Antecedents and Consequences of Emotional Labor in Head Coaches of NCAA
Division I Program
Dissertation
Presented in Partial Fulfillment of the Requirements for the Degree Doctor of Philosophy
in the Graduate School of The Ohio State University
By
Ye Hoon Lee, M.S.
Graduate Program in College of Education and Human Ecology
The Ohio State University
2012
Dissertation Committee:
Dr. Packianathan Chelladurai, Advisor
Dr. Brian Turner
Dr. Donna Pastore
Copyright by
Ye Hoon Lee
2012
Abstract
Emotional labor, defined as the regulation of both feelings and emotions to be
effective in jobs (Hochschild, 1983), is a topic that has not been addressed adequately in
sport management literature. Hochschild (1983) identified two emotional labor strategies
including surface acting (managing outward expressions to show appropriate emotions)
and deep acting (trying to experience appropriate feelings before expressing them). Zapf
(2002) also argued that automatic regulation (expressing appropriate emotions naturally
in a given situation) is the third category of emotional labor strategies. It is a critical issue
when we consider that sport management is concerned with production of services the
quality of which is largely determined by regulations of emotions by both parties in the
employee-client interface. The significance of emotional labor is even greater in the case
of those service providers who are also in leadership positions as in the case of coaches
and athletes. The coach-athlete relationship of intercollegiate athletics may be the
relevant area to investigate the nature of emotional labor in the sport setting.
This study investigated a working model of emotional labor in coach-athlete
relationship. The proposed model identified emotional intelligence (Mayer & Salovey,
1997) and affectivity (Watson, Clark, & Tellegen, 1988) as potential antecedents of a
coaches’ choice of emotional labor strategy. The current study also investigated the
differential impact of the emotional labor strategies on the individual outcomes of
emotional exhaustion (Maslach, 1982) and job satisfaction. To achieve this goal, the
study was performed in two stages. In the first stage, psychometric properties of the
ii
scales used for pilot tested. Based on the result from the pilot study, the questionnaires
were refined to improve their psychometric properties. In the second stage, the
confirmatory factor analysis and structural equation modeling were employed to test the
proposed hypotheses.
Using census method, questionnaires were distributed to coaches at NCAA
Division I universities in the United State via online. The results revealed that positive
affectivity negatively predicted all of the emotional labor strategies including surface
acting, deep acting, and automatic regulation while negative affectivity predicted only
surface acting negatively. Emotional intelligence predicted only automatic regulation.
Regarding the consequences of emotional labor, surface acting positively
predicted emotional exhaustion and negatively predicted job satisfaction. On the other
side, automatic regulation was negatively associated with emotional exhaustion and
positively associated with job satisfaction. However, it was found that deep acting had no
relationship with consequences. Finding discussed and practical implication, limitations,
and directions for future research were presented.
iii
Dedication
Dedicated to my loving and supportive family
iv
Acknowledgements
I realize that this dissertation would not have been completed without the support
and many individuals. While everyone who has contributed to the success of this work
may not be named, please know that you are remembered.
My sincere appreciation and gratitude is dedicated to my advisor, Dr. Chella for
his continuous support, positive influence, and inspiration throughout my studies. I have
learnt not only knowledge in sport management but also passion, dignity, and humility. It
has been my honor to be his protégé. Along with Dr. Chella, I would like to extend my
sincere appreciation for the positive contribution of time, effort, guidance, and
suggestions of other members of the committee – Dr. Brian Turner and Dr. Donna
Pastore.
Special thanks should be given to the individuals who made this project possible;
the head coaches in NCAA division I and II program. Their cooperation and willingness
to participate made this project possible.
I owe a great deal of love, gratitude, and appreciation to my parents, Keun Soo
Lee and Eui Ran Hwang, my brother, Jae Hoon Lee, my sister, Esther Lee, my sister-inlaw, Bona Lee, my brother-in-law, Sang Tae Lee, and my precious dog, Yamoo. If it had
not been for their unconditional love, sacrifice, and emotional support, this dissertation
would not have been possible.
v
Finally, I am deeply grateful to the grace of God with all my heart for his blessing,
benevolence, and guidance at every stage of my life. “But by the grace of God I am what
I am, and his grace toward me was not in vain. On the contrary, I worked harder than any
of them, though it was not I, but the grace of God that is with me (1 Corinthians 15:10).”
vi
Vita
July 12, 1979………………………………………………Born – Seoul, Korea
2006………………………………………………………..B.S. Physical Education, Seoul
National University
2009………………………………………………………..M.S. Sport Psychology,
Michigan State University
2010 to present…………………………………………….Graduate Teaching Associate,
The Ohio State University
Fields of Study
Major Field: Education
Cognate: Statistic
(Structural Equation Modeling)
vii
Table of Contents
Abstract…………………………………………………………………………………....ii
Dedication………………………………………………………………………………...iv
Acknowledgements………………………………………………………………………..v
Vita……………………………………………………………………………………....vii
List of Tables……………………………………………………………………………..xi
List of Figures…………………………………………………………………………...xiii
Chapters:
1. Introduction………………………………………………………………………..1
Background of Study……………………………………………………...3
Statement of the Problem………………………………………………….7
Purpose of the Study………………………………………………………9
Proposed Variables and Hypotheses………………………………………9
Significance of Study…………………………………………………….16
Operational Definition of Terms………………………………………....18
2. Literature Review………………………………………………………………...22
Emotional Labor………………………………………………………....22
Conceptualization of Emotional Labor…………………………………..25
Antecedents of Emotional Labor………………………………………...34
Consequences of Emotional Labor……………………………………....50
3. Method…………………………………………………………………………...64
Research Design…..….………………………………………………….64
viii
Sampling Method………………………………………………………...66
Pilot Study………………………………………………………………..67
Target Population………………………………………………………...69
Instrumentation………………………………………………………......73
Data Collection…………………………………………………………..77
Data analysis……………………………………………………………..78
4. Results……………………………………………………………………………80
Demographic Characteristics………………………………………….....80
Single-Group Confirmatory Factor Analysis………………………….....86
Second-Order Confirmatory Factor Analysis……………………………94
Single-Group Structural Equation Modeling………………………….....97
5. Discussion……………………………………………………………………....105
Overview of the Instruments…………………………………………...105
Significance Findings of the Study…………………………………......107
Implications……………………………………………………………..119
Limitations and Future Studies……………………………………........120
6. List of References……………………………………………………………....124
Appendices
A. Email – Pre-notification…………………………………………………….139
B. Email – Main study invitation………………………………………………141
C. Email – Follow-up……………………………………………………….....144
D. Positive Affectivity and Negative Affectivity Scale.………………………147
E. Emotional Intelligence Scale……………………………………………….149
ix
F. Emotional Labor Scale……………………………………………………...152
G. Emotional Exhaustion Scale………………………………………………..155
H. Job Satisfaction Scale……………………………………………………....157
I. Demographic Questionnaire………………………………………………..159
x
List of Tables
Table 2.1. Relationship between positive affectivity and emotional labor strategies…....37
Table 2.2. Relationship between negative affectivity and emotional labor strategies…...38
Table 2.3. Relationship between emotional intelligence and emotional labor strategies..50
Table 2.4. Relationship between surface acting and emotional exhaustion…………......59
Table 2.5. Relationship between deep acting and automatic regulation, and emotional
exhaustion……………………………………………………………………………......60
Table 2.6. Relationship between surface acting and job satisfaction………………........63
Table 2.7. Relationship between deep acting and automatic regulation, and job
satisfaction…………………………………………………………………………….....63
Table 3.1. Reliability measures from the pilot study………………………………….....68
Table 3.2. Number of coaching position at NCAA Division I program………………....71
Table 4.1. Demographic variable frequencies for respondents…………………………..82
Table 4.2. Gender of the team of respondents…………………………………………...83
Table 4.3. The number of respondents and the total number of coaches by sports……...83
Table 4.4. Comparison of early to late respondents……………………………………...85
Table 4.5. Factor loadings for each item and Cronbach’s coefficient and average variance
extracted for each factor……………………………………………………………….....88
Table 4.6. A summary of the single-group confirmatory factor analysis……………......89
Table 4.7. Means, standard deviations, and correlation for factors……………………...95
xi
Table 4.8. A summary of the second-order factor model for emotional intelligence
construct……………………………………………………………………………….....96
Table 4.9. Maximum likelihood estimates for second-order factor model………………96
Table 4.10. Maximum likelihood estimates for Model 1 and Model 2………………...100
Table 4.11. A summary of the single-group structural equation modeling…………….101
Table 4.12. Summary of results for study hypotheses………………………………….102
xii
List of Figures
Figure 1.1. A proposed model……………………………………………………………15
Figure 2.1. Grandey’s (2000) emotional regulation model of emotional labor………….32
Figure 4.1. First-order confirmatory factor analysis model for antecedent scales except
emotional intelligence construct……………………………………………………........90
Figure 4.2. First-order confirmatory factor analysis model for emotional labor strategy
scale………………………………………………………………………………………91
Figure 4.3. First-order confirmatory factor analysis model for consequences scales……92
Figure 4.4. Second-order confirmatory factor model of emotional intelligence…….......97
Figure 4.5. Path coefficients between latent variables for Model 1…………………….103
Figure 4.6. Path coefficients between latent variables for Model 2…………………….104
xiii
CHAPTER 1
INTRODUCTION
Emotion is a central part of our daily lives. People feel different emotions every
second of their lives, and the emotion actually influences their subsequent actions.
Regarding workplaces, the role of emotion can be even stronger because various factors,
including the interaction with supervisors, peers, and followers, generate affective
experiences that have potential to influence subsequent behaviors (Weiss & Cropanzano,
1996). In fact, most organizational theories attempted to de-value the exploration of
emotions in the past for two major reasons (Martin, Knopoff, & Beckman, 1998). First,
Western tradition tended to view emotions as the opposite side of rationality and
disorganized interruptions of mental ability, which were believed to obscure sound
judgment (Grandey, 2000). In the past, the importance of rationality had overweighed
emotionality, which in turn led to the belief that it was not necessary to study
emotionality. Second, emotions were deemed as an area difficult to study and measure
because all individuals experience different and subjective feeling states (Arvey, Renz, &
Watson, 1998). However, today, researchers have realized that the critical role of
emotions should be integrated in research on organizational behaviors in order to provide
a more comprehensive understanding of human behaviors in organizational settings
(Damasio, 1999; Kalat & Shiota, 2007). Although logical mind is valued in our culture,
the emotion shapes our behaviors outside as well as within the workplace. Research
1
studies in the field of human resource development have also followed this trend.
Scholars in this field recognized emotions as the main topic of interest and included
emotion as keyword in many subscripts (Callahan, 2000; Callahan & McCollum, 2002;
Kunnanatt, 2004; Landen, 2002; McEnrue & Groves, 2006). In reality, organizations
across the world, when looking for advantages in today’s competitive nature of work,
have also identified the critical role of emotions in organizational outcomes. As the direct
interactive experience between employees and customers has become more and more
important, employees’ emotional displays act as a critical variable enhancing favorable
experiences of customers. For example, Tsai (2001) found a positive relationship
between employees’ emotional display and customer satisfaction. Additionally, a number
of studies found a positive relationship between employees’ emotional display and
organizational outcomes, ranging from employee health and psychological well-being to
positive word-of-mouth, increased sales, customer service performance, and customer
satisfaction (e.g., Pugh, 2001; Rafaeli & Sutton, 1989; Tsai, 2001; Tsai & Huang, 2002).
These results suggest that employees’ emotional display that is appropriate for the
situation allows organizations to build a positive relationship with customers and provide
a major competitive advantage. As such, many service organizations attempted to impose
some restrictions on their employees’ emotional expressions to provide the right attitude,
as perceived by the customer (Grandey, 2000). As a result, a relatively new type of work
demand, known as emotional labor, has been introduced.
Hoschschild (1983), who first coined the term, defined emotional labor as
employees’ purposeful effort to produce, elicit, and express job-specific emotions in their
interaction with customers in order to achieve organizational goals. Emotional labor
2
(Hochschild, 1983) has received much attention from numerous occupations within the
service sector (e.g., waiters, call center employees, and nurses) that involves direct faceto-face or voice-to-voice customer contact. This labor is a response to the rules or
expectations regarding the appropriate display of emotions (Grandey, 2000). In other
words, within this interaction with customers, employees perform emotional labor in
accordance with the display rule, which serves as the standard for the acceptable
expression established by the organization.
Positive emotions, such as kindness and enthusiasm, are a common display rule in
service organization. In fact, most service organizations require service employees to
display positive emotions while suppressing negative emotions (Schaubroeck & Jones,
2000). Flight attendants whose profession requires emotional labor are a good example.
In their interaction with passengers, they need to display positive emotions as they make
every effort to smile even in difficult or unpleasant circumstances in which they are
experiencing negative emotions. For example, they need to suppress their true feelings,
such as anger or frustration, and cover it with positive emotions, even though they may
be facing offensive and disrespectful passengers. Unfortunately, they often struggle with
inconsistency between their true feelings and actual expressions, which in turn leads to
stress or negative well-being (Brotheridge & Grandey, 2002; Grandey, 2000; Hochschild,
1983).
Background of Study
Emotional labor (Hochschild, 1983) views emotions as a critical part of the
relation between service workers and customers and a core product produced by service
workers. Thus, in this concept, the quality of the service product depends on how
3
employees manage and express their emotions. The researchers have identified three
different kinds of emotional labor, including surface acting (Hochschild, 1983), deep
acting (Hochschild, 1983), and automatic regulation (Zapf, 2002). Surface acting
involves modifying one’s outward expressions without changing his or her inner feelings
while deep acting involves modifying one’s inner feelings to match their expressions in
order to adhere to display rules (Grandey, 2000). According to Hochschild (1983), “In
both cases, the actor has learned to intervene either in creating the inner shape of a
feeling or in shaping the outward appearance of one” (p. 36). However, Rafaeli and
Sutton (1989) stated that both strategies have different intentions. While deep acting,
which attempts to modify feelings that would follow the display rules are called “faking
in good faith” (p. 32), surface acting, because of its intention to be authentic to the
audience, is called “faking in bad faith” (p.32) because it intentionally fakes the
observable expressions to satisfy audiences. The last category of emotional labor is
automatic regulation, which reflects the process of expressing naturally felt
organizationally desired emotions (Zapf, 2002). In this case, individuals do not pay
attention and make a conscious effort to process emotional regulation while still
following the display rule. Zapf (2002) argued that sales person might automatically
smile whenever he or she meets customer effortlessly. Diefendorff, Croyle, and
Gosserand (2005) confirmed that automatic regulation was a distinctive strategy, which
has a potential to predict work outcomes.
There has been conclusive evidence that emotional labor is a significant factor
influencing favorable organizational outcome. Previous research studies have shown
enough conclusive evidence that would distinguish surface acting from deep acting in
4
their relation to outcomes. Specifically, surface acting has been found to be associated
with negative outcomes, such as personal inauthenticity, depersonalization, emotional
exhaustion, dissatisfaction, and burnout (Brotheridge & Grandy, 2002; Brotheridge &
Lee, 2002; Grandey, 2003; Grandey, Fisk, & Steiner, 2005). In contrast, deep acting
tends to relate to positive outcomes, such as personal authenticity, personal
accomplishment, job satisfaction, and performance (Brotheridge & Grandy, 2002;
Brotheridge & Lee, 2002; Grandey, 2003; Grandey et al., 2005). Accordingly, subsequent
research studies did not examine the predictability of automatic regulation on favorable
outcomes. Hennig-Thurau, Groth, Paul, and Gremler’s (2009) study found that
employee’s automatic regulation led to higher levels of positive affect among customers.
In the study of Dutch mathematic teachers, Naring, Briet, and Brouwers (2006) also
found that emotional consonance (corresponding with automatic regulation) has a
positive relationship with personal accomplishment (a subset of burnout). Finally,
Martinez-Inigo, Totterdell, Alcover, and Holman (2007) found that automatic regulation
is negatively related to emotional exhaustion and positively to job satisfaction.
To improve our understanding of emotional labor, the research realm has
extended to leadership position. The emotional labor research attempted to examine the
role of emotional labor on individual as well as organizational outcomes in supervisorsubordinates relationships in business settings and teacher-students relationship in
educational setting. Hochschild (1983) introduced three issues that are common to the
jobs requiring emotional labor. 1) They require voice or facial contact with the public, 2)
they require the worker to produce an emotional state in a client, and 3) the employer
exercises some control over the emotional activities of employees. Based on this standard,
5
she identified a number of jobs involving emotional labor, including managers and
administrators in this category. Actually, Gardner, Fischer, and Hunt (2009) viewed an
organizational leader as one who often attempts to regulate emotions to enhance
followers’ trust in the business setting.
Few studies showed the potential beneficial outcomes of leader positive emotion.
For instance, George and Bettenhausen (1990) found that positive moods experienced by
leaders was positively associated with group member’s pro-social behaviors and
negatively with group turnover rates. George (1995) also found that coaches with more
positive mood provided higher level of customer service compared to their counterparts.
Lewis (2000) also stated that both emotional expressions and emotional behaviors
important factors influencing positive or negative consequences of leader-follower
relationship. Specifically, he demonstrated that leader’s empathetic emotional
expressions determine the quality of relationship and allow leaders to receive favorable
outcomes, such as integrity as well as credibility, from followers. Moreover, Sy, Cote,
and Saavedra (2005) showed that when leaders displayed positive (negative) moods at
work, the emotional display induced followers to experience more positive (negative)
mood and generate more positive (negative) affections among group members, since
emotion is contagious. On the other side, most research studies on emotional labor in
leadership have focused on teaching profession, ranging from K-12 teachers (Hargreaves,
2000; Naring et al., 2006; Zapf & Holz, 2006) to college professors (Ogbonna & Harris,
2004; Zhang & Zhu, 2008).
Together, research topics have extended from exploring the validity of the
existence of emotional labor utilization among leaders to identifying the effectiveness of
6
appropriate emotional expressions on favorable organizational outcomes in leadership
paradigms. A leader’s ability to manage his or her emotions appropriately in the
interaction with followers may be a critical area to explore as part of the leadership
paradigm (Gardner et al., 2009).
Statement of Problem
Research studies in the field of organizational behavior and human resource have
paid increased interest to emotional labor. Liu, Prati, Perrewe, and Ferris (2008) stated
that emotional labor did exist and that increasing number of researchers has attempted to
identify the effects of emotional labor and its antecedents and consequences in various
professions. Furthermore, previous studies confirmed that leader emotions and emotional
displays are important factors to investigate in the leadership process (George, 2000). It
would be particularly important if leader emotion and emotional display influenced task
effectiveness as well as their well-being. However, according to the extensive literature
reviews, no studies focused on the nature of emotional labor in the sport settings. A coach
as a leader of athletic teams should also be considered a figure that performs leadership
role in competitive and training settings and is expected to regulate his/her emotions that
may negatively affect the team and the performance when interacting with athletes. Since
emotion is contagious (Hatfield, Cacioppo, Rapson, 1993), if a coach shows depressed
mood, disappointment, and sullen face in front of athletes, his or her team might become
depressed or disappointed, influencing the performance. To prevent these cases, coaches
need to suppress their negative emotions, such as disappointment and nervousness, and
conceal them with confidence and calmness to help athletes feel the corresponding
emotions before or during the competition.
7
Actually, practitioners have emphasized the importance of emotional labor in
intercollegiate athletic contexts. For example, John Wooden, one of the most famous and
greatest basketball coaches in NCAA history, talked about various challenges coaches
may face in handling emotions in coaching in his book. One of his arguments in the book
is that it is necessary for coaches to keep emotion under the control in order to generate
positive outcomes for teams, athletes, and themselves. As he stated, “Emotionalism
destroys consistency. A leader who is ruled by emotions, whose temperament is
mercurial, produces a team whose trademark is the roller coaster-ups and downs in
performance; unpredictability and undependability in effort and concentration; one day
good, the next day bad” (Wooden & Jamison, 2005, p. 107). Coach Wooden illustrated
the challenges that coaches may face at work and the importance of controlling emotions
during team performance.
In academic setting, Kimiecik and Gould (1987) interviewed James Counsilman,
a legendary Olympic swim coach, and found that he often felt nervous at major
competitions; although, he tried not to let his swimmers recognize his stress and genuine
feelings. Gould, Guinan, Greenleaf, and Chung (2002) also surveyed Olympic-level
coaches and found that their aim was to control of their own emotional state and mask
certain emotions from athletes. These research studies did not specify the term emotional
labor, but they indicated that emotional labor did exist in coaching context. However, no
research has investigated the nature of emotional labor strategies in sport settings.
Subsequently, the ways in which emotional labor in coaching affects coaches’ well-being
either positively and negatively are not clear, as literatures in other fields have indicated.
Furthermore, it is not clear how to increase the use of health-beneficial (i.e., deep acting
8
and automatic regulation) rather than health-detrimental (i.e., surface acting) emotional
labor strategies.
Purpose of this study
The purpose of this study is to identify antecedents (affectivity and emotional
intelligence) of emotional labor strategies (surface acting, deep acting, and automatic
regulation). Furthermore, since previous research has investigated the predictability of
emotional labor on certain outcomes, such as burnout and job satisfaction (see Grandey,
2000; Wharton, 1993), we investigated the relationship between emotional labor and
these outcomes. Through personal research, this will be the first study to examine the role
of affectivity and emotional intelligence in emotional labor and proposed consequences
in the sport setting simultaneously.
Proposed Variables and Hypotheses
Based on the previous evidence from research studies regarding emotional labor,
a model of emotional labor in coaching was developed. The model is illustrated in Figure
1.1. The reminder of this chapter will cover the various linkages in this model. It is
expected that each emotional labor strategy will be differentially related to proposed
antecedents and the proposed consequences.
