Process Model of Talent Management and Enterprise Competitiveness in Bosnia and Herzegovina
Bibliographic record
Abstract
Talent management is an essential area within human resource management and has been increasingly receiving attention over the past several decades. The focus of talent management is on the most crucial employees within an enterprise. Therefore, it is vital to have a specialized and tailored management system for them to maximize business results. This paper addresses the connection between talent management and enterprise competitiveness. It aims to examine the relationship between these two variables within the business environment of Bosnia and Herzegovina. This paper significantly contributes to both theory and practice because it proposes a new, more comprehensive process model of talent management based on a detailed analysis and synthesis of all available scientific and research works. Following this, the paper tests the proposed model in practice and measures its success by examining enterprise competitiveness. The research was conducted on 101 service enterprises in Bosnia and Herzegovina in the second quarter of 2023. Managers of service enterprises involved in human resource management were surveyed. The questionnaire was formulated based on a combination of existing research in the specified fields. The data were subjected to correlation and regression analysis, and the research results were presented according to the previously set objectives and hypotheses. The research results showed that talent management is a significant predictor of competitive advantage. Additionally, a positive impact on competitiveness was confirmed for each individual group of talent management activities presented in the proposed process model.
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How this classification was reachedexpand
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".