The influence of supply chain and human competence on improving the efficiency of small businesses in countries with developing economies
Bibliographic record
Abstract
To increase finance performance any small company needs to improve its management in supply chain and human competence (response). The main aim of our work is aimed at identifying universal marketing audit tools that could help small businesses to increase efficiency and profitability. A sample of 144 small businesses through a survey were used for research. The study applied the SmartPls program for finding marketing audit tools. Our research showed that supply chain management and response (reaction to feedback from employees or customers) are main tools of marketing audit to help improve a small business. We found that marketing audit has a positive effect on both current market efficiency and current profitability. Results also show that, despite the direct benefits of marketing audit, many small companies in emerging markets do not adopt audit due to lack of marketing knowledge. This study can be a reference for owners of small businesses in emerging markets. We developed novel tools of marketing audit that could help small businesses. This paper is an original contribution to managerial knowledge.
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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 | 0.002 | 0.010 |
| 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 | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".