Artificial Intelligence–Based Human Resource Management and Employee Performance
The Mediating Role of Digital Skills and the Moderating Effect of Technology Acceptance in Indonesian Manufacturing Firms
DOI:
https://doi.org/10.56442/ijble.v7i1.1328Keywords:
Artificial Intelligence–Based HRM; Employee Performance; Digital Skills; Technology Acceptance; Manufacturing IndustryAbstract
This study examines the effect of artificial intelligence–based human resource management (AI-based HRM) on employee performance with digital skills as a mediating variable and technology acceptance as a moderating variable. The research adopts a quantitative explanatory approach using a cross-sectional survey design. Data were collected from 300 permanent employees of manufacturing firms in Indonesia and analyzed using Partial Least Squares–Structural Equation Modeling (PLS-SEM). The results indicate that AI-based HRM has a significant positive effect on employee performance. Digital skills partially mediate the relationship between AI-based HRM and employee performance, while technology acceptance positively moderates the effect of AI-based HRM on digital skills. This study contributes to the AI–HRM literature by integrating the Resource-Based View and the Technology Acceptance Model and offers practical insights for manufacturing firms seeking to align AI adoption with employee digital capability development.
References
Barney, J. (1991). Firm resources and sustained competitive advantage. Journal of Management, 17(1), 99–120.
Bondarouk, T., & Brewster, C. (2022). Conceptualizing artificial intelligence in human resource management. Human Resource Management Review, 32(3), 100861.
Brougham, D., & Haar, J. (2018). Smart technology, artificial intelligence, robotics, and algorithms (STARA): Employees’ perceptions of our future workplace. Personnel Review, 47(2), 239–257.
Brynjolfsson, E., & McAfee, A. (2017). The business of artificial intelligence. Harvard Business Review, 95(4), 3–11.
Budhwar, P., & Debrah, Y. (2019). Human resource management in developing countries. Routledge.
Caligiuri, P., De Cieri, H., Minbaeva, D., Verbeke, A., & Zimmermann, A. (2020). International HRM insights for navigating the COVID-19 pandemic. Journal of International Business Studies, 51(1), 1–12.
Collins, C. J., et al. (2021). Artificial intelligence in people management: Current challenges and future directions. Academy of Management Perspectives, 35(4), 610–628.
Davis, F. D. (1989). Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS Quarterly, 13(3), 319–340.
Di Vaio, A., Palladino, R., Hassan, R., & Escobar, O. (2023). Artificial intelligence and business analytics: A systematic literature review. Technological Forecasting and Social Change, 186, 122170.
Farndale, E., Paauwe, J., & Boselie, P. (2021). An exploratory study of governance in HRM systems. Human Resource Management Journal, 31(1), 131–150.
Jarrahi, M. H. (2022). Artificial intelligence and the future of work: Human–AI symbiosis in organizational decision making. Journal of Business Research, 142, 618–629.
Kellogg, K. C., Valentine, M. A., & Christin, A. (2020). Algorithms at work: The new contested terrain of control. Administrative Science Quarterly, 65(2), 366–410.
Khatri, N., et al. (2021). Strategic human resource management and firm performance. International Journal of Human Resource Management, 32(15), 3127–3154.
Malik, A., Budhwar, P., & Srikanth, N. (2022). Artificial intelligence–driven human resource management: Toward a research agenda. Human Resource Management Journal, 32(2), 286–304.
Marler, J. H., & Boudreau, J. W. (2021). An evidence-based review of HR analytics. Human Resource Management Review, 31(2), 100748.
Meijerink, J., Bondarouk, T., & Lepak, D. (2021). Digital HRM and employee experience. Human Resource Management Review, 31(4), 100774.
Nguyen, T. T., Malik, A., & Budhwar, P. (2023). Digital skills and employee performance. Sustainability, 15(4), 2874.
Pfeffer, J. (1998). The human equation: Building profits by putting people first. Harvard Business School Press.
Schlicker, N., et al. (2023). Trust in artificial intelligence and employee behavior. Computers in Human Behavior, 139, 107495.
Sousa, M. J., & Rocha, Á. (2019). Digital learning and performance. Journal of Business Research, 94, 409–417.
Strohmeier, S., & Piazza, F. (2015). Artificial intelligence techniques in human resource management. Human Resource Management Review, 25(2), 169–181.
Tambe, P., Cappelli, P., & Yakubovich, V. (2019). Artificial intelligence in human resources management. California Management Review, 61(4), 15–42.
Teece, D. J. (2018). Business models and dynamic capabilities. Long Range Planning, 51(1), 40–49.
Van Laar, E., Van Deursen, A., Van Dijk, J., & De Haan, J. (2020). The digital skills framework. Computers in Human Behavior, 102, 103–120.
Venkatesh, V., & Davis, F. D. (2000). A theoretical extension of the technology acceptance model. Management Science, 46(2), 186–204.
Venkatesh, V., Morris, M., Davis, G., & Davis, F. (2003). User acceptance of information technology: Toward a unified view. MIS Quarterly, 27(3), 425–478.
Vrontis, D., Christofi, M., Pereira, V., Tarba, S., Makrides, A., & Trichina, E. (2022). Artificial intelligence, robotics, and HRM. Journal of Business Research, 140, 99–109.
Wright, P. M., & McMahan, G. C. (2011). Exploring human capital. Journal of Management, 37(4), 1024–1050.
Zhang, Y., & Chen, J. (2024). AI-enabled HRM systems and employee outcomes. International Journal of Manpower, 45(1), 77–95.
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