ISSN: 2222-6990
Open access
Purpose: This study investigates the impact of transformational leadership (TL) on the task performance (TP) of elderly care workers in Beijing, examining the mediating role of work engagement (WE) and the moderating effect of attitudes towards artificial intelligence (AAI). Design/methodology/approach: A quantitative design was adopted, using purposive sampling of 268 elderly care workers in Beijing, China. Data were analysed using SPSS and SmartPLS, with 5,000 resampling bootstraps employed within the Job Demands-Resources (JD-R) model to test the hypothesised relationships. Findings: TL positively influenced TP through WE, indicating a full mediation effect, as the direct relationship between TL and TP was not significant. AAI significantly moderated the relationship between WE and TP, with favourable AAI strengthening this link. These findings support the motivational processes proposed by the JD-R model and demonstrate that AAI, as a personal resource, can enhance the connection between engagement and performance. Research limitations/implications: This study is confined to elderly care institutions in Beijing, which may limit the generalisability of the findings. The cross-sectional design restricts causal inference, while self-reported data may introduce common method bias. Future research may adopt longitudinal or multi-source methodologies. Practical implications: Research findings indicate that while care service providers strengthen TL and employee engagement, they should prioritise fostering positive AAI. Systematic training, organisational support, and open communication can further enhance engagement, thereby improving AI application and TP. Originality/value: Within the context of elderly care, research examining the incorporation of AAI into the JD-R model remains scarce. This study contributes to understanding how TL and AAI jointly shape employees’ WE and TP.
Bakker, A. B., & Demerouti, E. (2017). Job demands–resources theory: Taking stock and looking forward. Journal of Occupational Health Psychology, 22(3), 273–285. https://doi.org/10.1037/ocp0000056
Bakker, A. B., & de Vries, J. D. (2021). Job Demands–Resources theory and self-regulation: New explanations and remedies for job burnout. Anxiety, Stress, & Coping, 34(1), 1–21. https://doi.org/10.1080/10615806.2020.1797695
Baruch, Y., & Holtom, B. C. (2008). Survey response rate levels and trends in organizational research. Human Relations, 61(8), 1139–1160. https://doi.org/10.1177/0018726708094863
Bass, B. M., & Avolio, B. J. (1994). Improving organizational effectiveness through transformational leadership. Thousand Oaks, CA: Sage Publications.
Bass, B. M., & Avolio, B. J. (1995). MLQ Multifactor Leadership Questionnaire: Technical report. Redwood City, CA: Mind Garden.
Bass, B. M., & Riggio, R. E. (2006). Transformational leadership (2nd ed.). Mahwah, NJ: Lawrence Erlbaum Associates.
Borman, W. C., & Motowidlo, S. M. (1993). Expanding the criterion domain to include elements of contextual performance. In N. Schmitt & W. C. Borman (Eds.), Personnel selection in organizations (pp. 71–98). San Francisco, CA: Jossey-Bass.
Brislin, R. W. (1970). Back-translation for cross-cultural research. Journal of Cross-Cultural Psychology, 1(3), 185–216. https://doi.org/10.1177/135910457000100301
Chen, Y., Wu, F., Zhang, W., Xing, W., Zhu, Z., Huang, Q., & Yuan, C. (2025). Perspectives on AI-driven nursing science among nursing professionals from China: A qualitative study. Nursing Reports, 15(6), 218. https://doi.org/10.3390/nursrep15060218
Chiminelli-Tomás, V., Tutte-Vallarino, V., Ferreira-Umpiérrez, A., Hernández-Morante, J. J., & Reche-García, C. (2025). Impact of nursing practice environments in work engagement and burnout: A systematic review. Healthcare, 13(7), 779. https://doi.org/10.3390/healthcare13070779
Chuang, L.-M., & Huang, S.-H. (2025). AI-supported healthcare technology resistance and behavioral intention: A serial mediation empirical study on the JD-R model and employee engagement. Systems, 13(4), 268. https://doi.org/10.3390/systems13040268
Corbeanu, A., & Iliescu, D. (2023). The link between work engagement and job performance: A meta-analysis. Journal of Personnel Psychology, 22(3), 111–122. https://doi.org/10.1027/1866-5888/a000316
Demerouti, E., Bakker, A. B., Nachreiner, F., & Schaufeli, W. B. (2001). The job demands-resources model of burnout. Journal of Applied Psychology, 86(3), 499–512. https://doi.org/10.1037/0021-9010.86.3.499
Faul, F., Erdfelder, E., Buchner, A., & Lang, A.-G. (2009). Statistical power analyses using G*Power 3.1: Tests for correlation and regression analyses. Behavior Research Methods, 41(4), 1149–1160. https://doi.org/10.3758/BRM.41.4.1149
Gnambs, T., Stein, J.-P., Appel, M., Griese, F., & Zinn, S. (2025). An economical measure of attitudes towards artificial intelligence in work, healthcare, and education (ATTARI-WHE). Computers in Human Behavior: Artificial Humans, 3, 100106. https://doi.org/10.1016/j.chbah.2025.100106
Hair, J. F., Hult, G. T. M., Ringle, C. M., & Sarstedt, M. (2017). A primer on partial least squares structural equation modeling (PLS-SEM) (2nd ed.). SAGE Publications.
