Cover
Preface
Part I: Methodology
1. 1 Designing and Conducting Laboratory Experiments
  1. 1.1 Why Use Laboratory Experiments?
  2. 1.2 Categories of Experiments
  3. 1.3 Some Prototypical Games
  4. 1.4 Established Good Practices for Conducting BOM Laboratory
  5. 1.5 Incentives
  6. 1.6 Deception
  7. 1.7 Collecting Additional Information
  8. 1.8 Infrastructure and Logistics
  9. References
2. 2 Econometrics for Experiments
  1. 2.1 Introduction
  2. 2.2 The Interaction Between Experimental Design and Econometrics
  3. 2.3 Testing Theory and Other Hypotheses: Classical Hypothesis Testing
  4. 2.4 Testing Theory and Other Hypotheses: Regression Analysis
  5. 2.5 Dependence of Observations
  6. 2.6 Subject Heterogeneity
  7. 2.7 Structural Estimation
  8. 2.8 Concluding Remarks
  9. Acknowledgments
  10. References
3. 3 Incorporating Behavioral Factors into Operations Theory
  1. 3.1 Types of Behavioral Models
  2. 3.2 Identifying Which Behavioral Factors to Include
  3. 3.3 Nesting the Standard Model
  4. 3.4 Developing Behavioral Operations Model
  5. 3.5 Modeling for Testable Predictions
  6. References
4. 4 Behavioral Empirics and Field Experiments
  1. 4.1 Going to the Field to Study Behavioral Operations
  2. 4.2 Analyzing the Data: Common Empirical Methods
  3. 4.3 Field Experiments (Creating the Data)
  4. 4.4 Conclusion: The Way Forward
  5. References
Part II: Classical Approaches to Analyzing Behavior
1. 5 Biases in Individual Decision‐Making
  1. 5.1 Introduction
  2. 5.2 Judgments Regarding Risk
  3. 5.3 Evaluations of Outcomes
  4. 5.4 Bounded Rationality
  5. 5.5 Final Comments and Future Directions
  6. Acknowledgments
  7. References
2. 6 Other‐regarding Behavior
  1. 6.1 Introduction
  2. 6.2 The Nature of Social Preferences
  3. 6.3 Models of Social Preferences
  4. 6.4 Fair Choice: Stability and Factors That Influence It
  5. 6.5 Reciprocal Choice
  6. 6.6 Trust and Trustworthiness
  7. 6.7 Summary: The Empirical Nature of Fair Choice
  8. References
3. 7 Behavioral Analysis of Strategic Interactions
  1. 7.1 Behavioral Game Theory
  2. 7.2 Behavioral Analysis of Principal–Agent Problems
  3. 7.3 Bargaining
  4. References
4. 8 Integration of Behavioral and Operational Elements Through System Dynamics
  1. 8.1 Introduction
  2. 8.2 Decision‐making in a Dynamic Environment
  3. 8.3 Principles (Guidelines) for Modeling Decision‐making
  4. 8.4 Grounded Development of Decision‐making Processes
  5. 8.5 Formulation Development and Calibration Example
  6. 8.6 Conclusion
  7. References
Part III: Applications within Operations Management
1. 9 Behavioral Foundations of Queueing Systems
  1. 9.1 Introduction and Framework
  2. 9.2 The Customer
  3. 9.3 The Server
  4. 9.4 The Manager
  5. 9.5 Testing Queueing Theory in the Laboratory
  6. 9.6 Conclusions and Future Research Opportunities
  7. References
2. 10 New Product Development and Project Management Decisions
  1. 10.1 Exploration: The Creative Process
  2. 10.2 Plan: From Creative to Reality
