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Theorizing Supply Chains with Qualitative Big Data and Topic Modeling
Author(s) -
Bansal Pratima Tima,
Gualandris Jury,
Kim Nahyun
Publication year - 2020
Publication title -
journal of supply chain management
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 3.75
H-Index - 92
eISSN - 1745-493X
pISSN - 1523-2409
DOI - 10.1111/jscm.12224
Subject(s) - big data , unpacking , relevance (law) , supply chain , computer science , function (biology) , data science , value (mathematics) , qualitative property , qualitative research , knowledge management , sociology , marketing , business , data mining , social science , political science , linguistics , philosophy , evolutionary biology , machine learning , biology , law
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.