R & D spending among Chinese SMEs: the role of business owners’ characteristics
Bibliographic record
Abstract
Purpose – Given that organizational decisions are made by individuals and thus shaped by their subjective and objective characteristics, the purpose of this paper is to examine the effect of SME business owners’ characteristics on their firms’ research and development (R & D) spending in a transition economy. Design/methodology/approach – The authors first build the arguments that, among small- and medium-sized enterprises (SMEs), business owners’ perceived importance of R & D-related activities, their education, related experiences, and social connections, should affect their firms’ R & D spending positively. Then the authors use a Chinese nationwide survey of private SMEs to test the arguments. Tobit regression analyses are conducted by taking Stata 12.0 as the statistic tool. Findings – The authors find that business owners’ perceived importance of R & D-related activities is positively associated with their firms’ R & D spending. In addition, better-educated owners and owners who have technology-related working experience tend to invest more in R & D activities. Finally, owners who have social connections, especially industrial connections, tend to spend more on R & D activities. Originality/value – This study improves the understanding of R & D spending determinants among SMEs. Going beyond general environmental determinants, it reveals the important agency role of SME owners, and thus contributes to a better understanding of how decisions leading to SME innovations are influenced by business owners’ perceptions and demographic characteristics.
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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 | 0.002 |
| 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 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".