Antecedents and processes leading to open innovation in SMEs
Bibliographic record
Abstract
Open Innovation (OI) literature related to Small and Medium Enterprises (SMEs) has often been fragmented, hindering the effective generalisation of findings. We conducted a systematic multidisciplinary literature review to explore OI in the context of SMEs across various regions. OI research in SMEs was most common in Europe, followed by China, Korea, South East Asia, Africa, South America and the Middle East. Surprisingly, very little research has been done in the USA, Canada, and the Indian subcontinent. We aimed to identify the commonalities and regional specifics in the antecedents and processes leading to OI. The review included one hundred research articles from the Web of Science database. Our findings revealed four common antecedents of OI across the firms: Human Factors, Technology, Policy, and Heterogeneity.Additionally, we identified three standard processes that lead to the OI phenomenon: firm-level partnerships, collaboration with academia, and intermediation. Though the antecedents and processes hold across firms of different sizes, human factors, policy and heterogeneity among the antecedents and intermediation as the process holds more significance to the SMEs as a sector. This research has important implications for academia and the industry. Firstly, we have responded to the research gap by increasing the generalisation of results across the regions. Secondly, different stakeholders may look for literature that is directly relevant to them. Thirdly, SME managers need to know the specific attitudes to facilitate OI effectively. Fourthly, the importance of intermediation has been identified as an effective process leading to OI. Finally, we have categorised future research directions under different clusters.
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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 | 0.007 | 0.045 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.006 | 0.005 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".