Crowdfunding: An Exploratory Study on Knowledge, Benefits and Barriers Perceived by Young Potential Entrepreneurs
Bibliographic record
Abstract
Crowdfunding (CF) has experienced impressive growth in recent years with the development of internet and information technologies that increased the participation of the “crowd” to fund entrepreneurial projects. Young entrepreneurs, especially well-qualified students, have recently begun to play a new role in the economy by launching new ventures in niche markets. The aim of the present paper is to provide a deeper understanding of CF among Portuguese young potential entrepreneurs as an alternative funding mechanism, by discussing its main characteristics and the perceived benefits and barriers that might drive young entrepreneurs to post a project on a CF platform or discourage its use. Through an online survey, we query well-qualified students about the knowledge they have about crowdfunding and benefits and barriers that can increase or reduce the possibility of funding to launch a new venture. The results show that potential young entrepreneurs have moderate knowledge about CF. Consequently, they are not able to explore all the business models available, specifically the models related to investment (lending and equity). The respondents perceive several benefits of the use of CF that go beyond the financial advantages, such as the communication of the project to a wider audience and the additional feedback from potential customers. The perceived barriers that could deter the use of CF are related to the implementation of the CF campaign, although contextual constraints have been mentioned.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".