Applying Grey Relational Analysis to Evaluate the Factors Affecting Innovation Capability: Evidence from Chinese High-Tech Industries
Bibliographic record
Abstract
For the studies on the innovation capability, there are many limitations in using traditional statistical techniques. The grey system theory proposed in this paper is to supplement the limitations of using traditional techniques and it is more suitable to figure out the significance of influencing factors for facilitating innovation capability. Based on the statistical data from Chinese high-tech industries, over the period 2006-2008, this paper used fifteen indicators affecting the innovation capability, and it applied grey relational analysis to find out the significant factors. The results show that expenditure and persons engaged in science and technology activities are the significant factors affecting innovation capability within Chinese high-tech industries, and the efficiency for input-output of resources is less significant factor, which implies that the efficiency for input-output within Chinese high-tech industries is lower, and its effect to facilitate Chinese high-tech industrial innovation capability is insignificant. In order to facilitate Chinese high-tech industrial innovation capability, the government and enterprises should pay enough attentions to not only the expenditure and personnel engaged in science and technology activities, but also enhancing the efficiency for input-output of technology resources. Key words: Innovation capability; Grey relational analysis; Chinese high-tech industries Resume: Pour les etudes sur la capacite d'innovation, il ya beaucoup de limitations dans l'utilisation de techniques statistiques traditionnelles. La theorie des systemes de gris proposees dans ce document est de completer les limites de l'utilisation des techniques traditionnelles et il est plus approprie pour comprendre l'importance de facteurs d'influence pour faciliter la capacite d'innovation. Base sur les donnees statistiques du chinois industries de haute technologie, sur la periode 2006-2008, ce papier utilise quinze indicateurs affectant la capacite d'innovation, et l'a applique l'analyse relationnelle grise pour decouvrir les facteurs significatifs. Les resultats montrent que les depenses et les personnes engagees dans des activites scientifiques et technologiques sont des facteurs importants qui affectent la capacite d'innovation au sein chinoise industries de haute technologie, et l'efficacite pour les entrees-sorties de ressources est un facteur moins important, ce qui implique que l'efficacite d'entrees-sorties au sein chinoise industries de haute technologie est plus faible, et son effet de faciliter chinoises de haute technologie capacite d'innovation industrielle est insignifiante. Afin de faciliter chinoises de haute technologie capacite d'innovation industrielle, le gouvernement et les entreprises devraient payer des attentions assez pour non seulement les depenses et le personnel engage dans les activites scientifiques et technologiques, mais aussi ameliorer l'efficacite des entrees-sorties de ressources technologiques. Mots cles: La capacite d'innovation; Gris analyse relationnelle; De la haute technologie de l’industrie chinoise
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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 | 0.006 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.007 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".