Ürün çeşitliliği ve karlılık durum çalışması: Vestel Tic. A.Ş.
Bibliographic record
Abstract
The aim of this study is to examine the effect of diversification on the profitability of a firm. To this end, Vestel Co. is examined by conducting an econometric analysis with panel data gathered from different departments of the firm. Importance and the most significant contribution of this study to the literature is that it analyzes a single firm in contrast to the studies including lots of firms operating in the same sector; and this allows us to examine the effect of diversification over time, through the firm’s lifetime. Data used in this study have been compiled from different sources within Vestel: Budget and Planning, Research and Strategic Analysis, Finance, and Law Departments starting from the first quarter of 1994 to second quarter of 2014. As the results show, there exists u-shape relationship between diversification and the firm’s profitability; i.e., with an increase in the level of diversification profitability also increases in the long run in case of related diversification. Although, the effect of intangible assets is negative on profitability in the short-run, its effect reverse and turn to positive in the long run. The most important result of this study is that, with related diversification, the firm gains profitability and enjoys its intangible assets in the long run.
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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 | 0.001 | 0.001 |
| 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 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.011 | 0.003 |
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".