Customer dependency in manufacturing SMEs: implications for R&D and performance
Bibliographic record
Abstract
In the now global business environment, SMEs are being subjected to increased pressures. In the manufacturing sector in particular, increased requirements for information and knowledge management, innovation, quality, and flexibility within new organisational forms such as the network enterprise entail organisational developments that can affect critical business processes, R&D in particular, and business performance. Hence, the customer dependency of manufacturing SMEs on certain important customers or the absence of diversification in their customer base can have significant impacts on the R&D activities, the productivity, and eventually the profitability of these organisations. Through an empirical study of 179 Canadian SMEs, it was found that more commercially dependent firms allocate more financial and human resources to product R&D. These firms are also less productive in that they have relatively fewer sales per employee. While customer dependency seems to negatively affect the SMEs’ profitability, firms whose product R&D activities are more intense report significantly higher gross margins. R&D activities could allow manufacturing SMEs to counter the influence of their major customers, by reversing the direction of commercial dependency, and thus to reduce their vulnerability.
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".