Buch, Englisch, 688 Seiten, Format (B × H): 157 mm x 235 mm, Gewicht: 1136 g
Buch, Englisch, 688 Seiten, Format (B × H): 157 mm x 235 mm, Gewicht: 1136 g
ISBN: 978-1-119-13830-3
Verlag: Wiley
A comprehensive review of behavioral operations management that puts the focus on new and trending research in the field
The Handbook of Behavioral Operations offers a comprehensive resource that fills the gap in the behavioral operations management literature. This vital text highlights best practices in behavioral operations research and identifies the most current research directions and their applications. A volume in the Wiley Series in Operations Research and Management Science, this book contains contributions from an international panel of scholars from a wide variety of backgrounds who are conducting behavioral research.
The handbook provides succinct tutorials on common methods used to conduct behavioral research, serves as a resource for current topics in behavioral operations research, and as a guide to the use of new research methods. The authors review the fundamental theories and offer frameworks from a psychological, systems dynamics, and behavioral economic standpoint. They provide a crucial grounding for behavioral operations as well as an entry point for new areas of behavioral research. The handbook also presents a variety of behavioral operations applications that focus on specific areas of study and includes a survey of current and future research needs. This important resource:
- Contains a summary of the methodological foundations and in-depth treatment of research best practices in behavioral research.
- Provides a comprehensive review of the research conducted over the past two decades in behavioral operations, including such classic topics as inventory management, supply chain contracting, forecasting, and competitive sourcing.
- Covers a wide-range of current topics and applications including supply chain risk, responsible and sustainable supply chain, health care operations, culture and trust.
- Connects existing bodies of behavioral operations literature with related fields, including psychology and economics.
- Provides a vision for future behavioral research in operations.
Written for academicians within the operations management community as well as for behavioral researchers, The Handbook of Behavioral Operations offers a comprehensive resource for the study of how individuals make decisions in an operational context with contributions from experts in the field.
Autoren/Hrsg.
Weitere Infos & Material
List of Contributors xvii
Preface xxi
Part I Methodology 1
1 Designing and Conducting Laboratory Experiments 3
Elena Katok
1.1 Why Use Laboratory Experiments? 3
1.2 Categories of Experiments 5
1.3 Some Prototypical Games 8
1.3.1 Individual Decisions 8
1.3.2 Simple Strategic Games 9
1.3.3 Games Involving Competition: Markets and Auctions 11
1.4 Established Good Practices for Conducting BOM Laboratory 12
1.4.1 Effective Experimental Design 13
1.4.2 Context 15
1.4.3 Subject Pool 16
1.5 Incentives 20
1.6 Deception 24
1.7 Collecting Additional Information 26
1.8 Infrastructure and Logistics 28
References 29
2 Econometrics for Experiments 35
Kyle Hyndman and Matthew Embrey
2.1 Introduction 35
2.2 The Interaction Between Experimental Design and Econometrics 37
2.2.1 The Average Treatment Effect 37
2.2.2 How to Achieve Randomization 38
2.2.3 Power Analysis 39
2.3 Testing Theory and Other Hypotheses: Classical Hypothesis Testing 42
2.3.1 Tests on Continuous Response Data 43
2.3.1.1 Parametric Tests 44
2.3.1.2 Nonparametric Tests 45
2.3.1.3 Testing for Trends 47
2.3.1.4 Bootstrap and Permutation Tests 48
2.3.1.5 An Illustration from Davis et al. (2011) 48
