- Email: [email protected]
Behavioral operations: The state of the field
Journal of Operations Management 31 (2013) 1–5
Contents lists available at SciVerse ScienceDirect
Journal of Operations Management journal homepage: www.elsevier.com/locate/jom
Editorial
Behavioral operations: The state of the field a r t i c l e
i n f o
Keywords: Behavioral operations Review
a b s t r a c t The field of behavioral operations has matured into an established area within the discipline of operations management. The field fills an essential void by laying the micro-foundations for the broader discipline of operations management. As such, the field examines a variety of topics and is methodologically diverse. © 2012 Elsevier B.V. All rights reserved.
Introduction Six years have passed since the publication of the last JOM special issue on behavioral operations (Bendoly and Schultz, 2006). That year also saw the first annual conference in behavioral operations. Since then the field has developed, broadened and matured. The annual conference has continued each year and grown. Members of the community have organized the INFORMS Section of Behavioral Operations Management and the POMS College of Behavior in Operations Management. Doctoral courses on behavioral operations are now taught at major research institutions and special issues in other top tier operations management journals have been published. It is safe to say that behavioral operations has become an accepted sub-field of the discipline of operations management. The value of behavioral operations lies in recognizing that almost all contexts studied within operations management contain people. There are managers making decisions, employees working in and improving processes, and customers buying products. It is tempting to treat these people mechanistically – managers making the best choices for their companies, employees diligently providing their best input and customers buying the product if the price is below an individual threshold. But reality bites. Managers may have the best of intentions, but are often unable to move their organization in the right direction. Employees may react to financial incentives, but are also concerned about status and fairness. Customers may respond not only to the value from the service or good they purchase, but also to non-monetary aspects of the process that delivered it. Behavioral operations starts at this micro-level to better understand behavior – ultimately enabling operations management to make better recommendations of how to design and improve processes and supply chains. Since the field is a departure from a mechanistic view of the organization, it has a relentlessly empirical focus – testing theoretical ideas for their robustness in the laboratory and in the real world. This focus pairs well with the Journal of Operations Management. In addition to the stellar papers themselves, this special issue as a collection of papers showcases several developments within the field. Substantively, the scope of the field is broadening. Many of the original papers in the field focused on inventory, either on stochastic ordering policy (i.e. newsvendor) or on supply chain (i.e. beer game) settings. The literature overview we provide in this 0272-6963/$ – see front matter © 2012 Elsevier B.V. All rights reserved. http://dx.doi.org/10.1016/j.jom.2012.12.001
introduction, as well as the papers published in the special issue, showcase a much broader variety of topics. These include revenue management, risk analysis and process improvement. Methodologically, while the field is often identified with experimental studies where subjects perform tasks on a computer, the papers in this special issue demonstrate its new directions. Our literature review shows behavioral work that includes survey research and mathematical modeling. Even within the context of experimental work, this special issue highlights that alternative experimental methods, such as eye movement studies or vignette-based research, are more frequently employed. We proceed in this introduction by briefly providing, and explaining the definition we used to circumscribe the boundaries of behavioral operations for this special issue. We then provide an up-to-date view of the field by presenting the results from a literature survey. Finally, we introduce the papers published in this special issue. We discuss both their contributions to the literature and highlight the novel methodological approaches they bring to the field.
1. Defining behavioral operations Our first task when starting work on the special issue was to decide which papers qualified as behavioral operations and which did not. A field that cannot define its boundaries risks being everything and therefore nothing. In German, the word ‘Trennschärfe’ captures our intentions best – a distinctive criterion that sharpens the vision and focus of the field. As Bob Hayes said, “a community of scholars, like any other community, requires a focus for its efforts, an integrating mechanism, a common ground that all can share and contribute to” (Hayes, 2000, pp. 106-107). With that intention, we attempt to provide such focus. We define behavioral operations as the study of potentially nonhyper-rational actors in operational contexts. In the simplest sense, behavioral operations must have an element of both operations and behavior. These criteria, however, require a closer definition. Research in behavioral operations requires an operations context. It is the richness and complexity of the context of our discipline that distinguishes us from research in organizational behavior. The goal of research in behavioral operations is not, for example, a
2
Editorial / Journal of Operations Management 31 (2013) 1–5
deeper understanding of leadership, fairness, emotions or motivation. We would be happy to accomplish these things, but they are not the goal. Rather, the goal of research in behavioral operations is a deeper understanding of operations processes. Which settings are valid contexts within the discipline of operations management is often debated. A pragmatic definition, such as Wickham Skinners guidance that operations management is essentially what real operations managers care about (Hayes, 2000), strikes us as relevant and valid. Our use of the term behavioral also requires focus. Many traditional papers in operations management do consider human behavior. However they predominantly model humans as hyperrational beings optimizing behavior toward a single monetary goal. We find value in considering other patterns of human action. There are three criteria that characterize hyper-rational actors: (A) they are mostly motivated by self-interest, usually expressed in monetary terms; (B) they act in a conscious, deliberate manner; and (C) they behave optimally for a specified objective function. A paper that in principle allows a deviation from either (A), (B) or (C) is sufficient to make a paper behavioral in nature. For example, Urda and Loch (2013) describe behavior as being motivated by social preferences, violating (A), and see emotions as key triggers of behavior, violating (B). Bendoly (2013) shows that decision makers in a simple revenue management task do not behave optimally, violating (C). Besides research being behavioral in nature and dealing with an operations context, a third aspect is necessary to limit the scope of the field of behavioral operations. Research in behavioral operations analyzes decisions, the behavior of individuals, or small groups of individuals. This constrains the unit of analysis to the micro-level. Separating a discipline into micro and macro is a standard practice in many academic areas. Economics shows this famous split between macro-economics, which is the study of whole economies, and micro-economics, which is the study of the parts of an economy, such as markets, organizations and individuals. Similarly, the field of organizations is divided into macro-organizations (i.e. organization theory), which studies the design, structure and performance of whole organizations, and micro-organizations (i.e. organizational behavior), which studies the components of such organizations, such as teams and individual employees. This split enables a division of labor, and a natural ability to theorize on different levels. We do not mean to imply that research at the organizational level does not add value. We merely suggest that the field of behavioral operations has a different emphasis. The risk of explicitly dividing the micro from the macro-perspective is that research in both areas becomes divergent and does not inform each other. There is high quality research that lives in both worlds, where micro issues inform macro outcomes. We have included one such paper in our special issue. Sawhney (2013) looks at the influence of training, job rotation and motivation (micro behavior) on used flexibility within a plant (macro operations). As the field of behavioral operations matures further, we expect to see a more explicit discussion about the distinction between micro and macro, and the links between these levels.
