Relationships Between Emotional Labor, Job Burnout, and Emotional Intelligence: An Analysis Combining Meta-analysis and Structural Equation Modeling

Relationships Between Emotional Labor, Job Burnout, and Emotional Intelligence: An Analysis Combining Meta-analysis and Structural Equation Modeling | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Relationships Between Emotional Labor, Job Burnout, and Emotional Intelligence: An Analysis Combining Meta-analysis and Structural Equation Modeling Yin-Che Chen, Zhi-Ling Huang, Hui-Chuang Chu This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4563859/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 18 Nov, 2024 Read the published version in BMC Psychology → Version 1 posted 4 You are reading this latest preprint version Abstract The present study adopted a meta-analysis design that incorporated structural equation modeling to explore the relationships between emotional labor (EL), job burnout (JB), and emotional intelligence (EI), and enable model validation. The results revealed that EL and JB were significantly and positively correlated, that EI was significantly and positively correlated with EL, and that EI was significantly and negatively correlated with JB. The SEM parameter estimation values were all positive, reaching the level of significance and meeting the basic fit criteria. The total effect size of EL on JB was 0.307, which was equal to the sum of the direct and indirect effect sizes (0.423–0.116). This result indicated that EL affected JB through EI, validating the presence of a moderating effect. Finally, the results were discussed, and practical suggestions were proposed. emotional labor job burnout emotional intelligence meta-analysis structural equation modeling Figures Figure 1 Figure 2 Figure 3 Figure 4 1. Introduction With the thriving development of organizational psychology and the increasing corporate emphasis on the emotional well-being of employees and their physiological and psychological health, scholars have increasingly explored emotional cultures, individual affective expressions, and work results in an organizational context to develop concrete and effective suggestions for corporate operations and management. Hochschild ( 1983 ) introduced the concept of emotional labor (EL) and the use of the organizational emotion norms as a standard for evaluating employee work performance. EL is regarded as a form of behavioral performance that aligns with emotion norms, indicating that emotions are an aspect of work. However, excessive EL or a dissonance between emotions norms and an individual’s personal emotions may induce stress or lead to negative results, such as perceived job burnout (JB), changes in work attitude, and a decline in service performance (Huang et al., 2014 ; Wu & Wang, 2005 ). Conservation of resources theory posits that when employees adhere to organizational emotion norms and adjust their emotions accordingly, they may expend considerable resources. Excessive emotional demands lead to JB, and a high EL may further exhaust an individual’s emotional resources, thereby causing them to experience various negative situations, including emotional exhaustion, depersonalization, and reduced personal accomplishment. These situations are regarded as the standard characteristics of JB (Wang & Wong, 2009). Thus, if an organization intends to prevent its employees from experiencing the negative effects of EL, the emotional abilities of its employees is a key factor to consider. In an organization, individuals with higher emotional intelligence (EI) are more adept at adjusting their emotions to environmental demands related to EL (Lee & Ok, 2012 ). Salovey and Mayer ( 1990 ) asserted that EI is a form of social intelligence that enables an individual to understand and manage interpersonal relationships. In addition to its role in influence an individual’s emotional management and behavior, EI represents an individual’s ability to control their emotions and cope with frustrations when they encounter challenging events. The present study explored the model architecture underlying the associations among EL, JB, and EI to determine the relationships between specific variables. A review of studies conducted in Taiwan and other Asian countries was conducted to integrate the model and summarize generalizable results that are relevant to organizations. Through the collection and collation of relevant literature, the present study determined that most studies related to EL, JB, and EI adopted a quantitative questionnaire survey design. Given the diverse outcomes reported in the literature, the present study adopted a meta-analysis research design and an integrated perspective to develop a theoretical model. Structural equation modeling (SEM) was used for validation, and a research model that combined meta-analysis and SEM (referred to as the “MASEM model”) was employed. Numerous scholars have adopted this model for research. Meta-analyses can overcome the disadvantages of traditional literature review methods by using statistical models to facilitate systematic integration and comments, thereby providing a focused and integrated architecture for studying specific topics. The meta-analysis model can be used to obtain objective quantitative information, assess the directions of the effects between variables, and determine the corresponding effect sizes (Wang, 1999 ). Furthermore, the emerging MASEM method can validate the relevance between variables and correlation matrices by examining a proposed theoretical model and assessing its model fit (Huang & Lin, 2007 ). Thus, the present study employed this research method to clarify and integrate literature findings and further explore the associations among EL, JB, and EI. The results and theoretical contribution of the present study are discussed, and practical suggestions are provided. 2. Literature Review 2.1. Emotional Labor The concept of EL was first proposed by Hochschild ( 1983 ), who defined EL as a process by which employees control and adjust their emotions during interactions with customers in the workplace to meet the normative demands of their organization with respect to personal emotional and behavioral expressions. This process enables the integration of smiles, emotions, and affective relationships into the products or services sold. EL can be classified as surface and deep acting on the basis of an individual’s acting status. Surface acting involves an individual receiving instructions regarding emotional regulation without changing their thoughts through internalization. An individual who engages in surface acting adheres to organizational emotion norms and exhibit the corresponding attitudes. Deep acting occurs when an individual not only conforms to emotion norms but also internalize such norms and adjust their emotions such that they can meet organizational demands. In the Chinese language, various terms can be used to convey the concept of EL. However, scholars have extended the work of Hochschild ( 1983 ) by focusing on the active emotional regulation of employees as well as the management and training implemented by organizations and leaders to enable their employees to meet organizational emotional demands. Most subsequent scholars have adopted said definition of EL for further extension and elaboration. Scholars worldwide have identified as EL as a service or a type of product on the basis of Hochschild’s ( 1983 ) finding regarding the positive and negative effects of EL on organizational development. 2.2. Job Burnout The concept of burnout was first proposed by Freudenberger ( 1975 ), who argued that burnout is not just a work-related status but one that an individual who is not affiliated with a unit may develop when they experience excessive demands on their resources, resulting in feelings of failure, weariness, and exhaustion. Freudenberger ( 1975 ) contended that the loss of charisma by a leader is the primary antecedent of burnout and that when a leader begins to doubt their ability to competently manage their subordinates, their subordinates may experience disappointment or develop low expectations toward their leader. Subsequently, the leader places excessive demands on the capabilities or resources of their subordinates to recover their charisma, thereby causing burnout and various forms of physiological and psychological discomfort. Maslach and Jackson ( 1981 ) contended that when employees experience increased levels of JB, they may become emotionally exhausted. Excessive emotional exhaustion may reduce the job dedication of employees and have psychological effects. In addition, the employees may treat customers as an inconvenience when they are providing services and switch from active to negative attitudes or even adopt depersonalized modes. The employees tend to engage in negative self-evaluation, expressing considerable dissatisfaction with themselves and their jobs with respect to customer interactions. Thus, Maslach and Jackson ( 1981 ) divided the aforementioned processes into three dimensions for measuring JB, namely, emotional exhaustion, depersonalization, and personal accomplishment, and they developed the Maslach Burnout Inventory (MBI) for assessing job burnout, which has been extensively applied by researchers. 2.3. Emotional Intelligence EI can be explored from the perspectives of emotion and intelligence. Salovey and Mayer ( 1990 ) defined emotions as a series of organized responses related to physiology, cognition, motive, and experience. Compared with moods, emotions have a shorter duration and are more intense. Emotions function as the positive or negative responses of an individual to internal and external events. Wechsler ( 1958 ) referred to intelligence as the comprehensive ability of an individual to respond appropriately to external environments by engaging in targeted thinking and rational decision-making. Salovey and Mayer ( 1990 ) defined EI as a form of social intelligence that enables an individual to understand and manage interpersonal relationships. This ability enables individuals to quickly classify their perceptions of life as emotions. In addition to facilitating internal emotional management, EI enables an individual to monitor the emotions of others and create an interactive atmosphere. This aspect of EI can be applied to predict future behavior. Salovey and Mayer ( 1990 ) proposed a conceptualized framework for using EI to evaluate emotions, the expression of personal emotions, the regulation of emotions, the application of emotions for flexible planning, the development of creative ideas, the transition of attention, and the generation of motivation. In addition, they argued that the aforementioned concepts allow emotions to fulfill positive functions. Specifically, compared with the expression of simple individual emotion, EI emphasizes identifying personal emotions and those of others as a basis for regulating and solving problems. 2.4. Relationships Between Emotional Labor, Job Burnout, and Emotional Intelligence 2.4.1. Relationship Between Emotional Labor and Job Burnout A review of EL- and JB-related studies revealed that surface acting involves behavioral performance that does not involve internal thoughts and easily leads to emotional dissonance an individuals, causing them to experience psychological stress and possibly emotional exhaustion. When an employee endeavors to adhere to organizational emotion norms and realizes that such norms contradict their personal thoughts, they tend to engage in surface acting, which places an excessive demand on their emotional resources, resulting in the development of JB (Lee & Chelladurai, 2016 ). EL can easily occur in occupations that require frequent interactions with customers. To maintain emotional bonds with customers, organizations place excessive demands on their employees’ emotional resources, which tends to cause job stress and lead to JB. When EL is implemented by organizations to control the emotions of their employees, emotion management becomes a method for these employees to adhere to organizational norms or pursue organizational goals. In the service industry, employees must often interact with customers. To achieve the goals that are expected of them, employees must often adapt their emotions when they engage in EL (Jeung et al., 2018 ). Numerous scholars investigated frontline service providers as research participants and reported that differences between individual EL and organizational emotional regulation tend to cause emotional dissonance among employees. In addition, emotional dissonance may cause emotional exhaustion and depersonalization, both of which are dimensions of JB. Researchers have reported that individual EL is positively correlated with JB (Wang & Wong, 2009; Wang et al., 2012 ). Hsu and Lee ( 2012 ) examined ground staff of an airline who experienced high EL in their study. The ground staff had to frequently interact with customers and adhere to strict emotion norms; thus, the study concluded that employees tend to develop JB when they are under the emotional control of their organizations. To alleviate such burnout, organizations can start by reducing the EL of their employees. Hsieh ( 2015 ) recruited hotel employees as participants and examined EL as a type of job demand. Conducted on the basis of job resources theory, their study revealed an association between EL and JB. Specifically, if employees were willing to undergo internalized changes with respect to emotion norms, their adopted deep acting was negatively correlated with their JB. By contrast, if the employees adopted surface acting while providing services to customers in accordance with organizational rules, their surface acting was positively correlated with their JB. Chen and Wu ( 2019 ) examined the counter staff of department stores to explore the roles of emotional discrepancy and emotional efforts in EL. Their results revealed that EL and JB are positively correlated. Employees who experience EL for a long period of time need to disguise their emotions or hide their true thoughts, which is a behavior that often depletes their emotional resources, reduces their personal accomplishment, and increases their JB. In summary, the present study hypothesized that EL is significantly and positively correlated with JB. If employees comply with organizational emotion norms over a long period of time and experience emotional dissonance, JB may occur because of the discrepancy between their internal thoughts and external acting. Thus, the following hypothesis was established: Hypothesis 1 EL is positively correlated with JB. 2.4.2. Relationship Between Emotional Intelligence and Emotional Labor Emotions were regarded as a display of individual behavior or a mental journey before Hochschild incorporated individual emotions and organizational norms into the concept of emotions and proposed the concept of EL, which considered the true emotions of employees, the emotional demands of enterprises, and actual dimensions of action transitions. Because the EL performed in an organization is regarded as a normative demand that can be managed and publicly displayed through various behavior, employees often express their emotional status through EL while interacting with others at work. Thus, employees who work in jobs that involve a high level of EL and possess high EI are regarded as employees who are highly competent at controlling and adapting their personal emotions to meet organizational demands. When employees accept emotion norms, they require more EI to adjust their personal feelings such that the depletion of their emotional and cognitive resources can be minimized (Miao et al., 2017 ). To build enjoyable and comfortable environments in an era that emphasizes customer orientation, organizations are increasingly focusing on emotion norms without considering the emotional expression modes of individuals or the processes by which they interact with others. Numerous scholars have explored the relationship between EI and EL (Man, 2002 ; Wu & Cheng, 2003 ). Service sector studies have identified EI as a special ability on the basis that employees are required to implement the EL strategies of their organizations. Employees engage in deep or surface acting while interacting with customers. Under resources conservation theory, EI is an internal emotional resource of employees. If employees can adequately apply EI to perform the EL that is required of them at work, they are likely to adhere to organizational emotion norms. Through perceived organizational support, employees can supplement the emotional resources depleted by EL (Wen et al., 2019 ). In addition, people with high EI can usually adapt their behavior in response to environmental changes, enabling them to adhere to emotion norms or display the emotions that are expected of them. Consequently, they EL load increases. Thus, in addition to the relationship between EI and EL, studies have often evaluated the effects of EI on the deep acting of employees and their EL loading; EI has also been studied as a moderating variable (Chu, 2013 ; Kao, 2011). EI not only affects the work of an employee but also facilitates the development of their interpersonal relationships if it is applied appropriately. Through behavioral performance, EI enables an employee to adjust or induce the emotional responses of others. Thus, an employee’s EI and EL acting behavior are positively correlated. If an employee has high EI, they can adequately implement emotional management depending on their emotional status to adjust their emotions through deep acting (Tsai, 2011 ). Yu (2019) examined employees of Taiwan International Ports Corporation, Ltd. to explore the effects of EI on EL and measured the surface acting and deep acting involved in the performance of EL. Their results revealed that the employees with higher EI were more adept at applying, regulating, and controlling their personal emotions. The emotional changes that they underwent at work enabled them to perform EL involving emotional concealment, surface emotional control, and frequent interactions. These results suggest that partial variables that influence the relationship between EI and EL are significantly and positively correlated. In summary, the present study hypothesized that EI and EL are significantly and positively correlated. Individuals with higher EI are more proficient at controlling their emotions and perceiving the emotions of others, thereby facilitating their rapid adaptation to environmental demands when they must adhere to organizational emotion norms. In the context of EL, they can adopt surface and deep acting to enhance the coherence and efficiency of their work and interpersonal relationships, thereby enabling higher levels of EL loading. Thus, the present study examined emotional loading in the context of EL and proposed the following hypothesis: Hypothesis 2 EI is positively correlated with El. 2.4.3. Relationship Between Emotional Intelligence and Job Burnout Studies have indicated that emotional dissonance considerably affects JB. The ability to implement effective emotional control or regulation appears to play a crucial role in preventing affective disorders among employees at work (Jeung et al., 2018 ). Thus, enterprises should emphasize emotion-related dimensions and consider how the physiological and psychological status of their employees may affect their personal performance and the overall performance of an enterprise. Through their interpersonal relationships and the improvement of their individual EI, employees can appropriately manage their external behavioral performance and reduce negative work outcomes. Studies have reported that employee stressors can originate from both organizational and individual factors related to work. Excessive stress loading may exhaust individuals physiologically, psychologically, and spiritually, resulting in JB. Furthermore, because organizations are an external factor that is difficult to change and control, and that EI varies between individuals, Hung ( 2008 ) examined employees of small and medium-sized businesses, and reported that EI and JB are significantly and negatively correlated; specifically, among the various dimensions of EI, emotion regulation was most correlated with JB, followed by emotional expression and emotional awareness. In addition, EI in the workplace can be classified into the three dimensions of emotional attention (i.e., the attention that an individual pays to the emotions of others), emotional understanding (i.e., the ability to understand, identify, and label the emotions of others), and emotional repair (i.e., the ability to regulate one’s emotions). A study reported that individuals with high EI can proficiently monitor the emotions of others, including the emotional responses of other personnel in the workplace. These individuals are also highly adept at understanding emotions and identifying the causes and effects of emotions, thereby reducing their work stress and alleviating various negative work effects and JB (Martínez-Monteagudo et al., 2019 ). In their study, Liao and Song ( 2018 ) examined mass rapid transit drivers who were prone to experiencing high job stress. Their study revealed that the appropriate application of EI by the drivers can effectively help them focus on their work and transform their stress into assistance, thereby helping them optimize their decision-making at work and to reduce their risk of developing JB. Their study also indicated that the EI and JB of the drivers were significantly and negatively correlated and that the application of EI reduced the development of JB when the drivers experienced job stress. In addition, their study employed the job requirements-resources model and asserted that employees with high EI can effectively allocate their job resources, quickly perceive emotions, and appropriately adjust their moods. These findings indicate that EI plays a role in moderating the relationship between job stress and JB. Chong and Yen (2021) examined tourism service workers who must adhere to stringent emotion norms to explore how the emotional transitions and adaptive abilities of amusement park workers affected their development of JB. Their study indicated that when individuals who are more adept at emotional regulation are also more capable of recognizing their personal emotions, addressing negative emotions, and, consequently, reducing their JB levels. Their results verified the presence of a negative relationship between EI and JB, implying that individuals with high EI can regulate their mindsets and emotions and are less likely to experience JB. Burnout syndrome is defined as a general sense of psychological discomfort involving low levels of self-esteem, motivation, and occupational commitment. Burnout is a persistent state characterize by depressive and negative emotions. Work and occupational factors may contribute to the development of burnout. However, when an individual has higher EI, they can more capably resist JB by managing their emotions and emotional information. Through emotional clarity, organizations can ensure that their employees can perceive emotions and reduce tension and psychological dissonance. In addition, the attention dimension of EI can help employees focus on their emotions and those of others, thereby increasing their performance and concentration at work. Skillful emotional regulation allows for the conversion of negative emotions into positive ones, which can substantially reduce the development of JB (Sánchez-Pujalte et al., 2021 ). In summary, the present study asserted that EI and JB are significantly and negatively correlated. When individuals have higher EI, they can more capably assess their personal emotional loading and adapt their emotions to regulate negative psychological perceptions and eliminate negative emotional thoughts. Such individuals are less likely to develop JB. Given these inferences, the present study proposed the following hypothesis: Hypothesis 3 EI is negatively correlated with JB. 2.4.4. Relationships Between Emotional Intelligence, Emotional Labor, and Job Burnout Most studies that investigated EI, EL, and JB have reported that individuals in an organization who are more adept at emotional regulation or adaption have a greater influence on their EL and JB. Thus, in addition to exploring the associations between variables, numerous studies have examined the moderating effect of EI on the relationship between EL and JB and reported diverse results. Taiwan-based studies that examined the moderating effect of EI on the relationship between EL and JB have identified significant correlations between EI and the other two variables. For example, a study reported that individual EI is positively correlated with workplace EL and that EI is negatively correlated with JB; however, the study asserted that the moderating effect of EI was different from the effect that was hypothesized (Chong & Yen, 2021). Liu et al. ( 2016 ) contended that environmental and individual factors affect the relationship between EL and JB. Individuals who are more adept at assessing emotions in the workplace can more competently address their negative psychological responses. Weng ( 2017 ) enrolled counseling psychologists as their study participants and reported that the psychologists who had to navigate the emotions of others while regulating their own personal emotions for an extended period of time in the workplace tended to perform increased EL and experience emotional exhaustion. In this context, their emotional awareness, sense of comfort, and emotion utilization could be influencing factors. Although these studies examined the moderating effects of EI on the relationship between EL and JB, their results revealed the correlations between variables but did not support their hypothesis regarding moderating effects. Studies conducted outside of Taiwan have also discussed the effect of EI on the relationship between EL and JB; however, their results differed from those of studies conducted in Taiwan. Hong and Lee ( 2016 ) examined a group of nurses and reported that when the nurses applied EI to fulfill their job requirements, they simultaneously engaged in EL and reduced their JB. Their results also indicated that EI mediates the relationship between EL and JB. Lee and Chelladurai ( 2016 ) discussed the three dimensions of EL (i.e., surface acting, deep acting, and genuine expression), defining genuine expression as an active or conscious process involving changing emotions and asserting that surface acting and deep acting can be influenced by an individual’s EI, thereby affecting their level of emotional exhaustion. Their study results verified that individuals who engage in more surface acting at work are more susceptible to emotional exhaustion because of the incongruity between their displayed emotions and genuine emotions. However, individuals with higher EI can more effectively regulate this process and reduce their JB. Scherer et al. ( 2020 ) argued that employees meet job requirements and adhere to emotion norms by engaging in EL, which is crucially influenced by the emotion management component of their individual EI. EI not only facilitates effective emotional expression but also functions as a resource for coping with social interactions in the workplace, implementing constructive and efficient conflict management, and buffering against negative emotions. Consequently, EI helps an individual reduce their emotional exhaustion. Their results also indicated that individual EI effectively moderates the relationship between surface acting and JB. In summary, on the basis of the results, hypotheses, and inferences of other studies, the present study hypothesized that an individual’s EI affects the relationship between EL and JB. When employees perform EL in accordance with the emotion-related rules of their organization, the effective application of personal EI to regulate emotions can reduce their risk of developing emotional exhaustion or JB. By contrast, individuals with low EI may experience more JB because of the EL they perform at work. Hypothesis 4 EI moderates the relationship between EL and JB. 2.5. Meta-analysis and structural equation modeling Meta-analyses (MAs) have been widely conducted since the 1970s. An MA involves collecting data from diverse sources, including clinical trials, observational studies, and individual records. An MA is a tool for integrating and organizing large amounts of diverse data systematically. An MA involves primary and secondary data analysis, and it addresses research questions derived from original research studies and collates the findings from multiple studies. An MA is often referred to as analysis of analyses (Glass, 1976 ). Numerous studies have been conducted to explore psychological outcomes. Although most of these studies have been sampled and systematically organized, the actual integration of such a great number of studies is rare. With the number of studies on popular topics continuing to increase, studies involving emerging and extensively studied issues require regular consolidation and summarization to assess their validity and outcomes. Therefore, to provide unbiased evaluations of existing evidence, scholars have started conducting quantitative MAs, which involve the quantification and integration of data from studies that covered similar topics but reported differing results. This research design enables scholars to understand various types of research pertaining to areas such as counseling, therapeutic effectiveness, and measurement outcomes (Glass, 1976 ; Ahn & Kang, 2018 ). Structural equation modeling (SEM) has experienced rapid development since the 1970s as a technique for describing and estimating the linear relationships between variables. SEM involves latent and measured variables. Latent variables are hypothetical constructs that cannot be directly measured. Thus, researchers often use measured variables and study the directed and undirected linear relationships between these variables. The techniques applied include path analyses, factor analyses, analyses of variance, and regression analyses (MacCallum & Austin, 2000 ; Jak, 2015 ). In SEM analyses, a technique that can test the relationships in a set of variables enables the measurement of all the hypothesized relationships between the variables. Fit indices are employed to perform overall evaluations without the necessity of collecting raw data. These indices can be directly applied to a covariance or correlation matrix (Jak, 2015 ). Researchers have employed SEM to conduct empirical research in various fields such as psychology, management, and other scientific disciplines. SEM enables researchers to examine the relationships between multiple concepts, assess the associations between variables on the basis of theoretical hypotheses, explore mediating effects, and evaluate the influence of multiple variables (Cheng, 2015 ). MacCallum and Austin ( 2000 ) conducted a synthesis of existing studies and discovered that SEM was commonly employed in fields such as applied psychology, organizational behavior, organizational psychology, and personnel psychology. Various studies have also adopted observational and experimental research designs. 