Motivations for International Open InnovatioN (IOI): the perspective of Quebec SMES in Africa
Bibliographic record
Abstract
Objective: This research aims to understand the motivations for SMEs from developed countries to engage in open innovation (OI) projects with partners in developing countries. \n \nMethodology: We adopted a qualitative approach and studied the case of 16 SMEs from Quebec that successfully carried out OI projects within African countries. \n \nRelevance: Despite the growing body of research on OI within SMEs, the international perspective of OI still needs to be explored. In particular, the context of developing countries has received limited attention, especially the motivations for SMEs from developed countries to undertake OI projects in developing countries. \n \nMain results: The results show that OI projects with African partners allow SMEs to integrate into African markets and acquire knowledge different from that of developed economies. These partnerships strengthen the overall organizational capacity of the SME beyond the acquisition of specific knowledge related to the innovation project. They also include social objectives to improve local communities living conditions. \n \nTheoretical contributions: By addressing the calls for research on OI within developing countries, this article expands the scope of OI in this context. It also contributes to the resource-based view theory by identifying integration within foreign networks as the main strategic resource, motivating SMEs from developed countries to initiate OI projects in developing countries. \n \nManagerial contributions: The study provides insights to SMEs from developed countries about the various reasons for implementing OI projects with partners in developing countries. It also offers them tailored advice to carry out such projects successfully.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.010 | 0.003 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".