The idiosyncrasy of research and development efficiency across types of small‐ and medium‐sized enterprises: evidence from Korea

Abstract

Small-and medium-sized enterprises (SMEs) are considered to be the main driving force behind economic and employment growth. However, the low productivity levels of SMEs cannot be explained by lower levels of R&D investment, but are instead a consequence of a lack of research and development (R&D) efficiency that would have been adequate for making firms more competitive. To investigate any differences in R&D efficiency that impact the company overall performance in economic and employment performance, we analyze various types of SMEs in Korea. This paper applies two estimation methods: data envelopment analysis and regression analysis. This study takes three relevant factors of output: (1) the number of patents, (2) the number of success in terms of technology development, and (3) the level of profit attained through new products. Additionally, the study's model incorporates three parts of input: (1) human resources, (2) development periods, and (3) R&D budget. In the context of this paper, a comparison of the factors regarding efficiency is performed based upon the following criteria: the level of novelty, the length of a product's life cycle, and the time period of a competitor emulation of the product created by original enterprise. Comparing the factors that influence the efficiency of types within each criterion is meaningful in analyzing the differences in efficiency outcome on the various types among SMEs. Our findings indicate that SMEs vary among types in terms of efficiency and that there are also differences causing factors according to types.

Innovation performance and influencing factors of low-carbon technological innovation under the global value chain: A case of Chinese manufacturing industry

“…Data Envelopment Analysis (DEA) method has been widely used to conduct input-output analysis for innovation performance (Chen, et al, 2016;Lee and Yoon, 2015). Because innovation process involves too many factors, it is better to view innovation process as a 'black box' and place the focus on innovation inputs and outputs (Chen and Guan, 2012;Kaihua and Mingting, 2014).…”

“…Innovation performance analysis of general innovation activities mainly focuses on a few selected input indicators such as R&D employees and R&D expenditure, and output indicators such as number of patents and turnover of new products (Cruz-Cázares et al, 2013;Lee and Yoon, 2015;Kaihua and Mingting, 2014;Moon, 2013). These selected input and output indicators seem adequate for general innovation performance evaluation.…”

The importance of large firms for generating economic value from subsidized technological innovation: A regional perspective

“…In particular, although larger firms may be less financially constrained in commercializing new technological achievements and hence more likely to use part of their subsidies as substitutes for investments they were going to make anyways, we expect them to use the additional investments stemming from these subsidies much more effectively than smaller firms when transforming scientific and technological achievements into industrial or commercial applications. Apart from financial inputs, commercializing innovations also requires continuous inputs of human resources and (production) technology (Lee and Yoon, 2015;Lewin et al, 2009). Large-scale enterprises have advantages in terms of their internal talent reserve and technical foundation (Bos-Brouwers, 2010), so that they have scale advantages in gaining resources for their business operations and commercialization activities (Ettlie and Rubenstein, 1987).…”

Dynamic analysis of the efficiency of research and development systems of South-European countries

“…Generally, two types of studies dedicated to this issue can be distinguished: the first type of studies analyzes R/D efficiency determinants, while the second type measures R/D efficiency. The techniques used in the second study group are the Malmquist Productivity Index [38], [20]; DEA [42], [25], [9], [16], [28], [2], [26]; Stochastic Frontier Analysis [42], [21] and Regression Analysis [26]. DEA is the most often used method to measure R/D efficiency, because it shows how to increase efficiency of inefficient decision-making units [10].…”