Exploring the Adoption Intention of Artificial Intelligence in Human Resource Management

Phan, Anh (2026) Exploring the Adoption Intention of Artificial Intelligence in Human Resource Management. Doctoral thesis, Birmingham City University.

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Abstract

Artificial Intelligence (AI) is increasingly reshaping Human Resource Management (HRM) through the automation of HR processes and the enhancement of decision-making capabilities. Despite its potential, the adoption of AI in HRM remains uneven, with persistent challenges related to organisational readiness, professional acceptance, and uncertainty surrounding AI-enabled systems.

This thesis investigates the key determinants influencing HR professionals’ intention to adopt AI technologies within HRM. Anchored in the Unified Theory of Acceptance and Use of Technology (UTAUT), the study extends existing technology adoption frameworks by incorporating the psychological factors of Perceived Risk (PR) and Status Quo Bias (SQB) to capture the socio-cognitive complexities of AI adoption in HR contexts. The research also addresses a recurring conceptual conflation between HR analytics, automation, and AI-powered HRM, which shapes HR professionals’ perceptions and adoption intentions.

A mixed-methods research design was employed across two sequential phases. Phase 1 involved qualitative research using thematic analysis of eighteen semi-structured interviews with HR professionals engaged with AI-enabled HR practices in the West Midlands, England. This phase confirmed the relevance of core UTAUT constructs—performance expectancy, effort expectancy, social influence, and facilitating conditions—while identifying perceived risk, status quo bias, and conceptual ambiguity surrounding AI as salient influences on adoption intention. Phase 2 comprised a quantitative study analysing 146 survey responses from HR professionals using SPSS. The quantitative findings refined the qualitative insights, demonstrating that social influence and status quo bias significantly influence AI adoption intention, whereas effort expectancy and facilitating conditions did not demonstrate statistical significance.

The findings indicate that performance expectancy and social influence are primary drivers of AI adoption in HRM, while heightened perceived risks, status quo bias, and the misinterpretation of AI as conventional HR analytics act as key barriers. Based on these results, the study proposes a context-specific AI adoption framework for HRM that integrates technological, social, and psychological dimensions of adoption. This research contributes empirical and conceptual insights to academic debates on AI adoption in HRM and provides practical guidance for policymakers, AI vendors, and HR practitioners seeking to support sustainable and responsible AI implementation.

Item Type:	Thesis (Doctoral)
Dates:	Date Event 25 March 2026 Accepted
Uncontrolled Keywords:	AI, HRM, adoption, Human Resource
Subjects:	CAH11 - computing > CAH11-01 - computing > CAH11-01-01 - computer science CAH11 - computing > CAH11-01 - computing > CAH11-01-05 - artificial intelligence CAH17 - business and management > CAH17-01 - business and management > CAH17-01-05 - human resource management
Divisions:	Business School > Management, Business and Marketing Doctoral Research College > Doctoral Theses Collection
Depositing User:	Louise Muldowney
Date Deposited:	13 Apr 2026 10:21
Last Modified:	13 Apr 2026 10:21
URI:	https://www.open-access.bcu.ac.uk/id/eprint/16964

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