Diffusion of innovation and trust in AI: Predictors of generative AI adoption among instructional leaders and management in higher education

The integration of Generative Artificial Intelligence (GenAI) in higher education has sparked interest in its potential to improve instructional leadership and academic management. This study explores the factors influencing instructional leaders’ adoption of GenAI by extending the diffusion of innovation (DOI) theory to include trust in AI as an additional explanatory factor. Data collected from 102 instructional leaders were analyzed using Pearson correlation and multiple regression. The results show that relative advantage, trialability, observability, and trust positively predict the adoption of GenAI, with trust being the strongest predictor. However, compatibility and complexity did not significantly affect adoption when other factors were considered. The findings suggest that adoption decisions are influenced by perceived benefits, opportunities for experimentation, visibility of outcomes, and confidence in AI's reliability and ethical use, rather than by alignment with existing practices. The study highlights key areas of application, including curriculum design, faculty feedback, student engagement, and professional development.