Analyzing E-Commerce Websites: A Quali-Quantitive Approach for the User Perceived Web Quality (UPWQ)
Bibliographic record
Abstract
<p>The electronic commerce (e-commerce) is an increasingly important phenomenon and this research deals with perceived quality from the users-customers point of view. The aim of this paper is to shed light on the critical factors determining the User Perceived Web Quality (UPWQ) for e-commerce.</p><p>We use the Pareto Chart as qualitative methodology able to identify the UPWQ considering not only technical features (ease of use, design, smart phone and tablet responsivity, information), but also emotional features such as trust, empathy, free shipping and discount. The Pareto chart main has the advantage to classify the selected features by their relevance.</p><p>We found that emotional features are more relevant than technical ones. Among the features we took into account, three alone (discount, free shipping and ease of use) determine about 70% of the user perceived web quality for e-commerce.</p><p>Our results have significant practical implication for managers involved in online business in order to better allocate resources to the most critical factors.</p>
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.021 | 0.044 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".