Tag Archives: Electronic word of mouth

The differential impact of brand loyalty on traditional and online word of mouth: The moderating roles of self-brand connection and the desire to help the brand

As we all might know, word of mouth (WOM) plays a significant role within the business field. Indeed, a popular refrain is mentioned by Merlo et al. (2014, p. 82) that “word of mouth is associated with increased customer loyalty”. Several studies (Matos & Rossi, 2008; Watson et al., 2015) have also confirmed this by their research on the relation between customer loyalty and WOM. In this paper, the authors went one step further in this aspect, and investigated how the relation between loyalty and WOM is ‘’affected by the shift from offline to online communication channels and from traditional, face-to-face conversations (i.e., in-person WOM) to online WOM(i.e., eWOM)” (Eelen, Özturan & Verlegh, 2017).

The authors conduct 4 studies in this paper. For the first study, they conduct a survey with regards to ten preselected consumer packaged goods (CPG) brands among 1061 consumers. They follow up with three experiments. In these experiments, they acquired a total of 1473 participants from Amazon MTurk that participated.

For the first experiment, the second study they conducted, the authors replicated the survey. This time however, instead of measuring they tried to manipulate brand loyalty and self-brand connection by asking participants theirselves to come up with brands they could think of.

The second experiment, the third study the authors conducted, focused on loyal consumers and their intentions to engage in in-person WOM and eWOM. The intentions were linked to their perceptions of social risk and psychological benefit. Lastly, the third experiment, the fourth study that was conducted, investigated the actual eWOM behavior of the participants (Eelen et al., 2017).

The findings of the article show that brand loyalty is positively related to word of mouth for CPG products, however, they also indicate that this relation is much weaker for eWOM than for in-person WOM. Another finding was that it appeared that loyal consumers were more willing to engage in eWOM, if there was a self-brand connection present. The last finding was based on the actual eWOM behavior, which indicated that consumers could be motivated to engage in eWOM if they were told that this would be helpful for the brand. It turned out that this approach was effective for loyal consumers, but not for the occasional users (consumers) of a brand (Eelen et al., 2017).

These findings may have several managerial implications. First of all, it provides marketers and brand managers with new insights. As these findings already implied, marketers and brand managers could consider an actionable strategy for stimulating their loyal customers to spread eWOM. Moreover, two specific types of motivation are considered when thinking about tackling this issue. First, the brands could link their customer engagement programs to the consumers’ need for self-presentation. Secondly, it could be done by strengthening the consumers’ identification with the brand. Additionally, brands could also provide customers with several eWOM tools that make it more applicable when the brand is top of mind (think of after purchasing etc.). Lastly, as can be concluded from the paper, that there was a positive relation between eWOM and the use of online media. Marketers may also think of targeting the customers that make heavily use social media, but are not necessarily brand loyal (Eelen et al., 2017).

A good example made in the article itself, was when the business Nutella, was able to organize an online action where its customers could design their own Nutella Jar. The customers were especially people who used social media a lot, but were triggered to also buy the brand, because they had to visit a store and purchase the jar of Nutella first before they could use the label (Eelen et al., 2017).

The article finds its strength in conducting a research that looks into the products side of in-person WOM and eWOM, instead of the service business. As it is also mentioned in the article, a lot of study has been done on the service business (58) but just a few on the product side (8). Another strength of the article would be the several manipulation checks and in-differences methods, to increase the robustness of the findings.  Lastly, the article is able to present convenient managerial implications and theoretical contributions (Eelen et al., 2017).

Now, when considering the weaknesses of the article, one of the most notable would be the fact that the authors tend to fail on exploring more possible differences between subtypes of eWOM. When thinking about the different online channels possible, e.g. Twitter vs. Facebook vs. brand site itself vs. store platform for reviews etc., these channels could all have different implications and customer usage of eWOM. To understand the differences more, the article could have studied the various dimensions that underlie these differences of the channel. Therefore, better insight could have be given to marketers, based on more specific channels for usage, also on which is more effective than the other (Eelen et al., 2017).


