Intellectual capital in Serbia’s hotel industry
Bibliographic record
Abstract
Purpose – The purpose of this study is to determine whether intellectual capital (IC) creates value in the Serbian hotel industry. Specifically, this paper examines to what degree IC and its key components affect the financial performance of hotels compared to physical and financial capital. Design/methodology/approach – The sample included all of the hotels that operated as independent entities in Serbia during 2009–2012. value-added intellectual coefficient was used to measure the level of IC contribution to value creation, which was linked to various measures of financial performance, including operating profit, return on equity, return on assets, profitability and employee productivity. Findings – Results indicate that after controlling for firm size and leverage, employee productivity and, to some extent, profitability were affected by human and structural capital. The research confirms that the financial performance of hotels in Serbia remains predominantly influenced by efficient use of physical capital. Research limitations/implications – The study’s generalizability is limited to the hotel sector within Serbia. Practical implications – Senior managers in the hotel industry must recognize the importance of managing both the physical aspects of their hotels and the intangible resources embedded in their employees and processes. Originality/value – The findings will aid recognition of the importance of investing in IC in hotel industry as a crucial element of achieving competitive advantage in the information age. Moreover, the findings suggest that long-term growth should not rely solely on physical and financial assets.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| 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".