Force field analysis on the adoption of artificial intelligence technology in human resource management

Yıl 2024, Cilt: 26 Sayı: Özel Sayı, 35 - 52, 21.10.2024

Nermin Kişi , Mehmet Akif Özer

https://doi.org/10.33707/akuiibfd.1406096

Öz

Technological innovations are recognized as a critical determinant of survival in a constantly changing business world with increasing globalization. Therefore, businesses of the modern era tend to utilize new technologies in their business processes. Artificial intelligence is one of the most discussed and widely used technologies today. The transition of businesses from their existing systems to these advanced technology systems requires a comprehensive planning process. In this respect, it is an important research topic to explore the key factors in adopting artificial intelligence-based applications in businesses. As the transition to the use of artificial intelligence in Human Resource Management (HRM) is a particularly complex and challenging process, more studies are needed on this topic. The aim of this study is to determine the driving and restraining forces affecting the adoption of artificial intelligence-based HRM practices. In order to achieve this aim, a systematic literature review method in line with the Force Field Analysis Model has been used in the study. The findings have revealed that the main driving forces encouraging the adoption of artificial intelligence in HRM are organizational preparedness and perceived benefits. Moreover, negative reactions to technological change, concerns regarding data privacy and security, algorithmic bias errors, and the lack of emotional intelligence have been identified as restraining forces in the adoption of artificial intelligence in HRM.

Anahtar Kelimeler

Organizational change, Human resource management, Artificial intelligence, Force field analysis

İnsan kaynakları yönetiminde yapay zekâ teknolojisinin benimsenmesi üzerine güç alanı analizi

Öz

Artan küreselleşme ile birlikte sürekli değişen iş dünyasında, teknolojik yenilikler hayatta kalmanın kritik bir belirleyicisi olarak kabul edilmektedir. Bu nedenle, modern çağın işletmeleri iş süreçlerinde yeni teknolojileri kullanmaya yönelmektedir. Günümüzde en çok tartışılan ve en yaygın kullanılan teknolojilerinden biri yapay zekâdır. İşletmelerin mevcut sistemlerinden bu ileri teknoloji sistemlerine geçişi kapsamlı bir planlama süreci gerektirir. Bu açıdan, işletmelerde yapay zekâya dayalı uygulamaları benimsemede temel faktörleri keşfetmek önemli bir araştırma konusudur. Özellikle, İnsan Kaynakları Yönetimi (İKY)’nde yapay zekâ kullanımına geçiş karmaşık ve zorlu bir süreç olduğundan, bu konuda daha fazla araştırma yapılmasına ihtiyaç duyulmaktadır. Bu çalışmanın amacı, yapay zekâya dayalı İKY uygulamalarının benimsenmesini etkileyen itici ve kısıtlayıcı güçleri belirlemektir. Bu amaca ulaşmak için, çalışmada Güç Alanı Analizi Modeli çerçevesinde sistematik literatür taraması yöntemi kullanılmıştır. Bulgular İKY’de yapay zekânın benimsenmesini teşvik eden temel itici güçlerin örgütsel hazırbulunuşluk ve algılanan faydalar olduğunu ortaya koymuştur. Ayrıca, teknolojik değişime karşı olumsuz tepkiler, veri gizliliği ve güvenliğiyle ilgili endişeler, algoritmik önyargıdan kaynaklanan hatalar ve duygusal zekâ eksikliği İKY’de yapay zekânın benimsenmesinde karşılaşılan kısıtlayıcı güçler olarak tanımlanmıştır.

