Government Public Services and Regional Digital Transformation for Sustainable Development: An Innovation Ecosystem Perspective

Abstract

Local governments have become key agents in advancing regional digital transformation in China. Drawing on a panel dataset of 30 provinces from 2009 to 2022, this study adopts an innovation ecosystem perspective. It explores how public service delivery interacts with market development, higher education institutions, and social entrepreneurship to support sustainable digital development. The results show that local public services have a strong positive impact on digital transformation. This effect is even greater when supported by other ecosystem components. These findings highlight the value of system-level collaboration in promoting inclusive, resilient, and sustainable regional growth. Rather than introducing a new theory, this study provides practical, context-specific evidence on how local governments and innovation ecosystems work together to support China’s digital transformation and long-term sustainability goals.

1. Introduction

As sustainable development becomes a central goal of national and regional strategies, China is entering a new era of digital transformation that aligns closely with long-term sustainability objectives [1,2]. This digital transformation process has been accelerated by the active role of local government, resulting in a broad impact of digital technologies on the entire nation. It is reshaping digital economies, governance, and society as a whole [3,4]. This transformation also holds significant potential for achieving sustainable development goals. Since the beginning of the 13th Five-Year Plan, China has established the world’s largest information and communication network. The share of fiber broadband users has increased from 56% at the end of 2015 to 94% in 2021, with over 120 million households covered by gigabit optical networks. The digital transformation in China is a complex process involving multiple actors [5,6]. At its core, it aims to enhance an ecosystem where residents live better lives, businesses grow, and governments deliver improved public services to support both innovation and sustainability. However, digital transformation varies significantly in pace and depth across all provinces in China [7]. Identifying the factors that drive these differences is essential for developing a more balanced and sustainable digital development strategy [8].

Recent studies emphasize the crucial role of local governments in driving digital transformation [9]. For instance, Pittaway and Montazemi (2020) show that strong leadership and strategic alignment within local governments are key to effective digital governance [10]. Chen et al. (2021) also show that government support, like subsidies and training, helps small service companies use digital technologies and grow sustainably [11]. While local governments can support digital transformation through favorable policies or government-sponsored programs, it has been recognized that they alone cannot achieve transformation [12]. Scholars of this stream suggest that regional digital transformation is a process that involves cross-sectoral coordination and dynamic interaction among various stakeholders to manage complex tasks and ensure sustainable outcomes [13]. For example, Nielsen and Jordanoski (2020) show that successful digital governance relies on formal intergovernmental coordination and inclusive collaboration across all levels of society [14]. In addition, Brunetti et al. (2020) highlight the importance of external enabling factors, such as collaborative partnerships and open innovation, which are critical for driving digital transformation across organizational boundaries [15]. Given the limits of single-actor perspectives, an ecosystem-based view can better capture the interplay among diverse forces [16].

The innovation ecosystem perspective provides further insights into this issue. The literature on regional innovation ecosystems emphasizes the importance of co-created relationships among governments, entrepreneurs, universities, and research institutions [17,18,19]. It suggests that local governments are not the only drivers of digital transformation but support it by providing public services for sustainable innovation [20,21,22]. These elements include markets, universities, and entrepreneurial actors. For example, Li (2022) shows that market turbulence affects how digital transformation impacts sustainability, underlining the role of market forces [23]. Evans et al. (2023) find that university–industry collaboration, based on trust and institutional support, helps drive digital transformation, but their study focuses only on bilateral ties [24]. Mukesh (2022) finds that digital transformation promotes both digital business model innovation and broader entrepreneurial innovation at the national level [25]. However, these studies examine single relationships or factors and do not explore how these elements interact as part of a larger system. Yet, existing studies have not fully addressed how these elements dynamically interact. Our study aims to fill this gap by exploring how local governments interact with other key components to promote digital transformation that aligns with sustainability goals. Based on the innovation ecosystem perspective, this study offers a more holistic analysis by highlighting how system-level collaboration supports both digitalization and sustainability. Traditional industrial cluster literature also highlights the value of system-level collaboration in promoting these goals.

Traditional literature on industrial clusters has focused on the geographical agglomeration of industrial elements, focusing on scale effects and spatial spillovers [26,27]. While this perspective provides valuable insights into regional transformation, it often overlooks the synergistic relationships formed through active collaboration among key actors. These relationships are essential not only for innovation but also for building sustainable regional capabilities. We argue that local government should coordinate and complement other elements in the regional innovation ecosystem to foster sustainable digital transformation [28,29].

Moreover, the existing literature on digital transformation has primarily focused on its economic impacts [30,31]. However, the influence of digital technologies extends beyond economic development and touches various domains such as public governance and social well-being [32,33]. The level of transformation varies across provinces, influenced not just by government input but also by the broader ecosystem [34]. Regional digital transformation is multidimensional, and its sustainable outcomes depend on more than just public services [35,36]. A better understanding of these interactions is essential for analyzing how innovation ecosystems can support comprehensive and sustainable regional development.

