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Empirical entrepreneurial ecosystem research: A guide to creating multilevel datasets

Year: 2024

Type: article

Abstract: Entrepreneurial ecosystems (EEs) are multilevel phenomena crucial for understanding and promoting productive entrepreneurship and economic development. The key insight of this study is that there is an actionable path to build and manage multilevel, longitudinal datasets for EE research, facilitating deeper insights into patterns and dynamics across different levels—often missed in single-source and cross-sectional data studies. It guides the integration of data spanning founders, firms, and socio-economic indicators from diverse sources, including archival records and self-reported data. Combining and triangulating these sources fills a significant methodological gap, supporting robust empirical EE analyses and enabling evidence-based policy formulation. • Presents a structured guideline for building multilevel EE datasets. • Combines firm-, founder-, and ecosystem-level data across spatial scales. • Enables evidence-based EE research and policymaking through data triangulation. • Offers dynamic data tracking for longitudinal analysis of EEs. • Provides a four-step approach to dataset creation replicable in other contexts.

Source: Journal of Business Venturing Insights

DOI: 10.1016/j.jbvi.2024.e00511

Author: Sophia Hess

Institution: University of Stuttgart

Language: English

Topic: Entrepreneurship Studies and Influences

Subfield: Management of Technology and Innovation

Field: Business, Management and Accounting

Domain: Social Sciences

SDG: Decent work and economic growth

Open Access status: hybrid

Estimated APC paid: $2,890