Methodological Redirections for an Evolutionary Approach of the External Business Environment
Bibliographic record
Abstract
The usual strategic analysis perceives the external business environment fragmentarily and without a coherent and unifying way. The three levels that a typical analysis of the external business environment involves are a) the macroenvironment and PEST analysis, b) mesoenvironment and “Porter’s diamond”, and c) industrial environment and “Porter’s five forces”. Contrary to the fragmentary analysis of the three levels, this article aims to counter-propose a restructured method of a unified and evolutionary analysis of the external business environment. After presenting the usual analytical handling of the external business environment in the three levels, we suggest that these are rather co-evolving than separate and autonomous spheres of analysis. Therefore, after introducing some elements of the evolutionary socioeconomic theory, we propose a systemic web that perceives the external environment of the socioeconomic organisations in dynamically unified and evolutionary terms. The systemic web conceptualises the approach of the external socioeconomic environment as an open and interactive system comprising three co-evolving spheres in the context of global dynamics: the institutional character of each spatially structured socioeconomic formation; the firm’s functions within the system; and the public-state intervention that contributes to the establishment and reproduction of the system. This conceptual redirection of the methodology of the external business environment can be useful for building an integrated strategic analysis that studies all “micro-meso-macro” components of the entire socioeconomic system.
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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 | 0.014 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.004 | 0.032 |
| Scholarly communication | 0.010 | 0.012 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.003 | 0.005 |
| 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".