Behavioral Operations Management: A Review of the Field

Source:
By:Rosa Hendijani

DOI: https://doi.org/10.30564/jpr.v1i3.736

Abstract:

Behavioral operations management (BOM) is one of the new areas in operations management. In the past 12 years, the field has made huge progress and researchers have become interested in this new perspective to solving operational problems. BOM is now one of the major subfields of operations management. In this paper, we examine and categorize areas of BOM based on the mainstream literature. Key areas include behavioral issues in new product development and project management, quality management, production management, inventory management, service operations, and forecasting. Studies in each area are divided into three subcategories, including OM context, individual attributes, heuristics, and biases, and individual differences. In OM context category, feedback and reward, training, work monitoring, teamwork and group decision making, goal setting, task assignment, and flexibility are among the main topics. In individual attributes, heuristics, and biases category, sunk cost effect and escalation of commitment, endowment effect, overprecision bias, planning fallacy, pull-to-center effect, anchoring and insufficient adjustment, and misperceptions of feedback are mainly discussed. In individual differences, analytic thinking and system thinking are mainly studied. New areas for research are suggested in each related section and are summarized in future directions and conclusion sections. In contexts such as new product development, project management, and inventory management, a shift to finding solution to performance improvement is beneficial instead of focusing on heuristics and biases and considering them as a deficiency in human decision making. Regarding individual differences category, a shift toward attributes other than cognitive abilities, such as global processing, creative thinking, and design thinking are recommended.

References:

[1] Erjavec, J. and Trkman, P.. Behavioral operations management: identification of its research program. International Journal of Services and Operations Management, In press, 2018. [2] Gans, N. and Croson, R.. Introduction to the special issue on behavioral operations. Manufacturing and Service Operations Management, 2008, 10(4): 563-565. [3] Katsikopoulos, K.V. and Gigerenzer, G.. Behavioral Operations Management: A Blind Spot and a Research Program. Journal of Supply Chain Management, 2013, 49(1): 3-7. [4] Bendoly, E., and Schultz, K. (Eds.) . Incorporating behavioral theory in OM empirical models. Journal of Operations Management, 2006, 24(6): 735–863 (Special issue). [5] Croson, R., Schultz, K., Siemsen, E. and Yeo, M.L.. Behavioral operations: the state of the field. Journal of Operations Management, 2013, 31(1-2): 1-5. [6] Zhao, X., Zhao, X. and Wu, Y.. Opportunities for research in behavioral operations management. International Journal of Production Economics, 2013, 1(142): 1-2. [7] Straub, D.. The value of scientometric studies: An introduction to a debate on IS as a reference discipline. Journal of the Association for Information Systems, 2006, 7(5): 241-245. [8] Hayes, R.H.. Toward a “new architecture” for POM. Production and Operations Management, 2000, 9(2): 105-110. [9] Bendoly, E., van Wezel, W. and Bachrach, D.G. (Eds.).. The handbook of behavioral operations management: Social and psychological dynamics in production and service settings. Oxford University Press, 2015. [10] Carter, C.R., Kaufmann, L. and Michel, A.. Behavioral supply management: a taxonomy of judgment and decision-making biases. International Journal of Physical Distribution and Logistics Management, 2007, 37(8): 631-669. [11] Tokar, T.. Behavioural research in logistics and supply chain management. The International Journal of Logistics Management, 2010, 21(1): 89-103. [12] Bendoly, E., Donohue, K., and Schultz, K.L.. Behavior in operations management: Assessing recent