INNOVATION COLLABORATIONS IN LOW-TO-MEDIUM TECH SMEs: THE ROLE OF THE FIRM’S INNOVATION ORIENTATION AND USE OF EXTERNAL INFORMATION
Bibliographic record
Abstract
This study articulates and tests the direct and indirect relationships between the company’s innovation orientation (IO), its collection and dissemination (C&D) of external information among the organisational members, and the level of success of its innovation collaborations involving customers, suppliers, and research organisations. Our conceptual framework is developed based on an integration of the literatures on organisational capabilities, marketing, innovation, and management control. We empirically test these relationships on a sample of 117 small-to-medium enterprises (SME) operating in Low-to-Medium-Tech (LMT) manufacturing industries. Partial Least Squares (PLS) results reveal that the relationship between the firm’s IO and the success of its customer collaborations is partially mediated by the C&D of external information. We also find that the relationship between the firm’s IO and the success of supplier collaborations is direct, and that the C&D of external information has no effect on the success of such collaborations. Finally the relationship between IO, C&D of external information and the success of research organisation collaborations is found to be indirect. Overall, these findings suggest that developing successful innovation collaborations in LMT sectors requires that SME managers start by building an internal culture that promotes innovation, learning and openness to the external environment.
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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 | 0.005 | 0.029 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.000 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".