Importantly, this study does not aim to propose a novel theory. Instead, it draws on the well-established literature on regional fiscal multipliers and extends this body of work to the context of China. Prior research has consistently shown that public expenditure promotes regional growth and resilience through various mechanisms. Chodorow-Reich (2019) highlights the importance of regional fiscal multipliers [37], while Silva (2021) emphasizes that limited fiscal space can weaken these effects and constrain credit provision [38]. This view is further supported by Dantas et al. (2023), who examine the role of government guarantees in influencing banks’ credit behavior [39]. While our findings align with these established insights, the main contribution of this study is to demonstrate their applicability in the Chinese context, using provincial-level data. In particular, we address a gap in the existing literature, which has primarily focused on national-level public service provision in China, by empirically exploring how such fiscal mechanisms operate across subnational regions [40]. By leveraging panel data from 30 Chinese provinces over 14 years, this study explores how local government public services interact with market institutions, higher education systems, and social entrepreneurship to promote regional digital transformation. We focus on digital transformation instead of just economic performance. This makes our work more relevant for policy. It also shows how China’s governance priorities are changing under the digital China strategy. In this sense, the study contributes not through theoretical novelty, but by offering context-specific, evidence-based insights into how decentralized public service delivery shapes inclusive and sustainable digital development in one of the world’s most complex governance environments.

The rest of the paper is organized as follows: Section 2 presents the literature review and hypothesis development. Section 3 explains the research methodology, including data sources, measurement, and econometric model. Section 4 presents the empirical results. Section 5 discusses the findings, contributions, limitations, and future research directions. The conclusion is then presented.

2. Literature Review and Hypothesis Development

This study adopts the innovation ecosystem perspective to examine the pivotal role of local government in advancing regional digital transformation. It offers a comprehensive analysis of the developmental processes within China’s regional digital transformation landscape. The analysis highlights that the efficacy of regional digital transformation is contingent not only upon the public services provided by local government but also on various other critical components within the regional innovation ecosystem.

2.1. The Supporting Role of Local Government in Regional Digital Transformation

Local governments are widely recognized as pivotal facilitators of regional digital transformation [41]. They play a supportive role in this transformation by delivering public services, which encompass investment in digital infrastructure, bolstering cybersecurity regulations, and enhancing the digital capabilities of local labor forces [42]. The effective provision of public services by local government establishes the foundational conditions necessary for regional digital transformation.

Firstly, local governments promote regional digital transformation through investments in essential digital infrastructure [43], such as high-speed internet access, broadband networks, and cybersecurity infrastructure. Digital infrastructure is crucial for enabling businesses, residents, and institutions to access and utilize digital tools. High-quality digital infrastructure provides the fundamental elements required for firms to leverage digital technology and reduces the costs associated with digital transformation strategies [44]. Moreover, it ensures the availability of digital technologies necessary for citizens. The absence of digital infrastructures often leads to significant inefficiencies and risks that impede the transformation process.

Secondly, local governments enhance regional digital transformation by establishing a clear and conducive regulatory framework that addresses the fundamental requirements of digital stakeholders [45]. Cybersecurity laws and regulations can eliminate regulatory hurdles, foster innovation, and safeguard data privacy and security [46]. This instills confidence among local businesses regarding the protection of their data and intellectual property, thereby bolstering their willingness to participate in the transformation process. Local government can streamline regulations and licensing procedures to facilitate local businesses’ digital transformation strategies. A favorable regulatory environment also attracts investment and spurs innovation in the digital sector.

Thirdly, public services delivered by local government improve the digital capabilities of local labor forces, enabling them to fully leverage digital technologies. Digital talents form the backbone of regional digital transformation [43]. Digital transformation becomes feasible only when the region possesses the necessary talents to acquire and harness digital technologies. Local government can provide training programs, workshops, and resources to help residents acquire digital skills. Promoting digital literacy among local labor forces is essential for ensuring full participation in the digital economy. Inadequate provision of public services can lead to digital divides and high entry costs for engaging in digital transformation. Therefore, we hypothesize that:

Hypothesis 1. 

The public services offered by a local government are positively associated with the digital transformation of a region.

2.2. The Joint Effect of Key Elements of the Regional Innovation Ecosystem

While local governments play a crucial role in driving regional digital transformation through public service delivery, their efforts alone may not suffice to generate successful outcomes [43]. To enhance effectiveness in fostering regional digital transformation, local government must be complemented by other key elements within the innovation ecosystem. Recent research has identified these key elements, with some of the most critical ones including markets, universities, and social entrepreneurs, as they form the framework for the innovation ecosystem [47]. These elements have proven to be particularly significant in China, as they contribute to rendering the regional innovation ecosystem more effective in cultivating an environment conducive to innovation and co-value creation [48,49,50].

