Organizational Citizenship Behavior as A Moderator in Employee Performance: A Study on Emotional Intelligence and Job Satisfaction
Bibliographic record
Abstract
This study aimed to determine and analyze: (1) the effect of emotional intelligence and job satisfaction on employee performance; (2) the effect of emotional intelligence on employee performance moderated by organizational citizenship behavior; and (3) the effect of job satisfaction on employee performance moderated by organizational citizenship behavior.This research uses associative research with a sample of 55 respondents who are North Sumatra National Land Agency Regional Office employees.Data of the study were collected through questionnaires, which contain a series of statements.In addition, Partial Least Square (SmartPLS) is used to assess and evaluate the five hypotheses proposed in this research as part of the data analysis strategies employed in this study.The study results show a positive and significant effect of emotional intelligence and job satisfaction on employee performance; there is no influence between emotional intelligence and employee performance moderated organizational citizenship behavior, and there is no influence between job satisfaction and employee performance moderated organizational citizenship behavior.Based on empirical evidence, it has been observed that enhancing employees' emotional stability can lead to improved job performance, hence enhancing overall organizational effectiveness.Furthermore, providing increased possibilities for employees to fulfill their job tasks will likely result in heightened job satisfaction.This study delineates the topic's significance for leaders in government offices and provides vital suggestions for future research.
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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 | 0.001 | 0.000 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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".