APPLICATION OF EGO DEPLETION THEORY TO LEADER HELPING: THE
DARK SIDE OF REACTIVE HELPING

By
Klodiana Lanaj

A DISSERTATION
Submitted to
Michigan State University
in partial fulfillment of the requirements
for the degree of
Business Administration - Organizational Behavior - Human Resource Management Doctor of Philosophy
2013

ABSTRACT
APPLICATION OF EGO DEPLETION THEORY TO LEADER HELPING: THE
DARK SIDE OF REACTIVE HELPING

By
Klodiana Lanaj
Research indicates that leaders are often approached by followers with help
requests for task-related and personal problems. In addition, theories of leadership
recognize that providing support and encouragement to followers is an important leader
behavior. Little research, however, has examined how helping followers may affect
leaders despite theoretical arguments that helping may deplete self-regulatory resources.
Drawing on Ego Depletion Theory, I propose that leader reactive helping – defined as
helping in response to direct requests for assistance by followers - depletes leaders’ selfregulatory resources. I also propose that helping with personal problems is more
depleting than helping with task-related problems. Depletion, on the other hand, is
expected to harm leaders’ work engagement and creativity because these activities
require self-regulatory resources. Helping role perceptions and prosocial motivation are
proposed to moderate the effects of reactive helping on depletion; whereas prosocial
impact and trait self-control are proposed to moderate the effects of state depletion on
work engagement and creativity. An experience sampling methodology is utilized to test
these research questions in a sample of middle and senior managers.

ACKNOWLEDGEMENTS

I am very thankful for these past five years in the Management Department. I
have worked with some amazing faculty and students and I have made friends for life. I
have also grown as a scholar and person and for that I must thank a number of people.
First and foremost I thank my advisor and mentor John Hollenbeck who has been
instrumental in my development as a student and scholar. I thank John for his wisdom,
passion for research, encouragement, and lightheartedness. I feel very blessed to call him
my mentor, advisor, and friend. This degree would not have been possible without his
support. I am also very thankful for the guidance of my committee members Linn Van
Dyne, Russ Johnson, and Fred Morgeson. I thank Linn for her passion for theory and care
for students. I thank Russ for taking a job at Michigan State University, thus making it
possible to work together. I thank Fred for his wealth of knowledge and thoughtprovoking questions. I’m also thankful to my fellow doctoral students and staff in the
Management Department who contributed to a wonderfully supportive working
environment. I thank my family who has been cheering me on across degrees and
continents – making you proud brings joy to my life. Finally, I am thankful to God for
opportunities to pursue my dreams and for good friends and purpose.

iii

TABLE OF CONTENTS

LIST OF TABLES ............................................................................................................ vi
LIST OF FIGURES ......................................................................................................... viii
INTRODUCTION ...............................................................................................................1
LITERATURE REVIEW ....................................................................................................5
Helping Behavior in Organizations: Key Distinctions ...........................................5
Proactive versus Reactive Helping .............................................................5
Task versus Personal Helping .....................................................................9
Task-Related and Personal Helping versus Initiating Structure and
Consideration ............................................................................................12
Positive versus Negative Effects Attributable to Helping .........................13
THEORETICAL BACKGROUND: EGO DEPLETION THEORY ...............................18
Depletion versus Alternative Constructs ..............................................................20
THEORETICAL DEVELOPMENT AND HYPOTHESES ............................................23
The Depleting Nature of Reactive Helping ...........................................................23
Moderators of the Effects of Reactive Helping on Depletion: Prosocial
Motivation ..............................................................................................................28
Moderators of the Effects of Reactive Helping on Depletion: Helping Role
Perceptions ............................................................................................................30
Consequences of Depletion: Work Engagement and Creativity ...........................35
Moderators of the Effects of Depletion on Its Consequences: Perceived Prosocial
Impact of Helping .................................................................................................40
Moderator of the Effects of State Depletion on Its Consequences: Trait SelfControl ..................................................................................................................43
Summary and Contributions .................................................................................45
METHODS .......................................................................................................................47
Sample ...................................................................................................................47
Procedure ..............................................................................................................47
One-Time Measures ..............................................................................................50
Daily Measures .....................................................................................................57
Confirmatory Factor Analyses ..............................................................................64
RESULTS .........................................................................................................................68
Test of Hypotheses ................................................................................................77
POST HOC ANALYSES ..................................................................................................88
Main Effects of Reactive Helping on Depletion ...................................................88
iv

Moderated Effects of Reactive Helping on Depletion: Prosocial Motivation ......92
Moderated Effects of Reactive Helping on Depletion: Job Experience ...............94
Outcomes of Depletion: Self-Rated Work Engagement .......................................99
Outcomes of Depletion: Daily Creativity ...........................................................102
Subordinates’ Perspective on Helping ................................................................105
Summary of Post Hoc Findings ..........................................................................107
DISCUSSION ..................................................................................................................109
Summary of Findings ..........................................................................................109
Reactive Helping .....................................................................................110
Outcomes of Depletion ...........................................................................113
Subordinates’ Perspective .......................................................................114
Strengths, Limitations, and Future Research ......................................................115
Conclusion and Implications ...............................................................................119
APPENDICES ................................................................................................................121
APPENDIX A .....................................................................................................122
APPENDIX B .....................................................................................................126
REFERENCES ...............................................................................................................128

v

LIST OF TABLES

Table 1. Data Collection Schedule ....................................................................................50
Table 2. One-Time (dispositional) Measures ....................................................................55
Table 3. Daily (within-person) Measures .........................................................................62
Table 4. Within-Individual Descriptive Statistics and Correlations .................................69
Table 5. Descriptive Statistics and Correlations for Daily Variables ...............................71
Table 6. Between-Individual Descriptive Statistics and Correlations ..............................73
Table 7. Parameter Estimates and Variance Composition of Level 1 Variables ..............76
Table 8. HLM Results for Predictors of Afternoon State Depletion (Hypotheses 1& 2) .77
Table 9. Moderating Effects of Prosocial Motivation (Hypothesis 4a, 4b) ......................78
Table 10. Moderating Effects of Reactive Helping Breadth (Hypothesis 5a and 5b) ......79
Table 11. Moderating Effects of Reactive Helping Efficacy (Hypothesis 6a and 6b) .....80
Table 12. HLM Results for Predictors of Daily Work Engagement (Hypothesis 7) ........81
Table 13. HLM Results for Predictors of Daily Creativity (Hypothesis 8) ......................81
Table 14. Moderating Effects of Task-Related Prosocial Impact for Daily Work
Engagement (Hypothesis 11a) ..........................................................................................83
Table 15. Moderating Effects of Task-Related Prosocial Impact for Daily Creativity
(Hypothesis 11b) ...............................................................................................................84
Table 16. Moderating Effects of Personal Prosocial Impact for Daily Work Engagement
(Hypothesis 12a) ...............................................................................................................85
Table 17. Moderating Effects of Personal Prosocial Impact for Daily Creativity
(Hypothesis 12b) ...............................................................................................................85
Table 18. Moderating Effects of Trait Self-Control for Work Engagement (Hypothesis
13) .....................................................................................................................................86
Table 19. Moderating Effects of Trait Self-Control for Creativity (Hypothesis 14) ........87

vi

Table 20. Post Hoc Analyses: Main Effects of Reactive Helping on Depletion ...............91
Table 21. Post Hoc Analyses: Moderating effects of Prosocial Motivation .....................93
Table 22. Post Hoc Analyses: Moderating effects of Job Experience ..............................96
Table 23. Post Hoc Analyses: Predictors of Daily Work Engagement ...........................100
Table 24. Post Hoc Analyses: Predictors of Daily Creativity .........................................100
Table 25. Post Hoc Analyses: Moderating Effects of Depletion ....................................101
Table 26. Post Hoc Analyses: Effects of Work Engagement on Daily Creativity .........103
Table 27. Post Hoc Analyses: Moderated Effects of Work Engagement .......................104
Table 28. Post Hoc Analyses: Subordinate Reactions to Leader Helping ......................106
Table 29. Post Hoc Analyses: Interactive Effects of Helping on Ratings of
Supportiveness ................................................................................................................106
Table 30. HLM Equations Testing Hypotheses ..............................................................122
Table 31. Summary of Hypotheses .................................................................................126

vii

LIST OF FIGURES

Figure 1. Proposed Model ...................................................................................................4
Figure 2. Moderating Effects of Prosocial Motivation .....................................................94
Figure 3. Moderating Effects of Job Experience: Task-Related Reactive Helping ..........98
Figure 4. Moderating Effects of Job Experience: Personal Reactive Helping .................98
Figure 5. Moderating Effects of Prosocial Helping Impact ............................................102
Figure 6. Moderating Effects of Trait Self-Control ........................................................104
Figure 7. Interactive Effects of Both Types of Helping ..................................................107

viii

INTRODUCTION
Proactive behavior, in its many different forms, has been identified as an
important predictor of both positive individual and organizational outcomes (Grant &
Ashford, 2008). Proactive behavior represents employee anticipatory acts that aim to
impact themselves, others, and/or their environments (Grant & Ashford, 2008).
Employees who engage in proactive behavior plan in advance, envision an outcome, and
interact with others in their environment to achieve that outcome (Grant & Ashford,
2008). Many common forms of proactive behavior involve searching for valuable
resources (Bamberger, 2009; Grant & Ashford, 2008). Help seeking, for example, is a
proactive behavior that facilitates acquisition of needed resources through “asking others
for assistance, information, advice, or support” (D. A. Hofmann, Z. K. Lei, & A. M.
Grant, 2009b, p. 1262).
Employee proactive behavior that aims to procure resources, such as help seeking,
triggers a potential need for a reactive behavior on the part of other employees. Engaging
in reactive behavior, however, may impair the functioning of the employees whose help
is being requested. For example, taking time and effort to help someone uses
psychological and emotional resources (Gailliot, 2010). Because these resources are
important for a variety of different behaviors (Muraven & Baumeister, 2000), assisting
others may have detrimental effects for helpers’ other work activities.
Although the literature on helping has begun to explore possible negative effects
of helping for the helper (e.g., Barnes et al., 2008), this literature has not specifically
differentiated between reactive and proactive helping. Some recent studies suggest that
helping behavior can impair individual-level and team-level outcomes (Bachrach, Powell,

1

Bendoly, & Richey, 2006; Barnes et al., 2008), however, this is likely to be most salient
for reactive helping acts that may not be anticipated or planned for on the part of the help
provider. Indeed, there is some evidence that compared to autonomous (e.g., volitional)
helping and no helping, non-autonomous (limited choice) helping is associated with
lower psychological resources (Weinstein & Ryan, 2010, Study 1, 2 and 4).
In addition, the literature on helping has not focused specifically on leaders.
Unlike a peer who might be able to ignore or avoid helping requests because they may
fall outside their own job descriptions, addressing many helping requests does fall within
the scope of leaders’ responsibilities (e.g., Morgeson, DeRue, & Karam, 2010; Yukl,
2010; Yukl, Gordon, & Taber, 2002). Little work, however, has examined how
responding to followers in need may affect leaders’ performance on other work activities.
This oversight is significant in light of evidence that leaders spend considerable time
helping followers with task-related and personal problems (Kaplan & Cowen, 1981).
The main purpose of this dissertation is to use Ego Depletion Theory (Baumeister,
Bratslavsky, Muraven, & Tice, 1998; Muraven & Baumeister, 2000) as a lens to
investigate the effects of leader daily reactive helping on leaders’ regulatory resources
and functioning. Building on Ego Depletion Theory, I propose that daily reactive helping
will deplete leaders’ self-regulatory resources and that helping with personal problems
will be more depleting than helping with task-related problems. Turning to consequences
of depletion, I suggest that state depletion will have negative effects on leaders’ work
engagement and creativity. Ego Depletion Theory suggests that these work activities
require regulatory resources and are likely to be susceptible to depletion of selfregulatory resources.

2

I draw on Ego Depletion Theory to also propose several moderators. More
specifically, I posit that helping role perceptions and prosocial motivation will moderate
the effects of reactive helping on state depletion. These expectations are informed by Ego
Depletion Theory, which posits that perceptions of an activity and motivation to achieve
social goals moderate the effects of activities that require resources on state depletion.
Turning to consequences of state depletion, I argue that prosocial impact and trait selfcontrol will moderate the effects of state depletion on work engagement and creativity.
Arguments by Ego Depletion Theory suggest that these constructs are likely to temper
the effects of state depletion on its consequences because they enable more efficient selfregulation. Figure 1 depicts the proposed conceptual model.

3

Figure 1
Proposed Model

Prosocial Motivation
Helping Breadth
Helping Efficacy

a

Trait Self-Control

Leader
Task-Related Help

Work Engagement
State Depletion

Leader
Personal Help

Creativity

Prosocial Impact

a

Note: prosocial motivation, helping breadth, helping efficacy, and trait self-control are level 2 (dispositional) variables. The rest of
the variables are level 1 (daily or within person variables).
For interpretation of the references to color in this and all other figures, the reader is referred to the electronic version of this
Dissertation.
4

LITERATURE REVIEW
Helping Behavior in Organizations: Key Distinctions
Helping is a cooperative affiliative - promotive behavior (Van Dyne, Cummings,
& Parks, 1995; Van Dyne & LePine, 1998), which consists of providing support,
suggestions, information, feedback, encouragement, and assistance with task-related and
personal problems (Anderson & Williams, 1996; Dudley & Cortina, 2008; Settoon &
Mossholder, 2002). Many organizational scholars have conceptualized helping as a
dimension of organizational citizenship behavior (OCB) (Bachrach, Powell, Collins, &
Richey, 2006; Marinova, Moon, & Van Dyne, 2010; Mossholder, Richardson, & Settoon,
2011; Organ, Podsakoff, & MacKenzie, 2006; Philip M. Podsakoff, MacKenzie, Paine, &
Bachrach, 2000; Van Dyne & LePine, 1998), which has been defined as “individual
behavior that is discretionary, not directly or explicitly recognized by the formal reward
system, and in the aggregate promotes the efficient and effective functioning of the
organization” (Organ et al., 2006, p. 3).
Proactive versus Reactive Helping. When considering reactive helping on the
part of the leader, the traditional conceptualization of helping as OCB is limiting for three
main reasons. First, defining helping as OCB does not account for helping acts that occur
as part of one’s job requirements as is the case in helping professions (e.g., nursing,
Hofmann et al., 2009b; Van Dyne & LePine, 1998) or for leaders. Second, helping as
OCB is considered extra role, however, the extent to which helping is perceived as in role
or extra role is likely to be a function of individual perceptions (Tepper, Lockhart, &
Hoobler, 2001); some form of helping may be perceived as in-role behavior and other
forms as extra-role (Van Dyne et al., 1995; Van Dyne, Kamdar, & Joireman, 2008; Van

5

Dyne & LePine, 1998), and research suggests that employees and supervisors may
struggle to distinguish between in-role and extra-role responsibilities (Morrison, 1994).
Third, traditional views of OCB posit that these acts contribute positively to
organizational outcomes (Organ et al., 2006). This view, however, does not take into
account the fact that helping may not always lead to positive consequences for the helper
(Bolino, Turnley, & Niehoff, 2004) and may even hurt helpers’ own performance (Barnes
et al., 2008). The limitations inherent in conceptualizing helping as OCB necessitate a
broader conceptualization of helping that does not include its potential consequences as
part of the definition (Bolino et al., 2004). For these reasons, I follow Anderson and
Williams’ (1996) approach and focus on reacting helping behaviors recognizing that
leaders are likely to vary in the extent to which they consider helping as in-role or extrarole.
Helping can be proactive or reactive in nature (Grant, Parker, & Collins, 2009;
Mossholder et al., 2011), and although the literature has not emphasized this distinction,
it is critical for several reasons. First, there is evidence that followers approach
supervisors with proactive help requests more often than coworkers, and thus, leaders are
likely to be the most frequent target of proactive help requests (Nadler, Ellis, & Bar,
2003). Little research, however, has studied the effects of leader helping on leader
functioning despite theoretical arguments that helping may deplete self-regulatory
resources and may consequentially harm leaders’ performance on other tasks (e.g.,
DeWall, Baumeister, Gailliot, & Maner, 2008).
Second, reactive helping is the most common form of helping behavior in that
helping occurs mostly in response to requests for assistance (Anderson & Williams,

6

1996; Burke, Weir, & Duncan, 1976; Geller & Bamberger, 2012; Grant & Hofmann,
2011a; Hofmann et al., 2009b; Mueller & Kamdar, 2011). For example, Grant and
Hofmann (2011b, p. 11) stated that “as much as 75–90% of all help in organizations … is
provided in response to a direct request from another person.” Most of the research
on helping, however, either does not acknowledge a distinction between proactive and
reactive helping (e.g., Anderson & Williams, 1996; Barnes et al., 2008; Mossholder et al.,
2011), or classifies helping as a proactive behavior (e.g., Den Hartog, De Hoogh, &
Keegan, 2007; McAllister, Kamdar, Morrison, & Turban, 2007), and little work has
specifically studied reactive helping.
Third, although helping behavior has been linked to some positive outcomes for
the helper such as improved mood and self-esteem (Glomb, Bhave, Miner, & Wall, 2011;
Williamson & Clark, 1989), this is less likely to be the case with leader reactive helping
for two main reasons. First, experimental work suggests that helping may improve mood
when people desire communal relationships (e.g., relationships with friends, family,
romantic involvement) more so than when they desire exchange relationships
(interactions among strangers and business acquaintances). For example, Williamson and
Clark (1989) found that when both exchange and communal conditions were considered,
there was no main effect of helping on mood (p < .05). They found, however, that helping
improved mood significantly in the communal condition, but that it deteriorated mood in
the exchange condition (Study 3). Customarily, interactions among leaders and followers
are more likely to be exchange oriented than communal in nature. Reactive leader
helping, therefore, is less likely to improve mood.

7

Second, most of the helping literature that has found positive effects of helping
has focused on proactive helping. Proactive helping, however, is motivated by different
needs and motives (Batson, 1990; Clary & Orenstein, 1991; Den Hartog et al., 2007; Van
Lange, Schippers, & Balliet, 2011; Weinstein & Ryan, 2010) whose fulfillment may
actually benefit helpers and perhaps offset some of the negative consequences of helping,
such as depletion of self-regulatory resources. For example, some organizational research
has linked helping with positive affect (Glomb et al., 2011), and positive affect has been
shown to offset depletion (Tice, Baumeister, Shmueli, & Muraven, 2007). Reactive
helping, however, is less likely to be associated with state positive affect because it is
given under more controlled settings at work and Ego Depletion Theory suggests that
exerting self-control in controlling settings is more depleting than self-control exercised
in more autonomous settings (Muraven, 2008; Muraven, Gagné, & Rosman, 2008).
In fact, Weinstein and Ryan, (2010) found that whereas daily autonomous helping
was positively associated with daily wellbeing, controlled (non-autonomous) daily
helping was not (Study 1). Autonomous helping represented instances when the helper
perceived to have choice or volition in acting and is conceptually similar to proactive
helping, whereas controlled helping represented acts motivated by desires to comply with
requests or please others and is conceptually similar to reactive helping. Compared to no
helping, daily controlled helping was associated with lower mean levels of wellbeing,
lower vitality, and lower self-esteem. The authors concluded that autonomous helping
was associated with increased wellbeing because it satisfied the needs of autonomy,
relatedness, and competence. Similarly, research on helping professions illustrates that
compared to volunteers, helping professionals report higher exhaustion (Gabassi, Cervai,

8

Rozbowsky, Semeraro, & Gregori, 2002), suggesting that the discretionary and maybe
intermittent nature of proactive helping may offset some of its negative consequences.
But even if reactive helping induces state positive affect, the hypotheses proposed
here posit that frequent reactive helping is likely to be depleting despite possible affective
boosts. These expectations are consistent with arguments by Ego-Depletion Theory that
affect does not mediate the impact of different activities that require self-control on
depletion (Muraven & Baumeister, 2000). For example, Muraven and Baumeister (2000)
stated that depletion is not caused or mediated by experience of mood and emotions, but
by efforts that deplete self-control resources.
Task versus Personal Helping. Research suggests that leaders may be
approached for two types of help: task-related and personal (Bamberger, 2009; House,
1981). Task-related help seeking represents solicitation of assistance that directly ties to
the fulfillment of job requirements and responsibilities (Bamberger, 2009). Personal help
seeking, instead, aims to address personal and emotional problems not directly linked to
job responsibilities but that may ultimately affect help seeker’s job performance
(Bamberger, 2009). The expected outcome of help seeking is receiving help and there are
two main types of helping: task-related and personal (Dudley & Cortina, 2008; Settoon &
Mossholder, 2002). Task-related helping involves providing instrumental support that
facilitates task-performance, whereas personal helping involves assisting with personal
problems and providing emotional support (Dudley & Cortina, 2008; Mossholder et al.,
2011).
Similar to previous research on helping, task-related helping in this dissertation is
defined as helping “with work-based issues and other less personal problems … likely to

9

involve informational support and instrumental assistance” (Dudley & Cortina, 2008,
p.1251). Some exemplars of task-related helping are behaviors such as providing workrelated advice, providing new perspectives on work-related problems, giving factual
information about work procedures or performance, assisting others with technical
aspects of procedures and regulations, and supplying information about fulfillment of job
roles and responsibilities (Burke et al., 1976; Kaplan & Cowen, 1981; Settoon &
Mossholder, 2002).
Personal helping, on the other hand, has been defined in the literature as “selfesteem maintenance and other, more personal, problem-solving behaviors… likely to
involve emotional support” (Dudley & Cortina, 2008, p.1251). Some exemplars of
personal helping are behaviors such as assisting with emotional or psychological health,
helping with personal feelings and intimate relationships, providing counseling, and
showing concern (Burke et al., 1976; Kaplan & Cowen, 1981; Settoon & Mossholder,
2002).
Personal helping, as defined here, has also been referred to as “emotional helping”
in the helping literature. For example, McGuire’s (1994) conceptualization of “emotional
helping” refers to behaviors such as providing moral support, providing comfort,
listening to problems, giving personal advice, cheering up, and showing concern.
Similarly, Bamberger (2009) defined emotional helping as “help aimed at facilitating the
resolution of problems that are more personal in nature, often involving relationship
problems or issues relating to an individual’s psychological well-being, and demanding
the sharing of often intimate thoughts and feelings” (p.57). These definitions of
“emotional helping” are similar to the definition of personal helping (Dudley & Cortina,

10

2008). The emotional aspect of personal helping refers to the sensitive nature of problems
and to the sharing of intimate thoughts, emotions, and experiences by the help seeker
(Bamberger, 2009). Emotional or personal helping episodes, therefore, may or may not
be accompanied with displays of emotions by the help seeker but the term “emotional
helping” is somewhat confusing. To avoid confusion and to be consistent with
predominant conceptualizations of helping in the literature, I refer to helping with
personal problems as “personal helping” rather than “emotional helping.”
In this dissertation I focus on the effects of both task-related and personal helping
on leaders’ functioning and behavior in the workplace. Both types of helping fall within
the formal job responsibilities of leaders and research has long recognized that helping
subordinates with task-related and personal problems is an important leader function
(Yukl, 2010). For example, Katz and Kahn (1978) argued that leaders have two important
functions: task direction and psychological supportiveness. Task direction refers to taskrelated behaviors that mainly support goal attainment, whereas psychological
supportiveness refers to socio-emotional leader behaviors that mainly support group
members (Katz & Kahn, 1978). Limited research, however, has investigated the effects
that reactive helping has on leaders despite evidence that leaders spend considerable time
helping. For example, Kaplan and Cowen (1981) found that most leader helping involved
task-related issues. Leaders, however, also spent approximately 2.5 hours a week (7% of
their working time) responding to requests for help with personal problems (e.g., marital
problems, problems with children, and with depression). Most of these issues (in 75% of
cases) were raised by subordinates and about 40% of leaders reported that followers
contacted them with personal problems during off-work hours as well.

