# The Effectiveness Evaluation Method of Regional Digital Economy Innovation Model Based on Intelligent Computing

Authors: Shifu Shan; Jingru Pan
Journal: Mathematical Problems in Engineering
 (2022)

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## Abstract

Digitalization is a long-standing concern in modern industrial engineering. At present, there is no widely recognized standard for the connotation and definition of digital economy, the scope of digital economy is difficult to be defined. This paper conducts an evaluation study on the development level of the digital economy, which mainly contains two parts: first, a comprehensive digital economy; second, a study on the influencing factors. This paper first summarizes the connotation of the digital economy and digital economy evaluation-related research, extracts the influencing factors of digital economy development, and adopts partial least squares (PLS) path model to verify the hypotheses from five dimensions: basic resources, integration development, innovation capacity, social benefits, and network security. It also has an indirect impact on integrated development by influencing innovation ability; innovation capability has a significant positive impact on convergence development; convergence development has a significant positive impact on social benefits; both convergence development and social benefits have a significant negative impact on cybersecurity. Finally, based on the above research results, corresponding countermeasures and suggestions are proposed for the development of the regional digital economy.


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## Body

## 1. Introduction

As a product of Internet technology, the digital economy has brought great changes to the socio-economic production and life styles, and a number of related research results have emerged in the academic field [1]. For the emerging digital economy, more scholars have focused on its development status, connotation and extension, and accounting issues. For example, Litvinenko [2] compared the concept of the digital economy in various countries and found that although the concept has been around for a long time, its connotation has been enriched over time and the boundaries have become more and more blurred, while the understanding of the digital economy is divided and the focus of each country is different.Digital enabling services, industry and agriculture, and 44 digital economy projects have been implemented to boost the development of high-end, green, intelligent, and integrated industries. The digital energy attached service industry has been upgraded. We will implement projects such as the Xinhua Department Store smart mall, e-commerce live broadcast base of the four shopping malls, and tourism big data platform, promote the in-depth application of information technology in the service industry, and comprehensively improve the intelligence level of the service industry, and promote industrial scientific and technological innovation. We will implement projects, such as Wolong green energy-saving transformer manufacturing plant, Zhongli new plate smart home manufacturing plant, and Internet + smart water, speed up the upgrading of control systems and technological transformation of energy conservation, consumption reduction, and emission reduction, and promote the transformation of old and new industrial kinetic energy. Efficient development of digital-enabled agriculture. We will implement projects, such as the Wantou intelligent organic pasture of Junhua company, the e-commerce agricultural products exhibition center in Yueyahu township, and the construction of the national digital agriculture innovation and application base, promote the popularization and application of data collection, measurement, and analysis technologies in the agricultural industry, and accelerate the digital transformation of agricultural production and operation.The digital economy is a kind of innovation, but it is different from innovation in the traditional sense [3]. Traditional innovation is the result of the systematic combination of production factors, the integration of the capabilities of a series of enterprise research and development, production, and manufacturing, and the focus is on technological innovation with independent intellectual property rights [4]. The digital economy, on the other hand, reflects an economic and social form that goes beyond the realm of information industry and Internet technology and transforms the organization of production by digitizing production factors [5]. For example, e-commerce, a model of digital economy, is an innovation of business model, which integrates information flow, capital flow, logistics, and commercial flow through the use of network technology to generate value-added. Some literature points out that the digital economy reconstructs the relationship between consumers and enterprises, leads to personalized and immediate demand for R&D and design, integrates R&D resources of enterprises, and promotes collaborative innovation [6]. In the era of digital economy, the integrated sharing of information resources will accelerate knowledge diffusion and promote innovation acceleration in the whole economic and social fields [7].Taken together, the digital economy is recognized as a driver of innovation. However, most of these analyses remain in the logical explanation. In addition, the key elements of the development of the digital economy are information and data, and the characteristics of information sharing can easily lead to data and information leakage and misuse, make intellectual property protection more difficult, and affect the incentive of enterprises to innovate, which may also affect the development of innovation. So, what kind of impact will the digital economy have on innovation capability? How much impact will it have? How does it affect? These questions have not been well documented and need to be answered by empirical analysis [8]. Therefore, this research can provide a theoretical basis for exploring new innovation influencing factors and provide better ideas for the implementation of national innovation-driven development strategy.Starting from the abovementioned issues and practical implications, this paper will study the impact of digital economy on the improvement of innovation capability. Compared with the existing studies, the innovations of this paper are as follows: first, we try to examine the influence of innovation from a new perspective. In order to comprehensively analyze the innovation impact of digital economy, this paper expands the sample as much as possible and selects 287 prefecture-level cities in China for regression analysis and verification of the impact of digital economy on innovation capability, trying to explore and find more factors that can promote innovation development; second, due to the lack of unified standards for accounting indicators of the digital economy and the difficulty of obtaining data, the existing literature on the measurement of the digital economy has the width and narrowness of the digital economy [9].


