An Analysis of the Organizational Commitment Theory Evolution Based on Mapping Knowledge Domains and the Research Trend
Bibliographic record
Abstract
Taking the articles related to organizational commitment from 1997 to 2012 in SCI or SSCI databases of Web of Science as the research objects and applying the information visualization methods,this article draws from the literature metrology perspective a scientific knowledge mapping to show that the research on organizational commitment theory has gotten more and more attention since 2005.The United States,Canada,UK and China have an important position and obvious advantage in the organizational commitment research.Through co-citation analysis,the paper studies the evolution process of the knowledge in this field.And through co-occurrence analysis of the keywords and detection analysis of the words of sudden appearance,it finds that the hot spot of organizational commitment research mainly focused on job satisfaction,turnover intention and employee satisfaction,and that the research fronts mainly centered on special groups,such as professional talents,and the influence of new-type management methods and human-based management on organizational commitment,etc.As an important attitude variable,organizational commitment can predict the staff's working performance,job satisfaction and job exit behavior;has an important practical significance for keeping the core staff in the enterprise and for performance management.
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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 | 0.004 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.016 | 0.019 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.006 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 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".