Affectivity
Affectivity, defined as the sum of individual mood states (Watson, Clark, &
Tellegen, 1988) appears to be associated with coaches’ emotional labor strategies.
Specifically, affectivity can be classified into two categories, including positive
affectivity (PA) and negative affectivity (NA). PA refers to the individual’s tendency to
experience positive emotions while NA corresponds to the individual’s tendency to
9
experience negative emotions (Watson & Clark, 1984). This study proposes that PA of
coaches relates negatively to surface acting and positively to deep acting. Indeed, a
number of studies found negative relationships between PA and surface acting (e.g.,
Austin, Dore, & O’Donovan, 2008; Brotheridge & Lee, 2003; Diefendorff et al., 2005;
Gosserand & Diefendorff, 2005). Concerning the relationship with deep acting, previous
literatures also found positive relationship with PA (Austin et al., 2008; Gosserand &
Diefendorff, 2005) while Brotheridge and Lee (2003) while Diefendorff and his
colleagues (2005) found no relationship. However, the researchers proposed a positive
relationship between them, since high PA coaches who are very active and enthusiastic
(Watson, 1988) would be more likely to change their inner feelings instead of engaging
in superficial and shallow strategy like surface acting.
Furthermore, coaches’ NA is proposed to have a positive relationship with surface
acting based on previous evidence (Austin et al., 2008; Brotheridge & Lee, 2003;
Diefendorff et al., 2005; Gosserand & Diefendorff, 2005). While most studies found no
relationship between NA and deep acting (e.g., Brotheridge & Lee, 2003; Diefendorff et
al., 2005), recent study revealed a negative association between NA and deep acting (e.g.,
Austin et al., 2008). High NA individuals are pessimistic, and they tend to view
themselves and world in negative way (Watson & Clark, 1984). Thus, high NA coaches
may not engage in deep acting strategy because they may not systematically try to solve
the problems upon experiencing problematic situation (i.e., deep acting). As such, we
proposed a negative association between the two variables.
10
H1. Positive affectivity will be negatively associated with surface acting
H2. Positive affectivity will be positively associated with deep acting
H3. Negative affectivity will be positively associated with surface acting
H4. Negative affectivity will be negatively associated with deep acting
Emotional intelligence
We also hypothesized that emotional intelligence was associated negatively with
surface acting and positively with deep acting and automatic regulation. Emotional
Intelligence is defined as the ability to perceive, express, understand, and regulate
emotions in the self and others (Mayer & Salovey, 1997). Previous studies have shown
that individuals with high emotional intelligence use less surface acting compared to
those with low emotional intelligence (Austin et al., 2008; Mikolajczak, Menil, &
Luminet, 2007). In addition, Daus, Rubin, Smith, and Cage (2005) found that emotionally
intelligent individuals showed deep acting more during interpersonal interactions.
Following studies replicated this result (e.g., Brotheridge, 2006b; Côté, 2005; Karim &
Weisz, 2010; Liu et al., 2008). Finally, Mikolajczak and colleagues’ study (2007) found a
positive association between positive consonance (i.e., automatic regulation) and
emotional intelligence. Based on these findings, we proposed that emotional intelligence
would have similar effect on coaches using different emotional labor.
H5. Emotional intelligence will be negatively associated with surface acting
H6. Emotional intelligence will be positively associated with deep acting.
H7. Emotional intelligence will be positively associated with automatic regulation.
11
Emotional exhaustion
Emotional exhaustion is the core component of job burnout and refers to a lack of
energy and emotional resource (Maslach, 1982). Emotionally exhausted people feel that
they are frustrated and depleted of all of their energy. A number of studies investigated
the relationship between emotional labor and emotional exhaustion. According to Zapf
(2002), emotion work is associated with emotional exhaustion while Grandey (2003) also
argued that both surface acting and deep acting positively influenced emotional
exhaustion. The current study proposed that surface acting and deep acting were
positively associated with emotional exhaustion while automatic regulation was
negatively associated with emotional exhaustion. A number of studies also found a
positive relationship between surface acting and emotional exhaustion (Abraham, 1998;
Brotheridge & Grandey, 2002; Brotheridge & Lee, 2003; Chau, Dahling, Levy, &
Diefendorff, 2009; Glomb & Tews, 2004; Grandey, 2003; Johnson & Spector, 2007;
Martinez-Inigo, Totterdell, Alcover, & Holman, 2007; Montgomery, Panagopolou, de
Wildt, & Meenks, 2006; Naring et al., 2006;). These studies argued that surface acting
requires employees’ conscious efforts to suppress their genuine emotions and fake unfelt
emotions that generate emotional exhaustion.
Regarding the relationship with deep acting, most studies found no relationship
between deep acting and emotional exhaustion (Brotheridge & Grandey, 2002;
Brotheridge & Lee, 2003; Totterdell & Holman, 2003). However, the current study
proposed the positive relationship between the two variables based on the Hochschild’s
(1983) argument that deep acting still requires individual’s conscious effort. During the
process of deep acting, individuals try to change the perception of the situation (Grandey,
12
2000) in order to meet the desired emotion required by the display rule. This conscious
effort is still is demanding and can lead to emotional exhaustion.
Finally, automatic regulation has been proposed to have a negative effect on
emotional exhaustion. In fact, the results concerning the relationship between automatic
regulation and emotional exhaustion have been mixed. Glomb and Tews (2004) found
that when employees expressed genuine negative emotions, they tended to experience
emotional exhaustion while positive genuine emotion did not relate to emotional
exhaustion. On the other hand, Mrtinez-Inigo and colleagues (2007) found that automatic
regulation is negatively associated with emotional exhaustion. In their study, coaches
with automatic regulation would not experience emotional exhaustion since the strategy
does not require conscious efforts to generate certain emotion that is required by the
display rule.
H8. Coach surface acting is positively associated with emotional exhaustion.
H9. Coach deep acting is positively associated with emotional exhaustion
H10. Coach automatic regulation is negatively associated with emotional
exhaustion.
Job satisfaction
Job satisfaction is another popular consequence used in emotional labor research.
Hochschild (1983) stated that emotional labor reduced workers’ job satisfaction when
their personal feelings were commoditized and exchanged like a property. However, the
previous results were mixed in that some found a positive relationship between emotional
13
work and job satisfaction (e.g., Adelman, 1995; Cote & Morgan, 2006; Wharton, 1993)
while others found a negative relationship (Morris & Feldman, 1997; Parkinson, 1991).
These mixed results may be due to the different operalization of the construct. As such,
subsequent studies assumed that emotional labor can be distinguished by surface acting
and deep acting and shared common view that surface acting is a detrimental health
strategy while deep acting is a beneficial health strategy (Brotherdige & Grandey, 2002;
Brotheridge & Lee, 2002; Judge, Woolf, & Hurst, 2009; Liu et al., 2008). Based on these
results, the current study expects surface and deep acting to have different effects on job
satisfaction.
First, job satisfaction is predicted to be negatively associated with surface acting.
Previous literatures indicated a negative relationship between surface acting and job
satisfaction (Bono & Vey, 2005; Cote & Morgan, 2002; Zhang & Zhu, 2008). These
studies stated that surface acting reduced job satisfaction among employees since it
generated emotional dissonance and a sense of inauthenticity. Additionally, individuals
with surface acting will also experience self-alienation and develop negative attitude
towards their jobs. On the contrary, deep acting and automatic regulation is predicted to
have positive associations with job satisfaction. The rationale behind this is that
performing deep acting and automatic regulation reduces the mismatch between felt
emotion and displayed emotion, which in turn reduces the negative effects of emotional
dissonance. Additionally, being able to modify inner feelings may give individuals a
sense of personal accomplishment and authenticity, thus create a feeling of job
satisfaction.
14
H11. Coach surface acting is negatively associated with job satisfaction.
H12. Coach deep acting is positively associated with job satisfaction
H13. Coach automatic regulation is positively associated with job satisfaction.
Figure 1.1. A proposed model of emotional labor in coaching
15
Significance of Study
One of the most important sectors of the sport industry in North America is
intercollegiate athletics. The increasing number of fans of various collegiate teams and a
broad range of media coverage reflects the influence and the importance of intercollegiate
athletics. One of the biggest reasons for its popularity is its ability to generate
entertainment values, which attract fans and media. As such, athletes who are the main
source of the entertainment values can be deemed as critical human resource for
intercollegiate athletics (Turner & Chelladurai, 2005).
Moreover, coaches play a critical role in producing entertainment values in
athletics because “they recruit athletes (i.e., mobilize the human resources), attempt to
develop them into excellent athletes (i.e., motivate and train them) and mold into the
effective teams (i.e., coordinate their efforts and activities)” (Turner & Chelladurai, 2005,
p. 194). Through this process, athletes are able to show spectacular performance,
enhancing their excitement and competitiveness, which in turn elevates the entertainment
value. Subsequently, coaches receive a great incentive from athletic department, as the
compensation is about 18% of athletic department expenditures at the NCAA Division I
level (Faulks, 2010). Given its importance, it is not surprising that scholars and
practitioners have attempted to identify constructs that would influence coaches’ wellbeing (e.g., job burnout) and job-related attitude (e.g., job satisfaction). This can become
particularly important when we consider that these consequences relate to coaches’
withdrawal behaviors from activities that they previously enjoyed and pursued (Smith,
1986).
16
It has been noted that in sports, frequent and intense interactions between coaches
and athletes predict coach burnout (Udry, Gould, Bridges, & Tuffey, 1997). It is possible
that this interaction involves a great deal of exchange of emotions, since it inevitably
involves frequent face-to-face and voice-to-voice contact. It is true that coach-athlete
relationship can evoke emotional displays, such as anger associated with athlete’s
misconduct, disappointments from losing a contest, nervousness before major
competitions, and joy from winning a contest. However, as one of the most important
roles of coaches is to motivate and empower athletes to achieve organizational goals, it is
critical for coaches to regulate their emotions and express appropriate emotions to
motivate these athletes.
Therefore, we expect that as coaches regulate those emotions, they may
negatively affect the team and the performance through emotional labor. In other words,
we believe that coaches are directing their emotional displays toward athletes in order to
motivate them and achieve desired goal. For instance, the coach may need to control the
anger or frustration aroused by team’s losing streak. Coaches who feel angry or frustrated
inside but appear calm and confident in front of athletes engage in surface acting. On the
other hand, coaches may feel calmness and confidence by attributing the losing streak to
bad luck rather than to team’s ability. Coaches may feel calmness and confidence
automatically, as a form of automatic regulation. As individual’s emotional display plays
a critical role in organizational and individual outcomes, it seems meaningful to study the
dynamics of emotional display among coaches and its relation to important consequences
in sports.
17
This paper may be the starting point for exploring coaches’ emotional
management in order to reveal the overall process of emotional labor in coaching.
Consequently, this study will contribute to the existing research on emotions, leaderships,
and coaching behaviors by examining an entirely new area for the emotional labor
research.
We hope that this paper will also offer recommendations for practitioners in the
field of coaching or human resource development. Due to the lack of sufficient scientific
evidence on the effect of emotional labor on the sport settings, this study will be one of
the first to emphasize the importance of emotional labor on coach well-being. By
providing support for the importance of emotional labor process in coaching, we may be
able to apply this concept to training in coaching and other education programs.
Furthermore, we hope that this study finds a way to reduce job burnout in coaching. If we
find significant relationship between coach emotional labor processes and job burnout,
practitioners may use this finding to reduce coaches’ job burnout through interventions
that would influence coaches’ emotional labor.
Operational Definition of Terms
Below are definitions for constructs included in this study that are less commonly
used in the literature.
1. Emotional labor is defined as the regulation of both feelings and expressions of
emotions in accordance with display rules to accomplish organizational goals
(Grandey, 2000). In the current study, emotional labor involves coaches’
regulation of both feelings and expressions of emotions to generate positive
emotions to motivate athletes.
18
a. Surface acting is defined as the modification of one’s outward appearance
in order to follow display rule (Hochschild, 1983). In this study, surface
acting involves coaches’ deliberate faking in observable facial expressions
by suppressing inner feelings in the interaction with athletes.
b. Deep acting is defined as the modification of one’s inner feelings that is
required by the display rule (Hochschild, 1983). In this study, deep acting
involves coaches’ conscious efforts to generate certain emotion that is
appropriate for the situation in the interaction with athletes.
c. Automatic regulation is defined as employees’ subjective feeling when
they do not experience mismatch between the naturally felt emotions and
emotions that is required by the display rules (Ashforth & Humphrey,
1983). In this study, automatic regulation measures coaches’ tendency to
express appropriate emotions (such as enthusiasm and calmness)
spontaneously although when they encounter the incidences which arouse
inappropriate emotions.
2. Emotional intelligence is defined as the ability to recognize the meanings of
emotions, and to reason and problem solve on the basis of them, and it involves
“the capacity to perceive emotions, assimilate emotion-related feelings,
understand the information of those emotions, and manage them” (Mayer &
Salovey, 1997; p. 267). In this study, emotional intelligence refers to coaches’
ability to perceive, understand, regulation, and utilize emotions in the self and
others in the work place.
a. The appraisal of emotion refers to the ability to identify one’s own
emotions (Mayer & Salovey, 1997).
19
b. The understanding of emotion of others refers to the ability to identify
other’s emotions and the sensitivity to emotions expressed by, or repressed
within others (Mayer & Salovey, 1997).
c. The regulation of emotion refers to managing emotions for a variety of
adaptive purposes (Mayer & Salovey, 1997).
d. The utilization of emotion refers to the ability to harness emotion to
facilitate various cognitive activities such as flexible planning, creative
thinking, redirected attention, and motivation (Mayer & Salovey, 1997).
3. Affectivity is defined as the sum total of individual’s mood states (Watson, Clark,
& Tellegen, 1988).
a. Positive affectivity is defined as the individual’s tendency to experience
positive emotions (Watson, Clark, & Tellegen, 1988). In this study,
positive affectivity measures the degree to which coaches feel positive
emotions in daily life
b. Negative affectivity is defined as the individual’s tendency to experience
negative emotions (Watson, Clark, & Tellegen, 1988). In this study,
negative affectivity measures the degree to which coaches experience
negative emotions in daily life.
4. Job satisfaction is defined as “the extent to which people life (satisfaction) or
dislike (dissatisfaction) their jobs” (Spector, 1997, p. 2). In this study, job
satisfaction involves coaches’ evaluation of their jobs.
5. Emotional exhaustion is defined as feelings of being emotionally overextended
and drained by one contact with other people” (Leiter & Maslach, 1998; p. 297).
20
In this study, emotional exhaustion measures coaches’ feeling of burnout in the
form of overwhelming exhaustion due to their jobs.
21
CHAPTER 2
LITERATURE REVIEW
This chapter has been divided into four sections: (a) Emotional Labor; (b)
Conceptualization of Emotional Labor; (c) Antecedents of Emotional Labor; and (d)
Consequences of Emotional Labor. Each section will discuss the existing literature and
conceptual approaches that provide the basis for the present study.
Emotional Labor
In 1983, Arlie Russell Hochschild first coined the term emotional labor in her
book “The Managed Heart: The Commercialization of Feeling” to refer to “the
management of feeling to create a publicly observable facial and bodily display” (p.7).
According to her, the management of emotions, deemed as the private and typical daily
task, becomes a part of the work role. Emotional labor involves service employees’
enhancement, faking, and suppression of emotions when interacting with customers in
order to improve organizational outcomes as well as their wage (Grandey, 2000). Those
employees engage in emotional labor in response to the display rules specified by the
organization. The display rule serves as the standard for appropriate expressions of
emotions. It actually identifies the emotions that employees should display and suppress
in their interaction with customers to be effective on their jobs (Ashforth & Humphrey,
1993; Grandey, 2000; Hochschild, 1983; Morris & Feldman, 1996). Wharton and
Erickson (1993) introduced three types of display rules, integrative, differentiating, and
22
masking. Integrative display rule means expressing positive emotions and encouraging
warm relationships with customers. For example, customer service employees are usually
expected to display positive emotions, such as cheerfulness, in their interaction with
customers to satisfy their customers. In addition, a salesperson whose goal is to sell cars
should display cheerfulness and friendly emotions to customers to generate positive
emotions in them since emotion is contagious (Gosserand & Diefendorff, 2005). In
contrast, differentiating display rule involves expressing negative emotions and driving
people away (e.g., fear, hate, anger). For instance, debt collectors and bouncers need to
express negative emotions, such as anger and aggressiveness, when they attempt to
receive money from borrowers. This negative emotion allows them to achieve their
objectives more effectively. Finally, masking emotional display rules are displays of
neutrality. In some professions, such as judge and physician, displaying calmness or
showing no emotions may be appropriate for achieving individuals’ or organizational
goals (Hochschild, 1983; Sutton, 1991). These rules of emotional expression can be
taught explicitly from the training materials or by observing other employees (Grandey,
2000). By requiring employees to follow the display rules, the emotional expression now
“has become a marketplace commodity with standards and rules dictating how and when
emotion should be expressed” (Opengart, 2005, p. 55).
There are many similarities between emotional labor and physical labor in the
sense that they require skills and experience and that they are often controlled by external
factors. Additionally, it is a type of labor because employees labor hard to suppress or
enhance their emotions to accomplish tasks and for a wage (Newman, Guy, & Mastracci,
23
2009). Thus, we can conclude that those purposeful efforts to express certain emotions at
work regardless of real feelings are called emotional labor.
There are three different kinds of emotional labor, such as surface acting, deep
acting, and automatic regulation (Ashforth & Humphrey, 1993; Grandey, 2000;
Hochschild, 1983; Morris & Feldman, 1996). Surface acting is the process of modifying
one’s expressions, such as putting smiles on a face, without changing their inner feelings
towards a certain display rule. In surface acting, employees need to suppress their felt
emotions and fake the required emotions dictated by the display rule. The second
strategy is deep acting, which corresponds to the process of actually trying to change
one’s feelings required by the display rules. That is, the individual tries to experience the
emotion that is appropriate for the situation. Thus, surface acting only manages
observable expressions, whereas deep acting attempts to change internal emotional states
to meet the organizational expectations (Grandey, 2000).
In addition, Ashforth and Humphrey (1993) proposed another category of
emotional labor, i.e., expression of genuine emotion (i.e., genuine acting) or automatic
regulation (Zapf, 2002). They argued that previous research has neglected the possibility
that employees are able to experience and display appropriate emotions spontaneously.
For instance, there is a possibility that social workers truly feel sympathetic toward an
abused child, which means it is not necessary for them to engage in surface acting or
deep acting. They labeled this type of expression emotional labor because employees are
still adopting organizationally required emotions. Diefendorff, Croyle, and Gosserand
(2004) confirmed that genuine acting or automatic regulation is a distinct type of emotion
24
regulation performed in an automatic way. Few research studies have investigated this
automatic regulation in association with organizational outcomes.
Conceptualization of Emotional Labor
Since Hochschild’s (1983) introduction of the concept of emotional labor, several
researchers (Ashforth & Humphrey, 1993; Grandey, 2000; Hochschild, 1983; Morris &
Feldman, 1996) have proposed the four main conceptualizations of emotional labor.
These researchers all agreed that every service organization has specific organizational
emotional display rules that serve as a guide for an appropriate emotional expression,
depending on job. In addition, they all addressed the importance of emotional
management in the workplace. These theories include Hochschild’s conceptualization
(1983), Ashforth and Humphrey’s conceptualization (1993), Morris and Feldman’s
conceptualization (1996), and Grandey’s conceptualization (2000). Although they are
based on common theoretical backgrounds, each perspective uses distinctive features to
explain emotional labor. Thus, to further understand emotional labor, the following
sections discuss the four main conceptualizations of emotional labor.
Hochschild’s (1983) Conceptualization
Hochschild (1983) first introduced the term emotional labor in her book ‘The
Managed Heart’. In this book, she discussed the new aspect of emotion and emotional
management in organizations. She defined emotional labor as “the management of
feeling to create a publicly observable facial and bodily display” to meet an
organizational goal (Hochschild, 1983, p.7). Hochschild (1983) suggested that emotional
management serves as a critical factor in organizational as well as individual success,
thus service employees should follow the organizational emotional display rules
25
developed by a certain job demands. For instance, customer service providers are
expected to display positive emotion, such as cheerfulness, while interacting with
customers in order to deliver satisfactory impression.
In her article, Hochschild (1983) described an employee as an actor, customer,
and audience while a workplace as the stage where the interaction takes place. She
identified two emotional labor strategies in the workplace, such as surface acting and
deep acting. Surface acting reflects an employee’s effort to modify one’s outward
expressions, such as putting a smile on a face to obey the display rule. Deep acting
reflects the process of an individual’s effort to experience emotions appropriate for the
situation. She argued that it was necessary for the service employees to perform one of
the two emotional labors to perform their jobs effectively.
Interestingly, she stressed the negative consequences of emotional labor such as
job stress and burnout. The rationale is that while employees are attempting to obey a
certain display rule, it possibly leads them to experience emotional dissonance, that is, the
separation of felt emotions from the displayed emotions. She pointed out that surface
acting was the main determinant of emotional dissonance because such a labor forced
employees to modify their true feelings to required feelings. Furthermore, the
psychological effort derived by engaging in both surface acting and deep acting can result
in detrimental effects on employees. In the qualitative study with flight attendants and bill
collectors, she investigated the negative effects of emotional labor and found that it
related to substance abuse, headaches, and absenteeism.
