Uncovering Synergy and Dysergy in Consumer Reviews: A Machine Learning Approach

Massive online text reviews can be a powerful market research tool for understanding consumer experiences and helping firms improve and innovate. This research exploits the rich semantic properties of text reviews and proposes a novel machine learning modeling framework that can reliably and efficiently extract consumer opinions and uncover potential interaction effects across these opinions, thereby identifying hidden and nuanced areas for product and service improvement beyond existing modeling approaches in this domain.

Measuring Founding Strategy

We introduce a novel approach to measure the founding strategic differentiation of startups and its relationship to follow-on performance. We use natural language processing and historical websites to estimate the similarity between the founding website of an individual startup, the historical website of public firms at the startup's founding year, and the founding website of other startups founded in the same year.

Aligning Differences: Discursive Diversity and Team Performance

How does cognitive diversity in a group affect its performance? Prior research suggests that group cognitive diversity poses a performance tradeoff: Diverse groups excel at creativity and innovation, but struggle to take coordinated action.

Predicting Risk Perception: New Insights from Data Science

We outline computational techniques for predicting perceptions of risk. Our approach uses the structure of word distribution in natural language data to uncover rich representations for a very large set of naturalistic risk sources. With the application of standard machine learning techniques, we are able to accurately map these representations onto participant risk ratings.