EXAMINING A MODEL OF CAREER ADVANCEMENT OF
FEMALE AND MALE ASSISTANT COACHES
By
Moe Machida

A DISSERTATION
Submitted to
Michigan State University
In partial fulfillment of the requirements
For the degree of
DOCTOR OF PHILOSOPHY
Kinesiology
2012

ABSTRACT
EXAMINING A MODEL OF CAREER ADVANCEMENT OF
FEMALE AND MALE ASSISTANT COACHES
By
Moe Machida

Since the inception of Title IX, women’s participation in sport has significantly increased.
However, there is a significant decline in the number of women in the coaching
profession (Acosta & Carpetner, 2012). Scholars claim that women’s lower intention to
advance their career may be contributing to their limited representation in leadership
positions (e.g., Cunningham et al., 2003; van Vianen & Keizer, 1996). As an exploratory
investigation, and informed by the existing literatures (e.g., DeRue & Wellman, 2009;
Dragoni et al., 2009; Machida et al., 2012; Moran-Miller & Flores, 2011) and theories
(Bandura, 1997; Van Velsor et al., 2010), the purposes of the present study were (a) to
examine the influences of a wide array of antecedents on assistant coaches’ intentions
to advance their career, and (b) to investigate possible gender differences. The
antecedents examined in the present study included leader competency, motivation to
lead, leader self-efficacy, outcome expectancy, developmental experiences which
consist of developmental challenges and head coach professional support (i.e.,
feedback, support, and mentoring), learning orientation, work-family and family-work
conflicts, and gender discrimination. Six hundred and seventy-three assistant coaches
who coach collegiate women’s teams and 245 of their head coaches participated in the
study. Data analyses were conducted in two main phases. First, structural equation
models were tested with the whole sample of assistant coaches (N = 673). Second,

using composite scores of the factors confirmed in the first phase, I conducted path
analyses with the sub-sample of assistant coaches who had head coaches’ evaluations
of assistant coaches’ leader competency (n = 245). Multiple indicators multiple causes
(MIMIC) models and multiple group analyses were conducted to examine gender
differences. The results from the two phases of the analyses were generally consistent.
Overall, the results showed that leader self-efficacy and outcome expectancy were
positively related to career intention through their effects on motivation to lead.
Developmental experiences, which consist of developmental challenges and head
coach professional support, were positively related to leader self-efficacy. Learning
orientation was positively related to engagement in developmental experiences. In
addition, the results showed that family-work conflict was directly and negatively related
to motivation to lead, while gender discrimination was negatively related to outcome
expectancy, which in turn was related to motivation to lead. The findings from the
present study also suggest possible gender differences in the roles of the factors
examined. Although women had higher outcome expectancy and motivation to lead
than men, women reported lower career intention, leader self-efficacy, and
developmental challenges than men. Theoretical and research implications as well as
practical implications of the findings are discussed.

This dissertation is dedicated to my family who has provided a tremendous amount of
support for my education in the United States for the last 10 years.

iv

ACKNOWLEDGEMENTS
First, I would like to express my great appreciation for the members of my dissertation
committee. To my advisor and the chair of my dissertation committee, Dr. Deborah
Feltz, thank you for your mentoring for the last four years. I appreciate the “challenges”
that you have given me, which have helped my development as a scholar. To Dr. John
Schaubroeck, thank you for introducing me to research on leadership and teaching me
its complex nature. Your enthusiasm for leadership research has kept me passionate
about the topic. To Drs. Martha Ewing and Daniel Gould, thank you for your deep
insights into this project. I have truly enjoyed learning from you.

Also, I would like to express my gratitude for the funding for this project provided by the
College of Education and the Graduate School.

And I would like to thank the assistant coaches and head coaches in the United States,
who participated in the study.

To my undergraduate research assistants, Kristen Kelsay, Christina Miller, Sarah Parks,
and Phillip Pratt, thank you for your assistance to the data collection. I have enjoyed
working with each one of you.

Lastly, to my family and friends, thank you for your support. Without you, I believe I
could not have done what I have done.

v

TABLE OF CONTENTS

LIST OF TABLES ………………………………………………………….…………..

ix

LIST OF FIGURES ………………………………………………………….…………

xi

CHAPTER 1
INTRODUCTION ……………………………………………………………………..…
Nature of the Problem ……………………………………………………………....
Purposes of the Study and Hypotheses………………………………………..….
Intention as a Predictor of Career Advancement ………………………...
Leader Competency …………………………………………..…………….
Motivation to Lead …………………………………………………..……….
Leader Self-Efficacy …………………………………………………..……..
Outcome Expectancy ………………………………………………………..
Developmental Experiences ………………………………………………..
Learning Orientation …………………………………………………………
Work-Family and Family-Work Conflicts and Gender Discrimination ….
Gender Comparison in the Hypothesized Model …………………………
Definitions ……………………………………………………………………………
Delimitations ……………………………………………..…………………………..

1
1
2
2
6
6
7
8
9
11
12
15
15
18

CHAPTER 2
LITERATURE REVIEW ……………………………………………………………….
Underrepresentation of Women in Athletic Careers ……………………………
Intention as a Predictor of Career Advancement ……………………………….
Antecedents of Career Intention ………………………………………………….
Leader Competency ……………………………………………….………..
Leading and Coaching...........………………………………..……….
Leader Competency ………………………………..…………….
Coach Competency …………………………………..…………..
Comparing Leader and Coach Competency …………………..
Gender Differences in Leader Competency ………………………..
Motivation to Lead ………………………………..…………………………
Gender Differences in Motivation to Lead ………………………….
Leader Self-Efficacy ………………………………..………………………..
Gender Differences in Leader Self-Efficacy ………………………..
Outcome Expectancy ………………………………..………………………
Gender Differences in Outcome Expectancy ………………………
Developmental Experiences ………………………………..………………
Developmental Challenges ……………………………..……………
Feedback (Assessment)……………………………..…...…………..
Support ………………………………..……………………………….
Mentoring Relationship ………………………………..………...

20
21
23
24
24
25
25
27
28
28
29
31
31
33
36
37
37
38
44
47
48

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Developmental Experiences as Antecedents ……………………...
Gender Differences in Developmental Experiences ………………
Learning Orientation ………………………………..………………………
Gender Differences in Learning Orientation ……………………….
Work-Family and Family-Work Conflicts and Gender Discrimination….
Gender Differences in Work-Family and Family-Work Conflicts
and Gender Discrimination …..………………………………..……..
Summary ………………………………..………………………………………………
CHAPTER 3
METHOD ……………………………….………………………………………………..
Participants ………………………………………………………………………….
Procedures ……………………………………………………………………..…...
Measurements …………………………………………..………………………….
Data Analyses ………………………………………………………………………
CHAPTER 4
RESULTS ………………………………………………………………….……………
Scale Reliabilities and Descriptive Statistics Results …………………………..
Fist Phase: Structural Equation Modeling ……………………………………….
Measurement Model Testing ……………………………………………….
Hypothesized Model Testing ……………………………………………….
MIMIC Model Analysis ………………………………………………………
Multiple Group Analysis ……………………………………………………..
Summary of the First Phase of the Analyses …………………………….
Second Phase: Path Analyses ………………………………..…………………..
Preliminary Testing of the Sample ………………………………..……….
Path Analyses ………………………………..………………………………
MIMIC Model Analysis ………………………………..……………………..
Multiple Group Analysis ………………………………..……………………
Summary of the Second Phase of the Analyses …………………………
Post-Hoc Supplementary OLS Regression Analyses …………………………
Curvilinear Effects of Leader Self-Efficacy ………………………………..
Curvilinear Effects of Developmental Challenges ………………………..
Interaction Effects of Developmental Challenges and Learning
Orientation ……………………………………………………………………
Interaction Effects of Developmental Challenges and Feedback,
Support, and Mentoring ……………………………………………………..
CHAPTER 5
DISCUUSSION ………………………………………………………………………….
Theoretical and Research Implications ………………………………..……….
Antecedents of Career Intention ………………………………..………….
Developmental Experiences ………………………………………………..
Learning Orientation ………………………………..……………………….
Work-Family and Family-Work Conflicts and Gender Discrimination ….

vii

50
53
56
58
59
65
66
68
68
73
73
78
81
81
85
86
86
98
104
110
110
110
118
128
132
139
140
140
143
146
150
155
155
155
158
160
161

Gender Differences in Antecedents of Career Intention ………………...
Practical Implications ………………………………..…………………………..
Study Limitations ………………………………..……………………………….
Conclusion ………………………………..………………………………………

163
164
167
170

APPENDICES
Appendix A: Assessment Instrument…………………….………..………..…..…
1. Demographic Questionnaire ………………………..……....….……
2. Intention to Advance Career …………………………………..….…
3. Leader Competency Scale ………………………………….….…...
4. Motivation to Lead Measure…………………………………….……
5. Leader Self-Efficacy Scale …………………………………………..
6. Outcome Expectancy Scale ……………………………….………...
7. Developmental Challenges Profile ………………………………….
8. Feedback Measure …………………..……………………………….
9. Support Measure ……………………………………………………..
10. Mentorship Quality and Leaning Measure …………………………
11. Learning Orientation Measure …………………..…………………..
12. Work-Family and Family-Work Conflicts Scale ……………………
13. Gender Discrimination Measure …………………………………….

172
173
178
179
182
184
187
188
192
193
194
196
197
198

REFERENCES …………………………………………………………………….……

199

viii

LIST OF TABLES

TABLE 1: Developmental Challenges ……………………………………………..

42

TABLE 2.1: Demographic Information of Assistant Coaches…………………..

70

TABLE 2.2: Demographic Information of Head Coaches ………………………

72

TABLE 3: Zero-Order Correlations and Descriptive Statistics of Study
Variables …………………………………………………………………….………

83

TABLE 4.1: SEM Model Results …………………………………………………

91

TABLE 4.2: Indirect Effects and Significance Using Normal Distribution: Sobel
Tests
………………………………………………………………………………………….

93

Table 4.3: Bootstrap Results for Indirect Effects …………………………………

95

TABLE 5.1: MIMIC Model Analysis Results ……………………………………….

101

TABLE 5.2: The Relationships Between Gender and Latent Variables
…………………………………………………………………………………………..

102

TABLE 6.1: Multiple Group Analysis: Female (n = 360) vs. Male (n= 216)…….

107

TABLE 6.2: Multiple Group SEM: Female (n = 360) vs. Male (n = 216) ………

109

TABLE 7.1: MIMIC Model Analysis Results: AC with Head Coach Ratings (n =
245) vs. AC without Head Coach Ratings (n = 428) ……………………………

113

TABLE 7.2: The Relationship between HCR (with or without Head Coach
Ratings) and Latent Variables ……………………………………………………...

114

TABLE 8.1: Multiple Group CFA: AC with HC Ratings (n = 245) vs. AC without
HC (n = 428) Ratings ………………………………………………………

116

TABLE 8.2: Multiple Group SEM: AC with Head Coach Ratings (n = 245) vs.
AC without Head Coach Ratings (n = 428) ……………………………………….

116

TABLE 9.1: Path Analyses (with Latent Variables of LC and FBSUPPMR),
with Measurement Errors Controlled (n = 245) …………………………………..

121

TABLE 9.2: Indirect Effects and Significance Using Normal Distribution: Sobel
Tests (n = 245) ………………………………………………………………………

123

ix

TABLE 9.3: Bootstrap Results for Indirect Effects (n = 245) ……………………

125

TABLE 10: MIMIC Analyses Results on Leader Competency (n = 245) ……….

130

TABLE 11: Multiple Group CFA, Leader Competency, Female (n = 163) vs.
Male (n = 80) …………………………………………………………………………

134

TABLE 12: Multiple Group Path Analyses, Female (n = 163), Male (n = 80) ….

137

TABLE 13: Curvilinear Effects of Leader Self-Efficacy …………………………..

142

TABLE 14: Curvilinear Effects of Developmental Challenges …………………..

145

TABLE 15: Interaction Effects of Developmental Challenges and Learning
Orientation ……………………………………………………………………………

148

TABLE 16: Interaction Effects of Developmental Challenges and Feedback,
Support, and Mentoring ……………………………………………………………..

152

x

LIST OF FIGURES

FIGURE 1: Hypothesized Model of Leader Development and Career
Advancement of Coaches ………………………………………………………….

5

FIGURE 2: Hypothesized Structural Equation Model Results ………..………..

97

FIGURE 3: Hypothesized Path Model Results… ………………………………..

127

FIGURE 4: Interaction Effect of Developmental Challenges and Learning
Orientation on Career Intention ………………………………………………...

150

FIGURE 5 ……………………………………………………………
FIGURE 6 ………………………………………………………………………
FIGURE 7 ……………………………………………………………………..



















xi

CHAPTER I

INTRODUCTION
Nature of the Problem
Research supports that women are as capable as men to lead (e.g., Eagly &
Carli, 2007; Eagly & Chin, 2010). However, there are very few women leaders,
especially in fields such as athletics that are considered “masculine” in nature. This
trend is salient worldwide and can be seen in the structure of many athletic
organizations, such as the International Olympic Committee (IOC) and the National
Collegiate Athletic Association (NCAA). Though small, women’s representation in
athletic professions such as collegiate athletic administration has shown steady
increase (Acosta & Carpenter, 2012). One athletic career that shows significant decline
in women’s representation is intercollegiate sport coaching. In 1972, when Title IX was
instituted, women coached over 90% of women’s teams. However, currently, women
coach only 42% of women’s teams. Thus, more than half of women’s teams are
coached by men, though less than 3% of men’s teams are coached by women (Acosta
& Carpenter, 2012).
Having female representation in the athletic profession has significant
implications for society. Scholars argue that female leaders in athletics can play a
crucial role in affecting the lives of athletes, especially young female athletes (e.g.,
Hasbrook, 1987; Hepler & Feltz, 2008). Although increased female representation in
leadership positions in athletics can have significant positive impacts, there are various
sociological and psychological challenges that are faced by women to advance in their

1

career (e.g., Bracken, 2009; Eagly & Carli, 2007; Epitropaki & Martin, 2004; Sartore &
Cunningham, 2007). Scholars in business and sport settings suggest that women’s and
men’s career development processes are different, and they have indicated that
programs that are specifically targeted to women are necessary to increase gender
equity in organizations (Greenhill, Auld, Cuskelly, & Hopper, 2009; Knoppers, 1987;
Leonard, 1989; Lovett & Lowry, 1994; Schwalbe, Godwin, Holden, Schrock, Thompson,
& Wolkomir, 2000; Shaw & Frisby, 2006; Stangl & Krane, 1998). However, there is little
empirical study that examined such gender differences. Thus, it is still unclear how
women’s leader development and career advancement processes might differ from
men’s, and how such gender differences may surface in the career development of
coaches.
Purposes of the Study and Hypotheses
Intercollegiate coaching is the athletic leadership profession that shows the
largest decline in women’s representation (Acosta & Carpenter, 2012). The purposes of
the present study were to examine the factors that influence intercollegiate coaches’
leader development and career advancement, and to investigate gender differences in
the process. I believe that the present study has significant implications for those who
study women’s leadership, including leadership in athletics, and for those organizations
and fields that struggle to increase women’s representation in leadership positions.
Intention as a Predictor of Career Advancement
Intention can be considered as the best predictor of individuals’ behaviors (Ajzen,
1991; Armitage & Conner, 2001; Gordin & Kok, 1996), and research supports this
relationship in various settings, such as health and safety (e.g., Elliot, Armitage, &

2

Baugham, 2003; Gordin & Kok, 1996), exercise (e.g., Dzewaltowski, Noble, & Shaw,
1990; Hagger, Chatzisarantis, & Biddle, 2001), and industry and organizations (e.g.,
Hurtz & Williams, 2009; Maurer & Palmer, 1999; McCarthy & Garavan, 2006). Though
there is a dearth of studies examining the intentions of leaders in career advancement,
a few studies suggest that females have lower intention than males to advance their
careers in general business (Gupta, Turban, & Bhawe, 2008; van Vianen & Keizer,
1996) as well as in athletics settings (Cunningham, Doherty, & Gregg, 2007;
Cunningham, Sagas, & Ashley, 2003). Intention may be critical, especially for women’s
career advancement, and can be one of the factors that explain why women are
underrepresented in leadership positions in athletics. Thus, the present study examined
intention to advance one’s coaching career as the proximate predictor of one’s career
advancement.
There is no empirical study to date in any field, including athletics, which
extensively examined the leaders’ career advancement model and explored possible
gender differences in the process. The present study was exploratory in nature; thus it
did not aim to propose a theoretical framework of leaders’ career advancement. Rather,
informed by past literatures in the field (e.g., DeRue & Wellman, 2009; Dragoni, Tesluk,
& Oh, 2009; Machida, Schaubroeck, & Feltz, 2012; Moran-Miller & Flores, 2011) as well
as various theoretical models and frameworks (Bandura, 1997; Van Velsor, McCauley,
& Ruderman, 2010), the present study examined possible influences of a wide array of
antecedents on coaches’ career intention, which was conceptualized to be the predictor
of their career advancement. I identified plausible facilitating antecedents including
developmental challenges, feedback, support, mentoring relationship, and learning

3

orientation. Common issues women face in advancing their careers, such as workfamily and family-work conflicts and gender discrimination, were also considered. The
hypothesized model is presented in Figure 1. In addition to testing the hypothesized
model, I examined the potential differences in the factors and roles of these factors in
the leader development and career advancement processes between women and men.

4

Figure 1. Hypothesized Model of Leader Development and Career Advancement of Coaches
5

Leader Competency
Leader development can be defined as the process by which individuals learn
leadership skills and develop the competencies critical for effectiveness in leadership
positions (Dragoni et al., 2009; Spreitzer, McCall, & Mahoney, 1997; Van Velsor et al.,
2010). Leader competency includes cognitive, interpersonal, business, and strategic
skills (Mumford, Campion, & Morgeson, 2007). If assistant coaches are properly being
developed as leaders and have high leader competency, they are more likely to pursue
their leadership careers and advance to higher leadership positions. Thus, leader
competency was examined as a direct predictor of career intention.
Motivation to Lead
Motivation to lead can be defined as “an individual differences construct that
affects a leader’s or leader-to-be’s decisions to assume leadership training, roles, and
responsibilities and that affect his or her intensity of effort at leading and persistence as
a leader” (Chan & Drasgow, 2001, p.482). Though research on motivation to lead is still
a relatively newly emerged area of inquiry, and few studies have examined it as a focal
variable, having high motivation to lead is considered critical for a leader’s success
(Chan & Drasgow, 2001; Kark & Van Dijk, 2007; Machida et al., 2012; Van Iddekinge,
Ferris, & Heffer, 2009). Low motivation to lead may, therefore, negatively affect the
development of coaches’ leader competency, which, in turn, may lower their intention to
advance in their career. Thus, I anticipated that motivation to lead indirectly relates to
career intention through leader competency.
Hypothesis 1. Motivation to lead positively relates to career intention through
leader competency.

6

In addition to this indirect relationship, I hypothesized that motivation to lead is
directly related to career intention. Leaders who are in an early stage of their career
may not yet possess high leader competency because they are in the midst of
developing their skills. However, if they have high motivation to lead, they may be able
to maintain high career intention in this developmental phase that poses many
difficulties and failure experiences to leaders. Motivation to lead is conceptualized to
influence leaders’ decisions to assume and take leadership roles (Chan & Drasgow,
2001). For leaders to put maximal efforts in developing and advancing their careers to
higher leadership positions, it may be critical to be able to maintain high motivation and
high career intention. Thus, I hypothesized that motivation to lead is not only related to
career intention through leader competency, but also it is directly related to career
intention.
Hypothesis 2. Motivation to lead directly and positively relates to career
intention.
Leader Self-Efficacy
The self-efficacy belief of the leader could play a vital role in leaders’ career
advancement. Self-efficacy is one of the largest areas of research in applied
psychology, including education, organizational psychology, and athletics (e.g., Chen &
Bliese, 2002; Feltz, Short, & Sullivan, 2008; Schunk, 1996; Stajkovic & Luthans, 1998),
although there is a paucity of studies that examine its role in leadership (Machida &
Schaubroeck, 2011). Leader self-efficacy can be defined as a person’s perception of his
or her “abilities, knowledge, and skills in areas needed to lead others effectively”
(Machida & Schaubroeck, 2011, p.460). Though past research has indicated that high

7

self-efficacy beliefs of leaders are associated with greater work performance and leader
effectiveness (e.g., Ng, Ang, & Chan, 2008; Paglis & Green, 2002; Stajkovic & Luthans,
1998), the role of self-efficacy in career advancement is still unclear.
Bandura (1997) argued that self-efficacy beliefs promote the learning of new
skills by affecting an individual’s actions, efforts, and persistence. The findings from past
research, which showed the linkage between leader self-efficacy and motivation to lead
(Chan & Drasgow, 2001; Machida et al., 2012), and between leader self-efficacy and
leader performance (Paglis & Green, 2002; Ng et al., 2008; Stajkovic & Luthans, 1998),
support this perspective. Therefore, I hypothesized that higher self-efficacy beliefs may
be positively associated with leaders’ motivation to lead others, which in turn may
increase their leader competency and their intention to advance career.
Hypothesis 3. Leader self-efficacy positively relates to leader competency
through motivation to lead.
Hypothesis 4. Leader self-efficacy positively relates to career intention through
motivation to lead.
Outcome Expectancy
In social-cognitive theory, Bandura (1997) explained the relationships between
self-efficacy beliefs and outcome expectancies: “perceived self-efficacy is a judgment of
one’s ability to organize and execute given types of performances, whereas an outcome
expectation is a judgment of the likely consequences such performances will produce”
(p.21). Social-cognitive theory argues that self-efficacy and outcome expectancy need
to be distinguished, because correct performance does not always lead to the
individual’s desired outcome. Thus, even if assistant coaches possess high self-efficacy

8

and high leadership skills, they might not expect that they will be able to obtain head
coaching positions because of a discriminatory environment or other barriers. Low
outcome expectancy may negatively affect coaches’ desire to lead others, which affect
their intention to advance in their careers. Past research suggests that outcome
expectancy plays a role in one’s career intention (Betz & Voyten, 1997; van Vianen,
1999), and the present study hypothesized that motivation to lead mediates this
relationship between outcome expectancy and career intention.
Hypothesis 5. Outcome expectancy positively relates to career intention through
motivation to lead.
Developmental Experiences
According to McCauley and colleagues (McCauley & Van Velsor, 2004; Van
Velsor et al., 2010), there are three components that constitute optimal developmental
experiences for leaders: developmental challenges, feedback (i.e., assessment), and
support. First, job assignments that challenge leaders to expand their comfort zone are
critical. Challenging experiences instill doubts in their current knowledge and skills and
motivate them to explore new information and strategies. Thus, challenging experiences
may cultivate a leader’s needs to learn and master the challenges to grow further. The
second component is assessment or process and performance feedback that leaders
obtain from multiple sources (e.g., themselves, supervisors, peers). Feedback gives
leaders information about their current strengths and weaknesses, and clarifies their
developmental needs and possible strategies to overcome challenges successfully.
The third important component, support, which can be in many forms (e.g., supervisor,
peers, family), can lessen the sense of insecurity and assist leaders in maintaining

9

confidence in the face of challenges. Such support includes the mentoring relationship
that leaders have with their supervisors. The importance of developmental experiences,
which include challenges, feedback, and support and mentoring relationship, is
demonstrated in research (e.g., Birdi, Allan, & Warr, 1997; DeRue & Wellman, 2009;
Hazucha, Hezlett, & Schneider, 1993; Higgins, 2000).
Past research indicates that developmental experiences play critical roles in
leader development. However, the mechanism by which these developmental
experiences result in development is still unclear. Bandura (1997) identified the four
main sources of self-efficacy: achievement experiences, verbal persuasion, vicarious
experiences, and physiological and affective states. Challenges, feedback, support, and
the mentoring relationship can be categorized into these sources proposed by Bandura,
thus have direct influence on self-efficacy. Machida et al. (2012) showed that
challenges, feedback, and support predict women’s leader self-efficacy, which in turn
predicts their motivation to lead, which is then related to their career ascendance. The
present study hypothesized that developmental experiences are positively related to
leader self-efficacy, and leader self-efficacy mediates the relationship between
developmental experiences and motivation to lead, which is related to leader
competency and career intention.
Hypothesis 6. Developmental challenges, feedback, support, and mentoring
relationship positively relate to leader self-efficacy.
Hypothesis 7. The relationships between developmental challenges, feedback,
support, and mentoring relationship, and motivation to lead are mediated by
leader self-efficacy.

10

In addition, the present study examined the direct relationship between these
developmental experiences and leader competency. Though self-efficacy is established
as a strong predictor of one’s behavior and performance, it is possible that leaders can
increase their leader competency without increasing their self-efficacy. Developmental
experiences may impact coaches’ leader competency directly. Leaders, who are in the
developmental phase, may be underestimating their abilities because of the challenges
they are facing, and they may be attending more to their deficits (Van Velsor et al,
2010). Thus, though leaders may have low self-efficacy, they may still demonstrate high
leader competency as long as they are exposed to quality developmental experiences.
The study hypothesized that developmental experiences have a direct and positive
relationship with leader competency.
Hypothesis 8. Developmental challenges, feedback, support, and mentoring
relationship quality directly relate to leader competency.
Learning Orientation
Scholars suggest that the amount of learning that occurs in similar
developmental experiences may differ among individuals, and such variation can be
attributed to individual differences in the individual’s ability to learn (Spreitzer et al.,
1997; Van Velsor et al., 2010). The ability to learn is “a complex combination of
motivational factors, personality factors, and learning tactics” (Van Velsor et al., 2010,
p.5). Such individual differences could include a construct such as learning orientation.
Learning orientation is referred to as an individuals’ focus on mastery of tasks, learning,
and increasing competence (Dweck, 1975, 1986). A high learning orientation fosters an

11

individual’s motivation to explore new strategies and perspectives, which would be
critical for the individual’s long-term development (Dweck, 1975, 1986).
Recent studies by DeRue and Wellman (2009) and Dragoni et al. (2009) support
the positive roles that learning orientation plays in leader development, and past
research in various settings reported a positive link between learning orientation and
self-efficacy (e.g., Bell & Kozolowski, 2002; VandeWalle, Cron, & Slocum, 2001; Vosloo,
Ostrow, & Watson, 2009). Further, McCauley and colleagues (McCauley & Van Velsor,
2004; Van Velsor et al., 2010) and empirical studies (e.g., Birdi et al., 1997; VandeWalle,
Ganesan, Challagalla, & Brown, 2000) suggest that learning orientation is positively
related to individuals’ engagement in developmental experiences. Thus, I hypothesized
that coaches’ learning orientation is related to their extent of engagement in
developmental experiences (e.g., developmental challenges, feedback, support, and
mentoring relationship), which in turn influences their leader self-efficacy.
Hypothesis 9. Learning orientation positively relates to developmental
challenges, feedback, support, and mentoring relationship.
Hypothesis 10. The relationship between learning orientation and leader selfefficacy is mediated by developmental challenges, feedback, support, and
mentoring relationship.
Work-Family and Family-Work Conflict and Gender Discrimination
Though developmentally challenging experiences may play positive roles in
leader development, women who aspire to be leaders have to deal with unique
challenges in addition to the challenges all leaders encounter (McCauley & Van Velsor,
2004). Challenges that are unique to women leaders may include extensive domestic

12

responsibilities at home, work-family and family-work conflicts (e.g., Bianchi, Milkie,
Sayer, & Robinson, 2000; Carlson, Kacmar, & Williams, 2000), and gender
discrimination in their organizations (e.g., Eagly & Carli, 2007; Epitropaki & Martin,
2004). These challenges are also documented in the research on women leaders in
athletics (e.g., Bracken, 2009; Greenhill et al., 2010; Leberman & Palmer, 2009). It is, in
fact, possible that these challenges are more evident in the field of athletics, which has
been historically male-dominated. It is important to consider these additional challenges
and how these challenges might affect coaches’ development and career advancement.
Adverse physiological and affective states are among the main sources of selfefficacy levels (Bandura, 1997); thus, I anticipated that coaches who have faced high
levels of obstacles, such as work-family and family-work conflicts, and gender
discrimination, are more likely to experience adverse physiological and affective
reactions (e.g., anxiety, exhaustion, frustration), which in turn reduce their motivation to
lead.
Hypothesis 11. Work-family and family-work conflicts and gender discrimination
negatively relate to leader self-efficacy.
Hypothesis 12. The relationships between work-family and family-work conflicts
and gender discrimination and motivation to lead are mediated by leader selfefficacy.
Furthermore, I anticipated that these challenges are not only related to leader
self-efficacy, but also are related to outcome expectancy, and in turn influence leaders’
desire to lead others. Even when they expect to face family interference with work, or
work interference with family, or anticipate their gender to be a hindrance for their

13

career advancement, they may still be able to maintain high self-efficacy as leaders
because they are aware that in the absence of these obstacles, they could be
successful. However, such obstacles that are rather uncontrollable may decrease their
expectancy to obtain the desirable outcomes, which in turn, reduces their motivation to
lead.
Hypothesis 13. Work-family and family-work conflicts and gender discrimination
negatively relate to motivation to lead through outcome expectancy.
In addition to the indirect relationship through leader self-efficacy and outcome
expectancy, I also hypothesized that work-family and family-work conflicts directly
influence leaders’ motivation to lead. Leaders’ motivation to lead may be significantly
and negatively affected when family and domestic responsibilities interfere with work
and vice versa, because such conflicts make it difficult for them to assume and take
responsibilities as leaders. This decrease in motivation to lead is not necessarily due to
a decrease in their confidence about their leadership skills or outcome expectancy.
Leaders may be able to maintain their strong sense of self-efficacy and possess high
outcome expectancy in the face of family interference with work or work interference
with family because they may be aware that they can be successful and achieve the
favorable career outcome if they are not faced with these constraints. Machida et al.
(2012) demonstrated that family-work conflict is negatively and directly related to
motivation to lead of women leaders in athletics, separately from its indirect influence
through leader self-efficacy. Thus, leader self-efficacy and outcome expectancy may not
be the only factors which directly influence motivation to lead; conflicts rising from
having family life may simply reduce coaches’ motivation to lead.

14

Hypothesis 14: There are direct negative relationships between work-family and
family-work conflicts and motivation to lead.
Gender Comparison in the Hypothesized Model
Past studies suggest that many people, especially males in power positions,
believe affirmative action programs are institutional discrimination against men
(Greenhil, et al., 2009; Leonard, 1989). However, scholars in business and sport
settings have indicated that programs that are specifically targeted to women are
necessary to eliminate the inequities in the workplace (Greenhill et al., 2009; Knoppers,
1987; Leonard, 1989; Lovett & Lowry, 1994; Schwalbe et al., 2000; Shaw & Frisby,
2006; Stangl & Krane, 1998). The current status of women in leadership and decisionmaking positions in athletics, as well as past studies on leader development, suggest
that it is imperative to examine how women can be developed as leaders and what
potential differences exist in the career advancement processes of women and men.
Such examination will increase understanding of general leader development issues,
inform programs and practices of developing leaders not only in the field of athletics but
also in other fields, and possibly increase the number of women who will hold higher
leadership positions in the future. Thus, in addition to testing the aforementioned
hypotheses, the present study examined gender differences in the hypothesized model.
Definitions
Developmental Challenges
Developmental challenges consist of five dimensions: (a) unfamiliar
responsibilities, (b) creating change, (c) high levels of responsibility, (d) managing
boundaries, and (e) managing diversity (McCauley et al., 1999).