2. State of the literature We used the definition from the previous section in order to identify papers published in behavioral operations between 2006 and 2011. We searched major outlets in operations management, including the Journal of Operations Management (JOM), Management Science (MS), Production and Operations Management (POM), Manufacturing & Service Operations Management (MSOM) and the Decision Sciences Journal (DSJ) for articles matching our definition of
Fig. 1. Distribution of behavioral operations publications.
behavioral operations. The resulting literature search yielded 100 Behavioral Operations papers from 2006 to 2011. Bendoly et al. (2006) provide a review of work prior to 2006. A list of these papers, with operations context, methodology and behavioral domain, is available from the authors upon request. Fig. 1 charts the frequency of behavioral operations publications in the five journals over the six years studied. Fig. 1 shows a general increase in the number of papers in behavioral operations. The exception to this general trend is 2008 which saw the MSOM Special Issue in Behavioral Operations. The number of publications has recently grown within Management Science, possibly due to the opening of new departments within that journal that focus on behavioral work. Similarly, the number of papers published within Decision Sciences has steadily grown. JOM played a critical role in supporting the field in its early years (’06–’08), especially by publishing the first special issue. However, as the number of other outlets has grown the journal has recently seen a decrease in publications in behavioral operations. We hope that this special issue will reverse this trend, and clearly signal that JOM is open to all forms of research in behavioral operations. We further categorized all papers into their operations context, as well as their methodology employed. Results from this analysis are shown in Fig. 2. Fig. 2 shows a large breadth of operations issues and research methodologies. While the field of behavioral operations is often associated with the study of inventory management and production management (Bendoly et al., 2006), only 18% of the papers found in our search were related to inventory and production decisions. A much larger number of manuscripts dealt with supply chain issues such as contracting or supplier relationships, product development issues such as ideation and design decisions, or quality issues such as error detection. Similarly, while the field is often equated with experimental research, less than 50% of the papers in our survey were experimental, speaking to the methodological variety existing within the field. The growth in method is especially important. Each research methodology in itself has limitations. A mix of methodologies allows for better triangulation of results. The literature review also shows an explosion in the diversity of the behavioral content. To demonstrate this we present a Wordle based on the behavioral domains we identified during our literature review. To identify behavioral domains, we used the title, keywords and abstracts and identified behavioral keywords mentioned by the authors. We were looking for theoretical perspective used (e.g. prospect theory) or the broad domain in which their work was positioned (e.g. social preferences). A Wordle is a graph that is based on a frequency count of words (see http://www.wordle.net). Words that are counted in larger frequencies are represented by a larger font size in the graph. The resulting graph is shown in Fig. 3. In our opinion, much of the early research identified with behavioral operations suffered from insufficient behavioral theory underpinnings. It focused on identifying gaps between theoretical models of what should happen and what did happen in practice. What theoretical grounding was used was generally restricted to a
Editorial / Journal of Operations Management 31 (2013) 1–5
Forecasting 4%
Other 9% Supply Chain 27%
Service 7%
Experiment - Game 11%
Modeling 6%
Conceptual 7%
Experiment - Other 4% Experiment - Decision Task 28%
Archival 14%
Inventory 8% Production 10%
3
Product Dev./Proj. Man. 17% Quality 11%
Qualitative/ Conceptual 15%
Survey 22%
Fig. 2. Operations context and methodology in behavioral operations.
