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Moderating role of hotel employees’ gender and job position on the relationship between emotional intelligence and emotional labor
International Journal of Hospitality Management 43 (2014) 47–52
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International Journal of Hospitality Management journal homepage: www.elsevier.com/locate/ijhosman
Research Note
Moderating role of hotel employees’ gender and job position on the relationship between emotional intelligence and emotional labor Hyo Sun Jung 1 , Hye Hyun Yoon ∗ Department of Culinary and Foodservice Management, College of Hotel & Tourism Management, Kyung Hee University, 1 Hoegi-dong, Dongdaemun-gu, Seoul 130-701, Republic of Korea
a r t i c l e
i n f o
Keywords: Emotional intelligence Emotional labor Employees’ diversity
a b s t r a c t The purpose of this study is to identify the effects of deluxe hotel employees’ emotional intelligence on their emotional labor, and the moderating effects of employees’ diversity (gender and job position) on the relationship between emotional intelligence and emotional labor. The results showed that the use of emotion (UOE) had the largest effect on surface acting during emotional labor, and self-emotion appraisal (SEA) had the largest effect on deep acting. In addition, the study found moderating effects of employees’ diversity on the relationship between emotional intelligence and emotional labor, and the effects of others’ emotion appraisal (OEA) on surface acting were shown to be significantly higher among female employees than among males. Furthermore, the effects of the use of emotions (UOE) on deep acting were larger in the FOH than in the BOH. However, results showed that the effects of regulation of emotion (ROE) on deep acting were significantly stronger in the BOH than in the FOH. © 2014 Elsevier Ltd. All rights reserved.
1. Introduction Emotional intelligence is the general ability to understand others’ emotions and to experience and express appropriate emotions (Mayer et al., 2002), whereas emotional labor is the positive expression of emotions related to duties in situations that should require an emotional response (Hochschild, 1979; Grandey, 2000). In particular, deep acting requires more effort in order to experience expressed norms in situations where emotional labor is performed, and it is regarded as a sort of emotional labor that can be much more easily expressed by those who have a high degree of emotional intelligence. Surface acting is defined as artificially controlling and expressing emotions to meet the norms of expression required by organizations, without actually feeling the emotions (Ashforth and Humphrey, 1993). What, then, is the relationship between emotional intelligence and emotional labor? Few studies have examined the relationship between emotional intelligence and emotional labor, and those have yielded different opinions on the subject (see Table 1). Karim and Weisz (2010), Lee and Ok (2012), and Prentice et al. (2013) suggested that employees’ emotional intelligence is positively related
∗ Corresponding author. Tel.: +82 2 961 9403; fax: +82 2 964 2537. E-mail addresses: [email protected] (H.S. Jung), [email protected] (H.H. Yoon). 1 Tel.: +82 2 961 2321; fax: + 82 2 964 2537. http://dx.doi.org/10.1016/j.ijhm.2014.08.003 0278-4319/© 2014 Elsevier Ltd. All rights reserved.
to emotional labor. Also, Brotheridge (2006) and Ramachandran et al. (2011) found that emotional intelligence has positive influence only upon deep acting out of emotional labor. On the other hand, Lee et al. (2010) and Kim et al. (2012) mentioned that emotional intelligence has positive influence upon deep acting, but has negative influence upon surface acting. Austin et al. (2008) and Psilopanagioti et al. (2012) noted that employees’ emotional intelligence has negative influence only upon surface acting. Also, Totterdell and Holman (2003), and Johnson and Spector (2007) argued that there is no significant relationship between emotional intelligence and emotional labor. In this way, diverse contradictory results exist in the relationship between emotional intelligence and emotional labor. However, present study synthesized the results of prior researches (Karim and Weisz, 2010; Lee and Ok, 2012) and then supposed that employees with excellent emotional intelligence will have even efficient emotional labor in a working situation. This is because the effective control in own emotion or other’s emotion and the excellent ability in regulating or utilizing emotion will lead even to very effective emotional labor in the inner aspect, as well as emotional labor in the superficial aspect. Also, the diversity of employees in organizations may be divided into surface-level diversity and deep-level diversity (Harrison et al., 2002; Robbins and Judge, 2013). Jackson et al. (2003) said that diversity represented to the distribution of personal attributes among interdependent employees of a workplace. Meanwhile, although groups of employees consisted of males in similar age
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Table 1 Previous studies on the relationship between emotional intelligence and emotional labor. Directions
Authors
Sample
Main results
Mikolajczak et al. (2007)
Nurses (124)
Karim and Weisz (2010)
Public organization (92)
Lee and Ok (2012)
Hotel (309)
Prentice et al. (2013)
Hospitality (578)
Individuals with higher trait EI scores experienced more positive consonance and performed less emotional effort than individuals with lower trait EI scores, in terms of both SA and DA. EI, DA, and SA were indicated to have positive correlation. Out of this, EI was mentioned to have positive influence upon DA. EI negatively affected emotional dissonance while positively affecting emotional effort, and suggested that EI contributed to positive expression of emotions. It suggested the positive relationship between EI and SA with saying that SA has positive relationship out of EI and EL, and that the influence of DA and SA upon burnout reduces in the more excellent EI.
