The Perspective Classification of Balanced Scorecard with Ontology Technique
DOI:
https://doi.org/10.32890/jict2024.23.3.4Keywords:
classification, ontology, balanced scorecard, performance management, case studyAbstract
The competition among Higher Education Institutions (HEIs), such as universities, worldwide is crucial. It is imperative for universities
to maintain high-quality education and achieve and maintain their world ranking. Therefore, they must utilise performance management tools in their performance improvement efforts. The Balanced Scorecard (BSC) is a quality tool for performance management in universities that helps them to enhance, increase, and maintain their overall performance and ranking. When their strategy changes, the performance indicators must be revised and rearranged in accordance with the BSC form. The information needed for decision-making cannot be immediately available because there are delays in updating the performance management system. This creates a gap between performance improvement in HEIs and the BSC. To eliminate this gap, the question we need to answer is how to automatically upload the updated BSC perspectives into the performance management system to streamline the decision-making automatically. This paper aims to present the Perspective Classification of Balanced Scorecard with Ontology (PCBSC-Onto) framework for automatically classifying performance indicators into BSC’s four perspectives using an ontological approach to support the dynamic capability of the management system’s performance. The balanced scorecard ontology and performance indicator ontology created through this research were applied in the PCBSC-Onto framework. The experiment’s results are based on the performance indicator data of a university in Thailand, and it presents how this framework and its algorithms contribute to increasing performance management ability in the HEI context. The accuracy rate of the PCBSC-Onto framework is 82.97% when compared to the accuracy of the modified Delphi method. The results reveal the accuracy of the proposed ontologies and algorithms
on the data from the case study.
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