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Can Universities Forge Organizational Readiness for AI Adoption? Evidence from Meta-Analysis
Citation. Tunkevichus, O. & Bagrationi, K. (2026). Can Universities Forge Organizational Readiness for AI Adoption? Evidence from Meta-Analysis. European Conference on Knowledge Management, 27(2), 1333–1339. https://doi.org/10.34190/eckm.27.2.5167
Summary. This meta-analysis synthesizes 28 empirical higher-education studies using PLS-SEM to identify predictors of AI adoption and organize them across individual, organizational, and institutional levels. It reports that most examined predictors have positive pooled associations with adoption, while substantial heterogeneity indicates that readiness depends on implementation context, AI solution type, and how readiness is operationalized.
Key claims
- Twelve of fourteen observed predictors had statistically significant positive pooled effects on AI adoption in the reviewed higher-education studies.
- Intention was the strongest reported proximal predictor of AI introduction (ES = 0.907, p < 0.001), while perceived usefulness, perceived ease of use, AI literacy, self-efficacy, social influence, competitive pressure, and organizational determinants also showed positive effects.
- High heterogeneity for organizational readiness (I² = 0.84) suggests that readiness effects vary across contexts, AI solution types, and measurement choices.
Evidence & limitations
- The review followed PRISMA and applied random-effects meta-analysis to 28 empirical higher-education studies using PLS-SEM; its conclusions are specific to that evidence base and sector.
- The authors report conceptual fragmentation and inconsistent modeling roles for readiness, so pooled effects should not be treated as a single context-invariant readiness model.