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Crowdsourcing New Product Ideas Under Consumer Learning

Author

Listed:
  • Yan Huang

    (Ross School of Business, University of Michigan, Ann Arbor, Michigan 48109)

  • Param Vir Singh

    (Tepper School of Business, Carnegie Mellon University, Pittsburgh, Pennsylvania 15213)

  • Kannan Srinivasan

    (Tepper School of Business, Carnegie Mellon University, Pittsburgh, Pennsylvania 15213)

Abstract

We propose a dynamic structural model that illuminates the economic mechanisms shaping individual behavior and outcomes on crowdsourced ideation platforms. We estimate the model using a rich data set obtained from IdeaStorm.com, a crowdsourced ideation initiative affiliated with Dell. We find that, on IdeaStorm.com, individuals tend to significantly underestimate the costs to the firm for implementing their ideas but overestimate the potential of their ideas in the initial stages of the crowdsourcing process. Therefore, the “idea market” is initially overcrowded with ideas that are less likely to be implemented. However, individuals learn about both their abilities to come up with high-potential ideas as well as the cost structure of the firm from peer voting on their ideas and the firm's response to contributed ideas. We find that individuals learn rather quickly about their abilities to come up with high-potential ideas, but the learning regarding the firm's cost structure is quite slow. Contributors of low-potential ideas eventually become inactive, whereas the high-potential idea contributors remain active. As a result, over time, the average potential of generated ideas increases while the number of ideas contributed decreases. Hence, the decrease in the number of ideas generated represents market efficiency through self-selection rather than its failure. Through counterfactuals, we show that providing more precise cost signals to individuals can accelerate the filtering process. Increasing the total number of ideas to respond to and improving the response speed will lead to more idea contributions. However, failure to distinguish between high- and low-potential ideas and between high- and low-ability idea generators leads to the overall potential of the ideas generated to drop significantly. This paper was accepted by Sandra Slaughter, information systems .

Suggested Citation

  • Yan Huang & Param Vir Singh & Kannan Srinivasan, 2014. "Crowdsourcing New Product Ideas Under Consumer Learning," Management Science, INFORMS, vol. 60(9), pages 2138-2159, September.
    • Handle: RePEc:inm:ormnsc:v:60:y:2014:i:9:p:2138-2159
    DOI: 10.1287/mnsc.2013.1879
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    References listed on IDEAS

