Moderating Effect of Social Capital on the Relationship Between HRM Practices and the Performance of Companies Listed on NSE
Bibliographic record
Abstract
Accessibility to broader information sources as well as the advancement of the quality of information, relevance, and timeliness is facilitated by social capital. These circumstances pave the way for individuals to improve their knowledge by interacting with coworkers daily. The study's main objective was to establish the moderating effect of social capital on the relationship between HRM practices and the performance of companies listed on NSE. The research was guided by the social-capital theory. This research utilized a descriptive research design and applied the positivist approach. The population of the study included 65 companies listed on the Nairobi Stock Exchange (NSE), and it employed both primary and secondary data, with secondary data consisting of the financial indicator Return on Assets (ROA). Questionnaires were employed to gather primary data, and descriptive and inferential statistics were utilized to analyze the data. The method utilized was linear regression. According to the findings, social capital has a moderating influence on the link between HRM practice configurations and company success on the NSE. This study recommends that training and development efforts be consistent, and that research institutes guarantee that training provided to employees is relevant to their needs. As a result, they should undertake a training need analysis to determine the program's relevance to learners. Research institutes must also ensure that they have the best possible resources.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".