Eelen, J., Özturan, P., & Verlegh, P. W. (2017). The differential impact of brand loyalty on traditional and online word of mouth: The moderating roles of self-brand connection and the desire to help the brand. International Journal of Research in Marketing, 34(4), 872-891.

Watson, G. F., Beck, J. T., Henderson, C. M., & Palmatier, R. W. (2015). Building, measuring, and profiting from customer loyalty. Journal of the Academy of Marketing Science, 43(6), 790–825. http://dx.doi.org/10.1007/s11747-015-0439-4.

Merlo, O., Eisingerich, A. B., & Auh, S. (2014). Why customer participation matters. MIT Sloan Management Review, 55(2), 81–88.

de Matos, C. A., & Rossi, C. A. V. (2008). Word-of-mouth communications in marketing: A meta-analytic review of the antecedents and moderators. Journal of the Academy of Marketing Science, 36(4), 578–596. http://dx.doi.org/10.1007/s11747-008-0121-1.


Which is Worse; the Product or the Reviewer? The Effect of Word of Mouth Attribution on Consumer Choice

It is no surprise that the consumer purchase environment has changed with the rise of the internet. Next to the fact that consumers don’t need to leave their house, can choose between a much larger (international) assortment of products, they are also exposed to significantly greater levels of information; consumer reviews, in particular. Nowadays, consumers have access to a large amount of word-of-mouth (WOM) – expressed in online consumer ratings – to make better informed purchase decisions (He & Bond, 2015).

Consumer Review Characteristics
He & Bond (2015) in their paper named ‘Why is the Crowd Divided? Attribution for Dispersion in Online Word of Mouth’, identified this phenomenon and they chose to focus not only average rating score, but also rating distribution. They authors built a framework that argues that when consumers are exposed to dispersed WOM, they attribute the dispersion on either product performance or reviewer characteristics (He & Bond, 2015). To extend existing literature further, He & Bond (2015) attempt to predict the attribution by looking at taste similarity.

Figure 1: High and low distribution example (Source: He & Bond, 2015)

Attributions for Dispersion
He & Bond (2015) argue that when consumers are aware of WOM dispersion, they will attempt to find an explanation. In principle, explanations behind dispersion can vary greatly. However, overall people’s attributions of the cause of WOM can be categorized in two ways: (1) product sources, or (2) reviewer characteristics.

Consumers could one one had attribute the cause of WOM to product sources – performance, in particular (He & Bond, 2015). However, different consumers might simply value product attributes differently, resulting in different reviews for the same product (He & Bond, 2015). Folkes (1988) indeed found that negative WOM is oftentimes perceived by consumers as incorrect product usage by the reviewer, rather than the product performance.

I hear you asking: when do consumers attribute WOM to a product source and when to reviewer characteristics?
The authors raise the principle of taste similarity. They argue that when consumers read WOM, they judge how similar the reviewers are to each other (He & Bond, 2015). When there is a high consensus (low dispersion) consumers are more likely to attribute the WOM to product sources (He & Bond, 2015). Alternatively, when consensus is low, consumers might be more likely to attribute WOM to reviewers (He & Bond, 2015).

Consequences of Attribution
When consumers attribute high-dispersion WOM to product performance, this might induce performance uncertainty, possibly resulting in a negative customer response (He & Bond, 2015). This effect might be less prominent with WOM attributed to reviewer characteristics and might even be beneficial to consumers to learn about their own preferences (He & Bond, 2015).

Based on the above, the authors formulated three hypotheses:
H1:       The negative influence of WOM dispersion on product evaluations is stronger for
taste-similar domains than for taste-dissimilar domains.

H2:       The moderating influence of taste similarity on product evaluations is mediated by attributions for WOM dispersion.

H3:      The moderating influence of taste similarity on effects of WOM dispersion will be stronger among consumers with greater openness to experience.

Schermafbeelding 2018-03-11 om 19.50.15Figure 2: Conceptual Model (Source: He & Bond, 2015)

The authors conducted four studies. Study 1 examined the effect of dispersion and product domain on consumer choice (He & Bond, 2015). Study 2 measured the effect of dispersion on attribution and purchase intention. Study 3 investigated the effect of different taste similarities within a product domain. Lastly, study 4 examined whether people that are open to new experiences react more positively in cases of high dispersion and taste dissimilarity.

