Behavioral operations management explores the interaction of human behaviors and operational systems and processes. Specifically, behavioral operations researchers are interested in identifying ways in which deviations from rational behaviors impact operational performance differently (for better or for worse), and the ways in which operations policies (such as lean inventories) impact behavior.
Operations management is concerned with the design, implementation, ongoing management, and improvement of processes for transforming inputs into outputs of value for customers. The normative solutions derived to accomplish operational goals often encompass simplistic behavioral assumptions; assumptions that do not accurately reflect the human variable in a process. Behavioral operations seeks an improved understanding of the anomalies resulting from human deviations from normative theory. It looks specifically at the "human variable" to explain breakdowns and failures of operations systems, as well as differences in performance that result from social, psychological, and cultural root causes. As a sub-discipline of operations management, the objective for behavioral operations researchers is to understand and predict interactions between social and psychological variables on the one hand, and operations management policies and decisions on the other. Given this understanding, behavioral operations researchers seek interventions to improve outcomes in the socio-technical system. Given the inherent and evolutionary nature of behavioral characteristics, some authors have claimed that operations managers will have greater success in adapting systems and processes to reflect human idiosyncrasy rather than expecting people to adapt to systems constraints (Gino and Pisano, 2008).
The focus of behavioral operations is both 1) on those behaviors that deviate from rational, normative theory in order to better design systems requiring human judgment and decision making well as; 2) the incorporation of behavioral or affective responses (e.g., motivation; individual differences) into operations management models in order to improve work outcomes such as productivity and satisfaction. It is first important to understand the assumptions underlying traditional operations management models. Broadly, these assumptions are borne from a model of rationality employed in the fields of psychology and economics, which describes the “rational man” as one who: 1) is self-interested and concerned with wealth accumulation; 2) makes conscious, cognitive, and deliberate decisions; 3) makes decisions with full information, and can discriminate useful information from that which is not; and 4) is optimizing (Simon, 1986). More specifically, Boudreau et al. (2003) provide us with the following summary of common behavioral assumptions to operations models:
- People are not a major factor
- People are deterministic and predictable
- Workers are independent
- Workers are “stationary” (no learning, fatigue, or problem solving occurs)
- Workers are not part of the product or service
- Workers are emotionless
- Work is perfectly observable
It has long been understood that humans are limited in their ability to collect and process information. When making decisions, especially complex decisions, human decision-makers fail to adhere to normative decision theories, but interestingly, appear to do so in systematic ways (Kahneman, Slovic and Tversky (1982). Moreover, a person’s social goals and collective behaviors impart clear influences on behavior. Theories fundamental to the areas of cognitive psychology, social psychology, and sociology offer rich insights to the phenomena observed in operations management.
The cognitive revolution in psychology was important because it recognized an 'operant' individual acting between a stimulus and response, capable of moderating the relationships between stimuli and responses which were previously believed to be mechanistic (Seligman and Maier, 1967). Psychological and Organizational Behavior models (see below) had to be developed which accounted for unobservable, affective and seemingly irrational responses from individuals. In particular, cognitive psychology addresses (among other things) an individual’s decision-making biases and use of heuristics as an attempt to overcome bounded rationality. Heuristics are mapped to deviations in the decision-making process, and often lead to biases which are mapped to deviations in decision outcomes (Bendoly et al, 2009). The anchoring and insufficient adjustment heuristic falls under this domain, and is employed when people attempt to estimate unknown data points. In an operations management context, orders for inventory may be anchored to the previous period’s demand and then insufficiently adjusted for the current period (Schweitzer and Cachon, 2000). Other behavioral regularities falling within the realm of cognitive psychology include framing effects, and the overconfidence effect.
Social psychology describes how an individual relates to other individuals, and specifically how individuals' actions are influenced by emotions (Loch and Wu, 2005) and motivation (Bendoly et al., 2009). Social behavioral theories help us understand why individuals act competitively or cooperatively with others. For example, those seeking status make decisions consistent with the achievement of recognition or higher hierarchical position relative to peers as an end goal. Status seeking as a social preference in operations management is observed in laboratory experiments where subjects are shown to be willing to sacrifice supply chain profits and efficiency in response to aggressive pricing by their supply chain partner; in other words, they are willing to forfeit their own profits to prevent the aggressor from achieving status (Loch and Wu, 2008). In addition to status, important social psychology facets include goal setting, feedback and controls, interdependence, and reciprocity.
Sociological theories define the context of interactions between individuals and groups, as well as the interactions between multiple groups, sometimes referred to as group dynamics. The concept of group-think fits within this body of knowledge, wherein one individual changes her beliefs to conform to the larger group consensus. A strong group identity, and associated group-think, is a common point in team life-cycles, and can prevent teams from accepting outside advice and incorporating external ideas. In operations management, product development teams can fall prey to this phenomenon and thus stall in creative and innovative efforts. Examinations of organizational and national cultural variations are important facets of work in this area as well.
Organizational behavior is an applied branch of psychology that seeks to understand human behavior in organizational settings. Organizational Behavior researchers have recognized that many of the variables studied in Operations Management, such as process enabling technology, lean inventories, and cross training are important to understanding that setting (Parker and Wall, 1998). However, Organizational Behavior researchers tend to take the Operations Management variables as given contexts in their study of behavioral variables such as motivation, job satisfaction and individual differences (in, e.g., responses to technology). Although Operations Management researchers as far back as the 1960s (such as Norman Dudley and Ezey Dar-El) sometimes worked in the interface between Organizational Behavior and Operations Management (especially looking at fatigue or individual differences in ability), a long period of focusing on normative models which ignored human behavior (as described above) characterized Operations Management research until the late 1990s (Doerr et al., 1996; Hayes and Hill, 2001; Schultz et al., 1998). Currently in Behavioral Operations, researchers seek to understand the implication of behavior (and the findings of Organizational Behavior research) for the design of the "context" variables which are within the domain of traditional Operations Management.
