News
Avi Seidmann – Racial Bias in Customer Service: Evidence from Twitter
Avi Seidmann, Digital Business Institute Fellow and Everett W. Lord Distinguished Faculty Scholar published a groundbreaking study that provides the Read more
Marshall Van Alstyne comments on misinformation in Boston Globe article
Professor Marshall Van Alstyne comments on misinformation in the Boston Globe. Who can be held responsible for stopping misinformation? The Read more
Andrei Hagiu blog – The Platform Chronicles
Andrei Hagiu, Dean's Research Scholar and Associate Professor, Information Systems, talks about the benefits of classifying network effects in his Read more
Andrei Hagiu – Financial Times – Amazon aggregators are walking into the dragon’s cave
FT article sites research from Andrei Hagiu, Associate Professor, Information Systems and Dean's Research Scholar. "Amazon has many ways of Read more
Managing AI
The Digital Business Institute recently hosted a showcase of papers in the MIS Quarterly Magazine special issue: Managing AI Read more
Should We Regulate Platforms?
Digital Business Institute Professor Marshall Van Alstyne hosted a panel of a dozen of the world's top scholars and regulators Read more
Telemedicine is the Greatest Medical Advancement of our Generation- Avi Seidmann
Telemedicine is the Greatest Medical Advancement of our Generation. Insights at Questrom interviews Avi Seidmann, Everett W. Lord Distinguished Faculty Read more
Don’t Let Platforms Commoditize Your Business – Andrei Hagiu
New article in Havard Business Review from Questrom's Andrei Hagiu, Dean's Research Scholar and Associate Professor of Information Systems. Read Read more
Marshall Van Alstyne interviewed by Summit Health
Summit Health interviews Marshall Van Alstyne, Questrom Chair Professor, Digital Business Faculty, and co-author of bestseller Platform Revolution about trends Read more
Error-riddled data sets are warping our sense of how good AI really is
Short read about how our understanding of progress in machine learning has been colored by flawed testing data. Error-riddled data Read more