Commenced in January 2007
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Edition: International
Paper Count: 32451
Factors Affecting Employee Decision Making in an AI Environment

Authors: Yogesh C. Sharma, A. Seetharaman


The decision-making process in humans is a complicated system influenced by a variety of intrinsic and extrinsic factors. Human decisions have a ripple effect on subsequent decisions. In this study, the scope of human decision making is limited to employees. In an organisation, a person makes a variety of decisions from the time they are hired to the time they retire. The goal of this research is to identify various elements that influence decision making. In addition, the environment in which a decision is made is a significant aspect of the decision-making process. Employees in today's workplace use artificial intelligence (AI) systems for automation and decision augmentation. The impact of AI systems on the decision-making process is examined in this study. This research is designed based on a systematic literature review. Based on gaps in the literature, limitations and the scope of future research have been identified. Based on these findings, a research framework has been designed to identify various factors affecting employee decision making. Employee decision making is influenced by technological advancement, data-driven culture, human trust, decision automation-augmentation and workplace motivation. Hybrid human-AI systems require development of new skill sets and organisational design. Employee psychological safety and supportive leadership influences overall job satisfaction.

Keywords: Employee decision making, artificial intelligence, environment, human trust, technology innovation, psychological safety.

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[1] Akter, S., McCarthy, G., Sajib, S., Michael, K., Dwivedi, Y. K., D’Ambra, J., & Shen, K. N. (2021). Algorithmic bias in data-driven innovation in the age of AI. International Journal of Information Management, 60, 102387.
[2] Bader, V., & Kaiser, S. (2019). Algorithmic decision-making? The user interface and its role for human involvement in decisions supported by artificial intelligence. Organization, 26(5), 655–672.
[3] Basu, S., Garimella, A., Han, W., & Dennis, A. (2021). Human decision making in AI augmented systems: Evidence from the initial coin offering market. Hawaii International Conference on System Sciences 2021 (HICSS-54).
[4] Cao, G., Duan, Y., Edwards, J. S., & Dwivedi, Y. K. (2021). Understanding managers’ attitudes and behavioral intentions towards using artificial intelligence for organizational decision-making. Technovation, 106, 102312.
[5] Charlwood, A., &Guenole, N. (2022). Can HR adapt to the paradoxes of artificial intelligence? Human Resource Management Journal.
[6] Darioshi, R., & Lahav, E. (2021). The impact of technology on the human decision-making process. Human Behavior and Emerging Technologies, 3(3), 391–400.
[7] De Cremer, D., & Kasparov, G. (2021). The ethics of technology innovation: A double-edged sword? AI and Ethics.
[8] Dymitrowski, A., &Mielcarek, P. (2021). Business model innovation based on new technologies and its influence on a company’s competitive advantage. Journal of Theoretical and Applied Electronic Commerce Research, 16(6), 2110–2128.
[9] Elgendy, N., Elragal, A., &Päivärinta, T. (2021). DECAS: A modern data-driven decision theory for big data and analytics. Journal of Decision Systems, 0(0), 1–37.
[10] Glikson, E., & Woolley, A. W. (2020). Human trust in artificial intelligence: Review of empirical research. Academy of Management Annals, 14(2).
[11] Haesevoets, T., De Cremer, D., Dierckx, K., & Van Hiel, A. (2021). Human-machine collaboration in managerial decision making. Computers in Human Behavior, 119, 106730.
[12] Höddinghaus, M., Sondern, D., & Hertel, G. (2021). The automation of leadership functions: Would people trust decision algorithms? Computers in Human Behavior, 116, 106635.
[13] Jain, J., & Gupta, S. (2022). AI in HR a Fairy Tale of Combining People, Process, and Technology in Managing the Human Resource. In Impact of Artificial Intelligence on Organizational Transformation (pp. 33–56). John Wiley & Sons, Ltd.
[14] Jarrahi, M. H., Newlands, G., Lee, M. K., Wolf, C. T., Kinder, E., & Sutherland, W. (2021). Algorithmic management in a work context. Big Data & Society, 8(2), 20539517211020332.
[15] Judeh, M. (2021). Effect of work environment on employee engagement: Mediating role of ethical decision-making. Problems and Perspectives in Management, 19(3), 221–229.
[16] Keding, C. (2021). Understanding the interplay of artificial intelligence and strategic management: Four decades of research in review. Management Review Quarterly, 71(1), 91–134.
[17] Keding, C., & Meissner, P. (2021). Managerial overreliance on AI-augmented decision-making processes: How the use of AI-based advisory systems shapes choice behavior in R&D investment decisions. Technological Forecasting and Social Change, 171, 120970.