Brotheridge (2006)
Customer service (188)
Ramachandran et al. (2011)
Resort (152)
Lee et al. (2010)
General company (401)
Kim et al. (2012)
Hotel (353)
Austin et al. (2008)
Students (Univ.) (247)
Psilopanagioti et al. (2012)
Physicians (130)
Totterdell and Holman (2003) Johnson and Spector (2007)
Call center (bank) (90) Customer service (176)
positive
EI −→ EL
positive
EI −→ DA
positive
EI −→ DA negative
EI −→ SA
negative
EI −→ SA
EI
Not significant
−→
EL
The higher the score of a person’s EI, the higher the possibility of DA, implying that EI is positively related only with DA during EL. EI had a significant positive correlation with DA during EL but had nothing to do with SA. It was noted that EI has positive influence upon DA, but has negative influence upon SA in a research of targeting general companies such as electronic, heavy industry, retail, finance, manufacturing, resort, and security. EI was differently related to SA and DA. Hotel front-line employees’ EI is positively related to DA, and negatively related to SA. It was claimed that EI and DA has no significant relationship and that EI has significantly negative influence only upon SA. EI and SA during EL were negatively correlated, and that the ability to appraise one’s own emotions had the largest effects. They argued that EI had no connection with EL. EI did not moderate the relationship between the EL strategies and personal outcomes
Note: Emotional intelligence (EI); emotional labor (EL); surface acting (SA); deep acting (DA).
groups and from the same ethnic group with the same religious conviction in the past, most work groups today are characterized by varieties in gender, nationality, ethnicity, age, education level, career paths, values, and personalities (Mikolajczak et al., 2007). In particular, Petrides and Furnham (2000) observed that females had greater ability to express their emotions than males, and Tamres et al. (2002) found that, although females experienced more stress than males in work situations, they dealt with the emotional aspects of such situations more effectively. Also, Jung and Yoon (2012) advised that the characteristics of job positions played a role in moderating the relationship between emotional intelligence and employees’ behavior. Moreover, studies have shown how employee diversity greatly affects performance in the hospitality industry (Sourouklis and Tsagdis, 2013). Therefore, employee diversity should be efficiently managed in order to enhance the productivity of organizations (Garib, 2013). In this study, the diversities of employees in the hotel industry were divided into surface diversity and deep diversity, and gender and job position were chosen as a representative surface factor and a representative deep factor, respectively. In the present study, emotional intelligence was divided into four sub-factors: others’ emotion appraisal, self-emotion appraisal, use of emotion, and regulation of emotion (Wong and Law, 2002). Also, based on Chu and Murrmann (2006)’s study, the current study divides traits of emotional labor into surface acting and deep acting. This study not only explores the sub-factors of emotional
intelligence that significantly affect employees’ emotional labor (H1,2) but also attempts to establish the moderating effects of the diversity of organization employees on the causal relationship between emotional intelligence and emotional labor (P1,2) (Fig. 1). 2. Research methodology The data used for the current study were collected from November through December 2012 from employees of deluxe hotel in Seoul of Korea. With the permission of the human resources manager, employees were provided with a voluntary survey and were asked by the researcher to complete the self-administered questionnaires. A pilot test of 50 employees was conducted to ensure the reliability of the scales to be used in a questionnaire, and several modifications were made based on feedback. In total, 500 questionnaires were distributed. After eliminating incomplete questionnaires, 308 usable questionnaires were obtained for processing – a response rate of almost 61.0%. The sample included 49.7% males and 50.3% females; 49.3% were 21–30 years of age. As job position, FOH (front of house) of directly facing customers accounted for 66.6%, and BOH (back of house) as the support position accounted for 33.4%. All participants had been working for five years or less in their current hotel (69.8%). Mayer and Salovey (1997, p. 10) define emotional intelligence more precisely as the “the ability to perceive accurately, appraise, and express emotion; the ability to access and/or generate feelings
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Fig. 1. The proposed model of emotional intelligence, emotional labor, and employees’ diversity.
when they facilitate thought; the ability to understand emotion and emotional knowledge; and the ability to regulate emotions to promote emotional and intellectual growth.” The emotional intelligence scale (Mayer and Salovey, 1997; Wong and Law, 2002) was used, containing 16 items based on a seven-point Likert-type scale. Also, Hochschild (1983, p. 7) defined emotional labor more precisely as the “management of feeling to create a publicly observable facial and bodily display.” This study divides traits of emotional labor into surface acting and deep acting (Diefendorff et al., 2005). Surface acting is the personal effort to suppress one’s own emotions and observe emotional expressions required by the organization. Deep acting means a more active effort to change one’s own emotions into the norm of emotional expressions of the organization. Emotional labor was measured using 8 items based on the work of Brotheridge and Lee (2003) and Chu and Murrmann (2006). 3. Results The confirmatory measurement models demonstrated the soundness of measurement properties (2 = 381.924; GFI = .908; RMR = .051). In addition, all standardized factor loadings exceeded .7, and each indicator t-value exceeded 8.0 (p < .001), the average variance extracted of all factors exceeded the recommended .50 threshold (See Table 2) (Fornell and Larcker, 1981). The structural equation model (SEM) fit was good (2 = 634.874; 2 /df = 2.601; GFI = .849; CFI = .926). To examine how employees’ emotional intelligence affects surface acting among the elements of emotional labor, hypothesis 1 was verified and accepted. Use of emotion (ˇ = .365), self-emotion appraisal (ˇ = .309), others’ emotion appraisal (ˇ = .298), and regulation of emotion (ˇ = .208) had significant effects on surface acting. Hypothesis 2 (i.e., employees’ emotional intelligence has a significant effect on deep acting among the elements of emotional labor) was accepted as well. Self-emotion appraisal (ˇ = .486), use of emotion (ˇ = .219), others’ emotion appraisal (ˇ = .188), and regulation of emotion (ˇ = .137; p < .05) had a significant effect on deep acting (See Table 3). A multi-group approach was used to test the moderating effects of the employees’ diversity (surface-level vs. deep-level) on emotional intelligence and emotional labor: 2 differences with two degrees of freedom were used to compare the two models (unconstrained and constrained) for each of the eight path coefficients, consecutively. The results of the moderating effects of employees’ surface-level diversity are shown in Table 4. The results showed that the effects of others’ emotion appraisal on surface acting were significantly stronger in the female (ˇ = .447) than in the male (ˇ = .156). Therefore,
proposition 1 was partially supported. Also, results of the moderating effects of employees’ deep-level diversity are shown in Table 5. The results showed that the effects of use of emotion on deep acting were significantly stronger in the FOH (ˇ = .335) than in the BOH (ˇ = −.066). Also, the results showed that the effects of regulation of emotion on deep acting were significantly stronger in the BOH (ˇ = .345) than in the FOH (ˇ = .021). Therefore, proposition 2 was partially supported.
