Efficiency Overload: A Dual-Mechanism Analysis of AI Adoption and Employee Wellbeing in Islamic Microfinance Institutions
Keywords:
AI Adoption, Employee Well-being, Workplace Safety, Techno-Overload, Task Optimization, Islamic Microfinance Institutions (BMT)
Abstract
The adoption of artificial intelligence (AI) in Baitul Maal wat Tamwil (BMT) institutions is expected to improve work efficiency, yet its impact on employees’ psychological wellbeing remains inconclusive. This study examines the effect of AI adoption on employee wellbeing through three mediating mechanisms by integrating the Job Demands-Resources (JD-R) and Conservation of Resources (COR) theories. Data were collected from 338 BMT employees in Purwokerto and Purbalingga using purposive sampling and analyzed with Partial Least Squares Structural Equation Modeling (PLS-SEM) in SmartPLS. The results indicate that AI adoption significantly increases task optimization, workplace safety, and techno-overload, but has no direct effect on employee wellbeing. Contrary to the hypotheses, task optimization negatively affected wellbeing by reducing human interaction and the perceived meaningfulness of work, whereas techno-overload positively influenced wellbeing because employees perceived it as eustress that encouraged competence development. Workplace safety showed no significant effect, as it was regarded as a basic organizational standard rather than a source of additional wellbeing. These findings suggest that AI influences employee wellbeing indirectly through employees’ psychological interpretations of technological change. The study extends JD-R and COR theories and highlights the importance of balancing digital efficiency with human interaction and psychosocial support during digital transformation.
References
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Aust, B., Møller, J. L., Nordentoft, M., Frydendall, K. B., Bengtsen, E., Jensen, A. B., Garde, A. H., Kompier, M., Semmer, N., Rugulies, R., & Jaspers, S. Ø. (2023). How effective are organizational-level interventions in improving the psychosocial work environment, health, and retention of workers? A systematic overview of systematic reviews. Scandinavian Journal of Work, Environment and Health, 49(5), 315–329. https://doi.org/10.5271/sjweh.4097
Bankins, S., Formosa, P., & Griep, Y. (2024). AI surveillance and employee wellbeing: A paradox of control and autonomy. Human Resource Management Review, 34(1), 100967.
Budhwar, P., Malik, A., De Silva, M. T. T., & Thevisuthan, P. (2022). Artificial intelligence–challenges and opportunities for international HRM: a review and research agenda. International Journal of Human Resource Management, 33(6), 1065–1097. https://doi.org/10.1080/09585192.2022.2035161
Califf, C. B., Sarker, S., & Sarker, S. (2023). The bright and dark sides of technostress: A mixed-methods study involving healthcare it1. MIS Quarterly: Management Information Systems, 44(2), 809–856. https://doi.org/10.25300/MISQ/2020/14818
Chang, P. C., Zhang, W., Cai, Q., & Guo, H. (2024). Does AI-Driven Technostress Promote or Hinder Employees’ Artificial Intelligence Adoption Intention? A Moderated Mediation Model of Affective Reactions and Technical Self-Efficacy. Psychology Research and Behavior Management, 17(February), 413–427. https://doi.org/10.2147/PRBM.S441444
Chowdhury, S., Dey, P., Joel-Edgar, S., Bhattacharya, S., Rodriguez-Espindola, O., Abadie, A., & Truong, L. (2023). Unlocking the value of artificial intelligence in human resource management through AI capability framework. Human Resource Management Review, 33(1). https://doi.org/10.1016/j.hrmr.2022.100899
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(January), 102887. https://doi.org/10.1016/j.ijinfomgt.2025.102887
Ciccarelli, M. (2024). AI-enabled safety systems and employee outcomes. Journal of Safety Research, 45, 45–57.
Demerouti, E., & Bakker, A. B. (2023). Job Demands-Resources Theory : Taking Stock and Looking Forward Job Demands – Resources Theory : Taking Stock and Looking Forward. Journal of Occupational Health Psychology, 22(September 2023), 273–285.
