My research explores how entrepreneurs build social relationships and engage in communication with their audiences to gain support for their innovations. In particular, the three essays of my dissertation examine different ways in which entrepreneurs can communicate the value of their innovations in the age of digitization.
In my dissertation, I use novel machine learning approaches to examine rich conversational data on Twitter and Product Hunt, an online community for discovering early-stage entrepreneurial products. Outside of my dissertation work, I also use big data on Twitter, Amazon, and the App Store to provide entrepreneurs with insights on how to leverage their digital relationships.
 Greve, Henrich R., & Jamie Seoyeon Song. 2017. “Amazon Warrior: How a Platform Can Restructure Industry Power and Ecology” in Advances in Strategic Management, vol. 37
 Bodner, Julia*, Jamie Seoyeon Song*, & Gabriel Szulanski. 2019. “Heuristics to Navigate Uncertainties: Interview with Professor Kathleen M. Eisenhardt” Journal of Management Inquiry, 28(3): 359-365
Other Working Papers
 Song, Jamie Seoyeon & Martin Gargiulo. “Divergent Opinions in Social Media and the Adoption of Cultural Products" – Academy of Management Journal (2nd Round Revise and Resubmit)
 Song, Jamie Seoyeon & Jason P. Davis. “What’s in a Name? Categorical and Idiosyncratic Identity of New Organizations in Nascent Markets”– Under review at Strategic Management Journal
 Song, Jamie Seoyeon. Going Beyond Conversational Partners: Entrepreneurs’ Framing and Audiences’ Support for Their Innovations – Manuscript in preparation for Administrative Science Quarterly
 Song, Jamie Seoyeon “Leveraging Ambiguity: Entrepreneurs’ Linguistic Ambiguity and Audiences' Support for Their Innovation" – Manuscript in preparation for Administrative Science Quarterly
Research in Progress
 Female entrepreneurs’ communication of their ventures (with Tatiana Lluent) – Data analysis
 Social relationships and development of ideas for innovation (with Gianluca Carnabuci and Linus Dahlander) – Designing experiments
 Optimal distinctiveness in product attributes (with Tian Chan and Yonghoon Lee) – Data analysis
 Using Reddit and Twitter conversations to detect meaning formation (with Hallie Cho) – Data analysis
 Using word embedding to detect knowledge communities – Data analysis