Abstract
High-impact entrepreneurship research stands or falls with data quality. Yet research design and data collection choices often force researchers into trade-offs among relevance, validity, and replicability. Reliance on existing databases constrains the questions we can study, while primary data collection to address new questions often struggles to deliver high-quality, large, and representative samples. Increasingly, the most tangible contributions come from unique, high-quality data that answer novel, important questions. We present a 5I framework (Invest, Integrate, Innovate, Incentivize, Impact), offering guidance for authors, reviewers, and editors to navigate these trade-offs and build unique datasets that enable relevant, valid, and replicable research.
| Original language | English |
|---|---|
| Pages (from-to) | 1295-1321 |
| Number of pages | 27 |
| Journal | Entrepreneurship Theory and Practice |
| Volume | 50 |
| Issue number | 5 |
| Early online date | 6 Apr 2026 |
| DOIs | |
| Publication status | Published - 30 Sept 2026 |
Bibliographical note
Publisher Copyright:© The Author(s) 2026
Acknowledgements
We gratefully acknowledge the thoughtful feedback and guidance from Per Davidsson, Ari Hyytinen, Mikko Rönkkö, Regan Stevenson, and Karl Wennberg. Their insightful comments significantly strengthened this work, but we remain responsible for any remaining errors. The authors used Grammarly and ChatGPT for language editing; all edits were reviewed by the authors, who take full responsibility for the final content.Funding
The authors received no financial support for the research, authorship, and/or publication of this article.
Keywords
- data
- entrepreneurship
- identification
- reproducibility
- research methods
- transparency
ASJC Scopus subject areas
- Business and International Management
- Economics and Econometrics
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