  3. 10.3 Execute: From Planning to Execution
  4. 10.4 Conclusions
  5. References
3. 11 Behavioral Inventory Decisions
  1. 11.1 Introduction
  2. 11.2 Nominal and Actual Order Quantities
  3. 11.3 Decision Biases
  4. 11.4 Utility Functions
  5. 11.5 Individual Heterogeneity
  6. 11.6 Other Inventory Models
  7. 11.7 Summary and Outlook
  8. Acknowledgments
  9. References
4. 12 Forecast Decisions
  1. 12.1 An Introduction to Forecasting Behavior
  2. 12.2 Judgment Biases in Point Forecasting
  3. 12.3 Judgment Biases in Forecasting Uncertainty
  4. 12.4 Organizational Forecasting Processes
  5. 12.5 Improving Judgmental Forecasting
  6. 12.6 Conclusion and Future Research Opportunities
  7. References
5. 13 Buyer–Supplier Interactions
  1. 13.1 Introduction
  2. 13.2 Coordination with Imperfect Information: The Beer Distribution Game
  3. 13.3 Relationships Under Incentive Conflicts: Contracting in Supply Chains
  4. 13.4 Contracting and Mechanism Design
  5. 13.5 Conclusion and Future Possibilities
  6. References
6. 14 Trust and Trustworthiness
  1. 14.1 Are There Any Business Case Studies Where Trust and Trustworthiness Matter?
  2. 14.2 What Is Trust?
  3. 14.3 What Is Trustworthiness?
  4. 14.4 How Can We Measure Trust and Trustworthiness?
  5. 14.5 What Are the Building Blocks of Trust and Trustworthiness?
  6. 14.6 Two Remarks on Research Methods (Optional)
  7. 14.7 Conclusion
  8. Appendix 14.A A Selected Overview of Additional Decision Games for Studying Trust
  9. References
7. 15 Behavioral Research in Competitive Bidding and Auction Design
  1. 15.1 Overview of Behavioral Operations Research on Auctions
  2. 15.2 What We Learned from Experimental Economics Literature on Forward Auctions
  3. 15.3 Buyer‐determined Auctions
  4. 15.4 Relationships and Moral Hazard in Auctions
  5. 15.5 Empirical Findings on Bidder Behavior, Judgment, and Decision‐making Bias
  6. 15.6 Supply Risk
  7. 15.7 Elements of Auction Design
  8. 15.8 Comparing and Combining Auctions with Negotiations
  9. 15.9 Ongoing and Future Directions
  10. References
8. 16 Strategic Interactions in Transportation Networks
  1. 16.1 Introduction
  2. 16.2 Experiments on Route Choice in Networks with Fixed Architecture
  3. 16.3 Experiments on Traffic Paradoxes
  4. 16.4 Experiments on the Pigou–Knight–Downs Paradox
  5. 16.5 Experiments on the Downs–Thomson Paradox
  6. 16.6 Experiments on the Braess Paradox
  7. 16.7 Discussion and Conclusions
  8. Acknowledgment
  9. References
9. 17 Incorporating Customer Behavior into Operational Decisions
  1. 17.1 How to Think About “Behaviors” in Operational Settings: Customer Journey Maps
  2. 17.2 The “Before” Behaviors
  3. 17.3 The “During” Behaviors
  4. 17.4 The “After” Behaviors
  5. 17.5 Concluding Remarks
  6. Acknowledgments
  7. References
10. 18 The Future Is Bright
  1. 18.1 Introduction
  2. 18.2 Current Research Trends
  3. 18.3 Emerging Behavioral Operations Topics
  4. 18.4 Final Remarks
  5. Acknowledgments
  6. References
Index
End User License Agreement

