Multinational Enterprises and People Management: Examining the Role of Artificial Intelligence

Dr. Pankaj Kumar

doi:https://doi.org/10.17492/jpi.focus.v10i2.1022305

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Multinational Enterprises and People Management: Examining the Role of Artificial Intelligence

Pankaj Kumar

Vol 10 , Issue 2 , July - December 2023 | Pages: 96-119 | Research Paper

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https://doi.org/10.17492/jpi.focus.v10i2.1022305

Published Online: December 15, 2023

Author Details ( * ) denotes Corresponding author

1. * Pankaj Kumar, Assistant Professor, Management Studies, DIT University, Dehradun, Dehradun, Uttarakhand, India (pankajmyhr@gmail.com)

AI explains computer systems that solve issues like humans. This article examines potential effects of artificial intelligence on human resources and people management also examines how artificial intelligence (AI) may affect personnel management and multinational enterprises (MNEs) doing business. When multinational organizations (MNEs) operate across borders and face multiple cultures, managing people becomes more complicated. AI-driven solutions are changing how multinational businesses (MNEs) recruit, train, motivate, and retain their global workforce. This research examines the benefits and drawbacks of incorporating artificial intelligence (AI) into HR practices for international organizations. This research aims to improve international human resource practices and assess their impact on multinational firms’ commercial success. This study suggests that AI’s potential for injustice and prejudice can be solved, despite many arguments about it. Human resource specialists require particular expertise and abilities to employ AI in people management

Keywords

Artificial Intelligence; Machine Learning; Human Resource Management; International Labour; Automation