Hochschild (1983) also introduced three common things for the jobs that require
emotional labor. 1) They require voice or facial contact with the public, 2) they require
26
the worker to produce an emotional state in a client, and 3) the employer exercises some
control over the emotional activities of employees. Based on these criteria, she identified
a number of jobs involving emotional labor and condensed them into six main categories:
a) professional and technical workers, b) managers and administrators, 3) sales workers,
4) clerical workers, 5) service workers who work inside private households, and 6)
service workers who work outside private households. By providing these related jobs,
Hochschild (1983) attempted to distinguish jobs that required emotional labor from jobs
that do not require emotional labor. However, she overlooked differences in the degree of
emotional demands according to different jobs, individual differences, and contextual
factors (Hochschild, 1983).
Ashforth and Humphrey’s (1993) Conceptualization
Ashforth and Humphrey (1993) defined emotional labor as the process of
expressing expected emotions in the interaction with customers. They agreed with
Hochschild’s (1983) idea that employees should engage in surface acting and deep acting
in order to adhere to display rules. However, Ashforth and Humphrey (1993) added a
third category of emotional labor called genuine acting. They stated that Hochschild
(1983) overlooked the possibility that employees are able to express appropriate emotions
spontaneously and naturally in their work places. They used a social worker as an
example, suggesting that he or she may feel sympathetic towards an abused child
naturally without engaging in surface acting and deep acting. These employees still
follow the display rules while showing genuine emotions as a form of emotional labor.
Ashforth and Humphrey (1993) also addressed the role of effort in emotional
labor. According to them, while surface acting and deep acting require some degree of
27
individual’s effort, emotional labor strategies can become routine, turning into an
effortless process due to the high frequency of such a service interaction. Hence, in
addition to perceiving emotional labor as an effortful process, individuals can engage in
emotional labor as an effortless process through a repetitive and habitual experience of
service interactions. This will allow employees to experience less negative consequences
of emotional labor compared to surface acting and deep acting.
Ashforth and Humphrey (1993) made a key contribution to emotional labor
research when they attempted to identify the consequences of surface acting and deep
acting rather than actual process. Ashforth and Humphrey (1993) argued that
Hochschild’s (1983) approach neglected to identify outcomes derived from emotional
labor, such as customer reaction. As such, they tried to look into the outcomes of
emotional labor. They emphasized that emotional labor research should focus on
observable emotional acting rather than internal emotional process because such acting
could possibly affect customer behavior. Rather than focusing on negative consequences
of emotional labor derived from emotional dissonance, they stressed positive effects of
emotional labor on job effectiveness and self-expression. Furthermore, they addressed the
importance of genuine issues in actual emotional labor strategies, since others can detect
their sincerity. This conceptualization suggests that positive effects of emotional labor
lead to more optimistic views of emotional labor.
Morris and Feldman’s (1996) Conceptualization
Morris and Feldman (1996) defined emotional labor as individuals’ effort, plan,
and control of appropriate emotions in order to adhere to required display rules during
interpersonal interactions. Followed by Ashforth and Humphrey’s (1993) contention,
28
Morris and Feldman (1996) also supported the role of effort in emotional labor. However,
they argued that employees need to put some effort in emotional labor even when there is
no mismatch between employee’s felt emotion and the organizational display rules. This
is necessary in order to ensure the consistence between the felt emotion and expressed
emotion within the desired display rules.
One of the contributions of Morris and Feldman’s (1996) conceptualization is that
they put more emphasis on the influence of individual characteristics and environmental
factors in the workplace on individuals’ emotional expressions. As such, this perspective
allows us to identify various contextual antecedents of individuals’ engagement in
emotional labor in terms of appropriateness. Morris and Feldman (1996) proposed a
model that contains many work-related factors, which affect emotional labor, such as the
explicitness of display rules and task routineness. In this model, four dimensions
influence emotional labor utilization.
The first dimension is the frequency of the emotional display, which refers to the
frequency with which employees and customers interact. Morris and Feldman (1996)
stated that the degree to which individuals interact with others affected the degree to
which employees engage in emotional labor. In other words, employees who interact with
customers more often require higher emotional labor engagement since they need to
adhere to display rules more. The second dimension is the duration and intensity of
required emotional displays. When the displayed emotion is longer and stronger, an
employee should pay more attention to managing his or her emotions. The third
dimension is the variety of emotions that should be expressed. When it is necessary to
express more emotions, there are more needs to manage those emotions. Finally, the
29
fourth dimension is emotional dissonance, which refers to the discrepancy between
emotions one is actually feeling and emotions one is actually displaying. A mismatch
between requires more emotional labor (Morris & Feldman, 1996). Morris and Feldman
(1996) argued that these four dimensions of emotional labor would influence employees’
well being, such as emotional exhaustion, negatively while emotional dissonance has a
negative effect on job satisfaction.
Furthermore, Morris and Feldman (1996) also proposed other antecedents of
emotional labor, such as individual difference variables, job characteristics, and
organizational characteristics. They identified gender and positive and negative
affectivity as individual difference variables while task routineness and job autonomy as
job-related antecedents. Organizational determinants include explicitness of display rules
and closeness of supervisor monitoring.
In their empirical examination of these antecedents, they found that task
routineness and job autonomy were most strongly associated with emotional labor.
Specifically, they reported a positive relationship between task routineness and frequency
of emotional labor, and emotional labor and negative relationship between duration and
emotional labor. In addition, emotional dissonance was the strongest factor influencing
consequences of emotional labor, as it related positively with emotional exhaustion and
negatively with job satisfaction (Morris & Feldman, 1997).
Grandey’s (2000) Conceptualization
Grandey (2000) defined emotional labor as the process of regulating both feelings
and expressions of emotions to meet the organizational goals. Based on Hoschschild’s
conceptualization, she agreed that emotional labor includes both surface acting and deep
30
acting. According to Gosserand (2003), Grandey’s conceptualization follows internal
emotion regulation approach rather than occupational categorization (Hochschild, 1983),
observable expressions of emotions (Ashforth & Humphrey, 1993), or characteristics of
the situation or emotional dissonance (Morris & Feldman, 1996).
Grandey (2000) proposed a model of emotional labor (see figure 2.1), which
consists of situational, individual, and organizational antecedents that affect emotional
labor as well as consequences of emotional labor. Situational determinants of emotional
labor include interaction expectation, such as the frequency, duration, variety of
interactions, and the display rules and affective events, such as positive and negative
events. Individual difference determinants include gender, emotional expressivity,
emotional intelligence, and affectivity. Organizational determinants are job autonomy,
supervisor support, and peer support. In addition, Grandey (2000) suggested burnout, job
satisfaction, job performance, and quitting behavior as consequences of emotional labor.
This model places more emphasis on surface acting and deep acting in the
emotional labor process because of three advantages (Grandey, 2000). First, focusing on
both surface acting and deep acting allows researchers to see both positive and negative
outcomes. For instance, there may be a negative relationship between surface acting and
job satisfaction while there could be a positive relationship between deep acting and job
satisfaction. The rationale behind this is that surface acting relates positively to emotional
dissonance while deep acting allows individuals to perceive personal achievement
through successfully adhering to organizational display rules. Thus, by focusing on both
surface act and deep act, future studies would be able to see both positive and negative
outcomes of emotional labor.
31
Figure 2.1. Grandey’s (2000) Emotional Regulation Model of Emotional Labor
Second, this conceptualization, which views emotional labor as the internal
management of emotions, indicates that emotional labor is a type of skill that can be
learned and developed (Grandey, Fisk, Mattila, Jansen, & Sideman, 2005). It allows
organizations to educate or train their employees to improve emotional regulation
strategies to display appropriate emotions. For instance, Grandey and colleagues (2005)
explained if an investigation demonstrated a positive relationship between deep acting
rather than surface acting and good customer service, organizations would train
employees to engage in deep acting. In addition, Grandey et al. (2005) also provided an
example of physicians who need to be cautious about becoming too emotional in their
interactions with patients. This kind of emotional involvement with patients might result
32
in job burnout. Thus, physicians should be trained or educated to control their emotions
to be effective on their jobs.
Finally, this model was based uniquely on emotional regulation model proposed
by Gross (1998a, 1998b). According to Gross’ (1998a) model, emotional regulation is
defined as “the processes by which individuals influence which emotions they have,
when they have them, and how they experience and express these emotions” (Gross,
1998b, p.275). This model posits that emotional cues lead to emotional response
tendencies (behavioral, experiential, and physiological), which in turn lead to emotional
responses. Accordingly, emotion regulation in this model includes two processes,
antecedent-focused process and response-focused process. The antecedent-focused
process is similar to deep acting in that individuals regulate the situation before the
creation of emotion. The response-focused process is consistent with surface acting in
that it involves modification of the observable signs of emotion (Gross, 1998a).
Antecedent-focused emotion regulation involves the modification of situation or
the perception of the situation in order to change his or her emotions (Gross, 1998a;
1998b). It is considered similar to deep acting because it attempts to change feelings,
which in turn influence expressions (Grandey, 2000). Gross (1998a, 1998b) proposed
four antecedent-focused strategies using this approach, situation selection (deciding or
avoiding a specific situation), situation modification (physically changing the situation),
attentional deployment (attempting to focus on other situations), and cognitive change
(reappraising the situation in order to interpret differently).
Response-focused emotion regulation, or response modulation, reflects
individual’s efforts to manage his or her emotion response tendencies that have already
33
happened. Individuals must change ones’ emotional expression rather than inner feelings.
Grandey (2000) stated that this approach is related to surface acting, since it involves
changing one’s outward expressions.
Together, these conceptualizations allow us to increase our understanding of
emotional labor or emotional regulation strategies internally as well as outwardly.
Specifically, the researcher adopted Grandey’s (2000) theoretical framework of
emotional labor as a guiding theory for the current study in order to investigate the
association of emotional labor with antecedents and consequences. As demonstrated in
Grandey’s (2000) model, individual characteristics, such as emotional intelligence and
affectivity, influence coaches’ emotional labor while different kinds of emotional labor
influence outcomes, such as emotional exhaustion and job satisfaction differently.
Antecedents of Emotional Labor
Gosserand and Diefendorff (2005) argued that individual’s dispositional factors
are important variables which influence the use of emotional labor. The proposed
antecedents for the current study include emotional intelligence, positive affectivity, and
negative affectivity. In the next sections, the proposed variables will be discussed.
Affectivity
Affectivity is defined as the sum of individual’s mood states (Watson, Clark, and
Tellegen, 1988). Watson (1988) classified affectivity into Positive affectivity (PA) and
Negative affectivity (NA) and argued that they are largely distinctive and independent
factors. Specifically, Watson defied PA and NA as
“one’s level of pleasurable engagement with the environment. High PA is
composed of terms reflecting enthusiasm (e.g., excited, enthusiastic), energy (e.g.,
34
active, energetic), mental alertness (e.g., alert, attentive), and determination (e.g.,
strong, determined). In contrast, NA is a general factor of subjective distress and
subsumes a broad range of aversive mood states, including distressed, nervous,
afraid, angry, guilty, and scornful” (p. 1020).
According to the Morris and Feldman’s (1996) conceptualization, individual’s
tendency to feel positive and negative affect has a potential to influence emotional
dissonance. They contended that then emotional dissonance would more likely happen if
there was a conflict between organizationally required emotions and employee’s
affectivity (positive or negative). As such, when there is congruence between display
rules and their affectivity, fewer negative outcomes will occur. Brotheridge and Lee
(2003) argued that individuals with high level of affectivity as opposed to low affectivity
might have more difficult time hiding and realigning their true feelings with surface
acting. As such, it might be more difficult for an employee with high level of positive
affectivity, such as bill collectors, to display negative emotions while an employee with
high level of negative affectivity may not be well fitted for some jobs, such as a job of
flight attendants.
Employees with PA will be more likely to interpret negative events more
positively and respond to the events actively. Consequently, they will put more effort into
changing their inner feelings required by the display rule, thus engage in deep acting.
Previous literatures supported this idea. For instance, in the study on developing
Emotional Scale (ELS), Brotheridge and Lee (2003) investigated the association between
PA and emotional labor to establish the convergent validity of the instrument.
Subsequently, they found a positive association between PA and surface acting. In the
35
study of 274 employed undergraduate students, Diefendorf, Croyle, and Gosserand (2005)
also found that PA was negatively associated with surface acting. Gosserand and
Diefendorff’s (2005) study supported these findings, showing that surface acting was
negatively associated with PA. Subsequently, Austin et al. (2008) found that PA was
negatively associated with surface acting. However, regarding the relationship between
PA and DA, the previous results are mixed. Brotheridge and Lee (2003) and Diefendorff,
Croyle, and Gosserand (2005) did not find significant relationship between these two
constructs. In contrast, Gosserand and Diefendorff (2005) and Austin et al. (2008) found
a positive relation between PA and deep acting. Table 2.1 summarizes previous
literatures on the relationship between PA and emotional labor strategies.
Interestingly, one study conducted by Diefendorff et al. (2005) investigated the
relationship between PA and the expression of naturally felt emotion (i.e., automatic
regulation). The result revealed that PA had a positive association with the expression of
naturally felt emotion strategy.
Regarding the relationship between NA and emotional labor strategies, it has been
reported that employees with high level of NA possibly experience more negative
emotion and react to the events more intensively (Grandey, 2000; Gosserand &
Diefendorff, 2005) or passively when facing negative events. Subsequently, they will not
attempt to change their inner feelings, which in turn lead them to engage in surface acting.
Indeed, previous literatures found a positive relationship between NA and surface acting
(Austin et al., 2008; Brotheridge & Lee, 2003; Diefendorff et al., 2005; Gosserand &
Diefendorff, 2005; Liu et al., 2008). This implies that high NA individuals are more
likely to fake or suppress their emotions than to modify their emotions to adhere to
36
display rules. Table 2.2 summarizes previous studies on the relationship between NA and
emotional labor strategies.
Table 2.1
Relationship between positive affectivity and emotional labor strategies
Study
Austin et al. (2008)
Brotheridge & Lee (2003)
Diefendorff et al. (2005)
Type of Emotional Labor
Relationship
Surface acting
Negative
Deep acting
Positive
Surface acting
Negative
Deep acting
No relationship
Surface acting
Negative
Deep acting
No relationship
Expression of naturally felt
Positive
emotion (Automatic regulation)
Gosserand & Diefendorff
(2005)
Surface acting
Negative
Deep acting
Positive
37
Table 2.2
Relationship between negative affectivity and emotional labor strategies
Study
Austin et al. (2008)
Brotheridge & Lee (2003)
Diefendorff et al. (2005)
Type of Emotional Labor
Relationship
Surface acting
Positive
Deep acting
Negative
Surface acting
Positive
Deep acting
No relationship
Surface acting
Positive
Deep acting
No relationship
Expression of naturally felt
No relationship
emotion (Automatic regulation)
Gosserand & Diefendorff
(2005)
Liu et al. (2008)
Surface acting
Positive
Deep acting
Positive
Surface acting
Positive
Deep acting
Negative
Emotional intelligence
Emotional intelligence is a growing concept used to predict an individual’s
behavior, especially in the area of business in which emotional intelligence has been
utilized to predict leadership and behavior of a leader in terms of group effectiveness.
Salovey and Mayer (1990) first defined emotional intelligence as the ability to perceive,
express, understand, and regulate emotions in the self and others. Following Salovey and
Mayer’s work, Goleman (1995) popularized this concept with his bestseller book
38
‘Emotional Intelligence: Why it can Matter More Than IQ’. Emotional intelligence has
been studied extensively in the business setting over the last decades. The research
indicates that emotional intelligence is an important aspect of leadership effectiveness
and characterizes great leaders (Sosik & Megerain, 1999). A high level of emotional
intelligence allows leaders to become aware of their own emotions, to identify the
emotions of the group and of the individual followers accurately, and to control their own
emotions.
Most research on the application of emotional intelligence is based on the one of
two models, the mixed model, which includes both mental abilities (such as emotional
self-awareness, empathy, problem-solving, impulse control) and self-reported personality
characteristics (such as mood, genuineness, warmth) (Sternberg, Forsythe, Hedlund,
Horvath, Wagner, Williams, Snook, & Grigorenko, 2000). In contrast, the ability model
of emotional intelligence represents a cognitive-emotional ability within an ability
framework measured by a maximum performance (IQ like) test consisting of
performance tasks requiring responses to be evaluated against predetermined scoring
criteria (Salovey & Mayer, 1990). As a mental skill or ability, emotional intelligence is
changeable and develops with experience (Mayer, 2001). However, it is important to
provide a thorough overview of both models of emotional intelligence. The most
commonly utilized mixed model approaches are reviewed below.
Mixed Models of Emotional Intelligence
Goleman (1995) conceptualized emotional intelligence as demonstrating “the
competencies that constitute self-awareness, self-management, social awareness, and
social skills at appropriate times and ways in sufficient frequency to be effective in the
39
situation” (Boyatzis, Goleman, & Rhee, 2000, p.344). Goleman used the 110-item selfreport Emotional Competence Inventory (ECI Version 2) to measure 20 competencies
that assess emotional intelligence and fall within four separate domains: self-awareness,
self-management, social awareness, and relationship management. However, several
studies (Conte, 2005; Mattews, Zeidner, & Roberts, 2004) have questioned the utility of
this measure in that it has not demonstrated enough validity and reliability, as it has been
found to have considerable overlap between with measures of the Big Five personality
factors (i.e., neuroticism, extraversion, openness to experience, agreeableness, and
conscientiousness).
Consistent with Goleman, Bar-On’s (1997) mixed model approach suggests that
emotional intelligence comprises an array of cognitive capabilities, competencies, and
skills, which influence one’s ability to be better at dealing with environmental demands
and pressures. In his model, Bar-On (1997) identified five broad dimensions subdivided
into 15 subscales as key factors of emotional intelligence: (a) intrapersonal (i.e.,
emotional self-awareness, assertiveness, self-regard, self-actualization, independence); (b)
interpersonal (i.e., interpersonal relationship, social responsibility, empathy); (c)
adaptability (i.e., problem solving, reality testing, flexibility); (d) stress-management (i.e.,
stress tolerance, impulse control); and (e) general mood (i.e., happiness, optimism).
Based on his model, Bar-On developed the Emotional Quotient Inventory (EQ-i;
Bar-On, 1997), which is a 133-item self-report measure assessing emotional intelligence.
According to Perez, Petrides, and Furnham (2005), EQ-i is one of the most widely used
measures of the trait emotional intelligence. However, they also argued that this measure
contains several unrelated facets (e.g., problem solving, reality testing, and independence)
40
and neglects many important ones (e.g., emotion perception, emotion expression, and
emotion regulation). In fact, previous studies on concurrent validity suggested
considerable overlap between the EQ-I and other psychological measures.
In addition to the ECI and the EQ-I, Schutte, Malouff, Hall, Haggerty, Cooper,
Golden, and Dornheim (1998) developed the Schutte Emotional Intelligence Scale (SEIS)
comprising 33 items measured on a 5-point Likert scale, which assesses the extent to
which an individual can identify, understand, harness, and regulate emotions in self and
others. Specifically, the SEIS consists of three subscales, the appraisal and expression of
emotion, the regulations of emotion, and the utilization of emotion, adopted from the
original conceptual ability model of Salovey and Mayer (1990):. Schutte and colleagues
reported that the measure showed an acceptable internal consistency (Cronbach's alpha
of .90) and 2 weeks test-retest reliability of .78.
Austin, Saklofske, Huang, and McKenny (2004) modified the SEIS because of a
lack of reverse-key items (Petrides & Furnham, 2000; Saklofske, Austin, & Minski,
2003). According to them, the previous measure contains relatively small number of
items, and a lack of reverse-key items could potentially confound emotional intelligence
score with acquiescent responding (Austin et al., 2004). The Modified Version of the
SEIS (MVSEIS) consists of 41 items, with 20 forward-keyed and 21 reverse-keyed items.
Austin and colleagues reported internal reliability of .85 for the entire scale and
reliabilities of .78 for regulation of emotions, .68 for utilization of emotion, and .76 for
appraisal of emotion subscales. They also reported that overall the MVSEIS correlated
highly (r = .66, p < .001) with the short version of Bar-On Emotional Quotient Inventory
(EQ-i:S, Bar-On, 1997). Moreover, Austin and his colleagues indicated that this modified
41
version is reasonably congruent with most theoretical approaches to the mixed emotional
intelligence.
Ability model of Emotional Intelligence
Unlike the mixed model, which suggests that emotional intelligence is a
combination of both trait and state characteristics, the ability model of emotional
intelligence conceptualizes the construct as a set of abilities that can be learned and
developed over time.
Mayer and Salovey (1997) defined emotional intelligence as,
“the capacity to reason about emotions, and of emotions to enhance thinking. It
includes the abilities to accurately perceive emotions, to access and generate
emotions so as to assist thought, to understand emotions and emotional
knowledge, and to reflectively regulate emotions so as to promote emotional and
intellectual growth” (p. 10).
Mayer and Salovey (1990) also argued a great deal of individual difference in the ability
to utilize his or her emotions to solve problems and proposed three conceptually related
mental processes involving emotional information. These processes include: (a) the
appraisal and expression of emotion; (b) the regulation of emotion; and (c) the utilization
of emotion. In this model, the first two branches (the appraisal and expression of emotion
and the regulation of emotion) can be classified into self and other while the first branch
(the appraisal and expression of emotion) can be divided into a verbal versus a nonverbal
domain. Additionally, the third branch (the utilization of emotion) consists of four subfactors, flexible planning, more creative thinking, the ability to (re-)direct attention, and a
tendency to motivate themselves and others, which reflect flexibility of individuals with
42
high emotional intelligence in their utilization of emotions. Moreover, this model
assumes that emotionally intelligent individuals are skilled in the following areas: (a)
perceiving and appraising their own emotions (e.g., identifying their own emotional
status); (b) expressing and communicating emotions precisely to others when appropriate
(e.g., sharing emotions with others during the interpersonal context); (c) recognizing
other’s emotions precisely and responding to them more adaptively (e.g., encourage
others appropriately); (d) regulating their own and others’ emotions effectively in order to
achieve certain goals (e.g., enhancing other’s mood to accomplish goals); and (e) using
their own emotions in order to solve problem related to emotion by motivating adaptive
behaviors (e.g., showing enthusiasm to encourage others) (Neuhauer & Freudenthaler,
2005).
