Author
Joe Zhu
Other affiliations: Nanjing Audit University, Southeast University, National Yunlin University of Science and Technology ...read more
Bio: Joe Zhu is an academic researcher from Worcester Polytechnic Institute. The author has contributed to research in topics: Data envelopment analysis & Returns to scale. The author has an hindex of 72, co-authored 231 publications receiving 19017 citations. Previous affiliations of Joe Zhu include Nanjing Audit University & Southeast University.
Papers published on a yearly basis
Papers
More filters
TL;DR: In this article, the authors present DEA Software Tools and Technology: A State-of-the-Art Survey with DEA Estimators and their Applications in Data Envelopment Analysis.
Abstract: -Preface W.W. Cooper, L.M. Seiford, J. Zhu. -1. Data Envelopment Analysis: History, Models and Interpretations W.W. Cooper, L.M. Seiford, J. Zhu. -2. Returns to Scale in DEA: R.D. Banker, W.W. Cooper, L.M. Seiford, J. Zhu. -3. Sensitivity Analysis in DEA: W.W. Cooper, Shanling Li, L.M. Seiford, J. Zhu. -4. Incorporating Value Judgments in DEA: E. Thanassoulis, M.C. Portela, R. Allen. -5. Distance Functions with Applications to DEA R. Fare, S. Grosskopf, G. Whittaker. -6. Qualitative Data in DEA W.D. Cook. -7. Congestion: Its Identification and Management with DEA W.W. Cooper, Honghui Deng, L.M. Seiford, J. Zhu. -8. Malmquist Productivity Index: Efficiency Change Over Time K. Tone. -9. Chance Constrained DEA: W.W. Cooper, Zhimin Huang, S.X. Li. -10. Performance of the Bootstrap for DEA Estimators and Iterating the Principle: L. Simar, P.W.Wilson. -11. Statistical Tests Based on DEA Efficiency Scores R.D. Banker, R. Natarajan. -12. Performance Evaluation in Education: Modeling Educational Production J. Ruggiero. -13. Assessing Bank and Bank Branch Performance: Modeling Considerations and Approaches J.C. Paradi, S. Vela, Zijiang Yang. -14. Engineering Applications of Data Envelopment Analysis: Issues and Opportunities: K.P. Triantis. -15. Benchmarking in Sports: Bonds or Ruth: Determining the Most Dominant Baseball Batter Using DEA T.R. Anderson. -16. Assessing the Selling Function in Retailing: Insights from Banking, Sales forces, Restaurants & Betting shops A.D. Athanassopoulos. -17. Health Care Applications: From Hospitals to Physicians, From Productive Efficiency to Quality Frontiers: J.A. Chilingerian, H.D. Sherman. -18. DEA Software Tools and Technology: A State-of-the-Art Survey R. Barr. -Notes about Authors. Author Index. Subject Index.
1,462 citations
TL;DR: It is shown that the standard DEA model can be used to improve the performance via increasing the desirable outputs and decreasing the undesirable outputs, and the linearity and convexity of DEA are preserved.
1,254 citations
TL;DR: In this article, the authors examined the performance of the top 55 U.S. commercial banks via a two-stage production process that separates profitability and marketability and uncovered substantial performance inefficiency in both dimensions.
Abstract: Utilizing recent developments in data envelopment analysis (DEA), this paper examines the performance of the top 55 U.S. commercial banks via a two-stage production process that separates profitability and marketability. Substantial performance inefficiency is uncovered in both dimensions. Relatively large banks exhibit better performance on profitability, whereas smaller banks tend to perform better with respect to marketability. New contextdependent performance measures are defined for profitability and marketability which employ a DEA stratification model and a DEA attractiveness measure. When combined with the original DEA measure, the context-dependent performance measure better characterizes the profitability and marketability of 55 U.S. commercial banks. The new approach identifies areas for improved bank performance over the two-stage production process. The effect of acquisition on efficiency and attractiveness is also examined.
878 citations
TL;DR: In this article, the authors address several issues related to the use of data envelopment analysis (DEA), including model orientation, input and output selection/definition, use of mixed and raw data, and number of inputs and outputs to use versus the number of DMUs.
Abstract: In this paper, we address several issues related to the use of data envelopment analysis (DEA). These issues include model orientation, input and output selection/definition, the use of mixed and raw data, and the number of inputs and outputs to use versus the number of decision making units (DMUs). We believe that within the DEA community, researchers, practitioners, and reviewers may have concerns and, in many cases, incorrect views about these issues. Some of the concerns stem from what is perceived as being the purpose of the DEA exercise. While the DEA frontier can rightly be viewed as a production frontier, it must be remembered that ultimately DEA is a method for performance evaluation and benchmarking against best-practice. DEA can be viewed as a tool for multiple-criteria evaluation problems where DMUs are alternatives and each DMU is represented by its performance in multiple criteria which are coined/classified as DEA inputs and outputs. The purpose of this paper is to offer some clarification and direction on these matters.
654 citations
Book•
31 Oct 2002
TL;DR: In this paper, the authors present DEA models for evaluating value chains Congestion Super Efficiency Sensitivity Analysis and its uses DEA Excel Solver and its use in DEA Excel solver.
Abstract: Basic DEA Models Measure-specific DEA Models Returns-to-Scale DEA with Preference Modelling Undesirable Measures Context-dependent Data Envelopment Analysis Benchmarking Models Models for Evaluating Value Chains Congestion Super Efficiency Sensitivity Analysis and Its Uses DEA Excel Solver.
632 citations
Cited by
More filters
TL;DR: In this paper, a coherent data-generating process (DGP) is described for nonparametric estimates of productive efficiency on environmental variables in two-stage procedures to account for exogenous factors that might affect firms’ performance.
2,915 citations
TL;DR: This paper addresses the "super-efficiency" issue of Data Envelopment Analysis by using the slacks-based measure (SBM) of efficiency, which the author proposed in his previous paper [European Journal of Operational Research 130 (2001) 498].
2,575 citations
31 Jan 2001
TL;DR: In this paper, the slacks-based measure (SBM) of efficiency was proposed to discriminate the efficient decision making units (DMUs) based on the existence of slacks.
Abstract: In most models of Data Envelopment Analysis (DEA), the best performers have the full efficient status denoted by unity (or 100%), and, from experience, we know that usually plural Decision Making Units (DMUs) have this “efficient status”. To discriminate between these efficient DMUs is an interesting subject. This paper addresses this “super-efficiency” issue by using the slacks-based measure (SBM) of efficiency, which the author proposed in his previous paper [European Journal of Operational Research 130 (2001) 498]. The method differs from the traditional one based on the radial measure, e.g. Andersen and Petersen model, in that the former deals directly with slacks in inputs/outputs, while the latter does not take account of the existence of slacks. We will demonstrate the rationality of our approach by comparing it with the radial measure of super-efficiency. The proposed method will be particularly useful when the number of DMUs are small compared with the number of criteria employed for evaluation.
1,979 citations
1,677 citations
TL;DR: A sketch of some of the major research thrusts in data envelopment analysis (DEA) over the three decades since the appearance of the seminal work of Charnes et al. is provided.
1,390 citations