Generative AI Adoption in Manufacturing Firm: The Roles of Technology, Environment, and Functional Context

This study investigates the determinants of generative AI adoption in manufacturing firms, with a focus on differences across functional departments. Drawing on an integrated framework that combines the Technology Acceptance Model (TAM) and the Technology–Organization–Environment (TOE) framework, we examine the effects of technological usefulness, environmental influence, and job tenure on the level of generative AI adoption. Using a hierarchical re- gression analysis based on a survey of 400 Korean manufacturing employees, the results indicate that both technologi- cal usefulness and environmental influence significantly enhance generative AI adoption, while job tenure does not have a meaningful impact. Although job function did not significantly moderate these relationships, production de- partments reported lower usage levels than marketing and R&D. Supplementary interviews suggest that adoption is shaped more by task characteristics than formal job roles. These findings underscore the importance of user-centered and task-aligned strategies for AI integration in industrial contexts, and offer theoretical and managerial implications for the effective deployment of generative AI technology.