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Journal of Applied Psychology
Can Becoming a Leader Change Your Personality? An
Investigation With Two Longitudinal Studies From a
Role-Based Perspective
Wen-Dong Li, Shuping Li, Jie (Jasmine) Feng, Mo Wang, Hong Zhang, Michael Frese, and Chia-Huei
Wu
Online First Publication, July 23, 2020. http://dx.doi.org/10.1037/apl0000808
CITATION
Li, W.-D., Li, S., Feng, J. (J.), Wang, M., Zhang, H., Frese, M., & Wu, C.-H. (2020, July 23). Can
Becoming a Leader Change Your Personality? An Investigation With Two Longitudinal Studies From
a Role-Based Perspective. Journal of Applied Psychology. Advance online publication.
http://dx.doi.org/10.1037/apl0000808
Can Becoming a Leader Change Your Personality? An Investigation With
Two Longitudinal Studies From a Role-Based Perspective
Wen-Dong Li
The Chinese University of Hong Kong
Shuping Li
The Hong Kong Polytechnic University
Jie (Jasmine) Feng
Rutgers University
Mo Wang
University of Florida
Hong Zhang
The Chinese University of Hong Kong
Michael Frese
Asia School of Business and Leuphana University of Lueneburg
Chia-Huei Wu
University of Leeds
Organizational research has predominantly adopted the classic dispositional perspective to under-
stand the importance of personality traits in shaping work outcomes. However, the burgeoning
literature in personality psychology has documented that personality traits, although relatively
stable, are able to develop throughout one’s whole adulthood. A crucial force driving adult
personality development is transition into novel work roles. In this article, we introduce a dynamic,
role-based perspective on the adaptive nature of personality during the transition from the role of
employee to that of leader (i.e., leadership emergence). We argue that during such role transitions,
individuals will experience increases in job role demands, a crucial manifestation of role expecta-
tions, which in turn may foster growth in conscientiousness and emotional stability. We tested these
hypotheses in two 3-wave longitudinal studies using a quasi-experimental design. We compared the
personality development of 2 groups of individuals (1 group promoted from employees into
leadership roles and the other remaining as employees over time), matched via the propensity score
matching approach. The convergent results of latent growth curve modeling from the 2 studies
support our hypotheses regarding the relationship between becoming a leader and subsequent small,
but substantial increases in conscientiousness over time and the mediating role of job role demands.
The relationship between becoming a leader and change of emotional stability was not significant.
This research showcases the prominence of examining and cultivating personality development for
organizational research and practice.
Keywords: personality change/development, leadership, job role demands, role transition
Editor’s Note. Christopher M. Berry served as the action editor for this
article.—GC
XWen-Dong Li, Department of Management, CUHK Business
School, The Chinese University of Hong Kong; Shuping Li, Department
of Management and Marketing, Faculty of Business, The Hong Kong
Polytechnic University; Jie (Jasmine) Feng, Department of Human
Resource Management, School of Management and Labor Relations,
Rutgers University; Mo Wang, Department of Management, War-
rington College of Business, University of Florida; Hong Zhang, De-
partment of Management, CUHK Business School, The Chinese Uni-
versity of Hong Kong; Michael Frese, Asia School of Business, and
Institute of Management and Organization, Leuphana University of
Lueneburg; Chia-Huei Wu, Leeds University Business School, Univer-
sity of Leeds.
Jie (Jasmine) Feng and Mo Wang contributed equally. We are indebted to
Wiebke Bleidorn, William Fleeson, Barry Gerhart, Mark Griffin, Jason Huang, Xu
Huang, Jiafang Lu, Chris Nye, Sharon Parker, Nilam Ram, Brent Roberts, Ted
Schwaba, Mabel Sieh, Zhaoli Song, Rob Tett, Jiexin Wang, Chi-Sum Wong, and
Mike Zyphur for insightful discussions. We thank Emilio Ferrer, Kevin Grimm,
Kris Preacher for invaluable suggestions on adopting latent change score model-
ing. We also appreciate helpful comments from Xiangyu Gao, Kenny Law, Hui
Liao, Wu Liu, Zhen Zhang, and participants from the Eighth Annual Symposium
of the Centre for Leadership & Innovation by the Hong Kong Polytechnic Uni-
versity and the Third Annual Tsinghua Leadership Forum by Tsinghua University
on earlier versions of this article. This study was supported by a direct grant of
research from CUHK Business School, the Chinese University of Hong Kong
awarded to Wen-Dong Li. We also thank Xin Zhang for her able assistance.
Correspondence concerning this article should be addressed to Wen-
Dong Li, Department of Management, CUHK Business School, The Chi-
nese University of Hong Kong, No. 12 Chak Cheung Street, Shatin, Hong
Kong, China. E-mail: oceanbluepsy@gmail.com
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This article is intended solely for the personal use of the individual user and is not to be disseminated broadly.
Journal of Applied Psychology
© 2020 American Psychological Association 2020, Vol. 2, No. 999, 000
ISSN: 0021-9010 http://dx.doi.org/10.1037/apl0000808
1
It’s not who you are underneath; it’s what you do that defines you.
—(Nolan, 2005, 1:11:09)
Personality traits, defined as relatively stable patterns of behav-
iors, thoughts, and feelings (Donnellan, Hill, & Roberts, 2015;
Johnson, 1997), have been featured prominently in organizational
research. Theory and research have demonstrated that personality
traits are able to predict a wide spectrum of work behaviors and
attitudes (e.g., Barrick & Mount, 1991; Berry, Ones, & Sackett,
2007; Chiaburu, Oh, Berry, Li, & Gardner, 2011; Colbert, Barrick,
& Bradley, 2014; House, Shane, & Herold, 1996; Ilies, Scott, &
Judge, 2006; Judge, Bono, Ilies, & Gerhardt, 2002; Oh & Berry,
2009; Ones, Dilchert, Viswesvaran, & Judge, 2007; Sackett,
Lievens, Van Iddekinge, & Kuncel, 2017; Schneider, 1987; Staw,
2004; Tett & Burnett, 2003).
The majority of the organizational personality literature has
assumed the position that personality traits cause work behaviors
and attitudes, not vice versa (Tasselli, Kilduff, & Landis, 2018).
An important reason lies perhaps in that this line of research has
been dominated by the classic dispositional perspective on person-
ality (McCrae & Costa Jr, 1999; McCrae et al., 2000). This
perspective postulates that the direction of causality travels only
from personality to life experiences, because personality traits are
“endogenous dispositions that follow intrinsic paths of develop-
ment essentially independent of environmental influences” (Mc-
Crae et al., 2000, p. 173).
However, recent research in personality psychology has docu-
mented that personality traits, although relatively stable, are able to
develop in adulthood as one adopts new life roles (for reviews, see
Bleidorn, Hopwood, & Lucas, 2018; Caspi, Roberts, & Shiner,
2005; Donnellan et al., 2015). Meta-analytic research has reported
significant mean-level changes of personality traits in middle and
old age, with a standardized mean difference, d, ranging from .06
to .41 (Roberts, Walton, & Viechtbauer, 2006). More recent meta-
analyses found substantial within-person variance in personality in
ESM research (N. P. Podsakoff, Spoelma, Chawla, & Gabriel,
2019). The rapid development of this dynamic perspective has
spawned a further reconceptualization of personality traits as den-
sity distributions of relevant states (Fleeson, 2001) and a recogni-
tion that both traits and states are needed for a more comprehen-
sive understanding of personality traits (Fleeson, 2004;
Jayawickreme, Zachry, & Fleeson, 2019). Nevertheless, organiza-
tional personality research has lagged behind. With the firm es-
tablishment of the importance of personality, the time seems ripe
to revisit the possibility that personality traits, though relatively
stable, may develop as people adapt to novel work roles (Tasselli
et al., 2018).
In this research, we adopt a role-based perspective and investi-
gate whether and how transitioning from an employee into a
supervisory role,
1
that is, leadership emergence (Barling, Christie,
& Hoption, 2010), may shape one’s personality development.
Assuming a leadership role in which one supervises subordinates
is important and meaningful to both the employee and the orga-
nization. For an employee, taking up a leadership role represents a
milestone in one’s career development (Hill, 2007; Wang & Wan-
berg, 2017) and has been regarded as the first step in the leadership
process (Bass & Bass, 2008). For organizations, promoting an
employee to a leadership position is a crucial step in planning
leadership succession (Kesner & Sebora, 1994).
When transitioning from employees to leadership roles, we
expect individuals to increase their conscientiousness and emo-
tional stability, two of the Big Five personality traits (Goldberg,
1990). Chiefly, as they shoulder broader responsibilities and play
more important roles in organizations (Fleishman et al., 1991;
Mintzberg, 1971; Yukl, 2012), novice leaders are expected to be
more conscientious than when they were employees—more effi-
cient, organized, vigilant, achievement-oriented, and dependable
to subordinates. Fulfilling the expectations and responsibilities
mandated by leadership roles also requires leaders to deal effec-
tively with uncertainties and changes. Therefore, leaders need to
be able to remain calm, and handle negative emotions in responses
to stress, which are characteristics of emotional stability. Over
time, such behavioral changes may consolidate and habituate,
leading to changes in personality traits (Caspi & Moffitt, 1993;
Roberts, Wood, & Caspi, 2008).
We do not formulate directional hypotheses on changes of
agreeableness, extraversion, and openness. Agreeableness has
been shown to have a weak correlation with leadership emergence
(Judge et al., 2002). Key subdimensions of extraversion—social
dominance and social vitality—may exhibit distinctive patterns of
change (Roberts et al., 2006). Although taking a supervisory
position may increase social dominance through enhancing confi-
dence and sense of power (Bandura, 1997; Keltner, Gruenfeld, &
Anderson, 2003), it may not strengthen social vitality. In fact,
being promoted into more powerful leadership roles may decrease
social vitality because novel leaders, after assuming more power,
may not think and feel from others’ perspectives (Keltner et al.,
2003). The extant literature points to conflicting predictions on
change of openness as well. Assuming a leadership role may
enhance openness because such a transition necessitates creatively
dealing with novel work tasks (Shalley, Gilson, & Blum, 2000).
Yet, new leaders may experience declines in openness because
they need to adhere to rules and routines to maintain stability and
consistency (Yukl, 2012). The conflicting mechanisms prevent us
from formulating directional hypotheses on changes of the three
personality traits.
We further examine a key underlying mechanism for the change
of personality traits—increases in job demands after adopting the
role of leaders. Job demands refer to the amount of various forms
of responsibilities associated with meeting the expectations of a
work role (Karasek, 1979). According to the theoretical work of
personality development by Caspi and Moffitt (1993), the unique
job demands embedded in leadership roles provide a strong reward
structure and social control mechanism for nascent leaders to
behave adaptively. As such, the novice leaders may modify their
behaviors, thoughts, and feelings to meet the new expectations.
These changes may habituate and generalize over time. Personality
changes may then ensue.
Using two national longitudinal studies with a quasi-experimental
design, this research makes three contributions. First, it sheds light on
what and how personality traits change over time after one assumes a
supervisory role. Given the debate on whether personality traits are
able to change in adulthood (e.g., Costa & McCrae, 2006), this
1
Following Mintzberg (1971, 2009), we do not distinguish leaders from
managers and supervisors here, although we acknowledge that in other
cases doing so may be more useful.
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2LI ET AL.
investigation serves as a direct test of the predictions from the classic
dispositional perspective and those based on the role-based theory of
personality development by Caspi and Moffitt (1993). Our findings
provide insight into which theory is more accurate in accounting for
personality change or the lack thereof.
Second, this research unravels why personality traits develop
after one transits from an employee into supervisory role through
the mediating role of increases of job demands. The literature on
personality development has been in its infancy in personality
psychology, and thus muss less is known about the mechanisms of
personality change (Roberts & Nickel, 2017). By examining the
mediation through changes of job role demands, our research
paves the way for future research to examine personality change as
the “one of the most vital outcomes of organizational experience”
(Tasselli et al., 2018, p. 483).
Third, by examining whether becoming a leader is related to
personality development over time, this research offers an alternative
perspective on the causal explanation of the relationship between
personality and leadership emergence. Previous research has typically
assumed that personality traits affect leadership emergence only (De-
rue, Nahrgang, Wellman, & Humphrey, 2011; Judge et al., 2002). The
current research challenges and complements this assumption by
showcasing that leadership emergence over time may also shape
personality adaptation. Coupled with previous research, the current
research may inspire future work to integrate the two seemingly
conflicting views and examine possible reciprocal relationships be-
tween personality and leadership (Bandura, 1997; Frese, 1982; Kohn
& Schooler, 1978).
Theoretical Background and Hypotheses
Theory and Research on Personality Development
Two major theoretical perspectives have emerged in the literature
on personality development (Costa Jr, McCrae, & Löckenhoff, 2019;
Specht et al., 2011). According to the classic trait perspective, envi-
ronmental factors cannot change adult personality traits because per-
sonality traits are endogenous and are only under the control of
biological maturation (McCrae & Costa Jr, 1999; McCrae et al.,
2000). Recently, a novel approach, the transactional perspective,
underscores the transactions between personality and the environment
(e.g., Caspi & Moffitt, 1993; Roberts, Caspi, & Moffitt, 2003; Roberts
et al., 2008). The transactional perspective postulates that the envi-
ronment can influence adult personality development, although rarely
dramatically; it also recognizes the role of personality traits in shaping
the environment. Empirical evidence from organizational research
(Tasselli et al., 2018; Woods, Wille, Wu, Lievens, & De Fruyt, 2019)
and the literature on personality psychology (Bleidorn et al., 2018;
Caspi et al., 2005; Donnellan et al., 2015) mostly supports this
middle-ground approach. The theoretical work on personality devel-
opment by Caspi and Moffitt (1993) represents such a transactional
perspective.
Caspi and Moffitt (1993) highlighted the importance of role
transitions in fostering personality development, because transi-
tions to novel roles “require persons to organize their activities
around new tasks” (p. 249). This theory predicts that personality
change occurs “when there is a strong press to behave” and “clear
information is provided about how to behave adaptively” (e.g.,
after assuming a leadership role; Caspi & Moffitt, 1993, p. 248).
Changes in behaviors, thoughts, and feelings may occur in re-
sponse to structured new expectations. Over time, it may promote
changes in patterns of behaviors, thoughts, and feelings, that is,
changes of personality traits (Donnellan et al., 2015; Johnson,
1997).
Role expectations and demands have been proposed as one
major form of such “strong pressure to behave” and inform “how
to behave” (Caspi & Moffitt, 1993, p. 248). Role theory suggests
that a role encompasses a variety of expectations set forth by
others and oneself regarding what is appropriate and what is not
(Biddle, 1979; Katz & Kahn, 1978). Role expectations serve as a
reward structure and a social control mechanism, such that appro-
priate behaviors are reinforced and inappropriate behaviors are
punished. Thus, when people assume new social roles, such as
leadership roles, the new set of role expectations requires them to
behave differently (Ilgen & Hollenbeck, 1991). Over time, appro-
priate behaviors will be reinforced, consolidated, and generalized,
leading to personality change in a bottom-up fashion (Caspi &
Moffitt, 1993).
