The Impact of Intangible Assets and R&D Expenditure on the Market Capitalization and EBITDA of Selected ICT Sector Enterprises in the European Union
Bibliographic record
Abstract
In the present paper, using the panel regression model with fixed effects, it was verified whether there is a relationship between intangible assets and R&D expenses on one side and the EBITDA level and market capitalization of 222 publicly listed companies from the Information and Communication Technology sector. Our research confirmed that in the group of companies from the ICT sector there is a medium of dependence of EBITDA on intangible assets and R&D expenditure. At the same time, at the level of the entire ICT sector, no relationship was found between the level of intangible assets and expenditures on research and development on the one hand, and the level of market capitalization of companies on the other. A similar lack of dependence was recorded in the ICT services and ICT manufacturing subsectors. In addition, there was a high correlation at the level of 74% between the level of intangible assets and R&D expenditure on the one hand and the EBITDA level obtained by companies from the ICT manufacturing sub-sector and the lack of such correlation in the ICT services sub-sector. Our research suggests that, in particular, the financial results of companies in the ICT sector may directly depend on the amount of expenses that companies incur in order to introduce new innovative solutions into their market offer.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".