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The Research on Scientific Innovation of Colleges and Universities

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The Research on Scientific Innovation of Colleges and Universities
The Research on Scientific Innovation of Colleges and Universities
Based on Fuzzy Integral Evaluation Method
XIE Nanbin, XIAO Jing
Shanwei Vocational and Technical College, Shanwei, Guangdong, P.R.China, 516600
[email protected]
Abstract: Scientific innovation ability of colleges and universities has become the key that universities
develop and gain competitive advantages. The paper evaluates the scientific innovation ability of the
university scientific and has a special significance for enhancing the overall competitiveness of the
university effectively. At the same time, the paper constructed a scientific innovation ability evaluation
index system of university with systematic, quantify and applicable based on the questionnaire and
expert’s screening according to the principle of evaluation index system design, which selected 31
elements from the following 5 areas: the investment ability of scientific innovation, the supporting
ability of scientific innovation, the production ability of scientific innovation, the transformation ability
of scientific innovation and the sustainable ability of scientific innovation as evaluation index. Proposed
evaluation method of university scientific innovation ability based on fuzzy integral that combined to
improved fuzzy integral evaluation method.
Keywords: fuzzy integral, university, scientific innovation, evaluation.
1 Introduction
Scientific Innovation ability of Colleges and Universities refer to the all-round abilities that coordinate
and promote the university basic subjects’ scientific research and the development of the emerging
disciplines based on the main element of university innovation system (teachers and researchers) in the
full use of modern information technology, which apply policies, laws, and organize all available
resources within the universities and the human, financial, material outside the universities, then put
knowledge, talent, technology, information, and advanced innovation management into university
scientific innovation system constantly. Scientific Innovation ability has become an important symbol to
measure a country's basic research and frontier areas original of high-tech innovation ability, the key
factor to measure the competitiveness of colleges and universities and the fundamental driving force of
colleges and universities development. The basic science research and breakthrough of university, and
emerging subjects’ research result constantly spurred to high-tech industry group that represent the
direction of industrial development in future, which is the inexhaustible source of strength. With the
rapid development of science and technology, China's scientific innovation work, especially the in basic
research burden will fall on the shoulders of colleges and universities. Therefore, the studies of
university scientific innovation have important theoretical and practical significance for promoting the
development of colleges and universities and enhance the competitiveness of the country.
For the evaluation studies of scientific innovation ability, foreign scholars mainly reflected in the
institutions assessment and performance evaluation, such as the Mc Guire [1] and others on the research
about university research productivity and reputation; Izadi, Johnes, Oskrochi and Crouchle[2] the
research about consumer cost of the UK universities; Mensah and Werner [3] the research about
cost-effectiveness of university and financial flexibility. Chinese scholars has a large number of
empirical studies and gain a wealth of research results on the evaluation of university scientific
innovation ability, such as Wang Zhangbao, Xu Zongwei and others builds a comprehensive
quantitative evaluation index system and put forward the comprehensive evaluation model and method
based on the analysis of scientific innovation ability in colleges and its constituent elements [4]; Wang,
Guangping, Kim Hao established indicators for evaluation of university scientific innovation and
conducted empirical analysis for scientific innovation in colleges by using factor analysis [5]; Liang Yan,
Geng Yan, Lin Yuwei, LI Xiangyin has been studied university scientificl innovation ability evaluation
system by the Hierarchy Analysis Method [6]; Wang Qing, Cao Zhaomin analyzed major internal and
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external environment that have the impact of Shanghai university scientific innovation ability by the
application of S (strengths) W (weaknesses) O (opportunities) T (threats) analysis methods in
management and put forward the development strategies of Shanghai university scientific innovation
ability[7]; Chen Yunping, Chen Linxin established evaluation index system of scientific innovation
ability, while did the dynamic comparative analysis to the trend of development and changes about
2000-2004 six provinces in central China's scientific innovation ability of universities by using principal
component analysis[8]. Domestic and foreign scholars’ research about scientific innovation ability
evaluation of university can be described as fruitful.
