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 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 | 0.009 | 0.033 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.136 | 0.134 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".