The Degree of Cooperation in International Strategic Alliances and Value Creation Outcomes: Empirical Study on Service Firms in Yemen
Bibliographic record
Abstract
International strategic alliances (ISAs) are one of the partnership options that may be used by firms in achieving their goals. It is an inter-firm cooperation where firms commit some of their resources and capabilities towards the alliance to create a competitive advantage. Yemeni services industry need to improve its competitiveness and the quality of services it provides to the people and therefore, forming a strategic alliances with foreign partners is considered one of the best strategies. Although the involvement of Yemeni’s service firms in ISAs are evidence, but there is no previous study conducted to determine whether these alliances do create value to the firms or not. Therefore, this study is conducted with the objectives of determining whether Yemeni’s service firms’ involvement in ISAs do create value to the firms or not and what kind of value creation outcomes are created by this ISAs. Alliances may also come in various forms with different degree of cooperation. This study will also look at the influence of the degree of cooperation is ISAs on the value creation outcomes. Survey of 214 service firms’ managers revealed that ISAs do create value to the organization with financial value top the list. Improvement in customer service and better return on investment (ROI) are among the most important advantage gain by these service firms from their involvement in ISAs. This study also posits a significant positive relationship between the degree of cooperation in ISAs and the value creation outcome.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
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".