We define an economic network as a linked set of entities, where links are created by actual realizations of shared economic outcomes between entities. We analyze the predictive information contained in a specific type of economic network, namely, a product network, where the links between products reflect aggregated information on the preferences of large numbers of individuals to co-purchase pairs of products. The product network therefore reflects a simple “smoothed” model of demand for related products. Using a data set containing more than 70 million observations of a nonstatic co-purchase network over a period of two years, we predict network entities' future demand by augmenting data on their historical demand with data on the demand for their immediate neighbors, in addition to network properties, specifically, local clustering and PageRank. To our knowledge, this is the first study of a large-scale dynamic network that shows that a product network contains useful distributed information for demand prediction. The economic implications of algorithmically predicting demand for large numbers of products are significant.
The content industry has been undergoing a tremendous transformation in the last two decades. We focus in this paper on recent changes in the form of social computing. Although the content industry has implemented social computing to a large extent, it has done so from a techno-centric approach in which social features are viewed as complementary rather than integral to content. This approach does not capitalize on users' social behavior in the website and does not answer the content industry's need to elicit payment from consumers. We suggest that both of these objectives can be achieved by acknowledging the fusion between content and community, making the social experience central to the content website's digital business strategy. We use data from Last.fm, a site offering both music consumption and online community features. The basic use of Last.fm is free, and premium services are provided for a fixed monthly subscription fee. Although the premium services on Last.fm are aimed primarily at improving the content consumption experience, we find that willingness to pay for premium services is strongly associated with the level of community participation of the user. Drawing from the literature on levels of participation in online communities, we show that consumers' willingness to pay increases as they climb the so-called "ladder of participation" on the website. Moreover, we find that willingness to pay is more strongly linked to community participation than to the volume of content consumption. We control for self-selection bias by using propensity score matching. We extend our results by estimating a hazard model to study the effect of community activity on the time between joining the website and the subscription decision. Our results suggest that firms whose digital business models remain viable in a world of "freemium" will be those that take a strategic rather than techno-centric view of social media, that integrate social media into the consumption and purchase experience rather than use it merely as a substitute for offline soft marketing. We provide new evidence of the importance of fusing social computing with content delivery and, in the process, lay a foundation for a broader strategic path for the digital content industry in an age of growing user participation.
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. We test this conjecture using the revenue distributions of books in over 200 distinct categories on Amazon.com and detailed daily snapshots of co-purchase recommendation networks in which the products of these categories are situated. We measure how much a product is influenced by its position in this hyperlinked network of recommendations using a variant of Google's PageRank measure of centrality. We then associate the average influence of the network on each category with the inequality in the distribution of its demand and revenue,quantifying this inequality using the Gini coefficient derived from the category's Lorenz curve. We establish that categories whose products are influenced more by the recommendation network have significantly flatter demand and revenue distributions, even after controlling for variation in average category demand, category size, and price differentials. Our empirical findings indicate that doubling the average network influence on a category is associated with an average increase of about 50 percent in the relative revenue for the least popular 20 percent of products, and with an average reduction of about 15 percent in the relative revenue for the most popular 20 percent of products. We also show that this effect is enhanced by higher assortative mixing and lower clustering in the network, and is greater in categories whose products are more evenly influenced by recommendations. The direction of these results persists over time, across both demand and revenue distributions, and across both daily and weekly demand aggregations. Our work illustrates how the microscopic economic data revealed by online networks can be used to define and answer new kinds of research questions, offers a fresh perspective on the influence of networked IT artifacts on business outcomes, and provides novel empirical evidence about the impact of visible recommendations on the long tail of electronic commerce.