This paper is motivated by our work with the Department of Energy’s Critical Material Institute (CMI), which funds national labs researchers to develop new technologies to assist with the realization of clean energy goals. The projects involve research by scientists, but their continued funding requires an estimate of the market value of their technical breakthroughs. We develop a Bayesian model of optimal industry capacity addition to capture the benefit of the technology, estimate the benefit by using data from experiments within CMI and explore the value of funding a portfolio of projects to impact an industry. The methodology can be applicable to many such lab to practice contexts, and provided insights for both managers at CMI and the scientists setting up milestones for their projects.

All content-sharing sites, including social media platforms, face the creation and spread of misinformation, which leads to wrong beliefs, a hyper-partisan atmosphere, and public harm by the users. Given the dire consequences of the misinformation on society, government agencies, academic researchers, and industrial entities address misinformation creation and distribution on social media platforms. Experts have suggested leveraging the “wisdom of crowds” to identify misinformation to address the scalability issue in other solutions such as professional fact-checking. However, the implication of such crowdsourcing programs on its participants is not carefully studied in the field. We take the first step and leverage the quasi-field experiment of Twitter’s Birdwatch program to investigate the causal effect of participating in the crowdsourcing program on the subsequent activities of the participants, including the propensity to generate content and create misinformation. Our results show that the users decrease the frequency of content creation and the diversity of the content and a lowered average content engagement from other users in such a program, suggesting that the program is built on a significant economic cost of lowered volume, diversity, and interestingness of the user-generated content. However, we find increased cognition in the content creation in the program, and statistically mixed results (some significant but the other not) regarding the content become less reading like misinformation. Those results partially imply the success of the program in dampening the generation of content with misinformation. Our empirical research contributes to the literature on crowdsourcing and misinformation and provides significant implications for social media platform managers.

The rise of the internet has precipitated the birth and downfall of numerous industries, but perhaps no industry has been transformed to the same degree as news production. Journalism has seen the rise of content aggregation, the proliferation of fake news through social media, and the decimation of local reporting capacity, all of which have served to hollow the newspaper industry. In this work, we examine the downstream effects of this decline industry on an outcome of significant theoretical and practical significance: political corruption. As newspapers are an important investigative arm of local communities, it is possible that the closure of community media will embolden corrupt actors who believe they are less likely to be detected following the closure of a local newspaper. To estimate any effect, we employ a difference in difference approach, exploiting the phased closure of major daily newspapers across the country. Results indicate a significant increase in federal corruption charges in federal districts following closure. Further, we observe no evidence that the rise in online newsvendors and the democratization of the press ameliorates this effect. This highlights the important role of the “fourth estate” in inhibiting corruption in governance and the need to conceptualize the punitive societal effects of the internet more expansively.

Key Words: Difference in Differences, Media, News, Political Corruption

Conventional law enforcement on traffic safety by human police deployment is often cost-ineffective due to information asymmetry and negative externality. Recent decades have seen the rise of automated enforcement (in the form of traffic surveillance cameras) on the road, yet there has been inadequate understanding and empirical evidence to assess its impact on traffic safety. To probe the role of automated enforcement, we study the dynamic changes in accidents where surveillance cameras are present or absent in a metropolitan in southern China. We collected and consolidated a dataset of traffic camera installations and road accidents between 2014 and 2016 and mapped them to the vicinity of comparable road intersections of this city. We analyze two camera types: advanced cameras (which detect various traffic violations through constant video capture and analytics enabled by the machine learning algorithms embedded) and conventional ones (which detect limited violations via electromagnetic devices that trigger temporary image capture). We employ an event study method and observe a disproportionate and persistent decrease in the number of accidents at the intersections installed with advanced cameras, albeit with no measurable effect from conventional cameras. We study the mechanisms and find that both the functions and the presence of automated enforcement improve traffic safety. These findings unravel the complexity and nuances in the role of automated enforcement in traffic safety.

With the increasing significance of cloud computing in today’s digital economy, the adverse environmental impacts of data centers have been at the forefront of the sustainability debate. While prior studies have focused on energy-efficient designs of data centers from technological and engineering perspectives, we have a limited understanding of how regulatory environments play a role in driving firms’ green innovation and subsequent value creation. Drawing upon the Porter Hypothesis, this study examines whether energy efficiency regulation for data centers can increase firms’ data center-related green innovation (i.e., green data center innovation) and subsequently their firm value. We exploit a natural experiment using the revision of California’s Title 24 in 2013, which imposed energy efficiency requirements to data centers. Using a difference-in-differences framework based on data from 347 US IT firms, we find that the regulation enactment is positively associated with the increase in the number of corporate patents relating to green data centers in California-headquartered IT firms, compared to non-California-based firms. Moreover, the California-headquartered IT firms have experienced a significant increase in firm value after this regulation went into effect, and this effect appears to be partially mediated by green data center innovation. The results highlight that green IT innovation can be a viable means to enhance firm value while complying with environmental regulations, which has meaningful implications for research and practice.

Keywords: Cloud computing, data centers, innovation, firm value, Porter Hypothesis, green IT, green IS, sustainability

We provide theory and evidence connecting successive waves of IT investment to the marketplace advantages enjoyed by large, digitally intensive firms. We present a model requiring firms to make sequential tech investments to move up the AI “stack” and to invest in new organizational capital at each stage. We extend growth accounting techniques to estimate productivity growth in producing intermediate technological goods within firms. Laggard firms incur additive adjustment costs when catching-up with frontier firms. Since firms use other technologies to produce frontier technological intermediate goods, advantages in earlier vintages of technology capital “stack” in producing frontier intermediates. We take this model to a new panel data set on firms’ technology investments. We demonstrate a progression in stages of major technology investments over the last two decades – databases, networks, cloud computing, data science, and AI – and demonstrate that adjustment costs across stages contribute to a “Matthew effect” in which firms with more developed digital capabilities face lower costs of remaining at the technology frontier. Over time, advantages in older technologies dissipate.

Keywords: AI, digitization, productivity, superstar firms, information technology, complements, market power, machine learning

We propose a generative, deep learning model—a Variational Autoencoder called “InnoVAE”— that situates the innovation recorded in patent text and features within an interpretable, latent vector space, enabling: a) dynamic, geometric interpretation, comparison, and visualization of the innovative content of filed patents and the strategies pursued by their issuing firms, b) spatial interpretation of factors of scientific innovation, c) the engineering of innovation factors predictive of firm value, and d) controlled, synthetic generation of novel patents.

Applying InnoVAE to a corpus and structured variables of computing systems patents granted over three decades, we show that 1) our engineered innovation characteristics are as predictive as the most widely used structured patent measures in terms of predicting firm value (Tobin’s q) and that 2) “breakthrough” patents, identified by operationalizing creative invention within our latent space, have more scientific impact but generate less economic value. Our findings illustrate the potential that interpretable and generative AI methods have for managerial applications, and provide an empirical prototype of deep learning as the invention of a method of invention (IMI).

