A Conceptual Framework of Iranian Consumer Trust in B2C Electronic Commerce
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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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.007 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".