Innovation Strategy after IPO: How Data Analytics Mitigates the IPO Penalty on Innovation

Lynn Wu, Operations, Information and Decisions, The Wharton School; Bowen Lou, Operations, Information and Decisions, The Wharton School; and Lorin Hitt, Operations, Information and Decisions, The Wharton School

Abstract: This proposal explores how the new generation of information technologies (IT) including AI, automation and data analytics, affect how firms innovate. In particular, we focus on the effect of innovation after a firm went through an IPO. Earlier work has documented that innovation characteristics in firms often become more incremental after IPO. We explore how data analytics can mitigate some of the disadvantage on innovation qualities post IPO. We hypothesize that using data analytics can significantly reduce certain innovation quality concerns post-IPO, especially when analytics is used to generate innovations that are a recombination of long-jump technologies.

Michelle Eckert is Marketing and Communications Coordinator for the Mack Institute, where she works to engage students, researchers, and corporate partners in opportunities for collaboration. Michelle received her B.A. in Art from Valparaiso University in 2007. Her background includes two AmeriCorps terms of service working to teach mathematics, computer literacy, and job readiness skills to out-of-school youth in Philadelphia, focusing particularly on promoting access to post-secondary education.