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Unit VII PowerPoint Presentation

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For this assignment,
you will compose a 10-slide PowerPoint presentation
. Select a contemporary leadership challenge in your workplace or any organization you are currently or previously associated with, and propose an optimal leadership model to apply for success. In doing so, your presentation should include the elements listed below.

· Explain why leadership of this organization is not functioning to its potential.

· In analyzing the leader, determine which characteristics and leadership style exhibited require improvement through reflection or training.

· Examine the role that effective communication will serve toward conveying the scope of the problem and resolution in the situation.

· Describe the exhibited behaviors by the leader, and explain whether they add value. 

· Determine what needs to change, and explain how your selected model will support success for the organization.

· Describe Tuckman’s teamwork performance stage, and recommend leader support to optimize conjoined efforts.

· Explain to what extent conflict is present, both positive to challenge the status quo and negative based on personal agenda pursuit.

Narration is optional. If you add narration, a transcript of your narration is required in the slide notes for each slide.

Utilizing the notes section for further information on each slide is optional. You may utilize bullets, lists, charts, tables, paragraphs, and images for your presentation. Ensure the presentation that you create is your own authentic work.

Please integrate a minimum of three peer-reviewed journal articles from the CSU Online Library to support your proposal for success. The required title and reference slides do not count toward the slide requirement. Adhere to APA Style when constructing this assignment, including in-text citations and references for all sources that are used.

Psychological Foundations of Leadership 1

  • Course Learning Outcomes for Unit VII
  • Upon completion of this unit, students should be able to:

    6. Examine characteristics leaders exhibit to achieve organizational objectives.
    6.1 Examine team-building skills.
    6.2 Relate the ability of a leader to communicate and resolve conflict to the psychological


    Learning Outcomes

  • Learning Activity
  • 6.1

  • Unit Lesson
  • Chapter 9
    Unit VII PowerPoint Presentation

    Unit Lesson
    Chapter 12
    Unit VII PowerPoint Presentation

  • Required Unit Resources
  • Chapter 9: Developing Teamwork

    Chapter 12: Communication and Conflict Resolution Skills

    Unit Lesson


    Welcome to Unit VII! As we rapidly progress toward the finish line, let’s pause and contemplate the wealth of
    knowledge that has been acquired thus far, as related to the psychological foundations of leadership. In this
    unit, we will explore key characteristics essential for leaders to conduct and execute critical functions. First,
    we will examine the acumen of skills necessary for a leader to effectively build high-performing teams.
    Second, we will further explore the key role of effective communication and characteristics needed for leader
    effectiveness. Third, we will investigate knowledge, skills, and abilities necessary to conduct negotiation and
    resolve conflict. Finally, we will summarize, synthesize, and discuss how the psychological foundations of
    leadership examined throughout this course directly relate to the aforementioned characteristics.

    Team Building

    What leadership characteristics are essential for building teams? DuBrin (2019) posits that leaders must be
    viewed through a lens of trustworthiness by followers. So, how does a leader demonstrate this critical
    characteristic? As we have all learned and observed from personal experience, teams evolve through
    specific stages. Let’s pause for a moment and reflect upon when we were a part of a team. Did you look to
    the leader to assess whether you could place your trust in the individual? From your personal observation,
    what did you evaluate?

    Teams assemble for a purpose and are driven to accomplish stated objectives. If a leader demonstrates and
    conveys conflicting information and views, this certainly can prove problematic on the journey to gain the trust
    of followers. Returning to our existing knowledge of the stages of team building, forming occurs first
    (Tuckman, 1965). Typically, this is where the leader assembles the followers to initiate conversion from a
    group to a team. Experience has shown that once trustworthiness is established, the self-identity of the team

    Psychological Challenges of Leadership

    Psychological Foundations of Leadership 2


    emerges (DuBrin, 2019). Consequently, the effective leader demonstrates the skill and ability to inspire and
    enables and empowers group members. These essential characteristics become the foundation for guiding
    the team through the next stage of building, storming (Tuckman, 1965).

    (Adapted from Tuckman, 1965)

    Characteristically, teams often assemble, and the effective leader is able to guide brainstorming that may
    lead to conflicting ideas. As a result, positive conflict emerges, resulting in establishing common ground,
    further leading to the third stage, norming (Tuckman, 1965). It is during this stage that the leader empowers
    followers to make decisions and to assume authority toward execution of steps realized through the planning
    process. Finally, the team is now positioned to perform (Tuckman, 1965). The final stage for team
    development is adjourning (Tuckman, 1965). Characteristics inherent with the effective leader in this stage
    relate to guiding members to anticipate and influence change, a critical characteristic that is requisite to
    achieving higher levels of performance. Next, let’s examine how communication and related characteristics
    are critical for leader success.


    Leaders can only influence and guide followers through highly effective communication skills. At first glance,
    the emphasis is placed on how the leader speaks and gains attention. DuBrin (2019) asserts that two key
    areas that are supported by the spoken word and effective communication rest upon the leader’s ability to
    demonstrate characteristics that lead to a climate of trust, which is supported by the cooperation theory.
    Again, calling on you to reflect on past experience, what key indicators did you look for in order to trust and
    cooperate with the initiative set forth by the leader? How would you rate and evaluate the importance of tone,
    eye contact, and body language, which are all critical evaluative measures?

    Let’s consider the other key function of leader communication, effective listening. Anecdotally, experience
    over the years leads to a sincere commitment to more actively listening to followers in organizations. Given
    the pressures of time and scope of assignments that teams face, there is often a shortfall of leader effort in
    this category. Returning to the stages of team building, effective listening through active inquiry is an essential
    characteristic for leaders to better understand followers and to build a guiding coalition. Next, we will explore
    conflict resolution and negotiation and required skills for leader efficacy.

    Conflict Resolution

    Leadership faces the inevitability of emerging conflict. Earlier, we examined the storming phase, where the
    leader demonstrating effective listening promoted emerging conflict in order to identify solutions that will

    Psychological Foundations of Leadership 3


    support the execution of the plan. Reflect upon a time when you were a part of a team where a conflict
    emerged. Did the leader demonstrate confidence—a critical characteristic in identifying disagreement—and
    actively listen to gain a better understanding? Moreover, did the leader seek to inspire and to recognize the
    value of contribution by each follower?

    Successful leaders are persuasive and demonstrate this characteristic by resolving conflict through win-win
    negotiation and serving the best interests of the team. Finally, integrity must be unilaterally observed and
    displayed by the leader in all dealings in order to demonstrate trust. Hence, effective leaders are
    communicative, not fearful of conflict, and committed to understanding differences, even through cross-
    cultural boundaries. Next, we will be assessing how each of the characteristics examined for team building,
    communication, and conflict resolution relate to the psychological foundations of leadership.


    We have examined a host of different characteristics essential for leader success in driving followers forward.
    First, the cognitive foundation of leadership necessitates demonstrated intelligence in order to assemble
    teams that are driven through effective communication. Second, the social foundation requires the effective
    leader to build relationships that are centered on trust and integrity in order to lead the team through
    challenging circumstances. Third, the organizational foundation necessitates performance by the leader to
    communicate, build teams, and resolve conflict to promote interrelationships that are supported by
    harmonious interaction. Finally, the industrial foundation centers upon developing the characteristics of the
    leader—cognitively, socially, and organizationally—while resting upon the importance of integrity,
    trustworthiness, self-discipline, and self-confidence to achieve the stated organizational strategy. Further,
    given that conflict can and will occur, the leader embraces each of the characteristics inherent with these
    foundations toward achieving success.


    DuBrin, A. J. (2019). Leadership: Research findings, practice, and skills (9th ed.). Cengage Learning.

    Tuckman, B. W. (1965). Developmental sequence in small groups. Psychological Bulletin, 63(6), 384–399.

      Course Learning Outcomes for Unit VII
      Learning Activity
      Required Unit Resources
      Unit Lesson
      Team Building
      Conflict Resolution

    InternationalLeadership Journal Summer 2019


    The Millennial Effect: A Multi-Generational

    Leadership Model*

    Darlene Andert
    Florida Gulf Coast University (Retired)

    Accounting for Profitability LLC

    George Alexakis
    Florida Gulf Coast University

    Robert C. Preziosi

    Nova Southeastern University (Emeritus)

    Each generation develops internal frameworks to understand the nature of effective
    leadership against a backdrop of significant contemporary national and global events—in
    effect, people are who they are based on the time in which they were raised. The civil
    rights movement, military behavioral models adopted by management, technological
    innovation, and mainstream media proliferation prompted the analysis of generational-
    based leadership perspectives in North America. Diverse trends and events also shaped
    the evolution of generational leadership mindsets in other countries. Arguably, there has
    been some development toward global paradigms of business leadership with the
    multiplying of business schools. The multi-generation leadership model presented in this
    article uses a systems theory lens to view the evolving leadership models from the
    traditionalists to the millennials in North America. It offers a broad temporal view and
    discusses the extent to which each the above events acted as variables that gained or lost
    critical mass in each respective generation. Based on the generational differences, this
    article offers some recommendations with regard to leadership development, talent
    management, and human resource practices in the new era and future foci for
    generational leadership research.

    Key words: generational leadership, millennials, multi-generational leadership model,
    systems thinking, workplace

    In modern society, traditional management principles can lose their efficiency

    (Binham et al., 2018). Leadership is one of the most popular topics for executive

    development programs, and the widely researched traditional approaches no

    longer significantly meet the needs of organizations or individuals (Moldoveanu &

    Narayandas, 2019). Karakas, Manisaligil, and Sarigollu (2015) spent seven years

    exploring the “benefits of reflective, creative, and collaborative spaces for

    millennials using practices from leadership and personal development courses”

    *Andert, D., Alexakis, G., & Preziosi, R. C. (2019). The millennial effect: A multi-generational

    leadership model. International Leadership Journal, 11(2), 32–63.

    International Leadership Journal Summer 2019


    (237) to understand and adjust for the differing needs of the next generation. In

    1950, Stogdill’s seminal work acknowledged that leadership exists among people

    in social situations, and that those who lead in one situation may not necessarily

    lead in another. Diversified leadership theories followed. Understanding good

    leadership’s composition, the factors contributing to future leader development

    and the consistent replication of leadership models still challenge scholars and

    practitioners nonetheless. In spite of an overabundance of scientific and

    anecdotal work, a myriad of leadership-related questions have remained

    unanswered (Gandolfi & Stone, 2018). Augmenting Stogdill’s groundbreaking

    original work, Michel and LeBreton (2010) introduced the concept of leadership

    coherence, which connotes that a leader’s behavior fluctuates in a consistent,

    reliable, and predictable idiographic manner across situations. Haeger and

    Lingham (2013) indicate that “leadership patterns are changing, not in theory, but

    through intergenerational collisions between leaders’ behaviors and

    interpretations from direct reports of what it means to lead” (1) Nonetheless, a

    challenge for practitioners is that contemporary theories for understanding the

    ways in which leaders can and should act in different situations typically depend

    on context and may introduce numerous generational contingencies. Massey

    (1979) posited early on that we are who we are based on the time in which we

    were raised. Barbuto and Gottfredson (2016) explored three generational

    cohorts, with particular emphasis on the millennial generation, estimated to be

    50% of the workforce by 2020. They stressed the necessary progression of

    general management and leadership practices needed to create an organization

    rich in human capital. They suggest that servant leadership is the optimal

    leadership style for the millennial generation and call for leadership process

    adjustments that are in agreement with the current generational realities.

    Conger and Kanungo’s (1998) landmark work demonstrates the complexity of

    the issues and explains that even a well-researched theory like transformational

    leadership, which depends on follower characteristics and emotions because

    leadership is a process of attribution, implies the need for a theory of

    followership. The authors conclude that people follow transformational leaders

    International Leadership Journal Summer 2019


    because they attribute to those leaders the capacity to impose order, security,

    and direction in an otherwise chaotic and threatening world. Naseer, Raja, Syed,

    Donia, and Darr (2015) indicate that people will follow even bad leaders when the

    leader–member exchange (LMX) is high and the perceived organizational politics

    (POP) is also high, thus discovering the dark side of leadership and the social

    impact and complexity of


    Defining the Generations and Their Differences

    Leadership research in the United States has spanned multi-generational

    realities, commencing with the veteran generation, who grew up during the war

    years, and culminating with the millennial generation, who were raised during the

    digital age. The significant events that occurred during their formative adolescent

    years greatly affect each generation (Myers & Sadaghiani, 2010). Expanded

    media news content and opinions, the codification of human rights legislation,

    and the timing of major military actions and the impact of returning veterans re-

    entering the workplace in critical mass substantively shaped each respective

    generation’s understanding of leadership and followership. The current

    leadership model of the millennial generation has progressed from that of the

    previous generation. The apparent shift from the time-of-war traditionalist view of

    leadership to a more dynamic and flexible leader/follower perspective is evident

    when viewed through the lens of the multi-generational leadership model.

    Howe and Strauss (2007) describe the word generation as a cohort group

    whose estimated span of life boundaries is fixed and thus develops a peer

    personality. While research on the topic of generational differences has

    dramatically increased in recent decades since multi-generational marketing is

    very important to advertisers and marketers (Williams & Page, 2011), employers

    and human resources professionals are equally concerned with how these

    differences play out in workplace leadership. While the workplace composition

    continues to shift, examining the interrelationships of workers of different

    generations who have different skills, attitudes, expectations, and learning styles

    increasingly makes sense (Helyer & Lee, 2012). This article uses the four






    International Leadership Journal Summer 2019


    existing generational cohorts that appear extensively in the literature as a

    framework for analyzing the evolution of leadership using a systems approach,

    acknowledging that significant societal events and the social learning experience

    (Bandura, 1971) frame normative understandings and redefine leadership both

    domestically and globally. The U.S. Census (Colby & Ortman, 2014) defines the

    generations as

    • traditionalists (also called the veteran generation; 1922–1943);

    • baby boomers (1943–1960, or traditionally defined as 1946–1964);

    • Generation X (also called Gen X; 1960–1980); and

    • millennials (also called Generation Y or Gen Y; 1980–2000).

    In 2018, after a decade of research, the Pew Research Center sought to “to keep

    the Millennial generation analytically meaningful” in order to “begin looking at

    what might be unique about the next cohort” and deconstructed the previous

    generational frameworks (Dimock, 2019, para. 5).

    [The] Pew Research Center decided a year ago to use 1996 as the last birth
    year for Millennials for our future work. Anyone born between 1981 and 1996
    (ages 23 to 38 in 2019) is considered a Millennial, and anyone born from 1997
    onward is part of a new generation (Dimock, 2019, para, 5).

    The newest delineation of the generations by the Pew Research Center is

    offered in Figure 1 below.

    Figure 1. Pew Center generational definitions
    Source: From “Defining Generations: Where Millennials End and Generation Z Begins,” by M.
    Dimock, January 17, 2019, Pew Research Center (https://www.pewresearch.org/fact-

    The generations defined

    The generations defined

    https://www.pewresearch.org/wp-content/uploads/2019/01/FT_19.01.17_generations_2019 ?w=640

    International Leadership Journal Summer 2019


    Millennial Characteristics

    The multi-generational leadership model serves as a framework for

    understanding the dynamic perspective of current leadership thinking to inform

    American and international businesses. From viewing leadership during the

    global financial crisis to the MTV icons who provided a distracting, fragmented,

    rap-video culture (Kaufman, 1993), the millennial experience during their

    formative years has considerably nullified the authority traditionally associated

    with leaders. The Pew Research Center altered the millennial generation

    boundaries in 2018 to encompass persons from 23 to 38 years of age who

    represent “more than one in three (35%) American labor force

    participants . . . making them the largest generation in the U.S. labor force (Fry,

    2018, para. 1). Current thinking, especially among millennials, prioritizes a

    balancing of personal and professional life (Ng & Gossett, 2013). They seek time

    to pursue personal interests and personal goals as a life priority (Alexander &

    Sysko, 2012). Campione (2015) states that

    factors affecting Millennials’ job satisfaction are those that negatively impact
    satisfaction, those that push Millennials out rather than positive factors that lure
    them in. And, although employers have become quite creative in some of their
    offerings to recruit Millennials, they have often failed to retain them. (69)

    They are technically adept and engaged in their communities using their

    expertise to fight for social justice (Gass & Bezold, 2013). Previous generations

    have not possessed “anything close to their upbeat, high-achieving, team-

    playing, and civic-minded reputation” (Howe & Strauss, 2003, 1). This assertive

    orientation challenges traditional leader-centric, hierarchical leader/follower

    theories. Katy Perry’s (2010) song “Firework,” with such lyrics as “Baby you’re a

    firework—come on show them what you’re worth,” is a popular theme song for

    much of today’s young generation. It represents an anthem of sorts and is

    consistent with the civic-mindedness attributed to the current generational focus.

    Pop singer Brandi Carlile’s lyrics for “The Joke: (2018) further cement this

    generation’s perspective “Let ‘em laugh while they can/Let ‘em spin, let ‘em

    scatter in the wind/I have been to the movies, I’ve seen how it ends/And the

    joke’s on them.” Millennials have strongly stated in corporate surveys and

    International Leadership Journal Summer 2019


    academic studies that freedom to do their work, how they want to do it, is very

    highly valued (Campione, 2015). Perhaps the preference represents the belief

    that the previous generation did not get it right. The current millennial focus is

    ostensibly a global generation, representing the most racially and ethnically

    diverse cohort in U.S. history (Howe & Strauss, 2003).

    With a keystroke or touch of a mobile telephone button, the current cohort can

    summon their networks and swiftly amass people to any location or event. In

    popular culture, this has led to the phenomenon of “flash mobs.” In political

    arenas, the consequences can be more eventful. For example, as far back as

    2011, the Egyptian revolution against the government served as an example of

    this generation’s civic will and leadership–followership dynamic. Youthful pro-

    democracy protesters used Twitter, YouTube, Facebook, and Twitpic to topple

    an authoritarian regime that previous generations were unable to alter

    (Ungerleider, 2011). The phenomenon compelled the mass news media to more

    accurately report relevant stories. Distrusting of mainstream media and

    established authorities, millennials not only supported the political revolution of

    the Bernie Sanders’ U.S. presidential campaign but literally used technology to

    gauge the accuracy of political claims (Uygur, 2016). PBS News Hour (2019)

    notes that by Election Day 2020, millennials will be a larger share of America’s

    adult population than baby boomers and destined to be politically wooed as an

    important factor in the upcoming elections. Most recently, the control of the

    media has radically changed with the origination of the Internet, mobile phones,

    and online social networking (Alexander & Sysko, 2012). The current generations

    have access to a highly expanded mainstream and independent media.

