Jing Gong

May 3 – Sirkka Jarvenpaa to Present “BioData Sourcing and Appropriation: The Case of Genomics”

April 25, 2019 By Jing Gong

BioData Sourcing and Appropriation: The Case of Genomics

Sirkka L. Jarvenpaa

Professor of Information Systems and Bayless/Rauscher Chair in Business Administration

McCombs School of Business, University of Texas at Austin

Friday, May 3, 2019

10:30 AM – 12:00 PM

Speakman Hall Suite 200

Abstract

Discussions of “big data” often focus on algorithms, decision-making, and visualization, without comparable attention to the data per se. Attention to data is also missing in studies on information systems sourcing and digital entrepreneurship. Research on data supply chains often takes an organizational perspective and focuses on privacy, ownership, and security rather than on the data and its use for varied market and nonmarket uses among heterogeneous users in an interorganizational or community context. This void in research on data sourcing and appropriation is unfortunate as data infrastructures are critical arenas for collaboration but also competition in many ecosystems involving data providers, intermediaries, and diverse users. Not just firms, but also communities and nations are competing to grow their data sources and appropriate value particularly in the area of genomics and health. The presentation explores biodata sourcing and appropriation with the specific focus on genomics data and their associated tensions. The study of data sourcing focuses on what is different in partnerships in biodata (genomics) sourcing from what is commonly focused in partnerships in IS sourcing. The study on bio data appropriation focuses on entrepreneurial companies’ strategies leveraging open genomics data.

Bio

Dr. Sirkka L. Jarvenpaa is Professor of Information Systems and Bayless/Rauscher Chair in Business Administration at the McCombs School of Business, The University of Texas at Austin. At The University of Texas at Austin, she serves as the Director of the Center for Business, Technology and Law. She has held many distinguished appointments such as the Marvin Bower Fellow at Harvard Business School. She was the first woman to hold the title of Finnish Distinguished (Fidipro) Professor. Her work has appeared in information systems, management, accounting, marketing, engineering, psychology, and anthropology journals. She recently published a co-authored book “Words Matter: Communicating Effectively in the New Global Office.” Dr. Jarvenpaa has received numerous best paper awards in information systems and management journals. Dr. Jarvenpaa serves or has served as the senior editor or editor-in-chief for several journals: Journal of Association for Information Systems, Journal of Strategic Information Systems, MIS Quarterly, Information Systems Research, Organization Science. She holds a B.S. in Business Administration from Bowling Green State University and Masters and Ph.D. degrees in Business Administration from University of Minnesota. She is a recipient of Association of Information Systems (AIS) Fellow and LEO Awards (LEO stands for Life Time Achievement of Exceptional Global Contributions in the field of information systems). She has been awarded three honorary doctorates.

April 30 – Gordon Burtch to Present “Estimating the Economic Impact of ‘Humanizing’ Customer Service Chatbots”

April 24, 2019 By Jing Gong

Estimating the Economic Impact of ‘Humanizing’ Customer Service Chatbots

Gordon Burtch

Associate Professor, Information & Decision Sciences

Carlson School of Management, University of Minnesota

Tuesday, April 30, 2019

12:30 PM – 2:00 PM

Speakman Hall Suite 200

Abstract

We consider the economic impacts of ‘humanising’ AI-enabled autonomous customer service agents (chat-bots). Implementing a field experiment in collaboration with a dual channel clothing retailer based in the United States, we automate a used clothing buy-back process, such that individuals engage with the retailer’s autonomous chatbot to describe the used clothes they wish to sell, obtain a price offer, and (if they accept the offer) print a shipping label to finalize the transaction. We causally estimate the impact on transaction conversion and price sensitivity from randomly exposing consumers to (1) exogenous variation in price offers, in tandem with (2) exogenously varied levels of chatbot anthropomorphism, operationalized by incorporating a random draw from a set of three anthropomorphic features: humor, communication delays and social presence. We provide evidence of a non-linear relationship, consistent with the ‘Uncanny Valley’ effect documented in the HCI-literature. That is, we show that while introducing either a small (1 treatment) or large (3 treatments) degree of anthropomorphism increases conversion rates substantially (on the order of 10% in the latter case), introducing only a moderate level (2 treatments) is counterproductive. Moreover, we show that a large degree of anthropomorphism (3 treatments) causally increases consumers’ price sensitivity. We argue that this latter effect occurs because, as a chatbot becomes more human-like, consumers shift from a price-taking mindset into a fairness evaluation or negotiating mindset. We discuss the implications for the implementation of AI-enabled autonomous agents in human-facing job roles, and customer service settings in particular.

