Prototyping dynamics: sharing multiple designs improves exploration, group rapport, and results
Abstract
Prototypes ground group communication and facilitate decision making. However, overly investing in a single design idea can lead to fixation and impede the collaborative process. Does sharing multiple designs improve collaboration? In a study, participants created advertisements individually and then met with a partner. In the Share Multiple condition, participants designed and shared three ads. In the Share Best condition, participants designed three ads and selected one to share. In the Share One condition, participants designed and shared one ad. Sharing multiple designs improved outcome, exploration, sharing, and group rapport. These participants integrated more of their partner's ideas into their own subsequent designs, explored a more divergent set of ideas, and provided more productive critiques of their partner's designs. Furthermore, their ads were rated more highly and garnered a higher click-through rate when hosted online.
Integrity signals
Registry
Cites
Statistics
Claims the paper makes
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
Subjective ratings may not fully capture objective performance metrics, such as actual click-through rates on social media platforms.
“Although subjective ratings are common proxies for real-world design effectiveness [26, 50], they may not fully capture objective performance metrics, such as actual click-through rates on social media platforms.”
From Decoupling of Usefulness and Novelty: Evaluating the Impact of Generative AI on Design Outputs and Novice Designers' Creative Thinking (2026) · cites this paper as [26]
The citation lists haven’t been fetched yet
References 67 · Cited by 138
- Authors
- Dow, S., Fortuna, J., Schwartz, D., Altringer, B., Schwartz, D., & Klemmer, S.
- Affiliations
- Stanford University (US)
- Harvard University (US)
- Venue
- Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
- Publisher
- ACM
- Published
- 2011-05-07
- Pages
- 2807-2816
- OpenAlex
- W2101658911
- Cited by
- 138
- References
- 67
- Citations by year
- 2026: 3 · 2025: 7 · 2024: 5 · 2023: 10 · 2022: 9 · 2021: 11 · 2020: 10 · 2019: 7 · 2018: 5 · 2017: 13 · 2016: 9 · 2015: 16 · 2014: 11 · 2013: 15 · 2012: 3
- FWCI
- 29.00
- Type
- conference paper
- Language
- en
- Open access
- closed
- Locations
- CHI Conference on Human Factors in Computing Systems (publishedVersion)
- Topics
- Physical Sciences › Engineering › Mechanical Engineering › Design Education and Practice
- Social Sciences › Psychology › Social Psychology › Team Dynamics and Performance
- Physical Sciences › Computer Science › Human-Computer Interaction › Innovative Human-Technology Interaction
- Keywords
- prototype development, group discussion, decision-making, design fixation, co-design, divergent thinking, design criticism, advertising design, click-through rate
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 ›
Are you an author, or do you see a problem with this page? Request a review