HRM-Driven Change Management and Its Impact on Reducing Technological Resistance in AI-IoT Adoption: A Regression Analysis Approach

Abstract

Purpose: This study examines how human resource management (HRM)-driven change management practices affect technological resistance in the context of artificial intelligence and Internet of Things (AI-IoT) adoption among manufacturing SMEs in Peninsular Malaysia.
Methodology/Design/Approach: A quantitative cross-sectional survey was conducted with 420 managers, IT officers, and HR practitioners from licensed manufacturing SMEs in Selangor, Penang, and Johor Bahru. Data were analysed using reliability testing, exploratory factor analysis (EFA), confirmatory factor analysis (CFA), hierarchical multiple regression, moderation analysis, and structural equation modelling (SEM).
Findings: HRM-driven change management , encompassing training and development, communication and participation, leadership support, and incentive alignment , significantly reduced technological resistance to AI-IoT adoption. Perceived organisational readiness partially mediated the relationship between HRM-driven change management and technological resistance. Technological self-efficacy moderated the relationship, such that the resistance-reducing effect of HRM-driven change management was stronger among employees with higher technological self-efficacy. The final SEM model explained 71.3% of the variance in technological resistance and demonstrated excellent fit.
Originality of the research: This study integrates HRM, change management, and technology adoption literatures within a single empirical framework and provides evidence from manufacturing SMEs in Malaysia , a context undergoing rapid Industry 4.0 transformation. The study offers a regression-grounded, HRM-centric perspective on overcoming technological resistance, an under-explored mechanism in AI-IoT adoption research.