Portugal’s Crowdfunding: A Systematic Literature Review
Bibliographic record
Abstract
This study aims to analyze and classify the evolution of crowdfunding in Portugal from 2014 to 2020, addressing the central question, “What is the evolution of literature on crowdfunding and its research focuses in Portugal?”. Additionally, it investigates, through the sub-question, if crowdfunding is perceived as an alternative form of financing. The methodology employs a systematic review, covering four thematic areas: (1) research focus—concepts; (2) research method—quantitative/qualitative identification; (3) geographical area—countries of study; (4) innovation—future research areas. The research begins with Google Scholar, followed by a more specific search of the B-On database, focusing on the Portuguese context. Results highlight the scarcity of research in Portugal, emphasizing the nascency of crowdfunding in the country. The study reveals the importance of investor behavior, influenced by platform security and regulations. Growth in crowdfunding in Portugal is anticipated, attracting multidisciplinary interest but emphasizing the need for more comprehensive studies. Despite limitations in data availability, the study provides valuable insights for entrepreneurs seeking alternative financing in Portugal, demonstrating crowdfunding as an alternative financing method. Integration of crowdfunding with technology, especially blockchain, is suggested as a potentially disruptive system, paving the way for future research and innovations.
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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 | 0.015 | 0.052 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.004 |
| Bibliometrics | 0.036 | 0.025 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".