pap 1

attached

Welcome to your first paper (out of five)!

Don't use plagiarized sources. Get Your Custom Essay on

pap 1

Just from $13/Page

Order Essay

Before I discuss Paper One, I want to jump ahead and mention your final paper, Paper Five. Your final paper in this course will be similar to an empirical manuscript that researchers submit to academic journals. It takes a lot of work, but we want to make the process as painless for you as possible. Rather than just turning in one final paper, you will turn in sections of the paper and get feedback on your work. You will then merge your first four papers together into one final fifth paper. Make sure to read over our feedback and incorporate changes we might suggest into your final paper!

While each paper focuses on a specific part of your manuscript, all papers have supporting materials to help you craft your paper. Each paper includes paper instructions (which are long and detailed but worth reading!), a grading rubric, a checklist, and an example paper from a prior semester. If you look over ALL FOUR items, your chances of getting a good grade will improve dramatically!

1). Paper I – Literature Review Study One Instructions (Social Loafing, Fall 2022)



2). Paper I – Literature review Grading Rubric



3). Paper I – Literature Review Checklist

 (If you can check “Yes” to all items in this checklist, your paper will be really good!)



4). Paper I Example Paper #1 – Counterfactual Thinking

 (This is from a student from a prior semester. It is a good example, but do not copy it as the topic differs. Includes helpful notes from your instructor)



5). Paper I Example Paper #2 – Facebook Apologies

· (This is also from a student from a prior semester)

· 6). Articles to Use for Paper I: For your first paper, you MUST cite to three of the following articles. You can cite four of them if you like, but you must find a fifth article to cite using PsycInfo. These are listed in no particular order, but make sure to read the abstracts to see how well they will fit in with your own paper. Some might be more relevant to your study than others.

Collective Effort Model and Group Cohesion (Karau)



Morality of Social Loafing (Mihelic)



Free Ride Flocking (Harding)



Group Size and Reward (Mefoh)

 

Motive Disposition (Hilkenmeier)

 

Partner Productivity and Social Loafing (Hamamoto)



Performance Knowledge (Lount)

 

Social Loafing in the Classroom (North)



Team Learning and Social Loafing (Gabelica)

Additional Supporting Material for Paper I

· 1).

APA Formatting – APA Example Paper

 (This is an “official” APA training paper you can find online. It provides another good example of how to format your paper)

 2). To find the surveys and Researcher Instructions (which contains the hypothesis), go to the

Assignment #4 – Study One Materials

 3).

Paper V – Example Paper #1 – Counterfactual Thinking

 (This is well beyond Paper I, but if you want to see how your Paper I will eventually fit in with your final course paper, I encourage you to look at this Paper V example paper)

 4).

Paper V – Example Paper #2 – Facebook Apologies

This article was downloaded by: [5.198.138.130] On: 28 May 2022, At: 10:40
Publisher: Institute for Operations Research and the Management Sciences (INFORMS)
INFORMS is located in Maryland, USA

Management Science

Publication details, including instructions for authors and subscription information:
http://pubsonline.informs.org

Working Harder or Hardly Working? Posting Performance
Eliminates Social Loafing and Promotes Social Laboring in
Workgroups
Robert B. Lount Jr., Steffanie L. Wilk

To cite this article:
Robert B. Lount Jr., Steffanie L. Wilk (2014) Working Harder or Hardly Working? Posting Performance Eliminates Social Loafing
and Promotes Social Laboring in Workgroups. Management Science 60(5):

1098

-1106. https://doi.org/10.1287/mnsc.2013.1820

Full terms and conditions of use: https://pubsonline.informs.org/Publications/Librarians-Portal/PubsOnLine-Terms-and-
Conditions

This article may be used only for the purposes of research, teaching, and/or private study. Commercial use
or systematic downloading (by robots or other automatic processes) is prohibited without explicit Publishe

approval, unless otherwise noted. For more information, contact permissions@informs.org.

The Publisher does not warrant or guarantee the article’s accuracy, completeness, merchantability, fitness
for a particular purpose, or non-infringement. Descriptions of, or references to, products or publications, or
inclusion of an advertisement in this article, neither constitutes nor implies a guarantee, endorsement, or
support of claims made of that product, publication, or service.

Please scroll down for article—it is on subsequent pages

With 12,500 members from nearly 90 countries, INFORMS is the largest international association of operations research (O.R.)
and analytics professionals and students. INFORMS provides unique networking and learning opportunities for individual
professionals, and organizations of all types and sizes, to better understand and use O.R. and analytics tools and methods to
transform strategic visions and achieve better outcomes.
For more information on INFORMS, its publications, membership, or meetings visit http://www.informs.org

http://pubsonline.informs.org

https://doi.org/10.1287/mnsc.2013.1820

https://pubsonline.informs.org/Publications/Librarians-Portal/PubsOnLine-Terms-and-Conditions

http://www.informs.org

MANAGEMENT SCIENCE
Vol. 60, No. 5, May 2014, pp. 1098–1106
ISSN 0025-1909 (print) � ISSN 1526-5501 (online) http://dx.doi.org/10.1287/mnsc.2013.1820

Working Harder or Hardly Working?

Posting

Performance Eliminates Social Loafing and
Promotes Social Laboring in Workgroups

Robert B. Lount Jr., Steffanie L. Wilk
Department of Management and Human Resources, Fisher College of Business, Ohio State University,

Columbus, Ohio 43210 {lount@fisher.osu.edu, wilk@fisher.osu.edu}

The current paper examines how posting performance—an act that triggers increased social comparisons
between workers—influences employees’ motivation when working in groups. In the study, posting

employee performance moderated the relationship between groupwork and employee motivation. When indi-
vidual performance was publicly posted in the workplace, employees working in a group performed better than
when working alone (i.e., social laboring); however, when individual performance was not posted, employees
working in a group performed worse than when working alone (i.e., social loafing). The findings shed light on
how social comparisons can have positive implications for employee performance in groups.

Keywords : organizational studies; effectiveness performance; motivation; productivity
History : Received September 14, 2011; accepted August 6, 2013, by Jesper Sørensen, organizations. Published

online in Articles in Advance January 2, 2014.

Introduction
Despite the widespread use of groups and teams in
organizations to perform both routine and specialized
tasks, concerns have long been raised that working in
a group has the potential to harm individual motiva-
tion. Numerous studies have documented that when
inputs are pooled together, group members experi-
ence a diffusion of responsibility where individuals
reduce their effort and become less productive com-
pared with when they work alone (for a review, see
Karau and Williams 1993); this phenomenon is often
referred to as social loafing or free riding. Although
social loafing is often described as a potential mal-
ady that can harm motivation when individuals work
in groups (Hackman 2002, Kerr and Tindale 2004), in
recent years, researchers have increasingly begun to
document instances of social laboring—when work-
ing in a group can lead individuals to increase their
effort and be more productive compared with individ-
uals working alone (for reviews, see Kerr and Hertel
2011, Larson 2010, Weber and Hertel 2007). Consid-
ering the frequency with which employees work in
groups, it is both practically and theoretically impor-
tant to help shed light on features in the workplace
that not only help to eliminate social loafing but also
may promote social laboring.

In the current paper, we examine how having
performance publicly compared with others in the
workplace affects employee motivation when indi-
viduals work in groups. We argue that when social

comparison processes become salient via the pres-
ence of posting performance, employees working in
groups will increase their motivation (i.e., social labor-
ing); however, when social comparison processes are
minimized via the absence of posting performance,
employees working in groups will be inclined to
reduce their task motivation (i.e., social loafing). We
test our predictions in a call center where employees
performed their work either alone or as a member
of a temporary workgroup. The setting allowed us to
directly examine how posting performance relates to
both social loafing and social laboring when employ-
ees worked in groups compared with when they
worked alone.

Social Bases of Motivation in Workgroups
For more than a century, scholars have documented
that working in a group can affect individuals’ levels
of task motivation (Köhler 1926, Ringelmann 1913).1

The positive or negative impact of working with
others on task motivation has been attributed to
two main psychological mechanisms: (1) perceived
instrumentality and (2) social comparison processes.
Whereas the former affects effort through an individ-
ual’s assessment of how necessary his or her con-
tribution is for the group to accomplish its task,
the latter affects effort through influencing the value

1 We define a group as two or more individuals whose inputs are
pooled together toward a common goal (Williams 2010).

1098

Lount and Wilk: Working Harder or Hardly Working?
Management Science 60(5), pp. 1098–1106, © 2014 INFORMS 1099

individuals place on how they perform relative to
other group members.

Research supporting social comparison theory
(Festinger 1954) has documented that individuals
generally prefer to compare favorably against others
(for reviews, see Buunk and Gibbons 2007, Suls and
Wheeler 2000). When performance comparisons are
made salient among group members, individual effort
can be influenced by the value that individuals place
on how their performance is evaluated by others in
the group, as well as by themselves. Given that indi-
viduals generally desire to be seen as valued mem-
bers of the groups to which they belong (Hogg 1996),
making performance comparisons salient allows indi-
viduals to both demonstrate and evaluate their rela-
tive value to the group (Harkins and Szymanski 1989).
Additionally, when individuals work together on a
shared task, concerns over being seen as the weakest
link, or the group member who is responsible for low-
ered performance, can fuel individual effort in groups
(Hertel et al. 2000). Moreover, providing explicit com-
parisons can increase competition, where individu-
als work harder in an effort to outperform others
(Lount and Phillips 2007). Taken together, when per-
formance comparisons are made salient in groups,
individuals can feel increasingly accountable for their
contributions; the comparisons lead individuals to
place increased value on performing well so that they
can be seen favorably. Accordingly, researchers have
argued that increasing the salience of performance
comparisons can help not only eliminate social loafing
(e.g., Karau and Williams 1993, Williams et al. 1981)
but also promote social laboring in groups (e.g., Kerr
et al. 2005, Lount et al. 2008).

Although promoting performance comparisons
within groups has the potential to improve effort, the
motivational potential of such performance compar-
isons depends on the perceived similarity of those
being compared (e.g., Festinger 1954, Harkins 1987,
Sanders et al. 1978). Within groups, comparisons with
others who are working on an identical task provide
information about group members’ relative abilities
and contributions, making performance comparisons
particularly relevant and increasing their motivational
potential (Suls et al. 2002). For instance, Harkins
(1987) documented that the task motivation of coac-
tors (i.e., individuals who work near one another but
who do not have a shared outcome) was larger when
they anticipated having their performance compared
with another person who was working on an identical
task (i.e., generating as many uses as possible for the
same object) compared with when they anticipated
being compared with someone performing a dissimi-
lar task (i.e., generating as many uses as possible for
a different object).

Although working alongside group members per-
forming an identical task has the potential to increase

motivation via social comparison processes, working
on a task identical to one’s teammates’ paradoxi-
cally also has the potential to harm individual moti-
vation and promote social loafing. Namely, working
on an identical task can lead individuals to lower
the perceived importance of their own contributions.
In other words, perceived instrumentality can be
harmed because individuals become aware that they
are not solely responsible for the performance on
the assigned task and that other group members can
make up for any one individual’s low performance
(Kerr 1983). Supporting the possibility that motivation
is harmed when individuals perform a task identical
to that of their teammates, Karau and Williams’ (1993)
meta-analysis on motivation in groups documented
social loafing effects to occur when group members
worked on identical tasks, whereas individual moti-
vation was not harmed when teammates worked
on different tasks. Taken together, basic research on
individual motivation in groups highlights that both
social comparison processes and concerns about per-
ceived instrumentality play a critical role in influenc-
ing effort when working on a task identical to one’s
teammates’ (Kerr et al. 2007, Hertel et al. 2008). As
such, when teammates perform the same task as one
another, understanding how to structure groupwork
to avoid the potential negative effects of decreased
perceptions of instrumentality will likely depend on
the how social comparison processes are affected by
the broader organizational context.

Whereas prior research has found evidence of social
loafing in the laboratory (for reviews, see Karau and
Williams 1993, Rutte 2003) and in simulated work
environments (e.g., Early 1989, Erev et al. 1993),
the degree to which such findings necessarily cor-
respond or predict the behavior of employees in
workgroups is unclear. Moreover, doubts have been
raised as to whether social loafing will necessarily
even occur among employees (e.g., Erez and Somech
1996) because there are a number of features present
in the workplace that may deter the propensity to
reduce task motivation when working with others
(e.g., desire for organization to perform well, poten-
tial for promotion, concerns over being laid off). To
date, the most frequently cited evidence of loafing
in organizational workgroups comes from survey-
based studies that have examined perceptions of loaf-
ing (e.g., George 1992, Liden et al. 2004). Perceptions
of loafing—although tapping into beliefs about group-
oriented behaviors (e.g., letting others do more of the
work)—do not necessarily reflect actual changes in
behavior (i.e., motivation losses) caused by working
on a shared task. Additionally, building on recent doc-
umentations of social laboring obtained from labora-
tory studies (e.g., Kerr and Hertel 2011) and in team
sports (e.g., Hüffmeier and Hertel 2012), it has been

Lount and Wilk: Working Harder or Hardly Working?
1100 Management Science 60(5), pp. 1098–1106, © 2014 INFORMS

suggested that the social setting may be structured
not only to eliminate social loafing but also to pro-
mote social laboring when employees work in groups
(van Dick et al. 2009).

We argue below that posting employee perfor-
mance in the workplace—an action that can help to
activate social comparison processes—constitutes a
critical component of the work context that can help
to eliminate social loafing and promote social laboring
when individuals work in groups.

The Current Study
The practice of posting performance is common in
many workplace settings and typically takes the form
of a manager posting one or more characteristics of
an individual’s performance in view of his or her
coworkers. Posting performance highlights that the
organization is aware of and cares about performance,
and it can signal to employees that they should also
care (Nordstrom et al. 1991). Prior work has shown
that employees perform better when they receive
feedback from management that allows them to com-
pare their performance to that of others (Blanes i
Vidal and Nossol 2011). Posting performance can
facilitate a culture of comparison, where performance
comparisons are a salient and relevant feature of
the workplace. Despite the potential for posting per-
formance to increase employee effort, the motiva-
tional benefits of the comparisons will likely depend
on against whom one is being compared (Festinger
1954, Harkins 1987). That is, whereas posting per-
formance may promote social comparison processes
in certain work settings (e.g., when coworkers per-
form identical tasks), they may not necessarily elicit
such processes when comparisons are deemed less
relevant (e.g., when coworkers perform dissimilar
tasks).

In the current study, we sought to examine how
performance posting would impact the relationship
between groupwork and employee motivation when
teammates worked on an identical task. Namely,
although working on a task identical to one’s team-
mates’ can harm individual motivation via reducing
perceptions of instrumentality, working under con-
ditions that heighten comparison processes may not
only be sufficient to eliminate the tendency reduce
one’s inputs but also promote the desire to per-
form especially well. Consistent with the expectation
that settings that make comparisons especially salient
should lead individuals to be driven to compare
favorably, we predicted that posting performance
would moderate the relationship between groupwork
and individual task motivation. When performance
was posted in the office, we expected social labor-
ing to occur (i.e., increased performance of employees
working in groups compared with when they work

alone). However, when performance was not posted,
we expected that a reduction in perceived instrumen-
tality would promote social loafing (i.e., decreased
performance of employees working in groups com-
pared with working alone).

To test our predictions in the workplace, it was nec-
essary to examine the performance of employees when
working alone compared with when they worked in
a group, and we examine whether performance in
these contexts differed as a function performance post-
ing. Accordingly, we examined the performance of
employees working at a call center who performed the
task of recruiting people to participate in a focus group
either alone or with other employees recruiting people
for the same focus group.2 While working in a group,
an employee’s number of recruits was pooled with
that of other group members, thereby creating out-
come interdependence among group members. The
basic nature of an individual’s work did not differ
while working alone or as a member of a group
because employees sat in a cubicle making phone calls
in both situations, providing us with an opportunity
to compare employee performance across these two
types of work settings.3

The study was conducted over a 12-week period,
6 weeks during which the company posted employee
performance weekly and 6 weeks during which the
company did not post employee performance. Across
these 12 weeks, employees were assigned, on a daily
basis, to work alone or with others completing the
same task.

Method
Organizational Setting
FocusCo (a pseudonym) is an organization that con-
ducts focus groups for clients. Focus groups are
used to test market products or product marketing
campaigns. Clients would contract with FocusCo to
recruit individuals, run the focus groups, and report

2 By alone, we simply mean that the individual does not have
interdependence with others. The current work environment (i.e.,
employees working at cubicles side by side) helps controls for
many of the psychological effects (e.g., arousal) attributable to
working in the presence of others that can affect individual perfor-
mance (Aiello and Douthitt 2001, Cottrell et al. 1968).
3 To argue that a process loss or gain in a group has occurred,
one needs to compare performance against a baseline (Hill 1982,
Tindale and Larson 1992). In the current study, the presence of
an individual baseline enabled us to examine whether employees
exert different levels of effort when they work in groups compared
with when they work alone. Given that the basic nature of the task
did not change when individuals worked alone or as a member
of a group, positive/negative changes in individual performance
across these two work arrangements should largely be attributable
to motivation gains/losses instead of coordination gains/losses
(Larson and Schaumann 1993, Steiner 1972).

Lount and Wilk: Working Harder or Hardly Working?
Management Science 60(5), pp. 1098–1106, © 2014 INFORMS 1101

back with results. Our data come from call center
employees responsible for recruiting individuals for
these focus groups. Employees contacted prospective
focus group members from a list of phone numbers
provided by the organization.

For each client project, a profile was created for
requested focus group members. The employees
were responsible for calling and quizzing prospec-
tive group members on the characteristics of the pro-
file (e.g., age, sex, exposure to a particular product,
user of a particular product). Although projects gener-
ally required similar participant characteristics, there
was variation depending on the project, making some
focus groups more time consuming to recruit par-
ticipants for than others. As such, managers gener-
ally kept track of project assignments to minimize
the chance that certain employees were regularly
assigned to more or less difficult projects than others
across time.

Before making phone calls, employees began each
new project by carefully studying the profile require-
ments. While on the phone, based on the responses
of the prospective focus group member, the employee
would determine whether he or she fit the profile. If
so, the employee would provide this individual with
information about the place and time of the focus
group and encourage him or her to attend. If the
recruit was able and willing to participate, this would
be considered a successful recruit for the employee.
However, if the responses provided indicated that the
prospective focus group member was not appropriate
for the project, the employee would end the call and
then proceed to the next call.

Employees worked on only one project at a time.
Each project had a quota to be met, and depending on
how managers assigned projects that day, individuals
worked either alone or simultaneously with others to
meet the quota. Assignment to whether one worked
alone or in a group was largely random, with some
oversight from managers with regard to workflow
scheduling. In any given week, employees would be
assigned to multiple projects, working either alone or
with others on a project. The project was considered
complete when the number of recruits met the quota,
which could take anywhere from less than a day to
several days.

Even though projects were of generally short dura-
tion, working together with one or more others cre-
ated a sense of group membership. When a project
was assigned to more than one person, the man-
ager met with the employees who were to be work-
ing on the same project, explained the project and
their joint goal, and encouraged them to keep track
of recruits among themselves to make sure they met
the quota and did not recruit too many participants.
As we observed in our site visits, employees did this

by informing each other of a successful recruit or
by checking in with one another throughout the day
to determine how many recruits had been achieved.
Moreover, group members tended to talk about “our
project” rather than “my project.” Management rein-
forced the group nature of the work by making the
entire group accountable for reaching the quota and
requiring all group members to continue to work on
that project until the quota was met. If an employee’s
recruit was discovered to be unqualified, the entire
group, not just the employee who recruited the par-
ticipant, would have to return to work on the project
until the quota was met, even if that meant putting
their newly assigned project on hold.

It was also observed in our site visits that, given
the high number of rejections employees faced when
making phone calls to recruit focus group members,
it was viewed as a positive event to complete a quota.
In addition, the time transitioning from one project to
the next provided an opportunity for a break from the
phones. Given the taxing nature of constantly talking,
team members who helped reach the quota were gen-
erally viewed favorably by their teammates.

We examined performance of employees across
12 weeks. For six consecutive weeks, individual per-
formance was posted at the end of each week in
a prominent spot in the office. At the end of the
six weeks with posting, the performance list was
removed from the office and the manager informed
employees that performance would no longer be
posted. Thus, performance data during the “post-
ing period” began the first day employees were
exposed to the performance list, and performance
data during the “no-posting period” began the first
day employees worked in the absence of the perfor-
mance list.4 The call center manager provided few
details to the employees about the posting or why it
was removed. The information posted included the
employee’s name, the number of people recruited for
focus groups that week, and the number of hours he
or she worked that week. The FocusCo primary per-
formance metric, recruits per hours worked, was also
clearly visible. The list was ordered sequentially on
the basis of employee performance, with the top per-
former’s information listed first.

4 The decision to have the first six weeks be the posting time period
and have the six weeks following be the no-posting period was
made for two reasons. First, managers wanted time to set up the
posting system and used the week preceding January 1 (i.e., when
the call center is closed) to do so. Second, the call center did not
want to compare performance during the holiday season (i.e., six
weeks preceding January 1) with performance following the holi-
days to avoid “holiday effects” because management believed that
people may be less interested in participating in focus groups dur-
ing the holidays.

Lount and Wilk: Working Harder or Hardly Working?
1102 Management Science 60(5), pp. 1098–1106, © 2014 INFORMS

Sample
For our analyses, we use data provided by the orga-
nization for all call center employees and projects
within the 12-week period noted above. The call cen-
ter organization was small and varied from around
20 to 25 employees. We created a sample to include
only employees who were in both the posting and
no-posting period for a final count of 21 employ-
ees (90% female with average tenure of 2.3 years).
Although the number of respondents is small, it is
consistent with other experience sampling studies
(e.g., 27 participants in Ilies and Judge 2002, 18 par-
ticipants in Totterdell and Holman 2003). From these
employees we obtained 737 observations across the
12 weeks, where each observation included perfor-
mance data for a particular project on a given day for
a particular employee. We focus on the project level
as employees would sometimes work on more than
one project in a given workday.

Measures

Recruits per Hour. The dependent variable is
FocusCo’s primary measure of productivity, the num-
ber of recruits per hour worked for each employee.
This was calculated for each day for each project
on which an employee was working. Given that the
task is easily learned and performance is determined
largely by persistence, consistent with other research
examining task motivation among call center workers
(e.g., Grant 2008), the number of recruits per hour the
employee obtained was operationalized as a behav-
ioral indication of effort.

Posting. A dichotomous variable (0 = no posting,
1 = posting) was created to indicate whether FocusCo
was posting or not during the time period.

Groupwork. We calculated the number of other
employees who were assigned the same project on the
same shift on the same day. There were either two,
three, or four people working on the same project
on the same shift, with the majority being two per
project per shift. We created a dichotomous variable
where 0 indicates the person was working alone on
this particular project that day and on that shift and a
1 indicates that the person was working with at least
one other person on this particular project that day
and that shift. Dichotomizing groupwork (0 = alone,
1 = group) is consistent with prior work showing that
the distinction between working alone versus work-
ing with at least one other person is a critical one
in distinguishing between individual and group con-
texts (Latané et al. 1979, Williams 2010).

Controls. We also controlled for the number of
days a person had been working on the cur-
rent project or task because research has shown
that experience can influence performance (Quinones
et al. 1995).

Analysis
Data were structured so that each line of data was
a separate project/day/person combination (thus,
projects were nested within days, which were nested
within person). Because of the nested nature of the
data, we chose to use hierarchical linear model-
ing (HLM) to account for the dependence among
the observations (Raudenbush and Bryk 2002). HLM
accounts for bias associated with dependence among
observations that occurs when data are nested within
units (multilevel models) by modeling both fixed and
random effects.

Results
To determine whether using HLM was empirically
justified, we calculated the intraclass correlation coef-
ficients ICC(1), which is a measure of the variance
that is attributable to the three levels, project, per-
son, and day, for our dependent variable. We found
that although there was significant variance in recruit
by hours that is explained at both the project (95%,
p < 00001) and the person (5%, p < 0005) levels, we found no significant variance in our dependent vari- able that was explained at the day level (0%, p > 0010).
These findings indicate that using HLM is an appro-
priate analytical technique and that two-level models
(project nested within person) are suitable for these
data (Kreft and De Leeuw 2002).

Means, standard deviations, and intercorrelations
of study variables are provided in Table 1. Before test-
ing our interaction hypothesis, we first examined the
main effects of posting and groupwork. The results
of the regression analyses showed that neither post-
ing (b = 0002, p = 0063) nor groupwork (b = −0002,
p = 0062) significantly predicted task performance.
As anticipated, however, the interaction term signif-
icantly predicted individual task performance (b =

0042, t = 4020, p < 0001) (see Table 2, Model 4). Explor- ing this interaction, we find that when posting was present, individuals working in groups performed better than when working alone (simple slope = 0019, t = 2018, p = 0003). However, the opposite pattern

Table 1 Means, Standard Deviations, and Intercorrelations Among
Study Variables

Variable Mean SD 1 2 3

1. Recruits per hour 0059 0061
2. Posting (1= posting) 0042 0049 0001
3. Groupwork (1= group) 0026 0044 −0003 0013∗∗

4. Days on project 1036 0072 −0004 0012∗∗ 0001

Notes. Within-person correlations between variables were obtained using
HLM analyses to account for nonindependence. N = 737 (pairwise, within-
person correlations).

∗p < 0005; ∗∗p < 0001.

Lount and Wilk: Working Harder or Hardly Working?
Management Science 60(5), pp. 1098–1106, © 2014 INFORMS 1103

Table 2 HLM Analyses of Recruits per Hour

Measure Model 1 Model 2 Model 3 Model 4

Intercept 0064∗∗∗ 0063∗∗∗ 0064∗∗∗ 0068∗∗∗

Days on project −0004 −0004 −0004 −0004
Groupwork (1= group) −0002 −0003 −0023∗

Posting (1= posting) 0002 0003 −0009
Groupwork×Posting 0042∗∗

�2 22033∗∗∗ 22039∗∗∗ 22064∗∗∗ 27043∗∗∗

Sample size (project per /person) 737 737 737 737

∗p < 0005; ∗∗p < 0001; ∗∗∗p < 00001.

occurred when posting was not present; individu-
als working in groups performed worse than when
working alone (simple slope = −0023, t = −4033, p < 0001).5 Taken together, these results supported our pri- mary hypothesis (see Figure 1).

Supplemental Analyses
We conducted additional analyses to help rule out
potential alternative explanations for our findings.
One might argue that the evidence of social loafing
while working in groups when there was no posting
was an artifact of the ordering of our data collection.
That is, the social loafing finding may be a reaction to
the removal of posting; thus, there is a concern that this
finding may not occur prior to the posting period. To
test whether social loafing was occurring prior to the
posting period, we ran HLM analyses on the entire
year of data (including all employees) prior to posting
and tested whether working in a group had a negative
effect on individual task performance. This allowed
us to maximize the use of our data and increase the
power of our analyses. Suggestive of social loafing,
working in a group was negatively associated with
individual task performance (b = −0010, t = −6004, p < 00001; person: n = 123, event: n = 51152). As an addi- tional check, we also ran the same analyses with a reduced sample of only those who were employed sometime during the period prior to posting as well as in the posting period. We find a similar pattern of results. Working in a group had a negative effect on individual task performance (b = −0004, t = −1077, p = 0008; person: n= 15, event: n= 11559).

Next, we sought to examine whether seasonal dif-
ferences may have accounted for the motivation gains
documented in our posting period. To test this we

5 The statistical interaction between groupwork × posting was also
significant when groupwork was entered as a continuous predic-
tor: b = 0042, t = 3068, p < 00001. Simple slope analyses showed that when posting was present, individuals working with more group members tended to perform better than when working with fewer group members (simple slope = 0015, t = 2075, p < 0001). When post- ing was not present, individuals working with more people tended to perform worse than when working with fewer people (simple slope = −0018, t = −1049, p = 0014).

Figure 1 Plot of Posting ×Groupwork for Recruits per Hour

0.25

0.35

0.45

0.55

0.65

0.75

0.85

Alone In group

R
ec

ru
its

p
er

h
ou

No posting

Posting

examined the same six-week period corresponding
to the posting period but in the year before, a time
when no posting was present. We ran HLM analy-
ses on these data, including all employees, and tested
whether working in a group had a negative effect on
task performance. Social loafing effects on individ-
ual task performance were observed as working in
a group was negatively related to performance (b =

−0013, t = −2025, p = 0002; person: n = 33, event: n =

640). Additionally, we ran the same analyses using the
full 12 weeks of data corresponding to our posting
and nonposting period but in the year prior. Similar
results were observed; working in a group was neg-
atively related to performance (b = −0010, t = −3034,
p < 00001; person: n = 49, event: n = 11458). As a final check, we also ran the same analyses with a reduced sample of only those who were employed in the 12 weeks in the corresponding year prior and also during the posting period. We again find a similar pattern of results: working in a group had a negative effect on task performance (b = −0008, t = −1064, p =

0010; person: n = 8, event: n = 346). Taken together,
these additional analyses help rule out the possibility
that our effects were likely solely attributable to sea-
sonal effects or that our loafing results were triggered
by the removal of posting performance.

We also wanted to examine whether individuals’
performance in groups during the nonposting period
was affected by the reputation of their teammates.
Whereas prior work has found that motivation is sen-
sitive to real-time differences in teammates’ perfor-
mance (e.g., Messé et al. 2002, Schultz et al. 2010),
expectations about differences in relative abilities may
also affect motivation in groups. By the end of the
six weeks of posting performance, employees may
have made a general observation of their coworkers’
performance, which may have influenced subsequent
levels of motivation while working with others. As
such, we computed an average performance value
for each employee during the posting period and

Lount and Wilk: Working Harder or Hardly Working?
1104 Management Science 60(5), pp. 1098–1106, © 2014 INFORMS

examined whether the average performance of one’s
group members affected individual performance in
the time period following posting. Analyses showed
that mean coworkers’ prior performance did not
influence the performance of individuals working in
groups (b = −0005, t = 0038, p = 0070). We also tested
both the interaction between an individual’s mean
performance during the posting period with their
average coworkers’ performance during posting and
for a possible curvilinear relationship. Neither the
interaction analysis (b = −0003, t = −0005, p = 0096)
nor the curvilinear test (b = 0052, t = 1019, p = 0024)
was significant. Taken together, these analyses sug-
gest that individual performance was not affected by
differences in performance reputation between group
members.

Discussion
Whereas the literature on motivation in groups has
regularly speculated that loafing may occur in orga-
nizational workgroups, the findings of this study
highlight that the relationship between working in
a group and individual motivation depends on the
broader work environment. That is, whether or not
the organization posted employee performance—an
act that triggers increased social comparisons between
workers—was found to moderate the relationship
between groupwork and employee motivation. Specif-
ically, whereas the absence of posting performance
in an office was associated with a decrease in task
performance for employees working in groups com-
pared with when they work alone (i.e., social loafing),
the presence of posting performance was associated
with an increase in task performance for employ-
ees working in groups compared with when they
work alone (i.e., social laboring). These findings high-
light the important role that social comparison pro-
cesses can play in influencing individual motivation,
and they provide support for the expectation that
group settings can either positively or negatively affect
employee motivation (Hackman 2002).

In addition to documenting how heightening social
comparisons processes can aid motivation in groups,
through examining differences in motivation as a
function of whether an employee worked alone or
in a group, our results demonstrate that employee
effort can be sensitive to groupwork. When perfor-
mance was not posted in the office, groupwork was
found to promote loafing. This finding is consistent
with prior work showing students (Erev et al. 1993)
and trainees (Early 1989) loafing at simulated work
tasks. However, whereas these prior studies showed
evidence of loafing when individual performance was
anonymous, in the current work setting, individual
contributions to the group task were not anonymous

because employees could track and compare their
performance with their fellow group members. With
conditions of anonymity frequently shown to elicit
loafing, one may anticipate that even higher levels of
loafing may have occurred if the work setting was
one where individual performance was completely
anonymous.

Despite showing that groupwork can elicit social
loafing among employees, it is important to highlight
that the results also show the potential of groupwork
to positively influence employee motivation. Namely,
posting performance in the office not only helped to
eliminate loafing but also resulted in motivation gains
in workgroups. This finding supports recent specu-
lation that managers and practitioners may actively
manage the broader work context to help elicit social
laboring in groups (van Dick et al. 2009).

The current study also helps further our under-
standing into potential boundary conditions about
when posting performance in the workplace may
impact individual performance. Specifically, there was
not an overall change in individual performance for
employees who worked alone during posting and
after posting was removed. Although this may appear
to be inconsistent with prior work linking posting
performance with increased task performance (e.g.,
Azmat and Iriberri 2010, Blanes i Vidal and Nos-
sol 2011), our findings suggest that the benefits of
performance comparisons in some organizations may
depend, in part, on being compared with others per-
forming the exact same task. Although individuals may
be compared on tasks that may appear similar on
the surface (e.g., two employees installing parts into
a car), if the tasks are not identical (e.g., one per-
son is installing seats and the other is installing steer-
ing wheels), publicly posting how long it took each
person to perform his or her task may be perceived
as irrelevant information, and therefore not aid in
improving employee motivation. Thus, merely post-
ing performance data in the office may be insufficient
to augment effort if employees do not see the perfor-
mance comparisons as personally relevant.

Finally, our results also contribute to the grow-
ing research on within-person performance, which
suggests that the same individuals will exert differ-
ent levels of effort depending on the context. Whereas
characteristics of the task (Fisher and Noble 2004)
and the behavior of others (Stewart and Nandkeolyar
2007) have been found to relate to within-person per-
formance variation, we find that working with others
on a shared task and posting performance matters as
well.

Limitations and Future Directions
It is important to note that there are likely bound-
ary conditions that influence motivation in work-
groups. Given that employees working in groups in

Lount and Wilk: Working Harder or Hardly Working?
Management Science 60(5), pp. 1098–1106, © 2014 INFORMS 1105

our study experienced minimal task interdependence
and worked together for short periods of time, the
groups we studied may differ from workgroups com-
monly found in other organizations (Kozlowski and
Bell 2003). Accordingly, group-level features that can
shape individual behavior (see Cronin et al. 2011),
such as cohesion, should be less influential in the
types of temporary workgroups examined in the cur-
rent study. As such, one should be cautious about
generalizing the motivational effects of groupwork
seen in the current study to all workgroups. For
instance, although we found evidence of social loafing
when the office did not post performance, there are
likely instances where employees would not loaf—
and may even display motivation gains—while work-
ing with others in a setting where performance is not
posted. Namely, employees working alongside rivals
(Kilduff et al. 2010) or those facing intergroup compe-
tition (Erev et al. 1993, Pettit and Lount 2010, Wittchen
et al. 2011) may be especially motivated, regardless of
whether the office posts employee performance.

Although we have argued that heightening social
comparison processes in workgroups promotes a
variety of psychological states argued to facilitate
increased performance (i.e., increased evaluation con-
cerns, self-evaluation, competition, and the desire to
be seen as a valued group member), the degree to
which each of these psychological states was acti-
vated cannot be determined in the current study.
Future research in settings involving daily changes
in one’s work environment (i.e., working alone ver-
sus working in a group) could capitalize on daily
surveys or surveys administered following the com-
pletion of each project. Such data may provide valu-
able insight into the psychological states responsible
for performance changes when individuals worked
alone versus in a group. Insight into psychologi-
cal mechanisms may also be garnered in the lab-
oratory. For instance, experimentally manipulating
posting performance (present versus not present),
groupwork (alone versus group), and task similarity
(identical versus different) may help to better under-
stand why posting performance is particularly moti-
vating in groups. Self-report data could be collected
in an experiment to help examine the degree to which
an increased desire to be seen in a positive light
by other group members underlies changes in moti-
vation while posting is present. Thus, although the
current study showed that performance posting was
associated with improved motivation for employees
working in groups, future work is needed to more
closely examine the specific psychological processes
that underlie these effects.

Conclusion
Consistent with a contingency-based approach to
understanding behavior in organizations, we have

argued in the current paper that employee motivation
can be both positively and negatively affected by work-
ing in a group. These findings highlight that factors
present in the broader organizational context, such as
whether or not performance is posted, can in part
help to explain when working in a group will help or
hurt employee motivation.

Acknowledgments
The authors thank Hillary Anger Elfenbein, Norbert Kerr,
Nathan Pettit, and three anonymous reviewers for their
helpful feedback on earlier versions of this paper.

References
Aiello JR, Douthitt EA (2001) Social facilitation from Triplett to elec-

tronic performance monitoring. Group Dynam. 5:163–180.
Azmat GN, Iriberri N (2010) The importance of relative perfor-

mance feedback information: Evidence from a natural experi-
ment using high school students. J. Public Econom. 94:435–452.

Blanes i Vidal J, Nossol M (2011) Tournaments without prizes: Evi-
dence from personnel records. Management Sci. 57:1721–1736.

Buunk AP, Gibbons FX (2007) Social comparison: The end of a the-
ory and the emergence of a field. Organ. Behav. Human Decision
Processes 102:3–21.

Cottrell NB, Wack DL, Sekerak GL, Rittle RH (1968) Social facilita-
tion of dominant responses by presence of others. J. Personality
Soc. Psych. 9:245–250.

Cronin MA, Weingart LR, Todorova G (2011) Dynamics in groups:
Are we there yet? Acad. Management Ann. 5:571–612.

Early PC (1989) Social loafing and collectivism: A comparison of
the United States and the People’s Republic of China. Admin.
Sci. Quart. 34:565–581.

Erev I, Bornstein G, Galili R (1993) Constructive intergroup com-
petition as a solution to the free rider problem: A field experi-
ment. J. Experiment. Soc. Psych. 29:463–478.

Erez M, Somech A (1996) Is group productivity loss the rule or
the exception? Effects of culture and group-based motivation.
Acad. Management J. 39:1513–1537.

Festinger L (1954) A theory of social comparison processes. Human
Relations 7:117–140.

Fisher CD, Noble CS (2004) A within-person examination of cor-
relates of performance and emotions while working. Human
Performance 17:145–168.

George JM (1992) Extrinsic and intrinsic origins of perceived social
loafing in organizations. Acad. Management J. 35:191–202.

Grant AM (2008) Does intrinsic motivation fuel the prosocial fire?
Motivational synergy in predicting persistence, performance,
and productivity. J. Appl. Psych. 93:48–58.

Hackman JR (2002) Leading Teams: Setting the Stage for Great Perfor-
mances (Harvard Business School Press, Boston).

Harkins SG (1987) Social loafing and social facilitation. J. Experi-
ment. Soc. Psych. 23:1–18.

Harkins SG, Szymanski K (1989) Social loafing and group evalua-
tion. J. Personality Soc. Psych. 56:934–941.

Hertel G, Kerr NL, Messé LA (2000) Motivation gains in perfor-
mance groups: Paradigmatic and theoretical developments on
the Köhler effect. J. Personality Soc. Psych. 79:580–601.

Hertel G, Niemeyer G, Clauss A (2008) Social indispensability or
social comparison: The why and when of motivation gains of
inferior group members. J. Appl. Soc. Psych. 38:1329–1363.

Hill GW (1982) Group vs. individual performance: Are N +1 heads
better than one? Psych. Bull. 91:517–539.

Lount and Wilk: Working Harder or Hardly Working?
1106 Management Science 60(5), pp. 1098–1106, © 2014 INFORMS

Hogg MA (1996) Social identity, self-categorization, and the small
group. Witte E, Davis J, eds. Understanding Group Behavior,
Vol. 2 (Lawrence Erlbaum Associates, Hillsdale, NJ), 227–253.

Hüffmeier J, Hertel G (2012) When the whole is more than the sum
of its parts: Motivation gains in the wild. J. Experiment. Soc.
Psych. 47:455–459.

Ilies R, Judge TA (2002) Understanding the dynamic relationships
among personality, mood, and job satisfaction: A field expe-
rience sampling study. Organ. Behav. Human Decision Processes
89:1119–1139.

Karau SJ, Williams KD (1993) Social loafing: A meta-analytic review
and theoretical integration. J. Personality Soc. Psych. 65:681–706.

Kerr NL (1983) Motivation losses in small groups: A social dilemma
analysis. J. Personality Soc. Psych. 45:819–828.

Kerr NL, Hertel G (2011) The Köhler group motivation gain: How
to motivate the “weak links” in a group. Soc. Personality Com-
pass 5:43–55.

Kerr NL, Tindale RS (2004) Group performance and decision mak-
ing. Annual Rev. Psych. 55:623–655.

Kerr NL, Messé LA, Park ES, Sambolec EJ (2005) Identifiability,
performance feedback and the Köhler effect. Group Processes
Internat. Relations 8:375–390.

Kerr NL, Messé LA, Seok DH, Sambolec EJ, Lount RB Jr, Park ES
(2007) Psychological mechanisms underlying the Köhler moti-
vation gain effect. Personality Soc. Psych. Bull. 33:828–841.

Kilduff GJ, Elfenbein HA, Staw BM (2010) The psychology of
rivalry: A relationally dependent analysis of competition. Acad.
Management J. 53 (5):943–969.

Köhler O (1926) Kraftleistungen bei Einzel- und Gruppenabeit
[Physical performance in individual and group situations].
Industrielle Psychotechnik 3:274–282.

Kozlowski SWJ, Bell BS (2003) Work groups and teams in organiza-
tions. Borman WC, Illgen DR, Klimoski RJ, eds. Comprehensive
Handbook of Psychology: Industrial and Organizational Psychology,
Vol. 12 (John Wiley & Sons, New York), 333–375.

Kreft I, De Leeuw J (2002) Introducing Multilevel Modeling (Sage,
London).

Larson JR Jr (2010) In Search of Synergy in Small Group Performance
(Psychology Press, New York).

Larson JR Jr, Schaumann L (1993) Group goals, group coordination,
and group member motivation. Human Performance 6:49–69.

Latané B, Williams KD, Harkins SG (1979) Many hands make light
the work: The causes and consequences of social loafing. J. Per-
sonality Soc. Psych. 37:822–832.

Liden RC, Wayne SJ, Jaworski RA, Bennett N (2004) Social loafing:
A field investigation. J. Management 30:285–304.

Lount RB Jr, Phillips KW (2007) Working harder with the out-group:
The impact of social category diversity on motivation gains.
Organ. Behav. Human Decision Processes 103:214–224.

Lount RB Jr, Park ES, Kerr NL, Messé LA, Seok D (2008) Evaluation
concerns and the Köhler effect: The impact of physical presence
on motivation gains. Small Group Res. 39:795–812.

Messé LA, Hertel G, Kerr NL, Lount RB Jr, Park ES (2002) Knowl-
edge of partner’s ability as a moderator of group motivation
gains: Exploration of the Köhler discrepancy effect. J. Personal-
ity Soc. Psych. 82:935–946.

Nordstrom R, Lorenzi P, Hall RV (1991) A review of public post-
ing of performance feedback in work settings. J. Organ. Behav.
Management 11:101–123.

Pettit NC, Lount RB Jr (2010) Looking down and ramping up: The
impact of status differences on effort in intergroup contexts.
J. Experiment. Soc. Psych. 46:9–20.

Quinones MA, Ford JK, Teachout MS (1995) The relationship
between work experience and job performance: A conceptual
and meta-analytic review. Personality Psych. 48(4):887–910.

Raudenbush SW, Bryk AS (2002) Hierarchical Linear Models: Applica-
tions and Data Analysis Methods (Sage, Thousand Oaks, CA).

Ringelmann M (1913) Recherches sur les moteurs animés: Travail
de l’homme. [Research on animate sources of power: The work
of man]. Ann. l’Instiuit National Agronomique 12:1–40.

Rutte CG (2003) Social loafing in teams. West MA, Tjosvold D,
Smith KG, eds. International Handbook of Organizational Team-
work and Cooperative Working (John Wiley & Sons, Chichester,
UK), 361–378.

Sanders GS, Baron RS, Moore DL (1978) Distraction and social com-
parison as mediators of social facilitation effects. J. Experiment.
Soc. Psych. 14:291–303.

Schultz KL, Schoenherr T, Nembhard D (2010) An example and
proposal concerning the correlation of working processing
times in parallel tasks. Management Sci. 56:176–191.

Steiner ID (1972) Group Processes and Productivity (Academic Press,
New York).

Stewart GL, Nandkeolyar AK (2007) Exploring how constraints
created by other people influence intraindividual variation in
objective performance measures. J. Appl. Psych. 92:1149–1158.

Suls J, Wheeler L (2000) Handbook of Social Comparison Processes:
Theory and Research (Kluwer Academic Publishers, New York).

Suls J, Martin R, Wheeler L (2002) Social comparison: Why, with
whom, and with what effect? Current Directions Psych. Sci.
11:159–163.

Tindale RS, Larson JR Jr (1992) Assembly bonus effect or typical
group performance? A comment on Michaelsen, Watson, and
Black (1989). J. Appl. Psych. 77:102–105.

Totterdell P, Holman D (2003) Emotion regulation in customer ser-
vice roles: Testing a model of emotional labor. J. Occupational
Health Psych. 8:55–73.

van Dick R, Tissington PA, Hertel G (2009) Do many hands make
light the work? How to overcome social loafing and gain moti-
vation in work teams. Eur. Bus. Rev. 21:233–245.

Weber B, Hertel G (2007) Motivation gains of inferior group
members: A meta-analytical review. J. Personality Soc. Psych.
93:973–993.

Williams KD (2010) Dyads can be groups (and often are). Small
Group Res. 41:268–274.

Williams KD, Harkins SG, Latané B (1981) Identifiability as a deter-
rent to social loafing: Two cheering experiments. J. Personality
Soc. Psych. 40:303–311.

Wittchen M, van Dick R, Hertel G (2011) Motivated information
processing during intergroup competition: A model of inter-
group competition effects on individual effort. Organ. Psych.
Rev. 1:257–272.

Ife PsychologIA; Volume 20 Number1, March 2012
Copyright ©2012 Ife Center for Psychological Studies/Services, Ile-Ife Nigeria.

229

EFFECTS OF GROUP SIZE AND EXPECTANCY OF REWARD ON

SOCIAL LOAFING

Philip C. Mefoh, PhD & Chinonso L. Nwanosike

Department of Psychology, University of Nigeria, Nsukka.

Abstract

There is a large body of evidence which have shown

that monitoring personal effort on group projects
reduces social loafing effects, but as the world gets

more complex with several co-operative tasks there is

a need to explore other variables that would inspire

group, rather than individual performance. This

experiment re-examined the prediction that performing
in large group would lead to social loafing behaviour,

the study also tested whether the promise of reward

would attenuate social loafing effect on a simple

experimental task. Forty (40) Igbo secondary school

students of south eastern Nigeria participated in the

experiment. They were aged 13 – 16 years (mean age =
14.53). Results indicated that performance was

significantly poorer in the group condition than in the

alone condition (p. <05); and reward significantly

attenuated social loafing effect in the group condition

(p. <05). These observations were discussed in relation to the prevailing challenge in team work.

Key words: Expectancy of reward, Group size, Max

Ringlemann, Individual performance, Personal effort.

Introduction

The Igbo speaking people of south eastern Nigeria knew that
individuals work less hard in groups than when working alone. The

Igbo proverb, ―ewu oha nwe n‘anwu n‘aguu‖ (meaning that a goat

owned by many individuals usually dies of hunger), predict that

sharing work with other people reduce individual performance. This

phenomenon of individuals exerting less effort when they work in a
group than when they work independently is labeled social loafing.

Social loafing is a pervasive characteristic of working in groups and

occurs in several different cultures (Gabrenya, Latane & Wang,

1983).

The first evidence of social loafing was demonstrated by a

French agricultural engineer, Max Ringlemann (Kravitz & Martin,

Ife PsychologIA; 20(1), March 2012

230

1986). Ringlemann asked participants to pull on a rope as hard as

they could. The participants pulled by themselves or with one, two,

or seven others. A sensitive gauge was used to measure how strongly
the participants pulled the rope. Participants‘ efforts pulling on the

rope were less when they worked in a group than when they

performed the task alone. Groups of two pulled at only 95 percent of

their capacity and groups of three and eight sank to 85 percent and

49 percent respectively. That is, as more individuals pulled on the

rope, the individuals exerted themselves less. From these
observations, Ringlemann observed that individuals perform below

their potential when working in a group (LaFasto & Larson, 2001).

Similarly, Latane, Williams and Harkins (1979) identified

social loafing among college students. Latane and colleagues had six

blindfolded college students sit in semi-circle. The students wore
headphone that blasted sounds of people shouting into their ears.

The experimental task was to shout as loud as possible while

listening to the headphone noise. On some trials, the students

believed that the other five students were also shouting. At other

trials, they believe that they were either shouting alone or with one

other student. In reality, only a student was performing on all the
trials. Consistent with the phenomenon of social loafing, when a

student thought one other person was shouting, the student shouted

82 percent as intensely as when alone, and when they believed all six

of them were shouting, they shouted 75 percent as intensely. As in

Ringlemann‘s study, output decreased with increased group
membership, due to social loafing.

Since these early observations, social loafing has been

identified in numerous other studies (e.g., De Vita, 2001; Hardy &

Latane, 1988; Karau & Williams, 1993; Price & Harrison, 2006;

Weldone & Gargano, 1988). The phenomenon have also been

documented in a host of behaviours to illustrate a principle that is
common in business, family, education, and in social gatherings that

harms the overall integrity and performance of a group by reducing

the level of output. The practical advice from most social loafing

research was that work groups should be designed so that each

individual‘s effort can be assessed independently of those of the
group to prevent social loafing (Carron, Burke, & Prapavessis, 2004).

This advice was based on the observation that when individual

performance was monitored within the group situation the

individuals worked just as hard as they did when they worked alone

(Latane, 1981). That is, people working by themselves think they are

responsible for completing a task, when they work in groups;

Mefoh, P.; Effects of Group Size and Expetancy of Reward on Social Loafing

231

however, this feeling of responsibility diffuses to others and their

performance effort declines (Comer, 1995; Piezon & Donaldson,

2005).
What happens when an individual‘s effort cannot be assessed

in a group activity? Many jobs today are very complex that only

group performance can be measured. Probably, one useful way to

attenuate social loafing effect on group projects (besides monitoring

personal effort) would be to develop a collectivistic culture. Not all

culture experience social loafing. Social loafing was more likely to
occur in societies where the focus is on the individual rather than the

group (Gabrenya, et. al. 1985). In a study comparing individualistic

values to collectivistic values, Earley (1989) found that social loafing

occurred with managers with individualistic orientation, while there

was no such occurrence with managers whose orientation was
focused on the group. In this study, the researchers predict that the

use of reward would enhance contributions in group product and

reduce social loafing.

The study has two simple objectives. The first was to

determine whether performance drop-off would occur in the group

condition and not in the alone condition; and the second was to
examine whether reward will attenuate social loafing effect in group

condition. As many studies indicated that there was some degree of

social loafing within every group, it was predicted that performance

would be poorer in the group condition. Similarly, because it has

been demonstrated time after time in highly controlled learning
experiments that reward increases the strength of a response and

increase its probability of being repeated in future (Eisenberga;

Armeli & Pretz, 1998), it was predicted that reward will reduce social

loafing effect in group conditions, and perhaps increase output.

Method

Participants

Participants for this experiment consist of forty (40) secondary school

students. All the participants were Igbo from the south eastern part

of Nigeria. The sex composition of the sample (17 males and 23
females) was representative of the student body from where the

sample was selected. The participants were selected by means of a

table of random numbers. Their ages ranges from 13 to 16 years (M =

14.53; SD = 2.93).

Ife PsychologIA; 20(1), March 2012

232

Figure 1

An example of the board matrix used in the experiment

Materials

The stimulus materials were 40 board matrices (5×5 ft.) similar to the

one shown in Figure 1. Each participant received 25 pieces of neatly

cut cardboard tiles and a board matrix. The blocks/cells spaces on

the matrices were in different shapes, some were square and others
rectangle. The two (2) shapes were randomly mixed on the board

matrix, such that there were 15 square and 10 rectangular shaped

cells. Latency was measured using a stop-watch.

Procedure

This experiment adopted a pre-test post-test paradigm. Before
the commencement of the experiment, participants were pre-

instructed that they would be tested twice. In the pre-test (alone)

condition, the entire participants received 25 pieces of carefully

marked cardboard tiles and a board matrix each. Thereafter, the

participants were randomly assigned into 2 groups and each group
was tested in different class rooms. Participants were required to fix

the tiles to the spaces marked on the board matrix according to the

shape (either a square or a rectangle). They were shown examples of

how to fix the tiles, and were informed that only tiles that were

properly fixed unto the designated spaces on the board matrix would

be counted as correct. They were told to fix as many tiles as they can.
Test time was 15s. After the trial, the tiles that were correctly fixed to

their specified positions were collected and counted. The participants

score was recorded and the tiles were returned back to the

participant for the next phase of the experiment.

The post-test (group) condition was the major interest of this
experiment. In this phase, one of the 2 groups earlier created was

Mefoh, P.; Effects of Group Size and Expetancy of Reward on Social Loafing

233

referred to as the reward group and the other, the no-reward group.

The variable, reward, was manipulated by simple instruction.

Participants in the reward condition were given the following
instruction:

―You will have to repeat the task again. This time, your
individual scores would be added together with those of
other members of your group to know which group

(referring to them and other participants in the next
classroom), will do better than the other. If your group

does well in this task, each one of you would get an ink
pen and an exercise book”.

Participants assigned to the no-reward condition were instructed

similarly, except that the statement promising reward was omitted.

The instruction given to the no-reward group stopped at:
“… This time, your individual scores would be added
together with those of other members of your group to
know which group will do better that than the other”.

The instruction given to the two reward groups (reward vs.

no-reward) was designed to create an illusion of judgment. That is,

participants were led to believe that each individual‘s score on the
task and those of other participants in the group would be added

together to obtain the group‘s total. During the post-test, the

researchers ensured that nothing untoward happened during the

experiment, and that all the participants performed the task strictly

to the rules. The groups‘ trials lasted for 15s. At the expiration of the
test interval, the researchers gathered all the properly fixed tiles from

the board matrices and sorted them according to the participant who

fixed them. All the tiles used in this experiment were carefully

marked with identification numbers, making it easy for the

researchers to tell which participant fixed a particular tile. Thus, a

participant‘s score in the pre-test (alone) condition and his or her
score on the post-test (group) condition were matched for

comparison.

After the pre- and post-test sessions, the researchers met

with all the participants in one of the classrooms used for the

experiment to explain the purpose of the test and why minimal
deception was employed. All the participants in the study were given

an ink pen and an exercise book, regardless of whether the

participant was in the reward or no-reward condition. The gifts were

considered adequate compensation for the time the participants

spent in the research process.

Ife PsychologIA; 20(1), March 2012

234

Design/Statistics

This experiment adopted a composite design in which all 40

participants performed in both alone and group conditions. However,
half that number was randomly assigned to the reward condition

while the other half were assigned to the no-reward condition.

Analysis of covariance (ANCOVA), because it provides a more

powerful test for pre-test post-test studies than difference score

(Dancey & Reidy, 2002), was adopted for data analysis.