### 1.1. Related Work

As for the research on the measurement of digital economy, due to the characteristics of digital economy such as virtual nature, high penetration, and external economy, accounting for digital economy should therefore reflect its penetration in various industries to provide policy makers with a basis for decision making, but there is no unified caliber on accounting for digital economy internationally [10]. In fact, in the early stage of digital economy development, ICT was mainly concentrated in the field of e-commerce, so most of the measurements of digital economy were basically centered on e-commerce. Using a comparative approach, [11] constructed a model for measuring the development level of the digital economy, incorporating factors such as smart infrastructure, promoting growth-led employment, and enhancing social vitality [12]. Suggested that two conventional value-added accounting methods, namely, the production method and the expenditure method, could be used to account for the size of the digital economy, but the study only proposed an idea and did not have a specific measurement framework. Asheim [13] explained the impact brought by AI technology from three aspects: total employment, employment population structure, and employment time series. Simmie and Martin [14] explained that in the digital economy, the value chain as well as the way of value creation has changed, and the supply-oriented business model is gradually shifting to a demand-oriented business model, where customers and vendors create value together. Baldwin and Von Hippel [15] have disrupted the R&D model of manufacturing, which can break the bottleneck of the innovation chain, improve the quality of the manufacturing chain, and optimize the efficiency of the supply chain.Khamparia et al. [16, 17] analysed that the digital economy can drive the expansion of China’s economy and that its impact on employment in technology-intensive manufacturing is stronger than that on labor-intensive and capital-intensive and that China’s digital transformation will lead to productivity gains and a speed-up in innovation. From a micro perspective, [18] argues that network resources in the digital economy have a positive impact on firm value and competitive advantage [19]. Found that B2C-type e-commerce platforms open channels for customers to participate in the manufacturing process of firms and trigger the codevelopment of new products between customers and firms. It was also verified in [20] that customer participation under the moderating effect of interface management contributes to the smooth development of an enterprise’s autonomous R&D activities.


