Cognitive and social comparison processes in brainstorming

Dugosh, Karen L.; Paulus, Paul B.

doi:10.1016/j.jesp.2004.05.009

Cited by 253 publications

(71 citation statements)

References 33 publications

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“…We also assessed the overall quality of each idea (judging both novelty and value). We assembled sets of three ideas that comprised both high quality and diverse theme, as both example quality and diversity have been shown to improve ideation performance [37,25,34,45].…”

Section: Examplesmentioning

confidence: 99%

Providing Timely Examples Improves the Quantity and Quality of Generated Ideas

Siangliulue

Chan

Gajos

et al. 2015

Proceedings of the 2015 ACM SIGCHI Conference on Creativity and Cognition

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Emerging online ideation platforms with thousands of example ideas provide an important resource for creative production. But how can ideators best use these examples to create new innovations? Recent work has suggested that not just the choice of examples, but also the timing of their delivery can impact creative outcomes. Building on existing cognitive theories of creative insight, we hypothesize that people are likely to benefit from examples when they run out of ideas. We explore two example delivery mechanisms that test this hypothesis: 1) a system that proactively provides examples when a user appears to have run out of ideas, and 2) a system that provides examples when a user explicitly requests them. Our online experiment (N=97) compared these two mechanisms against two baselines: providing no examples and automatically showing examples at a regular interval. Participants who requested examples themselves generated ideas that were rated the most novel by external evaluators. Participants who received ideas automatically when they appeared to be stuck produced the most ideas. Importantly, participants who received examples at a regular interval generated fewer ideas than participants who received no examples, suggesting that mere access to examples is not sufficient for creative inspiration. These results emphasize the importance of the timing of example delivery. Insights from this study can inform the design of collective ideation support systems that help people generate many high quality ideas.

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Section: Examplesmentioning

confidence: 99%

Providing Timely Examples Improves the Quantity and Quality of Generated Ideas

Siangliulue

Chan

Gajos

et al. 2015

Proceedings of the 2015 ACM SIGCHI Conference on Creativity and Cognition

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“…People might infer the desirable properties of a set of ideas from the example set that they saw. Here, an example set provides information about the performance of others, encouraging participants to match the properties of their own ideas to example sets [26,20]. While the two explanations involve very different mechanisms, they both support the value of presenting ideators with sets of creative and diverse examples.…”

Section: Creativity Of Generated Ideasmentioning

confidence: 93%

“…Results from a study of social influence processes in group brainstorming suggested that people are affected by information about the performance of others [26,20]. One can infer the overall performance of others from ideas that one sees and try to match with ideas of the same calibre.…”

Section: How Creative and Diverse Examples Affect Ideationmentioning

confidence: 99%

“…Our literature review reveals two criteria for an inspiring set of example ideas: creativity of individual examples and diversity of the set of examples. Compared to seeing a random selection of examples (as one might see when simply browsing ideas), seeing particularly creative (i.e., novel and valuable) ideation examples has been shown to improve both the creativity and diversity of ideas one generates [22,26,20]. Similarly, if the set of examples is diverse (i.e., if the ideas within the set were substantially different from each other), the diversity of generated ideas should also increase [25].…”

Section: Introductionmentioning

confidence: 99%

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Toward Collaborative Ideation at Scale

Siangliulue

Arnold

Gajos

et al. 2015

Proceedings of the 18th ACM Conference on Computer Supported Cooperative Work &Amp; Social Computing

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A growing number of large collaborative idea generation platforms promise that by generating ideas 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 particularly creative or diverse ideas from others can inspire you, but few scalable mechanisms exist to assess diversity. We contribute a new scalable crowd-powered method for evaluating the diversity of sets of ideas. The method relies on similarity comparisons (is idea A more similar to B or C?) generated by non-experts 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. People seeing the diverse sets of examples from our idea map generate more diverse ideas than those 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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“…Von Hippel's research have established the importance of user innovation (Von Hippel, 1978), while also addressing the by ideas of others (Leggett Dugosh et al, 2000;Nijstad, Stroebe, & Lodewijkx, 2002), by cognitive training (Dahl & Moreau, 2002;Iyer et al, 2009;Marakas & Elam, 1997), or even using special software (MacCrimmon & Wagner, 1994;Rangaswamy & Lilien, 1997;Verhaegen et al, 2011). We also know that some sorts of hybrid ideation bring superior results, being able to use collaborative and stimulating synergies, while eliminating the frequent pitfalls of the classic groupideation techniques, such as free-riding or production-blocking (Aiken, Sloan, Paolillo, & Motiwalla, 1997;Diehl & Stroebe, 1987).…”

Section: Current State Of Knowledgementioning

confidence: 99%