scholarly journals Coopetition for Sustainable Competitiveness: R&D Collaboration in Perspective of Productivity

2020 ◽  
Vol 12 (19) ◽  
pp. 7993
Author(s):  
Youngwook Ko ◽  
Yanghon Chung ◽  
Hangyeol Seo
Keyword(s):  
Data Envelopment Analysis ◽  
Propensity Score ◽  
Value Creation ◽  
New Technology ◽  
Innovation Process ◽  
Network Data ◽  
Data Envelopment ◽  
Value Appropriation ◽  
Analysis Methodology ◽  
Two Stages

This study explores the effect of coopetition on research and development (R&D) productivity in two stages of the innovation process: (1) value creation to develop new technology and (2) value appropriation to generate profits. Using a sample from the 2010 and 2014 Korea Innovation Survey, we applied the propensity score matching methodology to control selective bias and the two-stage network data envelopment analysis methodology to measure R&D productivity. Our findings indicate that firms who cooperate with competitors in the value creation stage have relatively higher R&D productivity than those who do not. In contrast, firms that pursue the coopetition strategy showed relatively low R&D productivity in the value appropriation stage. Overall, this study provides a better understanding of coopetition by demonstrating its various benefits, costs, and risks.

Efficiency Evaluation of China's Regional Innovation Process Based on a Network Data Envelopment Analysis Model with a Mixed Structure: Enlightenment from the Regional Tobacco Industry

2021 ◽  
Vol 7 (6) ◽  
pp. 5184-5196
Author(s):  
Zhu Yu ◽  
Yang Feng ◽  
Wang Dawei
Keyword(s):  
Data Envelopment Analysis ◽  
Tobacco Industry ◽  
Innovation Process ◽  
Data Envelopment Analysis Model ◽  
Network Data ◽  
Data Envelopment ◽  
Mixed Structure ◽  
Regional Innovation ◽  
Network Data Envelopment Analysis ◽  
Two Stages

Accurate measurement of regional efficiency is a prerequisite for effective management. Prior studies have expanded on the overall "black box" evaluation with two stages of research and development (R&D) and commercialization, opening up the internal structure of the regional innovation process, but ignoring the independent innovation activities of universities, research institutes, and firms in the R&D stage. We construct a mixed structure with two stages, three actors, and four subsystems, and conduct an empirical analysis of China's provincial samples from 2017 to 2019 by using the network data envelopment analysis (DEA) model. Results show that the efficiency of the R&D stage at the provincial level is generally higher than that of the commercialization stage. However, the three subsystems of the R&D stage perform poorly. Spearman’s rank correlation coefficient suggests that there is a significantly positive correlation between total regional efficiency and commercialization. In addition, we use the k-means method to divide 27 provinces into three clusters, setting a more appropriate improvement benchmark for inefficient provinces. Based on enlightenment of regional tobacco industry, we put forward some proposals for specific stage and specific subsystem.

scholarly journals Efficiency of Two-Stage Technological Innovation in High Patent-Intensive Industries That Considers Time Lag: Research Based on the SBM-NDEA Model

2020 ◽  
Vol 2020 ◽  
pp. 1-9
Author(s):  
Junzhong Zou ◽  
Wei Chen ◽  
Nan Peng ◽  
Xuan Wei
Keyword(s):  
Data Envelopment Analysis ◽  
Panel Data ◽  
Technological Innovation ◽  
Time Lag ◽  
Innovation Process ◽  
Indicator System ◽  
Data Envelopment ◽  
Input Output ◽  
Two Stages ◽  
Output Indicator

Considering time lag and accumulation of inputs and outputs, this paper adopts the superefficiency data envelopment analysis (DEA) model to study the technological innovation efficiency of high patent-intensive industries using panel data from 2007 to 2017. Given the characteristics and the actual circumstances of the industries, the innovation process is divided into two stages, and an input-output indicator system is established. The results show that the overall innovation efficiency level of high patent-intensive industries in China is increasing. However, the R&D achievements in technology are not quickly applied or sufficiently transformed.

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