    Millennials now teach others to use the media to create awareness of important

    civic causes. Examples include the “flash mob to end violence against women”

    (European Parliament, 2013) that occurred the week before a vote by the

    European Parliament aimed at combating violence against women and girls. In

    Aventura, Florida, millennial-aged local police officers advanced toward a holiday

    celebration flash mob dance and joined in, rather than dispersing the crowd

    (Wells, 2018), an expression of millennial egalitarianism.

    International Leadership Journal Summer 2019


    Millennials are the first “native” generation to technology while members of all

    other generations are described as “immigrants,” regardless of their technological

    proficiencies (Hershatter & Epstein, 2010, as cited in Alexander & Sysko, 2012).

    The historically unique circumstances (i.e., a younger generation possessing

    superior skills and knowledge) affect the flow of information. The situation

    equalizes opportunities and can lead to a more equitable redistribution of power

    among leaders and followers.

    With their highly collaborative nature, millennials seek constant interaction and

    feedback to assess progress. They seek consistent and constant interpersonal

    contact to move in partnership with others, fostered by an open access media. In

    2016, IBM joined many other large firms in eliminating annual appraisals for more

    frequent, real-time feedback for millennials and their entire workforce (Peck,

    2016). Their external locus of identity prompts a need for immediate feedback

    and almost continuous recognition and approval (Crumpacker & Crumpacker,

    2007, as cited in Gass & Bezold, 2013). Although most millennials are committed

    to their work and careers, they are reluctant to become general managers,

    largely because they see that new managers are often given lots of additional

    responsibility with very little additional support—and support is essential to them

    (Tulgan, 2011). Today, it is a hi-tech, constant-contact world in which action does

    not exist in isolation (Silverman, 2011). This is quite different from the first half of

    the century, when Stogdill’s (Bass, 1990) summative work framed the leadership

    assumptions and beliefs that considered:

    • leadership to occur when leaders do things to followers;

    • leadership to be hierarchy based and linked to an office;

    • leadership to make the crucial difference to organizational performance;

    • leadership to reside in an individual rather than the system, as the source

    or central to organizational accomplishment;

    • leaders to be different from other people; and

    • leaders to be the ones who shape the behavior of others in a desired

    direction and theorized to set the moral climate and culture of the

    organization or collective.

    International Leadership Journal Summer 2019


    By contrast, today’s workforce does not identify with being a corporate citizen

    destined to retire with the gold watch but would rather be a world citizen destined

    to retire after making a difference (Rhodes, 1983). Millennials’ expectations of

    government and their own career goals are different from those of other

    generations. The expectancies were cited as the main finding in a recent research

    report on Brexit (Weinbaum, Girven, & Oberholtzer, 2016). This workforce is fluid

    and mobile. POLITICO Magazine (Robertson & Henderson, 2018) began a series

    of articles around the working title “The United States of Millennials,” which

    explored how “the nation’s largest generation is transforming cities large and

    small” (para. 1) and remaking each to fit their own image and beliefs.

    Followers are indeed inspired by leaders who advocate for their moral freedom

    (Lemoine, Hartnell, & Leroy, 2019). The financial industry downfall was blamed

    on the traditionalist or greedy corporate senior executives, and the Egyptian

    revolution of the millennials represented grassroots, emergent, and alternating

    leadership (Andert, 2011). This brand of egalitarian and civic-minded leadership

    is becoming ubiquitous, as the multidisciplinary nature of the management field

    increasingly causes it to expand its sectors. Tesone (2003) best sums up the

    growing intricacy of the contemporary manager’s leadership responsibilities and

    challenges within an ever-widening group of products and service units with his

    aptly titled book, The Leadership Cat with the Management Hat.

    The purpose of this article is to compare and contrast generational influences

    on the research and definition of leadership. We use a systems thinking lens to

    view the various components of mainstream media, the codification of civil rights

    and human relations legislation, and military behavioral modeling on the

    perspectives of leadership from the traditionalist through millennial generations. It

    offers a cross-functional view of the future foci for generational leadership

    research, its applications, and implications on evolving organizational cultures.

    A Systems Look at Leadership

    Despite the frequent use of the term systems by academicians and management

    practitioners, there is little agreement as to what the term really means (Kefalas,

    International Leadership Journal Summer 2019


    2011). A system is “the name of an abstract concept, that of a complex whole

    entity of a particular kind,” according to Stacey, Griffin, and Shaw (2000, 46).

    Systems theory, or the systems approach, originated in the physical sciences,

    where it challenged the prevailing Cartesian orthodoxy by methodically testing

    instability, nonlinearity, and other complex variables of the natural world (Mingers

    & White, 2010). Kefalas (2011) describes this way of thinking (i.e., systems

    thinking) in the human organizational context:

    The systems approach sees organizations as organic systems that are in a
    continuous interaction with their external environment. This process of
    interaction is essentially a process of acquiring information about the changes
    in the external environment, evaluating the impact of these changes, and
    adapting the organization’s strategy, structure, and evolution. (370)

    Palaima and Skaržauskienė (2010) established a link between systems thinking

    and leadership performance. They empirically confirmed the theoretical insight that

    a systems thinking approach is most important when dealing with the salient

    conceptual strategic issues of an organization. This article uses systems thinking

    as a theoretical insight to examine the dynamic nature of leadership, based on the

    external changes affecting the human condition. It evaluates the evolution of those

    changes on the generations, implementing a renewing definition of leadership.

    Systems theory posits that a basic system consists of elements that function

    interdependently and of inputs, throughputs, and outputs (Katz & Kahn, 1978; St.

    Clair, Hunter, Cola, & Boland, 2018). Applied to generational cohorts, each

    generation receives new inputs (i.e., social realities and norms) or raw materials

    perpetuating the metaphorical systems cycle of life. The common inputs generally

    affect a generation’s perspectives on life and specifically affect the meaning and

    corresponding actions of the generation’s leadership. Coomes and DeBard (2004)

    suggest that history and popular culture could be a useful frame through which one

    can better understand various generations. Simply stated, the concept of

    generational differences is that the time that one went through secondary school

    generally affects one’s views regarding workplace matters (Raines, 2013). Teece

    (2018) suggests that the application of systems theory in management ran its

    course by the 1980s, yet today, its greater potential is for it to provide a holistic

    International Leadership Journal Summer 2019


    view of the business enterprise. This article supports the notion that systems

    theory applies as a holistic view of the workforce within business.

    Millennials have experienced major influencers since the time they could begin

    to conceptualize (i.e., preteen). They were affected by computers; mobile

    phones; school violence (e.g., Columbine); domestic terrorism (e.g., Oklahoma

    bombings and 9/11); celebrity scandals (e.g., O. J. Simpson and Bill Clinton);

    parental layoffs; and an ever-increasing diversity of languages, dual ethnicities,

    sexual alignment, and nontraditional families (Dwyer, 2009; Rhodes, 1983;

    Salahuddin, 2010). The millennial generation experienced the idea that the child

    is a central family focus, which substantially changes the input portion of the

    systems leadership equation. This generation’s influence on society is

    noticeable. So influenced by social media, today, millennials are ‘influencers”

    using Instagram, Twitter, and other forms of social media to make their mark on

    society (Izea, 2019). However, millennials’ leadership style is embryonic and yet

    to be fully discovered (Foot & Stoffman, 1998; Lancaster & Stillman, 2002;

    Sahadi, 2007; Zemke, Raines, & Filipczak, 1999) as this generation gains

    workplace status. Teamwork plays a main role as an input (Nicholas & Lewis,

    2008). The Egyptian government overthrow represents an influential outcome of

    a generation that can change situations with the touch of technology—

    summoning thousands of previously unknown participants to a cause (Malik,

    2014). The instant mass movement approach represents a leader–follower

    paradigm disintegration of sorts, as leaders and followers become less

    distinguishable. The roles can change within a person as well as among people.

    This current reality has been a deliberate evolution, quietly occurring though

    the maturation from the traditionalist through the baby boomer generation to the

    current generations of Gen X and Gen Y. Daft (2013) provides a review of the

    four eras of leadership theory development. Era 1 focuses on the greatness of

    the individual person. Era 2 emerged with Taylorism and classical management.

    It represents the formalization of rational thinking and organizational structuring.

    Era 3 emerged during the time of the quality management and organizational

    team structuring movements. Era 4 coincided with the increased societal

    International Leadership Journal Summer 2019


    consciousness of stewardship and servant leadership frameworks. The

    contemporary era, which we label Era 5, is the emergence of the dynamic

    interplay in the leader–follower relationship, characterized by distributive

    utilization of these roles. The primary role of the individual acting as a leader

    must be to develop a culture that enables individuals to coalesce around the

    shared purpose of the enterprise (Allio, 2012). There is some caution raised. The

    development of “I” among millennials needs to slow down, and maybe even

    reverse itself, so that leaders will be able to see issues and pending events from

    a total community perspective (Kets de Vries, 2019).

    All the eras described above are the result of systemic generational

    experiences. First, and most pronounced, are the changes that occurred in the

    early 1960s and beyond (see Figure 2 on the next page). Second, millennials are

    the first generation in quite some time to experience their formative years without

    a global war and the reinforcement of the military model as returning soldiers re-

    entered the workplace. In contrast, veterans returning from Vietnam underplayed

    their military backgrounds because the populace found disfavor with a war

    perceived to have been lost and unjust. For the first time, the military model was

    less influential in the workplace. In addition, the baby boomers began to be

    exposed from the mid-1960s on to the codification of the civil rights strife of the

    1950s, including the Equal Pay Act of 1963, Civil Rights Act of 1964, Age

    Discrimination in Employment Act of 1967, Pregnancy Discrimination Act of

    1978, Immigration Reform and Control Act of 1986, and Americans with

    Disabilities Act of 1990. Millennials benefited from the existence of all these

    enactments, but experienced none of the struggles associated with inducing the

    passing of these laws.

    The final element displayed in the multi-generational leadership model depicts

    the timing and expansion of the media messages that greatly influenced each

    generation. The media’s effects commenced in the late 1960s with the

    introduction of The Phil Donahue Show/Donahue (1967–1996); in the 1980s with

    the introduction of The Sally Jesse Raphael Show/Sally (1983–2002); and in the

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    1990s with Ricki Lake (1993–2004) and The Jerry Springer Show (1991–2019). This expansion of media brought

    about the expansion of content well beyond traditional dialogue. Millennials were fully exposed, and remain

    exposed, to the media’s timing, complexity, and sophistication.

    Geraldo (Geraldo Rivera show; 1987–1998)

    The Phil Donahue Show/Donahue (1967–1996) Ricki Lake (1993–2004)

    The Sally Jesse Raphael Show/Sally (1983–2002)

    The Jerry Springer Show (1991–present)

    World War I World War II Gulf War 2
    1914–1918 1939–1945 2003–2010

    Korean War Vietnam War Gulf War 1 Afghanistan War
    1950–1953 1955–1975 1990–1991 2007–present

    Cold War

    The Civic Rights Act of 1991; The Americans with Disabilities Act of 1990; Age Discrimination in Employment Act of 1967;

    Vocational Rehabilitation Act; Pregnancy Discrimination Act of 1978; Equal Pay Act of 1963; Title VII of the 1964 Civil Rights Act

    Figure 2. Multi-generational leadership model

    1910–1920 1920–1930 1930–1940 1940–1950 1950–1960 1960–1970 1970–1980 1980–1990 1990–2000 2000–2010 2010–2020

    Traditionalists Baby Boomers Generation X Millennials

    1910–1920 1920–1930 1930–1940 1940–1950 1950–1960 1960–1970 1970–1980 1980–1990 1990–2000 2000–2010 2010–2020

    The maturing baby boomers, Gen Xers, and millennials were fully exposed to

    the changes associated with talk television, with no subject being too outrageous.

    The full spectrum and velocity of information increased, and so too did the

    predatory behavior of criminals. The 1981 abduction and slaying of Adam Walsh

    brought national attention to a generation that needs to challenge those in

    authority (Thomas, 2008). The 2009 financial collapse and the 2011 Occupy Wall

    Street protest movement solidified the reaction against the establishment and

    authority. Each life event added to the generational cohort’s discernment of

    authority and leadership roles (Deal, 2007; Williams, 2007; Zemke et al., 1999).

    The above inputs shaped the respective generations. It made Gen Xers “not as

    a separate generation, but rather the concluding stages of the baby boom

    generation” (Foot & Stoffman, as cited in Dwyer, 2009, 103). The cohort sought a

    new work environment, as Gen X’s leadership style reflected fairness and

    competence (Houlihan, 2007; Salahuddin, 2010). It also created a generation

    that reportedly lacks the people skills of the previous generations, with a

    straightforwardness that may negatively affect others (Sahadi, 2007). Gen Xers

    are more concerned about productivity than the number of hours spent on the job

    (Houlihan, 2007). They view the idea of “face time” as inefficient, wasteful, and

    unnecessary. Generation X is characterized as the latchkey kids, independent

    (yet dependent on their parents), selfish or cynical, questioning authority,

    resilient, adaptable, culturally progressive, and technologically well informed,

    expecting immediate results and committing their attention to the team and the

    boss (Frandsen, 2009). Collectively, these are neither submissive followers nor

    traditional coercive leaders (Bass, 1990).

    The baby boomers, Gen Xers, and millennials seek a different understanding of

    leaders and leading that is based in a temporal systems lens. Salahuddin (2010)

    characterizes the summative differences among the generational understandings

    of leadership (see Table 1).

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    Table 1: Most Admired Leader Characteristics by Generation

    Characteristic Veteran Baby Boomer Generation X Millennial

    Ambitious 2 10 10 8

    Caring 4 4 3 10

    Competent 1 1 4 1

    Determined 9 9 5 2

    Forward-looking 10 2 5 5

    Honest 3 5 1 4

    Imaginative 6 6 7 9

    Inspiring 8 3 9 7

    Loyal 7 7 2 6

    Self-controlled 5 8 6 3
    Note: Bolding added for emphasis by authors. Source: Adapted from “Generational Differences
    Impact on Leadership Style and Organizational Success” by M. M. Salahuddin, 2010, Journal of
    Diversity Management, 5(2), p. 5. Copyright 2010 by the Clute Institute.

    Table 1 offers some patterns that help compare and contrast the view of

    leadership among the generations.

    • Baby boomers, Gen Xers, and millennials place greater value on

    ambitiousness than traditionalists.

    • Being determined and forward-looking are no longer considered valuable

    leadership characteristics by Gen Xers and millennials.

    • Being loyal and inspiring are reemerging as valued characteristics of


    • Being caring is a highly valued characteristic of millennials.

    Historically, the traditionalists valued quality, respect, and authority (Houlihan,

    2007). In its simplest form, traditional leadership research adopted the

    perspective of leadership as:

    • the nucleus of all social movements,

    • preeminent within a group of a few people,

    • a centralization of effort as an expression of the power of all,

    • influenced by the needs and wishes of the group,

    • the central focus of activity,

    • a position of high potential,

    • a primary agent, and

    • a person one pace ahead of the group (Stogdill & Bass, 1981).

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    By contrast, the current generation values individualism. Ironically, millennials

    embody core values that are similar to traditionalists in that they believe in

    collective action, are optimistic about the future, and trust in centralized authority

    of the organization, but only if it is caring (Houlihan, 2007; Howe & Strauss, 2003;

    Hughes & Fiehl, 2013). Coworker trust is a significant predictor of lowered

    turnover intention, while a trust-based environment encourages high levels of

    management having trust in employees and vice versa, which undoubtedly

    boosts organizational competitiveness (Semerciöz, Hassan, & Aldemir, 2011).

    The root of trust lies in strong relationships.

    Leadership and the Millennials

    Hewlett, Sherbin, and Sumberg (2009) describe how the oldest and youngest

    cohorts in the workplace demand many of the same things. They contend that

    millennials are powerfully reshaping work agendas. Harris (2011) asserts that the

    baby boomer leadership has failed, and millennials are displaying divergent

    leadership behaviors.

    The Millennials are entering and leaving college largely dedicated to issues that
    exceed self-interest. Millennials are the new service generation, and like a true
    Millennial, my first job out of college was working for the nonprofit I helped start.
    I want the world to be a better place, and I think dedicating at least part of your
    life to service is how we can make lasting change. This spirit runs at odds with
    the dominating zeitgeist of the Baby Boom Generation. (Harris, 2011, para. 7)

    Loehr (2013, as cited in Sullivan, 2013) warns that massive changes are coming

    to workplace demographics, and if leaders do not begin preparing now, they may

    find themselves outdone by a competitor whom they originally trained, or be left

    with employees who are ill-equipped to perform their duties. One example of

    such massive change is the #MeToo movement. It is fairly common knowledge

    that the election of a record number of women in the November 2018

    U.S. congressional elections will cause many changes. Sessa, Kabacoff, Deal,

    and Brown (2007) examined leisure time and electronic personal connections

    outside the workplace as the preferred friendship building arenas of what the

    authors call GenMe (i.e., millennials). Leadership may be less personal to them.

    This generation has lived through a series of Enron-like leadership debaucheries.

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    Sessa et al. (2007) provide some of the first empirical evidence of a generational

    shift in work values. Understanding the work values of these young individuals

    helps organizations appreciate leadership evolution and how to support the

    popular notion that leisure is a particularly salient work value for millennials

    compared to baby boomers. Leaders should accept that the shift toward leisure

    reflects the realities of the current work environment. Loehr (as interviewed in

    Sullivan, 2013) says that today’s leaders should overhaul their leaders in training,

    create an initiative and contribution culture, and fill the talent pipeline now.

    Therefore, which leadership theory is most likely to accommodate this new

    generation? Reviewing the leadership theories currently in use can be instructive.

    Stewardship (Block, 1993) and servant leadership (Greenleaf, 1977) are earlier

    models consistent with the millennial mindset, but rarely practiced in the

    workplace. With the flattening of organizational pyramids and the loss of

    management positions, most millennials are experiencing job enlargement and

    increased committee responsibilities. This group work will alter the focus of the

    zeitgeist leadership models and empowering leadership may be a form of

    leadership that is acceptable in multiple cultures (Thomas & Rahschulte, 2018).

    The multi-generational leadership model proposes that an egalitarian, fluid, and

    dynamic leadership paradigm must emerge along with increased awareness to

    address the changing generational expectations and shifting role of today’s

    leaders. Emergent leadership (Chaturvedi, Zyphur, Arvey, Avolio, & Larsson,

    2012), alternating leadership (Andert, 2011), grassroots leadership (Kezar,

    2011), and transcendental leadership (Alexakis, 2011) are leadership styles that

    fit the caring, democratic, and imaginative frameworks of millennials. These

    developing theories reflect a move away from a predominantly top-down

    management focus and toward a more egalitarian orientation with practical

    applications and global inclusiveness (i.e., not strictly U.S. based). Organizations

    today are flattening their pyramids and assigning workers as “team leaders,”

    replacing the traditional entry-level supervisor’s role. Millennials’ team-based,

    lackluster desire for organizational commitment complements a dynamic leader–

    follower role exchange. Emergent leadership allows for natural self-selection of

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    role diversity and permits millennials to select and re-select roles, as warranted.

