Factors Influencing Investments into Human Resources to Support Company Performance
Bibliographic record
Abstract
Human resources are very important in a business; however, the return on investment in human resources is longer than in fixed assets, so entrepreneurs frequently consider how much to actually invest. This article, based on primary research, examines the motivations for investment when a 20% profit is typically invested with a model return of around 14%. Those findings are supported by the results presented in Archetype models based on similarity clustering. The results are based on an empirical study (278 respondents, omnibus survey) in the Czech Republic. Moreover, the study concludes that the business experience positively influences human resource management and future development to increase the investment share. In essence, this article displays the paramount importance of human resources and human resource management in the international business environment, demonstrating that investments in human resources are crucial to the success of all businesses, positively and consistently supporting organizations’ performance, and entrepreneurship will continue to remain a vital component of the activities belonging to the post COVID-19 era. In addition, in an era governed by the influences specific to the knowledge-based society and the knowledge-based economy, in which intellectual capital will be considered one of the most relevant intangible assets of entities all over the world, the measurement of human resources investment will turn out to be essential for the success of all businesses, while taking the necessary steps in supporting sustainability, sustainability assessment and Sustainable Development Goals (SDGs).
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.005 | 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 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".