Mazuruse, Gideon ORCID: https://orcid.org/0000-0001-9390-978X, Nyagadza, Brighton ORCID: https://orcid.org/0000-0001-7226-0635, Chifurira, Retius, Muvuti, Ashley and Matsiwira, Last (2026) Artificial Intelligence (AI) adoption and satisfaction in management education research: An explanatory-predictive hybrid SEM-RF approach. Strategic Business Research. p. 100102. (In Press)

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Abstract

The accelerated diffusion of artificial intelligence (AI) tools in management education research presents both strategic opportunities and ethical challenges for business schools and higher education institutions. Even as AI applications promise enhanced analytical efficiency and research productivity, concerns regarding academic integrity, critical thinking development, and data confidentiality complicate their integration. Despite these tensions, empirical evidence explaining and predicting satisfaction with AI tools among management education researchers remains limited. Existing studies have focused largely on conceptual frameworks, ethical implications, often relying on student samples or cross-sectional studies. Consequently, little is known about the specific determinants of AI satisfaction among researchers. To address this gap, the present study investigates the determinants of user satisfaction with AI technologies within a management education context and develops a predictive framework to inform institutional decision-making. The study, based on a sample of 260 respondents, examined 9 key constructs. The Random Forest (RF) model was trained by running the bagging procedure on a dataset, and its performance was validated using out-of-bag error estimation to assess predictive accuracy. Structural Equation Modelling (SEM) findings reveal that perceived ease of use significantly enhances perceived usefulness, which in turn drives satisfaction with AI tools. Ethical concern attitudes are negatively associated with perceived usefulness, underscoring the managerial and pedagogical trade-offs inherent in AI adoption within business schools. The RF model complements the explanatory analysis by demonstrating strong predictive performance (R² = 0.73) and identifying perceived ease of use, access to AI technologies, and perceived usefulness as the most influential predictors of satisfaction. The convergence of theory-driven and machine learning results enhances the robustness and practical relevance of the findings. By integrating explanatory and predictive modelling, this study contributes to management education literature on digital transformation and responsible innovation. The findings offer actionable insights for business school leaders, curriculum designers, and policymakers seeking to support ethically-grounded, strategically-aligned AI integration in management research and education.

Item Type:	Article
Status:	In Press
DOI:	10.1016/j.sbr.2026.100102
School/Department:	London Campus
URI:	https://ray.yorksj.ac.uk/id/eprint/14131

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