From the current situation of research, the evaluation of China's university scientific innovation ability
has become increasingly mature, but the application of the new evaluation method in the evaluation
practice is very limited, and more are using the traditional scientific progress contribution rate as a
measure of the key indicators about science progress and innovation ability, some of the more classical
evaluation methods are still playing a leading role. Therefore, there are still many problems about the
evaluation of university scientific innovation ability in China, that mainly reflected in: (1) research
methods, lack of scientific innovation ability evaluation methods which are standardization, specific and
strong operational colleges and universities; (2) the existing scientific innovation ability evaluation
methods are not suitable. Manifested as an analysis of innovation in research universities are more and
inadequate attention for teaching research university or teaching university. For the comprehensive and
scientific evaluation of the scientific innovation ability, this paper evaluated and analyzed the
University's scientific innovation ability to established evaluation index system and evaluation model
and selected a province D University as a practical case by fuzzy integral evaluation method.
2 The Construction on Evaluation Index System of University Scientific
Innovation Ability
2.1 The Construction Principles on Evaluation Index System of University Scientific Innovation
Ability
Scientific innovation ability evaluation index of university is a tool to measure the strength of university
scientific innovation ability. To make such an evaluation tool effective and credible, evaluation results
can comprehensive, objective and scientific reflect the actual level of scientific innovation and
development trends of universities, in the construct of index system, we should select criterion and
establish index system in accordance with systems theory point of view and systematic analysis method,
seek indicator system can fully cover the content of various types of universities scientific innovation
and characteristics; in the choice of indicators, we should based on the full use of existing statistical
indicators and build some new indicators as innovative ideas in accordance with research purposes’
requirement, and seek to target system in the practical application process, we need to convenience,
simple, and operable. Particular, it shall follow the following principles:
2.1.1 Objectivity principle. The settings of evaluation index system should be as objectively as possible
to reflect and describe the activities of university scientific innovation activities in the whole process
and the law, scientific and accurately reveal the essential characteristic of university science innovation.
2.1.2 Systematic principle. Evaluation index system of scientific innovation ability is a comprehensive
integration of multiple subsystems, it must proceed from the overall system point of view and the
various indicators of pre-selected to be able to as an organic whole, which requires the establishment of
evaluation index system has coverage surface sufficiently.
2.1.3 The feasibility principle. Mainly include:
the availability of data, it can be obtained specific
data index through a simple process as much as possible use of existing information or be able to use
existing data; the measurable of data, requiring the various indicators which chooses from index
system can be able to carry out quantitative descriptions and can be measure and analyze the
determination according to a certain standard, the quantitative indicators data must to ensure their true,
reliable and effective; the operability of evaluation indicators, the number of indicator must be less
but excellent, the calculation method must be simple and easy to implement, then it need simple and
①
②
③
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workable in the practical application process,.
2.1.4 Strategic principle. The evaluation of universities scientific innovation is not only to analysis
innovation ability of past and present, but also explores and analyzes university innovation ability
system in future and potential ability. The setting indicator not only has realistic targets that measure the
results of university scientific innovation activities, but also has process indicators that reflected the
activities of university scientific innovation. They can comprehensively reflect the status of university
scientific l innovation ability and future development trends, with the function of forward-looking,
oriented, and can really play the evaluation, then guide the university's science and technology activities
in the right direction.
2.1.5 Comparability principle. Index system is comprehensive assessment for scientific innovation
ability throughout university system. It needs a reference values for measurement and evaluation and
can be horizontal comparison of evaluation results in various types of institutions, therefore the choice
of specific indicators at all levels must be the meaning of common indicators in colleges, statistics
caliber and scope of the line as far as possible, so that each indicator can be reflect one aspect of science
and technology activities.
2.1.6 Independence principle. It requires the various indicators must be relatively independent, same
lever indicators can not be mutually inclusive, avoid the duplication of target information as possible,
minimize to redundancy the information; each indicator can be a reflection of one aspect of science and
technology activities.
2.2 The Construction on Evaluation Index System of University Scientific Innovation Ability
According to the basic principles of the university scientific innovation ability evaluation index system’s
meaning and the establishment, think about the data’s availability and integrity, and through experts
consultation and discussion, to divided university scientific innovation ability into the input capacity of
scientific innovation, the upporting capacity of scientific innovation, the production capacity of
scientific innovation, the achievement transformation capacity of scientific innovation and the
sustainable innovation ability. These five parts are level two indicators, following are 31 level three
indicators, they consists of a evaluation index system that is systematic, quantified, and applicable to
university scientific innovation ability, in Table 1.
