Limits...
Analyzing the efficiency of small and medium-sized enterprises of a national technology innovation research and development program.

Park S - Springerplus (2014)

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.

View Article: 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.


Normal probability plot of DEA efficiency scores with the 95% CI (n1 = 139).
© Copyright Policy - open-access
Related In: Results  -  Collection

License
getmorefigures.php?uid=PMC4128955&req=5

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


Analyzing the efficiency of small and medium-sized enterprises of a national technology innovation research and development program.

Park S - Springerplus (2014)

Normal probability plot of DEA efficiency scores with the 95% CI (n1 = 139).
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4128955&req=5

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

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.

View Article: 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.