From planning to mature: On the success of open source projects

Comino, Stefano; Manenti, Fabio M.; Parisi, Maria Laura

doi:10.1016/j.respol.2007.08.003

Cited by 95 publications

(82 citation statements)

References 21 publications

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“…an increase in the number of recipients increases the total public goods provision but at a decreasing rate. This finding received empirical support in the context of online public goods, such as open-source software and online peer productive communities (Kandel and Lazear (1992), Comino et al (2007), Zhang and Zhu (2010), Algan et al (2013)). Comino et al (2007) find that the size of the "community of developers" in open-source projects increases the chances of progress but this effect decreases as the community gets larger.…”

Section: Related Researchmentioning

confidence: 86%

“…This finding received empirical support in the context of online public goods, such as open-source software and online peer productive communities (Kandel and Lazear (1992), Comino et al (2007), Zhang and Zhu (2010), Algan et al (2013)). Comino et al (2007) find that the size of the "community of developers" in open-source projects increases the chances of progress but this effect decreases as the community gets larger. Zhang and Zhu (2010) show the importance of the recipient group size for individual incentives for knowledge provision using exogenous variation in the recipient group size on Wikipedia (a block by the Chinese government).…”

Section: Related Researchmentioning

confidence: 86%

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Economic Downturn and Volunteering: Do Economic Crises Affect Content Generation on Wikipedia?

2015

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Die Dis cus si on Pape rs die nen einer mög lichst schnel len Ver brei tung von neue ren For schungs arbei ten des ZEW. Die Bei trä ge lie gen in allei ni ger Ver ant wor tung der Auto ren und stel len nicht not wen di ger wei se die Mei nung des ZEW dar.Dis cus si on Papers are inten ded to make results of ZEW research prompt ly avai la ble to other eco no mists in order to encou ra ge dis cus si on and sug gesti ons for revi si ons. The aut hors are sole ly respon si ble for the con tents which do not neces sa ri ly repre sent the opi ni on of the ZEW. AbstractIn this paper, we address the impact of surging unemployment on online public good provision.Specifically, we ask how drastically increased unemployment affects voluntary contributions of content to the online encyclopedia Wikipedia. We put together a monthly country-level data set, which combines country specific economic outcomes with data on contributions to the online encyclopedia. As a source of exogenous variation in the economic state we use the fact that European countries were affected by the financial crisis in the US in September 2008 with different intensity. For European countries, we find that the economic downturn is associated with more viewership, which channels higher participation of volunteers in Wikipedia expressed in editing activity and content growth. We provide evidence for increased information search online or online learning as a potential channel of the change in public goods provision, which is a potentially important side effect of the economic downturn.

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Section: Related Researchmentioning

confidence: 86%

Section: Related Researchmentioning

confidence: 86%

Economic Downturn and Volunteering: Do Economic Crises Affect Content Generation on Wikipedia?

2015

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“…The choice of software license can also have a detrimental influence on the success of a project [4]. They find that if more effort is necessary to complete the project, developers tend to choose less restrictive licenses such as those from the No-Copyleft or Weak-Copyleft categories as opposed to Strong-Copyleft.…”

Section: Success Factorsmentioning

confidence: 99%

“…The variables included have been previously identified in literature as affecting OSS success [3,11]. For time-invariant variables we have chosen those that define a project in general terms such as license [4], the operating system that can be used and the programming language [2] …”

Section: Time-invariant Variablesmentioning

confidence: 99%

“…What this paper proposes is that, through machine learning we can model which available project metrics are of importance in determining OSS project health. Our method differs from previous attempts at building a model, which were based on statistical correlations that approximated success factors without revealing how they actually influenced project's status [4][5][6].…”

Section: Introductionmentioning

confidence: 99%

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How Healthy Is My Project? Open Source Project Attributes as Indicators of Success

Piggot

Amrit

2013

Open Source Software: Quality Verification

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Abstract. Determining what factors can influence the successful outcome of a software project has been labeled by many scholars and software engineers as a difficult problem. In this paper we use machine learning to create a model that can determine the stage a software project has obtained with some accuracy. Our model uses 8 Open Source project metrics to determine the stage a project is in. We validate our model using two performance measures; the exact success rate of classifying an Open Source Software project and the success rate over an interval of one stage of its actual performance using different scales of our dependent variable. In all cases we obtain an accuracy of above 70% with one away classification (a classification which is away by one) and about 40% accuracy with an exact classification. We also determine the factors (according to one classifier) that uses only eight variables among all the variables available in SourceForge, that determine the health of an OSS project. IntroductionDetermining what makes a software project successful has been a research topic for well over 20 years. The first model that defined the factors influencing software success was published in 1992 by Delone and McLean [1], as the Information Systems Success Model. Since then there has been a considerable effort in research to determine what can be done to minimize project failure. However, factors that influence commercial projects differ from those known as FLOSS or Free/Libre Open Source Software. Attempts at remedying this gap have focused on statistical models that focus on certain aspects of a software development lifecycle. Only recently, has historical data been used to determine the changing nature of factors for success during a projects lifecycle [2]. In this paper we use machine learning in the form of decision trees, to predict the development stage of an Open Source project based on project metrics 1 , project constraints and circumstance. This model will serve as an indicator of OSS project health that will enable developers to determine accurately in what stage their 1 We use the terms metric and attribute to mean the same concept in this paper.

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Problem‐formulation and problem‐solving in self‐organized communities: How modes of communication shape project behaviors in the free open‐source software community

Foss

Frederiksen

Rullani

2015

Strategic Management Journal

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Research summary: Building on the problem‐solving perspective, we study behaviors related to projects and the communication‐based antecedents of such behaviors in the free open‐source software (FOSS) community. We examine two kinds of problem/project‐behaviors: Individuals can set up projects around the formulation of new problems or join existing projects and define and/or work on subproblems within an existing problem. The choice between these two behaviors is influenced by the mode of communication. A communication mode with little a priori structure is the best mode for communicating about new problems (i.e., formulating a problem); empirically, it is associated with project launching behaviors. In contrast, more structured communication fits subproblems better and is related to project joining behaviors. Our hypotheses derive support from data from the FOSS community. Managerial summary: We study how the way in which individuals communicate influence the project‐behaviors they engage in. We find that relatively unstructured communication is associated with the setting up new projects, while communication that is structured around an artifact is associated with joining projects. Our findings hold implications for understanding how management may influence project behaviors and problem‐solving: Firms that need to concentrate on more incremental problem‐solving efforts (e.g., because a sufficient number of attractive problems have already been defined) should create environments in which interaction is undertaken mainly via artifacts. On the other hand, if firms seek to generate new problems (e.g., new strategic opportunities), they should create environments in which open‐ended, verbal conversation is relatively more important than artifact‐based communication. Copyright © 2015 John Wiley & Sons, Ltd.

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From planning to mature: On the success of open source projects

Cited by 95 publications

References 21 publications

Economic Downturn and Volunteering: Do Economic Crises Affect Content Generation on Wikipedia?

Economic Downturn and Volunteering: Do Economic Crises Affect Content Generation on Wikipedia?

How Healthy Is My Project? Open Source Project Attributes as Indicators of Success

Problem‐formulation and problem‐solving in self‐organized communities: How modes of communication shape project behaviors in the free open‐source software community

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