The replication crisis that has deeply affected neighboring disciplines is now under scrutiny in operations management. Eight scholars (Davis et al.: A Replication Study of Operations Management Experiments in Management Science) took the initiative to examine the replicability of ten influential experimental articles. Their results were disturbing: only six articles were fully replicated, two were partially replicated, and two completely missed the mark. Such results raise questions about the robustness of our basic research. In light of these findings, a pressing question arises: Does supply chain management, a closely related discipline, face a similar challenge? As we chart the course forward, it is imperative that both operations and supply chain management embrace transparency, rigor, and accountability. Addressing this crisis head-on will ensure that our disciplines maintain credibility, relevance, and trustworthiness in the academic and business communities. It is clear that there is a need for more replication studies that can challenge existing work.
Davis, A.M., Flicker, B., Hyndman, K., Katok, E., Keppler, S., Leider, S., Long, X., & Tong, J.D. (2023). A Replication Study of Operations Management Experiments in Management Science. Management Science, 69(9), _-_. https://doi.org/10.1287/mnsc.2023.4866
How should researchers construct research questions for their academic work? One intuitive answer is by spotting a gap in the existing academic literature. This is certainly an effective approach that follows the Popperian scientific method. In addition to gap-spotting, there is a second approach that deserves a little more attention: problematization. Alvesson and Sandberg (2011) describe this approach in their famous article Generating Research Questions Through Problematization (a must read!). They write that “[t]he dominance of gap-spotting is surprising, given it is increasingly recognized that theory is made interesting and influential when it challenges assumptions that underlie existing literature.” This is what problematization does: it is about identifying and challenging assumptions that underlie existing theories and generating research questions that lead to the development of more interesting and influential theories. Of course, we will still need gap-spotting in the future. But I do believe that SCM research could benefit from more problematization.
Alvesson, M., & Sandberg, J. (2011). Generating Research Questions Through Problematization. Academy of Management Review, 36(2), 247–271. https://doi.org/10.5465/amr.2009.0188
In recent years, academic articles that use supply chain databases have become more and more common in SCM-related journals. Such databases (e.g., Bloomberg SPLC, FactSet Supply Chain Relationships, and Mergent Supply Chain) were originally not developed for use in academic research, but for use in business practice. However, they offer great potential for a better understanding of supply chains (or more precisely supply networks) and supply chain management and are therefore also very interesting for researchers. A recent article by Culot and her coauthors (2023) discusses these potentials and points out pitfalls for using supply chain databases in SCM research. The article is entitled Using Supply Chain Databases in Academic Research: A Methodological Critique and based on a review of previous studies using such databases, publicly available materials, interviews with information service providers, and the direct experience of the authors. I am sure this long-awaited article will serve as a reference for quantitative research relying on such databases for years to come.
Culot, G., Podrecca, M., Nassimbeni, G., Orzes, G., & Sartor, M. (2023). Using Supply Chain Databases in Academic Research: A Methodological Critique. Journal of Supply Chain Management, 59(1), 3–25. https://doi.org/10.1111/jscm.12294
This year’s Nobel Memorial Prize in Economics goes to David Card “for his empirical contributions to labour economics” and Joshua D. Angrist and Guido W. Imbens “for their methodological contributions to the analysis of causal relationships”. The Royal Swedish Academy of Sciences writes in a comprehensive article about the scientific background of this prize (PDF): “Taken together, […] the Laureates’ contributions have played a central role in establishing the so-called design-based approach in economics. This approach – aimed at emulating a randomized experiment to answer a causal question using observational data – has transformed applied work and improved researchers’ ability to answer causal questions of great importance for economic and social policy using observational data.” Similar to what is still widespread in SCM research today, the traditional approach to causal inference in economics relied on structural equation models at least until the 1980s, but, based on the laureates’ work on the local average treatment effect, natural experiments have become increasingly popular in economics. Unfortunately, almost no corresponding research exists in our discipline, but a certain number of natural experiments were carried out in related disciplines (e.g.; Lee & Puranam, 2017; Li & Zhu, 2021; Huang et al., 2021). Perhaps this Nobel Prize can serve as an inspiration for more natural experiments also in the SCM discipline?
Our discipline is still almost exclusively shaped by positivism. This is very surprising in view of the very complex social phenomena with which the discipline deals. However, recently I have noticed a (slowly) growing trend toward interpretivism. For example, Darby and her coauthors (2019) have discussed the set of questions interpretive research can address in SCM. Many SCM researchers may still be unsure of how best to conduct an interpretive study. Used to the structured approaches of positivist studies (e.g., Yin), we often would like to have a template in hand that shows us how to conduct an interpretive study. A new article by Mees-Buss and her coauthors (2021) argues that the inductive route to theory that templates (e.g., Gioia) offer do not address the challenges of interpretation. They argue that “a return to a hermeneutic orientation opens the way to more plausible and insightful theories based on interpretive rather than procedural rigor” and they offer “a set of heuristics to guide both researchers and reviewers along this path”.
