Motivated by the rise of social media platforms that achieve a fusion of content and community, we consider the role of word-of-mouth communications (WOM) structured through a network. Using a data set from YouTube, we examine how cascades of WOM interactions enhance the popularity of videos. We first estimate the impact of channel influence and other network parameters in initiating WOM communications. The probit estimation considers the selection effect in videos that are likely to be associated with a greater propensity to trigger WOM. We find that factors related to a channel's ability to be a connector and a translator is most likely to result in the incidence of WOM. We then examine how cascades of WOM conversations have persistent impacts on subsequent video popularity. Empirically, the main issue here is heterogeneity in the epidemic potential of a video. Since the threshold might vary across videos, we use a finite mixture model. We also conduct a simultaneous estimation using latent instrumental variables to address endogeneity from unobservables. Our research has implications for researchers and practitioners by highlighting how WOM travels through networks of influence and susceptibility in disseminating awareness, and holds insights in regard to designing social recommendation systems and identifying trending topics in social media. > >
Many software firms, especially mobile app providers, offer perpetually free basic products to users, but premiums are charged for access to the additional features or functionalities. While the free offering helps capture potential customers, it might cannibalize the sales of premium goods or services. This paper adopts a game theoretical approach to examine the impact of free offering on the competition between two firms in the presence of network effects. The firms can either offer a free core product and a paid service or offer them as a bundle. The core product has stand-alone value and can be used separately but the value-added service has no value without the core product. We derive the market equilibria and present conditions under which the free offering strategy outperforms the bundling strategy. We show that when a firm's core product has a sufficient advantage in product quality, it is better for this firm to sell the bundle but for the other to use free strategy. However, if the core products are similar in terms of quality, it is optimal for them to use the same strategies. Whether to offer a free product depends largely on the core products' quality. We also show that the firms may be caught in a prisoner's dilemma when both adopt the free strategy. Finally, we find that the profitability of the firm that offers a free product always increases in network effects intensity and market size, but this is not the case for the firm that sells the bundle. This study contributes to understanding the behavior of feature-limited free offering in a duopoly setting. Our findings also provide insights into the design of free product and the impact of network effects on the firms' offering decisions. > >
Social networks have been shown to affect health. Because online social networking makes it easier for individuals to interact with experientially similar others in regard to health issues and to exchange social support, there has been an increasing effort to understand how networks function. Nevertheless, little attention has been paid to how these networks are formed. In this paper, we examine the driving forces behind patients' social network formation and evolution. We argue that patients' health-related traits influence their social connections and that the patients' network layout is shaped by their cognitive capabilities and their network embeddedness. By studying longitudinal data from 1,322 individuals and their communication ties in an online healthcare social network, we find that firsthand disease experience, which provides knowledge of the disease, increases the probability that patients will find experientially similar others and establish communication ties. Patients' cognitive abilities, including the information load that they can process and the range of social ties that they can manage, however, limit their network growth. In addition, we find that patients' efforts to reach out for additional social resources are associated with their embeddedness in the network and the cost of maintaining connections. Practical implications of our findings are discussed.
In this paper, we investigate whether social support exchanged in an online healthcare community benefits patients’ mental health. We propose a nonhomogeneous Partially Observed Markov Decision Process (POMDP) model to examine the latent health outcomes for online health community members. The transition between different health states is modeled as a probability function that incorporates different forms of social support that patients exchange via discussion board posts. We find that patients benefit from learning from others and that their participation in the online community helps them to improve their health and to better engage in their disease self-management process. Our results also reveal differences in the influence of various forms of social support exchanged on the evolution of patients’ health conditions. We find evidence that informational support is the most prevalent type in the online healthcare community. Nevertheless, emotional support plays the most significant role in helping patients move to a healthier state. Overall, the influence of social support is found to vary depending on patients’ health conditions. Finally, we demonstrate that our proposed POMDP model can provide accurate predictions for patients’ health states and can be used to recover missing or unavailable information on patients’ health conditions.
