DETERMINANTS OF ARTIFICIAL INTELLIGENCE ADOPTION AMONG ACADEMIC AND NON-ACADEMIC STAFF OF AUCHI POLYTECHNIC, EDO STATE, NIGERIA
This study examined the determinants of artificial intelligence (AI) adoption among academic and non-academic staff of Auchi Polytechnic, Edo State, Nigeria. Specifically, it investigated the relationships between perceived usefulness, AI literacy, institutional support and AI adoption. The study adopted a descriptive survey research design. The population comprised 3,150 academic and non-academic staff, from which a sample of 354 respondents was determined using the Taro Yamane formula. Data were collected through a structured questionnaire and analysed using descriptive statistics, Pearson correlation and multiple regression analysis with SPSS Version 26. The hypotheses were tested at the 5% level of significance. The descriptive results showed that perceived usefulness had a mean score of 4.18, AI literacy recorded 3.65, institutional support had 2.84, while AI adoption recorded 3.92. The multiple regression results indicated that perceived usefulness (β = 0.396, p < 0.001), AI literacy (β = 0.339, p < 0.001) and institutional support (β = 0.246, p < 0.001) had significant positive relationships with AI adoption. The overall regression model was statistically significant (F = 150.29, p < 0.001), with an R² of 0.545, indicating that the three explanatory variables jointly accounted for 54.5% of the variation in AI adoption. Perceived usefulness was the strongest predictor, followed by AI literacy and institutional support. The study concluded that AI adoption among staff of Auchi Polytechnic is associated with perceived work-related benefits, employees’ AI competencies and the availability of institutional resources and support. It recommended that the Polytechnic promote practical AI applications in academic and administrative activities, strengthen staff AI literacy through regular training, and improve digital infrastructure, technical assistance and institutional guidelines for responsible AI use.
Authors
- I. MOHAMMAD UMOLE
- IMHABEKHAI OSHIOZEKHAI COSMAS
Institutions
- Auchi Polytechnic (NG)
Publication Details
- Journal
- International Journal of African Innovation and Multidisciplinary Research
- Published
- 2026-11-02
- DOI
- https://doi.org/10.70382/mejaimr.v14i2.0139
- Primary Topic
- Technology Adoption and User Behaviour
- Type
- article
- Field-Weighted Citation Impact
- 0.00