Learning Organizations and Employees’ Outcomes: A Perspective of Psychosocial Safety Climate
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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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 | 0.002 | 0.004 |
| 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 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.000 | 0.002 |
| 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 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".