Result
FIGURE 2

Strict attention was paid to how participants placed the tiles

on the spaces demarcated on the board matrices. Only tiles that were

correctly placed were counted as correct. Data obtained in the study

were analyzed with analysis of covariance. The descriptive statistics
showed that the number of tiles fixed by participants were more in

the pre-test (alone) condition (M=15.10; SD=2.27) than in the post-

test (group) condition (M=11.65; SD= 3.01). See Figure 2. An

evaluation of these scores showed that participants‘ scores on the

1A bar chart showing the mean number of tiles fixed in the alone and group

conditions

Alone Perf.

Group Perf.

Mefoh, P.; Effects of Group Size and Expetancy of Reward on Social Loafing

235

pre-test measure and their scores on the post-test measure differed

significantly, F (1, 37) = 4.82, P <.05). This result failed to reject the

first hypothesis that performance would be poorer in the group
condition.

The result further indicated that more tiles were fixed by

participants assigned to the reward condition (M=13.05; SD= 2.72)

than those assigned to the no-reward condition (M=10.25; SD =

2.63). The two reward conditions (i.e., reward vs. no-reward) differed

significantly on the post-test measure after adjustment for the pre-
test scores, F (1, 37) = 11.25, P <.05 (see Figure 3). This represented

an effect size of 0.23, showing that once pre-test scores were

partialled out, 23% of the variation in the number of tiles fixed on the

matrices can be accounted for by differing conditions of reward. This

result also failed to reject the second prediction that reward will
reduce social loafing effect in the group condition.

FIGURE 3

A related t-test was used to compare the scores obtained by

participants assigned to the post-test/reward condition with the

scores they obtained in the pre-test measure. If the mean score
obtained by participants in the post-test/reward condition was

meaningfully greater than the mean score obtained by the same

participants on the pre-test measure, it would mean that reward

increased productivity. This was not the case, instead the number of

1A bar chart showing the mean number of tiles fixed in the reward and no reward

conditions.

Reward Condition

No Reward condition

Ife PsychologIA; 20(1), March 2012

236

tiles fixed by participants in the post-test/reward condition were

fewer (261) compared to their performance in the pre-test measure

(307). Analysis of these data showed that the difference between the
group‘s post-test and pre-test scores was 2.15; and the t-value (3.25)

has an associated p-value of p<.004. This finding indicated that

although reward attenuated social loafing behavior, it not increase or

improve productivity.

Discussion
This study sought to determine whether performance drop-off

would occur in the group condition and not in the alone condition;

and to examine whether reward will attenuate social loafing effect in

the group condition and probably increase productivity. Result

obtained in relation to the first objective showed that participants
worked less hard in the group condition than in the alone condition.

This observation was consistent with previous studies (Karau &

Williams, 1995; Kravitz & Martin, 1986; Price & Harrison, 2006;

Weldon & Gargano, 1988), that social loafing engenders negative

consequences that affect groups. Like in most of those studies the

observation made in this experiment can be related to the theory of
diffusion of responsibility. People working alone think they are

personally responsible for task outcome; but when they work in

groups, this feeling of responsibility diffuses to others. Rothwell

(2004) argued that large group sizes can cause individuals to feel lost

in the crowed. In this experiment, participants were led to believe
that their scores on the post-test (group) condition would be added

together to those of nineteen other participants to arrive at the

group‘s total. Because the group size was too large, it was possible

that participants, especially those driven by their individuality felt

that their contribution will not be recognized, and this lowered their

motivation. When a group becomes very large, some people often feel
that their efforts are not needed or will not be recognized (Kerr,

1989).

The second objective that reward will attenuate social loafing

effect in participants who were expecting reward was confirmed. The

performance mean score of participants assigned to the reward and
no-reward conditions differed significantly in favour of participants

who were promised reward. The observation was consistent with

literature (e.g., Eisenbarger, Armeli & Pretz, 1998), that reward

control behaviour and facilitate performance. However, when an

individual believes that compensation was not allotted equally among

group members, the individual withdraws his/her individual efforts.

Mefoh, P.; Effects of Group Size and Expetancy of Reward on Social Loafing

237

In this experiment, because participants believed that distribution of

compensation would be equitable, they anticipated reward contingent

on group goal. This disposition probably reinforced the participants‘
desire to pursue group goals in order to benefit the group. That is,

participants in the reward condition were motivated to pursue group

goals and they had hoped that other group members will also

contribute to the group‘s performance.

The post hoc result showed that reward did not increase

productivity. Although reward significantly reduced social loafing
effect, it failed to increase performance/productivity on the

experimental task. In the experiment, the performance of the

participants assigned to the post-test/reward condition was poorer

compare to their pre-test scores. It can be argued that when reward

can be obtained just by belonging to a group, group members
sometimes become less committed to group goals, have lower levels

of performance, set lower goals for their own achievement and

constitute group whose performance suffers. Therefore, to use reward

to improve productivity, it should be merged with guiding individuals

to exhibit loyalty to group objectives. When people know the goal,

know how far they need to go and where the competition is, they are
more inclined to work towards the goals than if they did not have

that knowledge.

In conclusion, this experiment collaborated with many

previous studies that social loafing was a pervasive characteristic of

working in groups. However, social loafing is not inevitable. Latane
(1981) demonstrated that when individual performance was

monitored within the group situation, social striving rather than

social loafing occur. Creating structures where individual

contributions are observable, measurable and known to all team

members attenuate social loafing effect. This technique had remained

the best known solution to curbing social loafing effect in group
settings. But because the contemporary World is becoming more

complex by each passing day, and because several tasks are co-

operative in nature, personal effort may be difficult, if not impossible

to monitor in such settings. It was this realization that caused the

researchers to examine the effect of reward in attenuating social
loafing effect. The experiment demonstrated that reward attenuated

social loafing behavior on the experimental task, but did not quite

increase productivity. The finding tends to suggest that reward is

only but a single variable that may influence social loafing

behaviour/effect. This calls for more research, it may require an

integrative framework of analysis of the reward – other variable(s)

Ife PsychologIA; 20(1), March 2012

238

interactions to understand the relationship between reward and

productivity in social loafing research.

Mefoh, P.; Effects of Group Size and Expetancy of Reward on Social Loafing

239

References

Brooks, C. & Ammons, J. (2003): Free riding in group projects and

the effect of timing, frequency and specificity of criteria in
peer assessment. Journal of Education for Business, May-
June.

Carron, A. Burke, S. & Prapavessis, H. (2004): managing groups and
teams social loafing. Journal of Applied Sport Psychology, vol.
16, 41-58.

Comer, D.R. (1995): A model of social loafing in real work group.
Human Relations, vol. 48, 647-667.

Dancey, C.P. & Reidy, J. (2002): Statistics without Maths for
Psychology. Essex: Pearson Education.

De Vita, G. (2001): The use of group work in large and diverse
business management classes: Some critical issues. The
International Journal of Management Education, vol. 1 (3), 26-

34.

Earley, P.C. (1989): Social loafing and collectivism: A comparison of

the United States and the people‘s Republic of China.
Administrative Science Quarterly in Business, vol. 34, 565-

581.

Eisenberger, R. Armeli, S, & Pretz, J. (1998): Can the promise of
reward increase creativity? Journal of Personality and Social
Psychology, vol. 66 (6), 1116-1127.

Gabrenya, W.K., Latane, B & Wang, Y. (1983): Social loafing in cross-
cultural perspective: Chinese on Taiwan. Journal of Cross-
Cultural Psychology, vol. 14, 368-384.

Gabrenya, W.K. Jr, Wang, Y.E, & Latane, B. (1985): Social loafing on

an optimizing task: Cross-cultural differences among Chinese
and Americans. Journal of Cross –Cultural Psychology, vol. 16,

223-242.

Hardy, C.J. & Latane, B. (1988): Social loafing in Cheerleaders:
Effects of team membership and competition. Journal of Sport
and Exercise Psychology, vol. 10, 109-114.

Karau, S.J. & Williams, K.D. (1993): Social loafing: A meta-analytic
review and theoretical integration. Journal of Personality and
Social Psychology, vol. 65, 681-706.

Kerr, N.L. (1989): Illusions of efficacy: The effects of group size on

perceived efficacy in social dilemmas. Journal of Experimental
Social Psychology, vol. 25, 287-313.

Kravitz, D. A. & Martin, B. (1986): Ringlemann rediscovered: The
original article. Journal of Personality and Social Psychology,
vol. 50 (5), 936-941.

Ife PsychologIA; 20(1), March 2012

240

LaFasto, F. & Larson, C. (2001): When teams work best. Thousand

oaks, CA: sage Publications.
Latane, B. (1981): The Psychology of social impact. American

Psychologist, vol. 36, 343-356.

Latane, B., Williams, K. & Harkins, S. (1979): Many hands make light
the work: Causes and consequences of social loafing. Journal
of Personality and Social Psychology, vol. 27, 822-832.

Piezon, S.L., & Donaldson, R.L. (2005): Online groups and social
loafing: understanding student- group interaction. Online

Journal of Distance Learning Administration, vol. 8 (4).

Retrieved online July 7, 2007 http://www.westga.edu
/adistance/ojdla/winter 84/ piezon 84 htm.

Price, K.H & Harrison, D.A. (2006): Withholding inputs in team

context: Member composition, interaction process, evaluation
structure, and social loafing. Journal of Applied Psychology,
vol. 91 (6), 84 – 94.

Rothwell, D. (2004): In mixed company. California: Thomson

Wadsworth.
Weldone, E. & Gargano, G.M. (1988): Cognitive loading: The effects of

accountability and shared responsibility on cognitive effort.
Personality and Social Psychology Bulletin, vol. 14, 159 – 171.

http://www.westga.edu/

Reproduced with permission of the copyright owner. Further reproduction prohibited without permission.

Taking a Free Ride: How Team Learning Affects Social Loafing

Catherine Gabelica1, Sven De Maeyer2, and Michaéla C. Schippers3
1 Department of People, Organizations and Negotiation, IESEG School of Management

2 Department of Training and Education Sciences, University of Antwerp
3 Department of Technology and Operations Management, Rotterdam School of Management (RSM), Erasmus University Rotterdam

Although collaboration is increasingly required in today’s academic and work contexts, there are many
ways in which teamwork can be impaired by dysfunctional inefficiencies and process loss. An important
form of process loss is the tendency for individual members of a team to exert less effort than their fel-
low team members (i.e., social loafing). Since teams need to sustain the effort of team members as a col-
laborative resource, it is imperative to understand factors that shape social loafing in team tasks. This
study examines simultaneously the degree to which goal orientation and changes in team learning (i.e.,
shifts in collective knowledge) affect social loafing. The authors use a multiwave design to explain
changes in social loafing tendencies of 675 students working in teams. They conduct linear mixed
effects modeling to show that individual team members who belong to teams that score higher than
other teams on team learning throughout 9 weeks of teamwork experience a decrease in social loafing.
Although learning and performance orientations are significantly related to initial self- or peer-rated
social loafing, they cannot explain ensuing changes in social loafing. Results highlight the importance of
considering team-level dynamic properties when explaining fluctuations of motivation in teams.

Educational Impact and Implications Statement
Even though small group work has gradually progressed to being one of the dominant approaches
in the domain of learning and instruction and professional development, research shows that large
numbers of team members exhibit unco-operative behaviors such as social loafing (i.e., individuals’
tendency to expend less effort than their fellow team members). The results of a nine-week longitu-
dinal study with 675 students working in teams reveal that teams experiencing a steeper shift in
team’s collective knowledge (i.e., team learning) than other teams show a decrease in social loafing
tendencies over time. Additionally, they show that the learning and performance orientations of
individual members predict social loafing at the start of the collaboration. These findings help us
better understand how dynamic team-level properties can prevent individual members from engag-
ing in dysfunctional behaviors.

Keywords: teams, co-operative learning, collaboration, social loafing, team learning

In the past few decades, classroom instructors and professional
practitioners increasingly have used teams to improve learning and
achievement (Johnson & Johnson, 2015; Salas et al., 2007). Both
team and collaborative learning research are rooted in the principle
that to be effective, teams must collaborate to overcome barriers to
their interpersonal processes (Mathieu et al., 2019). That is, team
success depends on team members’ contributions to team out-
comes, such that low contributions to teamwork and motivational
losses are associated with low achievement (Kirschner, 2009).

Although many studies have investigated the factors that explain
how and why some teams outperform other teams (e.g., Kozlowski
& Ilgen, 2006), only a relatively smaller body of literature consid-
ers and explains social motivation losses, such as “social loafing”
(Kozlowski & Bell, 2013). Social loafing, which occurs when peo-
ple expend less effort than their fellow team members in team con-
texts (Karau & Williams, 1993), is common in classroom settings.
Most college graduates likely can recall instances in which they
worked collectively on projects that were graded on a team basis,
yet some team members “slacked off” and failed to put an equal
share of effort into achieving the team outcomes.

Social loafing theory (also known as collective effort theory)
(Karau & Williams, 1993) proposes explanations for why loafing
occurs, most of which cite structural reasons for withheld inputs or
team set-up factors, such as group size. While situational and dis-
positional variables can both drive social motivation (Toma &
Butera, 2015), relatively few social loafing models incorporate
team members’ individual differences, which may account for the
fact that the research evidence to date is limited and mixed (Karau

This article was published Online First October 25, 2021.
Catherine Gabelica https://orcid.org/0000-0003-0096-6940
Sven De Maeyer https://orcid.org/0000-0003-2888-1631
Michaéla C. Schippers https://orcid.org/0000-0002-0795-5454
Correspondence concerning this article should be addressed to Catherine

Gabelica, Department of People, Organizations and Negotiation, IESEG
School of Management, Rue de la Digue 3, 59000 Lille, France. Email:
c.gabelica@ieseg.fr

716

Journal of Educational Psychology

© 2021 American Psychological Association 2022, Vol. 114, No. 4, 716–733
ISSN: 0022-0663 https://doi.org/10.1037/edu0000713

T
hi
s
do
cu
m
en
ti
s
co
py
ri
gh
te
d
by

th
e
A
m
er
ic
an

Ps
yc
ho
lo
gi
ca
lA

ss
oc
ia
tio

n
or

on
e
of

its
al
lie
d
pu
bl
is
he
rs
.

T
hi
s
ar
tic
le
is
in
te
nd
ed

so
le
ly

fo
r
th
e
pe
rs
on
al
us
e
of

th
e
in
di
vi
du
al
us
er

an
d
is
no
tt
o
be

di
ss
em

in
at
ed

br
oa
dl
y.

https://orcid.org/0000-0003-0096-6940

https://orcid.org/0000-0003-2888-1631

https://orcid.org/0000-0002-0795-5454

mailto:c.gabelica@ieseg.fr

https://doi.org/10.1037/edu0000713

& Wilhau, 2020). Differences in goal orientations (learning vs.
performance) are expected to drive social motivation as they can
influence the extent to which individual members generally value
certain tasks (Karau & Wilhau, 2020). When team members hold
certain goal orientations (i.e., situated orientations for action in an
achievement task, Dweck, 1986), they are expected to display and
sustain low social loafing when they collaborate, specifically when
they attach value to task mastery, understanding, and growth (i.e.,
learning orientation). Individual members who focus on demon-
strating ability (i.e., performance orientation) might start their col-
laborative task with low social loafing because they seek to gain
favorable judgments from others. However, performance orienta-
tion might relate to increased social loafing over time, due to the
reception of team feedback and therefore, lower individual identi-
fiability (Karau & Wilhau, 2020).
Research in this area is even scarcer at the team level of analy-

sis. In a team, communication is directed toward a twofold pur-
pose: (a) to develop the interpersonal relations within the team,
and (b) to build a collective knowledge pool. However, most stud-
ies focus on the socioemotional side of teamwork (e.g., creation
and maintenance of cohesiveness and a sense of community;
Kreijns et al., 2003), while there is a larger gap in our understand-
ing of the influence of important sociocognitive mechanisms, such
as team learning, on social loafing. As teamwork enables individu-
als to merge their individual knowledge and skills to reach a com-
mon goal, it is characterized by the phenomenon of team learning.
Through team learning, defined as “a change in the team’s collec-
tive level of knowledge and skill produced by the shared experi-
ence of the team members” (Ellis et al., 2003, p. 822), individual
members’ knowledge is transformed and integrated into a collec-
tive knowledge pool (Van den Bossche et al., 2011). Thereby, by
progressively acquiring more complex knowledge and skills,
teams can overcome motivational barriers such as social loafing
(Raes et al., 2015; van Dick et al., 2009). In this sense, team learn-
ing and team motivation are closely related (Bell et al., 2012).
Importantly, social loafing develops over time during collabora-

tion. As such, time is a key factor. As team members socially
interact and initiate momentum on team tasks, they increase their
collective knowledge pool, which is expected to decrease social
loafing over time because of the whole team being involved in
maintaining a shared conception of problems they encounter.
However, the time factor is underresearched (Fransen et al., 2013;
Hofmann & Jones, 2005). Although many researchers have stud-
ied how groups develop into functional teams in organizational
settings (Kozlowski & Bell, 2013), data about why teams develop
differently and how different aspects of interaction are related are
limited (Fransen et al., 2013). Research studying teams in educa-
tional settings proposes that high-learning teams likely follow a
linear progressive development (Fransen et al., 2013; Johnson et
al., 2002) because of their specific features (e.g., restricted dura-
tion of teamwork, valence of deadlines and grades, low expertise
at the start of a collaboration). Accordingly, we expect student
teams to follow a linear progression, on average. However, build-
ing upon group socialization theory supporting that the relation-
ship between a team and its members also changes over time, we
propose that time spent in teams is not sufficient to explain
increases or decreases in social loafing. Teams also need to learn

(Bell et al., 2012). Consequently, we expect high-learning teams
to experience downward shifts in individual social loafing.

We first extend social loafing research by testing the impact of
the emergence of, and change in, team learning on social loafing
trajectories. We then connect these variables to person-related fac-
tors (i.e., goal orientations), moving back to the more proximal in-
dependent variables of our model. That is, we propose it is change
in learning during the collaborative process, rather than team
learning measured at a static point in time, and initial levels of
individual differences, that decrease social loafing over time. This
study makes several contributions to team motivation and team
learning literature by (1) investigating interdependent contexts of
naturally occurring teams; (2) using a multilevel, interactive
framework to analyze social loafing tendencies in teams that incor-
porates important but under-researched individual and team-level
factors; and (3) adopting a multiwave, multisource design to check
for patterns in how social loafing evolves over time and account
for differing perspectives on individual behavior in teams.

Literature Review and Hypotheses

Concept of Social Loafing

A team is a collection of individuals who work interdependently
to achieve a common goal and share responsibility for team out-
comes (Cohen & Bailey, 1997; Michaelsen et al., 2004). Individu-
als, in co-operative as opposed to competitive and individualistic
situations, tend to engage in more on-task behaviors and less off-
task, disruptive behaviors (Johnson & Johnson, 2015). However,
placing individuals in teams and having them work together does
not necessarily lead to co-operative efforts. Teamwork can also
generate dysfunctional inefficiencies such as “social loafing.”
According to the collective effort model (CEM), social loafing
refers to individuals’ behavioral tendencies to put forth less effort
than their teammates (Karau & Williams, 1993). It is believed to
occur in teams as a result of the presence of others as coactors who
combine their efforts on a collective task (Karau & Wilhau, 2020).

Social psychologists and organizational behavior researchers
conceptualize social loafing as an individual motivational con-
struct that operates in team contexts (Karau & Wilhau, 2020).
They categorize team motivation losses due to social loafing into
“interpersonal processes” in most team interaction classification
systems (Kozlowski & Bell, 2013; Mathieu et al., 2008). Interest-
ingly, in the literature on student engagement in educational psy-
chology, the notion of effort is included in definitions of both
cognitive and behavioral engagement (Fredricks et al., 2004). In
the definition of social loafing mentioned above, the notion of
effort is primarily behavioral, a matter of doing the work (or a fair
share of the work), and less of learning and mastering the task. We
could hence stipulate that social loafing is closer to the concept of
behavioral (dis)engagement (i.e., individuals’ active participation,
involvement and persistence in a learning activity) but applied to
team settings and with a strong notion of relative efforts (relative
to fellow team members) that is not specifically mentioned in the
engagement literature (Fredricks et al., 2004; Skinner et al., 2009).

Compared with the volume of research on individual motivation,
relatively little work has directly addressed social motivation and
social loafing in teams (Kozlowski & Bell, 2013). Prior research

TAKING A FREE RIDE 717

T
hi
s
do
cu
m
en
ti
s
co
py
ri
gh
te
d
by

th
e
A
m
er
ic
an

Ps
yc
ho
lo
gi
ca
lA

ss
oc
ia
tio

n
or

on
e
of

its
al
lie
d
pu
bl
is
he
rs
.

T
hi
s
ar
tic
le
is
in
te
nd
ed

so
le
ly

fo
r
th
e
pe
rs
on
al
us
e
of

th
e
in
di
vi
du
al
us
er

an
d
is
no
tt
o
be

di
ss
em

in
at
ed

br
oa
dl
y.

showed that social loafing leads to several negative team processes
and outcomes. It evokes distrust, lowered morale, and low team
cohesion and performance (Duffy & Shaw, 2000; Jassawalla et al.,
2008). Of moderate magnitude, it appears to be generalizable across
tasks and subject populations (Karau & Williams, 1997).
Because teams that display detrimental processes are likely to

sustain such negative interaction patterns over time (Webb & Cull-
ian, 1983), literature on teams has implicitly regarded social loafing
as a static rather than temporal variable (Aggarwal & O’Brien,
2008; Hofmann & Jones, 2005). However, we propose that social
loafing may be more dynamic than previously conceptualized. In
this article, we posit that social loafing is not a single, discrete act;
rather, individual team members may be inclined to contribute their
fair shares at different times or according to different tasks. Hence,
we view social loafing as a time-varying phenomenon that follows
different trajectories over time. Whereas the focus of prior work has
been on identifying the causes of social loafing at one point in time,
we oppose the idea of social loafing as a static phenomenon and
examine whether and how social loafing changes over time.

Operationalization of Social Loafing

To date, researchers have tended to capture the social loafing
tendencies of individual team members from a single-source per-
spective. Most previous studies on social loafing use self-ratings
of loafing; only a few studies have used peer ratings (e.g., Price et
al., 2006), and only one study, to our knowledge, has used both
peer and self-ratings (Stark et al., 2007).
Karau and Williams (1993) and Jassawalla et al. (2008) suggest

social loafing occurs without self-awareness and that loafers gen-
erally find it socially undesirable to admit they loafed on complet-
ing collective tasks; the authors’ argument draws on sources other
than team members themselves, that is, their teammates. Arguably,
according to human behavior concepts and theories (e.g., attribu-
tion theory, decision making, performance appraisal; Ilgen et al.,
1994), attitudes and behaviors depend largely on perceptions. We
can argue that social loafing starts to exert influence in a team
when other members perceive that some member who relies too
much on his or her teammates to accomplish his or her portion of
the work takes advantage of them while “unfairly” enjoying and/
or sharing the team outcome equally well with less work (Jassa-
walla et al., 2008; Schippers, 2014). This proposal is often used as
a main argument for measuring “perceived social loafing of
others” (i.e., an individual’s assessment of the others’ relative con-
tribution to the team; Piezon & Ferree, 2008; Zhu et al., 2019).
At the same time, some researchers (e.g., Cheng & Warren,

1999) question the reliability of peer evaluations, suggesting peer
ratings may suffer from a halo effect (Loughry et al., 2007), leni-
ency effect, or lack of skill in differentiating teammates. For exam-
ple, Davison et al. (2014) find that only high performers are able
to deliver evaluations of teammates that differentiate between
those who perform well and those who perform poorly. Peer rat-
ings also may be biased by friendships or personal dislikes (Bar-
clay & Harland, 1995). These limitations could lead to the use of
self-ratings of social loafing. For example, Price and colleagues
(2006) found in their study using both peer and self-ratings that
individuals were more inclined to highlight their own loafing than
the loafing of others. Another argument in favor of self-assessment
purports that questioning one’s own relative contribution to the

team can lead to disclosure of one’s beliefs about him or herself as
a team member (McCardle & Hadwin, 2015) and self-awareness
of one’s antisocial behavior (Simms & Nichols, 2014). Finally,
Conway and Lance (2010) claim that there are two major miscon-
ceptions about self versus other-ratings. The first misconception is
that other-report is superior to self-report measures. The second is
that relationships between self-reported constructs are always
upwardly biased. They contend that “rather than providing a more
accurate estimate of true relationships among constructs, relation-
ships estimated using different methods tend to be more attenuated
and less accurate as compared to same-method correlations” (Con-
way & Lance, 2010, p. 327). In sum, thus far, prior outcomes are
mixed with regard to which source best assesses social loafing.

Social Loafing and Contextual Factors

Researchers offer multiple explanations for why social loafing
takes place, with early work indicating that characteristics of the sit-
uation and individual members’ situational interpretations often
drive social loafing (Williams et al., 1981). Specifically, the social
loafing literature proposes that people engage in social loafing
mostly because of a decreased perceived accountability and
increased dispensability of effort experienced by team members
(Harkins, 1987; Price et al., 2006). Similarly, co-operative learning
research also demonstrates that positive goal interdependence and
individual responsibility and accountability are likely to reduce
social loafing (Buchs et al., 2015; Johnson & Johnson, 2009).

The robust presence of social loafing in teams has led research-
ers to identify not only its antecedents but also variables that might
moderate the tendency to engage in social loafing (Kozlowski &
Bell, 2013). Most authors note the influence of set-ups or work
designs to minimize social loafing (Erez & Somech, 1996; Koz-
lowski & Bell, 2013; Stark et al., 2007). For example, social loaf-
ing can be reduced by improving task management and reward
structures (George, 1992; Pearsall et al., 2010). Other strategies
that reduce individual tendencies to loaf include increasing team
familiarity and identifiability of individual members and decreas-
ing ‘team size’ (Erez & Somech, 1996; Lam, 2015). However, we
propose that work-design factors are not sufficient to explain
social loafing tendencies, because they also reside in individual
team members. Accordingly, individual-level factors may explain
individual differences in social loafing.

Social Loafing and Individual Differences

Individual differences have received less attention in social loaf-
ing research (Stark et al., 2007). As early as 1995; Comer began to
integrate team members’ attitudes and individual differences into
social loafing frameworks (Comer, 1995), but empirical evidence
and understanding of these factors remain relatively limited and
are mostly derived from laboratory settings (Karau & Wilhau,
2020). There is increasing evidence that individual differences can
explain the extent to which team members loaf (Bolin & Neuman,
2006; Morgeson et al., 2005); for example, those who believe they
are better than others (Huguet et al., 1999) are more likely to loaf,
whereas those with high levels of winning orientations and prefer-
ences for group work (Stark et al., 2007) and conscientious, agree-
able team members (Schippers, 2014; Tan & Tan, 2008) are less
likely to loaf.

718 GABELICA, DE MAEYER, AND SCHIPPERS

T
hi
s
do
cu
m
en
ti
s
co
py
ri
gh
te
d
by

th
e
A
m
er
ic
an

Ps
yc
ho
lo
gi
ca
lA

ss
oc
ia
tio

n
or

on
e
of

its
al
lie
d
pu
bl
is
he
rs
.

T
hi
s
ar
tic
le
is
in
te
nd
ed

so
le
ly

fo
r
th
e
pe
rs
on
al
us
e
of

th
e
in
di
vi
du
al
us
er

an
d
is
no
tt
o
be

di
ss
em

in
at
ed

br
oa
dl
y.

This angle of individual differences points to a need for addi-
tional hypotheses that include person-based factors that can pre-
vent or lower the occurrence and magnitude of social loafing
throughout team collaborations.
A person-related motivational factor that appears to have

received little research attention in the social loafing literature is
goal orientations, including learning orientation and performance
orientation (cf. Gagné & Zuckerman, 1999).

Learning Orientation and Performance Orientation

Work on goal orientations in team contexts is rooted in research
arguing that how people change a given situation into an effective
situation depends on their social motivation (Forgas et al., 2005;
Schippers, & Scheepers, 2020). Social motivation theories are
concerned with goal-directed behaviors that are aimed at, or cen-
tral to, social interaction (Carver & Scheier, 1998; De Dreu et al.,
2008). An assumption of social motivation is that one’s tendency
to collaborate and interact with others is largely driven by individ-
ual differences, in particular achievement goals (De Dreu et al.,
2008). In accordance with this premise, we can hypothesize that
goal orientations have important consequences for interpersonal
interactions and for behavior change, more specifically change in
social loafing. Since the purpose of the present article is to investi-
gate antecedents of social loafing viewed as a dynamic behavior
that emerges in social contexts, we expect members to bring their
behavior in line with their initial goals.
Much research on motivation in individual settings has examined

the basic concept of “goal” that accounts for how people intend to
behave (Locke et al., 1981). Goal-related motivation theories and
research have given rise to Achievement Goal Theory (AGT) that
focuses on the psychological features of goals and individuals’
intention beyond a goal (i.e., goal orientation, Pintrich, 2000). Sev-
eral goal structure models have emerged to explain the reasons for
achievement behaviors (Kaplan & Maehr, 2007). In early work,
goal structure is conceptualized as two-dimensional (Elliot, 2005).
Specifically, these models stipulate that people’s goals focus on
increasing competencies via learning (i.e., learning or mastery goals)
versus obtaining affirmative judgments about their competencies
(i.e., performance goals; Dweck, 1986, 1999). Despite the varying
terminologies, mastery versus performance goal orientations are closely
related to learning versus performance orientations.
Later research on achievement goal orientations proposes to con-

sider whether achievement goal orientations lead individuals to
approach or avoid a task (e.g., Elliot & Church, 1997). In the trichoto-
mous achievement goal framework, the performance goal orientation
construct is divided into a performance-approach goal orientation and
a performance-avoidance goal orientation. Individuals who are per-
formance-avoidance oriented are concerned with avoiding demon-
strating low ability, mostly in comparative terms (Urdan & Kaplan,
2020). Following this logic, Elliot and McGregor (2001) later pro-
pose a 2 3 2 model that adds a fourth goal orientation, a mastery-
avoidance orientation, whereby a learner’s goal is to avoid misun-
derstandings and mistakes. It implies a fear of failure that is rooted
in an intrapersonal rather than an interpersonal perspective.
Mastery or learning orientations generally relate to interest, per-

sistence, positive emotions, use of deep learning approaches, and,
under certain conditions, to achievement. In contrast, perform-
ance-avoidance goals relate to negative emotions, disengagement

in the face of obstacles, and low achievement. Performance-
approach goals are associated with higher achievement, and under
different circumstances, with more and less adaptive and maladap-
tive emotions and learning strategies (Payne et al., 2007; Rolland,
2012; Ramos et al., 2021; Urdan & Kaplan, 2020). Mastery-avoid-
ance goals have received less scrutiny than the other goals.
Although patterns of relations between mastery-avoidance goals
and outcomes are inconsistent, they are generally associated with
maladaptive outcomes (Madjar et al., 2011).

There is a growing number of studies incorporating AGT in
team and collaborative learning research (Poortvliet et al., 2009).
However, to understand how those operate in the context of team-
work, further empirical studies are needed (Lim & Lim, 2020). In
collaborative learning research, studies consistently show that
mastery orientation has positive effects on individual-level cogni-
tive and affective outcomes such as cognitive processing (e.g., Pat-
rick et al., 2008) and a handful of studies similarly demonstrate
positive effects on team behaviors such as other-regulation (Grei-
sel et al., 2018; Lee et al., 2010; Lim & Lim, 2020; Volet & Mans-
field, 2006) or feedback-seeking (Payne et al., 2007). By contrast,
inconsistent relationships have been found between performance
orientation and other-regulation. For example, some studies show
that performance orientation has negative (Lee et al., 2010), posi-
tive (Greisel et al., 2018), and no significant relationships (Lim &
Lim, 2020) with other-regulation, and feedback-seeking (Cellar et
al., 2010; Payne et al., 2007). Further, in situations of team prob-
lem solving, Poortvliet and colleagues (Poortvliet et al., 2007;
Poortvliet et al., 2012) show that performance orientation is related
to information retention and even thwarting behavior. Most studies
find no significant effects of the performance-avoidance goal ori-
entation on team constructs. Payne et al. (2007) find a negative
correlation with feedback-seeking in nonteam settings, whereas
Cellar et al. (2010) conclude that there is no significant relation
between the two constructs.

In the present study, we decided to focus exclusively on the
approach variants of the achievement goals because these are pre-
dictive of process variables in the collaborative learning literature,
while the avoidance goals have been less studied and seem to nei-
ther hinder nor promote collaboration (Lim & Lim, 2020). For the
sake of parsimony, we include learning and performance orienta-
tions in our multivariate approach, as the goal of our study is to
investigate whether the significant relationships found in this liter-
ature reproduce at the team level.

As stated, studies to date suggest that team members scoring
high on learning orientation tend to engage more in adaptive col-
laborative learning than members scoring low on this orientation.
However, the results on the effects of performance orientations in
collaborative contexts are mixed. Further, what is not yet clear in
this strand of research is how different goal orientations induce
differences in social loafing trajectories (Skinner et al., 2009).

Because they attach great value to hard work for its own sake,
achievement or personal growth, team members who are learning
oriented are also more likely to place value in specific collabora-
tive tasks, and hence, are less likely to loaf. This task value propo-
sition is consistent with the Collective Effort model that suggests
that individuals who view tasks as meaningful, important, or
intrinsically interesting are less likely to engage in social loafing
(Karau & Wilhau, 2020). Furthermore, individuals scoring high on
learning orientation seek more help from, and exchange more

TAKING A FREE RIDE 719

T
hi
s
do
cu
m
en
ti
s
co
py
ri
gh
te
d
by

th
e
A
m
er
ic
an

Ps
yc
ho
lo
gi
ca
lA

ss
oc
ia
tio

n
or

on
e
of

its
al
lie
d
pu
bl
is
he
rs
.

T
hi
s
ar
tic
le
is
in
te
nd
ed

so
le
ly

fo
r
th
e
pe
rs
on
al
us
e
of

th
e
in
di
vi
du
al
us
er

an
d
is
no
tt
o
be

di
ss
em

in
at
ed

br
oa
dl
y.

information with, their peers (Newman & Schwager, 1995).
Engaging in such help-seeking and information-sharing behaviors
implies that this team member lacks a particular skill that others
might have (Veenman et al., 2005). Thus, a learning orientation
likely encourages collaboration and discourages social loafing
(Poortvliet et al., 2009), such that it should associate positively
with a willingness to participate in a team activity, regardless of
effort identifiability. We propose that those with a high learning
orientation are less likely to loaf at the start and over the course of
collaborations.

H1: Team members with a higher learning orientation are less suscep-
tible to display social loafing throughout the collaboration.

Similarly, when the pursuit of performance goals is driven by
need for achievement, these goals might stimulate high relative
contribution in a team (Lim & Lim, 2020). Specifically, since
team members who endorse performance goals tend to strive to
demonstrate their competence to others, performance orientation
could be an effective motive in the short term, and hence, at the
start of a team project. However, this effect might change in the
long term (Brophy, 2005). In teams that have co-operative reward
structures (e.g., team scores), members with high levels of per-
formance orientation may not be able to use interpersonal stand-
ards, such as performance relative to their peers, to assess competence
in achievement situations (Elliot & McGregor, 2001; Pintrich,
2000). Also, in teams, their individual efforts may not be identifia-
ble, and feedback is generally provided at the team level. Since
reward and recognition are important for such people (Reeve,
2015), they might consequently develop a lowered sense of
accountability and thus greater tendencies to loaf over time. Addi-
tionally, if individuals compete with their teammates to establish
their abilities, they are less likely to collaborate, which may gener-
ate less harmonious social relations or augment disruptive behav-
ior (Butler, 1995; Midgley et al., 2001). Accordingly, it is likely
that a performance orientation does not encourage teamwork in
the long term but does elicit the growth of antisocial tendencies
such as social loafing (Poortvliet et al., 2009).

H2: Team members with a higher performance orientation are (a) less
susceptible to display social loafing at the start of a team collaboration,
(b) more susceptible to display social loafing over time.

Social Loafing and Team-Level Factors

Traditionally, psychological theories have mostly focused on indi-
vidual variables (e.g., personality traits, attitudes, values) in their
attempts to explain individual behavior, the underlying assumption
being that the causes of an individual’s behavior are inside the indi-
vidual. Social interdependence theory, on the other hand, postulates
that individual behavior can be explained by the interactions among
individuals that are inherently dynamic (Johnson & Johnson, 2015).
As such, this theory recognizes the critical role played by team fac-
tors in the completion of a team task for individual members. There
is a growing body of literature that recognizes the importance of
team-level factors during collaboration (Johnson & Johnson, 2009).
Despite this growing interest, in contrast to research on individual-
level antecedents of social loafing, there is much less information

about team-level factors that enable team members to resist social
loafing or decrease its intensity over time.

The collaborative learning literature supports that team commu-
nication can serve two complementary purposes,(a) building a pos-
itive and cohesive socioemotional climate (Bakhtiar et al., 2017;
Isohätälä et al., 2020) and (b) facilitating team cognitive processes
(Järvelä et al., 2016; Rogat & Linnenbrink García, 2011).

A few studies on social loafing focus on the socioemotional
aspects of teams, and they typically rely on laboratory work to
reveal, for example, that group cohesiveness reduces or eliminates
social loafing (e.g., Duffy & Shaw, 2000; Lam, 2015). This result
was replicated in field studies that affirm that social loafing in
teams relates to low team cohesiveness (Høigaard et al., 2006;
Liden et al., 2004). This finding usually is explained by high levels
of member identification with teams and concerns about team
welfare.

However, much less is known about the sociocognitive factors
that could substantially lower or even eliminate social loafing
(Erez & Somech, 1996; Lam, 2015). Whereas empirical work on
social loafing implies that team members simply add their individ-
ual inputs to produce team outcomes, teams are social systems that
evolve and create multiple solutions that stem from ongoing
knowledge sharing (Jassawalla et al., 2008). Despite this observa-
tion, there is a paucity of evidence on the extent to which team-
level differences in team learning during collaborative learning,
can explain differences in individual loafing behavior. The present
study aims to address this gap.

Social Loafing and

Team Learning

In response to the lack of research on team sociocognitive fac-
tors, we propose that individual team members reduce their loafing
tendencies when their teams increase their collective learning.
According to Wilson et al. (2007), team-level learning represents a
change in a team’s collective level of knowledge and skills. We
conceptualize team learning as an output of shared experience of
the team members, and more specifically, as a newly shared under-
standing of how the team should function and develop new knowl-
edge and skills about the team tasks (Ellis et al., 2003; Van den
Bossche et al., 2011).

We hence view team learning from a social constructivist per-
spective, according to which people create knowledge during
social interactions (Boud et al., 2001; Oliveira & Sadler, 2008).
Team learning is frequently compared with collaborative and
co-operative learning, though the concepts are not mutually exclu-
sive. In this article, team learning is not conceived as a structured
peer learning method but shares some conceptual similarities with
the two other constructs. As variations of “peer learning”, they all
incorporate features such as shared experiences and responsibil-
ities, positive interdependence, individual accountability, and pro-
motive interaction (Johnson & Johnson, 1999; Slavin, 2011).
However, team learning, typically studied in work settings, also
encompasses (a) the production of team-level outcomes, such as
collective knowledge (i.e., knowledge held by the team as its own
united entity) and team performance, (b) the main goal of success-
fully completing a given task, and (c) mutual accountability for
these outcomes (Dochy et al., 2014). While research on co-operative
and collaborative learning has recently shifted its attention to the
group as the unit of analysis, it has traditionally focused on outputs at

720 GABELICA, DE MAEYER, AND SCHIPPERS

T
hi
s
do
cu
m
en
ti
s
co
py
ri
gh
te
d
by

th
e
A
m
er
ic
an

Ps
yc
ho
lo
gi
ca
lA

ss
oc
ia
tio

n
or

on
e
of

its
al
lie
d
pu
bl
is
he
rs
.

T
hi
s
ar
tic
le
is
in
te
nd
ed

so
le
ly

fo
r
th
e
pe
rs
on
al
us
e
of

th
e
in
di
vi
du
al
us
er

an
d
is
no
tt
o
be

di
ss
em

in
at
ed

br
oa
dl
y.

the individual level (e.g., what do students learn?) (Fransen et al.,
2013; Vangrieken et al., 2016; Weinberger et al., 2007). As such,
both research lines complement each other when building an
understanding of the extent to which team members converge
toward increased collective knowledge.

Dynamics of Social Loafing and Team Learning

Teams are not static entities, but instead change in dynamic ways
over time. To clarify the determinants of social loafing, it is there-
fore necessary to consider temporal aspects of teams, which include
team development (i.e., changes in the team as a whole) and team
socialization (i.e., changes in the relationship between the team and
its members; Arrow et al., 2000; Levine & Moreland, 1994).
Group socialization theory stipulates that both the team and its

members are “potential influence agents” (Levine & Moreland,
1994, p. 306). This perspective purports that people change as a
function of the team that they join. Traditionally, small group
research focuses on the team perspective and overlooks how rela-
tions between a team and its members develop over time (Mathieu
et al., 2019). This theory recognizes that individual members’ con-
tribution levels change over time and that it can, in turn, change
the relationship between the team and its members. As such, the
effects of the team on an individual’s behavior can depend on
team socialization, reflecting changes over time. If changes in con-
tribution occur, this can result in divergence and the potential
exclusion of the individual from the team. However, the team can
also resocialize the individual (Levine et al., 2001). In line with
this theory, we posit that when contribution levels become increas-
ingly unequal, it creates a tension that can be repaired through
knowledge creation. The team can hence change the individual so
he or she can exert more effort toward the team goals. We hence
propose that the development of new knowledge and skills has the
potential to raise the satisfaction of the loafers’ needs.
Furthermore, there is a consensus across disciplines that teams as

a whole develop or change over time (Fransen et al., 2013;
Hommes et al., 2014; Kozlowski & Bell, 2013). Since team learn-
ing requires interactions between individuals, inherently, it is emer-
gent and dynamic and involves developmental progression
(Kozlowski & Bell, 2013). It supposes a shift in knowledge state—
a knowledge trajectory over time. As teams develop and evolve
from groups of individual members to become collectivities with
well-mapped repertoires of adaptive skills, the learning that
emerges is not only inextricably connected to the fundamentals of
social motivation but also changes over time (Goodman & Dabbish,
2011; Wiese & Burke, 2019).
Recently, more promising research efforts adopt a regulatory

approach to team learning. In this view, team learning takes a reg-
ulatory role. Team members respond to goal progress, adjust their
efforts and strategies and create newly shared understandings. In
turn, this role should benefit motivational processes (Bell et al.,
2012; Chen et al., 2009). However, the regulatory perspective on
team learning requires further consideration, especially with
regard to its relationship to motivation in team contexts. Using this
theoretical model, we posit that dynamics inherent to team learn-
ing shape motivational states that emerge over time. By increasing
team learning, teams can increase their effort and attention to team
goal accomplishment and strategies and thereby reduce process
losses in the form of social loafing. A steeper change in collective

knowledge is expected to lower social loafing because it would ne-
cessitate that the whole team devotes attention to integrating indi-
vidually held information into the team’s collective knowledge
state and maintaining a shared conception of a problem.

To join emerging efforts to explore the dynamic relationships
between team learning and social loafing, both conceptually and
empirically, we formulate and test the following hypothesis:

H3: An increase in team learning leads to a decrease in social loafing,
even when controlling for individual goal orientations.

In summary, though prior work on social loafing offers important
theoretical foundations pertaining to the reasons for social loafing
and the limiting conditions of its effects, several significant theoreti-
cal and practical gaps remain. First, it has traditionally conceptualized
social loafing as a stable rather than a dynamic construct. Second, it
has mostly focused on structural reasons and set-up factors to explain
social loafing. Relatively fewer studies have investigated other rea-
sons related to individual differences and team-level factors. Third,
even though goal orientation theory and social motivation theory pos-
tulate interrelations between goal orientations and motivation, the
effects of goals orientations on social loafing development have not
been closely examined. Fourth, research on team-level factors, and
more specifically on team sociocognitive factors, is even scarcer in
the social loafing literature. Furthermore, building upon social inter-
dependence theory and a regulatory approach of team learning (for
theoretical considerations) as well as on group socialization theory
and team development models (for temporal considerations), team
learning appears as a promising yet underexplored emergent socio-
cognitive mechanism that can initiate a downward shift in individual
social loafing. So far, however, there has been little discussion about
the power of team learning growth on change in social loafing. Fifth,
studies have tended to collect data from single sources, leaving room
for same-source biases. To address these research gaps on social loaf-
ing, our goal is to identify individual and team factors, namely goal
orientations and team learning, that together make up for this process
loss; to do so, we use a multilevel framework and research design
(Kozlowski & Klein, 2000), a repeated-measure design, and multiple
assessors of social loafing tendencies.

Method

Data and Sample

Participants in our study (n = 675) were first-year business stu-
dents attending a Research Methods course in their first trimester
at a Dutch university. They were required to form three- or four-
person teams (n = 195 teams, 105 three-person teams, 90 four-per-
son teams) to complete tasks. The course used a self-selection
approach in which students selected their own teams for the entire
trimester. At the start of the course, students completed an online
survey measuring the independent variables, that is, learning ori-
entation and performance orientation (T0). Throughout the trimes-
ter, they were required to complete three team assignments, which
counted toward their final grade. Just before they turned in each
assignment, students were contacted by e-mail and were asked to
fill out the online surveys individually to measure team learning
(T1, T2, T3) and social loafing (T1, T2, T3). They were briefed

TAKING A FREE RIDE 721

T
hi
s
do
cu
m
en
ti
s
co
py
ri
gh
te
d
by

th
e
A
m
er
ic
an

Ps
yc
ho
lo
gi
ca
lA

ss
oc
ia
tio

n
or

on
e
of

its
al
lie
d
pu
bl
is
he
rs
.

T
hi
s
ar
tic
le
is
in
te
nd
ed

so
le
ly

fo
r
th
e
pe
rs
on
al
us
e
of

th
e
in
di
vi
du
al
us
er

an
d
is
no
tt
o
be

di
ss
em

in
at
ed

br
oa
dl
y.

that the questionnaire was referring to the team assignments they
had just completed collaboratively. The assignments varied in dif-
ficulty. The first task consisted of choosing a research topic from a
list, reading the accompanying case, conducting a literature search
using different databases (e.g., Google Scholar, JSTOR), making
an inventory of relevant articles, and comparing their results. Most
students already were acquainted with literature search in data-
bases, so this task was not difficult. The second task, of greater dif-
ficulty, involved reading and analyzing a scientific article, building
a complex conceptual model of the variables measured in the arti-
cle, and formulating research hypotheses. The task was additive in
nature: it lacked structure, there were no clear strategies, and more
than one correct solution was possible. Finally, the third task, of
moderately high difficulty, required teams to read a case, compose
a research question, and answer scientific reasoning questions.
According to their research question, students had to select and
justify the most appropriate research method (e.g., experiment,
case study, survey study); define whether the aim of the study
would be to explore, explain, or describe (e.g., theory building vs.
theory testing); and justify which research strategy would be least
suitable for answering the research question. Teams also had to
formulate recommendations according to the case. In total, they
worked together for about nine weeks to complete these three
team assignments. All members received the same grade on their
assignments irrespective of individual effort, and performance
standards were clearly communicated.
The response rate for the first survey was 94.43% (729 stu-

dents); for the second survey, it was 91.58% (707 students); and
for the third survey, it was 88.60% (684 students). We omitted
from the analysis teams for which the data of two or three mem-
bers were missing. The final sample consisted of 675 students dis-
tributed in 195 teams. Of these respondents, 70.80% were men,
28.90% were women, and for .30%, information about gender was
missing. The mean age of the participants was 18.76 years (stand-
ard deviation [SD] = 1.48); 68.80% were Dutch, 24.60% indicated
non-Dutch nationality, and 6.60% did not indicate any nationality.

Procedure

Data were collected with an online survey, sent by two research
assistants. None of the authors nor the assistants were involved in
teaching the Research Methods class. Moreover, the teachers were
not aware of the purposes of the research. Because students filled out
the questionnaires before they received their grades for their tasks,
feedback on how well they did on the task did not affect their percep-
tions of the measured variables. Participants were briefed about the
purpose of the research and given the opportunity to opt out, but none
of the students did so, and the final sample remained unchanged.

Measures

Team Learning

To assess team learning, we used a 5-item scale from the input-
mediator-output-input (IMOI) model to grasp team dynamics, as
described by Ilgen et al. (2005) and adapted by Schippers et al.
(2013). Items included, for instance, “We learned from our mistakes
in our tasks,” “We learned how to improve at our tasks,” and “We
developed new knowledge or skills about our tasks” (1 = “strongly

disagree;” 5 = “strongly agree”). Team members rated these items
individually. Their responses were aggregated to the team level to
obtain team learning scores for each team at the three time points
when team learning was measured. The Cronbach’s alpha coeffi-
cients were .79 for time 1, .78 for time 2, and .80 for time 3.

Social Loafing Tendencies

We assessed social loafing tendencies with a 4-item measure
derived from a questionnaire developed by George (1992) and
adapted by Schippers (2014). We operationalized social loafing in
two ways: self-reported and peer-reported. Because both sources of
assessment have advantages and disadvantages, combining them
offers a valid alternative to using one source over the other. To our
knowledge, only Stark et al. (2007) use both self-ratings and peer
ratings to study social loafing, concluding that participants are more
willing to admit their own loafing behaviors than recognize the loaf-
ing of their teammates. However, they emphasize it is legitimate to
expect that social loafing appraisals from different perspectives
(e.g., self vs. teammates) will differ. Therefore, to account for dif-
fering perspectives on individual behavior in teams, we use the two
separate source measures of social loafing behavior.

We summed self-reported responses across items that asked team
members about the extent to which they “defer responsibilities they
should assume to other team members,” “put forth less effort than
other members of their team,” “prefer to let the other team members
do the work if possible,” and “put forth less effort on the assign-
ment when other team members are around to do the work” (1 =
“totally disagree;” 5 = “totally agree”). For this measure, the Cron-
bach’s alphas were of .85 for time 1, .88 for time 2, and .88 for time
3. With regard to peer-rated social loafing, team members wrote
down the names of their teammates and rated them on each of the
four items on a 5-point Likert scale using the same labels as the
self-report questionnaire. To justify aggregating the peer ratings, we
assessed interrater agreement within teams according to rwg values
(level of within-group agreement of the peer evaluation score for
each referent). Because these estimates produced very good indica-
tors of peer evaluation reliability (average rwg = .88), we averaged
the peer ratings and used these scores for all analyses in the study.
The Cronbach’s alphas were .87 for time 1, .87 for time 2, and .86
for time 3. In the coding, items were reversed, such that higher
scores indicated higher levels of social loafing tendencies.

Learning Orientation and Performance Orientation

To assess learning and performance orientations, we used the 8-
item scales developed by Button et al. (1996). Sample items for
learning orientation (a = .84) included “The opportunity to learn
new things is important to me,” and “I prefer to work on tasks that
force me to learn new things.” Sample items for performance ori-
entation (a = .74) included “I prefer to do things that I can do well
rather than things that I do poorly,” and “The opinions others have
about how well I can do certain things are important to me.”

Data Aggregation

We evaluated individual-level scores on the team learning scale
to justify aggregation to the team level. To assess within-group
heterogeneity, we calculated rWG(j) indices (James et al., 1984) for
each measurement time of team learning with a cutoff criterion of

722 GABELICA, DE MAEYER, AND SCHIPPERS

T
hi
s
do
cu
m
en
ti
s
co
py
ri
gh
te
d
by

th
e
A
m
er
ic
an

Ps
yc
ho
lo
gi
ca
lA

ss
oc
ia
tio

n
or

on
e
of

its
al
lie
d
pu
bl
is
he
rs
.

T
hi
s
ar
tic
le
is
in
te
nd
ed

so
le
ly

fo
r
th
e
pe
rs
on
al
us
e
of

th
e
in
di
vi
du
al
us
er

an
d
is
no
tt
o
be

di
ss
em

in
at
ed

br
oa
dl
y.

.70 (George, 1990). Using the uniform null and normal distribu-
tions (George & James, 1993), the average rWG(j) scores were
appropriate for T1 (rWG[j] = .99, SD = .52), T2 (rWG[j] = .99, SD = .52),
and T3 (rWG[j] = 98, SD = .55). We also calculated the intraclass cor-
relation coefficient, ICC(1), to identify the proportion of the var-
iance in the measures that could be explained by team
membership, and the ICC(2) to assess the reliability of the team
means for team learning (Bliese, 2000). All ICC(1) scores were
greater than 0, and their corresponding one-way analyses of var-
iance (ANOVAs) were significant at p , .05. Specifically, the
ICC(1) coefficients were .33 (T1), .41 (T2), .and 40 (T3). The ICC
(2) coefficients were .60 (T1), .68 (T2), and .66 (T3). The cutoff
level of .60 thus was attained for ICC(2) too (Glick, 1985). These
analyses provided sufficient support for aggregating our individ-
ual-level scores to the team level.

Hypotheses Testing

We conducted linear mixed effects modeling to examine the
degrees to which goal orientations affected initial levels and
growth of social loafing and the initial levels and growth of team
learning affected initial levels and growth of social loafing (Dun-
can et al., 2006). With this technique, we can examine average tra-
jectories, the pattern of change in individual and team constructs,
and variations across individuals and teams, as well as analyzing
the instigators of such variations (e.g., intercept or change in team
learning; Mathieu & Rapp, 2009). This method models the
repeated measures of an observed variable, reflecting the initial
status of individuals and the rate of change in the dependent varia-
bles across time periods. Moreover, it allows us to account for the
complex multilevel structure of the data. Repeated measures
(Level 1) were nested within individuals (Level 2) who were
nested within teams (Level 3). For these analyses, we used the
computing environment R Core Team (R) and the linear mixed-
effects models using “Eigen” and S4 package (LME4; Bates et al.,
2016).
In a first step, we modeled three unconditional-growth models

in which no predictors were included except the effects of time.
For both dependent variables (peer-rated and self-rated social loaf-
ing), we first estimated a model in which we assumed a linear
effect of time. In the second model, we allowed the intercepts and
the slope of time to vary from individual to individual. In the third
model, we allowed both the intercept and the slope of time to vary
from individual to individual and from team to team. We expected
between-team differences in both the initial scores and how social
loafing tendencies evolved over the three measurement occasions.
The Step 2 analyses tested the effects of goal orientations and

team learning (H1, H2, H3) on both dependent variables. We first
modeled the effects of learning and performance orientation on the
social loafing intercept and change. We compared the fit of this
model to the fit of the best unconditional model using a �2 log
likelihood test and the Akaike information criterion (AIC; lower
AIC values indicate better model fit). Then, we compared the fit of
the model with only individual-level factors against a model in
which we added team learning as a team-level explanatory factor,
to decide which fit the data best. Finally, to test H3, we con-
structed two measures of team learning: team learning initial states
and team learning growth throughout the trimester.