April 26 – Balaji Padmanabhan to Present “Showing to be Seen: Using Data Science to Discover TV Programs for Public Health Announcements”

April 18, 2019 By Jing Gong

Showing to be Seen: Using Data Science to Discover TV Programs for Public Health Announcements

Balaji Padmanabhan

Professor, Information Systems & Decision Sciences

Muma College of Business, University of South Florida

Friday, April 26, 2019

10:30 AM – noon

Speakman Hall Suite 200

Abstract

Television is a prominent channel for educating the public about chronic health conditions. This study presents a methodology for selecting TV programs for public health campaigns targeted to the at-risk individuals. Through high-dimensional analysis of a large dataset on TV viewership of the entire U.S. panel in 2016 the methodology first inductively discovers programs whose popularities are correlated with eight chronic conditions and risk factors. A series of nonparametric tests then examine the robustness of findings and verify that a significant portion of the correlations is genuine—that is, not all the discovered correlations are accidental due to the “curse of dimensionality.” We then use Facebook’s split testing platform and run a series of online experiments to compare the effectiveness of targeting the shows discovered by the methodology with those that were targeted by the major 2016 public health campaigns. The experimental results corroborate the potential value of the methodology, which opens up a potentially new set of programs for public health officials to consider in their efforts to combat a range of conditions that are significantly expensive both in human lives and cost to the economy.

April 19 – Alessandro Acquisti to Present “The Sense of Privacy”

April 15, 2019 By Jing Gong

The Sense of Privacy

Alessandro Acquisti

Professor of Information Technology and Public Policy, PwC William W. Cooper Professor of Risk and Regulatory Innovation

Heinz College, Carnegie Mellon University

Friday, April 19, 2019

10:30 AM – noon

Speakman Hall Suite 200

Abstract

Many factors affect privacy behavior in both conscious and unconscious manners. Some of those factors are sensorial cues: hearing, seeing, or smelling the presence of others. Human beings may be wired to react to those cues even when they do not carry information about actual trade-offs associated with privacy choices, and thus should not normatively influence privacy calculus. In four experiments (N=829), we examine the effect on privacy-relevant behavior (the disclosure of personal information) of sensorial cues signaling the presence of other humans, including cases when that presence does not materially affect risks or benefits associated with personal disclosures. Four types of sensorial cues (proximity, visual, auditory, and olfactory), each signaling the presence of another person around the participant’s physical space, produce a consistent and significant inhibitory effect on disclosure of personal, intimate information in an online survey. The findings suggest a visceral, and in part unconscious, influence of sensorial stimuli on privacy choices. We discuss the implications of the findings in the context of privacy (and security) decision making in a digital age, where physical cues human beings may have adapted to use for detection of threats may be absent or even manipulated by third parties.

