Factors influencing labor mobility in tourism companies in Khanh Hoa province

Hue University Journal of Science: Social Sciences and Humanities ISSN 2588-1213 Vol. 129, No. 6B, 2020, Tr. 77–90, DOI: 10.26459/hueuni-jssh.v129i6B.5767 * Corresponding: tambminh@gmail.com Submitted: 9-4-2020; Revised: 13-5-2020; Accepted: 14-5-2020. FACTORS INFLUENCING LABOR MOBILITY IN TOURISM COMPANIES IN KHANH HOA PROVINCE Bui Thi Tam*, Nguyen Thi Kim Yen School of Hospitality and Tourism, Hue University, 22 Lam Hoang St., Hue, Vietnam Abstract: Tourism labor mobility has drawn the keen interest of different parties due to its role in tourism development, in general, and tourism laborers’ well-being, in particular. To investigate the current situation of labor mobility in tourism in Vietnam, this study surveyed 220 staff members in 30 tourism companies in Khanh Hoa province. The results show the evidence of active labor mobility in tourism companies, especially in hotel and travel sectors. The more experience a person has, the more mobilized he/she is, especially upward occupational mobility. Five factors are found to have an impact on labor mobility in Khanh Hoa province, in which the highest importance is assigned to job satisfaction and organizational culture. The findings suggest useful managerial implications for better human resource development in Khanh Hoa tourism companies. Keywords: determinant, labor mobility, human resource, Khanh Hoa, tourism company 1. Introduction Labor mobility or labor movement refers to the ease that a person can change to a new job or workplace in an intra-industry or inter-industries. Labor mobility is considered a feature of the labor market in the tourism industry [16]. From the economic point of view, as an important production factor, labor is mobilized under different driving forces, such as labor demand and supply, salary payment, and career incentives. As such, labor mobility is known as the way to maximize the value of human capital and to promote economic development. At the corporate level, labor mobility has both positive and negative impacts on its performance. On the one hand, labor mobility pushes companies to pursue more efficient human resource management for acquiring a high-quality workforce. The better the human resource management policy is, the more chances to attract skilled laborers and to enhance job engagement the company will have. On the other hand, labor mobility causes the loss of the company’s human capital that requires much time and effort to compensate. At the individual Bui Thi Tam, Nguyen Thi Kim Yen Vol. 129, No. 6B, 2020 78 level, labor mobility is regarded as a change to a better occupational alternative, either economically or mentally, and either strategically or tactically [2, 8]. During the last decades, Vietnam tourism has experienced a high stable growth rate that nurtures the fast development of the tourism labor market and facilitates the labor movement within and across industries. While labor mobility is an intrinsic part of tourism dynamics, challenges of labor mobility in Vietnam tourism are increasingly dramatic because of unhealthy competition among tourism companies for skilled-laborers. The issue of tourism labor mobility has recently risen in tourism agendas for human resource development. The central questions are what the current situation of tourism labor mobility in Vietnam is, and what factors explain labor mobility in tourism. Unfortunately, little evidence is known for more efficient managerial solutions and policy implications. Taking the case of Khanh Hoa province and from a labor perspective, this study aims to examine the key factors influencing tourism labor mobility. Among preliminary efforts in this research domain in Vietnam, the results are expected to provide meaningful information for a better understanding of labor mobility in the context of Vietnam tourism development. 2. Labor mobility – concepts and determinants 2.1. Basic concepts The term ‘labor mobility’ refers to ‘the degree to which people are able and willing to move from one occupation to another or from one area to another’ (Cambridge Dictionary). Labor mobility is defined as the ability and the capacity of a laborer to change his/her job to a different place or a different occupation within or in different industries. Labor mobility can be defined in three different perspectives [20, 10, 24, 13]:  Labor mobility as the individual change of job refers to job movement of individual laborer within or across companies, inter or intra-industry (vertically and horizontally occupational mobility), in the same or different geographical regions (geographical mobility).  