2.2.1. Market Development

A robust market serves as a cornerstone in the regional innovation ecosystem [51], offering essential conditions and incentives for driving digital infrastructure investments and enhancing regulatory flexibility. A strong market signals stability and growth potential [52], attracting investments in digital infrastructure to address financial constraints faced by local governments. Abundant funding from venture capitalists, banks, and other investors becomes available in robust markets [53], providing multiple avenues for capital and financing options for digital infrastructure projects. Private companies and public-private partnerships are more inclined to invest in building and upgrading digital infrastructure when they anticipate high demand for digital services and favorable returns on investment.

In dynamic and rapidly evolving markets, policymakers may adopt a more agile regulatory approach, embracing iterative, adaptive policies that respond to changing technological landscapes and the need for digital transformation. In strong markets, intellectual property rights are really valuable. Policies and regulations, like those for patents, copyrights, and trademarks, are there to protect innovation and creativity. This protection incentivizes businesses to invest in research and development, enabling them to reap the rewards of their inventions throughout the digital transformation process [54].

A strong market often drives increased demand for skilled labor in the digital sector [55], attracting and retaining talented individuals crucial for regional digital transformation. Entrepreneurs, researchers, and skilled workers are drawn to regions with vibrant job markets and economic opportunities [56], further nurturing the innovation ecosystem. Individuals and companies can leverage public services delivered by local government, which may include education and workforce training programs aimed at equipping the workforce with requisite digital skills and knowledge. Therefore, we hypothesize the following:

Hypothesis 2. 

The positive effect of the public services delivered by local government on regional digital transformation is stronger when the local markets are more developed.

2.2.2. Higher Education Institutions

The literature on the innovation ecosystem highlights the pivotal role of higher education institutions, including universities and research institutions, in driving technological innovations [57]. Higher education institutions serve as catalysts for growth-driving innovation by establishing a dynamic nexus between scientific research and social and economic development [58]. The triple helix perspective emphasizes their collaborative interactions with local government and industries, forming a potent engine for technological advancement [59]. Collaborations between local government and higher education institutions yield new knowledge and technologies essential for digital transformation [60]. Higher education institutions address the knowledge and technological limitations of local government in enhancing digital infrastructure [61,62], significantly influencing regional efforts to develop high-quality infrastructures and expedite effective digital transformation.

Higher education institutions also influence local policies related to digital transformation [63]. They offer expertise and advocate for policies pertaining to improved broadband access, data privacy regulations, and digital inclusion initiatives. Consequently, regions with a higher concentration of higher education institutions provide greater access to the skills and knowledge necessary for digital transformation. Companies in such regions are better equipped to initiate and execute digital transformation projects, fostering increased engagement in digital transformation activities.

Furthermore, higher education institutions ensure a steady supply of human capital for regional digital transformation [64]. They offer continuing education and professional development programs, upskilling the existing workforce in digital competencies. This empowers individuals to actively participate in regional digital transformation by leveraging their knowledge and digital skills [65]. Simultaneously, companies benefit from access to a pool of talented individuals proficient in digital skills and literacy, enhancing their willingness and efficiency to participate in digital transformation initiatives. Consequently, the delivery of public services by the government becomes more effective in facilitating digital transformation.

Hypothesis 3. 

The positive effect of public service delivery by a local government on regional digital transformation is stronger when a region has higher education institutions.

2.2.3. Social Entrepreneurship

In regions characterized by a high level of social entrepreneurship, the supportive role of local government in digital transformation is accentuated. Regional digital transformation encompasses objectives related to both economic development and social welfare enhancement [66]. Digital technologies are deployed to address social issues concerning digital infrastructure development, cybersecurity, and digital skill education [67]. Due to financial and staffing constraints, local government may not be able to address all these social challenges single-handedly during the digital transformation process. Social entrepreneurship integrates digital technologies with business for social development, generating innovative social solutions [68].

Social entrepreneurs innovate creative solutions to pressing social issues pertaining to digital infrastructure, developing new technologies, business models, or approaches to enhance digital connectivity and accessibility in underserved regions. They bridge the gap between the public and private sectors, utilizing their societal embeddedness to assist local governments in funding digital infrastructure projects through co-investment or government procurement [69].

Moreover, social entrepreneurs advocate for policies and regulations supporting data privacy and protection. They collaborate with local government and regulatory bodies to shape policies safeguarding individuals and organizations from cyber threats [70]. Social enterprises play a vital role in promoting responsible digital technology practices, data ethics, and the development of technologies prioritizing user privacy and security [71]. This contributes to the removal of barriers and the creation of incentives for investment in digital transformation initiatives.

Regions with high levels of social entrepreneurship host numerous social enterprises that complement local government in delivering adequate public services, including healthcare and education [72]. Improved higher education and healthcare services enhance the human capital of the region, enabling more individuals to benefit from public services provided by local government and participate actively in digital transformation efforts.

Hypothesis 4. 

The positive effect of the delivery of public services by the local government on the regional digital transformation is stronger when a region has more social entrepreneurship.

We propose a conceptual framework to answer the research questions (See Figure 1).

