The Effect of Layout and Colour Temperature on the Perception of Tourism Websites for Mobile Devices
Bibliographic record
Abstract
In e-commerce, the user interface design of a website is critical to its success. However, there is limited research on how colour and layout design elements influence the perception of e-commerce websites for mobile devices. To bridge this gap, we conducted an empirical study to investigate, how the layout of information and colour temperature of an e-commerce tourism website for mobile device influence essential Technology Acceptance Model (TAM) user experience (UX) design attributes and intention to use the website. The results of our Partial Least Square Path Modelling (PLSPM) showed that both interface design elements significantly influence perceived aesthetics, perceived enjoyment, perceived usefulness and intention to use. Specifically, layout (list = 0 and grid = 1) positively influences perceived aesthetics and perceived enjoyment, while colour temperature negatively influences perceived usefulness and intention to use. The first finding suggests that in tourism website design for mobile devices, a grid layout of products and services provides a better hedonic user experience than a list layout. Moreover, the second finding suggests that cooler-temperature (blue and green) tourism websites are viewed by users as more useful than warmer-temperature (orange and red) tourism websites. We discuss the implications of these findings in the context of website UX design for mobile devices in the tourism domain.
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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 | 0.001 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".