    as
    1. Sridhar Narayanan & Puneet Manchanda, 2009. "Heterogeneous Learning and the Targeting of Marketing Communication for New Products," Marketing Science, INFORMS, vol. 28(3), pages 424-441, 05-06.
    2. Franke, Nikolaus & Hippel, Eric von, 2003. "Satisfying heterogeneous user needs via innovation toolkits: the case of Apache security software," Research Policy, Elsevier, vol. 32(7), pages 1199-1215, July.
    3. Oded Netzer & James M. Lattin & V. Srinivasan, 2008. "A Hidden Markov Model of Customer Relationship Dynamics," Marketing Science, INFORMS, vol. 27(2), pages 185-204, 03-04.
    4. Nitin Mehta & Surendra Rajiv & Kannan Srinivasan, 2003. "Price Uncertainty and Consumer Search: A Structural Model of Consideration Set Formation," Marketing Science, INFORMS, vol. 22(1), pages 58-84, June.
    5. Robert A. Lowe & Arvids A. Ziedonis, 2006. "Overoptimism and the Performance of Entrepreneurial Firms," Management Science, INFORMS, vol. 52(2), pages 173-186, February.
    6. Christian Terwiesch & Yi Xu, 2008. "Innovation Contests, Open Innovation, and Multiagent Problem Solving," Management Science, INFORMS, vol. 54(9), pages 1529-1543, September.
    7. Antonio E. Bernardo & Ivo Welch, 2001. "On the Evolution of Overconfidence and Entrepreneurs," Journal of Economics & Management Strategy, Wiley Blackwell, vol. 10(3), pages 301-330, September.
    8. Kevin J. Boudreau & Nicola Lacetera & Karim R. Lakhani, 2011. "Incentives and Problem Uncertainty in Innovation Contests: An Empirical Analysis," Management Science, INFORMS, vol. 57(5), pages 843-863, May.
    9. Lars Bo Jeppesen & Karim R. Lakhani, 2010. "Marginality and Problem-Solving Effectiveness in Broadcast Search," Organization Science, INFORMS, vol. 21(5), pages 1016-1033, October.
    10. John R. Hauser & Glen L. Urban, 1977. "A Normative Methodology for Modeling Consumer Response to Innovation," Operations Research, INFORMS, vol. 25(4), pages 579-619, August.
    11. Tülin Erdem & Michael P. Keane & Baohong Sun, 2008. "A Dynamic Model of Brand Choice When Price and Advertising Signal Product Quality," Marketing Science, INFORMS, vol. 27(6), pages 1111-1125, 11-12.
    12. Tülin Erdem & Michael P. Keane, 1996. "Decision-Making Under Uncertainty: Capturing Dynamic Brand Choice Processes in Turbulent Consumer Goods Markets," Marketing Science, INFORMS, vol. 15(1), pages 1-20.
    13. Gregory S. Crawford & Matthew Shum, 2005. "Uncertainty and Learning in Pharmaceutical Demand," Econometrica, Econometric Society, vol. 73(4), pages 1137-1173, July.
    14. Herz, Holger & Schunk, Daniel & Zehnder, Christian, 2014. "How do judgmental overconfidence and overoptimism shape innovative activity?," Games and Economic Behavior, Elsevier, vol. 83(C), pages 1-23.
    15. Schulze, Anja & Hoegl, Martin, 2008. "Organizational knowledge creation and the generation of new product ideas: A behavioral approach," Research Policy, Elsevier, vol. 37(10), pages 1742-1750, December.
    16. Lucas, Robert Jr, 1976. "Econometric policy evaluation: A critique," Carnegie-Rochester Conference Series on Public Policy, Elsevier, vol. 1(1), pages 19-46, January.
    17. Alberto Galasso & Timothy S. Simcoe, 2011. "CEO Overconfidence and Innovation," Management Science, INFORMS, vol. 57(8), pages 1469-1484, August.
    18. Juanjuan Zhang, 2010. "The Sound of Silence: Observational Learning in the U.S. Kidney Market," Marketing Science, INFORMS, vol. 29(2), pages 315-335, 03-04.
    19. Nitin Mehta & Xinlei (Jack) Chen & Om Narasimhan, 2008. "Informing, Transforming, and Persuading: Disentangling the Multiple Effects of Advertising on Brand Choice Decisions," Marketing Science, INFORMS, vol. 27(3), pages 334-355, 05-06.
    20. Vineet Kumar & Brett R. Gordon & Kannan Srinivasan, 2011. "Competitive Strategy for Open Source Software," Marketing Science, INFORMS, vol. 30(6), pages 1066-1078, November.
    21. Dan Lovallo & Colin Camerer, 1999. "Overconfidence and Excess Entry: An Experimental Approach," American Economic Review, American Economic Association, vol. 89(1), pages 306-318, March.
    22. Lakhani, Karim R. & von Hippel, Eric, 2003. "How open source software works: "free" user-to-user assistance," Research Policy, Elsevier, vol. 32(6), pages 923-943, June.
    23. Barry L. Bayus, 2013. "Crowdsourcing New Product Ideas over Time: An Analysis of the Dell IdeaStorm Community," Management Science, INFORMS, vol. 59(1), pages 226-244, June.
    24. Karan Girotra & Christian Terwiesch & Karl T. Ulrich, 2010. "Idea Generation and the Quality of the Best Idea," Management Science, INFORMS, vol. 56(4), pages 591-605, April.
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