Value-Based Performance Excellence Model for Higher Education Institutions in Malaysia: On Bayesian Structural Equation Modeling

• Main Authors: M. R., Abdul Hamid, Zainol, Mustafa, Nur Riza, Mohd Suradi, Fazli, Idris, Mokhtar, Abdullah, Megawati, Omar
• Format: Article
• Language: English
• Published: Universiti Teknologi MARA (UiTM) 2012
• Subjects: Q Science (General)
• Online Access: http://umpir.ump.edu.my/id/eprint/7702/1/Value-Based_Performance_Excellence_Model_for_Higher_Education_Institutions_in_Malaysia-_On_Bayesian_Structural_Equation_Modeling.pdf; http://umpir.ump.edu.my/id/eprint/7702/

Nowadays, universities throughout the world are striving hard to demonstrate their commitment to academic excellence, research, and professional services. In addition, they are determined to show their accountability by establishing the impact of their performance on the community, nation, and world. The common measures of academic performance of universities such as number of students enrolled in academic programmes, number of research publications, amount of research funding etc. are tangible and quantitative in nature. It should be noted, however, that intangible or qualitative measures such as students’ satisfaction with academic programmes and reputation of a university are examples of equally important yardsticks of academic performance. Other intangible academic performance indicators of universities should also include those yardsticks that are related to core values. For instance, ‘plagiarism’ in academic works is an example of a value-based indicator that reflects some departure from the core value of ‘academic integrity’. Therefore, the aim of this article is to empirically test a theoretical model of Value-based Performance Excellence indicators for Higher Education Institutions (IHLs) in Malaysia. A sample of 419 respondents from eleven Malaysian Public Universities was collected and the data were analysed to validate the model. Structural Equation Modeling (SEM) through Bayesian estimation was used in the analysis. The results of analysis showed the estimation using Bayesian estimation is comparatively close to maximum likelihood estimation. Therefore, the model is statistically valid and reliable.