An emerging body of evidence offers support for this theory of
personality development. For example, transitioning into one’s
first job was related to increases in conscientiousness (Specht,
Egloff, & Schmukle, 2011). Unemployment (Boyce, Wood, Daly,
& Sedikides, 2015) and retirement (Specht et al., 2011) were
related to decrease in conscientiousness.
Becoming a Leader and Changes in Conscientiousness
and Emotional Stability
A role-based perspective on personality development suggests
that transitioning from the role of employee into that of leader
enhances two key personality traits: conscientiousness and emo-
tional stability. Conscientiousness represents the tendency to be
dependable, efficient, organized, and achievement-motivated.
Emotional stability, the opposite of neuroticism, refers to the
tendency to remain calm and poised, and experience functional
emotional adjustment, especially under stressful situations. In
brief, as we elucidate in more detail below, a leadership role entails
taking responsibilities and fulfilling obligations to ensure adequate
performance of oneself, the direct subordinates, the work group,
and the organization (Bass & Bass, 2008; Hogan, Curphy, &
Hogan, 1994; Yukl, 2013). Such demands may include various
forms of work ranging from daily routines to novel and risky tasks
(Fleishman et al., 1991; Mintzberg, 1971; Yukl, 2012). Further-
more, leaders need to form committed, meaningful bonds with a
large number of stakeholders at work, including subordinates,
upper management, and those outside organizations (e.g., Floyd &
Wooldridge, 1992; Reitzig & Maciejovsky, 2015). Requirements
of such leadership roles motivate new leaders to behave accord-
ingly, with adequate behaviors reinforced and inappropriate ones
punished (Ilgen & Hollenbeck, 1991). To successfully meet these
novel role expectations, novice leaders need to be more efficient,
dependable, organized, and behave conscientiously; they also need
to be able to embrace greater challenges, better control and manage
emotions, and remain more poised and worry less in stressful
situations. Over time, those behavioral changes will consolidate
and generalize, leading to increases in conscientiousness and emo-
tional stability (Caspi & Moffitt, 1993).
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3
PERSONALITY CHANGE, LEADERSHIP, AND ROLE TRANSITION
Research on implicit theories of leadership provides further
support for the expectation that leadership roles necessitate indi-
vidual attributes pertaining to high levels of conscientiousness and
emotional stability. This line of research focuses on a central
question: What characteristics does a typical/effective leader have
(Lord, Foti, & De Vader, 1984). It demonstrates that when de-
scribing a typical leader, lay people often use such individual
characteristics as dedicated, disciplined, hardworking, strong, ex-
cellence oriented, and nonirritable (Den Hartog et al., 1999; Of-
fermann, Kennedy, & Wirtz, 1994). Such individual attributes map
well onto definitions of conscientiousness and emotional stability.
Taken in concert, we propose that:
Hypothesis 1: Being promoted to leadership positions is pos-
itively related to increases in conscientiousness over time.
Hypothesis 2: Being promoted to leadership positions is pos-
itively related to increases in emotional stability over time.
Assuming a Leadership Role and Increases in Job
Role Demands
Assuming a supervisory role tends to impose on nascent leaders
a large number of tasks and responsibilities (Ilgen & Hollenbeck,
1991). Our prediction on the relationship between assuming a
leadership role and increases in job role demands is derived mainly
from the literature on the nature of leadership roles and supervi-
sory work (Fleishman et al., 1991; Mintzberg, 1971; Yukl, 2012).
Given the prominence of leadership positions to the effectiveness
of employees, teams, and organizations, the obligations inherently
embedded in leadership role are of great significance to multiple
stakeholders (Bass & Bass, 2008; Yukl, 2013). In his seminal work
on analyzing daily activities of chief executives, Mintzberg (1971)
reported three major sets of roles associated with supervisory
work: information processing roles (e.g., serving as a central point
of collecting and disseminating information), interpersonal roles
(e.g., interacting with people inside and outside organizations), and
decision-making roles (e.g., decision making in face of uncertainty,
such as on initiating changes and allocating resources). Mintzberg
(1971) concluded that leaders tend to “perform a great quantity of
work at an unrelenting pace” (p. B-99). Fleishman et al. (1991)
summarized previous research on effective leadership behaviors and
conclude that there exist four major dimensions of leadership behav-
iors that resemble Mintzberg’s work: information search and struc-
turing, information use in problem solving, managing personnel re-
sources, and managing material resources. Yukl (2012) reviewed
more recent research on effective leadership behaviors and puts forth
four major categories of leadership behaviors: task-oriented,
relationship-oriented, change-oriented, and external.
Taken together, this line of research suggests that assuming
leadership roles requires job incumbents to take on a larger amount
of leadership responsibilities, often of greater significance to or-
ganizations, than when they were employees. Indeed, this notion
has been echoed by theoretical work and findings of research
showing that supervisory jobs are inherently characterized by high
levels of job demands (e.g., heavy workloads and long working
hours; e.g., Cavanaugh, Boswell, Roehling, & Boudreau, 2000;
Ganster, 2005; Hambrick, Finkelstein, & Mooney, 2005; Lee &
Ashforth, 1991; Li, Schaubroeck, Xie, & Keller, 2018). Thus, we
predict that:
Hypothesis 3: Being promoted to leadership positions is pos-
itively related to increases in job demands over time.
Increases in Job Role Demands as a Mediating
Mechanism
As we explained earlier, the theoretical work by Caspi and
Moffitt (1993) predicted that during role transitions, novel role
demands and expectations bring about ambiguity and unpredict-
ability. Given that individuals are motivated to restore a sense of
predictability and clarity, when clear and structured information is
provided, they tend to change their behaviors to adapt to the novel
expectations. Accumulation of behavioral changes over time may
facilitate personality development. Stated differently, changes in
role demands and expectations serve an important underlying
mechanism for the influences of role transitions on personality
change.
In the context of this research, new leadership roles likely
provide a strong situation (Davis-Blake & Pfeffer, 1989) for nov-
ice leaders to behave accordingly to cope with various demands
and responsibilities mandated by leadership obligations. Such new
demands and expectations generate strong pressure and motivation
for nascent leaders to adapt after their transitioning into leadership
roles. Thus, nascent leaders need to work diligently and efficiently,
be organized, challenge themselves, be dependable to subordinates
and other stakeholders, manage their emotions in face of stressful
situations, and be able to deal with uncertain and unpredictable
situations, probably at greater levels than when they were employ-
ees. Such behaviors map well onto the behavioral manifestations
of conscientiousness and emotional stability (Goldberg, 1990).
Over time, as novice leaders successfully enact new leadership
roles, such behaviors may consolidate and habituate, fostering
enhanced conscientiousness and emotional stability (Roberts et al.,
2008). Providing indirect support to this prediction, research has
shown that high job demands may serve as challenges to spur high
well-being, and superb performance (LePine, Podsakoff, & LeP-
ine, 2005; N. P. Podsakoff, LePine, & LePine, 2007).
Hypothesis 4: Increases in job demands mediate the relation-
ship between being promoted to leadership positions and
increases in conscientiousness (H4a) and emotional stability
(H4b).
An Overview of the Current Research
We tested our hypotheses in two three-wave longitudinal studies
with data from National Survey of Midlife in the United States
(MIDUS) and the Household, Income and Labor Dynamics in
Australia (HILDA) Survey. We capitalized on the advantages of
quasi-experimental designs (Cook, Campbell, & Peracchio, 1990;
Grant & Wall, 2009) by comparing the personality development of
two groups of participants (see Figure 1). A treatment group (i.e.,
becoming leaders group) was composed of participants who were
employees at Time 1, promoted into leadership positions by Time
2 (the transition occurred between Time 1 and Time 2), and
remained as leaders at Time 3. We then adopted a propensity score
matching approach (Austin, 2011; Haviland, Nagin, & Rosen-
baum, 2007) to generate an equivalent control group (i.e., the
nonleaders/always-employees group) comprising participants who
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4LI ET AL.
were employees throughout the three waves. The longitudinal
quasi-experimental design “mimics some of the particular charac-
teristics of a randomized controlled trial”(Austin, 2011, p. 399), is
able to “rule out many alternative explanations for development,
such as historical effects...andage-graded develop-
ment”(Schwaba & Bleidorn, 2019, p. 654) and thus allows us to
“strengthen causal inferences” (Grant & Wall, 2009, p. 655) for the
relationship between becoming a leader and subsequent personal-
ity development in a rigorous manner.
Time Lag in the Current Research and the Literature
on Personality Development
Theory and research on time and temporal issues suggest that
the identification of optimal time lags should be informed by
theoretical rationale, research evidence, and pragmatic concerns in
data collection (Dormann & Griffin, 2015; Mitchell & James,
2001; Ployhart & Vandenberg, 2010; Shipp & Cole, 2015). The-
oretically, time lags should be sufficient to allow an effect to arise
so that researchers can capture meaningful changes of a construct
of interest. The selection of time lags should also be in alignment
with prior research that have observed significant development of
the construct, or the lack thereof. Pragmatically, collecting longi-
tudinal data too frequently may cause participants’ fatigue and
boredom and thus compromise data quality. Thus, identifying the
optimal time lags requires researchers to balance all the above
concerns to develop an appropriate and feasible design to tackle
their research questions. In practice, however, because of the
dearth of theories on time and temporal issues in most areas of
organizational research (Dalal, Alaybek, & Lievens, 2020; Mitch-
ell & James, 2001; N. P. Podsakoff et al., 2019; Shipp & Cole,
2015), researchers tend to give greater weight to prior research
findings and feasibility of data collection in their decision.
In this research, we followed the above principles to seek
longitudinal data of appropriate time intervals to test our research
questions. Our selection of time intervals was informed by previ-
ous research on personality development (Roberts et al., 2006) and
recent work in longitudinal research (Dormann & Griffin, 2015;
Mitchell & James, 2001). Theoretically, the effect of life events on
personality change may take years to consolidate and materialize,
before it reaches its peak and decays (Donnellan et al., 2015;
Mitchell & James, 2001). A meta analytic study (Roberts et al.,
2006) has shown a positive correlation between the magnitude of
personality change and time interval, ranging from 1 year to 43
years. Thus, we rely on the above evidence and guidance to
identify the time frames in studying personality change.
In Study 1, we examined the direct relationship between becom-
ing a leader and subsequent changes in personality traits (Hypoth-
eses 1 and 2) with a time lag of 10 years. We then conducted Study
2, to further investigate the mediating role of change in job role
demands (Hypotheses 3 and 4) with a time lag of 4 years. Con-
vergent findings from the two studies with different contexts and
time intervals indicate the robustness of our conclusions.
Study 1
Method
Participants and procedure. Our research was approved by
the Survey and Behavioral Research Ethics Committee of the
Chinese University of Hong Kong (“Influences of becoming a
leader on personality change: A longitudinal investigation”, refer-
ence No. SBRE-19 –509 and “Influences of becoming a leader on
personality change: A validation study of personality scales”,
reference No. SBRE-19-749). We used data from the three-wave
MIDUS study in the United States in Study 1. MIDUS is a
longitudinal interdisciplinary research project on human well-
being and aging, which has been sponsored by MacArthur Foun-
dation Research Network and National Institute of Aging (P01-
AG020166 and U19-AG051426). The first wave of the MIDUS
data was collected from 1995 to 1996 from a national representa-
tive sample of the U.S. The same participants were contacted in the
second and third waves, which took place approximately 10 years
and 20 years later, respectively. In each of the three waves,
personality variables were collected through self-administered
questionnaires and leadership information phone interviews.
No research on a similar topic using MIDUS data has been pub-
lished. In this research, we included working individuals who pro-
vided complete data on gender, age, education level, and supervisor
roles across the three waves and at least one wave data on personality
variables. With complete information on leadership roles across time,
we were able to generate two groups of participants. The becoming
leaders group comprised those who were employees at Time 1 but
were promoted into supervisory positions by Time 2 and remained
supervisors at Time 3. We used a propensity score matching method
(Austin, 2011; Haviland et al., 2007) to form a nonleaders group with
employees across the three waves.
As suggested previously (Bliese & Ployhart, 2002; Little & Rubin,
2002; McArdle, 2009; Newman, 2009), we used all available data
with maximum likelihood (ML; also known as full information max-
imum likelihood [FIML]) estimation in Mplus. Newman (2014)
pointed out that using “all the available data” is the first principle of
missing data analysis (p. 384). In total, 90 participants were included
in the becoming leaders group (61 provided complete data) and 161
in the nonleaders group (128 provided complete data). Information on
demographic variables, income and personality variables at Time 1
for the two groups are reported in Table 1.
Measures.
Becoming a leader. Whether an employee became a leader
(i.e., leadership emergence) during the period of this research was
assessed with information on one’s leadership role occupancy at
Employee Leader
EmployeeEmployeeEmployee
Leader
promoted to
leadership
positions
Non-leaders group
Matched on age,
gender, education,
income and personality
at Time 1
Becoming-leaders group
Time 1 Time 2 Time 3
Figure 1. Change of leadership positions for the two groups of partici-
pants. Open dots denote a nonleader, employee position; closed dots denote
a leadership position. Becoming leaders group and nonleaders group were
matched via a propensity score matching on age, gender, education, in-
come, and the Big Five personality traits at Time 1.
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5
PERSONALITY CHANGE, LEADERSHIP, AND ROLE TRANSITION
the three measurement occasions. Prior leadership research has
assessed leadership role occupancy by asking participants whether
they held or had held supervisory roles (Day, Sin, & Chen, 2004;
Judge et al., 2002; Li, Arvey, & Song, 2011). In Sherman et al.’s
(2012) study, which provided the most useful point of reference for
the present research, leadership role occupancy was assessed with
the question, “Are you responsible for managing others?”.
Accordingly, leadership role occupancy was assessed using
responses to a question in the three waves of MIDUS survey: “Do
you supervise anyone on your main job?” Responses to the ques-
tion were converted into a variable indicating leadership roles (i.e.,
0⫽nonleaders, 1 ⫽leaders) at each time point. Such information
was further used to generate the variable of becoming a leader. An
individual was treated as becoming a leader if s/he was an em-
ployee at Time 1, was promoted into leadership positions by Time
2, and remained as supervisors at Time 3. These 90 individuals
formed the becoming leaders group, which was used as the treat-
ment group in our analyses (Cook et al., 1990; Grant & Wall,
2009).
We then adopted the propensity score matching approach (Aus-
tin, 2011; Haviland et al., 2007) to create an equivalent control
group (i.e., the nonleaders group). In total, 313 participants were
employees throughout the three waves. From these participants,
the control group was created using propensity score matching to
approximate the effect of randomization by matching values of
confounding factors between the treatment and the control group
(Austin, 2011). Specifically, R package MatchIt was used to create
propensity scores through a logistic regression where participants’
leadership status was predicted by the nine individual difference
variables (Ho, Imai, King, & Stuart, 2011), including age, gender,
education level, income, and the Big Five personality traits at Time
1. We used two-to-one matching in this study. For each participant
in the treatment group, the algorithm searched for up to two
participants from the control group who provided most similar
propensity scores based on the nine variables. Previous Monte
Carlo studies have shown that two-to-one matching was more
optimal than other matching methods in terms of avoiding sam-
pling bias (Austin, 2010). Further, following previous recommen-
dations (Austin, 2010, 2011), the search was conducted with a
caliper of width equal to 0.2 SD of the logit of the propensity score
for the treatment group participants. In other words, the difference
in the logit of the propensity score between the two groups in the
propensity-score-matched set was required to be less than 0.2 SD
of the treatment group participants. In the final analyses, 90
participants were included in the treatment group and 161 in the
generated equivalent control group.
