Identifying Factors Influencing Entry Mode Selection in Food Industry of Small and Medium-sized Enterprises (SMEs) in Iran
Bibliographic record
Abstract
As internationalization increases in today’s business society, it becomes ever more important for individual business to keep us with the development. The way a company ventures from its domestic market to new geographical markets and selecting the right entry modes are important decision that demands a lot of resources and planning. In the process of selecting entry modes a wide range of factors must be taken into consideration before making the final decision. To provide a better understanding of the impact of some internal and external factors on Iranian SMEs in food industry we chose a conceptual framework from Root and studied its variables in our sample. This model states that a) target country market factors, b) target country environmental factors c) target country production factors and d) home country factors as external factors and e) country production and f) company resource/commitment factors as internal factors have impact on the process of choosing entry modes. In order to collect data we use questionnaire. Our findings illustrated that all of the factors were mentioned in Root’s model had impact on selecting entry modes to a foreign country.
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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 | 0.004 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".