Replication without repeating ourselves: Addressing the replication crisis in operations and supply chain management research

Operations and supply chain management (OSCM) scholars have generally not engaged in the robust discourse over the extent and implications of science's replication crisis, perhaps because replication studies remain exceedingly rare in the discipline. In this manuscript, I encourage more replications. However, I also assume that such studies will remain rare. Therefore, it is argued that the replication crisis in OSCM can be partially addressed by conducting theory testing and refining research in a more systematic fashion that acknowledges researchers' and journals' incentives to publish new knowledge, while also efficiently helping to ensure that the foundations of that new knowledge are generalizable and not based on idiosyncratic, spurious, or questionable findings. Hence, I focus on ways to conduct a replication within a larger theory testing or refining study in a manner that makes it possible to explain the results, even if they differ from past research. This is achieved via enhanced transparency, designing research to include tests of nomological validity and account for the possibility that the results will not replicate, and more careful consideration of controls. The payoff from this investment in designing replication logic into theory testing and refining research would be that in the course of conducting normal science, we would be able to make much stronger claims about the veracity of our theories, facts, and predictions.

Automated summary

While replication studies remain rare in the field of Operations and Supply Chain Management, the author encourages more replication while acknowledging that they will likely continue to be uncommon. It is suggested that addressing the replication crisis in OSCM can be achieved through systematic theory testing and refining research, which takes into consideration researchers' and journals' incentives to publish new knowledge. This would lead to stronger claims about the veracity of theories, facts, and predictions in the field.