Keywords: Patents, Creativity, Generative AI, Artificial Intelligence, Variational Autoencoder, Innovation Space, Disentangled Representation Learning, Invention of a Method of Invention, Interpretability

In practice, the seller often engages in counterfactual thinking to compare her selected choice with other forgone alternatives. If a forgone alternative (ex post) generates a better outcome than the selected one, the seller experiences regret. We characterize the pricing decision of a regretful seller and find that, although regret leads the seller to set a price that is lower than that set by an unbiased seller, the regretful seller employs decision policies whose structure is similar to those of the unbiased seller: the price decreases with the remaining inventory and increases with the time-to-go. Interestingly, we find that the seller who has a greater number of goods does not necessarily receive greater revenue. We also have explored the concept of managerial regret in other revenue management contexts.

Many digital platforms have accrued enormous power and scale, leveraging cross-side network effects between the sides they connect (e.g., producers and consumers; or creators and viewers). Platforms motivate a diverse spectrum of producers, large and small, to participate by sharing platform revenue with them, predominantly under a linear revenue-sharing scheme with the same commission rate regardless of producer power or size. Under pressure from society, lawsuits, and antitrust investigations, major platforms have announced revenue sharing designs that favor smaller businesses. We develop a model of platform economics, and show that a small-business oriented (SBO) differential revenue sharing design can increase total welfare and outputs on the platform. While the small producers almost always benefit from the shift in revenue sharing design, large producers can also be better off under some conditions. More interestingly, we show that platforms are the most likely winner under a differential revenue sharing scheme. Hence, an intervention that ostensibly offers concessions and generous treatment to producers might well be self-serving for platforms and also good for the entire ecosystem.

Keywords: Platform, revenue-sharing, platform regulation, ecosystem design

Many digital platforms give users a bundle of goods sourced from numerous creators, generate revenue through consumption of these goods, and motivate creators by sharing of revenue. This paper studies the platform’s design choices and creators’ participation and supply decisions when users’ (viewers’) consumption of goods (content) is financed by third-party advertisers. The model specifies the platform’s scale: number of creators and content supplied and magnitudes of viewers, advertisers, and revenues. I examine how the distribution of creator capabilities affects market concentration among creators and how it can be influenced by platform design. Tools for ad management and analytics are more impactful when the platform has sufficient content and viewers but has low ad demand. Conversely, reducing viewers’ distaste for ads through better matching and timing—which can create win–win–win effects throughout the ecosystem—is important when the platform has strong demand from advertisers. Platform infrastructure improvements that motivate creators to supply more content (e.g., development toolkits) must be chosen carefully to avoid creating higher concentration among a few powerful creators. Investments in first-party content are most consequential when the platform scale is small and when it has greater urgency to attract more viewers. I show that revenue sharing is (only partly) a tug of war between the platform and creators because a moderate sharing formula strengthens the overall ecosystem and profits of all participants. However, revenue-sharing tensions indicate a need to extend the one-rate-for-all creators approach with richer revenue-sharing arrangements that can better accommodate heterogeneity among creators.

Keywords: platforms • content • revenue sharing • advertising • multisided markets • ecosystem • developers

Digital distribution of entertainment products has led to new opportunities for firms to extract revenues through a variety of different channels but also new challenges in determining how to best manage those channels given the potential for cannibalization. In particular, television networks and movie studios are faced with the challenge of negotiating fixed fee licensing deals for their content on streaming services such as Netflix without knowing what effect such deals will have on demand through other channels. In this research, we exploit the natural experiments of Netflix’s launches in Australia to determine the causal effect of licensing a piece of content to this service on paid digital downloads of that content. We find that licensing television shows to Netflix in the Australia caused digital sales of those shows to increase by an average of 37% in the years after Netflix’s launch relative to shows that were not licensed. In contrast, we find that licensing films to Netflix in Australia after its launch caused digital sales of those films to decrease by at least 56% relative to films that were not licensed. We also find evidence that films continue to experience a depressive effect on digital downloads even after they are removed from Netflix. Our results shed light on the effect that consumption through a subscription bundle has on a la carte purchases, and they also have practical implications for content owners negotiating deals for their content with streaming video services.

Personal data privacy is becoming an increasing concern, especially amongst the mobile app users. While the app sought permission to access user data, many times the approval process was obscure and non-transparent. With increasing push from the policy makers, most tech firms have been required to clearly disclose the data being collected on each user. In this effort, mobile operating system owners (e.g. Google Android and Apple iOS) mandated app developers to disclose what data their app is collecting during the use. Apple announced this policy enactment for all apps that were updated on or after December 14, 2020. Whether the privacy disclosure affects consumer demand remains a big unknown. We use this privacy label policy to evaluate the role of these labels on the demand of top selling/downloaded apps on the iPhone. We find that app developers are strategic in updates when they are collecting more intrusive user information. From empirical analysis, we find that disclosure of privacy label to collect “Data to Track You,” “Data Linked to You,” or “Data Not Linked to You” reduces app demand as measured from decline in ranking post label disclosure. The results are statistically significant for top free apps, insignificant for top paid apps, and mixed for top revenue grossing. We provide results for within category results and impact of individual data types in the results section.

When not available internally, firms increasingly outsource knowledge to a consultancy including design thinking, analytics, sustainability, and cybersecurity. We present a game theoretic model of a client (she) that outsources knowledge from a consultancy (he) to meet her project requirements by a given due date. The project requirements may include the development of prototypes, designs, software, and analytical solutions.

The consultant is the Stackelberg leader that sets of the price for outsourced knowledge; the client determines the amount of outsourcing and the amount of internal knowledge development necessary to complete her project on time. We explore three critical elements of the interplay between the consultant and the client: (i) the ability of the client to absorb the outsourced knowledge, (ii) uncertainty regarding the quantity of knowledge to outsource, (iii) the project completion may be late.

First, an extensive literature identifies drivers of absorptive capacity, which include the client’s existing knowledge and attributes of the prior relationship between the client and the consultant such as trust. We show that while more absorptive capacity always improves the client’s position, it may or may not benefit the consultant. Second, in knowledge intensive settings, projects may be novel so that uncertainty exists regarding the solution approach required. Surprisingly, we show that, under certain conditions, the client benefits from more project scope uncertainty. Third, a client may receive the consultant’s outsourced knowledge after the contractually agreed upon due date or may be slow to absorb that knowledge. Both drive uncertainty in the project completion. We show that the consultant charges a higher price if the client has less absorptive capacity or if the client incurs a higher penalty if the project is late.

Keywords: knowledge intensive services, knowledge outsourcing, absorptive capacity, uncertainty, game theory

With the rapid growth of e-commerce, financial products are being brought onto online platforms. However, due to the scarcity of data in this new product domain, online platforms face challenges in predicting users’ purchase behavior. In this paper, we study whether we can transfer” knowledge learned from the existing consumer goods domain to benefit the prediction in the domain of the financial products. With data provided by one of the largest online shopping platforms in China, we develop machine learning solutions to enable knowledge transfer. We show that users’ prior browsing and shopping history in consumer goods can significantly improve the prediction accuracy of users’ purchases of mutual funds for both the existing-user and the new-user scenarios. In addition, we study the heterogeneous prediction performance lifts on users with different socioeconomic statuses and investment risk preferences. Results show that information from the consumer goods domain has a higher prediction performance lift on users in the high socioeconomic group. Finally, we compare the effect of different sources of information on predicting users’ purchases of mutual funds. We find that users’ browsing and shopping history for consumer goods are more predictive than their profile features. Our findings and methods will be valuable to both the financial industry and online platforms that seek to expand their product domains.

