- Michael D. Smith
- Frederica De Stefano
- Alain Lemaire
- Tesary Lin
- David Yermack
- Rafael Pass
- Bobbie Cochrane
Michael D. Smith
Heinz College, Carnegie Mellon University
Friday, November 13, 2020
12:00 PM – 1:00 PM
"The Abundant University – Remaking Higher Education for a Digital World"
Over the past quarter century new digital technologies have transformed nearly every sector of our economy—in each case creating abundant access, choice, interactivity and personalization for consumers. The one industry remarkably absent from this transformation is higher education. As COVID accelerates a long overdue transformation of our market, and it’s time for us to imagine what abundant access, choice, interactivity, and personalization could look like in the context of our business. In this talk I will draw on my prior research into the transformation of the entertainment industry, and my current research into the delivery of online education to address three key questions facing the academy today:
- Why do today’s digital technologies threaten to our longstanding business model?
- How is the transformation of higher education likely to play out in the market?
- What should educational institutions—and IS researchers—do to respond to this opportunity?
Michael D. Smith is the J. Erik Jonsson Professor of Information Technology and Marketing at Co-Director of the Initiative for Digital Entertainment Analytics at Carnegie Mellon University’s Heinz College. He received a Bachelor of Science in Electrical Engineering (summa cum laude) and a Master of Science in Telecommunications Science from the University of Maryland, and a Ph.D. in Management Science from MIT’s Sloan School of Management. Professor Smith’s research uses economic and statistical techniques to analyze firm and consumer behavior in online markets, and he co-authored the book Streaming, Sharing, Stealing: Big Data and the Future of Entertainment (MIT Press, 2016) with Rahul Telang.
Federica De Stefano
Wharton School of Business, University of Pennsylvania
Tuesday, October 1st, 2019
1:00 PM – 2:30 PM
"Do more able managers capture the value that they create? Manager's abilities, unit performance, and compensation"
More able managers create more value for their employers. A central question in the study of strategic human capital is how much of that increased value is captured by those managers through their pay. We exploit unique restaurant and retail data to examine the relationship between persistent managerial contributions to profits and pay for middle and lower-level managers. Although we find substantial, persistent differences in managers’ contributions to store profitability, high-performing managers receive only 0.5 percent of the additional value that they create in higher pay. Additional analyses suggest that this decoupling between value creation and value capture may reflect compressed pay scales, ambiguity on the relative role of managers versus context in determining performance, and high-performing managers’ reluctance to leave the organization.
Federica De Stefano is a Postdoctoral Fellow in the Wharton People Analytics Initiative at the Wharton School of the University of Pennsylvania. Her research examines how firms create and capture value from human capital. In studying this topic, she has developed three interrelated streams of research that analyze: 1) the role of managers in shaping individual and organizational outcomes; 2) employee mobility; and 3) the use of non-standard work arrangements. Theoretically, her research seeks to speak to the literatures on human resource management and strategic human capital. Empirically, much of her work is quantitative and based on field studies in organizational contexts, with a focus on the restaurant industry. Federica holds a Ph.D. in Business Administration and Management, a M.Sc. in International Management, a BA in Management from Bocconi University in Milan (Italy), and a M.Sc. in Management Science and Corporate Management from Fudan University in Shanghai (China).
Tuesday, November 5th, 2019
11:30 am-1:00 pm
"The role of linguistic match between users and products"
In this paper, we examine how the linguistic similarity between the language used by reviewers of a product and prospective customers’ own writing style can be leveraged to assess the match between customers and products. Applying tools from machine learning, Bayesian statistics, and computational linguistics to a large-scale dataset from Yelp, we find that the closer the writing style of a restaurant’s past reviews are to a prospective customer’s writing style, the more likely that customer is to write a review for that restaurant. This effect holds across restaurant types and is driven by the linguistic similarity of positive past reviews. Further, we find that similarity with respect to words related to leisure (e.g., family, wine, beer, weekend), biology (e.g., eat, life, love), as well as swear words are most influential in creating a match between customers and restaurants. Finally, matching in writing styles commonly associated with neuroticism and openness also predicts a customer’s likelihood to write a review. The current research suggests that preferences for products can be inferred from the similarity between prospective customers’ linguistic style (as well as the language used by other customers) to describe a product. Our results suggest that recommendation agents should incorporate linguistic matching as part of the recommendation algorithm
Alain Lemaire is a quantitative marketing researcher. He works in the areas of Social Media, Crowdfunding, News Media, and Recommendation Engines. These areas are rich in data, primarily rich in unstructured data, and are largely unexplored in the marketing field. To study these domains, he uses tools from Machine Learning, Bayesian Statistics, and Econometric.
