Michael D. Smith

Professor Carnegie Mellon University

  • Pittsburgh PA

Michael D. Smith's research uses economic and statistical techniques to analyze firm and consumer behavior in online markets.

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Carnegie Mellon University

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Biography

Michael D. Smith is a Professor of Information Technology and Marketing at Carnegie Mellon University.

He received his Bachelors of Science in Electrical Engineering (summa cum laude) and his Masters of Science in Telecommunications Science from the University of Maryland, and received his Ph.D. in Management Science and Information Technology from the Sloan School of Management at MIT.

Professor Smith's research uses economic and statistical techniques to analyze firm and consumer behavior in online markets — specifically markets for digital information and digital media products. His research in this area has been published in leading Management Science, Economics, and Marketing journals and covered by professional journals including The Harvard Business Review and The Sloan Management Review and press outlets including The Economist, The Wall Street Journal, The New York Times, Wired and Business Week.

Professor Smith has received several awards for his teaching and research including the National Science Foundation’s prestigious CAREER Research Award, the 2009 and 2004 Best Teacher Awards in Carnegie Mellon’s Masters of Information Systems Management program, the best published paper award runner-up for Information Systems Research in 2006, and best paper nominations at the International Conference on Information Systems and the Hawaii International Conference on Systems Sciences. He was also recently selected as one of the top 100 “emerging engineering leaders in the United States” by the National Academy of Engineering. Professor Smith currently serves as a Senior Editor at Information Systems Research, and has previously served as an Associate Editor at Management Science and Management Information Systems Quarterly.

Prior to receiving his Ph.D., Professor Smith worked extensively in the telecommunications and information systems industries, first with GTE in their laboratories, telecommunications, and satellite business units and subsequently with Booz Allen and Hamilton as a member of their telecommunications client service team. While with GTE, Professor Smith was awarded a patent for research applying fuzzy logic and artificial intelligence techniques to the design and operation of telecommunications networks.

Areas of Expertise

Streaming
Digital Media Products
Consumer Behavior
Higher Education Policy

Media Appearances

Why Apple TV+ is offering a free weekend of binge-watching

Fox 2 Now  tv

2025-01-03

Michael D. Smith, a professor of information technology and public policy at Carnegie Mellon University, said the two-day window is not too short to ignore and not too long to satisfy all demand.

“This is not ‘I’m going to let you binge-watch this over the course of three or four days or a week or a couple weeks and then maybe you won’t subscribe next month,’” he said. “This is, ‘I’m giving you two days to explore my catalog. And I’m hoping that you’re going to find something in there that maybe you’ll binge. Maybe you’ll have time to binge the first six episodes, but it’s so cool you’ve got to come back and you’re going to be willing to subscribe to come back.’”

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The Tyson-Paul fight had tech issues. Can streaming handle more major live events?

NPR  online

2024-11-21

Morning Edition's A Martínez spoke to Michael Smith, a professor of information technology and policy at Carnegie Mellon University, who believes we should be optimistic about streaming's long-term foray into live events.

Why is it difficult to stream events like the Tyson-Paul fight to hundreds of millions of people?

Smith said the internet was designed for point-to-point communication, while broadcast channels were designed for multipoint communication. TV broadcasters, for example, transmit one signal via one shared channel to all TVs in their coverage area. Streaming services, in contrast, require a dedicated channel every time someone connects online to play an episode of a show, movie or live sports event.

"So we're trying to get the internet to do something it wasn't really designed to do," Smith said.

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Big Cable Networks Like HLN Are Failing, and Media Companies Can’t Stop Their Decline

Variety  online

2022-12-07

But the media companies will eventually have to find a way to dispose of lackluster cable properties. Some are being propped up with more sports telecasts. Others with devoted followings, like FX or TCM, are being transformed into curated hubs on streamers. At some point, many of the cable outlets will “become loss makers,” says EY’s Harrison. And the prognosis isn’t optimistic. “I think it’s entirely possible they die,” says Michael Smith, professor of information technology and marketing at Carnegie Mellon University’s Heinz College.

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Industry Expertise

Telecommunications
Consumer Goods
Consumer Services

Accomplishments

CAREER Research Award, National Science Foundation

n/a

Best Teacher Award, Carnegie Mellon’s Masters of Information Systems Management Program

2009, 2004

Runner-up, Best Published Paper Award, Information Systems Research

2006

Education

MIT Sloan School of Management:

Ph.D.

Management Science and Information Technology

University of Maryland at College Park

M.S.

Telecommunications

University of Maryland at College Park

B.S.

Electrical Engineering

Articles

Influence via ethos: On the persuasive power of reputation in deliberation online

Management Science

2023

Deliberation among individuals online plays a key role in shaping the opinions that drive votes, purchases, donations, and other critical offline behavior. Yet, the determinants of opinion change via persuasion in deliberation online remain largely unexplored. Our research examines the persuasive power of ethos—an individual’s “reputation”—using a seven-year panel of over a million debates from an argumentation platform containing explicit indicators of successful persuasion. We identify the causal effect of reputation on persuasion by constructing an instrument for reputation from a measure of past debate competition and by controlling for unstructured argument text using neural models of language in the double machine-learning framework. We find that an individual’s reputation significantly impacts their persuasion rate above and beyond the validity, strength, and presentation of their arguments.

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Internet Governance Through Site Shutdowns: The Impact of Shutting Down Two Major Commercial Sex Advertising Sites

Management Science

2022

In the two weeks after the U.S. Congress passed a package of anti-sex trafficking bills on March 21, 2018, two of the largest online commercial sex advertising platforms ceased operation. On March 23, Craigslist voluntarily removed their personals section, which had been dominated by advertisements for commercial sex. And on April 6, the Department of Justice seized Backpage.com, the largest online platform for commercial sex advertisements. Our research examines the impact of these shutdowns on a variety of important outcome variables, notably prostitution arrests and violence against women—variables that the prior literature has shown were impacted by the introduction of commercial sex advertising platforms. We employ a generalized difference-in-differences model by exploiting cross-city variation in the preshutdown usage of the two shuttered sites.

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Consumer Behavior in the Online Classroom: Using Video Analytics and Machine Learning to Understand the Consumption of Video Courseware

Journal of Marketing Research

2021

Video is one of the fastest growing online services offered to consumers. The rapid growth of online video consumption brings new opportunities for marketing executives and researchers to analyze consumer behavior. However, video also introduces new challenges. Specifically, analyzing unstructured video data presents formidable methodological challenges that limit the use of multimedia data to generate marketing insights. To address this challenge, the authors propose a novel video feature framework based on machine learning and computer vision techniques, which helps marketers predict and understand the consumption of online video from a content-based perspective.

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