Targe
t
layer
criteria
layer
Table 1. Evaluation index system of university scientific innovation ability
index layer (evaluation indicator)
variable
symbol
Scientific innovation ability of university Xi
The input
capacity of
scientific
innovation
X1
The
supporting
capacity of
scientific
innovation
X2
unit
Personnel number
X11
The number of associate professor and above in scientific
activities
The number of doctorates in scientific activities
X12
The amount of financial input
The amount of basic research inputs
The amount of technology services inputs
X14
X15
X16
The amount of Scientific instruments and equipment inputs
The proportion of government input in research funding
X17
X21
The proportion of businesses to invest in research funding
X22
science and technology projects in national and provincial
(ministerial) level
The number of key disciplines national and provincial
(ministerial) level
The number of key Laboratory of national and provincial
(ministerial) level
X23
Million
Perce
ntage
Perce
ntage
item
X24
number
X25
number
639
X13
number of
people
number of
people
number of
people
Million
Million
Million
The
production
capacity of
scientific
innovation
X3
The
achieveme
nt
transformat
ion
capacity of
scientific
innovation
X4
The
sustainable
innovation
ability X5
The amount of per capita library collection
X26
The total number of paper
The number of papers included inSCI EI ISTP
The number of books published in Science and Technology
area
Scientific achievement award on Provincial (ministerial) level
and above
The number of invention patents
X31
X32
X33
10000volu
me
perce
ntage
chapter
chapter
works
The rate of campus network coverage
X27
X34
item
X35
item
The number of contracts that patent sold
The actual income of the year the patent sold
The number of technology transfer contracts
The real income of the year that transfer technology
X41
X42
X43
X44
item
Million
item
Million
The conversion rate of scientific and technological
achievements
The contribution of scientific and technological progress to the
growth rate of GDP
X45
perce
ntage
perce
ntage
The rate of annual R & D personnel training
X51
The proportion of outstanding young scientists and technicians
account for Provincial (ministerial) level and above
X52
The ratio of science and technology funds investment to GDP
X53
The renewal rate of scientific instruments and equipment
X54
The rate of digestion and absorption of imported technology
X55
The rate of school-enterprise cooperation in production and
research
X56
、 、
X46
perce
ntage
perce
ntage
perce
ntage
perce
ntage
perce
ntage
perce
ntage
3 The Choice about the Evaluation Methods of Scientific Innovation Ability
University has many evaluation methods of scientific innovation ability. Fuzzy integral method is one of
the evaluation methods. The concept of fuzzy integral is proposed in 1974 firstly by Sugeno who is a
Japanese scholar [9], which provides an effective information fusion method. It is a nonlinear function of
fuzzy metric estimation. Fuzzy Integral is involved in vague method; the purpose of it is to find
indicators data and can be regarded as the maximum degrees of unity between a variety of information
sources of data and the importance of correlation. Fuzzy integral theory and its applications have rapid
development and wide application in the field of practice[10]. The designed university’s scientific
innovation ability indicators at all levels of the paper are not completely independent, that is, there is a
degree of relevance and interaction based on the characteristics and evaluation requirements of scientific
innovation capacity. Therefore, to evaluate the university science and technology innovation by using do
not need to assume additively and independence of the improved fuzzy integral. Improved fuzzy
comprehensive evaluation methods are as follows [11].
3.1 Determination of the evaluation index value
3.1.1 Dimensionless processing for indicators data. As in the design of scientific innovation index
system of universities, each original data values has a different dimension and units, some for absolute,
some for relative number, some for the average, some for the wan BMB, some for the percentage, some
for manmade and so on, so that they are not directly comparable. In order to eliminate the degrees of
non-public metric from the different caused by dimensional and dimensionless unit, we should do
640
non-dimensional treatment for evaluation index, i.e. the same degree quantitative of indicators. The
paper did non-dimensional treatment of quantitative indicators by the commonly used standard
statistical transformation method in statistics. The standard transformation method can be described as
follows: Suppose the number of evaluation objects for n, the number of evaluation index for m, then the
sample
data
matrix
composition
of
the
n-index
value
is
x
,
xi = {x1 , x 2 , L , xij } (i = 1,2, L , n; j = 1, 2, L, m ) . Measure and evaluation values xij for the
measurement I and evaluation of indicator j, using the quantitative approach of the same degree xij is as
follows:
Where X ij refers to indicator value after the same degree quantitative, x j and E j respectively
refer to the mean and variance of indicator j
3.1.2 Determine the value of qualitative indicators. Semantic value of qualitative indicators is given
through expert scoring. Experts in the scoring process of qualitative indicators values, the description of
X ij =
x ij − x j
(1)
Ej
them has a considerable degree ambiguity, so describe the subjective evaluation values by the concept of
trapezoidal fuzzy number expressed semantic value. Though the questionnaire survey, the experts give
the various qualitative indicators based on assessment of the semantic value.