Mayer and Salovey (1997) refined the original model and proposed a four-branch
ability model of emotional intelligence, which is known as the most influential
conceptualization among the proposed emotional intelligence models (Zeidner, Roberts,
& Matthews, 2008). The three dimensions of ‘appraisal and expression of emotion’,
‘regulation of emotion’, ‘utilization of emotion’ were retained in the original model while
one dimension, understanding emotion (e.g., ability to label emotions and understand
ambivalent feelings such as simultaneous feelings of love and hate), was added in the
revised model. Consequently, the revised model comprised four branches, including (a)
perceiving emotions, (b) utilization of emotion to facilitate thoughts, (c) understanding of
emotions, and (d) regulating emotions. The first branch (i.e., perceiving emotions) is
considered the most basic emotion-related skill while the fourth branch reflect the most
integrated process involving complex abilities.
43
The first branch (perception of emotion) involves receiving and recognizing
emotional information from the environment. This branch includes the ability to identify
emotions in self and others’ facial expressions, postural language, and voices, as well as
other communication channels, such as stories, music, or works of art (Mayer, Salovey,
& Caruso, 2004). Such capability to precisely recognize others’ emotion from their face
and voice can be the most basic branch because by accurately picking up others’ nonverbal behaviors, individuals can react to the situation in more adaptive way, which in
turn can enhance interpersonal relationships. This branch is deemed as the starting point
for more advanced branches.
The second branch (utilization of emotions to facilitate thoughts) involves the use
of emotions to enhance reasoning and various cognitive activities, such as thinking and
problem solving, which includes the ability to assimilate emotions into cognitive process
(Bracket, Rivers, Shiffman, Lerner, & Salovey, 2006). Regarding this branch, individuals
can use certain emotions (e.g., happiness) to direct the attention to important information
(e.g., focusing on work). In fact, Isen (2001) found that positive emotion, such as
cheerfulness and happiness, have a potential to yield clear-headed, well-organized, openminded, and flexible problem solving. Positive affect is also associated with good
interpersonal communication through enhanced social skills and kindness. Consequently,
individuals with high level of emotional intelligence can focus on positive emotions
intentionally in order to enhance persistence while facing challenges and stimulate
creativity in solving difficult problems (Carmeli, 2003).
The third branch (understanding emotion) involves the ability to analyze
emotional information, label them precisely, and realize the complicated relationship
44
between emotions and corresponding actions. This component gives individuals clues
about why they as well as others feel certain way by examining the causes, key factors,
and outcomes of emotions (Frijda, 1988).
The fourth branch (regulation of emotion) involves the ability to manage both
one’s and others’ emotions to enhance emotional and intellectual growth. This involves
the ability to reduce, enhance, or modify an emotional response in oneself and others
(Gross, 1998). This branch involves the most advanced skill and includes knowing how
to generate appropriate emotions by enhancing certain emotions and reducing counterpart
emotions.
Based on this conceptual model, Mayer, Caruso, and Salovey (1999) developed
the Multifactor Emotional Intelligence Scale (MEIS) with a total of 402 items and 12
subscales. However, this scale showed low reliability and only branch I (perception) and
IV (regulation) loaded well on emotional intelligence construct (Ciarrochi, Chan, &
Capupi, 2000). To solve these problems, the Mayer-Salovey-Caruso Emotional
Intelligence Test (MSCEIT) was developed with the total of 292 items and 12 subscales
(Mayer, Salovey, & Caruso, 2000).
Mayer and Salovey (1997) emphasized emotional intelligence as a cognitiveemotional ability within the functional, internal processes that ought to be measured by a
maximum performance (IQ like) test. In the maximum performance test, respondents are
instructed to choose the alternative that would best describe their actual behavior in the
situation (e. g., “You want to celebrate your birthday with some friends, but they tell you
that they have other plans.”) in which there is only one answer to respond the items.
45
Wong and Law (2002) developed a self-report measure of emotional intelligence
(WLEIS; Wong and Law Emotional Intelligence Scale), which contains four dimensions
of Mayer and Salovey’s (1997) conceptualization of emotional intelligence. In this
measure, the four dimensions include: (a) appraisal and recognition of emotion in the self
(perception of emotion), (b) appraisal and recognition of emotion in others
(understanding of emotion), (c) regulation of emotion in the self (regulation of emotion),
and (d) use of emotion to facilitate performance (utilization of emotion). Rather than
assessing individual’s ability to solve emotional problems, as Mayer and Salovey’s (1997)
measure intended, this instrument measures self-perceptions of emotional intelligence
and emotional self-efficacy.
According to Wong and Law (2002), employees with high level of emotional
intelligence are better at engaging in emotion regulation to satisfy organizational display
rules effectively with greater ease. Employees high on the first two dimensions (i.e.,
perception and appraisal of emotion in the self and others) are better at knowing their
own and others’ emotions. As such, they recognize appropriate emotions in a situation
that is aligned with display rules and provides a positive interactive experience for the
others. Moreover, employees high on the third and fourth dimensions are considered
skillful emotional laborers because those dimensions are related to the ability to regulate
emotions and use those emotions in more adaptive ways. They are better at quickly
adapting to and managing the conflict between felt emotions and expression emotions;
therefore, are more likely to express appropriate emotions. Accordingly, employees high
on emotional intelligence are more likely to utilize deep acting or automatic regulation, as
46
it is more effective and requires advanced strategies to produce appropriate emotions in
given situations.
Emotional intelligence and Emotional labor
One of the purposes of the current study is to examine the relationship between
emotional intelligence and emotional labor constructs. As such, reviewing previous
literatures on both constructs may enhance our understanding of both domains. In terms
of the relationship between emotional intelligence and emotional labor, Grandey (2000)
stated that emotional intelligence is a key individual difference variable, which influences
the levels and types of emotional labor employees perform. In addition, Opengart (2005)
discussed the potential connection between emotional intelligence and emotional labor,
suggesting that “the management and regulation of emotions also require the intelligence
to perceive, learn, and adjust behavior as necessary” (p.57). In addition, Cheung and
Tang (2007) suggested overlaps between some dimensions of emotional intelligence and
emotional labor. For example, “self-regulation of emotion, a key competency of
emotional intelligence, is similar to Grandey’s (2000) conceptualization of emotional
labor, which refers to the regulation of both feelings and expressions of emotion to be
effective on their job” (p. 76). As a matter of fact, previous research has shown that
individuals with high emotional intelligence use less surface acting compared to those
with low emotional intelligence (Austin, Dore, & O’Donovan, 2008; Mikolajczak, Menil,
& Luminet, 2007). These individuals are also less likely to exert emotional effort,
experience emotional dissonance, and experience job burnout (Mikelajxzak, Menil, &
Luminet, 2007). Mikelajxzak et al.’s (2007) study with nurses indicated that trait
emotional intelligence had a negative relationship with surface acting (β = -.31) and deep
47
acting (β = -.35). Interestingly, they also found a positive association between emotional
intelligence and positive consonance (i.e., automatic regulation, experiencing positive
emotion required by the display rule spontaneously). Additionally, Austin and her
colleagues (2008) found that emotional intelligence was negatively associated with
surface acting among undergraduate students. However, they found no association
between emotional intelligence and deep acting. On the contrary, Cote (2005) also found
that emotionally intelligent individuals showed deep acting more during interpersonal
interactions while Liu and his colleagues (2008) also found that emotional intelligence
related positively to deep acting among 574 employees and managers. However, Liu et al.
(2008) failed to find significant association between emotional intelligence and surface
acting. Additionally, Brotheridge (2006b) reported a positive relationship between
emotional intelligence and deep acting. In this particular study, workers with higher
levels of emotional intelligence were better at identifying emotional demands as part of
their work role and effectively displayed deep acting as a response of these situational
demands. In Pakistan, Karim and Weisz (2010) supported the previous evidence, which
suggested that emotional intelligence had a positive association with deep acting but no
association with surface acting. Finally, Daus, Rubin, Smith, and Cage (2004) conducted
a study with a sample of police and found that all four branches of ability model of
emotional intelligence related significantly to deep acting while surface acting related
only to one branch of emotional intelligence (understanding emotions). These mixed
findings (see Table 2.3) provide a relevant point for further exploration of the relationship
between the two constructs.
48
Furthermore, the current study posits that automatic regulation may be associated
with emotional intelligence. That is because individuals high in emotional intelligence are
more likely to recognize and understand emotional cues and information; consequently,
they may feel emotions that are appropriate for the situation in an automatic way
(Carmeli, 2003). For example, highly emotionally intelligent coaches may recognize
athlete’s emotion of frustration or nervousness immediately and interpret the situation in
a more adaptive way to benefit from that particular situation. Thus, they are able to react
to the situation quickly and encourage athletes with their enthusiasm.
Overall, since different emotional labor strategies are found to have different
influence on individuals’ well-being and performance, emotional intelligence may be a
critical characteristic that enables an individual to engage in appropriate emotional labor
strategies in a given situation (Feldman, Barret, & Gross, 2001).
49
Table 2.3
Relationship between emotional intelligence and emotional labor strategies
Study
Daus, et al. (2004)
Cote (2005)
Brotheridge (2006b)
Mikolajczak et al. (2007)
Type of Emotional Labor
Relationship
Surface acting
No relationship
Deep acting
Positive
Deep acting
Positive
Deep acting
Positive
Surface acting
Negative
Deep acting
Negative
Positive consonance (automatic
Positive
regulation)
Austin et al. (2008)
Liu et al. (2008)
Karim & Weisz (2010)
Surface acting
Negative
Deep acting
No relationship
Surface acting
No relationship
Deep acting
Positive
Surface acting
No relationship
Deep acting
Positive
Consequences of Emotional Labor
One of the Hochschild’s (1983) main arguments was that workers would
experience harmful consequences (e.g., psychological distress, burnout, and feelings of
inauthenticity) from continuous management of emotions in the workplace. Consistent
with this argument, much of the empirical evidence has supported the relationship
50
between emotional labor and negative outcomes. For example, Rafaeli and Sutton (1989,
1991) confirmed the existence of emotional labor in various occupations and asserted that
it had a significant effect on individuals’ psychological well-being, job performance, as
well as organizational outcomes. Additionally, Kinman (2007) found a positive
association between emotional labor and negative outcomes in the study of flight
attendants and telesales agents. However, they also found that the negative effect of
emotional labor could extend from the workplace to the home environment. Nevertheless,
other research studies have shown diverse consequences in which emotional labor is not
generally harmful to employees (e.g., Wharton, 1993). For example, Adelmann (1995)
did not find a relationship between emotional labor and job outcomes while Wharton
(1983) found a positive relationship between job satisfaction and emotional labor.
Together, the evidence regarding the relationship between emotional labor and
organizational outcomes is mixed. That is, the pattern is not as simple as one might
expect, in that emotional labor is sometimes harmful to individual well-being while
sometimes it is not. Johnson and Spector (2007) argued that utilizing different emotional
labor strategies accounts for the inconsistencies in these findings. In fact, Ashforth and
Tomiuk (2000) stated that it would be important to distinguish surface acting and deep
acting because each strategy involves different internal states and may have different
influence on employees’ well-being. Additionally, Brotheridge and Lee (2003), as well as
other researchers (e.g., Kruml & Geddes, 2000; Grandey, 2000), identified different
internal psychological processes involved in surface acting, deep acting, and automatic
regulation, which in turn influenced employee outcomes.
51
Thus, the following section discusses the proposed consequences of emotional
labor (i.e., job burnout and job satisfaction), which has been examined mostly up to date.
Burnout
Burnout is one of the most known phenomena in the helping professions, which
has received must attention globally (Maslach & Jackson, 1986). In those occupations,
employees who feel dissatisfied with their performance within their workplace, which
depletes their emotional resources due to stress, and consequently distance themselves
from their coworkers and customers are said to be burned-out professionally (Arlotto,
2002). Specifically, according to Schaufile and Enzmann (1998),
“Burnout is a metaphor. It is a state of exhaustion similar to smothering of a fire
or the extinguishing of the candle. Where there used to be a vital spark and the
flame of life was running bright. It is not dark and chilly. The fuel has been used
up and the energy backup is depleted (p. 1).”
After the Freudenberger’s (1974) study, an increased number of burnout research
studies have been conducted, showing that although common, burnout is an abnormal
response in people-oriented occupations. Most of the systematic research on burnout
began during the 1970’s and 1980’s, and the researchers agreed that burnout is a general
experience of physical, emotional, and mental exhaustion (Pines, 1981).
Maslach (1982) first clarified and developed a conceptual model of burnout as
well as the instrument (Maslach Burnout Inventory; MBI, 1986) to measure burnout.
According to Maslach, Jackson, and Leiter (1996), burnout “is a syndrome of emotional
exhaustion, depersonalization, and reduced personal accomplishment that can occur
among individuals who work with people in some capacity” (p. 4). Maslach (1982) also
52
developed the self-report questionnaire, the MBI, which includes the three dimensions
mentioned in the abovementioned definitions. Emotional exhaustion reflects a lack of
energy and feeling that one’s emotional resources are used up due to excessive emotional
demands from work. According to Leiter and Maslach (2001), this dimension reflects the
basic individual stress component of the syndrome. Depersonalization refers to a negative,
cynical, or excessively detached response to other people at work. This dimension
involves the interpersonal dimension of burnout. Finally, reduced personal
accomplishment reflects a reduced feeling of one’s capability and productivity at work
(Leiter & Maslach, 2001).
Burnout in Sport
Although the burnout research has started in the area of human service
professions, it has been applicable to other domains (Graf, 1992). Athletic context can be
one of them, since it shares many of the same characteristics that have been identified in
burnout studies (Vlahos, 1997). In fact, coaches, athletes, and sport psychologists have
been increasingly concerned with burnout (Raedeke, Lunney, & Venables, 2002), since
they are likely to experience burnout at some points of their lives (Vealey, Armstrong,
Comar, & Greenleaf, 1998). Furthermore, sport environment, characterized by high level
of competitiveness as well as physical and emotional exhaustion, is relevant for studying
burnout (Vealey et al., 1998). For instance, individuals who belong to sport teams have to
interact with others during training and competitions because it is necessary for team
cohesion and success. Moreover, public highly values and recognizes the personal
accomplishment in sport setting through winning and performance enhancement.
Pargman (1998) stated that burnout is prevalent in sport settings due to numerous
53
contributors, such as external pressure to win, insufficient fun, and unavoidable failures.
Specifically, Dale and Weinberg (1990) stated that coaching in the sport context involves
many stressors, including long duration of work, excessive expenditure of mental and
emotional energy, and high expectations from others. That is, coaches require a great
amount of physical and mental energy when engaging in long practice and have to deal
with pressures to win, which can cause potential burnout.
In fact, coaching is a challenging occupation characterized by constant pressure
and stress (Graf, 1992). Coaching context includes wide variety of stressors, such as
pressure to win, administrative and parental interference or indifference, disciplinary
problems, long work hours, continuous and often emotionally volatile interactions with
players, and attention or pressure from media coverage (Kelly & Gill, 1993; Weinberg &
Gould, 1999).
One of the biggest responsibilities of coaches is dealing with people such as
parents, boosters, administrators, staff, and athletes. Coaches must take the role of
motivator, counselor, advisor, and parental substitute. At this point, they are expected to
engage in emotional labor when interacting with those audiences while taking on various
roles requiring different types of emotional expressions. As such, coaching context may
be a relevant area in which the relationship between emotional labor and job burnout can
be examined.
Emotional exhaustion
Emotional exhaustion has been one of the constantly mentioned consequences of
burnout (e.g., Grandey 2003; Morris & Feldman, 1997; Hochschild, 1983). Specifically,
as a key component of burnout (Maslach, 1982), emotional exhaustion reflects a lack of
54
energy and a feeling that one’s emotional resources are used up due to excessive
emotional demands derived from the interaction with customers or clients (Saxton,
Phillips, & Blakeney, 1991).
In terms of the association with emotional labor, Wharton (1993) showed that
emotional labor led to negative consequences, such as emotional exhaustion. Specifically,
most studies indicated that surface acting has a significant influence on emotional
exhaustion (Abraham, 1998; Bono & Vey, 2005; Gross, 1998a; Johnson & Spector, 2007;
Kruml & Geddes, 2000; Morris & Feldman, 1997). The rationale behind this is that
surface acting requires more effort and sustained process while it also generates
emotional dissonance (i.e., a mismatch between felt emotion and displayed emotion)
(Ashforth & Humphrey, 1993; Diefendorff & Gosserand, 2003). One of the
characteristics of surface acting is the mismatch between individual’s real feelings and
expressed emotions, and it has been found to have an association with negative
psychological problems (Zapf, 2002). As such, surface acting can be taxing and
problematic as long as the faked emotional display is maintained. Deep acting, on the
other hand, allows individuals to change their inner feelings, which in turn reduces the
discrepancy between felt emotion and displayed emotion and should minimize emotional
strain.
Gross (1998a) found that surface acting elicits a stronger and longer physiological
response in the lab study. In this study, participants in two groups, suppression of
emotion (similar to surface acting) and reappraisal of emotion (similar to deep acting),
watched a disgusting video clip. The reaction of both groups was evaluated by self-report,
observations of others, and physiological responses. Interestingly, the results of both self-
55
report and observation indicated that individuals in both groups reduced their expressions
of disgust. However, groups that suppressed their emotion (i.e., surface acting group)
showed increased physiological response, such as higher finger temperature and heart
rate. This result indicates that suppression of emotion as a form of surface acting and
emotion reappraisal as a form of deep acting are successful in changing their outward
expressions while surface acting fails to manage cardiovascular responses although it can
still be detrimental. Another experimental study conducted by Goldin, McRae, Ramel,
and Gross (2008) compared two emotion regulation strategies, cognitive appraisal and
expressive suppression (deep acting and surface acting, respectively) in the context of
negative events. They found that appraisal reduced negative emotion experience while
suppression reduced only facial expressions. They further found that the two strategies
influence participants’ brain activity differently.
A number of empirical studies have supported the lab results. Kruml and Geddes
(2000) found that surface rather than deep acting was more strongly associated with
emotional exhaustion while Brotheridge and Grandey (2002) found that surface but not
deep acting was associated with three dimensions of burnout. Employees who faked their
true emotions while expressing faked emotions reported more emotional exhaustion,
more depersonalization, and reduced personal accomplishment. Zammunier and Galli
(2005) also found that surface acting was associated with a personal cost, which has a
potential to influence emotional exhaustion. Similarly, Montgomery et al. (2006) found a
positive relationship between surface acting and emotional exhaustion in a Dutch
governmental organization. However, deep acting did not relate to the proposed
consequence.
56
In the study of the relationship between teacher’s emotional labor strategies and
burnout, Naring and colleagues (2006) found a significant relationship of surface acting
with the burnout dimension of emotional exhaustion and depersonalization. Deep acting
was found to be a significant predictor of personal accomplishment. Using meta-analysis,
Bono and Vey (2005) also found a positive association between surface acting and
emotional exhaustion. The jobs of the more than 4,000 participants were first grouped
into 40 categories. Although police officers, firefighters, and security officers indicated
the strongest need to suppress emotions, teachers also frequently experienced emotion
suppression. They found that the need to hide emotions had a strong relationship with
emotional exhaustion.
However, the relationship between deep acting and job burnout is inconsistent.
Another study showed a negative correlation between deep acting and emotional
exhaustion (Johnson & Spector, 2007) while Grandey (2003) reported a positive relation
between deep acting and emotional exhaustion. Additionally, Diefendorff et al. (2008)
also found that surface acting and deep acting were positively associated with emotional
exhaustion among nurses who interacted with patients. However, most studies revealed
no significant relationship between deep acting and job burnout components (Glomb &
Tews, 2004; Goldberg & Grandey, 2007; Montgomery et al., 2006; Naring et al., 2006;
Totterdell & Holman, 2003).
Finally, coaches’ automatic display of regulation is expected to have a negative
relationship with emotional exhaustion. Only one study has examined connections
between automatic regulation and individual well-being. Martinez-Inigo and colleagues
(2007) found that employees’ automatic regulation was negatively associated with
57
emotional exhaustion. Since coaches’ automatic regulation barely requires effort and
emotional dissonance, we hypothesize that it will be negatively associated with emotional
exhaustion. Tables 2.4 and 2.5 summarize previous findings regarding the relationships
among surface acting, deep acting, automatic regulation, and emotional exhaustion.
In summary, the evidence regarding the relationship between surface acting and
emotional exhaustion shows that surface acting has a positive association with emotional
exhaustion. However, several studies have shown that deep acting is not significantly
related to emotional exhaustion. The previous literatures reported that deep acting
contributes to emotional exhaustion inconsistently. However, the current study
hypothesized that deep acting may relate positively to emotional exhaustion because it
still requires individuals to use their emotional resource and cognitive efforts in the
process of deep acting. In addition, to our knowledge, no studies examined the
relationship between automatic regulation and emotional exhaustion.