few areas of behavioral research such as bounded rationality and motivation theory. The field needed more work explaining how, why, and what could be done about these gaps (Bendoly et al., 2010). In contrast, the behavioral domains observed in our literature review show a larger diversity of theoretical underpinnings for understanding operations problems. Moritz et al. (2013) use dual process theory to understand decision making. Furthermore, Riedl et al. (2013) use procedural rationality to analyze supplier selection while Urda and Loch (2013) use social preferences to help understand aspects of work. While there is always room for improvement, we have seen significant progress in the field tying underlying theories of behavior to operations settings. We also see an increase in the identification of individual differences (heterogeneity) in behavioral responses to similar stimuli. An emphasis on the average response while ignoring heterogeneity can lead to serious errors in model development (Schultz et al., 2007). Moritz et al. (2013) show how a tendency for cognitive reflection changes inventory decisions. Vericourt et al. (2013) show differences in inventory decisions by gender and risk preference. We strongly support a move to report and study not only the average response of subjects, but the range and diversity of responses as well (see also Lau et al., in press for an example). We hope that editors and reviewers will continue to encourage the specific inclusion of behavioral theory, from many fields, and the analysis of heterogeneity of responses. 3. The content of this special issue Our special issue is an excellent example of the broadening of context, methodology and behavioral domain in our field. The articles here show a diversity of application beyond inventory
and production, a growth in behavioral domain past bounded rationality and motivation and broadening of methodology that complements the use of laboratory experiments. Contextual diversity. The field of behavioral operations encompasses the study of individual behavior, as well as the study of social preferences in groups (Loch and Wu, 2007). Urda and Loch (2013) tie social preferences more clearly to individual behavior through their study on emotions as pathways by which social preferences affect individual behavior. In their work, social preferences trigger emotions, which in turn regulate behavior. They test this framework in different operational contexts, such as process improvement and shift scheduling, and discuss far-reaching managerial implications. The concept of emotions has been understudied within behavioral operations, and this research provides a powerful framework to promote more research in this area. Given the publicity as well as the documented financial implications of supply chain risk (Hendricks and Singhal, 2005), risk management appears as a central task for supply chain managers. Two papers in our special issue examine this topic. Hora and Klaasen (2013) examine how operational risk management professionals assess and learn from operational risk events that happen to other companies. They show that these professionals apply a ‘benchmarking’ logic to this process, and are more likely to learn from companies that they see as market leaders. Tazelaar and Snijders (2013), using real-life cases, show that even experienced supply chain managers struggle with adequately assessing risk in the first place and are easily outperformed by a simple decision model. One area of research that should become vital for the field of behavioral operations is that of personnel assessment in supply chains. The field of human resources has a long tradition of
Fig. 3. Frequency count of behavioral domains.
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Editorial / Journal of Operations Management 31 (2013) 1–5
research in this area, but it is important to test which aspects of that theory are most applicable in the context of supply chain management. For that purpose, we need a better understanding of individual differences in behavioral operations, an area that has received only scant attention (Lau et al., in press). Two manuscripts in our special issue promise to lay a better foundation for such an understanding: Bearden et al. (2013) examine risk preferences and gender differences; Moritz et al. (2013) study the construct of cognitive reflection, i.e. the tendency of decision makers not to overly trust their intuition. Together these papers provide a first step to better understand the factors and traits that make for better supply chain managers. One of the most profound changes in supply chain management over the last 20 years is globalization. Supply chains span the globe in ways that were previously unimaginable. From a behavioral perspective, culture becomes important as a predictor and explaining factor of behavior in these settings (Loch and Wu, 2007). Our special issue contains two studies that involve cross-cultural comparisons. Cui et al. (2013) use a simple ordering task to, explore the differences in decision making between Chinese and U.S. managers. Riedl et al. (2013) compare supplier selection decisions in the same cross-cultural context. While ordering tasks are still a frequent context for research in behavioral operations, the field has started analyzing different decision contexts as well. One such area is revenue management, where a key task lies in dynamic pricing, i.e. decision makers constantly judging whether to adjust prices given that capacity expires at a certain point in the future. Bendoly (2013) studies two performance metrics in this task, and finds that while both metrics carry approximately similar information, the different framing of these metrics has a profound influence on individual behavior, and ultimately performance. Methodological diversity. In addition to variation in their substantive area, the papers in this special issue are methodologically diverse. The field of behavioral operations is often associated with laboratory experiments (Bendoly et al., 2006). In such experiments, participants are exposed to operational tasks, and the experimenters would systematically alter elements of the underlying task or information structure. A good summary of this line of research is given in Katok (2011). However, the field of behavioral operations is a content area, and not a methodological choice. The articles published in the special issue demonstrate that researchers in behavioral operations are branching out into different research methodologies in order to more deeply understand the operations management content. One prominent technique is vignette-based research. In this methodology, researchers describe a business scenario to participants, who then respond to the scenario by either making a choice, or by preparing a judgment on a subjective Likert scale. Manipulations involve adding, changing or deleting sentences in the scenario description. These are often weak manipulations, making results that are found more powerful. This technique has been successfully applied in the context of consumer research. Key challenges include the design of the vignettes (Rungtusanatham et al., 2011), and selection of the proper subject pool as respondents. Urda and Loch (2013) use student subjects in their study on emotions, as the research context does not require specific domain knowledge. If, however, domain specific knowledge is required, the subject pool needs to reflect this requirement. Hora and Klaasen (2013) draw on a pool of professional risk managers. A similar approach is also employed by Tazelaar and Snijders (2013), who cleverly develop their vignettes from cases they solicited using survey research. Observed behavior in a lab provides only limited information about the process of human judgment and decision making. One approach to dig deeper into the psychology of such processes