Results

Self-Rated Social Loafing

Level 1 Analyses: How Does Self-Rated Social Loafing
Change Over Time?

In the first step, we modeled an unconditional growth model in
which no predictors of social loafing were included, except the
effects of time (with the time variable coded such that the initial time
point = 0). We contrasted a model in which the slope of time could
vary from individual to individual (model 2) with a model in which
the effect of time was included only in the fixed part (model 1; see
Table 1). Model 2 achieved better fit. Some individuals loafed more
over time, some loafed less, and others stagnated. Moreover, the
model that added the team-level perspective of social loafing (model
3) attained an even better fit; the slope of time varied from individual
to individual and from team to team. Accordingly, this model pre-
dicts that social loafing evolves differently in different teams, such
that some teams show increased social loafing and other teams show
either no evolution or a decrease in social loafing.

Level 2 Analyses: How Do Learning Orientation, Performance
Orientation, and Team Learning Affect Self-Rated Social
Loafing?

In the second step, we investigated factors that may explain the
change trajectories of social loafing. This stage was fundamental
to understanding why some individuals loafed more or reduced
their loafing behaviors over time. In model 4, we tested only goal
orientation effects, whereas in model 5, we added the main effects
of team learning initial states at time 0 and team learning growth,
as well as the interaction effects between these variables. Finally,
in model 6, we added the control variable ‘team size’.

From the comparison of the various models tested, we conclude
that the multilevel model combining the three explanatory factors—

Table 1
Model Fit Statistics for the Five Models Fitted on Self-Rated Social Loafing

Model �2LL AIC v2 df p

Model 1 (growth only fixed effect) 1,635.4 1,645.4
Model 2 (M1 þ individual differences in growth) 1,492.7 1,506.7 142.67 2 ,.001
Model 3 (M2 þ team differences in growth) 1,485.9 1,503.9 6.78 2 .033
Model 4 (M3 þ goal orientation effects) 1,405.9 1,431.9 80.04 4 ,.001
Model 5 (M4 þ team learning effects) 1,394.8 1,424.8 11.07 2 .004
Model 6 (M5 þ team size) 1,389.5 1,421.5 5.04 1 .021

Note. �2LL = �2 log-likelihood, AIC = Akaike information criterion, df = degrees of freedom.

TAKING A FREE RIDE 723

T
hi
s
do
cu
m
en
ti
s
co
py
ri
gh
te
d
by

th
e
A
m
er
ic
an

Ps
yc
ho
lo
gi
ca
lA

ss
oc
ia
tio

n
or

on
e
of

its
al
lie
d
pu
bl
is
he
rs
.

T
hi
s
ar
tic
le
is
in
te
nd
ed

so
le
ly

fo
r
th
e
pe
rs
on
al
us
e
of

th
e
in
di
vi
du
al
us
er

an
d
is
no
tt
o
be

di
ss
em

in
at
ed

br
oa
dl
y.

learning orientation and performance orientations of individual
members and team learning (model 5)—showed better fit than
the model that accounted for individual-level factors only
(model 4), which was better than the unconditional model
(model 3). Finally, model 6 controlling for team size showed a
better fit than model 5. Table 2 displays the model 6 parameter
estimates.
Individual-Level Factors. Our results show that only learn-

ing orientation had stable effects over time. There was no signifi-
cant interaction with time. That is, though neither learning nor
performance orientation explained why some individuals differed
in their social loafing tendencies over time, learning orientation
did influence their initial states of social loafing. Partially consist-
ent with H1, learning orientation relates negatively to self-rated
social loafing but there are no time effects. We did not, however,
find support for H2.
Team-Level Factor. At the team level, the change in team

learning showed a negative effect on the change of social loafing.
Individual members who were part of teams that scored higher on
team learning throughout the nine weeks of teamwork scored
lower on social loafing. Thus, only change in team learning
explains variations of social loafing over time, thereby confirming
our H3 with regard to self-reported social loafing. Finally, there

was a significant effect of the control variable ‘team size’, showing
a higher degree of self-rated social loafing for members in teams
of 3 than for members in teams of 4.

Peer-Rated Social Loafing

Level 1 Analyses: How Does Self-Rated Social Loafing
Change Over Time?

Following the same procedure for self-rated social loafing,
we modeled unconditional growth without any predictors of
peer-rated social loafing except the effects of time. On average,
we found the same pattern of results. In model 3, in which we
allowed the slope of time to vary from individual member to
individual member, and from team to team, we found better fit
than model 2 (in which we allowed the slope of time to vary
from individual member to individual member), which was
better than model 1, in which we added only the effect of time
to the fixed part (see Table 3). Model 3 predicts that growth
trajectories in peer-rated social loafing differ from team to
team, such that some indicate increases in peer-rated social
loafing, but others show no change or decreases in peer-rated
social loafing.

Table 2
Parameter Estimates (Est.), Standard Errors (SE), and p-Values From Model 6 Fitted on Self-Rated Social Loafing

Parameter Est. SE p

Fixed part
Intercept * 3.171 0.235 ,.001
Time �0.018 0.014 .182
Team size (= 3) 0.543 0.234 .021
Performance orientation �0.015 0.045 .744
Learning orientation �0.387 0.045 ,.001
Team learning (initial score) �0.459 0.353 .194
Team learning (growth) 0.294 0.703 .676
Team Learning (initial score) 3 Time 0.189 0.187 .315
Team Learning (growth) 3 Time �1.181 0.378 .002

Random part
Individual level
Variance in intercepts 0.243
Variance in slopes 0.037
Correlation intercept slope �0.16

Team level
Variance in intercepts 0.002
Variance in slopes 0.006
Correlation intercept slope �1
Residual variance 0.048

* Reference category: team of size = 4.

Table 3
Model Fit Statistics for the Five Models Fitted on Peer-Rated Social Loafing

Model �2LL AIC v2 df p

Model 1 (growth only fixed effect) 2,632.6 2,642.6
Model 2 (M1 þ individual differences in growth) 2,512.2 2,526.2 120.403 2 ,.001
Model 3 (M2 þ team differences in growth) 2,484.6 2,502.6 27.555 2 ,.001
Model 4 (M3 þ goal orientation effects) 2,477.0 2,503.3 7.621 4 .107
Model 5 (M4 þ team learning effects) 2,452.8 2,482.8 24.216 2 ,.001
Model 6 (M5 þ team size) 2,445.7 2,477.7 7.122 1 ,.008

Note. �2LL = �2 log-likelihood, AIC = Akaike information criterion, df = degrees of freedom.

724 GABELICA, DE MAEYER, AND SCHIPPERS

T
hi
s
do
cu
m
en
ti
s
co
py
ri
gh
te
d
by

th
e
A
m
er
ic
an

Ps
yc
ho
lo
gi
ca
lA

ss
oc
ia
tio

n
or

on
e
of

its
al
lie
d
pu
bl
is
he
rs
.

T
hi
s
ar
tic
le
is
in
te
nd
ed

so
le
ly

fo
r
th
e
pe
rs
on
al
us
e
of

th
e
in
di
vi
du
al
us
er

an
d
is
no
tt
o
be

di
ss
em

in
at
ed

br
oa
dl
y.

Level 2 Analyses: HowDo Learning Orientation, Performance
Orientation, and Team Learning Affect Peer-Rated Social
Loafing?

In the second step, we tested whether variations of the direction
of change in peer-rated social loafing also can be explained by the
explanatory factors of our study. Model 4, in which we added the
effects of performance and learning orientations on social loafing
and their interaction effects with the time variable, did not have a
significantly better fit than the unconditional growth model (model
3); we concluded that both performance and learning orientations
have no significant effects on peer-rated social loafing. Accord-
ingly, we estimated model 5 in a more parsimonious way, keeping
the main effects of goal orientations in the model as control varia-
bles but removing the interaction effects with time. Thus, model 5
models the effects of team learning on peer-rated social loafing, af-
ter controlling for the main effects of the individual-level factors,
learning and performance orientations. It achieves a significantly
better fit than model 4. Finally, in model 6, in which we added
team size, we found better fit than model 5. Table 4 displays the
parameter estimates of this model.
Individual-Level Factors. Although we controlled for the

effects of goal orientations, the parameter estimates for the effects of
learning orientation were not significantly different from zero. By
contrast, performance orientation was found to be negatively related
to peer-rated social loafing (H2a) but there was no significant interac-
tion with time (H2b). This result differs from the self-rated data.
Thus, H1 and H2b are not confirmed for peer-rated social loafing.
Team-Level Factor. The initial level of team learning had a

significant negative effect on peer-rated social loafing at the start.
Therefore, in teams in which initial learning scores were higher,
lower social loafing was reported by peers. Over time, the change
of team learning (growth) had a negative interaction effect on
social loafing as rated by peers. That is, teams that increased their
team learning over time were able to counteract the negative
effects of social loafing tendencies perceived by the teammates;

these teams showed a decrease in peer-rated social loafing (H3).
There was also a significant effect of the control variable ‘team
size’, showing a higher degree of peer-rated social loafing for
members in teams of 3 than for members in teams of 4.

Discussion

Research in work and educational settings shows that simply ask-
ing individuals to collaborate does not necessarily lead to optimal
collaboration (Johnson & Johnson, 2014; Kozlowski & Bell, 2013).
Teamwork creates social motivational challenges that teams need to
overcome throughout their experiences (Järvelä & Järvenoja,
2011). Typically, motivational challenges in teams tend to lie in
individual members’ tendency to exert less effort than their team-
mates (i.e., social loafing), leading to process loss. This is highly
concerning as social loafing may intensify over time and lead to a
downward spiral of motivation and process losses. This article
aimed to examine how working on a team task shapes individual
members’ tendency to exert their fair share of effort. Our study
showed that teams that score high on team learning throughout nine
weeks of teamwork experience decreased social loafing.

The primary contribution of this article has been to account for
the temporal dynamics of social loafing and identify important
individual- and team-level factors that affect its development. In
doing so, our study produces three important sets of findings.

First, building on the collective effort model (CEM) and social
interdependence theory, we find that loafing tendencies are more
dynamic than previously thought. In a sample of temporary teams,
social loafing trajectories appear to fluctuate across individuals
and teams and even over a three-month period. Literature on teams
has implicitly considered social loafing as a static rather than tem-
poral variable (Aggarwal & O’Brien, 2008; Hofmann & Jones,
2005). Hence, this finding extends current knowledge about the
dynamic nature of social motivation losses in teams (Kozlowski &
Bell, 2013). Our finding is also consistent with the conceptualiza-
tion that social loafing behaves like effort exertion (as part of

Table 4
Parameter Estimates (Est.), Standard Errors (SE), and P-Values From Model 6 Fitted on Peer-Rated Social Loafing

Parameter Est. SE p

Fixed part
Intercept* 2.105 0.283 ,.001
Time 0.016 0.021 .449
Team size (= 3) 0.565 0.210 .007
Performance orientation �0.111 0.055 .045
Learning orientation 0.016 0.054 .770
Team learning (initial score) �1.292 0.505 .012
Team learning (growth) 0.912 1.008 .367
Team Learning (initial score) 3 Time 0.251 0.295 .455
Team Learning (growth) 3 Time �2.406 0.597 ,.001

Random part
Individual level
Variance in intercepts 0.255
Variance in slopes 0.048
Correlation intercept_slope 0.230

Team level
Variance in intercepts 0.061
Variance in slopes 0.025
Correlation intercept_slope �0.670
Residual variance 0.135

* Reference category: team size = 4.

TAKING A FREE RIDE 725

T
hi
s
do
cu
m
en
ti
s
co
py
ri
gh
te
d
by

th
e
A
m
er
ic
an

Ps
yc
ho
lo
gi
ca
lA

ss
oc
ia
tio

n
or

on
e
of

its
al
lie
d
pu
bl
is
he
rs
.

T
hi
s
ar
tic
le
is
in
te
nd
ed

so
le
ly

fo
r
th
e
pe
rs
on
al
us
e
of

th
e
in
di
vi
du
al
us
er

an
d
is
no
tt
o
be

di
ss
em

in
at
ed

br
oa
dl
y.

behavioral engagement) in individual learning situations, which
has been shown to be altered by situational constraints (Malmberg
& Martin, 2019). Researchers who investigated participation in
social settings more generally have successfully demonstrated that
participation can fluctuate over time (Cheung et al., 2008; Hewitt,
2005). Complementary research focused on SSRL in collaborative
learning has recently provided evidence that engagement in cogni-
tive interactions can vary from moment to moment (e.g., Isohätälä
et al., 2020). However, factors triggering fluctuations in participa-
tion in student-led team tasks are less well understood (Isohätälä
et al., 2020). Extending these lines of research on the temporal
aspects of participation into social loafing models allowed us to
verify that social loafing was also a fluctuating phenomenon across
individuals and teams and to identify factors contributing to those
fluctuations.
Second, building on previous research on the individual characteris-

tics that determine people’s tendencies to loaf (e.g., Charbonnier et
al., 1998; Schippers, 2014), our findings show that the learning orien-
tation of individual team members appears to have constant effects
for any member across time (H1). However, if we had measured
learning orientation repeatedly as well, we might have found some
time effects of other observations of learning orientation on social
loafing trajectories, in line with the strand of research that supports
goal orientation variability (Bernacki et al., 2014). Nevertheless, these
results corroborate the findings of recent work applying Achievement
Goal theory (AGT) to collaborative settings. In this work, cross-sec-
tional data reveal that learning orientation is positively related to col-
laborative (as opposed to antisocial) behaviors such as coregulating
the team processes and elaborating peers’ content (Greisel et al.,
2018; Lee et al., 2010). Note that an unanticipated finding of our
study is that self-reported learning orientation does not predict peer-
rated social loafing. This inconsistency may be due to differing per-
ceptions of attitudes and behaviors by different members of the team.
Finally, although the results of this study do not show any significant
effects of performance orientation on self-rated social loafing emer-
gence or development (H2), performance orientation is found to be
negatively related to initial peer-rated social loafing (H2a). This means
that individuals scoring high on performance orientation are rated by
their peers as low loafers at the first team assignment. This result may
be explained by the fact that, because they seek to obtain affirmative
judgments about their competencies, members scoring high on per-
formance orientation are perceived as behaving collaboratively. Since
the effect is not present for self-rated social loafing, these relationships
may partly be explained by differing perceptions of own versus other
behaviors, an interesting avenue for future research. These conflicting
results corroborate previous studies in the collaborative learning litera-
ture. In fact, contrary to learning orientation’s main motivative role in
collaborative learning, mixed results have been described so far
regarding performance orientation (e.g., Lim & Lim, 2020). Such
results raise the possibility that holding a performance orientation
might have different consequences in team contexts where only team
performance is measured, depending on the timing of the collabora-
tion (Muis & Edwards, 2009). Additionally, it might be that social
loafing is related to the avoidance variant of the achievement goals.
Performance-avoidance goals—engaging in a task with the goal of
avoiding revealing inabilities—have been shown to be related to dis-
engagement in individual learning situations (Urdan & Kaplan, 2020).
It can thus be suggested that the relationship between goal orien-

tation and social loafing is mediated by social goals, such as

building caring and committed relationships and belonging to a
team, and team orientation (Johnson & Johnson, 2009). Moreover,
social goals could increase or decrease the level of endorsement of
members’ goals throughout the process of teamwork (Bernacki et
al., 2014). For example, a high team orientation might explain how
self-interest is expanded to joint interest and how new goals are
crafted in collaborative situations, reducing the emergence of social
loafing. Reaching a shared clarity and consensus about the team’s
purpose and an alignment between individual and team goals could
hence help teams prevent dysfunctional inefficiencies such as social
loafing and optimize the use of the team capabilities (Johnson &
Johnson, 2015; Kayes et al., 2005). If this hypothesis holds true,
this will also suggest that social loafing can be changed and com-
pensated for by strengthening team members’ identification.

Third, the development of social loafing depends not only on
the passage of time but also on the increase of team learning that
arises and grows among team members. Importantly, our results
show that only team learning —a team-level concept—appears to
explain changes in the trajectories of social loafing, over and
above individual goal orientations. Consistent with our hypothesis
3, over time, an increase in team learning leads to a decrease in
social loafing. This finding is particularly significant in the context
of the complex nature of motivation loss and the dearth of research
that demonstrates a relationship between the emergence and
changes in team learning and social loafing (Bell et al., 2012). It
highlights the need to consider temporal aspects of teams, which
include not only team development (i.e., changes in the team as a
whole) but also team socialization (i.e., changes in the relationship
between the team and its members; Levine & Moreland, 1994).
Consequently, these results corroborate group socialization theory
introduced by Levine and Moreland (1994) who suggested that
individuals change as a function of the team that they join. This
theory provides a valuable temporal explanation of how individu-
als can become team members although, as noted by Kozlowski
and Bell (2013), there is a paucity of research focusing on team
socialization over time. Surprisingly, it is still widely believed that
over time, individuals striving for their own goals naturally develop
into team members of an autonomous team capable of adapting
itself to meet environmental contingencies (Kayes et al., 2005).
Furthermore, the present study extends our understanding of the
regulatory approach to team learning (Bell et al., 2012; Chen
et al., 2009) by linking the upgrade of shared knowledge to moti-
vational processes.

However, in the current study we could only infer that sociocog-
nitive interactions had occurred that led to a shift in team’s collec-
tive knowledge (Fransen et al., 2013). Prior research has identified
several factors that may influence why and how an increase in
team knowledge predicts ensuing changes in social loafing,
namely: (1) positive interdependence, (2) group processing behav-
iors, (3) perception of a team reward, and (4) socioemotional inter-
actions. Below, we elaborate on these four factors.

First, it is possible to hypothesize that positive interdependence
(i.e., team members’ perception that they can attain their goals
only if all other teammates promote each other’s efforts to achieve
the goals) is necessary for team learning to have positive effects
on social loafing. When individual members perceive positive
interdependence, they might realize that their efforts are required
for the team to create team knowledge and that they make a unique
contribution to their team. Positive interdependence is also posited

726 GABELICA, DE MAEYER, AND SCHIPPERS

T
hi
s
do
cu
m
en
ti
s
co
py
ri
gh
te
d
by

th
e
A
m
er
ic
an

Ps
yc
ho
lo
gi
ca
lA

ss
oc
ia
tio

n
or

on
e
of

its
al
lie
d
pu
bl
is
he
rs
.

T
hi
s
ar
tic
le
is
in
te
nd
ed

so
le
ly

fo
r
th
e
pe
rs
on
al
us
e
of

th
e
in
di
vi
du
al
us
er

an
d
is
no
tt
o
be

di
ss
em

in
at
ed

br
oa
dl
y.

to create a sense of responsibility and accountability for complet-
ing their share of work (Johnson & Johnson, 2015).
Second, we can argue that group processing enables team learn-

ing development. Several fields of research provide insights into
the nature of group processing. Social interdependence theory
states that group processing encompasses analyzing and imple-
menting actions to achieve the team’s goals (Johnson & Johnson,
2009). In social psychology, group-level information processing
(Hinsz et al., 1997) involves information sharing and use. Simi-
larly, in the research on socially shared regulation of learning
(SSRL), Volet and colleagues (2009) use the term ‘high-level cog-
nitive processing’ depicting behaviors such as elaborating, drawing
inferences, asking thought-provoking questions, and negotiating.
They are all presumed to contribute to the coconstruction of
knowledge, a core behavior that can augment team knowledge
(Van den Bossche et al., 2011). Hence, based on the premise that
shared regulation leads to increased shared knowledge (Lajoie &
Lu, 2011; Saab et al., 2012), and that shared regulation is scarce
when at least one member is disengaged (Isohätälä et al., 2017),
we could suggest that teams that experienced the steeper team
learning shifts in our study self-regulated their motivation and
cognition. Finally, these social regulation processes are similar to
team learning behaviors described in small group research. They
include reflecting on team processes and outcomes, asking ques-
tions, sharing and discussing ideas and divergences, and solving
them constructively to coconstruct new understandings and reach
mutual agreement (see Decuyper et al., 2010; for review).
Third, based on behavioral learning theories (Bandura, 1977)

that posit that individuals will work hard on tasks for which they
obtain a reward and exert less effort in tasks that yield no reward,
we could propose that an increase in team knowledge can be per-
ceived as a reward that make loafers work harder toward their
team goals.
Finally, team learning may affect social loafing through enhanced

concern with the team and its outcomes, higher sense of community
and/or higher cohesiveness (Lam, 2015). Both research on collabo-
rative learning and work in social psychology and organizational
behavior have raised the importance of socioemotional interactions
that complement sociocognitive interactions for successful team-
work (Isohätälä et al., 2017).
To summarize, our study complements past research by demon-

strating the salience of studying the temporal dynamics of group
motivational constructs and identifying team factors that eliminate
motivation losses in group endeavors (Aggarwal & O’Brien, 2008;
Bell et al., 2012). It therefore contributes to address the essential
issues of why teams develop differently and how different aspects
of interaction are connected at the individual and team levels
(Fransen et al., 2013). Examining these relationships over a pro-
longed period of time, and over many performance episodes, may
be a viable route for further research, which also should seek to
specify any boundary conditions for the present effects. Research
on other individual differences or contextual variables that might
explain different social loafing trajectories also is necessary. For
example, positive norms for co-operative work and constructive
behaviors (Buchs et al., 2015) could prevent social loafing. Specif-
ically, social norms that promote team goals, open lines of com-
munication, early resolution, and that expect everyone to work
hard will likely increase members’ motivation to contribute to the
team efforts. It is expected that the more those norms are shared,

the stronger would be the involvement of members in the team ac-
tivity (Levine & Moreland, 2004). Further, research on other
team-emergent mechanisms (e.g., trust, psychological safety, team
cohesion) that might minimize social loafing also is necessary.
Complementary research on regulated learning in social settings
has provided insightful evidence that team members need to
engage in regulated learning to develop joint knowledge construc-
tion (Järvelä et al., 2016; Malmberg et al., 2017). Growth in this
research field provides an exciting opportunity for researchers to
investigate teamwork by shedding light on metacognitive proc-
esses that are essential for overcoming motivational problems in
collaborative learning. By examining these phenomena empiri-
cally, we could gain a better grasp on the complexities of motiva-
tion in team settings. Such insights could assist the design and
application of interventions that stimulate behaviors and processes
that have been shown to be helpful in reducing the tendency to
engage in social loafing. If these results replicate across settings
(e.g., in workplaces) and tasks, the use of team exercises, feed-
back, incentives, and debriefing interventions arguably could
increase the use of effective behaviors and even reduce motivation
loss (Gabelica et al., 2014; Pritchard et al., 2008).

Limitations and Future Directions

Although obvious strengths of the current study are that we
tested the hypotheses with a large number of teams, over time and
in a context where social loafing often occurs, our study is not
without limitations. First, it is conceivable that there was a per-
cept–percept bias in the first model, that predicted changes in self-
rated loafing for testing relationships between variables from the
same questionnaire. However, we minimized the impact of this
bias by using temporal measurements (Podsakoff et al., 2003).
Although the instruments and constructs we use have been shown
to be both reliable and valid, self-reported team learning cannot
fully cover learning behaviors and strategies in which teams
engage. Because we did not systematically observe team learning
processes, the challenges for continuing research are to document
the processes that occur when individuals collaborate to solve
team tasks (Fransen et al., 2013), validate interventions to make
teams function as effectively as possible, and investigate the
impact of team learning and motivation processes on performance.
Closer examination of communication processes may help deter-
mine whether and how learning behaviors vary in quality and
affect social loafing. To address this issue, researchers could over-
lay qualitative analyses to clarify how the quality of specific learn-
ing processes (e.g., sharing information and knowledge; mutually
refining, building on, or modifying each original offer; reflecting
on team processes) increases or decreases over time in dynamic
episodes with social loafing tendencies (Goodman & Dabbish,
2011). The focus of these analyses should be on the sociocognitive
and socioemotional interactions that occur during teamwork, the
conditions under which they occur, what the effects of these inter-
actions are, and how they are interrelated (i.e., the interactions par-
adigm; Dillenbourg et al., 1996).

Second, our validated model differs slightly across the two sour-
ces of ratings of social loafing. We chose to investigate peer ratings
and self ratings independently, because prior outcomes are mixed
with regard to which source best assesses social loafing (Karau &
Williams, 1993; Stark et al., 2007). We find a different pattern

TAKING A FREE RIDE 727

T
hi
s
do
cu
m
en
ti
s
co
py
ri
gh
te
d
by

th
e
A
m
er
ic
an

Ps
yc
ho
lo
gi
ca
lA

ss
oc
ia
tio

n
or

on
e
of

its
al
lie
d
pu
bl
is
he
rs
.

T
hi
s
ar
tic
le
is
in
te
nd
ed

so
le
ly

fo
r
th
e
pe
rs
on
al
us
e
of

th
e
in
di
vi
du
al
us
er

an
d
is
no
tt
o
be

di
ss
em

in
at
ed

br
oa
dl
y.

(higher peer-rating means) than Stark et al. (2007; higher self-rating
means); their study participants were more willing to report their
own versus their teammates’ social loafing. However, the question
of the “true” score of social loafing remains unanswered. To mea-
sure actual social loafing, researchers would need to observe, re-
cord, and interpret accurately team members’ efforts (Lord, 1985;
Mulvey & Klein, 1998). Although perceptions of social loafing and
actual reduced effort may be associated, reduced effort also may
occur without the awareness of team members (Mulvey & Klein,
1998). If reduced efforts are not perceived by the team, they may
not affect team functioning and motivation. Therefore, perceptions
of social loafing require further research. Along these lines, it also
is important to note that prior work has provided empirical evidence
that peer appraisals are associated with reduced social loafing (e.g.,
Druskat & Wolff, 1999). In our study, because students’ evalua-
tions were completed early in the academic year and at multiple
points, social loafing may have been lower than expected in situa-
tions of no peer appraisal. As a result, the magnitude of the underly-
ing effects may be underestimated.
Third, we conducted our study with undergraduate student pro-

ject teams. Although past research on social loafing also has used
student samples (Alnuaimi et al., 2010; Gagné & Zuckerman,
1999; Schippers, 2014), we are cautious about the external validity
of our findings. Student groups sometimes work less as a team and
more as individual participants who complete separate portions of
their assigned task independently (Skilton et al., 2008). Moreover,
expertise distribution within teams in educational settings may be
limited, which may limit the inputs team members have available
to complete tasks and increase their dispensability. Researchers
should determine if our results generalize to employees who con-
stitute project teams outside formal educational settings (Price et
al., 2006). Although loafing tendencies usually are demonstrated
in laboratory settings (Huguet et al., 1999), team members in our
study were not role playing; rather, they were performing mean-
ingful tasks designed to be complex enough to demand team
efforts, have a team goal and reward (i.e., have positive interde-
pendence), and require that a few months be spent together. Our
student teams had assignments that required the cooperation and
coordination of team efforts across multiple meetings, but unlike
teams in a work context, they lacked the same history of common
experiences and identity (Karau & Hart, 1998).
Finally, the current study focused on just two goals assessed

once at the start of the collaboration. While focusing on learning
and performance goals is parsimonious, it fails to account for other
goals that are potentially important in achievement situations in
teams. Research within the achievement goal framework has pro-
liferated over the past years and more complex models have been
studied (i.e., trichotomous achievement goal framework and 2 3 2
achievement goal framework; Urdan & Kaplan, 2020). Addition-
ally, there has been growing evidence that achievement goals can
change across tasks (Bernacki et al., 2014; Fryer & Elliot, 2007).
Future research could capture variations in team members’
endorsement of achievement goals over time and relate these fluc-
tuations to social loafing trajectories. Researchers could verify if
throughout the process of team goal pursuit and regulation, indi-
vidual goal switches or intensification are related to increases or
drops of social loafing.
Despite these limitations, our research contributes to emerging

literature on the development of social loafing. By demonstrating

that the increase of team learning can lower the emergence of
social loafing, it provides further empirical support for the power
of team learning on individual behaviors (Gabelica et al., 2014;
Bell et al., 2012; Decuyper et al., 2010). It also provides an inte-
grative theoretical model that combines individual- and team-level
predictors of social loafing. It highlights the importance of other
team members and their interactive behaviors in determining indi-
vidual behavior. We hope this study stimulates further empirical
and theoretical research on the temporal dynamics of social
loafing.

Practical Implications

Any setting in which people’s efforts are merged into a single
output might be conducive to the demotivating effects of working
in teams. Teachers and trainers may experience withheld effort
that negatively affects not only teams’ but also classes’ perform-
ance and dynamics. In school settings, dealing with social loafing
and its consequences has become a time-consuming concern for
teachers who use team-based learning. An important challenge for
research and practice is to implement strategies for maximizing
team functioning and team learning, such that the potential of each
team’s resources can be fulfilled (Webb et al., 1998).

The outcomes of our study provide substantial insights for
designing and supporting teams in ways that reduce opportunistic
behavior such as social loafing (Tan & Tan, 2008). Our findings
underscore the positive effects of learning orientation on the level
of social loafing at the start of team activities. During team forma-
tion phases (Tuckman & Jenson, 1977), devoting specific attention
to favorable beliefs and appraisals of tasks and teams (high value
to the task and team) may reduce tension arising from the uneven
motivations that often occur in newly formed teams. Assessing
members’ goal orientations may help team managers and teachers
anticipate antisocial behaviors and take early action to build the
learning tones of their teams (Bunderson & Sutcliffe, 2003; Gagné
& Zuckerman, 1999).

When working in increasingly learning-oriented teams, individ-
ual members also may move away from individualistic concerns
and work harder when everyone’s ideas and contributions are val-
ued. This result is encouraging, because it suggests social loafing
is a changeable behavior that fluctuates over time. Stimulating
knowledge sharing and building, learning from prior mistakes, and
constructive team discussions may reduce social loafing tenden-
cies. By asking critical questions and introducing competing per-
spectives and interpretations, teachers and team facilitators can
broaden discussions and promote deeper team concern, commit-
ment, and engagement. This approach calls for the implementation
and evaluation of the motivational benefits of interventions that
facilitate team learning and maintain high learning opportunities
and challenges throughout team tasks (Gabelica et al., 2014; Hack-
man & Wageman, 2005). For example, emerging research indi-
cates that providing teams with feedback on how they have
performed, and inducing team reflection on what teams do and
how they do it, helps them become more effective, especially if
their initial team performance is low (Gurtner et al., 2007). The
co-operative learning literature also provides insight in this regard.
Buchs et al. (2015), for example, highlight the needs to better pre-
pare students for collaboration and use structured methods to en-
courage constructive interactions. Social interdependence theory

728 GABELICA, DE MAEYER, AND SCHIPPERS

T
hi
s
do
cu
m
en
ti
s
co
py
ri
gh
te
d
by

th
e
A
m
er
ic
an

Ps
yc
ho
lo
gi
ca
lA

ss
oc
ia
tio

n
or

on
e
of

its
al
lie
d
pu
bl
is
he
rs
.

T
hi
s
ar
tic
le
is
in
te
nd
ed

so
le
ly

fo
r
th
e
pe
rs
on
al
us
e
of

th
e
in
di
vi
du
al
us
er

an
d
is
no
tt
o
be

di
ss
em

in
at
ed

br
oa
dl
y.

traditionally presumed that team members had the necessary skills
to collaborate successfully (Johnson & Johnson, 2015). To achieve
team learning and effectiveness, it therefore appears necessary to
help teams construct shared understandings of task characteristics
and the team’s knowledge in early stages of teamwork (Fransen et
al., 2013). From a cognitive perspective, team members with poor
communication skills are less likely to benefit from team-based
learning, because they may not be able to share their ideas and
proposals with others; ask critical questions; reflect on their own
and team functioning; provide constructive criticism; or disagree
with elaborated argumentation (Kramarski, & Mevarech, 2003;
Webb & Farivar, 1994). Therefore, preparing newly formed teams
for collaboration by training them in team learning processes (e.g.,
shared reflection, coconstruction, high-level elaboration, construc-
tive disagreements, reaching agreement) that produce high team
performance (Webb et al., 1998) is a promising avenue for team
development.

References

Aggarwal, P., & O’Brien, C. L. (2008). Social loafing on group projects:
Structural antecedents and effect on student satisfaction. Journal of Market-
ing Education, 30(3), 255–264. https://doi.org/10.1177/0273475308322283

Alnuaimi, O. A., Robert, L. P., & Maruping, L. M. (2010). Team size, disper-
sion, and social loafing in technology-supported teams: A perspective on
the theory of moral disengagement. Journal of Management Information
Systems, 27(1), 203–230. https://doi.org/10.2753/MIS0742-1222270109

Arrow, H., McGrath, J. E., & Berdahl, J. L. (2000). Small groups as com-
plex systems: Formation, coordination, development, and adaptation.
Sage.

Bakhtiar, A., Webster, E. A., & Hadwin, A. F. (2017). Regulation and
socio-emotional interactions in a positive and a negative group climate.
Metacognition and Learning, 13(1), 57–90. https://doi.org/10.1007/
s11409-017-9178-x

Bandura, A. (1977). Self-efficacy: Toward a unifying theory of behavioral
change. Psychological Review, 84(2), 191–215. https://doi.org/10.1037/
0033-295X.84.2.191

Barclay, J. H., & Harland, L. K. (1995). Peer performance appraisals: The
impact of rater competence, rater location, and rating correctability on
fairness perceptions. Group & Organization Management, 20(1), 39–60.
https://doi.org/10.1177/1059601195201004

Bates, D. M., Maechler, M., Bolker, B., Walker, S., Christensen, R. H. B.,
Singmann, H., Dai, B., Grothendieck, G., & Green, P. (2016). lme4:
Linear mixed-effects models using S4 classes. R package version
1.1–12.

Bell, B. S., Kozlowski, S. W. J., & Blawath, S. (2012). Team learning: A
theoretical integration and review. In S. W. J. Kozlowski (Ed.), The
Oxford handbook of organizational psychology (pp. 859–909). Oxford
University Press.

Bernacki, M. L., Aleven, V., & Nokes-Malach, T. J. (2014). Stability and
change in adolescents’ task-specific achievement goals and implications
for learning mathematics with intelligent tutors. Computers in Human
Behavior, 37, 73–80. https://doi.org/10.1016/j.chb.2014.04.009

Bliese, P. D. (2000). Within-group agreement, non-independence, and reli-
ability: Implications for data aggregation and analysis. In K. J. Klein &
S. W. J. Kozlowski (Eds.), Multilevel theory, research, and methods in
organizations: Foundations, extensions, and new directions (pp.
349–381). Jossey-Bass.

Bolin, A. U., & Neuman, G. A. (2006). Personality, process, and perform-
ance in interactive brainstorming groups. Journal of Business and Psy-
chology, 20(4), 565–584. https://doi.org/10.1007/s10869-005-9000-7

Boud, D., Cohen, R., & Sampson, J. (2001). Peer Learning in Higher Edu-
cation: Learning from and with each other. Kogan.

Brophy, J. (2005). Goal theorists should move on from performance goals.
Educational Psychologist, 40(3), 167–176. https://doi.org/10.1207/
s15326985ep4003_3

Buchs, C., Gilles, I., Antonietti, J.-P., & Butera, F. (2015). Why students
need to be prepared to cooperate: A co-operative nudge in statistics
learning at university. Educational Psychology, 36(5), 956–974. https://
doi.org/10.1080/01443410.2015.1075963

Bunderson, J. S., & Sutcliffe, K. M. (2003). Management team learning
orientation and business unit performance. Journal of Applied Psychol-
ogy, 88(3), 552–560. https://doi.org/10.1037/0021-9010.88.3.552

Butler, R. (1995). Motivational and informational functions and consequen-
ces of children’s attention to peers’ work. Journal of Educational Psy-
chology, 87(3), 347–360. https://doi.org/10.1037/0022-0663.87.3.347

Button, S. B., Mathieu, J. E., & Zajac, D. M. (1996). Goal orientation in
organizational research: A conceptual and empirical foundation. Organi-
zational Behavior and Human Decision Processes, 67(1), 26–48. https://
doi.org/10.1006/obhd.1996.0063

Carver, C. S., & Scheier, M. F. (1998). On the self-regulation of behavior.
Cambridge University Press. https://doi.org/10.1017/CBO9781139174794

Cellar, D. F., Stuhlmacher, A. F., Young, S. K., Fisher, D. M., Adair,
C. K., Haynes, S., & Riester, D. (2010). Trait goal orientation, self-regu-
lation, and performance: A meta-analysis. Journal of Business and Psy-
chology, 26, 467–483. https://doi.org/10.1007/s10869-010-9201-6

Charbonnier, E., Huguet, P., Brauer, M., & Monteil, J. M. (1998). Social
loafing and self-beliefs: People’s collective effort depends on the extent
to which they distinguish themselves as better than others. Social Behav-
ior and Personality, 26(4), 329–340. https://doi.org/10.2224/sbp.1998
.26.4.329

Chen, G., Kanfer, R., DeShon, R. P., Mathieu, J. E., & Kozlowski, S. W. J.
(2009). The motivating potential of teams: Test and extension of Chen
and Kanfer’s (2006) cross-level model of motivation in teams. Organi-
zational Behavior and Human Decision Processes, 110(1), 45–55.
https://doi.org/10.1016/j.obhdp.2009.06.006

Cheng, W., & Warren, M. (1999). Peer teacher assessment of the oral and
written tasks of a group project. Assessment & Evaluation in Higher
Education, 24(3), 301–314. https://doi.org/10.1080/0260293990240304

Cheung, W. S., Hew, K. F., & Ng, S. L. C. (2008). Toward an understand-
ing of why students contribute in asynchronous online discussions. Jour-
nal of Educational Computing Research, 38(1), 29–50. https://doi.org/
10.2190/EC.38.1.b

Cohen, S. G., & Bailey, D. E. (1997). What makes teams work: Group effec-
tiveness research from the shop floor to the executive suite. Journal of Man-
agement, 23(3), 239–290. https://doi.org/10.1177/014920639702300303

Comer, D. (1995). A model of social loafing in real work groups? Human
Relations, 48(6), 647–667. https://doi.org/10.1177/001872679504800603

Conway, J. M., & Lance, C. E. (2010). What reviewers should expect from
authors regarding common method bias in organizational research. Jour-
nal of Business and Psychology, 25(3), 325–334. https://doi.org/10
.1007/s10869-010-9181-6

Davison, H. K., Mishra, V., Bing, M. N., & Frink, D. D. (2014). How indi-
vidual performance affects the variability of peer evaluations in class-
room teams: A distributive justice perspective. Journal of Management
Education, 38(1), 43–85. https://doi.org/10.1177/1052562912475286

De Dreu, C. K. W., Nijstad, B. A., & van Knippenberg, D. (2008). Moti-
vated information processing in group judgment and decision making.
Personality and Social Psychology Review, 12(1), 22–49. https://doi
.org/10.1177/1088868307304092

Decuyper, S., Dochy, F., & Van den Bossche, P. (2010). Grasping the
dynamic complexity of team learning: An integrative model for effective
team learning in organizations. Educational Research Review, 5(2),
111–133. https://doi.org/10.1016/j.edurev.2010.02.002

TAKING A FREE RIDE 729

T
hi
s
do
cu
m
en
ti
s
co
py
ri
gh
te
d
by

th
e
A
m
er
ic
an

Ps
yc
ho
lo
gi
ca
lA

ss
oc
ia
tio

n
or

on
e
of

its
al
lie
d
pu
bl
is
he
rs
.

T
hi
s
ar
tic
le
is
in
te
nd
ed

so
le
ly

fo
r
th
e
pe
rs
on
al
us
e
of

th
e
in
di
vi
du
al
us
er

an
d
is
no
tt
o
be

di
ss
em

in
at
ed

br
oa
dl
y.

https://doi.org/10.1177/0273475308322283

https://doi.org/10.2753/MIS0742-1222270109

https://doi.org/10.1007/s11409-017-9178-x

https://doi.org/10.1037/0033-295X.84.2.191

https://doi.org/10.1177/1059601195201004

https://doi.org/10.1016/j.chb.2014.04.009

https://doi.org/10.1007/s10869-005-9000-7

https://doi.org/10.1207/s15326985ep4003_3

https://doi.org/10.1080/01443410.2015.1075963

https://doi.org/10.1037/0021-9010.88.3.552

https://doi.org/10.1037/0022-0663.87.3.347

https://doi.org/10.1006/obhd.1996.0063

https://doi.org/10.1017/CBO9781139174794

https://doi.org/10.1007/s10869-010-9201-6

https://doi.org/10.2224/sbp.1998.26.4.329

https://doi.org/10.1016/j.obhdp.2009.06.006

https://doi.org/10.1080/0260293990240304

https://doi.org/10.2190/EC.38.1.b

https://doi.org/10.1177/014920639702300303

https://doi.org/10.1177/001872679504800603

https://doi.org/10.1007/s10869-010-9181-6

https://doi.org/10.1177/1052562912475286

https://doi.org/10.1177/1088868307304092

https://doi.org/10.1016/j.edurev.2010.02.002

Dillenbourg, P., Baker, M., Blaye, A., & O’Malley, C. (1996). The evolu-
tion of research on collaborative learning. In E. Spada & P. Reiman
(Eds.), Learning in Humans and Machine: Towards an interdisciplinary
learning science (pp. 189–211). Elsevier.

Dochy, F., Gijbels, D., Raes, E., & Kyndt, E. (2014). Team learning in
education and professional organisations. In S. Billett, C. Harteis, & H.
Gruber (Eds.), International handbook of research in professional and
practice-based learning (pp. 987–1020). Springer. https://doi.org/10
.1007/978-94-017-8902-8_36

Druskat, V. U., & Wolff, S. B. (1999). Effects and timing of developmen-
tal peer appraisals in self-managing work groups. Journal of Applied
Psychology, 84(1), 58–74. https://doi.org/10.1037/0021-9010.84.1.58

Duffy, M. K., & Shaw, J. D. (2000). The Salieri syndrome: Consequences
of envy in groups. Small Group Research, 31(1), 3–23. https://doi.org/
10.1177/104649640003100101

Duncan, T. E., Duncan, S. C., & Strycker, L. A. (2006). An introduction to
latent variable growth curve modeling. LEA.

Dweck, C. S. (1986). Motivational processes affecting learning. American
Psychologist, 41(10), 1040–1048. https://doi.org/10.1037/0003-066X.41
.10.1040

Dweck, C. S. (1999). Self-theories: Their role in motivation, personality,
and development. Psychology Press.

Elliot, A. J. (2005). A conceptual history of the achievement goal con-
struct. In A. J. Elliot & C. Dweck (Eds.), Handbook of competence and
motivation (pp. 52–72). Guilford Press.

Elliot, A. J., & McGregor, H. A. (2001). A 2 X 2 achievement goal frame-
work. Journal of Personality and Social Psychology, 80(3), 501–519.
https://doi.org/10.1037/0022-3514.80.3.501

Elliot, A. J., & Church, M. A. (1997). A hierarchical model of approach
and avoidance achievement motivation. Journal of Personality and
Social Psychology, 72(1), 218–232. https://doi.org/10.1037/0022-3514
.72.1.218

Ellis, A. P., Hollenbeck, J. R., Ilgen, D. R., Porter, C. O., West, B. J., &
Moon, H. (2003). Team learning: Collectively connecting the dots. Jour-
nal of Applied Psychology, 88(5), 821–835. https://doi.org/10.1037/
0021-9010.88.5.821

Erez, M., & Somech, A. (1996). Is group productivity loss the rule or the
exception? Effects of culture and group-based motivation. Academy of
Management Journal, 39(6), 1513–1537. https://doi.org/10.2307/257067

Forgas, J., Williams, K., & Laham, S. (2005). Social motivation: introduc-
tion and overview. In K. D. Williams & S. M. Laham (Eds.), Social
motivation: Conscious and unconscious processes (pp. 1–15). Cam-
bridge University Press.

Fransen, J., Weinberger, A., & Kirschner, P. (2013). Team effectiveness
and team development in CSCL. Educational Psychologist, 48(1), 9–24.
https://doi.org/10.1080/00461520.2012.747947

Fredricks, J. A., Blumenfeld, P. C., & Paris, A. H. (2004). School engage-
ment: Potential of the concept, state of the evidence. Review of Educational
Research, 74(1), 59–109. https://doi.org/10.3102/00346543074001059

Fryer, J. W., & Elliot, A. J. (2007). Stability and change in achievement
goals. Journal of Educational Psychology, 99(4), 700–714. https://doi
.org/10.1037/0022-0663.99.4.700

Gabelica, C., Van den Bossche, P., De Maeyer, S., Segers, M., &
Gijselaers, W. (2014). The effect of team feedback and guided reflexiv-
ity on team performance change. Learning and Instruction, 34, 86–96.
https://doi.org/10.1016/j.learninstruc.2014.09.001

Gagné, M., & Zuckerman, M. (1999). Performance and learning goal orien-
tations as moderators of social loafing and social facilitation. Small Group
Research, 30(5), 524–541. https://doi.org/10.1177/104649649903000502

George, J. M. (1990). Personality, affect, and behavior in groups. Journal
of Applied Psychology, 75(2), 107–116. https://doi.org/10.1037/0021
-9010.75.2.107

George, J. M. (1992). Extrinsic and intrinsic origins of perceived social
loafing in organizations. Academy of Management Journal, 35(1),
191–202. https://doi.org/10.2307/256478

George, J. M., & James, L. R. (1993). Personality, affect, and behavior in
groups revisited: Comment on aggregation, levels of analysis, and a
recent application of within and between analysis. Journal of Applied
Psychology, 78(5), 798–804. https://doi.org/10.1037/0021-9010.78.5.798

Glick, W. H. (1985). Conceptualizing and Measuring Organizational and
Psychological Climate: Pitfalls in Multilevel Research. Academy of
Management Review, 10(3), 601–616. https://doi.org/10.5465/amr.1985
.4279045

Goodman, P., & Dabbish, L. (2011). Methodological issues in measuring
group learning. Small Group Research, 42(4), 379–404. https://doi.org/
10.1177/1046496410385471

Greisel, M., Melzner, N., Kollar, I., & Dresel, M. (2018). How groups reg-
ulate their learning: The influence of achievement goals on self-, co- and
shared regulation strategies. Proceedings of the 13th International Con-
ference of the Learning Sciences: Rethinking Learning in the Digital
Age. Making the Learning Sciences Count.

Gurtner, A., Tschan, F., Semmer, N. K., & Nagele, C. (2007). Getting
groups to develop good strategies: Effects of reflexivity interventions on
team process, team performance, and shared mental models. Organiza-
tional Behavior and Human Decision Processes, 102(2), 127–142.
https://doi.org/10.1016/j.obhdp.2006.05.002

Hackman, J. R., & Wageman, R. (2005). A theory of team coaching. Acad-
emy of Management Review, 30, 269–287. https://doi.org/10.5465/amr
.2005.16387885

Harkins, S. (1987). Social loafing and social facilitation. Journal of Experi-
mental Social Psychology, 23(1), 1–18. https://doi.org/10.1016/0022
-1031(87)90022-9

Hewitt, J. (2005). Toward an understanding of how threads die in asyn-
chronous compute conferences. Journal of the Learning Sciences, 14(4),
567–589. https://doi.org/10.1207/s15327809jls1404_4

Hinsz, V. B., Tindale, R. S., & Vollrath, D. A. (1997). The emerging con-
ceptualization of groups as information processors. Psychological Bulle-
tin, 121(1), 43–64. https://doi.org/10.1037/0033-2909.121.1.43

Hofmann, D. A., & Jones, L. M. (2005). Leadership, collective personality,
and performance. Journal of Applied Psychology, 90, 509–522. https://
doi.org/10.1037/0021-9010.90.3.509

Høigaard, R., Safvenbom, R., & Tonnessen, F. E. (2006). The relationship
between group cohesion, group norms, and perceived social loafing in
soccer teams. Small Group Research, 37(3), 217–232. https://doi.org/10
.1177/1046496406287311

Hommes, J., Van den Bossche, P., de Grave, W., Bos, G., Schuwirth, L., &
Scherpbier, A. (2014). Understanding the effects of time on collabora-
tive learning processes in problem based learning: A mixed methods
study. Advances in Health Sciences Education: Theory and Practice,
19(4), 541–563. https://doi.org/10.1007/s10459-013-9487-z

Huguet, P., Charbonnier, E., & Monteil, J. (1999). Productivity loss in per-
formance groups: People who see themselves as average do not engage
in social loafing. Group Dynamics, 3(2), 118–131. https://doi.org/10
.1037/1089-2699.3.2.118

Ilgen, D. R., Hollenbeck, J. R., Johnson, M., & Jundt, D. (2005). Teams in
organizations: From input-process-output models to IMOI models. An-
nual Review of Psychology, 56, 517–543. https://doi.org/10.1146/
annurev.psych.56.091103.070250

Ilgen, D. R., Major, D. A., & Spencer, L. T. (1994). The cognitive revolu-
tion in organizational behavior. In J. Greenberg (Ed.), Organizational
Behavior: The State of the Science (pp. 1–22). Erlbaum.

Isohätälä, J., Näykki, P., & Järvelä, S. (2020). Cognitive and socio-emo-
tional interaction in collaborative learning: Exploring fluctuations in stu-
dents’ participation. Scandinavian Journal of Educational Research,
64(6), 831–851. https://doi.org/10.1080/00313831.2019.1623310

730 GABELICA, DE MAEYER, AND SCHIPPERS

T
hi
s
do
cu
m
en
ti
s
co
py
ri
gh
te
d
by

th
e
A
m
er
ic
an

Ps
yc
ho
lo
gi
ca
lA

ss
oc
ia
tio

n
or

on
e
of

its
al
lie
d
pu
bl
is
he
rs
.

T
hi
s
ar
tic
le
is
in
te
nd
ed

so
le
ly

fo
r
th
e
pe
rs
on
al
us
e
of

th
e
in
di
vi
du
al
us
er

an
d
is
no
tt
o
be

di
ss
em

in
at
ed

br
oa
dl
y.

https://doi.org/10.1007/978-94-017-8902-8_36

https://doi.org/10.1037/0021-9010.84.1.58

https://doi.org/10.1177/104649640003100101

https://doi.org/10.1037/0003-066X.41.10.1040

https://doi.org/10.1037/0022-3514.80.3.501

https://doi.org/10.1037/0022-3514.72.1.218

https://doi.org/10.1037/0021-9010.88.5.821

https://doi.org/10.2307/257067

https://doi.org/10.1080/00461520.2012.747947

https://doi.org/10.3102/00346543074001059

https://doi.org/10.1037/0022-0663.99.4.700

https://doi.org/10.1016/j.learninstruc.2014.09.001

https://doi.org/10.1177/104649649903000502

https://doi.org/10.1037/0021-9010.75.2.107

https://doi.org/10.2307/256478

https://doi.org/10.1037/0021-9010.78.5.798

https://doi.org/10.5465/amr.1985.4279045

https://doi.org/10.1177/1046496410385471

https://doi.org/10.1016/j.obhdp.2006.05.002

https://doi.org/10.5465/amr.2005.16387885

https://doi.org/10.1016/0022-1031(87)90022-9

https://doi.org/10.1207/s15327809jls1404_4

https://doi.org/10.1037/0033-2909.121.1.43

https://doi.org/10.1037/0021-9010.90.3.509

https://doi.org/10.1177/1046496406287311

https://doi.org/10.1007/s10459-013-9487-z

https://doi.org/10.1037/1089-2699.3.2.118

https://doi.org/10.1146/annurev.psych.56.091103.070250

https://doi.org/10.1080/00313831.2019.1623310

Isohätälä, J., Näykki, P., Järvelä, S., & Baker, M. J. (2017). Striking a bal-
ance: Socio-emotional processes during argumentation in collaborative
learning interaction. Learning, Culture and Social Interaction, 16, 1–19.
https://doi.org/10.1016/j.lcsi.2017.09.003

James, L. R., Demaree, R. J., & Wolf, G. (1984). Estimating withingroup
interrater reliability with and without response bias. Journal of Applied
Psychology, 69(1), 85–98. https://doi.org/10.1037/0021-9010.69.1.85

Järvelä, S., & Järvenoja, H. (2011). Socially constructed self-regulated
learning and motivation regulation in collaborative learning groups.
Teachers College Record, 113(2), 350–374.

Järvelä, S., Järvenoja, H., Malmberg, J., Isohätälä, J., & Sobocinski, M.
(2016). How do types of interaction and phases of self-regulated learn-
ing set a stage for collaborative engagement? Learning and Instruction,
43, 39–51. https://doi.org/10.1016/j.learninstruc.2016.01.005

Jassawalla, A. R., Malshe, A., & Sashittal, H. (2008). Student perceptions
of social loafing in undergraduate business classroom teams. Decision
Sciences Journal of Innovative Education, 6(2), 403–406. https://doi
.org/10.1111/j.1540-4609.2008.00183.x

Johnson, D. W., & Johnson, R. T. (1999). Making co-operative learning
work. Theory into Practice, 38(2), 67–74. https://doi.org/10.1080/
00405849909543834

Johnson, D. W., & Johnson, R. T. (2009). An educational psychology success
story: Social interdependence theory and co-operative learning. Educa-
tional Researcher, 38(5), 365–379. https://doi.org/10.3102/0013189X09
339057

Johnson, D. W., & Johnson, R. T. (2014). co-operative Learning in 21st
Century. Anales de Psicología, 30(3), 841–851. https://doi.org/10.6018/
analesps.30.3.201241

Johnson, D. W., & Johnson, R. T. (2015). Theoretical approaches to co-op-
erative learning. In R. Gillies (Ed.), Collaborative learning: Develop-
ments in research and practice (pp. 17–46). Nova.

Johnson, S. D., Suriya, C., Won Yoon, S., Berrett, J. V., & La Fleur, J.
(2002). Team development and group processes of virtual learning
teams. Computers & Education, 39(4), 379–393. https://doi.org/10
.1016/S0360-1315(02)00074-X

Kaplan, A., & Maehr, M. L. (2007). The contributions and prospects of
goal orientation theory. Educational Psychology Review, 19(2),
141–184. https://doi.org/10.1007/s10648-006-9012-5

Karau, S. J., & Wilhau, A. J. (2020). Social loafing and motivation gains
in groups: An integrative review. In S. J. Karau (Ed.), Individual motiva-
tion within groups: Social loafing and motivation gains in work, aca-
demic, and sports teams (pp. 3–51). Academic Press. https://doi.org/10
.1016/B978-0-12-849867-5.00001-X

Karau, S. J., & Hart, J. R. (1998). Group cohesiveness and social loafing:
Effects of a social interaction manipulation on individual motivation
within groups. Group Dynamics Theory Research and Practice, 2(3),
185–191. https://doi.org/10.1037/1089-2699.2.3.185

Karau, S. J., & Williams, K. D. (1993). Social loafing: A meta-analytic
review and theoretical integration. Journal of Personality and Social Psy-
chology, 65(4), 681–706. https://doi.org/10.1037/0022-3514.65.4.681

Karau, S. J., & Williams, K. D. (1997). The effects of group cohesiveness
on social loafing and social compensation. Group Dynamics, 1(2),
156–168. https://doi.org/10.1037/1089-2699.1.2.156

Kayes, A. B., Kayes, C., & Kolb, D. A. (2005). Experiential learning in
teams. Simulation & Gaming, 36(3), 330–354. https://doi.org/10.1177/
1046878105279012

Kirschner, P. A. (2009). Epistemology or pedagogy, that is the question. In
S. Tobias & T. M. Duffy (Eds.), Constructivist theory applied to instruc-
tion: Success or failure? (pp. 144–157). Routledge.

