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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.


Government R&D subsidy size comparisons with the 95% CIs of DEA efficiency scores (n1 = 139).
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Fig3: Government R&D subsidy size comparisons with the 95% CIs of DEA efficiency scores (n1 = 139).

Mentions: Because it is determined that the efficiency difference is not statistically significant between R&D collaboration types in Section 4.3, the sample of n1 = 139 can be pooled and analyzed in order to compare the efficiency between government subsidy sizes. Table 8 presents a summary of descriptive statistics of VRS DEA efficiency scores using the sample of n1 = 139. Figure 3 shows 95% CI of DEA efficiency scores for each government subsidy size. In terms of the mean of DEA efficiency scores, GS-Class1 has the largest, while GS-Class5 has the smallest. As for the dispersion, the last two sizes, such as GS-Class4 and GS-Class5, are the worst due to the largest dispersion measures. GS-Class4 has IQR = 0.512 and StDev = 0.264, and GS-Class5 has IQR = 0.429 and StDev = 0.271.Table 8


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

Park S - Springerplus (2014)

Government R&D subsidy size comparisons with the 95% CIs of DEA efficiency scores (n1 = 139).
© Copyright Policy - open-access
Related In: Results  -  Collection

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

Fig3: Government R&D subsidy size comparisons with the 95% CIs of DEA efficiency scores (n1 = 139).
Mentions: Because it is determined that the efficiency difference is not statistically significant between R&D collaboration types in Section 4.3, the sample of n1 = 139 can be pooled and analyzed in order to compare the efficiency between government subsidy sizes. Table 8 presents a summary of descriptive statistics of VRS DEA efficiency scores using the sample of n1 = 139. Figure 3 shows 95% CI of DEA efficiency scores for each government subsidy size. In terms of the mean of DEA efficiency scores, GS-Class1 has the largest, while GS-Class5 has the smallest. As for the dispersion, the last two sizes, such as GS-Class4 and GS-Class5, are the worst due to the largest dispersion measures. GS-Class4 has IQR = 0.512 and StDev = 0.264, and GS-Class5 has IQR = 0.429 and StDev = 0.271.Table 8

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.