Labor mobility as a migrant that may not be for an occupational reason.  Labor mobility as a flow that is often driven by market forces (domestic or international). From the economic point of view, labor mobility facilitates the flow of labor from one to another market under the labor market forces, by which it helps solve the issues of labor economics such as supply and demand, jobs and income, and employment and unemployment. In other words, labor mobility is a result of labor market operation that leads to maximizing the Jos.hueuni.edu.vn Vol. 129, No. 6B, 2020 79 labor value. The labor flows among regions and industries help to narrow the gaps in salary and payment [8, 25]. Labor mobility and career advancement have recently drawn much attention from researchers and practitioners. In some studies, labor mobility is recognized as a laborer’s strategy that refers to a purposeful change of work – called upward occupational mobility. They have to acquire knowledge, skills, and experience that are commonly expected to enable them to move upper the career ladder, either in the same or a different company. This argument sets labor mobility as ‘a linear, progressive, and upward movement’ [12, 14, 2]. On the contrary, many other studies support that labor mobility is not always ‘strategized’, but rather, it is commonly conditioned by sociocultural–spatial considerations and/or arrangements, such as a search for work-life balance, gender, and redistribution of domestic duties [8, 15]. As noted by Zampoukos [24, p. 63], ‘labor mobility and career paths must also be recognized as fragmented, happenstance, and erratic’. From the corporate point of view, labor mobility bears two sides of effects. On the one hand, labor mobility enables companies to recruit better people and enhance competitiveness. It motivates employees’ high work engagement to ensure their job position, better payment, and career opportunities. On the other hand, due to the shortage of qualified human resources, unhealthy competition among companies for high skilled laborers arises. The out-flow of skilled laborers causes a loss to companies since it costs their time and money to train or to search for the new ones. This is especially true in the tourism industry, where the shortage of high-quality laborers is prevailing. It is also important to note that the negative impacts of labor mobility become more severe in tourism where the labor market is characterized as low entry barriers and high seasonality. As noted in some studies [20, 24], labor mobility may merely be participation in paid jobs, and hence becomes a part of the labor market, or leave the labor market and become unemployment (temporary or permanent). As such, labor mobility is just a coping strategy. 2.2. Factors influencing tourism labor mobility Given the complex nature of labor mobility, factors influencing a laborer’s decision on mobility range from macro-environment factors (socio-cultural-economic development and market forces, transportation conveniences, etc.), organizational factors (corporate performance, policies for its employees, organizational culture, etc.) to personal factors (demographic characteristics, job awareness, motivation, etc.). With the focus on the individual perspective of labor mobility, the following discussion is mainly paid on personal-related factors, including Bui Thi Tam, Nguyen Thi Kim Yen Vol. 129, No. 6B, 2020 80 personal characteristics, awareness, and career investment related to occupational environment factors. – Individual human capital: It can be said that individual human capital is one of the most important determinants of labor mobility. The greater human capital a laborer possesses, the more job opportunities for him/her to take both occupational mobility and geographical mobility. Goldin [9, p. 56] notes that “human capital is defined as knowledge, skills, and health a person accumulates during their whole life, which enables them to be self-aware of their potentials as an active member in society”. In economics, human capital is used as an important measure for remuneration distribution. The value of human capital is derived through the labor market, where the skills and knowledge are marketed [6, 21, 10]. In other words, through the labor market, human capital is employed in