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The sample size of 10 was chosen in line with prior work around formative testing for usability, aiming to explore first, easy-to-find problems users encounter.
“Our sample size was chosen in line with prior work around formative testing for usability [19,43]: our goal is to explore the frst,”
From Why Johnny Can’t Prompt: How Non-AI Experts Try (and Fail) to Design LLM Prompts (2023) · cites this paper as [43]
The Sonar, Voice, and Sonoice strategies all received mean SUS scores above 80.
“The other three strategies all received an average score not only above the average (68) but above 80, which is considered a high score by past research (78, 79).”
From SONOICE! a Sonar–Voice dynamic user interface for assisting individuals with blindness and visual impairment in pinpointing elements in 2D tactile readers (2024) · cites this paper as [79]
Cites
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Handbook of Usability Testing: How to Plan, Design, and Conduct Effective Tests
Rubin, J. N. (1994) · Cited by 1,750 · see citing works
Perspectives of Curriculum Evaluation
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Handbook of Human Factors and Ergonomics
Salvendy, G. (2012) · Wiley eBooks · doi:10.1002/9781118131350 · Cited by 758 · see citing works
A Practical Guide to Usability Testing
Hass, C. (2019) · Springer eBooks · doi:10.1007/978-3-319-96906-0_6 · Cited by 666 · see citing works
A method to standardize usability metrics into a single score
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Self-citation(2008) · doi:10.1016/b978-0-12-373558-4.x0001-5 · Cited by 287 · see citing works
Correlations among prototypical usability metrics
Sauro, J., & Lewis, J. R. (2009) · CHI Conference on Human Factors in Computing Systems · doi:10.1145/1518701.1518947 · Cited by 221 · see citing works
Self-citationQualitative Market Research: A Practitioner's and Buyer's Guide
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The Ultimate Question: Driving Good Profits and True Growth
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Individual differences in human-computer interaction
Aykin, N. M., & Aykin, T. (1991) · Computers & Industrial Engineering · doi:10.1016/0360-8352(91)90009-u · Cited by 40 · see citing works
Handbook of Global User Research
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Applied Marketing and Social Research
Bradley, U. (1987) · Cited by 12 · see citing works
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Why Johnny Can’t Prompt: How Non-AI Experts Try (and Fail) to Design LLM Prompts
Zamfirescu-Pereira, J. D., Wong, R. Y., Hartmann, B., & Yang, Q. (2023) · Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems · doi:10.1145/3544548.3581388 · Cited by 893 · see citing works
Evaluating Large Language Models in Generating Synthetic HCI Research Data: a Case Study
Hämäläinen, P., Tavast, M., & Kunnari, A. (2023) · Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems · doi:10.1145/3544548.3580688 · Cited by 248 · see citing works
Mol, M., van Schaik, A., Dozeman, E., Ruwaard, J., Vis, C., Ebert, D. D., Etzelmueller, A., Mathiasen, K., Moles, B., Mora, T., et al. (2020) · BMC Psychiatry · doi:10.1186/s12888-020-02627-8 · Cited by 92 · see citing works
Shi, Y., Du, J., & Worthy, D. A. (2020) · Automation in Construction · doi:10.1016/j.autcon.2020.103367 · Cited by 82 · see citing works
Mol, M., van Genugten, C., Dozeman, E., van Schaik, D. J. F., Draisma, S., Riper, H., & Smit, J. H. (2019) · Journal of Clinical Medicine · doi:10.3390/jcm9010091 · Cited by 61 · see citing works
Patel, T., Ivo, J., Faisal, S., McDougall, A., Carducci, J., Pritchard, S., & Chang, F. (2020) · Journal of Medical Internet Research · doi:10.2196/18073 · Cited by 49 · see citing works
AntiFake: Using Adversarial Audio to Prevent Unauthorized Speech Synthesis
Yu, Z., Zhai, S., & Zhang, N. (2023) · Proceedings of the 2023 ACM SIGSAC Conference on Computer and Communications Security · doi:10.1145/3576915.3623209 · Cited by 44 · see citing works
Brüggemeier, B., Breiter, M., Kurz, M., & Schiwy, J. (2020) · Proceedings of the 2nd Conference on Conversational User Interfaces · doi:10.1145/3405755.3406122 · Cited by 12 · see citing works
Kim, J. W., Wang, K., Dorneich, M. C., Winer, E., Brown, L. J., & Whitehurst, G. (2026) · IEEE Transactions on Human-Machine Systems · doi:10.1109/thms.2026.3667033
- Authors
- Sauro, J., & Lewis, J. R.
- ORCID
- Lewis, James R.
- Venue
- Quantifying the User Experience
- Publisher
- Elsevier
- Published
- 2016
- Pages
- 9-18
- OpenAlex
- W3023945813
- Cited by
- 312
- References
- 13
- Citations by year
- 2026: 66 · 2025: 55 · 2024: 47 · 2023: 48 · 2022: 32 · 2021: 18 · 2020: 15 · 2019: 18 · 2018: 8 · 2017: 4 · 2016: 1
- FWCI
- 22.91
- Type
- book chapter
- Language
- en
- Open access
- closed
- Locations
- Elsevier eBooks (publishedVersion)
- Topics
- Physical Sciences › Computer Science › Human-Computer Interaction › Usability and User Interface Design
- Physical Sciences › Computer Science › Human-Computer Interaction › Innovative Human-Technology Interaction
- Physical Sciences › Computer Science › Human-Computer Interaction › Persona Design and Applications
- Keywords
- user research, usability testing, quantitative analysis, usability metrics, task completion rate, task completion time, user satisfaction, user interface problems
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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