Generative AI Adoption in Indian Commerce Education: A Systematic Review of Faculty Acceptance Determinants Using the Unified Theory of Acceptance and Use of Technology (UTAUT) Framework

This systematic review of secondary data provides a comprehensive, evidence-based understanding of the factors influencing faculty acceptance of Generative AI in Indian commerce higher education. The integrated UTAUT-TAM framework, extended with AI Literacy, Academic Integrity Concerns, and Disciplinary Relevance, offers robust explanatory power for the observed adoption patterns. Four definitive conclusions emerge. First, Performance Expectancy is the dominant driver of faculty acceptance, but its effect is discipline-dependent—stronger in data-intensive commerce subjects than in theory-intensive ones. Second, the AI Literacy gateway effect means that institutions must invest in substantial, sustained faculty training before expecting meaningful attitude shifts; superficial awareness campaigns are ineffective. Third, Academic Integrity Concerns constitute a genuine structural barrier that cannot be addressed through individual faculty persuasion but requires systemic assessment reform. Fourth, and most critically, the policy-practice gap between NEP 2020's AI integration vision and the ground reality of affiliated commerce colleges represents the single largest obstacle to sustainable adoption. For Indian higher education to realize the NEP 2020 vision of an AI-ready workforce, commerce faculty cannot remain the "missing middle" in the adoption chain—enthusiastic in principle but incapacitated in practice. Closing this gap requires coordinated action from regulators (UGC, AICTE, ICAI), university administrations, college leadership, and EdTech providers. The theoretical models are validated; the policy frameworks exist; what remains is the political will and resource commitment to translate intention into integration. Future primary research should test the extended model proposed in this study through large-scale surveys of Indian commerce faculty exclusively, supplemented by qualitative interviews to capture the nuanced phenomenology of AI-mediated pedagogical change. Longitudinal studies tracking faculty from initial training through sustained usage would illuminate the temporal dynamics of acceptance formation. Finally, experimental studies comparing AI-integrated versus traditional assessment outcomes in commerce education would provide the empirical foundation for resolving Academic Integrity Concerns through evidence rather than anxiety.

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