Exploring the Landscape of UX Subjective Evaluation Tools and UX Dimensions: A Systematic Literature Review (2010–2021)
Bibliographic record
Abstract
Abstract The quality of the User Experience (UX) with systems, products and services is now considered an indispensable part of success in the market. Users' expectations have increased in such a way that mere usability is no longer sufficient. While numerous UX subjective evaluation tools exist, there is little guidance on how to select or use these tools. Therefore, there is a need to provide a critical state of the art on the topic of subjective evaluation tools and the UX dimensions covered. In this study, we conducted a systematic literature review on UX subjective evaluation tools and the UX dimensions covering the period of 2010–2021 with an initial sample of 3831 publications, 325 of which were selected for the final analysis, to provide researchers and practitioners with the recent changes in the field of UX. Results showed that 104 different tools are available for UX evaluation, they can be classified as general or domain-specific, applicable for a wide variety of products and in total covering more than 300 UX dimensions. Our categorization of UX dimensions under 13 main dimensions (e.g. usability, utility, hedonic, emotion, sensory, etc.) showed that the informational, social, cognitive and physical dimensions appeared to be less frequently present in current tools. We argue that these four dimensions deserve more space in UX tools. Having a high number of UX evaluation tools can be confusing for evaluators, and they need some guidance for selecting and combining tools. Modularity is the emerging trend in the development of UX evaluation questionnaires (e.g. meCUE, UEQ+), bringing the benefits of being thorough, flexible, easy to use, low-cost and rapid, while avoiding overlapping of dimensions and providing comparability through the use of a similar format and rating scale. Finally, the need for having a comprehensive evaluation tool requires updating the set of included dimensions to accommodate for new generations of products and technologies.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.091 | 0.219 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.005 |
| Bibliometrics | 0.056 | 0.038 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.006 | 0.008 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".