Ethical Appraisals of Artificial Intelligence in MBA Education: Moral Concern, Social Pressure, Perceived Benefits, and AI Experience
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This cross-sectional study examined how MBA students appraise ethical concerns, practical benefits, social expectations, control, and institutional governance related to educational artificial intelligence (AI). The analysis used 275 eligible responses submitted by the prespecified study cut-off. Descriptive statistics, omega and Spearman-Brown reliability, exploratory factor analysis (EFA), rank-based and bootstrap robustness checks, and regression models of ordered AI experience were used. Three exploratory dimensions were retained in 90.4% of 500 bootstrap samples. A proportional-odds reference model improved on the null model, chi-square(6) = 17.81, p = .007, but the proportional-odds assumption was rejected, chi-square(6) = 17.81, p = .007; threshold-specific models were therefore primary. AI self-efficacy/control was associated with reporting at least moderate rather than none/minimal AI experience (OR = 1.51, 95% CI [1.13, 2.03], p = .005), and this result persisted after background adjustment, bootstrap validation, and response-quality screens. In the smaller extensive-experience comparison (n = 26), efficiency benefit and perceived ethical-issue frequency were positive exploratory correlates. Concern was highest for data privacy, while governance responses indicated comparatively limited knowledge of personal-data practices. Predictive performance was modest. The findings support clearer task-specific guidance, critical AI literacy, and transparent data practices, but the cross-sectional self-report design does not establish causal adoption effects or validated moral profiles
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