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Title: Performance measurement in UK universities : bringing in the stakeholders' perspectives using data envelopment analysis
Author: Sarrico, Claudia S.
ISNI:       0000 0001 3553 350X
Awarding Body: University of Warwick
Current Institution: University of Warwick
Date of Award: 1998
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This thesis is about performance measurement in higher education. It brings in different stakeholders' perspectives on performance measurement, in UK universities using data envelopment analysis. The introduction gives the background of the higher education sector in the UK at present and its history. It introduces the drive for performance measurement in higher education, and the motivation for the dissertation. The method data envelopment analysis is then described. The traditional use of performance indicators and peer assessment is reviewed and the use of DEA, instead of parametric techniques, is justified. The opportunity to use DEA in a somewhat different way than previously is identified. The novel proposed framework integrates in the same analysis the perspectives of three different levels of stakeholders. Firstly, the perspective of the applicant in the process of choosing a university to apply to; secondly, the perspective of the State that funds and evaluates university performance; and finally the institutional perspective. In the applicant's perspective, the use of DEA in university selection is compared to existing methods. The new approach devised recognises the different values of students and is empirically tested in a case study at a comprehensive school. This chapter clearly deals with a choice problem, and the link with MCDM is first approached. Finally, a comprehensive decision support system that includes DEA for university selection is arrived at. Then the relationship between the State and higher education over time is described, the current operational model explained and the future trends outlined. In order to measure performance, according to the mission and objectives of the state/ funding councils, a review of their three main remits is undertaken. The contribution of DEA to inform the State/ funding councils in their remit is then discussed. The problem of taking account of subject mix factor in the measurement of performance is dealt with, by linking the input/ output divide by means of virtual weights restrictions. It is shown how institutions can turn performance measurement to their own benefit, by using it as a formative exercise to understand the different expectations of them, by the two previous external evaluations. A methodology for institutional performance management is proposed that takes into account the external/ internal interfaces: the applicant/ institution, and state/ institution interfaces. The methodology is illustrated with an application to the University of Warwick. Virtual weights restrictions are widely used in this thesis, a reflection on its uses is offered. The reasons for mainly using virtual weights restrictions instead of absolute weights restrictions are explained. The use of proportional weights restrictions is reviewed, and the reasons for using simple virtual weights and virtual assurance regions in this thesis is ascertained. Alternatives to using virtual weights restrictions are considered, namely using absolute weights restrictions with a virtual meaning. The relationship between DEA and MCDM in this domain is elaborated upon. Several conclusions are arrived at and novel contributions are made to the knowledge of the subject treated: the importance of bringing in the perspectives of different stakeholders in an integrated approach; the contribution of DEA in choice problems; handling subject mix by means of virtual assurance regions; data availability policy is found to be inadequate; a more appropriate way of comparing departments within a university; and the superiority of virtual assurance regions to represent preference structures and link the input-output divide.
Supervisor: Not available Sponsor: Fundação para a Ciência e a Tecnologia (FCT)
Qualification Name: Thesis (Ph.D.) Qualification Level: Doctoral
EThOS ID:  DOI: Not available
Keywords: HA Statistics ; LB2300 Higher Education