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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.

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Helix: How Your DNA is Choosing Your Wine

Imagine that you really like pizza. You probably have a favourite pizza – and a favourite place to get it – right? Let’s say your favourite pizza is a margarita. When you get the pizza and eat it, you will probably like it. However, do you not sometimes think something could have been done differently? Maybe there should have been less cheese, maybe it’s too greasy, or maybe the temperature is just off? Does getting the perfect pizza every time sound like a dream to you?  Well, it’s time to wake up then, because consumer genomics start-up Helix is very close to realizing this concept.

But first, let’s back it up a bit.

What are Human Genomics?
The whole concept of human genomics started off in medicine. A patient’s DNA would be sequenced, which means that “the exact order of the four bases in a strand of DNA” would be determined (yourgenome, 2016). Why does this matter, you ask? Well, with the exact order of the composition of somebody’s DNA, doctors could tailor their treatment, medicine, and pretty much every factor that would impact a patient’s health (Farr, 2016). Probably the most popular case of DNA sequencing is that of Steve Jobs, who paid $100,000 in 2011 to sequence his DNA in an attempt to let doctors gain more insight into his sickness and try to help him more effectively (Farr, 2016). Next to the value of DNA sequencing in medicine, Illumina – the company whose supercomputers are behind 90% of DNA sequencing ever done – has identified a use for DNA sequencing outside of the medical field (Farr, 2016).

The Birth of Helix
Helix – an Illumina spin-off – is said to “democratize genomics” (Farr, 2016). Illumina has managed to bring the costs of DNA sequencing down tremendously – partly due to decreasing lab costs and more lenient regulatory decisions in the US (Farr, 2016; Teo, 2017). Where Steve Jobs paid $100,000 in 2011, a comparable procedure would now cost less than $1000 (Farr, 2016). According to helix, DNA sequencing can – next to provide more insight into diseases – discover other personal matters like your lifestyle, personality traits, taste senses, and much more (Farr, 2016). See where I am going with this?

Helix provides many different products. They – for now – offer six different product categories (Helix, 2018).

  • Ancestry: These products help you find out where your ancestors stem from, to hundreds of thousands of years back;
  • Entertainment: This is the fast-moving consumer goods section, if you will. Here, you can get for example a wine tailored to your taste perfectly;
  • Family: These products are mainly meant for families that want to grow, offering them fertility information;
  • Fitness: Here, Helix wants to help you to “reach your full potential” by designing the perfect workout routine;
  • Health: This is the more traditional use of DNA sequencing as explained in the previous section;
  • Nutrition: Lastly, the nutrition products let you design your perfect nutrition plan that suits your metabolism the best (Helix, 2018).


The Business Model
Helix has a new and unusual business model. As they work closely with Illumina, they have many valuable resources that help them analyse consumers’ whole DNA spectrum, whereas similar companies are able to only analyse part of it (Zhang, 2017). Consumers pay a one-time $80 fee to analyse their DNA and the rest is subsidized by Helix (Zhang, 2017). The consumers then choose what kind of products they would like to purchase, and Helix lets third-party companies create those products based on the genetic information Helix provides them (Zhang, 2017).
Helix has, in that sense, created an online platform with customers – on one hand – who gain access to the platform by letting their DNA be sequenced, and on the other hand the product developers (Molteni, 2017).
The business model is efficient in the sense that its platform brings together companies that offer very specialized, personalized products and consumers that are seeking such products and cannot find them in conventional retail channels. Customers benefit as they receive products that are tailored to their individual tastes to the maximum extent, and companies benefit as they cater to the customers. Also, as the companies get to know more and more about individual customers, they could use this information to develop tailored product recommendations. However, as will be explained in the next section, the efficiency of the business model might suffer from regulatory decisions and consumer privacy issues.

Talk About Personalized Products
Basically, Helix takes product personalization to the next level. Personalizing products has many advantages, for example customers’ craftsmanship is emphasized, and customers form a connection with the product if they have put effort into designing it (Nagle, 2017). However, writing your name on a wine label and getting the wine tailored to your DNA are two completely different things. Because DNA is pretty much as personal as you can get, there are potential drawbacks of the Helix business model. The first and most obvious issue is privacy concerns. If people are already freaking out about the cookies that are gathered on websites, why would they send their DNA to a company to get a product of which they could by a similar version in the supermarket?

Some companies using DNA sequencing store consumer data for “unspecified research” and might sell it to third parties (Niemiec & Howard, 2016: p.23). If consumers get suspicious about this, and privacy concerns rise through the roof, it might negatively impact Helix as well. Also, ethical issues such as discrimination based on DNA information are surfacing, too (Farr, 2016). Imagine that your life insurance gets to know your DNA information, this could highly impact the price you pay.

All in all, although customers like personalized products, the safety of information security measures – or even international regulations – need to be established before customers can completely trust the businesses.

The Future
In the future, Helix aims to create an “App Store” for their genomics products and services (Farr, 2016). They want to create the platform in such way that consumers can access their DNA information, browse the “App Store” to discover products that they like (Farr, 2016). The consumers just need to let their DNA be sequenced once – just like you create your Apple ID once – and can then browse the “App Store” as they wish (Farr, 2016). Helix compares their platform to the App Store rather than to Google Play, as they aim to review each seller, which is what Apple does do each app created, whereas Google takes a more lenient approach (Zhang, 2017). Right now, Helix already has 14 employees whose task it is to get to the bottom of the products developed by their featured companies (Zhang, 2017). The buzzword of the platform is that it is “dynamic” (Molteni, 2017). Helix wants to evolve and widen its platform as the research improves, resulting in more products and services to offer to their customers (Molteni, 2017).

So, if you ask Helix, the next time you eat a margarita, you will love it so much that you will feel it in your genes, literally.

Farr, C. (2016). Genetics Startup Helix Wants To Create A World of Personalized Products from Your DNA. Retrieved from: [Accessed February 16th, 2018]

Helix (2018). How It Works. Retrieved from: [Accessed February 16th, 2018]

Molteni, M. (2017). Helix’s Bold Plan To Be Your One Personal Genomics Shop. Retrieved from: [Accessed February 17th, 2018]

Nagle, T. (2017) How Personalized Goods are Shaping the Economy. Retrieved from: [Accessed February 17th, 2018]

Niemec, E. & Howard, H. C. (2016). Ethical Issues in Consumer Genome Sequencing: Use of Conumer’s Samples and Data. Applied & Transational Genomics, 8, pp.23-30.

Teo, G. (2017). The Second Coming of Consumer Genomics With 3 Predictions for the Future. Retrieved from: [Accessed February 17th, 2018]

YourGenome (2018). What is DNA Sequencing? Retrieved from: [Accessed February 16th, 2018]

Zhang, S. (2017). How Do You Know When a DNA Test is B.S.? Retrieved from: [Accessed February 17th, 2018]