Adamopoulou, E., & Moussiades, L. (2020). Chatbots: History, technology, and applications. Machine Learning with Applications, 2, 100006. Retrieved from https://doi.org/10.1016/j.mlwa.2020.100006.
Alshahrani, A., Dennehy, D., & Mäntymäki, M. (2022). An attention-based view of AI assimilation in public sector organizations: The case of Saudi Arabia. Government Information Quarterly, 39(4), 101617. Retrieved from https://doi.org/10.1016/j.giq.2021.101617.
Arrieta, A. B., Díaz-Rodríguez, N., Del Ser, J., Bennetot, A., Tabik, S., Barbado, A., García, S., Gil-López, S., Molina, D., Benjamins, R., Chatila, R., & Herrera, F. (2019). Explainable Artificial Intelligence (XAI): Concepts, taxonomies, opportunities and challenges toward responsible AI. Retrieved from http://arxiv.org/abs/1910.10045.
Ashok, M., Madan, R., Joha, A., & Sivarajah, U. (2022). Ethical framework for artificial intelligence and digital technologies. International Journal of Information Management, 62, 102433. Retrieved from https://doi.org/10.1016/j.ijinfomgt.2021.102433.
Bansal, H. & Khan, R. (2018). A review paper on human computer interaction. International Journal of Advanced Research in Computer Science and Software Engineering, 8(4), 53. Retrieved from https://doi.org/10.23956/ijarcsse.v8i4.630.
Basu, S., Majumdar, B., Mukherjee, K., Munjal, S., & Palaksha, C. (2023). Artificial intelligence–HRM interactions and outcomes: A systematic review and causal configurational explanation. Human Resource Management Review, 33(1), 100893. Retrieved from https://doi.org/10.1016/j.hrmr.2022.100893.
Bernhardt, A., Kresge, L., & Suleiman, R. (2023). The data-driven workplace and the case for worker technology rights. Industrial & Labor Relations Review, 76(1), 3–29. Retrieved from https://doi.org/10.1177/00197939221131558
Bhardwaj, G., Singh, S. V., & Kumar, V. (2020). An empirical study of artificial intelligence and its impact on human resource functions. 2020 International Conference on Computation, Automation and Knowledge Management (ICCAKM). Retrieved from https://ieeexplore.ieee.org/document/9051544.
Bollen, K. A. (2014). Structural equations with latent variables: Bollen/structural equations with latent variables. Ney York: John Wiley & Sons.
Bondarouk, T., & Brewster, C. (2016). Conceptualising the future of HRM and technology research. The International Journal of Human Resource Management, 27(21), 2652–2671. Retrieved from https://doi.org/10.1080/09585192.2016.1232296.
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.
Cantwell, J. & Janne, O. (2000, June). The role of multinational corporations and national states in the globalization of innovatory capacity: The European perspective. Technology Analysis & Strategic Management, 12(2), 243–262. Retrieved from https://doi.org/10.1080/713698463.
Cappzlli, P., Tambe, P., & Yakubovich, V. (2018). Artificial intelligence in human resources management: Challenges and a path forward. Retrieved from https://doi.org/10.2139/ssrn.3263878.
Chamorro-Premuzic, T., Polli, F., & Dattner, B. (2019). Building ethical AI for talent management. Harvard Business Review, 21, 1–15.
Ciulli, F., & Kolk, A. (2023, June). International Business, digital technologies and sustainable development: Connecting the dots. Journal of World Business, 58(4), 101445. Retrieved from https://doi.org/10.1016/j.jwb.2023.101445.
Colbert, A., Yee, N., & George, G. (2016). The digital workforce and the workplace of the future. Academy of Management Journal, 59(3), 731–739. Retrieved from https://doi.org/10.5465/amj.2016.4003.
Dabić, M., Maley, J. F., Švarc, J., & Poček, J. (2023). Future of digital work: Challenges for sustainable human resources management. Journal of Innovation & Knowledge, 8(2), 100353. Retrieved from https://doi.org/10.1016/j.jik.2023.100353.
De Soete, B., Lievens, F., & Druart, C. (2012). An update on the diversity-validity dilemma in personnel selection: A review. Psihologijske Teme, 21(3), 399–424.
Dobbe, R. (2022). System safety and artificial intelligence. 2022 ACM Conference on Fairness, Accountability, and Transparency. Retrieved from https://doi.org/10.48550/ar Xiv.2202.09292.
Dwivedi, Y. K., Hughes, L., Ismagilova, E., Aarts, G., Coombs, C., Crick, T., Duan, Y., Dwivedi, R., Edwards, J., Eirug, A., Galanos, V., Ilavarasan, P. V., Janssen, M., Jones, P., Kar, A. K., Kizgin, H., Kronemann, B., Lal, B., Lucini, B., … Williams, M. D. (2021). Artificial Intelligence (AI): Multidisciplinary perspectives on emerging challenges, opportunities, and agenda for research, practice and policy. International Journal of Information Management, 57, 101994. Retrieved from https://doi.org/10.1016/j.ijinfomgt.2019.08.002.
Einola, K., & Khoreva, V. (2023). Best friend or broken tool? Exploring the co‐existence of humans and artificial intelligence in the workplace ecosystem. Human Resource Management, 62(1), 117–135. Retrieved from https://doi.org/10.1002/hrm.22147.
Enholm, I. M., Papagiannidis, E., Mikalef, P. & Krogstie, J. (2022). Artificial intelligence and business value: A literature review. Information Systems Frontiers: A Journal of Research and Innovation, 24(5), 1709–1734. Retrieved from https://doi.org/10.1007/s10796-021-10186-w.
Enright, M. J. (2000, June). Regional clusters and multinational enterprises. International Studies of Management & Organization, 30(2), 114–138. Retrieved from https://doi.org/10.1080/00208825.2000.11656790.