3. Materials and Methods

3.1. Data Sources

This study utilizes panel data encompassing Chinese provinces spanning from 2009 to 2022 as the initial research sample. In order to enhance the reliability of our findings, Tibet, Hong Kong, Macao, and Taiwan were excluded due to substantial gaps in pertinent variable data. Missing values within the dataset were addressed through interpolation techniques. Further, the upper and lower 1% quartiles of all continuous variables are trimmed to reduce the impact of extreme values on the results. Subsequently, a total of 420 observations are obtained.

Given these data preparation steps, we believe that the resulting sample size is methodologically sound for empirical analysis. The 420 province-year observations, covering 30 provinces over 14 years, provide both spatial and temporal variation. Panel data with repeated measures improves the robustness of statistical estimation by increasing degrees of freedom and reducing multicollinearity. Moreover, previous studies in similar domains have relied on comparable or smaller datasets, for example, Hu et al. (2022) with 369 observations and Zhang and Cen (2023) with only 300, further supporting the adequacy of our sample size [73,74].

This analysis employs fixed-effect modeling technique, effectively controlling for the unobserved province-specific effects and partially alleviating the endogeneity problem. Statistical analysis is conducted utilizing Stata 18.0. The data on regional digital transformation comes from China Financial Yearbook. The data on government public services comes from Economy Prediction System. The data on market development comes from Statistical Yearbook of China. The data of higher education institutions comes from Compendium of Science and Technology Statistics for Higher Education Institutions. The data on social entrepreneurship comes from China Civil Affairs Statistical Yearbook. Other data come from China Statistical Yearbook.

3.2. Variable Selection

3.2.1. Explained Variable

Following the methodology outlined by Bruno et al. (2023), regional digital transformation (RDT) was measured by four key indicators [72]: (1) mobile phone penetration, (2) Internet broadband accessibility, (3) scale of software product revenue, and (4) total volume of telecom business.

3.2.2. Core Explanatory Variable

In alignment with the approach adopted by Islam (2015), the core explanatory variable encompasses dimensions including culture and education, healthcare, and infrastructure to gauge the provision of government public services (GPS) [75]. The government public services within a given province were measured by indicators as delineated in

Table 1. The measurement of government public services.

Government Public Service	Specific Indicators
Cultural education	Number of general secondary and elementary schools per 10,000 population
	Number of pupil-teacher ratios in general elementary schools per 10,000 population
	Number of teacher-student ratios in general secondary schools per 10,000 population
Health care	Number of hospitals and health centers per 10,000 people
	Number of hospital and health center beds per 10,000 people
	Doctors per 10,000 population
Infrastructure	Per capita residential water consumption (tons/person)
	Per capita residential electricity consumption (kWh/person)
	Per capita gas use by residents (cubic meters/person)

.

3.2.3. Moderating Variables

The NERI index, as devised by the National Economic Research Institute (NERI), is used to measure market development (MD) across provinces over time [47]. Widely recognized in existing studies, the NERI index is utilized to gauge the quality of the market environment within Chinese provinces [76]. In this study, a sub-index derived from the NERI index is employed to measure the proportion of economic resources allocated through market mechanisms rather than government intervention. The higher value indicates a greater reliance on market-based allocation of economic resources [77].

Higher education institutions (HEIs) consist of multidisciplinary universities, science and technology institutions, and other specialized educational establishments. Building upon the work of Chinta et al. (2016), HEIs are measured through three dimensions [78]: (1) the count of institutions; (2) the volume of scholarly publications originating from HEIs specializing in science, technology, engineering, and mathematics disciplines; and (3) the number of faculty members within these institutions in each province over time.

Social entrepreneurship (SE) is measured by the total number of registered social organizations in a given province in a given year. Social organizations in China include three types: civic associations, private non-business entities, and foundations. All these organizations are established with societal objectives such as poverty alleviation and social welfare [71].

3.2.4. Control Variables

Referring to the previous studies [79], this study incorporates the following control variables: (1) Residents’ Living Standard (RLS), measured by the mean wage of the employed populace; (2) Gross Domestic Product (GDP) growth rate, calculated as the end-period GDP relative to the base-period GDP; (3) Gross Population Density (GPD), measured by the proportion of the total permanent population of each province to its area; (4) Foreign Direct Investment (FDI), measured by the percentage of annual total utilized FDI in annual provincial GDP.

3.3. Model Settings

To test the impact of government public services on regional digital transformation, this study refers to relevant literature and constructs the following model:

RDT_it = α₀ + α₁GPS_it + α₂MD_it + α₃HEIs_it + α₄SE_it + α₅con_it + μ_i + λ_t + ε_it

(1)

where subscripts i and t denote province and year, respectively. The explained variable GPS_it represents the level of regional digital transformation of province i in year t. The core explanatory variable GPS_it represents the government public service of the province i in year t. MD, HEIs, and SE are moderating variables, market development, higher education institutions, and social entrepreneurship, respectively. The control variables con_it from a vector set of various control variables. Moreover, con stands for other control variables, μ_i is an individual fixed effect that controls the effects of individual characteristics that are not observed. Furthermore, λ_i is a time-fixed effect, which is used to control the time factor that does not vary with the region. ε_it denotes the random disturbances, and the regressions use clustering robust standard errors at the provincial level.