2
The method has recently been
used in research on personality change (e.g., Schwaba & Bleidorn,
2019).
Conscientiousness and emotional stability. MIDUS research-
ers assessed participants’ Big Five personality traits three times
with the Midlife Development Inventory (Lachman & Weaver,
1997). This inventory included personality items from previous
research (Goldberg, 1990) and has been used in previous research
(Human et al., 2013; Kornadt, 2016; Mu, Luo, Nickel, & Roberts,
2016; Turiano et al., 2012). Participants indicated the extent to
which they agreed or disagreed to the items on a four-point
response scale ranging from 1 (a lot)to4(not at all). Their
responses were coded such that higher scores reflect higher per-
sonality traits. Previous research on the factor structures of the
personality scales found significant cross-loadings for some items
and used different versions of the personality scales (Iveniuk,
Laumann, Waite, McClintock, & Tiedt, 2014; Zimprich, Alle-
mand, & Lachman, 2012). Based on these studies and research on
measurement invariance of the MIDUS personality scales (South,
Jarnecke, & Vize, 2018) and personality scales in general (Dong &
Dumas, 2020), conscientiousness and emotional stability were
evaluated in this study by four and three items respectively. Sam-
ple items were “organized” (conscientiousness), and “moody”
(emotional stability, negatively worded). Internal consistency co-
efficients (Cronbach’s alpha) for conscientiousness were .56, .48,
and .63, respectively for the three waves (the coefficients were
relatively low due to the use of a negatively worded item). The
emotional stability scale also demonstrated appreciable internal
consistency reliabilities (␣⫽.81, .73, and .69). All items are
displayed in the Appendix.
We conducted a validity study using an independent sample via
Amazon’s Mechanical Turk (MTurk, Buhrmester, Kwang, & Gos-
ling, 2011) to demonstrate the convergent validities and test-retest
reliabilities of the personality measures used in this study. We
invited 230 participants to complete online surveys twice with an
interval of one week. In total, 150 participants (average age was
35.81; 58.7% were male) completed both questionnaires with
usable data. Each questionnaire included measures of the Big Five
personality traits used in Study 1 (and also in Study 2), 44
personality items from the Big Five Inventory (John, Naumann, &
Soto, 2008), and the one hundred-item version of the Big Five
personality instrument from the International Personality Item
Pool (Goldberg et al., 2006). Results (see Table 2) show that
personality measures used in the first (and the second) study
correlated highly (ranging from .82 to .93) with corresponding
measures with Big Five Inventory and International Personality
Item Pool. Test–retest reliability coefficients ranged from .81 to
2
As Austin (2010) noted, “Because of the imposition of the constraint
that the logit of the propensity score of matched subjects could differ by,
at most, a fixed amount, it is possible that insufficient numbers of untreated
subjects will be available for matching to some treated subjects. Thus,
when using M:1 matching (M⬎1), it is conceivable that, although some
matched sets will contain Muntreated subjects, some matched sets will
contain fewer than Muntreated subjects” (p. 1094). This seems to be the
case for our current propensity matching (161:90 ⫽1.79:1).
Table 1
Mean Individual Characteristics at Time 1 for the Two Groups
After Propensity Score Matching (Study 1)
Matched individual
characteristics
Becoming
leaders group
(n⫽90)
Nonleaders
group
(n⫽161)
Age 37.10 37.66
Gender (% of males) 57.8 55.9
Education 3.28 3.21
Log transformed annual income 10.26 10.26
Conscientiousness 3.43 3.42
Emotion stability 2.82 2.78
Agreeableness 3.43 3.40
Extraversion 3.34 3.27
Openness 3.10 3.05
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6LI ET AL.
.90. The results suggest the personality measures used in this
research have sound psychometric properties.
Control variables. Gender, age, and education have been
found to be related to leadership emergence (Bass & Bass, 2008)
and personality development (Caspi et al., 2005; Donnellan et al.,
2015; Roberts & DelVecchio, 2000; Roberts et al., 2006). Al-
though propensity score matching generated in principle equal
mean levels of those variables across the two groups, their variance
may not necessarily be the same. In keeping with previous research
(e.g., Specht et al., 2011), we thus controlled for these variables to
rule out their influences more completely.
Analytical strategy. We adopted the latent growth curve
modeling approach (Chan, 1998; Ployhart & Vandenberg, 2010;
Preacher, Briggs, Wichman, & MacCallum, 2008) to test our
hypotheses. Univariate latent growth curve modeling was used to
model two parameters: intercept (i.e., starting point) and slope
(i.e., change). As shown in the right-hand side of Figure 2, a
personality variable is modeled with an intercept and a slope (the
same for job role demands).
We first performed univariate latent growth curve analyses. We
used a dummy leadership variable (i.e., 0 ⫽the nonleaders group,
1⫽the becoming leaders group) to predict the change parameters
(i.e., slopes). Significant coefficients of the leadership variable
provide direct support for the influences of becoming a leader on
personality change (Hypotheses 1 and 2). Consistent with previous
research (e.g., Chawla, MacGowan, Gabriel, & Podsakoff, 2020;
Newton, LePine, Kim, Wellman, & Bush, 2020; Sherf & Morri-
son, 2020), we used the following indices to assess model fit:
comparative fit index (CFI), root mean square error of approxi-
mation (RMSEA), and standardized root-mean-square residual
(SRMR).
Results
Scale independence and measurement equivalence. As sug-
gested in previous research (McArdle, 2009; Ployhart & Vanden-
berg, 2010; Preacher et al., 2008), we performed confirmatory
factor analyses to demonstrate the independence of research vari-
ables at each wave and measurement invariance tests of each
variable across time. Results show satisfactory model fit indices
for a two-factor model with conscientiousness and emotional sta-
bility (see Table 3). Thus, the personality variables were indepen-
dent from each other.
Table 2
Correlations Between the Personality Measures in the Current Research and Corresponding Personality Variables from IPIP and BFI
and Test-Retest Reliabilities in the Validation Study
Correlation
Conscientiousness Extroversion Agreeableness Emotional stability Openness
Study 1 Study 2 Study 1 Study 2 Study 1 Study 2 Study 1 Study 2 Study 1 Study 2
Correlation at Time 1
IPIP .86
ⴱⴱ
.86
ⴱⴱ
.87
ⴱⴱ
.91
ⴱⴱ
.92
ⴱⴱ
.90
ⴱⴱ
.92
ⴱⴱ
.85
ⴱⴱ
.85
ⴱⴱ
.86
ⴱⴱ
BFI .90
ⴱⴱ
.81
ⴱⴱ
.91
ⴱⴱ
.92
ⴱⴱ
.82
ⴱⴱ
.82
ⴱⴱ
.92
ⴱⴱ
.78
ⴱⴱ
.84
ⴱⴱ
.85
ⴱⴱ
Correlation at Time 2
IPIP .89
ⴱⴱ
.87
ⴱⴱ
.87
ⴱⴱ
.88
ⴱⴱ
.91
ⴱⴱ
.88
ⴱⴱ
.92
ⴱⴱ
.84
ⴱⴱ
.86
ⴱⴱ
.83
ⴱⴱ
BFI .92
ⴱⴱ
.86
ⴱⴱ
.92
ⴱⴱ
.93
ⴱⴱ
.83
ⴱⴱ
.79
ⴱⴱ
.93
ⴱⴱ
.78
ⴱⴱ
.85
ⴱⴱ
.85
ⴱⴱ
Test-retest reliability .83
ⴱⴱ
.89
ⴱⴱ
.90
ⴱⴱ
.92
ⴱⴱ
.89
ⴱⴱ
.84
ⴱⴱ
.90
ⴱⴱ
.89
ⴱⴱ
.81
ⴱⴱ
.84
ⴱⴱ
Note.N⫽150. IPIP ⫽International Personality Item Pool; BFI ⫽Big Five Inventory.
ⴱⴱ
p⬍.01.
T1 Job
demands
T2 Job
demands
T3 Job
demands
Intercept
Personality
Intercept
demands
Slope
demands
Slope
Personality
T1
Personality
T2
Personality
T3
Personality
εjd
εjd
εjd
ˢpers
ˢpers
ˢpers
1
1
1
0
1
2
1
1
1
0
1
2
Figure 2. Bivariate latent growth curve model for personality and job role demands. ε⫽residual variance.
jd ⫽job demands; pers ⫽personality; T1 ⫽Time 1; T2 ⫽Time 2; T3 ⫽Time 3.
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7
PERSONALITY CHANGE, LEADERSHIP, AND ROLE TRANSITION
We then compared model fit indices among three types of
measurement invariance, configural (i.e., form), metric (i.e., factor
loading), and scalar (i.e., intercept) equivalence (Vandenberg &
Lance, 2000) across the three time points. We followed Finkel
(1995) and correlated error terms of the same item across time. The
results (see Table 3) demonstrated sufficient measurement equiv-
alence for the measures used in Study 1, which is consistent with
previous research (e.g., Schwaba & Bleidorn, 2018).
Tests of hypotheses. The means, standard deviations, and
correlations among study variables are presented in Table 4. We
compared the means of each of the study variables across the
three waves as a preliminary examination of their changes and the
rank-order stability for the study variables (see Table 5). The
results show significant increases in emotional stability from Time
1 to Time 2 and Time 3 for both the becoming leaders group and
the nonleaders group. The becoming leaders group also experi-
enced significant increases in conscientiousness after Time 2,
whereas the nonleaders group seemed to experience reduced con-
scientiousness from Time 2 to Time 3. We also calculated the
effect sizes of the differences using Cohen’s d(1988) for repeated
measures. The differences in personality change were further
tested with latent growth curve modeling.
Hypotheses 1 and 2 predicted that becoming leaders is related to
significant subsequent increases in conscientiousness and emo-
tional stability over time. Results (see Table 6) show that becom-
ing a leader significantly correlated with increases in conscien-
tiousness (coefficient ⫽.08, p⬍.05, Model 1), but not with
increases in emotional stability (coefficient ⫽.04, p⬎.10, Model
2). We also calculated the effect size of the influence of becoming
a leader on personality change using the approach by Feingold
(2009, 2017). This approach produces an effect size index equiv-
alent to Cohen’s d(1988). The effect sizes were .37 and .10 for
conscientiousness and emotional stability respectively. Thus Hy-
pothesis 1 was supported but Hypothesis 2 was not.
We further plotted the development of conscientiousness for the
two groups (Figure 3A) with the means of conscientiousness (i.e.,
raw scores) across time. The becoming leaders group experienced
significant increases in conscientiousness (slope ⫽.08, p⬍.01)
across the three waves. However, the change in conscientiousness
for the nonleaders group was not significant (slope ⫽⫺.01, p⬎
.10). This result provides further evidence for the relationship
between becoming a leader and subsequent increases of conscien-
tiousness over time.
3
Supplementary analysis. We performed additional analyses
with an alternative leadership measure to supplement our rudimen-
tary measure of leadership role occupancy. Specifically, we used
an alternative leadership measure capturing span of control with an
item asking participants to report “How many people do you
supervise?”, if they had supervised others on their main job.
Results show that with this alternative measure, becoming a leader
had a significant impact on both increases in conscientiousness and
emotional stability. Thus, it seems that the alternative measure of
span of control is more sensitive in generating significant findings.
Study 2
Method
Participants and procedure. In Study 2, we used three-wave
longitudinal data from the HILDA Survey (Summerfield et al.,
3
Results show that influences of becoming a leader on changes of
agreeableness, openness, and extraversion were not significant. This was
also the case for Study 2.
Table 3
Model Fit Indices for Testing Measurement Invariance and Variable Independence for Study 1
Model
2
(df) CFI RMSEA SRMR ⌬CFI ⌬RMSEA ⌬SRMR
Conscientiousness
Configural invariance 72.25
ⴱⴱⴱ
(47) .965 .046 .063 — — —
Metric invariance 92.79
ⴱⴱⴱ
(53) .946 .055 .074 ⫺.019 .009 .011
Scalar invariance 114.95
ⴱⴱⴱ
(61) .926 .059 .076 ⫺.039 .013 .013
Emotional stability
Configural invariance 39.10
ⴱⴱⴱ
(21) .981 .059 .036 — — —
Metric invariance 43.97
ⴱⴱⴱ
(25) .980 .055 .035 ⫺.001 ⫺.004 ⫺.001
Scalar invariance 91.01
ⴱⴱⴱ
(31) .936 .088 .058 ⫺.045 .029 .022
CFA
Time 1 43.44
ⴱⴱⴱ
(13) .928 .097 .069 — — —
Time 2 35.93
ⴱⴱⴱ
(13) .902 .089 .069 — — —
Time 3 27.82
ⴱⴱⴱ
(13) .934 .068 .076 — — —
Note.N⫽251. CFI ⫽comparative fit index; RMSEA ⫽root mean square error of approximation; SRMR ⫽standardized root mean square residual;
CFA ⫽confirmatory factor analysis.
ⴱⴱⴱ
p⬍.001.
Table 4
Ms, SDs, and Correlations for Study 1 Variables
Variable MSD12 3 456
1. Conscientiousness T1 3.42 .44 —
2. Emotional stability T1 2.73 .79 .26 —
3. Conscientiousness T2 3.48 .42 .58 .10 —
4. Emotional stability T2 2.98 .70 .18 .59 .18 —
5. Conscientiousness T3 3.48 .45 .60 .26 .59 .19 —
6. Emotional stability T3 2.97 .67 .08 .58 .15 .70 .19 —
7. Becoming a leader
a
1.36 .48 .01 .03 ⫺.04 .00 .17 .08
Note.N⫽189 –251. Correlations ranging from .14 to .18 were signifi-
cant at p⬍.05; correlations from .19 to .77 were significant at p⬍.01.
T1 ⫽Time 1; T2 ⫽Time 2; T3 ⫽Time 3.
a
0⫽nonleaders group, 1 ⫽becoming leaders group.
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8LI ET AL.
2017; Wooden, Freidin, & Watson, 2002). The major purpose of
the HILDA study is to track economic conditions and health and
well-being of Australians over time. The survey started with an
initial sample of households that were representative of all Aus-
tralian households in 2001 and have since then retained its cross-
sectional representativeness over time (see Summerfield et al.,
2017). Members of each household have been traced annually. We
used data from the survey years of 2005, 2009, and 2013, when the
Big Five personality traits were assessed. Thus, the time interval
was 4 years. In these years, respondents also reported whether they
held leadership positions and their job role characteristics.
In our analyses, we selected working participants who provided
complete data on sex, age, education level, work status (e.g., full
time vs. part time), and supervisory roles across the three waves
and at least one wave of data on major study variables. As in Study
1, we included two groups (becoming leaders group and nonlead-
ers group) of participants based on complete information on su-
pervisory status in analyses and handled missing data with the ML
estimation in Mplus. Information on age, gender, education level,
pay and personality at Time 1 for the two groups after propensity
score matching was provided in Table 7.
Measures.
Becoming a leader. As in Study 1, becoming a leader was
assessed with information on leadership role occupancy across the
three waves. Participants were asked a question: “As part of your
job, do you normally supervise the work of other employees?”
