Visualization in Operations Management Research

Rahul Basole (), Elliot Bendoly (), Aravind Chandrasekaran () and Kevin Linderman ()
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Rahul Basole: Accenture AI, Atlanta, Georgia 30308
Elliot Bendoly: Operations and Business Analytics, The Ohio State University, Columbus, Ohio 43210
Aravind Chandrasekaran: Operations and Business Analytics, The Ohio State University, Columbus, Ohio 43210
Kevin Linderman: Penn State University, State College, Pennsylvania 16801

INFORMS Joural on Data Science, 2022, vol. 1, issue 2, 172-187

Abstract: The unprecedented availability of data, along with the growing variety of software packages to visualize it, presents both opportunities and challenges for operations management (OM) research. OM researchers typically use data to describe conditions, predict phenomena, or make prescriptions depending on whether they are building, testing, or translating theories to practice. Visualization, when used appropriately, can complement, aid, and augment the researcher’s understanding in the different stages of research (theory building, testing, or translating and conveying results). On the other hand, if used incorrectly or without sufficient consideration, visualization can yield misleading and erroneous claims. This article formally examines the benefits of visualization as a complementary method enhancing each stage of a broader OM research strategy by examining frameworks and cases from extant research in different OM contexts. Our discussion offers guidance with regard to researchers’ use of visual data renderings, particularly toward avoiding misrepresentation, which can arise with the incorrect use of visualization. We close with a consideration of emerging trends and their implications for researchers and practitioners as well as recommendations for both authors and reviewers, regardless of domain, in evaluating the effectiveness of visuals at each stage of research.

Keywords: data visualization; operations management; methods (search for similar items in EconPapers)
Date: 2022
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Persistent link: https://EconPapers.repec.org/RePEc:inm:orijds:v:1:y:2022:i:2:p:172-187

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