Qualitative Research in Behavioral Sciences

Qualitative Research in Behavioral Sciences

Behavioral Supply Chain Management: Conceptualizing Human Experiments in Logistics Research

Document Type : Review Article

Authors
Mohammad Milad Ahmadi
Elham Moghadamnia
Assistant professor, Department of Management, SR.C., Islamic Azad University, Tehran, Iran
10.22077/qrbs.2025.10230.1126
Abstract
Background: Decision-making in logistics and supply chain management is often influenced by cognitive biases and behavioral factors; however, much of the existing research in this field remains focused on rational models and quantitative optimization. As a result, the role of mental processes, human interactions, and participant perceptions within the supply chain has received limited attention.
Aim: The purpose of this study was to examine the role and function of behavioral experiments in logistics and supply chain research and to explain their capacity to generate valid empirical evidence for analyzing human decision-making. The emphasis was on demonstrating how laboratory scenario-based experiments and field experiments can contribute to refining and validating managerial theories and models.
Method: This study was conducted through a systematic literature review. Sources were searched in Scopus, Web of Science, Google Scholar, and domestic databases including SID, Magiran, and ISC using keywords such as "behavioral supply chain", "behavioral logistics", "behavioral experiment", and their Persian equivalents. By the end of the search period (August 2025 / Mordad 1404), a total of 73 studies had been identified, and after applying inclusion and exclusion criteria, 50 studies were selected for final analysis. Data were analyzed using a combined conceptual coding approach (open– axial– selective), through which eight key thematic components were extracted.
Results: The results indicate that deviations from rational decision-making, interpersonal effects, and subjective interpretation play significant roles in supply chain performance; a notable example is the bullwhip effect. Behavioral experiments particularly scenario-based experiments and decision-making simulations enable the controlled observation of such phenomena and the empirical testing of behavioral assumptions.
Conclusions: Incorporating behavioral experiments as part of a mixed-method research approach can strengthen the theoretical validity of decision-making models and enhance the design of managerial policies in supply chain contexts. These methods reduce the cost and risk associated with field studies while enabling more precise analysis of cognitive processes and human interactions.
Keywords
behavioral experiments
behavioral operations management
behavioral logistics
behavioral economics
supply chain management
management laboratory
Subjects

References

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Bachrach, D. G., & Bendoly, E. (2011). Rigor in behavioral experiments: A basic primer for supply chain management researchers. Journal of Supply Chain Management, 47(3), 5-8. https://doi.org/10.1111/j.1745-493X.2011.03230.x

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Bazerman, M. H., & Moore, D. A. (2013). Judgment in managerial decision making (8th ed.). Wiley. https://www.wiley.com/en-us/Judgment+in+Managerial+Decision+Making%2C+8th+Edition-p-9781118065709

Billeter, D., Kalra, A., & Loewenstein, G. (2011). Underpredicting learning after initial experience with a product. Journal of Consumer Research, 37(5), 723-736. https://doi.org/10.1086/655862

Biyalogorsky, E., Boulding, W., & Staelin, R. (2006). Stuck in the past: Why managers persist with new product failures. Journal of Marketing, 70(2), 108-121. https://doi.org/10.1509/jmkg.70.2.108

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Eckerd, S. (2016). Experiments in purchasing and supply management research. Journal of Purchasing and Supply Management, 22(4), 258-261. https://doi.org/10.1016/j.pursup.2016.08.002

