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Abstract:
What determines open source project success? In this study, we investigate the impact of network social capital - the benefits open source developers secure from their memberships in a developer collaboration network - on open source project success. We focus on one specific type of success as measured by the productivity of open source project team. Specific hypotheses are developed and tested on a longitudinal panel of 2378 projects hosted at Sourceforge. We find that network social capital is not equally accessible to or appropriated by all projects. Our main results are (1) teams with greater internal cohesion are more successful, (2) external cohesion (cohesion among the external contacts of a team) has an inverse U-shaped relationship with the project's success; moderate levels of external cohesion are the best for a project's success, rather than very low or very high levels of this variable, (3) the technological diversity of a contact also has the greatest benefit when it is neither too low nor too high, and (4) the number of direct and indirect external contacts are positively correlated with a project's success with the effect of the number of direct contacts being moderated by the number of indirect contacts. These results are robust to a number of control variables and alternate model specifications. Several theoretical and managerial implications are provided.

open source software development, social networks, productivity, social capital, project success, team composition

Abstract:
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 may even free-ride i.e. not distribute the content to others. As a result, generated demand may 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.

Peer to peer, P2P, product diffusion, P2P diffusion, supply-constrained diffusion

Abstract:
This paper is motivated by the success of You Tube, which is attractive to content creators and media companies for the potential to rapidly disseminate digital content. The tremendous variation in the success of videos posted online and the networked structure of interactions on You Tube lend itself to an inquiry about the role of social influence on content diffusion. Using a unique data set of video information and user information collected from You Tube, we find that evidence for a number of mechanisms by which social influence is transmitted, such as a preference for conformity, social learning and the role of innovators or opinion-makers. Such mechanisms of social interactions can play a huge role not only in the success of user-generated content, but also on the magnitude of that impact. 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 of content. Econometrically, the problem in identifying social influence is that individuals' choices depend in great part on 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 robust to potential self-selection according to user tastes, temporal heterogeneity and the reflection problem. Implications for researchers and managers are discussed.

Diffusion, Social Contagion, User-Generated Content, Social Networks, Reflection Problem

Abstract:
Peer-to-Peer (P2P) networks, a decentralized content distribution format in which users distribute media to each other, is fast gaining popularity for delivery of digital media such as music and videos. Product diffusion in P2P is unique because free riders ý users who download content from others in the network without redistributing it to others ý can create a supply constraint that results in the incomplete fulfillment of generated demand. P2P firms offer distribution referrals, i.e. payments to users who distribute content to others, to provide users with incentives to distribute content. In this paper, we study a P2P firm's optimal referral strategy. Starting with a simple model for media diffusion in P2P networks, we apply optimal control theory to determine a dynamic referral strategy. The diffusion model uniquely captures the role of the referral in addressing the supply constraint in P2P diffusion. We find that the referral strategy is governed by two main effects. Early in the diffusion, the referral strategy is dominated by a scarcity effect, namely that there exist very few users distributing the file in the work. Because the availability of users willing to distribute the file increases with time, the referral is nonincreasing with time during this phase. If the product is sufficiently diffused in the network, referral policy is dominated by a saturation effect later in the diffusion. In this Stage, the referral is non-decreasing with time in order to encourage sales which usually slow down late in the diffusion. In networks with significant free-riding, the optimal trajectory involves a very high referral at the beginning, followed by a decreasing trajectory. If the product is sufficiently diffused, the referral may start to increase in the final few periods due to the saturation effect mentioned above. Finally, we observe that firm profits under this dynamic strategy can be considerably higher than under a myopic referral policy. Our research represents a first step towards understanding marketing and operational issues in this emerging distribution format for entertainment goods and other digital media.

Peer to peer, P2P, product diffusion, dynamic referral, Internet marketing, networks and marketing

Abstract:
This study examines whether developers learn from their experience and from interactions with peers in OSS projects. A Hidden Markov Model (HMM) is proposed that allows us to investigate (1) the extent to which OSS developers actually learn from their own experience and from interactions with peers, (2) whether a developer's abilities to learn from these activities vary over time, and (3) to what extent developer learning persists over time. We calibrate the model on six years of detailed data collected from 251 developers working on 25 OSS projects hosted at Sourceforge. Using the HMM three 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 as the most important source of learning for developers across the three states. Developers in the medium learning state benefit 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 when in 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.

Open source software development, learning, communication, Hidden Markov Model

Abstract:
Open source software (OSS) development teams use informal communication to coordinate their work towards common goals. According to software engineering folklore, the architecture and the organization of the final product depend on the communication patterns of the contributors. Unlike in a formal organization, the communication network structures in an OSS project evolve unrestricted and unplanned. Little is known about the stability and efficiency of the communication structures that would evolve in OSS projects. In this paper, we use the connections model of the social networks theory by incorporating the salient features of OSS development to study the communications structures that might emerge in OSS projects. We characterize the stable and efficient structures. We find that for a given scenario there may exist several stable structures which are inefficient. We also find that there does not always exist a stable structure that is efficient. This can be explained by the fact that the stability of the structure is dependent on individual's maximization of self utility whereas the efficiency of the structure is dependent on maximization of group utility. In general, tension exists between stable and efficient structures because the players act in their self-interest rather than the group-interest. We discuss the results of the model in the context of OSS development. We also provide numerical simulation to illustrate the tension between stable and efficient networks. We further discuss implications of our results and provide directions for future research.