15

Developmental Experiences
Being consistent with McCauley and colleagues’ (McCauley & Van Velsor, 2004;
Van Velsor et al., 2010) leader development model, developmental experiences were
conceptualized to consist of three main components: developmental challenges,
feedback (i.e., assessment), and support.
Feedback (Assessment)
Feedback involves the performance and process evaluations that a leader
receives from multiple sources (e.g., themselves, supervisors, peers, employees,
family) (Van Velsor et al., 2010). For the present study, quality of feedback that
assistant coaches received from the head coaches was assessed.
Gender Discrimination
Gender discrimination in the present study referred to assistant coaches’
perception of gender discrimination in the workplace.
Intention to Advance Careers (Career Intention)
Being consistent with conceptual work (Ajzen, 1991) and past research on
intentions (e.g. Hurtz & Williams, 2009), the intentions to advance one’s career to a
head coaching position was measured with three types of intentions: desire (i.e., I want
to be a head coach), felt responsibility (i.e., I feel obligated to be a head coach), and
self-prediction (i.e., I will be a head coach).
Leader Competency
Leader competency was defined as the knowledge and skills necessary to lead
others successfully (Spreitzer et al., 1997; Dragoni et al., 2009). Leader competency
included cognitive, interpersonal, business, and strategic skills (Mumford et al., 2007).

16

Leader Self-Efficacy
Self-efficacy is defined as “beliefs in one’s capabilities to organize and execute
the courses of action required to produce given attainments” (Bandura, 1997, p. 3). For
the present study, leader self-efficacy was defined as leaders’ (assistant coaches’)
confidence in their capacities to lead others effectively (Machida & Schaubroeck, 2011).
Learning Orientation
Learning orientation is defined as an individual’s emphasis on mastery of tasks,
learning, and increasing competence (Dweck, 1975, 1986).
Mentoring Relationship
Mentoring relationship encompasses two dimensions: mentoring quality and
mentoring learning. Mentoring quality encompasses perceived satisfaction, benefits,
and the depths associated with the mentoring relationship, whereas mentoring learning
is learning that occurs in mentors (head coaches) and mentees (assistant coaches) in
the relationship (Allen & Eby, 2003).
Motivation to Lead
Motivation to lead can be defined as “an individual differences construct that
affects a leader’s or leader-to-be’s decisions to assume leadership training, roles, and
responsibilities and that affect his or her intensity of effort at leading and persistence as
a leader” (Chan & Drasgow, 2001, p.482). Motivation to lead consists of three subconstructs: affective (i.e., like to lead others), social-normative (i.e., lead for a sense of
responsibility), and non-calculative (i.e., lead without considering the costs and
benefits).
Outcome Expectancy

17

Outcome expectancy is defined as “a judgment of the likely consequences such
performances will produce” (Bandura, 1997, p.21). For the present study, assistant
coaches’ outcome expectancy toward obtaining head coaching positions was assessed.
Support
Support is defined as tangible and emotional assistance individuals receive from
others for their work (Van Velsor et al., 2010). It can be in many forms (e.g.,
supervisors, peers, employees, family, organization); for the present study, work
support that assistant coaches receive from head coaches was assessed.
Work-Family and Family-Work Conflicts
Work-family conflict is defined as “a form of interrole conflict in general demands
of, time devoted to, and strain created by the job interfere with performing family
responsibilities” (Netemeyer, Boles, & McMurrian, 1996, p.401), while family workconflict is defined as “a form of interrole conflict in which the general demands of, time
devoted to, and strain created by the family interfere with performing work-related
responsibilities” (Netemeyer et al., 1996, p.401).
Delimitations
The study was delimited to assistant coaches and their head coaches who coach
women’s teams in intercollegiate athletics (NCAA Division I, II, and III). Coaches who
coach women’s teams were selected because I aimed to make a meaningful
comparison between genders. As indicated, there are fewer than 3% of female coaches
who coach men’s teams. Men’s teams are deemed contextually different from women’s
teams. Coaches who participated in the present study were female and male, older than
18 years old, with various demographic characteristics (i.e., ethnicity, educational

18

attainment, marital status). Assistant coaching positions included both paid and unpaid
(volunteer) positions, and participants had varying degrees of experiences.

19

CHAPTER II

LITERATURE REVIEW
Women’s status in the workplace is still lower than men’s. The recent report
issued by the U.S. government shows that although women outnumber men in earned
college degrees and graduate education, women still earn 75% as much as their male
counterparts at all educational levels. Women are three times more likely than men to
work in administrative support jobs. Though women hold about 50% of middle
management positions in the United States (U.S. Bureau of Labor Statistics, 2010), few
women hold the top management and leadership positions. For example, as of 2011,
only 14 of Fortune 500 companies (Fortune, 2011) and 98 of 3049 publicly traded
companies (Petrecca, 2011) were led by female CEOs.
Research consistently shows that women and men are generally equally
qualified for leadership jobs (Eagly & Carli, 2007; Eagly & Chin, 2010), and some
studies have indicated that women tend to exhibit more effective leadership styles than
men (Eagly, Johannessen-Schmidt, & van Engen, 2003). Further, some scholars
suggest that leadership styles that women tend to employ are seen more favorably in
contemporary organizations (e.g., Eagly & Carli, 2007; Eagly & Chin, 2010). Despite
past studies that claim that women should be equally good leaders as men, if not better,
underrepresentation of women in leadership positions is still apparent across various
professions.
This chapter reviews the literature relevant to the present study. First, I review
the current status of women in athletic professions as well as the implications of

20

increased representation of women in athletic leadership positions. The discussion
focuses on the representation of women in the coaching profession, which has shown a
significant decline in the past three decades (Acosta & Carpenter, 2012), and also was
the population of interest in the present study. Second, I review research on career
intention, because it is considered the best predictor of career advancement in athletics
and was the focal dependent variable in the present study. The hypothesized model of
career advancement of coaches is presented. Finally, I review the literature on
predictors of career intentions. Plausible gender differences in the roles of each
antecedent are also discussed.
Underrepresentation of Women in Athletic Careers
Women are generally underrepresented in higher leadership positions; there are
very few women leaders, especially in fields such as athletics that are considered
“masculine” in nature. This trend is salient worldwide and can be seen in the structure of
many athletic organizations, such as the International Olympic Committee (IOC). The
IOC is the umbrella organization of all Olympic events, and perhaps is one of the most
influential sport organizations in the world. The IOC consists of 106 representatives
from 85 different countries (International Olympic Committee, 2012). However, there are
only 20 women representatives, and there are only two women out of 15 members on
the current IOC executive board. In addition, the IOC has never had a woman as
president in its history. The statistics of intercollegiate athletics in the United States also
suggest that women are significantly underrepresented in leadership and decision
making positions (Acosta & Carpenter, 2012). For example, women hold about 36% of
administrative positions in athletic administration; however, the percentages of women

21

are even less in senior management. Only 20% of athletic director positions in NCAA
registered institutions (Division I, II, and III) are held by women. In Division I institutions,
women hold 9% of such positions (Acosta & Carpenter, 2012).
Though small, women’s representation in positions such as collegiate athletic
administration have increased over the last 30 years (Acosta & Carpetner, 2012). Yet,
intercollegiate sport coaching is an athletic career that shows significant decline in
women’s representation. Women coached over 90% of women’s teams in 1972, when
Title IX was instituted. However, currently, women represent 43% of head coaching
positions in women’s teams. Thus, in the intercollegiate athletics, men coach more than
half of women’s teams, although women coach less than 3% of men’s teams (Acosta &
Carpenter, 2012).
Increasing female representation in the coaching profession has significant
implications. Scholars argue that female coaches can have a positive impact on the
lives of athletes, especially young female athletes (e.g., Hasbrook, 1987; Hepler & Feltz,
2008). Female coaches can serve as role models to these athletes, which impact their
participation in sport and physical activities (Hepler & Feltz, 2008; Marshall, 2001) and
their later advancement to athletic professions (Everhart & Chelladurai, 1998; MoranMiller & Flores, 2011). Demonstrating that women can be successful in male-dominated
fields such as athletics can challenge the dominant conception in society that women
cannot lead, facilitate the acceptance of female leaders in young people, and motivate
young girls to pursue a leadership career in athletics (Hepler & Feltz, 2008).
Though increasing female representation in leadership positions in athletics
could bring significant changes, women face various psychosocial difficulties and

22

challenges in advancing their career (e.g., Bracken, 2009; Eagly & Carli, 2007;
Epitropaki & Martin, 2004; Sartore & Cunningham, 2007). Scholars in business and
sport settings suggest that women’s and men’s career development processes may
differ, and advocate the need for programs that are specifically targeted to women in
order to increase women’s representation in leadership and to achieve gender equity in
organizations (Greenhill, Auld, Cuskelly, & Hopper, 2009; Knoppers, 1987; Leonard,
1989; Lovett & Lowry, 1994; Schwalbe, Godwin, Holden, Schrock, Thompson, &
Wolkomir, 2000; Shaw & Frisby, 2006; Stangl & Krane, 1998). However, there is little
empirical study that examined such gender differences; thus, it is still unclear how
women’s and men’s leader development and career advancement may be different, and
how the differences surface in the career development of intercollegiate sport coaches
where there is a decline in women’s representation.
Intention as a Predictor of Career Advancement
Intention can be considered as the best predictor of many individuals’ behaviors
(Ajzen, 1991; Armitage & Conner, 2001; Gordin & Kok, 1996), and this relationship
between intention and behaviors is established in various settings, such as health and
safety (e.g., Gordin & Kok, 1996; Elliot, Armitage, & Baugham, 2003), exercise (e.g.,
Dzewaltowski, Noble, & Shaw, 1990; Hagger, Chatzisarantis, & Biddle, 2001), and the
industries and organizations (e.g., Maurer & Palmer, 1999; Hurtz & Williams, 2009;
McCarthy & Garavan, 2006). Although there is a dearth of studies examining the
intention of leaders, a few studies suggest that, as compared to men, women have
lower intention for advanced careers in general business (Gupta, Turban, & Bhawe,
2008; van Vianen & Keizer, 1996), as well as in athletic settings (Cunningham, Sagas,

23

& Ashley, 2003; Cunningham, Doherty, & Gregg, 2007). Studies in athletics, which
include Cunningham et al. (2003, 2007) and the recent work by Moran-Miller and Flores
(2011), advocate that strong career intention is important for women’s career
development in athletics. Intention may be especially critical for women’s career
advancement, and can be one of the factors that explain why women are
underrepresented in leadership positions in athletics. Having career intention as the
proximate predictor of career advancement, I present a model of leader development
and career advancement of collegiate coaches in Figure 1.
Antecedents of Career Intention
Informed by theories (Bandura, 1997; Van Velsor, McCauley, & Ruderman,
2010) and existing literatures (e.g., Chan & Drasgow 2001; DeRue & Wellman, 2009;
Dragoni, Tesluk, & Oh, 2009; Machida, Schaubroeck, & Feltz, 2012), I identify the
antecedents of career intention. First. The relationships between career intention, and
leader competency and motivation to lead are explained. Drawing upon social-cognitive
theory (Bandura, 1997), I also discussed the roles of outcome expectancy and leader
self-efficacy. In addition, based on McCauley and colleagues’ leader development
model (Van Velsor et al., 2010), social-cognitive theory (Bandura, 1997), and past
literature, I discuss the effects of developmental experiences that include developmental
challenges, feedback, support, mentoring relationship, and learning orientation. I also
consider the influences of challenges such as work-family and family-work conflicts and
gender discrimination in the model.
Leader Competency

24

Leader development is defined as the process by which individuals learn
leadership skills and develop competencies necessary for effectiveness as leaders
(Dragoni et al., 2009; Van Velsor et al., 2010). Past research (e.g., Hazucha, Hezlett, &
Schneider, 1993) shows that leading or management skills are predictive of employees’
career advancement. If assistant coaches are properly being developed as leaders,
they might be more likely to pursue leadership careers and advance to higher
leadership positions. Thus, current leader competency can be a predictor of career
intention. Leader competency is defined as the knowledge and skills necessary for
successful leadership (Dragoni et al., 2009; Spreitzer, McCall, & Mahoney, 1997).
Leading and coaching. Studies of both leader and coach development were
reviewed due to the observation that there seems to be overlap between the factors that
affect leader and coach development. However, coaching and leading are conceptually
different. In fact, the literature seems to suggest that leading requires broader skill sets
than coaching.
Leader competency. Mumford, Campion, and Morgeson (2007) examined the
leadership skills that are required for leaders at different levels of organizational
leadership. The study identified four main leadership skill requirements, which include
cognitive skills, interpersonal skills, business skills, and strategic skills. Mumford et al.
considered cognitive skills as the basis of leadership skills, which are related to basic
cognitive capabilities (i.e., collecting, processing, and disseminating information, and
learning). These skills include oral and written communication skills, reading
comprehension skills, active learning skills, and critical thinking skills that allow leaders
to process new information and its implication.

25

The second category of leadership skill requirement is interpersonal skills, which
involve the social skills that are vital in interacting with and influencing others (Mumford
et al., 2007). Interpersonal skills encompass social perceptiveness, negotiation skills,
and persuasion skills, which are important in developing leaders’ awareness and
coordination of their own reactions and reactions of others. These skills enable them to
develop satisfying and productive relationships and influence others effectively to
accomplish teams’ and organization’s goals. Business skills is the third category of
leadership skills, which include the skills that are necessary to create an optimal work
context (Mumford et al., 2007). These skills include management of material, personnel,
and financial resources, and operation analysis. These skills assist leaders in making
critical decisions while they obtain and allocate resources.
The fourth category of leadership skills involves strategic skills, which are highly
conceptual skills that require a systems perspective “to understand complexity, deal
with ambiguity, and to effect influences in the organization” (Mumford et al., 2007,
p.157). These include visioning and system perception skills, which allow leaders to
have clear organizational objectives and ideas of when changes occur. Strategic skills
also encompass environmental scanning skills, which include identification of
downstream consequences and key causes, which in turn allow leaders to have a
deeper understanding of the environment. Strategic skills also encompass problem
identification skills, which assist leaders in determining the nature of organizational
problems, and the solution appraisals and objective evaluation skills that allow leaders
to actively evaluate and lay out the solutions for these problems. Mumford et al. (2008)
tested this model of four leadership skills in a sample of 1023 managers at different

26

organizational levels (i.e., junior, midlevel, and senior), and these four categories of
leadership skills were theoretically and empirically supported. In addition, DeRue and
Wellman (2009) successfully used these four leadership skills as taxonomy to measure
leadership skill development of managers.
Coach competency. Multiple theoretical frameworks have conceptualized
coaching effectiveness, including models such as the Multidimensional Model of
Leadership (Chelladurai & Saleh, 1978) and the Mediational Model of Leadership (Smoll
& Smith, 1989). The most recent framework proposed is Myers and colleagues’ (Myers,
Chase, Beauchamp, & Jackson, 2010; Myers, Feltz, Maier, Wolfe, & Reckase, 2006;
Myers, Feltz, & Wolfe, 2008) coaching competency model, which was developed based
on the work of coaching self-efficacy (Feltz, Chase, Moritz, & Sullivan, 1999; Myers,
Feltz, Chase, Reckase, & Hancok, 2008).
Myers and colleagues (Myers, Chase, et al., 2010; Myers, Feltz, Maier, et al.,
2006; Myers, Feltz, & Wolfe, 2008) defined coaching competency as a “head coach’s
ability to affect athletes’ learning and performance” (Myers, Chase, et al., 2010, p.478),
and they conceptualized it as a multidimensional construct that includes four main
dimensions. First is motivation, which is defined as the coach’s ability to effectively
influence the athletes’ motivation for their optimal performance. The second dimension,
game strategy, refers to the coach’s skills in leading athletes to desired outcomes at
competitions. The third dimension, technique, involves the coaches’ ability to instruct
athletes to improve their sport skills. Character building is the fourth dimension, which
refers to the coach’s skills in facilitating the athletes’ personal development and positive
attitudes toward sports.

27

Comparing leader and coach competency. From examining each dimension of
leader competency in Mumford et al. (2007) and coach competency in Myers et al.
(Myers, Chase, et al., 2010; Myers, Feltz, et al., 2006), one can observe that leader
competency encompasses a broader set of skills than coach competency does. For
example, the interpersonal skill dimension identified in Mumford et al. can be
considered as an overarching dimension that encompasses the motivation and
character building dimensions of coach competency in Myers et al. The dimensions
game strategy and technique in coach competency can be integrated into the dimension
in leader competency as business skills, which are associated with the individual’s skills
in creating an effective work context, conducting operational analysis, and managing
resources. Thus, it seems that the dimensions of coaching competency can be captured
in leader competency, although there are some dimensions in leader competency, such
as cognitive skills and strategic skills, that might not be completely captured in coach
competency.
In sum, the theories and models available in the coaching and leadership
literatures seem to suggest that leading requires a broader set of skills than coaching,
which implies that leadership skills can be considered as foundational skills for effective
coaching. Therefore, developing leader competency might be critical for assistant
coaches to acquire coach competency, and to develop and remain as an effective
coach throughout their career. Thus, I propose that leader competency is related to
coaches’ career intention.
Gender differences in leader competency. Research, in general, is in
agreement that women and men are generally equally qualified for leadership jobs

28

(Eagly & Carli, 2007; Eagly & Chin, 2010). Some studies have even indicated that
women tend to exhibit more effective leadership styles than men. A meta-analysis of 45
past studies on transformational, transactional, and laissez-faire leadership styles
reported that, as compared to men, women demonstrate more transformational
leadership behaviors and contingent reward behaviors, which are considered effective
leadership styles (Eagly et al., 2003). Further, scholars have suggested that women
adopt leadership styles that may be more favorable in contemporary organizations (e.g.,
Eagly & Carli, 2007; Eagly & Chin, 2010). Despite these past studies that claim that
women should be equally good leaders as men, if not better, women are
underrepresented in leadership positions (Eagly & Carli, 2007; Acosta & Carpenter,
2012). It is imperative to examine gender differences in the role of leader competency in
career advancement.
Motivation to Lead
Motivation to lead is “an individual differences construct that affects a leader’s or
leader-to-be’s decisions to assume leadership training, roles, and responsibilities and
that affect his or her intensity of effort at leading and persistence as a leader” (Chan &
Drasgow, 2001, p.482). Thus, the leader’s motivation to lead could play an important
role in leader development. Motivation to lead consists of three sub-constructs:
affective-identity motivation to lead, in which individuals are motivated to lead because
they like to lead others; social-normative motivation to lead, in which individuals are
motivated to lead for a sense of responsibility; and non-calculative motivation to lead, in
which individuals are motivated to lead without considering the costs and benefits
associated with taking the leadership roles. There is a dearth of studies examining the

29

effects of motivation on leader development (Chan & Drasgow, 2001; Kark & Van Dijk,
2007); however, it can be hypothesized that motivation to lead is critical in developing
leader competency. Chan and Drasgow (2001) conceptualized that motivation to lead
affects leaders’ decisions to assume the leadership role, to engage in leadership
training, and to persist in leading others. Chan and Drasgow’s study also showed that
motivation to lead is positively related to military recruits’ leadership potentials as rated
by their supervisors and peers. A recent study by Machida et al. (2012) also reported
that motivation to lead is positively related to women leaders’ career ascendance. Low
motivation to lead may, therefore, negatively affect the development of one’s leader
competency, which may lower their intention to advance their career. Thus, I anticipate
that motivation to lead is indirectly related to career intention through it is effect on
leader competency. Therefore, I put forth the following hypothesis:
Hypothesis 1. Motivation to lead positively relates to career intention through
leader competency.
In addition to this indirect relationship, I hypothesize that motivation to lead is
directly related to assistant coaches’ career intention. There is no study that has
examined this relationship; however, it can be assumed that leaders who are in an early
stage of their career may not yet possess high leader competency because they are in
the midst of developing skills. Nevertheless, if they have high motivation to lead, they
may be able to maintain high career intention in this developmental phase, which may
accompany many of the challenges and difficulties. Motivation to lead is conceptualized
to influence leaders’ decisions to assume and take leadership roles (Chan & Drasgow,
2001). Thus, I hypothesize that motivation to lead is not only indirectly related to career

30

intention through leader competency, but also motivation to lead is directly related to
career intention.
Hypothesis 2. Motivation to lead directly and positively relates to career
intention.
Gender differences in motivation to lead. Because women face unique
challenges in addition to general challenges that all leaders encounter in advancing and
sustaining their career (e.g., Eagly & Carli, 2007; McCaley & Van Velsor, 2004), it may
be more crucial for women to maintain strong motivation to lead in their developmental
process than it is for men. However, as indicated above, motivation to lead is a
relatively new area of inquiry, and there is no conceptual or empirical study that has
examined such gender differences – although Machida et al.’s (2012) study of women
administrators in athletics showed that motivation to lead may play a crucial role in
women’s career advancement. Gender differences in the intensity of motivation to lead
as well as the role of motivation to lead in assistant coaches’ career advancement need
to be examined.
Leader Self-Efficacy
Self-confidence or self-efficacy beliefs of leaders may play a vital role in the
development of leader competency. Self-efficacy is defined as “beliefs in one’s
capabilities to organize and execute the courses of action required to produce given
attainments” (Bandura, 1997, p. 3). It is conceptualized to influence the:
course of actions [people] choose to pursue, how much effort they put forth in
given endeavors, how long they will persevere in the face of obstacles and

31

failures, their resilience to adversity, [and] whether their thought patterns are selfhindering or self-aiding. (Bandura, 1997, p. 3)
Leader self-efficacy can be defined as individuals’ confidence in their capabilities to lead
others effectively (Machida & Schaubroeck, 2011).
Very few empirical studies have examined the relationship between leader selfefficacy and leaders’ performance (Anderson, Krajewski, Goffin, & Jackson, 2008;
Hannah, Avolio, Luthans, & Harms, 2008). Nonetheless, studies suggest that leader
self-efficacy is related to the effectiveness of the leaders as rated by others and to
greater work unit outcomes. For example, leader self-efficacy has been shown to be
associated with greater work-performance and leaders’ effectiveness (Paglis & Green,
2002; Stajkovic & Luthans, 1998), and with organizational performance (Wood &
Bandura, 1989).
Studies of the role of self-efficacy in the development of leaders are even scarcer
than studies of self-efficacy in relation to leadership outcomes. However, scholars have
suggested that leader self-efficacy is related to leader development (Hanna et al.,
2008). I further argue that motivation to lead explains the mechanism by which leader
self-efficacy influences leader development. Social cognitive theory (Bandura, 1997)
argues that one of the ways in which self-efficacy influences performance and learning
is through its effect on actions, efforts, and persistence. Empirical investigation also
provides support for this perspective. Chan and Drasgow’s (2001) study showed that
leader self-efficacy is positively related to motivation to lead, which, in turn, is positively
related to leaders’ potentials. Machida et al. (2011) also demonstrated that leader selfefficacy is positively related to motivation to lead, which is related to women leaders’

32

career ascendance. Thus, I hypothesized that leader self-efficacy is related to leader
competency through its effect on motivation to lead of coaches.
Hypothesis 3. Leader self-efficacy positively relates to leader competency
through motivation to lead.
In addition, self-efficacy has been linked to career intention. A study by Betz and
Voyten (1997) on 350 college students’ career explorations showed that career decision
making self-efficacy belief was negatively associated with career indecision and
positively associated with career exploration intentions. With a sample of 1,030
employees, van Vianen (1999) also reported that managerial self-efficacy was positively
related to ambition for a managerial position. Although the link between self-efficacy and
career intention is established to some degrees, the mechanism by which self-efficacy
results in changes in career intention is still unclear. As indicated by Bandura (1997),
self-efficacy influences one’s performance and learning through its impacts on actions,
effort, and persistence. Thus, for the present study, I hypothesized that leader selfefficacy influences motivation to lead, which, in turn, affects career intention, which was
conceptualized as a proximate predictor of coaches’ career advancement.
Hypothesis 4. Leader self-efficacy positively relates to career intention through
motivation to lead.
Gender differences in leader self-efficacy. Research suggests that women
leaders are usually less confident than their male counterparts (e.g., Hacket & Betz,
1981; Heilman, Simon, & Repper, 1987; Pallier, 2003). Hacket and Betz (1981)
proposed that women’s restrained positions at work may be, at least in part, attributed
to their low self-efficacy beliefs. Hacket and Betz’s claim is supported in research. For

33

example, the examination by Heilman et al. (1987), on women’s and men’s reactions to
affirmative action, showed that when selected on the basis of sex preference rather than
merit, women evaluate their performance more negatively and give themselves less
credits for successful outcomes. They also view themselves as less competent in
general leadership skills and show less desire to persist in their leadership roles.
Heilman et al. explained these results by claiming that women have lower confidence as
leaders, while men are generally more confident about their ability. Such differences
may create the discrepancy in their career attainment. The results were replicated in the
more recent study by Stewart and Shapiro (2000). Also, van Vianen and Keizer (1996)
reported that men demonstrate higher managerial self-efficacy, which in turn is related
to their higher ambition to be a manager than women.
The studies in sport settings also suggest that female leaders are less confident
than male leaders. Cunningham et al. (2007) showed that, although there are no gender
differences in the degrees of perceived barriers and support in becoming a head coach,
female assistant coaches show lower head coaching self-efficacy as compared to male
assistant coaches. Greenhill et al.’s (2009) investigation of females’ limited
representation in sport coaching also suggests that females may be underestimating
their capacity to be successful in the profession. Female coaches who participated in
the study indicated that they would not apply for higher coaching positions unless they
meet all the required qualifications, even if their experiences are extensive. Greenhill et
al. discussed that female coaches’ lack of self-confidence may be contributing to their
reluctance to make themselves available for positions, and their lack of self-confidence
may explain the underrepresentation of women in higher sport leadership positions.

34

Not only are the degrees of self-efficacy beliefs different, but also the roles of
self-efficacy in leader development may differ between genders. Klassen (2004)
reviewed and examined cross-cultural differences in efficacy beliefs. Comparison was
made between (a) Asian vs. Western and (b) Eastern Europe vs. Western Europe and
American. Past studies showed that self-efficacy is generally lower for individuals in the
non-Western group; however, Klassen suggested that these lower self-efficacy beliefs
do not necessarily lead to lower performance, and in some cases, lower self-efficacy is
more predictive of performance for the individuals from non-Western cultures.
What this review of studies by Klassen (2004) indicates is that there might be
cultural differences in the roles of self-efficacy in influencing performance. Women and
men are socialized in certain gender roles (e.g., Eagly & Carli, 2007; Eagly & Karau,
2002), and it is possible that the roles of self-efficacy are different for women and men
leaders. As indicated, studies suggest that women generally have lower self-efficacy as
compared to men (Hacket & Betz, 1981; Heilman et al., 1987; Pallier, 2003), although
research also indicates that females are as competent as males as leaders (e.g., Eagly
et al., 2003; Eagly & Carli, 2007). Thus, lower self-efficacy may not have a strong
impact on women’s competency as compared to men, or women may have a more
“realistic” sense of self-efficacy. Yet, self-efficacy is shown to be critical in increasing
career intention, which supposedly predicts career advancement (e.g., Cunningham et
al., 2003, 2007; Moran-Miller & Flores, 2011). In the development process as leaders,
women are required to deal with the challenges that they face at work, which include
general challenges as leaders and challenges that may be more evident for women,
such as work-family and family-work conflicts and gender discrimination (e.g., Bracken,

35

2009; Eagly & Carli, 2007; Epitropaki & Martin, 2004; Sartore & Cunningham, 2007).
Having high levels of leader self-efficacy may be more critical for women than it is for
men, in order to remain motivated to lead and advance in their career.
Outcome Expectancy
In social-cognitive theory, Bandura (1997) explained the relationships between
self-efficacy beliefs and outcome expectancies: “Perceived self-efficacy is a judgment of
one’s ability to organize and execute given types of performances, whereas an outcome
expectation is a judgment of the likely consequences such performances will produce”
(p.21). Social-cognitive theory argues that self-efficacy and outcome expectancy need
to be distinguished, because correct performance does not always lead to the
individual’s desired outcome. Thus, even if assistant coaches possess high self-efficacy
and high leadership skills, they might not expect that they would be able to obtain head
coaching positions. Past research suggests that outcome expectancy plays a role in
one’s career intention. In their study on career exploration of college students, Betz and
Voyten (1997) demonstrated that outcome expectancy is positively related to students’
career exploration intentions. In addition, van Vianen (1999) reported a positive
relationship between outcome expectancy and employees’ ambition for managerial
positions.
Although the relationship between outcome expectancy and career intention is
established to some degree, the mechanism by which outcome expectancy results in
changes in one’s intention has not been examined. I propose that outcome expectancy
negatively affects coaches’ desire to lead others, which, in turn, influences their
intention to advance careers. When individuals perceive that there is little chance that

36

they will obtain a higher leadership position, they may not be able to maintain high
motivation to lead, which lowers their career intention.
Hypothesis 5. Outcome expectancy positively relates to career intention through
motivation to lead.
Gender differences in outcome expectancy. There are few studies which have
systematically investigated gender differences in outcome expectancy and its effect on
career advancement. However, Cunningham et al. (2007) reported that female assistant
coaches expect less positive outcomes associated with being a head coach than their
male counterparts, although there are no significant gender differences in perceived
degrees of barriers and levels of support in becoming a head coach. Thus, there may
be gender differences in career outcome expectancy between women and men, which
may impact their differences in motivation to lead and career intention. In addition, the
role of outcome expectancy in career advancement may vary between women and
men. For women, having high outcome expectancy may be more crucial than for men
because past research indicates that women tend to hesitate to apply for jobs when
they believe they are not qualified and have little chance to receive an offer, even
though they have extensive experiences (Greenhill et al., 2009).
Developmental Experiences
Developmental work experiences may be critical for the development of leaders.
According to McCauley and colleagues (McCauley & Van Velsor, 2004; Van Velsor et
al., 2010), the work experiences that best facilitate leader development consist of three
components: developmental challenges, feedback (i.e., assessment), and support.

