The Industry 4.0, the Corporate Social Responsibility and the Impacts of Brand in the Digital Transformation
Bibliographic record
Abstract
The interest of my research is Digital Transformation and Corporate Social Responsibility as an involvement for the Brand. In this way the “product platform” contributes to the improvement of competitive position of the Business Unit and Branding”, between the marketing process: Customer Relationship Management—Product Development Management—Customer Satisfaction. Weberian vision of ideal type as a concept—ideal limit to illustrate the significant elements of its own empirical content, tends to identify an overcoming from the role of consumer as target to the analysis of the techniques of profiling the humanization of the customer with his needs, fears and aspirations. The research project consists of three parts. In the first a deepening on the wide literature of international scope, above all “made in USA” regarding the market orientation—Industry 4.0—platform: the origin, the internal organization, the management, the communication strategies. In the second part focuses instead on brand analysis, to reconstruct the main social and economic projects while assessing the internal analytical coherence, ideological value and deducing the most significant operational indications. The third, on the other hand, explores corporate social responsibility through small and medium-sized enterprises with market orientation, a socially responsible management-driven approach, strategic enterprise orientation and business management, resulting from different synergistic combinations.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.015 |
| Scholarly communication | 0.010 | 0.005 |
| Open science | 0.000 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".