Abstract

This study explores global perspectives of supply chain leaders on the adoption, usage, and impact of generative artificial intelligence (gen AI) in supply chain management (SCM) based on a quantitative analysis of survey responses from 104 industry professionals. It is utilizing the Diffusion of Innovation (DOI) Theory (E. M. Rogers, 2003), Technology-Organization-Environment-Ethics (TOEE) framework—an extension of the Technology-Organization-Environment (TOE) Framework (L. G. Tornatzky & Fleischer, 1990), incorporating ethics—and the Balanced Scorecard (BSC) Framework (Kaplan & Norton, 1996). Findings highlight the key technological, organizational, environmental, and ethical factors influencing adoption and reveal how ethical considerations—such as transparency, accountability, and fairness—shape implementation and perceived outcomes. The study further examines how gen AI enhances operational efficiency, decision-making, innovation, and ethical evaluation across the five main areas of supply chain operations: sourcing, planning, manufacturing, logistics, and customer service. While gen AI strengthens predictive insights, refines workflows, and enhances decision-making, challenges related to data integrity, governance, and ethical readiness persist. These insights provide organizations with strategic recommendations for integrating gen AI effectively while ensuring regulatory compliance and trust. By addressing both the benefits and ethical complexities of gen AI, this research contributes to the evolving discourse on AI governance and sustainable technological integration in global supply chains. Ultimately, by offering a structured analysis of industry perspectives, this study presents a roadmap for organizations seeking to optimize efficiency, innovation, and responsible AI adoption.

Library of Congress Subject Headings

Supply chain management—Technology; Generative artificial intelligence; Business logistics—Data processing; Artificial intelligence—Moral and ethical aspects

Date of Award

2025

School Affiliation

Graduate School of Education and Psychology

Department/Program

Education

Degree Type

Dissertation

Degree Name

Doctorate

Faculty Advisor

Seung Lee

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