11

Supervisors hold formal helping roles in their organizations and research indicates
that people in formal helping roles are perceived as being more accessible and as
possessing more expertise than other employees, which increases the likelihood that they
will be approached for help (e.g., Hofmann et al., 2009b). Perhaps an indication of this is
the fact that executives spend approximately half of their time in contact with
subordinates (Kurke & Aldrich, 1983). It is likely, therefore, that reactive helping is a
prevalent leader behavior and therefore it is important for research to investigate the
effects that it may have on leaders.
Task-Related and Personal Helping versus Initiating Structure and
Consideration. Leader reactive task-related and personal helping are conceptually
similar, but distinct constructs from initiating structure and consideration respectively.
Initiating structure consists of instrumental leader behaviors that facilitate group goal
attainment, such as planning, scheduling, communicating information, and criticizing
(Fleishman, 1953; Korman, 1966). Consideration, on the other hand, consists of relational
behaviors characterized by respect toward subordinates’ feelings and ideas, such as doing
personal favors, showing appreciating, being friendly and approachable (Fleishman,
1953; Korman, 1966). Similar to initiating structure, task-related helping consist of
instrumental acts and similar to consideration, personal helping consists of considerate
interpersonal acts.
Task-related and personal helping behaviors, however, differ from initiating
structure and consideration behaviors in two crucial ways. First, reactive helping is
problem focused in that leaders attempt to assist their followers with issues that they
bring to leaders. Initiating structure and consideration behaviors, on the other hand,

12

describe leaders’ general pattern of behavior or styles and they may or may not be
problem focused (e.g., Fleishman, 1953). In fact, one of the main criticisms of the Ohio
State Leadership Studies that developed initiating structure and consideration scales is
that they do not account for situational variables (Kerr, Schriesheim, Murphy, & Stogdill,
1974).
Second, the helping episodes considered here occur in response to help seeking,
however it is unclear if initiating structure and consideration behaviors are self-initiated
or if they occur in response to employee requests. For example, initiating structure items
such as “He talks about how much should be done” and “He sees to it that people under
him are working up to their limits” (Fleishman, 1953, p.3) could be either self-initiated
by leaders or they could occur in response to subordinate requests. Similarly,
consideration items such as “He is easy to understand” and “He treats all his foremen as
his equal” (Fleishman, 1953, p.3) could be either proactive or reactive in nature.
In sum, despite their conceptual similarities, reactive task-related and personal
helping differ from initiating structure and consideration because helping as
conceptualized here is problem focused and reactive in nature, whereas initiating
structure and consideration represent general leader styles that may or may not be
problem focused and reactive in nature.
Positive versus Negative Effects Attributable to Helping. The majority of
management research on helping has linked it to positive personal and organizational
outcomes. With regards to personal outcomes, research indicates that helping improves
performance evaluations, the amount of assistance that helpers get from others, and
helpers’ mood. I review these outcomes in the sections that follow.

13

There is empirical evidence that helping contributes to helpers’ performance
ratings. For example, Whiting, Podsakoff, and Pierce, (2008) conducted two
experimental studies where they investigated the effects of task, helping, and voice
behavior on performance appraisal decisions. They found that helping contributed to
performance appraisal ratings above and beyond the other behaviors. Similarly,
experimental work by Podsakoff, Whiting, Podsakoff, and Mishra (2011) studied the
effect of candidates’ propensity to exhibit certain citizenship behaviors on selection
decisions. They found that candidates’ endorsement of helping behaviors was positively
associated with other (e.g., recruiter) - rated competence and overall evaluation. In
addition, Podsakoff, Ahearne, and MacKenzie (1997) found that helping was positively
related to both performance quantity and quality in a sample of paper mill workers. Other
experimental and field work also suggests that altruistic behavior matters in the context
of performance evaluations and reward recommendations (Heilman & Chen, 2005;
MacKenzie, Podsakoff, & Paine, 1999).
Research findings suggest that helpers receive more help in return. Applying a
social exchange perspective to helping in a sample of food service employees, Lyons and
Scott (2012) found that employees who helped their coworkers received more help from
those coworkers in return. They explained that this is due to the norm of reciprocity that
helping others engenders in help receivers. Mueller and Kamdar (2011) examined the
moderating effect of helping on the relationship between help seeking and creativity.
Although this was not their main research question, they also reported that giving help
was positively associated with receiving help. Helping others seems to produce an

14

experience of obligation in those being helped who then reciprocate by assisting the
helper.
Helping has also been shown to contribute to helpers’ positive mood. Although
organizational research on the effects of helping on affective states is scarce (Grant &
Sonnentag, 2010), recent work has started to examine this association. For example,
Glomb and colleagues (2011) found that daily proactive helping was associated with
positive mood in a sample of professional and managerial employees. In nonworking
contexts (e.g., family and volunteering outside of work), proactive helping has been
associated with a decline in depressive symptoms, higher positive affect, and higher life
satisfaction (S. L. Brown, Brown, House, & Smith, 2008; Hecht & Boies, 2009; Poulin et
al., 2010; Weinstein & Ryan, 2010).
In order to be thorough, it is important to note that the positive effects of helping
are not conclusive. In fact, a few studies show that helping may not affect personal
outcomes. For example, in a diary study investigating predictors of helping in an
employee sample, Conway, Rogelberg, and Pitts (2009) did not find a direct effect of
helping on positive affect. In exploratory analyses, they found that helping was associated
with positive affect only for employees who were low in altruism. Their findings,
therefore, are inconsistent with those reported by Glomb and coauthors (2011). In two
experiments, Heilman and Chen (2005) found that helping behaviors did not contribute to
performance ratings or reward recommendations for women, but that they did for men. A
gender effect, however, has not been reported in similar other studies (e.g., Whiting et al.,
2008).

15

Although much attention has been paid to the positive outcomes of helping, recent
research has started exploring its negative consequences as well. This research shows that
helping can have detrimental effects on the helper. For example, Wright, George,
Farnsworth, and McMahan (1993) found that helping was negatively associated with task
performance. The authors argued that this negative association was because individuals
have a limited amount of resources that they can dedicate to either their in-role behaviors
or to helping. Similarly, Barnes and coauthors (2008) found that team members’ helping
detracted from their own taskwork, especially when the workload was evenly distributed
among team members. They argued that allocation of resources to helping behaviors
reduced the amount of resources that could be invested in helpers’ own tasks.
Venkataramani and Dalal (2007) studied helping and harming in a national sorority
chapter and found the two to be weakly but significantly positively correlated. They
argued that helping and harming may increase with the amount of interaction. Mueller
and Kamdar (2011) examined creativity in a sample of engineering teams and found that
helping was negatively related to helpers’ own creativity after controlling for help
received and team member status. They argued that helping diminishes the amount of
time and energy that employees can devote to creative tasks.
Research indicates that helping can have negative effects on other helper
outcomes. For example, Halbesleben, Harvey, and Bolino (2009) found that helping at
work contributed to time, strain, and behavioral work-family interferences. The authors
argued that investment of resources at work leaves fewer resources for activities at home.
In non-work settings, Poulin and coauthors (2010) found that helping was associated with
negative affect for caregivers who perceived low interdependence (e.g., low mutuality of

16

need and caregiver dependence on spouse) with an ailing spouse. In addition, employees
in helping professions are particularly prone to experiencing fatigue and burnout (Cordes
& Dougherty, 1993; Slatten, David Carson, & Carson, 2011).
With a few exceptions (e.g., studies by Barnes et al., 2008; Glomb et al., 2011;
Halbesleben et al., 2009; Mueller & Kamdar, 2011), organizational research has mostly
focused on the effects of helping on the group or recipient, and little on the effects of
helping on the helper (Spitzmuller, Van Dyne, & Ilies, 2008). As the above review
indicates, however, helping may have negative consequences for helpers. The literature
and theory related to the negative effects of helping are not nearly as well-developed as
the literature and theory for the positive effects of helping, but Ego Depletion Theory
(Baumeister et al., 1998; Muraven & Baumeister, 2000) may serve as a useful
conceptual framework for understanding why, when, where, and with whom (e.g.,
Bacharach, 1989) helpers are likely to encounter negative effects attributable to helping.
Ego Depletion Theory would suggest that leader reactive helping is likely to reduce
leaders’ regulatory resources. Regulatory resources are important for a host of other
leader behaviors.

17

THEORETICAL BACKGROUND: EGO DEPLETION THEORY
Ego Depletion Theory suggests that individuals exert control over the self in order to
behave in accordance with socially acceptable standards and expectations (Baumeister,
Gailliot, DeWall, & Oaten, 2006; Baumeister, Vohs, & Tice, 2007). Acts of self-control
represent the capacity of the self to override natural self-serving tendencies and steer
behavior towards more socially appropriate norms and behaviors as well as desired
outcomes (Baumeister, DeWall, Ciarocco, & Twenge, 2005). For example, people exert
self-control when they engage in decision making, self-presentation, or when dealing
with demanding partners (Baumeister, Vohs, et al., 2007). When exerting self-control,
people draw from a limited reservoir of resources, which may become depleted
(Baumeister & Vohs, 2007; Hagger, Wood, Stiff, & Chatzisarantis, 2010). Depletion
refers to “a temporary reduction in the self’s capacity or willingness to engage in
volitional action (including controlling the environment, controlling the self, making
choices, and initiating action) caused by prior exercise of volition” (Baumeister et al.,
1998, p. 1253). In sum, depletion is a state that “renders the self temporarily less able
and less willing to function normally or optimally” (Baumeister & Vohs, 2007, p. 2).
Self-control represents an inner resource similar to energy or strength (Baumeister &
Heatherton, 1996). This same resource is utilized for different activities such as
“regulating thoughts, controlling emotions, inhibiting impulses, sustaining physical
stamina, and persisting in the face of frustration or failure” (Schmeichel, Vohs, &
Baumeister, 2003, p. 33). Self-control, therefore, is a general purpose resource that is
necessary for a broad variety of acts that require self-regulation. Coping with stress (due
to continuous monitoring and focusing attention on threatening stimuli), dealing with

18

negative emotions (due to attempts at overriding, inhibiting, or altering negative
emotions), regulating attention, dieting (restraining impulses or desires), are a few more
examples of activities that require self-control (Muraven & Baumeister, 2000). Other
synonymous terms for self-control are “willpower,” “self-discipline,” and “selfregulation.” Self-regulation is a broader concept that encompasses both deliberate and
unconscious regulatory processes, whereas self-control represents deliberate and
conscious endeavors to manage the self (Hagger et al., 2010).
Ego Depletion Theory has five key tenets (Muraven & Baumeister, 2000). First, it
posits that self-control is necessary for executive functioning. Second, self-control is
limited and people possess a finite amount of resources. Third, all self-control activities
draw from the same pool of resources. Fourth, people vary in the amount of self-control
that they possess – people who possess more self-control are more likely to succeed in
endeavors that require self-control. Fifth, self-control is expendable and continual
exertions can lead to state depletion. Muraven and Baumeister (2000) likened self-control
to a muscle that becomes fatigued and thereby less functional when exerted. In sum, the
main prediction of Ego Depletion Theory is that sustained engagement in acts that require
self-control produces short-term self-regulatory deficiencies – or state depletion. In turn,
state depletion leads to decreased performance on subsequent acts that require selfcontrol.
Similar to other self-regulation theories (e.g., Higgins, 1998), Ego Depletion Theory
posits that behavior is affected by four main drivers: standards, monitoring, selfregulatory strength (e.g., resources), and motivation (Baumeister & Vohs, 2007). People
monitor their environment for cues about how their behavior compares to some internally

19

or externally valued standard. Then, they exert self-control and expend physical,
psychological, emotional, and cognitive resources to align their behaviors with these
valued standards. This process is likely to leave the self with fewer resources. Motivation,
however, compensates or substitutes for these expended resources and renders the self
less vulnerable to state depletion (Baumeister & Vohs, 2007).
In a helping context, this means that leaders are likely to monitor their working
environment for standards of helping (e.g., whether helping with a particular problem is
appropriate or possible). If leaders agree to help they are likely to exert self-control and
expend psychological, emotional, and cognitive resources during helping episodes.
Motivation may affect not only their decision to help, but also the helping process itself.
For example, leaders’ prosocial motivation to help may compensate or substitute for
expended resources and may render leaders less vulnerable to state depletion and more
able to self-regulate efficiently.
Depletion versus Alternative Constructs
Ego Depletion Theory posits that depletion due to exertion of self-control is different
from fatigue, lower self-efficacy, and negative affect. First depletion is not fatigue, which
has been defined as “a pervasive sense of tiredness or lack of energy that is not related
exclusively to exertion” (R. F. Brown & Schutte, 2006, p. 585). In their meta-analysis on
Ego Depletion Theory, Hagger and colleagues (2010) reported a corrected correlation of
.44 between depletion and fatigue. They argued that these constructs are distinct from
each other and that fatigue may be an outcome or a mediator of the effect of state
depletion on performance. In addition, experimental work testing the construct

20

distinctiveness of fatigue and depletion shows that depletion is not tantamount to fatigue
(Vohs, Glass, Maddox, & Markman, 2011).
Second depletion is not self-efficacy, which has been defined as “the conviction
that one can successfully execute the behavior required to produce the outcomes”
(Bandura, 1977, p. 193). Arguably, tasks that require self-control may result in poorer
performance on subsequent tasks not because of depletion but because of reduction in
self-efficacy perceptions. This idea, however, has been refuted in the depletion literature
(Hagger et al., 2010). First, a recent meta-analysis by Hagger et al. (2010) did not find
significant mean differences in self-efficacy perceptions between depleted and nondepleted participants across many experiments. Second, research has not found an
association between depletion and self-efficacy (Finkel et al., 2006; Gailliot &
Baumeister, 2007). Third, reduced self-efficacy for some tasks may not necessarily affect
performance on other tasks (Hagger et al., 2010). For example, reduced self-efficacy to
help with personal problems is unlikely to affect one’s self-efficacy to experience work
engagement.
Last depletion is not negative affect, defined as “a general dimension of subjective
distress and unpleasurable engagement that subsumes a variety of aversive mood states,
including anger, contempt, disgust, guilt, fear and nervousness” (Watson, Clark, &
Tellegen, 1988, p. 1063). Tasks that require self-control are demanding and effortful and
may induce negative emotions. In fact, the meta-analysis by Hagger et al. (2010) found a
small mean difference (d =.14) in negative affect between depleted and non-depleted
participants, and no significant difference in positive affect. The significant effect may be
due to the fact that in addition to investment of cognitive and psychological resources,

21

depletion may be partially due to exertion to improve negative mood (Muraven &
Baumeister, 2000).

22

THEORETICAL DEVELOPMENT AND HYPOTHESES
The Depleting Nature of Reactive Helping
Helping is a problem solving activity that requires exertion of energy and effort
(Gailliot, 2010). Helping involves several processes such as problem solving, behavioral
flexibility, perspective taking, social perceptiveness, and emotional management and
support (Dudley & Cortina, 2008) and Ego Depletion Theory posits that these processes
consume resources. For example, helpers engage in thought and action specific to
problems expressed by help seekers and problem solving requires exertion of self-control
(Schmeichel et al., 2003). Schmeichel and coauthors (2003) stated that “… using logic to
draw conclusions and implications from ideas, extrapolating from known facts to
make estimates about unknowns, and generating novel ideas may require active selfcontrol” (p.33). Because these processes are important to helping, responding to requests
for assistance is likely to deplete self-regulatory resources.
Helping is also likely to increase helpers’ perceptions of time pressure and to
reduce their cognitive flexibility (Mueller & Kamdar, 2011). Managing such negative
perceptions and emotions can also be depleting (Baumeister, Vohs, et al., 2007; Grandey,
Fisk, & Steiner, 2005). Furthermore, evolutionary theory suggests that people are
inherently selfish and think of their needs before the needs of others (Gailliot, 2010).
Helping, therefore, also consumes resources to override or curb selfish tendencies to
focus on one’s own tasks rather than to help others (DeWall et al., 2008; Gailliot, 2010).
Furthermore, leaders may need to sympathize and take the followers’ perspective
on issues that they bring to leaders. When agreeing to assist, leaders may also need to
switch mindsets from focusing on the tasks at hand to the problems raised by followers.

23

Both perspective taking and mindset switching are activities that deplete self-regulatory
resources (Ackerman, Goldstein, Shapiro, & Bargh, 2009; Hamilton, Vohs, Sellier, &
Meyyis, 2011). For example, switching from an approach to an avoidance mindset or
from an individualistic to a collectivistic mindset has been shown to deplete selfregulatory resources (Hamilton et al., 2011; Experiment 4 and 5).
Reactive helping acts can also be thought of as goal disruptive events or taskrelated obstacles that require behavioral flexibility for leaders who have to juggle other
daily activities (e.g., Kurke & Aldrich, 1983; Mintzberg, 1975). Research examining the
effects of externally induced obstacles indicates that they consume psychological
resources and cause negative mood and fatigue (Zohar, 1999). For example, Zohar,
Tzischinski, and Epstein (2003) studied the energy expended during goal disruptive or
goal enhancing events in a sample of hospital residents. Goal disruptive events were
defined as interpersonal or non-interpersonal work disruptions, diversion of time and
effort from the tasks at hand, or unforeseen difficulties. Goal enhancing events, on the
other hand, were defined as challenging or interesting opportunities and tasks. The
authors found that goal disruptive events predicted negative affect and fatigue. Goal
enhancing events also predicted fatigue but only under high workload levels. The overall
pattern of results suggests that goal disruptive and enhancing events increase fatigue
levels due to energy resource limitations (Zohar et al., 2003). This line of research
supports the general idea that responding to disruptions may consume resources. More
generally, the arguments presented thus far support the position that helping is likely to
deplete helpers’ self-regulatory resources.

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Additionally, personal helping is likely to be more depleting than task-related
helping. Ego Depletion Theory posits that discussion of intimate topics and
uncomfortable interactions deplete self-regulatory resources (Baumeister & Vohs, 2007;
Muraven & Baumeister, 2000; Richeson & Trawalter, 2005). This is because people
labor at managing their biases, their emotions, and the display of their emotions.
Attempts at managing and expressing emotions deplete self-regulatory resources
(Grandey et al., 2005; Scott & Barnes, 2011). In addition, helping with personal problems
necessitates emotional regulation because it involves giving emotional support and
dealing with uncomfortable problems (Dudley & Cortina, 2008). For example, leaders
may have to manage feelings of discomfort or surprise when subordinates seek help for
personal matters. Leaders may also have to labor at looking composed and sympathetic
and to show concern and courtesy to followers during personal helping episodes. These
activities require emotional regulation and are likely to be depleting.
Helping with personal problems also requires mindset switching from focusing on
work related activities to focusing on followers’ personal problems. As argued earlier,
mindset switching requires resources, but this may be even more effortful for personal
problems because these are less common for leaders (Kaplan & Cowen, 1981). Research
indicates that leaders are more comfortable to help followers with task-related problems
than with personal problems (Burke et al., 1976; Etzion, Adler, & Zeira, 1980). This may
be because helping with personal problems requires more emotional and psychological
resources.
Although a direct association between helping and depletion has yet to be
established, recent research on Ego Depletion Theory suggests that this is a possibility.

25

First, there is evidence that helping requires self-regulatory resources. For example,
DeWall, Baumeister, Gailliot, and Maner (2008) conducted several experiments to study
the effects of state depletion on helping and found that subjects who were depleted were
less likely to help others. They concluded that helping requires exertions by the self and
that it draws from the same resources as other behaviors that require self-control. Second,
literature on the effects of interpersonal activities on depletion suggests that demanding
interactions are depleting (Finkel et al., 2006). For example, Finkel and coauthors (2006)
found that demanding interactions lead to impaired self-regulation (e.g., depletion). They
coined the term “high maintenance interactions” for interdependent interpersonal
activities that require effortful coordination beyond what is required to complete the task
at hand (e.g., helping an emotionally distressed person with a personal problem,
Experiment 4). High maintenance interactions were more depleting than low maintenance
interactions and the effect remained even after controlling for task motivation and liking
for the partner (Finkel et al., 2006). Mood or self-efficacy did not account for the effect.
In one experiment, the authors compared high and low maintenance interactions to a
condition where participant did not interact with anybody else. The low maintenance
interaction was more depleting than the no interaction condition, but this difference did
not reach significance.
In addition, research on social psychology suggests that supporting others
emotionally and with personal problems taxes one’s own psychological and emotional
resources (Shumaker & Brownell, 1984). For example, Kinman, McFall, and Rodriguez
(2011) found that members of the clergy who performed emotional labor experienced
increased psychological distress and lower intrinsic job motivation. Magen and

26

Konasewich, (2011) studied the effects of helping among friends and found that
providing emotional support was associated with deteriorated mood states among
women. Similarly, Strazdins and Broom (2007) found that giving emotional help to
friends and family was positively associated with helpers’ depressive symptoms. They
argued that helping with personal matters (giving advice, talking about relationship
problems etc.) could be emotionally charged and thus strenuous for help providers. Taken
together, these arguments support the position that helping others with personal issues is
likely to deplete self-regulatory resources and that personal problems are likely to elicit
emotional states whose regulation also necessitates self-control resources.
In sum, although empirical research on the effects of interpersonal activities on
state depletion is in its infancy, both Ego Depletion Theory (e.g., Baumeister et al., 2007;
Gailliot, 2010) and organizational research that has taken a resource perspective on
helping (Halbesleben et al., 2009) suggest that helping is likely to deplete helpers’
regulatory resources. Furthermore, compared to task-related helping, helping with
personal problems is likely to be more depleting. This is because in addition to engaging
in resource intensive processes such as problem solving and switching mindsets, leaders
may also need to manage their own emotions and feelings during the helping process.
This adds another layer of complexity that enhances the resource taxing nature of
personal helping. Based on the conceptual arguments presented thus far, I hypothesize the
following:
Hypothesis 1: Reactive task-related helping will be positively associated with
state depletion, controlling for morning state depletion.