3. Research Methods 3.1. Scope of Literature To collect a substantial amount of data from the relevant literature, a search of several digital databases was conducted; these databases included the Airiti Library, National Digital Library of Theses and Dissertations in Taiwan, National Central Library Taiwan Periodic Literature, Web of Science, and ProQuest databases. To engage in comprehensive research collection, the keywords “emotional labor,” “job burnout,” and “emotional intelligence” were used as the variables of interest. To be eligible for inclusion in the present study, a relevant work must report the correlation coefficients for the three variables EI, EL, and JB, and the correlation coefficients between each pair of these variables. For the present study, the considered works included journal articles, master’s theses, and doctoral dissertations. To avoid the inclusion of duplicate studies, a thesis or dissertation that was submitted to and published in a journal was only counted as a single work. The research samples were selected on the basis of the considerations as follows. The data analyzed in the present study were collected from studies published before October 24, 2022. A total of 38 journal articles and 88 master’s theses or doctoral dissertations were identified. Studies that did not examine the pairwise associations between EI, EL, and JB, and qualitative studies were excluded. Journal articles, theses, and dissertations that did not provide the relevant correlation coefficient data were also excluded. The final sample of included literature comprised 24 journal articles and six master’s theses or doctoral dissertations. 3.2. Data Analyses The present study employed a MASEM model. First, an MA was conducted to collect the relevant literature and analyze the corresponding data, with the objective of exploring the relationships between variable pairs. Subsequently, an integration method was applied to analyze the research sample and data. Effect size was used as a quantitative measure to identify objective general conclusions from the collected literature. The statistical software Comprehensive Meta-Analysis (CMA) was used to convert the correlation coefficients between each variable pair (EL–JB, EL–EI, and JB–EI) into equivalent effect sizes to clarify the correlations between the examined variables and create referential plots for further analyses. A test of homogeneity was also conducted to determine how journal articles differed from master’s theses and doctoral dissertations. The relationships between EL, JB, and EI were explained to verify the relationships between each variable pair and among all three variables. Subsequently, SEM was employed to validate the model fit. Being a method that combines factor analysis and path analysis, SEM can be employed to validate all hypotheses and provide exploratory suggestions for validating model fit. 4. Results 4.1. Relationship Between Emotional Labor and Job Burnout After a literature screening was conducted, 12 research samples were included. The homogeneity test results revealed a Q value of 298.840, which was statistically significant ( p < 0.001) and indicated the presence of heterogeneity in the present study (i.e., the null hypothesis of homogeneity was rejected). Generally, in heterogeneous studies, random-effects models are used to perform interpretations. However, the small sample size of the present study did not allow for a meaningful interpretation of the results obtained from random-effects models. Therefore, a fixed-effects model was adopted. An I 2 value of 96.319 was obtained, indicating a high degree of heterogeneity. Therefore, the present study explained a high level of the heterogeneity, with the obtained mean explaining 96.319% of the variance. The effect size between EL and JB was 0.262, indicating a low degree of correlation. The corresponding 95% confidence interval (CI) ranged from 0.234 to 0.290. The estimated effect size was converted into a Z value of 17.453, which was statistically significant ( p < 0.001). Thus, the analysis results indicated a positive correlation between EL and JB (Table 1 ). To avoid the overestimation of effect sizes due to publication bias, a publication bias analysis of the included samples was conducted. First, a funnel plot was created for examination. Figure 1 presents the funnel plot for EL and JB, revealing that all 12 included articles were distributed on both sides of the funnel and formed a mostly symmetrical shape. No evidence of publication bias was found. To examine publication bias, the fail-safe N for the relationship between EL and JB was calculated and determined to be 721, indicating that 721 additional studies with nonsignificant results pertaining to EL and JB were required to overturn the findings of the present study. The tolerance interval (5K + 10, where K represents the total number of samples included in the MA) for the present study was 70 articles. According to Rosenthal ( 1991 ), when the fail-safe N exceeds the tolerance interval, unpublished, nonsignificant, and undiscovered studies not included in an MA do not affect its results. The fail-safe N in the present study was greater than the tolerance interval; thus, the present study had no publication bias with respect to relationship between EL and JB (Table 1 ). Table 1 Meta-analysis results for relationships between variables. Variable Number of articles Effect size 95% CI Z value Test of homogeneity Fail Safe N Lower limit Upper limit Q value I 2 EL and JB 12 0.262 0.234 0.290 17.453 *** 298.840 *** 96.319 721 EL and EI 12 0.288 0.259 0.316 18.698 *** 643.806 *** 98.291 1148 EI and JB 6 -0.188 -0.246 -0.128 -6.084 *** 23.442 *** 78.671 227 *** p < 0.001. 4.2. Relationship Between Emotional Labor and Emotional Intelligence After a literature screening was conducted, a total of 12 research samples were included. The homogeneity test revealed a Q value of 643.806, which was statistically significant ( p < 0.001) and indicated the presence of heterogeneity in the present study (i.e., the null hypothesis of homogeneity was rejected). In heterogeneous studies, random-effects models should be used for interpretation. However, the sample size was too small to yield meaningful interpretation of the results of random-effects models. Therefore, a fixed-effects model was adopted. An I 2 value of 98.291 was obtained, indicating a high degree of heterogeneity. Therefore, the present study explained a high level of heterogeneity, with the obtained mean explaining 98.291% of the variance. The effect size between EI and JB was 0.288, indicating a low degree of correlation. The corresponding 95% CI ranged from 0.259 to 0.316. The estimated effect size was converted into a Z value of 18.698, which was statistically significant ( p < 0.001). Thus, the analysis results indicated a positive correlation between EI and EL. The corresponding funnel plot revealed that all 12 included articles were distributed on both sides of the funnel and formed a mostly symmetrical shape (Fig. 2 ). No evidence of publication bias was found. The fail-safe N for the relationship between EI and EL was 1,148, indicating that 1,148 additional studies with nonsignificant results pertaining to EI and EL were required to overturn the findings of the present study. Given that the tolerance interval was 70 articles, the present study had no publication bias with respect to the relationship between EI and EL. 4.3. Relationship between Emotional Labor and Job Burnout After a literature screening was conducted, a total of six research samples were included. The homogeneity test result revealed a Q value of 23.442, which was statistically significant ( p < 0.001) and indicated the presence of heterogeneity in the present study (i.e., the null hypothesis of homogeneity was rejected). In heterogeneous studies, random-effects models should be used for interpretation. However, the sample size was too small to yield meaningful interpretation of the results of random-effects models. Therefore, a fixed-effects model was adopted. An I 2 value of 78.671 was obtained, indicating a high degree of heterogeneity. Therefore, the present study explained a high level of heterogeneity, with the obtained mean explaining 78.671% of the variance. The effect size between EI and JB was − 0.188, indicating a low degree of correlation. The corresponding 95% CI ranged from − 0.246 to − 0.128. The estimated effect size was converted into a Z value of − 6.084, which was statistically significant ( p < 0.001). Thus, the analysis results indicated a negative correlation between EI and JB. The corresponding funnel plot reveals that all the six included articles were distributed on both sides of the funnel and formed a mostly symmetrical shape (Fig. 3 ). No evidence of publication bias was found. The fail-safe N for the relationship between EI and JB was 227, indicating that 227 additional studies with nonsignificant results pertaining to EI and JB were required to overturn the findings of the present study. Given that the tolerance interval was 40 articles, the present study had no publication bias with respect to the relationship between EI and JB. 4.4. Validation of the Theoretical Models of Emotional Labor, Job Burnout, and Emotional Intelligence 4.4.1. Correlation Matrices, Harmonic Mean, and Reliability Between Emotional Labor, Job Burnout, and Emotional Intelligence Through the MA, the present study obtained a correlation coefficient matrix for the relationships between EI, EL, and JB (Table 2 ), and the matrix was used to validate the subsequent SEM. Because the number of samples varies between different variable pairs (i.e., EI, El, and JB in Table 3 ), to prevent the differences in sample sizes from affecting the results and to generate non-positive-definite problems, the present study followed the recommendations of Viswesvaran and Ones ( 1995 ) and converted the samples sizes (for the studies that examined the correlations between each variable pair) into harmonic means to obtain the overall sample size. The calculated harmonic mean of 2,083 was used for SEM validation. Table 2 Correlation coefficient matrix for relationships between EL, JB, and EI. Variable EI EL JB EI 1.000 EL 0.288 1.000 JB -0.188 0.262 1.000 Table 3 Sample sizes of studies that examined correlations between EL, JB, and EI. Variable EI EL EL 4013 JB 1045 4277 Table 4 presents the reliability results for the included studies with respect to each variable. The reliability values were used to calculate the mean reliability, residual, and root mean reliability of each study, and they were used in the SEM process. Table 4 Reliability of included studies with respect to EL, JB, and EI. Author Year EL JB EI Lin et al. 2015 0.84 Liu et al. 2016 0.939 0.949 0.956 Liu and Hsieh 2016 0.857 0.895 Chen et al. 2017 0.882 0.874 Chen and Wu 2019 0.901 0.92 Angeli et al 2015 0.86 Aeeun Jeon 2016 0.782 Liu et al. 2018 0.79 0.95 Kwon et al. 2021 0.79 0.94 Kim 2020 0.81 Lee and Ji 2018 0.75 Hung and Liu 2011 0.887 0.93 Hung 2008 0.834 0.873 Hsieh et al. 2008 0.59 0.87 Ponniah Ramana et al. 2016 0.95 0.87 Lin 2009 0.885 0.887 Wong 2017 0.893 0.913 0.897 Kamassi et al. 2019 0.919 Mean reliability 0.842 0.867 0.905 Residual 0.158 0.133 0.095 Root mean reliability 0.918 0.931 0.951 4.4.2. Validation of Theories Regarding the Relationships Between Emotional Labor, Job Burnout, and Emotional Intelligence 1. Basic fit standards Figure 4 presents that the parameter estimates for the relationships between EI and JB, EI and EL, and EL and JB, which are all positive and less than 1. The p -values were all significant, suggesting that the parameter estimation values conformed to a basic fit. The factor loadings for EI, EL, and JB were 0.951, 0.918, and 0.931, respectively, which slightly exceeded the factor loading level of 0.95 recommended by Bagozzi and Yi ( 1988 ); however, these values were within the acceptable range. According to the table summarizing the parameter estimates for latent variable errors, the error estimation values were positive, and the standard errors ranged between 0.031 and 0.037; this range was within the moderate range and conformed to the basic fit for moderate standard errors as proposed by Bagozzi and Yi ( 1988 ). These results revealed that the parameters of the proposed model met the criteria for acceptable parameter values, indicating a high-quality model. In summary, only the factor loading for EI (0.951) was slightly higher than the indicator norm of 0.95. The model error variance results did not reveal any negative value. That is, all values were positive and significant. The absolute value of the correlation coefficients between estimation parameters did not approach 1, and the standard error was not excessively high, satisfying the standard for basic fit. Thus, the proposed theoretical model was acceptable. 2. Overall model fit The goodness of fit index (GFI) value for the present study was 1 (a GFI value that is closer to 1 indicates a more favorable fit), which was higher than the recommended threshold of 0.9 and indicated an acceptable fit. Both the root mean square residual (RMR) and standardized root mean square residual (SRMR) were 0, that is, less than the suggested thresholds of 0.05 and 0.08, respectively; these results indicate that the fit criteria were satisfied. When SRMR equals 0, a perfect fit is achieved. Therefore, the absolute fit indices for the present study met the fit standards, indicating that the model of the present study exhibited a favorable fit. For the incremental fit indices, the normed fit index (NFI), incremental fit index (IFI), and comparative fit index (CFI) values were all 1, exceeding the fit criterion of 0.9. These values indicated that the incremental fit indices met the established criterion, demonstrating the favorable fit of the proposed model. For parsimony fit indices, the parsimonious normed fit index (PNFI) for the present study was 0, which did not exceed the threshold of 0.05, showing compliance with the fit index criterion. However, the Akaike information criterion (AIC), Bayesian information criterion (BIC), and expected cross-validation index (ECVI) are competitive model fit indices, and they could not be applied in the present study because the proposed model was not competitive. Therefore, no further discussion or comparison of AIC, BIC, and ECVI values are conducted in the present study. In summary, the overall fit analysis indicated that nearly all the fit indices met their respective fit criteria. The absolute fit indices and incremental fit indices met the fit criteria. Among the parsimony fit indices, the PNFI was the only index that did not meet the recommended standard. The results for the AIC, BIC, and ECVI were used in competitive models; thus, they were unsuitable for the proposed model in this study. In the present study, only a single parsimony fit index did not meet the recommended standard. These results indicated that the theoretical model fit was favorable such that a subsequent exploration could be conducted. 3. Fit of internal structure model The individual reliability values for EL, JB, and EI were higher than 0.5; that is, they all satisfied the recommended standard. The composite reliability was greater than 0.6, and the mean variance extracted of all variables was greater than 0.5; that is, these values all satisfied the recommended standard. These results indicate that the proposed model had a favorable internal structure model fit. The standardized residual test revealed that all values were less than 1.96, indicating that the internal structure model fit met the recommended standard and was favorable. 4.4.3. Moderating Effect of EI on Relationship Between Emotional Labor and Job Burnout To validate moderating effects, bootstrapping was conducted with a 95% interval and 1000 iterations. Table 5 presents the results for the moderating effect validation conducted using bootstrapping. Error modification percentage bootstrapping and percentage bootstrapping were performed to examine the related effects. The 95% CI did not include a zero value, and the p -value was significant, confirming the moderating effect of EI. These results indicated that EI moderated the relationship between EL and JB. Table 5 Bootstrapping validation of moderating effect. Parameter Method Estimation Lower limit Upper limit p -value EL→EI→JB Error modification percentage bootstrapping -0.116 -0.141 -0.093 0.001 Percentage bootstrapping -0.116 -0.140 -0.092 0.002 EL was verified to have a total effect, direct effect, and indirect effect on JB. The total effect of two variables in three variables was significant. The total effect size of EL on EI was 0.330, indicating a direct effect. The total effect size of EI on JB was − 0.352, indicating a direct effect and supporting the hypotheses proposed in the present study. No indirect effect was identified with respect to the relationships between EL and EI and between EI and JB. The total effect size of EL on JB was 0.307, which was the sum of the direct effect and indirect effect sizes (0.423 − 0.116). This result signified that EL affected JB through EI and that EI moderated the relationship between EL and JB, supporting Hypothesis 4 . 5. Discussion and Suggestions 5.1. Discussion 5.1.1. Relationship between Emotional Labor and Job Burnout The results of the present study revealed a significant and positive relationship between EL and JB, indicating that when employees experienced higher levels of EL, they also perceived an increase in JB. The present study assumed that EL is a type of job requirement, where employees are subjected to the intangible or tangible emotion norms of an organization and expected to provide services through face-to-face or vocal interactions. In this situation, their emotions are subject to organizational management. Furthermore, the emotions of employees can be cultivated through training such that they can display specific emotional expressions; this process is regarded as an aspect of EL (Hochschild, 1983 ). Depending on the occupations that are involved, an organization may require its employees to express specific emotions or service attitudes toward customers. If employees effectively display the positive emotions required of them, they can feel a sense of accomplishment through their emotional influence on customers. Therefore, EL is regarded as a performance indicator of service quality and overall impression, and it is closely related to the job outcomes of employees (Lin et al., 2015 ). However, when employees self-regulate their emotions, they often encounter constraints and need to display professionalism while hiding their true emotions. This process of excessive emotional suppression results in an inconsistency between internal thoughts and external expressions, and it can lead to the development of several types of JB including emotional exhaustion, depersonalization, and reduced sense of accomplishment. The faking of emotions depletes the energy of an employee and causes them to experience frustration, and it is also a main factor that contributes to work dissatisfaction (Liu & Hsieh, 2016 ; Lee & Chelladurai, 2016 ). According to conservation of resources theory, the level of an employee’s EL affects their emotional resources during emotional transitions. The dissonance between genuine emotions and emotion norms can lead to the development of JB. Hence, the present study hypothesized that EL positively affects JB (Hypothesis 1 ). 5.1.2. Relationship between Emotional Intelligence and Emotional Labor The results of the present study revealed a significant and positive relationship between EI and JB, indicating that when employees possess a higher level of EI, their adoption of EL coping behavior and their generation and perception of EL increase. On the basis of conservation of resources theory, the present study regarded EI as the internal emotional resources of an individual employee. EI encompasses the ability to perceive and evaluate one’s emotions and those of others and the ability to regulate and apply emotions. Thus, when employees have higher EI, they are more likely to adjust their emotional expressions through deep acting to align with their internalized emotions and organizational requirements when they engage in EL at work. This alignment allows them to gain recognition and psychological support, reduce the depletion of their emotional resources, and reinforce these resources (Yu, 2020 ; Wen et al., 2019 ). Moreover, the emotional demands of an organization can lead to the development of EL among its employees, and their individual adaptation is dependent on their EI. Employees with higher EI exhibit a higher sense of environmental safety and are more skilled at social interactions. They can effectively identify their genuine inner emotions and establish a connection between EL and their inner emotions. They are also more likely to engage in deep acting as an adaptive mechanism for managing their internal emotions. These individuals have greater EL loading levels. Thus, individuals with higher EI are regarded as more suitable candidates for undertaking EL (Lin, 2009 ; Weng, 2017 ). Therefore, given the extent to which individuals with higher EI engage in deep acting and their EL loading, the present study hypothesized that EI positively affects EL (Hypothesis 2 ). 5.1.3. Relationship Between Emotional Labor and Job Burnout The results of the present study confirmed that the proposed hypotheses are consistent with the findings of other studies. Several Taiwan-based studies reported that a significant and negative relationship existed between the EI and JB of nursing and rehabilitation professionals. Although these professions are associated with emotions related to safety, care, and joy, they must frequently confront the illness and mortality of patients. Thus, individuals with higher EI can regulate their emotions and mitigate negative emotions that cause psychological discomfort. Individuals with higher EI are more empathetic toward the emotions of others and are more adept at understanding and managing their own emotions and those of others. Thus, they are more likely to exhibit rational behavior, experience a sense of accomplishment, and experience less frustration caused by work and interpersonal interactions (Hsieh et al., 2008 ; Hsieh, 2010). Liao and Song ( 2018 ) examined mass rapid transit drivers with high job stressors, and their results indicated that the drivers with higher EI were more adept at using emotional information to guide appropriate work behavior, enabling them to effectively convert their job stress into opportunities for professional development. Moreover, individuals with higher EI exhibit higher emotional tolerance. They adopt positive and proactive attitudes when faced with setbacks at work and do not avoid job responsibilities; instead, they inspire themselves to achieve personal accomplishments, thereby reducing their JB. Similar findings have also been reported by studies conducted outside of Taiwan. Studies that examined teachers have discovered that the application of EI enabled teachers to effectively manage their job frustrations, implement emotional control, maintain harmonious relationships with their colleagues, achieve enhanced self-awareness, and improve their job performance. These studies highlighted that accomplishments and goal attainment are not only dependent on knowledge, skills, and experience, but also on the ability to manage one’s emotions. Successfully understanding oneself and others lead to improved outcomes and consolidates the importance of emotions and the negative relationship between EI and JB (Nur Sakinah Thomas et al., 2012 ). The similar results reported by both studies conducted within and outside of Taiwan correspond to the results of the present study; thus, Hypothesis 3 (i.e., EI negatively affects JB) is supported. These findings have been verified across various professions and can be extrapolated to various industry sectors. 5.1.4. Relationships between Emotional Labor, Job Burnout, and Emotional Intelligence The present study proposed a theoretical model and several hypotheses regarding EL, JB, and EI variables. On the basis of conservation of resources theory, the present study examined employee emotions as a job resource. Accordingly, when employees engaged in EL, having higher EI helped them adopt deep acting strategies to enhance their EL loading levels. The job requirements-resources model revealed that employees with higher EI were more adept at understanding others and regulating their own emotions, enabling them to adapt quickly to the emotion norms of their organizations and to alleviate the job stress and burnout caused by emotional dissonance. In other words, the EL of employees was affected by their level of EI, which in turn affected the reduction of JB. EI moderated the relationship between EL and JB. Individuals with high EL who experienced a discrepancy between emotional requirements and their own emotions were more likely to have a negative sense of JB. However, when employees engaged in EL, those with emotional awareness and the ability to perform effective emotion transitions experienced reduced JB as a result of regulated deep acting (Lee & Chelladurai, 2016 ). Both studies conducted within and outside of Taiwan have extensively discussed the relationship between high EL jobs, EI, and JB. Most studies have employed conservation of resources theory, which regards employee emotions as a type of job resource. If emotions are regarded as job requirements, they may promote emotional labor performance and mitigate the level of JB (Hong & Lee, 2016 ). The overall fit index results presented in the model fit summary table indicate a favorable fit between the proposed theoretical model and the collected data (Fig. 4 ). Nearly all the fit index results, including the absolute and incremental fit index results, met the evaluation criteria. The PNFI was the only index for which the results were slightly less than the recommended standard of 0.05. The results indicated that the basic model fit, overall model fit, and internal structure model fit of the proposed model were all satisfactory. Additionally, in the analysis of the moderating effects of EI, bootstrapping with 1000 iterations was performed with the CI being set to 95%. The results did not include a zero value, and the total effect of EL on JB was 0.307, which was equivalent to the sum of the direct effect (0.423) and the indirect effect (− 0.116) (0.423 − 0.116). This result verifies the moderating effect of EI; thus, Hypothesis 4 (i.e., EI moderates the relationship between EL and JB) is supported. 