Evaluation on the Management and Construction of Service-Oriented
Government in International Metropolis Based on Principal
Component Analysis and Matter-Element Model
Shuang Li
*
and Yiwei Zhang
Department of Tourism Management, Sanda University, Shanghai 201000, China
Keywords: Service-Oriented Government, International Metropolis, Principal Component Analysis, Matter-Element
Model.
Abstract: The management and construction of service-oriented government is becoming more and more important in
the governance of major cities in the world. Especially with the development of new technology, the
efficiency of service-oriented government construction and management has been rapidly improved. By
considering the effectiveness of construction, we can find out the problems, understand the gap between
cities, and further enhance the energy level and core competitiveness of cities. From four aspects of
financial and social expenditure, public service, e-government and information disclosure, 21 indicators are
selected to form the evaluation system of service-oriented government construction performance index.
Using SPSS software, the paper evaluates the construction effect of service-oriented government in seven
international metropolises, such as London and New York City, by means of principal component analysis
and matter element analysis. The results show that: the second echelon cities have obvious advantages in e-
government construction, and focus on the technical experience of "Government Online-offline Shanghai".
The gap between the two echelons is mainly reflected in the financial expenditure. The focus of service-
oriented government construction in China's international metropolis is to increase social financial
expenditure.
1 INTRODUCTION
With the economic globalization, New York,
London and other international metropolises play
important roles in the world, and gradually develop
into the world economic center, political and
cultural center. Service oriented government is a
kind of government which takes citizen service as
its purpose, which undertakes service responsibility
under the guidance of citizen standard and social
standard. It is established through legal procedures
and according to the will of citizens (Liu 2002).
Establishing a service-oriented government is not
only an important measure to achieve scientific
development and deal with various social problems,
but also a change of governance mode and
governance philosophy. It requires the government
to realize the transformation and change from
traditional to modern management system, operation
mechanism, management mode and other aspects.
We should pay attention to the functions of the
government in the social field and put the social
management and public service functions in an
increasingly important position. The construction of
service-oriented government can enhance the city's
energy level and core competitiveness, and
constantly improve the governance capacity and
governance level of the socialist modern
international metropolis.
The essence of the evaluation of the construction
effect of service-oriented government is the
comprehensive consideration of the construction
effect of each city's service-oriented government. It
can not only comprehensively evaluate the
comprehensive effect of the city in the construction
of service-oriented government, but also find out the
problems restricting the improvement of the effect
through the evaluation, so as to provide a reference
for the future government work (Li and Zheng
2020).
784
Li, S. and Zhang, Y.
Evaluation on the Management and Construction of Service-Oriented Government in International Metropolis Based on Principal Component Analysis and Matter-Element Model.
DOI: 10.5220/0011767700003607
In Proceedings of the 1st International Conference on Public Management, Digital Economy and Internet Technology (ICPDI 2022), pages 784-789
ISBN: 978-989-758-620-0
Copyright
c
2023 by SCITEPRESS – Science and Technology Publications, Lda. Under CC license (CC BY-NC-ND 4.0)
2 RESEARCH METHODS
2.1 Data Standardization
In order to promote the comparability between the
data, the initial data is dimensionless. For indicator
which is the larger, the more favorable, the
calculation formula of positive indicators is used;
For indicator which is the smaller, the more
favorable, the calculation formula of Negative
indicators is used, as follows
Positive indicator: x
=x
max(x
)
⁄
(1)
Negative indicator: x
=min(x
)x
⁄
(2)
Where x` represents index value after
standardization, x
ij
represents original index value,
x
j
represents column J indicators (Fan, Wang and
Zhou 2012).
2.2 Principal Component Analysis
Principal Component Analysis (PCA) is to solve the
eigenvalue and eigenvector of the correlation
coefficient matrix of the sample indicators,
transform multiple correlated indicators into a few
independent comprehensive indicators (i.e. principal
component), and analyze the standardized indicators.
The calculation process includes extracting principal
components according to the cumulative
contribution rate of principal component variance,
calculating the score and weight of each principal
component.
2.3 Matter-element Model
Matter Element Model is a new subject founded by
Chinese scholar Professor Cai Wen (Luo and Wu
2014), which studies the laws and methods of
solving incompatibility problems. It includes
establishing the matter-element model of service-
oriented government construction, establishing the
system of evaluation indicators, determining the
classical domain and matter-element matrix,
determining the correlation function and correlation
degree, calculating the comprehensive correlation
degree and determining the evaluation grade.
Table 1: Evaluation index system of service oriented government construction.
