Supply chain management research: Key elements of study design and statistical testing

Catherine A. Helmuth, Christopher W. Craighead, Brian L. Connelly, Donovan Y. Collier, Joe B. Hanna

Research output: Contribution to journalArticlepeer-review

16 Scopus citations

Abstract

Over the past three decades, supply chain management (SCM) has evolved from its origins as a nascent field of study to encompass construct definition, identification of the field's central issues, and establishment of its conceptual boundaries. At this point, a sufficient body of empirical SCM research has been put forward to allow for quantitative assessment of the field. Therefore, we examine three key elements of study design to assess what has happened, what is currently happening, and where we should be heading as a field. To do so, following a pattern of reviews in similar disciplines, we begin with an examination of effect sizes of the relationships under investigation. Results show that effect sizes in SCM research have marginally increased over time and that sub-domains within SCM that receive the most scholarly attention also have higher effect sizes. We also conduct a post hoc analysis of statistical power and empirically examine a range of factors and study contexts that could influence power. Findings suggest that average statistical power in SCM research exceeds the statistical power of most related disciplines and is particularly high in several unique contexts. Lastly, we find that measurement reliability and the use of control variables have increased over time, possibly suggesting the field has matured, instilling a degree of confidence in its research. Overall, our results show that SCM research is becoming more empirically rigorous, but we also uncover key areas that warrant improvement. We describe implications of our review for the design of future SCM empirical studies.

Original languageEnglish
Pages (from-to)178-186
Number of pages9
JournalJournal of Operations Management
Volume36
DOIs
StatePublished - May 1 2015
Externally publishedYes

Keywords

  • Effect size
  • Empirical research
  • Reliability
  • Statistical power
  • Supply chain

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