The Impact of Testimonials on Purchase Intentions in a Mock E-commerce Web Site
Bibliographic record
Abstract
Purchasing through virtual market is different from the process that takes place in the traditional market. In this market, things are less tangible and more threatening. Therefore, trust becomes crucial and it is established in a different way. This study examined the effect of testimonials on the level of trust in e-commerce. It also examined the impact of product touch level and price on the effect of testimonials. Two mock e-commerce sites were used, one with testimonials and the other without. The experimental approach simulated a complete shopping process with students whose age was between 21 and 30, on a fully functional website, with subjective and objective behavioral measures. The subjective measures were based on two questions that participants were asked along the experiment. The objective measures consisted of metrics such as navigation patterns in the site, number of products in the shopping cart, and readiness to enter credit card number. The presence of testimonials had a greater impact on users with little internet-based shopping experience, was associated with increased trust, and was more significantly pronounced for price than for product touch level. In addition, the results showed that a decreased level of trust was associated with higher prices. The impact of testimonials is accounted for in terms of history sharing and building an online community.
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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 | 0.003 | 0.062 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".