pap 1


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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


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Working Harder or Hardly Working? Posting Performance
Eliminates Social Loafing and Promotes Social Laboring in
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):



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Vol. 60, No. 5, May 2014, pp. 1098–1106
ISSN 0025-1909 (print) � ISSN 1526-5501 (online)

© 2014 INFORMS

Working Harder or Hardly Working?


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 {,}

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.

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).


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

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.

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

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.


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).

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.

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








Alone In group






No 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

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

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

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.

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.

The authors thank Hillary Anger Elfenbein, Norbert Kerr,
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Ife PsychologIA; Volume 20 Number1, March 2012
Copyright ©2012 Ife Center for Psychological Studies/Services, Ile-Ife Nigeria.




Philip C. Mefoh, PhD & Chinonso L. Nwanosike

Department of Psychology, University of Nigeria, Nsukka.


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.


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,


The first evidence of social loafing was demonstrated by a

French agricultural engineer, Max Ringlemann (Kravitz & Martin,

Ife PsychologIA; 20(1), March 2012


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


however, this feeling of responsibility diffuses to others and their

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

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.



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


Figure 1

An example of the board matrix used in the experiment


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.


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


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

―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


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



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.


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


Alone Perf.

Group Perf.

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


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

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.


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


Reward Condition

No Reward condition

Ife PsychologIA; 20(1), March 2012


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.

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,


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


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


interactions to understand the relationship between reward and

productivity in social loafing research.

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Carron, A. Burke, S. & Prapavessis, H. (2004): managing groups and
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Comer, D.R. (1995): A model of social loafing in real work group.
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Dancey, C.P. & Reidy, J. (2002): Statistics without Maths for
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Earley, P.C. (1989): Social loafing and collectivism: A comparison of

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Eisenberger, R. Armeli, S, & Pretz, J. (1998): Can the promise of
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Gabrenya, W.K., Latane, B & Wang, Y. (1983): Social loafing in cross-
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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
    Sven De Maeyer
    Michaéla C. Schippers
    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:


    Journal of Educational Psychology

    © 2021 American Psychological Association 2022, Vol. 114, No. 4, 716–733
    ISSN: 0022-0663
















    & 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

















    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.

















    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

















    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

    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

    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

















    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.


    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

















    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.


    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.


    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

















    .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.,
    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.


    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

    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.

















    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
    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.

















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

    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.


    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.

















    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
    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

















    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

















    (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

    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

















    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


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    Received August 19, 2020
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    Accepted August 17, 2021 n

















      Taking a Free Ride: How Team Learning Affects Social Loafing

      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


      Data and Sample



      Team Learning

      Social Loafing Tendencies

      Learning Orientation and Performance Orientation

      Data Aggregation

      Hypotheses Testing


      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?


      Limitations and Future Directions

      Practical Implications


    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


    Mitsuhiko Ishikawa, Department of

    Psychology, Graduate School of Letters,

    Kyoto University, Yoshida-Honmachi, Kyoto

    606-8501, Japan.


    Funding information

    Japan Society for the Promotion of Science,

    Grant/Award Number: 16H06301


    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


    (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


    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.


    • The social influence of partner’s productivity level on

    young children’s task performance was examined during



    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

    Inf Child Dev. 2021;30:e2213. © 2020 John Wiley & Sons, Ltd 1 of 14

    • Positive correlations were observed between children’s

    and partners’ performance in both experiments.


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



    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


    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


    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,

    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,

    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


    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,

    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


    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.


    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


    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


    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.


    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.


    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).


    The authors declare no conflicts of interest.


    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.


    The data sets generated and analysed during the current study are available from the corresponding author upon

    reasonable request.


    Mitsuhiko Ishikawa


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    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.

    14 of 14 HAMAMOTO ET AL.

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

      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


      4.1 Limitation








    1 3

    Journal of Business Ethics (2019) 160:713–727


    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

    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


    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

    Katarina Katja Mihelič

    1 Faculty of Economics, University of Ljubljana, Kardeljeva
    pl. 17, Ljubljana, Slovenia

    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.

