Empirical Analyses Using Secondary Supply Chain Data
研究了Bloomberg SPLC、FactSet和CompuStat三个常用供应链数据库，提出一个数据驱动的路线图，帮助研究者选择数据源、确定分析单位并处理内生性等计量问题，提升实证研究的可靠性和普适性。
ABSTRACT The increasing availability of secondary data on supply chain relationships has created new opportunities for empirical research in supply chain management. Datasets from sources including Bloomberg SPLC, FactSet, and CompuStat may support empirical analyses of decision‐making, strategic behaviors, governance mechanisms, and dynamics of inter‐organizational relationships of supply chains. However, the complexity and interconnectedness of supply chains and the inconsistent quality and coverage of supply chain data sources present empirical challenges, which have limited the scope and depth of current empirical research on supply chains. To address these challenges, this study investigates and compares the three commonly used supply chain databases and introduces a data‐focused roadmap for supply chain research using secondary data sources. This roadmap presents a process featuring data source selection, unit‐of‐analysis decisions, and appropriate econometric treatments, for example, endogeneity, selection bias, and correlated errors in supply chains, which contributes to the supply chain management literature by improving consistency, generalizability, and reliability in empirical supply chain research.