Identifying and Prioritizing the Contributory Factors to the Early Internationalization of International New Ventures in Halal Food Industry
Bibliographic record
Abstract
This study seeks to identify and prioritize the determining factors in the early internationalization of international new ventures in Halal food industry. In terms of nature and objective, this is an applied research employing quantitative methods. The process consists of two steps. First, the most important factors in the early internationalization of new ventures were identified through an inquiry into previous literature. Then, a questionnaire was devised and distributed among 80 managers and experts working in new Halal food ventures. Subsequently, the collected data were analyzed by PLS and SPSS software. Results indicate that factors such as the managerial characteristics, the company features, the network, the industry features, technology, company resources, country features, advertisement and the knowledge as well as Halal brands (form the participants’ perspective) have respectively contributed to the early internationalization of international new ventures in Halal food industry in Iran.
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How this classification was reachedexpand
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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".