Social Learning as a Microfoundation of Digital Entrepreneurial Ecosystems: Evidence from Decentralized Autonomous Organizations (DAOs)

Abstract

We identify a core microfoundation of digital entrepreneurial ecosystems (EE): social learning in the form of information cascades. Using decentralized autonomous organizations (DAOs) as a laboratory where micro-level coordination processes are directly observable on blockchains, we trace how individual-level voting behavior aggregates into ecosystem-level outcomes. Exploring hand-collected data covering 19,450,710 votes from 924,095 unique voters in 3,317 DAO governance proposals, we estimate economically strong information cascades that run from influential through early to late voters. Several contextual factors impede social learning in DAOs, including the presence of cybercriminals in the DAO community and market sentiment, while the decentralization of token ownership is an important prerequisite for information cascades to unfold. Finally, we document a link between the potency of information cascades and the financial performance of DAOs. Stronger information cascades are associated with higher market capitalization, trading volume, and abnormal cryptocurrency token returns. Overall, we contribute to the EE literature by demonstrating how observable micro-level learning processes triggered by influential agents in the network scale into ecosystem-level financial performance.