37

Developmental challenges. Challenging job assignments that force leaders to
expand their comfort zone are necessary components of work experiences (Van Velsor
et al., 2010). In a comfortable environment, leaders do not feel the urge to grow
because they become complacent and satisfied with existing knowledge and skills. On
the other hand, challenging experiences instill doubts in their current knowledge and
skills and motivate them to explore new information and strategies. Thus, challenging
experiences may cultivate leaders’ needs to learn and to take the challenges in stride
(Van Velsor et al., 2010).
McCauley, Ruderman, Ohlott, and Morrow (1994) developed and provided
validity evidence for the Developmental Challenge Profile (DCP) in response to the
need to measure developmental experiences for managers. Factor analyses of 692
managers yielded 15 DCP scales, which included: unfamiliar responsibilities, proving
yourself, developing new directions, inherited problems, reduction decisions, problems
with employees, high stakes, managing business diversity, job overload, handling
external pressure, influencing without direct authority, adverse business conditions, lack
of top management support, lack of personal support, and difficult boss. The study
showed the adequate internal consistency and test-retest reliability and confirmed the
factor structure. The study also showed the significant relationships between these
developmental challenges and reports of on-the-job learning, job transitions, objective
features of the jobs, and psychological states of the job incumbents, which provided
validity evidence.
Through the series of studies by McCauley and colleagues and their work at the
Center for Creative Leadership (McCauley, Ohlott, & Ruderman, 1999; Van Velsor et

38

al., 2010), DCP was further modified, and currently it includes 10 lower-order
dimensions with five higher-order dimensions or task-related characteristics that
increase the developmental values of the challenging work experiences (see Table 1).
Those five higher-order characteristics include: (a) unfamiliar responsibilities, (b)
creating change, (c) high levels of responsibility, (d) managing boundaries, and (e)
managing diversity. The first higher-order dimension, unfamiliar responsibilities, refers
to the degree to which individuals must handle responsibilities that are new or different
from their previous jobs. Individuals experience such challenges when they switch jobs
and obtain new responsibilities, or when there are significant changes in the senior
management and organizational structure. Unfamiliar responsibilities accompany novel
situations which break their routines and encourage them to adopt new tactics that
assist their growth. Also with unfamiliar responsibilities, individuals have to prove
themselves and re-gain credibility from their peers, followers, and superiors (McCauley
et al., 1994).
The second higher-order dimension, creating changes, involves three subdimensions of job characteristics: new directions, inherited problems, and problems with
employees (McCauley et al., 1999). New directions is the degree to which the job
assignment requires individuals to initiate a strategic change or a new project for the
organization. The dimension inherited problems refers to the degree to which the
assignment requires individuals to solve problems that existed before they were
assigned to the job. Problems with employees refers to the degree to which a job
assignment requires individuals to manage problem employees (e.g., who lack
experiences and motivation, or are resistant). Ambiguity associated with creating

39

changes motivates individuals to explore new strategies and set clear goals, which are
critical for their development (Dechant, 1990; McCauley et al., 1994).
The third higher-order dimension, high levels of responsibilities, is represented by
two sub-dimensions, high stakes and scope and scale (McCauley et al., 1999). The
dimension high stakes refers to the extent to which the job assignment encompasses
significant responsibilities through clear deadlines, pressure from superiors, and high
visibility, and through responsibilities for making decisions that significantly affect the
organization. Scope and scale is the degree to which the job assignment involves a
wide range of significant responsibilities. These components of the job assignment add
complexity to the job and require individuals to learn and improve their skills (Kelleher,
Finestone, & Kowy, 1986; McCauley et al., 1994).
Managing boundaries is the fourth higher-order dimension of developmental
challenges; it involves two aspects of job assignments: handling external pressures and
influencing without authority (McCauley et al., 1999). Handing external pressures is the
extent to which leaders need to manage relationships with and influence groups outside
the organization (e.g., clients, unions, government agencies, business partners).
Influence without authority is the degree to which the job assignment requires leaders to
influence people over whom they have no direct authority. Managing boundaries
requires leaders to improve their skills to build collaborative relationships with others
(Dechant, 1990; McCauley et al., 1994).
The last dimension, managing diversity, consists of two sub-dimensions: working
across cultures and working group diversity (McCauley et al., 1999). Working across
cultures refers to the extent to which the job assignment demands leaders to work with

40

people and groups from different cultures or different countries. Working group diversity
is the degree to which the job assignment requires leaders to work with and manage
people of both genders and different racial and ethnic backgrounds. Managing diversity
involves the responsibility for developing and making personnel decisions about a
diverse group of people. These experiences of managing diversity assist leaders to
develop an understanding of cross-cultural differences and diversity and to improve
their interpersonal adaptability (Spreitzer et al., 1997).

41

Table 1
Developmental Challenges (McCauley et al., 1999)
Higher Order SubScale

Lower Order Sub- Example Items
Scale

Experiencing a Job
Transition

Unfamiliar
Responsibilities

You lack experience important to
carrying out some aspect of your job.

Creating Change

New Directions

You have to carry out a major
reorganization as a result of a merger,
acquisition, downsizing, or rapid
growth.

Inherited
Problems

You inherited widespread morale
problems.

Problems with
Employees

Your direct reports resist your
initiatives.

High Stakes

You are responsible for decisive action
in a highly charged environment.

Scope and Scale

The job is potentially more than even a
good delegator can handle.

High Levels of
Responsibility

Managing
Boundaries

External Pressure You manage relationships with
government officials or regulatory
agencies.
Influence without
Authority

Managing Diversity

To accomplish a major portion of your
objectives, you must influence and
work with executives higher than your
immediate boss.

Work across
Cultures

You manage parts of the business that
are scattered across the world.

Work Group
Diversity

You have to get people from different
racial, religious, cultural, or ethnic
backgrounds to work together.

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Empirical studies support the effects of developmental challenges on leader
development. For example, DeRue and Wellman (2009) conducted a four-phase study
to examine the leadership skill development of managers. In the first phase, the authors
collected data on individual difference variables. During the second phase, the authors
conducted interviews with managers to collect specific examples and descriptions of job
experiences that affected their development as leaders. In the third phase, the
managers rated their developmental experiences using the DCP (McCauley et al., 1994,
1999). In the fourth phase, using the Mumford et al. (2007) taxonomy of leadership
skills, supervisors rated leadership skill development for each of the specific work
experiences identified by the managers in the second phase of the study. In the final
sample of 60 managers and 225 work experiences, the study reported that
developmentally challenging work experiences have a predominantly positive, inverted
U-shaped relationship with managers’ leadership skill development.
The study of Dragoni et al. (2009) also showed the benefits of developmental
challenges on managers’ leader competency. The developmental quality of managerial
assignments was measured using the DCP (McCauley et al., 1994, 1999). Managers’
end-state competencies scale (Spreitzer et al., 1997) was used to measure leader
competency. The results showed that the developmental quality of managerial
assignments had a positive association with end-state competencies, even after
accounting for the effects of tenure in job assignments and organization. A more recent
study by Dragoni et al. (2011) also reported that leaders’ developmental experiences,
as measured by the Leadership Experience Inventory (LEI; VanKatwyk & Laczo, 2004),
positively predicted strategic thinking competency as assessed by multiple exercises.

43

Though the role of developmental challenges in leader development is well
established in the management literature, there is no study to date that has specifically
examined the effects of challenges on leader development in sport. However, the
literature seems to be in agreement that certain experiences are essential in the
development of coaches. North (2010) examined the role and impact of the Coach
Development Officers’ program, using the data that were obtained from 46 coaches who
participated in the program. The results suggested that providing developmental
opportunities is instrumental in developing coaches. Also, Erickson, Côté, and FraserThomas (2007) proposed the stages of developmental experiences based on the data
collected from 19 retrospective interviews with high performance coaches. The stages
are (a) diversified early sport participation (i.e., participation in various team and
individual sports on a recreational basis); (b) competitive sport participation (i.e.,
participation in at least one sport at a competitive level and multiple leadership
opportunities); (c) highly competitive sport participation (i.e., competing at elite levels),
introductory coaching experiences, (e.g., coaching part-time at developmental level),
engagement in non-coaching activities (e.g., enrolling in graduate programs), and
interaction with mentor coaches; and lastly (d) coaching at an elite level (i.e., coaching
at high-performance level). This study suggested that certain experiences and
incremental challenges are crucial for high performance coaches’ development.
Feedback (Assessment). Feedback involves the assessment or process and
performance evaluations that leaders obtain from multiple sources (e.g., themselves,
supervisors, peers, employees, family) (Van Velsor et al., 2010). Feedback gives
leaders information about their current strengths and weaknesses, and clarifies their

44

developmental needs and possible strategies to overcome challenges successfully (Van
Velsor et al., 2010). Thus, feedback assists individuals in dealing with the uncertainty
they experience in challenging assignments (DeRue & Wellman, 2009). Although the
results from the studies on feedback interventions in enhancing performance have been
inconsistent, the meta-analysis of 131 studies, with 607 effect sizes indicated that there
is a generally positive relationship between feedback and performance (Kluger &
DeNisi, 1996). Both learning theories (e.g., Kanfer & Ackerman, 1989) and leadership or
leader development models (e.g., Avolio, 2004; Van Velsor et al., 2010) also suggest
the importance of feedback in the leader development process.
There are a limited number of empirical investigations on the role of feedback in
leader development; however, existing research supports its role. For example,
Hazucha et al. (1993) examined the effects of 360-degree feedback on the development
of managers after two years. The supervisors, peers, and subordinates completed the
Management Skill Profile (MSP) to provide feedback to managers. The study found that
access to feedback from peers, supervisors, and subordinates was related to significant
progress in individuals’ management skills as rated by both self and others. The recent
study by DeRue and Wellman (2009), which showed that there is a predominantly
positive, inverted U-shaped relationship between developmental challenges and
leadership skill development, also indicated the positive role of feedback in the
leadership skill development of managers. Though the statistics did not reach the
significance level, the results from DeRue and Wellman indicated that feedback
moderates the curvilinear relationship between developmental challenge and leadership
skill development. That is, there is a positive, concave downward curve relationship

45

between developmental challenges and leadership skill development for individuals who
have less feedback access; however, such a pattern is not present among individuals
with greater feedback access. The results suggest that feedback availability can counter
the negative impact that a high degree of challenges may have (DeRue & Wellman,
2009). A recent study by Machida et al. (2012) on women leaders in athletics did not
support such moderating influence of feedback, however, was not supported, although
their study still supported the importance of peer and supervisor feedback. Machida et
al. showed that the moderation effect of peer and supervisor feedback on the
relationship between developmental challenges and leader self-efficacy, motivation to
lead, and career ascendance are not significant. Instead, feedback has an influence on
leader self-efficacy separate from developmental challenges, which in turn, is related to
motivation to lead and career ascendance.
There is a scarcity of studies that have examined the effects of feedback on
leader development in sport, although giving feedback has been identified as one of the
critical behaviors for effective leadership in sport (e.g., Beam, Serwatka, & Wilson,
2004; Chelladurai & Saleh, 1980; Riemer & Chelladurai, 1995). The aforementioned
study by North (2010), on the role and impact of the Coach Development Officers’
program, indicated that feedback plays an important role in the effectiveness of the
program. Though it was not specific to leadership skill development in coaches, Write
and Côté (2003) examined the impact of social and contextual factors in the
development of leader-athletes. Six leader-athletes who competed on Canadian
intercollegiate sport teams participated in a three-part interview, which provided an indepth description of the activities they were involved in during their development. Write

46

and Côté reported that the social influences that play an instrumental role in the
development of athletes’ leadership skills include receiving feedback and
acknowledgement from parents and coaches. These studies provide support to the
importance of feedback in developing leaders in sport.
Support. The third important component in the developmental experience put
forth by McCauley and colleagues (McCauley & Van Velsor, 2004; Van Velsor et al.,
2010) is support. Developmental challenges provide leaders with opportunities to grow;
however, such experiences could have the most developmental values when there is
support available for leaders (Van Velsor et al., 2010). Support can be in many forms
(e.g., supervisors, peers, employees, family, organization) and can alleviate the sense
of insecurity and give leaders confidence in the face of challenges. Support assists
leaders in dealing with the struggles associated with challenges and development, and
it encourages them to gain a positive view of themselves (Van Velsor et al., 2010).
The importance of support and the relationship with others in leader development
is empirically supported. For example, Birdi, Allan, and Warr (1997) examined four
types of developmental activities and how participation in these activities differs
depending on various individual and environmental factors. The environmental factors
examined included perceived social support: management support, coworker support,
and non-work support from family and friends. The study showed that, though peer and
non-work support are not significantly related to employees’ participation in
developmental activities, supervisor or senior staff support is positively related to
employees’ participation in developmental activities. In addition, a study by Hazucha et
al. (1993) showed that supervisor support is positively related to managers’ self-

47

reported skill development, and organizational support is significantly related to the
managers’ skill development as rated by self and others. There are also positive
relationships between supervisor and organizational support and self-ratings of efforts
and engagement in development activities. A recent study by Machida et al. (2012) also
demonstrated that peer and supervisor support are positively related to leader selfefficacy, which in turn is related to motivation to lead and to career ascendance.
Mentoring relationship. One important form of support is the mentoring
relationship. The benefits of the mentoring process and other developmental
relationships in the workplace are well documented (e.g., Day, 2001; Higgins, 2000).
McCauley and colleagues (McCauley et al. & Van Velsor, 2004; Van Velsor et al., 2010)
argued that mentoring relationships not only facilitate career advancement, but also
provide psychological support to leaders. The leader development literature identifies
mentoring relationships as critical for leader development. In a review of contextual
factors that influence leadership development, Day (2001) discussed six specific
practices, including mentoring, that can be employed in an organization to facilitate
leadership development. Mentoring relationship could be formal (i.e., a mentor formally
assigned to a mentee in an organization-sponsored program), and mentoring
relationship also could be informal (i.e., mentoring encouraged by the organization)
(Day, 2001). Regardless of the formality of the mentoring relationship, scholars argue
that mentoring relationships could be a significant developmental experience which
supports developing leaders (Allen & Eby, 2003; Day, 2001; Ragins, Cotton, & Miller,
2000). Mentoring relationships also provide leaders with the opportunities to observe

48

and interact with members of senior management, which assists leaders to enhance
their strategic thinking skills that are critical for advancing their careers (Day, 2001).
Empirical evidence supports the effects of mentoring relationships on employees’
career success. For example, a meta-analysis by Allen, Eby, Poteet, and Lentz (2004)
reviewed the existing empirical studies regarding the career benefits associated with
mentoring. The results showed that both objective (i.e., compensation, promotion) and
subjective career outcomes (i.e., career satisfaction, expectation for advancement) are
related to two functions of mentoring: career-related and psychosocial mentoring,
though the effect sizes were small. Also, Lankau and Scandra (2002) show that
mentoring influences the learning and job attitudes of protégés. The results showed that
there is a positive relationship between vocational mentoring and protégés’ relational
learning (i.e., understanding of interpersonal relationships and interaction among the
organization), which is negatively associated with intentions to leave. In addition, role
modeling through mentoring is positively related to protégés’ personal skill
development, which, in turn, is negatively related to role ambiguity and positively related
to job satisfaction.
Further, a study by Ragins et al. (2000) indicated that the quality of the mentoring
relationship may be more critical than the mere availability of mentors. Results indicated
that protégés who are in highly satisfying relationship with their mentors have more
positive attitudes toward their job and career than protégés who are in dissatisfying
mentoring relationship and non-mentored individuals. However, the study found no
significant differences in attitudes between non-mentored individuals and individuals
who reported dissatisfaction or individuals who reported marginal satisfaction with their

49

mentors. Thus, this study suggests that the mere presence of a mentor does not lead
automatically to a positive outcome; it is the quality of the mentoring relationship that
matters.
There is a scarcity of studies that have systematically examined the effects of
support or mentoring on leader development in sport. The aforementioned studies in
sport, by North (2010) on coach development through the Coach Development Officers
program, and by Write and Côté (2003) on athletes’ leader development, have indicated
the importance of support from mentors, parents, or coaches in developing leaders. In
addition, the examination of the development of expert sport coaches by Nash and
Sproule (2009) provided insight into the role of the mentoring relationship in the leader
development of coaches. Using an interview approach, they examined the transition of
nine expert coaches through various stages in their careers. One of the critical factors in
their career development that was identified by these coaches included having
appropriate mentors at the initial stages of their coaching careers. Interaction with and
observing experienced coaches assist a developing coach to learn tactics and gain
knowledge that are critical for a successful coaching career.
Developmental experiences as antecedents. In sum, past research (DeRue &
Wellman, 2009; Dragoni et al., 2009; Hazcha et al., 1993) seems to suggest that
developmental experiences, which include developmental challenges, feedback,
support, and mentoring, play an integral role in developing the leadership skills of
individuals in various occupational fields, including athletics. Though the roles of
developmental experiences are documented in research, the mechanism by which
these developmental experiences result in leader development is still unclear. I

50

hypothesize that increase in leader self-efficacy can explain the relationship between
the developmental experiences and leader development.
According to Bandura (1997), there are four major sources of self-efficacy. The
first source is enactive mastery experiences, which are past success experiences and
considered the strongest sources of self-efficacy. The second source of self-efficacy is
vicarious experiences; individuals develop self-efficacy through observing the
performance of others. Verbal persuasion is the third source of self-efficacy; it occurs
when significant others (including oneself) demonstrate their support for one’s
capabilities to succeed. Fourth, physiological and affective states can also influence
self-efficacy, through the associations people make between their performances and
various levels of physiological arousal and emotions.
In relations to these four sources of self-efficacy proposed by Bandura (1997),
developmental challenges, and the successes and failures associated with these
experiences, could be considered the forms of enactive mastery experiences that can
significantly affect leaders’ self-efficacy. Feedback and support can be related to verbal
persuasion sources, where leaders increase their efficacy belief by the verbal
encouragement or support that they receive from others. Mentoring relationships can be
considered as verbal persuasion and vicarious experience sources of self-efficacy.
Modeling the mentors and receiving support from mentors could increase leaders’ selfefficacy.
There are a limited number of studies that have examined the sources of leader
self-efficacy (Hannah et al., 2008) or the relationships between developmental
experiences and leader self-efficacy. A study by van Vianen and Keizer (1996) showed

51

that managerial experience, verbal persuasion, vicarious experience, and physiological
and affective states are related to managerial self-efficacy in employees, which, in turn,
is related to their ambitions for managerial careers. In addition, Mellor, Barclay, Bulger,
and Kath (2006) reported that stewards’ verbal persuasion is positively related to union
members’ self-efficacy to serve as stewards.
In athletic context, North (2009) suggested that the confidence of coaches is
enhanced through regular interactions and contact with the mentor coach. A recent
study by Moran-Miller and Flores (2011) also showed that the quality of female role
models is a positive predictor of female college athletes’ coaching self-efficacy, which in
turn is positively related to their interest in coaching. In addition, Machida et al. (2012)
showed that challenges, feedback, and support predict women’s leader self-efficacy,
which is related to their desire to lead others, and ultimately their career ascendance.
Thus, I hypothesize that challenge, feedback, support, and mentoring relationships
should have direct effect on coaches’ leader self-efficacy, which in turn influences their
motivation to lead.
Hypothesis 6. Developmental challenges, feedback, support, and mentoring
relationship quality positively relate to leader self-efficacy.
Hypothesis 7. The relationships between developmental challenges, feedback,
support, and mentoring relationship quality, and motivation to lead are mediated
by leader self-efficacy.
In addition, there may be a direct relationship between these developmental
experiences and leader competency. Though self-efficacy is established as a strong
predictor of one’s behavior and performance, it is possible that leaders can demonstrate

52

high leader competency without possessing high self-efficacy. Leaders who are in the
developmental phase may be underestimating their abilities because of the challenges
they are facing, and they may be attending more to their deficits than their strengths
(Van Velsor et al, 2010). Also, as Klassen (2004) reported, the predictability of selfefficacy on performance may be different between cultures. Low self-efficacy does not
necessarily lead to lower performance in some individuals. As indicated by past
research, women in general tend to underestimate their ability and have lower selfefficacy than men, especially in male-dominated fields (e.g., Clifton & Gill, 1994; Lirgg,
George, Chase, & Ferguson, 1996), which may include athletics. Thus, although
coaches may have low leader self-efficacy, they may still demonstrate high leader
competency as long as they are exposed to quality developmental experiences.
Hypothesis 8. Developmental challenges, feedback, support, and mentoring
relationship quality directly relate to leader competency.
Gender differences in developmental experiences. Betz and Hackett (Betz,
2007; Hackett & Betz, 1981) argued that low self-efficacy beliefs that result from limited
access to the four main self-efficacy information sources (i.e., mastery, verbal
persuasion, vicarious learning, and affective and emotional state) proposed by Bandura
(1997) may be contributing to the underrepresentation of women in the workplace.
Though scarce, existing research does support this claim. van Vianen and Keizer
(1996) found that men reported higher degrees of managerial experiences and job tasks
and that they received more positive feedback or verbal persuasion than women, and
men showed higher managerial self-efficacy and ambitions for managerial careers than
women.

53

In addition, studies suggest that the roles of developmental experiences may differ
between women and men. Through an experimental study which examined the
reactions of recipients toward affirmative action, Stewart and Shapiro (2000) showed
that men have a greater desire to persist as leaders as compared to women. The study
also demonstrated that negative outcome feedback has a greater impact on women,
and women take larger responsibilities for task failures than men. Neubert and Taggar
(2004) examined how gender moderates the relationship between individual
characteristics, which include member network centrality (i.e., feeling included in other
team members’ network of advise and support), and emergence of informal leadership
in the context of intact manufacturing teams. Results showed that member network
centrality is important for the leadership emergence of both men and women, but it is
more critical for men than for women.
In addition, scholars suggested that mentoring experiences for women may be
different from mentoring experiences for white men (e.g., Rudeman & Hughes-James,
1998). The examination by Ragins and Cotton (1999) of the effects of gender
composition on the relationship of mentoring functions and career outcomes (i.e.,
promotion rate and compensation) indicated that the gender composition of the
mentoring relationship are related to reports of mentor functions and the career
outcomes of protégés. There is a positive and significant relationship between having a
history of primarily male mentors and a protégée’s compensation. Compensation is
significantly higher among female protégés with a history of male mentors than female
protégés with a history of female mentors. The results also suggested that male
protégés with male mentors may benefit the most from their mentoring relationship

54

(e.g., satisfaction, support for development, challenging job assignments,
compensation), while male protégés with female mentors may benefit the least. Female
protégés with a history of male mentors receive more promotions than their male
counterparts, although the compensation is significantly lower among female protégés.
A study by Ragins et al. (2000) examined the impact of protégés’ gender and the
combination of mentor and protégés’ gender on job attitudes (e.g., career commitment,
job satisfaction, turn over intentions). The study indicated that both men and women
benefit similarly in their job attitudes from a relationship with an informal mentor.
However, the study found significant differences in the role of a formal mentor in career
commitment. Higher career commitment was observed among men with formal mentors
than women with formal mentors. Further, significantly less career commitment is
reported among women with formal mentors than both men and women without
mentors.
In the athletics context, studies of gender differences in the roles of
developmental experiences are even scarcer. Although there was no male comparison,
Greenhill et al.’s (2009) aforementioned case study with female sport leaders suggested
that having formal and informal networks has a substantial impact on female coaches’
career pathways. Developing informal mentoring programs for female coaches is
deemed important. Greenhill et al. further suggested that women-only networks in
athletics (e.g., administrators and coaches) can encourage mutual support and
assistance, which may play a critical role in improving women’s positions in athletics.
Assigning mentor coaches to developing coaches may instill confidence that women
need to advance their career (Greenhill et al., 2009).

55

In addition to these findings from past studies, a recent study by Machida et al.
(2012) suggested that developmental challenges, feedback, and support may play a
critical role in women leaders’ career ascendance through their effects on leader selfefficacy and motivation to lead, although the study did not have a male sample
comparison. Thus, the past studies in both business (e.g., Allen et al., 2004; Birdi et al.,
1997; Hazucha et al., 1993) and sport settings (Machida et al., 2012; North, 2010; Write
& Côté, 2003) seem to suggest that developmental experiences can play a critical role
in leader development, and that there may be gender differences in their roles.
However, there has been a limited number of empirical studies that have examined
such gender differences.
Learning Orientation
Scholars (DeRue & Wellman, 2009; Dragoni et al., 2009; Van Velsor et al. 2010)
have argued that there is a variation in the degrees of learning that occurs from similar
developmental experiences, and such variation can be, in part, attributed to individual
differences in the ability to learn. The ability to learn is “a complex combination of
motivational factors, personality factors, and learning tactics” (Van Velsor et al., 2010,
p.5), which include constructs such as learning orientation (Dweck, 1986). Learning
orientation may be related to the degree of coaches’ engagement in developmental
experiences (i.e., developmental challenges, feedback, support, and mentoring), which,
in turn, relate to their leader self-efficacy.
Learning orientation is a psychological construct that has gained considerable
attention in leader development research (e.g., DeRue & Wellman, 2009; Dragoni et al.,
2009) and has consistently shown its positive role in leader development (e.g., DeRue &

56

Wellman, 2009; Dragoni et al., 2009). Learning orientation is defined as an emphasis on
learning, mastery, and increasing competence (Dweck, 1975, 1986). Learning or
mastery orientation has been linked to self-efficacy, which in turn relates to performance.
For example, VandeWalle, Cron, and Slocum (2001), in a setting of undergraduate
business courses, showed that students’ learning goal orientation is positively related to
their self-efficacy, which, in turn, positively relates to their exam performance. Further,
through an experimental study using a computer simulation task, Bell and Kozolowski
(2002) showed that there is a generally positive relationship between participants’
learning orientation and their self-efficacy. In another experimental study using a
simulated team decision-making task, Porter (2005) also found a significant and positive
relationship between team learning orientation and self-efficacy.
However, the mechanism by which learning orientation results in an increase of
self-efficacy is still unclear. A high learning orientation encompasses motivation to
investigate new methods and tactics, which may improve one’s capabilities for the
longer term (Dweck, 1975). McCauley and colleagues (McCauley & Van Velsor, 2004;
Van Velsor et al., 2010) argued that individuals’ ability to learn fosters motivation to
participate and facilitates engagement in developmental experiences, which in turn
influences their own development. Birdi et al. (1997) showed that employees’ learning
orientation is significantly related to participation in different types of developmental
activities (e.g., voluntary or involuntary learning activities, career-planning activities).
Also VandeWalle, Ganesan, Challagalla, and Brown (2000) demonstrated that
employees’ learning orientation is positively related to their perception of benefits
associated with feedback-seeking behaviors, while it is negatively related to perceptions

57

of costs associated with such behaviors. In addition, Porath and Bateman (2006)
reported that employees’ learning orientation is significantly and positively related to
engagement in proactive behaviors, which are related to higher performance.
In sport, learning or mastery orientation has generally shown a positive
relationship with the self-confidence of athletes or sport participants (e.g., Hall & Kerr,
1997; Vosloo, Ostrow, & Watson, 2009); however, its effect on individuals in athletic
careers has not been systematically examined. Informed by theories (Deweck, 1975;
McCaley & Van Velsor et al., 2010) as well as past research in the area, I hypothesize
that coaches’ learning orientation is related to their extent of engagement in
developmental experiences (e.g., developmental challenges, feedback, support, and
mentoring), which in turn influences their leader self-efficacy.
Hypothesis 9. Learning orientation positively relates to developmental
challenges, feedback, support, and mentoring relationship.
Hypothesis 10. The relationship between learning orientation and leader selfefficacy is mediated by developmental challenges, feedback, support, and
mentoring relationship.
Gender differences in learning orientation. Although no empirical study in a
leadership context has examined gender differences in learning orientation, studies in
educational settings show that the role of learning orientation may differ between
genders. For example, a meta-analysis by Severiens and ten Dam (1998) on 22 past
studies reported that there are significant gender differences in dimensions of students’
learning orientation. In addition, Page and Alexitch (2003) showed that there is a
positive relationship between learning orientation and academic performance for male

58

college students; however, such relationship was not observed among their female
counterparts. Although there are not enough empirical and conceptual foundations to
hypothesize the direction of gender differences in the role of learning orientation in
leader development, it seems crucial to consider gender in its examination. 

Work-Family and Family-Work Conflicts and Gender Discrimination
As the past research (e.g., DeRue & Wellman, 2009; Dragoni et al., 2009, 2011;
Hazucha et al., 1993) demonstrated, challenges may play positive roles in leader
development. However, leaders, especially women who aspire to be leaders, have to
deal with additional challenges which may not be necessarily developmental. There
have been a number of studies, in both general business and athletic careers, that have
examined the challenges women leaders face in advancing to and sustaining their
leadership roles (e.g., Braken, 2009; Eagly & Carli, 2007; Leberman & Palmer, 2009).
Such challenges include extensive domestic responsibilities at home, which
result in work-family and family-work conflicts. Although the difference has been
decreasing, time spent for housework by women is still significantly higher than men
(Bianchi et al., 2000; Sayer, Bianchi, & Robinson, 2004), and work-family and familywork conflicts could pose a significant challenge to women. Work-family conflict is
defined as “a form of interrole conflict in general demands of, time devoted to, and strain
created by the job interfere with performing family responsibilities” (Netemeyer, Boles, &
McMurrian, 1996, p.401). Family work-conflict is defined as “a form of interrole conflict
in which the general demands of, time devoted to, and strain created by the family
interfere with performing work-related responsibilities” (Netemeyer et al., 1996, p.401).
Research shows that work-family and family-work conflicts are related to job

59

satisfaction, family satisfaction, life satisfaction, organizational commitment (Carlson,
Kacmar, & Williams, 2000), job burn out, turn over intentions, job tension, and
psychological distress (Burke, 1988; Netemeyer et al., 1996). Although studies have
reported conflicting evidence about gender differences (e.g., Eagle, Miles, & Icenogle,
1997; Frone, Russell, & Barnes, 1996), work-family and family-work conflicts that
female employees experience are often shown to be significantly higher than their male
counterparts (e.g., Carlson et al., 2000; Williams & Alliger, 1994).
In sport settings, work-family and family-work conflicts can also be hindrances for
women’s career advancement. In a nation-wide study conducted by the NCAA
(Bracken, 2009), female coaches indicated time requirements and amount of travel as
the top two reasons for the lack of female representation in the coaching profession,
and family commitments as a top contributing factor for females’ hesitation to pursue a
coaching career. In addition, female athletic administrators identified time requirements
and family duties as the two top career-inhibiting factors for women in athletic
administration, with 73% of them agreed that athletic careers conflict with family duties
(Bracken, 2009).
Also, qualitative studies on women sport leaders show the possible effect of
work-family and family-work conflicts on their development and advancement as
leaders. For example, Leberman and Palmer (2009) conducted qualitative semistructured interviews with nine female sport leaders in New Zealand and reported that,
although these women developed strategies to cope with multiple constraints imposed
by family and work, they also noted their experiences of feeling guilt, exhaustion and
stress, lack of societal approval, and limited resources to deal with these challenges

60

throughout their careers. In addition, in examining the underrepresentation of women in
elite coaching professions, Greenhill et al.’s (2009) case study with sport leaders in
Queensland, Australia, also demonstrated that time and family commitments are one of
the dominant issues that negatively impact female coaches’ career progression.
While work-family and family-work conflicts impose significant constraints,
gender discrimination in organizations can also impose an additional challenge that
women need to overcome to attain and sustain a leadership position. Women generally
perceive gender to be a hindrance in their work more than men (Berdahl & Moore,
2006; Browne, 1997). Because people usually associate leaders with masculine
characteristics, women are often perceived to be inadequate for leadership positions
(Epitropaki & Martin, 2004). When women display traditional feminine characteristics
(e.g., weak, uncertain, ineffectual) and few masculine qualities (e.g., tough, decisive, or
competent), women often face difficulty in gaining acceptance as leaders (Eagly & Carli,
2007). However, when they demonstrate these masculine qualities, they also receive
criticisms (Eagly & Carli, 2007); these qualities conflict with people’s prescriptive
stereotypes of women (Bowles, Babcock, & Lei, 2007; Tepper, Brown, & Hunt, 1993).
Eagly and Carli describe this dilemma of women leaders as a “double bind” which
makes it difficult for women to advance their careers as leaders.
A study by Heilman, Wallen, Fuchs, and Tamkins (2004) showed the possible
biases toward women which may limit their career success. Through three experimental
studies, the results show that when women are recognized to be successful, they are
less favored. This trend is most evident when they are successful in a male-dominated
field. In addition, the results demonstrated that these negative reactions toward women

61

could significantly affect people’s recommendations for women’s career outcomes (i.e.,
salary, special career opportunities). Another experimental study by Gupta et al. (2008)
showed that the perceived gender stereotype may significantly influence women’s
career intention. With a sample of undergraduate business students, Gupta et al.
examined the effects of implicit and explicit activation of gender stereotypes on
women’s and men’s entrepreneurial intentions. Participants read a fictitious news article
about the characteristics of successful entrepreneurs. The article provided either (a) no
gender stereotypical information (i.e., control), (b) explicit masculine stereotypical
information, (c) implicit masculine stereotypical information, (d) explicit feminine
stereotypical information, (e) implicit feminine stereotypical information, or (f) nullified
stereotypical information on the characteristics of successful entrepreneurs. Though no
gender differences were observed when the stereotype was explicitly nullified (i.e.,
article said “entrepreneurs show characteristics of both men and women”),
entrepreneurial intention was significantly higher among men than women in the control
condition, where no explicit or implicit stereotypical information was presented; this
result suggests “the everyday gender gap” (Gupta et al., 2008, p. 1057) in an intention
to advance career in the field that is generally associated with masculine characteristics.
Literatures in sport also suggest that gender discrimination and stereotypes could
be significant barriers for women to advance to leadership positions. For example,
Sartore and Cunningham (2007) argued that gender discrimination and stereotyping
can be a great hindrance to women’s career in athletics in that it interrupts women’s
identity as leaders and results in self-limiting behaviors. The aforementioned study by
NCAA (Bracken, 2009) also reported that female coaches and athletic administrators

62

identify gender discrimination as one of the top reasons why women leave careers or
hesitate to enter into a career in athletics. In addition, a study with German female
volunteer sport organization leaders indicated that these leaders have experienced
gender discrimination and perceived it to be a significant barrier in sustaining and
advancing their careers (Pfiser & Rauke, 2006). Also, Manley, Greenlees, Thelwell, and
Smith (2010) showed that the gender of coaches possibly affects athletes’ perceptions
of coaches’ competency, which may have a great impact on their career advancement.
Competency ratings are significantly higher among male coaches as compared to
female coaches in terms of their competency in game-strategy and coaching technique.
Physiological and affective states are among the main determinants of selfefficacy levels (Bandura, 1997); thus, adverse physical and emotional reactions to workfamily and family-work conflicts and gender discrimination may negatively affect leaders’
self-efficacy levels. A recent study by Machida et al. (2012) showed that gender
discrimination and family-work conflict are not significantly related to women leader’s
self-efficacy, which is related to their desires to lead others. However, to date, Machida
et al. (2012) is the only study that has investigated this relationship, and their population
was limited to women athletic administrators. Based on Bandura’s claim and past
research in the field, which indicates that coaching jobs put significant constraints on
women (e.g., Bracken, 2009; Sartore & Cunningham, 2007), I hypothesized that
coaches who are facing high levels of such obstacles are more likely to experience
anxiety, frustration, and other adverse physiological and emotional reactions, which may
reduce leader self-efficacy, which in turn, may lower their motivation to lead.

