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.
Table of Contents
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
References 316
Part III Applications within Operations Management 323
9 Behavioral Foundations of Queueing Systems 325
Gad Allon and Mirko Kremer
9.1 Introduction and Framework 325
9.2 The Customer 327
9.2.1 Disutility of Waiting (cT) 328
9.2.1.1 Waiting Cost (cw, cs) 329
9.2.1.2 Waiting Time (Tw, Ts) 331
9.2.2 Quality (v) 332
9.2.3 Abandonments (ℙ(v ≥ θi)) 334
9.2.4 Arrivals (λ) 337
9.2.5 Queue Discipline (λ → w) 337
9.2.6 Service Speed (μ) 338
9.3 The Server 338
9.3.1 Work Speed (μ) 339
9.3.2 Work Content (w) 340
9.3.3 Work Sequence (λ → w) 341
9.3.4 Quality (v) 342
9.4 The Manager 343
9.4.1 Ambience 343
9.4.2 Capacity 344
9.4.3 Discipline 345
9.4.4 Incentives 346
9.4.5 Information 347
9.4.6 Layout 350
9.4.7 Task 352
9.5 Testing Queueing Theory in the Laboratory 353
9.6 Conclusions and Future Research Opportunities 356
References 359
10 New Product Development and Project Management Decisions 367
Yael Grushka‐Cockayne, Sanjiv Erat, and Joel Wooten
10.1 Exploration: The Creative Process 368
10.1.1 Brainstorming 370
10.1.2 Innovation Contests 372
10.1.3 Open Innovation 374
10.2 Plan: From Creative to Reality 376
10.2.1 Cognitive Process 378
10.2.2 Emotions 380
10.2.3 Incentives and Motivation 382
10.3 Execute: From Planning to Execution 382
10.4 Conclusions 385
References 387
11 Behavioral Inventory Decisions: The Newsvendor and Other Inventory Settings 393
Michael Becker‐Peth and Ulrich W. Thonemann
11.1 Introduction 393
11.2 Nominal and Actual Order Quantities 394
11.3 Decision Biases 396
11.3.1 Anchoring on the Mean Demand 402
11.3.2 Demand Chasing Heuristic 404
11.3.3 Quantal Choice Model 406
11.3.4 Debiasing the Decision Maker 410
11.4 Utility Functions 412
11.4.1 Risk Preferences 412
11.4.2 Loss Preferences 413
11.4.3 Prospect Theory 414
11.4.4 Mental Accounting 416
11.4.5 Inventory Error 417
11.4.6 Impulse Balance 419
11.5 Individual Heterogeneity 419
11.5.1 Professional Experience 420
11.5.2 Cognitive Reflection 420
11.5.3 Overconfidence 421
11.5.4 Gender 421
11.5.5 Culture 422
11.5.6 Online Platforms 422
11.6 Other Inventory Models 423
11.6.1 Nonobservable Lost Sales 423
11.6.2 Price Setting 423
11.6.3 Stochastic Supply 424
11.6.4 Multiple Newsvendors 424
11.6.5 Multiple Products 425
11.6.6 Multiple Periods 425
11.6.7 Economic Order Quantity Model 425
11.7 Summary and Outlook 426
11.7.1 So, What Have We Learned So Far? 426
11.7.2 What Is Still to Come? 427
Acknowledgments 428
References 428
12 Forecast Decisions 433
Paul Goodwin, Brent Moritz, and Enno Siemsen
12.1 An Introduction to Forecasting Behavior 433
12.1.1 Demand Forecasting 433
12.1.2 An Overview of Human Judgment in Demand Forecasting 435
12.1.3 Where Human Judgment May Add Value 437
12.2 Judgment Biases in Point Forecasting 438
12.2.1 Anchoring and Point Forecasting 438
12.2.2 System Neglect and Other Heuristics in Time Series Forecasting 441
12.3 Judgment Biases in Forecasting Uncertainty 442
12.3.1 Forecasting a Distribution 442
12.3.2 Additional Biases in Forecasting a Distribution 443