27

Hypothesis 2: Reactive personal helping with be positively associated with state
depletion, controlling for morning state depletion.
Hypothesis 3: Reactive personal helping will be more depleting than taskrelated helping, controlling for morning state depletion.
Moderators of the Effects of Reactive Helping on Depletion: Prosocial Motivation
Ego Depletion Theory acknowledges that certain individual differences and
contextual features may moderate the extent to which activities that require self-control
are subsequently associated with state depletion (Baumeister et al., 2007). For example,
Ego Depletion Theory suggests that heightened motivation to achieve a goal moderates
the effects of activities that require self-control on depletion (Baumeister, Vohs, et al.,
2007). Prosocial motivation coincides with a heightened motivation to meet the goal of
benefiting others (Grant, 2008; Grant & Berg, 2011) and should temper the effects of
reactive helping on state depletion. Prosocially motivated people care not only about their
personal outcomes, but also about the needs and welfare of others (Batson, 1987, 1990;
Beersma & De Dreu, 2005). They view interpersonal activities as cooperative endeavors
and assisting others as the right thing to do (Beersma & De Dreu, 2005). Scholars suggest
that helping for prosocial motives results in a number of self-benefits such as 1) aversive
arousal reduction (e.g., relieved feelings of distress experienced when someone else is in
trouble); 2) punishment avoidance (avoidance of feelings of shame or guilt for not
helping); and 3) personal and social reward (e.g., feeling good about oneself for helping).
These benefits may be either consciously intended or an unintended consequence of
pursuing the goal of ultimately benefiting others through one’s help (Batson, 1990).

28

Leaders who are motivated by prosocial desires to assist their followers are likely
to need less energy to overcome selfish tendencies because prosocially motivated people
subordinate their own interests often to help others (Grant & Mayer, 2009) and repeated
practice with an activity improves self-regulation (Baumeister et al., 1998). In addition,
leaders who are prosocially motivated help for personally meaningful reasons and may
experience less strain and conflicting emotions during helping. As a result, fewer
resources will be dedicated to managing negative feelings. Helping others for prosocial
motives is also likely to have self-affirming value for helpers. According to selfaffirmation theory, an event is self-affirming when it increases the perceived integrity and
moral adequacy of the self (Schmeichel & Vohs, 2009). People experience selfaffirmation when they perform activities that confirm their values and personal
relationships are important affirmation resources (Sherman & Cohen, 2006). Leaders who
are high in prosocial motivation remain true to self during helping episodes because these
represent activities that are personally relevant and congruent with their desires to benefit
others. Research indicates that self-affirmation “enables good self-control” (Schmeichel
& Vohs, 2009, p. 771), consequentially, leaders who are high in prosocial motivation
ought to experience less state depletion from helping. Consistent with the arguments
made thus far, I posit the following:
Hypothesis 4: Prosocial motivation will have a cross-level moderating effect on
the relation between daily reactive helping and state depletion such that a) taskrelated helping and b) personal helping will be less depleting for high versus low
prosocial motivation.

29

Moderators of the Effects of Reactive Helping on Depletion: Helping Role
Perceptions
Although the leadership research considers assisting followers to be an important
leader function (Morgeson et al., 2010; Yukl, 2010), some leaders may view helping with
task-related and personal problems as part of their job, whereas others may consider it as
going beyond the call of duty. In fact, research on helping indicates that people vary in
their helping role perceptions (Kamdar, McAllister, & Turban, 2006; McAllister et al.,
2007). Arguments by Ego Depletion Theory suggest that helping breadth role perceptions
and helping efficacy role perceptions are likely to influence relations between reactive
helping and state depletion. It is possible; therefore, that these two helping role
perceptions may moderate the extent to which helping is associated with state depletion.
Because the two helping types are distinct constructs that involve dealing with
different sets of problems (Dudley & Cortina, 2008; Settoon & Mossholder, 2002),
helping breath and efficacy perceptions ought to be specific to helping type. Thus, taskrelated helping breadth perceptions refer to perceptions that helping with task-related
issues is an expected part of one’s job, whereas personal helping breadth perceptions
refer to perceptions that helping with personal problems is an expected part of one’s job.
Task-related helping efficacy, on the other hand, is defined as perceptions that one is
capable to help with task-related problems, whereas personal helping efficacy perceptions
capture perceptions of confidence that one is capable to help with personal problems.
Helping role perceptions specific to task-related and personal helping are constructs
concordant with either task-related or personal helping and ought to be considered as
moderators between their respective helping form and state depletion. For example, task-

30

related helping breadth is concordant with task-related helping and ought to be
considered as a moderator between task-related helping and state depletion, whereas
personal helping breath is concordant with personal helping and ought to be considered
as a moderator between personal helping and state depletion. In the following sections I
review the theoretical arguments for why helping breadth perceptions and helping
efficacy perceptions are likely to moderate the effects of helping on state depletion.
Helping breadth role perceptions: As previously discussed, helping breadth refers
to perceptions that helping is an expected part of one’s job (McAllister et al., 2007).
Research on Ego Depletion Theory suggests that feeling forced or pressured to exert selfcontrol for external reasons leads to greater state depletion (Moller, Deci, & Ryan, 2006;
Muraven, 2008; Muraven et al., 2008). Muraven (2008) tested these ideas in an
experimental setting and found that subjects who resisted eating cookies for controlled
reasons were more depleted afterwards than subjects who resisted eating cookies for
more autonomous reasons. The authors suggested that Ego Depletion Theory ought to be
revised to account for the fact that exertion of self-control due to external expectations is
more depleting than exertion of self-control for more volitional reasons.
Similarly, work by Muraven, Gagné, and Rosman (2008) suggests that feeling
pressured to exert self-control for external reasons is more depleting than exerting selfcontrol for volitional reasons because the latter consumes fewer resources. This is
partially due to the subjective vitality (e.g., energy) experienced while exerting
autonomous self-control. People experience vitality when they engage in behaviors that
feel self-driven and vitality replenishes depleted resources. Leaders who perceive to have
high helping breadth are likely to experience limited volition and vitality and to become

31

more depleted by helping (e.g., Muraven, 2008). Hence, helping breadth is likely to
moderate the strength of the relationship between reactive helping and depletion such that
this relation will be stronger for high versus low helping breadth perceptions.
Hypothesis 5a: Task-related helping breadth will have a cross-level moderating
effect on the relation between daily task-related helping and state depletion such
that the relation will be stronger for high versus low task-related helping
breadth.
Hypothesis 5b: Personal helping breadth will have a cross-level moderating
effect on the relation between daily personal helping and state depletion such
that the relation will be stronger for high versus low personal helping breadth.
Helping efficacy role perceptions: Helping efficacy refers to perceptions of one’s
competence to help (McAllister et al., 2007). Judgments of self-efficacy are based on four
sources of information: past experience; vicarious learning through observing others’
actions; verbal persuasion that one possesses the required capabilities for a particular
activity; and psychological states (Bandura, 1982). Leaders who have high helping selfefficacy are likely to have faced similar task or emotional helping requests and to have
been successful in the past. Furthermore, Ego Depletion Theory suggests that self-control
may get stronger with exercise (Gailliot, Plant, Butz, & Baumeister, 2007; Hagger et al.,
2010; Muraven, Baumeister, & Tice, 1999). It is likely; therefore, that high helping selfefficacy may weaken the effects of reactive helping on depletion because leaders with
high helping efficacy are likely to have built up resources from helping experiences in
their past.

32

People who possess higher helping self-efficacy are also likely to mobilize their
motivation and cognitive resources more efficiently and to choose more efficient
analytical strategies for performance (e.g., Bandura & Wood, 1989; Gist & Mitchell,
1992). In fact, self-efficacy plays a central role in self-regulation because it facilitates
personal agency and determines the amount of effort that people are willing to exert for
any given endeavor (Bandura, 1991). People high in self-efficacy are committed to their
courses of actions and derive intrinsic motivation from mastering challenges (Bandura,
1991). Therefore, in addition to being efficient at utilizing their resources, leaders high in
helping self-efficacy are also likely to be intrinsically motivated by helping requests.
Leaders high in helping self-efficacy, therefore, ought to become less depleted by helping
episodes.
To summarize, leaders who are high in helping self-efficacy may be less
vulnerable to depletion due to helping because: they are likely to have had more
experience with helping, and because they are likely to be intrinsically motivated to help
and more efficient at self-regulating during helping. For these reasons I expect that leader
helping self-efficacy will moderate the effect of reactive helping on depletion such that
this relationship will be weaker for leaders who are high versus low in helping selfefficacy. Because efficacy refers to judgments of one’s capabilities for a specific task
(Gist & Mitchell, 1992), and because task-related and personal helping refer to two
different categories of behaviors, I differentiate between task-related and personal
helping efficacy perceptions. The following hypotheses summarize my arguments:
Hypothesis 6a: Task-related helping efficacy will have a cross-level moderating
effect on the relation between daily task-related helping and state depletion such

33

that the relation will be weaker for high versus low task-related helping
efficacy.
Hypothesis 6b: Personal helping efficacy will have a cross-level moderating
effect on the relation between daily personal helping and state depletion such
that the relation will be weaker for high versus low personal helping efficacy.
Although dyadic interactions (e.g., a specific helping episode between a leader
and a follower) are beyond the scope of this dissertation, it is likely that characteristics of
the target of helping may affect the extent to which helping depletes leaders. For
example, helping a peer may be associated with less depletion than helping a subordinate
because leaders are likely to experience more volition when they help a peer (e.g.,
responding to a peer’s request for help is likely to fall outside one’s job responsibilities)
and Ego Depletion Theory suggests that volition is energizing (Muraven, 2008).
Similarly, the quality of the leader-member exchange (LMX) between leader and
follower may moderate the extent to which helping is depleting. A leader who has a high
LMX relationship with a follower, for example, may experience less discomfort and
depletion from helping that follower with personal problems.
Furthermore, some followers may be more dependent on their leaders and ask for
help more frequently than others (Nadler, 1998). Helping dependent followers may be
more depleting than helping followers who approach their leaders more selectively. This
is because in addition to spending energy on helping acts, leaders may also need to exert
self-control to manage negative feelings of exasperation and impatience from having to
respond to many help requests from the same subordinate.

34

In this dissertation, I conceptualize reactive helping as the sum of helping
episodes undertaken by leaders in response to their followers’ help seeking requests
during a given work day. As argued so far, target characteristics are likely to matter and
may strengthen (e.g., dependent followers) or weaken (high LMX follower) the relation
between a particular helping episode and depletion due to that episode. Because I look at
the sum of daily helping episodes across all subordinates and depletion associated with
all these helping episodes, the effects of reactive helping on depletion reported in this
dissertation are likely to yield conservative estimates. Target specification will be a
natural evolution for the literature studying the effects of reactive helping on depletion.
Consequences of Depletion: Work Engagement and Creativity
Effective performance at work requires investment of psychological, emotional,
and cognitive resources. Depleted leaders, however, possess a diminished amount of
resources that they can dedicate to work activities. It is possible; therefore, that state
depletion due to helping may impair leaders’ performance on other work activities.
Although there is some evidence in the management literature that depletion affects work
activities, this research has focused rather narrowly on counterproductive work behaviors.
It is important to review this literature here, however, because it clarifies how Ego
Depletion Theory has been applied in the management literature so far.
Wagner, Barnes, Lim, and Ferris (2012) drew on Ego Depletion Theory to argue a
relationship between sleep quantity and quality and employee cyberloafing. They
suggested that sleep deprivation impaired employees’ ability to recover depleted
resources and in turn left them with fewer self-control resources to resist temptations to
cyberloaf while at work. Consistent with their expectations, they found that employees

35

cyberloafed (e.g., visited Facebook, ESPN, YouTube, and other non-work related
websites) more frequently following the switch to daylight saving time.
Perhaps in a more direct test of Ego Depletion Theory, Christian and Ellis (2011)
found that depletion due to poor sleep quality had a direct effect on workplace deviance
in a sample of nurses. In another study testing Ego Depletion Theory, Barnes, and
coauthors (2011) posited that employees are more likely to engage in unethical behavior
when their self-control resources are diminished. Across several studies they found that
impaired self-control due to insufficient sleep was positively related to unethical
behavior. Similar findings were reported by Gino, Schweitzer, Mead, and Ariely (2011)
who conducted a number of experiments and found that depletion of self-regulatory
resources increased individuals’ propensity to behave dishonestly. Resisting unethical
behavior requires self-control resources but depleted individuals have fewer resources left
to identify and refrain from unethical acts. Finally, findings by Thau and Mitchell (2010)
suggest that supervisor abuse depletes employees’ self-regulatory resources and may
result in employee work deviance.
Ego Depletion Theory suggests that depletion is likely to have a broad range of
effects in the workplace (Baumeister et al., 1998; DeWall et al., 2008; DeWall,
Baumeister, Mead, & Vohs, 2011; Muraven & Baumeister, 2000). Thus, focusing only on
counterproductive work behaviors as consequences of depletion is limiting because other
important work activities require self-control resources as well. For example, theory
would suggest that depletion is likely to affect work engagement and creativity because
these activities require resources. Work engagement has been defined as the
“simultaneous investment of personal energies in the experience or performance of work”

36

(Christian, Garza, & Slaughter, 2011, p. 95). Employees who are engaged experience
high levels of energy, are enthusiastically involved in their jobs, and are motivated to
strive towards challenging goals (Bakker & Leiter, 2010). Energy, however, is an
expendable resource and deleted individuals possess less of it (Baumeister, Muraven, &
Tice, 2000). Consequentially, depleted individuals may not have the necessary resources
to experience engagement at work.
Work engagement has been shown to fluctuate daily and researchers have argued
that personal resources predict daily work engagement (e.g., Bakker, 2011; Kahn, 1990;
Sonnentag, 2003; Sonnentag, Mojza, Demerouti, & Bakker, 2012). For example,
Sonnentag, Dormann, and Demerouti, (2010) presented a theoretical model of daily work
engagement and identified resource level as a predictor of daily work engagement. In
addition, empirical work suggests that replenishment of resources affects daily work
engagement suggesting that self-regulatory resources are an important predictor of work
engagement (e.g., Sonnentag, 2003; Sonnentag et al., 2012). Although a direct
relationship between state depletion and work engagement has yet to be investigated,
there are theoretical reasons to expect that state depletion will be negatively related to
leaders’ daily work engagement. This research question has practical implications for
leaders and organizations because work engagement has been shown to impact OCB and
task performance (e.g., Christian et al., 2011; Crawford, LePine, & Rich, 2010).
In addition to work engagement, state depletion is also likely to impair leader
creativity. Creativity refers to “coming up with fresh ideas for changing products,
services, and processes so as to better achieve the organization’s goals” (Amabile,
Barsade, Mueller, & Staw, 2005, p. 367). Creativity has been shown to fluctuate daily

37

(Amabile et al., 2005; Binnewies & Wörnlein, 2011; Ohly & Fritz, 2010; To, Fisher,
Ashkanasy, & Rowe, 2012) and Ego Depletion Theory suggests that creative endeavors
require energy and that state depletion may impair daily creativity (Baumeister,
Schmeichel, DeWall, & Vohs, 2007; Baumeister & Tierney, 2011). For example,
Baumeister (2005, p. 82) stated that “Conscious, controlled processes offer great
flexibility, enabling people to deal in thoughtful, creative ways with a remarkable broad
range of events and circumstances. But they are expensive, in the sense that they require
energy and effort.” In one of the few studies on creativity and state depletion, Baumeister
and colleagues (2007, Study 4) found that depleted participants generated less creative
solutions to a particular problem compared to participants who were not depleted. These
preliminary findings indicate that state depletion may hinder daily creativity.
Creativity has important implications for leaders because leaders need to engage
in creative problem solving to facilitate the fulfillment of group and organizational goals
(Morgeson et al., 2010; M. D. Mumford, Zaccaro, Harding, Jacobs, & Fleishman, 2000).
Thus, creativity ought to be studied as a consequence of state depletion for practical
reasons too. Illustrating this point, a recent survey of 1,500 chief executives conducted by
IBM's Institute for Business Value demonstrated that CEOs selected creativity as the
most important leadership competency for future successful organizational performance
(Kern, 2010). Similarly, endorsing the sentiment that creativity is an important leader
behavior, Dyer, Gregersen, & Christensen, (2011, p. 7) stated that “Innovative companies
are almost always led by innovative leaders. Let us say this again: Innovative companies
are always led by innovative leaders. The bottom line: if you want innovation, you need
creativity skills within the top management team of your company.” Surprisingly,

38

however, very little research has studied leader creativity and most studies have focused
on non-managerial employees (e.g., Amabile et al., 2005; Binnewies & Wörnlein, 2011;
Ohly & Fritz, 2010; To et al., 2012). This is a serious omission given the theoretical and
practical relevance of leader creativity.
To summarize, Ego Depletion Theory suggests that regulatory resources are likely
to predict both work engagement and creativity. Consequentially, I expect state depletion
to be negatively related to these two outcomes. The following two hypotheses reflect
these expectations:
Hypothesis 7: State depletion will be negatively related to daily work
engagement.
Hypothesis 8: State depletion will be negatively related to daily creativity.
So far, I have argued that reactive helping will induce state depletion in leaders,
which means that leaders will possess fewer self-regulatory resources to devote to other
important work activities. For example, work engagement and creativity represent
important leader activities that require self-regulatory resources. In order to experience
work engagement, leaders need self-regulatory resources to become absorbed and
experience vigor at work. Similarly, to come up with novel solutions to problems leaders
need self-regulatory resources because these facilitate cognitive flexibility. Creativity,
therefore, is likely to also be sensitive to diminished regulatory resources.
The conceptual arguments presented thus far suggest that reactive helping acts
deplete self-regulatory resources, and consequentially diminish work engagement and
creativity. State depletion, therefore, is expected to mediate the negative effects of
reactive helping on work engagement and creativity. These expectations are consistent

39

with one of the main premises of Ego Depletion Theory that investment of regulatory
resources on some behaviors will leave fewer resources for other activities (Baumeister et
al., 2000). Consistent with the above arguments, I propose the following hypotheses:
Hypothesis 9: State depletion will mediate the effects of daily task-related
helping on a) daily work engagement, and b) daily creativity.
Hypothesis 10: State depletion will mediate the effects of daily personal helping
on a) daily work engagement, and b) daily creativity.
Moderators of the Effects of Depletion on Its Consequences: Perceived Prosocial
Impact of Helping
There is evidence that leaders monitor their success, and success of the helping
effort should matter, but this has been largely ignored in the helping literature to date. For
example, the helpers in Kaplan and Cowen’s (1981) sample perceived themselves to be
moderately effective (Mean = 5.5 on an 8 point scale) and felt that they were
encouraging, supportive, and sympathetic. Some leaders, however, also reported feeling
puzzled, helpless, uncomfortable, and frustrated and rated helping followers with certain
personal problems (e.g., marital) as particularly difficult to handle. With few exceptions
(Barnes et al., 2008; Glomb et al., 2011; Weinstein & Ryan, 2010), most of the helping
research has looked at antecedents of helping and little research has considered helping
outcomes, especially perceptions about helping effectiveness (Clary & Orenstein, 1991).
Perceptions related to helping experiences, however, are likely to affect the extent to
which leaders’ state depletion affects their other work behaviors.
Perceived prosocial impact captures employees’ subjective beliefs of whether or
not their help benefited others at work (Grant & Campbell, 2007). Focusing attention on

40

how actions facilitate positive outcomes reduces leaders’ attention to the negative aspects
of helping (e.g., Grant & Campbell, 2007; Grant & Sonnentag, 2010). This is important
because focusing on negative aspects of one’s work leads to reduced psychological
resources and energy (Fritz & Sonnentag, 2006) as well as to negative emotions, whose
management requires further exertion of self-control resources (Baumeister, Vohs, et al.,
2007). Consideration of positive aspects of one’s work, on the other hand, has the
potential to help regain lost resources or to acquire new resources (Fritz & Sonnentag,
2006). For example, Grant and Campbell (2007) found that perceived prosocial impact
protected against burnout (a marker of depletion). Similarly, Grant and Sonnentag (2010)
found that prosocial impact buffered against emotional exhaustion associated with
negative task evaluations. They argued that prosocial impact compensates for negative
aspects of work task. Building on these arguments and findings, it is possible that
perceptions of impacting others through one’s help may safeguard leaders’ regulatory
resources because they divert leaders from dwelling on the negative aspects of helping
(e.g., effort) and motivate leaders to focus on the positive aspects of one’s help instead
(e.g., benefiting others) (Grant & Sonnentag, 2010).
Sonnentag and Grant (2012) conceptualized perceived prosocial impact as a
positive affective work event. Recently, scholars have argued theoretically and shown
empirically that positive work events energize employees. For example, Gross and
coauthors (2011) found that positive events were negatively related to end-of-work
fatigue (defined by the authors as an outcome of depleted resources) for employees who
experienced chronic social stressors such as interpersonal tension with others. It is
possible; therefore, that helping events that are high in prosocial impact may energize

41

employees and may restore depleted resources. This expectation is consistent with
arguments by Ego Depletion Theory that positive experiences facilitate self-regulation
even when the self is depleted (Tice et al., 2007).
Perceptions of prosocial impact are inherently tied to the type of tasks that one
performs, and researchers have studied perceptions of prosocial impact across different
jobs (Grant, 2012; Sonnentag & Grant, 2012). For example, Sonnentag and Grant (2012)
studied perceptions of prosocial impact in a sample of firefighters and rescue workers;
Grant (2012) examined perceptions of prosocial impact in a sample of governmental
employees; whereas Grant and Campbell (2007) investigated perceptions of prosocial
impact in samples of transportation service employees, secretaries, and school teachers.
Clearly, perceptions of prosocial impact are specific to one’s work behaviors.
Similarly, because task related helping and personal helping are unique constructs
that deal with distinct types of employee problems (Dudley & Cortina, 2008), perceptions
of prosocial impact ought to be specific to the helping type. For example, a leader may
perceive that his or her help with task-related problems was particularly effective on a
given day, but that helping subordinates with personal problems was not. A global
evaluation of prosocial impact of helping would not capture these important nuances. For
these reasons, I differentiate between prosocial impact perceptions for task-related
helping, which refers to one’s perceptions that helping with task-related problems was
beneficial to subordinates; and prosocial impact perceptions for personal helping, which
refers to one’s perceptions that helping with personal problems was beneficial to
subordinates. Perceptions of prosocial impact for task-related helping and perceptions of
prosocial impact for personal helping are concordant constructs with task-related and

42

personal helping respectively, compared to a global evaluation of one’s perceived
prosocial impact of helping. Consideration of these two types of prosocial impact
perceptions ought to provide a clearer understanding of the effects that depletion has on
work outcomes.
In sum, perceptions of prosocial impact for task-related helping and perceptions
of prosocial impact for personal helping should moderate the effects of depletion on work
engagement and creativity because these perceptions encourage people to focus on the
positive aspects of task-related and personal helping (Grant & Sonnentag, 2010) and
because they constitute positive work experiences. Focusing on the positive aspects of
one’s work safeguards resources, whereas positive experiences facilitate and improve
self-regulation when resources are diminished (Tice et al., 2007). In line with these
arguments, I posit the following hypotheses:
Hypothesis 11: Daily perceived prosocial impact of task-related helping will
moderate the effect of state depletion on a) daily work engagement and b) daily
creativity such that these relations will be weaker when perceived prosocial
impact of task-related helping is high versus low.
Hypothesis 12: Daily perceived prosocial impact of personal helping will
moderate the effect of state depletion on a) daily work engagement and b) daily
creativity such that these relations will be weaker when perceived prosocial
impact of personal helping is high versus low.
Moderator of the Effects of State Depletion on Its Consequences: Trait Self-Control
Because some individuals have more self-control resources than others (Tangney,
Baumeister, & Boone, 2004), the effects of state depletion on daily work engagement and

43

creativity may depend on trait self-control. Hagger and colleagues (2010) argued that
individuals who are high in trait self-control are likely to have more resources remaining
after engaging in depleting tasks. The authors stated that trait self-control may “moderate
the ego-depletion effect” (p. 500).
Whereas Hagger et al. (2010) could not test this effect in their meta-analysis,
there is some empirical evidence that trait self-control may serve as a buffer against state
depletion. For example, Muraven, Collins, Shiffman, and Paty (2005) studied the effect
of daily self-control demands on alcohol intake and found that people who were high in
trait self-control were better at managing their drinking levels after a high level of daily
self-control demands. Similarly, DeWall, Baumeister, Stillman, and Gailliot (2007)
studied the effects of state depletion on aggression and found that the effect was
moderated by trait self-control. Participants who were high in trait self-control were less
likely to express intentions of responding aggressively.
Overall, these studies suggest that individuals high in self-control have a larger
amount of resources at their disposal. Thus, although engaging in reactive helping is
likely to be associated with state depletion, leaders high in trait self-control are also likely
to have a more extended pool of resources at their disposal (Hagger et al., 2010), which
may protect their other activities from the negative effects of state depletion. Consistent
with these arguments, I propose the following:
Hypothesis 13: The negative association between sate depletion and daily work
engagement will be weaker for leaders who are high versus low in trait-state
control.

