Analysis of Trends in Research on Emotional Labor in Hotel and Resort Sectors in Korea using Semantic Network Analysis

Jangheon Han¹; Hyejin Jung^2*

¹Humanitas College, Kyung Hee University, Korea.
²College of Hotel and Tourism Management, Kyung Hee University, Korea.

Abstract

This study analyzed emotional labor in the hotel and resort industry through semantic network analysis and convergence of iteration correlation (CONCOR) analysis. The scope of the study was limited to academic papers on the hotel and resort industry published by the Korea Citation Index (KCI) between 2006 and 2022. This included papers from the journals Tourism & Leisure Research (26), Korean Journal of Hospitality & Tourism (23), Korean Journal of Tourism Research (19), International Journal of Tourism & Hospitality Research (18), Tourism Research (14), and other journals (74). Having identified the major research trends, visualization was carried out to confirm the topics closely related to emotional labor, which identified the subjects of emotional labor, “employees,” job factors such as “job stress,” and negative psychological variables such as “emotional exhaustion” with high frequency and centrality. The CONCOR analysis revealed four main clusters: “job impact factors,” “service impact factors,” “supervisor impact factors,” and “psychological impact factors.” The results offer suggestions for future research directions on emotional labor in hotels and resorts, as well as implications for academic and practical applications.

Keywords:Emotional labor, Hotel & resort, Research trends, Semantic network analysis, CONCOR analysis, Text-mining.

 

1. Introduction

Cesar Ritz, the founder of the Ritz-Carlton hotel chain, famously said, “The customer is never wrong.” However, due to the wide reach of social media and the ability for information to spread rapidly, it is increasingly difficult to manage a positive brand image. In particular, it is difficult for service employees to actively cope with customer power abuse at the service contact point. The interactions with such customers are rarely seen by others, as they are carried out in relative privacy during the service encounter. However, due to recent changes in the social environment, cases of employee abuse at the hands of customers and supervisors are increasingly being exposed [1]. In line with this social trend, in 2018 Korea’s National Assembly passed a revision to the Industrial Safety and Health Act in the form of the “Emotional Workers Protection Act.” In 2019, the Ministry of Employment and Labor distributed a manual for the protection of customer service workers’ health in the hotel and resort industry, to protect workers and guarantee their rights as employees.

Arlie Russell Hochschild, an American sociologist, first introduced the concept of the “emotional labor” employees perform at the point of service contact in his book “The Managed Heart” [2]. Similar to actors who attempt to control their own emotions and express false emotions, service employees experience extreme stress and various adverse psychological effects [3]. In addition, if there is a discrepancy between discipline and emotion within the organization, a lack of “emotional coordination” can lead to burnout and job dissatisfaction [4]. Customers’ demands of service companies are increasing; thus, to ensure sustainable business performance, managing employees’ emotional labor is as important as managing the organizational unit [5].

Research on employees’ emotional labor began in the early 2000s, with the hotel and resort industry being an important area of study. Starting with a comparative study between Korea and the United States on hotel employees’ emotional labor and job stress in 2006 [6], studies on the effects of organizational behavior such as job burnout and satisfaction, stress, organizational commitment, turnover intention, and customer orientation have followed [7, 8].

Emotional labor can cause emotional exhaustion. From a service company’s perspective, proper emotional management is a very important factor. A lack of emotional management leads to a decline in the relationship between those serving customers and the customers themselves; for the employee, it often leads to negative outcomes, such as long-term leave or turnover [9]. In addition, although protection laws have been enacted to protect employees against emotional labor, their implementation involves social awareness, effective educational programs, and various levels of in-depth research on emotional labor [10]. In this context, in preparation for the renaissance of the hotel and resort industry in the post-COVID era, analyses focusing on emotional labor are expected to provide helpful insight into better management and employee retention practices to support service employees.

Therefore, in this study, we have conducted an analysis of emotional labor in the hotel and resort industry among papers registered in the Korea Citation Index (KCI) from 2006 to 2022. Research trends in this area were analyzed by applying semantic network and convergence of iteration correlation (CONCOR) analysis to topics relating to emotional labor in hotels and resorts. Based on our analysis, we propose future research directions and implications for emotional labor management.

