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.

Video sharing social media platforms, such as YouTube, offer an effective way to deliver medical information. Few studies have identified evidence-backed digital therapeutics as well as technology-enabled interventions to improve the ease with which patients can retrieve medical information to manage chronic conditions. We propose an interdisciplinary lens that synthesizes deep learning methods with themes emphasized in Information Systems (IS) and Healthcare Informatics (HIS) research to examine user engagement with encoded medical information in YouTube videos. We first use a bidirectional long short-term memory (BLSTM) method to identify medical terms in videos and then classify videos based on whether they encode a high or low degree of medical information. We then employ principal component analysis on aggregate video data to discover three dimensions of collective engagement with videos: non-engagement, selective attention driven engagement, and sustained attention driven engagement. Videos with low medical information result in non-engagement; at the same time, videos with a greater amount of encoded medical information struggle to maintain sustained attention driven engagement. Our study provides healthcare practitioners and policymakers with a nuanced understanding of how users engage with medical information in video format. Our research also contributes to enhancing current public health practices by promoting normative guidelines for educational video content enabling management of chronic conditions.

Applying the lens of service operations strategy, we discuss the theoretical underpinnings of the sharing economy. “Sharing economy” services represent much more than dynamic, real-time buyer and seller matching; they also enable enhanced and novel forms of service operations value creation. Today’s technology-mediated sharing economy platforms enable shared service offerings between service providers and receivers–many of whom are neither employees nor customers of the platform firm. Characteristically, throughout the production process — from inception to distribution to consumption — peers are active co-producers of goods and services. Therefore, the sharing economy is, indeed, a paradigm shift in service business model and delivery system structure and it warrants new service operations strategies. We start by offering a general discussion of the sharing economy relative to service operations. Next, we offer a new conceptual typology as a first attempt to lay the groundwork, in part, for addressing strategic operational issues with this type of structure. Specifically, the typology relates platforms to three salient dimensions—(1) primary sharing function types, (2) generic transaction types, and (3) noneconomic versus economic incentive mechanisms—that address the nature and inherent complexities of different combinations of platform attributes. We conclude with a research agenda to inspire future sharing economy services typology research and understanding of related service operations implications.

Despite the steady growth of online education, student engagement and retention rates online have lagged relative to physical classrooms. In this research, we develop a content genome framework, based on machine learning and computer vision techniques, to understand how online video content influences student engagement rates. We then apply this framework in a unique dataset provided by Masterclass.com consisting of 771 online course videos and more than 2.6 million viewing records from 225,580 students. Our analysis shows that readily observable characteristics of course delivery can be used to accurately predict student engagement. Our results provide managerial implications for online education platforms to optimize online course video content design and improve student engagement. The findings and methods in this study also shed light on how to advance management research using unstructured video data in other contexts such as video marketing and entertainment analytics.

Note: Joint work with Mi Zhou, George Chen, and Pedro Ferreira.

We investigate digital customer care in the Telecommunications industry. The primary goal is to determine an optimal strategy to manage customer sentiment on social media sites such as Twitter. We also aim to identify factors and external events that can influence the effectiveness of customer care. Recently, managing customer sentiment (particularly on social media) has become crucial as more customers have started to use social media to seek help from firms. Our study uses data consisting of sentiments expressed by customers directed at Twitter’s service accounts of four major U.S. telecommunication- service providers: AT&T, Verizon, Sprint and T-Mobile. To understand the antecedents of digital customer care, we model a diffusion process of customer sentiment over time. This diffusion process is influenced (or controlled) by the firm through the strategy employed to respond to customer tweets. The main benefit of our methodology is that it can be used in a prescriptive sense (specifically, to improve digital customer care), rather than merely for prediction (e.g., to forecast customer sentiment). The parameters of the controlled diffusion process are estimated to shed several insights into digital customer care in this industry. First, we find a clear separation among the firms in terms of digital customer care effectiveness. Second, we find that good customer care is not merely a matter of responding to customer tweets: T-Mobile and Sprint have high response rates, but are low on effectiveness. Third, the quality of digital customer care that customers expect varies across firms: Customers of higher priced firms (e.g., Verizon and AT&T) expect better customer care. Fourth, seemingly unrelated events (such as signing an exclusive contract with a celebrity) can impact digital customer care. These events can be firm-initiated or exogenous. Our study has important implications for managers as it can help firms determine the optimal strategy to influence customer sentiment.

The study also helps firms anticipate the impact of external events on digital customer care and adjust the response strategy to accommodate these events.

As medical crowdfunding becomes increasingly popular but many fundraising campaigns fail to collect sufficient donations for patients in need, it is important to explore how to craft an effective fundraising campaign. While in practice a majority of fundraising campaigns are narrated from the third-person perspective, little prior research examines if it is optimal to narrate the campaigns from the third-person perspective. In the present research, we draw upon multiple theories in the literature and propose that the relative effectiveness of the first- vs. third-person perspective is contingent on patient gender. Empirically, we conduct a randomized field experiment on a leading medical crowdfunding platform in China. The empirical results show that the third-person perspective is more effective in motivating donation-related behaviors for male-patient fundraising campaigns, whereas the first-person perspective is more effective for female-patient fundraising campaigns. Furthermore, we find that, to a large extent, the choice of the narrative perspectives matters more for potential donors who do not have many historical donations and have fewer friends with donations on the focal fundraising campaigns. The findings generate important theoretical and managerial implications for medical crowdfunding.

Qualcomm, the largest cellphone chipmaker in the world, was recently fined RMB 6.088 billion (approximately $975 million) by the Chinese government for alleged anti-competitive conducts including requiring downstream phone manufacturers to cross-license their patents to Qualcomm and its customers. Qualcomm’s cross-licensing practice has also received similar charges or scrutiny in South Korea, Japan, European Union, and the United States. Motivated by this practice, we study the impacts of cross-licensing in a supply chain in which an upstream supplier requires its downstream competing manufacturers to cross-license. We find that, contrary to common belief, cross-licensing may incentivize more innovation investment by the weak manufacturer. In addition, besides the weak manufacturer, even the strong one may benefit from cross-licensing under certain conditions. However, the supplier does not always benefit from conducting the cross- licensing practice. We show that cross-licensing does not always hurt social welfare or consumer surplus as it is accused for. We also find that allowing manufacturers to charge each other royalties benefit manufacturers at the cost of the supplier and consumers. Our results shed light on how cross-licensing affects innovation, profits and welfare, which have managerial implications to firms in high-tech industries, as well as to policy makers around the world.

This paper investigates the threshold Luce model, a recently proposed choice model with a threshold for the consideration-set formation. Under the threshold Luce model, consumers first form their consideration set: If an alternative with significantly low utility is dominated by another one, it will not be included in the consideration set. The threshold Luce model can alleviate the independence of irrelevant alternatives (IIA) property and allow more flexible substitution patterns. We characterize the optimal strategy and develop efficient solutions for price and assortment optimization problems. Under the threshold Luce model, the price competition may have zero, one, two, or infinite Nash equilibria, depending on the magnitude of the threshold effect. Moreover, we also develop an efficient estimation method to calibrate the threshold Luce model. Our numerical study on synthetic and real data sets shows that the new model can improve the goodness of fit and prediction accuracy of consumer choice behavior. Thus, the threshold effect should be taken into account in decision making if it exists.

With the rise of misinformation epidemic fueled by coordinated efforts by humans and bots, this study aims to empirically investigate how bot-assisted attention manipulations in online commenting platforms impact the diffusion of targeted specific opinions. We focus on Naver (a Yahoo-like online platform in Korea) accounts used by a bot-assisted abuser to sway public opinion about the South Korean government. Using ground-truth of the abuser accounts externally verified by court proceedings, we capture the entire dynamic interaction between regular users and malicious users at an individual user level and reveal that there are two forces at work, the herding effect and the correction effect. Further, we conduct counterfactual analyses to evaluate several governance options, namely input control, behavior control, and output control, to mitigate the adverse consequences of online opinion manipulations by abusers. As it is increasingly difficult for people to tell truth apart from manipulated opinions, transparency on an online public square remains a huge concern. This study will provide valuable implications to managers and policy makers to estimate the consequences of malicious behaviors.

Mobile communications technologies and online platforms have enabled large-scale consumer-to- consumer (C2C) sharing of their under-utilized products. A product owner’s self-use values can differ over time, and in a period of low self-use value, the consumer may rent out her product in a product-sharing market. In response to consumer-to-consumer product sharing, many manufacturers (e.g., General Motors, BMW) have entered the product-sharing market to provide their own rental services in addition to outright sales to consumers. This paper develops an analytical framework to study a manufacturer’s optimal entry strategy in the product-sharing market and the economic implications of its entry. Our analysis shows that when C2C sharing has a low transaction cost and the manufacturer’s marginal cost of production is not very high, the manufacturer will find it not optimal to offer its own rental services to consumers. In contrast, when the transaction cost for C2C sharing is high or the manufacturer’s marginal cost of production is high, the manufacturer should offer enough units of the products for rental to squeeze out C2C sharing (in expectation). When the transaction cost for C2C sharing and the manufacturer’s marginal cost are both in the middle ranges, the manufacturer’s rental services and the C2C sharing will coexist, in which case the manufacturer’s entry in the product-sharing market may reduce the total number of units of the product in the whole market but increase the consumer surplus and the social welfare. Furthermore, we find that, to maximize the total profit from both the retail market and the sharing market, it may be more efficient for the manufacturer to adjust its quantity offered for rental rather than its retail price in the retail market, i.e., it may be best for the manufacturer to keep the same optimal retail price that it would choose in the absence of the sharing market, but use how much direct rental services to offer to respond to the C2C product sharing.

App developers face a significant challenge in generating market demand. Given their resource constraints, app developers need insights into which features of their products are the most important drivers of demand. Categorizing features of an app as intrinsic or social, we focus on the role played by these features in different parts – head, body and tail – of the demand distribution. Using data from the iOS platform for a large number of apps, we extract intrinsic and social features from a panel of version release notes using a hierarchical deep learning model. We determine how these extracted features impact the number of downloads. We find that social features increase the demand for tail apps. However, social features along with intrinsic features help the head apps, as they help get the word out about the new capabilities. Our results underscore the heterogeneity in the effect of product features on app demand, and reveal the demand drivers and underlying consumer behavior in the app selection process. The key contribution of our research is to demonstrate that the choice of features made by app developers is critical to their success, and that there is no one-size-fits-all approach that works in all parts of the demand distribution.

We study the pricing and profit implications of smart technologies that can predict consumers’ real-time needs and customize the offering according to the predictions. In a two-period monopoly model with dynamic pricing and random consumer preferences, we find that when prediction accuracy is low, the firm adopts a conservative pricing strategy and smartness benefits both the consumer and the firm through reducing the consumer’s usage cost. As accuracy increases, the firm raises the second-period price, which makes the consumer less inclined to engage in initial consumption. Under certain conditions, the reduction of the first-period price or demand can lower total profit and consumer surplus to levels below those with the traditional technology. The dynamic inconsistency in our model differs significantly from the “ratchet effect” in the behavior- based pricing literature, as our main results would not hold if the consumer’s needs were not random. When prediction accuracy is high, smart technology once again outperforms traditional technology in terms of firm profit. Our results challenge the conventional wisdom that it is always profitable to increase accuracy when it comes to using data to predict consumer preferences.

We study the pricing and profit implications of smart technologies that can predict consumers’ real-time needs and customize the offering according to the predictions. In a two-period monopoly model with dynamic pricing and random consumer preferences, we find that when prediction accuracy is low, the firm adopts a conservative pricing strategy and smartness benefits both the consumer and the firm through reducing the consumer’s usage cost. As accuracy increases, the firm raises the second-period price, which makes the consumer less inclined to engage in initial consumption. Under certain conditions, the reduction of the first-period price or demand can lower total profit and consumer surplus to levels below those with the traditional technology. The dynamic inconsistency in our model differs significantly from the “ratchet effect” in the behavior- based pricing literature, as our main results would not hold if the consumer’s needs were not random. When prediction accuracy is high, smart technology once again outperforms traditional technology in terms of firm profit. Our results challenge the conventional wisdom that it is always profitable to increase accuracy when it comes to using data to predict consumer preferences.

Because sharing economy platforms have less control on the assets and labor of the providers on the platform, they face unique challenges in implementing platform-wide changes. We discuss some of these challenges in the context of a platform changing its pricing from being completely decentralized to being completely centralized. Our study reveals that the negative effects of provider retaliation due to platform changes may outweigh the potential benefits of centralization. We offer ways to mitigate provider retaliation while retaining the benefits of centralization.

Note: Joint work with Apostolos Filippas and Arun Sundararajan.

Businesses have widely used email ads to directly send promotional information to consumers. While email ads serve as a convenient channel that allows firms to target consumers online, are they effective in increasing offline revenues for firms that predominantly sell in brick-and-mortar stores? Is the effect of email ads, if any, heterogeneous across different consumer segments? If so, on which consumers is the effect highest? In this research, we address these questions using a unique high-dimensional observational dataset from one of the largest retailers in the US, which links each consumer’s online behaviors to the item-level purchase records in physical stores. We use a doubly robust estimator (DRE) that incorporates nonparametric machine learning methods and allows us to perform causal estimation on observational data. Using the DRE we find that receiving email ads can increase a consumer’s spending in physical stores by approximately $11.82. Additionally, we find that the increased offline sales result from increased purchase probability and a wider variety of products being purchased. Further, we use a data-driven approach to demonstrate that the effect of email ads is heterogeneous across different consumer segments. Interestingly, the effect is highest among consumers who have fewer interactions with the focal retailer recently (i.e., lower email opening frequency). Overall, our results suggest a reminder effect of email ads. Receiving email ads from the retailer can generate awareness and remind the consumer of the retailer’s offerings of various products and services, which gradually increase the consumer’s purchase probability in the retailer’s physical stores. These findings have direct implications for marketers to improve their digital marketing strategy design and for policy makers who are interested in evaluating the economic impact of prevalent email advertising.

The successful introduction of radical new products is fraught with challenges including technical uncertainty (can the firm create the new product) and time-based competition (how fast can the firm enter the marketplace). To alleviate these challenges, a firm may choose to redirect its NPD efforts and transfer a portion of the knowledge developed for the radical product to improve an existing product in its portfolio. While the market value of the existing product improves, the knowledge transfer also reduces the market potential of the radical new product. We analyze a model to provide a deep understanding of conditions that drive a firm to undertake knowledge transfer from a radical new product under development to an existing product improvement project. Importantly, we evaluate these decisions in the context of the ability of the existing product improvement team to integrate the knowledge transfer (i.e., the team’s absorptive capacity) and thereby enhance the market value of the existing product.

Vibhanshu Abhishek is an Associate Professor of Information Systems the Paul Merage School of Business, University of California – Irvine. His research focuses on the effect of emerging technologies on consumers’ behavior, business strategy and market structure. He is particularly interested in multi-channel coordination and examines issues in multi-channel retail, advertising and pricing. He studies how consumers respond to different forms of advertising and how companies can strategically use new advertising channels to connect with their consumers. He also examines the dynamics of e-commerce marketplaces and their interaction with traditional retail. Dr. Abhishek has published in top management journals like Operations Research, Marketing Science, Management Science, MIS Quarterly and Journal of Interactive Marketing. He is also a recipient of the Google Faculty research award, Adobe Faculty grant, Flipkart research grand and has won several awards like the ISA-INFORMS best paper award, CIST best student paper award and the ISS Nunamaker-Chen Dissertation award. His research has been cited in popular press outlets such as the Sloan Management Review, NY Times, Forbes, Fortune, Pittsburgh Post Gazette, Seattle times and Wall Street Journal. He has worked with several firms including McKinsey & Co., Sequoia Capital, Pirates, LEGO, Adobe, FICO, IBM and Omnicom and advises hi-tech startups. He received a PhD in Operations and Information Management and a M.A. in Statistics from the Wharton School, University of Pennsylvania. He also holds a B.Tech in Computer Science from IIT Kanpur. Before joining UC Irvine, Dr. Abhishek was an Assistant Professor of Information Systems at the Heinz College, Carnegie Mellon University.

Ashish Agarwal is an Associate Professor in Information, Risk and Operations Management Department at the McCombs School of Business, U T Austin. Prior to joining U T Austin, Ashish obtained his PhD in Information Systems from Tepper School of Business, Carnegie Mellon University. He also holds a Bachelors in Engineering from Indian Institute of Technology, Mumbai (India), and an MS in Engineering from Massachusetts Institute of Technology. His research interests include sponsored search, online information and investment markets, social media advertising, economics of app ecosystems, and network analysis. His papers have been accepted in several leading conferences and journals in information systems and marketing, including Management Science, Information Systems Research and Journal of Marketing Research. He serves on the editorial board for Management Science.

