AI-Driven HRM and Employee Performance: The Mediating Role of HR Agility in Industrial Companies
DOI:
https://doi.org/10.61255/jeemba.v4i1.861Keywords:
Artificial Intelligence, Human Resource Management, HR Agility, Employee Performance, PLS-SEMAbstract
Purpose - This study examines the effect of AI-driven human resource management on employee performance and investigates the mediating role of HR agility in industrial companies in West Java, Indonesia.
Design/methodology/approach – A quantitative explanatory design was employed using a cross-sectional survey of 214 employees from industrial firms. Data were collected through structured questionnaires and analyzed using Structural Equation Modeling with Partial Least Squares (SEM-PLS) via SmartPLS to test both direct and indirect relationships among the constructs.
Findings – The results indicate that AI-driven human resource management has a positive and significant effect on HR agility and employee performance. HR agility also significantly improves employee performance. Furthermore, HR agility partially mediates the relationship between AI-driven human resource management and employee performance, indicating that the effectiveness of AI-based HR practices depends on the organization’s ability to develop adaptive and responsive HR systems.
Limitations – This study is limited by its cross-sectional design, reliance on self-reported data, and focus on industrial companies in a single regional context, which may restrict generalizability.
Originality/value – This study contributes to digital HRM and dynamic capability literature by positioning HR agility as a key mechanism through which AI-driven HR practices enhance employee performance in an emerging economy context.
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References
Arslan, A., Golgeci, I., Khan, Z., Al-Tabbaa, O., & Hurmelinna-Laukkanen, P. (2022). Artificial intelligence-driven innovation in industrial firms: The role of knowledge sourcing and firm capabilities. Technovation, 114, 102420. https://doi.org/10.1016/j.technovation.2021.102420
Bondarouk, T., & Brewster, C. (2016). Conceptualising the future of HRM and technology research. The International Journal of Human Resource Management, 27(21), 2652–2671. https://doi.org/10.1080/09585192.2016.1232296
Brougham, D., & Haar, J. (2018). Smart technology, artificial intelligence, robotics, and algorithms (STARA): Employees’ perceptions of our future workplace. Journal of Management & Organization, 24(2), 239–257. https://doi.org/10.1017/jmo.2016.55
Budhwar, P., Malik, A., De Silva, M. 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. https://doi.org/10.1080/09585192.2022.2035161
Chatterjee, S., Chaudhuri, R., Vrontis, D., & Thrassou, A. (2021). The impact of artificial intelligence on organizational effectiveness: The role of human resource management. The International Journal of Human Resource Management, 32(19), 4183–4210. https://doi.org/10.1080/09585192.2020.1871393
Doz, Y. L., & Kosonen, M. (2010). Embedding strategic agility: A leadership agenda for accelerating business model renewal. Long Range Planning, 43(2–3), 370–382. https://doi.org/10.1016/j.lrp.2009.07.006
Felipe, C. M., Roldán, J. L., & Leal-Rodríguez, A. L. (2017). Impact of organizational culture values on organizational agility. Sustainability, 9(12), 2354. https://doi.org/10.3390/su9122354
Hair, J. F., Hult, G. T. M., Ringle, C. M., & Sarstedt, M. (2022). A primer on partial least squares structural equation modeling (PLS-SEM) (3rd ed.). Sage.
Hair, J. F., Risher, J. J., Sarstedt, M., & Ringle, C. M. (2019). When to use and how to report the results of PLS-SEM. European Business Review, 31(1), 2–24. https://doi.org/10.1108/EBR-11-2018-0203
Jöhnk, J., Weißert, M., & Wyrtki, K. (2021). Ready or not, AI comes: An interview study of organizational AI readiness factors. Business & Information Systems Engineering, 63(1), 5–20. https://doi.org/10.1007/s12599-020-00676-7
Johnson, R. D., Lukaszewski, K. M., & Stone, D. L. (2020). The evolution of the field of human resource information systems: Co-evolution of technology and HR processes. Human Resource Management Review, 30(4), 100718. https://doi.org/10.1016/j.hrmr.2019.100718
Kellogg, K. C., Valentine, M. A., & Christin, A. (2020). Algorithms at work: The new contested terrain of control. Academy of Management Annals, 14(1), 366–410. https://doi.org/10.5465/annals.2018.0174
Khin, S., & Ho, T. C. F. (2019). Digital technology, digital capability and organizational performance. International Journal of Innovation Science, 11(2), 177–195. https://doi.org/10.1108/IJIS-08-2018-0083
Koopmans, L., Bernaards, C. M., Hildebrandt, V. H., van Buuren, S., van der Beek, A. J., & de Vet, H. C. W. (2014). Improving the individual work performance questionnaire using rasch analysis. Journal of Applied Measurement, 15(2), 160–175.