Bio

Alessandro Acquisti is a Professor of Information Technology and Public Policy at the Heinz College, Carnegie Mellon University (CMU), and the PwC William W. Cooper Professor of Risk and Regulatory Innovation. He is the director of the Peex (Privacy Economics Experiments) lab at CMU, and the co-director of Carnegie Mellon’s CBDR (Center for Behavioral and Decision Research). Alessandro investigates the economics of privacy. His studies have spearheaded the investigation of privacy and disclosure behavior in online social networks, and the application of behavioral economics to the study of privacy and information security decision making. Alessandro has been the recipient of the PET Award for Outstanding Research in Privacy Enhancing Technologies, the IBM Best Academic Privacy Faculty Award, the IEEE Cybersecurity Award for Innovation, Heinz College School of Information’s Teaching Excellence Award, and numerous Best Paper awards. His studies have been published in journals, books, and proceedings across a variety of fields, including Science, Proceedings of the National Academy of Science, Management Science, Journal of Economic Literature, Marketing Science, Journal of Consumer Research, Journal of Personality and Social Psychology, and Journal of Experimental Psychology. Alessandro has testified before the U.S. Senate and House committees on issues related to privacy policy and consumer behavior, and has been frequently invited to consult on privacy policy issues by various government bodies, including the White House’s Office of Science and Technology Policy and the Council of Economic Advisers, the Federal Trade Commission, the National Telecommunications and Information Administration, and the European Commission. Alessandro’s findings have been featured in national and international media outlets, including the Economist, the New York Times, the Wall Street Journal, the Washington Post, the Financial Times, Wired.com, NPR, CNN, and 60 Minutes; his TED talks on privacy and human behavior have been viewed over 1.2 million times online. His 2009 study on the predictability of Social Security numbers was featured in the “Year in Ideas” issue of the NYT Magazine (the SSNs assignment scheme was changed by the US Social Security Administration in 2011). Alessandro holds a PhD from UC Berkeley, and Master degrees from UC Berkeley, the London School of Economics, and Trinity College Dublin. He has held visiting positions at the Universities of Rome, Paris, and Freiburg (visiting professor); Harvard University (visiting scholar); University of Chicago (visiting fellow); Microsoft Research (visiting researcher); and Google (visiting scientist). He has been a member of the National Academies’ Committee on public response to alerts and warnings using social media, he is a member of the Board of Regents of the National Library of Medicine (NLM), and he is a Carnegie Fellow (inaugural class).

March 29 – Olivia Sheng to Present “Do Fit Opinions Matter? The Impact of Fit Context on Online Product Returns”

March 20, 2019 By Jing Gong

Do Fit Opinions Matter? The Impact of Fit Context on Online Product Returns

Olivia Sheng

Presidential Professor and Emma Eccles Jones Presidential Chair of Information Systems

David Eccles School of Business, University of Utah

Friday, March 29, 2019

10:30 AM – noon

Speakman Hall Suite 200

Abstract

Product fit uncertainty is cited as one of the top reasons for high online product return rates. In this study, we examine the informational role of online product fit opinions in helping reduce purchase errors that ultimately lead to product returns. Fit describes how well a product suits a consumer’s needs, and the value of a product drops sharply when it deviates from a consumer’s ideal fit. We investigate the impact of two types of structured product fit information – fit valence and fit reference – on online product returns of apparel goods, which is a product category especially sensitive to fit. Using the lens of advice-taking, we illustrate how the context (i.e. fit reference) of fit opinions helps a consumer better interpret fit valence. A change in the product review system at an online retailer allows us to use a natural experiment to examine the causal effects of such fit information from multiple advisors. We find that the availability of mere fit valence (e.g. whether the apparel is true to size or runs large/small) increases product returns; rather, it is the combination of fit valence and fit reference that drives a significant reduction in product returns. Applying generalized propensity score analysis, we observe a dynamic treatment effect and a 19.86% decrease in product return probability due to the combined fit information. The treatment effect increases when 1) a customer shares similar fit-related needs with the reviewers, and 2) the valence of fit opinions is dispersed. More interestingly, our results suggest that when fit context is provided, negative fit valence is especially helpful in reducing product returns. This study makes theoretical contributions to the understanding of advice-taking in matters of fit. Our findings are generalizable to situations where fit attributes dominate the product evaluation process. We provide business implications to online sellers grappling with high product return rates.

Bio

Olivia Sheng is Presidential Professor and Emma Eccles Jones Presidential Chair of Information Systems at the David Eccles School of Business, University of Utah. She also directs the Global Knowledge Management Center (http://gkmc.utah.edu) to seek research and education extension of business analytics, and organizes one of the first academic conferences on business intelligence/analytics (http://winterbaconf.org) annually.

