[Marketing] Nah Lee - Patient text reviews and preference estimation

Professor Nah Lee of Sungkyunkwan University&rsquo;s SKK GSB has published a paper in the prestigious marketing journal, &#39;Marketing Letters&#39;, demonstrating that non-clinical aspects lead to greater impact on patient satisfaction than clinical aspects. Prof. Lee, with a co-author Richard Staelin (Duke University&#39;s Fuqua School of Business), analyzed a set of 317 thousand Google reviews of U.S. acute care hospitals.<br /> <br /> <br /> <br /> Abstract&nbsp;<br /> <br /> The goal of this paper is to illustrate how customer text reviews can be used to identify (a) the factors underlying consumers&rsquo; preference for a product offering and (b) the magnitude of each of these factors on the consumers&rsquo; overall assessment of the product offering experience. The authors do this using approximately 317k Google patient reviews for U.S. acute care hospitals. They first analyze the texts using Natural Language Processing and find eleven valenced topics well-describe the types of healthcare experiences. Then, after describing the structure of these reviews, they use regression analysis to estimate the magnitude of each type of experience on the patient&rsquo;s overall evaluation of the experience after adjusting for any halo effect associated with the dominantly discussed topic, which has the potential of influencing the impact of the other discussed experiences. The authors conclude by providing numerous managerially significant insights coming from these analyses.

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[Marketing] Nah Lee - Vertical versus Horizontal Variance in Online Reviews and Their Impact on Demand

Professor Nah Lee&#39;s paper, &quot;Vertical versus Horizontal Variance in Online Reviews and Their Impact on Demand,&quot; has been accepted for publication in the Journal of Marketing Research, a top-tier marketing journal.<br /> <br /> <br /> <br /> Abstract&nbsp;<br /> <br /> 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 that although increased variance in consumer reviews about taste mismatch normally decreases subsequent demand, it can increase demand when mean ratings are low and/or quality variance is high. In contrast, increased variance in quality always decreases subsequent demand, although this effect is moderated by the amount of variance in tastes. 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 reliably distinguish quality from taste evaluations from two subsets of reviews of size 5,000 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. These responses are 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 allowing us to estimate the two types of review variances. Finally, we estimate the effects of these variances in overall ratings on establishment sales, again finding support for our theoretical results.

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