A Comparison of Evaluation Networks and Collaboration Networks in Open Source Software Communities

The open source software (OSS) development communities have experienced rapid growth in recent years. Previous social network studies on OSS communities focused on collaboration relationships. However, information about how OSS community members perceive each other is largely ignored. In this study, we report an empirical investigation of the evaluation network in an online OSS community which includes over 11,800 OSS projects and more than 94,330 developers. A collaboration network is modeled from this data set and analyzed for comparison purposes. We find the evaluation network is significantly different from collaboration network in average degree, average path length and fragmentation rate. Furthermore, we argue that the evaluation networks can be used to locate expertise - skillful developers in OSS communities and capture important social relationships among the developers missed in the collaboration network. These characteristics of the evaluation network may benefit the research of OSS development communities and expert recommendation systems

Discovering Determinants of Project Participation in an Open Source Social Network

Successful open source software projects often require a steady supply of self motivated software developers. However, little work has been done from a relational/network perspective to study the factors that drive the developers to participate in OSS projects. In this paper, we investigate the participation dynamics in a social network, particularly in an online open source community called Ohloh. Through a REST-based API, we collected information about 11,530 open source software projects involving 94,330 developers. Using social network analysis and statistical analysis methods, we examine a set of social and technical factors in the Ohloh dataset, which we define as the determinants that significantly influence the developers’ participation choices. We found that the determinants include (1)homophily in programming language, (2)project mutual acquaintance, and (3)project age. In addition, our research findings provide the possibility of predicting developers’ participation choices based on the discovered determinants, and therefore can have important implications for OSS project management and in designing social network enabled recommendation systems

Perceived Social Norms, Token Rewards, and Cooperation in Decentralized Autonomous Organizations (DAOs)

Decentralized autonomous organizations (DAOs) offer a novel paradigm, fostering members’ decentralized cooperation towards collective goals. Central to this are token rewards, aligning individuals’ interests with DAO’s collective goals to enhance cooperation. We introduce a theoretical model proposing that DAO members’ perceived social norms impact the effectiveness of this token-based interest alignment mechanism by influencing members’ tendencies to hold tokens, subsequently affecting their cooperative behaviors. By analyzing data collected from the prominent social DAO, Steem, our empirical findings validated this proposition. Our study stands at the forefront of elucidating the complex interplay between economic incentives and social motivations in DAOs, particularly the interest alignment mechanism. Moreover, based on the basic rationales of profit-sharing arrangements in traditional organizations, we transpose this understanding to the context of DAOs, offering a nuanced articulation of the interest alignment mechanism, which is absent in the current DAO literature

CAN FIRMS IMPROVE PERFORMANCE THROUGH EXTERNAL CONTRIBUTIONS TO THEIR OPEN-SOURCE SOFTWARE PROJECTS?

A growing number of firms are developing open-source software (OSS) projects to get external contributions from developers unaffiliated with them. We investigate the impact of external contributions to a firm’s OSS projects on its performance measured by Tobin’s q and how the amount of comment activities within the firm’s OSS projects moderates this effect. Using a panel of 536 publicly listed firms over 2011-2019, we find that external contributions to a firm’s OSS projects have a positive impact on the Tobin’s q value of the firm. Moreover, this performance effect is strengthened when there are more comment activities within the firm’s OSS projects. Our study contributes to the literature and generates managerial implications for firms and OSS communities

The Impacts of Lockdown on Open Source Software Contributions during the COVID-19 Outbreak

We leverage the lockdown of Wuhan, China in January 2020 in response to COVID-19 as a natural experiment to study its impacts on individuals’ contributions to open source software (OSS) on GitHub – the world’s largest OSS platform. We find that Wuhan developers’ contributions decreased by 10.2% relative to those in Hong Kong, Macau, and Taiwan (HMT) regions in the five weeks after the lockdown. Moreover, the contributions of Wuhan developers who interacted more with local developers on GitHub were reduced more after the lockdown. We conjecture that the lack of face-to-face (F2F) collaboration for Wuhan developers is the main driver of their reduced contributions, providing important insights for OSS platforms and stakeholders

HOW MICROBLOG FOLLOWER NETWORKS AFFECT OPEN SOURCE SOFTWARE PROJECT SUCCESS

Successful open source software (OSS) projects require efficient communication means and a steady supply of voluntary developers. Microblogging, as well as the follower network it generates, is becoming increasingly popular as an emerging Web 2.0 communication technology in many online OSS communities. However, little is known about how microblogging follower networks affect OSS project success. Based on theories drawn from the social network domain, OSS and virtual team research,we hypothesized two follower network mechanisms – preferential attachment and structural holes – which may significantly affect OSS project success, by improving knowledge sharing and attracting more skillful developers. We plan to empirically study a microblog follower network in a large online OSS community, aiming to examine the impacts of the two hypothesized follower network mechanisms on OSS project success. Our potential findings may provide insights for OSS project managers to better manage microblog communications and thereby achieve project success

Understanding Moderators of Peer Influence for Engineering Viral Marketing Seeding Simulations and Strategies

Seeding as an emerging viral marketing strategy requires a better understanding on how various contextual factors that embedded in social networks affect peer influence and product diffusion. Realistic simulations for seeding need to incorporate empirical insights about the complexities (various moderators) and dynamics (temporal changes) of peer influence by analyzing real-world data. We analyze the impacts of peer influence moderators in a large-scale phone call network of 0.48 million customers with 364 million calls and 3.9 million video-on-demand purchases, to design empirical models and engineer data-driven simulations of product diffusion, as well as developing and evaluating seeding strategies. We intend to contribute to existing research by 1) enriching the theoretical and empirical understanding of peer influence moderators for stakeholders, 2) combining econometric models and analyses with data-driven simulations towards a complex system approach for devising and evaluating effective seeding strategies in different scenarios

An Information Diffusion-Based Recommendation Framework for Micro-Blogging

Micro-blogging is increasingly evolving from a daily chatting tool into a critical platform for individuals and organizations to seek and share real-time news updates during emergencies. However, seeking and extracting useful information from micro-blogging sites poses significant challenges due to the volume of the traffic and the presence of a large body of irrelevant personal messages and spam. In this paper, we propose a novel recommendation framework to overcome this problem. By analyzing information diffusion patterns among a large set of micro-blogs that play the role of emergency news providers, our approach selects a small subset as recommended emergency news feeds for regular users. We evaluate our diffusion-based recommendation framework on Twitter during the early outbreak of H1N1 Flu. The evaluation results show that our method results in more balanced and comprehensive recommendations compared to benchmark approaches

Manipulating Multiple Order Parameters via Oxygen Vacancies: The case of Eu0.5Ba0.5TiO3-{\delta}

Controlling functionalities, such as magnetism or ferroelectricity, by means of oxygen vacancies (VO) is a key issue for the future development of transition metal oxides. Progress in this field is currently addressed through VO variations and their impact on mainly one order parameter. Here we reveal a new mechanism for tuning both magnetism and ferroelectricity simultaneously by using VO. Combined experimental and density-functional theory studies of Eu0.5Ba0.5TiO3-{\delta}, we demonstrate that oxygen vacancies create Ti3+ 3d1 defect states, mediating the ferromagnetic coupling between the localized Eu 4f7 spins, and increase an off-center displacement of Ti ions, enhancing the ferroelectric Curie temperature. The dual function of Ti sites also promises a magnetoelectric coupling in the Eu0.5Ba0.5TiO3-{\delta}.Comment: Accepted by Physical Review B, 201