Exploring psychological well‐being in business and economics arena: A bibliometric analysis
Bibliographic record
Abstract
Background: Recent events like the global pandemic and geopolitics leading to war bring to bear the evergreen importance of psychological well-being (PWB) among workers and how it can further influence business growth and performance. Furthermore, the complexity of today's job requirements has created enormous life pressures for individuals, negatively hurting their PWB. Method: This article took the format of a literature review of existing research work by pursuing the keywords in the SCOPUS database to retrieve the articles published on PWB in the field of business and economics from 1978 to 2022. The data were analyzed to elaborate, interpret and graphically display the results, in particular, authors, sources, documents, and social structure of the existing bibliography. The Bibliometrix R package is used for robust analysis of retrieved data. Results: The findings showed that the last decade saw a rise in scholarly work on PWB. However, in 2021, its sharp expansion stalled. It further revealed that academics from four countries had a significant role in accessing PWB in the business and economics fields, namely the United States, the United Kingdom, Australia, and Canada. The reports also indicate themes such as mental health, coronavirus disease 2019 (COVID-19), and depression are emerging themes, whereas niche themes include unemployment, quality of life, and job loss. Conclusion: This study suggests these new areas be studied in contemporary literature to provide cogent room to improve policy decisions on PWB within the business world.
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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 | 0.011 | 0.070 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.220 | 0.294 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".