Time: Friday, 28 October 2022, 10:30–12:00
Associate Professor of Operations, Information and Decisions
The Wharton School, The University of Pennsylvania
We examine the role of AI analytics in facilitating innovation in firms that have gone through IPO. Using patent data on over 1,000 publicly traded firms, we find that firms acquiring AI analytics capability post-IPO experience less of a decline in innovation quality compared to similar firms that have not acquired that capability. This effect is 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—a form of innovation that is especially well supported by analytics. By examining three main mechanisms that hampered post-IPO innovation, we find that AI analytics can ameliorate the pressure to meet short-term financial goals and disclosure requirements. However, it has limited effect in addressing managerial incentives. For firms with long product cycles, the disclosure effect is reduced to a greater extent than it is for those with short cycles. Overall, our results show the importance of examining technology as a critical input factor in innovation. We show that the increased deployment of analytics may reduce some of the innovative penalties suffered by IPOs, and that investors and managers can potentially mitigate post-IPO reductions in innovative output by directing capital acquired in the IPO process to the acquisition of AI analytics capabilities.
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.