GENERATIVE AI ADOPTION BY SMES: DRIVERS, BARRIERS AND PERFORMANCE OUTCOMES IN NIGERIA
Abstract The rapid emergence of Generative Artificial Intelligence (GenAI) is creating new opportunities for small and medium-sized enterprises (SMEs) to improve business processes, innovation, productivity, and competitiveness. However, the factors encouraging or limiting Generative AI adoption and its performance outcomes among SMEs in Nigeria remain underexplored. This study examines Generative AI adoption by SMEs in Nigeria, focusing on its drivers, barriers, and performance outcomes. A cross-sectional survey research design was employed, with data collected from 200 SME owners, managers, and employees using a structured questionnaire titled Generative AI Adoption and SME Performance Questionnaire (GAIA-SPQ), validated with a reliability coefficient of 0.86. Data were analyzed using correlation analysis at a 0.05 level of significance. Findings reveal that drivers of Generative AI significantly influence its adoption, while barriers also significantly affect adoption negatively. The study further finds that Generative AI use significantly affects SME performance, particularly in relation to productivity and innovation. The study concludes that Generative AI provides important opportunities for improving SME performance, although effective adoption requires favourable conditions and the reduction of implementation barriers. It is recommended that SMEs strengthen their technological capabilities, develop relevant digital skills, and adopt cost-effective Generative AI solutions to improve productivity, innovation, and competitiveness.
Authors
- Chinwe C. Ebenezer-Nwokeji
- Sunday Eze Amakodi
- V/REV DR CHIKEZIE IHEOMA
- KARACHI C. ISIAWUIKE
- OGWUCHE GABRIEL DR SHAIBU
- DR ECHETAMA C. FORSTINA
- DR MBAEGBU R. E. V.
- DR NMONWU LUCKY PAUL.
Institutions
- Federal University of Technology Owerri (NG)
- Imo State University (NG)
- Alvan Ikoku Federal University of Education, Owerri (NG)
- Imo State Polytechnic Omuma (NG)
Publication Details
- Journal
- Zenodo (CERN European Organization for Nuclear Research)
- Published
- 2026-09-30
- DOI
- https://doi.org/10.5281/zenodo.23051862
- Primary Topic
- AI in Service Interactions
- Type
- article
- Field-Weighted Citation Impact
- 0.00