Key words: cross-domain recommendation, cross-domain consumer behavior, e-commerce, transfer learning

We develop a model for a regional decision-maker to analyze the requirement of medical equipment capacity in the early stages of a spread of infections. We use the model to propose and evaluate ways to manage limited equipment capacity. Early-stage infection growth is captured by a stochastic differential equation (SDE) and is part of a two-period community spread and shutdown model. We use the running-maximum process of a geometric Brownian motion to develop a performance metric, probability of breach, for a given capacity level. Decision-maker estimates costs of economy vs. health and the time till the availability of a cure; we develop a heuristic rule and an optimal formulation that use these estimates to determine required medical equipment capacity. We connect the level of capacity to a menu of actions, including the level and timing of shutdown, shutdown effectiveness, and enforcement. Our results show how these actions can compensate for the limited medical equipment capacity in a region. We next address the sharing of medical equipment capacity across regions and its impact on the breach probability. In addition to traditional risk-pooling, we identify a peak-timing effect depending on when infections peak in different regions. We show that equipment sharing may not benefit the regions when capacity is tight. A coupled SDE model captures the messaging coordination and movement across regional borders. Numerical experiments on this model show that, under certain conditions, such movement and coordination can synchronize the infection trajectories and bring the peaks closer, reducing the benefit of sharing capacity.

Key words: COVID-19 pandemic, Capacity planning, Stochastic differential equations, Optimal stopping

Health insurance companies (HIC) have come to recognize that misalignments between patients’ choices of providers and the HIC’s cost-effective provider preferences can result in significant cost to the HIC. Such misalignments may occur either because enrollees are unaware of their options or because they do not have an incentive to choose the cost-effective provider. This work examines how an insurer can exert effort and offer cash rewards to nudge patients towards cost-effective providers. We build a stylized analytical model that captures the salient aspects of the insurer’s decision problem while considering the key drivers of its enrollee’s choice of providers. With this framework, we analyze the HIC’s optimal effort and reward, individually and jointly, under different cost-share structures (e.g., copayment and coinsurance). Finally, we rigorously compare (both analytically and numerically) and assess the savings that each approach can achieve. When coinsurance is high, HIC prefers effort over reward. Conversely, the cash reward is better when coinsurance is low and the price difference between the two providers is high. With copayment, the HIC prefers to use cash reward when the price difference is high; otherwise, it exerts effort. This implies that neither a reward-only nor an effort-only approach uniformly outperforms the other. Additionally, we find conditions when a joint effort and cash reward approach could perform better than the individual approaches, indicating the two mechanisms serve as tactical complements. This work shows that an HIC would benefit from adopting a framework that tailors the nudge (effort or reward or both) based on the cost-share structure and the relative magnitude of related costs. Specifically, these results suggest that an insurance company may have to set up an apparatus to implement both the reward and the effort-based mechanisms, judiciously choosing one over the other for different healthcare procedures and geographies.

Key words: Cost reduction, Healthcare, Cash Incentive, Effort, Provider choice, Coordination

Dr. Cheryl Aasheim is a Professor of Information Technology at Georgia Southern University. She will become part of the Warrington College of Business Information Systems and Operations Management Department as a Clinical Research Associate Professor in August of this year. Her research interests include e-commerce, text analytics, online consumer reviews and IS education. She is on the editorial review board for Decision Sciences Journal of IS Education and Journal of Computer Information Systems. She teaches in the areas of text analytics, data mining and programming with a focus on data analytics.

Professor Hemant K. Bhargava is an academic leader in economic modeling and analysis of technology-based business and markets. His research focuses on decision analytics and how the distinctive characteristics of technology goods influences specific elements of operations, marketing, and competitive strategy, and the implications it holds for competitive markets and technology-related policy. He has examined deeply these issues in specific industries including platform businesses, information and telecommunications industries, healthcare, media and entertainment, and electric vehicles. Bhargava earned a Ph.D. in Information Systems, Operations, and Economics from the Wharton School of the University of Pennsylvania. He has an MBA from the Indian Institute of Management, Bangalore, and a B.S. in Mathematics from Delhi University.

Dr. Doosti has received his Ph.D. in Business Administration from the Foster School of Business at the University of Washington. His research interests lie at the intersection of information systems, marketing, and digital economics. His current work explores competition, demand, customer behavior and platform design in domains of information systems such as e-commerce, digital products, crowdsourcing, social media, and mobile economy.

Cheryl Gaimon holds the Esther and Edward Brown Chair and specializes in the area of operations management (OM). She initiated establishment of the OM Program and served as first the OM Area Coordinator for seven years. She was a core participant in the development of the interdisciplinary Management of Technology (MoT) Certificate Program and currently serves as that program’s director. She has taught courses at the undergraduate, masters, and PhD levels as well as in executive programs.

Professor Gaimon’s teaching and research considers how a firm manages its knowledge-based resource capabilities (which include (i) people, (ii) manufacturing and service technologies, (iii) processes and procedures, (iv) materials, and (v) information) in environments characterized by innovations in science and technology, global competition, and a dynamic marketplace. In particular, her research and teaching addresses new product and process development, implementation of new technology, and sustainable operations. Due to the complexity and time pressure of developing innovations that are successful in the marketplace, Professor Gaimon also addresses knowledge outsourcing and alliances/partnerships. She teaches courses in innovation and management of technology. Her research has appeared in journals including Management Science, Operations Research, Organization Science, and Production and Operations Management.

Professor Garg’s research uses economic and statistical techniques to analyze information flow in digital platforms and networked structures. More specifically, Professor Garg’s research spans following four broad areas: 1) diffusion of digital content across networks, 2) digital marketing strategies for social and mobile commerce, 3) role of digital technologies in labor markets and entrepreneurship, and 4) identification of business value of data streams generated by digital technologies (blockchain, NFT, IoT, AR/VR, etc).

With his research, Professor Garg has been helping various for-profit, non-profit, and government organizations to develop data enabled digital strategies and public policies. Professor Garg’s research has appeared in academic journals like Management Science, MIS Quarterly (MISQ), Information Systems Research (ISR), Production and Operations Management (POM), Journal of Management Information Systems (JMIS), and various other journals and peer reviewed conference proceedings. His work has received media coverage in Forbes, Fortune, Austin Statesman, Dallas Morning News, Pittsburgh Post-Gazette, Medium, and more.

Professor Garg is a public speaker and has frequently talked about the future of workforce, entrepreneurship, technology innovation and adoption, digital media marketing, mobile commerce, and social networks. Professor Garg has taught course on big data, data enabled business insights, business analytics, digital strategy and transformation, interactive marketing and more.

