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Inhaltsverzeichnis |
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1;Handbook on Data
Envelopment Analysis;3
1.1;Preface;7
1.2;About the Authors;11
1.3;Contents;21
1.4;Contributors;23
1.5;Chapter 1: Data Envelopment Analysis: History, Models, and Interpretations
;27
1.5.1;1.1 Introduction;27
1.5.2;1.2 Background and History;29
1.5.3;1.3 CCR Model;33
1.5.4;1.4 Extensions to the CCR Model;44
1.5.4.1;1.4.1 Nondiscretionary Inputs and Outputs;44
1.5.4.2;1.4.2 Categorical Inputs and Outputs;46
1.5.4.3;1.4.3 Incorporating Judgment or A Priori Knowledge;47
1.5.4.4;1.4.4 Window Analysis;49
1.5.5;1.5 Allocative and Overall Efficiency;52
1.5.6;1.6 Profit Efficiency;55
1.5.7;1.7 Recent Developments;60
1.5.8;1.8 Conclusions;62
1.5.9;References;62
1.6;Chapter 2:
Returns to Scale in DEA
;66
1.6.1;2.1 Introduction;66
1.6.2;2.2 RTS Approaches with BCC Models;68
1.6.3;2.3 RTS Approaches with CCR Models;73
1.6.4;2.4 Most Productive Scale Size;79
1.6.5;2.5 Additive Models;82
1.6.6;2.6 Multiplicative Models;86
1.6.7;2.7 Summary and Conclusion;91
1.6.8;Appendix;93
1.6.9;References;94
1.7;Chapter 3:
Sensitivity Analysis in DEA
;96
1.7.1;3.1 Introduction;96
1.7.2;3.2 Sensitivity Analysis Approaches;97
1.7.2.1;3.2.1 Algorithmic Approaches;98
1.7.2.2;3.2.2 Metric Approaches;98
1.7.2.3;3.2.3 Multiplier Model Approaches;102
1.7.2.4;3.2.4 A Two-Stage Alternative;106
1.7.2.5;3.2.5 Envelopment Approach;109
1.7.3;3.3 Summary and Conclusion;115
1.7.4;References;115
1.8;Chapter 4:
Choices and Uses of DEA Weights;117
1.8.1;4.1 Introduction;117
1.8.2;4.2 Using Price Information;120
1.8.3;4.3 Reflecting Meaningful Trade-Offs;122
1.8.4;4.4 Incorporating Value Information and Managerial Goals;125
1.8.5;4.5 Choosing From Alternate Optima;129
1.8.6;4.6 Looking for Non-zero Weights;133
1.8.7;4.7 Avoiding Large Differences in the Values of Multipliers;136
1.8.8;4.8 Improving Discrimination and Ranking Units;139
1.8.9;4.9 Conclusions;144
1.8.10;References;146
1.9;Chapter 5:
Malmquist Productivity Indexes and DEA;151
1.9.1;5.1 Introduction;151
1.9.2;5.2 DEA Technologies;152
1.9.3;5.3 Projecting onto the Frontier;156
1.9.4;5.4 Productivity Indexes;162
1.9.5;5.5 A Dynamic Malmquist Productivity Index;170
1.9.6;References;172
1.10;Chapter 6:
Qualitative Data in DEA;174
1.10.1;6.1 Introduction;174
1.10.2;6.2 Problem Settings Involving Ordinal Data;175
1.10.2.1;6.2.1 Ordinal Data in RandD Project Selection;175
1.10.2.2;6.2.2 Efficiency Performance of Korean Telephone Offices;177
1.10.3;6.3 Modeling Ordinal Data;179
1.10.3.1;6.3.1 Permissible Worth Vectors;182
1.10.3.2;6.3.2 Criteria Importance;186
1.10.4;6.4 Solutions to Applications;187
1.10.4.1;6.4.1 RandD Project Efficiency Evaluation;187
1.10.4.2;6.4.2 Evaluation of Telephone Office Efficiency;188
1.10.5;6.5 Problem Settings and Issues Involving Qualitative Data;189
1.10.5.1;6.5.1 Implementation of Robotics: Identifying Efficient Implementers;190
1.10.5.2;6.5.2 A Fair Model for Aggregating Preferential Votes;190
1.10.5.3;6.5.3 Multiple Criteria Decision Modeling: Ordinal Data, Criteria Importance, and Criteria Clearness;191
1.10.5.3.1;6.5.3.1 Evaluating Vendors for Complex Systems;192
1.10.5.3.2;6.5.3.2 Country Risk Evaluation;192
1.10.5.3.3;6.5.3.3 Mutual Fund Selection;193
