Measurement Mode of Smart Government based on DPSIR Model
under the Background of Smart City
Danyang Liu, Chunmei Han* and Kaiqiao Yang
People's Public Security University of China, Beijing, China
Keywords: Smart Government, Smart City, Measurement Mode, The DPSIR Model, Analytic Hierarchy Process.
Abstract: Data-driven and technology empowerment have become the key to the innovation of government affairs in
the information age, but government affairs are not fully mature, and there is a lack of a scientific rating
system for dynamic monitoring and feedback. This paper evaluates smart government construction as the
research subject and combines the DPSIR model and sustainable development theory to build an indicator
system for smart government. At the same time, the analytic hierarchy process (AHP) is used to allocate the
weight of the indicator system, and then optimize the system through the Delphi method. Based on the
integration of interdisciplinary theories, it is of great significance to explore the measurement mode of smart
government construction in the new period, realize the real-time evaluation of smart government construction,
and effectively improve the efficiency of government governance and public service, which can help improve
government management and services in China.
1 INTRODUCTION
Since IBM (International Business Machines
Corporation) put forward the vision of building Smart
City in 2008, the concept of “Smart” appeared in
public. Smart government, an indispensable part of
constructing smart cities, is the brain and nervous
system of the whole smart city (Guo, 2016, Liu, 2016,
Yu, 2016, Hu, 2016, Sang, 2016), and there is no lack
of research and discussions on smart government in
academia. “Smart government”, analyzed from
morphemes, can be divided into two parts - “smart”
and “government affairs.” “Government affairs” is
the working form of its vertical development, and
“smart government” is the way to build a government
that can use information and communication
technologies to solve essential problems better
(Mellouli, 2014, Luna-Reyes, 2014, Zhang, 2014).
“Smart” is a high-level model of its horizontal
development, and smart government has been
envisioned as an adaptive evolution of government in
academia. Unlike previous government work, Smart
Government can integrate existing new-generation
information technologies such as cloud computing,
big data and artificial intelligence (AI) to integrate
various management departments and management
modules. It can reduce the possibility of “Business
Process Silos” by establishing effective internal
business collaboration, while reducing the “Data
Divide” and improving the efficiency and quality of
government services by building a highly
interconnected working mechanism and service
platform (Kankanhalli, 2019, Charalabidis, 2019,
Mellouli, 2019).
Although the overall construction of smart
government in China has achieved initial success,
there are still some problems, such as lack of
government planning, incomplete organizational
structure, low coordination efficiency, low social
participation. The effect of comprehensive
construction needs to be further improved. Building a
perfect evaluation indicator system can provide value
orientation for smart government. However, the
current evaluation indicator system mainly focuses
on the breakthrough of smart government in
technology and government performance. Less
consideration is given to the influence of social and
environmental benefits on performance evaluation.
The smart government evaluation system based on
sustainable development has a big system view of
evaluation, which will lay a good foundation for the
construction of smart government.
452
Liu, D., Han, C. and Yang, K.
Measurement Mode of Smart Government based on DPSIR Model under the Background of Smart City.
DOI: 10.5220/0011185100003440
In Proceedings of the International Conference on Big Data Economy and Digital Management (BDEDM 2022), pages 452-459
ISBN: 978-989-758-593-7
Copyright
c
2022 by SCITEPRESS – Science and Technology Publications, Lda. All rights reserved
2 APPLICATION OF THE DPSIR
MODEL FROM THE
PERSPECTIVE OF
SUSTAINABLE
DEVELOPMENT
2.1 Explanation of the DPSIR Model
and Sustainable Development
The Brundtland Commission released Our Common
Future in 1987, also known as the Brundtland Report,
which put forward “sustainable development” for the
first time and won international recognition. It was
defined as “development that meets the needs of the
present without compromising the ability of future
generations to meet their own needs” (Castro, 2021,
Lopes, 2021). Based on previous academic views, we
summarize and foster new meanings to sustainable
development - common, coordinated, fair, efficient,
and people-oriented.
The DPSIR model comprises five rating
indicators, including driving forces, pressures, states,
impacts, and responses, constituting a natural system
evaluation indicator system (Zhao, 2021, Fang, 2021,
Liu, 2021, Liu, 2021). Each type plays a different role
in the system, respectively, and there exists both a
distinction and a correlation between the five
elements and an established logical relationship,
while the five types are divided into several
categories of indicators.
