The Role of AI in Enhancing HRM Practices A Comparative Study Across Industries
DOI:
https://doi.org/10.62207/yqwrzp72Keywords:
Artificial Intelligence, Human Resource Management, Employee Engagement, Productivity, Industrial, SystematicAbstract
Application of artificial intelligence (AI) in Human Resource Management (HRM) is increasingly important in various industries to increase employee engagement and productivity. However, the impact of AI in HRM varies based on the specific characteristics of each industry, such as technology, manufacturing, services, and healthcare, demanding a targeted approach. This research aims to provide a comprehensive analysis of the role of AI in increasing employee engagement and productivity through a systematic literature review that examines research methods, industry distribution, and contextual factors that influence the effectiveness of AI in HRM. Using PRISMA methodology, a number of studies that met the inclusion and exclusion criteria were selected for analysis. The research results show that AI has a positive impact on employee engagement and productivity, but the impact varies across industries, influenced by organizational culture, skills requirements, and ethical and legal regulations. These findings provide guidance for HRM practitioners in effectively adopting AI according to the unique needs of each sector.
References
Agarwal, A. (2022). Ai adoption by human resource management: a study of its antecedents and impact on hr system effectiveness. Foresight, 25(1), 67-81. https://doi.org/10.1108/fs-10-2021-0199
Alasmri, N. and Basahel, S. (2022). Linking artificial intelligence use to improved decision-making, individual and organizational outcomes. International Business Research, 15(10), 1. https://doi.org/10.5539/ibr.v15n10p1
Alsaif, A. and Aksoy, M. (2023). Ai-hrm: artificial intelligence in human resource management: a literature review. Journal of Computing and Communication, 2(2), 1-7. https://doi.org/10.21608/jocc.2023.307053
Arslan, A., Cooper, C., Khan, Z., Gölgeci, İ., & Ali, I. (2021). Artificial intelligence and human workers interaction at team level: a conceptual assessment of the challenges and potential hrm strategies. International Journal of Manpower, 43(1), 75-88. https://doi.org/10.1108/ijm-01-2021-0052
Belhadi, A., Mani, V., Kamble, S., Khan, S., & Verma, S. (2021). Artificial intelligence-driven innovation for enhancing supply chain resilience and performance under the effect of supply chain dynamism: an empirical investigation. Annals of Operations Research, 333(2-3), 627-652. https://doi.org/10.1007/s10479-021-03956-x
Braganza, A., Chen, W., Canhoto, A., & Sap, S. (2021). Productive employment and decent work: the impact of ai adoption on psychological contracts, job engagement and employee trust. Journal of Business Research, 131, 485-494. https://doi.org/10.1016/j.jbusres.2020.08.018
Brock, J. and Wangenheim, F. (2019). Demystifying ai: what digital transformation leaders can teach you about realistic artificial intelligence. California Management Review, 61(4), 110-134. https://doi.org/10.1177/1536504219865226
Cahyani, P. and Siswanto, S. (2019). The effect of transformational leadership on employee performance through employee engagement. JMM Unram - Master of Management Journal, 8(2), 203-211. https://doi.org/10.29303/jmm.v8i2.440
Chen, H., Li, L., & Chen, Y. (2020). Explore success factors that impact artificial intelligence adoption on telecom industry in china. Journal of Management Analytics, 8(1), 36-68. https://doi.org/10.1080/23270012.2020.1852895
Chen, Z. (2022). Artificial intelligence-virtual trainer: innovative didactics aimed at personalized training needs. Journal of the Knowledge Economy, 14(2), 2007-2025. https://doi.org/10.1007/s13132-022-00985-0
Fitri, D. (2023). Enhancing employee productivity through technology system ai-based approaches. Proceeding of the International Seminar on Business Economics Social Science and Technology (Isbest), 3(1). https://doi.org/10.33830/isbest.v3i1.1236
