Investing in Data Quality for High-Impact Entrepreneurship Research
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.
Authors
- Tomasz Mickiewicz (ORCID: https://orcid.org/0000-0001-5261-5662)
- Markku Maula (ORCID: https://orcid.org/0000-0002-7030-5059)
- Johan Wiklund
- Silvio Vismara
Institutions
- University of Bergamo (IT)
- IMT School for Advanced Studies Lucca (IT)
- Aston University (GB)
- Syracuse University (US)
- Aalto University (FI)
Publication Details
- Journal
- Entrepreneurship Theory and Practice
- Published
- 2026-04-06
- DOI
- https://doi.org/10.1177/10422587261435916
- Primary Topic
- Entrepreneurship Studies and Influences
- Type
- article
- Field-Weighted Citation Impact
- 0.00