Theorizing Supply Chains with Qualitative Big Data and Topic Modeling
探讨了定性大数据在供应链理论研究中的价值，重点介绍主题建模方法如何从海量文本中识别新构念，并分析构念间关系以解释供应链的涌现、运行和适应过程。
The availability of Big Data has opened up opportunities to study supply chains. Whereas most scholars look to quantitative Big Data to build theoretical insights, in this paper we illustrate the value of qualitative Big Data. We begin by describing the nature and properties of qualitative Big Data. Then, we explain how one specific method, topic modeling, is particularly useful in theorizing supply chains. Topic modeling identifies co‐occurring words in qualitative Big Data, which can reveal new constructs that are difficult to see in such volume of data. Analyzing the relationships among constructs or their descriptive content can help to understand and explain how supply chains emerge, function, and adapt over time. As topic modeling has not yet been used to theorize supply chains, we illustrate the use of this method and its relevance for future research by unpacking two papers published in organizational theory journals.