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.

Conclusion: 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.