What configurations of structures facilitate supply chain learning? A supply chain network and complexity perspective
本研究采用模糊集定性比较分析，考察供应链网络与复杂性因素如何共同影响供应链学习行为，发现弱关系是必要条件，强关系也有促进作用，适度复杂性有利于学习。
Purpose This study explores how factors arising from supply chain (SC) network and complexity work together in supply chain learning (SCL) behavior. Design/methodology/approach Fuzzy set qualitative comparative analysis, which is an emerging configurational analysis method, was adopted to examine the complex combination of five influencing factors. The data were collected using a two-stage survey. First, the authors selected seven typical firms with an awareness of SCL. Second, questionnaires were sent to the partners of the seven selected firms, and 156 valid questionnaires were obtained from 76 firms. Findings Drawing on emergent insights from the initiative, the authors find that multiple configurations of SC network and complexity lead to high SCL. Specifically, weak ties are necessary conditions of such learning, while strong ties are also conducive to this. Moreover, a moderate SC complexity is conducive to SCL. Practical implications This study enriches the understanding of SCL and provides new insights for SC management practitioners to take measures to improve it. Originality/value This study addresses the lack of in-depth understanding of the antecedent conditions of SCL in the literature. It establishes an integrated and comprehensive theoretical framework of such learning based on contingency theory. Additionally, this study incorporates ambidextrous SCL (i.e. creation capability and dispersion capacity). An overall prototype of SCL capability is proposed on SC network and complexity theory.