Dhakal, S. P., & Mahmood, M. N. (2025). A scientometric analysis of three decades of research on workplace psychosocial hazards: Implications for policy and practice. Journal of Safety Research, 93(January 2024), 79–89. https://doi.org/10.1016/j.jsr.2025.02.011
Diener, E., Oishi, S., & Tay, L. (2018). Advances in subjective well-being research. Nature Human Behaviour, 2(4), 253–260. https://doi.org/10.1038/s41562-018-0307-6
Dima, J., Gilbert, M. H., Dextras-Gauthier, J., & Giraud, L. (2024). The effects of artificial intelligence on human resource activities and the roles of the human resource triad: opportunities and challenges. Frontiers in Psychology, 15(June), 1–15. https://doi.org/10.3389/fpsyg.2024.1360401
Dwivedi, Y. K., Kshetri, N., Hughes, L., Slade, E. L., Jeyaraj, A., Kar, A. K., Baabdullah, A. M., Koohang, A., Raghavan, V., Ahuja, M., Albanna, H., Albashrawi, M. A., Al-Busaidi, A. S., Balakrishnan, J., Barlette, Y., Basu, S., Bose, I., Brooks, L., Buhalis, D., … Wright, R. (2023). “So what if ChatGPT wrote it?” Multidisciplinary perspectives on opportunities, challenges and implications of generative conversational AI for research, practice and policy. International Journal of Information Management, 71. https://doi.org/10.1016/j.ijinfomgt.2023.102642
Enholm, I. M., Papagiannidis, E., Mikalef, P., & Krogstie, J. (2022). Artificial intelligence and business value: A literature review. Information Systems Frontiers, 24(5), 1709–1734.
Falco, A., Girardi, D., & Dal Corso, L. (2023). Safety climate and employee wellbeing in digital work environments. Safety Science, 158, 105978. https://doi.org/https://doi.org/10.1016/j.ssci.2022.105978
Gagné, M., Parker, S. K., Griffin, M. A., Dunlop, P. D., Knight, C., Klonek, F. E., & Parent-Rocheleau, X. (2022). Understanding and shaping the future of work with self-determination theory. Nature Reviews Psychology, 1(7), 378–392. https://doi.org/10.1038/s44159-022-00056-w
Giuntella, O., Konig, J., & Stella, L. (2025). Artificial intelligence and the wellbeing of workers. Scientific Reports, 15(1), 1–14. https://doi.org/10.1038/s41598-025-98241-3
Hair, J. F., Hult, G. T. M., Ringle, C. M., & Sarstedt, M. (2021). A primer on partial least squares structural equation modeling (PLS-SEM).
Hannigan, T. R. (2024). Artificial inteligence and the future of work: A systematic review. https://doi.org/https://doi.org/10.5465/annals.2022.0027
Hansson, H. J., Härgestam, L., & Martínez, S. B. (2026). AI Adoption and Employee Wellbeing in Swedish Organizations : (May).
Hasan, R., & Ali, M. (2023). Digital transformation in Islamic financial institutions. Journal of Islamic Accounting and Business Research, 14(5), 789–805.
Hillebrand, L., Raisch, S., & Schad, J. (2025). Managing With Artificial Intelligence: an Integrative Framework. Academy of Management Annals, 19(1), 343–375. https://doi.org/10.5465/annals.2022.0072
Hobfoll, S. E., Halbesleben, J., Neveu, J. P., & Westman, M. (2023). Conservation of resources in the organizational context: The reality of resources and their consequences. Annual Review of Organizational Psychology and Organizational Behavior, 5(November 2022), 103–128. https://doi.org/10.1146/annurev-orgpsych-032117-104640
Ip, E., Srivastava, R., Lentz, L., Jasinoski, S., & Anderson, G. S. (2025). Antecedents of Workplace Psychological Safety in Public Safety and Frontline Healthcare: A Scoping Review. International Journal of Environmental Research and Public Health, 22(6). https://doi.org/10.3390/ijerph22060820
Jaiswal, A., Arun, C. J., & Varma, A. (2023). Rebooting employees: upskilling for artificial intelligence in multinational corporations. International Journal of Human Resource Management, 33(6), 1179–1208. https://doi.org/10.1080/09585192.2021.1891114
Javanmardi, S., Rappelt, L., Zangenberg, S., Heinke, L., Baumgart, C., Niederer, D., & Freiwald, J. (2025). Effectiveness of workplace health promotion programs for industrial workers: a systematic review. BMC Public Health, 25(1). https://doi.org/10.1186/s12889-025-21365-8
Jaya, U. A., Priyana, I., Kusnara, H. P., Somantri, B., & Wijaya, Z. R. (2026). AI Adoption , Work Pressure , and Psychological Resilience : Effects on Job Satisfaction and Employee Performance. 14(1). https://doi.org/10.37641/jimkes.v14i1.4482
Kim, B. J., Kim, M. J., & Lee, J. (2025). The dark side of artificial intelligence adoption: linking artificial intelligence adoption to employee depression via psychological safety and ethical leadership. Humanities and Social Sciences Communications, 12(1). https://doi.org/10.1057/s41599-025-05040-2
La Torre, G., Manai, M. V., Meucci, S., Lucente, A., Picerno, A., Ammirati, S., & De Sio, S. (2026). Artificial intelligence and occupational health and safety: a systematic review. Journal of Public Health (Germany). https://doi.org/10.1007/s10389-026-02738-8
Likert, R. (1932). “Technique for the Measurement of Attitudes, A.” Encyclopedia of Research Design, 140, 1–55. https://doi.org/10.4135/9781412961288.n454
Lițan, D. E. (2025). The Impact of Technostress Generated by Artificial Intelligence on the Quality of Life: The Mediating Role of Positive and Negative Affect. Behavioral Sciences, 15(4). https://doi.org/10.3390/bs15040552
Liu, N.-C., Wang, Y.-C. W., & Lin, Y.-T. (2024). Fintech adoption and employee adaptation. Technological Forecasting and Social Change, 195, 122789.
