MIS Distinguished Speaker Series

Temple University

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Nov 4 – Ramesh Sharda – “Network-based Health Analytics”

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Time: Friday, 4 November 2022, 10:30–12:00
Room: LW420

Ramesh Sharda
Chuck and Kim Watson Chair
Vice Dean for Grad Programs and Research
Regents Professor of Management Science and Information Systems
ConocoPhillips Chair of Technology Management
Spears School of Business
Oklahoma State University
https://business.okstate.edu/directory/16728.html

Abstract

Networks have been around a long time, but analytics/data science projects seldom use network level properties directly in analyzing a problem. We illustrate how network measures can be used to help inform medical decision-making. Examples include network applications in descriptive, predictive, and prescriptive analytics: Applications of network metrics in health analytics – comorbidities; descriptive analytics in health demographics based upon comorbidities; incorporating comorbidities to predict hospital lengths of stay; clique modeling to determine identify diseases combinations that impact mortality, etc.

Bio

Ramesh Sharda is the Vice Dean for Research and the Watson Graduate School of Management, Watson/ConocoPhillips Chair and a Regents Professor of Management Science and Information Systems in the Spears School of Business at Oklahoma State University. He has coauthored two textbooks (Analytics, Data Science, and Artificial Intelligence: Systems for Decision Support, 11th edition, Pearson and Business Intelligence, Analytics, and Data Science: A Managerial Perspective , 4th Edition, Pearson). His research has been published in major journals in management science and information systems including Management Science, Operations Research, Information Systems Research, JMIS, EJIS, Decision Support Systems, Interfaces, INFORMS Journal on Computing, and many others. He is a member of the editorial boards of journals such as the Decision Support Systems, Decision Sciences, ACM Database, and Information Systems Frontiers. He served as the Executive Director of Teradata University Network through 2020 and was inducted into the Oklahoma Higher Education Hall of Fame in 2016. Ramesh is a Fellow of INFORMS and AIS. He was the winner of 2020 OSU Eminent Faculty Award. Ramesh also won the Fulbright Distinguished Chair Award at Aalto University in Finland for 2022-2023.

Oct 28 – Lynn Wu – “Innovation Strategy after IPO: How AI Analytics Spurs Innovation after IPO”

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Time: Friday, 28 October 2022, 10:30–12:00
Room: LW420

Lynn Wu
Associate Professor of Operations, Information and Decisions
The Wharton School, The University of Pennsylvania
https://oid.wharton.upenn.edu/profile/wulynn/

Abstract

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.

Bio

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.