In the past, Professor Garg has been on faculty at The University of Texas at Austin, worked at organization like National Instruments, Jacobs Engineering (CH2M Hill), JPL, Infosys, and more. For his contributions to the field of technology and engineering, Professor Garg was nominated and named a senior member of IEEE. Professor Garg received his PhD from the School of Information Systems and Management, Carnegie Mellon University. He received graduate degree in Public Policy and Management from Carnegie Mellon University, in Computer Science, and in Electrical Engineering, both from University of Southern California. He received undergraduate degree in Electrical Engineering from Indian Institute of Technology, Banaras Hindu University, Varanasi.

Dr. Brad N. Greenwood is an Associate Professor of Information Systems and Operations Management at George Mason University. He joined the faculty at Mason from the University of Minnesota’s Carlson School of Management where he was an Associate Professor of Information and Decision Sciences. Previously, he has also served on the faculty at Temple University’s Fox School of Business and the University of Maryland’s Smith School of Business. Dr. Greenwood’s research examines the intended and unintended consequence of innovation, and how access to the resulting information affects welfare at the interface between business, technology, and social issues, notably in the contexts of healthcare and entrepreneurship. He is currently an Associate Editor at Management Science and his work has been published in such leading outlets as: The Proceedings of the National Academy of Sciences, Administrative Science Quarterly, Management Science, Organization Science, Information Systems Research, Productions and Operations Management, MIS Quarterly, the Communications of the ACM, the Strategic Management Journal, the Journal of Business Ethics, the Journal of Law, Economics, and Organization, the Journal of Medical Internet Research, and PLoS ONE.

His corporate experience includes nearly eight years as a deputy project manager and analyst for CACI International, a mid-sized consulting firm in the greater Washington DC Metro Area. He received his Bachelor’s degree in Information Technology and Management Information Systems from Rensselaer Polytechnic Institute. Dr. Greenwood also received: a Master’s of Business Administration in IT Consulting from the University of Notre Dame; a Master’s of Informational Technology from Virginia Polytechnic Institute; and a PhD in Decision, Operations and Information Technology, with a minor in Strategic Management, from the University of Maryland, College Park.

Yu “Jeffrey” Hu is Sharon A. and David B. Pearce Professor at Georgia Institute of Technology’s Scheller College of Business. He is also a Digital Fellow at MIT’s Initiative on Digital Economy. He is an expert on big data, analytics, digital economy, digital transformation, electronic commerce, omni-channel retailing, offline commerce, social media, and fintech. His research uses econometric, machine learning, and analytical models to study consumer behaviors in environments such as electronic commerce, omni-channel retailing, offline commerce, social media, mobile app, fintech, and healthcare. He coauthored the first paper discovering the “Long Tail” phenomenon in Internet markets and the first paper proving the value of social media in predicting stock markets. He has been an expert, consultant, or advisor for many large companies, and has taught many top executives. He is frequently invited to speak at industry conferences. He has served as a Co-Director of Business Analytics Center and Associate Director of Master of Science in Analytics. His research has been published in top journals such as Management Science, Information Systems Research, Review of Financial Studies, Marketing Science, MIT Sloan Management Review, Economic Inquiry, International Journal of Industrial Organization, and Management Information Systems Quarterly. His research has been discussed and cited by media outlets such as Wall Street Journal, New York Times, Reuters Bloomberg, InformationWeek, Wired Magazine, TIME Magazine, Forbes, INC. Magazine, The Telegraph, National Public Radio, SeekingAlpha.com, Bankrate.com, etc. His papers have been adopted for classroom use by many top universities around the world.

Professor Iyer is the Susan Bulkeley Butler Chair in Operations Management at the Krannert School of Management. He is the Department head of the management department and Senior Associate Dean in the Krannert School of Management. He is currently the Director of DCMME (Dauch Center for the Management of Manufacturing Enterprises) and the GSCMI (Global Supply Chain Management Initiative). From 2012 to 2016, he was the Director of Purdue NExT – a University wide modular online interactive courses for global distribution. He was the Associate Dean for Graduate Programs (2011-2013) and Director of DCMME and the founding Director of GSCMI (2006-2011) at the Krannert School of Management. Previously, he was Purdue University Faculty Scholar from 1999-2004. His teaching and research interests are operations and supply chain management. Professor Iyer’s research currently focuses on analysis of supply chains including the impact of promotions on logistics systems in the grocery industry, and analysis of the impact of competitors on operational management models and the role of supply contracts. His other topics of study include inventory management in the fashion industry, effect of supplier contracts, and use of empirical data sets in operations management model building.

Professor Apurva Jain teaches and conducts research in the area of Supply Chain Management at the Department of Information Systems and Operations Management, Foster School of Business, University of Washington, Seattle.

His main research interests are in the areas related to managing capacities and inventories in supply chains. Some of the recent topics he has worked on include the following: Managing medical equipment capacity during pandemics, Inventory optimization in distribution & fulfillment systems, Product differentiation and supply segmentation in food retailing, Visibility of supply information and retailer-supplier collaboration, Integration of online and offline channels in omnichannel retailing, Forecasting of social-media-driven demand spikes in apparel retailing, New technology adoption (RFID, IOT) in retailing; and Rental inventory management. He enjoys working on industry-sponsored research projects, such as a Unilever-sponsored project on collaborative differentiation in supply chains and a Gates Foundation project on tiered pricing in pharmaceutical supply chains.

His research in these areas has been published in leading research journals like Operations Research, Management Science, Manufacturing and Service Operations Management, and Production & Operations Management. He was the program chair of the POMS conference and the conference chair of the MSOM conference, the two premier research conferences in Operations Management. He is currently an associate editor of the Decision Sciences Journal.

DK holds a Bachelor’s degree in Computer Science from Columbia University (Machine Learning Focus), a Master’s degree in Statistics (Master’s Thesis: Johnson-Lindenstrauss Lemma and its Effect on Supervised Learning) from Yale University and PhD from the Operation, Information and Decisions department of the Wharton School (Thesis: Three Essays in Big Data Consumer Analytics in E-Commerce). Before academia, DK worked at 4 tech start-ups and Blackrock as a quantitative software engineer and at Thomson Reuters as an ML contractor building a natural language processing engine for financial data. His work has been published at journals and conferences including Management Science, Information Systems Research, Journal of Marketing Research, AAAI, AIES, and WWW. He is a recipient of ISS Gordon B Davis Young Scholar, 2021 Marketing Science Institute Young Scholar, CDO Magazine Academic Data Leader, ISR Best Paper Award, MSBA Teaching Award, Management Science Distinguished Service Award, and Best Conference Paper Awards. His work is supported by organizations such as Adobe, Bosch Institute, Google Cloud, Marketing Science Institute, McKinsey & Co, Nvidia, and Net Institute.