44

Hypothesis 14: The negative association between sate depletion and daily
creativity will be weaker for leaders who are high versus low in trait-state
control.
Summary and Contributions
This dissertation aims to make a number of theoretical contributions. First, it
contributes to research on helping. Whereas most organizational research has studied the
effects of helping on the receiver and the workgroup, little work has studied the effects of
helping on the helper (Spitzmuller et al., 2008). In addition, most research has studied
proactive helping, which is volitional and employees may decide to help when they have
the necessary resources or to fulfill certain needs (e.g.,Weinstein & Ryan, 2010).
However, little is known about how helping in response to proactive requests for
assistance affects the helper. This is relevant because theory suggests that helping may
deplete the helper and may harm his or her performance on other tasks (DeWall et al.,
2008; Gailliot, 2010). This dissertation is the first to investigate this possibility for
leaders.
Second, many interpersonal proactive behaviors are regarded as valuable
employee initiatives (Grant & Ashford, 2008). For example, management scholars have
asserted that in-role performance is not sufficient anymore, but that “organizational
survival and success depend on proactivity” (Grant et al., 2009, p. 31). Many of these
proactive initiatives often target others who are expected to perform reactive behaviors,
but it is unclear how responding to proactive behaviors may affect respondents. It is
possible that responding to proactive behaviors may tax employees’ self-regulatory

45

resources and deter them from other perhaps more important work tasks. I investigate
these possibilities here by studying reactive helping in response to proactive help seeking.
Third, this dissertation contributes to research on leadership by utilizing Ego
Depletion Theory to make a set of novel predictions about the effects of reactive helping
on leaders’ self-regulatory resources and other work behaviors. Management research has
recently started to apply Ego Depletion Theory to work contexts but this research has
taken a rather narrow approach by primarily focusing on how depletion impairs people’s
ability to refrain from unethical or abusive behaviors (e.g.,Christian & Ellis, 2011; Mead,
Baumeister, Gino, Schweitzer, & Ariely, 2009; Thau & Mitchell, 2010). This dissertation
provides a broader application of Ego Depletion Theory by considering other relevant
work activities such as work engagement and creativity, which are important because
they contribute to effective work performance (Christian et al., 2011; Rich, Lepine, &
Crawford, 2010).
Fourth, this dissertation relies on a method that allows study of a temporal
phenomenon like state depletion. More specifically, I use an experience sampling
methodology (Wheeler & Reis, 1991) and hierarchical linear modeling (HLM,
Raudenbush & Bryk, 2002) to test the hypotheses that are posited in this dissertation.
HLM is appropriate because it accounts for the non-independence of data and it allows
individuals to serve as their own control, thus eliminating unmeasured betweenindividual confounds. Last, the practical implications of this dissertation may be
substantial. If reactive helping is shown to deplete leaders, then leaders and organizations
need to become aware of ways in which they can prevent or counteract depletion
associated with reactive helping.

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METHODS
Sample
The sample consisted of 77 mid to high level managers enrolled in a weekend
MBA course. Seventy eight percent of participants (61 people) were male. The ethnic
composition of the sample was as follows: 66% of participants (51 people) were white,
13% (10) were Asian, 13 % (10) were Black or African American, 5% (4) were Hispanic
or Latino, and 2 participants selected “Other” as their ethnicity. The average age of
participants was 38.6 years (SD = 7.8). Their average experience in managerial or
supervisor positions was 9.8 years (SD = 6.8), average tenure in current job was 3.9 years
(SD = 3.4), and average experience in current occupational domain was 13.7 years (SD =
7.3). Participants had on average 7 subordinates (SD = 6) and they worked an average of
52 (SD = 9.1) hours a week. Participants held a variety of occupational positions such as
director of global marketing, research manager, regional operations director, sales
manager, human resources manager, chief financial officer, and hospital president among
others. Of the 77 leaders, 74 provided sufficient data to be included in multilevel
analyses.
Procedure
I designed an experience sampling study for theoretical and empirical reasons.
With regards to theoretical reasons, Ego Depletion Theory specifically recognizes that
state depletion is a temporal phenomenon that varies within people and across times and
circumstances (Muraven & Baumeister, 2000). Because one’s ability to self-regulate
varies daily, ESM is an appropriate methodology. Empirically, ESM is appropriate

47

because prior research has shown that the focal variables considered in this dissertation
vary daily (e.g., Glomb et al., 2011; Grant & Sonnentag, 2012; Sonnetag, 2003).
I used an experience sampling method to collect data over three work weeks.
Reis and Wheeler (1991) suggested that a two work-week period is likely to represent an
accurate portrayal of social interactions. More specifically, the authors stated that “the 2week record-keeping period is assumed to represent a stable and generalizable estimate of
social life” (p. 287). Recently, however, scholars have expanded the timeframe for
experience sampling studies to three work weeks (see Bono, Glomb, Shen, Kim, & Koch,
in press). In order to ascertain whether a two or three week timeframe was appropriate for
the current study, I conducted a search in Web of Science for experience sampling studies
published in the Academy of Management Journal, Journal of Applied Psychology, and
Personnel Psychology for 5 years (2008-2012) using the keywords “experience
sampling,” “diary,” and “within person.” I also manually searched the in-press articles (in
press as of May 15th 2012) of these three journals.
The search yielded 24 published articles that used experience sampling
methodology. The average number of work days in the study designs of these articles
was 10.9 with a standard deviation of 6.4 days. The average frequency of daily
measurement was 2.5 with a standard deviation of 1.4 measurements. These trends
support my expectations that experience sampling of 15 work days with 2 daily
measurements is within the accepted bounds of publishing standards of similar studies in
these three journals.
I collected data using online Qualtrics surveys. Approximately one week prior to
the start of the daily surveys, I emailed leaders a one-time survey, which measured

48

demographics and stable differences (trait self-control, helping breadth, and helping
efficacy). In the one-time survey, I also asked leaders to provide contact information for
up to five subordinates. I started the daily surveys approximately a week after the onetime survey. I emailed leaders two surveys each day for 15 consecutive working days. I
sent the morning survey at 7 AM and the afternoon survey between 3:45 and 4 PM. The
morning survey measured state depletion, positive affect, and negative affect. The
afternoon survey measured task-related reactive helping, personal reactive helping,
prosocial impact for task-related reactive helping, prosocial impact for personal reactive
helping, state depletion, daily work engagement, and daily creativity. On those same 15
work days, I also emailed a survey to leaders’ subordinates between 3:45 and 4:00 PM
each day. In this survey, I measured subordinates’ ratings of leaders’ work engagement
and creativity on that day. Ratings of work engagement and creativity from subordinates
ought to alleviate issues of common method bias (P. M. Podsakoff, MacKenzie, Lee, &
Podsakoff, 2003).
After the data collection was complete, each leader received an individualized
feedback report, which displayed how their mood and helping behaviors varied over the
15 days relative to the class average. These data and trends were also discussed in class.
After the data collection was complete, followers were entered in a raffle to win $25. One
in 10 subordinates won $25 and checks were mailed to winners approximately two
months after the data collection was completed. Participation was completely voluntary
in that leaders and followers could quit completing the surveys at any point if they so
desired. The data was also confidential in that leaders had access only to their own
responses. All other information presented to leaders was aggregated either at the leader

49

(e.g., their subordinate ratings) or class level and was not identifiable. Table 1 depicts the
data collection procedure.
Table 1
Data Collection Schedule

Data Collected
Timeline
Source

Phase 1
Stable Differences
1 time
Leader

Phase 2
Daily Surveys
15 work days
Leader (twice a day)
Followers (once a day)

Over the three study weeks, leaders completed 68% of the morning surveys and
65% of the afternoon surveys. On average, leaders completed the morning survey at 9:06
AM (SD = 1:41 hrs.), and the afternoon survey at 5:42 PM (SD = 1:47 hrs.). The average
time elapsed between completion of the morning and afternoon survey was 8:26 hrs. (SD
= 2:15 hrs.). Over the same 15 work days, subordinates completed 76% of the daily
surveys. On average, subordinates completed the afternoon survey at 4:24 PM (SD = 1:30
hrs.). These response rates are similar to published experience sampling studies (Daniels,
Boocock, Glover, Hartley, & Holland, 2009; Scott & Barnes, 2011; Scott, Colquitt,
Paddock, & Judge, 2010).
One-Time Measures
Task-related reactive helping breadth: I measured task-related reactive helping
breadth with three items adapted from McAllister and coauthors (2007). Following
procedures described by McAllister et al. (2007), participants were shown the three items
for task-related reactive helping and were directed to indicate whether each statement
reflected an expected part of their job. An example item for task-related reactive helping
breadth is: “This behavior is an expected part of my job: Helping subordinates who ask

50

for my help with difficult assignments.” The response format was on a five point scale (1
= “Strongly Disagree” 5 = “Strongly Agree”). Coefficient alpha for task-related helping
breath was α = 0.90.
Task-related reactive helping efficacy: to measure task-related reactive helping
efficacy, I used procedures described by McAllister and coauthors (2007). Participants
rated how efficacious they felt to perform each of the task-related reactive helping items
(response format was: 1 = “Strongly Disagree” 5 = “Strongly Agree”). An example item
for task-related reactive helping efficacy is: “I am completely confident in my capabilities
when engaging in this behavior: Helping subordinates who ask for my help with difficult
assignments.” Coefficient alpha for task-related helping efficacy was α = 0.89.
Personal reactive helping breadth: following procedures described by McAllister
and coauthors (2007), I measured personal reactive helping breadth with three items.
Participants were shown the three items per personal reactive helping and were asked to
indicate their agreement on a five-point response format where 1 = “Strongly Disagree”
and 5 = “Strongly Agree.” An example item for personal reactive helping breath is: “This
behavior is an expected part of my job: Helping subordinates who come to me with their
personal problems and worries.” Coefficient alpha for personal reactive helping breadth
was α = 0.83.
Personal reactive helping efficacy: I measured personal reactive helping efficacy
with three items following procedures by McAllister and coauthors (2007). An example
item for personal helping efficacy is: “I am completely confident in my capabilities when
engaging in this behavior: Helping subordinates who come to me with their personal
problems and worries.” Participants indicated their agreement on a five point scale, where

51

1 = “Strongly Disagree” and 5 = “Strongly Agree.” Coefficient alpha for personal helping
efficacy was α = 0.88.
Prosocial motivation: in this dissertation, prosocial motivation refers to
contextual prosocial motivation, which is a relatively stable perception. Grant and Berry
stated that (2011, p. 3): “Contextual prosocial motivation refers to an employee’s desire
to benefit a particular category of other people through a particular occupation, job, or
role.” Hence, the prosocial motivation items used in this dissertation reflect leaders’
prosocial motivation to help their subordinates who seek their help at work. This
approach is consistent with published operationalizations of prosocial motivation (Grant,
2008). Furthermore, research indicates that those who help with task-related problems
also help with personal issues (Settoon & Mossholder, 2002). For this reason, I collected
one overall prosocial motivation measure.
I measured prosocial motivation with three items adapted from Grant (Grant,
2006, 2008). Items were changed to reflect leaders’ prosocial motivation to respond to
their subordinates’ proactive requests for help. An example item is: “Overall, why are
you motivated to help subordinates who ask for your help? Because I care about
benefiting my subordinates through my help.” Participants responded to each item using
a five-point response format where 1 = “Strongly Disagree” and 5 = “Strongly Agree.”
Coefficient alpha for prosocial motivation was α = 0.88.
Trait self-control: ten (out of 13) items from the brief version of the scale
developed by Tangney, Baumeister, and Boone, (2004) were used to measure trait selfcontrol. I decided not to include the following three items: “I am lazy,” “I say
inappropriate things,” and “Sometimes I can’t stop myself from doing something, even if

52

I know it is wrong.” I removed these items because they sounded pejorative and had the
potential to offend or upset respondents as well as to deter participation. Participants
indicated their agreement with each of the other 10 statements along a five-point response
format where 1 = “Strongly Disagree” and 5 = “Strongly Agree.” An example item is: “I
am good at resisting temptation.” Internal consistency as indicated by coefficient alpha
was α = 0.79.
In their scale validation piece, Tangney and coauthors (2004) showed empirically
that trait self-control is a distinct construct from the Big Five personality traits (Goldberg,
1990, 1992). A closer look at the construct definitions of trait self-control and the Big
Five personality traits, however, suggests that trait self-control is conceptually similar to
conscientiousness and emotional stability. Trait self-control refers to people’s capacity
and willingness to change how they think, feel, or behave in ways that are appropriate for
a given situation. Conscientiousness refers to people’s tendencies to be dependable and
responsible. Costa and coauthors suggested that consciousness encompasses aspects of
both proactivity and inhibition, in that highly conscientious people are competent,
orderly, and dutiful on one hand, as well as achievement oriented, self-disciplined, and
deliberate in their actions on the other hand (Costa Jr, McCrae, & Dye, 1991). Hence,
similar to people who are high in trait self-control, people who are high in consciousness
are able to exert control over their environment (e.g., the aspect of order) as well as
themselves (e.g., self-discipline). Conceptually, therefore, trait self-control and
conscientiousness share similarities.
Despite their similarities, trait self-control is conceptually and empirically distinct
from conscientiousness. For example, different from conscientiousness, trait self-control

53

captures people’s ability to change and adapt the self to their environment.
Conscientiousness, however, refers mostly to people’s tendency to maintain order in their
surrounding environment. Adaptability of the self, therefore, is a unique aspect of trait
self-control not captured by conscientiousness. In support of the idea that trait selfcontrol and conscientiousness are distinct constructs, Tangney and coauthors (2004)
showed that trait self-control and conscientiousness share only about 25 percent of their
variance (correlation between self-control and conscientiousness was r = .48).
Trait self-control is also conceptually similar to emotional stability because the
latter refers to people’s ability to exert control over their moods and emotional reactions
(Costa & McCrae, 1987). Trait self-control, however, is a broader construct that
encompasses not only the self’s ability to control emotions but also the self’s ability to
control thought and behavior. In support of their construct distinctiveness, Tangney et al.
(2004) reported a correlation of only r =.42 between trait self-control and emotional
stability. These arguments suggest that trait self-control is a distinct construct from
conscientiousness and emotional stability and ought to have unique moderating effects on
the relations between state depletion and its consequences.
Work experience: it is possible that work experience may affect the extent to
which reactive helping is associated with state depletion. Ego Depletion Theory suggests
that experience with an activity that requires self-control (e.g., dieting) improves people’s
ability to exert self-control over time (Muraven, Tice, & Baumeister, 1998). Experienced
leaders may have helped subordinates with similar problems before and may become less
depleted by helping episodes than leaders who have less experience. Work experience,
therefore, may moderate the extent to which task-related and personal reactive helping

54

are associated with state depletion such that these effects may be weaker for leaders with
high versus low work experience. Although not a formal part of my model, I explored
these possibilities in post-hoc analyses.
To do so, I measured work experience including experience as a leader and
experience in current role. I measured experience as a leader was with one item adapted
from work by Stam and Elfring (2008): “How many years have you held
managerial/supervisory positions?” I measured experience in the current role with the
following item adapted from Jokisaari and Nurmi (2009): “How many years have you
worked in your current job.” The items for all one-time measures are listed in Table 2.
Table 2
One-Time (dispositional) Measures
Task-Related Helping Breadth:
This behavior is an expected part of my job:
1. Helping subordinates who ask for my help with difficult
assignments.
2. Helping subordinates who ask for my help with heavy
work loads.
3. Going out of my way to help subordinates who ask for my
help with work-related problems.

McAllister et al.,
(2007)
1= strongly disagree
2= disagree
3 = neither agree nor
disagree
4 = agree
5 = strongly agree

Task-Related Helping Efficacy:
I am completely confident in my capabilities when engaging in
this behavior:
1. Helping subordinates who ask for my help with difficult
assignments.
2. Helping subordinates who ask for my help with heavy
work loads.
3. Going out of my way to help subordinates who ask for my
help with work-related problems.

McAllister et al.,
(2007)
1= strongly disagree
2=disagree
3 = neither agree nor
disagree
4 = agree
5 = strongly agree

55

Table 2 (cont’d)
Personal Helping Breadth:
This behavior is an expected part of my job:
1. Helping by listening to subordinates who come to me
because they have to get something off their chest.
2. Helping subordinates who come to me with their personal
problems and worries.
3. Helping by taking a personal interest in subordinates who
ask for my help with personal problems.

McAllister et al.,
(2007)
1= strongly
disagree
2= disagree
3 = neither agree
nor disagree
4 = agree
5 = strongly agree

Personal Helping Efficacy:
I am completely confident in my capabilities when engaging in this
behavior.
1. Helping by listening to subordinates who come to me
because they have to get something off their chest.
2. Helping subordinates who come to me with their personal
problems and worries.
3. Helping by taking a personal interest in subordinates who
ask for my help with personal problems.

McAllister et al.,
(2007)
1= strongly
disagree
2= disagree
3 = neither agree
nor disagree
4 = agree
5 = strongly agree

Prosocial Motivation:
“Why are you motivated to help subordinates who ask for your
help?”
1. Because I care about benefiting my subordinates through my
help.
2. Because I want to have a positive impact on my
subordinates through my help.
3. Because it is important to me to do good for my
subordinates through my help.

Adapted from
Grant (2008)
1= strongly
disagree
2 = disagree
3 = neither agree
nor disagree
4 = agree
5 = strongly agree

Work Experience:
Experience as a leader: “How many years have you held
managerial/supervisory positions?”
Experience in current role (job experience): “How many years
have you worked in your current job?”

Stam and Elfring
(2008)

56

Jokisaari and
Nurmi (2009)

Table 2 (cont’d)
Brief Self-control scale:
1. I am good at resisting temptation.
2. I have a hard time breaking bad habits (R).
3. I do certain things that are bad for me, if they
are fun (R).
4. I refuse things that are bad for me.
5. I wish I had more self-discipline (R).
6. People would say that I have iron selfdiscipline.
7. Pleasure and fun sometimes keep me from
getting work done (R).
8. I have trouble concentrating (R).
9. I am able to work effectively toward long-term
goals.
10. I often act without thinking through all the
alternatives (R).
(R) Reversed Items

Tangney, Baumeister, and
Boone, (2004)
1= very slightly or not at all
2 = a little
3 = moderately
4 = quite a bit
5 = very much

Daily Measures
State depletion: I used five items from the State Self-Control Scale (Twenge,
Muraven, & Tice, 2004) to measure morning and afternoon state depletion. Higher scores
on this scale indicate that self-resources are depleted. Participants responded to these
items along a five-point response format where 1 = “Very slightly or not at all” and 5 =
“Very much.” An example item is: “I feel drained right now.” Average coefficient alpha
for morning state depletion was α = 0.92, and average coefficient alpha for afternoon
state depletion was α = 0.93.
Reactive helping: selection of the helping items used in this dissertation was
driven by two considerations. First, I did not want to overburden participants with long
surveys because they had to complete surveys at work twice a day, once in the morning
and once in the afternoon. Keeping the surveys short had the potential to improve the

57

response rate as well as the data quality. Thus, I decided to use three items for taskrelated reactive helping and three items for personal reactive helping.
Second, I selected helping items that most accurately capture the literature’s
definitions for task-related helping and personal helping (e.g., Cortina & Dudley, 2008).
As an example, I decided to exclude the following items by Settoon and Mossholder
(2002): “Make an extra effort to understand the problems faced by coworkers” and
“Show coworkers where to go to get what they need.” Settoon and Mossholder (2002)
argued that the item “Make an extra effort to understand the problems faced by
coworkers” refers to personal helping, whereas the item “Show coworkers where to go to
get what they need” refers to task-related helping. These items are ambiguous, however,
in that they may refer to helping with both task-related and personal issues. In order to
improve the conceptual and empirical distinctiveness of the two helping types, I dropped
ambiguous items such as these and decided to use items that are clearly task-related (e.g.,
Today, I helped subordinates who asked for my help with difficult assignments), or
personal (e.g., Today, I helped subordinates who came to me with their personal
problems and worries) (adapted from Settoon & Mossholder, 2002).
Reactive task-related helping: I adapted three items from the scale developed by
Settoon and Mossholder, (2002) to measure reactive task-related helping. Items were
changed to reflect daily task-related helping acts, as well as to capture reactive taskrelated helping acts only. Each afternoon, participants were asked to indicate the
frequency with which they had engaged in task-related helping behaviors at work that
day. Daily frequency was measured on a 6 point scale where 1 = “Never” and 6 = “Five
or more times.” An example item is: “Today, I helped subordinates who asked for my

58

help with difficult assignments.” Average internal consistency of task-related helping
across all study days as indicated by Cronbach’s alpha was α = 0.91.
Reactive personal helping: I measured reactive personal helping with three items
adapted from Settoon and Mossholder (2002). Similar to task-related helping, each
afternoon participants indicated the frequency with which they had helped with personal
issues at work that day. Items were adapted to reflect reactive beaviors. An example item
is: “Today, I helped subordinates who came to me with their personal problems and
worries.” The response format ranged from 1 “Never” to 6 “Five or more times.”
Average reliability of the scale was α = 0.93.
Prosocial impact of reactive helping: I measured state prosocial impact for task
related reactive helping with three items initially developed by Grant (2006) and recently
published by Sonnentag and Grant (2012). After responding to the helping items,
participants were asked to provide an evaluation of the prosocial impact of those helping
acts. A sample item is: “I feel that my help with the above issues made a positive
difference in subordinates’ lives today.” Participants indicated their agreement with these
three items along a five point scale where 1 = “Strongly Disagree” and 5 = “Strongly
Agree.” Average internal consistency for prosocial impact of reactive task-related
helping was α = 0.95; and average internal consistency for prosocial impact of reactive
personal helping was α = 0.97.
Work engagement: I adapted six items from the scale developed by Rich and
coauthors (2010) to measure daily work engagement. Compared to other measures of
work engagement, this scale operationalizes Kahn’s (1990) conceptualization of work
engagement as investment of physical, cognitive, and emotional energy at work (Rich et

59

al., 2010). Kahn’s (1990) conceptualization dovetails well with Ego Depletion Theory’s
predictions that depleted resources are likely to hinder one’s ability to be physically,
cognitively, and emotionally vested in other activities. Hence, two items from each of
these three dimensions were used to measure work engagement. Items were rated by
leaders’ subordinates each afternoon. Leaders’ subordinates expressed their agreement
with each item along a five-point scale (1 = “Strongly Disagree” to 5 = “Strongly
Agree”). An example item is: “Today, John Doe exerted a lot of energy on the job.”
Average coefficient alpha was α = 0.93.
In return for participating in this study, each leader received a personalized
feedback report, which contained information about how his or her behavior compared to
the average of the other leaders. To increase self-awareness, I also provided leaders with
information about how their own assessment of daily work engagement and creativity
differed from ratings by subordinates. Self-ratings of work engagement were collected
from leaders with the same 6 items as above. An example item is: “Today, I exerted a lot
of energy on the job.” Average coefficient alpha for self-ratings of work engagement was
α = 0.89. Self-ratings of daily work engagement are discussed in post-hoc analyses.
Creativity: I measured daily creativity with four items adapted from Zhou and
George (2001). Each afternoon, subordinates rated their supervisor’s daily creativity by
indicating their agreement with items such as “Today, Jane Doe came up with creative
solutions to problems” (response format was 1 = “Strongly Disagree” to 5 = “Strongly
Agree”). Average coefficient alpha was α = 0.95. Each afternoon, leaders also rated their
own creativity with the same four items. The average coefficient alpha for self-ratings of
daily creativity was α = 0.93.