Garg, S., Sinha, S., Kar, A. K., & Mani, M. (2022). A review of machine learning applications in human resource management. International Journal of Productivity and Performance Management, 71(5), 1590–1610. Retrieved from https://doi.org/10.1108 ijppm-08-2020-0427.
George, D. G. (2019). Integration of artificial intelligence in human resource. International Journal of Innovative Technology and Exploring Engineering, 9(2), 5069–5073. Retrieved from https://doi.org/10.35940/ijitee.l3364.129219.
Guedhami, O., Knill, A., Megginson, W. L., & Senbet, L. W. (2022, September 6). The dark side of globalization: Evidence from the impact of COVID-19 on multinational companies. Journal of International Business Studies, 53(8), 1603–1640. Retrieved from https://doi.org/10.1057/s41267-022-00540-8
Haefner, N., Wincent, J., Parida, V., & Gassmann, O. (2021). Artificial intelligence and innovation management: A review, framework, and research agenda. Technological Forecasting and Social Change, 162, 120392. Retrieved from https://doi.org/10.1016/ j.techfore.2020.120392
Hassan, A. (2022). The role of artificial intelligence in human resources. In Future of Organizations and Work after the 4^th Industrial Revolution (pp. 57–80). Springer International Publishing. Retrieved from https://link.springer.com/chapter/10.1007/978-3-030-99000-8_4.
Jarrahi, M. H., Lutz, C., Boyd, K., Oesterlund, C., & Willis, M. (2022). Artificial intelligence in the work context. Journal of the Association for Information Science and Technology, 74(3), 303-310. Retrieved from https://doi.org/10.1002/asi.24730.
Kaushal, N., Kaurav, R. P. S., Sivathanu, B., & Kaushik, N. (2023). Artificial intelligence and HRM: Identifying future research agenda using systematic literature review and bibliometric analysis. Management Review Quarterly, 73(2), 455–493. Retrieved from https://doi.org/10.1007/s11301-021-00249-2.
Kochan, T. (2021). Artificial intelligence and the future of work: A proactive strategy. AI Magazine, 42(1), 16-24.
Krakowski, S., Luger, J. & Raisch, S. (2023). Artificial intelligence and the changing sources of competitive advantage. Strategic Management Journal, 44(6), 1425–1452. Retrieved from https://doi.org/10.1002/smj.3387.
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. Retrieved from https://doi.org/10.1108/mrr-03-2020-0168.
Kullu, F. D. & Raj, A. X. (2018). Artificial intelligence optimism vs pessimism: A Conceptual insight. LIBA Journal of Management, 1(2), 25–30.
Larsson, A. S. & Edwards, M. R. (2022). Insider econometrics meets people analytics and strategic human resource management. The International Journal of Human Resource Management, 33(12), 2373–2419. Retrieved from https://doi.org/10.1080/ 09585192.2020.1847166.
Loureiro, S. M. C., Guerreiro, J., & Tussyadiah, I. (2021). Artificial intelligence in business: State of the art and future research agenda. Journal of Business Research, 129, 911–926. Retrieved from https://doi.org/10.1016/j.jbusres.2020.11.001
Lu, Y., Zheng, H., Chand, S., Xia, W., Liu, Z., Xu, X., Wang, L., Qin, Z., & Bao, J. (2022). Outlook on human-centric manufacturing towards Industry 5.0. Journal of Manufacturing Systems, 62, 612–627. Retrieved from https://doi.org/10.1016/ j.jmsy.2022.02.001.
Luo, X., Tong, S., Fang, Z., & Qu, Z. (2019). Frontiers: Machines vs. Humans: The impact of artificial intelligence chatbot disclosure on customer purchases. Marketing Science, 38(6), 913-1084. Retrieved from https://doi.org/10.1287/mksc.2019.1192.
Malik, A., Budhwar, P., Patel, C., & Srikanth, N. R. (2020, December 17). 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. Retrieved from https://doi.org/10.1080/09585192.2020.1859582.
Manalo, H., & Manalo, M. R. (2021). Leading people and technology in the 4^th IR. Retrieved from https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3925567.
Markoff, J. (2016). Machines of loving grace: The quest for common ground between humans and robots. New York: HarperCollins Publishers.
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. Retrieved from https://doi.org/10.1080/09585192.2016.1244699.
Matsakis, L. (2019). At an outback steakhouse franchise, surveillance blooms. Retrieved from https://www.wired.com/story/outback-steakhouse-presto-vision-surveillance/
Maznevski, M. L., & Chudoba, K. M. (2000). Bridging space over time: Global virtual team dynamics and effectiveness. Organization Science, 11(5), 473–492. Retrieved from https://doi.org/10.1287/orsc.11.5.473.15200.
McCartney, S., & Fu, N. (2022). Promise versus reality: A systematic review of the ongoing debates in people analytics. Journal of Organizational Effectiveness People and Performance, 9(2), 281-311. Retrieved from https://doi.org/10.1108/joepp-01-2021-0013
McDonald, R. P. & Bollen, K. A. (1990). Structural equations with latent variables. Journal of the American Statistical Association, 85(412), 1175. Retrieved from https://doi.org/10.2307/2289630.
Mehrabi, N., Morstatter, F., Saxena, N., Lerman, K., & Galstyan, A. (2022). A survey on bias and fairness in machine learning. ACM Computing Surveys, 54(6), 1–35. Retrieved from https://doi.org/10.1145/3457607.
Meske, C., Bunde, E., Schneider, J., & Gersch, M. (2022). Explainable artificial intelligence: Objectives, stakeholders, and future research opportunities. Information Systems Management, 39(1), 53–63. Retrieved from https://doi.org/10.1080/ 10580530.2020.1849465.
