My research explores how entrepreneurs can communicate the value of their innovations in the age of digitization. In particular, my three-essay dissertation sponsored by Strategy Research Foundation investigates different ways in which entrepreneurs can engage in interactive conversations with their audiences on online platforms to gain support for their innovations. I propose that, contrary to our previous understanding, entrepreneurs may benefit from frames that are different from those of their conversational partners, ambiguous, or controversial.
To test these ideas, 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.
Job Market Paper
My job market paper, “Going Beyond Conversational Partners: Entrepreneurs’ Framing and Audiences’ Support for Their Innovations,” examines how entrepreneurs engage in interactive conversations with audience members to gain their support for entrepreneurs’ innovation. Prior research on framing and social movements suggests that entrepreneurs can garner support for their innovation by using interpretative lenses or frames that are similar to those shared among their conversational partners. I argue, however, that entrepreneurs would benefit from introducing frames that are different from those of their conversational partners when entrepreneurs aim to convince those observing conversations in a public domain like social media or online community platforms. By doing so, entrepreneurs would open up new perspectives in understanding the innovation for the broader observers, thereby expanding the frame that is used to understand the innovation.
I test my predictions using a novel and extensive database of more than 500K time-stamped comments, 1.035 million users’ characteristics, and over 100K products’ metadata between on Product Hunt, an online community for discovering early-stage entrepreneurial products. I develop an approach that builds on the neural-network word-embedding model, a machine learning algorithm for natural language processing, to capture frames used by entrepreneurs and audience members in their conversations. This method enables us to accurately capture continuous relationships among words based on their contextual meanings. Overall, this study contributes to our understanding of the dynamic and interactive process of entrepreneurial framing and how it cultivates meaning and catalyzes action for a wider audience.
 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. “Controversy Sells? The Effect of Controversy in Social Media on the Adoption of Cultural Products" – Organization Science (under review)
 Song, Jamie Seoyeon “Leveraging Ambiguity: Entrepreneurs’ Linguistic Ambiguity and Audiences' Support for Their Innovation" – Manuscript in preparation for Administrative Science Quarterly
 Song, Jamie Seoyeon & Jason P. Davis. “What’s in a Name? Categorical and Idiosyncratic Identity of New Organizations in Nascent Markets” – Manuscript in preparation for the Academy of Management Journal
Research in Progress
 Song, Jamie Seoyeon & Gokhan Ertug. “Co-Constructing Meaning around Innovation: Entrepreneurial Intervention in Community Discourse and Entrepreneurial Success” – Data analysis
 Song, Jamie Seoyeon. "Using Word Embedding to Detech Knowledge Communities" – Data analysis