Organizational Learning, Its Antecedents and Consequences From the Perspective of Teachers From Public Higher Education Institutions
Bibliographic record
Abstract
The general objective of this article is to measure and analyze the correlations that involve organizational learning, its antecedents, and consequences from the performance perspective, based on the perception of professors from public higher education institutions. In terms of objectives, the research method is classified as descriptive. As for the procedures, a survey was carried out, and the approach to the problem is quantitative. Data collection was carried out through a structured questionnaire. The research was based on the perception of 600 professors. The results highlight that the constructs of knowledge acquisition and transformation, organizational learning culture, and affective organizational commitment are significant and positive antecedents that influence learning at an organizational level. Additionally, the correlation between organizational learning and performance is significant and positive. We emphasize the elaboration of the proposed integrated model that considered the significant and positive correlations between organizational learning culture and team cohesion; team cohesion and affective organizational commitment; team cohesion and knowledge acquisition and transformation; and knowledge acquisition and transformation and organizational learning culture; organizational learning culture and affective organizational commitment; knowledge acquisition and transformation and affective organizational commitment. We concluded that the validation of the proposed integrated model is a contribution of research to science and social and administrative practice. However, the research substantiates aspects of the antecedents and consequences of organizational learning with contributions to managers and meets academic and social aspirations. Other variables could be tested, and other groups of respondents could be investigated.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".