1.10.5.3.4;6.5.3.4 Ordinal Data in Multicriteria Modeling: Evaluation in Terms of Subsets of Criteria;193
1.10.6;6.6 Discussion;194
1.10.7;References;194
1.11;Chapter 7:
Congestion: Its Identification and Management with DEA;196
1.11.1;7.1 Congestion;196
1.11.2;7.2 Comparison of Two Literatures on Congestion;201
1.11.3;7.3 Färe, Grosskopf, and Lovell (FGL) Approach;201
1.11.4;7.4 Cooper, Thompson, and Thrall (CTT) Approach;205
1.11.4.1;7.4.1 A Numerical Example;207
1.11.5;7.5 A Unified Additive Model;209
1.11.6;7.6 Estimating the Output Effects of Congestion;211
1.11.7;7.7 Extensions;214
1.11.8;References;215
1.12;Chapter 8:
Slacks-Based Measure of Efficiency;217
1.12.1;8.1 Introduction;217
1.12.2;8.2 The SBM Model;218
1.12.2.1;8.2.1 Production Possibility Set;218
1.12.2.2;8.2.2 Input-Oriented SBM;219
1.12.2.3;8.2.3 Output-Oriented SBM;220
1.12.2.4;8.2.4 Nonoriented SBM;221
1.12.2.5;8.2.5 An Illustrative Example of SBM Models;222
1.12.2.6;8.2.6 The Dual Program of the SBM Model;223
1.12.3;8.3 Extensions of the SBM Model;224
1.12.3.1;8.3.1 Variable Returns-to-Scale Model;224
1.12.3.2;8.3.2 Weighted-SBM Model;225
1.12.3.3;8.3.3 Super-SBM Model;226
1.12.3.4;8.3.4 An Illustrative Example of Super-SBM Models;227
1.12.4;8.4 Further Extensions;227
1.12.4.1;8.4.1 Dealing with Nonpositive Data in the SBM Models;227
1.12.4.2;8.4.2 Variations of the SBM Models;229
1.12.4.3;8.4.3 A Compromise of Radial and Nonradial Measures of Efficiency;230
1.12.5;8.5 Concluding Remarks;230
1.12.6;References;231
1.13;Chapter 9:
Chance-Constrained DEA;232
1.13.1;9.1 Introduction;232
1.13.2;9.2 Efficiency and Efficiency Dominance;233
1.13.3;9.3 Stochastic Dominance and Joint Chance Constrained Efficiency;236
1.13.3.1;9.3.1 Potential Uses;239
1.13.4;9.4 Stochastic Efficiency in Marginal Chance Constrained Models;244
1.13.5;9.5 Satisficing DEA Models;252
1.13.6;9.6 Concluding Remarks;258
1.13.7;References;259
1.14;Chapter 10:
Performance of the Bootstrap for DEA Estimators and Iterating the Principle;262
1.14.1;10.1 Introduction;262
1.14.2;10.2 Efficiency and the Theory of the Firm;263
1.14.3;10.3 Estimation;265
1.14.4;10.4 A Statistical Model;268
1.14.5;10.5 Some Asymptotic Results;269
1.14.6;10.6 Bootstrapping in DEA/FDH Models;271
1.14.7;10.7 Implementing the Bootstrap;274
1.14.8;10.8 Monte Carlo Evidence;279
1.14.9;10.9 Enhancing the Performance of the Bootstrap;287
1.14.10;10.10 Conclusions;289
1.14.11;References;290
1.15;Chapter 11:
Statistical Tests Based on DEA Efficiency Scores;293
1.15.1;11.1 Introduction;293
1.15.2;11.2 Hypothesis Tests When Inefficiency is the Only Stochastic Variable;295
1.15.2.1;11.2.1 Statistical Foundation for DEA;295
1.15.2.2;11.2.2 Efficiency Comparison of Two Groups of DMUs;296
1.15.2.3;11.2.3 Tests of Returns to Scale;297
1.15.2.4;11.2.4 Tests of Allocative Efficiency;299
1.15.2.5;11.2.5 Tests of Input Separability;301
1.15.3;11.3 Hypothesis Tests for Situations Characterized by Shifts in Frontier;302
1.15.4;11.4 Hypothesis Tests for Composed Error Situations;306
1.15.4.1;11.4.1 Tests for Efficiency Comparison;307
1.15.4.2;11.4.2 Tests for Evaluating the Impact of Contextual Variables on Efficiency;308
1.15.4.3;11.4.3 Tests for Evaluating the Adequacy of Parametric Functional Forms;311
1.15.5;11.5 Concluding Remarks;313
