Online Word-of-Mouth: Ratings, sentiments and sales rank


Introduction

Online Word-of-Mouth (referred to as OWOM for the remainder of this blog) is increasingly important for business because consumers use OWOM information to search, evaluate and choice a product (Hu, et al., 2013). Online ratings and reviews are a form of user generated content and, a more narrow term, OWOM. This blog evaluates the most important theoretical and practical findings of an article by Hu et al. (2013).

Whereas earlier research has primarily focused on the valence, variance and volume of reviews and their impact on sales, this article examines the interplay between ratings, sentiments and sales rank. Ratings refer to the numerical evaluation of the product. The sentiment of a review is based on the terms used in the review and is classified with a score between 1 and 5 (similar to the rating scale). Lastly, the sales rank is the rank of the book on Amazon, implying the sales.

Theoretical relevance

The article provides evidence that ratings do not impact the sales rank directly. However, ratings influence the sales rank through sentiments. At the same time, sentiments impact the provided ratings and sales rank directly. The results of the research, including significant levels, are graphically displayed in figure 1. It’s very interesting that the data doesn’t provide a direct correlation between ratings and sales rank, because several previous studies did show a correlation between those variables. Therefore, this study provides additional knowledge to our understanding of OWOM.

Theory

Figure 1 – The results of the study. Significance: *** p<.001; ** p<.01; *p<.05. Source: (Hu, et al., 2013, p. 19)

Practical relevance

The findings related to the indirect impact of ratings imply a sequential decision making process. Hu et al. (2013) suggest that consumers use ratings to screen products, and limit the number of products in their evaluation. Thereafter, consumers use reviews to evaluate the limited number of products and make a final decision. This suggestion could be utilized by businesses by showing the average rating of a product on the category page, as already done by retailers such as Amazon and Walmart. Besides, retailers could somehow present ratings more obvious during early stages in the buying process (i.e. search & awareness) and present reviews or the sentiment more obvious later in the process (i.e. evaluation & purchase). Besides, retailers could provide a sentiment score, because evaluating all the reviews by humans require great cognitive information processing.

Discussion point

Although the research contributes to the literature of OWOM, I do have a remark. The article is published in 2013 but the findings are based on data that is collected between September 2005 and January 2006. I question the relevance of the data compared to the current situation in the market. Computers have become more advanced and consumer might analyze content differently due to convenience with online shopping. Besides, no explanation is given why the data was collected during that period.

All in all, the article contributes to the literature of OWOM and provides relevant practices that can be applied by platform providers to optimize their OWOM strategy.

 

Written by: Ivar van der Lugt              418691iL
This article is related to session 5: Post-consumption Word-of-Mouth.

Sources

Hu, N., Koh, N. & Reddy, S., 2013. Ratings lead you to the product, reviews help you clinch it? The mediating role of online review sentiments on product sales. Decision Support Systems, Volume 57, pp. 42-53.

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