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Crowd-sourced idea filtering with Bag of Lemons: the impact of the token budget size

Author

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  • Gafari Lukumon

    (University Mohammed VI Polytechnic
    Ecole Normale Supérieure (ENS-PSL))

  • Mark Klein

    (University Mohammed VI Polytechnic
    Massachusetts Institute of Technology)

Abstract

Identifying the best ideas from the vast volumes generated by open innovation engagements is costly and often time-consuming. One approach is to engage crowds in filtering the ideas, not just generating them. Klein and Garcia, 2015 proposed a “BOL” approach that is better (in terms of accuracy and speed) at idea filtering than other filtering methods such as a conventional Likert approach. The idea behind this approach (BOL) is that it asks the crowd to distribute a fixed budget of tokens that eliminate bad ideas rather than select good ones. In this paper, we explain why BOL works better than other filtering methods using empirical experiments (with n = 850 subjects). Also, we present the effect of the token budget size on idea-filtering engagement and found, among others, that the accuracy of a filter depends on the token budget size.

Suggested Citation

  • Gafari Lukumon & Mark Klein, 2023. "Crowd-sourced idea filtering with Bag of Lemons: the impact of the token budget size," DECISION: Official Journal of the Indian Institute of Management Calcutta, Springer;Indian Institute of Management Calcutta, vol. 50(2), pages 205-219, June.
    • Handle: RePEc:spr:decisn:v:50:y:2023:i:2:d:10.1007_s40622-023-00349-w
    DOI: 10.1007/s40622-023-00349-w
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