Dr. Meng Li is an Associate Professor, SCM PhD coordinator, and Bauer Fellow at C.T. Bauer College of Business, University of Houston. His recent research interests focus on managerial behavior, platform, and Al. His research has appeared in Management Science, Manufacturing and Service Operations Management, Production and Operations Management, and Strategic Management Journal, among others. He is a Guest Editor and Senior Editor for Production and Operations Management, and an Associate Editor for Decision Sciences Journal.

Hari Natarajan is Professor of Management and Vice Dean of Business Programs at the Miami Herbert Business School. Previously, he served as the Academic Director of MHBS’ Global Executive MBA program. Hari received his undergraduate degree in Engineering from the Indian Institute of Technology, Madras, and MS and PhD degrees (with dual titles in Business Administration and Operations Research) from the Pennsylvania State University’s Smeal College of Business, where he won the Alumni Association Award for his doctoral dissertation. Hari’s research develops and applies optimization models to support practical decision-making in supply chains and service networks. He has worked on collaborative research projects with several companies including Armstrong, Capital One, Corning, Florida Panthers, Intcomex, and Uponor Aldyl. He has also been a technical advisor for analytics firms such as Marketics and LatentView and serves as a non-executive director on the board of MetaCorp. His research has appeared in top journals and has won awards including the Institute of Industrial and Systems Engineering Transactions Best Paper Award. His work has been supported by the eBusiness Research Center and the Center for Supply Chain Research at Penn State, the Center for International Business Education and Research, and the James W. McLamore Award (now Provost’s Research Award) at the University of Miami.

Min-Seok Pang is an Associate Professor of Management Information Systems and Milton F. Stauffer Research Fellow at Fox School of Business, Temple University. He has received a B.S. in Industrial Engineering and an M.S. in Management from Korea Advanced Institute of Science and Technology (KAIST) and holds a Ph.D. in Business Administration from University of Michigan. His research interests include, among others, strategic management of information technology in the public sector and technology-enabled public policies. His research has been published in top-tier academic journals such as Management Science, MIS Quarterly, Information Systems Research (ISR), and Strategic Management Journal. He received an AIS Best Information Systems Publication Award, an ISR Best Published Paper Award, INFORMS ISS Sandra Slaughter Early Career Award, and MIS Quarterly Outstanding Associate Editor of the Year Award. He currently serves as a Senior Editor for Journal of the Association for Information System and an Associate Editor for MIS Quarterly.

Dr. Jiyong Park is an assistant professor of information systems at the Bryan School of Business and Economics, University of North Carolina at Greensboro. Jiyong holds a Ph.D. in Management Engineering (Major: Information Systems) at Korea Advanced Institute of Science and Technology (KAIST) and a B.A. in Industrial and Management Engineering at Pohang University of Science and Technology (POSTECH). His research interests lie at the intersection between economic and societal aspects of information systems and technology. He is especially interested in economics of information systems, societal impacts of information technology, and the future of work. His works are presented at leading academic conferences, including International Conference on Information Systems (ICIS), Workshop on Information Systems and Economics (WISE), and INFORMS Conference of Information Systems and Technology (CIST), among others. At UNCG, he teaches Principles of Predictive Analytics.

Karthik Ramachandran is an Associate Professor and Area Coordinator of Operations Management in Georgia Institute of Technology’s Scheller College of Business. His research focuses on the operational and organizational aspects of innovation related to product design and development, technology management, and operations strategy. His work uses analytical models, complemented by case-based, experimental, and empirical methods, to study operational issues that are critical in the successful commercialization of innovations.

Karthik’s research has appeared journals such as Management Science, Manufacturing & Service Operations Management (MSOM), Production & Operations Management (POM), and IIE Transactions. His book chapters, case studies, and pedagogical material are available upon request. Karthik serves is a Senior Editor of Production and Operations Management, and Associate Editor of Decision Sciences. At Georgia Tech, he teaches courses in New Product Development, Collaborative Innovation, and Entrepreneurship. Karthik is passionate about entrepreneurship and he has mentored dozens of startups through the CREATE-X initiative at Georgia Tech.

Karthik obtained his Ph.D. in Supply Chain & Operations Management and M.S. in Operations Research from the University of Texas at Austin. He was also a Research Associate at the University of California, San Diego. Previously, he did his B.S. in Mechanical Engineering at the Indian Institute of Technology, Madras (Chennai).

Jingchuan Pu is an Assistant Professor in the Department of Supply Chain and Information Systems at Smeal College of Business, the Pennsylvania State University. He received his Ph.D. in Information Systems from Warrington College of Business, the University of Florida. Jingchuan has received Ph.D. Outstanding Teaching Award from Warrington College of Business and Nunamaker-Chen Dissertation Award (Runner-up) from INFORMS Information Systems Society.

Prasanna (Sonny) Tambe is an Associate Professor of Operations, Information and Decisions at the Wharton School at the University of Pennsylvania. His research focuses on the economics of technology and labor. Recent research projects focus on 1) understanding how firms compete for software developers, 2) how software engineers choose technologies in which to specialize, and 3) how AI is transforming HR management. Much of this research has uses Internet-scale data sources to measure labor market activity at novel levels of granularity. His published papers have analyzed data from online job sites and other labor market intermediaries that generate databases of fine-grained information on workers’ skills and career paths or on employers’ job requirements. He is a co-author of “The Talent Equation: Big Data Lessons for Navigating the Skills Gap and Building a Competitive Workforce,” published by McGraw Hill in 2013.

Jinyang Zheng is an assistant professor at the Krannert School of Management at Purdue University. His research primarily focuses on exploring the economic impact and uncovering the mechanisms of business strategies that are related to or enabled by novel information technologies. His current research interests consist of three major streams, including economics of AI, user-generated unstructured content, online two-sided market, and smart city. Methodologically, he applies causal inference, including both micro-econometrics and graphical models, empirical IO, and unstructured data analysis. His research has been published in premier business journals, such as Management Science and Information Systems Research, and has won multiple awards or nominations at premier conferences, including ICIS, WISE, CIST, and CSWIM. Jinyang develops and teaches the courses of Analyze Unstructured Data (Business Analytics Master Program course), and Data Tech for Research (Ph.D. course), and has taught Database Systems (Undergraduate core course). He has been continuously listed as an outstanding & distinguished teacher at Krannert. He graduated from the University of Washington in 2017. Prior to that, he graduated from the Department of Statistics at Fudan University in 2013.

The goal of any supply chain is to make the pie bigger, and determine how to best split it (the “pie” being the cumulative supply chain profits, which, in healthcare, stem from creating high-quality patient outcomes at low cost). Our analytical model suggests the pie becomes bigger when the healthcare provider invests optimally in process improvement, but the provider’s investment depends on the payment scheme offered by the insurer (e.g., fee-for-service, a fixed fee for each given case, capitation, or some mix of these across patients). Perhaps counterintuitively, our model suggests that when the insurer naively sets the payment scheme, its profit is maximized when it offers the provider a somewhat-balanced mix of payments. However, if the insurer and provider coordinate the supply chain, then the provider more heavily invests in process improvement and the size of the pie grows – which in turn yields benefits to the insurer and provider alike as well as to patients in the form of higher quality of care at reduced cost. Finding the right scheme is a function of a number of parameters, such as the provider’s fixed versus variable cost structure and the provider’s ability to capture latent demand as it reduces waste, thereby freeing up capacity.