63

Hypothesis 11. Work-family and family-work conflict and gender discrimination
negatively relate to leader self-efficacy.
Hypothesis 12. The relationships between work-family and family-work conflict
and gender discrimination and motivation to lead are mediated by leader selfefficacy.
Furthermore, I anticipated that these challenges are not only related to leader
self-efficacy, but are also related to outcome expectancy, and this, in turn influences
coaches’ desire to lead others. Even when coaches face family interference with work
or work interference with family, or perceive their gender to be a hindrance for their
career advancement, they may still be able to maintain their self-efficacy as leaders
because they are aware that they could be successful if not imposed on by these
constraints. However, such obstacles, which are rather uncontrollable, may decrease
coaches’ expectancy to obtain the desirable outcomes, which in turn may reduce their
motivation to lead.
Hypothesis 13. Work-family and family-work conflicts and gender discrimination
negatively relate to motivation to lead through outcome expectancy.
In addition to the indirect relationship through leader self-efficacy and outcome
expectancy, I also hypothesize that work-family and family-work conflicts directly
influence women’s motivation to lead. Leaders’ motivation to lead may be significantly
and negatively affected when family and domestic responsibilities interfere with work
and vice versa, because such conflicts make it difficult for them to assume and take
responsibilities as leaders. This reduction in motivation to lead may not be necessarily
attributed to a decrease in their confidence about their leadership skills or outcome

64

expectancy. As indicated, leaders may be able to maintain their strong sense of selfefficacy in the face of family interference with work or work interference with family.
Also, leaders may be able to possess high outcome expectancy in such challenges
because they may anticipate that they can effectively manage these constraints.
Machida et al. (2012) demonstrated that family-work conflict is negatively and directly
related to motivation to lead of women leaders in athletics, although its indirect influence
on motivation to lead through leader self-efficacy is not significant. Thus, in addition to
leader self-efficacy and outcome expectancy, family duties which leaders have outside
work may directly affect their desire to be leaders.
Hypothesis 14: There are direct negative relationships between work-family and
family-work conflicts and motivation to lead.
Gender differences in work-family and family-conflicts and gender
discrimination. In sum, past research has identified common challenges, such as
work-family and family-work conflicts and gender discrimination, faced by women
leaders in athletics. However, no study to date has simultaneously studied their
plausible differential effects on women’s and men’s career advancement. Although past
research on gender differences in work-family and family-work conflicts are inconclusive
(e.g., Carlson et al., 2000; Eagle et al., 1997; Frone et al., 1996; Williams & Alliger,
1994), higher degrees of work-family and family-work conflict are often observed among
female employees than among their male counterparts (e.g., Carlson et al., 2000;
Williams & Alliger, 1994). Studies of gender discrimination in the workplace seem to
indicate that women may be more affected by gender discrimination than men (e.g.,
Browne, 1997; Gupta et al., 2003; Heilman et al., 2004). In addition to the

65

developmental challenges as leaders, it is important to consider these additional
challenges and how these challenges might contribute to women and men’s
development as leaders in the coaching profession. Women and men might have
different responses to the same challenges; thus such gender differences need to be
examined.
Summary
An intention can be the best predictor of career advancement (Ajzen, 1991;
Armitage & Conner, 2001; Gordin & Kok, 1996; Moran-Miller & Flores, 2011). Past
research and theories (Bandura, 1997; Van Velsor et al., 2010) have identified potential
antecedents which include leader competency, motivation to lead, leader self-efficacy,
outcome expectancy, developmental experiences (which consist of developmental
challenges, feedback, support, and mentoring relationship), learning orientation, and
challenges such as work-family and family-work conflicts and gender discrimination. I
put forth several hypotheses in explaining their potential roles in leaders’ career
advancement.
The past studies on leader development and career advancement suggest that
there may be gender differences in the responses to these antecedents. Athletics has
historically been a male-dominated field, which has struggled to increase women’s
representation in higher leadership positions (e.g., IOC, 2012; Acosta & Carpenter,
2012). Despite its implications for increasing women’s representation (e.g., Hepler &
Feltz, 2008), collegiate coaching is a profession where a decline of women’s
representation is significant (Acosta & Carpenter, 2012). There is an obvious lack of a
systematic examination of the career advancement process in athletics and an

66

investigation of gender differences in its process. Thus, it is imperative to investigate the
roles of the aforementioned antecedents in the career advancement of collegiate
coaches, and to examine the gender differences. Such study may have significant
theoretical as well as practical implications, not only for athletics but also for any fields
struggling to increase women’s representation.

67

CHAPTER III

METHOD
Participants
Nine hundred and six assistant coaches who coach collegiate women’s teams
accessed the online survey. The responses from 673 assistant coaches and 245 of their
head coaches were utilized for the subsequent analyses. Five hundred and seventy six
assistant coaches indicated their gender: 360 assistant coaches (62.5%) were females
and 216 (37.5%) assistant coaches were males. There were 128 female and 117 male
head coaches. The mean age of the assistant coaches was 30.16 years (SD = 8.62).
On average, the assistant coaches indicated 6.41 years of coaching experience (SD =
5.84) and 3.75 years (SD = 3.57) of working for their current head coaches. As
presented in Table 2.1, there were gender differences in age, coaching experiences,
and years working with the current head coach. Independent sample t-tests showed that
the males were significantly older and had longer years of coaching experiences and
longer years of working with the current head coaches than their female counterparts (p
< .001). About 90% of the female and male assistant coaches were Caucasian. The five
highest numbers of female assistant coaches were seen in basketball (n = 71), softball
(n = 62), volleyball (n = 52), soccer (n = 39), and indoor (n = 35) and outdoor track (n =
37). For the male assistant coaches, the five highest numbers were seen in indoor (n =
86) and outdoor track (n = 90), cross country (n = 53), basketball (n = 27), and soccer (n
= 27). In addition, more than 70% of the female coaches were single, and only 16% of

68

them had children, while less than 50% of male coaches were single, with about 46% of
them had children. .
The mean age of the head coaches was 42.72 years (SD = 10.99). Head
coaches, on average, indicated that they had 16.41 years of coaching experience (SD =
9.65). As reported in Table 2.2, about 90% of the head coaches were Caucasian. In
addition, about 68% of head coaches were married and more than 50% of them had
children.

69

Table 2.1
Demographic Information of Assistant Coaches

Age

Female
M
SD
27.81
5.87

Male

Total

M
35.23

SD
10.29

M
30.16

SD
8.62

Coaching Experience

5.17

4.66

8.43

6.90

6.41

5.84

Years Working with the
Current Head Coach

3.32

3.35

4.48

3.83

3.75

3.57

Female
n
%

Male

Total

n

%

n

%

0
71
0
25
0
27
0
6
8
5
13
0
12
1
39
62
23
7
35
37
52
2
1
6

0
19.7
0
6.9
0
7.5
0
1.7
2.2
1.4
3.6
0
3.3
0.3
10.8
17.2
6.4
1.9
9.7
10.2
14.4
0.6
0.3
1.7

1
27
0
53
2
3
3
4
0
4
3
0
7
2
27
12
14
6
86
90
18
1
0
3

0.5
12.4
0
24.4
0.9
1.4
1.4
1.8
0
1.8
1.4
0
3.2
0.9
12.4
5.5
6.5
2.8
39.6
41.5
8.3
0.5
0
1.4

1
98
1
78
2
31
3
10
8
9
16
0
19
3
67
74
39
13
121
127
72
3
1
8

0.1
10.6
0.1
8.4
0.2
3.4
0.3
1.1
0.9
1.0
1.7
0
2.1
0.3
7.2
8.0
4.2
1.4
13.1
13.7
7.8
0.3
0.1
0.9

295
61

82.9
17.1

100
114

46.1
52.5

399
177

69.3
30.7

a

Sport
Baseball
Basketball
Bowling
Cross country
Fencing
Field hockey
Football
Golf
Gymnastics
Ice hockey
Lacrosse
Rifle
Rowing
Skiing
Soccer
Softball
Swimming & Diving
Tennis
Indoor track
Outdoor track
Volleyball
Water polo
Wrestling
Other sports
Gender of the Team
Women’s team only
Both women’s and men’s teams

70

Table 2.1 (cont’d)
Female
n
%
Ethnicity
African American
Asian
Caucasian
Hispanic
Native American
Pacific Islander
Interracial
Other
Highest Educational Attainment
High School Diploma or GED
Associate Degree
Bachelor’s Degree
Master’s Degree
Doctoral Degree
Other
Current Institution
NCAA Division I
NCAA Division II
NCAA Division III
Other
Current Position
Associate head coach
Full time (paid) assistant coach
Part time (paid) assistant coach
Volunteer (Unpaid) assistant coach
Graduate assistantship
Other
Marital Status
Single
Married or Cohabiting
Number of Children
None
1
2
3
4
more than 4

Male

Total

n

%

n

%

12
3
320
8
4
4
3
6

3.3
0.8
88.9
2.2
1.1
1.1
0.8
1.7

15
5
183
3
1
4
1
4

6.9
2.3
84.7
1.4
0.5
1.9
0.5
1.9

27
8
505
11
5
8
4
10

4.7
1.4
87.4
1.9
0.9
1.4
0.7
1.7

4
0
170
165
6
16

1.1
0
47.1
45.7
1.7
4.4

8
4
95
97
7
5

3.7
1.9
44
44.9
3.2
2.3

12
4
265
265
13
21

2.1
0.7
45.7
45.7
2.2
3.6

147
81
128
5

40.7
22.4
35.5
1.4

110
36
70
1

50.7
16.6
32.3
0.5

259
117
119
6

44.6
20.1
34.3
1.0

21
179
81
21
50
8

5.8
49.7
22.5
5.8
13.9
2.2

25
107
56
10
15
4

11.5
49.3
25.8
4.6
6.9
1.8

47
287
138
33
66
12

8.1
49.2
23.7
5.7
11.3
2.1

266
95

73.7
26.3

101
115

46.8
53.2

368
212

63.4
36.6

303
13
16
3
3
0

83.9
3.6
4.4
0.8
0.8
0

117
28
35
19
3
3

53.9
13.7
17.1
9.3
1.5
1.5

422
41
52
22
6
3

77.3
7.5
9.5
4.0
1.1
0.6

a

Notes. Participants indicated all sports they currently coach. The total number of the
responses may not equal to 673; this is due to missing data on the demographic
information.
71

Table 2.2
Demographic Information of Head Coaches
Frequency
n
%
Gender
Female
Male
Ethnicity
African American
Asian
Caucasian
Hispanic
Native American
Pacific Islander
Interracial
Other
Highest Educational Attainment
High School Diploma or GED
Associate Degree
Bachelor’s Degree
Master’s Degree
Doctoral Degree
Other
Marital Status
Single
Married or Cohabiting
Number of Children
None
1
2
3
4
more than 4

128
117

52.2
47.8

7
2
216
4
6
1
2
3

2.9
0.8
89.6
1.7
2.5
0.4
0.8
1.2

2
1
57
173
8
3

0.8
0.4
23.4
70.9
3.3
1.2

77
166

31.7
68.3

104
30
55
34
8
7

43.7
12.6
23.1
14.3
3.4
2.9

Notes. The total number of the responses may not equal to 245; this is due to missing
data on the demographic information.

72

Procedures
The study was based on data collected from two sources: assistant coaches and
head coaches. Assistant coaches who coach women’s collegiate sport teams were
recruited from the National Collegiate Athletic Association (NCAA) registered institutions
(Division I, II, and III). The list of NCAA registered institutions was obtained from the
NCAA website. Undergraduate research assistants and I obtained assistant coaches’
names and emails from each institution’s website. Then, the web link to the online
survey was emailed directly to the assistant coaches’ emails; 6,404 emails were sent.
Assistant coaches completed the survey, which included questionnaires that measured
their intention to advance their careers (i.e., career intention), motivation to lead, leader
self-efficacy, outcome expectancy, developmental challenges, feedback, support,
mentoring relationship, learning orientation, work-family and family-work conflicts, and
gender discrimination. The order of questionnaires and the order of items were
randomized for each participant. Assistant coaches were offered $5 Starbucks Gift
cards for completing the whole survey. Assistant coaches provided their head coaches’
names and contact information (i.e., email address). I then emailed the head coaches
the web link to the online survey for evaluating the assistant coaches’ leader
competency and the mentoring relationship.
Measurements
Demographic questionnaire. Demographic information on both the assistant
and head coaches was obtained. Selected items included age, educational background,
years in coaching, and sport currently coaching (see Appendix A-1).

73

Intention to advance career (i.e., career intention). Three distinct
conceptualizations of intention (Hurtz & Williams, 2009) to advance careers were
measured, which included (a) the desire (“I want”) to advance (3 items, e.g., “I want to
become a university head coach during my career in athletics”), (b) the self-predicted
likelihood (“I will”) of advancing (3 items, e.g., “It is likely that I will actively seek
opportunities to become a university head coach during my career in athletics”), and (c)
the felt responsibility (“I should”) to advance (3 items, e.g., “Every assistant coach
should become a university head coach during his/her career in athletics”). Assistant
coaches responded to each item on a 7-point scale, ranging from 1 (“strongly disagree”)
to 7 (“strongly agree”). See Appendix A-2 for this questionnaire.
Leader competency. Leader competency was assessed by the head coaches
under whom the assistant coaches worked at the time of the study. Mumford, Campion,
and Morgeson’s (2007) leadership skill taxonomy was used. This scale consists of four
skill dimensions: cognitive skills (6 items, e.g., “speaking: talking to others to convey
information effectively”), interpersonal skills (4 items, e.g., “persuasion: persuading
others to change their minds or behavior”), business skills (4 items, e.g., “management
of personnel resources: motivating, developing, and directing people as they work”),
and strategic skills (7 items, e.g., “identification of downstream consequences:
determining the long-term outcomes of a change in operations”). Prior to the data
collection, the relevancy of each item to the field of athletic coaching was assessed by
three experienced coaches, and items and sub-scales were modified based on their
feedback. For example, in the item “management of material resources: obtaining and
seeing to the appropriate use of equipment, facilities, and materials needed to do

74

certain work,” the phrase “to do certain work” was replaced by “to have a successful
program.” To capture the coaching profession’s specific competencies, four items from
the Coaching Efficacy Scale (CES; Feltz, Chase, Moritz, & Sullivan, 1999) representing
each of four subscales of CES were also included (e.g., “Developing athletes’ ability”).
Head coaches indicated the extent to which their assistant coaches showed the
competence in each of the skills, on a scale of 1 (“not at all”) to 5 (“to a very large
extent”). See Appendix A-3 for the questionnaire.
Motivation to lead. The 27-item motivation to lead (Chan & Drasgow, 2001)
measure was used to assess participants’ motivation to lead. The motivation to lead
measure consists of three sub-scales: affective-identity (e.g., “Most of the time I prefer
being a leader rather than a follower when working in a group”), non-calculative (e.g., “I
would agree to lead others even if there are no special rewards or benefits with that
role”), and social-normative (e.g., “I have been taught that I should always volunteer to
lead others if I can”). Assistant coaches responded to the items based on a scale of 1
(“strongly disagree”) to 5 (“strongly agree”) (see Appendix A-4).
Leader self-efficacy. The assistant coaches’ leader self-efficacy was assessed
with the same items used for the leader competency measures constructed for this
study, based on the leadership skill taxonomy of Mumford et al. (2007) and CES (Feltz,
Chase, Moritz, & Sullivan, 1999). Assistant coaches indicated their confidence on a
scale of 1 (“not at all confident”) to 5 (“totally confident”) that they can be effective in
these dimensions (see Appendix A-5).
Outcome expectancy. To assess the career outcome expectancy of assistant
coaches, I designed a measure based on past research in the area (Betz & Voyten,

75

1997; Hutrz & Williams, 2009; van Vianen, 1999). The measure consists of 4 items
(e.g., “If I had the skills to be a head coach, I will be able to get a head coaching job in
the future”). Assistant coaches responded to each item on a scale of 1 (“strongly
disagree”) to 5 (“strongly agree”). See Appendix A-6 for the questionnaire.
Developmental challenge. The extent of engagement in developmental
challenges was measured by the Developmental Challenge Profile (DCP: McCauley,
Ohlott, & Ruderman, 1999). DCP consists of five higher order subscales. The example
items for each subscale are presented in Table 1. Assistant coaches responded to each
item based on a scale of 1 (“not at all descriptive”) to 5 (“extremely descriptive”) (see
Appendix A-7). Prior to the data collection, the relevancy of each item to the field of
athletic coaching was assessed by three experienced coaches. The sub-scale “work
across culture,” which captures the extent to which leaders are required to work with
people from different cultures and in different countries, was eliminated based on the
consistent feedback from these experienced coaches that this was not relevant to the
collegiate coaching profession. A few modifications were made to the items. For
example, the item “the customer base you work with is extremely varied” was modified
to “the customer base (e.g., supporters, fans, alums) you work with is extremely varied.”
Feedback. To measure the quality of feedback from supervisors and peers, the
9-item Informal Feedback Measure (Newstrom, Monczka, & Reif, 1974) was used. Each
item was preceded by the following: “The feedback I receive from a head coach at work
has tended to be …” Assistant coaches rated the feedback they received from head
coaches on the scale of 1 to 7. Each item is associated with a set of two anchors. For
example, on one item, assistant coaches evaluated the quality of feedback on a scale of

76

1 being “vague” and 7 being “clear”. On another item, they responded based on 1 being
“weak” and 7 being “strong” (see Appendix A-8).
Support. Perceived support was measured by the Work Support measure
(Haynes, Wall, & Bolden, 1999). The measure consisted of four items and was
developed to assess perception of peer-support in the workplace. However, for the
purpose of this study, it was used to measure perception of head coaches’ support by
replacing the word “colleagues” with “head coaches.” Participants responded to each
item which began with “To what extent can you count on your head coach to…” (e.g.,
“Count on your head coach to listen to you when you need to talk about problems at
work?”), based on a scale of 1 to 5 (1 = “not at all”, 5 = “completely”) (see Appendix A9).
Mentoring relationship. The 10-item measure developed by Allen and Eby
(2003) was used to assess mentorship quality and learning. Assistant coaches and
head coaches indicated their agreement with the statement on each item, based on a 5point scale that ranged from 1 (“strongly disagree”) to 5 (“strongly agree”). Sample items
included “The mentoring relationship between my mentor/protégé (replaced by the word
“head coach/assistant coach” and I was very effective,” and “My mentor/protégé
(replaced by the word “head coach/assistant coach”) and I were “co-learners” in the
mentoring relationship.” Higher scores indicated better mentorship quality and
mentorship learning (see Appendix A-10).
Learning orientation. Learning orientation of assistant coaches was assessed
by VandeWalle’s (1997) five-item measure of learning goal orientation (e.g., “I often look
for opportunities to develop new skills and knowledge”). Assistant coaches responded

77

to each item on a scale of 1 (“strongly disagree”) to 5 (“strongly agree”) (see Appendix
A-11).
Work-family and family-work conflicts. The 10-item work-family and familywork conflict scale (Netemeyer, Boles, & McMurrian, 1996) was used to measure the
constraints posed to coaches by having both work and family life. A sample item in the
dimension of work-family conflict is “The demands of my work interfere with my home
and family life.” A sample item in the dimension of family-work conflict is “I have to put
off doing things at work because of demands on my time at home.” Assistant coaches
indicated their agreement to items on a scale of 1 (“strongly disagree”) to 7 (“strongly
agree”) (see Appendix A-12).
Gender discrimination. A four-item perceived gender discrimination scale
(Foley, Hang-Yue, & Wong, 2005; Sanchez & Brock, 1996) was used to capture
participants’ perceptions of gender discrimination at work. An example item is “At work, I
sometimes feel that my gender is a limitation.” Assistant coaches indicated their
agreements on each item on a five-point scale (1 = “Strongly disagree”, 5 = “Strongly
agree”) (see Appendix A-13).
Data Analyses
Because I had limited participation from the head coaches, there was a
significant amount of missing data on leader competency. Thus, data analyses were
conducted in two main phases. First, I tested a structural equation model (SEM) using
the whole sample of assistant coaches (N = 673) to confirm the measurements and
structures without including leadership competency as measured by their head
coaches. Then, using the factors and structures that were confirmed in the first steps, I

78

tested a path model with the sub-sample that had complete data (n = 245), which
included the head coaches’ evaluations of the leader competency of the assistant
coaches. As supplementary analyses, I conducted Multiple Indicator Multiple Cause
(MIMIC) model, as well as multiple group analysis, for testing heterogeneity and the
measurement and structural equivalence of factors between samples of female and
male assistant coaches.
In each phase, the models were first examined for overall fit as evaluated by
various model fit indices, including chi-square. The chi-square test was interpreted with
caution because scholars claim that the value is reliant on sample size and likely to be
affected by model complexity and deviations from multivariate normality (Hu & Bentler,
1998). Thus, model evaluations considered the following indices: Comparative Fit Index
(CFI: Bentler, 1990), Tucker-Lewis Index (TLI: Bentler & Bonett, 1980), and Root Mean
Squared Error of Approximation (RMSEA: Browne & Cudeck, 1993). The indirect effects
were examined using Sobel tests and bootstrap confidence intervals of the indirect
effects based on 1,000 resamplings. For these latter tests, a 95% confidence interval
that does not include zero indicates a significant indirect effect. In comparing models,
changes in overall model fits were evaluated. In addition, the MIMIC models as well as
the multiple group analysis were conducted to test for potential gender differences in the
model. MIMIC models test for the heterogeneity and measurement equivalence
between groups, while the multiple group analysis tests for group differences in
measurements and structural parameters. MIMIC models and multiple group analysis
were conducted in order to have rather comprehensive understandings of potential
gender differences in the model. As post-hoc supplementary analyses, ordinary least

79

square (OLS) regression analyses were conducted to test for the potential curvilinear
and interaction effects among factors.




80

CHAPTER IV

RESULTS
The purposes of this study were to examine a career advancement model of
collegiate assistant coaches, and to investigate the gender differences in the model.
This chapter presents the results in four main sections. The first section presents the
descriptive statistics results. The second section presents the first phase of testing that
used structural equation modeling. In this section, I evaluate the hypothesized as well
as alternative models. Also, multiple indicators multiple causes (MIMIC) model and
multiple group analyses results are presented. In the next section, the results of the
path model are presented. This section presents the results of the path model using the
composite scores of the factors confirmed in the first phase. MIMIC model and multiple
group models results are also presented. The section that follows the path analysis is a
presentation of the results from post-hoc supplementary ordinary least square (OLS)
regression, which tested possible curvilinear effects and interaction effects.
Scale Reliabilities and Descriptive Statistics Results
The alpha coefficients of the scales, the descriptive statistics, and zero-order
correlations among study variables are presented in Table 3. As indicated in the table,
all scales used in the present study provided adequate level of internal consistency. In
addition, the results showed that assistant coaches scored relatively higher than the
mid-point of the scale on career intention, leader competency, motivation to lead, leader
self-efficacy, outcome expectancy, feedback, support, mentoring relationship, and workfamily conflict, while they scored relatively lower on family-work conflict and gender

81

discrimination. There were significant positive correlations between the present study’s
key dependent variable of career intention and (a) motivation to lead, (b) leader selfefficacy, (c) outcome expectancy, (d) developmental challenges, (e) learning
orientation, and (f) gender discrimination. There was a negative correlation between
career intention and support. In terms of the gender differences in the study variables,
the results showed that women were lower in career intention, leader self-efficacy, and
developmental challenges, although they were higher in motivation to lead and outcome
expectancy as compared to their male counterparts.

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Table 3
Zero-Order Correlation and Descriptive Statistics of Study Variables
Variables

M

SD

1

1. Career Intention (Scale 1-7)

5.12

1.54

.91

2. Leader Competency (Scale 1-5)

4.02

.60

-.04

.96

3. Motivation to Lead (Scale 1-5)

3.98

.38

.19***

.08

.84

4. Leader Self-Efficacy (Scale 1-5)

4.02

.49

.22***

.05

.35***

.93

5. Outcome Expectancy (Scale 1-5)

4.22

.65

.29***

.03

.30***

.27***

.77

6. Developmental Challenges (Scale 5-15)

11.99

2.89

.21***

-.02

.09*

.21***

.08

.92

7. Feedback (Scale 1-7)

5.36

1.05

-.04

.14*

.19***

.16***

.05

-.07

8. Support (Scale 1-5)

4.21

.91

-.10*

.10

.08*

.10*

.07

-.13**

9. Mentoring Relationship (Scale 1-5)

3.91

.91

-.03

.13

.18***

.15***

.12**

-.03

10. Learning Orientation (Scale 1-5)

4.36

60

.19***

.04

.36***

.34***

.24***

.13**

11. Work-family conflict (Scale 1-5)

4.39

1.53

.05

.11

-.01

.03

-.08

.27***

12. Family-work conflict (Scale 1-5)

2.56

1.15

-.03

.12

- .14**

.02

-.15***

.10*

13. Gender Discrimination (Scale 1-5)

2.03

.97

.10**

-.00

-.08

-.02

.20***

.16**

14. Gender (1 = Female, 0 = Male)

.62

.48

-.13**

-.01

-.12**

-.12**

.11*

-.11**

83

2

3

4

5

6

Table 3 (cont’d)
Variables

7

8

9

10

11

12

13

14

1. Career Intention (Scale 1-7)
2. Leader Competency (Scale 1-5)
3. Motivation to Lead (Scale 1-5)
4. Leader Self-Efficacy (Scale 1-5)
5. Outcome Expectancy (Scale 1-5)
6. Developmental Challenges (Scale 5-15)
7. Feedback (Scale 1-7)

.86

8. Support (Scale 1-5)

.69***

.91

9. Mentoring Relationship (Scale 1-5)

.73***

.70***

.96

10. Learning Orientation (Scale 1-5)

-.08*

.02

-.15***

.89

11. Work-family conflict (Scale 1-5)

-.13**

-.14***

-.11**

.01

.92

12. Family-work conflict (Scale 1-5)

-.10*

-.04

-.05

-.06

.35***

.89

13. Gender Discrimination (Scale 1-5)

-.21***

-.19***

-.18***

-.08

.22***

.24***

.88

14. Gender (1 = Female, 0 = Male)

.02

-.01

.03

-.02

-.06

-.06

.06

N/A

Notes. In bold on the diagonal indicate the internal consistency reliability of the scale. For the mentoring relationship
measure, assistant coaches’ ratings on mentoring relationship were reported.
*** p < .001. ** p < .01. * p < .05


84

First Phase: Structural Equation Modeling
To analyze the data from the total sample of assistant coaches (N = 673), I used
structural equation modeling (SEM) with Mplus Version 6.1 (Muthén & Muthén, 2010).
The model was tested using a covariance matrix and full-information maximum
likelihood (FIML) which handles missing cases under Missing at Random (MAR)
assumptions. When the data on all study variables were missing or the data on
covariates or group information (i.e., gender in the present study) were missing, the
cases were excluded. Following the procedure outlined by Landis, Beal, and Tesluk
(2000), I randomly combined items into composites (i.e., parcels) for all constructs,
except for outcome expectancy (4 items), supervisor support (4 items), gender
discrimination (4 items), and learning orientation (5 items) that had fewer number of
items, for which I used all items as indicators. The 10-item mentoring relationship and
quality measure was reduced to 4 parcels (one 2-item and one 3-item parcels for each
of the two subscales). Assistant coaches’ responses on their mentoring relationship with
their head coaches were utilized. For feedback, there were three 3-item parcels. For
career intention (i.e., intention to advance career), leader self-efficacy, developmental
challenge, and motivation to lead, parcels were the theoretical conceptual dimensions
identified by their developers. Thus, each of the three dimensions of career intention,
five dimensions of leader self-efficacy, five dimensions of developmental challenge, and
three dimensions of motivation to lead was represented by a parcel. Work-family (5
items) and family-work conflicts (5 items) were treated as two separate latent
constructs, according to the past research that confirmed the two-factor structure
(Netemeyer, Boles, & McMurrian, 1996).