findings and revisiting old assumptions. Journal of operations management, 2006, 24(6): 737-752. [13] Gino, F. and Pisano, G.. Toward a theory of behavioral operations. Manufacturing and Service Operations Management, 2008, 10(4): 676-691. [14] Bendoly, E., Croson, R., Goncalves, P. and Schultz, K.. Bodies of knowledge for research in behavioral operations. Production and Operations Management, 2009, 19(4): 434-452. [15] Boudreau, J., Hopp, W., McClain, J.O. and Thomas, L.J.. On the interface between operations and human resources management. Manufacturing and Service Operations Management, 2003, 5(3): 179-202. [16] Chow, C.W. and Haddad, K.M.. Relative performance evaluation and risk taking in delegated investment decisions. Decision Sciences, 1991, 22(3): 583-593. [17] Sengupta, K. and Abdel-Hamid, T.K.. Alternative conceptions of feedback in dynamic decision environments: an experimental investigation. Management Science, 1993, 39(4): 411-428. [18] Basadur, M., Graen, G.B. and Scandura, T.A.. Training effects on attitudes toward divergent thinking among manufacturing engineers. Journal of Applied Psychology, 1986, 71(4): 612-617. [19] Mitchell, T.R. and Silver, W.S.. Individual and group goals when workers are interdependent: Effects on task strategies and performance. Journal of applied psychology, 1990, 75(2): 185-193. [20] Whyte, G.. Diffusion of responsibility: Effects on the escalation tendency. Journal of Applied Psychology, 1991, 76(3): 408-415. [21] Schmidt, J.B., Montoya‐Weiss, M.M. and Massey, A.P.. New product development decision‐making effectiveness: Comparing individuals, face‐to‐face teams, and virtual teams. Decision sciences, 2001, 32(4): 575-600. [22] Garland, H.. Throwing good money after bad: The effect of sunk costs on the decision to escalate commitment to an ongoing project. Journal of Applied Psychology, 1990, 75(6): 728-731. [23] Garland, H., Sandefur, C.A. and Rogers, A.C.. De-escalation of commitment in oil exploration: When sunk costs and negative feedback coincide. Journal of Applied Psychology, 1990, 75(6): 721-727. [24] Kahneman, D., Knetsch, J.L. and Thaler, R.H.. Anomalies: The endowment effect, loss aversion, and status quo bias. Journal of Economic perspectives, 1991, 5(1): 193-206. [25] Loch, C.H. and Wu, Y.. Behavioral operations management. Foundations and Trends® in Technology, Information and Operations Management, 2007, 1(3): 121-232. [26] Knetsch, J.L. and Sinden, J.A.. Willingness to pay and compensation demanded: Experimental evidence of an unexpected disparity in measures of value. The Quarterly Journal of Economics, 1984, 99(3): 507-521. [27] Schmidt, J.B. and Calantone, R.J.. Escalation of commitment during new product development. Journal of the academy of marketing science, 2002, 30(2): 103-118. [28] Moore, D.A. and Healy, P.J.. The trouble with overconfidence. Psychological review, 2008, 115(2): 502-517. [29] Connolly, T. and Dean, D.. Decomposed versus holistic estimates of effort required for software writing tasks. Management Science, 1997, 43(7): 1029-1045. [30] Frederick, S., Loewenstein, G. and O'donoghue, T.. Time discounting and time preference: A critical review. Journal of economic literature, 2002, 40(2): 351-401. [31] O'Donoghue, T. and Rabin, M.. Incentives for procrastinators. The Quarterly Journal of Economics, 1999, 114(3): 769-816. [32] Paich, M. and Sterman, J.D.. Boom, bust, and failures to learn in experimental markets. Management Science, 1993, 39(12): 1439-1458. [33] Bendoly, E.. System dynamics understanding in projects: Information sharing, psychological safety, and performance effects. Production and operations management, 2014, 23(8): 1352-1369. [34] Fischer, H. and Gonzalez, C.. Making sense of dynamic systems: how our understanding of stocks and flows depends on a global perspective. Cognitive science, 2016, 40(2): 496-512. [35] Weinhardt, J.M., Hendijani, R., Harman, J.L., Steel, P. and Gonzalez, C.. How analytic reasoning style and global thinking