Crowd-sourced idea filtering with Bag of Lemons: the impact of the token budget size
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40622_2023_349_ReferencePDF.pdf
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53a4075c51c678166b511ccf19858354
Author(s)
Lukumon, Gafari
Klein, Mark
Date Issued
July 8, 2023
Publisher
Springer India
Citation
Lukumon, Gafari and Klein, Mark. 2023. "Crowd-sourced idea filtering with Bag of Lemons: the impact of the token budget size."
Version
Author's final manuscript
Abstract
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.
MIT Department
Massachusetts Institute of Technology. Center for Collective Intelligence
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Article is made available in accordance with the publisher's policy and may be subject to US copyright law. Please refer to the publisher's site for terms of use.
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DOI of Published Version
https://doi.org/10.1007/s40622-023-00349-w
