The DOI matches its Crossref record; 1 of the 1 references it cites were found in a registry or an index.
Metrics for measuring ideation effectiveness
Overview
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Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
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Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
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Citations
Cites
Jansson, D. G., & Smith, S. M. (1991) · Design Studies · doi:10.1016/0142-694x(91)90003-f · Cited by 1,087
Self-citation
Cited by
The Effects of Generative AI on Design Fixation and Divergent Thinking
Wadinambiarachchi, S., Kelly, R. M., Pareek, S., Zhou, Q., & Velloso, E. (2024) · Proceedings of the CHI Conference on Human Factors in Computing Systems · doi:10.1145/3613904.3642919 · Cited by 320
“Moreover, fluency, variety, and originality were lower in the AI-supported group compared to the baseline condition.”
What inspires designers? Preferences on inspirational approaches during idea generation
Gonçalves, M., Cardoso, C., & Badke-Schaub, P. (2014) · Design Studies · doi:10.1016/j.destud.2013.09.001 · Cited by 295
The effect of AI-based inspiration on human design ideation
Kim, J., & Maher, M. L. (2023) · International Journal of Design Creativity and Innovation · doi:10.1080/21650349.2023.2167124 · Cited by 114
Overcoming design fixation: Design by analogy studies and nonintuitive findings
Moreno, D. P., Blessing, L. T., Yang, M. C., Hernández, A. A., & Wood, K. L. (2016) · Artificial Intelligence for Engineering Design, Analysis and Manufacturing · doi:10.1017/s0890060416000068 · Cited by 79
A computational tool for creative idea generation based on analogical reasoning and ontology
Han, J., Shi, F., Chen, L., & Childs, P. R. N. (2018) · Artificial Intelligence for Engineering Design, Analysis and Manufacturing · doi:10.1017/s0890060418000082 · Cited by 78
Metadata
- Authors
- Shah, J. J., Smith, S. M., & Vargas-Hernandez, N.
- Venue
- Design Studies
- Publisher
- Elsevier BV
- Published
- 2003-03
- Volume
- 24
- Issue
- 2
- Pages
- 111-134
- ISSN
- 0142-694X (ISSN-L)
- OpenAlex
- W2019998121
- Cited by
- 1,095
- References
- 1
- FWCI
- 24.19
- Type
- journal article
- Language
- en
- Open access
- closed
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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How to cite
Cite CoChat for the checks on this page, and the paper for its own findings.
- This page
- CoChat. (2026). Citation check: Metrics for measuring ideation effectiveness. https://app.cochat.ai/papers/10.1016/s0142-694x(02)00034-0
- The paper
- Shah, J. J., Smith, S. M., & Vargas-Hernandez, N. (2003). Metrics for measuring ideation effectiveness. Design Studies. https://doi.org/10.1016/s0142-694x(02)00034-0
Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
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.