Responses to the question were coded (i.e., 0 ⫽nonleaders, 1 ⫽
leaders) for each time point. Such information then was used to
identify whether an employee at Time 1 became a leader by Time
2 and remained as a leader at Time 3. A total of 431 individuals
(342 provided complete data) were identified and they formed the
becoming leaders group.
Propensity score matching method was adopted to generate an
equivalent control group, the nonleaders group with equivalent
levels of age, gender, education level, pay, and personality traits at
Time 1. After propensity score matching, the nonleader control
group included 818 participants (675 provided complete data).
Conscientiousness and emotional stability. Big Five person-
ality traits were assessed using descriptive adjectives from Saucier
(1994), which are based on Goldberg’s (1990) scale of Big Five
personality traits. Participants were asked to indicate the extent to
which they agreed or disagreed to the adjectives on a response
scale ranging from 1 (strongly disagree)to7(strongly agree).
Consistent with the approach adopted in Study 1 in constructing
scales, conscientiousness and emotional stability were captured by
three and four items, respectively. Sample items were “orderly”
(conscientiousness), and “moody” (emotional stability, negatively
worded). Internal consistency coefficients for conscientiousness
were .73, .75, and .78, respectively for the three waves. The
coefficients were also appreciable for emotional stability (␣⫽.73,
.74, and .72). The Appendix shows all the items.
Job role demands. Participants’ work role related job de-
mands were assessed using a scale of three questions (␣⫽.72, .72,
and .75, respectively) adapted from the Job Content Questionnaire
(Karasek, 1979; Karasek et al., 1998) on a 7-point scale ranging
from 1 (strongly disagree)to7(strongly agree). The three items
are “I have to work fast in my job,” “I have to work very intensely
in my job,” and “I don’t have enough time to do everything in my
job.” Job demands have been widely used in previous research to
reflect the amount of various types of workloads and responsibil-
ities associated with work roles in organizations (Ganster & Rosen,
2013; Hambrick et al., 2005; N. P. Podsakoff et al., 2007; Son-
nentag & Frese, 2012).
Control variables. Participants’ gender, age, education, and
full-time work status (full time vs. part time) may be related to
both leadership emergence (Bass & Bass, 2008), job role demands
(Ganster & Rosen, 2013; Sonnentag & Frese, 2012), and person-
Table 5
Means, Mean-Level Differences, and Rank-Order Stabilities for Personality Traits (Study 1)
Study variable
M/SD Effect size (Cohen’s d) Rank-order stability
T1 T2 T3 d
12
d
13
d
23
r
12
r
13
r
23
Becoming leaders group
Conscientiousness 3.43/.45 3.46/.42 3.59/.38 .05 .36
ⴱ
.43
ⴱⴱ
.60
ⴱⴱ
.43
ⴱⴱ
.68
ⴱⴱ
Emotional stability 2.80/.68 3.03/.62 3.01/.59 .43
ⴱⴱⴱ
.46
ⴱⴱⴱ
.01 .52
ⴱⴱ
.55
ⴱⴱ
.67
ⴱⴱ
Nonleaders group
Conscientiousness 3.45/.44 3.51/.40 3.44/.47 .13 .08 ⫺.19 .56
ⴱⴱ
.68
ⴱⴱ
.58
ⴱⴱ
Emotional stability 2.81/.70 2.98/.62 2.92/.61 .33
ⴱⴱⴱ
.24
ⴱ
⫺.10 .63
ⴱⴱ
.59
ⴱⴱ
.71
ⴱⴱ
Note.N⫽161 for the nonleaders group and 90 for the becoming leaders group. d-coefficients indicate standardized differences in mean level between
measurement occasions: positive values signify mean-level increases and negative values mean-level decreases. r-coefficients indicate correlations of a
variable between two measurement occasions. T1 ⫽Time 1; T2 ⫽Time 2; T3 ⫽Time 3.
ⴱ
p⬍.05.
ⴱⴱ
p⬍.01.
ⴱⴱⴱ
p⬍.001.
Table 6
Results of Latent Growth Curve Models: Study 1
Predictor
Slope of conscientiousness
(Model 1), Coefficient
(SE)
Slope of emotional
stability (Model
2), Coefficient
(SE)
Becoming a leader .08
ⴱ
(.03) .04 (.05)
Model fit indices
2
(df) 9.16 (6) 17.88
ⴱ
(5)
CFI .986 .946
RMSEA .046 .101
SRMR .074 .024
Note.N⫽251 (90 for the becoming leaders group and 161 for the
non-leaders group). Becoming a leader: 0 ⫽nonleaders group, 1 ⫽
becoming leaders group. CFI ⫽comparative fit index; RMSEA ⫽root
mean square error of approximation; SRMR ⫽standardized root mean
square residual. Slopes indicate changes and intercepts indicate starting
points.
ⴱ
p⬍.05.
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9
PERSONALITY CHANGE, LEADERSHIP, AND ROLE TRANSITION
ality development (Caspi et al., 2005; Donnellan et al., 2015;
Roberts & DelVecchio, 2000; Roberts et al., 2006). In keeping
with previous research (e.g., Specht et al., 2011), we thus included
them in analyses to rule out their influences more completely
because their variance may not be necessarily the same across the
two groups. When testing the indirect effects of becoming a leader
on personality change through increases in job role demands, we
controlled the starting point (i.e., intercept) of job demands and the
starting point of personality traits in predicting changes of consci-
entiousness (Bleidorn, 2012; Hudson, Roberts, & Lodi-Smith,
2012).
Analytical strategy. We used the latent growth curve model-
ing approach (Chan, 1998; Ployhart & Vandenberg, 2010;
Preacher et al., 2008) in Study 2. Univariate latent growth curve
models were estimated to test Hypotheses 1, 2, and 3. To test the
mediation hypothesis (Hypothesis 4), we performed bivariate (with
a personality trait and job role demands, see Figure 2) latent
growth curve modeling with a binary leadership variable indicat-
ing becoming leader or nonleaders group (i.e., 0 ⫽the nonleaders
group, 1 ⫽the becoming leaders group). We also tested the
indirect effect of becoming a leader on change of personality
through change of job demand and calculated the confidence
interval.
Results
Scale independence and measurement equivalence. We
conducted confirmatory factor analyses to demonstrate the inde-
pendence of study variables at each wave of data collection and
measurement equivalence of each variable across time (McArdle,
2009; Ployhart & Vandenberg, 2010; Preacher et al., 2008). Re-
sults show that a three-factor model (conscientiousness, emotional
stability, and job role demands) fit the data well at each wave (see
Table 8). Thus, the variables in Study 2 were sufficiently distinct
from each other.
Then we compared three types of measurement invariance,
configural (i.e., form), metric (i.e., factor loading), and scalar (i.e.,
intercept) equivalence (Vandenberg & Lance, 2000) across the
three measurement occasions. Results show appreciable measure-
ment equivalence over time.
Tests of hypotheses. Table 9 displays the means, standard
deviations, and correlations among Study 2 variables. We con-
ducted a preliminary examination of changes in personality traits
and job role demands by comparing their means across time (see
Table 10). The results show significant increases of conscientious-
ness and job demands over time for both the leader and nonleaders
group. The nonleaders group experienced significant increases in
emotional stability.
We first examined Hypotheses 1 and 2 on the relationship
between becoming a leader and subsequent changes of conscien-
tiousness and emotional stability. Recall that we tested these
relationships using leadership as a binary variable (i.e., 0 ⫽non-
leaders group and 1 ⫽becoming leaders group). Results (Model 1,
Table 11) reveal that becoming a leader was significantly related to
increases in conscientiousness (coefficient ⫽.07, p⬍.05), lend-
ing support to Hypothesis 1. The effect size (Feingold, 2009, 2017)
was .12. The relationship between becoming a leader and change
of emotional stability was not significant (coefficient ⫽⫺.01, p⬎
.10, Model 4; effect size ⫽⫺.02). Thus Hypothesis 2 received no
support.
We plotted the change of conscientiousness for the two groups
with the means of conscientiousness across time (Figure 3B).
Although the nonleaders group experienced significant increases
Table 7
Mean Individual Characteristics at Time 1 for the Two Groups
after Propensity Score Matching (Study 2)
Matched individual
characteristics
Becoming leaders
group
(n⫽431)
Nonleaders
group
(n⫽818)
Age 34.01 34.82
Gender (% of males) 53.1 49.9
Education 5.57 5.68
Log transformed annual income 10.11 10.10
Conscientiousness 4.90 4.91
Emotion stability 4.85 4.87
Agreeableness 5.28 5.30
Extraversion 4.64 4.63
Openness 4.42 4.38
Figure 3. Mean trends for conscientiousness and job role demands (based
on raw scores). (A) Mean trends for conscientiousness in Study 1. (B)
Mean trends for conscientiousness in Study 2. (C) Mean trends for job role
demands in Study 2.
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10 LI ET AL.
in conscientiousness over time (slope ⫽.08, p⬍.001), the
becoming leaders group exhibited greater increases (slope ⫽.16,
p⬍.001).
Hypotheses 3 stated that after becoming leaders, individuals’ job
role demands increase. This hypothesis was supported by a sig-
nificant relationship between the leadership variable and changes
in job role demands (coefficient ⫽.17, p⬍.001, Model 2 of Table
11; effect size ⫽.27). This finding is corroborated by the result of
plotting the change of job demands for the two groups over time
(Figure 3C). The becoming leaders group experienced greater
increases in job demands (slope ⫽.22, p⬍.001) than the non-
leaders group (slope ⫽.06, p⬍.05).
Hypothesis 4 dealt with the mediating role of change in job role
demands in the relationship between becoming a leader and per-
sonality changes. Because the relationship between becoming a
leader and increases in emotional stability was not significant, the
mediation hypothesis was tested only with conscientiousness. In
the analyses, we used the leadership variable, the slope of job role
demands, the intercept of job role demands, and the intercept of
conscientiousness to predict the slope of conscientiousness. The
influence of the leadership variable became nonsignificant (coef-
ficient ⫽⫺.01, p⬎.10, Model 3 of Table 11), whereas the
influence of the slope of job role demands was still significant
(coefficient ⫽.52, p⬍.05). The indirect effect was .071 (95%
confidence interval [.006, .192]). Thus, the results support the
mediating role of changes of job demands in the relationship
between becoming a leader and change in conscientiousness. Hy-
pothesis 4 was partially supported.
General Discussion
Inspired by the burgeoning literatures on personality develop-
ment, this study adopted a role-based perspective of personality
development at work and examined what, how, and why person-
ality traits may develop after one’s adoption of novel leadership
roles. In a recent review, Tasselli et al. (2018) pointed out that one
important reason for the dearth of organizational research on
personality change is that “researchers have tended to render such
Table 8
Model Fit Indices for Testing Measurement Invariance and Variable Independence for Study 2
Model
2
(df) CFI RMSEA SRMR ⌬CFI ⌬RMSEA ⌬SRMR
Conscientiousness
Configural invariance 26.25 (21) .999 .014 .013 — — —
Metric invariance 28.79 (25) .999 .011 .017 .000 ⫺.003 .004
Scalar invariance 81.22 (31) .988 .036 .031 ⫺.011 .022 .018
Emotional stability
Configural invariance 117.22
ⴱⴱⴱ
(47) .986 .035 .026 — — —
Metric invariance 129.18
ⴱⴱⴱ
(53) .985 .034 .033 ⫺.001 ⫺.001 .007
Scalar invariance 141.88
ⴱⴱⴱ
(61) .984 .033 .032 ⫺.002 ⫺.002 ⫺.006
Job demands
Configural invariance 55.43
ⴱⴱⴱ
(21) .991 .036 .024 — — —
Metric invariance 64.63
ⴱⴱⴱ
(25) .989 .036 .031 ⫺.002 .000 .007
Scalar invariance 100.89
ⴱⴱⴱ
(31) .981 .043 .039 ⫺.010 .007 .015
CFA
Time 1 155.91
ⴱⴱⴱ
(32) .956 .058 .041 — — —
Time 2 155.16
ⴱⴱⴱ
(32) .956 .058 .041 — — —
Time 3 119.07
ⴱⴱⴱ
(32) .971 .049 .035 — — —
Note.N⫽1,249. CFI ⫽comparative fit index; RMSEA ⫽root mean square error of approximation; SRMR ⫽standardized root mean square residual;
CFA ⫽confirmatory factor analysis.
ⴱⴱⴱ
p⬍.001.
Table 9
Ms, SDs, and Correlations for Study 2 Variables
Variable MSD 123456789
1. Conscientiousness T1 4.92 1.14 —
2. Emotional stability T1 4.88 1.11 .20 —
3. Conscientiousness T2 5.03 1.11 .65 .18 —
4. Emotional stability T2 4.93 1.10 .20 .61 .22 —
5. Conscientiousness T3 5.15 1.14 .60 .17 .72 .15 —
6. Emotional stability T3 4.96 1.09 .24 .57 .25 .64 .22 —
7. Job demands T1 4.37 1.32 .02 ⫺.07 .01 ⫺.03 .04 ⫺.06 —
8. Job demands T2 4.45 1.32 ⫺.01 ⫺.05 .03 ⫺.07 .05 ⫺.06 .44 —
9. Job demands T3 4.61 1.35 ⫺.05 ⫺.08 ⫺.03 ⫺.10 ⫺.02 ⫺.10 .39 .53 —
10. Becoming a leader
a
1.35 .48 ⫺.01 ⫺.02 .06 ⫺.03 .07 ⫺.02 .05 .20 .18
Note.N⫽1,014 –1,249. Correlations ranging from .06 to .08 were significant at p⬍.05; correlations from .09 to .72 were significant at p⬍.01. T1 ⫽
Time 1; T2 ⫽Time 2; T3 ⫽Time 3.
a
0⫽nonleaders group, 1 ⫽becoming leaders group.
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11
PERSONALITY CHANGE, LEADERSHIP, AND ROLE TRANSITION
change impossible by definition” (p. 44). This may have to do with
the influence by the Five Factor theory of personality. Personality
psychology has gone through a similar period of development. But
recently, examining personality development has gained momen-
tum in personality psychology (for reviews, see Bleidorn et al.,
2018; Caspi et al., 2005; Donnellan et al., 2015). Heeding a recent
call (Tasselli et al., 2018), we investigated changes in conscien-
tiousness and emotional stability during leadership emergence. We
hope this research will stimulate more future research on person-
ality development at work.
Implications for Theory and Research
Our opening quote from Batman Begins suggests that what
people do may shape their personality traits. Consistently, results
from both studies revealed that after becoming leaders, individuals
enhanced their levels of conscientiousness, meaning that they
became more dependable, organized, and efficient. To perform
various job responsibilities and obligations embedded in leader-
ship roles, nascent leaders appear to be dictated by the structured
role expectations to behave more conscientiously (Caspi & Mof-
fitt, 1993). Successful enactment of leadership roles over time may
facilitate the conscientious behaviors to be habituated and gener-
alized. The changes of behavior patterns essentially give rise to
changes in conscientiousness.
