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Cited by 20Knowledge Management and SharingExpert finding and Q&A systemsOpen Source Software Innovations

The DOI matches its Crossref record; 1 of the 1 references it cites were found in a registry or an index.

Building Community Knowledge In Online Competitions: Motivation, Practices and Challenges

Cheng, Ruijia, Zachry, Mark·2020·Proceedings of the ACM on Human-Computer Interaction
DOI 10.1145/3415250
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Abstract

Knowledge building is a prevalent feature in open online systems, but it is challenging to motivate participants to contribute and to maintain quality in the participants' contributions. Open online competitions, where participants compete for prizes with knowledge artifacts, offer a potential design model for online systems to incentivize community knowledge building activities. However, while there is evidence that participants contribute to public knowledge and share it during competitions, it remains unclear how and why they do so. In this study, through interviews with 14 participants in Kaggle Competitions, we investigate participants' motivation, practices, and challenges when contributing to community knowledge under a competitive structure. We find that competitive mechanisms impact expert and beginner participants very differently in their public knowledge building behaviors. Experts contribute to shared knowledge in order to compete for reputation, while they tend to form their own niches and only share knowledge artifacts that are abstract and not usable by less experienced participants. Beginners are often driven away from contributing to shared knowledge because of their vulnerable social image. We leverage Scardamalia's framework for Knowledge Building Communities to discuss the different challenges and opportunities that competitive design brings to expert and beginner participants. We offer design implications for effectively implementing competitive mechanis…

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  • This type of qualitative design probe study is common in formative research on human-AI interaction, as our goal is not to settle on specific design elements, but rather to explore what is possible in the design space of introducing user communities to AI code generation tools.

    “Not measured by quantitative metrics, this type of qualitative design probe study is common in formative research on human-AI interaction (e.g., [ 13 , 57 ]), as our goal is not to settle on specific design elements, but rather to explore what is possible in the design space of introducing user communities to AI code generation tools.”

    From "It would work for me too": How Online Communities Shape Software Developers' Trust in AI-Powered Code Generation Tools (2024) · cites this paper as [13]

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Metadata

Authors
Cheng, R., & Zachry, M.
Affiliations
University of Washington (US)
ORCID
Venue
Proceedings of the ACM on Human-Computer Interaction
Publisher
Association for Computing Machinery (ACM)
Published
2020-10-14
Volume
4
Issue
CSCW2
Pages
1-22
ISSN
2573-0142 (ISSN-L)
DOI
10.1145/3415250
OpenAlex
W3093839314
Cited by
20
References
1
Citations by year
2026: 1 · 2025: 3 · 2024: 4 · 2023: 1 · 2022: 8 · 2021: 3
FWCI
3.59
Type
journal article
Language
en
Open access
closed
Licence
www.acm.org/publications/policies/copyright_policy#Background (vor)
Locations
Proceedings of the ACM on Human-Computer Interaction (publishedVersion)
Topics
  • Social Sciences › Decision Sciences › Information Systems and Management › Knowledge Management and Sharing
  • Physical Sciences › Computer Science › Information Systems › Expert finding and Q&A systems
  • Physical Sciences › Computer Science › Computer Science Applications › Open Source Software Innovations
Keywords
knowledge construction, online competition, Kaggle competition, competition mechanism, participant motivation, community knowledge sharing, reputation, knowledge building communities, online learning platforms

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