    The notion is analogous to self-leadership, which is “apparently not only

    beneficial for individual performance, but fosters team members’ teamwork

    (proficiency), leads to better adaptation to changes in the team environment

    (adaptivity) and, especially, encourages participation in the improvement of the

    team’s procedures (proactivity)” (Hauschildt & Konradt, 2012, 164).

    Grassroots leadership, as demonstrated during the Egyptian revolution, allows

    civic-minded millennials to circumvent the weaknesses of traditional authoritarian

    leadership and act in a manner that aligns more with inclusive beliefs. Alternating

    leadership aligns with the team-oriented nature of millennials versus the

    autocratic-oriented inclinations of previous generations that color millennials’

    reality. Finally, transcendental leadership focuses on personal development,

    beginning with leader self-motivation toward peak performance, causing workers

    to do the same as employee behavior is often indicative of superiors, and

    organizational goals to be met or exceeded (Ling, Lin, & Wu, 2016). Similarly, the

    transcendent follower expresses competence in terms of their management of

    relations with self, others, and the organization (e Cunha, Rego, Clegg, & Neves,

    2013). The effect of each generation’s experience redefines the leadership

    paradigm. Though the generational realities are still developing for millennials,

    researchers and business professionals would be wise to consider the lens

    through which this generation views the workplace as unique and unlike previous

    leadership perspectives.

    Facilitating the Millennial Leadership Paradigm

    Tulgan (2011) indicates that the following aids offer millennials the support and

    guidance needed to effectively learn and practice general management and

    leadership principles:

    • Explain that a new role carries with it real authority. A huge new

    responsibility should not be accepted lightly.

    • Describe for new leaders exactly what their new leadership responsibilities

    look like beyond extra paperwork. Explain the “people work” in detail and

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    create standard operating procedures for managing that focuses on the

    basics (e.g., articulating employee expectations, following up regularly,

    tracking performance closely in writing, and holding people accountable).

    • Formally deputize any new leader, no matter how small the project or how

    short the duration of the leadership role. Announce the new leadership to

    the whole team, articulate the nature of this person’s new authority, and

    explain the standard operating procedures for management that the new

    leader has been tasked to follow.

    • Check in regularly (preferably daily) with new leaders and review the

    standard operating procedures for managing people. Ask about the

    management challenges that new leaders are facing. Reinforce their new

    authority with the team and every individual on the team.

    • Pay close attention every step of the way, and evaluate new leaders in their

    new roles. With the right amount of guidance and support, most people

    who are very good at their jobs and committed to their work and career

    have the ability to grow into strong, competent leaders.

    The transcendental leadership model holds that the leader’s role is that of a

    facilitator in the motivation process without using punishments and rewards to

    manipulate or coerce (Alexakis, 2011). Transcendental leaders invariably provide

    corporate social responsibility beyond their organizations’ domains; an appealing

    orientation that decidedly attracts and sustains millennial workers and managers.

    As in servant leadership, the leader can be most effective when fostering, aiding,

    supporting, collaborating, abetting, easing, promoting, cultivating, nurturing,

    sponsoring, and otherwise advancing the motivational level that is intrinsically

    present within every person (Alexakis, 2011). Both the baby boomer and Gen

    Xer mentor and the millennial mentee can benefit as leadership skills are

    developed and advanced. Harris (2011) reports that “the Millennial Generation

    will be the most educated, and is the most service-minded generation, in

    American History. [They] are also the most diverse” (para. 5).

    Some empirical studies challenge the popular media concerning the vast

    generational differences (Deal, 2007) and have determined that a true tipping

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    point of change has been reached (Levenson, 2010). Contrary to many popular

    press articles, the results of Zabel, Biermeier-Hanson, Baltes, Early, and

    Shepard (2017) indicate that there are no generational differences in work ethic

    between millennials and other generations. Their research supports Costanza

    and Finkelstein’s (2015) broader contention that little actual empirical evidence

    exists of generational differences in work attitudes. Others (e.g., Macky, Gardner,

    & Forsyth, 2008) call for more research that controls for age and time period.

    Deal, Altman, and Rogelberg (2010) sought a new direction that helps the

    research and practitioner communities alike understand the realities of

    generational similarities and differences so that there may be less reliance on

    urban myths and stereotyping. Yet, the generation gap “endures as a staple

    American political and social analysis” (Samuelson, 2010, para. 1). In 2000,

    Howe and Strauss’s seminal work (as cited in Kowske, Rasch, & Wiley, 2010)

    described millennials as having seven distinguishable traits, including being:

    • special, vital, and full of promise for themselves and for the future of the

    society and the world;

    • sheltered from being smothered in their formative years with safety rules

    and devices;

    • confident, because of their trust and optimism;

    • team-oriented, due to being raised in sports teams and group learning;

    • achievement-oriented, which is the result of higher school standards and

    an instilled sense of accountability;

    • pressured by the desire to excel and do well; and

    • conventional, rather than rebellious.

    Meister and Willyerd (2010) list the “top five things millennials want to learn” from

    their employers. Leadership is the third item below “technical skills in my area of

    expertise” and “self-management and personal productivity” (Meister & Willyerd,

    2010, in graphic).

    According to Reinhardt, 2010, millennials recognize that they are

    the youngest generation of current employees, say they understand the latest
    technological devices, have the ability to multi-task, [and] have plenty of

    International Leadership Journal Summer 2019


    energy. They also recognize that their positive outlook on life, need for social
    interaction and immediate results in their work advancement might be seen as
    weaknesses by older colleagues. (para. 11)

    The millennial generation can develop its leadership lens by executing the

    following guidelines:

    • Balance caring with authority when modeling sound leadership.

    • Be imaginative and inclusive.

    • Provide structure within an informal workplace atmosphere.

    • Apply media and technology to everyday activities whenever appropriate.

    • Be attentive and sensitive to use of face time only when it makes sense.

    • Add teamwork and collaboration to the schedule whenever prudent.

    • Provide for social opportunities and connections.

    Companies such as Virgin, Google, Facebook, and SAS have gained notoriety

    for moving well beyond the workforce commoditization paradigms that endorsed

    command over human beings (Heilbroner, 1986) toward enacting an evolved or

    enlightened systemic perspective—managing human resource synergy. The

    approach points to an expression of a positive systems approach (i.e., holistic

    perspective) to understand how to maximize the unique qualities of all current

    workplace generations. Such interactive, generationally inclusive human

    resource dimensions positively influence the modern organizational culture, in

    contrast to the cost-oriented staffing practices of the early 20th century.

    Leadership development cannot be separated from the context and culture of

    organizational design (Swensen, Gorringe, Caviness, & Peters, 2016). The

    paradigm shift toward human resource synergy is illustrated clearly by SAS CEO,

    Dr. James Goodnight, who noted that “when 95 percent of a company’s assets

    drive out the front gate every night, the CEO must see to it that they return the

    following day” (Semerciöz et al., 2011).

    Conclusions and Recommendations

    Researchers and practitioners can be certain that the definition and application of

    leadership is changing temporally and reactive to significant systemic

    International Leadership Journal Summer 2019


    generational events and experiences. What is obtainable for the current

    workplace is to help mirror and model the emerging leadership changes

    understood by millennials. Dwyer (2009) offers that “understanding the

    differences may enable management to structure strategies and transformation

    techniques to motivate employees to the full extent of their skills and abilities to

    support the realization of organizational goals and objectives” (101). These

    workers will be the next generation of leaders. Where, then, does the future

    leadership style of the up-and-coming generation stand? According to Gass and

    Bezold (2013):

    leadership must also create a workplace culture that is collaborative and
    empowers employees. Leaders must also show that they respect their
    employees as individuals, openly communicating with them including listening
    respectfully to what they have to say. Finally, [sic] leaders need to be open,
    trusting and ethical. (691)

    Each generation enters the workplace with an understanding and expectation

    of leadership roles and processes based on the summative experiences and

    major events occurring during their respective formative years. Millennials, as

    generations before, arrive in the workplace with a set of predetermined realities

    that have shaped their beliefs and values related to what constitutes leader,

    leading, and leadership. As millennials learn to lead, Ancona and Bresman

    (2018) note that they begin to build knowledge, skills, and applications around a

    set of capabilities: sense-making, relating, visioning, inventing, and building

    credibility. The authors go on to say that building credibility is central to the other

    four. Others (e.g., Groysberg, Lee, Price & Cheng, 2018) suggest that strategy

    and culture are the most important focal points for an organization’s success.

    The media, human rights legislation, and military experience have played a major

    role on each generation’s perceived reality. Millennials’ experiences, media

    shrewdness, civic-mindedness, and collaborative nature will continue to mature,

    transform, and refine. Leaders, researchers, and practitioners can frame the

    future and the resultant redefinition of leadership for this next generation through

    a systems thinking approach, working through conceptualizing strategic issues of

    the organization as offered by the multi-generational leadership model. This

    International Leadership Journal Summer 2019


    model seeks to abandon the commoditization of employees offered by early

    economic and human relations theorists. It further seeks to abandon the

    constructivist cost approach that mechanistically can result in organizational

    cultures focused on managing procedure, paperwork, and processes versus

    creating synergy through people. The synergy caused by generational

    intermingling cannot help but affect employers, sectors, and higher education

    institutions (Helyer & Lee, 2012). Ultimately, the root of trust lies in individual

    relationships, which create an institutional phenomenon beyond interpersonal

    relationships (Semerciöz et al., 2011).

    Temporal context represents the lens through which millennials view

    leadership. An old lens empties the current reality. Millennials are clearly

    rebuffing the norm established by earlier generations (Campione, 2015).

    Consequently, researchers and practitioners alike need to reevaluate and frame

    (Daft, 2013) an Era 5 of leadership, as millennials dynamically role-switch

    between being leaders and followers in pursuit of an egalitarian expression of

    leadership discrete from the role of management. Participatory decision making

    in the Era 5 sharing economy (e.g., Airbnb and Uber) necessitates

    interdependent decisions coupled with highly collaborative interactions pointing

    to a robust leader–follower team dynamic.

    Likewise, it is time to expand stewardship and servant leadership to include

    emergent, grassroots, distributed, and alternating leadership styles as the

    potentialities of the millennial generation’s redefinition of leadership unfolds.

    Researchers must continue to identify and analyze the new generation’s

    predominant leadership paradigm. They must consider an inclusive lens that allows

    for a lessening of hierarchical-based, hero-worshipping leadership expressions. The

    new focus should increase the fluidity of the role exchange between the leader and

    follower. Current generations seek more flexibility when selecting the leader role—

    and equally seek the follower role, as needed, when needed.

    Limitations of the Research and Future Directions

    The above analysis has been limited to North American examples in the

    descriptions of the factors influencing generational cohorts. As Nayar (2013)

    International Leadership Journal Summer 2019


    notes, the challenges of millennial leadership are of more than passing interest to

    economically emerging nations, such as India. The authors propose that similar

    principles may apply in other nations. The specifics of how these will result in and

    influence leadership development requires further examination. China is now

    becoming more powerful, and its new leaders are often media subjects. The

    events and trends that have influenced their mindsets undeniably merit

    investigation. As noted by Wang and Chee (2011), these include the earlier

    Western influences, the Soviet legacy, the Cultural Revolution, the resurgence of

    classic models, and the rise of new entrepreneurs such as Jack Ma of Alibaba. In

    Western Europe (e.g., Germany and the United Kingdom), leadership models

    and the generational influences include the austerities of the 1950s, economic

    revitalization, reunification, Thatcherism, and so forth. The authors hope and trust

    that the research described in this article contributes to the debate as to how to

    view and approach the complex study of leadership development globally.

    Generation Z (also known as iGeneration) includes those born between

    approximately 2000 and 2020. They were not included in this article because

    they have not yet entered the workforce in large numbers. In addition, little peer-

    edited research currently exists on Generation Z. Future research on

    generational differences pertaining to workplace leadership should include the

    most recent generational cohort.


    Alexakis, G. (2011). Transcendental leadership: The progressive hospitality
    leader’s silver bullet. International Journal of Hospitality Management, 30(3),
    708–713. doi:10.1016/j.ijhm.2010.12.005

    Alexander, C. S., & Sysko, J. M. (2012). A study of the cognitive determinants of

    generation Y’s entitlement mentality. Academy of Educational Leadership
    Journal, 16(2), 63–68. Retrieved from

    Allio, R. J. (2012). Leaders and leadership—Many theories, but what advice is

    reliable? Strategy & Leadership, 41(1), 4–14.



    International Leadership Journal Summer 2019


    Ancona, D., & Bresman, H. (2018, November 14). The five key capabilities of
    effective leadership. Leadership & Organisations. Insead. Retrieved from

    Andert, D. (2011). Alternating leadership as a proactive organizational

    intervention: Addressing the needs of the baby boomers, generation Xers and
    millennials. Journal of Leadership, Accountability, and Ethics, 8(4), 67–83.

    Bandura, A. (1971). Social learning theory. New York, NY: General Learning.

    Barbuto, J. E., Jr., & Gottfredson, R. K. (2016). Human capital, the millennial’s

    reign, and the need for servant leadership. Journal of Leadership Studies,
    10(2), 59–63.

    Bass, B. M. (1990). Stogdill’s handbook of leadership: Revised and expanded

    edition. New York, NY: The Free Press.

    Binham, O. T., Kilbourne, S., Jucle, G., Giselli, E., Stogdill, R., & Bennis, U.

    (2018). Leadership as an effective management tool. ТРУДИ, 6.

    Block, P. (1993). Stewardship: Choosing service over self-interest. San

    Francisco, CA: Berrett-Koehler.

    Burns, J. M. (1978). Leadership. New York, NY: Harper & Row.

    Campione, W. A. (2015). Corporate offerings: Why aren’t millennials staying?

    Journal of Applied Business and Economics, 17(4), 60–75.

    Carlile, B. The joke [Video file]. Retrieved from


    Chaturvedi, S., Zyphur, M. J., Arvey, R. D., Avolio, B. J., & Larsson, G. (2012).

    The heritability of emergent leadership: Age and gender as moderating
    factors. The Leadership Quarterly, 23(2), 219–232.

    Colby, S. L., & Ortman, J. M. (2014, May). The baby boom cohort in the United

    States: 2012 to 2060—Population estimates and projections. U.S. Census
    Bureau Current Populations Reports (P25-1141). U.S. Department of
    Commerce Economics and Statistics Administration, U.S. Census Bureau.
    Retrieved from https://www.census.gov/prod/2014pubs/p25-1141

    Conger, J. A., & Kanungo, R. N. (1998). Charismatic leadership in organizations.

    San Francisco, CA: Jossey-Bass.




    International Leadership Journal Summer 2019


    Coomes, M. D., & DeBard, R. (Eds.). (2004). Serving the millennial generation:
    New directions for student services (No. 106). New York, NY: John Wiley &

    Costanza, D. P., & Finkelstein, L. M. (2015). Generationally based differences in

    the workplace: Is there a there there? Industrial and Organizational
    Psychology, 8(3), 303–323. doi:10.1017/iop.2015.15

    Daft, R. L. (with Lane, P. G.). (2011). Management (11th ed.). Mason, OH: South-

    Western Cengage Learning.

    Deal, J. J. (2007). Retiring the generational gap: How employees young and old

    can find common ground. San Francisco, CA: Jossey-Bass.

    Deal, J. J., Altman, D. G., & Rogelberg, S. G. (2010). Millennials at work: What

    we know and what we need to do (If anything). Journal of Business and
    Psychology, 25(2), 191–199.

    Dimock, M. (2019, January 17). Defining generations: Where millennials end and

    generation Z begins. Pew Research Center. Retrieved from

    Dwyer, R. J. (2009). Prepare for the impact of the multi-generational workforce!

    Transforming Government People, Process and Policy, 3(2), 101–110.

    e Cunha, M. P., Rego, A., Clegg, S., & Neves, P. (2013). The case for

    transcendent followership. Leadership, 9(1), 87–106.

    European Parliament. (2013, February 4). Flash mob to end violence against

    women [Video file]. Retrieved from

    Foot, D. K., & Stoffman, D. (1998). Boom, bust and echo: Profiling from the

    demographic shift in the new millennium (2nd ed.). Toronto, Canada:
    MacFarlane, Walter & Ross.

    Frandsen, B. M. (2009, February 1). Leading by recognizing generational

    differences. Long-Term Living, 58(2), 34–35.

    Fry, R. (2018, April 11). Millennials are the largest generation in the U.S. labor

    force. Pew Research Center. Retrieved from

    Defining generations: Where Millennials end and Generation Z begins

    Defining generations: Where Millennials end and Generation Z begins

    Millennials are the largest generation in the U.S. labor force

    Millennials are the largest generation in the U.S. labor force

    International Leadership Journal Summer 2019


    Gandolfi, F., & Stone, S. (2018). Leadership, leadership styles, and servant
    leadership. Journal of Management Research, 18(4), 261–269.

    Gass, E., & Bezold, M. P. (2013). Generation Y, shifting funding structures, and

    health care reform: Reconceiving the public health paradigm through social
    work. Social Work in Public Health, 28(7), 685–693.


    Greenleaf, R. K. (1977). Servant leadership. Mahwah, NJ: Paulist Press.

    Groysberg, B., Lee, J., Price, J., & Cheng, J. Y.-J. (2018). The leader’s guide to
    corporate culture: How to manage the eight critical elements of organizational
    life. Harvard Business Review, 96(1), 44–52.

    Haeger, D. L., & Lingham, T. (2013). Intergenerational collisions and leadership

    in the 21st century. Journal of Intergenerational Relationships, 11(4), 1–18.

    Harris, L., Jr. (2011, July 28). After the baby boom leadership fail. Brain Food.

    Retrieved from http://archives.hypervocal.com/politics/2011/baby-boom-

    Hauschildt, K., & Konradt, U. (2012). The effect of self-leadership on work role

    performance in teams. Leadership, 8(2), 145–168.

    Heilbroner, R. (1986). The nature and logic of capitalism. New York, NY: W. W.


    Helyer, R., & Lee, D. (2012). The twenty-first century multiple generation

    workforce: Overlaps and differences but also challenges and benefits.
    Education + Training, 54(7), 565–578. doi:10.1108/00400911211265611

    Hewlett, S. A., Sherbin, L., & Sumberg, K. (2009). How gen Y & boomers will

    reshape your agenda. Harvard Business Review, 87(7/8), 71–76.

