The Effect of Emotional Intelligence on Four Dimensions of knowledge Conversion in Selected Industrial Organizational
Bibliographic record
Abstract
There is a relation between Emotional intelligence, knowledge management and culture of each organization. In this research the impact of organizational cultures have been studied. The methodology has been used for this research was descriptive. According to type and size of their projects, organizational culture was estimated as bureaucratic in seven organizations. The Quinn organizational culture questionnaire along with several interviews with managers verified the bureaucratic culture in four organizations. The applied tool for data collection was a questionnaire consisting of 33 questions. Moreover, the sample size was 344 employees in four organizations. To investigate the reliability of the questionnaire the Cronbach’s alpha value has been measured and the validity has been confirmed by the field. Moreover, according to Goleman’s emotional intelligence model the five factors have been measured in the selected organizations. Also the knowledge management‘s Model presented by Nonaka and Takeuchi has been used by considering four presented elements. The results demonstrated that in the bureaucratic cultures, externalization and combination are in a proper status. Analyzing the research data depicted the relationship between different dimensions of emotional intelligence and the ability of individuals in different aspects of converting the knowledge. For example Social skill and empathy ability of individuals have a positive and significant relationship with socialization.
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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 | 0.001 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".