Déterminants de la confiance du consommateur vis-à-vis d’un marchand Internet non familier : une approche par le rôle des tiers
Bibliographic record
Abstract
Lack of trust has been identified as the major barrier to the growth of electronic commerce. Building trust is essential for the success of web vendors, especially less-known ones. If trust is a sine qua non condition of e-commerce development, then understanding its determinants should be a primary concern for web merchants. This research explores trust within the context of business-to-consumer e-commerce. More specifically, we focus on the role of third parties in building trust. Much recent research has been concerned with web merchant characteristics, web site characteristics or consumer’s characteristics. Third parties could be, however, key elements in trust development, particularly when the consumer is not familiar with the web merchant. The focus of this research is on three potential signals of a web merchant’s trustworthiness: trust seals (awarded by a third-party organization), testimonies from satisfied past buyers (selected by the web merchant and displayed on the web site) and partnership with a well-known web vendor. An experiment was set up to test the hypothesis. For this purpose, a fictive travel web site was created. Data collected on 304 consumers. Results show that: - Trust seals and testimonies have a positive impact on consumer’s trust - Internet perceived risk is a moderator of the impact of the presence of testimonies on consumer’s trust, - Trust is a determinant of the intention to buy on the web site, the intention to recommend the web site and the intention to visit the web site again.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.007 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 0.001 |
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".