Generative artificial intelligence (GenAI) revolution: A deep dive into GenAI adoption

Kumar, Aman; Shankar, Amit; D. Hollebeek, Linda; Behl, Abhishek; Marc Lim, Weng

Abstract

This study examines key reasons (for and against) that influence business-to-business (B2B) managers’ intention to adopt generative artificial intelligence (GenAI). We also investigate how GenAI adoption influences firm performance, along with the moderating effect of ethical leadership. Study 1 undertakes a series of in-depth interviews, yielding a set of hypotheses that are tested in Study 2. A total of 277 responses was collected from respondents in the USA, the UK, Canada, India, Australia, Malaysia, and Japan to test the proposed model using structural equation modeling. The findings highlight that need for uniqueness, information completeness, convenience, and deceptiveness significantly impact GenAI adoption. The results also highlight that GenAI adoption boosts firm performance. Finally, ethical leadership was found to moderate the effect of GenAI adoption on firm performance. This study enriches the GenAI, technology adoption, and behavioral reasoning theory literatures while also providing pertinent insights for firms intending to adopt GenAI.

Citation

Kumar, A., Shankar, A., D. Hollebeek, L., Behl, A., & Marc Lim, W. (2025). Generative artificial intelligence (GenAI) revolution: A deep dive into GenAI adoption. Journal of Business Research, 189, Article 115160. https://doi.org/10.1016/j.jbusres.2024.115160

• Journal Article Type: Article
• Acceptance Date: Dec 17, 2024
• Online Publication Date: Jan 2, 2025
• Publication Date: 2025-02
• Deposit Date: Jan 13, 2025
• Journal: Journal of Business Research
• Print ISSN: 0148-2963
• Publisher: Elsevier
• Peer Reviewed: Peer Reviewed
• Volume: 189
• Article Number: 115160
• DOI: https://doi.org/10.1016/j.jbusres.2024.115160
• Keywords: Artificial intelligence; Generative artificial intelligence; Generative AI; GenAI; Adoption; Behavioral reasoning theory; Mixed methods
• Public URL: https://keele-repository.worktribe.com/output/1044127
• Publisher URL: https://www.sciencedirect.com/science/article/pii/S0148296324006647?via%3Dihub