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 xviiPreface xxiPart I Methodology 11 Designing and Conducting Laboratory Experiments 3Elena Katok1.1 Why Use Laboratory Experiments? 31.2 Categories of Experiments 51.3 Some Prototypical Games 81.3.1 Individual Decisions 81.3.2 Simple Strategic Games 91.3.3 Games Involving Competition: Markets and Auctions 111.4 Established Good Practices for Conducting BOM Laboratory 121.4.1 Effective Experimental Design 131.4.2 Context 151.4.3 Subject Pool 161.5 Incentives 201.6 Deception 241.7 Collecting Additional Information 261.8 Infrastructure and Logistics 28References 292 Econometrics for Experiments 35Kyle Hyndman and Matthew Embrey2.1 Introduction 352.2 The Interaction Between Experimental Design and Econometrics 372.2.1 The Average Treatment Effect 372.2.2 How to Achieve Randomization 382.2.3 Power Analysis 392.3 Testing Theory and Other Hypotheses: Classical Hypothesis Testing 422.3.1 Tests on Continuous Response Data 432.3.1.1 Parametric Tests 442.3.1.2 Nonparametric Tests 452.3.1.3 Testing for Trends 472.3.1.4 Bootstrap and Permutation Tests 482.3.1.5 An Illustration from Davis et al. (2011) 482.3.1.6 When to Use Nonparametric Tests 502.3.2 Tests on Discrete Response Data 502.4 Testing Theory and Other Hypotheses: Regression Analysis 522.4.1 Ordinary Least Squares: An Example from Davis et al. (2011) 522.4.2 Panel Data Methods 552.4.2.1 Dynamic Panel Data Models: The Example of Demand Chasing 572.4.3 Limited Dependent Variable Models 602.4.3.1 Binary Response Data 612.4.3.2 Censored Data 622.4.3.3 Other Data 632.5 Dependence of Observations 632.5.1 A “Conservative” Approach 642.5.2 Using Regressions to Address Dependence 662.5.2.1 Higher Level Clustering 672.5.2.2 How Many Clusters 682.6 Subject Heterogeneity 682.6.1 Multilevel Analysis: Example Implementation 702.7 Structural Estimation 712.7.1 Model Selection 732.7.2 An Illustration 752.7.3 A Word on Standard Errors 762.7.4 Subject Heterogeneity: Finite Mixture Models 782.8 Concluding Remarks 80Acknowledgments 84References 843 Incorporating Behavioral Factors into Operations Theory 89Tony Haitao Cui and Yaozhong Wu3.1 Types of Behavioral Models 903.1.1 Nonstandard Preferences 903.1.2 Nonstandard Decision‐making 963.1.3 Nonstandard Beliefs 1003.2 Identifying Which Behavioral Factors to Include 1003.2.1 Robustly Observed 1033.2.2 One/A Few Factors Explain Many Phenomena 1043.2.3 Boundaries and Observed Behavioral Factors 1043.3 Nesting the Standard Model 1063.3.1 Reference Dependence 1063.3.2 Social Preferences and Comparison 1073.3.3 Quantal Response Equilibrium 1083.3.4 Cognitive Hierarchy in Games 1093.3.5 Learning 1093.3.6 Overconfidence 1103.4 Developing Behavioral Operations Model 1103.4.1 Parsimony Is Still Important 1103.4.2 Adding One Versus Many Behavioral Factors 1113.5 Modeling for Testable Predictions 114References 1154 Behavioral Empirics and Field Experiments 121Maria R. Ibanez and Bradley R. Staats4.1 Going to the Field to Study Behavioral Operations 1214.1.1 External Validity and Identification of Effect Size 1224.1.2 Overcome Observer Bias 1234.1.3 Context 1234.1.4 Time‐based Effects 1244.1.5 Beyond Individual Decision‐making 1254.2 Analyzing the Data: Common Empirical Methods 1264.2.1 Reduced Form Analysis of Panel Data 1264.2.2 Difference in Differences 1294.2.3 Program or Policy Evaluations 1304.2.4 Regression Discontinuity 1314.2.5 Structural Estimation 1324.3 Field Experiments (Creating