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Journal of Business and Psychology (2024) 39:755–778
https://doi.org/10.1007/s10869-023-09906-7
ORIGINAL PAPER
Leader Identity ontheFly: Intra‑personal Leader Identity Dynamics
inResponse toStrong Events
KarolinaW.Nieberle1 · BryanP.Acton2· SusanneBraun3· RobertG.Lord3· Yue(Angelique)Fu4
Accepted: 28 July 2023 / Published online: 5 September 2023
© The Author(s) 2023
Abstract
Recent theorizing challenges the notion that leadership, and especially leader identities, is static. Yet, we know little about
the dynamics that characterize how leader identities change within individuals across short periods of time. The current
work integrates theorizing on temporal dynamics in leadership research with event systems theory to describe and predict
day-to-day shifts (i.e., unidirectional, sudden changes) and dynamic ebb and flow patterns (i.e., multidirectional, potentially
nonlinear changes over multiple days) of individuals’ leader identities. Specifically, we argue that the experience of strong
(i.e., novel, disruptive, extraordinary) daily events facilitates positive leader identity shifts, and that over time, the resulting
identity ebb and flows are more pronounced in unfamiliar compared to familiar contexts. We collected experience sampling
data from 69 young adults at a university in the UK across seven-day periods at three different time points during the aca-
demic year (1159 data points). Using dynamical systems modeling, we analyze the velocity (i.e., rate of change) and the
acceleration (i.e., change in velocity) parameters of individuals’ leader identity dynamics. We find that (a) on a daily level,
strong events prompt positive shifts in leader identity, and that (b) over time, chains of stronger and weaker events provoke
similar patterns of leader identity ebb and flows. However, these relationships are not stronger in unfamiliar compared to
familiar contexts. Our research informs the theoretical understanding of events and short-term leader identity dynamics. We
discuss implications for theory and research, in particular how events can trigger leader identity formation.
Keywords Ebb and flow· Events· Dynamical systems modeling· Leader identity· Young adults
Nothing is absolute. Everything changes, everything moves, eve-
rything revolves, everything flies and goes away — Frida Kahlo.
Social-cognitive theory seeks to explain how one’s leader
identity becomes salient during a particular period of time
(Epitropaki etal., 2017). One explanation is that individuals
scan their environment for relevant cues, which they inte-
grate with more enduring information about their leadership
related past, present, and future (Ashforth & Schinoff, 2016;
Kivetz & Tyler, 2007; Shaughnessy & Coats, 2018). Leader
identity describes the extent to which individuals define
themselves as “being a leader” during a specific period of
time. When a leader identity is more salient than “being a
follower,” individuals think, feel, and act like leaders (Jen-
nings etal., 2021; Lanaj etal., 2021a, b).
Scholars agree that, similar to other identities (e.g., entre-
preneurial; Tripathi etal., 2020), leader identities are mal-
leable and fluctuate within individuals over short periods
of time (Epitropaki etal., 2017; Lord & Chui, 2017; Lord
etal., 2016). This view contrasts to earlier research in which
leader identities have been treated as relatively stable intra-
personal attributes (e.g., Johnson etal., 2012; Kwok etal.,
2018; Venus etal., 2019), which develop over longer periods
such as weeks or months (Day & Sin, 2011; Middleton etal.,
2019; Miscenko etal., 2017). For example, leader identities
have been examined from the perspective of skill acquisition
Additional supplementary materials can be found on the Open
Science Framework (https:// osf. io/ wrvye/).
* Karolina W. Nieberle
karolina.w.nieberle@durham.ac.uk
1 Department ofPsychology, Durham University, Upper
Mountjoy, South Road, DurhamDH13LE, UK
2 School ofManagement, Binghamton University,
Binghamton, NY, USA
3 Durham University Business School, Durham University,
Durham, UK
4 Alliance Manchester Business School, The University
ofManchester, Manchester, UK
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756 Journal of Business and Psychology (2024) 39:755–778
1 3
in response to training (Lang etal., 2021; Wallace etal., 2021)
and predicted by individual differences (e.g., learning orienta-
tion, motivation to lead; Kwok etal., 2021; Middleton etal.,
2019). Recent experience sampling studies (Jennings etal.,
2021; Lanaj etal., 2021a, b; Lanaj etal., 2021a, b), however,
begin to tap into short-term changes in individuals’ leader
identities, showing that leader identities are sensitive to con-
textual stimulation (e.g., self-reflection exercises; Jennings
etal., 2021; Lanaj etal., 2021a, b). These short-term changes
have been described as unidirectional, sudden, and discon-
tinuous leader identity shifts, which happen from one day to
another. Over time, however, multiple of these smaller shifts
combined may form a dynamic and potentially nonlinear pat-
tern of leader identity ebb and flows (McClean etal., 2019).
Knowing why and how leader identities change in the
form of short-term shifts and ebb and flows is essential for
understanding whether and when a person is likely to exhibit
leadership during a particular period of time. We maintain
that a strong (person-level) leader identity alone does not
fully predict the extent to which a person is likely to exhibit
leadership on a specific day. In addition to individuals’ gen-
eral self-schema as a leader, exhibiting leadership requires
that their leader identity is salient on a given day and is more
salient than other possible identities (e.g., being a follower).
Defining individuals’ leader identity relative to their follower
identity sets a baseline to determine if leadership is going to
be the identity that most likely drives cognition, motivation,
and behavior. We thus define an active leader identity as the
extent to which “being a leader” is more salient than “being
a follower” during a specific period of time. According to
event systems theory, strong (i.e., novel, disruptive, extraor-
dinary) events have the potential to prompt positive leader
identity shifts (increases in leader identity activation) as they
require individuals to actively make sense of who they are
(Hammond etal., 2017; Hoffman & Lord, 2013; Morge-
son etal., 2015). Although qualitative findings indicate that
strong events can trigger identity change (e.g., Hennekam
etal., 2021), research that explains and quantifies the factors
that facilitate such changes in leader identities over shorter
periods of time remains very limited.
Our research challenges leader identity change as primar-
ily a long-term, gradual development process. Instead, we
suggest that leader identities are contextually malleable and
that the intra-personal changes in leader identity relative to
a follower identity are best characterized as a dynamic pat-
tern of identity ebbs and flows (McClean etal., 2019). We
argue that the extent to which an individual’s leader identity
is salient relative to their follower identity varies around a
typical identity state (i.e., an identity-equilibrium), produc-
ing small identity shifts from day to day. Over time, the
resulting leader identity dynamics can be quantified by the
velocity (i.e., rate of change) and acceleration (i.e., change
in velocity) at which intra-personal change occurs.
In sum, building on temporal (McClean etal., 2019) and
event system theorizing (Hoffman & Lord, 2013; Morge-
son etal., 2015), we argue that strong daily events prompt
individuals’ leader identities to positively shift away from
their identity-equilibrium. Over time, the dynamic changes
in event strength will predict a similar pattern of changes
in leader identity. Table1 provides an overview of our key
study constructs.
Our research makes several contributions. First, we
quantify how short-term changes in leader identities occur
by examining the daily shifts, and the ebb and flow pat-
terns that these intra-personal dynamics follow over time
(McClean etal., 2019). We seek to explain how leader iden-
tity ebb and flows are put in motion through experiences of
events and in different contexts (Hoffman & Lord, 2013;
Morgeson etal., 2015). Events are triggers for sensemak-
ing about situated identities (Ashforth & Schinoff, 2016;
Hennekam etal., 2021), and our research shows that strong
events prompt positive shifts in daily leader identities. As
events do not happen in isolation but follow each other in
chains or event clusters (Morgeson etal., 2015), we exam-
ine the implications of chains of weak and strong events
for leader identity ebb and flows. Thus, we are able to test
whether event clusters function as accelerators of individu-
als’ sensemaking processes and over time have the potential
to shift individuals away from their chronic leader identity
equilibrium.
Second, time is a critical element of leader identity
change (Epitropaki etal., 2017; Hammond etal., 2017; Lord
& Chui, 2017; Lord etal., 2016). Although the importance
of time for organizations has been widely recognized (Agu-
inis & Bakker, 2021; Ancona etal., 2001; Castillo & Trinh,
2018; Shipp & Jansen, 2021), there remains scope for the
empirical integration of time and change into (leadership)
research methods. We contribute to the modeling of time
in leadership and organizational behavior research (Fischer
etal., 2017; McClean etal., 2019; McCormick etal., 2020).
Short-term variation within persons is often ignored and
considered as an error, rather than being explained scien-
tifically (Lord etal., 2015). Instead, we apply principles of
dynamical systems modeling (Boker, 2001; Boker & Nes-
selroade, 2002; Cole etal., 2017) in order to model two key
elements, velocity (i.e., rate of change) and acceleration (i.e.,
change in velocity), which quantify how leader identities
shift in ebb and flow patterns within individuals and over
short periods of time (McClean etal., 2019).
Third, we aim to offer insights into the intra-personal
relationships between leader and follower identities. Cur-
rent theorizing suggests that leadership and followership
are part of a larger network of self-schemas (Epitropaki
etal., 2017; Lord & Chui, 2017). As such, both identities
should be considered as drivers of motivation and behavior
(Acton etal., 2019; Lord etal., 2016). Yet, current research
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757Journal of Business and Psychology (2024) 39:755–778
1 3
does not explain how the potentially conflicting elements
of leader and follower identities relate to each other (Epi-
tropaki etal., 2017). For example, individuals may engage
in an intra-personal process of dynamic leader and follower
identity switching (Sy, 2010; Sy & McCoy, 2014). We intro-
duced the leader–follower identity grid (LFIG) to assess
leader and follower identities. Our measurement approach
reflects that both leadership and followership schemas can
(but need not) be active to inform an individual’s identity
on a particular day. Our analysis of general (person-level)
and within-person relationships between leader and follower
identities addresses whether the two identities can be active
at the same time or whether activation of one identity likely
de-activates the other identity.
