Measuring the Efficiency of Turkish SMEs: A Data Envelopment Analysis Approach
Bibliographic record
Abstract
<p>Small and medium-sized enterprises (SMEs) that are undertaking significant roles in the development of the economy of Turkey and in the increase of the production and employment encounter with many problems. Financial problems take an important place among them. The resources that could be used by SMEs in meeting the financing needs to be limited require the efficient use of these resources. In this study, the resource activities of SMEs have been studied with Data Envelopment Analysis (DEA). In analysis short-term liabilities, long-term liabilities and equity values of the enterprises that are quoted continuously on SME Industrial Index within 2011-2014 have been used as input variables; and the sales revenue and net profit values have been used as output variables. The total efficiency values of each decision making unit have been attained with the use of CCR model according to the years, technical efficiency values of them have been attained with the use of BCC model and the scale efficiency values of them have been attained by comparing these values to each other. As a result, it has been determined that those providing resource efficiency are only a few among the enterprises that are proceeded in the BIST SME Industrial Index; and these enterprises could reach their existent sales revenue and net profit numbers with less resources. In this respect it has been revealed that the SMEs that have problems in providing credit and not having strong equity structure are not able to make use of their own resources efficiently.</p>
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".