is to use biometric research. This involves measuring involuntary responses of the human body, such as eye pupil dilation or blink rates. This approach has a long tradition in psychology and marketing research, and a more recent tradition in economics as well. With the recent use of webcams and specialized software, these studies no longer require expensive specialized equipment. Bendoly (2013) uses this method to examine the physiological responses to different performance metrics in a repeated revenue management task. A more subjective, but much richer approach to studying the process of human judgment and decision making lies in verbal protocol analysis which requires participants to ‘think aloud’ while working on their task in an experiment, instead of silently making their judgments and decisions. Common criticisms against this approach are that people have difficulties articulating their thought-process, or that making them do so changes the thoughtprocess itself. However extensive research has shown that when following the right protocol, such criticism can be overcome (Ericsson and Simon, 1993, see also Payne, 1994 for a short list of best practices). Cui et al. (2013) use this approach for a crosscultural comparison of decision making in a simple ordering context. While the field of empirical operations management has a long standing tradition in psychometric research (and the Journal of Operations Management has been instrumental in supporting this tradition), the field of behavioral operations has rarely made use of these measurements. One reason may lie in the fact that psychometric measures are out-of-task, i.e. they correspond to responses of participants to questions that are posed separately from the actual operations task. The behavioral economics tradition (which has inspired much work in behavioral operations) often uses in-task measures instead, i.e. key individual variables such as risk aversion or loss aversion are estimated from observed choices in the experiment. While in-task measures have the merit of being based on revealed preferences, out-of-task measures are less model dependent. Using them as correlates in the context of experiments or surveys is a technique that will enrich the field of behavioral operations. Bearden et al. (2013) employ psychometric measurement to study risk aversion in a simple ordering context. Moritz et al. (2013) employ a psychometric measure to study thinking style in a similar context. Riedl et al. (2013) employ classic psychometric measurements in their survey research to study supplier selection decisions. 4. Conclusion The field of behavioral operations has reached a new stage in its life, moving beyond its previous limitations on topics and research methodologies. This special issue shows a healthy growth in the operational contexts, research methodologies and behavioral domains of our field. This is welcome and exciting. The inherent creativity we have seen while editing this special issue emphasize that the field has become a vibrant and established domain within operations management, and speaks well of its future contributions. Acknowledgments We would like to thank the previous Editors in Chief, Morgan Swink and Ken Boyer, for initiating the special issue, and the current Editors in Chief, Dan Guide and Tom Choi, for their continuing support. The special issue received 80 submissions; we thank all submitting authors for their support of the special issue. Nine of these submissions were accepted for publication in the special issue. The average number of days between submission and response for papers that were sent for review was 78 days. We
Editorial / Journal of Operations Management 31 (2013) 1–5
thank the numerous reviewers, who allowed us to provide detailed feedback and short cycle times despite the large number of submissions received. Reviewers Vishal Agarwal Gopesh Anand Mark Baratt Neil Bearden Elliott Bendoly Gary Bolton Matt Bowler Stephen Brammer David Cantor Craig Carter Raul Chao Kay-Yut Chen Edward Cokely Andrew Davis Ken Doerr Karen Donohue David Drake Jennifer Dunn Stephanie Eckerd Feryal Erhun Tianjun Feng Joy Field Nagesh Gavirneni Uri Gneezy Paulo Goncalves John Gray Dan Guide Reidar Hagtvedt Sean Handley Manpreet Hora Elena Katok Ted Klastorin Thomas Kull Nelson Lau Alec Levenson Min Li Kevin Linderman Miguel Lobo John MacDonald Susan Meyer-Goldstein Jürgen Mihm Brent Moritz Suresh Muthulingam Sriram Narayanan Javad Nasiry Ingrid Nembhard Julie Niederhoff Rogelio Oliva Nektarios Orianopoulos Steve Powell Yufei Ren Denise Rousseau Brooke Saladin Fabrizio Salvador Tobias Schönherr Manuel Sosa Sri Talluri Doug Thomas Anita Tucker
Georgetown University of Illinois Arizona State INSEAD Emory University of Texas at Dallas Oklahoma State Warwick Business School Iowa State Arizona State University of Virginia HP Labs Michigan Tech Cornell Naval Postgraduate School University of Minnesota Harvard Michigan State University of Maryland Stanford Fudan Boston College Cornell University of California at San Diego University of Lugano Ohio State Penn State University of Alberta Rutgers Georgia Tech University of Texas at Dallas University of Washington Arizona State INSEAD University of Southern California University of Minnesota University of Minnesota INSEAD Michigan State University of Minnesota INSEAD Penn State Cornell Michigan State HKUST Yale Syracuse Texas A&M Cambridge Dartmouth Union College Carnegie-Mellon Wake Forest IE Business School Michigan State INSEAD Michigan State Penn State Harvard
Mark van Oyen Liana Vittorino Cynthia Wallin Joseph Wang Noel Watson Scott Webster Elliott Weiss Diana Wu Zhaohui Wu Yaozhong Wu Zach Zacharia
5 University of Michigan University of Victoria Brigham Young National Taiwan MIT-Zaragoza Syracuse University of Virginia University of Kansas Oregon State National University of Singapore Lehigh
References Bendoly, E., Croson, R., Goncalves, P., Schultz, K., 2010. Bodies of knowledge for research in behavioral operations. Production and Operations Management 19 (4), 434–452. Bendoly, E., Donohue, K., Schultz, K., 2006. Behavioral operations management: assessing recent findings and revisiting old assumptions. Journal of Operations Management 24, 737–752. Bendoly, E., Schultz, K. (Eds.), 2006. Incorporating behavioral theory in OM empirical models. Journal of Operations Management 24 (6), 735–863 (Special issue). Ericsson, K.A., Simon, H., 1993. Protocol Analysis: Verbal Reports as Data. MIT Press, Cambridge, MA. Hayes, R.H., 2000. Toward a “new architecture” for POM. Production and Operations Management 9 (2), 105–110. Hendricks, K.B., Singhal, V.R., 2005. An empirical analysis of the effect of supply chain disruptions on long-run stock price performance and equity risk of the firm. Production and Operations Management 14 (1), 35–52. Katok, E., 2011. Using laboratory experiments to build better operations management models. Foundations and Trends in Technology, Information, and Operations Management 5 (1), 1–86. Lau, N., Hasija, S., Bearden, J.N. Newsvendor pull-to-center reconsidered. Decision Support Systems, in press. Loch, C., Wu, Y., 2007. Behavioral operations management. Foundations and Trends in Technology, Information, and Operations Management 1 (3), 121–232. Payne, J.W., 1994. Thinking aloud: insights into information processing. Psychological Science 5 (5), 241–244. Rungtusanatham, M., Wallin, C., Eckerd, S., 2011. The vignette in a scenario based role playing experiment. Journal of Supply Chain Management 47 (3), 9–16. Schultz, K., Robinson, L., Thomas, L.J., McClain, J.O., 2007. The use of framing in inventory decisions. Working paper, Johnson School Research Paper Series No. 02-07.