the Data) 1334.3.1 Experimental Design 1334.3.2 Field Sites and Organizational Partners 1374.3.3 Ethics and Human Subject Protocol 1394.4 Conclusion: The Way Forward 140References 141Part II Classical Approaches to Analyzing Behavior 1495 Biases in Individual Decision‐Making 151Andrew M. Davis5.1 Introduction 1515.2 Judgments Regarding Risk 1545.2.1 The Hot‐Hand and Gambler’s Fallacies 1555.2.2 The Conjunction Fallacy and Representativeness 1575.2.3 The Availability Heuristic 1595.2.4 Base Rate Neglect and Bayesian Updating 1625.2.5 Probability Weighting 1635.2.6 Overconfidence 1655.2.7 Ambiguity Aversion 1675.3 Evaluations of Outcomes 1695.3.1 Risk Aversion and Scaling 1695.3.2 Prospect Theory 1725.3.2.1 Framing 1745.3.3 Anticipated Regret 1755.3.3.1 Reference Dependence 1775.3.4 Mental Accounting 1775.3.5 Intertemporal Choice 1795.3.6 The Endowment Effect 1815.3.7 The Sunk Cost Fallacy 1825.4 Bounded Rationality 1845.4.1 Satisficing 1845.4.2 Decision Errors 1865.4.3 System 1 and System 2 Decisions 1885.4.4 Counterpoint on Heuristics and Biases 1895.5 Final Comments and Future Directions 191Acknowledgments 193References 1936 Other‐regarding Behavior: Fairness, Reciprocity, and Trust 199Gary E. Bolton and Yefen Chen6.1 Introduction 1996.1.1 What Is Other‐regarding Behavior? 1996.1.2 Why Other‐regarding Behavior Is Important? 1996.1.3 Two Types of Triggers 2016.2 The Nature of Social Preferences 2016.2.1 The Central Role of Fairness and the Approach to Studying It in Behavioral Economics 2016.2.2 Fairness in the Ultimatum and Dictator Games 2036.2.3 Reciprocity in the Gift Exchange Game 2046.2.4 The Trust Game 2056.2.5 The Role of Institutions in Other‐regarding Behavior 2066.3 Models of Social Preferences 2086.3.1 What Can These Models Explain: Dictator and Ultimatum Games 2116.3.2 What Can These Models Explain: Gift Exchange and Trust Games 2116.3.3 What Can These Models Explain: The Market Game 2126.3.4 An Intention‐based Reciprocity Model 2126.4 Fair Choice: Stability and Factors That Influence It 2146.4.1 Example: Quantitative Estimates of Social Preferences 2146.4.2 Factors That Influence Fair Choice 2156.4.2.1 Stake Size 2156.4.2.2 Incomplete Information About Pie Size 2206.4.2.3 Entitlements 2206.4.2.4 Social Distance and Physiological Features 2216.4.2.5 Procedural Fairness 2216.5 Reciprocal Choice 2226.5.1 Economic Incentives May Harm the Intrinsic Reciprocity 2226.5.2 Wage Levels and Firm Profits Affect the Reciprocity 2226.5.3 Worker’s Population Affect the Degree of Reciprocity 2236.5.4 Do the Experimental Results with Imitated Effort Hold When the Effort Is Real? 2236.5.5 Maintaining Reputation Is One Motive to Trigger and Sustain Reciprocity 2246.5.6 Institutional Tit for Tat 2256.6 Trust and Trustworthiness 2266.6.1 Building Blocks of Trust and Trustworthiness 2266.6.2 Innate Triggers for Trust and Trustworthiness: Other‐regarding Preferences 2276.7 Summary: The Empirical Nature of Fair Choice 227References 2297 Behavioral Analysis of Strategic Interactions: Game Theory, Bargaining, and Agency 237Stephen Leider7.1 Behavioral Game Theory 2387.1.1 Accurate Beliefs 2397.1.2 Best Responses 2427.1.3 Strategic Sophistication 2447.1.4 Coordination Games and Equilibrium Selection 2477.1.5 Repeated Games 2497.1.6 Applications in Operations Management 2527.2 Behavioral Analysis of Principal–Agent Problems 2537.2.1 Response to Financial Incentives 2547.2.2 Financial Incentives in Other Settings: