The impact of career capital on sustainable competitive advantage: The mediating role of human resource management capabilities
Bibliographic record
Abstract
This study aims to clarify the effect of career capital on the sustainable competitive advantage when human resource management’s capabilities work as a mediator variable in construction companies specializing in concrete in Amman. The total number of companies are (23), the companies that showed cooperation and filled out the questionnaire were (17) companies. The study population consisted of (1400) employees from various administrative levels. Applying the study to the whole study population is difficult due to its large size; thus, a simple random sampling method is used. (302) questionnaires were distributed, the retrieved questionnaires valid for analysis were (250) and constituted of (83%). The unstructured interviews were adopted to define the study problem in the construction companies specialized in concrete. The questionnaire is the tool for measuring the study variables by collecting data and then analyzing those using descriptive and inferential statistics. The most important study results are that in the construction companies specialized in concrete industries in Amman; the level of career capital was high, and the level of sustainable competitive advantage was medium. At the same time, the results showed that the level of human resource management capabilities was high. The results also indicate that human resource management's capabilities partially mediate the impact of career capital and the sustainable competitive advantage. Human resource management's capabilities contributed to increasing this impact; this means that human resources management's capabilities play an essential role in activating the relationship between career capital and sustainable competitive advantage.
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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 | 0.005 |
| 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 | 0.003 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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".