Relation between attitudes towards artificial intelligence and employee well-being in Serbia
DOI:
https://doi.org/10.71159/icemit2512BKeywords:
artificial intelligence, employee wellbeing, regression analysis, SerbiaAbstract
The main aim of this paper is to investigate the relationship between attitude towards artificial intelligence (AI) and employee well-being in Serbia. Since previous studies showed that AI can have different effects not only on workplace behavior, but on other elements of employee life, we used employee well-being (EWB) as a construct that is composed of workplace, psychological, and life well-being. Regarding the methodology, we used a quantitative approach to investigate the proposed relation. The sample of the research contains 266 responses from employees in Serbia, who work in the service sector, mostly in IT, consulting, and banking industries, in privately owned companies. Also, most of the respondents are highly educated, from the northern part of the country, the Autonomous Province of Vojvodina. The questionnaires used in the research were adapted from the AI attitude scale with 4 questions, while the employee well-being scale was adapted from an 18-question scale. Additionally, we added several demographic questions to the final questionnaire. The Spearman’s correlation and multiple regression model were used to investigate the effects of different control and independent variables on employee well-being.
References
Adil, M. S., & Baig, M. (2018). Impact of job demands-resources model on burnout and employee's well-being: Evidence from the pharmaceutical organisations of Karachi. IIMB Management Review, 30(2), 119-133. https://doi.org/10.1016/j.iimb.2018.01.004
Arboh, F., Boateng, F. G., Anokye, R., & Asumeng, M. (2024). From fear to empowerment: The impact of employees’ AI awareness on workplace well-being – a new insight from the JD-R model. International Journal of Healthcare Management. Advance online publication. https://pubmed.ncbi.nlm.nih.gov/39875341/
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
Brynjolfsson, E., & McAfee, A. (2017). Machine, platform, crowd: Harnessing our digital future. W. W. Norton & Company.
Chuang, Y. T., Chiang, H. L., & Lin, A. P. (2025). Insights from the Job Demands–Resources Model: AI's dual impact on employees’ work and life well-being. International Journal of Information Management, 83, 102887. https://doi.org/10.1016/j.ijinfomgt.2025.102887
Cohen, J. (1988). Statistical power analysis for the behavioral sciences (2nd ed.). Lawrence Erlbaum Associates.
Daugherty, P. R., & Wilson, H. J. (2024). Human+ Machine, Updated and Expanded: Reimagining Work in the Age of AI. Harvard Business Press.
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.
García-Madurga, M. Á., Gil-Lacruz, A. I., Saz-Gil, I., & Gil-Lacruz, M. (2024). The role of artificial intelligence in improving workplace well-being: A systematic review. Businesses, 4(3), 389-410. https://doi.org/10.3390/businesses4030024
Giuntella, O., Konig, J., & Stella, L. (2025). Artificial intelligence and the wellbeing of workers. Scientific Reports, 15(1), 20087. https://doi.org/10.1038/s41598-025-98241-3
Grassini, S. (2023). Development and validation of the AI attitude scale (AIAS-4): a brief measure of general attitude toward artificial intelligence. Frontiers in Psychology, 14, 1191628. https://doi.org/10.3389/fpsyg.2023.1191628
Guest, D. E. (2017). Human resource management and employee well‐being: Towards a new analytic framework. Human Resource Management Journal, 27(1), 22-38. https://doi.org/10.1111/1748-8583.12139
Gupta, R. (2024). Impact of artificial intelligence (AI) on human resource management (HRM). International Journal for Multidisciplinary Research, 6(3), 25-30.