5.2. Suggestions 5.2.1. Management Implications The results revealed a significant and positive relationship between EL and JB. Therefore, in terms of management practices, organizations should establish emotion-related rules to meet customer service expectations with respect to emotional expression. They should also examine other factors that may affect employees, such as their work environment, the target recipients of a service, and the occurrence of unexpected events. These measures enable organizations to mitigate the negative effects of EL on the development of JB in their employees. For employee training, organizations should focus on teaching deep acting strategies for emotional expression. In contrast to masking or surface acting, this approach can encourage employees to naturally express specific emotions that can positively influence their colleagues and customers. It can also substantially reduce the development of JB among employees at work. The results indicated a positive relationship between EI and EL. This relationship encompasses the adoption of deep acting strategies by employees and an increase in their level of EL loading. Therefore, businesses should not only consider basic requirements during the selection process but also incorporate an EI assessment or observe the ability of candidates to express themselves and monitor their surroundings during conversations. To facilitate talent management, organizations must consider EI in terms of personnel promotion and relocation decisions. A manager must not only set emotion norms but also understand the challenges that their employees face and the process of emotional transition that they undergo during EL. Employees with higher EI can empathize with people in these situations and offer support to other employees who need it. This method can effectively propagate an organization’s emotional demands among its personnel. The results revealed a negative relationship between EI and JB, indicating that employees with higher EI generate less JB and perceive lower levels of JB. Therefore, organizations should consider incorporating EI as a predictor of employee performance and burnout during their selection process. Additionally, organizations can help employees enhance their EI through education and training, thereby cultivating their ability to perceive the emotions of others and anticipate the needs of their customers before any requests are made. This measure can foster enthusiasm and a sense of accomplishment among employees at work. Furthermore, implementing job rotation and task transitions can help employees cultivate their EI and reduce their JB through the accumulation of practical experience. The present study verified the significant moderating effect of EI on the relationship between EL and JB. When employees have higher EI, they can more effectively adapt to the emotion norms within their organization. They can also transform their EL into genuine experiences and engage in deep acting to mitigate JB. In other words, an employee’s level of EI can affect their reduction of JB resulting from EL. Therefore, organizations should use EI as a criterion for assessing the self-awareness and personal emotional management of candidates during the selection process. EI can predict how candidates adapt to EL in future job roles and their ability to cope with work-related stress. For candidates who demonstrate high levels of professional competency and the potential to improve their EI, an organization should provide training programs that incorporate EI training. These programs should incorporate experiential learning to enhance the emotional regulation and resilience of employees in the face of setbacks. When human resources teams identify negative work outcomes caused by EL or EI-related JB, they can use their organizations’ internal resources to implement job rotation or job redesign. Practical experiences can be leveraged to cultivate the EI of employees. Additionally, providing counseling and care for employees can contribute to talent retention. If an organization can promote methods that emphasize positive emotions to guide employees to accept challenges with a positive attitude, the EI levels of the organization’s teams can be increased, and a positive atmosphere can be created. Subsequently, the JB caused by EL can be reduced, thereby reducing turnover intention. 5.2.2. Future Suggestions EL, JB, and EI have been studied over a long period of time. Scholars have proposed various definitions and explored various perspectives relating to these constructs. For example, EL is often classified as surface acting and deep acting, but some scholars have argued that an individual’s genuine emotions should also be regarded as form of emotional display and be established as a third category of EL. Scholars have also proposed alternative definitions other than the aforementioned categories. However, in the present MA study, the analysis focused on exploring the correlations among variables by examining a single variable without separately integrating and validating individual subdimensions through SEM. Given the integrity of the dimensions, future studies should focus on extending the topics explored in the present study; this can be achieved by exploring the subdimensions of each variable to clarify in-depth research conceptions and further verifying the consistency of the relationships between single variables and subdimension variables. The literature review revealed that a strong connection exists between EL, JB, and EI in both academic and practical contexts. The related topics, such as organizational culture, leadership style, and the work stress that influences employee development, are frequently discussed in organizational settings. Various studies have explored work outcome–related topics such as turnover intention, retention willingness, and job satisfaction. Therefore, future studies can use the findings of the present study as a theoretical foundation for exploring other moderating variables and their corresponding effect sizes. Given that the present study focused on EL, JB, and EI, which are individual-level variables, future studies should examine organizational-level or group-level variables to expand the research on EL, JB, and EI and facilitate the development of detailed recommendations for practical measures. The research methods employed in the present study combined MA and SEM. The initial data collection step involved identifying studies that investigated the variables of interest and conducting data analysis through MA. Subsequently, on the basis of the correlations between each variable pair, the fit of the SEM was validated. This integrated approach focused on effect sizes, which is a quantitative measure. However, to obtain a more comprehensive understanding of the relationships examined in the present study, future studies should collect qualitative data through methods such as interviews, observations, or other methods used in the relevant qualitative studies. Through the application of various research methods in conjunction with quantitative analysis, the reliability of the validation results can be enhanced. To research EL, JB, and EI, researchers have employed various definitions and scales depending on the dimensions of the variables that were examined. For instance, to measure EL, some scholars have defined it on the basis of its acting aspect, whereas others have classified EL on the basis of interactions or diversity-related dimensions. Similarly, for EI, researchers have used scales that assess personal emotions and the emotions of others and the ability of an individual to evaluate and apply emotions. Researchers may be inclined to adopt scales with a higher reliability for evaluation, and they often prefer scales that were developed during the early stage of variable content development. Therefore, given the need to align past research with the current context, researchers should compare newer and older scales or prioritize the analysis of scales developed in more recent years. Furthermore, study samples vary across studies. Thus, to account for the potential influence of occupational differences in job content on research outcomes, researchers should consider obtaining consistent samples or focusing on specific professions to facilitate data integration. The present study considered the uniformity of regions and the similarity of cultures in data collection by selecting studies from Taiwan and the Asian region. Future studies should consider cultural differences and employ a cross-cultural research design to collect data from both Taiwan and international sources. In addition to studies from the Asian region, studies from Western countries can be included. Furthermore, expanding literature sources to include foreign dissertations and theses can increase the comprehensiveness of the relevant research and enable the extension of findings to various countries and industries, thereby providing a more comprehensive interpretation with major managerial implications. Declarations Ethical approval： This noninterventional study did not require ethical approval due to its design, nor did it take place within any private or protected space. Therefore, no specific permissions were required to conduct the study in the geographical regions specific to this study. Informed consent： The primary methodology employed in this study is meta-analysis, which does not involve the use of research participants' information. Therefore, informed consent for this study is not required. Competing Interests: The researchers declare no competing interests. Data Availability Statement: The datasets analyzed for the current study are available from the corresponding author upon reasonable request. References Ahad, R., Mustafa, M. Z., Mohamad, S., Abdullah, N. H. S., & Nordin, M. N. (2021). Work attitude, organizational commitment and emotional intelligence of Malaysian vocational college teachers. Journal of Technical Education and Training , 13 (1), 15-21. https://doi.org/10.30880/jtet.2021.13.01.002 Ahn, E., & Kang, H. (2018). Introduction to systematic review and meta-analysis. Korean Journal of Anesthesiology , 71 (2), 103-112. https://doi.org/10.4097/kjae.2018.71.2.103 Ashforth, B. E., & Humphrey, R. H. (1993). Emotional labor in service roles: The influence of identity. Academy of Management Review , 18 (1), 88-115. https://doi.org/10.2307/258824 Awa, W. L., Plaumann, M., & Walter, U. (2010). Burnout prevention: A review of intervention programs. Patient Education and Counseling , 78 (2), 184-190. https://doi.org/10.1016/j.pec.2009.04.008 Bagozzi, R. P., & Yi, Y. (1988). On the evaluation of structural equation models. Journal of the Academy of Marketing Science , 16 , 74-94. Brackett, M. A., Mayer, J. D., & Warner, R. M. (2004). Emotional intelligence and its relation to everyday behaviour. Personality and Individual Differences , 36 (6), 1387-1402. https://doi.org/10.1016/S0191-8869(03)00236-8 Chang, Y. C., Wang, C. H., Lin, W. L., & Chen, K. J. (2021). Relationship between work passion and job burnout of tour managers: Mediating effect of affectivity. Journal of Tourism and Leisure Studies , 27 (1), 67-103. https://doi.org/10.6267/JTLS.202104_27(1).0003 Chen, C. H., & Wu, P. Y. (2019). The study of job stress, emotional labor, and job burnout for department store staff. Journal of Tourism and Travel Research , 14 (2), 41-57. Chen, D. L., Yu, S. Y., & Wu, C. H. (2017). The study of 119 dispatchers’ emotional labor, job satisfaction and job burnout. Journal of Management Information and Computing , 6 (3), 176-185. https://doi.org/10.6285/MIC.6(S3).15 Chen, L. W. (2013). The study of the antecedents and consequences of emotional labor strategies on service quality: A case study of nursing personnel (Unpublished master’s thesis). Southern Taiwan University of Science and Technology. Chen, S. H., & Hsu, M. S. (2011). The study of relationship between the faculty's emotional labor and job burnout in the university. Journal of Kaohsiung Normal University , 30 , 1-15. https://doi.org/10.7060/KNUJST.201106.0001 Chen, Y. J. (2012). The relations of teacher's emotion intelligence, teacher-student relationship, and emotion regulation of lower level students. Journal of Early Childhood Education and Care Research, 9 , 79-101. https://doi.org/10.6471/JECEC.201207.0079 Cheng, C. P. (2015). Power analysis of testing path coefficients in structural equation modeling. Journal of Human Resource Management , 15 (3), 75-96. https://doi.org/10.6147/JHRM.2015.1503.04 Chien, S. W. (2014). A study on the relationship between perceived transformational leadership of kindergarten principals and emotional labor among early childhood educators (Unpublished master’s thesis). National Chung Cheng University. Chu, C. Y. (2013). The Impact of hospital nurses' emotional labor on job satisfaction and burnout. Taiwan Journal of Public Health , 32 (3), 266-278. https://doi.org/10.6288/TJPH2013-32-03-08 Cordes, C. L., & Dougherty, T. W. (1993). A review and an integration of research on job burnout. Academy of Management Review , 18 (4), 621-656. https://doi.org/10.2307/258593 Côté, S., & Miners, C. T. (2006). Emotional intelligence, cognitive intelligence, and job performance. Administrative Science Quarterly , 51 (1), 1-28. https://doi.org/10.2189/asqu.51.1.1 Fan, C. W. (2006). The study of relationship between organizational structures and teacher's job burnout in junior high school. School Administration , (44), 1-25. https://doi.org/10.6423/HHHC.200607.0001 Freudenberger, H. J. (1975). The staff burn-out syndrome in alternative institutions. Psychotherapy: Theory, Research & Practice , 12 (1), 73-82. https://doi.org/10.1037/h0086411 Friedman, I. A. (1995). School principal burnout: The concept and its components. Journal of Organizational Behavior , 16 (2), 191-198. https://doi.org/10.1002/job.4030160209 Gao, X. H. (2011). A study of the emotional labor, emotional intelligence and job satisfaction—Healing style products as moderators (Unpublished master’s thesis). Tainan University of Applied Sciences. https://doi.org/10.6821/TUT.2011.00083 Gardner, H. (1983). Frames of mind. Basic books . New York. Glass, G. V. (1976). Primary, secondary, and meta-analysis of research. Educational Researcher , 5 (10), 3-8. Goffman, E. (1959). Presentation of self in everyday life . New York: Overlook Press. Goleman, D. (1996). Emotional intelligence: Why it can matter more than IQ . Bloomsbury Publishing. Gosserand, R. H. (2003). An examination of individual and organizational factors related to emotional labor . Louisiana State University and Agricultural & Mechanical College. Grandey, A. A. (2000). Emotional regulation in the workplace: A new way to conceptualize emotional labor. Journal of Occupational Health Psychology , 5 (1), 95-110. https://doi.org/10.1037/1076-8998.5.1.95 Grandey, A. A., & Gabriel, A. S. (2015). Emotional labor at a crossroads: Where do we go from here. Annual Review of Organizational Psychology and Organizational Behavior , 2 (1), 323-349. https://doi.org/10.1146/annurev-orgpsych-032414-111400 Grandey, A. A., & Melloy, R. C. (2017). The state of the heart: Emotional labor as emotion regulation reviewed and revised. Journal of Occupational Health Psychology , 22 (3), 407-422. https://doi.org/10.1037/ocp0000067 Ho, J. M., Huang, Y. C., Chen, J. H., & Chuang, M. J. (2005). A study of the relationship between job demand-control-support model and burnout among customs officials. Journal of Human Resource Management , 5 (2), 19-37. https://doi.org/10.6147/JHRM.2005.0502.02 Hochschild, A. R. (1983). The managed heart: Commercialization of human feeling . Berkeley: University of California Press. Hong, E., & Lee, Y. S. (2016). The mediating effect of emotional intelligence between emotional labour, job stress, burnout and nurses' turnover intention. International Journal of Nursing Practice , 22 (6), 625-632. https://doi.org/10.1111/ijn.12493 Hou, S. Z., Fan, H. L., & Lien, W. C. (2015). Sincere or insincere? Exploring the relationship between emotional labor and innovative behavior. NTU Management Review , 25 (3), 67-99. https://doi.org/10.6226/NTUMR.2015.JUN.OB07 Hsieh, C. J., Chang, H. W., Chou, Y. J., & Chang, J. (2008). The relationship between emotional intelligence and burnout in nurses. Journal of New Taipei Nursing , 10 (2), 11-24. https://doi.org/10.6540/NTJN.2008.2.002 Hsieh, C. S., & Chan, H. S. (2006). A study of the employees' job burnout situation on hairdressing enterprise. Journal of Chien Kuo Technology University , 25 (3), 71-94. https://doi.org/10.6995/JCTU.200604.0071 Hsieh, H. H. (2015). A study of the relations between emotional labor and burnout among hotel workers. Island Tourism Research , 8 (1), 25-51. Hsu, J. Y., & Chan, S. Y. (2014). Investigating the relationship between authoritarian leadership and subordinate's emotional labor. Journal of Human Resource Management , 14 (4), 31-55. https://doi.org/10.6147/JHRM.2014.1404.02 Hsu, S. W., & Li, X. L. (2012). Study of emotional labor and job attitude of the ground staff in the airline-job burnout as a mediator. Journal of Furen University Welfare and Health , 18 (2), 1-26. https://doi.org/10.29440/FJJHE.201212.0001 Hsu, S. W., Chang, H. Y., Cheng, S. S., Luo, M. G., & Cheng, R. (2020). Does employees' emotional intelligence matter? Study of leisure adjustment strategy moderating effect on emotional intelligence, working pressure and happiness in international tourist hotels. Journal of Business and Management Technology , 21 (2), 147-176. Hsu, S. W., Lin, W. T., & Su, H. W. (2010). A study of job stress, burnout and turnover intention of employees in the banquet hall of international tourist hotels-Social support as a moderator. Journal of Commerce and Management Quarterly , 11 (2), 209-240. https://doi.org/10.30083/CMQ.201006.0002 Hsu, Y. H., Yang, C. C., Chang, Y. C., Huang, L. L., Lin, C. H., Huang, J. C., & Yeh, D. F. (2021). The association between work-family conflict, social support and burnout among nursing staff. Journal of Healthcare Management , 22 (1), 5-22. https://doi.org/10.6174/JHM.202103_22(1).5 Hu, H. L., Zhang, B. Q., Lin, J. H., & Chen, S. Z. (2021). Emotional labor of the registered nurse: A qualitative study. Journal of Occupational Medicine and Health , 28 (3), 269-279. Huang, B. Y., & Lin, S. H. (2007). A study of stress response caused by personality and social support: Combining psychometric meta-analysis and structural equation modeling. Journal of Educational Psychology , 39 (2), 263-294. https://doi.org/10.6251/BEP.20070604 Huang, D. C., Ji, N. W., Chang, Y. T., & Kuo, W. C. (2014). Antecedents, consequences, and potential moderators of emotional labor: A meta-analytic investigation. Journal of Human Resource Management , 14 (3), 93-134. https://doi.org/10.6147/JHRM.2014.1403.04 Huang, Y. C., Huang, Y. H., Liu, J. Y., & Chen, J. H. (2008). The moderating effects of self-efficacy on the relationships of demand-Control model and burnout. Journal of Management Practice and Theory , 2 (1), 37-51. https://doi.org/10.29916/JMPP.200803.0003 Huang, Y. F. (2016). Mask effect in public service: A perspective of emotional labor. Public Administration Quarterly , 8 (4), 55-74. Huang, Y. H., Huang, Y. C., Chen, J. H., & Du, P. L. (2015). The reciprocal effects of psychosocial job characteristics, burnout and health among nurses: A panel study. Taiwan Journal of Public Health , 34 (2), 204-217. https://doi.org/10.6288/TJPH201534103090 Hung, M. C., & Liu, Y. (2011). The impact of job burnout on job performance-Examining the moderating effects of emotional intelligence and perceived supervisor support. Proceedings of the Conference on Defense Management , 1119-1138. http://dx.doi.org/10.29497/GFGL.201106.1119 Hung, P. S. (2008). A study of the relationship among employee assistance programs (EAPs), emotional intelligence and employee reflections (Unpublished master’s thesis). Chung Yuan Christian University. https://doi.org/10.6840/CYCU.2008.00107 Jak, S. (2015). Meta-analytic structural equation modelling (pp. 1-88). Dordrecht, Neth Springer. James, N. (1989). Emotional labour: Skill and work in the social regulation of feelings. The Sociological Review , 37 (1), 15-42. https://doi.org/10.1111/j.1467-954X.1989.tb00019.X Jen, W. L., Tu, R. T., Wu, Y. J., & Tseng, W. L. (2010). Moderating effects of emotional intelligence of city bus drivers on the job stress model. Transportation Journal , 22 (4), 415-445. https://doi.org/10.6383/JCIT.201012.0415 Jeon, A. (2016). The effect of pre-flight attendants’ emotional intelligence, emotional labor, and emotional exhaustion on commitment to customer service. Service Business, 10 (2), 345-367. ht t ps://do i. o rg/10. 1007/s11628 - 015- 0273- 2 Jeung, D.-Y., Kim, C., & Chang, S.-J.. (2018). Emotional labor and burnout: A review of the literature. Yonsei Medical Journal , 59 (2), 187-193. https://doi.org/10.3349/ymj.2018.59.2.187 Jian, J. Z. (1998). The tendencies and activators of nurse' working emotions. Nursing Research , 6 (5), 417-426. https://doi.org/10.7081/NR.199810.0417 Ji‐Soo Kim. (2020). Emotional labor strategies, stress, and burnout among hospital nurses: A path analysis. Journal of Nursing Scholarship , 52 (1), 105-112. https://doi.org/10.1111/jnu.12532 Kristensen, T. S., Borritz, M., Villadsen, E., & Christensen, K. B. (2005). The copenhagen burnout inventory: A new tool for the assessment of burnout. Work & Stress , 19 (3), 192-207. https://doi.org/10.1080/02678370500297720 Kwon, C.-Y., Lee, B., Kwon, O.-J., Kim, M.-S., Sim, K.-L., & Choi, Y.-H. (2021). Emotional labor, burnout, medical error, and turnover intention among South Korean nursing staff in a university hospital setting. International Journal of Environmental Research and Public Health , 18 (19), 1-14. https://doi.org/10.3390/ijerph181910111 Lee, H. J. (2021). Relationship between emotional labor and job satisfaction: Testing mediating role of emotional intelligence on South Korean public service employees. Public Organization Review , 21 (2), 337-353. https://doi.org/10.1007/s11115-020-00490-5 Lee, J. J., & Ok, C. (2012). Reducing burnout and enhancing job satisfaction: Critical role of hotel employees’ emotional intelligence and emotional labor. International Journal of Hospitality Management , 31 (4), 1101-1112. https://doi.org/10.1016/j.ijhm.2012.01.007 Lee, Y. H. (2019). Emotional labor, teacher burnout, and turnover intention in high-school physical education teaching. European Physical Education Review , 25 (1), 236-253. https://doi.org/10.1177/1356336X17719559 Lee, Y. H., & Chelladurai, P. (2016). Affectivity, emotional labor, emotional exhaustion, and emotional intelligence in coaching. Journal of Applied Sport Psychology , 28 (2), 170-184. https://doi.org/10.1080/10413200.2015.1092481 Leiter, M. P., & Maslach, C. (1988). The impact of interpersonal environment on burnout and organizational commitment. Journal of Organizational Behavior , 9 (4), 297-308. https://doi.org/10.1002/job.4030090402 Li, C. C., Yeh, W. Z., Yu, Z., Hsu, R. G., Hung, Q. E., & Shih, P. C. (2022). The influence of transformational leadership, transactional leadership, ethical climate, and emotional intelligence on the performance of housing agents: The mediation effect of trust in supervisor. Journal of Taiwan Land Research , 25 (1), 23-61. Lian, C. G., Huang, J. X., Zhang, Y. J., & Ruan, Y. T. (2022). Work convenience or stress? The relationship between the work-related use of instant messaging apps beyond work hours and job burnout in teachers and the moderating role of job values. Journal of Educational Psychology , 53 (3), 717-743. https://doi.org/10.6251/BEP.202203_53(3).0009 Liao, W. J., & Song, Z. J. (2018). The relationship between MRT drivers' job stress and job burnout: The moderating effects of emotional intelligence and perceived investment in human resource development. Journal of Management Practice and Theory , 12 (1), 1-25. https://doi.org/10.29916/JMPP.201806_12(1).0001 Lin, C. C. (2018). The study of examining the effect of role stress and job burnout on well-being of junior high school directors. School Administration , (115), 91-120. https://doi.org/10.6423/HHHC.201805_(115).0005 Lin, F. Y., Shen, H. T., Liao, J. Z., & Chan, C. W. (2013). Meta-analysis of evidence. Journal of Long-Term Care , 17 (2), 81-88. https://doi.org/10.6317/LTC.201308_17(2).0002 Lin, H. H. (2009). The influence of emotional labor on job satisfaction for the service personnel over the counter district office, Taichung city ： The moderating effect of emotional intelligence (Unpublished master’s thesis). National Taichung University of Science and Technology. https://doi.org/10.6826/NUTC.2009.00029 Lin, S. P. (2000). Development of the organizational emotional labor burden scale. Management Review , 8 (3), 427-447. Lin, S. P., Yang, N. C., & Chang, S. H. (2000). A case study on emotion rules, socialization processes, and related issues of emotion labor management. Journal of Business and Management Technology , 1 (1), 67-86. Lin, Y. N., & Yang, W. R. (2008). The impact of multidimensional sense of humor on self-esteem, job burnout, and job satisfaction: A study of frontline service employees. Journal of Business and Management Technology , 9 (4), 417-442. https://doi.org/10.30083/CMQ.200812.0001 Lin, Y. S., Huang, W. H., Chang, S. H., & Lin, L. Y. (2015). Surface and deep acting performance affect job burnout and turnover intention: The moderating effect of sense of humor. Journal of Outdoor Recreation , 28 (1), 73-103. https://doi.org/10.6130/JORS.2015.28(1)3 Lin, Z. Y., & Chang, W. H. (2018). The structural relationship between emotional labor and cynicism of full-time international tourist hotels employees. Journal of Tourism Industry and Aviation Management , 1 (1), 22-42. https://doi.org/10.6862/JTIAM.201807_1(1).0002 Liu, T. Y., Lee, C. R., Su, C. H., & Wu, P. L. (2016). Relationship among emotional labor, emotional intelligence and job burnout of public health staff. Policy and Human Resources Management , 7 (2), 89-114. https://doi.org/10.29944/PPM.201612_7(2).0003 Liu, Y. H., & Hsieh, C. W. (2016). Negative display rules and job burnout: Investigating the mediation effect of emotional dissonance and the moderation effect of service climate using a sample of Taipei city government employees. Chinese Public Administration Review , 22 (2), 81-100. https://doi.org/10.6635/cpar.2016.22(2).04 Liu, Y. H., & Liu, W. W. (2009). Exploring relationships among emotional labor, job stress, and coping behaviors in nurses. Journal of Medical and Health Sciences , 11 (2), 98-115. https://doi.org/10.6563/TJHS.2009.11(2).3 Lopes, P. N., Brackett, M. A., Nezlek, J. B., Schütz, A., Sellin, I., & Salovey, P. (2004). Emotional intelligence and social interaction. Personality and Social Psychology Bulletin , 30 (8), 1018-1034. https://doi.org/10.1177/0146167204264762 MacCallum, R. C., & Austin, J. T. (2000). Applications of structural equation modeling in psychological research. Annual Review of Psychology , 51 , 201-226. https://doi.org/10.1146/annurev.psych.51.1.201 Man, L. F. (2002). The study of emotional labor burden and job outcomes among emotional labor workers: The moderating effects of emotional intelligence and job characteristics (Unpublished master’s thesis). Providence University. Martínez-Monteagudo, M. C., Inglés, C. J., Granados, L., Aparisi, D., & García- Fernández, J. M. (2019). Trait emotional intelligence profiles, burnout, anxiety, depression, and stress in secondary education teachers. Personality and Individual Differences , 142 , 53-61. https://doi.org/10.1016/j.paid.2019.01.036 Martins, A., Ramalho, N., & Morin, E. (2010). A comprehensive meta-analysis of the relationship between emotional intelligence and health. Personality and Individual Differences , 49 (6), 554-564. https://doi.org/10.1016/j.paid.2010.05.029 Maslach, C., & Jackson, S. E. (1981). The measurement of experienced burnout. Journal of Organizational Behavior , 2 (2), 99-113. https://doi.org/10.1002/job.4030020205 Maslach, C., & Leiter, M. P. (2016). Understanding the burnout experience: Recent research and its implications for psychiatry. World Psychiatry , 15 (2), 103-111. https://doi.org/10.1002/wps.20311 Maslach, C., & Leiter, M. P. (2017). Understanding burnout: New models. The handbook of stress and health: A guide to research and practice (pp. 36-56). https://doi.org/10.1002/9781118993811.ch3 Maslach, C., Schaufeli, W. B., & Leiter, M. P. (2001). Job burnout. Annual Review of Psychology , 52 (1), 397-422. https://doi.org/10.1146/annurev.psych.52.1.397 Mayer, J. D., & Salovey, P. (1993). The intelligence of emotional intelligence. Intelligence , 17 (4), 433-442. https://doi.org/10.1016/0160-2896(93)90010-3 Mayer, J. D., & Salovey, P. (1997). What is emotional intelligence? In P. Salovey & D. Sluyter (Eds.), Emotional development and emotional intelligence: Implications for educators (pp. 3-31). New York: Basic Books. Mayer, J. D., Caruso, D. R., & Salovey, P. (2016). The