To mitigate the endogeneity concerns the fixed-effect models, namely that the lagged dependent variable can be correlated with the error term [80]. Dynamic panel data models are also employed to test the hypotheses as a robustness check [81]. It is appropriate for the data, where the number of provinces (30 provinces) is larger than the time period (20 years), the explained variable is dynamic, and the core explanatory variable is not strictly exogenous. System Following prior studies [82], the two-step system GMM for the dynamic panel data models is used to address endogeneity bias.

4. Results

Table 2. Descriptive statistics.

Variable Name	Obs.	Mean	Std. Dev.	Min	Max
RDT	420	1.668	0.868	0.095	2.984
GPS	420	8.199	0.646	6.069	9.758
MD	420	1.828	0.317	0.846	2.387
HEIs	420	4.108	0.604	2.356	5.119
SE	420	9.668	0.741	7.811	11.480
RLS	420	10.87	0.377	10.090	12.030
GDP	420	1.106	0.073	0.750	1.299
GPD	420	5.205	7.361	0.077	39.380
FDI	420	6.446	1.392	3.157	9.880

shows the descriptive statistics of the main variables. As can be seen from the table, the minimum regional digital transformation value is 0.095, the maximum value is 2.984, the mean value is 1.668, and the standard deviation is 0.868, indicating that the overall level of regional digital transformation in China is low, and the regional digital transformation varies greatly, showing the characteristics of imbalance and insufficiency. Details of descriptive statistics for other variables are shown in Table 2.

As shown in

Table 3. Correlation analysis and VIF test.

Variables	(1)	(2)	(3)	(4)	(5)	(6)	(7)	(8)	(9)	VIF
RDT	1.000									2.78
GPS	0.715 ***	1.000								3.65
MD	0.846 ***	0.650 ***	1.000							4.27
HEIs	0.719 ***	0.732 ***	0.539 ***	1.000						4.18
SE	0.661 ***	0.841 ***	0.566 ***	0.718 ***	1.000					3.17
RLS	0.359 ***	0.535 ***	0.501 ***	0.059	0.179 *	1.000				2.93
GDP	−0.083	−0.246 **	−0.208 **	−0.037	−0.117	−0.384 ***	1.000			1.67
GPD	0.374 ***	0.225 **	0.495 ***	0.214 **	0.050	0.380 ***	−0.037	1.000		1.22
FDI	0.845 ***	0.715 ***	0.912 ***	0.587 ***	0.629 ***	0.511 ***	−0.167 *	−0.26 **	1.000	5.33

*** p < 0.01, ** p < 0.05, * p < 0.1, standard errors in parentheses.

, most variables are significantly correlated, which provides preliminary support for the subsequent analysis. However, the correlation coefficients remain relatively low, suggesting that the relationships among the variables are not strong enough to distort the regression results. To further ensure robustness, we conducted variance inflation factor (VIF) tests for all fixed-effect models. None of the VIF values exceeded the commonly accepted threshold of 10, indicating that multicollinearity does not pose a significant concern. In addition, all continuous independent variables were mean-centered to reduce potential collinearity.

Table 4. Results of baseline regression analysis.

Variables	Model1	Model 2	Model 3	Model 4	Model 5	Model 6
GPS (H1)		0.112 ***	0.054 **	0.068 **	0.046 **	0.114 ***
		(0.005)	(0.043)	(0.094)	(0.074)	(0.001)
MD			−0.014			−0.015
			(−0.112)			(−0.129)
GPS*MD (H2)			0.090 ***			0.090 ***
			(0.002)			(0.007)
HEIs				−0.001		−0.001
				(−0.236)		(−0.135)
GPS*HEIs (H3)				0.086 ***		0.085 ***
				(0.007)		(0.000)
SE					−0.000	−0.000
					(−0.112)	(−0.102)
GPS*SE (H4)					0.082 ***	0.082 ***
					(0.009)	(0.002)
Control variables	YES	YES	YES	YES	YES	YES
Year FE	YES	YES	YES	YES	YES	YES
Area FE	YES	YES	YES	YES	YES	YES
_cons	−5.900 ***	0.211	1.924 ***	1.582 ***	0.904	0.849
	(−0.402)	(0.443)	(0.573)	(0.593)	(0.531)	(0.574)
N	420	420	420	420	420	420
R2	0.359	0.469	0.591	0.593	0.587	0.597
R2_a	0.356	0.465	0.571	0.575	0.582	0.592

*** p < 0.01, ** p < 0.05, standard errors in parentheses.

represents the results of the empirical analysis, including the primary and moderating effects. The model includes the province-fixed effects to control for the unobserved and time-invariant heterogeneity across provinces, as well as the year-fixed effects to control for unobserved cycles, thereby mitigating the omitted variable problem. The R-squared values of all the regression models are around 0.5, indicating that the goodness of fit of these models is satisfactory. All variables are in their logarithmic forms to reduce the impact of heteroscedasticity.

