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Article

Mechanisms Influencing the Digital Transformation Performance of Local Governments: Evidence from China

by
Wei Zhou
1,
Zhijie Lyu
2 and
Shixiang Chen
1,3,*
1
School of Political Science and Public Administration, Wuhan University, Wuhan 430072, China
2
Zhou Enlai School of Government, Nankai University, Tianjin 300071, China
3
Local Government Public Service Innovation Research Center, Wuhan University, Wuhan 430072, China
*
Author to whom correspondence should be addressed.
Systems 2024, 12(1), 30; https://doi.org/10.3390/systems12010030
Submission received: 11 December 2023 / Revised: 11 January 2024 / Accepted: 16 January 2024 / Published: 17 January 2024
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Abstract

:
The transformation of the government into a digital entity is imperative, serving not only as a catalyst for the modernization of China’s governance system and capacity but also as a cornerstone for advancing the digital economy and the establishment of a digital China. This paper presents a multi-level analytical framework designed to assess the digital transformation performance of local governments. Utilizing a dataset comprising macro-regional and micro-individual data from Hubei province, we conduct an extensive analysis to examine the underlying mechanisms that influence the digital transformation performance of local governments and employ the hierarchical linear model (HLM) as the primary analytical instrument. The results of our analysis show that individual-level government–citizen interactions, government image, and district-level department collaborative capacities exert substantial and positive influences on the digital transformation performance of local governments. Furthermore, it is worth noting that department collaborative capacity plays a significant and positive moderating role in the relationship between government image and the digital transformation performance of local governments. These findings not only offer valuable insights for optimizing policy formulation but also contribute to a more comprehensive understanding of the mechanisms underlying the digital transformation performance of local governments.