Cheryl Gaimon is the Esther and Edward Brown Chair and a Regents Professor in the Scheller College of Business at the Georgia Institute of Technology. She initiated establishment of the Operations Management (OM) Program and was a core participant in the development of an interdisciplinary Certificate Program in the Management of Technology (MOT) (currently serving as that program’s director). Professor Gaimon’s research and teaching consider how a firm manages its knowledge-based assets (including people, manufacturing and service technologies, processes and procedures, materials and information) in environments characterized by innovations in science and technology, global competition and a dynamic marketplace. Particular topics of interest include outsourcing knowledge, innovation and new product development strategies (such as knowledge creation and transfer), and environmental process improvement. Her research has appeared in journals including Management Science, Manufacturing and Service Operations Management, Operations Research, Organization Science, and Production and Operations Management. Professor Gaimon is a Fellow of the Production and Operations Management Society (POMS). She served as the President of POMS in 2008-2009. She is the recipient of the “Sushil K. Gupta POMS Distinguished Service Award” in 2014; the “2014 Brady Family Award for Faculty Research Excellence” given by the Scheller College.. Professor Gaimon is the Management of Technology Department Editor for Production and Operations Management. Formerly, she was Associate Editor for Management Science, Senior Editor of Manufacturing and Service Operations Management, Department Editor of IIE (Institute of Industrial Engineers) Transactions, and Department Editor of IEEE Transactions on Engineering Management.

Sang Pil Han is an Associate Professor of Information Systems in the W. P. Carey School of Business at the Arizona State University. Han is interested in studying how firms gain useful insights and competitive advantages from big-data and business analytics. He is especially interested in topics related to mobile analytics, mobile apps, mobile marketing, and social media. Han’s recent research focuses on addiction to mobile social apps, mobile targeting, mobile content consumption modeling and mobile media planning. In his research, he relies upon empirical research methods including econometric analyses, hierarchical Bayesian modeling, dynamic structural modeling and randomized field experiments. His papers were published in top-tier journals such as Management Science, Management Information Systems Quarterly, Information Systems Research, among others. He currently serves an Associate Editor at Information Systems Research.

Tingliang Huang is an Associate Professor in the Carroll School of Management at Boston College, Massachusetts, and an honorary faculty member at the University College London, UK. He has research interests in modeling consumer behavior, the interface of operations and marketing, service operations, and business analytics. He has published in leading journals such as Marketing Science, Management Science, Manufacturing & Service Operations Management, and Production and Operations Management. His research has been recognized by some research awards, such as the 2018 POMS Wickham Skinner Early-Career Research Accomplishments Award, the 2015 Wickham Skinner Best Paper Award published in Production and Operations Management, the 2018 Most Influential Service Operations Paper Award, and the 2016 INFORMS Service Science Best Student Paper Award. Tingliang obtained a PhD in Operations Management from the Kellogg School of Management, Northwestern University, an MS from the University of Minnesota, and a BS from the University of Science and Technology of China (USTC).

Srikanth Jagabathula is Visiting Associate Professor of Technology and Operations Management Unit at Harvard Business School and Associate Professor (on leave) at the Department of Information, Operations, and Management Sciences at Leonard N. Stern School of Business of New York University. Professor Jagabathula’s research interests broadly lie at the intersection of operations, machine learning, and marketing. His current work broadly focuses on developing data-driven modeling and learning techniques with the goal of improving the accuracy of operational decision-making. The objective of his research is to obtain easy-to-use techniques for a wide range of managerial decisions: the right products to design, the right products and prices to offer to customers, and the right quantity of each product to carry. More broadly he is interested in understanding how to handle and extract useful insights from the large quantities of data being generated by businesses. He has received a number of awards recognizing his work, including the NSF CAREER Award, the Wickham Skinner Early-Career Research Accomplishments Award from the Production and Operations Management Society, best student paper awards in operations and machine learning conferences, best master’s thesis award, and the President of India Gold Medal in 2006. Professor Jagabathula received a B.Tech. degree in Electrical Engineering from IIT Bombay, and an S.M. degree and Ph.D. in Electrical Engineering and Computer Science from the Massachusetts Institute of Technology.

Baojun Jiang is an associate professor of marketing at the Olin Business School at Washington University in St. Louis. He received a B.A. in economics and physics from Grinnell College, an M.S. in physics and an M.S. in electrical engineering from Stanford University, an M.B.A. from the University of Texas at Austin, and an M.S. and Ph.D. in information systems from Carnegie Mellon University. His current research interests include the sharing economy, platform-based business models, competitive strategy, behavioral economics, and marketing-operations interface. His research has been published in top-tier journals such as Marketing Science, Management Science, Journal of Marketing Research, and Production and Operations Management. He was selected as a 2017 MSI Young Scholar by the Marketing Science Institute, and serves as a Senior Editor at Production and Operations Management and is on the Editorial Review Boards of Journal of Marketing Research and Marketing Science.

Xinxin Li is an Associate Professor of Operations and Information Management at the School of Business at the University of Connecticut. Her research interests are in economics of information systems with an emphasis on the implications of new technologies to consumer welfare, firm pricing and competitive strategies. Her work has been published in Management Science, Information Systems Research, MIS Quarterly, Marketing Science, Strategic Management Journal, among other journals. She received her Ph.D. from the Wharton School at University of Pennsylvania and her B.S. from Tsinghua University in China.

Xitong Li is an Associate Professor in the department of Information Systems and Operations Management, HEC Paris, France. His recent research interests include the economic and social impacts of using online data/information, and innovative technologies using online data and services. His research has appeared, or is forthcoming, in Information Systems Research, MIS Quarterly, Journal of Management Information Systems, ACM Transactions on Internet Technology, ACM Transactions on Multimedia Computing Communications and Applications, IEEE Transactions on Engineering Management, IEEE Transactions on Automation Science and Engineering, IEEE Transactions on Systems, Man & Cybernetics, IEEE Communications Magazine, and other leading international journals and conference proceedings. His research has recently been granted by ANR AAPG France (solo PI, Agence Nationale de la Recherche in France, equivalent to National Science Foundation in the U.S.) for three years of 2018-2020. He won the Best Paper Award at the 46th Hawaii International Conference on System Sciences (HICSS) in 2013 and the OCIS Best Paper Award Finalist at the 77th Academy of Management (AoM) Annual Meeting in 2017. He received his Ph.D. in management from the MIT Sloan School of Management, and his Ph.D. in engineering from Tsinghua University.

Dr. Vijay Mookerjee is a Professor and Charles and Nancy Davidson Professor of Information Systems at Naveen Jindal School of Management in UT Dallas. He has earned his bachelor of engineering from Nagpur University in India. Prior to joining UT Dallas in 2001, he taught at the University of Washington, where he received the PhD Mentor of the Year award. He earned his PGDM in systems and marketing from the Indian Institute of Management. He earned his PhD in management with a major in management information systems from Purdue University.

His research interests include social networks, managerial issues in information security, optimal software development methodologies, storage and cache management, content delivery systems, and the economic design of expert systems and machine learning systems. In 2011, his research on how companies can improve the online forum experience for customers won the Best Paper award at the Conference on Information Systems and Technology, which is held in conjunction with the Institute for Operations Research and the Management Sciences (INFORMS) national meeting. In the co-authored paper, Mookerjee, his wife, Dr. Radha Mookerjee, and another colleague created a computer program that can help companies determine when they should provide expert input to customers using online forums.

Mookerjee said he is most proud of being named a fellow of the Information Systems section of INFORMS. He is senior editor of Information Systems Research. He serves as an associate editor of several leading journals, including Decision Support Systems, Management Science: Information Systems Department, INFORMS Journal on Computing: Telecommunications and E- Commerce Area, Information Technology and Management and Journal of Data Management. He has published in several journals, including Information Systems, Computer Science, and Operations Research. He has been involved with the Workshop on Information Technologies and Systems (WITS), serving as co-chair of the WITS workshop in Australia in 2000.

Aleda Roth is a Distinguished Professor at Clemson University. Before then, she held the W.P. Carey Endowed Chair at ASU and the Mary Farley Lee Endowed Chair at UNC-Chapel Hill. She also held faculty positions at Duke and Boston University; and invited visiting scholar positions, including London Business School, IESA, Xi’an Jiaotong, Católica Portuguesa, WHU (Germany), INSEAD (Singapore), and others. Aleda is an internationally recognized thought leader in manufacturing, supply chain and service operations strategy. Her research is motivated by theoretical and practical explanations of how firms can best deploy their operations, global supply chains, and technology strategies for “triple-aim” performance – competitive advantage, sustainability and public well-being. She addresses strategic and policy impacts of emerging paradigms, such as service innovation/design strategies in the sharing economy, hospitality, health care, humanitarian and other sectors She proposes new business models for supply chain and operations, including business synergies of responsible/sustainable operations; strategic sourcing strategies for resiliency to political risk and mitigating quality risks in food, pharmaceuticals and other consumer products. With over 250 publications (106 refereed), Aleda’s work ranks #2 in the

world for publications in premier journals: POM, JOM, MSOM & Mgt. Sci. from 2001-2015 and 7th worldwide in service management research. Aleda has received over 100 research honors since earning her doctorate in 1986. Most recently, Aleda was honored to be selected for the 2018 Inaugural Class of Clemson University’s Research, Scholarship and Artistic Achievement Award and the 2015-2016 Senior Scholar Research Excellence Award (Clemson College of Business); and was named an Academic Scholar for Cornell University’s Institute for Healthy Futures. In 2014, Aleda was honored with the Award for the Advancement of Women in Operations Research and Management Sciences (OR/MS); in 2013, was inducted into the Inaugural Class of Texas A&M Institute for Advanced Study (TIAS) as an Eminent Scholar. Aleda served the first woman elected as President of the Production and Operations Management Society; was elected as a Distinguished Fellow of her three professional societies; and holds senior editorial leadership roles. She served as a subject matter expert for NSF grant reviews, the NY State Attorney General’s Office, the National Academies of Sciences, Engineering and Medicine and the National Center for Health Statistics. She serves on the Executive Advisory Committee of the U.S. Manufacturing Competitiveness Initiative; and since 1991, as an industry-sponsored member of the Conference Board’s Business Performance Excellence Council. She received over $2.75 million in external research funding. Since 1991, Aleda is a member of Conference Board’s Business Performance Excellence Council; and she served on the Executive Advisory Board of the Council on Competitiveness’ U.S. Manufacturing Initiative. She has consulted with numerous corporate entities, including Nestle (Vevey), J&J, GE, Baxter, IBM, National Center for Manufacturing Sciences, Smith & Nephew (UK), Deloitte, Accenture, E&Y, etc. Aleda earned her doctorate from Ohio State University in production and operations management and BS in psychology; and holds a MSPH in biostatistics from the UNC-Chapel Hill. Prior to her doctorate, she worked for more than 10 years in health care, holding senior research and top management positions.

Michael D. Smith is the J. Erik Jonsson Professor of Information Technology and Marketing at Carnegie Mellon University’s Heinz College and co-director of CMU’s Initiative for Digital Entertainment Analytics. He received a Bachelors of Science in Electrical Engineering (summa cum laude) and a Masters of Science in Telecommunications Science from the University of Maryland, and received a Ph.D. in Management Science from the Sloan School of Management at MIT. His research specializes in entertainment analytics, marketing, and technology management and he is a co-author of the book Streaming, Sharing, Stealing: Big Data and the Future of Entertainment (MIT Press, September 2016).

Anjana Susarla is an Associate Professor of Information System at Broad College of Business in Michigan State University. Anjana Susarla earned an undergraduate degree in Mechanical Engineering from the Indian Institute of Technology, Chennai; a graduate degree in Business Administration from the Indian Institute of Management, Calcutta; and Ph.D. in Information Systems from the University of Texas at Austin. Before attending graduate school, she worked in Enterprise Resource Planning (ERP) consulting. Her research interests include the economics of information systems, social media analytics and the economics of artificial intelligence. Her work has appeared in several academic journals and peer-reviewed conferences such as Academy of Management Conference, IEEE Computer, Conference on Knowledge Discovery and Data Mining, Information Systems Research, International Conference in Information Systems, Journal of Management Information Systems, Management Science and MIS Quarterly. She has served on and serves on the editorial boards of Electronic Commerce Research and Applications, Information Systems Research, MIS Quarterly and the Journal of Database Management. She has been a recipient of the William S. Livingston Award for Outstanding Graduate Students at the University of Texas, a Steven Schrader Best Paper Finalist at the Academy of Management, the Association of Information Systems Best Publication Award, a Runner-Up for Information Systems Research Best Published Paper Award 2012; and the Microsoft Prize by the International Network of Social Networks Analysis Sunbelt Conference. She has worked in consulting and led experiential projects with several companies. Her op-eds and work have been quoted in several media outlets such as the Associated Press, Newsweek, The Conversation, Sirius XM, World Economic Forum, Chicago Tribune, Salon and Pew Research. She has also been a speaker at public forums such as the SXSW and the United States Institute of Peace.

Dr. Ruxian Wang is currently an associate professor at Johns Hopkins University, Carey Business School. Before returning to academia, he worked in Hewlett-Packard Company for several years as a research scientist. He received Ph.D. from Columbia University, M.S. and B.S. from Nanjing University. His research and teaching interests include operations management, revenue management, pricing, consumer choice models, data-driven decision making. His research articles appeared in several journals, such as Management Science, Manufacturing & Service Operations Management, Operations Research, Production and Operations Management.

Patient care data, such as Electronic Health Records (EHR) and health insurance claims, create a unique opportunity to improve clinical practice by analyzing patterns across patients and providing actionable insights. This work provides a methodology to identify subpopulations for whom certain patterns of medical care have led to significantly anomalous health outcomes. We provide a general framework to identify these anomalous patterns of care and provide an empirical analysis using health insurance data. We detect interventions in patient care (currently in terms of medications) that have significantly affected health outcomes either negatively (in which case they may represent suboptimal care that should be identified and corrected) or positively (in which case they may represent new, previously undiscovered best care practices). We hope that this work will eventually lead to better patient outcomes as measured by various factors such as the number of future hospital visits, length of stay in the hospital, fewer complications due to additional secondary diseases, etc. This will further help both in terms of improving patient health and reducing health care costs. The methodological contributions of this work are in developing novel machine learning methods to identify effective treatments for specific subpopulations from observational data. This is an important and challenging problem which is more generally applicable to the social science literature.

Consumers often rely on product reviews to make purchase decisions, but how consumers use review content in their decision making has remained a black box. In the past, extracting information from product reviews has been a labor intensive process that has restricted studies on this topic to single product categories or those limited to summary statistics such as volume, valence, and ratings. This paper uses deep learning natural language processing techniques to overcome the limitations of manual information extraction and shed light into the black box of how consumers use review content. With the help of a comprehensive dataset that tracks individual-level review reading, search, as well as purchase behaviors on an e-commerce portal, we extract six quality and price content dimensions from over 500,000 reviews, covering nearly 600 product categories. The scale, scope, and precision of such a study would have been impractical using human coders or classical machine learning models. We achieve two objectives. First, we describe consumers’ review content reading behaviors. We find that although consumers do not read review content all the time, they do rely on review content for products that are expensive or of uncertain quality. Second, we quantify the causal impact of content information of read reviews on sales. We use a regression discontinuity in time design and leverage the variation in the review content seen by consumers due to newly added reviews. To extract content information, we develop two deep learning models: a full deep learning model that predicts conversion directly and a partial deep learning model that identifies content dimensions. Across both models, we find that aesthetics and price content in the reviews significantly affect conversion across almost all product categories. Review content information has a higher impact on sales when the average rating is higher and the variance of ratings is lower. Consumers depend more on review content when the market is more competitive, immature, or when brand information is not easily accessible. A counterfactual simulation suggests that re-ordering reviews based on content can have the same effect as a 1.6% price cut for boosting conversion.

Note: Joint work with Dokyun Lee and Kannan Srinivasan.

Customers are increasingly turning to social media for help. According to a recent report by Twitter, over 5.5M customer service-related tweets are generated per month. In this work, we aim to explore two questions: 1) understanding and explaining firms’ strategy when engaging complaining customers on Twitter, and 2) predicting customer complaint behavior on Twitter. Specifically, for the first question, we consider how firms’ customer engagement strategy is influenced by their expectations for how their customer-service interactions will lead to sentiment broadcast about the firm. We particularly focus on how politeness, a linguistic factor indicating how a customer is questioning or complaining rather than the content of a query, affects firms’ customer service engagement strategy. We develop a novel machine-learning methodology to measure politeness from tweets. Using this approach, our estimation results show several interesting results, including that firms are more likely to respond to more polite customers, and that this effect is augmented for customers with high social status. However, firms are more likely to engage impolite customers with a high social status in a private channel such as through direct messaging. This behavior is justified by evidence that customer politeness predicts the nature of sentiment customers broadcast about the firm. On the other hand, for the second question, we focus on how to predict whether a customer will become a complainer, defined as those who have experienced unsatisfactory and decided to take public negative actions towards the firm. We propose a novel machine learning framework based on Non-negative Matrix Factorization (NMF) techniques for this task. To regularize the model learning process, several prior knowledge on the on the antecedents of customer complaint behavior are incorporated, which includes customer personality and firm’s characteristics. We implement our approach using Alternating Least Squares (ALS) framework and show that our approach significantly outperforms state-of-the-art techniques in predicting which firm the customer is going to engage on Twitter and whether the customer is a complainer.