R. Croson University of Texas at Arlington, Arlington, TX, United States K. Schultz Air Force Institute of Technology, Wright Patterson Air Force Base, OH, United States E. Siemsen ∗ University of Minnesota, Minneapolis, MN, United States M.L. Yeo Loyola University Maryland, Baltimore, MD, United States ∗ Corresponding
author. E-mail address: [email protected] (E. Siemsen)
Contents lists available at SciVerse ScienceDirect
Journal of Operations Management journal homepage: www.elsevier.com/locate/jom
Editorial
Behavioral operations: The state of the field a r t i c l e
i n f o
Keywords: Behavioral operations Review
a b s t r a c t The field of behavioral operations has matured into an established area within the discipline of operations management. The field fills an essential void by laying the micro-foundations for the broader discipline of operations management. As such, the field examines a variety of topics and is methodologically diverse. © 2012 Elsevier B.V. All rights reserved.
Introduction Six years have passed since the publication of the last JOM special issue on behavioral operations (Bendoly and Schultz, 2006). That year also saw the first annual conference in behavioral operations. Since then the field has developed, broadened and matured. The annual conference has continued each year and grown. Members of the community have organized the INFORMS Section of Behavioral Operations Management and the POMS College of Behavior in Operations Management. Doctoral courses on behavioral operations are now taught at major research institutions and special issues in other top tier operations management journals have been published. It is safe to say that behavioral operations has become an accepted sub-field of the discipline of operations management. The value of behavioral operations lies in recognizing that almost all contexts studied within operations management contain people. There are managers making decisions, employees working in and improving processes, and customers buying products. It is tempting to treat these people mechanistically – managers making the best choices for their companies, employees diligently providing their best input and customers buying the product if the price is below an individual threshold. But reality bites. Managers may have the best of intentions, but are often unable to move their organization in the right direction. Employees may react to financial incentives, but are also concerned about status and fairness. Customers may respond not only to the value from the service or good they purchase, but also to non-monetary aspects of the process that delivered it. Behavioral operations starts at this micro-level to better understand behavior – ultimately enabling operations management to make better recommendations of how to design and improve processes and supply chains. Since the field is a departure from a mechanistic view of the organization, it has a relentlessly empirical focus – testing theoretical ideas for their robustness in the laboratory and in the real world. This focus pairs well with the Journal of Operations Management. In addition to the stellar papers themselves, this special issue as a collection of papers showcases several developments within the field. Substantively, the scope of the field is broadening. Many of the original papers in the field focused on inventory, either on stochastic ordering policy (i.e. newsvendor) or on supply chain (i.e. beer game) settings. The literature overview we provide in this 0272-6963/$ – see front matter © 2012 Elsevier B.V. All rights reserved. http://dx.doi.org/10.1016/j.jom.2012.12.001
introduction, as well as the papers published in the special issue, showcase a much broader variety of topics. These include revenue management, risk analysis and process improvement. Methodologically, while the field is often identified with experimental studies where subjects perform tasks on a computer, the papers in this special issue demonstrate its new directions. Our literature review shows behavioral work that includes survey research and mathematical modeling. Even within the context of experimental work, this special issue highlights that alternative experimental methods, such as eye movement studies or vignette-based research, are more frequently employed. We proceed in this introduction by briefly providing, and explaining the definition we used to circumscribe the boundaries of behavioral operations for this special issue. We then provide an up-to-date view of the field by presenting the results from a literature survey. Finally, we introduce the papers published in this special issue. We discuss both their contributions to the literature and highlight the novel methodological approaches they bring to the field.
