A Comprehensive Framework to Evaluate Websites: Literature Review and Development of GoodWeb
Bibliographic record
Abstract
BACKGROUND: Attention is turning toward increasing the quality of websites and quality evaluation to attract new users and retain existing users. OBJECTIVE: This scoping study aimed to review and define existing worldwide methodologies and techniques to evaluate websites and provide a framework of appropriate website attributes that could be applied to any future website evaluations. METHODS: We systematically searched electronic databases and gray literature for studies of website evaluation. The results were exported to EndNote software, duplicates were removed, and eligible studies were identified. The results have been presented in narrative form. RESULTS: A total of 69 studies met the inclusion criteria. The extracted data included type of website, aim or purpose of the study, study populations (users and experts), sample size, setting (controlled environment and remotely assessed), website attributes evaluated, process of methodology, and process of analysis. Methods of evaluation varied and included questionnaires, observed website browsing, interviews or focus groups, and Web usage analysis. Evaluations using both users and experts and controlled and remote settings are represented. Website attributes that were examined included usability or ease of use, content, design criteria, functionality, appearance, interactivity, satisfaction, and loyalty. Website evaluation methods should be tailored to the needs of specific websites and individual aims of evaluations. GoodWeb, a website evaluation guide, has been presented with a case scenario. CONCLUSIONS: This scoping study supports the open debate of defining the quality of websites, and there are numerous approaches and models to evaluate it. However, as this study provides a framework of the existing literature of website evaluation, it presents a guide of options for evaluating websites, including which attributes to analyze and options for appropriate methods.
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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 | 0.129 | 0.157 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.005 | 0.006 |
| Bibliometrics | 0.067 | 0.048 |
| Science and technology studies | 0.006 | 0.008 |
| Scholarly communication | 0.018 | 0.026 |
| Open science | 0.005 | 0.012 |
| Research integrity | 0.005 | 0.005 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".