60

Positive and negative affect: the literature identifies state positive and negative
affect as important antecedents of daily creativity (Amabile et al., 2005; Binnewies &
Wornlein, 2011; To et al., 2012). Therefore, in order to assess a cleaner relation between
state depletion and daily creativity, morning state positive and negative affect were
entered as control variables in the equations predicting daily creativity. State positive and
negative affect were each measured in the morning with four items from the short form of
the PANAS scale (Mackinnon et al., 1999). Leaders were asked to indicate the extent to
which certain emotions captured how they felt at that moment, on a scale of 1 to 5, where
1 = “Very slightly or not at all” and 5 = “Very much.” Sample items for state positive
affect are “inspired” and “excited” and sample items for negative affect are “distressed”
and “upset.” The average internal reliability across all study days for state positive affect
was α = 0.93, and the average internal reliability for state negative affect was α = 0.82.
Helping difficulty: task difficulty has been shown to relate to depletion, but the
nature of this relationship is unclear (Hagger et al., 2010). It is possible, for example,
that helping difficulty may affect the extent to which reactive helping is associated with
state depletion. To explore these possibilities in post-hoc supplementary analyses, I
measured perceptions of helping difficulty for task-related and personal reactive helping
with the following item: “On average, how difficult did you find helping your
subordinates with the above problems today?” Participants rated helping difficulty
(separately for task-related and personal) along a five point scale where 1 = “Not at all
difficult” and 5 = “Very difficult.” The items for the daily measures are presented in
Table 3.

61

Table 3
Daily (within-person) Measures
Measure

Positive Affect
1. Inspired
2. Excited
3. Enthusiastic
4. Determined

Source
Morning (Control) Measures
Items from (MacKinnon et
al., 1999)
1= very slightly or not at all
2 = a little
3 = moderately
4 = quite a bit
5 = very much

Negative Affect
1. Afraid
2. Nervous
3. Upset
4. Distressed

Items from (MacKinnon et
al., 1999)
1= very slightly or not at all
2 = a little
3 = moderately
4 = quite a bit
5 = very much

State Depletion:
1. I feel drained right now.
2. My mind feels unfocused right now.
3. Right now, it would take a lot of effort for me to
concentrate on something.
4. Right now, my mental energy is running low.
5. Right now, I feel like my willpower is gone.

Adapted from Christian
and Ellis (2011)
1 = very slightly or not at all
2 = a little
3 = moderately
4 = quite a bit
5 = very much

Afternoon Measures
State Depletion:
1. I feel drained right now.
2. My mind feels unfocused right now.
3. Right now, it would take a lot of effort for me to
concentrate on something.
4. Right now, my mental energy is running low.
5. Right now, I feel like my willpower is gone.

62

Adapted from Christian
and Ellis (2011)
1 = very slightly or not at all
2 = a little
3 = moderately
4 = quite a bit
5 = very much

Table 3 (cont’d)
Reactive Task-Related Helping:
1. Today, I helped subordinates who asked for my
help with difficult assignments.
2. Today, I helped subordinates who asked for my
help with heavy work loads.
3. Today, I went out of my way to help subordinates
who asked for my help with work-related
problems.

Adapted from Settoon and
Mossholder, (2002)
1 = Never
2 = Once
3 = Twice
4 = Three times
5 = Four times
6 = Five or more times

Prosocial Impact for Task-Related Helping:
1. I feel that my help with the above issues made a
positive difference in subordinates’ lives today.
2. I am very conscious of the positive impact that
my help with the above issues had on
subordinates today.
3. I am very aware of the ways in which my help
with the above issues benefited subordinates
today.

Adapted from Grant, (2008);
Sonnentag & Grant, (2012)
1= strongly disagree
2=disagree
3 =neither agree nor disagree
4 = agree
5 = strongly agree

Reactive Personal Helping:
Adapted from Settoon and
1. Today, I helped by listening to subordinates who Mossholder, (2002)
1 = Never
came to me because they had to get something off
2 = Once
their chest.
2. Today, I helped subordinates who came to me
3 = Twice
with their personal problems and worries.
4 = Three times
3. Today, I helped by taking an interest in
5 = Four times
subordinates who asked for my help with
6 = Five or more times
personal problems.
Prosocial Impact for Personal Helping:
1. I feel that my help with the above issues made a
positive difference in subordinates’ lives today.
2. I am very conscious of the positive impact that
my help with the above issues had on
subordinates today.
3. I am very aware of the ways in which my help
with the above issues benefited subordinates
today.

63

Adapted from Grant, (2008);
Sonnentag & Grant, (2012)
1= strongly disagree
2=disagree
3 =neither agree nor disagree
4 = agree
5 = strongly agree

Table 3 (cont’d)
Work Engagement:
1. Today, my supervisor exerted a lot of energy on
the job.
2. Today, my supervisor strived hard to complete
his/her job.
3. Today, my supervisor felt positive about his/her
job.
4. Today, my supervisor was excited about his/her
job.
5. Today, my supervisor was focused on his/her job.
6. Today, my supervisor was absorbed by his/her
job.

Items from Rich et al.,
(2010)
1= strongly disagree
2=disagree
3 =neither agree nor disagree
4 = agree
5 = strongly agree

Creativity:
1. Today, my supervisor came up with creative
solutions to problems.
2. Today, my supervisor exhibited creativity on the
job.
3. Today, my supervisor had a fresh approach to
problems.
4. Today my supervisor came up with new and
practical ideas to improve performance.

Creativity (adapted from
Zhou & George, 2001)
1= strongly disagree
2=disagree
3 =neither agree nor disagree
4 = agree
5 = strongly agree

Task-Related Helping Difficulty:
On average, how difficult did you find helping your
subordinates with the above problems today?

1= not at all difficult
2 = a little difficult
3 = moderately difficult
4 = quite difficult
5 = very difficult

Personal Helping Difficulty:
On average, how difficult did you find helping your
subordinates with the above problems today?

1= not at all difficult
2 = a little difficult
3 = moderately difficult
4 = quite difficult
5 = very difficult

Confirmatory Factor Analyses
Before testing the hypotheses, I examined the construct validity of the level 1
measures by conducting within-person confirmatory factor analyses. I performed these
analyses using AMOS 20.0 after centering all item scores at each participant’s mean item
scores. This approach is considered appropriate when conducting confirmatory factor
64

analyses with experience sampling data (Scott et al., 2010; Sonnentag & Grant, 2012).
First, I examined the factor structure of the task and personal reactive helping variables
by comparing a two factor structure where items for task-related reactive helping loaded
on one factor and the items for personal reactive helping loaded on another factor to a
one-factor model were all six items loaded on one factor. The model fit for the two-factor
model was acceptable (CFI = 0.99,TLI = 0.96, RMSEA = 0.07). The model fit for the one
factor model was not acceptable (CFI = 0.61, TLI = 0.08, RMSEA = 0.35). The chisquare difference test showed that the two factor model was a significantly better fit to
2

the data than a one factor model (∆χ = 1084, ∆ d.f. = 1, p < .00). These analyses support
the construct distinctiveness of task and personal reactive helping.
Next, I examined the factor distinctiveness of the two helping constructs from the
two prosocial impact variables. More specifically, I compared a four factor model where
the items for task related reactive helping, personal reactive helping, prosocial impact of
task-related reactive helping, and prosocial impact of personal reactive helping loaded on
four separate constructs, to a two factor model where items for task-related reactive
helping and prosocial impact of task-related reactive helping loaded on one factor, and
items for personal reactive helping and prosocial impact of personal reactive helping
loaded on another factor. The four-factor model fit the data significantly better than a two
2

factor model (∆χ = 1973, ∆ d.f. = 5, p < .00). The model fit for the four-factor model
was CFI = 0.98,TLI = 0.97, RMSEA = 0.05, and the model fit for the two factor model
was CFI = 0.71,TLI = 0.57, RMSEA = 0.20.
Given the moderate within-person correlation between prosocial impact of task
and personal helping (r = 0.46), I also compared a two factor model where their items
65

loaded on two separate constructs to a one factor model where all items loaded on one
factor. The fit for the two factor model was CFI = 0.99,TLI = 0.98, RMSEA = 0.06,
whereas the fit for the one factor model was CFI = 0.62,TLI = 0.21, RMSEA = 0.38. A
2

two factor model fit the data significantly better than a one factor model (∆χ = 1296, ∆
d.f. = 1, p < .00), thus supporting the construct distinctiveness of these two variables.
I also examined the factor structure of the two helping constructs, the two
prosocial factors, morning affect, and morning depletion in the same model. These
analyses confirmed structures that supported the distinctiveness of these constructs. For
example, the 7 factor structure fit the data considerable better than a 6 factor structure
that combined depletion and negative affect into one overall factor. The fit for the 7factor model was CFI = 0.97,TLI = 0.95, RMSEA = 0.04, whereas the model fit for the 6
factor model that combined morning negative affect and depletion into one construct was
CFI = 0.95, TLI = 0.93, RMSEA = 0.06. The fit for the 7-factor model was significantly
2

better than that for the 6-factor model (∆χ = 234, ∆ d.f. = 6, p < .00).
Finally, the average within person correlation between subordinate-rated daily
work engagement and creativity was relatively high (r = 0.74). For this reason I compared
a two factor model where the items for daily work engagement loaded on one factor, and
the items for creativity loaded on another factor to a one-factor model where all items
loaded on one overall factor. The fit for a two-factor model was CFI = 0.95, TLI = 0.93,
RMSEA = 0.11, whereas the fit for a one factor model was CFI = 0.83, TFI = 0.69,
RMSEA = .22. The chi-square difference test showed that a two factor model fit the data
2

significantly better than a one-factor model (∆χ = 2873, ∆ d.f. = 1, p < .00). Although

66

the within-person correlation was high for these two variables, their items load on two
different constructs.

67

RESULTS
Tables 4 to 6 show the means, standard deviations, and inter-correlations of the
variables at the within- and between person level respectively. In order to account for the
non-independent nature of the data (e.g., daily observations nested within leaders), I
conducted analyses using hierarchical linear modeling (HLM) (Raudenbush & Bryk,
2002). Before testing the hypotheses, I ran null models for all level 1 (e.g., daily)
variables in order to ascertain that the use of HLM was indeed appropriate in this context.
Null models estimate the partitioning of the total variance in the level 1 variables into
within and between individual variance components. HLM is only appropriate when there
is a considerable amount of within-individual variance in the daily variables. As Table 7
shows, there was considerable variance at the within-person level in all level 1 variables
(between 34% and 73%), justifying the use of HLM for the analyses.
In all HLM models, level 1 predictors were centered at the mean of the
participants. This implies that participants’ means for these variables across all study
days were zero. Thus, between-participant mean differences across all study days for
these variables were also zero. Individual- mean centering, therefore, removes all
between-level variance from level 1 variables. This approach allows for a better
understanding of the within-individual relations among variables because the effects are
not confounded by between-individual differences. Centering at the individual mean is a
common practice used in experience sampling studies (e.g., Judge, Scott, & Ilies, 2006;
Scott et al., 2010). Stable individual differences (e.g., role perceptions and trait selfcontrol) were grand-mean centered - raw metrics or grand mean centering provide
equivalent models (Hofmann & Gavin, 1998).

68

Table 4
Within-Individual Descriptive Statistics and Correlations

1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16

Variable
Positive Affect (AM)
Negative Affect (AM)
State Depletion (AM)
State Depletion (PM)
Task-Related R Helping
Personal R Helping
Prosocial Impact Task
Prosocial Impact Personal
Daily Engagement (subs)
Daily Creativity (subs)
Daily Engagement (self)
Daily Creativity (self)
Task-Helping Difficulty
Personal Helping Difficulty
Interactions w/Subordinates
Time (PM)

M
3.51
1.35
1.33
1.73
2.33
1.66
3.73
3.42
3.89
3.73
3.91
3.70
1.55
1.30
2.56
17.79

SD
0.89
0.42
0.36
0.55
0.79
0.70
0.59
0.57
0.41
0.41
0.56
0.57
0.55
0.39
0.63
1.09

1

2

-.24**
-.40**
-.13**
.02
.05
.03
.06
-.02
.03
.16**
.11**
.06
-.05
.00
-.02

69

.31**
.19**
.06
-.01
-.07
-.10*
.06
.02
-.06
.02
.03
.03
.08
-.02

3

.23**
.06
.02
-.03
-.00
.06
.03
-.12**
-.11**
-.01
.08
.05
.05

4

.07
.08*
-.05
.01
.06
.01
-.08*
-.07
.19**
.07
.04
.13**

a

5

.46**
.52**
.25**
.17**
.14**
.25**
.24**
.33**
.15**
.24**
.08*

6

.37**
.54**
.06
.03
.15**
.17**
.13**
.26**
.06
.04

7

.39**
.14**
.11**
.23**
.18**
.25**
.09*
.18**
.06

8

.04
.02
.10*
.09*
.04
.17**
.07
.04

Table 4 (cont’d)

9
10
11
12
13
14
15
16

Variable
Daily Engagement (subs)
Daily Creativity (subs)
Daily Engagement (self)
Daily Creativity (self)
Task-Helping Difficulty
Personal Helping Difficulty
Interactions w/Subordinates
Time (PM)

M
3.89
3.73
3.91
3.70
1.55
1.30
2.56
17.79

SD
0.41
0.41
0.56
0.57
0.55
0.39
0.63
1.09

9

10

.74**
.10*
.02
.09*
-.00
.67**
-.03

11

12

13

14

15

.15**
.05
.12**
-.01
.67**
-.02

.40**
.10**
.02
.13**
.06

.11**
.03
.08
.04

.29**
.09*
.08*

.02
-.07

-.08

a

All variables are within-individual variables. To accomplish this all daily variables were centered at the person level before the

correlations were computed. N = 730-862. R = Reactive. Time (PM) = Time when afternoon survey was completed. Subs =
subordinate rated variables. Means and standard deviations are based on between-individual scores. * p <.05, ** p < .01.

70

Table 5
Descriptive Statistics and Correlations for Daily Variables

1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16

Variable
Positive Affect (AM)
Negative Affect (AM)
State Depletion (AM)
State Depletion (PM)
Task-Related R Helping
Personal R Helping
Prosocial Impact Task
Prosocial Impact Personal
Daily Engagement (subs)
Daily Creativity (subs)
Daily Engagement (self)
Daily Creativity (self)
Task-Helping Difficulty
Personal Helping Difficulty
Interactions w/Subordinates
Time (PM)

M
3.51
1.36
1.35
1.76
2.36
1.65
3.69
3.38
3.88
3.74
3.86
3.68
1.56
1.30
2.56
17.71

SD
1.02
0.59
0.59
0.84
1.25
0.98
0.84
0.82
0.58
0.59
0.75
0.81
0.83
0.63
0.93
1.78

1

a

2

71

4

5

6

7

8

.38**
.26**
.12**
.12**
-.05
.00
-.03
-.01
-.06
-.01
.10*
.11*
.07
-.01

-.22**
-.45**
-.21**
.10*
.18**
.26**
.23**
.13**
.09*
.53**
.46**
-.01
-.01
.00
-.01

3

.37**
.06
.02
-.10*
-.04
-.11**
-.05
-.26**
-.20**
.16**
.16**
.00
.04

.00
.03
-.12**
-.01
-.08*
-.08
-.15**
-.11**
.13**
.07
-.04
.09*

.52**
.51**
.31**
.22**
.17**
.19**
.24**
.39**
.29**
.24**
.13**

.33**
.51**
.13**
.07
.07
.14**
.12**
.30**
.07
.08*

.61**
.21**
.20**
.40**
.35**
.21**
.11**
.11**
.10*

.12**
.10*
.30**
.25**
.05
.16**
.04
.07

Table 5 (cont’d)

9
10
11
12
13
14
15
16

Variable
Daily Engagement (subs)
Daily Creativity (subs)
Daily Engagement (self)
Daily Creativity (self)
Task-Helping Difficulty
Personal Helping Difficulty
Interactions w/Subordinates
Time (PM)

M
3.88
3.74
3.86
3.68
1.56
1.30
2.56
17.71

SD
0.58
0.59
0.75
0.81
0.83
0.63
0.93
1.78

9
.81**
.24**
.14**
.15**
.10*
.67**
.06

10

11

12

13

14

15

.20**
.12**
.15**
.07
.69**
.04

.61**
.12**
.06
.10*
.02

.13**
.07
.08*
.02

.52**
.09*
.04

.09*
-.04

.02

a

All variables are daily variables. N = 730-862. R = Reactive. Time (PM) = Time when afternoon survey was completed. Means

and standard deviations are based on daily scores (These results were based on the raw daily data which combines within and
between person variances). Subs = subordinate rated variables.* p <.05, ** p < .01.

72

Table 6
Between-Individual Descriptive Statistics and Correlations

1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24

Variable
Positive Affect (AM)
Negative Affect (AM)
State Depletion (AM)
State Depletion (PM)
Task-Related R Helping
Personal R Helping
Prosocial Impact Task
Prosocial Impact Personal
Daily Engagement (subs)
Daily Creativity (subs)
Daily Engagement (self)
Daily Creativity (self)
Task-Helping Difficulty
Personal Helping Difficulty
Interactions w/Subordinates
Time (PM)
Task Helping Breadth
Task Helping Efficacy
Personal Helping Breadth
Personal Helping Efficacy
Prosocial Motivation
Trait Self-Control
Job Experience
Managerial Experience

M
3.51
1.35
1.33
1.73
2.33
1.66
3.73
3.42
3.89
3.73
3.91
3.70
1.55
1.30
2.56
17.79
4.48
4.41
3.96
4.17
4.67
3.81
3.88
9.76

SD
0.89
0.42
0.36
0.55
0.79
0.70
0.59
0.57
0.41
0.41
0.56
0.57
0.55
0.39
0.63
1.09
0.72
0.65
0.74
0.65
0.50
0.56
3.39
6.79

1
-.22
-.46**
-.28*
.03
.01
.43**
.34**
.16
.10
.77**
.71**
.00
-.04
-.05
-.09
.14
.24*
.32**
.25*
.32**
.22
-.04
.18

2

.45**
.33**
.12
.17
-.06
.12
-.14
-.07
-.03
-.01
.04
.09
-.04
.03
-.11
-.29*
-.16
-.08
-.24*
-.06
-.08
-.30**

73

3

.50**
-.02
-.02
-.20
-.09
-.29*
-.15
-.30*
-.27*
.25*
.27*
-.01
-.02
-.19
-.15
-.19
-.02
-.32**
-.23*
.06
-.14

4

-.06
.02
-.22
-.06
-.24*
-.18
-.25*
-.17
.04
.06
-.05
.00
-.05
-.11
-.06
-.13
-.09
-.29*
-.01
-.22

a

5

.57**
.47**
.35**
.31*
.26*
.14
.27*
.44**
.45**
.41**
.18
.15
-.01
.16
-.04
.03
.09
-.06
-.13

6

7

.30**
.51**
.15
.10
.04
.13
.08
.33**
.02
.10
-.04
-.10
.19
.17
-.11
.17
.02
-.06

.83**
.20
.22
.58**
.54**
.14
.11
.02
.15
.02
.05
.34**
.30**
.25*
.24*
-.05
.05

8

.13
.11
.47**
.44**
.02
.11
-.07
.07
-.09
-.11
.31**
.38**
.10
.22
-.04
.03

Table 6 (Cont’d)

9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24

Variable
Daily Engagement (subs)
Daily Creativity (subs)
Daily Engagement (self)
Daily Creativity (self)
Task-Helping Difficulty
Personal Helping Difficulty
Interactions w/Subordinates
Time (PM)
Task Helping Breadth
Task Helping Efficacy
Personal Helping Breadth
Personal Helping Efficacy
Prosocial Motivation
Trait Self-Control
Job Experience
Managerial Experience

M
3.89
3.73
3.91
3.70
1.55
1.30
2.56
17.79
4.48
4.41
3.96
4.17
4.67
3.81
3.88
9.76

SD
0.41
0.41
0.56
0.57
0.55
0.39
0.63
1.09
0.72
0.65
0.74
0.65
0.50
0.56
3.39
6.79

9
.89**
.23
.19
.20
.22
.64**
.08
.02
-.06
-.02
-.12
-.09
.07
-.03
.08

10

.17
.17
.19
.18
.70**
.10
.04
-.01
-.07
.01
-.04
.04
.02
.07

74

11

12

13

14

15

16

.76**
.13
.10
-.03
-.03
.09
.25*
.36**
.24*
.32**
.41**
-.05
.20

.14
.09
.08
-.01
.13
.11
.22
.12
.33**
.33**
-.11
.09

.81**
.21
.01
-.06
-.09
-.11
-.30**
-.21
-.02
-.06
-.03

.23
.06
-.11
-.13
-.10
-.24*
-.19
-.01
.05
.10

.18
.11
.12
-.10
-.12
.07
.00
.16
.13

.20
.19
-.05
.04
.08
.15
.26*
.01

Table 6 (Cont’d)

17
18
19
20
21
22
23
24

Variable
Task Helping Breadth
Task Helping Efficacy
Personal Helping Breadth
Personal Helping Efficacy
Prosocial Motivation
Trait Self-Control
Job Experience
Managerial Experience

M
4.48
4.41
3.96
4.17
4.67
3.81
3.88
9.76

SD
0.72
0.65
0.74
0.65
0.50
0.56
3.39
6.79

17
.62**
.19
.00
.22
-.04
.20
.05

18

.34**
.23*
.42**
.10
.31**
.20

19

.50**
.52**
.16
.15
.29*

20

.33**
.33**
.22
.31**

21

22

.07
.19
.28*

.00
.19

23

45**

a

N = 68-77. Variables 1 through 16 are within-individual variables. Variables 17-24 are between individual variables. Correlations

are based on between-individual scores, where variables 1 to 16 were aggregated at the individual level. R = Reactive. Means and
standard deviations are based on between-individual scores. Sub = subordinate rated variables. * p <.05, ** p < .01.

