Supply chain data analytics and supply chain agility: a fuzzy sets (fsQCA) approach

Abstract

Purpose

Practitioners and researchers have reached a consensus that supply chain analytics is a strong determinant for desirable organizational outcomes such as supply chain performance and agility. The purpose of this paper is to examine a configural combination (i.e. causal recipes) subsuming supply chain data analytics, firmsize, age and annual sales to predict supply chain agility based on knowledge-based theory.

Design/methodology/approach

Survey data (n = 215) were obtained from firms operating in the United Arab Emirates. Consequently, fuzzy sets qualitative comparative analysis (fsQCA) technique was applied to the data to establish causal recipes that are necessary and sufficient to achieve high scores of supply chain agility.

Findings

Results from fsQCA support the major tenets of complexity theory that several configural combinations (i.e. supply chain data analytics, firm size, firm age and annual sales) are sufficient and necessary conditions for achieving higher scores of supply chain agility.

Originality/value

This study is first of its kind in understanding the association between supply chain data analytics and agility with fsQCA technique. This research also offers a headway for supply chain managers in identifying configural combinations of antecedents manifesting high scores for supply chain agility. Implications for theory and practice are illustrated as well as future research course.

Citation

Shamout, M.D. (2020), "Supply chain data analytics and supply chain agility: a fuzzy sets (fsQCA) approach", International Journal of Organizational Analysis, Vol. 28 No. 5, pp. 1055-1067. https://doi.org/10.1108/IJOA-05-2019-1759