Decoding the Trust–Tech Nexus in AI-Driven Academic Marketing: A Multi-Paradigm Machine Learning Analysis of Stakeholder Decision-Making Confidence

The growing presence of AI in academic marketing is reshaping the way HEIs (Higher Education Institutions) are engaging their stakeholders. The benefits that come from uti-lizing AI-driven elements to improve operational efficiency only come to fruition when stakeholders trust those integrated elements. The relationship between stakeholder per-ceptions and their confidence in decision making on AI-enabled marketing activities is examined by this study through the application of a multi-paradigm machine-learning framework. This study analyzed the responses of 200 stakeholders using a combination of survey data and provided greater strength to the findings by combining multiple respons-es. Five different paradigms were used: Linear Regression, Ridge Regression, Support Vector Regression, Random Forest, and Gradient Boosting to evaluate stakeholder data and predict their behavior. The results suggest that Ridge Regression provided the most stable baseline for prediction; however, the ensemble models were able to capture many critical non-linear dynamics that were overlooked by linear model approaches. Trust in AI tools and personalised advertising is a dominant factor in stakeholder confidence, where-as the institutional perception of stakeholders acts as a structural moderator. These find-ings provide empirical validation of the Trust-Tech Nexus and demonstrate that for stakeholders to fully realise the benefits of AI-driven personalised advertising, institution-al credibility is essential.