Identifying the Importance of UX Dimensions for Different Software Product Categories
Bibliographic record
Abstract
Abstract Billions of users around the world use mobile applications and computer software to achieve their professional and personal goals. This situation drives User Experience (UX) researchers and practitioners to assess the importance of UX dimensions across different products, to facilitate the design, development and evaluation of new products. To that end, this study surveyed a group of 200 end users and 8 UX experts from Canada to document the importance of 21 UX dimensions for 15 software product categories. The results confirmed that the importance of UX dimensions varies between product categories. Comparing the findings to those of similar studies conducted in Germany and Indonesia revealed that, while culture influences the rating of UX dimensions, the importance of UX dimensions is still determined by the product category. Comparisons between the importance ratings of UX dimensions between end users and experts and within end users were not significant in 77% and 97% of cases, respectively. Results showed that task-based product categories rely more on pragmatic dimensions (i.e. functionality and usability) while leisure-based products value hedonic dimensions (i.e. pleasure) as well. This study benefits researchers and practitioners by enabling them to select the most important UX dimensions for evaluating their products. CCS CONCEPTS: • Human-centered computing • Human-Computer Interaction (HCI) • HCI design and evaluation methods. Additional Keywords and Phrases: User experience, UX dimension, UX evaluation, culture.
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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 | 0.009 | 0.043 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".