Preface	6
	Contents	10
	Contributors	12
	Chapter 1: Ranking Decision Making Units: The Cross-Efficiency Evaluation	15
	1.1 Introduction	15
	1.2 Ranking Methods in DEA	17
	1.3 The Cross-Efficiency Evaluation: The Standard Approach	18
	1.4 The Choice of DEA Weights in Cross-Efficiency Evaluations	20
	1.4.1 Ranking Ranges and Cross-Efficiency Intervals	25
	1.4.2 Illustrative Example	27
	1.5 The Aggregation of Cross-Efficiencies	30
	1.5.1 Illustrative Example (Cont.)	33
	1.6 Other Uses	34
	1.6.1 Identification of Mavericks and All-Round Performers	34
	1.6.2 Classification of DMUs and Benchmarking	35
	1.6.3 Fixed Cost and Resource Allocation	35
	1.7 Extensions	36
	1.7.1 Cross-Efficiency Evaluation with Directional Distance Functions	36
	1.7.2 Cross-Efficiency Evaluation with Multiplicative DEA Models	36
	1.7.3 Cross-Efficiency Evaluation Under VRS	37
	1.7.4 Fuzzy Cross-Efficiency Evaluation	38
	1.7.5 Game Cross Efficiency	39
	1.8 Conclusions	39
	References	40
	Chapter 2: Data Envelopment Analysis for Measuring Environmental Performance	44
	2.1 Introduction	44
	2.2 Environmental DEA Technology	45
	2.3 Models for Measuring Environmental Performance	48
	2.3.1 Environmental Efficiency Index	48
	2.3.2 Environmental Productivity Index	50
	2.3.3 Other Developments	51
	2.4 Case Study	52
	2.4.1 Data	52
	2.4.2 Results and Discussions	53
	2.4.2.1 EEI Analysis	53
	2.4.2.2 EPI Analysis	58
	2.5 Conclusion	59
	References	61
	Chapter 3: Input and Output Search in DEA: The Case of Financial Institutions	63
	3.1 Introduction	63
	3.2 Efficiency Modeling in Financial Institutions	65
	3.3 A Case Study: American Banks	67
	3.3.1 The Data Set: Three Inputs and Three Outputs	68
	3.3.1.1 Labor	68
	3.3.1.2 Physical Capital	68
	3.3.1.3 Deposits	68
	3.3.1.4 Interest and Non-interest Income	71
	3.3.1.5 Loans	71
	3.3.2 DEA Specification Searches Using Multivariate Methods	79
	3.3.3 Results Visualization and Strategic Pattern Identification	85
	3.3.4 Dissecting the Efficiency Score	94
	3.4 Conclusions	95
	References	96
	Chapter 4: Multi-period Efficiency Measurement with Fuzzy Data and Weight Restrictions	100
	4.1 Introduction	100
	4.2 Crisp Network DEA with Weight Restrictions	102
	4.3 Fuzzy Multi-period Efficiency with Weight Restrictions	106
	4.4 Example	111
	4.5 Conclusion	120
	References	121
	Chapter 5: Pitching DEA Against SFA in the Context of Chinese Domestic Versus Foreign Banks	123
	5.1 Introduction	123
	5.2 Conceptual Framework	125
	5.2.1 Chinese Banking Sector	125
	5.2.2 Modeling Performance to Estimate Bank Efficiency	127
	5.2.3 Contextual Variables	128
	5.3 Data and Method	129
	5.3.1 Data	129
	5.3.2 Data Envelopment Analysis (DEA)	132
	5.3.3 Stochastic Frontier Analysis (SFA)	135
	5.4 Results and Analysis	137
	5.4.1 Testing for Scale Inefficiency Using DEA	137
	5.4.2 Main DEA Results	138
	5.4.2.1 Core Model (Single-Output BCC-O)	138
	5.4.2.2 Extended Model (Two-Output BCC-O)	139
	Overall Potential Improvements Identified by DEA Using the Extended Model	140
	Assessing the Marginal Role of the Output Variables in DEA: Efficiency Contribution Measures (ECM) for the Extended Model	140
	5.4.3 SFA Results	142
	5.4.3.1 Core Model (Single-Output Translog Function)	142