Henseler, J., Ringle, C. M., & Sarstedt, M. (2015). A new criterion for assessing discriminant validity in variance-based structural equation modeling. Journal of the Academy of Marketing Science, 43, 115-135. doi: 10.1007/s11747-014-0403-8
Hung, J. (2023). Smart elderly care services in China: Challenges, progress, and policy development. Sustainability, 15(1), 178. https://doi.org/10.3390/su15010178
Judge, T. A., & Piccolo, R. F. (2004). Transformational and transactional leadership: A meta-analytic test of their relative validity. Journal of Applied Psychology, 89(5), 755–768. https://doi.org/10.1037/0021-9010.89.5.755
Juyumaya, J., & Torres, J. P. (2023). Effects of transformational leadership and work engagement on managers’ creative performance. Baltic Journal of Management, 18(1), 34–53. https://doi.org/10.1108/BJM-11-2021-0449
Koopmans, L., Bernaards, C. M., Hildebrandt, V. H., Schaufeli, W. B., de Vet, H. C. W., & van der Beek, A. J. (2013). Development of an individual work performance questionnaire. International Journal of Productivity and Performance Management, 62(1), 6–28. https://doi.org/10.1108/17410401311285273
Krijgsheld, M., Tummers, L. G., & Scheepers, F. E. (2022). Job performance in healthcare: A systematic review. BMC Health Services Research, 22(1), 149. https://doi.org/10.1186/s12913-021-07357-5
Manresa, A., Sammour, A., Mas-Machuca, M., Chen, W., & Botchie, D. (2025). Humanizing GenAI at work: bridging the gap between technological innovation and employee engagement. Journal of Managerial Psychology, 40(5), 472-492. https://doi.org/10.1108/JMP-05-2024-0356
McManus, H., Dundon, T., & Lavelle, J. (2025). “Workin for a livin”: Mediating the role of perceived support, work engagement, and organizational citizenship behavior in the hospitality sector. International Journal of Hospitality Management, 126, 103983. https://doi.org/10.1016/j.ijhm.2024.103983
Melkas, H., Hennala, L., Pekkarinen, S., & Kyrki, V. (2020). Impacts of robot implementation on care personnel and clients in elderly-care institutions. International Journal of Medical Informatics, 134, 104041. https://doi.org/10.1016/j.ijmedinf.2019.104041
Ministry of Human Resources and Social Security of the People’s Republic of China, & Ministry of Civil Affairs of the People’s Republic of China. (2019). National occupational skill standards: Elderly care worker (2019 edition). Beijing, China.
Na, E., Jung, Y., & Kim, S. (2023). How do care service managers and workers perceive care robot adoption in elderly care facilities? Technological Forecasting and Social Change, 187, 122250. https://doi.org/10.1016/j.techfore.2022.122250
National Bureau of Statistics of China. (2024, February 29). Statistical communiqué of the People’s Republic of China on the 2023 national economic and social development. Retrieved from http://www.stats.gov.cn/sj/zxfb/202402/t20240228_1947915.html
National Health Commission of the People’s Republic of China. (2021). Action plan for the development of the smart health and elderly care industry (2021–2025). Beijing, China: National Health Commission. Retrieved from https://www.nhc.gov.cn/lljks/c100158/202110/a871dfd3e1fd45b89e9af1f40e44ba12/files/1733127377368_21866.pdf
Nguyen, T.-M., & Malik, A. (2022). Impact of knowledge sharing on employees’ service quality: The moderating role of artificial intelligence. International Marketing Review, 39(3), 482–508. https://doi.org/10.1108/IMR-02-2021-0078
Office of Beijing Municipal Working Committee on Ageing, Beijing Association on Ageing, & China Philanthropy Research Institute, Beijing Normal University. (2024, October 11). Beijing elderly career development report (2023). Beijing Municipal Civil Affairs Bureau. Retrieved from https://mzj.beijing.gov.cn/attach/0/e579da4186d440e7890d97711cc87d91.pdf
Platania, S., Morando, M., Gruttadauria, S. V., & Koopmans, L. (2024). The individual work performance questionnaire: Psychometric properties of the Italian version. European Journal of Investigation in Health, Psychology and Education, 14(1), 49–63. https://doi.org/10.3390/ejihpe14010004
Podsakoff, P. M., MacKenzie, S. B., Lee, J. Y., & Podsakoff, N. P. (2003). Common method biases in behavioral research: A critical review of the literature and recommended remedies. Journal of Applied Psychology, 88(5), 879–903. https://doi.org/10.1037/0021-9010.88.5.879
Saks, A. M. (2006). Antecedents and consequences of employee engagement. Journal of Managerial Psychology, 21(7), 600–619. https://doi.org/10.1108/02683940610690169
