This paper examines the differential impact of variances in the quality and taste comments found in online customer reviews on firm sales. Using an analytic model, we show increased variance in consumer reviews about taste mismatch only decreases subsequent demand when mean ratings are high and/or quality variance is low and can even increase demand when mean ratings are low and/or quality variance is high. In contrast, increased variance in quality always decreases subsequent demand. Since these theoretical demand effects are predicated on the assumption that consumers can differentiate between the two sources of variation in ratings, we conduct a survey that demonstrates that subjects are indeed able to distinguish quality from taste evaluations from two subsets of 5,000 reviews taken from our larger datasets of reviews for 4,305 restaurants and 3,460 hotels. We use these responses to construct sets of reviews that we use in a controlled laboratory experiment on restaurant choice, finding strong support for our theoretical predictions. They were also used to train classifiers using a bag-of-words model to predict the degree to which each review in the larger datasets relates to quality and/or taste. Finally, we estimate the effects of the two types of variance in overall ratings on establishment sales, again finding support for our theoretical results.

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High-Skilled Emigration

Product Recalls

  • “Effect of Perceived Quality and Other Recall Characteristics on Abnormal Firm Performance: Empirical Evidence from U.S. Automobile Recalls.” 2013.

Bayesian Weather Forecasting

(with National Weather Service, under NSF Grant ATM-0641572, PI: Roman Krzysztofowicz)
  • “Bayesian Fusion of Ensemble and High Resolution Forecasts.” (with Roman Krzysztofowicz)

  • “Sufficient Statistics of Ensemble Forecast for Bayesian Processor of Ensemble.” (with Roman Krzysztofowicz and Zack Armentrout)

  • “Stochastic Properties of Ensemble Forecast: A Bayesian Perspective.” (with Roman Krzysztofowicz and Zack Armentrout)