Intellectual Capital and Financial Performance in Serbia
Bibliographic record
Abstract
Purpose This research paper explores the impact of intellectual capital (IC) and its various components on financial performance of 100 Serbian companies within the real sector (which includes all companies in the Serbian economy not including banking and insurance). Design/methodology/approach The performance measures used were net profit, operating revenues, operating profit, return on equity (ROE), and return on assets (ROA), whereas IC efficiency was measured using value added intellectual coefficient (VAIC). A multiple‐regression model was used to assess the relationship among individual components of VAIC and financial performance. Findings Net profit, operating revenue, and operating profit are not the consequence of the efficient use of IC in Serbian companies. On the other hand, human and structural capital affect ROE and ROA, whereas physical capital influences ROE. Research limitations/implications VAIC is an accounting measure of performance and therefore does not provide an adequate framework for analyzing synergy between human, structural, and physical capital. In addition, the model fails to offer adequate analysis for those companies that have negative values for equity and operating profit. Practical implications The presented results are especially useful for further research regarding the role and significance of IC for Serbian companies. By focusing on adequate IC management and use, the Serbian economy's competitiveness level would increase. Originality/value This paper is original as no previous empirical work on IC and its effects on financial performance have been carried out among Serbian companies in the real sector. Copyright © 2013 John Wiley & Sons, Ltd.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 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".