~
~
f1 = f j X ik
{ ( )
}
k = 1,L , g ; i = 1,L , dg k ; j = 1,L, y
(2)
~
f j X ik refers to the trapezoidal fuzzy number, expressed as (aik , bik , c ik , d ik ) , aik ∈ [0 1] ,
~
bik ∈ [0 1] , cik ∈ [0 1] , d ik ∈ [0 1] . f j X ik are the semantic values that number i qualitative
( )
( )
indicators under evaluation level X k for the expert j , dg k is the number of qualitative indicators
under the evaluation levels X k , y is the number of experts. Calculate the fuzzy values of qualitative
indicators through integrated views of various experts.
We can obtained the value of qualitative indicators through fuzzy computing. There are many methods
to change fuzzy numbers into clearly value, but each of conversion formulas has its advantages and
disadvantages. Delgado who pointed out it is inappropriate that using a single conversion formula for
fuzzy, because the calculation is too simplified and can not be effectively verified. Therefore, Delgado
and others recommend choosing a variety of fuzzy methods and conversing fuzzy after comprehensive
consideration. So this study considered change the fuzzy into clear value comprehensive by using three
kinds of commonly used fuzzy formula: the relative distance formula (G1 ) , the center value
method (G2 ) , as well as the gravity value method (G3 ) .So we can let each qualitative indicator
separately solved fuzzy values to changed into clear values, and get all the qualitative indicators values.
{f ( X )
k
i
k = 1,2, L , g ; i = 1, 2, L , dg k
}
(3)
3.2 The calculation of evaluation values in various evaluation levels by improved fuzzy integral
fuzzy density and determination of values λ. Through a questionnaire survey, all levels of values λ and
fuzzy density values in various dimensions of evaluation index are given by the experts based on
attention degree (fuzzy density) and setting principles of value λ.
{ ( ) k = 1,2,L, g; i = 1,2,L, g ; j = 1,2,L, y}
λ = {λ k = 1,2,L, g ; j = 1,2, L, y}
(5)
w1 = w j X ik
k
( 4)
k
j
( ) is the fuzzy density of the number i evaluation index
w j X ik
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X ik under the evaluation level X k
by the expert number j.
λkj
is the value λ given from evaluation level X k by the expert number j.
By combining the suggestions of various experts, we can get value λ of all evaluation levels and fuzzy
density values of evaluation index in all evaluation levels. According to formula (1) we can get fuzzy
({ }
)
k
measure under evaluation level X k respectively: wλ X i , i = 1, 2, L, g k . Reorder by size
about
each
index
( )
f X
value
k
i
under
evaluation
k
ij
Xk
level
( ) ≥ L ≥ f (X ) ≥ L ≥ f (X ). Get evaluation values of evaluation levels X
f X
k
i
k
igk
k
,
by using
fuzzy integral formula. Repeat the above methods to find the evaluation value of all evaluation levels:
f = { f ( X k )}.
3.3 Comprehensive Evaluation
Calculate comprehensive evaluation value X by the fuzzy integral method based on attention degree
from the experts (fuzzy density) firstly, then determined the value λ by the principle of values λ, the
fuzzy density values {w( X k )} of each evaluation levels, and the evaluation values { f ( X k )} of all
the evaluation values.
4 Conclusion
This paper has constructed evaluation index system of university scientific innovation ability, which is
systematic, quantify, the applicable. I proposed the integrate evaluation methods based on fuzzy integral
of university scientific innovation capabilities according to the actual situation of university scientific
combined with the improved fuzzy integral evaluation method. Think that university should be
established to help connect the main object and subordinate object of scientific innovation environment,
based on the local scientific development, enhance their ability to support scientific innovation,
scientific achievements transformation capacity and promote the scientific innovation through the
mechanism innovation.
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