Schermafbeelding 2018-03-11 om 19.51.49

Figure 3: Experimental Conditions Example (Source: He & Bond, 2015)

All hypotheses were supported. Consumers were more willing to accept dispersed WOM when the product domain was represented by dissimilar tastes (He & Bond, 2015). Also, the second experiment revealed that participants preferred low dispersion, but were much more tolerant of dispersion in product environments with dissimilar tastes. Furthermore, study 2 proved taste similarity was indeed responsible for the attribution process by which consumers attribute WOM to either product or reviewer. Study 3 revealed that when consumers know tastes are dissimilar in a product domain, negative attitude towards the product will drop, whereas negative attitude will increase when they perceive the tastes to be similar (He & Bond, 2015). Finally, study 4 revealed that participants with a low level of openness had lower product evaluations on average. Participants high in openness were more positive about WOM dispersion, given dispersion was attributed to reviewer characteristics (He & Bond, 2015).

Strengths & Weaknesses
One strength of this paper is that it takes a new approach to explaining consumer reaction on WOM dispersion. Where previous literature focused on reference dependence as explanation of negative consumer response (uncertainty increase), this research takes it back a step and provides understanding of how consumers actually perceive the WOM by explaining their attribution process. Also, taste similarity was not included in any prior research, while He & Bond (2015) proved this to be an important mediator. Furthermore, the authors highlighted that where reference dependence argues dispersed WOM is mostly negative, this is not always the case (He & Bond, 2015).

A limitation is that all experimental conditions showed consumer reviews on a 10-point scale. The authors do not test whether the found effects are still present in different length scales. For example, consumers might perceive responses on a 5-point scale as dispersed more easily, as the values are closer together. If the majority of people is very high up in the 10-point scale and then 1 individual gives a 1-star rating, this might have less effect than with 5-point scales. The authors themselves identify the possible effect of different WOM dispersion perception with different physical appearances of the scales (He & Bond, 2015; Graham, 1937). The authors could create more experimental conditions where they also alter scale length, to better the generalizability of their study, as not every company uses the same scale length.

Furthermore, the authors took into consideration similarity between reviewers, but not similarity between the consumer and the reviewers. The results might possibly differ if the consumer finds out the reviewers are drastically different from him/herself (or very similar). An improvement could be to include experimental conditions where the ‘average reviewer profile’ is described, so people could judge how similar they are to the reviewers – and of course explicitly state this to make it measurable.

So, now that you know more about review scales; on a scale from 1 to 10, how likely are you to interpret the reviews the same way as you did before reading this post?

Folkes, V. S. (1988). Recent Attribution Research in Consumer Behavior: A Review and New Directions. Journal of Consumer Research, 14(4), 548 – 565.

Graham, J. L. (1937). Illusory Trends in the Observations of Bar Graphs. Journal of Experimental Psychology, 20(6): 597 – 608.

He, S. X. & Bond, S. D. (2015). Why is the crowd divided? Attribution for dispersion in online word of mouth. Journal of Consumer Research, 41(6), 1509 – 1527.

Continue reading Which is Worse; the Product or the Reviewer? The Effect of Word of Mouth Attribution on Consumer Choice

How Trending Status and Online Ratings Affect Prices of Homogeneous Products

The Internet and Word-of-Mouth (WOM)

Ever since the inception of the Internet, consumers have benefited from extensive opportunities to share their evaluations of products online. Most e-commerce platforms allow consumers to review products, and an increasing number of opinion platforms have been introduced that offer online consumer ratings and reviews. Furthermore, most online retailers are now listing and selling trending products, defined as products that large groups of individuals are currently purchasing or discussing (Kocas and Akkan, 2016). In their article “How trending status and online ratings affect prices of homogeneous products”, Kocas and Akkan explore the pricing implications of these reviews and trending status. The following research questions result:

RQ1: How do standardised average prices vary with product popularity (measured by the trending status)? 

RQ2: When controlled for popularity, how do standardized average prices vary with average consumer ratings?

