‘If you know yourself and know your enemy, you need not fear the result of a hundred battles’ (Sun Tzu, Art of War)
Online retailers currently implement and leverage a variety of sales support tools. This article provides insight in consumer (users) behaviour on those e-commerce websites (Adomavicius and Tuzhilin 2005). It looks at different factors that might affect consumer behaviour (specifically consumers’ decisions whether or not to buy a product) (Hennig-Thurau et al. 2012). In this research the effect of two things on sales of a product is examined. One is recommendation systems (generated by the firm) and the other is online review systems (generated by customers). Previous literature has primarily focused on both loose effects; this study particularly looks at how these two factors might combine together to affect consumers’ behaviour.
Recommendation systems are where each product will give you recommendations to other (similar) products (Oestreicher-Singer and Sundararajan 2012). This study argues that we should see recommendations systems as networks with a number of different products linked to each other. This study seeks to analyse the whole network of related product referrals. The author predicts that the position of all the different products within a network might affect the sales of a product. Any product near to the centre of the recommendation network will get more attention. So, if the Product A is near to the centre of the network it will get more sales. If the competing product (or normally products) is near to the centre these will get more attention, taking attention away from the Product A, and so Product A will get less sales.
Online review systems (eWOM: electronic word-of-mouth) are where users of this site write a review of a product. This study observes when the consumers is shopping on the site, they can see both reviews of the product they are considering and also all reviews of other recommended products. The author predicts that the fact that they can see these other reviews creates competition, which makes the customer less likely to buy the particularly product.
The empirical analysis are performed with collected data from Amazon.com on 1.740 randomly selected books within four categories (programming, business, health and guide books) over a period from two years. This empirical analysis yields three major findings. The research has shown that recommendation systems intensify the competition between products. The authors state that the products which are linked to recommendation systems generate more sales if they have a central place in the referral recommendation network. There have been extensive findings that these sales gains are impeded by improvements in the reviews (eWOM) of competing products. This indicates that a positive eWOM received by a competing book worsen the rank of the focal book.
The main limitation of this study is that they used data at aggregate level, and not at individual consumer level. Therefore, the collected data does not track the actual activity of reviews/recommendations. This is a missing link for virtually all eWOM studies. The solution to this limitation could may be collecting click-stream data to better connect behavior and action.
Adomavicius, G., and Tuzhilin, A. 2005. “Towards the Next Generation of Recommender Systems: A Survey of the State-of- the-Art and Possible Extensions,” IEEE Transactions on Knowl- edge and Data Engineering (17:6), pp. 734-749.
Hennig-Thurau, T., Marchand, A., and Marx, P. 2012. “Can Auto- mated Group Recommender Systems Help Consumers Make Better Choices?,” Journal of Marketing (76:5), pp. 89-109.
Oestreicher-Singer, G., and Sundararajan, A. 2012. “Recommen- dation Networks and the Long Tail of Electronic Commerce,” MIS Quarterly (36:1), pp. 65-83