The Influence of Personality on Mobile Web Credibility
Bibliographic record
Abstract
Research has shown that the perceived credibility of a website is critical to its success. However, little is known about how individual differences influence this important factor of web design. In this paper, we investigate how personality traits affect the perceived credibility of a website in the mobile domain. Using a sample of 323 participants, we developed a model showing how the Big Five personality traits influence the perceived credibility of a website through its perceived aesthetics and perceived usability. Our model reveals that Agreeableness is the strongest predictor of aesthetics and/or usability, followed by Conscientiousness. This suggests that the more agreeable and/or the more conscientious users are easily more satisfied aesthetically and usability-wise by a mobile websites than the less agreeable and/or the less conscientious users respectively. Consequently, designers of mobile sites may have to do more in user interface design in order to attract the less agreeable and/or the less conscientious users to their sites based on its hedonic (aesthetics-inspired) and utilitarian (usability-inspired) appeal.
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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 | 0.002 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".