2.3.1.6 When to Use Nonparametric Tests 50
2.3.2 Tests on Discrete Response Data 50
2.4 Testing Theory and Other Hypotheses: Regression Analysis 52
2.4.1 Ordinary Least Squares: An Example from Davis et al. (2011) 52
2.4.2 Panel Data Methods 55
2.4.2.1 Dynamic Panel Data Models: The Example of Demand Chasing 57
2.4.3 Limited Dependent Variable Models 60
2.4.3.1 Binary Response Data 61
2.4.3.2 Censored Data 62
2.4.3.3 Other Data 63
2.5 Dependence of Observations 63
2.5.1 A “Conservative” Approach 64
2.5.2 Using Regressions to Address Dependence 66
2.5.2.1 Higher Level Clustering 67
2.5.2.2 How Many Clusters 68
2.6 Subject Heterogeneity 68
2.6.1 Multilevel Analysis: Example Implementation 70
2.7 Structural Estimation 71
2.7.1 Model Selection 73
2.7.2 An Illustration 75
2.7.3 A Word on Standard Errors 76
2.7.4 Subject Heterogeneity: Finite Mixture Models 78
2.8 Concluding Remarks 80
Acknowledgments 84
References 84
3 Incorporating Behavioral Factors into Operations Theory 89
Tony Haitao Cui and Yaozhong Wu
3.1 Types of Behavioral Models 90
3.1.1 Nonstandard Preferences 90
3.1.2 Nonstandard Decision-making 96
3.1.3 Nonstandard Beliefs 100
3.2 Identifying Which Behavioral Factors to Include 100
3.2.1 Robustly Observed 103
3.2.2 One/A Few Factors Explain Many Phenomena 104
3.2.3 Boundaries and Observed Behavioral Factors 104
3.3 Nesting the Standard Model 106
3.3.1 Reference Dependence 106
3.3.2 Social Preferences and Comparison 107
3.3.3 Quantal Response Equilibrium 108
3.3.4 Cognitive Hierarchy in Games 109
3.3.5 Learning 109
3.3.6 Overconfidence 110
3.4 Developing Behavioral Operations Model 110
3.4.1 Parsimony Is Still Important 110
3.4.2 Adding One Versus Many Behavioral Factors 111
3.5 Modeling for Testable Predictions 114
References 115
4 Behavioral Empirics and Field Experiments 121
Maria R. Ibanez and Bradley R. Staats
4.1 Going to the Field to Study Behavioral Operations 121
4.1.1 External Validity and Identification of Effect Size 122
4.1.2 Overcome Observer Bias 123
4.1.3 Context 123
4.1.4 Time-based Effects 124
4.1.5 Beyond Individual Decision-making 125
4.2 Analyzing the Data: Common Empirical Methods 126
4.2.1 Reduced Form Analysis of Panel Data 126
4.2.2 Difference in Differences 129
4.2.3 Program or Policy Evaluations 130
4.2.4 Regression Discontinuity 131
4.2.5 Structural Estimation 132
4.3 Field Experiments (Creating the Data) 133
4.3.1 Experimental Design 133
4.3.2 Field Sites and Organizational Partners 137
4.3.3 Ethics and Human Subject Protocol 139
4.4 Conclusion: The Way Forward 140
References 141
Part II Classical Approaches to Analyzing Behavior 149
5 Biases in Individual Decision-Making 151
Andrew M. Davis
5.1 Introduction 151
5.2 Judgments Regarding Risk 154
5.2.1 The Hot-Hand and Gambler’s Fallacies 155
5.2.2 The Conjunction Fallacy and Representativeness 157
5.2.3 The Availability Heuristic 159
5.2.4 Base Rate Neglect and Bayesian Updating 162
5.2.5 Probability Weighting 163
5.2.6 Overconfidence 165
5.2.7 Ambiguity Aversion 167
5.3 Evaluations of Outcomes 169
5.3.1 Risk Aversion and Scaling 169
5.3.2 Prospect Theory 172
5.3.2.1 Framing 174
5.3.3 Anticipated Regret 175
5.3.3.1 Reference Dependence 177
5.3.4 Mental Accounting 177
5.3.5 Intertemporal Choice 179
5.3.6 The Endowment Effect 181
5.3.7 The Sunk Cost Fallacy 182
5.4 Bounded Rationality 184
5.4.1 Satisficing 184
5.4.2 Decision Errors 186
5.4.3 System 1 and System 2 Decisions 188
5.4.4 Counterpoint on Heuristics and Biases 189
5.5 Final Comments and Future Directions 191
Acknowledgments 193
References 193
6 Other-regarding Behavior: Fairness, Reciprocity, and Trust 199
Gary E. Bolton and Yefen Chen
6.1 Introduction 199
6.1.1 What Is Other-regarding Behavior? 199