2. Theoretical Background

2.1. Concept, Characteristics, and Components of Emotional Labor

In the past, from the perspective of manufacturing-based organizational behavior, emotions were not classified as significant. However, as the service economy continues to expand, we are beginning to realize that the feelings of service employees are an important factor in understanding and interpreting human behavior more broadly [11]. Emotions are described as operational responses to internal or external events that can facilitate cognitive abilities [12]. Understanding emotions makes it easier to understand the behavior of employees within the organizational environment and is increasingly important from the perspective of human resource development [13]. In service companies and operations, emotions strongly affect employees’ interactions with customers; often, employees must suppress their feelings to provide positive feedback to customers [14].

Using the crew of Delta Airlines as an example, Hochschild [2] first explained emotional labor as the process of suppressing one’s emotions to induce the desired customer reaction, and she divided emotional labor into “deep acting” and “surface acting” constructs. Ashforth and Humphrey [15] defined the act of socially necessary emotional expression in the context of service provision as emotional labor. Morris and Feldman [16] defined emotional labor as the level of planning, effort, and control required by the organization of employees in the service delivery process. Diefendorff, et al. [17] defined it as expressing emotions in accordance with the regulations and norms established by the organization rather than those felt by individual employees at the service interface. In general, emotional labor can be described as controlling and commercializing employees’ emotions to ensure they are expressed in the form desired by the organization as part of the employees’ duties [14]. Emotional labor has multi-dimensional characteristics, which can be divided into four categories: emotional attentiveness, emotional expression frequency, emotional expression diversity, and emotional dissonance [16]. In a recent study, emotional labor was examined in terms of the frequency and diversity of emotional expression and emotional dissonance [18].

There are two main components of emotional labor: surface acting and deep acting. Surface acting refers to keeping the emotions of the employees in mind and managing the surface actions to match the emotional expressions required by the organization [19]. On the other hand, deep acting is the effort to not only match the surface actions with the organization’s emotional expression demands but also to change the internal emotions to meet the organization’s needs [20]. A hotel company’s service contact employees try to meet service standards, performing emotional labor to include internal acts as well as surface acts, as they should always provide customers with a bright attitude, active mind, sincere greeting and response, and polite tone [21]. However, having to respond positively to a customer while under duress often leads to job burnout and an increase in turnover [18].

2.2Emotional Labor Research Trends

The study of emotional labor in the hotel and resort industry is a research topic in the field of organizational behavior in hotel management (see, e.g., Kim [6] comparative study of the emotional labor of Korean and American hotel employees). We investigate this here using KCI registered studies. Emotional labor research in hotels and resorts is commonly divided according to service locations and tasks. In addition, internal factors, such as inhibition of job immersion, emotional depletion, and increased stress caused by the emotional labor of employees at service contact points, as well as an increase in turnover, have received much attention [22-28] .

In addition to the quantitative hotel and resort emotional labor studies mentioned above, qualitative studies have also been conducted. Kim [29] studied the psychology and phenomenon of hotel employees’ emotional labor; the results showed that hotel employees suffer from various forms of emotional labor that are closely related to the hotel’s cost factors and hotel use information through online blogs. Emotional labor can cause hotel workers to experience extreme stress and professional skepticism; however, confiding in close colleagues seems to provide some relief.

Lim [30] conducted a subjectivity study using a Q- methodology to investigate the emotional labor of hotel employees. Here, 41 P samples were categorized into 40 Q statements on surface behavioral items, loyal behavioral items, principled efforts, artificial efforts, and four recognition types of emotional labor. In particular, despite some similarities, perspectives on emotional labor and the approach to responding to customers differed considerably.

Kim [31] studied the direction of hotel service manual development considering hotel emotional labor. Three main conclusions were summarized: it is necessary to learn the regulations and management practices concerning emotional labor from Europe and Japan, as part of emotional labor management in foreign service industries; it is important to reflect on the changes outlined in the Industrial Safety and Health Act as part of the emotional labor management of the Korean service industry, and self-leadership pre-education and application of the customer service contact enhances confidence and self-efficacy and improves customer orientation.

