A Study on the Factors That Influence Innovation Activities of Spanish Big Firms
Bibliographic record
Abstract
The main goal of this research is to study the role of several factors and firms’ resources that could have had an impact on the development of innovative activities of Spanish big firms, exploring how these factors can help to achieve success through innovation and improving business performance. We propose a new model to analyze the relationships between a set of organizational, technological, financial and information-based resources, as well as other aspects such as company’s cooperation. We employ a Structural Equation Model and the PLS technique in order to validate the theoretical model proposed in this research. The data come from the Spanish National Statistics Institute’s Survey on Firms Technological Innovation. The sample is composed by 2224 observations referred to firms with 200 or more workers. The main results show that human and financial resources and cooperation affect positively R&D activities. At the same time R&D, information management and technological resources have a positive effect on innovation. Finally, R&D activities, innovation results (product and process innovation) and information management influence business results.
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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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".