MAPPING THE FIELD: A BIBLIOMETRIC ANALYSIS OF EMPLOYEE VOICE
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.
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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 | 0.010 | 0.059 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.149 | 0.167 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.008 | 0.007 |
| 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".