Unorganized retail stores in emerging economies, unlike conventional organized retail stores, are rarely equipped with point-of-sale devices. This leads to lack of data on retail sales and inventories that are crucial for effective upstream decisions made by the supplier. Existing studies in the area of inventory management have assumed availability of censored demand data (in the form of sales observations) and inventory data (order up-to levels, inventories, stockout indicators, etc.). Our study, motivated by unorganized retailing of health commodities in rural India, assumes the availability of only replenishment data. We begin by observing that replenishments are related to downstream sales through the retailer’s inventory policy. We make structural and parametric assumptions about demand and inventory policy, and develop a methodology built on the Expectation-Maximization algorithm to jointly estimate demand and the inventory policy of the retailer. We further explore values of these estimates in improving availability.

We investigate the sustainability of Accountable Care Organizations (ACO) and study their performance under the Medicare Shared Savings Program’s (MSSP) incentive mechanisms for population health management in the United States. Based on a national sample of ACOs studied during a five-year period between 2013 and 2017, we empirically examine the role of selection and learning/improvement effects on the sustainability of ACOs with respect to shared savings and losses incurred. Our results indicate the existence of selection effects and improvements over time among MSSP ACOs, and imply that both effects can explain dropout behavior among ACOs, and ACO switching behavior from low-risk to high-risk models. Our research also explores the determinants of ACOs’ decisions to drop out, remain, or switch to high-risk incentive models that are part of the MSSP. Our results suggest that a myopic policy of moving ACOs to a high-risk (two-sided) model may lead to unintended consequences, such as ACO dropout, which in turn, may have detrimental effects on overall patient population health. We contribute to the rich literature on incentives and their impact on the economics of healthcare, and our results can better inform policy makers on proposed changes to MSSP with respect to the role of incentives in enhancing ACO participation.

A rich literature on digital product piracy has examined the impact of pricing and protection efforts; however, extant research has generally ignored the underlying platform’s role – the two- sided market that facilitates the creation and consumption of the digital good. Through a platform model of competition in the video games industry, our paper captures the intrinsic relationship between platforms, game developers and gamers, and the impact of piracy. Our analysis throws light on an important result – that under standard licensing models, console profits are independent of piracy protection efforts thus providing a first theoretical explanation for the indifferent (to piracy) behavior of many console makers. In order to spread the risk from piracy, we introduce a form of revenue sharing where the console maker gets a commission for every legit copy downloaded. Our results suggest that this regime improves both gamer and developer welfare from licensing the platform as well as the social welfare of the duopoly.

Patients are frequently asked to share their personal health information for research. However, researchers have not assessed how electronic informed consents (e-consents) should be designed to increase long-term patient satisfaction with their consent decisions and understanding of the research to which they are consenting. The objective of this study was to compare the effects on patient consent experiences of three e-consent designs: (1) standard consent containing federally required information; (2) consent with additional on-demand details about the proposed research; and (3) a consent with on-demand details plus factual messages designed to enhance trust in research institutions and processes. We conducted a three-arm, parallel-group, single-blinded, randomized controlled trial from November 2017 to November 2018. The study was conducted in four outpatient family medicine clinics in north-central Florida. Participants were English- speaking, adult patients recruited from clinic waiting rooms. A total of 1,242 patients were approached; 734 completed the consent; 510 completed six-month follow-up. Using a tablet computer, participants were randomized to 1) a standard e-consent (standard), 2) an e-consent containing standard information plus hyperlinks to additional on-demand details (on-demand), or 3) an e-consent containing standard information, on-demand hyperlinks, and factual messages about data protections and researcher training (trust- enhanced). The primary study outcomes were satisfaction with consent decision (1-5 scale) and subjective understanding (0-100), measured via survey immediately, at 1 week, and at 6 months post-consent. Participants averaged 45.5 years of age; 66.3% were female. At 6-month follow-up, compared to standard consent, participants who used the on-demand consent reported greater satisfaction (B = 0.43; SE = 0.09; P <.001) and subjective understanding (B = 18.04; SE = 2.58; P <.001). At 6-month follow-up, compared to the on-demand consent, participants who used the trust-enhanced consent reported greater satisfaction (B= 0.9; SE = 1.0; P < .001) and subjective understanding (B = 32.2; SE = 2.6, P < .001). Six months after consenting to share their health records for research, patients who used e-consents with on-demand research details and trust-enhancing messages reported higher satisfaction and understanding. Research institutions should consider developing and further validating e-consents that interactively deliver information beyond that required by federal regulations, including facts that may enhance patient trust in research.

Television is a prominent channel for informing and educating the public about risk behaviours, chronic conditions, and other concerns. This study presents an inductive three-step methodology that is intended to help campaigns complement the lists of TV programs they select to target with their awareness messages on the nation’s epidemics like drug overdose, smoking, binge drinking, and STDs. Through high-dimensional analysis of large data on TV viewership of the entire US panel in 2016, the methodology first discovers the episodic programs whose popularities are correlated with eight risk behaviours and chronic conditions, and benchmarks the correlations against those that exist between the socio-economic status and health conditions. A series of nonparametric tests then examine the robustness of the findings and verify that a significant portion of the correlations is genuine. In the last step, the methodology applies Facebook’s split (A/B) testing platform to experimentally test the practical value of the discovered correlations in public communication. Under two series of experimental studies, we conducted 53 independent online experiments and compared the inductively discovered programs with (1) those that were most frequently targeted by the major campaigns in 2016 (e.g., Tips From Former Smokers), and (2) random, yet similarly popular TV programs. The experimental results empirically corroborate the potential value of the inductively discovered correlations in reaching the audience intended for awareness messages. Overall, the findings indicate the significant potential of the proposed methodology in helping public officials in their efforts to combat conditions that are expensive both in human lives and cost to the economy.

Although many brokerage firms are using the Internet to facilitate stock transactions, little is known about whether and how the use of the online channel affects customer performance and, more importantly, how investors’ risk preferences moderate this effect on performance. This research investigates online channel usage and customer performance using a unique data set of more than 7,000 customer accounts over a 44-month period (January 2010 – August 2013) at a leading Chinese brokerage firm. The findings reveal that online channel usage is associated with higher performance in customers’ portfolio returns. Importantly, these effects differ across investors with different risk preferences; specifically, risk-averse investors earn higher profits than risk-neutral investors from using the online channel. Further analyses indicate that the higher performance for risk-averse investors, relative to that of the risk-neutral, can be explained by their higher trading frequency, but lower trading volume. In contrast, risk-seeking investors profit less from online channel usage because of their high level of both trading frequency and trading volume. We discuss the implications for research and practice.

We investigate the impact of pre-existing offline data on online decision making and answer the following fundamental question: under what conditions offline data improves online learning and decision making? We demonstrate our results in the context of dynamic pricing both for parametric and non-parametric models.