1.15.6;References;314
1.16;Chapter 12:
Modeling DMU´s Internal Structures: Cooperative and Noncooperative Approaches;316
1.16.1;12.1 Introduction;316
1.16.2;12.2 Two-Stage Processes;318
1.16.3;12.3 Centralized Model;319
1.16.4;12.4 Stackelberg Game;322
1.16.5;12.5 DEA Model for General Multistage Serial Processes Via Additive Efficiency Decomposition;325
1.16.6;12.6 General Multistage Processes;328
1.16.6.1;12.6.1 Parallel Processes;328
1.16.6.2;12.6.2 Nonimmediate Successor Flows;329
1.16.7;12.7 Conclusions;330
1.16.8;References;331
1.17;Chapter 13:
Assessing Bank and Bank Branch Performance;333
1.17.1;13.1 Introduction;333
1.17.2;13.2 Performance Measurement Approaches in Banking;334
1.17.2.1;13.2.1 Ratio Analysis;334
1.17.2.2;13.2.2 Frontier Efficiency Methodologies;335
1.17.2.3;13.2.3 Other Performance Evaluation Methods;336
1.17.3;13.3 Data Envelopment Analysis in Banking;336
1.17.3.1;13.3.1 Banking Corporations;337
1.17.3.1.1;13.3.1.1 In-Country;337
1.17.3.1.2;13.3.1.2 Cross-Country Studies;338
1.17.3.2;13.3.2 Bank Branches;339
1.17.3.2.1;13.3.2.1 Small Number of Branches;339
1.17.3.2.2;13.3.2.2 Large Number of Branches;340
1.17.3.2.3;13.3.2.3 Branch Studies Incorporating Service Quality;341
1.17.3.2.4;13.3.2.4 Unusual Banking Applications of DEA;341
1.17.4;13.4 Model Building Considerations;342
1.17.4.1;13.4.1 Approaching the Problem;342
1.17.4.2;13.4.2 Input or Output?;342
1.17.4.3;13.4.3 Too Few DMUs/Too Many Variables;343
1.17.4.4;13.4.4 Relationships and Proxies;344
1.17.4.5;13.4.5 Outliers;344
1.17.4.6;13.4.6 Zero or Blank?;345
1.17.4.7;13.4.7 Size Does Matter;345
1.17.4.8;13.4.8 Too Many DMUs on the Frontier;346
1.17.4.9;13.4.9 Environmental Factors;346
1.17.4.10;13.4.10 Service Quality;347
1.17.4.11;13.4.11 Validating Results;347
1.17.5;13.5 Banks as DMUS;347
1.17.5.1;13.5.1 Cross-Country/Region Comparisons;348
1.17.5.2;13.5.2 Bank Mergers;349
1.17.5.2.1;13.5.2.1 Selecting Pairs of Branch Units for Merger Evaluation;351
1.17.5.2.2;13.5.2.2 Defining a Strategy for Hypothetically Merging Two Bank Branches;351
1.17.5.2.3;13.5.2.3 Developing Models for Evaluating the Overall Performance of Merged Units Through the Selection of Appropriate Input and Output Variables
;352
1.17.5.2.4;13.5.2.4 Calculating Potential Efficiency Gains;352
1.17.5.2.5;13.5.2.5 Identifying Differences in Cultural Environments Between the Merging Banks;352
1.17.5.2.6;13.5.2.6 Calculating Potential Synergies;353
1.17.5.3;13.5.3 Temporal Studies;354
1.17.5.3.1;13.5.3.1 The Models;354
1.17.5.3.2;13.5.3.2 Window Analysis;355
1.17.5.3.3;13.5.3.3 Malmquist Productivity Index;357
1.17.6;13.6 Bank Branches as DMUS;361
1.17.6.1;13.6.1 The Production Model;362
1.17.6.2;13.6.2 Profitability Model;363
1.17.6.3;13.6.3 Intermediation Model;364
1.17.6.4;13.6.4 Model Results;365
1.17.6.5;13.6.5 Senior Management Concerns;366
1.17.6.6;13.6.6 A Two-Stage Process;367
1.17.6.7;13.6.7 Targeted Analysis;368
1.17.6.8;13.6.8 New Role of Bank Branch Analysis;369
1.17.6.9;13.6.9 Environmental Effects;370
1.17.7;13.7 Validation;372
1.17.7.1;13.7.1 Validating a Method with Monte Carlo Simulation;372
1.17.8;13.8 Conclusions;373
1.17.9;References;374
1.18;Chapter 14:
Engineering Applications of Data Envelopment Analysis;380
1.18.1;14.1 Background and Context;381