Model 2 represents the base model. We are examining the influence of governmental public services on regional digital transformation. The coefficient of government support is positive and statistically significant at the 1% level, suggesting that governmental public services are positively associated with regional digital transformation, supporting hypothesis H1.

Models 3–5 in Table 4 test the research hypotheses H2, H3, and H4. The results of Model 3 indicate that the degree of market development has a positive moderating effect (0.090, p < 0.01), supporting hypothesis H2. Model 4 results indicate that the regression coefficient of the interaction term between government public services and higher education institutions is significantly positive (0.086, p < 0.01), suggesting that higher education institutions have a positive moderating effect, thereby validating hypothesis H3. The results of Model 5 indicate that the regression coefficient of the interaction term between government public services and the level of social entrepreneurship is significantly positive (0.082, p < 0.01), suggesting that the level of social entrepreneurship has a positive moderating effect, supporting hypothesis H4.

4.1. Robustness Tests

In order to ensure the robustness of results, the sub-indicators of regional digital transformation were replaced by the regional digital infrastructure environment and the utilization of digital technology. The results are presented in columns (1)–(2) of

Table 5. Results of robustness check.

Variables	(1)	(2)	(3)	(4)
	Regional Digital Infrastructure Replacement y	Digital Technology Applications Replacement y	y One Period Behind	Excluding Specific Samples
GPS	0.166 ***	0.088 ***	−0.142 ***	1.289 ***
	(0.007)	(0.002)	(−0.005)	(0.000)
Control Variable	YES	YES	YES	YES
Year FE	YES	YES	YES	YES
Area FE	YES	YES	YES	YES
_cons	0.527	0.939 **	1.137 **	0.834 *
	(0.472)	(0.429)	(0.519)	(0.537)
N	420	420	408	372
Number of years	12	12	12	12
R2	0.487	0.507	0.405	0.494
R2_a	0.483	0.504	0.392	0.491

*** p < 0.01, ** p < 0.05, * p < 0.1, standard errors in parentheses.

. It is noteworthy that the direction of the coefficients of the government public services and the level of significance did not change significantly. This indicates that the conclusions of the baseline regression analysis are not affected by the choice of explanatory variables.

Considering that digital transformation is a gradual process and may have a certain lag on government public services, this paper replaces the current indicator for regional digital transformation used in the benchmark regression with its lagged one-period term (L.RDT). The estimation results are reported in column (4) of Table 5. The regression results indicate a significant positive correlation at the 1% level between government public services and regional digital transformation. This finding is consistent with the previous results and further validates the robustness of the regression results.

Municipalities directly under the central government have a higher administrative level and authority than other categories of cities. They typically exhibit a higher level of economic development than other administrative districts. Therefore, it is reasonable to assume that these municipalities also have a relatively high level of governmental public services and a significant degree of regional digital transformation [83]. As a result, this paper excludes the sample of municipalities, including Beijing, Tianjin, Shanghai, and Chongqing, and re-estimates the results as shown in column (5) of Table 5. The robustness check further confirms that government public services significantly promote regional digital transformation.

To further assess the robustness of the positive association between regional digital transformation (RDT) and public service provision (GPS), we implement the parametric bounding approach proposed by Oster (2019) [84], following the empirical framework adopted in Dantas et al. (2023) and Verner and Gyöngyösi (2020) [39,85].

Specifically, we compare two OLS specifications with a common simplified model. The simplified model, which excludes all control variables and fixed effects, produces a coefficient estimate of $\dot{\beta }$ = 0.215 and $\dot{R}$ = 0.507. The fully specified model, incorporating relevant covariates (RLS, GDP, GPD, FDI) and time-fixed effects, yields $\tilde{\beta }$ = 0.143 with $\tilde{R}$² = 0.750. Under the assumption that selection on unobservables is proportional to selection on observables, and setting the maximum R-squared value ($R_{max}^2=\min (1, \mathsf{\Pi }$ × $\tilde{R}$²) as 1.3 × $\tilde{R}$² = 0.975 and 2.0 × $\tilde{R}$² = 2.000, respectively, the bounding values of ${\beta }_{\Pi }^{\mathrm{*}}$ are calculated as 0.1736 and 0.1981 (see

Table 6. Government Public Services and Regional Digital Transformation—Bounds for Robustness to Proportional Selection on Unobservables.