Related Theory

Research in marketing and economics have shown that it is profitable for retailers to sell popular products at a discount as advertising the low price is an effective and cheap method to inform consumers of the extra surplus they could get by purchasing these products (Elberse, 2008). In the present study, trending is considered an indicator of product popularity as well as a costless form of advertising – trending products signal desirability and potential positive surplus to consumers (Hosken and Reiffen, 2004). Hence, one can assume that trending products are priced lower by retailers, as the resulting increase in demand more than likely compensates for the decrease in marginal revenue per item sold.

Furthermore, several studies have shown that positive ratings and reviews have a positive effect on sales (Baek et al., 2012). Similarly to trending status, high ratings can act as a signal of desirability. Hence one can reasonably assume that highly rated products should be priced lower by retailer for the same reason as aforementioned.

Formally stated,

H1: Retailers randomize prices of products independently. The average and minimum profit-maximizing prices for the trending products are lower than the prices for non-trending products given identical average consumer ratings.

H2: The average and minimum profit-maximizing prices for the product with higher average consumer ratings are lower than the product with lower average consumer ratings given identical trending status.


This study analyses data gathered from 24 of the 28 categories of books available on Amazon.com from May 25 to September 13, 2011 and includes a sample of 466’190 books. Both hypotheses are supported by the experiment, showing that a trending product should be priced lower than other products in order to exploit the higher number of browsers these trending items attract. Similarly, highly rated products lead to a higher conversion rate (from browsing to purchasing) and, hence deserve lower prices.

Strengths & Weaknesses

5-stars-no-padding Whereas several studies have examined the impact of viral characteristics of products on consumer behaviour and pricing policies, this study is the first to empirically examine the influence of trending status on pricing online in a field experiment with a large dataset. Similarly, whereas several studies have examined the impact of online reviews on consumer behaviour, no prior work has examined how online reviews and ratings affect prices of homogeneous goods. A strong point of this paper is that it acts on these 2 gaps to provide novel findings, and tangible and actionable insights to practitioners.

5-stars-no-padding  Another strength is that this paper provides a detailed methodology, which is complemented by an appendix as well as a detailed explanation of the economic foundations behind the theory (including formulas). This level of details increases the academic relevance of the paper, and allows other researcher to easily replicate the experiments, hence facilitating continuous research on the topic.

1 star    One of the weaknesses of this study is the fact that it only examines one type of products – books. Several studies (e.g. Abdullah-Al-Mamun and Robel, 2014) have shown that price sensitivity varies from one product category to another. Similarly, product reviews are generally more important for certain types of products than others. For instance, for a product such as a microwave, personal taste doesn’t really matter, hence one could expect product reviews to be more important as it provides an objective evaluation. However, for a product such as a science-fiction book, personal taste is important, hence the influence of product reviews is likely lower. Thus, it would be beneficial to replicate this study while taking into account category- and product-specific features as a predictor of prices. This can easily be done by replicating the experiment with more product categories on Amazon, and would validate the robustness of this study’s findings across product categories.

1 star    A second weakness of this paper is the fact that it examines the impact of online ratings by relying only on single-dimensional rating schemes. Online platforms display reviews using a variety of formats, and many platforms provide separate ratings for different product attributes. Research has shown that multi-dimensional and single-dimensional rating schemes in online review platforms have different impact on consumers (Tunc et al., 2017). Similarly, this study only looks at the ratings but not at the content of the review. However, studies have shown that the latter can influence consumer behaviour. Both these factors can influence the conversion rate from browser to buyer (Mudambi and Schuff, 2010) and thus the profitability of retailers. Hence, it would be interesting to replicate the present research in the context of multi-dimensional rating schemes, and take into account the actual content of online reviews.


We have seen that there are significant advantages to demand-based pricing for popular products with a relatively high market share. Hence, online retailers should monitor signs of trending as they act as a positive desirability signal that increases the demand of price-comparing consumers. By responding to trending signs and adjusting their prices, retailers can optimise their profits. Nevertheless, managers should be cautious of the research findings and conduct further experiments when applying them to products other than books. Finally, managers should be careful about the pace at which they adjust their prices – popularity status can change extremely quickly, but consumers will not react well to frequent price changes.