1.18.2;14.2 Research Issues and Opportunities;384
1.18.2.1;14.2.1 Evaluating Design Alternatives;384
1.18.2.2;14.2.2 Disaggregated Process Evaluation and Improvement: Opening the ``Input/Output Transformation Box´´;385
1.18.2.3;14.2.3 Hierarchical Manufacturing System Performance;386
1.18.2.4;14.2.4 Data Measurement Imprecision in Production Systems;389
1.18.2.5;14.2.5 Dynamical Production Systems;391
1.18.2.6;14.2.6 Visualization of the DEA Results: Influential Data Identification;395
1.18.3;14.3 A DEA-Based Approach Used for the Design of an Integrated Performance Measurement System;396
1.18.4;14.4 Selected DEA Engineering Applications;399
1.18.4.1;14.4.1 Four Applications;399
1.18.4.1.1;14.4.1.1 Evaluating Efficiency of Turbofan Jet Engines (Bulla et al. 2000);399
1.18.4.1.2;14.4.1.2 Measurement and Monitoring of Relative Efficiency of Highway Maintenance Patrols (Cook et al. 1990, 1994) and of Highway Maintenance Operations (Ozbek et al. 2010a, b)6
;400
1.18.4.1.3;14.4.1.3 Data Envelopment Analysis of Space and Terrestrially Based Large Scale Commercial Power Systems for Earth (Criswell and Thompson 1996)
;401
1.18.4.1.4;14.4.1.4 The Relationship of DEA and Control Charts (Hoopes and Triantis 2001);401
1.18.4.2;14.4.2 The Effect of Environmental Controls on Productive Efficiency;402
1.18.4.3;14.4.3 The Performance of Transit Systems;403
1.18.4.4;14.4.4 Other Engineering Applications of DEA;404
1.18.5;14.5 Systems Thinking Concepts and Future DEA Research in Engineering;405
1.18.5.1;14.5.1 The Need for Operational Thinking;405
1.18.5.2;14.5.2 Contribution to Performance Measurement Science;406
1.18.5.3;14.5.3 Relationship of the DEA Model with the Real World;407
1.18.6;References;408
1.19;Chapter 15:
Applications of Data Envelopment Analysis in the Service Sector;420
1.19.1;15.1 Introduction;420
1.19.2;15.2 Universities1
;423
1.19.2.1;15.2.1 Introduction;423
1.19.2.2;15.2.2 Conceptual Framework;424
1.19.2.3;15.2.3 Research Design;426
1.19.2.4;15.2.4 Results and Analysis;428
1.19.2.5;15.2.5 Concluding Remarks;429
1.19.3;15.3 Hotels2
;430
1.19.3.1;15.3.1 Introduction;430
1.19.3.2;15.3.2 Conceptual Framework;431
1.19.3.3;15.3.3 A Quick Guide to Selecting Inputs and Outputs;433
1.19.3.4;15.3.4 A Numerical Example;433
1.19.3.5;15.3.5 Concluding Remarks;435
1.19.4;15.4 Real Estate Agents3
;436
1.19.4.1;15.4.1 Introduction;436
1.19.4.2;15.4.2 Research Design;437
1.19.4.3;15.4.3 Analysis of Results;439
1.19.4.4;15.4.4 Concluding Remarks;441
1.19.5;15.5 Commercial Banks5
;442
1.19.5.1;15.5.1 Introduction;442
1.19.5.2;15.5.2 Conceptual Framework;443
1.19.5.2.1;15.5.2.1 Modeling Profit Efficiency;443
1.19.5.2.2;15.5.2.2 Key Profit Centers and Estimating Corresponding Data;444
1.19.5.3;15.5.3 Methodology;446
1.19.5.3.1;15.5.3.1 Overview of Network DEA;446
1.19.5.3.2;15.5.3.2 Network Slacks-Based Measure of Efficiency;449
1.19.5.3.3;15.5.3.3 Data and Simulation;450
1.19.5.4;15.5.4 Results and Analysis;451
1.19.5.4.1;15.5.4.1 Profit Efficiency Using Traditional DEA (SBM);451
1.19.5.4.2;15.5.4.2 Profit Efficiency Using Network SBM and Simulated Profit Center Data;452
1.19.5.5;15.5.5 Concluding Remarks;454
1.19.6;15.6 Epilogue;455
1.19.7;References;457
1.20;Chapter 16:
Health-Care Applications: From Hospitals to Physicians, from Productive Efficiency to Quality Frontiers;461
