A survey on how different factors impact entrepreneurs' success in food industry
Bibliographic record
Abstract
In this paper, we present an empirical study to detect important factors influencing the success of entrepreneurs who were active in food industry in Tehran, Iran. The proposed study selects a sample of 174 people out of 318 entrepreneurs who were involved in this industry and distributed a questionnaire, which consists of two groups of questions among them. The first group of questions is associated with personal characteristics of the survey people and the second group of questions are related to different financial, infrastructure and supply chain management categories. The study defines entrepreneurs' mental desirability of success in terms of 15 different questions and asks them to provide their insights in terms of five Likert based responses. The results of questions are analyzed using Pearson correlation test and the preliminary results indicate that, among personal characteristics, education and age play important roles on the success of a business plan. The other observation is that the easier entrepreneurs can get loans and financial support, the higher abilities to absorb new customers and the higher chance of success for absorbing new financial resources. Distributions of sales, compared with competing products as well as distribution of after sales service for customers are negatively associated with infrastructures. The rate of success in using new technologies and supply chain management are correlated, which means the better supply chain, the better achievement to information technology.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| 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".