https://orcid.org/0009-0002-7488-4040
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Abstract
The development of digital economy is a strategic choice to grasp the revolution of new science and technology and the new opportunities of industrial reform. The development of digital economy depends on the good support of policy and theoretical system. Therefore, the quantitative evaluation of policy texts provides the basis of decision-making and the suggestions of path optimization for the formulation and improvement of digital economy policy of China. By selecting the text of digital economy policy issued by China government, the paper constructs a quantitative evaluation model of digital economy policy using the methods of content analysis and text mining. The empirical research results show that the overall design evaluation of the selected policy is relatively reasonable. Six policies were evaluated as excellent and two as acceptable. In view of the problems such as lack of predictive policy in the policy type, lack of specific policy in the policy timeliness, imbalance in the use of policy guarantee, and lack of comprehensive coverage in the policy objectives, the paper puts forward corresponding countermeasures and suggestions.
Citation: Hong S, Wang T, Fu X, Li G (2024) Research on quantitative evaluation of digital economy policy in China based on the PMC index model. PLoS ONE 19(2): e0298312. https://doi.org/10.1371/journal.pone.0298312
Editor: Jiachao Peng, Wuhan Institute of Technology, CHINA
Received: March 16, 2023; Accepted: January 21, 2024; Published: February 15, 2024
Copyright: © 2024 Hong et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Data Availability: All relevant data are within the paper and its Supporting Information files.
Funding: The National Social Science Foundation of China, 21BJL073, A/Prof. Xuebin Tian. The funder had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
Competing interests: The authors have declared that no competing interests exist.
Introduction
As a new economic form, digital economy plays a significant role in driving industrial reform and enabling high-quality development. It is an important embodiment of national comprehensive strength in the digital age and an important engine for building a modern economic system. Influenced by the revolution of new science and technology and industrial reform, the development situation of digital economy of China is undergoing profound changes, and the trend of digital transformation is inevitable. Meanwhile, many countries have issued strategic plans one after another. The purpose is to create new competitive advantages and reshape the new international pattern in the digital era. From the perspective of theoretical logic and practical experience, building a new development pattern of digital economy requires the guidance and support of relevant policies under the background of industrial reform. At present, the research on digital economy policy of China mainly focuses on policy evolution, path optimization and enterprise innovation. However, there are few researches on digital economy from the perspective of policy text quantification.
Digital economy refers to high-tech development, business and social transformation, and information changes that drive economic growth. Inadequate funding of information and computing infrastructure may be a significant challenge in the transformation of the digital economy [1]. Improving the efficiency and volume of foreign investment is the effective way to promote the digital economy [2], and strengthen the application of key technologies of digital economy in specific regions [3]. With the acceleration of the digital development process, developing digital infrastructure and expanding the scope of digital applications will be conducive to the digital economy [4]. In fact, the new and old features of the digital economy are accurately grasped, which is conducive to opening up new market space [5]. Digital economy will greatly reduce market friction, but it also poses the new challenges to the effective operation of the market [6]. National policy priorities for digital economy development need to be determined and the countries need to increase support for the digital transformation process [7]. Furthermore, the countries need to consider all aspects of formulating cluster policies, which include the types of authoritarian and liberal [8]. There is clear evidence indicating the importance of digital flow, but enterprises have cleverly covered up the digital flow, while national policies and rules have failed to produce effective responses so far [9]. The competition policy in the digital economy should be based on sound theory and strict evidence analysis, and it is best to summarize it with the method of law and economics [10]. The digital economy is changing the competitive conditions of the digital market, and the new conditions of market competition and the new business models pose problems for regulators [11]. In addition, the growth of digital economic aggravates digital exclusion, inequality, unfavorable integration and other digital hazards [12]. Exploring policy options to alleviate a series of new challenges emerging in the digital economy and implementing precise policies, are the important tasks for coping with the Corona Virus Disease 2019 (COVID-19) and achieving sustainable economic development [13, 14].