6.1.2 Why Other-regarding Behavior Is Important? 199
6.1.3 Two Types of Triggers 201
6.2 The Nature of Social Preferences 201
6.2.1 The Central Role of Fairness and the Approach to Studying It in Behavioral Economics 201
6.2.2 Fairness in the Ultimatum and Dictator Games 203
6.2.3 Reciprocity in the Gift Exchange Game 204
6.2.4 The Trust Game 205
6.2.5 The Role of Institutions in Other-regarding Behavior 206
6.3 Models of Social Preferences 208
6.3.1 What Can These Models Explain: Dictator and Ultimatum Games 211
6.3.2 What Can These Models Explain: Gift Exchange and Trust Games 211
6.3.3 What Can These Models Explain: The Market Game 212
6.3.4 An Intention-based Reciprocity Model 212
6.4 Fair Choice: Stability and Factors That Influence It 214
6.4.1 Example: Quantitative Estimates of Social Preferences 214
6.4.2 Factors That Influence Fair Choice 215
6.4.2.1 Stake Size 215
6.4.2.2 Incomplete Information About Pie Size 220
6.4.2.3 Entitlements 220
6.4.2.4 Social Distance and Physiological Features 221
6.4.2.5 Procedural Fairness 221
6.5 Reciprocal Choice 222
6.5.1 Economic Incentives May Harm the Intrinsic Reciprocity 222
6.5.2 Wage Levels and Firm Profits Affect the Reciprocity 222
6.5.3 Worker’s Population Affect the Degree of Reciprocity 223
6.5.4 Do the Experimental Results with Imitated Effort Hold When the Effort Is Real? 223
6.5.5 Maintaining Reputation Is One Motive to Trigger and Sustain Reciprocity 224
6.5.6 Institutional Tit for Tat 225
6.6 Trust and Trustworthiness 226
6.6.1 Building Blocks of Trust and Trustworthiness 226
6.6.2 Innate Triggers for Trust and Trustworthiness: Other-regarding Preferences 227
6.7 Summary: The Empirical Nature of Fair Choice 227
References 229
7 Behavioral Analysis of Strategic Interactions: Game Theory, Bargaining, and Agency 237
Stephen Leider
7.1 Behavioral Game Theory 238
7.1.1 Accurate Beliefs 239
7.1.2 Best Responses 242
7.1.3 Strategic Sophistication 244
7.1.4 Coordination Games and Equilibrium Selection 247
7.1.5 Repeated Games 249
7.1.6 Applications in Operations Management 252
7.2 Behavioral Analysis of Principal–Agent Problems 253
7.2.1 Response to Financial Incentives 254
7.2.2 Financial Incentives in Other Settings: Monitoring, Tournaments, and Teams 256
7.2.3 Reciprocity and Gift Exchange 258
7.2.4 Nonmonetary Incentives 262
7.2.5 Applications in Operations Management 263
7.3 Bargaining 264
7.3.1 Theoretical Approaches 265
7.3.2 Economics Experiments: Free-form Bargaining 266
7.3.3 Economics Experiments: Structured Bargaining 268
7.3.4 Economics Experiments: Multiparty Negotiations 270
7.3.5 Psychology Experiments: Biases in Negotiations 271
7.3.6 Applications in Operations Management 272
References 273
8 Integration of Behavioral and Operational Elements Through System Dynamics 287
J. Bradley Morrison and Rogelio Oliva
8.1 Introduction 287
8.2 Decision-making in a Dynamic Environment 289
8.3 Principles (Guidelines) for Modeling Decision-making 293
8.3.1 Principle of Knowability 294
8.3.2 Principle of Correspondence 295
8.3.3 Principle of Requisite Action 296
8.3.4 Principle of Robustness 296
8.3.5 Principle of Transience 297
8.4 Grounded Development of Decision-making Processes 298
8.4.1 Archival Cases 301
8.4.2 Ethnography 301
8.4.3 Field Studies 302
8.4.4 Interviews 302
8.4.5 Time Series and Econometric Methods 303
8.4.6 Experimental Results and Decision-making Theory 304
8.5 Formulation Development and Calibration Example 304
8.5.1 Erosion of Service Quality 304
8.5.1.1 Employees’ Effort Allocation 306
8.5.1.2 Decision Rule in Context 310
8.5.2 Dynamic Problem Solving 311
8.5.2.1 Clinicians’ Cue Interpretation 311
8.5.2.2 Decision Rule in Context 313
8.6 Conclusion 313
Refere