We seek to develop a recommender system that takes into account supply chain constraints regarding the availability of a product while nudging the customers to purchase it. The motivation to study this problem was because a firm faced availability constraints for one of its products but the available quantities still exceeded the current demand. To identify customers to nudge, we develop a Support Vector Machine (SVM) approach to rank order the customers based on their propensity to purchase the product. The underlying notion in our approach is that Type I errors, to be defined in the paper, in our classifier are not necessarily problematic but are potential nudging targets. Also, as a consequence, traditional ways of evaluating classifiers (with Type I and Type II errors) are not appropriate. Therefore, we conduct a field experiment to evaluate how well the identified customers are nudged through information and/or couponing. We find that, in terms of the successful nudges, our SVM-based approach performed better than other approaches. The experiment also generated insights about when couponing as opposed to information is more effective when nudging.

In this talk I will first introduce a broad class of deep learning models and show that they can learn useful hierarchical representations from large volumes of high-dimensional data with applications in information retrieval, object recognition, and speech perception. I will next introduce models that can find semantically meaningful representations of words, learn to read documents and answer questions about their content. I will further introduce the notion of “Memory” as being a crucial part of an intelligent agent’s ability to plan and reason in partially observable environments and demonstrate a deep reinforcement learning agent that can learn to store arbitrary information about the environment over long time lags. I will show that on several tasks these models significantly improve upon many of the existing techniques.

With the availability of large-scale network data, peer influence in social networks can be more rigorously examined and understood than before. Peer influence can arise from immediate neighbors in the network (formally defined as cohesion or direct ties with one-hop neighbors) and from indirect peers who share common neighbors (formally defined as structural equivalence or indirect ties with two-hop neighbors). While the literature examined the role of each peer influence (direct or indirect) separately, the study of both peer network effects acting simultaneously was ignored, largely due to methodological constraints. This paper attempts to fill this gap by evaluating the simultaneous effect of both direct and indirect peer influences in technology adoption in the context of Caller Ring Back Tone (CRBT) in a cellular telephone network, using data from 200 million calls by 1.4 million users. Given that such a large-scale network makes traditional social network analysis intractable, we extract many densely-connected and self- contained subpopulations from the network. We find a regularity in these subpopulations in that they consist either of about 200 nodes or about 500 nodes. Using these sub-populations and panel data, we analyze direct and indirect peer influences using a novel auto-probit model with multiple network terms (direct and indirect peer influence, with homophily as a control variable). Our identification strategy relies on Bramoullé et al.’s (2009) spatial autoregressive model, allowing us to identify the direct and indirect peer influences on each of the extracted subpopulations. We use meta-analysis to summarize the estimated parameters from all subpopulations. The results show CRBT adoption to be simultaneously determined by both direct and indirect peer influence (while controlling for homophily and centrality). Robustness checks show model fit to improve when both peer influences are included. The size and direction of the two peer influences, however, differ by group size. Interestingly, indirect peer influence (structural equivalence) plays a negative role in diffusion when group size is about 200, but a positive role when group size is about 500. The role of direct peer influence (cohesion), on the other hand, is always positive, irrespective of group size. Our findings imply that businesses must design different target strategies for large versus small groups: for large groups, businesses should focus on consumers with both multiple one-hop and two-hop neighbors; for small groups, businesses should only focus on consumers with multiple one-hop neighbors.

Psychometric measures, including health numeracy, subjective literacy, and perceptions of trust and anxiety related to physicians, have been shown to have a profound impact on the treatment outcomes for various health conditions, including depression, diabetes, PTSD, and cardiovascular disease. In many cases, these effects have been even more pronounced amongst health-disparate populations. However, effectively collecting and measuring such covariates in a timely and unobtrusive manner has proven elusive in real-world settings. In this study, leveraging survey text and users’ text communication through online forums and text messaging-based channels, we propose a novel deep learning architecture to infer numeracy, literacy, trust, and anxiety in an unobtrusive, privacy-preserving manner. Our architecture incorporates novel embeddings for linguistic representational richness, key demographic dimensions (e.g., race, education, and income levels), and structural psychometric similarities. Preliminary results across thousands of subjects in comparison with existing state-of-the-art methods demonstrate the potential efficacy and utility of the proposed method. Our results have important downstream implications for mobile health platforms and community-engagement-based interventions designed to drive positive outcomes, particularly for health-disparate populations.

Firms seek to better understand heterogeneity in the customer response to marketing campaigns, which can boost customer targeting effectiveness. Motivated by the success of modern machine learning techniques, this paper presents a framework that leverages deep-learning algorithms and field experiment response heterogeneity to enhance customer targeting effectiveness. We recommend firms run a pilot randomized experiment and use the data to train various deep-learning models. By incorporating recurrent neural nets and deep perceptron nets, our optimal deep- learning model can capture both temporal and network effects in the purchase history, after addressing the common issues in most predictive models such as imbalanced training, data sparsity, temporality, and scalability. We then apply the learned optimal model to identify customer targets from the large amount of remaining customers with the highest predicted purchase probabilities. Our application with a large department store on a total of 2.8 million customers supports that optimal deep-learning models can identify higher-value customer targets and lead to better sales performance of marketing campaigns, compared to industry common practices of targeting by past purchase frequency or spending amount. We demonstrate that companies may achieve sub-optimal customer targeting not because they offer inferior campaign incentives, but because they leverage worse targeting rules and select low-value customer targets. The results inform managers that beyond gauging the causal impact of marketing interventions, data from field experiments can also be leveraged to identify high-value customer targets. Overall, deep-learning algorithms can be integrated with field experiment response heterogeneity to improve the effectiveness of targeted campaigns.

We consider the problem faced by a service platform that needs to match supply with demand, but also to learn attributes of new arrivals in order to match them better in the future. We introduce a benchmark model with heterogeneous workers and jobs that arrive over time. Job types are known to the platform, but worker types are unknown and must be learned by observing match outcomes. Workers depart after performing a certain number of jobs. The payoff from a match depends on the pair of types and the goal is to maximize the steady-state rate of accumulation of payoff. Our main contribution is a complete characterization of the structure of the optimal policy in the limit that each worker performs many jobs.

The platform faces a trade-off for each worker between myopically maximizing payoffs (exploitation) and learning the type of the worker (exploration). This creates a multitude of multi- armed bandit problems, one for each worker, coupled together by the constraint on availability of jobs of different types (capacity constraints). We find that the platform should estimate a shadow price for each job type, and use the payoffs adjusted by these prices, first, to determine its learning goals and then, for each worker, (i) to balance learning with payoffs during the “exploration phase”, and (ii) to myopically match after it has achieved its learning goals during the “exploitation phase.”

In this project, we study the problem of providing recommendations to the students that help them in their studies. To address this problem, we present an approach of providing recommendations of remedial learning materials to the students that fills the gaps in their knowledge of the subject in the courses that they take. According to this method, we first identify gaps in student’s mastery of various course topics. Then we identify those items from the library of assembled learning materials that help us to fill those gaps, and then we recommend these identified materials to the student. We show empirically through A/B testing that this approach leads to better performance results, as measured by student’s total score on the final exam across the Personalized, Non- personalized and the Control groups and by improvement of student’s average score on that exam in comparison to the previously taken courses. The proposed method is scalable since it can be applied to a large number of students across many courses.

We investigate the economic impact of images and lower-level image factors that influence property demand in Airbnb. Using Difference-in- Difference analyses on a nine-month Airbnb panel dataset spanning 8,211 properties, we find that units with verified photos (taken by Airbnb photographers) generate approximately 7% more demand, or $4,141 per year on average. Leveraging computer vision techniques to classify the image quality of more than 380,000 photos, we show that 52.5% of this effect comes from the high image quality of verified photos. Next, we identify 12 image attributes from photography and marketing literature to further quantify (using computer algorithms) and characterize unit images to evaluate the economic impact of these human-interpretable attributes. The results suggest that these attributes have a direct impact on demand even after controlling for many observables and thus there is significant value in optimizing images in e-commerce settings. From an academic standpoint, we provide one of the first large-scale empirical evidence that directly connects systematic lower-level and interpretable image attributes to demand. This contributes to, and bridges, the photography and marketing (e.g., staging) literature, which has traditionally ignored the demand side (photography) or did not implement systematic characterization of images (marketing). Lastly, these results provide immediate insights for housing and lodging e-commerce managers (of Airbnb, hotels, realtors, etc.) to optimize product images for increased demand.

The capacity to evaluate and rank expert workers by their decision quality has substantial practical value. Yet, when no ground truth information exists, such evaluation typically requires enlisting peer-experts and may be prohibitively costly in many important settings. In this work, we formulate a data science problem of Ranking Expert decision makers’ unobserved Quality (REQ) based exclusively on historical decisions data and without resorting to evaluation by other experts. The REQ problem is particularly challenging because the correct decisions in our settings are unknown (unavailable), and because some of the information used by the decision makers may not be available for retrospective evaluation. We propose a machine-learning-based approach for the REQ problem and evaluate it on several datasets. We evaluate our approach under a variety of settings and data domains, and we find that it yields robust performance: our REQ method often yields superior ranking of expert workers and is otherwise comparable to the best alternative approach. As such, our method constitutes a de-facto benchmark for future research on the REQ problem. In addition, we explore analytically and empirically the conditions under which our approach is particularly advantageous in practice. Finally, we discuss and develop an approach for a related challenge of ranking decision-making entities, such as business units or service providers that are unable (or unwilling) to share their decision data. For this variant of the REQ problem as well our proposed approach yields effective ranking.

Konstantin Bauman is an Assistant Professor at the Management Information Systems Department of the Fox School of Business, Temple University. Konstantin’s research interests lie in the area of technical information systems (design science). Within technical IS, he is focusing on the fields of quantitative modeling and data science. In particular, Konstantin works on developing novel machine learning methods for predicting customer preferences, and designing novel approaches to recommender systems that provide better personalized advice to the customers. Before joining Fox School of Business, Konstantin Bauman worked as a postdoctoral research scientist in the IOMS Department at the Leonard N. Stern School of Business, NYU. Konstantin also worked in industry as a head of machine learning group at the research department of Yandex LLC. where he dealt with large-scale machine learning and data science problems on a daily basis. Konstantin received his M.S. in Mathematics from Moscow State University in 2008, his M.S. in Data Mining from Moscow Institute of Physics and Technology in 2009, and his Ph.D. in Geometry and Topology from the Mathematical Department of Moscow State University in 2012.

Yuheng Hu is an Assistant Professor in the Department of Information and Decision Sciences at University of Illinois at Chicago. Before UIC, he was a Research Staff Member at IBM Research. Yuheng obtained his Ph.D in Computer Science from Arizona State University. His research interest is at the intersection of Machine Learning, Social Media Analytics, and Online Markets. His research has been published in premier journals such as IEEE Transactions on Knowledge and Data Engineering, and top conferences such as ACM CHI, IJCAI, and AAAI. His work has won multiple honors, including Best Paper Nominations at INFORMS CIST, the ACM CHI conference, INFORMS eBusiness Best Paper Award, and a letter of commendation from The President of Arizona State University. His research has also been widely covered by national and international media outlets such as ABC, PBS, WIRED magazine, and The Sydney Morning Herald.

Vijay Kamble is an Assistant Professor of Information and Decision Sciences in the College of Business Administration at University of Illinois at Chicago. From 2015-2017 he was a postdoctoral researcher in the Society and Algorithms Lab in the Management Science and Engineering Department at Stanford University. He obtained his PhD in Electrical Engineering and Computer Science from UC Berkeley in 2015. His research interests are in modeling and algorithm design surrounding the themes of pricing, learning, and incentives in online labor markets and service platforms.

Karthik Kannan is the Thomas Howatt Chaired Professor in Management at Purdue’s Krannert School of Management. He is the academic director for the MBA programs (two-year MBA, STEM-MBA, Weekend MBA), academic co-director for MS in BAIM (Business Analytics and Information Management), and co-director for BIAC (Business Information and Analytics Center).

He researches and teaches aspects related to “Designing for Human Instincts.” Specifically, he is interested in understanding and designing systems — products, processes, or policies — that exploit human instincts and biases in order to nudge behavior. He has published papers in several leading management journals and conferences, and has also won some best paper awards. He also serves/has served in editorial capacities. He is also a CERIAS Fellow and Krannert’s Faculty Fellow. For 2017-18, he has been awarded the prestigious Jefferson Science Fellowship by the National Academies of Sciences and Engineering.

Prior to joining Purdue, he obtained his PhD in information systems, MS in Electrical and Computer Engineering, and MPhil in Public Policy and Management all from Carnegie Mellon University. Before joining the graduate school, he worked with Infosys Technologies for a couple of years. His undergraduate degree is in Electrical and Electronics Engineering from NIT Trichy (formerly, REC Trichy).

Jingjing Li is an assistant professor of information technology in the McIntire School of Commerce at the University of Virginia. She received her Ph.D. from the Leeds School of Business, the University of Colorado at Boulder. Her research interests relate to machine learning and big data analytics, with applications in e-commerce, platform business, healthcare, search engine, user- generated contents, and recommender systems. She is currently working on multiple machine learning projects funded through the National Science Foundation. Her work on Big Data has received the AWS Research Grant and Microsoft Research Azure Award. Before joining the McIntire School, she was a Scientist at Microsoft, where she proposed and implemented several large-scale machine learning solutions for numerous Microsoft products such as Xbox One, Windows 8 Search Charm, Windows Phone App Store, Cortana, and Bing Entity Search.

Xiao Liu is an Assistant Professor of Marketing at New York University Stern School of Business. Professor Liu’s research focuses on quantitative marketing and empirical industrial organization, with a particular interest in consumer financial service innovations, high-tech marketing and machine learning. She received her B.S. in Finance from Tsinghua University and her Ph.D. in Marketing from Carnegie Mellon University Tepper School of Business.

Maytal Saar-Tsechansky is an Associate Professor of Information, Risk and Operations Management at the McCombs School of Business, The University of Texas at Austin, and a co- founder of Sweetch — a mobile health startup firm. She received her Ph.D. from New York University’s Stern School of Business. Her research interests include machine learning and Artificial Intelligence methods for data-driven decision-making. Her research has been published in the Journal of Finance, Management Science, Information Systems Research, Journal of Machine Learning Research, and Machine Learning Journal, among other venues. Maytal’s research has been supported by both government and industry, including the National Science Foundation, SAP, and the Israeli Science Ministry. In recent years she has served on the editorial boards of the Machine Learning Journal, the Information Systems Research (ISR) journal, the INFORMS Journal on Computing, and she is a frequent Program Committee member in the premier machine learning, data mining, artificial intelligence, and Information Systems conferences. At McCombs, Maytal has developed and taught popular machine learning and data mining courses tailored for business students.

Ruslan Salakhutdinov received his PhD in machine learning (computer science) from the University of Toronto in 2009. After spending two post-doctoral years at the Massachusetts Institute of Technology Artificial Intelligence Lab, he joined the University of Toronto as an Assistant Professor in the Department of Computer Science and Department of Statistics. In February of 2016, he joined the Machine Learning Department at Carnegie Mellon University as an Associate Professor.

Ruslan’s primary interests lie in deep learning, machine learning, and large-scale optimization. His main research goal is to understand the computational and statistical principles required for discovering structure in large amounts of data. He is an action editor of the Journal of Machine Learning Research and served on the senior programme committee of several learning conferences including NIPS and ICML. He is an Alfred P. Sloan Research Fellow, Microsoft Research Faculty Fellow, Canada Research Chair in Statistical Machine Learning, a recipient of the Early Researcher Award, Connaught New Researcher Award, Google Faculty Award, NVIDIA’s Pioneers of AI award, and is a Senior Fellow of the Canadian Institute for Advanced Research.

Dr. Param Vir Singh is the Carnegie Bosch Associate Professor of Business Technologies and Director of PNC Center for Financial Services Innovation at the Tepper School of Business, Carnegie Mellon University. He is the recipient of the Informs’ Information Systems Society’s prestigious Sandy Slaughter Early Career award.

Dr. Singh’s research includes economic aware analysis of user generated content and structural modeling of user behavior in new economy. His interests are focused on digital economy, in particular, sharing economy, crowdsourcing, social media, enterprise 2.0, fintech and mobile. Much of his work follows the econo-mining principle where he applies machine learning, deep learning, computer vision, natural language processing techniques along with rich economic theories to analyze policy/design questions.