## 1.1. Related Work

As for the research on the measurement of digital economy, due to the characteristics of digital economy such as virtual nature, high penetration, and external economy, accounting for digital economy should therefore reflect its penetration in various industries to provide policy makers with a basis for decision making, but there is no unified caliber on accounting for digital economy internationally [10]. In fact, in the early stage of digital economy development, ICT was mainly concentrated in the field of e-commerce, so most of the measurements of digital economy were basically centered on e-commerce. Using a comparative approach, [11] constructed a model for measuring the development level of the digital economy, incorporating factors such as smart infrastructure, promoting growth-led employment, and enhancing social vitality [12]. Suggested that two conventional value-added accounting methods, namely, the production method and the expenditure method, could be used to account for the size of the digital economy, but the study only proposed an idea and did not have a specific measurement framework. Asheim [13] explained the impact brought by AI technology from three aspects: total employment, employment population structure, and employment time series. Simmie and Martin [14] explained that in the digital economy, the value chain as well as the way of value creation has changed, and the supply-oriented business model is gradually shifting to a demand-oriented business model, where customers and vendors create value together. Baldwin and Von Hippel [15] have disrupted the R&D model of manufacturing, which can break the bottleneck of the innovation chain, improve the quality of the manufacturing chain, and optimize the efficiency of the supply chain.Khamparia et al. [16, 17] analysed that the digital economy can drive the expansion of China’s economy and that its impact on employment in technology-intensive manufacturing is stronger than that on labor-intensive and capital-intensive and that China’s digital transformation will lead to productivity gains and a speed-up in innovation. From a micro perspective, [18] argues that network resources in the digital economy have a positive impact on firm value and competitive advantage [19]. Found that B2C-type e-commerce platforms open channels for customers to participate in the manufacturing process of firms and trigger the codevelopment of new products between customers and firms. It was also verified in [20] that customer participation under the moderating effect of interface management contributes to the smooth development of an enterprise’s autonomous R&D activities.


## 2. Comprehensive Evaluation of the Development Level of Regional Digital Economy

Regional cities and municipalities and the evaluation process are shown in Figure1.Figure 1
Comprehensive evaluation process.People’s risk refers to the risk of loss caused by people due to their own behavioral faults or improper self-management. In this paper, people’s risk is divided into two parts: people’s action behavior risk and people's self-management risk. Sports venue risk refers to the risk of loss caused by people due to improper organization and maintenance or poor execution of sports venues. In this paper, sports venue risk is divided into two parts: sports venue safety management risk and sports venue medical supervision risk. Site equipment risk refers to the risk of loss caused by unfavorable factors in the object that objectively exists. In this paper, the risk of venue equipment is divided into two parts: the risk of activity venue and the risk of sports equipment. . In this paper, the external environment risk is divided into two parts: natural environment risk and man-made environment risk.Combined whitening is a common form of surface injury caused by external forces resulting in loss between normal connections as shown in Figure2. It is divided into semidislocation and complete dislocation. The former is the surface portion of the dislocated joint, while the latter joint surface is completely dislocated from its original position. The main cause of joint dislocation is an incorrect landing position after a fall [20]. The first treatment is analgesic and antishock should require the injured person to remain quiet and not move. Fracture is the destruction of the integrity and continuity of the bone, local swelling, pain, and functional impairment when fractured. There are two types of fractures: one is without skin injury, and the other is open fracture. The upper and lower joints of such fracture sites can be fixed with plastic, wood, etc. In the case of cervical fractures, the fracture should be fixed in the postinjury position and should not be bent, stretched, or rotated. Several people work together to bring the injured person to the plank, fix both sides of the head with sandbags or rolled-up clothes, and tie the injured person to several broadband planks. The analysis of the steps to prevent the risk of injury in sports is shown in Figure 3.Figure 2
Sports training risk factors classification chart.Figure 3
Risk prevention chart for sports injuries.In order to establish a systematic risk assessment system for sports injuries, it is necessary to decompose the decision object based on the idea of decomposition method, and build a hierarchical model accordingly. The basic level of the model: decision target refers to the final derived result; criterion layer is the criteria for the model judgment to make a decision; program layer refers to each specific program limited by the decision target and criterion layer. According to the above, the hierarchical analysis model is constructed as shown in Figure4.Figure 4
Hierarchical decision model for high risk factors.The relative importance of the above four factors was compared between two under the constraints of the criterion level. The importance ratings of coaches and athletes on the seven major exposures of the four risk categories were first arithmetically averaged in terms of categories and then weighted in terms of the ratio of the number of coaches and athletes participating in the study to obtain the final values of the matrix factors.