Our finding that transitioning into leadership roles was related to
subsequent increases in conscientiousness only, not other Big Five
personality traits, is consistent with previous research. For exam-
ple, Bleidorn (2012) reported that transitioning from school to
work resulted in increases only in conscientiousness of the Big
Five personality trait. Specht et al. (2011) found that among the
Big Five, only conscientiousness increased (decreased) when peo-
ple started the first jobs (retired). Our finding is also consistent
with research showing that conscientiousness is the best predictor
of job performance (Barrick & Mount, 1991) as well as one of the
best predictors of leadership (Derue et al., 2011; Judge et al., 2002;
Oh & Berry, 2009).
It is important to note that our findings were obtained with a
quasi-experimental design (Cook et al., 1990; Grant & Wall, 2009)
by comparing personality development of two groups of individ-
uals, one becoming leaders group and one nonleaders group. The
propensity score matching method (Austin, 2011; Haviland et al.,
2007) was adopted to ensure that participants in the two groups
were in principle equal in terms of age, gender, education level,
income, and the Big Five personality traits at Time 1. Thus, using
Table 10
Means, Mean-Level Differences, and Rank-Order Stabilities for Personality Traits and Job Role Demands (Study 2)
Study variable
M/SD Effect size (Cohen’s d) Rank-order stability
T1 T2 T3 d
12
d
13
d
23
r
12
r
13
r
23
Becoming leaders group
Conscientiousness 4.94/1.12 5.19/1.07 5.28/1.08 .25
ⴱⴱⴱ
.31
ⴱⴱⴱ
.10
ⴱ
.61
ⴱⴱ
.53
ⴱⴱ
.67
ⴱⴱ
Emotional stability 4.85/1.09 4.91/1.05 4.94/1.07 .06 .09 .04 .64
ⴱⴱ
.56
ⴱⴱ
.62
ⴱⴱ
Job role demands 4.49/1.33 4.85/1.22 4.99/1.27 .24
ⴱⴱⴱ
.33
ⴱⴱⴱ
.12 .35
ⴱⴱ
.34
ⴱⴱ
.55
ⴱⴱ
Non-leaders group
Conscientiousness 4.96/1.12 5.00/1.12 5.10/1.17 .05 .16
ⴱⴱⴱ
.13
ⴱⴱⴱ
.68
ⴱⴱ
.64
ⴱⴱ
.74
ⴱⴱ
Emotional stability 4.88/1.12 4.96/1.10 4.99/1.10 .07
ⴱ
.09
ⴱⴱ
.02 .59
ⴱⴱ
.57
ⴱⴱ
.65
ⴱⴱ
Job role demands 4.31/1.37 4.24/1.32 4.47/1.33 ⫺.05 .11
ⴱ
.17
ⴱⴱⴱ
.48
ⴱⴱ
.40
ⴱⴱ
.50
ⴱⴱ
Note.N⫽431 for the becoming leaders group and 818 for the nonleaders group. d-coefficients indicate standardized differences in mean level between
measurement occasions: positive values signify mean-level increases and negative values mean-level decreases. r-coefficients indicate correlations of a
variable between two measurement occasions. T1 ⫽Time 1; T2 ⫽Time 2; T3 ⫽Time 3.
ⴱ
p⬍.05.
ⴱⴱ
p⬍.01.
ⴱⴱⴱ
p⬍.001.
Table 11
Results of Latent Growth Curve Models: Study 2
Predictor
Slope of conscientiousness
(Model 1), Coefficient
(SE)
Slope of job role demands
(Model 2), Coefficient
(SE)
Slope of conscientiousness
(Model 3), Coefficient
(SE)
Slope of emotional stability
(Model 4), Coefficient
(SE)
Becoming a leader .07
ⴱ
(.03) .17
ⴱⴱⴱ
(.05) ⫺.01 (.06) ⫺.01 (.03)
Intercept of job demands — — ⫺.03 (.04) —
Intercept of conscientiousness — — ⫺.03 (.04) —
Slope of job demands — — .52
ⴱ
(.21) —
Model fit indices
2
(df) 5.33 (6) 40.29
ⴱⴱⴱ
(6) 55.66
ⴱⴱⴱ
(21) 2.10 (6)
CFI 1.00 .956 .985 1.00
RMSEA .000 .068 .036 .000
SRMR .008 .032 .025 .005
Note.N⫽1,249 (431 for the becoming leaders group and 818 for the nonleaders group). Slopes indicate changes and intercepts indicate starting points.
Becoming a leader: 0 ⫽nonleaders group, 1 ⫽becoming leaders group. CFI ⫽comparative fit index; RMSEA ⫽root mean square error of approximation;
SRMR ⫽standardized root mean square residual.
ⴱ
p⬍.05.
ⴱⴱⴱ
p⬍.001.
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12 LI ET AL.
this method allowed us to rule out alternative explanations that the
pretreatment differences between the two groups may drive the
difference in personality change. The strengths of design and
analyses ensure the robustness of our findings.
The findings that assuming leadership roles was related to
subsequent increases in one’s conscientiousness also speak to the
leadership literature. Leadership research (Derue et al., 2011;
Judge et al., 2002) has primarily assumed that the causal interpre-
tation of the relationships between personality and leadership
emergence is that personality predicts leadership emergence. In
this vein, our findings challenge and complement the dominant
view by providing an alternative explanation that becoming lead-
ers may also shape personality traits. We reckon that our findings
do not necessarily suggest that the previous dominant assumption
on the causality of the relationship between personality traits and
leadership, which is based on the five factor model, is incorrect.
We encourage future work to integrate the two different perspec-
tives and examine the possibility of reciprocal relationships be-
tween personality traits and leadership (Kohn & Schooler, 1978;
Li, Li, Fay, & Frese, 2019).
We did not observe significant findings on changes in emotional
stability. Roberts et al. (2006) found that emotional stability pla-
teaus between about age 40 and 50. This finding appears to be
what we found for in Study 1: Emotional stability did not change
significantly from Time 2 to Time 3. The average age of partici-
pants in Study 1 ranged from about 40 to 50. In Study 2, the leader
group exhibited no significant change in emotional stability. Their
average age was also roughly within the range of 40 to 50. Future
research could examine the reasons for the specific patterns of
change in emotional stability during this period more closely and
may also look into individual difference in the pattern of change in
personality.
We found that changes of job role demands mediated the rela-
tionship between becoming a leader and change of conscientious-
ness. The literature on personality development has been in its
infancy in examining mechanisms for personality change (Roberts
& Nickel, 2017). So far, past research has examined influences of
major life events, such as having the first job, marriage, and
unemployment on personality development (Bleidorn et al., 2018).
Among the limited research on personality development at work,
researchers have looked into influences of job satisfaction, job
characteristics, job insecurity, income, and occupational status
(e.g., Li, Fay, Frese, Harms, & Gao, 2014; Li et al., 2019; Roberts
et al., 2003; Sutin, Costa Jr, Miech, & Eaton, 2009; Sutin & Costa,
2010; Wu & Griffin, 2012; Wu, Wang, Parker, & Griffin, 2020).
Recent macro organizational research has shown increases in CEO
cognitive complexity with increases in CEO job tenure (Graf-
Vlachy, Bundy, & Hambrick, 2020). Our study extends this line of
research by probing personality development after occurrence of a
nonnormative event, becoming a leader, and more importantly,
revealing a key underlying mechanism through change in job role
demands. Future research should examine how other types of work
role transitions (e.g., assuming the first job, job rotations, becom-
ing self-employed) and work experiences (e.g., adoption of artifi-
cial intelligence technology and teleworking) engender personality
adaptation.
The effect sizes observed in the current research for change of
conscientiousness seem small according to the conventional rule of
thumb. This suggests that becoming a leader might not change an
unconscientious person into a highly conscientious one.
4
Yet, the
small effect sizes are consistent with findings of previous research
in both personality psychology (Roberts et al., 2006) and effect
sizes observed in organizational research in general (Bosco, Agui-
nis, Singh, Field, & Pierce, 2015). As pointed out previously
(Prentice & Miller, 1992; Roberts, Kuncel, Shiner, Caspi, & Gold-
berg, 2007), small effect sizes do not necessarily mean that such
research findings have no practical significance at all. This raises
the question why we did not record more changes in conscien-
tiousness. Personality traits are relatively stable, and also prone to
change (Donnellan et al., 2015; Johnson, 1997). Personality
changes are often not dramatic, because of other mechanisms that
may promote personality stability. For example, people may ac-
tively avoid novel environments or simply do not make “social and
emotional investment that would result in change” (Roberts et al.,
2008, p. 390). Moreover, not all the people react to the same
change in the same manner and what we discovered in this paper
was a general trend. Future research can examine individual dif-
ferences in the speed, timing, and magnitude of personality
changes.
It should be noted that our research does not provide a definite
answer to the question whether the classic dispositional perspec-
tive of personality traits or a role-based transactional perspective
of personality development is more accurate in accounting for
personality development. In fact, there seems still an ongoing
debate on the major determinants of personality trait development
in the state-of-art of research in personality psychology (Costa Jr
et al., 2019). We concur with personality psychologists (e.g.,
Bleidorn et al., 2019; Costa Jr et al., 2019; Nye & Roberts, 2019)
and organizational scholars (e.g., Li et al., 2014; Tasselli et al.,
2018; Woods et al., 2019; Wu et al., 2020) that more research
endeavors should be devoted to this intriguing and fruitful line of
inquiry in organizational research.
Study Strengths, Limitations, and Directions for
Future Research
Adopting a role-based perspective by integrating research from
personality psychology and the literature on leadership, we tested
our hypotheses with two three-wave longitudinal studies from two
countries (Taylor, Li, Shi, & Borman, 2008) across approximately
eight and 20 years. We also adopted a quasi-experimental design
comparing two groups of individuals matched with their individual
difference variables at Time 1. The strengths and convergent
findings contribute to the robustness of our conclusions. Never-
theless, this study is also limited in several ways, which point to
directions for future research. The first limitation is related to the
abbreviated measure of the broad Big Five personality trait, al-
though this practice has been widely adopted in research on
personality change (Boyce et al., 2015; Lucas & Donnellan, 2011;
Roberts & Nickel, 2017; Specht et al., 2014). The validation study
demonstrated that our personality scales were valid and reliable.
Prior research suggests that different subdimensions of the Big
Five personality traits may show different patterns of change
(Roberts et al., 2006). If feasible and when a fine-grained lower
level model of personality is identified, future research should use
4
We thank our action editor for this comment.
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13
PERSONALITY CHANGE, LEADERSHIP, AND ROLE TRANSITION
longer scales to capture more delicate personality change such as
changes of facets or nuances (Mõttus, Kandler, Bleidorn, Ri-
emann, & McCrae, 2017).
5
Second, using self-report measures of personality, although ad-
opted as a dominant approach in personality research (Ones et al.,
2007; Roberts et al., 2007), raises the possibility whether social
desirability may potentially account for the significant findings
(P. M. Podsakoff, MacKenzie, Lee, & Podsakoff, 2003). Partici-
pants moving into leadership roles may think that they need to be
more conscientious, rather than they actually become more con-
scientious.
6
However, if this is true, then those moving into lead-
ership roles may also think that they need to be more emotionally
stable. However, results for changes on emotional stability were
not significant. We urge future research to use other-report per-
sonality assessments if feasible (Connelly & Ones, 2010).
Third, conducting secondary analyses of public data limited
our capability to test our theorization of the role-based perspec-
tive of personality change. Although we show that job role
demands serve as an underlying mechanism for personality
change during leadership emergence, assuming leadership roles
may also change other aspects of work, such as job control (Li
et al., 2018). We tested the moderating role of change in job
control in Study 2 in the relationships between change of job
demands and changes of conscientiousness and emotional sta-
bility, which might be suggested by the job demands-control
model (Karasek, 1979). The results were not significant. Mul-
tiple possible mediators might also be a plausible reason for
observing nonsignificant results for changes of emotional sta-
bility. Sound theories of personality development based on
work experiences have yet to be developed in organizational
research, which renders it difficult to examine multiple medi-
ating mechanisms. We encourage future research to develop
theories and explicitly examine other aspects of work that
becoming a leader may change and integrate the role-based
perspective and job demands-control model.
Fourth, following previous research (Day et al., 2004; Judge
et al., 2002), we examined a crude form of leadership experi-
ences, transitioning from the role of employees to that of
leaders, on personality development. Leadership is multifaceted
and may include various leadership styles. That said, our sen-
sitivity analyses using an alternative measure of leadership,
span of control, generated more visible and substantial results.
In this vein, the analyses based on the crude leadership measure
may present conservative tests of our hypotheses. Future re-
search should investigate influences of specific leadership be-
haviors at multiple organizational levels (e.g., first line leaders
and CEOs) on changing individual characteristics in the long
run (Day & Dragoni, 2015).
Fifth, time lag is a thorny issue in longitudinal research. Theory
and research suggest that identifying optimal time lags should be
informed by theoretical rationale, past research evidence, and the
feasibility of data collection (Dormann & Griffin, 2015; Mitchell
& James, 2001; Ployhart & Vandenberg, 2010; Shipp & Cole,
2015). Although our selection of time intervals was informed by
theory and empirical research (Dormann & Griffin, 2015; Mitchell
& James, 2001; Roberts et al., 2006), the selection of time lags
might not be optimal. As pointed out by our anonymous reviewers,
it is possible that during the 4- or 10-year time lag, many other
important life events may occur, which then may dilute the influ-
ence of becoming a leader on personality change (Cohen, Cohen,
West, & Aiken, 2003; Dormann & Griffin, 2015; Mitchell &
James, 2001). However, if this is true, then our research likely
represents a more conservative examination of the influence of
becoming a leader. Thus, the significant relationships between
becoming a leader and the associated change of conscientiousness
afterwards suggest the robustness of the findings. Related, recent
longitudinal research suggests collecting more waves of data to
examine more nuanced changes in personality traits and other
variables at work (Bleidorn et al., 2019; Donnellan et al., 2015;
Ployhart & Vandenberg, 2010). Because investigation of person-
ality change in organizational research is still in its infancy, and it
is not always pragmatic to collect longitudinal data across years for
organizational researchers, it seems not uncommon to find re-
search using two waves or three waves of data. Although we
believe that such two-wave or three-wave research is still valuable
to advance this line of research, we encourage researchers to make
their efforts to collect more waves of data in their investigations in
the future if feasible. We concur with Podsakoff and colleagues
(N. P. Podsakoff et al., 2019) that researchers should conduct more
comprehensive studies to examine the effect of time more explic-
itly in the future.
Sixth and related, because of ethical and feasibility concerns, we
were not able to conduct a field experiment with random assign-
ment and strong manipulation of our independent variable, becom-
ing a leader, to draw more definitive causal inferences. Thus, we
cannot draw causal inferences. As suggested by our anonymous
reviewer, it seems possible that some events might have occurred
between Time 1 and Time 2 for participants in the becoming
leaders group, which caused their increases in conscientiousness
and prompted them into leadership roles later on. We examined
this possibility of reverse causality. We used available data in the
two studies with participants who were employees at Time 1 and
Time 2, but some were promoted into leadership positions by Time
3 and the rest remained as employees at Time 3. We adopted latent
change score modeling (McArdle, 2001, 2009; Selig & Preacher,
2009) to model personality change from Time 1 to Time 2, and
then used such a change variable to predict leadership status at
Time 3. Findings from the two studies revealed that changes in
conscientiousness from Time 1 to Time 2 did not significantly
predict leadership emergence at Time 3. Although such analyses
might not be ideal tests of reverse causality, the findings seem to
suggest that reverse causality is not a serious problem.