His research has won several best paper awards and his research is frequently cited by mainstream media such as Forbes, InformationWeek, CIO magazine, and Business Week. He has been frequently invited by professional/academic societies such as Informs/CIST to provide tutorials on topics including deep learning and structural modeling. Dr. Singh is Associate editor at Management Science and a Senior editor at Information Systems Research. His research articles have appeared in several journals including Management Science, Marketing Science, Information Systems Research, Management Information Systems Quarterly, Organization Science and ACM Transaction of Software Engineering and Methodologies.

Sriram Somanchi is an Assistant Professor of Business Analytics at Mendoza College of Business. His research focuses on bridging the gap between machine learning and social science problems. His interests include developing computationally efficient statistical machine learning algorithms for pattern detection in massive, complex data and demonstrating the practical utility of applying these approaches to real-world problems. He has worked in the area of event and pattern detection in the domains of disease surveillance, clinical health, finance and law enforcement. Somanchi also is interested in leading the development of machine learning and data-mining methods to enable data-driven decision making in organizations and public policy agencies. Somanchi has a PhD in Information Systems and Management from Heinz College at Carnegie Mellon University. He is a graduate from Machine Learning Department at CMU, and earned an M.E in computer science from the Indian Institute of Science, Bangalore, India.

Bin Zhang is an assistant professor in the Department of Management Information Systems, University of Arizona, and a visiting research fellow at Carnegie Mellon University. He is also an affiliated member of Artificial Intelligence Lab, University of Arizona. Bin received his Ph.D. degree in Information Systems Management from Carnegie Mellon University, and a Master’s degree in Machine Learning, from the School of Computer Science at CMU. His primary research interests are large social network analysis and statistical modeling for network problems. Bin’s research projects have been funded by federal and national agencies such as NSF, NSFC, and NIH. His work has appeared in premier information systems journals such as Information Systems Research. Bin also has experience in the Internet industry at companies like Yahoo! and has designed architectures of online ERP systems in the software industry.

Kunpeng Zhang (KZ) is a researcher in the area of large-scale data analytics with particular focuses on mining social media data through machine learning, network analysis, and natural language processing techniques. He is currently Assistant Professor in department of Information Systems at the Smith School of Business, University of Maryland, College Park. He received his Ph.D. in Computer Science from Northwestern University. He published papers in the area of social media, text mining, network analysis, and information systems on top conference and journals. He serves as program committees for many international conferences and currently is Associate Editor for Electronic Commerce research journal.

Does hospital advertising influence patient choice? W e examine 305,000 individual patient-level visits over 24 months in Massachusetts to answer this question. We find that patients are positively influenced by hospital advertising; seeing a television advertisement for a given hospital makes a patient more likely to select that hospital. However, we observe significant heterogeneity in patient response depending on insurance status, medical condition, and demographic factors like age, gender, and race. We find that advertising is effective in drawing patients who live further away, allowing hospitals to increase their market share. Our demand model allows us to study the impact of a ban on hospital advertising, an intervention recently considered by policy makers. Our policy simulation shows that banning hospital advertising can not only affect patient travel distances and redistribute market share, but also hurt patient health outcomes through increased hospital readmissions. The effect of a ban also varies according to hospital and market characteristics, such as hospital type and the geographic configuration of hospitals.

Hospitalization of patients with chronic diseases poses a significant burden on the healthcare system. This is partially attributed to the complex medical regimes and failure of engaging patients through greater self-monitoring and care coordination between patients and providers. Recently, patient portals have become popular as platforms that provide patients with online access to their medical records and serve as a tool to communicate with health care providers for medication refills, appointment scheduling, viewing lab tests, and provider inquiries. Despite the popularity of patient portals, there is a paucity of research on the influence of patient usage of portals on their health outcomes. We draw on a unique dataset of patient-level usage of portals, across a twelve-year period, to examine the association between usage and incidence of subsequent hospitalizations. Our results indicate that portal usage is associated with improvements in patient health outcomes along multiple dimensions, including the frequency and incidence of hospital and ED visits, readmission risk, and length of stay. This represents one of the first studies to conduct a detailed analysis of patient-level usage of portals and study their effect on health outcomes.

Note: Joint work with Chenzhang Bao, Bruce Meyer, Kirk Kirksey, and Harpreet Singh.

We collected panel data about patients suffering from chronic diseases from four urban locations of a large tertiary care hospital system. Our goal is to use Machine Leaning (ML) methods to predict the nature and extent of deterioration in the condition of patients over time. In this study, we extract panel data from the EMRs of 1893 patients that suffered from Type II diabetes. We find that when it comes to predicting key indicators of disease progression, ML methods outperform regression models and structural correlational estimates based on simple clinical factors. We combine ML techniques with physician assessments to create a two-stage filtering model of diagnosis that promises to deliver superior outcomes of both efficiency and effectiveness in care delivery.

Note: Joint work with Praveen Pathak.

Surgical robot is now a proven high tech innovation that promises new possibilities for advancing surgical care delivery. Robot-assisted surgery is becoming accepted by health care providers and patients. Among the promised benefits of robot-assisted surgery is that it will reduce the outcome variation in surgical care delivery across surgeons performing the same surgical procedure (e.g., hysterectomy) – a variation that is considered to be normal when a surgical procedure is performed without the assistance of a robot. Even though the extant literature provides insights into the learning of surgeons and surgical teams for procedures performed without the assistance of robots, little is known about surgeon and surgical team learning in the context of robot-assisted surgeries.

We report the findings of a longitudinal field study on (da Vinci) robot-assisted surgery at a multi-specialty hospital that investigates into: (i) the outcome variation across surgeons performing a surgical procedure, and (ii) surgeon and surgical team learning. The study period is 5 years starting from2008, the point-in-time of initial installation of the (da Vinci) surgical robot in the hospital, to 2013. The study sample contains data on 1380 robot-assisted surgeries performed during the 5-year study period by 18 surgeons in the hospital belonging to two medical specialties: urology and obstetrics and gynecology (OB/GYN). The key contributions of this study are in demonstrating that: (i) a surgical robot reduces the outcome variations in surgical care delivery across surgeons performing the same surgical procedure; and (ii) the learning mechanism in the context of robot-assisted surgery is more nuanced than cumulative volume-based learning – specifically, given particular levels of surgical volume, individual learning of a surgeon depends significantly on the regularity with which the surgeon performs robot-assisted surgeries; and surgeons learn faster with increasing complexity of surgeries. The study also contributes towards demonstrating the interdependency between duration and quality out come of robot-assisted surgical care delivery, there by providing new insights into the speed versus quality debate in managing health care operations.

Note: Joint work with Scott Bosch, Ujjal Kumar Mukherjee, and Shoubhik Sinha.

EPMs shift greater financial risk to hospitals and make them responsible for realizing target savings. Savings are strongly affected by medical practice norms, selection and standardization of treatment modalities and clinical pathways, and education/training of support staff, all of which fall under the purview of doctors and other health professionals. Gainsharing plans serve to align the incentives of hospitals, doctors, and other health professionals by sharing rewards and risks. Our research objective is to study the design of gainsharing contracts that are simple to implement, pass the waiver test, mitigate agency costs, and inefficiencies caused by different levels of risk tolerance. We customize principal-agent models to our setting to obtain optimal parameters of easy-to-implement gainsharing plans and evaluate their performance relative to a benchmark that will be realized with no agency cost.

Note: Joint work with Mili Mehrotra, and Xiaoxu Tang.

The past decade has witnessed a significant, systematic and pervasive ICT-biased shift in R&D in many firms. Specifically, firms in diverse industries are systematically reallocating innovation efforts towards more digital products, services and business models. For example, cars today incorporate a variety of software to run engines, control safety features, identify current coordinates of drivers and guide them to their destinations, and integrate with satellite, mobile and GPS networks. Emerging innovations such as driverless cars further emphasize this shift. Similar examples abound in a variety of industries ranging from aircrafts to oil and gas exploration to financial services. In this study, we assess market returns to ICT-centric innovation across a range of industries. A limitation in the identification of these returns is that the adoption of ICT-centric innovation is endogenous and likely to be highly responsive to performance incentives. W e use group-based trajectory (GBT) models to identify the returns to ICT-centric innovation, contingent on prior performance. GBT models, a statistical technique based on finite mixture models, can be used to discern heterogeneity based on an attribute of interest, especially when the heterogeneity changes or evolves over time, and when there is a lack of theoretical guidance to discern the basis of heterogeneity. We find that returns to ICT- centric innovation are moderated by the performance trajectory of the firm prior to adoption. Specifically, investments in ICT-centric innovation by firms with high intangible returns or greater market expectations of growth are more highly valued. ICT-centric innovation is more disruptive to firms with low intangible returns and greater market expectations of profitability.

We analyze the anatomy of a digital advertising supply chain. Our analytical model involves an advertising agency, which uses a network of large publishers, and an exchange with real time bidding and smaller, specialized publishers. Using a Stackelberg game, we analyze the decisions in both the network and the exchange, and their impact on the profitability of the agency as well as that of the supply chain. We use a proprietary dataset from a campaign carried out by a publicly traded, multinational online advertising agency to explore the vertical(intra-channel) and horizontal (inter-channel) interactions, and analyze the impact of interactions between channel structures and pricing models on the advertising agency’s decisions and campaign performance. We find that there are quantifiable vertical interactions and horizontal synergies, the failure to account for which may lead to overspending on some actions while underspending on others. Specifically, our results indicate that incorporating such interactions and synergies in the agency’s decision making increases the overall supply chain profit by 50% over the status quo. In addition, with feasible information and profit sharing schemes, the supply chain profit can more than double, getting closer to the profit level of a theoretically ideal, but practically infeasible, fully integrated supply chain. We further provide a new rationale for the online advertising agency as an intermediary; by combining information from multiple channels in its decision making and structuring contracts appropriately, the agency enables the supply chain to achieve higher levels of efficiency that would be impossible to attain if the advertising channels act individually. This goes beyond the current economic rationalization of the agency based on economies of scale and lowering transaction costs, which led the agency to an organizational structure focused on vertical media buying. We propose that the agencies should instead be organized by campaigns in order to monetize the substantial benefits derived from cross-channel information sharing and decision making. We also find empirically that the presence of complementarity and acting on it are separate issues, and that in the agency we study, the decision makers in the two channels are unaware of the strong synergies that exist in this domain.

Note: Joint work with Genaro J. Gutierrez and Changseung Yoo.

Electronic visit (e-visit), which allows patients and primary care providers communicating through secure messages sent from web patient portals, has enabled virtual care delivery as an alternative to traditional office visits for selected and non-urgent medical issues. To help identify the conditions that e-visits lead to improved care delivery efficiency and patient access, we modeled the dynamics of in-office waiting time and appointment backlog using single server priority queue and discrete-time bulk-service queues, respectively, and developed numerical methods for computing system performance metrics. Service system intensity, effectiveness of e-visit, and popularity of e-visits are identified as the key factors that impact primary care delivery efficiency and patient accessibility to care. The insights obtained from the models provide guidance to care providers who are engaged in facilitating e-visits to apprehend the influence of the novel care delivery channel on their established practices.

Does a provider’s technology support strategy influence its buyers’ post-adoption IT service use? We study this question in the context of cloud infrastructure services. The provider offers two levels of support, basic and full. Under basic support, the provider handles simple service quality issues. Under full support, the provider also offers education, training, and personalized guidance through two-way interactions with buyers. Using unique data on public cloud infrastructure services use by 22,179 firms from March 2009 to August 2012 and fixed effects dynamic panel data models, we find that buyers who opt for full support use 34.38% more of the service as well as increase the fraction of servers they run in parallel by 3.56 percentage points relative to those who do not. Furthermore, buyers who opt to switch back to basic support from full support continue using 15.58% more of the service and have a proportion of servers running in parallel 4.36 percentage points higher compared to buyers who have never accessed full support. We also find that in the long-run these effects of support on volume and efficiency of usage do not disappear. These findings provide suggestive evidence of buyer learning as a result of provider support.

Note: Joint work with G. F. Retana, C. Forman, M. Niculescu, and D.J. Wu.

This paper investigates the dynamics between actors in the hospital-patient-law firm triad in influencing medical malpractice lawsuits. Using agency theory, we establish the incentives for each actor in the triad, and then using information processing, operational transparency and economic theory, we describe the mechanisms through which information technology, communication quality, and law firm concentration influence each actor’s incentives. Specifically, we argue that technology will have a direct impact on reducing lawsuits. In addition, technology will also complement both communication quality between caregivers and patients as well as law firm concentration in reducing lawsuits. We combine data on 168 hospitals in the state of Florida from 2007-11 in order to investigate the triad. Results indicate that although information technology does not have a direct impact on number of lawsuits it does complement communication quality. We observe an interesting tradeoff when examining competitive intensity of law firms, with technology helping reduce lawsuits in high intensity counties while increasing them in low intensity counties. A post-hoc analysis looking at the impact of HIT and communication quality on different caregivers (physicians vs nurses) reveals that increased technology complements high physician communication quality but not nurse communication quality with respect to lawsuits. Our results remain robust under different model specifications and operationalization of key variables. Together these results provide a better understanding behind the drivers and interesting insights on mechanisms to reduce lawsuits.

Note: Joint work with Luv Sharma.

Ravi Aron, PhD (Information Systems, Leonard N. Stern School of Business, New York University) joined the Johns Hopkins Carey Business School in 2009. He is an Associate Professor in the research track with expertise in the areas of information technology strategy, healthcare strategy and healthcare information systems. He was the Sloan Industry Studies Fellow in 2008 – 2009 and a Senior Fellow at the Phyllis Mack Center for Technology and Innovation at The Wharton School from 2006 to 2013.

Dr. Aron’s research explores the use of IT in transforming the delivery of health care. In particular, his research explores the role played by information and communication technologies in making more clinically effective care less costly to deliver. He has also studied the impact of IT in enabling collaboration between specialist decision makers across both geographical boundaries and boundaries of the firm. His current research investigates how decision-making by clinicians and other specialists is influenced by algorithms and machine inference systems.

Dr. Aron is an advisor to several multi-specialty, tertiary care hospitals in Asia and startups in the health care domain. He has also advised several governmental and policy making agencies in India and Singapore and has worked with the government of Mauritius. He has given talks on the strategic use of IT to achieve business transformation in several countries including the US, UK, India, Singapore, Chile, Peru, Spain, Thailand, Mauritius, South Korea, Philippines and Japan. He was an invited participant and a session chair at the World Economic Forum at Davos in 2005 and 2006.

Dr. Indranil Bardhan is a Professor of Management Information Systems in the Jindal School of Management at the University of Texas at Dallas. He has previously served as Visiting Professor in the Department of Clinical Sciences at the University of Texas Southwestern Medical School. At UT Dallas, Dr. Bardhan teaches courses in the MBA and Executive MBA programs, MS programs in MIS and Healthcare Management, as well as a PhD course for MIS doctoral students. His research and teaching interests focus on the measurement of information technology-driven productivity improvements in the healthcare sector. Specifically, Dr. Bardhan’s research seeks to evaluate the impact of health IT initiatives on the cost and quality of health care delivery. He has collaborated with researchers from the UT Southwestern Medical Center and the Dallas Fort Worth Hospital Council on several research studies related to development of predictive models for readmissions of congestive heart failure patients.

Dr. Bardhan has more than ten years of management consulting experience, most recently as a Principal with PricewaterhouseCoopers Consulting. He has advised senior IT and business executives of Fortune 500 companies on a wide array of IT consulting projects. He has served as an Associate or Senior Editor at several prestigious academic journals. Dr. Bardhan research has been published in the major academic journals and has received more than 1,200 citations. He has also served as a conference co-chair and track chair of several major academic information systems-related conferences. Dr. Bardhan holds a Ph.D. in Management Science and Information Systems from the McCombs School of Business at the University of Texas at Austin, a Master of Engineering degree from Penn State University, and a Bachelor of Technology degree from India.

Anitesh Barua is an Assistant Professor for the Department of Information, Risk, and Operations Management at the University of Texas at Austin. Anitesh Barua received his B.E. from Jadavpur University (India), and his M.S. and Ph.D. from Carnegie Mellon University. His research and teaching interests include measuring business value of information technology, analyzing strategic information technology investments, enterprise modeling using information economics, and economics of software development and maintenance.

Professor Bharadwaj joined the Goizueta Business School in 1995 from Texas A&M University where she received her Ph.D. degree in Management Information Systems with a minor in Computer Science. She also holds an M.B.A and a B.S. degree in Mathematics. Before pursuing her doctoral studies, Anandhi worked as an information systems consultant at NIIT, a world- wide IT consulting firm and was responsible for IT systems development and executive training for clients world-wide.

