The influence of Organizational Climate and Organizational Socialization on Knowledge Management: An empirical study in banking sector of Pakistan
Bibliographic record
Abstract
The basic objective of the empirical study is to identify the influence of organizational socialization (OS) and organizational climate (OC) on knowledge management (KM) among the banking sector of Pakistan. The above said sector is selected as population of the current research. By using the simple random sampling technique, different branches of public banks and private banks are selected as a sample. 270 questionnaires were circulated to top level managers and middle level managers. 240 questionnaires were filled by employees and used for analysis. The overall response rate was 89%. Different statistical techniques i.e. Pearson’s correlation analysis, multiple regression analysis and reliability analysis are applied on collected data. The results of Pearson’s correlation analysis shows that there is positive relationship between organizational climate (OC), organizational socialization (OS), knowledge management (KM), its dimensions i.e. knowledge sharing (KS) and knowledge application (KA). Moreover, regression analysis’s results explain that organizational socialization is strong predictor of knowledge management as compare to organizational climate. From the management point of view, the results give clear clue to Pakistan’s banking sector must understand the importance of organizational socialization, organizational climate for the purpose of knowledge management. In future researches, data may be collected to other sectors like telecom industry, textile industry and education sector etc. for more generalizing the results. Moreover, researches some other variables like social interaction, perceived organizational support, and perceived supervisor support may also be conducted.
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How this classification was reachedexpand
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 | 0.002 | 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 | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".