ABSTRACT
Research has explored different ways of improving crowd ideation, such as presenting examples or employing facilitators. While such support is usually generated through peripheral tasks delegated to crowd workers who are not part of the ideation, it is possible that the ideators themselves could benefit from the extra thought involved in doing them. Therefore, we iterate over an ideation system in which ideators can perform one of three peripheral tasks (rating originality and usefulness, similarity, or idea combination) on demand. In controlled experiments with workers on Mechanical Turk, we compare the effects of these secondary tasks to simple idea exposure or no support at all, examining usage of the inspirations, fluency, breadth, and depth of ideas generated. We find tasks to be as good or better than exposure, although this depends on the period of ideation and the fluency level. We also discuss implications of inspiration size, homogeneity, and frequency.
- Teresa M. Amabile. 1983. The social psychology of creativity: A componential conceptualization. Journal of personality and social psychology 45, 2: 357.Google ScholarCross Ref
- Michael S. Bernstein, Greg Little, Robert C. Miller, Björn Hartmann, Mark S. Ackerman, David R. Karger, David Crowell, and Katrina Panovich. 2010. Soylent: a word processor with a crowd inside. In Proceedings of the 23nd annual ACM symposium on User interface software and technology, 313--322. Retrieved January 11, 2016 from http://dl.acm.org/citation.cfm?id=1866078 Google ScholarDigital Library
- Joel Chan, Steven Dang, and Steven P. Dow. 2016. Comparing Different Sensemaking Approaches for LargeScale Ideation. Retrieved March 11, 2016 from http://joelchan.me/files/2016-chi-sensemaking-ideation.pdfGoogle Scholar
- Joel Chan, Steven Dang, and Steven P. Dow. 2016. Improving Crowd Innovation with Expert Facilitation.Google Scholar
- Lydia B. Chilton, Greg Little, Darren Edge, Daniel S. Weld, and James A. Landay. 2013. Cascade: Crowdsourcing taxonomy creation. In Proceedings of the SIGCHI Conference on Human Factors in Computing Systems, 1999-- 2008. Retrieved January 11, 2016 from http://dl.acm.org/citation.cfm?id=2466265 Google ScholarDigital Library
- Arthur Cropley. 2006. In praise of convergent thinking. Creativity research journal 18, 3: 391--404.Google Scholar
- Alan R. Dennis and Joseph S. Valacich. 1993. Computer brainstorms: More heads are better than one. Journal of applied psychology 78, 4: 531.Google ScholarCross Ref
- Alan R. Dennis and Mike L. Williams. 2003. Electronic Brainstorming: Theory, Research, and Future Directions. In Group creativity: Innovation through collaboration. Oxford University Press.Google Scholar
- Michael Diehl and Wolfgang Stroebe. 1987. Productivity loss in brainstorming groups: Toward the solution of a riddle. Journal of Personality and Social Psychology 53, 3: 497--509.Google ScholarCross Ref
- Karen Leggett Dugosh, Paul B. Paulus, Evelyn J. Roland, and Huei-Chuan Yang. 2000. Cognitive stimulation in brainstorming. Journal of Personality and Social Psychology 79, 5: 722--735.Google ScholarCross Ref
- Beth A. Hennessey and Teresa M. Amabile. 2010. Creativity. Annual Review of Psychology 61, 1: 569--598.Google ScholarCross Ref
- Alex Ivanov and Dianne Cyr. 2006. The Concept Plot: a concept mapping visualization tool for asynchronous webbased brainstorming sessions. Information Visualization 5, 3: 185--191.Google ScholarCross Ref
- D. G. Jansson and S. M. Smith. 1991. Design Fixation. Design Studies 12, 1: 3--11.Google ScholarCross Ref
- Aniket Kittur, Ed H. Chi, and Bongwon Suh. 2008. Crowdsourcing user studies with Mechanical Turk. In Proceedings of the SIGCHI conference on human factors in computing systems, 453--456. Retrieved August 17, 2015 from http://dl.acm.org/citation.cfm?id=1357127 Google ScholarDigital Library
- Aniket Kittur, Boris Smus, Susheel Khamkar, and Robert E. Kraut. 2011. Crowdforge: Crowdsourcing complex work. In Proceedings of the 24th annual ACM symposium on User interface software and technology, 43-- 52. Retrieved August 4, 2015 from http://dl.acm.org/citation.cfm?id=2047202 Google ScholarDigital Library
- Nicholas W. Kohn, Paul B. Paulus, and YunHee Choi. 2011. Building on the ideas of others: An examination of the idea combination process. Journal of Experimental Social Psychology 47, 3: 554--561.Google ScholarCross Ref
- Aaron Kozbelt, Ronald A. Beghetto, and Mark A. Runco. 2010. Theories of creativity. The Cambridge handbook of creativity: 20--47.Google Scholar
- Filip Krynicki. 2014. Methods and models for the quantitative analysis of crowd brainstorming. Retrieved April 5, 2016 from https://uwspace.uwaterloo.ca/handle/10012/8347Google Scholar