### 2.1. Indicator Data Collection and Pre-Processing

Based on the constructed comprehensive evaluation index system, the regional statistical yearbook, regional prefecture-level cities’ statistical yearbook, regional Internet development report, regional Internet security report, regional intellectual property annual report, regional municipal governments’ information disclosure annual report, and other reports were widely consulted and combined with the data from the website of the Alliance for the Development of Integration of Two Systems; four years of data from 2014 to 2017 were selected from regional municipalities.After the data collection was completed, the data of the number of government information disclosure in Suzhou from 2014–2016 and 2018 were used for fitting. The comparison of the results of multiple fitting methods is shown in Table1.Table 1
Fitting results of the data of the number of government public information in Suzhou in 2017.
Fitting methodR2FormulaEstimateIndex0.8887y=258855e0.0777x369949Linear0.8922y=29428x+254303368811Logarithmic0.9455y=76107lnx+269656375055Binomial0.9633y=−6968x2+73351x+204542382224Power0.9501 yy=272756x0.225375029According to the fitting results,R2 is used as the judging criterion, and because the binomial fit has the largest R2, the binomial fit result is selected as the indicator data of the number of government public information in Suzhou in 2017.Since each indicator data have different levels and orders of magnitude, the original data of the indicators are standardized in the paper, and the original data are converted to values between 0 and 1. The standardization method uses linear maximum-minimum standardization. Since the index system contains negative indicators, a different approach is used for standardization.Withy years, n evaluation objects, and m evaluation indicators, xθij∗, the original values of the indicators are used; the standardized values of the indicators are denoted by xθij; in all y years is denoted by xjmin, where θ denotes the year θ=1,2,…,y, j denotes the evaluation index j=1,2,...,m.(1)xjmax=maxxθij∗|1≤θ≤y,1≤i≤n,xjmin=minxθij∗|1≤θ≤y,1≤i≤n.Most of the indicators in the indicator system are positive indicators, and the larger the value of these indicators, the better.x denotes the standardized value, and the formula used to standardize the positive indicators is.(2)xθij=xθij∗−xjminxjmax−xjmin.The network security indicators in the indicator system are all negative indicators, and the smaller the value of such indicators, the better, so the negative indicators are standardized using the following formula:(3)xθij=xjmax−xθij∗xjmax−xjmin.


## 2.1. Indicator Data Collection and Pre-Processing

Based on the constructed comprehensive evaluation index system, the regional statistical yearbook, regional prefecture-level cities’ statistical yearbook, regional Internet development report, regional Internet security report, regional intellectual property annual report, regional municipal governments’ information disclosure annual report, and other reports were widely consulted and combined with the data from the website of the Alliance for the Development of Integration of Two Systems; four years of data from 2014 to 2017 were selected from regional municipalities.After the data collection was completed, the data of the number of government information disclosure in Suzhou from 2014–2016 and 2018 were used for fitting. The comparison of the results of multiple fitting methods is shown in Table1.Table 1
Fitting results of the data of the number of government public information in Suzhou in 2017.
Fitting methodR2FormulaEstimateIndex0.8887y=258855e0.0777x369949Linear0.8922y=29428x+254303368811Logarithmic0.9455y=76107lnx+269656375055Binomial0.9633y=−6968x2+73351x+204542382224Power0.9501 yy=272756x0.225375029According to the fitting results,R2 is used as the judging criterion, and because the binomial fit has the largest R2, the binomial fit result is selected as the indicator data of the number of government public information in Suzhou in 2017.Since each indicator data have different levels and orders of magnitude, the original data of the indicators are standardized in the paper, and the original data are converted to values between 0 and 1. The standardization method uses linear maximum-minimum standardization. Since the index system contains negative indicators, a different approach is used for standardization.Withy years, n evaluation objects, and m evaluation indicators, xθij∗, the original values of the indicators are used; the standardized values of the indicators are denoted by xθij; in all y years is denoted by xjmin, where θ denotes the year θ=1,2,…,y, j denotes the evaluation index j=1,2,...,m.(1)xjmax=maxxθij∗|1≤θ≤y,1≤i≤n,xjmin=minxθij∗|1≤θ≤y,1≤i≤n.Most of the indicators in the indicator system are positive indicators, and the larger the value of these indicators, the better.x denotes the standardized value, and the formula used to standardize the positive indicators is.(2)xθij=xθij∗−xjminxjmax−xjmin.The network security indicators in the indicator system are all negative indicators, and the smaller the value of such indicators, the better, so the negative indicators are standardized using the following formula:(3)xθij=xjmax−xθij∗xjmax−xjmin.


