Determinants of European telecom operators' capital structure
Bibliographic record
Abstract
In recent years, the telecommunications services sector has made a remarkable contribution to the global economy, thereby attracting the interest of researchers. This study aims to examine the relationship between total leverage and its main components (short-term and longterm leverage) and firm-specific and country-specific factors affecting the capital structure of European telecom operators during 2009-2020. The observed period, beginning right after the world economic crisis in 2008, was characterized by a stable economy and the expansion of mobile communications, and the Internet and multimedia services. We used dynamic panel regression models with 9 explanatory and three dependent variables and concluded that liquidity, profitability, sales growth, assets turnover, cost of debt, and non-debt tax shield had a significant influence on the capital structure of European telecom operators. We found that total leverage and long-term leverage significantly depend on their previous year's values. Tangibility, size of firm, and country GDP growth rate were not significantly associated with the capital structure of telecom operators within the observed period. The findings about a dominant negative impact of liquidity and profitability, and the positive impact of sales growth on leverage, are in line with the postulations of the pecking-order theory. This study can be helpful to managers and other stakeholders in improving their understanding of the factors affecting the capital structure of telecom operators.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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 | 0.001 |
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 teacher head, 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".