Intellectual Capital and Financial Performance in Serbia
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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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 | 0.001 |
| 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) | 0.001 | 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 it