University Students’ Perceptions of Artificial Intelligence Regulation in Business Decision-Making

This study aims to examine university students’ perceptions of artificial intelligence (AI) regulation in business decision-making, focusing on the roles of perceived risks, trust, transparency, legal liability, and the effectiveness of regulatory measures in shaping acceptance of AI-based decisions. Understanding these perceptions is crucial, as students represent the next generation of professionals, managers, and policymakers who will interact with AI in organisational and societal contexts. A structured questionnaire was administered to 304 students across multiple faculties, capturing data on perceived AI risks, trust, transparency and audit practices, legal liability, regulatory effectiveness, and AI acceptance in business. The study applied PLS-SEM to assess the measurement and structural models, supported by exploratory factor analysis (EFA) and normality tests. Results indicate that transparency and audit practices significantly enhance trust, while perceived liability and regulatory effectiveness demonstrate stronger relationships with AI acceptance. Although trust represents an important theoretical dimension of AI governance, its direct effect on acceptance was not statistically significant in this study. Perceived risks negatively affect adoption, whereas the perceived effectiveness of legal and regulatory measures indirectly reduces risk perceptions, thereby fostering acceptance. Legal and business liability provides complementary institutional reinforcement, although its direct effect on acceptance is modest. These findings highlight the interconnected roles of cognitive, relational, and institutional factors in shaping AI acceptance in business decision-making. The study offers practical insights for promoting responsible AI adoption, emphasising transparent, explainable, and auditable systems to build trust, alongside stronger regulatory frameworks to reduce risks and ensure accountability. The research extends trust theory to AI contexts and provides empirical evidence on students as emerging stakeholders, while suggesting directions for future research.