Table 2 Properties of the measurement model. Construct (Cronbach’s alpha)
Standardized loadings
t-Value
CCR
AVE
Others’ emotion appraisal (.866) EI1 EI2 EI3 EI4
.737 .793 .833 .783
Fixed 13.192 13.777 13.030
.802
.619
Self-emotion appraisal (.872) EI5 EI6 EI7 EI8
.754 .806 .865 .753
Fixed 14.134 15.143 13.129
.820
.633
Use of emotion (.901) EI9 EI10 EI11 EI12
.842 Fixed 17.858 17.552 17.757
.694
.862 .828 .819 .825
Regulation of emotion (.921) EI13 EI14 EI15 EI16
.882 Fixed 18.730 20.638 20.136
.744
.867 .834 .882 .870
Surface acting (.929) EL1 EL2 EL3 EL4
.888 Fixed 18.714 20.355 20.411
.764
.835 .857 .902 .903
Deep acting (.895) EL5 EL6 EL7 EL8
.751 .830 .856 .866
Fixed 14.790 15.284 15.477
.844
.683
Note: Composite construct reliability (CCR); average variance extracted (AVE). 2 = 381.924 (df = 237) p < .001; CMIN/DF = 1.611; goodness of fit index (GFI) = .908; normed fit index (NFI) = .931; tucker Lewis index (TLI) = .968; comparative fit index (CFI) = .972; incremental fit index (IFI) = .973; root mean square residual (RMR) = .051; root square error of approximation (RMSEA) = .045; *** p < .001.
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Table 3 Structural parameter estimates. Hypothesized path (stated as alternative hypothesis) H1: Emotional intelligence → surface acting H1a Others’ emotion appraisal → SA H1b Self-emotion appraisal → SA H1c Use of emotion → SA H1d Regulation of emotion → SA H2: Emotional intelligence → deep acting H2a Others’ emotion appraisal → DA H2b Self-emotion appraisal → DA H2c Use of emotion → DA H2d Regulation of emotion → DA Goodness-of-fit statistics
Standardized coefficients
t-Value
Results
Supported
.298
5.165***
.309
5.410***
.365
6.466***
.208
3.892*** Supported
.188
3.283**
.486
7.711***
.219
3.910***
.137
2.496*
2 (244) = 634.874 (p < .001) CMIN/DF = 2.601 GFI = .849 NFI = .885 CFI = .926 RMSEA = .072
Note: Surface acting (SA); deep acting (DA); goodness of fit index (GFI); normed fit index (NFI); comparative fit index (CFI); root mean square error of approximation (RMSEA). * p < .05. ** p < .01. *** p < .001.
4. Discussion This study sought to examine the effects of the emotional intelligence of deluxe hotel employees on emotional labor. The study found that all the dimensions of emotional intelligence, in order of influence, had a significant positive effect on surface acting and deep acting (Mikolajczak et al., 2007; Karim and Weisz, 2010; Lee & Ok, 2012, Prentice et al., 2013). Given this result, it may be deduced that employees with high emotional intelligence perform surface acting better because they utilize their emotions well, and their excellent ability to understand their own emotions and to make
an effort to experience the emotions they express positively affect deep acting. In considering the moderating effects of employees’ diversity (gender and job position) on the relationship between emotional intelligence and emotional labor, the effects of others’ emotion appraisal on surface acting were shown to be significantly higher among female than among males. This result means that females’ greater ability to perceive and understand others’ emotions exerts a significantly greater effect on their surface acting during emotional labor than for males, indicating that the relationship between the two variables is much closer for females than for males. Also, the effects of the use of emotions on deep acting were larger in the FOH position than in the BOH. Since the FOH position performs emotional labor through direct contact with customers, their ability to utilize emotions is assumed to have larger effects on deep acting in this position than in the BOH. On the other hand, the effects of the regulation of emotions on deep acting were larger in the BOH. This is assumed to be because employees in the BOH who spend more time with their colleagues than with customers, have relatively lower ability to regulate their emotions than employees in the FOH. Implications that can be inferred from the present study are as follows. The study was an initial attempt to examine the causal relationship between emotional intelligence and emotional labor in employees in the hospitality industry. Emotional intelligence and emotional labor are abilities that are essential for those employees that provide customer contact at the front line of service, an area that has not been studied hitherto. Furthermore, a few studies that have been conducted by other researchers have shown conflicting results regarding the directivity between emotional intelligence and emotional labor. The present study has provided empirical results regarding methods of performing emotional labor in relation to emotional intelligence, by examining concrete directivity and causal relationships between four sub-factors of emotional intelligence and two sub-factors of emotional labor. This study showed that employees of hospitality industries with excellent emotional intelligence performed surface acting to a high degree during emotional labor. Therefore, the results of the present study can be considered meaningful in that they identified positive relationships between the two variables. This is assumed to be because, although emotional intelligence is important to utilize and regulate one’s own emotions while perceiving and understanding others’, emotional labor can be effectively performed after perceiving and understanding solely one’s own emotions. This study suggests that variations in employees’ emotional intelligence should be taken into account in managing employees at the organization level. Specifically, strategies should be established to manage employees’ emotional intelligence and emotional labor through education