Anahtar Kelimeler

Örgütsel değişim, İnsan kaynakları yönetimi, Güç alanı analizi, Yapay zekâ

Kaynakça

• Agarwal, A. (2023). 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
• Ahmed, O. (2018). Artificial intelligence in HR. International Journal of Research and Analytical Reviews, 5(4), 971-978. https://doi.org/10.31221/osf.io/cfwvm
• Al-Alawi, A. I., Sanosi, S. K. & Althawadi, A. H. (2021). Effects of technology and digital innovations on the human resources ecosystem. 2021 International Conference on Decision Aid Sciences and Application, 7th- 8th December 2021, Online, 502-510, https://doi.org/10.1109/DASA53625.2021.9682279.
• Almarashda, H., Baba, I., Ramli, A., Memon, A. & Rahman, I. (2021). Human resource management and technology development in artificial intelligence adoption in the UAE energy sector. Journal of Applied Engineering Sciences, 11(2) 69-76. https://doi.org/10.2478/jaes-2021-0010
• Arslan, A., Cooper, C., Khan, Z., Golgeci, I. & Ali, I. (2022). 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
• Aydın, E. & Turan, M. (2023). An AI-based shortlisting model for sustainability of human resource management. Sustainability, 15(3), 1-15. https://doi.org/10.3390/su15032737
• Baldegger, R., Caon, M. & Sadiku, K. (2020). Correlation between entrepreneurial orientation and implementation of AI in human resources management. Technology Innovation Management Review, 10(4), 72-79. http://doi.org/10.22215/timreview/1348
• Bankins, S. (2021). The ethical use of artificial intelligence in human resource management: A decision-making framework. Ethics and Information Technology, 23, 841-854. https://doi.org/10.1007/s10676-021-09619-6
• 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, 857-875. https://doi.org/10.1007/s10796-021-10223-8
• Bhatt, P. (2022). AI adoption in the hiring process-important criteria and extent of AI adoption. Foresight, 25(1), 144-163. https://doi.org/10.1108/FS-07-2021-0144
• Böhmer, N. & Schinnenburg, H. (2023). Critical exploration of AI-driven HRM to build up organizational capabilities. Employee Relations, 45(5), 1057-1082. https://doi.org/10.1108/ER-04-2022-0202
• 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. https://doi.org/10.1080/09585192.2022.2035161
• Capatina, A., Bleoju, G., Matos, F. & Vairinhos, V. (2017). Leveraging intellectual capital through Lewin’s Force Field Analysis: The case of software development companies. Journal of Innovation & Knowledge, 2(3), 125-133. https://doi.org/10.1016/j.jik.2016.07.001
• Chilunjika, A., Intauno, K. & Chilunjika, S. R. (2022). Artificial intelligence and public sector human resource management in South Africa: Opportunities, challenges and prospects. SA Journal of Human Resource Management, 20, 1-12. https://doi.org/10.4102/sajhrm.v20i0.1972
• Chowdhury, S., Dey, P., Joel-Edgar, S., Bhattacharya, S., Rodriguez-Espindola, O., Abadie, A. & Truong, L. (2023). Unlocking the value of artificial intelligence in human resource management through AI capability framework. Human Resource Management Review, 33(1), 1-21. https://doi.org/10.1016/j.hrmr.2022.100899
• Eubanks, B. (2022). Artificial intelligence for HR: Use AI to support and develop a successful workforce (2nd. Ed.). Kogan Page, London, N.Y.
• Goswami, M., Jain, S., Alam, T., Deifalla, A. F, Ragab, A. E. & Khargotra, R. (2023). Exploring the antecedents of AI adoption for effective HRM practices in the Indian pharmaceutical sector. Frontiers in Pharmacology, 14, 1-14. https://doi.org/10.3389/fphar.2023.1215706
• Harwood, T. G. & Garry, T. (2003). An overview of content analysis. The Marketing Review, 3, 479-498.
• Hmoud, B. (2021). The adoption of artificial intelligence in human resource management. Forum Scientiae Oeconomia, 9(1), 105-118. https://doi.org/10.23762/fso_Vol9_no1_7
• Hossin, M. S., Ulfy, M. A., Ali, I. & Karim, M. W. (2021). Challenges in adopting artificial intelligence (AI) in HRM practices: A study on Bangladesh perspective. International Fellowship Journal of Interdisciplinary Research, 1(1), 66-73. https://doi.org/10.5281/zenodo.4480245
• Hussain, S. M., Ahmad, N., Fazal, F. & Menegaki, A. N. (2023). The impact of female directorship on firm performance: A systematic literature review. Review of Managerial Science. https://doi.org/10.1007/s11846-023-00677-2
• IBM (2021, October). The business case for AI in HR: Insights and tips on getting started. https://www.ibm.com/downloads/cas/A5YLEPBR
• Islam, M., Mamun, A. A., Afrin, S., Ali Quaosar, G. M. A. & Uddin, Md. A. (2022). Technology adoption and human resource management practices: The use of artificial intelligence for recruitment in Bangladesh. South Asian Journal of Human Resources Management, 9(2), 324-349. https://doi.org/10.1177/23220937221122329