Haenlein, M., & Kaplan, A. (2019). A brief history of artificial intelligence: On the past, present, and future of artificial intelligence. California Management Review, 61(4), 5-14. https://doi.org/10.1177/0008125619864925
Han, M., Hu, E., Zhao, J., & Shan, H. (2025). High performance work systems and employee performance: the roles of employee well-being and workplace friendship. Human Resource Development International, 28(1), 36-55. https://doi.org/10.1080/13678868.2023.2268488
Jin, G., Jiang, J., & Liao, H. (2024). The work affective well-being under the impact of AI. Scientific Reports, 14(1), 25483. https://doi.org/10.1038/s41598-024-75113-w
Kaplan, A., & Haenlein, M. (2019). Siri, Siri, in my hand: Who’s the fairest in the land? On the interpretations, illustrations, and implications of artificial intelligence. Business Horizons, 62(1), 15-25. https://doi.org/10.1016/j.bushor.2018.08.004
Lane, M., Williams, M., & Broecke, S. (2023). The impact of AI on the workplace: Main findings from the OECD AI surveys of employers and workers. OECD Social, Employment and Migration Working Papers, No. 288, OECD Publishing, Paris, https://doi.org/10.1787/ea0a0fe1-en.
Mäkikangas, A., Kinnunen, U., Feldt, T., & Schaufeli, W. (2016). The longitudinal development of employee well-being: A systematic review. Work & stress, 30(1), 46-70. https://doi.org/10.1080/02678373.2015.1126870
Malik, A., Budhwar, P., & Kazmi, B. A. (2023). Artificial intelligence (AI)-assisted HRM: Towards an extended strategic framework. Human Resource Management Review, 33(1), 100940. https://doi.org/10.1016/j.hrmr.2022.100940
Mendy, J., Jain, A., & Thomas, A. (2025). Artificial intelligence in the workplace–challenges, opportunities and HRM framework: a critical review and research agenda for change. Journal of Managerial Psychology, 40(5), 517-538. https://doi.org/10.1108/JMP-05-2024-0388
Soori, M., Arezoo, B., & Dastres, R. (2023). Artificial intelligence, machine learning and deep learning in advanced robotics, a review. Cognitive Robotics, 3, 54-70. https://doi.org/10.1016/j.cogr.2023.04.001
Soulami, M., Benchekroun, S., & Galiulina, A. (2024). Exploring how AI adoption in the workplace affects employees: a bibliometric and systematic review. Frontiers in Artificial Intelligence, 7, 1473872. https://doi.org/10.3389/frai.2024.1473872
Valtonen, A., Saunila, M., Ukko, J., Treves, L., & Ritala, P. (2025). AI and employee wellbeing in the workplace: An empirical study. Journal of Business Research, 199, 115584. https://doi.org/10.1016/j.jbusres.2025.115584
Wamba-Taguimdje, S. L., Fosso Wamba, S., Kala Kamdjoug, J. R., & Tchatchouang Wanko, C. E. (2020). Influence of artificial intelligence (AI) on firm performance: the business value of AI-based transformation projects. Business Process Management Journal, 26(7), 1893-1924. https://doi.org/10.1108/BPMJ-10-2019-0411
Wright, T. A. (2006). To be or not to be [happy]: The role of employee well-being. Academy of Management Perspectives, 20(3), 118-120. https://doi.org/10.5465/amp.2006.21903486
Wright, T. A., & Huang, C. C. (2012). The many benefits of employee well‐being in organizational research. Journal of Organizational Behavior, 33(8), 1188-1192. https://doi.org/10.1002/job.1828
Zheng, X., Zhu, W., Zhao, H., & Zhang, C. H. I. (2015). Employee well‐being in organizations: Theoretical model, scale development, and cross‐cultural validation. Journal of Organizational Behavior, 36(5), 621-644. https://doi.org/10.1002/job.1990
Zirar, A., Ali, S. I., & Islam, N. (2023). Worker and workplace Artificial Intelligence (AI) coexistence: Emerging themes and research agenda. Technovation, 124, 102747. https://doi.org/10.1016/j.technovation.2023.102747
Downloads
Published
How to Cite
Conference Proceedings Volume
Section
License
Copyright (c) 2025 International Scientific Conference on Economics, Management and Information Technologies

This work is licensed under a Creative Commons Attribution 4.0 International License.