more efficient ways that promote labor mobility. On the contrary, some studies prove that although well-trained and high skilled laborers often gain more chances for job mobility, it (mobility) may also cause a higher risk to them. In most cases, the greater the individual human capital is, the less chance for labor mobility will become [21, 17]. This is common in the industries where the labor market has low entry conditions, and the tourism labor market is a typical case. – Personal characteristics: Personal-related determinants such as personality, gender, age, and work experience are common factors influencing decision-making on job movement. The impacts of these determinants are multidimensional and mostly in connection with their social role, culture, and personality. For instance, when deciding on job change, a woman may be more bounded by her family duties. Likewise, younger people may gain more opportunities for mobility than those who are older. This is not because of age discrimination (although it really does in most cases, depending on the nature of work), but rather it is often related to such factors as family-work balance, social ties, and career investment [7, 15, 8]. Besides, the ability to mobilize due to different demographic characteristics commonly manifests in the need and motives in taking job moves. For some people, a change of job is mainly driven by the payment level, while for many others it is for self-esteem and self- actualization, as indicated in the Maslow’s hierarchy of needs [18]. These arguments account for the possibility of relating personal-related factors with the career goal. – Job awareness and investment: job awareness refers to the personal viewpoint and attitude toward a career, which manifests the individual consciousness and will, motivation, and affection toward his/her work [4, 14]. High job awareness will motivate the individual to invest in their career development and job engagement that become the key to their career success. It is reasonable to claim that laborers hardly leave their jobs where they have spent much time Jos.hueuni.edu.vn Vol. 129, No. 6B, 2020 81 and effort to learn and to advance themselves in work performance, and as a result, they are gaining better opportunities for career advancement and remuneration. Otherwise, laborers have to invest in their human capital to improve their job opportunities and hence labor mobility [20, 17]. In other words, job awareness and investment work as the ‘pull and push’ factors for labor mobility. – Organizational culture: In the business world today, building a strong organizational culture is well-recognized as one of the strategic solutions to maintain competitive advantages, especially in service industries. An organization with a transparent and cooperative working environment is usually a target for numerous talents. Most studies in job engagement and organizational loyalty prove that organizational culture works as a key determinant that has a positive impact on job engagement [3, 5, 17]. – Job satisfaction: A literature review on labor mobility shows that the more satisfied with the job, the less chance a laborer leaves their organization [20, 19]. Although in different work contexts, the predictors of job satisfaction may be different, and the common factors include payment, work environment, the suitability of work, a chance for career advancement, etc. Obviously, there seem to be some overlaps in examining the determinants of labor mobility, and it is undeniable that job satisfaction appears to be an important construct explaining labor mobility. Besides, some other factors can be found, including the structure labor market and institutional and legal factors related to labor welfares [8, 13]. However, as mentioned above, these external and macro-environmental factors are only taken to some extent for discussion. The analytical framework of the factors influencing labor mobility applied in this study is illustrated in Figure 1. Bui Thi Tam, Nguyen Thi Kim Yen Vol. 129, No. 6B, 2020 82 Figure 1. Factors influencing labor mobility in tourism companies in Khanh Hoa province 3. Research methods 3.1. Site description and sampling techniques Located along Southern Central Coastal Zone, Khanh Hoa is well-endowed with tourism resources, especially famous beaches and islands that indulge beach lovers. Tourism is set as a leading economic sector to promote socio-economic development in the province. According to Khanh Hoa statistics, in 