Artificial Intelligence Adoption and Managerial Decision-Making Effectiveness: The Mediating Role of Information-Driven Decision in a Malaysian Private Healthcare Provider

Abstract

This study investigates the impact of artificial intelligence (AI) adoption on managerial decision-making effectiveness, with Information-Driven Decision Capability (IDC) as a mediating variable, within the context of private healthcare providers in Malaysia. As AI technologies become increasingly integrated into healthcare operations, understanding how these systems influence managerial decision-making has become increasingly important. A quantitative, cross-sectional, and explanatory research design was adopted. Data were collected from managerial-level employees using a structured questionnaire through purposive sampling. A total of 153 valid responses were analysed using Statistical Package for the Social Sciences (SPSS) to examine the proposed relationships and mediation effects. The findings reveal that Perceived Usefulness (PU), Perceived Ease of Use (PEOU), and AI Reliability (REL) have significant positive relationships with Information-Driven Decision Capability (IDC). In addition, IDC has a strong positive effect on Managerial Decision-Making Effectiveness (MDME). The results further show that PU and REL have direct significant effects on MDME, while PEOU does not directly influence MDME. Mediation analysis confirms that IDC partially mediates the relationships between PU and REL with MDME, while fully mediating the relationship between PEOU and MDME. These findings suggest that the effectiveness of AI in managerial decision-making is not solely dependent on technology adoption, but also on managers’ capability to interpret and utilise AI-generated insights effectively. This study contributes to the literature by identifying IDC as a key mechanism linking AI adoption and decision-making effectiveness, particularly within private healthcare organisations.