Organizational Legitimacy: A Bibliometric Analysis of Web of Science Database
Bibliographic record
Abstract
This study is an attempt to conduct a bibliometric review in the above-mentioned field. A bibliometric review is conducted using 644 articles from the Web of Science core collection database. The article evaluates the research article distribution based on time and geography. It also highlights notable articles, authors and journals. We make use of techniques like citation analysis, co-citation analysis and co-occurrence analysis. Software Vos Viewer and Biblioshiny are used. The article also explores the theme development in the area. The volume of publications has seen steady growth since 2003 but has seen rapid growth post 2020. On analysing the country-wide distribution, we realize that the top five positions are occupied by USA, England, China, Canada and Spain. In terms of the major sources of these publications, the list of journals has spread across various disciplines such as accounting, human resource management, economics, ethics, etc. Trending topics like corporate social responsibility, sustainability, financial performance, corruption, risk and trust whose linkages with legitimacy have gained emphasis in recent times have been highlighted. Further, the analysis emphasizes areas with research scope like social media and legitimacy, corporate governance and legitimacy, institutional pressures, etc. Additionally, co-citation clusters were developed to represent the distribution of the thematic structure. The article provides various suggestions for academicians and managers/policymakers.
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How this classification was reachedexpand
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | Bibliometrics Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Observational | low |
| gpt | Bibliometrics Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Other design | high |
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 | 0.011 | 0.063 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.171 | 0.170 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.008 | 0.005 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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, unvalidatedLabeled directly by 2 models reading the full record.
The models disagree on parts of this classification; every voice is preserved in the section at the end of the page.
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".