When does data envelopment analysis outperform a naïve efficiency measurement model?

Büschken, Joachim

doi:10.1016/j.ejor.2007.09.026

Cited by 13 publications

(5 citation statements)

References 35 publications

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“…Therefore, according to Spearman's rank correlation analysis, there are no big differences between the results for the original method (EWSM) and the WSM Conjoint and Conjoint DEA results. This conclusion is consistent with Buschken's claim [65] that the naive model almost perfectly replicates the DEA efficiency scores for constant return-to-scales and low input-output data heterogeneity. Therefore, it can be concluded that the heterogeneity of input-output data is essential to take advantage of the DEA capability.…”

Section: Verification Of the Dea Resultssupporting

confidence: 90%

“…Teacher ranking has become increasingly objective over the past few years thanks to new methods, a systematic approach and well-developed, organized academic networks and databases [65]. Mester [66] states that the leading indicators for the metric of teachers' scientific research work are: number of citations, h-index, and i10 index.…”

Section: Assessment Of the Research Efficiencymentioning

confidence: 99%

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Using Data Envelopment Analysis and Multi-Criteria Decision-Making Methods to Evaluate Teacher Performance in Higher Education

et al. 2020

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This paper proposes an approach that combines data envelopment analysis (DEA) with the analytic hierarchy process (AHP) and conjoint analysis, as multi-criteria decision-making methods to evaluate teachers’ performance in higher education. This process of evaluation is complex as it involves consideration of both objective and subjective efficiency assessments. The efficiency evaluation in the presence of multiple different criteria is done by DEA and results heavily depend on their selection, values, and the weights assigned to them. Objective efficiency evaluation is data-driven, while the subjective efficiency relies on values of subjective criteria usually captured throughout the survey. The conjoint analysis helps with the selection and determining the relative importance of such criteria, based on stakeholder preferences, obtained as an evaluation of experimentally designed hypothetical profiles. An efficient experimental design can be either symmetric or asymmetric depending on the structure of criteria covered by the study. Obtained importance might be a guideline for selecting adequate input and output criteria in the DEA model when assessing teachers’ subjective efficiency. Another reason to use conjoint preferences is to set a basis for weight restrictions in DEA and consequently to increase its discrimination power. Finally, the overall teacher’s efficiency is an AHP aggregation of subjective and objective teaching and research efficiency scores. Given the growing competition in the field of education, a higher level of responsibility and commitment is expected, and it is therefore helpful to identify weaknesses so that they can be addressed. Therefore, the evaluation of teachers’ efficiency at the University of Belgrade, Faculty of Organizational Sciences illustrates the usage of the proposed approach. As results, relatively efficient and inefficient teachers were identified, the reasons and aspects of their inefficiency were discovered, and rankings were made.

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Section: Verification Of the Dea Resultssupporting

confidence: 90%

Section: Assessment Of the Research Efficiencymentioning

confidence: 99%

Using Data Envelopment Analysis and Multi-Criteria Decision-Making Methods to Evaluate Teacher Performance in Higher Education

et al. 2020

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“…Stochastic frontier approaches (SFAs) and data envelopment analyses (DEAs) are the main methods used to measure total factor productivity in academic circles; this can avoid the structural deviation caused by the misunderstanding of production function, such as occurs in the traditional accounting method and SFA method [54][55][56]. In view of this, the traditional DEA model cannot solve the problem of relaxation variable, and when the efficiency of efficient decision-making units is 1, the units cannot be distinguished well.…”

Section: The Measurement and Decomposition Of The Green Total Factor ...mentioning

confidence: 99%

Can China’s Campaign-Style Environmental Regulation Improve the Green Total Factor Productivity?

Du,

Zhang,

Wang

2023

Sustainability

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The central environmental inspection policy serves as a pivotal instrument for environmental regulation in China, closely intertwined with the nation’s economic and social development into a greener model. Based on the urban data of China from 2004 to 2018, this paper employs a regression discontinuity design to empirically test the inherent mechanism of the central environmental inspection policy’s impact on green total factor productivity, and attempts to analyze its impact on technological progress from the perspective of a bias towards technological advancement. This study found that central environmental inspections can significantly improve green total factor productivity, the mechanism behind this improvement being through the enhancement of technological progress, while having a negative impact on technical efficiency. Additionally, we found that the impact of policies on technological progress is mainly through increasing the magnitude of technological progress, rather than favoring technological progress. The results of this research provide reasonable suggestions for the Chinese government to revise their environmental inspection system.

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“…As we had only one output measure, caveat of correlation between output items was avoided and dimensions kept low in out DEA model. Both Büschken (2009) and Lin (2007) argued lower amount of dimensions to be key for valid efficiency evaluation. Based on Büschken (2009) it is more important to have heterogeneity between different parameter values than to have numerous inputs and outputs at use.…”

Section: Paper Mill's Distribution Efficiencymentioning

confidence: 99%

“…Both Büschken (2009) and Lin (2007) argued lower amount of dimensions to be key for valid efficiency evaluation. Based on Büschken (2009) it is more important to have heterogeneity between different parameter values than to have numerous inputs and outputs at use.…”

Section: Paper Mill's Distribution Efficiencymentioning

confidence: 99%

Paper mill's distribution efficiency to emerging East European markets

Hilmola

Hämäläinen

Hujala

2014

Industrial Management &Amp; Data Systems

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Purpose – European paper industry has been struggling with margins and profitability for more than decade time period. At typical in markets of west, paper product demand is at long-term decline, mostly driven by continuously increasing internet use. However, in emerging markets demand still exists, and in Europe numerous small markets in east have even some growth available. The paper aims to discuss these issues. Design/methodology/approach – The authors analyse in this research work with longitudinal data (period of 2002-2009) from one large Finnish paper mill and data envelopment analysis (DEA) approach, how distribution efficiency to selected eight East European markets has evolved. Findings – In general distribution efficiency has improved, but this has taken place in step-wise manner rather than being linear year-to-year development (year 2006 found to be the threshold). Reason is mostly in better management of transportation costs, and in particular lower monthly deviation of these costs. It is surprising that case paper mill has been able to manage transportation costs in rapidly increasing energy cost environment so efficiently. Maybe European Union enlargement of 2004 and 2007 has had its effects on distribution efficiency. Research limitations/implications – The research is limited to the deliveries of one paper mill located in Finland. Also East European markets in the early periods of this study were emerging papers markets, and distribution practices were clearly evolving. Practical implications – Based on the study East European paper market distribution should give more attention on transportation cost control, and trying to find solutions to minimize it with low monthly fluctuation. Originality/value – Very few studies exist from East European distribution issues, and particularly that of paper industry. Also used quantitative method of DEA is relatively new in this context and gives valuable insights for the distribution efficiency development.

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When does data envelopment analysis outperform a naïve efficiency measurement model?

Cited by 13 publications

References 35 publications

Using Data Envelopment Analysis and Multi-Criteria Decision-Making Methods to Evaluate Teacher Performance in Higher Education

Using Data Envelopment Analysis and Multi-Criteria Decision-Making Methods to Evaluate Teacher Performance in Higher Education

Can China’s Campaign-Style Environmental Regulation Improve the Green Total Factor Productivity?

Paper mill's distribution efficiency to emerging East European markets

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