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Beyond the Matrix: Visual Methods for Qualitative Network Research
Author(s) -
Jaspersen Lena J.,
Stein Christian
Publication year - 2019
Publication title -
british journal of management
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 2.407
H-Index - 108
eISSN - 1467-8551
pISSN - 1045-3172
DOI - 10.1111/1467-8551.12339
Subject(s) - organizational network analysis , social network (sociolinguistics) , social network analysis , computer science , data collection , data science , situated , qualitative research , typology , qualitative property , network analysis , visual research , knowledge management , management science , sociology , artificial intelligence , world wide web , social science , machine learning , social media , art , organizational learning , physics , quantum mechanics , anthropology , economics , visual arts
Network research remains dominated by approaches involving the analysis of numerical data stored in data matrices with the aim of identifying the effects of hidden social structures. While such research has advanced our understanding of social networks at the inter‐personal, inter‐unit and inter‐organizational level, repeated calls have been made for network research to attend to the situated meanings attached to both relationships and network structures. In this article, we advance a nascent literature on qualitative methods for social network analysis by drawing together developments in visual network research from across the social sciences. We introduce a typology of three visual methods for the collection of network data using network maps: participatory network mapping , network map interviews and visual network surveys . Drawing on three empirical examples from our research in the inter‐organizational domain, we demonstrate how these methods can be used for the collection of qualitative and quantitative relational data, and how they can be triangulated with other qualitative methods and social network analysis. We evaluate the merits and limitations of the methods presented and conclude that visual network research is a useful addition to existing methods for network research in business and management studies.