Volume 07 Issue 12 December 2024
1Wui San Taslim, 2Titik Rosnani, 3Rizky Fauzan
1,2Faculty of Economics and Business, Tanjungpura University, Pontianak, Indonesia
3Faculty of Management, Tanjungpura University, Pontianak, Indonesia
1ORCID https://orcid.org/0009-0006-2805-8548
2ORCID https://orcid.org/0000-0001-5376-6155
3ORCID http://orcid.org/0000-0002-4983-3658
DOI : https://doi.org/10.47191/ijsshr/v7-i12-46Google Scholar Download Pdf
ABSTRACT
This study investigates the intricate relationship between work automation, employee performance, and human resource decision-making in the context of increasing artificial intelligence (AI) adoption. Drawing on social systems theory, contingency theory, and cognitive load theory, we propose a conceptual framework that explores the direct and indirect effects of work automation on HR decision-making, with employee performance as a mediating factor. A quantitative survey of 122 managerial-level employees from technology, manufacturing, and financial sectors across 18 countries was conducted. Using Partial Least Squares Structural Equation Modelling, we tested hypotheses examining the relationships among variables. Results reveal that work automation indirectly influences HR decision-making through employee performance. The study introduces the concept of Emotionally Aware AI Decision Making (EA-AIDM) as a critical factor in leveraging AI for effective HR decision-making. EA-AIDM emerges as a significant mediator between work automation and HR decision-making, offering a novel approach to integrating AI technology with human factor considerations in HR practices. Our findings suggest that organisations should adopt a holistic approach to work automation implementation in HRM, balancing technological advancements with employee performance considerations. This research contributes to the growing body of literature on AI in HRM by providing empirical evidence on the mediating role of EA-AIDM and offers practical insights for organisations navigating the evolving landscape of work in the age of AI.
KEYWORDS:Work Automation, Employee Performance, HR Decision Making, Artificial Intelligence, Emotionally Aware AI Decision Making
REFERENCES1) Abdolmaleki, A., Movahedi, M., Lau, N., & Reis, L. P. (2013). A distributed cooperative reinforcement learning method for decision making in fire brigade teams. Lecture Notes in Artificial Intelligence, 7500, 248.
2) Al-Alawi, A. I., Naureen, M., Alalawi, E. I., & Naser Al-Hadad, A. A. (2021). The role of artificial intelligence in recruitment process decision-making. In 2021 International Conference on Decision Aid Sciences and Application (DASA) (pp. 197-203). IEEE.
3) 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.
4) Chandra, M. (2016). Artificial intelligence and the future of knowledge workers. In 2016 5th International Conference on Reliability, Infocom Technologies and Optimization (ICRITO) (p. 44). IEEE.
5) Couger, J. D., & McIntyre, S. C. (1988). Causes of motivational problems among AI managers. In Proceedings of the ACM SIGCPR Conference on Management of Information Systems Personnel (pp. 72-77). ACM.
6) Creswell, J. W. (2015). A concise introduction to mixed methods research. SAGE Publications.
7) Haga, A., Tomida, Y., Yamashita, A., & Matsubayashi, K. (2019). Analysis of internal social media for in-house job training aimed at improving the efficiency of human-resource development. In Proceedings - 2019 International Conference on Technologies and Applications of Artificial Intelligence (TAAI). IEEE.
8) Hair, J. F., Hult, G. T. M., Ringle, C. M., & Sarstedt, M. (2017). A primer on partial least squares structural equation modeling (PLS-SEM) (2nd ed.). Sage Publications.
9) Hampton, A. J., & DeFalco, J. A. (2022). The frontlines of artificial intelligence ethics: Human-centric perspectives on technologies advance (1st ed.). Routledge.
10) Jain, D., Makkar, S., Jindal, L., & Gupta, M. (2020). Uncovering employee job satisfaction using machine learning: A case study of Om Logistics Ltd.
11) Jankovic, M., Cardinal, J. S. L., & Bocquet, J. C. (2015). Context management in collaborative decision making in complex design projects. International Journal of Product Development, 20(4), 286.
12) Jetha, A., Shamaee, A., Bonaccio, S., Gignac, M. A. M., Tucker, L. B., Tompa, E., Bültmann, U., Norman, C. D., Banks, C. G., & Smith, P. M. (2021). Fragmentation in the future of work: A horizon scan examining the impact of the changing nature of work on workers experiencing vulnerability. American Journal of Industrial Medicine, 64(8), 649-666.
13) Kaplan, J. (2015). Humans need not apply: A guide to wealth and work in the age of artificial intelligence. Yale University Press.
14) Koopmans, L., Bernaards, C. M., Hildebrandt, V. H., Schaufeli, W. B., de Vet, H. C. W., & van der Beek, A. J. (2011). Conceptual frameworks of individual work performance: A systematic review. Journal of Occupational and Environmental Medicine, 53(8), 856-866.
15) Lawrence, P. R., & Lorsch, J. W. (1967). Differentiation and integration in complex organizations. Administrative Science Quarterly, 12(1), 1.
16) Li, J., He, R., & Wang, T. (2022). A data-driven decision-making framework for personnel selection based on LGBWM and IFNs. Applied Soft Computing, 126, Article 109100.
17) Luhmann, N. (1984). Social systems. Stanford University Press.
18) Ma, H., & Wang, J. (2021). Application of artificial intelligence in intelligent decision-making of human resource allocation. In The 2020 International Conference on Machine Learning and Big Data Analytics for IoT Security and Privacy: SPIoT-2020 (Vol. 1282, p. 207). Springer.
19) 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.
20) Marvin, G., Jackson, M., & Alam, M. G. R. (2021). A machine learning approach for employee retention prediction. In TENSYMP 2021 - 2021 IEEE Region 10 Symposium. IEEE.
21) Mekala, M., Viswanathan, P., Srinivasu, N., & Varma, G. (2019). Accurate decision-making system for mining environment using Li-Fi 5G technology over IoT framework. In 2019 International Conference on Contemporary Computing and Informatics (IC3I) (pp. 74-79). IEEE.
22) Nguyen, L. A., & Park, M. (2022). Artificial intelligence in staffing. Vision.
23) Pourkhodabakhsh, N., Mamoudan, M. M., & Bozorgi-Amiri, A. (2023). Effective machine learning, meta-heuristic algorithms and multi-criteria decision making to minimizing human resource turnover. Applied Intelligence, 53(12), 16309-16331.
24) 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, Article 1014434.
25) Strich, F., Mayer, A-S., & Fiedler, M. (2021). What do I do in a world of artificial intelligence? Investigating the impact of substitutive decision-making AI systems on employees' professional role identity. Journal of the Association for Information Systems, 22(2), 304-324.
26) 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.
27) Sweller, J. (1988). Cognitive load during problem solving: Effects on learning. Cognitive Science, 12(2), 257-285.
28) Tong, S., Jia, N., Luo, X., & Fang, Z. (2021). The Janus face of artificial intelligence feedback: Deployment versus disclosure effects on employee performance. Strategic Management Journal, 42(9), 1600-1631.