Importance-Performance Matrix Analysis (IPMA) of Business Intelligence Indicators for Higher Education Evaluation Using Intellectual Property Components
Keywords:
Intellectual Property, Importance-Performance Matrix Analysis (IPMA) , Higher Education, Business IntelligenceAbstract
This study aimed to develop a model for evaluating higher education performance through IPMA of business intelligence indicators by integrating intellectual property components to improve the management of universities’ intangible assets. The study employed an applied, descriptive-analytical design with an exploratory mixed-method approach. The qualitative phase used the Delphi technique with 20 academic experts, and the quantitative phase included 193 university experts selected via Cochran’s formula. Data were collected through a researcher-made questionnaire and analyzed using ATLAS.TI and SMARTPLS software. IPMA was used to rank components, and partial least squares structural equation modeling assessed relationships among variables. Five main dimensions—intellectual property criteria, business intelligence criteria in higher education, higher education evaluation indicators, support procedures in higher education, and higher education outlook—were identified. All structural paths were significant (p<0.05). The “higher education outlook” showed the highest importance (0.730), while “support procedures” had the highest performance (71.418). All constructs demonstrated acceptable convergent and discriminant validity and reliability. Integrating intellectual property components into business intelligence frameworks can serve as a powerful tool for assessing and improving higher education quality. This approach enhances the management of intangible assets, strengthens innovation and competitiveness, and supports achieving strategic goals and sustainable development in higher education institutions.
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