    Houlihan, A. (2007, November). How to effectively lead different generations in

    the workplace. Reliable Plant. Retrieved from

    Howe, N., & Strauss, W. (2007). The next 20 years: How customer and

    workforce attitudes will evolve. Harvard Business Review, 85(7/8), 41–52.

    Howe, N., & Strauss, W. (2003). Millennials go to college (2nd ed.). Great Falls,

    VA: Lifecourse Associates.

    Hughes, T., & Fiehl, S. (2013, October). Talking ‘bout my generation. Inside




    International Leadership Journal Summer 2019


    Learning Technologies & Skills. 45–46. Retrieved from

    Izea. (2019). Influencer marketing: Top millennial influencers. Retrieved from


    Karakas, F., Manisaligil, A., & Sarigollu, E. (2015). Management learning at the

    speed of life: Designing reflective, creative, and collaborative spaces for
    millennials. The International Journal of Management Education, 13(3), 237–

    Katz, D., & Kahn, R. L. (1978). The social psychology of organizations (2nd ed.).

    New York, NY: Wiley.

    Kaufman, P. (Producer & Director). (1993). Rising sun [Motion picture]. United

    States: 20th Century Fox.

    Kefalas, A. G. (2011). On systems thinking and the systems approach. World

    Futures: The Journal of New Paradigm Research, 67(4–5), 343–371.

    Kets de Vries, M. F. R. (2019, January 18). Have we reached the limit of

    individualism? [Web log post]. INSEAD.

    Kezar, A. (2011). Grassroots leadership: Encounters with power dynamics and

    oppression. International Journal of Qualitative Studies in Education, 24(4),

    Kowske, B. J., Rasch, R., & Wiley, J. (2010). Millennials’ (lack of) attitude

    problem: An empirical examination of generational effects on work attitudes.
    Journal of Business and Psychology, 25(2), 265–279.

    Lancaster, L. C., & Stillman, D. (2002). When generations collide: Who they are.

    Why they clash. How to solve the generational puzzle at work. New York, NY:

    Lemoine, G. J., Hartnell, C., & Leroy, H. (2019). Taking stock of moral

    approaches to leadership: An integrative review of ethical, authentic, and
    servant leadership. Academy of Management Annals, 13(1), 148–187.

    Levenson, A. R. (2010). Millennials and the world of work: An economist’s

    perspective. Journal of Business and Psychology, 25(2), 257–264.






    International Leadership Journal Summer 2019


    Ling, Q., Lin, M., & Wu, X. (2016). The trickle-down effect of servant leadership

    on frontline employee service behaviors and performance: A multilevel study
    of Chinese hotels. Tourism Management, 52, 341–368.

    Macky, K., Gardner, D., & Forsyth, S. (2008). Generational differences at work:

    Introduction and overview. Journal of Managerial Psychology, 23(8), 857–

    Malik, N. (2014). Revolutionizing the revolution: An examination of social media’s

    role in the Egyptian Arab spring (Undergraduate honors thesis). Retrieved
    from the Undergraduate Honors Thesis Collection of the Digital Commons at
    Butler University (Paper 197). Retrieved from

    Massey, M. (1979). The people puzzle: Understanding yourself and others.

    Reston, VA: Reston.

    Meister, J. C., & Willyerd, K. (2010, May 1). Mentoring millennials. Harvard

    Business Review. Retrieved from https://hbr.org/2010/05/mentoring-

    Michel, J. S., & LeBreton, J. M. (2010). Leadership coherence: An application of

    personality coherence theory to the study of leadership. Personality and
    Individual Differences, 50(5), 688–694. doi:10.1016/j.paid.2010.12.018

    Mingers, J., & White, L. (2010). A review of the recent contribution of systems

    thinking to operational research and management science. European Journal
    of Operational Research, 207(3), 1147–1161. doi:10.1016/j.ejor.2009.12.019

    Moldoveanu, M., & Narayandas, D. (2019). The future of leadership

    development. Harvard Business Review, 97(2), 40–48.

    Myers, K. K., & Sadaghiani, K. (2010). Millennials in the workplace: A

    communication perspective on millennials’ organizational relationships and
    performance. Journal of Business and Psychology, 25(2), 225–238.

    Naseer, S., Raja, U., Syed, F., Donia, M. B., & Darr, W. (2015). Perils of being

    close to a bad leader in a bad environment: Exploring the combined effects of
    despotic leadership, leader member exchange, and perceived organizational
    politics on behaviors. The Leadership Quarterly, 27(1), 14–33.

    Nayar, V. (2013). Handing the keys to Gen Y. Harvard Business Review, 91(5),





    International Leadership Journal Summer 2019



    Ng, E. W., & Gossett, C. W. (2013). Career choice in Canadian public service: An

    exploration of fit with the millennial generation. Public Personnel
    Management, 42(3), 337–358. doi:10.1177/0091026013495767

    Nicholas, A., & Lewis, J. (2008). Millennial attitudes toward books and e-books.

    Faculty and Staff—Articles & Papers, Digital Commons at Salve Regina
    University. Retrieved from

    Palaima, T., & Skaržauskienė, A. (2010). Systems thinking as a platform for

    leadership performance in a complex world. Baltic Journal of Management,
    5(3), 330–355.

    PBS News Hour. (2019). Politics. The game for 2020 Democrats: wooing

    millennials. Retrieved from https://www.pbs.org/newshour/politics/the-game-

    Peck, E. (2016, February 2). The dreaded annual performance review inches

    closer to extinction. Huffington Post. Retrieved from

    Perry, K. (2010, October 28). Firework [Video file]. Retrieved from


    Raines, C. (2013, March 14). Claire Raines on 10 predictions for Generation Z

    [Web log post]. AMACOM Books Blog. Retrieved from

    Reinhardt, E. (2010). The challenge of managing a multigenerational workplace.

    The Business Journal, 24(21), 1-5.

    Rhodes, S. R. (1983). Age-related differences in work attitudes and behavior: A

    review and conceptual analysis. Psychological Bulletin, 93(2), 328–367.

    Robertson, D., & Henderson, T. (2018, April 26). The United States of

    millennials. POLITICO Magazine. Retrieved from

    Sahadi, J. (2007, August 29). CEO pay: 364 times more than workers.

    CNNMoney.com. Retrieved from







    Claire Raines on 10 Predictions for Generation Z

    Claire Raines on 10 Predictions for Generation Z



    International Leadership Journal Summer 2019



    Salahuddin, M. M. (2010). Generational differences impact on leadership style

    and organizational success. Journal of Diversity Management, 5(2), 1–6.

    Samuelson, R. J. (2010, March 15). The real generation gap: Young adults are

    getting slammed. Newsweek, 155(11).

    Semerciöz, F., Hassan, M., & Aldemir, Z. (2011). An empirical study on the role

    of interpersonal and institutional trust in organizational innovativeness.
    International Business Research, 4(2), 125–136. doi:10.5539/ibr.v4n2p125

    Sessa, V. I., Kabacoff, R. I., Deal, J., & Brown, H., (2007). Generational

    differences in leader values and leadership behavior. The Psychologist-
    Manager Journal, 10, 47–74.

    Silverman, D. (2011). Interpreting qualitative data (4th ed.). London, United

    Kingdom: Sage.

    Stacey, R. D., Griffin, D., & Shaw, P. (2000). Complexity and management: Fad

    or radical challenge to systems thinking? East Sussex, United Kingdom:
    Psychology Press.

    St. Clair, D. P., Hunter, G. K., Cola, P. A., & Boland, R. J. (2018). Systems-savvy

    selling, interpersonal identification with customers, and the sales manager’s
    motivational paradox: A constructivist grounded theory approach. Journal of
    Personal Selling & Sales Management, 38(4), 391–412.

    Stogdill, R. M. (1950). Leadership, membership and organization. Psychological

    Bulletin, 47(1), 1–14. doi:10.1037/h0053857

    Stogdill, R. M., & Bass, B. M. (1981). Stogdill’s handbook of leadership: A survey

    of theory and research. New York, NY: Free Press.

    Sullivan, K. (2013). Prepare your organization now for the workforce of the future.

    Nonprofit Business Advisor, 285, 1.

    Swensen, S., Gorringe, G., Caviness, J., & Peters, D. (2016). Leadership by

    design: Intentional organization development of physician leaders. Journal of
    Management Development, 35(4), 549–570. doi:10.1108/JMD-08-2014-0080

    Teece, D. (2018). Dynamic capabilities as (workable) management systems

    theory. Journal of Management & Organization, 24(3), 359–368.



    International Leadership Journal Summer 2019



    Tesone, D. V. (2003). The leadership cat with the management hat. Upper

    Saddle River, NJ: Pearson Custom.

    Thomas, D., & Rahschulte, T. (2018). The moderating effects of power distance

    and individualism/collectivism on empowering leadership, psychological
    empowerment, and self-leadership in international development
    organizations. International Leadership Journal, 10(3), 3–39.

    Thomas, P. (2008, December 16). Case closed: Police ID Adam Walsh killer.

    ABC News. https://abcnews.go.com/Archives/video/dec-16-2008-adam-

    Tulgan, B. (2011). Generation Y: All grown up and now emerging as new

    leaders. Journal of Leadership Studies, 5(3), 77–81. doi:10.1002/jls.20237

    Ungerleider, N. (2011, January 25). Massive Egyptian protests powered by

    YouTube, Twitter, Facebook, Twitpic. Fast Company. Retrieved from

    Uygur, C. (2016). Why millennials love Bernie Sanders. The Huffington Post.

    Retrieved from http://www.huffingtonpost.com/cenk-uygur/why-millennials-

    Wang, B. X., & Chee, H. (2011). Chinese leadership. Basingstoke, United

    Kingdom: Palgrave MacMillan.

    Weinbaum, C., Girven, R. S., & Oberholtzer, J. (2016). The millennial generation:

    Implications for the intelligence and policy communities. RAND Corporation.

    Wells, D. (2018, December 21). Florida cops break up, then join flash mob at

    shopping mall. Retrieved from https://fox13now.com/2018/12/21/florida-cops-

    Williams, K. C., & Page, R. A. (2011). Marketing to the generations. Journal of

    Behavioral Studies in Business, 3(1), 1–17.

    Williams, R. B. (2007, February 21). Gen X will change work culture. National

    Post, p. 3.

    Zabel, K. L., Biermeier-Hanson, B. J., Baltes, B. B., Early, B. J., & Shepard, A.

    (2017). Generational differences in work ethic: Fact or fiction? Journal of
    Business and Psychology, 32(3), 301–315. doi:10.1007/s10869-016-9466-5









    International Leadership Journal Summer 2019


    Zemke, R., Raines, C., & Filipczak, B. (1999). Generations at work: Managing

    the clash of veterans, boomers, Xers, and nexters in your workplace. Toronto,
    Canada: AMACOM.

    Darlene Andert, EdD, is retired from the faculty of Florida Gulf Coast University’s Lutgert
    College of Business in Fort Myers, although she continues to conduct research. She
    earned an EdD in Human and Organizational Development from The George Washington
    University. Dr. Andert’s research interests include leadership, group dynamics, and
    organizational development with a focus on human resource development. She can be
    reached at dandert@fgcu.edu.

    George Alexakis, EdD, is a full professor at Florida Gulf Coast University’s Lutgert
    College of Business in Fort Myers. His leadership background includes administrative,
    consulting, and operations positions with academic institutions, corporate organizations,
    and small business partnerships. He has provided management training for a variety of
    organizations domestically and internationally. Dr. Alexakis is currently conducting
    research in the areas of transcendental leadership, organizational dynamics, and
    pedagogical advancements as they relate to the management disciplines. He can be
    reached at galexaki@fgcu.edu.

    Robert C. Preziosi, DPA, is a professor emeritus of leadership and human resources
    management in the Wayne Huizenga College of Business and Entrepreneurship at Nova
    Southeastern University with the Public Administration Department. In December 2000,
    her was named Professor of the Decade by the school. In a recent book, North American
    Adult Educators, he was named 1 of 50 quintessential adult educators of the 21st century.
    He is on the editorial boards of Employment Relations Today, the Journal of Applied
    Management and Entrepreneurship, and the Employee Responsibilities and Rights
    Journal. Six of his books are on the shelves at the Baker Library at the Harvard Business
    School. He can be reached at preziosi@nova.edu.




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    Research Article
    Construction of the Enterprise Human Resource Quality
    Evaluation System Based on the WICS Leadership Mode


    Na Zong

    Huaxin College of Hebei Geo University, Hebei 050700, China

    Correspondence should be addressed to Na Zong; 18407233@masu.edu.cn

    Received 31 March 2022; Revised 23 April 2022; Accepted 6 May 2022; Published 7 June 2022

    Academic Editor: Vijay Kumar

    Copyright © 2022 Na Zong. ­is is an open access article distributed under the Creative Commons Attribution License, which
    permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

    With the advancement of society and economy, the market competition among various businesses has become increasingly �erce.
    Nowadays, if businesses want to grow in the face of adversity, they must move forward boldly and make use of abundant human
    resources to fuel their growth. Human resource management is becoming increasingly important. As a result, this paper develops
    an enterprise human resource quality assessment system based on the WICS leadership model. ­e di�erences between the WICS
    model and the traditional management model are �rst compared in this paper. ­e requirements of the WICS model in human
    resource management are then described. Furthermore, this paper proposes a human resource evaluation algorithm that
    combines data-driven and WICS models to address the current human resource cost evaluation algorithm’s low accuracy and
    poor e�ect. ­e simulation results show that the proposed algorithm can re�ect changing human resource cost characteristics,
    improve human resource cost evaluation results, and obtain better results than other human resource cost evaluation models and
    has a wide range of applications.

    1. Introduction

    ­e term “leadership” is increasingly being used as a new
    term in corporate human resource quality evaluations
    (EHRQA). A model with strong leadership can help busi-
    nesses attract talent, reduce internal con�ict at work, boost
    productivity, and foster a positive work environment. ­e
    corporate market is becoming increasingly competitive as
    the information age progresses [1–6]. ­ere is de�nitely a
    struggle for talent and resources going on behind the scenes
    of this matchup. ­e loss of enterprise talents has emerged as
    a signi�cant factor impeding the development of businesses.
    ­e factors that have contributed to this occurrence warrant
    careful investigation. ­e EHRQA technique, which is part
    of the leadership model, is being implemented progressively
    in order to alter the old talent management strategy.
    Comparing the leadership model to the typical EHRQA
    approach, the leadership model places a greater emphasis on
    the applicability of employees to the organization and pays
    more attention to the workability and performance of
    employees while at work [7–12]. Evaluating employees’

    initiative, creativity capacity, and cooperation ability, among
    other traits, allows them to maximize their own initiative
    and maximize the value of their own abilities, thus enabling
    the �rm to enter a new stage of development [13, 14].

    Davi-Mc Clelland, a Harvard University professor, was
    the �rst to introduce the concept of leadership, which was in
    1973. According to any traits that can be consistently
    measured or counted, the notion of leadership refers to a
    sharp division between outstanding and ordinary people at
    work, which can be measured or tallied [15–17]. Examples of
    such divisions include work motivation, workplace attitude
    or values, personality traits and cognition as well as self-
    image, expertise in a speci�c subject, professional abilities,
    and so on. We must identify and separate outstanding
    performers from those who perform below average, reuse
    outstanding leaders, and scienti�cally cultivate ordinary
    ability workers, so that they can assume leadership roles.
    Currently, the concept of the leadership model is based on
    leadership, which means that enterprises place a strong
    emphasis on analyzing the leadership level of employees as
    well as job requirements, and the standards for human

    Mathematical Problems in Engineering
    Volume 2022, Article ID 3259403, 8 pages





    resource employees are defined in terms of “quality” and
    measured in terms of “quantity.”

    +e cost of human resources is a significant component
    of EHRQA [18, 19]. If the cost of human resources cannot be
    accurately estimated, it will result in a significant waste of
    human resources, a huge number of lost manpower op-
    portunities, and an increase in the operating costs of the
    organization as a result. +e cost of EHRQA is therefore
    directly tied to the survival of the organization, and the study
    in EHRQA is of significant importance [20–25].

    Despite this, some businesses, particularly in China, fail
    to factor in human resource costs when making operational
    decisions. Due to the planned economy’s influence, many
    enterprises’ ideas and concepts have lagged behind tech-
    nological advancements, highlighting the EHRQA problem.
    Over the last decade, the EHRQA problem has garnered
    increasing attention from domestic research institutions and
    scholars, resulting in the development of a large number of
    EHRQA algorithms [26–31]. +e majority of EHRQA is
    completed manually, which is the most prevalent method.
    Because of the presence of human elements and the poor
    objectivity of the evaluation results, the EHRQA results are
    blind to a certain extent and it is difficult to acquire the ideal
    EHRQA results. With the existence of human factors and
    poor objectivity of the assessment findings, using EHRQA
    algorithms such as the gray model and neural network, some
    researchers have argued that by defining EHRQA indicators,
    collecting matching EHRQA data, and developing EHRQA
    models, they can get better outcomes than manual tech-
    niques in terms of EHRQA results. +e research has pro-
    gressed to the point where data on human resource costs has
    been amassed, and a considerable amount of EHRQA data
    has emerged, which serves as the foundation for data mining
    in the field of human resource cost assessment. Chaos theory
    is a data-driven strategy that may be used to extract the
    changing characteristics of situations from large amounts of
    data. It is also a new technology for EHRQA modeling that is
    being developed [32, 33].

    +e continual appearance of quality difficulties in my
    country’s economic market has intensified people’s attention
    to quality management, resulting in the notion of overall
    quality management becoming more prevalent as the times
    have demanded it [34–37]. Personnel have a considerable
    impact on the output quality of enterprise products and
    services, and EHRQA has emerged as an important com-
    ponent of total quality management practice in the process.
    Due to the short time span in which comprehensive quality
    management has been implemented in human resource
    management, there are still some issues that need to be
    addressed. Because of this, it is critical to investigate the role
    of human resource management in the process of com-
    prehensive quality management [38–40].

    +is paper proposes a data-driven EHRQA algorithm in
    order to improve EHRQA’s accuracy. +e results of this
    paper demonstrate that the algorithm can accurately capture
    the changing characteristics of human resource costs, im-
    prove the results of human resource cost assessment, and
    outperform existing human resource cost assessment
    models (DDW).

