Using critical realism and abduction to navigate theory and data in operations and supply chain management research

Operations and supply chain management (OSCM) is a theoretically and philosophically fragmented field. Researchers must consider how they use theory and explain empirical phenomena. This paper aims to use critical realism to introduce more coherence into this fragmented field.,This paper draws on existing critical realism and abduction literature and this study uses a research process from two PhD projects to investigate critical realism’s role in OSCM research. This paper uses a narrative approach to collect data over a long timeframe, capturing data not commonly used in OSCM research.,Research that struggles to bridge the gap between theory and data benefits from critical realism, which provides a philosophy and associated methods to identify a suitable theory and guide researchers when they encounter obstacles. While clear steps often outline established methods, researchers are sometimes unable to identify when their research process has reached an obstacle. This paper argues that such obstacles can be treated as “crossroads” offering new research opportunities when correctly evaluated and addressed.,Importantly, researchers should be able to reflect upon their own research processes, enabling a better understanding of these processes and the discovery of new research directions. Researchers can use critical realism, abduction and systematic combining to bridge the divide between theory and data in OSCM.,This paper contributes to the field’s discussion regarding the roles of critical realism and abduction, synthesizing multiple academic sources, highlighting critical realism’s importance and providing a novel means of addressing difficulties in navigating an eclectic research area. This paper offers a philosophical alternate to the field, which is often instead considered from a positivistic standpoint. The paper is valuable to researchers in the OSCM field, who can use the research to improve their selection of data and theories, as well as their understanding of their own research processes.