This paper divides the research on emotional labor into earlier and later time periods. A total of 86 studies were conducted from 2006 to 2015; the focus was on identifying the causal relationship between variables directly linked to the departure of employees, such as turnover, with variables directly linked to corporate performance, such as job exhaustion due to emotional labor, job satisfaction, job attitude, organizational immersion, and organizational conflict. A total of 88 studies were conducted from 2016 to 2022; during this period, the studies considered the impact of various regulatory variables and the moderating effects of variables relating to the support perception of employees within the organization (e.g., supervisor and co-worker support), organizational support perception, and organizational trust. In addition, some of the studies examined the moderating effect of employees’ psychological variables, such as personality, self-efficacy, intrinsic motivation, DISC, behavior type, resilience, and understanding of others, as well as the effects of general characteristics such as the rank and age of employees.

2.3.Analysis of Semantic Network and Related Prior Studies

Qualitative research has been conducted by applying content analysis [32, 33]. In recent studies relating to the research trends, a growing number of studies apply network analysis to ensure more objectivity [24]. Semantic network analysis is based on prior developments in social network analysis [34]. Semantic network analysis is a method of collecting information that appears on a topic and simultaneously interpreting detailed meanings by analyzing the strength of the connections between keywords [34]. Many recent studies have applied semantic network analysis to the hotel and resort sectors. Choi [35] conducted an awareness analysis of integrated resorts using semantic network analysis and concluded that Korea’s integrated resorts are strongly recognized as theme parks, hotels, and resorts based on the accommodation and facilities, and topics such as travel, sea, and pensions are highly centered around tourism at the family level. Park and Choi [23] used this method to establish a plan to revitalize membership programs to improve hotel profitability. Practical topics relating to hotel membership, such as discounts, benefits, reservations, and promotions, were found to be central, which can be explained by users thinking that specific discounts and benefits are important to hotel membership. Choi [36] used it to analyze hotel management research trends and identified basic factors, including employees, customers, and hotel services. Human resource factors, such as hotel workers, job performance, organizational immersion, and turnover, are key areas of study in the hotel and resort industry. Here, the trends of emotional labor in hotels and resorts were analyzed through a literature search; based on the findings of the semantic network and CONCOR analysis, we discuss future research directions.

3. Research Methods

3.1. Subject of Analysis

A total of 174 studies on emotional labor were collected after searching for “Hotel Emotional Labor” and “ Resort Emotional Labor” in the integrated search window of the Korea Research Foundation’s KCI. In all, 86 were confirmed from 2006 to 2015 and 88 from 2016 to 2022, with the following breakdown according to journal name: Tourism & Leisure Research (26), Korean Journal of Hospitality & Tourism (23), Korean Journal of Tourism Research (19), International Journal of Tourism & Hospitality Research (18), Tourism Research (14), Journal of Foodservice Management (11), Journal of Tourism Sciences (8), Journal of Hotel & Resort (7), The Journal of the Korea Contents Association (5), Northeast Asia Tourism Research (4), Culinary Science & Hospitality Research (4), and Other journals (8).

3.2. Research Methods and Procedures

The semantic network and CONCOR analysis consisted of a total of five steps [37]. Step 1 was a text mining procedure that targeted keywords highly related to emotional labor. We used the KrKwic program to classify the subject words by noun. The total number of words collected through the KrKwic program was 393, with a total of 166 subject words appearing more than twice. Next, the parts containing the same topic words were calculated by integrating them. A total of 42 keywords were selected during the final text mining process. Step 2 was the process of conducting frequency analysis of the distinguished noun unit keywords; the importance of keywords relating to emotional labor was confirmed through frequency analysis. Step 3 involved creating a relational matrix using the KrKtitle program for the important keywords obtained from the frequency analysis. In Step 4, the relational matrix was applied to the UCINET program to analyze the central and semantic network and conduct CONCOR. Step 5 was the visualization of the results using the Net Draw program.

4. Research Results

4.1. Frequency Analysis and Central Analysis Results

After the text mining procedure, frequency analysis was conducted on the 42 selected keywords. Among the emotional labor-related keywords, the most frequently used keyword, excluding hotels, was “Employee” (50), followed by “Moderating Effect” (30), “Turnover Intention” (28), “Job Satisfaction” (25), “Worker” (21), “Job Burnout” (21), “Staff” (21), “Job Stress” (19), “Service” (18), and “Customer Orientation” (15).