Online user forums for technical support are being widely adopted by IT firms to supplement traditional customer support channels. Customers benefit from having an additional means of product support, while firms benefit by lowering the costs of supporting a large customer base. Typically these forums are populated with content generated by users, consisting of questioners (solution seekers) and solvers (solution providers). While questioners can be expected to keep returning as long as they can find answers, firms must employ different means in order to recognize and encourage the contributions of solvers. We identify and compare the impact of two widely adopted recognition mechanisms on the philanthropic behavior of solvers. In the first mechanism, feedback-based recognition, solver contribution is evaluated by questioners. In the second mechanism, quantity-based recognition, all contributions are weighted equally regardless of questioner feedback. We draw on the pro-social behavior literature to identify four drivers of solver contribution: (1) peer recognition, (2) image motivation, (3) social comparison, and (4) social exposure. We show that the choice of recognition mechanism strongly influences a solver’s problem-solving behavior, highlighting the importance of the firm’s decision in this regard. We address issues of solvers self-selecting a type of recognition mechanism by using propensity score analysis in order to show that solver behavior is a result of forum conditioning. We also study the impact of the recognition mechanism on forum quality and the effectiveness of support to draw comparative analytics.
Giving away trial software is a common practice for software developers to maximize the exposure of their products to potential consumers and to minimize the consumers' uncertainty about software quality. There are two types of free trials: (1) freeware, which consists of very basic features of focal software without a time lock, and (2) trialware, which has the full functionality of focal software with a time lock. In this paper, we study what factors make some free-trial software attract more potential adopters than others. Our empirical model under the traditional Bass-type diffusion examines the effects of the different types of free-trial software and ratings on consumer software sampling and reveals the dynamics of sampling over time. Using free-trial software downloading data on Download.com, we observe that the consumer software sampling process can be described by the theory of information diffusion. We find that user ratings affect sampling performance positively and that third-party ratings need to be positive to be effective. Finally, our results do not show any discernible differences between freeware and trialware with regard to their impact on sampling performance. This study contributes to the understanding of software free-trial practice from the perspective of consumer sampling growth of different types of free trials. Our findings can help design free-trial strategies to extrapolate the extent of consumer awareness of focal software and effectively convey its quality information to potential customers.
This paper is motivated by the success of YouTube, which is attractive to content creators as well as corporations for its potential to rapidly disseminate digital content. The networked structure of interactions on YouTube and the tremendous variation in the success of videos posted online lends itself to an inquiry of the role of social influence. Using a unique data set of video information and user information collected from YouTube, we find that social interactions are influential not only in determining which videos become successful but also on the magnitude of that impact. We also find evidence for a number of mechanisms by which social influence is transmitted, such as (i) a preference for conformity and homophily and (ii) the role of social networks in guiding opinion formation and directing product search and discovery. Econometrically, the problem in identifying social influence is that individuals' choices depend in great part upon the choices of other individuals, referred to as the reflection problem. Another problem in identification is to distinguish between social contagion and user heterogeneity in the diffusion process. Our results are in sharp contrast to earlier models of diffusion, such as the Bass model, that do not distinguish between different social processes that are responsible for the process of diffusion. Our results are robust to potential self-selection according to user tastes, temporal heterogeneity and the reflection problem. Implications for researchers and managers are discussed.
Cooperative caching is a popular mechanism to allow an array of distributed caches to cooperate and serve each others' Web requests. Controlling duplication of documents across cooperating caches is a challenging problem faced by cache managers. In this paper, we study the economics of document duplication in strategic and nonstrategic settings. We have three primary findings. First, we find that the optimum level of duplication at a cache is nondecreasing in intercache latency, cache size, and extent of request locality. Second, in situations in which cache peering spans organizations, we find that the interaction between caches is a game of strategic substitutes wherein a cache employs lesser resources towards eliminating duplicate documents when the other caches employs more resources towards eliminating duplicate documents at that cache. Thus, a significant challenge will be to simultaneously induce multiple caches to contribute more resources towards reducing duplicate documents in the system. Finally, centralized decision making, which as expected provides improvements in average latency over a decentralized setup, can entail highly asymmetric duplication levels at the caches. This in turn can benefit one set of users at the expense of the other, and thus will be challenging to implement.