The theory of public policy evaluation is a theoretical framework for assessing policies, aimed at determining their effectiveness, feasibility, sustainability, and fairness. Its scope of application encompasses various domains, including government policies, social policies, and economic policies. On one hand, this theory enhances the scientific and practical aspects of policy formulation by offering a scientific and systematic approach to evaluating policy effects. On the other hand, evaluation outcomes assist policymakers in comprehending the effects of policy implementation, thereby enabling policy adjustments and improvements based on the evaluation results. Existing methods for public policy evaluation each possess distinctive characteristics, providing crucial references for assessments related to digital economic policies. Due to the distinct scopes of application for each method, evaluation outcomes also exhibit slight variations. The grey relational degree model exhibits subjectivity and substantial error in determining optimal values for various indicators. The slow convergence of the Back Propagation (BP) neural network hampers the precision of policy evaluation. The fuzzy comprehensive evaluation method lacks objectivity in assessing indicators. It’s noteworthy that current policy evaluation research excessively emphasizes ex-post evaluation, paying insufficient attention to the policies themselves. In contrast, the Policy Modeling Consistency (PMC) index model showcases a more prominent comprehensive evaluation effect. This method underscores the internal consistency of policies, offering advantages such as diversity assessment, pliability of indicators, and distinguishable gradations. Additionally, serving as a model for evaluating policy texts, it boasts traits like low cost and ease of operation, which mitigate subjectivity during the evaluation process. While assessing the merits and demerits of individual policies, it better reveals policy disparities across different periods or regions through policy comparisons.
The PMC index model has a very wide range of applications, mainly embodied in regional sustainable development policy [15], green development policy [16], new energy vehicle industry policy [17], carbon emissions and carbon neutral policy [18], manufacturing development policy [19], watershed ecological compensation policy [20], public health emergency policy [21], waste classification management policy [22], disaster relief policy [23], pig price regulation policy [24], etc. It involves the fields of development, land, environmental protection, energy, industry, ecology, health, waste, disaster relief, and livelihood. Furthermore, relevant research focuses on policy timeliness, policy issuing institutions, policy audience scope, policy type, policy intensity, policy social benefits, policy incentives and constraints, etc. The overall design, improvement space, optimization path, theoretical expansion, decision-making basis, countermeasures and suggestions, management practice, technical support and evaluation of corresponding policies will be discussed. To sum up, it is very necessary and valuable to use the PMC index model to conduct policy evaluation on digital economy of China.
Compared with other methods, the PMC index model has important advantages. The evaluation dimensions are rich, and by adding evaluation dimensions instead of calculating indicator weights, it effectively avoids indicator weight errors and subjective evaluation biases, making the evaluation results more objective and accurate. Focusing on pre-policy evaluation, analyzing key content and keywords in policy texts, fills the gap in research and analysis of policy content. The PMC index evaluation method can be applied to both single industry policies and regional policy systems.
The research objective of this paper is to employ the PMC index model’s research methodology to provide an in-depth interpretation of China’s digital economic policies. Building upon text mining and content analysis, this study employs a combined quantitative and qualitative approach to thoroughly explore the strengths and weaknesses of China’s digital economic policies. Subsequently, it presents corresponding optimization paths and recommendations, aiming to furnish scientific decision-making foundations for expediting the development of China’s digital economic market.
The digital economy policy is a series of policy tool combinations that promote the development of the digital economy, involving multiple dimensions such as policy nature, policy objectives, policy content, policy effects, etc. How to objectively evaluate is a very important issue. At present, there is relatively little literature on the evaluation of digital economy policies, and policy evaluation is the most important link in the process of public policy formulation and management [25, 26]. It uses relevant research methods to systematically measure and judge the effectiveness of policy intervention and implementation [27]. Through the evaluation of digital economy policies, not only can scientific judgments be made on the value of the policies themselves [28], but also the actual effects of policy formulation and implementation can be tested [29]. Therefore, in view of this, this article combines digital economy policy sample analysis, text mining, and PMC index model to construct a quantitative evaluation index system for digital economy policies. It conducts sample analysis and text mining on China’s digital economy policies, and quantitatively evaluates and analyzes typical digital economy policy texts at the central and local levels, in order to provide decision-making basis for the improvement of relevant policies and the formulation of new policies.
The contributions of this study are as follows:
Quantitative assessment of national and provincial-level digital economic policy texts in China is conducted through the utilization of content analysis and text mining methods. This endeavor aims to gain deeper insights into the ongoing trends within China’s digital economic industry. Moreover, it seeks to furnish optimized recommendations for the formulation and refinement of future policies. Simultaneously, from an empirical standpoint, a quantitative evaluation of China’s digital economic policies is performed. The outcomes of policy evaluation serve as a reference for standardizing and enhancing the practicality of the future digital economic industry policy evaluation system, thereby facilitating the high-quality development of China’s digital economic sector.