2.2 Applicability Analysis of DPSIR
From the perspective of sustainable development, the
construction of smart government can be regarded as
an organic system of smart government composed of
multiple subsystems. DPSIR can be applied to
systematic evaluation, and the effect evaluation of
smart government construction can be carried out
from the perspective of a large-scale system. First, the
relationship between the elements of DPSIR can
dynamically show the effectiveness of each part,
instead of describing the research object statically and
in isolation. It emphasizes the influence of human
factors on the environment and the environment’s
response to the system (Ruan, 2019, Li, 2019, Zhang,
2019, Liu, 2019). Secondly, DPSIR can deal with
some qualitative information, which is convenient for
selecting a more suitable mathematical method for
calculation and getting more operable and dominant
results. Finally, the theoretical framework of DPSIR
model theory can provide an excellent theoretical
basis for the construction of smart government
evaluation system, and has strong applicability.
Therefore, DPSIR is selected as the supporting theory
of the evaluation system in this paper.
3 DIMENSION CONSTRUCTION:
A MEASURING TOOL FOR
THE EFFECT IN SMART
GOVERNMENT
CONSTRUCTION
3.1 The Significance and Role of
Dimension in Evaluation System
Construction
Dimension is a coordinate for understanding the
whole thing and a thinking method for analyzing
things. The evaluation of the effect of smart
government construction needs corresponding
indicators to measure. Scattered and messy indicators
cannot support evaluation work. By classifying the
corresponding indicators and dividing them into
different dimensions, we build an effective indicator
evaluation system, find the relationship between
evaluation objects, and thus carry out scientific and
reasonable evaluation (Wang, 2020) Based on orderly
integration of the value goal and content orientation
of the effect evaluation of smart government
construction, dimension analysis can more
objectively reflect the connotation of smart
government construction effect evaluation, enhance
the logicality, directivity and accuracy of indicator
system selection, and scientifically construct the
indicator of smart government construction effect
evaluation.
3.2 The Establishment and
Interpretation of the Evaluation
Dimension of Smart Government
This paper analyzes the construction of smart
government from five dimensions of DPSIR, which
can reflect the causal relationship between
dimensions and the feedback principle contained.
Human factors have become “driving forces” to
promote system change, thereby giving birth to
“pressure” that affects the development direction of
the system. Under pressure, the smart government
system presents a corresponding “state”, and under
the comprehensive action of the first three indicators,
the smart government construction has an “impact”.
Measurement Mode of Smart Government based on DPSIR Model under the Background of Smart City
453
Finally, the actors of smart government construction
formulate relevant laws, policies or plans as
“responses” for “driving forces”, “pressures”,
“states” and “impacts”. These five indicators
comprehensively reflect the comprehensive effect of
smart government construction, and are five
dimensions of establishing an evaluation indicator
system.
Driving forces reflect the main factors that
promote the system when environmental and social
factors act on it for a long time.
Pressures evaluate the state and ability of the
government to cope with risks and challenges in its
work, focusing on the technology and system of
promoting the construction of smart government.
States are the performance of smart government
under the combined action of driving forces and
pressures, aiming to highlight the research object’s
most essential characteristics.
Impacts are evaluated based on the promotion of
smart government to the whole government work in
the exploration process and the effectiveness of
improvement.
Responses refer to measures taken by various
actors in response to the evaluation of the smart
government construction to promote a more adapted
and better functioning of smart government in the
current situation.
4 INDICATOR SYSTEM: A TOOL
FOR MEASURING THE
EFFECT OF SMART
GOVERNMENT
CONSTRUCTION
From the perspective of DPSIR, the construction
effect of smart government is comprehensively
considered from five indicators. In essence, based on
the theory of sustainable development, the evaluation
connotation of sustainable construction is transferred
to smart government construction, an innovative
attempt in the interdisciplinary evaluation and mutual
learning. We standardize and guide the construction
of indicator systems under the guidance of the
evaluation connotation of sustainable circular
development. In terms of operability, the formulation
of indicators focuses on application; Therefore, based
on completing the construction of the indicator
system and assigning weights to it, this paper uses the
review of existing literature and visits to surveys to
give a careful consideration of various factors
affecting the application of indicators, to give a more
systematic indicator evaluation model and reference
to the use of indicators, and to provide an idea for the
application of indicators by evaluation subjects.
