The Formation of the Conflicts Management Models of the Strategic Alliances under the Conditions of the Globalization
Bibliographic record
Abstract
The processes of globalization led to the fact that the world markets began to create new business forms actively, one of which is a strategic alliance. The conditions of the strategic alliances functioning uncertainty associated with a complex system of management and organization, conflicts arising from the work dissatisfaction and high uncertainty of possible outcomes. Conflicts may lead to the dissolution of the alliance at any stage of its formation and development. The influence of conflict can be minimized by choosing the most stable forms of corporations’ interaction. Objective: to develop and substantiate models which allow managing conflicts of strategic alliances on conditions of uncertainty of the current globalization state stage. The result of this study is to construct two basic models of conflict management as a factor of the strategic alliances’ stability: the state model allows identifying and eliminating the causes of the dissolution of strategic alliances in the conflict environment and forecasting model of variant forms of the corporations’ interaction, leading to a stable development. For the formation and stability of the models a systematic approach is used as well as factor analysis and methods of the decisions’ making. To control the conflicts, is to control the stability of strategic alliances: to predict the result of the activities, plan the development, to eliminate the problem quickly. The models, formed within the work allow predicting the result of the strategic alliances’ activities: a stable development, consolidation or merger, dissolution.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".