1. Defining behavioral operations Our first task when starting work on the special issue was to decide which papers qualified as behavioral operations and which did not. A field that cannot define its boundaries risks being everything and therefore nothing. In German, the word ‘Trennschärfe’ captures our intentions best – a distinctive criterion that sharpens the vision and focus of the field. As Bob Hayes said, “a community of scholars, like any other community, requires a focus for its efforts, an integrating mechanism, a common ground that all can share and contribute to” (Hayes, 2000, pp. 106-107). With that intention, we attempt to provide such focus. We define behavioral operations as the study of potentially nonhyper-rational actors in operational contexts. In the simplest sense, behavioral operations must have an element of both operations and behavior. These criteria, however, require a closer definition. Research in behavioral operations requires an operations context. It is the richness and complexity of the context of our discipline that distinguishes us from research in organizational behavior. The goal of research in behavioral operations is not, for example, a
2
Editorial / Journal of Operations Management 31 (2013) 1–5
deeper understanding of leadership, fairness, emotions or motivation. We would be happy to accomplish these things, but they are not the goal. Rather, the goal of research in behavioral operations is a deeper understanding of operations processes. Which settings are valid contexts within the discipline of operations management is often debated. A pragmatic definition, such as Wickham Skinners guidance that operations management is essentially what real operations managers care about (Hayes, 2000), strikes us as relevant and valid. Our use of the term behavioral also requires focus. Many traditional papers in operations management do consider human behavior. However they predominantly model humans as hyperrational beings optimizing behavior toward a single monetary goal. We find value in considering other patterns of human action. There are three criteria that characterize hyper-rational actors: (A) they are mostly motivated by self-interest, usually expressed in monetary terms; (B) they act in a conscious, deliberate manner; and (C) they behave optimally for a specified objective function. A paper that in principle allows a deviation from either (A), (B) or (C) is sufficient to make a paper behavioral in nature. For example, Urda and Loch (2013) describe behavior as being motivated by social preferences, violating (A), and see emotions as key triggers of behavior, violating (B). Bendoly (2013) shows that decision makers in a simple revenue management task do not behave optimally, violating (C). Besides research being behavioral in nature and dealing with an operations context, a third aspect is necessary to limit the scope of the field of behavioral operations. Research in behavioral operations analyzes decisions, the behavior of individuals, or small groups of individuals. This constrains the unit of analysis to the micro-level. Separating a discipline into micro and macro is a standard practice in many academic areas. Economics shows this famous split between macro-economics, which is the study of whole economies, and micro-economics, which is the study of the parts of an economy, such as markets, organizations and individuals. Similarly, the field of organizations is divided into macro-organizations (i.e. organization theory), which studies the design, structure and performance of whole organizations, and micro-organizations (i.e. organizational behavior), which studies the components of such organizations, such as teams and individual employees. This split enables a division of labor, and a natural ability to theorize on different levels. We do not mean to imply that research at the organizational level does not add value. We merely suggest that the field of behavioral operations has a different emphasis. The risk of explicitly dividing the micro from the macro-perspective is that research in both areas becomes divergent and does not inform each other. There is high quality research that lives in both worlds, where micro issues inform macro outcomes. We have included one such paper in our special issue. Sawhney (2013) looks at the influence of training, job rotation and motivation (micro behavior) on used flexibility within a plant (macro operations). As the field of behavioral operations matures further, we expect to see a more explicit discussion about the distinction between micro and macro, and the links between these levels.
2. State of the literature We used the definition from the previous section in order to identify papers published in behavioral operations between 2006 and 2011. We searched major outlets in operations management, including the Journal of Operations Management (JOM), Management Science (MS), Production and Operations Management (POM), Manufacturing & Service Operations Management (MSOM) and the Decision Sciences Journal (DSJ) for articles matching our definition of
Fig. 1. Distribution of behavioral operations publications.
behavioral operations. The resulting literature search yielded 100 Behavioral Operations papers from 2006 to 2011. Bendoly et al. (2006) provide a review of work prior to 2006. A list of these papers, with operations context, methodology and behavioral domain, is available from the authors upon request. Fig. 1 charts the frequency of behavioral operations publications in the five journals over the six years studied. Fig. 1 shows a general increase in the number of papers in behavioral operations. The exception to this general trend is 2008 which saw the MSOM Special Issue in Behavioral Operations. The number of publications has recently grown within Management Science, possibly due to the opening of new departments within that journal that focus on behavioral work. Similarly, the number of papers published within Decision Sciences has steadily grown. JOM played a critical role in supporting the field in its early years (’06–’08), especially by publishing the first special issue. However, as the number of other outlets has grown the journal has recently seen a decrease in publications in behavioral operations. We hope that this special issue will reverse this trend, and clearly signal that JOM is open to all forms of research in behavioral operations. We further categorized all papers into their operations context, as well as their methodology employed. Results from this analysis are shown in Fig. 2. Fig. 2 shows a large breadth of operations issues and research methodologies. While the field of behavioral operations is often associated with the study of inventory management and production management (Bendoly et al., 2006), only 18% of the papers found in our search were related to inventory and production decisions. A much larger number of manuscripts dealt with supply chain issues such as contracting or supplier relationships, product development issues such as ideation and design decisions, or quality issues such as error detection. Similarly, while the field is often equated with experimental research, less than 50% of the papers in our survey were experimental, speaking to the methodological variety existing within the field. The growth in method is especially important. Each research methodology in itself has limitations. A mix of methodologies allows for better triangulation of results. The literature review also shows an explosion in the diversity of the behavioral content. To demonstrate this we present a Wordle based on the behavioral domains we identified during our literature review. To identify behavioral domains, we used the title, keywords and abstracts and identified behavioral keywords mentioned by the authors. We were looking for theoretical perspective used (e.g. prospect theory) or the broad domain in which their work was positioned (e.g. social preferences). A Wordle is a graph that is based on a frequency count of words (see http://www.wordle.net). Words that are counted in larger frequencies are represented by a larger font size in the graph. The resulting graph is shown in Fig. 3. In our opinion, much of the early research identified with behavioral operations suffered from insufficient behavioral theory underpinnings. It focused on identifying gaps between theoretical models of what should happen and what did happen in practice. What theoretical grounding was used was generally restricted to a
Editorial / Journal of Operations Management 31 (2013) 1–5
Forecasting 4%
Other 9% Supply Chain 27%
Service 7%
Experiment - Game 11%
Modeling 6%
Conceptual 7%
Experiment - Other 4% Experiment - Decision Task 28%
Archival 14%
Inventory 8% Production 10%
3
Product Dev./Proj. Man. 17% Quality 11%
Qualitative/ Conceptual 15%
Survey 22%
Fig. 2. Operations context and methodology in behavioral operations.
