PURPOSE - This study examines how Emotion Artificial Intelligence (Emotion AI) influences the quality of Human Resource (HR) decision-making through the mediating role of Emotionally Aware AI Decision Making (EA-AIDM). EA-AIDM is introduced as a socio-technical construct that reflects AI systems, capacity to detect, interpret, and respond to human emotions in HR contexts.

METHODOLOGY - Using a quantitative design, the study applied Partial Least Squares Structural Equation Modeling (PLS-SEM) to analyze responses from 122 HR professionals representing technology, manufacturing, and financial sectors. Participants were selected through purposive and stratified sampling, with inclusion criteria such as managerial roles and experience using AI-driven HR systems. Analyses included reliability, validity, factor loadings, and mediation testing.

FINDING - Results reveal that AI adoption has no direct impact on HR decision-making (β = 0.178, p = 0.085) but exerts a significant indirect influence through EA-AIDM (β = 0.364, p < 0.001), indicating partial mediation. Among EA-AIDM indicators, context awareness and risk aversion showed the strongest effects, while emotion detection was weakest (mean = 2.69). These findings underscore the importance of designing emotionally aware AI that balances analytical precision with empathy to achieve ethical and effective HR decisions

Bankins, S., Formosa, P., Griep, Y., & Richards, D. (2022). AI Decision Making with Dignity? Contrasting Workers’ Justice Perceptions of Human and AI Decision Making in a Human Resource Management Context. Information Systems Frontiers, 24(3), 857–875. https://doi.org/10.1007/s10796-021-10223-8

Barcellini, F. (2022). The Design of “Future Work” in Industrial Contexts: Lessons Learned from Worker–Technology Cooperation and Work Transformation Management. In Managing Future Challenges for Safety (pp. 75–83). Springer International Publishing. https://doi.org/10.1007/978-3-031-07805-7_10

Bilan, S., Šuleř, P., Skrynnyk, O., Krajňáková, E., & Vasilyeva, T. (2022). Systematic Bibliometric Review Of Artificial In℡Ligence Technology In Organizational Management, Development, Change And Culture. Business: Theory and Practice, 23(1), 1–13. https://doi.org/10.3846/btp.2022.13204

Burnett, J. R., & Lisk, T. C. (2019). The Future of Employee Engagement: Real-Time Monitoring and Digital Tools for Engaging a Workforce. International Studies of Management & Organization, 49(1), 108–119. https://doi.org/10.1080/00208825.2019.1565097

Charlwood, A., & Guenole, N. (2022). Can HR adapt to the paradoxes of artificial intelligence? Human Resource Management Journal, 32(4), 729–742. https://doi.org/10.1111/1748-8583.12433

Koopmans, L., Bernaards, C. M., Hildebrandt, V. H., Schaufeli, W. B., de Vet Henrica, C. W., & van der Beek, A. J. (2011). Conceptual Frameworks of Individual Work Performance: A Systematic Review. Journal of Occupational & Environmental Medicine, 53(8), 856–866. https://doi.org/10.1097/jom.0b013e318226a763

Malik, A., Budhwar, P., Patel, C., & Srikanth, N. R. (2022). May the Bots Be with You! Delivering HR Cost-Effectiveness and Individualised Employee Experiences in an MNE. The International Journal of Human Resource Management, 33(6), 1148–1178. https://doi.org/10.1080/09585192.2020.1859582

McCarthy, J. (1955). A Proposal for the Dartmouth Summer Research Project on Artificial Intelligence.

Mesquita, B., & Frijda, N. H. (1992). Cultural variations in emotions: A review. Psychological Bulletin, 112(2), 179–204. https://doi.org/10.1037//0033-2909.112.2.179

Picard, R. W. (1997). Affective Computing. The MIT Press. https://doi.org/10.7551/mitpress/1140.001.0001

Pillai, R., & Sivathanu, B. (2020). Adoption of artificial intelligence (AI) for talent acquisition in IT/ITeS organizations. Benchmarking: An International Journal, 27(9), 2599–2629. https://doi.org/10.1108/bij-04-2020-0186

Rožman, M., Oreški, D., & Tominc, P. (2022). Integrating artificial intelligence into a talent management model to increase the work engagement and performance of enterprises. Frontiers in Psychology, 13. https://doi.org/10.3389/fpsyg.2022.1014434

Strohmeier, S. (Ed.). (2022). Handbook of Research on Artificial Intelligence in Human Resource Management. In Figures. Edward Elgar Publishing. https://doi.org/10.4337/9781839107535

Su, Y.-S., Suen, H.-Y., & Hung, K.-E. (2021). Predicting behavioral competencies automatically from facial expressions in real-time video-recorded interviews. Journal of Real-Time Image Processing, 18(4), 1011–1021. https://doi.org/10.1007/s11554-021-01071-5

Tiwari, S. (2023). Atlas of AI: power, politics and the planetary costs of artificial intelligence Atlas of AI: power, politics and the planetary costs of artificial intelligence , by Kate Crawford, New Haven, CT, Yale University Press Books, 2021, 336 pp., 28 (hardback), ISBN 9780300209570: By Kate Crawford, New Haven, CT, Yale University Press Books, 2021, 336 pp., 28 (hardback), ISBN 9780300209570. Journal of Cyber Policy, 8(1), 131–133. https://doi.org/10.1080/23738871.2023.2237981

Wissemann, A. K., Pit, S. W., Serafin, P., & Gebhardt, H. (2022). Strategic Guidance and Technological Solutions for Human Resources Management to Sustain an Aging Workforce: Review of International Standards, Research, and Use Cases. JMIR Human Factors, 9(3), e27250. https://doi.org/10.2196/27250