Schaufeli, W. B., Salanova, M., González-Romá, V., & Bakker, A. B. (2002). The measurement of engagement and burnout: A two sample confirmatory factor analytic approach. Journal of Happiness Studies, 3(1), 71–92. https://doi.org/10.1023/A:1015630930326
Schaufeli, W. B. (2017). Applying the Job Demands-Resources model: A “how to” guide to measuring and tackling work engagement and burnout. Organizational Dynamics, 46(2), 120–132. https://doi.org/10.1016/j.orgdyn.2017.04.008
Taddeo, M., & Floridi, L. (2021). How AI can be a force for good: An ethical framework to harness the potential of AI while keeping humans in control. In B. C. Stahl, V. Yaghmaei, & M. Jirotka (Eds.), The ethics of artificial intelligence (pp. 9–28). Cham, Switzerland: Springer. https://doi.org/10.1007/978-3-030-81907-1_7
Tariq, K., Tahir, H., Malik, U., Shafiq, A., & Khan, M. (2025). Attitudes and readiness to adopt artificial intelligence among healthcare practitioners in Pakistan’s resource-limited settings. BMC Health Services Research, 25, 1031. https://doi.org/10.1186/s12913-025-13207-5
Topol, E. J. (2019). High-performance medicine: The convergence of human and artificial intelligence. Nature Medicine, 25(1), 44–56. https://doi.org/10.1038/s41591-018-0300-7
Venkatesh, V., Morris, M. G., Davis, G. B., & Davis, F. D. (2003). User acceptance of information technology: Toward a unified view. MIS Quarterly, 27(3), 425–478. https://doi.org/10.2307/30036540
Wang, J., Wang, Z., Alam, M., Murad, M., Gul, F., & Gill, S. A. (2022). The impact of transformational leadership on affective organizational commitment and job performance: The mediating role of employee engagement. Frontiers in Psychology, 13, 831060. https://doi.org/10.3389/fpsyg.2022.831060
Wang, W., Chen, L., Xiong, M., & Wang, Y. (2023). Accelerating AI adoption with responsible AI signals and employee engagement mechanisms in health care. Information Systems Frontiers, 25, 2239–2256. https://doi.org/10.1007/s10796-021-10154-4
Wong, K. L. Y., Hung, L., Wong, J., Park, J., Alfares, H., Zhao, Y., Mousavinejad, A., Soni, A., & Zhao, H. (2024). Adoption of artificial intelligence-enabled robots in long-term care homes by health care providers: Scoping review. JMIR Aging, 7(1), e55257. https://doi.org/10.2196/55257
World Bank. (2021). World Bank support to aging countries: An independent evaluation. Independent Evaluation Group. Washington, DC: World Bank. Retrieved from https://documents1.worldbank.org/curated/en/672701626792774286/World-Bank-Support-to-Aging-Countries-An-Independent-Evaluation
Xanthopoulou, D., Bakker, A. B., Demerouti, E., & Schaufeli, W. B. (2007). The role of personal resources in the job demands–resources model. International Journal of Stress Management, 14(2), 121–141. https://doi.org/10.1037/1072-5245.14.2.121
Xanthopoulou, D., Bakker, A. B., Demerouti, E., & Schaufeli, W. B. (2009). Work engagement and financial returns: A diary study on the role of job and personal resources. Journal of Occupational and Organizational Psychology, 82(1), 183–200. https://doi.org/10.1348/096317908X285633
Xinhua. (2025, January 10). China accelerates elderly care reforms as population ages. The State Council of the People’s Republic of China. https://english.www.gov.cn/news/202501/10/content_WS67805ca4c6d0868f4e8eea64.html
Yuan, X., & Gao, Y. (2020). Demographic transition and economic miracles in China: An analysis based on demographic perspective. International Journal of Economic Policy Studies, 14(1), 25–45. https://doi.org/10.1007/s42495-019-00030-0
Zhang, S., & Zhang, Z. (2023). Human resource dilemma and intelligent choice of elderly care service in China. Journal of Xi’an Jiaotong University (Social Sciences), 43(3), 152–163. https://doi.org/10.15896/j.xjtuskxb.202306013
Zhao, Y., & Li, J. (2024). Opportunities and challenges of integrating artificial intelligence in China’s elderly care services. Scientific Reports, 14, 9254. https://doi.org/10.1038/s41598-024-60067-w
Yang, W., & Yusof, R. (2026). The Impact of Transformational Leadership on the Task Performance of Elderly Care Workers: The Mediating Role of Work Engagement and the Moderating Role of Attitudes toward Artificial Intelligence. International Journal of Academic Research in Business and Social Sciences, 16(3), 1050–1068.
Copyright: © 2026 The Author(s)
Published by HRMARS (www.hrmars.com)
This article is published under the Creative Commons Attribution (CC BY 4.0) license. Anyone may reproduce, distribute, translate and create derivative works of this article (for both commercial and non-commercial purposes), subject to full attribution to the original publication and authors. The full terms of this license may be seen at: http://creativecommons.org/licences/by/4.0/legalcode