Impact of Digital Government on Digital Transformation of Enterprises from the Perspective of Urban Economic Sustainable Development
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1. Introduction
The digital economy, based on digital information technologies such as big data, the Internet of Things, blockchain, cloud computing, and artificial intelligence, is rapidly changing the world, bringing the economic interests of countries and regions all over the world closer together. Digital technology is integrated into all areas of China’s economy, and China’s overall industrial structure has undergone digital upgrading. The scale of the Chinese enterprise digital economy has jumped from 11 trillion yuan in 2012 to 50.2 trillion yuan in 2022, and its proportion of gross domestic product (GDP) has risen from 21.6 percent to 41.5 percent over the same period, ranking second in the world for many consecutive years.
2. Literature Review
2.1. Enterprise Digital Transformation
2.2. Digital Government
2.3. Summary
In summary, existing studies have investigated the influencing factors affecting the digital transformation of enterprises and the related consequences of digital government from multiple perspectives and scenarios, achieving fruitful research results. However, the existing literature has paid less attention to the impact of digital government on enterprises’ digital transformation, and few scholars have discussed the economic consequences of digital government and enterprises’ digital transformation from the perspective of urban economic development. This study attempts to explore the drivers of enterprise digital transformation from the perspective of digital government. This study also examines, from the perspective of enhancing enterprise digitalization through digital government construction, the mechanisms through which digital government promotes urban economic development to better understand the economic effects generated by digital government at the macro level and micro level.
3. Theoretical Analysis and Hypothesis
3.1. Digital Government and Enterprise Digital Transformation
Digital governments facilitate enterprise digital transformation.
3.2. Digital Government and Enterprise Digital Transformation: The Mediating Effects of Urban Business Environment
Digital governments facilitate enterprise digital transformation by improving the business environment.
3.3. Digital Government and Enterprise Digital Transformation: The Mediating Effects of Information Search Costs
Digital government can effectively reduce the information search costs for enterprises. Specifically, on the one hand, digital government aggregates information data from various government departments, breaks through the barrier of the “digital divide”, opens up efficient channels for the circulation of enterprise information, and reduces the costs incurred by enterprises in the original information search process. On the other hand, digital government integrates the “fragmented” information scattered across government departments, improving the integrity of information. Through strict data governance rules and systems, conducting multi-source verification of data quality, and clarifying authoritative data sources, digital government ensures the authenticity and accuracy of data. By sharing coordination mechanisms, digital government responds to enterprises’ demands for data sharing in a timely manner, ensuring the timeliness of enterprises’ information acquisition. The information disclosed by digital government is authoritative, authentic, and timely, reducing enterprises’ information search costs.
Digital governments facilitate enterprise digital transformation by reducing information search costs.
3.4. Digital Government and Urban Economic Sustainable Development
Digital governments facilitate urban economic sustainable development.
3.5. Digital Government and Urban Economic Sustainable Development: The Mediating Effects of Enterprise Digital Transformation
Digital governments facilitate urban economic sustainable development by improving enterprise digital transformation.
4. Materials and Methods
4.1. Research Sample and Data Source
In this study, the selected sample is representative, mainly in the following aspects: (1) the rationality of the time range selection, considering that the establishment of big data management bureaus in various provinces was concentrated from 2014 to 2021, selecting the 2012–2022 listed companies in the Shanghai and Shenzhen stock markets as the sample so that the sample period covers the changes before and after the establishment of big data management bureaus, which helps to examine the evolution of enterprise digitization. (2) The diversity of sample sources, with listed companies in the Shanghai and Shenzhen stock markets as the sample, covering enterprises from different industries and regions to ensure the diversity of the sample and help to comprehensively reflect the changes in enterprise digitization and the degree of sustainable urban economic development. (3) The prudence of sample processing, achieved by excluding financial, ST, and ST* listed companies; excluding samples with missing main research variables; and conducting Winsorization on all continuous variables. Various bias factors were considered in the sample processing process, improving the quality of the sample. (4) The reliability of data sources was ensured using authoritative data sources such as the China Stock Market and Accounting Research (CSMAR) database, China Information (CNINF), the National Bureau of Statistics, provincial statistical bureaus, and the Economy Prediction System (EPS) database, as well as manually querying corporate annual reports to obtain some uncovered indicators, ensuring the reliability and comprehensiveness of the data. Stata 17.0 software was used to clean, organize, and analyze the above data. A total of 36,188 company-year samples were initially formed.
4.2. Econometric Model
In Model 1, the dependent variable represents the degree of digital transformation of enterprise i in year t; the independent variable is the digital government, which is the treatment group when the province in which the enterprise i is located has set up an urban big data management bureau in year t; and it is the control group otherwise. FirmFE represents the fixed effect of the firm, YearEF represents the fixed effect of the year, IndEF represents the fixed effect of the industry, and ε represents the random error term. α represents the constant term. β and γ are model estimation parameters, and this study focuses on the parameter β. A significantly positive β indicates that the digital governments significantly facilitate enterprise digital transformation.
