With the increasing use of recommender systems in various application domains, many algorithms have been proposed for improving the accuracy of recommendations. Among various other dimensions of recommender systems performance, long-tail (niche) recommendation performance remains an important challenge, due in large part to the popularity bias of many existing recommendation techniques. In this study, we propose CORE, a cosine-pattern-based technique, for effective long-tail recommendation. Comprehensive experimental results compare the proposed approach both to classical, widely-used recommendation algorithms and to specialized long-tail recommendation baselines, and demonstrate its practical benefits in accuracy, flexibility, and scalability, in addition to the superior long-tail recommendation performance.
Gedas Adomavicius is a professor in the Department of Information and Decision Sciences at the Carlson School of Management, University of Minnesota, where he also holds the Larson Endowed Chair for Excellence in Business Education. He received his PhD degree in computer science from New York University. His general research interests revolve around computational techniques for aiding decision-making in information-intensive environments and include personalization technologies and recommender systems, machine learning and data analytics, and electronic market mechanisms. His research has been published in a number of leading academic journals in information systems and computer science, including Information Systems Research, MIS Quarterly, Management Science, Journal of Operations Management, IEEE Transactions on Knowledge and Data Engineering, ACM Transactions on Information Systems, and Data Mining and Knowledge Discovery, and has been cited more than 28,000 times to date (according to Google Scholar). He has received several research grants from major funding institutions, including the U.S. National Science Foundation CAREER award for his research on personalization technologies. He has served on the editorial boards of several leading academic journals, including as Senior Editor for Information Systems Research and MIS Quarterly. In 2017, Prof. Adomavicius received the INFORMS Information Systems Society’s Distinguished Fellow Award. At the Carlson School of Management, he has taught analytics-related courses in the undergraduate, MBA, MSBA, PhD, and Executive Education programs and has served in several administrative roles, including as the chair of the Information and Decision Sciences Department.
]]>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.
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.
]]>Increasingly, artificial intelligence (AI) serves as a frontline operator, while humans perform backend operations. Despite growing firms in the gig economy employing a business model with AI as the default service provider, the literature is limited regarding the impact of such a model on employee income. To address such limitations, this research proposes an AI-first service framework, which asserts that AI initially attempts to solve tasks, but upon unsatisfactory service outcome, customers pay a fee for employee assistance with such tasks. To empirically investigate our proposed framework, we partner with an AI-first learning app, where AI and tutors are the default and on-request service providers, respectively. We use tutor-level observational data and find that as AI, the default service provider, becomes more effective, tutor income is mediated by changes in task volume and margin, but that the pathways differ depending on employee expertise. Findings from our granular data show that, as AI effectiveness increases, tasks that are difficult due to their broad coverage across topics are passed on to tutors such that low-expertise (vs. high-expertise) tutors accept fewer tasks. We also conduct a field experiment at the customer level to show that customers are willing to pay for extra assistance from tutors, despite receiving free service from AI, for tasks that are difficult due to in-depth knowledge required within a topic. We discuss the theoretical implications of our findings and practical ramifications to effectively manage and develop human competency in the era of an AI-driven economy.
Sang-Pil Han is an Associate Professor of Information Systems in the W. P. Carey School of Business at Arizona State University. His research focuses on artificial intelligence, digital platforms, and business analytics. His research has been published in top-tier academic journals such as Management Science, Management Information Systems Quarterly, Information Systems Research and Journal of Marketing, and featured in Harvard Business Review and BBC News. He has received grants from the Marketing Science Institute and Wharton Interactive Media Initiative, the NET Institute, the Wharton Customer Analytics Initiative, the Korea Research Foundation, the Hong Kong General Research Fund, as well as private companies. At ASU, he was a Co-Faculty Director for the Master of Science in Business Analytics program. He served as an Associate Editor at Information Systems Research. He advises a variety of organizations, including tech startups like Mathpresso, a leading AI-powered education platform, and RoundIn, an online golf learning platform, as well as non-profits like Simple Steps, a 501c3 organization that assists female immigrant talent in achieving their professional goals. In his spare time, he enjoys playing golf with his wife and two daughters.
