April 1, 2022

Innovation Strategy After IPO: How AI and Data Analytics Mitigate the Post-IPO Decline in Innovation

11:00 AM - 12:30 PM

SB1 2321 (Judy Rosener Flexible Classroom)

  • Host(s): Assistant Professor Tingting Nian
  • Speaker(s): Lynn Wu, Associate Professor of Operations, Information and Decisions
  • University: University of Pennsylvania, Wharton School of Business
We examine how data analytics can facilitate innovation in firms that have gone through an initial public offering (IPO). It has been documented that an IPO is associated with a decline in innovation despite the infusion of capital from the IPO that should have spurred innovation. By assembling and analyzing multi-year panel data at the firm level, we find that firms that possess or acquire data analytics capability has sustained the rate of innovation compared to similar firms that have not acquired that capability. This effect is even greater when only machine learning capabilities are considered. Moreover, we find this sustained rate of innovation is driven principally by the continued development of innovations that combine existing technologies into new ones – the form of innovation that are especially well-supported by analytics. By examining the three main mechanisms that inhibit firm innovation after IPO—short-term financial pressure, cost of disclosure requirements, and managerial incentives, we find that data analytics can ameliorate the pressure from meeting short-term financial goals and disclosure requirements, but analytics is limited in addressing managerial incentives. Overall, our results suggest that the increased deployment of analytics may reduce some of the innovative penalties of IPOs, and that investors and managers can potentially mitigate post-IPO reductions in innovative output by directing newly acquired capital to the acquisition of analytics capabilities.