Materials and methods
Data source
Any single indicator can be misleading, but if multiple composite indicators point to the same result, it can provide a more accurate judgment of the evaluated thing. The PMC index first determines the meaning and level of variables at all levels, and then evaluates and analyzes the policy’s advantages and disadvantages through the aggregated consistency level. This method attempts to find highly saturated secondary variables that can characterize policy characteristics and assign consistent weights to these variables to avoid subjective limitations. In addition, all variables are binary balanced, greatly simplifying the complexity of PMC index calculation.
This article uses a composite analysis method that combines policy sample analysis, text mining, and PMC index model. On the basis of sorting out China’s digital economy policies, text mining is used to search for important and highly correlated text data, which forms a component of secondary indicators. Unstructured text data is transformed into structured and readable data, and the PMC index model is used to conduct quantitative evaluation research on digital economy policies in central and local areas of China.
The selection criteria for digital economic policies are as follows: To ensure the comprehensiveness and authority of the policy sample content, the focus is primarily on national and provincial-level digital economic policy texts. National-level policies, being overarching documents, provide stronger guidance and standardization, serving as crucial foundations for provincial policy formulation, guiding and constraining provincial policies. The selection of provincial policies considers their coordination with the central government and effectiveness. City-level and county-level policies are often extensions and supplements to provincial policies, and therefore, they are not included in the sample selection.
The initial policy search is conducted using the "PKU Law" (Peking University Legal Information Retrieval System) legal professional database with the title "Digital Economy," specifying the policy category as currently effective, and the retrieval date as January 1, 2023. Informal decision documents such as "letters" and "administrative licensing approvals" are excluded, focusing on formal decision documents like laws, regulations, resolutions, orders, opinions, and notifications. To ensure accuracy, verification and supplementation are performed on the official websites of the central government and various provincial governments. Finally, a manual full-text review is employed to eliminate policies with little relevance to the research theme. In total, 37 policy texts were retrieved (detailed information in S1 and S2 Appendices), including 5 national-level policies and 32 provincial-level policies. The main distribution results are presented in Table 1.
Model construction
Variable classification and parameter identification.
With the help of the text mining software ROSTCM.6, the 37 selected policy texts related to the digital economy were processed to extract representative words from them. Since the policy content pertains to the digital economy, it is necessary to manually remove high-frequency words such as "digital" and "economy," as well as adverbs and verbs that have no obvious effect on the results, such as "development," and so on. After eliminating the aforementioned words, 60 high-frequency effective words were finally identified, as shown in Table 2. This provides an important reference for setting secondary variables.
Adhering to the modeling principles of the PMC index model and grounded in the "Omnia Mobilis" [30] hypothesis, which posits interconnectivity among entities while not disregarding any existing variables, equal weights are assigned to both primary and secondary variables.
Compared with the traditional economy, the digital economy focuses on the application of products and the extension of services, is demand oriented, focuses on discovering potential and intangible user needs, provides personalized services, and creates user value. Due to the existence of digital technology, more consumers are involved in the production and consumption of products, so the dominant position of the digital economy is relatively unclear.
The digital economy is essentially a technology economy paradigm, which is an optimal practice model for economy and society based on technological innovation. It responds to structural crises caused by technological changes through institutional changes, thus forming a relatively stable and sustainable behavior. The digital economy is driven by digital knowledge and information as key production factors, modern information networks as the main carrier, and the efficient utilization of information and communication technology as an important driving force for efficiency improvement and economic optimization. Therefore, introducing data elements and changing social production methods can provide an important experimental environment for expanding current economic and management theories; The development of the digital economy requires new theories from economics and management to explain and promote the construction of new theories.
Since China first included the digital economy in the Two Sessions Report in 2017, the national level has attached increasing importance to promoting the development of the digital economy. To address various practical issues in the development of the digital economy, various Chinese ministries have issued a series of policies and regulations. The introduction of numerous policies has also made the policy and regulatory system in this field increasingly complex, leading to ineffective implementation and poor coordination in the policy integration environment. Policy analysis is the main basis for the abolition, reform, and establishment of digital economy policies. The primary task of policy analysis is to identify and conceptualize the problems that need to be solved, and policy problems stem from unresolved practical problems within the current policy system. The evaluation and analysis based on digital economy policy texts have important practical value and significance. Therefore, when quantitatively evaluating China’s digital economy policies, this article will focus on distinguishing the advantages and disadvantages of current policies based on existing practical problems, providing decision-making basis for the improvement of relevant policies and the formulation of new policies.
