Public Innovation Policy and Other Determinants of Innovativeness in Poland
Notice bibliographique
Résumé
IntroductionInnovation is now regarded as a key factor of development of both businesses and economies. According to the neoclassical theory of economic growth, only technical progress is able to sustain long-term of economies in terms of per capita income (Solow, 1994). In developed countries, the share of Total Factor Productivity (TFP) reflecting technical progress in economic is about 60-80 per cent depending on the period for which the analysis is conducted (Hayami & Godo, 2005). The share of TFP in economic in Poland in the period 1999-2005 was 82 per cent (Siemek-Filus, 2008). The share of TFP in the of value added in industry and construction in Poland in the years 20022008 was 65 per cent(Wojnicka-Sycz, 2013). This means that Poland is already reflecting the path of development characteristic of developed countries and determined by factors such as innovation, human capital, and knowledge.The main weakness of the neoclassical model is that technological progress is outside the economic system - it is an exogenous variable, and thus the model does not include the possibility to influence technological progress. This drawback has been overcome by the so called theory of economic growth proposed by Romer and Lucas, in which a huge role in the of productivity is attributed to human capital, knowledge and learning by doing (Romer, 1990). Robert Lucas proved the right of rising revenues from knowledge at the level of society, but declining at the company level (Lucas, 2010). The new theory shows that technological progress and innovation can be effectively influenced, for example, by instruments of innovation and industrial policy.In Poland, since 2000, public innovation policy has become very important. It is executed at several levels: domestic, regional and, to a lesser extent, sectorial and local. Innovation policy in Poland is implemented via scientific, industrial and entrepreneurshippromoting policies as well as by means of regional policies carried out by regions themselves and on the domestic level by the ministry responsible for regional development. Moreover, some cities, especially metropolises, engage in pro-innovative activities like creation of science and technology parks. At all these levels most of the activities connected with innovation policy are co-financed by the European Union's structural funds. The European Union's structural funds support such activities as investment in modern technology and equipment in firms, acquiring patents, joint innovative projects between enterprises and scientific institutions, activities of business clusters or technology transfer centres, creation of laboratories for tenants of science and technology parks. The instruments of public innovation policy in Poland are thus varied and comprise innovation grants for firms, proinnovative institutions and universities for different purposes like investment and R&D staff's work, special loans, tax exemptions, creation of pro-innovative infrastructure like technology parks, preparation of regional and domestic innovation strategies, securing of intellectual property rights, promotion of knowledge networks, etc. Still, the amount of money available for support of innovativeness is low in comparison with the most developed countries and it is mainly channelled by means of policy connected with the European Union's support in the form of structural funds. Poland ranks on the European Innovation Scoreboard 2015 in the group of moderate innovators among some other former communistic and Mediterranean countries with results lower than the European Union's average. Efforts in innovativeness and R&D of these countries will depend on whether the European Union as a whole reaches the indicators of R&D&I of its main competitors like the USA or Japan, especially whether the share of R&D in Gross Domestic Product reaches the order of 3 per cent. …
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,001 | 0,005 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,001 |
| Bibliométrie | 0,002 | 0,002 |
| Études des sciences et des technologies | 0,000 | 0,001 |
| Communication savante | 0,002 | 0,001 |
| Science ouverte | 0,000 | 0,001 |
| Intégrité de la recherche | 0,000 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,007 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».