Abstract
The evolution of artificial intelligence and varying perspectives on its integration within the supply chain management landscape tend to influence organisations’ ability to adapt to changing market conditions and maintain relevance and competitiveness. Using a quantitative approach, this study explored the drivers of artificial intelligence adoption in Nigeria’s supply chain management landscape. A survey questionnaire was the primary means of collecting quantitative data from 80 local supply chain practitioners, which was analysed through statistical tests. Results from the study established support and leadership from senior management, availability of technological infrastructure, and regulatory framework and regulatory considerations as the foremost drivers of AI adoption in Nigeria’s supply chain landscape. The study's findings provide valuable insights for policymakers, industry practitioners, and academic researchers. The study posits that fostering a conducive environment for AI implementation, addressing regulatory ambiguities, and enhancing technological capabilities will be imperative for unlocking the full benefits of AI in Nigeria's supply chain management landscape.
Publisher
Bussecon International Academy
Reference104 articles.
1. Aayog, N. I. T. I. (2018). National strategy for artificial intelligence. Discussion Paper. https://niti.gov.in/sites/default/files/2019-01/NationalStrategy-for-AI-Discussion-Paper.pdf. [Accessed January 15, 2024].
2. Abdulquadri, A., Mogaji, E., Kieu, T. A., & Nguyen, N. P. (2021). Digital transformation in financial services provision: A Nigerian perspective to the adoption of chatbot. Journal of Enterprising Communities: People and Places in the Global Economy, 15(2), 258-281. https://doi.org/10.1108/JEC-06-2020-0126.
3. Aboelmaged, M. G. (2014). Predicting e-readiness at firm-level: An analysis of technological, organisational and environmental (TOE) effects on e-maintenance readiness in manufacturing firms. International Journal of Information Management, 34(5), 639-651. https://doi.org/10.1016/j.ijinfomgt.2014.05.002.
4. Ahmad, T., Zhang, D., Huang, C., Zhang, H., Dai, N., Song, Y., & Chen, H. (2021). Artificial intelligence in sustainable energy industry: Status quo, challenges and opportunities. Journal of Cleaner Production, 289, 125834. https://doi.org/10.1016/j.jclepro.2021.125834.
5. Alsheibani, S., Cheung, Y., & Messom, C. (2018). Artificial Intelligence Adoption: AI-readiness at Firm-Level. PACIS, 4, 231-245.