Employing Ruiz’s [31] research methodology, the 10 primary variables for the variable setting in the PMC model of digital economy policy have been established. These 10 primary variables are as follows: Policy Type (X1), Policy Effectiveness (X2), Policy Level (X3), Policy Felids (X4), Policy Guarantee (X5), Policy Audience (X6), Policy Objectives (X7), Policy Evaluation (X8), Policy Perspective (X9), and Policy Publicity (X10). Concurrently, through text mining and considering the current state of digital economic development, and drawing insights from the research of scholars Kuang [32], Liu [33], Hu [34] and Yang [35], the Chinese digital economic policy PMC model variables were formulated. This encompasses 10 primary variables and 45 secondary variables, and the evaluation criteria are in binary form, with detailed outcomes presented in Table 3. The main procedural steps are as follows: assigning values to policy data based on corresponding indicators, inputting these values into a multi-input-output table, calculating the PMC index for policy texts, and subsequently plotting the PMC surface graph.
Table of multiple-input-output.
The parameter configuration for the PMC index model primarily employs a binary approach, assuming that the importance of each secondary variable for input-output is equal, thereby effectively considering each variable. When the expressions regarding the policy under evaluation align with the corresponding evaluation criteria of a given secondary variable, that secondary variable is assigned a value of 1; conversely, it is assigned 0. This approach ensures that every variable is adequately accounted for. Combined with the specific conditions for the variables of the PMC index model of digital economic policy of China, the table of multi-input-output is established, as shown in Table 4.
Empirical study on policy evaluation
Selection of evaluation objects
The index model of PMC is designed to objectively consider all secondary variables without any special requirements for the evaluation object. It conducts quantitative evaluation on any digital economic policy, but subjective deviation should be minimized when selecting policy samples to be evaluated [36]. Therefore, in this study, a simple random sampling method was employed to select 8 policies for evaluation from the pool of 37 digital economic policies. They are recorded as P1, P2, P3, P4, P5, P6, P7 and P8 respectively. There are 3 national policies and 5 provincial policies. The specific results are shown in Table 5.
Calculation of PMC index
The calculation of the PMC index model revolves around four aspects [37]. First, inputting the primary and secondary variables from the previous text into a multi-input-output table according to Formula (1). Second, sequentially assigning values to the 45 secondary variables in the digital economic policy multi-input-output table using Formula (2). The values of the secondary variables follow a [0,1] distribution, with a value of 1 assigned if they meet the evaluation criteria and 0 if they do not. Third, calculating the values of the primary variables based on Formula (3). After assigning values to the secondary variables that follow a [0,1] distribution in the digital economic policy PMC index model, the sum of the secondary variable scores is obtained. Then, dividing the sum by the number of secondary variables contained in the respective primary variable yields the arithmetic mean, representing the value of that primary variable. Finally, using Formula (4) to sum the values of each primary variable in the digital economic policy PMC index model, the overall PMC index for each digital economic policy is obtained. The detailed calculation formulas are as follows:
(1)
(2)
(3)
As shown in the formula, t is the primary variable, j is the secondary variable and T(Xtj) is the number of secondary indicators under the primary indicator.
According to the formula (1)-(4), the calculation results are brought into Table 4. Therefore, the table of multi-input-output of digital economic policies of China is finally obtained, as shown in Table 6. Simultaneously, referring to Ruiz’s classification standards for policies, such as 9–10 (perfect), 7–8.99 (excellent), 5–6.99 (acceptable), and 0–4.99 (defective). Finally, the PMC index and evaluation grade of digital economic policy are determined, as shown in Table 7.
Surface plot of PMC.
The surface plot can show the quantitative results more intuitively and the differences between various policies. The fluctuation degree of the surface plot can be used to judge the gaps of the policies. The smaller the fluctuation degree is, the more reasonable the internal structure of the policy is, and the more detailed the policy is.
