Essays on Online Reviews in Service Industry

Loading...
Thumbnail Image

Date

2022

Journal Title

Journal ISSN

Volume Title

Repository Usage Stats

139
views
111
downloads

Abstract

This dissertation investigates the impact of online reviews on downstream demand and firm behavior, for service products with multi-faceted attributes and for which complicated information about those attributes can be found in online text reviews. The two essays in this dissertation examines the review content and its importance for two different product types, both of which comprise an important part of the service sector.

The first essay examines the differential impact of variances in the quality and taste comments found in online restaurant and hotel 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. Finally, we estimate the effects of the two types of variance in overall ratings on establishment sales, again finding support for our theoretical results.

The second essay explores the association between changes in clinical performance and online hospital reviews. Despite the surging increase in the public awareness and usage of online patient reviews for healthcare services, little is known about how these reviews affect healthcare provider's behavior. To this goal, we study the clinical performance of 2,773 U.S. hospitals in recent years when reviews are widely available and examine how it has changed compared to the performance in the pre-review era. To this end we analyze over 300k Google reviews. Our overarching premise is that multiple measures of patient reviews, including the content focus of the reviews (i.e., clinical or non-clinical), and the valence and variance of the content, may be associated with the changes in the quality of care provided. Of particular interest is the heterogeneity of hospital responses across covariates such as the hospital’s characteristics, patient socio-demographic variables, and the degree of competition facing the hospital. Using an exploratory data analysis approach and causal forests, we identify a number of variables that might influence the association between review variables and changes in hospital performance, including the helpfulness of the reviews, competitive intensity, hospital size, and education level and age of the population the hospital serves. Interestingly, provider type, ownership structure, and whether or not the hospitals write response comments to the online reviews have not materially influenced the association between review variables and changes in hospital performance. We offer insights on how the impact of the review variables is affected by meaningful covariates.

Description

Provenance

Subjects

Citation

Citation

Lee, Nah Youn (2022). Essays on Online Reviews in Service Industry. Dissertation, Duke University. Retrieved from https://hdl.handle.net/10161/25267.

Collections


Dukes student scholarship is made available to the public using a Creative Commons Attribution / Non-commercial / No derivative (CC-BY-NC-ND) license.