Finally, our research contributes to the understanding of
leader identity dynamics during the critical time period of
young adulthood (Liu etal., 2021; Shaughnessy & Coats,
2018; Zaar etal., 2020). Young adults may experience strong
events as “shocks” (Crawford etal., 2019) that can cause
uncertainty and liminality (Hawkins & Edwards, 2015),
which may hinder their development. Conversely, strong
events (or chains of strong and weak events) may function as
developmental opportunities for identity play and work that
can ultimately help to build or solidify longer-term leader
identities (Ashforth & Schinoff, 2016; Ibarra, 1999; Ibarra
& Obodaru, 2020). Thus, our research of short-term leader
identity dynamics holds the potential to inform approaches
to leader (identity) development (Day & Dragoni, 2015),
particularly for young adults. It can advise educational
institutions and employers on the types of experiences (e.g.,
strong and weak events) that they should support to foster
young adults’ development.
Table 1 Key concepts and definitions in our research
ConceptDefinitionIllustration References
Event strength Events are discrete and discontinuous units of activity that
occur in a specific time and location, and that diverge
from the routine features of the environment.
Event strength describes the extent to which an event is
salient, commands attention, and stands out of the day-to-
day routine. Event strength is characterized by the three
features novelty (i.e., differs from one's current or past
experiences), disruptiveness (i.e., reflects a discontinuity),
and extraordinariness (i.e., questions established orders).
Hoffman &
Lord, 2013;
Morgeson et
al., 2015
Leader identity
dynamics
We define an activeleader identity as the extent to which
‘being a leader’ is more salient than ‘being a follower’
during a particular period of time.
Leader identity dynamics are characterized by multiple
smaller shifts in an individual’s active leader identity over
shorter periods of time (i.e., ebb and flows). Leader
identity dynamics represent the pattern of variability that
individuals experience around their typicalleader identity
level (i.e., their equilibrium). These dynamics can be
quantified via the velocity and the acceleration at which
change occurs.
McClean et
al., 2019;
Lang et al.,
2021; Xu et
al., 2020
Velocity Velocity describes the rate at which change in leader
identity occurs over a specified unit of time (e.g., day-to-
day). As such, it indicates how quickly change in leader
identity occurs over time.
Boker, 2001;
Boker &
Nesselroade,
2002
Acceleration Acceleration indicates whether the velocity is increasing
or decreasing over a specified unit of time. As such, the
acceleration indicates whether the rate of change in leader
identity is accelerating, decelerating, or remaining
constant.
Boker, 2001;
Boker &
Nesselroade,
2002
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758 Journal of Business and Psychology (2024) 39:755–778
1 3
Theory andHypotheses
Leader Identity Dynamics
Leader identity has been described as a momentary state
(Ashford & DeRue, 2012), as being created on-the-spot
(Lord & Chui, 2017; Lord etal., 2016), frequently moving
(DeRue & Ashford, 2010; Sveningsson & Alvesson, 2003),
and dynamically shifting within individuals (Epitropaki
etal., 2017; Lord etal., 2020). Building on these perspec-
tives, we conceptualize leader identity dynamics as the intra-
personal changes in an individual’s leader identity activation
over time. We apply the term dynamics (or “ebb and flow”;
McClean etal., 2019) to describe change processes over time
(Lang etal., 2021) and specifically focus on describing and
explaining small changes over shorter periods of time (Xu
etal., 2020).
Individuals are motivated to establish and re-establish
both who they are (I am a leader) and who they are not (I
am not a leader; Watson, 2009). As such, leader identities
can be understood as equilibria that result from individuals’
momentary sensemaking of the extent to which they do or
do not feel like a leader. The equilibrium describes the indi-
vidual’s typical leader identity state (Boker, 2015; Deboeck,
2013). Internal and external cues can affect the identity-equi-
librium state. The cues are integrated into the momentary
self-concept with the goal to understand “‘Who am I in this
situation?’ and ‘what should I do?’” (Epitropaki etal., 2017,
p. 107). Accordingly, leader identities may change due to new
situations or events that the individual encounters (Ashforth
& Schinoff, 2016; Lord etal., 2015). However, over time, the
net effect of such changes is often the formulation of a new
equilibrium of leader identity; this might closely follow the
experience and sensemaking associated with strong events
(Hoffman & Lord, 2013; Morgeson etal., 2015).
Qualitative research that describes identity formation
as a process of identity work (i.e., maintaining, adapting,
shaping, or revising an existing identity) and play (i.e.,
exploring possible new identities; Brown, 2015; Bysh etal.,
2022a; Ibarra & Petriglieri, 2010) supports the perspective
of identity-as-equilibrium. For example, individuals revise
and deconstruct their existing identities and experiment with
new ones, rendering some more salient than others (e.g.,
Nicholson & Carroll, 2013; Sveningsson & Alvesson, 2003).
Building on these findings, we argue that the extent to which
individuals see themselves as leaders varies around their
typical level of leader identity (i.e., the equilibrium).
Events andLeader Identity Dynamics
Events reflect discrete and discontinuous units of activity that
occur in a specific time and location (Hoffman & Lord, 2013;
Morgeson etal., 2015). Events can disrupt routines, so that
individuals need to adjust their behaviors to the new require-
ments, as well as interpret and connect these new experiences
with their previous ones (Hoffman & Lord, 2013; Morgeson
& DeRue, 2006; Wallace etal., 2021), as such events, and
especially strong events, are a promising avenue to explain
change in leadership and identity processes (Bednar etal.,
2020; Hoffman & Lord, 2013; Morgeson etal., 2015).
Event strength focuses on the impact that events have on
individuals. Event strength is defined as the extent to which
an event is salient, commands attention, and stands out of the
day-to-day routine (Morgeson etal., 2015). The stronger the
events, the more they prompt controlled information process-
ing and influence individuals’ affect, cognition, and behavior
(Crawford etal., 2019; Hoffman & Lord, 2013; Morgeson,
2005; Morgeson & DeRue, 2006; Morgeson etal., 2015).
Event strength is characterized by the three features nov-
elty (i.e., the event differs from current or past experiences),
disruptiveness (i.e., the event reflects a discontinuity), and
extraordinariness (i.e., the event questions established
orders).1 These three event features function independently,
such that for example a novel event (e.g., meeting a new
co-worker) is not necessarily disruptive. However, the event
features combine in an additive fashion such that their con-
fluence determines the overall strength of an event and the
impact it likely has on individuals (Morgeson etal., 2015).
Table2 provides a construct definition for each event feature,
and it explains the mechanisms through which they impact
individuals’ affect, cognition, and behavior.
We argue that strong events trigger leader identity dynam-
ics as individuals respond with experimenting and negotiat-
ing their identities (Hammond etal., 2017; Hoffman & Lord,
2013). Strong events have been referred to as awakening
events (Seibert etal., 2021), trigger events (Bednar etal.,
2020), or even shocks (Crawford etal., 2019; Hennekam
etal., 2021; Lee & Mitchell, 1996). They can represent
“significant points of tension, change, or challenge” (Lanka
etal., 2020, p. 382) for individuals’ self-perceptions as a
leader. Strong events increase the need for sensemaking and
reflection (Bednar etal., 2020; Crawford etal., 2019; Mait-
lis & Christianson, 2014), which in turn can be a source
of leader identity change (e.g., Jennings etal., 2021; Lanaj
etal., 2019, 2021a, b). In particular, recent work shows that
strong events during work transitions prompt individuals to
explore new behaviors in pursuit of their identity aspirations
(Seibert etal., 2021). Furthermore, strong events such as the
1 Morgeson etal., (2015) further include criticality (i.e., high amount
of personal significance) as dimension of event strength. While our
research does not directly include event criticality, criticality is inher-
ent in our measurement as we asked individuals to rate the most
important event on a given day. Supplementary analyses can be found
in an Online Supplementary Material at OSF.
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759Journal of Business and Psychology (2024) 39:755–778
1 3
confinement during COVID-19 pandemic have been shown
to trigger individuals’ identity reconstruction (Hennekam
etal., 2021).
Daily Events andChange inLeader Identity Activation
Identities change during periods of transition (Ashforth
& Schinoff, 2016; Ibarra, 1999; Ibarra & Obodaru, 2020).
Emerging adulthood (i.e., the late teens and twenties) is
such a time of transition (Liu etal., 2021; Zaar etal., 2020).
During this period, individuals are particularly inclined to
seek leadership-related experiences that inform their identi-
ties (Liu etal., 2021; Murphy & Johnson, 2011; Zaar etal.,
2020). Emerging adulthood also provides opportunities for
strong daily events to occur, such as through educational
experiences, leisure activities, volunteering, or first experi-
ences with internships and jobs (Liu etal., 2021). Strong
events unsettle current leadership-related perceptions
because they create tensions or challenges to who one is
(Lanka etal., 2020), trigger self-related sensemaking and
reflection (Bednar etal., 2020; Crawford etal., 2019; Mait-
lis & Christianson, 2014), and prompt identity exploration
(Hennekam etal., 2021; Seibert etal., 2021).
Identity and sensemaking are interdependent. A salient
identity facilitates sensemaking. At the same time, sense-
making in response to strong events precipitates a search
for meaning that informs identity (Crawford etal., 2019;
Weick etal., 2005). The stronger daily events are, the more
they depart from an individual’s established cognitive sche-
mas and scripts. For a strong daily event, individuals can no
longer follow their previously learned cognitive patterns.
Rather, they are prompted to explore different perspectives
and to construct novel interpretations of a situation, which
promotes higher levels of self-complexity, an important
ingredient for leader development (Hannah etal., 2009).
Strong daily events and the resulting sensemaking can be
seen as opportunities for leadership. Individuals are required
to actively depart from their prior knowledge and take over
responsibility for adapting or newly creating their cognitive
(and/or behavioral) response. In that sense, strong events
require individuals to be active and adopt leadership to some
degree. The fact that strong daily events require individuals
to be active makes it likely that the salience of individuals’
leader identity increases. Contrarily, experiencing weaker
daily events means that events are rather familiar, ordinary,
and non-disruptive. For these events, individuals can rec-
ognize learned patterns and follow their previous routines.