Table 4 Moderating effects of gender (surface-level diversity). Male (N = 153)
OEA → SA SEA → SA UOE → SA ROE → SA OEA → DA SEA → DA UOE → DA ROE → DA
Female (N = 155)
Standardized coefficients
t-Value
Standardized coefficients
t-Value
.156 .439 .315 .230 .212 .426 .255 .202
1.975* 5.011*** 3.851*** 2.986** 2.665** 5.055*** 3.217** 2.649**
.447 .201 .373 .177 .189 .541 .195 .035
5.215*** 2.698** 4.936*** 2.440* 2.320* 5.805*** 2.497* .448
Unconstrained model chi-square (df = 488)
Constrained model chi-square (df = 489)
2 (df = 1)
933.995 933.995 933.995 933.995 933.995 933.995 933.995 933.995
942.068 935.955 934.106 934.011 934.002 934.397 934.600 935.383
8.073* 1.960 .111 .016 .007 .402 .605 1.388
Note: 2 /df = 1.914; GFI = .799; NFI = .840; CFI = .916; RMSEA = .055; others’ emotion appraisal (OEA), self-emotion appraisal (SEA), use of emotion (UOE), regulation of emotion (ROE), surface acting (SA), deep acting (DA). * p < .05. ** p < .01. *** p < .001.
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Table 5 Moderating effects of job position (deep-level diversity). BOH (N = 103)
OEA → SA SEA → SA UOE → SA ROE → SA OEA → DA SEA → DA UOE → DA ROE → DA
FOH (N = 205)
Standardized coefficients
t-Value
Standardized coefficients
t-Value
.319 .348 .481 .168 .213 .537 −.066 .345
2.971** 3.851*** 5.040*** 1.994* 2.186* 5.685*** −.773 3.974***
.298 .293 .306 .211 .164 .470 .335 .021
4.296*** 4.025*** 4.403*** 3.096** 2.429* 5.888*** 4.792*** .306
Unconstrained model chi-square (df = 488)
Constrained model chi-square (df = 489)
2 (df = 1)
953.634 953.634 953.634 953.634 953.634 953.634 953.634 953.634
954.634 954.392 954.447 954.335 954.699 954.882 961.922 959.107
1.000 .758 .813 .701 1.605 1.248 8.288* 5.473*
Note: 2 /df = 1.954; GFI = .801; NFI = .841; CFI = .914; RMSEA = .056; others’ emotion appraisal (OEA), self-emotion appraisal (SEA), use of emotion (UOE), regulation of emotion (ROE), surface acting (SA), deep acting (DA). * p < .05. ** p < .01. *** p < .001.
or training, and methods should be devised for responding reasonably and efficiently when emotions experienced by employees and emotions expressed at service contact points are not suitably matched. Programs should be developed to train employees who perform emotional labor to understand the point of view of the customer, in order to improve their ability to express emotion. In addition, the fact that employees with excellent emotional intelligence will eventually be expected to perform emotional labor effectively should be reflected on the process of recruitment. The present study thus makes a contribution to the body of research by drawing attention to the fact that, since the quality of emotional labor in the hospitality industry varies with employees’ emotional intelligence, in-depth understanding of emotional labor and the perception of performing methods are necessary to achieve the desired corporate outcomes and the highest degree of customer satisfaction. In the present study, gender was chosen as a surfacelevel diversity and job position was chosen as a deep-level diversity, in order to examine their moderating effects on the relationship between emotional intelligence and emotional labor. It may also be assumed that, since the variable gender is a surface-level diversity, it supports only surface-level emotional labor. In particular, an employee’s position in the industry is significant because, unlike positions in general companies, there are clear differences between the characteristics of FOH and BOH, and internal factors such as personal preference and values are reflected in employees’ choice of job position in the hospitality industry. Given that females have better ability to understand and perceive others’ emotions than males and are sensitive to the regulation and change of emotions, it may be assumed that their surface acting is also better than that of males (Petrides and Furnham, 2000). Moderating effects were also found from the positions in which employees worked. The FOH position’s excellent ability to regulate emotions was shown to be associated with excellent deep-level ability for emotional expression. By contrast, the BOH positions, who have no direct contact with customers, have relatively lower ability to regulate their emotions, but their deep-level emotional labor improves along with their ability to regulate emotions. However, several limitations of the study need to be mentioned. First, since the variables of emotional intelligence and emotional labor used in the present study were derived from studies conducted with general enterprises, the distinctive business characteristics of the hospitality industry may not have been reflected. Second, the subjects of emotional labor are customers, who would produce more objective data, whereas all the questions in the present study regarding independent variables and dependent variables were answered by employees. Accordingly, limitations might exist due to common method bias. In future
studies, surveys should be conducted by matching questionnaires for employees to those aimed at customers. Fourth, although only gender and job position were examined as employee diversity variables in the present study, in future studies, moderating effects should be examined using more direct and concrete diversity variables such as personality, values, tenure, and cultural factors. Finally, although emotional labor should be analyzed through longitudinal studies, since it can vary due to momentary emotions or moods such as stress, the present study was conducted as a cross-sectional study for reasons of time and cost. In future, more practical results should be extracted from longitudinal studies.
