A three-stage DEA model to evaluate learning-teaching technical efficiency: Key performance indicators and contextual variables

Fuentes, Ramón; Fuster, Begoña; Bañuls, Adelaida Lillo

doi:10.1016/j.eswa.2015.11.022

Cited by 57 publications

(51 citation statements)

References 45 publications

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“…Also, Johnes [98] (p. 281) reports a mean efficiency of 92.51% regarding 130 UK universities with six inputs and three outputs, also with the lowest individual university efficiency levels around 60%. See also the comprehensive review of DEA studies in education by Fuentes, Fuster, and Lillo-Banuls [99] (pp. [91][92][93] regarding the applied inputs and outputs which are highly influential for the calculated efficiency results (in comparison with the number of observed DMU).…”

Section: Discussionmentioning

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: Discussionmentioning

confidence: 99%

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

Klumpp

2018

Education Sciences

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“…Some examples are safety of the project site [66][67][68], geographic location of projects [69], environmental impacts [70] and satisfaction of community, client or customer [71,72]. Moreover, the quality and variety of materials and goods used in a project [73,74], number of active labor force and salaries [75][76][77] and experience and scientific levels of employees in the project [78,79] are amongst other effective measures of project performance. Risks and operational risks, in particular, are also important indicators that many researchers have taken into account [23,63,80].…”

Section: Literature Reviewmentioning

confidence: 99%

A Novel Approach for Evaluation of Projects Using an Interval–Valued Fuzzy Additive Ratio Assessment (ARAS) Method: A Case Study of Oil and Gas Well Drilling Projects

et al. 2018

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Abstract:The beginning of the 21st-century resulted in a more developed multi-attribute decisionmaking (MADM) tool and inspired new application areas that have resulted in discoveries in sustainable construction and building life cycle analysis. Construction and civil engineering stand for the central axis of a body consisting of a multidisciplinary (multi-dimensional) world with ties to disciplines constituting the surface, and with the disciplines, as a consequence, tied to each other. When dealing with multi-attribute decision-making problems generally multiple solutions exist, especially when there is a large number of attributes, and the concept of Pareto-optimality is inefficient. The symmetry and structural regularity are essential concepts in many natural and man-made objects and play a crucial role in the design, engineering, and development of the world. The complexity and risks inherent in projects along with different effective indicators for success and failure may contribute to the difficulties in performance evaluation. In such situations, increasing the importance of uncertainty is observed. This paper proposes a novel integrated tool to find a balance between sustainable development, environmental impact and human well-being, i.e., to find symmetry axe with respect to goals, risks, and constraints (attributes) to cope with the complicated problems. The concept of "optimal solution" as the maximum degree of implemented goals (attributes) is very important. The model is built using the most relevant variables cited in the reviewed project literature and integrates two methods: the Step-Wise Weight Assessment Ratio Analysis (SWARA) method and a novel interval-valued fuzzy extension of the Additive Ratio Assessment (ARAS) method. This model was used to solve real case study of oil and gas well drilling projects evaluation. Despite the importance of oil and gas well drilling projects, there is lack of literature that describes and evaluates performance in this field projects. On the other hand, no structured assessment methodology has been presented for these types of projects. Given the limited research on performance evaluation in oil & gas well-drilling projects, the research identifies a set of performance criteria and proposes an evaluation model using fuzzy Delphi method. An illustrative example shows that the proposed method is a useful and alternative decision-making method.

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“…We calculate MPI by the performance of each DMU at different periods using the technology of a base period. Fare et al (1994) stated that a distance function in input-oriented MPI's approach can effectively measure the productivity change of a given DMUs between time t and t+1 as shown in Equation (2):…”

Section: Malmquist Productivity Index (Mpi) Modelmentioning

confidence: 99%

“…MPI was introduced as a theoretical index by Caves, Christensen, and Diewert (1982) and popularized as an empirical index by Fare, Grosskopf, Norris, and Zhang (1994). We calculate MPI by the performance of each DMU at different periods using the technology of a base period.…”

Section: Malmquist Productivity Index (Mpi) Modelmentioning

confidence: 99%

“…S. Liu, Lu, Lu, & Lin, 2013). Several studies have demonstrated that DEA applications involve a wide range of contexts such as education (Fuentes, Fuster, & Lillo-Bañuls, 2016;Lee & Worthington, 2016), banking (Sahin, Gokdemir, & Ozturk, 2016;Tsolas & Charles, 2015) and agriculture (KOČIŠOVÁ, 2015;Shrestha, Huang, Gautam, & Johnson, 2016). Therefore, this study deployed the DEA to calculate the efficiency of the rubber production in southern Thailand.…”

Section: Introductionmentioning

confidence: 99%

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Application of Data Envelopment Analysis and Malmquist Index on the Measurement of Efficiency and Productivity: A Case Study of Rubber Production in Southern Thailand

Parichatnon

Maichum

Peng

2017

Pijss

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A three-stage DEA model to evaluate learning-teaching technical efficiency: Key performance indicators and contextual variables

Cited by 57 publications

References 45 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

A Novel Approach for Evaluation of Projects Using an Interval–Valued Fuzzy Additive Ratio Assessment (ARAS) Method: A Case Study of Oil and Gas Well Drilling Projects

Application of Data Envelopment Analysis and Malmquist Index on the Measurement of Efficiency and Productivity: A Case Study of Rubber Production in Southern Thailand

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