]]>Older workers use organizational IT in qualitatively different ways than their younger counterparts. In particular, they often focus on a few core features of an IT instead of exploiting the full range of features. This behavior is indicative of the significant reduction in post-adoptive IT use that occurs as the workforce ages. However, since the causes of this reduction remain unclear, managers and systems designers have difficulty addressing the problem of the underuse of technologies by the aging workforce. By means of a serial mediation model, this research note argues that the reduction in post-adoptive IT use among older workers—and specifically the reduction in extended feature usage—is caused by the decline of fluid intelligence that occurs with aging and by the impact of this decline on the ability of users to learn about new features. To test the model, data were collected from younger and older users of Microsoft Excel. Different measures for extended feature usage were employed for triangulation purposes. To reinforce the confidence in the study results even more and to yield broader implications for the post-adoptive use of IT, intention to explore and user innovation with IT were also brought into play as outcome measures. In addition, the triangulation strategy was based on different measures for age and for the primary mediating variable. The results supported the model and indicated some important ways that managers and systems designers can help older workers use more features of workplace IT despite the decline in their fluid intelligence.
Stefan Tams holds the Professorship in Technology and Aging at HEC Montréal, Canada, where he is an associate professor of information systems. His current research interests focus on the roles of age and stress in IT use. His work has appeared in several scientific journals, including MIS Quarterly, Journal of the Association for Information Systems, European Journal of Information Systems, and Journal of Strategic Information Systems, among others. His research has been featured in The Wall Street Journal and other outlets.
]]>Abstract
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
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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.
]]>Lynn Wu
Associate Professor of Operations, Information and Decisions
The Wharton School, The University of Pennsylvania
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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.
]]>Michelle Carter
Associate Professor
Washington State University
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Abstract
In recent years, organizations of every shape and size have embraced the “whole self” movement, which encourages employees to show up authentically in the workplace. The movement dovetails with diversity, equity, and inclusion (DEI) programs and organizations’ use of social media to signal allyship with historically disadvantaged groups. Such signaling encourages job candidates to follow suit, as a means of demonstrating their trustworthiness and confidence to prospective employers. However, while social media allyship can lead to positive outcomes for organizations, it may not benefit individuals. Cybervetting research cautions against taking a public stand on potentially sensitive social issues in case it negatively affects perceptions of job suitability. Thus, the “whole self” movement creates an interesting conundrum: on one hand, organizations may view social media allyship positively; on the other, it could prove detrimental to individuals if the stance taken is not aligned with the values of hiring agents who use online content to evaluate job candidates. In this presentation, Michelle Carter will discuss research that takes an identity perspective to explore hiring agents’ views on the effectiveness of social media allyship in general, and for individuals’ job prospects.
Bio
Dr. Michelle Carter is an associate professor in the Carson College of Business at Washington State University and an affiliate associate professor in the Information School at the University of Washington. Michelle’s research focuses on information technologies’ involvement in identity and social change, factors that shape IT usage behaviors, and information systems management. Her work has appeared in MIS Quarterly, the European Journal of Information Systems, the Journal of the Association for Information Systems, the Journal of Information Technology, as well as other journals, books, and conference proceedings. Michelle is an associate editor for the Journal of the Association for Information Systems (JAIS) and a senior editor of the upcoming JAIS special issue on technology and social inclusion. She is a past-president of the Association for Information Systems (AIS) Special Interest Group on Social Inclusion and previously chaired the AIS committee on diversity and inclusion. Michelle is a Distinguished Member – Cum Laude of the AIS and was recognized for her research and service contributions to the IS field as a 2016 recipient of the AIS Early Career Award. In 2021, Michelle was elected to serve on the AIS Council as Vice President for Special Interest Groups and Colleges.