Employee Acceptance of Generative AI in HR Decision-Making: An Extended Technology Acceptance Model Approach

Using Partial Least Squares Structural Equation Modelling, data on the Technology Acceptance Model (TAM) of 350 employees and HR specialists were used to develop an extended model of the TAM in the domain of generative artificial intelligence (AI) as applied in HR decision-making. The most salient finding of the study is that trust in AI is the most significant driver of employee acceptance of AI in HR systems. Other important positive antecedents of employee acceptance are perceived usefulness, organizational support, AI literacy, and perceived ease of use, while perceived risk is a significant barrier to employee acceptance. As evidenced by its 68.4% explanatory power, this integrated model of the TAM has strong explanatory capability and supports the theoretical coherence of extending the TAM into the GenAI-HRM context. Overall, the study provides valuable insights into AI adoption frameworks and offers suggestions for how organizations can achieve responsible AI adoption. In order to achieve the transformative potential of technology through GenAI as a strategic HR capability, it will be necessary to build trust with employees and create literacy through investment, as well as establish a risk mitigation and require institutional endorsement in order to ensure both employee health and well-being and organizational equity. Additionally, future research must also continue examining these dynamics in different contexts over time using longitudinal designs. This will track how the nature of how humans and AI interact is evolving in the contemporary workplace.

Bhattacherjee, A. (2012). Social science research: Principles, methods, and practices (2nd ed.). University of South Florida. 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. The International Journal of Human Resource Management, 33(6), 1065–1097. Carolus, A., Koch, M. J., Straka, S., Latoschik, M. E., & Wienrich, C. (2023). MAILS — Meta AI Literacy Scale: Development and testing of an AI literacy questionnaire based on a comprehensive AI competency model. Computers in Human Behavior: Artificial Humans, 1(1), 100014. Cheng, Y., & Hackett, R. D. (2021). A critical review of algorithms in HRM: Definition, theory, and practice. Human Resource Management Review, 31(1), 100698. Cheng, X., Su, L., Luo, X., Benitez, J., & Cai, S. (2022). The good, the bad, and the ugly: Impact of analytics and artificial intelligence-enabled personal information collection on privacy and participation in ridesharing. European Journal of Information Systems, 31(3), 339–363. Choung, H., David, P., & Ross, A. (2023). Trust in AI and its role in the acceptance of AI technologies. International Journal of Human–Computer Interaction, 39(9), 1727–1739. Chou, S. W., Min, H. T., Chang, Y. C., & Lin, C. T. (2021). Understanding continuance intention of knowledge creation using extended expectation–confirmation theory: An empirical study of Taiwan and China online communities. Behaviour & Information Technology, 29(6), 557–570. Cohen, J. (1992). A power primer. Psychological Bulletin, 112(1), 155–159.