Health at birth is an important predictor of long-term outcomes and the wide variations in Cesarean section rates raise concerns about the quality of maternal care and have important cost implications. While information technology (IT) holds great promise for improving healthcare quality while lowering costs, it is far from certain whether and how electronic health records improve maternal and infant well-being and lower future risk in outcomes. This study constructed a unique dataset that includes rich information about medical interventions as well as a battery of maternal and birth outcomes to investigate the effect of electronic health records in the context of intergenerational health. We find evidence that hospitals’ electronic sharing of health records with outside providers is associated with an overall decrease in the probability of maternal complications and babies going to neonatal intensive care unit (NICU) as well as fewer unnecessary procedure uses in low risk deliveries.

Predictive algorithms have an increasingly important role in supporting the day-to-day operations of healthcare organizations. Yet, fully realizing the value of algorithms lies critically in the opportunity to re-engineer the related processes and redefine roles in ways that make organizations more effective. We will present two interrelated studies around algorithm-enabled process innovation (AEPI) and value creation. Our context is an AEPI effort focused on early identification and treatment of a deadly clinical condition known as sepsis. In the first part, using a rich set of clinical and nonclinical data from a hospital system, we examine the relationship between sepsis AEPI and patient mortality. Overall, we demonstrate that sepsis AEPI is effective in reducing mortality and it does so through timely diagnostic (i.e., lactates) and therapeutic (i.e., antibiotics) interventions. As time goes by, however, the timeliness of these interventions partially lapses and so does the sepsis AEPI’s reduction impact on mortality. In the second part, we seek a prescriptive answer to designing alerting mechanism to account for individual risk factors and providers’ compliance behavior. We formulate the problem of determining when to alert sepsis as a discrete- time, finite-horizon Markov Decision Process and structurally characterize threshold policies. We demonstrate how the proposed alerting mechanism can further improve outcomes in the hospital.

Although significant research has examined how technology can intensify racial and other outgroup biases, limited work has been devoted to the role information systems can play in abating them. Racial biases are particularly worrisome in healthcare, where underrepresented minorities suffer disparities in access to care, quality of care, and clinical outcomes. In this paper, we examine the role clinical decision support systems (CDSS) play in attenuating systematic biases among black patients, relative to white patients, in rates of amputation and revascularization stemming from diabetes mellitus. Using a panel of inpatient data and a difference in difference approach, results suggest that CDSS adoption significantly shrinks disparities in amputation rates across white and black patients; with no evidence that this change is simply delaying eventual amputations. Results suggest that this effect is driven by changes in treatment care protocols that match patients to appropriate specialists, rather than altering within physician decision-making. These findings highlight the role information systems and digitized patient care can play in promoting unbiased decision making by structuring and standardizing care procedures.

Health wearables in combination with gamification enable interventions that have the potential to increase physical activity—a key determinant of health. However, the extant literature does not provide conclusive evidence on the benefits of such gamification and how these benefits will vary across individuals and gamification features. In this paper, we investigate the effect of Fitbit leaderboards on the number of steps taken by the user. Using a unique dataset of Fitbit wearable users, some of whom participate in a leaderboard, we find that leaderboards lead to a 370 (3.5%) step increase in the users’ daily physical activity. However, we find that the benefits of leaderboard are highly heterogeneous. Surprisingly, we find that those who were highly active prior to adoption are hurt by leaderboards and walk 630 fewer steps daily post-adoption (a 5% relative decrease). In contrast, those who were sedentary prior to adoption benefited substantially from leaderboards and walked an additional 300 steps daily after adoption (a 15% relative increase). We also find that the number of other active users on the leaderboard increased benefit but that this benefit decreased with additional users. Finally, we observe that strong prior performance on the leaderboard positively impacted subsequent physical activity. Overall, our results point to generally positive, but nuanced, benefits of gamification enabled by health wearables. In a non-trivial proportion of cases, individuals opt into variants of these interventions with negative effects on their physical activity.

Idris Adjerid is an associate professor in Business Information Technology at the Pamplin College of Business at Virginia Tech. He received his Ph.D. in information systems and management from Carnegie Mellon University and earned both an MBA and a bachelor’s degree in business information technology from Virginia Tech. His research uses econometric methods as well as lab and field experiments and consists of two, often overlapping, streams. The first stream focuses on the economics of privacy, with a focus on the intersection of behavioral economics and privacy decision-making. The second stream focuses on the economics of health care technologies. His research has been published in leading journals, including Management Science, Information Systems Research, MIS Quarterly, American Psychologist, and ACM Computing Surveys. His work and expert commentary have been cited by numerous outlets in the popular press, including The New York Times, the Wall Street Journal, the Washington Post, Wired, Politico, and USA Today.

Dr. Hilal Atasoy is an Assistant Professor at the Fox School of Business, Temple University. She has a Ph.D. and a Master’s degree in Economics from the University of Illinois, Urbana-Champaign. Her research analyzes how information systems and the associated flow of information across providers affect healthcare costs and quality, network externalities of health information technology, and the effects of digital protocols on clinical decision-making. Dr. Atasoy’s research was published in leading outlets such as Management Science, Information Systems Research, Industrial and Labor Relations Review and The Annual Review of Public Health.

Sezgin Ayabakan is an assistant professor in the Management Information Systems Department at Temple University’s Fox School of Business. He graduated from the University of Texas at Dallas with a Ph.D. in Management Science in 2014. Sezgin received his M.S. degree in Industrial and Systems Engineering from the University of Florida in 2008, and a B.S. degree in Industrial Engineering from Bilkent University, Turkey. His broader research interests focus on the impact of health information technology and analytics on the cost and quality of healthcare delivery, as well as the business value of information systems on firm performance.

Mehmet U. S. Ayvaci is an Associate Professor in the Jindal School of Management at the University of Texas Dallas. His research broadly addresses the grave inefficiencies in healthcare. Particularly, he studies how to better utilize the available information and resources in supporting operations in healthcare organizations. His research addresses three related themes typically within healthcare: (1) algorithmic decision making in the context of human-in-the-loop predictive algorithms and pitfalls, (2) economics of information and information technology, and (3) medical informatics/health economics using applied machine learning, cost-effectiveness, and comparative effectiveness methods.

Dr. Ramnath K. Chellappa is Associate Dean and Goizueta Foundation Term Professor of Information Systems & Operations Management at the Goizueta Business School, Emory University. He is also the founding Academic Director of the Master of Science in Business Analytics program at Goizueta. He was previously a Caldwell Research Fellow at Goizueta, Emory and SRITNE Distinguished Academic Fellow and Visiting Professor at the Indian School of Business, Hyderabad. Prior to joining Emory University, Prof. Chellappa served on the faculty of Marshall School of Business, University of Southern California. Prof. Chellappa’s expertise is in the fields of electronic markets, pricing, digital goods piracy and economics of information security and privacy. In addition to being widely published in these areas, his work has also received multiple best paper awards in premier conferences. His research methods include analytical modeling, empirical modeling and social network analysis. He also serves/has served on the editorial boards of Information Systems Research and MIS Quarterly. He was previously the president of the INFORMS Information Systems Society. Prof. Chellappa also works closely with the industry on the managerial aspects of information technology driven issues and frequently serves as a litigation expert on technology-related cases. He is often quoted in the popular media on information privacy and security related issues. Prof. Chellappa has received numerous awards for teaching, mostly recently the Provost’s Distinguished Teaching Award for Excellence in Graduate and Professional Education. He received his PhD from the McCombs School of Business at the University of Texas in Austin in where his work provided the first scholarly definition of the term “Cloud Computing”.