Her research focuses on predictive and prescriptive analytics to address the needs in healthcare, marketing, social media, business relationship and performance, human resource, products and operations management. Her research has received funding from U.S. Food and Drug Administration, National Science Foundation, Overstock, Yahoo!, U.S. Army, IBM Tivoli, Toshiba Corp., Sun Microsystems, SAP University Alliance, and Wasatch Advisors. Currently, she engages companies in Utah such as Backcountry, Utah Transit Authority, University of Utah Health Services, and Intermountain Healthcare Company to collaborate on research and capstone projects related to big data analysis. Along with her collaborators, she was a recipient of the 2017 INFORMS Design Science Award. Her research publications received various media attention and professional recognitions.

Dr. Sheng received the B.S. degree from the National Chiao Tung University in Taiwan, R.O.C. and the Master’s and Ph.D. Degrees in Computers and Information Systems from the University of Rochester. Prior to University of Utah, she was on faculty of Management Information Systems at the University of Arizona since 1985 and was the Department Head from 1997 to 2002. Since 1995, Dr. Sheng visited and taught globally at different institutions including Hong Kong University of Science and Technology, Tokyo Institute of Technology, Shanghai Jiao Tung University, and Molde University College in Norway.

March 22 – Raghu Santanam to Present “Multi-Channel Utilization in Goal-Pursuit Platforms”

February 28, 2019 By Jing Gong

Multi-Channel Utilization in Goal-Pursuit Platforms

Raghu Santanam

Department Chair / McCord Chair in Business / Professor of Information Systems

W. P. Carey School of Business, Arizona State University

Friday, March 22, 2019

10:30 AM – noon

Speakman Hall Suite 200

Abstract

With increasing ubiquity of mobile devices, goal-pursuit platforms have seen tremendous development as they provide individuals important capabilities for achieving user goals. Assisting users in effectively and efficiently attaining their goal is the key to the success of both the users’ and the goal-pursuit platforms. In this research, we developed a set of hypotheses based on affordance and goal pursuit theory to predict how multi-channel adoption improves goal pursuit. We utilized a unique proprietary database from a goal-pursuit platform based in the U.S. to construct a panel data set. Based on the data set, we leverage a quasi-natural experiment to examine the effect of multi-channel adoption on goal pursuit. The empirical results show that multi-channel adoption improves overall goal pursuit intensity and activeness. Most notably, compared with active users, previously inactive users show higher goal pursuit intensity after the mobile channel was made available. Further, we also observe preliminary evidence of a complementary relationship between PC and mobile channels and we demonstrate that multi-channel adoption creates monetization benefits for the goal-pursuit platforms.

February 22 – Joey F George to Present “Media, Culture & Deception”

February 18, 2019 By Jing Gong

Media, Culture & Deception

Joey F George

Associate Dean for Research / John D. DeVries Endowed Chair in Business / Professor of Information Systems

Ivy College of Business, Iowa State University

Friday, February 22, 2019

10:30 AM – noon

Speakman Hall Suite 200

Abstract

As the world “gets smaller” and more people engage in cross-cultural communications, their ability to successfully separate truth from deception can be critically important. Yet it is challenging. While deceptive communication has been studied for decades, some areas are not well understood. In particular, two areas that could benefit from further research concern the effects of cultural differences and communication media on deception and its detection. Building on developments in theories of deception and its detection, we examine the question: How do differences in culture between senders and receivers affect deception detection, especially where the deceptive communication occurs across different media? To address this question, stimulus materials from recorded interviews were created featuring participants from the United States, Spain, and India. Three stimulus sets were created, one each in American English, Spanish, and Indian English, and each consisting of 32 interview snippets. Half of the snippets were honest and half were dishonest. Each snippet represented one of four media: full audio-visual, video only, audio only, and text only. Veracity judges were also recruited from the same three countries as the interview participants, to independently observe and evaluate the communication both within their culture and across other cultures. Evidence was found that different combinations of cultural and media effects affected the accuracy of deception detection.

Reference:

George, Joey F.; Gupta, Manjul; Giordano, Gabriel; Mills, Annette; Tennant, Vanessa M.; and Lewis, Carmen C.. 2018. “The Effects of Communication Media and Culture on Deception Detection Accuracy,” MIS Quarterly, (42: 2) pp.551-575.