Gaye, B., Zhang, D., & Wulamu, A. (2021). Sentiment classification for employees reviews using regression vector- stochastic gradient descent classifier (rv-sgdc). Peerj Computer Science, 7, e712. https://doi.org/10.7717/peerj-cs.712
Harlianto, J. and Rudi, R. (2023). Promote employee experience for higher employee performance. International Journal of Professional Business Review, 8(3), e0827. https://doi.org/10.26668/businessreview/2023.v8i3.827
Hui, G. (2023). Remodeling the traditional fashion industry in the era of industry 4.0. International Journal of Global Optimization and Its Application, 2(3), 165-178. https://doi.org/10.56225/ijgoia.v2i3.259
Joshi, A., Sekar, S., & Das, S. (2023). Decoding employee experiences during pandemic through online employee reviews: insights to organizations. Personnel Review, 53(1), 288-313. https://doi.org/10.1108/pr-07-2022-0478
Kar, S., Kar, A., & Gupta, M. (2021). Modeling drivers and barriers of artificial intelligence adoption: insights from a strategic management perspective. Intelligent Systems in Accounting Finance & Management, 28(4), 217-238. https://doi.org/10.1002/isaf.1503
Kryscynski, D., Reeves, C., Stice-Lusvardi, R., Ulrich, M., & Russell, G. (2017). Analytical abilities and the performance of hr professionals. Human Resource Management, 57(3), 715-738. https://doi.org/10.1002/hrm.21854
Li-jun, W., Zhou, Y., & Zheng, G. (2022). Linking digital hrm practices with hrm effectiveness: the moderate role of hrm capability maturity from the adaptive structuration perspective. Sustainability, 14(2), 1003. https://doi.org/10.3390/su14021003
Malik, A., Budhwar, P., Mohan, H., & Srikanth, N. (2022). Employee experience –the missing link for engaging employees: insights from an mne's ai‐based hr ecosystem. Human Resource Management, 62(1), 97-115. https://doi.org/10.1002/hrm.22133
Malik, N., Tripathi, S., Kar, A., & Gupta, S. (2021). Impact of artificial intelligence on employees working in industry 4.0 led organizations. International Journal of Manpower, 43(2), 334-354. https://doi.org/10.1108/ijm-03-2021-0173
Mantello, P., Ho, T., Nguyen, M., & Vuong, Q. (2021). My boss the computer: a bayesian analysis of socio-demographic and cross-cultural determinants of attitude toward the non-human resource management. SSRN Electronic Journal. https://doi.org/10.2139/ssrn.3772076
Maryati, S., Panjaitan, N., & Wang, L. (2022). Do organizational culture and human resources management practices decrease turnover intention in microfinance company?. Binus Business Review, 13(2), 147-157. https://doi.org/10.21512/bbr.v13i2.8012
MASIH, D. (2023). Enhancing employee efficiency and performance in industry 5.0 organizations through artificial intelligence integration. EEL, 13(4), 300-315. https://doi.org/10.52783/eel.v13i4.589
NAGI, F., Salih, R., Alzubaidi, M., Shah, H., Alam, T., Shah, Z., … & Househ, M. (2023). Applications of artificial intelligence (ai) in medical education: a scoping review.. https://doi.org/10.3233/shti230581
Naldi, S., Alexsander, D., & Purnomo, M. (2021). Interrelatedness between organizational culture and human resource management in the context of corporate entrepreneurship. The Winners, 22(1). https://doi.org/10.21512/tw.v22i1.6996
Nurlia, N. (2023). Ai implementation impact on workforce productivity : the role of ai training and organizational adaptation. Escalate, 1(01), 01-13. https://doi.org/10.61536/escalate.v1i01.6
Paesano, A. (2021). Artificial intelligence and creative activities inside organizational behavior. International Journal of Organizational Analysis, 31(5), 1694-1723. https://doi.org/10.1108/ijoa-09-2020-2421
Pandey, D. (2023). Enhancing productivity: artificial intelligence’s effect on productivity of nepalese large-scale organizations. Asian Journal of Economics Business and Accounting, 23(24), 47-57. https://doi.org/10.9734/ajeba/2023/v23i241186
Rahmadani, V., Schaufeli, W., Ivanova, T., & Osin, E. (2019). Basic psychological need satisfaction mediates the relationship between engaging leadership and work engagement: a cross‐national study. Human Resource Development Quarterly, 30(4), 453-471. https://doi.org/10.1002/hrdq.21366