The premise of constructing surface plot of PMC is to calculate the matrix of PMC. The matrix of PMC is a 3×3 matrix, composed of 9 primary variables. Currently, there are 10 primary variables. However, the X10 of primary variable does not have any secondary variable, and its policy scores are all 1. Therefore, the X10 of the primary variable is eliminated under the premise of considering matrix symmetry. Finally, the 3×3 matrix is constructed by 9 primary variables. It can more intuitively show the consistency and rationality within the policy. The calculation of PMC surface is shown in formula (5).
The PMC surface of digital economy policy of China is shown in Fig 1. The X axis corresponds to 1, 2 and 3 in the Fig 1, and the Y axis corresponds to series 1, 2 and 3. The Z axis represents the score of the PMC index of the policy to be evaluated. Different color blocks represent different scores of the primary variables. The convex part of the surface plot indicates that there is a high score. The concave part of the surface plot indicates that there is a low score. The advantages and disadvantages of a certain policy can be seen from the comparison among the eight policies. The PMC surfaces of the policies of P1, P3, P4, P5, P6 and P8 have a certain degree of concavity and convexity, indicating that the internal consistency is relatively high and the structure is relatively reasonable. The PMC surfaces of the policies of P7 and P2 have an obvious fluctuation trend, indicating that the internal consistency is low, and the policy is not detailed enough, and the overall score is low.
Analysis of quantitative results.
Among the 8 selected digital economic policies, there are 6 excellent policies (2 national policies and 4 provincial policies) and 2 acceptable policies (1 national policy and 1 provincial policy). Furthermore, there is no perfect policy or bad policy. The specific ranking of these policies is as follows P1>P4>P8>P5>P6>P3>P7>P2. On the whole, the digital economy policy of China is scientific and reasonable. The central government and the local governments have a strong sense of coordination in policy formulation. It has effectively promoted the preliminary establishment of the data resource element market and the rapid development of the digital economy industry in China. However, it is worth noting that the lack of perfect policies indicates that the quality of current digital economic policies still has some room for improvement.
The radar map is made according to the average value of 9 primary variables in the 8 policies, which can intuitively and clearly show the shortcomings of digital economic policies of China, as shown in Fig 2. It is an important aspect that needs attention in the process of formulating digital economy policies in the future.
The average value of policy type X1 is 0.85, which shows that digital economy policy of China has a strong role in supervision, suggestion, description and guidance. The average value of policy effectiveness X2 is 0.33, which shows there is no effective connection among long-term, medium-term and short-term in the process of formulating digital economy policies. Too much attention is paid to the development of medium and short term policies, ignoring the long term implementation of policies. The average value of policy level X3 is 0.33. It shows that the issuing units of relevant policies are mainly independent departments and lack of joint issuing organs. The average value of policy areas X4 is 0.71. It shows that the current digital economy policy covers a wide range of fields and involves a deeper level. The average value of policy guarantee X5 is 0.82. It shows that China has used many kinds of security methods to formulate digital economy policies. The average value of policy audience X6 is 0.97. It shows that the digital economy policy involves a wide audience. The average value of policy objectives X7 is 0.83, which shows that the digital economy policy has clear objectives. The average value of policy evaluation X8 is 0.98. It fully shows that the formulation of relevant policies is based on sufficient, reasonable planning and scientific scheme. The average value of policy perspective X9 is 0.5. It shows that the macro and micro levels are not fully involved in the formulation of relevant policies, and the combination of macro and micro is low.
The PMC index of the fourteenth five year plan for digital economy is 7.74. The rank of P1 is No. 1, and its ranking is excellent. Only X4 of the primary variables is below the mean value. The planning outline of digital economy development of China has fully played a leading role, reflected the confidence and the ability of accelerating the cultivation and development of the digital economy, promoting industrial reform, and enhancing comprehensive competitiveness. Therefore, if the policy is to be improved, the indicator of X4 should be given priority.
The PMC index of the promotion regulations of digital economy of Jiangsu Province is 7.61. The rank of P4 is No. 2, and its ranking is excellent. Only X1 of the primary variables is below the mean value. The rank of this policy is No. 1 among the five provincial policies selected, and its score exceeds two national policies. It means that the policy makers consider comprehensively and reasonably when designing policies. Resource elements, digital industry, public service and governance system are considered comprehensively. However, the X1 of the primary variables does not involve prediction, and it becomes the optimization object in future.
