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Lynn Wu:
Innovation Strategy After IPO: How Data Analytics Mitigates the Post-IPO Decline in Innovation

Lynn Wu: Innovation Strategy After IPO: How Data Analytics Mitigates the Post-IPO Decline in Innovation
October 18, 2021
Virtual Event
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Professor Lynn Wu of The Wharton School joined S-DEL Director Erik Brynjolfsson on October 18, 2021, to discuss how data analytics mitigates the post-IPO decline in innovation.


Abstract

Innovation Strategy After IPO:
How Data Analytics Mitigates the Post-IPO Decline in Innovation

We examine the role of data analytics in facilitating 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. Using patent data for over 2,000 firms, we find that firms that possess or acquire data analytics capability experience a smaller decline in innovation compared to similar firms that have not acquired that capability. Moreover, we find this sustained rate of innovation is driven principally by the continued development of innovations that either combine existing technologies into new ones or reuse existing innovations by applying them to new problem domains—both forms of innovation that are especially well-supported by analytics. Our results suggest that the increased deployment of analytics may reduce some of the innovation decline 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.


About Lynn Wu

Lynn Wu is an associate professor (with tenure) at the Wharton School. She teaches MBA, undergraduate and PhD classes about the use and impact of emerging technologies on business.

Her research examines how emerging information technologies, such as artificial intelligence and analytics, affect innovation, business strategy, and productivity. Specifically, her work follows three streams. In the first stream, she examines how data analytics and artificial intelligence affect firm innovation, business strategy, labor demand, and productivity for both large firms and startups. In her second stream, she studies how enterprise social media and online platforms affect work performance, career trajectories, entrepreneurship success, and the formation of new type of biases that arise from using technologies. In her third stream of research, Lynn leverages fine-grained nanodata available through online digital traces to predict economic indicators such as real estate trends, labor trends and product adoption.

Lynn has published articles in economics, management and computer science. Her work has been widely covered by media outlets, including, NPR, the Wall Street Journal, Businessweek, New York Times, Forbes, and The Economist. She has won numerous awards such as Early Career awards from INFORMS and AIS, best paper awards from Information System Research, AIS, ICIS, HICSS, CHITA, and Kauffman. She has also won the Dean’s teaching award.

Lynn received her undergraduate degrees from MIT (Finance and Computer Science), her master’s degree from MIT (Computer Science) and her Ph.D. from MIT Sloan School of Management (Management Science). Lynn has experiences working with a variety of firms in the technology industry (e.g. IBM, SAP, Google, Facebook etc), government agencies and think tanks (e.g the World Bank, the Russel Sage Foundation). She has also consulted and advised several startups. Prior to academia, she was a software engineer and a research scientist at MIT AI lab and IBM.

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