Improving The Institutional Research and Publication Performances Using Clustering Algorithm
Performance measurement is an activity that measures the performance of an organization based on a standardized benchmark. Performance measurement activities should be implemented more focused and balanced by taking into account the size and capabilities of an organization in order to be implemented...
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| Main Authors: | , , |
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| Format: | Monograph |
| Language: | English |
| Published: |
UUM
2021
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| Subjects: | |
| Online Access: | https://repo.uum.edu.my/id/eprint/30115/1/13869.pdf https://repo.uum.edu.my/id/eprint/30115/ |
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| Summary: | Performance measurement is an activity that measures the performance of an organization based on a standardized benchmark. Performance measurement activities should be implemented more focused and balanced by taking into account the size and capabilities of an organization in order to be implemented more efficiently. Looking at the situation in higher education institutions, performance measurement is very important to ensure excellence and sustainability. Among the things that need to be measured are university research and development (R&D) activities as well as teaching and learning activities. University’s R&D activities involve research, publishing as well as innovation and commercialization. All these elements are closely related and have continuity with each other to ensure that it can achieve the level of university ranking globally. Accordingly, the study will determine the dominant factors to ensure the excellence of the university's R&D performance. This study will involve case study methods at Universiti Utara Malaysia (UUM) which includes aspects of university R&D performance as well as several aspects that include research and publications. The main need for this study is to enhance and improve university achievement in the context of R&D in order to achieve a good position globally. Accordingly, the application of quantitative approaches through data mining clustering techniques is seen to be more structured and comprehensive highlighting five main clusters among the academia. Each cluster has its own feature which may inform the management to establish the policy relating to the talent management according to their respective strength. This is to ensure that the decisions made are more comprehensive so that the decision-making process will be more rational and consistent based on the objectives that have been set. In addition, through data mining methods, it is also seen to ensure that the aspect of matching the abilities and background of the school is taken into account during the division of KPIs. Therefore, the decision-making process will be more efficient and equal to ensure the excellence and sustainability of R&D university. |
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