Entrepreneurship and Crowdfunding in Lebanon: ABC Model of Attitude
Bibliographic record
Abstract
In light of insufficient traditional methods of funding of entrepreneurial projects and ventures in Lebanon, crowdfunding is proposed as an alternative funding mechanism. Crowdfunding is a way for entrepreneurs to fund their projects via dedicated online platforms where the needed capital is accumulated from small contributions from a large number of interested individuals: the crowd. Since the people are at the center, it is vital to address their attitudes toward participating as funders. For this aim, the researcher derives from previous studies and the present literature a conceptual framework that proposes to explain what impacts attitudes toward participating as funders in crowdfunding. The model is tested via a questionnaire targeted to 457 workers located in the four main regions of Lebanon. Results of the statistical analysis confirmed the proposed model and confirmed 3 out of 4 of the hypotheses pertaining to the variables (enthusiasm, trust, and experience) and gave insights concerning awareness, understanding, and attitudes regarding crowdfunding in Lebanon. The study provides recommendations for improvement of the status of crowdfunding in Lebanon for the benefit of entrepreneurs, SMEs, and the economy.
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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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".