Bibliographic record
Abstract
The continued rise in SMEs’ corruption-related activities results in uncertainty around their ability to sustainably contribute to economic growth, leaving SMEs financially fragile and exposed to problems associated with trade credit management resulting in business exits. Given that little research has been conducted on how corruption affects smaller businesses while corruption’s impact on SMEs’ trade credit management effectiveness remains largely unexamined, the study aims to determine the impact of corruption on SMEs’ trade credit management effectiveness. By addressing this unanswered research gap, SMEs could be better equipped to understand how corruption affects their trade credit management in support of their overall finances. The study employed a quantitative research design with purposive sampling using a survey by administrating 10450 online questionnaires tested by a sample of 450 SMEs across South Africa. The result aligns with expectations around corruption being detrimental to SMEs’ trade credit management effectiveness while also indicating, unexpectedly, SMEs’ willingness to partake in corruption, given that SMEs benefit from increased effectiveness in managing trade credit. The study adds to the existing literature on corruption and SMEs’ trade credit management while also providing anti-corruption recommendations to SMEs that are dependent on trade credit. In so doing, SMEs could be better equipped to understand how corruption affects their trade credit management to support their overall finances contributing to improved SME creation rates and fostering entrepreneurship as a pivotal mechanism for improving South Africa’s sustainable development goals.
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 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 | 0.004 | 0.024 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".