85

Measurement Model Testing
The test of the measurement model excluded any predictive paths against the
whole sample of assistant coaches (N = 673). The model fit the data well, χ2 (N = 673,
1089) = 2152.81, CFI = .95, TLI = .94, RMSEA = .038; however, high correlations
between feedback and support (.76), feedback and mentoring relationship (.80), and
support and mentoring relationship (.74) were observed. In McCauley et al.’s (McCauley
& Van Velsor, 2004; Van Velsor, McCauley, & Ruderman, 2010) model of leader
development, these components of developmental experiences, feedback, support, and
mentoring relationship, capture interactions and learning from others, who, in the
present study, are the head coaches that supervise the assistant coaches. Thus, the
decision was made to create a new latent variable “head coach professional support”
combining these three factors (i.e., feedback, support, and mentoring relationship) using
each composite score as an indicator. The model fit the data well, χ2 (N = 673, 754) =
1510.44, CFI = .95, TLI = .94, RMSEA = .039. Therefore I proceeded to test the
hypothesized model with this congeneric measurement of the hypothesized factors.
Hypothesized Model Testing
Model 1. I began by testing the structural model in which all antecedent variables
(i.e., developmental challenges, head coach professional support, learning orientation,
work-family and family-work conflicts, and gender discrimination) are indirectly related to
career intention through the mediator variables: developmental challenges, head coach
professional support, leader self-efficacy, outcome expectancy, and/or motivation to
lead (see Model 1 in Table 4.1, 4.2, and 4.3). As indicated in Table 4.1, the results
support the hypothesized positive, direct relationship between motivation to lead and

86

career intention (Hypothesis 2). Outcome expectancy was also positively related to
career intention. Developmental challenges and head coach professional support were
significantly and positively related to leader self-efficacy, which supports Hypothesis 6.
Learning orientation was positively related to developmental challenges and head coach
professional support, which supports Hypothesis 9. However, I did not find support for
the relationship between leader self-efficacy, and work-family and family-work conflicts
and gender discrimination. Thus, Hypothesis 11 was not supported.
Indirect effects were analyzed with the Sobel test (Table 4.2) and bootstrap
confidence intervals (Table 4.3). Results provided support for some of the hypothesized
indirect effects. The mediating effect of motivation to lead was significant for the
relationship between leader self-efficacy and career intention (Hypothesis 4), as well as
for the relationship between outcome expectancy and career intention (Hypothesis 5).
Also the results showed that developmental challenges and head coach professional
support were positively related to motivation to lead through leader self-efficacy
(Hypothesis 7). In addition, the indirect relationships between learning orientation and
leader self-efficacy through developmental challenges and head coach professional
support were supported (Hypothesis 10). However, a mediating influence of leader selfefficacy on the relationships between motivation to lead, and work-family and familywork conflicts and gender discrimination were not significant (Hypothesis 12). The
mediating effects of outcome expectancy on the relationship between motivation to lead
and work-family and family-work conflicts and gender discrimination were partially
supported (Hypothesis 13). The results showed that gender discrimination was
negatively related to motivation to lead through outcome expectancy; however, the

87

mediating influence of outcome expectancy was not found for the relationships between
family-work and work-family conflicts and motivation to lead. For the complete results,
see Table 4.2.
Overall, the results supported most of the hypothesized direct and indirect paths.
The exceptions included (a) the direct relationships between work-family and familywork conflicts and gender discrimination, and leader self-efficacy; (b) the indirect
relationships between work-family and family-work conflicts and gender discrimination,
and motivation to lead through leader self-efficacy; and (c) the indirect relationship
between work-family and family-work conflicts and motivation to lead through outcome
expectancy.
Model 2. I then proceeded to add hypothesized direct effects of work-family and
family-work conflicts on motivation to lead as indicated in Hypothesis 14 (see Model 2 in
Table 4.1, 4.2, and 4.3, and Figure 2). Adding these two direct effects did not make
substantial changes in CFI, TLI, and RMSEA (.01, .00, and .00 respectively). The
results also showed partial support for Hypothesis 14. The relationship between familywork conflict and motivation to lead was negative and significant, as stated in
Hypothesis 14; however, there was no significant relationship between work-family
conflict and motivation to lead. All hypothesized direct and indirect paths that were
supported in Model 1 remained significant in Model 2, except for the path between
family-work conflict and outcome expectancy; there was a small change in the beta
coefficient (.01), which changed the p-value from .05 to .07.
Model 3. Next, I added all direct effects from antecedent variables (i.e.,
developmental challenges, head coach professional support, learning orientation, work-

88

family and family-work conflicts, gender discrimination) on all outcome and mediating
variables, which included career intention, motivation to lead, leader self-efficacy, and
outcome expectancy (see Model 3 in Table 4.1, 4.2, and 4.3). This model fit the data
well and showed improvement in all the model fit indices, as compared to Model 2. CFI,
TLI, and RMSEA differences were .017, .018, and .006, respectively. The relationships
shown significant in Model 2 remained significant in Model 3; however, by adding these
direct effects, motivation to lead was no longer significantly related to career intention.
The corresponding Sobel test and bootstrap results indicated that the indirect effects of
leader self-efficacy and outcome expectancy on career intention through motivation to
lead were no longer significant.
Estimating the direct effects of five latent variables (i.e., leader self-efficacy,
outcome expectancy, developmental challenges, head coach professional support, and
gender discrimination) on career intention in Model 3 might have reduced the indirect
effects of leader self-efficacy and outcome expectancy on career intention through
motivation to lead. Each of these five latent variables had a significant direct relationship
with career intention. Comparison was made between the fit of this model (Model 3) and
a model in which the relationship between motivation to lead and career intention was
set to equal its value obtained in Model 2. Although there was no large decrease in CFI,
TLI, and RMSEA (.001, .001, and .001 respectively), the chi-square model fit worsened,
Δχ2 (1) = 14.03, p < .001. This drop in the model fit may imply that the direct effects of
these variables substantially reduced the relationship between motivation to lead and
career intention. Thus, it is likely that this hypothesized indirect effects of the antecedent
variables became no longer significant due to the substantial weakening of the

89

relationship between motivation to lead and career intention after estimating the direct
effects of these variables on career intention.
I conclude that Model 2 is best supported. First, Model 2 is a more parsimonious
model as compared to Model 3, and Model 3’s fit was not a considerable improvement
over Model 2. In addition, there is no strong theoretical basis to predict that leader selfefficacy, outcome expectancy, developmental challenges, head coach professional
support, and gender discrimination have direct relationships with career intention
unrelated to motivation to lead. Further, estimating these direct effects substantially
reduced the magnitude of relationships between motivation to lead and career intention
to a degree that it was no longer significant.

90

Table 4.1
SEM Model Results
Model 1 Model 2 Model 3 Model 4 Model 5 Model 6
Direct Effects on CA
MTL -- > CA
LSE -- > CA
DC-- > CA
FBSUPPMR -- > CA
WFC -- > CA
FWC -- > CA
GD -- > CA
LO -- > CA
OE -- > CA

.28***
-

.27***
-

.05
.12*
.18***
-.12**
-.03
.01
.13**
.02
.26***

.28***
-

.28***
-

.28***
-

Direct Effects on MTL
LSE -- > MTL
OE -- > MTL
DC -- > MTL
FBSUPPMR -- > MTL
WFC -- > MTL
FWC -- > MTL
GD -- > MTL
LO -- > MTL

.42***
.33***
-

.42***
.30***
.08
-.16**
-

.26***
.19***
-.01
.11*
.07
-.16**
.01
.27***

.45***
.27***
.08
-.13**
-

.45***
.27***
.08
-.13**
-

.43***
.30***
.07
-.14**
-

Direct Effects on LSE
DC -- > LSE
FBSUPPMR -- > LSE
WFC -- > LSE
FWC -- > LSE
GD -- > LSE
LO -- > LSE

.37***
.17**
-.06
.05
-.03
.

.37***
.17**
-.06
.05
-.03
-

.26***
.13**
-.07
.07
.00
.32***

.37***
.18***
-.08
.06
.01
-

.37***
.18***
-.08
.07
.01
-

.37***
.18**
-.06
.06
-.03
-

Direct Effects on OE
DC -- > OE
FBSUPPMR -- > OE
WFC -- > OE
FWC -- > OE
GD -- > OE
LO -- > OE
LSE -- > OE

-.01
-.10*
-.20*
-

-.01
-.09
-.20***
-

.11**
.01
-.06
-.08
-.18***
.11*
.31***

-.00
-.07
-.22***
-

-.00
-.07
-.22***
-

-.02
-.08
-.20***
-

91

Table 4.1 (cont’d)
Model 1 Model 2 Model 3 Model 4 Model 5 Model 6
Direct Effect of DC
and FBSUPPMR
DC -- > LO
FBSUPPMR -- > LO

.22***
.12**

.22***
.12**

.19***
.11*

.23***
.10*

.23***
.10*

.22***
.12***

Effects of Gender
Gender -> CA
Gender -> MTL
Gender -> LSE
Gender -> OE
Gender -> DC
Gender -> FBSUPPMR
Gender -> WFC
Gender -> FWC
Gender -> LO
Gender -> LSE_I

-

-

-

-.15***
.19***
-.11**
.12**
-.09*
.03
-.05
-.08
-.03
-

-.15***
.20***
-.14**
.12**
-.09*
.03
-.05
-.08
-.03
.16***

-

-

-

-

-

-

-.01
.05
-.07
-.04
.11*
-.01
-.05
-.03
.00
.02

1922.31
780
.923
.915
.047

1913.00
778
.924
.915
.047

1639.49
761
.941
.933
.041

2038.85
816
.912
.903
.051

2012.77
815
.914
.905
.050

2073.93
816
.915
.906
.048

Effects of HCR
HCR -> CA
HCR -> MTL
HCR -> LSE
HCR -> OE
HCR -> DC
HCR -> FBSUPPMR
HCR -> WFC
HCR -> FWC
HCR -> LO
Fit Statistics
Chi-Square
Degrees of Freedom
CFI
TLI
RMSEA

Notes. CA = Career intention, MTL = Motivation to lead, LSE = Leader self-efficacy, OE
= Outcome expectancy, DC = Developmental challenges, FBSUPPMR = Latent
construct combining feedback, support, and mentoring relationship (i.e., head coach
professional support), WFC = Work-family conflict, FWC = Family work-conflict, GD =
Gender discrimination, LO = Learning orientation, HCR = Head coach ratings available

92

Table 4.2
Indirect Effects and Significance Using Normal Distribution: Sobel Tests
Sobel Tests
LSE --> MTL --> CA
Model 1
Model 2
Model 3
Model 4
Model 5
Model 6
OE --> MTL --> CA
Model 1
Model 2
Model 3
Model 4
Model 5
Model 6
DC --> LSE --> MTL
Model 1
Model 2
Model 3
Model 4
Model 5
Model 6
FBSUPPMR --> LSE --> MTL
Model 1
Model 2
Model 3
Model 4
Model 5
Model 6
WFC --> LSE --> MTL
Model 1
Model 2
Model 3
Model 4
Model 5
Model 6
FWC --> LSE --> MTL
Model 1
Model 2
Model 3
Model 4
Model 5
Model 6

Value

SE

z

p

.12
.12
.01
.13
.13
.12

.03
.03
.02
.03
.03
.03

4.55
4.48
.82
4.62
4.62
4.50

.00
.00
.41
.00
.00
.00

.09
.08
.01
.08
.08
.08

.02
.02
.01
.02
.02
.02

4.06
3.81
.82
3.53
3.54
3.82

.00
.00
.41
.00
.00
.00

.16
.16
.07
.17
.17
.16

.03
.03
.02
.03
.03
.03

5.55
5.57
3.48
5.72
5.72
5.60

.00
.00
.00
.00
.00
.00

.07
.07
.03
.08
.08
.08

.02
.02
.01
.02
.02
.02

3.41
3.43
2.51
3.63
3.64
3.54

.00
.00
.01
.00
.00
.00

-.02
-.03
-.01
-.04
-.04
-.03

.02
.02
.01
.02
.02
.02

-1.14
-1.24
-1.34
-1.63
-1.66
-1.29

.26
.21
.18
.10
.10
.20

.02
.02
.01
.03
.03
.03

.02
.02
.01
.02
.02
.02

.94
1.12
1.37
1.42
1.45
1.28

.35
.26
.17
.16
.15
.20

93

Table 4.2 (cont’d)
Sobel Tests
GD --> LSE --> MTL
Model 1
Model 2
Model 3
Model 4
Model 5
Model 6
WFC-- > OE-- > MTL
Model 1
Model 2
Model 3
Model 4
Model 5
Model 6
FWC -- > OE -- > MTL
Model 1
Model 2
Model 3
Model 4
Model 5
Model 6
GD -- > OE -- > MTL
Model 1
Model 2
Model 3
Model 4
Model 5
Model 6
LO -- > DC -- > LSE
Model 1
Model 2
Model 3
Model 4
Model 5
Model 6
LO -- > FBSUPPMR -- > LSE
Model 1
Model 2
Model 3
Model 4
Model 5
Model 6

Value

SE

z

p

-.01
-.01
.00
.00
.00
-.01

.02
.02
.01
.02
.02
.02

-.55
-.56
.04
-.22
-.22
-.57

.59
.58
.97
.83
.83
.57

.00
-.00
-.01
-.00
-.00
-.01

.02
.02
.01
.01
.01
.02

-.13
-.03
-1.17
-.08
-.08
-.41

.90
.82
.24
.94
.94
.69

-.03
-.03
-.02
-.02
-.02
-.02

.02
.02
.01
.01
.01
.02

-1.86
-1.74
-1.54
-1.46
-1.46
-1.59

.06
.08
.13
.14
.14
.11

-.07
-.06
-.04
-.06
-.06
-.06

.02
.02
.01
.02
.02
.02

-3.54
-3.43
-2.73
-3.43
-3.44
-3.49

.00
.00
.01
.00
.00
.00

.08
.08
.05
.08
.08
.08

.02
.02
.02
.02
.02
.02

4.02
4.02
3.31
4.04
5.05
4.06

.00
.00
.00
.00
.00
.00

.02
.02
.02
,02
.02
.02

.01
.01
.01
.01
.01
.01

2.20
2.20
1.97
1.89
1.89
2.23

.03
.03
.05
.06
.06
.03

94

Table 4.3
Bootstrap Results for Indirect Effects
Indirect Effects
LSE --> MTL --> CA
Model 1
Model 2
Model 3
Model 4
Model 5
Model 6
OE --> MTL --> CA
Model 1
Model 2
Model 3
Model 4
Model 5
Model 6
DC --> LSE --> MTL
Model 1
Model 2
Model 3
Model 4
Model 5
Model 6
FBSUPPMR --> LSE --> MTL
Model 1
Model 2
Model 3
Model 4
Model 5
Model 6
WFC --> LSE --> MTL
Model 1
Model 2
Model 3
Model 4
Model 5
Model 6
FWC --> LSE --> MTL
Model 1
Model 2
Model 3
Model 4
Model 5
Model 6

M

SE

95% CI LL

95% CI UL

.12
.12
.01
.13
.13
.12

.03
.03
.02
.03
.03
.03

.05
.05
-.02
.02
.06
.05

.19
.18
.05
.20
.20
.19

.09
.08
.01
.08
.08
.08

.02
.02
.01
.02
.02
.02

.04
.03
-.02
.02
.02
.03

.15
.14
.04
.13
.13
.14

.16
.16
.07
.17
.17
.16

.03
.03
.02
.03
.03
.03

.09
.09
.02
.10
.10
.09

.22
.22
.11
.23
.23
.22

.07
.07
.03
.08
.08
.08

.02
.02
.01
.02
.02
.02

.02
.02
.00
.03
.03
.03

.12
.12
.06
.13
.13
.13

-.02
-.03
-.01
-.04
-.04
-.03

.02
.02
.01
.02
.02
.02

-.07
-.07
-.05
-.08
-.08
-.07

.02
.02
.01
.01
.01
.02

.02
.02
.01
.03
.03
.03

.02
.02
.01
.02
.02
.02

-.03
-.03
-.01
-.02
-.02
-.02

.07
.07
.05
.08
.08
.07

95

Table 4.3 (cont’d)
Indirect Effects
GD --> LSE --> MTL
Model 1
Model 2
Model 3
Model 4
Model 5
Model 6
WFC-- > OE-- > MTL
Model 1
Model 2
Model 3
Model 4
Model 5
Model 6
FWC -- > OE -- > MTL
Model 1
Model 2
Model 3
Model 4
Model 5
Model 6
GD -- > OE -- > MTL
Model 1
Model 2
Model 3
Model 4
Model 5
Model 6
LO -- > DC -- > LSE
Model 1
Model 2
Model 3
Model 4
Model 5
Model 6
LO -- > FBSUPPMR -- > LSE
Model 1
Model 2
Model 3
Model 4
Model 5
Model 6

M

SE

95% CI LL

95% CI UL

-.01
-.01
.00
.00
.00
-.01

.02
.02
.01
.02
.02
.02

-.05
-.05
-.02
-.05
-.05
-.05

.03
.03
.02
.04
.04
.03

.00
-.00
-.01
-.00
-.00
-.01

.02
.02
.01
.01
.01
.02

-.03
-.03
-.03
-.03
-.03
-.03

.03
.03
.01
.03
.03
.02

-.03
-.03
-.02
-.02
-.02
-.02

.02
.02
.01
.01
.01
.02

-.07
-.06
-.03
-.05
-.05
-.06

.01
.01
.00
.01
.01
.01

-.07
-.06
-.04
-.06
-.06
-.06

.02
.02
.01
.02
.02
.02

-.11
-.10
-.07
-.10
-.10
-.10

-.02
-.02
-.00
-.02
-.02
-.02

.08
.08
.05
.08
.08
.08

.02
.02
.02
.02
.02
.02

.03
.03
.02
.03
.03
.03

.14
.14
.09
.14
.14
.14

.02
.02
.02
,02
.02
.02

.01
.01
.01
.01
.01
.01

.00
.00
.00
-.01
-.01
.00

.05
.05
.03
.04
.04
.05

96

Figure 2. Hypothesized Structural Equation Model Results
97

MIMIC Model Analysis
MIMIC model analysis tests for the heterogeneity and measurement equivalence
between groups, and can be defined as confirmatory analysis with a covariate (Muthén,
1989). The MIMIC model tests mean differences in latent factors and may be a
preferable approach to testing measurement equivalence when the sample size is
relatively small. MIMIC model analysis results are presented in Table 5.1. Participants
who did not indicate their gender in the survey were excluded from the analysis (n = 97).
To test for the measurement equivalence between samples of male and female
assistant coaches, I first regressed all latent variables on gender (Male = 0, Female = 1)
in a measurement model (Model 2 in Table 5.1). As shown in Table 5.1, the model fit
well to the data, although the model fit significantly worsened from the measurement
model that does not include gender as a covariate (Model 1 in Table 5.1). This change
in the model fit can be attributed to the increase in degrees of freedom that resulted
from adding a covariate in the model. The relationships between gender covariate and
each of the latent variables were examined to test for the heterogeneity in factors (i.e.,
mean differences in latent factors) between male and female assistant coaches (see
Table 5.2). The significant relationships between a gender covariate and latent variables
indicate that factor means are different between genders. As shown in Table 5.2, there
were significant negative relationships between gender and career intention and leader
self-efficacy. There were also significant positive relationships between gender and
motivation to lead as well as outcome expectancy. These results indicate that male
assistant coaches had higher means on career intention and leader self-efficacy factors

98

as compared to female assistant coaches, while female assistant coaches had higher
means on motivation to lead and outcome expectancy factors.
Next, I examined if there were any significant direct relationships between any of
the indicators of latent variables and a gender covariate. The modification indices, which
indicate a chi-square change if the parameter is freed, were evaluated to see if
regressing any of the indicators of latent variables on the gender covariate significantly
improved the model fit. A significant direct relationship between the indicator and a
covariate not related to the factor and a significant improvement in the model fit by
estimating this relationship indicate a measurement non-equivalence (Muthén, 1989).
Considering the sample size and biases involved in the chi-square value, only large
modification indices (MI > 20) with large expected standardized parameter changes
(EPC) were considered (Saris, Satorra, & Sorbon, 1987). The modification index in
Model 2 showed that regressing one of the indicators of leader self-efficacy (i.e., leader
self-efficacy about interpersonal skills) on gender could improve the fit of the model (MI
= 24.42, EPC = .16). Thus, in the next model, the direct path from gender to this
particular indicator of leader self-efficacy was included (Model 3 in Table 5.1). As Table
5.1 presents, there was a significant chi-square drop in Model 3 as compared to Model
2, Δχ2 (1) = 68.83, p < .001, though as Table 5.2 indicates, the magnitudes of the paths
between gender and latent factors remained relatively unchanged. The direct path
between gender and this indicator (i.e., leader self-efficacy about interpersonal skills) of
leader self-efficacy factor was significant and positive, which suggests that female
assistant coaches have higher leader self-efficacy about interpersonal skills than their
male counterparts. However, as indicated, the relationship between the leader self-

99

efficacy factor and gender was negative and significant. Given that this indicator of
leader self-efficacy about interpersonal skills had a positive loading on the leader selfefficacy factor, females may be expected to have higher means on leader self-efficacy
about interpersonal skills than males although females have lower means than males
on the overall leader self-efficacy factor. Thus, this particular indicator of leader selfefficacy may not be equivalent between female and male groups. However, it is
important to note that the improvement in CFI, TLI, and RMSEA was not substantial
(.002, .002, and .000, respectively). Therefore, measurement equivalence between
genders may still be supported.

100

Table 5.1
MIMIC Model Analysis Results
ChiSquare

Degrees of
Freedom

CFI

TLI

RMSEA Chi-square
difference.

1510.44

754

.949

.942

.039

Model 2. CFA
controlled for
gender

1553.82

786

.945

.937

.041

43.38***
(32)

Model 3. CFA
controlled for
gender, with a
direct effect of
gender on LSE_I

1528.50

785

.947

.939

.041

25.32***(1)

Model 1. CFA

Notes: *** p < .001, ** p < .01, * p < .05

101

Table 5.2
The Relationships Between Gender and Latent Variables
Direct Effects of Gender

Model 2. CFA
controlled for gender

Model 3. CFA controlled
for gender, with a direct
effect of gender on LSE_I

Gender -> CA

-.11**

-.11**

Gender -> MTL

.16**

.16**

Gender -> LSE

-.14**

-.17***

Gender -> OE

.11*

.11*

Gender -> DC

-.07

-.07

Gender -> FBSUPPMR

.02

.02

Gender -> WFC

-.05

-.05

Gender -> FWC

-.08

-.07

Gender -> GD

.06

.06

Gender -> LO

-.03

-.03

Gender -> LSE_I

-

.16***

Notes: LSE_I = Leader Self-Efficacy about Interpersonal Skills
*** p < .001, ** p < .01, * p < .05

102

Model 4. Because the results from the MIMIC model analysis showed that latent
means may differ between genders, I tested a hypothesized structural model controlling
for the effects of gender. Models with and without the direct path from gender to leader
self-efficacy about interpersonal skills were examined. The structural model controlled
for the effects of gender on latent variables only (Model 4 in Table 4.1, 4.2, and 4.3),
which produced an acceptable model fit, χ2 (N = 576, 816) = 2038.85 CFI = .912, TLI =
.903 RMSEA = .050. Gender was significantly and positively related to motivation to
lead and outcome expectancy, while gender was significantly and negatively related to
career intention, leader self-efficacy, and developmental challenges. In comparing the
model (Model 4 in Table 4.1, 4.2, and 4.3) and the model without controlling for gender
(Model 2 in Table 4.1, 4.2, and 4.3), it appears that controlling for the effects of gender
did not make drastic changes in the magnitude of the hypothesized direct and indirect
relationships between the factors. However, the indirect relationship between learning
orientation and leader self-efficacy through head coach professional support was no
longer significant; there was a slight change in the Sobel test value, from .021 to .018,
which made the p-value change from .028 to .059. The bootstrap confidence interval
included 0. This may be attributed to the significant, negative direct effects of gender on
leader self-efficacy. Female assistant coaches had a lower mean on the leader selfefficacy factor than their male counterparts.
I then proceeded to test a model including a direct path from gender to an
indicator of leader self-efficacy: leader self-efficacy about interpersonal skills (Model 5 in
Table 4.1, 4.2, and 4.3). Again the relationships between latent factors remained
unchanged as compared to Model 2 and Model 4. As in the measurement model

103

comparison, the chi-square value improved as compared to the model in which gender
only related to latent variables (Model 4), Δχ2 (1) = 26.08, p < .001. However, the other
model fit statistics show only slight improvements (CFI: .002, TLI: .002, and RMSEA:
.001).
In sum, the MIMIC model analysis showed that there were some gender
differences in latent means. Females showed significantly lower career intention, leader
self-efficacy, and developmental challenges, and higher outcome expectancy and
motivation to lead. Controlling for gender effects did not make substantial changes in
the magnitude of relationships between latent variables in the hypothesized model.
There was also a significant direct relationship between one indicator of leader selfefficacy (leader self-efficacy about interpersonal skills) and gender, unrelated to the
leader self-efficacy latent factor. However, both the measurement and the structural
models showed that adding this direct path did not make substantial changes in the
model fit, nor in the relationships between latent factors.
Multiple Group Analysis
In addition to MIMIC model analysis, I tested potential gender differences by
conducting multiple group analysis (Joreskog, 1971; Muthén, 1989). Multiple group
analysis tests the potential gender differences in measurements (i.e., factor loadings,
intercepts) and structures (i.e., latent means, variance, covariance, structural paths).
Because multiple group analysis usually requires a large sample size for each group
(Muthén, 1989) and the sample size for this study may not have been sufficient and
lacked power (Female: n = 360, Male: n = 216), the results are interpreted with
cautions.

104

First, measurement equivalence between two groups (i.e., female and male
assistant coaches) was tested. Establishing measurement equivalence should precede
testing for structural equivalence (Vandenberg & Lance, 2000). Testing measurement
equivalence followed six steps: (a) fitting the model separately in each female and male
group to confirm that the model fits to each group; (b) fitting the model in all groups
simultaneously, allowing all parameters to be freed; (c) fitting the model in all groups
holding factor loadings equal to test the equivalence of factor loadings; (d) fitting the
model in all groups holding factor loadings and intercepts equal to test the equivalence
of the intercepts; (e) fitting the model in all groups, holding factor loadings, intercepts,
factor variance, and covariance equal to test the equivalence of factor and covariance;
and (f) fitting the model in all groups, holding factor loadings, intercepts, factor variance,
covariance, and latent means equal to test the equivalence of latent means (Muthén &
Muthén, 2007). Once measurement equivalence was established, I proceeded to test
equivalence in the structural paths, by comparing the constrained model and baseline
model where all the structural paths were freely estimated. Models were examined for
improvement/worsening in overall fit as evaluated by change in chi-square, CFI, TLI,
and RMSEA. The chi-square value is biased to the sample size (Hu & Bentler, 1998);
thus, it was interpreted with caution. Although there are no definite cut-points for
significant changes in TLI and RMSEA, the change in CFI of .01 was considered a
significant change (Vandenberg & Lance, 2000).
The model results for the measurements are presented in Table 6.1. In Step 1, I
fit the model to the female and male samples separately. Participants who did not
indicate their gender in the survey were excluded from the analysis (n = 98). As

105

indicated in Table 6.1, the model shows the acceptable fit to both the female and male
data. In Step 2, I fit both female and male models simultaneously, allowing all
parameters to be freed. The model showed the acceptable fit to the data. In Step 3, the
model was fit to both female and male groups, holding factor loadings equal, to test the
equivalence of the factor loadings. As shown in the table, worsening in the chi-square
difference was significant, Δχ2 (32) = 65.16, p < .001; however, there were only slight
differences in CFI, TLI, and RMSEA values between the models (.003, .000, and .000,
respectively). I concluded that the changes in fit in the constrained model in Step 3 were
not substantial; thus, equivalence of the factor loadings between female and male
groups was confirmed.
In Step 4, factor loadings as well as intercepts were held constant across groups.
Chi-square differences between the model in Step 3 and Step 4 were significant, Δχ2
(32) = 105.398, p < .001; however, CFI, TLI, and RMSEA were not significantly affected
(changes were .005, .004, and 001 respectively). Thus, I concluded that equivalence for
the intercepts across groups was confirmed. Next, in Step 5, I held factor loadings,
intercepts, variance, and covariances constant across groups. Change in chi-square
from Step 4 was significant, Δχ2 (56) = 95.32, p < .001; however, changes in CFI, TLI,
and RMSEA were small (.003, .000, and .000, respectively). Thus, the equivalence for
variance and covariances were supported. Lastly, in Step 6, factor loadings, intercepts,
variance, covariance, and latent means were constrained to be same across groups.
Again the chi-square difference was significant, Δχ2 (9) = 58.99, p < .001; however, CFI,
TLI, and RMSEA differences were small (.003, .003, and 000, respectively). The
equivalence of latent means between two groups was confirmed.

106

Table 6.1
Multiple Group Analysis: Female (n = 360) vs. Male (n = 216)
Model

ChiSquare

Degrees
of
Freedom

CFI

TLI

RMS
EA

Step 1.a.

Male group

1252.39

775

.908

.898

.053

Step 1.b.

Female group

1485.80

775

.920

.911

.050

Step 2.

Multiple group
(parameters
freed)

2738.19

1550

.916

.906

.052

Step 3.

Multiple group
(invariant
loading)

2803.35

1582

.913

.906

.052

65.16***
(32)

Step 4.

Multiple group
(invariant
loading and
intercepts)

2908.75

1614

.908

.902

.053

105.40***
(32)

Step 5.

Multiple group
(invariant
loading,
intercept,
variance and
covariance)

3004.07

1670

.905

.902

.053

95.32***
(56)

Step 6.

Multiple group
(invariant
loading,
variance,
covariance,
latent mean)

3063.06

1679

.902

.899

.053

58.99***
(9)

Notes. *** p < .001, ** p < .01, * p < .05

107

Chisquare
difference

Because measurement equivalence between female and male groups was
confirmed, I then proceeded to test for the equivalence in the structural paths
hypothesized in the study. Results are presented in Table 6.2. First, I tested a model
constraining loadings, intercept, variance, covariance, and latent means to be equal
across two groups, but freeing the structural paths. As shown in Table 6.1, the model fit
was acceptable. Next, I tested a model holding everything including structural paths
constant. The chi-square change in this model from Step 1 was significant, Δχ2 (15) =
29.41, p = .01, but the values in CFI, TLI, and RMSEA remained relatively unchanged
(.001, .000, and 000, respectively). Thus, I concluded that structural equivalence
between male and female groups was supported.
In sum, the multiple group analysis suggested that there was measurement as
well as structural equivalence between samples of female and male assistant coaches.