Hoffman & Lord, (2013, p. 561) describe such routine events
as “substitutes for leadership,” that is, as events that require
individuals not to show leadership action for success.
Overall, we propose that on days when young adults expe-
rience strong events, their daily leader identity will devi-
ate from their typical identity level, such that short-term
Table 2 Conceptual definition of the event features that characterize the strength of event
Feature Definition Mechanism Reference
Novelty The extent to which an event is a new or unexpected expe-
rience that differs from current or past experiences
Novelty drives individuals to actively make sense because
no established routines or scripts are available to guide
the cognitive and behavioral response to anew or unex-
pected experience
Crawford etal., (2019); Hoffman & Lord, (2013);
Morgeson (2005); Morgeson etal. (2015)
Disruptiveness The extent to which an event is a discontinuity that dis-
rupts the predictable flow of day-to-day experiences
Disruptiveness requires individuals to actively make sense
because they need to identify in which ways to adapt
to the discontinuity and how to adjust and change their
actions
Morgeson & DeRue, (2006); Morgeson etal., (2015)
Extraordinariness The extent to which an event questions or threatens estab-
lished orders of functioning
Extraordinariness drives individuals to actively make
sense because these events take the form of opportuni-
ties or threats which require the use of new scripts and
schemas
Hoffman & Lord, (2013)
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760 Journal of Business and Psychology (2024) 39:755–778
1 3
changes in leader identity activation become observable. In
particular, we expect that the stronger the daily events, the
more young adults’ leader identity will shift in a positive
direction away from their equilibrium (i.e., increased leader
identity activation).
Hypothesis 1: The strength of daily events relates posi-
tively to changes in leader identity activation.
Events andLeader Identity Dynamics Over Time
Individuals experience different events over time. Events
that follow each other can be similarly strong (e.g., two
strong or weak events after each other) or differ in their
strength (e.g., a strong event is followed by a weak event
or vice versa). According to event systems theory, the
extent to which different events vary in strength over time
will inform how likely they elicit change (Morgeson etal.,
2015). Similarly, Hoffman & Lord, (2013) pointed out that
event sequences should be considered in order to accurately
interpret their consequences for leadership dynamics. In
fact, experimental research on painful events demonstrates
that what mattered most for participants’ experienced pain
was not the intensity of pain associated with events but
the pattern of change in an event’s pain intensity (Ariely,
1998).
We argue that leader identity dynamics increase with
the abruptness of change in event strength. Over time, the
extent to which leader identities are salient for individuals
likely varies around a typical identity state so that peri-
ods of relative identity stability alternate with periods of
identity change (Ibarra & Petriglieri, 2010). We previously
theorized that strong daily events result in a positive shift
in leader identity activation (Hypothesis 1). In addition
to that, we argue that over time the rate of change and
the acceleration in an individual’s leader identity is pre-
dicted by the experienced rate of change in event strength.
Individuals are more likely to be affected by strong events
where these events are clearly noticeable and differ in
their strength from previous events. That is, the more the
strength of events shifts (i.e., high rate of change, mean-
ing that stronger and weaker events are rapidly occurring
after each other), the more an individual’s leader identity
will be likely to shift away from their equilibrium. Based
on Hypothesis 1, we would expect these leader identity
shifts around the equilibrium to occur in the same direc-
tion as the event strength. That is, over time increases in
event strength should predict increases in leader identity
activation. Similarly, over time decreases in event strength
should predict decreases in leader identity activation. In
contrast, when the strength of events remains relatively
stable over time (i.e., low rate of change, with events of
similar strength following each other), there will be no
noticeable difference in event strength for individuals
such that their identities remain in similar states. That is,
previously active leadership identities will remain active,
or previously active follower identities will maintain their
activation.
Finally, acceleration of leader identity is a core concept of
dynamical systems modeling (Chow etal., 2005). Whereas
velocity represents how much leader identities shift, accel-
eration represents how fast these identity shifts occurs, that
is, whether the rate of change itself is decreasing (decel-
eration), increasing (acceleration), or remaining stable over
time. We argue that increases in event strength over time
will affect not only how much leader identities shift, but also
how fast these shifts occur. The higher the rate of change in
event strength, the more noticeable are strong events and
the more likely their experience will really kick in, such that
individuals will move faster away from their leader identity
equilibrium. Thus, we would argue that an increase in event
strength over a period of time should provoke an increasing
rate of change in leader identity shifts.
In sum, we propose that a higher rate of change in event
strength results in leader identity dynamics with a pro-
nounced ebb and flow pattern over time, characterized by
high velocity and acceleration. That is, the more the strength
of events changes over time, the more likely individuals’
leader identity is to shift (i.e., higher rate of change) at an
increasing rate (i.e., acceleration).
Hypothesis 2: Over time, the rate of change in event
strength (i.e., event strength velocity) is positively related
to the rate of change in leader identity activation (i.e.,
leader identity velocity).
Hypothesis 3:Over time, the rate of change in event
strength (i.e., event strength velocity) is positively related
to the acceleration in leader identity activation.
Contexts andLeader Identity Dynamics
Contexts are essential to the impact of events: The more an
event fits the emerging needs of a developmental stage, the
larger its potential impact (Morgeson etal., 2015). Young
adults’ developmental needs for leadership may change as
they become more accustomed to previously unfamiliar con-
texts (Ashforth & Schinoff, 2016; Ashforth etal., 2014).
Upon entering unfamiliar contexts (e.g., at university),
young adults do not have a framework to guide their per-
ceptions and behaviors (Hirsh etal., 2012). Their identities
will be more malleable, which renders the identity-relevant
cues in their environment more salient (Ashforth & Schinoff,
2016). Thus, in unfamiliar contexts, young adults are more
likely to be receptive to external cues that can inform their
leader identities. In contrast, in familiar contexts, young
adults may have already formed situated leader identities and
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761Journal of Business and Psychology (2024) 39:755–778
1 3
thus strong events may be less likely to unsettle these identi-
ties. In sum, we argue that the relationship between changes
in event strength and young adults’ leader identity dynamics
(i.e., velocity, acceleration) will be stronger in unfamiliar as
compared to familiar contexts.
Hypothesis 4: The positive relationship between the rate
of change in event strength (i.e., event strength veloc-
ity) with (a) the rate of change (i.e., velocity) and (b) the
acceleration of leader identity will be stronger in unfamil-
iar as compared to familiar contexts.
Method
Data were analyzed using R, version 4.0.0 (R Core Team,
2021). The analysis code and the Online Supplementary
Material (OSM) containing additional analyses can be
accessed at https:// osf. io/ wrvye/.
Research Context
We conducted an experience sampling study with under-
graduate students at a large university based in the United
Kingdom (UK).2 At three times of the academic year
2019–2020, we collected one week of daily data correspond-
ing to the three terms of one academic year in the UK. At
time 1 (t1), the first term of the academic year in October/
November 2019, participants experienced various new situ-
ations (e.g., starting university courses and projects; meet-
ing instructors and fellow students) and social roles (e.g.,
engaging in formal college activities; becoming members
in sport and leisure clubs). Thus, t1 represents an unfamil-
iar context. At time 2 (t2), participants returned after the
winter break to enter their second term of study in January
2020. They returned to previously established roles and tasks
(e.g., in college or sports clubs) and resumed their studies.
Thus, t2 represents a familiar context. Finally, at time 3 (t3),
the third term started in May 2020 and was shaped by the
COVID-19 pandemic. The campus shut down, and students
were taught and examined virtually. The social distancing
guidelines restricted face-to-face interactions to a minimum,
resulting in feelings of isolation (Hamza etal., 2021). In
addition, many students relocated to their home countries.
Accordingly, while originally proposed to be the most famil-
iar context, t3 represented an unexpected context for which
we did not have propositions.
Participants andProcedures
We collected self-report data via a baseline survey at the
onset of the academic year and a series of daily surveys at
the onset of each of the three time points (i.e., academic
terms) with seven measurements (Monday to Sunday),
respectively. The baseline survey assessed socio-demo-
graphics and construct validation variables for our meas-
ure of leader identity. We blended a daily assessment of
identity activation with an episodic experience sampling
approach (Beal & Gabriel, 2019). Participants rated one
discrete daily event along multiple features (Hoffman &
Lord, 2013). Such event-based assessments help to access
episodic (i.e., context-specific) rather than semantic (i.e.,
context-independent) memories, which improves the accu-
racy of ratings (Hansbrough etal., 2020; Hoffman & Lord,
2013; Shondrick etal., 2010). This approach allowed us to
capture important daily events, which we expected to drive
leader identity dynamics.
The daily surveys were sent out at 5 pm and assessed
participants’ most important daily events, as well as their
leader and follower identities on the respective day. Self-
report data were most appropriate since participants them-
selves were in the best position to evaluate their daily expe-
rience of events as well as their leader identities (McClean
etal., 2019).
We recruited students in collaboration with one college
at the University (via email, flyers, and approaching them in
common areas). As reimbursement for completing a mini-
mum of 75% of daily surveys, participants entered a lottery
for a formal college event and received a leadership reflec-
tion certificate and £20 ($25). We received 110 baseline
surveys with 78 participants at t1 (458 assessments; 84% of
daily surveys completed), 60 at t2 (371 assessments; 88%
of daily surveys completed), and 54 at t3 (346 assessments;
92% of daily surveys completed).
For our analyses, we included participants with a mini-
mum of two daily assessments per time point, resulting in
a sample of 69 participants for t1 (449 assessments, 93%
of daily surveyscompleted), 56 participants for t2 (367
assessments, 93.6% of daily surveyscompleted), and 51
participants for t3 (343 assessments, 96.1% of daily sur-
veyscompleted). Out of the 69 participants, 55.1% were
female (42% male, 2.9% other) with an average age of 19.24
years (SD = 1.22, ranging from 18 to 23 years). Most were in
their first year of studies (60.9%; 17.4% second year; 18.8%
third year; 1.4% fourth year; 1.4% missing), 68.1% lived in
the college, and 26.1% currently or formerly held a formal
position within the college (e.g., year-group representa-
tive). Some participants had prior work experience (44.9%)
of 2.47 years on average (SD = 1.55, ranging from 0.4 to
6 years). Out of those with prior work experience, 41.9%
reported that they had supervised others.
