Development of a two-dimensional productivity measurement model for higher learning institutions
Measuring the performance of higher learning institutions (HLIs) is a must for these institutions to stay competitive and to move forward.Initiatives towards constructing a more appropriate and accurate measurement is vital. This paper focuses on formation of a productivity model that consists of...
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| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
2016
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| Subjects: | |
| Online Access: | http://repo.uum.edu.my/20252/1/IRMM%206%20S7%202016%2091%2094.pdf http://repo.uum.edu.my/20252/ https://www.econjournals.com/index.php/irmm/article/view/3177 |
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| Institution: | Universiti Utara Malaysia |
| Language: | English |
| Summary: | Measuring the performance of higher learning institutions (HLIs) is a must for these institutions to stay competitive and to move forward.Initiatives towards constructing a more appropriate and accurate measurement is vital. This paper focuses on formation of a productivity model that consists of efficiency and effectiveness dimensions by utilizing a non parametric method, data envelopment analysis (DEA). The identification of suitable input,
output and outcome variables were done prior to the development of the model.The proposed model is validated by measuring the productivity of 16
public universities in Malaysia for year 2008. However, due to unavailability of one variable data, an estimate was used as a proxy to represent the real data.The results show average efficiency and effectiveness scores are 0.817 and 0.900 respectively and 0.754 is the overall productivity score.A total of six universities were both efficient and effective. The formation of this performance model would work as a complement method to the existing performance methods or as an alternative method in monitoring the level of performance of HLIs especially for the Malaysia public HLIs.The proposed model could be adopted in a different field or sector after priori identification of suitable and related variables of the selected context. |
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