## 3. Entropy Weighting

In information theory, information and entropy are measures of the degree of order and disorder of a system, respectively. Applied to evaluation, the greater the entropy value of an indicator, the greater the amount of information the indicator can provide, and the indicator should be given a higher weight. The entropy method is an objective assignment method, which has a stronger mathematical theoretical basis than the subjective assignment method, excludes the subjective interference of the evaluator, and is more accurate and scientific.This paper studies the comprehensive evaluation of multiple indicators for multiple objects and years. In order to make the evaluation results of each year comparable with each other, time variables are added when using the entropy weighting method to calculate the index weights.


### 3.1. Entropy Weighting Method Calculation Process

Let there bey years, n evaluation objects, and m evaluation indicators, and use xθij to denote the standardized indicator values, where θ denotes the year θ=1,2,...,y.(1)
Theith evaluation object(4)pθij=xθij∑θ=1y∑i=1nxθij.(2)
Calculate the entropy valueej(5)ej=∑θ=1y∑i=1npθijlnpθij,amongthen,k=Inyn.dj=1−ej.(3)
Thejth indicator wj(6)wj=dj∑j=1mdj=1−ejm−∑j=1mej.From this, the weights of each indicator can be calculatedW=w1,w2,...,wm.


### 3.2. Comprehensive Evaluation of Digital Economy

The TOPSIS is a multiobjective decision making method proposed by C. L. Hwang and K. Yoon, and the TOPSIS method was fused with the entropy method by [12].The TOPSIS evaluation method is applied in this evaluation study as follows:(1)
Determine ideal solution(7)x+=x1+,x2+,…,xm+=maxxθij|j∈J1,minxθij|j∈J2|1≤θ≤y,1≤i≤n,,x−=x1−,x2−,…,xm−=minxθij|j∈J1,maxxθij|j∈J2|1≤θ≤y,1≤i≤n,.Among them,J1 is the positive indicator and J2 is the negative indicator.Since the original data have been standardized, the maximum value is 1 and the minimum value is 0 after the standardization of positive indicators; the minimum value is normalized to 1 and the maximum value is normalized to 0 after the standardization of positive indicators.(2)
Calculate the distanceDi+ of the evaluation object to the positive ideal solution x+ and the distance Di− of the negative ideal solution x−, respectively,(8)Di+=∑θ=1y∑j=1mwjxθij−x+2,i=1,2,…,n,(9)Di−=∑θ=1y∑j=1mwjxθij−x−2,i=1,2,…,n,wherewj is the weight of the jth indicator calculated by the entropy weighting method.(3)
Calculate the relative posting progress of each evaluation objectCi∗.(10)Ci∗=Di−Di++Di−,i=1,2,…,n.Among them,Ci∗∈0,1.According to equations (8) to (10), the relative fit of each evaluation object can be obtained.The final posting degree as the evaluation result of digital economy development level ranges from 0 to 1, and the larger the value, the higher the digital economy development level.


