Barriers to innovation in Spanish rural Small and Medium-Sized Enterprises
Bibliographic record
Abstract
In the context of globalisation, innovation has been recognized as a key driver of Europe’s national and regional economies, whether rural or not. Nevertheless, rural firms are considered less innovative than firms in urban agglomerations. Rural areas represent three-quarters of the land of the OECD countries and are home to a quarter of its population. Moreover, small and medium enterprises (SMEs) are the backbone of the economy. \nThis paper reviews the barriers to innovation indicated throughout literature, brings out what constitutes the main barriers in rural SMEs and presents an understanding of some of the factors that determine the position of these firms in responding to new requirements. \nData were collected through a questionnaire for managers of 511 SMEs in a rural area of Spain. Statistical analysis was performed with SPSS software package. The results identify key factors that hinder innovation in rural SMEs, namely those related to economic reasons, such as high costs of the innovation or the difficulty to obtain financial resources, and risk aversion issues. Specific research related to the study of innovation barriers in SMEs firms in rural areas is limited. Therefore, this paper fills this research gap by expanding the body of knowledge in the field of rural SMEs innovation and provides further evidence on this phenomenon. The results also offer relevant insights for managers and policy makers when formulating and implementing strategies to diminish innovation barriers in rural SMEs.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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".