4.1 The Construction and Optimization
of the Indicator System
4.1.1 Preliminary Construction of
Evaluation Indicator System
The goal of this paper is to evaluate the effect of smart
government construction. Constructing the
corresponding indicator evaluation system is the
basis of construction effect evaluation. This paper
grasps the research status of smart government
through literature collection, grasps the primary
evaluation methods, and constructs the evaluation
indicator system to carry out a continuous
longitudinal dynamic evaluation. According to
DPSIR, the evaluation system of smart government
construction is divided into five dimensions: driving
forces, pressures, states, impacts and responses. A
preliminary evaluation indicator system is obtained
according to the above-mentioned structure and
authoritative literature work (see Table 1). The
system includes five first-class indicators, 11 second-
class indicators, 29 third-class indicators and several
main observation points corresponding to them.
Table 1: Preliminary smart government evaluation system.
First-class
indicator
Second-class
indicator
Third-class
indicator
Driving
forces
Economic drives
Growth in fiscal
revenues
Proportion of
special funds to
fiscal expenditure
Fund
management and
expenditure status
Technology-
driven
Intensity of
investment in
R&D
ICT patent and
conversion rate of
scientific and
technological
achievements
Pressures Data information
Data information
security
Data information
sharing
Compatibility
Compatibility of
information
system
Acceptance of
staff on the new
mode
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454
Overall
management
ability of leaders
States
Communication
Support
Foundation
Construction of
network
infrastructure
Technology for
platform
operation and
maintenance
management
system
Platform for
internal
collaboration
Development of
efficient office
system
Development of
intelligent
decision system
Development of
automatic
supervision
system
Public service
platform
Degree of
information
disclosure
Usage of platform
Impacts
Efficient
government wor
k
Convenient
mobile office
Concise
working
procedures
Precise
prediction and
decision
Real-time
discrimination of
monitoring
mechanism
Interaction of
social services
Personalization of
government
services
Satisfaction
with complaint
resolution
Visualization of
data analysis
Responses
Responses to
policy adjustment
Frequency of
policy
introduction
Authority of
policies
Degree of policy
implementation
Responses to
talent cultivation
Training system
for technical
professionals
Feedback on staff
training
Some observation points of indicators: 1.
Intensity of the investment in research and
development (R&D): equivalent to economic
support, which is a powerful guarantee for improving
innovation from scientific research (scientific
research funding support); 2. ICT patents and the rate
of technology transfer: the proportion of scientific
and technological achievements successfully applied
in information and communication technology
patents to the total number of scientific research
achievements in the statistical cycle (original
support); 3. Pressure from data information security:
information laws and regulations, data confidentiality
norms, data leakage penalties and data storage forms.
4.1.2 Optimization of the Evaluation
Indicator System
In this paper, when optimizing the evaluation system
of the effectiveness of smart government, we
compare various methods of screening indicators.
Finally, we decided to use the Delphi method to
screen them, using anonymous feedback. After
extensive consultation with experts, after collating,
summarizing and counting, we again conduct
centralized feedback so that the indicators can reach
a relatively optimal state while the opinions gradually
converge (Pr 2021, Bbtdc 2021, Ga 2021, Nm 2021).
When using the Delphi method of testing, the number
of expert groups is generally not less than 10. In order
to avoid the influence of subjectivity, this paper
selected ten people working in government agencies
and those who have expertise in e-government to
issue expert questionnaires. Ten copies were returned
in both rounds of the screening process, and the
positive coefficient of experts was 100%.
4.1.2.1 Screening Indicators by the
Delphi Method in the First Round
Firstly, the importance of the first edition of Rickett’s
five-point scale is measured, that is, one is very
unimportant, two is not important, three is average,
four is important, and five is very important. The
maximum value of each indicator is max and the
minimum value is min, and then the mean value C,
standard deviation s and dispersion coefficient C.V of
each indicator are calculated respectively. When 0.1
≤ C.V ≤ 0.2, the importance of the indicator
meets the requirements.