12.4 Organizational Forecasting Processes 443
12.4.1 Forecasting Between Organizations 443
12.4.2 Some Best Practices for Organizational Forecasting 444
12.5 Improving Judgmental Forecasting 445
12.5.1 Providing Feedback and Guidance 445
12.5.2 Using Appropriate Elicitation Methods 446
12.5.3 Obtaining Forecasts from Groups 448
12.5.4 Interacting with Statistical Methods 449
12.6 Conclusion and Future Research Opportunities 452
References 453
13 Buyer-Supplier Interactions 459
Kay‐Yut Chen and Diana Wu
13.1 Introduction 459
13.2 Coordination with Imperfect Information: The Beer Distribution Game 460
13.2.1 Behavioral Explanations for the Bullwhip Effect 460
13.2.2 Remedies for the Bullwhip Behavior 466
13.3 Relationships Under Incentive Conflicts: Contracting in Supply Chains 468
13.3.1 Contracts Under Stochastic Demand 469
13.3.2 Contracts with Deterministic Demand 474
13.3.3 Contracts and Asymmetric Information 475
13.3.4 Contracts and Bargaining Protocols 477
13.3.5 Impact of Noncontractual Decisions on Channel Relationships 479
13.4 Contracting and Mechanism Design 480
13.4.1 The Traditional Rational Perspective 480
13.4.2 The Behavioral Perspective 481
13.4.3 Behavioral Mechanism Design 482
13.5 Conclusion and Future Possibilities 482
References 484
14 Trust and Trustworthiness 489
Özalp Özer and Yanchong Zheng
14.1 Are There Any Business Case Studies Where Trust and Trustworthiness Matter? 490
14.2 What Is Trust? 494
14.3 What Is Trustworthiness? 496
14.4 How Can We Measure Trust and Trustworthiness? 498
14.4.1 The Investment Game 498
14.4.2 The Forecast Sharing Game 500
14.4.3 Why Do We Use Different Games to Study Trust and Trustworthiness? 503
14.5 What Are the Building Blocks of Trust and Trustworthiness? 504
14.6 Two Remarks on Research Methods (Optional) 509
14.6.1 Spontaneous (One Shot) Versus Reputation (Repeated) 509
14.6.2 Can We Model Trust and Trustworthiness Analytically? 510
14.7 Conclusion 512
Appendix 14.A A Selected Overview of Additional Decision Games for Studying Trust 515
References 519
15 Behavioral Research in Competitive Bidding and Auction Design 525
Wedad Elmaghraby and Elena Katok
15.1 Overview of Behavioral Operations Research on Auctions 525
15.1.1 Auction Basics 526
15.2 What We Learned from Experimental Economics Literature on Forward Auctions 527
15.2.1 Tests of Revenue Equivalence 527
15.2.1.1 Sealed‐bid First Price vs. Dutch 527
15.2.1.2 Sealed‐Bid Second Price vs. English 528
15.2.2 Why Is Bidding Too Aggressive in Sealed‐bid Auctions 528
15.2.3 Auctions with Asymmetric Bidders 529
15.3 Buyer‐ determined Auctions 530
15.3.1 The Basic Model of Auctions with Nonprice Attributes 531
15.3.2 The Effect of Nonprice Attribute Information 531
15.4 Relationships and Moral Hazard in Auctions 532
15.4.1 Reputation in Auctions 532
15.4.2 Trust and Trustworthiness in Buyer‐determined Auctions 534
15.5 Empirical Findings on Bidder Behavior, Judgment, and Decisionmaking Bias 534
15.5.1 Starting Prices and Herding Behavior 536
15.5.2 Reference Prices in Auctions 537
15.6 Supply Risk 542
15.6.1 Supplier Selection Under Supply Risk 542
