Change management and organizational performance: current key trends
Bibliographic record
Abstract
This paper provides a comprehensive review of the literature on the relationship between change management and organizational performance through bibliometric and thematic analysis. Using the Web of Science database, 304 publications were analyzed with VOS viewer and biblioshiny in R-studio. The study examines author engagement, keyword trends, co-authorship networks, and conceptual structures. Findings reveal a growing research interest in this field across various geographical regions. The United States and China are the most influential countries, while Appelbaum from Concordia University in Canada is the most productive researcher. The research’s conceptual structure focuses on four key aspects: human, organizational, technological, and leadership. Co-word analysis categorizes topics based on their intensity, specialization, and level of development, distinguishing well-researched areas from emerging and underexplored themes. This approach provides a clearer understanding of thematic and temporal developments in the field. By mapping the conceptual landscape, this study highlights key research areas and identifies future directions for exploration. The findings contribute to a deeper understanding of how change management impacts organizational performance and offer insights for scholars and practitioners interested in advancing the field.
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.
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 | 0.013 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.017 | 0.035 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.011 | 0.013 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".