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Analyzing the efficiency of small and medium-sized enterprises of a national technology innovation research and development program.
Bottom Line:
However, the R&D collaboration type, "SME-University-Laboratory" Joint-Venture was superior to the others, achieving the largest median and the smallest interquartile range of DEA efficiency scores.Second, the differences in the efficiency were statistically significant between government R&D subsidy sizes, and the phenomenon of diseconomies of scale was identified on the whole.As the government R&D subsidy size increases, the central measures of DEA efficiency scores were reduced, but the dispersion measures rather tended to get larger.
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PubMed Central - PubMed
Affiliation: Department of Business Administration, Baekseok University, Cheonan, ROK 330-704 Korea.
ABSTRACT
This study analyzes the efficiency of small and medium-sized enterprises (SMEs) of a national technology innovation research and development (R&D) program. In particular, an empirical analysis is presented that aims to answer the following question: "Is there a difference in the efficiency between R&D collaboration types and between government R&D subsidy sizes?" Methodologically, the efficiency of a government-sponsored R&D project (i.e., GSP) is measured by Data Envelopment Analysis (DEA), and a nonparametric analysis of variance method, the Kruskal-Wallis (KW) test is adopted to see if the efficiency differences between R&D collaboration types and between government R&D subsidy sizes are statistically significant. This study's major findings are as follows. First, contrary to our hypothesis, when we controlled the influence of government R&D subsidy size, there was no statistically significant difference in the efficiency between R&D collaboration types. However, the R&D collaboration type, "SME-University-Laboratory" Joint-Venture was superior to the others, achieving the largest median and the smallest interquartile range of DEA efficiency scores. Second, the differences in the efficiency were statistically significant between government R&D subsidy sizes, and the phenomenon of diseconomies of scale was identified on the whole. As the government R&D subsidy size increases, the central measures of DEA efficiency scores were reduced, but the dispersion measures rather tended to get larger. No MeSH data available. |
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Fig4: Normal probability plot of DEA efficiency scores with the 95% CI (n1 = 139). Mentions: Again, in terms of DEA efficiency scores associated with the sample of n1 = 139, the Normality assumption should be rejected based on Figure 4 as well as two test statistics such as AD = 3.024*** (P-value < 0.005) and KS = 0.132*** (P-value < 0.010). Table 9 shows KW test results on DEA efficiency scores with the sample of n1 = 139 in Figure 4. Based on H = 27.04*** (P-value = 0.000), the efficiency difference between government subsidy sizes is statistically significant ().Figure 4 |
View Article: PubMed Central - PubMed
Affiliation: Department of Business Administration, Baekseok University, Cheonan, ROK 330-704 Korea.
No MeSH data available.
