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What Is Needed for the Sustainable Success of OSS Projects: Efficiency Analysis of Commit Production Process via Git

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  • Jaeyoon Song

    (College of Business Administration, Seoul National University, Seoul 08826, Korea)

  • Changhee Kim

    (College of Business Administration, Incheon National University, Incheon 22012, Korea)

Abstract

The purpose of this study is to investigate the relative efficiency of open source software projects, and to analyze what is needed for their sustainable success. The success of open source software is known to be attributable to a massive number of contributors engaging in the development process. However, an efficient open source software project is not guaranteed simply by active participation by many; a coordination mechanism is needed to seamlessly manage the multi-party collaboration. On this basis, this study aimed to examine the internal regulatory processes based on Git and GitHub, which serve as such a mechanism, and redefine the efficiency of open source software projects to fully reflect them. For this purpose, a two-stage data envelopment analysis was used to measure the project efficiency reflecting the internal processes. Moreover, this study considered the Kruskal–Wallis test and Tobit regression analysis to examine the effects of the participation by many on an open source software project based on the newly defined efficiency. Results show that a simple increase in contributors can be poisonous in terms of the efficiency of open source software projects.

Suggested Citation

  • Jaeyoon Song & Changhee Kim, 2018. "What Is Needed for the Sustainable Success of OSS Projects: Efficiency Analysis of Commit Production Process via Git," Sustainability, MDPI, vol. 10(9), pages 1-13, August.
    • Handle: RePEc:gam:jsusta:v:10:y:2018:i:9:p:3001-:d:165463
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    References listed on IDEAS

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    4. Chih-Hai Yang & Hsuan-Yu Lin & Chiang-Ping Chen, 2014. "Measuring the efficiency of NBA teams: additive efficiency decomposition in two-stage DEA," Annals of Operations Research, Springer, vol. 217(1), pages 565-589, June.
    5. R. D. Banker & A. Charnes & W. W. Cooper, 1984. "Some Models for Estimating Technical and Scale Inefficiencies in Data Envelopment Analysis," Management Science, INFORMS, vol. 30(9), pages 1078-1092, September.
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    Cited by:

    1. Hou, Shengjie & Zhang, Xiang & Yi, Biyi & Tang, Yi, 2022. "Public attitudes on open source communities in China: A text mining analysis," Technology in Society, Elsevier, vol. 71(C).

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