2 The university has a collegiate system, where all students belong to
a college for the duration of their study. While the academic depart-
ments deliver the formal teaching, the colleges play a key role in pro-
viding the social contexts in which students engage in extra-curricular
activities.
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762 Journal of Business and Psychology (2024) 39:755–778
1 3
Measurement
Event Strength
We prompted participants to recall the most important event
that happened to them during the day and to describe it in
a few sentences. Participants then rated the event along the
three features of event strength (Hoffman & Lord, 2013;
Morgeson etal., 2015): novelty (Was the event familiar to
you? From 1 = familiar to 7 = novel); disruptiveness (Did
the event disrupt your normal routine? From 1 = not at all
to 7 = highly disruptive); extraordinariness (Was it an ordi-
nary or an extraordinary event? From 1 = very ordinary to
7 = very extraordinary).
Based on the theoretical premise that all three event fea-
tures add equally to the experienced strength of events, we
combined the three ratings to measure event strength via
mean scores. The multilevel correlations (i.e., days nested
within individuals) of the three event ratings were moder-
ate to strong across t1 (0.40–0.46), t2 (0.43–0.61), and t3
(0.49–0.57). We further calculated the correlation of each of
the event features with the composite score (Table3). Cor-
relations were high, indicating that all three event features
substantially contributed to event strength (see OSM for
results from additional confirmatory factor analysis). Cron-
bach’s α for the event strength measure was on average 0.69
(t1), 0.76 (t2), and 0.78 (t3). Findings from intraclass cor-
relations (ICC2) further indicated that the event strength rat-
ings were reliable for individuals over time (ICC(2)t1 = 0.56;
ICC(2)t2 = 0.67, ICC(2)t3 = 0.66) (Bliese, 2000).
Leader Identity
A leader identity is an aggregated construct that is composed of
multiple and interrelated leadership self-schemas (Epitropaki
etal., 2017). Scholars have argued that these schemas are entan-
gled within a larger network of self-schemas containing follow-
ership schemas (Epitropaki etal., 2017; Lord & Chui, 2017). As
such, bothleader and follower identities should be considered
simultaneously to determine which identity is more salient to
drive motivation and behavior (Acton etal., 2019; Lord etal.,
2016). We thus operationalized leader identity as the level of
leader identity that goes beyond a follower identity. To do so, our
analysis used participants’ leader identity scores as the criterion
variable, while introducing their follower identity scores as a
separate predictor. This parcels out the variance in participants’
leader identity that is explained by their follower identity. As
such, we predict the extent to which a leader identity is salient
or active beyond a follower identity.3
We used the leader–follower identity grid (LFIG) to
assess daily leader and follower identity. We built upon Sy
and colleagues’ work (Sy & McCoy, 2014; Sy & Reiter-
Palmon, 2018) for the LFIG, which is similar to the Affect
Grid (Russell etal., 1989). The LFIG comprises a two-
dimensional space: follower (x-axis) and leader (y-axis),
resulting from participants’ response to the question “Today,
I considered myself a…”: 1 = not at all to 10 = very much so.
With a single click, participants indicated both their daily
leader and follower identities (Fig.1). A short video instruc-
tion prior to the study ensured participants’ familiarity with
the response format. At the outset of the survey, we indicated
that leadership and followership not only are roles or posi-
tions but also concern how one feels about oneself in social
contexts, and that both are equally valuable.
We validated the LFIG via the baseline data, which assessed
participants’ general leader and follower identities with 10-item
Likert scales (Hiller, 2005; Rus etal., 2010), respectively (items
in Appendix). For the baseline assessment, participants indicated
a leader identity strength of 6.36 (SD = 2.09), and a follower
identity strength of 5.43 (SD = 5.43), as measured via the LFIG.
Correlations between the baseline assessments of the LFIG and
the Likert scales were strong (r = 0.73 for leader identity; r = 0.71
for follower identity, both p < 0.001).
Analyses
We tested our hypotheses with dynamical systems modeling
(DSM; Boker, 2001; Boker & Nesselroade, 2002). DSM
utilizes differential equations (i.e., equations that relate to
a variable and its derivatives) to study intra-personal vari-
ability (Bisconti etal., 2006; Boker & Nesselroade, 2002).
Building on DSM, we used the residual score of each par-
ticipants’ daily assessment of our focal variables (i.e., event
strength, leader identity, follower identity) to assess their
deviation from their equilibrium as a measure for their intra-
personal variability. While residuals are traditionally viewed
as noise, they carry meaningful information about within-
person variability (Bisconti etal., 2004, 2006; Nesselroade,
Table 3 Descriptive statistics of the event features and their correla-
tions with the overall event strength measure
Means (M) and standard deviations (SD) are calculated across the
three study contexts
Event features M SD Time 1
(unfamil-
iar)
Time 2
(familiar)
Time 3
(COVID-
19)
Novelty 3.58 1.91 .51 .62 .63
Disruptiveness 4.01 1.79 .49 .52 .59
Extraordinary 3.62 1.67 .53 .62 .65
3 As a robustness check, we repeated our analysis without includ-
ing follower identity as a control variable and report the findings in
the OSM. Excluding follower identity as a control variable did not
change the results.
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763Journal of Business and Psychology (2024) 39:755–778
1 3
1991). Based on these residual scores, we calculated the first
and second derivatives to characterize the velocity (i.e., the
rate of change) and acceleration (i.e., change in velocity)
of each participant’s intra-personal dynamics in our focal
variables. By relying on residuals and their derivatives,
we overcome disadvantages associated with the common
approach in psychology and management to measure intra-
personal variability via the person-level standard deviation
(e.g., Johnson etal., 2012). The person-level standard devia-
tion is disadvantageous when aiming to describe variability
over time (Deboeck, 2009; Deboeck etal., 2008; Wang etal.,
2012), as the same standard deviation may result from dif-
ferent ebb and flow patterns (illustration in Fig.2); as such,
relying on it may overlook fine-grained information such
as the frequency of change (Deboeck etal., 2008; McClean
etal., 2019). Table4 summarizes the advantages of the DSM
approach in comparison to more traditional approaches, such
as growth modeling.
Obtaining Residual Scores
As the first step, we calculated each person’s daily deviation
from their equilibrium. To do so, we obtained each person’s
daily residuals from their estimated linear trend in our focal
variables (i.e., event strength, leader identity, follower iden-
tity), separately for each time point. Using a linear imputa-
tion function based on the R package “imputeTS” (Gasimova
etal., 2014; Moritz & Bartz-Beielstein, 2017), we imputed
missing data for each time point separately and performed
linear mixed effect modeling. These models included a sep-
arate intercept and slope for each person. Residuals were
calculated as a form of level-1 residuals since data points
were nested within individuals (i.e., lowest level). Thus,
Fig. 1 The leader–follower identity grid (LFIG). Note. The LFIG was
applied as a daily measure with the instruction: We are interested in
knowing how you felt about yourself today. Did you consider yourself
more of a leader, a follower, none, or both? Please click at the posi-
tion that best represents how you considered yourself today. We are
asking you to rate on both leader and follower simultaneously with a
“click”. Today, I considered myself a...
Fig. 2 Three hypothetical persons with different ebb and flow pat-
terns. Note. Figure adapted from Deboeck, (2009) and applied to our
study. Three figures with different identity ebb and flows based on
different arrangements of seven assessments. The variance of all three
time series data is the same (s2 = 9.55). However, if calculating dif-
ference scores for each time series (xt − xt−1), which are related to the
estimates of the first derivative (i.e., velocity), the variance of these
difference scores differs largely (s2a = 5.50, s2b = 34.83, s2c = 22.00)
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764 Journal of Business and Psychology (2024) 39:755–778
1 3
Table 4 Summary of the DSM approach for answering questions about leader identity dynamics
Concept Dynamical Systems Approach Traditional Approaches (e.g., longitudinal growth modeling)
Predicted score Focal question: What predicts a person’s equilibrium state?
The predicted score in a regression equation is an individual’s equilibrium (i.e.,
typical state). In a regression line representing leader identity over time, a predicted
value is each value on the regression line
Example: The “typical” level of leader identity that a person tends to shift back to
over a specific period of time (e.g., days, weeks). Therefore, the predicted value
represents the reference point used to detect a shift rather than treated on its own
like in more traditional approaches
Focal question: What is the current level of leader identity?
The predicted score in a regression equation is what is traditionally used to measure
the expected level of identity. The predicted values are the values on the regression
line. This can only evaluate overall trends, but not smaller shifts. Additionally, tradi-
tional approaches utilize the predicted value in a regression solely
Residual score Focal question: What predicts how much a person’s assessment deviates from their
equilibrium state?
The residual score represents the extent to which an observed score has fluctuated
from an equilibrium at the one-time point. Larger scores represent larger shifts
from the equilibrium
Example: A positive (negative) sign indicates that the level of leader identity shifts
above (below) the individual’s leader identity equilibrium
Focal question: What error is observed in the data?
The residual is treated as an error term and thus is not directly incorporated into mod-
els of change. This ignores the potentially meaningful shifts from equilibrium states
and what predicts them
Change over time Focal questions: How can we quantify a person’s intra-personal fluctuations around
their equilibrium over time?
Two change parameters are used: velocity, acceleration. The velocity represents the
rate of change at which fluctuations around a person’s equilibrium occur across a
defined unit of time. The acceleration captures whether and how this rate of change
at which fluctuations occur is changing over time
Example: Larger velocity scores represent larger fluctuations of a person’s leader
identity around their leader identity equilibrium. Positive (negative) acceleration
indicates that these fluctuations are accelerating (decelerating) over time. An accel-
eration of zero indicates that a person’s fluctuations around their leader identity
equilibrium remain stable across time
Focal question: How can we quantify a person’s overall change in the level of leader
identity over time?
