Strategic Alliance Success Factors: A Literature Review on Alliance Lifecycle
Bibliographic record
Abstract
Objectives. The research aims to investigate how firms can achieve alliance success. In global markets, the alliance failure rate is very high. This study will try to understand why, facing with such a high failure rate, more and more firms decide to enter or form strategic alliances. It appears necessary to identify key factors and show how firms can successfully manage them in each phase of alliance lifecycle.Methodology. For this study, a qualitative approach was adopted, in order to explore and understand the research problem. The issues of alliance success factors is investigated through the analysis of the existing literature, focusing in particular on the last two decades.Findings. By reviewing several theoretical perspectives, we identified alliance success factors and showed what kind of relevance they have in each phase of alliance lifecycle. It was found that strategic alliances develop through three phases. Alliance success lies on successful management of key factors, involved in each phase.Research Limits. Research deals with the issues of alliance success factors at the level of a single alliance and not at the level of an alliance portfolio. Further research should extend the analysis perspective.Managerial Implications. Firms involved in a strategic alliance should consider several critical aspects. For the entire alliance lifecycle, they have to look for a high degree of fit with their own partners. Another important aspect is related to the risk of opportunistic behavior, which could be reduced through the choice of an appropriate governance form and the development of social capital.
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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 | 0.004 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.010 | 0.011 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| 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".