Antecedents of Adoption Intention for AI-Driven Wearable Technology in Malaysian Healthcare

Abstract

This study reports the pilot phase of a larger doctoral investigation into the adoption of AI-driven wearable technology in Malaysian healthcare. Guided by an integration of Diffusion of Innovations theory and the Technology Acceptance Model, the pilot tested seven direct effect hypotheses linking compatibility, relative advantage, trialability, complexity, observability, perceived ease of use, and perceived usefulness to adoption intention. Data were collected from healthcare sector respondents at a professional conference in Kuala Lumpur in August 2026 (N = 93, after removing two duplicates from 95 raw responses). Partial least squares structural equation modeling was used for its suitability to prediction oriented research. The structural model explained 60.0 percent of the variance in adoption intention. Perceived usefulness (beta = 0.261), perceived ease of use (beta = 0.201), observability (beta = 0.174), compatibility (beta = 0.162), and complexity (beta = -0.152) significantly predicted adoption intention (all p < .05); relative advantage and trialability did not. Composite reliability exceeded 0.78 for all nine constructs, and heterotrait monotrait ratios for all 36 construct pairs fell below the conservative 0.85 threshold as point estimates, but bootstrap confidence intervals crossed 0.85 for six of the eight adoption intention pairs, so discriminant validity for that construct remains provisional. Perceived regulatory support was retained for reliability assessment but not tested as a moderator here, a test reserved for the full study. Findings offer preliminary support for the framework and considerations for the full scale study.