Trust plays an important role in supply chain management research (see some of my previous posts, e.g. The More Trust the Better! Really?, The Evolution of Trust). An article by Free (2008), titled Walking the Talk? Supply Chain Accounting and Trust among UK Supermarkets and Suppliers, asks: “How are calculative practices implicated in the constitution of trust in the UK retail sector?” This leads to two principal findings: First, “existing definitions of trust need to be more tightly and coherently elaborated to be applicable in the inter-organizational context”. The author proposes “a set of trust constructs that reflects both institutional phenomena (system trust) and personal and interpersonal forms of trust (trust, trusting behaviours, trustworthiness and trusting disposition)”. Second, “trust can be invoked in both ritualistic and instrumental ways”. Here, the author suggests “that the simple dichotomy of trust and distrust […] should be expanded to embrace manipulation and the use of trust as a discursive resource”.
Free, C. (2008). Walking the Talk? Supply Chain Accounting and Trust among UK Supermarkets and Suppliers. Accounting, Organizations and Society, 33 (6), 629–662. https://doi.org/10.1016/j.aos.2007.09.001
Today, I present Mentzer et al.’s (2001) must-read article, Defining Supply Chain Management. The authors demonstrate that, “although definitions of SCM differ across authors […], they can be classified into three categories”: (1) SCM as a management philosophy (= supply chain orientation), which involves a systems approach to viewing the supply chain as a whole, a strategic orientation toward cooperative efforts, and a customer focus; (2) SCM as an implementation of a management philosophy, which involves seven activities such as “mutually sharing information”; and (3) SCM as a set of management processes, which includes processes such as “customer relationship management” and “order fulfillment”. The article also contains a useful definition of SCM as “the systemic, strategic coordination of the traditional business functions and the tactics across these business functions within a particular company and across businesses within the supply chain, for the purposes of improving the long-term performance of the individual companies and the supply chain as a whole”.
Mentzer, J.T., DeWitt, W., Keebler, J.S., Min, S., Nix, N.W., Smith, C.D. & Zacharia, Z.G. (2001). Defining Supply Chain Management. Journal of Business Logistics, 22 (2), 1–25. https://doi.org/10.1002/j.2158-1592.2001.tb00001.x
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