ability model of emotional intelligence principles and updates. Emotion Review , 8 (4), 290-300. https://doi.org/10.1177/1754073916639667 Miao, C., Humphrey, R. H., & Qian, S. (2017). A meta‐analysis of emotional intelligence and work attitudes. Journal of Occupational and Organizational Psychology , 90 (2), 177-202. https://doi.org/10.1111/joop.12167 Min, C. H., Wu, Y. F., & Hsu, S. J. (2015). The effects of employees’ emotional intelligence and job satisfaction on organizational citizenship behavior in the high-tech industry. Journal of Management Information and Computing , 4 (2), 108-127. https://doi.org/10.6285/MIC.4(2).09 Morris, J. A., & Feldman, D. C. (1996). The dimensions, antecedents, and consequences of emotional labor. The Academy of Management Review , 21 (4), 986-1010. https://doi.org/10.2307/259161 Petrides, K. V., & Furnham, A. (2000). On the dimensional structure of emotional intelligence. Personality and Individual Differences , 29 (2), 313-320. Ramana, P., Sambasivanb, M., & Kumar, N. (2016). Counterproductive work behavior among frontline government employees: Role of personality, emotional intelligence, affectivity, emotional labor, and emotional exhaustion. Journal of Work and Organizational Psychology , 32 (1),25-37. https://doi.org/10.1016/j.rpto.2015.11.002 Rosenthal, R. (1991). Meta-analysis: A review. Psychosomatic Medicine , 53 (3), 247-271. Rossie, A. M., Perrewe, P. L., Sauter, S. L., & Jex, S. M. (2006). Stress and quality of working life: Current perspectives in occupational health . Charlotte, NC: Information Age Publishing Salovey, P., & Mayer, J. D. (1990). Emotional intelligence. Imagination, Cognition and Personality , 9 (3), 185-211. https://doi.org/10.2190/DUGG-P24E-52WK-6CDG Sánchez-Pujalte, L., Mateu, D. N., Etchezahar, E., & Gómez Yepes, T. (2021). Teachers’ burnout during COVID-19 pandemic in Spain: Trait emotional intelligence and socioemotional competencies. Sustainability , 13 (13), 1-11. https://doi.org/10.3390/su13137259 Schaufeli, W. B., & Greenglass, E. R. (2001). Introduction to special issue on burnout and health. Psychology & health , 16 (5), 501-510. https://doi.org/10.1080/08870440108405523 Scherer, S., Zapf, D., Beitler, L. A., & Trumpold, K. (2020). Testing a multidimensional model of emotional labor, emotional abilities, and exhaustion: A multilevel, multimethod approach. Journal of Occupational Health Psychology , 25 (1), 46-67. https://doi.org/10.1037/ocp0000166 Shah, M. K., Gandrakota, N., Cimiotti, J. P., Ghose, N., Moore, M., & Ali, M. K. (2021). Prevalence of and factors associated with nurse burnout in the US. JAMA Network Open , 4 (2). https://doi.org/10.1001/jamanetworkopen.2020.36469 Sharpe, D. (1997). Of apples and oranges, file drawers and garbage: Why validity issues in meta-analysis will not go away. Clinical Psychology Review , 17 (8), 881-901. https://doi.org/10.1016/S0272-7358(97)00056-1 Tang, Y. T. (2009). The effects of employee perception of supervisor's emotional intelligence on innovative behavior. Journal of Quality , 16 (6), 461-478. Thomas, N. S., Mousavy, S., Mukundan, J., & Nimehchisalem, V. (2012). The relationship between burnout and trait emotional intelligence among secondary school teachers in Malaysia. International Journal of Applied Linguistics and English Literature , 1 (4), 15-23. Tsai, Y. C. (2011). A study on the relationship between emotional intelligence and emotional labor of elementary school teachers (Unpublished master’s thesis). National Taipei University of Education. https://doi.org/10.6344/NTUE.2011.00461 Tyng, C. M., Amin, H. U., Saad, M. N. M., & Malik, A. S. (2017). The influences of emotion on learning and memory. Frontiers in Psychology , 8 , 1454. https://doi.org/10.3389/fpsyg.2017.01454 Viswesvaran, C., & Ones, D. S. (1995). Theory testing: Combining psychometric meta‐analysis and structural equations modeling. Personnel Psychology , 48 (4), 865-885. Wang, C. H., & Weng, C. Y. (2009). Relationships among emotional labor, job burnout and job satisfaction of the tour leaders: Moderating effects of role identity. Journal of Outdoor Recreation , 22 (4), 23-53. https://doi.org/10.6130/JORS.2009.22(4)2 Wang, C. H., Lo, H. F., Deng, S. Y., & Lin, C. W. (2012). Emotional labor, job burnout and job satisfaction of frontline employees in the hospitality industry. Journal of Hospitality, Tourism, and Leisure Studies , 9 (3), 151-170. https://doi.org/10.6572/JHT.9(3).3 Wang, H. Y., Huang, D. X., & Wei, L. M. (2014). The relations among teachers' exercise participation, emotional intelligence and job stress at elementary schools. Journal of Sports, Leisure, and Hospitality Research , 9 (4), 88-102. https://doi.org/10.29429/JSLHR. 2014129(4).05 Wang, H. Y., Liu, R. W., & Yeh, M. G. (2018). Effectively performing systematic review and meta-analysis. T aiwan Journal of Clinical Pharmacy , 26 (1), 1-10. https://doi.org/10.6168/FJCP.201801_26(1).0001 Wang, S. H. (1999). The concept of meta-analysis and its application in nursing. Nursing Research , 7 (2), 183-192. https://doi.org/10.7081/NR.199904.0183 Wang, Y. L. (2018). Research on the correlation between preschool teacher's emotional service, job burnout and turnover intention. Journal of Meihe Education , 37 (1), 9-40. Wechsler, D. (1958). The measurement and appraisal of adult intelligence. Williams & Wilkins Co. Wen, J. F., & Cui, L. Y. (2001). A study on work-family conflicts and job burnout of female professionals in high-tech companies: Effects of social support. Management Review , 20 (4), 65-91. https://doi.org/10.6656/MR.2001.20.4.CHI.65 Wen, J., Huang, S. S., & Hou, P. (2019). Emotional intelligence, emotional labor, perceived organizational support, and job satisfaction: A moderated mediation model. International Journal of Hospitality Management , 81 , 120-130. https://doi.org/10.1016/j.ijhm.2019.01.009 Weng, C. Y. (2017). A study on the correlation among emotional labor, emotional intelligence, and emotional exhaustion of counseling psychologists (Unpublished master’s thesis). National Dong Hwa University. Wharton, A. S. (2009). The sociology of emotional labor. Annual Review of Sociology , 35 , 147-165. https://doi.org/10.1146/annurev-soc-070308-115944 Wortman, P. M. (1994). Judging research quality . The handbook of research synthesis , 97-109. Wu, H. H., & Lin, C. C. (2011). The Effect of social support on the junior high school teacher's burnout: Role stress and self-efficacy as a mediator. Journal of Education Research, National Chiayi University , (26), 25-56. https://doi.org/10.6474/NCUJER.201103.0025 Wu, P. L. (2016). A study on the relationship between emotional labor and job burnout among public health personnel: The moderating effects of personality traits and emotional intelligence (Unpublished master’s thesis). National Kaohsiung Normal University. Wu, Q. S., & Lin, T. Y. (2005). Educational term: Emotional labor. Educational Resources and Research , 65 , 136. Wu, S. R., & Wang, X. H. (2005). Emotional labor and its related factors in clinical nurses. Evidence-Based Nursing , 1 (4), 243-252. https://doi.org/10.6225/JEBN.1.4.243 Wu, Z. Y. (2008). Abusive supervision and emotional exhaustion: The mediating effects of subordinate justice perception and emotional labor. Chinese Journal of Psychology , 50 (2), 201-221. https://doi.org/10.6129/CJP.2008.5002.06 Wu, Z. Y. (2008). The mediating process of trust and the moderating effect of emotional intelligence on a Chinese supervisor's authoritarianism leadership and a subordinate's job satisfaction and organizational commitment. Indigenous Psychological Research in Chinese Societies , (30), 3-63. https://doi.org/10.6254/2008.30.3 Wu, Z. Y., & Cheng, B. S. (2003). Emotion studies in organizations in Taiwan: A critical review. Applied Psychology Research , (19), 137-173. Wu, Z. Y., & Cheng, B. S. (2006). The relationships among frequency of encountering difficult customers, perceived service training utility, emotional labor, and emotional exhaustion-The viewpoint of "conservation of resources theory". Journal of Management , 23 (5), 581-599. https://doi.org/10.6504/JOM.2006.23.05.04 Xie, M. S. (2010). The impact of individual's emotional intelligence and gender on the job demands-resources and job burnout relationship: Evidence from community mental rehabilitation institution staff (Unpublished master’s thesis). Ching Yun University. Yen, K. L., & Li, Y. H. (2008). A analysis on the contents and the related studies of emotional labor and burnout and revelation of elementary school administrators. School Administration , (57), 1-37. https://doi.org/10.6423/HHHC.200809.0001 Yin, H., Huang, S., & Chen, G. (2019). The relationships between teachers’ emotional labor and their burnout and satisfaction: A meta-analytic review. Educational Research Review , 28 . https://doi.org/100283.10.1016/j.edurev.2019.100283 Yu, C. L. (2020). A study on the correlation among employees' emotional intelligence, emotional labor, and job satisfaction: A case study of port operational management authority (Unpublished master’s thesis). National Kaohsiung University of Science and Technology. Zhang, H. C., Liu, J. W., & Yang, H. N. (2010). Exploring the effect of psychological contract on work-life balance: The moderating roles of social support and emotional intelligence. Journal of Human Resource Management , 10 (3), 1-24. https://doi.org/10.6147/JHRM.2010.1003.01 Zhang, H. C., Liu, J. W., Xu, H. M., Miao, M. Z., & Tong, Z. L. (2011). The relationships between supervisor's mood contagion and employee's innovative behavior: The mediating roles of work engagement and burnout. Journal of Human Resource Management , 11 (2), 1-24. https://doi.org/10.6147/JHRM.2011.1102.02 Zhang, L. Y., & Yan, Y. Y. (2021). Relationships among emotional labor, job burnout, and job satisfaction of amusement park employees: Emotional intelligence as a moderator. Journal of Hospitality, Tourism and Leisure Studies , 18 (1), 69-89. https://doi.org/10.6572/JHT.202106_18(1).0004 Zhong, C. D., & Hsieh, M. H. (2008). Emotional intelligence and negotiation behavior: Negotiation strategy, personal attraction and negotiation outcomes. Journal of Management , 25 (5), 525-548. https://doi.org/10.6504/JOM.2008.25.05.04 Additional Declarations No competing interests reported. Cite Share Download PDF Status: Published Journal Publication published 18 Nov, 2024 Read the published version in BMC Psychology → Version 1 posted Editorial decision: Revision requested 19 Jun, 2024 Editor assigned by journal 16 Jun, 2024 Submission checks completed at journal 16 Jun, 2024 First submitted to journal 11 Jun, 2024 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-4563859","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":316438873,"identity":"549b5f39-cf2b-467d-9f49-b37ec1cd0114","order_by":0,"name":"Yin-Che Chen","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA/UlEQVRIiWNgGAWjYDACZjBiSABSB8ACBiCChzgtbAkgvgRMiwQBXSAtPAbEaTE4znvwc0GFXR6/dM/XzTwMd+rMJRIYH7xtY6gzOIBDy2G+ZOkZZ5KLJeec3XZzBsMzCcsZCcyGc9uA1uHUwmPGzNt2IHHDjdxtNz4wHJYwuJHAJs0L1GKGV8u/A4n7b+Q8u5EA0cL+m7CWBqAtEjlscFuY8WmRPMxjLM1zLDlxxo00s5szDA5LbjjzsFlyzjkJyf04tPCdP2P4mafGLrF/RvKz2zwVh/kNjicf/PCmzIZfsgG7FgVUo8CRwghSizsm5XEYNQpGwSgYBaMAAQA97FtU9wSyIgAAAABJRU5ErkJggg==","orcid":"","institution":"National Tsing Hua University","correspondingAuthor":true,"prefix":"","firstName":"Yin-Che","middleName":"","lastName":"Chen","suffix":""},{"id":316438874,"identity":"1a31b541-1a26-435a-93bd-3cda7a310d44","order_by":1,"name":"Zhi-Ling Huang","email":"","orcid":"","institution":"National Tsing Hua University","correspondingAuthor":false,"prefix":"","firstName":"Zhi-Ling","middleName":"","lastName":"Huang","suffix":""},{"id":316438875,"identity":"c8aa2502-cb10-4bdf-8e23-2ec410578bfb","order_by":2,"name":"Hui-Chuang Chu","email":"","orcid":"","institution":"National Tsing Hua University","correspondingAuthor":false,"prefix":"","firstName":"Hui-Chuang","middleName":"","lastName":"Chu","suffix":""}],"badges":[],"createdAt":"2024-06-11 11:51:35","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4563859/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4563859/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1186/s40359-024-02167-w","type":"published","date":"2024-11-18T15:56:50+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":59476745,"identity":"d0c54ef9-f07d-4c86-ae1c-08a3a76bdfd8","added_by":"auto","created_at":"2024-07-02 09:05:30","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":196784,"visible":true,"origin":"","legend":"\u003cp\u003eFunnel plot of EL and JB analysis results\u003c/p\u003e","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-4563859/v1/d50049f2f4e1f0b58e848429.png"},{"id":59477194,"identity":"d2257124-a79d-4747-8b72-cc8ba3871c05","added_by":"auto","created_at":"2024-07-02 09:13:30","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":112645,"visible":true,"origin":"","legend":"\u003cp\u003eFunnel plot of EI and EL analysis results\u003c/p\u003e","description":"","filename":"floatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-4563859/v1/fd65609011b87ca906de5095.png"},{"id":59476743,"identity":"c4e49816-3311-4384-a52b-8485e58653b5","added_by":"auto","created_at":"2024-07-02 09:05:30","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":94096,"visible":true,"origin":"","legend":"\u003cp\u003eFunnel plot of EI and JB analysis results.\u003c/p\u003e","description":"","filename":"floatimage3.png","url":"https://assets-eu.researchsquare.com/files/rs-4563859/v1/9285616d21ce215ff9b88e46.png"},{"id":59476746,"identity":"3a5d2443-1a6a-4df5-98dc-4b09207062f0","added_by":"auto","created_at":"2024-07-02 09:05:30","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":38938,"visible":true,"origin":"","legend":"\u003cp\u003ePath chart and standardized parameter estimates of proposed theoretical model.\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-4563859/v1/c43a2833dda06f5c59540a04.png"},{"id":69834481,"identity":"d64156ce-1f9a-45ac-af18-abe9309122de","added_by":"auto","created_at":"2024-11-25 16:00:42","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1682346,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4563859/v1/1e041d4d-0235-4853-a667-c3f60527ca50.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Relationships Between Emotional Labor, Job Burnout, and Emotional Intelligence: An Analysis Combining Meta-analysis and Structural Equation Modeling","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eWith the thriving development of organizational psychology and the increasing corporate emphasis on the emotional well-being of employees and their physiological and psychological health, scholars have increasingly explored emotional cultures, individual affective expressions, and work results in an organizational context to develop concrete and effective suggestions for corporate operations and management. Hochschild (\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e1983\u003c/span\u003e) introduced the concept of emotional labor (EL) and the use of the organizational emotion norms as a standard for evaluating employee work performance. EL is regarded as a form of behavioral performance that aligns with emotion norms, indicating that emotions are an aspect of work. However, excessive EL or a dissonance between emotions norms and an individual\u0026rsquo;s personal emotions may induce stress or lead to negative results, such as perceived job burnout (JB), changes in work attitude, and a decline in service performance (Huang et al., \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; Wu \u0026amp; Wang, \u003cspan citationid=\"CR123\" class=\"CitationRef\"\u003e2005\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eConservation of resources theory posits that when employees adhere to organizational emotion norms and adjust their emotions accordingly, they may expend considerable resources. Excessive emotional demands lead to JB, and a high EL may further exhaust an individual\u0026rsquo;s emotional resources, thereby causing them to experience various negative situations, including emotional exhaustion, depersonalization, and reduced personal accomplishment. These situations are regarded as the standard characteristics of JB (Wang \u0026amp; Wong, 2009). Thus, if an organization intends to prevent its employees from experiencing the negative effects of EL, the emotional abilities of its employees is a key factor to consider. In an organization, individuals with higher emotional intelligence (EI) are more adept at adjusting their emotions to environmental demands related to EL (Lee \u0026amp; Ok, \u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e2012\u003c/span\u003e). Salovey and Mayer (\u003cspan citationid=\"CR97\" class=\"CitationRef\"\u003e1990\u003c/span\u003e) asserted that EI is a form of social intelligence that enables an individual to understand and manage interpersonal relationships. In addition to its role in influence an individual\u0026rsquo;s emotional management and behavior, EI represents an individual\u0026rsquo;s ability to control their emotions and cope with frustrations when they encounter challenging events. The present study explored the model architecture underlying the associations among EL, JB, and EI to determine the relationships between specific variables. A review of studies conducted in Taiwan and other Asian countries was conducted to integrate the model and summarize generalizable results that are relevant to organizations.\u003c/p\u003e \u003cp\u003eThrough the collection and collation of relevant literature, the present study determined that most studies related to EL, JB, and EI adopted a quantitative questionnaire survey design. Given the diverse outcomes reported in the literature, the present study adopted a meta-analysis research design and an integrated perspective to develop a theoretical model. Structural equation modeling (SEM) was used for validation, and a research model that combined meta-analysis and SEM (referred to as the \u0026ldquo;MASEM model\u0026rdquo;) was employed. Numerous scholars have adopted this model for research. Meta-analyses can overcome the disadvantages of traditional literature review methods by using statistical models to facilitate systematic integration and comments, thereby providing a focused and integrated architecture for studying specific topics. The meta-analysis model can be used to obtain objective quantitative information, assess the directions of the effects between variables, and determine the corresponding effect sizes (Wang, \u003cspan citationid=\"CR112\" class=\"CitationRef\"\u003e1999\u003c/span\u003e). Furthermore, the emerging MASEM method can validate the relevance between variables and correlation matrices by examining a proposed theoretical model and assessing its model fit (Huang \u0026amp; Lin, \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2007\u003c/span\u003e). Thus, the present study employed this research method to clarify and integrate literature findings and further explore the associations among EL, JB, and EI. The results and theoretical contribution of the present study are discussed, and practical suggestions are provided.\u003c/p\u003e"},{"header":"2. Literature Review","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1. Emotional Labor\u003c/h2\u003e \u003cp\u003eThe concept of EL was first proposed by Hochschild (\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e1983\u003c/span\u003e), who defined EL as a process by which employees control and adjust their emotions during interactions with customers in the workplace to meet the normative demands of their organization with respect to personal emotional and behavioral expressions. This process enables the integration of smiles, emotions, and affective relationships into the products or services sold. EL can be classified as surface and deep acting on the basis of an individual\u0026rsquo;s acting status. Surface acting involves an individual receiving instructions regarding emotional regulation without changing their thoughts through internalization. An individual who engages in surface acting adheres to organizational emotion norms and exhibit the corresponding attitudes. Deep acting occurs when an individual not only conforms to emotion norms but also internalize such norms and adjust their emotions such that they can meet organizational demands. In the Chinese language, various terms can be used to convey the concept of EL. However, scholars have extended the work of Hochschild (\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e1983\u003c/span\u003e) by focusing on the active emotional regulation of employees as well as the management and training implemented by organizations and leaders to enable their employees to meet organizational emotional demands. Most subsequent scholars have adopted said definition of EL for further extension and elaboration. Scholars worldwide have identified as EL as a service or a type of product on the basis of Hochschild\u0026rsquo;s (\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e1983\u003c/span\u003e) finding regarding the positive and negative effects of EL on organizational development.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2. Job Burnout\u003c/h2\u003e \u003cp\u003eThe concept of burnout was first proposed by Freudenberger (\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e1975\u003c/span\u003e), who argued that burnout is not just a work-related status but one that an individual who is not affiliated with a unit may develop when they experience excessive demands on their resources, resulting in feelings of failure, weariness, and exhaustion. Freudenberger (\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e1975\u003c/span\u003e) contended that the loss of charisma by a leader is the primary antecedent of burnout and that when a leader begins to doubt their ability to competently manage their subordinates, their subordinates may experience disappointment or develop low expectations toward their leader. Subsequently, the leader places excessive demands on the capabilities or resources of their subordinates to recover their charisma, thereby causing burnout and various forms of physiological and psychological discomfort. Maslach and Jackson (\u003cspan citationid=\"CR83\" class=\"CitationRef\"\u003e1981\u003c/span\u003e) contended that when employees experience increased levels of JB, they may become emotionally exhausted. Excessive emotional exhaustion may reduce the job dedication of employees and have psychological effects. In addition, the employees may treat customers as an inconvenience when they are providing services and switch from active to negative attitudes or even adopt depersonalized modes. The employees tend to engage in negative self-evaluation, expressing considerable dissatisfaction with themselves and their jobs with respect to customer interactions. Thus, Maslach and Jackson (\u003cspan citationid=\"CR83\" class=\"CitationRef\"\u003e1981\u003c/span\u003e) divided the aforementioned processes into three dimensions for measuring JB, namely, emotional exhaustion, depersonalization, and personal accomplishment, and they developed the Maslach Burnout Inventory (MBI) for assessing job burnout, which has been extensively applied by researchers.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e2.3. Emotional Intelligence\u003c/h2\u003e \u003cp\u003eEI can be explored from the perspectives of emotion and intelligence. Salovey and Mayer (\u003cspan citationid=\"CR97\" class=\"CitationRef\"\u003e1990\u003c/span\u003e) defined emotions as a series of organized responses related to physiology, cognition, motive, and experience. Compared with moods, emotions have a shorter duration and are more intense. Emotions function as the positive or negative responses of an individual to internal and external events. Wechsler (\u003cspan citationid=\"CR114\" class=\"CitationRef\"\u003e1958\u003c/span\u003e) referred to intelligence as the comprehensive ability of an individual to respond appropriately to external environments by engaging in targeted thinking and rational decision-making. Salovey and Mayer (\u003cspan citationid=\"CR97\" class=\"CitationRef\"\u003e1990\u003c/span\u003e) defined EI as a form of social intelligence that enables an individual to understand and manage interpersonal relationships. This ability enables individuals to quickly classify their perceptions of life as emotions. In addition to facilitating internal emotional management, EI enables an individual to monitor the emotions of others and create an interactive atmosphere. This aspect of EI can be applied to predict future behavior. Salovey and Mayer (\u003cspan citationid=\"CR97\" class=\"CitationRef\"\u003e1990\u003c/span\u003e) proposed a conceptualized framework for using EI to evaluate emotions, the expression of personal emotions, the regulation of emotions, the application of emotions for flexible planning, the development of creative ideas, the transition of attention, and the generation of motivation. In addition, they argued that the aforementioned concepts allow emotions to fulfill positive functions. Specifically, compared with the expression of simple individual emotion, EI emphasizes identifying personal emotions and those of others as a basis for regulating and solving problems.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003e2.4. Relationships Between Emotional Labor, Job Burnout, and Emotional Intelligence\u003c/h2\u003e \u003cdiv id=\"Sec7\" class=\"Section3\"\u003e \u003ch2\u003e2.4.1. Relationship Between Emotional Labor and Job Burnout\u003c/h2\u003e \u003cp\u003eA review of EL- and JB-related studies revealed that surface acting involves behavioral performance that does not involve internal thoughts and easily leads to emotional dissonance an individuals, causing them to experience psychological stress and possibly emotional exhaustion. When an employee endeavors to adhere to organizational emotion norms and realizes that such norms contradict their personal thoughts, they tend to engage in surface acting, which places an excessive demand on their emotional resources, resulting in the development of JB (Lee \u0026amp; Chelladurai, \u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e2016\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eEL can easily occur in occupations that require frequent interactions with customers. To maintain emotional bonds with customers, organizations place excessive demands on their employees\u0026rsquo; emotional resources, which tends to cause job stress and lead to JB. When EL is implemented by organizations to control the emotions of their employees, emotion management becomes a method for these employees to adhere to organizational norms or pursue organizational goals. In the service industry, employees must often interact with customers. To achieve the goals that are expected of them, employees must often adapt their emotions when they engage in EL (Jeung et al., \u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). Numerous scholars investigated frontline service providers as research participants and reported that differences between individual EL and organizational emotional regulation tend to cause emotional dissonance among employees. In addition, emotional dissonance may cause emotional exhaustion and depersonalization, both of which are dimensions of JB. Researchers have reported that individual EL is positively correlated with JB (Wang \u0026amp; Wong, 2009; Wang et al., \u003cspan citationid=\"CR109\" class=\"CitationRef\"\u003e2012\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eHsu and Lee (\u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e2012\u003c/span\u003e) examined ground staff of an airline who experienced high EL in their study. The ground staff had to frequently interact with customers and adhere to strict emotion norms; thus, the study concluded that employees tend to develop JB when they are under the emotional control of their organizations. To alleviate such burnout, organizations can start by reducing the EL of their employees. Hsieh (\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2015\u003c/span\u003e) recruited hotel employees as participants and examined EL as a type of job demand. Conducted on the basis of job resources theory, their study revealed an association between EL and JB. Specifically, if employees were willing to undergo internalized changes with respect to emotion norms, their adopted deep acting was negatively correlated with their JB. By contrast, if the employees adopted surface acting while providing services to customers in accordance with organizational rules, their surface acting was positively correlated with their JB. Chen and Wu (\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2019\u003c/span\u003e) examined the counter staff of department stores to explore the roles of emotional discrepancy and emotional efforts in EL. Their results revealed that EL and JB are positively correlated. Employees who experience EL for a long period of time need to disguise their emotions or hide their true thoughts, which is a behavior that often depletes their emotional resources, reduces their personal accomplishment, and increases their JB.