Promoting food industry supply chain agility and performance through the development of big data analytics: based on PLS-SEM and fsQCA approaches

Purpose The COVID-19 pandemic, geopolitical conflicts, anti-globalization and the digital economy have led to accelerated changes in the market, forcing companies to use big data to achieve precise and agile product or service delivery, thereby improving performance. Existing research has not yet explored the mechanisms for data-driven supply chain agility and supply chain performance. Based on dynamic capacity theory and organizational information processing theory, this paper constructs a conceptual model to investigate how big data analytics can facilitate the implementation of high-level supply chain agility and performance through customer integration, internal integration and collaborative knowledge creation. Design/methodology/approach We collected a sample of the Chinese food industry and conducted an empirical study using partial least squares structural equation model (PLS-SEM) and fuzzy set qualitative comparative analysis (fsQCA). Findings The results show that big data analytics has an impact on supply chain agility through three paths. Moreover, big data analytics capability and supply chain agility are considered dynamic capabilities and the effect of configuration under different conditions is empirically tested. Four solutions to improve the performance of the supply chain are obtained. Practical implications This research sheds light on the implementation process of big data-driven supply chain performance, which is of good theoretical and practical value for expanding the theory of organizational information processing and helping enterprises achieve high-level agile supply and performance. Originality/value We provide a new perspective on supply chain agility by exploring the antecedents of supply chain agility and its impact on supply chain performance from the perspective of information processing and dynamic capabilities. Existing studies have not focused on the role of big data analytic capabilities in improving supply chain agility. The purpose of this study is to attempt to establish a clear relationship between the three mediating paths (customer integration, internal integration and collaborative knowledge creation) between big data analytics capabilities and supply chain agility. In addition, we use the empirical methods of PLS-SEM and fsQCA to better substantiate the conclusions.