Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Faculty Publications Article Faculty Publications -Articles Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Creator An entity primarily responsible for making the resource Sivathanu, Brijesh; Pillai, Rajasshrie; Rana, Nripendra P. Title A name given to the resource Reimagining Automotive Manufacturing with Generative AI: A Technolology-Organization-Environment Perspective Date A point or period of time associated with an event in the lifecycle of the resource 01-01-2025 Source A related resource from which the described resource is derived Journal of Computer Information Systems; Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.1080/08874417.2025.2554850" target="_blank" rel="noreferrer noopener">https://doi.org/10.1080/08874417.2025.2554850</a> <br /><br /><a href="https://www.scopus.com/pages/publications/105016811173?origin=resultslist" target="_blank" rel="noreferrer noopener">https://www.scopus.com/pages/publications/105016811173?origin=resultslist</a> Coverage The spatial or temporal topic of the resource, the spatial applicability of the resource, or the jurisdiction under which the resource is relevant Sivathanu B., Christ University, Bangalore, India; Pillai R., Chandragupt Institute of Management Patna, Patna, India; Rana N.P., Queens University Belfast, Belfast, United Kingdom, Humanities and Social Sciences Research Center (HSSRC), Deanship of Scientific Research, Imam Mohammad Ibn Saud Islamic University, Riyadh, Saudi Arabia, College of Business &amp; Economics, University of Johannesburg, Johannesburg, South Africa Description An account of the resource 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-AIs transformative potential in the sector. 2025 The Author(s). Published with license by Taylor &amp; Francis Group, LLC. Subject The topic of the resource data quality; Generative AI; perceived Gen-AI hallucination potential; PLS-SEM; potential use; TOE Publisher An entity responsible for making the resource available Taylor and Francis Ltd. Relation A related resource ISSN: 8874417; Language A language of the resource English Type The nature or genre of the resource Article Rights Information about rights held in and over the resource All Open Access; Green Open Access; Hybrid Gold Open Access Format The file format, physical medium, or dimensions of the resource online