Promoting Innovation in SMEs in Developing Countries: A Case Study of Costa Rica's PROPYME Program
Notice bibliographique
Résumé
ABSTRACTThe Program of Support for Small and Medium Enterprises (PROPYME) in Costa Rica was initiated in 2002 by the Ministry of Science and Technology and is ongoing as of 2015. It provides nonrefundable grants for small and medium sized enterprises to develop innovation-related projects, including RD a critical problem which is also observed in other Latin American countries (Lederman et al., 2014). In recent years some large multinational companies have engaged in advanced manufacturing in high technology industries, where they have progressively upgraded the value-added of their operations and increased R&D investments (OECD, 2012). However, the vast majority of firms, especially small and medium-sized enterprises (SMEs), hardly invest in innovation. Strong obstacles to innovation at the firm level, as identified by industry surveys and expert assessments, include limited managerial and technical skills, organizational rigidity, insufficient information about markets and technologies, lack of access to finance, obsolete infrastructure, and insufficient collaboration on innovation among firms and between firms and universities or public research centers.Since the creation of the Costa Rican Ministry of Science and Technology (MICIT) in 1990, the promotion of science, technology and innovation has become a top priority on the Government's agenda (MICIT, 2011). The PROPYME fund was instituted in 2002 with the belief that without government intervention, investment by SMEs in innovation, technology adoption, and skills development, would be suboptimal. MICIT is responsible for the design, implementation and funding of the program, through its National Council for Scientific and Technological Research (CONICIT).The PROPYME fund addresses the key bottlenecks facing the national innovation system: low innovation in SMEs, insufficient collaboration in R&D between firms, lack of collaboration with universities, and low training in firms (Monge et al., 2010). The program excludes large firms, focusing instead on promoting innovation and skills development in SMEs, defined as firms with less than 100 employees. The grants are provided only to SMEs that have been in operation for more than six months.The government decided to provide grants for innovative projects because relying on market forces alone resulted in suboptimal investment in innovation by SMEs. In Costa Rica the private sector accounts for about a third of total R&D, while in more technologically advanced countries the figure is around two-thirds. The PROPYME program aims to reverse this over-reliance on public sector R&D. The program design assumed that public grants produce an additionality effect, that is, increased expenditures by SMEs on innovation - expenditures that would not occur without the public funding incentive. …
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 distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,005 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,002 | 0,006 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,001 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 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 tête enseignante, 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 ».