75

Table 7
a

Parameter Estimates and Variance Composition of Level 1 Variables

1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16

Variable
Positive Affect (AM)
Negative Affect (AM)
State Depletion (AM)
State Depletion (PM)
Task-Related R Helping
Personal R Helping
Prosocial Impact of Task
Prosocial Impact of Personal
Daily Work Engagement (subs)
Daily Creativity (subs)
Daily Work Engagement (self)
Daily Creativity (self)
Task-Related Helping Difficulty
Personal-Helping Difficulty
Interactions w/Subordinates
Time (PM)

Intercept
b00
3.52**
1.36**
1.34**
1.75**
2.35**
1.67**
3.73**
3.43**
3.89**
3.73**
3.91**
3.70**
1.55**
1.30**
2.56**
17.7**

Within-Individual
Variance
2
(e )
0.37
0.20
0.24
0.47
0.98
0.51
0.38
0.39
0.20
0.21
0.26
0.35
0.40
0.28
0.80
2.34

a

Between-Individual
Variance
2
(r )
0.72**
0.15**
0.11**
0.25**
0.55**
0.44**
0.32**
0.29**
0.15**
0.15**
0.29**
0.30**
0.27**
0.12**
0.27**
0.84**

Percentage
of WithinIndividual
Variance
34%
57%
68%
65%
64%
54%
55%
57%
57%
58%
47%
54%
59%
69%
70%
73%

2

N = 730-862. b00 represents the average level of the variable across individuals. e represents the within-individual variance and,

2

r the between-individual variance in the variable. Percentage of within-individual variance was computed as the ratio of the
within-individual variance/(within + between variance). Subs = subordinate rated variables. * p <.05, ** p < .01.

76

Test of Hypotheses
Hypotheses 1 and 2 posited that task-related reactive helping and personal reactive
helping would each be associated with an increase in state depletion. Because task and personal
reactive helping were moderately correlated at the within person level (r = 0.46), I entered them
simultaneously in the HLM regressions predicting state depletion. In these analyses, I also
controlled for morning state depletion. This approach assesses whether task and personal reactive
helping during the day are associated with a change in depletion levels. Similar analyses have
been conducted by other organizational scholars who have studied daily effects of activities that
require psychological resources (e.g., Scott & Barnes, 2011). In contrast to predictions, neither
task (B = 0.00, p > .05) nor personal (B = 0.06, p > .05) reactive helping were associated with a
positive change in state depletion. Thus, Hypotheses 1 and 2 were not supported. The HLM
results are presented in Table 8. Given that the main effects for task and personal reactive
helping were not significant, these coefficients are not significantly different from 0 or each
other. Hence, Hypothesis 3 was not supported either.
Table 8
a

HLM Results for Predictors of Afternoon State Depletion (Hypotheses 1 and 2)
Predictor
Intercept (b00)

B

s.e.

1.75

0.07

25.44**

Morning State Depletion (b01)

0.36

0.07

5.38**

Task Related Reactive Helping (b02)

0.00

0.03

-0.11

Personal Reactive Helping (b03)

0.06

0.05

1.12

a

t

All level 1 predictors were centered at persons’ means, N = 588. Coefficients (Bs) are

unstandardized effect sizes. * p <.05, ** p < .01.

77

Hypotheses 4a and 4b stated that the association between task-related reactive helping
and personal reactive helping would be contingent on prosocial motivation, such that these
associations would be weaker for high versus low prosocial motivation. The results of the HLM
regressions testing these two hypotheses are shown in Table 9. The association between taskrelated reactive helping and depletion was not moderated by prosocial motivation (B = -0.04, p >
.05), thus failing to support Hypothesis 4a. Likewise, the association between personal reactive
helping and state depletion was not moderated by helping motivation (B = -0.12, p > .05), failing
to support Hypothesis 4b. In sum, the effects of the two types of helping on state depletion were
not contingent on prosocial motivation.
Table 9
a

Moderating Effects of Prosocial Motivation (Hypothesis 4a, 4b)

Criterion: Afternoon State Depletion
B
s.e.
t

Predictor
Intercept (b00)
Level 2 predictors
Prosocial Motivation (b01)
Level 1 predictors
State Depletion (AM) (b10)

1.74

0.07

25.70**

-0.13

0.16

-0.84

0.36

0.07

Task-Related Reactive Helping (b20)

-0.01

0.03

-0.17

Personal Reactive Helping (b30)
Cross-level predictors
Prosocial Motivation X Task Helping (b21)

0.05

0.05

1.07

-0.04

0.06

-0.70

Prosocial Motivation X Personal Helping (b31)

-0.12

0.12

-1.03

a

5.51**

All level 1 predictors were centered at persons’ means; level 2 variables were grand-

mean centered. Level 1 N = 588. Level 2 N = 71. Coefficients (Bs) are unstandardized
effect sizes. * p <.05, ** p < .01.

78

Hypotheses 5a and 5b posited that the associations between task-related reactive helping
and personal reactive helping would be moderated by task-related reactive helping breadth and
personal reactive helping breadth respectively. HLM tests failed to support these hypotheses.
More specifically, as shown in Table 10, task-related reactive helping breadth did not moderate
the association between task-related reactive helping and depletion (B = -0.03, p > .05) failing to
support Hypothesis 5a. Similarly, personal reactive helping breadth did not moderate the
association between personal helping and depletion, failing to support Hypothesis 5b (B = -0.03,
p > .05).
Table 10
a

Moderating Effects of Reactive Helping Breadth (Hypothesis 5a and 5b)

Predictor
Intercept (b00)
Level 2 predictors
Task-Related Reactive Helping Breadth (b01)

Criterion: Afternoon State Depletion
B
s.e.
t
1.75

0.07

25.45**

0.02

0.07

0.28

Personal Reactive Helping Breadth (b02)
Level 1 predictors
Morning State Depletion (b10)

-0.07

0.10

-0.72

0.36

0.07

5.29**

Task-Related Reactive Helping (b20)

0.00

0.03

-0.05

Personal Reactive Helping (b30)
Cross-level predictors
Task Helping Breadth X Task-Related Helping (b21)

0.06

0.06

1.07

-0.03

0.05

-0.54

Personal Helping Breadth X Personal Helping (b32)

-0.03

0.08

-0.04

a

All level 1 predictors were centered at persons’ means, level 2 variables were grand-

mean centered. Level 1 N = 588. Level 2 N = 71. Coefficients (Bs) are unstandardized
effect sizes. * p <.05, ** p < .01.

79

Hypotheses 6a and 6b predicted that the expected positive association between taskrelated reactive helping and personal reactive helping would be weakened by helping efficacy.
These expectations were not supported. Task-related reactive helping efficacy did not moderate
the relation between task-related reactive helping and depletion (B = -0.01, p > .05), failing to
support Hypothesis 6a. Furthermore, personal reactive helping efficacy did not moderate the
association between personal reactive helping and depletion (B = -0.02, p > .05), failing to
support Hypothesis 6b. The HLM regressions are presented in Table 11.
Table 11
a

Moderating Effects of Reactive Helping Efficacy (Hypothesis 6a and 6b)

Predictor
Intercept (b00)
Level 2 predictors
Task-Related Reactive Helping Efficacy (b01)

Criterion: Afternoon State Depletion
B
s.e.
t
1.75

0.07

25.34**

0.01

0.10

0.11

Personal Reactive Helping Efficacy (b02)
Level 1 predictors
Morning State Depletion (b10)

-0.18

0.11

-1.64

0.35

0.07

5.32**

Task-Related Reactive Helping (b20)

0.00

0.03

-0.13

Personal Reactive Helping (b30)
Cross-level predictors
Task Efficacy X Task-Related Helping (b21)

0.06

0.06

0.91

-0.01

0.06

-0.22

Personal Efficacy X Personal Helping (b32)

-0.02

0.13

-0.19

a

All level 1 predictors were centered at persons’ means; level 2 variables were grand-

mean centered. Level 1 N = 588. Level 2 N = 71. Coefficients (Bs) are unstandardized
effect sizes. * p <.05, ** p < .01.
Consistent with predictions by Ego Depletion Theory that depletion of resources ought to
diminish performance on other work behaviors that require resources, Hypothesis 7 stated that
80

state depletion would be negatively related to daily work engagement. As Table 12 shows, state
afternoon depletion was not related to leaders’ daily work engagement as rated by subordinates
(B = 0.04, p > .05), failing to support Hypothesis 7.
Table 12
a

HLM Results for Predictors of Daily Work Engagement (Hypothesis 7)
Predictor
Intercept (b00)

B

s.e.

3.93

0.05

78.52**

Afternoon State Depletion (b01)

0.04

0.03

1.51

a

t

All level 1 predictors were centered at persons’ means; N = 589. Coefficients (Bs) are

unstandardized effect sizes. * p <.05, ** p < .01.
Similarly, Hypothesis 8 predicted that depletion would be negatively related to daily
creativity. As Table 13 shows, controlling for state positive and negative affect, afternoon state
depletion was not related to daily creativity as rated by subordinates, (B = 0.02, p > .05), failing
to support Hypothesis 8.
Table 13
a

HLM Results for Predictors of Daily Creativity (Hypothesis 8)
Predictor
Intercept (b00)

B

s.e.

3.76

0.05

71.86**

Morning Positive Affect (b01)

0.02

0.03

0.87

Morning Negative Affect (b02)

-0.01

0.04

-0.22

Afternoon State Depletion (b01)

0.02

0.02

0.69

a

t

All level 1 predictors were centered at persons’ means; N = 589. Coefficients (Bs) are

unstandardized effect sizes. * p <.05, ** p < .01.

81

Testing mediation. The mediation hypotheses (9 and 10) posited that state depletion
would mediate the effects of task and personal reactive helping on daily work engagement and
creativity. I tested these hypotheses with the procedure recommended by Krull and MacKinnon
(1999), which was specifically developed for non-independent data. Krull and MacKinnon
(1999) recommended that a Sobel (1982) test is appropriate to establish whether mediation (the
indirect effect) is significant. This procedure is popular among organizational scholars who have
used HLM in experience sampling and multilevel studies (Chowdhury & Endres, 2010;
Dimotakis, Conlon, & Ilies, 2012; Dimotakis, Scott, & Koopman, 2011; Levin, Walter, &
Murnighan, 2011; Scott & Barnes, 2011).
To test Hypotheses 9 and 10, I examined whether state depletion mediated the effects of
task and personal reactive helping on work engagement and creativity as rated by subordinates.
Given that task and personal reactive helping did not have a main effect on state depletion, I did
not expect depletion to mediate their effects on these two outcomes. Supporting these
expectations, state depletion did not mediate the effects of task related reactive helping on daily
work engagement (Sobel z = 0.71, p > .05), nor the effects of personal reactive helping on daily
work engagement (Sobel z = 1.13, p > .05) thus failing to support Hypotheses 9a and 10a.
Similarly, state depletion failed to mediate the effects of task-related reactive helping (Sobel z =
0.47, p > .05) and personal reactive helping (z = -0.40, p > .05) on daily creativity failing to
support Hypotheses 9b and 10b. In sum, the mediation Hypotheses 9 and 10 were not supported.
Hypothesis 11 predicted that prosocial impact of task-related reactive helping would
moderate the effects of depletion on daily work engagement, such that this association would be
weaker for high versus low prosocial impact of task-related helping (Hypothesis 11a) and on
daily creativity, such that this effect would be weaker for high versus low prosocial impact of

82

task-related helping (Hypothesis 11b). Tables 14 and 15 summarize the HLM regressions testing
these hypotheses. As shown in Table 14, the interaction of state depletion with prosocial impact
of task-related reactive helping did not predict daily work engagement (B = 0.06, p > .05), thus
failing to support Hypothesis 11a. Failing to support Hypothesis 11b, the effect of depletion on
daily creativity was not moderated by prosocial impact of task-related reactive helping (B = 0.03,
p > .05) (see Table 15). Interestingly, prosocial impact of task-related reactive helping had a
positive main effect on both daily work engagement and daily creativity rated by subordinates.
Table 14
Moderating Effects of Task-Related Prosocial Impact for Daily Work Engagement
a

(Hypothesis 11a)
Predictor
Intercept (b00)

B

s.e.

3.93

0.05

77.54**

Afternoon State Depletion (b01)

0.05

0.03

1.77

Prosocial Impact of Task R Helping (b02)

0.12

0.03

4.11*

Depletion X Prosocial Impact of Task R (b03)

0.06

0.05

1.13

a

t

All level 1 predictors were centered at persons’ means, N = 583, R = Reactive.* p <.05,

** p < .01.

83

Table 15
Moderating Effects of Task-Related Prosocial Impact for Daily Creativity (Hypothesis
a

11b)
Predictor
Intercept (b00)

B

s.e.

3.76

0.05

71.08

Afternoon State Depletion (b01)

0.03

0.03

1.00

Prosocial Impact of Task R Helping (b02)

0.08

0.04

2.12*

Depletion X Prosocial Impact of Task R (b03)

0.03

0.05

0.61

State Positive Affect (b04)

0.02

0.03

0.63

State Negative Affect (b05)

0.00

0.03

-0.28

a

t

All level 1 predictors were centered at persons’ means, N = 486, R = Reactive.* p <.05,

** p < .01.
Hypothesis 12a predicted that the association between depletion and daily work
engagement would be weaker when prosocial impact of personal reactive helping is high versus
low. As shown in Table 16, this expectation was not supported (B = -0.04, p > .05). Hypothesis
12b predicted that the association between depletion and daily creativity would be weaker for
high versus low prosocial motivation of personal helping. This hypotheses was also not
supported (B = -0.05, p > .05) (see Table 17).

84

Table 16
Moderating Effects of Personal Prosocial Impact for Daily Work Engagement (Hypothesis
a

12a)

Predictor
Intercept (b00)

B

s.e.

3.93

0.05

78.64

Afternoon State Depletion (b01)

0.04

0.03

1.64

Prosocial Impact of Personal R Helping (b02)

0.03

0.03

1.14

Depletion X Prosocial Impact of Personal R (b03)

-0.04

0.04

-0.86

a

t

All level 1 predictors were centered at persons’ means; N = 583, R = Reactive. * p <.05,

** p < .01.
Table 17
a

Moderating Effects of Personal Prosocial Impact for Daily Creativity (Hypothesis 12b)
Predictor
Intercept (b00)

B

s.e.

3.76

0.05

71.87**

Afternoon State Depletion (b01)

0.02

0.03

0.78

Prosocial Impact of Personal R Helping (b02)

0.03

0.03

1.20

-0.05

0.04

-1.35

State Positive Affect (b04)

0.02

0.03

0.63

State Negative Affect (b05)

0.00

0.03

-0.13

Depletion X Prosocial Impact of Personal R (b03)

a

t

All level 1 predictors were centered at persons’ means, N = 486, R = Reactive. * p <.05,

** p < .01.
Hypothesis 13 posited that the association between depletion and daily work engagement
would depend on trait self-control. People high in trait self-control have a larger pool of selfcontrol resources. Hence, I expected that these people would be less vulnerable to the negative
consequences of depletion. Results of HLM regressions are summarized in Table 18. In contrast

85

to expectations, trait self-control did not moderate the association between afternoon state
depletion and daily creativity rated by subordinates (B = 0.01, p > .05). Thus, Hypothesis 13 was
not supported.
Table 18
a

Moderating Effects of Trait Self-Control for Work Engagement (Hypothesis 13)

Predictor
Intercept (b00)
Level 2 predictors
Trait Self-Control (b01)
Level 1 predictors
Afternoon State Depletion (b10)
Cross-level predictors
Depletion X Trait Self-Control (b11)
a

B

Criterion: Work Engagement
s.e.
t

3.93

0.05

79.16**

0.06

0.08

0.74

0.04

0.03

1.44

0.01

0.04

0.31

All level 1 predictors were centered at persons’ means; level 2 variables were grand-

mean centered. Level 1 N = 584. Level 2 N = 66. Coefficients (Bs) are unstandardized
effect sizes. * p <.05, ** p < .01.
Finally, Hypothesis 14 posited that the effects of depletion on daily creativity rated by
subordinates would also depend on trait self-control (Table 19). Controlling for state positive and
negative affect at level 1, the effects of depletion on daily creativity were not contingent on trait
self-control (B = 0.04, p > .05). Hypothesis 14, therefore, was also not supported.

86

Table 19
a

Moderating Effects of Trait Self-Control for Creativity (Hypothesis 14)

Predictor
Intercept (b00)
Level 2 predictors
Trait Self-Control (b01)
Level 1 predictors
State Positive Affect (b02)

B

Criterion: Creativity
s.e.
t

3.76

0.05

72.21**

0.06

0.09

0.68

0.03

0.03

0.93

State Negative Affect (b03)

0.00

0.03

-0.28

Afternoon State Depletion (b40)
Cross-level predictors
Depletion X Trait Self-Control (b41)

0.03

0.03

0.94

0.04

0.04

0.82

a

All level 1 predictors were centered at persons’ means; level 2 variables were grand-

mean centered. Level 1 N = 487. Level 2 N = 64. Coefficients (Bs) are unstandardized
effect sizes. * p <.05, ** p < .01.
In sum, none of the hypotheses proposed in this dissertation were supported when tested
with the proposed HLM regressions (e.g., regression that did not include many control
variables). The HLM regressions that were used to test all hypotheses are included in the
Appendix. Theory and research suggests that several of the constructs examined in this study are
moderately associated with each other. Hence, the relatively simple manner in which I initially
proposed to test my hypotheses may not reliably and appropriately account for spurious variables
that were not included as control variables, but that may have interfered with the associations
examined here. It is possible that the large amount of within-person variance in depletion (e.g.,
65% of variance) may be due to a host of within-person phenomena. In attempts to further
examine the within-person associations between reactive helping and depletion, I conducted a set
of rigorous post hoc analyses, which are outlined in the subsequent sections of this manuscript.
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POST HOC ANALYSES
Main Effects of Reactive Helping on Depletion
Although Ego Depletion Theory suggests that reactive helping is likely to consume selfregulatory resources, I did not find main effects for task-related and personal reactive helping on
changes in state depletion. It is possible that the HLM regressions that I constructed to test
Hypotheses 1 and 2 omitted substantive variables that relate to the dependent variable,
independent variables, or both dependent and independent variables. Such spurious variables
could have suppressed the true associations between reactive helping and depletion. Not
including substantive control variables in the HLM regressions, therefore, may explain why I did
not find main effects for reactive helping on state depletion. To test for this possibility, I drew
from theory and research and selected control variables that ought to relate to the dependent
variable, independent variables, or both. I then regressed task-related and personal reactive
helping on state depletion controlling for morning state depletion, and these new control
variables.
Specifically, I decided to control for positive and negative affect, prosocial impact of
reactive helping, time when the afternoon survey was completed, and average amount of daily
interactions with subordinates because theory and empirical research suggest that these variables
share significant variance with the dependent and/or independent variables.
I controlled for daily positive and negative affect because work by Glomb and coauthors
(2011) suggests that employees may decide to help in attempts to either maintain a positive
mood or to alleviate negative mood. Furthermore, Ego Depletion Theory also posits that both
mood maintenance and management consumes self-regulatory resources (Baumeister et al.,
2000). So there are theoretical reasons to expect that morning affect could affect both reactive

88

helping (the independent variables) and state depletion (the dependent variable) in this model.
Consequentially, I decided to control for morning positive and negative affect in the equations
involving state depletion.
Work by Grant and Sonnentag (2012) conceptualizes perceptions of prosocial impact of
helping as positive affective events and Ego Depletion Theory (Tice et al., 2007) posits that
positive events replenish depleted resources. Furthermore in a recent conceptual piece, Lilius
(2012) argued that prosocial impact is a restorative resource. She explained that most interactions
simultaneously consume and generate regulatory resources and that “simultaneous consideration
of both dimensions will illuminate that certain interactions will be experienced as more
restorative than depleting with short and longer-term implications…” (p.571). Thus,
simultaneous consideration of both 1) the depleting nature of helping and 2) prosocial impact of
helping is appropriate in this current context. For these reasons, I decided to control for prosocial
impact of both task and personal reactive helping in the HLM regressions that tested the effects
of reactive helping on state depletion.
Empirical evidence suggests that leaders spend one-third to half of their day in contact
with subordinates (Kurke & Aldrich, 1983; Mintzberg, 1975), yet not all of these interactions
represent helping behaviors. Indeed, my analyses show that, on average, leaders engage in taskrelated helping slightly more than once a day, and in personal helping slightly less than once a
day. In addition, Ego Depletion Theory suggests that other interpersonal events (e.g., meetings)
could be depleting (e.g., Finkel et al., 2006). For this reason, I decided to control for the average
daily amount of interactions that leaders had with subordinates on any given day. This was
operationalized as the average of the following item rated by subordinates: “How much did you

89

interact with Jane Doe today at work” (response format: 1 = “Not at All” to 5 = “Very Much”).
The average daily interaction reported by subordinates was 2.5 (SD = 1.1).
Finally, Ego Depletion Theory states that “most forms of self-regulation failure escalate
over the course of the day” (Baumeister, 2003, p. 283) because resources are consumed by
multiple ongoing daily activities. For this reason, I controlled for the time of day when leaders
responded to items about state depletion each afternoon (e.g., the time of day when the afternoon
survey was completed). The later in the day leaders completed the afternoon survey, the more
likely it is that they were depleted from other work-related activities beyond reactive helping.
Time in the afternoon was operationalized as the time when afternoon responses were submitted
to the Qualtrics survey system. Other experience sampling studies also control for the time of
day when surveys are completed (e.g., Scott & Barnes, 2011).
Table 20 summarizes the HLM regressions that control for morning affect, prosocial
impact, average daily interactions with subordinates, and time of day when depletion was
reported each afternoon. Compared to the null model, this model explains 42% of the withinperson variance in afternoon depletion. The two types of reactive helping explain 15% of the
within person variance in depletion. Thus, this model has considerable explanatory power.
As Table 20 illustrates, in this model personal reactive helping had a main positive effect
on afternoon depletion (B = 0.14, p < .05). This means that leaders experience an increase in
depletion on days when they engage in more than their average amount of personal reactive
helping. Task-related reactive helping, however, did not have a main effect on state depletion.
With regards to the control variables, negative affect was associated with an increase in depletion
levels at the day level. This finding is consistent with arguments by Ego Depletion Theory that
attempts at managing negative mood may deplete resources. Interestingly, average daily

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interactions with subordinates was also associated with an increase in depletion. This finding
resonates with arguments by Ego Depletion Theory that interpersonal events are effortful and
may deplete resources. This is especially likely to be the case in work contexts where most
interactions among leaders and their subordinates are work-focused rather than leisurely in
nature.
Table 20
Post Hoc Analyses: Main Effects of Reactive Helping on Depletion
Predictor
Intercept (b00)

a

B

s.e.

t

1.73

0.07

24.77**

State Positive Affect (b10)

-0.11

0.07

-1.59

State Negative Affect (b20)

0.24

0.07

Prosocial Impact of Task R (b30)

-0.08

0.05

-1.55

Prosocial Impact of Personal R (b40)

-0.07

0.07

-1.10

Time Afternoon (b50)

0.08

0.02

4.14**

Daily Interactions with Subordinates (b60)

0.08

0.03

2.64*

Morning State Depletion (b70)

0.23

0.09

2.70*

-0.02

0.04

0.14

0.06

Task-Related Reactive Helping (b80)
Personal Reactive Helping (b90)
a

3.24*

-0.38
2.41*

All level 1 predictors were centered at persons’ means; Level 1 N = 473. Level 2 N =

64. Coefficients (Bs) are unstandardized effect sizes, R = Reactive helping. * p <.05, ** p
< .01.
In sum, these post hoc analyses fail to support Hypothesis 1, which stated that taskrelated reactive helping had a main effect on stated depletion, but supported Hypothesis 2, which
predicted that personal helping would be associated with a positive change in depletion of selfregulatory resources.