According to the expert consultation, we can see
that most indicators in the preliminary indicator
evaluation system meet the requirements, which
shows that the system tends to be good. There are 12
problematic indicators, of which the amount of
revenue growth, the proportion of special funding to
financial expenditure, staff acceptance of the new
mode, the usage of platform, communication
technology support, efficiency of government work,
and satisfaction with complaint resolution need to be
Measurement Mode of Smart Government based on DPSIR Model under the Background of Smart City
455
adjusted, while the authority of policies, the state of
funding management and expenditure, the ability of
leaders to co-ordinate and manage, the visualization
of data analysis and the real-time discrimination of
monitoring mechanisms need to be deleted.
The advice given by experts is that the evaluation
indicator system should have a strong correlation
when choosing indicators to prevent generalization.
The monitoring mechanism and response mechanism
need to be constantly adjusted and improved
according to the smart government's actual situation
and specific content. The usage of the platform is
relatively general, which should be decomposed into
two indicators. And it is more appropriate to evaluate
the coverage of the platform and the usage habits of
platform users. The study draws on expert opinion to
adjust the indicator system accordingly, and conducts
a second round of indicator screening again.
4.1.2.2 Screening Indicators by Expert
Judgment in the Second Round
The steps and methodology of the second round are
the same as those of the first round. The revised
indicator system has won the consensus of experts.
4.1.2.3 Inspection of Coordination
Degree of Expert Opinions
Kendall’s W synergy coefficient will be used to
calculate the consistency test of expert opinions.
When the same evaluator has the same rating, the
calculation formula of W is as follows:
𝑊=
(
)
∑
（1）
T
=
∑
(n
−n
)/12
（2）
N refers to the number of indicators to be
evaluated, and K represents the number of people
who scored;
S is the sumR_iof squares of the deviation and the
sum of the grades evaluated by an evaluated object
and the average of all these sums;
Here, m_i is the number of their repeated grades
in the evaluation result, and n_ij is the number of the
same levels of the evaluator. When W is between 0
and 1, and the closer it is to 1, the higher the
consistency of expert opinion and the more
reasonable the evaluation results, and vice versa. The
synergy coefficient of expert opinions of the two
rounds W is calculated by SPSS, as shown in Table
2.
Table 2: Calculation results of the test in coordination.
W-value
Round-1 Scoring 0.152
Round-2 Scoring 0.381
According to Table 4, the synergy coefficient of
the first round in Round-1 Scoring is 0.152, and
Round-2 Scoring is 0.381, indicating that the
coordination between expert opinions increases. In
addition, the significance value P of the two rounds
of synergy coefficient is far less than0.05, which
shows that the synergy coefficient is significant. That
is, the evaluation results are consistent.
4.2 The Calculation and Allocation of
Indicator Weights
The allocation of indicator weights is an essential link
in building the evaluation indicator system of the
effect of smart government construction, which has a
great impact on the evaluation quality of the indicator
system. Therefore, the allocation methods of
indicator weights need to be compared repeatedly and
carefully selected to make the final allocation of
indicator weights objective, accurate, scientific and
reasonable. Calculating indicator weight can be
summarized into the following four types: 1.
Information enrichment method, mainly represented
by factor analysis and principal component analysis;
2. Digital relative size analysis method, mainly
represented by AHP hierarchy method and pecking
order diagram method; 3. Using the amount of
information, that is, the amount of information
carried by data, mainly taking entropy method as an
example; 4. Analyze data volatility or correlation,
mainly taking CRITIC, independence and
information weight as examples.
Each of the above methods has its characteristics
and advantages. Through practical application
analysis, this indicator system’s indicator weight
allocation method is mainly AHP hierarchical
method. Thomas L. Saaty developed this decision-
making method. It combines qualitative and
quantitative methods to analyze complex analysis
objectives hierarchically, making subjective
evaluation objective and simplifying complex
problems. AHP can make the problem organized and
hierarchical, make each indicator easy to analyze
quantitatively, carry out the simple sorting
calculation, effectively determine the weight of each
evaluation indicator, and obtain more accurate
results.
Through the research and analysis of AHP,
according to the principle and calculation method of
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456
AHP, the following indicator weight allocation steps
are carried out:
First, design a questionnaire for experts for the
evaluation indicator system of smart government
construction effect, which includes the first-level,
second-level, and third-level indicators, and compare
the importance of influencing factors in pairs at the
same level.