	5.4.3.2 Extended Model (Two-Output Translog Function)	146
	5.4.4 Comparing DEA and SFA Results	146
	5.5 Concluding Remarks	149
	References	151
	Chapter 6: Assessing Organizations´ Efficiency Adopting Complementary Perspectives: An Empirical Analysis Through Data Envelop...	154
	6.1 Introduction	155
	6.2 DEA and MDS Methodologies: A Brief Overview	156
	6.2.1 The Data Envelopment Analysis Method	156
	6.2.2 The Multidimensional Scaling Method	157
	6.3 Data and Selection of Indicators	158
	6.3.1 Our Sample	158
	6.3.2 Inputs and Outputs Employed in the DEA Analysis	159
	6.3.3 Indicators Included in the MDS Analysis	160
	6.4 Studying HEIs´ Efficiency by Means of Data Envelopment Analysis: Results	161
	6.5 Combining DEA and MDS Methodologies: Results	163
	6.5.1 Preliminary Insights	163
	6.6 Results	168
	6.7 Concluding Remarks	171
	Appendix: List of Universities Included in the Analysis and Their Acronyms	172
	References	173
	Chapter 7: Capital Stock and Performance of RandD Organizations: A Dynamic DEA-ANP Hybrid Approach	176
	7.1 Introduction	177
	7.2 Literature Review	179
	7.2.1 Current Status of Taiwanese RandD Organizations	179
	7.2.2 DEA Applications in RandD Organizations	180
	7.3 Research Design	181
	7.3.1 Three-Stage Value-Creation Process of RandD Organizations	181
	7.3.2 Data Selection and Description	183
	7.3.3 Dynamic Extension of Network Slack-Based Measure DEA Model	184
	7.4 Results and Discussions	187
	7.4.1 Performance Analysis in Value-Creation Process	187
	7.4.2 The Relationship Between Capital Stock and RandD Organizations Performance	190
	7.5 Conclusions	192
	References	193
	Chapter 8: Evaluating Returns to Scale and Convexity in DEA Via Bootstrap: A Case Study with Brazilian Port Terminals	196
	8.1 Introduction	196
	8.2 Efficiency Measurement and RTS Characterization	198
	8.2.1 Measuring Efficiency Scores Under Different Orientations and Frontiers	198
	8.2.2 Scaling or RTS Characterization	201
	8.2.3 Orientation Impact on RTS Characterization	202
	8.3 Estimation and Bootstrapping in DEA	203
	8.3.1 Estimation	203
	8.3.2 Bootstrapping Method	205
	8.4 Case Study: Brazilian Port Terminals	206
	8.5 Results	210
	8.5.1 Initial Estimates	210
	8.5.2 Preliminary Statistics Tests on Initial Estimates	213
	8.5.2.1 Testing for Model Specification	214
	8.5.2.2 Testing for Differences Between Container and Bulk Terminals	214
	8.5.2.3 Testing for Relevant Inputs and Outputs	215
	8.5.2.4 Testing for Outliers	216
	8.5.3 Bootstrapped Efficiency Scores and Convexity Assumption	217
	8.5.4 RTS Characterizations: CIs for SI and uo	218
	8.5.5 Discussion	220
	8.6 Conclusions	220
	References	221
	9: DEA and Cooperative Game Theory	224
	9.1 Introduction	224
	9.2 Cooperative Game Theory	225
	9.2.1 Bargaining Problems	225
	9.2.1.1 The Nash Solution	226
	9.2.1.2 The Kalai-Smorodinsky Solution	228
	9.2.2 Transferable Utility Games	229
	9.2.2.1 The Core and Related Concepts	230
	9.2.2.2 The Shapley Value	230
	9.2.2.3 The Least Core and the Nucleolus	232
	9.3 Nash Bargaining Approaches to DEA	232
	9.4 TU Cooperative Game Approaches to DEA	236
	9.5 Further Potential Applications	239
	9.5.1 Nash Decomposition for Process Efficiency in Multistage Production Systems	240
	9.5.2 DEA Production Games	242
