ISSN: 2222-6990
Open access
Purpose: AI-based decision-support systems are expanding in Malaysian healthcare, yet doctors’ utilisation remains inconsistent due to concerns about reliability, workflow alignment, and task complexity. This study examines how task and technology characteristics influence Task-Technology Fit (TTF) and, subsequently, AI-based medical decision-making (AIIM). It also tests the mediating role of TTF and the moderating effect of Personal Innovativeness in IT (PIIT). Design/methodology/approach: A cross-sectional online survey was conducted with 197 medical doctors in Malaysia who had experience using AI-based tools. Validated 7-point Likert scales measured Task Characteristics (TAC), Technology Characteristics (TEC), TTF, PIIT, and AIIM. Purposive sampling was used, and hypotheses were tested via PLS-SEM. Findings: TAC positively influences TTF (? = 0.380, p = 0.001), contrary to the hypothesised negative path, while TEC positively influences TTF (? = 0.610, p = 0.000). TTF significantly enhances AIIM (? = 0.296, p = 0.000). Mediation analysis shows a positive indirect effect of TAC on AIIM through TTF (? = 0.281, p = 0.000), whereas TEC shows a positive and significant indirect effect (? = 0.673, p = 0.000), confirming TTF’s mediating role. PIIT significantly strengthens the TTF–AIIM relationship (? = 0.132, t = 3.143, p = 0.000). Research limitations: This study used purposive sampling and focused only on doctors in Selangor, limiting generalisability. Future studies should include broader and multi-state samples. Control variables such as decision type, doctor level, and clinical experience were not included; accounting for these factors may better isolate the influences of TAC, TEC, TTF, and PIIT on AIIM. Implications: The study advances TTF theory by showing that task and technology characteristics shape AI-based medical decision making, with TTF acting as a mediator and PIIT strengthening this relationship. Practically, healthcare institutions and AI developers should prioritise AI tools that integrate smoothly with existing Malaysian clinical systems (e.g., MySejahtera Health Records, Teleprimary Care, EMRs) to improve adoption. Doctors with lower PIIT may benefit from structured training, peer support, and low-risk AI “sandbox” environments. Socially, effective AI use can improve patient safety, diagnostic accuracy, workflow efficiency, and job satisfaction, contributing to a more sustainable healthcare workforce and greater public trust in AI. Originality/value: This study is among the first in Malaysia to examine how TAC and TEC jointly influence AIIM through TTF. It establishes TTF as a mediator and PIIT as a key individual level moderator. The study also reframes the traditional TTF outcome of “performance impacts” into a domain specific measure, AIIM, capturing accuracy, efficiency, confidence, diagnostic reasoning, and error reduction. This provides new theoretical insights and practical guidance for strengthening AI adoption in clinical settings.
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