Noy, S., & Zhang, W. (2023). Experimental evidence on the productivity effects of generative artificial intelligence. Science, 381(6654), 187–192. https://doi.org/10.1126/science.adh2586
Parent-Rocheleau, X., & Parker, S. K. (2022). Algorithms as work designers: How algorithmic management influences the design of jobs. Human Resource Management Review, 32(3), 100838. https://doi.org/10.1016/j.hrmr.2021.100838
Parker, S. K., & Grote, G. (2023). Automation, Algorithms, and Beyond: Why Work Design Matters More Than Ever in a Digital World. Applied Psychology, 71(4), 1171–1204. https://doi.org/10.1111/apps.12241
Pereira, V., Hadjielias, E., Christofi, M., & Vrontis, D. (2023). A systematic literature review on the impact of artificial intelligence on workplace outcomes: A multi-process perspective. Human Resource Management Review, 33(1), 100857. https://doi.org/10.1016/j.hrmr.2021.100857
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
Ragu-Nathan. (2023). Technostress and employee outcomes: A meta-analysis. MIS Quarterly: Management Information Systems, 47(2), 789–820.
Rahayu, S. (2023). The impact of artificial intelligence on education: Opportunities and challenges. Jurnal Educatio FKIP UNMA, 9(4), 2132–2140. https://doi.org/https://doi.org/10.31949/educatio.v9i4.6110
Raisch, S., & Krakowski, S. (2021). Artificial Intelligence and Management: The Automation-Augmentation Paradox. Academy of Management Review, 46(1), 192–210. https://doi.org/10.5465/2018.0072
Robbins & Judge. (2017). Essentials of organizational behavior. Pearson Education, 3.
Roczniewska, M., Smoktunowicz, E., Calcagni, C. C., von Thiele Schwarz, U., Hasson, H., & Richter, A. (2021). Beyond the Individual: A Systematic Review of the Effects of Unit-Level Demands and Resources on Employee Productivity, Health, and Well-Being. Journal of Occupational Health Psychology, 27(2), 240–257. https://doi.org/10.1037/ocp0000311
Routray, R., Choudhary, P., & Sinha, V. (2025). Intelligent technology and enhanced well-being: can artificial intelligence mitigate digital overload? Future Business Journal, 11(1). https://doi.org/10.1186/s43093-025-00691-8
Schulte, P., Saute, S., Pandalai, S., Tiesman, H., & Chosewood, L. (2024). An urgent call to address work‐related psychosocial hazards and improve worker well‐being (pp. 499–514). https://doi.org/10.1002/ajim.23583
Sekaran & Bougie, R. (2016). Research methods for business: A skill-building approach.
Shaik, AS, Alshibani, SM, Mishra, S., Papa, A., & Cuomo, MT (2023). Does learning from innovation failure enhance innovation performance? A quantitative investigation of small businesses. Technovation, 127, 102818.
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(November), 1–16. https://doi.org/10.3389/frai.2024.1473872
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Published
2026-08-17
How to Cite
Yogi Andrean Maulana, Fatmah Bagis, Rahayu, T. S. M., & Kharismasyah, A. Y. (2026). Efficiency Overload: A Dual-Mechanism Analysis of AI Adoption and Employee Wellbeing in Islamic Microfinance Institutions. International Journal of Education, Vocational and Social Science , 5(03), 7737-7767. https://doi.org/10.63922/ijevss.v5i03.5922
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