List of Tables

Chapter 01
1. Table 1.1 The instrument to elicit risk preferences in Holt and Laury (2002).
Chapter 02
1. Table 2.1 Results of hypothesis tests using data from Davis et al. (2011).
2. Table 2.2 Hypothesis testing with regressions; Davis et al. (2011).
3. Table 2.3 Illustration of lagged dependent variables in panel data.
4. Table 2.4 Simulated regressions: mean anchoring and subject heterogeneity.
5. Table 2.5 Structural estimation of newsvendor decisions From Bolton and Katok (2008).
6. Table 2.6 Estimation results for mixture model on mean anchoring.
Chapter 03
1. Table 3.1 Toolbox of model components to explain phenomena: “standard” vs. “behavioral.”
Chapter 05
1. Table 5.1 Sample lottery choice risk aversion elicitation task (50–50 chance between $5.50 and $4.50 for Option A and 50–50 chance between $X and $1.00 for Option B).
2. Table 5.2 Recreation of table 1 from Rabin (2000).
Chapter 06
1. Table 6.1 Summary of the experimental designs and observations – average first offers, rejection rates, and disadvantageous counter offers – for each bargaining study in the samples.
Chapter 09
1. Table 9.1 Framework.
Chapter 11
1. Table 11.1 Overview of the behavioral newsvendor.
2. Table 11.2 Performances of demand chasing detection approaches – aggregated data (Lau and Bearden 2013).
Chapter 13
1. Table 13.1 List of behavioral papers discussed in Chapter 12.
2. Table 13.2 Theoretical formulations of commonly studied supply chain contracts.
Chapter 16
1. Table 16.1 Equilibrium predictions, social optimum predictions, and mean observed choice frequencies by information condition RCC and SCC in Mak, Gisches, and Rapoport (2015).
2. Table 16.2 Equilibrium predictions and mean observed choice frequencies in the basic and augmented networks in experiment 2 of Rapoport et al. (2009).
3. Table 16.3 Predicted and observed distributions of route choice frequencies and associated travel costs by game type and network type in Rapoport et al. (2014).
Chapter 17
1. Table 17.1 Consumer utility calculations.

