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Abstract:
The sustained increase in different forms of electronic interaction over the last decade has led to the emergence of a number of electronic and visible networks that connect consumers, products and businesses. In this paper, we conjecture that the visibility of these networks influences a wide variety of choices and outcomes in electronic markets, and analyze the nature and extent of influence that their visibility induces. We do so by developing an extended model of social or "peer" effects that separates network-induced social influence from demand correlations that are caused by product complementarity, by product characteristics, and by self-selected groupings. Our main analytical result provides a simple set of conditions under which the influence that visible social networks have on demand can be econometrically identified, and we show that our conditions place very minimal empirical restrictions on the structure of the networks that define the pattern of social influence. We estimate this model using data about the demand and co-purchase networks for over 250,000 books offered on Amazon.com over the period of a year. Our empirical results show that the explicit visibility of a co-purchase relationship more than triples the average influence that complementary products have on each others' demand. Furthermore, the magnitude of this social influence is higher for more popular books, for more recently published books, and varies in counter-intuitive ways with changes in pricing, secondary market activity and assortative mixing across product categories. Our paper presents new evidence quantifying the role of network "position" and the influence of visible social networks in electronic markets, highlighting the power of basing virtual shelf position or slotting on consumer preferences that are revealed through shared purchasing patterns. It also offers new results for the identification of peer effects which reverse the impossibility issue associated with the reflection problem of Manski (1993), establishing in the process that robust identification is simplified considerably when there are multiple overlapping networks mediating peer influence.

networks, social networks, peer effects, electronic commerce, ecommerce, recommender systems, identification, selection, influence

Abstract:
The effective management of digital rights is the central challenge in many industries making the transition from physical to digital products. We present a new model that characterizes the value of these digital rights when products are sold both embedded in tangible physical artifacts, and as pure digital goods, and when granting rights permitted by one's digital rights management (DRM) platform may affect the extent of digital piracy. Our model indicates that in the absence of piracy, digital rights should be unrestricted, since a seller can use its pricing strategy to optimally balance sales between physical and digital goods. However, the threat of piracy limits the extent to which digital rights should be granted: the value of digital rights is determined not only by their direct effect on the quality of legal digital goods, but by a differential piracy effect that can lower a seller's pricing power. When the latter effect is sufficiently high, granting digital rights can have a detrimental effect on value - our model indicates that this kind of effect is more likely to be observed for digital rights that aim to replicate the consumption experience of physical goods, rather than enhancing a customer's digital experience. We test the predictions of our analytical model using data from the ebook industry. Our empirical evidence supports our theoretical results, showing that four separate digital rights each have an economically significant impact on ebook prices, and establishing that the digital rights which aim to replicate physical consumption while increasing the threat of piracy are the ones that have negative impact on seller value. We also show that if the pricing of a digital good is keyed off that of an existing tangible good, optimal pricing changes for the former should be more nuanced, rather than simply mirroring changes in the price of the latter, and we discuss the effect of the technological sophistication of potential customers on optimal pricing and rights management. Our results represent new evidence of the importance of an informed and judicious choice of the different digital rights granted by a DRM platform, and provide a new framework for guiding managers in industries that are progressively being digitized.

digital piracy, intellectual property, digital rights management, DRM, piracy, ebooks, electronic book, hedonic price, copyright, IP, law

Abstract:
It has been conjectured that the peer-based recommendations associated with electronic commerce lead to a redistribution of demand from popular products or "blockbusters" to less popular or "niche" products, and that electronic markets will therefore be characterized by a "long tail" of demand and revenue. In this paper, we develop a novel method to test this conjecture and we report on results contrasting the demand distributions of books in over 200 distinct categories on Amazon.com. Viewing each product as having a unique position in a hyperlinked network of recommendations between products that is analogous to shelf position in traditional commerce, we quantify the extent to which a product is influenced by its recommendation network position by using a variant of Google's PageRank measure of centrality. We then associate the average level of network influence on each category with the inequality in the distribution of its demand and revenue, quantifying this inequality using the Gini coefficient derived from the categories' Lorenz curve. We establish that categories whose products are influenced more by recommendations have significantly flatter demand distributions, even after controlling for variations in average category demand, the category's size and measures of price dispersion. Our empirical findings indicate that doubling the average influence of recommendations on a category is associated with an average increase in the relative demand for the least popular 20% of products by about 50%, and a average reduction in the relative demand for the most popular 20% by about 12%. We also show that this effect is enhanced when there is assortative mixing in the recommendation network, and in categories whose products are more evenly influenced by recommendations. The direction of these results persist across time, across both demand and revenue distributions, and across both daily and weekly demand aggregations. Our work offers new ideas for assessing the influence of networks on demand and revenue patterns in electronic commerce, and provides new empirical evidence supporting the impact of visible recommendations on the long tail of electronic commerce.

networks, social networks, electronic commerce, ecommerce, recommender systems, influence, gini coefficient

Abstract:
We define an economic network as a linked set of entities, where linksare created by actual realizations of shared economic outcomes betweenentities. Such networks are becoming increasingly prevalent on theInternet, an example being the copurchase netwok on Amazon whereentities are books and links designate which pairs were purchasedsimultaneously. Our dataset covers a diverse set of books spanning over 400 categories over a period of three years with a total of over 70 million observations. To our knowledge, this is the first large scalestudy showing that an economic network contains useful predictiveinformation that is distributed in the network. We show that an economicnetwork contains predictive information. Specifically, we demonstratethat an entity’s future demand is more accurately predicted bycombining its historical demand with that of its neighbors than byconsidering its demand alone. In other words, if you want to know whatyour state will be in the future, consider what is happening to yourneighbors now. This result could apply to other economic networks whereoutcomes of sets of entities tend to be related.

network effects, economic networks, copurchase networks, predictive models, data mining