AI adoption by human resource management: a study of its antecedents and impact on HR system effectiveness

• Published in: Foresight (Cambridge) Vol. 25; no. 1; pp. 67 - 81
• Main Author: Agarwal, Alpana
• Format: Journal Article
• Language: English
• Published: Bradford Emerald Publishing Limited 16-03-2023; Emerald Group Publishing Limited
• Subjects: Artificial intelligence; Automation; Competition; Competitive advantage; Decision making; Human resource management; Literature reviews; Model testing; Pandemics; Perceptions; Performance management; Statistical analysis; System effectiveness; Technology; Technology adoption; India; Technology adoption; AI adoption; TTF model; Artificial intelligence; TOE model; HR effectiveness
• ISSN: 1463-6689; 1465-9832; 1463-6689
• DOI: 10.1108/FS-10-2021-0199

Purpose
The purpose of this study is to explore and examine the determinants of artificial intelligence (AI) adoption by human resource management (HRM). Further, the impact of AI adoption by HR department on their effectiveness has also been tested.
Design/methodology/approach
A model explaining the antecedents of AI adoption by HRM is proposed in this study. The proposed model is based on task–organization–environment and task–technology fit models. A two-step partial least square-based structural equational modelling (PLS-SEM) has been used for testing the model. Data was collected from 210 HRM employees (only senior level or specialized HR positions), working in IT firms located in Delhi-NCR region.
Findings
Literature review shows that among others, organizational preparedness, perceived benefits and technology readiness determine AI adoption which in turn can make HR system more effective. Results of PLS-SEM support all hypothesized relationships and validate the proposed model.
Originality/value
Considering paucity of research on antecedents of AI adoption by human resource department, this study adds significantly to the body of knowledge. Additionally, based on the findings of statistical analysis, certain AI-related recommendations are given to HRM.