The Handbook of Behavioral Operations

KAREN DONOHUE, PHD, is Board of Overseers Professor of Supply Chain and Operations in the Carlson School of Management at the University of Minnesota. ELENA KATOK, PHD, is Ashok & Monica Mago Professor of Operations Management in the Naveen Jindal School of Management at the University of Texas at Dallas, and a Co-Director of the Center and Laboratory for Behavioral Operations and Economics. STEPHEN LEIDER, PHD, is Associate Professor of Technology and Operations in the Ross School of Business at the University of Michigan.

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