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Contents lists available at ScienceDirect
International Journal of Hospitality Management journal homepage: www.elsevier.com/locate/ijhosman
Research Note
Moderating role of hotel employees’ gender and job position on the relationship between emotional intelligence and emotional labor Hyo Sun Jung 1 , Hye Hyun Yoon ∗ Department of Culinary and Foodservice Management, College of Hotel & Tourism Management, Kyung Hee University, 1 Hoegi-dong, Dongdaemun-gu, Seoul 130-701, Republic of Korea
a r t i c l e
i n f o
Keywords: Emotional intelligence Emotional labor Employees’ diversity
a b s t r a c t The purpose of this study is to identify the effects of deluxe hotel employees’ emotional intelligence on their emotional labor, and the moderating effects of employees’ diversity (gender and job position) on the relationship between emotional intelligence and emotional labor. The results showed that the use of emotion (UOE) had the largest effect on surface acting during emotional labor, and self-emotion appraisal (SEA) had the largest effect on deep acting. In addition, the study found moderating effects of employees’ diversity on the relationship between emotional intelligence and emotional labor, and the effects of others’ emotion appraisal (OEA) on surface acting were shown to be significantly higher among female employees than among males. Furthermore, the effects of the use of emotions (UOE) on deep acting were larger in the FOH than in the BOH. However, results showed that the effects of regulation of emotion (ROE) on deep acting were significantly stronger in the BOH than in the FOH. © 2014 Elsevier Ltd. All rights reserved.
1. Introduction Emotional intelligence is the general ability to understand others’ emotions and to experience and express appropriate emotions (Mayer et al., 2002), whereas emotional labor is the positive expression of emotions related to duties in situations that should require an emotional response (Hochschild, 1979; Grandey, 2000). In particular, deep acting requires more effort in order to experience expressed norms in situations where emotional labor is performed, and it is regarded as a sort of emotional labor that can be much more easily expressed by those who have a high degree of emotional intelligence. Surface acting is defined as artificially controlling and expressing emotions to meet the norms of expression required by organizations, without actually feeling the emotions (Ashforth and Humphrey, 1993). What, then, is the relationship between emotional intelligence and emotional labor? Few studies have examined the relationship between emotional intelligence and emotional labor, and those have yielded different opinions on the subject (see Table 1). Karim and Weisz (2010), Lee and Ok (2012), and Prentice et al. (2013) suggested that employees’ emotional intelligence is positively related
∗ Corresponding author. Tel.: +82 2 961 9403; fax: +82 2 964 2537. E-mail addresses: [email protected] (H.S. Jung), [email protected] (H.H. Yoon). 1 Tel.: +82 2 961 2321; fax: + 82 2 964 2537. http://dx.doi.org/10.1016/j.ijhm.2014.08.003 0278-4319/© 2014 Elsevier Ltd. All rights reserved.
to emotional labor. Also, Brotheridge (2006) and Ramachandran et al. (2011) found that emotional intelligence has positive influence only upon deep acting out of emotional labor. On the other hand, Lee et al. (2010) and Kim et al. (2012) mentioned that emotional intelligence has positive influence upon deep acting, but has negative influence upon surface acting. Austin et al. (2008) and Psilopanagioti et al. (2012) noted that employees’ emotional intelligence has negative influence only upon surface acting. Also, Totterdell and Holman (2003), and Johnson and Spector (2007) argued that there is no significant relationship between emotional intelligence and emotional labor. In this way, diverse contradictory results exist in the relationship between emotional intelligence and emotional labor. However, present study synthesized the results of prior researches (Karim and Weisz, 2010; Lee and Ok, 2012) and then supposed that employees with excellent emotional intelligence will have even efficient emotional labor in a working situation. This is because the effective control in own emotion or other’s emotion and the excellent ability in regulating or utilizing emotion will lead even to very effective emotional labor in the inner aspect, as well as emotional labor in the superficial aspect. Also, the diversity of employees in organizations may be divided into surface-level diversity and deep-level diversity (Harrison et al., 2002; Robbins and Judge, 2013). Jackson et al. (2003) said that diversity represented to the distribution of personal attributes among interdependent employees of a workplace. Meanwhile, although groups of employees consisted of males in similar age
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Table 1 Previous studies on the relationship between emotional intelligence and emotional labor. Directions
Authors
Sample
Main results
Mikolajczak et al. (2007)
Nurses (124)
Karim and Weisz (2010)
Public organization (92)
Lee and Ok (2012)
Hotel (309)
Prentice et al. (2013)
Hospitality (578)
Individuals with higher trait EI scores experienced more positive consonance and performed less emotional effort than individuals with lower trait EI scores, in terms of both SA and DA. EI, DA, and SA were indicated to have positive correlation. Out of this, EI was mentioned to have positive influence upon DA. EI negatively affected emotional dissonance while positively affecting emotional effort, and suggested that EI contributed to positive expression of emotions. It suggested the positive relationship between EI and SA with saying that SA has positive relationship out of EI and EL, and that the influence of DA and SA upon burnout reduces in the more excellent EI.