Change is considered linearly. That is, the rate of change of the predicted level of
leader identity is observed, but not how much the levels of leader identity are fluc-
tuating around an equilibrium. Questions about change around an equilibrium state
cannot be answered
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765Journal of Business and Psychology (2024) 39:755–778
1 3
the residuals were centered at the person level and aligned
more closely to the raw residuals found within ordinary least
squares regressions. Residuals represented the difference in
an individual’s observed response from the one that would
be predicted by their overall regression line at t1, t2, or t3
respectively4:
These residuals served as the data for computing the
dynamic parameters in our focal variables (i.e., velocity,
acceleration).
Testing forTime‑Dependent Processes
As the second step, we tested the core assumption that the
within-person residuals of our focal variables have a sys-
tematic ordering and are not random (Boker & Nesselroade,
2002). We simulated 1000 scrambled versions of the residual
data5 and applied a Kolmogorov–Smirnov test to compare
them to the original data. If the scrambled data is signifi-
cantly different from the original data, one can assume that
the residuals in the original data have a systematic time-
ordering. At all three time points, the residuals of our focal
variables in the original data were significantly different
from those in the scrambled datasets (Table5).
Velocity andAcceleration Parameters
As the third step, based on the residual scores, we calcu-
lated approximations to the first and the second derivatives
of our focal variables for each time point separately. The
first derivative expresses the direction and rate of change
(i.e., velocity), with higher values indicating a higher rate of
change and positive (negative) values indicating an increase
(decrease). Applied to leader identity, positive velocity
values thus indicate increased leader identity activation,
whereas negative values indicate reduced leader identity
activation. The second derivative expresses changes in the
first derivative (i.e., whether the rate of change is acceler-
ating or decelerating; Bisconti etal., 2006; Boker & Nes-
selroade, 2002). Positive values indicate acceleration,
e
ij =yij −
(
B0+
𝛽1x1ij
)
−
U
j
i
=
day i
j
=
person j
negative values deceleration, and a value of zero indicates
a constant pattern of intra-personal leader identity shifts.
Following previous guidelines (e.g., Boker & Nes-
selroade, 2002; Deboeck, 2009), we applied the Gen-
eralized Orthogonal Local Derivative (GOLD) method
(Deboeck, 2010) to estimate the derivatives via a time-
delay embedded matrix, which contains lagged replica-
tions of the original data. That is, the original data is reor-
ganized such that short time-ordered sequences of the data
appear as rows of a matrix (Boker etal., 2018; Chow etal.,
2016). For the structure of this matrix (i.e., the number
of row/columns), one decision relates to the number of
consecutive measurements to be included. Past literature
suggested to select smaller “bursts” within the larger time
frame (Bisconti etal., 2006; Boker & Nesselroade, 2002),
with a minimum of four measurements for accurate esti-
mations of the second derivative (Boker etal., 2018). We
thus chose sequences of four consecutive measurements
(e.g., four consecutive measurements of leader identity:
LI1, LI2, LI3, LI4) to construct the time-delay embedded
matrix based on which the derivatives were estimated:
Based on this matrix, we estimated the first (i.e., veloc-
ity) and the second (i.e., acceleration) derivative from
each sequence of four occasions of measurement (i.e.,
each row) within each of our three time points, respec-
tively. At each time point, an individual’s velocity and
acceleration scores were thus estimated based on four sets
of 4-day sequences each.
LI
(4)=
⎡
⎢
⎢
⎢
⎣
LI1LI 2LI3LI 4
LI2LI 3LI4LI 5
LI3LI 4LI5LI 6
LI
4
LI
5
LI
6
LI
7
⎤
⎥
⎥
⎥
⎦
Table 5 Comparison of residuals from the original data to scrambled
data sets
Residuals were obtained for each person separately for each of the
three time points. We simulated 1000 scrambled versions of the resid-
ual data and compared them to the original data via a Kolmogorov–
Smirnov test. Significant differences imply that the residuals in our
data have a systematic time-ordering and are not random
** p < .001
Time point Residual Scores 5% CI of D95% CI of D
t1 Leader identity 0.72** 0.74**
Follower identity 0.73** 0.76**
Event Strength 0.28** 0.30**
t2 Leader identity 0.75** 0.77**
Follower identity 0.73** 0.76**
Event Strength 0.25** 0.28**
t3 Leader identity 0.68** 0.72**
Follower identity 0.69** 0.73**
Event Strength 0.22** 0.25**
4 We include a visual representation of the leader identity residuals
in the OSF repository.
5 To calculate residuals for the scrambled data, we utilized a linear
regression approach (Gasimova etal., 2014) used previously to avoid
model convergence issues.
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766 Journal of Business and Psychology (2024) 39:755–778
1 3
Hypothesis Testing
We retrieved two datasets. The first contained individuals’
daily event strength and their daily leader and follower identity
residuals, at each of the three time points respectively. We used
this dataset to test Hypothesis 1. The second dataset included
individuals’ velocity (i.e., event strength velocity, leader and
follower identity velocity) and acceleration parameters (i.e.,
leader and follower identity acceleration) at each of the three
time points, respectively. We used this dataset to test Hypoth-
eses 2 to 4. As both datasets were nested across three levels
(i.e., days or sequences nested in time points, nested in indi-
viduals), we utilized 3-level random coefficient modeling via
the lme4 R package (Bates etal., 2015).
Results
Daily Events
Tables6 and 7 display examples of daily events. In Table6,
examples of strong and weak events are displayed for each
time point. In order to better understand the daily events,
we coded them along two identity-relevant dimensions that
comprise (a) their domain (i.e., work, home, community;
Hammond etal., 2017) and (b) their level of inclusiveness
(i.e., personal, relational, collective; Brewer & Gardner,
1996; Hammond etal., 2017; Lord & Hall, 2005). Table7
gives prototypical examples for daily events within each of
these dimensions, and Fig.3 illustrates their relative fre-
quencies at each time point.
Descriptive Analyses
The means, standard deviations, and correlations among
the focal study variables (i.e., event strength, leader iden-
tity, follower identity) at the person-level are presented in
Table8. The ICC(1) demonstrated that at all time points,
there was substantial variance at the within-person level
for event strength (ICC(1) = 0.17, 0.23, 0.23), leader
identity (ICC(1) = 0.39, 0.33, 0.47), and follower identity
(ICC(1) = 0.29, 0.39, 0.55).
After calculating each individual’s leader identity equilib-
rium within each time point, we compared these equilibria
ranges to the general leader identity strength that participants
had indicated in the baseline survey. As shown in Fig.4, the
Table 6 Examples of young adults’ stronger and weaker daily events at each time point
Examples of the most important events that young adults indicated in their daily surveys. Event strength is indicated in brackets and calculated
based on the ratings of event novelty, disruptiveness, and extraordinariness
Stronger events Weaker events
Time 1 (Unfamiliar)
• [When playing pool, I] potted 4 balls in a row including the black
in pool (7)
• Organised all of my stuff and tidied my room (1)
• Took my friend to [Accident & Emergency] along with another girl
helping who I don’t really know but is her friend (7)
• I went to the library alone to do some dissertation research (1)
• Did a big rowing thing for charity (6.67) • Chatting in the kitchen with my housemates (1.33)
• Came home for the first time since moving to [Name of the city the
university is placed] via a six-hour overnight bus journey (6.33)
• I had a dissertation meeting with my supervisor to discuss what I am
going to learn about next (1.33)
• I developed my social media presence (6.0) • Watched TV with a friend (1.67)
Time 2 (Familiar)
• A friend had a serious accident last night while a lot of us were pre-
sent and so today has been digesting that and making sure everyone
is ok (7)
• I had a Business Ethics seminar on trust in business practice which
was very interesting (1)
• I had a meeting with the [student committee] president to discuss
an issue (6.67)
• I started working on a summative assignment [an assessment of
learning] for one of my modules (1)
• Went on a date and it went really well (6.33) • Did some discrete homework (1)
• Completed a research project field trip (6.33) • Going to strength and conditioning [refers to sports training] (1.33)
• Mentored a young person (6.33) • Saw a good friend and [it] was nice to catch up (1.67)
Time 3 (COVID-19)
• My grandma had a stroke (7) • Ate lunch with my family (1)
• Took a walk outside for the first time in two months (6.33) • Took my dog for a walk in the park (1)
• Had an argument with my girlfriend (6.33) • Played games with friends online (1.33)
• Had my first online exam and completed around half of it (6.33) • Facetimed my boyfriend (1.33)
• Social distanced street party (6) • Read a book (1.67)
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767Journal of Business and Psychology (2024) 39:755–778
1 3
Table 7 Examples of young adults’ daily events as characterized by their domain(work, home, community) and level of inclusiveness(personal, relational, collective)
Level of inclusiveness (Brewer & Gardner, 1996)
Personal Relational Collective
The personal self is a part of the differentiated, individuated
self-concept. Events at the personal level represent the
basic social motivation of self-interest. These events are
often experienced alone.
The relational self is a part of the self-concept derived from
connections and role relationships with significant others.
Events at the relational level represent the basic social
motivation of benefitting others. These events often take
place in meaningful relationships with others.
The collective self corresponds to the concept of social identity
as represented in social identity and self-categorization theory.
Events at the collective level represent the basic social motiva-
tion of collective welfare. These events often take place in a
collective.
Work domain: Events directly related to formal university requirements or work (e.g., internships, course attendance, studying, exams)
• Getting a bonus for my performance in a job
• I completed my first session as a tutor
• I submitted a summative essay
• Revising for my exam
• Taking a video interview for internship
• Bar shift I’m a supervisor and was training a new member
is staff
• I talked to a friend on my course about revision and we
decided to make a shared document for putting our notes on
• I went to my seminar without my friend. I tried to persuade
her to go also but she did not
• I worked through a past paper with my friend
• Mentored a young person
• Chaired my first [student committee] meeting
• I led a group revision session
• I took part in a team briefing for a field trip tomorrow.