15.6.2 Qualification Screening and Incumbency 542
15.7 Elements of Auction Design 543
15.7.1 Reserve Prices 543
15.7.2 Ending Rules 544
15.7.3 Bid Increments and Jump Bidding 545
15.7.4 Rank‐based Feedback 545
15.7.5 Multisourcing 546
15.8 Comparing and Combining Auctions with Negotiations 547
15.8.1 Sequential Mechanism 547
15.8.2 Post‐auction Negotiation 548
15.8.3 Multiunit Setting 550
15.9 Ongoing and Future Directions 550
References 552
16 Strategic Interactions in Transportation Networks 557
Amnon Rapoport and Vincent Mak
16.1 Introduction 557
16.1.1 Basic Notions and Chapter Organization 558
16.2 Experiments on Route Choice in Networks with Fixed Architecture 559
16.2.1 Selten et al. (2007) 561
16.2.2 Mak, Gisches, and Rapoport (2015) 562
16.2.3 Summary 564
16.3 Experiments on Traffic Paradoxes 564
16.4 Experiments on the Pigou-Knight-Downs Paradox 565
16.4.1 Morgan, Orzen, and Sefton (2009) 566
16.4.2 Hartman (2012) 567
16.4.3 Summary 567
16.5 Experiments on the Downs-Thomson Paradox 568
16.5.1 Denant‐Boèmont and Hammiche (2010) 568
16.5.2 Dechenaux, Mago, and Razzolini (2014) 568
16.5.3 Summary 569
16.6 Experiments on the Braess Paradox 569
16.6.1 Morgan, Orzen, and Sefton (2009) 570
16.6.2 Rapoport et al. (2009) 572
16.6.3 Gisches and Rapoport (2012) 574
16.6.4 Rapoport, Gisches, and Mak (2014) 575
16.6.5 Rapoport, Mak, and Zwick (2006) 576
16.6.6 Summary 578
16.7 Discussion and Conclusions 579
Acknowledgment 581
References 581
17 Incorporating Customer Behavior into Operational Decisions 587
Anton Ovchinnikov
17.1 How to Think About “Behaviors” in Operational Settings: Customer Journey Maps 588
17.1.1 What Are the Main Kinds of Behaviors to Think About? 590
17.2 The “Before” Behaviors 591
17.3.1 Assortment Management 596
17.3.2 Inventory 597
17.3.3 Quality 599
17.3.4 Location 600
17.3.5 Physical Facility Design and “Atmospherics” 600
17.3.6 Virtual “Facility” Design 601
17.3.7 Price Optimization and Dynamic Pricing 601
17.3.8 Dynamic Pricing 602
17.3.9 New Product Introductions 605
17.3.10 Product Reuse, Returns, and Recycling 606
17.3.11 Summary of the “During” Behaviors 606
17.4 The “After” Behaviors 607
17.5 Concluding Remarks 612
Acknowledgments 612
References 612
18 The Future Is Bright: Recent Trends and Emerging Topics in Behavioral Operations 619
Karen Donohue and Kenneth Schultz
18.1 Introduction 619
18.2 Current Research Trends 620
18.2.1 Methodological Observations 621
18.2.2 OM Context Observations 624
18.3 Emerging Behavioral Operations Topics 627
18.3.1 Behavioral Issues in Healthcare Operations 627
18.3.1.1 Current Research Examples 628
18.3.1.2 Future Research Needs 630
18.3.2 Behavioral Issues in Retail Operations 632
18.3.2.1 Current Research Examples 633
18.3.2.2 Future Research Needs 634
18.3.3 Behavioral Issues in Social and Sustainable Operations 636
18.3.3.1 Current Research Examples 638
18.3.3.2 Future Research Needs 639
18.3.4 Behavioral Issues in Supply Chain Risk 640
18.3.4.1 Current Research Examples 641
18.3.4.2 Future Research Needs 642
18.4 Final Remarks 643
Acknowledgments 645
References 645
Index 653