The PMC index of the promotion regulations of digital economy of Zhejiang Province is 7.54. The rank of P8 is No. 3, and its ranking is excellent. Only X1 and X4 of the primary variables are below the mean value. Different from the P1 of the national policy, the P8 of the provincial policy significantly weakens the prediction of the digital economy industry, and more considers the content of the micro level. Therefore, the optimization path is from X1 to X4 in future.
The PMC index of the promotion regulations of digital economy of Hebei Province is 7.50. The rank of P5 is No. 4, and its ranking is excellent. Only X1 and X7 of the primary variables are below the mean value. The full and detailed of policy guarantee reflects the confidence and determination. However, too many policy objectives focus on industrial digitization and digital industrialization, which does not reflect equal and inclusive public services. Therefore, the optimization path is from X1 to X7 in future.
The PMC index of the promotion regulations of digital economy of Henan Province is 7.41. The rank of P6 is No. 5, and its ranking is excellent. Only X1 and X7 of the primary variables are below the mean value. The same as P5, the policy types do not include predictability, and the policy objectives do not cover digital public services. For Henan, the supporting role of public services and social governance should be strengthened when making policies. Furthermore, the service system should be optimized to promote the development of digital economy in the region and build a strong province of digital economy. Therefore, the optimization path is from X1 to X7 in future.
The PMC index of guiding opinions on developing digital economy, stabilizing and expanding employment is 7.36. The rank of P3 is No. 6, and its ranking is excellent. X4, X5, and X7 of the primary variables are below the mean value. The main goal of policy formulation is to achieve higher quality and full employment, and constantly expand employment innovation space. However, there are few policy guarantees, and the policy objectives fail to reflect the improvement of governance system of digital economy. Therefore, the optimization path is from X5 to X4 to X7 in future.
The PMC index of the promotion regulations of digital economy of Guangdong Province is 6.85. The rank of P7 is No. 7, and its ranking is acceptable. X1, X4, and X7 of the primary variables are below the mean value. The difference between X7 and the mean value is the largest. It indicates that the policy objective setting needs to be strengthened. It is necessary to further optimize policies to promote high-quality economic development in combination with the documents and regulations of national strategic and the regional industry situation. Therefore, the optimization path is from X7 to X4 to X1 in future.
The PMC index of guidelines for foreign investment and cooperation in digital economy is 6.45. The rank of P2 is No. 8, and its ranking is acceptable. Six of the primary variables are lower than the mean value such as X1, X4, X5, X6, X7, and X8. This policy is mainly guided by foreign investment and cooperation, and its content is not as comprehensive and detailed as other policies to be evaluated. The difference between X6 and the mean value is the largest, and the difference between X7 and the mean value is the smallest. Therefore, the optimization path is X6 ‐ X4 ‐ X8 ‐ X5 ‐ X1 ‐ X7 in future.
Conclusions and suggestions
Conclusions
This paper conduct content analysis and text mining on 37 digital economic policies. Then, the PMC index model of digital economic policy of China was constructed. Therefore, the empirical analysis was conducted on the eight selected representative policies. The main research conclusions are as follows.
Firstly, the overall design of digital economy policy of China is more reasonable and comprehensive. The average of PMC index of the eight evaluated policy texts is 7.31. There are the 6 policies of P1, P3, P4, P5, P6, and P8 evaluated as excellent. Among them, the provincial policies account for a large proportion. There are the 2 policies of P2 and P7 evaluated as acceptable. The provincial policies account for half. It fully shows that China has fully considered the current situation and future development trend of the industry when formulating digital economy policies, and has promoted the construction of development pattern of digital economy under the background of industrial reform.
Secondly, according to the multi-input-output of digital economic policies, the digital economic policy of China still has a lot of room for improvement. The policy type of X1 mainly focuses on supervision, advice, description and guidance, and lacks prediction. Only the national policies of P1 and P3 involve prediction, while the provincial policies of P4, P5, P6, P7, and P8 do not involve prediction. Although the policy effectiveness of X2 involves the policies of long-term, mid-term and short-term, but the single policy does not well reflect the policies combination of long-term, mid-term and short-term. In fact, perfect policies should effectively combine the long-term planning with the short-term goals. The overall score of the policy guarantee of X5 is high. However, there is a serious lack of legal protection. Among the 8 policies to be evaluated, only P3 involves policy guarantee. The policy objectives of X7 does not highlight the function of public service especially for the provincial policies. Therefore, it is not conducive to the further improvement of the overall service level.