few areas of behavioral research such as bounded rationality and motivation theory. The field needed more work explaining how, why, and what could be done about these gaps (Bendoly et al., 2010). In contrast, the behavioral domains observed in our literature review show a larger diversity of theoretical underpinnings for understanding operations problems. Moritz et al. (2013) use dual process theory to understand decision making. Furthermore, Riedl et al. (2013) use procedural rationality to analyze supplier selection while Urda and Loch (2013) use social preferences to help understand aspects of work. While there is always room for improvement, we have seen significant progress in the field tying underlying theories of behavior to operations settings. We also see an increase in the identification of individual differences (heterogeneity) in behavioral responses to similar stimuli. An emphasis on the average response while ignoring heterogeneity can lead to serious errors in model development (Schultz et al., 2007). Moritz et al. (2013) show how a tendency for cognitive reflection changes inventory decisions. Vericourt et al. (2013) show differences in inventory decisions by gender and risk preference. We strongly support a move to report and study not only the average response of subjects, but the range and diversity of responses as well (see also Lau et al., in press for an example). We hope that editors and reviewers will continue to encourage the specific inclusion of behavioral theory, from many fields, and the analysis of heterogeneity of responses. 3. The content of this special issue Our special issue is an excellent example of the broadening of context, methodology and behavioral domain in our field. The articles here show a diversity of application beyond inventory
and production, a growth in behavioral domain past bounded rationality and motivation and broadening of methodology that complements the use of laboratory experiments. Contextual diversity. The field of behavioral operations encompasses the study of individual behavior, as well as the study of social preferences in groups (Loch and Wu, 2007). Urda and Loch (2013) tie social preferences more clearly to individual behavior through their study on emotions as pathways by which social preferences affect individual behavior. In their work, social preferences trigger emotions, which in turn regulate behavior. They test this framework in different operational contexts, such as process improvement and shift scheduling, and discuss far-reaching managerial implications. The concept of emotions has been understudied within behavioral operations, and this research provides a powerful framework to promote more research in this area. Given the publicity as well as the documented financial implications of supply chain risk (Hendricks and Singhal, 2005), risk management appears as a central task for supply chain managers. Two papers in our special issue examine this topic. Hora and Klaasen (2013) examine how operational risk management professionals assess and learn from operational risk events that happen to other companies. They show that these professionals apply a ‘benchmarking’ logic to this process, and are more likely to learn from companies that they see as market leaders. Tazelaar and Snijders (2013), using real-life cases, show that even experienced supply chain managers struggle with adequately assessing risk in the first place and are easily outperformed by a simple decision model. One area of research that should become vital for the field of behavioral operations is that of personnel assessment in supply chains. The field of human resources has a long tradition of
Fig. 3. Frequency count of behavioral domains.
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research in this area, but it is important to test which aspects of that theory are most applicable in the context of supply chain management. For that purpose, we need a better understanding of individual differences in behavioral operations, an area that has received only scant attention (Lau et al., in press). Two manuscripts in our special issue promise to lay a better foundation for such an understanding: Bearden et al. (2013) examine risk preferences and gender differences; Moritz et al. (2013) study the construct of cognitive reflection, i.e. the tendency of decision makers not to overly trust their intuition. Together these papers provide a first step to better understand the factors and traits that make for better supply chain managers. One of the most profound changes in supply chain management over the last 20 years is globalization. Supply chains span the globe in ways that were previously unimaginable. From a behavioral perspective, culture becomes important as a predictor and explaining factor of behavior in these settings (Loch and Wu, 2007). Our special issue contains two studies that involve cross-cultural comparisons. Cui et al. (2013) use a simple ordering task to, explore the differences in decision making between Chinese and U.S. managers. Riedl et al. (2013) compare supplier selection decisions in the same cross-cultural context. While ordering tasks are still a frequent context for research in behavioral operations, the field has started analyzing different decision contexts as well. One such area is revenue management, where a key task lies in dynamic pricing, i.e. decision makers constantly judging whether to adjust prices given that capacity expires at a certain point in the future. Bendoly (2013) studies two performance metrics in this task, and finds that while both metrics carry approximately similar information, the different framing of these metrics has a profound influence on individual behavior, and ultimately performance. Methodological diversity. In addition to variation in their substantive area, the papers in this special issue are methodologically diverse. The field of behavioral operations is often associated with laboratory experiments (Bendoly et al., 2006). In such experiments, participants are exposed to operational tasks, and the experimenters would systematically alter elements of the underlying task or information structure. A good summary of this line of research is given in Katok (2011). However, the field of behavioral operations is a content area, and not a methodological choice. The articles published in the special issue demonstrate that researchers in behavioral operations are branching out into different research methodologies in order to more deeply understand the operations management content. One prominent technique is vignette-based research. In this methodology, researchers describe a business scenario to participants, who then respond to the scenario by either making a choice, or by preparing a judgment on a subjective Likert scale. Manipulations involve adding, changing or deleting sentences in the scenario description. These are often weak manipulations, making results that are found more powerful. This technique has been successfully applied in the context of consumer research. Key challenges include the design of the vignettes (Rungtusanatham et al., 2011), and selection of the proper subject pool as respondents. Urda and Loch (2013) use student subjects in their study on emotions, as the research context does not require specific domain knowledge. If, however, domain specific knowledge is required, the subject pool needs to reflect this requirement. Hora and Klaasen (2013) draw on a pool of professional risk managers. A similar approach is also employed by Tazelaar and Snijders (2013), who cleverly develop their vignettes from cases they solicited using survey research. Observed behavior in a lab provides only limited information about the process of human judgment and decision making. One approach to dig deeper into the psychology of such processes