Monitoring, Tournaments, and Teams 2567.2.3 Reciprocity and Gift Exchange 2587.2.4 Nonmonetary Incentives 2627.2.5 Applications in Operations Management 2637.3 Bargaining 2647.3.1 Theoretical Approaches 2657.3.2 Economics Experiments: Free‐form Bargaining 2667.3.3 Economics Experiments: Structured Bargaining 2687.3.4 Economics Experiments: Multiparty Negotiations 2707.3.5 Psychology Experiments: Biases in Negotiations 2717.3.6 Applications in Operations Management 272References 2738 Integration of Behavioral and Operational Elements Through System Dynamics 287J. Bradley Morrison and Rogelio Oliva8.1 Introduction 2878.2 Decision‐making in a Dynamic Environment 2898.3 Principles (Guidelines) for Modeling Decision‐making 2938.3.1 Principle of Knowability 2948.3.2 Principle of Correspondence 2958.3.3 Principle of Requisite Action 2968.3.4 Principle of Robustness 2968.3.5 Principle of Transience 2978.4 Grounded Development of Decision‐making Processes 2988.4.1 Archival Cases 3018.4.2 Ethnography 3018.4.3 Field Studies 3028.4.4 Interviews 3028.4.5 Time Series and Econometric Methods 3038.4.6 Experimental Results and Decision‐making Theory 3048.5 Formulation Development and Calibration Example 3048.5.1 Erosion of Service Quality 3048.5.1.1 Employees’ Effort Allocation 3068.5.1.2 Decision Rule in Context 3108.5.2 Dynamic Problem Solving 3118.5.2.1 Clinicians’ Cue Interpretation 3118.5.2.2 Decision Rule in Context 3138.6 Conclusion 313References 316Part III Applications within Operations Management 3239 Behavioral Foundations of Queueing Systems 325Gad Allon and Mirko Kremer9.1 Introduction and Framework 3259.2 The Customer 3279.2.1 Disutility of Waiting (cT) 3289.2.1.1 Waiting Cost (cw, cs) 3299.2.1.2 Waiting Time (Tw, Ts) 3319.2.2 Quality (v) 3329.2.3 Abandonments (ℙ(v ≥ θi)) 3349.2.4 Arrivals (λ) 3379.2.5 Queue Discipline (λ → w) 3379.2.6 Service Speed (μ) 3389.3 The Server 3389.3.1 Work Speed (μ) 3399.3.2 Work Content (w) 3409.3.3 Work Sequence (λ → w) 3419.3.4 Quality (v) 3429.4 The Manager 3439.4.1 Ambience 3439.4.2 Capacity 3449.4.3 Discipline 3459.4.4 Incentives 3469.4.5 Information 3479.4.6 Layout 3509.4.7 Task 3529.5 Testing Queueing Theory in the Laboratory 3539.6 Conclusions and Future Research Opportunities 356References 35910 New Product Development and Project Management Decisions 367Yael Grushka‐Cockayne, Sanjiv Erat, and Joel Wooten10.1 Exploration: The Creative Process 36810.1.1 Brainstorming 37010.1.2 Innovation Contests 37210.1.3 Open Innovation 37410.2 Plan: From Creative to Reality 37610.2.1 Cognitive Process 37810.2.2 Emotions 38010.2.3 Incentives and Motivation 38210.3 Execute: From Planning to Execution 38210.4 Conclusions 385References 38711 Behavioral Inventory Decisions: The Newsvendor and Other Inventory Settings 393Michael Becker‐Peth and Ulrich W. Thonemann11.1 Introduction 39311.2 Nominal and Actual Order Quantities 39411.3 Decision Biases 39611.3.1 Anchoring on the Mean Demand 40211.3.2 Demand Chasing Heuristic 40411.3.3 Quantal Choice Model 40611.3.4 Debiasing the Decision Maker 41011.4 Utility Functions 41211.4.1 Risk Preferences 41211.4.2 Loss Preferences 41311.4.3 Prospect Theory 41411.4.4 Mental Accounting 41611.4.5 Inventory Error 41711.4.6 Impulse Balance 41911.5 Individual Heterogeneity 41911.5.1 Professional Experience 42011.5.2 Cognitive Reflection 42011.5.3 Overconfidence 42111.5.4 Gender 42111.5.5 Culture 42211.5.6 Online Platforms 42211.6 Other Inventory Models 42311.6.1 Nonobservable Lost