• Jaiswal, A., Arun, C. J. & Varma, A. (2022). Rebooting employees: Upskilling for artificial intelligence in multinational corporations. The International Journal of Human Resource Management, 33(6), 1179-1208. https://doi.org/10.1080/09585192.2021.1891114
• Johnson, B. A. M., Coggburn, J. D. & Llorens, J. J. (2022). Artificial intelligence and public human resource management: Questions for research and practice. Public Personnel Management, 51(4), 538-562. https://doi.org/10.1177/00910260221126498
• 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, 5-20. https://doi.org/10.1007/s12599-020-00676-7
• Konovalova, V., Mitrofanova, E., Mitrofanova, A. & Gevorgyan, R. (2022). The impact of artificial intelligence on human resources management strategy: Opportunities for the Humanisation and Risks. Wisdom, 2(1), 88-96. https://doi.org/10.24234/wisdom.v2i1.763
• Kshetri, N. (2021). Evolving uses of artificial intelligence in human resource management in emerging economies in the Global South: Some preliminary evidence. Management Research Review, 44(7), 970-990. https://doi.org/10.1108/MRR-03-2020-0168
• Lawler, J. J., & Elliot, R. (1996). Artificial intelligence in HRM: An experimental study of an expert system. Journal of Management, 22(1), 85-111. https://doi.org/10.1177/014920639602200104
• Levi, D. & Lawn, M. (1993). The driving and restraining forces which affect technological innovation in organizations. The Journal of High Technology Management Research, 4(2), 225-240. https://doi.org/10.1016/1047-8310(93)90006-2
• Lewin, K. (1951). Field theory in social science: Selected theoretical papers. Harper & Row, New York, NY.
• Mak, A. H. N. & Chang, R. C. Y. (2019). The driving and restraining forces for environmental strategy adoption in the hotel industry: A force field analysis approach. Tourism Management, 73, 48-60. https://doi.org/10.1016/j.tourman.2019.01.012
• Malik, A., Budhwar, P. & Kazmi, B. A. (2023). Artificial intelligence (AI)-assisted HRM: Towards an extended strategic framework. Human Resource Management Review, 33(1), 1-16. https://doi.org/10.1016/j.hrmr.2022.100940
• Mathew, S., Oswal, N. & Ateeq, K. (2021). Artificial intelligence (AI): Bringing a new revolution in human resource management (HRM). Grenze International Journal of Engineering and Technology, Jan Issue, 211-218.
• McKinsey & Company (2022, December). The state of AI in 2022- and a half decade in review. https://www.mckinsey.com/~/media/mckinsey/business%20functions/quantumblack/our%20insights/the%20state%20of%20ai%20in%202022%20and%20a%20half%20decade%20in%20review/the-state-of-ai-in-2022-and-a-half-decade-in-review.pdf?shouldIndex=false
• Mehrotra, S. & Khanna, A. (2022). Recruitment through AI in selected Indian companies. Metamorphosis, 21(1), 31-39. https://doi.org/10.1177/097262252110662
• Mittal, N. & Gujral, H. K. (2020). Realization of artificial intelligence in human resource management best practices. Mukt Shabd Journal, 9(7), 2786-2795.
• Mohamad, Z., Chaudhary, R., Iyer, S., Sutarto & Khan, S. (2022). A probing: How artificial intelligence is beneficial in different fields of human resource management. Pacific Business Review (International), 15(4), 80-89.
• Ose, S. O. (2016). Using Excel and Word to structure qualitative data. Journal of Applied Social Science, 10(2), 147-162. https://doi.org/10.1177/1936724416664948
• Palos-Sanchez, P. R., Baena-Luna, P., Badicu, A. & Infante-Moro, J. C. (2022). Artificial intelligence and human resources management: A bibliometric analysis. Applied Artificial Intelligence, 36(01), 3628-3655. https://doi.org/10.1080/08839514.2022.2145631
• Pan, Y., Froese, F., Liu, N., Hu, Y. & Ye, M. (2022). The adoption of artificial intelligence in employee recruitment: The influence of contextual factors. The International Journal of Human Resource Management, 33(6), 1125-1147. https://doi.org/10.1080/09585192.2021.1879206
• Panda, P. (2020). The future transformation of HR functions which apstole by artificial intelligence. Brindavan Journal of Management and Computer Science, 1(1), 127-135.
• Parikh, N. (2021, October 14). Understanding bias in AI-enabled hiring. https://www.forbes.com/sites/forbeshumanresourcescouncil/2021/10/14/understanding-bias-in-ai-enabled-hiring/?sh=410312cd7b96
• Prikshat, V., Patel, P., Varma, A. & Ishizaka, A. (2022). A multi-stakeholder ethical framework for AI-augmented HRM. International Journal of Manpower, 43(1), 226-250. https://doi.org/10.1108/IJM-03-2021-0118