Next, we examined the centrality of the degree analysis. Degree centrality analysis is an indicator of the number of connections between the main nodes of networks [34]. Excluding “Emotion” and “Hotel,” “Job” (0.095), “Employee” (0.071), “Relationship” (0.040), “Moderating Effect” (0.038), “Exhaustion” (0.038), “Stress” (0.032), “Job Satisfaction” (0.030), “Turnover Intention” (0.029), “Service” (0.026), “Staff” (0.025), and “Job Stress” (0.024) were revealed in the analysis.

Keyword	Frequency	Percentage	Keyword	Frequency	Percentage
Hotel	68	12.0	Social	8	1.5
Employee	50	9.5	Support	8	1.5
Moderating Effect	30	5.7	Job Attitude	8	1.5
Turnover Intention	28	5.3	Organizational Citizenship Behavior	7	1.3
Job Satisfaction	25	4.7	Emotional Exhaustion	6	1.1
Worker	21	4.0	Service Encounter	6	1.1
Job Burnout	21	4.0	Psychological	6	1.1
Staff	21	4.0	Resort	5	0.9
Job Stress	19	3.6	Supervisor	5	0.9
Service	18	3.4	Conflict	4	0.8
Customer Orientation	15	2.8	Restaurant	4	0.8
Exhaustion	15	2.8	Job	4	0.8
Emotional Intelligence	14	2.6	Job Engagement	4	0.8
Relationship	14	2.6	Job Characteristics	4	0.8
Deluxe Hotel	13	2.5	Emotion	3	0.6
Mediating Effect	12	2.3	Rapport	3	0.6
Stress	12	2.3	Supervisor Support	3	0.6
Emotional Dissonance	9	1.7	Food and Beverage	3	0.6
Organizational Commitment	9	1.7	Subjectivity	3	0.6
Emotional Exhaustion	8	1.5	Prosocial	3	0.6
Internal Marketing	8	1.5	Service Commitment	2	0.4

Next, a closeness centrality analysis was performed. This analysis can confirm whether the main node between two networks is close to emotional labor [34]. The closeness centrality analysis identified “Job” (95.349), “Moderating Effect” (91.111), “Employee” (87.234), “Service” (83.673), “Worker” (83.673), “Relationship” (87.234), “Exhaustion” (87.234), “Turnover Intention” (80.392), “Deluxe Hotel” (77.358), “Job Satisfaction” (75.926), and “Customer Orientation” (75.926). The frequency analysis results are shown in Table 1. The connection and closeness centrality analysis results are summarized in Table 2. The semantic network analysis results are shown in Figure 1.

Keyword	Degree	Closeness	Keyword	Degree	Closeness
Hotel	0.126	100	Social	0.01	69.492
Employee	0.071	87.234	Support	0.014	73.214
Moderating Effect	0.038	91.111	Job Attitude	0.006	60.294
Turnover Intention	0.029	80.392	Organizational Citizenship Behavior	0.004	61.194
Job Satisfaction	0.03	75.926	Emotional Exhaustion	0.009	63.077
Worker	0.022	83.673	Service Encounter	0.003	59.42
Job Burnout	0.018	73.214	Psychological	0.004	60.294
Staff	0.025	82	Resort	0.006	68.333
Job Stress	0.024	69.492	Supervisor	0.008	70.69
Service	0.026	83.673	Conflict	0.003	61.194
Customer Orientation	0.017	75.926	Restaurant	0.004	62.121
Exhaustion	0.038	87.234	Job	0.095	95.349
Emotional Intelligence	0.008	70.69	Job Engagement	0.005	61.194
Relationship	0.04	87.234	Job Characteristics	0.002	55.405
Deluxe Hotel	0.02	77.358	Emotion	0.144	100
Mediating Effect	0.015	71.93	Rapport	0.003	57.746
Stress	0.032	71.93	Supervisor Support	0.004	61.194
Emotional Dissonance	0.013	65.079	Food and Beverage	0.013	70.69
Organizational Commitment	0.007	63.077	Subjectivity	0.001	52.564
Emotional Exhaustion	0.006	62.121	Prosocial	0.003	56.944
Internal Marketing	0.002	56.164	Service Commitment	0.002	56.164

Figure 1.Visualization of the result of the semantic network analysis.

