Impacto en las redes sociales de las Grandes Empresas Españolas: Reputación Corporativa, Integridad y Comportamiento Ético
Bibliographic record
Abstract
Corporate Reputation, CR, is currently a high intangible value for companies. Its recognition, measurement and effective management are the key factors to corporate survival and sustainability over time. But this effort is not simple, the relationship with their stakeholders is changing due to new technologies, labor relations, environmental issues, corporate image, branding and business ethics. Therefore, it is necessary to analyze the relationship between the CR and its various components. This paper presents a study where the relationship between integrity and CR perceived by the public in social networks and media on-line is analyzed. For this purpose a sample of Spanish companies were selected according to their profit size. The results of this study allow us to understand into what extent the ethics of large companies manage to influence their perception and whether or not this CR received through social networks penalize their attractiveness or admiration in the social media. This partnership will provide a new perspective on how integrity performs in the current scenario between companies and their stakeholders over Internet. Ultimately, this research shows if citizens and companies, human person and legal person, act and express their behaviour in social networks under the same ethical level. It also helps to prove the tendency —one of the two or both— to contribute to the improvement and recovery of the lack of trust in the business market. Only then can we maintain a solid CR in a complex and changing global environment in which the human being, as a natural or legal person, is the protagonist.
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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 | 0.004 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.007 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 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".