\u003c/p\u003e \u003cp\u003eIn summary, the present study hypothesized that EL is significantly and positively correlated with JB. If employees comply with organizational emotion norms over a long period of time and experience emotional dissonance, JB may occur because of the discrepancy between their internal thoughts and external acting. Thus, the following hypothesis was established:\u003c/p\u003e \u003cp\u003e \u003cstrong\u003eHypothesis 1\u003c/strong\u003e \u003cp\u003e \u003cb\u003eEL is positively correlated with JB.\u003c/b\u003e \u003c/p\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section3\"\u003e \u003ch2\u003e2.4.2. Relationship Between Emotional Intelligence and Emotional Labor\u003c/h2\u003e \u003cp\u003eEmotions were regarded as a display of individual behavior or a mental journey before Hochschild incorporated individual emotions and organizational norms into the concept of emotions and proposed the concept of EL, which considered the true emotions of employees, the emotional demands of enterprises, and actual dimensions of action transitions. Because the EL performed in an organization is regarded as a normative demand that can be managed and publicly displayed through various behavior, employees often express their emotional status through EL while interacting with others at work. Thus, employees who work in jobs that involve a high level of EL and possess high EI are regarded as employees who are highly competent at controlling and adapting their personal emotions to meet organizational demands. When employees accept emotion norms, they require more EI to adjust their personal feelings such that the depletion of their emotional and cognitive resources can be minimized (Miao et al., \u003cspan citationid=\"CR90\" class=\"CitationRef\"\u003e2017\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eTo build enjoyable and comfortable environments in an era that emphasizes customer orientation, organizations are increasingly focusing on emotion norms without considering the emotional expression modes of individuals or the processes by which they interact with others. Numerous scholars have explored the relationship between EI and EL (Man, \u003cspan citationid=\"CR80\" class=\"CitationRef\"\u003e2002\u003c/span\u003e; Wu \u0026amp; Cheng, \u003cspan citationid=\"CR126\" class=\"CitationRef\"\u003e2003\u003c/span\u003e). Service sector studies have identified EI as a special ability on the basis that employees are required to implement the EL strategies of their organizations. Employees engage in deep or surface acting while interacting with customers. Under resources conservation theory, EI is an internal emotional resource of employees. If employees can adequately apply EI to perform the EL that is required of them at work, they are likely to adhere to organizational emotion norms. Through perceived organizational support, employees can supplement the emotional resources depleted by EL (Wen et al., \u003cspan citationid=\"CR116\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). In addition, people with high EI can usually adapt their behavior in response to environmental changes, enabling them to adhere to emotion norms or display the emotions that are expected of them. Consequently, they EL load increases. Thus, in addition to the relationship between EI and EL, studies have often evaluated the effects of EI on the deep acting of employees and their EL loading; EI has also been studied as a moderating variable (Chu, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2013\u003c/span\u003e; Kao, 2011).\u003c/p\u003e \u003cp\u003eEI not only affects the work of an employee but also facilitates the development of their interpersonal relationships if it is applied appropriately. Through behavioral performance, EI enables an employee to adjust or induce the emotional responses of others. Thus, an employee\u0026rsquo;s EI and EL acting behavior are positively correlated. If an employee has high EI, they can adequately implement emotional management depending on their emotional status to adjust their emotions through deep acting (Tsai, \u003cspan citationid=\"CR105\" class=\"CitationRef\"\u003e2011\u003c/span\u003e). Yu (2019) examined employees of Taiwan International Ports Corporation, Ltd. to explore the effects of EI on EL and measured the surface acting and deep acting involved in the performance of EL. Their results revealed that the employees with higher EI were more adept at applying, regulating, and controlling their personal emotions. The emotional changes that they underwent at work enabled them to perform EL involving emotional concealment, surface emotional control, and frequent interactions. These results suggest that partial variables that influence the relationship between EI and EL are significantly and positively correlated.\u003c/p\u003e \u003cp\u003eIn summary, the present study hypothesized that EI and EL are significantly and positively correlated. Individuals with higher EI are more proficient at controlling their emotions and perceiving the emotions of others, thereby facilitating their rapid adaptation to environmental demands when they must adhere to organizational emotion norms. In the context of EL, they can adopt surface and deep acting to enhance the coherence and efficiency of their work and interpersonal relationships, thereby enabling higher levels of EL loading. Thus, the present study examined emotional loading in the context of EL and proposed the following hypothesis:\u003c/p\u003e \u003cp\u003e \u003cstrong\u003eHypothesis 2\u003c/strong\u003e \u003cp\u003e \u003cb\u003eEI is positively correlated with El.\u003c/b\u003e \u003c/p\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section3\"\u003e \u003ch2\u003e2.4.3. Relationship Between Emotional Intelligence and Job Burnout\u003c/h2\u003e \u003cp\u003eStudies have indicated that emotional dissonance considerably affects JB. The ability to implement effective emotional control or regulation appears to play a crucial role in preventing affective disorders among employees at work (Jeung et al., \u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). Thus, enterprises should emphasize emotion-related dimensions and consider how the physiological and psychological status of their employees may affect their personal performance and the overall performance of an enterprise. Through their interpersonal relationships and the improvement of their individual EI, employees can appropriately manage their external behavioral performance and reduce negative work outcomes. Studies have reported that employee stressors can originate from both organizational and individual factors related to work. Excessive stress loading may exhaust individuals physiologically, psychologically, and spiritually, resulting in JB. Furthermore, because organizations are an external factor that is difficult to change and control, and that EI varies between individuals, Hung (\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e2008\u003c/span\u003e) examined employees of small and medium-sized businesses, and reported that EI and JB are significantly and negatively correlated; specifically, among the various dimensions of EI, emotion regulation was most correlated with JB, followed by emotional expression and emotional awareness.\u003c/p\u003e \u003cp\u003eIn addition, EI in the workplace can be classified into the three dimensions of emotional attention (i.e., the attention that an individual pays to the emotions of others), emotional understanding (i.e., the ability to understand, identify, and label the emotions of others), and emotional repair (i.e., the ability to regulate one\u0026rsquo;s emotions). A study reported that individuals with high EI can proficiently monitor the emotions of others, including the emotional responses of other personnel in the workplace. These individuals are also highly adept at understanding emotions and identifying the causes and effects of emotions, thereby reducing their work stress and alleviating various negative work effects and JB (Mart\u0026iacute;nez-Monteagudo et al., \u003cspan citationid=\"CR81\" class=\"CitationRef\"\u003e2019\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eIn their study, Liao and Song (\u003cspan citationid=\"CR66\" class=\"CitationRef\"\u003e2018\u003c/span\u003e) examined mass rapid transit drivers who were prone to experiencing high job stress. Their study revealed that the appropriate application of EI by the drivers can effectively help them focus on their work and transform their stress into assistance, thereby helping them optimize their decision-making at work and to reduce their risk of developing JB. Their study also indicated that the EI and JB of the drivers were significantly and negatively correlated and that the application of EI reduced the development of JB when the drivers experienced job stress. In addition, their study employed the job requirements-resources model and asserted that employees with high EI can effectively allocate their job resources, quickly perceive emotions, and appropriately adjust their moods. These findings indicate that EI plays a role in moderating the relationship between job stress and JB.\u003c/p\u003e \u003cp\u003eChong and Yen (2021) examined tourism service workers who must adhere to stringent emotion norms to explore how the emotional transitions and adaptive abilities of amusement park workers affected their development of JB. Their study indicated that when individuals who are more adept at emotional regulation are also more capable of recognizing their personal emotions, addressing negative emotions, and, consequently, reducing their JB levels. Their results verified the presence of a negative relationship between EI and JB, implying that individuals with high EI can regulate their mindsets and emotions and are less likely to experience JB.\u003c/p\u003e \u003cp\u003eBurnout syndrome is defined as a general sense of psychological discomfort involving low levels of self-esteem, motivation, and occupational commitment. Burnout is a persistent state characterize by depressive and negative emotions. Work and occupational factors may contribute to the development of burnout. However, when an individual has higher EI, they can more capably resist JB by managing their emotions and emotional information. Through emotional clarity, organizations can ensure that their employees can perceive emotions and reduce tension and psychological dissonance. In addition, the attention dimension of EI can help employees focus on their emotions and those of others, thereby increasing their performance and concentration at work. Skillful emotional regulation allows for the conversion of negative emotions into positive ones, which can substantially reduce the development of JB (S\u0026aacute;nchez-Pujalte et al., \u003cspan citationid=\"CR98\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). In summary, the present study asserted that EI and JB are significantly and negatively correlated. When individuals have higher EI, they can more capably assess their personal emotional loading and adapt their emotions to regulate negative psychological perceptions and eliminate negative emotional thoughts. Such individuals are less likely to develop JB. Given these inferences, the present study proposed the following hypothesis:\u003c/p\u003e \u003cp\u003e \u003cstrong\u003eHypothesis 3\u003c/strong\u003e \u003cp\u003e \u003cb\u003eEI is negatively correlated with JB.\u003c/b\u003e \u003c/p\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section3\"\u003e \u003ch2\u003e2.4.4. Relationships Between Emotional Intelligence, Emotional Labor, and Job Burnout\u003c/h2\u003e \u003cp\u003eMost studies that investigated EI, EL, and JB have reported that individuals in an organization who are more adept at emotional regulation or adaption have a greater influence on their EL and JB. Thus, in addition to exploring the associations between variables, numerous studies have examined the moderating effect of EI on the relationship between EL and JB and reported diverse results.\u003c/p\u003e \u003cp\u003eTaiwan-based studies that examined the moderating effect of EI on the relationship between EL and JB have identified significant correlations between EI and the other two variables. For example, a study reported that individual EI is positively correlated with workplace EL and that EI is negatively correlated with JB; however, the study asserted that the moderating effect of EI was different from the effect that was hypothesized (Chong \u0026amp; Yen, 2021).\u003c/p\u003e \u003cp\u003eLiu et al. (\u003cspan citationid=\"CR75\" class=\"CitationRef\"\u003e2016\u003c/span\u003e) contended that environmental and individual factors affect the relationship between EL and JB. Individuals who are more adept at assessing emotions in the workplace can more competently address their negative psychological responses. Weng (\u003cspan citationid=\"CR117\" class=\"CitationRef\"\u003e2017\u003c/span\u003e) enrolled counseling psychologists as their study participants and reported that the psychologists who had to navigate the emotions of others while regulating their own personal emotions for an extended period of time in the workplace tended to perform increased EL and experience emotional exhaustion. In this context, their emotional awareness, sense of comfort, and emotion utilization could be influencing factors. Although these studies examined the moderating effects of EI on the relationship between EL and JB, their results revealed the correlations between variables but did not support their hypothesis regarding moderating effects.\u003c/p\u003e \u003cp\u003eStudies conducted outside of Taiwan have also discussed the effect of EI on the relationship between EL and JB; however, their results differed from those of studies conducted in Taiwan. Hong and Lee (\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2016\u003c/span\u003e) examined a group of nurses and reported that when the nurses applied EI to fulfill their job requirements, they simultaneously engaged in EL and reduced their JB. Their results also indicated that EI mediates the relationship between EL and JB. Lee and Chelladurai (\u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e2016\u003c/span\u003e) discussed the three dimensions of EL (i.e., surface acting, deep acting, and genuine expression), defining genuine expression as an active or conscious process involving changing emotions and asserting that surface acting and deep acting can be influenced by an individual\u0026rsquo;s EI, thereby affecting their level of emotional exhaustion. Their study results verified that individuals who engage in more surface acting at work are more susceptible to emotional exhaustion because of the incongruity between their displayed emotions and genuine emotions. However, individuals with higher EI can more effectively regulate this process and reduce their JB. Scherer et al. (\u003cspan citationid=\"CR100\" class=\"CitationRef\"\u003e2020\u003c/span\u003e) argued that employees meet job requirements and adhere to emotion norms by engaging in EL, which is crucially influenced by the emotion management component of their individual EI. EI not only facilitates effective emotional expression but also functions as a resource for coping with social interactions in the workplace, implementing constructive and efficient conflict management, and buffering against negative emotions. Consequently, EI helps an individual reduce their emotional exhaustion. Their results also indicated that individual EI effectively moderates the relationship between surface acting and JB.\u003c/p\u003e \u003cp\u003eIn summary, on the basis of the results, hypotheses, and inferences of other studies, the present study hypothesized that an individual\u0026rsquo;s EI affects the relationship between EL and JB. When employees perform EL in accordance with the emotion-related rules of their organization, the effective application of personal EI to regulate emotions can reduce their risk of developing emotional exhaustion or JB. By contrast, individuals with low EI may experience more JB because of the EL they perform at work.\u003c/p\u003e \u003cp\u003e \u003cstrong\u003eHypothesis 4\u003c/strong\u003e \u003cp\u003e \u003cb\u003eEI moderates the relationship between EL and JB.\u003c/b\u003e \u003c/p\u003e \u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003e2.5. Meta-analysis and structural equation modeling\u003c/h2\u003e \u003cp\u003eMeta-analyses (MAs) have been widely conducted since the 1970s. An MA involves collecting data from diverse sources, including clinical trials, observational studies, and individual records. An MA is a tool for integrating and organizing large amounts of diverse data systematically. An MA involves primary and secondary data analysis, and it addresses research questions derived from original research studies and collates the findings from multiple studies. An MA is often referred to as analysis of analyses (Glass, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e1976\u003c/span\u003e). Numerous studies have been conducted to explore psychological outcomes. Although most of these studies have been sampled and systematically organized, the actual integration of such a great number of studies is rare. With the number of studies on popular topics continuing to increase, studies involving emerging and extensively studied issues require regular consolidation and summarization to assess their validity and outcomes. Therefore, to provide unbiased evaluations of existing evidence, scholars have started conducting quantitative MAs, which involve the quantification and integration of data from studies that covered similar topics but reported differing results. This research design enables scholars to understand various types of research pertaining to areas such as counseling, therapeutic effectiveness, and measurement outcomes (Glass, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e1976\u003c/span\u003e; Ahn \u0026amp; Kang, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2018\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eStructural equation modeling (SEM) has experienced rapid development since the 1970s as a technique for describing and estimating the linear relationships between variables. SEM involves latent and measured variables. Latent variables are hypothetical constructs that cannot be directly measured. Thus, researchers often use measured variables and study the directed and undirected linear relationships between these variables. The techniques applied include path analyses, factor analyses, analyses of variance, and regression analyses (MacCallum \u0026amp; Austin, \u003cspan citationid=\"CR79\" class=\"CitationRef\"\u003e2000\u003c/span\u003e; Jak, \u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). In SEM analyses, a technique that can test the relationships in a set of variables enables the measurement of all the hypothesized relationships between the variables. Fit indices are employed to perform overall evaluations without the necessity of collecting raw data. These indices can be directly applied to a covariance or correlation matrix (Jak, \u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). Researchers have employed SEM to conduct empirical research in various fields such as psychology, management, and other scientific disciplines. SEM enables researchers to examine the relationships between multiple concepts, assess the associations between variables on the basis of theoretical hypotheses, explore mediating effects, and evaluate the influence of multiple variables (Cheng, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). MacCallum and Austin (\u003cspan citationid=\"CR79\" class=\"CitationRef\"\u003e2000\u003c/span\u003e) conducted a synthesis of existing studies and discovered that SEM was commonly employed in fields such as applied psychology, organizational behavior, organizational psychology, and personnel psychology. Various studies have also adopted observational and experimental research designs.\u003c/p\u003e \u003c/div\u003e"},{"header":"3. Research Methods","content":"\u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003e3.1. Scope of Literature\u003c/h2\u003e \u003cp\u003eTo collect a substantial amount of data from the relevant literature, a search of several digital databases was conducted; these databases included the Airiti Library, National Digital Library of Theses and Dissertations in Taiwan, National Central Library Taiwan Periodic Literature, Web of Science, and ProQuest databases. To engage in comprehensive research collection, the keywords \u0026ldquo;emotional labor,\u0026rdquo; \u0026ldquo;job burnout,\u0026rdquo; and \u0026ldquo;emotional intelligence\u0026rdquo; were used as the variables of interest. To be eligible for inclusion in the present study, a relevant work must report the correlation coefficients for the three variables EI, EL, and JB, and the correlation coefficients between each pair of these variables. For the present study, the considered works included journal articles, master\u0026rsquo;s theses, and doctoral dissertations. To avoid the inclusion of duplicate studies, a thesis or dissertation that was submitted to and published in a journal was only counted as a single work. The research samples were selected on the basis of the considerations as follows. The data analyzed in the present study were collected from studies published before October 24, 2022. A total of 38 journal articles and 88 master\u0026rsquo;s theses or doctoral dissertations were identified. Studies that did not examine the pairwise associations between EI, EL, and JB, and qualitative studies were excluded. Journal articles, theses, and dissertations that did not provide the relevant correlation coefficient data were also excluded. The final sample of included literature comprised 24 journal articles and six master\u0026rsquo;s theses or doctoral dissertations.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003e3.2. Data Analyses\u003c/h2\u003e \u003cp\u003eThe present study employed a MASEM model. First, an MA was conducted to collect the relevant literature and analyze the corresponding data, with the objective of exploring the relationships between variable pairs. Subsequently, an integration method was applied to analyze the research sample and data. Effect size was used as a quantitative measure to identify objective general conclusions from the collected literature. The statistical software Comprehensive Meta-Analysis (CMA) was used to convert the correlation coefficients between each variable pair (EL\u0026ndash;JB, EL\u0026ndash;EI, and JB\u0026ndash;EI) into equivalent effect sizes to clarify the correlations between the examined variables and create referential plots for further analyses. A test of homogeneity was also conducted to determine how journal articles differed from master\u0026rsquo;s theses and doctoral dissertations. The relationships between EL, JB, and EI were explained to verify the relationships between each variable pair and among all three variables. Subsequently, SEM was employed to validate the model fit. Being a method that combines factor analysis and path analysis, SEM can be employed to validate all hypotheses and provide exploratory suggestions for validating model fit.\u003c/p\u003e \u003c/div\u003e"},{"header":"4. Results","content":"\u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003e4.1. Relationship Between Emotional Labor and Job Burnout\u003c/h2\u003e \u003cp\u003eAfter a literature screening was conducted, 12 research samples were included. The homogeneity test results revealed a Q value of 298.840, which was statistically significant (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001) and indicated the presence of heterogeneity in the present study (i.e., the null hypothesis of homogeneity was rejected). Generally, in heterogeneous studies, random-effects models are used to perform interpretations. However, the small sample size of the present study did not allow for a meaningful interpretation of the results obtained from random-effects models. Therefore, a fixed-effects model was adopted. An I\u003csup\u003e2\u003c/sup\u003e value of 96.319 was obtained, indicating a high degree of heterogeneity. Therefore, the present study explained a high level of the heterogeneity, with the obtained mean explaining 96.319% of the variance. The effect size between EL and JB was 0.262, indicating a low degree of correlation. The corresponding 95% confidence interval (CI) ranged from 0.234 to 0.290. The estimated effect size was converted into a Z value of 17.453, which was statistically significant (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Thus, the analysis results indicated a positive correlation between EL and JB (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eTo avoid the overestimation of effect sizes due to publication bias, a publication bias analysis of the included samples was conducted. First, a funnel plot was created for examination. Figure\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e presents the funnel plot for EL and JB, revealing that all 12 included articles were distributed on both sides of the funnel and formed a mostly symmetrical shape. No evidence of publication bias was found. To examine publication bias, the fail-safe N for the relationship between EL and JB was calculated and determined to be 721, indicating that 721 additional studies with nonsignificant results pertaining to EL and JB were required to overturn the findings of the present study. The tolerance interval (5K\u0026thinsp;+\u0026thinsp;10, where K represents the total number of samples included in the MA) for the present study was 70 articles. According to Rosenthal (\u003cspan citationid=\"CR95\" class=\"CitationRef\"\u003e1991\u003c/span\u003e), when the fail-safe N exceeds the tolerance interval, unpublished, nonsignificant, and undiscovered studies not included in an MA do not affect its results. The fail-safe N in the present study was greater than the tolerance interval; thus, the present study had no publication bias with respect to relationship between EL and JB (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eMeta-analysis results for relationships between variables.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"9\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eVariable\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eNumber of articles\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eEffect size\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e95% CI\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eZ value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e \u003cp\u003eTest of homogeneity\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eFail Safe N\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eLower limit\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eUpper limit\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eQ value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eI\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEL and JB\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.262\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.234\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.290\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e17.453\u003c/p\u003e \u003cp\u003e***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e298.840\u003c/p\u003e \u003cp\u003e***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e96.319\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e721\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEL and EI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.288\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.259\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.316\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e18.698\u003c/p\u003e \u003cp\u003e***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e643.806\u003c/p\u003e \u003cp\u003e***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e98.291\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e1148\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEI and JB\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-0.188\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-0.246\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-0.128\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-6.084\u003c/p\u003e \u003cp\u003e***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e23.442\u003c/p\u003e \u003cp\u003e***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e78.671\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e227\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003e***\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001.