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Multiple Dimensions of University Governance and Performance: The Case of UK Higher Education

Basha, Faisal Hani H (2020) Multiple Dimensions of University Governance and Performance: The Case of UK Higher Education. Doctoral thesis, University of Huddersfield.

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Abstract

This study is a unique systematic quantitative effort to decipher and interpret the complex multi-dimensional associations and trade-offs between university governance and performance in the UK. It is unprecedented in its scope and breadth and breaks with traditional discourses in corporatised university governance that are the extant paradigm. A seven element multi-theoretical framework is used to investigate the large decade-long collated empirical data set of university-year governance-performance metrics in the country. Five distinct subsumed research objectives are targeted in the study. These are used to explicate empirical insights about the trade-offs involved in the four inter-related aspects overlooked by the current corpus of governance performance research here, i.e., the multiple dimensions in either construct, the quality assurance/cultural connotations therein, the embedded process like elements and longitudinal relationships. The study, thus, richly expands the body of knowledge in university governance and performance.

Singularly in the thesis 31 variables capturing varied dimensions of university governance and performance for the eleven years between 2005 and 2015 are collected mixing hand written means with other standard approaches. Across this data horizon, in a sample consisting of 132 UK universities, these variables are extensively analysed, resulting in a variegated, comprehensive and distinctive panoply of triangulated findings. From a methodological perspective the research is largely knit using eclectic and advanced regression analysis. However, it is not limited to this. The project splices the empirical UK data set in innovative ways and coalesces critical discussions and narratives across univariate, bivariate, multivariate pooled and panel analyses. While GLS Fixed Effects regressions are the base model chosen, a series of five other sensitivity-assessing regressions and a battery of related tests are done to achieve full academic rigour.

The thesis uncovers strong robust evidence for the multi-dimensional and complex links between UK university governance and performance. In six different complex models of multivariate regressions it finds a range of nuanced complex yet highly tractable and explainable relationships between multidimensional governances and performances of the UK university.

These findings allow for novel contributions to the body of knowledge. Thus, uniquely the thesis conceptualises university governance and performance crafting holistic definitions of either construct. It expands the vocabulary of the discourse using theory to identify five new missing dimensions of university governance. By analysing the rich panel data set of several governance performance variables, it establishes an inflexion point to differentiate the future research trajectory in this area. Providing a robust basis for the existing normative and argumentative policy literature it assesses the credibility of the many scholarly critiques. The thesis also formally tests the validities of many existing and recent policy changes introduced in UK HEI and uncovers trade-offs and complexities that may have been missed by regulators. Finally, it provides an empirical basis for key concepts in governance such as culture/quality assurance concerns, process like characteristics and teaching/learning regimes. These novel contributions result in a highly original set of recommendations to university governors, HEI regulators and future researchers.

Item Type: Thesis (Doctoral)
Subjects: L Education > LB Theory and practice of education > LB2300 Higher Education
Schools: Huddersfield Business School
Depositing User: Christine Morelli
Date Deposited: 18 Feb 2021 13:23
Last Modified: 18 Feb 2021 13:23
URI: http://eprints.hud.ac.uk/id/eprint/35348

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