Date of Conferral
4-9-2025
Date of Award
April 2025
Degree
Doctor of Business Administration (D.B.A.)
School
Business Administration
Advisor
Meridith Wentz
Abstract
Higher education institutions face challenges in integrating predictive analytics due to data silos and resistance to adoption, limiting their ability to improve student outcomes and operational efficiency. This issue concerns institutional leaders, as ineffective data use can lead to declining retention rates, financial strain, and missed opportunities for growth. Grounded in the composite conceptual framework of the technology acceptance model and the diffusion of innovations theory, the purpose of this qualitative pragmatic inquiry study was to explore effective strategies to implement predictive analytics tools to reduce costs and improve student outcomes. The study included three higher education leaders in Ontario with experience using or managing predictive analytics. Data were collected using semistructured interviews and publicly available documents. Through thematic and template analysis, four themes were identified: (a) challenges in integration, (b) stakeholder alignment, (c) practical applications, and (d) institutional support. A key recommendation is for higher education leaders to implement targeted training programs that enhance stakeholder engagement, ensuring faculty and administrators can effectively utilize predictive analytics for informed decision-making. The implications for positive social change include the potential for higher education leaders to leverage data-driven insights to improve student retention and promote equitable access to educational resources, fostering more inclusive and effective learning environments.
Recommended Citation
Nadpurajah, Laxshaa, "Strategies to Improve Higher Education Institutional Performance Through Predictive Analytics Implementation" (2025). Walden Dissertations and Doctoral Studies. 17580.
https://scholarworks.waldenu.edu/dissertations/17580