List of Illustrations

Chapter 01
1. Figure 1.1 Payoffs in the prisoner’s dilemma game (Flood 1958).
2. Figure 1.2 An example of a 2 × 2 full factorial design.
3. Figure 1.3 Mean order quantities in the Bolton, Ockenfels, and Thonemann (2012) experiment.
4. Figure 1.4 Distribution of offers in the Forsythe, Horowitz, and Sefton (1994) study. (a) Dictator game with pay, (b) ultimatum game with pay, (c) dictator game without pay, and (d) ultimatum game without pay.
5. Figure 1.5 Summary of Holt and Laury (2002) data. (a) Low real payoffs (solid line with dots) compared with hypothetical payoffs (thin lines) and risk‐neutral benchmark (dashed line). (b) Real payoffs: low (solid line with dots), 20× (squares), 50× (diamonds), 90× (triangles), risk neutral (dashed).
Chapter 02
1. Figure 2.1 Histogram of difference in reserve prices: 1 versus 4 bidders.
2. Figure 2.2 Summary data for newsvendor order quantities by optimal order quantity. (a) Histogram and (b) summary statistics.
3. Figure 2.3 Predicted choice probabilities for various structural models. (a) High profit condition and (b) low profit condition.
Chapter 03
1. Figure 3.1 The S‐shaped value function in prospect theory.
Chapter 05
1. Figure 5.1 Chapter 5 structure and included topics.
2. Figure 5.2 Median judgments (on a logarithmic scale) for the “stops” problem (the “committees” problem is another example not highlighted in this chapter).
3. Figure 5.3 Probability weighting function example with β = 0.65 using the form w(p) = p^β/(p^β + (1 − p)^β)^1/β.
4. Figure 5.4 Calibration curves from four half‐range, general knowledge tasks.
5. Figure 5.5 Value function of prospect theory.
Chapter 06
1. Figure 6.1 Amounts offered to the recipient in dictator and ultimatum games (one observation per proposer).
2. Figure 6.2 Wage and effort levels in the gift exchange game.
3. Figure 6.3 Money sent and returned in the trust game with initial endowments of $10.
4. Figure 6.4 Best offers in the market game.
5. Figure 6.5 Bolton–Ockenfels utility function with c = 1 and a_i/b_i = 0.25.
6. Figure 6.6 A comparison of observed average opening offers to pecuniary equilibrium opening offers. The serrated 45° line indicates where prediction and observation match. The horizontal serrated line marks equal division offers. The solid line is the regression line (numbers in parentheses are two‐sided p‐values).
7. Figure 6.7 Probability for the proposer to make an offer (top) and probability for the responder to accept such an offer (bottom), observations vs. model.
8. Figure 6.8 Average opening offers are predicted quite satisfactorily across experiments and treatments. The solid line in the graph is the regression line. The numbers in brackets are two‐sided p‐values. If we drop the extreme observation (truncation game), the resulting regression estimate is Observed = 0.97 Predicted + 0.02; R² = 0.333.
Chapter 07
1. Figure 7.1 Ultimatum Game.
2. Figure 7.2 Example stag hunt and minimum effort coordination games.
3. Figure 7.3 Example Prisoner’s Dilemma game.
4. Figure 7.4 Two‐stage alternating offer game.
Chapter 08
1. Figure 8.1 Decision‐making as a feedback process.
2. Figure 8.2 Forrester’s representation of three databases.
3. Figure 8.3 Feedback structure of erosion of service quality model (partial).
4. Figure 8.4 Time per order (partial model estimation).
5. Figure 8.5 Reinforcing feedback loop that leads to fixation.
Chapter 11
1. Figure 11.1 Order quantities for the high‐ and low‐critical‐ratio settings in Bolton and Katok (2008) and trained managers in Bolton et al. (2012).
2. Figure 11.2 Expected order quantities for different levels of anchoring and demand chasing for uniform demand between 0 and 100.
3. Figure 11.3 The effect of random errors for uniform demand between 0 and 100.
4. Figure 11.4 Order quantities for different decision biases and utility functions for uniformly distributed demand between 0 and 100 for different critical ratios.
Chapter 14
1. Figure 14.1 Experimental results in Berg et al. (1995).
2. Figure 14.2 Retailers’ and suppliers’ decisions in the forecast sharing game.
3. Figure 14.A.1 The trust game in Snijders and Keren (1999).
4. Figure 14.A.2 Three decision games in Bohnet and Zeckhauser (2004).
5. Figure 14.A.3 The trust‐building game in Ho and Weigelt (2005).
Chapter 16
1. Figure 16.1 Representations of the networks discussed in Pigou (1920) and Roughgarden (2005), where x denotes the number of users traversing the bottom edge. (a) When the bottom route has a linear cost function and (b) when the bottom route has a non‐linear cost function.
2. Figure 16.2 The eight‐route network in the experiment conducted by Mak, Gisches, and Rapoport (2015) (n = 18).
3. Figure 16.3 The networks in Morgan, Orzen, and Sefton (2009)’s experiment on the PKD paradox (n = 8).
4. Figure 16.4 The networks in Morgan, Orzen, and Sefton (2009)’s experiment on the Braess paradox (n = 8).
5. Figure 16.5 The basic (left‐hand side) and augmented (right‐hand side) networks in Rapoport et al. (2009) (n = 18). (a) Experiment 1 and (b) experiment 2.
6. Figure 16.6 The two networks in Rapoport, Gisches, and Mak (2014) (n = 18).
Chapter 17
1. Figure 17.1 360‐degree view of customer experience and the relevant behavioral operations issues. *Behavior in waiting lines (queueing) is a subject of Chapter 9 and is therefore omitted here.
2. Figure 17.2 Consumer segmentation under deterministic utility.
3. Figure 17.3 Consumer segmentation under random utility.
Chapter 18
1. Figure 18.1 Behavioral operations publications by OM Journal and year.
2. Figure 18.2 Behavioral operations publications by Methodology.
3. Figure 18.3 Behavioral operations publications by OM Context.
4. Figure 18.4 Breakdown of behavioral assumptions by OM Context.

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Wiley Series in
Operations Research and Management Science

Operations Research and Management Science (ORMS) is a broad, interdisciplinary branch of applied mathematics concerned with improving the quality of decisions and processes and is a major component of the global modern movement towards the use of advanced analytics in industry and scientific research. The Wiley Series in Operations Research and Management Science features a broad collection of books that meet the varied needs of researchers, practitioners, policy makers, and students who use or need to improve their use of analytics. Reflecting the wide range of current research within the ORMS community, the Series encompasses application, methodology, and theory and provides coverage of both classical and cutting edge ORMS concepts and developments. Written by recognized international experts in the field, this collection is appropriate for students as well as professionals from private and public sectors including industry, government, and nonprofit organization who are interested in ORMS at a technical level. The Series is comprised of four sections: Analytics; Decision and Risk Analysis; Optimization Models; and Stochastic Models.

