Assessment of Artificial Intelligence adoption among Lecturers in Nigerian Universities: Examining Capability, Opportunity and Motivation – Behavioural (COM-B) Framework
DOI:
https://doi.org/10.64348/zije.2026448Abstract
This study examined the behavioural determinants of Artificial Intelligence (AI) adoption among university lecturers in Southwest Nigeria using the Capability–Opportunity–Motivation Behaviour (COM-B) framework. It analysed how lecturers’ competence, institutional conditions, and motivational drivers shape AI integration in teaching, research, and academic practice. A descriptive cross-sectional survey design was employed, drawing on data from 2,145 lecturers across federal, state, and private universities using a structured questionnaire with satisfactory reliability (α = 0.79–0.84). Data were analysed using descriptive statistics, t-tests, Pearson correlation, and multiple regression. Findings revealed moderate capability (mean: 2.06–2.50), indicating strong digital literacy but limited institutional training, highlighting the need for structured capacity-building and continuous professional development. Institutional opportunity was also moderate yet uneven (mean: 1.85–2.35), constrained by funding gaps, governance inefficiencies, and weak digital infrastructure, underscoring the importance of improved policy coherence and investment. Motivation was relatively strong (mean: 2.00–2.52), driven by perceived teaching benefits and professional identity, though influenced by social and institutional factors, suggesting the need for incentives, recognition, and collaborative support systems. AI use was evident in teaching and research but limited in assessment and institutional engagement, revealing an intention–practice gap and the need for embedding AI into formal workflows. Capability significantly influenced adoption (t = 2.41, p < 0.05), while opportunity (r = 0.48) and motivation (r = 0.66) were strong predictors, explaining 58% of variance. The study recommends integrated institutional strategies to strengthen capacity, expand opportunities, and sustain motivation for equitable AI adoption.
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