58
Table 2.4
Relationship between surface acting and emotional exhaustion
Study
Type of Emotional Labor
Relationship
Abraham (1998)
Surface acting
Positive
Kruml & Geddes (2000)
Surface acting
Positive
Brotheridege & Grandey (2002)
Surface acting
Positive
Brotheridge & Lee (2003)
Surface acting
Positive
Grandey (2003)
Surface acting
Positive
Totterdell & Holman (2003)
Surface acting
Positive
Glomb & Tews (2004)
Surface acting
Positive
Zammunier and Galli (2005)
Surface acting
Positive
Montgomery et al. (2006)
Surface acting
Positive
Naring et al. (2006)
Surface acting
Positive
Martinez-Inigo et al. (2007)
Surface acting
Positive
Johnson & Spector (2007)
Surface acting
Positive
Chau et al. (2009)
Surface acting
Positive
Philipp & Schupbach (2010)
Surface acting
Positive
Hulsheger & Schewe (2011)
Surface acting
Positive
59
Table 2.5
Relationship between deep acting and automatic regulation, and emotional exhaustion
Study
Type of Emotional Labor
Relationship
Brotheridge & Grandey (2002)
Deep acting
No relationship
Brotheridge & Lee (2003)
Deep acting
No relationship
Grandey (2003)
Deep acting
Positive
Totterdell & Holman (2003)
Deep acting
No relationship
Johnson & Spector (2007)
Deep acting
Negative
Philipp & Schupbach (2010)
Deep acting
Negative
Martinez-Inigo, Totterdell,
Automatic regulation
Negative
Alcover, & Holman (2007)
Job satisfaction
Job satisfaction is an employees’ evaluative judgment about his or her job.
Grandey (2000) argued that emotional labor has a potential to influence job attitudes.
Early research on the relationship between emotional labor and job satisfaction showed
both positive (Adelmann, 1995; Wharton, 1993) and negative relationships (Abraham,
1998; Morris & Feldman, 1997). These mixed results may be due to the different
emotional labor strategies displayed by employees. Kruml and Geddes (2000) explicated
that when individuals engage in surface acting, they may experience feeling of
inauthenticity through the suppression of felt emotions, which will result in more
negative consequences (i.e., job dissatisfaction and intentions to quit) for the individual
60
when compared to deep acting (Hochschild, 1983; Brotheridge & Lee, 2002; Grandey,
2003).
In fact, Wolcott-Burnam (2004) found a negative relationship between surface
acting and job satisfaction and a positive relationship between deep acting and job
satisfaction. Additionally, Parkinson (1991) stated that employees experience decreased
job satisfaction when their true feelings are masked (i.e., surface acting). Similarly,
Pugliesi (1999) found a negative association between surface acting toward customers
and job satisfaction among university employees. Cote and Morgan (2002) found that the
suppression of unpleasant emotions (i.e., surface acting) decreased job satisfaction and
increased the intention to quit in their longitudinal study of various occupations.
Specifically they found that when employees suppress their negative emotions (such as
anger, fear, and sadness) towards customers, coworkers, and supervisors, the employees
experienced a decreased job satisfaction and increased intention to quit. Additionally,
Grandey (2003) investigated the relationship between emotional labor and job
satisfaction among university administrative employees and found a negative association
between surface acting and job satisfaction. She reasoned that the feeling of
inauthenticity caused by surface acting strategy might result in the decreased job
satisfaction. The meta-analysis conducted by Bono and Vey (2005) revealed a negative
relationship between emotional labor and job satisfaction while indicating that deep
acting had no significant influence on job satisfaction. Finally, Diefendorff and
colleagues (2008) found that surface acting related to job satisfaction negatively. Tables
2.6 and 2.7 summarize previous literatures.
61
While surface acting has consistently shown a negative association with job
satisfaction, the relationship between deep acting and job satisfaction is less clear.
Hochschild (1983) argued that any organizational management of emotions leads to job
dissatisfaction. If an employee is not naturally experiencing the organizationally desired
emotion in a certain situation, he/she must exert extra effort in order to meet the display
rules. This extra effort may be unpleasant and lead to dissatisfaction.
Brotheridge and Grandey (2002) found a positive relation between deep but not
surface acting and personal accomplishment (which is thought to be a key determinant of
job satisfaction; Hackman & Lawler, 1971; Locke & Latham, 1990). In addition, Kruml
and Geddes (2000) found that higher levels of effort in emotion regulation (conceptually
similar to deep acting) related positively to personal accomplishment while higher levels
of emotional dissonance (conceptually similar to surface acting) related negatively to
personal accomplishment. Because deep acting related positively to personal
accomplishment and did not lead to emotional dissonance, there is reason to believe that
deep acting relates positively to job satisfaction.
Furthermore, automatic regulation display is expected to have a positive
association with job satisfaction because theoretically, it requires no psychological efforts
when interacting with athletes and generates no emotional dissonance. Martinez-Inigo
and colleagues (2007) also found a positive relationship between automatic regulation
and job satisfaction. Tables 2.6 and 2.7 summarize the existing evidence regarding the
relationships among surface acting, deep acting, automatic regulation, and job
satisfaction.
62
Table 2.6
Relationship between surface acting and job satisfaction
Study
Type of Emotional Labor
Relationship
Parkinson (1991)
Surface acting
Negative
Pugliesi (1999)
Surface acting
Negative
Grandey (2003)
Surface acting
Negative
Kruml & Geddes (2000)
Surface acting
Negative
Cote & Morgan (2002)
Surface acting
Negative
Grandey (2003)
Surface acting
Negative
Wolcott-Burnam (2004)
Surface acting
Negative
Bono & Vey (2005)
Surface acting
Negative
Hulsheger & Schewe (2011)
Surface acting
Negative
Table 2.7
Relationship between deep acting and automatic regulation, and job satisfaction
Study
Type of Emotional Labor
Relationship
Kruml & Geddes (2000)
Deep acting
Positive
Brotheridge & Grandey (2002)
Deep acting
Positive
Martinez-Inigo et al. (2007)
Automatic regulation
Positive
63
CHAPTER 3
METHOD
This chapter discusses the procedures used to examine emotional labor, emotional
intelligence, affectivity, emotional exhaustion, and job satisfaction among intercollegiate
coaches. This chapter consists of five sections: (a) Research design; (b) Sampling method;
(c) Target population; (c) Instrumentation; (d) Data collection; (e) Data analysis.
Research Design
The function of research design is to guide researchers to answer the research
question as unambiguously as possible based on the obtained evidence. Good research
design assists in understanding and interpreting the results of the study and allows
researchers to obtain usable results (Wiersma & Jurs, 2005). Quantitative research design
has been dominating the social science research since the nineteenth century (De Vaus,
2001). According to Wiersma and Jurs (2005), quantitative research describes
phenomena in numbers and measures instead of words in order to determine the
relationship, effects, and causes. In the past, science has often utilized quantitative
methods of analysis because it produces reliable and objective data validated by
standardized instruments (De Vaus, 2001). Accordingly, the current study can be
classified as quantitative because it used quantitative data gathered through the use of
survey in order to identify the relationships among the proposed constructs. However, it
64
is critical to note that the current research did not randomly assign participants to
treatment groups and did not manipulate any variables, as experimental and quasiexperimental approaches would. Therefore, the results derived by the current study
cannot provide evidence of a cause-effect relationship.
The current study also adopted the survey research procedure. Survey research
often uses questionnaires or interviews to ask questions regarding the characteristics,
attitudes, behaviors, or opinions of a specific population. By doing this, the researchers
can describe the status quo as well as determine the relationships and effects occurring
between variables. Several types of survey research include phone interviews, internetbased survey, and directly administered questionnaires (Ary, Jacobs, Razavieh, &
Sorensen, 2006). Specifically, the current study used an internet-based survey (web-based
survey). An internet-based survey has a number of advantages. First, web survey allows
researchers to reduce various costs related to paper printing, postage, package mail-out
process, and data entry. Second, when using web surveys, it takes less time to collect the
data compared to mail surveys (Singleton & Straits, 2005). It is important to note that
when using mail surveys, it take at least a few weeks to complete the data collection.
Third, researchers using web surveys are able to survey larger sample sizes and collect
data from broader geographical areas with lower cost (Dillman, 2000). Additionally,
Dillman (2000) stated that web surveys allow a more dynamic interaction between
participants and instrument while other researchers (e.g., Ary et al., 2006; Gratton &
Jones, 2004) valued a quick response time along with the guaranteed anonymity and
reduced response bias.
65
However, web surveys also contain several drawbacks. According to Singleton
and Straits (2005), the most serious weakness of web surveys would be a coverage error.
They also indicated that specific populations are more likely to have internet access
compared to other populations. For instance, Couper (2000) argued that “College
graduates are 16 times more likely than others to have internet access, and black and
Hispanic households are only about 40 percent as likely as white households to have
home internet access” (p. 471). Since the current study used head coaches in universities,
a coverage error concern was lessened. Moreover, Couper (2000) emphasized that web
surveys had lower response rates compared to traditional mail surveys. To check nonresponse error, the researcher compared early to late respondents, as Miller and Smith
(1983) suggested. Late respondents have been shown to have similar characteristics with
non-respondents. Thus, the study could possibly achieve the generalization if there is no
significant difference between early respondents and late respondents.
Sampling Method
Singleton and Straits (2005) defined sampling as “the process of selecting a subset
of cases (i.e., sample) in order to draw conclusions about the entire set (i.e., target
population)” (p.146). Sampling is generally adopted when it is impossible to include all
members of a population in research studies. In that case, researchers typically select a
sample representative of a larger population that would allow them to generalize the
results. It has been said that random sampling ensures the representativeness from a
mathematical perspective. Random sample involves what is called probability sampling,
which means that all members of the population have the same chance of being selected.
66
When one sample is selected using this method, all other members of the population had
the same probability of being selected (Wiesma & Jurs, 2005).
However, probability sampling procedures are not always feasible or desirable.
Random sampling may not be possible when a researcher cannot access an entire group.
For example, selecting a random sample from all graduating middle school seniors in the
United States would be impossible. In that case, nonprobability sampling can be adopted,
which is very different from probability sampling in the way it selects samples.
According to Singleton and Straits (2005), nonprobability sampling is defined as a
“process of case selection other than random selection” (p. 132). Unlike probability
sampling, this method allows some members of the population to have a greater chance
of being selected. Although it is difficult to say that findings obtained from analyzing
nonprobability sample can be generalized to target population, it is still adopted in many
research studies due to its feasibility. Finally, a census method refers to collecting data
from an entire population of individuals (Singleton & Straits, 2005). In many research
studies, it has been said that it is simply not feasible to include all members of a
population due to time and effort required for this method (Wiersma & Jurs, 2005).
However, the current study decided to use a census method because the personal
information of entire population has been obtained from the commercial website and the
web survey enabled the researchers to contact every member with minimal time and
effort. Additional reasons for adopting this method are discussed in target population
section.
67
Pilot Study
Before the main study, a pilot study was conducted in order to establish the
validity and the reliability of the survey questionnaire. Face and content validity of the
items were reviewed by three sport management professors who have expertise in the
research on leadership and practical coaching experience. Based on the comments and
suggestions from these individuals, the survey items were modified to make better of the
survey items.
Table 3.1
Reliability measures from the pilot study
Factor
Cronbach’s α
No. of Items
SURFACE ACTING
.72
4
DEEP ACTING
.46
4
AUTOMATIC REGULATION
.76
4
Appraisal of Emotion
.90
4
Understanding of Emotion
.85
4
Regulation of Emotion
.92
4
Utilization of Emotion
.88
4
POSITIVE AFFECTIVITY
.83
5
NEGATIVE AFFECTIVITY
.78
5
EMOTIONAL EXHAUSTION
.94
5
JOB SATISFACTION
.72
3
EMOTIONAL INTELLIGENCE
68
With the refined questionnaire items, a pilot study was conducted. After the
approval from The Ohio State University Human Subject Review Committee, a survey
invitation email was sent to 500 randomly selected head coaches at NCAA Division II
programs. They were asked to click a web survey link to fill out the questionnaire
answers. Upon completing the survey, the answers were sent and saved automatically
under the researcher’s web survey account. A total of 49 coaches were responded from
the 500 coaches (9.8% response rate). However, six of them were unusable because they
did not complete the questionnaires. Thus, a total of 43 responses out of 500 coaches
(8.6 % response rate) were used for a pilot study to check the reliability of the
instrumentation. Cronbach’s alphas which represent internal consistency was used to
measure reliability (Hair, Black, Babin, Anderson, & Tatham, 2006). The pilot study
results are presented in Table 3.1. Al the internal consistency measures were acceptable
except for the deep acting (α = .44). The final instrument was refined in order to improve
reliability using data obtained from the pilot study and will be discussed in the instrument
section.
Target Population
The target population comprised the athletic head coaches from National
Collegiate Athletic Association (NCAA) Division I program. The head coaches were
chosen as samples in this study based on their significant role in organizational outcomes.
According to Turner (2001), head coaches of intercollegiate athletic teams are resemble
mid-level managers in a general business setting. Specifically, Turner pointed out “the
supervisory responsibility for the actions and performance of certain individuals (team
members, assistant coaches, and support personnel” (p. 1) and their direct effect on the
69
team’s effectiveness. The researcher obtained the list of all coaches in this program, with
the most updated job information from a website (http://www.collegecoachesonline.com).
The number of coaches in different sports is listed in Table 3.2.
A total of 6,806 athletic head coaching positions at Division I programs were
identified on the abovementioned website. However, contact information for 1,888
coaches was duplicated while transferring the data from the website to Selectsurvey.net
system. Therefore, 4,918 coaches participated in the current study initially. After the first
sending, 232 emails bounced back because of invalid addresses, full mailboxes, or no
longer in the coaching position. Therefore, excluding additional 232 addresses resulted in
4,686 coaches who were invited to participate in the study.
The census method was chosen because of the possible low response rate derived
by web-survey techniques. Henning (2009) stated that case response rates for web survey
tend to range from 0% to 20%, and the researcher speculated the estimated response rate
for the current study of 10%. That is because the survey was distributed in the middle of
March, when most universities have spring breaks and a large number of coaches are off
duty. Generally, Hair, Black, Babin, Anderson, & Tatham (2006) recommended
collecting at least 300 samples to run structural equation modeling properly. Therefore,
10% out of 4,686 total coaches would yield approximately 450 participants, which would
be enough to conduct structural equation modeling. In addition, Dillman, Smyth, and
Christian (2009) recommended that having 5 – 10 respondents per scale item would
ensure a reliable statistical analysis of an instrument. The current study used 42 scale
items and thus, it would require 420 respondents to yield reliable analysis. Since 10% out
70
of 4,686 total coaches would yield approximately 460 participants, our sample size is
large enough to conduct structural equation modeling properly and obtain reliable results.
Overall, 464 Division I coaches returned the completed questionnaires (response
rate of 9.9%). Of the 464 prospective responses, 34 responses were excluded from the
study because the participants did not respond to all items or they completed the
questionnaires inaccurately (e.g., putting the same response for most of items). In the
case of a few cases of missing data, missing data was replaced using mean replacement.
The next chapter discusses the methods used to control non-response error. The final
sample size for the current study was 430 coaches (9.1%).
Table 3.2.
Number of Coaching Position at NCAA Division I Program
Sport
The Number of Coaching Position
Men’s Baseball
298
Men’s Basketball
345
Men’s Cross Country
306
Men’s Fencing
21
Men’s Diving
124
Men’s Football
245
Men’s Golf
294
Men’s Gymnastics
16
Men’s Ice Hockey
59
Men’s Indoor Track
210
Men’s Lacrosse
58
Men’s Rifle
12
Men’s Skiing
11
Men’s Soccer
205
71
Continued
Table 3.2 continued
Men’s Swimming
133
Men’s Tennis
261
Men’s Track
280
Men’s Volleyball
25
Men’s Water Polo
22
Men’s Wrestling
72
Women’s Basketball
343
Women’s Bowling
31
Women’s Cross Country
334
Women’s Diving
164
Women’s Fencing
23
Women’s Golf
237
Women’s Gymnastics
61
Women’s Field Hockey
78
Women’s Ice Hockey
35
Women’s Indoor Track
252
Women’s Lacrosse
87
Women’s Rifle
12
Women’s Rowing
81
Women’s Skiing
12
Women’s Soccer
315
Women’s Softball
287
Women’s Swimming
191
Women’s Tennis
319
Women’s Track
315
Women’s Volleyball
328
Women’s Water Polo
33
Total
6806
72
Instrumentation
Affectivity (Watson, Clark, & Tellgen, 1988).
This study used the modified version of Positive and Negative Affectivity Scales
(PANAS; Watson, Clark, & Tellgen, 1988; Appendix D) to measure coach affectivity
using a five-point Likert format, ranging from very slightly or not at all to extremely.
Higher scores on positive or negative affectivity indicate higher levels of positive and
negative traits, respectively. The original scale included twenty items, ten items for
positive affectivity and ten items for negative affectivity; however, the scale for the
current study contains eight items, four items for positive affectivity and another four
items for negative affectivity. The items include eight emotion words for each type of
affectivity (e.g., positive affectivity: interested and excited; negative affectivity: scared
and upset). Watson et al. (1988) reported acceptable internal consistency reliability for
both the positive and negative affectivity scales (α = .87, α = .87).
Emotional Intelligence (Wong & Law, 2002).
Current study used the 16-item Wong and Law Emotional Intelligence Scale
(WLEIS; Wong & Law, 2002; Appendix E) to assess individual differences in the ability
to identify and regulate emotions in the self and others. There are four items for each of
the four dimensions in a six-point Likert format, ranging from 1 = strongly disagree to 6
= strongly agree: A sample item for self-emotion appraisal would be “I have a good sense
of why I have certain feelings most of the time; other-emotion appraisal (e.g., “I am a
good observer of others’ emotions”); regulation of emotion (e.g., “I am able to control
my temper and handle difficulties rationally”); and use of emotion (e.g., “I always set
goals for myself and then try my best to achieve them”). High average scores will
73
indicate high levels of emotional intelligence. This measure showed minimal correlations
with a measure of IQ by Eysenck (1990), which supports its discriminant validity.
Surface acting (Brotheridge & Lee, 2003; Gross & John, 2003)
Surface acting scale (Appendix F; Items 1, 4, 7, & 10) was developed by the
researcher which contains modified items from the two established scales. This was
performed in order to broadly cover the emotional labor strategies of surface acting and,
thus, gain a better understanding of them. Surface acting can be achieved through
emotive faking and suppression. Two items from Brotheridge and Lee’s (2003)
Emotional Labour Scale (ELS) cover the suppression and one item from Brotheridge and
Lee’s ELS as well as one item from Gross and John’s (2003) Emotion Regulation
Questionnaire (ERQ) cover the emotive faking – for a total of four items that assess the
surface acting construct. In employing a five-point Likert response scale (1 = never, 5 =
always), participants are asked to respond to the stem “On an average day at practice and
competition, how often do you do each of the following when interacting with athletes?”
Higher average scores on each of the subscales represent higher levels of the dimension
being assessed. Therefore, the surface acting dimension consists of four items that
measure the extent to which the coach express emotions that are not felt and suppress
feelings that conflict with display rules. Two of the items from the surface acting
dimension address suppression, while the other two items address emotive faking. The
following represents a sample item from the surface acting subscale, “Hide my true
feelings about a situation” for the suppression dimension and “Pretend to have emotions
that I didn’t really have” for the emotive faking dimension.
74
Deep acting (Brotheridge & Lee, 2003)
Deep acting was measured using ELS (Brotheridge & Lee, 2003; Appendix F;
Item 2, 5, & 8). Three items in the deep acting subscale assess how much a coach has to
modify inner feelings to comply with display rules. Sample items from the deep acting
subscale are “Make an effort to actually feel the emotions that I need to display to others”
and “When I’m faced with a stressful situation, I make myself think about it in a way that
helps me stay calm.” In employing a five-point Likert response scale (1 = never, 5 =
always), participants are asked to respond to the stem “On an average day at practice and
competition, how often do you do each of the following when interacting with athletes?”
Higher average scores on each of the subscales represent higher levels of the dimension
being assessed. This scale showed an acceptable internal reliability alpha of .82
(Brotheridge & Lee, 2003).
Automatic Regulation (Cukur, 2009)
Since there was no available scale for automatic regulation at the time of
investigation, three items from the automatic regulation (Appendix F; Item 3, 6, & 9)
were developed for the current study. Participants rated to what extent to they express
their spontaneous emotions that is appropriate for the situation in an automatic way. In
employing a five-point Likert response scale (1 = never, 5 = always), sample items from
the automatic regulation is, “Experience spontaneously the positive emotions (such as
confidence and enthusiasm) I express when athletes make a big mistake,” and “I
spontaneously feel the emotions I have to show to others.”
75
Emotional Exhaustion (Maslach & Jackson, 1986).
A modified version of the emotional exhaustion subscale of the Maslach Burnout
Inventory (1986; Appendix G) were used in the current study. The original scale included
nine items, yet researcher used a shorter version of this scale which consist of five items.
The measure assesses how often respondents report feeling the symptoms of emotional
exhaustion at work. A sample item is, “I feel emotionally drained at work.” The scale
employs a seven-point Likert format that ranges from never to every day. Higher mean
scores on this measure suggest high levels of emotional exhaustion. The wording of the
scales will be changed from “work” to “coaching” to increase the face validity of the
measure for coaches (i.e., “I feel emotionally drained at coaching”). Also, the words
“recipient” and “people” will be replaced by “athletes”. Previous research has shown that
this change in wording had no effect on the psychometric properties of the scales (Kelley,
1994; Kelley & Gill, 1993).
Job Satisfaction (Cammann, Fichman, Jenkins, & Klesh, 1979; Spector, 1985)
Coach job satisfaction were measured by the modified version of Job Satisfaction
Subscale of Michigan Organizational Assessment Questionnaire (Cammann, Fichman,
Jenkins, & Klesh, 1979) and Spector’s (1985) Job Satisfaction Survey. Two items from
Job Satisfaction Subscale of Michigan Organizational Assessment Questionnaires were
adopted while one item from Job Satisfaction Survey (Spector, 1985) was adopted for
this study. The current measure (Appendix H) consists of three items that assess overall
job satisfaction. Response options are based on six-point Likert scale where one
corresponds to strongly disagree and six corresponds to strongly agree. A higher mean
score indicates overall satisfaction with the job. A sample item is, “All in all, I’m
76
satisfied with my job” from Job Satisfaction Subscale of Michigan Organizational
Assessment Questionnaire and “My job is enjoyable” from Job Satisfaction Survey.
