Moderating Roles of Review Credibility and Author Popularity on Book Sales

User reviews have become a popular source to assess the quality of products in consumers\u27 purchasing decision. New insights into the effect of user reviews on product sales can be derived from examining review credibility and author popularity in our example of book sales from Amazon.com. We found that (1) average rating of reviews and diversity of ratings positively affect book sales, but (2) high diversity weakens the effect to sales, showing a quadratic effect of diversity on sales. In addition, our results suggest evidence that (3) review credibility and author popularity moderate the positive association of average rating of reviews and diversity of ratings on sales. Finally, (4) consumers seem to pay more attention to reviews for digital books than for paper books

The Effect of Identity Disclosure on Reliability and Efforts Provision in Online Review Systems

As consumers rely on online customer reviews to assess the quality of products, firms often try to manipulate the reviews to attract consumers. If review sites fail to maintain reliability, firms are less likely to be motivated in improving quality of products. To alleviate fakery, online review providers have designed several identity disclosure mechanisms. The purpose of this research is to explore the role of identity disclosure on (1) reliability of online review systems and (2) subsequent efforts provision. We employ an incentive-aligned laboratory experiment based on a simple model of review systems. As theory of social pressure predicts, our results show that identity disclosure hurt the reliability of review systems, but not necessarily efforts provision. The current paper is a research in progress that aims to better understand the role of identity disclosure in online review systems

Better Understanding Emotions in Texts: A Cognitive Hybrid Deep Learning Approach

With the widespread use of online platforms, people frequently share their thoughts and opinions via emotionally charged texts. For online service providers and researchers, these user-generated contents serve as valuable resource in analyzing individual’s view as well as providing personalized services. However, many of the existing methodologies and labeled dictionary datasets are lacking when there are multiple co-existing discrete emotions embedded in texts. In this research-in-progress study, we propose a new Cognitive Joint Attention Neural Network (CJANN) model inspired by the human’s cognitive process in reading texts. This model also incorporates three layers of attention modules to measure the level of emotion provision for eight discrete emotions. The proposed deep learning model outperforms other widely used models

Effect of Displayed Ratings on Provision of Emotional Online Reviews

Consumers are frequently sharing digitalized opinions such as online reviews to aid purchase decision of others. While each online review is supposed to portray an individual’s unique and genuine experience, previous consumers’ reviews may to some extent influence others’ subsequent reviews when the next consumers perceive the previous ones to be misleading. This study aims to examine the causal impact of previous average rating on provision of emotional text in a subsequent online review, especially when displayed rating is off from its true rating. Using online review data collected from Yelp, we apply natural language processing to measure the amount of emotions in each review and, then, use a regression discontinuity design by taking advantage of the Yelp’s rounded rating display. Our preliminary results suggest that, when the displayed rating is different from the true rating, subsequent reviews tend to include more emotional text. Specifically, a higher displayed rating leads to a sharp increase in provision of negative emotions such as sadness, fear, disgust, and anger

Predicting Post-adoption Usage of Information Technology: A Large-scale Data Analysis of Mobile App Download and Usage Behavior

The patterns of technology usages are the actual reflection of user preference and value assessment over the technology. In the context of mobile app downloads and usages, we show that users’ early-time app usage patterns are important predictors for their continued usages and usage intensity of the app. Using the large-scale mobile app download and usage data, we develop and empirically validate prediction models for continued usages and usage intensity of apps with early-time usage patterns right after the download of an app such as first-usage time, secondusage time, revisit time, and in-app activities. We also consider possible heterogeneity among user groups and app characteristics in our model and discuss the interplay between user and technology for explaining post-adoption user behaviors