A network DEA quantity and quality-orientated production model: An application to Australian university research services

Lee, Boon L.; Worthington, Andrew C.

doi:10.1016/j.omega.2015.05.014

Cited by 115 publications

(67 citation statements)

References 52 publications

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“…Specific calculation methods, such as data envelopment analysis (DEA) window analysis as well as DEA Malmquist index, are employed for such questions. As Parteka and Wolszczak-Delacz [13] (p. 68) outline, this may overcome shortcomings of former analysis perspectives which had focused mainly on static efficiency analysis results [14][15][16][17][18][19][20][21][22][23]. The underlying technique for efficiency measurement is the DEA introduced by Charnes, Cooper, and Rhodes [24] in the basic form with constant returns to scale (CRS) and extended by Banker, Charnes, and Cooper [25] with variable returns to scale (VRS).…”

Section: Introductionmentioning

confidence: 99%

The Index Number Problem with DEA: Insights from European University Efficiency Data

Klumpp

2018

Education Sciences

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An increasing effort has been put into dealing with the question of time-series analysis regarding institutional efficiency, including in the area of higher education. Universities are important institutions for economies and societies and are expected to provide excellence as well as efficiency in their processes and outputs. This is reflected in the context of an increased global competitive environment by more refined international university rankings. Combining the two areas, this paper points towards a methodological challenge in comparing different ranking datasets for their use in a data envelopment analysis (DEA) Malmquist index time-series efficiency analysis, namely, index-based data compared to additive data. The problem is discussed in a theoretical framework and complemented with an empirical application: calculations for 70 European universities with budget and staff input data and different ranking output data for the timeframe of 2011-2016 show that there is no evidence for a specific index data problem. Important implications regarding university management and higher education policies are outlined. Efficiency improvements among the analyzed universities are significant but also unevenly distributed and not easy to obtain for individual institutions.

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Section: Introductionmentioning

confidence: 99%

The Index Number Problem with DEA: Insights from European University Efficiency Data

Klumpp

2018

Education Sciences

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“…DEA is widely applied to efficiency measurement of decision making units (DMUs) in various industries, including agriculture [6,7], industry [8,9] and service industry-such as finance [10,11], education [12,13], retail [14,15], healthcare service and so on. Thereby, in the field of healthcare service, the applications of DEA method mainly focus on the simple efficiency measurement of medical and healthcare institutions or organizations.…”

Section: Introductionmentioning

confidence: 99%

Performance Measurement of Healthcare Service and Association Discussion between Quality and Efficiency: Evidence from 31 Provinces of Mainland China

2017

Sustainability

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Performance of healthcare service includes quality and efficiency, so there is inevitably an association between them. In general, it is believed that there is a trade-off between quality and efficiency; however, we prove that it is not completely accurate. We take the quality as an additional output to measure the healthcare service efficiency creatively, and discuss the association between quality and efficiency based on the effect of quality on relative efficiency. Firstly, we use TOPSIS method to calculate relative quality index values of healthcare service of 31 provinces of mainland China. This practice ensures the consistent comparison of 31 provinces' qualities. Subsequently, we measure the 31 provinces' relative performances and efficiencies of healthcare service with consideration of quality or not, by constructing DEA models. Then, we analyze the association between quality and efficiency from each group of the national, east, central and west, and conclude that the association between them is different along with their advantage degrees of quality and efficiency. Finally, we use the Tobit regression method to test 12 environment variables' net impacts on efficiency values both with consideration of quality or not. The results indicate that the main drivers of healthcare service efficiency are different when we take quality as an additional output or not. This article contributes to the field of performance measurement of healthcare service, puts forward a new method to integrate quality and efficiency and provides management guidelines.

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“…The efficiency is relative, as it is based on a comparison between DMU, chosen for the analysis. Each object is described by multiple inputs and outputs (Suzuk, Nijkamp, 2011;Appa, Williams, 2002;Lee, Worthington, 2016).…”

Section: Methodsmentioning

confidence: 99%

Effectiveness of National Airlines in Europe – the DEA Approach

Żółtaszek

Pisarek

2016

Folia Oeconomica Stetinensia

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National airlines operate in a highly competitive environment. EU airlines face a challenge to compete with low cost carriers, as a result of the liberalization process in the sector. European flag airlines of non-EU member states, not benefiting from liberalization, are forced to compete internationally. This research is focused on national carriers, as they provide the majority of service to and from central and regional airports. Therefore, to establish the most efficient entities on the passenger air transport market, DEA (Data Envelopment Analysis) methodology, has been utilized. The purpose of this paper is to evaluate the effectiveness of 29 chosen national airlines in Europe in the year 2013, using the DEA approach, to pinpoint the subset of fully-efficient market leaders, as well as potential sources of inefficiency, among less effective carriers. The analysis incorporates information on inputs (e.g. fleet, number of employees, number of countries and airports served) and outputs (revenue, annual passengers carried, load factor). The results show that more than 40% (12 of 29) researched airlines are effective and the other 34% are near-efficient. Moreover, outcomes suggest that "going big" may not increase effectiveness. It is harder to achieve full efficiency for big carriers than small ones.

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A network DEA quantity and quality-orientated production model: An application to Australian university research services

Cited by 115 publications

References 52 publications

The Index Number Problem with DEA: Insights from European University Efficiency Data

The Index Number Problem with DEA: Insights from European University Efficiency Data

Performance Measurement of Healthcare Service and Association Discussion between Quality and Efficiency: Evidence from 31 Provinces of Mainland China

Effectiveness of National Airlines in Europe – the DEA Approach

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