- Richard L. Marsh, Joshua D. Landau, and Jason L. Hicks. 1996. How examples may (and may not) constrain creativity. Memory & cognition 24, 5: 669--680.Google Scholar
- Brent A. Nelson, Jamal O. Wilson, David Rosen, and Jeannette Yen. 2009. Refined metrics for measuring ideation effectiveness. Design Studies 30, 6: 737--743.Google ScholarCross Ref
- Charlan J. Nemeth, Bernard Personnaz, Marie Personnaz, and Jack A. Goncalo. 2004. The liberating role of conflict in group creativity: A study in two countries. European Journal of Social Psychology 34, 4: 365--374.Google ScholarCross Ref
- Bernard A. Nijstad, Michael Diehl, and Wolfgang Stroebe. 2003. Cognitive Stimulation and Interference in Idea-Generating Groups. In Group Creativity: Innovation Through Collaboration. Oxford University Press.Google Scholar
- Bernard A. Nijstad and Wolfgang Stroebe. 2006. How the group affects the mind: A cognitive model of idea generation in groups. Personality and social psychology review 10, 3: 186--213.Google Scholar
- Bernard A. Nijstad, Wolfgang Stroebe, and Hein FM Lodewijkx. 2002. Cognitive stimulation and interference in groups: Exposure effects in an idea generation task. Journal of experimental social psychology 38, 6: 535--544.Google ScholarCross Ref
- Alex F. Osborn. 1963. Applied imagination; principles and procedures of creative problem-solving. Scribner, New York.Google Scholar
- Jonathan A. Plucker and Matthew C. Makel. 2010. Assessment of Creativity. In The Cambridge Handbook of Creativity, James C. Kaufman and Robert J. Sternberg (eds.). Cambridge University Press, Cambridge, 48--73. Retrieved November 29, 2016 from http://ebooks.cambridge.org/ref/id/CBO9780511763205A0Google Scholar
- Pao Siangliulue, Kenneth C. Arnold, Krzysztof Z. Gajos, and Steven P. Dow. 2015. Toward Collaborative Ideation at Scale: Leveraging Ideas from Others to Generate More Creative and Diverse Ideas. 937--945. Google ScholarDigital Library
- Pao Siangliulue, Joel Chan, Steven P. Dow, and Krzysztof Z. Gajos. 2016. IdeaHound: Improving Largescale Collaborative Ideation with Crowd-Powered Real-time Semantic Modeling. 609--624. Google ScholarDigital Library
- Pao Siangliulue, Joel Chan, Krzysztof Z. Gajos, and Steven P. Dow. 2015. Providing Timely Examples Improves the Quantity and Quality of Generated Ideas. 83--92. Google ScholarDigital Library
- Steven M. Smith. 2003. The constraining effects of initial ideas. In Group creativity: Innovation through collaboration, Paul B. Paulus and Bernard A. Nijstad (eds.). Oxford University Press, New York, NY, US, 15--31.Google Scholar
- Lixiu Yu, Aniket Kittur, and Robert E. Kraut. 2014. Distributed analogical idea generation: inventing with crowds. 1245--1254. Google ScholarDigital Library
- Lixiu Yu, Aniket Kittur, and Robert E. Kraut. 2014. Searching for analogical ideas with crowds. 1225--1234. Google ScholarDigital Library
- Lixiu Yu and Jeffrey V. Nickerson. 2011. Cooks or cobblers?: crowd creativity through combination. In Proceedings of the SIGCHI conference on human factors in computing systems, 1393--1402. Retrieved October 12, 2015 from http://dl.acm.org/citation.cfm?id=1979147 Google ScholarDigital Library
Index Terms
- The Effect of Peripheral Micro-tasks on Crowd Ideation
Recommendations
Scalable Crowd Ideation Support through Data Visualization, Mining, and Structured Workflows
CSCW '17 Companion: Companion of the 2017 ACM Conference on Computer Supported Cooperative Work and Social ComputingAs the size of innovation communities increases, methods of supporting their creativity need to scale as well. Our research proposes the integration of three scalable techniques into a crowd ideation system: 1) data visualization, 2) structured ...
Creative Computing for Bespoke Ideation
COMPSAC '15: Proceedings of the 2015 IEEE 39th Annual Computer Software and Applications Conference - Volume 01Today, idea generation is an extremely important activity for both academic researchers and industrial groups. A considerable number of applications and research studies have been made in the past years in order to increase the effectiveness of idea ...
Directed Diversity: Leveraging Language Embedding Distances for Collective Creativity in Crowd Ideation
CHI '21: Proceedings of the 2021 CHI Conference on Human Factors in Computing SystemsCrowdsourcing can collect many diverse ideas by prompting ideators individually, but this can generate redundant ideas. Prior methods reduce redundancy by presenting peers’ ideas or peer-proposed prompts, but these require much human coordination. We ...
Comments