• Small group work in my tutorial, I had to explain our
thoughts to the rest of the group
• We were allocated into different group to prepare presentation
in today’s seminar
Home domain: Events linked to the home domain, such as home arrangements (e.g., food shopping, cleaning), activities in leisure time (e.g., cinema, shopping, sports), and activities with fam-
ily and friends (e.g., partner, parents)
• Beat my 5K running time
• I ran the furthest I ever have
• Today I found my clothes are not neatly arranged and then
organized them in the closet.
• Today I decided to stop eating meat
• Went to see doctor because I’m ill
• I caught up with my friends while having brunch together
• I paid highly attention to my parents’ healthy condition
• I visited a friend in hospital
• Today is Mother’s Day. I prepared a meal and bought
flowers for my mother
• Went on a second date with the guy I’ve been seeing
• Creating a menu in Spanish with my group
• Got invited into a group for housing next year
• I prepared a dinner for my family
• Telling someone they won’t be in our house
• Zoom call with lots of my extended family telling their
memories of the war
Community domain: Events during leisure time that are linked to communities (e.g., charities, college activities unrelated to university work, sports club, religious communities)
• I attended a [university student society] assembly meeting
and applied for a position
• I had [university] trials this morning to make the first team,
and I really tried my best, but I don’t think I played as well
as I could have so I’m quite disappointed in that aspect.
• I went to church in the evening and the sermon was on num-
bers and the fulfilment of god’s promises. I found it intriguing
as I had never considered that book that way before
• My sports training got moved but no one had told me about
it so I ended up just running around for ages trying to find it
• Had brunch with some rowing friends
• I celebrated the Chinese spring festival with my friend
• I was praying the Rosary with my girlfriend when I noticed
that she was feeling tired, so I started putting on her
shoes and we went out for a prayer walk to finish it with
a breath of fresh air
• I went to go and watch my friend play for the [university
sports team]
• Reading the bible with a friend
• Conducting my duties as a sports club executive
• Hanging the poster for [a campaign for re-evaluating the
costs of college accommodation]
• Helping organise/lead the Remembrance Sunday parade
• Phone meeting about an outreach strategy for a charity I’m
involved in. Spoke to the staff member responsible about
where it came from and how it fits with our work
• Volunteering at a homeless shelter
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768 Journal of Business and Psychology (2024) 39:755–778
1 3
leader identity equilibrium scores were related to the gen-
eral leader identity strength, especially at time 1. However,
there was also not a clear overlap among the general leader
identity strength and the leader identity equilibria, suggest-
ing that the general leader identity strength as indicated via
the baseline survey may not necessarily represent where a
person tends to drift back to empirically.
Hypothesis Testing
We hypothesized that the strength of daily events would
relate positively to leader identity shifts (Hypothesis 1). To
test this prediction, we utilized the person-centered leader
and follower identity residuals, which represented each par-
ticipant’s daily deviation from their equilibrium in leader
and follower identity respectively. Using 3-level multilevel
modeling (i.e., days nested in time points, nested in per-
sons), we regressed the daily leader identity residual onto
daily event strength, while controlling for the daily follower
identity residual. Results supported Hypothesis 1 as, after
accounting for follower identity, daily event strength posi-
tively predicted change in leader identity (
𝛽
= 0.14, 95%
CI [0.08, 0.21]. t(240) = 4.60, p < 0.001). That is, stronger
daily events predicted increases in leader identity away from
equilibrium.
Hypothesis 2 proposed that over time, the rate of change
in event strength (i.e., event strength velocity) positively
relates to the rate of change in leader identity (i.e., leader
identity velocity). Using 3-level multilevel modeling (i.e.,
4-day sequences nested in time points, nested in persons), we
regressed leader identity velocity onto event strength veloc-
ity, while controlling for follower identity velocity. Results
supported Hypothesis 2 as over time, after accounting for
the rate of change in follower identity, the rate of change in
event strength predicted the rate of change in participants’
leader identity (
𝛽
= 0.23, 95% CI [0.11, 0.35], t(701) = 3.73,
p < 0.001). Over time, increases in event strength thus pre-
dicted increases in leader identity activation.
Hypothesis 3 predicted that over time, the rate of change
in event strength (i.e., event strength velocity) positively
relates to acceleration in leader identity. Again, we utilized
3-level multilevel modeling with event strength velocity as
predictor for leader identity acceleration, while controlling
for follower identity acceleration. Results did not support
Hypothesis 3, although the trend was in the predicted direc-
tion. Over time, when accounting for acceleration in follower
identity, the rate of change in event strength was not signifi-
cantly related to the acceleration in leader identity activation
(
𝛽
= 0.22, 95% CI [-0.03, 0.47], t(701) = 1.71, p = 0.089).
For Hypothesis 4, we predicted that at t1 (unfamiliar
context) as compared to t2 (familiar context), the positive
relationship between event strength velocity with (a) leader
identity velocity and (b) leader identity acceleration would
be stronger. For t3 (COVID-19), we did not have specific
predictions. Results for Hypothesis 4a are shown in Table9.
The event strength velocity predicted leader identity veloc-
ity for each time point (t1:
𝛽
= 0.23, p = 0.03; t2:
𝛽
= 0.23,
p = 0.03; t3:
𝛽
= 0.22, p = 0.04), and model fit statistics did
not indicate the expected differences between unfamiliar (t1)
and familiar (t2) contexts. Thus, Hypothesis 4a was not sup-
ported. Results for Hypothesis 4b are shown in Table 10.
The event strength velocity was not related to leader identity
acceleration at any time point (t1:
𝛽
= 0.17, p = 0.452; t2:
𝛽
= 0.33, p = 0.122; t3:
𝛽
= 0.14, p = 0.551). These results
did not support Hypothesis 4b.
Exploratory Analysis: Relationship Between Leader
andFollower Identities
Our study offers exploratory insights into the intra-personal
relationships between leader and follower identities and
identity dynamics, respectively. Correlations at the person-
level indicated that leader and follower identities were posi-
tively related at t3 (r = . 36, p < 0.05), but unrelated at t1 or
t2 (r = 0.13 and .22). This suggests that while generally the
strength of participants’ leader identity was not dependent on
their follower identity, during the COVID-19 pandemic, the
strength of leader and follower identities was co-dependent.
Furthermore, for our main analyses, we used follower
identity as a covariate that predicts leader identity. At all
three time points, leader identity velocity was negatively
related to follower identity velocity (see Table9). This
suggests that increases in individuals’ leader identity co-
occurred with decreases in their follower identity and vice
versa. Findings further showed that at t1 and t2 (but not t3),
leader identity acceleration was positively related to follower
identity acceleration (see Table10). That is, the more indi-
viduals’ leader identity de-stabilized (i.e., increased in rate
of change), the more their follower identity de-stabilized as
well. However, at t3 (COVID-19), there was no relationship
between leader and follower identity acceleration.
Discussion
Time and context are critical to leader identity change
(Epitropaki etal., 2017; Hammond etal., 2017; Lord
& Chui, 2017; Lord etal., 2016). Integrating temporal
approaches to leadership (McClean etal., 2019) with
event systems theories (Hoffman & Lord, 2013; Morge-
son etal., 2015), we demonstrate that the experience of
stronger and weaker events provokes variability in indi-
viduals’ leader identities, best described as intra-personal
dynamics in the form of leader identity ebb and flows.
Our findings support that strong (i.e., novel, disruptive,
extraordinary) daily events predict positive shifts in
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769Journal of Business and Psychology (2024) 39:755–778
1 3
leader identities, making them more salient relative to a
follower identity. Moreover, we show that over time, the
more events changed in strength, the more individuals’
leader identity changed too. As such, the more strong and
weak daily events alternated, the more individuals experi-
enced similar shifts in their leader identity activation. We
did not find this relationship to be stronger in unfamiliar
as compared to familiar contexts. Furthermore, we found
exploratory evidence for the intra-personal co-occurrence
of leader and follower identities, such that changes in
leader and follower identities were negatively related (i.e.,
negative relation of velocity) while the de-stabilization
of leader and follower identities were positively related
(i.e., positive relation of acceleration). That is, leader and
follower identities shifted together and were aligned in
increasing or decreasing their rate of change. However,
change was in opposite directions, such that positive
shifts in leader identity co-occurred with negative shifts
in follower identity.
Theoretical Contributions
Our study makes several contributions. First, we explain and
quantify why and how short-term changes in leader identity
occur. Scholars have argued that leader identities are highly
dynamic states that fluctuate within short periods of time
(DeRue & Ashford, 2010; Epitropaki etal., 2017; Lord &
Chui, 2017; Lord etal., 2016), and more recently, studies
provided quantitative support for this notion (Jennings etal.,
2021; Lanaj etal., 2019, 2021a, b). Yet, more can be done to
understand the patterns that these intra-personal dynamics
follow and how they are put in motion.
One of our key contributions is that we characterize
individuals’ leader identity dynamics as ebb and flow pat-
terns that are affected by the experience of strong events.
According to our findings, strong daily events shifted indi-
viduals away from their leader-identity equilibrium, that is,
to see themselves more as a leader than they usually did.
When considered over time, changes in event strength fur-
ther provoked corresponding changes in leader identity. For
example, shifts upwards in event strength from equilibrium
predicted similar shifts upwards in leader identity from equi-
librium. Similarly, shifts in an individual’s event strength
may become more negative across a series of days. This
illustrates a decrease in event strength, and our results sug-
gest that a person’s leader identity would also become less
salient across that period.
