MAPPING THE FIELD: A BIBLIOMETRIC ANALYSIS OF EMPLOYEE VOICE
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Objective: There is limited literature that discussed the trend of ‘employee voice’. Hence, this bibliometric analysis is aimed to evaluate the global research growth to retrieve and analyze the publication on ‘employee voice’. The bibliometric analysis is used to search the database of Scopus from the oldest publication in 1986 to the recent publication in 2019. The objectives were to evaluate the trend of ‘employee voice’ research, details of co-authorship, leading institutions and countries, top scholars, and leading author keywords. Methodology: This study used VOS Viewer 1.6.11 to analyze and visualize the global research trend on ‘employee voice’ in analyzing the bibliographic data. Bibliometric maps were retrieved from VOS Viewer 1.6.11. Results: This study retrieved 443 journal articles from the Scopus database from 1986 to 2019. The publication’s trend revealed that the number of publications has been increasing steadily since 2005. The leading countries in ‘employee voice’ research are the United Kingdom and the United States. Among the fifteen leading universities, five of them were from the world’s top 150 universities. Among the keywords, ‘voice behavior’ has the most linkage with ‘employee voice’, which indicated that employee voice is active in the business and management field compared to other fields such as nursing and psychology. According to the author keywords analysis, ‘promotive voice’ and ‘prohibitive voice’ were found to become a potential concerned area in the future as they started to receive attention in 2017. Implication: This paper can be beneficial for academicians, organizations, and business policymakers in understanding the global trend of ‘employee voice’ besides discovering the future directions and opportunities for future studies.
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.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.010 | 0.015 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 it