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    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

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    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


    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).


    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

    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.”


    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.”


    This construct was measured using a semantic differential
    scale (Beck and Ajzen 1991) based on five pairs of adjec‑
    tives (e.g., good/bad).


    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/


    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.”

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    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.


    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

    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

    (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

    The values of AVE appear diagonally and below the diagonal are the squared correlations between the

    Social loafing

    Social loafing

    Mindfulness Moral mean‑


    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.


    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.


    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.


    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


    See Table 4.

    Table 4 Measurement scales

    Variable Composite reli‑


    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…

    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

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    • Reaping the Fruits of Another’s Labor: The Role of Moral Meaningfulness, Mindfulness, and Motivation in Social Loafing
    • Abstract


      Theoretical Background

      Origins of the Term and Conceptualization

      Determinants of Social Loafing

      Conceptual Model and Hypotheses


      Sample and Procedure


      Moral Meaningfulness





      Analytical Procedure


      Descriptive Statistics

      Measurement Model

      Structural Model and Hypotheses Testing



      Limitations and Future Research




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    Educational Psychology

    ISSN: 0144-3410 (Print) 1469-5820 (Online) Journal homepage:

    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:

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    Published online: 01 Jul 2010.

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    Educational Psychology, Vol. 20, No. 4, 2000

    Social Loa® ng in a Co-operative Classroom

    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



    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.


    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.


    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

    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.


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    Journal of Marketing Education
    2018, Vol. 40(2) 117 –127
    © The Author(s) 2017
    Reprints and permissions:
    DOI: 10.1177/0273475317708588


    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

    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.

    Students of a Feather “Flocked”
    Together: A Group Assignment Method
    for Reducing Free-Riding and Improving
    Group and

    Individual Learning Outcomes

    Lora Mitchell Harding1

    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.

    free-riding, social loafing, group project, teamwork, student motivation, learning approaches and issues, marketing education

    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

    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,

    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

    The Flocking Method of Group

    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

    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
    Hypothesis 3: The effect of group assignment method on
    group and individual learning outcomes will be mediated
    by perceived free-riding.


    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 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
    (, 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.

    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).


    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

    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)

    (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

    0.15 (0.16) 86% (0.04) 79% (0.08)

    (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

    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.


    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 +

    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)
    Group assignment γ01 0.082* (0.036) 0.082† (0.038) −0.043† (0.022) −0.048** (0.015)
    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



    ] = 0.002, SE = 0.001, CI [.0003,

    .006]). Thus, Hypothesis 3 is generally supported.


    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,

    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

    −0.04 0.01 .006

    Mediator variable model (group grade)
    Perceived free-riding d

    −0.08 0.04 .067

    Group assignment a

    0.01 0.01 .012
    Dependent variable model (individual grade)
    Perceived free-riding b

    0.08 0.06 .201

    Group grade b

    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).


    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.


    The author received no financial support for the research, author-
    ship, and/or publication of this article.


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    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-
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    Maranto, R., & Gresham, A. (1998). Using “world series shares”
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    March, J. G. (1954). Group norms and the active minority. American
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    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
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    Mello, J. A. (1993). Improving individual member accountability in
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    17, 253-259.

    Metheny, D., & Metheny, W. (1997). Enriching technical courses
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    Michaelsen, L. K., & Black, R. H. (1994). Building learning teams:
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    Muller, T. E. (1989). Assigning students to groups for class proj-
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    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,

    Slavin, R. E. (1990). Research on cooperative learning: Consensus
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    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 “


    ” 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.








    General Paper Format



    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


    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

    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

    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?



    References Page

    Title for the references page

    1. Do references start on their own page?

    2. Is the word “References” centered? Is it in

    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



    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.