    2. The Specific Application of the Leadership
    Model in EHRQA

    Adopting the leadership model provides a new perspective
    and a solid foundation for the EHRQA’s work, clarifies the
    human resources departments’ fundamental responsibilities,
    and establishes a solid foundation for enterprise develop-
    ment. Meanwhile, it provides a solid foundation for the
    company’s personnel recruiting, job assignment, employee
    training and development, promotion and reward, and other
    activities, and it heralds the start of a new era in human
    resource development.

    2.1. Employee Recruitment. When hiring personnel under
    the traditional paradigm, businesses place a greater emphasis
    on evaluating candidates’ academic qualifications as well as
    their expertise and abilities. As a result, such inspections
    have the disadvantage of not delving deeply into the char-
    acteristics of employees, which is negative, because both
    internal character qualities and employee characteristics are
    in the process of long-term development. Marketing social
    positions are tough to adapt to for people with avariety of
    personality types, such as quiet and sensitive personalities,
    who have received extensive long-term training in a timely
    manner. As a result, if deep-level features of employees are
    ignored, even long-term employee training and investment
    training may be ineffective in retaining personnel. +is is a
    significant waste of training resources for businesses. +e
    features of WICS talents, on the other hand, are taken into
    consideration throughout the selection process. Regarding
    employees, we thoroughly investigate their fundamental
    requirements and features, pay close attention to their fit for
    certain positions, ensure that employees can find their dream
    employment, and limit the waste of training resources
    caused by high employee turnover.

    2.2. Work Assignment. +e traditional job assignment is
    based on a lack of available positions in the organization and
    is centered on affairs, whereas the employee job assignment
    under the guidance of the leadership model is centered on
    observing the components of the work, analyzing the
    characteristics of leadership, and evaluating the performance
    of employees, among other things. Leadership tasks are
    related to their positions in order to more effectively identify
    talent and develop appropriate career planning and com-
    pensation designs for employees.

    2.3. Staff Training. Employee training in the traditional
    sense is primarily concerned with introducing employees to
    the job topic and improving their workability. In accordance
    with the new model, employee training is based on the
    principle of people-centeredness. +is company provides
    employees with specialized training that is based on their
    own quality conditions, as well as training that is tailored to
    their own personal development. It assists employees in
    enhancing their own deficiencies while simultaneously re-
    ducing training requirements. +e time-consuming steps of

    2 Mathematical Problems in Engineering

    the content, increased publicity and training of corporate
    culture, and instilling a strong feeling of professional belief
    and work confidence in new employees are all important

    2.4. Performance Appraisal. +e fundamental criterion of
    the leadership model is the ability to discriminate between
    the signs of exceptional talent and those of ordinary talent.
    One must establish performance appraisal indicators on the
    basis of this information, improve performance appraisal
    standards by making them more scientific and standardized,
    and implement systematic performance appraisal standards
    to more accurately reflect employees’ work performance,
    allowing outstanding employees to be recognized and
    rewarded in a timely manner, as well as being beneficial to
    employees and motivating and increasing the motivation of
    the employees.

    2.5. Career Promotion. Achieving career advancement is
    something that every corporate employee hopes and expects
    to happen. It is the direct result of the employees’ efforts, and
    it signifies that the employees’ abilities and professional
    development have advanced to a new level. It is beneficial in
    inspiring people to enhance their work abilities, to actively
    work hard, and to contribute to the improvement of the
    overall competitiveness of the organization.

    2.6. Future-Oriented EHRQA. Traditional EHRQA has the
    issue of being static and backward-looking, which makes it
    ineffective. It focuses on the historical job performance, as
    well as the performance of job seekers and employees in the
    future. One’s prior success, on the other hand, cannot be
    compared to his potential contribution to the organization
    in the future. Good performance in the previous year does
    not necessarily imply great ability nor does it imply that the
    employee will be able to adapt to the company’s future
    strategy and culture and continue producing and contrib-
    uting in the same manner in the future. It is vital to im-
    plement a future-oriented human resource assessment in
    order to increase the organization’s strategic flexibility.

    Future-oriented personnel evaluation is not simply a
    reversal of traditional evaluation; rather, it is a transfor-
    mation of traditional evaluation. It necessitates not only the
    evaluation of past and present performance but also the
    evaluation of the behavior of obtaining performance and
    then the inference of the assessee’s ability to adapt to the
    future from the behavior performance of the assessee.
    Traditional techniques of personnel selection are concerned
    with determining the degree to which the candidate’s
    existing knowledge, ability, and experience matches the
    degree of knowledge, ability, and experience required by the
    target position, and using this information as the selection
    criteria. While there is nothing wrong with selecting talents
    in this manner in order to quickly adapt to job requirements
    when the external business environment is relatively stable,
    when the external business environment is constantly
    changing, or when the company is in a stage of rapid

    development, it is possible to select talents in accordance
    with the requirements of existing positions. It will diminish
    the adaptability of the organization, which means that it will
    reduce the firm’s strategic flexibility as well.

    Companies must consider the demand for talents for the
    role in the future when hiring, and they must select job
    applicants based on the talent requirements required for
    future opportunities in order to increase strategic flexibility.
    Nokia Corporation had hidden concerns about the unex-
    pected future instability of the industry at the beginning of
    this century in the consideration and strategic layout of
    talent selection, and it used this as a starting point. People
    who are adaptable to future development and change have
    been identified as the primary target of talent recruitment.
    Instead of focusing on the most competent talents available
    at the time, this strategy allows the company to make swift
    adjustments when faced with organizational changes and
    significant changes in the industrial environment, thereby
    avoiding the creation of a talent crisis in the first place.

    How is a future-oriented human resource assessment
    conducted? By establishing standards, such as the Nokia’s
    “two-dimensional” model, the universal competency model
    based on future change and development, as well as industry
    and organizational characteristics, lays the groundwork for
    assessment. +e organization’s use of a professional com-
    petency model enables it to conduct a future-oriented hu-
    man resource evaluation. Human resource evaluation in the
    future requires enterprise managers to have strong strategi


    analysis capabilities. +ey can contribute actively to strategy
    formulation and analysis of the organizational environment.
    Businesses should focus on predicting the evaluation object’s
    ability and performance in future situations from a timely
    perspective when conducting talent evaluation activities
    such as recruitment, selection, and assessment, following the
    establishment of talent evaluation standards. Following the
    evaluation, it is critical to adjust the prediction level of the
    evaluation standard in accordance with actual employee
    performance in order to improve the forecast’s accuracy and
    thus the evaluation’s effectiveness.

    2.7. 1e Importance of Establishing a Training System.
    Enterprises have largely recognized the necessity of training,
    but the majority of training sessions are conducted on an
    emergency basis, frequently in response to group difficulties
    in management or when performance has been slow for a
    lengthy period of time. Retraining is an after-the-fact
    remedy when it comes to increasing strategic flexibility,
    according to this approach. If a company wishes to achieve
    “longevity,” the concept of adapting to “cure” is a “disease-
    prevention” approach, which involves developing a forward-
    looking training system and strengthening the strategic
    flexibility of the company, resulting in a driving force for
    long-term development.

    +e forward-looking training system is comprised of two
    components. +e first component is forward-looking
    training based on personal development, and the second
    component is forward-looking training based on organi-
    zational change. Employees’ knowledge of their own

    Mathematical Problems in Engineering 3

    personal development may increase, which may lead to a
    demand for training in the form of a work transfer, job
    promotion, or job skills enhancement as a result of this
    increased awareness. After comparing the existing wor


    skills of employees with the potential future work skills
    requirements, businesses can �nally determine their training
    requirements. In some circumstances, even though the
    current work performance of employees is satisfactory to the
    �rm, there is still a gap between the requirements of the
    organization’s plan and the current work performance of
    employees, which must be addressed in advance through

    Organizational change occurs as a result of a variety of
    factors, including competition and technological innovation,
    stagnation in industry development, strategic goal adjust-
    ment, the evolution of the enterprise life cycle, and natural
    and man-made disasters. Human resource managers can
    anticipate this transition to the greatest extent possible,
    enabling them to provide training support with a high
    degree of match during the strategic planning and pro-
    motion stages. Investing in this type of forward-thinking
    training bene�ts the organization by assisting and pro-
    moting the development and implementation of the overall
    plan. It contributes to employees’ long-term ability and
    competency development.

    3. The DDW Model

    ­is paper proposes the DDW model for EHRQA. ­e
    model structure is shown in Figure 1.

    EHRQA concerns are in�uenced by a variety of factors,
    including human resource introduction policies and in-
    centives, as well as the operational state of the business. ­e
    implicit change trend provides a credible foundation for
    human resource cost assessment modeling. Chaos theory is a
    widely used data-driven method. It is possible to invert the
    changing trend of human resource costs using phase space
    reconstruction technology, and a learning sample of human
    resource costs can be generated. As a result of the experi-
    ment, chaos theory is applied to human resource cost data in
    this work and a multidimensional human resource cost time
    series is created.

    Let the historical EHRQA data of a certain enterprise be
    x(tj), j � 1,2, · · · ,n{ }, n represents the number of EHRQA
    samples, and the original EHRQA data is transformed into a
    more accurate EHRQA data by determining the delay time λ
    and the embedding dimension m of x(tj). Regular EHRQA
    cost data is as follows:

    X(l) �[x(l),x(l + λ), · · · ,x(l +(m − 1)λ)],
    l � 1,2, · · · ,M.


    According to the results of the analysis of (1), the values
    of the variables λ and m of x(tj) are extremely important for
    accurately estimating the cost of human resources, where λ
    represents the time interval between data points and m
    represents that multiple data points are related to the current
    human resource cost. ­e optimal value λ of human resource
    cost data should be determined using the CC method, and

    the optimal value m of human resource cost should be
    determined using the CAO algorithm.

    ­e steps to determine λ are as follows:

    (1) Setting two EHRQA data as X(i) � [x(i), x(i + λ),
    · · · ,x(i + (m − 1)λ)] and X(j) � [x(j), x(j + λ),
    · · · ,x(j + (m − 1)λ)], the distance between them is

    rij �‖X(i) − X(j)‖. (2)

    (2) When calculating the value of a human resource cost
    assessment, the critical radius r is used to de�ne its
    valid range, the data points within the critical radius
    are statistically sensitive, the logarithmic ratio of
    statistical data points by the associated integral is
    used, and the calculation formula is as follows:

    C(m,N,r,λ) �

    M(M − 1)

    H(r − ‖X(i) − X(j)‖), (3)

    where H(r − ‖X(i) − X(j)‖) is

    H(x) �


    { . (4)

    In accordance with the critical radius, we divide the
    complete EHRQA data set into t subhuman resource cost
    assessment data sequences, with the following results:

    S(m,r,λ) �




    Cl(m,r,λ) − Cl(m,r,λ)[ ]

    m{ }. (5)

    ­e di�erence between the data is

    ΔS(m,l) � max S m,rj,λ( )[ ] − min S m,rj,λ( )[ ]. (6)


    Human resource cost data

    Chaos Analysis

    Training set

    Extreme training set

    Particle Swarm
    Optimization Algorithm


    whether to quit



    Figure 1: structure of our framework.

    4 Mathematical Problems in Engineering

    ΔS(l) �


    ΔS(m,t). (7)

    IfΔS(l) gets the �rst minimum value, it means that the λ
    value at this time is the optimal EHRQA data delay time.

    ­e steps to determine m are as follows: the ith
    reconstructed human resource cost assessment data is
    Xi(m + 1), and its nearest neighbor EHRQA data vector is
    Xn(i,m)(m + 1); then,

    α(i,m) �
    Xi(m + 1) − Xn(i,m)(m + 1)

    Xi(m) − Xn(i,m)(m)


    i � 1,2, · · · ,N − mλ.



    E(m) �

    N − mλ

    N− mλ

    α(i,m). (9)

    Suppose there are k EHRQA data in total, they form a
    data set Sk (xp,tp){ }


    p�1, and the EHRQA data set after

    chaotic processing is xp � xp,xp+1, · · ·xp+m− 1{ }

    wheretp � xp+m and m is the embedding dimension; we can

    βTk βk +


    εTk ε( ),

    s.t. tp � ∑

    βkf αkxp + bk( ) − εkp � 1,2, · · ·k

     .



    l w,ε,βk( ) �
    βTk βk +


    εTk ε − w Hkβk − Tk − ε( ),

    s.t. tp � ∑

    βkf αkxp + bk( ) − εkp � 1,2, · · · ,k

     .



    β1 βi βL

    Output Node

    L Hidden Nodes

    n Input Nodes


    (ai, bi)

    l n


    Figure 2: ­e structure of ELM.

    0 50 100 150 200











    Figure 3: Comparison of di�erent models.

    Mathematical Problems in Engineering 5

    Let the partial derivative of (11) be 0; then,


    ⟶ βTk � wHk


    ⟶ cεT + w � 0


    ⟶Hkβk − T − ε � 0


    . (12)

    Let t and x be the input and output of EHRQA, re-
    spectively; then the extreme learning machine of EHRQA is

    t � ∑

    βkf αkx + bk( ). (13)

    ­e extreme learning machine is shown in Figure 2.

    4. Results

    For the purpose of evaluating the performance of the DDW
    algorithm, an EHRQA data set has been selected as the study
    object. ­ere are a total of 300 data points in this set, which
    have been normalized to the range of 0–1, as shown in
    Figure 3. As a validation set, we choose 100 data points.

    Figure 3 illustrates how chaos theory was used to obtain
    the EHRQA data. ­is demonstrates that the EHRQA data
    exhibits a certain degree of temporal correlation, as indi-
    cated by the ideal EHRQA data with T�5 and m�5. The
    original EHRQA data is processed and normalized to the
    time interval T�5 and m�5. ­ere are EHRQA learning
    samples available.

    Figure 4 and 5 illustrate EHRQA results obtained using
    the algorithm described in this article. As can be seen, the
    model presented in this research is capable of accurately
    predicting the changing trend in EHRQA and producing

    0 1 9 17 25 33 41 49 57 65 73 81 89 97 10














    Train predict

    Figure 4: Train results.


    1 5 9 13 17 21 25 29 33 37 41 45 49 53 57 61 65 69 73 77 81 85 89 93 97

    Train predict

    Figure 5: Test results.

    6 Mathematical Problems in Engineering

    near-perfect EHRQA results. ­e �ndings demonstrate that
    chaos theory and extreme learning can be integrated into
    EHRQA research and that the resulting human resource cost
    assessment is reliable.

    As part of the e�ort to make the experimental �ndings
    of the human resource cost assessment algorithm in this
    study comparable, the BPNN, ARIMA, and SVM algo-
    rithms are utilized as comparison algorithms and the RMSE
    and MAPE algorithms are employed to evaluate EHRQA
    results, respectively. ­e comparison results are shown in
    Figure 6.

    It can be seen that the DDW method outperforms the
    other methods on both metrics. Among the four methods,
    BPNN has the worst performance, followed by SVM, and
    ARIMA is slightly lower than the DDW model.

    5. Conclusion

    ­e enterprise-wide human resource quality assessment
    (EHRQA) is a critical metric for determining an organi-
    zation’s human resource management e�ectiveness. Due to
    the fact that EHRQA is dependent on factors such as �-
    nancial resources, reputation, personnel age and degree
    structure, and other criteria, as well as the local human
    resources introduction policy, the EHRQA procedure is
    quite complex. To improve the accuracy of EHRQA, an
    algorithm based on the data-driven and WICS leadership
    model is developed and chaos theory is incorporated to
    analyze the original data for the human resource cost as-
    sessment and establish human resource costs. We evaluate
    the learning samples to ascertain the underlying charac-
    teristics of the data’s variance. In addition, the extreme
    learning machine is used to learn the data for the human
    resource cost evaluation, and as a result of this learning, the
    human resource cost evaluation algorithm is developed. ­is
    is the outcome of the cost-bene�t analysis of human

    Data Availability

    ­e data sets used to support the �ndings of this study are
    available from the author upon request.

    Conflicts of Interest

    ­e author declares no con�icts of interest.


    ­e paper was supported by (1) Human Resources and Social
    Security Issues in the Hebei Province, the Research on the
    Optimization of Human Resource Management Professional
    Talent Training Model from the Perspective of the Big
    Data—Taking Huaxin College as a case study, under JRSHZ-
    2021-01077, and (2) Human Resources and Social Security
    Issues in the Hebei Province, Discussion on the Path of
    Improving the Young Teachers Instructional Leadership in
    Colleges and Universities in the Hebei Province, under


    [1] R. Mishra, R. Singh, and T. Papadopoulos, “Linking Digital
    Orientation and Data-Driven Innovations: A SAP-LAP
    Linkages Framework and Research propositions,” IEEE
    Transactions on Engineering Management, vol. 2, pp. 1–13,

    [2] K. J. Feldman, M. Lopez, and M. Gagliardi, “Using a data-
    driven organizational improvement model to engage an in-
    terdisciplinary team in transforming a public women’s health
    clinic,” Patient Experience Journal, vol. 1, no. 2, pp. 87–93,

    [3] L. Zheng, “Optimize CSCL activities based on a data-driven
    approach,” Lecture Notes in Educational Technology, Data-
    Driven Design for Computer-Supported Collaborative Learn-
    ing, Springer, Berlin, Germany, pp. 147–162, 2021.

    [4] J. L. Barton, Development and Initial Validation of a Measure
    for Early Childhood Program Readiness for Data Driven De-
    cision[D], University of Kansas, Lawrence, KS, USA, 2019.

    [5] L. Cao and C. Zhang, “Domain-driven data mining,” Inter-
    national Journal of Data Warehousing and Mining, vol. 2,
    no. 4, pp. 49–65, 2006.

    [6] O. Sadovskyi, T. Engel, R. Heininger, and M. Böhm, “Analysis
    of Big Data Enabled Business Models Using a Value Chain
    perspective,” in Proceedings of the Multikonferenz Wirt-
    schaftsinformatik (MKWI 2014), pp. 1126–1137, Tagungs-
    band, February 2014.

    [7] H. L. Crenshaw, What Matters in Data Use: Examining Equity
    and Data Driven Decision Making in Diverse Elementary
    schools[D], University of Illinois at Urbana-Champaign,
    Champaign, IL, USA, 2016.

    [8] S. R. Carroll, D. Rodriguez-Lonebear, and A. Martinez,
    “Indigenous data governance: strategies from United States
    native nations[J],” Data Science Journal, vol. 18, 2019.

    [9] C. Jenkins, E. Harris, B. Krumm, and K. Curry, “Cultivating a
    global mindset in leadership preparation: contextual impli-
    cations[J],” Journal of International Economic Law, vol. 2,
    no. 3, p. n3, 2012.