Demographic Information
The demographic items (Appendix I) will ask the respondents to report their
gender, ethnicity, age, type of sports, years worked for the organization, and years
worked in coaching. Specifically, years worked in coaching will be utilized as the
measurement for past experience variable.
Data Collection
The research obtained the approval from the Human Subjects Institutional Review
Board at The Ohio State University in order to protect human subjects. As mentioned
above, an online survey was utilized for the current study. Selectsurvey.net software was
used for the online questionnaire which is available through the College of Education and
Human Ecology at The Ohio State University. The researcher sent a pre-notification email (Appendix A) with information which contained the overall description of the study
and the upcoming study schedule, and encouraged the participation in the second week of
March. The questionnaires were distributed one week after the pre-notification e-mails
were sent out as Kent and Turner (2002) recommended. Email message (Appendix B)
containing the purpose, procedure of the research and a survey link preceded the
questionnaire. This message was initiated with the informed consent which assures
confidentiality, the voluntary nature of participation in the study, and which encourages
the participants to answer the items as honestly as possible. Since it was a web-based
survey, as soon as the respondents completed the questionnaire online, the responses
were directly forwarded to the researcher. Anonymity were also insured because the
77
participants did not leave any identifying information and they will be told that only
group data were used in the analyses rather than individual results. Follow-up emails
(Appendix C) were sent out after one-week of initial emailing to encourage participation
and to remind participants of the deadline. This was done in order to deal with the
problems associated with the low response rate that online survey usually has
(Yammarino, Skinner, & Childers, 1991).
Data Analysis
Data analysis was conducted in a two-step process. In the first step of data
analysis, the calculation of descriptive statistics for the used variables was conducted and
the reliability of the subscales of each instrument will be investigated using SPSS 19.0.
Alpha coefficients greater than .70 are assumed to be adequate for internal consistency in
the field of social science (Nunnally & Bernstein, 1994). Pearson correlations were also
calculated for all variables to determine if there was a sufficient relationship between
each variable. According to Kline (2005), correlations between constructs should not
exceed .85 in order for the constructs to have discriminant validity. However, correlations
higher than .85 are accepted if the constructs have been theoretically supported to be
distinct from each other (Hair et al., 1998).
In the second step, the Structural Equation Modeling (SEM) technique that is
available through LISREL 8.80 (Joreskog & Sorbom, 2007) was utilized to test both
measurement models and structural models. To test the hypotheses, the researcher used
Anderson and Gerbing’s (1988) two-step approach which examine a measurement model
first and then a series of structural models. Specifically, a single confirmatory factor
analysis (CFA) was conducted on the latent variables (i.e., emotional intelligence,
78
affectivity, emotional labor, job satisfaction, and emotional exhaustion) in order to
identify how well observed variables and individual items define the corresponding
factors. Based on the results, the researcher refined the model. In order to determine how
well the individual items define the corresponding factors, the researcher followed
Steven’s (1996) suggestion that items with factor loadings less than .40 should be
dropped. LISREL 8.80 (Joreskog & Sorbom, 2007) also provides the following measure
of fit for the measurement model: (a) relative chi-square (the ratio of chi-square to
degrees of freedom); (b) root mean square error of approximation (RMSEA); (c) standard
root mean residual (SRMR); (d) comparative fit index (CFI), and tucker lewis index (TLI;
also known as NNFI). The researcher used the maximum likelihood estimation to
evaluate the fit of the model. For relative chi-square, the value less than 3.0 are preferred
and represent good data-model fit (Monro, 2005). For CFI and TLI, values higher
than .90 are considered to have a good fit (Hair et al., 1998). In addition, RMSEA and
SRMR values less than .06 indicates a close fit of the model and values less than .08
indicates a reasonable fit. Meanwhile, values greater than .10 indicates a poor fit and the
model should not be considered (Hu & Bentler, 1999).
Second, the proposed structural relationship was tested by assessing structural
coefficients of the relationship among constructs in the hypothesized model. With this
analysis, the researchers identified three things: a) the significance of the relationship; b)
the strength of relationships among constructs (i.e., structural coefficients); c) the
direction of relationships among constructs (i.e., positive or negative relationship).
79
CHAPTER 4
RESULTS
This chapter presents the results in three main sections. The first section describes
the demographic characteristics of the sample along with the analysis for controlling for
non-response error. The second section presents the results of the single-group
confirmatory factor analysis. At this point, overall measurement model fit, factor loadings,
construct validity, and the reliability of the scales are discussed. Finally, results of singlegroup structural equation modeling are presented. The direction and significance of
individual path coefficients and the overall model fit are discussed in relation to proposed
hypotheses.
Demographic Characteristics
The demographic characteristics of the 430 participants are shown in Table 4.1. A
total of 464 coaches agreed to participate in this study. Of the 464 prospective responses,
34 responses were disqualified from the study because the participants did not respond to
all the items. In the case of a few cases of missing data, missing data was replaced using
mean replacement.
Among the respondents, the majority of the respondents were males (65.3%) and
the largest group of the respondents was those whose age range was between 41 and 50.
Further, more than 85% of the respondents were Caucasian. Coaches had average of 4
hours of contact hours with athletes per day, ranging from one hour to 14 hours.
80
Additionally, the coaches had 19.50 years of coaching experience while the
average number of years the coaches worked in the current athletic department was 9.95
years ranging from 1 year to 43 years. The number of years the coaches in the coaching
profession ranged from 1 year to 50 years.
Among 278 male coaches, 106 respondents (38.1%) coached men’s team, 101
respondents (36.3%) coached women’s team, and 71 respondents (25.6%) coached both
men’s and women’s teams. In addition, among 132 female coaches, only one respondent
(.7%) coached male team, 113 respondents (85.6%) coached female teams, and 18
respondents (13.6%) coached both teams. Table 4.2 illustrates the gender of the team of
the survey respondents. Additionally, Table 4.3 illustrates the number of respondents and
the total number of coaches by sports.
Non-Response Error
As a way of controlling for non-response error, t-tests comparing the means of
early to late respondents’ responses for each factor were conducted as suggested by
Miller and Smith (1993). There is evidence to show that late respondents are similar to
non-respondents. Thus the results can be generalized to the total population if the early
and late respondents did not differ in the measured factors. As Table 4.4 shows, there
was no difference between early and late respondents in any factors (p > .05) which leads
to the conclusion that non-response error is not a problem with this study.
81
Table 4.1
Demographic Variable Frequencies for Respondents (N = 430)
Variable
Frequency
Percent
Gender
Male
281
65.3
Female
133
30.9
21-30
26
6.2
31-40
121
28.3
41-50
136
31.1
51-60
104
24.2
61-70
23
5.5
Above 70
1
.2
African American
19
4.4
Asian
9
2.1
Caucasian
375
87.2
Hispanic
7
1.6
American Indian
2
.5
High School
2
.5
Community College
2
.5
Bachelor Degree
192
44.7
Master Degree
206
47.9
Doctorate Degree
10
2.6
Age
Ethnicity
Education
82
Table 4.2
Gender of the Team of Respondents (N = 430)
Gender of Coach
Team Gender
Male (N = 278)
Female (N = 132)
Male Team (N = 107)
106
1
Female Team (N = 214)
101
113
Both Team (N = 89)
71
18
Table 4.3
The number of respondents and the total number of coaches by sports
The Number of Coaching
Sport
# of Respondents
Position
Men’s Baseball
22
298
Men’s Basketball
8
345
Men’s Cross Country
23
306
Men’s Fencing
1
21
Men’s Diving
22
124
Men’s Football
11
245
Men’s Golf
11
294
Men’s Gymnastics
2
16
Men’s Ice Hockey
10
59
Men’s Lacrosse
2
58
Men’s Rifle
7
12
Men’s Skiing
2
11
Men’s Soccer
9
205
Men’s Swimming
35
133
Men’s Tennis
19
261
Men’s Track
25
280
Men’s Volleyball
3
25
Continued
83
Table 4.3 Continued
Men’s Water Polo
1
22
Men’s Wrestling
6
72
Women’s Basketball
26
343
Women’s Bowling
2
31
Women’s Cross Country
26
334
Women’s Diving
22
164
Women’s Fencing
2
23
Women’s Golf
16
237
Women’s Gymnastics
7
61
Women’s Field Hockey
15
78
Women’s Ice Hockey
2
35
Women’s Indoor Track
1
252
Women’s Lacrosse
9
87
Women’s Rifle
7
12
Women’s Skiing
2
12
Women’s Soccer
23
315
Women’s Softball
19
287
Women’s Swimming
46
191
Women’s Tennis
18
319
Women’s Track
31
315
Women’s Volleyball
45
328
Women’s Water Polo
4
33
Women’s Olympic Sports
2
271
544
6806
Total
84
Table 4.4
Comparison of early (N = 235) to late respondents (N = 195)
Factors
df
t-value
Sig.
Mean Difference
APP
430
1.508
.088
.117
UND
430
.791
.622
.059
REG
430
-.824
.444
-.072
UTIL
430
-.038
.711
-.002
PA
430
.232
.616
.014
NA
430
.120
.368
.008
SA
430
-.404
.494
-.024
DA
430
1.210
.306
.086
AR
430
1.139
.373
.072
EE
430
1.410
.587
-.192
JS
430
1.666
.239
.156
Note. Negative t-value indicates that late respondents had a higher mean compared to
early respondents.
APP = Appraisal of emotion; UND = Understanding of emotion; REG = Regulation of
emotion; UTIL = Utilization of emotion; PA = Positive affectivity; NA = Negative
affectivity;
SA = Surface acting; DA = Deep acting; AR = Automatic regulation;
EE = Emotional exhaustion; JS = Job satisfaction.
85
Single-Group Confirmatory Factor Analysis
A single-group confirmatory factor analysis was conducted first to examine how
well the observed variables define the corresponding latent variables in the measurement
model. Model 1 was an initial measurement model while Model 2 was a modified model
wherein two items with insufficient factor loading were dropped.
Table 4.5 presents the results of the results of singe-group confirmatory factor
analysis of 11 factors (antecedents, emotional labor, and consequences) showed a goodfitting model (χ2 /df = 1359.52/764 = 1.779; RMSEA = .043; SRMR = .054; TLI = .97;
CFI = .97). The chi-square divided by degrees of freedom was less than 3.0. The value of
RMSEA was below .05 and the values of TLI and CFI were above .95. Although the
model showed a good fit and a good reliability, threats to convergent validity were
detected.
First of all, one item (SA4) did not load on the construct well in this model (λ = .25) indicating that this item did not define the underlying the corresponding construct
well. According to Steven (1996), the item with factor loading below .40 should be
removed. As such, the item 4 in surface acting (i.e., “When I am feeling negative
emotions, I make sure not to express them”) was dropped from further analyses.
Additionally, the current study showed that AVE value for negative affectivity, surface
acting, deep acting, and automatic regulation were lower than acceptable value of .50.
Fornell and Lacker (1981) argued that the average variance extracted (AVE) is the one
which indicates convergent validity. For negative affectivity, factor loading of one item
(NA1) showed relatively small values (.43) and measurement error for this item was very
high (.82). As such, this item was deleted to improve AVE. After deleting the item, AVE
86
was improved to .58. However, surface acting, deep acting, and automatic regulation
were retained for further analysis since these are the main constructs in the current study.
After dropping two items (SA4 & NA1), a new CFA was conducted. The
modified test of the Model 2 resulted in slight changes in the LISREL output which still
showed a good-fitting model (χ2 /df = 1226.73/685 = 1.791; RMSEA = .043; SRMR
= .048; TLI = .97; CFI = .97). The factor loadings of each item, Cronbach’s coefficient (α)
and AVEs for each factor for both Model 1 and Model 2 are shown in the Table 4.5. The
final result of the single-group models tested is summarized in Table 4.6. As shown, the
items in the modified model all defined the latent variables well by showing that all
factor loadings were above .40, as suggested by Stevens (1996). Additionally, the
Cronbach’s coefficients were acceptable for all factors in that they were all above .70, the
cut point suggested by Nunnally and Bernstein (1994). The measurement model for the
antecedents, emotional labor strategies, and consequences for Model 2 is represent in
Figure 4.1, 4.2, and 4.3. Yet, measurement model for emotional intelligence construct is
not represented in Figure 4.1, but in the later section because it’s the second-order factor
model.
87
Table. 4.5
Factor loadings (λ) for each item and Cronbach’s coefficient (α) and average variance
extracted (AVE) for each factor
Factor loading (λ)
Factor
Items
Appraisal of Emotion
APP1
APP2
APP3
APP4
Understanding of Emotion
UND1
UND2
UND3
UND4
Regulation of Emotion
REG1
REG2
REG3
REG4
Utilization of Emotion
UTIL1
UTIL2
UTIL3
UTIL4
Positive Affectivity
PA1
PA2
PA3
PA4
Negative Affectivity
NA1
NA2
NA3
NA4
Surface Acting
SA1
SA2
SA3
SA4
Model 1
Model 2
.60
.87
.79
.70
.60
.87
.79
.70
.57
.83
.58
.85
.76
.87
.61
.92
.74
.55
.77
.76
.60
.72
.78
.75
.43
.79
.60
.86
.67
.80
.58
.24
88
Alpha (α)
.82
AVE
.54
.78
.52
.86
.63
.77
.50
.80
.51
.78
.58
.71
.47
.57
.83
.59
.85
.76
.87
.61
.92
.74
.55
.77
.76
.58
.73
.80
.74
.75
.58
.93
.66
.83
.54
Continued
Table 4.5 Continued
Factor loading (λ)
Factor
Items
Model1
Deep Acting
DA1
DA2
DA3
Automatic Regulation
AR1
AR2
AR3
Emotional Exhaustion
EE1
EE2
EE3
EE4
EE5
Job Satisfaction
JS1
JS2
JS3
.60
.73
.70
.69
.67
.73
Model 2
Alpha (α)
AVE
.71
.46
.73
.49
.89
.63
.92
.79
.60
.74
.69
.67
.67
.74
.83
.78
.83
.80
.71
.82
.78
.84
.81
.71
.89
.85
.92
.89
.85
.92
Table 4.6
A Summary of the Single-Group CFA
χ2/ df
RMSEA
SRMR
TLI
CFI
Model 1
1.779
.043
.054
.97
.97
Model 2
1.791
.043
.048
.97
.97
89
Figure 4.1 First-order confirmatory factor analysis model for antecedent scales except
emotional intelligence construct
90
Figure 4.2 First-order confirmatory factor analysis model for emotional labor strategies
scales
91
Figure 4.3 First-order confirmatory factor analysis model for consequences scales
Discriminant validity
In order to establish the discriminant validity of the constructs, correlations were
used (Table 4.7). According to Kline (2005), correlation between constructs which
exceeds .85 indicates a lack of discriminant validity. The largest value of the correlation
is .58 between APP and UTIL. However, as Table 4.7 shows none of the constructs lacks
92
discriminant validity because the correlations between them did not exceed .85. As such,
all of the constructs were kept for the further analysis. As a more stringent way, Fornell
and Larcker (1981) proposed that it is necessary for AVE for each construct to be larger
than the squared correlation between two constructs including the one with AVE. From
Table 4.7, the biggest squared correlation between the two constructs was .36 between
Appraisal of emotion construct and Utilization of emotion construct (r = .600). Since
AVE values for Appraisal of emotion and Utilization of emotion are .54 and .50,
respectively, none of them had the problem with the test for discriminant validity.
Regarding the correlations, positive affectivity was significantly correlated with
all of the emotional labor strategies, negatively with surface acting and positively with
deep acting and automatic regulation, respectively. Negative affectivity was positively
and significantly correlated with surface acting while negatively and significantly
correlated with automatic regulation. Regarding relationship between emotional labor and
the consequences, surface acting had a positive and significant relationship with
emotional exhaustion, while it had a negative and significant relationship with job
satisfaction. Automatic regulation showed the reverse results in that it had a negative and
significant relationship with emotional exhaustion but positive relationship with job
satisfaction. However, the results revealed that deep acting did not show any significant
association with either of the outcome variables.
In addition, Table 4.7 shows how coaches in NCAA Division I institutions
engages in different kinds of emotional labor strategies and their well-being status. As
seen, the coaches who participated in the study consider themselves as high on emotional
intelligence (M = 4.96 for APP; M = 4.61 for UND; M = 4.66 for REG; M = 5.12 for
93
UTIL) and rated themselves as positive affectivity individuals (M = 3.96). Automatic
regulation strategies was the strategy which coaches used most (M = 3.57) followed by
deep acting (M = 3.30) and surface acting (M = 2.97). Finally, they were mostly satisfied
with their jobs as well (M = 5.17).
Second-Order Confirmatory Factor Analysis
A second-order confirmatory factor analysis model is represented when some
higher-order factor explain first-order factors based on theory which supports their
hierarchical relationship (Schmaker & Lomax, 2010). In this study, the measurement
model for emotional intelligence is second-order factor model (see Figure 4.4) because
theoretically, appraisal of emotion, understanding of emotion, regulation of emotion, and
utilization of emotion loaded in a higher order factor (i.e., emotional intelligence). Thus,
it is important to look at how well the first-order factors represent emotional intelligence
construct as a next step. The results indicated that the second-order factor model had an
acceptable fit as shown in Table 4.8. In addition, Table 4.9 shows the factor loadings of
each first-order factor for the second-order factor.
94
Table 4.7.
Means (M), Standard deviations (SD), and correlation for factors
M
SD
2
3
4
5
Correlation
6
95
Factor
1. APP
4.96
.78
1
1.00
7
8
9
10
2. UND
4.61
.75
.513**
1.00
3. REG
4.66
.88
.529**
.416**
1.00
4. UTIL
5.12
.76
.600**
.324*
.418**
1.00
5. PA
3.96
.61
.309**
.221**
.251**
.405**
1.00
6. NA
2.04
.68
-.274**
-.087
-.239**
-.205**
-.168**
1.00
7. SA
2.97
.59
-.148*
-.040
-.182**
-.121*
-.291**
.249**
1.00
8. DA
3.30
.71
.099*
.076
.024
.124*
.219**
.042
-.094
1.00
9. AR
3.57
.63
.278**
.192**
.264**
.182**
.323**
-.143*
-.190**
.309*
1.00
10. EE
2.47
1.36
-.212**
.023
-.178**
-.145*
-.411**
.378**
.418**
-.075
-.210**
1.00
11. JS
5.17
.94
.367**
.218**
.272**
.369**
.425**
-.245**
-.245**
.067
.204**
-.517**
* Significant at .05 level ** Significant at .01 level
APP = Appraisal of Emotion; UND = Understanding of Emotion; REG = Regulation of Emotion; UTIL = Utilization of
Emotion;
PA = Positive Affectivity; NA = Negative Affectivity; SA = Surface Acting; DA = Deep Acting; AR = Automatic Regulation;
EE = Emotional Exhaustion; JS = Job Satisfaction
11
1.00
Table 4.8
A summary of the second-order factor model for Emotional Intelligence construct
RMSEA
χ2/ df
SRMR
TLI
(90% CI)
.063
Model
2.58
.044
.97
(.054; .072)
CFI
Table 4.9
Maximum likelihood estimates for second-order factor model
2nd-order factor
λ
t-value
.95
.65
.69
.72
12.01
9.18
12.20
11.88
1st-order factor
Emotional Intelligence
Appraisal of Emotion (APP)
Understanding of Emotion (UND)
Regulation of Emotion (REG)
Utilization of Emotion UTIL)
96
.98
Figure 4.4. Second-order confirmatory factor model of Emotional Intelligence
Single-Group Structural Equation Modeling
Once it was established that the measurement model had an acceptable fit, SEM
was conducted next to examine structural relationships among latent variables. A brief
description of the models tested follows. Model 1 was a full LISREL model which
included both measurement and structural models. Model 2 was a modified full LISREL
which deleted insignificant relationships and added an additional path among latent
variables in Model 1.
97
The result of SEM for Model 1 showed that the model fit reasonably well (χ2 /df =
869.88/334 = 2.60; RMSEA = .063; SRMR = .083; TLI = .94; CFI = .95). Then, the
researcher followed a specification search procedure and modification indices (MI) in the
LISREL output suggest that the model needs to add the path from positive affectivity to
automatic regulation to make a better model fit. The rationale for this modification will
be presented in the discussion section. Additionally, several insignificant relationships
between latent variables were detected. Regarding the antecedents, the relationship
between emotional intelligence and surface acting (β = .00; t = .57) and deep acting (β
= .07; t = 1.00) was insignificant. In addition, the relationship between negative
affectivity and deep acting (β = .09; t = 1.54) was also insignificant. Regarding the
consequences of emotional labor, the relationships of deep acting with emotional
exhaustion (β = .04; t = .71) and job satisfaction (β = -.03; t = -.35) were insignificant. As
deep acting had no significant relationships, for the sake of parsimony, the second model
was retained as the final model.
The fit of the modified Model 2 with the data was improved and reached a
reasonable fit (χ2 /df = 731.97 / 264 = 2.77; RMSEA = .067; SRMR = .079; TLI = .95;
CFI = .95). The fit indices suggest that the chi-square divided by degrees of freedom was
still less than 3.0. The value of RMSEA and SRMR was below .08 and the values of TLI
and CFI were above .95.