This finding has important implications for the role of
events in leader identity development (e.g., leader identity play
and work). In line with prior theorizing, our findings identi-
fied a distinctive “event cluster” (Morgeson etal., 2015) that
describes how leader identities shift when individuals experi-
ence sequences of weaker and stronger events following each
other. Knowing that events facilitate leader identity dynam-
ics suggests that experiencing strong events is a powerful
sensemaking source that may ultimately inform individuals’
formation of new typical leader identity states (i.e., new iden-
tity equilibrium). When a strong event renders an individu-
als’ leader identity more salient than other possible identities
(e.g., follower identity), individuals will be channeled to see
their environment through the lens of their leader identity and
encode information most effectively when it aligns with the
salient identity (i.e., “I am a leader”). Each time a strong event
prompts a positive shift in a person’s leader identity activa-
tion, this person is likely to build leadership skills and experi-
ences. As such, the events that happen on a day-to-day basis
Fig. 3 Relative frequencies of event domain and level of inclusive-
ness at each time point. Note. Two independent coders (i.e., the first
author and a research assistant) categorized the daily events for their
domain (1100 events categorized; 6% of events too ambiguous to
categorize) and their level of inclusiveness (967 events were catego-
rized; 17% were too ambiguous to categorize). The inter-coder relia-
bility via Cohen’s Kappa (Cohen, 1960) was .86 for event domain and
.65 for event level of inclusiveness, indicating perfect (event domain)
and satisfactory (event level of inclusiveness) agreement (MacPhail
et al., 2016). After the initial coding, the first authors met with the
research assistant to discuss disagreeing event coding, and to reach
agreement. The domain percentages are calculated based on 429
(time 1), 347 (time 2), and 324 (time 3) events. The percentages for
the level of inclusiveness are calculated based on 377 (time 1), 288
(time 2), and 302 (time 3) events
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770 Journal of Business and Psychology (2024) 39:755–778
1 3
can consolidate into the longer-term knowledge an individual
has about leadership and themselves. At the same time, the
identity ebb and flows that happens over short periods of time
around a person’s leader identity equilibrium can further result
in the sudden emergence of a new leader identity equilibrium.
Dynamic systems theory argues that systems (such as a person’s
self-concept) change due to energy, and that systems require an
increase in energy so that “new ordered structures may spon-
taneously appear that were not formerly apparent” (Thelen &
Smith, 1998; p. 272). Applied to our study, this means that
the higher the variability in a person’s leader identity activa-
tion over a short period of time, the more there is energy as
Table 8 Means, standard deviations, and correlations of study variables
M and SD are used to represent mean and standard deviation, respectively. Between-person correlations are based on N = 69
a Gender is coded as 0 = male, 1 = female
* p < .05; **p < .01
Variable M SD 1 2 3 4 5 6 7 8 9 10
1. Gendera1.57 0.5
2. Age 19.24 1.22 − .26*
3. Leader identity (t1) 5.15 1.54 − 0.09 0.08
4. Leader identity (t2) 5.06 1.6 − 0.14 0.04 .59**
5. Leader identity (t3) 4.13 1.77 − 0.02 − 0.09 .47** .73**
6. Follower identity (t1) 4.66 1.49 0.12 0.05 0.13 0.08 − 0.12
7. Follower identity (t2) 4.37 1.61 − 0.04 0.21 0.22 0.22 0.16 .74**
8. Follower identity (t3) 3.78 1.93 − 0.04 − 0.11 0.14 0.22 .36* .54** .71**
9. Event strength (t1) 3.72 0.8 − 0.25 − 0.12 0.13 0.23 0.06 0.02 0.05 0.13
10. Event strength (t2) 3.79 0.91 − 0.17 0.03 − 0.04 .35* 0.24 − 0.04 0.05 0.13 .63**
11. Event strength (t3) 3.62 0.81 − 0.12 − 0.14 0.03 0.05 0.14 − 0.15 − 0.01 0.2 .64** .70**
Fig. 4 Participants’ general leader identity strength (baseline survey)
and leader identity equilibria (times 1–3). Notes: Figure represents
the general (between-person) leader identity strength and the empiri-
cally determined leader identity equilibrium for each participant that
provided data at all time points. The general leader identity strength
was assessed via the baseline survey at the onset of the study. The
leader identity equilibria represent the predicted values of leader
identity based on linear trend line of a person’s values in each of the
three time points. Y-axis was ordered by the general leader identity
strength
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771Journal of Business and Psychology (2024) 39:755–778
1 3
greater cognitive and emotional processing is needed. With
increasing cognitive and emotional processing, the spontaneous
emergence of a new and previously unknown leader identity
equilibrium becomes more likely. In that sense, with increasing
leader identity dynamics, it is more likely that current identity
equilibria de-stabilize. Figure5 illustrates how the leader iden-
tity dynamics that arise from a chain of successive events can
de-stabilize an individual’s leader identity state and inform a
new leader identity equilibrium. Based on this theorizing, an
important implication from this work is that effective leadership
may occur at the level of events, not at the level of long-term
stable traits.
Knowing how sequences of stronger and weaker events
put leader identity formation in motion has further impli-
cations for how identities inform dynamic leadership pro-
cesses, such as when leadership behaviors vary over short
periods of time (Kelemen etal., 2020). Strong events in the
environment seem to de-stabilize the individual’s typical
identity level (Nicholson & Carroll, 2013) such that new
identities — as potential drivers of leadership behavior —
become salient. This corresponds to Barsalou, (2008) who
argued that concepts such as identity are tightly coupled to
situational features because they prepare the individual for
action. This implies that self-views are situated, and that
deviations from an identity equilibrium could be precipitated
by the features and response requirements of a particular
event. In other words, leader identities and events are linked
by the demands for actions and the fact that the person is
part of a situation or event. This is particularly true if people
perceive the event as meaningful and personally significant.
Second, we contribute to the theoretical understanding
of how individuals form their leader identities during the
critical time period of young adulthood (Liu etal., 2021;
Shaughnessy & Coats, 2018; Zaar etal., 2020). The pro-
cess of forming one’s identity is complex, as identities
are constantly under construction (Ashforth & Schinoff,
2016; DeRue & Ashford, 2010; Swann etal., 2009). Our
research implies that young adults’ leader identities change
through strong daily events, especially when alternating
with weak events. Such experiences not only trigger iden-
tity exploration, but also likely feed into their longer term
identity formation and development processes (Shalley
etal., 2004). Qualitative findings from biographical analy-
ses of outstanding leaders suggest that different types and
contents of events experienced during young adulthood
shaped their leadership pathways (Ligon etal., 2008). We
Table 9 Event strength velocity as predictor of leader identity velocity (Hypothesis 4a)
*p < .05; **p < .01
Time 1 (unfamiliar) Time 2 (familiar) Time 3 (COVID-19)
Predictors Estimates CI pEstimates CI pEstimates CI p
(Intercept) 0.04 − 0.07 to 0.14 0.493 0.02 − 0.09 to 0.13 0.708 0.02 − 0.09 to 0.10 0.921
Follower identity velocity − 0.2 − 0.32 to − 0.08 0.001** − 0.2 − 0.34 to − 0.06 0.006** − 0.18 − 0.33 to − 0.04 0.015
Event strength velocity 0.23 0.02 to 0.44 0.029* 0.23 0.03 to 0.44 0.027* 0.22 0.01 to 0.42 0.042*
Model fit statistics
R2 (marginal) 0.056 0.053 0.050
RMSE 0.869 0.826 0.721
AIC 725.988 570.363 460.471
Table 10 Event strength velocity as predictor of leader identity acceleration (Hypothesis 4b)
*p < .05; ***p < .001
Time 1 (unfamiliar) Time 2 (familiar) Time 3 (COVID-19)
Predictors Estimates CI pEstimates CI pEstimates CI p
(Intercept) − 0.06 − 0.28 to 0.17 0.621 − 0.14 − 0.36 to 0.08 0.214 0.01 − 0.21 to 0.23 0.932
Follower identity acceleration − 0.15 − 0.27 to − 0.03 0.018* − 0.23 − 0.35 to − 0.10 < 0.001*** − 0.13 − 0.28 to 0.02 0.086
Event strength velocity 0.17 − 0.27 to 0.61 0.452 0.33 − 0.09 to 0.74 0.122 0.14 − 0.32 to 0.60 0.551
Model fit statistics
R2 (marginal) 0.023 0.063 0.015
RMSE 1.856 1.657 1.562
AIC 1141.968 879.482 777.828
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772 Journal of Business and Psychology (2024) 39:755–778
1 3
expand these insights by showing quantitative evidence
that short-term variation in leader identities is triggered
by strong events. When leader identities form, they may
represent the contextual experiences associated with the
strong events that individuals encounter. Leadership self-
knowledge is in part represented as a contextually embod-
ied structure that includes the environment, in addition
to the human body and brain (Lord & Shondrick, 2011).
Building on our results, future research could test whether
the physiological experience of strong events might inform
young adults’ case-based knowledge of leadership, which
has been argued as a key requisite for leadership develop-
ment (Mumford etal., 2017). Contrary to our expectations,
strong events impacted leader identities in both unfamil-
iar and familiar contexts, which emphasizes the power of
experiencing strong events for leader identity change irre-
spective of individuals’ familiarity with the context. It may
further emphasize that for young adults, leader identities
are consistently under construction even when they operate
in well-known contexts.
Third, we contribute to understanding the importance
of time in leadership research (e.g., Ancona etal., 2001;
Castillo & Trinh, 2018). Scholars have offered advanced
conceptual and measurement frameworks (e.g., Aguinis &
Bakker, 2021; Shipp & Jansen, 2021; Shipp & Richardson,
2021). Our modeling of velocity and acceleration parameters
informs the theoretical understanding of short-term intra-
personal dynamics in leadership (Kelemen etal., 2020). Par-
ticularly, modeling the velocity and acceleration of leader
identity dynamics supports ebb and flow approaches in
leadership research. As such, our research offers a nuanced
perspective on how short-term and non-linear changes can
be described (McClean etal., 2019).
