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Cited by 204

The DOI matches its Crossref record; 7 of the 7 references it cites were found in a registry or an index.

Looking Inside the Fishbowl of Creativity: Verbal and Behavioral Predictors of Creative Performance

Ruscio, John, Whitney, Dean M., Amabile, Teresa M.·1998·Creativity Research Journal

Abstract

This study set out to identify specific task behaviors that predict observable product creativity in three domains and to identify which of those behaviors mediate the well-established link between intrinsic motivation and creativity. One-hundred fifty-one undergraduate students completed a motivational measure and were later videotaped while engaging in tasks in three different domains: problem solving (a structure-building activity), art (collage making), and a writing (an American Haiku poem). Behavioral coding and think-aloud protocol analysis yielded reliable measures that, when empirically combined form task process indicators, strongly predicted judge-rated product creativity in each domain. One of the indicators, involvement in the task, served as a mediator of intrinsic motivation's positive influence one creativity. Other indicators reflect domain-relevant skills and creativity-relevant processes, lending support to the componential model of creativity. Theoretical and methodological implications for future creativity research are discussed.

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Metadata

Authors
Ruscio, J., Whitney, D. M., & Amabile, T. M.
Venue
Creativity Research Journal
Published
1998-07
Volume
11
Issue
3
Pages
243-263
ISSN
  • 1040-0419 (ISSN-L)
  • 1532-6934
OpenAlex
W2054704221
Cited by
204
References
7
FWCI
4.74
Type
journal article
Language
en
Open access
closed

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CoChat. (2026). Citation check: Looking Inside the Fishbowl of Creativity: Verbal and Behavioral Predictors of Creative Performance. https://app.cochat.ai/papers/10.1207/s15326934crj1103_4
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Ruscio, J., Whitney, D. M., & Amabile, T. M. (1998). Looking Inside the Fishbowl of Creativity: Verbal and Behavioral Predictors of Creative Performance. Creativity Research Journal. https://doi.org/10.1207/s15326934crj1103_4

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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).

Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.