Tracking AI’s Impact on Creativity, Leadership, and Innovation
Insights shared at Columbia University’s AI Summit show how the technology is redefining the creative process and influencing executive decision making.
Insights shared at Columbia University’s AI Summit show how the technology is redefining the creative process and influencing executive decision making.
A new study by Professor Lori Yue and her co-authors reveals how app developers acquiring smaller third-party apps in the iOS App Store create powerful synergies that discourage new competitors from entering the market.
Analyzing the language of social media users reveals surprising predictors of fake news sharing, offering new strategies to curb misinformation and foster healthier online communities.
Discover the articles that captivated readers and highlighted Columbia Business School's impactful thought leadership and groundbreaking insights.
New CBS research shows that writing an online review covering both the emotional and rational aspects of a bad experience can help consumers feel better — and make them more likely to give the business another try.
Using natural language processing, Professor Olivier Toubia and his co-researchers have found that the way people write, no matter the topic, can reveal clues about how well they might do in the future—whether in school, work, or other areas.
The technology poses significant challenges to electoral integrity and has sparked a race between those attempting to manipulate information and those striving to uphold democratic values, argues Professor Bruce Kogut.
New Research Shows U.S. Voters’ Ability to Identify Real News Hinges on Education and Income, Not Political Alignment
David has over 15 years of experience investing in distressed, special situations and all-weather credit strategies, including as a Partner and Portfolio Manager of Standard General, LP. and Sunago Capital Partners LP. He also serves as Executive Chairman of Turning Point Brands, Inc. (NYSE: TPB), a Director of National Cinemedia, Inc.
Jacopo Perego is a Class of 1967 Associate Professor of Business in the Economics Division at Columbia Business School. His research specializes in the economics of information, the analysis of how economic agents strategically acquire, use, and share information. His work primarily focuses on topics such as the optimal design of information policies, the competitive provision of information, and strategic communication. Prior to joining Columbia, he was a Postdoctoral Associate at the Cowles Foundation, Yale University.
Kristen Lane is a faculty member in the Marketing Division at Columbia Business School. Her research focuses on the psychology of (mis)information. Specifically, her work examines the social and identity-based processes that drive what people read, believe, and share online. Her findings inform efforts to understand and reduce the spread of misleading and deceptive information.
Sharad Devarajan is a media entrepreneur, producer and creator. His most recent company, Graphic India, is the culmination of his lifelong dream to launch superheroes and genre stories that tap into the unique creativity and culture of India but appeal to audiences worldwide.
Professor Seave is a Principal of Quantum Media, the New York City based consulting firm focused on marketing and strategic planning for media and entertainment companies as well as nonprofits. As a Quantum Media principal, she has led numerous consulting engagements since 1998 and has provided senior-level management consulting services to many companies in a broad range of assignments.
With nearly 90 academic publications, over 50 students, half a dozen patents, and nearly 10 million online followers, Moran Cerf is one of the leaders in the research and applications of neuroscience in business.
Cerf holds a PhD in neuroscience (Caltech), an MA in Philosophy, and a BSc in Physics (Tel-Aviv University. He has taught leadership and marketing at NYU and the Kellogg School of Management, where he was a professor of neuroscience and business for nearly a decade.
Sandra Matz takes a Big Data approach to studying human behavior in a variety of business-related domains. She combines methodologies from psychology and computer science – including machine learning, experimental designs, online surveys, and field studies – to explore the relationships between people’s psychological characteristics (e.g. their personality) and the digital footprints they leave with every step they take in the digital environment (e.g. their Facebook Likes or their credit card transactions).
Professor Ansari's research addresses customer relationship management, e-commerce personalization and targeting, social network modeling, and Bayesian models of consumer actions. He is currently working on the use of machine learning methods for Big-Data settings in marketing. Prior to joining Columbia, Professor Ansari was at the University of British Columbia, Canada. He has several publications in leading journals in marketing and allied fields.
Dr. Mohamed Hussein is a faculty member in the Marketing Division at Columbia Business School. Using survey experiments, conjoint analysis, and natural language processing techniques, he studies the psychology of persuasion, politics, and the intersection of the two. Dr. Hussein’s research has been published in top-tier academic journals, including the Journal of Consumer Research, Journal of Experimental Psychology: General, and Personality and Social Psychology Review.