Finally, our exploratory findings offer insights into the
intra-personal relationship between leader and follower
identities. Our results showed that, generally, leader and
follower identities were not related (i.e., no relationship in
person-level correlations at t1 and t2). However, changes in
individuals’ leader and follower identities were co-occur-
ring, such that when individuals’ leader identity increased
over time, their follower identity decreased (i.e., a negative
relationship in the velocity parameters). This finding aligns
with theoretical assumptions that the cognitive schemas
individuals hold about leadership are stored within a larger
network of domain-specific schemas that include follower-
ship (Lord & Chui, 2017; Lord etal., 2016, 2020), and our
exploratory findings support this view. The two identities
are unlikely to be active at the same time so that activation
of one identity de-activates (or at least de-emphasizes) the
other identity. Which of these identities becomes salient is
arguably driven by the events that individuals encounter as
well as the relational cues they meet in a specific situation. It
would be of interest to understand if the finding that solely-
one-identity-is-active is unique for young adults who are in
a sensitive period with regards to leader identity develop-
ment or if it is transferrable to individuals in late or mid-
adulthood. Furthermore, we found that the more one identity
de-stabilized, the more the other identity de-stabilized too
Fig. 5 Leader identity dynamics and formation of a new leader identity equilibrium
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773Journal of Business and Psychology (2024) 39:755–778
1 3
(i.e., positive relationship in the acceleration parameters for
t1 and t2). That is, while leader and follower identities may
not be active at the same time, the changes in their identity
equilibria are intertwined. A person’s leader and follower
identity equilibria thus remain connected even though acti-
vating a leader (follower) identity at a particular period of
time likely de-activates a follower (leader) identity. Based on
this finding, we may conclude that when individuals form a
new typical leader identity state, this will have consequences
for their typical follower identity state, too.
The positive association between acceleration in leader
and follower identities may further indicate that some events
destabilize both identities. In sum, these exploratory find-
ings could be interpreted as an occurrence of leader–fol-
lower identity switching, that is, the intra-personal process
of dynamically switching between leader and follower iden-
tities (Sy, 2010; Sy & McCoy, 2014).
Practical Implications
Practically, our research highlights the importance of
strong events for young adults’ leader identities. McCall,
(2004, p. 127) notes that “while experience is at the heart
of [leadership] development, not all experiences are cre-
ated equal.” Our research pinpoints the experience of
strong events as developmental opportunities that can
shape a leader identity. Although such events might be
seen as “shocks” (Crawford etal., 2019) that can cause
uncertainty and liminality (Hawkins & Edwards, 2015),
our findings imply that young adults should seek out con-
texts that offer the potential to experience strong (i.e.,
novel, disruptive, extraordinary) events that prompt them
to explore new leader identity states. Likewise, our results
can inform organizations about the importance of events
for developing leaders, especially for their new members
(e.g., in graduate schemes). In addition to assessing readi-
ness for leadership development (an individual difference
approach; Kwok etal., 2021), organizations might do well
to focus on the types of events that are part of leader-
ship development experiences. Since identities can facili-
tate leadership motivation and behavior, fostering leader
identities has become increasingly important (Hawk-
ins & Edwards, 2015; Wallace etal., 2021; Zaar etal.,
2020). Playfulness has been argued as a key to leader
identity development as it intrinsically drives individu-
als to seek out surprise and uncertainty and try out new
identities (Bysh etal., 2022a, b; Kark, 2011). We advise
organizations to offer safe spaces for leader identity play,
which will allow especially less experienced members to
embrace strong events with a discovery-oriented mindset
(Petriglieri, 2010).
Furthermore, our findings inform a better practical under-
standing of the ways in which the COVID-19 pandemic
affected young adults’ identities. We found that during the
pandemic as compared to the other two time points, the
strength of young adults’ leader and follower identities was
lower, and positively related. Furthermore, the relationship
between their acceleration parameters was diminished. The
pandemic may have unsettled previously more stable notions
of young adults’ leader identities and affected the extent to
which they were able to integrate leadership and follow-
ership schemas into their self-concepts. One interpretation
is that the experiences of social isolation (e.g., university
campus closure, virtual learnings, loss of in-person social
activities) may have reduced self-perceptions of leadership,
throwing young adults back to earlier stages of their iden-
tity formation (Ashforth, 2020; Gibson etal., 2021). The
fact that young adults’ leader and follower identities were
positively related during COVID-19 pandemic emphasizes
the role of social interactions for both leader and follower
identities, and that contexts can create liminal experiences
of being betwixt and between leader and follower identities
(Ibarra & Obodaru, 2016). This may mean that in contexts
of extensive social isolation, some situations may restrict
the experience of any identity related to leadership and fol-
lowership (e.g., I feel neither as a leader nor as a follower),
while other situations may render both identities salient
(e.g., I feel as being both a leader and a follower). Dur-
ing the COVID-19 context, young adults may have lacked a
clearly salient identity that differentiates being a leader from
being a follower.
Limitations andFuture Directions
The contributions of our study need to be considered
in reference to its limitations, which can inform future
research. The longitudinal character of the study (i.e.,
multiple daily assessments across three time points dur-
ing one academic year) presented challenges for partici-
pant recruitment and retention. Similar to other studies
that relied on principles of DSM (e.g., Bisconti etal.,
2006), our participant sample was comparatively small.
For ecological reasons, we restricted daily assessments
to seven days per timepoint. While this corresponds to
the recommended minimum of six data points (Boker,
2001), future research may determine the leader identity
dynamics across multiple, consecutive weeks (e.g., for
newcomers in organizations).
Our research relied on self-assessments which is the typi-
cal approach for measuring identities. We aimed to coun-
teract potential rating biases by accessing episodic rather
than semantic memory for the recall and rating of their daily
event experience (Hansbrough etal., 2020). Nevertheless,
future research could add objective and other-rated outcomes
of leader identity change (e.g., study or career success, pro-
motion, leadership development activities).
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774 Journal of Business and Psychology (2024) 39:755–778
1 3
While the focus of our work was on drivers of intra-per-
sonal leader identity dynamics, future work may incorporate
outcomes of leader identity dynamics. One interesting ques-
tion will be, whether momentary increases in young adults’
leader identities strengthen their leadership motivation
orwhether this would require that young adults reach more
stable notions of leader identity. Furthermore, how success-
fully participants dealt with strong events may moderate the
effects of events on leader and follower identity activation. As
DeRue & Ashford, (2010) emphasize, the leader identity con-
struction process depends on both claims and grants, which
may depend on how successful the outcomes of events were.
In terms of future research perspectives, we further
hope to inform studies of leader–follower identity switch-
ing such as when leadership is shared between multiple
individuals (Adriasola & Lord, 2020; Denis etal., 2012;
Sy & McCoy, 2014). Connectionist perspectives suggest
that identities are complex and composed of multiple ele-
ments that form part of an interconnected cognitive net-
work (Hanges etal., 2000; Lord & Hall, 2005; Lord &
Shondrick, 2011; Lord etal., 2001). Both leadership and
followership have been argued to be part of such a larger
network of self-schemas, which raises the question how
individuals manage “the dual (follower vs. leader) tensions
and successful integration of both identities in one’s self-
concept” (Epitropaki etal., 2017, p. 120). In fact, schemas
of leadership and followership (i.e., implicit leadership and
followership theories; Epitropaki etal., 2013; Lord etal.,
2020; Offermann & Coats, 2018; Sy, 2010) share com-
munalities (e.g., being enthusiastic, hard-working, moti-
vated), which may suggest a co-activation of identities. At
the same time, they also differ (e.g., domineering vs. easily
influenced; educated vs. uneducated), which may suggest
that active leader identities inhibit aspects of a momentary
follower identity. Future research can apply our approach
and LFIG measure to study specific work events (e.g., posi-
tive or negative interactions with managers, co-workers,
subordinates) that may trigger individuals to switch from
a follower to a leader identity and vice versa.
Conclusion
We examine the ebb and flow patterns that characterize
intra-personal leader identity dynamics. Integrating perspec-
tives on temporal dynamics in leadership research with event
system theory we find that strong daily events drive positive
shifts in young adults’ leader identities, making leadership
more salient than followership. Over time, chains of stronger
and weaker events provoke leader identity dynamics in the
form of ebb and flows with the potential to result in the
emergence of new leader identity equilibria. We hope our
study will spark new perspectives and research on events
and the dynamic patterns of leader and follower identity
formation.
Appendix
Original reference Items for leader (follower) identity
measures
Hiller, (2005) I am a leader (follower)
Hiller, (2005), Rus etal., (2010) I see myself as a leader (follower)
Hiller, (2005) If I had to describe myself to
others, I would include the word
“leader (follower)”
Hiller, (2005) I prefer being seen by others as a
leader (follower)
Rus etal., (2010) Being a leader (follower) is impor-
tant to who I am
Rus etal., (2010) Being a leader (follower) is a
central part of who I am
Rus etal., (2010) I am a typical leader (follower)
Rus etal., (2010) I am exemplary of other leaders
(followers)
Rus etal., (2010) I identify with other leaders (fol-
lowers)
Rus etal., (2010) I enjoy being a leader (follower)
Items for leader and follower identities were measured in the baseline
survey and rated on two separate Likert scales (Instruction: When
thinking about yourself, how well does each of the following state-
ments describe you in general?) on seven-point Likert scales, ranging
from (1) not descriptive at all to (7) extremely descriptive
Supplementary Information The online version contains supplemen-
tary material available at https:// doi. org/ 10. 1007/ s10869- 023- 09906-7.
Acknowledgements We thank Susan Frenk, Principal of St Aidan’s
College, Durham University (UK) for her support in realizing the study,
and Ella Mai Vreugdenhil for support in coding the qualitative data.
We further thank Steve Boker, Paul Hanges, and Jonas W. B. Lang
for their helpful comments on earlier versions of the manuscript. A
previous version was presented at the 2021 Academy of Management
Annual Meeting (Virtual), and the 2022 Interdisciplinary Perspectives
of Leadership Symposium (Greece).
Funding This research was supported by the grant “Advancing Leader-
ship Research” (W911NF-18–2-0049) U.S. Army Research Institute
for the Behavioral and Social Sciences (ARI).
Data Availability Data for this study are not available because they are
proprietary based on the data management plan submittedfor ethical
approval to the University at which data was collected and to the funder
of this research. The analysiscode has been made available at the Open
Science Framework and can be assessed at https:// osf. io/ wrvye/.
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775Journal of Business and Psychology (2024) 39:755–778
1 3
Declarations
Disclaimer The views, opinions, and/or findings contained in this paper
are those of the authors and shall not be construed as an official Depart-
ment of the Army position, policy, or decision, unless so designated
by other documents.
Conflict of Interest The authors declare no competing interests.
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