[18] Kim, J., Dibrell, C., Kraft, E., & Marshall, D. (2021). Data analytics and performance: The moderating role of intuition-based HR management in major league baseball. Journal of Business Research, 122, 204–216.
[19] Landers, R. N., & Marin, S. (2021). Theory and technology in organizational psychology: A review of technology integration paradigms and their effects on the validity of theory. Annual Review of Organizational Psychology and Organizational Behavior, 8(1), 235–258.
[20] Langer, M., & Landers, R. N. (2021). The future of artificial intelligence at work: A review on effects of decision automation and augmentation on workers targeted by algorithms and third-party observers. Computers in Human Behavior, 123, 106878.
[21] Ma, H., Gao, Q., Li, X., & Zhang, Y. (2022). AI development and employment skill structure: A case study of China. Economic Analysis and Policy, 73, 242–254.
[22] Mahmud, H., Islam, A. K. M. N., Ahmed, S. I., &Smolander, K. (2022). What influences algorithmic decision-making? A systematic literature review on algorithm aversion. Technological Forecasting and Social Change, 175, 121390.
[23] Malik, N., Tripathi, S. N., Kar, A. K., & Gupta, S. (2021). Impact of artificial intelligence on employees working in industry 4.0 led organizations. International Journal of Manpower. Advance online publication.
[24] Margherita, A. (2021). Human resources analytics: A systematization of research topics and directions for future research. Human Resource Management Review, 100795.
[25] Mele, C., Russo Spena, T., Kaartemo, V., &Marzullo, M. L. (2021). Smart nudging: How cognitive technologies enable choice architectures for value co-creation. Journal of Business Research, 129, 949–960.
[26] Meng, J., & Berger, B. K. (2019). The impact of organizational culture and leadership performance on PR professionals’ job satisfaction: Testing the joint mediating effects of engagement and trust. Public Relations Review, 45(1), 64–75.
[27] Parent-Rocheleau, X., & Parker, S. K. (2021). Algorithms as work designers: How algorithmic management influences the design of jobs. Human Resource Management Review, 100838.
[28] Pendleton, D., Derbyshire, P., & Hodgkinson, C. (2021). The Future of Work. In D. Pendleton, P. Derbyshire, & C. Hodgkinson (Eds.), Work-Life Matters: Crafting a New Balance at Work and at Home (pp. 57–74). Springer International Publishing.
[29] Pereira, V., Hadjielias, E., Christofi, M., &Vrontis, D. (2021). A systematic literature review on the impact of artificial intelligence on workplace outcomes: A multi-process perspective. Human Resource Management Review, 100857.
[30] Salman, S. F. A., & Sankar, J. P. (2021). The relationship between employee empowerment and perceived employee job performance among the hospitality sector in the kingdom of Bahrain: The case of three star hotels in Bahrain. IKSP Journal of Innovative Writings, 1(2), Article 2.
[31] Schlicker, N., Langer, M., Ötting, S. K., Baum, K., König, C. J., & Wallach, D. (2021). What to expect from opening up ‘black boxes’? Comparing perceptions of justice between human and automated agents. Computers in Human Behavior, 122, 106837.
[32] Shet, Sateesh. V., Poddar, T., Wamba Samuel, F., & Dwivedi, Y. K. (2021). Examining the determinants of successful adoption of data analytics in human resource management – A framework for implications. Journal of Business Research, 131, 311–326.
[33] Tomprou, M., & Lee, M. K. (2022). Employment relationships in algorithmic management: A psychological contract perspective. Computers in Human Behavior, 126, 106997.
[34] Trunk, A., Birkel, H., & Hartmann, E. (2020). On the current state of combining human and artificial intelligence for strategic organizational decision making. Business Research, 13(3), 875–919.
[35] Ulfert, A.-S., Antoni, C. H., &Ellwart, T. (2022). The role of agent autonomy in using decision support systems at work. Computers in Human Behavior, 126, 106987.
[36] Waldkirch, M., Bucher, E., Schou, P. K., &Grünwald, E. (2021). Controlled by the algorithm, coached by the crowd – how HRM activities take shape on digital work platforms in the gig economy. The International Journal of Human Resource Management, 32(12), 2643–2682.
[37] Wijayati, D. T., Rahman, Z., Fahrullah, A., Rahman, M. F. W., Arifah, I. D. C., &Kautsar, A. (2022). A study of artificial intelligence on employee performance and work engagement: The moderating role of change leadership. International Journal of Manpower. Advance online publication.
[38] Ferrer, X., Nuenen, T. van, Such, J. M., Coté, M., &Criado, N. (2021). Bias and discriminationin AI: A cross-disciplinary perspective. IEEE Technology and Society Magazine,40(2), 72–80.
[39] Zeng H, Zhao L and Zhao Y (2020) Inclusive leadership and taking-charge behavior: Rolesof psychological safety and thriving at work. Frontiers in Psychology, 11:62.
[40] Zhou, L., Paul, S., Demirkan, H., Yuan, L., Spohrer, J., Zhou, M., &Basu, J. (2021). Intelligence augmentation: Towards building human-machine symbiotic relationship. AIS Transactions on Human-Computer Interaction, 13(2), 243–264.