1. Introduction

The onset of the Fourth Industrial Revolution has propelled human society into a new digital epoch. Moreover, the swift advancement of digital technologies has led to a myriad of economic, social, and political repercussions, catalyzing robust global digital transformation. The advent of digital transformation has revolutionized public engagement in governance and government responsiveness [1], introducing novel challenges to the conventional bureaucratic framework and the vertical governance model. In response to evolving governance requirements and the shifting environmental dynamics in the contemporary era, countries around the world have begun to introduce digital technologies into government management, launching a number of government digital transformation policies that are aligned with the development of IT. The Chinese government also places paramount emphasis on the pivotal role of digitalization in facilitating the comprehensive transformation of national governance. The government has embarked on a series of strategic endeavors aimed at catalyzing the digital transformation of public administration. The digital transformation of local governments plays an ever more crucial role in advancing the execution of strategic initiatives. This effort is pivotal to bolstering the modernization of the national governance system and its capacity and is an inevitable requisite for propelling the growth of the digital economy and the construction of Digital China.
In practice, the overall level of global e-government development is rising. According to survey data from the United Nations, the average value of the E-Government Development Index (EGDI) has increased from 0.549 in 2018 to 0.610 in 2022, and of the 193 member states, 133 countries were anticipated to be at a “high level” or “very high level” in 2022, an increase of 22 countries from 2018. Many countries have implemented reforms of government functions and institutions in line with digital transformation and are actively realizing the online provision of public services, providing support to respond to the multiple needs of members of society. Despite the Chinese government’s relatively delayed entry into the realm of digital transformation, it has demonstrated rapid progress in recent years, capitalizing on its latecomer advantage and enhancing its overall performance. China’s E-Government Development Index (EGDI) has ascended to the 43rd position globally, accompanied by an online service index that has reached the classification of a “very high level”. As the central pivot, the national government service platform has been established, and the national integrated government service platform is now in its preliminary stages of development. Furthermore, local governments have undertaken a multitude of digital transformation initiatives. For instance, Shanghai has initiated the establishment of an “integrated online platform” and “one-network unified management”, while in Zhejiang province, reforms encompass the “running at most once” policy and the development of the “city brain”. These initiatives have not only spurred innovation in the governance strategies of local governments but have also refined the digital transformation of local government functions. Nonetheless, uneven local government digital transformation performance still exists across countries [2,3]. Hence, it is evident that certain latent mechanisms continue to operate, necessitating further exploration.
At the theoretical level, scholars have dedicated their efforts to discerning the underpinnings of factors that impact the digital transformation performance of local governments. Existing studies have typically cleaved into two distinct analytical perspectives. Some scholars posit that disparities in the digital transformation performance of local governments stem from macro-level factors. Enhancing this performance hinges on the allocation and utilization of various resources, encompassing financial resources, professional talents [4], data components [5], and regulatory frameworks [6]. Furthermore, factors such as economic development levels, institutional context [7], inter-governmental competition, inter-governmental learning [8], and infrastructure development [9] constitute significant motivating forces. Conversely, other scholars delve into the micro-level perspective, scrutinizing the pivotal roles of public attitudes [10], subjective norms [11], perceived ease of use, and perceived usefulness [12]. It is evident that extant research offers valuable theoretical insights into comprehending the factors influencing the digital transformation performance of local governments. Nevertheless, they have yet to incorporate both macro-regional and micro-individual factors within a unified model to explore their joint mechanisms of action.
In recent years, scholars have increasingly recognized that the digital transformation of government necessitates the collaborative efforts of various stakeholders, including government departments, enterprises, and the general public. As the digital transformation process progresses, a novel dynamic has emerged between local governments and the public, facilitating the seamless execution of collaborative efforts and the realization of value co-creation. This underscores the centrality of government–citizen interactions in the realm of collaborative endeavors and their pivotal role in shaping and enhancing governance performance. However, extant research has yielded diverse findings: some studies suggest that government–citizen interactions have a beneficial impact, enhancing governance performance [13]. Conversely, some scholars contend that government–citizen interactions might engender conflict between governance principles and efficiency, potentially leading to public dissatisfaction [14]. In contrast, other studies propose that government–citizen interactions exert a limited influence on governance performance [15]. It is apparent a more intricate and multifaceted causal relationship exists between government–citizen interactions and government performance within the context of local government digital transformation. Thus, further empirical investigations are imperative to scrutinize and reconcile the existing disparities in theoretical perspectives.
As China’s ongoing administrative system reform progresses, a discernible shift is observed in the governing philosophy of the government, transitioning from an era of “omnipotent government” to a “limited government” and from a “management-oriented government” to a “service-oriented government”. This transformation is also reflected in a shift in governing behavior, moving from power-centric to citizen-centric approaches. Concurrently, local governments are increasingly recognizing the pivotal role played by reputation mechanisms, with government image being a noteworthy example. In the realm of organizational reputation research, the relationship between corporate reputation and performance has been well established. Corporate reputation has been shown to make substantial contributions to corporate growth and performance enhancement [16]. However, a limited number of studies have examined the relationship between government image and the digital transformation performance of local governments. Thus, there is a clear need for further research in this area.
A synthesis of local government transformation practices and theoretical studies reveals that local governments with nearly identical organizational structures and institutional environments have demonstrated different levels of digital transformation performance [17]. Dynamic capability theory offers valuable insights in terms of addressing this issue. This theory underscores the necessity for organizations to continually adapt their capabilities in response to external environmental changes to facilitate transformation and achieve a competitive advantage [18]. The digital transformation of government represents a complex and dynamic process, demanding that local governments reconfigure the collaborative capacity among government departments to effectively respond to shifts in the external environment and, consequently, enhance the performance of the digital transformation process. Nonetheless, the current body of empirical research exploring the relationship between departmental collaborative capacity and the digital transformation performance of local governments remains in need of further enrichment.
In summary, this study aims to bridge existing research gaps by integrating macro and micro perspectives and constructing a multi-level analytical framework to assess the digital transformation performance of local governments. A comprehensive analysis of the impact mechanisms at both district and individual levels was conducted. Utilizing objective data gathered from 13 prefectural-level administrative regions in Hubei province and questionnaire responses from 1446 members of the public, a hierarchical linear model analysis method was employed to empirically investigate the influence mechanisms of government–citizen interactions, government image, and department collaborative capacity on the digital transformation performance of local governments. This approach serves a dual purpose: firstly, it mitigates the limitations of drawing one-sided conclusions stemming from single-level data analysis. Secondly, it vividly portrays the progress made in the realm of Chinese local government digital transformation initiatives while providing robust empirical support for its sustainable and enduring development.