### 3.3. Comprehensive Evaluation Results

The evaluation results of digital economy development level can rank the evaluation objects, and the evaluation results are shown in Table2 and Figure 5.Table 2
Comprehensive evaluation results of regional digital economy development level.
City2014201520162017AverageNanjing0.55560.55870.64020.75980.6301Suzhou0.41150.44470.48890.58290.4820Wuxi0.27070.28860.32240.40550.3355CHangzhou0.19810.21310.28770.39290.2731Yangzhou0.11550.13350.25010.36220.2155Nantong0.15150.15020.18590.26680.1931Zhenjiang0.14650.15310.19870.25110.1907Taizhou0.08970.10550.15090.25440.1511Xuzhou0.07660.08280.17160.22210.1355Yancheng0.06580.07550.11430.15980.1031Suqian0.07220.10510.06880.13250.0933Huai'an0.05210.06110.07050.09880.0801Lianyungang0.04990.18660.24180.08150.2239Average0.17390.18660.24220.38390.2273Figure 5
Line graph of the regional digital economy development level.The comprehensive evaluation results show that the regional digital economy development level is relatively obvious, which shows that four cities are in a relatively backward position. The relatively large gap between the evaluation results of the digital economy in different cities and regions indicates that the unbalanced development of digital economy in the region is relatively serious.According to the evaluation results, the regional digital economy development level can be divided into three levels, as shown in Table3, the first echelon is the high-level cities whose four year average exceeds 0.2236 (i.e., the average of all cities); the second echelon is the medium level cities whose average is less than 0.2236 but more than 0.15; and the third echelon is other lower level cities.Table 3
Regional digital economy development level echelon.
EchelonsIncluding citiesHigh levelNanjing, Suzhou, Wuxi, and CHangzhouMedium levelYangzhou, Nantong, Zhenjiang, and TaizhouLow levelXuzhou, Yancheng, Suqian, Huai'an, and LianyungangThe radar diagram of the evaluation results of the five first-level indicators for each city in the high-level region is shown in Figure6. Where BR indicates basic resources; ID indicates integration development; SB indicates social benefits; IA indicates innovation capacity; and CS indicates cyber security.Figure 6
Radar chart of indicators at the level of high-level regions.From the perspective of basic resources dimension, Nanjing, Suzhou, Wuxi, and Changzhou are decreasing in order, but the evaluation results of each city are improving year by year, Wuxi and Changzhou have relatively lower scores in basic resources, and the progress is not as obvious as Nanjing and Suzhou; from the perspective of integration development, all cities are improving and the progress is gradually increasing, and Nanjing is still the leader of integration development with the latest data. From the social benefit dimension, Nanjing is in the absolute lead, which should be attributed to the fact that Nanjing has created more jobs in the information industry, while the social benefit dimension of Suzhou and Wuxi is not outstanding and is the lowest level among the five dimensions of their own development, which has become an obvious disadvantage to Changzhou; from the viewpoint of innovation capability, Suzhou is in the absolute lead and the other three cities are at similar levels, the same is true in terms of innovation capability; all cities have not improved significantly and are in a relatively stable state; in the cyber security dimension, Changzhou Wuxi is relatively high and stable, while Nanjing Suzhou has relatively low and unstable evaluation results, indicating that Nanjing Suzhou has relatively more unstable factors in the cyber security dimension. In general, the high-level regions have better digital economy development, and the characteristics of their respective development are relatively obvious.The radar chart of the medium level regional level indicators is shown in Figure7. Among them, the basic resource dimension is not outstanding and the progress is not significant, relatively speaking, the progress of Nantong is relatively large; from the perspective of integration development, all four cities have progressed and the progress is relatively obvious, Yangzhou and Taizhou are obviously better than Nantong and Zhenjiang; from the social benefit dimension, the development level of all four cities is not high, and the progress is not significant between four years and is in a relatively stable state; from the perspective of innovation capacity, all four cities are basically in the same level range. In addition to Yangzhou, Taizhou is slightly worse, and the other three cities have almost no significant progress; network security dimension, the evaluation results of the four cities in the medium level area are relatively high, but there is a certain regressive trend when the other dimensions grow, the network security evaluation results are declining.Figure 7
Radar map of indicators at the medium level.The radar diagram of the first-level indicators for low-level regions is shown in Figure8. Except for the relatively high evaluation results of the network security dimension, the scores of the other four dimensions are low, among which the shortcomings common to the five cities of basic resources and integration development, Xuzhou’s scores in the other four dimensions belong to the higher level in this echelon, but it is obvious that Xuzhou’s network security score is significantly regressed. The level of innovation capacity of all cities is not high, but Yancheng city has grown significantly.Figure 8
Radar map of indicators at the low-level area level.