Second, invite relevant experts to fill out the
questionnaire and recycle it after filling it out. A total
of 10 questionnaires were distributed and ten were
recovered, with an effective rate of 100%. After
recovery, integrate and summarize the survey data of
the questionnaire, and prepare for the next analysis.
Thirdly, using “yaahp” to calculate the indicator
weight, firstly, the single hierarchical ranking is
carried out, that is, for a particular factor in the
previous layer, the ranking of the importance of each
factor in this level. There is the general hierarchical
ranking, the ranking from the highest level to the
lowest level in turn, and the ranking weight process
of determining the relative importance of all factors
in a certain level to the general goal. After the
calculation, the indicator weight is analyzed by
consistency test to judge whether it conforms to
logical consistency, and the inconsistent indicator
data is screened and adjusted to get the specific data
of the final indicator weight.
Table 3: Weight allocation of smart government evaluation
indicator system.
First-
grade
indicator
Second-grade
indicator
Third-grade indicator
Driving
forces
0.2111
Economic Drives
0.1156
Growth of fiscal revenue
0.0422
Special funds for smart
government account for
0.0734% of fiscal
expenditure
Technology Drives
0.0955
Input intensity of
research and
development (R&D)
0.0394
The rate of technology
transfer and ICT patents
0.0566
Pressures
0.11435
Data information
pressure 0.06775
Data information
security pressure
0.04316
Data information sharing
pressure 0.02459
Compatible
pressure 0.0466
Compatibility of
Information system
0.0311
User acceptance of the
new mode 0.0155
States
0.2734
Construction of
basic support
0.06625
Construction of network
infrastructure 0.03934
Technology for platform
operation and
maintenance
management system
0.02316
Platform for
internal
collaboration
0.12365
Development of efficient
office system 0.05734
Development of
Intelligent Decision
System 0.04124
Development of
Automatic supervision
system 0.02507
Public Service
Platform 0.0835
Information publicity
0.03967
Scope of Platform
promotion 0.0217
Depth of platform
application 0.02213
Impacts
0.19095
Smart Government
0.12174
Convenient mobile
office 0.0563
Concise working
procedure 0.04156
Accurate forecast and
decision-making
0.02388
Interactive social
services 0.06921
Personalization of
government services
0.04135
Satisfaction of appeal
resolution 0.02786
Responses
0.21025
Responses to
policy adjustment
0.07851
Frequency of policy
introduction 0.04722
Degree of Policy
implementation 0.03129
Talent training
response 0.13174
Technical professionals
training system 0.04392
Staff training feedback
adjustment 0.0878
Measurement Mode of Smart Government based on DPSIR Model under the Background of Smart City
457
Some indicator observation points: 1. Research
and development (R & D) investment intensity:
equivalent to scientific research economic support,
refers to the ratio of R&D investment to GDP and is
a powerful guarantee for improving scientific
research innovation strength (scientific research
funding support); 2. ICT patents and conversion rate
of scientific and technological achievements: the
proportion of scientific and technological
achievements successfully applied in information and
communication technology patents to the total
number of scientific research achievements in the
statistical cycle (original support); 3. Data
information security pressure: information laws and
regulations, data confidentiality norms, data leakage
penalties and data storage forms.
4.3 Model of the Indicator System and
Its Use
Finally, after screening, optimization and weight
allocation, the evaluation system of smart
government construction effect gets five first-level
indicators, 11 second-level indicators and 25 third-
level indicators. Furthermore, all levels of indicators
are qualitative indicators in the evaluation indicator
system constructed in this paper, which can be
measured by qualitative methods, with high
operability and high data availability. As a new work
direction, smart government often lacks experts who
can accurately grasp the specific contents and
objectives of smart government construction, which
cannot ensure that the selected experts can carry out
credible sample extraction and evaluation, so it is not
feasible to adopt expert bid evaluation method.
Compared with the previous method, the
questionnaire survey method is more operable, and
the results are relatively scientific and credible. In the
practical application of the indicator system, the
research group adopts the method of questionnaire
survey, taking the staff of government agencies as the
sampling objects, scientifically and reasonably
determining the sampling number and obtaining data,
which is convenient for the subsequent calculation,
processing and analysis of the obtained data.
