Does Technology Transfer Help Small and Medium Companies? Empirical Evidence from Korea
Bibliographic record
Abstract
We challenge the view that technology transfer from big companies to small and medium (SM) size companies helps SM companies to prosper. With a large dataset of SM companies in Korea, we utilize the stochastic production frontier (SPF) model to examine the productivity of inputs and the generalized linear model (GLM) to compare business performance between two groups of SM companies: SM companies that receive technology transfer and those that do not receive technology transfer from big companies. The empirical results demonstrate that the transfer of technology from big companies to SM companies help SM companies to enjoy productivity of capital. Nonetheless, SM companies receiving technology transfer were found to underperform in terms of labor productivity and profit margin compared to their counterparts. We further investigate the reasons why SM companies receiving technology transfer from big companies underperform relative to their counterparts, and our findings shows that the former do not export much of their product and face more difficulties such as lower price for their products imposed by big companies than the latter. By identifying the negative rather than the conventionally assumed positive effect of technology transfer, this paper contributes to the literature on the relationship between technology transfer and SM companies’ prosperity in the case of Korea. Our findings have important implications for how SM companies should strategize and rethink about the clauses embedded in the transfer of technology that they receive from big companies because technology transfer plays as a barrier to their prosperity.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".