2018, Khanh Hoa received 6.3 million tourist arrivals with a growth rate of 15.6% compared with 2017, with a rise in revenue by 27.8%. The tourism industry creates employment for more than 2,500 people, in which hotels and restaurants account for more than 81%. High dynamic tourism development in Khanh Hoa makes it well fitted for this study. According to Khanh Hoa Tourism Department (2019), there are 111 hotels, ranked 3 to 5 stars, and 128 travel agencies and tour operators in the locality. In this first study examining labor mobility at the individual level, the authors planned to extend the sample size to more than 200 to facilitate different techniques of analysis, given the minimum sample size calculated according to Yamane’s formula is 96 [23]. To ensure the presentation of key tourism sectors in the sample, the stratified random sampling method was used. 230 questionnaires were delivered to staff members in 30 selected tourism companies in Khanh Hoa, from which 220 questionnaires were valid (Refers to Appendix 1 for the profiles of respondents and their working sectors). Jos.hueuni.edu.vn Vol. 129, No. 6B, 2020 83 3.2. Data of analysis From the conceptual framework shown in Figure 1, 20 questions were designed to measure the people’s opinions on the factors influencing labor mobility in tourism companies in Khanh Hoa province. The statistical package SPSS 22.0 and the method of Exploratory Factor Analysis (EFA) were used to explore the factors influencing the labor mobility in tourism companies. The Cronbach’s Alpha coefficient for reliability check and the Kaiser-Meyer-Olkin (KMO) test for adequacy of sampling data were examined. Besides, the statistic descriptive analysis was used to summarize the respondents’ ratings and perceptions of the factors influencing their labor mobility. The analysis of variance (ANOVA) was applied to compare the mean differences among the testing groups. As a condition for carrying out the ANOVA, the Levene statistics were calculated to test the homogeneity of group variances. 4. Findings and discussions 4.1. Current situation of labor mobility in tourism companies in Khanh Hoa It can be said that given no official statistics of tourism labor mobility is available at both the industry and enterprise level, surveys on individuals’ labor mobility are regarded as the best way to investigate the current situation of tourism labor mobility. The results show that the top management people in the hotel sector are highly experienced, with an average of 9.13 years working in this sector, 3.67 years of experience in the travel sector, and/or 3.89 years in the F&B sector (Table 1). Besides, some of them had worked many years in other tourism businesses (7 years on average) or other business sectors (4.92 years). Although these people account for less than 10 percent of the sample, it reveals the evidence of inter-industry mobility. Compared with those in the hotel sector, it takes the top management people in the travel sector a shorter time to reach their position. On average, they needed about 7.85 years of experience in the travel sector, 4.92 years of experience in the hotel sector, and about 5.5 years in other tourism services. The top management people in the travel sector change their job approximately 3.62 times compared with 3.45 times of those in the hotel sector (Table 1). For the middle management group, on average, people spent a long time in the hotel sector compared with those in other tourism sectors, with lower mobility. The situation is also similar for the lower management level (team leader/supervisor), who work longer in the hotel sector than those in other sectors. However, it is slightly different for the base-line people who spend more years in the travel and F&B sectors than in the hotel sector, which may support the argument that labor mobility is higher in the hotel sector than in others. Bui Thi Tam, Nguyen Thi Kim Yen Vol. 129, No. 6B, 2020 84 Table 1. Classification of labors by work positions and work experiences in different tourism sectors Current work position Section of work Years of experience by sectors Times of mobility Hotel Travel F&B Other tourism services Other business Public sector Top management level Hotel 9.13 3.67 3.89 7.00 4.92 3.57 3.45 TO/TA 5.50 7.85 2.25 5.50 5.14 4.67 3.62 F&B – 2.00 4.00 – – 5.00 3.00 Average 8.38 6.30 3.43 5.88 5.00 4.00 3.50 Middle management level Hotel 7.44 16.00 3.20 6.17 1.33 5.00 2.88 TO/TA 5.00 4.64 12.00 7.00 6.17 6.25 