Analyzing supply chain uncertainty to deliver sustainable operational performance: Symmetrical and asymmetrical modeling approaches

Abstract

The purpose of this study is to analyze different types of supply chain uncertainties and suggest strategies to deal with unexpected contingencies to deliver superior operational performance (OP) using symmetrical and asymmetrical modeling approaches. The data were collected through a survey given to 146 supply chain managers within the fast moving consumer goods industry in Thailand. Symmetrical modeling is applied via partial least squares structural equation modeling (PLS-SEM) in order to assess the theoretical relationships among the latent variables, while asymmetrical modeling is applied via fuzzy set qualitative comparative analysis (fsQCA) to emphasize their combinatory causal relation. The empirical results support the theory by highlighting the mediating effect of supply chain strategy (SCS) in the relation between supply chain uncertainty (SCU) and firms' OP and, hence, deliver business sustainability for the firms, demonstrating that the choice of SCS should not be an "either-or" decision. This research contributes by providing an illustration of a PLS-SEM and fsQCA based estimation for the rapidly emerging field of sustainable supply chain management. This study provides empirical support for resource dependence theory (RDT) in explaining the relation between SCU and SCS, which leads to sustainable OP. From a methodological standpoint, this study also illustrates predictive validation testing of models using holdout samples and testing for causal asymmetry.