Artificial intelligence in personnel management: the development of APM model. Issue 4 (4th November 2020)

• Record Type: Journal Article
• Authors: Chang, Kirk
• Is Part Of: Bottom line. Volume 33:Issue 4(2020)
• Page Start: 377
• Page End: 388
• Publication Date: 2020-11-04
• Subjects: Artificial intelligence -- AI -- Personnel management -- Career opportunity -- Job replacement -- Manager subordinate relationship; Library finance -- Periodicals; Libraries -- Economic aspects -- Periodicals; 025.11
• DOI: 10.1108/BL-08-2020-0055
• Languages: English
• ISSNs: 0888-045X

Abstract

Abstract : Purpose: Managers have mixed views of how artificial intelligence (AI) affects personnel management (PM). The purpose of this paper is to identify potential knowledge gap and bring new insights to the AI-personnel-management literature. Design/methodology/approach: Both applicability and theoretical perspectives are adopted to critically discuss the constraint and opportunity of AI in PM. Tables and narrative analysis are used to clarify the role of AI in managerial practices. Findings: Research findings have helped to develop a new model titled AI in Personnel Management (APM). The APM model unfolds itself in three levels, followed by potential outcome. The three levels comprise "organizational, managerial and individual job levels, " and the outcome comprises "organizational performance, employees' well-being and staff turnover rate". Research limitations/implications: The APM model helps managers to understand the implication of AI in their workplace. With better understanding of AI's implication, managers are more likely to develop appropriate AI-driven managerial policies, which in turn benefit employees and their organizations. The APM model acts as a reference guide, helping managers to evaluate the AI's constraint and opportunity in their managerial practices. Originality/value: The APM model is valuable and informative to the academic researchers, as it has first responded to Malik et al. (2019)'s call (re: the absence of AI and management literature), and, more importantly, it has advanced the knowledge of AI–management relationship, supporting scholars to further understand the role of AI in PM.