Reimagining Automotive Manufacturing with Generative AI: A Technology-Organization-Environment Perspective

Abstract

Generative AI (Gen-AI) is transforming industries, and the automotive sector is no exception. Auto Component Manufacturing (AutoCM) organizations face strong pressure to adopt Gen-AI for competitiveness, yet little research has examined adoption factors in this context. This study investigates the determinants of Gen-AI adoption in AutoCM firms using the Technology-Organization-Environment framework. A model was developed and tested through survey data from 490 managers, validated via PLS-SEM. The findings reveal key predictors of adoption: perceived intelligence, data quality, absorptive capacity, IT infrastructure, top management commitment, vendor support, competitive pressure, and government support. Results further show that adoption intention drives potential use of Gen-AI, but this relationship is weakened by concerns over Gen-AI hallucination. This research identifies crucial drivers of Gen-AI adoption in AutoCM, extends TOE framework literature, and provides actionable insights for practitioners to strategically harness Gen-AI’s transformative potential in the sector.

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