7
Further-
more, using the propensity score matching approach “mimics some
of the particular characteristics of a randomized controlled trial”
(Austin, 2011, p. 399), to minimize alternative explanations caused
by preexisting group differences and to “strengthen causal infer-
ences” (Grant & Wall, 2009, p. 655). Schwaba and Bleidorn
(2019) concluded that “Propensity-score matching can thus rule
out many alternative explanations for development, such as his-
torical effects (e.g., development because of the 2008 global re-
cession), and age-graded development” (p. 654). We urge future
research, if feasible, to adopt alternative designs and methods (e.g.,
5
We are indebted to our anonymous reviewer for pointing this out.
6
We thank our anonymous reviewer for this comment.
7
These results did not mean that conscientiousness cannot predict lead-
ership emergence. Previous research on this issue uses a different design,
which has been primarily cross-sectional in nature.
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14 LI ET AL.
the latent change score approach) to gauge the robustness of our
findings.
8
Practical Implications
Findings of this research provide important implications for
both organizations and employees in better planning leadership
succession and managing career development. Leadership succes-
sion has been deemed as a crucial issue for the sustainability of
organizations (Kesner & Sebora, 1994). The finding that becoming
a leader enhanced one’s conscientiousness has important implica-
tions. Given the importance of conscientiousness for leadership
(Judge et al., 2002), promoting an employee into a leadership
position may have a potential to induce a virtuous cycle: Becoming
a leader may enhance one’s level of conscientiousness, which in
turn may further enhance his or her leadership effectiveness.
However, two caveats may surface. First, Judge, Piccolo, and
Kosalka (2009) pointed out that highly conscientious employees
may not be able to adapt to new environments well and may also
fall short of creativity. Second, the relationship between consci-
entiousness and job performance may be curvilinear (Le et al.,
2011), suggesting a diminishing marginal utility of the benefits of
conscientiousness. Balancing the benefits and possible dark sides
associated with increases of conscientiousness in leaders may be
an important task for organizations. Organizations may implement
special training for their leaders to better adapt to volatile envi-
ronments and improve flexibility.
Our findings also have important implications for leadership
development. Organizations may consider assigning employees
with informal leadership roles as a form of stretch experiences to
prod their employees to develop leadership capabilities. This may
in the long run facilitate development of behaviors and traits
related to conscientiousness and prepare the leader for the future
tasks. The majority of the literature on leadership development has
concentrated on leaders’ skill and identity development via chal-
lenging work experiences (DeRue & Wellman, 2009; Dragoni, Oh,
Vankatwyk, & Tesluk, 2011; Dragoni, Tesluk, Russell, & Oh,
2009; Lord, Day, Zaccaro, Avolio, & Eagly, 2017). We encourage
organizations to broaden the scope and content of leader develop-
ment to include personality development and strive for “more
holistic forms of leader development” (Day & Dragoni, 2015, p.
144).
Our findings also have important implications for employees
in managing their careers. Given that becoming a leader repre-
sents a milestone for one’s career development (Baruch &
Bozionelos, 2010; Wang & Wanberg, 2017), assuming leader-
ship roles seems a natural step for employees to climb up the
corporate ladder. In this regard, our findings provide employees
another perspective to consider and evaluate their career devel-
opment decisions. We found becoming a leader was related to
subsequent increases in conscientiousness over time. Although
offering benefits on one’s health (Bogg & Roberts, 2004),
having a high level of conscientiousness may come at a cost of
becoming less adaptable and less creative (Judge et al., 2009).
Furthermore, increase in job role demands mediated the rela-
tionship between becoming a leader and increase in conscien-
tiousness. Research on work stress has shown that job demands,
although maybe perceived as challenges (N. P. Podsakoff et al.,
2007), are resource-depleting and thus detrimental to well-
being (Sonnentag & Frese, 2012). Being mindful of the benefits
and costs may help employee to make more judicious decision
to pursue careers as leaders.
Conclusion
The majority of extant organizational personality research
has taken the position that personality traits influence work
experiences, not vice versa. Although this view, which has been
shaped by the Five Factor theory of personality, seems parsi-
monious, it cannot account for the accumulating empirical
evidence that adults’ personality traits continue to develop as
people adapt to new life/work roles. We found that a role-based
perspective of personality development helps explain the
change in personality traits when people transition into leader-
ship roles from employees. Work roles play a crucial role in
socializing individuals (Frese, 1982; Nicholson, 1984). We
hope this study can stimulate more future research on the notion
that “people are both producers and products of social systems”
(Bandura, 1997, p. 6).
8
We thank our anonymous reviewer for this comment.
References
Austin, P. C. (2010). Statistical criteria for selecting the optimal number of
untreated subjects matched to each treated subject when using many-to-
one matching on the propensity score. American Journal of Epidemiol-
ogy, 172, 1092–1097. http://dx.doi.org/10.1093/aje/kwq224
Austin, P. C. (2011). An introduction to propensity score methods for
reducing the effects of confounding in observational studies. Multivar-
iate Behavioral Research, 46, 399 – 424. http://dx.doi.org/10.1080/
00273171.2011.568786
Bandura, A. (1997). Self-efficacy: The exercise of control. New York, NY:
Freeman.
Barling, J., Christie, A., & Hoption, C. (2010). Leadership. In S. Zedeck
(Ed.), Handbook of industrial and organizational psychology: Vol. 1.
Building and developing the organization (pp. 183–240). Washington,
DC: American Psychological Association.
Barrick, M. R., & Mount, M. K. (1991). The Big Five personality dimen-
sions and job performance: A meta-analysis. Personnel Psychology, 44,
1–26. http://dx.doi.org/10.1111/j.1744-6570.1991.tb00688.x
Baruch, Y., & Bozionelos, N. (2010). Career issues. In S. Zedeck (Ed.),
APA handbook of industrial and organizational psychology (Vol. 2, pp.
67–113). Washington, DC: American Psychological Association.
Bass, B. M., & Bass, R. (2008). The Bass handbook of leadership: Theory,
research, and managerial applications (4th ed.). New York, NY: Free
Press.
Berry, C. M., Ones, D. S., & Sackett, P. R. (2007). Interpersonal deviance,
organizational deviance, and their common correlates: A review and
meta-analysis. Journal of Applied Psychology, 92, 410 – 424. http://dx
.doi.org/10.1037/0021-9010.92.2.410
Biddle, B. J. (1979). Role theory: Expectations, identities, and behaviors.
New York, NY: Academic Press.
Bleidorn, W. (2012). Hitting the road to adulthood: Short-term personality
development during a major life transition. Personality and Social
Psychology Bulletin, 38, 1594 –1608. http://dx.doi.org/10.1177/
0146167212456707
Bleidorn, W., Hill, P. L., Back, M. D., Denissen, J. J. A., Hennecke, M.,
Hopwood, C. J., . . . Roberts, B. (2019). The policy relevance of
personality traits. American Psychologist, 74, 1056 –1067. http://dx.doi
.org/10.1037/amp0000503
This document is copyrighted by the American Psychological Association or one of its allied publishers.
This article is intended solely for the personal use of the individual user and is not to be disseminated broadly.
15
PERSONALITY CHANGE, LEADERSHIP, AND ROLE TRANSITION
Bleidorn, W., Hopwood, C. J., & Lucas, R. E. (2018). Life events and
personality trait change. Journal of Personality, 86, 83–96. http://dx.doi
.org/10.1111/jopy.12286
Bliese, P. D., & Ployhart, R. E. (2002). Growth modeling using random
coefficient models: Model building, testing, and illustrations. Organiza-
tional Research Methods, 5, 362–387. http://dx.doi.org/10.1177/
109442802237116
Bogg, T., & Roberts, B. W. (2004). Conscientiousness and health-related
behaviors: A meta-analysis of the leading behavioral contributors to
mortality. Psychological Bulletin, 130, 887–919. http://dx.doi.org/10
.1037/0033-2909.130.6.887
Bosco, F. A., Aguinis, H., Singh, K., Field, J. G., & Pierce, C. A. (2015).
Correlational effect size benchmarks. Journal of Applied Psychology,
100, 431– 449. http://dx.doi.org/10.1037/a0038047
Boyce, C. J., Wood, A. M., Daly, M., & Sedikides, C. (2015). Personality
change following unemployment. Journal of Applied Psychology, 100,
991–1011. http://dx.doi.org/10.1037/a0038647
Buhrmester, M., Kwang, T., & Gosling, S. D. (2011). Amazon’s Mechan-
ical Turk: A new source of inexpensive, yet high-quality, data? Per-
spectives on Psychological Science, 6, 3–5. http://dx.doi.org/10.1177/
1745691610393980
Caspi, A., & Moffitt, T. E. (1993). When do individual differences matter?
A paradoxical theory of personality coherence. Psychological Inquiry, 4,
247–271. http://dx.doi.org/10.1207/s15327965pli0404_1
Caspi, A., Roberts, B. W., & Shiner, R. L. (2005). Personality develop-
ment: Stability and change. Annual Review of Psychology, 56, 453– 484.
http://dx.doi.org/10.1146/annurev.psych.55.090902.141913
Cavanaugh, M. A., Boswell, W. R., Roehling, M. V., & Boudreau, J. W.
(2000). An empirical examination of self-reported work stress among
U.S. managers. Journal of Applied Psychology, 85, 65–74. http://dx.doi
.org/10.1037/0021-9010.85.1.65
Chan, D. (1998). The conceptualization and analysis of change over time:
An integrative approach incorporating longitudinal mean and covariance
structures analysis (LMACS) and multiple indicator latent growth mod-
eling (MLGM). Organizational Research Methods, 1, 421– 483. http://
dx.doi.org/10.1177/109442819814004
Chawla, N., MacGowan, R. L., Gabriel, A. S., & Podsakoff, N. P. (2020).
Unplugging or staying connected? Examining the nature, antecedents,
and consequences of profiles of daily recovery experiences. Journal of
Applied Psychology, 105, 19 –39. http://dx.doi.org/10.1037/apl0000423
Chiaburu, D. S., Oh, I.-S., Berry, C. M., Li, N., & Gardner, R. G. (2011).
The five-factor model of personality traits and organizational citizenship
behaviors: A meta-analysis. Journal of Applied Psychology, 96, 1140 –
1166. http://dx.doi.org/10.1037/a0024004
Cohen, J. (1988). Statistical power analysis for the behavioral sciences
(2nd ed.). Hillsdale, NJ: Erlbaum.
Cohen, J., Cohen, P., West, S. G., & Aiken, L. S. (2003). Applied multiple
regression/correlation analysis for the behavioral sciences (3rd ed.).
Mahwah, NJ: Erlbaum.
Colbert, A. E., Barrick, M. R., & Bradley, B. H. (2014). Personality and
leadership composition in top management teams: Implications for or-
ganizational effectiveness. Personnel Psychology, 67, 351–387. http://
dx.doi.org/10.1111/peps.12036
Connelly, B. S., & Ones, D. S. (2010). An other perspective on personality:
Meta-analytic integration of observers’ accuracy and predictive validity.
Psychological Bulletin, 136, 1092–1122. http://dx.doi.org/10.1037/
a0021212
Cook, T. D., Campbell, D. T., & Peracchio, L. (1990). Quasi-
experimentation. In M. Dunnette & L. Hough (Eds.), Handbook of
industrial and organizational psychology (2nd ed., Vol. 1, pp. 491–576).
Palo Alto, CA: Consulting Psychologists Press.
Costa, P. T., Jr., & McCrae, R. R. (2006). Age changes in personality and
their origins: Comment on Roberts, Walton, and Viechtbauer (2006).
Psychological Bulletin, 132, 26 –28. http://dx.doi.org/10.1037/0033-
2909.132.1.26
Costa, P. T., Jr., McCrae, R. R., & Löckenhoff, C. E. (2019). Personality
across the life span. Annual Review of Psychology, 70, 423– 448. http://
dx.doi.org/10.1146/annurev-psych-010418-103244
Dalal, R. S., Alaybek, B., & Lievens, F. (2020). Within-person job per-
formance variability over short timeframes: Theory, empirical research,
and practice. Annual Review of Organizational Psychology and Orga-
nizational Behavior, 7, 421– 449. http://dx.doi.org/10.1146/annurev-
orgpsych-012119-045350
Davis-Blake, A., & Pfeffer, J. (1989). Just a mirage: The search for
dispositional effects in organizational research. The Academy of Man-
agement Review, 14, 385– 400. http://dx.doi.org/10.5465/amr.1989
.4279071
Day, D. V., & Dragoni, L. (2015). Leadership development: An outcome-
oriented review based on time and levels of analyses. Annual Review of
Organizational Psychology and Organizational Behavior, 2, 133–156.
http://dx.doi.org/10.1146/annurev-orgpsych-032414-111328
Day, D. V., Sin, H.-P., & Chen, T. T. (2004). Assessing the burdens of
leadership: Effects of formal leadership roles on individual performance
over time. Personnel Psychology, 57, 573– 605. http://dx.doi.org/10
.1111/j.1744-6570.2004.00001.x
Den Hartog, D. N., House, R. J., Hanges, P. J., Ruiz-Quintanilla, S. A.,
Dorfman, P. W., Abdalla, I. A.,...Zhou, J. (1999). Culture specific and
cross-culturally generalizable implicit leadership theories: Are attributes
of charismatic/transformational leadership universally endorsed? The
Leadership Quarterly, 10, 219 –256. http://dx.doi.org/10.1016/S1048-
9843(99)00018-1
Derue, D. S., Nahrgang, J. D., Wellman, N., & Humphrey, S. E. (2011).
Trait and behavioral theories of leadership: An integration and meta-
analytic test of their relative validity. Personnel Psychology, 64, 7–52.
http://dx.doi.org/10.1111/j.1744-6570.2010.01201.x
Derue, D. S., & Wellman, N. (2009). Developing leaders via experience:
The role of developmental challenge, learning orientation, and feedback
availability. Journal of Applied Psychology, 94, 859 – 875. http://dx.doi
.org/10.1037/a0015317
Dong, Y., & Dumas, D. (2020). Are personality measures valid for differ-
ent populations? A systematic review of measurement invariance across
cultures, gender, and age. Personality and Individual Differences, 160,
109956. http://dx.doi.org/10.1016/j.paid.2020.109956
Donnellan, M. B., Hill, P. L., & Roberts, B. W. (2015). Personality
development across the life span: Current findings and future directions.
In M. Mikulincer, P. R. Shaver, M. L. Cooper, & R. J. Larsen (Eds.),
APA handbook of personality and social psychology: Vol. D: Person-
ality processes and individual differences (pp. 107–126). Washington
DC: American Psychological Association.
Dormann, C., & Griffin, M. A. (2015). Optimal time lags in panel studies.