Michel, R. S., Belur, V., Naemi, B., & Kell, H. J. (2019). Graduate admissions practices: A targeted review of the literature. ETS Research Report Series, 2019(1), 1-18.
Mökander, J., Morley, J., Taddeo, M., & Floridi, L. (2021). Ethics-based auditing of automated decision-making systems: Nature, scope, and limitations. Science and Engineering Ethics, 27(4), 44. Retrieved from https://doi.org/10.1007/s11948-021-00319-4.
Morgan, B. (2018). Robots will take our jobs and we need a plan: 4 scenarios for the future. Forbes. Retrieved from https://www.forbes.com/sites/blakemorgan/ 2018/09/05/robots-will-take-our-jobs-and-we-need-a-plan-4-scenarios-for-the-future/?sh =f0e66006db43.
Narula, R. & Dunning, J. H. (2010, September). Multinational enterprises, development and globalization: Some clarifications and a research agenda. Oxford Development Studies, 38(3), 263–287. Retrieved from https://doi.org/10.1080/13600818.2010.505684.
Palos-Sánchez, P. R., Baena-Luna, P., Badicu, A. & Infante-Moro, J. C. (2022). Artificial intelligence and human resources management: A bibliometric analysis. Applied Artificial Intelligence: AAI, 36(1). Retrieved from https://doi.org/10.1080/ 08839514.2022.2145631
Peña, F. (1988). Artificial intelligence is coming. European Management Journal, 6(2), 173–177. Retrieved from https://doi.org/10.1016/0263-2373(88)90028-x.
Petricevic, O., & Teece, D. J. (2019, October 28). The structural reshaping of globalization: Implications for strategic sectors, profiting from innovation, and the multinational enterprise. Journal of International Business Studies, 50(9), 1487–1512. Retrieved from https://doi.org/10.1057/s41267-019-00269-x.
Raisch, S., & Krakowski, S. (2021). Artificial intelligence and management: The automation–augmentation paradox. Academy of Management Review, 46(1), 192–210. Retrieved from https://doi.org/10.5465/amr.2018.0072.
Rajesh, D. S., Kandaswamy, M. U., & Rakesh, M. A. (2018). The impact of artificial intelligence in talent acquisition lifecycle of organizations. International Journal of Engineering Development and Research, 6(2), 709–717.
Reilly, P. (2018). The impact of artificial intelligence on the HR function. Retrieved from https://www.employment-studies.co.uk/system/files/resources/files/mp142_The_ impact_of_Artificial_Intelligence_on_the_HR_function-Peter_Reilly.pdf.
Russell, S. (2016). Artificial intelligence: A modern approach (Global Edition). England: Pearson Education Limited.
Samek, W., Montavon, G., Vedaldi, A., & Hansen, L. K. (2019). Explainable AI: interpreting, explaining and visualizing deep learning (Vol. 11700). Springer Nature.
Shneiderman, B. (2020). Human-centered artificial intelligence: Reliable, safe & trustworthy. International Journal of Human-Computer Interaction, 36(6), 495–504.
Steidl, M., Felderer, M., & Ramler, R. (2023). The pipeline for the continuous development of artificial intelligence models—Current state of research and practice. The Journal of Systems and Software, 199, 111615. Retrieved from https://doi.org/10.1016/j.jss.2023.111615.
Tariq, M. U. (2023). Role of artificial intelligence in the enabling sustainable supply chain management during COVID-19. International Journal of Services and Operations Management, 44(1), 115–135.
Veluchamy, R., Sanchari, C. & Gupta, S. (2021). Artificial intelligence within recruitment: eliminating biases in human resource management. Artificial Intelligence, 8(3), 348-358.
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. Retrieved from https://doi.org/10.1080/ 09585192.2020.1871398
Wang, A., Liu, A., Zhang, R., Kleiman, A., Kim, L., Zhao, D., & Russakovsky. (2022). REVISE: A tool for measuring and mitigating bias in visual datasets. International Journal of Computer Vision, 130(7), 1790–1810.
Weber, M., Beutter, M., Weking, J., Böhm, M., & Krcmar, H. (2022). AI Startup business models: Key characteristics and directions for entrepreneurship research. Business & Information Systems Engineering, 64(1), 91–109. Retrieved from https://doi.org/10.1007/s12599-021-00732-w
West, J. C. (2015). The second machine age: Work, progress, and prosperity in a time of brilliant technologies. Psychiatry, 78(4), 380–383. Retrieved from https://doi.org/10.1080/00332747.2015.1105632.
Yawalkar, M. V. V. (2019). A study of artificial intelligence and its role in human resource management. International Journal of Research and Analytical Reviews (IJRAR), 6(1), 20–24.
Yoo, Y., Boland, R. J., Jr, Lyytinen, K., & Majchrzak, A. (2012). Organizing for innovation in the digitized world. Organization Science, 23(5), 1398–1408. Retrieved from https://doi.org/10.1287/orsc.1120.0771.
Yuganthini, P., Vigneswari, A., Jancy, S., Mercypaulselvan, AntoPraveena, M. D., & Vijiamuthamary. (2021). Activity tracking of employees in industries using computer vision. 2021 5^th International Conference on Trends in Electronics and Informatics (ICOEI).
Zou, M., Zhou, Y. & Williams, M. (2022). In search of the ‘buffering’ effect in the job demands-control model: The role of teamwork HRM practices and occupations. Economic and Industrial Democracy. Retrieved from https://doi.org/10.1177/ 0143831X221128345.
Crawford, K. (2021). The atlas of AI: Power, politics, and the planetary costs of artificial intelligence. Yale University Press.
Hutchinson, B., & Mitchell, M. (2019). 50 years of test (un) fairness: Lessons for machine learning. In Proceedings of the conference on fairness, accountability, and transparency (pp. 49–58).