Reexamining VC Investment Data. How Precise Are They Really?
Many strategy and entrepreneurship scholars rely on investment datasets from providers like Pitchbook, LSEG, and Crunchbase. Despite their polished appearance, these datasets were built for practitioners-not researchers-and may embed systemic bias. This bias can distort our understanding of the innovation landscape and even alter research results.
Join us Friday at noon in Copenhagen for PDW #15284: The Illusion of Precision: A Reexamination of VC Investment Data (https://cdmcd.co/vaAMxb) Together, we'll assess the scope of measurement error, selection bias, and temporal drift in private company transaction data. This session features leading scholars who will share techniques for leveraging primary sources and explore how new tools, including large language models, may reduce reliance on commercial databases.
When: Friday, 25 July, 12:00–14:00 CET
Where: Bella Center: Hall D – D3-m11
Sponsors: STR; ENT; and Research Methods (RM)
Presenters:
• Millard Jay Habegger, University of Maryland
• Florence Honoré, University of Wisconsin
• Bekhzod Khoshimov, NYU Abu Dhabi
• David Kirsch, University of Maryland
• Yuan Shi, Cornell University
• David Waguespack, University of Maryland
We'll open the PDW with a panel discussion on the potential problems of private company transaction datasets that will include "Research Bites" on data inconsistencies, followed by roundtables led by presenters. These sessions will explore data use cases and best practices to improve rigor in empirical work.
Open to all AoM attendees-no pre-registration required.
------------------------------
Jay Habegger
Doctoral Student
University of Maryland College Park
Hyattsville MD
------------------------------