Promoting Innovation in SMEs in Developing Countries: A Case Study of Costa Rica's PROPYME Program
Bibliographic record
Abstract
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. …
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.006 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".