A 59 Years (1962-2021) Bibliometric Analysis of Organizational Support Research Articles
Bibliographic record
Abstract
Organizational support is an administrative concern that emphasises well-being and appreciates employees’ contributions. This study aims to determine the revolution of organizational support through published articles. Thisstudy employed 3 bibliometric analysis methods: descriptive analysis using Microsoft Excel, citation analysis using Publish or Perish software, and VOSviewer visualisation mapping. A total of 5527 articles within the last six decades (1962 - 2021) were retrieved from the Scopus database for analysis. A majority of the articles were published in the United States of America in the areas of Business, Management, and Accounting. Based on the review, the annual article publication trend has \nsignificantly increased each year. The University of Toronto was the most influential institution publishing organizational support articles, whereas, Robert Eisenberger is a wellknown author in this area. Also, the International Journal of Human Resource Management is an active source in this topic and Taylor & Francis is a well-known thriving publisher. “Perceived Organizational Support” is the most popular article in this field. The information obtained from this study can be utilised by organizations, organizational policymakers, and researchers to identify future research gaps.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.052 | 0.143 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 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".