In light of increasing diversity and complexity of the research methods used in OM, the Empirical Research Methods in Operations Management department sets out to serve our research community in two ways: (1) publishing manuscripts about how empirical research methods are used in OM and (2) performing method checks for incoming manuscripts before they go to a topical department. Both roles align with the journal’s evolution in recent years in terms of methodological rigor. To do so, the department also trains methods reviewers.
How do we determine if a study is methodologically focused and hence suitable for this department to handle? Recognizing that it is difficult to delineate a catch-all standard, we attempt to shed some light via the “Develop-Review-Import” classification. That is, suitable methods manuscripts can be broadly categorized into three classes (examples in parenthesis):
Developing a new empirical methodology for an OM problem (Ilk et al. 2020, Ketzenberg et al. 2020, Pak et al. 2020, Petropoulos et al. 2018, Shang et al. 2020). Studies utilizing prediction, forecasting, and machine learning models as part of their method development process naturally fit under this class. While such research may go to the appropriate topical department, due to their methodological nature, they can also be assigned to the methods department depending on the novelty of the method.
Reviewing how well the empirical OM community is applying a method or doing a certain type of inference (Brusco et al. 2012, Ketokivi 2019, Lu et al. 2018, Malhotra et al. 2014, Peng and Lai 2012, Rungtusanatham et al. 2014). This class of articles typically starts with a comprehensive survey of published papers using the focal method, then provides a summary of issues identified, and follows up with recommendations for improvement.
Importing a specific (class of) methodology into OM from other disciplines that has potential applications in OM. The emphasis for this class is on the extent of applications in OM and the accompanied demonstration. Review and Import classes are expected to have some overlap: A new methodology might be imported to address an existing OM problem better. The demonstration of this improvement inevitably requires some review of published papers. A classic example is Brown et al. (2005).
What’s in common for all three classes is that (1) the focus is on the methods and (2) the empirical context is OM. Below are some examples of articles that are unlikely to fit with the department’s mission. In the “Develop” class, articles that propose very general methodological approaches that broadly apply across business and economic disciplines would fit better in methodological journals. In the “Review” class, simple descriptive reviews are likely to be rejected. Manuscripts in this class must identify specific problems or areas of improvement and propose actionable guidelines on how current OM research could improve. In the “Import” class, the main challenge is that novelty of a method itself is insufficient. Authors need to clearly explain how the imported method represents an improvement over current methods or can address important OM questions that current methods cannot.
The second role of the department is to support other departments by performing method reviews, which usually consist of a quick review to identify any major mistakes or weaknesses in the research design or analysis before the article is sent to regular peer review. A pool of method-expert reviewers maintained by the department conducts such checks. Authors whose submissions have been sent for a preliminary methods review are often asked to revise their methodology or analysis to better prepare the manuscript for peer review.
Department Editorial
References
Brown L, Gans N, Mandelbaum A, Sakov A, Shen H, Zeltyn S, Zhao L (2005) Statistical Analysis of a Telephone Call Center: A Queueing-Science Perspective. Journal of the American Statistical Association 100(469):36–50.
Brusco MJ, Steinley D, Cradit JD, Singh R (2012) Emergent clustering methods for empirical OM research. Journal of Operations Management 30(6):454–466.
Ilk N, Shang G, Goes P (2020) Improving customer routing in contact centers: An automated triage design based on text analytics. Journal of Operations Management 66(5):553–577.
Ketokivi M (2019) Avoiding bias and fallacy in survey research: A behavioral multilevel approach. Journal of Operations Management 65(4):380–402.
Ketzenberg ME, Abbey JD, Heim GR, Kumar S (2020) Assessing customer return behaviors through data analytics. Journal of Operations Management:joom.1086.
Lu G, Ding XD, Peng DX, Hao-Chun Chuang H (2018) Addressing endogeneity in operations management research: Recent developments, common problems, and directions for future research. Journal of Operations Management 64(1):53–64.
Malhotra MK, Singhal C, Shang G, Ployhart RE (2014) A critical evaluation of alternative methods and paradigms for conducting mediation analysis in operations management research. Journal of Operations Management 32(4):127–137.
Pak O, Ferguson M, Perdikaki O, Wu S (2020) Optimizing stock‐keeping unit selection for promotional display space at grocery retailers. Journal of Operations Management 66(5):501–533.
Peng DX, Lai F (2012) Using partial least squares in operations management research: A practical guideline and summary of past research. Journal of Operations Management 30(6):467–480.
Petropoulos F, Kourentzes N, Nikolopoulos K, Siemsen E (2018) Judgmental selection of forecasting models. Journal of Operations Management 60(1):34–46.
Rungtusanatham M, Miller JW, Boyer KK (2014) Theorizing, testing, and concluding for mediation in SCM research: Tutorial and procedural recommendations. Journal of Operations Management 32(3):99–113.
Shang G, McKie EC, Ferguson ME, Galbreth MR (2020) Using transactions data to improve consumer returns forecasting. Journal of Operations Management 66(3):326–348.