    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

    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:

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    Note: Green text boxes contain explanations of APA 7’s paper formatting guidelines…

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    …while blue text boxes contain directions for writing and citing in APA 7.

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    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.


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    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.


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    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.


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    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).

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    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.

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    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.


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    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.




    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

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    Note that both the running head and the page number continue on the pages that follow the title.



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    The word “Abstract” should be centered and bolded at the top of the page.


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    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.

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    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.

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    The main paragraph of the abstract should not be indented.


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    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.



    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).

    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.


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    By contrast, here, we’ve merely paraphrased an idea from the external source. Thus, no location or page number is required.


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    The paper’s title is bolded and centered above the first body paragraph. There should be no “Introduction” header.


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    When listing multiple citations in the same parenthetical, separate them with semicolons.


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    For sources with two authors, use an ampersand (&) between the authors’ names rather than the word “and.”


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    Spell out abbreviations the first time you use them, except in cases where the abbreviations are very well-known (e.g., “CIA”).



    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

    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).


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    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.”


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    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

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    Third level headings are flush left, bolded, written in title case, and italicized.



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    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


    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.

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    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).



    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.

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    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.



    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


    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).


    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

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    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.


    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

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    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.

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    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


    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.

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    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.


    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.

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    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


    Clear instruction 7 Whether the teacher is able to

    explain class topics

    comprehensibly, provide clear sets

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    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


    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

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    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)


    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”

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    American Association of University Professors. Background facts on contingent faculty


    American Association of University Professors. (2018, October 11). Data snapshot: Contingent

    faculty in US higher ed. AAUP Updates.


    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.

    Anderson, K., & Miller, E. D. (1997). Gender and student evaluations of teaching. PS: Political

    Science and Politics, 30(2), 216–219.

    Armstrong, J. S. (1998). Are student ratings of instruction useful? American Psychologist,

    53(11), 1223–1224.

    Attiyeh, R., & Lumsden, K. G. (1972). Some modern myths in teaching economics: The U.K.

    experience. American Economic Review, 62(1), 429–


    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–


    Basow, S. A. (1995) Student evaluations of college professors: When gender matters. Journal of

    Educational Psychology, 87(4), 656–665.

    Becker, W. (2000). Teaching economics in the 21st century. Journal of Economic Perspectives,

    14(1), 109–120.

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    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.

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    Note that sources in online academic publications like scholarly journals now require DOIs or stable URLs if they are available.




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    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”).

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    Source with organizational author.


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    Source with two authors.


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    Shortened DOI.



    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–


    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.

    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.

    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.

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    Print book.


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    Second edition of a print book.


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    Academic article for which a DOI was unavailable.


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    Chapter in an edited collection.



    Dizney, H., & Brickell, J. (1984). Effects of administrative scheduling and directions upon

    student ratings of instruction. Contemporary Educational Psychology, 9(1), 1–7.

    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–


    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).

    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.

    Gaff, J. G., & Simpson, R. D. (1994). Faculty development in the United States. Innovative

    Higher Education, 18(3), 167–76.

    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.

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    Sample ICALT Items Rephrased for HET

    Suite Sample ICALT Item HET Phrasing

    Safe learning environment The teacher promotes mutual


    Does the teacher promote mutual


    Classroom management The teacher uses learning time


    Does the teacher use learning time


    Clear instruction The teacher gives feedback to


    Does the teacher give feedback to


    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


    Does the teacher adapt the

    instruction to the relevant

    differences between pupils?

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    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.



    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


    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

    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.

    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.

    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 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)

    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.

    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)

    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

    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 …

    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!

    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:

    2. Center for Writing:


    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).

    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)

    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!)

    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).

    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).

    italicize the name of the journal article (
    Journal of Personality)

    e. give the volume number, also in

    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

    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

    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
    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!



    Title Page Criteria

    (5 points)

    1. Header (in ALL CAPS)
    2. Page number
    3. Descriptive Title (in

    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


    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

    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

    Meets 0 criteria


    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



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