The Effect Of Digital Hr Transformation And Work Flexibility On Employee Performance Through Employee Engagement With The Moderating Role Of Employee Well-Being In The Hospitality Sector
DOI:
https://doi.org/10.33751/jhss.v10i2.157Keywords:
Digital HR Transformation, Work Flexibility, Employee Engagement, Employee Well-being, Employee Performance, HospitalityAbstract
The increasing integration of digital technology into organizational activities has significantly reshaped human resource management practices, particularly within the hospitality industry. Alongside these technological developments, changing work patterns and greater demands for workplace flexibility have encouraged organizations to rethink how employees are managed, engaged, and supported. This study investigates the influence of digital HR transformation and work flexibility on employee performance through employee engagement, while also examining the moderating role of employee well-being in the Indonesian hospitality sector. A quantitative explanatory approach was employed in this research. Data were collected through questionnaires distributed to employees working in hotels, restaurants, and tourism-related businesses. The data were analyzed using Structural Equation Modeling–Partial Least Squares (SEM-PLS). In this model, digital HR transformation and work flexibility function as independent variables, employee engagement acts as a mediating variable, employee performance serves as the dependent variable, and employee well-being is positioned as a moderating variable. The findings are expected to indicate that both digital HR transformation and work flexibility contribute positively to employee engagement. Employees who experience supportive digital systems and adaptive working arrangements are more likely to feel emotionally connected to their work and organization. Higher levels of engagement are also expected to improve employee performance and mediate the relationship between organizational practices and performance outcomes. Furthermore, employee well-being is predicted to strengthen the relationship between employee engagement and employee performance. This study contributes to the growing body of literature on digital-based Strategic Human Resource Management by offering an integrated framework that combines technological, psychological, and organizational perspectives. Practically, the findings are expected to assist hospitality organizations in designing more adaptive and employee-centered HR strategies in the digital era.
References
[1] R. Wu and Z. Yu, “Investigating Users’ Acceptance of the Metaverse with an Extended Technology Acceptance Model,” Int. J. Hum. Comput. Interact., vol. 40, no. 19, pp. 5810–5826, Oct. 2024, doi: 10.1080/10447318.2023.2241295.
[2] R. M. Ryan and E. L. Deci, “Self-Determination Theory and The Facilitation of Intrinsic Motivation, Social Development, and Well-Being,” American Psychologist, vol. 55, no. 1, pp. 68–78, 2000, doi: 10.1037/0003-066X.55.1.68.
[3] Bakker, A. B, Demerouti, and Evangelia, “Article Selected Job Demands–Resources Theory: Taking Stock and Looking Forward,” J. Occup. Health Psychol., vol. 22, no. 3, pp. 273–285, 2017, Accessed: May 11, 2026. [Online]. Available: https://psycnet.apa.org/buy/2016-48454-001
[4] S. L. Albrecht, A. B. Bakker, J. A. Gruman, W. H. Macey, and A. M. Saks, “Employee Engagement, Human Resource Management Practices and Competitive Advantage,” Journal of Organizational Effectiveness: People and Performance, vol. 2, no. 1, pp. 7–35, Mar. 2015, doi: 10.1108/JOEPP-08-2014-0042.
[5] S. Chowdhury, P. Budhwar, and G. Wood, “Generative Artificial Intelligence in Business: Towards a Strategic Human Resource Management Framework,” British Journal of Management, vol. 35, no. 4, pp. 1680–1691, Oct. 2024, doi: 10.1111/1467-8551.12824.
[6] A. Keegan and J. Meijerink, “HRM Practices for Value Creation and Value Capture in Online Labour Platform Ecosystems: Towards an Integrative Perspective,” in A Research Agenda for Strategic Human Resource Management, Edward Elgar Publishing, 2022, pp. 167–194. doi: 10.4337/9781800883802.00017.