Open source software development, Social networks, Efficiency, Stability, communication structure, coordination structure.

Abstract:
Over the last few years, open source software (OSS) development has gained a huge popularity and has attracted a large variety of developers under its fold. 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. We incorporate developer heterogeneity in the network based on their informative value. We find that, for a given scenario, there may exist several stable structures which are inefficient. We also find that there may not always exist a stable structure that is efficient. This can be explained by the fact that the stability of the structure is dependent on the developer's maximization of self utility whereas the efficiency of the structure is dependent on the maximization of group utility. In general, a tension exists between the stability and efficiency of structures because developers act in their self interest rather than the group interest. We find, whenever there is such a tension, the stable structure is either under-connected across types or over-connected within type of developers from an efficiency perspective. Empirically, we use the latent class model and analyze two real-world OSS projects hosted at Sourceforge.net. For each project, different types of developers and a stable structure is identified, which fits well with the predictions of our model. We further discuss implications of our results and provide directions for future research.

social networks, network formation, game theory, open source, software, knowledge worker, teams

Abstract:
Cooperative caching is a popular mechanism to allow an array of distributed caches to cooperate and serve each others' web requests. Monitoring and controlling duplication of documents across cooperating caches is a challenging operational problem faced by cache managers. In this paper, we analyze optimal duplication in a game-theoretic setting with two cooperating caches. We have three primary findings. First, our results suggest that intermediate levels of duplication - greater than that of CARP, a protocol that allows no duplication, but less than that of ICP, a protocol that does not monitor duplication - are desirable. Second, the game is a game of strategic substitutes wherein an increase in duplication by one cache results in a decrease in duplication by the other. Thus, a cache that can credibly signal that it is incapable of monitoring duplication levels can get higher contribution from other caches i.e., get other caches to eliminate more duplicate documents. Finally, decentralized decision-making by selfish caches can be quite inefficient (i.e., result in higher average latency) relative to the socially optimal solution. At the same time, the socially optimal solution can be highly asymmetric even when caches are symmetric and thus may not be acceptable to the cache that has to contribute the most resources. These factors should be accounted for when contracts for cooperative caching are structured by independent ISPs.

Content Delivery, Caching, game theory

Abstract:
Distributing freeware or trialware is a common practice of most publishers in the software market in order to increase potential users' experience prior to their final purchasing. Third-party rating of new software is an objective quality reference to the users as well. We develop an empirical framework to analyze the impact of freeware/trialware and third-party rating on software sampling. Our results show that freeware is not always a dominant strategy to enhance the users' software sampling and outperformed by trialware when the quality gap between them is high enough to the users in some software categories. Third-party rating reflects more credible information to the users compared to anonymous users' rating. As the sampling acts as risk relievers for final purchasing, our findings demonstrate how actual marketing strategies work in the software market. We have used various estimation methods to address endogeneity issues in our model.

Product sampling, Freeware, Trialware, Third-party rating, Software

Abstract:
Dynamic project review embraces changes and accepts the idea that software requirements will evolve throughout a project. Dynamic requirement reviews can incorporate new market and customer information and fix initial design and development shortcomings. We study the optimal timing of dynamic requirement reviews for software projects. We find that when alignment cost is incremental, the reviews should be conducted with an even time interval. When the alignment cost is cumulative, the reviews should be conducted more frequently at earlier stages of the project. We solve closed-form solutions for the case that the decay function is linear. Our results show that as system decays more rapidly, requirement reviews should be conducted more frequently. As requirement reviews take a longer time, the reviews should occur less frequently.

software engineering, process analysis, requirement reviews, optimal policy

Abstract:
Product sampling is a part of promotion tools to increase the sales of goods. Especially, the marginal sampling cost for consumer is close to zero in software market since consumers may download a freeware or trial version of the focal product easily through the internet. Unlike other consumable goods, sampling is an inexpensive strategy to software vendors. Hence, it is common practice in software markets. This research builds a multilevel model to illustrate categorical heterogeneity in software sampling and the effects of sampling strategy on sampling performance among software categories. Using the data collected from download.com, we find that: 1) sampling intensity is different among software categories; 2) trial version strategy outperforms freeware strategy in some categories, but on average its effect on sampling performance is lower than that of freeware strategy by 19%. These results serve to provide guidance for software vendors to choose the better sampling strategy.

Software, Product Sampling, Freeware, Trialware, Bayesian Inference