4.3. Definition of Variables
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Dependent variables: The enterprise digital transformation is represented by DCG. According to the study of Wu et al. [4], text analysis was used to count 76 enterprise digital word frequencies in five dimensions, namely, artificial intelligence, blockchain, cloud computing, big data, and the use of digital technology, to measure enterprise digital transformation according to the ratio of the number of word frequencies reflecting the digital transformation of enterprises in the annual report to the total number of words in the annual report (DCG).
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Independent variables: Digital government (DGov), identified by both the regional dimension and the temporal dimension, is the cross-multiplier of policy and treat. This study manually queried regarding the establishment of big data administration bureaus of China’s 31 provinces’, municipalities’, and autonomous regions’ governments, and matched the listed companies. If the sample company’s province sets up a big data administration bureau, it should be assigned to the experimental group, and its variable “Policy” takes the value of 1. Otherwise, it belongs to the control group, and its variable “Policy” takes the value of 0. In the year of the establishment of the urban big data administration bureau and the following years, the sample company’s variable “Treat” takes the value of 1; otherwise, it takes the value of 0.
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Control variables: Drawing on the published literature [58,59], control variables were selected as follows. At the micro level, enterprise size (Size), financial leverage (Lev), return on assets (ROA), accounts receivable turnover ratio (Rec), inventory level (Inv), growth level (Grow), management shareholding level (SHARE), board size (BOARD), and shareholding concentration (Shrhfd) were selected in order to control the impact of differences in enterprise’s individual characteristics, development capability, financial performance, and equity structure on enterprise digital transformation. At the macro level, industrial structure (CS), fiscal deficit ratio (Deficit), and GDP per capita (PREGDP) were selected in order to control the impact of regional industrial structure, economic development level, and fiscal revenue on enterprise digital transformation. The specific meanings and measures of the variables in this study are listed in Table 1.
5. Results
5.1. Descriptive Statistics
5.2. Multiple Regression Analysis
5.2.1. Parallel Trend Test
5.2.2. Benchmark Regression
5.3. Endogeneity Test
5.3.1. Placebo Test
5.3.2. Multi-Period DID Propensity Score Matching Method (PSM-DID)
5.3.3. Instrumental Variable (IV) Method
5.4. Robustness Test
To enhance the reliability of the empirical findings, this study substitutes the dependent variables, replaces the independent variable, and introduces a one-period lag to the independent variables. This approach aims to assess the robustness of the fundamental empirical results pertaining to Hypothesis H1.
5.4.1. Replace the Dependent Variable
5.4.2. Replace the Independent Variable
5.4.3. Dependent Variable Lagged by One Period
5.5. Mechanism Test
5.5.1. Mechanism Test: Urban Business Environment
5.5.2. Mechanism Test: Information Search Costs
5.6. Heterogeneity Analysis
5.6.1. Regional Location: Distinguishing between Eastern, Central, and Western Cities
5.6.2. Administrative Level: Distinguishing between Provincial Capitals and Non-Provincial Capitals
5.6.3. Economic Development: Distinguishing Cities by GDP Level
6. Economic Consequences: Contribution to the Level of Urban Economic Sustainable Development
7. Discussion
Building digital governments in cities can achieve a win–win situation, speeding up the process of enterprise digital transformation and contributing to the sustainable development of the urban economy. Our research results show that the effect of digital government policies stimulating enterprise digital transformation exhibits significant regional differences; the different regional locations, administrative levels, and economic development levels of the city all affect the effectiveness of the policy. Nevertheless, the improvement in the business environment and the reduction in information search costs brought about by digital government greatly promote enterprise digital transformation. Empirical results indicate that the business environment of cities and the information search costs resulting from their geographical locations significantly affect the degree of enterprise digital transformation. Digital government construction enhances the sustainable development momentum within cities in two aspects: firstly by improving the business environment, and secondly by reducing the information search costs brought about by the geographical distance of cities, both of which significantly promote enterprise digital transformation. Furthermore, enterprise digital transformation plays a significant role in promoting sustainable economic growth in cities. Economic growth is an important aspect of urban sustainable development, demonstrating that digital government construction promotes sustainable urban economic development.
From the perspective of improving the business environment and reducing information search costs, digital government is beneficial for enterprise digital transformation and the sustainable development of the urban economy. Digital government can improve the business environment in multiple ways, including enhancing government efficiency, integrating administrative services, reducing administrative burdens on enterprises, and optimizing market conditions through data sharing mechanisms. In terms of information search costs, digital government aggregates and improves the quality of information, reducing the information search costs for enterprises. This stimulates investment in digital technology by enterprises, promotes enterprise digital transformation, and enhances the quality of decision making and the profitability of enterprises. Overall, the improvement in government and enterprise efficiency, as well as the enhancement of urban administrative services and market conditions, all contribute to saving urban resources and improving the efficiency of urban resource allocation. The growth of digital technology applications in governments and enterprises plays a role in promoting enterprise digital transformation and advancing the sustainable development of the urban economy in various aspects.
