Statistical power of structural equation models in SCM research

• We analyze the statistical power of structural equation models in SCM research.
• 32% of all 988 SEM applications have too little power.
• Another 43% of all applications exhibit excessive power.
• Inadequate statistical power may lead to either Type I or Type II errors.
• We emphasize the importance of adequate power and give advice on how to achieve it.

Prior research has emphasized the relevance of adequate statistical power for covariance-based structural equation modeling (CSEM). Nevertheless, reviews in domains other than supply chain management (SCM) found that the magnitude of power tends to be inadequate. This finding is worrisome because statistical power directly affects the meaningfulness of the conclusions based on CSEM. The issue is particularly relevant for the field of SCM in light of the increasing use of CSEM. An investigation of the statistical power of CSEM published in seven major SCM journals since 1999 confirms this criticism. Specifically, an analysis of 988 applications of CSEM indicates that 32% of all applications have too little power, increasing the probability of Type II errors, and that another 43% of all applications exhibit excessive power, increasing the probability of Type I errors. This paper emphasizes the importance of adequate statistical power for CSEM in SCM.