Kozlowski, S. W. J., & Bell, B. S. (2008). Team learning, development,
and adaptation. In V. I. Sessa & M. London (Eds.),Work group learning
(pp. 15–44). LEA.

Kozlowski, S. W. J., & Bell, B. S. (2013). Work groups and teams in
organizations: Review update. In N. Schmitt & S. Highhouse (Eds.),

Handbook of psychology, vol. 12: Industrial and organizational psy-
chology (2nd ed., pp. 412–469). Wiley.

Kozlowski, S. W. J., & Ilgen, D. R. (2006). Enhancing the effectiveness of
work groups and teams. Psychological Science in the Public Interest,
7(3), 77–124. https://doi.org/10.1111/j.1529-1006.2006.00030.x

Kozlowski, S. W. J., & Klein, K. J. (2000). A multilevel approach to
theory and research in organizations: Contextual, temporal, and emer-
gent processes. In K. J. Klein & S. W. J. Kozlowski (Eds.), Multilevel
theory, research, and methods in organizations: Foundations, exten-
sions, and new directions (pp. 3–90). Jossey-Bass.

Kramarski, B., & Mevarech, Z. R. (2003). Enhancing mathematical rea-
soning in the classroom: The effects of co-operative learning and meta-
cognitive training. American Educational Research Journal, 40(1),
281–310. https://doi.org/10.3102/00028312040001281

Kreijns, K., Kirschner, P. A., & Jochems, W. (2003). Identifying the pit-
falls for social interaction in computer-supported collaborative learning
environments: A review of the research. Computers in Human Behavior,
19(3), 335–353. https://doi.org/10.1016/S0747-5632(02)00057-2

Lajoie, S. P., & Lu, J. (2011). Supporting collaboration with technology:
Does shared cognition lead to coregulation in medicine? Metacognition
and Learning, 7(1), 45–62. https://doi.org/10.1007/s11409-011-9077-5

Lam, C. (2015). The role of communication and cohesion in reducing social
loafing in group projects. Business and Professional Communication
Quarterly, 78(4), 454–475. https://doi.org/10.1177/2329490615596417

Lee, E., Yoon, J., & Kim, M. (2010). Analysing participation types and
developing instructional strategies of wiki-based collaborative learning
by considering learners’ goal orientation and self-esteem. The Korean
Journal of Educational Methodology Studies, 22(4), 43–66.

Levine, J. M., & Moreland, R. L. (1994). Group socialization: Theory and
research. European Review of Social Psychology, 5(1), 305–336. https://
doi.org/10.1080/14792779543000093

Levine, J. M., & Moreland, R. L. (2004). Collaboration: The social context
of theory development. Personality and Social Psychology Review, 8(2),
164–172. https://doi.org/10.1207/s15327957pspr0802_10

Levine, J. M., Moreland, R. L., & Choi, H.-S. (2001). Group socialization
and newcomer innovation. In M. Hogg & R. S. Tindale (Eds.), Black-
well handbook of social psychology: Group processes (pp. 86–106).
Blackwell.

Liden, R. C., Wayne, S. J., Jaworski, R. A., & Bennett, N. (2004). Social
loafing: A field investigation. Journal of Management, 30(2), 285–304.
https://doi.org/10.1016/j.jm.2003.02.002

Lim, J. Y., & Lim, K. Y. (2020). Co-regulation in collaborative learning:
Grounded in achievement goal theory. International Journal of Educa-
tional Research, 103(3), 101621. https://doi.org/10.1016/j.ijer.2020.101621

Locke, E. A., Shaw, K. N., Saari, L. M., & Latham, G. P. (1981). Goal set-
ting and task performance: 1969–1980. Psychological Bulletin, 90(1),
125–152. https://doi.org/10.1037/0033-2909.90.1.125

Lord, R. G. (1985). An information processing approach to social percep-
tions, leadership, and behavioral measurement in organizations.
Research in Organizational Behavior, 7, 87–128.

Loughry, M. L., Ohland, M. W., & Moore, D. D. (2007). Development of
a theory-based assessment of team member effectiveness. Educational
and Psychological Measurement, 67(3), 505–524. https://doi.org/10
.1177/0013164406292085

Madjar, N., Kaplan, A., & Weinstock, M. (2011). Clarifying mastery-avoid-
ance goals in high school: Distinguishing between intrapersonal and task-
based standards of competence. Contemporary Educational Psychology,
36(4), 268–279. https://doi.org/10.1016/j.cedpsych.2011.03.003

Malmberg, J., Järvelä, S., & Järvenoja, H. (2017). Capturing temporal and
sequential patterns of self, co- and socially shared regulation in the con-
text of collaborative learning. Contemporary Educational Psychology,
49(1), 160–174. https://doi.org/10.1016/j.cedpsych.2017.01.009

Malmberg, L. E., & Martin, A. J. (2019). Processes of students’ effort
exertion, competence beliefs and motivation: Cyclic and dynamic effects

TAKING A FREE RIDE 731

T
hi
s
do
cu
m
en
ti
s
co
py
ri
gh
te
d
by

th
e
A
m
er
ic
an

Ps
yc
ho
lo
gi
ca
lA

ss
oc
ia
tio

n
or

on
e
of

its
al
lie
d
pu
bl
is
he
rs
.

T
hi
s
ar
tic
le
is
in
te
nd
ed

so
le
ly

fo
r
th
e
pe
rs
on
al
us
e
of

th
e
in
di
vi
du
al
us
er

an
d
is
no
tt
o
be

di
ss
em

in
at
ed

br
oa
dl
y.

https://doi.org/10.1016/j.lcsi.2017.09.003

https://doi.org/10.1037/0021-9010.69.1.85

https://doi.org/10.1016/j.learninstruc.2016.01.005

https://doi.org/10.1111/j.1540-4609.2008.00183.x

https://doi.org/10.1080/00405849909543834

https://doi.org/10.3102/0013189X09339057

https://doi.org/10.6018/analesps.30.3.201241

https://doi.org/10.1016/S0360-1315(02)00074-X

https://doi.org/10.1007/s10648-006-9012-5

https://doi.org/10.1016/B978-0-12-849867-5.00001-X

https://doi.org/10.1037/1089-2699.2.3.185

https://doi.org/10.1037/0022-3514.65.4.681

https://doi.org/10.1037/1089-2699.1.2.156

https://doi.org/10.1177/1046878105279012

https://doi.org/10.1111/j.1529-1006.2006.00030.x

https://doi.org/10.3102/00028312040001281

https://doi.org/10.1016/S0747-5632(02)00057-2

https://doi.org/10.1007/s11409-011-9077-5

https://doi.org/10.1177/2329490615596417

https://doi.org/10.1080/14792779543000093

https://doi.org/10.1207/s15327957pspr0802_10

https://doi.org/10.1016/j.jm.2003.02.002

https://doi.org/10.1016/j.ijer.2020.101621

https://doi.org/10.1037/0033-2909.90.1.125

https://doi.org/10.1177/0013164406292085

https://doi.org/10.1016/j.cedpsych.2011.03.003

https://doi.org/10.1016/j.cedpsych.2017.01.009

of learning experiences within school days and school subjects. Contem-
porary Educational Psychology, 58, 299–309. https://doi.org/10.1016/j
.cedpsych.2019.03.013

Mathieu, J. E., & Rapp, T. L. (2009). Laying the foundation for successful
team performance trajectories: The roles of team charters and perform-
ance strategies. Journal of Applied Psychology, 94(1), 90–103. https://
doi.org/10.1037/a0013257

Mathieu, J. E., Gallagher, P. T., Domingo, M. A., & Klock, E. A. (2019).
Embracing complexity: Reviewing the past decade of team effectiveness
research. Annual Review of Organizational Psychology and Organiza-
tional Behavior, 6(1), 17–46. https://doi.org/10.1146/annurev-orgpsych
-012218-015106

Mathieu, J. E., Maynard, M. T., Rapp, T. L., & Gilson, L. L. (2008). Team
effectiveness 1997-2007: A review of recent advancements and a
glimpse into the future. Journal of Management, 34(3), 410–476.
https://doi.org/10.1177/0149206308316061

McCardle, L., & Hadwin, A. F. (2015). Using multiple, contextualized
data sources to measure learners’ perceptions of their self-regulated
learning. Metacognition and Learning, 10(1), 43–75. https://doi.org/10
.1007/s11409-014-9132-0

Michaelsen, L., Bauman Knight, A., & Fink, L. D. (Eds.). (2004). Team-
based learning: A transformative use of small groups in college teach-
ing. Stylus.

Midgley, C., Kaplan, A., & Middleton, M. (2001). Performance-approach
goals: Good for what, for whom, under what circumstances, and at what
cost? Journal of Educational Psychology, 93(1), 77–86. https://doi.org/
10.1037/0022-0663.93.1.77

Morgeson, F. P., Reider, M. H., & Campion, M. A. (2005). Selecting indi-
viduals in team settings: The importance of social skills, personality
characteristics, and teamwork knowledge. Personnel Psychology, 58(3),
583–611. https://doi.org/10.1111/j.1744-6570.2005.655.x

Muis, K. R., & Edwards, O. (2009). Examining the stability of achieve-
ment goal orientation. Contemporary Educational Psychology, 34(4),
265–277. https://doi.org/10.1016/j.cedpsych.2009.06.003

Mulvey, P. W., & Klein, H. J. (1998). The impact of perceived loafing and
collective efficacy on group goal processes and group performance.
Organizational Behavior and Human Decision Processes, 74(1), 62–87.
https://doi.org/10.1006/obhd.1998.2753

Newman, R. S., & Schwager, M. T. (1995). Students’ help seeking during
problem solving: Effects of grade, goal, and prior achievement. Ameri-
can Educational Research Journal, 32(2), 352–376. https://doi.org/10
.3102/00028312032002352

Oliveira, A. W., & Sadler, T. D. (2008). Interactive patterns and conver-
gence of meaning during student collaborations in science. Journal of
Research in Science Teaching, 45(5), 634–658. https://doi.org/10.1002/
tea.20211

Patrick, H. S., Van Joolingen, W. R., Savelsbergh, E. R., & van Hout-
Wolters, B. (2008). Motivation and performance within a collaborative
computer-based modelling task: Relations between students’ achieve-
ment goal orientation, self-efficacy, cognitive processing, and achieve-
ment. Contemporary Educational Psychology, 33(1), 58–77. https://doi
.org/10.1016/j.cedpsych.2006.12.004

Payne, S. C., Youngcourt, S. S., & Beaubien, J. M. (2007). A meta-analytic
examination of the goal orientation nomological net. Journal of Applied
Psychology, 92(1), 128–150. https://doi.org/10.1037/0021-9010.92.1.128

Pearsall, M. J., Ellis, A. P. J., & Bell, B. S. (2010). Building the infrastruc-
ture: The effects of role identification behaviors on team cognition de-
velopment and performance. Journal of Applied Psychology, 95(1),
192–200. https://doi.org/10.1037/a0017781

Piezon, S., & Ferree, W. (2008). Perceptions of social loafing in online
learning groups: A study of public university and U.S. Naval War Col-
lege students. International Review of Research in Open and Distance
Learning. Advance online publication. https://doi.org/10.19173/irrodl
.v9i2.484

Pintrich, R. R. (2000). Multiple goals, multiple pathways: The role of goal
orientation in learning and achievement. Journal of Educational Psy-
chology, 92(3), 544–555. https://doi.org/10.1037//0022-O663.92.3.544

Podsakoff, P. M., MacKenzie, S. B., Lee, J. Y., & Podsakoff, N. P. (2003).
Common method biases in behavioral research: A critical review of the
literature and recommended remedies. Journal of Applied Psychology,
88(5), 879–903. https://doi.org/10.1037/0021-9010.88.5.879

Poortvliet, P. M., Janssen, O., Van Yperen, N. W., & Van de Vliert, E.
(2009). The joint impact of achievement goals and performance feed-
back on information giving. Basic and Applied Social Psychology, 31,
197–209. https://doi.org/10.1080/01973530903058276

Poortvliet, P. M., Anseel, F., Janssen, O., Van Yperen, N. W., & Van de
Vliert, E. (2012). Perverse effects of other-referenced performance goals
in an information exchange context. Journal of Business Ethics, 106(4),
401–414. https://doi.org/10.1007/s10551-011-1005-8

Poortvliet, P. M., Janssen, O., Van Yperen, N. W., & Van de Vliert, E.
(2009). Low ranks make the difference: How achievement goals and
ranking information affect cooperation intentions. Journal of Experi-
mental Social Psychology, 45(5), 1144–1147. https://doi.org/10.1016/j
.jesp.2009.06.013

Poortvliet, P. M., Janssen, O., Van Yperen, N. W., & Van de Vliert, E.
(2007). Achievement goals and interpersonal behavior: How mastery
and performance goals shape information exchange. Personality and
Social Psychology Bulletin, 33(10), 1435–1447. https://doi.org/10.1177/
0146167207305536

Price, K. H., Harrison, D. A., & Gavin, J. H. (2006). Withholding inputs in
team contexts: Member composition, interaction processes, evaluation
structure, and social loafing. Journal of Applied Psychology, 91(6),
1375–1384. https://doi.org/10.1037/0021-9010.91.6.1375

Pritchard, R. D., Harrell, M. M., DiazGranados, D., & Guzman, M. J.
(2008). The productivity measurement and enhancement system: A
meta-analysis. Journal of Applied Psychology, 93(3), 540–567. https://
doi.org/10.1037/0021-9010.93.3.540

Raes, E., Kyndt, E., Decuyper, S., Van den Bossche, P., & Dochy, F.
(2015). An exploratory study of group development and team learning.
Human Resource Development Quarterly, 26(1), 5–30. https://doi.org/
10.1002/hrdq.21201

Ramos, A., De Fraine, B., & Verschueren, K. (2021). Learning goal orien-
tation in high-ability and average-ability students: Developmental trajec-
tories, contextual predictors, and long-term educational outcomes.
Journal of Educational Psychology, 113(2), 370–389. https://doi.org/10
.1037/edu0000476

Reeve, J. (2015). Understanding motivation and emotion (6th ed.). Wiley.
Rogat, T., & Linnenbrink-García, L. (2011). Socially shared regulation in

collaborative groups: An analysis of the interplay between quality of
social regulation and group processes. Cognition and Instruction, 29(4),
375–415. https://doi.org/10.1080/07370008.2011.607930

Rolland, R. G. (2012). Synthesizing the evidence on classroom goal struc-
tures in middle and secondary schools: A meta-analysis and narrative
review. Review of Educational Research, 82(4), 396–435. https://doi
.org/10.3102/0034654312464909

Saab, N., Van Joolingen, W. R., & Van Hout-Wolters, B. (2012). Support
of the collaborative inquiry learning process: influence of support on
task and team regulation. Metacognition and Learning, 7(1), 7–23.
https://doi.org/10.1007/s11409-011-9068

Salas, E., Stagl, K. C., Burke, C. S., & Goodwin, G. F. (2007). Fostering
team effectiveness in organizations: Toward an integrative theoretical
framework. In B. Shuart, W. Spaulding, & J. Poland (Eds.), Modeling
complex systems (pp. 185–243). University of Nebraska Press.

Schippers, M. C. (2014). Social loafing tendencies and team performance:
The compensating effect of agreeableness and conscientiousness. Acad-
emy of Management Learning & Education, 13(1), 62–81. https://doi
.org/10.5465/amle.2012.0191

732 GABELICA, DE MAEYER, AND SCHIPPERS

T
hi
s
do
cu
m
en
ti
s
co
py
ri
gh
te
d
by

th
e
A
m
er
ic
an

Ps
yc
ho
lo
gi
ca
lA

ss
oc
ia
tio

n
or

on
e
of

its
al
lie
d
pu
bl
is
he
rs
.

T
hi
s
ar
tic
le
is
in
te
nd
ed

so
le
ly

fo
r
th
e
pe
rs
on
al
us
e
of

th
e
in
di
vi
du
al
us
er

an
d
is
no
tt
o
be

di
ss
em

in
at
ed

br
oa
dl
y.

https://doi.org/10.1016/j.cedpsych.2019.03.013

https://doi.org/10.1037/a0013257

https://doi.org/10.1146/annurev-orgpsych-012218-015106

https://doi.org/10.1177/0149206308316061

https://doi.org/10.1007/s11409-014-9132-0

https://doi.org/10.1037/0022-0663.93.1.77

https://doi.org/10.1111/j.1744-6570.2005.655.x

https://doi.org/10.1016/j.cedpsych.2009.06.003

https://doi.org/10.1006/obhd.1998.2753

https://doi.org/10.3102/00028312032002352

https://doi.org/10.1002/tea.20211

https://doi.org/10.1016/j.cedpsych.2006.12.004

https://doi.org/10.1037/0021-9010.92.1.128

https://doi.org/10.1037/a0017781

https://doi.org/10.19173/irrodl.v9i2.484

https://doi.org/10.1037//0022-O663.92.3.544

https://doi.org/10.1037/0021-9010.88.5.879

https://doi.org/10.1080/01973530903058276

https://doi.org/10.1007/s10551-011-1005-8

https://doi.org/10.1016/j.jesp.2009.06.013

https://doi.org/10.1177/0146167207305536

https://doi.org/10.1037/0021-9010.91.6.1375

https://doi.org/10.1037/0021-9010.93.3.540

https://doi.org/10.1002/hrdq.21201

https://doi.org/10.1037/edu0000476

https://doi.org/10.1080/07370008.2011.607930

https://doi.org/10.3102/0034654312464909

https://doi.org/10.1007/s11409-011-9068

https://doi.org/10.5465/amle.2012.0191

Schippers, M. C., Homan, A. C., & van Knippenberg, D. L. (2013). To
reflect or not to reflect: Prior team performance as a boundary condition
of the effects of reflexivity on learning and final team performance.
Journal of Organizational Behavior, 34(1), 6–23. https://doi.org/10
.1002/job.1784

Schippers, M. C., & Scheepers, A. W. A. (2020). Individual motivation,
team learning and performance in collaborative academic contexts. In
S. J. Karau (Ed.), Individual motivation within groups: Social loafing
and motivation gains in work, academic, and sports teams (pp. 81–108).
Academic Press. https://doi.org/10.1016/B978-0-12-849867-5.00003-3

Simms, A., & Nichols, T. (2014). Social loafing: A review of the literature.
Journal of Management Policy and Practice, 15(1), 58.

Skilton, P. F., Forsyth, D., & White, O. J. (2008). Interdependence and
integration learning in student project teams: Do team project assign-
ments achieve what we want them to? Journal of Marketing Education,
30(1), 57–65. https://doi.org/10.1177/0273475307312198

Skinner, E. A., Kindermann, T. A., Connell, J. P., Wellborn, J. G. (2009).
Engagement and disaffection as organizational constructs in the dynam-
ics of motivational development. In K. R. Wenzel & A. Wigfield (Eds.),
Educational psychology handbook series. Handbook of motivation at
school (p. 223–245). Routledge/Taylor & Francis Group.

Slavin, R. E. (2011). Instruction based on co-operative learning. In R.
Mayer (Ed.), Handbook of research on learning and instruction (pp.
344–360). Taylor and Francis.

Stark, E. M., Shaw, J. D., & Duffy, M. K. (2007). Preference for group
work, winning orientation, and social loafing behavior in groups. Group
& Organization Management, 32(6), 699–723. https://doi.org/10.1177/
1059601106291130

Tan, H. H., & Tan, M. L. (2008). Organizational citizenship behavior and
social loafing: The role of personality, motives, and contextual factors.
The Journal of Psychology, 142(1), 89–108. https://doi.org/10.3200/
JRLP.142.1.89-112

Tan, H. H., & Tan, M. L. (2008). Cooperation versus competition effects
on information sharing and use in group decision-making. Social and
Personality Psychology Compass, 142(1), 89–467.

Toma, C., & Butera, F. (2015). Cooperation versus competition effects on
information sharing and use in group decision making. Social and Per-
sonality Psychology Compass, 9(9), 455–467. https://doi.org/10.1111/
spc3.12191

Tuckman, B. W., & Jensen, M. A. C. (1977). Stages of small-group devel-
opment revisited. Group & Organization Studies, 2(4), 419–427. https://
doi.org/10.1177/105960117700200404

Urdan, T., & Kaplan, A. (2020). The origins, evolution and future direc-
tions of achievement goal theory. Contemporary Educational Psychol-
ogy, 61, 101862https://doi.org/10.1016/j.cedpsych.2020.101862

Van den Bossche, P., Gijselaers, W., Segers, M., Woltjer, G., & Kirschner,
P. A. (2011). Team Learning: Building Shared Mental Models. Instruc-
tional Science, 39(3), 283–301. https://doi.org/10.1007/s11251-010-9128-3

van Dick, R., Tissington, P. A., & Hertel, G. (2009). Do many hands make
light work? How to overcome social loafing and gain motivation in

work teams. European Business Review, 21(3), 233–245. https://doi.org/
10.1108/09555340910956621

Vangrieken, K., Dochy, F., & Raes, E. (2016). Team learning in teacher
teams: Team entitativity as a bridge between teams-in-theory and teams-
in-practice. European Journal of Psychology of Education, 31(3),
275–298. https://doi.org/10.1007/s10212-015-0279-0

Veenman, S., Denessen, E., van den Akker, A., & van der Rijt, J. (2005).
Effects of co-operative learning program on the elaborations of students
during help seeking and help-giving. American Educational Research
Journal, 42(1), 115–115. https://doi.org/10.3102/00028312042001115

Volet, S., & Mansfield, C. (2006). Group work at university: Significance
of personal goals in the regulation strategies of students with positive
and negative appraisals. Higher Education Research & Development,
25(4), 341–356. https://doi.org/10.1080/07294360600947301

Volet, S. E., Summers, M., & Thurman, J. (2009). High-level co-regulation
in collaborative learning: How does it emerge and how is it sustained?
Learning and Instruction, 19(2), 128–143. https://doi.org/10.1016/j
.learninstruc.2008.03.001

Webb, N., & Cullian, L. (1983). Group interaction and achievement in
small groups: Stability over time. American Educational Research Jour-
nal, 20(3), 411–423. https://doi.org/10.3102/00028312020003411

Webb, N. M., Nemer, K. M., Chizhik, A. W., & Sugrue, B. (1998). Equity
issues in collaborative group assessment: Group composition and per-
formance. American Educational Research Journal, 35(4), 607–651.
https://doi.org/10.3102/00028312035004607

Webb, N., & Farivar, S. (1994). Promoting helping behavior in co-opera-
tive small groups in middle school mathematics. American Educa-
tional Research Journal, 31(2), 369–396. https://doi.org/10.3102/
00028312031002369

Weinberger, A., Stegmann, K., & Fischer, F. (2007). Knowledge conver-
gence in collaborative learning: Concepts and assessment. Learning and
Instruction, 17(4), 416–426. https://doi.org/10.1016/j.learninstruc.2007
.03.007

Wiese, C. W., & Burke, C. S. (2019). Understanding Team Learning Dy-
namics Over Time. Frontiers in Psychology, 10, 1417. https://doi.org/10
.3389/fpsyg.2019.01417

Williams, K., Harkins, S., & Latane, B. (1981). Identifiability as a deter-
rent to social loafing: Two cheering experiments. Journal of Personality
and Social Psychology, 40(2), 303–311. 0022-3514/814002-0303100.

Wilson, J. M., Goodman, P. S., & Cronin, M. (2007). Group learning.
Academy of Management Review, 32(4), 1041–1059. https://doi.org/10
.5465/amr.2007.26585724

Zhu, M., Singh, S., & Wang, H. (2019). Perceptions of social loafing dur-
ing the process of group development. International Journal of Organi-
zation Theory and Behavior, 22(4), 350–368. https://doi.org/10.1108/
IJOTB-04-2018-0049

Received August 19, 2020
Revision received July 10, 2021

Accepted August 17, 2021 n

TAKING A FREE RIDE 733

T
hi
s
do
cu
m
en
ti
s
co
py
ri
gh
te
d
by

th
e
A
m
er
ic
an

Ps
yc
ho
lo
gi
ca
lA

ss
oc
ia
tio

n
or

on
e
of

its
al
lie
d
pu
bl
is
he
rs
.

T
hi
s
ar
tic
le
is
in
te
nd
ed

so
le
ly

fo
r
th
e
pe
rs
on
al
us
e
of

th
e
in
di
vi
du
al
us
er

an
d
is
no
tt
o
be

di
ss
em

in
at
ed

br
oa
dl
y.

https://doi.org/10.1002/job.1784

https://doi.org/10.1016/B978-0-12-849867-5.00003-3

https://doi.org/10.1177/0273475307312198

https://doi.org/10.1177/1059601106291130

https://doi.org/10.3200/JRLP.142.1.89-112

https://doi.org/10.1111/spc3.12191

https://doi.org/10.1177/105960117700200404

https://doi.org/10.1016/j.cedpsych.2020.101862

https://doi.org/10.1007/s11251-010-9128-3

https://doi.org/10.1108/09555340910956621

https://doi.org/10.1007/s10212-015-0279-0

https://doi.org/10.3102/00028312042001115

https://doi.org/10.1080/07294360600947301

https://doi.org/10.1016/j.learninstruc.2008.03.001

https://doi.org/10.3102/00028312020003411

https://doi.org/10.3102/00028312035004607

https://doi.org/10.3102/00028312031002369

https://doi.org/10.1016/j.learninstruc.2007.03.007

https://doi.org/10.3389/fpsyg.2019.01417

https://doi.org/10.5465/amr.2007.26585724

https://doi.org/10.1108/IJOTB-04-2018-0049

Literature Review and Hypotheses

Concept of Social Loafing

Operationalization of Social Loafing

Social Loafing and Contextual Factors

Social Loafing and Individual Differences

Learning Orientation and Performance Orientation

Social Loafing and Team-Level Factors

Social Loafing and Team Learning

Dynamics of Social Loafing and Team Learning

Method

Data and Sample

Procedure

Measures

Team Learning

Social Loafing Tendencies

Learning Orientation and Performance Orientation

Data Aggregation

Hypotheses Testing

Results

Self-Rated Social Loafing

Level 1 Analyses: How Does Self-Rated Social Loafing Change Over Time?

Level 2 Analyses: How Do Learning Orientation, Performance Orientation, and Team Learning Affect Self-Rated Social Loafing?

Peer-Rated Social Loafing

Level 1 Analyses: How Does Self-Rated Social Loafing Change Over Time?

Level 2 Analyses: How Do Learning Orientation, Performance Orientation, and Team Learning Affect Peer-Rated Social Loafing?

Discussion

Limitations and Future Directions

Practical Implications

References

R E S E A R CH A R T I C L E

Examining the social influence of reputation for
partner productivity level on the collaborative task
performance of young children

Hikaru Hamamoto1 | Risa Mizobata1 | Mitsuhiko Ishikawa1 |

Shoji Itakura2

1Department of Psychology, Graduate School of Letters, Kyoto University, Kyoto, Japan

2Center for Baby Science, Doshisha University, Kizugawa, Japan

Correspondence

Mitsuhiko Ishikawa, Department of

Psychology, Graduate School of Letters,

Kyoto University, Yoshida-Honmachi, Kyoto

606-8501, Japan.

Email: ishikawa.mitsuhiko.23r@st.kyoto-u.ac.jp

Funding information

Japan Society for the Promotion of Science,

Grant/Award Number: 16H06301

Abstract

Humans adjust behaviour in the presence of others in a phe-

nomenon called social influence, which can be categorized

into social facilitation (promotional effects) and social

loafing

(inhibitory effects). The study examined whether the produc-

tivity level of the partner in individual and collaborative tasks

produced a social influence on children’s task

performance.

The participants were aged 4- to 6-years-old and grouped as

high-productivity, low-productivity, and control according to

the productivity level of the partners. Experiments 1 and

2 assigned adults and young peers as partners, respectively.

Children’s performance in the collaborative task decreased

compared with the individual task. This effect was

unobserved when the adult partner was highly productive

but occurred regardless of productivity level when the part-

ner was a peer. Positive correlations were also observed

between children’s and partners’ performance in both experi-

ments. The results thus suggested that young children adjust

their behaviour based on the partner’s actions.

Highlights

• The social influence of partner’s productivity level on

young children’s task performance was examined during

collabo

rative task.

• Children performed individual and collaborative tasks with

an adult (Experiment 1) or child partner (Experiment 2).

Received: 7 May 2020 Revised: 25 October 2020 Accepted: 21 November 2020

DOI: 10.1002/icd.2213

https://doi.org/10.1002/icd.2213

https://orcid.org/0000-0002-5141-6511

http://wileyonlinelibrary.com/journal/icd

https://doi.org/10.1002/icd.2213

• Positive correlations were observed between children’s

and partners’ performance in both experiments.

K E YWORD S

children, joint action, social facilitation, social influence, social

loafing

1 | INTRODUCTION

When people work on tasks, the presence or absence of others influences their behaviour, attitudes, and emotions.

In general, these effects are referred to as social influence (Raven, 1965). Previous studies suggested that social influ-

ence can be categorized into two types, namely, social facilitation and social loafing (Harkins, 1987).

1.1 | Social facilitation

Social facilitation is a phenomenon in which the amount of work is increased when working as a group compared

with working alone on a task (Guerin, 2010). Previous studies on adults report social facilitation during task perfor-

mance in laboratory settings (Aiello & Svec, 1993; Woods, Dautenhahn, & Kaouri, 2005). For instance, using memory

tasks, Meade, Nokes, and Morrow (2009) demonstrate that when two participants memorized a given scenario at

the same time, they showed better performance compared with memorizing alone.

From the developmental perspective, previous studies show that children between 1- and 3-years-old can engage

in cooperative behaviours in which multiple people act towards the same goal (for a review, see Brownell, 2011). For

example, when two toddlers collaborate to remove a toy from a box or cylinder, 24-month-old dyads succeeded multi-

ple times, whereas 18-month-old dyads did so infrequently. However, 12-month-old dyads were unsuccessful. These

results suggest that children may collaborate from approximately 2 years of age (Brownell & Carriger, 1990).

Moreover, researchers illustrate that in cooperative situations, as in the case of adults, social facilitation occurs

in early childhood. The research on social facilitation in children dates back to Triplett (1898). When instructed to

turn a fishing rod reel, Triplett reported that children performed the task faster when working with others than work-

ing alone. Furthermore, Arterberry, Cain, and Chopko (2007) focused on 5-year-old children who were instructed to

complete puzzles of varying levels of difficulty either alone or with a peer partner. Half the children were informed

that they were being evaluated, whereas half were not. The results revealed that the children demonstrated social

facilitation with the easy puzzle because they displayed better task performance with a partner compared with work-

ing alone and with awareness of being evaluated. Park and Lee (2015) examined social facilitation in preschoolers

using a classification task, in which multiple objects were classified according to their features, and a perspective-

taking task, in which the participants’ imagined the thoughts and feelings of characters in a story. Children conducted

tasks either alone or with a peer partner. Both tasks were performed better when working with a partner than work-

ing individually, and social facilitation occurred in both tasks. Thus, developmental studies revealed that social facilita-

tion during collaborative tasks can be observed in children from the age of 5-years-old.

1.2 | Social loafing

Alternatively, social loafing is a phenomenon in which individuals expend less effort when working collectively than

individually (Karau & Williams, 1993). In a study with adult participants, Latané, Williams, and Harkins (1979)

2 of 14 HAMAMOTO ET AL.

reported that when asked to enter a room alone or in groups and clap and shout to make as much noise as possible,

the participants made less noise in the group than the alone condition. When asked to wear headphones and blind-

folds, such that they were unaware of how others in the group were doing their tasks, and were asked to shout as

loudly as possible, the participants made less noise in the group than the alone condition. This finding indicated that

social loafing occurred when working with

others.

Social loafing may occur in preschool children as well. In the abovementioned experiment by Arterberry

et al. (2007), children performed poorly on the easy puzzle when working with a peer partner compared with working

alone and if they were unaware of being evaluated. Thus, studies on adults and young children suggest that simply

performing tasks with others cannot determine whether social influence leads to behaviours consistent with facilita-

tion or loafing.

1.3 | Partner characteristics and social influence in young children

In the fields of educational and developmental psychology, studies on social influence tended to focus on promoting

cooperative behaviour and improving learning in young children. These studies suggested that social facilitation of

learning efficiency is likely to occur when preschoolers and young children learn new skills and knowledge by work-

ing with highly skilled infants or others who are more capable, such as adults (Piaget, 1968; Rogoff, 1990, 2003;

Vygotsky, 1978; for reviews, see Azmitia, 1988; Foley, Ratner, & House, 2002; Forman, Minick, & Stone, 1993;

Garton & Pratt, 2001; Maynard & Martini, 2005; Pine & Messer, 1998; Tudge & Winterhoff, 1993).

Partner characteristics may be influential not only in learning situations but also in adjusting the social influence

of collaborative tasks. Haux, Engelmann, Herrmann, and Tomasello (2017) reported that children were instructed to

select a puppet with which to play a game and asked which puppet helps a child participant to win. The children

based their choice on information about the puppet’s social characteristics (linguistically instructed reliability). The

study further demonstrated that 5-year-old children gave negative information more weight, thus strongly avoiding

puppets characterized as unreliable. A partner’s characteristics can influence partner selection in a collaborative task.

Thus, the partner’s characteristics can also be considered to influence performance in a collaborative task. Specifi-

cally, a possibility exists that social facilitation, such as that of learning as demonstrated in previous studies

(Maynard & Martini, 2005), may be more likely to occur when a task is performed with a partner who is likely to

achieve a high result for the goal (e.g., a partner with high productivity, competence, or reliability).

Moreover, previous studies illustrated that the ability of partners to work together influences learning efficiency

(Garton & Pratt, 2001). However, the mechanism in which partner characteristics influence performance efficiency

in collaborative tasks is unclear (i.e., partner’s social influence). Nevertheless, the notion that partner characteristics

as determinants of the occurrence of social facilitation or social loafing is possible. Thus, the present study aimed to

examine how partner characteristics influence performance efficiency in collaborative tasks. In two experiments,

information about the partner’s productivity level was manipulated to examine how this aspect influences the perfor-

mance efficiency of the other

partner.

The objective of the study was to investigate the social influence of information about a partner’s productivity

level on the task performance of young children. Previous studies illustrated that social facilitation and loafing start

at approximately 5 years of age (Arterberry et al., 2007; Park & Lee, 2015). In the present study, children aged 4 to

6 years underwent two experiments. Scholars have suggested that young children learn new things more efficiently

by working with a partner who is more capable (Azmitia, 1988; Foley et al., 2002; Garton & Pratt, 2001; Pine &

Messer, 1998; Tudge & Winterhoff, 1993). Therefore, the study predicted that the performance of children will

improve when performing a task with a highly capable partner on the premise that the ability of a partner influences

children’s learning efficiency in collaborative tasks. In Experiment 1, young children were paired with adult partners

in a collaborative task. Capability was considered different between adult and child partners; thus, the children were

paired with peer partners in Experiment 2.

HAMAMOTO ET AL. 3 of 14

2 | EXPERIMENT 1

In Experiment 1, young children were paired with adult partners to examine the social influence of informa-

tion about partner characteristics (level of productivity) on the children’s task performance. Previous studies

proposed that 5-year-old children exhibit social facilitation and loafing (Arterberry et al., 2007; Park &

Lee, 2015). Thus, the study recruited children aged 4 to 6 years as participants. The objective of the present

study was to clarify the social influence of partner characteristics (productivity level) by comparing the differ-

ence in the number of stickers that participants can place in 1 min in an individual task compared with a

collaborative task.

2.1 | Methods

2.1.1 | Participants

The participants consisted of a total of 81 children (mean age 68 months, SD = 7.83, range = 56–80 months),

which included 17 4-year-old children (10 boys and 7 girls), 36 5-year-old children (18 boys and 18 girls), and 28

6-year-old children (13 boys and 15 girls). Eight additional children participated in the experiment but were

excluded from analysis because data could not be obtained or due to procedural deficiencies. All participants

were Japanese. The sample size was determined according to Arterberry et al. (2007), who examined the social

influence on child behaviour with between-subject factors. In addition, post hoc power analysis was conducted

with G*Power (Erdfelder, Faul, & Buchner, 1996) using the effect size of interaction between groups and task

conditions. The result indicated that with the present sample, 95% power with alpha at .05 was achieved for

determining the interaction between groups and task conditions. All participants attended a public kindergarten in

Osaka or Kyoto Prefecture. Two university students participated in the experiment as adult partners for the col-

laborative task. The adult partners were balanced between high and low productivity. The parents of the child

participants were briefed on the purpose and content of the research and provided written informed consent for

the participation of the children. The Research Ethics Review Board of Department of Psychology, Kyoto Univer-

sity, Japan, approved the experimental protocol.

2.1.2 | Materials and experimental environment

A board with an illustration of a tree (printed on A4 paper) and round stickers (approximately 1.5 cm) were used for

the task. The stickers used by the adult partners were marked with black dots to distinguish from those used by the

child participants. To control the task time, the experimenter measured time using a stopwatch.

2.1.3 | Experimental conditions

The child participants were given information about the productivity level of their partner (i.e., high-productivity,

low-productivity, and control), and condition was treated as a between-subject factor.

The participants were randomly assigned to one of the three groups: high-productivity = 25 participants, low-

productivity = 27 participants, and control group = 29 participants.

In the high-productivity group, the participants were informed that their partner was good at applying stickers.

However, the opposite information was provided for the low-productivity group. In the control group, the partici-

pants were not given any information about the ability of their partners to apply stickers.

4 of 14 HAMAMOTO ET AL.

2.1.4 | Procedure

Three people were present during the experiment, namely, one child participant, one adult partner, and one experi-

menter. The child participant and adult partner sat side-by-side in chairs during the task. When the child participant

worked on the individual task, the adult partner sat in the same room with their back to the child. The experimenter

sat on the opposite side and watched them work on their tasks. On the desk, a sheet of stickers was provided for

the child and adult to use in the task. The adult partners used stickers with black dots to distinguish from those used

by the child participants. Both were given instructions to perform two tasks, namely, an individual task and a collabo-

rative task. The order of the tasks was counterbalanced.

2.1.5 | Individual task

Before the individual task, the experimenter explained to the children that their task was to place a round sticker on

the board with the illustration of a tree. The participants were then asked whether they were good at placing stickers

on the board, to which they responded that they were either good or bad. The children were then instructed to apply

as many stickers to the board as possible within 1 min. To motivate the children to engage in the task, they were told

that the total number of stickers for one child will be compared with those applied by other children. While engaging

in the individual task, the adult partners turned their backs to avoid seeing the children work on the task. Task time

was measured with the stopwatch, and the children’s actions during the task were recorded.

2.1.6 | Joint task

The child participant and adult partner sat side by side. The experimenter explained to both that the task was to

place round stickers on the board with the tree illustration. The participants were then asked whether they were

good at placing stickers on the board. After answering, the participants in the high- and low-productivity groups

were asked whether they were also good at placing stickers on the board. In the high-productivity group, the adult

partners replied that they are good at applying stickers, whereas those in the low-productivity group replied that

they are bad at applying stickers.

To ensure understanding of their partner’s productivity level, the child participants were asked whether their

partner was good at applying stickers. The question was repeated until the participant’s answer matched the part-

ner’s productivity level. Approximately 77% of the children provided correct answers to the partner’s productivity

level at the first instance. In the control group, the adult partners were not asked about their productivity level.

Instead, they immediately moved to the task instruction.

The experimenter explained to the children and adults that they should work together to place as many stickers on

the board as possible in 1 min. They were also told that their total number of stickers will be compared with those

applied by other child participants. During the joint task, the adult partner maintained the speed of applying stickers. The

adult partners in the high-productivity, low-productivity, and control groups applied approximately 20 (mean = 21.56,

SD = 3.77), 10 (mean = 9.11, SD = 1.96), and 15 stickers (mean = 14.59, SD = 4.50) stickers, respectively.

The task time was measured with the stopwatch, and the participants’ actions during the task were recorded.

2.2 | Results

The Shapiro–Wilk test was used to assess the normality of the distributions. All data presented a normal distribution.

Table 1 shows the average number of stickers placed by the participants for each condition. A repeated measures

HAMAMOTO ET AL. 5 of 14

ANOVA was conducted to examine differences in the mean number of stickers across groups (high-productivity,

low-productivity, and control), task conditions (individual and joint), and age (4-, 5-, and 6-years-old). As a result, a

significant interaction between groups and task conditions was observed, F(2,72) = 3.717, p = .29, ηp
2 = .094. After-

ward, a series of Bonferroni-corrected follow-up pairwise comparisons were performed. The number of stickers

applied in the joint task was significantly decreased than the individual task in the low-productivity (p = .006,

ηp
2 = .101) and control (p = .017, ηp

2 = .076) groups. No difference between task conditions was found for the high-

productivity group (p = .343, ηp
2 = .013).

In addition, a significant main effect of condition was found, whereas the number of stickers applied in the joint

task was significantly decreased than the individual task, F(2,72) = 4.902, p = .030, ηp
2 = .064. This result suggests

that the child participants exhibited social loafing in the collaborative task. No other interactions and main effects

reached significance: group × task conditions × age: F(2,72) = 1.963, p = .109, ηp
2 = .098; task conditions × age: F

(2,72) = 1.043, p = .358, ηp
2 = .028; group: F(2,72) = 0.316, p = .730, ηp

2 = .009; age: F(2,72) = 0.431, p = .651,

ηp
2 = .012.

The speed of the adult partner’s behaviour during the collaborative task can influence the child participants’ per-

formance, a correlation analysis was conducted to examine whether a relationship exists between the number of

stickers placed by the child participants and those by the adult partners. The correlation was significant and positive

(r = 0.521, p < .001), which indicates that the child participants adjusted their performance to match the performance

of the adult partners (Figure 1).

For further examination of the children’s adjustment of their performance to their partner, the study examined

whether the partners placed fewer stickers in the joint task than those placed by the children in the individual task.

The t test indicated that the partners in the joint task placed fewer stickers than in the individual task (partner aver-

age vs. individual average: 14.91 vs. 16.95, p = .017). This finding suggests that the children adjusted their perfor-

mance to that of their partner.

TABLE 1 Average number of stickers
placed by participants for each condition
in Experiment 1

Task Group Age Mean SD

Individual High productivity 4-years-old 13.0 3.6

5-years-old 17.0 2.8

6-years-old 16.0 5.5

Low-productivity 4-years-old 19.3 3.8

5-years-old 15.9 4.2

6-years-old 18.4 4.4

Control 4-years-old 14.9 4.5

5-years-old 18.4 3.3

6-years-old 18.2 6.4

Joint High productivity 4-years-old 16.7 4.7

5-years-old 15.7 3.2

6-years-old 16.3 3.9

Low-productivity 4-years-old 17.2 5.1

5-years-old 15.0 4.1

6-years-old 14.7 3.5

Control 4-years-old 12.4 4.7

5-years-old 18.1 3.5

6-years-old 15.7 5.5

6 of 14 HAMAMOTO ET AL.

2.3 | Discussion

Experiment 1 aimed to examine whether the task performance of the child participants was influenced by informa-

tion about the productivity level of their adult partners during the collaborative task. The study predicted that the

children’s performance will be facilitated when performing tasks with a high-productivity partner. The results illus-

trated that the children’s performance during the collaborative task was decreased in the low-productivity or control

group. Correlation analysis between the number of stickers applied by children compared to that of adult partners

during the collaborative task indicated that the more stickers the adults applied, the more stickers the children

applied.

Experiment 1 demonstrated that young children may engage in social loafing when performing collaborative

tasks with adult partners who are not highly productive. The results of the correlation analysis denoted that children

were influenced by the behaviour of their partners in the collaborative task. Joint actions are defined as a social

interaction in which two or more people spatiotemporally coordinate their actions to cause an environmental change

(Sebanz, Bekkering, & Knoblich, 2006). A previous study on cooperative behaviour reported that even when partners

were not instructed to synchronize their behaviour, the behaviour of each partner was automatically synchronized.

For example, synchronization has been reported in repetitive physical exercises, such as leg shaking (Schmidt, Care-

llo, & Turvey, 1990), hand-rocking vibrators (Schmidt & Turvey, 1994), rocking chairs (Richardson, Marsh, Isenhower,

Goodman, & Schmidt, 2007), finger swinging (Oullier, De Guzman, Jantzen, Lagarde, & Scott Kelso, 2008), and walk-

ing (Zivotofsky, Gruendlinger, & Hausdorff, 2012; Zivotofsky & Hausdorff, 2007). In Experiment 1 of the present

study, the participants were only instructed to place as many stickers as possible. No further instructions were pro-

vided regarding the order and speed of sticker placement. The children may have voluntarily monitored the behav-

iour of the adult partners during the collaborative tasks and may have been influenced by the engagement of the

partner’s behaviours.

Thus, Experiment 1 demonstrated that young children were influenced by their partner’s behaviour because the

children exhibited social loafing during the collaborative task with adult partners. However, previous studies

F IGURE 1 Correlation between the number of stickers sealed by partners and participants. The vertical axis
shows the number of seals for the participants during the task, whereas the horizontal axis denotes the number of
seals for the partner. The dotted line indicates a linear approximation straight line

HAMAMOTO ET AL. 7 of 14

suggested that social loafing occurs when high expectations are placed on the ability of partners and teammates dur-

ing collaborative tasks (Hardy & Crace, 1991a, 1991b). Therefore, the notion that social loafing was induced because

the partner was an adult is possible regardless of information about their productivity level. In Experiment 2, same-

age young children (peers) were assigned as partners to exclude the influence of the partner as an adult.

3 | EXPERIMENT 2

In Experiment 2, a peer partner was assigned to eliminate the possibility that social loafing occurred because the

partner was an adult.

3.1 | Method

3.1.1 | Participants

A total of 80 children (mean age = 63 months, SD = 5.87 months, range = 54–77 months) participated in Experiment

2. This group included 26 4-year-old children (10 boys and 16 girls), 46 5-year-old children (23 boys and 23 girls),

and 8 6-year-old children (5 boys and 3 girls). No participants took part in Experiments 1 and 2. Thirteen additional

children participated in Experiment 2 but were excluded from analysis as either data could not be obtained when

children interrupted the task before completing it or due to procedural deficiencies. The participants were Japanese.

The sample size was determined to match Experiment 1. All participants attended a public kindergarten in Osaka

Prefecture. The parents were informed about the purpose and content of the research, who provided written

informed consent for the participation of their children. Information about the productivity level of the partners

(high-productivity, low-productivity, and control) was treated as a between-subject factor. The participants were

divided into three groups, namely, high-productivity (n = 26), low-productivity (n = 28), and control (n = 26).

3.1.2 | Procedure

The same task in Experiment 1 was used in Experiment 2. Its only difference from Experiment 1 was that peers were

assigned as partners in the collaborative task. The actual productivity of the peer partner was not manipulated in

Experiment 2. Only one of the children was given information about the partner’s productivity by the experimenter;

thus only one data point per pair was acquired. The other child of the pair who was not given information of the

partner’s productivity was instructed to work together with the partner to place as many stickers on the board as

possible in 1 min. In addition, children from the productivity groups were told that their total number of stickers will

be compared with those of other child participants. The children were randomly assigned as pairs in a kindergarten

to avoid the effects of familiarity. To ensure understanding of their partner’s productivity level, the child participants

were asked whether their partner was good at applying stickers. The question was repeated until the participant’s

answer matched the partner’s productivity level. Approximately 72% of the children provided the correct answer at

the first instance.

3.2 | Results

The Shapiro–Wilk test was used to assess the normality of the distributions. All data presented a normal distribution.

Table 2 displays the average number of stickers placed by the participants for each condition. A repeated measures

8 of 14 HAMAMOTO ET AL.

ANOVA was used to examine differences in the mean number of stickers across groups (high-productivity, low-pro-

ductivity, and control), task conditions (individual and joint), and age (4-, 5-, and 6-years-old). A significant main effect

of condition was observed, and the number of stickers applied in the joint task was significantly decreased than the

individual task, F(2,72) = 4.902, p = .030, ηp
2 = .064. The result suggests that the child participants exhibited social

loafing in the collaborative task. In addition, the results revealed a significant main effect of age, F(2,72) = 7.296,

p = .01, ηp
2 = .169. and 4-year-old children applied less stickers than 5- (p = .002) and 6-year-old (p = .009) children.

No other interactions and main effects reached significance: group × task conditions × age: F(2,72) = 0.747, p = .527,

ηp
2 = .030; group × task conditions: F(2,72) = 0.852, p = .431, ηp

2 = .023; task conditions × age: F(2,72) = 0.418,

p = .660, ηp
2 = .011; group: F(2,72) = .324, p = .724, ηp

2 = .009.

The speed of the partner’s behaviour during the collaborative task can possibly influence the participants’ perfor-

mance. Thus, correlation analysis was conducted to examine whether a relationship exists between the number of

stickers placed by the participants and those of their partners (Figure 2). The correlation was significant and positive

(r = 0.292, p = .009), which indicates that the more stickers the partner placed, the more stickers the participant

placed (Figure 2).

For further examination of children’s adjustment of their performance to their partner, the study examined

whether partners placed fewer stickers in the joint task than those placed by children in the individual task. The

t test indicated that the partners in the joint task placed fewer stickers than those in the individual task (partner aver-

age vs. individual average: 19.59 vs. 21.32, p = .009), which suggests that children adjust their performance to their

partner.

3.3 | Discussion

Experiment 2 aimed to investigate whether the task performance of the children was influenced by information

about the productivity level of their peer partner during the collaborative task.

TABLE 2 Average number of stickers
placed by participants for each condition
in Experiment 2

Task Group Age Mean SD

Individual High productivity 4-years-old 20.6 5.2

5-years-old 21.4 3.4

6-years-old 23.5 2.4

Low-productivity 4-years-old 19.4 4.1

5-years-old 22.4 3.5

6-years-old 20.7 4.1

Control 4-years-old 17.2 5.9

5-years-old 22.6 3.4

6-years-old 24.3 4.3

Joint High productivity 4-years-old 17.2 3.8

5-years-old 19.2 3.6

6-years-old 18.5 2.4

Low-productivity 4-years-old 17.1 3.8

5-years-old 20.4 3.3

6-years-old 18.5 3.9

Control 4-years-old 14.6 7.3

5-years-old 20.0 3.2

6-years-old 22.5 3.0

HAMAMOTO ET AL. 9 of 14

In this experiment, information about the partner’s productivity level did not influence the difference in the

participants’ performance in the collaborative task compared with the individual task. This finding was inconsis-

tent with the hypothesis that performing tasks with a highly productive partner will facilitate performance. Analy-

sis of the performance of the participants during the collaborative task indicated that the number of stickers

decreased in the collaborative compared with the individual task regardless of the partner’s level of productivity.

In other words, even when the partner was a young peer, social loafing was observed in children. Correlation

analysis of Experiments 1 and 2 revealed that the participants’ performance was associated with their partner’s

performance in the collaborative task, as evidenced by the participants’ tendency to place more stickers relative

to their partners.

4 | GENERAL DISCUSSION

In Experiments 1 and 2, children exhibited social loafing behaviour except when the adult partner was highly produc-

tive. Seemingly, whether the partner was an adult or a peer had a stronger social influence than information about

the partner’s productivity level.

In both experiments, children generally demonstrated social loafing during the collaborative task. Harkins (1987)

pointed to the presence or absence of evaluation concerns as a factor for social loafing. The author argued that a

reduction in individual evaluation concerns in the co-behavioural setting was a factor in the occurrence of social

loafing. In addition, Arterberry et al. (2007) empirically demonstrated that the presence or absence of evaluation is a

determinative factor for whether social loafing or social facilitation occurs in 5-year-old children. The authors further

revealed that children showed social facilitation when individual performance was evaluated by an observer during

the collaborative task, whereas social loafing occurred when their performance was not evaluated during the collabo-

rative task.

F IGURE 2 Correlation between the numbers of stickers affixed by partners and participants. The vertical axis
denotes the number of seals for the participants during the task, whereas the horizontal axis indicates the number of
seals for the partner

10 of 14 HAMAMOTO ET AL.

Harkins (1987) argued that social influence is associated with an individual’s concerns about how their perfor-

mance will be evaluated against others and examined how evaluation concerns influence social influence under four

conditions, namely, (a) individual and no evaluation, (b) individual and evaluated, (c) collaborative and no evaluation,

and (d) collaborative and evaluated. As a result, the participants displayed better performance in the collaborative

condition when performance was evaluated than in the individual and evaluated condition and individual and no

evaluated condition. This finding indicates the occurrence of social facilitation. Conversely, when participants were

not evaluated in the collaborative condition, the performance of the group was lower than that under the evaluated

condition, which indicates social loafing. In other words, the presence or absence of evaluation concerns about per-

formance is considered to be a factor for the occurrence of social facilitation or loafing. A recent study examining

social influence on child behaviour in a cooperative context with a modified version of the marshmallow test demon-

strated that children were more willing to invest effort by delaying gratification in a cooperative context compared

with an individual task (Koomen, Grueneisen, & Herrmann, 2020). Thus, the present study inferred that children may

be more engaging in the collaborative context when they can expect later rewards, such as positive evaluation by

others.

The study did not evaluate the performance of individuals in the collaborative task as the main objective was to

examine the social influence of information about a partner’s productivity level. Instead, the children were instructed

that their total number of pairs of stickers will be compared with those of other child participants. Therefore, a possi-

bility exists that social loafing occurred as a whole. Previous studies have reported that 5-year-old children behave

altruistically and distribute more of their stickers to others when observers are watching their behaviour compared

to when nobody is watching (Engelmann, Herrmann, & Tomasello, 2012, 2016; Leimgruber, Shaw, Santos, &

Olson, 2012). Children may have more awareness of evaluation concerns when observers evaluate their individual

performance.

For Experiments 1 and 2, a correlation was observed between the number of stickers placed by the child partici-

pants and those placed by adult/peer partners. This finding indicates that the number of stickers placed by the child

participants is dependent on the behaviour of their partner for both experiments. The notion that the participants

were more influenced by what was actually happening in front of them than by verbally provided information before

the task is possible. Children did not show significant social loafing when the partner was a highly productive adult.

Thus, children may have coordinated their actions during jointly working on the task. The current results may suggest

that social influence in terms of the children’s performance is dependent on their partners’ actions.

