Bibliographic record
Abstract
Aims: This paper aims at highlighting the importance of e-commerce adoption in Iran and refers to its technical, social-cultural and managerial implementation challenges, and by providing a ranking for each dimension of these challenges, wants to know solving or at least minimizing the adverse effects of which one has the most influenceon implementation of e-commerce in Iran.Study design:Quantitative research design.Place and Duration of Study: Iran, in 2013.Methodology: In this research system dynamics approach was used and the necessary data collected from previous researches, then analyzed by VENSIM software.Since the most of projects in Iran are short-term projects, a 5 year interval used for data analysis with the change rate of 20%.Because 20% is the lowest rate that best represents the effects of applied changes.Results: Data analysis showed that reducing every dimension of each challenge by 20% will have a great effect on the implementation of e-commerce in Iran.Conclusion:The research findings revealed that the shortage of internet service providers (as a technical challenge), Officials and decision makers' lack of familiarity with the structure and function of e-commerce (as a social-cultural challenge) and lack of strategic management (as a managerial challenge) are the most important implementation challenges of e-commerce in Iran
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.003 | 0.001 |
| Insufficient payload (model declined to judge) | 0.948 | 0.929 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".