## 3.1. Entropy Weighting Method Calculation Process

Let there bey years, n evaluation objects, and m evaluation indicators, and use xθij to denote the standardized indicator values, where θ denotes the year θ=1,2,...,y.(1)
Theith evaluation object(4)pθij=xθij∑θ=1y∑i=1nxθij.(2)
Calculate the entropy valueej(5)ej=∑θ=1y∑i=1npθijlnpθij,amongthen,k=Inyn.dj=1−ej.(3)
Thejth indicator wj(6)wj=dj∑j=1mdj=1−ejm−∑j=1mej.From this, the weights of each indicator can be calculatedW=w1,w2,...,wm.


## 3.2. Comprehensive Evaluation of Digital Economy

The TOPSIS is a multiobjective decision making method proposed by C. L. Hwang and K. Yoon, and the TOPSIS method was fused with the entropy method by [12].The TOPSIS evaluation method is applied in this evaluation study as follows:(1)
Determine ideal solution(7)x+=x1+,x2+,…,xm+=maxxθij|j∈J1,minxθij|j∈J2|1≤θ≤y,1≤i≤n,,x−=x1−,x2−,…,xm−=minxθij|j∈J1,maxxθij|j∈J2|1≤θ≤y,1≤i≤n,.Among them,J1 is the positive indicator and J2 is the negative indicator.Since the original data have been standardized, the maximum value is 1 and the minimum value is 0 after the standardization of positive indicators; the minimum value is normalized to 1 and the maximum value is normalized to 0 after the standardization of positive indicators.(2)
Calculate the distanceDi+ of the evaluation object to the positive ideal solution x+ and the distance Di− of the negative ideal solution x−, respectively,(8)Di+=∑θ=1y∑j=1mwjxθij−x+2,i=1,2,…,n,(9)Di−=∑θ=1y∑j=1mwjxθij−x−2,i=1,2,…,n,wherewj is the weight of the jth indicator calculated by the entropy weighting method.(3)
Calculate the relative posting progress of each evaluation objectCi∗.(10)Ci∗=Di−Di++Di−,i=1,2,…,n.Among them,Ci∗∈0,1.According to equations (8) to (10), the relative fit of each evaluation object can be obtained.The final posting degree as the evaluation result of digital economy development level ranges from 0 to 1, and the larger the value, the higher the digital economy development level.