Social influence has been examined from a wide range of contexts; however, a partner’s actions may have strong

effects on a child’s behaviour. Butler and Walton (2013) found that children persisted longer in a task they believed

to be cooperative than individualistic. The authors suggested that an early emerging drive to engage in collaborative

activities leads to children’s motivation to work with others. However, the current results indicated that the children

adjusted their behaviour to the actual partner’s actions, which suggested that monitoring a partner’s actions may

strongly influence children’s engagement in joint tasks. Warneken, Gräfenhain, and Tomasello (2012) proposed that

communicating the intent to cooperate and sharing goals between partners can lead individuals to coordinate their

efforts. Thus, children may perceive a partner’s intent to achieve the goal by monitoring the partner’s actions and

coordinating their performance. Social influence in joint tasks that enable children to monitor a partner’s actions may

be strongly influenced by the actual partner’s performance.

4.1 | Limitation

In our paradigm, the children were able to observe their partner’s actions during the task. The presence of a partner

may have been a distraction that slowed the participants’ actions. Thus, future studies should use a collaborative task

in which children and their partner work towards a joint goal but are unaware of each other’s contribution to the

HAMAMOTO ET AL. 11 of 14

task. Alternatively, a control condition should be modified, such that the child and partner each engage in an individ-

ual task simultaneously, and the partner’s productivity level is the same as that in the collaborative task.

The current study revealed a positive correlation between the children’s and partner’s performance. Thus, high-

productivity partners may induce social facilitation. In the current study, high-productivity adult partners placed

approximately 20 stickers during the joint task, which has not been evaluated as extremely highly productive. In

future studies, a wider range of partner’s productivity levels should be considered for examining social loafing and

social facilitation.

5 | CONCLUSION

The child participants demonstrated social loafing except when the partner was a highly productive adult. Correlation

analyses indicated that the number of stickers placed by the child participants tended to reflect their partners’ per-

formance. Thus, young children may spontaneously monitor the behaviour of others and coordinate their behaviour

accordingly when performing collaborative tasks with others.

ACKNOWLEDGEMENTS

We appreciate the cooperation of all families that agreed to participate in this study. We thank Hiroki Yamamoto

and Hika Kuroshima for their help with this study. We also thank the anonymous reviewers and colleagues who have

provided us useful feedback or helped to conduct experiments. This work was funded by a grant to Shoji Itakura

from the Japan Society for the Promotion of Science (Grant No. 16H06301).

CONFLICT OF INTEREST

The authors declare no conflicts of interest.

AUTHOR CONTRIBUTIONS

Hikaru Hamamoto and Mitsuhiko Ishikawa developed the study concept and analysed the data. Hikaru Hamamoto

and Risa Mizobata conducted the experiments, which were supervised by Shoji Itakura. Hikaru Hamamoto wrote the

first draft of this study. Mitsuhiko Ishikawa finalized the manuscript. All authors approved the experiment design and

discussed the results.

DATA AVAILABILITY STATEMENT

The data sets generated and analysed during the current study are available from the corresponding author upon

reasonable request.

ORCID

Mitsuhiko Ishikawa https://orcid.org/0000-0002-5141-6511

REFERENCES

Aiello, J. R., & Svec, C. M. (1993). Computer monitoring of work performance: Extending the social facilitation framework to

electronic presence 1. Journal of Applied Social Psychology, 23(7), 537–548. https://doi.org/10.1111/j.1559-1816.1993.
tb01102.x

Arterberry, M. E., Cain, K. M., & Chopko, S. A. (2007). Collaborative problem solving in five-year-old children: Evidence of

social facilitation and social loafing. Educational Psychology, 27(5), 577–596. https://doi.org/10.1080/

01443410701308755

Azmitia, M. (1988). Peer interaction and problem solving: When are two heads better than one? Child Development, 59(1),

87–96. https://doi.org/10.2307/1130391

12 of 14 HAMAMOTO ET AL.

https://orcid.org/0000-0002-5141-6511

https://doi.org/10.1111/j.1559-1816.1993.tb01102.x

https://doi.org/10.1080/01443410701308755

https://doi.org/10.2307/1130391

Brownell, C. A. (2011). Early developments in joint action. Review of Philosophy and Psychology, 2, 193–211. https://doi.org/
10.1007/s13164-011-0056-1

Brownell, C. A., & Carriger, M. S. (1990). Changes in cooperation and self-other differentiation during the second year. Child

Development, 61, 1164–1174. https://doi.org/10.1111/j.1467-8624.1990.tb02850.x
Butler, L. P., & Walton, G. M. (2013). The opportunity to collaborate increases preschoolers’ motivation for challenging tasks.

Journal of Experimental Child Psychology, 116, 953–961. https://doi.org/10.1016/j.jecp.2013.06.007
Engelmann, J. M., Herrmann, E., & Tomasello, M. (2012). Five-year olds, but not chimpanzees, attempt to manage their repu-

tations. PLoS One, 7(10), e48433. https://doi.org/10.1371/journal.pone.0048433

Engelmann, J. M., Herrmann, E., & Tomasello, M. (2016). The effects of being watched on resource acquisition in chimpan-

zees and human children. Animal Cognition, 19, 147–151. https://doi.org/10.1007/s10071-015-0920-y
Erdfelder, E., Faul, F., & Buchner, A. (1996). GPOWER: A general power analysis program. Behavior Research Methods, Instru-

ments, & Computers, 28(1), 1–11. https://doi.org/10.3758/BF03203630
Foley, M. A., Ratner, H. H., & House, A. T. (2002). Anticipation and source-monitoring errors: Children’s memory for collabo-

rative activities. Journal of Cognition and Development, 3(4), 385–414. https://doi.org/10.1207/S15327647JCD3,4-02

Forman, E. A., Minick, N., & Stone, C. A. (1993). Contexts for learning: Sociocultural dynamics in children’s development. Oxford,

England: Oxford University

Press.

Garton, A. F., & Pratt, C. (2001). Peer assistance in children’s problem solving. British Journal of Developmental Psychology,

19(2), 307–318. https://doi.org/10.1348/026151001166092
Guerin, B. (2010). Social facilitation. The Corsini Encyclopedia of Psychology, 4, 1629–1630. https://doi.org/10.1002/

9780470479216.corpsy0890

Hardy, C. J., & Crace, R. K. (1991a). Social support within sport. Sport Psychology Training Bulletin, 3(1), 1–8.
Hardy, C. J., & Crace, R. K. (1991b). The effects of task structure and teammate competence on social loafing. Journal of

Sport and Exercise Psychology, 13(4), 372–381. https://doi.org/10.1123/jsep.13.4.372
Harkins, S. G. (1987). Social loafing and social facilitation. Journal of Experimental Social Psychology, 23, 1–18. https://doi.

org/10.1016/0022-1031(87)90022-9

Haux, L., Engelmann, J. M., Herrmann, E., & Tomasello, M. (2017). Do young children preferentially trust gossip or firsthand

observation in choosing a collaborative partner? Social Development, 26(3), 466–474. https://doi.org/10.1111/sode.
12225

Karau, S. J., & Williams, K. D. (1993). Social loafing: A meta-analytic review and theoretical integration. Journal of Personality

and Social Psychology, 65(4), 681–706. https://doi.org/10.1037/0022-3514.65.4.681
Koomen, R., Grueneisen, S., & Herrmann, E. (2020). Children delay gratification for cooperative ends. Psychological Science,

31(2), 139–148. https://doi.org/10.1177/0956797619894205
Latané, B., Williams, K., & Harkins, S. (1979). Many hands make light the work: The causes and consequences of social

loafing. Journal of Personality and Social Psychology, 37(6), 822–832. https://doi.org/10.1037/0022-3514.37.6.822
Leimgruber, K. L., Shaw, A., Santos, L. R., & Olson, K. R. (2012). Young children are more generous when others are aware of

their actions. PLoS One, 7, e48292. https://doi.org/10.1371/journal.pone.0048292

Maynard, A. E., & Martini, M. I. (2005). Learning in cultural context: Family, peers, and school. New York: Kluwer.

Meade, M. L., Nokes, T. J., & Morrow, D. G. (2009). Expertise promotes facilitation on a collaborative memory task. Memory,

17(1), 39–48. https://doi.org/10.1080/09658210802524240
Oullier, O., De Guzman, G. C., Jantzen, K. J., Lagarde, J., & Scott Kelso, J. A. (2008). Social coordination dynamics: Measuring

human bonding. Social Neuroscience, 3(2), 178–192. https://doi.org/10.1080/17470910701563392
Park, J., & Lee, J. (2015). Dyadic collaboration among preschool-age children and the benefits of working with a more socially

advanced peer. Early Education and Development, 26(4), 574–593. https://doi.org/10.1080/10409289.2015.995567
Piaget, J. (1968). Six psychological studies (Translation by A. Tenzer). New York: Vintage Books.

Pine, K. J., & Messer, D. J. (1998). Group collaboration effects and the explicitness of children’s knowledge. Cognitive Devel-

opment, 13(1), 109–126. https://doi.org/10.1016/S0885-2014(98)90023-5
Raven, B. H. (1965). Social influence and power. In I. D. Steiner & M. Fishbein (Eds.), Current studies in social psychology

(pp. 371–382). New York, NY: Holt, Rinehart & Winston.

Richardson, M. J., Marsh, K. L., Isenhower, R. W., Goodman, J. R., & Schmidt, R. C. (2007). Rocking together: Dynamics of

intentional and unintentional interpersonal coordination. Human Movement Science, 26(6), 867–891. https://doi.org/10.
1016/j.humov.2007.07.002

Rogoff, B. (1990). Apprenticeship in thinking: Cognitive development in social context. Oxford, England: Oxford University

Press.

Rogoff, B. (2003). The cultural nature of human development. Oxford, England: Oxford University Press.

Schmidt, R. C., Carello, C., & Turvey, M. T. (1990). Phase transitions and critical fluctuations in the visual coordination of

rhythmic movements between people. Journal of Experimental Psychology: Human Perception and Performance, 16(2),

227–247. https://doi.org/10.1037/0096-1523.16.2.227

HAMAMOTO ET AL. 13 of 14

https://doi.org/10.1007/s13164-011-0056-1

https://doi.org/10.1111/j.1467-8624.1990.tb02850.x

https://doi.org/10.1016/j.jecp.2013.06.007

https://doi.org/10.1371/journal.pone.0048433

https://doi.org/10.1007/s10071-015-0920-y

https://doi.org/10.3758/BF03203630

https://doi.org/10.1207/S15327647JCD3,4-02

https://doi.org/10.1348/026151001166092

https://doi.org/10.1002/9780470479216.corpsy0890

https://doi.org/10.1123/jsep.13.4.372

https://doi.org/10.1016/0022-1031(87)90022-9

https://doi.org/10.1111/sode.12225

https://doi.org/10.1037/0022-3514.65.4.681

https://doi.org/10.1177/0956797619894205

https://doi.org/10.1037/0022-3514.37.6.822

https://doi.org/10.1371/journal.pone.0048292

https://doi.org/10.1080/09658210802524240

https://doi.org/10.1080/17470910701563392

https://doi.org/10.1080/10409289.2015.995567

https://doi.org/10.1016/S0885-2014(98)90023-5

https://doi.org/10.1016/j.humov.2007.07.002

https://doi.org/10.1037/0096-1523.16.2.227

Schmidt, R. C., & Turvey, M. T. (1994). Phase-entrainment dynamics of visually coupled rhythmic movements. Biological

cybernetics, 70(4), 369–376. https://doi.org/10.1007/BF00200334
Sebanz, N., Bekkering, H., & Knoblich, G. (2006). Joint action: Bodies and minds moving together. Trends in Cognitive Sci-

ences, 10(2), 70–76. https://doi.org/10.1016/j.tics.2005.12.009
Triplett, N. (1898). The dynamogenic factors in pacemaking and competition. The American Journal of Psychology, 9(4),

507–533. https://doi.org/10.2307/1412188
Tudge, J. R. H., & Winterhoff, P. A. (1993). Can young children benefit from collaborative problem solving? Tracing the

effects of partner competence and feedback. Social Development, 2, 242–259. https://doi.org/10.1111/j.1467-9507.
1993.tb00016.x

Vygotsky, L. S. (1978). In M. Cole, V. John-Steiner, S. Scribner, & E. Souberman (Eds.), Mind in society. Cambridge, MA: Har-

vard University Press.

Warneken, F., Gräfenhain, M., & Tomasello, M. (2012). Collaborative partner or social tool? New evidence for young chil-

dren’s understanding of joint intentions in collaborative activities. Developmental Science, 15(1), 54–61. https://doi.org/
10.1111/j.1467-7687.2011.01107.x

Woods, S., Dautenhahn, K., & Kaouri, C. (2005, June). Is someone watching me? Consideration of social facilitation effects

in human-robot interaction experiments. In 2005 international symposium on computational intelligence in robotics and

automation (pp. 53–60). Espoo, Finland: IEEE. https://doi.org/10.1109/CIRA.2005.1554254
Zivotofsky, A. Z., Gruendlinger, L., & Hausdorff, J. M. (2012). Modality-specific communication enabling gait synchronization

during over-ground side-by-side walking. Human Movement Science, 31(5), 1268–1285. https://doi.org/10.1016/j.

humov.2012.01.003

Zivotofsky, A. Z., & Hausdorff, J. M. (2007). The sensory feedback mechanisms enabling couples to walk synchronously: An

initial investigation. Journal of Neuroengineering and Rehabilitation, 4(1), 28. https://doi.org/10.1186/1743-0003-4-28

How to cite this article: Hamamoto H, Mizobata R, Ishikawa M, Itakura S. Examining the social influence of

reputation for partner productivity level on the collaborative task performance of young children. Inf Child

Dev. 2021;30:e2213. https://doi.org/10.1002/icd.2213

14 of 14 HAMAMOTO ET AL.

https://doi.org/10.1007/BF00200334

https://doi.org/10.1016/j.tics.2005.12.009

https://doi.org/10.2307/1412188

https://doi.org/10.1111/j.1467-9507.1993.tb00016.x

https://doi.org/10.1111/j.1467-7687.2011.01107.x

https://doi.org/10.1109/CIRA.2005.1554254

https://doi.org/10.1016/j.humov.2012.01.003

https://doi.org/10.1186/1743-0003-4-28

https://doi.org/10.1002/icd.2213

Examining the social influence of reputation for partner productivity level on the collaborative task performance of young …

1 INTRODUCTION

1.1 Social facilitation

1.2 Social loafing

1.3 Partner characteristics and social influence in young children

2 EXPERIMENT 1

2.1 Methods

2.1.1 Participants

2.1.2 Materials and experimental environment

2.1.3 Experimental conditions

2.1.4 Procedure

2.1.5 Individual task

2.1.6 Joint task

2.2 Results

2.3 Discussion

3 EXPERIMENT 2

3.1 Method

3.1.1 Participants

3.1.2 Procedure

3.2 Results

3.3 Discussion

4 GENERAL DISCUSSION

4.1 Limitation

5 CONCLUSION

ACKNOWLEDGEMENTS

CONFLICT OF INTEREST

AUTHOR CONTRIBUTIONS

DATA AVAILABILITY STATEMENT

REFERENCES

Vol.:(0123456789)

1 3

Journal of Business Ethics (2019) 160:713–727
https://doi.org/10.1007/s10551-018-3933-z

ORIGINAL PAPER

Reaping the Fruits of Another’s Labor: The Role of Moral
Meaningfulness, Mindfulness, and Motivation in Social Loafing

Katarina Katja Mihelič1 · Barbara Culiberg1

Received: 30 August 2017 / Accepted: 28 May 2018 / Published online: 5 June 2018
© Springer Science+Business Media B.V., part of Springer Nature 2018

Abstract
Despite the popularity of teams in universities and modern organizations, they are often held back by dishonest actions, social
loafing being one of them. Social loafers hide in the crowd and contribute less to the pooled effort of a team, which leads to
an unfair division of work. While previous studies have mostly delved into the factors related to the task or the group in an
attempt to explain social loafing, this study will instead focus on individual factors. Accordingly, the aim is to investigate the
determinants of social loafing attitudes, namely moral meaningfulness and mindfulness in a university setting. We further
examine the relationship between attitudes and intentions and introduce the moderating role of motivation in the attitude–
intention link. The findings from a sample of 319 business students reveal that both mindfulness and moral meaningfulness
are negatively related to loafing attitudes, while attitudes positively predict social loafing intentions. In addition, we find that
extrinsic motivation strengthens the relationship between social loafing attitudes and intentions.

Keywords Academic misconduct · Moral meaningfulness · Mindfulness · Motivation · Social loafing · University

Introduction

Working in teams has become a common feature of contem‑
porary working lives (Kozlowski and Bell 2013; Schlossberg
2016). It is therefore not surprising that in higher educa‑
tion institutions team/group projects have gained traction
in “preparing students for the real world” (Forehand et al.
2016, p. 62) as they mirror the experiences of the modern
workplace. However, while teamwork is popular in univer‑
sities and businesses alike, it is not immune to individual
dishonest behavior such as social loafing which occurs when
individuals expend less work effort when they form part of
a group than they do when working individually (George
1992; Kidwell and Bennett 1993; Latané et al. 1979). Draw‑
ing on assertions by Bennett and Naumann (2005), social
loafing is problematic for two main reasons: (1) an indi‑
vidual is not delivering what is expected of him/her; and (2)
when observed by others, incidences of slacking off may

decrease motivation and trigger a negative performance
spiral among peers. The negative effects of social loafing
manifest in teams and organizations as diminished cohe‑
siveness, hindered work processes, greater dissatisfaction
with results (Monzani et al. 2014), lower potency (Duffy and
Shaw 2000) and reduced motivation of other members (Price
et al. 2006). This all diminishes a team’s productivity and
efficiency (Comer 1995; Karau and Williams 1995; Bennett
and Naumann 2005). Because it results in adverse conse‑
quences for individuals and societies, it has been dubbed a
“social disease” (Latané et al. 1979, p. 831). In line with this
reasoning, social loafing is a moral issue since it is a choice
that “involves modifying the life plan of another individual
or group of individuals” (Morris 2004, p. 353).

Understanding what drives social loafing is relevant
nowadays for educators teaching ethics in business schools
and managers who encourage and advocate teamwork.
Social loafing can be addressed through ethics educa‑
tion and development at university, in order to potentially
repress this questionable practice early on and discourage
its occurrence in future employment. It has been suggested
that the relationship between ethics instruction and ethi‑
cal perception and behavior is affected by an individual’s
personal characteristics (Wang and Calvano 2015). As is
evident from recent research (May and Luth 2013; Floyd

* Barbara Culiberg
Barbara.culiberg@ef.uni‑lj.si

Katarina Katja Mihelič
Katja.mihelic@ef.uni‑lj.si

1 Faculty of Economics, University of Ljubljana, Kardeljeva
pl. 17, Ljubljana, Slovenia

http://crossmark.crossref.org/dialog/?doi=10.1007/s10551-018-3933-z&domain=pdf

714 K. K. Mihelič, B. Culiberg

1 3

et al. 2013; Wang and Calvano 2015), the diverse effects of
business ethics education have the makings of a viable set
of predictors for explaining ethically questionable situations,
including social loafing. For example, moral meaningfulness
was initially approached as an outcome of business ethics
education (May and Luth 2013), but has the potential to
serve as an antecedent in social loafing decision‑making
since it describes the relative importance of ethics in an
individual’s life. Lampe and Engleman‑Lampe (2012, p. 99)
also emphasized the role of mindfulness, conceptualized as
present‑centered attention and awareness (Brown and Ryan
2003), by stressing “a growing need to educate students
about how the mind works in ways that can help or hinder
making ethical decisions.” Moreover, when Ballantine et al.
(2016) recently demonstrated that students’ ethical decisions
are a result of their learning approaches (which incorporate
their motivation), business ethics educators were alerted to
the complex role of student motivation in academic miscon‑
duct. Motivation, referring to a set of energetic forces that
initiate, direct, and sustain action (Pinder 2008), may not
only serve as a predictor, as studies of academic misconduct
(Davy et al. 2007) suggest, but also as a moderator, as shown
in studies of job‑related intentions and performance (Dysvik
and Kuvaas 2010, 2011). Following these recent research
developments, the objective of this study is to examine (a)
moral meaningfulness and mindfulness as novel antecedents
of social loafing attitudes; and (b) motivation as a modera‑
tor in the social loafing attitudes–intentions relationship. By
introducing these constructs to the domain of social loafing,
this paper strives to link the literature on business ethics
education with ethically questionable behaviors.

Because social loafing takes place in various settings,
such as at work and at university, separate research streams
have developed to examine this issue. Accordingly, organi‑
zational scholars have studied employees’ social loafing in
an effort to better understand what causes a team’s subop‑
timal performance (e.g., Meyer et al. 2016; Kidwell and
Valentine 2009). In addition, considerable attention has
also been devoted to this topic in the higher education
setting (e.g., Jassawalla et al. 2009; Schippers 2014) by
treating it as a form of academic misconduct. Throughout
the years, the efforts of both research streams have sought
to understand the conditions that give rise to social loafing
tendencies. To this end, studies have largely focused on
factors that describe either the task or the team charac‑
teristics and its relationships, i.e., among team members
or with team leaders (e.g., Meyer et al. 2016; Murphy
et al. 2003; Kidwell and Valentine 2009). Considerably
less attention has been devoted to understanding how the
characteristics of an individual team member impact their
decision to withhold effort. Conversely, a recent meta‑
review of ethical decision‑making studies in business
pointed out that individual factors have been extensively

studied in business ethics literature and there is a trend
towards identifying new individual factors which could
work as antecedents or moderators in the decision‑making
process (Lehnert et al. 2015). In this regard, it is important
to consider the findings of Hoon and Tan (2008) where
one individual factor, i.e., conscientiousness, significantly
predicted the occurrence of social loafing, while group
and task characteristics did not. Social loafing research in
higher education has lately further attested to the relevance
of other individual factors (e.g., Schippers 2014), thus
sowing the seeds for further scholarly investigations. In
light of the apparent relevance and potential of novel indi‑
vidual factors and their underrepresentation in previous
social loafing studies, this study will focus on social loaf‑
ing as a type of academic misconduct and complement this
thinking with research from the business ethics research
stream, its rich background and the more recent develop‑
ments therein. Hence, this study adopts an intra‑individual
lens while examining this phenomenon by focusing on the
“bad apples” (Kish‑Gephart et al. 2010), i.e., the individu‑
als, their characteristics, and their decision‑making.

The study aims to make the following unique contribu‑
tions to the literature. First, considering social loafing is a
moral issue, we contribute to the literature on academic mis‑
conduct by proposing two promising constructs with ethical
ties as antecedents to social loafing attitudes. In this way,
we answer a recent call (Cronan et al. 2015) to identify the
determinants of attitudes in studies of academic integrity
violations. We thus introduce mindfulness, which has been
advocated as an important decision‑making determinant in
ethical situations (Ruedy and Schweitzer 2010; Schuh et al.
2017) and also holds the potential to predict social loafing
attitudes. In addition, recent recommendations highlighting
the need to explore how mindfulness influences ethical con‑
duct in concert with other individual‑level characteristics
(Schuh et al. 2017) are followed. Hence, moral meaning‑
fulness is introduced in order to explain social loafing by
looking into the way ethics is considered in students’ daily
lives at university.

Second, considering the important role of motivation
found in previous studies of academic misconduct (e.g.,
Davy et al. 2007; Jordan et al. 2013; Rettinger et al. 2004),
it seems likely that the effects of attitudes on the intentions
of social loafing may differ depending on the level of student
motivation. Therefore, this study contributes to this line of
research and provides a more nuanced understanding of the
attitude–intention link by introducing extrinsic motivation
as a moderator, which takes account of the fact that the fun‑
damental underpinnings of social loafing are motivational
(George 1992; Shepperd 1993). The inclusion of extrinsic
motivation also responds to the appeal by Smith et al. (2001)
to identify other individual variables as moderators of social
loafing effects.

715Reaping the Fruits of Another’s Labor: The Role of Moral Meaningfulness, Mindfulness, and…

1 3

Third, an incremental contribution refers to the position‑
ing of social loafing in academic misconduct research, which
has mostly focused on cheating and plagiarism (Bing et al.
2012; McCabe et al. 2006; Cronan et al. 2015; Lawson 2004;
Ballantine et al. 2016; Fida et al. 2016), while giving consid‑
erably less attention to other practices. Therefore, this study
contributes to a more comprehensive understanding of social
loafing as a dishonest student act. According to research‑
ers, this phenomenon has surprisingly been neglected (Jas‑
sawalla et al. 2009) even though students express concerns
about social loafing and its consequences such as the quality
of the work that is submitted and lower grades (Clark and
Baker 2011).

The remainder of the paper offers an overview of the
social loafing phenomenon and its characteristics, followed
by a review of the empirical studies that investigated loafing
determinants in the educational and organizational settings.
Thereafter, a conceptual model of social loafing intentions
among business students is proposed and argumentation for
the proposed hypotheses is developed. The empirical study’s
research design is then described and the results of hypoth‑
eses testing are provided. The “Discussion” section offers an
interpretation of the findings through the lens of academic
misconduct and business ethics education and outlines prac‑
tical suggestions for reducing the incidence of loafing at uni‑
versity. The paper concludes with limitations and suggests
opportunities for future scholarly exploration of the social
loafing phenomenon.

Theoretical Background

Origins of the Term and Conceptualization

In the early 1880s, a professor of agricultural engineering
Max Ringelmann designed a rope‑pulling task for his stu‑
dents and measured their performance. What he observed
was a worse performance when the number of students pull‑
ing the rope increased. In other terms, when working with
their peers students were more likely to slack off, which dec‑
ades later came to be known as the Ringelmann effect (Krav‑
itz and Martin 1986). It was not until the late 1970s that this
pioneering study was replicated in a series of experiments
demonstrating that students who performed alone clapped
their hands and shouted more intensely than those who did
the same thing as part of a group. The presence of other
people led them to reduce their effort in clapping and cheer‑
ing (Latané et al. 1979; Williams et al. 1981). It was then
that Latané and colleagues coined the term social loafing.1

When social loafing occurs at university, it is a form of
academic misconduct because it creates an unfair academic
advantage for an individual or an unfair academic disadvan‑
tage for other members of the academic community (Uni‑
versity of California). The problem of social loafing is that
students hide in the crowd and contribute less to the pooled
effort of a team, particularly in situations where the potential
for evaluation is low (Hoon and Tan 2008). As a result, pro‑
fessors obtain a false representation of an individual student’s
(i.e., loafer’s) efforts, while other members of the team need
to compensate for the resulting slack so they obtain better
results. After having experienced loafing by team members,
students may form a negative attitude towards teamwork
which may manifest itself in the form of a reluctance to work
in teams even later on in future employment.

Teamwork at university enables interpersonal interactions
while simultaneously fostering adaptability and tolerance.
However, those students who have had negative experiences
with teamwork mostly associate it with “frustration” (Hall
and Buzwell 2012, p. 7). The main source of this frustration
is often the problem of social loafing, and as a result students
complain about the team members’ unequal contributions.
Jassawalla and colleagues found that students perceive loaf‑
ers as those that (a) do less and slack off, (b) do poor quality
work for the team, and (c) engage in distractive and disrup‑
tive behaviors (Jassawalla et al. 2009). Students therefore
turn to lecturers to report how certain team members spend
minimal amounts of time and effort while at the same time
expecting to be rewarded equally, which seems unfair to
those who do their fair share of the work (Hall and Buzwell
2012; Jassawalla et al. 2009; Clark and Baker 2011). Indeed,
a loafer profits from being a member of groups where the
successful completion of a project leads to equal rewards for
all members (Comer 1995). In situations where the individ‑
ual contributions are difficult to evaluate, it is relatively easy
for a loafer to evade his/her job or task (Karau and Williams
1993; Price et al. 2006; Williams et al. 1981).

Determinants of Social Loafing

Social loafing is widespread and can be found in both the
organizational as well as educational setting, across gender
and age and in various occupations and different cultures

1 In day‑to‑day discussions we often hear the term “free riding” when
describing someone slacking off on team assignments. Social loafing
and free riding are similar in that they both entail effort being withheld,
i.e., investing less than full effort when performing a task (Bennett and

Naumann 2005; Kidwell and Bennett 1993). Loafers put in less effort
when working in a group compared to the effort they devote when work‑
ing individually due to reduced identifiability or evaluation (Latané
et al. 1979; Kidwell and Bennett 1993). Free riders enjoy the benefits
of belonging to a group while they are simultaneously unburdened by
the equivalent costs of providing those benefits. The decision to free ride
resides in comparing the benefits of contributing to a group’s goals and
the benefits obtained by free riding (Albanese and Van Fleet 1985).

Footnote 1 (continued)

716 K. K. Mihelič, B. Culiberg

1 3

(Karau and Williams 1993). This makes it of interest to
researchers studying academic and workplace misconduct.
Therefore, this section offers an overview of the main deter‑
minants that have previously been investigated in relation to
social loafing.

In terms of the team and task characteristics associated
with loafing, field studies among employees have established
that group size and a lack of cohesiveness (Liden et al. 2004)
exacerbate loafing, whereas team identification and a posi‑
tive group context (Kidwell and Valentine 2009; Høigaard
et al. 2013) reduce it. A study conducted among lower‑level
employees from an electronics firm and a machinery pro‑
ducing firm demonstrated that higher task interdependence,
lower task visibility and lower distributive justice predicted
social loafing (Liden et al. 2004). In the educational setting,
social loafing was more prevalent in larger groups and was
present in situations where the task/project was greater in
scope and more comprehensive, thus carrying a considerable
weight in the overall grade (Aggarwal and O’Brien 2008).

As far as interpersonal exchanges are concerned, exist‑
ing evidence reveals that a higher quality of leader‑member
exchange decreased social loafing among manufacturing
employees (Murphy et al. 2003). Further, analyzing teams
of blue‑collar workers in German companies, Meyer and col‑
leagues (2016) found that social loafing was most prevalent in
teams with strong homogeneous subgroups within the team
(i.e., strong faultlines). A study involving students working
on an idea‑generation task demonstrated that students with
high and average levels of interpersonal trust engaged in
social loafing (Williams and Karau 1991). Scholars have also
explored one’s perceptions and attributions of peers’ loafing.
Australian students, for example, attributed loafing to peers’
apathy and laziness (Hall and Buzwell 2012). Similarly, Jasa‑
walla et al. (2009) inquired about students’ perceptions of
their peers’ loafing and found that students attributed loaf‑
ers’ slacking off to apathy and their disruptive behavior to
social disconnectedness. The way individuals perceive the
loafing of others also affects their own behavior. In particu‑
lar, empirical studies demonstrate that perceiving that other
members loaf, or even anticipating that other members will
loaf, increases the incidence of loafing (Karau and Williams
1993; Jackson and Harkins 1985). Contrary to these find‑
ings from the workplace, a study involving students found
that perceptions of peers’ loafing reduced one’s own loafing
(Jassawalla et al. 2009), i.e., social compensation.

Next, we turn to the individual variables that drive social
loafing. Existing organizational studies have found that indi‑
vidual job satisfaction has a negative effect on social loafing
among military reserve personnel (Kidwell and Valentine
2009). Similarly, satisfaction with the management of a
company negatively predicted loafing among hotel employ‑
ees in China, turnover intentions contributed to more social
loafing, while affective organizational commitment was not

significantly associated with loafing (Luo et al. 2013). In
an academic setting, students with a low need for cognition
loafed more on a vigilance task (i.e., paying attention to
dots appearing on a computer screen), confirming the need
for cognition as a moderator (Smith et al. 2001). Authors of
two studies involving undergraduate students incorporated
personality characteristics in their research and found that
conscientiousness had a significant negative effect on loaf‑
ing (Hoon and Tan 2008; Ferrari and Pychyl 2012). More
recently, Schippers (2014) revealed that conscientiousness
as well as agreeableness do indeed compensate for loafing
tendencies among Dutch undergraduate students and thereby
affect a team’s overall performance less. Further, Duffy and
Shaw (2000) report that the presence of envy enhances loaf‑
ing, while a student’s felt responsibility contributes to less
slacking (Hoon and Tan 2008). Negative affectivity does
not have a significant influence on social loafing (Murphy
et al. 2003). The range of factors explored in social loafing
studies is wide, but there is a deficit of individual factors
with stronger ethical ties. In the following section, we fill
this void by developing a conceptual model that explains an
individual student’s loafing intention.

Conceptual Model and Hypotheses

This study draws on insights from early theoretical frame‑
works of social loafing (Comer 1995; Kidwell and Bennett
1993) and recent additions to social loafing theory, which
pointed to the explanatory power of individual factors
(Schippers 2014; Jassawalla et al. 2009) by focusing on the
drivers of social loafing through an intra‑individual view.
In the model, the proposed constructs are linked to the atti‑
tude–intention relationship of social loafing which stems
from the basic proposition that attitudes shape intentions,
which in turn lead to behavior (Bentler and Speckart 1979;
Ajzen 1991). Attitudes are conceptualized as one’s disposi‑
tion to respond favorably or unfavorably to social loafing,
while intentions indicate how much of an effort people plan
to exert in order to loaf (Ajzen 2002).

To explain how students form their attitudes towards
social loafing, we propose a pair of constructs that describe
how students approach tasks (i.e., mindfulness) and ethics
(i.e., moral meaningfulness) in their daily lives. While their
handling of tasks helps determine the role of their state of
mind in this situation, the role ethics plays in students’ lives
is important because they are dealing with a moral issue. By
including mindfulness, the psychological state of conscious‑
ness that reflects how present and engrossed students are
when they perform required tasks, student involvement in
team tasks is taken into account. Mindfulness may help indi‑
viduals withdraw from automatic thoughts and unhealthy
behavior (Brown and Ryan 2003). More specifically, being

717Reaping the Fruits of Another’s Labor: The Role of Moral Meaningfulness, Mindfulness, and…

1 3

aware of what is going on during teamwork leads students to
refrain from automatically performing their tasks and to also
pay attention to their peers’ engagement in tasks. We further
focus on the role ethics plays in student life to account for the
fact that social loafing may be viewed as a moral issue (Mor‑
ris 2004) since students may benefit at the expense of others.
Given that social loafing holds negative consequences for
other team members, we include moral meaningfulness to
complement mindfulness as an antecedent of social loafing
attitudes and account for its moral aspects.

Based on existing theorizing on the role of motivation
in teams (Shepperd 1993) and following Comer’s propo‑
sitions (1995), we introduce motivation as a moderator in
the attitude–intention relationship. In this way, we strive to
capture student engagement in their studies as this repre‑
sents the context in which social loafing occurs. Integrating
these three different aspects of social loafing from an indi‑
vidual’s perspective allows for a more comprehensive and
balanced understanding of the social loafing phenomenon.
In what follows, argumentation for each of the hypotheses
is provided.

The first hypothesis relies on previous studies on aca‑
demic misconduct that highlight various moral anteced‑
ents of attitudes such as moral obligation (Beck and Ajzen
1991), idealism (Etter et al. 2006), and moral perspective
(Eisenberg 2004). Considering these findings and the recent
developments in literature on business ethics education (May
et al. 2014), we first introduce an antecedent of social loaf‑
ing attitudes that captures how much meaning an individual
gains from behaving ethically, namely moral meaningfulness
(May et al. 2014). In an educational context, moral meaning‑
fulness reflects the value of ethics in students’ lives and the
extent to which ethical actions are integrated into their iden‑
tities (May and Luth 2013). Hanson et al. (2017) denoted
how relevant the students’ sense of moral right, practical
consequence and moral worth are in their ethical decisions,
particularly in the way students determine moral meaning.
Following May et al.’s (2014) reasoning, high levels of
moral meaningfulness should motivate students to recognize
ethical dilemmas, such as social loafing, and resolve them.
When students encounter and form their attitudes towards
social loafing, they may be influenced by the meaning they
have obtained from behaving ethically at university. By hav‑
ing gained meaning from ethical behavior, students may be
averse to ethically questionable practices like social loafing
since morally oriented students have been shown to hold less
favorable attitudes regarding academic misconduct (Eisen‑
berg 2004). In line with this proposition, we believe that a
higher level of moral meaningfulness leads to more negative
attitudes to social loafing.

Hypothesis 1 Moral meaningfulness is negatively related to
social loafing attitudes.

The second hypothesis concerns the relationship between
mindfulness and attitudes to social loafing. When an individ‑
ual is in a mindful state, they are alert, aware of the present
moment and focus their attention on what is currently going
on. In other words, mindful people have greater external
awareness and are attentive to daily occurrences (Dane and
Brummel 2014). They are also more selfless than mindless
individuals (Hunter and McCormick 2008), exhibit a strong
orientation to caring for others (Good et al. 2016) and dis‑
play fewer Machiavellian tendencies (Krishnakumar and
Robinson 2015). This suggests they are unlikely to delib‑
erately engage in selfish acts such as investing less effort
than peers in a team. Because mindfulness increases other‑
oriented and prosocial behaviors (Good et al. 2016), it can
be speculated that it leads to the formation of more negative
attitudes about withholding effort. In this sense, mindful‑
ness should protect individuals from developing favorable
attitudes towards acts that put others at a disadvantage. For
the reasons outlined above, we hypothesize:

Hypothesis 2 Mindfulness is negatively related to social
loafing attitudes.

A highly recognized relationship in academic miscon‑
duct studies is the link between attitudes and intentions (e.g.,
Beck and Ajzen 1991; Stone et al. 2009; Harding et al. 2007;
Mayhew et al. 2009). It has been suggested that the more
favorable an individual’s attitudes to dishonest behavior
are, the stronger their intention to perform such behavior
(Ajzen 1991). Researchers have well documented that when
students have more positive attitudes towards academic mis‑
conduct, their intentions will be more positive (Stone et al.
2009; Lim and See 2001). Although this relationship has
not been tested for social loafing, it may also apply here.
Students who regard social loafing as something positive
are more likely to engage in social loafing. Therefore, we
propose that:

Hypothesis 3 Social loafing attitudes are positively related
to social loafing intentions.

In addition to the above foci, we sought to further under‑
stand the link between attitudes and intentions. To do so,
we turned to Comer’s theorizing since his model of social
loafing (Comer 1995) speculated that task motivation may
moderate the links between social loafing and the predictive
factors. When someone works on a task and is extrinsically
motivated, they are focused on attaining an outcome that
is separate from performing the task. They chiefly perform
the task because it has an instrumental value for them (Ryan
and Deci 2000). Several studies lend support for the theo‑
rized role of motivation in intentions. For example, research
on ethically questionable consumer behavior has endorsed

718 K. K. Mihelič, B. Culiberg

1 3

a concept similar to motivation, i.e. involvement, which
describes the relevance of an object, situation or action for
an individual (Celsi and Olson 1988) as a moderator in the
link between attitudes and intention (Kos Koklic et al. 2014).
Further, George (1992) previously showed that salespeople’s
intrinsic task involvement was predictive of less social loaf‑
ing. It seems plausible then that when a student needs to
complete a group assignment and is doing so for extrinsic
reasons (e.g., obtaining a grade exclusively in order to pass
a course), their attitudes towards social loafing will translate
into intentions more strongly than for those students with
lower extrinsic motivation levels. We assume that extrinsic
motivation would strengthen the relationship between atti‑
tudes and intentions to engage in loafing.

Hypothesis 4 The relationship between social loafing
attitudes and intentions is moderated by extrinsic motiva‑
tion: The higher the extrinsic motivation, the stronger the
relationship.

Methods

Sample and Procedure

Data were collected from business students in a European
business school holding accreditations from three institu‑
tions: the Association to Advance Collegiate Schools of
Business (AACSB), EFMD Quality Improvement System
(EQUIS) and the Association for MBAs (AMBA). As part
of their courses there, undergraduate students are regularly
required to develop group projects during the semester in
which they may themselves loaf or encounter their peers’
social loafing. In addition, they often participate in ad hoc
team assignments during lectures.

Students were invited to take part in this study at the
beginning of seminar sessions of a core undergraduate
course. They were informed of the study’s purpose by teach‑
ing assistants, who also instructed them about the research
protocol. Then, teaching assistants distributed the paper‑
and‑pencil surveys. Participation in the study was voluntary
and students could withdraw at any point during the data
collection. Anonymity was guaranteed as no identification
information was required.

After initially inspecting the data, 12 questionnaires were
removed from the analysis due to substantial missing values
and/or a central tendency error. Therefore, the final sample is
comprised of 319 usable responses. Altogether, 57% of the
study participants were female and 43% were male, with an
average age of 19.73 years (SD = 1.13).

Measures

Unless otherwise indicated, the survey items used a five‑
point Likert scale with the anchors: 1‑strongly disagree and
5‑strongly agree. Established and previously validated scales
(listed in Table 4 in Appendix) were used to measure the
constructs.

Moral Meaningfulness

Four items were used to assess this construct (May et al.
2014) after being adapted to fit a university context (i.e., the
item “work” was replaced by the term “school”). A sample
item is: “Maintaining high morals/ethics brings me meaning
at school.”

Mindfulness

Trait mindfulness was measured with five items from the
Mindful Attention Awareness Scale (MAAS) (Brown and
Ryan 2003). They form the validated short version scale
used in previous studies (Osman et al. 2016; Smith et al.
2017). All items were reverse‑coded in order to evaluate how
often an individual experiences mindless states. Response
anchors ranged from: 1‑almost never to 5‑almost always.
After recoding, higher values indicate an elevated level of
trait mindfulness. A sample item is: “I do jobs or tasks auto‑
matically without being aware of what I’m doing.”

Attitudes

This construct was measured using a semantic differential
scale (Beck and Ajzen 1991) based on five pairs of adjec‑
tives (e.g., good/bad).

Intentions

Three items were used to measure social loafing inten‑
tions (Chen and Tung 2009). Items were adapted to fit the
social loafing context. A sample item is: “In future group
projects, I may let my peers carry out some of my tasks/
responsibilities.”

Motivation

The Academic Motivation Scale (Vallerand et al. 1989)
was used to measure extrinsic motivation (i.e., four items
for extrinsic motivation—external regulation). Participants
were asked why they study and then offered statements to
respond to. A sample item is: “Because with only a high
school degree I would not find a high‑paying job later on.”

719Reaping the Fruits of Another’s Labor: The Role of Moral Meaningfulness, Mindfulness, and…

1 3

Analytical Procedure

This study used a structural equation modeling procedure to
test the hypotheses proposed in the conceptual model. This
procedure differs from other multivariate procedures in that
it assumes a confirmatory approach to the data analysis and
takes account of the measurement error present in social
science research because the manifest indicators do not per‑
fectly represent their underlying, latent factor. Aside from
this, it considers the strength of the individual relationships
with respect to other paths between factors and evaluates a
model of relationships as a whole (Byrne 2012).

The effects were estimated using the latent moderated
structural equation method, a more recent approach in
structural equation modeling that enables the modeling of
interactions of latent variables that are measured with con‑
tinuous indicators. The analyses were performed with the
M‑Plus program, version 7.4 (Muthén and Muthén 2012).
The structural equation modeling proceeded in the following
steps. We began the analysis by estimating the measurement
model and inspecting the goodness‑of fit indices in order to
evaluate to what extent the model fits the data. Then com‑
posite reliabilities and average variance extracted (AVE) of
the respective scales were calculated, followed by tests to
evaluate the convergent and discriminant validity. Next, we
proceeded by estimating the structural model, which allowed
us to examine the magnitude of the relationships between the
factors as demonstrated by the regression coefficients. As
regards the interaction effect, the chosen approach uses raw
data of the indicators of the moderating factor to estimate
the effect. It does not require a product term to be formed
between the antecedent and moderator in order to create
the latent interaction factor (Klein and Moosbrugger 2000).
Finally, to evaluate the model fit we compared two nested
models, whereby one included the moderating effect while
the other one did not.

Results

This section begins with a presentation of the descriptive
statistics. It then continues by testing the measurement and
structural models and concludes by examining the statistical
significance of the proposed relationships.

Descriptive Statistics

Table 1 provides an overview of descriptive statistics, Cron‑
bach’s alpha, and untabulated correlations between the vari‑
ables. Among the study variables, extrinsic motivation had
the highest mean value (4.20), suggesting that business stu‑
dents find outcomes outside of studies (e.g., a high‑paying
and prestigious job) to be important motivating factors. The
mean values for moral meaningfulness and mindfulness are
3.80 and 3.27, respectively, while the lowest mean values
pertain to social loafing intentions (2.12) and attitudes (1.60),
on average indicating that business students hold relatively
negative views of social loafing.

Measurement Model

To establish the validity and reliability of the scales, we
first performed a series of confirmatory factor analyses,
which are reported in Table 2. We inspected the factor load‑
ings and their significance levels for each underlying factor
independently and dropped one indicator of the mindful‑
ness construct that exhibited a low loading (i.e., 0.293).
The proposed five‑factor solution demonstrated an excel‑
lent fit with the data (χ2 = 239.833 (df = 160; p = 0.000);
CFI = 0.962; TLI = 0.955; RMSEA = 0.040; SRMR = 0.042).
The RMSEA and SRMR were well below the 0.8 threshold,
while CFI and TLI were well above the required 0.9 values
(Kline 2016; Hu and Bentler 1998). The standardized factor
loadings were all significant and ranged between 0.629 and
0.882 for extrinsic motivation, 0.480 and 0.780 for moral
meaningfulness, 0.510 and 0.792 for loafing intention, 0.578
and 0.756 for attitudes, and from 0.533 to 0.681 for mind‑
fulness. This attests to the convergent validity of the indi‑
cators. The proposed measurement model was superior to

Table 1 Descriptive statistics,
Cronbach’s alpha, and
correlations among the study
variables

n = 319; Scales adopted a 5‑point response format with 1 indicating the minimum value and 5 the maxi‑
mum value. Reliabilities are displayed in the third column
**p < 0.01. *p < 0.05

Variable M SD α 1 2 3 4 5

1. Social loafing intention 2.12 0.86 0.66 1
2. Social loafing attitude 1.60 0.69 0.80 0.50** 1
3. Mindfulness 3.27 0.73 0.72 − 0.21** − 0.20** 1
4. Moral meaningfulness 3.80 0.74 0.79 − 0.34** − 0.32** 0.14* 1
5. Extrinsic motivation 4.20 0.79 0.88 − 0.04 − 0.04 − 0.09 0.04 1

720 K. K. Mihelič, B. Culiberg

1 3

the one‑factor model (χ2 = 1590.879 (df = 170; p = 0.000);
CFI = 0.327; TLI = 0.924; RMSEA = 0.165; SRMR = 0.191).
To further examine the reliability of the constructs, we com‑
puted the composite reliability which exceeded the required
value of 0.7 for all constructs (Nunnally and Bernstein
2007; Hair et al. 2010). In addition, the values of AVE for
the constructs were around 0.45 and above (see Table 3),
which is reasonable (Netemeyer et al. 2003). In order to
determine discriminant validity, we followed Fornell and
Larcker’s (1981) approach where we compared the AVE of
each construct with the shared variance (i.e., the squared

correlation) between the constructs (Farrell 2010). Because
the values of AVE were higher than the squared correla‑
tions between the constructs, discriminant validity was
confirmed (see Table 3). We ran an additional confirma‑
tory factor analysis whereby social loafing attitudes and
intentions were collapsed into one factor and the resulting
model fit was poorer than the proposed 5‑factor model at
p = 0.001 (χ2 = 313.727 (df = 164; p = 0.000); CFI = 0.929;
TLI = 0.918; RMSEA = 0.055; SRMR = 0.049; Δ = 73.894),
providing further evidence of discriminant validity.

Table 2 Results of confirmatory
factor analyses

n = 319
CFI comparative fit index, TLI Tucker‑Lewis index, RMSEA root‑mean‑square error of approximation,
SRMR standardized root mean square residual
***p < 0.001 a Harman’s single factor model: all variables combined into a single factor b Social loafing attitudes and social loafing intentions collapsed into a single factor

Model Chi square (χ2) (df, p) Δχ2 CFI TLI RMSEA SRMR

Full measurement
model, five factors

239.833
(df = 160; p = 0.000)

– 0.962 0.955 0.040 0.042

Model A, one factora 1590.879
(df = 170; p = 0.000)

1351.046*** 0.327 0.924 0.165 0.191

Model B, four factorsb 313.727
(df = 164; p = 0.000)

73.894*** 0.929 0.918 0.055 0.049

Table 3 Discriminant validity
matrix

The values of AVE appear diagonally and below the diagonal are the squared correlations between the
constructs

Social loafing
intention

Social loafing
attitude

Mindfulness Moral mean‑
ingfulness

Extrinsic
motiva‑
tion

Social loafing intention 0.45
Social loafing attitude 0.25 0.46
Mindfulness 0.04 0.04 0.42
Moral meaningfulness 0.12 0.10 0.02 0.50
Extrinsic motivation 0.00 0.00 0.01 0.00 0.62

Fig. 1 Results of the hypotheses testing. Note Unstandardized regression coefficients are reported. *Significant at p ≤ 0.05. Arrow indicates sup‑
ported hypothesis

721Reaping the Fruits of Another’s Labor: The Role of Moral Meaningfulness, Mindfulness, and…

1 3

Structural Model and Hypotheses Testing

Next, the relationships proposed in the conceptual model
were tested and the results are summarized in Fig. 1. As
standard model fit indicators are not available for latent inter‑
action models, the model is evaluated based on the follow‑
ing information criteria: Loglikelihood Value = − 7384.381,
free parameters = 68; Akaike Information Criterion
(AIC) = 14904.762. Hypothesis 1 assumed a negative rela‑
tionship between moral meaningfulness and social loafing
attitudes and was supported (γ = − 0.26, p < 0.01). Further, Hypothesis 2 proposed that mindfulness is negatively related to social loafing attitudes. A significant regression coefficient provides support for this hypothesis (γ = − 0.17, p < 0.05). Hypothesis 3 contends that more favorable attitudes to social loafing are associated with greater social loafing intentions (γ = 0.76, p < 0.01), which is supported. Finally, the find‑ ings provide support for the moderating effect of extrinsic motivation, which was proposed in Hypothesis 4 (γ = 0.26, p < 0.01). Extrinsic motivation strengthens the relationship between attitudes and intentions. To evaluate the model fit and further establish the significance of the moderating effect, we compared nested models with and without inter‑ action. The fit indices for the model without the interaction yielded a poorer fit (Loglikelihood Value = − 7386.366, free parameters = 67; Akaike (AIC) = 14906.732). The loglikeli‑ hood difference between the two nested models was signifi‑ cant (3.974) at the 5‑percent level for one degree of freedom. Based on this, we can conclude that the model with interac‑ tion is superior and more parsimonious and can be accepted.

Discussion

Business ethics educators argue that the key to reducing aca‑
demic misconduct is through business ethics instruction (Wang
and Calvano 2015). However, in order to facilitate change, edu‑
cators need to be familiar with the individual characteristics
that influence such behavior. By considering the outcomes of
business ethics education as predictors of academic miscon‑
duct, i.e., social loafing, this study attempts to add to earlier
work by authors who emphasized the role of moral meaning‑
fulness (May and Luth 2013) and mindfulness (Pandey et al.
2018) in pedagogical interventions that shape moral reasoning.
Unlike demographic factors, these factors are potentially open
to manipulation either through a specific business ethics course
or university ethical guidelines and codes of conduct (Jordan
2001). A better understanding of the drivers of social loafing
intentions through an intra‑individual lens may offer the tools
to address individual students’ academic misconduct. More
specifically, while prior research on academic misconduct
concentrated on cheating and plagiarism, this study instead
looked at the dishonest act of social loafing which has become

a significant concern not only for universities and professors
who encourage teamwork and students who are required to
participate in teams as part of their studies, but also for manag‑
ers as the future employers of graduates. Because social loaf‑
ing is a complex phenomenon, this study drew on knowledge
from different research fields and referred to three distinct
factors that shape an individual’s decision to loaf, namely
how involved students are in tasks (i.e., mindfulness), ethics
(i.e., moral meaningfulness) and their studies in general (i.e.,
motivation). The study contributed to the literature on aca‑
demic misconduct and business ethics education by present‑
ing mindfulness and moral meaningfulness as novel predictors
and introducing an alternative role for extrinsic motivation to
explain social loafing.

The current study established the importance of moral
meaningfulness in social loafing (Hypothesis 1) as it found
that students who draw meaning from ethics in their stud‑
ies will hold more negative attitudes towards social loafing.
If students find doing the right thing important, they will
find withholding effort more unacceptable because it brings
unfair advantages to loafers. Existing research on academic
misconduct considers various ethical factors, such as ethi‑
cal orientation (Allmon et al. 2000) and moral philosophies
(Sierra and Hyman 2008). Accordingly, this study extends
their findings with an alternative concept that considers the
meaning of ethics in a particular, i.e., educational, setting.
May et al. (2013) reported that moral meaningfulness is
influenced by business ethics education, and more attention
should thus be devoted to ethical issues in class by encourag‑
ing discussions on ethics and providing ethical guidelines.
In addition, Lau (2010) demonstrated the significant value
of ethics education in improving students’ overall ethical
orientation. Our results suggest that providing students with
opportunities to increase the value of ethics in their lives, to
see it as meaningful and, ultimately, to make ethics a part of
their identities could lead students to constructively resolve
moral issues (May and Luth 2013), i.e., social loafing.

Moreover, the relationship between mindfulness and
social loafing attitudes was negative (Hypothesis 2), con‑
firming that students who are more observant, selfless,
and guided by other‑oriented motives perceive social loaf‑
ing more negatively. This finding extends the reasoning of
Valentine et al. (2010) who approached mindfulness as an
outcome of ethical guidelines in a health care institution,
whereas in this study mindfulness was treated as an anteced‑
ent, thereby establishing its position in business ethics edu‑
cation literature, where the interest in mindfulness is grow‑
ing (Good et al. 2016; Schuh et al. 2017). Extant research
namely indicates that mindfulness not only makes people
more aware of their own behavior, but also of occurrences in
their environment (Brown and Ryan 2003; Dane and Brum‑
mel 2014). In turn, the realization of how hard their peers
intend to work makes students more attentive to the amount

722 K. K. Mihelič, B. Culiberg

1 3

of effort they themselves invest (Williams and Karau 1991).
Therefore, the act of benefiting from other students’ work is
less acceptable in the eyes of a mindful student. In line with
scholars’ suggestions that mindfulness is “a stance of greater
objectivity” (Adair and Fredrickson 2015, p. 198), mindful
students would consider the issues of fairness, equality and
justice when distributing the set tasks among peers. This
corresponds with research indicating that mindfulness leads
to more ethical decision‑making and less cheating (Ruedy
and Schweitzer 2010). While previous empirical work has
established mindfulness as a relevant determinant of proso‑
cial behaviors, showing how it facilitates more positive rela‑
tionships at work (Good et al. 2016; Hunter and McCor‑
mick 2008), our findings indicate that mindfulness can also
contribute to reducing undesirable behaviors which greatly
hinder a team’s effectiveness. By exploring the ‘dark side’
of teamwork, we expand the current understanding of the
beneficial role of mindfulness in shaping more negative atti‑
tudes to social loafing.

