Science Mapping of Perceived Organizational Support: A Bibliometric Analysis Approach
Bibliographic record
Abstract
The purpose of this study is to obtain related topic and information regarding perceived organizational support. In conducting this study, the researcher utilized Scopus database for data mining and extraction. A total of 816 articles were discovered in the Scopus database accessed on July, 2023. Afterward, VOSviewer is utilized for scientific mapping and analysis of publication performance such as identify the contributions of authors, journals, countries, and author keywords number. Several countries have contributed to publications of perceived organizational support. The data reveals that the US contributes the most to this publishing subject, followed by China, India, the UK, Australia, Malaysia, Pakistan, South Korea, Canada, and Turkey. Most works on the topic are from these nations. The "International Journal of Human Resource Management" has the most important influence on this topic based on article document output. While, "Journal of Management" has the greatest number of citations. This topic's top five authors write the most articles and Stinglhamber F. is the most prominent author in perceived organizational support study. Meanwhile, the most referenced author in this field is Eisenberger R., with 1,300 citations. This research can serve as a point of reference for future research pertaining to POS topic.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.063 | 0.345 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.004 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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; both teacher heads agree on what is shown here.
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".