3.18 F&B 1.00 – 9.50 – 4.00 13.00 3.00 Average 6.75 5.58 5.88 6.50 4.50 6.63 3.00 Group leader/ supervisor Hotel 6.59 4.83 2.70 1.86 2.60 2.71 2.53 TO/TA 3.00 6.38 2.00 2.50 5.67 3.50 2.65 F&B 4.75 3.00 4.85 6.71 14.00 7.00 2.46 Average 6.10 5.83 3.83 3.75 5.08 3.917 2.55 Base-line staff Hotel 3.84 4.43 3.20 2.29 3.50 3.44 2.38 TO/TA 2.33 4.90 2.00 – 2.50 5.33 2.10 F&B 2.00 1.00 7.88 4.00 2.00 3.33 3.38 Average 3.64 4.50 5.05 7.56 3.00 3.80 2.37 Source: Authors’ survey, Aug. – Oct. 2019 Note: TO/TA = Tour operator/Travel agency; F&B = Foods and Beverage. The analysis reveals some interesting patterns of labor mobility in the tourism companies in Khanh Hoa. Intra-industry mobility is common in Khanh Hoa, where the hotel and travel sectors are the two main destinations of tourism employment. Longer years of engagement in one sector show that vertical occupational mobility is higher in that sector, and here the hotel sector is the typical case. The horizontal mobility occurs across tourism sectors and inter-industry, which supports the argument that tourism works as a destination of employment [20, 22]. The long years of experience at the lower level of management and base- Jos.hueuni.edu.vn Vol. 129, No. 6B, 2020 85 line people in the F&B sector may be due to the low job entry conditions. This might be the result of the movement of the skilled and qualified management people from the F&B to the hotel and TO/TA sectors. 4.2. Factors influencing labor mobility in tourism companies in Khanh Hoa The EFA method was performed with 20 measurement variables. Cronbach’s Alpha coefficient of 0.876, with no value of corrected item-total correlation below 0.3, shows high scale reliability. The KMO value of 0.822, with the significant level of Bartlett’s test at 0.000, indicates the sampling adequacy and the relevancy of applying EFA. The Principal Components extraction with the Varimax rotation method (for uncorrelated factors) results in the Kaiser Criterion with the Eigenvalue greater than 1 and the factor loading greater than 0.50 for the factor selection [11]. This first EFA results in 5 factors. However, there is one variable with a factor loading below 0.5 and should be excluded. Hence, the second analysis of EFA was performed with 19 variables. This analysis results in 5 factors with the KMO value of 0.811 and 66.80% of the total variances are explained. The Cronbach’s Alpha coefficients are greater than 0.7 for all factors (Table 2), indicating high scale reliability. The results show that all 5 factors suggested in the conceptual framework (Figure 1) are kept, and their statistical analysis is summarized in Table 3. The data reveal that tourism laborers in Khanh Hoa express their strong agreement with all determinants of labor mobility, signifying that these factors have a high strong influence on their choice of mobility. The results also indicate that the laborers’ perceptions of their mobility are not statistically different (p < 0.05) among the determinant groups, except for education. It is evident that ‘Job satisfaction’ is the most influencing factor on labor mobility, followed by ‘Organizational culture’, ‘Individual work attitude’, and ‘Tourism job opportunities’. ‘Job awareness and investment’ is the least influential. This is understandable in the context of Vietnam tourism and Khanh Hoa, in particular. The Table 2. The results of EFA analysis on determinants of tourism labor mobility in Khanh Hoa Factors extracted Number of variables Cronbach’s Alpha coefficients Factor 1. Individual work attitude 5 0.810 Factor 2. Job awareness and investment 4 0.847 Factor 3. Organizational culture 4 0.783 Factor 4. Job satisfaction 3 0.791 Factor 5. Tourism job opportunities 3 0.774 Bui Thi Tam, Nguyen Thi Kim Yen Vol. 129, No. 6B, 2020 86 industry has gained an impressive high growth for decades. This rate promotes high demand for tourism laborers with relatively easy entry barriers, as aforementioned. The high agreement rate for ‘Job satisfaction’ and ‘Organizational culture’ is supported by the common situation that tourism labor seems to outrank these factors in making a job choice [1, 5]. These results imply further support to the above-mentioned argument that labor mobility is a progressive and upward movement [14, 2], although contextual differences should be recognized and examined. The test results show a statistically significant difference among educational groups for four factors, except for ‘Job awareness and investment’. The