Anandhi currently serves as the Department Editor for the IS track in Management Science and is a Senior Editor for Information Systems Research she has also served as an Associate Editor of MIS Quarterly (2002-2004) and the Journal of AIS. Her research has been published in journals such as Management Science, Information Systems Research, MIS Quarterly, Journal of MIS, Production and Operations Management, and IEEE Transactions on Engineering Management.

Professor Gupta received his Ph.D. degree in Management Sciences from the University of Waterloo. He also holds an M.A.Sc in Industrial Engineering from the University of Windsor, and a B.Tech Mechanical Engineering (India). His research focuses on stochastic models for supply chain and health operations management. Specifically, his research and consulting interests include supply chain management, healthcare capacity management, payment innovations, and health policy, revenue management in manufacturing and service industries, and design and operational control of manufacturing systems.

Professor Gupta is on assignment at the National Science Foundation, where he serves as a program director for the Service, Manufacturing, and Operations Research program in the division of Civil, Mechanical, and Manufacturing Innovation of the Directorate for Engineering.

Diwas KC is interested in understanding and improving the performance of service systems, with a particular focus on healthcare delivery organizations. Professor KC draws on concepts and tools from operations management, economics, behavioral psychology, and statistics to examine productivity, quality and capacity management. His research has identified a number of factors related to the design and organization of work, including workload, specialization, task variety, multitasking, and learning that impact worker as well as firm-level productivity and quality. A distinct but complementary stream of his research has also explored techniques for improving capacity management and patient flow in various healthcare settings, including ICUs, emergency departments and outpatient clinics.

Professor KC teaches MBA electives in Management Science in Spreadsheets, and Healthcare Operations and Technology Management, a course that he developed. Professor KC received his Ph.D. from the Wharton School of Business, University of Pennsylvania, his MS in Management Science and Engineering from Stanford University and ScB in Electrical Engineering from Brown University.

Sridhar Narasimhan is Co-Director -Business Analytics Center (BAC), and Professor of IT Management, Scheller College of Business. The BAC sponsored the Business Analytics and Big Data Industry Forum on March 20, 2015 and supports our MBA, BSBA, and MS Analytics programs.

Professor Narasimhan is the founder and first Area Coordinator of the nationally ranked Information Technology Management area. In fall 2010 he was the Acting Dean and led the College in its successful AACSB Maintenance of Accreditation effort. He was Senior Associate Dean from 2007 through 2015. He has chaired College’s Reappointment, Promotion, and Tenure Committee from 2003-2007.

Professor Narasimhan was Co-PI (with Sandy Slaughter) on a grant of over $650,000 in funding (2010-14) to study the FACE project (US Navy). Together with Professor Saby Mitra, he developed the IT Management Partnership program. He has led various task forces that have revamped the degree programs in the Scheller College of Business. He has developed and has taught the MBA IT Practicum course since 2003 and worked with executives to offer projects from organizations that include: AT&T, Bank of America, Coca-Cola, Coca-Cola Enterprises, InterContinental Hotels, Southern Company, Iron Planet, Microsoft, NCR, HD Supply, and Dell SecureWorks.

Professor Narasimhan received his Ph.D. degree in Business Administration from Ohio State University, and holds a M.S. in Computers and Information Systems from University of Rochester.

Kingshuk K. Sinha serves as the Department Chair and Professor in the Supply Chain and Operations Department, and is the holder of the Mosaic Company – Jim Prokopanko Professorship in Corporate Responsibility at the Carlson School of Management, University of Minnesota. He also serves as a Graduate Faculty on the inter-disciplinary Bioinformatics and Computational Biology Programs of the University of Minnesota. His degrees include a Ph.D. in Management and an M.S. in Petroleum Engineering from The University of Texas at Austin, and a B.Tech (Honors) in Petroleum Engineering from the Indian School of Mines. Before beginning his graduate studies at The University of Texas at Austin he worked for the offshore production and engineering planning groups of Dubai Petroleum Company (operated by Conoco Inc.) in United Arab Emirates.

Dr. Sinha’s scholarly pursuits are committed to advancing the areas of Management of Technology and Innovation, Global Supply Chain Management, Quality Management, Health Care Supply Chain Management, Responsible Supply Chain Management and Big Data Analytics. Among the most recent scholarly recognitions he has received include the Carlson School of Management Annual Faculty Research Award in 2011 and the second place winner of the 2012 Production and Operations Management Society’s College of Health Care Operations Best Paper Competition. Other recognitions include the Journal of Operations Management’s Best Paper Award, the Stan Hardy Award for the Best Published Paper in Operations Management, the Decision Sciences Institute’s Best Theoretical and Empirical Research Paper Award, the Decision Sciences Journal’s Best Paper Award, and the IBM Best Paper Award from the Production and Operations Management Society.

He has served as the Director of the Joseph M. Juran Center for Leadership in Quality, Founding Academic Director of the Medical Industry Leadership Institute (MILI), Ph.D. Program Coordinator and the MBA Program Coordinator, University of Minnesota Senate, and the Carlson School’s Faculty Consultative Committee and Appointments Committee. His roles in professional societies have included serving as the Program Chair for the Annual Meeting of the Production and Operations Management Society in San and as a Board Member and Secretary of the Society. He has served as a Senior Editor of the Production and Operations Management journal. He currently serves as the Senior Editor of the newly created Industry Studies and Public Policy Department of the Production and Operations Management journal. He also serves as a Senior Editor of the Decision Sciences journal.

Carrie C. Queenan (Ph.D., Georgia Tech) is an Assistant Professor of Management Science at the Moore School of Business, University of South Carolina. Prior to returning to academia, Dr. Queenan was a process engineer with Shell Chemical and an operations strategy analyst with Siemens.

Dr. Queenan researches service operations with a primary focus on healthcare operations and how technology can enable more efficient and effective care. Dr. Queenan’s research has been accepted for publication in journals such as Production and Operations Management, Journal of Operations Management, Interfaces, and others. She earned the POMS’ College of Service Operations Most Influential Paper Award for her research. She serves as an Associate Editor within the Healthcare Operations Department at JOM and is on the Editorial Review Board for POMS.

Xiang Zhong is an Assistant Professor of Industrial and Systems Engineering at the Herbert Wertheim College of Engineering, University of Florida. Dr. Zhong received her Ph.D. degree in Industrial and Systems Engineering from the University of Wisconsin-Madison. She also holds an M.S. in Statistics from the University of Wisconsin-Madison, and a B.S. in Automation from Tsinghua University. Dr. Zhong is a member of the Institute of Industrial and Systems Engineers (IISE), Institute of Electrical and Electronics Engineers (IEEE), and the Institute for Operations Research and the Management Sciences (INFORMS).

Dr. Zhong primary research interests are in the area of Healthcare Systems Engineering (HSE), which is an emerging branch of Engineering intended to investigate the fundamental principles governing healthcare systems operations, and utilize them for analysis, continuous improvement, control, and design. The goal of her research is to develop rigorous quantitative models and first- principle-based methods for healthcare system operations management and improvement, and apply the results on the hospital or clinic floor.

We develop and analyze a model where a firm observes the evolution of a demand-surge over a short time-period. The firm’s decisions about inventory, quality and delivery influence the evolution of the demand surge over short-term and have impact on the level of long-term demand it may experience. The firm must determine the time and quantity for ordering inventory to meet the surge and must choose between sources that differ in their quality-levels and delivery-times.

The model is inspired by the experience of a US-based apparel firm that enjoyed a social-media driven demand surge that originated from a few high-profile positive reviews in the press. The sourcing choices made by the firm to satisfy the demand surge influenced how consumers perceived the quality and delivery performance. These consumer perceptions dynamically influenced the spread of the demand through social networks.

Beyond this specific example, the model captures the basic features of an increasingly-wider set of business contexts in which a firm must observe and respond to sudden shifts in demand-volumes. We situate the model in relation to information diffusion models in Marketing and Information Systems literature and to some recent work related to capacitated diffusion models in Operations literature.

We frame the model around a sequence of time epochs: first, the firm observes an event that may trigger the evolution of a demand surge; second, after observing the early evolution, the firm reacts by deciding its order-sizes from different sources; third, firm receives material against it orders, uses this material to satisfy demand and observes the long-term impact of its choices. We model the evolution of the demand as a diffusion curve. As time progresses and the firm observes the demand evolution, it can learn about the parameters of the diffusion process. We show how to analyze the model and optimize the timing and order-size decisions for the firm. We use these results to develop insights into the value of waiting to gather more information about the surge before acting. We propose ways to influence the probability of a demand surge and once it starts, ways to influence its shape. We also compare the relative effectiveness of the two operational levers of quality and inventory availability that are used to respond to the surge. Based on input parameters estimated from public information, a computational study is employed to confirm the robustness of these insights with respect to changes in the modeling assumptions.

Firms have traditionally used price and advertising to signal product quality when consumers initially are not well-informed about qualities of competing sellers. In the last two decades, the Internet has made it more feasible for buyers to connect with new sellers and products which they cannot inspect before purchase. But the Internet also provides abundant external sources of information about sellers’ product qualities, including online review and ratings systems, search engines, user forums, online social networks, expert opinions etc.

This paper examines how the availability of external information to consumers impacts sellers’ use of price and advertising as signaling instruments, and thereby how it impacts market prices. We demonstrate a rich and complex interaction between the informational roles of price, advertising, and the external information environment. First, contrary to expectation, better information sometimes may have no impact at all on firms’ pricing strategy or consumer welfare. Second, when price alone is sufficient as a signaling instrument, we find that better external information about product quality acts as a substitute, hence reduces the level of price distortion (i.e., increase) needed for signaling. But, external information may alter firms’ mix of signaling instruments, motivating firms to place more weight on price and less on (the more expensive instrument) advertising. This shift causes an increase in market prices when there is an increase in the quality of external information available to buyers. Surprisingly, therefore, better information is not always a boon to buyers because it can lead to higher prices when both price and advertising are needed to signal quality. Even when external information impacts price in the expected direction (reduction), our work adds a new explanation beyond the prior understanding that search costs affect prices by changing the level of competition.

With the increasingly open global economy and advanced technologies, companies have emerged as supply chain orchestrators, linking buying firms’ needs with dispersed manufacturers worldwide. In addition to facilitating material and information flows, these orchestrators provide financial assistance to players in the supply network, where needed. For example, some third-party logistics providers (3PLs) perform the procurement function for small and medium sized buyers, in addition to their traditional shipping services. The 3PLs can often obtain payment delay arrangements from the financially stronger manufacturers, which in turn can be partially extended to the buyers. Hence, the procurement service includes partial financing for the buyers. The question is, to what extent does this practice benefit all parties in the chain? To address this question, we explicitly model the cash dynamics in a supply chain consisting of a manufacturer, several buyers, and a 3PL firm. We characterize the Pareto zone, where all firms benefit from the 3PL’s procurement service. We show that the Pareto zone grows as the number of buyers increases. We also show that, under leadership by the 3PL, the supply chain profit is higher than under leadership by the manufacturer. We find that the intermediary role of the 3PL is crucial, in that its benefit vanishes if the manufacturer chooses to grant payment delay to the buyers directly. This analysis demonstrates how cash dynamics intimately interact with material and information flows in a supply chain. Although our model focuses on a 3PL’s procurement service, the modeling ideas and insights can be extended to other types of supply chain orchestrators.

We leverage a temporary block of the Chinese microblogging platform Sina Weibo due to political events to estimate the causal effect of user-generated microblogging content on product demand in the context of TV show viewership. Using a set of difference-in-differences regressions, we show viewership decreased more strongly in geographical areas with a higher Sina Weibo penetration, and only for shows with a high activity level on Sina Weibo. We quantify the effect on viewership in units of comments on tweets (comments were disabled during the block) by instrumenting the number of relevant comments with a dummy for the time period of the block, and find an elasticity of 0.02. In terms of the behavioral mechanism, we find more pre-show microblogging activity increases demand, whereas the ability to engage in microblogging during show time as a complementary activity to TV consumption does not affect product demand.

Joint work with Stephan Seiler, and Wenbo Wang.

Despite a substantial increase in the Development Assistance for Health (DAH) over the last two decades, many developing countries have fallen significantly short of the Millennium Development Goals (MDGs) set forth by the UN in 2000. The below-par progress towards the health targets has frequently been attributed to the mismatch between supply and demand due to the supply-side barriers and demand-side constraints prevalent in developing countries. It is important for the organizations managing the supply chains for health programs in these countries to carefully prioritize and balance the funding allocated to coordinate supply and demand to achieve maximal impact. In this paper, we analyze how budget-constrained organizations should allocate the available funding between procuring inventory and engaging in demand mobilization in order to maximize program coverage. We provide analytical results and several insights based on our computational study regarding how the funding allocation decision and program coverage change with the budget and operating environment. In many developing country health programs, funding allocation is supply-side focused. However, we show that by optimally allocating funding between the supply and demand sides, program coverage can be improved significantly, sometimes by as much as 100%, relative to the supply-side focused strategy. In many cases, demand mobilization may be carried out by local agents including community health workers on ground, and for those situations, we identify the optimal performance-based contract to motivate the agent. We demonstrate that amongst all possible contracts, a bonus contract is optimal to motivate the agent when the reservation price is zero. When the agent’s reservation price is non-zero, the optimal contract closely resembles a bonus contract. In addition to identifying the optimal contract, our analysis informs when an organization might benefit from having a physical presence on ground to directly engage in demand mobilization. We find that the benefits from having a physical presence on ground are mostly insignificant in settings where demand mobilization is relatively inexpensive. However, as the cost of demand mobilization goes up, having the ability to directly engage in demand mobilization could lead to significant gains in program coverage.

Joint work with Jay Swaminathan.

We investigate the impact of collaborative filtering recommender algorithms (e.g., Amazon.com’s “Customers who bought this item also bought”), commonly used in e-commerce, on sales volume and diversity. We use data from a randomized field experiment on movie sales run by a top retailer in North America. For sales volume, we show that different algorithms have differential impacts. Purchase-based collaborative filtering (“Customers who bought this item also bought”) causes a 25% lift in views and a 35% lift in the number of items purchased over the control group (no recommender). In contrast, View-based collaborative filtering (“Customers who viewed this item also viewed”) shows only a 3% lift in views and a 9% lift in the number of items purchased, albeit not statistically significant. For sales diversity, we find that collaborative filtering algorithms cause individuals to discover and purchase a greater variety of products but push users to the same set of titles, leading to concentration bias at the aggregate level. We show that this differential impact on individual versus aggregate diversity is caused by users exploring into only a few ’pathway’ popular genres. That is, the recommenders were more effective in aiding discovery for a few popular genres rather than uniformly aiding discovery in all genres. For managers, our results inform personalization and recommender strategy in e-commerce. From an academic standpoint, we provide the first empirical evidence from a randomized field experiment to help reconcile opposing views on the impact of recommenders.

Innovation and technological advancements are eliminating constraints on online education. In this paper, we focus on the decision of whether in a competitive setting, a university should offer an online program to complement its current on-campus offering. Since competition between universities could be moderated by subjective assessments such as rankings, we also examine how reputation effects (through rankings) moderate the decision to offer online program. Online program offerings in some cases could also result in the emergence of external markets and this leads us to provide guidelines on the threshold external market sizes required for offering online programs. Our analysis assumes a duopoly setting where the universities play a two-stage game. In the first stage each university simultaneously decides on whether to offer an online program, and in the second stage based on these decisions, the universities decide on the level of content and program match and corresponding equilibrium price for both the on-campus and online program offering.

Our results are that in most cases, regardless of the strategy adopted by the competitor, supplementing the on-campus offering with the online program offering is the preferred option for a university. The only condition under which this might not be the case is when the relative effort differential between the two program offerings and the size of the uncovered market is very small. From a design perspective, we find that content match between the on-campus and online program serves as a mechanism to induce increased coverage of the market through the on-campus program offering. The relative equilibrium prices are such that online program should always be offered at a lower price than the on-campus program.

When we consider reputation effects in our analysis, all the general results hold with one exception. This is for the case when the higher reputed university chooses to complement its on-campus program with an online program while the lower ranked university chooses not to do so, then under certain conditions, the equilibrium market price for the online program is greater than the on-campus program offered by the lower ranked university. For the case where market externalities emerge when online programs are introduced, we are able to provide insights into the threshold market sizes necessary for both universities to supplement their existing on-campus program with an online program.

Joint work with Vashkar Ghosh and Asoo J. Vakharia.