Panel A: Comparison of Baseline OLS Without Controls and Full OLS with Covariates
	Simplified model		All controls model		$R_{max}^2$		Bounding values
Outcome	$\dot{\beta }$	$\dot{R}$2	$\tilde{\beta }$	$\tilde{R}$2	Π = 1.3	Π = 2.0	${\beta }_{\Pi =1.3}^{\mathrm{*}}$	${\beta }_{\Pi =2.0}^{\mathrm{*}}$
RDT on DIG	0.215	0.507	0.139	0.622	0.8086	2.000	0.1626	0.1798
Panel B: OLS without time-fixed effects vs. OLS with time-fixed effects
	Simplified model		All controls model		$R_{max}^2$		Bounding values
Outcome	$\dot{\beta }$	$\dot{R}$2	$\tilde{\beta }$	$\tilde{R}$2	Π = 1.3	Π = 2.0	${\beta }_{\Pi =1.3}^{\mathrm{*}}$	${\beta }_{\Pi =2.0}^{\mathrm{*}}$
RDT on DIG	0.215	0.507	0.143	0.750	0.975	2.000	0.1736	0.1981

).

These bounds remain positive and statistically meaningful, indicating that the estimated effect of digitalization on public service provision is not likely to be overturned by omitted variable bias. Even under conservative assumptions regarding unobservable selection, the direction and significance of the estimated coefficient remain robust.

4.2. Endogeneity Tests

We used the instrumental variables to address the endogeneity issues, which could be caused by reverse causality, omitted variables, and measurement errors. With reference to previous studies [86], the lagged one period of government public services (L.GPS) is selected as the instrumental variable for government public services (GPS) to address the endogeneity issue in the model, and the L.GPS is transformed into logarithmic form. First, the regression estimation is conducted using the two-stage least squares method, and the estimation results are presented in columns (1)–(2) of Table 6. The regression results from the first stage indicate that the estimated coefficients of L.GPS are significantly positive at the 1% level. In the second stage of regression results, the estimated coefficients of the core explanatory variables are significant at the 1% level. The results show that there is a strong positive correlation between instrumental variables and government public services.

The system GMM model is further utilized to address the endogeneity problem. The static panel fixed-effects model is extended to a dynamic panel fixed-effects model by introducing the lagged one period of the explanatory variables in the equation. The estimated coefficients for government public services are still significantly positive at the 1% level. The results show that after addressing potential endogeneity issues, government public services continue to have a significant positive impact on promoting regional digital transformation. This finding further confirms the robustness of the baseline regression results (see

Table 7. Endogeneity test results.

Variable	(1)	(2)	(3)
	2SLS-First	2SLS-Second	SYS-GMM
L.GPS	1.477 ***	1.447 ***	4.222 ***
	(0.006)	(0.003)	(0.005)
RLS	1.771 ***	−0.815 ***	0.176
	(0.008)	(−0.01)	(0.191)
GDP	0.826	2.084 **	−0.773
	(0.215)	(0.038)	(−1.23)
GPD	−0.0344 ***	0.013	0.180 ***
	(0.000)	(1.28)	(0.003)
FDI	0.937 ***	0.954 ***	0.109
	(0.007)	(0. 008)	(0.75)
_cons	−0.560 ***	0.102	−0.456 ***
	(4.338)	(0.434)	(−3.382)
N	408	408	408
R2_a	0.508	0.466	0.473

*** p < 0.01, ** p < 0.05, standard errors in parentheses.

).

4.3. Heterogeneity Analyses

The results of the data analysis indicate significant variations in the extent of digital transformation among provinces. The southeastern coastal region exhibits the strongest comprehensive strength, the central region has experienced rapid development in recent years, and the western region is the weakest due to inherent environmental limitations. Utilizing the division method of the National Bureau of Statistics (NBS), the 30 provinces, municipalities, and autonomous regions under study are categorized into eastern, central, and western regions. According to the full sample validation method, various regional test models have been established, and the empirical results are presented in

Table 8. Regression results of regional differences.

Variable	(1)	(2)	(3)
	Eastern Part	Central Region	Western Region
GPS	0.163 ***	0.344 ***	0.267 ***
	(0.004)	(0.008)	(0.005)
RLS	2.629 ***	−0.017	−1.158
	(0.001)	(−1.012)	(−1.43)
GDP	−1.265	0.751	2.217 **
	(−1.38)	(0.482)	(0.003)
GPD	−0.050 ***	−0.502 ***	−0.045 ***
	(−0.006)	(−0.006)	(−0.002)
FDI	0.780 ***	0.666 ***	1.341 ***
	(0.003)	(0.004)	(0.007)
Year FE	YES	YES	YES
Area FE	YES	YES	YES
_cons	−28.630 ***	−20.440	1.416
	(−7.40)	(−1.09)	(0.15)
N	143	104	143
Number of years	13	13	13
R2	0.820	0.368	0.761

*** p < 0.01, ** p < 0.05, standard errors in parentheses.

. The regression results indicate variations in the facilitation effect of government public services and regional digital transformation across the eastern, central, and western regions. When government public services increase by 1%, the level of regional digital transformation in the eastern, central, and western regions increases by 0.163%, 0.344%, and 0.267%, respectively. Government public services in the central region have the most significant impact on facilitating regional digital transformation. This suggests that government public services have a greater impact on driving the development of regional digital transformation in the central and western regions compared to the eastern region.

