University|09-18-2026

Social network analysis in entrepreneurship research

Newly published in Review of Managerial Science: our systematic review of how social network analysis (SNA) is applied methodologically in entrepreneurship research.

We assessed 53 articles from Q1 and Q2 journals published between 2005 and 2025, organised along four dimensions: study overview, network configurations, data collection and preparation, and data analysis.

The central finding is a gap between theory and method. Entrepreneurial networks are described in theory as dynamic and relationally complex, yet they are studied mostly cross-sectionally and in purely structural terms. Nearly half of the studies drew on a single data source, longitudinal designs remain rare, and the analysis usually stops at density and degree centrality. 21 studies do not even report which software was used.

From these findings we derive recommendations on data collection, metric selection and software use that address the specific gaps identified rather than restating generic standards.

A big thank you to Hanna Frei and Dominik K. Kanbach!

Abstract:

Social network analysis (SNA) offers an analytical toolkit for researching the relational structures that shape entrepreneurial processes. However, it remains comparatively underutilized in entrepreneurship research, and where it is applied, the methodological choices do not always match the demands of the field’s own theoretical foundations. To address these shortcomings, this paper systematically reviews how SNA has been methodologically applied in entrepreneurship research and to what extent current practices align with the requirements of structural embeddedness, social capital, and processual perspectives on entrepreneurial networking. The review is based on 53 articles published between 2005 and 2025 in journals ranked in the first two quartiles (Q1 and Q2) of the Journal Citation Reports and indexed in the Web of Science Core Collection. The findings are organized across four dimensions: study overview, network configurations, data collection and preparation, and data analysis. The review reveals that the field relies heavily on cross-sectional designs, single data sources, and a narrow set of conventional metrics, primarily density and degree centrality. This creates a systematic mismatch between the dynamic, relationally complex nature of entrepreneurial networks as described by theory and the static, structurally focused methods used to study them. Nearly half of the reviewed studies drew on only one data source, limiting the ability to capture the relational depth that social capital theory emphasizes. Longitudinal designs, which are essential for testing processual accounts of network evolution, remain scarce. Based on these findings, the paper develops a methodologically grounded framework with recommendations for data collection, metric selection, and software use that are tied to the specific gaps identified in the review.

Reference:

Frei, H., Tiberius, V. & Kanbach, D. K. (2026). Social network analysis in entrepreneurship research: A systematic review of methodological practices and an agenda for theory-method alignment. Review of Managerial Science, in press. doi:10.1007/s11846-026-01054-5