Regarding the attitude–intention link (Hypothesis 3), atti‑
tudes to social loafing had a significant positive influence
on loafing intentions. While this result is not surprising, it
provides additional support for this relationship in the grow‑
ing academic misconduct literature (e.g., Stone et al. 2009;
Mayhew et al. 2009; Harding et al. 2007), where the studies
demonstrated the positive influence of attitudes on intentions
to engage in various types of academic misconduct (Stone
et al. 2009) or cheating in particular (Mayhew et al. 2009).
By transferring this link to the social loafing context and
confirming its significance, we demonstrate the relevance of
attitudes in the formation of intentions to loaf, and the need
to consider this relationship by researchers as well as busi‑
ness ethics educators, who are trying to limit and prevent it.

Finally, we found that extrinsic motivation is a significant
moderator between attitudes and intentions (Hypothesis 4).
By corroborating the moderating role of motivation, this
study complements the findings of previous studies where
motivation was positioned as an antecedent of academic mis‑
conduct (Davy et al. 2007) and critical thinking (Howard
et al. 2015). Building on this knowledge, this study advances
the understanding of the role of motivation in academic
misconduct, i.e., social loafing. Jordan (2001) found both
motivation and attitudes were related to cheating behavior.
Furthering this insight, our findings indicate that for students
with a more pronounced extrinsic motivation to study, the
positive link between attitudes and intentions is stronger. In
other terms, students whose main inclination to study stems
from extrinsic factors (e.g., a desire to obtain a high salary
or a better job later on) will be more likely to loaf if they
evaluate it positively. These results suggest that extrinsic
motivation drives students with positive attitudes to be will‑
ing to put even more effort into social loafing.

Implications

It is important to gauge business students’ attitudes not only
to understand their behavior in student teams, but because
students are the employees and managers of tomorrow, who
are likely to transfer their work ethic and habits as well as
experiences they acquire at university, including their way
of working in teams, to the work environment. In times
when higher education institutions and organizations are
increasingly attempting to prevent harmful misconduct and
facilitate the ethical development of their students, our study
offers the following implications for universities and teach‑
ing practices.

First, we concur with researchers who have emphasized
the importance of teaching about ethical challenges at uni‑
versity (Lau 2010; de los Reyes et al. 2017), as we found
support for the role of moral meaningfulness in social loaf‑
ing attitudes, thereby providing pertinent implications for
business ethics education. Students could be educated about
academic misconduct as well as misconduct in business in
order to facilitate their ethical development (Hanson et al.
2017). Universities could integrate ethics into courses across
disciplines and stress the importance of ethical behavior by
presenting real‑life cases of ethical lapses and discussing
with students the ethical implications of different courses of
action. Students could also study novels and—through story‑
telling—grow both professionally and personally (Michael‑
son 2016). Universities could organize keynote speeches on
ethical issues by people who are recognized as role models
in the business community. In this way, students would be
equipped to solve the problems that arise during their studies
and become better prepared to tackle challenges pertaining
to misbehavior at work.

Second, our results indicate that mindfulness facilitates
the formation of more critical attitudes to social loafing.
Therefore, students could be guided towards a more mind‑
ful state in their team tasks, which would make them more
observant of their own and others’ loafing incidences. One
way to encourage students to be more present and pay atten‑
tion to the occurrences in their immediate environment is
to make them acquainted with mindfulness training that,
aside from benefiting teamwork, improves working memory
capacity and reading comprehension (Mrazek et al. 2013).
Acquainting students with the mindfulness concept and inte‑
grating mindfulness exercises into the curriculum could lead
to more transformational learning, as recently demonstrated
in a pedagogical innovation in an MBA leadership course
(Kuechler and Stedham 2017). With regard to teamwork,
students could be encouraged to consciously observe them‑
selves while performing their role and to reflect on a particu‑
lar team task. This could be achieved by using diaries (e.g.,
weekly logs) in which students reflect on their experiences
both in and outside of class (e.g., during meetings with team

723Reaping the Fruits of Another’s Labor: The Role of Moral Meaningfulness, Mindfulness, and…

1 3

members). These short reflective exercises could draw atten‑
tion to the ethical aspects of students’ own behavior as well
as that of their peers. As mindfulness is also related to com‑
mitment (Zivnuska et al. 2016), such practices could have
broader beneficial effects reflected in increased engagement
in class.

Third, the present study found that students who find
social loafing beneficial (i.e., have positive attitudes to
social loafing) are more likely to shirk responsibility (i.e.,
have positive loafing intentions); therefore, educators should
make it clear that social loafing is unacceptable. One way
of doing this is to instruct students both on their duty to
achieve objectives in a team project as well as on the equal
distribution of tasks. The code of ethics and active discus‑
sions in class could point out the negative consequences of
social loafing such as procrastination (Ferrari and Pychyl
2012), lower grades or even the failure to submit projects.
The negative consequences a loafer causes for fellow team
members should also be outlined. Universities could also
rely on the following quote by Confucius (Jia and Jia 2017):
“don’t do unto others what you don’t want others to do unto
you” in presenting this issue to students.

Fourth, as the link between social loafing attitudes and
intentions is stronger for extrinsically motivated students,
educators could pay attention to students’ motivation when
attempting to identify potential loafers. While extrinsic
motivation per se does not have a negative connotation, it is
problematic if, in conjunction with other factors, it results
in individual actions that bring negative consequences for
other people (e.g., peers, co‑workers). In teaching business
ethics, it would be worthwhile to foster discussions about
student motivation and the potential academic misconduct
resulting from it because that would help increase students’
self‑awareness about their own motivation in a particular
course, thereby enabling them to make more informed deci‑
sions regarding course selection (Cole et al. 2004). On a
more general note, given the presence of team projects dur‑
ing studies, students could be frequently reminded of the dos
and don’ts in teamwork and the characteristics of effective
team members as well as those members who take advantage
of others’ efforts while withholding their own. This should
be followed by an outline of possible courses of action to
ensure an equal division of tasks and the successful comple‑
tion of a project.

Limitations and Future Research

While the present study provides a meaningful contribution
to the existing literature on social loafing, it is not without
limitations. The first concerns the cross‑sectional design
which limits the ability to firmly establish causality. Second,
the sample was relatively homogenous (i.e., undergraduate
students from a single business school in one geographic

location). In future research scholars could cross‑validate
the proposed conceptual model on samples of students from
other disciplines and countries. Related to this, it was not our
intention to examine the influence of various demographic
characteristics on social loafing, hence future studies could
focus on other characteristics such as full‑ and part‑time
studies and the duration and type of work experience to
account for the possible differences. Level of study, namely
the differences between undergraduate, graduate and PhD
students could also be examined. This would allow research‑
ers to make more general conclusions about the nature of the
phenomenon in question. Moreover, more attention could
also be devoted to analyzing relationships through a cross‑
cultural lens by evaluating how prone social loafing is to the
influence of individualist versus collectivist values.

In the current study, social loafing intentions were
measured in team projects whereby the act of withholding
effort mainly occurs outside of class. Yet, aside from group
projects, social loafing can also occur during lectures, as
exemplified by students not preparing for class, not reading
the assigned materials, and relying on peers to participate
in class discussions and share knowledge. Future research
could thus also concentrate on understanding what causes
social loafing in class.

This study was conducted in a higher education institu‑
tion and therefore certain constructs, i.e., moral meaningful‑
ness, attitudes, and intentions, were adapted to this setting.
The values of the reliability coefficients for the social loafing
intention scale were very close to the cut‑off values, imply‑
ing that its use in future studies should be reconsidered.
Moreover, the values of AVE suggest that the transfer of
measurement scales from other settings to examine social
loafing may be problematic. This could be solved in future
studies by using alternative scales for the chosen constructs.
Alternatively, future research could replicate the research
design in the workplace setting in order to test whether the
model and its relationships hold there as well. In addition,
because of the deficiencies in social loafing literature, the
study’s approach was focused as it only introduced a narrow
set of individual factors. The interaction between individual
and group‑related factors could also provide a promising
area for investigation.

Conclusion

Engaging in social loafing, a form of academic miscon‑
duct, not only impairs the learning process of the loafer, but
decreases the motivations of his/her peers to participate in
team tasks. If students experience frustration in team assign‑
ments during their studies and end up doing extra work to
compensate for loafers, they might develop unfavorable atti‑
tudes towards collaborating with other people. Once in the

724 K. K. Mihelič, B. Culiberg

1 3

workplace, these previous negative experiences can lead to
an overall reluctance to become involved in teams and to
then favor individual assignments. With ever more work in
companies being performed in teams, knowing more about
what drives social loafing at university is both timely and
important for business ethics educators. The present study
provides a fresh insight into the individual factors triggering
social loafing intentions.

Acknowledgements The authors would like to thank the students who
participated in the study.

Compliance with Ethical Standards

Conflict of interest The authors declare that they have no conflict of
interest.

Appendix

See Table 4.

Table 4 Measurement scales

Variable Composite reli‑
ability

Item

Social loafing intention 0.70 In future group projects, I may let my peers carry out some of my tasks/responsibilities
If I had the opportunity, I would let my peers carry out some of my study assignments for me
I would never let my peers do some of my study assignments for me

Social loafing attitude 0.81 I find social loafing…
Good–bad
Pleasant–unpleasant
Foolish–wise
Useful–useless
Unattractive–attractive

Mindfulness 0.73 It seems I am “running on automatic” without much awareness
I run through activities without being really attentive to them
I get so focused on the goal I want to achieve that I lose touch with what I am doing right

now to get there—(dropped item)
I do jobs or tasks automatically, without being aware of what I’m doing
I find myself doing things without paying attention

Moral meaningfulness 0.80 Maintaining high morals/ethics brings me meaning at school
I find that doing the “right thing” at school is personally meaningful for me
Doing the ethical thing gives me purpose at school
Behaving consistently with my morals is quite important to me

Extrinsic motivation 0.89 Why do you study?
Because with only a high‑school degree I would not find a high‑paying job later on
In order to obtain a more prestigious job later on
Because I want to have “the good life” later on
In order to have a better salary later on

725Reaping the Fruits of Another’s Labor: The Role of Moral Meaningfulness, Mindfulness, and…

1 3

References

Adair, K. C., & Fredrickson, B. L. (2015). Be open: Mindfulness pre‑
dicts reduced motivated perception. Personality and Individual
Differences, 83(1), 198–201.

Aggarwal, P., & O’Brien, C. L. (2008). Social loafing on group pro‑
jects: Structural antecedents and effect on student satisfaction.
Journal of Marketing Education, 30(3), 255–264.

Ajzen, I. (1991). The theory of planned behavior. Organizational
behavior and human decision processes, 50(2), 179–211.

Ajzen, I. (2002). Perceived behavioral control, self‑efficacy, locus of
control, and the theory of planned behavior. Journal of Applied
Social Psychology, 32(4), 665–683.

Albanese, R., & Van Fleet, D. D. (1985). Rational behavior in
groups: The free‑riding tendency. Academy of Management
Review, 10(2), 244–255.

Allmon, D. E., Page, D., & Roberts, R. (2000). Determinants of
perceptions of cheating: Ethical orientation, personality and
demographics. Journal of Business Ethics, 23(4), 411–422.

Ballantine, J. A., Guo, X., & Larres, P. (2016). Can future managers
and business executives be influenced to behave more ethi‑
cally in the workplace? The impact of approaches to learning
on business students’ cheating behavior. Journal of Business
Ethics, 1–14.

Beck, L., & Ajzen, I. (1991). Predicting dishonest actions using the
theory of planned behavior. Journal of Research in Personal-
ity, 25(3), 285–301.

Bennett, N., & Naumann, S. E. (2005). Understanding and prevent‑
ing shirking, job neglect, social loafing, and free riding. In R.
E. Kidwell Jr. & C. L. Martin (Eds.), Managing organizational
deviance (pp. 113–130). Thousand Oaks, CA: Sage Publications.

Bentler, P. M., & Speckart, G. (1979). Models of attitude–behavior
relations. Psychological Review, 86(5), 452–464.

Bing, M. N., Davison, H. K., Vitell, S. J., Ammeter, A. P., Garner, B.
L., & Novicevic, M. M. (2012). An experimental investigation
of an interactive model of academic cheating among business
school students. Academy of Management Learning & Educa-
tion, 11(1), 28–48.

Brown, K. W., & Ryan, R. M. (2003). The benefits of being present:
Mindfulness and its role in psychological well‑being. Journal
of Personality and Social Psychology, 84(4), 822–848.

Byrne, B. M. (2012). Structural equation modeling with Mplus:
Basic concepts, applications, and programming. Abingdon:
Routledge.

Celsi, R. L., & Olson, J. C. (1988). The role of involvement in attention
and comprehension processes. Journal of Consumer Research,
15(2), 210–224.

Chen, M.‑F., & Tung, P.‑J. (2009). The moderating effect of perceived
lack of facilities on consumers’ recycling intentions. Environ-
ment and Behavior, 42(6), 824–844.

Clark, J., & Baker, T. (2011). It‘s not fair: Cultural attitudes to social
loafing in ethnically diverse groups. Intercultural Communica-
tion Studies, XX(1), 124–140.

Cole, M. S., Feild, H. S., & Harris, S. G. (2004). Student learning
motivation and psychological hardiness: Interactive effects on
students’ reactions to a management class. Academy of Manage-
ment Learning & Education, 3(1), 64–85.

Comer, D. R. (1995). A model of social loafing in real work groups.
Human Relations, 48(6), 647–667.

Cronan, T. P., Mullins, J. K., & Douglas, D. E. (2015). Further under‑
standing factors that explain freshman business students’ aca‑
demic integrity intention and behavior: Plagiarism and sharing
homework. Journal of Business Ethics, 1–24.

Dane, E., & Brummel, B. J. (2014). Examining workplace mindful‑
ness and its relations to job performance and turnover intention.
Human Relations, 67(1), 105–128.

Davy, J. A., Kincaid, J. F., Smith, K. J., & Trawick, M. A. (2007). An
examination of the role of attitudinal characteristics and moti‑
vation on the cheating behavior of business students. Ethics &
Behavior, 17(3), 281–302.

de los Reyes, G., Kim, T. W., & Weaver, G. R. (2017). Teaching eth‑
ics in business schools: A conversation on disciplinary differ‑
ences, academic provincialism, and the case for integrated peda‑
gogy. Academy of Management Learning & Education, 16(2),
314–336.

Duffy, M. K., & Shaw, J. D. (2000). The Salieri syndrome conse‑
quences of envy in groups. Small Group Research, 31(1), 3–23.

Dysvik, A., & Kuvaas, B. (2010). Exploring the relative and combined
influence of mastery‑approach goals and work intrinsic motiva‑
tion on employee turnover intention. Personnel Review, 39(5),
622–638.

Dysvik, A., & Kuvaas, B. (2011). Intrinsic motivation as a moderator
on the relationship between perceived job autonomy and work
performance. European Journal of Work and Organizational
Psychology, 20(3), 367–387.

Eisenberg, J. (2004). To cheat or not to cheat: Effects of moral perspec‑
tive and situational variables on students’ attitudes. Journal of
Moral Education, 33(2), 163–178.

Etter, S., Cramer, J. J., & Finn, S. (2006). Origins of academic dishon‑
esty: Ethical orientations and personality factors associated with
attitudes about cheating with information technology. Journal of
Research on Technology in Education, 39(2), 133–155.

Farrell, A. M. (2010). Insufficient discriminant validity: A comment
on Bove, Pervan, Beatty. and Shiu (2009). Journal of Business
Research, 63(3), 324–327.

Ferrari, J. R., & Pychyl, T. A. (2012). “If I wait, my partner will do
it”: The role of conscientiousness as a mediator in the relation
of academic procrastination and perceived social loafing. North
American Journal of Psychology, 14(1), 13–24.

Fida, R., Tramontano, C., Paciello, M., Ghezzi, V., & Barbaranelli,
C. (2016). Understanding the interplay among regulatory self‑
efficacy, moral disengagement, and academic cheating behav‑
iour during vocational education: A three‑wave study. Journal
of Business Ethics 1–16.

Floyd, L. A., Xu, F., Atkins, R., & Caldwell, C. (2013). Ethical out‑
comes and business ethics: Toward improving business ethics
education. Journal of Business Ethics, 117(4), 753–776.

Forehand, J. W., Leigh, K. H., Farrell, R. G., & Spurlock, A. Y.
(2016). Social dynamics in group work. Teaching and Learning
in Nursing, 11(2), 62–66.

Fornell, C., & Larcker, D. F. (1981). Structural equation models with
unobservable variables and measurement error: Algebra and
statistics. Journal of Marketing Research, 382–388.

George, J. M. (1992). Extrinsic and intrinsic origins of perceived
social loafing in organizations. Academy of Management Jour-
nal, 35(1), 191–202.

Good, D. J., Lyddy, C. J., Glomb, T. M., Bono, J. E., Brown, K.
W., Duffy, M. K., et al. (2016). Contemplating mindfulness at
work: An integrative review. Journal of Management, 42(4),
114–142.

Hair, J. F., Black, W. C., Babin, B. J., Anderson, R. E., & Tatham, R.
L. (2010). Multivariate data analysis (Vol. 7). Upper Saddle
River, NJ: Prentice Hall.

Hall, D., & Buzwell, S. (2012). The problem of free‑riding in group
projects: Looking beyond social loafing as reason for non‑con‑
tribution. Active Learning in Higher Education, 14(1), 37–49.

Hanson, W. R., Moore, J. R., Bachleda, C., Canterbury, A., Franco,
C., Marion, A., et al. (2017). Theory of moral development of
business students: Case studies in Brazil, North America, and

726 K. K. Mihelič, B. Culiberg

1 3

Morocco. Academy of Management Learning & Education,
16(3), 393–414.

Harding, T. S., Mayhew, M. J., Finelli, C. J., & Carpenter, D. D.
(2007). The theory of planned behavior as a model of academic
dishonesty in engineering and humanities undergraduates. Eth-
ics & Behavior, 17(3), 255–279.

Høigaard, R., Boen, F., De Cuyper, B., & Peters, D. M. (2013).
Team identification reduces social loafing and promotes social
laboring in cycling. International Journal of Applied Sports
Science, 25(1), 30–40.

Hoon, H., & Tan, T. M. L. (2008). Organizational citizenship behav‑
ior and social loafing: The role of personality, motives, and
contextual factors. The Journal of Psychology, 142(1), 89–108.

Howard, L. W., Tang, T. L.‑P., & Austin, M. J. (2015). Teaching criti‑
cal thinking skills: Ability, motivation, intervention, and the
Pygmalion effect. Journal of Business Ethics, 128(1), 133–147.

Hu, L., & Bentler, P. M. (1998). Fit indices in covariance structure
modeling: Sensitivity to underparameterized model misspeci‑
fication. Psychological Methods, 3(4), 424–453.

Hunter, J., & McCormick, D. W. (2008). Mindfulness in the work‑
place: An exploratory study. In SE Newell (Facilitator), Weick-
ian Ideas. Symposium conducted at the annual meeting of the
Academy of Management, Anaheim, CA.

Jackson, J. M., & Harkins, S. G. (1985). Equity in effort: An expla‑
nation of the social loafing effect. Journal of Personality and
Social Psychology, 49(5), 1199–1206.

Jassawalla, A., Sashittal, H., & Sashittal, A. (2009). Students’ per‑
ceptions of social loafing: Its antecedents and consequences in
undergraduate business classroom teams. Academy of Manage-
ment Learning & Education, 8(1), 42–54.

Jia, Y., & Jia, X. L. (2017). A dialogic approach to intercultural
conflict management and harmonious relationships. In X. Dai
& G. M. Chen (Eds.), Conflict management and intercultural
communication. The art of intercultural harmony. Oxon:
Routledge.

Jordan, A. E. (2001). College student cheating: The role of motivation,
perceived norms, attitudes, and knowledge of institutional policy.
Ethics & Behavior, 11(3), 233–247.

Jordan, J., Brown, M. E., Treviño, L. K., & Finkelstein, S. (2013).
Someone to look up to executive–follower ethical reasoning and
perceptions of ethical leadership. Journal of Management, 39(3),
660–683.

Karau, S. J., & Williams, K. D. (1993). Social loafing: A meta‑analytic
review and theoretical integration. Journal of personality and
social psychology, 65(4), 681–706.

Karau, S. J., & Williams, K. D. (1995). Social loafing: Research find‑
ings, implications, and future directions. Current Directions in
Psychological Science, 4(5), 134–140.

Kidwell, R. E., & Bennett, N. (1993). Employee propensity to withhold
effort: A conceptual model to intersect three avenues of research.
Academy of Management Review, 18(3), 429–456.

Kidwell, R. E., & Valentine, S. R. (2009). Positive group context, work
attitudes, and organizational misbehavior: The case of withhold‑
ing job effort. Journal of Business Ethics, 86(1), 15–28.

Kish‑Gephart, J. J., Harrison, D. A., & Treviño, L. K. (2010). Bad
apples, bad cases, and bad barrels: Meta‑analytic evidence about
sources of unethical decisions at work. Journal of Applied Psy-
chology, 95(1), 1–31.

Klein, A., & Moosbrugger, H. (2000). Maximum likelihood estima‑
tion of latent interaction effects with the LMS method. Psycho-
metrika, 65(4), 457–474.

Kline, R. B. (2016). Principles and practice of structural equation
modeling (4th edn.). New York: The Guilford Press.

Kos Koklic, M., Vida, I., Bajde, D., & Culiberg, B. (2014). The study
of perceived adverse effects of digital piracy and involvement:

Insights from adult computer users. Behaviour & Information
Technology, 33(3), 225–236.

Kozlowski, S. W. J., & Bell, B. S. (2013). Work groups and teams in
organizations: Review update. In N. Schmitt & S. Highhouse
(Eds.), Handbook of psychology. Industrial and organizational
psychologoy (Vol. 12, pp. 412–469). Hoboken: Wiley.

Kravitz, D. A., & Martin, B. (1986). Ringelmann rediscovered: The
original article. Journal of Personality and Social Psychology,
50(5), 936–941.

Krishnakumar, S., & Robinson, M. D. (2015). Maintaining an even
keel: An affect‑mediated model of mindfulness and hostile work
behavior. Emotion, 15(5), 579–589.

Kuechler, W., & Stedham, Y. (2017). Management Education and
Transformational Learning: The Integration of Mindfulness in
an MBA Course. Journal of Management Education, 1–26.

Lampe, M., & Engleman‑Lampe, C. (2012). Mindfulness‑based busi‑
ness ethics education. Academy of Educational Leadership Jour-
nal, 16(3), 99–130.

Latané, B., Williams, K., & Harkins, S. (1979). Many hands make light
the work: The causes and consequences of social loafing. Journal
of Personality and Social Psychology, 37(6), 822–832.

Lau, C. L. (2010). A step forward: Ethics education matters! Journal
of Business Ethics, 92(4), 565–584.

Lawson, R. A. (2004). Is classroom cheating related to business stu‑
dents’ propensity to cheat in the” real world”? Journal of Busi-
ness Ethics, 49(2), 189–199.

Lehnert, K., Park, Y., & Singh, N. (2015). Research note and review
of the empirical ethical decision‑making literature: Boundary
conditions and extensions. Journal of Business Ethics, 129(1),
195–219.

Liden, R. C., Wayne, S. J., Jaworski, R. A., & Bennett, N. (2004).
Social loafing: A field investigation. Journal of Management,
30(2), 285–304.

Lim, V. K., & See, S. K. (2001). Attitudes toward, and intentions to
report, academic cheating among students in Singapore. Ethics
& Behavior, 11(3), 261–274.

Luo, Z., Qu, H., & Marnburg, E. (2013). Justice perceptions and drives
of hotel employee social loafing behavior. International Journal
of Hospitality Management, 33, 456–464.

May, D. R., & Luth, M. T. (2013). The effectiveness of ethics educa‑
tion: A quasi‑experimental field study. Science and Engineering
Ethics, 19(2), 545–568.

May, D. R., Luth, M. T., & Schwoerer, C. E. (2014). The influence of
business ethics education on moral efficacy, moral meaningful‑
ness, and moral courage: A quasi‑experimental study. Journal of
Business Ethics, 124(1), 67–80.

Mayhew, M. J., Hubbard, S. M., Finelli, C. J., Harding, T. S., & Car‑
penter, D. D. (2009). Using structural equation modeling to vali‑
date the theory of planned behavior as a model for predicting stu‑
dent cheating. The Review of Higher Education, 32(4), 441–468.

McCabe, D. L., Butterfield, K. D., & Treviño, L. K. (2006). Academic
dishonesty in graduate business programs: Prevalence, causes,
and proposed action. Academy of Management Learning & Edu-
cation, 5(3), 294–305.

Meyer, B., Schermuly, C. C., & Kauffeld, S. (2016). That’s not my
place: The interacting effects of faultlines, subgroup size, and
social competence on social loafing behaviour in work groups.
European Journal of Work and Organizational Psychology,
25(1), 31–49.

Michaelson, C. (2016). A novel approach to business ethics education:
Exploring how to live and work in the 21st century. Academy of
Management Learning & Education, 15(3), 588–606.

Monzani, L., Ripoll, P., Peiró, J. M., & Van Dick, R. (2014). Loafing
in the digital age: The role of computer mediated communica‑
tion in the relation between perceived loafing and group affective
outcomes. Computers in Human Behavior, 33, 279–285.

727Reaping the Fruits of Another’s Labor: The Role of Moral Meaningfulness, Mindfulness, and…

1 3

Morris, D. (2004). Defining a moral problem in business ethics. Jour-
nal of Business Ethics, 49(4), 347–357.

Mrazek, M. D., Franklin, M. S., Phillips, D. T., Baird, B., & Schooler,
J. W. (2013). Mindfulness training improves working memory
capacity and GRE performance while reducing mind wandering.
Psychological Science, 24(5), 776–781.

Murphy, S. M., Wayne, S. J., Liden, R. C., & Erdogan, B. (2003).
Understanding social loafing: The role of justice perceptions and
exchange relationships. Human Relations, 56(1), 61–84.

Muthén, L. K., & Muthén, B. O. (2012). Mplus User’s Guide (7th edn.).
Los Angeles, CA: Muthén & Muthén.

Netemeyer, R. G., Bearden, W. O., & Sharma, S. (2003). Scaling pro-
cedures: Issues and applications. Thousand Oaks, CA: Sage
Publications.

Nunnally, J. C., & Bernstein, I. H. (2007). Psychometric theory
(3rd edn.). New York: McGraw Hill.

Osman, A., Lamis, D. A., Bagge, C. L., Freedenthal, S., & Barnes,
S. M. (2016). The mindful attention awareness scale: Further
examination of dimensionality, reliability, and concurrent valid‑
ity estimates. Journal of Personality Assessment, 98(2), 189–199.

Pandey, A., Chandwani, R., & Navare, A. (2018). How can mindfulness
enhance moral reasoning? An examination using business school
students. Business Ethics: A European Review, 27(1), 56–71.

Pinder, C. C. (2008). Work motivation in organizational behavior (2nd
edn.). New York: PsychologyPress.

Price, K. H., Harrison, D. A., & Gavin, J. H. (2006). Withholding
inputs in team contexts: Member composition, interaction
processes, evaluation structure, and social loafing. Journal of
Applied Psychology, 91(6), 1375–1384.

Rettinger, D. A., Jordan, A. E., & Peschiera, F. (2004). Evaluating the
motivation of other students to cheat: A vignette experiment.
Research in Higher Education, 45(8), 873–890.

Ruedy, N. E., & Schweitzer, M. E. (2010). In the moment: The effect
of mindfulness on ethical decision making. Journal of Business
Ethics, 95, 73–87.

Ryan, R. M., & Deci, E. L. (2000). Self‑determination theory and the
facilitation of intrinsic motivation, social development, and well‑
being. American Psychologist, 55(1), 68–78.

Schippers, M. C. (2014). Social loafing tendencies and team perfor‑
mance: The compensating effect of agreeableness and consci‑
entiousness. Academy of Management Learning & Education,
13(1), 62–81.

Schlossberg, M. (2016). Google experiment reveals the single most
important quality for teamwork. Business insider. Retrieved from
http://www.busin essin sider .com/googl e‑revea ls‑how‑to‑have‑
the‑perfe ct‑team‑2016‑2.

Schuh, S. C., Zheng, M. X., Xin, K. R., & Fernandez, J. A. (2017). The
interpersonal benefits of leader mindfulness: A serial mediation

model linking leader mindfulness, leader procedural justice
enactment, and employee exhaustion and performance. Journal
of Business Ethics, 1–19.

Shepperd, J. A. (1993). Productivity loss in performance groups: A
motivation analysis. Psychological Bulletin, 113(1), 67–81.

Sierra, J. J., & Hyman, M. R. (2008). Ethical antecedents of cheating
intentions: Evidence of mediation. Journal of Academic Ethics,
6(1), 51–66.

Smith, B. N., Kerr, N. A., Markus, M. J., & Stasson, M. F. (2001).
Individual differences in social loafing: Need for cognition as a
motivator in collective performance. Group Dynamics: Theory,
Research, and Practice, 5(2), 150–158.

Smith, O. R. F., Melkevik, O., Samdal, O., Larsen, T. M., & Haug,
E. (2017). Psychometric properties of the five‑item version of
the Mindful Awareness Attention Scale (MAAS) in Norwegian
adolescents. Scandinavian Journal of Public Health, 45(4),
373–380.

Stone, T. H., Jawahar, I., & Kisamore, J. L. (2009). Using the theory of
planned behavior and cheating justifications to predict academic
misconduct. Career Development International, 14(3), 221–241.

University of California, B. Definitions & Examples of Academic Mis‑
conduct. Retrieved May 2017, from http://sa.berke ley.edu/condu
ct/integ rity/defin ition .

Valentine, S., Godkin, L., & Varca, P. E. (2010). Role conflict, mindful‑
ness, and organizational ethics in an education‑based healthcare
institution. Journal of Business Ethics, 94(3), 455–469.

Vallerand, R. J., Blais, M. R., Brière, N. M., & Pelletier, L. G. (1989).
Construction et validation de l’Échelle de Motivation en Édu‑
cation (EME)/Construction and validation of the Motivation
toward Education Scale. Canadian Journal of Behavioural Sci-
ence/Revue canadienne des sciences du comportement, 21(3),
323–349.

Wang, L. C., & Calvano, L. (2015). Is business ethics education effec‑
tive? An analysis of gender, personal ethical perspectives, and
moral judgment. Journal of Business Ethics, 126(4), 591–602.

Williams, K. D., Harkins, S. G., & Latané, B. (1981). Identifiability as
a deterrant to social loafing: Two cheering experiments. Journal
of Personality and Social Psychology, 40(2), 303–311.

Williams, K. D., & Karau, S. J. (1991). Social loafing and social com‑
pensation: The effects of expectations of co‑worker performance.
Journal of Personality and Social Psychology, 61(4), 570–581.

Zivnuska, S., Kacmar, K. M., Ferguson, M., & Carlson, D. S. (2016).
Mindfulness at work: Resource accumulation, well‑being, and
attitudes. Career Development International, 21(2), 106–124.

http://www.businessinsider.com/google-reveals-how-to-have-the-perfect-team-2016-2

http://sa.berkeley.edu/conduct/integrity/definition

Reproduced with permission of copyright owner. Further reproduction
prohibited without permission.

Reaping the Fruits of Another’s Labor: The Role of Moral Meaningfulness, Mindfulness, and Motivation in Social Loafing

Abstract

Introduction

Theoretical Background

Origins of the Term and Conceptualization

Determinants of Social Loafing

Conceptual Model and Hypotheses

Methods

Sample and Procedure

Measures

Moral Meaningfulness

Mindfulness

Attitudes

Intentions

Motivation

Analytical Procedure

Results

Descriptive Statistics

Measurement Model

Structural Model and Hypotheses Testing

Discussion

Implications

Limitations and Future Research

Conclusion

Acknowledgements

References

Full Terms & Conditions of access and use can be found at
https://www.tandfonline.com/action/journalInformation?journalCode=cedp20

Educational Psychology

ISSN: 0144-3410 (Print) 1469-5820 (Online) Journal homepage: https://www.tandfonline.com/loi/cedp20

Social Loafing in a Co-operative Classroom Task

Adrian C. North , P. Alex Linley & David J. Hargreaves

To cite this article: Adrian C. North , P. Alex Linley & David J. Hargreaves (2000) Social
Loafing in a Co-operative Classroom Task, Educational Psychology, 20:4, 389-392, DOI:
10.1080/01443410020016635

To link to this article: https://doi.org/10.1080/01443410020016635

Published online: 01 Jul 2010.

Submit your article to this journal

Article views: 2344

View related articles

Citing articles: 2 View citing articles

https://www.tandfonline.com/action/journalInformation?journalCode=cedp20

https://www.tandfonline.com/loi/cedp20

https://www.tandfonline.com/action/showCitFormats?doi=10.1080/01443410020016635

https://doi.org/10.1080/01443410020016635

https://www.tandfonline.com/action/authorSubmission?journalCode=cedp20&show=instructions

https://www.tandfonline.com/doi/mlt/10.1080/01443410020016635

https://www.tandfonline.com/doi/citedby/10.1080/01443410020016635#tabModule

Educational Psychology, Vol. 20, No. 4, 2000

Social Loa® ng in a Co-operative Classroom
Task

ADRIAN C. NORTH & P. ALEX LINLEY, School of Psychology, University of

Leicester, UK

DAVID J. HARGREAVES, Roehampton Institute, Digby Stuart College, London, UK

ABSTRACT Social loa® ng refers to the tendency for individuals to reduce their own personal

input when performing as part of group. This phenomenon may be problematic if it exists in

educational contexts, given a current emphasis on group collaborative classroom activities. The

present study investigated whether social loa® ng existed in a collaborative educational task,

employing groups of three and eight participants. The results indicated that individuals

working within the smaller groups were more productive than those working in larger groups,

consistent with the social loa® ng hypothesis. Future research should determine whether the

detrimental effects on students’ collaborative performance attributable to social loa® ng are

justi® able in terms of gains accrued in other (e.g. interpersonal) domains.

Social loa® ng refers to the tendency for individuals to progressively reduce their
personal input to a collaborative task as group size increases. The concept has been
employed in explanations of phenomena as diverse as the creative musical output of
Lennon and McCartney (Jackson & Padgett, 1982) and competitive swimming perfor-
mances (Everett et al., 1992). More generally, social loa® ng pervades several cultures
(e.g. Gabrenya et al., 1983; but also Earley, 1989; Gabrenya et al., 1985), and has been
demonstrated in a range of cognitive tasks (e.g. Pratarelli & McIntyre, 1994); be-
havioural tasks (e.g. Anshel, 1995); and participant groups (e.g. Karau & Williams,
1993). Others, however, have argued that the effects of social loa® ng might be
mitigated by social facilitation effects (see, e.g. Bartis et al., 1988; Harkins, 1987;
Jackson & Williams, 1985), such that larger groups can sometimes perform at a higher
level than do smaller groups.

Several explanations and interpretations of social loa® ng have been put forward.
These include the withholding of effort because others are perceived as doing so also,
such that one does not wish to be perceived as the ª suckerº (Robbins, 1995); lack of
motivation owing to the low value one attaches to one’ s individual contribution

ISSN 0144-3410 print; ISSN 1469-5820 online/00/040389-04 Ó 2000 Taylor & Francis Ltd
DOI: 10.1080/01443410020016635

390 A. C. North et al.

(Shepperd, 1993); expectations of the performance of one’ s co-workers (Williams &
Karau, 1991); reward incentives (Shepperd & Wright, 1989); and a diffusion of
individual responsibility for subsequent outcomes (Weldon & Gargano, 1988).

The present study was designed to assess the potential impact of group size on social
loa® ng in the context of an educational task. Current educational policy in the UK
emphasises the importance of group collaboration as a means of (a) developing a variety
of transferable interpersonal skills; and (b) assisting the development of less able
students (see, e.g. Topping, 1992). The implications of social loa® ng for this policy are
very real: an emphasis on collaborative exercises may well promote, for instance,
interpersonal skills, but might also inspire social loa® ng on the part of individual group
members. In short, the educational gains accrued through collaborative classroom
activities may come at the expense of individual attainment on the speci® c task at hand.

Therefore, while debate may still continue regarding the existence of and precise
mechanisms underlying social loa® ng, the phenomenon may have potential implica-
tions for educational practice. The present study concerned these potential implica-
tions. During the course of a timetabled lecture, university undergraduates were asked
to engage in a collaborative word-generation task, which they were told was to illustrate
a point to be made later in the lecture. Participants completed the task in groups of
either three or eight, and it was predicted that individuals working in the smaller groups
would generate more words per person than would individuals working in larger
groups.

Method

Participants

One hundred and ten ® rst-year psychology undergraduates (55 males, 55 females) took
part in the experiment. Their mean age was 19.24 years (SD 5 1.90 years). Participants
were attending a compulsory lecture.

Design

Participants were assigned to either one of 10 groups consisting of eight people or one
of 10 groups consisting of three people. Males and females were represented equally
within each of these two situations, and sex distribution was also equal within each
group of eight participants, mathematical impossibility precluding such a distribution
within the groups of three.

Procedure

Upon arrival in a compulsory timetabled lecture, participants were arranged into
groups of either three or eight. It was explained that this was so they could take part in
a task that would illustrate an important point to be made later as part of the lecture.
Participants were instructed that they had 15 minutes to generate as many words as
possible which contained the letters T-O-N. Participants were informed that these
letters had to occur in the order stated, but that other letters could be interspersed
between them. Proper nouns were ineligible. The letters T-O-N were selected given the
very large number of eligible words that participants could nominate. Participants were
also advised that the illustrative utility of the task was in no way contingent upon the

Social Loa® ng in the Classroom 391

number of words generated. One participant in each group was responsible for keeping
count of the number of words generated, but was also allowed to generate words.
Participants were then instructed to retire from the lecture hall, and ® nd a location
where they would be able to work together without disturbance: they were asked to
begin their return journey to the lecture theatre precisely 15 minutes after beginning the
task.

Results and Discussion

The mean number of words generated by each participant in each group was calculated
as the total number of words produced by the group divided by the number of people
in that group. These means indicated the productivity of individuals within each group.
An independent t-test was then carried out to test for differences in these values
between the groups of three participants and the groups of eight participants. The result
of this was signi® cant [t (18) 5 7.27, p , 0.001]. The mean number of words produced
by individual participants working in a group of three was 13.86 words (SD 5 2.62
words), and the corresponding value for individual participants working in groups of
eight was 6.75 words (SD 5 1.64).

The results indicate the existence of social loa® ng in the larger groups of participants,
offering support for the experimental hypothesis. On face value this seems to suggest
that students should not be encouraged to participate in collaborative tasks in the
classroom. However, the present results do not support this simplistic conclusion. Put
simply, they suggest merely that there are disadvantages as well as advantages to
classroom collaborative activities: they do not demonstrate that the detrimental effects
on productivity attributable to social loa® ng are greater than the positive effects on
students attributable to the development of e.g. interpersonal skills during the course of
the task. Future research should also examine the extent to which the present results
could be replicated following variations in (a) the type of task in which participants
engage (e.g. cognitive, behavioural, or creative tasks); and (b) the size of the group in
which the task is completed (since there may be optimum group sizes for different
tasks). In conclusion, the present results indicate that social loa® ng does seem to occur
in collaborative classroom activities, but future research should determine whether its
detrimental effects on students’ performance are nevertheless worth incurring, and
whether these effects obtain across a range of tasks and group sizes.

Correspondence: Adrian North, School of Psychology, University of Leicester, University
Road, Leicester LE1 7RH, UK.

REFERENCES

ANSHEL, M.H. (1995) Examining social loa® ng among elite female rowers as a function of task duration
and mood, Journal of Sport Behavior, 18, pp. 39± 49.

BARTIS, S., SZYMANSKI, K. & HARKINS, S.G. (1988) Evaluation and performance: a two-edged knife,
Personality and Social Psychology Bulletin, 14, pp. 242± 251.

EARLEY, P.C. (1989) Social loa® ng and collectivism: a comparison of the United States and the
People’ s Republic of China, Administrative Science Quarterly, 34, pp. 565± 581.

EVERETT, J.J., SMITH, R.E. & WILLIAMS, K.D. (1992) Effects of team cohesion and identi® ability on
social loa® ng in relay swimming performance, International Journal of Sport Psychology, 23, pp.
311± 324.

392 A. C. North et al.

GABRENYA, W.K., LATANE, B. & WANG, Y. (1983) Social loa® ng in cross-cultural perspective: Chinese
on Taiwan, Journal of Cross Cultural Psychology, 14, pp. 368± 384.

GABRENYA, W.K., WANG, Y. & LATANE, B. (1985) Social loa® ng on an optimizing task: cross-cultural
differences among Chinese and Americans, Journal of Cross Cultural Psychology, 16, pp. 223± 242.

HARKINS, S.G. (1987) Social loa® ng and social facilitation, Journal of Experimental Social Psychology, 23,
pp. 1± 18.

JACKSON, J.M. & PADGETT, V.R. (1982) With a little help from my friend: social loa® ng and the
Lennon± McCartney songs, Personality and Social Psychology Bulletin, 8, pp. 672± 677.

JACKSON, J.M. & WILLIAMS, K.D. (1985) Social loa® ng on dif® cult tasks: working collectively can
improve performance, Journal of Personality and Social Psychology, 49, pp. 937± 942.

KARAU, S.J. & WILLIAMS, K.D. (1993) Social loa® ng: a meta-analytic review and theoretical integration,
Journal of Personality and Social Psychology, 65, pp. 681± 706.

PRATARELLI, M.E. & MCINTYRE J.A. (1994) Effects of social loa® ng on word recognition, Perceptual and

Motor Skills, 78, pp. 455± 464.
ROBBINS, T.L. (1995) Social loa® ng on cognitive tasks: an examination of the ª sucker effectº , Journal

of Business and Psychology, 9, pp. 337± 342.
SHEPPERD, J.A. (1993) Productivity loss in performance groups: a motivation analysis, Psychological

Bulletin, 113(1), pp. 67± 81.
SHEPPERD, J.A. & WRIGHT, R.A. (1989) Individual contributions to a collective effort: an incentive

analysis, Personality and Social Psychology Bulletin, 15, pp. 141± 149.
TOPPING, K. (1992) Co-operative learning and peer tutoring: an overview, The Psychologist, 5, pp.

151± 161.
WELDON, E. & GARGANO, G.M. (1988) Cognitive loa® ng: the effects of accountability and shared

responsibility on cognitive effort, Personality and Social Psychology Bulletin, 14, pp. 159± 171.
WILLIAMS, K.D. & KARAU, S.J. (1991) Social loa® ng and social compensation: the effects of expecta-

tions of co-worker performance, Journal of Personality and Social Psychology, 61, pp. 570± 581.

https://doi.org/10.1177/0273475317708588

Journal of Marketing Education
2018, Vol. 40(2) 117 –127
© The Author(s) 2017
Reprints and permissions:
sagepub.com/journalsPermissions.nav
DOI: 10.1177/0273475317708588
journals.sagepub.com/home/jmed

Article

Group-based teaching and learning is ubiquitous across
undergraduate and graduate business curricula (Chapman &
Van Auken, 2001; see also, Batra, Walvoord, & Krishnan,
1997; Huff, Cooper, & Jones, 2002), and with any type of
group work, free-riding—a form of social loafing in which
free-riding group members reap the rewards of nonloafing
members without exerting comparable effort (Comer,
1995)—is a concern (McCorkle et al., 1999; Strong &
Anderson, 1990). Several methods for reducing free-riding
in student groups have been explored (see, Karau & Williams,
1993 for a review). Particular emphasis has been placed on
the use of periodic peer evaluations for curbing and sanction-
ing free-riders (e.g., Aggarwal & O’Brien, 2008; Brooks &
Ammons, 2003; Druskat & Wolff, 1999); however, less
attention has been given to how group assignment may con-
tribute to free-riding.

The present research examines an instructor-driven
method of group assignment, herein called the flocking
method, designed to improve both students’ motivation and
ability to contribute to the group. In particular, students are
flocked, or matched, by the instructor according to their
schedule availability and willingness to devote time to the

course, such that motivated students (i.e., those who plan to
devote more time to the course) are grouped with other
motivated students with similar schedules, whereas unmo-
tivated students (i.e., those who plan to devote less time to
the course) are grouped with other unmotivated students
with similar schedules. Based on a review of the determi-
nants of free-riding, it is proposed that assigning students
with similar motivation levels and schedules to the same
group will reduce many of the temptations and obstacles
commonly associated with free-riding (Hall & Buzwell,
2012), resulting in more equitable contributions across
group members. It is also proposed that this reduction in
free-riding will, in turn, lead to better student learning out-
comes—not only on the group project, but also with regard
to students’ individual understanding of the course content

708588 JMDXXX10.1177/0273475317708588Journal of Marketing EducationHarding
research-article2017

1Belmont University, Nashville, TN, USA

Corresponding Author:
Lora Mitchell Harding, Jack C. Massey College of Business, Belmont
University, 1900 Belmont Boulevard, Nashville, TN 37212-3757, USA.
Email: lora.harding@belmont.edu

Students of a Feather “Flocked”
Together: A Group Assignment Method
for Reducing Free-Riding and Improving
Group and

Individual Learning Outcomes

Lora Mitchell Harding1

Abstract
Group-based teaching and learning is ubiquitous across undergraduate and graduate business curricula, and with any type of
group work, free-riding—a form of social loafing in which free-riding group members reap the rewards of nonloafing members
without exerting comparable effort—is a concern. This research examines a group assignment method, herein called the
flocking method, designed to reduce free-riding by improving students’ motivation and availability to contribute to the group.
A quasi-experiment is described in which students were flocked, or matched, according to their schedule availability and
willingness to devote time to the course, such that motivated students (i.e., those who planned to devote more time) were
grouped with other motivated students with similar schedules, whereas unmotivated students were grouped with other
unmotivated students with similar schedules. Compared with self-selected groups, students in flocked groups not only
reported less free-riding, they also performed better on group and individual assignments, indicating an actual reduction in
free-riding. Additionally, compared with the most prominent methods for reducing free-riding examined in literature, the
flocking method of group assignment reduces resource demands on the instructor and students, making it as efficient to
implement as it is effective. Limitations and directions for future research are discussed.

Keywords
free-riding, social loafing, group project, teamwork, student motivation, learning approaches and issues, marketing education
issues

https://us.sagepub.com/en-us/journals-permissions

https://journals.sagepub.com/home/jmed

mailto:lora.harding@belmont.edu

http://crossmark.crossref.org/dialog/?doi=10.1177%2F0273475317708588&domain=pdf&date_stamp=2017-05-16

118 Journal of Marketing Education 40(2)

on which the group project is based (Slavin, 1990; see also,
Kimber, 1996; Webb, 1997).

In the next sections, the literatures on free-riding and
social loafing are reviewed, with emphasis placed on the
causes of free-riding and corresponding methods of reducing
it. Next, the rationale behind the flocking method of group
assignment is described, drawing from research on the con-
sequences of free-riding and social loafing for other group
members to formulate hypotheses. The results of a quasi-
experiment in which flocked groups are compared with self-
selected groups in an undergraduate marketing research
course are then presented, providing support for the proposi-
tion that students in flocked groups perceive less free-riding
and enjoy better group and individual learning outcomes.
Finally, the contributions and limitations of the research are
discussed, including the benefits and drawbacks of the flock-
ing method of group assignment.

Methods for Reducing Free-Riding in
Groups

The phenomenon of social loafing, defined as “a decrease in
individual effort due to the social presence of other[s]”
(Latané, Williams, & Harkins, 1979, p. 823), was first recog-
nized over a century ago when Ringelmann (see, Latané
et al., 1979, for a discussion) reported that the collective
effort that groups exerted during a rope-pulling exercise was
less than the sum of the group members’ individual efforts
when pulling the rope alone. Social loafing is particularly
problematic when a loafing group member profits from, or
free-rides on, the efforts of nonloafing members, thereby
“deriving benefits . . . that are disproportionately larger than
his or her contributions to the group” (Comer, 1995, p. 649;
see also, Albanese & Van Fleet, 1985). Although several
causes of free-riding have been identified (see, Comer, 1995;
Hall & Buzwell, 2012; Karau & Williams, 1993; Strong &
Anderson, 1990, for reviews), most are predictive of a
decrease in the free-rider’s motivation to contribute to the
group (as opposed to, for example, a decline in intragroup
coordination; Ingham, Levinger, Graves, & Peckham, 1974).
For instance, people are more likely to free-ride when they
believe it will be difficult to identify their individual contri-
butions (George, 1992; Harkins & Jackson, 1985; Jones,
1984; Karau & Williams, 1993; Liden, Wayne, Jaworski, &
Bennett, 2004; Price, Harrison, & Gavin, 2006; K. D.
Williams, Harkins, & Latané, 1981), when they expect other
group members to perform well (Karau & Williams, 1993;
K. D. Williams & Karau, 1991), when the group task is of
low personal meaningfulness (e.g., not intrinsically interest-
ing or consequential to their personal outcomes; Karau &
Williams, 1993; K. D. Williams & Karau, 1991), or when
they feel they are dispensable with little to uniquely contrib-
ute (Harkins & Petty, 1982; Kerr & Bruun, 1983; Price et al.,
2006; Weldon & Mustari, 1988).

Although motivational drivers have received the most
attention, free-riding may also be a product of factors that
constrain one’s ability to contribute to the group, such as lim-
ited scholarly aptitude (real or perceived; Börjesson et al.,
2006; Freeman & Greenacre, 2011; Kerr, 1983; March, 1954;
see also, Karau & Williams, 1997), differing work styles or
time constraints (i.e., availability; Hall & Buzwell, 2012).
For instance, Freeman and Greenacre (2011) find that when
students with limited scholarly aptitude are perceived to be
unwilling to exert comparable effort, they are often ostra-
cized by other group members and effectively blocked from
contributing to the group. Likewise, students with schedules
that routinely prohibit them from meeting with the group
may be willing but, unfortunately, unable to contribute fully
(Hall & Buzwell, 2012). Thus, free-riding is not always a
consequence of one’s willingness to contribute—it may also
be a consequence of one’s ability to contribute.

Several methods for reducing free-riding in groups have
been explored, with particular emphasis on the use of peri-
odic peer evaluations for curbing and sanctioning free-riders
(Mello, 1993; Strong & Anderson, 1990; D. L. Williams,
Beard, & Rymer, 1991). In particular, research shows that
multiple peer evaluations (Aggarwal & O’Brien, 2008;
Brooks & Ammons, 2003), implemented early and with spe-
cific evaluative criteria (Brooks & Ammons, 2003), can
reduce free-riding and improve perceived group outcomes
(Druskat & Wolff, 1999). Peer evaluations are especially
effective at reducing free-riding when used to reward and
sanction individual group members through individualized
group project grades (Cook, 1981; Kench, Field, Agudera, &
Gill, 2009; Jalajas & Sutton, 1984; Maranto & Gresham,
1998; Mello, 1993; D. L. Williams et al., 1991), firing free-
riding members (Abernethy & Lett, 2005), and so on. Other
methods for reducing free-riding have been identified,
including the use of self-evaluations (Harkins & Szymanski,
1988; Szymanski & Harkins, 1987), and the formation of
smaller groups (Aggarwal & O’Brien, 2008; Alnuaimi,
Robert, & Maruping, 2010; Ingham et al., 1974; Karau &
Williams, 1993; Kerr & Bruun, 1981, 1983; Latané et al.,
1979; Liden et al., 2004). However, little attention has been
given to how group assignment may contribute to free-riding
(cf. Bacon, Stewart, & Anderson, 2001; Karau & Hart, 1998;
Karau & Williams, 1993, 1997; Liden et al., 2004; Muller,
1989).

The limited research that has considered the role of group
assignment generally recommends that students form their
own groups. Indeed, self-selection is associated with higher
initial cohesion, motivation to resolve interpersonal conflicts
and established group norms—factors which are predictive
of reduced free-riding (Bacon, Stewart, & Silver, 1999; see
also, Mello, 1993; Strong & Anderson, 1990). Furthermore,
empirical work has shown that self-selection is predictive of
superior group dynamics (Chapman, Meuter, Toy, & Wright,
2006), positive group experiences (Bacon et al., 1999;

Harding 119

Chapman et al., 2006; Mahenthiran & Rouse, 2000), and
higher grades (Mahenthiran & Rouse, 2000).

However, it is important to note that these advantages are
in comparison with randomly-assigned groups; only one
article, to the author’s knowledge, has compared self-selected
with instructor-assigned groups, and no difference in per-
ceived social loafing was found (Aggarwal & O’Brien,
2008). One reason for this null effect may be that the
researchers did not empirically distinguish between groups
assigned randomly and those formed on more strategic bases
(e.g., skill sets, personality types); thus, the effect of each
instructor-assignment method may have nullified the other.
Alternately, Aggarwal and O’Brien note that some instruc-
tors may have used instructor-assignment and self-selection
simultaneously. Regardless, it is unclear why students in
self-selected (vs. instructor-assigned) groups did not report a
lower incidence of social loafing, as predicted. Furthermore,
self-selected groups may suffer from a lack of diversity
(Jalajas & Sutton, 1984), critical task-related skills (Blowers,
2003), cohesion due to tight-knit subgroups (Michaelsen &
Black, 1994), and task focus during meetings (Chapman
et al., 2006), which may impede performance. Thus, the
question remains as to whether self-selection is indeed the
preferred group assignment method for reducing free-riding
or, alternately, if and when instructor-assignment to groups is
preferable.

The Flocking Method of Group
Assignment

The present research examines the flocking method, an
instructor-driven method of group assignment designed to
reduce free-riding by improving students’ motivation and
ability to contribute to the group. As previously discussed,
although there is a robust literature on the motivational ante-
cedents of free-riding and social loafing (Comer, 1995;
Karau & Williams, 1993; Strong & Anderson, 1990), free-
riding is not always a result of apathy or a deliberate lack of
effort—that is, one’s willingness to contribute to the group.
Numerous other factors may instead diminish one’s ability to
contribute, such as time constraints (see, Hall & Buzwell
2012, for a review). Thus, a group assignment method
designed to reduce free-riding would ideally address both
variables.

It is postulated that flocking, or matching, students with
similar motivation levels (i.e., willingness to contribute) and
schedules (i.e., ability to contribute) will reduce many of the
temptations and obstacles commonly associated with free-
riding (Hall & Buzwell, 2012; Mello, 1993). For instance, it
becomes more difficult for a student to willfully free-ride
when other group members are similarly unmotivated; like-
wise, it is easier to avoid unintentionally free-riding when
other group members have similar availability to meet. Thus,
it is proposed that the flocking method of group assignment

will maximize students’ motivation and ability to contribute,
resulting in more equitable contributions (i.e., less free-rid-
ing) in flocked (vs. self-selected) groups. Specifically, it is
hypothesized that

Hypothesis 1: Students in flocked (vs. self-selected)
groups will perceive less free-riding.

