Division I Program. Dissertation. Presented in Partial Fulfillment of the Requirements for the Degree Doctor of Philosophy

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1 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

2 Copyright by Ye Hoon Lee 2012

3 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

4 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

5 Dedication Dedicated to my loving and supportive family iv

6 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

7 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

8 Vita July 12, 1979 Born Seoul, Korea B.S. Physical Education, Seoul National University 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

9 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 Literature Review...22 Emotional Labor...22 Conceptualization of Emotional Labor..25 Antecedents of Emotional Labor...34 Consequences of Emotional Labor Method...64 Research Design viii

10 Sampling Method...66 Pilot Study..67 Target Population...69 Instrumentation...73 Data Collection..77 Data analysis Results 80 Demographic Characteristics...80 Single-Group Confirmatory Factor Analysis...86 Second-Order Confirmatory Factor Analysis 94 Single-Group Structural Equation Modeling Discussion Overview of the Instruments Significance Findings of the Study Implications..119 Limitations and Future Studies List of References Appendices A. Pre-notification.139 B. Main study invitation 141 C. Follow-up D. Positive Affectivity and Negative Affectivity Scale. 147 E. Emotional Intelligence Scale.149 ix

11 F. Emotional Labor Scale G. Emotional Exhaustion Scale..155 H. Job Satisfaction Scale I. Demographic Questionnaire..159 x

12 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

13 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 Maximum likelihood estimates for Model 1 and Model Table A summary of the single-group structural equation modeling.101 Table Summary of results for study hypotheses.102 xii

14 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 Figure 4.6. Path coefficients between latent variables for Model xiii

15 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

16 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

17 (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

18 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

19 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

20 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

21 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

22 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

23 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

24 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

25 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

26 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

27 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

28 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

29 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

30 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

31 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

32 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

33 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

34 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

35 In this study, emotional exhaustion measures coaches feeling of burnout in the form of overwhelming exhaustion due to their jobs. 21

36 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

37 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

38 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

39 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

40 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

41 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

42 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

43 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

44 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

45 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

46 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

47 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

48 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

49 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

50 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

51 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 Type of Emotional Labor Relationship Austin et al. (2008) Brotheridge & Lee (2003) Surface acting Deep acting Surface acting Deep acting Negative Positive Negative No relationship Diefendorff et al. (2005) Gosserand & Diefendorff (2005) Surface acting Deep acting Expression of naturally felt emotion (Automatic regulation) Surface acting Deep acting Negative No relationship Positive Negative Positive 37

52 Table 2.2 Relationship between negative affectivity and emotional labor strategies Study Type of Emotional Labor Relationship Austin et al. (2008) Brotheridge & Lee (2003) Surface acting Deep acting Surface acting Deep acting Positive Negative Positive No relationship Diefendorff et al. (2005) Gosserand & Diefendorff (2005) Liu et al. (2008) Surface acting Deep acting Expression of naturally felt emotion (Automatic regulation) Surface acting Deep acting Surface acting Deep acting Positive No relationship No relationship Positive Positive Positive 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

53 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

54 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

55 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

56 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

57 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

58 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

59 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

60 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

61 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

62 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

63 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

64 Table 2.3 Relationship between emotional intelligence and emotional labor strategies Study Type of Emotional Labor Relationship Daus, et al. (2004) Cote (2005) Brotheridge (2006b) Mikolajczak et al. (2007) Surface acting Deep acting Deep acting Deep acting Surface acting Deep acting Positive consonance (automatic regulation) No relationship Positive Positive Positive Negative Negative Positive Austin et al. (2008) Liu et al. (2008) Karim & Weisz (2010) Surface acting Deep acting Surface acting Deep acting Surface acting Deep acting Negative No relationship No relationship Positive No relationship 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

65 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

66 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

67 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

68 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

69 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

70 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

71 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

72 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

73 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

74 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, Alcover, & Holman (2007) Automatic regulation Negative 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

75 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

76 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

77 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

78 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

79 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

80 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

81 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

82 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 EMOTIONAL INTELLIGENCE 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

83 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 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

84 team s effectiveness. The researcher obtained the list of all coaches in this program, with the most updated job information from a website ( 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 s 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

85 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 Continued

86 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

87 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

88 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

89 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

90 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

91 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 e- mail (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 s were sent out as Kent and Turner (2002) recommended. 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

92 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 s (Appendix C) were sent out after one-week of initial ing 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 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

93 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

94 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

95 Additionally, the coaches had 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

96 Table 4.1 Demographic Variable Frequencies for Respondents (N = 430) Variable Frequency Percent Gender Age Ethnicity Education Male Female Above African American Asian Caucasian Hispanic American Indian 2.5 High School 2.5 Community College 2.5 Bachelor Degree Master Degree Doctorate Degree