## 3.3. Comprehensive Evaluation Results

The evaluation results of digital economy development level can rank the evaluation objects, and the evaluation results are shown in Table2 and Figure 5.Table 2
Comprehensive evaluation results of regional digital economy development level.
City2014201520162017AverageNanjing0.55560.55870.64020.75980.6301Suzhou0.41150.44470.48890.58290.4820Wuxi0.27070.28860.32240.40550.3355CHangzhou0.19810.21310.28770.39290.2731Yangzhou0.11550.13350.25010.36220.2155Nantong0.15150.15020.18590.26680.1931Zhenjiang0.14650.15310.19870.25110.1907Taizhou0.08970.10550.15090.25440.1511Xuzhou0.07660.08280.17160.22210.1355Yancheng0.06580.07550.11430.15980.1031Suqian0.07220.10510.06880.13250.0933Huai'an0.05210.06110.07050.09880.0801Lianyungang0.04990.18660.24180.08150.2239Average0.17390.18660.24220.38390.2273Figure 5
Line graph of the regional digital economy development level.The comprehensive evaluation results show that the regional digital economy development level is relatively obvious, which shows that four cities are in a relatively backward position. The relatively large gap between the evaluation results of the digital economy in different cities and regions indicates that the unbalanced development of digital economy in the region is relatively serious.According to the evaluation results, the regional digital economy development level can be divided into three levels, as shown in Table3, the first echelon is the high-level cities whose four year average exceeds 0.2236 (i.e., the average of all cities); the second echelon is the medium level cities whose average is less than 0.2236 but more than 0.15; and the third echelon is other lower level cities.Table 3
Regional digital economy development level echelon.
EchelonsIncluding citiesHigh levelNanjing, Suzhou, Wuxi, and CHangzhouMedium levelYangzhou, Nantong, Zhenjiang, and TaizhouLow levelXuzhou, Yancheng, Suqian, Huai'an, and LianyungangThe radar diagram of the evaluation results of the five first-level indicators for each city in the high-level region is shown in Figure6. Where BR indicates basic resources; ID indicates integration development; SB indicates social benefits; IA indicates innovation capacity; and CS indicates cyber security.Figure 6
Radar chart of indicators at the level of high-level regions.From the perspective of basic resources dimension, Nanjing, Suzhou, Wuxi, and Changzhou are decreasing in order, but the evaluation results of each city are improving year by year, Wuxi and Changzhou have relatively lower scores in basic resources, and the progress is not as obvious as Nanjing and Suzhou; from the perspective of integration development, all cities are improving and the progress is gradually increasing, and Nanjing is still the leader of integration development with the latest data. From the social benefit dimension, Nanjing is in the absolute lead, which should be attributed to the fact that Nanjing has created more jobs in the information industry, while the social benefit dimension of Suzhou and Wuxi is not outstanding and is the lowest level among the five dimensions of their own development, which has become an obvious disadvantage to Changzhou; from the viewpoint of innovation capability, Suzhou is in the absolute lead and the other three cities are at similar levels, the same is true in terms of innovation capability; all cities have not improved significantly and are in a relatively stable state; in the cyber security dimension, Changzhou Wuxi is relatively high and stable, while Nanjing Suzhou has relatively low and unstable evaluation results, indicating that Nanjing Suzhou has relatively more unstable factors in the cyber security dimension. In general, the high-level regions have better digital economy development, and the characteristics of their respective development are relatively obvious.The radar chart of the medium level regional level indicators is shown in Figure7. Among them, the basic resource dimension is not outstanding and the progress is not significant, relatively speaking, the progress of Nantong is relatively large; from the perspective of integration development, all four cities have progressed and the progress is relatively obvious, Yangzhou and Taizhou are obviously better than Nantong and Zhenjiang; from the social benefit dimension, the development level of all four cities is not high, and the progress is not significant between four years and is in a relatively stable state; from the perspective of innovation capacity, all four cities are basically in the same level range. In addition to Yangzhou, Taizhou is slightly worse, and the other three cities have almost no significant progress; network security dimension, the evaluation results of the four cities in the medium level area are relatively high, but there is a certain regressive trend when the other dimensions grow, the network security evaluation results are declining.Figure 7
Radar map of indicators at the medium level.The radar diagram of the first-level indicators for low-level regions is shown in Figure8. Except for the relatively high evaluation results of the network security dimension, the scores of the other four dimensions are low, among which the shortcomings common to the five cities of basic resources and integration development, Xuzhou’s scores in the other four dimensions belong to the higher level in this echelon, but it is obvious that Xuzhou’s network security score is significantly regressed. The level of innovation capacity of all cities is not high, but Yancheng city has grown significantly.Figure 8
Radar map of indicators at the low-level area level.


## 4. Conclusion

In this paper, we evaluate the level of digital economy development research, first summarize the connotation of the digital economy and digital economy evaluation-related research, distill the influence factors of digital economy development, use PLS to verify the hypothesis analysis, and the research results show that the basic resources have a large impact on integration development, not only has a direct positive impact but also has an indirect impact on integration development by affecting the innovation capacity.



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*Source: 8136437-2022-07-22.xml*