	References	245
	Chapter 10: Measuring Bank Performance: From Static Black Box to Dynamic Network Models	249
	10.1 Introduction	250
	10.2 Selective Literature Review	251
	10.2.1 Network DEA and Dynamic DEA	251
	10.2.2 Bank Production and Risk	253
	10.3 Preliminaries	254
	10.3.1 Black-Box Technology	254
	10.3.2 Network Technology with Bad Outputs	255
	10.3.3 Dynamic Technology with Carryovers	256
	10.3.4 Dynamic-Network Technology	258
	10.4 DEA Implementation	260
	10.5 A Choice of Variables and Regulatory Constraints	268
	10.5.1 Variable Selection: An Example	268
	10.5.2 Imposing Bank Regulatory Constraint	269
	10.6 A Summary	271
	References	271
	Chapter 11: Evaluation and Decomposition of Energy and Environmental Productivity Change Using DEA	275
	11.1 Introduction	276
	11.2 Luenberger Productivity Indicator and Its Decomposition	278
	11.3 DEA Model for Energy and Environmental Efficiency Measurement	285
	11.4 Application to China´s Regional Energy and Environmental Productivity Change	289
	11.4.1 Data and Variables	290
	11.4.2 Results and Discussions	293
	11.5 Conclusions	303
	References	304
	Chapter 12: Identifying the Global Reference Set in DEA: An Application to the Determination of Returns to Scale	306
	12.1 Introduction	307
	Part I: On Identification of the Global Reference Set	308
	Part II: On Determination of the RTS	310
	12.2 Background	311
	12.2.1 Technology Set	311
	12.2.2 The RAM Model	312
	12.3 Identifying the Global Reference Set (GRS)	312
	12.3.1 Definition of the GRS	312
	12.3.2 Properties of the GRS	314
	12.3.3 Identification of the GRS	316
	12.3.4 Properties of the Proposed Approach	320
	12.3.5 Numerical example	321
	12.4 Determination of Returns to Scale (RTS)	323
	12.4.1 Definition of RTS for an Inefficient DMU	323
	12.4.2 Determination of RTS Via the BCC Model	323
	12.4.3 Determination of RTS Via the CCR Model	325
	12.4.4 Numerical Example	326
	12.4.4.1 Determining RTS Statuses of the DMUs Using Algorithm I	327
	12.4.4.2 Determining RTS Statuses of the DMUs Using Algorithm II	327
	12.5 Empirical Application	328
	12.5.1 Evaluation of Schools via the RAM Model	329
	12.5.2 Determining RTS Statuses of the Efficient Schools	329
	12.5.3 Determining RTS Statuses of the Inefficient Schools	329
	12.6 Summary and Concluding Remarks	333
	References	334
	Chapter 13: Technometrics Study Using DEA on Hybrid Electric Vehicles (HEVs)	338
	13.1 Introduction	339
	13.2 Methodology	339
	13.3 Research Model and Dataset	342
	13.3.1 TFDEA Parameters	342
	13.3.1.1 Input Variable	342
	13.3.1.2 Output Variables	343
	13.3.1.3 Categorical Parameter	344
	13.3.2 Dataset	344
	13.4 Analysis of the Technological Advancement Patterns	346
	13.4.1 Two-Seaters and Compact Segments: ``Stagnated´´	347
	13.4.2 Midsize Segment: ``Flourishing´´	348
	13.4.3 Large Segment: ``Emerging´´	349
	13.4.4 SUV Segment: ``Forging Ahead´´	351
	13.4.5 Minivan Segment: ``Crossover´´	351
	13.4.6 Pickup Truck Segment: ``Steady´´	352
	13.5 Conclusion	352
	Appendix: 2013 State-of-the-Art Frontiers of Different HEV Segments	353
	References	354
	Chapter 14: A Radial Framework for Estimating the Efficiency and Returns to Scale of a Multi-component Production System in DEA	357