108

Table 6.2
Multiple Group SEM: Female (n = 360) vs. Male (n = 216)

Model

ChiSquare

Degrees
of
Freedom

CFI

TLI

RMSEA Chisquare
difference

Step 1

Multiple group
(invariant
loadings,
intercepts,
variance,
covariance, and
latent mean),
structural paths
freed

3347.20

1687

.882

.880

.058

Step 2

Multiple group
(invariant
loadings,
intercepts,
variance,
covariance, and
latent mean),
structural paths
held constant

3376.61

1702

.881

.880

.058



Notes: *** p < .001, ** p < .01, * p < .05

109

29.41*
(15)

Summary of the First Phase of Analysis
The structural equation model of the total sample of assistant coaches (N = 673)
showed that leader self-efficacy and outcome expectancy were related to career
intention through motivation to lead. As hypothesized, leader self-efficacy was positively
related to developmental challenges and head coach professional support which
captured feedback, support, and mentoring from the head coaches. The relationships
between these antecedents and motivation to lead were mediated by leader selfefficacy. Gender discrimination was negatively related to motivation to lead through
outcome expectancy, and family-work conflict also had a direct and negative
relationship with motivation to lead. In addition, learning orientation was positively
related to leader self-efficacy through developmental challenges and head coach
professional support though the indirect effect of learning orientation on leader selfefficacy through head coach professional support was no longer significant after
accounting for the effect of gender. MIMIC model analysis and multiple group analysis
generally supported the measurement and structural equivalence between the samples
of female and male assistant coaches; however, MIMIC model analysis indicated that
there may have been latent mean differences between females and males, and thus
this potential gender difference continued to be considered in the analyses in the
second phase, where I tested path models with a sample of assistant coaches who had
the evaluation of their leadership competency completed by the head coaches.
Second Phase: Path model
Preliminary Testing of the Sample

110

Prior to proceeding to the path analyses, I tested potential differences between
assistant coaches whose leadership skills were evaluated by their head coaches (n =
245) and assistant coaches whose leadership skills were not evaluated by their head
coaches (n = 428). Similar to testing the gender differences, MIMIC model as well as
multiple group analysis were conducted.
Assistant coaches who had their leadership skills rated by head coaches were
coded as 1, and assistant coaches who did not have their leadership skills rated by
head coaches were coded as 0. The MIMIC model analysis results are presented in
Table 7.1; there were no substantial changes in model fit as compared to a
measurement model that does not include a covariate. Also there were no paths
between the factor indicator and a covariate that had a large modification index (MI >
20). There was a significant positive relationship between the covariate and head coach
professional support, which indicates that assistant coaches who had their leadership
skills evaluated by their head coaches had a higher latent mean on this factor. Thus,
measurement equivalence between the groups was confirmed; however, the results
indicated that there might have been a latent mean difference on this one factor. The
structural model results which included this variable as a covariate are presented in
Table 4.1, 4.2, and 4.3 (Model 6). There were no substantial changes in the magnitudes
of the relationships between the latent factors as compared to the hypothesized model
that does not include the covariate. Again, the relationship between the covariate and
head coach professional support was positive and significant. In sum, the results
indicated that measurement equivalence between these groups can be retained;

111

however, there may have been a latent mean difference in head coach professional
support factor.

112

Table 7.1
MIMIC Model Analysis Results: AC with Head Coach Ratings (n = 245) vs. AC without
Head Coach Ratings (n = 428)
ChiSquare

Model 1. CFA
Model 2. CFA
controlled for
HCR

Degrees of
Freedom

CFI

TLI

RMSEA Chisquare
difference

1510.44

754

.949

.942

.039

1555.07

786

.948

.941

.038

Notes. AC = Assistant Coaches

113

44.625
(32), p =
.06

Table 7.2
The Relationship Between HCR (with or without Head Coach Ratings) and Latent
Variables
Direct Effects of HCR

Model 2. CFA
controlled for HCR

HCR -> CA

.00

HCR -> MTL

.05

HCR -- > LSE

-.06

HCR -> OE

.02

HCR -> DC

-.01

HCR -> FBSUPPMR

.11**

HCR -> WFC

-.01

HCR -> FWC

-.05

HCR -> GD

-.03

HCR -> LO

.00

Notes. See Model 6 in Table 4.1, 4.2, and 4.3 for the structural model results.
*** p < .001, ** p < .01, * p < .05

114

Multiple group analysis results are presented in Table 8.1 and 8.2. I followed the
steps described in Phase 1 in testing measurement and structural equivalence between
genders. As indicated in the tables, holding the loadings, intercept, variance and
covariance, latent means, and structural paths equal across groups did not make
substantial changes in the fit statistics as compared to the less constrained models.
Thus, multiple group analysis indicated that the measurements as well as the structural
equivalence between the groups of assistant coaches whose leadership skills were
rated by their head coaches and those whose leadership was not rated by their head
coaches can be retained.
In sum, the results from both the MIMIC model and the multiple group analysis
support that there are measurement as well as structural equivalence between the
groups of assistant coaches whose leadership skills were rated by their head coaches
and whose leadership skills were not rated by their head coaches. However, it is
important to note that the MIMIC model results indicated a latent mean difference in
head coach professional support. This is considered a limitation of the chosen analysis
approach in the second phase of the analyses.

115

Table 8.1
Multiple Group CFA: AC with Head Coach Ratings (n = 245) vs. AC without Head
Coach Ratings (n = 428) Ratings
ChiSquare

Degrees of
Freedom

CFI

TLI

RMSEA Chi-Square
Difference

1346.84

775

.903

.893

.055

1580.29

775

.913

.903

.049

2. Multiple group
(free parameters)
3. Multiple group
model (loading
constant across
group)

2927.13

1550

.909

.899

.051

2968.41

1582

.908

.900

.051

41.28 (32)

4. Multiple group
(loading and
intercept held
constant)

3018.90

1614

.907

.901

.051

50.50*(32)

5. Invariant factor
variance and
covariance

3112.73

1670

.907

.902

.051

93.83**(56)

6. Invariant factor
variance,
covariance, and
mean

3120.90

1679

.905

.902

.051

8.17(9)

1.a. AC with HC
ratings
1.b. Female group

Notes. *** p < .001, ** p < .01, * p < .05

116

Table 8.2
Multiple Group SEM: AC with Head Coach Ratings (n = 245) vs. AC without Head
Coach Ratings (n = 428)
ChiSquare

Degrees of
Freedom

CFI

TLI

RMSEA Chi-Square
Difference

1. Multiple group
(loading and
intercept held
constant,
variance,
covariance,
mean), structural
path freed

3448.72

1687

.884

.881

.056

2. Multiple group
(loading and
intercept held
constant,
variance,
covariance,
mean), coefficient
held constant

3456.63

1694

.884

.883

.055

Notes. *** p < .001, ** p < .01, * p < .05

117

7.91 (15)

Path Analysis
For path analysis in the second phase of analyses, a latent variable was created
for leader competency which was not included in the first phase, using five subscale
scores as indicators. A measurement model was conducted for leader competency. The
model fit the data well, χ2 (N = 244, 4) = 4.36, CFI = 1.00, TLI = .99, RMSEA = .019.
Then, I tested a path model with the sub-sample (n = 245) including a latent
factor of leader competency. Composite scores were used for the factors that were
confirmed in the first phase. This approach was taken considering the number of free
parameters in the model and the small sample size. Measurement errors were
calculated for each variable by (1-alpha coefficient) x variance, and were controlled in
the model by loading the calculated measurement error to each variable. I continued to
use a combined latent variable for feedback, support, and mentoring (i.e., “head coach
professional support”) in this analysis because this was to better represent the construct
than simply aggregating these variables. Again, the model fits were evaluated using chisquare, CFI, TLI, and RMSEA. Due to the smaller sample size, the chi-square value
was weighed more importantly as compared to Phase 1. Path model results are
presented in Table 9.1, and the corresponding indirect effect results are presented in
Table 9.2 and 9.3.
The hypothesized model was retested with this smaller sample size (see Model 1
in Table 9.1, 9.2, and 9.3 and Figure 2). The results generally supported the hypotheses
supported in the first phase. Motivation to lead was directly and positively related to
career intention (Hypothesis 2). Leader self-efficacy and outcome expectancy positively
related to career intention through motivation to lead (Hypothesis 4 and 5).

118

Developmental challenges and head coach professional support were related to leader
self-efficacy (Hypothesis 6), and the relationships between these developmental
antecedents and motivation to lead were mediated by leader self-efficacy (Hypothesis
7). In addition, learning orientation significantly and positively related to developmental
challenges and head coach professional support, which provided support for Hypothesis
9. The indirect effect of learning orientation on leader self-efficacy through
developmental challenges was found; however, such indirect effect through head coach
professional support was not significant. Thus, Hypothesis 10 was partially supported.
Also, there were no significant relationships between leader self-efficacy and workfamily and family-work conflicts or gender discrimination (Hypothesis 11), and the
relationships between these variable and motivation to lead were not mediated by
leader self-efficacy (Hypothesis 12).
The results from the first phase of analyses partially supported Hypothesis 13 by
showing that gender discrimination was negatively related to motivation to lead through
outcome expectancy. The results from the path analysis in the second phase supported
such indirect effects of gender discrimination; however, the indirect effects of workfamily and family-work conflicts were still not significant. The direct effect of family-work
conflict on motivation to lead remained significant; however, the effect of work-family
conflict was still not significant, which again partially supported Hypothesis 14.
Regarding hypotheses involving leader competency, Hypothesis 1, which
predicted that motivation to lead positively relates to career intention through leader
competency, was not supported. In addition, leader self-efficacy was not positively
related to leader competency through motivation to lead. Thus, Hypothesis 3 was not

119

supported. Further, the results did not support Hypothesis 8, which predicted that
developmental challenges and feedback, support, and mentoring relationship (i.e., head
coach professional support) directly relate to leader competency. The relationship
between developmental challenges and leader competency was not significant. Head
coach professional support had a positive, direct relationship with leader competency;
however, the coefficient did not reach the significance level (p = .066), which may be
attributed to lack of power resulted from a small sample size.
In sum, the results from the path analyses supported the hypothesized paths that
were supported in the first phase of the analyses, except for the indirect effect of
learning orientation on leader self-efficacy through head coach professional support.
The results did not provide support for hypotheses regarding the added variable, leader
competency, although I found a positive trend in the relationship between a combined
latent factor of feedback, support, and mentoring (i.e., head coach professional support)
and leader competency.

120

Table 9.1
Path Analyses (with Latent Variables of LC and FBSUPPMR), with Measurement Errors
Controlled (n = 245)
Model 1

Model 2

Model 3

Direct Effects on CA
MTL -- > CA
LC -- > CA

.22**
-.03

.26***
-.04

.26**
-.04

Direct Effects on LC
MTL -- > LC
DC -- > LC
FBSUPPMR -- > LC

.05
.01
.14 (p = .057)

.06
-.01
.14 (p = .054)

.06
-.01
.14 (p = .052)

Direct Effects on MTL
LSE -- > MTL
OE -- > MTL
WFC -- > MTL
FWC -- > MTL

.44***
.25**
.04
-.20**

.47***
.22**
.03
-.19**

.47***
.22***
.03
-.19**

Direct Effects on LSE
Challenges -->LSE
FBSUPPMR --> LSE
WFC -- > LSE
FWC --> LSE
GD --> LSE

.29**
.23**
-.06
.07
.03

.28***
.24***
-.06
.07
.04

.28**
.24**
-.06
.07
.04

Direct Effects on OE
WFC -- > OE
FWC -- > OE
GD-- > OE

.03
-.05
-.23**

.02
-.04
-.24**

.02
-.04
-.24**

Direct Effects of Challenges
and FBSUPPMR
LO -- > Challenges
LO -- > FBSUPPMR

.26***
.15*

.27***
.14*

.27***
.14*

121

Table 9.1 (cont’d)
Model 1

Fit Statistics
Chi-Square
Degrees of Freedom
CFI
TLI
RMSEA

-.18**
.21***
-.05
.14*
-.16*
.09
-.00
-.03
.03
-.09
.13**
.11*

354.83
115
.847
.796
.092

273.66
102
.888
.850
.083

Model 3

-.18**
.21***
-.05
.14*
-.16*
.09
-.00
-.03
.03
-.07

Effects of Gender
Gender -> CA
Gender -> MTL
Gender -> LSE
Gender - > OE
Gender -> DC
Gender -> FBSUPPMR
Gender -> WFC
Gender -> FWC
Gender -> LO
Gender -> LC
Gender -> LC_COG
Gender -> LC_I

Model 2

344.35
113
.852
.800
.092

Notes. LC = Leader Competency, LC_COG = Leader Competency about Cognitive
Skills, LC_I = Leader Competency about Interpersonal Skills
*** p < .001, ** p < .01, * p < .05

122

Table 9.2
Indirect Effects and Significance Using Normal Distribution: Sobel Tests (n = 245)
Sobel Tests
MTL -- > LC -- > CA
Model 1
Model 2
Model 3
LSE -- > MTL -- > LC
Model 1
Model 2
Model 3
LSE --> MTL --> CA
Model 1
Model 2
Model 3
OE --> MTL --> CA
Model 1
Model 2
Model 3
DC --> LSE --> MTL
Model 1
Model 2
Model 3
FBSUPPMR --> LSE --> MTL
Model 1
Model 2
Model 3
WFC --> LSE --> MTL
Model 1
Model 2
Model 3
FWC --> LSE --> MTL
Model 1
Model 2
Model 3
GD --> LSE --> MTL
Model 1
Model 2
Model 3
WFC-- > OE-- > MTL
Model 1
Model 2
Model 3

Value

SE

z

p

-.00
-.00
-.00

.00
.01
.01

-.37
-.47
-.48

.71
.64
.63

.02
.03
.03

.03
.04
.04

.64
.65
.79

.52
.52
.43

.10
.12
.12

.03
.04
.04

2.90
3.31
3.31

.00
.00
.00

.06
.06
.06

.02
.02
.03

2.29
2.30
2.30

.02
.02
.02

.13
.13
.13

.04
.04
.04

3.46
3.40
3.40

.00
.00
.00

.10
.11
.11

.03
.04
.04

2.98
3.14
3.14

.00
.00
.00

-.03
-.03
-.03

.03
.03
.03

-.84
-.80
-.80

.40
.42
.42

.03
.04
.04

.03
.03
.03

.94
1.02
1.02

.35
.31
.31

.01
.02
.02

.03
.03
.03

.41
.58
.58

.68
.56
.56

.01
.01
.01

.02
.02
.02

.34
.25
.25

.73
.80
.80

123

Table 9.2 (cont’d)
Sobel Tests
FWC -- > OE -- > MTL
Model 1
Model 2
Model 3
GD -- > OE -- > MTL
Model 1
Model 2
Model 3
LO -- > DC -- > LSE
Model 1
Model 2
Model 3
LO -- > FBSUPPMR -- > LSE
Model 1
Model 2
Model 3

Value

SE

z

p

-.01
-.01
-.01

.02
.02
.02

-.54
-.52
-.52

.59
.60
.60

-.06
-.06
-.05

.03
.03
.03

-2.18
-2.12
-2.12

.03
.03
.03

.08
.08
.08

.03
.03
.03

2.59
2.59
2.59

.01
.01
.01

.03
.04
.04

.02
.02
.02

1.72
1.69
1.69

.09
.09
.09

Notes. *** p < .001, ** p < .01, * p < .05

124

Table 9.3
Bootstrap Results for Indirect Effects (n = 245)
Indirect Effects
MTL -- > LC -- > CA
Model 1
Model 2
Model 3
LSE -- > MTL -- > LC
Model 1
Model 2
Model 3
LSE --> MTL --> CA
Model 1
Model 2
Model 3
OE --> MTL --> CA
Model 1
Model 2
Model 3
DC --> LSE --> MTL
Model 1
Model 2
Model 3
FBSUPPMR --> LSE --> MTL
Model 1
Model 2
Model 3
WFC --> LSE --> MTL
Model 1
Model 2
Model 3
FWC --> LSE --> MTL
Model 1
Model 2
Model 3
GD --> LSE --> MTL
Model 1
Model 2
Model 3
WFC-- > OE-- > MTL
Model 1
Model 2
Model 3

M

SE

95% CI LL

95% CI UL

-.00
-.00
-.00

.00
.01
.01

-.01
-.02
-.02

.01
.01
.01

.02
.03
.03

.03
.04
.04

-.04
-.04
-.04

.08
.10
.10

.10
.12
.12

.03
.04
.04

.03
.04
.04

.17
.20
.20

.06
.06
.06

.02
.02
.03

.00
.00
.00

.11
.11
.11

.13
.13
.13

.04
.04
.04

.05
.05
.05

.20
.21
.21

.10
.11
.11

.03
.04
.04

.02
.03
.03

.19
.20
.20

-.03
-.03
-.03

.03
.03
.03

-.10
-.10
-.10

.04
.04
.04

.03
.04
.04

.03
.03
.03

-.04
-.04
-.04

.10
.10
.10

.01
.02
.02

.03
.03
.03

-.05
-.05
-.05

.08
.09
.09

.01
.01
.01

.02
.02
.02

-.04
-.03
-.03

.05
.04
.04

125

Table 9.3 (cont’d)
Indirect Effects
FWC -- > OE -- > MTL
Model 1
Model 2
Model 3
GD -- > OE -- > MTL
Model 1
Model 2
Model 3
LO -- > DC -- > LSE
Model 1
Model 2
Model 3
LO -- > FBSUPPMR -- > LSE
Model 1
Model 2
Model 3

M

SE

95% CI LL

95% CI UL

-.01
-.01
-.01

.02
.02
.02

-.06
-.05
-.05

.04
.03
.03

-.06
-.06
-.05

.03
.03
.03

-.12
-.11
-.11

.00
.00
.00

.08
.08
.08

.03
.03
.03

.01
.01
.01

.14
.14
.14

.03
.04
.04

.02
.02
.02

-.02
-.02
-.02

.09
.09
.09

126

Figure 3. Hypothesized Path Model Results

127

MIMIC Model Analysis
As in the first phase, I tested the measurement and structural equivalence
between genders using the MIMIC model as well as multiple group analysis, following
the steps described in the first phase. Because the measurement equivalence of leader
competency was not established in the first phase, MIMIC analysis was first conducted
on this factor. The results are presented in Table 10. As indicated in the table, there
were significant chi-square changes when controlling for gender (see Model 2), Δχ2 (4)
= 13.69, p = .01. Substantial changes in CFI, TLI, and RMSEA were observed (.012,
.021, and .053). Gender was not significantly related to the latent mean of leader
competency factor (β = -.05, p = .49). The modification index indicated that there may
be a significant chi-square improvement by estimating the path from gender to an
indicator of leader competency about cognitive skills (MI = 4.22, EPC = .09). Estimating
this path improved the model fit significantly (see Model 3), Δχ2 (1) = 4.37, p = .04.
Again, gender was not significantly related to the latent variable of leader competency
(β = -.05, p = .43). The relationship between gender and the indicator of leader
competency about cognitive skills was positive and significant (β = .11, p = .04). The
model fit index in Model 3 indicated that estimating the path between gender and an
indicator of leadership skills competency about interpersonal skills can improve the fit
significantly (MI = 4.22, EPC = .09). Thus, in the next step, I included this path (Model
4). As compared to Model 3, the model fit (Model 4) improved significantly Δχ2 (1) =
5.56, p = .02. CFI, TLI, and RMSEA differences were .006, .006, and .025, respectively.
The relationship between gender and the latent variable of leader competency was not
significant (β = -.07, p = .32). However, the relationships between gender and the

128

indicators of leader competency about cognitive skills and interpersonal skills were
positive and significant (β = .13, p = .01, β = .11, p = .02, respectively). Thus, the MIMIC
analysis results indicated that there may have been some measurement nonequivalence in the leader competency factor. In sum, there was no gender difference in
factor mean; however, females may be expected to have higher means on leader
competency about cognitive and interpersonal skills.

129

Table 10
MIMIC Analyses Results on Leader Competency (n = 245)
ChiSquare

Degrees of
Freedom

CFI

Model 1. CFA

4.36

4

1.000 .999

.019

Model 2. CFA
controlled for
gender

18.05

8

.988

.978

.072

13.69** (4)

Model 3. CFA
controlled for
gender (regress
LC_COG on
GENDER)

13.68

7

.992

.983

.063

4.366* (1)

Model 4. CFA
controlled for
gender (regress
LC_COG and
LC_I on
GENDER)

8.12

6

.998

.994

.038

5.56* (1),
9.925** (2)

Notes. *** p < .001, ** p < .01, * p < .05



130

TLI

RMSEA ChiSquare
Difference

MIMIC model results from the first phase of the analyses showed that there may
be some latent mean differences in factors. As a next step, I added the effects of
gender to the hypothesized model (see Model 2 in Table 9.1, 9.2, and 9.3). In addition
to regressing the leader competency factor on gender, the direct paths from gender to
two of the factor indicators were added (see Model 3 in Table 9.1, 9.2, and 9.3) because
the preceding MIMIC analysis on this factor showed that there may have been
measurement non-equivalence on these two indicators. Adding the direct effects of
gender did not change the significance of the relationships between variables.
The results from the first phase of the analyses showed that there were negative
relationships between gender and career intention, leader self-efficacy, and
developmental challenges. As indicated in Table 9.1 (see Model 2 and Model 3), though
the relationship between gender and leader self-efficacy was no longer significant, the
relationship between gender and career intention and developmental challenges
remained negative and significant in the path analysis in the second phase. Thus,
female assistant coaches showed lower means on these variables than male assistant
coaches. Also the results from the first phase of the analyses indicated that gender was
significantly and positively related to motivation to lead and outcome expectancy. Thus,
females showed higher means on these factors than males. These relationships
remained significant in the path analysis in the second phase. As in the preceding
MIMIC analysis on leader competency, the results from Model 2 (see Table 9.1) showed
that the relationship between gender and leader competency was not significant,
although the relationships between gender and the two indicators of leader competency

131

(cognitive skills and interpersonal skills) were positive and significant (see Model 3 in
Table 9.1).
In sum, adding the direct effect of gender in the path analysis did not make
substantial changes in the magnitudes of the hypothesized relationships. Also, there
were a few significant direct effects of gender on factors in the model. Yet, it is important
to note that the results may need to be interpreted with caution because the model fit
significantly worsened out of the acceptable model fit range when including gender, χ2
(N = 244, 113) = 344.35, CFI = .85, TLI = .80, RMSEA = .092. Regardless, the results
seem to be concordant and to supplement the findings from the first phase of the
analyses.
Multiple Group Analysis
In addition to MIMIC model, multiple group analysis was conducted. As in the
MIMIC model, multiple group analysis was first conducted on leader competency in
order to establish the measurement equivalence. The results are presented in Table 11.
Unlike the multiple group analysis conducted in the first phase of the analyses, chisquare differences were considered more important in this analysis given the smaller
sample size. As indicated in the table, holding intercept of leader competency factor
worsens the model fit significantly as compared to less constrained model, Δχ2 (4) =
14.55, p = .01; CFI, TLI, and RMSEA differences were .012, .008, and .014, respectively
(see Step 4 in Table 11). Thus, freeing intercepts was considered. The modification
index showed that freeing the intercept of one indicator of leadership skills competency
(i.e., cognitive skills) may improve the model fit (MI = 6.20, EPC = .07). Thus, in the next
step (Step 4.a. in Table 11), this intercept was freed. The model fit improved; however,

132

the chi-square difference was still significant, Δχ2 (3) = 8.18, p = .05. CFI, TLI, and
RMSEA differences were .006, .003, and .005. The modification index showed that
freeing an additional intercept (i.e., interpersonal skills) may further improve the model
fit (MI = 5.60, EPC = .08). Freeing this intercept (Step 4.b. in Table 11) did improve the
model fit. The chi-square difference was no longer significant, Δχ2 (2) = 2.46, p = .29.
CFI, TLI, and RMSEA differences were .000, .002, and .005. Thus, partial intercept
equivalence between genders was established. Factor and latent mean equivalence
was established in the next steps. Thus, the results from multiple group analysis support
the partial measurement equivalence between genders for leader competency.

133

Table 11
Multiple Group CFA, LC only, Female (n = 163) vs. Male (n = 80)
ChiSquare

Degrees of
Freedom

CFI

TLI

RMS
EA

1.a. Female group

13.95

5

.985

.971

.105

1.b. Male group

15.11

5

.959

.918

.158

2. Multiple group
(free parameters)

27.63

10

.980

.959

.120

3. Multiple group
model (invariant
loading)

29.33

14

.982

.975

.095

1.70 (4)

4. Multiple group
(invariant loading
and intercept)

43.88

18

.970

.967

.109

14.55***(4)

4.a. Multiple group
(invariant loading
and intercept
except for
LC_COG)

37.51

17

.976

.972

.100

8.18* (3)

4.b. Multiple group
(invariant loading
and intercept
except for LC_COG
and LC_I)

31.79

16

.982

.977

.090

2.46 (2)

5. Multiple group
(invariant factor
variance)

34.68

17

.980

.977

.093

2.89 (1)

6. Multiple group
(invariant factor
variance and factor
mean)

35.77

18

.979

.977

.090

1.10 (1)

Notes. *** p < .001, ** p < .01, * p < .05

134

Chi-Square
Difference

In the next step, I tested the equivalence in the hypothesized paths between
genders. As in the path analysis (n = 245), composite variables were used for the
factors that were established in the first phase, except for a combined latent variable of
feedback, support, and mentoring (i.e., head coach professional support). The latent
variable of leader competency was included and the intercepts of cognitive skills and
interpersonal skills were freed, considering the results from the preceding measurement
model. The results are presented in Table 12. First, loadings, intercepts (except for
leadership skills about cognitive and interpersonal skills), factor variance, and latent
means were held equal while freeing all coefficients. In Step 2, still freeing all
hypothesized coefficients, factor covariances were held equal in addition to all other
parameters. Constraining covariance did not make significant changes in the chisquare, Δχ2 (6) = .95, p = .99, or in any other fit statistics (CFI, TLI, and RMSEA
differences were .004, .008, and .003 respectively). Thus, factor covariance equivalence
was supported. In Step 3, all hypothesized coefficients and other parameters were
constrained to be equal. This made significant changes in the model fit, Δχ2 (19) =
40.84, p = .003 and other fit statistics (CFI, TLI, and RMSEA differences were .015,
.003, and .001, respectively). Thus, each hypothesized path was freed one by one to
examine where this difference could be attributed.
As Table 12 shows, there were significant improvements in the model fit when I
freed (a) the path from family-work conflict to outcome expectancy, and (b) the path
from gender discrimination to outcome expectancy. Freeing these two paths in the
model significantly improved the model fit, Δχ2 (2) = 11.06, p = .001 (CFI, TLI, and
RMSEA differences were .006, .005, and .001, respectively). Family-work conflict was

135

not significantly related to outcome expectancy for females (b =.03, p = .46), while for
males, the relationship was negative and significant (b = -.14, p = .03). In addition, there
was no significant relationship between gender discrimination and outcome expectancy
for females (b = -.08, p = .13); however, the relationship was negative and significant for
males (b = -.28, p = .001).
In sum, the multiple group analysis seems to suggest that there may be gender
differences in the paths between (a) outcome expectancy and family-work conflict, and
(b) outcome expectancy and gender discrimination. However, it is important to note that
the fit of the model was not satisfactory, and these patterns were not found in the first
phase of the analyses where I tested the model on the whole sample of assistant
coaches. Also, although I made the effort to lower the number of free parameters in the
model by using composite variables, sample size was small and there were twice as
many female assistant coaches (n = 163) as male assistant coaches (n = 80). Multiple
group model analysis requires a sufficient size of the sample in all groups (Muthén &
Muthén, 2007); thus, the results need to be interpreted with caution.

136

Table 12
Multiple Group Path Analyses, Female (n = 163), Male (n = 80)
ChiDegrees
Square of
Freedom

CFI

TLI

RMSEA Chi-Square
Difference

1. Multiple group (invariant
loading, intercept, except
for LC_COG, LC_I,
invariant variance and
latent means, and
invariant variance, means
for latent variables) all
coefficient freed.

447.64

229

.856

.837

.089

2. Multiple group (invariant
loading, intercept, except
for LC_COG, LC_I,
invariant variance,
covariance and latent
means) all hypothesized
coefficient freed.

448.60

235

.860

.845

.086

.95 (6)

3. Multiple group (invariant
loading, intercept, except
for LC_COG, LC_I,
invariant variance,
covariance and latent
means), ALL hypothesized
paths held constant.

489.44

254

.845

.842

.087

40.84 (19)**

4. CA ON MTL (1) freed

489.43

253

.845

.840

.088

.002 (1)

5. CA ON LC (2) freed

488.05

253

.846

.841

.087

1.39 (1)

6. LC ON MTL (3) freed

487.98

253

.846

.841

.087

1.45 (1)

7. LC ON FBSUPPMR (4)
freed

488.56

253

.845

.841

.088

.87 (1)

8. LC ON DC (5) freed

487.32

253

.846

.842

.087

2.12 (1)

137

Table 12 (cont’d)
ChiDegrees
Square of
Freedom
489.08 253

CFI

TLI

.845

.841

RMSEA ChiSquare
Difference
.088
.36 (1)

486.29

253

.847

.842

.087

3.14 (1)

11. LSE ON WFC (8) freed 488.34

253

.845

.841

.087

1.10 (1)

12. LSE ON FWC (9) freed 488.89

253

.845

.841

.088

.54 (1)

13. LSE ON GD (10) freed

489.10

253

.845

.841

.088

.34 (1)

14. MTL ON LSE (11)
freed

486.10

253

.847

.843

.087

3.34 (1)

15. MTL ON OE (12) freed

485.93

253

.847

.843

.087

3.51 (1)

16. MTL ON WFC (13)
freed

488.84

253

.845

.841

.088

.59 (1)

17. MTL ON FWC (14)
freed

489.42

253

.845

.840

.088

1.02 (1)

18. OE ON WFC (15)
freed

488.27

253

.845

.841

.087

1.17 (1)

19. OE ON FWC (16)
freed

482.33

253

.849

.845

.086

7.11** (1)

20. OE ON GD (17) freed

483.47

253

.849

.844

.087

5.96* (1)

21. DC ON LO (18) freed

487.26

253

.846

.842

.087

2.18 (1)

22. FBSUPPMR ON LO
(19) freed

487.57

253

.846

.842

.087

1.87 (1)

23. OE ON FWC (16) and
OE ON GD (17) freed

478.37

252

.851

.847

.086

11.06**
(2)

9. LSE ON DC (6) freed
10. LSE ON FBSUPPMR
(7) freed

Notes. *** p < .001, ** p < .01, * p < .05

138

Summary of the Second Phase of the Analyses
With a sample of assistant coaches who had the evaluation of leadership skills
by their head coaches (n = 245), I tested a path model using the composite scores of
the factors confirmed in the first phase of the analyses and a latent factor of leader
competency, as well as a combined factor of feedback, support, and mentoring (i.e.,
head coach professional support). The results provided support for hypotheses that
were supported in the first phase, except for the hypothesis regarding the indirect effect
of learning orientation on leader self-efficacy through head coach professional support.
There were no significant relationships between leader competency and motivation to
lead, career intention, leader self-efficacy, or developmental challenges. Leader
competency was also positively and directly predicted by head coach professional
support when controlled for the effect of gender.
The results from the MIMIC model and the multiple group analysis were
consistent in suggesting that there was measurement non-equivalence on leadership
skills competency between genders, specifically on the cognitive skills and interpersonal
skills indicators. Further, the MIMIC model suggested that there were gender
differences in means of career intention, motivation to lead, developmental challenges,
and outcome expectancy. Females had lower career intention and developmental
challenges, and higher motivation to lead and outcome expectancy. In addition, multiple
group analysis suggested that outcome expectancy was negatively significantly related
to family-work conflict and gender discrimination for males, but not for females.
Considering the small sample size and the unsatisfactory model fit, these results need

139

to be interpreted with caution although they provide insights into possible gender
differences in the career advancement process.
Post-Hoc Supplementary OLS Regression Analyses
I also conducted post-hoc supplementary analyses of plausible curvilinear
relationships as well as interaction effects using OLS regression analyses. Specifically, I
tested (a) curvilinear relationships between leader self-efficacy and motivation to lead,
leader competency, and career intention; (b) curvilinear relationships between
developmental challenges and leader self-efficacy, motivation to lead, leader
competency, and career intention; (c) interaction effects of learning orientation and
developmental challenges on leader self-efficacy, motivation to lead, leader competency
and career intention, and (d) interaction effects of developmental challenges, and
support, feedback, and mentoring on leader self-efficacy, motivation to lead, leader
competency, and career intention.
Curvilinear Effects of Leader Self-Efficacy
Scholars claim that leader self-efficacy generally has positive impacts; however,
an extremely high level of leader self-efficacy can potentially be detrimental to leaders’
development because it may inhibit their efforts in development activities (Machida &
Schaubroeck, 2011; Shipman & Mumford, 2011). Thus, using OLS regression analyses,
I tested the curvilinear relationships between leader self-efficacy and three key
dependent variables in the present study: (a) motivation to lead, (b) leader competency,
and (c) career intention. After first entering leader self-efficacy, I entered the square of
leader self-efficacy in the second step. Squared leader self-efficacy did not significantly
2

increase the amount of variance explained in motivation to lead (ΔR = .002, p = .26

140

2

2

ns), leader competency (ΔR = .001, p = .66 ns), or career intention (ΔR = .002, p = .29
ns), after controlling for the effect of leader self-efficacy. I also tested for the potential
direct effect of gender on the relationship. Entering the effect of gender in the first step
did not change the magnitude of the effect of squared term of leader self-efficacy on
motivation to lead, leader competency, or career intention (See Table 13 for complete
results).

