Tag Archives: WOM

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

Making the most out of your marketing efforts in the context of eWOM

Think about it, who is your favourite advisor when it comes to finding your next, undiscovered restaurant? Your mum or perhaps your best friend? Sometimes they might not give the best advice, luckily you have kind strangers who write reviews online, which you can consult. Review platforms such as Yelp.com, enable people to write and read reviews on products and/or services. But how do businesses handle marketing efforts in the context of electronic word-of-mouth (eWOM)? This is an important question for managers since the rise of the Internet has changed how they allocate marketing expenditure, turning more to online advertising (Lu et al. 2013), but is this effective? To answer this question Lu et al. research the influence of promotional marketing on third-party review platforms.

What was their approach?
Lu et al. examined the impact of online coupons, keyword sponsored search and eWOM on weekly restaurants’ sales using a three-year panel study. They focused on restaurants since going out to dinner is a high-involvement service and eWOM is particularly important for high-involvement products and/or services (Gu et al. 2012). With high-involvement products customers spend considerable time searching for information before purchasing. Lu et al. collected their data from one of the largest restaurant review websites in China. Online coupons are displayed on this platform and the keyword sponsored search works as follows: restaurants buy keywords and when users search for restaurants using that keyword, the restaurants will be displayed at the top of the platform’s search results.

Key insights
One of the key insights Lu et al. found is that both promotional marketing and eWOM have a significant impact on sales. Keyword sponsored search and eWOM have a positive impact on sales. Likewise, offering online coupons has a positive impact on sales, however this relationship is not present for coupon value, indicating that the presence of online coupons is more important than their value since it increases awareness among users (Leone and Srinivasan 1996). Another key insight is that interaction between eWOM and promotional marketing is significant. The interaction between eWOM and coupon offerings is negative, indicating that they substitute one another and thus only one is needed to attract sales. On the other hand, the interaction between eWOM and keyword sponsored search is positive, indicating that they complement one another and together increase sales. Furthermore, if you would use both promotional marketing tools simultaneously, this would negatively impact sales since too many marketing tools  at the same time is experienced as too intrusive by customers. Altogether, these insights highlight different sources of information, with different levels of credibility, while still both sharing the power to inform and attract customers.

Looking to promote your business?
The study’s strength is that it presents some very useful advices when it comes to using promotional marketing in the context of eWOM. First of all, it is good to know that allowing promotional marketing activities on third-party platforms does not hurt the platform’s credibility and thus indicates some interesting marketing possibilities. According to Lu et al., you should stimulate users to generate more positive eWOM since this increases sales. Businesses could use online coupons to get customers’ attention, but if the volume of eWOM is high, this tool becomes less effective. In the case of high eWOM volume, businesses should rather buy keywords to increase sales. However, businesses should not use these two promotional marketing tools simultaneously since this decreases sales, rather they should focus on the tool that is most suitable for them.

Although these insights are useful, managers should note the study’s weaknesses. One of these weaknesses is the study’s generalisability. Firstly, the study only included restaurants from Shanghai, while other academics indicate the presence of cross-cultural differences (King et al. 2014). Secondly, the study focused on high-involvement products, while many studies examine low-involvement products, e.g. books and films, and find that eWOM has a significant impact on sales (Chevalier and Mayzlin 2006; Duan et al. 2008). Thirdly, the study focused on one platform, while other studies indicate that eWOM across platforms can impact sales (Gu et al. 2012). Therefore, future research could focus on whether the study’s results also apply cross-culturally, across different product and across different platforms. Another weakness of this study is the limited dimensions of eWOM and promotional marketing captured. For instance, Chavelier and Maryzlin (2006) indicate that the length of reviews also influences customers’ purchasing behaviour. Besides, the measurement of promotional marketing is two-fold, while other options such as banners or pop-up ads also exist. Future research could therefore investigate whether results differ for other promotional marketing tools and if adding more dimensions for eWOM might indicate different results. To conclude, although the paper has some weaknesses, it does not overturn the practical implications, managers should however be cautious and decide whether the study applies to their specific situation or if their situation deviates from the study’s setting.

Chevalier, J.A. and D. Mayzlin (2006) ‘The Effect of Word of Mouth on Sales: Online Book Reviews’, Journal of Marketing Research 43(3): 345-354.

Duan, W., B. Gu and A.B. Whinston (2008) ‘The dynamics of online word-of-mouth and product sales – An empirical investigation of the movie industry’, Journal of Retailing 84(2): 233-242.

Gu, B., J. Park and P. Konana (2012) ‘Research Note – The Impact of External Word-of-Mouth Sources on Retailer Sales of High-Involvement Products’, Information Systems Research 23(1): 182-196.

King, R.A., P. Racherla and V.D. Bush (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.

Leone, R.P. and S.S. Srinivasan (1996) ‘Coupon face value: Its impact on coupon redemptions, brand sales, and brand profitability’, Journal of Retailing 72(3): 273-289.

Lu, X., S. Ba, L. Huang and Y. Feng (2013) ‘Promotional Marketing or Word-of-Mouth? Evidence from Online Restaurant Reviews’, Information Systems Research 24(3): 596-612.


The Central Role of Engagement in Online Communities


(noun) emotional involvement or commitment


You might haven’t noticed but in one way or the other we’ve all interacted on or with an online community. Whether it was while searching for travel routes, computer settings or in a fashion context. Chances are you read some posts until you found what you were looking for and then left the page without contributing. You are not alone in this, 90% of users never or rarely contributes, while 9% contribute 10% of the content and 1% contribute 90% of the content. This is commonly referred to as the 90-9-1 rule. But how can online communities encourage more people to create content and to help recruit others?