1.20.1;16.1 Introduction;461
1.20.2;16.2 Brief Background and History;463
1.20.2.1;16.2.1 Acute General Hospitals and Academic Medical Centers;464
1.20.2.2;16.2.2 Nursing Homes;465
1.20.2.3;16.2.3 Department Level, Team-Level, and General Health-Care Studies;466
1.20.2.4;16.2.4 Physician-Level Studies;467
1.20.2.5;16.2.5 Data Envelopment Analysis Versus Stochastic Frontier Analysis;468
1.20.2.6;16.2.6 Reviewer Comments on the Usefulness of DEA;469
1.20.2.7;16.2.7 Summary;470
1.20.3;16.3 Health-Care Models;471
1.20.3.1;16.3.1 Clinical Efficiency Definitions;471
1.20.3.2;16.3.2 How to Model Health-Care Providers: Hospitals, Nursing Homes, Physicians;472
1.20.3.3;16.3.3 Managerial and Clinical Efficiency Models;473
1.20.3.3.1;16.3.3.1 Medical Center and Acute Hospital Models: Examples of Managerial Efficiency;475
1.20.3.3.2;16.3.3.2 Nursing Homes: Another Example of Managerial Efficiency;476
1.20.3.3.3;16.3.3.3 Primary Care Physician Models: An Example of Clinical Efficiency;477
1.20.3.4;16.3.4 Hospital Physician Models: Another Example of Clinical Efficiency;478
1.20.3.5;16.3.5 Profitability Models: A Nursing Home Example;479
1.20.4;16.4 Special Issues for Health Applications;481
1.20.4.1;16.4.1 Defining Models from Stakeholder Views;481
1.20.4.2;16.4.2 Selecting Appropriate Health-Care Outputs and Inputs: The Greatest Challenge for DEA;483
1.20.4.2.1;16.4.2.1 Take Two Aspirin and Call Me in the Morning;483
1.20.4.2.2;16.4.2.2 Using DEA to Adjust Outputs for Patient Characteristics and Case mix;484
1.20.4.3;16.4.3 Should Environmental and Organizational Factors Be Used as Inputs?;485
1.20.4.4;16.4.4 Problems on the Best Practice Frontier: A Physician Example;485
1.20.4.4.1;16.4.4.1 The Concept of a Preferred Practice Cone or Quality Assurance Region;487
1.20.4.4.2;16.4.4.2 Constant Versus Variable Returns to Scale;488
1.20.4.4.3;16.4.4.3 Scale and Scope Issues;489
1.20.4.5;16.4.5 Analyzing DEA Scores with Censored Regression Models;489
1.20.5;16.5 New Directions: From Productive Efficiency Frontiers to Quality-Outcome Frontiers;492
1.20.5.1;16.5.1 A Field Test: Combining Outcome Frontiers and Efficiency Frontiers;495
1.20.6;16.6 A Health DEA Application Procedure: Eight Steps;497
1.20.6.1;16.6.1 Step 1: Identification of Interesting Health-Care Problem and Research Objectives;497
1.20.6.2;16.6.2 Step 2: Conceptual Model of the Medical Care Production Process;498
1.20.6.3;16.6.3 Step 3: Conceptual Map of Factors Influencing Care Production;498
1.20.6.4;16.6.4 Step 4: Selection of Factors;498
1.20.6.5;16.6.5 Step 5: Analyze Factors Using Statistical Methods;499
1.20.6.6;16.6.6 Step 6: Run Several DEA Models;499
1.20.6.7;16.6.7 Step 7: Analyze DEA Scores with Statistical Methods;499
1.20.6.8;16.6.8 Step 8: Share Results with Practitioners and Write It Up;500
1.20.7;16.7 DEA Health Applications: Do´s and Don´ts;500
1.20.7.1;16.7.1 Almost Never Include Physicians As a Labor Input;500
1.20.7.2;16.7.2 Use Caution When Modeling Intermediate and Final Hospital Outputs;501
1.20.7.3;16.7.3 Do Check the Distribution of DEA Scores and Influence of Best Practice Providers on Reference Sets;503
1.20.8;16.8 A Final Word;504
1.20.9;References;506
1.21;Index;510
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