Abstract
A growing number of large online collaborative idea generation platforms promise that by ideating together, people can create better ideas than any would have alone. But how might these platforms best leverage the number and diversity of contributors to help each contributor generate even better ideas? Prior research suggests that seeing examples of ideas generated by others can improve one's own ideation outcomes, but the benefit may depend on how creative and how diverse (i.e., different from each other) these examples are. There already exist scalable crowd-powered mechanisms to evaluate creativity of individual ideas, but few options exist for assessing the diversity of ideas. We contribute a new scalable crowd-powered method for evaluating the diversity of sets of ideas. The method relies on simple comparisons of similarity (is idea A more similar to B or C?) generated by a large number of non-expert contributors. We build on prior work on multidimensional scaling and active similarity learning techniques to create an abstract spatial idea map. Our validation study reveals that human raters agree with the estimates of dissimilarity derived from our idea map as much or more than they agree with each other. The results of our main experiment demonstrate that diverse sets of examples generated automatically using our idea map improve the diversity of ideas generated by participants compared to seeing randomly selected examples. Our results also corroborate findings from prior research showing that people presented with creative examples generated more creative ideas than those who saw a set of random examples. We see this work as a step toward building more effective online systems for supporting large scale collective ideation.
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Citation Information
Pao Siangliulue, Kenneth C. Arnold, Krzysztof Z. Gajos, and Steven P. Dow. Toward collaborative ideation at scale: Leveraging ideas from others to generate more creative and diverse ideas. In Proceedings of the 18th ACM Conference on Computer Supported Cooperative Work & Social Computing, CSCW '15, pages 937-945, New York, NY, USA, 2015. ACM.
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