A bibliometric review of job satisfaction and organizational commitment in businesses area literatures
Bibliographic record
Abstract
BACKGROUND: The bibliometric analysis and systematic appraisal of research on job satisfaction and organizational commitment in administrative and technical studies in the study show substantial efficacy, opening the path for future research in this subject. OBJECTIVE: The goal of this literature review is to identify important ideas that have the potential to influence job satisfaction and organizational commitment, as well as to provide the groundwork for future research in this field using bibliometric analysis. METHODS: This study used a bibliometric review approach to examine Web of Science papers on job satisfaction and organizational commitment. RESULTS: Performance, Impact, Transformational Leadership, Citizenship Behavior, Employee Performance, Organizational Justice, Job Satisfaction, Turnover, Psychological Empowerment, Organizational Commitment, Normative Commitment, Empowerment, and Turnover Intentions were the most frequently used terms in research on job satisfaction and organizational commitment, according to the survey. The bulk of these publications were published in the United States, China, Turkey, South Korea, Canada, Indonesia, Pakistan, Taiwan, and Jordan. IMPLICATIONS: The findings of the research may be used to generate articles on work satisfaction and organizational commitment in the field of market discipline, notably in the domains of business and technology.
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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 | 0.018 | 0.092 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.004 |
| Bibliometrics | 0.187 | 0.204 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".