Abdullah-Al-Mamun, M. K. R., & Robel, S. D. (2014). A Critical Review of Consumers’ Sensitivity to Price: Managerial and Theoretical Issues. Journal of International Business and Economics, 2(2), 01-09.

Baek, H., Ahn, J., & Choi, Y. (2012). Helpfulness of online consumer reviews: Readers’ objectives and review cues. International Journal of Electronic Commerce, 17(2), 99-126.

Brynjolfsson, E., Hu, Y., & Smith, M. D. (2010). Research commentary—long tails vs. superstars: The effect of information technology on product variety and sales concentration patterns. Information Systems Research, 21(4), 736-747.

Elberse, A. (2008). Should you invest in the long tail?. Harvard business review, 86(7/8), 88.

Hosken, D., & Reiffen, D. (2004). How retailers determine which products should go on sale: Evidence from store-level data. Journal of Consumer Policy, 27(2), 141-177.

Kocas, C., & Akkan, C. (2016). How Trending Status and Online Ratings Affect Prices of Homogeneous Products. International Journal of Electronic Commerce, 20(3), 384-407.

Mudambi, S. M., & Schuff, D. (2010). Research note: What makes a helpful online review? A study of customer reviews on Amazon. com. MIS quarterly, 185-200.

Tunc, M. M., Cavusoglu, H., & Raghunathan, S. (2017). Single-Dimensional Versus Multi-Dimensional Product Ratings in Online Marketplaces.

Increasing sales? Focus on word of mouth marketing is not enough!

Coolblue is an online retailer in the Netherlands and Belgium. They started in 1999 and now have 332 specialized webshops and 7 physical shops in the Benelux. This makes Coolblue one of Netherlands biggest webshops. And they are doing quite good when looking at their awards: Best webshop in the Netherlands in 2011, 2012, 2013, 2015 and second place in 2014. Looking at their social media platforms they are performing well with an online fan base of more than 204.000 fans. In 2013, Coolblue was the highest entering company in the Best Social Media Award with a 3th place. In 2014 they even came closer with becoming NL’s best social media with a second place. On social media they perform outstanding, having great word of mouth marketing and looking at their growth in revenue with last year growth of 45% a TV campaign is an interesting step. In January 2015, Coolblue launched their first TV campaign. But why does Coolblue need a TV campaign while they have such good functioning social media platforms and became huge with word of mouth marketing?

In today’s world it is almost impossible to think about living without social media. Everybody knows or at least has some vague idea about what social media are, and at the same time almost everybody is involved in it daily. Facebook, Twitter, Instagram and so on are examples that will most likely ring a bell for practically everybody. If we look at Coolblue, they use their Facebook page in such a way that we can all watch and follow the company. Mostly it is used at keeping customers up to date and informed about new product, but they also use it to amuse their audience with entertaining content. With people who liked Coolblue’s social media content they want to use e-Word of mouth marketing to create more brand recognition. Brand recognition is a problem for Coolblue, which they can’t solve with word of mouth behavior only. Coolblue’s CEO Pieter said:’’ Untill now we have been growing for years with word of mouth marketing, but now it’s time to grow even faster with our new tv campaign. Then they can experience our service and products, and most of the time our customers are so impressed word of mouth behavior will follow automatically.’’ Also when we take a look at Google trend with Coolblue (blue) and their biggest competitors, Bol.com (red) & Wehkamp (yellow), we see that Coolblue is still way less popular.


If we look at their social media strategy nowadays their focus on social media platforms are on (professional) fans, and sometimes even a focus group within this fan base (Example: People who love cats). These fans are people who know Coolblue, already bought a product at Coolblue and experienced their service & delivery propositions. These fans show word of mouth behavior, but clearly it is not enough to become as big as their competitors if we look at brand recognition. Looking at this Coolblue case, word of mouth marketing (online & offline combined) can grow a company with cheap marketing. But if you want to become de biggest, you still have to invest in old fashioned marketing to increase brand recognition at the mass.

http://www.facebook.com/coolblue- http://www.google.nl/trends/explore#q=coolblue

There are two kinds of people, which one are you?