Rožman, M., Oreški, D., & Tominc, P. (2023). Artificial-intelligence-supported reduction of employees’ workload to increase the company’s performance in today’s vuca environment. Sustainability, 15(6), 5019. https://doi.org/10.3390/su15065019
Rožman, M. (2023). Agility and artificial intelligence adoption: small vs. large enterprises. Naše Gospodarstvo/Our Economy, 69(4), 26-37. https://doi.org/10.2478/ngoe-2023-0021
Sabil, S. (2023). Identification of hrm improvement strategy using artificial intelligence in modern economic development. International Journal of Professional Business Review, 8(6), e01835. https://doi.org/10.26668/businessreview/2023.v8i6.1835
Sekar, S. (2023). Unlocking the voice of employee perspectives: exploring the relevance of online platform reviews on organizational perceptions. Management Decision, 61(11), 3408-3429. https://doi.org/10.1108/md-11-2022-1509
Shah, S. (2023). Building a culture-conscious workforce. International Journal of Culture and Education, 1(3). https://doi.org/10.59600/ijcae.v1i3.15
Sithambaram, R. and Tajudeen, F. (2022). Impact of artificial intelligence in human resource management: a qualitative study in the malaysian context. Asia Pacific Journal of Human Resources, 61(4), 821-844. https://doi.org/10.1111/1744-7941.12356
Song, Z., Gu, Q., & Wang, B. (2019). Creativity-oriented hrm and organizational creativity in china. International Journal of Manpower, 40(5), 834-849. https://doi.org/10.1108/ijm-05-2016-0108
Szajna, A. and Kostrzewski, M. (2022). Ar-ai tools as a response to high employee turnover and shortages in manufacturing during regular, pandemic, and war times. Sustainability, 14(11), 6729. https://doi.org/10.3390/su14116729
Tambe, P., Cappelli, P., & Yakubovich, V. (2019). Artificial intelligence in human resources management: challenges and a path forward. California Management Review, 61(4), 15-42. https://doi.org/10.1177/0008125619867910
Tucker, E. (2020). Driving engagement with the employee experience. Strategic Hr Review, 19(4), 183-187. https://doi.org/10.1108/shr-03-2020-0023
Wang, Q. (2023). The impact of ai on organizational employees: a literature review. Journal of Education Humanities and Social Sciences, 19, 45-53. https://doi.org/10.54097/ehss.v19i.10955
Wijayati, D., Rahman, Z., Fahrullah, A., Rahman, M., Arifah, I., & Kautsar, A. (2022). A study of artificial intelligence on employee performance and work engagement: the moderating role of change leadership. International Journal of Manpower, 43(2), 486-512. https://doi.org/10.1108/ijm-07-2021-0423
Wirtz, J., Patterson, P., Kunz, W., Gruber, T., Lu, V., Paluch, S., … & Martins, A. (2018). Brave new world: service robots in the frontline. Journal of Service Management, 29(5), 907-931. https://doi.org/10.1108/josm-04-2018-0119
Yu, L., Li, Y., & Fan, F. (2023). Employees’ appraisals and trust of artificial intelligences’ transparency and opacity. Behavioral Sciences, 13(4), 344. https://doi.org/10.3390/bs13040344
Zafar, A. (2023). Unleashing ai potential in human resource management (a case study of corporate sector in karachi). Global Journal for Management and Administrative Sciences, 4(1), 65-89. https://doi.org/10.46568/gjmas.v4i1.182
Zhang, B., Liu, L., Cooke, F., Zhou, P., Sun, X., Zhang, S., … & Bai, Y. (2022). The boundary conditions of high-performance work systems–organizational citizenship behavior relationship: a multiple-perspective exploration in the chinese context. Frontiers in Psychology, 12. https://doi.org/10.3389/fpsyg.2021.743457
Zhou, Y. (2023). The dark side of ai-enabled hrm on employees based on ai algorithmic features. Journal of Organizational Change Management, 36(7), 1222-1241. https://doi.org/10.1108/jocm-10-2022-0308
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Copyright (c) 2024 Eko Putro Wibowo, Zakhi Bailatul Nur Avian, Frandy Putra Perdamen Tarigan, Irwan Syah Erlangga, Andy Soenanta (Author)

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