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Generative artificial intelligence (GenAI) revolution: a deep dive into GenAI adoption
Indian Institute of Management, Ranchi, India.
Indian Institute of Management Vishakhapatnam, India.
Umeå University, Faculty of Social Sciences, Umeå School of Business and Economics (USBE), Business Administration. Sunway Business School, Sunway University, Malaysia; Vilnius University, Lithuania; Tallinn University of Technology, Estonia; University of Johannesburg, South Africa.ORCID iD: 0000-0002-1282-0319
Keele Business School, Keele University, United Kingdom.
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2025 (English)In: Journal of Business Research, ISSN 0148-2963, E-ISSN 1873-7978, Vol. 189, article id 115160Article in journal (Refereed) Published
Abstract [en]

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.

Place, publisher, year, edition, pages
Elsevier, 2025. Vol. 189, article id 115160
Keywords [en]
Adoption, Artificial intelligence, Behavioral reasoning theory, GenAI, Generative AI, Generative artificial intelligence, Mixed methods
National Category
Business Administration
Identifiers
URN: urn:nbn:se:umu:diva-234007DOI: 10.1016/j.jbusres.2024.115160ISI: 001398724700001Scopus ID: 2-s2.0-85214228464OAI: oai:DiVA.org:umu-234007DiVA, id: diva2:1926685
Available from: 2025-01-13 Created: 2025-01-13 Last updated: 2025-04-24Bibliographically approved

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Hollebeek, Linda D.

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