Lengnick-Hall, C. A., Beck, T. E., & Lengnick-Hall, M. L. (2011). Developing a capacity for organizational resilience through strategic human resource management. Human Resource Management Review, 21(3), 243–255. https://doi.org/10.1016/j.hrmr.2010.07.001
Margherita, A. (2022). Human resources analytics: A systematization of research topics and directions for future research. Human Resource Management Review, 32(2), 100795. https://doi.org/10.1016/j.hrmr.2020.100795
Marler, J. H., & Boudreau, J. W. (2017). An evidence-based review of HR analytics. The International Journal of Human Resource Management, 28(1), 3–26. https://doi.org/10.1080/09585192.2016.1244699
Meijerink, J., Bondarouk, T., & Lepak, D. P. (2021). Digital HRM: A new stage of e-HRM and the role of human resource management in digital transformation. Journal of Management Studies, 58(5), 1310–1338. https://doi.org/10.1111/joms.12681
Minbaeva, D. (2021). Disrupted HR? Human resource management in the digital age. Human Resource Management Journal, 31(1), 1–8. https://doi.org/10.1111/1748-8583.12313
Muduli, A. (2016). Exploring the facilitators and mediators of workforce agility: An empirical study. Management Research Review, 39(12), 1567–1586. https://doi.org/10.1108/MRR-10-2015-0236
Pan, S. L., Cui, M., Qian, J., & Li, M. (2022). Digital transformation and organizational agility: The mediating role of strategic flexibility. Management Decision, 60(13), 1–18. https://doi.org/10.1108/MD-09-2021-1259
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/amr.2018.0184
Sherehiy, B., & Karwowski, W. (2014). The relationship between work organization and workforce agility in small manufacturing enterprises. International Journal of Industrial Ergonomics, 44(3), 466–473. https://doi.org/10.1016/j.ergon.2013.11.002
Stone, D. L., Deadrick, D. L., Lukaszewski, K. M., & Johnson, R. (2015). The influence of technology on the future of human resource management. Human Resource Management Review, 25(2), 216–231. https://doi.org/10.1016/j.hrmr.2015.01.002
Strohmeier, S. (2020). Digital human resource management: A conceptual clarification. German Journal of Human Resource Management, 34(3), 345–365. https://doi.org/10.1177/2397002220921131
Tallon, P. P., & Pinsonneault, A. (2011). Competing perspectives on the link between strategic information technology alignment and organizational agility: Insights from a mediation model. MIS Quarterly, 35(2), 463–486. https://doi.org/10.2307/23044052
Teece, D. J., Peteraf, M., & Leih, S. (2016). Dynamic capabilities and organizational agility: Risk, uncertainty, and strategy in the innovation economy. California Management Review, 58(4), 13–35. https://doi.org/10.1525/cmr.2016.58.4.13
Ulrich, D., & Dulebohn, J. H. (2015). Are we there yet? What’s next for HR? Human Resource Management, 54(2), 187–204. https://doi.org/10.1002/hrm.21609
Upadhyay, A. K., & Khandelwal, K. (2018). Applying artificial intelligence: Implications for recruitment. Strategic HR Review, 17(5), 255–258. https://doi.org/10.1108/SHR-07-2018-0051
Van den Broek, E., Sergeeva, A., & Huysman, M. (2021). When the machine meets the expert: An ethnography of developing AI for hiring. MIS Quarterly, 45(3), 1557–1580. https://doi.org/10.25300/MISQ/2021/15435
Vrontis, D., Christofi, M., Pereira, V., Tarba, S., Makrides, A., & Trichina, E. (2022). Artificial intelligence, robotics, advanced technologies and human resource management: A systematic review. The International Journal of Human Resource Management, 33(6), 1237–1266. https://doi.org/10.1080/09585192.2020.1871398
Wamba-Taguimdje, S.-L., Fosso Wamba, S., Kala Kamdjoug, J. R., & Tchatchouang Wanko, C.-E. (2020). Influence of artificial intelligence on firm performance: The business value of AI-based transformation projects. Business Process Management Journal, 26(7), 1893–1924. https://doi.org/10.1108/BPMJ-10-2019-0411
Wiblen, S., Marler, J. H., & Boudreau, J. W. (2024). Human resource analytics and the changing nature of HR decision-making. Human Resource Management Journal, 34(1), 45–63. https://doi.org/10.1111/1748-8583.12501
Zang, Y., Ye, X., Wang, M., & Arar, K. (2023). Artificial intelligence adoption and employee outcomes: A systematic review and future research agenda. Technological Forecasting and Social Change, 196, 122833. https://doi.org/10.1016/j.techfore.2023.122833
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