\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec17\" class=\"Section2\"\u003e \u003ch2\u003e4.2. Relationship Between Emotional Labor and Emotional Intelligence\u003c/h2\u003e \u003cp\u003eAfter a literature screening was conducted, a total of 12 research samples were included. The homogeneity test revealed a Q value of 643.806, which was statistically significant (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001) and indicated the presence of heterogeneity in the present study (i.e., the null hypothesis of homogeneity was rejected). In heterogeneous studies, random-effects models should be used for interpretation. However, the sample size was too small to yield meaningful interpretation of the results of random-effects models. Therefore, a fixed-effects model was adopted. An I\u003csup\u003e2\u003c/sup\u003e value of 98.291 was obtained, indicating a high degree of heterogeneity. Therefore, the present study explained a high level of heterogeneity, with the obtained mean explaining 98.291% of the variance. The effect size between EI and JB was 0.288, indicating a low degree of correlation. The corresponding 95% CI ranged from 0.259 to 0.316. The estimated effect size was converted into a Z value of 18.698, which was statistically significant (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Thus, the analysis results indicated a positive correlation between EI and EL. The corresponding funnel plot revealed that all 12 included articles were distributed on both sides of the funnel and formed a mostly symmetrical shape (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). No evidence of publication bias was found. The fail-safe N for the relationship between EI and EL was 1,148, indicating that 1,148 additional studies with nonsignificant results pertaining to EI and EL were required to overturn the findings of the present study. Given that the tolerance interval was 70 articles, the present study had no publication bias with respect to the relationship between EI and EL.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec18\" class=\"Section2\"\u003e \u003ch2\u003e4.3. Relationship between Emotional Labor and Job Burnout\u003c/h2\u003e \u003cp\u003eAfter a literature screening was conducted, a total of six research samples were included. The homogeneity test result revealed a Q value of 23.442, which was statistically significant (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001) and indicated the presence of heterogeneity in the present study (i.e., the null hypothesis of homogeneity was rejected). In heterogeneous studies, random-effects models should be used for interpretation. However, the sample size was too small to yield meaningful interpretation of the results of random-effects models. Therefore, a fixed-effects model was adopted. An I\u003csup\u003e2\u003c/sup\u003e value of 78.671 was obtained, indicating a high degree of heterogeneity. Therefore, the present study explained a high level of heterogeneity, with the obtained mean explaining 78.671% of the variance. The effect size between EI and JB was \u0026minus;\u0026thinsp;0.188, indicating a low degree of correlation. The corresponding 95% CI ranged from \u0026minus;\u0026thinsp;0.246 to \u0026minus;\u0026thinsp;0.128. The estimated effect size was converted into a Z value of \u0026minus;\u0026thinsp;6.084, which was statistically significant (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Thus, the analysis results indicated a negative correlation between EI and JB. The corresponding funnel plot reveals that all the six included articles were distributed on both sides of the funnel and formed a mostly symmetrical shape (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). No evidence of publication bias was found. The fail-safe N for the relationship between EI and JB was 227, indicating that 227 additional studies with nonsignificant results pertaining to EI and JB were required to overturn the findings of the present study. Given that the tolerance interval was 40 articles, the present study had no publication bias with respect to the relationship between EI and JB.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec19\" class=\"Section2\"\u003e \u003ch2\u003e4.4. Validation of the Theoretical Models of Emotional Labor, Job Burnout, and Emotional Intelligence\u003c/h2\u003e \u003cdiv id=\"Sec20\" class=\"Section3\"\u003e \u003ch2\u003e4.4.1. Correlation Matrices, Harmonic Mean, and Reliability Between Emotional Labor, Job Burnout, and Emotional Intelligence\u003c/h2\u003e \u003cp\u003eThrough the MA, the present study obtained a correlation coefficient matrix for the relationships between EI, EL, and JB (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e), and the matrix was used to validate the subsequent SEM. Because the number of samples varies between different variable pairs (i.e., EI, El, and JB in Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e), to prevent the differences in sample sizes from affecting the results and to generate non-positive-definite problems, the present study followed the recommendations of Viswesvaran and Ones (\u003cspan citationid=\"CR107\" class=\"CitationRef\"\u003e1995\u003c/span\u003e) and converted the samples sizes (for the studies that examined the correlations between each variable pair) into harmonic means to obtain the overall sample size. The calculated harmonic mean of 2,083 was used for SEM validation.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eCorrelation coefficient matrix for relationships between EL, JB, and EI.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariable\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eEI\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eEL\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eJB\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.288\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eJB\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-0.188\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.262\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eSample sizes of studies that examined correlations between EL, JB, and EI.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariable\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eEI\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eEL\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e4013\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eJB\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1045\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e4277\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e presents the reliability results for the included studies with respect to each variable. The reliability values were used to calculate the mean reliability, residual, and root mean reliability of each study, and they were used in the SEM process.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eReliability of included studies with respect to EL, JB, and EI.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAuthor\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYear\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eEL\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eJB\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eEI\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLin et al.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2015\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.84\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLiu et al.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2016\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.939\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.949\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.956\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLiu and Hsieh\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2016\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.857\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.895\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eChen et al.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2017\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.882\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.874\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eChen and Wu\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2019\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.901\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.92\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAngeli et al\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2015\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.86\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAeeun Jeon\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2016\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.782\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLiu et al.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2018\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.79\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.95\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eKwon et al.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2021\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.79\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.94\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eKim\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2020\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.81\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLee and Ji\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2018\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.75\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHung and Liu\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2011\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.887\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.93\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHung\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2008\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.834\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.873\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHsieh et al.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2008\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.59\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.87\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePonniah Ramana et al.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2016\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.95\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.87\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLin\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2009\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.885\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.887\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWong\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2017\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.893\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.913\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.897\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eKamassi et al.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2019\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.919\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMean reliability\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.842\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.867\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.905\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eResidual\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.158\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.133\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.095\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRoot mean reliability\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.918\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.931\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.951\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec21\" class=\"Section3\"\u003e \u003ch2\u003e4.4.2. Validation of Theories Regarding the Relationships Between Emotional Labor, Job Burnout, and Emotional Intelligence\u003c/h2\u003e \u003c/div\u003e \u003c/div\u003e\n\u003ch3\u003e1. Basic fit standards\u003c/h3\u003e\n\u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003eFigure \u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e presents that the parameter estimates for the relationships between EI and JB, EI and EL, and EL and JB, which are all positive and less than 1. The \u003cem\u003ep\u003c/em\u003e-values were all significant, suggesting that the parameter estimation values conformed to a basic fit. The factor loadings for EI, EL, and JB were 0.951, 0.918, and 0.931, respectively, which slightly exceeded the factor loading level of 0.95 recommended by Bagozzi and Yi (\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e1988\u003c/span\u003e); however, these values were within the acceptable range.\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eAccording to the table summarizing the parameter estimates for latent variable errors, the error estimation values were positive, and the standard errors ranged between 0.031 and 0.037; this range was within the moderate range and conformed to the basic fit for moderate standard errors as proposed by Bagozzi and Yi (\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e1988\u003c/span\u003e). These results revealed that the parameters of the proposed model met the criteria for acceptable parameter values, indicating a high-quality model.\u003c/p\u003e \u003cp\u003eIn summary, only the factor loading for EI (0.951) was slightly higher than the indicator norm of 0.95. The model error variance results did not reveal any negative value. That is, all values were positive and significant. The absolute value of the correlation coefficients between estimation parameters did not approach 1, and the standard error was not excessively high, satisfying the standard for basic fit. Thus, the proposed theoretical model was acceptable.\u003c/p\u003e\n\u003ch3\u003e2. Overall model fit\u003c/h3\u003e\n\u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003eThe goodness of fit index (GFI) value for the present study was 1 (a GFI value that is closer to 1 indicates a more favorable fit), which was higher than the recommended threshold of 0.9 and indicated an acceptable fit. Both the root mean square residual (RMR) and standardized root mean square residual (SRMR) were 0, that is, less than the suggested thresholds of 0.05 and 0.08, respectively; these results indicate that the fit criteria were satisfied. When SRMR equals 0, a perfect fit is achieved. Therefore, the absolute fit indices for the present study met the fit standards, indicating that the model of the present study exhibited a favorable fit. For the incremental fit indices, the normed fit index (NFI), incremental fit index (IFI), and comparative fit index (CFI) values were all 1, exceeding the fit criterion of 0.9. These values indicated that the incremental fit indices met the established criterion, demonstrating the favorable fit of the proposed model. For parsimony fit indices, the parsimonious normed fit index (PNFI) for the present study was 0, which did not exceed the threshold of 0.05, showing compliance with the fit index criterion. However, the Akaike information criterion (AIC), Bayesian information criterion (BIC), and expected cross-validation index (ECVI) are competitive model fit indices, and they could not be applied in the present study because the proposed model was not competitive. Therefore, no further discussion or comparison of AIC, BIC, and ECVI values are conducted in the present study.\u003c/p\u003e \u003cp\u003eIn summary, the overall fit analysis indicated that nearly all the fit indices met their respective fit criteria. The absolute fit indices and incremental fit indices met the fit criteria. Among the parsimony fit indices, the PNFI was the only index that did not meet the recommended standard. The results for the AIC, BIC, and ECVI were used in competitive models; thus, they were unsuitable for the proposed model in this study. In the present study, only a single parsimony fit index did not meet the recommended standard. These results indicated that the theoretical model fit was favorable such that a subsequent exploration could be conducted.\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e\n\u003ch3\u003e3. Fit of internal structure model\u003c/h3\u003e\n\u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003eThe individual reliability values for EL, JB, and EI were higher than 0.5; that is, they all satisfied the recommended standard. The composite reliability was greater than 0.6, and the mean variance extracted of all variables was greater than 0.5; that is, these values all satisfied the recommended standard. These results indicate that the proposed model had a favorable internal structure model fit. The standardized residual test revealed that all values were less than 1.96, indicating that the internal structure model fit met the recommended standard and was favorable.\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e \u003cdiv id=\"Sec25\" class=\"Section2\"\u003e \u003ch2\u003e4.4.3. Moderating Effect of EI on Relationship Between Emotional Labor and Job Burnout\u003c/h2\u003e \u003cp\u003eTo validate moderating effects, bootstrapping was conducted with a 95% interval and 1000 iterations. Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e presents the results for the moderating effect validation conducted using bootstrapping. Error modification percentage bootstrapping and percentage bootstrapping were performed to examine the related effects. The 95% CI did not include a zero value, and the \u003cem\u003ep\u003c/em\u003e-value was significant, confirming the moderating effect of EI. These results indicated that EI moderated the relationship between EL and JB.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab5\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eBootstrapping validation of moderating effect.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eParameter\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMethod\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eEstimation\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eLower limit\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eUpper limit\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cem\u003ep\u003c/em\u003e-value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eEL\u0026rarr;EI\u0026rarr;JB\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eError modification percentage bootstrapping\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-0.116\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-0.141\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-0.093\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePercentage bootstrapping\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-0.116\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-0.140\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-0.092\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.002\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eEL was verified to have a total effect, direct effect, and indirect effect on JB. The total effect of two variables in three variables was significant. The total effect size of EL on EI was 0.330, indicating a direct effect. The total effect size of EI on JB was \u0026minus;\u0026thinsp;0.352, indicating a direct effect and supporting the hypotheses proposed in the present study. No indirect effect was identified with respect to the relationships between EL and EI and between EI and JB. The total effect size of EL on JB was 0.307, which was the sum of the direct effect and indirect effect sizes (0.423\u0026thinsp;\u0026minus;\u0026thinsp;0.116). This result signified that EL affected JB through EI and that EI moderated the relationship between EL and JB, supporting Hypothesis \u003cspan refid=\"FPar7\" class=\"InternalRef\"\u003e4\u003c/span\u003e.\u003c/p\u003e \u003c/div\u003e"},{"header":"5. Discussion and Suggestions","content":"\u003cdiv id=\"Sec27\" class=\"Section2\"\u003e \u003ch2\u003e5.1. Discussion\u003c/h2\u003e \u003cdiv id=\"Sec28\" class=\"Section3\"\u003e \u003ch2\u003e5.1.1. Relationship between Emotional Labor and Job Burnout\u003c/h2\u003e \u003cp\u003eThe results of the present study revealed a significant and positive relationship between EL and JB, indicating that when employees experienced higher levels of EL, they also perceived an increase in JB. The present study assumed that EL is a type of job requirement, where employees are subjected to the intangible or tangible emotion norms of an organization and expected to provide services through face-to-face or vocal interactions. In this situation, their emotions are subject to organizational management. Furthermore, the emotions of employees can be cultivated through training such that they can display specific emotional expressions; this process is regarded as an aspect of EL (Hochschild, \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e1983\u003c/span\u003e). Depending on the occupations that are involved, an organization may require its employees to express specific emotions or service attitudes toward customers. If employees effectively display the positive emotions required of them, they can feel a sense of accomplishment through their emotional influence on customers. Therefore, EL is regarded as a performance indicator of service quality and overall impression, and it is closely related to the job outcomes of employees (Lin et al., \u003cspan citationid=\"CR73\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). However, when employees self-regulate their emotions, they often encounter constraints and need to display professionalism while hiding their true emotions. This process of excessive emotional suppression results in an inconsistency between internal thoughts and external expressions, and it can lead to the development of several types of JB including emotional exhaustion, depersonalization, and reduced sense of accomplishment. The faking of emotions depletes the energy of an employee and causes them to experience frustration, and it is also a main factor that contributes to work dissatisfaction (Liu \u0026amp; Hsieh, \u003cspan citationid=\"CR76\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Lee \u0026amp; Chelladurai, \u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). According to conservation of resources theory, the level of an employee\u0026rsquo;s EL affects their emotional resources during emotional transitions. The dissonance between genuine emotions and emotion norms can lead to the development of JB. Hence, the present study hypothesized that EL positively affects JB (Hypothesis \u003cspan refid=\"FPar4\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec29\" class=\"Section3\"\u003e \u003ch2\u003e5.1.2. Relationship between Emotional Intelligence and Emotional Labor\u003c/h2\u003e \u003cp\u003eThe results of the present study revealed a significant and positive relationship between EI and JB, indicating that when employees possess a higher level of EI, their adoption of EL coping behavior and their generation and perception of EL increase. On the basis of conservation of resources theory, the present study regarded EI as the internal emotional resources of an individual employee. EI encompasses the ability to perceive and evaluate one\u0026rsquo;s emotions and those of others and the ability to regulate and apply emotions. Thus, when employees have higher EI, they are more likely to adjust their emotional expressions through deep acting to align with their internalized emotions and organizational requirements when they engage in EL at work. This alignment allows them to gain recognition and psychological support, reduce the depletion of their emotional resources, and reinforce these resources (Yu, \u003cspan citationid=\"CR131\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Wen et al., \u003cspan citationid=\"CR116\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). Moreover, the emotional demands of an organization can lead to the development of EL among its employees, and their individual adaptation is dependent on their EI. Employees with higher EI exhibit a higher sense of environmental safety and are more skilled at social interactions. They can effectively identify their genuine inner emotions and establish a connection between EL and their inner emotions. They are also more likely to engage in deep acting as an adaptive mechanism for managing their internal emotions. These individuals have greater EL loading levels. Thus, individuals with higher EI are regarded as more suitable candidates for undertaking EL (Lin, \u003cspan citationid=\"CR69\" class=\"CitationRef\"\u003e2009\u003c/span\u003e; Weng, \u003cspan citationid=\"CR117\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). Therefore, given the extent to which individuals with higher EI engage in deep acting and their EL loading, the present study hypothesized that EI positively affects EL (Hypothesis \u003cspan refid=\"FPar5\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec30\" class=\"Section3\"\u003e \u003ch2\u003e5.1.3. Relationship Between Emotional Labor and Job Burnout\u003c/h2\u003e \u003cp\u003eThe results of the present study confirmed that the proposed hypotheses are consistent with the findings of other studies. Several Taiwan-based studies reported that a significant and negative relationship existed between the EI and JB of nursing and rehabilitation professionals. Although these professions are associated with emotions related to safety, care, and joy, they must frequently confront the illness and mortality of patients. Thus, individuals with higher EI can regulate their emotions and mitigate negative emotions that cause psychological discomfort. Individuals with higher EI are more empathetic toward the emotions of others and are more adept at understanding and managing their own emotions and those of others. Thus, they are more likely to exhibit rational behavior, experience a sense of accomplishment, and experience less frustration caused by work and interpersonal interactions (Hsieh et al., \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2008\u003c/span\u003e; Hsieh, 2010). Liao and Song (\u003cspan citationid=\"CR66\" class=\"CitationRef\"\u003e2018\u003c/span\u003e) examined mass rapid transit drivers with high job stressors, and their results indicated that the drivers with higher EI were more adept at using emotional information to guide appropriate work behavior, enabling them to effectively convert their job stress into opportunities for professional development. Moreover, individuals with higher EI exhibit higher emotional tolerance. They adopt positive and proactive attitudes when faced with setbacks at work and do not avoid job responsibilities; instead, they inspire themselves to achieve personal accomplishments, thereby reducing their JB. Similar findings have also been reported by studies conducted outside of Taiwan. Studies that examined teachers have discovered that the application of EI enabled teachers to effectively manage their job frustrations, implement emotional control, maintain harmonious relationships with their colleagues, achieve enhanced self-awareness, and improve their job performance. These studies highlighted that accomplishments and goal attainment are not only dependent on knowledge, skills, and experience, but also on the ability to manage one\u0026rsquo;s emotions. Successfully understanding oneself and others lead to improved outcomes and consolidates the importance of emotions and the negative relationship between EI and JB (Nur Sakinah Thomas et al., \u003cspan citationid=\"CR104\" class=\"CitationRef\"\u003e2012\u003c/span\u003e). The similar results reported by both studies conducted within and outside of Taiwan correspond to the results of the present study; thus, Hypothesis \u003cspan refid=\"FPar6\" class=\"InternalRef\"\u003e3\u003c/span\u003e (i.e., EI negatively affects JB) is supported. These findings have been verified across various professions and can be extrapolated to various industry sectors.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec31\" class=\"Section3\"\u003e \u003ch2\u003e5.1.4. Relationships between Emotional Labor, Job Burnout, and Emotional Intelligence\u003c/h2\u003e \u003cp\u003eThe present study proposed a theoretical model and several hypotheses regarding EL, JB, and EI variables. On the basis of conservation of resources theory, the present study examined employee emotions as a job resource. Accordingly, when employees engaged in EL, having higher EI helped them adopt deep acting strategies to enhance their EL loading levels. The job requirements-resources model revealed that employees with higher EI were more adept at understanding others and regulating their own emotions, enabling them to adapt quickly to the emotion norms of their organizations and to alleviate the job stress and burnout caused by emotional dissonance. In other words, the EL of employees was affected by their level of EI, which in turn affected the reduction of JB. EI moderated the relationship between EL and JB.