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Research on Comprehensive Evaluation of Science And 2020 2nd International Conference on Education, Economics and Information Management (EEIM 2020) ISBN: 978-1-60595-684-8 Research on Comprehensive Evaluation of Science and Technology Innovation Ability of Universities in Jiangxi Province Hong JI1,2,a,* and Jin-jin WANG2 1Jiangxi Normal University Science and Technology College, Gongqingcheng, Jiangxi, China 2School of Education, Jiangxi Normal University, Nanchang, Jiangxi, China [email protected] *Corresponding author Keywords: Regional Colleges and Universities, Technological Innovation Ability, Factor Analysis Method, Evaluation System. Abstract. The strength of scientific and technological innovation is an important criterion to measure the comprehensive strength of a country. Improving the strength of scientific and technological innovation is an important part of improving the country's comprehensive national strength and enhancing its international voice. As an important part of the national scientific research team, universities play an important role in improving the national technological innovation strength. This paper constructs an evaluation index system for the technological innovation ability of colleges in Jiangxi Province, and selects 34 universities in Jiangxi Province as samples, and uses factor analysis and SPSS23 statistical analysis software to analyze the technological innovation capabilities of the sample universities. The results show that the overall input and output capacity of technological innovation is relatively low, which is far behind that of universities in other developed provinces, especially key universities. 1. Introduction As an important part of the national technology innovation system, universities undertake the functions of integrating, creating, processing, and disseminating knowledge, producing high-tech achievements, promoting high-tech industrialization, transforming scientific and technological achievements, and cultivating a large number of technological innovation talents. In the process of implementing new industrialization, optimizing the industrial structure, and transforming the economic development mode, Jiangxi Province needs strong scientific and technological strength as a support force, and must continuously increase investment in technological innovation of universities, and use "use innovation to make cities prosper, use innovation to make cities develop faster" as the development benchmark , it can give full play to the driving role of innovation, so that universities can play an important role in promoting the transformation of economic development mode and promoting regional economic growth. Constructing and optimizing the evaluation system of the technological innovation capability of universities in Jiangxi Province and comprehensively evaluating the scientific and technological innovation strength of the universities in Jiangxi Province have an important influence in promoting the construction of regional technological innovation capabilities. 2. Principles for the Design of Evaluation Indexes for Technological Innovation Ability of Jiangxi Universities The establishment of a scientific and effective evaluation index system for scientific and 94 technological innovation capabilities of universities is very important to enrich and develop the theoretical connotation of national technological innovation capabilities, strengthen the basic support for innovation capabilities, and improve the power of scientific and technological progress in order to promote regional economic growth. Universities should make full use of their own advantages, strengthen school-enterprise, school-school cooperation, continuously improve the structure of production, education and research, and fully develop the potential for technological innovation.[1] 2.1. Scientific Principle The principle of scientificity requires that research must be developed scientifically, and that information and data are true, correct and objective. Evaluation indicators are also established under the guidance of scientific thinking and analysis with real and reliable data. 2.2. Operability Principle The principle of operability requires that the established evaluation index system of technological innovation capabilities of universities is easy to collect and quantify, and the conclusions drawn are universal. The established evaluation indicators should be used for future innovation ability evaluation. 2.3. Comprehensive Principle The evaluation of scientific and technological innovation capabilities of universities is a comprehensive, systematic, and highly hierarchical work, covering all aspects from the local to the whole. 3. The Design of the Evaluation Index for the Scientific and Technological Innovation Ability of Universities in Jiangxi Province The technological innovation of universities should include three parts: talents, knowledge and material resources. According to the perspective based on the process of university technology innovation, technology innovation of university mainly includes input and output. This article follows the above principles and establishes a set of indicator systems, including two first-level indicators: scientific and technological innovation input capacity and scientific and technological innovation output capacity; five secondary indicators ： human resource input capacity, technological funding input capacity, project input capacity, technological writing output capacity , technological output capacity; 15 three-level indicators: the number of scientific research personnel, scientific research personnel with senior professional titles, research and development personnel, full-time equivalent personnel in research and development, technological funds allocated in the current year, internal expenditures of technological funds in the current year, total number of topics, number of people invested in the current year, and current year of the topic Appropriation funds, project expenditures in the current year, number of monographs, published academic papers, number of appraisal results, number of technology transfers and contracts signed, actual income of the year of technology transfer. 95 4. Analysis of the Scientific and Technological Innovation Ability of Universities in Jiangxi Province 4.1. Data Sources and Research Methods The factor analysis method was proposed by the British psychologist Spearman, which refers to the method of extracting a few unrelated factors from variables with complex relationships. This paper selects the method of factor analysis and uses SPSS23 software to analyze the selected sample data of 34 universities in Jiangxi Province in 2017, and comprehensively evaluate the technological innovation capabilities of universities in Jiangxi Province. The research data in this article comes from the "2017 University Science and Technology Statistics Collection".[2] 4.2. KMO and Bartlett Sphericity Test Before performing factor analysis, KMO and Bartlett's sphericity test must be performed first. The value is between 0 and 1. The closer the KMO is to 1, the stronger the correlation between the variables. After testing, the KMO test value is 0.728, indicating that the correlation between the index variables is strong, and the effect of factor analysis is better at this time. 4.3. Analysis of Eigenvalue and Variance Contribution The KMO test proves that the index system constructed is highly relevant, so the factor analysis method is used to calculate the initial eigenvalues, cumulative contribution rate, and cumulative contribution rate of the index system after rotation.[3] The initial eigenvalues of the two principal components extracted are 10.423 and 1.625, both of which are greater than 1, and their contribution rates are 69.484% and 10.835% respectively. The cumulative contribution rate is 80.319%, which exceeds 80%.The principal components are named F1 and F2, and are used as new indicators to evaluate the scientific and technological innovation capabilities of universities in Jiangxi Province. 4.4. Factor Loading Matrix The above data has not been standardized, so the factors cannot be compared, and no reasonable explanation can be made for the extracted common factors. Here, the maximum variance method is used to rotate the data to obtain the rotation component matrix, and further explain the common factors.The details are shown in Table 1. Table 1. Rotation component matrix. ingredient coding index F1 F2 c1 Teaching and research staff .892 -.068 c2 Number of teaching and research personnel with senior professional titles .878 .217 c3 Research and development staff .939 .239 c4 Research and development full-time equivalent personnel .939 .238 c5 Science and technology funds allocated in the year .780 .547 c6 Internal expenditure of science and technology expenditures .787 .520 c7 Total number of topics .687 .477 c8 Number of participants in the project during the year .941 .236 c9 Funds allocated for the project that year .738 .583 c10 Expenditure for the project in the current year .704 .553 c11 Number of monographs .427 .612 c12 Number of academic papers published .881 .238 c13 Number of identification results -.026 .784 c14 Number of technology transfer contracts signed .175 .835 c15 Actual income for the year of technology transfer .653 .516 96 Table 1 shows that the principal component F1 has a large load on indicators such as C1(teaching and research personnel), C2 (the number of teaching and research personnel with senior professional titles), C3 (research and development
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