The modified model showed that emotional intelligence had a significant positive
association with automatic regulation (β = .26; t = 3.84). In addition, positive affectivity
had a significant negative association with surface acting (β = -.46; t = -6.27) and positive
association with automatic regulation (β = .33; t = 4.62). Negative affectivity and surface
98
acting (β = .29; t = 4.80). Regarding the consequences of different emotional labor
strategies, surface acting had a significant and positive relationship with emotional
exhaustion (β = .65; t = 8.57) and negative relationship with job satisfaction (β = -.45; t =
-6.78). Additionally, automatic regulation had a significant and negative relationship with
emotional exhaustion (β = -.11; t = -2.13) and significant positive relationship with job
satisfaction (β = .2; t = 3.44). Maximum likelihood (ML) estimates (i.e., standardized
estimates) and fit indices for the initial model and modified model are shown in the Table
4.10 and Table 4.11.
Overall, the hypothesized structural model consisted of antecedents (emotional
intelligence, positive affectivity, and negative affectivity), emotional labor strategies
(surface acting and automatic regulation), and consequences (emotional exhaustion and
job satisfaction). The results indicated that eight of the thirteen hypotheses were
supported (Table 4.12).
Regarding the antecedents of emotional labor, the maximum likelihood estimate
of the structural coefficient showed that emotional intelligence had no significant
relationship with surface acting (β = .00; t = .042) and deep acting (β = .07; t = .97),
which rejected hypothesis 1 and 2. Additionally, the positive and significant association
between emotional intelligence and automatic regulation (β = .26; t = 3.84) was found,
supporting hypothesis 3. Next, positive affectivity was found to have a significant and
positive relationship with surface acting (β = -.46; t = -6.27), deep acting (β = .26; t =
3.32), and automatic regulation (β = .33; t = 3.84) among Division I coaches, supporting
hypothesis 4 and hypothesis 5. However, the path between positive affectivity and deep
acting were dropped for the modified model because deep acting construct did not predict
99
the proposed consequences, which in turn resulted in dropping deep acting construct.
Negative affectivity was found to be positively related to surface acting (β = .29; t = 4.80),
supporting hypothesis 6 but there was no significant relationship with deep acting (β
= .09; t = 1.48), rejecting hypothesis 7.
Regarding the consequences of different emotional labor strategies, as expected,
surface acting had a significant and positive relationship with emotional exhaustion (β
= .65; t = 8.57) and negative relationship with job satisfaction (β = -.45; t = -6.78),
supporting hypothesis 8 and 9. Deep acting had no significant relationship with emotional
exhaustion (β = .04; t = .80) and job satisfaction (β = -.03; t = -.54), rejecting hypothesis
10 and 11. Additionally, automatic regulation had a significant and negative relationship
with Emotional exhaustion (β = -.11; t = -2.13) and positive relationship with job
satisfaction (β = .20; t = .3.44), supporting hypothesis 12 and 13. Table 4.13 shows the
overall results of the tested hypotheses. Structural relationship for Model 1 and Model 2
are shown in Figure 4.5 and Figure 4.6, respectively.
Table 4.10
A Summary of the Single-Group SEM
χ2/ df
RMSEA
SRMR
TLI
CFI
Model 1
2.60
.064
.085
.94
.95
Model 2
2.77
.067
.079
.95
.95
100
Table 4.11
Maximum Likelihood Estimates for Model 1 and Model 2
Estimates
β
S.E.
t
Model 1
Emotional Intelligence
Surface Acting
.00
.054
.042
Emotional Intelligence
Deep Acting
.07
.063
.97
Emotional Intelligence
Automatic Regulation
.44
.054
6.78
Positive Affectivity
Surface Acting
-.49
.11
-5.93
Positive Affectivity
Deep Acting
.26
.11
3.32
Negative Affectivity
Surface Acting
.29
.047
4.68
Negative Affectivity
Deep Acting
.09
.053
1.48
Surface Acting
Emotional Exhaustion
.68
.20
8.82
Surface Acting
Job Satisfaction
-.48
.10
-7.28
Deep Acting
Emotional Exhaustion
.04
.12
.80
Deep Acting
Job Satisfaction
-.03
.077
-.54
Automatic Regulation
Emotional Exhaustion
-.10
.12
-2.02
Automatic Regulation
Job Satisfaction
.19
.079
3.39
Emotional Intelligence
Automatic Regulation
.26
.055
3.84
Positive Affectivity
Surface Acting
-.46
.098
-6.27
Positive Affectivity
Automatic Regulation
.33
.19
4.62
Negative Affectivity
Surface Acting
.29
.047
4.80
Surface Acting
Emotional Exhaustion
.65
.19
8.57
Surface Acting
Job Satisfaction
-.45
.10
-6.78
Automatic Regulation
Emotional Exhaustion
-.11
.13
-2.13
Automatic Regulation
Job Satisfaction
.20
.084
3.44
Model 2
101
Table 4.12
Summary of Results for Study Hypotheses
Hypothesis
Results
H1. Emotional intelligence will be negatively associated with surface
Not supported
acting
H2. Emotional intelligence will be positively associated with deep acting
H3. Emotional intelligence will be positively associated with automatic
Not supported
Supported
regulation
H4. Positive affectivity will be negatively associated with surface acting
Supported
H5. Positive affectivity will be positively associated with deep acting
Supported
H6. Negative affectivity will be positively associated with surface acting
Supported
H7. Negative affectivity will be negatively associated with deep acting
Not supported
H8. Surface acting will be positively associated with emotional exhaustion
Supported
H9. Surface acting will be negatively associated with job satisfaction
Supported
H10. Deep acting will be positively associated with emotional exhaustion
Not supported
H11. Deep acting will be negatively associated with job satisfaction
Not supported
H12. Automatic regulation will be negatively associated with emotional
Supported
exhaustion
H13. Automatic regulation will be positively associated with job
satisfaction
102
Supported
103
Figure 4.5. Path coefficients between latent variables for Model 1
Insignificant relationships are not represented in the figure
104
Figure 4.6. Path coefficients between latent variables for Model 2
CHAPTER 5
DISCUSSION
The purpose of this study was to identify antecedents and consequences of
coaches’ emotional labor to gain a more comprehensive understanding of the relationship
between sports coaches’ emotional labor and individual outcomes. The researcher first
examined (a) the validity and the reliability of the scales used; (b) the measurement
model with single-group CFA; and (c) the structural model which consists of coaches’
dispositional antecedents, emotional labor strategies, and individual outcomes with
single-group SEM. This chapter will provide an overview of the scale and the study
findings, followed by the discussion of theoretical and practical implications of the study.
The chapter will also address the limitations of the study as well as recommendations for
future studies.
Overview of the Instruments
As an initial step, the researcher examined the psychological properties of
instruments used in the current study. The validity of the scale consists of the content
validity and the construct validity including convergent validity and discriminant validity.
First of all, the questionnaire employed in the study was sent to a panel of experts
consisting of two sport management professors and one sport management Ph.D. student
who confirmed the content validity of the scales used.
Discriminant validity was examined by the correlations among the proposed
factors. The researcher confirmed discriminant validity of the scales used by showing
105
that none of the correlations between constructs exceeded .85, the cut-off point suggested
by Kline (2005). Additionally, the reliability estimates for all factors were acceptable
because Cronbach’s alphas of all constructs exceeded the cut-off value of .70 suggested
by Nunnally (1978).
The convergent validity was examined by factor loadings of each construct and
values of AVE via CFA. The initial CFA showed that the factor loading value of one
item (SA4) was lower than .40, the cutoff point proposed by Stevens (1996) and,
therefore, was dropped. In addition, AVE values for negative affectivity, surface acting,
deep acting, and automatic regulation were lower than the threshold of .50, proposed by
Fornell and Larcker (1981). The result indicated that the factor loading of NA4 was close
to .40 (.43) and the measurement error of this item was large (.86). Subsequently, the
researcher dropped this item and AVE for the negative affectivity scale was improved
to .58. Additionally, AVE values for SA (.47), DA (.46), and AR (.49) were slightly
lower than recommended value of .50. However, the researcher decided to keep these
scales because Ping (2009) suggests that AVE value which is a few points lower than the
acceptable value (.50) may not always be fatal for a model test. He stated that as long as
the latent variable showed an acceptable reliability (Cronbach alpha), it would
demonstrate sufficient convergent validity. Additionally, Ping (2009) argued that
researchers can ignore low AVE when testing a “first-time” model since the first-time
study usually utilizes new measures in a new model. He also stated that an AVE slightly
lower than .50 might be acceptable if there is no major problems with discriminant
validity. This study is the “first-time” study regarding coaches’ emotional labor strategies
in the sport setting and surface acting and deep acting measures were distributed to
106
athletic coaches for the first time while automatic regulation measures were developed by
the researcher based on the previous scales. Additionally, since the instruments showed a
good discriminant validity and reliability, the researcher concluded that the low AVEs for
surface acting, deep acting, and automatic regulation may not be a critical problem for
this study. In the following sections, I identify and discuss the significant findings of the
study.
Significant Findings of the Study
Structural relationship between the Proposed Antecedents and Emotional Labor
Positive Affectivity and Automatic Regulation.
In evaluating the structural model, the modification indices suggested an
additional path from positive affectivity to automatic regulation to make the model fit the
data better. Inclusion of the path showed that positive affectivity had a significant and
positive impact on automatic regulation. It means if a coach has a predisposition to be
positive, enthusiastic, and active, they are more likely to manage their emotion in an
automatic way while displaying appropriate emotions than a coach low in positive
affectivity. Diefendorff et al (2005) found that extraversion, similar to positive affectivity,
significantly predicted the display of naturally felt emotions. They reasoned that
individuals high in extraversion were better at displaying positive emotions since they
tend to experience this emotion more often.
An explanation provided by Lyubomirsky, King, and Diener (2005) is more
applicable to the coaching context. They suggested that one of the characteristics related
to positive affect include effective coping with challenge and stress. That is, individuals
with positive affectivity may engage in effective coping strategy with stress or challenges.
107
According to them, positive emotion leads individuals to approach rather than to avoid
and to seek out and undertake new goals. Indeed, Aspinwall (1998) recognized the role of
positive affect as a resource on coping and self-regulation. Empirically, Miller and
Schnoll (2000) examined the relationship between personality variables as predictors and
coping strategies. The result revealed that positive affectivity was strongly associated
with active coping strategy which refers to either behavioral or psychological responses
designed to change the nature of the stressor itself or how one thinks about it (De Rijk, Le
Blanc, Schaufeli, & De Jonge, 1998). It means Individuals with active coping strategy
may usually attempt to change the perception of the stressor which is similar with the
process of deep acting strategy. Thus, it implies that coaches with high PA may engage in
more deep acting strategy (as the current study also found). Moreover, through repetition,
they will be more likely to engage in automatic regulation as it becomes stable and
require little effort. Together, it is possible to conclude that high positive affectivity
coaches are more likely to engage in the active emotional labor strategy such as deep
acting and ultimately automatic regulation strategy.
Positive Affectivity and Emotional Labor
PA was significantly associated with both surface acting and deep acting in the
present study. First of all, the result showed that PA was negatively associated with
surface acting (β = -.47) as hypothesized. This result is consistent with previous finding
by Grandey and Brotheridge (2002), Brotheridge and Lee (2003), and Diefendorff et al.
(2005) who found a negative correlation between the two variables. The result indicated
that high PA coaches characterized as active, enthusiastic and attentive are less likely to
manage their emotions by surface acting than low PA coaches characterized as sad and
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lethargic. In other words, high PA coaches tend not to engage in emotionally superficial
surface acting such as faking their emotions or suppressing inappropriate emotions.
Positive affectivity refers to one’s level of pleasurable engagement with the
environment. While those high on PA exhibit high energy, full concentration, and high
enthusiasm, those low on PA are characterized by sadness, lethargy, and distress (Watson,
1988). As such, individuals with high PA tend to experience more often positive
emotions such as enthusiasm and optimism (Costa & McCrae, 1992). Additionally,
according to Watson and Clark (1984), PA is associated with individual’s ability to
process emotional information accurately and efficiently, to solve problems, make plans,
and achieve in one’s life. Based on these characteristics, it is reasonable to conclude that
high PA coaches who tend to be active and systematic will not engage in superficial and
shallow strategy like surface acting. Additionally, coaches need to display positive
emotions such as enthusiasm and high energy more often in their interaction with athletes
to motivate and empower them. As such, high PA coaches who tend to show those
positive emotions across time and situations will have less of a need to surface act since
their affective state is often congruent with appropriate emotions in most given situations.
The present study showed that PA was positively associated with deep acting as
hypothesized. This is consistent with Johnson’s (2004) and Gosserand and Diefendorff
(2005)’s findings. As one of the characteristics of PA is full concentration (Watson,
1988), Jex and Spector (1996) argued that PA increases individual’s attentional focus
which is the core component of deep acting. Employees with deep acting use four
different kinds of strategies including situational selection, situational modification,
attention deployment and cognitive change (Grandey, 2000). Among them, attention
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deployment refers to focusing one’s attention on the positive aspect of situation. Grandey
(2000) illustrated that “attentional deployment is done by thinking about event that call
up the emotions that one needs in that situation (p. 99)”. As such, high PA coaches may
be better at shifting inner feelings by focusing on other aspects of the situation based on
their ability of full concentration. Overall, the result indicates that every coach may
experience negative emotions in response to difficult athletic events. However, in these
situations, high PA coaches may be more likely to attempt to modify their inner feelings
through attention deployment to experience appropriate emotions (deep acting) than low
PA coaches.
Negative Affectivity and Emotional Labor
The results also showed that NA was significantly related to surface acting while
its relationship with NA was insignificant. The finding of a significant positive
relationship between NA and surface acting support earlier findings (e.g., Brotheridge &
Grandey, 2002; Brotheridge & Lee, 2003; Diefendorff et al. (2005); and Grandey, 2002).
These findings show that high NA coaches whose natural dispositions include pessimism
and anxiousness are more likely to engage in surface acting to manage their emotions and
expressions in their interaction with athletes. The present study also hypothesized that
NA would be negatively associated with deep acting but this was not the case. The results
showed that there was no significant relationship between NA and deep acting.
Johnson and Gross (2007) suggested that individuals who are high in Neuroticism
(i.e., high NAs) are more likely to believe they cannot change or control their emotions
due to their pessimistic characteristic. Similarly, Watson and Clark (1984) found that
people with high NA who view themselves and the world around them in more negative
110
way, may feel helpless and not attempt to actively change the situation. Thus, employees
who are high in NA are more likely to perform minimum role requirements (Bell &
Luddington, 2006) and use surface acting, which entails only the change of facial
expression, gestures, and voice tone (Ashforth & Humphrey, 1993) without modifying
inner emotion (i.e., deep acting).
Emotional Intelligence and Emotional Labor
The results showed that there was a significant positive relationship between
emotional intelligence and automatic regulation (β = .44; t = 6.96). That is, coaches with
high levels of emotional intelligence were more likely to use automatic regulation
strategies in their interaction with athletes. Zapf (2002) stated that automatic regulation
was the employees’ spontaneous expression of an emotion that is naturally felt. For
instance, a coach who displays enthusiasm without a conscious effort is most likely to
motivate and encourage the athletes during a very close game. It is a useful strategy
because expression of such an emotion is contagious. Otherwise, the athletes may be
emotionally stirred enough to perform well.
According to Mayer and Salovey (1997), emotional intelligence involves “the
ability to perceive emotions, to access and generate emotions so as to assist thought, to
understand emotions and emotional knowledge, and to reflectively regulate emotions so
as to promote emotional and intellectual growth” (p. 5). Individuals with high level of
emotional intelligence are better at comprehending what’s going on and understanding
other’s emotions. They are also flexible and better at regulating emotions and expressing
adaptive emotions in a given situation (Mayer & Salovey, 1997). Due to such skills,
individuals with high level of emotional intelligence are expected to be more effective at
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identifying an emotion that is appropriate in any given situations (i.e., display rule) and
display them accordingly. Although coaches encounter a myriad of incidents which
evoke extreme emotions, those coaches with high emotional intelligence will be able to
remain calm and display appropriate emotions.
In fact, Fabian (1999) argued that emotional intelligence is having the ability
while emotional work (i.e., emotional labor) is a way of acting based on that ability. This
implies that emotionally intelligent individuals (i.e., high-level ability) are more likely to
engage in effective emotional labor strategy (i.e., high-level performance). It is like
individuals with good athletic ability who display more effective and better performance
naturally compared to those with bad athletic ability. They may have better speed,
reaction time, coordination of movement, and agility which is the basic component of
sport skills (Liguori, 2009) compared to counterparts. Similarly, Cote, Miners, and Moon
(2006) argue that individuals with high emotional intelligence have a broader choice of
selecting emotional labor strategies and, thus, are more likely to choose the appropriate
strategy. In the emotional labor context, automatic regulation is considered the most
effective strategy because it enhances one’s well-being compared to other strategies
(Martinez-Inigo et al., 2007). Therefore, it is a logical conclusion that highly emotionally
intelligent individuals may engage in more effective emotional labor strategy such as
automatic regulation. This view is supported by the present results.
It is critical to note that those coaches who have not received any training in
emotional control and labor have to rely on their own ability to shape their behavior in
emotionally laden contexts. Thus, those coaches high on emotional intelligence are likely
to be good at emotional regulation than the others in which case the labor will become
112
automatic and requires little effort (Cote et al., 2006). Therefore, an individual with high
level of emotional intelligence may report only high level of emotional regulation (i.e.,
automatic regulation), not because they do not engage in emotional labor strategies such
as surface acting and deep acting but it becomes routine and automatic process. The
current study showed that emotional intelligence as one’s ability may be a critical
individual characteristic to perform the necessary emotional work.
However, the result also revealed that emotional intelligence had no significant
relationship with surface acting in the current study although the researcher posited the
negative relationship between them. This result is not consistent with earlier research
(e.g., Austin et al. 2008; Brotheridge, 2006b; Mikolajczak et al.2007) which found a
negative and significant association between emotional intelligence and surface acting.
Further, a lack of significant association between emotional intelligence and deep acting
is also in contradiction to the previous findings of a positive relationship between them
(e.g., Cote, 2005; Daus et al., 2004).
The insignificant relationship between emotional intelligence, and surface acting
and deep acting is unexpected. It may be due to the coaching occupation being different
from other occupations. Previous studies investigating the relationship between EI and
emotional labor strategies were carried out in conventional service organizations where
the display rule is typically integrative (i.e., positive emotion). However, in the sport
setting, the coaching occupation require coaches to display enthusiasm, calmness,
coldness, and even anger to create certain organizational climate to enhance the
performance of individuals and teams. As there are no specific display rules for athletic
coaches, they do have considerable latitude in expressing their emotions. As a matter of
113
fact, Gardner and his colleagues (2009) stated that leaders with more power and
prestigious occupation (i.e., lawyers, doctors, and physicians) have more freedom from
the display rules because their roles are necessary for their clients and followers. Given
the fact that athletic coaches also have great authority over the operation of the team (e.g.
over the selection and utilization of team members), they have the power over athletes
and, therefore, they have more freedom from display rules. Further, in so far as the
athletic context has no specific display rules and as coaches have no obligations to
generate any specific emotions, emotional intelligence may have nothing to do with the
controlled emotional labor strategies (i.e., surface acting and deep acting). Other
individual difference characteristics such as affectivity, personality, or situational
demands may have the potential to influence on these controlled labors.
Another possible explanation for the insignificant relationship between emotional
intelligence on the one hand and surface acting and deep acting on the other may be
rooted in the emotional intelligence measure used. Wong & Law’s (2002) measure
composed of four dimensions of emotional intelligence based on Mayer and Salovey’s
(2002) ability model of emotional intelligence. The correlation among constructs (see
Table 4.6) showed that different dimensions of emotional intelligence had significant
correlations with surface acting and deep acting. Three of the four dimensions of
emotional intelligence (i.e., appraisal of emotion, regulation of emotion, and utilization of
emotion) still had negative associations with surface acting while one branch (i.e.,
understanding of emotion) failed to have an association. Also, only two dimensions of
emotional intelligence (i.e., appraisal of emotion and utilization of emotion) had a
positive association with deep acting whereas the other dimensions (i.e., understanding of
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emotion and regulation of emotion) did not. As such, it is possible that since some of the
dimensions did not correlate with surface acting and deep acting, the overall results
showed no association between the two variables. It is reasonable to suggest that future
research may use different emotional intelligence scales or use each subscale of
emotional intelligence to examine the relationship with emotional labor.
Structural Relationship between Emotional Labor and Consequences
It was generally expected that the emotional labor strategies would have opposing
relationships with the well-being outcomes. Grandey (2003) reports the need to
distinguish surface acting from deep acting given that these dimensions of emotional
labor do not have a uniform impact on outcome variables. Therefore it was hypothesized
that (a) there would be a positive relationship between surface acting and emotional
exhaustion and a positive relationship between deep acting and emotional exhaustion (b)
there would be a negative relationship between surface acting and job satisfaction and a
positive relationship between deep acting and job satisfaction. It was also hypothesized
that (c) there would a negative relationship between automatic regulation and emotional
exhaustion; (d) there would be a positive relationship between automatic regulation and
job satisfaction.
Emotional Labor and Emotional Exhaustion
In the current study, both surface acting and automatic regulation were
significantly associated with emotional exhaustion but in opposite directions. First of all,
the results indicated that coaches who reported faking or suppressing their emotions
reported high level of emotional exhaustion which is consistent with previous research
(Brotheridge & Grandey, 2002; Grandey, 2003; Johnson & Spector, 2007; Montgomery
115
et al., 2006; Näring et al., 2006; Kruml & Geddes, 2000). This relationship can be
explained by conservation of resource theory (Hobfoll, 1988). COR theory suggests that
individuals would experience burnout if they depleted all of their inner resources and
cannot regain them. According to Baumeister, Bratslavsky, Muraven, and Tice (1998),
purposeful self-control and regulatory processes require effort and lead to depletion of
mental resources. When individuals engage in surface acting, they need to constantly
monitor their actual and desired behaviors. As such, surface acting as a form of
regulatory process is an effortful process which drains mental resources. Since surface
acting involves making an effort to suppress genuine emotions and displaying inauthentic
emotional expressions continuously, employees may feel energy depletion and fatigue in
the process (Hulsheger & Schewe, 2011). Therefore, surface acting is expected to impair
coaches’ well-being and increase emotional exhaustion.
