A Bibliometric Analysis of Two Decades of Global Research on Organizational Ambidexterity using the Scopus Database
Bibliographic record
Abstract
Modern organizations face dynamism and due to which come across various performance challenges. Ambidexterity, which is the organizational balance among exploration and exploitation related activities, has gained significant attention in recent times on a global scale and is applicable in multiple domains. To advance the organizational ambidexterity understanding in an integrated and holistic manner, the current bibliometric analysis evaluates the globally published research conducted on organizational ambidexterity incorporating varied expressions. Using the Scopus database, the current research accumulated 282 journal articles from 1996 until 2018. The analysis of this research highlights, organizational ambidexterity research publications experienced a considerable upward momentum since year 2014 and onwards, with more than 40 papers per annum between 2015-2018. Top contributing institutions in organizational ambidexterity domain come from the United States, the United Kingdom, China, Spain, and Italy. Moreover, top-cited papers are from authors in the United States, the United Kingdom, and Netherlands. Most importantly, VOS viewer software is used to analyze co-authorship, author keyword co-occurrences, and network strength. The United States and the United Kingdom have the strongest link strength, followed by Canada, China, and South Korea, and Norway.
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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 | 0.006 | 0.035 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.226 | 0.262 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".