Target layer Criterion layer Evaluating indicator
Index
attribute
Achievement of
service oriented
government
construction
The level of financial
social expenditure
Proportion of social expenditure x
/（%） Ⅴ
Growth rate of proportion of social expenditure in recent
three years x
/（%）
Ⅴ
Public service level
Safety index x
Ⅴ
Health care index x
Ⅴ
pollution index x
⋀
Traffic flow index x
Ⅴ
Quality of life index x
Ⅴ
Business collaboration x
/(item)
Ⅴ
Service Quantity x
/(item)
Ⅴ
E-government level
Unified hotline / email x
Ⅴ
APP x
Ⅴ
Facebook x
Ⅴ
Twitter x
Ⅴ
Instagram x
Ⅴ
Weibo x
Ⅴ
WeChat x
Ⅴ
The earliest legislative year x
/(year) ⋀
Degree of information
disclosure
laws and regulations x
/(item)
Ⅴ
Information disclosure data organization x
/(item)
Ⅴ
data set x
/(item)
Ⅴ
Data category x
/(item)
Ⅴ
Note: In the indicator attribute column, "V" indicates that the larger the indicator, the better; "⋀" indicates that the smaller
the indicator, the better.
Evaluation on the Management and Construction of Service-Oriented Government in International Metropolis Based on Principal
Component Analysis and Matter-Element Model
785
3 EMPIRICAL RESEARCH
3.1 Indicator System and Data Sources
According to the eigenvalue size and variance
cumulative contribution rate to extract principal
components, table 2 shows that the cumulative
contribution rate of the first five principal
components reaches 97.909%, so we can use these
five principal components to make principal
component analysis on the construction effect of
service-oriented government. According to the
principal component load matrix, the five principal
components are divided into financial expenditure
factor, e-government factor, public service quantity
factor, information disclosure factor and public
service quality factor.
3.2 Dimension Reduction
The software spss22.0 was used for principal
component analysis to obtain the characteristic
value, variance contribution rate and the cumulative
variance contribution rate of the sample indexes.
The cumulative contribution rate extracts the main
components according to the characteristic value
and variance cumulative contribution rate. The five
principal components can be used to analyze the
construction effect. According to the load matrix of
principal component, the five main components are
divided into fiscal expenditure factor, e-government
factor, public service quantity factor, information
disclosure factor, and public service quality factor.
3.3 Effectiveness Evaluation
3.3.1 Determine the Matter-element of
Service-oriented Government
Construction
Service-oriented government construction effect R,
service-oriented government construction effect
characteristic C and characteristic value V constitute
the matter element of service-oriented government
construction effect.
R=
Nc
v
c
v
⋮⋮
c
v
=
R
R
⋮
R
(3)
Where R is the matter-element of the
construction effect of n-dimensional service-
oriented government, r = (n, C, V).
3.3.2 Classic Domain and Node Domain
Matter-Element Matrix for
Determining the Effectiveness of
Service-Oriented Government
Construction
The classical domain matter-element matrix of the
construction effect is expressed as follows:
R
＝M
,c
,V
=
⎣
⎢
⎢
⎢
⎡
M
c
V
c
V
⋮⋮
c
V
⎦
⎥
⎥
⎥
⎤
=
⎣
⎢
⎢
⎢
⎡
M
c
<a
,b
>
c
<a
,b
>
⋮⋮
c
<a
,b
>
⎦
⎥
⎥
⎥
⎤
(4)
Table 2: Principal component factor scores of the construction effect.
City
Score of each principal component
Fiscal expenditure
facto
r
(
v1
)
e-Government
facto
r
(
v2
)
Public service
q
uantit
y
facto
r
(
v3
)
Information
disclosure facto
r
(
v4
)
Public service
q
ualit
y
facto
r
(
v5
)
New York 4.134 2.566 2.953 0.202 0.549
London 4.508 1.085 -0.230 0.241 0.346
Hong Kong 4.519 1.135 -0.038 0.044 0.822
Tokyo -1.687 4.814 1.109 0.657 0.389
Beijing -2.312 3.514 1.670 0.495 0.269
Shenzhen -2.245 3.002 2.059 -0.732 -0.058
Shanghai -1.964 3.601 1.913 0.704 0.223
weighting
facto
r
0.423 0.228 0.179 0.110 0.061
Note: the weight coefficient is equal to the proportion of each principal component variance contribution rate to the total
variance contribution rate.
ICPDI 2022 - International Conference on Public Management, Digital Economy and Internet Technology
786
Where R
is the classical domain entity, M
represents the j-level of the construction effect of
the service-oriented government (J = 1, 2, 3, 4), c
indicates the i-th evaluation index. The interval <
a
,b
> is the range of the value ( V
)
corresponding to the evaluation level J, which is the
classical domain.The classical domain is the value
range of evaluation grade, which is the basis of
matter element evaluation. According to the
extension of the construction effect of service-
oriented government, it is divided into four
levels(M
-M
), which is qualitatively described as
excellent, good, medium and poor.
3.3.3 The Domain Matter Element Matrix
for Determining the Construction
Effect of Service-oriented Government
The domain matter-element matrix of the
construction effect of service-oriented government
is expressed as follows:
R
＝N
,c
,V
=
⎣
⎢
⎢
⎢
⎡
N
c
<a
,b
>
c
<a
,b
>
⋮⋮
c
<a
,b
>
⎦
⎥
⎥
⎥
⎤
(5)
Where R
is called nodal domain matter element.