5. Discussion

Adopting a perspective rooted in the regional innovation ecosystem, this study utilizes a provincial-level panel dataset (2009–2022) from China to examine the role of local governments in promoting regional digital transformation. The study yields the following conclusions: (1) The delivery of public service by local governments significantly promotes digital transformation, and this result remains robust across multiple empirical tests. (2) This effect is further amplified when three key components of the regional innovation ecosystem—market development, higher education institutions, and social entrepreneurship—interact with and complement government public service delivery. These results highlight the importance of synergic effects within the innovation ecosystem for achieving inclusive and sustainable development.

5.1. Theoretical Implication

The theoretical contributions of this study encompass three main facets. First, it enriches the literature on local government and digital transformation by offering provincial-level evidence. Prior studies have largely emphasized the central government’s role, while this study demonstrates that decentralized public service delivery can play a significant role in promoting digitalization. The findings indicate that local governments act not only as service providers but also as enablers of collaborative and sustainable digital development. By introducing a subnational and ecosystem-based perspective, the study enhances the current understanding of how local governance interacts with other ecosystem actors to support China’s evolving digital transformation agenda [87].

Second, the study extends the application of the innovation ecosystem framework to the digital transformation context. It highlights that local government interacts not in isolation, but in synergy with other critical ecosystem components—including market development, higher education institutions, and social entrepreneurship—to foster regional digital transformation. Rather than focusing on isolated dyadic relationships, this research emphasizes the system-level coordination among diverse actors, contributing to the understanding of synergic effects in complex governance settings.

Third, this study advances the theoretical discourse on system-level capacities by recognizing the importance of non-technical enablers in driving regional digital transformation. In contrast to traditional models that prioritize technological capabilities or industrial agglomeration, our findings stress that institutional trust, knowledge co-creation, and inclusive governance are equally important for building a resilient and adaptive innovation ecosystem. This adds to the understanding of how innovation ecosystems evolve to address broader sustainability goals beyond economic outcomes. Collectively, these contributions provide an integrated framework for analyzing the multidimensional drivers of regional digital transformation, bridging the gap between digital governance, innovation collaboration, and sustainable regional development.

5.2. Practical Implication

This study provides practical guidance for policymakers working to advance digital transformation and sustainable growth at the regional level. First, local governments must forge robust partnerships with diverse organizations, including universities, research institutions, enterprises, and non-profit organizations, to establish digital innovation platforms. These platforms can serve not only as drivers of technological advancement but also as enablers of inclusive and sustainable innovation. Local government can tap into the specialized expertise of universities and research institutions, collaborate with enterprises to pool resources, form innovation alliances, and drive digital technology adoption across various industries. Offering innovation incentives, research funding, and tax breaks can incentivize collaboration among different organizations, fostering a mutually beneficial environment for long-term digital and sustainable development.

Second, local government should intensify efforts to construct a unified digital market that supports both economic efficiency and sustainability. Given the strategic importance of digital transformation, establishing a unified digital market is crucial for optimizing resource allocation, reducing systemic fragmentation, and promoting coordinated industrial and ecological development. Government can enact policies to promote digital technology standardization, reduce market entry barriers, and facilitate the seamless flow of digital products and services. Moreover, establishing a regulatory framework for digital transformation is essential to uphold market order, encourage innovation, and foster the healthy and sustainable development of the digital economy.

Third, local government must nurture and support enterprises to become the linchpins of sustainable digital transformation through targeted innovation and entrepreneurship policies. By establishing entrepreneurship incubators, providing venture capital, streamlining entrepreneurial processes, and implementing other supportive measures, local government can stimulate entrepreneurial activity and drive innovation in digital technology applications. Furthermore, enhancing intellectual property protection and safeguarding enterprises’ legitimate rights in digital innovation will not only strengthen technological competitiveness but also encourage more companies to engage in responsible and sustainable digital transformation efforts.

5.3. Research Limitations and Future Research

This study has several limitations that open up avenues for further research. First, the empirical model does identify key parts of the innovation ecosystem, like market development, higher education institutions, and social entrepreneurship. However, it does not consider more detailed factors such as local governance capacity and regional innovation culture, which may affect local government effectiveness in different situations. Future research could incorporate qualitative methods or mixed approaches to explore these contextual variations. Second, the analysis is limited to the overall impact of public services on regional digital transformation, without distinguishing between specific types of digital technologies (e.g., AI, big data, smart infrastructure). Further studies could compare how different technological pathways interact with ecosystem elements to support differentiated development strategies. Third, this study adopts a cross-sectional provincial perspective. Future research could explore intra-provincial disparities, especially between urban and rural areas, to better understand how synergic effects evolve at finer spatial scales. In addition, longitudinal studies may help assess how innovation ecosystems adapt over time in response to policy changes and digital disruptions.