November 9 – Ali Tafti to Present “Leveraging Covariates in Randomized Experiments Guided by Causal Structure”

November 5, 2018 By Jing Gong

Leveraging Covariates in Randomized Experiments Guided by Causal Structure

Ali Tafti

Associate Professor of Information and Decision Sciences

College of Business, University of Illinois at Chicago

Friday, November 9, 2018

10:30 AM – noon

Speakman Hall Suite 200

Abstract

Researchers using randomized controlled trial (RCT) experiments often subgroup or condition on auxiliary variables that are not the randomized treatment variables. There are many good reasons to condition on auxiliary variables—also referred to as control variables or covariates— in randomized experiments. In particular, designing and conducting RCTs is costly to researchers and subjects. Therefore, it’s important to derive greater value from RCTs that are conducted; measuring not just the average treatment effect (ATE), but also finding more nuanced insights about the underlying theoretical mechanisms and generalizing the inferences. Unfortunately, there are many confusing and even contradictory guidelines on the use of subgroups or auxiliary variables in RCTs. For example, the common wisdom is that post-treatment variables (i.e. those that are ex-post to the treatment variable) should not be conditioned on. However, such variables can provide valuable information, and can in many cases be properly utilized. Using causal diagrams, and applying a few simple rules based upon Judea Pearl’s causal diagramming framework, we explain how researchers can leverage covariates without biasing their causal inferences. We provide guidelines for using subgroups and auxiliary variables in randomized experiments, focusing on some well-known digital experiments featured in the Information Systems literature.

October 19 – Maytal Saar-Tsechansky to Present “More For Less: Adaptive Labeling Payments in Online Labor Markets”

October 11, 2018 By Jing Gong

More For Less: Adaptive Labeling Payments in Online Labor Markets

Maytal Saar-Tsechansky

Associate Professor

McCombs School of Business, University of Texas at Austin

Friday, October 19, 2018

10:30 AM – noon

Speakman Hall Suite 200

Abstract

In many predictive tasks where human intelligence is needed to label training instances, online crowdsourcing markets have emerged as promising platforms for large-scale, cost-effective labeling. However, these platforms also introduce challenges that must be addressed for these opportunities to materialize. In particular, it has been shown that different trade-offs between payment offered to labelers and the quality of labeling arise at different times, possibly as a result of different market conditions and even the nature of the tasks themselves. Because the underlying mechanism giving rise to different trade-offs is not well understood, for any given labeling task and at any given time, it is not known which labeling payments to offer in the market so as to produce accurate models cost-effectively. Effective and robust methods for dealing with these challenges are essential to enable a growing reliance on these promising and increasingly popular labor markets for large-scale labeling. In this talk I will first present a new data science problem, Adaptive Labeling Payment (ALP): how to learn and sequentially adapt the payment offered to crowd labelers before they undertake a labeling task, so as to produce a given (machine learning) predictive model performance cost-effectively. I will then present our approach to address the problem and a rich set of results that demonstrate its performance under a variety of market settings. We also show that the method is highly versatile and can acquire more labels of lower quality (and cost) under some market conditions, while pursuing fewer and higher quality labels in other settings. Overall, our method yields significant cost savings and robust performance; as such, it can be used as a benchmark for future mechanisms to determine cost-effective payments.

Bio

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. Her research focuses on developing machine learning (ML) and artificial intelligence (AI) methods to improve decision-making and to benefit people, organizations, and society. Most of her work aims to augment ML & AI by bringing to bear the problems that machine learning and AI inform in practice and the context in which learning itself occurs, with the goal of effectively dealing with the constraints and taking advantage of the opportunities presented in these environments. Her research integrates business, machine learning and artificial intelligence, and she has addressed challenges in different domains, including health care, smart electricity grid, fraud detection, finance, and emerging forms of work, such as online labor markets. Maytal received her Ph.D. from New York University’s Stern School of Business. 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, Decision Sciences, 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 applied machine learning and data mining courses tailored to business students.