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Names: Donohue, Karen, (Karen L.), editor. | Katok, Elena, 1966– editor. | Leider, Stephen, editor.
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Description: Hoboken, NJ : John Wiley & Sons, 2019. | Series: Wiley series in operations research and management science | Includes bibliographical references and index. |
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List of Contributors

Gad Allon
The Wharton School
University of Pennsylvania
Philadelphia, PA
USA

Michael Becker‐Peth
Rotterdam School of Management
Erasmus University
Rotterdam
The Netherlands
and
Department of Supply Chain Management and Management Science
University of Cologne
Cologne
Germany

Gary E. Bolton
Naveen Jindal School of Management
University of Texas at Dallas
Richardson, TX
USA

Kay‐Yut Chen
College of Business
University of Texas at Arlington
Arlington, TX
USA

Yefen Chen
School of Economics and Management
Beihang University
Beijing
China

Tony Haitao Cui
Carlson School of Management
University of Minnesota
Minneapolis, MN
USA

Andrew M. Davis
Samuel Curtis Johnson Graduate School of Management, Cornell SC Johnson College of Business
Cornell University
Ithaca, NY
USA

Karen Donohue
Carlson School of Management
University of Minnesota
Minneapolis, MN
USA

Wedad Elmaghraby
Robert H. Smith School of Business
University of Maryland
College Park, MD
USA

Matthew Embrey
Department of Economics
University of Sussex
Falmer, Brighton
UK

Sanjiv Erat
Rady School of Management
University of California‐San Diego
La Jolla, CA
USA

Paul Goodwin
School of Management
University of Bath
Bath
UK

Yael Grushka‐Cockayne
Darden School of Business
University of Virginia
Charlottesville, VA
USA

Kyle Hyndman
Naveen Jindal School of Management
University of Texas at Dallas
Richardson, TX
USA

Maria R. Ibanez
Kellogg School of Management
Northwestern University
Evanston, IL
USA

Elena Katok
Naveen Jindal School of Management
University of Texas at Dallas
Richardson, TX
USA

Mirko Kremer
Frankfurt School of Finance and Management
Frankfurt
Germany

Stephen Leider
Ross School of Business
University of Michigan
Ann Arbor, MI
USA

Vincent Mak
Cambridge Judge Business School
University of Cambridge
Cambridge
UK

Brent Moritz
Smeal College of Business
Pennsylvania State University
University Park, PA
USA

J. Bradley Morrison
Brandeis International Business School
Brandeis University
Waltham, MA
USA

Rogelio Oliva
Mays Business School
Texas A&M University
College Station, TX
USA

Anton Ovchinnikov
Smith School of Business
Queen’s University
Kingston, Ontario
Canada

Özalp Özer
Naveen Jindal School of Management
University of Texas at Dallas
Richardson, TX
USA

Amnon Rapoport
School of Business Administration
University of California‐Riverside
Riverside, CA
USA

Kenneth Schultz
Xenia, OH
USA

Enno Siemsen
Wisconsin School of Business
University of Wisconsin‐Madison
Madison, WI
USA

Bradley R. Staats
Kenan‐Flagler Business School
University of North Carolina‐Chapel Hill
Chapel Hill, NC
USA

Ulrich W. Thonemann
Department of Supply Chain Management and Management Science
University of Cologne
Cologne
Germany

Joel Wooten
Moore School of Business
University of South Carolina
Columbia, SC
USA

Diana Wu
School of Business
University of Kansas
Lawrence, KS
USA

Yaozhong Wu
National University of Singapore Business School
Singapore
Singapore

Yanchong Zheng
Sloan School of Management
Massachusetts Institute of Technology
Cambridge, MA
USA

Preface

Behavioral operations is a field of study within operations management (OM) that examines how individuals behave and make decisions within operational settings. The individuals of interest can vary from managers making cognitively challenging inventory decisions, to workers performing repetitive tasks in a fulfillment center, to customers reacting to stocking decisions within a store. By better understanding what these individuals do and why, behavioral operations can also provide insight into how to improve operational settings and policies in light of behavioral tendencies. The field has grown tremendously in recent years, broadening the types of operational settings explored and the research methodologies employed. The idea for this handbook grew from our own desire to document major developments within the behavioral operations field so far, and to provide inspiration and tools for continued advancement.