Psychological Methods, 20, 489 –505. http://dx.doi.org/10.1037/
met0000041
Dragoni, L., Oh, I. S., Vankatwyk, P., & Tesluk, P. E. (2011). Developing
executive leaders: The relative contribution of cognitive ability, person-
ality, and the accumulation of work experience in predicting strategic
thinking competency. Personnel Psychology, 64, 829 – 864. http://dx.doi
.org/10.1111/j.1744-6570.2011.01229.x
Dragoni, L., Tesluk, P. E., Russell, J. E. A., & Oh, I. S. (2009). Under-
standing managerial development: Integrating developmental assign-
ments, learning orientation, and access to developmental opportunities in
predicting managerial competencies. Academy of Management Journal,
52, 731–743. http://dx.doi.org/10.5465/amj.2009.43669936
Feingold, A. (2009). Effect sizes for growth-modeling analysis for con-
trolled clinical trials in the same metric as for classical analysis. Psy-
chological Methods, 14, 43–53. http://dx.doi.org/10.1037/a0014699
This document is copyrighted by the American Psychological Association or one of its allied publishers.
This article is intended solely for the personal use of the individual user and is not to be disseminated broadly.
16 LI ET AL.
Feingold, A. (2017). Meta-analysis with standardized effect sizes from
multilevel and latent growth models. Journal of Consulting and Clinical
Psychology, 85, 262–266. http://dx.doi.org/10.1037/ccp0000162
Finkel, S. E. (1995). Causal analysis with panel data. Thousand Oaks, CA:
SAGE. http://dx.doi.org/10.4135/9781412983594
Fleeson, W. (2001). Toward a structure- and process-integrated view of
personality: Traits as density distribution of states. Journal of Person-
ality and Social Psychology, 80, 1011–1027. http://dx.doi.org/10.1037/
0022-3514.80.6.1011
Fleeson, W. (2004). Moving personality beyond the person-situation de-
bate: The challenge and the opportunity of within-person variability.
Current Directions in Psychological Science, 13, 83– 87. http://dx.doi
.org/10.1111/j.0963-7214.2004.00280.x
Fleishman, E. A., Mumford, M. D., Zaccaro, S. J., Levin, K. Y., Korotkin,
A. L., & Hein, M. B. (1991). Taxonomic efforts in the description of
leader behavior: A synthesis and functional interpretation. The Leader-
ship Quarterly, 2, 245–287. http://dx.doi.org/10.1016/1048-
9843(91)90016-U
Floyd, S. W., & Wooldridge, B. (1992). Middle management involvement
in strategy and its association with strategic type: A research note.
Strategic Management Journal, 13(S1), 153–167. http://dx.doi.org/10
.1002/smj.4250131012
Frese, M. (1982). Occupational socialization and psychological develop-
ment: An underemphasized research perspective in industrial psychol-
ogy. Journal of Occupational Psychology, 55, 209 –224. http://dx.doi
.org/10.1111/j.2044-8325.1982.tb00095.x
Ganster, D. C. (2005). Executive job demands: Suggestions from a stress
and decision-making perspective. The Academy of Management Review,
30, 492–502. http://dx.doi.org/10.5465/amr.2005.17293366
Ganster, D. C., & Rosen, C. C. (2013). Work stress and employee health:
A multidisciplinary review. Journal of Management, 39, 1085–1122.
http://dx.doi.org/10.1177/0149206313475815
Goldberg, L. R. (1990). An alternative “description of personality”: The
big-five factor structure. Journal of Personality and Social Psychology,
59, 1216 –1229. http://dx.doi.org/10.1037/0022-3514.59.6.1216
Goldberg, L. R., Johnson, J. A., Eber, H. W., Hogan, R., Ashton, M. C.,
Cloninger, C. R., & Gough, H. G. (2006). The international personality
item pool and the future of public-domain personality measures. Journal
of Research in Personality, 40, 84 –96. http://dx.doi.org/10.1016/j.jrp
.2005.08.007
Graf-Vlachy, L., Bundy, J., & Hambrick, D. C. (2020). Effects of an
advancing tenure on CEO cognitive complexity. Organization Science.
Advance online publication. http://dx.doi.org/10.1287/orsc.2019.1336
Grant, A. M., & Wall, T. D. (2009). The neglected science and art of
quasi-experimentation: Why-to, when-to, and how-to advice for organi-
zational researchers. Organizational Research Methods, 12, 653– 686.
http://dx.doi.org/10.1177/1094428108320737
Hambrick, D. C., Finkelstein, S., & Mooney, A. C. (2005). Executive job
demands: New insights for explaining strategic decisions and leader
behaviors. The Academy of Management Review, 30, 472– 491. http://
dx.doi.org/10.5465/amr.2005.17293355
Haviland, A., Nagin, D. S., & Rosenbaum, P. R. (2007). Combining
propensity score matching and group-based trajectory analysis in an
observational study. Psychological Methods, 12, 247–267. http://dx.doi
.org/10.1037/1082-989X.12.3.247
Hill, L. A. (2007). Becoming the boss. Harvard Business Review, 85,
48 –56, 122.
Ho, D. E., Imai, K., King, G., & Stuart, E. A. (2011). MatchIt: Nonpara-
metric preprocessing for parametric causal inference. Journal of Statis-
tical Software, 42, 1–28. http://dx.doi.org/10.18637/jss.v042.i08
Hogan, R., Curphy, G. J., & Hogan, J. (1994). What we know about
leadership. Effectiveness and personality. American Psychologist, 49,
493–504. http://dx.doi.org/10.1037/0003-066X.49.6.493
House, R. J., Shane, S. A., & Herold, D. M. (1996). Rumors of the death
of dispositional research are vastly exaggerated. The Academy of Man-
agement Review, 21, 203–224. http://dx.doi.org/10.5465/amr.1996
.9602161570
Hudson, N. W., Roberts, B. W., & Lodi-Smith, J. (2012). Personality trait
development and social investment in work. Journal of Research in
Personality, 46, 334 –344. http://dx.doi.org/10.1016/j.jrp.2012.03.002
Human, L. J., Biesanz, J. C., Miller, G. E., Chen, E., Lachman, M. E., &
Seeman, T. E. (2013). Is change bad? Personality change is associated
with poorer psychological health and greater metabolic syndrome in
midlife. Journal of Personality, 81, 249 –260. http://dx.doi.org/10.1111/
jopy.12002
Ilgen, D. R., & Hollenbeck, J. R. (1991). The structure of work: Job design
and roles. In M. D. Dunnette & L. M. Hough (Eds.), Handbook of
industrial and organizational psychology (2nd ed., Vol. 2, pp. 165–207).
Palo Alto, CA: Consulting Psychologists Press.
Ilies, R., Scott, B. A., & Judge, T. A. (2006). The interactive effects of
personal traits and experienced states on intraindividual patterns of
citizenship behavior. Academy of Management Journal, 49, 561–575.
http://dx.doi.org/10.5465/amj.2006.21794672
Iveniuk, J., Laumann, E. O., Waite, L. J., McClintock, M. K., & Tiedt, A.
(2014). Personality measures in the national social life, health, and aging
project. Journals of Gerontology Series B: Psychological Sciences and
Social Sciences, 69(Suppl. 2), S117–S124.
Jayawickreme, E., Zachry, C. E., & Fleeson, W. (2019). Whole trait theory:
An integrative approach to examining personality structure and process.
Personality and Individual Differences, 136, 2–11. http://dx.doi.org/10
.1016/j.paid.2018.06.045
John, O. P., Naumann, L. P., & Soto, C. J. (2008). Paradigm shift to the
integrative Big Five trait taxonomy: History, measurement, and concep-
tual issues. In O. P. John, R. W. Robins, & L. A. Pervin (Eds.),
Handbook of personality: Theory and research (pp. 114 –158). New
York, NY: Guilford Press.
Johnson, J. A. (1997). Units of analysis for the description and explanation
of personality. In R. Hogan, J. A. Johnson, & S. R. Briggs (Eds.),
Handbook of personality psychology (pp. 73–93). San Diego, CA: Ac-
ademic Press. http://dx.doi.org/10.1016/B978-012134645-4/50004-4
Judge, T. A., Bono, J. E., Ilies, R., & Gerhardt, M. W. (2002). Personality
and leadership: A qualitative and quantitative review. Journal of Applied
Psychology, 87, 765–780. http://dx.doi.org/10.1037/0021-9010.87.4.765
Judge, T. A., Piccolo, R. F., & Kosalka, T. (2009). The bright and dark
sides of leader traits: A review and theoretical extension of the leader
trait paradigm. The Leadership Quarterly, 20, 855– 875. http://dx.doi
.org/10.1016/j.leaqua.2009.09.004
Karasek, R. A. (1979). Job demands, job decision latitude, and mental
strain: Implications for job redesign. Administrative Science Quarterly,
24, 285–308. http://dx.doi.org/10.2307/2392498
Karasek, R., Brisson, C., Kawakami, N., Houtman, I., Bongers, P., &
Amick, B. (1998). The Job Content Questionnaire (JCQ): An instrument
for internationally comparative assessments of psychosocial job charac-
teristics. Journal of Occupational Health Psychology, 3, 322–355. http://
dx.doi.org/10.1037/1076-8998.3.4.322
Katz, D., & Kahn, R. L. (1978). The social psychology of organizations
(2nd ed.). New York, NY: Wiley
Keltner, D., Gruenfeld, D. H., & Anderson, C. (2003). Power, approach,
and inhibition. Psychological Review, 110, 265–284. http://dx.doi.org/
10.1037/0033-295X.110.2.265
Kesner, I. F., & Sebora, T. C. (1994). Executive succession: Past, present
& future. Journal of Management, 20, 327–372. http://dx.doi.org/10
.1177/014920639402000204
Kohn, M. L., & Schooler, C. (1978). The reciprocal effects of the substan-
tive complexity of work and intellectual flexibility: A longitudinal
assessment. American Journal of Sociology, 84, 24 –52. http://dx.doi
.org/10.1086/226739
This document is copyrighted by the American Psychological Association or one of its allied publishers.
This article is intended solely for the personal use of the individual user and is not to be disseminated broadly.
17
PERSONALITY CHANGE, LEADERSHIP, AND ROLE TRANSITION
Kornadt, A. E. (2016). Do age stereotypes as social role expectations for
older adults influence personality development? Journal of Research in
Personality, 60, 51–55. http://dx.doi.org/10.1016/j.jrp.2015.11.005
Lachman, M. E., & Weaver, S. L. (1997). The Midlife Development
Inventory (MIDI) personality scales: Scale construction and scoring.
Waltham, MA: Brandeis University.
Le, H., OH, I.-S., Robbins, S. B., Ilies, R., Holland, E., & Westrick, P.
(2011). Too much of a good thing: Curvilinear relationships between
personality traits and job performance. Journal of Applied Psychology,
96, 113–133. http://dx.doi.org/10.1037/a0021016
Lee, R., & Ashforth, B. E. (1991). Evaluating two models of burnout
among supervisors and managers in a public welfare setting. Journal of
Health and Human Resources Administration, 13, 508 –527.
LePine, J. A., Podsakoff, N. P., & LePine, M. A. (2005). A meta-analytic
test of the challenge stressor-hindrance stressor framework: An expla-
nation for inconsistent relationships among stressors and performance.
Academy of Management Journal, 48, 764 –775. http://dx.doi.org/10
.5465/amj.2005.18803921
Li, W. D., Fay, D., Frese, M., Harms, P. D., & Gao, X. Y. (2014).
Reciprocal relationship between proactive personality and work charac-
teristics: A latent change score approach. Journal of Applied Psychol-
ogy, 99, 948 –965. http://dx.doi.org/10.1037/a0036169
Li, W. D., Li, S., Fay, D., & Frese, M. (2019). Reciprocal relationships
between dispositional optimism and work experiences: A five-wave
longitudinal investigation. Journal of Applied Psychology, 104, 1471–
1486. http://dx.doi.org/10.1037/apl0000417
Li, W. D., Schaubroeck, J. M., Xie, J. L., & Keller, A. C. (2018). Is being
a leader a mixed blessing? A dual-pathway model linking leadership role
occupancy to well-being. Journal of Organizational Behavior, 39, 971–
989. http://dx.doi.org/10.1002/job.2273
Li, W. D., Arvey, R. D., & Song, Z. (2011). The influence of general
mental ability, self-esteem and family socioeconomic status on leader-
ship role occupancy and leader advancement: The moderating role of
gender. The Leadership Quarterly, 22, 520 –534. http://dx.doi.org/10
.1016/j.leaqua.2011.04.009
Little, R. J., & Rubin, D. B. (2002). Statistical analysis with missing data.
New York, NY: Wiley. http://dx.doi.org/10.1002/9781119013563
Lord, R. G., Day, D. V., Zaccaro, S. J., Avolio, B. J., & Eagly, A. H.
(2017). Leadership in applied psychology: Three waves of theory and
research. Journal of Applied Psychology, 102, 434 – 451. http://dx.doi
.org/10.1037/apl0000089
Lord, R. G., Foti, R. J., & De Vader, C. L. (1984). A test of leadership
categorization theory: Internal structure, information processing, and
leadership perceptions. Organizational Behavior & Human Perfor-
mance, 34, 343–378. http://dx.doi.org/10.1016/0030-5073(84)90043-6
Lucas, R. E., & Donnellan, M. B. (2011). Personality development across
the life span: Longitudinal analyses with a national sample from Ger-
many. Journal of Personality and Social Psychology, 101, 847– 861.
http://dx.doi.org/10.1037/a0024298
McArdle, J. J. (2001). A latent difference score approach to longitudinal
dynamic structural analysis. In R. Cudeck, S. D. Toit, & D. Sörbom
(Eds.), Structural equation modeling: Present and future. A Festschrift
in honor of Karl Jo¨ reskog (pp. 341–380). Lincolnwood, IL: Scientific
Software International.
McArdle, J. J. (2009). Latent variable modeling of differences and changes
with longitudinal data. Annual Review of Psychology, 60, 577– 605.
http://dx.doi.org/10.1146/annurev.psych.60.110707.163612
McCrae, R. R., & Costa, P. T., Jr. (1999). A five-factor theory of person-
ality. In A. Pervin & O. P. John (Eds.), Handbook of personality: Theory
and research (2nd ed., pp. 139 –153). New York, NY: Guilford Press.
McCrae, R. R., Costa, P. T., Jr., Ostendorf, F., Angleitner, A., Hrebícková,
M., Avia, M. D.,...Smith, P. B. (2000). Nature over nurture: Tem-
perament, personality, and life span development. Journal of Personality
and Social Psychology, 78, 173–186. http://dx.doi.org/10.1037/0022-
3514.78.1.173
Mintzberg, H. (1971). Managerial work: Analysis from observation. Man-
agement Science, 18(2), B97–B110. http://dx.doi.org/10.1287/mnsc.18
.2.B97
Mintzberg, H. (2009). Managing. San Francisco, CA: Berrett-Koehler
Publishers.
Mitchell, T. R., & James, L. R. (2001). Building better theory: Time and
the specification of when things happen. Academy of Management
Review, 26, 530 –547. http://dx.doi.org/10.5465/amr.2001.5393889
Mõttus, R., Kandler, C., Bleidorn, W., Riemann, R., & McCrae, R. R.
(2017). Personality traits below facets: The consensual validity, longi-
tudinal stability, heritability, and utility of personality nuances. Journal
of Personality and Social Psychology, 112, 474 – 490. http://dx.doi.org/
10.1037/pspp0000100
Mu, W., Luo, J., Nickel, L., & Roberts, B. W. (2016). Generality or
specificity? Examining the relation between personality traits and mental
health outcomes using a bivariate bi-factor latent change model. Euro-
pean Journal of Personality, 30, 467– 483. http://dx.doi.org/10.1002/per
.2052
Newman, D. A. (2009). Missing data techniques and low response rates:
The role of systematic nonresponse parameters. In C. E. Lance & R. J.
