{"title":"Empirical entrepreneurial ecosystem research: A guide to creating multilevel datasets","authors":"Sophia Hess","doi":"10.1016/j.jbvi.2024.e00511","DOIUrl":null,"url":null,"abstract":"<div><div>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.</div></div>","PeriodicalId":38078,"journal":{"name":"Journal of Business Venturing Insights","volume":"23 ","pages":"Article e00511"},"PeriodicalIF":0.0000,"publicationDate":"2024-11-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of Business Venturing Insights","FirstCategoryId":"1085","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S2352673424000635","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"Business, Management and Accounting","Score":null,"Total":0}
引用次数: 0
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.