Mapping Generative AI Adoption Across Sectors: A Systematic Literature Review and Development of the SDTR Theory

Md Maruf Islam; Ishraque Hossain Chowdhury

doi:10.56532/mjsat.v6i3.632

Authors

Md Maruf Islam Dept. of Finance, Real Estate and Decision Science, Wichita State University, Wichita, USA https://orcid.org/0009-0002-3193-8141
Ishraque Hossain Chowdhury Dept. of Management, University of Texas Rio Grande Valley. Edinburg, USA https://orcid.org/0000-0002-1669-150X

DOI:

https://doi.org/10.56532/mjsat.v6i3.632

Keywords:

Generative AI, Sectoral Innovation, Digital Transformation, Technology Adoption

Abstract

Generative AI adoption varies markedly across economic sectors, creating uneven digital transformation that affects competitive dynamics, inequality, and sustainable development. This study develops and empirically validates the Sectoral Digital Transformation Readiness (SDTR) theory, providing a framework for understanding GenAI adoption, regulatory paradoxes, and innovation ecosystems in 13 major sectors. Using PRISMA-guided review and advanced cluster and network analyses of 97 studies (2023–2025), four sector clusters are identified: Digital Pioneers, Regulated Innovators, Industrial Transformers, and Service Adapters. Silhouette analysis (0.73) and ANOVA (F=45.2, p<0.001) show the SDTR framework explains 78% of sectoral variance and reveals that regulatory clarity, not just permissiveness - drives adoption. The research advances theory by integrating institutional and outcome dimensions, and it provides policy guidance for cluster-specific governance promoting digital equity and responsible AI aligned with the UN Sustainable Development Goals.

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