Socialization in an Open Source Software Community: A Socio-Technical Analysis

How to Stimulate Continuous Innovative Knowledge Contribution? Mediation by Self-Efficacy and Moderation by Knowledge Level

Taking the perspective of innovative knowledge management, this study aimed to investigate the stimulation mechanism of continuous innovative knowledge contribution (CIKC). Through a quantitative study conducted in an open innovation community, we modeled a stimulus-organism-response framework to conduct a path analysis from the external environment to internal cognition, and then to knowledge contribution, and filled in the gaps in the mediating and moderating mechanisms. We focused on the stimuli of knowledge contribution, in view of both quantity and quality. Panel data from six periods in one year was collected for dynamic analysis, and we used the fixed effect model to test our hypotheses of mediation effect, moderation effect, and mediated-moderation effect. There were some interesting findings, showing that user's self-efficacy plays a partial mediating role in the quantity, rather than the quality, of CIKC; meanwhile, the users' knowledge level plays a moderating role, and there is a negative moderating mechanism of knowledge level in the process from participation value to the quality of CIKC.

Publishing publicly available interview data: an empirical example of the experience of publishing interview data

In September 2021 I made a collection of interview transcripts available for public use under a CreativeCommons license through the Princeton DataSpace. The interviews include 39 conversations I had with gig workers at AmazonFlex, Uber, and Lyft in 2019 as part of a study on automation efforts within these organizations. I made this decision because (1) I was required to contribute to a publicly available data set as a requirement of my funding and (2) I saw it as an opportunity to engage in the collaborative qualitative science experiments emerging in Science and Technology studies. This article documents my thought process and step-by-step design decisions for designing a study, gathering data, masking it, and publishing it in a public archive. Importantly, once I decided to publish these data, I determined that each choice about how the study would be designed and implemented had to be assessed for risk to the interviewee in a very deliberate way. It is not meant to be comprehensive and cover every possible condition a researcher may face while producing qualitative data. I aimed to be transparent both in my interview data and the process it took to gather and publish these data. I use this article to illustrate my thought process as I made each design decision for this study in hopes that it could be useful to a future researcher considering their own data publishing process.

The Duckbot: A system for automated imaging and manipulation of duckweed

Laboratory automation can boost precision and reproducibility of science workflows. However, current laboratory automation systems are difficult to modify for custom applications. Automating new experiment workflows therefore requires development of one-off research platforms, a process which requires significant time, resources, and experience. In this work, we investigate systems to lower the threshold to automation for plant biologists. Our approach establishes a direct connection with a generic motion platform to support experiment development and execution from a computational notebook environment. Specifically, we investigate the use of the open-source tool-changing motion platform Jubilee controlled using Jupyter notebooks. We present the Duckbot, a machine customized for automating laboratory research workflows with duckweed, a common multicellular plant. The Duckbot comprises (1) a set of end-effectors relevant for plant biology, (2) software modules which provide flexible control of these tools, and (3) computational notebooks which make use of these tools to automate duckweed experiments. We demonstrate the Duckbot's functionality by automating a particular laboratory research workflow, namely, duckweed growth assays. The Duckbot supports setting up sample plates with duckweed and growth media, gathering image data, and conducting relevant data analysis. We discuss the opportunities and limitations for developing custom laboratory automation with this platform and provide instructions on usage and customization.

Community Vital Signs: Measuring Wikipedia Communities’ Sustainable Growth and Renewal

Wikipedia is an undeniably successful project, with unprecedented numbers of online volunteer contributors. After 2007, researchers started to observe that the number of active editors for the largest Wikipedias declined after rapid initial growth. Years after those announcements, researchers and community activists still need to understand how to measure community health. In this paper, we study patterns of growth, decline and stagnation, and we propose the creation of 6 sets of language-independent indicators that we call “Vital Signs”. Three focus on the general population of active editors creating content: retention, stability, and balance; the other three are related to specific community functions: specialists, administrators, and global community participation. We borrow the analogy from the medical field, as these indicators represent a first step in defining the health status of a community; they can constitute a valuable reference point to foresee and prevent future risks. We present our analysis for eight Wikipedia language editions, and we show that communities are renewing their productive force even with stagnating absolute numbers; we observe a general lack of renewal in positions related to special functions or administratorship. Finally, we evaluate our framework by discussing these indicators with Wikimedia affiliates to support them in promoting the necessary changes to grow the communities.

Inequalities in Open Source Software Development: Analysis of Contributor's Commits in Apache Software Foundation Projects

While researchers are becoming increasingly interested in studying OSS phenomenon, there is still a small number of studies analyzing larger samples of projects investigating the structure of activities among OSS developers. The significant amount of information that has been gathered in the publicly available open-source software repositories and mailing-list archives offers an opportunity to analyze projects structures and participant involvement. In this article, using on commits data from 263 Apache projects repositories (nearly all), we show that although OSS development is often described as collaborative, but it in fact predominantly relies on radically solitary input and individual, non-collaborative contributions. We also show, in the first published study of this magnitude, that the engagement of contributors is based on a power-law distribution.