Vandenberg (Eds.), Statistical and methodological myths and urban
legends (pp. 7–36). New York, NY: Routledge.
Newman, D. A. (2014). Missing data: Five practical guidelines. Organi-
zational Research Methods, 17, 372– 411. http://dx.doi.org/10.1177/
1094428114548590
Newton, D. W., LePine, J. A., Kim, J. K., Wellman, N., & Bush, J. T.
(2020). Taking engagement to task: The nature and functioning of task
engagement across transitions. Journal of Applied Psychology, 105,
1–18. http://dx.doi.org/10.1037/apl0000428
Nicholson, N. (1984). A theory of work role transitions. Administrative
Science Quarterly, 29, 172–191. http://dx.doi.org/10.2307/2393172
Nolan, C. (Director). (2005). Batman begins [Film]. Burbank, CA: Warner
Bros.
Nye, C. D., & Roberts, B. W. (2019). A neo-socioanalytic model of
personality development. In B. B. Baltes, C. W. Rudolph, & H. Zacher
(Eds.), Work across the lifespan (pp. 47–79). London, UK: Academic
Press. http://dx.doi.org/10.1016/B978-0-12-812756-8.00003-7
Offermann, L. R., Kennedy, J. K., Jr., & Wirtz, P. W. (1994). Implicit
leadership theories: Content, structure, and generalizability. The Lead-
ership Quarterly, 5, 43–58. http://dx.doi.org/10.1016/1048-
9843(94)90005-1
Oh, I.-S., & Berry, C. M. (2009). The five-factor model of personality and
managerial performance: Validity gains through the use of 360 degree
performance ratings. Journal of Applied Psychology, 94, 1498 –1513.
http://dx.doi.org/10.1037/a0017221
Ones, D. S., Dilchert, S., Viswesvaran, C., & Judge, T. A. (2007). In
support of personality assessment in organizational settings. Personnel
Psychology, 60, 995–1027. http://dx.doi.org/10.1111/j.1744-6570.2007
.00099.x
Ployhart, R. E., & Vandenberg, R. J. (2010). Longitudinal research: The
theory, design, and analysis of change. Journal of Management, 36,
94 –120. http://dx.doi.org/10.1177/0149206309352110
Podsakoff, N. P., LePine, J. A., & LePine, M. A. (2007). Differential
challenge stressor-hindrance stressor relationships with job attitudes,
turnover intentions, turnover, and withdrawal behavior: A meta-analysis.
Journal of Applied Psychology, 92, 438 – 454. http://dx.doi.org/10.1037/
0021-9010.92.2.438
Podsakoff, N. P., Spoelma, T. M., Chawla, N., & Gabriel, A. S. (2019).
What predicts within-person variance in applied psychology constructs?
An empirical examination. Journal of Applied Psychology, 104, 727–
754. http://dx.doi.org/10.1037/apl0000374
This document is copyrighted by the American Psychological Association or one of its allied publishers.
This article is intended solely for the personal use of the individual user and is not to be disseminated broadly.
18 LI ET AL.
Podsakoff, P. M., MacKenzie, S. B., Lee, J. Y., & Podsakoff, N. P. (2003).
Common method biases in behavioral research: A critical review of the
literature and recommended remedies. Journal of Applied Psychology,
88, 879 –903. http://dx.doi.org/10.1037/0021-9010.88.5.879
Preacher, K. J., Briggs, N. E., Wichman, A. L., & MacCallum, R. C.
(2008). Latent growth curve modeling. Los Angeles, CA: Sage. http://
dx.doi.org/10.4135/9781412984737
Prentice, D. A., & Miller, D. T. (1992). When small effects are impressive.
Psychological Bulletin, 112, 160 –164. http://dx.doi.org/10.1037/0033-
2909.112.1.160
Reitzig, M., & Maciejovsky, B. (2015). Corporate hierarchy and vertical
information flow inside the firm—A behavioral view. Strategic Man-
agement Journal, 36, 1979 –1999. http://dx.doi.org/10.1002/smj.2334
Roberts, B. W., Caspi, A., & Moffitt, T. E. (2003). Work experiences and
personality development in young adulthood. Journal of Personality and
Social Psychology, 84, 582–593. http://dx.doi.org/10.1037/0022-3514
.84.3.582
Roberts, B. W., & DelVecchio, W. F. (2000). The rank-order consistency
of personality traits from childhood to old age: A quantitative review of
longitudinal studies. Psychological Bulletin, 126, 3–25. http://dx.doi
.org/10.1037/0033-2909.126.1.3
Roberts, B. W., Kuncel, N. R., Shiner, R., Caspi, A., & Goldberg, L. R.
(2007). The power of personality: The comparative validity of person-
ality traits, socioeconomic status, and cognitive ability for predicting
important life outcomes. Perspectives on Psychological Science, 2,
313–345. http://dx.doi.org/10.1111/j.1745-6916.2007.00047.x
Roberts, B. W., & Nickel, L. (2017). A critical evaluation of the neo-
socioanalytic model of personality. In J. Specht (Ed.), Personality de-
velopment across the life span (pp. 157–177). Elsevier. http://dx.doi.org/
10.1016/B978-0-12-804674-6.00011-9
Roberts, B. W., Walton, K. E., & Viechtbauer, W. (2006). Patterns of
mean-level change in personality traits across the life course: A meta-
analysis of longitudinal studies. Psychological Bulletin, 132, 1–25.
http://dx.doi.org/10.1037/0033-2909.132.1.1
Roberts, B. W., Wood, D., & Caspi, A. (2008). The development of
personality traits in adulthood. In O. P. John, R. W. Robins, & L. A.
Pervin (Eds.), Handbook of personality: Theory and research (3rd ed.,
pp. 375–398). New York, NY: Guilford Press.
Sackett, P. R., Lievens, F., Van Iddekinge, C. H., & Kuncel, N. R. (2017).
Individual differences and their measurement: A review of 100 years of
research. Journal of Applied Psychology, 102, 254 –273. http://dx.doi
.org/10.1037/apl0000151
Saucier, G. (1994). Mini-markers: A brief version of Goldberg’s unipolar
big-five markers. Journal of Personality Assessment, 63, 506 –516.
http://dx.doi.org/10.1207/s15327752jpa6303_8
Schneider, B. (1987). The people make the place. Personnel Psychology,
40, 437– 453. http://dx.doi.org/10.1111/j.1744-6570.1987.tb00609.x
Schwaba, T., & Bleidorn, W. (2018). Individual differences in personality
change across the adult life span. Journal of personality, 86, 450 – 464.
http://dx.doi.org/10.1111/jopy.12327
Schwaba, T., & Bleidorn, W. (2019). Personality trait development across
the transition to retirement. Journal of Personality and Social Psychol-
ogy, 116, 651– 665. http://dx.doi.org/10.1037/pspp0000179
Selig, J. P., & Preacher, K. J. (2009). Mediation models for longitudinal
data in developmental research. Research in Human Development, 6(2–
3), 144 –164. http://dx.doi.org/10.1080/15427600902911247
Shalley, C. E., Gilson, L. L., & Blum, T. C. (2000). Matching creativity
requirements and the work environment: Effects on satisfaction and
intentions to leave. Academy of Management Journal, 43, 215–223.
Sherf, E. N., & Morrison, E. W. (2020). I do not need feedback! Or do I?
Self-efficacy, perspective taking, and feedback seeking. Journal of Ap-
plied Psychology, 105, 146 –165. http://dx.doi.org/10.1037/apl0000432
Sherman, G. D., Lee, J. J., Cuddy, A. J. C., Renshon, J., Oveis, C., Gross,
J. J., & Lerner, J. S. (2012). Leadership is associated with lower levels
of stress. Proceedings of the National Academy of Sciences of the United
States of America, 109, 17903–17907. http://dx.doi.org/10.1073/pnas
.1207042109
Shipp, A. J., & Cole, M. S. (2015). Time in individual-level organizational
studies: What is it, how is it used, and why isn’t it exploited more often?
Annual Review of Organizational Psychology and Organizational Be-
havior, 2, 237–260. http://dx.doi.org/10.1146/annurev-orgpsych-
032414-111245
Sonnentag, S., & Frese, M. (2012). Stress in organizations. In N. Schmitt
& S. Highhouse (Eds.), Handbook of psychology: Vol. 12.Industrial and
organizational psychology (2nd ed., pp. 560 –592). Hoboken, NJ: Wiley.
South, S. C., Jarnecke, A. M., & Vize, C. E. (2018). Sex differences in the
Big Five model personality traits: A behavior genetics exploration.
Journal of Research in Personality, 74, 158 –165. http://dx.doi.org/10
.1016/j.jrp.2018.03.002
Specht, J., Bleidorn, W., Denissen, J. J., Hennecke, M., Hutteman, R.,
Kandler, C.,...Zimmermann, J. (2014). What drives adult personality
development? A comparison of theoretical perspectives and empirical
evidence. European Journal of Personality, 28, 216 –230. http://dx.doi
.org/10.1002/per.1966
Specht, J., Egloff, B., & Schmukle, S. C. (2011). Stability and change of
personality across the life course: The impact of age and major life
events on mean-level and rank-order stability of the Big Five. Journal of
Personality and Social Psychology, 101, 862– 882. http://dx.doi.org/10
.1037/a0024950
Staw, B. M. (2004). The dispositional approach to job attitudes: An
empirical and conceptual review. In B. Schneider & B. Smitt (Eds.),
Personality and organization (pp. 163–191). Mahwah, NJ: Erlbaum.
Summerfield, M., Bevitt, A., Freidin, S., Hahn, M., LA, N., Macalalad, N.,
. . . Wooden, M. (2017). HILDA user manual—Release 16. Retrieved
from https://melbourneinstitute.unimelb.edu.au/__data/assets/pdf_file/
0006/2597865/HILDA-User-Manual-Release-16.0_LATEST.pdf
Sutin, A. R., & Costa, P. T., Jr. (2010). Reciprocal influences of personality
and job characteristics across middle adulthood. Journal of Personality,
78, 257–288. http://dx.doi.org/10.1111/j.1467-6494.2009.00615.x
Sutin, A. R., Costa Jr., P. T., Miech, R., & Eaton, W. W. (2009). Person-
ality and career success: Concurrent and longitudinal relations. Euro-
pean Journal of Personality, 23, 71– 84.
Taylor, P. J., Li, W. D., Shi, K., & Borman, W. C. (2008). The transport-
ability of job information across countries. Personnel Psychology, 61,
69 –111. http://dx.doi.org/10.1111/j.1744-6570.2008.00106.x
Tasselli, S., Kilduff, M., & Landis, B. (2018). Personality change: Impli-
cations for organizational behavior. The Academy of Management An-
nals, 12, 467– 493. http://dx.doi.org/10.5465/annals.2016.0008
Tett, R. P., & Burnett, D. D. (2003). A personality trait-based interactionist
model of job performance. Journal of Applied Psychology, 88, 500 –517.
http://dx.doi.org/10.1037/0021-9010.88.3.500
Turiano, N. A., Pitzer, L., Armour, C., Karlamangla, A., Ryff, C. D., &
Mroczek, D. K. (2012). Personality trait level and change as predictors
of health outcomes: Findings from a national study of Americans
(MIDUS). The Journals of Gerontology: Series B: Psychological Sci-
ences and Social Sciences, 67, 4 –12. http://dx.doi.org/10.1093/geronb/
gbr072
Vandenberg, R. J., & Lance, C. E. (2000). A review and synthesis of the
measurement invariance literature: Suggestions, practices, and recom-
mendations for organizational research. Organizational Research Meth-
ods, 3, 4 –70. http://dx.doi.org/10.1177/109442810031002
Wang, M., & Wanberg, C. R. (2017). 100 years of applied psychology
research on individual careers: From career management to retirement.
Journal of Applied Psychology, 102, 546 –563. http://dx.doi.org/10
.1037/apl0000143
Wooden, M., Freidin, S., & Watson, N. (2002). The household, income and
labour dynamics in Australia (HILDA) survey: Wave 1. The Australian
This document is copyrighted by the American Psychological Association or one of its allied publishers.
This article is intended solely for the personal use of the individual user and is not to be disseminated broadly.
19
PERSONALITY CHANGE, LEADERSHIP, AND ROLE TRANSITION
Economic Review, 35, 339 –348. http://dx.doi.org/10.1111/1467-8462
.00252
Woods, S. A., Wille, B., Wu, C., Lievens, F., & De Fruyt, F. (2019). The
influence of work on personality trait development: The Demands-
Affordances TrAnsactional (DATA) model, an integrative review, and
research agenda. Journal of Vocational Behavior, 110, 258 –271. http://
dx.doi.org/10.1016/j.jvb.2018.11.010
Wu, C. H., & Griffin, M. A. (2012). Longitudinal relationships between
core self-evaluations and job satisfaction. Journal of Applied Psychol-
ogy, 97, 331–342. http://dx.doi.org/10.1037/a0025673
Wu, C. H., Wang, Y., Parker, S. K., & Griffin, M. A. (2020). Effects of
chronic job insecurity on Big Five personality change. Journal of Ap-
plied Psychology. Advance online publication. http://dx.doi.org/10
.1037/apl0000488
Yukl, G. (2012). Effective leadership behavior: What we know and what
questions need more attention. The Academy of Management Perspec-
tives, 26, 66 – 85. http://dx.doi.org/10.5465/amp.2012.0088
Yukl, G. (2013). Leadership in organizations (8th ed.). Upper Saddle
River, NJ: Pearson Prentice Hall.
Zimprich, D., Allemand, M., & Lachman, M. E. (2012). Factorial structure
and age-related psychometrics of the MIDUS personality adjective items
across the life span. Psychological Assessment, 24, 173–186. http://dx
.doi.org/10.1037/a0025265
Appendix
Personality Items Adopted in the Current Research
Items Used in the Big Five Personality Measure in
Study 1
Please indicate how well each of following descriptive adjec-
tives describes you (1 ⫽a lot,4⫽not at all)?
Conscientiousness:Organized,Responsible,Hardworking, and
Careless (negatively worded)
Emotional stability:Moody (negatively worded), Worrying
(negatively worded), and Nervous (negatively worded)
Agreeableness:Caring,Soft-hearted, and Sympathetic
Extraversion:Outgoing,Lively,Active, and Talkative
Openness:Creative,Imaginative,Intelligent,Curious,Sophis-
ticated, and Adventurous
Items Used in the Big Five Personality Measure in
Study 2
Please indicate how well each of the following describes you
(1 ⫽strongly disagree,7⫽strongly agree).
Conscientiousness:Orderly,Disorganized (negatively
worded), and Efficient
Emotional stability:Moody (negatively worded), Envious
(negatively worded), Touchy (negatively worded), and Tempera-
mental (negatively worded)
Agreeableness:Sympathetic,Kind,Cooperative, and Warm
Extraversion:Shy (negatively worded), Quite (negatively
worded), and Bashful (negatively worded)
Openness:Creative,Deep,Philosophical, and Intellectual
Received May 2, 2019
Revision received June 7, 2020
Accepted June 10, 2020 䡲
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