The Interplay of Aesthetics, Usability and Credibility in Mobile Website Design and the Effect of Gender
Bibliographic record
Abstract
In human-computer interaction, aesthetics, usability and credibility are key factors in the design of a successful website. Specifically, aesthetics has been identified as one of the main drivers of web credibility. However, in the mobile domain, large-scale research, cutting across cultures and continents, which is key to the generalizability of findings, is scarce. To bridge this gap, we conducted a multicultural study among 526 participants from 5 continents: Africa, Asia, North America, South America and Europe. Using four systematically designed mobile websites, we investigated: (1) the interrelationships among aesthetics, usability and credibility; and (2) the moderating effect of gender. Our results, based on partial least square path modeling, reveal that: (1) perceived aesthetics is stronger than perceived usability in predicting the perceived credibility of mobile websites; and (2) gender moderates the effect of perceived aesthetics on perceived usability, with this effect being stronger for males than for females. Our findings underscore the need for designers to pay closer attention to aesthetics in designing successful mobile websites, as their visual appeal, irrespective of gender, enhances their perceived ease of use and credibility. These findings are noteworthy because, given the usability challenges posed by the relatively small-screen size of the mobile device, designers may be tempted to focus on designing easy-to-use websites only, while downplaying their visual appeal. Such a decision may adversely impact the overall credibility of their websites going by users’ first impression.
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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 | 0.007 | 0.045 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".