Evaluating innovation capability of Chinese listed companies based on comprehensive methods

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Yanrong Hu ◽  
Hongjiu Liu
Keyword(s):  
Principal Component Analysis ◽  
Comprehensive Evaluation ◽  
Principal Component ◽  
Listed Companies ◽  
Component Analysis ◽  
Innovation Capability ◽  
Content Type ◽  
Innovation Ability ◽  
Copeland Method ◽  
Coefficient Method

Purpose This paper aims to evaluate innovation capability of companies based on comprehensive methods. Design/methodology/approach This paper used principal component analysis (PCA), kernel principal component analysis (KPCA) and principal component cluster (PCC) analysis to analyze the listed companies’ innovation capability. On these bases, mean method, Borda method, Copeland method, alienation coefficient method and fuzzy Borda method were used separately for the comprehensive evaluation. Findings The results show that the comprehensive evaluation can overcome the shortage of the single method and improve the reliability of the innovation ability evaluation. In addition, the method also reveals that the innovation ability of the listed companies is closely related to the innovation investment and their industry and further regional economic development level of each province (city and area). Originality/value This paper uses PCA, KPCA and PCC to evaluate and study their innovation ability. On the basis of these, five methods (mean method, Copeland method, Borda method, divorced coefficient method and fuzzy Borda method) are applied respectively to combine the sort results of PCA, KPCA and PCC. The results show that combination methods have better theoretical and practical significance for innovation ability.

Evaluation of Regional Technological Innovation Efficiency Based on Principal Component Analysis and DEA

2010 ◽  
Vol 33 ◽  
pp. 378-382 ◽  
Author(s):  
Hong Mei Chen ◽  
Zhi Yong Wu ◽  
Wei Jin
Keyword(s):  
Principal Component Analysis ◽  
Technological Innovation ◽  
Comprehensive Evaluation ◽  
Evaluation Model ◽  
Returns To Scale ◽  
Principal Component ◽  
Component Analysis ◽  
Comprehensive Evaluation Model ◽  
Innovation Ability ◽  
Determining Factor

Regional technological innovation ability is increasingly becoming the determining factor to attain an international competitive advantage for the areas, as well as to achieve regional economic growth and development. But in the same time of raising the regional technological innovation ability, it is also necessary to continuously improve the efficiency of regional technological innovation, so as to increase the resources efficiency. We evaluate the efficiency of regional technology innovation for China's 30 provinces and municipalities using comprehensive evaluation model combined principal component analysis and DEA. According to evaluation results, we think that the overall efficiency for regional technological innovation is at the low level, and most areas is at the stage of increasing returns to scale, that account for a common problem of insufficient investment in the process of regional technological innovation.

A principal component analysis of the determinants of spatial disparity between rural and urban localities of Ghana

2017 ◽  
Vol 44 (6) ◽  
pp. 715-731 ◽  
Author(s):  
Ivy Drafor
Keyword(s):  
Principal Component Analysis ◽  
Rural Areas ◽  
Urban Areas ◽  
Principal Component ◽  
Component Analysis ◽  
Spatial Inequality ◽  
Necessary Condition ◽  
Spatial Disparity ◽  
Content Type ◽  
Rural And Urban

Purpose The purpose of this paper is to analyse the spatial disparity between rural and urban areas in Ghana using the Ghana Living Standards Survey’s (GLSS) rounds 5 and 6 data to advance the assertion that an endowed rural sector is necessary to promote agricultural development in Ghana. This analysis helps us to know the factors that contribute to the depravity of the rural sectors to inform policy towards development targeting. Design/methodology/approach A multivariate principal component analysis (PCA) and hierarchical cluster analysis were applied to data from the GLSS-5 and GLSS-6 to determine the characteristics of the rural-urban divide in Ghana. Findings The findings reveal that the rural poor also spend 60.3 per cent of their income on food, while the urban dwellers spend 49 per cent, which is an indication of food production capacity. They have low access to information technology facilities, have larger household sizes and lower levels of education. Rural areas depend a lot on firewood for cooking and use solar/dry cell energies and kerosene for lighting which have implications for conserving the environment. Practical implications Developing the rural areas to strengthen agricultural growth and productivity is a necessary condition for eliminating spatial disparities and promoting overall economic development in Ghana. Addressing rural deprivation is important for conserving the environment due to its increased use of fuelwood for cooking. Absence of alternatives to the use of fuelwood weakens the efforts to reduce deforestation. Originality/value The application of PCA to show the factors that contribute to spatial inequality in Ghana using the GLSS-5 and GLSS-6 data is unique. The study provides insights into redefining the framework for national poverty reduction efforts.

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