    [10] E. Haak, J. Ubacht, M. Van den Homberg, S. Cunningham,
    and B V D. Walle, “A Framework for Strengthening Data
    Ecosystems to Serve Humanitarian purposes,” in Proceedings
    of the 19th annual international conference on digital gov-
    ernment research: governance in the data age, pp. 1–9, New
    York, NY, USA, 2018.








    Figure 6: Comparison of RMSE and MAPE with di�erent

    Mathematical Problems in Engineering 7

    [11] X. Tapia, “Effective Initiative Development and Implementa-
    tion in Higher Education: A Case of the Excellence in Part-
    nerships Initiative in the College of Business,” Administration
    at California State Polytechnic University, vol. 5, 2018.

    [12] K. M. Augustyniak, “Identifying and cultivating leadership
    potential in school psychology: a conceptual framework,”
    Psychology in the Schools, vol. 51, no. 1, pp. 15–31, 2014.

    [13] N. H. Arzt and S. M. Salkowita, “Evolution of Public Health
    Information Systems: Enterprise-Wide Approaches,” A Consul-
    tation Paper for the State of Utah Department of Health, vol. 2,

    [14] M. Okafor, D. F. Sarpong, A. Ferguson, and D. Satcher,
    “Improving health outcomes of children through effective
    parenting: model and methods,” International Journal of
    Environmental Research and Public Health, vol. 11, no. 1,
    pp. 296–311, 2014.

    [15] T. Porter-O’Grady and K. Malloch, “Evidence-Based Lead-
    ership: Solid Foundations for Management Practices,” In-
    troduction to Evidence-Based Practice in Nursing and Health
    Care, vol. 6, 301 pages, 2009.

    [16] S. Friedman, M. C. Krause, K. Pethe et al., “Managing the
    COVID-19 pandemic using quality improvement principles: a
    New York city pediatric primary care experience,” Pediatric
    Quality & Safety, vol. 6, no. 3, 2021.

    [17] S. I. H. Shah, V. Peristeras, and I. Magnisalis, “Government
    (Big) data ecosystem: definition, classification of actors, and
    their roles[J],” International Journal of Computer and Infor-
    mation Engineering, vol. 14, no. 4, pp. 102–114, 2020.

    [18] A. H. Hirai, W. M. Sappenfield, R. M. Ghandour, S. Donahue,
    V. Lee, and M. C. Lu, “+e collaborative improvement and
    innovation network (CoIIN) to reduce infant mortality: an
    outcome evaluation from the US south, 2011 to 2014,”
    American Journal of Public Health, vol. 108, no. 6, pp. 815–
    821, 2018.

    [19] K. Fatherree and N. Hart, “Funding the evidence act: options
    for allocating resources to meet emerging data and evidence
    needs in the federal government,” Data Foundation; Bipar-
    tisan Policy Center, vol. 3, Article ID 3766925, 2019.

    [20] M. Bruening, A. Z. Udarbe, E. Yakes Jimenez, P. Stell Crowley,
    D. C. Fredericks, and L. A. Edwards Hall, “Academy of nu-
    trition and dietetics: standards of practice and standards of
    professional performance for registered dietitian nutritionists
    (competent, proficient, and expert) in public health and
    community nutrition,” Journal of the Academy of Nutrition
    and Dietetics, vol. 115, no. 10, pp. 1699–1709, 2015.

    [21] G. +ün, S. Zielinski, and K. Velikov, “Access-enabling Ar-
    chitectures: New Hybrid Multi-Modal Spatial Prototypes
    towards Resource and Social Sustainability,” USDOT Region V
    Regional University Transportation Center, vol. 1, 2016.

    [22] M. Azeem, L. J. Mataruna-Dos-Santos, and R. B. A. Moalla,
    “Confirmatory model of the workplace creativity in higher
    education[J],” International Journal of Recent Technology and
    Engineering, vol. 8, no. 2, pp. 426–425, 2019.

    [23] S. Bharara, S. Sabitha, and A. Bansal, “Application of learning
    analytics using clustering data Mining for Students’ dispo-
    sition analysis,” Education and Information Technologies,
    vol. 23, no. 2, pp. 957–984, 2018.

    [24] B. Shin and P. B. Lowry, “A review and theoretical explanation
    of the ’Cyberthreat-Intelligence (CTI) capability’ that needs to
    be fostered in information security practitioners and how this
    can be accomplished,” Computers & Security, vol. 92, Article
    ID 101761, 2020.

    [25] G. Debebe, “Navigating the double bind: t,” Cogent Business &
    Management, vol. 4, no. 1, Article ID 1313543, 2017.

    [26] K. DeSalvo, B. Hughes, M. Bassett et al., “Public Health
    COVID-19 Impact Assessment: Lessons Learned and Com-
    pelling needs,” NAM perspectives, vol. 2021, Article ID
    34532688, 2021.

    [27] R. Colchamiro, R. A. Edwards, C. Nordstrom et al., “Mobi-
    lizing community resources to enhance postdischarge support
    for breastfeeding in Massachusetts (USA),” Journal of Human
    Lactation, vol. 31, no. 4, pp. 631–640, 2015.

    [28] M. Azeem, L. J. Mataruna-Dos-Santos, and R. B. Abdallah,
    “Proposing Revised KHDA Model of School Improvement:
    Identification of Factors for Sustainable Performance of
    Dubai Private Schools,” Sustainable Development and Social
    Responsibility, Springer, Berlin, Germany, pp. 173–202, 2020.

    [29] M. Helmy, S. Mazen, and I. M. Helal, “Analytical study on
    building a comprehensive Big data management maturity
    framework,” International Journal of Integrated Supply
    Management, vol. 20, no. 1, 2022.

    [30] N. K. Cottam, An Assessment of Policies Adopted in Indiana
    School Districts as a Result of the 2004 Child Nutrition and
    WIC Reauthorization Act, Indiana University, Bloomington,
    IN, USA, 2013.

    [31] Z. Chen, Y. Li, Y. Wu, and J. Luo, “+e transition from
    traditional banking to mobile internet finance: an organiza-
    tional innovation perspective-a comparative study of Citibank
    and ICBC,” Financial Innovation, vol. 3, no. 1, pp. 1–16, 2017.

    [32] G. S. J. Burch and N. Anderson, “Personality as a predictor of
    work-related behavior and performance: recent advances and
    directions for future research,” International Review of In-
    dustrial and Organizational Psychology 2008, vol. 23, no. 8,
    pp. 261–305, 2008.

    [33] W. Clark, S. N. Welch, S. H. Berry et al., “California’s historic
    effort to reduce the stigma of mental illness: the mental health
    services act,” American Journal of Public Health, vol. 103,
    no. 5, pp. 786–794, 2013.

    [34] J. Choukhno, M. Klarin, and T. Kosyaeva, “Shift from edu-
    cation to development: leaders and coaches in search of shared
    wisdom,” International Journal of Cognitive Research in Science,
    Engineering and Education, vol. 4, no. 2, pp. 23–29, 2016.

    [35] S. Goldrick-Rab and C. Cady, “Supporting community college
    completion with a culture of caring: A case study of Amarillo
    College,” Retrieved April, vol. 12, 2018.

    [36] L. Haynes-Maslow, L. Andress, S. Jilcott Pitts et al., “Argu-
    ments used in public comments to support or oppose the US
    department of agriculture’s minimum stocking requirements:
    a content analysis,” Journal of the Academy of Nutrition and
    Dietetics, vol. 118, no. 9, pp. 1664–1672, 2018.

    [37] A. J. Sturges, Z. Huysmans, W. Way, and A. Goodson,
    “Examining the role of high school athletic directors in
    promoting leadership development in high school student-
    athletes,” Journal for the Study of Sports and Athletes in Ed-
    ucation, vol. 14, no. 1, pp. 58–81, 2020.

    [38] E. G. Martin, N. Helbig, and G. S. Birkhead, “Opening health
    data,” Journal of Public Health Management and Practice,
    vol. 21, no. 5, pp. E1–E7, 2015.

    [39] F. M. Manzira and F. Bankole, “Application of Social Media
    Analytics in the Banking Sector to Drive Growth and Sus-
    tainability: A Proposed Integrated framework,” in Proceedings
    of the 2018 Open Innovations Conference (OI), pp. 223–233,
    IEEE, Johannesburg, South Africa, November 2018.

    [40] N. Levina and M. Arriaga, “Distinction and status production
    on user-generated content platforms: using bourdieu’s theory
    of cultural production to understand social dynamics in
    online fields,” Information Systems Research, vol. 25, no. 3,
    pp. 468–488, 2014.

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    articles for individual use.




    Tom Ahn and Amilcar A. Menichini

    A s the economic impact of the COV ID-19 pa ndemic lingers, w ith the speed of
    recovery still uncertain, the state of the civilian labor market will impact the public
    sector. Specifica lly, the relatively stable and insulated jobs in the Department of
    Defense (DoD) are expected to be perceived as more attractive for the near future.
    This implies changes in DoD worker quit behavior that present both a cha llenge
    and an opportunity for the DoD leadership in retaining high-qua lity, experienced
    ta lent. The authors use a unique panel dataset of DoD civilian acquisition workers
    a nd a dyna mic progra mming approach to simulate the impact of the pa ndemic
    on employee retention rates under a variety of recovery scenarios. Their findings
    posit that workers will choose not to leave the DoD while the civilian sector suffers

     Image designed by Nicole Brate




    from the impact of the pa ndemic. This a llows leadership to more ea sily reta in
    experienced workers. However, once the civilian sector has recovered enough, these
    same workers quit at an accelerated rate, making gains in ta lent only temporary.
    These results imply that while the DoD can take short-run advantage of negative
    shocks to the civilian sector to retain and attract high-qua lity employees, long-run
    retention will be achieved through more fundamenta l reforms to personnel policy
    that make DoD jobs more attractive, no matter the state of the civilian labor market.

    DOI: https://doi.org/10.22594/dau.21-871.29.0


    Keywords: Dynamic Retention Model, Dynamic Programming Model, Optimal Personnel


    Acquisition Workforce


    Tom Ahn and Amilcar A. Menichini

    A s the economic impact of the COV ID-19 pa ndemic lingers, w ith the speed of
    recovery still uncertain, the state of the civilian labor market will impact the public
    sector. Specifica lly, the relatively stable and insulated jobs in the Department of
    Defense (DoD) are expected to be perceived as more attractive for the near future.
    This implies changes in DoD worker quit behavior that present both a cha llenge
    and an opportunity for the DoD leadership in retaining high-qua lity, experienced
    ta lent. The authors use a unique panel dataset of DoD civilian acquisition worker


    a nd a dyna mic progra mming approach to simulate the impact of the pa ndemic
    on employee retention rates under a variety of recovery scenarios. Their findings
    posit that workers will choose not to leave the DoD while the civilian sector suffers

    52 Defense ARJ, January 2022, Vol. 29 No. 1 : 50 – 7


    Optimal Talent Management of the Acquisition Workforce in Response to Covid-19 https://www.dau.edu

    The initial impact of the COVID-19 pandemic on the U.S. civilian labor
    market was massive, with the unemployment rate spiking to 15% in May
    2020. W hi le most world economies contracted in 2020, there is some
    consensus among economists of a relatively robust recovery in the near
    future, with average global economic growth projected to be about 5.5% in
    2021 (International Monetary Fund, 2021). In the United States, the unem-
    ployment rate has already recovered partway since the nadir. However, the
    trajectory of recovery remains unclear, depending on a host of public health
    programs, government stimulus, and the macroeconomic environment.
    While the civilian labor market has seen extraordinary swings in employ-
    ment numbers, the government sector has been somewhat immune to the
    short-term effects of the pandemic. We examine the potentia l impacts of
    the g yrations and continuing uncertainty in the civilian labor market on
    the labor ma rket decisions of public-sector employees, focusing on the
    civilian Defense Acquisition Workforce in the Department of Defense (DoD).
    Historically, senior DoD leadership has been concerned with losing quali-
    fied senior civilian workers to the private sector. However, the labor market
    impact of COVID-19 may present a pressing need to adjust personnel policy,
    as well as an opportunity to leverage the stability of DoD positions to com-
    pete against the draw of more lucrative salaries at private firms.
    We solve a dynamic programming model of worker retention behavior, where
    long-lasting shocks in the civilia n labor ma rket a re explicitly modeled.
    Retention behavior refers to the employee’s decision to remain on the job to
    which currently assigned (i.e., Defense Acquisition Workforce, as defined in
    this article) from one period to the next. By shocks, we mean sudden, unpre-
    dictable events that affect the civilian labor market. Shocks in principle can
    be positive (such as unanticipated government stimulus) or negative (such

    as the COVID-19 pandemic). Many researchers model such shocks
    as temporary, with their effects on the economy dissipating

    after one period. Our model allows for negative shocks
    to slowly recover through time. A f ter ca librat-

    ing the model pa ra meters to the Defense
    Acquisition Workforce using a unique

    panel administrative personnel
    dataset that tracks the civilian
    DoD labor force over t he spa n
    of 30 yea rs, we simulate civ i l-
    ia n-side la bor m a rket shock s
    t h at c or re spond t o e c onom ic
    recoveries of varying speeds and
    forecast the retention behavior
    of the workforce.

    53Defense ARJ, January 2022, Vol. 29 No. 1 : 50 – 77

    January 202


    We find that a persistent negative shock to the civilian sector (plus insula-
    tion of the government/DoD labor market from the shock)—in our case, the
    COVID-19 pandemic—leads workers to deva lue jobs in the private sector
    in the short-run and remain in the government sector for a longer period of
    time. Depending on the severity and persistence of the shock, it may take
    more than a decade for workers to return to valuing civilian jobs as they did
    before the pandemic. This relative increase in attractiveness of government
    jobs is only temporary, however, and workers accelerate their exit from the
    government sector into the private sector once the economic recovery is well
    underway. That is, the retention rate when the economy recovers turns out
    to be lower than the rate that would have prevailed had the global pandemic
    not occurred.

    The sections that follow review the relevant literature and describe in more
    detail the labor market impact of COVID-19 on the private sector and the
    long-run career trajectories of the typical Defense Acquisition Workforce
    employee. Further discussion explains the dynamic programming model,
    describes the data, and calibrates the model parameters. Final discussion
    considers potential COVID-19 scenarios, projects behavior of the workforce
    under differing scenarios of economic recovery, and states our conclusions.

    Literature Review
    Employee retention ha s been studied ex tensively in the personnel

    economics literature. Most studies have been theoretic in nature or have
    focused on the private sector due to data availability (Barron et al., 2006;
    Fallick et al., 2006; Gibbons & Katz, 1991; Lazear, 1986; Wilson, 1969; among
    many others). One strand of the literature examines retention issues in the
    DoD, focusing on active-duty soldiers and officers at inflection points in
    their careers, such as the end of the first Service obligation or promotion
    (Goldberg, 2001; Warner, 1995). Others study the impact of reenlistment
    bonuses (Hattiangadi et al., 2004), civilian sector options (Fullerton, 2003),
    and nonmonetary job characteristics (Golding & Gregory, 2002).

    While the civilian labor market has seen
    extraordinary swings in employment
    numbers, the government sector has been
    somewhat immune to the short-term effects
    of the pandemic.

    54 Defense ARJ, January 2022, Vol. 29 No. 1 : 50 – 77

    Optimal Talent Management of the Acquisition Workforce in Response to Covid-19 https://www.dau.edu

    Our article complements the literature on retention issues in the Defense
    Acquisition Workforce. Guo et a l. (2014) a nd A hn and Menichini (2021)
    investigated t he demog raphic factors a ssociated w it h hig her Defense
    Acquisition Workforce employee retention, such as performance ratings
    and education. Focusing on retention strategies, Schwartz et al. (2016) ana-
    lyzed the pay flexibilities authorized by Congress and the Office of Personnel
    M a n a gement t o en h a nc e ret ent ion of t a lent e d D efen s e A c qu i sit ion
    Workforce personnel. Alternatively, Kotzian (2009) proposed organizational
    culture and leadership style as effective strategies to increase retention in
    the long-run. In line with Kotzian, Jenkins (2009) suggested that, instead of
    monetary benefits, workplace satisfaction and organizational commitment
    should be the focus of the leadership to achieve highly qualified employee
    retention. Dobriansky (2009) noted the stability of government positions
    as a draw for workers compared to the private sector.

    Our article is a lso related to the literature using the Dynamic Retention
    Model (DRM) to study employee stay/leave decisions in the government
    sector. For instance, Asch et al. (2013) used the DRM to analyze how policy
    changes affect retention decisions during the transition period between the
    old and the new regulations (e.g., impact of changes in retirement policy).

    The Impact on Unemployment
    Arising from Covid-1


    The short-run impact of COVID-19 has been extraordinary, with the
    nation’s unemployment rate spiking to almost 15% from near historical lows
    (3.5%) in 2 months. As Figure 1 shows, even during the Great Recession,
    the nation’s unemployment rate peaked at 10.6%. As a further reference,
    we added a yellow line in Fig ure 1 showing the previously recorded a ll-
    time high in monthly unemployment rate from the U.S. Bureau of Labor
    Statistics, which was about 11% at the end of 1982. The Cong ressiona l

    55Defense ARJ, January 2022, Vol. 29 No. 1 : 50 – 77

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    Budget Office (CBO) projects that the U.S. economy will grow 4.6% in 2021,
    after contracting 3.5% in 2020. These are significantly upwardly revised
    estimates from its report in July 2020, when the CBO projected a growth
    rate of 4%. Correspondingly, employment has recovered sharply since May
    2020 (CBO, 2021)


    U.S. Unemployment Rate






































































    Note. Raw data from Bureau of Labor Statistics


    AWF Retention by Gender

    Years of Service
    0 5 10 15 20 25



















    Note. Adapted from Ahn and Menichini (2019).

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    Government workers
    Financial activities

    Education and health services

    Self-employed workers
    Professional and business services

    Wholesale and retail trade

    Transportation and utilities
    Other services


    Leisure and hospitality
    Mining, quarrying, and oil and gas extraction

    0 5 10 15 20

    Note. Raw data from Bureau of Labor Statistics
    However, it remains unclear when the economy can return to “business-as-
    usual” and how much vigor it will have on the rebound. Public health factors
    such as the efficacy of vaccines and their distribution, the spread of more
    infectious variants of COVID-19, and sustained use of masks and socia l
    distancing until herd immunity is reached, will all play a role. In addition,
    the recovery of the rest of the world; additional federal, state, and local fiscal
    stimuli; as well as permanent changes in the economy, such as expanded
    work-from-home and reconfiguration of global supply chains, may impact
    the private-sector labor market for years to come.