\u003c/p\u003e \u003cp\u003eIndividuals with high EL who experienced a discrepancy between emotional requirements and their own emotions were more likely to have a negative sense of JB. However, when employees engaged in EL, those with emotional awareness and the ability to perform effective emotion transitions experienced reduced JB as a result of regulated deep acting (Lee \u0026amp; Chelladurai, \u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). Both studies conducted within and outside of Taiwan have extensively discussed the relationship between high EL jobs, EI, and JB. Most studies have employed conservation of resources theory, which regards employee emotions as a type of job resource. If emotions are regarded as job requirements, they may promote emotional labor performance and mitigate the level of JB (Hong \u0026amp; Lee, \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2016\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe overall fit index results presented in the model fit summary table indicate a favorable fit between the proposed theoretical model and the collected data (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). Nearly all the fit index results, including the absolute and incremental fit index results, met the evaluation criteria. The PNFI was the only index for which the results were slightly less than the recommended standard of 0.05. The results indicated that the basic model fit, overall model fit, and internal structure model fit of the proposed model were all satisfactory. Additionally, in the analysis of the moderating effects of EI, bootstrapping with 1000 iterations was performed with the CI being set to 95%. The results did not include a zero value, and the total effect of EL on JB was 0.307, which was equivalent to the sum of the direct effect (0.423) and the indirect effect (\u0026minus;\u0026thinsp;0.116) (0.423\u0026thinsp;\u0026minus;\u0026thinsp;0.116). This result verifies the moderating effect of EI; thus, Hypothesis \u003cspan refid=\"FPar7\" class=\"InternalRef\"\u003e4\u003c/span\u003e (i.e., EI moderates the relationship between EL and JB) is supported.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec32\" class=\"Section2\"\u003e \u003ch2\u003e5.2. Suggestions\u003c/h2\u003e \u003cdiv id=\"Sec33\" class=\"Section3\"\u003e \u003ch2\u003e5.2.1. Management Implications\u003c/h2\u003e \u003cp\u003eThe results revealed a significant and positive relationship between EL and JB. Therefore, in terms of management practices, organizations should establish emotion-related rules to meet customer service expectations with respect to emotional expression. They should also examine other factors that may affect employees, such as their work environment, the target recipients of a service, and the occurrence of unexpected events. These measures enable organizations to mitigate the negative effects of EL on the development of JB in their employees. For employee training, organizations should focus on teaching deep acting strategies for emotional expression. In contrast to masking or surface acting, this approach can encourage employees to naturally express specific emotions that can positively influence their colleagues and customers. It can also substantially reduce the development of JB among employees at work.\u003c/p\u003e \u003cp\u003eThe results indicated a positive relationship between EI and EL. This relationship encompasses the adoption of deep acting strategies by employees and an increase in their level of EL loading. Therefore, businesses should not only consider basic requirements during the selection process but also incorporate an EI assessment or observe the ability of candidates to express themselves and monitor their surroundings during conversations. To facilitate talent management, organizations must consider EI in terms of personnel promotion and relocation decisions. A manager must not only set emotion norms but also understand the challenges that their employees face and the process of emotional transition that they undergo during EL. Employees with higher EI can empathize with people in these situations and offer support to other employees who need it. This method can effectively propagate an organization\u0026rsquo;s emotional demands among its personnel.\u003c/p\u003e \u003cp\u003eThe results revealed a negative relationship between EI and JB, indicating that employees with higher EI generate less JB and perceive lower levels of JB. Therefore, organizations should consider incorporating EI as a predictor of employee performance and burnout during their selection process. Additionally, organizations can help employees enhance their EI through education and training, thereby cultivating their ability to perceive the emotions of others and anticipate the needs of their customers before any requests are made. This measure can foster enthusiasm and a sense of accomplishment among employees at work. Furthermore, implementing job rotation and task transitions can help employees cultivate their EI and reduce their JB through the accumulation of practical experience.\u003c/p\u003e \u003cp\u003eThe present study verified the significant moderating effect of EI on the relationship between EL and JB. When employees have higher EI, they can more effectively adapt to the emotion norms within their organization. They can also transform their EL into genuine experiences and engage in deep acting to mitigate JB. In other words, an employee\u0026rsquo;s level of EI can affect their reduction of JB resulting from EL. Therefore, organizations should use EI as a criterion for assessing the self-awareness and personal emotional management of candidates during the selection process. EI can predict how candidates adapt to EL in future job roles and their ability to cope with work-related stress. For candidates who demonstrate high levels of professional competency and the potential to improve their EI, an organization should provide training programs that incorporate EI training. These programs should incorporate experiential learning to enhance the emotional regulation and resilience of employees in the face of setbacks. When human resources teams identify negative work outcomes caused by EL or EI-related JB, they can use their organizations\u0026rsquo; internal resources to implement job rotation or job redesign. Practical experiences can be leveraged to cultivate the EI of employees. Additionally, providing counseling and care for employees can contribute to talent retention. If an organization can promote methods that emphasize positive emotions to guide employees to accept challenges with a positive attitude, the EI levels of the organization\u0026rsquo;s teams can be increased, and a positive atmosphere can be created. Subsequently, the JB caused by EL can be reduced, thereby reducing turnover intention.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec34\" class=\"Section3\"\u003e \u003ch2\u003e5.2.2. Future Suggestions\u003c/h2\u003e \u003cp\u003eEL, JB, and EI have been studied over a long period of time. Scholars have proposed various definitions and explored various perspectives relating to these constructs. For example, EL is often classified as surface acting and deep acting, but some scholars have argued that an individual\u0026rsquo;s genuine emotions should also be regarded as form of emotional display and be established as a third category of EL. Scholars have also proposed alternative definitions other than the aforementioned categories. However, in the present MA study, the analysis focused on exploring the correlations among variables by examining a single variable without separately integrating and validating individual subdimensions through SEM. Given the integrity of the dimensions, future studies should focus on extending the topics explored in the present study; this can be achieved by exploring the subdimensions of each variable to clarify in-depth research conceptions and further verifying the consistency of the relationships between single variables and subdimension variables.\u003c/p\u003e \u003cp\u003eThe literature review revealed that a strong connection exists between EL, JB, and EI in both academic and practical contexts. The related topics, such as organizational culture, leadership style, and the work stress that influences employee development, are frequently discussed in organizational settings. Various studies have explored work outcome\u0026ndash;related topics such as turnover intention, retention willingness, and job satisfaction. Therefore, future studies can use the findings of the present study as a theoretical foundation for exploring other moderating variables and their corresponding effect sizes. Given that the present study focused on EL, JB, and EI, which are individual-level variables, future studies should examine organizational-level or group-level variables to expand the research on EL, JB, and EI and facilitate the development of detailed recommendations for practical measures.\u003c/p\u003e \u003cp\u003eThe research methods employed in the present study combined MA and SEM. The initial data collection step involved identifying studies that investigated the variables of interest and conducting data analysis through MA. Subsequently, on the basis of the correlations between each variable pair, the fit of the SEM was validated. This integrated approach focused on effect sizes, which is a quantitative measure. However, to obtain a more comprehensive understanding of the relationships examined in the present study, future studies should collect qualitative data through methods such as interviews, observations, or other methods used in the relevant qualitative studies. Through the application of various research methods in conjunction with quantitative analysis, the reliability of the validation results can be enhanced.\u003c/p\u003e \u003cp\u003eTo research EL, JB, and EI, researchers have employed various definitions and scales depending on the dimensions of the variables that were examined. For instance, to measure EL, some scholars have defined it on the basis of its acting aspect, whereas others have classified EL on the basis of interactions or diversity-related dimensions. Similarly, for EI, researchers have used scales that assess personal emotions and the emotions of others and the ability of an individual to evaluate and apply emotions. Researchers may be inclined to adopt scales with a higher reliability for evaluation, and they often prefer scales that were developed during the early stage of variable content development. Therefore, given the need to align past research with the current context, researchers should compare newer and older scales or prioritize the analysis of scales developed in more recent years. Furthermore, study samples vary across studies. Thus, to account for the potential influence of occupational differences in job content on research outcomes, researchers should consider obtaining consistent samples or focusing on specific professions to facilitate data integration.\u003c/p\u003e \u003cp\u003eThe present study considered the uniformity of regions and the similarity of cultures in data collection by selecting studies from Taiwan and the Asian region. Future studies should consider cultural differences and employ a cross-cultural research design to collect data from both Taiwan and international sources. In addition to studies from the Asian region, studies from Western countries can be included. Furthermore, expanding literature sources to include foreign dissertations and theses can increase the comprehensiveness of the relevant research and enable the extension of findings to various countries and industries, thereby providing a more comprehensive interpretation with major managerial implications.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e"},{"header":"Declarations","content":"\u003cp\u003eEthical approval：\u003c/p\u003e\n\u003cp\u003eThis noninterventional study did not require ethical approval due to its design, nor did it take place within any private or protected space. Therefore, no specific permissions were required to conduct the study in the geographical regions specific to this study.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eInformed consent：\u003c/p\u003e\n\u003cp\u003eThe primary methodology employed in this study is meta-analysis, which does not involve the use of research participants\u0026apos; information. Therefore, informed consent for this study is not required. \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eCompeting Interests:\u003c/p\u003e\n\u003cp\u003eThe researchers declare no competing interests.\u003c/p\u003e\n\u003cp\u003eData Availability Statement:\u003c/p\u003e\n\u003cp\u003eThe datasets analyzed for the current study are available from the corresponding author upon reasonable request.\u003cbr\u003e\u0026nbsp;\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eAhad, R., Mustafa, M. Z., Mohamad, S., Abdullah, N. H. S., \u0026amp; Nordin, M. N. (2021). Work attitude, organizational commitment and emotional intelligence of Malaysian vocational college teachers. \u003cem\u003eJournal of Technical Education and Training\u003c/em\u003e, \u003cem\u003e13\u003c/em\u003e(1), 15-21. https://doi.org/10.30880/jtet.2021.13.01.002\u003c/li\u003e\n\u003cli\u003eAhn, E., \u0026amp; Kang, H. (2018). Introduction to systematic review and meta-analysis. \u003cem\u003eKorean Journal of Anesthesiology\u003c/em\u003e, \u003cem\u003e71\u003c/em\u003e(2), 103-112. https://doi.org/10.4097/kjae.2018.71.2.103\u003c/li\u003e\n\u003cli\u003eAshforth, B. E., \u0026amp; Humphrey, R. H. (1993). Emotional labor in service roles: The influence of identity. \u003cem\u003eAcademy of Management Review\u003c/em\u003e, \u003cem\u003e18\u003c/em\u003e(1), 88-115. https://doi.org/10.2307/258824\u003c/li\u003e\n\u003cli\u003eAwa, W. L., Plaumann, M., \u0026amp; Walter, U. (2010). Burnout prevention: A review of intervention programs. \u003cem\u003ePatient Education and Counseling\u003c/em\u003e, \u003cem\u003e78\u003c/em\u003e(2), 184-190. https://doi.org/10.1016/j.pec.2009.04.008\u003c/li\u003e\n\u003cli\u003eBagozzi, R. P., \u0026amp; Yi, Y. (1988). On the evaluation of structural equation models. \u003cem\u003eJournal of the Academy of Marketing Science\u003c/em\u003e, \u003cem\u003e16\u003c/em\u003e, 74-94.\u003c/li\u003e\n\u003cli\u003eBrackett, M. A., Mayer, J. D., \u0026amp; Warner, R. M. (2004). Emotional intelligence and its relation to everyday behaviour. \u003cem\u003ePersonality and Individual Differences\u003c/em\u003e, \u003cem\u003e36\u003c/em\u003e(6), 1387-1402. https://doi.org/10.1016/S0191-8869(03)00236-8\u003c/li\u003e\n\u003cli\u003eChang, Y. C., Wang, C. H., Lin, W. L., \u0026amp; Chen, K. J. (2021). Relationship between work passion and job burnout of tour managers: Mediating effect of affectivity. \u003cem\u003eJournal of Tourism and Leisure Studies\u003c/em\u003e,\u003cem\u003e 27\u003c/em\u003e(1), 67-103. https://doi.org/10.6267/JTLS.202104_27(1).0003\u003c/li\u003e\n\u003cli\u003eChen, C. H., \u0026amp; Wu, P. Y. (2019). The study of job stress, emotional labor, and job burnout for department store staff. \u003cem\u003eJournal of Tourism and Travel Research\u003c/em\u003e, \u003cem\u003e14\u003c/em\u003e(2), 41-57.\u003c/li\u003e\n\u003cli\u003eChen, D. L., Yu, S. Y., \u0026amp; Wu, C. H. (2017). The study of 119 dispatchers\u0026rsquo; emotional labor, job satisfaction and job burnout. \u003cem\u003eJournal of Management Information and Computing\u003c/em\u003e, \u003cem\u003e6\u003c/em\u003e(3), 176-185. https://doi.org/10.6285/MIC.6(S3).15\u003c/li\u003e\n\u003cli\u003eChen, L. W. (2013). \u003cem\u003eThe study of the antecedents and consequences of emotional labor strategies on service quality: A case study of nursing personnel \u003c/em\u003e(Unpublished master\u0026rsquo;s thesis). Southern Taiwan University of Science and Technology.\u003c/li\u003e\n\u003cli\u003eChen, S. H., \u0026amp; Hsu, M. S. (2011). The study of relationship between the faculty\u0026apos;s emotional labor and job burnout in the university. \u003cem\u003eJournal of Kaohsiung Normal University\u003c/em\u003e, \u003cem\u003e30\u003c/em\u003e, 1-15. https://doi.org/10.7060/KNUJST.201106.0001\u003c/li\u003e\n\u003cli\u003eChen, Y. J. (2012). The relations of teacher\u0026apos;s emotion intelligence, teacher-student relationship, and emotion regulation of lower level students. \u003cem\u003eJournal of Early Childhood Education and Care Research,\u003c/em\u003e \u003cem\u003e9\u003c/em\u003e, 79-101. https://doi.org/10.6471/JECEC.201207.0079\u003c/li\u003e\n\u003cli\u003eCheng, C. P. (2015). Power analysis of testing path coefficients in structural equation modeling. \u003cem\u003eJournal of Human Resource Management\u003c/em\u003e, \u003cem\u003e15\u003c/em\u003e(3), 75-96. https://doi.org/10.6147/JHRM.2015.1503.04\u003c/li\u003e\n\u003cli\u003eChien, S. W. (2014). \u003cem\u003eA study on the relationship between perceived transformational leadership of kindergarten principals and emotional labor among early childhood educators\u003c/em\u003e (Unpublished master\u0026rsquo;s thesis). National Chung Cheng University.\u003c/li\u003e\n\u003cli\u003eChu, C. Y. (2013). The Impact of hospital nurses\u0026apos; emotional labor on job satisfaction and burnout. \u003cem\u003eTaiwan Journal of Public Health\u003c/em\u003e, \u003cem\u003e32\u003c/em\u003e(3), 266-278. https://doi.org/10.6288/TJPH2013-32-03-08\u003c/li\u003e\n\u003cli\u003eCordes, C. L., \u0026amp; Dougherty, T. W. (1993). A review and an integration of research on job burnout. \u003cem\u003eAcademy of Management Review\u003c/em\u003e, \u003cem\u003e18\u003c/em\u003e(4), 621-656. https://doi.org/10.2307/258593 \u003c/li\u003e\n\u003cli\u003eC\u0026ocirc;t\u0026eacute;, S., \u0026amp; Miners, C. T. (2006). Emotional intelligence, cognitive intelligence, and job performance. \u003cem\u003eAdministrative Science Quarterly\u003c/em\u003e, \u003cem\u003e51\u003c/em\u003e(1), 1-28. https://doi.org/10.2189/asqu.51.1.1 \u003c/li\u003e\n\u003cli\u003eFan, C. W. (2006). The study of relationship between organizational structures and teacher\u0026apos;s job burnout in junior high school. \u003cem\u003eSchool Administration\u003c/em\u003e, (44), 1-25. https://doi.org/10.6423/HHHC.200607.0001\u003c/li\u003e\n\u003cli\u003eFreudenberger, H. J. (1975). The staff burn-out syndrome in alternative institutions. \u003cem\u003ePsychotherapy: Theory, Research \u0026amp; Practice\u003c/em\u003e, \u003cem\u003e12\u003c/em\u003e(1), 73-82. https://doi.org/10.1037/h0086411 \u003c/li\u003e\n\u003cli\u003eFriedman, I. A. (1995). School principal burnout: The concept and its components. \u003cem\u003eJournal of Organizational Behavior\u003c/em\u003e, \u003cem\u003e16\u003c/em\u003e(2), 191-198. https://doi.org/10.1002/job.4030160209 \u003c/li\u003e\n\u003cli\u003eGao, X. H. (2011). \u003cem\u003eA study of the emotional labor, emotional intelligence and job satisfaction\u0026mdash;Healing style products as moderators\u003c/em\u003e (Unpublished master\u0026rsquo;s thesis). Tainan University of Applied Sciences. https://doi.org/10.6821/TUT.2011.00083\u003c/li\u003e\n\u003cli\u003eGardner, H. (1983). \u003cem\u003eFrames of mind. Basic books\u003c/em\u003e. New York.\u003c/li\u003e\n\u003cli\u003eGlass, G. V. (1976). Primary, secondary, and meta-analysis of research. \u003cem\u003eEducational Researcher\u003c/em\u003e, \u003cem\u003e5\u003c/em\u003e(10), 3-8.\u003c/li\u003e\n\u003cli\u003eGoffman, E. (1959). \u003cem\u003ePresentation of self in everyday life\u003c/em\u003e. New York: Overlook Press.\u003c/li\u003e\n\u003cli\u003eGoleman, D. (1996). \u003cem\u003eEmotional intelligence: Why it can matter more than IQ\u003c/em\u003e. Bloomsbury Publishing.\u003c/li\u003e\n\u003cli\u003eGosserand, R. H. (2003). \u003cem\u003eAn examination of individual and organizational factors related to emotional labor\u003c/em\u003e. Louisiana State University and Agricultural \u0026amp; Mechanical College.\u003c/li\u003e\n\u003cli\u003eGrandey, A. A. (2000). Emotional regulation in the workplace: A new way to conceptualize emotional labor. \u003cem\u003eJournal of Occupational Health Psychology\u003c/em\u003e, \u003cem\u003e5\u003c/em\u003e(1), 95-110. https://doi.org/10.1037/1076-8998.5.1.95\u003c/li\u003e\n\u003cli\u003eGrandey, A. A., \u0026amp; Gabriel, A. S. (2015). Emotional labor at a crossroads: Where do we go from here. \u003cem\u003eAnnual Review of Organizational Psychology and Organizational Behavior\u003c/em\u003e, \u003cem\u003e2\u003c/em\u003e(1), 323-349. \u003cbr\u003e https://doi.org/10.1146/annurev-orgpsych-032414-111400\u003c/li\u003e\n\u003cli\u003eGrandey, A. A., \u0026amp; Melloy, R. C. (2017). The state of the heart: Emotional labor as emotion regulation reviewed and revised. \u003cem\u003eJournal of Occupational Health Psychology\u003c/em\u003e, \u003cem\u003e22\u003c/em\u003e(3), 407-422. https://doi.org/10.1037/ocp0000067 \u003c/li\u003e\n\u003cli\u003eHo, J. M., Huang, Y. C., Chen, J. H., \u0026amp; Chuang, M. J. (2005). A study of the relationship between job demand-control-support model and burnout among customs officials.\u003cem\u003e Journal of Human Resource Management\u003c/em\u003e, \u003cem\u003e5\u003c/em\u003e(2), 19-37. https://doi.org/10.6147/JHRM.2005.0502.02\u003c/li\u003e\n\u003cli\u003eHochschild, A. R. (1983). \u003cem\u003eThe managed heart: Commercialization of human feeling\u003c/em\u003e. Berkeley: University of California Press.\u003c/li\u003e\n\u003cli\u003eHong, E., \u0026amp; Lee, Y. S. (2016). The mediating effect of emotional intelligence between emotional labour, job stress, burnout and nurses\u0026apos; turnover intention. \u003cem\u003eInternational Journal of Nursing Practice\u003c/em\u003e, \u003cem\u003e22\u003c/em\u003e(6), 625-632. https://doi.org/10.1111/ijn.12493\u003c/li\u003e\n\u003cli\u003eHou, S. Z., Fan, H. L., \u0026amp; Lien, W. C. (2015). Sincere or insincere? Exploring the relationship between emotional labor and innovative behavior.\u003cem\u003e NTU Management Review\u003c/em\u003e, \u003cem\u003e25\u003c/em\u003e(3), 67-99. https://doi.org/10.6226/NTUMR.2015.JUN.OB07\u003c/li\u003e\n\u003cli\u003eHsieh, C. J., Chang, H. W., Chou, Y. J., \u0026amp; Chang, J. (2008). The relationship between emotional intelligence and burnout in nurses. \u003cem\u003eJournal of New Taipei Nursing\u003c/em\u003e, \u003cem\u003e10\u003c/em\u003e(2), 11-24. https://doi.org/10.6540/NTJN.2008.2.002\u003c/li\u003e\n\u003cli\u003eHsieh, C. S., \u0026amp; Chan, H. S. (2006). A study of the employees\u0026apos; job burnout situation on hairdressing enterprise. \u003cem\u003eJournal of Chien Kuo Technology University\u003c/em\u003e, \u003cem\u003e25\u003c/em\u003e(3), 71-94. https://doi.org/10.6995/JCTU.200604.0071\u003c/li\u003e\n\u003cli\u003eHsieh, H. H. (2015). A study of the relations between emotional labor and burnout among hotel workers. \u003cem\u003eIsland Tourism Research\u003c/em\u003e, \u003cem\u003e8\u003c/em\u003e(1), 25-51.\u003c/li\u003e\n\u003cli\u003eHsu, J. Y., \u0026amp; Chan, S. Y. (2014). Investigating the relationship between authoritarian leadership and subordinate\u0026apos;s emotional labor. \u003cem\u003eJournal of Human Resource Management\u003c/em\u003e, \u003cem\u003e14\u003c/em\u003e(4), 31-55. https://doi.org/10.6147/JHRM.2014.1404.02\u003c/li\u003e\n\u003cli\u003eHsu, S. W., \u0026amp; Li, X. L. (2012). Study of emotional labor and job attitude of the ground staff in the airline-job burnout as a mediator. \u003cem\u003eJournal of Furen University Welfare and Health\u003c/em\u003e, \u003cem\u003e18\u003c/em\u003e(2), 1-26. https://doi.org/10.29440/FJJHE.201212.0001\u003c/li\u003e\n\u003cli\u003eHsu, S. W., Chang, H. Y., Cheng, S. S., Luo, M. G., \u0026amp; Cheng, R. (2020). Does employees\u0026apos; emotional intelligence matter? Study of leisure adjustment strategy moderating effect on emotional intelligence, working pressure and happiness in international tourist hotels. \u003cem\u003eJournal of Business and Management Technology\u003c/em\u003e, \u003cem\u003e21\u003c/em\u003e(2), 147-176.\u003c/li\u003e\n\u003cli\u003eHsu, S. W., Lin, W. T., \u0026amp; Su, H. W. (2010). A study of job stress, burnout and turnover intention of employees in the banquet hall of international tourist hotels-Social support as a moderator.\u003cem\u003e Journal of Commerce and Management Quarterly\u003c/em\u003e, \u003cem\u003e11\u003c/em\u003e(2), 209-240. https://doi.org/10.30083/CMQ.201006.0002\u003c/li\u003e\n\u003cli\u003eHsu, Y. H., Yang, C. C., Chang, Y. C., Huang, L. L., Lin, C. H., Huang, J. C., \u0026amp; Yeh, D. F. (2021). The association between work-family conflict, social support and burnout among nursing staff. \u003cem\u003eJournal of Healthcare Management\u003c/em\u003e, \u003cem\u003e22\u003c/em\u003e(1), 5-22. https://doi.org/10.6174/JHM.202103_22(1).5\u003c/li\u003e\n\u003cli\u003eHu, H. L., Zhang, B. Q., Lin, J. H., \u0026amp; Chen, S. Z. (2021). Emotional labor of the registered nurse: A qualitative study. \u003cem\u003eJournal of Occupational Medicine and Health\u003c/em\u003e, \u003cem\u003e28\u003c/em\u003e(3), 269-279.\u003c/li\u003e\n\u003cli\u003eHuang, B. Y., \u0026amp; Lin, S. H. (2007). A study of stress response caused by personality and social support: Combining psychometric meta-analysis and structural equation modeling. \u003cem\u003eJournal of Educational Psychology\u003c/em\u003e, \u003cem\u003e39\u003c/em\u003e(2), 263-294. https://doi.org/10.6251/BEP.20070604\u003c/li\u003e\n\u003cli\u003eHuang, D. C., Ji, N. W., Chang, Y. T., \u0026amp; Kuo, W. C. (2014). Antecedents, consequences, and potential moderators of emotional labor: A meta-analytic investigation. \u003cem\u003eJournal of Human Resource Management\u003c/em\u003e, \u003cem\u003e14\u003c/em\u003e(3), 93-134. https://doi.org/10.6147/JHRM.2014.1403.04\u003c/li\u003e\n\u003cli\u003eHuang, Y. C., Huang, Y. H., Liu, J. Y., \u0026amp; Chen, J. H. (2008). The moderating effects of self-efficacy on the relationships of demand-Control model and burnout. \u003cem\u003eJournal of Management Practice and Theory\u003c/em\u003e, \u003cem\u003e2\u003c/em\u003e(1), 37-51. https://doi.org/10.29916/JMPP.200803.0003\u003c/li\u003e\n\u003cli\u003eHuang, Y. F. (2016). Mask effect in public service: A perspective of emotional labor. \u003cem\u003ePublic Administration Quarterly\u003c/em\u003e, \u003cem\u003e8\u003c/em\u003e(4), 55-74.\u003c/li\u003e\n\u003cli\u003eHuang, Y. H., Huang, Y. C., Chen, J. H., \u0026amp; Du, P. L. (2015). The reciprocal effects of psychosocial job characteristics, burnout and health among nurses: A panel study. \u003cem\u003eTaiwan Journal of Public Health\u003c/em\u003e, \u003cem\u003e34\u003c/em\u003e(2), 204-217. https://doi.org/10.6288/TJPH201534103090\u003c/li\u003e\n\u003cli\u003eHung, M. C., \u0026amp; Liu, Y. (2011). The impact of job burnout on job performance-Examining the moderating effects of emotional intelligence and perceived supervisor support. \u003cem\u003eProceedings of the Conference on Defense Management\u003c/em\u003e, 1119-1138. http://dx.doi.org/10.29497/GFGL.201106.1119\u003c/li\u003e\n\u003cli\u003eHung, P. S. (2008).\u003cem\u003e A study of the relationship among employee assistance programs (EAPs), emotional intelligence and employee reflections\u003c/em\u003e (Unpublished master\u0026rsquo;s thesis). Chung Yuan Christian University. https://doi.org/10.6840/CYCU.2008.00107\u003c/li\u003e\n\u003cli\u003eJak, S. (2015). \u003cem\u003eMeta-analytic structural equation modelling\u003c/em\u003e (pp. 1-88). Dordrecht, Neth Springer.\u003c/li\u003e\n\u003cli\u003eJames, N. (1989). Emotional labour: Skill and work in the social regulation of feelings. \u003cem\u003eThe Sociological Review\u003c/em\u003e, \u003cem\u003e37\u003c/em\u003e(1), 15-42. \u003cbr\u003e https://doi.org/10.1111/j.1467-954X.1989.tb00019.X\u003c/li\u003e\n\u003cli\u003eJen, W. L., Tu, R. T., Wu, Y. J., \u0026amp; Tseng, W. L. (2010). Moderating effects of emotional intelligence of city bus drivers on the job stress model. \u003cem\u003eTransportation Journal\u003c/em\u003e, \u003cem\u003e22\u003c/em\u003e(4), 415-445. https://doi.org/10.6383/JCIT.201012.0415\u003c/li\u003e\n\u003cli\u003eJeon, A. (2016). The effect of pre-flight attendants\u0026rsquo; emotional intelligence, emotional labor, and emotional exhaustion on commitment to customer service. \u003cem\u003eService Business, 10\u003c/em\u003e(2), 345-367. ht t ps://do i. o rg/10. 1007/s11628 - 015- 0273- 2\u003c/li\u003e\n\u003cli\u003eJeung, D.-Y., Kim, C., \u0026amp; Chang, S.