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Knowledge Market Design: A Field Experiment at Google Answers

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  • YAN CHEN
  • TECK‐HUA HO
  • YONG‐MI KIM

Abstract

In a field experiment at Google Answers, we investigate the performance of price‐based online knowledge markets by systematically manipulating prices. Specifically, we study the effects of price, tip, and a reputation system on both an answerer's effort and answer quality by posting real reference questions from the Internet Public Library on Google Answers under different pricing schemes. We find that a higher price leads to a significantly longer, but not better, answer, while an answerer with a higher reputation provides significantly better answers. Our results highlight the limitation of monetary incentives and the importance of reputation systems in knowledge market design.

Suggested Citation

  • Yan Chen & Teck‐Hua Ho & Yong‐Mi Kim, 2010. "Knowledge Market Design: A Field Experiment at Google Answers," Journal of Public Economic Theory, Association for Public Economic Theory, vol. 12(4), pages 641-664, August.
    • Handle: RePEc:bla:jpbect:v:12:y:2010:i:4:p:641-664
    DOI: 10.1111/j.1467-9779.2010.01468.x
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    References listed on IDEAS

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    Cited by:

    1. Anya Savikhin Samek & Roman Sheremeta, 2014. "Recognizing contributors: an experiment on public goods," Experimental Economics, Springer;Economic Science Association, vol. 17(4), pages 673-690, December.
    2. repec:clg:wpaper:2013-27 is not listed on IDEAS
    3. Anya Savikhin & Roman Sheremeta, 2010. "Visibility of Contributions and Cost of Information: An Experiment on Public Goods," Working Papers 10-22, Chapman University, Economic Science Institute.
    4. Luís Cabral & Lingfang (Ivy) Li, 2015. "A Dollar for Your Thoughts: Feedback-Conditional Rebates on eBay," Management Science, INFORMS, vol. 61(9), pages 2052-2063, September.
    5. Regner, Tobias, 2014. "Social preferences? Google Answers!," Games and Economic Behavior, Elsevier, vol. 85(C), pages 188-209.
    6. Dan Li & Longying Hu, 2017. "Exploring the effects of reward and competition intensity on participation in crowdsourcing contests," Electronic Markets, Springer;IIM University of St. Gallen, vol. 27(3), pages 199-210, August.
    7. Yan Chen & Grace Jeon & Yong-Mi Kim, 2014. "A day without a search engine: an experimental study of online and offline searches," Experimental Economics, Springer;Economic Science Association, vol. 17(4), pages 512-536, December.
    8. Anya Samek & Roman Sheremeta, 2013. "Recognizing Contributors and Cost of Information: An Experiment on Public Goods," Artefactual Field Experiments 00430, The Field Experiments Website.
    9. Martin Grossmann, 2021. "Entry regulations and optimal prize allocation in parallel contests," Review of Economic Design, Springer;Society for Economic Design, vol. 25(4), pages 289-316, December.
    10. Yan Chen & Joseph Konstan, 2015. "Online field experiments: a selective survey of methods," Journal of the Economic Science Association, Springer;Economic Science Association, vol. 1(1), pages 29-42, July.
    11. Tracy Xiao Liu & Jiang Yang & Lada A. Adamic & Yan Chen, 2014. "Crowdsourcing with All-Pay Auctions: A Field Experiment on Taskcn," Management Science, INFORMS, vol. 60(8), pages 2020-2037, August.
    12. Zhanwen Shi & Erbao Cao & Kai Nie, 2023. "Capacity pooling games in crowdsourcing services," Electronic Commerce Research, Springer, vol. 23(2), pages 1007-1047, June.
    13. Benjamin Edelman, 2012. "Earnings And Ratings At Google Answers," Economic Inquiry, Western Economic Association International, vol. 50(2), pages 309-320, April.
    14. Jing Wang & Gen Li & Kai-Lung Hui, 2022. "Monetary Incentives and Knowledge Spillover: Evidence from a Natural Experiment," Management Science, INFORMS, vol. 68(5), pages 3549-3572, May.
    15. Chih-Hung Peng & Dezhi Yin & Han Zhang, 2020. "More than Words in Medical Question-and-Answer Sites: A Content-Context Congruence Perspective," Information Systems Research, INFORMS, vol. 31(3), pages 913-928, September.

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