• Prikshat, V., Malik, A. & Budhwar, P. (2023). AI-augmented HRM: Antecedents, assimilation and multilevel consequences. Human Resource Management Review, 33(1), https://doi.org/10.1016/j.hrmr.2021.100860.
• Qahtani, E. H. A. L. & Alsmairat, M. A. K. (2023). Assisting artificial intelligence adoption drivers in human resources management: A mediation model. Acta Logistica, 10(1), 141-150. https://doi.org/10.22306/al.v10i1.371
• Qamar, Y., Agrawal, R. K., Samad, T. A. & Chiappetta Jabbour, C. J. (2021). When technology meets people: The interplay of artificial intelligence and human resource management. Journal of Enterprise Information Management, 34(5), 1339-1370. https://doi.org/10.1108/JEIM-11-2020-0436
• Rodgers, W., Murray, J. M., Stefanidis, A., Degbey, W. Y. & Tarba, S. Y. (2023). An artificial intelligence algorithmic approach to ethical decision-making in human resource management processes. Human Resource Management Review, 33(1), 1-19. https://doi.org/10.1016/j.hrmr.2022.100925
• Saxena, A. (2020). The growing role of artificial intelligence in human resource. EPRA International Journal of Multidisciplinary Research, 6(8), 152-158. https://doi.org/10.36713/epra2013
• Schuller, D. & Schuller, B. W. (2018). The age of artificial emotional intelligence. Computer, 51(9), 38-46. https://doi.org/10.1109/MC.2018.3620963
• Schwering, R. E. (2003). Focusing leadership through force field analysis: New variations on a venerable planning tool. Leadership & Organization Development Journal, 24(7), 361-370. https://doi.org/10.1108/01437730310498587
• Singh, A. & Shaurya, A. (2021). Impact of artificial intelligence on HR practices in the UAE. Humanities and Social Sciences Communications, 8, 1-9. https://doi.org/10.1057/s41599-021-00995-4
• Sithambaram, R. A. & Tajudeen F. P. (2023). Impact of artificial intelligence in human resource management: A qualitative study in the Malaysian context. Asia Pacific Journal of Human Resources, 61, 821-844. https://doi.org/10.1111/1744-7941.12356
• Song, Y. & Wu, R. (2021). Analysing human-computer interaction behaviour in human resource management system based on artificial intelligence technology. Knowledge Management Research & Practice, https://doi.org/10.1080/14778238.2021.1955630
• Sriram, V. P., Mathur, A., Aarthy C, C. J., Basumatary, B., Mamgain, P. & Pallathadka, H. (2021). Model based using artificial intelligence to overcome the human resource problem in the healthcare industry. Annals of the Romanian Society for Cell Biology, 25(4), 3980-3992.
• Swanson, D. J. & Creed, A. S. (2014). Sharpening the focus of force field analysis. Journal of Change Management, 14(1), 28-47. https://doi.org/10.1080/14697017.2013.788052
• 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
• Tanantong, T. & Wongras, P. A. (2024). UTAUT-based framework for analyzing users’ intention to adopt artificial intelligence in human resource recruitment: A case study of Thailand. Systems, 12(1), 1-27. https://doi.org/10.3390/systems12010028
• Thomas, J. (1985). Force field analysis: A new way to evaluate your strategy. Long Range Planning, 18(6), 54-59. https://doi.org/10.1016/0024-6301(85)90064-0
• Tsymbaliuk, S., Vasylyk, A. & Bilyk, O. (2022). Artificial intelligence in HR: Practices and prospects of the spread in Ukraine. The Eurasia Proceedings of Science, Technology, Engineering & Mathematics (EPSTEM), 17, 152-160. https://doi.org/10.55549/epstem.1176076
• Weber, P. (2023). Unrealistic optimism regarding artificial intelligence opportunities in human resource management. International Journal of Knowledge Management, 19(1), 1-19. https://doi.org/10.4018/IJKM.317217
• World Economic Forum (2023, May). Future of jobs report 2023. https://www3.weforum.org/docs/WEF_Future_of_Jobs_2023.pdf
• Xiao, Q., Yan, J. & Bamber, G. J. (2023). How does AI-enabled HR analytics influence employee resilience: Job crafting as a mediator and HRM system strength as a moderator. Personnel Review, https://doi.org/10.1108/PR-03-2023-0198
• Yang, Y. (2022). Artificial intelligence-based organizational human resource management and operation system. Frontiers in Psychology, 13, 1-11. https://doi.org/10.3389/fpsyg.2022.962291
• Zahidi, F., Imam, Y., Hashmi, A. U. & Baig, M. M. (2020). Impact of artificial intelligence on HR management-A review. Dogo Rangsang Research Journal, 10(6), 92-99.
• Zhang, J. & Yuan, Y. (2022). Multi-dimensional post competency evaluation model in human resource management under the background of artificial intelligence. Mathematical Problems in Engineering, 2022, 1-10. https://doi.org/10.1155/2022/9730127