What Do We Know about Crowdfunding and P2P Lending Research? A Bibliometric Review and Meta-Analysis
Bibliographic record
Abstract
In the era of fintech, businesses using technology other than traditional banks are providing financial services. Crowdfunding and peer-to-peer (P2P) lending are two of the most exciting financial innovations of the twenty-first century. In this paper, we use a bibliometric review and meta-analysis to understand the academic research on crowdfunding and P2P lending. Our findings show that the research on this topic has grown a lot in terms of publications since 2013 and the maximum mean total citations were observed in the year 2014. We provide the details about the most influential authors based on total citations, authors with the greatest number of publications, the most influential documents, significant journal sources, highest single country production, multiple country production, and important affiliations. We further apply the network analysis and visualisation techniques wherein we provide the details of the citation analysis of documents, co-citation analysis of authors, and co-occurrence analysis of author keywords. Finally, we provide the future directions of the research on this burgeoning topic.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.008 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.002 |
| Bibliometrics | 0.053 | 0.057 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.002 |
| 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; both teacher heads agree on what is shown here.
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".