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Hypothesis 3 stated that compared to task-related reactive helping, engaging in personal
reactive helping would be more depleting because of the sensitive and uncomfortable nature of
personal helping episodes. To examine whether this expectation was supported in post-hoc
analyses, I computed and compared the standardized regression coefficients of task and personal
reactive helping using the Hotelling- Williams t-test. Originally developed by Steiger (1980), this
test establishes whether two dependent regression coefficients are significantly different from
each other. The Hotelling-Williams t-test showed that the difference between the coefficients for
task-related and personal reactive helping is statistically significant (Hotelling-Williams t = 3.78, p < .01). The main effect of personal helping on depletion, therefore, was significantly
stronger than that of task-related helping, supporting Hypothesis 3.
Moderated Effects of Reactive Helping on Depletion: Prosocial Motivation
I used the HLM regression model that included the controls described above to test
Hypotheses 4-6. In these post-hoc analyses, I found partial support for Hypothesis 4 only. More
specifically, Hypothesis 4 stated that prosocial motivation would have a cross-level moderating
effect on the relation between reactive helping and depletion such that a) task-related helping and
b) personal helping would be less depleting for high versus low prosocial motivation. The
regression model is shown in Table 21. Prosocial motivation moderated only the association
between personal reactive helping and state depletion (B = 0.25, p < .05). The plot of this
interaction – depicted in Figure 2 - shows that the positive association between personal reactive
helping and state depletion is weaker for leaders high in prosocial motivation. These findings
support Hypothesis 4a. I computed the pseudo R-square which showed that prosocial motivation
explained 25% of the variance in the random slope of personal reactive helping.

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Table 21
Post Hoc Analyses: Moderating Effects of Prosocial Motivation

a

Criterion: Afternoon Depletion
B
s.e.
t

Predictor
Intercept (b00)
Level 2 predictors
Prosocial Motivation (b01)
Level 1 predictors
State Positive Affect (b10)

1.74

0.07

25.36**

-0.31

0.13

-2.44*

-0.11

0.07

-1.57

0.26

0.08

Prosocial Impact of Task R (b30)

-0.08

0.05

-1.50

Prosocial Impact of Personal R (b40)

-0.06

0.07

-0.84

Time Afternoon (b50)

0.08

0.02

4.10**

Daily Interactions w/Subordinates (b60)

0.08

0.03

2.72*

Morning State Depletion (b70)

0.24

0.09

2.84*

-0.01

0.04

0.13

0.06

2.36*

0.02

0.06

0.25

-0.25

0.08

-3.01*

State Negative Affect (b20)

Task-Related Reactive Helping (b80)
Personal Reactive Helping (b90)
Cross-level predictors
Task-Related Helping X Motivation (b81)
Personal Helping X Motivation (b91)
a

3.43*

-0.31

All level 1 predictors were centered at persons’ means; level 2 variables were grand-

mean centered. Level 1 N = 473. Level 2 N = 64. Coefficients (Bs) are unstandardized
effect sizes, R = Reactive helping. * p <.05, ** p < .01.

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Figure 2
Moderating Effects of Prosocial Motivation

Low Prosocial Motivation

High Prosocial Motivation

State Depletion

2

1.5
Low Personal Reactive Helping

High Personal Reactive Helping

Moderated Effects of Reactive Helping on Depletion: Job Experience
Ego Eepletion theory suggests that people’s ability to self-regulate works like a muscle,
in that repeated exposure to similar situations ought to strengthen one’s ability to self-regulate.
Ego Depletion Theory suggests that experience improves people’s ability to self-regulate over
time (Muraven et al., 1998). For example, leaders who have been on the job for a longer period
of time may have helped subordinates with similar problems in previous occasions. Job
experience, therefore, may moderate the extent to which task-related and personal reactive
helping are associated with state depletion such that these effects may be weaker for leaders with
high versus low work experience.

94

Although not a formal part of my conceptual model, I explored these possibilities in posthoc supplementary analyses. I measured job experience in the one-time survey with the
following item: “How many years have you worked in your current job.” I then examined
whether the associations posited in Hypotheses 1 and 2 were moderated by leader job
experience. These regressions (see Table 22) show that job experience moderates the association
between task-related reactive helping and state depletion (B21 = 0.03, p < .05), and marginally
moderates the relation between personal reactive helping and state depletion (B31 = -0.03, p <
0.10). Job experience explained 26% of the variance in the random slope of task-related reactive
helping, and 4.7% in the slope of personal reactive helping.

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Table 22
a

Post Hoc Analyses: Moderating Effects of Job Experience

Criterion: Afternoon Depletion
B
s.e.
t

Predictor
Intercept (b00)
Level 2 predictors
Job Experience (b01)
Level 1 predictors
State Positive Affect (b10)

1.73

0.07

24.96**

-0.02

0.02

-1.04

-0.12

0.07

-1.71

0.24

0.08

Prosocial Impact of Task R (b30)

-0.07

0.05

-1.48

Prosocial Impact of Personal R (b40)

-0.08

0.07

-1.11

Time Afternoon (b50)

0.07

0.02

4.15**

Daily Interactions w/Subordinates (b60)

0.07

0.03

2.50*

Morning State Depletion (b70)

0.24

0.09

2.76*

Task-Related Reactive Helping (b80)

-0.01

0.04

Personal Reactive Helping (b90)
Cross-level predictors
Task-Related Helping X JExp (b81)

0.15

0.06

2.56*

0.03

0.01

4.10**

-0.03

0.01

State Negative Affect (b20)

Personal Helping X JExp (b91)
a

3.20*

-0.23

-1.80

All level 1 predictors were centered at persons’ means; level 2 variables were grand-

mean centered. Level 1 N = 473. Level 2 N = 64. Coefficients (Bs) are unstandardized
effect sizes, R = Reactive Helping. JExp = Job experience. * p <.05, ** p < .01.
In order to examine the pattern of these associations, I plotted the interaction of taskrelated reactive helping and job experience (see Figure 3). Figure 3 suggest that there is a
positive association between task-related reactive helping and depletion for leaders who are high
in job experience, but a negative association for leaders who are low in job experience (e.g., taskrelated reactive helping seems to be replenishing for these leaders). Following procedures by
Preacher, Curran, and Bauer (2006), I conducted tests of simple slopes, which revealed that both
96

slopes were significant. The slope for leaders high in job experience (i.e., 1 sd above the mean)
was significant (B = 0.10, z = 2.47, p < .05), and the slope for leaders low in job experience (i.e.,
1 sd below the mean) was also significant (B = -0.11, z = -2.27, p < .05). Overall, these results
suggest that task-related reactive helping is depleting for leaders high in job experience, but
replenishing for leaders low in job experience. I discuss implications of these findings in the
discussion section.
I also plotted the interaction of personal reactive helping and job experience to examine
the pattern of these associations (see Figure 4). Although the interaction was marginally
significant (p = 0.08), Figure 4 suggests that the association between personal reactive helping
and depletion is stronger for leaders low in job experience. Consistent with arguments by Ego
Depletion Theory, therefore, job experience seems to buffer the depleting effects of personal
reactive helping. Tests of simple slopes revealed that the slope for leaders low in job experience
was significant (B = 0.24, z = 3.17, p < 05), whereas the slope for leaders high in job experience
was not significant (B = 0.06, z = 0.0, p < .05). Overall, these results indicate that personal
reactive helping is depleting for leaders low in job experience only.

97

Figure 3
Moderating Effects of Job Experience: Task-Related Reactive Helping
Low Job Experience

High Job Experience

State Depletion

2

1.5
Low Task Reactive Helping

High Task Reactive Helping

Figure 4
Moderating Effects of Job Experience: Personal Reactive Helping
Low Job Experience

High Job Experience

State Depletion

2

1.5
Low Personal Reactive Helping
98

High Personal Reactive Helping

I also examined whether managerial experience and helping difficulty moderated the
extent to which task and personal reactive helping were associated with state depletion. I did not
find effects for moderation. Managerial experience however had a main effect on state depletion
such that leaders with more managerial experience experienced, on average, less overall state
depletion. I also found a main effect of task-related helping difficulty on state depletion. Personal
helping difficulty did not affect depletion.
Outcomes of Depletion: Self-Rated Work Engagement
Originally, I proposed that state depletion would be negatively related to daily work
engagement (Hypothesis 7) and daily creativity (Hypothesis 8) as rated by subordinates. I did not
find support for these two hypotheses. As mentioned previously, in addition to collecting
subordinate ratings of leaders’ work engagement and creativity, I also measured self-ratings of
these behaviors. Entertaining the possibility that leaders may have a more accurate understanding
of their own daily work engagement and creativity than their subordinates do, I reran the
regressions that tested Hypotheses 7 and 8 with self-rated measures of work engagement and
creativity. Although privy to common method bias, self-ratings of work engagement and
creativity are the most common assessment of these variables in experience sampling studies
(Bakker & Xanthopoulou, 2009; Binnewies & Wornlein, 2011; Bledow, Schmitt, Frese, &
Kuhnel, 2011; Ohly & Fritz, 2010). In support of Hypothesis 7, these supplementary analyses
showed that depletion was negatively related to self-rated daily work engagement (B = -0.06, p <
.05). The results are summarized in Table 23.

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Table 23
a

Post Hoc Analyses: Predictors of Daily Work Engagement
Predictor
Intercept (b00)

B

s.e.

3.91

0.07

59.00**

Afternoon State Depletion (b01)

-0.06

0.03

-2.17*

a

t

All level 1 predictors were centered at persons’ means, N = 715. * p <.05, ** p < .01.

Depletion, however, was not related to self-rated daily creativity. These results are shown in
Table 24.
Table 24
Post Hoc Analyses: Predictors of Daily Creativity

a

Predictor
Intercept (b00)

B

s.e.

3.71

0.07

50.51**

Morning Positive Affect (b01)

0.10

0.04

2.71*

Morning Negative Affect (b02)

0.10

0.04

2.36*

Afternoon State Depletion (b01)

-0.05

0.05

a

t

-1.28

All level 1 predictors were centered at persons’ means, N = 588. * p <.05, ** p < .01.

I retested the mediation Hypothesis 9b using these updated analyses. More specifically, I
used procedures by Krull and MacKinnon (1999) to examine whether state depletion mediated
the effect of personal reactive helping on self-rated daily work engagement. I did not find
support for this hypothesis (Sobel z = -1.6, p =.11). A reason for this non-finding may be because
the effects of helping on state depletion are moderated by job experience and helping motivation
on one hand, but also because the effects of depletion on work engagement are moderated by
prosocial impact, as I explain in the subsequent sections.

100

Hypotheses 11a and 12a suggested that the effects of depletion on daily work
engagement would be contingent on prosocial impact of personal helping and prosocial impact
of task-related helping. Given that prosocial impact of task and prosocial impact of personal
reactive helping were moderately correlated (r = .39), I reran these analyses by entering these
variables simultaneously in the regression that predicted self-ratings of daily work engagement.
These regressions are shown in Table 25. I found that the effects of depletion on daily work
engagement were contingent on prosocial impact of personal helping only. Figure 4 portrays the
pattern of this interaction. The negative effect of depletion on daily work engagement was
weaker when prosocial impact of personal helping was low. This finding is consistent with
Hypothesis 12a.
Table 25
Post Hoc Analyses: Moderating Effects of Depletion

Predictor
Intercept (b00)

a

Criterion: Daily Work Engagement
B
s.e.
t
3.91

0.07

59.86**

Afternoon State Depletion (b10)

-0.05

0.02

-1.90

Prosocial Impact of Task R (b20)

0.16

0.04

3.95**

Prosocial Impact of Personal R (b30)

0.02

0.03

0.55

Depletion X Impact of Task (b40)

0.00

0.08

-0.05

Depletion X Impact of Personal (b50)

0.14

0.06

a

2.46*

All level 1 predictors were centered at persons’ means, N = 588, R = Reactive Helping.*

p <.05, ** p < .01.

101

Figure 5
Moderating Effects of Prosocial Helping Impact
4.2
Low Personal Helping Impact

High Personal Helping Impact

Work Engagement

4.1

4

3.9

3.8

3.7

Low Depletion

High Depletion

Outcomes of Depletion: Daily Creativity
Ego Depletion Theory suggests that creativity is a resource intensive process. For this
reason, I proposed that depletion of self-regulatory resources would have a direct negative effect
on leaders’ daily creativity. My HLM analyses, however, showed that state depletion was not
related to daily creativity either when it was self-rated or rated by subordinates. As reported
previously, state depletion had a negative effect on self-rated daily work engagement. Research
at the between person level indicates that work engagement is related to creativity (Hakanen,
Perhoniemi, & Toppinen-Tanner, 2008). The more engaged employees are at work, the more
they are able to come up with innovative and creative ideas. It is possible; therefore, that state
depletion may affect daily creativity through work engagement. To test this possibility, I
regressed daily creativity (rated by subordinates) on daily work engagement controlling for daily

102

affect. Table 25 contains the results of these regressions. As shown in Table 26, daily work
engagement had a main effect on daily creativity as rated by coworkers (B = 0.14, p < .05).
Table 26
Post Hoc Analyses: Effects of Work Engagement on Daily Creativity

Predictor
Intercept (b00)

Criterion: Daily Creativity
B
s.e.

a

t

0.05

71.91**

State Positive Affect (b10)

0.01

0.03

0.19

State Negative Affect (b20)

0.01

0.03

0.32

Daily Work Engagement (b30)
a

3.76**

0.14

0.06

2.46*

All level 1 predictors were centered at persons’ means; N = 485. * p <.05, ** p < .01.

A one-tailed Sobel test showed that work engagement mediated the effects of depletion
on daily creativity (z = -1.70, p < .05). Depletion, therefore, has a negative indirect effect on
creativity through daily work engagement.
Another set of results further support the idea that creativity is a resource intensive
process. More specifically, I examined whether the effect of work engagement on creativity was
contingent on trait self-control. People high in trait self-control have a larger pool of resources at
their disposal and they are more efficient at self-regulating their resources. Hence, the impact of
daily work engagement on creativity ought to be stronger for people who are high versus low in
trait self-control. The regressions for these analyses are shown in Table 27 and the interaction
graph is shown in Figure 6. The effect of self-ratings of daily work engagement on daily
creativity rated by subordinates was stronger for people who were high in trait self-control (B =
0.16, p < .05).

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Table 27
a

Post Hoc Analyses: Moderated Effects of Work Engagement
Criterion: Daily Creativity
B
s.e.

Predictor
Intercept (b00)
Level 2 Predictor
Trait Self-Control (b01)
Level 1 Predictors
State Positive Affect (b10)

t

3.76**

0.05

71.33**

0.06

0.09

0.65

0.02

0.03

0.44

State Negative Affect (b20)

0.01

0.03

0.66

Daily Work Engagement (b30)
Cross Level Predictor
Work Engagement X Self-Control

0.16

0.06

2.87*

0.16

0.07

2.28*

a

All level 1 predictors were centered at persons’ means; N = 485. * p <.05, ** p < .01.
Figure 6
Moderating Effects of Trait Self-Control

4

Low Trait Self-Control

High Trait Self-Control

Daily Creativity

3.9

3.8

3.7

3.6

3.5

Low Work Engagement

High Work Engagement

104

Subordinates’ Perspective on Helping
Although not a formal part of my dissertation model, I also looked at the effects of
reactive task and personal helping on followers’ perceptions of leader supportiveness. If leaders
respond to employee requests for help on a given day, than subordinates ought to rate these
leaders as more supportive. I measured leader supportiveness with three items adapted from the
Leader Behavior Description Questionnaire (Stogdill, Goode, & Day, 1962). Subordinates
indicated their agreement (1 = “Strongly Disagree” to 5 = “Strongly Agree”) to the following
three items every afternoon: “Today, Jane Doe showed concern for work group members,”
“Today, Jane Doe was friendly and approachable to work group members,” “Today, Jane Doe
looked out for the personal welfare of group members.” Average internal reliability was α =
0.91. Because subordinate-rated supportiveness and work engagement were highly correlated (r
= .77), I controlled for leaders’ work engagement when examining the effects of reactive helping
on leader supportiveness. Controlling for daily work engagement, reactive task-related helping
had a positive main effect on ratings of leader supportiveness (B = 0.06, p < .05).
Surprisingly, personal reactive helping did not have a main effect on ratings of leader
supportiveness. As Table 28 shows, the coefficient of personal helping had a negative sign. This
prompted me to examine the interactive effects of task and personal reactive helping on ratings
of daily leader supportiveness. I centered task and personal reactive helping at the person level
and then computed an interaction term for each day. I found that task and personal reactive
helping interacted such that task-related reactive helping had a stronger effect on daily ratings of
leader supportiveness on days when leaders engaged in low (vs. high) personal reactive helping.
These findings are suggestive of the fact that personal helping episodes may consume other
leader resources that diminish the value of task-related reactive helping to subordinates. For

105

example, helping a lot with both task and personal problems may diminish the quality of the help
given to subordinates. The results of the regressions are shown in Tables 28 and 29, and the
interaction graph in Figure 7.
Table 28
Post Hoc Analyses: Subordinate Reactions to Leader Helping

Criterion: Daily Leader Supportiveness
B
s.e.
t

Predictor
Intercept (b00)

4.06**

0.02

2.95*

0.04

-0.78

0.81

Daily Work Engagement (b30)

56.93**

-0.03

Personal Reactive Helping (b20)

0.07

0.06

Task Related Reactive Helping (b10)

a

a

0.06

13.52**

All level 1 predictors were centered at persons’ means; N at level 1 = 380 *; N at level 2

= 40. p <.05, ** p < .01.
Table 29
Post Hoc Analyses: Interactive Effects of Helping on Ratings of Supportiveness

Predictor
Intercept (b00)

a

Criterion: Daily Leader Supportiveness
B
s.e.
t
4.07**

0.07

59.97**

0.05

0.02

2.60*

-0.03

0.04

-0.65

Daily Work Engagement (b30)

0.79

0.06

13.17**

Task X Personal Helping (b40)

-0.05

0.02

-2.97*

Task Related Reactive Helping (b10)
Personal Reactive Helping (b20)

a

All level 1 predictors were centered at persons’ means, N at level 1 = 380, N at level 2

= 40. *p <.05, ** p < .01.

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Figure 7
Interactive Effects of Both Types of Helping

Supportive Leadership

4.5

Low Personal Helping

High Personal Helping

4

3.5

Low Task Related Helping

High Task Related Helping

Summary of Post Hoc Findings
These post hoc analyses showed that personal reactive helping is associated with an
increase in depletion, controlling for several theory-informed variables (state affect, prosocial
impact, time, and coworker interactions). This effect is weaker for leaders high in job experience
and for leaders high in helping motivation. Task-related reactive helping is replenishing for
leaders low in job experience, but depleting for leaders high in job experience. Depletion, on the
other hand, has a negative effect on self-ratings of daily work engagement. This negative
association is weakened when prosocial impact of personal reactive helping is high (vs. low).
Furthermore, daily work engagement is positively related to daily creativity as rated by
followers and it marginally mediates the effects of depletion on daily creativity. The effect of
daily work engagement on creativity rated by subordinates is stronger for people who are high

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(vs. low) in trait self-control. Finally, I examined subordinate ratings of leader supportiveness. I
found that reactive task-related helping contributed to subordinate perceptions of leader
supportiveness. Performance of personal helping, on the other hand, detracted from these
perceptions. More specifically, task-related reactive helping was associated with perceptions of
leader supportiveness only on days when leaders performed little personal reactive helping. This
finding may suggest that in addition to self-regulatory resources, personal helping may also
consume other important resources.

