Learning Organizations and Employees’ Outcomes: A Perspective of Psychosocial Safety Climate
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
Change in organizations becomes an essential element for the attainment of competitive advantage and survival of organizations in a highly competitive environment. This study investigates the direct influence of learning organizations on organizational innovation and affective commitment to change. Moreover, this study also examines the moderating role of psychosocial safety climate between the relationship of learning organizations and organizational innovation and affective commitment to change. 303 permanent employees from the manufacturing and service sectors participated in this study for the data collection purpose, and data was collected by adopting the time-lag technique through a self-administered process. The data analysis was performed using MS Excel, SPSS, and AMOS. The study's findings evidenced the direct influence of learning organizations on organizational innovation and affective commitment to change. Moreover, a higher psychosocial safety climate enhances the organizational innovation and affective commitment to change in learning organizations. The present research findings are helpful for the management of manufacturing and service sector organizations that by utilizing the concept of the learning organization, they can enhance the level of organizational innovation and affective commitment to change. Moreover, the psychosocial safety climate of the organization also plays a vital role in this regard. The present study highlights the importance of learning organizations to enhance organizational innovation and affective commitment to change by modifying their schemata through a psychosocial safety climate.
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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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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