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QCA and the harnessing of unstructured qualitative data
Author(s) -
Nishant Rohit,
Ravishankar M.N.
Publication year - 2020
Publication title -
information systems journal
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 2.635
H-Index - 89
eISSN - 1365-2575
pISSN - 1350-1917
DOI - 10.1111/isj.12281
Subject(s) - qualitative comparative analysis , qualitative research , computer science , coding (social sciences) , data science , qualitative property , complement (music) , qualitative analysis , management science , sociology , engineering , social science , machine learning , biochemistry , chemistry , complementation , gene , phenotype
Abstract This paper proposes qualitative comparative analysis (QCA) as a novel method to harness unstructured data sets such as publicly available reports and news articles. It shows how QCA and conventional qualitative IS research can complement each other. In particular, it demonstrates how qualitative IS research can combine typical qualitative coding techniques with a specific type of QCA, namely crisp‐set QCA (csQCA). The paper illustrates how QCA offers qualitative IS research an innovative approach to explicate the combination of conditions associated with particular outcomes. Drawing on an empirical study of green IS, it showcases the potential of QCA to harness large unstructured qualitative material and generate deeper insights about emerging IS phenomena. The paper also highlights how QCA can contribute to the data collection and analysis stages of qualitative IS research .