Determinants oOf Trust in B2c E-Commerce and their Relationship with Consumer Online Trust: A Case of Ekaterinburg, Russian Federation
Bibliographic record
Abstract
Previous research has established that online shopping is the function of a series of consumer evaluation and assessment of e-commerce sites. However, building and maintaining trust in the virtual environment is indeed a complex process and by extension takes time to fully achieve it. This takes the form of assessing the technology as well as the trustworthiness of the vendor in delivering its promises to the customers The aim of this research was to investigate specific determinants or factors that influence consumer online trust in the B2C e-commerce with a focus on Ekaterinburg, Russian Federation consumers. In other words, constructs such as perceived security, perceived privacy, perceived third party assurance, perceived reputation, perceived familiarity and perceived website quality and their relationship with online trust in the B2C context were studied. In conducting the research, a convenience sampling technique was adopted in carrying out the survey. Questionnaires were distributed to the target respondents and the data was analyzed using SPSS version 24. A PearsonâÂÂs correlation was used to test the six hypotheses identified in this study. Meanwhile, the results showed that five of the hypotheses were statistically significant with p 0.05, making us to reject the hypothesis as it was not statistically significant. Furthermore, a multiple regression analysis was also conducted in order to ascertain which of the constructs have a major influence with reference to consumer online trust. The results provided evidence that perceived security has the greatest influence on online trust for EkaterinburgâÂÂs consumer. This was followed closely by perceived reputation and finally perceived privacy. This clearly shows that EkaterinburgâÂÂs e-commerce consumer population considers these factors to be very imperative in engendering their trust in the virtual B2C e-commerce environment. These findings complement previous research findings in the domain of e-commerce trust. Keywords : E-commerce trust; B2C e-commerce; Perceived security; Perceived reputati
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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 | 0.002 | 0.000 |
| 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 | 0.001 | 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".