[7] A. S. Şimşek and H. Ateş, “The Extended Technology Acceptance Model for Web 2.0 Technologies in Teaching,” Innoeduca. International Journal of Technology and Educational Innovation, vol. 8, no. 2, pp. 165–183, Dec. 2022, doi: 10.24310/innoeduca.2022.v8i2.15413.
[8] S. Hamouche, “Human Resource Management and The COVID-19 Crisis: Implications, Challenges, Opportunities, and Future Organizational Directions,” Journal of Management & Organization, vol. 29, no. 5, pp. 799–814, Sep. 2023, doi: 10.1017/jmo.2021.15.
[9] G. Muridzi and S. Dhliwayo, “A Systematic Literature Review on Employee Engagement as a Strategic Driver of Organisational Performance in the Public Sector,” 2026, pp. 233–248. doi: 10.1007/978-3-032-13384-7_17.
[10] L. C. Wei and C. M. Lee, “The Role of Flexible Work Arrangements in Enhancing Employee Well- Being and Productivity: A Study on SDG 8 Implementation in Tech Companies,” Journal of Business and Social Sciences, vol. 2024, p. 18, Nov. 2024, Accessed: May 11, 2026. [Online]. Available: https://iuojs.intimal.edu.my/index.php/jobss/article/view/601
[11] N. V. Shifrin and J. S. Michel, “Flexible Work Arrangements and Employee Health: A Meta-Analytic Review,” Work Stress, vol. 36, no. 1, pp. 60–85, Jan. 2022, doi: 10.1080/02678373.2021.1936287.
[12] I. Ghozali, Structural Equation Modeling Metode Alternatif dengan Partial Least Squares (PLS). Semarang: Badan Penerbit Universitas Diponegoro, 2014.
[13] A. Jaiswal, C. J. Arun, and A. Varma, Rebooting Employees: Upskilling for Artificial Intelligence in Multinational Corporations. Routledge, 2023. doi: 10.4324/9781003377085-5.
[14] S. Bankins, A. C. Ocampo, M. Marrone, S. L. D. Restubog, and S. E. Woo, “A Multilevel Review of Artificial Intelligence in Organizations: Implications for Organizational Behavior Research and Practice,” J. Organ. Behav., vol. 45, no. 2, pp. 159–182, Feb. 2024, doi: 10.1002/job.2735.
[15] E. Edeh, W.-J. Lo, and J. Khojasteh, “Review of Partial Least Squares Structural Equation Modeling (PLS-SEM) Using R: A Workbook,” Struct. Equ. Modeling, vol. 30, no. 1, pp. 165–167, Jan. 2023, doi: 10.1080/10705511.2022.2108813.
[16] B. P. Contreras, A. N. Hoffmann, and T. A. Slocum, “Ethical Behavior Analysis: Evidence-Based Practice as a Framework for Ethical Decision Making,” Behav. Anal. Pract., vol. 15, no. 2, pp. 619–634, Jun. 2022, doi: 10.1007/s40617-021-00658-5.
[17] A. Austen and K. Piwowar-Sulej, “An Integrative Literature Review on Sustainability-Oriented HRM: How Multilevel Thinking May Help to Extend the State of the Art,” Journal of Change Management, vol. 25, no. 3, pp. 229–259, Jul. 2025, doi: 10.1080/14697017.2025.2526367.
[18] R. Grassinger, M. Landberg, S. Määttä, K. Vasalampi, and S. Bieg, “Interplay of Intrinsic Motivation and Well-Being at School,” Motiv. Emot., vol. 48, no. 2, pp. 147–154, Apr. 2024, doi: 10.1007/s11031-024-10057-2.
[19] A. S. Mastana, “A Primer on Partial Least Squares Structural Equation Modeling (PLS-SEM),” Wisdom Journal of Humanities and Social Sciences, vol. 2, no. 1(January), pp. 62–64, Jan. 2025, Accessed: May 11, 2026. [Online]. Available: https://so19.tci-thaijo.org/index.php/WJHS/article/view/1373