From the perspective of the heterogeneous characteristics of cities, factors such as the regional location, administrative level, and economic development level of cities all influence the effectiveness of digital government in promoting enterprise digital transformation. In China, the digital infrastructure construction levels in the central and western regions significantly lag behind that in the eastern region. Empirical results indicate that the policy stimulus of digital government on enterprise digital transformation is more pronounced in the less developed central and western regions. The results of grouping cities based on GDP levels once again validated this conclusion, with the policy effects of digital government being more pronounced in economically underdeveloped cities with lower GDPs. Additionally, the policy effectiveness in provincial capital cities is more pronounced compared to non-provincial capital cities. This may be attributed to their role as provincial political, economic, and cultural centers, where urban management is often more mature and specialized. Furthermore, they possess richer research institutions, educational resources, and talent, leveraging these advantages in urban management and human resources to benefit digital government in driving enterprise digital transformation.
Enterprise digital transformation plays a crucial role in the sustainable development of urban economy. It fosters knowledge sharing and technological innovation, overcoming the limitations imposed by geographical factors on technology spillovers. Moreover, it reduces operational costs and business risks for enterprises while enhancing decision-making quality. Empirical results also validate the enhancement of total factor productivity in cities due to enterprise digital transformation. In summary, the application of digital technology not only drives the sustained and stable development of enterprises, but also promotes technological innovation, contributing to the sustainable growth of urban economy.
The possible marginal contributions of this article are as follows. (1) Previous studies on the factors influencing enterprise digital transformation have mainly focused on investor characteristics, asset allocation, corporate governance, etc. Few studies have explored how governments promote enterprise digital transformation. Those that have often examined government fiscal policies, infrastructure construction, and intellectual property protection, with few scholars researching digital government as a factor influencing enterprise digital transformation. This article innovatively adopts digital government as a research perspective to investigate its effect on enterprise digital transformation, thus expanding the research field. (2) Previous articles on digital government have mostly explained the value, elements, and paths of digital government transformation from a theoretical perspective. Existing empirical studies on digital government often use government digital development indices formed by subjective evaluation as proxies for measuring digital government, which tend to be subjective. In contrast to previous empirical studies, this article innovatively uses the establishment of the urban big data administration bureau as a proxy variable for digital government, reducing the subjectivity of indicator measurement and providing methods and experiences for subsequent academic research on the economic consequences of digital government. (3) This article innovatively identifies two mechanisms paths, business environment and information search costs, and explores the impact of urban heterogeneity on the effectiveness of digital government policies. It reports in-depth research on the inherent mechanisms and logic of digital government in promoting enterprise digital transformation, thereby opening the “black box” between digital government and enterprise digitalization transformation.
8. Conclusions
In this study, we investigated the impact of digital government on enterprise digital transformation and its effect on the sustainable development of the urban economy. The research findings are as follows. (1) Digital government significantly promotes enterprise digital transformation. Improving the urban business environment and reducing the information search costs brought by urban geographical location are the two paths through which it operates. (2) The promotional effect of digital government on enterprise digital transformation is more pronounced in the central and western regions, provincial capitals, and economically underdeveloped cities compared to the eastern regions, non-provincial capitals, and economically developed cities. (3) Digital government construction promotes the sustainable development of urban economies, and enterprise digital transformation plays an intermediary role.
Based on the research conclusions of this article, the following recommendations are proposed. (1) Governments should further improve database resources by breaking down data barriers and coordinating the integration of data resources. (2) Governments should promote administrative process reform through digital technology, clarify the digital responsibilities and organizational structure of government departments, make government processes transparent and open, and promote the coordination between government responsibilities and data governance. (3) Enterprises should actively build the production methods, business forms, technological foundations, and organizational structures needed for digital transformation and provide suitable digital operation carriers. (4) Governments should actively enhance the level of urban management, formulate policies to attract research institutions and talent, and better leverage the policy effects of digital government.
This research is not without limitations. Firstly, the measurement methods for digital government need further refinement. The existing literature has not established a unified standard for identifying digital government. In this study, a multi-period Difference-in-Differences (DID) method is employed to identify the impact of digital government on urban economic development. The use of this method for identifying digital government is objective and less influenced by subjective factors. The method distinguishes between the presence and absence of digital government construction but does not reflect the “high” and “low” differences in construction levels. In future research, the multi-period DID method can be combined with indicators of e-government development, data collection methods, and other approaches to more comprehensively depict digital government. Secondly, solely exploring the impact of digital government on urban sustainable development from an economic development perspective is not comprehensive enough. Urban sustainable development involves multiple aspects, such as economic, social, and environmental factors. While economic development is undoubtedly one aspect of urban sustainable development, social and environmental aspects are also crucial components of urban sustainable development. The impact of digital government policies on urban entrepreneurial vitality, urban technological innovation capabilities, urban carbon emissions, etc., are all topics worth delving into in future research.
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