TY - JOUR
T1 - When crowds give you lemons
T2 - Filtering innovative ideas using a diverse-bag-of-lemons strategy
AU - Lykourentzou, Ioanna
AU - Ahmed, Faez
AU - Papastathis, Costas
AU - Sadien, Irwyn
AU - Papangelis, Konstantinos
N1 - Publisher Copyright:
© 2018 Association for Computing Machinery.
PY - 2018/11
Y1 - 2018/11
N2 - 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.
AB - 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.
KW - Diversity
KW - Filtering
KW - Open innovation
UR - http://www.scopus.com/inward/record.url?scp=85066417488&partnerID=8YFLogxK
U2 - 10.1145/3274384
DO - 10.1145/3274384
M3 - Article
AN - SCOPUS:85066417488
SN - 2573-0142
VL - 2
JO - Proceedings of the ACM on Human-Computer Interaction
JF - Proceedings of the ACM on Human-Computer Interaction
IS - CSCW
M1 - 115
ER -