	14.1 Introduction	358
	14.2 Radial Performance Measurement for a Multi-component System	360
	14.2.1 Basic Model	361
	14.2.2 Theoretical Connection with Black-Box Approach	363
	14.3 Procedure for Estimating the Returns to Scale	368
	14.4 Theoretical Connection Between Black Box Approach and Multi-component Approach	374
	14.5 Application	375
	14.5.1 Efficiency	376
	14.5.2 Returns to Scale	381
	14.6 Summary and Conclusion	382
	Appendix	383
	References	389
	Chapter 15: DEA and Accounting Performance Measurement	391
	15.1 Introduction	391
	15.2 Accounting Information	392
	15.3 Accounting Ratios for Performance Measurement	395
	15.4 Accounting Information and Its Interpretation in Productivity Measurement	398
	15.4.1 Model 1: Production Process	400
	15.4.2 Model 2: Firm Financial Efficiency Model	401
	15.4.3 Model 3: Funding Efficiency Model	401
	15.5 Indexing Dollar Values and Translation of Foreign Currencies	402
	15.6 Activity-Based Costing and DEA: Congenial Twins	404
	15.7 DEA and the Balanced Scorecard: A New Approach to an Old Problem	408
	15.8 Understanding Contextual Performance to ``Do Better´´	410
	15.9 Summary	415
	References	415
	Chapter 16: DEA Environmental Assessment (I): Concepts and Methodologies	419
	16.1 Introduction	420
	16.2 Literature Review	422
	16.3 Underlying Concepts for DEA Environmental Assessment	422
	16.3.1 Abbreviations and nomenclatures	422
	16.3.2 Natural and Managerial Disposability	423
	16.3.3 Unification Between Natural and Managerial Disposability	424
	16.3.4 Desirable Congestion (DC)	426
	16.4 Unified Efficiency	427
	16.4.1 Unified Efficiency (UE)	427
	16.4.2 Unified Efficiency under Natural Disposability (UEN)	430
	16.4.3 Unified Efficiency under Managerial Disposability (UEM)	431
	16.4.4 Unified Efficiency under Natural and Managerial Disposability (UENM)	432
	16.4.5 Unified Efficiency under Natural and Managerial Disposability: UENM(DC) with a Possible Occurrence of Desirable Congest...	434
	16.5 Investment Strategy	435
	16.6 Empirical Study	436
	16.7 Conclusion and Future Extensions	442
	References	449
	Chapter 17: DEA Environmental Assessment (II): A Literature Study	451
	17.1 Introduction	452
	17.2 DEA Environmental Assessment	453
	17.3 Disposability Concepts	456
	17.4 Electric Power Industry	462
	17.5 Petroleum and Coal Industries	463
	17.6 Agriculture, Fishery, Manufacturing and Transportation Industries	464
	17.7 Economic Development and Corporate Strategy	465
	17.8 Methodology Developments	466
	17.9 Conclusion	468
	References	469
	Chapter 18: Corporate Environmental Sustainability and DEA	488
	18.1 Introduction	488
	18.2 Corporate Environmental Sustainability	489
	18.3 Theory Testing and Statistical Inferencing with DEA: An Environmental Perspective	490
	18.3.1 Financial and Environmental Performance Relationship	491
	18.3.2 Ecological Efficiency and Technological Disposition Relationship	492
	18.3.3 Environmental Practices, Performance and Risk Management	493
	18.4 Benchmarking and Key Performance Indicators with DEA	494
	18.5 Multiple Criteria Decision Making with DEA	496
	18.5.1 Justifying and Choosing Environmental Technologies	497
	18.6 Future Research Directions	498
	18.7 Conclusion	500
	References	501
	Index	504