    The impact of such changes to the private sector will inevitably affect the
    public sector, especia lly for the civilia n workforce within the DoD. The
    combination of uncertainty in the private sector and a comparatively stable
    government sector is expected to alter their long-term career trajectories.
    Figure 2 shows the retention rate of Defense Acquisition Workforce work-
    ers, adapted from Ahn and Menichini (2019). The sample covers September
    1987 to December 2018. Approximately 30% of workers leave the DoD after
    about 8 years of service. After approximately 25 years of experience, roughly

    While job stability has always been a draw for
    the government sector, the state of the economy
    as well as the continuing uncertainty about the
    speed of economic recovery should make jobs in
    the DoD relatively much more attractive

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    three-qua r ters of employees have lef t. W hile it is undeniably tr ue that
    some employee turnover is beneficial (for instance, to jettison low-quality
    or unmotivated employees and bring in fresh ta lent), DoD leadership has
    consistently expressed a desire to hold on to highly skilled and experienced
    civilian workers (e.g., Department of Navy, 2018).
    W hile the shock of COVID-19 has been felt in a lmost ever y sector of the
    labor market, the government sector has notably been shielded from the
    worst of the impact. Figure 3 shows that, as of November 2020, government
    workers experienced an unemployment rate around 4%. This rate is lower
    than workers in the education and health services fields, who have received
    much wider media coverage of labor shortages due to the health risks from
    their proximity to the pandemic.

    While job stability has always been a draw for the government sector, the
    state of the economy as well as the continuing uncertainty about the speed
    of economic recover y should ma ke jobs in the DoD relatively much more
    attractive. Indeed, this argument parallels what has been known for a long
    time in military recruiting: demand for military jobs is countercyclical to
    the state of the civilian economy. With the backdrop of this large, negative,
    persistent, and unpredictable shock to the civilian labor market, we model
    the long-r un labor ma rket decisions of civ i lia n DoD employees using a
    dynamic programming framework.

    I n t h i s sec t ion , we descr ibe t he d i f ferent pa r t s of t he D y n a m ic

    Programming Model of employee retention that will be used to produce
    policy simulations.
    We assume Defense Acquisition Workforce workers are rational decision
    ma kers who ma ke career choices to ma ximize utility over their lifetime.
    The individual evaluates, at each decision point, all the costs and benefits
    involved in each possible choice, including pecunia r y as well as nonpe-
    cuniar y elements, which we describe in the following discussion. At the

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    beginning of each period (i.e., 1 yea r in this a r ticle), the worker chooses
    between leaving the Defense Acquisition Workforce to continue a career
    in the private sector or remaining in the public sector one more period. In
    addition, given that we observe in our data that only about 6% of workers
    who leave the Defense Acquisition Workforce return at a later date, plus the
    difficulty in discerning why they left (and why they returned), we further
    assume that leaving the Defense Acquisition Workforce is a n irreversi-
    ble decision.
    We next describe all the costs and benefits (including monetary and non-
    monetary elements) that the individual trades off in every decision point.
    We assume that the pecuniary components include:

    • Defense Acquisition Workforce compensation, including basic
    pay, health insurance, locality adjustment, bonuses

    • Compensation in the private sector

    We also assume the Defense Acquisition Workforce employee is included
    in the Civil Service Retirement System (CSRS), and model public retire-
    ment accordingly. W hile our dataset conta ins employees from both the
    discontinued CSRS and the current Federal Employee Retirement System
    (FERS), we model the CSRS because more individuals belong to that system
    in our sample. For employees working in the private sector, we assume they
    are contributing to a 401(k) plan where the employer matches up to 10% of
    gross pay. As we note in the data section, the moda l Defense Acquisition
    Workforce employee has a bachelor’s degree or above and earns close to
    $100,000 at the highest paygrade attained. Workers with these characteris-
    tics in the civilian sector most often have employer matching 401(k) options.
    The nonpecuniary components refer to the individual’s taste or preference
    for a job in the Defense Acquisition Workforce versus a career in the pri-
    vate sector. These components attempt to capture the taste of those agents
    who prefer the higher predictability and stability of public sector employ-
    ment, even at the cost of a lower salary compared to the private sector, and
    vice versa. To capture these relative preferences, we use taste parameters
    reflecting monetary-equivalent preferences for careers in the private versus
    the public sectors.

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    In particular, we use the following notation to construct the dynamic model:
    • Wt

    m i nd icat es compen sat ion i n t he Defen se Acqu isit ion
    Workforce (including all pecuniary components) in period t

    • Wt
    c denotes compensation in the private sector in period t

    • ωm is the public sector taste parameter, which captures the
    monetar y-equiva lent preference for a career in the Defense

    Acquisition Workforce

    • ωc is the private sector taste parameter, which captures the
    monetary-equivalent preference for a private sector career

    • T denotes the labor time horizon (number of working periods
    before final retirement)

    • β = 11 + r is the discount factor, where r represents the subjective
    discount rate

    • εtm and εtc are the random shocks affecting government and
    civilian jobs, respectively, in period t

    • E [.|εt – 1] indicates the expectation operator, given the shock in
    the previous period

    The ma ximization problem faced by the Defense Acquisition Workforce
    worker can be described by the following set of equations:

    L = Wt

    c + ωc+ βE [ Vt

    +1|εtc ] + εtc (1)

    S = Wt
    m + ωm+ βE [ Vt+1 |εtc,εtm ] + εtm, and (2)

     = Max [ Vt

    S ] (3)

    In these equations, superscript S denotes the agent ’s choice to continue
    working one more period in the Defense Acquisition Workforce (i.e., S =
    Stay). Alternatively, super-index L indicates the individual’s choice to quit

    Given that we observe in our data that only
    about 6% of workers who leave the Defense
    Acquisition Workforce return at a later
    date, plus the difficulty in discerning why
    they left (and why they returned), we further
    assume that leaving the Defense Acquisition
    Workforce is an irreversible decision.

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    the Defense Acquisition Workforce job to continue a career in the private
    sector (i.e., L = Leave). Therefore, Vt

    S denotes the (present) value of remain-
    ing in the public sector one more period, while Vt

    L indicates the (present)
    value of switching to the private sector. Equation (3) implies that the indi-
    vidual will decide to be part of the Defense Acquisition Workforce force in
    every period in which Vt

    S > Vt
    L and will leave the force as soon as the opposite

    is true. In economics terms, the va lue of leaving the Defense Acquisition
    Workforce, Vt

    L, represents the opportunity cost of choosing to stay in the
    public sector one more period.
    Regarding stochastic variables εtm and εtc, we assume they are independent
    and mean reverting over time (t dimension). The specification of the random
    shocks is the following:

    εtc= μc + ρc εt-1c  + τtc, τtc~ N ( 0, σc2 ) (4)
    εtm= μm + ρm εt-1m  +  τtm, τtm~ N ( 0, σm2  ) , and (5)

    τtc independent of τtm (6)

    That is, the random shocks evolve independently of each other, oscillating
    around their own long-run (unconditional) mean over time. In the context of
    equations (1)–(3), these stochastic variables could be interpreted as sudden
    and unpredicted events impacting the civilian and private sector salaries
    (i.e., Wt

    m a nd Wt
    c, respectively) over time, stemming from, for insta nce,

    fluctuations in the business cycle. As we describe later, we use these random
    variables to introduce the COVID-19 shock. Ashenfelter and Card (1982)
    found that nominal wages are well represented by autoregressive models of
    order 1, also known as AR(1) processes. In this type of model, the forecast
    of the variable of interest is based on the current value of the variable. For
    instance, the prediction of nominal wages in the next period would be based

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    on the current value of nominal wages. Over time, random variables follow-
    ing A R(1) processes oscillate around their long-run means. Accordingly,
    equations (4) and (5) define AR(1) representations for the random shocks.
    These AR(1) processes play an important role for our main results as they
    allow shocks to persist over time, that is, to gradually fade as time passes.
    As we explain in more detail later, we use parameter ρ to define the speed at
    which the economy (i.e., wages) recovers from a shock (e.g., from the COVID-
    19 outbreak). In terms of the optimization problem described in equations
    (1)–(3), random shocks εtm and εtc indicate state variables observed by the
    Defense Acquisition Workforce worker at the time of the decision.

    Data Description
    and Model Calibration

    In this section, we describe the Defense Acquisition Workforce sample
    as well as the selection and calibration of the parameter values necessary
    to implement the Dynamic Programming Model described previously. In
    the next section, we show those parameters provide a good approximation
    of the long-run labor market outcomes for the representative worker in the
    Defense Acquisition Workforce.

    Data: The Acquisition Workforce
    The Defense Acquisition Workforce comprises approximately 150,000

    employees, covering the period September 1987–December 2018. Civilians
    make up about 90% of the workforce, while active-duty Service members
    make up the remaining 10%. The Defense Acquisition Workforce’s mission is
    the “timely and cost-effective development and delivery of warfighting capa-
    bilities to America’s combat forces” (DoD, 2015). The Defense Acquisition
    Workforce was responsible for overseeing the equipping and sustaining of
    the nation’s military, spending over $1 trillion from FY 2016 to FY 2021.
    About 26% of the Defense Acquisition Workforce belongs to the engineer-
    ing career field, followed by contracting at 19%. Historically, the Defense
    Acquisition Workforce was sharply reduced in size and capability during
    the 1990s. The DoD started to rebuild the Defense Acquisition Workforce
    in 2008 and increased it by approximately 50,000 employees over 13 years.
    For this analysis, we restrict our sample to workers who were ever in the
    contracting, industria l property management, or purchasing fields. Our
    sample workers were born after January 1, 1950, but before December 31,
    1980. Workers with birthdates outside this range are either too old, in that
    the environment in which they made their labor decisions may not reflect

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    current jobs in the Defense Acquisition Workforce; or too young, in that
    these workers have not had time to make labor decisions that are pivotal to
    their careers. Restricting the sample nets us over 2 million worker-month
    records, with over 13,000 unique workers tracked through their careers.
    Table 1 presents some summary statistics for our sample.


    Variables Mean Std. Dev. Min Max

    Female 0.632

    Minority 0.278

    Disability 0.202

    Prior Military Service 0.619

    Has Bachelor’s Degree 0.547

    Has Postgraduate Degree 0.332

    Gained Additional Education in AWF 0.441

    Career Length in AWF (in years) 12.0 (8.6) 0.1 25.8

    Age at Entry 33.0 (8.2) 15 65

    Age at Exit 48.2 (10.55) 20 68

    Position Type: Professional 0.657

    (Ever Held) Technical 0.245

    Blue-Collar 0.018

    White-Collar 0.297

    Ever Rated Not Fully Satisfactory 0.575

    Highest Salary 95,143.67 (30,410.74) 27,397 189,600

    Observations 13,590

    The workforce is predominantly white and female. Over half the workforce
    has a bachelor’s degree or above. Compared to the civilian sector, careers
    in the Defense Acquisition Workforce are stable, with the average career
    length lasting well over a decade. This workforce is also highly paid, with the
    average employee earning almost $100,000 toward the end of their career.
    On average, workers in this sector begin their career at age 33, which indi-
    cates that the position in the Defense Acquisition Workforce is not their first
    job. In fact, a large number of these workers have prior military experience.
    To rigorously assess the impact of the civilia n sector on the attractive-
    ness of the DoD position, every employee in the dataset must be “assigned”
    and can expect to earn a civilian wage. To accomplish this, we estimate a
    hedonic regression using the Outgoing Rotation Group (ORG) of the Current
    Population Survey (CPS). As this dataset contains a representative sample
    of workers in the United States, including, most importantly, those who are

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    in the government sector, it is possible to make an apples-to-apples compar-
    ison with workers in the private sector. (See Ahn and Menichini [2021] for
    a detailed description.)
    We run a hedonic regression using the individual socio-demographic char-
    acteristics, professional and education experience, and locality indicators
    from the ORG of the CPS, which broadly match the Defense Acquisition
    Workforce variables summarized in Table 1, to obtain predicted civilian
    and government sector wages. The difference in the wages across private
    and public sectors, conditioned on individual characteristics, defines the
    government sector “wage penalty.”

    Calibration Results
    Before simulating the model described in equations (1)–(3), we define the

    parameter values, which we show in Table 2 and subsequently describe. We
    can observe in Table 2 that all parameter values, except compensation, are
    constant over the career of the Defense Acquisition Workforce employee.


    Parameter Value





    T 25

    β 0.95

    ωm 1.2782

    ωc 1

    μm 0

    μc 0

    ρm 0.90

    ρc 0.90

    σm 0.005

    σc 0.005

    As we described earlier, estimates from the hedonic regressions suggest that
    income in the private sector (i.e., Wt

    c) is, on average, around 17.61% higher
    than in the Defense Acquisition Workforce (i.e., Wt

    m) for individuals with
    similar characteristics. For this reason, after initially normalizing Wt

    m = 1,
    we let Wt

    c = 1.1761. We then add the income from the different retirement
    systems; thus, compensation changes over time. The data described earlier
    also show that the longest observed labor time horizon among all individuals

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    is 25 years. For that reason, we let T = 25. The subjective discount factor is
    assumed to be 0.95, implying an interest rate of 5.26%, which is similar to
    the average 30-Year T-Bond Constant Maturity Rate reported by the Federal
    Reserve Bank of St. Louis for the period covered by the dataset.

    Regarding the taste parameters, we ca librated parameter ωm so that the
    survival curve predicted by the model approximates the empirical survival
    curve as closely as possible via grid search (we show the graphical results of
    this calibration in the next section). In more technical terms, the calibration
    exercise searches for the value of ωm that minimizes the summed squared
    distance between the points of the empirical Defense Acquisition Workforce
    survival curve and the points of the survival curve predicted by the model.
    As Table 2 displays, we normalize ωc= 1 and, from the calibration exercise,
    we obtain ωm= 1.2782. These values are similar to those estimated by Ahn
    and Menichini (2021), and imply that the representative Defense Acquisition
    Workforce employee prefers the Defense Acquisition Workforce over the
    private sector.
    The remaining parameter values in Table 2 refer to the stochastic process of
    the random variables εtm and εtc . We follow Ashenfelter and Card (1982) to
    define the parameter values that govern the AR(1) processes of those terms.
    Accordingly, we let parameters μm and μc be equal to zero, we assume values
    of 0.005 for the standard deviation of the random shocks, σm and σc, and let
    the mean-reversion coefficients, ρm and ρc, be equa l to 0.9. These va lues
    depict the historical behavior of the shocks. In particular, those observed
    va lues of the mean-reverting coefficients suggest that wages have a high
    level of persistence over time; thus, the effects of shocks require a long time
    to disappear.

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    Model Solution and Policy Simulations
    In this section, we describe our policy simulations to forecast evolution

    in the behavior of the representative Defense Acquisition Workforce worker
    under a number of scenarios with differing speed rates of economic recovery
    from a large, abrupt, and unanticipated negative impact (i.e., COVID-19) to
    the private sector. This is a major systematic event that adversely affects
    all sectors of the economy, except for the public or government sector, which
    we assume keeps its employment constant (in fact, any future unanticipa-
    ted national shock to the economy and/or public health that is concentrated
    mainly in the private sector can be expected to operate in a similar manner).
    The assumption that the government sector is not affected by the shock
    is consistent with the assumption of independent random shocks in equa-
    tion (6).
    Concisely, we introduce a large negative civilian shock at a point in time.
    Then, we allow the system to recover and converge back to the steady state.
    We sta r t a na lyzing retention behavior assuming the economy recovers
    according to the empirical historical speed. However, given the observed
    recovery from the current pandemic seems to be, so far, much faster than
    normal, we also study the retention implications of different scenarios for
    the speed of recovery. We “control” the speed of recovery of the economy
    by setting the autoregressive term, ρ, which controls the velocity at which
    shocks gradually disappear over time.

    W hile the private sector goes through its g yrations, at ever y period the
    representative Defense Acquisition Workforce agent in our model surveys
    the current state of the private sector, forecasts the evolution of the state
    of the economy, and makes the ex ante optimal decision to stay or leave the
    Defense Acquisition Workforce. We describe the simulation procedure in
    more detail next.
    We solve the model described in equations (1)–(3) via backward induction.
    (See Rust [1987] for an empirical treatment.) That is, we start from the final
    period (i.e., t  = T = 25) and decide whether to stay one more (final) period in

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    the Defense Acquisition Workforce or to leave for the private sector. We then
    move one period backward (i.e., t = 24) and select to stay one more period or
    to leave the Defense Acquisition Workforce, considering the value from the
    optimal decision in period T = 25. We continue moving backward, deciding
    rationally in every period, until we reach the present period (i.e., t  = 0). This
    solution characterizes the retention behavior of a representative Defense
    Acquisition Workforce employee in all possible states of the economy.


    AWF Retention

    Years of Service
    0 5 10 15 20 25

    Dynamic Retention Model

    Acquisition Workforce


    AWF Retention
    Years of Service
    0 5 10 15 20 25
    Dynamic Retention Model
    Acquisition Workforce

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    We then stochastically simulate the model forward (i.e., over the 25 years
    of work) 100,000 times, which produces the stay/leave decisions of 100,000
    employees in all possible different situations over the labor period. These
    si mu lat ion s su m ma r i ze t he ret ent ion behav ior of t he represent at ive
    employee, which we show in Figure 4. The figure exhibits the ca librated,
    model-predicted sur viva l cur ve of the representative individua l ( purple
    line) and displays the cumulative probability of the worker staying in the
    Defense Acquisition Workforce after a certain period of time. For example,
    the figure suggests that the likelihood that the employee is still part of the
    Defense Acquisition Workforce after 10 years is about 65%. The figure also
    shows the empirical survival curve for the Defense Acquisition Workforce
    employees (yellow line) from the data described previously, suggesting that
    the calibrated model predicts actual behavior quite closely. While Figure
    4 displays the retention behavior of a representative Defense Acquisition
    Workforce employee, each demographic group described in Table 1 would
    have its own survival curve.

    Associated with the previous sur viva l cur ves are the yearly, model-pre-
    dicted probabilities of leaving the Defense Acquisition Workforce, which we
    show as the blue line in Figure 5. The retention rate is relatively high every
    year, as is shown by the fact that the likelihood of leaving is always below
    10% per year, and below 5% in the great majority of years. In addition, the
    probability of leaving is high initially, and diminishes through time before
    increasing again toward the end of the individua l’s career. For instance,
    the probability that the employee depa r ts from the Defense Acquisition
    Workforce in year 10 is around 2%. As before, we also show the empirical
    likelihood of leaving (yellow line) for comparison purposes.
    We then proceed to shock the model with a large negative random draw on
    the civilian side (i.e., εtc ) at year 10. The shock is equivalent to 3 standard
    deviations below the mean and is intended to capture the large effect of the
    sudden appearance of COVID-19. In economic terms, given the calibration
    shown in Table 2, this shock could be interpreted as a roughly 1.5% reduction

    Clearly, the historical coefficient implies
    it would easily take a decade or more to
    return to normality. However, a year after the
    appearance of the virus, the economy seems
    to be recovering much faster than suggested
    by historical terms.

