Employees’ change in perception when artificial intelligence integrates with human resource management: a mediating role of AI-tech trust

Abstract

Purpose

This research explores and examines the change in perception artificial intelligence (AI) technology can bring in various human resources (HR) functions [(perception of change that AI can create in the talent acquisition (PAITA), perception of change that AI can create in the training and development (PAITD), perception of change that AI can create in the performance assessment (PAIPA) and perception of change that AI can create in the pay and rewards (PAIPR)] and its impact on intention to adopt AI by HR professionals. Additionally, as the literature on trust in AI is scanty, the mediation influence of AI-tech trust was also examined.

Design/methodology/approach

Cross-sectional data were gathered from 264 HR professionals from Indian e-commerce organizations. The model has been tested using a two-step partial least squares-based, structural equational modeling (PLS-SEM) technique.

Findings

AI uses algorithms for creating accurate and trustworthy information databases; it also enables quick data access and transmission, which enhances HR functions. Employees’ perception of the change that AI can bring to various HR functions significantly impacts the adoption of AI in HR. Additionally, AI-tech trust positively mediates all the hypothesized relationships.

Originality/value

Based on stimulus-organism-response (S-O-R) and affordance theory, this study significantly increases the understanding of how employees perceive changes in various HR functions as a result of AI implementation and how much they trust the AI technology. This study also addresses the lack of research on AI integration in HR, with a special focus on developing countries.

Citation

Arora, M. and Mittal, A. (2024), "Employees’ change in perception when artificial intelligence integrates with human resource management: a mediating role of AI-tech trust", Benchmarking: An International Journal, Vol. ahead-of-print No. ahead-of-print. https://doi.org/10.1108/BIJ-11-2023-0795