Booth School of Business, University of Chicago
Wednesday, October 30th, 2019
1:00 PM – 2:30 PM
"Valuing Intrinsic and Instrumental Preferences for Privacy"
In this paper, I propose a framework for understanding why and to what extent people value their privacy. In particular, I distinguish between two motives for protecting privacy: the intrinsic motive, that is, a “taste” for privacy; and the instrumental motive, which reflects the expected economic loss from revealing one’s “type” specific to the transactional environment. Distinguishing between the two preference components not only improves the measurement of privacy preferences across contexts, but also plays a crucial role in developing inferences based on data voluntarily shared by consumers. Combining a two-stage experiment and a structural model, I measure the dollar value of revealed preference corresponding to each motive, and examine how these two motives codetermine the composition of consumers choosing to protect their personal data. The compositional differences between consumers who withhold and who share their data strongly influence the quality of firms’ inference on consumers and their subsequent managerial decisions. Counterfactual analysis investigates strategies firms can adopt to improve their inference: Ex ante, firms can allocate resources to collect personal data where their marginal value is the highest. Ex post, a consumer’s data-sharing decision per se contains information that reflects how consumers self-select into data sharing, and improves aggregate-level managerial decisions. Firms can leverage this information instead of imposing arbitrary assumptions on consumers not in their dataset.
Tesary is a PhD candidate in quantitative marketing at the University of Chicago Booth School of Business. Her research focuses on how information and data moderate the relationship between firms and consumers. Her recent projects examine consumer privacy preferences, the impact of privacy policies, and analytics tools that adapt to the modern information environment.
New York University, Stern School of Management
Thursday, May 16, 2019
10:30 am -12:00 pm
Bronfman Bldg. Room 410
"Initial Coin Offerings: Financing Growth with Cryptocurrency Token Sales"
Initial coin offerings (ICOs) have emerged as a new mechanism for entrepreneurial finance, with parallels to initial public offerings, venture capital, and pre-sale crowdfunding. In a sample of more than 1,500 ICOs that collectively raise $12.9 billion, we examine which issuer and ICO characteristics predict success, measured using real outcomes (employment and issuer failure) and financial outcomes (token liquidity and volume). Success is associated with disclosure, credible commitment to the project, and quality signals. An instrumental variables analysis finds that ICO token exchange listing causes higher future employment, indicating that access to liquidity has important real consequences for the enterprise.
New York University, Stern School of Management, Cornell University
Friday, May 10, 2019
10:30 am -12:00 pm
Bronfman Bldg. Room 310
"The Blockchain Protocol: Analysis and Challenges"
We present an overview and analysis of Nakamoto’s famous blockchain protocol which underlies the Bitcoin cryptocurrency, demonstrating that the protocol satisfies some desirable security properties, assuming that a majority of the participants are honest (and as long as the parameters of the protocol are appropriate set as a function of the communication network). We next investigate challenges associated with Nakamoto’s protocol—game-theoretic stability and “wasteful” proof of work—and present new blockchain protocols that overcome them.
Senior Technical Lead for Blockchain Capital Market Solutions at IBM
Tuesday, April 16, 2019
11:30 AM – 1:00 PM
Bronfman Bldg. Room 410
Bobbie Cochrane is an experienced Senior Research Scientist with a demonstrated history of working in the information technology and services industry. Her research involves Blockchain, Scalability, IBM DB2,Cloud, Computer Science, and Enterprise Software.