Mees-Buss, J., Welch, C., & Piekkari, R. (2021), From Templates to Heuristics: How and Why to Move Beyond the Gioia Methodology. Organizational Research Methods, in print. https://doi.org/10.1177/1094428120967716
In her insightful Nature comment Rein in the Four Horsemen of Irreproducibility, Dorothy Bishop describes how threats to reproducibility, recognized but unaddressed for decades, might finally be brought under control, by avoiding what she refers to as “the four horsemen of the reproducibility apocalypse”: publication bias, low statistical power, P-value hacking and HARKing (hypothesizing after results are known). In the video below she makes several important points. My perception is that the SCM research community does not take the reproducibility debate seriously enough.
Some time ago, an editorial of Nature Human Behaviour has highlighted that “[the] quest for positive results encourages numerous questionable research practices […] such as HARKing (hypothesizing after the results are known) and P-hacking (collecting or selecting data or statistical analyses until non-significant results become significant)”. To counteract these very serious problems, that make theory-testing research almost useless, the journal has adopted the registered report format, which “shift[s] the emphasis from the results of research to the questions that guide the research and the methods used to answer them”. Similarly, the European Journal of Personality has recently announced to support the registered report format, too: “In a registered report, authors create a study proposal that includes theoretical and empirical background, research questions/hypotheses, and pilot data (if available). Upon submission, this proposal will then be reviewed prior to data collection, and if accepted, the paper resulting from this peer-reviewed procedure will be published, regardless of the study outcomes.” I can only hope that SCM journals will quickly catch up with this development in other fields.
Several journals have already reacted to the p value debate. For example, an ASQ essay provides suggestions that not only every editor should read. Another example are the policies published by SMJ: SMJ “will no longer accept papers for publication that report or refer to cut-off levels of statistical significance (p-values)”. Instead, “authors should report either standard errors or exact p-values (without asterisks) or both, and should interpret these values appropriately in the text”. “[T]he discussion could report confidence intervals, explain the standard errors and/or the probability of observing the results in the particular sample, and assess the implications for the research questions or hypotheses tested.” SMJ will also require authors to “explicitly discuss and interpret effect sizes of relevant estimated coefficients”. It might well be that we are currently observing the beginning of the end of null-hypothesis statistical tests. And it might only be a matter of time before other journals, also SCM journals, require authors to remove references to statistical significance and statistical hypothesis testing and, ultimately, to remove p values from their manuscripts.
The P value debate has revealed that hypothesis testing is in crisis – also in our discipline! But what should we do now? Nature recently asked influential statisticians to recommend one change to improve science. Here are five answers: (1) Adjust for human cognition: Data analysis is not purely computational – it is a human behavior. So, we need to prevent cognitive mistakes. (2) Abandon statistical significance: Academia seems to like “statistical significance”, but P value thresholds are too often abused to decide between “effect” (favored hypothesis) and “no effect” (null hypothesis). (3) State false-positive risk, too: What matters is the probability that a significant result turns out to be a false positive. (4) Share analysis plans and results: Techniques to avoid false positives are to pre-register analysis plans, and to share all data and results of all analyses as well as any relevant syntax or code. (5) Change norms from within: Funders, journal editors and leading researchers need to act. Otherwise, researchers will continue to re-use outdated methods, and reviewers will demand what has been demanded of them.
Leek, J., McShane, B.B., Gelman, A., Colquhoun, D., Nuijten, M.B. & Goodman, S.N. (2017). Five Ways to Fix Statistics. Nature, 551 (2), 557-559. DOI: 10.1038/d41586-017-07522-z
Academics and students often have very different ideas in mind when they talk about case study research. Indeed, case studies in SCM research are not alike and several different case study research designs can be distinguished. A recent article by Ridder (2017), titled The Theory Contribution of Case Study Research Designs, provides an overview of four common approaches. First, there is the “no theory first” type of case study design, which is closely connected to Eisenhardt’s methodological work. The second type of research design is about “gaps and holes”, following Yin’s guidelines. This type of case study design is what can be seen in SCM journals maybe most often. A third design deals with a “social construction of reality”, which is represented by Stake. Finally, the reason for case study research can also be to identify “anomalies”. A representative scholar of this approach is Burawoy. Each of these four approaches has its areas of application, but it is important to understand their unique ontological and epistomological assumptions. A very similar overview is provided by Welch et al. (2011).
Ridder, H.G. (2017). The Theory Contribution of Case Study Research Designs. Business Research, 10 (2), 281-305. https://doi.org/10.1007/s40685-017-0045-z