97 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) Female Team (N = 214) Both Team (N = 89) Table 4.3 The number of respondents and the total number of coaches by sports Sport # of Respondents The Number of Coaching Position Men s Baseball Men s Basketball Men s Cross Country Men s Fencing 1 21 Men s Diving Men s Football Men s Golf Men s Gymnastics 2 16 Men s Ice Hockey Men s Lacrosse 2 58 Men s Rifle 7 12 Men s Skiing 2 11 Men s Soccer Men s Swimming Men s Tennis Men s Track Men s Volleyball Continued

98 Table 4.3 Continued Men s Water Polo 1 22 Men s Wrestling 6 72 Women s Basketball Women s Bowling 2 31 Women s Cross Country Women s Diving Women s Fencing 2 23 Women s Golf Women s Gymnastics 7 61 Women s Field Hockey Women s Ice Hockey 2 35 Women s Indoor Track Women s Lacrosse 9 87 Women s Rifle 7 12 Women s Skiing 2 12 Women s Soccer Women s Softball Women s Swimming Women s Tennis Women s Track Women s Volleyball Women s Water Polo 4 33 Women s Olympic Sports Total

99 Table 4.4 Comparison of early (N = 235) to late respondents (N = 195) Factors df t-value Sig. Mean Difference APP UND REG UTIL PA NA SA DA AR EE JS 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

100 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 (χ 2 /df = /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

101 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 (χ 2 /df = /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

102 Table. 4.5 Factor loadings (λ) for each item and Cronbach s coefficient (α) and average variance extracted (AVE) for each factor Factor Factor loading (λ) Items Model 1 Model 2 Alpha (α) AVE Appraisal of Emotion APP APP APP APP Understanding of Emotion UND UND UND UND Regulation of Emotion REG REG REG REG Utilization of Emotion UTIL UTIL UTIL UTIL Positive Affectivity PA PA PA PA Negative Affectivity NA1.43 NA NA NA Surface Acting SA SA SA SA Continued

103 Table 4.5 Continued Factor Factor loading (λ) Items Model1 Model 2 Alpha (α) AVE Deep Acting DA DA DA Automatic Regulation AR AR AR Emotional Exhaustion EE EE EE EE EE Job Satisfaction JS JS JS Table 4.6 A Summary of the Single-Group CFA χ 2 / df RMSEA SRMR TLI CFI Model Model

104 Figure 4.1 First-order confirmatory factor analysis model for antecedent scales except emotional intelligence construct 90

105 Figure 4.2 First-order confirmatory factor analysis model for emotional labor strategies scales 91

106 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

107 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

108 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

109 95 Table 4.7. Means (M), Standard deviations (SD), and correlation for factors M SD Correlation Factor APP UND ** REG **.416** UTIL **.324*.418** PA **.221**.251**.405** NA ** ** -.205** -.168** SA * ** -.121* -.291**.249** DA * *.219** AR **.192**.264**.182**.323** -.143* -.190**.309* EE ** ** -.145* -.411**.378**.418** ** JS **.218**.272**.369**.425** -.245** -.245** ** -.517** 1.00 * 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

110 Table 4.8 A summary of the second-order factor model for Emotional Intelligence construct χ 2 RMSEA / df SRMR TLI CFI (90% CI).063 Model (.054;.072) Table 4.9 Maximum likelihood estimates for second-order factor model 2nd-order factor 1st-order factor λ t-value Emotional Intelligence Appraisal of Emotion (APP) Understanding of Emotion (UND) Regulation of Emotion (REG) Utilization of Emotion UTIL)

111 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

112 The result of SEM for Model 1 showed that the model fit reasonably well (χ 2 /df = /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 (χ 2 /df = / 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

113 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 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

114 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 Model

115 Table 4.11 Maximum Likelihood Estimates for Model 1 and Model 2 Estimates β S.E. t Model 1 Emotional Intelligence Surface Acting Emotional Intelligence Deep Acting Emotional Intelligence Automatic Regulation Positive Affectivity Surface Acting Positive Affectivity Deep Acting Negative Affectivity Surface Acting Negative Affectivity Deep Acting Surface Acting Emotional Exhaustion Surface Acting Job Satisfaction Deep Acting Emotional Exhaustion Deep Acting Job Satisfaction Automatic Regulation Emotional Exhaustion Automatic Regulation Job Satisfaction Model 2 Emotional Intelligence Automatic Regulation Positive Affectivity Surface Acting Positive Affectivity Automatic Regulation Negative Affectivity Surface Acting Surface Acting Emotional Exhaustion Surface Acting Job Satisfaction Automatic Regulation Emotional Exhaustion Automatic Regulation Job Satisfaction

116 Table 4.12 Summary of Results for Study Hypotheses Hypothesis H1. Emotional intelligence will be negatively associated with surface Results Not supported acting H2. Emotional intelligence will be positively associated with deep acting Not supported H3. Emotional intelligence will be positively associated with automatic 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 Supported satisfaction 102

117 103 Figure 4.5. Path coefficients between latent variables for Model 1 Insignificant relationships are not represented in the figure

118 104 Figure 4.6. Path coefficients between latent variables for Model 2