MIS Distinguished Speaker Series

Temple University

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Innovation

Mar 24 – Shaila Miranda – “Setting an IT Innovation Agenda: The Practice Repertoire of Bots in a Blockchain Discourse”

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Time: Friday, 24 March 2023, 10:30–12:00
Room: LW420

Abstract

Communities make sense of social issues through discourse. An “issue” is a matter of potential concern. Issues can involve public policy or innovations. Issues do not exist prior to a discourse, but rather are the product of sensemaking and social construction through discourse. This constitutive nature of community discourse has been noted for information technology (IT) innovation. Through discourse, actors learn vicariously about the innovation, without needing to invest in it. Through discourse, actors advance diverse frames about the innovation, advocating for competing innovations or versions of an innovation – or even subverting the innovation. Prior research has highlighted the distinctive role of mass media in drawing attention to social issues and filtering information about them to shape public opinion. Discourse now takes place on digital mass media, where social bots abound. Though researchers have noted the role played by such bots in other venues, we lack understanding of the role they play in IT innovation discourses. Our study therefore asks: How do social bots participate in an IT innovation discourse? To address this question, we studied seven years of a Twitter blockchain discourse. Because our aim was to isolate the distinctive role of bots, we limited our investigation to discourse occurring in a single geographical area – Australia – to reduce confounds by cultural factors. Using text mining in a computational theory construction approach, we observed social bots to evince three sets of practices: innovation spotlighting, innovation framing, and innovation visibilizing practices. We theorize how this practice repertoire shapes an innovation discourse, i.e., by contributing to setting the agenda for the IT innovation. As the number of social bots grows, understanding how they shape innovation discourses will be essential to key innovation stakeholders and policymakers.

Bio

Shaila M. Miranda is the W.P. Wood Professor of MIS at the Price College of Business, the University of Oklahoma. She has a doctorate in Management Information Systems from the University of Georgia and an M.A. in Sociology from Columbia University. Her research focuses primarily on public discourse and shared meaning in the arenas of digital activism and innovation. She employs a combination of qualitative and computational inductive techniques. Shaila has published a book, Social Analytics, through Prospect Press and her research has appeared in journals such as the MIS Quarterly, Information Systems Research, Journal of Management Information Systems, Small Group Research, Information and Management, and Data Base. She serves as Senior Editor for MIS Quarterly and previously has served as Senior Editor for Information Systems Research.

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.

Apr 2 – Terence Saldanha to present “Information Technology Firms, and Revenue and Profit Stall: Theory and Empirical Evidence”

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Information Technology Firms, and Revenue and Profit Stall: Theory and Empirical Evidence

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Terence Saldanha

Assistant Professor
Terry College of Business
University of Georgia

Friday, Apr 2

10 – 11 am | Zoom

(send an email to ayabakan@temple.edu to get the Zoom link)

Abstact:

Slowdown in revenue or profit growth, what we call revenue stall or profit stall, is a key concern for any firm. This study examines the stall phenomenon and proposes three hypotheses to explain how Information Technology (IT) firms differ from non-IT firms in revenue stall and profit stall. First, we hypothesize that IT firms are more susceptible to revenue stall and profit stall than non-IT firms. Second, we hypothesize that coordination investments reduce revenue stall susceptibility and profit stall susceptibility to a greater extent in IT firms than in non-IT firms. Third, we hypothesize that innovation investments reduce revenue stall susceptibility and profit stall susceptibility to a greater extent in IT firms than in non-IT firms. Our analyses of a unique secondary longitudinal data of over 1200 large public U.S. firms from 1950-2015 supports our hypotheses. Our results are robust to endogeneity and alternative ways of measuring stall susceptibility. In further exploratory analysis, we find that revenue stall susceptibility mediates the effect of coordination investment and innovation investment on profit stall susceptibility. We also use our models to predict stall susceptibility, and we find a positive and high correlation between the predicted and actual values of stall susceptibility. Overall, our study contributes to theory and managerial practice by uncovering a non-intuitive finding that although IT firms are more susceptible than non-IT firms to revenue stall and profit stall, IT firms are more responsive than non-IT firms to investments in coordination and innovation that reduce stall susceptibility.