Conceptual distance matters when building on others' ideas in crowd-collaborative innovation platforms

Authors:
Joel Chan;Steven Dow;Christian Schunn
Affiliations:
University of Pittsburgh, Pittsburgh, PA, USA;Carnegie Mellon University, Pittsburgh, PA, USA;University of Pittsburgh, Pittsburgh, PA, USA
Venue:
Proceedings of the companion publication of the 17th ACM conference on Computer supported cooperative work & social computing
Year:
2014

Citing 2
Cited 0

Latent dirichlet allocation

The Journal of Machine Learning Research
The efficacy of prototyping under time constraints

Proceedings of the seventh ACM conference on Creativity and cognition

Quantified Score

Hi-index	0.00

Visualization

Abstract

In crowd-collaborative innovation platforms, other contributors' ideas can serve as sources of inspiration for creative ideas, but what patterns of interactions with others' ideas are most helpful? We investigate the hypothesis that building on inspiration sources that are conceptually far from one's target domain are most helpful, a popular hypothesis with mixed empirical support. We predict the success rate of 2,344 ideas for 12 different design challenges in a collaborative Web-based innovation platform based on their cited sources' conceptual distance from the target domain (measured using probabilistic topic modeling of the ideas). Surprisingly, we find that innovators who cite conceptually near sources of inspiration achieve a higher success rate than those who prefer far sources. We discuss implications for research and development of crowd-collaborative innovation platforms.