Quantitative Models for Performance Evaluation and Benchmarking

Zhu, Joe

doi:10.1007/978-1-4757-4246-6

Cited by 266 publications

(59 citation statements)

References 59 publications

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“…This model -and its many variants and extensions -has found thousands of applications in the scientific literature over the past decades. We refer here to various overview publications, such as Charnes et al (1994), Zhu (2003), Färe et al (1998), Ray (2004), Cooper et al (2006), Zhu, Cook (2007), Suzuki, Nijkamp (2016a,b), Susuki, Nijkamp (2017), and Suzuki et al (2010Suzuki et al ( , 2015.…”

Section: Data Envelopment Analysis (Dea)mentioning

confidence: 99%

Effective Clusters as Territorial Performance Engines in a Regional Development Strategy - A Triple-Layer DEA Assessment of the Aviation Valley in Poland

Kourtit¹,

Nijkamp²,

Suzuki³

2017

REGION

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Abstract. In the past decades, a new concept has been introduced in the regional development literature, viz. economic-technological clusters. A wealth of studies has been published on the conceptual, operational and policy foundation, and relevance of this concept, especially in relation to previously developed regional growth concepts, such as industrial districts, industrial complexes, or growth centers. In the present paper, clusters will be regarded as the spatial foci of sustainable territorial performance strategies and synergetic actions by both public and private actors. This paper aims to address the relevance of cluster concepts for an effective regional development policy, based on the notion of territorial capital. It does so by introducing a new concept, viz. effective cluster, in which spatial-economic synergy, local/regional concentration of industry, and the supporting role of territorial capital are regarded as the main determinants of a highly performing cluster in a given territory. The effective cluster concept will be illustrated and tested on the basis of a field study on the aviation and aerospace cluster 'Dolina Lotnicza' in the Podkarpackie region in South-East Poland. This is one of the most vibrant high-tech clusters in the country. Rather than providing a critical assessment of the specific development strategy of the Podkarpackie region, this study will show the added value of a new general conceptual framework based on effective clusters. A novel approach based on a triple-layer architecture will be adopted here, viz. a quantitative comparative analysis of the 16 Polish 'voivodships' (main administrative regions in the country, at a NUTS-2 level), a benchmark analysis of the 25 counties ('powiats') within the Podkarpackie voivodship (at a NUTS-4 level), and an effective industrial cluster analysis on the basis of the individual aviation firms located in the Podkarpackie region. In each step, an extended Data Envelopment Analysis (DEA), characterized by a merger of a Slack-Based Measure (SMB) and a super-efficiency (SE) DEA model, will be used in order to achieve an unambiguous ranking of the various regions or relevant Decision Making Units (DMUs) in the area concerned. The study will employ an extensive database based on field work among the individual actors in the cluster, in combination with a broadly composed territorial-capital database for the areas under study. The paper will be concluded with some strategic policy lessons. 39 40 K. Kourtit, P. Nijkamp, S. Suzuki

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Section: Data Envelopment Analysis (Dea)mentioning

confidence: 99%

Effective Clusters as Territorial Performance Engines in a Regional Development Strategy - A Triple-Layer DEA Assessment of the Aviation Valley in Poland

Kourtit¹,

Nijkamp²,

Suzuki³

2017

REGION

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“…The variables used to identify the frontiers of non-bankrupt and bankrupt groups need to be the same, that is, one variable used as output for non-bankruptcy will be applied as input for the bankrupt group and vice versa. The major reason is that DUMs identified as the benchmarks of each group, will be used as variablebenchmarks [16] to evaluate all companies, including non-bankruptcy (notated as G 1 ) and bankruptcy (notated as G 2 ) to classify their membership based on the same measurements. …”

Section: Methodsmentioning

confidence: 99%

“…To establish a general discriminant rule that a company can be classified to a des- ignated group if it is dominated only by a correspondent frontier, the stratification model [16] is further applied to deal with the overlap problem.…”

Section: Methodsmentioning

confidence: 99%

Consideration of Uneven Misclassification Cost and Group Size for Bankruptcy Prediction

Kuo

2013

AJIBM

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Despite a larger number of approaches developed for predicting bankruptcy over the past three decades, rare research has considered the effects of misclassification cost and group size. Uneven cost of misclassification results from Type I (misclassify a healthy company as a failure) and Type II errors (misclassify a failed company as healthy), which are seldom considered. Without accounting for unevenness in misclassification cost, the classifier is developed based on minimizing total misclassification errors to improve the hit-ratio for classification performance. This not only results in poor decision capability, but also causes bias towards the larger group. This paper explores the issues of uneven misclassification costs and imbalanced group size by applying an asymmetric-stratified data envelopment analysis to bankruptcy prediction. The results show a tradeoff between hit-ratio and misclassification cost when Type II error cost is ten times over that of Type I, that is, the higher the hit-ratio is, the greater the resulting misclassification costs are. By incorporating different proportions of Type II error costs to Type I into the classification procedures, the proposed approach provides greater flexibility to decision makers for credit evaluation or bankruptcy prediction based on different risk attitudes and situations.

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“…Specifically, DEA permits us to evaluate, appraise and rank CTAs in a risk-return framework without using traditional indices. Among the first studies in DEA to propose the evaluation of CTA classifications followed by the development of the benchmark models were Wilkens and Zhu (2001) and Zhu (2003).…”

Section: Introductionmentioning

confidence: 99%

“…Benchmarking models (Zhu, 2003) permits us to establish the relative standings of CTAs under investigations by ranking them in terms of efficiency. In other words, DEA can be seen as a benchmarking instrument because the "best practices" frontier is used as the "gold standard" of efficiency.…”

Section: Introductionmentioning

confidence: 99%

Evaluation of Commodity Trading Advisors using fixed and variable and benchmark models

Gregoriou

Chen

2006

Ann Oper Res

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This paper examines the performance of Commodity Trading Advisors (CTAs) using fixed and variable benchmarking models. In order to avoid the troublesome passive and active commodity and managed futures benchmarks (indices) when examining the performance of CTAs, we innovate by using data envelopment analysis (DEA). Because this alternative class has non-linear returns due to long/short positions, and derivatives (i.e., dynamic trading strategies), DEA can alleviate the problems usually associated with these indices. The effectiveness of using benchmarking models in a DEA setting will provide investors with an alternative technique in assessing the performance and identifying efficient CTAs.

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Quantitative Models for Performance Evaluation and Benchmarking

Cited by 266 publications

References 59 publications

Effective Clusters as Territorial Performance Engines in a Regional Development Strategy - A Triple-Layer DEA Assessment of the Aviation Valley in Poland

Effective Clusters as Territorial Performance Engines in a Regional Development Strategy - A Triple-Layer DEA Assessment of the Aviation Valley in Poland

Consideration of Uneven Misclassification Cost and Group Size for Bankruptcy Prediction

Evaluation of Commodity Trading Advisors using fixed and variable and benchmark models

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