Furthermore, it is proposed that a reduction in free-riding
will lead to better group and individual learning outcomes
for all students. It is fairly apparent why this may be the case
for “low motivation” free-riding students: Those who other-
wise would have ridden on the coattails of more motivated
group members are not given the chance to do so; thus, they
are forced to exert more effort themselves (Karau & Williams,
1993; K. D. Williams & Karau, 1991). It may also be that
“low motivation” and “low ability” students (i.e., those who
are more likely to free-ride) are better able to contribute to
the group because their contributions are no longer blocked
by more motivated and/or able group members (Freeman &
Greenacre, 2011; see also, Börjesson et al., 2006; Kerr, 1983;
March, 1954). This enhanced ability to contribute may, in
turn, lead them to become more engaged. In either case,
increased effort on the group project is anticipated to trans-
late not only to better group learning outcomes, but also to a
better individual understanding of the course content on
which the group project is based (Slavin, 1990; see also,
Kimber, 1996; Webb, 1997; cf. Bacon, 2005).

It is also anticipated that a reduction in free-riding will
improve the learning outcomes of non-free-riding students.
This conjecture is based on research on the consequences of
free-riding and social loafing for other group members (see,
Comer, 1995 for a review). As implied by the definition of
free-riding, other group members often pick up free-riders’
slack by increasing their own efforts (Huff et al., 2002; Liden
et al., 2004; K. D. Williams & Karau, 1991). This is most
likely to occur when free-riding is attributed to a lack of abil-
ity on the part of the free-rider (e.g., free-riding members are
trying to pull their weight but are unable to because of their
limited scholarly aptitude, schedule availability, etc.; Karau
& Williams, 1997). However, when free-riding is perceived
to stem from an unwillingness to contribute, as is often the
case (Hall & Buzwell, 2012), the consequences are often less
favorable. In particular, non-free-riders may fall victim to
“disheartened” loafing (Comer, 1995)—that is, reducing
their own efforts with the belief that, no matter the caliber of
their own contributions, they will not be able to pick up the
slack of free-riding members. Alternately, they may succumb
to “retributive” loafing (aka the sucker effect; Kerr, 1983;
see also, Comer, 1995; Jackson & Harkins, 1985)—that is,
choosing to reduce their efforts rather than be a “sucker” who
picks up the slack of others. In either case, perceptions of
free-riding often diminish the motivation of otherwise moti-
vated group members; thus, if perceptions of free-riding

120 Journal of Marketing Education 40(2)

were reduced by creating groups in a manner that minimizes
the willingness and ability to free-ride, the motivation and
efforts of non-free-riding students should remain high.
Again, this heightened effort should lead to better perfor-
mance on the group project and, thus, a better individual
understanding of the course content on which the group proj-
ect is based. Therefore, it is hypothesized that

Hypothesis 2: Students in flocked (vs. self-selected)
groups will have better group and individual learning
outcomes.
Hypothesis 3: The effect of group assignment method on
group and individual learning outcomes will be mediated
by perceived free-riding.

Method

To test these hypotheses, two group assignment methods
(flocking vs. self-selection) are compared using a between-
participants, quasi-experimental design. Data were collected
over a two-year period in an undergraduate marketing research
course with a substantial group work component. In particular,
whereas 50% of the total course grade is based on a variety of
individual assessments (i.e., four multiple-choice exams, α =
.80; 16 true/false quizzes, count top 10, α = .74; two home-
work assignments with correct/incorrect answers, α = .60), the
remaining 50% of the total course grade is based on a semes-
ter-long, multideliverable, client-based group research project.
The group project consists of five major deliverables: (1) a
research request agreement, (2) a research proposal, (3) a
research presentation to the class, (4) a research presentation
to the client, and (5) a final research report. The research pre-
sentation to the client is not graded, per se; rather, students
receive full credit once the instructor receives client confirma-
tion that the presentation took place (all groups reported in this
research received full credit for the client presentation). In
contrast, all other group project deliverables are graded using
detailed rubrics (α = .61; see, Web Appendix for rubrics, avail-
able online at http://journals.sagepub.com/home/jmd). The
purpose of the group project is to enable students to apply the
concepts discussed in class in a real-world setting, thereby
deepening their understanding of those concepts.

In the first academic year (fall and spring semesters;
2014-2015), students were instructed to form their own
groups, which ranged in size from four to six students.
Because research shows that the benefits of self-selection for
subjective group experiences and other outcomes are stron-
gest when students know each other prior to selecting their
own groups (Bacon, Stewart, & Stewart-Belle, 1998), some
insight into how well-acquainted students were prior to
forming groups is warranted. This research was conducted at
a smaller private teaching university with a mostly tradi-
tional, full-time undergraduate population. This course is
typically taken in one’s senior year, which increases the

likelihood that students have already taken one or more
upper-level marketing courses together—courses which
range in size from 20 to 30 students. Anecdotally, most stu-
dents appear to know each other on the first day of class and,
in the self-selection condition, appear to choose group mem-
bers with whom they are reasonably familiar. In the subse-
quent academic year (fall and spring semesters; 2015-2016),
students were instead assigned to groups using the flocking
method of matching students based on motivation and avail-
ability; again, groups ranged in size from four to six students.
All other elements of the course—organization, content,
assignments, and grading—were identical.

The flocking method was implemented using CATME
(www.catme.org), a web-based team management system
that facilitates group formation using responses to an online
survey (Hrivnak, 2013; see also, Bacon, 2014; Loughry,
Ohland, & Woehr, 2014). Survey questions can be custom-
ized and their impact on group formation weighted using an
11-point scale (1 = group dissimilar; 6 = ignore; 11 = group
similar). Although additional questions were included in the
survey, only CATME’s preset questions regarding schedule
availability and the number of hours one plans to devote to
the course were weighted in the formation of groups.
Specifically, students were asked (a) “Please check the times
that you are busy and unavailable for group work” (98 one-
hour blocks between 8:00 am and 10:00 pm each day,
Monday through Sunday; i.e., CATME question titled,
“Schedule”) and (b) “In this course, you intend to work how
many hours per week outside of class (not counting lectures
or labs)?” (1 = 1 hour per week; 2 = 2-4 hours; 3 = 5-7 hours;
4 = 8-10 hours; 5 = whatever it takes; i.e., CATME question
titled, “Commitment Lvl”). Weights of 11 and 9 were desig-
nated for the availability and motivation questions, respec-
tively, such that students assigned to any given group were
similar on these variables. Whereas students were told that
the CATME survey would be used to place them into groups
with others who shared similar schedules, the motivational
component of group assignment was not disclosed.

Perceived free-riding was assessed at the end of each
semester using a confidential peer-evaluation form in which
students individually reported the relative contributions of
each of their group members, including themselves, using a
constant-sum scale (100 points). Little variation in the num-
ber of points allocated to each member represents a low inci-
dence of perceived free-riding, whereas high variation
represents the opposite. Because the number of points repre-
senting equitable contribution varies according to the size of
the group, a coefficient of variation (σ/µ) was computed by
dividing the standard deviation of the set of points allocated
by a given student to his or her group members by the mean
of that set (Muller, 1989). Thus, the smaller (larger) the coef-
ficient of variation, the more (less) equally students were
perceived to contribute to the group, indicating a lower
(higher) incidence of free-riding.

http://journals.sagepub.com/home/jmd

www.catme.org

Harding 121

Finally, student learning outcomes were assessed by
examining group and individual grades, which were col-
lapsed across the various group and individual assignments
in the course. Although grades are an imperfect indicator of
student learning, the number and variety of assessments used
in this course serves to minimize the failure of any particular
assessment to satisfactorily reflect student learning.
Furthermore, grades are widely regarded in the literature as a
valid proxy for actual (vs. perceived) student learning
(Sitzmann, Ely, Brown, & Bauer, 2010; see also, Bacon,
2016; Clayson, 2009).

Results

Prior to testing the conjecture that flocking (vs. self-selec-
tion) reduces free-riding and, thus, improves student learning
outcomes, the two group assignment conditions were com-
pared on four demographic variables: gender, grade point
average (GPA), class standing, and number of absences. A
series of analyses of variance (ANOVAs) and Pearson chi-
square analyses indicated that the conditions are equivalent
on all four variables (see, Table 1); nevertheless, all subse-
quent analyses were conducted using these variables as
covariates.

Perceived Free-Riding

To determine whether flocking (vs. self-selection) reduces
free-riding (Hypothesis 1), individual students’ perceptions
of free-riding (i.e., the coefficient of variation) were

submitted to a one-way between-participants ANOVA. This
analysis yielded a main effect of group assignment, such that
free-riding was reduced (i.e., contribution variance was
lower) when student groups were flocked (vs. self-selected):
M = 0.07 versus 0.15; F(1, 79) = 8.01, p = .01, ηp2 = .09; see,
Table 2. This effect remained significant in a subsequent
analysis of covariance (ANCOVA) with gender, GPA, class
standing, and number of absences included as covariates:
F(1, 75) = 6.78, p = .01, ηp2 = .08. Because individual stu-
dents are nested within groups, supplemental hierarchical
linear modeling (HLM) analyses were conducted to control
for any possible confounding effects of group-level factors
(see, Table 3 for HLM results; students were modeled at
Level 1 and groups at Level 2; Raudenbush & Bryk, 2002).
Three models were estimated: (1) an empty model with no
predictors (M1), which provides a baseline estimate of the
impact of group-level factors on the dependent variable; (2)
a treatment model (M2), which examines the main effect of
group assignment on the dependent variable after controlling
for other group-level factors; (3) a treatment + covariates
model (M3), which examines the robustness of the main
effect of group assignment on the dependent variable after
controlling for gender, GPA, class standing, number of
absences, and other group-level factors (see, Table 3).

First, as shown in M1, group-level factors had a signifi-
cant impact on perceived free-riding ( γ00 = 0.11, p < .001), accounting for one fifth of the variance in the dependent variable (intraclass correlation coefficient = .20). However, as seen in M2, even after controlling for group-level factors ( γ00 = 0.07, p = .02), the main effect of group assignment

Table 1. Group Characteristics.

Group assignment condition Semester Group Group size Gender (female) GPA Class (senior) Absences

Self-selection Fall 1 5 80% (0.45) 3.31 (0.36) 100% (0.00) 3.20 (1.64)
2 6 33% (0.52) 3.42 (0.31) 83% (0.41) 2.00 (1.26)
3 5 80% (0.45) 3.46 (0.38) 100% (0.00) 2.60 (2.30)
4 4 50% (0.58) 3.50 (0.27) 50% (0.58) 2.25 (1.50)
Spring 5 5 60% (0.55) 3.27 (0.49) 80% (0.45) 6.60 (1.14)
6 6 50% (0.55) 3.24 (0.36) 67% (0.52) 2.67 (1.75)
7 6 67% (0.52) 3.22 (0.28) 83% (0.41) 3.83 (2.23)
8 6 33% (0.52) 3.26 (0.38) 100% (0.00) 4.17 (1.17)
Collapsed within condition 5.38 (0.74) 56% (0.50) 3.33 (0.34) 84% (0.37) 3.42 (2.06)

Instructor-assignment
(flocking method)

Fall 9 6 83% (0.41) 3.55 (0.44) 100% (0.00) 4.00 (2.10)
10 6 33% (0.52) 2.96 (0.21) 100% (0.00) 6.50 (2.81)

11 6 83% (0.41) 3.35 (0.24) 100% (0.00) 3.17 (2.32)
12 4 50% (0.58) 3.23 (0.49) 50% (0.58) 4.25 (2.22)
Spring 13 5 40% (0.55) 3.47 (0.39) 80% (0.45) 3.20 (2.77)
14 6 83% (0.41) 3.51 (0.36) 50% (0.55) 2.83 (2.04)
15 6 17% (0.41) 3.03 (0.55) 100% (0.00) 3.67 (3.20)
16 5 60% (0.55) 3.45 (0.28) 60% (0.55) 4.40 (2.51)
Collapsed within condition 5.50 (0.76) 57% (0.50) 3.31 (0.41) 82% (0.39) 4.00 (2.58)
Significance test for difference between conditions (p

value, two-tailed)
F(1, 14) = 0.11,

p = .74
χ2 (1, N = 87) = 0.01,

p = .93
F(1, 85) = 0.02,

p = .89
χ2 (1, N = 87) = 0.06,

p = .81
F(1, 85) = 1.35,

p = .25

Note. Standard deviations indicated in parentheses; n = 87 students and 16 groups.

122 Journal of Marketing Education 40(2)

on free-riding was significant ( γ01 = 0.08, p = .04); in fact,
more than one third (39%) of between-group variance in
perceived free-riding was accounted for by group assign-
ment. Finally, as seen in M3, the main effect of group
assignment on free-riding remained marginally significant
( γ01 = 0.08, p = .06) when group averages for gender, GPA,

Table 2. Descriptive Statistics for Dependent Variables.

Group assignment
condition Semester Group

Perceived variability of
contributions (free-riding) Group grade Average individual grade

Self-selection Fall 1 0.10 (0.22) 88% 79% (0.10)
2 0.14 (0.10) 87% 84% (0.04)
3 0.08 (0.12) 87% 82% (0.08)
4 0.30 (0.19) 90% 85% (0.10)
Spring 5 0.12 (0.11) 83% 78% (0.09)
6 0.06 (0.14) 88% 75% (0.06)
7 0.21 (0.24) 87% 78% (0.07)
8 0.24 (0.08) 76% 74% (0.07)
Collapsed within

condition
0.15 (0.16) 86% (0.04) 79% (0.08)

Instructor-assignment
(flocking method)

Fall 9 0.00 (0.00) 95% 87% (0.07)
10 0.16 (0.12) 83% 79% (0.04)

11 0.12 (0.07) 90% 80% (0.04)
12 0.05 (0.08) 81% 78% (0.08)
Spring 13 0.11 (0.11) 95% 87% (0.08)
14 0.01 (0.02) 93% 92% (0.01)
15 0.10 (0.09) 83% 80% (0.10)
16 0.02 (0.04) 90% 85% (0.05)
Collapsed within

condition
0.07 (0.09) 89% (0.05) 84% (0.07)

Note. Standard deviations indicated in parentheses; n = 87 students and 16 groups.

Table 3. Hierarchical Linear Modeling Results.

Predictors

Dependent variables

Perceived variability of contributions (free-riding) Individual grade

M1 (empty
model) M2 (treatment)

M3 (treatment
+ covariates)

M1 (empty
model) M2 (treatment)

M3 (treatment +
covariates)

Intercept γ00 0.111** (0.020) 0.070* (0.025) 0.070* (0.026) 0.814** (0.012) 0.835** (0.016) 0.839** (0.011)
Treatment
Group assignment γ01 0.082* (0.036) 0.082† (0.038) −0.043† (0.022) −0.048** (0.015)
Covariates
Gender γ02 −0.140 (0.109) −0.029 (0.044)
GPA γ03 −0.022 (0.170) 0.207** (0.069)
Class standing γ04 0.027 (0.104) −0.027 (0.043)
Number of absences γ05 0.001 (0.018) −0.002 (0.007)
Group-level variance (between-group) τ00 0.004 (0.002) 0.002 (0.002) 0.003 (0.003) 0.001 (0.001) 0.001 (0.001) 0.000 (0.000)
Individual-level variance (within-group) σ2 0.014** (0.003) 0.014** (0.003) 0.014** (0.003) 0.005** (0.001) 0.005** (0.001) 0.005** (0.001)
Deviance −96.119 −96.119 −85.218** −195.138 −192.786** −187.263**

Note. Standard errors indicated in parentheses; n = 87 students and 16 groups.
†p < .10. *p < .05. **p < .01.

class standing, and number of absences were added to the
model (covariates, ps >.20; other group-level factors, γ00 =
0.07, p = .03). Collectively, these results provide support
for Hypothesis 1 by showing that students in flocked (vs.
self-selected) groups perceive less free-riding, even after
holding other group-level factors constant.

Harding 123

Group Learning Outcomes
To determine whether flocking (vs. self-selection) leads to
better student learning outcomes (Hypothesis 2), group
grades and individual grades were examined in turn. First, a
one-way ANOVA assessing the impact of group assignment
on group grades revealed that, although the difference was
not significant due to the relatively small number of groups
(n = 16), students in flocked (vs. self-selected) groups per-
formed directionally better on the group project: M = 88.57

versus 85.72; F(1, 14) = 1.22, p = .29, ηp2 = .08; see, Table
2. This main effect became marginally significant in a sub-
sequent ANCOVA when group averages for gender, GPA,
class standing, and number of absences were included as
covariates: F(1, 10) = 3.82, p = .08, ηp2 = .28. To guard
against the possibility of overfitting given the number of
parameters relative to the sample size, covariates were
removed from the model sequentially. First, the main effect
of group assignment remained marginally significant when
class standing, which has the smallest effect size of the
four covariates ( ηp2 = .01), was removed from the model:
F(1, 11) = 4.09, p = .07, ηp2 = .27. Furthermore, this mar-
ginal main effect remained robust when gender (second
smallest effect size; ηp2 = .08) was also removed from the
model: F(1, 12) = 3.91, p = .07, ηp2 = .25). Finally, the
main effect of group assignment remained marginally sig-
nificant when number of absences ( ηp2 = .11) was removed
from the model, leaving only GPA (ηp2 = .22) as a covari-
ate: F(1, 13) = 3.25, p = .10, ηp2 = .20. Thus, overfitting
does not appear to be an issue. Collectively, these results
provide preliminary support for Hypothesis 2 by showing
that students in flocked (vs. self-selected) groups have
directionally better group learning outcomes.

Individual Learning Outcomes

Next, to determine whether flocking (vs. self-selection) leads
to better individual learning outcomes (Hypothesis 2), indi-
vidual grades were examined using a one-way ANOVA. As
predicted, students in flocked (vs. self-selected) groups per-
formed better on the individual component of their grades:
M = 83.59 versus 79.12; F(1, 85) = 7.39, p = .01, ηp2 = .08;
see, Table 2. Furthermore, when all covariates were included
in a subsequent ANCOVA, this effect remained significant:
F(1, 81) = 19.81, p < .001, ηp2 = .20. Again, to control for any possible confounding effects of group-level factors, supple- mental HLM analyses were conducted (see, Table 3). First, as with perceived free-riding, group-level factors had a signifi- cant impact on individual grades ( γ00 = 0.81, p < .001), accounting for nearly one fourth of the variance in the depen- dent variable (intraclass correlation coefficient = .24; see, M1). However, as seen in M2, even after controlling for group-level factors ( γ00 = 0.84, p < .001), the main effect of group assignment on individual grades was marginally signifi- cant ( γ01 = −0.04, p = .08); in fact, one fourth (25%) of

between-group variance in individual grades was accounted
for by group assignment. Finally, as seen in M3, the main
effect of group assignment on individual grades was signifi-
cant ( γ01 = −0.05, p = .003) when group averages for gender,
GPA, class standing, and number of absences were added to
the model (GPA γ03 = 0.21, p = .004; all other covariates, ps
>.50; other group-level factors, γ00 = 0.84, p < .001). Collectively, these results provide additional support for Hypothesis 2 by showing that students in flocked (vs. self- selected) groups have better individual learning outcomes, even after holding other group-level factors constant.

Mediation Analyses

To determine whether perceptions of free-riding mediate the
relationship between group assignment and group and indi-
vidual learning outcomes (Hypothesis 3), mediation models
for group grades and individual grades were examined in
turn (see, Table 4 for complete path analyses; abbreviated
results reported here). A mediation analysis for group grades
was conducted first (Hayes, 2013, PROCESS Model 4), with
group assignment (1 = flocking; −1 = self-selection) entered
into the model as the independent variable, free-riding (i.e.,
the coefficient of variation; centered) entered as the proposed
mediator, and group grade entered as the dependent variable
(see, Figure 1; top panel). As predicted, flocking was associ-
ated with reduced free-riding (i.e., lower contribution vari-
ance) and reduced free-riding was associated with higher
group grades. Most important, a bias-corrected, 95% confi-
dence interval (CI) based on 5,000 bootstrap samples
revealed that group assignment had an indirect effect on
group grade through the proposed mediator, free-riding
(indirect effect [ab] = 0.003, standard error [SE] = 0.002, CI
[0.001, 0.008]), which indicates that free-riding mediated the
effect of group assignment on group grades.

A parallel analysis was conducted for individual grades,
which revealed that, although flocking was associated
with reduced free-riding and higher individual grades,
perceived free-riding alone did not mediate the relation-
ship between group assignment and individual learning
outcomes (indirect effect [ab] = −0.001, SE = 0.003, CI
[−0.009, 0.004]). However, it was postulated that the
effect of free-riding on individual learning may be indi-
rect, such that it is only when students contribute more
equally (and, thus, perform better) on the group project
that they learn more as individuals. Indeed, a multiple
mediator path analysis (Hayes, 2013, PROCESS Model 6)
with group assignment entered as the independent vari-
able, free-riding and group grade (centered) entered as
serial mediators, and individual grade entered as the
dependent variable (see, Figure 1; bottom panel) showed
that the benefits of reduced free-riding for group learning
extended to individual learning outcomes, such that when
students contributed more equally to the group, the

124 Journal of Marketing Education 40(2)

increased learning that took place through the applied
group project translated to a better individual understand-
ing of the course material on which the project was based
(indirect effect [a

1
d

21
b

2
] = 0.002, SE = 0.001, CI [.0003,

.006]). Thus, Hypothesis 3 is generally supported.

Discussion

Free-riding is a pervasive issue in courses that include a sig-
nificant group work component, and although several meth-
ods for reducing free-riding have been examined, little
empirical insight has been provided as to how group assign-
ment may contribute. The present research examines an
instructor-driven method of group assignment, the flocking
method, designed to improve students’ motivation and abil-
ity to contribute to the group. In particular, students are
flocked, or matched, according to their self-reported willing-
ness and availability to contribute, such that unmotivated
(motivated) students are grouped with other unmotivated
(motivated) students with similar schedules. As predicted,
the results reveal that students in flocked (vs. self-selected)
groups not only report less free-riding, they also perform bet-
ter on both group and individual assignments, indicating an
actual reduction in free-riding. This latter finding is notable
as the preponderance of past metrics for gauging the impact
of free-riding in groups have been perceptual (Aggarwal &
O’Brien, 2008; Brooks & Ammons, 2003; Liden et al., 2004;
Muller, 1989; Price et al., 2006; cf. Asmus & James, 2005;
Dommeyer, 2012), which is problematic as meta-analyses
have found little to no correlation between actual and per-
ceived learning measures (Sitzmann et al., 2010; see also,
Bacon, 2011; Clayson, 2009). Thus, in addition to identify-
ing an effective method for reducing free-riding in groups,
this research also addresses the recent call for pedagogical
researchers to attend more closely to actual outcomes (Bacon,
2016).

Additionally, compared with the most commonly exam-
ined methods for reducing free-riding in groups—periodic
peer evaluations and corresponding individualized group
project grades (Strong & Anderson, 1990; D. L. Williams
et al., 1991)—the flocking method of group assignment
reduces resource demands on the instructor and students,
making it as efficient to implement as it is effective. In par-
ticular, once flocked groups are formed using CATME’s
web-based team management system, no further oversight
by the instructor or periodic evaluations by students are
required. One potential drawback of the flocking method
compared with periodic peer evaluations is that it may not
serve as an early warning system for free-riding students.
However, by grouping students with similar motivation lev-
els and schedules together, the flocking method is designed
to reduce many of the temptations and obstacles commonly
associated with free-riding (Hall & Buzwell, 2012), thereby
better equipping students to avoid free-riding altogether.
Nonetheless, it would be interesting to explore whether peri-
odic peer evaluations, which are also facilitated by CATME,
might further reduce free-riding when used in combination
with the flocking method.

Periodic peer evaluations may also provide more nuanced
insight into how the flocking method works, allowing future

Table 4. Mediation Model Coefficients.

Variable Path Β SE p

Single mediator model
Mediator variable model (perceived free-riding)
Group assignment a −0.04 0.01 .006
Dependent variable model (group grade)
Perceived free-riding b −0.08 0.04 .067
Group assignment c′ 0.01 0.01 .012
Dependent variable model (individual grade)
Perceived free-riding b 0.03 0.07 .606
Group assignment c′ 0.02 0.01 .011

Serial mediator model
Mediator variable model (perceived free-riding)
Group assignment a

1
−0.04 0.01 .006

Mediator variable model (group grade)
Perceived free-riding d

21
−0.08 0.04 .067

Group assignment a
2

0.01 0.01 .012
Dependent variable model (individual grade)
Perceived free-riding b

1
0.08 0.06 .201

Group grade b
2

0.61 0.17 .001
Group assignment c′ 0.01 0.01 .095

Note. SE = Standard error.

Figure 1. Mediation models for group and individual learning
outcomes. IE = indirect effect; TE = total effect; DE = direct effect.
mp < .10. *p < .05. **p < .01. ***p < .001.

Harding 125

research to examine, for instance, whether free-riding is
reduced in the early stages of a group project or, rather,
diminishes over time. Along these lines, it would have been
useful to reassess students’ motivation near the end of the
group project to ascertain whether motivation levels
increased for all students throughout the course of the proj-
ect, as anticipated. Because controlling for group-level fac-
tors did not eliminate the main effect of group assignment on
free-riding, it indeed appears that “low motivation” groups
experienced the same motivational (and learning outcome)
benefits as “high motivation” groups. Nonetheless, a post-
measure of motivation would have strengthened the empiri-
cal support for the flocking method.

Although the quasi-experimental methodology used in
this research bolsters confidence in the causal claim that the
flocking method reduces free-riding, which in turn improves
group and individual learning outcomes, the method of group
assignment used each semester (i.e., flocking vs. self-selec-
tion) was not randomly determined. Thus, although efforts
were made to ensure and verify that the treatment and control
conditions were comparable (e.g., both conditions included
fall and spring semesters; other elements of the course were
held constant; no differences in gender, GPA, class standing,
or number of absences were found), it is possible that the
group assignment conditions differed in ways that were nei-
ther anticipated nor detected. An additional limitation of this
research is that the bases by which students were flocked, or
matched (i.e., their stated motivation and availability to con-
tribute to the group), were not manipulated independently.
As a result, the relative impacts of these criteria on perceived
free-riding and student learning cannot be distinguished.
Because it is possible that one criterion may have been more
consequential than the other (or, alternately, that the criteria
only work in tandem to reduce free-riding), future research
might study these criteria independently using a 2 (motiva-
tion: flocked vs. not flocked) × 2 (availability: flocked vs.
not flocked) between-participants design.

The flocking method of group assignment advanced in
this research raises questions about diversity (Milliken &
Martins, 1996), and creativity (Hülsheger, Anderson, &
Salgado, 2009) in groups. These constructs are closely linked
as creativity and other positive group outcomes are more
prevalent in groups that are diversified on task-related vari-
ables such as skill sets (Blowers, 2003; Cummings, 2004;
Katzenbach & Smith, 1993; Metheny & Metheny, 1997;
Michaelsen & Black, 1994; Milliken & Martins, 1996; Price
et al., 2006), and educational backgrounds (Beheshtian-
Ardekani & Mahmood, 1986; Muller, 1989)—likely because
diversity on these variables increases the cognitive diversity
of the group (Hoever, Van Knippenberg, Van Ginkel, &
Barkema, 2012; Kurtzberg, 2005; Wang, Kim, & Lee, 2016).
Therefore, some attention to diversity should be paid in
group assignment. One might argue that the flocking method
runs counter to this premise; however, this need not be the

case. A sophisticated approach to the flocking method might
ensure that group members are (a) similar in terms of their
motivation and ability to contribute to the group, which
serves to safeguard against free-riding, but also (b) diverse in
terms of their task-related skills and backgrounds, which
serves to foster creativity.

Nevertheless, it would be useful to identify criteria by
which flocking (i.e., matching) might have the opposite effect
on free-riding. For instance, there is evidence which suggests
that assigning groups to include students from a mix of educa-
tional backgrounds (Beheshtian-Ardekani & Mahmood, 1986;
Muller, 1989), or with a diversity of skill sets (Blowers, 2003;
Katzenbach & Smith, 1993; Metheny & Metheny, 1997;
Michaelsen & Black, 1994; Price et al., 2006) may reduce
feelings of dispensability (Price et al., 2006), which is a moti-
vational antecedent of free-riding (Harkins & Petty, 1982;
Karau & Williams, 1993; Kerr & Bruun, 1983; Price et al.,
2006; Weldon & Mustari, 1988). Thus, although flocking stu-
dents with similar others may not be universally effective at
reducing free-riding, doing so strategically on variables related
to students’ motivation and ability to contribute to the group
can have a positive effect on student participation and learn-
ing—desirable outcomes that are likely to be augmented on
group projects that are heavily weighted in students’ grades
(Aggarwal & O’Brien, 2008; Bacon et al., 1999).

Acknowledgments

The author wishes to thank the reviewing team, D. Lee Warren and
Joe F. Alexander for their valuable comments.

Declaration of Conflicting Interests

The author declared no potential conflicts of interest with respect to
the research, authorship, and/or publication of this article.

Funding

The author received no financial support for the research, author-
ship, and/or publication of this article.

References

Abernethy, A. M., & Lett, W. L., III. (2005). You are fired! A
method to control and sanction free riding in group assign-
ments. Marketing Education Review, 15, 47-54.

Aggarwal, P., & O’Brien, C. L. (2008). Social loafing on group
projects: Structural antecedents and effect on student satisfac-
tion. Journal of Marketing Education, 30, 255-264.

Albanese, R., & Van Fleet, D. D. (1985). Rational behavior in
groups: The free-riding tendency. Academy of Management
Review, 10, 244-255.

Alnuaimi, O. A., Robert, L. P., & Maruping, L. M. (2010). Team
size, dispersion, and social loafing in technology-supported
teams: A perspective on the theory of moral disengagement.
Journal of Management Information Systems, 27, 203-230.

Asmus, C. L., & James, K. (2005). Nominal group technique, social
loafing, and group creative project quality. Creativity Research
Journal, 17, 349-354.

126 Journal of Marketing Education 40(2)

Bacon, D. R. (2005). The effect of group projects on content-related
learning. Journal of Management Education, 29, 248-267.

Bacon, D. R. (2011). Comparing direct versus indirect measures
of the pedagogical effectiveness of team testing. Journal of
Marketing Education, 33, 348-358.

Bacon, D. R. (2014). The editor’s corner. Journal of Marketing
Education, 36, 3-4.

Bacon, D. R. (2016). Reporting actual and perceived student learn-
ing in education research. Journal of Marketing Education, 38,
3-6.

Bacon, D. R., Stewart, K. A., & Anderson, E. S. (2001). Methods
of assigning players to teams: A review and novel approach.
Simulation & Gaming, 32, 6-17.

Bacon, D. R., Stewart, K. A., & Silver, W. S. (1999). Lessons from
the best and worst student team experiences: How a teacher
can make the difference. Journal of Management Education,
23, 467-488.

Bacon, D. R., Stewart, K. A., & Stewart-Belle, S. (1998). Exploring
predictors of student team project performance. Journal of
Marketing Education, 20, 63-71.

Batra, M. M., Walvoord, B. E., & Krishnan, K. S. (1997). Effective
pedagogy for student-team projects. Journal of Marketing
Education, 19, 26-42.

Beheshtian-Ardekani, M., & Mahmood, M. A. (1986). Development
and validation of a tool for assigning students to groups for
class projects. Decision Sciences, 17, 92-113.

Blowers, P. (2003). Using student skill self-assessments to get
balanced groups for group projects. College Teaching, 51,
106-110.

Börjesson, P. O., Hamidian, A., Kubilinskas, E., Richter, U., Weyns,
K., & Ödling, P. (2006). Free-riding in group work—mecha-
nisms and countermeasures. Seventh Educational Inspiration
Conference. Retrieved from https://www.lth.se/fileadmin/lth/
genombrottet/konferens2006/p_o_b_rjesson_mfl

Brooks, C. M., & Ammons, J. L. (2003). Free riding in group proj-
ects and the effects of timing, frequency, and specificity of cri-
teria in peer assessments. Journal of Education for Business,
78, 268-272.

Chapman, K. J., Meuter, M., Toy, D., & Wright, L. (2006). Can’t we
pick our own groups? The influence of group selection method
on group dynamics and outcomes. Journal of Management
Education, 30, 557-569.

Chapman, K. J., & Van Auken, S. (2001). Creating positive group
project experiences: An examination of the role of the instruc-
tor on students’ perceptions of group projects. Journal of
Marketing Education, 23, 117-127.

Clayson, D. E. (2009). Student evaluations of teaching: Are they
related to what students learn? A meta-analysis and review of
the literature. Journal of Marketing Education, 31, 16-30.

Comer, D. R. (1995). A model of social loafing in real work groups.
Human Relations, 48, 647-667.

Cook, R. W. (1981). An investigation of student peer evaluation on
group project performance. Journal of Marketing Education,
3(1), 50-52.

Cummings, J. N. (2004). Work groups, structural diversity, and
knowledge sharing in a global organization. Management
Science, 50, 352-364.

Dommeyer, C. J. (2012). A new strategy for dealing with social
loafers on the group project: The segment manager method.
Journal of Marketing Education, 34, 113-127.

Druskat, V. U., & Wolff, S. B. (1999). Effects and timing of
developmental peer appraisals in self-managing work groups.
Journal of Applied Psychology, 84, 58.

Freeman, L., & Greenacre, L. (2011). An examination of socially
destructive behaviors in group work. Journal of Marketing
Education, 33, 5-17.

George, J. M. (1992). Extrinsic and intrinsic origins of perceived
social loafing in organizations. Academy of Management
Journal, 35, 191-202.

Hall, D., & Buzwell, S. (2012). The problem of free-riding in group
projects: Looking beyond social loafing as reason for non-
contribution. Active Learning in Higher Education, 14, 37-49.

Harkins, S. G., & Jackson, J. M. (1985). The role of evaluation in
eliminating social loafing. Personality and Social Psychology
Bulletin, 11, 457-465.

Harkins, S. G., & Petty, R. E. (1982). Effects of task difficulty and
task uniqueness on social loafing. Journal of Personality and
Social Psychology, 43, 1214.

Harkins, S. G., & Szymanski, K. (1988). Social loafing and self-
evaluation with an objective standard. Journal of Experimental
Social Psychology, 24, 354-365.

Hayes, A. F. (2013). Introduction to mediation, moderation, and
conditional process analysis: A regression-based approach.
New York, NY: Guilford Press.

Hrivnak, G. A. (2013). CATME smarter teamwork. Academy of
Management Learning & Education, 12, 679-681.

Hoever, I. J., Van Knippenberg, D., Van Ginkel, W. P., & Barkema,
H. G. (2012). Fostering team creativity: Perspective taking
as key to unlocking diversity’s potential. Journal of Applied
Psychology, 97, 982.

Huff, L. C., Cooper, J., & Jones, W. (2002). The development and
consequences of trust in student project groups. Journal of
Marketing Education, 24, 24-34.

Hülsheger, U. R., Anderson, N., & Salgado, J. F. (2009). Team-
level predictors of innovation at work: A comprehensive meta-
analysis spanning three decades of research. Journal of Applied
Psychology, 94, 1128.

Ingham, A. G., Levinger, G., Graves, J., & Peckham, V. (1974).
The Ringelmann effect: Studies of group size and group per-
formance. Journal of Experimental Social Psychology, 10,
371-384.

Jackson, J. M., & Harkins, S. G. (1985). Equity in effort: An expla-
nation of the social loafing effect. Journal of Personality and
Social Psychology, 49, 1199.

Jalajas, D. S., & Sutton, R. I. (1984). Feuds in student groups:
Coping with whiners, martyrs, saboteurs, bullies, and dead-
beats. Journal of Management Education, 9(4), 94-102.

Jones, G. R. (1984). Task visibility, free riding, and shirking:
Explaining the effect of structure and technology on employee
behavior. Academy of Management Review, 9, 684-695.

Karau, S. J., & Hart, J. W. (1998). Group cohesiveness and social
loafing: Effects of a social interaction manipulation on indi-
vidual motivation within groups. Group Dynamics: Theory,
Research, and Practice, 2, 185.

Karau, S. J., & Williams, K. D. (1993). Social loafing: A meta-ana-
lytic review and theoretical integration. Journal of Personality
and Social Psychology, 65, 681-706.

Karau, S. J., & Williams, K. D. (1997). The effects of group cohe-
siveness on social loafing and social compensation. Group
Dynamics: Theory, Research, and Practice, 1, 156-168.

https://www.lth.se/fileadmin/lth/genombrottet/konferens2006/p_o_b_rjesson_mfl

Harding 127

Katzenbach, J. R., & Smith, D. K. (1993). The discipline of teams.
Boston, MA: Harvard Business School Press.

Kench, P. L., Field, N., Agudera, M., & Gill, M. (2009). Peer
assessment of individual contributions to a group project:
Student perceptions. Radiography, 15, 158-165.

Kerr, N. L. (1983). Motivation losses in small groups: A social
dilemma analysis. Journal of Personality and Social
Psychology, 45, 819-828.

Kerr, N. L., & Bruun, S. E. (1981). Ringelmann revisited: Alternative
explanations for the social loafing effect. Personality and
Social Psychology Bulletin, 7, 224-231.

Kerr, N. L., & Bruun, S. E. (1983). Dispensability of member effort
and group motivation losses: Free-rider effects. Journal of
Personality and Social Psychology, 44, 78-94.

Kimber, D. (1996). Collaborative learning in management edu-
cation: Issues, benefits, problems and solutions: A literature
review. Melbourne, Victoria, Australia: Royal Melbourne
Institute of Technology University.

Kurtzberg, T. R. (2005). Feeling creative, being creative: An
empirical study of diversity and creativity in teams. Creativity
Research Journal, 17, 51-65.

Latané, B., Williams, K., & Harkins, S. (1979). Many hands make
light the work: The causes and consequences of social loafing.
Journal of Personality and Social Psychology, 37, 822-832.

Liden, R. C., Wayne, S. J., Jaworski, R. A., & Bennett, N. (2004).
Social loafing: A field investigation. Journal of Management,
30, 285-304.

Loughry, M. L., Ohland, M. W., & Woehr, D. J. (2014). Assessing
teamwork skills for assurance of learning using CATME team
tools. Journal of Marketing Education, 36, 5-19.

Mahenthiran, S., & Rouse, P. J. (2000). The impact of group selec-
tion on student performance and satisfaction. International
Journal of Educational Management, 14(6), 255-265.

Maranto, R., & Gresham, A. (1998). Using “world series shares”
to fight free riding in group projects. PS: Political Science &
Politics, 31, 789-791.

March, J. G. (1954). Group norms and the active minority. American
Sociological Review, 19, 733-741.

McCorkle, D. E., Reardon, J., Alexander, J. F., Kling, N. D., Harris,
R. C., & Iyer, R. V. (1999). Undergraduate marketing students,
group projects, and teamwork: The good, the bad, and the
ugly? Journal of Marketing Education, 21, 106-117.

Mello, J. A. (1993). Improving individual member accountability in
small work group settings. Journal of Management Education,
17, 253-259.

Metheny, D., & Metheny, W. (1997). Enriching technical courses
with learning teams. College Teaching, 45, 32-35.

Michaelsen, L. K., & Black, R. H. (1994). Building learning teams:
The key to harnessing the power of small groups in higher

education. Collaborative Learning: A Sourcebook for Higher
Education, 2, 65-81.

Milliken, F. J., & Martins, L. L. (1996). Searching for common
threads: Understanding the multiple effects of diversity in
organizational groups. Academy of Management Review, 21,
402-433.

Muller, T. E. (1989). Assigning students to groups for class proj-
ects: An exploratory test of two methods. Decision Sciences,
20, 623-634.

Price, K. H., Harrison, D. A., & Gavin, J. H. (2006). Withholding
inputs in team contexts: Member composition, interaction
processes, evaluation structure, and social loafing. Journal of
Applied Psychology, 91, 1375-1384.

Raudenbush, S. W., & Bryk, A. S. (2002). Hierarchical linear
models: Applications and data analysis methods (2nd ed.).
Thousand Oaks, CA: Sage.

Sitzmann, T., Ely, K., Brown, K. G., & Bauer, K. N. (2010). Self-
assessment of knowledge: A cognitive learning or affective
measure? Academy of Management Learning & Education, 9,
169-191.

Slavin, R. E. (1990). Research on cooperative learning: Consensus
and controversy. Educational Leadership, 47(4), 52-54.

Strong, J. T., & Anderson, R. E. (1990). Free-riding in group proj-
ects: Control mechanisms and preliminary data. Journal of
Marketing Education, 12(2), 61-67.

Szymanski, K., & Harkins, S. G. (1987). Social loafing and self-
evaluation with a social standard. Journal of Personality and
Social Psychology, 53, 891-897.

Wang, X. F., Kim, T., & Lee, D. (2016). Cognitive diversity and
team creativity: Effects of team intrinsic motivation and trans-
formational leadership. Journal of Business Research, 69,
3231-3239.

Webb, N. M. (1997). Assessing students in small collaborative
groups. Theory into Practice, 36, 205-213.

Weldon, E., & Mustari, E. L. (1988). Felt dispensability in groups
of coactors: The effects of shared responsibility and explicit
anonymity on cognitive effort. Organizational Behavior and
Human Decision Processes, 41, 330-351.

Williams, D. L., Beard, J. D., & Rymer, J. (1991). Team projects:
Achieving their full potential. Journal of Marketing Education,
13(2), 45-53.

Williams, K. D., Harkins, S. G., & Latané, B. (1981).
Identifiability as a deterrent to social loafing: Two cheering
experiments. Journal of Personality and Social Psychology,
40, 303-311.

Williams, K. D., & Karau, S. J. (1991). Social loafing and social
compensation: The effects of expectations of co-worker per-
formance. Journal of Personality and Social Psychology, 61,
570-581.

Checklist – Paper One: Study One

Literature Review

Use the check sheet below to make sure your paper is the best it can be! Make sure you answer “

Yes

” to all questions before submitting your paper or you will lose points! Please note that the 7th Edition of the American Psychological Association Publication Manual has some flexibility in terms of language, font, spacing, and other items, but that papers in this course MUST adhere to the guidelines listed before.

Yes

Header

Yes

General Paper Format

Yes

1. Is
everything in your paper (including headers, the main body of your study one literature review, and references) in 12 point Times New Roman font?

2. Is
everything in your paper double spaced, including references (here I mean the spacing above and below each line, not the spaces following a period)?

3. Do you have one inch margins on all sides of the paper (one inch from the top of the page, one inch from the bottom, and one inch from each side)

4. Are the first lines of all paragraphs indented roughly ½ inch?

5. Are your paragraphs aligned left? (That is, text should be flush left, with lines lining up on the left of the page, but text should NOT line up on the right side of the page – it should look ragged)

6. Do you need help figuring out how to configure a word document in APA format (inserting headers, page numbers, indents, etc.)? If YES or NO, I recommend watching this video which walks you through setting up an APA formatted paper!

Title page

Header

1. Is your header title in ALL CAPS, and is it a shorter version of your real title?

2. Is your Running head in 12 point Times New Roman font?

3. Do you have a page number that is flush right (also in 12 point Times New Roman font)?

4. Is your header title 50 characters or less (including spaces and punctuation)?

Title / Name / Institution

1. Is your title focused and short, avoiding unnecessary words and abbreviations that serve no purpose (as recommended by the APA)?

2. Does your title describe your general paper theme (while avoiding something bland like “Paper One: Literature Review”)? Note that your header should be a shorter version or your title (For example, the first few words are fine)

3. Do all title words with three letters or more start with a capital letter?

4. Is your title in
bold?

5. If your title is longer than one line, is it double-spaced (like everything else in your paper)?

6. Are your name and institution correct?

7. Are your title, name, and institution elements centered and in 12 point Times New Roman font?

8. Does your title start three or four lines under the margin at the top of the page?

9. Are there two spaces between your paper title and your name?

Literature Review

1. Is your header title present and
identical to your header from the title page?

2. Is your header title in ALL CAPS and 12 point Times New Roman font?

3. Do you have a page number starting on page 2

Title for the literature review

1. Do you have the
identical title you used on the title page rewritten at the top of your literature review (including being in
bold)?

2. Is this title centered?

Main body of the literature review

1. Does your literature review start broadly, giving a brief overview of the paper to come?

2. Does your literature review start to narrow down toward your hypotheses?

3. Do your paragraphs transition from one to the next? (That is, avoid simply listing studies you read. Tie them together. How does Study A in paragraph A relate to Study B in paragraph B?)

4. Does your paper end in your very specific hypotheses? (You will lose a lot of points if your paper doesn’t provide the specific predictions!)

5. Did you make sure your predictions are written in the past tense?

6. Is your paper at least two pages long (not including the hypotheses)?

Citations for the literature review

1. Did you cite a minimum of 5 citations? (Note that you can give a lot of detail for some articles you cite but only a sentence or two for others. How much detail you go into depends on how important the article is in helping your support your hypotheses)

2. Are your citations in APA format (That is, ONLY the last name of the author(s) and date of publication)?

a. Note that you do NOT include first names, initials, or the title of the article the authors wrote when citing. That information belongs in the references pages only.

b. Also note that you only use an ampersand – the & symbol – when it occurs within parentheses. In other instances, use the word “and”

3. If you quoted, did you provide a page number for the direct quote?

4. If you paraphrased in any way, did you cite the source of that information?

5. Did you cite everything that sounded like it was factual information?

6. Did you make sure the period follows the citation rather than coming before it?

7. If there are two authors, did you cite both of them? If in parentheses, did you use the & symbol? If outside of parentheses, did you use the word “and”?

8. If there are three or more authors in the same citation, did you use the phrase et al. every time you cited them?

Yes

References Page

Title for the references page

1. Do references start on their own page?

2. Is the word “References” centered? Is it in
bold?

References – Make sure these are in APA format!

1. Are references listed in alphabetical order (starting with the last name of the first author listed)?

2. Are all citations from the literature review referenced?

3. Is the first line of the reference flush left while subsequent lines are indented (Note: Use the ruler function for this. DO NOT simply tab)?

4. Did you use the “&” symbol when listing more than one author name?

5. Did you include the date of publication

6. For article references, is the article title (which is not italicized) present, with only the first word and proper names starting with a capital letter?

7. For article references, is the name of the journal present with all major words starting with a capital letter (Note: this journal title is italicized)?

8. For article references, is the volume number italicized

9. For article references, are the page numbers present (not italicized)

10. For article references, is the DOI present

Yes

Writing Quality

1. Did you proofread your paper, go to the writing center, go to the research methods help center, or use the Pearson writer to make sure your paper flows well?

2. Did you use the past tense (which is recommended, since your papers in this class will reflect work you already did rather than work you will do)?

3. Did you use a scientific / objective terms like “people”, “participants”. “users”, “readers”, etc. (as opposed to subjective words like “you”, “we”, “me”, “I”, or “us”, etc.)? Note that you can use the word “I” when referring to your own work.

A NOVEL TEACHER EVALUATION MODEL 1

Branching Paths: A Novel Teacher Evaluation Model for Faculty Development

Kim A. Park,1 James P. Bavis,1 and Ahn G. Nu2

1Department of English, Purdue University

2Center for Faculty Education, Department of Educational Psychology, Quad City University

Author Note

Kim A. Park https://orcid.org/0000-0002-1825-0097

James P. Bavis is now at the MacLeod Institute for Music Education, Green Bay,

WI. We have no known conflict of interest to disclose.

Correspondence concerning this article should be addressed to Ahn G. Nu, Department

of Educational Psychology, 253 N. Proctor St., Quad City, WA, 09291. Email:

agnu@quadcityu.com

jforte
Text Box
Note: Green text boxes contain explanations of APA 7’s paper formatting guidelines…

jforte
Text Box
…while blue text boxes contain directions for writing and citing in APA 7.

jforte
Text Box
The running head is a shortened version of the paper’s title that appears on every page. It is written in all capitals, and it should be flush left in the document’s header. No “Running head:” label is included in APA 7. If the paper’s title is fewer than 50 characters (including spaces and punctuation), the actual title may be used rather than a shortened form.

jforte
Line

jforte
Text Box
Page numbers begin on the first page and follow on every subsequent page without interruption. No other information (e.g., authors’ last names) are required.

jforte
Line

jforte
Text Box
The paper’s title should be centered, bold, and written in title case. It should be three or four lines below the top margin of the page. In this sample paper, we’ve put three blank lines above the title.

jforte
Line

jforte
Text Box
Authors’ names appear two lines below the title. They should be written as follows:
First name, middle initial(s), last name.
Omit all professional titles and/or degrees (e.g., Dr., Rev., PhD, MA).

jforte
Text Box
Authors’ affiliations follow immediately after their names. If the authors represent multiple institutions, as is the case in this sample, use superscripted numbers to indicate which author is affiliated with which institution. If all authors represent the same institution, do not use any numbers.

jforte
Text Box
Author notes contain the following parts in this order:
1. Bold, centered “Author Note” label.
2. ORCID iDs
3. Changes of author affiliation.
4. Disclosures/ acknowledgments
5. Contact information.
Each part is optional (i.e., you should omit any parts that do not apply to your manuscript, or omit the note entirely if none apply).
Format each item as its own indented paragraph.

jforte
Line

jforte
Text Box
ORCID is an organization that allows researchers and scholars to register professional profiles so that they can easily connect with one another. To include an ORCID iD in your author note, simply provide the author’s name, followed by the green iD icon (hyperlinked to the URL that follows) and a hyperlink to the appropriate ORCID page.

jforte
Line

A NOVEL TEACHER EVALUATION MODEL 2

Abstract

A large body of assessment literature suggests that students’ evaluations of their teachers

(SETs) can fail to measure the construct of teaching in a variety of contexts. This can

compromise faculty development efforts that rely on information from SETs. The disconnect

between SET results and faculty development efforts is exacerbated in educational contexts

that demand particular teaching skills that SETs do not value in proportion to their local

importance (or do not measure at all). This paper responds to these challenges by proposing an

instrument for the assessment of teaching that allows institutional stakeholders to define the

teaching construct in a way they determine to suit the local context. The main innovation of this

instrument relative to traditional SETs is that it employs a branching “tree” structure populated

by binary-choice items based on the Empirically derived, Binary-choice, Boundary-definition

(EBB) scale developed by Turner and Upshur for ESL writing assessment. The paper argues

that this structure can allow stakeholders to define the teaching construct by changing the order

and sensitivity of the nodes in the tree of possible outcomes, each of which corresponds to a

specific teaching skill. The paper concludes by outlining a pilot study that will examine the

differences between the proposed EBB instrument and a traditional SET employing series of

multiple-choice questions (MCQs) that correspond to Likert scale values.

Keywords: college teaching, student evaluations of teaching, scale development, EBB

scale, pedagogies, educational assessment, faculty development

jforte
Text Box
Note that both the running head and the page number continue on the pages that follow the title.

jforte
Line

jforte
Text Box
The word “Abstract” should be centered and bolded at the top of the page.

jforte
Line

jforte
Text Box
An abstract quickly summarizes the main points of the paper that follows it. The APA 7 manual does not give explicit directions for how long abstracts should be, but it does note that most abstracts do not exceed 250 words (p. 38). It also notes that professional publishers (like academic journals) may have a variety of rules for abstracts, and that writers should typically defer to these.

jforte
Text Box
Follow the abstract with a selection of keywords that describe the important ideas or subjects in your paper. These help online readers search for your paper in a database.
The keyword list should have its first line indented. Begin the list with the label “Keywords:” (note the italics and the colon). Follow this with a list of keywords written in lowercase (except for proper nouns) and separated by commas. Do not place a period at the end of the list.

jforte
Text Box
The main paragraph of the abstract should not be indented.

jforte
Line

jforte
Text Box
By standard convention, abstracts do not contain citations of other works. If you need to refer to another work in the abstract, mentioning the authors in the text can often suffice. Note also that some institutions and publications may allow for citations in the abstract.

jforte
Line

A NOVEL TEACHER EVALUATION MODEL 3

Branching Paths: A Novel Teacher Evaluation Model for Faculty Development

“Faculty evaluation and development cannot be considered separately,” writes Michael

Theall, noting that “Evaluation without development is punitive, and development without

evaluation is guesswork” (2017, p. 91). As the practices that constitute modern programmatic

faculty development have evolved from their humble beginnings to become commonplace

features of university life (Lewis, 1996), a variety of tactics to evaluate the proficiency of

teaching faculty for development purposes have likewise become commonplace. These include

measures as diverse as peer observations, the development of teaching portfolios, and

evaluations of student performance.

One such measure, the student evaluation of teacher (SET), has been virtually

ubiquitous since at least the 1990s (Wilson, 1998). Though records of SET-like instruments can

be traced to work at Purdue University in the 1920s (Remmers & Brandenburg, 1927), most

modern histories of faculty development suggest that their rise to widespread popularity went

hand-in-hand with the birth of modern faculty development programs in the 1970s, when

universities began to adopt them in response to student protest movements criticizing

mainstream university curricula and approaches to instruction (Lewis, 1996; Gaff & Simpson,

1994; McKeachie, 1996). By the mid-2000s, researchers had begun to characterize SETs in

terms like “…the predominant measure of university teacher performance […] worldwide”

(Pounder, 2007, p. 178). Today, SETs play an important role in teacher assessment and faculty

development at most universities (Davis, 2009). Recent SET research practically takes the

presence of some form of this assessment on most campuses as a given; Spooren,

Vandermoere, Vanderstraeten, and Pepermans, for instance, merely note that that SETs can be

found at “almost every institution of higher education throughout the world” (2017, p. 130).

Darwin refers to them as “an established orthodoxy” and as a “venerated,” “axiomatic”

institutional presence (2012, p. 733).

jforte
Text Box
Here, we’ve borrowed a quote from an external source, so we need to provide the location of the quote in the document (in this case, the page number) in the parenthetical.

jforte
Line

jforte
Text Box
By contrast, here, we’ve merely paraphrased an idea from the external source. Thus, no location or page number is required.

jforte
Line

jforte
Text Box
The paper’s title is bolded and centered above the first body paragraph. There should be no “Introduction” header.

jforte
Line

jforte
Text Box
When listing multiple citations in the same parenthetical, separate them with semicolons.

jforte
Line

jforte
Text Box
For sources with two authors, use an ampersand (&) between the authors’ names rather than the word “and.”

jforte
Line

jforte
Text Box
Spell out abbreviations the first time you use them, except in cases where the abbreviations are very well-known (e.g., “CIA”).

jforte
Line

A NOVEL TEACHER EVALUATION MODEL 4

Moreover, SETs do not only help universities direct their faculty development efforts.

They have also come to occupy a place of considerable institutional importance for their role in

personnel considerations, informing important decisions like hiring, firing, tenure, and promotion.

Seldin (1993, as cited in Pounder, 2007) puts the percentage of higher educational institutions

using SETs as important factors in personnel decisions at roughly 86 percent. A 1991 survey of

department chairs found 97% used student evaluations to assess teaching performance (US

Department of Education). Since the mid-late 1990s, a general trend towards comprehensive

methods of teacher evaluation that include multiple forms of assessment has been observed

(Berk, 2005). However, recent research suggests the usage of SETs in personnel decisions is

still overwhelmingly common, though hard percentages are hard to come by, perhaps owing to

the multifaceted nature of these decisions (Galbraith et al., 2012; Boring et al., 2017). In certain

contexts, student evaluations can also have ramifications beyond the level of individual

instructors. Particularly as public schools have experienced pressure in recent decades to adopt

neoliberal, market-based approaches to self-assessment and adopt a student-as-consumer

mindset (Darwin, 2012; Marginson, 2009), information from evaluations can even feature in

department- or school-wide funding decisions (see, for instance, the Obama Administration’s

Race to the Top initiative, which awarded grants to K-12 institutions that adopted value-added

models for teacher evaluation).

