Understanding the drivers of organizational business performance from the human capital perspective
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 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 it