Whose online reviews to trust? – Understanding reviewer trustworthiness and its impact on business

The advent of the Internet has radically changed the way in which consumers can receive information about products they consider purchasing. While years ago we could only trust the opinion of our relatives and friends who have already used the product in question, or the information provided by the seller (though highly likely to be biased), nowadays we have an easy access to a great number of online reviews for various products and services. In popular websites like Amazon, TripAdvisor and IMDB people can find reviews for everything from camping lanterns, to places where to eat in Mexico City, or the new movie starring Leo diCaprio. This has become possible due to the rise of the so-called electronic word-of-month (e-WOM), or the phenomenon of people sharing with peers their opinions and experiences with certain products and services over the Internet.

Studies show that the impact of online reviews on sales of products and services is considerable (Forman and Wiesenfeld, 2008; Duan and Winston, 2008). A number of scholars claim that factors such as the length of the online review, the style of writing and the content affect the strength of the influence of the review on the purchasing decision of the customer (Otterbacher, 2009; Liu et al., 2008). However, little focus has been put on the impact of the person who writes the reviews.

With this in mind, Banerjee et al. (2017) took the research endeavor to investigate whether the overall trustworthiness of reviewers has any impact on the number of customers visiting the business that has been reviewed, and also which reviewer characteristics determine the trustworthiness of the reviewer. In order to achieve this goal, the authors used as a foundation the Source Credibility Theory (SCT), which argues that the trustworthiness of the information source improves the perceived credibility of the source, and as a consequence the persuasiveness of the communication in online reviews. Using a dataset with more than 2.2 million observations from Yelp.com the authors tested two sets of hypotheses, one assessing whether the online reputation of the business leads to higher business patronages and whether this is moderated by the trustworthiness of the reviewers, and the second set testing what factors impact the trustworthiness of the reviewers.

Summary of findings



1a: Review-based online reputation of a business is positively associated with the patronages generated by the business.


1b: Average perceived trustworthiness of reviewers reviewing a business positively moderates the association between online reputation and patronages of the business.


2a: Reviewer’s positivity in rating businesses is positively associated with the perceived reviewer trustworthiness.


2b: The amount of reviewer’s involvement in reviewing businesses is positively associated with the perceived reviewer trustworthiness.


2c: Reviewer’s experience in an online review website is positively associated with the perceived reviewer trustworthiness.



2d: Reviewer’s reputation in an online review website is positively associated with the perceived reviewer trustworthiness.


2e: Reviewer’s competence in writing useful reviews is positively associated with the perceived reviewer trustworthiness.


2f: Reviewer’s sociability as perceived by other users of a review website is positively associated with the perceived reviewer trustworthiness.


Table 1. Summary Results

Relevance, strengths and weaknesses

A strong point of the paper by Banerjee et al. (2017) can be traced to the academic contributions that it makes. The study is the first to integrate in one model various reviewer characteristics that have an impact on the trustworthiness. Contrary to previous studies that examine the phenomenon based on one or two characteristics, the authors of the paper use six different attributes; hence they study trustworthiness from various different angles and provide a more complete understanding of the factors that influence it. Additionally, the paper has strong managerial implications, as it presents evidence that businesses are impacted by their online reputation. Therefore, management should encourage the regular submission of reviews from their customers, but should also ensure that their customers are satisfied with their products, so that the reviews submitted are positively inclined. Moreover, as the study finds that trustworthiness of the reviewer increases the strength of the relationship between online business reputation and business patronages, managers should try to find ways in which they can encourage the most trustworthy reviewers to submit reviews.

On the other hand, the study does not come without limitations. A weakness of the paper is that when it considers the impact of online review reputation of a business, they only account for the increase in the business patronages, measured as the number of check-ins, but they do not provide evidence whether this increase leads to more tangible benefits such as increase in sales. Additionally, although a number of characteristics are considered that impact the trustworthiness of the reviewer, the list is arguably far from exhaustive. Certain cues such as whether the profile of the reviewer has a picture, for example, were ignored by the authors but can arguably also impact the findings of the study. Finally, the paper is based on data solely from one website, which decreases the generalizability of the findings. This, however, provides directions for future research, as the findings made by this paper can be tested in other websites/platforms where the importance of online reviews is high, such as TripAdvisor, Booking.com, etc.

Sources used:

Banerjee, S., Bhattacharyya, S. and Bose, I. (2017). Whose online reviews to trust? Understanding reviewer trustworthiness and its impact on business. Decision Support Systems, 96, pp.17-26.

Duan, W., Gu, B. and Whinston, A. (2008). Do online reviews matter? — An empirical investigation of panel data. Decision Support Systems, 45(4), pp.1007-1016.

Forman, C., Ghose, A. and Wiesenfeld, B. (2008). Examining the Relationship Between Reviews and Sales: The Role of Reviewer Identity Disclosure in Electronic Markets. Information Systems Research, 19(3), pp.291-313.

Liu, Y., Huang, X., An, A. and Yu, X. (2008). Modeling and Predicting the Helpfulness of Online Reviews. 2008 Eighth IEEE International Conference on Data Mining.

Otterbacher, J. (2009). ‘Helpfulness’ in online communities. Proceedings of the 27th international conference on Human factors in computing systems – CHI 09.








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