Business Ecosystem, a Secured Strategy to Gain Competitive Advantage According to SMOCS Model
Bibliographic record
Abstract
Attitude to the organizations has been changed over the time. Nowadays by increasing changes in business environments, borders between industries have almost been removed. According to James Moore (1993), organizational activities space is now an ecosystem one in which different businesses from different industries have mutual interactions as well as their survivals extensively depend on each other. These concepts are thoroughly propounded in business ecosystem approach.This paper reviewed different types of making strategic decision by using SMOCS Model was presented by Smida 1995 and showed which consequences and results shall be gained in each type for business ecosystem. It also showed scientific and applicable methods of making strategic decision according to SMOCS Model. So each business ecosystem may choose one of strategic decision making types as per situation and its expectation from the results. The method which applied in this research also authorized us to study a concurrent and simultaneous decision making in three main and important variables (resources, objectives and environmental conditions).Results of this research may help managers to make strategic decision in critical situations and also propose effective and useful offers to make decision. It raises knowledge and awareness level in making decision.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".