Brotheridge (2006)
Customer service (188)
Ramachandran et al. (2011)
Resort (152)
Lee et al. (2010)
General company (401)
Kim et al. (2012)
Hotel (353)
Austin et al. (2008)
Students (Univ.) (247)
Psilopanagioti et al. (2012)
Physicians (130)
Totterdell and Holman (2003) Johnson and Spector (2007)
Call center (bank) (90) Customer service (176)
positive
EI −→ EL
positive
EI −→ DA
positive
EI −→ DA negative
EI −→ SA
negative
EI −→ SA
EI
Not significant
−→
EL
The higher the score of a person’s EI, the higher the possibility of DA, implying that EI is positively related only with DA during EL. EI had a significant positive correlation with DA during EL but had nothing to do with SA. It was noted that EI has positive influence upon DA, but has negative influence upon SA in a research of targeting general companies such as electronic, heavy industry, retail, finance, manufacturing, resort, and security. EI was differently related to SA and DA. Hotel front-line employees’ EI is positively related to DA, and negatively related to SA. It was claimed that EI and DA has no significant relationship and that EI has significantly negative influence only upon SA. EI and SA during EL were negatively correlated, and that the ability to appraise one’s own emotions had the largest effects. They argued that EI had no connection with EL. EI did not moderate the relationship between the EL strategies and personal outcomes
Note: Emotional intelligence (EI); emotional labor (EL); surface acting (SA); deep acting (DA).
groups and from the same ethnic group with the same religious conviction in the past, most work groups today are characterized by varieties in gender, nationality, ethnicity, age, education level, career paths, values, and personalities (Mikolajczak et al., 2007). In particular, Petrides and Furnham (2000) observed that females had greater ability to express their emotions than males, and Tamres et al. (2002) found that, although females experienced more stress than males in work situations, they dealt with the emotional aspects of such situations more effectively. Also, Jung and Yoon (2012) advised that the characteristics of job positions played a role in moderating the relationship between emotional intelligence and employees’ behavior. Moreover, studies have shown how employee diversity greatly affects performance in the hospitality industry (Sourouklis and Tsagdis, 2013). Therefore, employee diversity should be efficiently managed in order to enhance the productivity of organizations (Garib, 2013). In this study, the diversities of employees in the hotel industry were divided into surface diversity and deep diversity, and gender and job position were chosen as a representative surface factor and a representative deep factor, respectively. In the present study, emotional intelligence was divided into four sub-factors: others’ emotion appraisal, self-emotion appraisal, use of emotion, and regulation of emotion (Wong and Law, 2002). Also, based on Chu and Murrmann (2006)’s study, the current study divides traits of emotional labor into surface acting and deep acting. This study not only explores the sub-factors of emotional
intelligence that significantly affect employees’ emotional labor (H1,2) but also attempts to establish the moderating effects of the diversity of organization employees on the causal relationship between emotional intelligence and emotional labor (P1,2) (Fig. 1). 2. Research methodology The data used for the current study were collected from November through December 2012 from employees of deluxe hotel in Seoul of Korea. With the permission of the human resources manager, employees were provided with a voluntary survey and were asked by the researcher to complete the self-administered questionnaires. A pilot test of 50 employees was conducted to ensure the reliability of the scales to be used in a questionnaire, and several modifications were made based on feedback. In total, 500 questionnaires were distributed. After eliminating incomplete questionnaires, 308 usable questionnaires were obtained for processing – a response rate of almost 61.0%. The sample included 49.7% males and 50.3% females; 49.3% were 21–30 years of age. As job position, FOH (front of house) of directly facing customers accounted for 66.6%, and BOH (back of house) as the support position accounted for 33.4%. All participants had been working for five years or less in their current hotel (69.8%). Mayer and Salovey (1997, p. 10) define emotional intelligence more precisely as the “the ability to perceive accurately, appraise, and express emotion; the ability to access and/or generate feelings
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Fig. 1. The proposed model of emotional intelligence, emotional labor, and employees’ diversity.
when they facilitate thought; the ability to understand emotion and emotional knowledge; and the ability to regulate emotions to promote emotional and intellectual growth.” The emotional intelligence scale (Mayer and Salovey, 1997; Wong and Law, 2002) was used, containing 16 items based on a seven-point Likert-type scale. Also, Hochschild (1983, p. 7) defined emotional labor more precisely as the “management of feeling to create a publicly observable facial and bodily display.” This study divides traits of emotional labor into surface acting and deep acting (Diefendorff et al., 2005). Surface acting is the personal effort to suppress one’s own emotions and observe emotional expressions required by the organization. Deep acting means a more active effort to change one’s own emotions into the norm of emotional expressions of the organization. Emotional labor was measured using 8 items based on the work of Brotheridge and Lee (2003) and Chu and Murrmann (2006). 3. Results The confirmatory measurement models demonstrated the soundness of measurement properties (2 = 381.924; GFI = .908; RMR = .051). In addition, all standardized factor loadings exceeded .7, and each indicator t-value exceeded 8.0 (p < .001), the average variance extracted of all factors exceeded the recommended .50 threshold (See Table 2) (Fornell and Larcker, 1981). The structural equation model (SEM) fit was good (2 = 634.874; 2 /df = 2.601; GFI = .849; CFI = .926). To examine how employees’ emotional intelligence affects surface acting among the elements of emotional labor, hypothesis 1 was verified and accepted. Use of emotion (ˇ = .365), self-emotion appraisal (ˇ = .309), others’ emotion appraisal (ˇ = .298), and regulation of emotion (ˇ = .208) had significant effects on surface acting. Hypothesis 2 (i.e., employees’ emotional intelligence has a significant effect on deep acting among the elements of emotional labor) was accepted as well. Self-emotion appraisal (ˇ = .486), use of emotion (ˇ = .219), others’ emotion appraisal (ˇ = .188), and regulation of emotion (ˇ = .137; p < .05) had a significant effect on deep acting (See Table 3). A multi-group approach was used to test the moderating effects of the employees’ diversity (surface-level vs. deep-level) on emotional intelligence and emotional labor: 2 differences with two degrees of freedom were used to compare the two models (unconstrained and constrained) for each of the eight path coefficients, consecutively. The results of the moderating effects of employees’ surface-level diversity are shown in Table 4. The results showed that the effects of others’ emotion appraisal on surface acting were significantly stronger in the female (ˇ = .447) than in the male (ˇ = .156). Therefore,
proposition 1 was partially supported. Also, results of the moderating effects of employees’ deep-level diversity are shown in Table 5. The results showed that the effects of use of emotion on deep acting were significantly stronger in the FOH (ˇ = .335) than in the BOH (ˇ = −.066). Also, the results showed that the effects of regulation of emotion on deep acting were significantly stronger in the BOH (ˇ = .345) than in the FOH (ˇ = .021). Therefore, proposition 2 was partially supported.
