A REVIEW OF STAKEHOLDER DYNAMICS
Bibliographic record
Abstract
Stakeholder relationships are a critical part of any businesses success, and should be properly managed to provide the best outcome for the firm during its market lifecycle. Our aim is to emphasize the importance of stakeholders along the firm lifecycle and present cultural dimensions of decision making with an overview of cultural aspects. The paper builds upon on the previous research of authors focusing on the very dynamic High-tech market in Canada. Our main statement is that the role and importance of stakeholders are changing and to show the directions or focus of stakeholders influence changes according to the stages in this market the interactions with, and among stakeholders’ changes remarkably depending on where the firm is in its market evolution or penetration. We also show the importance of stakeholders in the decision making process and analyzing the European market it can be seen that different cultural background even can affect the decisions also in a market with more similar background like Hungary and Germany. Market environment dictates the profile of the stakeholder ecosystem, and this reflects the profile of the marketplace. The relationship is mutually beneficial, or mutually destructive, and proper management is the only way to shift the latter to the former.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".