A variety of methodological approaches may be employed to investigate behavioral operations issues. While the literature may trend towards one method over a period of time – the first decade of the 21st century has witnessed a spike in laboratory-based behavioral research – researchers have leveraged a diverse arsenal of methodologies to tackle behavioral operations problems and should continue to seek substantive and creative approaches. Some examples of previous research using the more prominent methodologies are provided below; for a more comprehensive overview of recent methods applied please visit the Archive of OM Behavioral Research page.
- Human experiments
- Industrial (Hawthorne studies, see Mayo, 1949)
- Laboratory (Bullwhip stream, see Sterman, 1989)
- Situational (Mantel, Tatikonda, & Liao, 2006)
- Video (Seawright & Sampson, 2007)
- Mathematical modeling (Doerr, Klastorin and Magazine, 2000)
- Survey (Powell, 1995)
- Case study (Bendoly & Cotteleer, 2008)
Bendoly, E. 2011. Linking task conditions to physiology and judgment errors in RM systems. Production and Operations Management 20 (6), 860-876
Bendoly, E. 2013. Real-time Feedback and Booking Behavior in the Hospitality Industry: Moderating the Balance between Imperfect Judgment and Imperfect Prescription. Journal of Operations Management
Bendoly, E., Hur, D. 2007. Bipolarity in reactions to operational ‘constraints’: OM bugs under an OB lens. Journal of Operations Management 25 (1), 1-13
Bendoly, E., & Cotteleer, M.J. (2008). Understanding behavioral sources of process variation following enterprise system deployment. Journal of Operations Management 26 (1), 23-44.
Bendoly, E., Croson, R., Goncalves, P, & Schultz, K. (2010). Bodies of knowledge for research in behavioral operation. Production & Operations Management 19 (5), forthcoming.
Bendoly, E., Prietula, M. 2008. In “the zone”: The role of evolving skill and transitional workload on motivation and realized performance in operational tasks. International Journal of Operations & Production Management 28 (12), 1130-1152
Boudreau, J., Hopp, W., McClain, J.O., & Thomas, L.J. (2003). On the interface between operations and human resources management. Manufacturing & Service Operations Management 5 (3), 179-202.
Doerr, Mitchell, Klastorin & Brown (1996), The Impact of Material Flow Policies and Goals on Job Outcomes, Journal of Applied Psychology, V. 81, no. 2, pp. 142-152.
Doerr, Klastorin & Magazine (2000), "Synchronous Unpaced Flow Lines with Worker. Differences and Overtime Cost," Management Science, V. 46, no. 3, pp. 421-435.
Gino, F., & Pisano, G. (2008). Toward a theory of behavioral operations. Manufacturing & Service Operations Management, 10 (4), 676-691.
Hays, Julie M. and Arthur V. Hill ( 2001), "A Preliminary Investigation of the Relationships Between Employee Motivation/Vision, Service Learning, and Perceived Service Quality " Journal of Operations Management, 19 (3), 335-349
Kahneman, D. Slovic P. and A. Tversky (1982). Judgment under uncertainty: Heuristics and biases. Cambridge University Press.
Loch, C.H., & Wu, Y. (2005). Behavioral operations management. Foundations and Trends in Technology, Information and Operations Management, 1 (3), 121-232.
Loch, C.H., & Wu, Y. (2008). Social preferences and supply chain performance: An experimental study. Management Science, 54 (11), 1835-1849.
Mantel, S.P., Tatikonda, M.V., & Liao, Y. (2006). A behavioral study of supply manager decision-making: Factors influencing make versus buy evaluation. Journal of Operations Management, 24 (6), 822-838.
Mayo, E. (1949). Hawthorne and the Western Electric Company, The Social Problems of an Industrial Civilisation, Routledge.
Powell, T.C. (1995). Total quality management as competitive advantage: A review and empirical study. Strategic Management Journal, 16 (1), 15-37.
Schultz, K. L., Juran, D. C., Boudreau, J. W., McClain, J. O., & Thomas, L. J. (1998). Modeling and worker motivation in JIT production systems. Management Science, December, 44, (12), Part 1 of 2, 1595 - 1607.
Schweitzer, M. and Cachon, G. (2000). Decision Bias in the Newsvendor Problem. Management Science, V. 46(3), 404-420.
Seawright, K.K., & Sampson, S.E. (2007). A video method for empirically studying wait-perception bias. Journal of Operations Management, 25 (5), 1055-1066.
Seligman, M.E.P. and Maier, S.F. (1967). Failure to escape traumatic shock. Journal of Experimental Psychology, 74, 1–9.
Simon, H.A. (1986). Rationality in psychology and economics. The University of Chicago Press, Chicago.
Sterman, J.D. (1989). Modeling managerial behavior: Misperceptions of feedback in a dynamic decision making experiment. Management Science, 35 (3), 321-339.
Note on editorial credentials: Those involved in authoring and curating this article include the founder of the Behavioral Dynamics in Operations Management (BDOM) network, editor of the Journal of Operations Management's special issue on Behavior Issues in OM, winner of the 2008-09 JOM best paper (specifically for an empirical behavioral OM article), and author of Bodies of Knowledge in Behavioral Operations (POMS journal 2010).