AI-Driven Decision Support Beneath Uncertainty: A Hybrid Bayesian–PLS Model for Systemic Sustainability Innovation

This study examines Responsible Decision-Making (RADM) in AI-enabled sustainability within tertiary education under conditions of uncertainty and complex interdependence. Conventional analytical approaches are limited in such settings because they typically explain behavioural relationships without adequately modelling uncertainty. To address this limitation, the study proposes an AI-driven Decision Support System (DSS) based on a hybrid probabilistic framework integrating PLS-SEM with Bayesian Network (BN) inference. The framework combines structural analysis with probabilistic reasoning in a unified, interpretable system capable of modelling conditional dependencies among decision variables. Data were collected from 713 academic leaders in tertiary education institutions in Saudi Arabia. The model examines the effects of AI-Driven Sustainable Value (AISV), Responsible AI Ease of Use (RAIU), Institutional Sustainability Support (ISS), Ethical Leadership Norms (ELN), Responsible AI Competence (RAC), and AI Risk and Hallucination Awareness (ARHA) on Responsible Decision-Making and Sustainability Impact Performance (GGIP). The results indicate that ELN and ARHA have significant positive effects on RADM, while AISV and RAIU also contribute positively to decision quality. In contrast, ISS and RAC do not demonstrate significant direct effects on RADM. However, ISS shows indirect effects through contextual and cognitive pathways. The findings further suggest that awareness of uncertainty and AI-related risks plays a more influential role in decision quality than technical competence alone. The model demonstrates strong explanatory power (R2 = 0.64) and acceptable predictive capability (R2 = 0.48). Bayesian inference further indicates that sustainability outcomes improve under favourable institutional and cognitive conditions. Overall, the framework provides an interpretable and scalable DSS that supports scenario-based evaluation and probabilistic decision analysis under uncertainty. The findings are specific to the institutional context examined in this study. Although the framework may have relevance to other organisational environments characterised by uncertainty and complex decision structures, no external or cross-contextual validation was conducted. Therefore, the findings should be interpreted with appropriate contextual caution.