Sales 42311.6.2 Price Setting 42311.6.3 Stochastic Supply 42411.6.4 Multiple Newsvendors 42411.6.5 Multiple Products 42511.6.6 Multiple Periods 42511.6.7 Economic Order Quantity Model 42511.7 Summary and Outlook 42611.7.1 So, What Have We Learned So Far? 42611.7.2 What Is Still to Come? 427Acknowledgments 428References 42812 Forecast Decisions 433Paul Goodwin, Brent Moritz, and Enno Siemsen12.1 An Introduction to Forecasting Behavior 43312.1.1 Demand Forecasting 43312.1.2 An Overview of Human Judgment in Demand Forecasting 43512.1.3 Where Human Judgment May Add Value 43712.2 Judgment Biases in Point Forecasting 43812.2.1 Anchoring and Point Forecasting 43812.2.2 System Neglect and Other Heuristics in Time Series Forecasting 44112.3 Judgment Biases in Forecasting Uncertainty 44212.3.1 Forecasting a Distribution 44212.3.2 Additional Biases in Forecasting a Distribution 44312.4 Organizational Forecasting Processes 44312.4.1 Forecasting Between Organizations 44312.4.2 Some Best Practices for Organizational Forecasting 44412.5 Improving Judgmental Forecasting 44512.5.1 Providing Feedback and Guidance 44512.5.2 Using Appropriate Elicitation Methods 44612.5.3 Obtaining Forecasts from Groups 44812.5.4 Interacting with Statistical Methods 44912.6 Conclusion and Future Research Opportunities 452References 45313 Buyer–Supplier Interactions 459Kay‐Yut Chen and Diana Wu13.1 Introduction 45913.2 Coordination with Imperfect Information: The Beer Distribution Game 46013.2.1 Behavioral Explanations for the Bullwhip Effect 46013.2.2 Remedies for the Bullwhip Behavior 46613.3 Relationships Under Incentive Conflicts: Contracting in Supply Chains 46813.3.1 Contracts Under Stochastic Demand 46913.3.2 Contracts with Deterministic Demand 47413.3.3 Contracts and Asymmetric Information 47513.3.4 Contracts and Bargaining Protocols 47713.3.5 Impact of Noncontractual Decisions on Channel Relationships 47913.4 Contracting and Mechanism Design 48013.4.1 The Traditional Rational Perspective 48013.4.2 The Behavioral Perspective 48113.4.3 Behavioral Mechanism Design 48213.5 Conclusion and Future Possibilities 482References 48414 Trust and Trustworthiness 489Özalp Özer and Yanchong Zheng14.1 Are There Any Business Case Studies Where Trust and Trustworthiness Matter? 49014.2 What Is Trust? 49414.3 What Is Trustworthiness? 49614.4 How Can We Measure Trust and Trustworthiness? 49814.4.1 The Investment Game 49814.4.2 The Forecast Sharing Game 50014.4.3 Why Do We Use Different Games to Study Trust and Trustworthiness? 50314.5 What Are the Building Blocks of Trust and Trustworthiness? 50414.6 Two Remarks on Research Methods (Optional) 50914.6.1 Spontaneous (One Shot) Versus Reputation (Repeated) 50914.6.2 Can We Model Trust and Trustworthiness Analytically? 51014.7 Conclusion 512Appendix 14.A A Selected Overview of Additional Decision Games for Studying Trust 515References 51915 Behavioral Research in Competitive Bidding and Auction Design 525Wedad Elmaghraby and Elena Katok15.1 Overview of Behavioral Operations Research on Auctions 52515.1.1 Auction Basics 52615.2 What We Learned from Experimental Economics Literature on Forward Auctions 52715.2.1 Tests of Revenue Equivalence 52715.2.1.1 Sealed‐bid First Price vs. Dutch 52715.2.1.2 Sealed‐Bid Second Price vs. English 52815.2.2 Why Is Bidding Too Aggressive in Sealed‐bid Auctions 52815.2.3 Auctions with Asymmetric Bidders 52915.3 Buyer‐ determined Auctions 53015.3.1 The Basic Model of Auctions with