Efficiency of Entrepreneurial Universities in India: A Data Envelopment Analysis

Loganathan, Muralidharan; Subrahmanya, M.H. Bala

doi:10.1007/s13132-022-00897-z

Cited by 8 publications

(6 citation statements)

References 42 publications

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“…Based on another finding from this study, teaching efficiency scores showed a significant difference in favor of research universities, but no significant difference was observed based on university ownership. This finding partially supports previous research indicating that efficiency scores are not significantly influenced by institutional ownership (Foltz et al, 2012; Loganathan & Subrahmanya, 2022; Mousa & Ghulam, 2019). The study revealed a significant difference in research efficiency scores, favoring research and public universities.…”

Section: Discussionsupporting

confidence: 90%

“…While some previous studies have shown that efficiency scores do not differ according to institutional ownership (Foltz et al, 2012; Loganathan & Subrahmanya, 2022; Mousa & Ghulam, 2019), others have shown that they do (Casani et al, 2014; Kao & Pao, 2009; Tran & Villano, 2017). In this study, it was tested whether the efficiency scores differed according to (1) the type of university and (2) the structure of the university.…”

Section: Methodsmentioning

confidence: 92%

“…This analysis covers teaching, research, patents, and third mission activities. Loganathan and Subrahmanya (2022) analyzed the efficiency of 28 Indian universities with DEA. In this study, third mission activities were included as part of the process while evaluating the efficiency of universities.…”

Section: Introductionmentioning

confidence: 99%

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Teaching, research, and third mission efficiency of universities: A data envelopment analysis and hybrid multi‐criteria decision‐making approach

Maral,

Çetin

2024

Manage Decis Econ

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Previous studies on the efficiency of higher education institutions have generally approached the functions of higher education from a limited perspective, and the studies have followed traditional methods. The aim of this study is to determine the teaching, research, and third mission technical efficiency of universities, to rank efficient universities, to identify potential areas for improvement, and to examine the determinants of efficiency scores and the differences in scores in terms of various variables. In this study, the efficiency of 187 universities was measured by data envelopment analysis. Efficiency criteria were weighted by both objective and subjective multi‐criteria decision‐making (MCDM) methods and ranked by different MCDM methods. Beta and Linear regression were performed to determine the factors on efficiency scores. According to the results of the study, the most efficient universities are in the fields of research, teaching, and third mission, respectively. Budget criterion is the most important input criterion. Number of graduates, number of publications, and number of social responsibility projects are the most important output criteria. Having an older history positively predicts teaching and research efficiency. The size of universities and being located in developed regions negatively predict research and third mission efficiency. There is a positive relationship between rankings in teaching, research, and third mission. This research provides more comprehensive information to universities by considering teaching, research, and third mission efficiency separately. Second, this research proposes a new methodology for higher education efficiency measurement by integrating DEA and hybrid MCDM. Finally, this research provides new insights by analyzing factors related to teaching, research, and tertiary mission efficiency.

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Section: Discussionsupporting

confidence: 90%

Section: Methodsmentioning

confidence: 92%

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Teaching, research, and third mission efficiency of universities: A data envelopment analysis and hybrid multi‐criteria decision‐making approach

Maral,

Çetin

2024

Manage Decis Econ

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“…These aforementioned studies have been followed up by other authors in recent years. In measuring efficiency based on DEA analysis, the authors of the most recent studies include: Loganthan and Subrahmanya (2022), Zarrin (2022), Zhao et al (2022), Chen and Shu (2021).…”

Section: Literature Reviewmentioning

confidence: 99%

DEA models as a tool for evaluating and measuring the efficiency of public universities

Krajčo,

Habánik,

Grenčíková

2024

Economics &Amp; Sociology

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Nowadays, the issue of measuring economic and non-economic efficiency is related not only to the enterprise itself but also to the organizations providing education. Measuring the economic efficiency of an educational institution cannot be done as clearly as in the case of enterprises whose mission is to produce products and services for the purpose of selling them. In the case of educational institutions in the form of public universities, profit cannot be considered as the main objective. Universities only report data stipulated by the current legislation, thus measuring efficiency is a rather challenging matter with ambiguous quantification. This study analyzes the efficiency of public universities through some publicly available indicators of their activities. The selection of indicators was made based on the analysis of previous scientific works that dealt with the issue. The basic selection criteria were whether the indicators were verifiable and unquestionable. The work focused on the construction of a model based on DEA analysis of efficiency at constant returns to scale. Descriptive statistics, correlation analysis, cluster analysis, deduction, induction, comparison, and synthesis were also used. Through these methods, it was possible to construct a general model for evaluating the efficiency of public universities.

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“…[12], [13], agriculture e.g. [14]- [16], education [17], [18], etc. Several software systems implement DEA models to collect information about relative productivity.…”

Section: Introductionmentioning

confidence: 99%

Second-order conic programming for data envelopment analysis models

Mourad¹

2022

PEN

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Data envelopment analysis (DEA) is a widely used benchmarking technique. Its strength stems from the fact that it can include several inputs and outputs of not necessarily the same type to evaluate efficiency scores. Indeed, the aforesaid method is based on mathematical optimization. This paper constructs a second-order conic optimization problem unifying several DEA models. Moreover, it presents an algorithm that solves the former problem, and provides a MATLAB function associated with it. As far as known, no MATLAB function solves DEA models. Among different types of DEA, this function can handle deterministic, Malmquist index, and stochastic models. In fact, DEA is involved in various practical applications, thus, this work will provide some possible future extensions, not only for MATLAB but also for any programming software in applications of decision science and efficiency analysis.

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Efficiency of Entrepreneurial Universities in India: A Data Envelopment Analysis

Cited by 8 publications

References 42 publications

Teaching, research, and third mission efficiency of universities: A data envelopment analysis and hybrid multi‐criteria decision‐making approach

Teaching, research, and third mission efficiency of universities: A data envelopment analysis and hybrid multi‐criteria decision‐making approach

DEA models as a tool for evaluating and measuring the efficiency of public universities

Second-order conic programming for data envelopment analysis models

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