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References
Ahmadi, M. M., & Hendijani, R. (2023). Experimental research methodology in behavioral science studies with a focus on its applications in management and economics. Journal of Administrative Studies and Researches, 4(16), 8-25. https://srb.iau.ir/faculty/file/download/articlesInPublications/1721507899-smtc-jasr-v4n16p1-fa.pdf
Ahmadi, M. M., Hendijani, R., & Alikhanzadeh, A. (2020). Experimental research and its application in the development of knowledge in management. Strategic Management of Organizational Knowledge, 3(2), 5-49. https://doi.org/10.47176/smok.2020.1155
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Bachrach, D. G., & Bendoly, E. (2011). Rigor in behavioral experiments: A basic primer for supply chain management researchers. Journal of Supply Chain Management, 47(3), 5-8. https://doi.org/10.1111/j.1745-493X.2011.03230.x
Baron, R. M., & Kenny, D. A. (1986). The moderator– mediator variable distinction in social psychological research: Conceptual, strategic, and statistical considerations. Journal of Personality and Social Psychology, 51(6),1173-1182. https://doi.org/10.1037//0022-3514.51.6.1173
Bazerman, M. H., & Moore, D. A. (2013). Judgment in managerial decision making (8th ed.). Wiley. https://www.wiley.com/en-us/Judgment+in+Managerial+Decision+Making%2C+8th+Edition-p-9781118065709
Billeter, D., Kalra, A., & Loewenstein, G. (2011). Underpredicting learning after initial experience with a product. Journal of Consumer Research, 37(5), 723-736. https://doi.org/10.1086/655862
Biyalogorsky, E., Boulding, W., & Staelin, R. (2006). Stuck in the past: Why managers persist with new product failures. Journal of Marketing, 70(2), 108-121. https://doi.org/10.1509/jmkg.70.2.108
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Campbell, D. T. (1957). Factors relevant to the validity of experiments in social settings. Psychological Bulletin, 54(4), 297-312. https://psycnet.apa.org/doi/10.1037/h0040950
Carter, C. R., Rockwood, R. F., Patel, P. C., Bachrach, D., Bendoly, E., DuHadway, S., & Kaufmann, L. (2024). Experiments in supply chain management research: A systematic review and future directions. Journal of Business Logistics, 45(3), e12382. https://doi.org/10.1111/jbl.12382
Corbin, J., & Strauss, A. (2015). Basics of qualitative research: Techniques and procedures for developing grounded theory. Sage Publications. https://doi.org/10.4135/9781452230153
Croson, R., & Donohue, K. (2006). Behavioral causes of the bullwhip effect and the observed value of inventory information. Management science, 52(3), 323-336. https://doi.org/10.1287/mnsc.1050.0436
Croson, R., Schultz, K. L., Siemsen, E., & Yeo, M. L. (2013). Behavioral operations: The state of the field. Journal of Operations Management, 31(1-2), 1-5. https://doi.org/10.1016/j.jom.2012.12.001
Dazeh, N., Sotudeh, R., Hagh Parast, A. A., & Hirad, A. (2025). Identifying and ranking behavioral health indicators of auditors of the court of accounts of the republic of iran. Qualitative Research in Behavioral Sciences (QRBS), 4(1), 1-16. https://doi.org/10.22077/qrbs.2025.8779.1085
De Beuckelaer, A., & Wagner, S. M. (2012). Small sample surveys: Increasing rigor in supply chain management research. International Journal of Physical Distribution & Logistics Management, 42(7), 615-639. https://doi.org/10.1108/09600031211258129
Deck, C., & Smith, V. (2013). Using laboratory experiments in logistics and supply chain research. Journal of Business Logistics, 34(1), 6-14. https://doi.org/10.1111/jbl.12006
Deshpande, R., & Webster, F.E. (1989). Organizational culture and marketing: Defining the research agenda. Journal of Marketing, 53(1), 3-15. https://doi.org/10.1177/002224298905300102
Donohue, K., & Siemsen, E. (2011). Behavioral operations: Applications in supply chain management. In J. J. Cochran (Ed.), Wiley encyclopedia of operations research and management science (pp. 1-12). John Wiley & Sons. https://doi.org/10.1002/9780470400531.eorms0103
Douglas, M. A., Quade, M. J., Dickey, E. C., & Neubert, M. J. (2025). The ethics of sustainability in supply chain relationships: Through the lens of supply chain integrity. Journal of Business Logistics, 46(4), e70043. https://doi.org/10.1111/jbl.70043
Eckerd, S. (2016). Experiments in purchasing and supply management research. Journal of Purchasing and Supply Management, 22(4), 258-261. https://doi.org/10.1016/j.pursup.2016.08.002
Elghannam, A., & Mesias, F. (2019). Short food supply chains from a social media marketing perspective: A consumer- oriented study in Spain. New Medit, 18(1), 79-90. https://newmedit.ciheam.org/2019/03/15/short-food-supply-chains-from-a-social-media-marketing-perspective-a-consumer-oriented-study-in-spain/
Ellinger, A. E., & Chapman, K. (2016). IJPDLM’s 45th anniversary: A retrospective bibliometric analysis and future directions. International Journal of Physical Distribution & Logistics Management, 46(1), 2-18. https://doi.org/10.1108/IJPDLM-12-2015-0301
Eneqvist, E., & Karvonen, A. (2021). Experimental governance and urban planning futures: Five strategic functions for municipalities in local innovation. Urban Planning, 6(1), 183-194. https://doi.org/10.17645/up.v6i1.3396
Fahimnia, B., Pournader, M., Siemsen, E., Bendoly, E., & Wang, C. (2019). Behavioral operations and supply chain management: A review and literature mapping. Decision Sciences, 50(6), 1127-1183. https://doi.org/10.1111/deci.12369
Fawcett, S., Waller, M., & Bowersox, D. (2011). Cinderella in the c- suite: Conducting influential research to advance the logistics and supply chain disciplines. Journal of Business Logistics, 32(2), 115-121. https://doi.org/10.1111/j.2158-1592.2011.01010.x
Fildes, R., Goodwin, P., Lawrence, M., & Nikolopoulos, K. (2009). Effective forecasting and judgmental adjustments: An empirical evaluation and strategies for improvement in supply- chain planning. International Journal of Forecasting, 25(1), 3-23. https://doi.org/10.1016/j.ijforecast.2008.11.010
Fisher, R. J. (1993). Social desirability bias and the validity of indirect questioning. Journal of Consumer Research, 20(2), 303-315. https://doi.org/10.1086/209351
Frankel, R., Bolumole, Y. A., Eltantawy, R. A., Paulraj, A., & Gundlach, G. T. (2008). The domain and scope of SCM's foundational disciplines insights and issues to advance research. Journal of Business Logistics, 29(1), 1-30. https://doi.org/10.1002/j.2158-1592.2008.tb00066.x
Giannakis, M., & Papadopoulos, T. (2016). Supply chain sustainability: A risk management approach. International Journal of Production Economics, 171(4), 455-470. https://doi.org/10.1016/j.ijpe.2015.06.032
Gneezy, U., & Imas, A. (2017). Lab in the field: Measuring preferences in the wild. In A. V. Banerjee & E. Duflo (Eds.), Handbook of economic field experiments (pp. 439-464). North- Holland. https://ssrn.com/abstract=2811026
Goodarzi, M. & Ghorbani, M. H. (2019). A look at existing laboratories in world faculty of management. Science and Technology Policy Letters, 9(3), 17-26. https://dor.isc.ac/dor/20.1001.1.24767220.1398.09.3.2.0
Grant, A. M., & Wall, T. D. (2008). The neglected science and art of quasi experimentation: Why- to, when- to, and how- to advice for organizational researchers. Organizational Research Methods, 12(4), 653-686. https://doi.org/10.1177/1094428108320737
Hart, C. (1998). Doing a literature review: Releasing the social science research imagination. Sage Publication. https://uk.sagepub.com/en-gb/eur/doing-a-literature-review/book287758
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  • Receive Date 09 September 2025
  • Revise Date 04 December 2025
  • Accept Date 08 December 2025