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    in the civ ilia n sa la r y, Wt
    c, while the public sector sa la r y, Wt

    m, rema ins
    unchanged. The fact that the random shocks ( both εtm and εtc ) are mean
    rever ting over time implies that the impact of the negative shock on the
    civilian salary gradually disappears as time passes. As mentioned before,
    the speed of return to the pre-shock state will depend on the mean-reversion
    coefficient, ρ.


    Years of Service
    0 5 10 15 20 25

















    Recovery Scenario 1 (Rho = 0.3)
    Recovery Scenario 2 (Rho = 0.5)
    Recovery Scenario 3 (Rho = 0.7)
    Historical Recovery (Rho = 0.9)


    Years of Service
    0 5 10 15 20 25

    No COVID-19 Shock
    Recovery Scenario 1 (Rho = 0.3)
    Recovery Scenario 2 (Rho = 0.5)
    Recovery Scenario 3 (Rho = 0.7)
    Historical Recovery (Rho = 0.9)

    In Figure 6 we show, given the initia l negative shock, how the shocks are
    expected to evolve over time for four different va lues of the coefficient of
    mean-reversion. The purple bars depict the historical case, which is based

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    on the obser ved historica l mea n-reversion coeff icient of ρ = 0.9. Clea rly,
    the historica l coefficient implies it would easily ta ke a decade or more to
    return to normality. However, a year after the appearance of the virus, the
    economy seems to be recovering much faster than suggested by historical
    terms. We attempt to capture the faster rebound by reducing the coefficient
    of mean-reversion (i.e., via a quicker dissipation of the shock). Accordingly,
    we analyze three different scenarios featuring dissimilar speeds of recov-
    ery, a ll of which are faster than the historica l speed. Scenario 1, with the
    blue bars and ρ = 0.3, represents the case of a relatively faster return to the
    pre-COVID economy. On the other hand, the yellow bars in scenario 3, with
    ρ = 0.7, reflect a slower recovery to normality as compared to scenario 1. In
    between are the red bars of scenario 2, showing an intermediate speed of
    recovery with ρ = 0.5. Even in the more optimistic recovery scenario 1, the
    effects of the large negative shock clearly remain in place for some years.
    While we acknowledge that the magnitude and persistence of the shocks
    are speculative, they are informed by very recent (and ongoing) research.
    Many scholars are currently attempting to forecast the long-run impact of
    COVID-19 on the economy. (See Petrosky-Nadeau and Valetta [2020], for
    an example of such ongoing research.)

    The effect on retention behavior of the representative Defense Acquisition
    Workforce worker can be observed in Figure 7. The figure shows that, during
    the initia l 10 yea rs, the retention behavior is equiva lent to the blue line
    in Fig ure 4. At yea r 10, the COV ID-19 shock happens, a nd the retention
    behavior changes considerably. As mentioned before, we study the reten-
    tion behavior in four different contexts. The green line shows the retention
    impact of the virus under historical terms (i.e., ρ = 0.9). The other lines depict
    the expected retention behavior for three faster rates of economic recovery
    (i.e., ρ = 0.3, ρ = 0.5, and ρ = 0.7 for recovery scenarios 1, 2, and 3, respectively).
    In all cases, a kink and sudden flattening of the curve is evident, suggesting
    that individua ls stay longer in the Defense Acquisition Workforce in a n

    70 Defense ARJ, January 2022, Vol. 29 No. 1 : 50 – 77

    Optimal Talent Management of the Acquisition Workforce in Response to Covid-19 https://www.dau.edu

    attempt to avoid the sharp negative effect of the virus shock on the civilian
    labor market. Depending on the speed of recovery, it might take a substan-
    tia l amount of time for the employee to return to the pre-shock retention
    behavior. For instance, in the historica l case it ta kes around 10 years for
    the representative employee to return to the previrus retention behavior,
    while in scenarios 1, 2, and 3, the return to normality takes roughly 2, 3, and
    5 years, respectively. These long-lasting effects on retention behavior have
    important implications for the hiring policies of the public sector.
    It is worth noting that the time required to return to the “original” behavior
    specified previously does not mean that all workers will choose to delay leav-
    ing the Defense Acquisition Workforce by several years due to the impact
    of COVID-19. Instead, all employees will process the negative shock in the
    civilian economy as making the Defense Acquisition Workforce job more
    attractive. Until the shock fully dissipates, the DoD position will be more
    attractive tha n if no globa l pa ndemic had occurred. However, given the
    substantial wage premium in the civilian sector, the pandemic shock does
    not need to completely disappear before workers who were planning to move
    to the civilian sector resume their plans.


    Years of Service
    0 5 10 15 20 25












    No COVID-19 Shock
    Recovery Scenario 1 (Rho = 0.3)
    Recovery Scenario 2 (Rho = 0.5)
    Recovery Scenario 3 (Rho = 0.7)
    Historical Recovery (Rho = 0.9)

    To complement the ana lysis of the return to the pre-COVID context, we
    present Figure 8. The figure shows the model-predicted yearly probabilities
    of leaving the Defense Acquisition Workforce for the four different values of
    parameter ρ. The green line shows the retention behavior in the historical
    recovery scenario, confirming that it takes around 10 years to return to the
    pre-COVID retention behavior (the latter is represented by the no-COVID-
    19-shock blue line). The red, yellow, and purple lines, reflecting faster speeds

    71Defense ARJ, January 2022, Vol. 29 No. 1 : 50 – 77

    January 2022

    of economic rebound, suggest that around 2, 3, and 5 years, respectively, are
    required to eliminate the effects of the COVID-19 shock on retention. In all
    four scenarios, the likelihood of leaving the Defense Acquisition Workforce
    goes roughly to zero in the year of the shock, and then slowly starts to return
    to the no-shock levels as time passes and the effects of the shock dissipate.

    It is also important to note that, after the return to normality, the probability
    of leaving is higher in the slower recovery scenarios and lower in the faster
    rebound scenarios. More generally, after the COVID-19 shock dissipates, in
    all cases with shock, the likelihood of leaving is higher than in the no-shock
    case, with that probability increasing in parameter ρ. Indeed, the slower the
    recovery from the pandemic (i.e., higher ρ value), the larger the magnitude
    of exit probability after the recovery. This outcome suggests that, as more
    people decide to stay longer in the Defense Acquisition Workforce during
    the pandemic, when the economy returns to norma l the pent-up demand
    to leave for the private sector is expressed as a higher likelihood of leaving
    in the later years. This implies an opportunity as well as a problem for the
    Defense Acquisition Workforce leadership. While a slower recovery may
    induce more employees to stay longer, it cannot be a permanent solution to
    retain high-ability workers. A higher ρ will result in a much sharper exit of
    workers from the Defense Acquisition Workforce once the civilian economy

    Given the substantial wage premium in the
    civilian sector, the pandemic shock does
    not need to completely disappear before
    workers who were planning to move to the
    civilian sector resume their plans.

    72 Defense ARJ, January 2022, Vol. 29 No. 1 : 50 – 77

    Optimal Talent Management of the Acquisition Workforce in Response to Covid-19 https://www.dau.edu

    To reta in these workers, f unda menta l (a nd traditiona l) personnel pol-
    icy reforms will be required. For example, a pay increase or expansion of
    benefits before the civilian sector fully recovers may permanently induce
    senior workers to remain in the Defense Acquisition Workforce. Similarly,
    a one-time retention bonus, set far enough into the future when the civilian
    economy is back to normal, could prevent that exit.


    Years of Service
    0 5 10 15 20 25



    No COVID-19 Shock, No Bonus
    Historical Recovery, No Bonus
    Historical Recovery, Including Bonus

    Although a full analysis of the available policy reforms is outside the scope
    of this article, we show with more detail one particular way by which that
    expected long-term effect could be counteracted. In particular, we analyze
    the effect of a one-time bonus on the probabilit y of leaving the Defense
    Acquisition Workforce when the economy returns to normality. We assume
    the bonus is equivalent to 25% of the individual’s monthly salary and is paid
    at year-of-service 25 (with the virus shock occurring at year 10). Figure 9
    shows the main results of this exercise. The expected bonus has a fairly
    sma ll effect on employee retention in the early- and mid-career years, as
    the retention rates a re a lmost equiva lent w ith a nd w ithout the bonus.

    73Defense ARJ, January 2022, Vol. 29 No. 1 : 50 – 77

    January 2022

    However, as expected, the effect of the bonus is more visible in the f ina l
    years of the employee’s career, when the economy has fully recovered from
    the COV ID-19 shock. Without the bonus ( purple line), the likelihood of
    leaving is substantially higher than with the bonus (yellow line), suggesting
    that, indeed, a bonus would induce experienced employees to stay longer in
    the Defense Acquisition Workforce after the recovery. Fina lly, the bonus
    is just one of the tools available to the Defense Acquisition Workforce to
    affect individua l retention behavior (for instance, sa lary raises would be
    another useful tool).

    As of early 2021, the overall unemployment rate in the United States

    stands at 6.2%—an 8-percentage point decrease in just 8 months from the
    worst unemployment rate in almost 90 years arising from the COVID-19
    global pandemic, yet still almost double the unemployment rate from just
    one year ago. While the recovery has been as dramatic as the decline, the
    future remains very much in doubt. For example, in December 2020, payrolls
    shrank by 140,000. Outlook has considerably brightened since, but whiplash
    in the long-run forecast of economic recovery itself adds uncertainty to
    future labor market prospects in the civilian market.

    In this environment, we a na lyzed the potentia l impact of the economic
    re c over y on t he l a b or m a rket t r aje c t or y of t he D efen s e A c qu i sit ion
    Workforce. The contrast in stability of jobs in the government compared to
    the private sector should increase the attractiveness of DoD jobs, especially
    if the recovery proves to be slow or unpredictable. We built and calibrated a
    dynamic programming model of employee retention behavior, analyzed the
    impact of a negative persistent shock to the civilian sector, and simulated
    different recovery paths.

    Forward-looking leaders should regard these
    simulation results not as predictions of the
    future, but as guides to help set personnel
    policies that are flexible and adjustable,
    and even take advantage of gyrations in the
    civilian economy.

    74 Defense ARJ, January 2022, Vol. 29 No. 1 : 50 – 77

    Optimal Talent Management of the Acquisition Workforce in Response to Covid-19 https://www.dau.edu

    The la rger the magnitude of the negative shock to the civilia n economy,
    the more our results show that government positions become more attrac-
    tive; while the slower the economic recovery, the more highly employees
    may value government positions compared to the prepandemic period for
    several years.

    W hi le t his env ironment ca n increa se retention of t he avera ge worker
    from the Defense Acquisition Workforce, leadership should understa nd
    that, eventually, recovery of the civilian sector will push down the relative
    desirability of government jobs. This may lead to a speedy exodus of many
    senior-level workers who were being held back due to economic uncer-
    tainty. Personnel planning without considering the temporary increment
    in retention at the beginning of the shock may lead to overhiring, especially
    at t he ju n ior-level s . Conver sely,
    short-sighted reductions in hiring
    due t o t he i n it i a l i mpa c t s of t he
    negative shock may lead to a hol-
    lowing out of the workforce, once
    the shock impact wa nes. In addi-
    tion, as the economy recovers, there
    m ay b e f u nd a ment a l s t r uc t u r a l
    cha nges to t he labor ma rket t hat
    rem a i n , ch a n g i n g t he v a lu at ion
    of b ot h gover n ment a nd pr iv at e
    sector jobs in unpredictable ways.
    For wa rd-look i n g lea ders shou ld
    regard these simulation results not
    as predictions of the future, but as
    guides to help set personnel policies
    that are flexible and adjustable, and
    even take advantage of gyrations in
    the civilian economy.

    Learn more about this article by
    watching Dr. Tom Ahn and Dr.
    Amilcar Menichini’s presentation,
    Optimal Long-Run
    Talent Management
    of the DoD AWF in
    Response to


    75Defense ARJ, January 2022, Vol. 29 No. 1 : 50 – 77

    January 2022

    Ahn, T., & Menichini, A. (2019). Acquisition research program sponsored report

    series: Retention analysis modeling for the acquisition workforce (Report
    No. NPS-HR-20-001). Naval Postgraduate School. https://dair.nps.edu/

    Ahn, T., & Menichini, A. (2021). Acquisition research program sponsored report series:
    Retention analysis modeling for the acquisition workforce II (Report No. NPS-
    HR-21-031). https://dair.nps.edu/bitstream/123456789/4317/3/NPS-HR-21-031.

    Asch, B. J., Mattock, M. G., & Hosek, J. (2013). A new tool for assessing workforce
    management policies over time. RAND. https://www.rand.org/pubs/research_

    Ashenfelter, O., & Card, D. (1982). Time series representations of economic variables
    and alternative models of the labour market. The Review of Economic Studies,
    49(5), 761–781. https://doi.org/10.2307/2297188

    Barron, J., Berger, M., & Black, D. (2006). Selective counteroffers. Journal of Labor
    Economics, 24(3), 385–409. https://doi.org/10.1086/504275

    Congressional Budget Office. (2021). An overview of the economic outlook: 2021 to
    2031. https://www.cbo.gov/system/files/2021-02/56965-Economic-Outlook

    Department of Defense. (2015). DoD acquisition workforce strategic plan—FY 2016–
    FY 2021. https://www.hci.mil/docs/dod_acq_workforce_strat_plan_fy16_fy21.

    Department of Navy. (2018). DoN acquisition workforce FY 19–24 strategic plan.

    Dobriansky, J. (2009). Acquisition workforce challenge–Motivation for government
    vs. industry employment. Defense Acquisition Review Journal, 16(1), 69–83.

    Fallick, B., Fleishman, C., & Rebitzer, J. (2006). Job-hopping in Silicon Valley: Some
    evidence concerning the microfoundations of a high-technology cluster. Review
    of Economics and Statistics, 88(3), 472–481. https://doi.org/10.1162/rest.88.3.472

    Fullerton, R. L. (2003). An empirical assessment of U.S. Air Force pilot attrition.
    Defense and Peace Economics, 14 (5), 343–355. https://doi.org/10.1080/

    Gibbons, R., & Katz, L. (1991). Layoffs and lemons. Journal of Labor Economics, 9(4),
    351–380. https://doi.org/10.1086/298273

    Goldberg, M. (2001). A survey of enlisted retention: Models and findings (CRM
    D0004085.A2/Final). Center for Naval Analyses. https://www.cna.org/CNA_

    Golding, H., & Gregory, D. (2002). Sailors’ willingness to complete sea tours: Does
    money matter? (Report No. CRM D0006886.A2/Final). Center for Naval
    Analyses. https://www.cna.org/CNA_files/PDF/D0006886.A2

    Guo, C., Hall-Partyka, P., & Gates, S. M. (2014). Retention and promotion of high-
    quality civil service workers in the DoD acquisition workforce. RAND. https://

    Hattiangadi, A., Lee, G., & Quester, A. (2004). Recruiting Hispanics: The Marine Corps
    experience final report (Report No. CRM D0009071.A2/Final). Center for Naval
    Analyses. https://www.cna.org/CNA_files/PDF/D0009071.A2

    76 Defense ARJ, January 2022, Vol. 29 No. 1 : 50 – 77

    Optimal Talent Management of the Acquisition Workforce in Response to Covid-19 https://www.dau.edu

    International Monetary Fund. (2021, January). World economic outlook update.

    Jenkins, A. K. (2009). Keeping the talent: Understanding the needs of engineers
    and scientists in the defense acquisition workforce. Defense Acquisition Review
    Journal, 16(1), 18–32. https://www.dau.edu/library/arj/ARJ/arj50/ARJ50

    Kotzian, M. (2009). Leadership and cultural change: The challenge to acquisition
    workforce retention. Defense Acquisition Review Journal, 16(1), 32–52. https://

    Lazear, E. (1986). Raids and offer matching. Research in Labor Economics, 8, 141–165.

    Petrosky-Nadeau, N., & Valetta, R. (2020). Unemployment paths in a pandemic
    economy [Working paper]. Federal Reserve Bank of San Francisco. https://doi.

    Rust, J. (1987). Optimal replacement of GMC bus engines: An empirical model of
    Harold Zurcher. Econometrica, 55(5), 999–1033. https://doi.org/10.2307/1911259

    Schwartz, M., Francis, K. A., & O’Connor, C. V. (2016). The Department of Defense
    acquisition workforce: Background, analysis, and questions for Congress.
    Congressional Research Service. https://apps.dtic.mil/sti/citations/AD1014172

    Warner, J. (1995). The economics of military manpower. In K. Hartley & T. Sandler
    (Eds.), Handbook of Defense Economics, vol. 1 (pp. 347–398). Elsevier. https://

    Wilson, R. (1969). Competitive bidding with disparate information. Management
    Science, 15(7), 446–518. https://www.jstor.org/stable/2628640?origin=

    77Defense ARJ, January 2022, Vol. 29 No. 1 : 50 – 77

    January 2022

    Author Biographies

    Dr. Amilcar A . Menichini
    i s a n A s sociat e P rofes sor i n t he Gra duat e S chool of Defen se
    Ma na gement at t he Nava l Postg raduate School. Before joining
    t he Nava l Pos t g ra duat e School , he s t ud ied t o at t a i n h i s Ph D
    in Fina nce f rom t he Universit y of A r izona . Dr. Menichini ha s
    published in The Financial Review, Review of Quantitative Finance
    and Accounting, a nd Souther n Economic Jour nal.

    (E-mail address: aamenich@nps.edu)

    The views expressed in this article are those of the author(s) alone and not of
    the Department of Defense. Reproduction or reposting of articles from Defense

    Acquisition Research Journal should credit the author(s) and the journal.

    Dr. Tom Ahn
    i s a n A s s i s t a nt P r ofe s s or i n t h e G r a du a t e S c h o ol of D e fe n s e
    Ma na gement at t he Nava l Postg raduate School. A f ter ser v ing in
    The R epublic of Korea A r my for 3 yea rs, he completed a 2-yea r
    p o s t – do c t or a t e p o s it ion a t D u k e Un iv er s it y. He t a u g ht a t t he
    Un iver sit y of K ent uck y for 7 ye a r s a nd h a s b e en a t t he Nav a l
    Postg raduate School since 2017. Dr. A hn ha s published in Journal
    of Econometrics, Journal of Business and Economic Statistics, a nd

    Jour nal of Urban Economics.

    (E-mail address: sahn1@nps.edu)

    This content is in the Public Domain.

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