Crowdsourcing is an increasingly common method used for new product development in large engineering-focused companies. While effective at generating a large number of ideas, previous research has noted that there is not an efficient mechanism to sort ideas based on the sponsor's desired outcomes. Without such a mechanism, the sponsor is left to evaluate ideas individually in a labor-intensive effort. This paper evaluates the extent to which information revealed by the crowd during the course of a crowdsourcing event can be used to accurately predict sponsor selection of submitted ideas. The praxis reviews current literature relevant to new product development, innovation management, and crowdsourcing as well as methods for efficient sorting. Using a quantitatively-based methodology, the author develops and evaluates several predictive models using various attributes of the crowd reaction to crowdsourced ideas. Ultimately, the praxis proposes a model that can significantly reduce the burden of sorting through submissions and determining the submissions which merit further review.
|Advisor:||Guillaume-Joseph, Gina, Gideon, Alan K.|
|Commitee:||Etemadi, Amir, Householder, Russell W., Murphree, Edward L.|
|School:||The George Washington University|
|School Location:||United States -- District of Columbia|
|Source:||DAI-B 79/07(E), Dissertation Abstracts International|
|Subjects:||Management, Operations research|
|Keywords:||Crowdsourcing, Innovation management, Logistic regression, New product development, Open innovation|
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