We consider a large buyer who uses delayed payments as a mechanism to mitigate supplier moral hazard. Moral hazard in the supply chain arises because the buyer prefers shorter lead times that require the supplier to exert costly effort that is unobservable. For a cash-constrained supplier, a delayed payment raises the possibility of bankruptcy due to default and therefore incentivizes the supplier to exert effort. Bankruptcy has negative long term consequences for both the supplier and the buyer. While the supplier ceases operations and may incur a bankruptcy cost, the buyer incurs the cost involved with choosing another supplier. Thus, the optimal payment structure from the buyer’s viewpoint (principal) has to manage the tradeoff between supplier moral hazard and bankruptcy. The supplier (agent) chooses the effort level for timely delivery while factoring in the probability of bankruptcy. We model this as an infinite-horizon principal-agent game. We show that suppliers with high cost of effort are able to use the threat of bankruptcy to extract better payment terms (less or no delay) from the buyer and also exert less or no effort than what would be optimal for a supply chain as a whole. We show that a payment structure that involves a bonus payment for timely delivery combined with a delayed payment coordinates the supply chain. This payment structure effectively implies buyer cost-sharing in the supplier’s effort, contingent on adequate supplier performance. Our results provide managers with a roadmap on when and how to implement delayed payments as a function of different supplier parameters such as the cost of operational effort and the wholesale price.

Media platforms are characterized by significant and opposing cross-side network externalities from consumers and advertisers. Moreover, agents join one platform (single-home) in some instances but multiple platforms (multi-home) in other cases. In this paper, we investigate how cross-side network externalities and homing possibilities shape competing media platforms’ pricing strategies and profits. Counter to our naive intuition, a platform’s profits increase with consumers’ dislike for advertising but decrease with advertisers’ desire for consumers when agents on both sides of the market single-home. We obtain this result because the cross-side externalities moderate the intensity of competition between platforms. However, when agents on both sides can multi-home, the results are reversed because the cross-side externalities no longer moderate the competition between the two platforms. If agents on only one side of the market can multi-home, then the results crucially hinge on the relative size of the two externalities. Turning attention to pricing strategies, we find that even when consumers are heterogeneous in their sensitivity to advertising, both platforms do not simultaneously adopt a customized pricing strategy for consumers and at least one platform pursues a uniform pricing strategy if agents single-home on both sides of the market. However, multi-homing agents turn the platforms to local monopolists and induce them to adopt a symmetric customized pricing strategy when the two segments of consumers are quite heterogeneous in their sensitivity to advertising, and a symmetric uniform pricing strategy otherwise. Finally, when only advertisers multi-home, we observe a symmetric customized pricing strategy (unlike in a single-homing model), asymmetric pricing strategies (unlike in a multi-homing model) or a symmetric uniform pricing strategy depending on the relative size of the cross-side network effects.

It is estimated that the amount of discarded electronic products, such as mobile devices, cameras, and computers, in the US alone has increased from 3 million tons in 2008 to 9 million tons in 2012. This increasing volume, advances in recycling technologies, and product design improvements have made recycling of those items a burgeoning business. To ensure that certain recycling standards are met, several states have requirements dictating that electronic waste (e-waste) recyclers have to be certified with one of the two main recycling standards, i.e., e-Stewards or the Responsible Recycling (R2) standard. The former, however, is more stringent (e.g., incineration, prison labor, export are limited or prohibited) and therefore would lead to higher unit cost of recycling. On the other hand, it may result in higher collection volumes, as environmentally conscious consumers may prefer having their used electronics recycled by a recycler with higher standards. We observe that in the US, there are 107 recyclers certified with e-Stewards and 490 recyclers certified with R2. In practice, we observe that recyclers do not collect e-waste from consumers directly. Consumers drop off their e-waste at a collector, who then sells these items to a recycler for a fee. Alternatively, given that most e-waste is in fact in working condition (e.g., hard drives, RAM, LCD monitors, etc.), many collectors also sell these items on a secondary market such as e-Bay and Craigslist. In fact, this has become an important revenue source for collectors due to higher margins than selling as scrap to recyclers. In this paper, we aim to understand when a recycler would choose a more stringent (high-type) certification over a less stringent one (low-type). How would recyclers’ economies of scale (EoS) in processing e-waste and collectors’ reselling in secondary market affect recyclers’ pricing and choice of certification? To that end, we model competition between two e-waste recovery channels, each containing a recycler and a collector. In a two-stage model, each recycler first chooses its certification level (high or low) and the wholesale price it will pay to its collector. Then each collector determines what fraction of its collection volume to sell to its recycler, and what fraction to sell in the secondary market. Consumers who are environmentally conscious prefer to take their e-waste to a high-type rather than a low-type recovery channel. Therefore, the e-waste recovery channels compete both in the secondary market, and for collection of e-waste from the consumer population. We find that the collectors’ engagement in secondary market and the recyclers’ EoS in unit processing cost are critical to the recyclers’ equilibrium choice of certification. When the recyclers’ EoS is small, as expected, the recyclers choose the high-type certification only when the additional processing cost of high-type certificate is sufficiently low. Surprisingly, when the recyclers’ EoS is strong, they choose high-type certification when additional processing cost is sufficiently high. This counter-intuitive result is a direct consequence of the secondary market. Moreover, the recyclers encounter prisoners’ dilemma when both of them choose high-type certification. Finally, we find that increase in the total recycling volume from the consumers always benefits the recyclers’, but it may actually lower the collectors’ profitability.

Joint work with Y-T. Lin, W. Xiao, and M. Jin.

Ram Bala is an Assistant Professor of Operations Management & Information Systems at the Leavey School of Business, Santa Clara University. He holds a Ph.D. in Management Science from the UCLA Anderson School of Management. He studies pricing and resource allocation decisions for the software and pharmaceutical industries in dynamic, competitive markets using the mathematical techniques of operations research and game theory. One line of enquiry is the impact of upgrades and versions on pricing and other operational variables in innovation-intensive industries. He also looks at resource allocation decisions in services, particularly promotional effort, such as sales force and advertising. His work has been published in several top tier peer-reviewed journals including Management Science, Marketing Science, Information Systems Research, Production and Operations Management and the Journal of Revenue and Pricing Management. He has also presented at several prestigious conferences across different functional areas, emphasizing the cross-functional nature of his research. Before Santa Clara, he was a faculty member in Operations Management at the Indian School of Business. Prior to joining academia, he consulted for several firms in the area of pharmaceutical marketing analytics.

Gangshu (George) Cai joined the Leavey School of Business in Fall 2012 as an associate professor in the OMIS department, Santa Clara University. He is the Faculty Director of Graduate Business Programs.

Professor Cai’s research interests include competitive channel and supply chain management, interface between operations management and marketing, and supply chain financing. His scholarship has been supported by multiple organizations, including the National Science Foundation and the National Natural Science Foundation of China. Professor Cai’s work has appeared in leading academic journals, such as Production and Operations Management and Marketing Science. He holds a patent on an auction algorithm. He is the recipient of the Best Paper Award of Fifth International Conference on Electronic Commerce, Kansas State University President’s Faculty Development Award, CBA Fellowship, CBA Outstanding Contributions in Research Award, and Santa Clara University Dean’s Award for Scholarship Excellence.

He has been the co-chair of the annual International Workshop on Supply Chain Management in Shanghai, China, since 2014, the chair of Supply Chain and Internet Financing Research Center and Annual Meeting in Dalian, China, and the Shanghai Thousand Talent Program Distinguished Exert since 2015.

Professor Cai has also taught at Texas A&M International University and Kansas State University, and interned with the T.J. Watson Research Center at IBM in New York. He has won multiple teaching awards in both public and private universities, including Ralph Reitz Outstanding Teaching Award in Kansas State University (one per year school-wide), multiple Dean’s Award for Teaching Excellence in Santa Clara University, and the Leavey Impact Award for Teaching (at most one per year school-wide for contributions over the preceding five years).

Professor Cai received his B.S. in physics from Peking University and his M.S. in business statistics and economics from the Guanghua School of Management at Peking University. He earned his Ph.D. in operations research and computer science from North Carolina State University. He is an Associate Editor of Decision Science Journal and a Senior Editor of Production and Operations Management Journal.

Dr. Gökçe Esenduran is an assistant professor of operations management. She joined the Fisher faculty in 2010 after receiving her PhD in operations, technology and innovation management from the University of North Carolina at Chapel Hill, where she also taught operations management. Her research investigates the profitability and efficiency of environmental operations driven by regulations or market competition. Her research also has implications for policy makers about the design of efficient environmental regulations. Her work has been accepted for publication in Production and Operations Management, Decision Sciences, Journal of Supply Chain Management, and Business Horizons. Dr. Esenduran teaches an MBA elective called “Sustainable Operations” which she created in 2013 and PhD Seminars on sustainable operations and game theory. She also teaches Introduction to Operations Management in the undergraduate program. She serves as an ad-hoc referee for Management Sciences, Manufacturing and Service Operations Management, Production and Operations Management, Decision Sciences, European Journal of Operational Research, and Naval Research Logistics. Dr. Esenduran is serving as the co-chair of Environmental Operations track in 2016 POMS. She has also served as the chair of marketing and operations management interface track in 2014 POMS, and as co-chair of 2014 DSI Doctoral Dissertation Competition.

Juan Feng is an associate professor in the Department of Information Systems in the College of Business at the City University of Hong Kong. She holds a B.A. in economics from Renmin University of China, and a PhD in Business Administration from Pennsylvania State University, with a dual degree in Operations Research. Before joining City U, she worked as assistant professor at University of Florida. She serves on the editorial board of Decision Support Systems, and AE for E-Commerce Research and Applications and Journal of Electronic Commerce Research. She has published in journals such as Information Systems Research, Management Science, Marketing Science, Production and Operations Management, Informs Journal on Computing, etc.

Kartik Hosanagar is a Professor of Technology and Digital Business at The Wharton School of the University of Pennsylvania. Kartik’s research work focuses on the digital economy, in particular Internet media, Internet marketing and e-commerce. He serves as a Senior Editor at the journals Information Systems Research and MIS Quarterly.

Kartik has been recognized as one of the world’s top 40 business professors under 40. He is a six-time recipient of MBA or Undergraduate teaching excellence awards at the Wharton School. His research has received several best paper awards at conferences. Kartik is a cofounder of Yodle Inc, a venture-backed firm that has been listed among the top 50 fastest growing private firms in the US. He has served on the advisory boards of Milo (acq. by eBay) and Monetate and is involved with other startups as either an investor or board member. Kartik is a co-host of the SiriusXM show The Digital Hour which airs on Mondays at 5 pm ET on SiriusXM Channel 111.

Kartik graduated at the top of his class with a Bachelors degree in Electronics Engineering and a Masters in Information Systems from Birla Institute of Technology and Sciences (BITS, Pilani), India, and he has an MPhil in Management Science and a PhD in Management Science and Information Systems from Carnegie Mellon University.

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 research interests are primarily in the areas related to managing capacities and inventories in Supply Chains. Topics he has worked on include, among others, the following: Production-Inventory models with a mixture of demand with different characteristics, Availability of supply information and its impact on the buyer and supplier performances, Dual Channel models with interactions between consumers’ channel choices and inventory decisions, Rental inventory models with decreasing demand and multiple demand classes, Technology adoption in buyer-supplier networks, and Replenishment ordering decisions in continuous-time models. His research in these areas has been published in leading research journals like Operations Research, Management Science and Manufacturing and Service Operations Management. He also publishes articles in business press, most recently in International Commerce Review. He is currently an Associate Editor of the Decision Sciences Journal. He is currently working on a Unilever-sponsored project on collaborative differentiation in supply chains. He has been involved in student projects and in research projects with Seattle-based companies like Amazon.com, Starbucks, Boeing and Microsoft. He teaches courses in the areas of core Operations, Process Improvement, Inventory & Supply Chain, and Sourcing in the undergraduate program, full-time and part-time MBA programs and in the professional Masters programs. He has won Foster school awards for his teaching. He is the Director of the Master of Supply Chain Management Program that is being launched at the Foster School of Business. He is the also the past elected chair of the Faculty Council at the Foster School of Business. He has a Ph.D. in Operations Management from the Krannert School of Management, Purdue University. He has undergraduate and graduate degrees in Industrial Engineering from IIT-Roorkee and National Institute of Industrial Engineering, India, respectively. Before joining the academia, he has worked in consumer packaged goods manufacturing and as an Operations Management consultant in Asia.

Gulver is a PhD candidate at the Information Systems and Operations Management Department at the Warrington College of Business Administration, University of Florida. She holds a B.S. degree in Industrial Engineering from the Istanbul Technical University in Turkey and received an M.S. in Industrial and Systems Engineering and a Masters in Statistics from the University of Florida. Prior to entering the doctoral program she worked as a consultant in the finance industry. Gulver has taught Managerial Decisions Analysis II course in the undergraduate program at UF. Her current research interests include channel selection decisions, channel coordination and competition between online and social network enabled channels as well as panel data analysis to predict the winning bid and search for collusion affects in sealed-bid contracts.

Karthik Natarajan is an assistant professor of Supply Chain and Operations at the Carlson School of Management, University of Minnesota. Natarajan received his Ph. D. in Operations from the Kenan-Flagler Business School at the University of North Carolina (UNC) at Chapel Hill.

Natarajan’s research interests are in humanitarian and non-profit operations with a specific focus on managing and improving health care delivery systems in resource-constrained settings. Natarajan actively collaborates with and consults for several non-profit organizations including the U.S. Agency for International Development (USAID), OneVillage Partners (OVP) and Compatible Technology International (CTI). His research has appeared in the Manufacturing and Service Operations Management journal, and he is an ad hoc reviewer for several journals including Management Science, Manufacturing and Service Operations Management, Production and Operations Management and Decision Sciences.

Woochoel Shin is an assistant professor of marketing, at the Warrington College of Business Administration, University of Florida. He received his Ph.D. in marketing from the Fuqua School of Business, Duke University. His research interests include competitive strategies in online advertising and competitive product policy. His work on these topics has been published in Marketing Science, Management Science, and the Journal of Marketing Research.

Song Yao is an Assistant Professor of Marketing and the McManus Research Chair at the Kellogg School of Management, Northwestern University. Professor Yao has won the Paul Green Best Paper Award and the John Howard Dissertation Award, both of which are sponsored by the American Marketing Association. He was also the finalist for the Frank Bass Outstanding Dissertation Award in 2011 and 2012, and the John Little Best Paper Award in 2009 and 2011.

Professor Yao’s research interests include quantitative marketing, online marketing, auctions, pricing, competitive strategy, and customer management. With a methodological and theoretical orientation of empirical microeconomics, his substantive research focuses on network effects, especially in the context of new media such as online retailing and online advertising. His publications appear in leading academic journals, including Marketing Science.

Professor Yao received his Ph.D. in Business Administration from Duke University, M.A. in Economics from the University of California, Los Angeles, and B.A. in Economics from Renmin University of China.

Markdown optimization continues to be a key opportunity for retailers, especially for short life cycle products. As retailers have increased the breadth of their assortments and the proportions of seasonal and store brand merchandise, and shortened product seasons, the need for rigorous markdown pricing has only heightened. Existing literature includes a number of papers that focus on various aspects of this problem. We extend this literature in two ways. First, we explicitly incorporate the “inventory effect” on the pricing problem. In other words, we allow the sales rate to depend not only on price and seasonality, but also inventory levels. Second, we model the problem for the entire retail chain. This presents the possibility of inter-store shipments as another lever to enhance revenue. Dynamic pricing and inventory allocation strategies are determined using optimal control methods. In this presentation, we report our findings about the properties of the pricing and inventory policies, and illustrate key insights using numerical examples.

Consider a retailer using sponsored search marketing together with dynamic pricing. The retailer’s bid on a search keyword affects the retailer’s rank among the search results. The higher the rank, the higher the customer traffic and the customers’ willingness-to-pay will be. Thus, the question arises: When a retailer bids higher to attract more customers, should the accompanying price also decrease (to strengthen the bid’s effect on demand) or increase (to take advantage of higher willingness-to-pay)? We find that the answer depends on how fast the retailer increases its bid. In particular, as the end of the season approaches, the optimal bid exhibits smooth increases followed by big jumps. The optimal price increases only when the optimal bid increases sharply, including the instances where the bid jumps up. Such big jumps arise, for example, when the customer traffic is an S-shaped function of the retailer’s bid.

[Joint work with Shengqi Ye and Shanshan Hu]

We consider a decentralized two-period supply chain in which a manufacturer produces a product and sells it through a retailer facing a price-dependent demand. We assume that the second period production cost declines linearly in the first-period production, but with a random learning rate. In a decentralized supply chain, we assume that the retailer may or may not have the option to carry inventory. The retailer may carry strategic inventory to mitigate the manufacturer’s monopoly power in the second period. With symmetric market sizes and no holding cost, the manufacturer and the supply chain are both better off when the retailer carries inventory. However, the retailer prefers to carry inventory only when the learning effect is not significant. Therefore, the learning mitigates the value of retailer’s strategic inventory. With symmetric market sizes and no learning, the manufacturer is always better off with inventory. The retailer is better off with inventory when the inventory holding cost is low. The retailer is better off not carrying inventory when holding cost is medium. When the inventory holding cost exceeds a threshold, the retailer will not carry inventory even if he has the option to so.

