Consumer trust in e-commerce web sites
Bibliographic record
Abstract
Trust is at once an elusive, imprecise concept, and a critical attribute that must be engineered into e-commerce systems. Trust conveys a vast number of meanings, and is deeply dependent upon context. The literature on engineering trust into e-commerce systems reflects these ambiguous meanings; there are a large number of articles, but there is as yet no clear theoretical framework for the investigation of trust in e-commerce. E -commerce, however, is predicated on trust; indeed, any e-commerce vendor that fails to establish a trusting relationship with their customers is doomed. There is a very clear need for specific guidance on e-commerce system attributes and business operations that will effectively promote consumer trust. To address this need, we have conducted a meta-study of the empirical literature on trust in e-commerce systems. This area of research is still immature, and hence our meta-analysis is qualitative rather than quantitative. We identify the major theoretical frameworks that have been proposed in the literature, and propose a qualitative model incorporating the various factors that have been empirically found to influence consumer trust in e-commerce. As this model is too complex to be of practical use, we explore subsets of this model that have the strongest support in the literature, and discuss the implications of this model for Web site design. Finally, we outline key conceptual and methodological needs for future work on this topic.
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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 | 0.006 | 0.029 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".