141

Table 13
Curvilinear Effects of Leader Self-Efficacy
Dependent
Variable
Motivation
to Lead

B
Step 1 (N = 592)
LSE
Step 2
LSE
LSE squared
Step 1 (N = 573)
LSE
Gender
Step 2
LSE
Gender
LSE squared

SE

β

ΔR

2

R

2

.124***
.27***

.03

.35***
.002

-.09
.05

.32
.04

.125***

-.11
.47
.157***

.29***
.13***

.03
.03

.38***
.17***
.004

-.21
.13***
.06

.31
.03
.04

.161***

-.28
.17***
.66

Leader
Competency
Step 1 (N = 243)
LSE
Step 2
LSE
LSE squared
Step 1 (N = 243)
LSE
Gender
Step 2
LSE
Gender
LSE squared

.002
.01

.08

.05

-.30
.05

.81
.10

-.25
.30

.06
-.01

.08
.08

.05
-.01

.001

.003

.002
.001
-.30
-.01
.05

.81
.08
.10

142

-.26
-.01
.30

.003

Table 13 (cont’d)
Dependent
Variable
Career
Intention

B

Step 1 (N = 595)
LSE
Step 2
LSE
LSE squared
Step 1 (N = 573)
LSE
Gender
Step 2
LSE
Gender
LSE squared

SE

β

ΔR

2

R

2

.048
.70***

.13

.22***
.002

-.74
.18

1.36
.17

-.23
.45

.64***
-.32*

.13
.13

.050

.20***
-.10*

.056
.002
-.70
-.32*
.17

1.36
.13
.17

.057

-.22
-.10*
.43

Notes. LSE = Leader Self-Efficacy
*** p < .001, ** p < .01, * p < .05
Curvilinear Effects of Developmental Challenges
Based on DeRue and Wellman’s (2009) study, which reported a curvilinear,
inverted U-shaped relationship between developmental challenges and managers’
leadership skills development, I also tested the curvilinear effects of developmental
challenges on the key dependent variables: (a) leader self-efficacy, (b) motivation to
lead, (c) leader competency, and (d) career intention. The developmental challenges
variable was entered in the first step, followed by the square of this variable in the
second step. The squared variable did not significantly increase the amount of variance
2

2

explained in leader self-efficacy (ΔR = .002, p = .31 ns) or motivation to lead (ΔR =
.000, p = .80 ns), although it significantly increased the amount of variance explained in
2

2

leader competency (ΔR = .022, p = .02) and career intention (ΔR = .017, p = .00). This

143

indicates a curvilinear relationship between developmental challenges and leader
competency and career intention. Though the variance explained in leader competency
2

by the model was too small to reach the significance (R = .022, p = .07), the results
suggested that there was a U-shaped relationship between developmental challenges
and leader competency, which is indicated by a non-significant beta coefficient between
non-squared term of developmental challenges and leader competency in the first step
(β = -.02, p = .79), and a negative beta coefficient between non-squared term of
developmental challenges and leader competency (β = -.88, p = .02) and a positive beta
coefficient between squared developmental challenges and leader competency in the
second step (β= .88, p = .02). On the other hand, the results suggest that the
relationship between developmental challenges and career intention is a predominantly
positive, inverted U-relationship, which is indicated by a positive beta coefficient
between non-squared developmental challenges and career intention in the first step (β
= .21, p = .00) and the second step (β = 1.00, p < .001), and a negative beta coefficient
between the squared term of developmental challenges and career intention (β = -.81, p
= .001) in the second step. These curvilinear relationships between developmental
challenges and leader competency and career intention were also observed when
gender was included in the analysis (See Table 14).

144

Table 14
Curvilinear Effects of Developmental Challenges
Dependent
Variable
Leader SelfEfficacy

B
Step 1 (N = 584)
DC
Step 2
DC
DC squared
Step 1 (N = 570)
DC
Gender
Step 2
DC
Gender
DC squared

Motivation
to Lead

Step 1 (N = 586)
DC
Step 2
DC
DC squared
Step 1 (N = 571)
DC
Gender
Step 2
DC
Gender
DC squared

Leader
Step 1 (N = 243)
Competency
DC
Step 2
DC
DC squared
Step 1 (N = 242)
DC
Gender
Step 2
DC
Gender
DC squared

SE

β

ΔR

2

R

2

.043
.04***

.01

.21***
.002

-.01
.00

.04
.00

.045

-.04
.25
.052

.03***
-.10*

.01
.04

.20***
-.10*
.001

-.00
-.10*
.00

.04
.04
.00

-.03
-.10*
.22

.01*

.01

.09*

.00
.00

.03
.00

.053

.03
.06

.008
.000

.008

.023
.01*
.10*

.01
.03

.10*
.13**

.01
.10**
.00

.03
.03
.00

.04
.13**
.06

-.00

.01

-.02

-.19*
.01*

.08
.00

-.88*
.88*

.00
-.01

.01
.08

-.02
-.01

-.19*
-.02
.01*

.08
.08
.00

-.89*
-.02
.87*

.000

.024

.000
.022*

.022

.000
.022*

145

.023

Table 14 (cont’d)
Dependent
Variable
Career
Intention

B
Step 1 (N = 591)
DC
Step 2
DC
DC squared
Step 1 (N = 571)
DC
Gender
Step 2
DC
Gender
DC squared

SE

β

.11***

.02

.54***
-.02**

.13
.01
.02
.13

.19***
-.10*

.58***
-.32*
.02***

.13
.13
.01

2

1.00***
-.81**

.10***
-.32*

2

.21***

1.07***
-.10*
-.89***

ΔR

R

.042
.017**

.059

.051
.021***

.072

Notes. DC = Developmental Challenges
*** p < .001, ** p < .01, * p < .05

Interaction Effects of Developmental Challenges and Learning Orientation
DeRue and Wellman (2009) and Dragoni, Tesluk, and Oh (2009) suggested that
learning orientation may moderate the relationship between developmental challenges
and leadership skill development. Thus, I also tested the interaction effects of learning
orientation and developmental challenges on (a) leader self-efficacy, (b) motivation to
lead, (c) leader competency, and (d) career intention. The independent (i.e.,
developmental challenges) and the moderator (i.e., learning orientation) variables were
mean centered. The interaction terms were created between centered scores of
developmental challenge and learning orientation. After first entering the main effects of
the developmental challenges and learning orientation, I entered an interaction variable
in the second block. The block of interaction did not significantly increase the amount of

146

2

variance explained in leader self-efficacy (ΔR = .000, p = .61 ns), motivation to lead
2

2

(ΔR = .001, p = .34 ns), or leader competency (ΔR = .001, p = .66 ns); however,
entering the interaction term significantly increased the amount of variance explained in
2

career intention (ΔR = .008, p = .02). Developmental challenges as well as learning
orientation had significant positive relationships with career intention, although the
interaction terms of developmental challenges and learning orientation had a negative
relationship with career intention. The interaction effect of developmental challenges
and learning orientation on career intention is plotted in Figure 4. This interaction effect
remained significant when gender was included in the model (see Table 15).

147

Table 15
Interaction Effects of Developmental Challenges and Learning Orientation
Dependent
Variable
Leader SelfEfficacy

B
Step 1 (N = 584)
DC
LO
Step 2
DC
LO
DCxLO
Step 1 (N = 570)
DC
LO
Gender
Step 2
DC
LO
Gender
DCxLO

Motivation
to Lead

Step 1 (N = 582)
DC
LO
Step 2
DC
LO
DCxLO
Step 1 (N = 571)
DC
LO
Gender
Step 2
DC
LO
Gender
DCxLO

SE

β

.03***
.26***

.01
.03

2

2

.16***
.31***

ΔR

R

.139***
.000
.03***
.26***
.01

.01
.03
.01

.139***

.16***
.31***
.02
.153***

.03***
.27***
-.09*

.01
.03
.04

.15***
.32***
-.09*
.000

.03***
.26***
-.09*
.01

.01
.03
.04
.01

.153***

.15***
.32***
-.09*
.02
.129***

.01
.22***

.01
.03

.05
.35***

.01
.23***
-.01

.01
.03
.01

.05
.36***
-.04

.01
.23***
.11**

.01
.03
.03

.05
.36***
.14**

.01
.23***
.10**
-.01

.01
.03
.03
.01

.04
.37***
.14**
-.05

.001

.130***

.151***

.002




148

.154***

Table 15 (cont’d)
Dependent
Variable
Leader
Step 1 (N = 243)
Competency
DC
LO
Step 2
DC
LO
DC xLO
Step 1 (N = 242)
DC
LO
Gender
Step 2
DC
LO
Gender
DC xLO
Career
Intention

Step 1 (N = 589)
DC
LO
Step 2
DC
LO
DC xLO
Step 1 (N = 571)
DC
LO
Gender
Step 2
DC
LO
Gender
DCxLO

B

SE

β

ΔR

2

R

2

.002
-.01
.05

.01
.07

-.03
.05

-.01
.05
.01

.01
.07
.02

.03
.05
.03

.001

.003

.003
-.01
.05
-.02

.01
.07
.08

-.03
.05
-.01
.000

-.01
.05
-.02
.01

.01
.07
.08
.02

.003

-.03
.05
-.01
-.03
.070

.10***
.42***

.02
.10

.19***
.16***

.10***
.48***
-.07*

.02
.11
.03

.18***
.18***
-.10*

.09***
.42***
-.32*

.02
.11
.13

.17***
.16***
-.10*

.09***
.47***
-.32*
-.07*

.02
.11
.13
.03

.16***
.18***
-.10*
-.10*

.008*

.078

.077***

.009*

Notes. DC = Developmental Challenges, LO = Learning Orientation
*** p < .001, ** p < .01, * p < .05.

149

.085***

Figure 4. Interaction Effect of Developmental Challenges and Learning Orientation on
Career Intention

Interaction Effects of Developmental Challenges and Feedback, Support, and
Mentoring
McCauley and colleagues (McCauley & Van Velsor, 2004; Van Velsor, McCauley
& Ruderman, 2010) and the study by DeRue and Wellman (2009) suggested that
feedback, support, and mentoring may moderate the relationship between
developmental challenges and leader development. Thus, I also tested the interaction
effects of learning orientation and developmental challenges on (a) leader self-efficacy,
(b) motivation to lead, (c) leader competency, and (d) career intention. Feedback,
support, and mentoring relationship variables were aggregated due to the observation in
the main analyses that there may be a multicollinearity problem. The independent (i.e.,
developmental challenges) and the moderator (i.e., feedback/support/mentoring)
variables were mean centered. I created an interaction variable between centered
150

scores of developmental challenges and feedback/support/mentoring. After first entering
the main effects of the developmental challenges and feedback/support/mentoring, I
entered the interaction variable in the second block. The block of interaction did not
2

significantly increase the amount of variance explained in leader self-efficacy (ΔR =
2

2

.002, p = .29 ns), motivation to lead (ΔR = .003, p = .21 ns), leader competency (ΔR =
2

.009, p = .14 ns), or career intention (ΔR = .003, p = .15). Controlling for gender also
did not make significant changes in the relationships (see Table 16 for the complete
results). Thus, the results suggest that the interaction effects of feedback, support, and
mentoring and developmental challenges were not significant.

151

Table 16
Interaction Effects of Developmental Challenges and Feedback, Support, and Mentoring
Dependent
Variable
Leader SelfEfficacy

B
Step 1 (N = 576)
FBSUPPMR
DC
Step 2
FBSUPPMR
DC
DCxFBSUPPMR
Step 1 (N = 569)
FBSUPPMR
DC
Gender
Step 2
FBSUPPMR
DC
Gender
DCxFBSUPPMR

Motivation
to Lead

Step 1 (N = 575)
FBSUPPMR
DC
Step 2
FBSUPPMR
DC
DCxFBSUPPMR
Step 1 (N = 570)
FBSUPPMR
DC
Gender
Step 2
FBSUPPMR
DC
Gender
DCxFBSUPPMR

SE

β

.03***
.04***

.01
.01

2

2

.17***
.22***

ΔR

R

.074***
.002
.04***
.04***
-.00

.01
.01
.00

.076***

.19***
.23***
-.05
.082***

.03***
.04***
-.10*

.01
.01
.04

.17***
.21***
-.10*
.002

.04***
.04***
-.10*
-.00

.01
.01
.04
.00

.083***

.19***
.22***
-.09*
-.04
.041***

.03***
.01*

.01
.01

.18***
.10*

.03***
.02**
-.00

.01
.01
.00

.20***
.11**
-.05

.03***
.02**
.10**

.01
.01
.03

.18***
.11**
.13**

.03***
.02**
.10**
-.00

.01
.01
.03
.00

.20***
.12**
.13**
-.05

.003

.044***

.056***

.002

152

.058***

Table 16 (cont’d)
Dependent
Variable
Leader
Step 1 (N = 243)
Competency
FBSUPPMR
DC
Step 2
FBSUPPMR
DC
DCxFBSUPPMR
Step 1 (N = 242)
FBSUPPMR
DC
Gender
Step 2
FBSUPPMR
DC
Gender
DCxFBSUPPMR
Career
Intention

Step 1 (N = 577)
FBSUPPMR
DC
Step 2
FBSUPPMR
DC
DCxFBSUPPMR
Step 1 (N = 571)
FBSUPPMR
DC
Gender
Step 2
FBSUPPMR
DC
Gender
DCxFBSUPPMR

B

SE

β

.04
-.00

.02
.01

2

2

.14
-.01

ΔR

R

.019
.009
.04
.00
-.01

.02
.01
.01

.17
.02
-.10

.04*
-.00
-.03

.02
.01
.08

.028

.14*
-.02
-.02

.020

.009
.04*
.00
-.01
-.01

.02
.02
.08
.01

.028

.17*
.02
-.00
-.10
.045***

-.03
.11***

.03
.02

-.05
.20***

-.04
.10***
.01

.03
.02
.01

-.07
.19***
.06

.003

.048***

.054***
-.04
.10***
-.32*

.03
.02
.13

-.06
.19***
-.10*

-.05
.10***
-.33*
.01

.03
.02
.13
.01

-.08
.18***
-.10*
-.06

.004

.058***



Notes. DC = Developmental Challenges, FBSUPPMR = Feedback, Support, and
Mentoring
*** p < .001, ** p < .01, * p < .05.

153

In sum, the post-hoc supplementary OLS regression analyses showed that the
curvilinear effects of developmental challenges on leader competency and career
intentions were significant. The results suggested that there was a U-shaped
relationship between developmental challenges and leader competency while there was
a predominantly positive, inverted U-shaped relationship between developmental
challenges and career intention. Interaction effects of learning orientation and
developmental challenges were not significant except the interaction effect of learning
orientation and developmental challenges on career intention. This effect was also
significant when controlled for the effect of gender. The results also show that
developmental challenges and learning orientation had positive effects on career
intention, and the interaction of developmental challenges and learning orientation had
a negative effect on career intention.

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Chapter V

DISCUSSION
Although there has been a significant increase in women’s sport participation
since Title IX was enacted, scholars have shown that there has been a decline in
women’s representation in the collegiate coaching profession (Acosta & Carpenter,
2012). Informed by the existing literature (e.g., DeRue & Wellman, 2009; Dragoni,
Tesluk, & Oh, 2009; Machida, Schaubroeck, & Feltz, 2012; Moran-Miller & Flores, 2011)
and theories (Bandura, 1997; Van Velsor, McCauley, & Ruderman, 2010), the purpose
of the present study was to examine a model of career advancement of collegiate
assistant coaches, having intention to advance career (i.e., career intention) as the
proximate predictor of coaches’ career advancement. Past studies indicate that women
may have lower intention to advance their careers, which may contribute to their
underrepresentation in higher leadership positions (Gupta, Turban, & Bhawe, 2008; van
Vianen & Keizer, 1996). However, no study to date has simultaneously examined a
wide array of possible facilitating and inhibiting antecedents of career intention for
female and male coaches. The present study investigated possible gender differences
in the roles of antecedents in predicting career intention. In this chapter, I discuss the
results in relation to the hypotheses, and I highlight gender differences that emerged in
findings.
Theoretical and Research Implications
Antecedents of Career Intention
Scholars (e.g., Ajzen, 1991; Armitage & Conner, 2001; Gordin & Kok, 1996) have
suggested that intention is the best predictor of many behaviors. The present study
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investigated career intention as a proximate predictor of coaches’ career advancement
and examined its antecedents. The results of the first phase of analyses on the whole
sample of assistant coaches (N = 673) and the second phase of analyses on the subsample of assistant coaches (n = 245), which included their head coaches’ participation
in the study, suggested that although leader competency did not mediate the
relationship between motivation to lead and assistant coaches’ career intention as
hypothesized, motivation to lead was directly and positively related to career intention.
Moreover, the present study suggested that leader self-efficacy and outcome
expectancy play an important mediating role in coaches’ career advancement. The
results indicate that leader self-efficacy may influence coaches’ career intention through
its effect on motivation to lead. In addition, there was a separate influence of outcome
expectancy on career intention, and the results also indicate that this effect of outcome
expectancy on career intention may be mediated by motivation to lead.
Bandura (1997) argued that self-efficacy and outcome expectancy are
conceptually different; individuals can be highly efficacious about their abilities but may
not expect that they will be able to obtain desirable outcomes because of external
factors that are outside of their control (e.g., family, discrimination/biases). Self-efficacy
and outcome expectancy may have separate influences on individuals’ motivation and
behaviors. The present study supports Bandura’s claim. The link between leader selfefficacy and motivation to lead is well established (Chan & Drasgow, 2001; Machida et
al., 2012), and past research showed that outcome expectancy is related to career
intention (van Vianen, 1999). The present study advances the literature by examining
leader self-efficacy and outcome expectancy simultaneously, showing their unique

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contribution to increases in career intention through their effects on motivation to lead.
Further, the present study shows that these indirect effects of leader self-efficacy and
outcome expectancy on career intention through motivation to lead are present even
after accounting for possible gender differences.
The present study found no relationships between motivation to lead and leader
competency, or between leader competency and career intention. Thus, the mediating
role of leader competency in the relationship between motivation to lead and career
intention was not supported. This finding may be attributed to the small sample size (n =
245) and its lack of power. In addition, it is possible that the assistant coaches were in
the midst of the process of developing their leader competency; thus, motivation to lead
is a better predictor of their career intention. Also, a leader’s end-state competency,
which indicates a leader’s potential (Spreitzer et al., 1997), or the leadership potential
measure used by Chan and Drasgow (2001), may have a different relationship with
motivation to lead and career intention, and future studies should investigate this
relationship further by using multiple measures of leader competency.
In the post-hoc analyses, I examined the curvilinear effects of leader self-efficacy
on the key dependent variables (i.e., motivation to lead, leader competency, and career
intention), according to Bandura’s (1997) and scholars’ claims that very high levels of
self-efficacy may hinder a leader’s effectiveness and development (Shipman &
Mumford, 2011; Machida & Schaubroeck, 2011). The present study does not support
such curvilinear effects, even after controlling for the effect of gender. The findings are
consistent with the results from Machida et al. (2012), which also did not find a
curvilinear effect of leader self-efficacy on motivation to lead or on career ascendance in

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women leaders in athletics. Shipman and Mumford (2011) found that leaders’
awareness of their own deficiencies is important for their performance during tasks such
as planning and vision formulation. Machida and Schaubroeck (2011) also argued that it
is critical for leaders to manage their self-efficacy for optimal leader development.
Future studies should further investigate the roles that leader self-efficacy plays in
leader development and career advancement.
Developmental Experiences
The results from the present study indicated that developmental experiences,
which consist of developmentally challenging job assignments and head coach
professional support (i.e., feedback, support, and mentoring relationship), were
positively related to assistant coaches’ leader self-efficacy. This result suggests that
developmental challenges and head coach professional support are important sources
of self-efficacy information for coaches’ leader self-efficacy. Machida et al. (2012)
reported a similar relationship in a sample of women leaders in athletic administration.
Further, in the present study, similar findings were observed after controlling for the
effects of gender.
The present study also supports the mediating role of leader self-efficacy in the
relationship between developmental experiences and motivation to lead. The results
show that higher degrees of developmental experiences have positive influences on
leader self-efficacy, which in turn, have a positive impact on motivation to lead, even
after taking account of the influences of gender. This finding is consistent with
Bandura’s (1997) claim that self-efficacy is a cognitive mechanism which explains the
relationship between self-appraisal information (i.e., sources) and individuals’

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motivation. It also provides further support for Machida et al.’s (2012) finding that leader
self-efficacy mediates the relationship between developmental experiences and
motivation to lead of women in athletic administration.
In addition, the post-hoc analyses suggest that there is a U-shaped relationship
between developmental challenges and leader competency. This relationship was
supported even when controlled for the effect of gender. Past study by DeRue and
Wellman (2009) suggested that managers experience the most development when they
are moderately challenged at work, and there is a decline in development at the low and
high levels of challenges; thus, there was an inverted U-shaped relationship between
developmental challenges and development. The result from the present study is
inconsistent with the past research finding in showing that coaches demonstrate the
lowest leader competency when they experience the moderate level of developmental
challenges. This result is difficult to interpret from a theoretical perspective. The present
study employed a cross-sectional research design; thus, it only measured the current
competency of coaches and did not measure the “development” of the competency. As
DeRue and Wellman showed, the relationship between developmental challenges and
leader “development” may be an inverted-U shaped relationship for coaches, although
the relationship between developmental challenges and the “current” competency may
not mirror this relationship. Future study should consider a longitudinal study to help
clarify the relationship.
Further, the post-hoc regression analyses indicate that there is an inverted Ushaped relationship between developmental challenges and career intention. The result
is interpretable from a leader development perspective. Developmental challenges are

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generally positively related to leaders’ career intention; however, when leaders
experience a great deal of challenges, they may reduce their intent to advance their
careers. This diminishing return of developmental challenge is supported in Derue and
Wellman (2009) which showed a predominantly positive, inverted U-shaped relationship
between developmental challenges and managers’ leader skill development. However,
as noted, in the present study, developmental challenges had a U-shaped relationship
with coaches’ current leader competency. The role of developmental challenges
warrants further investigation.
Learning Orientation
The present study hypothesized that coaches’ learning orientation positively
relates to their engagement in developmental experiences (i.e., developmental
challenges and head coach professional support which include feedback, support, and
mentoring relationship). This hypothesis was fully supported even after accounting for
gender differences. This result is consistent with scholars’ claim that individuals’
capacity to learn, or their learning orientation, significantly affects individuals’ learning in
the developmental process (Dweck, 1975, 1986; McCauley & Van Velsor, 2004; Van
Velsor, McCauley & Ruderman, 2010), and with the findings from past research (e.g.,
Birdi, Allan, & Warr, 1997; VandeWalle, Ganesan, Challagalla, & Brown, 2000) which
also showed the positive link between learning orientation and engagement in
developmental activities.
Individuals’ learning orientation has been linked to higher self-efficacy (e.g., Bell
& Kozolowski, 2002; VandeWalle, Cron, & Slocum, 2001; Vosloo, Ostrow, & Watson,
2009). However, the mechanism by which learning orientation results in increased self-

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efficacy has not been examined in past research. In the present study, I hypothesized
the meditating roles of developmental experiences in the relationship between learning
orientation and leader self-efficacy. This hypothesis was partially supported; the indirect
effect of learning orientation on leader self-efficacy through developmental challenges
was significant. However, such an indirect effect, through head coach professional
support was no longer significant after controlling for the effect of gender and in the path
analyses with a sub-sample of assistant coaches (n = 245). The direct effect of gender
on leader self-efficacy may have made changes in the relationship. Gender is
significantly related to leader self-efficacy; female assistant coaches reported lower selfefficacy beliefs than male assistant coaches. Though it did not reach the significance
level, follow-up multiple group analysis suggests that head coach professional support
may have a positive relationship with female coaches’ leader self-efficacy, but not male
coaches’. This result needs to be interpreted with caution; however, it is possible that
female assistant coaches may benefit more from receiving head coach professional
support in increasing their self-efficacy than male assistant coaches do in the field of
athletic coaching, which is historically male-dominated. As the results indicate, leader
self-efficacy may have a significant impact on coaches’ career intention. Thus, it may be
crucial for assistant coaches, especially female coaches, to receive quality professional
support from head coaches to increase their self-efficacy, which may have a great
impact on their career. The different roles that professional support from supervisors
(i.e., feedback, support, and mentoring) play in women’s and men’s leader development
and career advancement need to be further investigated.
Work-Family and Family-Work Conflicts and Gender Discrimination

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The results from the present study show that challenges such as work-family and
family-work conflicts and gender discrimination are not significant sources of leaders
self-efficacy information even though they are often identified as significant challenges
for women to advance their career to leadership positions (e.g., Bracken, 2009; Eagly &
Carli, 2007). Machida et al. (2012) also reported that work-family and family-work
conflicts and gender discrimination do not significantly relate to leader self-efficacy of
women leaders in athletics. The findings suggest that both women and men may be
able to maintain their confidence to lead in the face of these challenges.
Though these challenges do not have substantial impacts on leader self-efficacy,
they seem to significantly affect coaches’ outcome expectancy and motivation to lead. I
hypothesized that family-work and work-family conflicts and perception of gender
discrimination negatively relate to outcome expectancy, which in turn, relates to
motivation to lead. This hypothesis was partially supported; I found a significant indirect
effect of gender discrimination on motivation to lead through outcome expectancy,
although such indirect effects were not found for work-family and family-work conflicts. I
found similar results after controlling for the effect of gender. It is possible that women
and men who are in the coaching professions have been prepared to deal with these
conflicts related to family duties, and thus consider these challenges as manageable,
which assists them in maintaining high expectations to obtain desirable outcomes (i.e.,
head coaching position) in the future. However, perception that an uncontrollable factor
such as gender may be a hindrance to their career may significantly reduce their
expectations, which, in turn, may diminish their motivation to lead.

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The follow-up multiple group analysis in the second phase of the analyses, with a
sub-sample of assistant coaches, suggests that the relationship between family-work
conflict and perception of gender discrimination and outcome expectancy may differ
between genders. Family-work conflict and gender discrimination are negatively related
to men’s outcome expectancy, but not for women. However, as indicated in Chapter 4,
the results need to be interpreted with caution because of the small sample size in the
analysis (Female: n = 163; Male: n = 80) and such patterns were not found in the first
phase of the study, which utilized the whole sample of assistant coaches (Female: n =
360; Male: n = 216). Future studies should further investigate the roles of these
challenges in the outcome expectancy of women and men.
Though the results did not support the hypotheses that work-family and familywork conflicts negatively affect leader self-efficacy or outcome expectancy, which in turn
affect motivation to lead, I found that family-work conflict was negatively and directly
related to motivation to lead. This result is consistent with the findings from Machida et
al. (2012), which found a similar relationship between family-work conflict and
motivation to lead in a sample of women leaders. The present study further suggests
that this relationship may hold for both women and men; women’s and men’s desire to
lead others may be equally affected by such conflicts. Women and men in coaching
professions may be able to maintain their self-efficacy and expectation for a desirable
outcome; however, the perception that family may interfere with work may significantly
decrease their motivation to persist as leaders. Regardless of gender, such conflict may
make it difficult for individuals to take on leadership roles.
Gender Differences in Antecedents of Career Intentions

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Overall, taking account of the effect of gender did not significantly affect the
relationships among the factors in the model except for the indirect relationship between
learning orientation and leader self-efficacy through head coach professional support in
the first phase of the analyses, and except for the direct relationships between familywork conflict and perception of gender discrimination and outcome expectancy in the
second phase of the analyses. However, multiple indicators multiple causes (MIMIC)
models indicated that there are significant gender differences observed in the factor
means. Being consistent with past research findings (Cunningham, Sagas, & Ashley,
2003; Gupta et al., 2008; Pallier, 2003; van Vianen & Keizer, 1996), women reported
lower career intention and leader self-efficacy. In addition, women reported lower
exposure to developmental challenges, which has not been empirically tested in past
research, but is consistent with scholars’ claim that women may be given fewer
opportunities to participate in developmentally challenging experiences (Betz, 2007;
Hackett & Betz, 1981). However, female coaches reported higher motivation to lead and
outcome expectancy than their male counterparts. Though such gender difference in
motivation to lead has not been empirically investigated, this finding of gender
difference in outcome expectancy contradicts the results from the past research
(Cunningham Doherty, & Gregg, 2007). This gender difference in outcome expectancy
may be attributed to the context in which the study took place. The present study only
focused on assistant coaches who coach women’s teams. Men may have felt
disadvantaged due to the assumption that women are favored for the coaching
positions in women’s teams.
Practical Implications

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The findings from the present study offer several implications for practices in the
field. First, the results suggest that leader self-efficacy may have a significant effect on
assistant coaches’ career intention through its effect on motivation to lead, and leader
self-efficacy may be a stronger predictor of assistant coaches’ career intention than their
actual leader competency. Thus, for coaches who are in a developmental phase,
especially females who tend to underestimate their abilities, it may be crucial for head
coaches and administrators to engage in deliberate efforts to increase their self-efficacy
beliefs, in order to facilitate their development and advancement in their coaching
career.
In increasing coaches’ leader self-efficacy, the present study shows that
developmental challenges, feedback, support, and the mentoring relationship can be
important sources of both women’s and men’s leader self-efficacy, which may play an
important role in increasing their motivation to lead, which in turn increases their
intention to advance their career in the coaching profession. The results also suggest
that female coaches may benefit more from receiving quality feedback, support, and
mentoring. Also, supporting scholars’ claims (Betz, 2007; Hackett & Betz, 1981), the
present study indicates that female coaches may be less exposed to developmentally
challenging job assignments, the result of which may have, in part, contributed to their
lower level of self-efficacy and career intention as compared to their male counterparts.
Thus, especially for female assistant coaches’ career advancement, it is imperative for
those who are mentoring them to give regular feedback and continuous support, and to
make conscious efforts to provide them with developmentally challenging experiences

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(e.g., providing them with new and greater responsibilities, the opportunities to create
changes and work with diverse group of people) that can nurture their self-efficacy.
Further, the findings suggest that fostering a learning orientation may be critical
for leaders’ development. Consistent with scholars’ claims (McCauley & Van Velsor,
2004; Van Velsor et al., 2010), the present study suggests that individuals’ learning
orientation may significantly impact one’s extent of engagement in developmental
activities, which may be important in fostering self-efficacy beliefs and leader
competency. Highly learning oriented individuals may seek more developmental
experiences than less learning oriented individuals. Especially for collegiate coaching
professions where winning and competitive records are highly emphasized, it is critical
for the organization, higher management, and direct supervisors (e.g., head coaches) to
emphasize mastery and learning in the work environment, rather than just the tangible
outcomes and short-term successes of assistant coaches.
In addition, the results from the present study indicate that both female and male
coaches may be able to maintain their self-efficacy in the face of challenges such as
family-work and work-family conflicts and gender discrimination. This result is
encouraging in that it indicates their capability to deal effectively with these challenges,
which are often cited as significant barriers in the workplace (e.g., Carlson et al., 2000;
Heilman et al., 2004; Netemeyer et al., 1996; Sartore & Cunningham, 2007; Sayer et al.,
2004). However, there is a significant negative effect of perceived gender discrimination
on motivation to lead through outcome expectancy, and family-work conflict is directly
and negatively related to motivation to lead, which is significantly related to career
intention.