4.2. CONCOR Analysis Results

CONCOR is an analysis method based on the type of relationship between the keywords highly related to emotional labor; it repeatedly derives results based on the correlations used to classify groups of keywords [38]. Detailed trends in the study of emotional labor can be examined by clustering keywords, and the meaning can be interpreted in depth by classifying clusters into central and surrounding clusters. A total of four cluster groups of keywords were found. The first cluster included Job Characteristics, Job Engagement, Job Stress, and Job Burnout; the cluster name given was “Job Impact Factors.” The second cluster included Service Commitment, Service Encounter, and Internal Marketing, and the cluster was named “Service Impact Factors.” The third cluster included Supervisor Support, Supervisor, and Conflict, and the cluster was named “Supervisor Impact Factors.” The fourth cluster included Psychological, Emotional Exhaustion, Emotional Dissonance, and Emotional Exhaustion, and the cluster was named “Psychological Impact Factors.” The CONCOR analysis results are summarized in Table 3.

Theme	Major Keywords
Job impact factors	Job Characteristics, Job Engagement, Job Stress, Job Burnout, Organizational Commitment, Turnover Intention
Service impact factors	Service Commitment, Service Encounter, Internal Marketing, Subjectivity
Supervisor impact factors	Supervisor Support, Supervisor, Staff, Conflict, Support, Social
Psychological impact factors	Psychological, Emotional Exhaustion, Emotional Dissonance, Emotional Intelligence

5. Discussion and Conclusion

The attitudes and emotions of employees who respond to customers during service encounters are key factors affecting customer experience and the overall perception of the company, with the potential to increase the company’s competitive advantage. In this context, smooth emotional labor management is a factor that contributes to better service and is key to creating a stable working environment for employees. This study examined the flow of research across 174 emotional labor studies of hotels and resorts from the literature published in the period 2006–2022, using semantic networks and CONCOR analysis. The results can be summarized as follows. The frequency analysis identified “Employee,” “Moderating Effect,” “Turnover Intention,” “Job Satisfaction,” “Worker,” “Job Burnout,” “Staff,” “Job Stress,” “Service,” “Customer Orientation,” “Exhaustion,” and “Emotional Intelligence” as the subjects and variables most frequently found in emotional labor research. In particular, words relating to the impact of emotional labor on jobs, such as “Job Satisfaction/Job Burnout/Job Stress,” along with the subjects of emotional labor, such as “Employee/Worker/Staff,” accounted for a large proportion. Negative psychological variables associated with emotional labor, such as “Emotional Dissonance/Emotional Exhaustion,” have also often served as research topics in the broader area of emotional labor.

The centrality analysis identified “Job,” “Relationship,” “Exhaustion,” “Stress,” and “Job Satisfaction” as having a high degree of centrality. A job refers to the work of an employee who is the subject of emotional labor, and emotional labor is greatly influenced by the business relationships with customers and supervisors. In addition, “Job,” “Moderating Effect,” “Service,” and “Turnover Intention” were confirmed to have a high degree of closeness centrality. The moderating effect refers to a methodology applied in diverse emotional labor-related studies. The CONCOR analysis divided the cluster of emotional labor-related research keywords into four groups: “Job Impact Factors,” “Service Impact Factors,” “Supervisor Impact Factors,” and “Psychological Impact Factors.” Based on this division, future research on emotional labor in the hotel and resort industry will likely encompass these four areas. The implications of this study can be summarized as follows. First, eight to nine studies are carried out on this topic each year. Choi [36] analyzed research trends relating to hotel management and also found that human resource research on employees is the most frequent type of hotel management research. Our main results support those findings and are expected to serve as a basis for a more detailed study of research trends relating to hotel management. In addition, it is expected that research on emotional labor will continue to have not only academic but also practical implications due to the increased demand for hotels and resorts in the post-COVID-19 period. Therefore, the main results relating to emotional labor research trends can be used as basic data in the field of emotional labor research in the future. Second, because employees are greatly affected by emotional labor caused by their managers, more specific manuals on emotional labor management and self-management will be important. This is in line with the conclusions of Kim [31]. For reference, the health protection manual for customer service workers distributed by the Ministry of Employment and Labor deals with mental and physical countermeasures and solutions to emotional labor caused by customers. It is important to also recognize the seriousness of emotional labor caused by supervisors and to reduce any adverse effects.

6. Limitations and Future Research

This study was conducted using research published between 2006 and 2022 on KCI registered sites. In future studies, it would be meaningful to analyze this topic’s trends in international journals and draw implications through comparative analysis with domestic journals.

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