Factors influencing proactiveness in supply chain risk identification: A fuzzy-set qualitative comparative analysis

Abstract

Nowadays, organizations pay more attention to introducing proactiveness for managing sudden unprecedented events like COVID-19. Previous studies focused on reactive and predictive methodologies for risk identification. In today's data-driven era, the adoption of proactive approaches is significantly increasing to achieve rapid recovery and accurate decision-making. However, analyzing the influencing factors and their interdependencies for addressing successful risk identification mechanisms is scarce in the literature. Researchers and social scientists adopted the fuzzy-set approach as a useful methodology to do statistical inferences from experts' surveys or interviews. Given this, we exploited fuzzy-set qualitative comparative analysis (fsQCA) to bring out the necessary conditions leading to the expected outcome. The dataset on the missing aspects was obtained based on a literature survey, supply chain (SC) related articles from social media, and discussions with experts. As a result, we aim to investigate the role of four influencing factors of SC risk, namely predicting future risks, continuous monitoring, external factors, and risk prioritization. The findings show the necessary and sufficient configurations of the causal conditions that lead to proactive risk identification. The solutions indicate that all four factors are perceived as necessary conditions. The mechanisms which focus on contemporary risk prediction and continuous monitoring are more likely to be effective. Further, some combinations highlight the significance of external factors like natural disasters (e.g., COVID-19). To the best of our understanding, there is no evidence in the previous studies that investigates the influencing factors of risk identification based on salient configurations using fsQCA. Given this, the findings of this research allow us to understand the essential factors that bring proactiveness to the risk management process.