Tag Archives: electronic commerce

The Visible Hand? Demand Effects of Recommendation Networks in Electronic Markets

When you watch a movie on Netflix, when you listen to music on Spotify, when you watch clips on Youtube, when you search for connections on LinkedIn or when you are shopping online on Bol.com, it appears often that you come across a sentence like: “you might also like”, “people you may know”, “customers who bought this item, also bought…”. Sometimes this suggestion might interest you and you click on it, but that is not always the case. Why do these recommendations appear and how do companies find the correct ones to recommend? What do these products have in common and how do they influence each other?


This paper of Oestreicher-Singer and Sundararajan (2012) explains these questions by focusing on online product or copurchase networks. In these networks, related products that each have their own network position, are linked to each other. The associations among products, and thus the product’s virtual shelf positions, are visible to the customers through recommendation hyperlinks. The main result states that this visibility of networks can cause a threefold average increase in the influence that complementary products, and thus not only recommended products, have on each other’s demand levels and that it amplifies the shared purchasing of complementary products.

To come to this result, data about the copurchase network of 250,000 books sold on Amazon.com is collected, which is used to test how demand levels are related. The data are tested on three types of possible correlations in demand. Firstly the visible network neighbours with explicit visible hyperlinks. Secondly the complementary products without visible hyperlinks, but with related demand which is controlled for unobserved sources of complementarity that might exist regardless of a visible hyperlink by constructing three alternative sets of complementary products and finally the similar environmental conditions with similar individual or environmental characteristics of the products. Besides these types of correlations, the study found that demand is also affected by the product’s individual characteristics of price, secondary market offers, vintage, in-degree and assortative mixing.

Strength of this study is that a real life setting is used to test the interdependencies, which increases the validity. Downside however is that the study is only about books. For the generalizability, it would be better to also look at other products and services such as movies, cameras or clothing.

The most remarkable outcome, besides the main result, is probably that this visibility has a stronger influence on newer and more popular products, because they ‘use’ the attention of their network position more efficiently. Recently published products are more influenced by neighbouring products, because the effect of observational learning on sales will be smaller when a consumer already has a strong prior idea of a product. Additionally, the conversion rate of recommendations that originate from more popular products is higher and sequentially the same level of total incoming traffic from fewer, more popular sources is associated with higher demand.

This study is important, because as the importance of electronic commerce continues to grow, the ability to control cross-product effects in electronic markets has become a key strategic marketing lever for firms, especially with new and popular products, and that is exactly why you see recommendation sentences when you shop online.



Oestreicher-Singer, G., Sundararajan, A. (2012) The Visible Hand? Demand Effects of Recommendation Networks in Electronic Markets. Management Science 58(11):1963-1981.

Tsekouras, D. (2017) ‘Session 2: Personalization & Product Recommendations, Rotterdam: Rotterdam School of Management (9th of February 2017).

Technology Usage and Online Sales: An Empirical Study (De, Hu and Rahmad, 2010)

Many internet retailers offer their customers advanced technology features to enhance the shopping experience, such as search functions and recommendation systems. However, how do these technologies influence consumers’ shopping behavior? Does the way these consumers use these technologies influence sales or their purchasing patterns?

Information systems, such as search and recommendation technologies are used to enhance the customer experience, by reducing the steps required to come to a preferred product. Furthermore this systems help the consumer to discover products that they would not have sought out otherwise. In the consumer journey, the consumer passes the “information search” stage before the stage of “alternative evaluation” and “purchase”. And during this information search, consumers first try to activate prior knowledge, before acquiring external sources. There are two types of products: products that are displayed in a company’s advertisement, also called promoted products; or products which are not displayed in any advertisement, called non-promoted products. Some consumers search for a specific product, with an exact name. This is called “direct search”. Some consumers do not know where they are looking for and type just a word, like “dress”, this is called “non-direct search”. Consumers who look for a product with a direct search often used their prior knowledge.


Interesting to see, is that consumers who use direct search influence online sales, however, they only affect promoted products. This indicates that consumers who are encountered with promoted products through advertisements, use that prior knowledge to directly search for the products to purchase it. Furthermore, direct search is negatively related with non-promoted products. Furthermore, recommendation systems positively influence online sales, for both types of products. However, the recommendation system works stronger in categories with many products, than in categories with a few products. An explanation might be, it is likely that consumers lack prior knowledge of a large proportion of the product assortment and therefore find provided recommendations more beneficial. An unexpected finding is that non-direct search has no influence on consumers’ purchase behavior.

These findings are interesting for internet retailers, but it proves that it is beneficial to invest in advanced technology features, such as search and recommendation systems because this will lead to higher levels of sales. Furthermore, if online retailers want to increase sales via search systems it is suggested to also promote products because otherwise the tool only enhances the customer experience.

An example that successfully adopted these technology systems is Amazon. Amazon is one of the largest online retailers in the world that sells almost everything. Another advanced technology feature that Amazon uses is collaborative filtering systems, where the consumers get information about what other consumers bought after buying a particular product: “Consumers who bought this product also bought this…”. Unfortunately, this study has not included this technology feature. However, no worries, other research has proven that these systems also increases the diversity and amount of products purchased by a consumer (Lee and Hosanagar, 2015).

However, findings from this paper still show the importance of investments in information technologies, as it influences consumers purchase behavior. Furthermore internet companies are continuing to develop more sophisticated search and recommendation systems, which is a good trend.

De, P., Hu, Y.J. and Mohammed, R.S. (2010) ‘Technology Usage and Online Sales: An Emperical Study’, Management Science, 56, 11: pp. 1930-1945.

Lee, D. and Hosanagar, K. (2015) ‘People Who Liked This Study Also Liked: An Emperical Investigation of the Impact of Recommender Systems on Sales Diversity’, available online from: https://papers.ssrn.com/sol3/papers2.cfm?abstract_id=2603361 [14 February 2017].