Understanding the drivers of organizational business performance from the human capital perspective
Bibliographic record
Abstract
Abstract The purpose of this study is to understand the drivers of organizational business performance from the perspective of human capital. Data were collected from 691 employees working in 15 North American credit unions. The model was developed and tested by means of the Partial Least Squares Structural Equation Modeling technique. This study illuminates an underexplored mechanism driving the association between transformational leadership and business performance based on several theoretical frameworks such as leader–member exchange theory, the conservation of resources theory, the heuristic model of employee turnover, equity theory, and capital‐based view. The findings indicate that transformational leaders provide their subordinates with constructive feedback and offer training and development (T&D) opportunities, which are the key factors driving employee job satisfaction. Employee job satisfaction curtails turnover intention, which, in turn, reduces human capital outflow and, consequently, increases business performance. Managers should always act as true transformational leaders and provide their subordinates with relevant performance feedback and ample T&D opportunities. Workers who undergo T&D at the expense of their organization become more loyal and are less likely to leave even though they become more marketable. Organizations are recommended to administer periodic employee satisfaction surveys and prevent the exodus of human capital, which may be difficult to replenish.
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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 | 0.003 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".