Artificial intelligence in personnel management: the development of APM model

Article


Chang, K. 2020. Artificial intelligence in personnel management: the development of APM model. The Bottom Line. 33 (4), pp. 377-388. https://doi.org/10.1108/BL-08-2020-0055
AuthorsChang, K.
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.

JournalThe Bottom Line
Journal citation33 (4), pp. 377-388
ISSN0888-045X
Year2020
PublisherEmerald
Accepted author manuscript
License
File Access Level
Anyone
Digital Object Identifier (DOI)https://doi.org/10.1108/BL-08-2020-0055
Publication dates
Online03 Nov 2020
Print12 Nov 2020
Publication process dates
Accepted29 Sep 2020
Deposited07 Jun 2023
Copyright holder© 2020, The Author(s)
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