[Joint work with Tao Li (Santa Clara University) and Suresh P. Sethi (University of Texas at Dallas)]

We develop a model to analyze technology adoption in a supply chain. While the basic model is general, we are motivated by the case of RFID adoption in supply chains. We consider firms on two levels of the supply chain: supplier firms and buyer firms. Industry experience with RFID adoption suggests that some firms, especially on the supply side, have a great amount of uncertainty in estimating the benefits of RFID adoption. This uncertainty is reduced as other supplier firms adopt RFID and information from their experiences becomes available. In addition, the benefit a supplier firm may see by adopting RFID is dependent on the number of its buyers who have already adopted. Thus, at any given time, the estimate of benefit for a supplier depends on the number of supplier firms and number of buyer firms who have already adopted the technology. We seek to capture this dependence and analyze its effect on the adoption of a new technology like RFID.

Our model follows a stream of technology adoption literature and makes a contribution to it. McCardle (1985) considered the technology adoption decision for a firm using a dynamic model where, in each period, information can be purchased to update the estimate of adoption benefit. Ulu and Smith (2009) recently extended that model to consider general probability distributions for benefit and general information signals. Chatterjee and Eliashberg (1990) presented a model to explicitly capture the effect of uncertainty on the firm’s utility and to aggregate individual firms’ decisions to produce a diffusion curve. Another related paper is Whang (2010). None of these papers consider the effect of other adoptee firms on the availability of information signals. The impact of buyer firms’ adoption decisions on the supplier firm’s benefits is also new to this literature. In the following, we briefly discuss a firms’ decision model, our analytical results, and the extension to a population model. We very briefly describe an empirical study to test insights generated from the analytical model.

[Joint work with Daeheon Choi]

We study the real-time effects of chatter on Twitter of upon the trading volume and stock price of Nasdaq 100 firms. We obtain real-time feeds from Twitter with messages mentioning each firm, and obtain data on trading volume and stock price from Yahoo! Finance (http://finance.yahoo.com) at ten-minute intervals. Empirical results suggest that there is a predictive relationship between a spike of Twitter activity associated with a firm and the subsequent spike in the trading volume and stock price for that firm. We take particular interest in the lag time between spikes in Twitter activity and the reaction in the financial markets to events, because even just a few minutes of lag time suggests that the Twitter activity may be informative.

This study has two primary objectives as a research contribution. First, the study represents a microscope upon the diffusion of information in social networks that becomes observable at a resolution of minutes. Second, the approach allows a better understanding how market value is influenced by events, which may reveal interdependencies between firms. Unlike traditional time-series approaches that remove spikes as anomalies in the data, we treat spikes as central to the analysis because they represent real reactions to news and have tangible impacts that should not be ignored, and they reveal some competitive dynamics within industries, for example, when product announcements of one firm impact their suppliers or rivals.

[Joint work with Ali Tafti and Ryan Zotti]

Customers often evaluate products at brick-and-mortar stores to identify their “best fit” product, but end up buying this product not at the store but at a competing online retailer to take advantage of lower prices. This free-riding behavior by customers is referred to as “showrooming.” We analyze three strategies to counter the effect of showrooming that may improve profits for the brick-and-mortar stores: (a) price matching, (b) making product matching harder between the brick-and-mortar store and the online retailer, and (c) charging customers for showrooming. We show that only the last two strategies may improve profits of the brick-and-mortar stores. We also present an analysis to illustrate when a particular strategy, (b) or (c), does better than the other.

[Joint work with Subodha Kumar and Jagmohan S. Raju]

A retailer cannot sell more than it has in stock; therefore its sales observations are a censored representation of the underlying demand process. When a retailer or other firm forecasts demand based on past sales observations, the firm requires an estimation approach that accounts for this censoring. Several authors have analyzed demand learning in censored environments, but they assume that inventory levels are known and hence that stockouts are observed. However, for a variety of reasons, firms often do not know how many units are available to meet demand. In this paper, we investigate the impact of this unknown on demand estimation and subsequent stocking levels in a censored environment. When the firm does not account for inventory uncertainty when estimating demand, we discover a systematic downward bias in demand estimates. We propose and test a heuristic prescription that relies on a single error statistic and sharply reduces this bias. Our work identifies and measures a new component of the value of tracking technologies such as RFID.

For many products, some (uninformed) consumers need to experience the touch and feel in order to determine their valuations. When consumers also differ in their shopping costs, we show that heterogeneous product assortment breadth among two competing retailers can emerge as an equilibrium. Specifically, we consider a market with two products and two retailers, and show conditions under which there exists an equilibrium in which one retailer carries a full-line and the other sells one product only, even though the demand structure for the two products is symmetric and the cost structures of the two retailers are the same. Under this equilibrium, the full-line retailer expands the market demand by attracting the uninformed consumers with large shopping costs and the limited-line retailer passes on the savings from a streamlined assortment to the informed consumers by setting a lower price. Therefore, the two retailers soften the competition between them and both achieve higher profits than they could earn with symmetric assortments.

[Joint work with Stephen M. Gilbert (University of Texas at Austin)]

In this paper, we designed and implemented a randomized field experiment using the VoD (video on demand) system of a large European cable provider. This experiment ran live for half- year during 2012. Its goal was to determine how user choices are affected by social signals (e.g. “likes”) and order in which movies appear. At the VoD system of this cable provider, movies are displayed in decreasing orders of “likes” they receive from users in last two weeks. Therefore, our experiment measures the impact of rank and likes on sales of movies in VoD by experimentally manipulating the order in which the movies appear.

We find that a movie buried in full VoD catalog (i.e. does not appear directly in the menu) sells four times less than when it shows up on the main screen of the VoD system. In short, a promotion to the main screen increases sales by four times. Within the menu, a promotion of one rank increases sales by 4%, on average. More likes also increase sales, but in a nonlinear fashion.

More importantly, we find some evidence the promoting a movie to a higher rank leads to the movie selling as much as the movie in its true rank. This suggests that users are getting all the information about movies from the ranking in the VOD system. In short, there is little social loss in manipulating (within some limits) ranks. On the other hand, movies that are demoted to a lower rank sell more than the true movies at those ranks. This suggests that once movies become “popular” they carry that information with them even when demoted. This suggests that quicker turnover of movies in menu might be beneficial to Cable operator.

We are working on analyzing the results at the household level as well.

[Joint work with Miguel Godinho de Matos (Technical University of Lisbon), Pedro Ferreira (Carnegie Mellon University), and Michael Smith (Carnegie Mellon University)]

Advance selling (AS) has been observed in practice as an effective marketing and sales operations strategy when selling new products or services to consumers. In recent years, many retailers invest in advanced technologies and innovative business models to develop the AS capability. We study the impact of such capability on supply chain players, manufacturers and retailers. A supply chain consisting of a manufacturer and a retailer under a wholesale price contract is modeled. The retailer can decide whether or not to sell in advance to consumers through pre-orders. AS generates more sales and also reduces demand uncertainty for the retailer. For a fixed wholesale price, we show that it benefits the retailer to have the AS capability. However, it may hurt the manufacturer’s profit. This is because reduced demand uncertainty for the retailer from advance selling may lead to reduced order quantity from the retailer to the manufacturer. However, if the manufacturer can adjust the wholesale price depending on whether or not the retailer has AS capability, we show that the retailer’s AS capability always benefits the manufacturer but may hurt the retailer. The manufacturer can adjust her wholesale price to manipulate the retailer on AS decisions. When the retailer has the option or capability to sell in advance, the manufacturer may raise the wholesale price to force the retailer to sell in advance and also take the AS benefits from the retailer. We demonstrate that having the option or capability of AS can backfire on the double marginalization problem and hurt the retailer’s profit, the supply chain profit, and social welfare, while always benefiting the manufacturer.

[Joint work with Zhan Pang (Lancaster University)]

In this paper, we investigate two important issues in assortment modeling: the impact of (1) ignoring stock-out substitution and (2) using an incorrectly specified choice model, on the optimal assortment and profits. We quantify their effects in terms of the maximum percentage gap from the optimal solution and study its variation across a wide range of values for key parameters specifying the problem. My research reveals several interesting insights. First, we find that an incorrectly specified choice model has a much higher impact on the optimal assortment profits as compared to ignoring stock-out substitution. This is significant, since traditionally, much of the focus in OM literature has been on the latter. Second, we find that ignoring stock-out substitution does not reduce the optimal assortment profits significantly. In fact, simple newsvendor based heuristics performs extremely well in spite of the fact that they incorporate the impact of stock-out substitution imperfectly. The findings of this paper have significant implications for retail assortment practice.

There has been a debate raging in the management literature on the payoff associated with investments in both Research & Development (R&D) and information technology. In this study, we investigate how information technology investments moderate the impact of R&D spending on firm performance. We begin with a simple analytical model that provides the basis of our empirical model and hypotheses. We test our hypotheses using archival data from 1998-2004 for a panel of 80 large firms across three industries. Our empirical results suggest that IT spending has a significant moderating effect on the relationship between R&D and firm profitability, especially in the high-tech and pharmaceutical industries. These results shed new light on the interaction relationship between R&D and firm performance, especially in knowledge-intensive industries where the interaction effect of R&D and IT is accentuated.

[Joint work with Indranil Bardhan and Gokcen Arkali]

Research in economics has studied quality-differentiated product line and pricing decisions through vertical differentiation models, albeit largely for physical goods (Mussa and Rosen 1978). Such quality differentiation for information goods is also called versioning, where a vendor provides different qualities or versions of a good which sell at different prices (Varian 1997). While a recent research has suggested that the shape of consumer utilities and marginal costs affect a vendor’s versioning decisions (Bhargava and Choudhary 2008), others have demonstrated the need for unique price schedules that are different from those of physical goods (Sundararajan 2004). An important assumption built into utility functions in extant models is that consumers enjoy “free disposal,” i.e., more of a good cannot make a consumer worse off (Mas-Colell, et al. 1995). However, for many information goods and services, consumers’ utility in product features is not strictly increasing as they suffer from usage related constraints, e.g., software consumption is intrinsically associated with memory usage. For example, even for the same price, a consumer may prefer a smaller bundle of Word and Excel, rather than the entire MS Office package as installing and using greater number of features may put a strain on his resources. Similarly, iPod users are restricted in the number of songs that they can store from both a storage-size and searchability point of view. Consideration of this usage constraint becomes increasingly important for mobile devices where both storage and memory come into play. This assumption of free disposal, usually represented by a monotonic utility function (even if concave in many cases), is increasingly being questioned in the case of information goods. While, a recent research in IS has examined goods with no-free-disposal (NFD) through contracts for personalization services under privacy constraints (Chellappa and Shivendu 2007), there is generally little or no research in this area where a realistic abstraction of information goods consumption has been proposed. Our research addresses these gaps through a comprehensive analysis of an information goods vendor’s product-line (versioning) and pricing decisions under multiple scenarios including when there is no free disposal.

[Co-authored with Amit Mehra]

Electronic commerce is growing rapidly in the recent years. Yet, various surveys of online customers continue to indicate that a significant percentage of customers were not satisfied with their online purchase experience. More research is clearly needed to better understand what affects customer evaluations of their online experience and their online satisfaction, especially in the face of uncertainty. Using BizRate data, this study empirically investigates the importance of product and retailer uncertainty in a customer’s online purchase decision as well as the uncertainty-reduction effects of retailer characteristics. We find that both types of uncertainty have a negative impact on customer satisfaction. However, customers are more concerned about retailer uncertainty than product uncertainty. A retailer’s service quality, website design, and pricing play important roles in mitigating the negative impact of uncertainties. Specifically, service quality is shown to mitigate the negative impact of retailer uncertainty in online markets. Website design helps reduce product uncertainty when experience goods are involved. Our findings also reveal that higher price signals higher retailer quality and consumers are willing to pay a price premium to get certain quality assurance.

[Co-authors: Jifeng Luo, Shanghai Jiao Tong University; Sulin Ba, University of Connecticut]

The paper integrates the literatures on online word-of-mouth and the Long Tail effect to examine the inter-relationship between music blogs and consumer music sampling behavior. We draw from prior Long Tail research examining books and music albums that has focused on sales, and examine an alternate form of consumption unique to information goods — online sampling. Based on novel click-through data from a leading music blog aggregator, we find that the patterns of consumption through online sampling is different from the patterns of consumption through sales and the relationship between music blogging and music sampling differs between the “body” and the “tail” of music sales. Our results suggest blog users are more likely to try music that has been suggested by blogs that are more influential in the blogging community and that the impact of blog influence is stronger in the tail than in the body. We further find that popularity also drives sampling, but that this effect is stronger in the body than the tail. Popularity and blog influence are substitutes, in that the impact of blogs on sampling is stronger for less popular music. Put together, the results shed new light on the impact of blogs on consumer choice and on the Long Tail of online music sampling.

[Co-authored with Jui Ramaprasad]

This study develops a stochastic model to capture developer learning dynamics in open source software projects (OSS). A Hidden Markov Model (HMM) is proposed that allows us to investigate (1) the extent to which individuals actually learn from their own experience and from interactions with peers, (2) whether an individual’s abilities to learn from these activities vary as she evolves/learns over time, and (3) to what extent individual learning persists over time. We calibrate the model on six years of detailed data collected from 251 developers working on 25 OSS projects hosted at Sourceforge. Using the HMM three latent learning states (high, medium, and low) are identified and the marginal impact of learning activities on moving the developer between these states is estimated. Our findings reveal different patterns of learning in different learning states. Learning from peers appears as the most important source of learning for developers across the three states. Developers in the medium learning state benefit most through discussions that they initiate. On the other hand, developers in the low and the high states benefit the most by participating in discussions started by others. While in the low state, developers depend entirely upon their peers to learn whereas when in medium or high state they can also draw upon their own experiences. Explanations for these varying impacts of learning activities on the transitions of developers between the three learning states are provided. The HMM modeling of this study contributes to the development of theoretically grounded understanding of learning behavior of individuals. Such a theory and associated findings have important managerial and operational implications for devising interventions to promote learning in a variety of settings.

[Joint work with Param Vir Singh (CMU) and Nara Youn (U of Iowa)]

Performance-based advertising is becoming increasingly popular in the online advertising industry, where advertisers pay the publisher only when an “action” (e.g., a click-through or a purchase) is generated by the advertisement. In this paper, we study two important questions: (1) Can this emerging advertising scheme perform one of the fundamental functions of advertising—signaling product quality?, and (2) Compared with traditional impression-based advertising, what is the impact of performance-based advertising on advertisers’ signaling behavior and publishers’ advertising revenue?

We argue that, unlike traditional impression-based advertising where total advertising expenditure is determined by advertising exposure (e.g., air time on TV or number of lines in newspapers), total advertising expenditure under performance-based advertising is determined by the amount of actions generated by consumers, which is not directly observable by the viewer of the advertisement. Our analysis also reveals that two critical factors significantly affect the signaling function of performance-based advertising: (1) The demand uncertainty factor, which measures advertisers’ uncertainty about their potential market, and (2) the advertising performance over-measure factor, which describes the extent to which product performance accounts for advertising performance. We find that the uncertainty factor facilitates, but the over-measure factor impedes (or even destroys) the signaling function of performance-based advertising.

[Co-authored with Jinhong Xie]

We introduce a model that explores how to manage knowledge of the product and process design teams throughout a new product development (NPD) project. The timing and the extent of knowledge that each team embeds in the NPD project determine the features and functionality of the new product and process. As a result, the knowledge developed and deployed during the NPD project drives the expected net revenue earned over the product’s life cycle.

A manager impacts the levels of knowledge of both the product and process design teams over time through knowledge development (KD) of the product design team as well as knowledge transfer (KT) between teams. Knowledge development includes activities such as prototyping, simulation or attending training courses. Knowledge transfer occurs in a variety of ways including face-to-face meetings, electronic communications, and the transfer of employees. While KD and KT are pursued to increase product and process team knowledge and ultimately drive net revenue, in the short-term these investments may uncover errors that reduce the value of the knowledge previously embedded in the NPD project. When errors are uncovered, rework is triggered which simply directs the manager to pursue additional knowledge creation through KD or KT.

We show the manager should follow one of two optimal strategies for the pursuit of KD of the product design team and KT between teams. First, consider the situation where the level of knowledge of the product design team is relatively high and the level of knowledge of the process design team is relatively low at the outset of the NPD project. We find that the manager pursues KD of the product team and KT to the process team at initially high rates that decrease throughout the NPD project. We refer to this as the front-loading strategy. In contrast, the manager optimally delays her peak efforts of KT to the product design team until later when the process design team has more knowledge. This strategy is referred to as the delay strategy.

We show that design changes triggered when errors are uncovered during KD or KT significantly impact the rates and the timing of KD for the product design team and KT between teams. Lastly, we show that, depending on drivers of expected net revenue, the manager’s pursuit of KD may actually substitute for KT (or vice versa) or KD and KT may possess a complementary relationship.