However, while SETs play a crucial role in faulty development and personnel decisions

for many education institutions, current approaches to SET administration are not as well-suited

to these purposes as they could be. This paper argues that a formative, empirical approach to

teacher evaluation developed in response to the demands of the local context is better-suited

for helping institutions improve their teachers. It proposes the Heavilon Evaluation of Teacher,

or HET, a new teacher assessment instrument that can strengthen current approaches to

faculty development by making them more responsive to teachers’ local contexts. It also

jforte
Text Box
Here, we’ve made an indirect or secondary citation (i.e., we’ve cited a source that we found cited in a different source). Use the phrase “as cited in” in the parenthetical to indicate that the first-listed source was referenced in the second-listed one. Include an entry in the reference list only for the secondary source (Pounder, in this case).

jforte
Line

jforte
Text Box
Here, we’ve cited a source that does not have a named author. The corresponding reference list entry would begin with “US Department of Education.”

jforte
Line

jforte
Text Box
Sources with three authors or more are cited via the first-listed author’s name followed by the Latin phrase “et al.” Note that the period comes after “al,” rather than “et.”

jforte
Line

jforte
Text Box
Note: For the sake of brevity, the next page of the original paper was cut from this sample document.

A NOVEL TEACHER EVALUATION MODEL 6

The following sections of the paper should clarify this argument. A review of relevant

literature will outline how researchers have defined the teaching construct, concluding that it is

multifaceted and highly subject to the local context. It will also briefly describe prevailing trends

in SET administration and give insight on empirical scale development, which offers a way to

create assessment instruments that are more sensitive to the local context. The Materials and

Methods section, which follows, will propose a pilot study that compares the results of the

proposed instrument to the results of a traditional SET (and will also provide necessary

background information on both of these evaluations). The paper will conclude with a discussion

of how the results of the pilot study will inform future iterations of the proposed instrument and,

more broadly, how universities should argue for local development of assessments.

Literature Review

Effective Teaching: A Contextual Construct

The validity of the instrument this paper proposes is contingent on the idea that it is

possible to systematically measure a teacher’s ability to teach. Indeed, the same could be said

for virtually all teacher evaluations. Yet despite the exceeding commonness of SETs and the

faculty development programs that depend on their input, there is little scholarly consensus on

precisely what constitutes “good” or “effective” teaching. It would be impossible to review the

entire history of the debate surrounding teaching effectiveness, owing to its sheer scope—such

a summary might need to begin with, for instance, Cicero and Quintilian. However, a cursory

overview of important recent developments (particularly those revealed in meta-analyses of

empirical studies of teaching) can help situate the instrument this paper proposes in relevant

academic conversations.

Meta-analysis 1. One core assumption that undergirds many of these conversations is

the notion that good teaching has effects that can be observed in terms of student achievement.

A meta-analysis of 167 empirical studies that investigated the effects of various teaching factors

jforte
Text Box
Second-level headings are flush left, bolded, and written in title case.
Third level headings are flush left, bolded, written in title case, and italicized.

jforte
Line

jforte
Text Box
Fourth-level headings are bolded and written in title case. They are also indented and written in-line with the following paragraph.

jforte
Line

A NOVEL TEACHER EVALUATION MODEL 7

on student achievement (Kyriakides et al., 2013) supported the effectiveness of a set of

teaching factors that the authors group together under the label of the “dynamic model” of

teaching. Seven of the eight factors (Orientation, Structuring, Modeling, Questioning,

Assessment, Time Management, and Classroom as Learning Environment) corresponded to

moderate average effect sizes (of between 0.34–0.41 standard deviations) in measures of

student achievement. The eighth factor, Application (defined as seatwork and small-group tasks

oriented toward practice of course concepts), corresponded to only a small yet still significant

effect size of 0.18. The lack of any single decisive factor in the meta-analysis supports the idea

that effective teaching is likely a multivariate construct. However, the authors also note the

context-dependent nature of effective teaching. Application, the least-important teaching factor

overall, proved more important in studies examining young students (p. 148). Modeling, by

contrast, was especially important for older

students.

Meta-analysis 2. A different meta-analysis that argues for the importance of factors like

clarity and setting challenging goals (Hattie, 2009) nevertheless also finds that the effect sizes

of various teaching factors can be highly context-dependent. For example, effect sizes for

homework range from 0.15 (a small effect) to 0.64 (a moderately large effect) based on the level

of education examined. Similar ranges are observed for differences in academic subject (e.g.,

math vs. English) and student ability level. As Snook et al. (2009) note in their critical response

to Hattie, while it is possible to produce a figure for the average effect size of a particular

teaching factor, such averages obscure the importance of context.

Meta-analysis 3. A final meta-analysis (Seidel & Shavelson, 2007) found generally

small average effect sizes for most teaching factors—organization and academic domain-

specific learning activities showed the biggest cognitive effects (0.33 and 0.25, respectively).

Here, again, however, effectiveness varied considerably due to contextual factors like domain of

study and level of education in ways that average effect sizes do not indicate.

jforte
Text Box
When presenting decimal fractions, put a zero in front of the decimal if the quantity is something that can exceed one (like the number of standard deviations here). Do not put a zero if the quantity cannot exceed one (e.g., if the number is a proportion).

jforte
Line

A NOVEL TEACHER EVALUATION MODEL 8

These pieces of evidence suggest that there are multiple teaching factors that produce

measurable gains in student achievement and that the relative importance of individual factors

can be highly dependent on contextual factors like student identity. This is in line with a well-

documented phenomenon in educational research that complicates attempts to measure

teaching effectiveness purely in terms of student achievement. This is that “the largest source of

variation in student learning is attributable to differences in what students bring to school—their

abilities and attitudes, and family and community” (McKenzie et al., 2005, p. 2). Student

achievement varies greatly due to non-teacher factors like socio-economic status and home life

(Snook et al., 2009). This means that, even to the extent that it is possible to observe the

effectiveness of certain teaching behaviors in terms of student achievement, it is difficult to set

generalizable benchmarks or standards for student achievement. Thus is it also difficult to make

true apples-to-apples comparisons about teaching effectiveness between different educational

contexts: due to vast differences between different kinds of students, a notion of what

constitutes highly effective teaching in one context may not apply in another. This difficulty has

featured in criticism of certain meta-analyses that have purported to make generalizable claims

about what teaching factors produce the biggest effects (Hattie, 2009). A variety of other

commentators have also made similar claims about the importance of contextual factors in

teaching effectiveness for decades (see, e.g., Theall, 2017; Cashin, 1990; Bloom et al., 1956).

The studies described above mainly measure teaching effectiveness in terms of

academic achievement. It should certainly be noted that these quantifiable measures are not

generally regarded as the only outcomes of effective teaching worth pursuing. Qualitative

outcomes like increased affinity for learning and greater sense of self-efficacy are also important

learning goals. Here, also, local context plays a large role.

jforte
Text Box
To list a few sources as examples of a larger body of work, you can use the word “see” in the parenthetical, as we’ve done here.

jforte
Line

A NOVEL TEACHER EVALUATION MODEL 9

SETs: Imperfect Measures of Teaching

As noted in this paper’s introduction, SETs are commonly used to assess teaching

performance and inform faculty development efforts. Typically, these take the form of an end-of-

term summative evaluation comprised of multiple-choice questions (MCQs) that allow students

to rate statements about their teachers on Likert scales. These are often accompanied with

short-answer responses which may or may not be optional.

SETs serve important institutional purposes. While commentators have noted that there

are crucial aspects of instruction that students are not equipped to judge (Benton & Young,

2018), SETs nevertheless give students a rare institutional voice. They represent an opportunity

to offer anonymous feedback on their teaching experience and potentially address what they

deem to be their teacher’s successes or failures. Students are also uniquely positioned to offer

meaningful feedback on an instructors’ teaching because they typically have much more

extensive firsthand experience of it than any other educational stakeholder. Even peer

observers only witness a small fraction of the instructional sessions during a given semester.

Students with perfect attendance, by contrast, witness all of them. Thus, in a certain sense, a

student can theoretically assess a teacher’s ability more authoritatively than even peer mentors

can.

While historical attempts to validate SETs have produced mixed results, some studies

have demonstrated their promise. Howard (1985), for instance, finds that SET are significantly

more predictive of teaching effectiveness than self-report, peer, and trained-observer

assessments. A review of several decades of literature on teaching evaluations (Watchel, 1998)

found that a majority of researchers believe SETs to be generally valid and reliable, despite

occasional misgivings. This review notes that even scholars who support SETs frequently argue

that they alone cannot direct efforts to improve teaching and that multiple avenues of feedback

are necessary (Seldin, 1993; L’hommedieu et al., 1990).

A NOVEL TEACHER EVALUATION MODEL 10

Finally, SETs also serve purposes secondary to the ostensible goal of improving

instruction that nonetheless matter. They can be used to bolster faculty CVs and assign

departmental awards, for instance. SETs can also provide valuable information unrelated to

teaching. It would be hard to argue that it not is useful for a teacher to learn, for example, that a

student finds the class unbearably boring, or that a student finds the teacher’s personality so

unpleasant as to hinder her learning. In short, there is real value in understanding students’

affective experience of a particular class, even in cases when that value does not necessarily

lend itself to firm conclusions about the teacher’s professional abilities.

However, a wealth of scholarly research has demonstrated that SETs are prone to fail in

certain contexts. A common criticism is that SETs can frequently be confounded by factors

external to the teaching construct. The best introduction to the research that serves as the basis

for this claim is probably Neath (1996), who performs something of a meta-analysis by

presenting these external confounds in the form of twenty sarcastic suggestions to teaching

faculty. Among these are the instructions to “grade leniently,” “administer ratings before

tests” (p. 1365), and “not teach required courses” (p. 1367). Most of Neath’s advice reflects an

overriding observation that teaching evaluations tend to document students’ affective feelings

toward a class, rather than their teachers’ abilities, even when the evaluations explicitly ask

students to judge the latter.

Beyond Neath, much of the available research paints a similar picture. For example, a

study of over 30,000 economics students concluded that “the poorer the student considered his

teacher to be [on an SET], the more economics he understood” (Attiyeh & Lumsden, 1972). A

1998 meta-analysis argued that “there is no evidence that the use of teacher ratings improves

learning in the long run” (Armstrong, p. 1223). A 2010 National Bureau of Economic Research

study found that high SET scores for a course’s instructor correlated with “high

contemporaneous course achievement,” but “low follow-on achievement” (in other words, the

jforte
Text Box
This citation presents quotations from different locations in the original source. Each quotation is followed by the corresponding page number.

jforte
Line

A NOVEL TEACHER EVALUATION MODEL 11

students would tend to do well in the course, but poor in future courses in the same field of

study. Others observing this effect have suggested SETs reward a pandering, “soft-ball”

teaching style in the initial course (Carrell & West, 2010). More recent research suggests that

course topic can have a significant effect on SET scores as well: teachers of quantitative

courses (i.e., math-focused classes) tend to receive lower evaluations from students than their

humanities peers (Uttl & Smibert, 2017).

Several modern SET studies have also demonstrated bias on the basis of gender

(Basow, 1995; Anderson & Miller, 1997), physical appearance/sexiness (Ambady & Rosenthal,

1993), and other identity markers that do not affect teaching quality. Gender, in particular, has

attracted significant attention. One recent study examined two online classes: one in which

instructors identified themselves to students as male, and another in which they identified as

female (regardless of the instructor’s actual gender) (Macnell et al., 2015). The classes were

identical in structure and content, and the instructors’ true identities were concealed from

students. The study found that students rated the male identity higher on average. However, a

few studies have demonstrated the reverse of the gender bias mentioned above (that is, women

received higher scores) (Bachen et al., 1999) while others have registered no gender bias one

way or another (Centra & Gaubatz, 2000).

The goal of presenting these criticisms is not necessarily to diminish the institutional

importance of SETs. Of course, insofar as institutions value the instruction of their students, it is

important that those students have some say in the content and character of that instruction.

Rather, the goal here is simply to demonstrate that using SETs for faculty development

purposes—much less for personnel decisions—can present problems. It is also to make the

case that, despite the abundance of literature on SETs, there is still plenty of room for scholarly

attempts to make these instruments more useful.

A NOVEL TEACHER EVALUATION MODEL 12

Empirical Scales and Locally-Relevant Evaluation

One way to ensure that teaching assessments are more responsive to the demands of

teachers’ local contexts is to develop those assessments locally, ideally via a process that

involves the input of a variety of local stakeholders. Here, writing assessment literature offers a

promising path forward: empirical scale development, the process of structuring and calibrating

instruments in response to local input and data (e.g., in the context of writing assessment,

student writing samples and performance information). This practice contrasts, for instance, with

deductive approaches to scale development that attempt to represent predetermined theoretical

constructs so that results can be generalized.

Supporters of the empirical process argue that empirical scales have several

advantages. They are frequently posited as potential solutions to well-documented reliability

and validity issues that can occur with theoretical or intuitive scale development (Turner &

Upshur, 1995; Turner & Upshur, 2002; Brindley, 1998). Empirical scales can also avoid issues

caused by subjective or vaguely-worded standards in other kinds of scales (Brindley, 1998)

because they require buy-in from local stakeholders who must agree on these standards based

on their understanding of the local context. Fulcher et al. (2011) note the following:

Measurement-driven scales suffer from descriptional inadequacy. They are not sensitive

to the communicative context or the interactional complexities of language use. The level

of abstraction is too great, creating a gulf between the score and its meaning. Only with

a richer description of contextually based performance, can we strengthen the meaning

of the score, and hence the validity of score-based inferences. (pp. 8–9)

There is also some evidence that the branching structure of the EBB scale specifically

can allow for more reliable and valid assessments, even if it is typically easier to calibrate and

use conventional scales (Hirai & Koizumi, 2013). Finally, scholars have also argued that

jforte
Text Box
Quotations longer than 40 words should be formatted as block quotations. Indent the entire passage half an inch and present the passage without quotation marks. Any relevant page numbers should follow the concluding punctuation mark. If the author and/or date are not referenced in the text, as they are here, place them in the parenthetical that follows the quotation along with the page numbers.

jforte
Line

A NOVEL TEACHER EVALUATION MODEL 13

theory-based approaches to scale development do not always result in instruments that

realistically capture ordinary classroom situations (Knoch, 2007, 2009).

The most prevalent criticism of empirical scale development in the literature is that the

local, contingent nature of empirical scales basically discards any notion of their results’

generalizability. Fulcher (2003), for instance, makes this basic criticism of the EBB scale even

as he subsequently argues that “the explicitness of the design methodology for EBBs is

impressive, and their usefulness in pedagogic settings is attractive” (p. 107). In the context of

this particular paper’s aims, there is also the fact that the literature supporting empirical scale

development originates in the field of writing assessment, rather than teaching assessment.

Moreover, there is little extant research into the applications of empirical scale development for

the latter purpose. Thus, there is no guarantee that the benefits of empirical development

approaches can be realized in the realm of teaching assessment. There is also no guarantee

that they cannot. In taking a tentative step towards a better understanding of how these

assessment schema function in a new context, then, the study described in the next section

asks whether the principles that guide some of the most promising practices for assessing

students cannot be put to productive use in assessing teachers.

Materials and Methods

This section proposes a pilot study that will compare the ICaP SET to the Heavilon

Evaluation of Teacher (HET), an instrument designed to combat the statistical ceiling effect

described above. In this section, the format and composition of the HET is described, with

special attention paid to its branching scale design. Following this, the procedure for the study is

outlined, and planned interpretations of the data are discussed.

jforte
Text Box
When citing multiple sources from the same author(s), simply list the author(s), then list the years of the sources separated by commas.

jforte
Line

jforte
Text Box
Note: For the sake of brevity, the next page of the original paper was cut from this sample document.

A NOVEL TEACHER EVALUATION MODEL 15

On the ICaP SET, students must indicate whether they strongly agree, agree, disagree,

strongly disagree, or are undecided. These thirty Likert scale questions assess a wide variety

of the course and instructor’s qualities. Examples include “My instructor seems well-prepared

for class,” “This course helps me analyze my own and other students’ writing,” and “When I

have a question or comment I know it will be respected,” for example.

One important consequence of the ICaP SET within the Purdue English department is

the Excellence in Teaching Award (which, prior to Fall 2018, was named the Quintilian or,

colloquially, “Q” Award). This is a symbolic prize given every semester to graduate instructors

who score highly on their evaluations. According to the ICaP site, “ICaP instructors whose

teaching evaluations achieve a certain threshold earn [the award], recognizing the top 10% of

teaching evaluations at Purdue.” While this description is misleading—the award actually goes

to instructors whose SET scores rank in the top decile in the range of possible outcomes, but

not necessarily ones who scored better than 90% of other instructors—the award nevertheless

provides an opportunity for departmental instructors to distinguish their CVs and teaching

portfolios.

Insofar as it is distributed digitally, it is composed of MCQs (plus a few short-answer

responses), and it is intended as end-of-term summative assessment, the ICaP SET embodies

the current prevailing trends in university-level SET administration. In this pilot study, it serves

as a stand-in for current SET administration practices (as generally conceived).

The HET

Like the ICaP SET, the HET uses student responses to questions to produce a score

that purports to represent their teacher’s pedagogical ability. It has a similar number of items

(28, as opposed to the ICaP SET’s 34). However, despite these superficial similarities, the

instrument’s structure and content differ substantially from the ICaP SET’s.

jforte
Text Box
Italicize the anchors of scales or responses to scale-like questions, rather than presenting them in quotation marks. Do not italicize numbers if the scale responses are numbered.

jforte
Line

A NOVEL TEACHER EVALUATION MODEL 16

The most notable differences are the construction of the items on the text and the way

that responses to these items determine the teacher’s final score. Items on the HET do not use

the typical Likert scale, but instead prompt students to respond to a question with a simple

“yes/no” binary choice. By answering “yes” and “no” to these questions, student responders

navigate a branching “tree” map of possibilities whose endpoints correspond to points on a 33-

point ordinal scale.

The items on the HET are grouped into six suites according to their relevance to six

different aspects of the teaching construct (described below). The suites of questions

correspond to directional nodes on the scale—branching paths where an instructor can move

either “up” or “down” based on the student’s responses. If a student awards a set number of

“yes” responses to questions in a given suite (signifying a positive perception of the instructor’s

teaching), the instructor moves up on the scale. If a student does not award enough “yes”

responses, the instructor moves down. Thus, after the student has answered all of the

questions, the instructor’s “end position” on the branching tree of possibilities corresponds to a

point on the 33-point scale. A visualization of this structure is presented in Figure 1.

A NOVEL TEACHER EVALUATION MODEL 17

Figure 1

Illustration of HET’s Branching Structure

Note. Each node in this diagram corresponds to a suite of HET/ICALT items, not to a single item.

aBecause it is inclusive of both “1” and “32” but contains no “0,” the HET uses a 32-point scale.

jforte
Text Box
Tables and figures are numbered sequentially (i.e., 1, 2, 3 …). They are identified via a second-level heading (flush-left, bold, and title case) followed by an italic title that briefly describes the content of the table or figure.

jforte
Line

jforte
Text Box
Table and figure notes are preceded by the label “Note.” written in italics. General notes that apply to the entire table should come before specific notes (indicated with superscripted lowercase letters that correspond to specific locations in the figure or table.
Table notes are optional.

jforte
Rectangle

jforte
Text Box
32

jforte
Typewritten Text
a

jforte
Line

A NOVEL TEACHER EVALUATION MODEL 18

The questions on the HET derive from the International Comparative Analysis of

Learning and Teaching (ICALT), an instrument that measures observable teaching behaviors for

the purpose of international pedagogical research within the European Union. The most recent

version of the ICALT contains 32 items across six topic domains that correspond to six broad

teaching skills. For each item, students rate a statement about the teacher on a four-point Likert

scale. The main advantage of using ICALT items in the HET is that they have been

independently tested for reliability and validity numerous times over 17 years of development

(see, e.g., Van de Grift, 2007). Thus, their results lend themselves to meaningful comparisons

between teachers (as well as providing administrators a reasonable level of confidence in their

ability to model the teaching construct itself).

The six “suites” of questions on the HET, which correspond to the six topic domains on

the ICALT, are presented in Table 1.

Table 1

HET Question Suites

Suite # of Items Description

Safe learning environment 4 Whether the teacher is able to

maintain positive, nonthreatening

relationships with students (and to

foster these sorts of relationships

among students).

Classroom management 4 Whether the teacher is able to

maintain an orderly, predictable

environment.

Clear instruction 7 Whether the teacher is able to

explain class topics

comprehensibly, provide clear sets

jforte
Text Box
Tables are formatted similarly to figures. They are titled and numbered in the same way, and table-following notes are presented the same way as figure-following notes. Use separate sequential numbers for tables and figures. For instance, this table is presented as Table 1 rather than as Table 2, despite the fact that Figure 1 precedes it.

jforte
Line

A NOVEL TEACHER EVALUATION MODEL 19

Suite # of Items Description

of goals for assignments, and

articulate the connections between

the assignments and the class

topics in helpful ways.

Activating teaching methods 7 Whether the teacher uses strategies

that motivate students to think about

the class’s topics.

Learning strategies 6 Whether teachers take explicit steps

to teach students how to learn (as

opposed to merely providing

students informational content).

Differentiation 4 Whether teachers can successfully

adjust their behavior to meet the

diverse learning needs of individual

students.

Note. Item numbers are derived from original ICALT item suites.

The items on the HET are modified from the ICALT items only insofar as they are phrased

as binary choices, rather than as invitations to rate the teacher. Usually, this means the addition

of the word “does” and a question mark at the end of the sentence. For example, the second

safe learning environment item on the ICALT is presented as “The teacher maintains a relaxed

atmosphere.” On the HET, this item is rephrased as, “Does the teacher maintain a relaxed

atmosphere?” See Appendix for additional sample items.

As will be discussed below, the ordering of item suites plays a decisive role in the teacher’s

final score because the branching scale rates earlier suites more powerfully. So too does the

“sensitivity” of each suite of items (i.e., the number of positive responses required to progress

upward at each branching node). This means that it is important for local stakeholders to

participate in the development of the scale. In other words, these stakeholders must be involved

jforte
Text Box
When a table is so long that it stretches across multiple pages, repeat the column labels on each new page. Most word processors have a feature that does this automatically.

jforte
Line

jforte
Text Box
In addition to presenting figures and tables in the text, you may also present them in appendices at the end of the document. You may also use appendices to present material that would be distracting or tedious in the body of the paper. In either case, you can use simple in-text references to direct readers to the appendices.

jforte
Line

A NOVEL TEACHER EVALUATION MODEL 20

in decisions about how to order the item suites and adjust the sensitivity of each node. This is

described in more detail below.

Once the scale has been developed, the assessment has been administered, and the

teacher’s endpoint score has been obtained, the student rater is prompted to offer any textual

feedback that s/he feels summarizes the course experience, good or bad. Like the short

response items in the ICaP SET, this item is optional. The short-response item is as follows:

• What would you say about this instructor, good or bad, to another student considering

taking this course?

The final four items are demographic questions. For these, students indicate their grade

level, their expected grade for the course, their school/college (e.g., College of Liberal Arts,

School of Agriculture, etc.), and whether they are taking the course as an elective or as a

degree requirement. These questions are identical to the demographic items on the ICaP SET.

To summarize, the items on the HET are presented as follows:

• Branching binary questions (32 different items; six branches)

o These questions provide the teacher’s numerical score

• Short response prompt (one item)

• Demographic questions (four items)

Scoring

The main data for this instrument are derived from the endpoints on a branching ordinal

scale with 33 points. Because each question is presented as a binary yes/no choice (with “yes”

suggesting a better teacher), and because paths on the branching scale are decided in terms of

whether the teacher receives all “yes” responses in a given suite, 32 possible outcomes are

possible from the first five suites of items. For example, the worst possible outcome would be

five successive “down” branches, the second-worst possible outcome would be four “down”

jforte
Text Box
Note: For the sake of brevity, the next few pages of the original paper were cut from this sample document.

A NOVEL TEACHER EVALUATION MODEL 27

References

American Association of University Professors. Background facts on contingent faculty

positions. https://www.aaup.org/issues/contingency/background-facts

American Association of University Professors. (2018, October 11). Data snapshot: Contingent

faculty in US higher ed. AAUP Updates. https://www.aaup.org/news/data-snapshot-

contingent-faculty-us-higher-ed#.Xfpdmy2ZNR4

Ambady, N., & Rosenthal, R. (1993). Half a minute: Predicting teacher evaluations from thin

slices of nonverbal behavior and physical attractiveness. Journal of Personality and

Social Psychology, 64(3) 431–441. http://dx.doi.org/10.1037/0022-3514.64.3.431

Anderson, K., & Miller, E. D. (1997). Gender and student evaluations of teaching. PS: Political

Science and Politics, 30(2), 216–219. https://doi.org/10.2307/420499

Armstrong, J. S. (1998). Are student ratings of instruction useful? American Psychologist,

53(11), 1223–1224. http://dx.doi.org/10.1037/0003-066X.53.11.1223

Attiyeh, R., & Lumsden, K. G. (1972). Some modern myths in teaching economics: The U.K.

experience. American Economic Review, 62(1), 429–

443. https://www.jstor.org/stable/1821578

Bachen, C. M., McLoughlin, M. M., & Garcia, S. S. (1999). Assessing the role of gender in

college students’ evaluations of faculty. Communication Education, 48(3), 193–

210. http://doi.org/cqcgsr

Basow, S. A. (1995) Student evaluations of college professors: When gender matters. Journal of

Educational Psychology, 87(4), 656–665. http://dx.doi.org/10.1037/0022-0663.87.4.656

Becker, W. (2000). Teaching economics in the 21st century. Journal of Economic Perspectives,

14(1), 109–120. http://dx.doi.org/10.1257/jep.14.1.109

jforte
Text Box
Start the references list on a new page. The word “References” (or “Reference,” if there is only one source), should appear bolded and centered at the top of the page. Reference entries should follow in alphabetical order. There should be a reference entry for every source cited in the text.

jforte
Text Box
Note that sources in online academic publications like scholarly journals now require DOIs or stable URLs if they are available.

jforte
Line

jforte
Text Box
All citation entries should be double-spaced. After the first line of each entry, every following line should be indented a half inch (this is called a “hanging indent”).

jforte
Text Box
Source with organizational author.

jforte
Line

jforte
Text Box
Source with two authors.

jforte
Line

jforte
Text Box
Shortened DOI.

jforte
Line

28 A NOVEL TEACHER EVALUATION MODEL

Benton, S., & Young, S. (2018) Best practices in the evaluation of teaching. Idea paper, 69.

Berk, R. A. (2005). Survey of 12 strategies to measure teaching effectiveness. International

Journal of Teaching and Learning in Higher Education, 17(1), 48–62.

Bloom, B. S., Englehart, M. D., Furst, E. J., Hill, W. H., & Krathwohl, D. R. (1956). Taxonomy of

educational objectives: The classification of educational goals. Addison-Wesley

Longman Ltd.

Brandenburg, D., Slinde, C., & Batista, J. (1977). Student ratings of instruction: Validity and

normative interpretations. Research in Higher Education, 7(1), 67–

78. http://dx.doi.org/10.1007/BF00991945

Carrell, S., & West, J. (2010). Does professor quality matter? Evidence from random

assignment of students to professors. Journal of Political Economy, 118(3), 409–432.

https://doi.org/10.1086/653808

Cashin, W. E. (1990). Students do rate different academic fields differently. In M. Theall, & J. L.

Franklin (Eds.), Student ratings of instruction: Issues for improving practice. New

Directions for Teaching and Learning, 43, 113–121.

Centra, J., & Gaubatz, N. (2000). Is there gender bias in student evaluations of

teaching? The Journal of Higher Education, 71(1), 17-33.

https://doi.org/10.1080/00221546.2000.11780814

Davis, B. G. (2009). Tools for teaching (2nd ed.). Jossey-Bass.

Denton, D. (2013). Responding to edTPA: Transforming practice or applying

shortcuts? AILACTE Journal, 10(1), 19–36.

jforte
Text Box
Print book.

jforte
Line

jforte
Text Box
Second edition of a print book.

jforte
Line

jforte
Text Box
Academic article for which a DOI was unavailable.

jforte
Line

jforte
Text Box
Chapter in an edited collection.

jforte
Line

A NOVEL TEACHER EVALUATION MODEL 29

Dizney, H., & Brickell, J. (1984). Effects of administrative scheduling and directions upon

student ratings of instruction. Contemporary Educational Psychology, 9(1), 1–7.

https://doi.org/10.1016/0361-476X(84)90001-8

DuCette, J., & Kenney, J. (1982). Do grading standards affect student evaluations of teaching?

Some new evidence on an old question. Journal of Educational Psychology, 74(3), 308–

314. https://doi.org/10.1037/0022-0663.74.3.308

Edwards, J. E., & Waters, L. K. (1984). Halo and leniency control in ratings as influenced by

format, training, and rater characteristic differences. Managerial Psychology, 5(1), 1–16.

Fink, L. D. (2013). The current status of faculty development internationally. International

Journal for the Scholarship of Teaching and Learning, 7(2).

https://doi.org/10.20429/ijsotl.2013.070204

Fulcher, G. (2003). Testing second language speaking. Pearson Education.

Fulcher, G., Davidson, F., & Kemp, J. (2011). Effective rating scale development for speaking

tests: Performance decision trees. Language Testing, 28(1), 5–29.

https://doi.org/10.1177/0265532209359514

Gaff, J. G., & Simpson, R. D. (1994). Faculty development in the United States. Innovative

Higher Education, 18(3), 167–76. https://doi.org/10.1007/BF01191111

Hattie, J. (2008). Visible learning: A synthesis of over 800 meta-analyses relating to

achievement. Routledge.

Hoffman, R. A. (1983). Grade inflation and student evaluations of college courses. Educational

and Psychological Research, 3(3), 151–160. https://doi.org/10.1023/A:101557981

jforte
Text Box
Note: For the sake of brevity, the next few pages of the original paper were cut from this sample document.

A NOVEL TEACHER EVALUATION MODEL 33

Appendix

Sample ICALT Items Rephrased for HET

Suite Sample ICALT Item HET Phrasing

Safe learning environment The teacher promotes mutual

respect.

Does the teacher promote mutual

respect?

Classroom management The teacher uses learning time

efficiently.

Does the teacher use learning time

efficiently?

Clear instruction The teacher gives feedback to

pupils.

Does the teacher give feedback to

pupils?

Activating teaching methods The teacher provides interactive

instruction and activities.

Does the teacher provide interactive

instruction and activities?

Learning strategies The teacher provides interactive

instruction and activities.

Does the teacher provide interactive

instruction and activities?

Differentiation The teacher adapts the instruction

to the relevant differences between

pupils.

Does the teacher adapt the

instruction to the relevant

differences between pupils?

jforte
Text Box
Appendices begin after the references list. The word “Appendix” should appear at the top of the page, bolded and centered. If there are multiple appendices, label them with capital letters (e.g., Appendix A, Appendix B, and Appendix C). Start each appendix on a new page.

jforte
Line

jforte
Text Box
Paragraphs of text can also appear in appendices. If they do, paragraphs should be indented normally, as they are in the body of the paper.

jforte
Text Box
If an appendix contains only a single table or figure, as this one does, the centered and bolded “Appendix” replaces the centered and bolded label that normally accompanies a table or figure.
If the appendix contains both text and tables or figures, the tables or figures should be labeled, and these labels should include the letter of the appendix in the label. For example, if Appendix A contains two tables and one figure, they should be labeled “Table A1,” “Table A2,” and “Figure A1.” A table that follows in Appendix B should be labeled “Table B1.” If there is only one appendix, use the letter “A” in table/figure labels: “Table A1,” “Table A2,” and so on.

STUDY ONE LITERATURE REVIEW INSTRUCTIONS 1

STUDY ONE LITERATURE REVIEW INSTRUCTIONS 10

Instructions for Paper I: Study One Literature Review Instructions (Worth 25 Points)

Ryan J. Winter

Florida International University

Pay attention to the title page formatting above (header, page number, title, my name, and my institution)! This is the same format that YOU will use for your own title page (minus this text box, of course)!

Paper I: Table of Contents

Item

Page #

Title Page

Table of Contents

Purposes of Paper I – Study One Literature Review

1. The psychological purpose (Paper overview)

2. The APA formatting purpose

3. The writing purpose

Notes About Paper I – Study One Literature Review

Formal Paper Instructions

The Title Page (5 points)

1. The header

2. The title

3. Your personal information

The Abstract (Not needed for this paper)

The Literature Review (12 points)

1. The first page of the literature review

2. Citations for the literature review

3. The content of the literature review

References (6 points)

1. APA reference page formatting

2. Number of required references

3. APA formatting for references

Writing Quality (2 points)

Other Helpful Hints

Your study hypotheses (Social Loafing Study)

Paper I Grade Rubric

Note: Right-click on the page number to open a link to the content in these instructions

(Select “Open Hyperlink)

Purposes of Paper I: Study One Literature Review

1).
The psychological purpose (Paper overview)

This paper serves several purposes, the first of which is helping you gain insight into research papers in psychology. As this may be your first time reading and writing papers in psychology, one goal of Paper I is to give you insight into what goes into such papers. This study one-literature review paper will help you a). better understand the psychology topic chosen for the semester (Social Loafing, or Loafing for short), b). learn about the various sections of an empirical research report by reading
five peer-reviewed articles (that is, articles that have a Title Page, Abstract, Literature Review, Methods Section, Results Section, and References Page), and c). use information gathered from research articles in psychology to help support your hypotheses for your first study this semester (Loafing). You will also write a second literature review later in the semester (for study two), so think about Paper I as the
first segment of your semester long paper. I highly recommend looking at the example Paper V to see what your final paper will look like. It will give you a good idea about how this current Paper I (as well as Papers II, III and IV) all fit together to form your final paper of the semester (Paper V).

In this current paper (Paper I), you will read five research articles, often summarizing what the authors did and found, and using that information to help support your Loafing study hypotheses. IMPORTANT: Yes, you need five references, but note that you can spend a lot of time (a page or two!) summarizing one reference but only a sentence or two summarizing others. Thus
spend more time on the more relevant articles!

For Paper I, start the paper broadly and then narrow your focus (think about the hourglass example provided in the lecture). My suggestion is to give a brief overview of your paper topic in your opening paragraph, hinting at the research variables that you plan to look at for study one. Your next paragraphs will review prior research (that is, the five references required for this paper). Make sure that you draw connections between these references rather than just listing them. Use smooth transitions between paragraphs and build a case that supports your study predictions. Your final paragraphs will use the research you just summarized to support your research hypothesis. And yes, that means
you MUST include your study one predictions in Paper I (which we provided in the researcher instructions, the debriefing statement, and at the end of these instructions. Use those predictions! They go at the end of your Paper I). A good hint is to look at the literature reviews of the articles that you are using as references as you write your own paper! See what those authors did in their literature reviews and mimic their literature review style. Keep in mind that Paper I will end with your hypothesis (and your references) rather than moving directly into your study methods. In Paper II, you will pick the topic up again and discuss your study methods, results, and discussion. Paper I thus merely leads up to your study one.

2).
The APA formatting purpose

The second purpose of Paper I: Study One Literature Review is to teach you proper American Psychological Association (APA) formatting. In the instructions below, I will tell you how to format your paper using APA style. There are a lot of very specific requirements in APA papers, so pay attention to the instructions below as well as your APA Formatting powerpoint presentation! Keep in mind that the research methods classes at FIU now use the 7th edition of the APA formatting manual.

3).
The writing purpose

Finally, Paper I is intended to help you grow as a writer. Few psychology classes give you the chance to write papers and receive feedback. This class will! We will give you extensive feedback on your first few papers in terms of content, spelling, and grammar. You will even be able to revise aspects of Paper I and include that content in future papers (notably Papers III and V). My hope is that you eventually craft a final paper that could be submitted to an empirical journal. Thus write your paper for readers who are familiar with APA style and methods but note that they may not know much about your specific study topic. Your job is to
educate them on the topic (Loafing) and make sure they understand how your study design advances the field of psychology. In other words, your reader will be knowledgeable about research methodology but not your specific topic. Teach them about your topic, not methods.

In fact, your final paper in this class (Paper V), might be read by another professor at FIU and not your instructor / lab assistant. Thus write your Paper I for that “other” reader – a person who may know NOTHING about your Loafing topic and your specific study but is familiar with the mechanics of APA formatted papers and research methodology.

Notes About Paper I – Study One Literature Review

Note #1: The plagiarism limit for Paper I is 30%. This excludes any overlap your paper might have regarding citations, references, and hypotheses. Make sure your paper falls under 30% (or 35% if including your predictions). Also note that when you upload your paper to Canvas, Canvas does a plagiarism check through turn-it-in.com. If you submit your paper early (before others submit), your score might be low. If you happen to resubmit an updated version, your score might go up, sometimes dramatically.
This is common. The later you submit, the more papers your submission is compared to, which might seem to inflate your plagiarism score. Do not be alarmed, but feel free to reach out to your instructor if it concerns you.

Note #2: I am looking for 2.5 pages
minimum for Paper I (around 850 words), including your study predictions (2 pages without predictions, or around 650 words), but that is the bare minimum. If it is only 2 pages, it better be really, really good. I do not think I could write Paper I in less than three pages and do the research topic justice, so aim for 3 to 4 pages.

Note #3: Because the study topic changes each semester, I revise these paper instructions each semester. You might see some text in blue. I do that because it is easier for me to make sure I update that specific information. Unless otherwise noted, just ignore the blue color itself.

Instructions for Paper I: Study One Literature Review Instructions (Worth 25 Points)

Students: Below are lengthy instructions on how to write your study one literature review. There is also a checklist document in Canvas, which I
HIGHLY recommend you print out and “check off” before submitting your paper (Your graders are sticklers for APA format, so make sure it is correct! We mark off if you have a misplaced “&” or fail to italicize something that needs italics, so carefully review all your work and
use the checklist! It WILL help you get a good grade). Also look at the example paper in Canvas. It will show you what we expect. We use the 7th Edition of the Publication Manual of the American Psychological Association for all paper formatting in this class (though note that
we adhere to the professional APA paper formatting, not the separate student formatting version also present in the APA publication manual).

Yes, the information below is long, boring, and detailed. I know. I got bored writing it! While I am sorry for the length, you will appreciate the detail as you write your paper. Take ten minutes to thoroughly read these instructions. It will save you lost points in the future!

The Title Page (5 Points)

1.
The header: You must have a header and page number on each page of your paper. This header will be identical on all pages (though the page number will increase)

a. If you do not know how to insert headers, ask your instructor or watch this very helpful video!

b. The header goes at the top of the paper.

i. Use “Insert Headers” or click on the top of the page to open the header. Alternatively, click anywhere at the top of the page and it should open the headers.

ii. Your header title is simply a shortened version of your original paper title. You can use a few words or a phrase from the title or create a new header title altogether. Just make sure it is in ALL CAPS. This short header
should be no more than 50 characters including spaces and punctuation

1. Note: The phrase “Running head” is no longer used with the 7th edition of the APA publication manual. Do not use it in the header

iii. Insert a page number as well. The header is flush left, but the page number is flush right. The page number for the title page is … 1!

iv. This same header will appear on every page of your document, including the title page.

c. Want an example header? Look at the title page of these instructions! You can use any title you want depending on your own preferences (For example, imagine I use the title, “To Loaf or Not to Loaf: That Is The Question” on my title page. I can use a short version of this for the header title: LOAF).

d. Your Title Page will be on page 1

The title: Your Title itself should be
three or four lines below the margin at the top of the page. Again, see my “Title” page on the first page of this current document as an example of the placement. For your title, you must come up with a title that helps describe your study one. Do NOT put “Paper One” or a variation of “Literature Review” for your title. Rather, think about the titles you saw in PsycInfo. Titles need to let the reader know what YOUR paper involves, so make your title descriptive.

a. Your title must also be in
bold text. Make sure that every word with four or more letters starts with a capital letter. You can use lower-case letters for words like “and”, “with”, “the”, but in general start each title word with a capital letter.

Your personal information: Your name (First and Last) and the name of your institution (FIU) are beneath the title. For this class, use your name (and ONLY your name).

a. You can also refer to the APA Format powerpoint for formatting guidance, but I suggest looking at the example papers, too. There some from prior students in this course as well as one based on a document provided by the APA. Most have comments and notes to direct you toward correct formatting.

b. Your name is placed two spaces
below your title.

c.
Double space everything! This includes all title page information

The Abstract?

You DO NOT need an abstract for Paper I. Because the abstract needs to summarize the study results, you cannot write it until you run your studies. So, omit the abstract until Paper V.

The Literature Review (12 points)

1.
The first page of the literature review (Page 2)

a. Make sure you have the same information in the header that you have on the title page (short title and page number). Of course, page 2 should have the number “2” in the header for the page number. (You are currently reading page six of these instructions, so you can see the number 6 in the header, since it is the sixth page).

b. The original title of your paper from the title page is
repeated on the first line of page two, centered. It is IDENTICAL to the title on your title page, including the
bold font type. Just copy and paste it from your title page!

c. The beginning text for your paper follows on the very next line.

Citations for the literature review

a.
Minimum citations: Your paper must cite a minimum of five (5) empirical research articles that are based on studies conducted in psychology. That is, each of the five citations must have a literature review, a methods section, a results section, a conclusion or discussion section, and references.

i. For Paper I, you MUST use
at least three of the articles provided in the Canvas folder. You can use four if you like, but only three are required. For your fifth article, you
must find a new one on your own (using library resources). There are some other requirements for this fifth article that you must follow:

1. First—and to reiterate—remember that the fifth article cannot be any of those found in the Canvas folder.

2. Second, for your fifth article, it can be based on a wide variety of topics, including Social Loafing, The Ringlemann Effect, Deindividuation, Group Work, Accountability, Teamwork. Social Learning, Group versus Individual Performance, etc.

a. Trust me, there are TONS of topics can use in your paper. Just make sure it is relevant to your study. It does not have to be about Loafing. Use your best judgment. Get creative!

b. For a nice / simple Social Loafing overview, go to …

https://www.simplypsychology.org/social-loafing.html

3. Finally, you can have more than five references if you want, but you must have a
minimum of five references.

APA citation format and examples: Proper citations must be made in the paper – give credit where credit is due, and do not make claims that cannot be validated. For citations (in-text referrals to other study authors), make sure you:

i. avoid using author first names, initials, or the title of the article the author(s) wrote

ii. include the last name of the author followed by the date of publication. If there is one author, use that author’s name every time you cite. If there are two authors, use both author names every time you cite. If there are three or more authors, use the last name of the first author every time you cite followed by the phrase et al. to replace other authors.

1. One author example:

a. “According to Piper (2020) …”

b. “The author found XYZ (Piper, 2020).”

2. Two authors example:

a. “According to Piper and Holmes (2020) …”

b. “The authors found XYZ (Piper & Holmes, 2020).”

3. Three + authors example:

a. “According to Piper et al. (2020) …”

b. “The authors found XYZ (Piper et al., 2020).”

iii. Consider an author that you did not personally read, but the article was cited by an author that you did read (That is, imagine you read Evans, not Piper, but you want to discuss what Piper found). Use the format below:

1. “Piper et al. (2020, as cited by Evans, 2021) found that …”

2. For easy reference, note that the date of the “as cited by” paper should be after the publication of the paper they cited!

c.
Direct quotes: If you use a direct quote, make sure to provide a page number for where you found that quote when citing the article. For example, “… as Piper found” (2020, p. 234). However, do not directly quote too often.
In fact, you cannot have more than two direct quotes for Paper I. If you do, you will lose “writing quality” points. In fact, I prefer that you not quote at all.

i. I prefer paraphrasing, but still cite even when you paraphrase.

ii. If you need help with paraphrasing or writing, there are lots of resources available to you at FIU, including the writing center and the center for academic success. Visit their websites for more info:

1. Center for Academic Success:

https://cfas.fiu.edu/

2. Center for Writing:

https://case.fiu.edu/writingcenter/

The content of the literature review

a. Your study one literature review should use prior research as a starting point, narrowing down the main theme of your specific project – think about the hourglass example from the APA Formatting Lecture.

b. The last part of your literature review should narrow down even further to focus on your own study, eventually ending with your study hypotheses. However, DO NOT go into specific details about your methods. You will talk about your specific study methods in Paper II in a few weeks.

i. Again, to be clear, at the end of your paper you MUST provide your specific predictions/hypotheses (See the last page of these instructions).

c.
The literature review must have a minimum of two (2) full pages of text NOT INCLUDING THE HYPOTHESES (2.5 pages if you include the hypotheses, or around 850 words).
If your paper is only two pages, it better be really, really good. I do not think I could do this paper topic justice in fewer than three pages, so if your paper is not at least three pages, I doubt it will get a good grade. The maximum for the literature review is five pages. Two to five pages gives you flexibility. With the predictions, title page, and the reference page, I expect a minimum of 4.5 pages to a maximum of 7.5 pages, but good papers will be around 6 pages.

References (6 points)

1.
APA reference page formatting:

a. The
References section starts on its own page, with the word
References centered and in
bold font. Use proper APA format here (or lose points!)

2.
Number of required references: All five references that you cited in the literature review
must be in this section (if you cited more than five articles, then there should be more than five references, which is fine in this paper).

a. Remember, at least three references must come from the Canvas article folder, one can come from either Canvas or library resources (PsycInfo), while the last one cannot come from Canvas. Only peer-reviewed articles are allowed for paper one (no books, journals, websites, or other secondary resources).

3.
APA formatting for references: For references, make sure you:

a. use alphabetical ordering (start with the last name of the first author)

b. use the authors’ last names but only the initials of their first/middle name

c. give the date in parentheses – e.g. (2020).

d.
italicize the name of the journal article (
Journal of Personality)

e. give the volume number, also in
italics

f. give the page numbers (not italicized) for articles

g. provide the doi (digital object identifier) if present (not italicized)

Writing Quality (2 Points)

Writing quality includes proper grammar and spelling. This is a college level, scientific paper, and we hold you to high standards. I recommend getting feedback on your paper from the Pearson Writer program prior uploading it on Canvas or going to the Writing Center at FIU for some proofreading help. Below are some hints to help you with writing quality:

1. Avoid using personal pronouns like “you”, “us”, “we”, “our”, and “I”. You can use those when discussing your predictions for your own study, but avoid these pronouns when discussing the work of other authors. Stick with terms like “people”, participants”, “humans”, “users”, or similar

2. Avoid direct quotes. We prefer paraphrasing. But either way, make sure to cite the source of the information you are quoting / paraphrasing

3. Do not center or full justify your paragraphs. Use the “left justification” option. That is, select this button

Other Helpful Hints

The above information is required for your paper, but I wanted to give you some
hints / tips about writing your literature review. Students often struggle with this first paper, but hopefully this will give you some good directions:

1. First, remember that you need 5 references, all of which MUST be peer-reviewed (three from the Canvas folder and one or two that you find on your own using PsycInfo). You can check mark the “peer-reviewed” option in PsycInfo to guarantee that you get peer-reviewed articles!

2. Second, I do not expect a lengthy discussion for each and every article that you cite. You might spend a page talking about Article A and a sentence or two discussing Article B. The amount of time you spend describing an article should be proportional to how important that article is in helping you defend your hypotheses. If a prior study looks a lot like your study, I expect you to spend more time discussing it. If an article you read simply supports a general idea that ties into your study, you can easily mention it in a sentence or two without delving into a lot of detail. Tell a good story in your literature review, but only go into detail about plot elements that have a direct bearing on your study! Again, look at the literature review articles that you are citing. How did those authors set up their literature reviews, and how did they summarize the studies they read for their literature reviews? Want a valuable hint? Look at their in-text citations, too. Sometimes they cite six different studies in the same sentence. You can do the same, as long as the citations have the same general information. That is six citations taken care of all on a single sentence, one more than required! For example, consider the paragraph below, copied from a published paper. There are six references in this single paragraph!

3. Third, make sure to proofread, proofread, proofread! I recommend using the Pearson Writer for help, but note that their suggestions are just that – suggestions. It is up to you to make sure the flow of your paper is easy to understand. You can download a free 90 day trial of the Pearson writer at

https://www.pearson.com/us/higher-education/products-services-teaching/learning-engagement-tools/pearson-writer.html

4. Fourth, look at the supporting documents for this paper. There is a checklist, a grade rubric, and an example paper. All will give you more information about what we are specifically looking for as well as a visual example of how to put your papers together.

5. Fifth, note that you have a lot of help available to you. You can go to the Research Methods Help Center (which is hosted by research methods instructors and teaching assistants). You can go to the Writing Center in the Green Library (at MMC) and get help with writing quality or get online Writing Center help. You can attend workshops from the Center for Academic success (CfAS) focusing on APA formatting, paraphrasing, and statistics. Your instructor might even be willing to give you extra credit for using these resources, so make sure to ask your instructor about it.

Study One Hypotheses

1.
What are your hypotheses?

a. This paper is all about supporting your hypotheses. Know what your hypotheses are BEFORE you write your paper, as it will help you determine how much time to spend on each article you are citing. My suggestion is to spend some time describing the nature of Social Loafing (Define it and discuss its origins), and then talk about studies that looked at this area. Use those studies to help you defend your own study hypothesis. That is, “Since they found X in this prior study, that helps support the hypothesis in my study”.

b. Do you remember your hypotheses? Okay, I will be really helpful here. BELOW are your study hypotheses. Use your literature review to support these hypotheses! Just remember that the rest of your paper needs to be at least
two full pages
NOT INCLUDING
the hypotheses below.

We have two predictions. First, if participants are told that their individual total score will be the basis of the “best performance”, then they will attempt to solve more problems than those who are told their score will be pooled with the scores of two other participants (resulting in either a group total score or a group average score), with no differences expected between these two group-based conditions. Second, since prior research suggests that people tend to think that they themselves do not engage in social loafing, we predict that all participants—regardless of their study condition—will agree that they completed more math problems than the average participant.”

In other words, participants who think their individual contributions are more identifiable will work harder on the task than those who think their contribution is pooled with others, but all participants will think they worked equally hard.

Paper I Grade Rubric

Note: Use the Paper Checklist, too! It is much more detailed than this grading rubric!

Criteria

Ratings

Title Page Criteria

(5 points)

1. Header (in ALL CAPS)
2. Page number
3. Descriptive Title (in
bold)

4. Your Name
5. Your University
6. Perfect APA formatting

Meets all criteria

5 points

Meets at least 5 criteria

4 points

Meets at least 4 criteria

3 points

Meets at least 3 criteria

2 points

Meets 0 to 2 criteria

0points

Literature Review

Study One Criteria

(12 points)

1. Starts broad and narrows.
2. Presents info clearly, educating the reader
3. Has smooth transitions between paragraphs
4. Includes 5 citations in APA format (minimum)
5. Concludes with study one predictions
6. Is 2 pages (minimum) excluding the predictions

Meets all criteria

12 points

Meets at least 5 criteria

9 to 11 points

Meets at least 4 criteria

6 to 8 points

Meets at least 3 criteria

3 to 5 points

Meets 0 to 2 criteria

0 to 2
points

Reference Section Criteria (6 points)

1. Includes 5 references (minimum)
2. References are listed alphabetically (by first author)
3. All references are in APA format (all but the first line indented, journal name in italics, title has mostly lower-case letters except the first word / proper nouns, authors first and middle names use initials, etc.)

Meets all criteria (No APA errors)

6 points

Meets 3 criteria (but with some APA errors)

5 points

Meets 2 criteria

3 to 4 points

Meets 1 criteria

1 to 2
point

Meets 0 criteria

0
points

Writing Quality Criteria

(2 points)

1. Uses proper spelling and punctuation
2. Has good transition between sentences
3. Includes good detail that informs the reader about important information in each paper section
4. Avoids plagiarism

Meets all criteria

2 points

Meets 3 criteria

1 to 2 points

Meets 2 criteria

0 to 1 points

Meets 0 to 1 criteria

0 points

image1

image2

Continue to order Get a quote

Calculate the price of your order

Type of paper needed:

Pages:

550 words

Academic level:

We'll send you the first draft for approval by September 11, 2018 at 10:52 AM

Total price:

$26

The price is based on these factors:

Academic level

Number of pages

Urgency

Basic features

Free title page and bibliography
Unlimited revisions
Plagiarism-free guarantee
Money-back guarantee
24/7 support

On-demand options

Writer’s samples
Part-by-part delivery
Overnight delivery
Copies of used sources
Expert Proofreading

Paper format

275 words per page
12 pt Arial/Times New Roman
Double line spacing
Any citation style (APA, MLA, Chicago/Turabian, Harvard)

Our guarantees

Delivering a high-quality product at a reasonable price is not enough anymore.
That’s why we have developed 5 beneficial guarantees that will make your experience with our service enjoyable, easy, and safe.

Money-back guarantee

You have to be 100% sure of the quality of your product to give a money-back guarantee. This describes us perfectly. Make sure that this guarantee is totally transparent.

Zero-plagiarism guarantee

Each paper is composed from scratch, according to your instructions. It is then checked by our plagiarism-detection software. There is no gap where plagiarism could squeeze in.

Free-revision policy

Thanks to our free revisions, there is no way for you to be unsatisfied. We will work on your paper until you are completely happy with the result.

Confidentiality Guarantee

Your email is safe, as we store it according to international data protection rules. Your bank details are secure, as we use only reliable payment systems.

Fair-cooperation guarantee

By sending us your money, you buy the service we provide. Check out our terms and conditions if you prefer business talks to be laid out in official language.

24/7 Support

Our specialists are always online to help you! We are available 24/7 via live chat, WhatsApp, and phone to answer questions, correct mistakes, or just address your academic fears.

See our T&Cs

Live Chat +1(978) 822-0999 Email WhatsApp

pap 1

Checklist – Paper One: Study One

Literature Review

Instructions for Paper I: Study One Literature Review Instructions (Worth 25 Points)

Recent Posts

Recent Comments

Calculate the price of your order

Our guarantees

Money-back guarantee

Zero-plagiarism guarantee

Free-revision policy

Confidentiality Guarantee

Fair-cooperation guarantee

24/7 Support

pap 1

Checklist – Paper One: Study One Literature Review

Instructions for Paper I: Study One Literature Review Instructions (Worth 25 Points)

Recent Posts

Recent Comments

Calculate the price of your order

Our guarantees

Money-back guarantee

Zero-plagiarism guarantee

Free-revision policy

Confidentiality Guarantee

Fair-cooperation guarantee

24/7 Support

Checklist – Paper One: Study One

Literature Review