This study develops a stochastic model to capture developer learning dynamics in open source software projects (OSS). A hidden Markov model (HMM) is proposed that allows us to investigate (1) the extent to which individuals learn from their own experience and from interactions with peers, (2) whether an individual's ability to learn from these activities varies as she evolves/learns over time, and (3) to what extent individual learning persists over time. We calibrate the model based on six years of detailed data collected from 251 developers working on 25 OSS projects hosted at Sourceforge. Using the HMM, three latent learning states (high, medium, and low) are identified, and the marginal impact of learning activities on moving the developer between these states is estimated. Our findings reveal different patterns of learning in different learning states. Learning from peers appears to be the most important source of learning for developers across the three states. Developers in the medium learning state benefit the most through discussions that they initiate. On the other hand, developers in the low and the high states benefit the most by participating in discussions started by others. While in the low state, developers depend entirely upon their peers to learn, whereas in the medium or high state, they can also draw upon their own experiences. Explanations for these varying impacts of learning activities on the transitions of developers between the three learning states are provided. The HMM is shown to outperform the classical learning curve model. The HMM modeling of this study contributes to the development of a theoretically grounded understanding of learning behavior of individuals. Such a theory and associated findings have important managerial and operational implications for devising interventions to promote learning in a variety of settings.
What determines the success of open source projects? In this study, we investigate the impact of network social capital on open source project success. We define network social capital as the benefits open source developers secure from their membership in developer collaboration networks. We focus on one specific type of success as measured by the rate of knowledge creation in an open source project. Specific hypotheses are developed and tested using a longitudinal panel of 2,378 projects hosted at SourceForge. We find that network social capital is not equally accessible to or appropriated by all projects. Our main results are as follows. First, projects with greater internal cohesion (that is, cohesion among the project members) are more successful. Second, external cohesion (that is, cohesion among the external contacts of a project) has an inverse U-shaped relationship with the project’s success; moderate levels of external cohesion are best for a project’s success rather than very low or very high levels. Third, the technological diversity of the external network of a project also has the greatest benefit when it is neither too low nor too high. Fourth, the number of direct and indirect external contacts positively affects a project’s success such that the effect of the number of direct contacts is moderated by the number of indirect contacts. These results are robust to several control variables and alternate model specifications. Several theoretical and managerial implications are provided.
In peer-to-peer (P2P) media distribution, users obtain content from other users who already have it. This form of decentralized product distribution demonstrates several unique features. Only a small fraction of users in the network are queried when a potential adopter seeks a file, and many of these users might even free-ride, i.e., not distribute the content to others. As a result, generated demand might not always be fulfilled immediately. We present mixing models for product diffusion in P2P networks that capture decentralized product distribution by current adopters, incomplete demand fulfillment and other unique aspects of P2P product diffusion. The models serve to demonstrate the important role that P2P search process and distribution referrals-payments made to users that distribute files-play in efficient P2P media distribution. We demonstrate the ability of our diffusion models to derive normative insights for P2P media distributors by studying the effectiveness of distribution referrals in speeding product diffusion and determining optimal referral policies for fully decentralized and hierarchical P2P networks.
Over the past few years, open source software (OSS) development has gained a huge popularity and has attracted a large variety of developers. According to software engineering folklore, the architecture and the organization of software depend on the communication patterns of the contributors. Communication patterns among developers influence knowledge sharing among them. Unlike in a formal organization, the communication network structures in an OSS project evolve unrestricted and unplanned. We develop a non-cooperative game-theoretic model to investigate the network formation in an OSS team and to characterize the stable and efficient structures. Developer heterogeneity in the network is incorporated based on their informative value. We find that there may exist several stable structures that are inefficient and there may not always exist a stable structure that is efficient. The tension between the stability and efficiency of structures results from developers acting in their self-interest rather than the group interest. Whenever there is such tension, the stable structure is either underconnected across types or overconnected within type of developers from an efficiency perspective. We further discuss how an administrator can help evolve a stable network into an efficient one. Empirically, we use the latent class model and analyze two real-world OSS projects hosted at SourceForge. For each project, different types of developers and a stable structure are identified, which fits well with the predictions of our model. Overall, our study sheds light on how developer abilities and incentives affect communication network formation in OSS projects.