[Co-authored with Gulru F. Ozkan and Stylianos Kavadias]

A new product often targets high-end customers by accentuating performance with regard to a key attribute – we refer to this as “steepening” the core product performance. However, an alternate strategy is to “flatten” performance along the core attribute dimension while heightening it along an alternate dimension (Christensen’s notion of disruption). Our two- firm model suggests that depending on market conditions, both firms should steepen, both should flatten, or one should steepen and the other flatten while differentiating substantively on core performance (this differs from the general Max-Min solution). Given our finding that the optimal strategy is highly dependent on the fraction of customers who are “overshot” by the core performance, it is somewhat surprising to simultaneously find that firms realize limited payoffs from marketing efforts that alter customer perceptions of overshoot. On the other hand, design efforts that intensify the degree of steepening or flattening are more profitable.

[Co-authored with Bo van der Rhee, Nyenrode University, The Netherlands, and Weiyu Tsai, University of Utah]

The model of Open Innovation is thriving in various forms. I investigate “brokered innovation contests” (BIC) where firms such as InnoCentive act as intermediaries between companies with problems to solve (seekers) and individuals offering solutions (solvers). BIC differ from lead user innovation and open source software in particular ways, making it a unique problem.

Previous research has found that often the problems require some expertise to solve and that having a higher number of solvers is better for the seeker. However, solvers have many choices of problems as well as outside interests. What motivates them to participate? Possible motivations found in empirical research include money, status, free time, signaling, and intrinsic motivation such as enjoying problem solving. I incorporate previous empirical findings into a model of solver participation with the goal of understanding how to design BIC. Focusing on signaling motivations, I find a trade-off for the BIC sponsor where more and better solutions require a high number of solvers and the value of the signal to the solver decreases as more solvers participate.

Many software firms offer a fully functional version of their products free of charge, with a limited trial time, to ease consumers’ uncertainty about the functionalities of their products and to help the diffusion of their new software. Reduced uncertainty raises consumers’ willingness to pay and leads to a larger network of software buyers. Because of the positive network effect, the increased installed base brings a higher value to the software and allows the firm to set a higher price for its software. However, offering free trial software with time lock will result in a free ride to those users who have only short period of usage, inevitably cannibalizing some demand of the firm’s commercial software. This paper examines the tradeoff between the effects of reduced uncertainty and demand cannibalization, and aims to uncover the conditions under which software firms should introduce the time-locked free trial software. We find that when consumers’ prior belief of product functionality is relatively low and when the network effect is not very strong, it is more profitable for the software firm to offer time-locked free trial software. If the software firm has the strategic option of providing free trial software with limited functionalities, we show that the time-locked free trial is preferred when the network effect is modest.

[Co-authored with Kenny Cheng]

The vast majority of inventory management research assumes that the inventory manager knows with certainty his inventory position. Recent empirical research, however, calls this assumption into question and reveals the reality of inventory management in practice: inventory records do not necessarily match the physical inventory on the shelf. Radio Frequency Identification (RFID) has been proposed as a solution to the problem of record inaccuracy. Instead, our interest is in intelligent inventory management tools that mitigate the costs of record inaccuracy, even without investment in RFID.

We study an inventory system with imperfect inventory records and unobserved lost sales. Record inaccuracies in our model are assumed to arise via an “invisible” demand process that perturbs physical inventory but is unobserved by the inventory manager. When inventory records are inaccurate, the true inventory level on the shelf is a random variable from the perspective of the inventory manager. We propose tracking inventory using a Bayesian Inventory Record (BIR), a probability distribution that evolves over time to reflect the inventory manager’s beliefs about the true inventory level, given replenishment and sales observations.

We formulate the problem of optimal BIR-based replenishment as a partially observed Markov decision process (POMDP). We analyze one- and two-period versions of the problem, isolating and interpreting impacts of record inaccuracy and invisible demand on the replenishment decision. In our setting, replenishment decisions in different time periods are coupled for two reasons: (1) because leftover inventory persists between periods, and (2) because replenishment decisions impact the shape of the BIR. The latter reason we call an “information effect,” and we find that it typically incentivizes a forward-looking inventory manager to stock less than he otherwise would. In this way, our research connects with known results on demand learning with censored observations, where an analogous information effect incentivizes an inventory manager to stock more.

We examine information-sensitive replenishments over longer horizons using an approximate POMDP algorithm inspired by the artificial intelligence literature. In numerical experiments, we find that our approximate POMDP algorithm achieves lower average cost than the myopic policy by ordering less. The approximate POMDP algorithm also achieves lower BIR standard deviations on average, suggesting that an information effect at least partially explains the difference between the myopic and forward-looking policies.

In this talk we characterize some of the operational benefits of item-level RFID in a retail environment. We examine a retail operation with backroom and shelf stock under the assumption of multiple replenishment and sales periods. Backroom stock is replenished according to a periodic-review order-up to policy and shelf stock is replenished continually from the backroom.

Replenishment decisions are made based on demand forecasts that are updated in each sales period based on previous sales. The influence of item-level RFID is two-fold: first, it directly affects the amount of products sold. Second, it indirectly affects the retailer’s demand forecast: more products sold mean a higher demand forecast, which means a higher order-up to level in the backroom. We derive the optimal order-up to levels for backroom stocking for both the RFID and no-RFID cases, and we examine the relative magnitude of the direct (i.e., sales) and indirect (i.e., forecast-driven order-up to levels) effects on expected retailer profit. A numerical study of the dynamics of this system reveals several insights that are of managerial interest.

In this paper we focus on retail demand and supply chains exploiting RFID enabled retail facilities. Currently, in retail facilities RFID implementation is mostly limited to either back store portals for case identification. Some rare implementations are geared for item level identification, such as Gillette’s smart shelves for its disposable razors. As technology progresses and costs diminish, there will be ever more potential for large scale deployment of item-level RFID in retail outlets. Furthermore, as triangulation capabilities expand, such RFID implementations will gradually enable real-time three-dimensional positioning of tagged items through retail facilities. As technology progresses, the potential for real-time management of retail facilities exploiting RFID generated live positional information. Yet the adoption of these technologies will depend strongly on the value generated through the retail demand and supply chain, from the consumers to the manufacturers.

Our team has developed the LiveRetail experimental platform for enabling the experimentation of real-time management of RFID equipped retail facilities. It combines a retail facility configurator, an agent-based retail simulator and a web-connected real-time retail management cockpit.

In the paper we first present the architecture and functionality of the LiveRetail platform. Second we then describe the key learnings from our early experimentation with the platform relative to value generation through the retail demand and supply chain. Third we extrapolate from our early findings so as to project the potential impact of item-level RFID on large retail networks, large consumer goods manufacturers, and consumers.

[Joint work with Angel Ruiz and Driss Hakimi]

The importance of material flow management for a profit-maximizing firm has been well-articulated in the supply chain literature. We demonstrate in our analytical model that a firm must also actively manage information flows within the supply chain, which translates to controlling what it knows, as well as what its competitors and suppliers know.

Our model of a supply chain consists of two horizontally competing firms sourcing from the same supplier. One firm (the ‘incumbent’) takes a lead in introducing a new product in the market, the demand for which is uncertain. The incumbent can invest in obtaining demand information not directly accessible to its competition. The second firm (the ‘entrant’) follows the incumbent in the market with the same or a perfectly substitutable product. Both firms source a component of the product from the (common) supplier. Now, if the incumbent has acquired information, his order to the supplier is likely to reflect some of that information. The supplier in turn could leak the incumbent’s order information to the entrant. This structure in its barest form captures the essence of numerous examples of supplier-driven leakage, highlighted as a leading supply chain risk in multiple surveys.

We formally show that the supplier always leaks the incumbent’s order information to the entrant. As a result, when the incumbent acquires information, its drive to control information flows within the supply chain can trigger operational losses through material flow distortion. Hence the firm may prefer not to acquire information even when it is costless to do so. However, if acquired, demand information is always disseminated in the supply chain, aided by leakage. This result is in stark contrast to the extant literature which argues that demand information is not shared in similar settings. Thus, in equilibrium, information asymmetry is dissipated in the supply chain – either all firms are privy to demand information or none are. Our results underscore the importance of Strategic Information Management – actively managing the supply chain’s information flows, and making trade-offs with material flows where appropriate, in order to maximize profits.

[Joint work with Krishnan S. Anand]

In many inventory control contexts, inventory levels are only partially (i.e., not fully) observed. We discuss the recent developments in the partially observed inventory systems and the associated models. In these models, the inventory level or the customer demand is observed via surrogates (signals). The system state turns out to be the conditional distribution of the inventory/demand given a history of these signals. In some models, this history can be summarized by several statistics called sufficient statistics. For example, the information delay and some censored demand models accept sufficient statistics. When no sufficient statistic exists, we are forced to approximate the conditional distribution.

An option is to approximate the conditional distribution with its mean and variance. This methodology is applied to the zero-balance walk model where the demand is not observed, the inventory level is noticed when it reaches zero, the unmet demand is lost, and replenishment orders are decided so as to minimize the total discounted costs over an infinite horizon. This problem has an infinite-dimensional state space, which makes it difficult to obtain a simple optimal policy. We compare approximations that are based on the mean/variance or just the mean of the inventory level. The mean based approximation has the customary dynamic programming equation of the fully observed problem, while the mean/variance based approximation has a novel equation that resembles a mixture of equations of the fully and partially observed problems. Value functions of both the mean/variance based policy and the mean based policy can be used to obtain lower bounds for the actual cost, but the bound obtained from the former policy is stronger. Moreover, the former policy coincides with the latter policy when the variance of the inventory level is zero. Hence, the mean/variance based policy generalizes the policy of the fully observed problem.

Another option is to solve the actual problem by using numerical methods (such as a finite family of polynomials) to represent the conditional distribution. We report a preliminary comparison of the mean/variance based policy and the numerical solutions.

Our methodologies can be used to evaluate the benefit of technologies, such as RFID tags, from the inventory management point of view. These technologies provide richer, real- time information to inventory managers in the form of more accurate measures of inventory or more signals. In a sense, they make a partially observed problem more of a “fully observed problem”. The difference between the optimal cost of the partially observed problem and that of the fully observed problem is (a bound on) the benefit of the technology. This benefit can be used to make an objective case against or for the technology. The objectivity here is critical for companies hesitantly considering new technology implementations like RFID tags.

[Joint work with Alain Bensoussan, Suresh Sethi]

It is understood that technology can bring great improvements to the supply chain. The latest technology to be promising new efficiencies is RFID. While the possibilities this technology brings seem to be clear to most, the efficiency gain remains vague. This study looks at the global supply chain operations of a large cruise ship company and measures the potential efficiency gains resulting from the application of RFID in three scenarios. Scenario 1 (S1) is a pallet level RFID tagging for pallets from the cruise company logistics center to the ships and case level tagging for express items from the logistics center to the ships. Scenario 2 (S2) is the application of pallet level RFID tagging across the supply chain involving all major suppliers, as well as the logistics center using RFID for pallet identification. Finally, scenario 3 (S3) considers case level for everything from suppliers to the logistics center and directly to the ships. For each of these scenarios we consider two possibilities for the cost of deployment: 1) that the cruise company bears the entire tagging cost or 2) that the cost of tags is shared with the suppliers. To establish the feasibility of these scenarios, a cost benefit analysis of the RFID application was conducted using a time study of current processes. Only direct benefits such as labour reduction and reduction in material resources were considered for this study; intangible benefits such as visibility and coordination improvements were not assigned a dollar value.

It was found that RFID tags in themselves can easily generate sufficient benefits to pay for the tags cost. However, cost amortization for the infrastructure to support RFID tag usage for components such as antennas and handheld scanners is considerable. In this context, the results show that using RFID technology can only generate substantial direct net benefits when two or more actors in the supply chain share the costs and benefits of a case level tagging deployment. However, for other scenarios to gain approval, the cost of the technology must further come down in order to generate an acceptable return on investment. Lastly, it was found that the key for a positive return on investment is not the scale of operations but the products flow density within the supply chain. The main contribution of this research is the examination of the application of technology and RFID in a global service supply chain. For practitioners, it provides a fresh look at RFID and technology costs and benefits.

[Joint work with Simon Véronneau]

We consider a single-product periodic-review inventory system. In each period, we assume that the system faces two types of uncertain demand, recorded and unrecorded; the recorded demand refers to the paying customers whose transactions are updated in the system whereas the unrecorded demand refers to the reduction of inventory without being updated in the system, either due to information system incapability (unrecorded sales) or pilferage (loss). Any demand that cannot be satisfied immediately upon arrival is lost, and incurs a corresponding lost sales penalty cost. Due to the presence of unrecorded demand, the actual and the recorded inventory levels may disagree, but the managerial decisions, such as inventory counting and replenishment, must be made solely on the recorded inventory level. In our model, we assume that whenever inventory stocks out, the manager incurs a fixed penalty cost, and becomes informed of the stock-out event. Furthermore, we assume that inventory counting is costly, but is necessarily performed as a part of the inventory replenishment process.

While the actual inventory level at a given period depends on the entire history of the observed process (inventory records), we identify that sufficient information is captured by the pair of (i) the recorded inventory level and (ii) the number of periods since the last inventory correction. Under mild technical conditions, we obtain several monotonicity structural results, relating the actual inventory level and the recorded inventory levels, which are useful developing the structure of the optimal policy. If the unrecorded demand consists of unrecorded sales and the inventory cost is charged based on the maximum storage capacity, then we show the optimality of a two-parameter policy, called the (l, S) policy. If unrecorded demand is either unrecorded sales or loss and the inventory cost is charged based on the actual inventory level in each period, then we identify a sufficient condition for the optimality of (l, S) policy, which is shown to be optimal in our numerical experiments.

RFID is moving from early stages of slap-and-ship to integration with existing systems and business applications. It is the latter that will yield a return on investment. In addition to existing business applications such as promotions execution we discuss in details two new novel applications.

We present new models that capture real-time status of shipments and make optimal inventory control decisions. In addition, we show analytically that RFID real-time data yield better inventory control policies than the traditional setting. RFID data can also be explored in expediting replenishment orders. We introduce so-called sequential systems, which have nicely structured policies. The regular and expediting orders follow a base stock type policy.

Hurricane Katrina in 2005 crippled much of the U.S. oil drilling and refining capacity, and as a result, demand for gasoline nationwide was very volatile in the days and weeks following the storm. On the other hand, production was quite stable, since drillers and refiners were operating at their (newly reduced) capacity. This is the opposite of the classical bullwhip effect (BWE), in which demand/order volatility increases as one moves upstream in the supply chain. We postulate the existence of a “reverse bullwhip effect” (RBWE) that occurs during and immediately after supply disruptions.

We introduce two analytical models to demonstrate the existence of the RBWE. In the first, we assume that a single buyer procures product from a single seller that is subject to disruptions in the form of capacity shocks. A change in capacity causes a change in the price of the product. If the buyer anticipates further price changes in the future due to a prolonged disruption, he may purchase a quantity that differs from the quantity specified by his steady-state demand curve. We provide conditions under which the variance of (an approximation of) the order quantity exceeds the variance of the capacity, and therefore that the RBWE occurs. We also prove that the magnitude of the RBWE increases with either the severity or the duration of the disruption.

Our second model examines buying patterns when multiple retailers compete for scarce product from a single supplier. This model is based on the “rationing game” discussed by Lee, et al. (1997), who argue that the BWE occurs between the retailers and their customers (i.e. the retailers’ orders are more volatile than their customers’ demands). We examine this claim more closely, verifying it under certain conditions and questioning it under others. Furthermore, we argue that the capacity uncertainty causes the RBWE to occur in the upstream portion of the supply chain; that is, that the retailers’ orders are more volatile than the supplier’s orders. Finally, we consider an alternate pricing structure in which the retailers pay for every unit ordered, plus a separate price for units actually received. This pricing structure discourages retailers from inflating their orders too severely. We demonstrate that this pricing structure causes a Nash equilibrium of order quantities to exist where it otherwise would not, and we prove the resulting existence of the (R)BWE.

[Joint work with Zuo-Jun Max Shen, Ying Rong]

Many countries limit the influence of foreign cultural products such as music, film, and television programs to protect their cultural identify. Commonly observed tools include Quotas, tariffs, and subsidies. However, the advances in digital technology create new avenues, such as internet, for consumers to access foreign entertainment programs. This calls a re-examination of the effectiveness of these traditional tools. We create a unified analytical framework to study the impact of digital technology on cultural protection policies. We find that the performances of these tools are greatly affected by the quality difference between domestic and foreign entertainment programs (through both traditional channel and Internet), and quota produces the least social welfare no matter whether there is leakage through internet.

[Joint work with Kenny Cheng, Jane Feng, Gary Koehler]