Middle Managers and Dilemmas in the Organisation
Bibliographic record
Abstract
Middle managers, it turns out, can assist a business in implementing significant changes and achieving its vision and goals by facilitating top management’s communication with lower managers or subordinates. This study aims to analyse bibliometric measures relevant to middle managers and dilemmas by examining publishing trends, delving into information about the trending of authors’ keywords, probing into conceptual evolution, and determining future research directions for this subject using the Scopus database. This study uses three bibliometric software to analyse the bibliographic data: ScientoPy, VOSviewer, and SciMAT. Over 56 years, this study found that publication growth is minimal, with the number of top publications being 32 in 2020. Scopus bibliographic databases depicted the United States as the most active country, with 114 publications co-authored by authors from the United Kingdom, Canada, Russian Federation, Italy, and Hong Kong. Denmark has published 29% of the total publications based on the last two years’ publications. The analysis of the authors’ keywords found that “Middle-managers”, “Leadership”, “Middle Management”, “Management,” and “Organisational change” were enumerated in the top five. The keyword “Middle-managers” has a close association with “leadership”, “management”, and “organisational change”. Based on thematic evolution, “Health”, “Anchoring-effect”, “Developing-countries”, “Communication-skills”, and “Health-care” became the newly emerging themes found from 2011 to 2022. In conclusion, this study could contextualise prior research on this subject and build a scientifically sound evidence-based practice paradigm for future research. Also, this study will expand our understanding of middle managers and associated dilemmas.
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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 | 0.028 | 0.077 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.030 | 0.035 |
| Science and technology studies | 0.007 | 0.013 |
| Scholarly communication | 0.026 | 0.023 |
| Open science | 0.001 | 0.010 |
| Research integrity | 0.002 | 0.002 |
| 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, 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".