relate to understanding stocks and flows. Journal of Operations Management, 2015, 39: 23-30. [36] Moritz, B.B., Hill, A.V. and Donohue, K.L.. Individual differences in the newsvendor problem: Behavior and cognitive reflection. Journal of Operations Management, 2013, 31(1-2): 72-85. [37] Moritz, B., Siemsen, E. and Kremer, M.. Judgmental forecasting: Cognitive reflection and decision speed. Production and Operations Management, 2014, 23(7): 1146-1160. [38] Wendel, S.. Designing for behavior change: Applying psychology and behavioral economics. Sebastopol, CA: O'Reilly Media, 2013. [39] Brown, T.. Design thinking. Harvard business review, 2008, 86(6): 84-94. [40] Dunne, D. and Martin, R.. Design thinking and how it will change management education: An interview and discussion. Academy of Management Learning and Education, 2006, 5(4): 512-523. [41] Juran, J.M.. A history of managing for quality: The evolution, trends, and future directions of managing for quality. Milwaukee, WI: ASQC Quality Press,1995: 597. [42] Stewart, D.M. and Grout, J.R.. The human side of mistake‐proofing. Production and Operations Management, 2001, 10(4): 440-459. [43] Mason, M.A. and Redmon, W.K.. Effects of immediate versus delayed feedback on error detection accuracy in a quality control simulation. Journal of organizational behavior management, 1993, 13(1): 49-83. [44] Larson, J.R. and Callahan, C.. Performance monitoring: How it affects work productivity. Journal of Applied Psychology, 1990, 75(5): 530-538. [45] Stanton, J.M. and Barnes-Farrell, J.L.. Effects of electronic performance monitoring on personal control, task satisfaction, and task performance. Journal of Applied Psychology, 1996, 81(6): 738. [46] Soman, D. and Shi, M.. Virtual progress: The effect of path characteristics on perceptions of progress and choice. Management Science, 2003, 49(9): 1229-1250. [47] Pei, B.K. and Reneau, J.H.. The effects of memory structure on using rule‐based expert systems for training: a framework and an empirical test. Decision Sciences, 1990, 21(2): 263-286. [48] Iravani, S.M., Van Oyen, M.P. and Sims, K.T.. Structural flexibility: A new perspective on the design of manufacturing and service operations. Management Science, 2005, 51(2): 151-166. [49] Suri, R.. Quick response manufacturing: a companywide approach to reducing lead times. CRC Press, 1998. [50] Suri, R.. Quick response manufacturing: A competitive strategy for the 21st century. In Proceedings of the 2002 POLCA Implementation workshop, 2002, 141. [51] Suri, R.. It's about time: the competitive advantage of quick response manufacturing. Productivity Press, 2010. [52] Robison, A.G. and Robinson, M.M.. On the tabletop improvement experiments of Japan. Production and Operations Management, 1994, 3(3): 201-216. [53] Pyzdek, T. and Keller, P.A.. The Six Sigma handbook: a complete guide for green belts, black belts, and managers at all levels. McGraw-Hill Companies, 2010. [54] Plous, S.. The psychology of judgment and decision making. Mcgraw-Hill Book Company, 1993. [55] Deming, W.E.. Quality, productivity, and competitive position. MIT Center for Advanced Engineering, Cambridge, MA: Massachusetts Institute of Technology Center for Advanced Engineering Study, 1986. [56] Gully, S.M., Payne, S.C., Koles, K. and Whiteman, J.A.K.. The impact of error training and individual differences on training outcomes: an attribute-treatment interaction perspective. Journal of Applied Psychology, 2002, 87(1): 143-155. [57] Ghosh, D. and Ray, M.R.. Risk, ambiguity, and decision choice: Some additional evidence. Decision Sciences, 1997, 28(1): 81-104. [58] Bachrach, D.G., Powell, B.C., Bendoly, E. and Richey, R.G.. Organizational citizenship behavior and performance evaluations: Exploring the impact of task interdependence. Journal of applied psychology, 2006, 91(1): 193-201. [59] Huber, V. L. and Brown, K.A.. Human resource issues in cellular manufacturing: A sociotechnical analysis. Journal of Operations Management, 