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Open Content, Linus’ Law, and Neutral Point of View

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  • Shane Greenstein

    (Harvard Business School, Boston, Massachusetts 02163)

  • Feng Zhu

    (Harvard Business School, Boston, Massachusetts 02163)

Abstract

The diffusion of the Internet and digital technologies has enabled many organizations to use the open-content production model to produce and disseminate knowledge. While several prior studies have shown that the open-content production model can lead to high-quality output in the context of uncontroversial and verifiable information, it is unclear whether this production model will produce any desirable outcome when information is controversial, subjective, and unverifiable. We examine whether the open-content production model helps achieve a neutral point of view (NPOV) using data from Wikipedia’s articles on U.S. politics. Our null hypothesis builds on Linus’ Law, which argues that with enough eyeballs, all bugs are shallow. Our findings are consistent with a narrow interpretation of Linus’ Law, namely, a greater number of contributors to an article makes an article more neutral. No evidence supports a broad interpretation of Linus’ Law. Moreover, several empirical facts suggest the law does not shape many articles. The majority of articles receive little attention, and most articles change only mildly from their initial slant. Our study provides the first empirical evidence on the limit of Linus’ Law. While many organizations believe that they could improve their knowledge production by leveraging communities, we show that in the case of Wikipedia, there are aspects, such as NPOV, that the community does not always achieve successfully.

Suggested Citation

  • Shane Greenstein & Feng Zhu, 2016. "Open Content, Linus’ Law, and Neutral Point of View," Information Systems Research, INFORMS, vol. 27(3), pages 618-635.
  • Handle: RePEc:inm:orisre:v:27:y:2016:i:3:p:618-635
    DOI: 10.1287/isre.2016.0643
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    Cited by:

    1. Shane Greenstein & Grace Gu & Feng Zhu, 2021. "Ideology and Composition Among an Online Crowd: Evidence from Wikipedians," Management Science, INFORMS, vol. 67(5), pages 3067-3086, May.

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