This was one of the questions that led Ray and Morris (2014) to conduct their research. More specifically, their goal was to introduce the concept of engagement, which drives pro-social behaviors in the context of open, non-binding online communities. Prior research has extensively recognized the role of engagement in communities, interestingly online community engagement has not been explicitly conceptualized, modeled, measured, or analyzed as a mediating construct in the information systems literature. This paper is the first to do so.

Building on Ma and Agarwal’s (2007) framework the authors propose a model that shows the central role of community engagement and how it relates to different outcomes (Figure 1). Data was collected from 301 users of online communities and structural equation modelling was used to test the proposed model. The developed framework recognizes that online communities are unique socio-technological environments in which engagement succeeds. In particular, members primarily contribute to and re-visit an online community out of a sense of engagement.

Screen Shot 2017-03-10 at 16.04.04

The authors find that members must feel engaged with the online community to actually create content and that members who merely feel satisfied can still help the online community by saying things that might help recruit others. In addition, they found that self-identity verification (the extent to which the way you see yourself matches the way others see you) has an indirect effect on knowledge contribution through engagement. Furthermore,  this paper provide evidence that engagement also mediates the effect between knowledge self-efficacy (the belief that you have the ability and expertise to contribute) and intention to contribute.

The main strength of the paper is its methodology. The authors have applied several models and control variables to ensure valid results. The main managerial implication for community managers is to help members enhance their self-identity, which eventually will lead to more contribution. They can do so by creating signals for members either by letting them choose a badge themselves or by automatically creating signals from prior activities and achievements such as for example”300+ posts on Data Science”.

In conclusion, this Ray and Morris (2014) found evidence that merely satisfaction is not enough to encourage consumers to actively contribute to online communities, but that engagement plays a central role. To get back to the main question raised in the introduction, the key to promoting pro-social behavior (creating content and recruiting others) in online communities is to create the right balance of engagement and satisfaction.



Ray, S., Kim, S. S., & Morris, J. G. (2014). The central role of engagement in online communities. Information Systems Research, 25(3), 528-546.

Ma, M., & Agarwal, R. (2007). Through a glass darkly: Information technology design, identity verification, and knowledge contribution in online communities. Information systems research, 18(1), 42-67.

How to understand Word-of-Mouth marketing in online communities?

Word-of-mouth (WOM) marketing is also known as social media marketing and leads to an intentional influence of consumer-to-consumer communication. Many marketers and sociologists recognize the importance of WOM as it affects many purchase decisions. WOM marketing is continuously changing as the Internet becomes more powerful; the accessibility, reach and transparency have empowered marketers to monitor WOM as never before.

The transformation of WOM
The researchers provide three WOM models before they discuss the research questions. These models are used as basic knowledge and as conceptual models in the paper.

Markets change so marketing theories should change as well to accommodate them. A review of the development of WOM is given in below and consists of three models. All three models currently coexist, and each pertains to different circumstances.

Model A
This model assumes that WOM occurs naturally among customers when marketers bring a new product to the market and perform an effective product notification through promotions and advertisement.


Model B
This model assumes that some consumers are viewed as opinion leaders. Marketers could target these opinion leaders to influence them with advertising and promotions. All the other consumers need to be influenced with advertising and promotions as well.


Model C
This model assumes that marketers have become more interested in directly managing WOM through targeted one-to-one communication programs. Marketers see consumers as co-producers of the value and meaning of WOM as the communication is produced in consumer network. This influence is creative and even hard to resist.


Research questions and findings
Three research questions are answered so that further understanding of the network coproduction model (model C) can be developed. The three questions are as follows; How do communities respond to community-oriented WOMM? What patterns do WOM communicator strategies assume? And Why do they assume these patterns? A blog-based campaign in six North American cities is used to answer the three research questions.

The findings indicate that differences are observed in the way the members of online communities respond to WOMM campaigns. The researchers introduce a new narrative model to show that a network of communication offers four different communication strategies; evaluation, embracing, endorsement and explanation. This is also shown in below. Each of them is influenced by character narrative, communication forum, communal norms, and the nature of the marketing promotion. Thus, WOM marketing does not simply increase marketing messages, but the messages are altered in the process of embedding them.


The main strength of this paper is that the researchers provide a standpoint for both theoretical and managerial implications. For the theoretical part, the researchers focused on the motivations to participate in the bold new world of network coproduction of WOM. These motivations are more complex, culturally embedded and influenced by communities with moral hazard. This research is more extensively compared with previous research that indicates that consumers engage in online communication because of altruism, reciprocity or to gain a higher status (Dichter et al., 1966).
The managerial part offers several practical suggestions for managers and marketers who employ WOM marketing techniques. The paper convinces managers and marketers to understand that WOM marketing techniques should be presented in a way that it is congruent with the ongoing character narratives, communication forums, norms and WOM environment and that their provided new narrative model should be considered.

A downside of this article is that the gathered data consists of textual, online blogs. The interpretation and analyzing of the most important parts of these textual blogs takes a lot of time and effort. The researchers are completely dependent on the participants’ productivity and writing skills. I would suggest the researchers to use different studies as well (case study, interviews etc.), especially in combination with textual blog data.
The last note is that the limitations of this paper have not been discussed. I would suggest to include the limitations to make the paper more reliable.

Kozinets, R. V., De Valck, K., Wojnicki, A. C., & Wilner, S. J. (2010). Networked narratives: Understanding word-of-mouth marketing in online communities. Journal of marketing, 74(2), 71-89.

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