A conceptual model for the internet's impact on marketing in Iran
Bibliographic record
Abstract
Purpose The potential impact of the internet on marketing is a common topic of discussion in marketing literature. However, there is still much debate on exactly what impact it will have on developing countries. To perceive this impact world‐wide, it is essential to spread some light to the less investigated parts of the globe as well. This study aims to reach a better conceptual model for the internet's impact on marketing in Iran, by examining Iranian marketing managers' perceptions of the internet's impact on their key marketing activities and comparing the results with other parts of the world. Design/methodology/approach The paper employs a cross‐sectional research design involving self‐administered delivery and collection surveys to Iranian marketing decision makers. Findings The findings suggest that the impact of these new media will be to cause firms to redefine markets, marketing activity and value creation, although these changes are not dramatic. Research limitations/implications The findings reported here are snapshots in time. The internet's rapid evolution and growth demand that regular tracking with longitudinal designs be implemented. Practical implications Firms need to make changes in the marketing activities and market definitions and to try to reach the new ways that are created through the internet. Originality/value A modified model for the world wide web marketing that seems to be more realistic for developing countries is presented.
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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) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".