Dr. Min Chen’s expertise lies in healthcare analytics, health economics, and outcome research. Her work has addressed issues relevant to the economics, organization, and regulation of the U.S. health care system, with a focus on using large-scale datasets and state-of-the-art techniques to measure policy impact and healthcare quality. Dr. Chen’s research has been published in high impact peer-reviewed journals and featured in media outlets. She has recently been appointed as a research economist by the National Bureau of Economic Research and invited to serve as a co-investigator on an NIH and AHRQ funded grant. In addition to her academic experiences, Dr. Chen worked as an economic consultant at Charles River Associates and has gained valuable practical experiences working with government agencies and various healthcare organizations. Dr. Chen holds a B.A. in Economics from the Renmin University of China, as well as a Master’s degree in Public Policy from the University of Chicago and a Ph.D. in Managerial Economics & Strategy from the Kellogg School of Management at Northwestern University.

Dr. Chris Harle is a Professor in the Department of Health Outcomes and Biomedical Informatics, and the Chief Research Information Officer (CRIO) at UF Health. Dr. Harle’s research focuses on the design, adoption, use, and value of health information systems. His primary interest is in understanding how information technology-mediated communication tools affect consumer, patient, and provider decisions and behavior. Recently, with funding from Pfizer, the National Institutes of Health (NIH), and the Agency for Healthcare Research and Quality (AHRQ), his research has focused on developing clinical decision support tools to support primary care clinicians in chronic pain care and opioid prescribing. Other recent research, funded by the NIH, focuses on developing interactive electronic informed consent processes for obtaining broad consent from patients to share their electronic health records for research studies. Dr. Harle holds an MS in Decision and Information Sciences from the University of Florida’s Warrington College of Business Administration and a PhD in information systems and management from Carnegie Mellon University’s H. John Heinz III College.

Aditya Jain’s primary research interests include retail operations, supply chain management and healthcare operations. Aditya earned his PhD in Operations Management from the Simon School of Business at University of Rochester, and served as a faculty member at Indian School of Business and the Kellogg School of Northwestern University prior to joining Baruch College. His industry experience includes consulting work with fashion retailer through the retail/supply chain analytics company he co-founded and ran for some years in India.

Sunil Mithas is a World Class Scholar and Professor at the Muma College of Business. Mithas has taught at the Robert H. Smith School of Business at the University of Maryland, and has held visiting positions at the UNSW Business School, Sydney; the University of Mannheim in Germany; HKUST, Hong Kong; and in the Graduate School of Management at the University of California, Davis. He earned his PhD from the Ross School of Business at the University of Michigan and an engineering degree from IIT, Roorkee. He is among top information systems scholars in the world and his interdisciplinary work in information systems, strategy, marketing, and operations management has appeared in premier business journals. He has worked on research or consulting assignments with organizations such as A. T. Kearney, Ernst & Young, Johnson & Johnson, the Social Security Administration, and the Tata Group; and is a frequent speaker at industry events for senior leaders. Identified as an MSI Young Scholar by the Marketing Science Institute, he is the author of the books Digital Intelligence: What Every Smart Manager Must Have for Success in an Information Age and Dancing Elephants and Leaping Jaguars: How to Excel, Innovate, and Transform Your Organization the Tata Way. Mithas is a Senior Editor of MIS Quarterly, and Production and Operations Management; Department Editor of Management Business Review; and serves on or has served on editorial boards of Information Systems Research, and Journal of Management Information Systems. His papers have won best-paper awards, and have been featured in practice-oriented publications such as MIT Sloan Management Review, Bloomberg, and CIO.com.

Balaji Padmanabhan is the Anderson Professor of Global Management, the Director of the Center for Analytics & Creativity and a professor in the Information Systems and Decision Sciences Department. Previously, he served as the chair of the department. He has created and taught undergraduate, MBA/MS, and doctoral courses in areas related to AI and machine learning, business/data analytics and computational thinking. In his work, he designs analytics-driven algorithms to solve business problems. Padmanabhan’s specific interests and expertise include AI and machine learning, designing analytics-driven algorithms for business applications, managing analytics, building and evaluating predictive models, patterns discovery in data, business value of analytics, enabling citizen data science and applications of analytics in churn, health care, recommender systems, fraud detection and elections. He often works with industry partners on applied research and has worked with more than twenty firms on various machine learning and analytics initiatives, often with a focus on innovative applications to drive business value. His research has been published in the premier computer science and business journals and conferences including ACM KDD Proceedings, ACM RecSys, ACM Transactions on MIS, Big Data, Decision Support Systems, IEEE Transactions on Knowledge and Data Engineering, Information Systems Research, the INFORMS Journal on Computing, JAMIA, Management Science and MIS Quarterly. He serves on the editorial board and program committees of many leading academic journals and conferences in the field. Padmanabhan earned a PhD from the Stern School of Business at New York University and a B.Tech in computer science from Indian Institute of Technology Madras.

Glen Schmidt’s research focuses on new product development and supply chain management. He is a Department Chair and David Eccles Professor of Business at the University of Utah, with research and teaching materials that have been recognized in award competitions at INFORMS and POMS.

David Simchi-Levi is a Professor of Engineering Systems at MIT. He is considered one of the premier thought leaders in supply chain management and business analytics. His Ph.D. students have accepted faculty positions in leading academic institutes including U. of California Berkeley, Carnegie Mellon U., Columbia U., Cornell U., Duke U., Georgia Tech, Harvard U., U. of Illinois Urbana-Champaign, U. of Michigan, Purdue U. and Virginia Tech. Professor Simchi-Levi is the current Editor-in-Chief of Management Science, one of the two flagship journals of INFORMS. He served as the Editor-in-Chief for Operations Research (2006-2012), the other flagship journal of INFORMS and for Naval Research Logistics (2003-2005). He is an INFORMS Fellow, MSOM Distinguished Fellow and the recipient of the 2014 INFORMS Daniel H. Wagner Prize for Excellence in Operations Research Practice; 2014 INFORMS Revenue Management and Pricing Section Practice Award; 2009 INFORMS Revenue Management and Pricing Section Prize and Ford 2015 Engineering Excellence Award. He was the founder of LogicTools which provided software solutions and professional services for supply chain optimization. LogicTools became part of IBM in 2009. In 2012 he co-founded OPS Rules, an operations analytics consulting company. The company became part of Accenture in 2016. In 2014, he co-founded Opalytics, a cloud analytics platform company focusing on operations and supply chain intelligence. The company became part of the Accenture Applied Intelligence in 2018.