The nodal domain matter element ( V
=<
a
,b
>) is the magnitude range of the feature. P
stands for the whole evaluation level of service-
oriented government construction.
3.3.4 Determination of Matter Element to
be Evaluated
The matter element ( R
) of the object to be
evaluated (N
) is expressed as:
R
=
N
c
v
c
v
⋮⋮
c
v
(6)
3.3.5 Determination of Correlation Function
and Correlation Degree
The correlation function of service-oriented
government construction performance indicator (K
)
is defined as:
K
=
(,
)
|
|
,X∈X
(,
)
,
(,
)
,X∉X
(7)
Where ρ
(
X, X
)
represents the distance between
the point X and the finite interval X
=
a
,b
,
ρX, X
represents the distance between point X
and the finite interval X
= a
,b
, X represents
the characteristic value of matter-element of the
construction effect of service-oriented government
to be evaluated, X
represents the value range of
classical domain matter-element, X
represents the
value range of node domain matter-element.
3.3.6 Calculate the Comprehensive
Correlation Degree and Determine the
Evaluation level
Comprehensive correlation degree of the object to
be evaluated with respect to grade J.
K
(
N
)
=
∑
a
K
(x
) (8)
When K(x) ≥1.0, it means that the object to be
evaluated exceeds the standard level. The larger the
value, the greater the degree of exceeding the
standard; When 0≤K (x)<1.0, it means that the
object to be evaluated conforms to the standard level.
The larger the value is, the closer it is to the upper
limit of the standard level; When -1.0 ≤ K(x) ＜0, it
means that the object to be evaluated does not
conform to the standard level, but has the conditions
to be converted to the standard level, and the larger
the value is, the easier it is to be converted, so it can
be judged that it belongs to the standard level; When
K(x)＜-1.0, it means that the object to be evaluated
does not meet the requirements of standard object,
Table 3: Evaluation results of service oriented government construction.
City
Relevancy
Grade Echelon
Excellent Good Medium Poor
New York 0.099 0.132 0.044 0.000 good
The first echelon London 0.000 0.089 0.072 0.004 good
Hong Kong 0.000 0.120 0.010 0.001 good
Tokyo 0.000 0.044 0.176 0.000 medium
The second echelon
Beijing 0.000 0.060 0.148 0.000 medium
Shenzhen 0.000 0.084 0.124 0.008 medium
Shanghai 0.000 0.126 0.171 0.000 medium
Evaluation on the Management and Construction of Service-Oriented Government in International Metropolis Based on Principal
Component Analysis and Matter-Element Model
787
and does not have the conditions to convert to
standard level.
Figure 1: Radar chart of evaluation results of service-
oriented government construction
3.4 Evaluation Results
According to the matter-element evaluation model,
we can calculate the evaluation results of service-
oriented government construction in seven cities,
and the overall evaluation level is general. Among
them, New York, London and Hong Kong are in the
first echelon, while Beijing, Shanghai, Shenzhen
and Tokyo are in the second echelon.
4 CONCLUSION
By comparing the principal component factor scores
of the first-echelon cities and the second-echelon
cities (Figure 1), it can be found that although the
second-echelon cities are slightly better than the
first-echelon cities in the number of e-government
and public services, the first-echelon cities are far
ahead of the second-echelon cities in the financial
expenditure factor. In particular, as China's first
government service brand, "Government Online-
offline Shanghai" ranked first in the investigation
and evaluation report on the integrated government
service capacity of provincial governments (2021).
By 2020, 357 reform measures had been
implemented, 3197 intervention matters had been
involved, and 150 million cumulative number of
work was done. The reform of "Government Online-
offline Shanghai" is also advancing in depth,
changing from technology driven to system driven
(as shown in Figure 2), so as to realize the
innovation of governance mode, the reconstruction
of governance mode and the reconstruction of
governance system.In addition, there is little gap
between cities in terms of public service quality and
information disclosure. The gap between the two
echelon cities is mainly reflected in financial
expenditure. Therefore, the focus of the construction
of service-oriented government in China's
international mega cities in the future should be the
improvement and improvement of financial social
expenditure.
ACKNOWLEDGMENTS
This work was financially supported by the General
Project of of philosophy and social science planning
of Shanghai (2018bck001).
Big data
center
Relevant departments
Service desk
User
Department
data
Department
data
Department
data
Large database
Event
database
Handling
database
Material
database
Knowledge
database
PC
Four terminals
APP
Alipay Wechat applet
Data calling
Data
cleaning
Data
integration
Data
processing
Data
governance
Input Central
system
Output
Figure 2: The technical architecture of “Government Online-offline Shanghai”.
ICPDI 2022 - International Conference on Public Management, Digital Economy and Internet Technology
788
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Evaluation on the Management and Construction of Service-Oriented Government in International Metropolis Based on Principal
Component Analysis and Matter-Element Model
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