In essence, this handbook aims to be a comprehensive resource on behavioral operations research for those already active in the field, as well as those new to it. We believe this latter audience is particularly important, as many OM scholars from a variety of backgrounds are beginning to conduct behavioral research. The handbook is designed to help new behavioral researchers learn more about the field and how such research is conducted. For veterans, the handbook offers extensive literature reviews of the many OM application areas studied within behavioral operations and highlights promising new areas of interest.

In order to provide a comprehensive research guide, the handbook is divided into three main parts. Part I provides tutorials on common research methods used to conduct behavioral research. Part II reviews major insights and frameworks from sister disciplines of psychology, behavioral economics, and system dynamics that serve as important grounding for behavioral research. Part III focuses on specific application areas within OM, providing a survey of current findings and a vision for future research needs.

Part I consists of four chapters. The first chapter discusses laboratory experiments as a methodology, including strengths and weaknesses, as well as best practices for experimental research on operations‐specific issues. The second chapter covers statistical and econometric techniques used to analyze experimental data, using examples and highlighting common practices from current research. Chapter 3 provides a framework for how to incorporate insights from behavioral experiments into operational modeling, as a key goal of behavioral operations is to develop more accurate models of operational decision‐making. Finally, Chapter 4 discusses behavioral research using two other empirical methods: statistical analysis of field data and field experiments. We hope that Part I will serve as a key methodological resource for individuals new to behavioral research, as well as those looking to further expand the behavioral methods they use.

Part II includes four chapters that highlight important results and frameworks from related fields in psychology, behavioral economics, and system dynamics that have important potential application in OM. Chapter 5 focuses on individual decision‐making topics: judgements regarding risk, evaluation of outcomes in various decision settings, and bounded rationality in decision‐making. Chapter 6 discusses decisions in a social context and the importance of other regarding behaviors such as fairness, trust, and reciprocity in interactions between individuals and firms. Chapter 7 discusses strategic interactions, including bargaining and negotiations, behavioral game theory (including tests of solution concepts and strategic sophistication), and behavioral and experimental analysis of incentive and agency problems. Finally, Chapter 8 discusses the system dynamics approach, which studies the interplay between the physical aspects of an operational system and the decision‐making of actors within that system. We hope that Section II will provide an entry point for readers who want to learn about other fields of behavioral research.

Part III includes ten chapters surveying major current areas of research within behavioral operations, with the final chapter highlighting emerging application areas. These chapters each provide an organizing framework for their OM application, survey major behavioral developments, and discuss future directions for these topics. The first four chapters cover research on key operations tasks such as queueing systems (Chapter 9), project management and product development (Chapter 10), inventory management (Chapter 11), and forecasting (Chapter 12). The next four chapters discuss operational settings involving multiple firms: buyer–supplier relationships (Chapter 13), trust and trustworthiness in transactions between firms (Chapter 14), sourcing and procurement auctions (Chapter 15), and transportation networks (Chapter 16). Chapter 17 examines the role that behavioral research on consumers can play in OM, including applications in retail operations and dynamic pricing. Finally, Chapter 18 describes recent trends in behavioral operations and includes short surveys on behavioral work within several emerging topics including healthcare operations, retail operations, social and environmental operations, and supply chain risk. We hope Part III will be helpful for current behavioral researchers looking for a comprehensive survey of OM applications and for the broader OM audience wishing to see how behavioral operations has already informed their specific OM area of interest. For both audiences, we hope these examples will inspire contributions to a continued stream of behavioral research that impacts both OM theory and practice in the years to come.

Karen Donohue, Elena Katok, and Stephen Leider (Editors)