is to use biometric research. This involves measuring involuntary responses of the human body, such as eye pupil dilation or blink rates. This approach has a long tradition in psychology and marketing research, and a more recent tradition in economics as well. With the recent use of webcams and specialized software, these studies no longer require expensive specialized equipment. Bendoly (2013) uses this method to examine the physiological responses to different performance metrics in a repeated revenue management task. A more subjective, but much richer approach to studying the process of human judgment and decision making lies in verbal protocol analysis which requires participants to ‘think aloud’ while working on their task in an experiment, instead of silently making their judgments and decisions. Common criticisms against this approach are that people have difficulties articulating their thought-process, or that making them do so changes the thoughtprocess itself. However extensive research has shown that when following the right protocol, such criticism can be overcome (Ericsson and Simon, 1993, see also Payne, 1994 for a short list of best practices). Cui et al. (2013) use this approach for a crosscultural comparison of decision making in a simple ordering context. While the field of empirical operations management has a long standing tradition in psychometric research (and the Journal of Operations Management has been instrumental in supporting this tradition), the field of behavioral operations has rarely made use of these measurements. One reason may lie in the fact that psychometric measures are out-of-task, i.e. they correspond to responses of participants to questions that are posed separately from the actual operations task. The behavioral economics tradition (which has inspired much work in behavioral operations) often uses in-task measures instead, i.e. key individual variables such as risk aversion or loss aversion are estimated from observed choices in the experiment. While in-task measures have the merit of being based on revealed preferences, out-of-task measures are less model dependent. Using them as correlates in the context of experiments or surveys is a technique that will enrich the field of behavioral operations. Bearden et al. (2013) employ psychometric measurement to study risk aversion in a simple ordering context. Moritz et al. (2013) employ a psychometric measure to study thinking style in a similar context. Riedl et al. (2013) employ classic psychometric measurements in their survey research to study supplier selection decisions. 4. Conclusion The field of behavioral operations has reached a new stage in its life, moving beyond its previous limitations on topics and research methodologies. This special issue shows a healthy growth in the operational contexts, research methodologies and behavioral domains of our field. This is welcome and exciting. The inherent creativity we have seen while editing this special issue emphasize that the field has become a vibrant and established domain within operations management, and speaks well of its future contributions. Acknowledgments We would like to thank the previous Editors in Chief, Morgan Swink and Ken Boyer, for initiating the special issue, and the current Editors in Chief, Dan Guide and Tom Choi, for their continuing support. The special issue received 80 submissions; we thank all submitting authors for their support of the special issue. Nine of these submissions were accepted for publication in the special issue. The average number of days between submission and response for papers that were sent for review was 78 days. We
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thank the numerous reviewers, who allowed us to provide detailed feedback and short cycle times despite the large number of submissions received. Reviewers Vishal Agarwal Gopesh Anand Mark Baratt Neil Bearden Elliott Bendoly Gary Bolton Matt Bowler Stephen Brammer David Cantor Craig Carter Raul Chao Kay-Yut Chen Edward Cokely Andrew Davis Ken Doerr Karen Donohue David Drake Jennifer Dunn Stephanie Eckerd Feryal Erhun Tianjun Feng Joy Field Nagesh Gavirneni Uri Gneezy Paulo Goncalves John Gray Dan Guide Reidar Hagtvedt Sean Handley Manpreet Hora Elena Katok Ted Klastorin Thomas Kull Nelson Lau Alec Levenson Min Li Kevin Linderman Miguel Lobo John MacDonald Susan Meyer-Goldstein Jürgen Mihm Brent Moritz Suresh Muthulingam Sriram Narayanan Javad Nasiry Ingrid Nembhard Julie Niederhoff Rogelio Oliva Nektarios Orianopoulos Steve Powell Yufei Ren Denise Rousseau Brooke Saladin Fabrizio Salvador Tobias Schönherr Manuel Sosa Sri Talluri Doug Thomas Anita Tucker
Georgetown University of Illinois Arizona State INSEAD Emory University of Texas at Dallas Oklahoma State Warwick Business School Iowa State Arizona State University of Virginia HP Labs Michigan Tech Cornell Naval Postgraduate School University of Minnesota Harvard Michigan State University of Maryland Stanford Fudan Boston College Cornell University of California at San Diego University of Lugano Ohio State Penn State University of Alberta Rutgers Georgia Tech University of Texas at Dallas University of Washington Arizona State INSEAD University of Southern California University of Minnesota University of Minnesota INSEAD Michigan State University of Minnesota INSEAD Penn State Cornell Michigan State HKUST Yale Syracuse Texas A&M Cambridge Dartmouth Union College Carnegie-Mellon Wake Forest IE Business School Michigan State INSEAD Michigan State Penn State Harvard
Mark van Oyen Liana Vittorino Cynthia Wallin Joseph Wang Noel Watson Scott Webster Elliott Weiss Diana Wu Zhaohui Wu Yaozhong Wu Zach Zacharia
5 University of Michigan University of Victoria Brigham Young National Taiwan MIT-Zaragoza Syracuse University of Virginia University of Kansas Oregon State National University of Singapore Lehigh
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R. Croson University of Texas at Arlington, Arlington, TX, United States K. Schultz Air Force Institute of Technology, Wright Patterson Air Force Base, OH, United States E. Siemsen ∗ University of Minnesota, Minneapolis, MN, United States M.L. Yeo Loyola University Maryland, Baltimore, MD, United States ∗ Corresponding
author. E-mail address: [email protected] (E. Siemsen)