higher the education level is, the higher agreement on the influences of these factors takes place. Likewise, a high statistically significant difference among the age groups is found on ‘Job awareness and investment’, with a mean of 3.99 for those aged above 45 compared with those of the younger (Means range from 3.01 to 3.28). The groups of higher age, higher education, and higher current positions all show a higher agreement on the influence of ‘Tourism job opportunities’. Looking back to the discussion on human capital, one can see that the more human capital an individual laborer accumulates, the more considerations they will make when deciding labor mobility. This provides very important managerial implications for human resource management in tourism companies. Proper policies should be in place for retaining their talent pool of laborers. Table 3. Factors influencing the labor mobility in tourism companies in Khanh Hoa Province Factors Mean1 Significant level by groups (p-value) Gender Age Marital status Education Current position Work section 1. Individual work attitudes 3.91 0.130 0.035 0.085 0.002 0.069 0.420 2. Job awareness and investment 3.41 0.295 0.000 0.078 0.302 0.542 0.422 3. Organizational culture 3.98 0.176 0.161 0.065 0.024 0.678 0.276 4. Job satisfaction 4.09 0.252 0.847 0.914 0.002 0.891 0.391 5. Tourism job opportunities 3.78 0.416 0.085 0.003 0.018 0.007 0.062 Source: Authors’ survey, Aug. – Oct. 2019 Note: (1) The Likert’s scale from 1 (completely disagree) to 5 (completely agree). Jos.hueuni.edu.vn Vol. 129, No. 6B, 2020 87 5. Conclusions Tourism labor mobility has drawn much attention from different parties due to its roles in tourism development, company competitiveness, and laborers’ well-being. For tourism companies, labor mobility works as ‘a pull factor’ attracting the talent pool of laborers, which helps enhance their competitiveness. On the other hand, the out-flow of laborers may cause the loss of the company’s human capital that becomes increasingly challenge nowadays. For an individual laborer, labor mobility involves the changes in their jobs in terms of horizontal or vertical occupation mobility, or geographical mobility. No matter what form it takes, labor mobility is for their better-off in terms of money, work-life balance, and/or career development. Therefore, understanding the factors influencing an individual’s labor mobility deserves to receive due attention. The findings of this study show that along with the high growth rate of tourism development in Khanh Hoa, labor mobility has become prevailing, especially in the hotel and travel sectors. Some interesting patterns of labor mobility are found, including inter-industry and intra-industry mobility, of which more vertical occupational mobility occurs in the hotel and travel sector than in others. Five factors of labor mobility are examined, and the highest importance is given to job satisfaction and organizational culture, which should become focal points in human resource development in tourism companies in Khanh Hoa. With the limited sample size, this study provides some preliminary empirical evidence of labor mobility in the tourism industry. The following directions are suggested for further studies on tourism labor mobility, including 1) extension of this research domain by different levels for better understanding of labor mobility in the tourism industry; 2) replication of this study with a larger sample size will be helpful for in-depth analyses of labor mobility, as well as interdependence among determinants of labor mobility, from which more theoretical and practical implications can be made. REFERENCES 1. Adler, H. and Lu. Y. T. (2009). Career goals and expectations of hospitality and tourism students in China. Journal of Teaching in Travel & Tourism, 9(1–2), pp. 63–80. 2. Alberti, G. (2014). Mobility strategies, ‘mobility differentials’ and ‘transnational exit’: the experiences of precarious migrants in London’s hospitality jobs. Work, Employment & Society, 28, 865–881. 3. Allen and Meyer (1990). The measurement and antecedents of affective, continuance and normative commitment to the organization. Journal of Occupation Psychology, 63, 1–18. Bui Thi Tam, Nguyen Thi Kim Yen Vol. 129, No. 6B, 2020 88 4. Arthur, M. B., Hall, D. T. and Lawrence, B. S. (1989). Handbook of career theory. Eds. Cambridge University Press. 5. Aziri B. (2011). Job Satisfaction: A literature review. Journal of management research and practice, 3(4), pp 77–86. 