Are Digital Rights Valuable? Theory and Evidence from the eBook Industry

The effective management of digital rights is a crucial challenge in many industries making the transition from physical to digital products. We present an economic model that characterizes the value of digital rights when products are sold both embedded in tangible physical artifacts and as pure digital goods, and when granting digital rights may also 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 their 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 their effect on the differential quality of legal and pirated digital goods. When the latter effect is negative, granting digital rights may 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 a significant impact on ebook prices, and establishing that those two that are most strongly associated with digital piracy have a negative impact on seller value. We also show, both analytically and empirically, that sellers should increase the prices of digital goods as the prices of their physical counterparts increase, but decrease them as the technological sophistication of their potential customers increases. Our results represent new evidence of the importance of an informed and judicious choice of the different digital rights permitted by one’s DRM platform, and provide a framework for guiding managers in industries that are progressively being “digitized.”NYU, Stern School of Business, IOMS Department, Center for Digital Economy Researc

Are Digital Rights Valuable? Theory and Evidence from eBook Pricing

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 oneâs 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 sellerâs pricing power. When the latter effect is sufficiently high, granting digital rights can have a detrimental effect on value â 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 customerâs 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.Information Systems Working Papers Serie

WHO PAYS PREMIUM IN THE AGE OF FREE SERVICES? FINDINGS FROM A MEDIA WEBSITE

The challenge for many media websites is converting users from free to fee. In order to encourage user participation and engagement with the websites many of them have provided consumers with a virtual community wherein the user can create an on-site identity, make friends, and interact with other consumers. We study the interplay between users’ functional and social behavior on media sites and their willingness to pay for premium services. We use data from Last.fm, a site offering both music consumption and social networking features. The basic use of Last.fm is free and premium services are provided for a fixed subscription fee. While the premium services mainly improve the content consumption experience, we find that willingness to pay for premium services is strongly associated with the level of social activity of the user, and specifically, the community activity of the user. Our results represent new evidence of the importance of introducing community and social activities as drivers for consumers\u27 willingness to pay for online services

Paying for Content or Paying for Community? The Effect of Social Involvement on Subscribing to Media Web Sites

Many sites have recently begun to encourage user participation and provide consumers with a virtual community wherein the user can create an on-site identity, make friends, and interact with other consumers. We study the interplay between users’ functional and social behavior on media sites and their willingness to pay for premium services. We use data from Last.fm, a site offering both music consumption and social networking features. The basic use of Last.fm is free and premium services are provided for a fixed subscription fee. While the premium services mainly improve the content consumption experience, we find that willingness to pay for premium services is strongly associated with the level of social activity of the user, and specifically, the community activity of the user. Our results represent new evidence of the importance of introducing community and social activities as drivers for consumers\u27 willingness to pay for online services

Social looks can be deceiving— How social cues affect information disclosure for high-trust users?

Across different domains, a growing number of websites are incorporating social features. This study shows that the mere presence of social cues on a website (such as functions for “liking” content or commenting) can cause users who perceive the website as trustworthy to expose themselves to potentially harmful consequences. We carried out an experiment utilizing a YouTube-like video platform that provides the opportunity to study users’ behaviors and perceptions in a realistic, controlled environment. Our results show that, among users who were primed to perceive the website as trustworthy (as opposed to untrustworthy), those who were exposed to social features disclosed more personal information compared with users who were not exposed. Moreover, among high-trust participants, the effect of social features on information disclosure was mediated by participants’ perception that they can connect to other people on the platform. Moreover, the presence of social cues did not influence participants’ privacy concerns

THE QUEST FOR CONTENT: THE INTEGRATION OF PRODUCT NETWORKS AND SOCIAL NETWORKS IN ONLINE CONTENT EXPLORATION

Without the guidance of traditional marketing campaigns, how do consumers find good usergenerated- content online? Recently, websites have begun to present content through combined product and social networks, linked by hyperlinks. We focus on the role of this dual network structure in facilitating ill-defined exploration of the content space. We first analyze the YouTube.com and show that user pages have unique structural properties and act as content brokers in the dual network. Using simulation analysis we show that random rewiring of the product network cannot replicate the brokering effect of the self-organizing social network. Finally, we introduce an experiment in which consumers browse a YouTube-based website that offers video recommendations through different networks. Using survival analysis we show that the dual network structure leads to faster access to “good” content and to overall higher satisfaction. Our work suggests that integrating a selforganizing social network with product networks significantly improves content exploration

Spreading the Oprah Effect: The Diffusion of Demand Shocks in a Recommendation Network

We study the magnitude and persistence of the diffusion of exogenous demand shocks on an ecommerce recommendation network. The demand shocks are generated by book reviews on the Oprah Winfrey Show and in the NYTimes, and the recommendation network is generated by Amazon’s copurchase network. We find a strikingly high level of diffusion of exogenous shock through such networks. Neighboring books experience a dramatic increase in their demand levels, even though they are not actually featured on the review. An average of 40% of neighbors, even 4 clicks away see a statistically significant increase in their demand levels; this effect is indicative of the depth of contagion in online recommendation networks following exogenous shocks. We also document how clustered networks “trap” a higher fraction of the contagion closer to the reviewed book, and we provide summaries of the persistence and relative magnitude of the demand inflation of the neighborhood

Using Retweets to Shape our Online Persona: a Topic Modeling Approach

Online socializing technologies have given rise to new social behaviors. We focus on the effect of reiteration tool (tools that enable us to redistribute a copy of content that others have posted) on users’ online persona building. Specifically, we study retweeting behavior on Twitter and ask: (1) Do users expand the breadth of topics they discuss? (2) Do users change the distribution of the topics they discuss? (3) Does the behaviors of experts differ from those of non-experts? We use data about 2,435 non-expert users, and 415 expert users and the users whom they follow and use LDA topic modeling to derive the topics in both self-tweets and re-tweets. We find that users rarely add new topics when retweeting and they do not alter significantly the distribution of topics. Also, this tendency is stronger among expert users, indicating that they rely more on their own words for impression management

Are Digital Rights Valuable? Theory and Evidence from eBook Pricing

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 oneâs 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 sellerâs pricing power. When the latter effect is sufficiently high, granting digital rights can have a detrimental effect on value â 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 customerâs 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.Information Systems Working Papers Serie

Prediction in Economic Networks: Using the Implicit Gestalt in Product Graphs

We define an economic network as a linked set of products, where links are created by realizations of shared outcomes between entities. We analyze the predictive information contained in an increasingly prevalent type of economic network, a “product network” that links the landing pages of goods frequently co-purchased on e-commerce websites. Our data include one million books in 400 categories spanning two years, with over 70 million observations. Using autoregressive and neural-network models, we demonstrate that combining historical demand of a product with that of its neighbors improves demand predictions even as the network changes over time. Furthermore, network properties such as clustering and centrality contribute significantly to predictive accuracy. To our knowledge, this is the first large-scale study showing that a non-static product network contains useful distributed information for demand prediction, and that this information is more effectively exploited by integrating composite structural network properties into one’s predictive models