However, this found significant and negative association between the form of
automatic regulation and emotional exhaustion (β = -.10, t = -2.02). The result indicates
that coaches who used automatic regulation were less likely to experience emotional
exhaustion. Mauss, Cook, and Gross (2007) suggested that that automatic emotion
regulation may become cost-free process. In their experimental study, the result indicated
that automatic anger regulation successfully decreased participant’s anger experience.
They argued that this was because of the low cost of regulating emotions. In the present
context, we can say that coaches with automatic regulation did not have to expend any
psychological and cognitive efforts, and consequently, they did not use up their emotional
resources which, in turn, led to less emotional exhaustion. By the same token, since
automatic regulation will not generate any emotional dissonance deemed as a proxy for
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emotional exhaustion, the strategy of automatic regulation had the effect of reducing
emotional exhaustion.
It was surprising that the hypothesis that deep acting would positively correlate to
emotional exhaustion was not supported (β = .04, t = .80). Given the empirical and
theoretical evidence in the literature that deep acting has been found to have a negative or
positive relationship with emotional exhaustion, the result of this relationship is quite
unexpected. However, Kim (2008) asserts that this relationship has been somewhat
debatable. For instance, according to conservation of resource theory, deep acting would
positively relate to emotional exhaustion due to the energy and cognitive efforts involved
to modify inner feelings (Liu et al., 2008). Specifically, Liu et al. (2008) argued that deep
acting require “a great deal of mental energy in the form of motivation, engagement, and
role internalization” (p. 2416), thus demands more psychological efforts from employees.
The researcher actually hypothesized the positive relationship between the two variables
based on the above assumptions. However, on the flip side, there would be a negative
relationship between them because deep acting is more authentic and thus reduces
emotional dissonance (Brotheridge & Lee, 2002). Therefore, deep acting may in fact
reduce emotional exhaustion. Further, those who use deep acting may receive positive
feedback from counterparts which, in turn, may lead to less exhaustion. As such, the
relationship between the two variables can be mixed. Future research may investigate the
independent and joint influences of deep acting and emotional dissonance on emotional
exhaustion.
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Emotional Labor and Job Satisfaction
Surface acting, deep acting, and automatic regulation were posited to be
associated with job satisfaction. The results indicated that surface acting was found to
have significant and negative association with job satisfaction. This result is consistent
with Grandey’s (2002) finding of a negative relationship between surface acting and job
satisfaction. Previous research also has supported the negative association between job
satisfaction and emotional dissonance, which is conceptually similar to surface acting in
that employees who surface act are likely to experience emotional dissonance (e.g.,
Abraham, 1998; Bono & Vey, 2005; Hulsheger & Schewe, 2011; Morris & Feldman,
1997). Specifically, Bono and Vey’s (2005) meta-analysis found surface acting to have
consistent relations to low job satisfaction, consequently leading to a high intention to
quit (Cote & Morgan, 2002).
A possible explanation for this relationship is the emergence of emotional
dissonance coaches can experience through the use of surface acting. It has been said that
surface acting generates emotional dissonance which refers to a mismatch between the
felt emotion and the expressed emotion (Hochschild, 1983). This is a very uncomfortable
state and may have a potential to decrease coaches’ job satisfaction. Furthermore, surface
acting drives coaches to feel the sense of inauthenticity due to the faking nature of
surface acting. Subsequently, coaches will experience self-alienation, as well as develop
negative attitudes toward their jobs.
Finally, automatic regulation was found to have a significant and positive impact
on job satisfaction (β = .65) among coaches. This result is consistent with Martinez-Inigo
et al.’s (2000) finding of a positive association between the two variables. Individuals
118
with automatic regulation may not experience emotional dissonance and also feel a sense
of authenticity because their felt emotion and expressed emotion are congruent which in
turn lead to increase job satisfaction.
Implications
Results of the present study also offer several important practical implications.
First of all, the significant connection between emotional labor strategies and individual
outcomes such as emotional exhaustion and job satisfaction indicates that coaches’ ability
to regulate emotions may benefit them in the long-run (Gross & John, 2003). As such,
athletic departments need to pay a special attention on coaches’ emotional experience for
their well-being. There has been little attention on coaches’ experience related to
emotions compared to athletes. However, previous literatures regarding coaches’
emotions have shown that coaches need to sufficiently regulate their emotions that are
not appropriate in a given situation for themselves and athletes (Gould, Guinan,
Greenleaf, & Chung, 2002; Kimiecik & Gould, 1987). The current study provided
evidence that this regulation process can enhance or harm their well-being and job-related
attitude.
More specifically, based on the result that surface acting was associated with
more negative outcomes (low levels of emotional exhaustion and low levels of job
satisfaction), athletic departments may implement emotional labor training programs in
order to encourage employees to avoid surface acting strategy during their interactions
with athletes. The result indicated that coach who suppress their true emotions and fake
the emotion they do not have experience a great degree of health problems. As such,
coaches should be informed to avoid this regulation process. Additionally, the training
119
program should be designed to encourage coaches to use automatic regulation during in
their professions. Grandey, Fisk, Mattila, Jansen, and Sideman (2005) argued that
emotional labor is a type of labor or skill and it can be trained or developed through
repetition. As such, the program can be designed to lead coaches to deliberately
experience the situation which generate aversive mood (through video clip or articles)
and help them regulate the aversive emotions and express them in more adaptive ways.
Through repetition, it may become more stable and require less effort from time to time.
In indirect way, as the current study showed that emotional intelligence is positively
related to automatic regulation, athletic department may provide emotional intelligence
training which help them engage in automatic regulation to coaches to reduce the
potential negative outcomes.
Without the intervention program, coaches also attempt to engage in automatic
regulation processes at work in everyday life. But how can they do that? Recent research
suggests that social and situational cues encountered in everyday life can activate and
affect individual’s behaviors (Aarts & Dijksterhuis, 2000, 2003). Aarts and Dijksterhuis
(2003) found that individuals became silent and spoke more quietly when they found the
pictures of libraries which illustrate the environment associated with the norm to be quiet.
By extension, coaches should post certain cues which activate their willingness to engage
in automatic regulation in their works. The example may include “Don’t fake your
emotions today”, “Be confident and calm for team, athletes, and myself”, “emotionalism
destroys consistency” in their office.
120
Limitation and Future Studies
The major weakness of the current study is its cross-sectional design. Although
the researcher hypothesized the relationship among constructs based on the previous
literatures (Grandey, 2000; Hochschild, 1983), the cause-effect relationship cannot be
inferred. That is because the current study did not randomly assign participants to
treatment groups nor manipulate any variables as experimental and quasi-experimental
approaches. For example, the current study found that coaches with surface acting were
more likely to be emotionally exhausted in work places. However, it is possible to think
that coaches who were emotionally exhausted were more likely to display superficial
emotional labor strategy like surface acting due to their fatigue. The future study may
conduct longitudinal design and examine the feedback loops between emotional labor
and individual outcomes for further understanding about the directions of these
relationships.
Additionally, the problematic nature of the self-reported measurement exists. The
results of the current study were totally based on respondents’ perceptions. The score
may be over estimated or under estimated, or others may think of them differently in their
emotional labor. Future studies may try to reduce the possible bias by using the perceived
version of different audiences such as assistant coaches and athletes, especially for
emotional labor utilization. Although the participants knew that the survey was
anonymous, it was possible that they might still want to give socially desirable responses
when rating their affectivity and emotional labor and to rate themselves in a favorable
way.
121
In terms of the role of emotional labor on individual outcomes, these relationships
might be influenced by several moderating variables. Grandey, Fisk, and Steiner (2005)
found that when employees feel high job autonomy in their jobs, it would minimize the
negative effects of emotional labor and reduce emotional exhaustion among them. In
addition, the Duke, Goodman, Treadway, and Breland (2009) also found that high
perceived organizational support played a moderating role in the relationship between
emotional labor and emotional exhaustion. They found that perceived organizational
support reduced the negative impact of surface acting on the proposed outcomes. Future
study may consider the role of these variables in the relationship between emotional labor
and its outcomes.
Although this study focused only on the individual outcomes of coaches, the
studies investigating whether emotional labor of coach affects the coaching effectiveness
are recommended. That is, a study which examine the task effectiveness of emotional
labor on such outcomes as team performance (e.g., win-loss record), athlete’s perceived
organizational climate, athlete’s satisfaction, and athlete’s trust toward coaches are
recommended.
Finally, the future study may consider the role of anger expression in coaching
context. Anger expression may be considered a form of the genuine negative expression
(Mahoney, Buboltz, Buckner, & Doverspike, 2011). In this process, individuals express
their true negative emotions such as anger or frustration upon experiencing the
corresponding negative emotions. Unlike most professions which prevent anger
expression to customers, anger expressions in coaching setting may be beneficial for the
teams as a means of achieving its objectives since sport setting has no specific display
122
rules. As a matter of fact, previous literatures found the positive role of anger on athletes’
successful performance (Cockerill, Nevill, & Lyons, 1991; Terry & Slade, 1995) and on
athletes’ self-confidence (Lane, Terry, & Lane, 1996) in athletic context. Given the
assumption that emotion is contagious (Hatfield et al., 1996), coaches’ anger expression
may be able to transmit to athletes and may lead to successful performance and increased
self-confidence among them. On the flip side, however frequent anger expressions were
found to have a negative impact on individual’s well-being and physical health
(Baumeister & Exline, 2000; Mayer & Salovey, 1995). As such, given the fact that
coaches’ anger may play a critical role in their outcomes as well as athletes’ outcomes, it
may be relevant area to study in coaching context.
Conclusion
The current study tested a model of emotional labor including dispositional
antecedents and consequences among intercollegiate coaches. Results showed that that
positive affectivity predicted all the proposed emotional labor strategies while negative
affectivity predicted only surface acting negatively. Emotional intelligence was also
found to be positively related to automatic regulation. Also, different emotional labor
strategies have shown to have different consequences for coaches. Specifically, surface
acting was shown to be a health-detrimental strategy while automatic regulation was
found to be a health-beneficial strategy. Considering the negative and positive
consequences coaches may experience as a result of surface acting and automatic
regulation, respectively, this study provided valuable insight both for theory and practice.
123
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138
APPENDIX A
Email – Pre-notification
139
March 9, 2012
Dear Coach:
Let me introduce myself and a research proposal in which you are invited to participate.
My name is Ye Hoon Lee and I am a Ph.D. candidate in Sport Management at The Ohio
State University and my doctoral advisor is Dr. Packianathan Chelladurai.
The research is concerned with emotional labor associated with coaching. Coaching is an
emotion-laden occupation in which many kinds of incidents evoke emotional outbursts
(e.g., athletes poor play and/or misconduct, winning or losing, referees’ bad calls,
negative media attention, and so on). It is understood that coaches are masters in
managing their own emotions and those of their athletes. Yet, the dynamics of emotions
in coaching have not been studied adequately. I propose to investigate the emotional
regulation strategies of intercollegiate coaches and their effects on coaches’ well-being.
By means of this brief introduction, I am requesting that you kindly participate in this
research project. Please note that participation in this study is voluntary and your nonparticipation will not be known to anybody. Your participation would, however, be
greatly appreciated and is crucial to the success of this research endeavor. I believe that
the results of this research will help improve the quality of coaching as well as enhance
the well-being of coaches.
If you will let me know, I will send you the results of the study and the recommendations
thereof.
You will be receiving in the week of March 12, another email which includes a more
detailed description of the study and an internet link connecting to a brief (10-15 minutes)
web survey questionnaire.
I thank you in advance for your time and assistance in this important project.
If you have any questions, please feel free to contact Ye Hoon Lee at [email protected]
or 517-420-4166 or Dr. Packianathan Chelladurai at [email protected].
Sincerely,
Ye Hoon Lee
Ph.D. candidate
Ohio State University
Dr. Packianathan Chelladurai
Professor
Ohio State University
140
APPENDIX B
Email – Main Study
141
March, 16, 2012
Dear Coach:
We are inviting you to participate in our research that identifies the antecedents and the
consequences of emotional labor in intercollegiate coaches at the Division I level. In this
study, emotional labor refers to the coaches’ regulation of both feelings and expressions
of emotions in their interaction with athletes to motivate and empower them. More
specifically, we intend to examine how coaches engage in emotional labor and how the
different kinds of emotional labor strategies affect your well-being such as emotional
exhaustion and job satisfaction. It is expected that the questionnaire will take
approximately 10-15 minutes to complete.
The link to the survey is:
https://surveys.ehe.osu.edu/TakeSurvey.aspx?EID=981B9llB038B019l6B39mB34M3M
B74J
If you do not wish to respond to this survey, please click on the link below to decline:
https://surveys.ehe.osu.edu/DeclineSurvey.aspx?EID=981B9llB038B019l6B39mB34M3
MB74J
Your participation in this study is completely voluntary. You may refuse to participate
and/or withdraw from participation at any time without prejudice or penalty. The
anonymity of your responses is guaranteed. The survey does not allow us to identify
responders in any way. Additionally, No information will be shared with anyone
associated with the team, including administrators, colleagues, and athletes. For questions,
concerns, complaints, or if you feel you have been harmed as a result of study
participation, you may contact Ye Hoon Lee at [email protected] and Dr. Packianathan
Chelladurai at [email protected]. For questions about your rights as a participant in
this study, or to discuss other study related concerns or complains with someone who is
not part of the research team, please contact Ms. Sandra Meadows in the Office of
Responsible Research Practices at 1-800-678-6251.
Return of the questionnaire will be considered your consent to participate. Your
cooperation is greatly appreciated. Thank you very much for participating in this study.
Sincerely,
Ye Hoon Lee
Ph.D candidate
Ohio State University
142
Dr. Packianathan Chelladurai
Professor
Ohio State University
143
APPENDIX C
Email – Follow - Up
144
March, 23, 2012
Dear Coach,
You should have already received an email containing a web survey questionnaire link
concerning the antecedents and the consequences of emotional labor in intercollegiate
athletic head coaches at the Division I level. More specifically, we intend to examine how
coaches engage in emotional labor and how the different kinds of emotional labor
strategies affect your well-being such as emotional exhaustion and job satisfaction.To get
an accurate view of emotional labor within intercollegiate sport, your input is vitally
important. A better understanding of this issue may be of interest to coaches, athletic
directors and administrators, and other university personnel since our primary goal is to
improve the quality of coaching as well as enhance the well-being of coaches.
If you have already completed the web survey, please accept our sincere thanks. If you
have not yet filled it out, please do so within one week. The questionnaire should only
take 10-15 minutes for you to complete. You can access the questionnaire by clicking on
the following link:
https://surveys.ehe.osu.edu/TakeSurvey.aspx?EID=981B9llB038B019l6B39mB345lKB7
4J
Your participation in this study is completely voluntary. You may refuse to participate
and/or withdraw from participation at any time without prejudice or penalty. Please be
assured that the survey software program in this study allows for anonymous collection
of data. The survey does not allow us to identify responders in any way. Additionally, no
information will be shared with anyone associated with the team, including
administrators, colleagues, and athletes. The results of the study will not be linked to any
individual or institution, and any discussion will be based only on group data.
If you did not receive the web survey link or if you were not connected to the web survey
questionnaire after clicking the link, please feel free to contact either Ye Hoon Lee at
[email protected] or Dr. Packianathan Chelladurai at [email protected] to solve
the problems. In addition, for questions about your rights as a participant in this study or
to discuss other study-related concerns or complaints with someone who is not part of the
research team, you may contact Ms. Sandra Meadows in the Office of Responsible
Research Practices at 1-800-678-6251.
Return of the questionnaire will be considered your consent to participate. Your
cooperation is greatly appreciated. Thank you very much for participating in this study in
advance.
Sincerely,
Ye Hoon Lee, M.S.
Ph.D candidate
Ohio State University
145
Dr. Packianathan Chelladurai
Professor
Ohio State University
146
APPENDIX D
Positive Affectivity and Negative Affectivity Scale (Watson, Clark, & Tellegen, 1988)
147
Please check one response for each item
that best indicates how you feel on average
Not at all
A little
Moderately
Quite a bit
Extremely
1
Interested
1
2
3
4
5
2
Excited
1
2
3
4
5
3
Upset
1
2
3
4
5
4
Scared
1
2
3
4
5
5
Enthusiastic
1
2
3
4
5
6
Inspired
1
2
3
4
5
7
Jittery
1
2
3
4
5
8
Afraid
1
2
3
4
5
148
APPENDIX E
Wong & Law’s Emotional Intelligence Scale(Wong & Law, 2002)
149
Please select the number for each
statement that reflects the extent to
which you agree / disagree with each
of the following statements
Strongly Disagree
Disagree
Slightly Disagree
Slightly Agree
Agree
Strongly Agree
1
I have a good sense of why I have
certain feelings most of the time.
1
2
3
4
5
6
2
I always know my friends’ emotions.
1
2
3
4
5
6
3
I am able to control my temper.
1
2
3
4
5
6
4
I always try to achieve goals I set for
myself.
1
2
3
4
5
6
5
I have good understanding of my own
emotions.
1
2
3
4
5
6
6
I am a good observer of others’
emotions.
1
2
3
4
5
6
7
I am quite capable of controlling my
own emotions.
1
2
3
4
5
6
8
I always tell myself I am a competent
person.
1
2
3
4
5
6
9
I really understand what I feel.
1
2
3
4
5
6
10
I am sensitive to the feelings and
emotions of others.
1
2
3
4
5
6
150
Continued
Appendix Continued
11
I can always calm down quickly when I
am very angry.
1
2
3
4
5
6
12
I am a self-motivated person.
1
2
3
4
5
6
13
I always know whether or not I am
happy.
1
2
3
4
5
6
14
I have good understanding of the
emotions of people around me.
1
2
3
4
5
6
15
I have good control of my own
emotions.
1
2
3
4
5
6
16
I would always encourage myself to try
my best.
1
2
3
4
5
6
151
APPENDIX F
Emotional Labor Scale (Brotheridge & Lee, 2003; Gross & John, 2003)
152
Never
Rarely
Sometimes
Often
Always
1
Resist expressing your true feeling.
1
2
3
4
5
2
Make an effort to actually feel the emotions that
you need to display to others.
1
2
3
4
5
3
Experience spontaneously the positive emotions
(such as confidence and enthusiasm) I express
when athletes make a big mistake.
1
2
3
4
5
4
Hide your true feelings about a situation.
1
2
3
4
5
5
Try to actually experience the emotions that you
must show.
1
2
3
4
5
6
I spontaneously feel the emotions I have to
show to others.
1
2
3
4
5
7
Pretend to have emotions that you do not really
have.
1
2
3
4
5
8
Really try to feel the emotions I have to show as
part of my job
1
2
3
4
5
On an average day at work, how frequently
do you do each of the following when
interacting with athletes
153
Continued
Appendix Continued
9
Experience spontaneously the positive emotions
(such as confidence and enthusiasm) I express in
a critical situation during a game.
1
2
3
4
5
10
When I am feeling negative emotions, I make
sure not to express them.
1
2
3
4
5
154
APPENDIX G
Emotional Exhaustion Scale (Maslach & Jackson, 1986)
155
Never
A few times a year
or less
Once a week
A few times a week
Everyday
1
I feel emotionally drained at
coaching
0
1
2
3
4
5
6
2
I feel used up at the end of the day
0
1
2
3
4
5
6
3
I feel fatigued when I get up in the
morning and have to face another
day on the coaching
0
1
2
3
4
5
6
4
I feel burned out from coaching
0
1
2
3
4
5
6
5
I feel frustrated on coaching
0
1
2
3
4
5
6
156
Once a month or
less
A few times a
month
Please select the one number for
each question that comes closest
to reflecting your opinion about
it
APPENDIX H
Job Satisfaction Scale (Cammann, Fichman, Jenkins, & Klesh, 1979; Spector, 1985)
157
Please select the number for each
statement that reflects the extent to
which you agree / disagree with each
of the following statements
Strongly Disagree
Disagree
Slightly Disagree
Slightly Agree
Agree
Strongly Agree
1
In general, I like my job.
1
2
3
4
5
6
2
All in all, I am satisfied with my job.
1
2
3
4
5
6
3
My job is enjoyable.
1
2
3
4
5
6
158
APPENDIX I
Demographic Questionnaire
159
Please complete the following questions:
1. Your gender
2. Your age
(Male
(
Female)
)
3. Your ethnicity
a. White / Caucasian
b. Black / African American
c. Hispanic
d. American Indian / Alaska Native
e. Hawaiian / Pacific Islander
f. Asian American
g. Other (
)
4. Your educational background
a. High School
b. Community College Degree
c. Bachelor Degree
d. Master Degree
e. Doctorate Degree
f. Other (
)
5. The sports you coach
a. What kind of sport(s)? (
)
b. Gender of the team (Male or Female) you coach? (
)
6. Your average hour per day that you are in direct contact with your athletes
(
) hours per day
160
7. Your year of coaching including this year? (
) years
8. How many years have you worked for the current team? (
9. Other responsibilities other than coaching?
) years
(Yes No)
a. If yes, what are these responsibilities? (
)
10. Please comment on your emotional experience as an intercollegiate coach and its
influence on you and your team will be appreciated.
(
)
Thank you for your help
161