An exploration study to detect important factors influencing internet marketing: A case study of food industry
Bibliographic record
Abstract
Internet marketing plays an important role on profitability of organizations, it can build a bridge between customers and business owners and anyone could purchase products and services through internet.In this paper, we present an empirical investigation to detect important factors influencing internet marketing on Iranian food industry, named Shahrvand.The proposed study selects 280 out of 1040 managers who were involved in this industry during the year of 2012.Structural equation modeling has been performed to detect important factors including internal/external factors, ease of use and electronic marketing.Cronbach alphas have been calculated for these four items were mostly above 0.80, which validated the overall questionnaire of the survey.The results indicate that among internal factors, knowledge management, organizational culture and resources influence on acceptance of internet marketing, while these factors do not show any meaningful impact on ease of use.In addition, external factors including trend on market growth, competition and infrastructure influence on ease of use and acceptance of internet marketing but infrastructure and competition do not impact on ease of internet marketing.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".