Do you prefer Coke or Pepsi? Do you eat your burger with cheese or without? And what about coffee, Americano or espresso? Zomato ensures that every meal, for users with all kinds of preferences, is a great experience.

Zomato is an India-based restaurant directory startup, that provides detailed information regarding restaurants nearby, including scanned menus, and also user’s reviews and photos of their gastronomic experiences. Zomato also includes real-time information about the restaurant and lets users book tables through its iOS and Android apps.

The image bellow summarizes Zomato’s key features:

Source: zomato.com/portugal
Source: zomato.com/portugal

Zomato has 1,398,900 listed restaurants in 22 countries. With the recent acquisition of Urbanspoon, Zomato will break into the US market, competing against services like Foursquare and Yelp.

The business model is quite simple, Zomato hires people to visit restaurants and to send the data to the team, including up to date information on new openings and scanned copies of menus. Users can then share photos of their dishes and evaluate restaurants in order to help other users decide where to eat.

The detailed informations available for each restaurant is then a result of the combined inputs of both the Zomato’s team and the users.

Example of a restaurant in Lisbon. Source: zomato.com/portugal
Layout of the information available for each restaurant.  Source: zomato.com/portugal

At Zomato, user’s evaluation takes the form of both a rating, using a 5-points scale and a review. Given that consumers with more extreme opinions (very satisfied or dissatisfied) are more likely to rate (Li and Hitt, 2008), most restaurants have a score of either close to 1 or higher than 4, as  the image bellow exemplifies.

Results of restaurantes for breakfast in Lisbon. Source: zomato.com/portugal
Results of restaurants in Lisbon. Source: zomato.com/portugal

Product rating is crucial for Zomato given that it is an integral element of online businesses especially for experience goods (Tsekouras, 2015) and because they are a reflection of product quality (Hu et al., 2009). Also, consumers tend to trust more opinions derived from others customers than information provided by the vendors themselves (Chevalier and Mayzlin, 2006), which is why having a high number of reviews is a key success factor for Zomato.

Social surroundings are then of crucial importance given that the success of Zomato relies on the degree to which there is interaction between users, through comments, ratings and a community creation of “foodies”, and the degree to which network effects take place i.e. where a good or service becomes more valuable because more people use it (Katz and Shapiro, 1994).  As according to Grönroos and Voima (2013), the customer’s well-being of Zomato is increased through the process, as more user’s feedback is available for each restaurant.

As a startup, Zomato relies in eWOM in order to attract new users and generate brand awareness. Unlike traditional WOM, eWOM has much broader effects, in part because there is no need to have a pre existing connection between the sender and the receiver. As so, eWOM applies to Zomato since it operates in an online context whereas traditional WOM typically happens in a face-to-face context (King et al., 2014).

Zomato is providing value for the consumers whilst the consumers are also creating value for each other, through their evaluations and photos. This reflects a finding in the article by Saarijarvi et al (2013), which states that it is important to evaluate what kind of value is co-created and for whom, meaning that value can have a different meaning for different actors in the co-creation process.

Zomato also generates great value for restaurants. In fact it is one of the most cost-effective high-impact marketing platform for dining establishments.


Check out the best place for you at https://www.zomato.com/!






Chevalier, J. A. and D. Mayzlin (2006). “The Effect of Word of Mouth on Sales: Online Book Re- views.” Journal of Marketing Research 43(3), 345–354.

Grönroos, C., & Voima, P. (2013). Critical service logic: making sense of value creation and co-creation. Journal of the Academy of Marketing Science, 41(2), 133-150

Katz, M.L. & Shapiro, C. (1994). Systems Competition and Network Effects, The Journal of Economic Perspectives, 8(2), 93-115.

King, R.A., Racherla, P., & Bush, V.D. (2014). What We Know and Don’t Know About Online Word-of-Mouth: A Review and Synthesis of the Literature. Journal of Interactive Marketing, 28(3), 167-183

Li, X., & Hitt, L. M. (2008). Self-selection and information role of online product reviews. Infor- mation Systems Research, 19(4), 456-474.