1991, 10(1): 138-159. [60] Schultz, K.L., McClain, J.O. and Thomas, L.J.. Overcoming the dark side of worker flexibility. Journal of Operations Management, 2003, 21(1): 81-92. [61] Audia, G., Kristof-Brown, A., Brown, K.G., and Locke, E.A.. Relationship of goals and microlevel work processes to performance on a multipath manual task. Journal of Applied Psychology, 1996, 81(5): 483. [62] Doerr, K.H., Mitchell, T.R., Klastorin, T.D. and Brown, K.A.. Impact of material flow policies and goals on job outcomes. Journal of Applied Psychology, 1996, 81(2): 142-152. [63] Doerr, K.H., Mitchell, T.R., Schriesheim, C.A., Freed, T. and Zhou, X.. Note: Heterogeneity and variability in the context of flow lines. Academy of Management Review, 2002, 27(4): 594-607. [64] Hirst, M.K.. Intrinsic motivation as influenced by task interdependence and goal setting. Journal of Applied Psychology, 1988, 73(1): 96-101. [65] Kerr, N.L., Messé, L.A., Seok, D.H., Sambolec, E.J., Lount Jr, R.B. and Park, E.S.. Psychological mechanisms underlying the Köhler motivation gain. Personality and Social Psychology Bulletin, 2007, 33(6): 828-841. [66] Doerr, K.H., Freed, T., Mitchell, T.R., Schriesheim, C.A. and Zhou, X.T.. Work flow policy and within-worker and between-workers variability in performance. Journal of Applied Psychology, 2004, 89(5): 911. [67] Schultz, K.L., Juran, D.C. and Boudreau, J.W.. The effects of low inventory on the development of productivity norms. Management Science, 1999, 45(12): 1664-1678. [68] Aiello, J.R. and Kolb, K.J.. Electronic performance monitoring and social context: Impact on productivity and stress. Journal of Applied Psychology, 1995, 80(3): 339-353. [69] Schultz, K.L., Juran, D.C., Boudreau, J.W., McClain, J.O. and Thomas, L.J.. Modeling and worker motivation in JIT production systems. Management Science, 1998, 44(12-part-1): 1595-1607. [70] Gino, F. and Staats, B.R.. Driven by social comparisons: How feedback about coworkers’ effort influences individual productivity. Harvard Business School NOM Unit Working Paper, 2011, 11-078. [71] Kernan, M.C. and Lord, R.G.. Effects of valence, expectancies, and goal-performance discrepancies in single and multiple goal environments. Journal of applied psychology, 1990, 75(2): 194-203. [72] Bolton, G.E. and Katok, E.. Learning by doing in the newsvendor problem: A laboratory investigation of the role of experience and feedback. Manufacturing and Service Operations Management, 2008, 10(3): 519-538. [73] Schweitzer, M.E. and Cachon, G.P.. Decision bias in the newsvendor problem with a known demand distribution: Experimental evidence. Management Science,2000, 46(3): 404-420. [74] Croson, D., Croson, R. and Ren, Y.. How to manage an overconfident newsvendor. 2008. http://cbees. utdallas, edu/papers/CrosonRenCmsonMS2008, pdf [75] Porteus, E.L.. Foundations of Stochastic Inventory Theory. Stanford Business Books, Stanford, CA, 2002. [76] Bostian, A.A., Holt, C.A. and Smith, A.M.. Newsvendor “pull-to-center” effect: Adaptive learning in a laboratory experiment. Manufacturing and Service Operations Management, 2008, 10(4): 590-608. [77] Lee, Y.S. and Siemsen, E.. Task decomposition and newsvendor decision making. Management Science, 2016, 63(10): 3226-3245. [78] Benzion, U., Cohen, Y., Peled, R. and Shavit, T.. Decision-making and the newsvendor problem: an experimental study. Journal of the Operational Research Society, 2008, 59(9): 1281-1287. [79] Kremer, M., Minner, S. and Van Wassenhove, L.N.. Do random errors explain newsvendor behavior?. Manufacturing and Service Operations Management, 2010, 12(4): 673-681. [80] Lurie, N.H. and Swaminathan, J.M.. Is timely information always better? The effect of feedback frequency on decision making. Organizational Behavior and Human decisión processes, 2009, 108(2): 315-329. [81] Ren, Y. and Croson, R.. Overconfidence in newsvendor orders: An experimental study. Management Science, 2013, 59(11): 2502-2517. [82] Su, X.. Bounded rationality in newsvendor