Intellectual capital and financial performance in the Serbian ICT industry
Bibliographic record
Abstract
Purpose – The purpose of this paper is to examine whether intellectual capital (IC) creates value in the Serbian information communication technology (ICT) sector. More specifically, it examines the degree to which IC and its key components affect the financial performance of selected ICT companies compared to effects on physical and financial capital. Design/methodology/approach – The analysis included 13,989 Serbian ICT companies during 2009-2013. Value-added intellectual coefficient (VAIC) was used to measure the level of IC contribution to value creation. Measures of financial performance used in the study were return on equity, return on assets, return on invested capital, profitability, and asset turnover. Findings – Results indicate that, when using firm size and leverage as control variables, only capital-employed efficiency has significant effect on financial performance. Finally, the research confirms that there were no significant differences in financial performance among different ICT subsectors. Research limitations/implications – Main research limitation is related to the disadvantages of VAIC as the measure of IC’s contribution to value creation. Practical implications – Owners and managers of Serbian ICT companies must recognize the importance of managing both the physical capital and the intangible resources embedded in their employees and processes. Originality/value – This is the first paper to examine comprehensively the impact of IC on financial performance in the ICT sector in a transitional economy. This study differs from prior studies in that the authors analyzed every company that operated in Serbian ICT sector.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 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".