Table 2 Properties of the measurement model. Construct (Cronbach’s alpha)
Standardized loadings
t-Value
CCR
AVE
Others’ emotion appraisal (.866) EI1 EI2 EI3 EI4
.737 .793 .833 .783
Fixed 13.192 13.777 13.030
.802
.619
Self-emotion appraisal (.872) EI5 EI6 EI7 EI8
.754 .806 .865 .753
Fixed 14.134 15.143 13.129
.820
.633
Use of emotion (.901) EI9 EI10 EI11 EI12
.842 Fixed 17.858 17.552 17.757
.694
.862 .828 .819 .825
Regulation of emotion (.921) EI13 EI14 EI15 EI16
.882 Fixed 18.730 20.638 20.136
.744
.867 .834 .882 .870
Surface acting (.929) EL1 EL2 EL3 EL4
.888 Fixed 18.714 20.355 20.411
.764
.835 .857 .902 .903
Deep acting (.895) EL5 EL6 EL7 EL8
.751 .830 .856 .866
Fixed 14.790 15.284 15.477
.844
.683
Note: Composite construct reliability (CCR); average variance extracted (AVE). 2 = 381.924 (df = 237) p < .001; CMIN/DF = 1.611; goodness of fit index (GFI) = .908; normed fit index (NFI) = .931; tucker Lewis index (TLI) = .968; comparative fit index (CFI) = .972; incremental fit index (IFI) = .973; root mean square residual (RMR) = .051; root square error of approximation (RMSEA) = .045; *** p < .001.
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Table 3 Structural parameter estimates. Hypothesized path (stated as alternative hypothesis) H1: Emotional intelligence → surface acting H1a Others’ emotion appraisal → SA H1b Self-emotion appraisal → SA H1c Use of emotion → SA H1d Regulation of emotion → SA H2: Emotional intelligence → deep acting H2a Others’ emotion appraisal → DA H2b Self-emotion appraisal → DA H2c Use of emotion → DA H2d Regulation of emotion → DA Goodness-of-fit statistics
Standardized coefficients
t-Value
Results
Supported
.298
5.165***
.309
5.410***
.365
6.466***
.208
3.892*** Supported
.188
3.283**
.486
7.711***
.219
3.910***
.137
2.496*
2 (244) = 634.874 (p < .001) CMIN/DF = 2.601 GFI = .849 NFI = .885 CFI = .926 RMSEA = .072
Note: Surface acting (SA); deep acting (DA); goodness of fit index (GFI); normed fit index (NFI); comparative fit index (CFI); root mean square error of approximation (RMSEA). * p < .05. ** p < .01. *** p < .001.
4. Discussion This study sought to examine the effects of the emotional intelligence of deluxe hotel employees on emotional labor. The study found that all the dimensions of emotional intelligence, in order of influence, had a significant positive effect on surface acting and deep acting (Mikolajczak et al., 2007; Karim and Weisz, 2010; Lee & Ok, 2012, Prentice et al., 2013). Given this result, it may be deduced that employees with high emotional intelligence perform surface acting better because they utilize their emotions well, and their excellent ability to understand their own emotions and to make
an effort to experience the emotions they express positively affect deep acting. In considering the moderating effects of employees’ diversity (gender and job position) on the relationship between emotional intelligence and emotional labor, the effects of others’ emotion appraisal on surface acting were shown to be significantly higher among female than among males. This result means that females’ greater ability to perceive and understand others’ emotions exerts a significantly greater effect on their surface acting during emotional labor than for males, indicating that the relationship between the two variables is much closer for females than for males. Also, the effects of the use of emotions on deep acting were larger in the FOH position than in the BOH. Since the FOH position performs emotional labor through direct contact with customers, their ability to utilize emotions is assumed to have larger effects on deep acting in this position than in the BOH. On the other hand, the effects of the regulation of emotions on deep acting were larger in the BOH. This is assumed to be because employees in the BOH who spend more time with their colleagues than with customers, have relatively lower ability to regulate their emotions than employees in the FOH. Implications that can be inferred from the present study are as follows. The study was an initial attempt to examine the causal relationship between emotional intelligence and emotional labor in employees in the hospitality industry. Emotional intelligence and emotional labor are abilities that are essential for those employees that provide customer contact at the front line of service, an area that has not been studied hitherto. Furthermore, a few studies that have been conducted by other researchers have shown conflicting results regarding the directivity between emotional intelligence and emotional labor. The present study has provided empirical results regarding methods of performing emotional labor in relation to emotional intelligence, by examining concrete directivity and causal relationships between four sub-factors of emotional intelligence and two sub-factors of emotional labor. This study showed that employees of hospitality industries with excellent emotional intelligence performed surface acting to a high degree during emotional labor. Therefore, the results of the present study can be considered meaningful in that they identified positive relationships between the two variables. This is assumed to be because, although emotional intelligence is important to utilize and regulate one’s own emotions while perceiving and understanding others’, emotional labor can be effectively performed after perceiving and understanding solely one’s own emotions. This study suggests that variations in employees’ emotional intelligence should be taken into account in managing employees at the organization level. Specifically, strategies should be established to manage employees’ emotional intelligence and emotional labor through education