The evaluation of the effect of smart government
construction needs to use an evaluation method that
can describe the whole system of the evaluation
object and analyze the complex representations of the
evaluation object hierarchically. The multi-indicator
comprehensive evaluation method is an evaluation
system that uses a specific model and evaluates the
research object based on the existing indicator
system. The fuzzy comprehensive evaluation method
is one of the commonly used methods of
comprehensive evaluation method. Fuzzy
mathematics is used to comprehensively consider
various factors affecting the evaluation of smart
government construction, and membership function
relationship is used to describe the fuzzy boundaries
among various factors (Zhou 2021, Cai 2021, Xu
2021, Wang 2021, Jiang 2021, Zhang 2021).
Although there is no lack of traditional
comprehensive evaluation method in indicator
evaluation, it is more appropriate to adopt fuzzy
comprehensive evaluation method because of the
complexity of intelligent government construction
factors and the fuzziness of evaluation influencing
factors and the objective conditions that the indicators
provided in this paper have many qualitative
indicators.
The specific application steps of the indicator
calculation method are as follows:
1) Determine the factor set to judge the effect of
smart government construction.
2) According to the needs of evaluation, the
evaluation set is given. According to the evaluation
demand of smart government construction effect,
according to Likert’s five-point scale, the general
evaluation set includes five evaluation elements: very
significant, relatively significant, significant, not
significant and highly insignificant.
3) List the membership function relationship.
Statistics and analysis of the questionnaire data,
determine the functional relationship between each
evaluation value and evaluation factor value, and
form a comprehensive evaluation fuzzy relationship
matrix.
4) Determine the weight set of evaluation factors.
The weights of the indicators determined in the
previous article can be directly taken as the weights
of the evaluation factors, and the weights of all the
evaluation factors constitute the weight set of the
evaluation factors.
5) Solve the fuzzy comprehensive evaluation
matrix. Using the membership function relationship
and the weight set of evaluation factors, the fuzzy
synthesis operation uses the primary factor
determination type.
6) By normalizing the fuzzy comprehensive
evaluation set, the final membership set is obtained.
7) Determine the final comment according to the
principle of maximum membership degree.
By using fuzzy comprehensive evaluation method
to process the data obtained from the questionnaire,
understand their subjective feelings according to the
scores of the five-point scale, synthesize the weight
assignment of each indicator, and finally get the
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458
comment with the maximum membership degree
after calculation, which is the actual application
process of the evaluation indicator system of smart
government construction effect.
5 CONCLUSION
This paper tries to establish the effect evaluation
system and scientific empowerment of intelligent
government affairs construction, In the next step of
research, it is necessary to conduct an empirical study
on the indicator system, transform the “theoretical”
indicator into the “practical” indicator, and select the
pilot area to evaluate the effect of smart government
construction based on controllable research scope and
available data. Evaluate the current process of smart
government construction more accurately, and
continue to follow up to achieve dynamic monitoring;
In further research, according to practical feedback
and empirical analysis, scientific theories and
algorithms can be used to form the target value of
staged evaluation indicators, accurately draw the
baseline of indicators, and better realize the
measurement of the construction effect of smart
government affairs.
Under the brand-new information age
background, the smart government platform plays a
ubiquitous role and is the core node of the whole
government network. The government reconstructs
the business flow through informationization and
promotes the “connectivity” of data (Lv, 2018, Li,
2018, Wang, 2018, Zhang, 2018, Hu, 2018, Feng,
2018). Promoting smart government cannot be
separated from monitoring and evaluating its
construction effect. Open up an evaluation system for
the construction effect of smart government affairs
from the new perspectives of “sustainable
development” and “smart ecology”, Accord to that
analysis logic of “driving-force-pressure-state-
influence-response”, Multi-dimensional and timely
monitoring of the construction process of smart
government affairs, Guided by the evaluation results,
dynamically adjusting the platform construction is
not only conducive to expanding the monitoring and
evaluation path in the field of smart government
affairs, but also conducive to grasping the big engine
of data empowerment, to comprehensively improve
the government affairs management and service level
of our government, improve the government affairs
efficiency, and promote the modernization of the
national governance system and governance capacity.
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