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.

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All analyses in this paper were done on a data set that was collected in November 2016 (going back 6 months). When analyzing this data set, we assumed that tweets appear in a chronological order. However, in February 2016, Twitter changed the home timeline layout to highlight certain tweets, in which the user is likely to be interested. This means that during our collection period, users were not necessarily presented with tweets in strict chronological order. We control for this potential source of bias by rerunning our main analysis on a previously collected data set. This data set includes tweets of 2,435 core users (and their followings) from September 2015 (going back 6 six), that is, prior to the policy change. All results obtained for this data set are indeed similar in direction, magnitude, and significance to those obtained from the 2016 data set. While the 2015 data set circumvents bias related to Twitter's policy change, it has a different limitation: The filters used to choose the core users were somewhat stricter than those used in the 2016 collection, as follows: (1) To be included in our 2015 data set, a user had to have posted at least 200 retweets and self-tweets in the 6-month period. Additionally, we filtered out users with fewer than 15 retweets or fewer than 15 self-tweets in each of the three months prior to the date of collection. (2) We filtered out users with exceptionally high and low (top or bottom 15%) numbers of followers or followings. While each dataset suffers from its own limitations, these limitations do not overlap, enabling us to suggest that the consistency of our results between the two complementary data sets offers robustness to our results. Tables A1 to A6 present the main results for H1, H2, and H3 for the 2015 data set.

O nline commercial interactions have increased dramatically over the last decade, leading to the emergence of networks that link the electronic commerce landing pages of related products to one another. Our paper conjectures that the explicit visibility of such "product networks"can alter demand spillovers across their constituent items. We test this conjecture empirically using data about the copurchase networks and demand levels associated with more than 250,000 interconnected books offered on Amazon.com over the period of one year while controlling for alternative explanations of demand correlation using a variety of approaches. Our findings suggest that on average the explicit visibility of a copurchase relationship can lead to up to an average threefold amplification of the influence that complementary products have on each others' demand levels. We also find that newer and more popular products "use" the attention they garner from their network position more efficiently and that diversity in the sources of spillover further amplifies the demand effects of the recommendation network. Our paper presents new evidence quantifying the role of network position in electronic markets and highlights the power of basing (virtual) shelf position on consumer preferences that are explicitly revealed through shared purchasing patterns.

Digital technologies have made networks ubiquitous. A growing body of research is examining these networks to gain a better understanding of how firms interact with their consumers, how people interact with each other, and how current and future digital artifacts will continue to alter business and society. The increasing availability of massive networked data has led to several streams of inquiry across fields as diverse as computer science, economics, information systems, marketing, physics, and sociology. Each of these research streams asks questions that at their core involve “information in networks”—its distribution, its diffusion, its inferential value, and its influence on social and economic outcomes. We suggest a broad direction for research into social and economic networks. Our analysis describes four kinds of investigation that seem most promising. The first studies how information technologies create and reveal networks whose connections represent social and economic relationships. The second examines the content that flows through networks and its economic, social, and organizational implications. A third develops theories and methods to understand and utilize the rich predictive information contained in networked data. A final area of inquiry focuses on network dynamics and how information technology affects network evolution. We conclude by discussing several important cross-cutting issues with implications for all four research streams, which must be addressed if the ensuing research is to be both rigorous and relevant. We also describe how these directions of inquiry are interconnected: results and ideas will pollinate across them, leading to a new cumulative research tradition.