-J.. (2018). Emotional labor and burnout: A review of the literature. \u003cem\u003eYonsei Medical Journal\u003c/em\u003e, \u003cem\u003e59\u003c/em\u003e(2), 187-193. https://doi.org/10.3349/ymj.2018.59.2.187\u003c/li\u003e\n\u003cli\u003eJian, J. Z. (1998). The tendencies and activators of nurse\u0026apos; working emotions. \u003cem\u003eNursing Research\u003c/em\u003e, \u003cem\u003e6\u003c/em\u003e(5), 417-426. https://doi.org/10.7081/NR.199810.0417\u003c/li\u003e\n\u003cli\u003eJi‐Soo Kim. (2020). Emotional labor strategies, stress, and burnout among hospital nurses: A path analysis. \u003cem\u003eJournal of Nursing Scholarship\u003c/em\u003e, \u003cem\u003e52\u003c/em\u003e(1), 105-112. https://doi.org/10.1111/jnu.12532\u003c/li\u003e\n\u003cli\u003eKristensen, T. S., Borritz, M., Villadsen, E., \u0026amp; Christensen, K. B. (2005). The copenhagen burnout inventory: A new tool for the assessment of burnout. \u003cem\u003eWork \u0026amp; Stress\u003c/em\u003e, \u003cem\u003e19\u003c/em\u003e(3), 192-207. https://doi.org/10.1080/02678370500297720\u003c/li\u003e\n\u003cli\u003eKwon, C.-Y., Lee, B., Kwon, O.-J., Kim, M.-S., Sim, K.-L., \u0026amp; Choi, Y.-H. (2021). Emotional labor, burnout, medical error, and turnover intention among South Korean nursing staff in a university hospital setting. \u003cem\u003eInternational Journal of Environmental Research and Public Health\u003c/em\u003e,\u003cem\u003e 18\u003c/em\u003e(19), 1-14. https://doi.org/10.3390/ijerph181910111\u003c/li\u003e\n\u003cli\u003eLee, H. J. (2021). Relationship between emotional labor and job satisfaction: Testing mediating role of emotional intelligence on South Korean public service employees. \u003cem\u003ePublic Organization Review\u003c/em\u003e, \u003cem\u003e21\u003c/em\u003e(2), 337-353. https://doi.org/10.1007/s11115-020-00490-5\u003c/li\u003e\n\u003cli\u003eLee, J. J., \u0026amp; Ok, C. (2012). Reducing burnout and enhancing job satisfaction: Critical role of hotel employees\u0026rsquo; emotional intelligence and emotional labor. \u003cem\u003eInternational Journal of Hospitality Management\u003c/em\u003e, \u003cem\u003e31\u003c/em\u003e(4), 1101-1112. https://doi.org/10.1016/j.ijhm.2012.01.007\u003c/li\u003e\n\u003cli\u003eLee, Y. H. (2019). Emotional labor, teacher burnout, and turnover intention in high-school physical education teaching. \u003cem\u003eEuropean Physical Education Review\u003c/em\u003e, \u003cem\u003e25\u003c/em\u003e(1), 236-253. https://doi.org/10.1177/1356336X17719559\u003c/li\u003e\n\u003cli\u003eLee, Y. H., \u0026amp; Chelladurai, P. (2016). Affectivity, emotional labor, emotional exhaustion, and emotional intelligence in coaching. \u003cem\u003eJournal of Applied Sport Psychology\u003c/em\u003e, \u003cem\u003e28\u003c/em\u003e(2), 170-184. https://doi.org/10.1080/10413200.2015.1092481\u003c/li\u003e\n\u003cli\u003eLeiter, M. P., \u0026amp; Maslach, C. (1988). The impact of interpersonal environment on burnout and organizational commitment. \u003cem\u003eJournal of Organizational Behavior\u003c/em\u003e, \u003cem\u003e9\u003c/em\u003e(4), 297-308. https://doi.org/10.1002/job.4030090402\u003c/li\u003e\n\u003cli\u003eLi, C. C., Yeh, W. Z., Yu, Z., Hsu, R. G., Hung, Q. E., \u0026amp; Shih, P. C. (2022). The influence of transformational leadership, transactional leadership, ethical climate, and emotional intelligence on the performance of housing agents: The mediation effect of trust in supervisor. \u003cem\u003eJournal of Taiwan Land Research\u003c/em\u003e, \u003cem\u003e25\u003c/em\u003e(1), 23-61.\u003c/li\u003e\n\u003cli\u003eLian, C. G., Huang, J. X., Zhang, Y. J., \u0026amp; Ruan, Y. T. (2022). Work convenience or stress? The relationship between the work-related use of instant messaging apps beyond work hours and job burnout in teachers and the moderating role of job values. \u003cem\u003eJournal of Educational Psychology\u003c/em\u003e, \u003cem\u003e53\u003c/em\u003e(3), 717-743. https://doi.org/10.6251/BEP.202203_53(3).0009\u003c/li\u003e\n\u003cli\u003eLiao, W. J., \u0026amp; Song, Z. J. (2018). The relationship between MRT drivers\u0026apos; job stress and job burnout: The moderating effects of emotional intelligence and perceived investment in human resource development. \u003cem\u003eJournal of Management Practice and Theory\u003c/em\u003e, \u003cem\u003e12\u003c/em\u003e(1), 1-25. https://doi.org/10.29916/JMPP.201806_12(1).0001\u003c/li\u003e\n\u003cli\u003eLin, C. C. (2018). The study of examining the effect of role stress and job burnout on well-being of junior high school directors. \u003cem\u003eSchool Administration\u003c/em\u003e, (115), 91-120. https://doi.org/10.6423/HHHC.201805_(115).0005\u003c/li\u003e\n\u003cli\u003eLin, F. Y., Shen, H. T., Liao, J. Z., \u0026amp; Chan, C. W. (2013). Meta-analysis of evidence. \u003cem\u003eJournal of Long-Term Care\u003c/em\u003e, \u003cem\u003e17\u003c/em\u003e(2), 81-88. https://doi.org/10.6317/LTC.201308_17(2).0002\u003c/li\u003e\n\u003cli\u003eLin, H. H. (2009). \u003cem\u003eThe influence of emotional labor on job satisfaction for the service personnel over the counter district office, Taichung city\u003c/em\u003e\u003cem\u003e：\u003c/em\u003e\u003cem\u003eThe moderating effect of emotional intelligence\u003c/em\u003e (Unpublished master\u0026rsquo;s thesis). National Taichung University of Science and Technology. https://doi.org/10.6826/NUTC.2009.00029\u003c/li\u003e\n\u003cli\u003eLin, S. P. (2000). Development of the organizational emotional labor burden scale. \u003cem\u003eManagement Review\u003c/em\u003e, \u003cem\u003e8\u003c/em\u003e(3), 427-447.\u003c/li\u003e\n\u003cli\u003eLin, S. P., Yang, N. C., \u0026amp; Chang, S. H. (2000). A case study on emotion rules, socialization processes, and related issues of emotion labor management. \u003cem\u003eJournal of Business and Management Technology\u003c/em\u003e, \u003cem\u003e1\u003c/em\u003e(1), 67-86.\u003c/li\u003e\n\u003cli\u003eLin, Y. N., \u0026amp; Yang, W. R. (2008). The impact of multidimensional sense of humor on self-esteem, job burnout, and job satisfaction: A study of frontline service employees. \u003cem\u003eJournal of Business and Management Technology\u003c/em\u003e, \u003cem\u003e9\u003c/em\u003e(4), 417-442. https://doi.org/10.30083/CMQ.200812.0001\u003c/li\u003e\n\u003cli\u003eLin, Y. S., Huang, W. H., Chang, S. H., \u0026amp; Lin, L. Y. (2015). Surface and deep acting performance affect job burnout and turnover intention: The moderating effect of sense of humor. \u003cem\u003eJournal of Outdoor Recreation\u003c/em\u003e, \u003cem\u003e28\u003c/em\u003e(1), 73-103. https://doi.org/10.6130/JORS.2015.28(1)3\u003c/li\u003e\n\u003cli\u003eLin, Z. Y., \u0026amp; Chang, W. H. (2018). The structural relationship between emotional labor and cynicism of full-time international tourist hotels employees. \u003cem\u003eJournal of Tourism Industry and Aviation Management\u003c/em\u003e, \u003cem\u003e1\u003c/em\u003e(1), 22-42. https://doi.org/10.6862/JTIAM.201807_1(1).0002\u003c/li\u003e\n\u003cli\u003eLiu, T. Y., Lee, C. R., Su, C. H., \u0026amp; Wu, P. L. (2016). Relationship among emotional labor, emotional intelligence and job burnout of public health staff. \u003cem\u003ePolicy and Human Resources Management\u003c/em\u003e, \u003cem\u003e7\u003c/em\u003e(2), 89-114. https://doi.org/10.29944/PPM.201612_7(2).0003\u003c/li\u003e\n\u003cli\u003eLiu, Y. H., \u0026amp; Hsieh, C. W. (2016). Negative display rules and job burnout: Investigating the mediation effect of emotional dissonance and the moderation effect of service climate using a sample of Taipei city government employees. \u003cem\u003eChinese Public Administration Review\u003c/em\u003e, \u003cem\u003e22\u003c/em\u003e(2), 81-100. https://doi.org/10.6635/cpar.2016.22(2).04\u003c/li\u003e\n\u003cli\u003eLiu, Y. H., \u0026amp; Liu, W. W. (2009). Exploring relationships among emotional labor, job stress, and coping behaviors in nurses. \u003cem\u003eJournal of Medical and Health Sciences\u003c/em\u003e,\u003cem\u003e 11\u003c/em\u003e(2), 98-115. https://doi.org/10.6563/TJHS.2009.11(2).3\u003c/li\u003e\n\u003cli\u003eLopes, P. N., Brackett, M. A., Nezlek, J. B., Sch\u0026uuml;tz, A., Sellin, I., \u0026amp; Salovey, P. (2004). Emotional intelligence and social interaction. \u003cem\u003ePersonality and Social Psychology Bulletin\u003c/em\u003e, \u003cem\u003e30\u003c/em\u003e(8), 1018-1034. https://doi.org/10.1177/0146167204264762\u003c/li\u003e\n\u003cli\u003eMacCallum, R. C., \u0026amp; Austin, J. T. (2000). Applications of structural equation modeling in psychological research. \u003cem\u003eAnnual Review of Psychology\u003c/em\u003e, \u003cem\u003e51\u003c/em\u003e, 201-226. https://doi.org/10.1146/annurev.psych.51.1.201\u003c/li\u003e\n\u003cli\u003eMan, L. F. (2002). \u003cem\u003eThe study of emotional labor burden and job outcomes among emotional labor workers: The moderating effects of emotional intelligence and job characteristics\u003c/em\u003e (Unpublished master\u0026rsquo;s thesis). Providence University.\u003c/li\u003e\n\u003cli\u003eMart\u0026iacute;nez-Monteagudo, M. C., Ingl\u0026eacute;s, C. J., Granados, L., Aparisi, D., \u0026amp; Garc\u0026iacute;a- Fern\u0026aacute;ndez, J. M. (2019). Trait emotional intelligence profiles, burnout, anxiety, depression, and stress in secondary education teachers. \u003cem\u003ePersonality and Individual Differences\u003c/em\u003e, \u003cem\u003e142\u003c/em\u003e, 53-61. https://doi.org/10.1016/j.paid.2019.01.036\u003c/li\u003e\n\u003cli\u003eMartins, A., Ramalho, N., \u0026amp; Morin, E. (2010). A comprehensive meta-analysis of the relationship between emotional intelligence and health. \u003cem\u003ePersonality and Individual Differences\u003c/em\u003e, \u003cem\u003e49\u003c/em\u003e(6), 554-564. https://doi.org/10.1016/j.paid.2010.05.029\u003c/li\u003e\n\u003cli\u003eMaslach, C., \u0026amp; Jackson, S. E. (1981). The measurement of experienced burnout. \u003cem\u003eJournal of Organizational Behavior\u003c/em\u003e, \u003cem\u003e2\u003c/em\u003e(2), 99-113. https://doi.org/10.1002/job.4030020205\u003c/li\u003e\n\u003cli\u003eMaslach, C., \u0026amp; Leiter, M. P. (2016). Understanding the burnout experience: Recent research and its implications for psychiatry. \u003cem\u003eWorld Psychiatry\u003c/em\u003e, \u003cem\u003e15\u003c/em\u003e(2), 103-111. https://doi.org/10.1002/wps.20311\u003c/li\u003e\n\u003cli\u003eMaslach, C., \u0026amp; Leiter, M. P. (2017). Understanding burnout: New models. \u003cem\u003eThe handbook of stress and health: A guide to research and practice\u003c/em\u003e (pp. 36-56). https://doi.org/10.1002/9781118993811.ch3\u003c/li\u003e\n\u003cli\u003eMaslach, C., Schaufeli, W. B., \u0026amp; Leiter, M. P. (2001). Job burnout. \u003cem\u003eAnnual Review of Psychology\u003c/em\u003e, \u003cem\u003e52\u003c/em\u003e(1), 397-422. https://doi.org/10.1146/annurev.psych.52.1.397\u003c/li\u003e\n\u003cli\u003eMayer, J. D., \u0026amp; Salovey, P. (1993). The intelligence of emotional intelligence. \u003cem\u003eIntelligence\u003c/em\u003e, \u003cem\u003e17\u003c/em\u003e(4), 433-442. https://doi.org/10.1016/0160-2896(93)90010-3\u003c/li\u003e\n\u003cli\u003eMayer, J. D., \u0026amp; Salovey, P. (1997). \u003cem\u003eWhat is emotional intelligence?\u003c/em\u003e In P. Salovey \u0026amp; D. Sluyter (Eds.), Emotional development and emotional intelligence: Implications for educators (pp. 3-31). New York: Basic Books.\u003c/li\u003e\n\u003cli\u003eMayer, J. D., Caruso, D. R., \u0026amp; Salovey, P. (2016). The ability model of emotional intelligence principles and updates. \u003cem\u003eEmotion Review\u003c/em\u003e, \u003cem\u003e8\u003c/em\u003e(4), 290-300. https://doi.org/10.1177/1754073916639667\u003c/li\u003e\n\u003cli\u003eMiao, C., Humphrey, R. H., \u0026amp; Qian, S. (2017). A meta‐analysis of emotional intelligence and work attitudes. \u003cem\u003eJournal of Occupational and Organizational Psychology\u003c/em\u003e, \u003cem\u003e90\u003c/em\u003e(2), 177-202. https://doi.org/10.1111/joop.12167\u003c/li\u003e\n\u003cli\u003eMin, C. H., Wu, Y. F., \u0026amp; Hsu, S. J. (2015). The effects of employees\u0026rsquo; emotional intelligence and job satisfaction on organizational citizenship behavior in the high-tech industry. \u003cem\u003eJournal of Management Information and Computing\u003c/em\u003e, \u003cem\u003e4\u003c/em\u003e(2), 108-127. https://doi.org/10.6285/MIC.4(2).09\u003c/li\u003e\n\u003cli\u003eMorris, J. A., \u0026amp; Feldman, D. C. (1996). The dimensions, antecedents, and consequences of emotional labor. \u003cem\u003eThe Academy of Management Review\u003c/em\u003e, \u003cem\u003e21\u003c/em\u003e(4), 986-1010. https://doi.org/10.2307/259161\u003c/li\u003e\n\u003cli\u003ePetrides, K. V., \u0026amp; Furnham, A. (2000). On the dimensional structure of emotional intelligence.\u003cem\u003e Personality and Individual Differences\u003c/em\u003e, \u003cem\u003e29\u003c/em\u003e(2), 313-320.\u003c/li\u003e\n\u003cli\u003eRamana, P., Sambasivanb, M., \u0026amp; Kumar, N. (2016). Counterproductive work behavior among frontline government employees: Role of personality, emotional intelligence, affectivity, emotional labor, and emotional exhaustion. \u003cem\u003eJournal of Work and Organizational Psychology\u003c/em\u003e, \u003cem\u003e32\u003c/em\u003e(1),25-37. https://doi.org/10.1016/j.rpto.2015.11.002\u003c/li\u003e\n\u003cli\u003eRosenthal, R. (1991). Meta-analysis: A review. \u003cem\u003ePsychosomatic Medicine\u003c/em\u003e, \u003cem\u003e53\u003c/em\u003e(3), 247-271.\u003c/li\u003e\n\u003cli\u003eRossie, A. M., Perrewe, P. L., Sauter, S. L., \u0026amp; Jex, S. M. (2006). \u003cem\u003eStress and quality of working life: Current perspectives in occupational health\u003c/em\u003e. Charlotte, NC: Information Age Publishing\u003c/li\u003e\n\u003cli\u003eSalovey, P., \u0026amp; Mayer, J. D. (1990). Emotional intelligence. \u003cem\u003eImagination, Cognition and Personality\u003c/em\u003e, \u003cem\u003e9\u003c/em\u003e(3), 185-211. https://doi.org/10.2190/DUGG-P24E-52WK-6CDG\u003c/li\u003e\n\u003cli\u003eS\u0026aacute;nchez-Pujalte, L., Mateu, D. N., Etchezahar, E., \u0026amp; G\u0026oacute;mez Yepes, T. (2021). Teachers\u0026rsquo; burnout during COVID-19 pandemic in Spain: Trait emotional intelligence and socioemotional competencies. \u003cem\u003eSustainability\u003c/em\u003e, \u003cem\u003e13\u003c/em\u003e(13), 1-11. https://doi.org/10.3390/su13137259\u003c/li\u003e\n\u003cli\u003eSchaufeli, W. B., \u0026amp; Greenglass, E. R. (2001). Introduction to special issue on burnout and health. \u003cem\u003ePsychology \u0026amp; health\u003c/em\u003e, \u003cem\u003e16\u003c/em\u003e(5), 501-510. https://doi.org/10.1080/08870440108405523\u003c/li\u003e\n\u003cli\u003eScherer, S., Zapf, D., Beitler, L. A., \u0026amp; Trumpold, K. (2020). Testing a multidimensional model of emotional labor, emotional abilities, and exhaustion: A multilevel, multimethod approach. \u003cem\u003eJournal of Occupational Health Psychology\u003c/em\u003e, \u003cem\u003e25\u003c/em\u003e(1), 46-67. https://doi.org/10.1037/ocp0000166\u003c/li\u003e\n\u003cli\u003eShah, M. K., Gandrakota, N., Cimiotti, J. P., Ghose, N., Moore, M., \u0026amp; Ali, M. K. (2021). Prevalence of and factors associated with nurse burnout in the US. \u003cem\u003eJAMA Network Open\u003c/em\u003e, \u003cem\u003e4\u003c/em\u003e(2). https://doi.org/10.1001/jamanetworkopen.2020.36469\u003c/li\u003e\n\u003cli\u003eSharpe, D. (1997). Of apples and oranges, file drawers and garbage: Why validity issues in meta-analysis will not go away. \u003cem\u003eClinical Psychology Review\u003c/em\u003e, \u003cem\u003e17\u003c/em\u003e(8), 881-901. https://doi.org/10.1016/S0272-7358(97)00056-1\u003c/li\u003e\n\u003cli\u003eTang, Y. T. (2009). The effects of employee perception of supervisor\u0026apos;s emotional intelligence on innovative behavior. \u003cem\u003eJournal of Quality\u003c/em\u003e, \u003cem\u003e16\u003c/em\u003e(6), 461-478.\u003c/li\u003e\n\u003cli\u003eThomas, N. S., Mousavy, S., Mukundan, J., \u0026amp; Nimehchisalem, V. (2012). The relationship between burnout and trait emotional intelligence among secondary school teachers in Malaysia. \u003cem\u003eInternational Journal of Applied Linguistics and English Literature\u003c/em\u003e, \u003cem\u003e1\u003c/em\u003e(4), 15-23.\u003c/li\u003e\n\u003cli\u003eTsai, Y. C. (2011). \u003cem\u003eA study on the relationship between emotional intelligence and emotional labor of elementary school teachers \u003c/em\u003e(Unpublished master\u0026rsquo;s thesis). National Taipei University of Education. https://doi.org/10.6344/NTUE.2011.00461\u003c/li\u003e\n\u003cli\u003eTyng, C. M., Amin, H. U., Saad, M. N. M., \u0026amp; Malik, A. S. (2017). The influences of emotion on learning and memory. \u003cem\u003eFrontiers in Psychology\u003c/em\u003e, \u003cem\u003e8\u003c/em\u003e, 1454. https://doi.org/10.3389/fpsyg.2017.01454 \u003c/li\u003e\n\u003cli\u003eViswesvaran, C., \u0026amp; Ones, D. S. (1995). Theory testing: Combining psychometric meta‐analysis and structural equations modeling. \u003cem\u003ePersonnel Psychology\u003c/em\u003e, \u003cem\u003e48\u003c/em\u003e(4), 865-885.\u003c/li\u003e\n\u003cli\u003eWang, C. H., \u0026amp; Weng, C. Y. (2009). Relationships among emotional labor, job burnout and job satisfaction of the tour leaders: Moderating effects of role identity. \u003cem\u003eJournal of Outdoor Recreation\u003c/em\u003e, \u003cem\u003e22\u003c/em\u003e(4), 23-53. https://doi.org/10.6130/JORS.2009.22(4)2\u003c/li\u003e\n\u003cli\u003eWang, C. H., Lo, H. F., Deng, S. Y., \u0026amp; Lin, C. W. (2012). Emotional labor, job burnout and job satisfaction of frontline employees in the hospitality industry. \u003cem\u003eJournal of Hospitality, Tourism, and Leisure Studies\u003c/em\u003e, \u003cem\u003e9\u003c/em\u003e(3), 151-170. https://doi.org/10.6572/JHT.9(3).3\u003c/li\u003e\n\u003cli\u003eWang, H. Y., Huang, D. X., \u0026amp; Wei, L. M. (2014). The relations among teachers\u0026apos; exercise participation, emotional intelligence and job stress at elementary schools. \u003cem\u003eJournal of Sports, Leisure, and Hospitality Research\u003c/em\u003e, \u003cem\u003e9\u003c/em\u003e(4), 88-102. https://doi.org/10.29429/JSLHR. 2014129(4).05\u003c/li\u003e\n\u003cli\u003eWang, H. Y., Liu, R. W., \u0026amp; Yeh, M. G. (2018). Effectively performing systematic review and meta-analysis. T\u003cem\u003eaiwan Journal of Clinical Pharmacy\u003c/em\u003e, \u003cem\u003e26\u003c/em\u003e(1), 1-10. https://doi.org/10.6168/FJCP.201801_26(1).0001\u003c/li\u003e\n\u003cli\u003eWang, S. H. (1999). The concept of meta-analysis and its application in nursing. \u003cem\u003eNursing Research\u003c/em\u003e,\u003cem\u003e 7\u003c/em\u003e(2), 183-192. https://doi.org/10.7081/NR.199904.0183\u003c/li\u003e\n\u003cli\u003eWang, Y. L. (2018). Research on the correlation between preschool teacher\u0026apos;s emotional service, job burnout and turnover intention. \u003cem\u003eJournal of Meihe Education\u003c/em\u003e, \u003cem\u003e37\u003c/em\u003e(1), 9-40.\u003c/li\u003e\n\u003cli\u003eWechsler, D. (1958). \u003cem\u003eThe measurement and appraisal of adult intelligence.\u003c/em\u003e Williams \u0026amp; Wilkins Co.\u003c/li\u003e\n\u003cli\u003eWen, J. F., \u0026amp; Cui, L. Y. (2001). A study on work-family conflicts and job burnout of female professionals in high-tech companies: Effects of social support. \u003cem\u003eManagement Review\u003c/em\u003e, \u003cem\u003e20\u003c/em\u003e(4), 65-91. https://doi.org/10.6656/MR.2001.20.4.CHI.65\u003c/li\u003e\n\u003cli\u003eWen, J., Huang, S. S., \u0026amp; Hou, P. (2019). Emotional intelligence, emotional labor, perceived organizational support, and job satisfaction: A moderated mediation model. \u003cem\u003eInternational Journal of Hospitality Management\u003c/em\u003e, \u003cem\u003e81\u003c/em\u003e, 120-130. https://doi.org/10.1016/j.ijhm.2019.01.009\u003c/li\u003e\n\u003cli\u003eWeng, C. Y. (2017). \u003cem\u003eA study on the correlation among emotional labor, emotional intelligence, and emotional exhaustion of counseling psychologists\u003c/em\u003e (Unpublished master\u0026rsquo;s thesis). National Dong Hwa University.\u003c/li\u003e\n\u003cli\u003eWharton, A. S. (2009). The sociology of emotional labor. \u003cem\u003eAnnual Review of Sociology\u003c/em\u003e, \u003cem\u003e35\u003c/em\u003e, 147-165. https://doi.org/10.1146/annurev-soc-070308-115944\u003c/li\u003e\n\u003cli\u003eWortman, P. M. (1994). Judging research quality\u003cem\u003e. The handbook of research synthesis\u003c/em\u003e, 97-109.\u003c/li\u003e\n\u003cli\u003eWu, H. H., \u0026amp; Lin, C. C. (2011). The Effect of social support on the junior high school teacher\u0026apos;s burnout: Role stress and self-efficacy as a mediator. \u003cem\u003eJournal of Education Research, National Chiayi University\u003c/em\u003e, (26), 25-56. https://doi.org/10.6474/NCUJER.201103.0025\u003c/li\u003e\n\u003cli\u003eWu, P. L. (2016). \u003cem\u003eA study on the relationship between emotional labor and job burnout among public health personnel: The moderating effects of personality traits and emotional intelligence\u003c/em\u003e (Unpublished master\u0026rsquo;s thesis). National Kaohsiung Normal University.\u003c/li\u003e\n\u003cli\u003eWu, Q. S., \u0026amp; Lin, T. Y. (2005). Educational term: Emotional labor. \u003cem\u003eEducational Resources and Research\u003c/em\u003e, \u003cem\u003e65\u003c/em\u003e, 136.\u003c/li\u003e\n\u003cli\u003eWu, S. R., \u0026amp; Wang, X. H. (2005). Emotional labor and its related factors in clinical nurses. \u003cem\u003eEvidence-Based Nursing\u003c/em\u003e, \u003cem\u003e1\u003c/em\u003e(4), 243-252. https://doi.org/10.6225/JEBN.1.4.243\u003c/li\u003e\n\u003cli\u003eWu, Z. Y. (2008). Abusive supervision and emotional exhaustion: The mediating effects of subordinate justice perception and emotional labor. \u003cem\u003eChinese Journal of Psychology\u003c/em\u003e, \u003cem\u003e50\u003c/em\u003e(2), 201-221. https://doi.org/10.6129/CJP.2008.5002.06 \u003c/li\u003e\n\u003cli\u003eWu, Z. Y. (2008). The mediating process of trust and the moderating effect of emotional intelligence on a Chinese supervisor\u0026apos;s authoritarianism leadership and a subordinate\u0026apos;s job satisfaction and organizational commitment. \u003cem\u003eIndigenous Psychological Research in Chinese Societies\u003c/em\u003e, (30), 3-63. https://doi.org/10.6254/2008.30.3\u003c/li\u003e\n\u003cli\u003eWu, Z. Y., \u0026amp; Cheng, B. S. (2003). Emotion studies in organizations in Taiwan: A critical review. \u003cem\u003eApplied Psychology Research\u003c/em\u003e, (19), 137-173.\u003c/li\u003e\n\u003cli\u003eWu, Z. Y., \u0026amp; Cheng, B. S. (2006). The relationships among frequency of encountering difficult customers, perceived service training utility, emotional labor, and emotional exhaustion-The viewpoint of \u0026quot;conservation of resources theory\u0026quot;. \u003cem\u003eJournal of Management\u003c/em\u003e, \u003cem\u003e23\u003c/em\u003e(5), 581-599. https://doi.org/10.6504/JOM.2006.23.05.04\u003c/li\u003e\n\u003cli\u003eXie, M. S. (2010). \u003cem\u003eThe impact of individual\u0026apos;s emotional intelligence and gender on the job demands-resources and job burnout relationship: Evidence from community mental rehabilitation institution staff\u003c/em\u003e (Unpublished master\u0026rsquo;s thesis). Ching Yun University.\u003c/li\u003e\n\u003cli\u003eYen, K. L., \u0026amp; Li, Y. H. (2008). A analysis on the contents and the related studies of emotional labor and burnout and revelation of elementary school administrators.\u003cem\u003e School Administration\u003c/em\u003e, (57), 1-37. https://doi.org/10.6423/HHHC.200809.0001\u003c/li\u003e\n\u003cli\u003eYin, H., Huang, S., \u0026amp; Chen, G. (2019). The relationships between teachers\u0026rsquo; emotional labor and their burnout and satisfaction: A meta-analytic review. \u003cem\u003eEducational Research Review\u003c/em\u003e, \u003cem\u003e28\u003c/em\u003e. https://doi.org/100283.10.1016/j.edurev.2019.100283\u003c/li\u003e\n\u003cli\u003eYu, C. L. (2020). \u003cem\u003eA study on the correlation among employees\u0026apos; emotional intelligence, emotional labor, and job satisfaction: A case study of port operational management authority\u003c/em\u003e (Unpublished master\u0026rsquo;s thesis). National Kaohsiung University of Science and Technology.\u003c/li\u003e\n\u003cli\u003eZhang, H. C., Liu, J. W., \u0026amp; Yang, H. N. (2010). Exploring the effect of psychological contract on work-life balance: The moderating roles of social support and emotional intelligence. \u003cem\u003eJournal of Human Resource Management\u003c/em\u003e, \u003cem\u003e10\u003c/em\u003e(3), 1-24. https://doi.org/10.6147/JHRM.2010.1003.01\u003c/li\u003e\n\u003cli\u003eZhang, H. C., Liu, J. W., Xu, H. M., Miao, M. Z., \u0026amp; Tong, Z. L. (2011). The relationships between supervisor\u0026apos;s mood contagion and employee\u0026apos;s innovative behavior: The mediating roles of work engagement and burnout. \u003cem\u003eJournal of Human Resource Management\u003c/em\u003e, \u003cem\u003e11\u003c/em\u003e(2), 1-24. https://doi.org/10.6147/JHRM.2011.1102.02\u003c/li\u003e\n\u003cli\u003eZhang, L. Y., \u0026amp; Yan, Y. Y. (2021). Relationships among emotional labor, job burnout, and job satisfaction of amusement park employees: Emotional intelligence as a moderator. \u003cem\u003eJournal of Hospitality, Tourism and Leisure Studies\u003c/em\u003e, \u003cem\u003e18\u003c/em\u003e(1), 69-89. https://doi.org/10.6572/JHT.202106_18(1).0004\u003c/li\u003e\n\u003cli\u003eZhong, C. D., \u0026amp; Hsieh, M. H. (2008). Emotional intelligence and negotiation behavior: Negotiation strategy, personal attraction and negotiation outcomes. \u003cem\u003eJournal of Management\u003c/em\u003e, \u003cem\u003e25\u003c/em\u003e(5), 525-548. https://doi.org/10.6504/JOM.2008.25.05.04\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"bmc-psychology","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"psyo","sideBox":"Learn more about [BMC Psychology](http://bmcpsychology.biomedcentral.com/)","snPcode":"","submissionUrl":"","title":"BMC Psychology","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"emotional labor, job burnout, emotional intelligence, meta-analysis, structural equation modeling","lastPublishedDoi":"10.21203/rs.3.rs-4563859/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4563859/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThe present study adopted a meta-analysis design that incorporated structural equation modeling to explore the relationships between emotional labor (EL), job burnout (JB), and emotional intelligence (EI), and enable model validation. The results revealed that EL and JB were significantly and positively correlated, that EI was significantly and positively correlated with EL, and that EI was significantly and negatively correlated with JB. The SEM parameter estimation values were all positive, reaching the level of significance and meeting the basic fit criteria. The total effect size of EL on JB was 0.307, which was equal to the sum of the direct and indirect effect sizes (0.423\u0026ndash;0.116). This result indicated that EL affected JB through EI, validating the presence of a moderating effect. Finally, the results were discussed, and practical suggestions were proposed.\u003c/p\u003e","manuscriptTitle":"Relationships Between Emotional Labor, Job Burnout, and Emotional Intelligence: An Analysis Combining Meta-analysis and Structural Equation Modeling","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-07-02 09:05:25","doi":"10.21203/rs.3.rs-4563859/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2024-06-19T13:44:20+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2024-06-16T11:53:10+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2024-06-16T11:52:21+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Psychology","date":"2024-06-11T11:48:53+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-psychology","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"psyo","sideBox":"Learn more about [BMC Psychology](http://bmcpsychology.biomedcentral.com/)","snPcode":"","submissionUrl":"","title":"BMC Psychology","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"292ebf90-0c77-4f40-af21-e1f1581b2ecf","owner":[],"postedDate":"July 2nd, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2024-11-25T15:58:26+00:00","versionOfRecord":{"articleIdentity":"rs-4563859","link":"https://doi.org/10.1186/s40359-024-02167-w","journal":{"identity":"bmc-psychology","isVorOnly":false,"title":"BMC Psychology"},"publishedOn":"2024-11-18 15:56:50","publishedOnDateReadable":"November 18th, 2024"},"versionCreatedAt":"2024-07-02 09:05:25","video":"","vorDoi":"10.1186/s40359-024-02167-w","vorDoiUrl":"https://doi.org/10.1186/s40359-024-02167-w","workflowStages":[]},"version":"v1","identity":"rs-4563859","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4563859","identity":"rs-4563859","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}