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The imagination market

Author

Listed:
  • Christina Ann LaComb

    (GE Global Research Center)

  • Janet Arlie Barnett

    (GE Global Research Center)

  • Qimei Pan

    (New York State Department of Transportation, Office of Information Services)

Abstract

Information markets are typically used as prediction tools, aggregating opinions about the likelihood of future events, or as preference indicators, identifying participants’ product preferences. However, the basic information market concept is more widely applicable. In our experiment, we utilized information markets within the domains of idea generation and group decisioning. Participants were allowed to propose ideas regarding potential technology research areas; these ideas were represented as securities on a virtual financial market. Participants were able to trade shares of technology ideas over the course of 3 weeks, resulting in the market identifying the “best” idea as the highest priced security. Our findings suggest that information markets for idea generation result in more ideas and more participants than traditional idea generation techniques; however, using markets to rank ideas may be no better than other methods of idea ranking. Additional benefits include providing immediate feedback, allowing visibility of all ideas to all contributors, and being a fun mechanism for consensus building.

Suggested Citation

  • Christina Ann LaComb & Janet Arlie Barnett & Qimei Pan, 2007. "The imagination market," Information Systems Frontiers, Springer, vol. 9(2), pages 245-256, July.
    • Handle: RePEc:spr:infosf:v:9:y:2007:i:2:d:10.1007_s10796-007-9024-9
    DOI: 10.1007/s10796-007-9024-9
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    References listed on IDEAS

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    Cited by:

    1. Li Chen & Paulo Goes & Wynd Harris & James Marsden & John Zhang, 2010. "Preference Markets for Innovation Ranking and Selection," Interfaces, INFORMS, vol. 40(2), pages 144-153, April.
    2. Ivo Blohm & Christoph Riedl & Johann Fuller & Orhan Koroglu & Jan Marco Leimeister & Helmut Krcmar, 2012. "The Effects of Prediction Market Design and Price Elasticity on Trading Performance of Users: An Experimental Analysis," Papers 1204.3457, arXiv.org.
    3. Chulhwan Chris Bang, 2015. "Information systems frontiers: Keyword analysis and classification," Information Systems Frontiers, Springer, vol. 17(1), pages 217-237, February.
    4. Ivo Blohm & Christoph Riedl & Johann Füller & Jan Marco Leimeister, 2016. "Rate or Trade? Identifying Winning Ideas in Open Idea Sourcing," Information Systems Research, INFORMS, vol. 27(1), pages 27-48, March.
    5. Patrick Buckley & Fergal O’Brien, 0. "The effect of malicious manipulations on prediction market accuracy," Information Systems Frontiers, Springer, vol. 0, pages 1-13.
    6. Patrick Buckley & Fergal O’Brien, 2017. "The effect of malicious manipulations on prediction market accuracy," Information Systems Frontiers, Springer, vol. 19(3), pages 611-623, June.

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