Impact of Average Rating on Social Media Endorsement: The Moderating Role of Rating Dispersion and Discount Threshold


This is a review of paper “Impact of Average Rating on Social Media Endorsement: The Moderating Role of Rating Dispersion and Discount Threshold” written by Xitong Li (2018)

Facebook launched the “Like” button in February 2009. Since then, more and more social media platforms, such as Twitter, LinkedIn and Instagram, started with introducing this service for their users. This liking function can be of great value for companies using these platforms for advertising. According to a study of Li and Wu (2013), one additional Facebook Like on a sponsor ad averagely will increase the company’s revenue by 215 dollars. It is therefore very interesting for companies to investigate in the motivating factors, that cause people to like and endorse a product since it can be an important extra source of revenue. How to encourage more users to involve in a product endorsement has therefore become increasingly essential to every company in terms of strategic marketing.

Why do people endorse products?

When a user clicks the “Like” button for a sponsored product, the product will automatically be shared to his or her Facebook friends. So the main reason for people to endorse a product is to inform their friends about a good deal. Users are willing to share and endorse a product to friends if they think it is a recommendable product or they want to show their interesting for this product publicly. For some users, social media endorsement is an uneconomical bargain since they should put their self-image at risk and may not get any monetary compensation. For instance, the self-image risk arises when they endorse a product with low quality. It is therefore important for people to make sure that the deal or product they are promoting to their friends is of high quality. A method often used to get knowledge on the quality is the average rating. The research therefore investigates how online reviews about restaurants affect social media endorsement of deal vouchers sold by the restaurants.   

Research Questions

While the average rating had been studied previously, how features of averaging rating will be the cause of social media endorsement were still unclear. Li (2017) attempted to start from the rating dispersion and discount threshold to investigate how these two features affect social media endorsement. To be specific, the author hopes the paper enables to answer the following two questions,

(1) Does a higher average of review ratings about the restaurants increase social media endorsement (Facebook Likes) of deal vouchers?

(2) Do rating dispersion moderate the effect of average rating on social media endorsement?

Previous studies show two possible motivations to drive consumers’ sharing on social media endorsement, which are increasing social capital (Lin et al, 2001) and enhancing self-image (Akerlof and Kranton, 2000). A higher average rating can signal to customers that the product gain recognition from the mainstream market and customers are more willing to endorse it to their friends. However, what a large dispersion of review rating of a product means to customers can have two opposite conjectures. On the one side, a large dispersion of review rating may send a signal to customers that the product has high uncertainty on its quality (Feldman and Lynch, 1988).  On the other side, a large dispersion of review rating may imply the product is unique and niche that is more attractive to customers with well-matched preference (Clemons et al 2006, Sun 2012).

Research design

The author chose the daily-deal businesses as the research setting and the restaurant industry as the object of research. Data of restaurant deals were collected from two sources, a data set provided by Byers et al. (2012) that consists of a nationwide sample of deals across 19 major cities of the United States, and a commercial daily-deal aggregator. To exclude restaurants that may not exist or too small, the author also checked whether the profile of a restaurant can be found on Yelp. Com or not. Finally, a cross-sectional data set that includes 2,545 restaurant deals and 129,129 individual review ratings has been generated. The author regards Facebook likes endorsed for a product deal as the dependent variable and review ratings on Yelp.com as the independent variable of this paper.

Findings

The main findings of this paper are:

•    The average rating increases consumers’ endorsements via Facebook for restaurants with enough reviews.

•    The effect of average rating on social media endorsement is greater for restaurants with more dispersed review ratings.

The first finding thus confirms the expected behavior of consumers which is that a higher review rating is associated with a perceived higher quality. This makes people more willing to endorse the product, since their risk of sacrificing their self-image or their social capital is lower. The second finding is quite surprising, since it indicates that people value more dispersion in a rating over a pure opinion.

Strengths and weaknesses

One of the strengths of the paper is that it takes place in a real life setting and uses real life data. Additionally. the researcher ensures a causal relationship between the dependent and independent variable by using a regression discontinuity (RD) design. Another strength is that, it gives interesting insights on a topic where existing views exist, which can be helpful for firms using social media endorsements. Weaknesses of the paper are that it only focuss on one specific business area, making it harder to generalize the findings to other fields. Next to that the research only uses likes as endorsement measure, however in the current social media era, there are other ways to endorse such as sharing or commenting which are not included.

Managerial implications and research implications

The research generates some interesting insights on the effect of the review rating. It can be valuable to know for company to know what impact their rating has on their social media advertisements, since these advertisements can generate large amounts of additional revenue. It is therefore of great importance for companies to make sure that their average review rating is high. Secondly it generates especially important new insight for companies with niche products, that have a more dispersed rating. For these companies, it is more useful to make use of social media advertising, since they can benefit from the endorsement effect most. It can be insight full to do future research on the effect of review ratings in other business areas and to investigate what other factors can influence the social media endorsement of consumers to test if the research also stands for other services and products.

Reference

Akerlof GA, Kranton RE (2000) Economics and identity. Quart. J. Econom. 115(3):715–753.

Byers JW, Mitzenmacher M, Zervas G (2012) Daily deals: Prediction, social diffusion, and reputational ramifications. Proc. Fifth ACM Internat. Conf. Web Search Data Mining (WSDM’12) (ACM, New York), 543–552.

Clemons EK, Gao GG, Hitt LM (2006) When online reviews meet hyper differentiation: A study of the craft beer industry. J. Man-agement Inform. Systems 23(2):149–171.

Feldman JM, Lynch JG (1988) Self-generated validity and other effects of measurement on belief, attitude, intention, and behavior. J. Appl. Psych. 73(3):421–435.

Li X, Wu L (2013) Measuring effects of observational learning and social-network word-of-mouth (WOM) on the sales of daily–deal vouchers. Proc. 46th Hawaii Internat. Conf. System Sci. (HICSS), Maui, HI, 2908–2917.

Lin N, Cook KS, Burt RS (2001) Social Capital: Theory and Research (Transaction Publishers, New Brunswick, NJ).

Sun M (2012) How does the variance of product ratings matter? Management Sci. 58(4):696–707.

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