A Critical Review of Artificial Intelligence in People Management

Kirk Chang (), Kuotai Cheng and Stephanie Sandland
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Kirk Chang: University of East London
Kuotai Cheng: National Tsinghua University
Stephanie Sandland: University of East London

Chapter Chapter 3 in HRM 5.0, 2024, pp 35-56 from Springer

Abstract: Abstract Artificial Intelligence (AI) seems omnipotent and ubiquitous in the workplace, but managers and employeesEmployee have mixed views about its merits in practice. In this chapter, the opportunities and constraints of AI-driven managementAI-driven Management policy and practices are critically discussed, through an in-depth review of both conceptual research and empirical study, drawing on three theoretical perspectives (i.e., job replacement, psychological contract, and demands-resources theories). Following the theories-informed analyses, four meaningful discoveries are revealed. These are: (i) a manager’s experiences, attitude, and understanding of AI are highly influential, which in turn may affect the design and implementation of AI-driven managementAI-driven Management policies; (ii) the utility of AI-driven managementAI-driven Management policies does not necessarily guarantee productivity and should not be treated as an elixir to fix poor employeeEmployee performance; (iii) managers are liable to learn AI and understand its characteristics prior to the implementation of AI-driven managementAI-driven Management policies; where applicable, a dry-run or pilot practice should be arranged in advance; and (iv) managers should remain cautious of AI’s influence in their managerial practices, as AI has the potential to become a job-demand or job-resource, affecting their employeesEmployee either psychologically or behaviorally. In conclusion, the current chapter aims to offer new unique insights to AI-driven managementAI-driven Management literature, hence advancing knowledge in the field of AI policymaking and managerial practices. Limitations and suggestions for future studies are also discussed.

Date: 2024
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DOI: 10.1007/978-3-031-58912-6_3

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