models. Manufacturing and Service Operations Management, 2008, 10(4): 566-589. [83] Forrester, J.W.. Industrial Dynamics. A major breakthrough for decision makers. Harvard business review, 1958, 36(4): 37-66. [84] Lee, H.L., Padmanabhan, V. and Whang, S.. Information distortion in a supply chain: the bullwhip effect. Management science, 1997, 43(4): 546-558. [85] Cachon, G. and Terwiesch, C.. Matching supply with demand. McGraw-Hill Publishing. [86] Sterman, J.D.. Modeling managerial behavior: Misperceptions of feedback in a dynamic decision making experiment. Management science, 1989, 35(3): 321-339. [87] Croson, R. and Donohue, K.. Behavioral causes of the bullwhip effect and the observed value of inventory information. Management science, 2006, 52(3): 323-336. [88] Croson, R., Donohue, K., Katok, E. and Sterman, J.. Order stability in supply chains: coordination risk and the role of coordination stock. Production and Operations Management, 2014, 23(2): 176-196. [89] Narayanan, A. and Moritz, B.B.. Decision making and cognition in multi‐echelon supply chains: An experimental study. Production and Operations Management, 2015, 24(8): 1216-1234. [90] Fitzsimmons, J.A. and Fitzsimmons, M.J.. Service management: Operations, strategy, information technology. New York, NY: McGraw-Hill, 2011, 7th ed. [91] Katz, K.L., Larson, B.M. and Larson, R.C.. Prescription for the waiting in line blues: Entertain, enlighten, and engage. Sloan Management Review, 1991, 32(2): 44-53. [92] Nie W.. Waiting: integrating social and psychological perspectives in operations management. Omega-International Journal of Management Science, 2000, 28(6): 611-629. [93] Antonides, G., Verhoef, P.C. and Van Aalst, M.. Consumer perception and evaluation of waiting time: A field experiment. Journal of Consumer Psychology, 2002, 12(3): 193-202. [94] Bailey, N. and Areni, C.S.. When a few minutes sound like a lifetime: Does atmospheric music expand or contract perceived time?. Journal of Retailing, 2006, 82(3): 189-202. [95] Milliman, R.E.. The influence of background music on the behavior of restaurant patrons, Journal of Consumer Research, 1986, 13(2): 286-289. [96] Yalch, R. and Spangenberg, E.. Effects of store music on shopping behavior. Journal of Consumer Marketing, 1990, 7(2): 55-63. [97] Borges, A., Herter, M.M. and Chebat, J.C.. “It was not that long!”: The effects of the in-store TV screen content and consumers emotions on consumer waiting perception. Journal of Retailing and Consumer Services, 2015, 22: 96-106. [98] McDonnell, J.. Music, scent and time preferences for waiting lines. International Journal of Bank Marketing, 2007, 25(4): 223-237. [99] Bae, G. and Kim, D.Y.. The effects of offering menu information on perceived waiting time. Journal of Hospitality Marketing and Management, 2014, 23(7): 746-767. [100] Luo, H., Wang, J., Han, X. and Zeng, D.. The impact of filler interface on online users' perceived waiting time. In Service Systems and Service Management (ICSSSM), 2015 12th International Conference on, IEEE, 2015: 1-5. [101] Hauss, D.. Queue science helps retailers recover revenue at checkout. RetailTouchpoints.com. 8 August, 2008 [102] Smith, D.R. and Whitt, W.. Resource sharing for efficiency in traffic systems. Bell System Technical Journal, 1981, 60(1): 39-55. [103] Shunko, M., Niederhoff, J. and Rosokha, Y.. Humans are not machines: The behavioral impact of queueing design on service time. Management Science, 2017, 64(1): 453-473. [104] Hendijani, R. and Bischak, D.P.. The effect of social relationships on the rates of referral to specialists. International Journal of Operations and Production Management, 2016, 36(4): 384-407. [105] Ariely, D. and Carmon, Z.. Gestalt characteristics of experiences: The defining features of summarized events. Journal of Behavioral Decision Making, 2000, 13(2): 191-201. [106] Baumgartner, H., Sujan, M. and Padgett, D.. Patterns of affective reactions to advertisements: The integration of moment-to-moment