Generally, customers seek for quality information about a product before purchasing it. The emergence of the internet has facilitated convenient access to a variety of information sources to obtain this quality information such as consumer generated ratings and reviews. These consumer-generated product evaluations are generally found on portal (e.g. google.com), retailer (e.g. Amazon), manufacturer (e.g. Nike) or product evaluation websites (e.g. yelp.com). These evaluations have strong effects on consumer persuasion, willingness to pay and trust (Tsekouras, lecture 2017). However, not all customer reviews have the same effect on purchase decisions as some reviews are perceived more helpful than others (Chen et al, 2008).
Building on this research, Baek et al, 2012 tried to determine which factors influence the perceived helpfulness of online reviews. In addition, they researched which factors are more important depending on the purpose of reading a review.
Furthermore, the paper extends previous research by considering two ways of looking at online reviews. Customers may take both peripheral and central cues into account when determining whether a review is helpful. Persuasion through the peripheral route requires less cognitive effort, hereby readers focus on more accessible information (e.g. the author of the review). Persuasion though the central route requires more cognitive effort, hereby customers focus on the content of the message.
Data collection occurred through Amazon.com on a subset of 23 products, from a variety of categories. For these products they collected the reviews and information related to the reviewer. The final dataset included 15.059 online consumer reviews written by 1,796 reviewers. Review helpfulness is measured though customers who rated whether the review was helpful or not.
The results show that a reviews’ helpfulness is affected by how inconsistent a review rating is with the average rating for that product, whether or not the review is written by a high-ranked reviewer, the length of the review message and the number of negative words included in the review message. The former finding is consistent with the negativity bias stating that negative reviews tend to be more salient than positive reviews (Tsekouras, lecture 2017).
Furthermore, it shows that customers assess the helpfulness of a review merely with central cues when they buy search goodsnd high-priced products. On the other hand, they use more peripheral cues when buying experience goods and low-priced products.
So what does this imply?
The findings of this research raise several practical managerial implications for firms, which I consider the main strength of the research. However, some implications may rely on the goal of the retailer, which I will elaborate on below. The findings may help web designers and marketers to design and shape their reviewing systems in such a way that review helpfulness is maximized. When more helpful reviews are written, the success of their service may increase as it leads to more customers using their service and increased sales (Chen et al. 2008; Chevalier and Mayzlin, 2006).
First, as it is shown that high-ranked reviewers are more credible to readers, firms may want to request and incentivise these reviewers to review their products more often. This is already done by Amazon, as they send top reviewers free merchandise to review (Chow, 2013), which has its pros and cons in my opinion. On the one hand, these top reviewers may not take into account factors such customer service, which in my opinion is an important factor in evaluating whether or not to buy the product. On the other hand, the purchasing bias and under-reporting bias are mitigated, which may result in a more ‘true’ rating as these biases normally result in a skewed product rating distribution (Hu et al, 2009). However, this ‘true’ rating may therefore differ from the average rating, which in turn decreases – as found in the study– the perceived helpfulness of the review. Consequently, I think this issue could be a very interesting field for further research.
Furthermore, to increase review helpfulness, a division between high-priced, low-priced, search and experience goods could be made. For example for high-priced and search goods the firm may want to encourage customers to write detailed messages, whereas for low-priced and experience goods reviewer credibility and review rating should emphasized more.
In addition, online retailers may face a trade-off between perceived helpfulness and positivity of a review. Some retailers encourage customers to write positive reviews, however this undermines the perceived usefulness of the review, which in turn may decrease the number of customers using the retailers’ service. Therefore, in my opinion in the long run it would be more helpful to encourage customers to write honest reviews.
Finally, I would like to make a suggestion for improvement. As review helpfulness is measured only though the customers who voted on whether a review was helpful or not, the findings might be less generalizable for the customers who did not vote. Consequently, the researchers may want to conduct an experiment to increase generalizability.
Baek, H.; Ahn, J.; and Choi, Y. Helpfulness of online consumer reviews: Readers’ objectives and review cues. International Journal of Electronic Commerce, 17, 2 (2012), 99-126.
Chen, P. Y., Dhanasobhon, S., & Smith, M. D. (2008). All reviews are not created equal: The disaggregate impact of reviews and reviewers at amazon. com.
Chevalier, J.A., and Mayzlin, D. The effect of word of mouth on sales: Online book reviews. Journal of Marketing Research, 43, 3 (2006), 345–354.
Hu, N., J. Zhang, and Paul A. Pavlou. (2009) “Overcoming The J-Shaped Distribution Of Product Reviews”. Communications of the ACM 52.10h: 144.
Tsekouras, D. (2 March 2017), Lecture Customer Centric Digital Commerce, “Post-consumption Worth of Mouth”.
NPR.org. (2013). Top Reviewers On Amazon Get Tons Of Free Stuff. [online] Available at: http://www.npr.org [Accessed 4 Mar. 2017].