Table 4 Moderating effects of gender (surface-level diversity). Male (N = 153)
OEA → SA SEA → SA UOE → SA ROE → SA OEA → DA SEA → DA UOE → DA ROE → DA
Female (N = 155)
Standardized coefficients
t-Value
Standardized coefficients
t-Value
.156 .439 .315 .230 .212 .426 .255 .202
1.975* 5.011*** 3.851*** 2.986** 2.665** 5.055*** 3.217** 2.649**
.447 .201 .373 .177 .189 .541 .195 .035
5.215*** 2.698** 4.936*** 2.440* 2.320* 5.805*** 2.497* .448
Unconstrained model chi-square (df = 488)
Constrained model chi-square (df = 489)
2 (df = 1)
933.995 933.995 933.995 933.995 933.995 933.995 933.995 933.995
942.068 935.955 934.106 934.011 934.002 934.397 934.600 935.383
8.073* 1.960 .111 .016 .007 .402 .605 1.388
Note: 2 /df = 1.914; GFI = .799; NFI = .840; CFI = .916; RMSEA = .055; others’ emotion appraisal (OEA), self-emotion appraisal (SEA), use of emotion (UOE), regulation of emotion (ROE), surface acting (SA), deep acting (DA). * p < .05. ** p < .01. *** p < .001.
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Table 5 Moderating effects of job position (deep-level diversity). BOH (N = 103)
OEA → SA SEA → SA UOE → SA ROE → SA OEA → DA SEA → DA UOE → DA ROE → DA
FOH (N = 205)
Standardized coefficients
t-Value
Standardized coefficients
t-Value
.319 .348 .481 .168 .213 .537 −.066 .345
2.971** 3.851*** 5.040*** 1.994* 2.186* 5.685*** −.773 3.974***
.298 .293 .306 .211 .164 .470 .335 .021
4.296*** 4.025*** 4.403*** 3.096** 2.429* 5.888*** 4.792*** .306
Unconstrained model chi-square (df = 488)
Constrained model chi-square (df = 489)
2 (df = 1)
953.634 953.634 953.634 953.634 953.634 953.634 953.634 953.634
954.634 954.392 954.447 954.335 954.699 954.882 961.922 959.107
1.000 .758 .813 .701 1.605 1.248 8.288* 5.473*
Note: 2 /df = 1.954; GFI = .801; NFI = .841; CFI = .914; RMSEA = .056; others’ emotion appraisal (OEA), self-emotion appraisal (SEA), use of emotion (UOE), regulation of emotion (ROE), surface acting (SA), deep acting (DA). * p < .05. ** p < .01. *** p < .001.
or training, and methods should be devised for responding reasonably and efficiently when emotions experienced by employees and emotions expressed at service contact points are not suitably matched. Programs should be developed to train employees who perform emotional labor to understand the point of view of the customer, in order to improve their ability to express emotion. In addition, the fact that employees with excellent emotional intelligence will eventually be expected to perform emotional labor effectively should be reflected on the process of recruitment. The present study thus makes a contribution to the body of research by drawing attention to the fact that, since the quality of emotional labor in the hospitality industry varies with employees’ emotional intelligence, in-depth understanding of emotional labor and the perception of performing methods are necessary to achieve the desired corporate outcomes and the highest degree of customer satisfaction. In the present study, gender was chosen as a surfacelevel diversity and job position was chosen as a deep-level diversity, in order to examine their moderating effects on the relationship between emotional intelligence and emotional labor. It may also be assumed that, since the variable gender is a surface-level diversity, it supports only surface-level emotional labor. In particular, an employee’s position in the industry is significant because, unlike positions in general companies, there are clear differences between the characteristics of FOH and BOH, and internal factors such as personal preference and values are reflected in employees’ choice of job position in the hospitality industry. Given that females have better ability to understand and perceive others’ emotions than males and are sensitive to the regulation and change of emotions, it may be assumed that their surface acting is also better than that of males (Petrides and Furnham, 2000). Moderating effects were also found from the positions in which employees worked. The FOH position’s excellent ability to regulate emotions was shown to be associated with excellent deep-level ability for emotional expression. By contrast, the BOH positions, who have no direct contact with customers, have relatively lower ability to regulate their emotions, but their deep-level emotional labor improves along with their ability to regulate emotions. However, several limitations of the study need to be mentioned. First, since the variables of emotional intelligence and emotional labor used in the present study were derived from studies conducted with general enterprises, the distinctive business characteristics of the hospitality industry may not have been reflected. Second, the subjects of emotional labor are customers, who would produce more objective data, whereas all the questions in the present study regarding independent variables and dependent variables were answered by employees. Accordingly, limitations might exist due to common method bias. In future
studies, surveys should be conducted by matching questionnaires for employees to those aimed at customers. Fourth, although only gender and job position were examined as employee diversity variables in the present study, in future studies, moderating effects should be examined using more direct and concrete diversity variables such as personality, values, tenure, and cultural factors. Finally, although emotional labor should be analyzed through longitudinal studies, since it can vary due to momentary emotions or moods such as stress, the present study was conducted as a cross-sectional study for reasons of time and cost. In future, more practical results should be extracted from longitudinal studies.
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