Chief executive pay in UK higher education: the role of university performance

Johnes, Jill; Virmani, Swati

doi:10.1007/s10479-019-03275-2

Cited by 19 publications

(27 citation statements)

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“…… The evidence suggests that the presence of highly-paid staff impacts positively on VC pay. (Bachan & Reilly, 2015, p. 69) This finding was confirmed by Johnes and Virmani (2020).…”

Section: Remuneration and Governancementioning

confidence: 73%

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Paying the piper: the governance of vice-chancellors’ remuneration in Australian and UK universities

Boden

Rowlands

2020

Higher Education Research & Development

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There is a long-running cyclical public debate in the UK and Australia about the level of vice-chancellors' remuneration in publicly funded universities. Whilst governments may promise greater oversight, little appears to change. Similar trends are emerging in some other European countries. This article critically considers the determination of vice-chancellors' remuneration as a governance issue. In the context of corporatised public universities, we consider how reforms in university governance may have contributed to the shifting nature and levels of vicechancellors' remuneration in Australia and the UK. We argue that this area requires urgent governance reform.

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“…… The evidence suggests that the presence of highly-paid staff impacts positively on VC pay. (Bachan & Reilly, 2015, p. 69) This finding was confirmed by Johnes and Virmani (2020).…”

Section: Remuneration and Governancementioning

confidence: 73%

“…Research demonstrates rapid and relatively recent increases in VCs' remuneration with little connection between pay and performance (Bachan & Reilly, 2015;Johnes & Virmani, 2020). Further, there is a demonstrated link between these increases and formulaic pay-setting mechanisms such as benchmarking (Gschwandtner & McManus, 2018).…”

Section: Introductionmentioning

confidence: 99%

Paying the piper: the governance of vice-chancellors’ remuneration in Australian and UK universities

Boden

Rowlands

2020

Higher Education Research & Development

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“…where E ev , E e d , E t , E p , and E c , respectively, represent the online course demand, learners' psychological characteristics, learning motivation, learning interest, and the selection form of higher education course learning content and w evi , w e du , w tra , w pri , and w con , respectively, represent learners' preference coefficient for higher education course task activities [16]; learning effect of the optimized selection of teaching content of higher education courses at a certain stage is given by…”

Section: Update Of Higher Education Curriculum Design Processmentioning

confidence: 99%

“…. , ω T n ) calculated by formula (16), and then sort the comprehensive decisionmaking attribute values of each higher education course to obtain the optimal course of higher education, and establish the optimal course design model of higher education, as shown in Figure 1.…”

Section: Establishing the Optimal Curriculum Design Model Of Higher Educationmentioning

confidence: 99%

Research on Higher Education Evaluation and Decision-Making Based on Data Mining

Feng

2021

Scientific Programming

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Educational data mining is concerned with developing methods to explore the data from educational environments which provides insights that help in understanding the learning process and improving the educational outcomes. The evaluation and decision-making methods of higher education resources ignore the number of specific basic systems of resource evaluation and decision-making, resulting in the low accuracy of evaluation and decision-making. Therefore, a research on higher education evaluation and decision-making based on data mining is proposed. We analyze the application of big data in the field of higher education and design its optimal curriculum design model. We calculate the phased teaching task objectives of higher education curriculum, form its curriculum teaching guidance according to the influence degree between learners’ learning progress and learners’ thinking limitations, and obtain the learning effect produced by the optimal selection of curriculum teaching content. Then the probability of learners completing the structured teaching goal is calculated, so as to establish the optimal curriculum design model of higher education. Finally, we obtain the quantitative values of different experiences, extract the main influencing factors of resource evaluation and decision-making, and carry out higher education resource evaluation and decision-making analysis on this basis. The experimental results show that the research method improves the flexibility and universal applicability of higher education evaluation and decision-making, achieving an evaluation accuracy of above 90% and with below 7% error rate.

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“…Using data on 149 VCs over the period 2009-2017 Johnes and Virmani (2019) examined the relationship between university performance and VC pay. They employed three measures of university performance: a measure of managerial efficiency, performance in media produced university rankings and a measure of financial stability.…”

Section: Literature On Vice Chancellors' Paymentioning

confidence: 99%