Product Innovation: Path to Sustainable Competitive Advantage with Use of Environmental, Social and Governance Principles
Bibliographic record
Abstract
Purpose of the study: This study aims to analyze the relationship between product innovation and strategic resources used by the furniture enterprises, under the perspective of sustainable competitive advantage, with the intention to identify the resources previous to innovation. Methodology/approach: The method used in this research is a quantitative and descriptive study, through a survey, applied to 1067 companies in Brazilian furniture industry. The data analysis occurred through Structural Equation Modeling. Originality/Relevance: Product innovation and strategic resources are capable of providing great potential for economic transformation. It is strategically acknowledged that there is a relationship between product innovation and the use of resources, however, there are as yet insufficient empirical studies to determine which resources influence product innovation. Another important aspect is to evaluate the influence of Environmental, Social and Governance sustainability precepts in the development of new products. Key findings: In the empirical study, noticed that Product Innovation results from the use of resources, which configures innovation antecedents. Theoretical/methodological contributions: This study contributes the advancement of science, which can be used to analyze the antecedents of Product Innovation, which pointed out that companies with strategic resources can expand the capacity of innovation by generating sustainable Product Innovation, which leads to the success of a new product. Social contributions/to management: This study can contribute to managerial decisions, as the results indicate that the Success in New Product Development proved to be an essential form of competitive advantage, comparing the results of Product Innovation with competitors and relating this performance to the use of Environmental, Social and Governance principles.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".