Nonprice Attributes 53115.3.2 The Effect of Nonprice Attribute Information 53115.4 Relationships and Moral Hazard in Auctions 53215.4.1 Reputation in Auctions 53215.4.2 Trust and Trustworthiness in Buyer‐determined Auctions 53415.5 Empirical Findings on Bidder Behavior, Judgment, and Decisionmaking Bias 53415.5.1 Starting Prices and Herding Behavior 53615.5.2 Reference Prices in Auctions 53715.6 Supply Risk 54215.6.1 Supplier Selection Under Supply Risk 54215.6.2 Qualification Screening and Incumbency 54215.7 Elements of Auction Design 54315.7.1 Reserve Prices 54315.7.2 Ending Rules 54415.7.3 Bid Increments and Jump Bidding 54515.7.4 Rank‐based Feedback 54515.7.5 Multisourcing 54615.8 Comparing and Combining Auctions with Negotiations 54715.8.1 Sequential Mechanism 54715.8.2 Post‐auction Negotiation 54815.8.3 Multiunit Setting 55015.9 Ongoing and Future Directions 550References 55216 Strategic Interactions in Transportation Networks 557Amnon Rapoport and Vincent Mak16.1 Introduction 55716.1.1 Basic Notions and Chapter Organization 55816.2 Experiments on Route Choice in Networks with Fixed Architecture 55916.2.1 Selten et al. (2007) 56116.2.2 Mak, Gisches, and Rapoport (2015) 56216.2.3 Summary 56416.3 Experiments on Traffic Paradoxes 56416.4 Experiments on the Pigou–Knight–Downs Paradox 56516.4.1 Morgan, Orzen, and Sefton (2009) 56616.4.2 Hartman (2012) 56716.4.3 Summary 56716.5 Experiments on the Downs–Thomson Paradox 56816.5.1 Denant‐Boèmont and Hammiche (2010) 56816.5.2 Dechenaux, Mago, and Razzolini (2014) 56816.5.3 Summary 56916.6 Experiments on the Braess Paradox 56916.6.1 Morgan, Orzen, and Sefton (2009) 57016.6.2 Rapoport et al. (2009) 57216.6.3 Gisches and Rapoport (2012) 57416.6.4 Rapoport, Gisches, and Mak (2014) 57516.6.5 Rapoport, Mak, and Zwick (2006) 57616.6.6 Summary 57816.7 Discussion and Conclusions 579Acknowledgment 581References 58117 Incorporating Customer Behavior into Operational Decisions 587Anton Ovchinnikov17.1 How to Think About “Behaviors” in Operational Settings: Customer Journey Maps 58817.1.1 What Are the Main Kinds of Behaviors to Think About? 59017.2 The “Before” Behaviors 59117.3.1 Assortment Management 59617.3.2 Inventory 59717.3.3 Quality 59917.3.4 Location 60017.3.5 Physical Facility Design and “Atmospherics” 60017.3.6 Virtual “Facility” Design 60117.3.7 Price Optimization and Dynamic Pricing 60117.3.8 Dynamic Pricing 60217.3.9 New Product Introductions 60517.3.10 Product Reuse, Returns, and Recycling 60617.3.11 Summary of the “During” Behaviors 60617.4 The “After” Behaviors 60717.5 Concluding Remarks 612Acknowledgments 612References 61218 The Future Is Bright: Recent Trends and Emerging Topics in Behavioral Operations 619Karen Donohue and Kenneth Schultz18.1 Introduction 61918.2 Current Research Trends 62018.2.1 Methodological Observations 62118.2.2 OM Context Observations 62418.3 Emerging Behavioral Operations Topics 62718.3.1 Behavioral Issues in Healthcare Operations 62718.3.1.1 Current Research Examples 62818.3.1.2 Future Research Needs 63018.3.2 Behavioral Issues in Retail Operations 63218.3.2.1 Current Research Examples 63318.3.2.2 Future Research Needs 63418.3.3 Behavioral Issues in Social and Sustainable Operations 63618.3.3.1 Current Research Examples 63818.3.3.2 Future Research Needs 63918.3.4 Behavioral Issues in Supply Chain Risk 64018.3.4.1 Current Research Examples 64118.3.4.2 Future Research Needs 64218.4 Final Remarks 643Acknowledgments 645References 645Index 653