responses into overall judgments. Journal of Marketing Research, 1997: 219-232. [107] Dasu, S. and Chase, R.B.. Designing the soft side of customer service. MIT Sloan Management Review, 2010, 52(1): 33-39. [108] Chase, R.B. and Dasu, S.. Want to perfect your company's service? Use behavioral science. Harvard business review, 2001, 79(6): 78-84. [109] Mills, R.T. and Krantz, D.S.. Information, choice, and reactions to stress: A field experiment in a blood bank with laboratory analogue. Journal of Personality and Social Psychology, 1979, 37(4): 608-620. [110] Oliva, R. and Watson, N.. Managing functional biases in organizational forecasts: A case study of consensus forecasting in supply chain planning. Production and operations Management, 2009, 18(2): 138-151. [111] Lawrence, M., Goodwin, P., O'Connor, M. and Önkal, D.. Judgmental forecasting: A review of progress over the last 25 years. International Journal of forecasting, 2006, 22(3): 493-518. [112] Boulaksil, Y. and Franses, P.H.. Experts' stated behavior. Interfaces, 2009, 39(2): 168-171. [113] Sanders, N.R. and Manrodt, K.B.. The efficacy of using judgmental versus quantitative forecasting methods in practice. Omega, 2003, 31(6): 511-522. [114] Nakano, M.. Collaborative forecasting and planning in supply chains: The impact on performance in Japanese manufacturers. International Journal of Physical Distribution and Logistics Management, 2009, 39(2): 84-105. [115] Krajewski, L.J., Ritzman, L.P. and Malhotra, M.K.. Operations management: Processes and supply chains. Pearson Education: Harlow, UK, 2013, 10th Ed.. [116] Wagenaar, W.A. and Sagaria, S.D.. Misperception of exponential growth. Perception and Psychophysics, 1975, 18(6): 416-422. [117] Wagenaar, W.A. and Timmers, H.. The pond-and-duckweed problem; Three experiments on the misperception of exponential growth. Acta Psychologica, 1979, 43(3): 239-251. [118] Feiler, D.C., Tong, J.D. and Larrick, R.P.. Biased judgment in censored environments. Management Science, 2013, 59(3): 573-591. [119] Tversky, A. and Kahneman, D.. Judgment under uncertainty: Heuristics and biases. science, 1974, 185(4157): 1124-1131. [120] Beach, L.R., Barnes, V.E. and Christensen‐Szalanski, J.J.. Beyond heuristics and biases: A contingency model of judgemental forecasting. Journal of forecasting, 1986, 5(3): 143-157. [121] Tversky, A. and Kahneman, D.. Availability: A heuristic for judging frequency and probability. Cognitive psychology, 1973, 5(2): 207-232. [122] Nisbett, R.E. and Ross, L.. Human inference: Strategies and shortcomings of social judgment. Englewood Cliffs, NJ: Prentice-Hall, 1980. [123] Repenning, N.P. and Sterman, J.D.. Nobody ever gets credit for fixing problems that never happened: creating and sustaining process improvement. California management review, 2001, 43(4): 64-88. [124] Repenning, N.P. and Sterman, J.D.. Capability traps and self-confirming attribution errors in the dynamics of process improvement. Administrative Science Quarterly, 2002, 47(2): 265-295. [125] Sterman, J., Oliva, R., Linderman, K.W. and Bendoly, E.. System dynamics perspectives and modeling opportunities for research in operations management. Journal of Operations Management, 2015, 39-40: 1-5. [126] Loch, C.H. and Wu, Y.. Behavioral operations management. Foundations and Trends® in Technology, Information and Operations Management, 2007, 1(3): 121-232. [127] Gigerenzer, G., Hertwig, R. and Pachur, T.. Heuristics: The Foundations of Adaptive Behavior. New York, NY: Oxford University Press, 2011. [128] Payne, J.W., Bettman, J.R. and Johnson, E.J.. The Adaptive Decision Maker. Cambridge, UK: Cambridge University Press, 1993. [129] Förster, J. and Dannenberg, L.. GLOMOsys: A systems account of global versus local processing. Psychological Inquiry, 2010, 21(3): 175-197. [130] Bertels, H.M.. You’ve Been Framed! The Effect of Opportunity and Prosocial Framing on the Novelty and Usefulness of Student Solutions. Journal of Management Education, 2018, 42(5): 650-689.