Miklos Sarvary is the Carson Family Professor of Business and the faculty lead for the Media and Technology Program at Columbia Business School. Miklos' broad research agenda focuses on media and information marketing. His most recent papers study ad blocking, online marketplace design and content bundling on social media. Previously, he worked on user-generated content, online/mobile advertising and media and telecommunications competition.
Harry Mamaysky is a Professor of Professional Practice at Columbia Business School, where he serves as the Director of the Program for Financial Studies. He is also on the Steering Committee of the Columbia-IBM Center for Blockchain and Technology. Harry teaches capital markets and asset pricing to MBA, Masters and PhD students, as well as Executive Education courses on the use of text data in finance, and on corporate bonds. He has consulted for a quantitative investment firm and for a nationally recognized statistical rating organization.
Professor Netzer's expertise centers on one of the major business challenges of the data-rich environment: developing quantitative methods that leverage data to gain a deeper understanding of customer behavior and guide firms' decisions. He focuses primarily on building statistical and econometric models to measure consumer preferences and understand how customer choices change over time, and across contexts. Most notably, he has developed a framework for managing firms' customer bases through dynamic segmentation.
One of the most crucial aspects and significant levers that gaming companies possess in designing digital games is setting the level of difficulty, which essentially regulates the user’s ability to progress within the game. This aspect is particularly significant in free-to-play (F2P) games, where the paid version often aims to enhance the player’s experience and to facilitate faster progression.
With unprecedented access to consumer information, firms are increasingly interested in designing highly effective data-driven targeting policies based on detailed consumer data. The current standard for implementing such policies involves the “test-then-learn” approach, where randomized experiments are used to estimate the differential impact of marketing interventions on various customers. However, this method fails to incorporate the firm’s ultimate business objectives, leading to inefficient experimentation and suboptimal targeting strategies.
In a data economy, transactions of goods and services generate data, which is stored, traded and depreciates. How are the economics of this economy different from traditional production economies? How do these differences matter for measurement of GDP, firm values, depreciation rates, welfare and externalities? We incorporate active experimentation and data as an
To investigate general patterns in news information in the United States, we combine a protocol for identifying major political news stories, 11 monthly surveys with 15,000 participants, and a model of news discernment. When confronted with a true and a fake news story, 47 percent of subjects confidently choose the true story, 3 percent confidently choose the fake story, and the remaining half are uncertain. Socioeconomic differences are associated with large variations in the probability of selecting the true news story.
Big data technologies change the way in which data and human labor combine to create knowledge. Is this a modest technological advance or a data revolution? Using hiring and wage data, we show how to estimate firms' data stocks and the shape of their knowledge production functions. Knowing how much production functions have changed informs us about the likely long-run changes in output, in factor shares, and in the distribution of income, due to the new, big data technologies.
‘Moral hazard’ links geoengineering to mitigation via the fear that either solar geoengineering (solar radiation management, SRM) or carbon dioxide removal (CDR) might crowd out the desire to cut emissions. Fear of this crowding-out effect ranks among the most frequently cited risks of (solar) geoengineering. We here test moral hazard versus its inverse in a large-scale, revealed-preference experiment (n~340,000) on Facebook and find little to no support for either outcome. For the most part, talking about SRM or CDR does not motivate our study population to support a large U.S.
Strategies for coping with businesses that face the declining demand of late life-cycle products are
revisited in light of the enhanced competitive capabilities made possible by access to the World
Wide Web and connectivity to the Internet. Presumably endgame competitors may draw upon a
wider variety of implementation options on both the demand and supply sides when serving the
highly-connected markets reached via Internet access. Results are posited to be mixed since supply-
How can we measure the extent to which data-intensive firms are using their market power? Economists typically look to markups as evidence of market power. Using a simple model with firms that price risk in their capital allocation and production decisions, we highlight the competing forces that make markups an unreliable measure of data-derived market power. Instead, we show how markups measured at different levels of aggregation reflect data and distinguish data from other intangible investments.
In light of the widely discussed political divide and increasing societal polarization, we investigate in this paper whether the polarization of political ideology extends to consumers’ preferences, intentions, and purchases. Using three different data sets—the publicly available social media data of over three million brand followerships of Twitter users, a YouGov brand-preference survey data set, and Nielsen scanner panel data—we assess the evolution of brand-preference polarization.