Using supply chain databases in academic research: A methodological critique
系统批判了使用Bloomberg SPLC等供应链数据库的三大问题：数据与研究问题不匹配、分类错误导致数据不准、仅含公开披露关系导致代表性不足，并给出改进指南，对使用二手数据的供应链研究者有重要参考价值。
Abstract This article outlines the main methodological implications of using Bloomberg SPLC, FactSet Supply Chain Relationships, and Mergent Supply Chain for academic purposes. These databases provide secondary data on buyer–supplier relationships that have been publicly disclosed. Despite the growing use of these databases in supply chain management (SCM) research, several potential validity and reliability issues have not been systematically and openly addressed. This article thus expounds on challenges of using these databases that are caused by (1) inconsistency between data, SCM constructs, and research questions ( data fit ); (2) errors caused by the databases' classifications and assumptions ( data accuracy ); and (3) limitations due to the inclusion of only publicly disclosed buyer–supplier relationships involving specific focal firms ( data representativeness ). The analysis is based on a review of previous studies using Bloomberg SPLC, FactSet Supply Chain Relationships, and Mergent Supply Chain, publicly available materials, interviews with information service providers, and the direct experience of the authors. Some solutions draw upon established methodological literature on the use of secondary data. The article concludes by providing summary guidelines and urging SCM researchers toward greater methodological transparency when using these databases.