Suggestions
Considering the uniqueness of the digital economy and in conjunction with the primary research findings, the subsequent recommendations are proposed.
Firstly, China’s digital economic policies predominantly emphasize supervision, suggestions, descriptions, and guidance, while lacking predictive aspects. A scientifically grounded and rational prediction is instrumental in cultivating the digital economy’s factor market and directing standardized development. Policies encompassing predictive mechanisms can facilitate broader societal involvement and stimulate industrial growth at various levels. Moreover, they can enhance the efficacy of governmental departments, augmenting their functional capabilities and executive authority, thus refining decision-making efficiency.
Secondly, the selected policies primarily center on mid-term and long-term leading strategies. Long-term policies exhibit a strategic, forward-looking nature that fosters a favorable business climate for the digital economy sector. However, these policies hold a broader guiding influence while offering limited guidance in specific domains. Consequently, the implementation of specialized policies can enhance the standardization of the digital economy sector. Simultaneously, it is imperative to establish effective linkages and dynamic synergies among long-term, mid-term, and short-term policies, instead of solely pursuing rapid short-term policy outcomes.
Thirdly, policy guarantee is a means and measure taken by the government to achieve a certain set goal. Good policy guarantees can successfully protect the digital economy. At present, the legislation in the digital field should be expedited, and the digital governance framework should be optimized. Sound laws are conducive to optimizing the business environment and protecting the legitimate rights and interests of consumers. Attracting enterprises and talents from all over the country could promote the development of technology and the accumulation of capital, and improve the overall social welfare level.
Finally, pay attention to the role of stakeholders in the digital economy. In the era of digital economy, stakeholders have already extended to employees, users, and partners. Stakeholders should not only include shareholders, but also employees in the future. We should not only focus on shareholders, but also on employees. Only with excellent employees can we create greater value for the enterprise and achieve better development. The digital economy is built on the basis of data, and enterprises earn more profits by collecting user data. Enterprises should be regulated while using data to earn more profits. Regulators need to play a more important role in protecting user privacy and data usage. Protecting privacy requires stricter regulations to ensure it. The government is facing a new market composed of an increasing number of platforms, which poses an unprecedented challenge to its market governance. On the basis of a scientific understanding of the new economic form of the digital economy and a rational analysis of the two growth paths of the digital economy, the government should redesign the content of government management functions in the digital economy and further play a positive role.
Research limitations and future trends
It is important to note that this study primarily quantitatively evaluated China’s national and provincial-level digital economic policies, excluding policies at the municipal and county levels. Due to variations in the level of digital industry development among different municipalities, a unified calculation standard has not yet been established. Therefore, future research should aim to expand the sample size and diversify the selection of subjects. Additionally, a comprehensive cross-analysis of policy content can be conducted from different dimensions by integrating policy tool theories. This approach can offer decision-makers objective and feasible solutions. Additionally, in terms of policy evaluation methods, since this study exclusively employed the PMC index model, the results are relatively singular. In the future, a more comprehensive approach should be considered, incorporating various evaluation methods rather than relying solely on a single approach.
Building upon the research findings of this paper and considering the current development of China’s digital economic industry, future studies can be directed towards the following aspects:
On one hand, it is essential to accelerate the integration of the digital economy with the real economy. As the digital economy and its systems become increasingly sophisticated, policies need to be appropriately adjusted, encompassing goals, positioning, and tools. Focusing on digital industrialization and industry digitization, policies should provide broader application scenarios for digital economic development, amplifying and multiplying the impact of digital technologies on economic growth.
On the other hand, emphasis should be placed on innovation-driven development. Presently, there are noticeable disparities in the development of the digital economy across the eastern, central, and western regions of China. Innovation-driven approaches can bridge these regional gaps, serving as a crucial lever for regional coordination. This strategy enables the utilization of developed digital economic regions’ driving roles and innovation advantages, enhances regional collaborative innovation capabilities, and promotes high-quality development of the digital economic industry.
Lastly, in order to achieve the policy objectives of digital industrialization and industry digitization, internal demand needs to be constantly improved and external demand needs to be fully utilized. The development of digital economy is long-term and systematic. Therefore, it is needed to accelerate the establishment of data element market, promote the equality and benefits of public services, improve the governance system of the digital economy, eliminate the digital divide and lay a solid foundation for building a good modern market system of digital economy.
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