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The past research suggests that organization-wide initiatives to reduce these
challenges for employees may be effective. For example, Kelly, Moen, and Tranby
(2011) showed that the ROWE (Results-Only Work Environment) initiative at one of the
Fortune 500 companies is effective in reducing employees’ work-family conflict and in
improving work-family fit. The ROWE initiative aims at shifting the organizational culture
to a more flexible one in terms of “when and where employees do their work” (Kelly et
al., 2011, p.269). Kelly et al. suggested that ROWE impacts employees’ perceptions
about schedule control, which has a significant influence on reducing work-family
conflicts. Also, as noted, men often perceive affirmative action programs to be
institutionalized discrimination toward men (Greenhil, et al., 2009; Leonard, 1989), and
such attitudes may limit acceptance of women in coaching profession. Through a metaanalysis of past studies on attitudes toward affirmative action programs, Harrison,
Kravitz, Mayer, Leslie, and Lev-Arey (2006) showed that organizations’ justification for
affirmative action programs is related to employees’ positive attitude toward such
programs. Thus, Harrison et al. suggested that organizations should openly discuss any
affirmative action policy which is taking place in the organization in order to increase the
positive attitudes of employees toward such a policy, rather than merely pointing out the
underrepresentation of certain groups; providing the justification and reasoning for
increasing organizational diversity is critical. Therefore, organization-wide practices
such as ROWE and an open discussion of increasing diversity in the organization may
be encouraged in order to assist coaches to maintain their motivation to lead, which is
critical for their career advancement.
Study Limitations

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No study is without limitations. Although this study was based on well-established
theories, multiple sources of data (i.e., both assistant coaches and head coaches), and
a diverse sample of participants (e.g., both genders, various sport teams, various
coaching experiences), there are limitations to the study that need to be addressed.
First is the present study’s limited sample size. Although I was able to obtain a rather
large sample of assistant coaches, I experienced difficulty with obtaining their head
coaches’ participation, which limited the choice of analytical procedures. The findings
from the first phase of the analyses, which utilized structural equation model analyses
on the whole sample of assistant coaches, were generally consistent with the findings
from the path analyses, which utilized the composite scores of the variables confirmed
in the first phase with a sub-sample of assistant coaches whose head coaches also
participated in the study. However, the sample may have been biased; the present
study shows that assistant coaches in this sub-sample perceived better quality
professional support from their head coaches. In addition, the path analyses in the
second phase show less than an acceptable fit and lack power; thus they need to be
interpreted with caution. Future study should consider using a larger sample in order to
have a more comprehensive understanding of the plausible factors that influence
coaches’ career advancement.
In addition, the present study only used supervisors’ (i.e., head coaches’)
evaluations as a measure of leader competency. This method of measurement may be
subject to biases, and such biases may have affected the results of the present study,
which showed that there were very small relationships between leader competency and
other study variables, although the hypotheses were based on established theories and

168

past research findings. Future study should consider multiple measurements of leader
competency, which could include others’ (e.g., athletes, an athletic department)
evaluations of leader competency, and alternative measures such as leader end-state
competency (Spreitzer et al., 1997), which indicates a leader’s potential to be
successful in future leadership positions, and field behavioral observations and
assessments (Patton, 2003) of leadership skills. Developing a leader competency
measure specific to the coaching profession should also provide significant research
and practical implications.
Although the study examined a wide range of factors and underlying
mechanisms by which leader development may occur and through which coaches
increase their intentions to advance their careers, there might be other factors that
directly influence the process, or mediate or moderate the relationships. For example,
variables proposed in the Theory of Planned Behaviors (TPB: Ajzen, 1991; Hurtz &
Willias, 2009) could have impact on assistant coaches’ intentions to advance their
career. The TPB posits that the intention is influenced by three factors: attitude toward
the action, subjective norms, and perceived control (Ajzen, 1991; Hurtz & Williams,
2009). The TPB model has been supported in predicting individuals’ intentions and
behaviors in various settings, such as exercise (Hagger, Chatzisarantis, & Biddle, 2001;
Dzewaltowski, Noble, & Shaw, 1990) and health (e.g., Gordin & Kok, 1996; Elliot,
Armitage, & Baugham, 2003), and in business and organizational settings (e.g., Hurtz &
Williams, 2009; Kolvereid, 1996; Krueger, Reilly, & Carsrud, 2000; Maurer & Palmer,
1999; McCarthy & Garavan, 2006). Future studies need to investigate how these TPB
related factors play roles in one’s career advancement.

169

Scholars and a considerable number of studies indicate that an intention is the
best predictor of behavior (Ajzen, 1991; Armitage & Conner, 2001; Gordin & Kok, 1996),
and the present study measured career intention as a proximate predictor of career
advancement of assistant coaches. However, an ultimate goal of researchers and
practitioners is to predict actual behaviors, and the present study is limited in its ability
to make this connection between intention and actual career advancement. Although
difficult, longitudinal study which follows the career paths of assistant coaches would be
beneficial in investigating this relationship.
Lastly, because of the cross-sectional nature of the study, strong causational
conclusions cannot be made. For example, it is possible that assistant coaches engage
in more developmentally challenging job assignments because they are highly
efficacious about their leadership skills, rather than that their high exposure to
developmentally challenging job assignments leads to higher self-efficacy. This
possibility cannot be ruled out by the research design employed in the present study.
Longitudinal study such as described above may be useful in clarifying this causal
relationship.
Conclusion
In various work contexts, women are significantly underrepresented in higher
leadership positions (e.g., Acosta & Carpenter, 2012; Eagly & Carli, 2007) although they
are, in fact, equally competent as men as leaders (e.g., Eagly et al., 2003). This trend
may be more evident in a male-dominated field such as athletics. Past research
indicates that women may have lower intention to advance their career as compared to
men in general business (Gupta et al., 2008; van Vianen & Keizer, 1996), as well as in

170

athletic settings (Cunningham et al., 2003, 2007), and this lower intention may be
contributing to women’s limited representation in leadership positions.
Although women’s sport participation has significantly increased since Title IX,
women’s representation in collegiate coaching has shown a decline in the past three
decades (Acosta & Carpenter, 2012). The results from the present study suggest that
developmental experiences, which consist of developmental challenges, feedback,
support, and mentoring relationship, are critical in increasing the leader self-efficacy of
collegiate coaches, which is, in turn, related to their desire to lead others, and ultimately
their career intention. Also, learning orientation is positively related to engagement in
developmental experiences. In addition, the present study found that challenges such
as family-work conflict and gender discrimination may significantly and negatively affect
their outcome expectancy and motivation to lead, which in turn affects their career
intention.
The findings from the present study also suggest possible gender differences in
the factors examined. Although women have higher outcome expectancy and
motivation to lead than men, women express lower career intention, leader self-efficacy,
and developmental challenges than men. Further investigations into the process of
coaches’ career advancement are warranted; however, the present study provides
significant insights into facilitating as well as inhibiting factors of both women’s and
men’s advancement in leadership careers, and it provides practical implications for
leader development practices, both in general business and in the athletic context.



171

APPENDIX A: ASSESSMENT INSTRUMENTS

172

Appendix A-1: Demographic Questionnaire
For assistant coaches
1. Sport (check sport that you currently coach)
a. Baseball
b. Basketball
c. Bowling
d. Cross Country
e. Fencing
f. Field Hockey
g. Football
h. Golf
i. Gymnastics
j. Ice Hockey
k. Lacrosse
l. Rifle
m. Rowing
n. Skiing
o. Soccer
p. Softball
q. Swimming & Diving
r. Tennis
s. Track & Field (Indoor)
t. Track & Field (Outdoor)
u. Volleyball
v. Water Polo
w. Wrestling
x. Other
2. Team that you coach is
a. Men’s team
b. Women’s team
c. Both men and women’s teams
3. My position as an assistant coach is
a. Volunteer (Unpaid)
b. Full time (Paid)
c. Part time (Paid)
d. My Graduate Assistantship
e. Other
4. Do you have any employment other than your assistant coaching position?
a. No
b. Yes __________________
5. Years in collegiate sport coaching (counting this year) ___________________
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6. Years coaching the current team (counting this year) _____________________
7. Years working for the current head coach (counting this year)
_____________________
8. Name of the head coach ___________________________ (this information is only
used for this particular research purpose. Your responses to the questions on this
survey will NOT be revealed)
9. Contact information (email) of the head coach _________________________ (this
information is only used for this particular research purpose. Your responses to the
questions on this survey will NOT be revealed)
10. Your Age _____
11. Your Gender _____
12. Your Ethnicity
a. African American
b. Asian
c. Caucasian
d. Hispanic
e. Native American
f. Pacific Islander
g. Interracial
h. Others __________
13. What is the level of your most recent educational attainment?
a. High School Diploma or GED
b. Associate Degree
c. Bachelor’s Degree
d. Master’s Degree
e. Doctoral Degree
f. Other
14. The team you currently work for is in:
a. NCAA Division I institution
b. NCAA Division II institution
c. NCAA Division III institution
d. Other ______________
15. Number of athletes in the team you coach ___________
16. Number of athletes you directly oversee ___________

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17. Your marital status
a. Single
b. Married or cohabiting
i. For how long? ___________
18. Number of children you have
a. None
b. 1
c. 2
d. 3
e. 4
f. Other _________

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Demographic Questionnaire
For head coaches
1. Name of assistant you evaluated: _________________________ (this information is
only used for this particular research purpose. Your responses to the questions on
this survey will NOT be revealed)
2. Do you have any employment other than your head coaching position?
a. No
b. Yes __________________
3. Years in collegiate sport coaching (counting this year) ___________________
4. Years coaching the current team (counting this year) _____________________
5. Number of assistant coaches you over see ___________________
6. Your Age _____
7. Your Gender _____
8. Your Ethnicity
a. African American
b. Asian
c. Caucasian
d. Hispanic
e. Native American
f. Pacific Islander
g. Interracial
h. Others __________
9. What is the level of your most recent educational attainment?
a. High School Diploma or GED
b. Associate Degree
c. Bachelor’s Degree
d. Master’s Degree
e. Doctoral Degree
f. Other
10. Number of athletes in the team you coach ___________
11. Number of athletes you directly oversee ___________
12. Your marital status
a. Single
b. Married or cohabiting
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i. For how long? ___________
13. Number of children you have
a. None
b. 1
c. 2
d. 3
e. 4
f. Other _________

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Appendix A-2: Intention to Advance Career
For assistant coaches
General Feelings about Becoming a University Head Coach in Future
Strongly
disagree

1

2

3

4

5

6

7

8

9

I want to become a university
head coach during my career in
athletics.
I would like to become a
university head coach during
my career in athletics.
I'm not very motivated to
become a university head
coach during my career in
athletics..
It is likely that I will actively
seek opportunities to become a
university head coach during
my career in athletics.
I intend to become a university
head coach during my career in
athletics..
I will definitely become a
university head coach during
my career in athletics.
It is my duty as an assistant
coach to become a university
head coach during my career in
athletics.
I would feel guilty if I did not
become a university head
coach during my career in
athletics.
Every assistant coach should
become a university head
coach during his/her career in
athletics..

Slightly
disagree

Slightly
agree

Strongly
agree

1

2

3

4

5

6

7

1

2

3

4

5

6

7

1

2

3

4

5

6

7

1

2

3

4

5

6

7

1

2

3

4

5

6

7

1

2

3

4

5

6

7

1

2

3

4

5

6

7

1

2

3

4

5

6

7

1

2

3

4

5

6

7

178

Appendix A-3: Leader Competency Scale
For head coaches
To what extent does this assistant coach show the competence in each of these skills?
Not at
all
Cognitive skills

1

2

3

4

To a
very
large
extent
5

1

Speaking (talking to others to convey
information effectively)

1

2

3

4

5

2

Active listening (listening to what other
people are saying and asking questions as
appropriate)

1

2

3

4

5

3

Writing (communicating effectively in
writing as appropriate for the needs of the
audience)

1

2

3

4

5

4

Reading comprehension (understanding
written sentences and paragraphs in work
related documents, such as compliance
documents)

1

2

3

4

5

5

Active learning (working with new
information to grasp its implications)

1

2

3

4

5

6

Critical thinking (using logic and analysis
to identify the strengths and weaknesses
of different approaches)

1

2

3

4

5

Interpersonal skills

1

2

3

4

5

7

Social perceptiveness (being aware of
others’ reactions and understanding why
they react as they do)

1

2

3

4

5

8

Coordination (adjusting actions in relation
to others’ actions)

1

2

3

4

5

9

Negotiation (bringing others together to
reconcile differences)

1

2

3

4

5

10

Persuasion (persuading others to change
their minds or behavior)

1

2

3

4

5

179

Business skills
11

Operations analysis (analyzing team
needs and performance requirements to
create a successful program)

1

2

3

4

5

12

Management of personnel resources
(motivating, developing, and directing
others)

1

2

3

4

5

13

Management of financial resources
(determining how money will be spent to
have a successful program)

1

2

3

4

5

14

Management of material resources
(obtaining and seeing to the appropriate
use of equipment, facilities, and materials
needed to have a successful program)

1

2

3

4

5

Strategic skills
15

Visioning (developing an image of how a
program should work under ideal
conditions)

1

2

3

4

5

16

Program perception (determining when
important changes have occurred in a
program or are likely to occur)

1

2

3

4

5

17

Program evaluation (looking at many
indicators of a team’s performance, taking
into account their accuracy)

1

2

3

4

5

18

Identification of downstream
consequences (determining the long-term
outcomes of a change in a program)

1

2

3

4

5

19

Identification of key causes (identifying the
things that must be changed to achieve a
goal)

1

2

3

4

5

20

Problem identification (identifying the
nature of problems)

1

2

3

4

5

21

Solution appraisal (observing and
evaluating the outcomes of problem
solution to identify lessons learned or
redirect efforts)

1

2

3

4

5

Coaching Skill

180

1

Motivates his or her athletes

1

2

3

4

5

2

Makes critical decision during competition

1

2

3

4

5

3

Develops athletes’ ability

1

2

3

4

5

4

Instill attitudes of respect for others

1

2

3

4

5

181

Appendix A-4: Motivation to Lead Measure
For assistant coaches
Please indicate the extent of your agreement to each of the statement:
Strongly
Disagree
1

2
3
4
5
6

Most of the time I prefer being a
leader rather than a follower
when working in a
group.
I am only interested in leading a
group if there are clear
advantages for me.
I feel that I have a duty to lead
others if I am asked.
I am the type of person who is
not interested in leading others.
I will never agree to lead if I
cannot see any benefits of
accepting that role.
I agree to lead whenever I am
asked or nominated by the other
members.

Disagree

Neutral

Agree

Strongly
Agree

1

2

3

4

5

1

2

3

4

5

1

2

3

4

5

1

2

3

4

5

1

2

3

4

5

1

2

3

4

5

7

I am definitely not a leader by
nature.

1

2

3

4

5

8

I would only agree to be a group
leader if I know I can benefit from
that role.

1

2

3

4

5

9

I was taught to believe in the
value of leading others.

1

2

3

4

5

10

I am the type of person who likes
to be in charge of others.

1

2

3

4

5

1

2

3

4

5

1

2

3

4

5

1

2

3

4

5

11

12

13

I would agree to lead others even
if there are no special rewards or
benefits with that
role.
It is appropriate for people to
accept leadership roles or
positions when they are asked.
I believe I can contribute more to
a group if I am a follower rather
than a leader.

182

Strongly
Disagree
14
15

I would want to know ‘what’s in it
for me’ if I am going to agree to
lead a group.
I have been taught that I should
always volunteer to lead others if
I can.

Disagree

Neutral

Agree

Strongly
Agree

1

2

3

4

5

1

2

3

4

5

16

I usually want to be the leader in
the groups that I work in.

1

2

3

4

5

17

I never expect to get more
privileges if I agree to lead a
group.

1

2

3

4

5

18

It is not right to decline leadership
roles.

1

2

3

4

5

1

2

3

4

5

1

2

3

4

5

1

2

3

4

5

1

2

3

4

5

1

2

3

4

5

1

2

3

4

5

1

2

3

4

5

19

20

21
22

23

24
25

I am the type who would actively
support a leader but prefers not
to be appointed as
leader.
If I agree to lead a group, I would
never expect any advantages or
special benefits.
It is an honor and privilege to be
asked to lead.
I have a tendency to take charge
in most groups or teams that I
worked in.
I have more of my own problems
to worry about than to be
concerned about the rest
of the group.
People should volunteer to lead
rather than wait for others to ask
or vote for them.
I am seldom reluctant to be the
leader of a group.

26

Leading others is really more of a
dirty job than rather than an
honourable one.

1

2

3

4

5

27

I would never agree to lead just
because others voted for me.

1

2

3

4

5

183

Appendix A-5: Leader Self-Efficacy
For assistant coaches
How confident are you in these skills?
Not at
all
Cognitive skills

1

2

3

4

To a
very
large
extent
5

1

Speaking (talking to others to convey
information effectively)

1

2

3

4

5

2

Active listening (listening to what other
people are saying and asking questions as
appropriate)

1

2

3

4

5

3

Writing (communicating effectively in
writing as appropriate for the needs of the
audience)

1

2

3

4

5

4

Reading comprehension (understanding
written sentences and paragraphs in work
related documents, such as compliance
documents)

1

2

3

4

5

5

Active learning (working with new
information to grasp its implications)

1

2

3

4

5

6

Critical thinking (using logic and analysis
to identify the strengths and weaknesses
of different approaches)

1

2

3

4

5

Interpersonal skills

1

2

3

4

5

7

Social perceptiveness (being aware of
others’ reactions and understanding why
they react as they do)

1

2

3

4

5

8

Coordination (adjusting actions in relation
to others’ actions)

1

2

3

4

5

9

Negotiation (bringing others together to
reconcile differences)

1

2

3

4

5

10

Persuasion (persuading others to change
their minds or behavior)

1

2

3

4

5

184

Business skills
11

Operations analysis (analyzing team
needs and performance requirements to
create a successful program)

1

2

3

4

5

12

Management of personnel resources
(motivating, developing, and directing
others)

1

2

3

4

5

13

Management of financial resources
(determining how money will be spent to
have a successful program)

1

2

3

4

5

14

Management of material resources
(obtaining and seeing to the appropriate
use of equipment, facilities, and materials
needed to have a successful program)

1

2

3

4

5

Strategic skills
15

Visioning (developing an image of how a
program should work under ideal
conditions)

1

2

3

4

5

16

Program perception (determining when
important changes have occurred in a
program or are likely to occur)

1

2

3

4

5

17

Program evaluation (looking at many
indicators of a team’s performance, taking
into account their accuracy)

1

2

3

4

5

18

Identification of downstream
consequences (determining the long-term
outcomes of a change in a program)

1

2

3

4

5

19

Identification of key causes (identifying the
things that must be changed to achieve a
goal)

1

2

3

4

5

20

Problem identification (identifying the
nature of problems)

1

2

3

4

5

21

Solution appraisal (observing and
evaluating the outcomes of problem
solution to identify lessons learned or
redirect efforts)

1

2

3

4

5

Coaching Skill

185

1

Motivating your athletes

1

2

3

4

5

2

Making critical decision during competition

1

2

3

4

5

3

Developing athletes’ ability

1

2

3

4

5

4

Instilling attitudes of respect for others in
your athletes

1

2

3

4

5

186

Appendix A-6: Outcome Expectancy Measure
For assistant coaches
Please indicate your agreement to the following statement.

1

2

3

4

If I had the skills to be
a head coach, I will be
able to get a head
coaching job in the
future.
In the future, I will not
be able to be a head
coach even if I have
the skills to be one.
Having good
leadership skills can
help me get a head
coaching job in the
near future.
If I were to seek a
head coaching
position in the future, I
am sure I can get one.

Strongly
disagree
1

Disagree

Neutral

Agree

2

3

4

Strongly
agree
5

1

2

3

4

5

1

2

3

4

5

1

2

3

4

5

187

Appendix A-7: Developmental Challenge Profile
For assistant coaches
Please indicate the extent of which the item describes your current job as an assistant
coach for your team:
Not at all
descriptive

1

2

You lack experience important
to carrying out some aspect of
your job. (e.g., financial
analysis, negotiation, or
budgeting)
You have to carry out a major
reorganization as a result of a
budget cut, downsizing, or rapid
growth.

Slightly
Descriptive

Moderately
Descriptive

Quite
Descriptive

Extremely
descriptive

1

2

3

4

5

1

2

3

4

5

3

You inherited widespread
morale problems.

1

2

3

4

5

4

Your direct reports resist your
initiatives.

1

2

3

4

5

1

2

3

4

5

1

2

3

4

5

1

2

3

4

5

1

2

3

4

5

1

2

3

4

5

1

2

3

4

5

1

2

3

4

5

5

6

7

8

10

11

12

Your success or failure in this
job will be evident to higher
management (i.e., athletic
directors).
This job is a dramatic increase
in scope for you (managing
significantly more people,
dollars, sites, functions, and so
forth)
The customer base (e.g.,
supporters, fans, alums) you
work with is extremely varied.
You have to coordinate action
across dispersed sites over
which you have no direct
authority.
In terms of demographic
variables, you have a diverse
group of direct reports (i.e.,
athletes).
You have to manage
something, such as a function,
technology, or market, with
which you are unfamiliar.
You have to make major
strategic changes in the
program – its direction,
structure, technology systems,
or operations.

188

13

14

15

16

17

18

You need to restore the
credibility of your team with the
rest of the department.
There is an interpersonal
conflict between you and at
least one of your key direct
reports (i.e., athletes).
You are responsible for
decisive action in a highly
charged or pressured
environment.
The job is potentially more than
even a good delegator can
handle.
To achieve your most important
goals, you must influence
people outside the program
(e.g., supporters, suppliers)
To achieve your most important
goals, you must influence peers
at similar levels in other units,
teams, divisions, and so forth.

1

2

3

4

5

1

2

3

4

5

1

2

3

4

5

1

2

3

4

5

1

2

3

4

5

1

2

3

4

5

20

You are part of a diverse work
group.

1

2

3

4

5

21

Others question whether you
are “ready” for this job.

1

2

3

4

5

1

2

3

4

5

1

2

3

4

5

1

2

3

4

5

1

2

3

4

5

1

2

3

4

5

1

2

3

4

5

1

2

3

4

5

1

2

3

4

5

22

23

24

25
26
27
28

30

You are trying something the
program has never tried before;
no one knows for sure how to
do it or how it will come out.
To succeed in this job you have
to dismantle the strategy your
predecessor has established.
Your athletes are used to doing
things the way they have
always been done and are
reluctant to change.
You are being tested by higher
management (i.e., athletic
directors)
You are responsible for
numerous different
technologies, or services.
You manage relationships with
conference officials or
regulatory agencies.
Achieving your goals depends
on how well you handle internal
politics.
You are responsible for
developing leaders from
different ethnic groups of
athletes.

189

31

32

Compared to previous job
incumbents, you do not have
the credentials, background, or
experience expected for this
job.
This job includes launching new
program ventures, such as,
new functions or groups, new
plans or concepts, or new
facilities.

1

2

3

4

5

1

2

3

4

5

33

Your team unit has a record of
poor performance.

1

2

3

4

5

34

Key members of your team unit
are incompetent, demotivated,
or performing poorly.

1

2

3

4

5

35

There are clear deadlines by
which your key objectives in the
program must be
accomplished.

1

2

3

4

5

36

You are responsible for multiple
functions or groups.

1

2

3

4

5

37

You must deal with diverse
supporters, fans, or alums.

1

2

3

4

5

1

2

3

4

5

1

2

3

4

5

1

2

3

4

5

1

2

3

4

5

38

39

40

41

To accomplish a major portion
of your program’s goals, you
must influence and work with
executives (e.g., athletic
directors) higher than your
immediate boss.
You manage parts of the
business that are scattered
across the world.
You have to get athletes from
different racial, religious,
cultural, or ethnic backgrounds
to work together.
This job is no less than a
change in your career direction,
that is, you are doing a type of
work dramatically different from
what you have done before.

42

You have to create or establish
new policies or procedures.

1

2

3

4

5

43

You must solve major problems
a predecessor created.

1

2

3

4

5

44

Some of your key direct reports
(i.e., athletes) lack the
experience to do their jobs
without close supervision from

1

2

3

4

5

190

you.

45

46

47

48

49

50

There is pressure to complete a
major piece of your job quickly.
This job puts you under
constant pressure; there are
seldom any periods to “catch
your breath”.
You have to carry out formal
negotiations with an outside
body, such as, conference
officials, supporters, or
regulatory agencies
A great deal of coordination
with other department’s units or
functions is required.
Your job requires
understanding the traditions
and values of people from
different cultures.
You must make personnel
decisions about athletes who
differ from you in background.

1

2

3

4

5

1

2

3

4

5

1

2

3

4

5

1

2

3

4

5

1

2

3

4

5

1

2

3

4

5

191

Appendix A-8: Feedback Measure
For assistant coaches
The feedback I receive from a head coach at work has tended to be …
1

2

3

4

5

Fair*
Weak
Good*
Soft
Valuable*
Pleasant*
Vague
Supportive*
Frequent*

6

7
Unfair
Strong
Bad
Hard
Worthless
Unpleasant
Clear
Unsupportive
Infrequent

*reverse scoring

192

Appendix A-9: Support Measure
For assistant coaches
The following questions ask about the extent to which your head coach provides you
with help or support.

To what extent can you:

1

2

3

4

Count on your head
coach to listen to you
when you need to talk
about problems at work?
Count on your head
coach to back you up at
work?
Count on your head
coach to help you with a
difficult task at work?
Really count on your
head coach to help you
in a crisis situation at
work, even though they
would have to go out of
their way to do so?

Not at all

To a
small
extent

Neither
great nor
small
extent

To a
great
extent

Complete
ly

1

2

3

4

5

1

2

3

4

5

1

2

3

4

5

1

2

3

4

5

193

Appendix A-10: Mentorship Quality and Mentorship Learning Measure
For assistant coaches
Please indicate your agreement to the following statements in the relationship with your head
coach.
Strongly
disagree

Strongly
agree

Items measuring relationship quality
1

The mentoring relationship
between my head coach and I was
very effective.
2
I am very satisfied with the
mentoring relationship my head
coach and I developed.
3
I effectively utilized my head coach
as a mentor.
4
My head coach and I enjoyed a
high-quality relationship.
5
Both my head coach and I
benefited from the mentoring
relationship.
Items measuring relationship
learning

1

2

3

4

5

1

2

3

4

5

1

2

3

4

5

1

2

3

4

5

1

2

3

4

5

1
2

1
1

2
2

3
3

4
4

5
5

1

2

3

4

5

1

2

3

4

5

1

2

3

4

5

3
4
5

I learned a lot from my head coach.
My head coach gave me a new
perspective on many things.
My assistant coach and I were “colearners” in the mentoring
relationship.
There was reciprocal learning that
took place between my head coach
and I.
My head coach shared a lot of
information with me that helped my
own professional development.

194

For head coaches
Please indicate your agreement to the following statements in the relationship with your
assistant coach indicated in the email.
Strongly
disagree

Strongly
agree

Items measuring relationship quality
1

The mentoring relationship
between this assistant coach and I
was very effective.
2
I am very satisfied with the
mentoring relationship this
assistant coach and I developed.
3
I effective utilized this assistant
coach as a mentee.
4
This assistant coach and I enjoyed
a high-quality relationship.
5
Both this assistant coach and I
benefited from the mentoring
relationship.
Items measuring relationship
learning

1

2

3

4

5

1

2

3

4

5

1

2

3

4

5

1

2

3

4

5

1

2

3

4

5

1

I learned a lot from this assistant
coach.

1

2

3

4

5

2

This assistant coach gave me a
new perspective on many things.
This assistant coach and I were
“co-learners” in the mentoring
relationship.
There was reciprocal learning that
took place between this assistant
coach and I.
This assistant coach shared a lot of
information with me that helped my
own professional development.

1

2

3

4

5

1

2

3

4

5

1

2

3

4

5

1

2

3

4

5

3
4
5




195

Appendix A-11: Learning Orientation Measure
For assistant coaches
Please indicate the extent of your agreement to each of the statement:
Strongly
Disagree Neutral
Disagree
1

2
3
4
5

I am willing to select a
challenging work
assignment that I can learn
a lot from.
I often look for
opportunities to develop
new skills and knowledge.
I enjoy challenging and
difficult tasks at work
where I'll learn new skills.
For me, development of my
work ability is important
enough to take risks.
I prefer to work in
situations that require a
high level of ability and
talent.

Agree

Strongly
Agree

1

2

3

4

5

1

2

3

4

5

1

2

3

4

5

1

2

3

4

5

1

2

3

4

5

196

Appendix A-12: Work-Family and Family-Work Conflicts Scale
For assistant coaches
Strongly
disagree

Slightly
disagree

Slightly
agree

Strongly
agree

Work-family conflict
1

The demands of my work
interfere with my home and
2 The amount of time my job
takes up makes it difficult to
fulfill family responsibilities.
3 Things I want to do at home
do not get done because of
the demands my job puts on
me.
4 My job produces strain that
makes it difficult to fulfill
family duties.
5 Due to work-related duties, I
have to make changes to my
plans for family activities.
Family-work conflict

1

2

3

4

5

6

7

1

2

3

4

5

6

7

1

2

3

4

5

6

7

1

2

3

4

5

6

7

1

2

3

4

5

6

7

1

1

2

3

4

5

6

7

1

2

3

4

5

6

7

1

2

3

4

5

6

7

1

2

3

4

5

6

7

1

2

3

4

5

6

7

2

3

4

5

The demands of my family
or spouse/partner interfere
with work-related activities.
I have to put off doing
things at work because of
demands on my time at
home.
Things I want to do at work
don’t get done because of
the demands of my family
or spouse/partner.
My home life interferes with
my responsibilities at work
such as getting to work on
time, accomplishing daily
tasks, and working
overtime.
Family-related strain
interferes with my ability to
perform job-related duties.

197

Appendix A-13: Gender Discrimination Measure
For assistant coaches
Please indicate the extent of your agreement to each statement.
Strongly
Disagree
1

2

3

4

At work, I sometimes feel that my
gender is a limitation.
At work, many people have
stereotypes about my gender and treat
me as if they were true.
At work, I sometimes feel that people
actively try to stop me from advancing
because of my gender.
At work, I feel that others exclude me
from their activities because of my
gender.

Disagree

Neutral

Agree

Strongly
Agree

1

2

3

4

5

1

2

3

4

5

1

2

3

4

5

1

2

3

4

5

198

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