research-article

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

Authors:

Mark ZachryAuthors Info & Claims

Proceedings of the ACM on Human-Computer Interaction, Volume 4, Issue CSCW2

Article No.: 179, Pages 1 - 22

https://doi.org/10.1145/3415250

Published: 15 October 2020 Publication History

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 mechanisms that could benefit both expert and beginner participants in future knowledge building systems.

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Cited By

Cheng RWang RZimmermann TFord D(2024)“It would work for me too”: How Online Communities Shape Software Developers’ Trust in AI-Powered Code Generation ToolsACM Transactions on Interactive Intelligent Systems10.1145/365199014:2(1-39)Online publication date: 9-Mar-2024
https://dl.acm.org/doi/10.1145/3651990
Peng ZChen QShen ZMa XOulasvirta A(2024)DesignQuizzer: A Community-Powered Conversational Agent for Learning Visual DesignProceedings of the ACM on Human-Computer Interaction10.1145/36373218:CSCW1(1-40)Online publication date: 26-Apr-2024
https://dl.acm.org/doi/10.1145/3637321
Duan PWarner JLi YHartmann B(2024)Generating Automatic Feedback on UI Mockups with Large Language ModelsProceedings of the CHI Conference on Human Factors in Computing Systems10.1145/3613904.3642782(1-20)Online publication date: 11-May-2024
https://dl.acm.org/doi/10.1145/3613904.3642782
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Index Terms

Building Community Knowledge In Online Competitions: Motivation, Practices and Challenges
1. Human-centered computing
  1. Collaborative and social computing
    1. Empirical studies in collaborative and social computing

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cover image Proceedings of the ACM on Human-Computer Interaction

Proceedings of the ACM on Human-Computer Interaction Volume 4, Issue CSCW2

CSCW

October 2020

2310 pages

EISSN:2573-0142

DOI:10.1145/3430143

Editor:
Jeff Nichols
Apple Inc.

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Copyright © 2020 ACM.

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Publication History

Published: 15 October 2020

Published in PACMHCI Volume 4, Issue CSCW2

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Cited By

Cheng RWang RZimmermann TFord D(2024)“It would work for me too”: How Online Communities Shape Software Developers’ Trust in AI-Powered Code Generation ToolsACM Transactions on Interactive Intelligent Systems10.1145/365199014:2(1-39)Online publication date: 9-Mar-2024
https://dl.acm.org/doi/10.1145/3651990
Peng ZChen QShen ZMa XOulasvirta A(2024)DesignQuizzer: A Community-Powered Conversational Agent for Learning Visual DesignProceedings of the ACM on Human-Computer Interaction10.1145/36373218:CSCW1(1-40)Online publication date: 26-Apr-2024
https://dl.acm.org/doi/10.1145/3637321
Duan PWarner JLi YHartmann B(2024)Generating Automatic Feedback on UI Mockups with Large Language ModelsProceedings of the CHI Conference on Human Factors in Computing Systems10.1145/3613904.3642782(1-20)Online publication date: 11-May-2024
https://dl.acm.org/doi/10.1145/3613904.3642782
Twyman MMurić GZheng W(2023)Positioning in a collaboration network and performance in competitions: a case study of KaggleJournal of Computer-Mediated Communication10.1093/jcmc/zmad02428:4Online publication date: 29-Jun-2023
https://doi.org/10.1093/jcmc/zmad024
Jiang YBáez MBenatallah B(2023)Towards Scaling External Feedback for Early-Stage Researchers: A Survey StudyCooperative Information Systems10.1007/978-3-031-46846-9_18(329-346)Online publication date: 30-Oct-2023
https://dl.acm.org/doi/10.1007/978-3-031-46846-9_18
Usman MAhmad FHabib UCheema AAftab MAhmad M(2022)Combining Latent Factor Model for Dynamic Recommendations in Community Question Answering ForumsComputational Intelligence and Neuroscience10.1155/2022/71916572022Online publication date: 1-Jan-2022
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Hadi Mogavi RZhang YHaq EWu YHui PMa X(2022)What Do Users Think of Promotional Gamification Schemes? A Qualitative Case Study in a Question Answering WebsiteProceedings of the ACM on Human-Computer Interaction10.1145/35551246:CSCW2(1-34)Online publication date: 11-Nov-2022
https://dl.acm.org/doi/10.1145/3555124
Cheng RHill B(2022)Many Destinations, Many Pathways: A Quantitative Analysis of Legitimate Peripheral Participation in ScratchProceedings of the ACM on Human-Computer Interaction10.1145/35551066:CSCW2(1-26)Online publication date: 11-Nov-2022
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Lottridge DQuehl KBentley FSilverman MOng MDickard MWhite BPlaut E(2022)Ubiquitous News Experienced Alone: Interviews with Americans and their DevicesProceedings of the ACM on Human-Computer Interaction10.1145/35129756:CSCW1(1-29)Online publication date: 7-Apr-2022
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Micallef NArmacost VMemon NPatil S(2022)True or False: Studying the Work Practices of Professional Fact-CheckersProceedings of the ACM on Human-Computer Interaction10.1145/35129746:CSCW1(1-44)Online publication date: 7-Apr-2022
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