Artificial Intelligence (AI) adoption and leadership as predictors of strategic planning effectiveness in select Philippine higher-education institutions
Corresponding email: rlraydanas@nu-clark.edu.ph
A B S T R A C T
This study is a quantitative descriptive–correlational examination of the relationships among perceived Artificial Intelligence (AI) adoption, leadership behaviors, and strategic planning effectiveness in selected private higher-education institutions in the Philippines. Anchored on the Technology Acceptance Model (TAM) and the Full Range Leadership Model, the study explored how academic leaders’ perceptions of AI integration and leadership practices are associated with perceived strategic planning outcomes. Data were collected from a total of 76 academic leaders from selected private higher-education institutions in Manila, Philippines, using an adapted self-report questionnaire. Descriptive results indicated moderate levels of AI adoption, leadership behaviors leaning towards transformational leadership, and high perceived strategic planning effectiveness. Tests of correlation revealed significant positive associations among the three variables, with leadership behaviors showing the strongest relationship with perceived strategic planning effectiveness. Multiple regression analysis further indicated that perceived AI adoption and leadership behaviors jointly accounted for a substantial proportion of the variance in perceived strategic planning effectiveness, with leadership behaviors demonstrating a stronger statistical association than AI adoption. The findings suggest that academic leaders who perceive higher levels of AI integration and who report more effective leadership practices also tend to perceive their institutions’ strategic planning processes as more effective. While the results underscore the importance of aligning digital transformation initiatives with leadership practices in higher education, the reliance on self-reported measures warrants cautious interpretation. Future studies may benefit from multi-source and longitudinal designs to further examine these relationships.