Alliance termination research: a bibliometric review and research agenda
Bibliographic record
Abstract
Purpose The purpose of this study is to examine the existing literature and evaluate the theories, characteristics, context and methods of alliance termination research published from 1992 to 2019. This study also aims to identify the gaps in the literature and recognize directions for future research focusing on alliance termination research. Design/methodology/approach The main research methods followed in this study are bibliometric review, citation analysis, co-citation analysis and cluster analysis. Findings The main findings of this study are the most cited articles, most productive journals and most productive countries. The results show that a total of 100 research articles were published between 1992 and 2019. The maximum number of publications were observed during 2011–2019. The article “Knowledge, bargaining power, and the instability of international joint ventures” (Inkpen and Beamish, 1997) was the most cited article and the “ Academy of Management Review ” was the most prominent journal, with 847 citations. The USA, France, the UK, Singapore and Canada are the most productive countries. The study also includes the analysis of the network of co-citation of references and co-occurrence of keywords in the context of alliance termination research. Originality/value To the best of authors’ knowledge, this study seems to be the first to perform bibliometric review and analysis in the area of alliance termination research. Therefore, it can help academicians and practitioners to identify the research trends and gaps in the alliance termination literature on which future research can be performed. Overall, this research paper leads to a better understanding of the alliance termination research and offers new insights into strategic management studies.
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 | 0.070 | 0.180 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.154 | 0.174 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.015 | 0.013 |
| Open science | 0.002 | 0.006 |
| 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".