All posts by elisabethkuyf

Helpfulness of online consumer reviews: Readers’ objectives and review cues.

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., retailer (e.g. Amazon), manufacturer (e.g. Nike) or product evaluation websites (e.g. 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 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.

Conceptual framework and hypotheses confirmation

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”. (2013). Top Reviewers On Amazon Get Tons Of Free Stuff. [online] Available at: [Accessed 4 Mar. 2017].


ARTO – The Easiest Way to Discover Art You Love

As the housing market continues to grow, the walls of  new homes need some decoration. You decide you want a nice piece of art but where to start, what is your personal taste? Even if you know your preferences, you may find it rather difficult to describe them to find the type of art you are looking for. Now there is ARTO Gallery, the application that helps you find your perfect art match!

What is ARTO?    

picture5ARTO Gallery, launched in 2016 by Jonie Oostveen who was a former Director Business Development at Spotify, is a new way to discover and buy art from your mobile device. The app helps experts as well as non-experts to discover great paintings based on their personal preferences, while also giving artists a platform to sell and promote their artworks. The platform offers art of both independent artists and galleries but also partners with renowned museums (e.g. the Rijks Museum and the National Gallery of Art).  Currently the platform has a database of 5.500 artists and more than 20.000 pieces, which is expected to grow to 50.000 by the end of the year.

How does it work?      

Once you open the app, you will see your “Art Stream”. Customers participate as active “co-creators”(Tsekouras, lecture 2017) by indicating per artwork whether they like it or not through swiping it left or right. The more the customer swipes, the better ARTO Gallery understands their personal preferences. By using the Artificial Intelligent “Art Recommendation Engine” ARTO can recommend artworks from their database to the customer. Furthermore, it is possible to stream artwork to your TV to imagine how the piece would look in your room, ask questions to the artists and see similar artworks to the ones you liked. Artists pay for the service if a transaction takes place as ARTO Gallery charges the artists’ account a certain percentage of the value of artworks sold. For customers the platform is free of charge.

ARTO Gallery impression

Efficiency Criteria

ARTO is the first two-sided personalized platform that connects art seekers to galleries and artists. The platform maximizes the joint profitability of both partners (Carson et al., 1999). On one side, art seekers are now enabled to find artworks they love more efficiently as their choice overload is reduced by personalized recommendations. Moreover, the invested effort is relatively low as using the application requires much less time than browsing gallery websites.
On the other side, galleries and artists have easy and low cost access to a new customer base, increasing their potential returns. Moreover, through this process ARTO Gallery allows the long tail of art to grow (Brynjolfsson et al., 2006) as more customers discover unfamiliar artwork styles while artists and galleries can adapt their supply to this.

Futhermore, I consider criterion for the feasibility of required reallocations  (Carson et al., 1999) to be met. However, evaluating the institutional environment, I would say that legally there is the largest threat for platform abuse. One might want to sell copies of paintings as if it is original work (intellectual property issues), which may both harm the potential buyer as well as the reputation of ARTO Gallery as a platform. Nonetheless, ARTO Gallery addressed this by their notifications of copyright infringement. Moreover, artists are carefully screened before they are allowed to upload work on the platform. In addition, artists and galleries are always responsible for their own artworks and personal information may be handed over to legal institutions at all times.


I think the ARTO Gallery application is unique and considerably differs from its competition by offering personalized art recommendations. This feature makes it a great way to discover your personal taste and new artworks, while it offers a platform for art suppliers to expose their works. However, as the application is launched recently it is difficult to tell whether it will establish itself into the marketplace. I think first some marketing investments need to be done as the application is still quite unknown, but there is definitely potential!


Brynjolfsson, E., Hu, Y., and Smith, M.D. 2006. From Nichees to Riches: Anotomy of the Long Tail. Management Review 47(4) 67-71.

Carson, S. J., Devinney, T. M., Dowling, G. R., & John, G. (1999). Understanding institutional designs within marketing value systems. Journal of Marketing, 115-130

Arto Gallery.(2017, February 24). Our Company. Retrieved from Arto.Gallery

Artworldforum. (2017, February 24). press-arto-gallery. Retrieved from

Tsekouras, D. (2 February 2017), Lecture Customer Centric Digital Commerce, “Introduction to value co-creation”.

Will the global village fracture into tribes? Recommender systems and their effects on consumer fragmentation

Netflix, Spotify and Amazon and many other companies recommend personalized content to best suit the customers’ preferences and increase consumption or sales (De et al., 2007). Recommender systems are used for different types of media such as movies, books, music, news and television. Moreover, they strongly affect what customers view and buying behavior. Many positive effects of personalization are known such as reduction of information overload, increasing relevance and loyalty (Tsekouras, lecture 2017).

However, personalization may also have drawbacks. As the internet becomes more and more specific to our interests, this “ hyperspecification” may fragment users, reduce shared experience and narrow media consumption. For example, fully personalizing news sites may mean that we no longer see the same news articles. Therefore one could argue that recommender systems will create fragmentation and will cause users to have less in common.
On the other hand, recommenders may have the opposite effect as they share information among customers who otherwise would not have communicated. The paper presents empirical evidence on whether recommender systems fragment or homogenize customers.

In an observational two-group experiment the behavior of the participants before and after recommendations is compared. Recommendations are provided in iTunes and the user experience is personalized through mostly content-based recommendations and a small part collaborative data.

Interestingly, contrary to the author’s expectations, recommendation systems increase purchase similarity (homogeneity among customers) and therefore do not increase fragmentation. Customers tend to purchase more after being exposed to personalized recommendations due to the volume effect, which in turn increases the chance of customers purchasing the same product.
Furthermore, customers buy a more similar mix of products after recommendations due to the product-mix effect. Consequently, customer networks become denser and smaller. However, it is important to note that the recommender system does not recommend the same items to many users but that the diversity of items consumed increases.

Within business, the findings of the paper confirm that recommendation tools can help marketers to increase sales but also alter the product mix customers buy. Off course, these are some of the the reason why numerous retailers (e.g. Netflix, Amazon, Spotify) use these recommendation systems.
However, what I find more interesting about the paper is the application of recommendation systems in the context of society. As we increase to spend time online and recommendation systems become more advanced, I think this is an interesting application. Similarly, I think it would be interesting to research to what extent the proposed homogenization of customers has implications for society and (business) cultures.
Fragmentation may have negative implications for society as when people share little information filter bubbles and echo chambers may exist. However, in business context, I think there are situations in which fragmentation of customers may provide better results than homogeinity . For example in the context of innovation, crowdsourcing and generating new business ideas, a variety of views and opinions may yield to better and more refreshing ideas from different anchors. In this case, it would not be desirable if every customer listens to the same music and reads the same news articles and books, as different point of views are needed. I think for companies who want to generate new ideas from their customers, the homogenization of their customer base might be something to take into account.


De P, Hu YJ, Rahman MS (2010) Technology usage and online sales: An empirical study. Management Sci. 56(11):1930–1945.

Hosanagar, K., Fleder, D., Lee, D., & Buja, A. (2013). Will the global village fracture into tribes? Recommender systems and their effects on consumer fragmentation. Management Science60(4), 805-823.

Tsekouras, D. (10 February 2017), Lecture Customer Centric Digital Commerce, “Personalization & Product Recommendations.