Quantifying dynamical spillover in co-evolving multiplex networks

Multiplex networks (a system of multiple networks that have different types of links but share a common set of nodes) arise naturally in a wide spectrum of fields. Theoretical studies show that in such multiplex networks, correlated edge dynamics between the layers can have a profound effect on dynamical processes. However, how to extract the correlations from real-world systems is an outstanding challenge. Here we introduce the Multiplex Markov chain to quantify correlations in edge dynamics found in longitudinal data of multiplex networks. By comparing the results obtained from the multiplex perspective to a null model which assumes layers in a network are independent, we can identify real correlations as distinct from simultaneous changes that occur due to random chance. We use this approach on two different data sets: the network of trade and alliances between nation states, and the email and co-commit networks between developers of open source software. We establish the existence of "dynamical spillover" showing the correlated formation (or deletion) of edges of different types as the system evolves. The details of the dynamics over time provide insight into potential causal pathways.

STRUCTURAL CHANGES ASSOCIATED WITH TEMPORAL DISPERSION IN SOFTWARE DEVELOPMENT TEAMS: EVIDENCE FROM OPEN SOURCE SOFTWARE PROJECT TEAMS

Collaboration structure and temporal dispersion (TD) in teams have been studied independently so far. This study uses Media Synchronicity Theory (MST) to derive hypotheses positing that the structure of collaboration networks in distributed teams changes when those teams are more temporally dispersed. The empirical test of hypotheses using ordinary least squares with archival data from 230 open source software (OSS) projects shows that the collaboration structure networks of those OSS teams that are more temporally dispersed are sparser and more centralised, and these associations are stronger in those teams exhibiting higher relative performance. Theoretical and practical consequences are discussed.

Modeling Relational Events

Sequences of relational events underlie much empirical research on organizational relations. Yet relational event data are typically aggregated and dichotomized to derive networks that can be analyzed with specialized statistical methods. Transforming sequences of relational events into binary network ties entails two main limitations: the loss of information about the order and number of events that compose each tie and the inability to account for compositional changes in the set of actors and/or recipients. In this article, we introduce a newly developed class of statistical models that enables researchers to exploit the full information contained in sequences of relational events. We propose an extension of the models to cater for sequences of relational events linking different sets of actors. We illustrate the empirical application of relational event models in the context of a free/open source software project with the aim to explain the level of effort produced by contributors to the project. We offer guidance in the interpretation of model parameters by characterizing the social processes underlying organizational problem solving. We discuss the applicability of relational events models in organizational research.

Socialization in Open Source Software Projects: A Growth Mixture Modeling Approach

The success of open source software (OSS) projects depends heavily on the voluntary participation of a large number of developers. To remain sustainable, it is vital for an OSS project community to maintain a critical mass of core developers. Yet, only a small number of participants (identified here as ‘‘joiners’’) can successfully socialize themselves into the core developer group. Despite the importance of joiners’ socialization behavior, quantitative longitudinal research in this area is lacking. This exploratory study examines joiners’ temporal socialization trajectories and their impacts on joiners’ status progression. Guided by social resource theory and using the growth mixture modeling (GMM) approach to study 133 joiners in 40 OSS projects, the authors found that these joiners differed in both their initial levels and their growth trajectories of socialization and identified four distinct classes of joiner socialization behavior. They also found that these distinct latent classes of joiners varied in their status progression within their communities. The implications for research and practice are correspondingly discussed.

Exploring the Impact of Soclo-Technlcal Core-Periphery Structures in Open Source Software Development

In this paper we apply the social network concept of core-periphery structure to the socio-technical structure of a software development team. We propose a socio-technical pattern that can be used to locate emerging coordination problems in Open Source projects. With the help of our tool and method called TESNA, we demonstrate a method to monitor the socio-technical core-periphery movement in Open Source projects. We then study the impact of different core-periphery movements on Open Source projects. We conclude that a steady core-periphery shift towards the core is beneficial to the project, whereas shifts away from the core are clearly not good. Furthermore, oscillatory shifts towards and away from the core can be considered as an Indication of the instability of the project. Such an analysis can provide developers with a good Insight into the health of an Open Source project. Researchers can gain from the pattern theory, and from the method we use to study the core-periphery movements.

Membership Claims and Requests: Conversation-Level Newcomer Socialization Strategies in Online Groups

Early socialization experiences have a long-term impact on newcomers’ satisfaction, performance, and intention to stay in a group. We know that newcomers proactively shape their own socialization, but we know little about the behavioral tactics they employ, or how the words they choose affect their acceptance by the group. The present article highlights three common conversational strategies of newcomers to online groups: (a) group-based membership claims, in which newcomers describe initial participation in the group; (b) identity-based membership claims, in which they describe their similarity to the group’s focal social category; and (c) information requests, in which they ask for help. Using machine learning to identify these conversational strategies automatically in 12,000 newcomers’ messages to approximately 100 online groups, we find that they are correlated with increased group responsiveness. We follow this analysis with two controlled field experiments to demonstrate that when individuals attest to previous group participation and make specific requests for information, community responsiveness increases, but claims of shared identity with the group have no impact.