When crowds give you lemons: Filtering innovative ideas using a diverse-bag-of-lemons strategy

Ioanna Lykourentzou, Faez Ahmed, Costas Papastathis, Irwyn Sadien, Konstantinos Papangelis

Research output: Contribution to journalArticlepeer-review

7 Citations (Scopus)

Abstract

Following successful crowd ideation contests, organizations in search of the “next big thing” are left with hundreds of ideas. Expert-based idea filtering is lengthy and costly; therefore, crowd-based strategies are often employed. Unfortunately, these strategies typically (1) do not separate the mediocre from the excellent, and (2) direct all the attention to certain idea concepts, while others starve. We introduce DBLemons – a crowd-based idea filtering strategy that addresses these issues by (1) asking voters to identify the worst rather than the best ideas using a “bag of lemons” voting approach, and (2) by exposing voters to a wider idea spectrum, thanks to a dynamic diversity-based ranking system balancing idea quality and coverage. We compare DBLemons against two state-of-the-art idea filtering strategies in a real-world setting. Results show that DBLemons is more accurate, less time-consuming, and reduces the idea space in half while still retaining 94% of the top ideas.

Original languageEnglish
Article number115
JournalProceedings of the ACM on Human-Computer Interaction
Volume2
Issue numberCSCW
DOIs
Publication statusPublished - Nov 2018
Externally publishedYes

Keywords

  • Diversity
  • Filtering
  • Open innovation

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