A Conceptual Framework of Iranian Consumer Trust in B2C Electronic Commerce
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
B2C in the developing countries is not yet a normalcy as compared to the developed countries. In this paper, we attempt to improve trust of B2C in Iran. A number of hypotheses are outlined to prove the theories that could improve the trust. A set of questionnaires was designed to reflect hence test the hypotheses. Various related factors are tested in the collective Iranian culture. From the survey, it was found that recommendations by close friends and families are known as an influencing factor on reputation because of the collective culture. In addition, the type of payment is illustrated as an influencing factor on trust as well. Based on the findings, a refined model of Iran Trust Model (ITM) is derived. The model considers the antecedents and the consequences of trust in Iran. A prototype was implemented and tested, in which the prototype – in the form of an e-commerce website that was developed adhering to the model, for a number of weeks. This study examines antecedents and consequences of trust in Iran. Type of payment and reputation are known as the antecedents that related positively to trust. Trust has a negative relationship with risk and a positive relationship with attitude.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.006 |
| Open science | 0.000 | 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 it