This study explores the factors influencing artificial intelligence (AI)-driven decision-making proficiency (AIDP) among management students, focusing on foundational AI knowledge, data literacy, problem-solving, ethical considerations and collaboration skills. The research examines how these competencies enhance self-efficacy and engagement, with curriculum design, industry exposure and faculty support as moderating factors. This study aims to provide actionable insights for educational strategies that prepare students for AI-driven business environments.
The research adopts a hybrid methodology, integrating partial least squares structural equation modeling (PLS-SEM) with artificial neural networks (ANNs), using quantitative data collected from 526 management students across five Indian universities. The PLS-SEM model validates linear relationships, while ANN captures nonlinear complexities, complemented by sensitivity analyses for deeper insights.
The results highlight the pivotal roles of foundational AI knowledge, data literacy and problem-solving in fostering self-efficacy. Behavioral, cognitive, emotional and social engagement significantly influence AIDP. Moderation analysis underscores the importance of curriculum design and faculty support in enhancing the efficacy of these constructs. ANN sensitivity analysis identifies problem-solving and social engagement as the most critical predictors of self-efficacy and AIDP, respectively.
The study is limited to Indian central universities and may require contextual adaptation for global applications. Future research could explore longitudinal impacts of AIDP development in diverse educational and cultural settings.
The findings provide actionable insights for curriculum designers, policymakers and educators to integrate AI competencies into management education. Emphasis on experiential learning, ethical frameworks and interdisciplinary collaboration is critical for preparing students for AI-centric business landscapes.
By equipping future leaders with AI proficiency, this study contributes to societal readiness for technological disruptions, promoting sustainable and ethical decision-making in diverse business contexts.
To the author’s best knowledge, this study uniquely integrates PLS-SEM and ANN to analyze the interplay of competencies and engagement in shaping AIDP. It advances theoretical models by linking foundational learning theories with practical AI education strategies, offering a comprehensive framework for developing AI competencies in management students.
