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A study of the impacts of positive/negative feedback on collective wisdom—case study on social bookmarking sites

Author

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  • Yuan-Chu Hwang

    (National United University)

  • Soe-Tsyr Yuan

    (National Chengchi University)

  • Jung-Hui Weng

    (National Chengchi University)

Abstract

The core spirit for web 2.0 is collective wisdom (i.e., the contribution of users, and the creation of value through the interaction between users). Social bookmarking sites integrate all kind of contents on the Internet (especially those generated by users), and play the role of pivot between content production and consumption. This paper mainly investigates how the positive/negative feedbacks would impact the quality of the collective wisdom within the autonomous service environments (i.e., the social bookmarking sites). Our research findings show that the performance of social bookmarking sites has a tradeoff between collective filtering (i.e., results of positive feedbacks) and front page update frequency that should be carefully managed for ensuring the good quality in collective wisdom and service performance. Moreover, the negative feedback could also shape the collective wisdom and stabilize the system performance. The research findings are believed to provide some managerial guidelines for web 2.0 sites design and operations.

Suggested Citation

  • Yuan-Chu Hwang & Soe-Tsyr Yuan & Jung-Hui Weng, 2011. "A study of the impacts of positive/negative feedback on collective wisdom—case study on social bookmarking sites," Information Systems Frontiers, Springer, vol. 13(2), pages 265-279, April.
    • Handle: RePEc:spr:infosf:v:13:y:2011:i:2:d:10.1007_s10796-009-9186-8
    DOI: 10.1007/s10796-009-9186-8
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    References listed on IDEAS

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    1. Francis Heylighen, 1999. "Collective Intelligence and its Implementation on the Web: Algorithms to Develop a Collective Mental Map," Computational and Mathematical Organization Theory, Springer, vol. 5(3), pages 253-280, October.
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    Cited by:

    1. Jiayin Qi & Chao Zhu & Yanwu Yang, 2014. "Recommendations based on Social Relationships in Mobile Services," Systems Research and Behavioral Science, Wiley Blackwell, vol. 31(3), pages 424-436, May.
    2. Kawaljeet Kaur Kapoor & Kuttimani Tamilmani & Nripendra P. Rana & Pushp Patil & Yogesh K. Dwivedi & Sridhar Nerur, 2018. "Advances in Social Media Research: Past, Present and Future," Information Systems Frontiers, Springer, vol. 20(3), pages 531-558, June.
    3. Chulhwan Chris Bang, 2015. "Information systems frontiers: Keyword analysis and classification," Information Systems Frontiers, Springer, vol. 17(1), pages 217-237, February.
    4. Steven D. Silver, 2021. "Dynamics of Negative Evaluations in the Information Exchange of Interactive Decision-Making Teams: Advancing the Design of Technology-Augmented GDSS," Information Systems Frontiers, Springer, vol. 23(6), pages 1621-1642, December.

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