Challenges Msmes Face And Benefits In The Adoption Of Open Collaborative Innovation: A Principal Component Approach
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
This study investigates the use of open collaborative innovation practices by MSMEs in Botswana and uses the opinions of a stratified sample of 206 MSMEs’ owners/managers to identify the benefits and challenges that MSMEs face in engaging in open collaborative innovations to scale up their businesses. The results show that majority of the enterprises (81.1%) were not engaged in open collaborative innovations, had owners/managers with first degree qualification or below (59.7%), lacked access to financial resources, and had below 5 years of experience (69.4%) in running the enterprises. Using the Principal component analysis, the identified benefits are increased financial revenue, improved market strategy, better incentives and improved knowledge; while the challenges are lack of networks, lack of financial support, market demands and previous innovation experiences. The study recommends that there is need for agencies charged with MSME Development in Botswana (LEA, CEDA) to sensitize MSMEs to engage in open collaborative innovation to enhance growth; enterprises should be encouraged to collaborate with universities to bridge the gap in the lack of qualified research experts with PhD ; policies to enable the MSMEs access finance for the businesses and protect the intellectual property rights abuse of businesses are imperative; and policies that would protect micro and small enterprises from unnecessary market competition with larger enterprises need to be put in place.
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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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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 it