A Bibliometric Analysis of Borrowers’ Behavior
Bibliographic record
Abstract
Understanding borrowers’ behavior is essential in making lending decisions, strengthening financial inclusion, and alleviating poverty. This research adopts a bibliometric approach to provide an overview of the borrower’s behavior relative to the selected literature. Bibliometric analysis quantifies the impact and quality of scientific production. This study reviewed 989 articles obtained from SCOPUS and published from 1987 to 2023. Data were cleaned, formatted, and analyzed using VOS viewer (1.6.19) and the R-Bibliometrix package. The research established an increased interest in borrowers’ behavior among scholars. Nonetheless, it is overshadowed by studies in lending behavior, microfinance, banking, peer-to-peer lending, and fintech. The scholarly focus is mainly on the supply side of the credit industry with little regard to demand-side dynamics, such as borrowers’ decision-making processes, which can affect the performance of credit facilities. This study recommends that further studies on credit facility demand-side dynamics should be carried out to understand the drivers of borrowers’ decisions.
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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 | 0.077 | 0.095 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| 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; 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".