6. Becker, G. (1975). Human Capital. New York: National Bureau of Economic Research. 7. Bui Thi Tam and Nguyen Hoang Thuy Vy (2019). Factors influencing women career advancement in hotel industry in Hue City. Hue University Journal of Science: Social Sciences and Humanities, 128(6D), pp. 5–18. (In Vietnamese). 8. Datta, K., McIlwaine, C., Evans, Y., Herbert, J., May, J. & Wills, J. (2007). From coping strategies to tactics: London's low-pay economy and migrant labour. British Journal of Industrial Relations, 45, 404– 432. 9. Goldin, C. (2016). “Human Capital”. In: Handbook of Clinometric. Diebolt Claude and Micheal John Haupert, Eds. pp. 55–86. Berlin: Springer. 10. Gill, N., Caletrio, J. & Mason, V. (2011). Introduction: mobilities and forced migration. Mobilities 6, 301–316. 11. Hair, J. F., Anderson, R. E., Tatham, R. L. & Black, W. C. (2008). Multivariate Data Analysis, (7th ed.). New Jersey: Prentice-Hall Publishers. 12. Hagan, J., Lowe, N. & Quingla, C. (2011). Skills on the move: rethinking the relationship between human capital and immigrant economic mobility. Work and Occupations, 38, 149178. 13. Ha Thi Minh Duc (2019). The mobility of skilled labors in Vietnam within ASEAN community. Ph.D. Dissertation. Institute of Social Sciences, Vietnam Academy of Social Sciences. 14. Iskander, N., Riordan, C. & Lowe, N. (2013). Learning in place: Immigrants spatial and temporal strategies for occupational advancement. Journal of Economic Geography, 89(1), 53–75. 15. James, A. (2014). Work-life ‘balance’ and gendered (im)mobilities of knowledge and learning in high- tech regional economies. Journal of Economic Geography, 14, 483–510. 16. Ladkin, A. (2014). Labor Mobility and Labor Market Structures in Tourism. In: Lew, A. A., Hall, M. C. & Williams, A. M. (Eds.), Companion to Tourism (pp. 132–142). Chichester: Wiley-Blackwell. 17. Ladkin, A. and Kichuk, A. (2017). Talent Management in Hospitality and Tourism. In: Horner, S., ed. Talent Management in Hospitality and Tourism. Oxford, UK: Goodfellow Publishers. ISBN 978-1- 910158-67-8. 18. Maslow, A. H. (1943). A Theory of Human Motivation. In Psychological Review, 50, 370–396. 19. Saari, L. M., Judge, T., A. (2004). Employee Attitudes and Job Satisfaction. Human Resource Management, 43(4), 395–407. Jos.hueuni.edu.vn Vol. 129, No. 6B, 2020 89 20. Szivas, E., Riley, M. and Airey, D. (2003). Labor mobility into tourism: Attraction and Satisfaction. Annals of Tourism Research, 30(1), pp. 64–76. 21. Szivas, E. (1997). A study of labor mobility into tourism: The case of Hungary. Ph.D. dissertation, Hungary: University of Surrey. 22. UNWTO (2017). Tourism for Development. Discussion paper on the occasion of the International Year of Sustainable Tourism for Development 2017. [internet]. Available at: Accessed 4th Jan., 2020. 23. Yamane, T. (1967). Statistics: An Introductory Analysis, 2nd Edition, New York: Harper and Row. 24. Zampoukos, K. (2018). Hospitality Workers and the Relational Spaces of Labor (Im)mobility. Tourism Geographies. 20(1), 49–66, DOI: 10.1080/14616688.2017.1331259. 25. Zampoukos, K. & Ioannides, D. (2015). Making difference within the hotel: labor mobility and the internationalization of reproductive work. In: Jordhus-Lier, D. & Underthun, A. (Eds.), A Hospitable World? Organizing work and workers in hotels and tourist resorts (pp. 39–51). London and New York: Routledge. Bui Thi Tam, Nguyen Thi Kim Yen Vol. 129, No. 6B, 2020 90 Appendix 1. Respondent profiles Groups Numbers Percent (%) Groups Numbers Percent (%) Gender Marital status Male 107 48,6 Single 72 32,7 Female 111 50,6 Married 143 65,0 Others 2 0,9 Divorce 5 2,3 Sum 220 100 Sum 220 100 Age group Education level 18–24 years old 17 7,7 High school and below 21 9,5 25–34 years old 82 37,3 Vocational, college certificate 67 30,5 35–44 years old 68 30,9 Undergraduates 116 52,7 45–54 years old 44 20,0 Post-graduates 16 7,3 Above 54 9 4,1 Sum 220 100 Sum 220 100 Specialization Working sector Tourism 53 34,0 Hotel 133 60,5 Hospitality 18 11,5 Travel agencies 55 25,0 Business administration 18 11,5 Food and beverage 24 10,0 Other majors 67 42,9 Other tourism services 8 3,6 Sum 156 100 Sum 220 100 Number of jobs changed Current position No change at all 19 8,8 Management board 37 16,8 1 time 26 12,0 Head/vice-head of department 29 13,2 2 times 42 19,4 Team leader, supervisor 83 37,7 3 times 61 28,1 Base-line staff 71 32,3 4 times 40 18,3 Sum 220 100 More than 4 times 29 13,4 Source: The authors’ survey, Aug. – Oct. 2019