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DISCUSSION
Most of the leadership research has taken a top-down approach to leader-follower
interactions by focusing mostly on how leader behavior affects followers. Leaders, however,
spend considerable time in contact with their subordinates and respond often to their requests for
help. Yet, little is known about how responding to help requests affects leaders’ ability to selfregulate at work. Informed by Ego Depletion Theory, the purpose of this study was to examine
the effects of task-related and personal reactive helping on leaders’ self-regulatory resources and
subsequent behavior. The main premise of this study was that responding to follower help
requests consumes self-regulatory resources, which subsequently impairs leaders’ work
engagement and creativity. To empirically test the research questions proposed in this
dissertation, I conducted an experience sampling study and collected data from 77 leaders and up
to five of their subordinates over three work weeks. In the discussion of this study I first
overview the main findings, focusing primarily on the post-hoc analyses. I then discuss strengths,
limitations, and ideas for future research.
Summary of Findings
As I previously reviewed in the results section, the hypotheses that I originally proposed
to test in this dissertation were not supported. These null findings may have occurred for
theoretical or empirical reasons. With regards to theory, Ego Depletion Theory has
predominantly been tested in laboratory settings where participants’ self-regulatory resources are
measured right after they participated in a resource-intensive experiment. It is possible that selfregulatory resources fluctuate rapidly within person and as a result depletion of self-regulatory
resources may be best measured right after a helping event. In this study, however, I was not able
to look at individual helping events. Rather, I looked at the totality of helping events that

109

occurred in a given day and their impact on leaders’ depletion of self-regulatory resources as
measured at the end of the work day. Arguably, leaders are exposed to a host of daily work
activities that may consume as well as generate resources and not examining helping right after it
occurred (e.g., at the event level) may have masked the true association between reactive helping
and depletion of self-regulatory resources. On the other hand, I did find that both task-related and
personal reactive helping consume resources for certain groups of leaders. For example, in posthoc analyses I found that personal reactive helping is depleting for leaders low in job experience
and for leaders high in job experience. The way I originally tested the main effects of reactive
helping on depletion, therefore, may represent conservative tests of Ego Depletion Theory in a
work context.
With regards to empirical reasons for the null effects, it is possible that control variables
(e.g., confounds, contaminants, suppressors), which affect the dependent variable and/or the
independent variables may have interfered and masked the true associations examined here. For
these reasons, I drew on theory and prior research to select a number of control variables shown
to affect the dependent variable and/or the independent variables. I retested all hypotheses with
these new regression models in post hoc analyses and found support for several of them. I review
these findings in the subsequent sections.
Reactive Helping
The primary question that I sought to address in this dissertation is whether task and
personal reactive helping deplete leaders’ self-regulatory resources. Results from this study
suggest that personal reactive helping had a main effect on state depletion. This effect was
unique to personal helping and independent of several control variables such as affect and
perceived impact. The effects of personal reactive helping on depletion, however, were weaker

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for leader high in prosocial motivation. These findings are consistent with propositions by Ego
Depletion Theory. Ego Depletion theory posits that sensitive and uncomfortable social
interactions deplete resources, which dovetails well with the main effect of personal reactive
helping on depletion. Furthermore, this theory also suggests that motivation to achieve a social
goal moderates deletion. Prosocial motivation coincides with a heightened motivation to benefit
others (Grant & Berg, 2011), and was found to buffer the depleting effects of personal reactive
helping.
The findings for task-related reactive helping were more complicated. Results suggested
that task related reactive helping was associated with an increase in depletion for leaders who
were high (vs. low) in job experience. In contrast, task-related reactive helping was associated
with a decrease in depletion for leaders who were low (vs. high) in job experience. Thus, helping
with task-related problems is depleting for leaders with more experience in a particular job but is
replenishing for leaders with less experience. These findings are inconsistent with predictions by
Ego Depletion Theory.
More specifically, Ego Depletion Theory suggests that experience with a particular
activity renders that activity less depleting over time (Muraven, 2010; Muraven et al., 1998).
According to this theory, self-control operates like a muscle and repeated exposure to an activity
ought to render this muscle less vulnerable to depletion. The findings of this dissertation,
however, challenge this theoretical assumption. In contrast to predictions by Ego Depletion
Theory, I found that leaders with more job experience are depleted even more by reactive taskrelated helping and that leaders with less job experience are in fact replenished rather than
depleted by task-related reactive helping.

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There are several potential explanations for these findings. In terms of understanding the
depleting effects for leaders with more job experience, it may be that experienced leaders resent
having to repeatedly help with the same task-related problems. They may expect their
subordinates to have accumulated the necessary know-how to deal with task-related issues. They
may also derive less satisfaction from tackling issues that they have addressed before. Thus, taskrelated helping episodes may be less engaging and interesting for experienced leaders. Managing
feelings of resentment and frustration may render helping with task-related issues even more
depleting for experienced leaders. The absence of data to properly examine these explanatory
mechanisms, however, calls for future research that explores these possibilities.
In terms of leaders new to their job (e.g., low in job experience), they may be replenished
by task-related reactive helping for two main reasons. First, when a leader who has not been on
the job for long is approached for help with task-related problems by subordinates, that leader is
likely to experience self-affirmation as a leader in that new environment. Theoretical and
empirical work on Ego Depletion Theory suggests that self-affirmation is energizing (Lilius,
2012; Schmeichel & Vohs, 2009). Thus, even though engaging in task-relating helping may
consume resources, the experience of affirmation as a leader may more than offset depletion and
energize new leaders.
Task-related helping may be energizing also because these events represent interesting
learning opportunities for leaders who are new on the job. By helping a subordinate solve a work
problem, leaders may acquire a new understanding of the task, subordinates’ skills, or the
organization. Indeed, there is some experimental evidence that engaging in an interesting task
replenishes resources even when the task is complex and requires effort (Thoman, Smith, &
Silvia, 2011). Whether self-affirmation or learning are explanatory mechanism for the

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replenishing effects of task-related helping remain empirical questions that ought to be examined
in the future.
With regards to other activities that consume resources, I also found a main effect of
daily interactions on state depletion. Leaders reported an increase in state depletion on days
when they interacted with their subordinates more than their average. This finding is consistent
with arguments by Ego Depletion Theory that demanding interactions consume resources (Finkel
et al., 2006). Although the measure that I used to assess interactions does not distinguish between
different types of leader-follower interactions, it is safe to assume that most leader-follower
interactions are work-related rather than leisurely in nature and ought to consume resources.
Outcomes of Depletion
One of the main predictions of Ego Depletion Theory is that depletion of self-regulatory
resources due to an activity impairs performance on other activities that require similar
resources. Work engagement and creativity are work behaviors likely to be affected by depletion
of self-regulatory resources. Consistent with expectations, I found that depletion had a negative
effect on daily work engagement, but this effect was weakened when prosocial impact of
personal reactive helping was high (vs. low). This is because prosocial impact is a positive
affective event likely to replenish resources and to buffer the negative effects of depletion
(Lilius, 2012; Sonnentag & Grant, 2012).
Although I did not find a main effect of depletion on daily creativity, results suggested
that creativity is a resource intensive process. For example, work engagement was positively
related to daily creativity and partially mediated the effect of depletion on daily creativity.
Furthermore, the effect of daily work engagement on creativity was stronger for leaders high in
trait self-control. The examination of daily creativity as a resources intensive process, however,

113

is a relatively new idea in the management research and future studies ought to replicate these
results in different contexts. For example, the linkage between resources and creativity may be
more relevant in occupations where creativity is particularly important such as in product
development or in marketing.
Subordinates’ Perspective
Although not a focal part of this dissertation, I was also interested in examining
subordinate reactions to leader reactive helping. In order to do so, I collected subordinate daily
ratings of leader supportiveness. Subordinates seemed to appreciate task-related reactive helping.
More specifically, daily task-related reactive helping contributed positively to subordinate
perceptions of leader supportiveness. Surprisingly, however, these effects were weakened when
leaders also performed high personal reactive helping. Personal reactive helping, therefore, may
be associated with other costs for the leader. Perhaps the quality of helping diminishes when
leaders help a lot with both task and personal problems. This may in turn reduce the benefit that
subordinates derive from task-related helping.
In summary, the findings of this dissertation paint a rather complex picture of the costs
and benefits of leader reactive helping. On one hand, personal reactive helping was associated
with an increase in depletion of self-self-control resources. On the other hand, prosocial impact
of personal reactive helping buffered the negative effect of depletion on work engagement. Thus,
although personal reactive helping is costly, the prosocial impact of personal helping seems to
offset its costs. Similarly, although task-related reactive helping was detrimental for leaders who
had been on the job for a longer time, it energized leaders who were less experienced. There is a
need for more research that explores the underlying mechanisms for the depleting and
replenishing effects of task-related helping.

114

Strengths, Limitations, and Future Research
This dissertation has several strengths. First, its focus on reactive helping addresses a
limitation in existing research. Organizational research has predominantly conceptualized
helping as a proactive behavior, and the general consensus in this literature is that proactive
behavior has positive effects for the actor, recipient, and the organization (Grant & Ashford,
2008). Many forms of proactive behavior, however, often necessitate a reactive behavior by the
person they target. Reactive helping, for example occurs in response to proactive help-seeking,
defined as acts of asking others for assistance, information, advice, and support (D. A. Hofmann,
Z. Lei, & A. M. Grant, 2009a). The helping literature has not specifically focused on reactive
helping. This is surprising because as much as 75-90% of all help in organizations is purported to
be in response to a direct request from another person (Grant & Hofmann, 2011). Hence, most
helping is reactive in nature. Examining the effects of reactive helping on the helper is relevant
particularly in light of arguments by Ego Depletion Theory that helping consumes resources and
may be detrimental to the helper (Gailliot, 2010).
The second strength of this study is its focus on leader outcomes. Responding to help
requests with both task and personal problems tends to fall within the scope of leader
responsibilities (Morgeson et al., 2010; Toegel, Kilduff, & Anand, 2012; Yukl, 2010). For
example, in offering problem solving advice to managers, Yukl (2010) wrote: “If a person’s
performance is being affected by personal problems (e.g., family problems, financial problems,
substance abuse) be prepared to offer assistance…” (Yukl, 2010, p.133). Despite evidence that
leaders spend considerable time responding to requests for help with personal and task related
problems (Kaplan & Cowen, 1981), we know surprisingly little about how reactive helping

115

affects leaders. This dissertation shows that responding to help requests with personal and taskrelated issues has consequences for leaders’ regulatory resources and other work behaviors.
Another strength of this dissertation is the study design. This research employed multiple
sources of data to capture discrete experiences at work over three work weeks. I measured
demographics and between individual differences approximately a week prior to the start of the
daily surveys. I then surveyed participants over a period of 15 consecutive working days.
Moreover, I was able to measure variables at different times each day. I measured leaders’ affect
and state depletion in the morning, which allowed me to test whether daily helping behaviors
were associated with a change in depletion that was independent of affect. I also measured daily
ratings of leaders’ work engagement and creativity from their subordinates. This approach
provides a more robust test of the study hypotheses while minimizing common method bias as a
potential explanation for my findings.
Despite these strengths, this study has several limitations that should be noted. First, this
research relied on self-reported measures of task and personal helping events. Self-reported
measures are subject to recall and memory biases and are not perfect measures of their
underlying constructs. Some of these concerns were mitigated by the fact that leaders responded
to questions about reactive helping every day. Their ability to recall what happened that day at
work is unlikely to have been markedly affected by recall and memory biases. Furthermore,
person-centered HLM analyses effectively control for between-person differences (e.g., response
desirability) that may otherwise confound relations among daily variables.
A second limitation of this study involves the manner in which the helping behaviors
were rated. Leaders indicated the frequency with which they performed reactive task and
personal helping. This approach did not capture the actual nature of helping episodes. For

116

example, I did not collect data on how long the helping episodes took, or whether leaders helped
one subordinate several times, or several subordinates only once a day. These nuances are
important and may have confounded some of the findings of this dissertation. Given the nature of
the sample, however, I was constrained in the number of items that I could measure each day.
The participants were busy leaders who may have refused to participate if the surveys were long
and burdensome. Future research that discriminates between different types of helping events
would shed further light onto the findings reported here.
A third limitation of this study has to do with data collected from subordinates.
Participation in this study was voluntary and both leaders and subordinates could quit at any time
if they so decided. In addition, all participants were assured that their data were confidential.
However, it is possible that subordinates felt obligated to participate or that they doubted that
their responses were confidential. This may have inflated their ratings of leaders’ daily work
engagement and creativity. However, this does not seem to be the case when one compares
leader self-ratings with subordinate ratings of the same variables. For example, the average of
daily work engagement self-rated by leaders was very similar to the daily average of leader daily
work engagement rated by subordinates (3.86 vs. 3.88 respectively). Similarly, the average daily
creativity self-rated by leaders was very similar to leader daily creativity rated by subordinates
(3.68 vs. 3.88).
A fourth limitation is that some of the significant findings derived in the post-hoc
analyses of this study are sensitive to the inclusion of multiple control variables in the HLM
regressions. Although I based the selection of the control variables on prior theory and research,
it is possible, for example that the main effect of personal reactive helping on depletion may not
replicate in other samples. It is worth noting, however, that the moderated effects of reactive

117

helping hold even when no controls are included in the HLM regressions. Thus, both types of
helping are depleting for specific groups of leaders: task-related helping remains depleting for
leaders high in job experience, whereas personal helping remains depleting for leaders low in job
experience.
In addition to the above methodological limitations, this study has several conceptual
limitations. First, only task-related and personal reactive helping behaviors were assessed. It is
unclear whether proactive helping would have had the same effects on leaders’ self-regulatory
resources. Future research ought to examine the effects of reactive helping relative to proactive
helping in terms of their effects on self-regulatory resources.
Second, I only focused on creativity and work engagement as leader behaviors in this
study. The selection of these variables was informed by theoretical arguments that both
behaviors require resources. However, it is likely that other leader behaviors may be vulnerable
to depletion. For example, leaders’ decision making quality may be affected by depletion of selfregulatory resources. Future research ought to examine such other outcomes.
Another conceptual limitation was the moderators examined in this study. The findings
illustrated that helping role perceptions did not moderate the effects of reactive helping on
depletion, but that prosocial motivation and job experience did. It is possible that other betweenindividual differences may either amplify or buffer the effects of reactive helping on depletion.
For example, leaders’ need for autonomy, competence, and relatedness may moderate the extent
to which helping episodes affect leaders both at the between and within person level. For
example, leaders high in need for relatedness and competence may be energized by both types of
helping, whereas leaders high in autonomy may experience more depletion. These research
questions remain to be investigated in the future.

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Conclusion and Implications
This study offers several contributions to the management literature, which I highlight in
this section. First, this research acknowledges that responding to help request by subordinates is
a daily activity for leaders. For example, my results suggested that leaders respond to help
requests with task issues a little more than once a day and with personal requests about once a
day. Responding to help requests has implications for leaders, subordinates, and the
organizations.
Leaders ought to be aware of the effects that reactive helping has on their daily selfregulatory resources. To the extent possible, leaders can then use that knowledge to properly
manage help requests. For example, leaders can decide to help with personal problems just
before lunch break because workday breaks help recover depleted resources (Trougakos, Beal,
Green, & Weiss, 2008). Scheduling times when subordinates can approach leaders with help
requests or when leaders agree to help may not always be feasible. For instance, some task and
personal problems are time sensitive and require immediate attention. In such situations
subordinates may play a more important role in alleviating the depleting effects of reactive
helping. For example, my results suggested that prosocial impact of task and personal helping
played an important role in replenishing self-regulatory resources and in buffering the negative
effects of depletion on other work activities. It may be beneficial for organization to promote a
culture where subordinates are encouraged to express gratitude when they receive help from
leaders. Perceptions of prosocial impact are likely to be reinforced in such instances and the
depleting effects of reactive helping may be lessened or even offset.
This study identified a set of between individual differences that moderate the extent to
which helping episodes affected self-regulatory resources. For example, leaders high in prosocial

119

motivation were not as affected by personal problems as were leaders low in prosocial
motivation. Similarly, leaders low in job experience were energized by task episodes, but were
more depleted by personal helping episodes. On the other hand, experienced leaders were more
depleted by task-related helping but less by personal helping. This information could be
incorporated in training programs that educate leaders about the costs of helping behaviors. For
example, in orientation trainings for new leaders organizations may share information about the
costs of helping and may even discourage new leaders from helping with personal problems.
Although refraining from helping may not always be feasible, being aware about the costs of
helping may improve leaders’ ability to self-regulate at work.
The findings as well as the limitations of this dissertation represent opportunities for
future research. As previously mentioned it is unclear why task-related helping acts are
energizing for inexperienced leaders but depleting for experienced leaders. Future research
should seek to identify meaningful and theoretically driven mediators that explain these
processes. Similarly, future research should examine how other leader-follower interactions (e.g.,
follower expressions of voice, information sharing etc.) affect leaders’ self-regulatory resources
and whether leader-follower relationship quality moderates these effects. In conclusion, this
dissertation is only the first step in examining how leader-follower interactions affect leader’s
self-regulation at work. It is my hope that this study motivates future work in the area of leader
self-regulation.

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APPENDICES

121

APPENDIX A
Table 30
HLM Equations Testing Hypotheses
Hypothesis
Hypothesis 1,
2, 3

Equation
Level 1:
State Depletion (afternoon)ij = β0j + β1j(Morning
Depletionij) + β2j(Task-Related Helpingij) +
β3(Personal Helpingij) + rij

Hypothesis
4a,b

Level 2:
βij = γi0 + Uij
Level 1:
State Depletion (afternoon)ij = β0j + β1j(Morning
Depletionij) + β2j(Task-Related Helpingij) +
β3(Personal Helpingij) + rij
Level 2:
β0j = γ00 + γ01(Prosocial Motivation) + U0j
β1j = γ10 + U1j
β2j = γ20 + γ21(Prosocial Motivation) + Uij

Hypothesis
5a,b

β3j = γ30 + γ31(Prosocial Motivation) + Uij
Level 1:
State Depletion (afternoon)ij = β0j + β1j(Morning
Depletionij) + β2j(Task-Related Helpingij) +
β3(Personal Helpingij) + rij
Level 2:
β0j = γ00 + γ01(Task-Related Helping Breadth) +
γ02(Personal Helping Breadth) + U0j
β1j = γ10 + U1j
β2j = γ20 + γ21(Task-Related Helping Breadth) + Uij
β3j = γ30 + γ31(Personal Helping Breadth) + Uij

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Table 30 (cont’d)
Hypothesis
6a,b

Level 1:
State Depletion (afternoon)ij = β0j + β1j(Morning
Depletionij) + β2j(Task-Related Helpingij) +
β3(Personal Helpingij) + rij
Level 2:
β0j = γ00 + γ01(Task-Related Helping Efficacy) +
γ02(Personal Helping Efficacy) + U0j
β1j = γ10 + U1j
β2j = γ20 + γ21(Task-Related Helping Efficacy+ Uij

Hypothesis 7

β3j = γ30 + γ31(Personal Helping Efficacy) + Uij
Level 1:
Work Engagementij = β0j + β1j(Afternoon Depletionij)

Hypothesis 8

+ rij
Level 1:
Creativityij = β0j + β1j(Afternoon Depletionij) +
β2j(State Positive Affectij) + β3j(State Negative
Affectij) + rij

Hypothesis 9a,
10a

Level 2:
βij = γi0 + Uij
Level 1:
Work Engagementij = β0j + β1j(Afternoon Depletionij)
+ β2j(Task-Related Helpingij) + β3(Personal
Helpingij) + rij

Hypothesis 9b,
10b

Level 2:
βij = γi0 + Uij
Level 1:
Creativityij = β0j + β1j(Afternoon Depletionij) +
β2j(Task-Related Helpingij) + β3(Personal Helpingij) +
β4j(State Positive Affectij) + β5j(State Negative
Affectij) + rij
Level 2:
βij = γi0 + Uij
123

Table 30 (cont’d)
Hypothesis
11a

Level 1:
Work Engagementij = β0j + β1j(Afternoon Depletionij)
+ β2 (Perceived Prosocial Impact of Task-Related
Helping) + β3j(Perceived Prosocial Impact of TaskRelated Helping *Afternoon Depletionij) + rij

Hypothesis
11b

Level 2:
βij = γi0 + Uij
Level 1:
Creativityij = β0j + β1j(Afternoon Depletionij) + β2j
(Perceived Prosocial Impact of Task Related Helping)
+ β3j(Perceived Prosocial Impact of Task Related
Helping *Afternoon Depletionij) + β4j(State Positive
Affectij) + β5j(State Negative Affectij) + rij

Hypothesis
12a

Level 2:
βij = γi0 + Uij
Level 1:
Work Engagementij = β0j + β1j(Afternoon Depletionij)
+ β2j (Perceived Prosocial Impact of Personal Helping)
+ β3j(Perceived Prosocial Impact of Personal Helping
*Afternoon Depletionij) + rij

Hypothesis
12b

Level 2:
βij = γi0 + Uij
Level 1:
Creativityij = β0j + β1j(Afternoon Depletionij) + β2j
(Perceived Prosocial Impact of Personal Helping) +
β3j(Perceived Prosocial Impact of Personal
Helping*Afternoon Depletionij) + β4j(State Positive
Affectij) + β5j(State Negative Affectij) + rij
Level 2:
βij = γi0 + Uij

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Table 30 (cont’d)
Hypothesis 13

Level 1:
Work Engagementij = β0j + β1j(Afternoon Depletionij)
+ rij
Level 2:
β0j = γ00 + γ01(Trait Self-Control) + U0j

Hypothesis 14

β1j = γ10 + γ11(Trait Self-Control) + Uij
Level 1:
Creativityij = β0j + β1j(Afternoon Depletionij) +
β2j(State Positive Affectij) + β3j(State Negative
Affectij) + rij
Level 2:
β0j = γ00 + γ01(Trait Self-Control) + U0j
β1j = γ10 + γ11(Trait Self-Control) + Uij

125

APPENDIX B
Table 31
Summary of Hypotheses

Hypotheses
Hypothesis 1: Reactive task-related helping will be positively
associated with state depletion, controlling for morning state depletion.
Hypothesis 2: Reactive personal helping with be positively associated
with state depletion, controlling for morning state depletion.
Hypothesis 3: Reactive personal helping will be more depleting than
task-related helping, controlling for morning state depletion.
Hypothesis 4: Prosocial motivation will have a cross-level moderating
effect on the relation between reactive helping and depletion such that
a) task-related helping and b) personal helping will be less depleting for
high versus low prosocial motivation.
Hypothesis 5a: Task-related helping breadth will have a cross-level
moderating effect on the relation between task-related helping and state
depletion such that the relation will be stronger for high versus low
task-related helping breadth.
Hypothesis 5b: Personal helping breadth will have a cross-level
moderating effect on the relation between personal helping and state
depletion such that the relation will be stronger for high versus low
personal helping breadth.
Hypothesis 6a: Task-related helping efficacy will have a cross-level
moderating effect on the relation between task-related helping and state
depletion such that the relation will be weaker for high versus low taskrelated helping efficacy.
Hypothesis 6b: Personal helping efficacy will have a cross-level
moderating effect on the relation between personal helping and state
depletion such that the relation will be weaker for high versus low
personal helping efficacy.

126

Table 31 (cont’d)
Hypothesis 7: State depletion will be negatively related to daily work
engagement.
Hypothesis 8: State depletion will be negatively related to daily
creativity.
Hypothesis 9: State depletion will mediate the effects of task-related
helping on a) work engagement, and b) creativity.
Hypothesis 10: State depletion will mediate the effects of personal
helping on a) work engagement, and b) creativity.
Hypothesis 11: Perceived prosocial impact of task-related helping will
moderate the effect of state depletion on a) work engagement and b)
creativity such that these relations will be weaker when perceived
prosocial impact of task-related helping is high versus low.
Hypothesis 12: Perceived prosocial impact of personal helping will
moderate the effect of state depletion on a) work engagement and b)
creativity such that these relations will be weaker when perceived
prosocial impact of personal helping is high versus low.
Hypothesis 13: The negative association between sate depletion and
daily work engagement will be weaker for leaders who are high versus
low in trait-state control.
Hypothesis 14: The negative association between sate depletion and
daily creativity will be weaker for leaders who are high versus low in
trait-state control.

127

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128

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