Saarijärvi, H., Kannan, P.K., & Kuusela, H. (2013). Value co-creation: theoretical approaches and practical implications. European Business Review, 25(1), 6-19.

Tsekouras, D. (2015) Variations On A Rating Scale: The Effect On Extreme Response Tendency In Product Ratings, working paper.


Electronic Word of Mouth and Amazon.com

Online star ratings

Figure 1: ‘Understanding’ online star ratings

Amblee and Bui (2011) have researched the effect of electronic word of mouth (eWOM) on the sales of digital microproducts. They studied amazon.com ‘shorts’: short stories (e-books) that are made available for a set price of 49 cents. They classify these shorts as digital microproducts.

This article focusses on their study on de effect of social commerce on product reputation and sales (hypothesis 1). Amblee and Bui (2011) studied this effect from three different perspectives: Valence (how positive or negative a rating is), the presence of a rating versus no rating and volume (thus the amount of ratings). Interestingly, they do not find a significant correlation between valence of the rating and sales. Amblee and Bui (2011) suggest that this might be due to the generally positive ratings, and thus a low variance between positive and negative ratings. Second, they also find that the presence of a rating is a good predictor of higher sales, as compared to no ratings. Furthermore, they also find a significant correlation between the volume of ratings and the volume of sales.

In their discussion, Amblee and Bui (2011) propose a better scoring system which allows users to score the e-book on different dimensions such as content, writing style and so on. While this suggestion might lead to a bigger variance between ratings, I wonder if it would have a positive effect on sales in the end. Amblee and Bui (2011) point out that the majority of past research on valence suggests that valence is not a reliable predictor of sales. However, by including more ratings to fill in like Amblee and Bui (2011) suggest, you might achieve a negative effect: that customers are no longer willing to fill in the rating. And the importance of the presence of the rating, and moreover the volume is exactly what is found to be so important to spark sales by Amblee and Bui (2011).

Fast forward to 2015. Did Amazon.com change the rating system? I went to Amazon.com and I filtered on short stories and on kindle editions. This is what I found:

Amazon ratings of Shorts 2015

Figure 2: Average customer review of shorts on Amazon.com, 2015

Amazon blog2

Figure 3: Layout of customer reviews of shorts on Amazon.com, 2015.

This shows that the great majority of the ratings is still very high (roughly 84% of the ratings are 4 stars or more).  However, it seems like Amazon has added some space for customers to motivate their rating. Furthermore customers can also identify reviews as helpful, and Amazon shows me the most helpful reviews first. By using this system, Amazon.com leaves it up to its customers if they want to motivate their rating (and spend more time rating a book) or not. What do you think, has the system improved? Do you think it will lead to more sales? Please comment below!


  • Amblee, N. and Bui, T. (2011) ‘Harnessing the influence of social proof in online shopping: The effect of electronic word of mouth on sales of digital microproducts’, International Journal of Electronic Commerce, Vol. 16, No. 2, pages 91-113, DOI 10.2753
  • Featured image: GTP Headlines, accessed 31-03-2015, http://news.gtp.gr/2014/11/25/amazon-com-launch-travel-service-enter-the-world-online-booking/
  • Figure 1: quora.com, accessed 01-04-2015  http://qph.is.quoracdn.net/main-qimg-80cd8d435bc1eed5a48d8732857f5aa7?convert_to_webp=true
  • Figure 2: Amazon.com, accessed 31-03-2015, http://www.amazon.com/s/ref=sr_nr_p_n_feature_browse-b_2?fst=as%3Aoff&rh=n%3A283155%2Cn%3A%211000%2Cn%3A17%2Cn%3A10300%2Cn%3A10307%2Cp_n_feature_browse-bin%3A618073011&bbn=10307&ie=UTF8&qid=1427828706&rnid=618072011
  • Figure 3: Amazon.com, accessed 31-03-2015,http://www.amazon.com/When-Fall-Love-Blue-Series-ebook/product-reviews/B00HE1PEZO/ref=cm_cr_dp_see_all_summary?ie=UTF8&showViewpoints=1&sortBy=byRankDescending