2. Literature Review and Research Hypotheses

2.1. Government–Citizen Interaction, Government Image, and Digital Transformation Performance of Local Governments

The digital transformation of governments is underway in countries around the world, driven by supranational agreements such as the Tallinn Declaration on e-Government. The digital transformation of governments has garnered considerable attention from both theoretical and practical circles. This focus has prompted rigorous theoretical refinement and practical implementation strategies. The benefits of leveraging data and technology for the digital transformation of government are almost limitless [19]. For example, Singapore’s PLANET project has been instrumental in improving the public transportation system [20]. Additionally, local governments have contributed to the development of a distinctive Chinese model for government digital transformation. This model is characterized by the adept coordination and management of data resources across diverse domains, facilitating correlation analysis of potential issues. Consequently, this data-driven approach has replaced empirical decision making with more scientifically grounded methods, yielding more effective governance outcomes [21]. Government digital transformation can effectively control tax evasion and improve fraud prevention [22]. Government digital transformation also proved to be very relevant during the COVID-19 crisis [23]. In addition, government digital transformation can improve the efficiency and effectiveness of public service provision, government transparency, and public satisfaction [24]. However, the digital transformation process of the government is not straightforward and faces numerous obstacles. These obstacles can be broadly categorized into structural and cultural impediments. Structural impediments include rules, capabilities, resources, etc. [25,26], while cultural impediments include the norms, perceptions, and expectations of public administration [27,28]. Based on existing research, it can be found that the influencing factors for the success of government digital transformation can be roughly divided into environmental elements and organizational elements. Environmental factors include the political system, technological conditions, information infrastructure, etc. [9,29], while organizational factors include resource conditions, leadership qualities, etc. [30,31]. The existing literature delves into the intricacies of government digital transformation, encompassing its core principles, constituent elements, models, practical challenges, and corresponding solutions. Notably, the focal point of scholarly inquiry has transitioned from “what constitutes government digital transformation” to the more pivotal question of “how to effectively realize government digital transformation.” Scholars have come to recognize that the successful execution of government digital transformation necessitates a dual approach: the optimization of the internal governance model and the cultivation of a conducive external environment.
In recent years, performance evaluation has progressively gained prominence as a focal point within the realm of administrative reform [32]. This emphasis is particularly pronounced in the context of local governments, which are integral components of the national governance system that have attracted increasing scholarly attention. Through extensive long-term experience in public management, two predominant performance assessment models have emerged. One model relies on objective metrics derived from cost–benefit analyses, while the other hinges on subjective assessments by the public. Notably, previous research has revealed that the subjective perceptions of the public hold greater explanatory power in the context of performance evaluation when compared with efficiency evaluations based on cost–benefit analyses [33]. Furthermore, an excessive focus on technical rationality and internal administrative efficiency, at the expense of neglecting the subjective sentiments of the public, may impede the transformation of government functions. Within the context of the government digital transformation process, the satisfaction and recognition of the public as the ultimate end-users serve as the foundational criteria for evaluating the performance of local government digital transformation initiatives. Consequently, measuring the digital transformation performance of local governments from the vantage point of the public’s subjective perceptions offers a more comprehensive reflection of the public value, centering on the preferences and expectations of the public.
In the late 1970s, E. Ostrom first utilized the concept of cooperative production to describe the cooperative production relationship between government departments as suppliers of public services and the public as consumers of public services [34]. She believed that integrating the resources of the government and the public could enhance the quality and effectiveness of public services on the one hand and, on the other hand, reduce government financial expenditures while obtaining higher service effectiveness. In the fields of political science and public administration, the theory of cooperative production has been widely applied to the study of citizen participation in public service provision, providing theoretical support for improving the digital transformation of local governments. The digital transformation of local governments encompasses not only the progressive reconfiguration of organizational structures, the reengineering of business processes, the standardization of administrative functions, and the innovation of public services through the integration of digital technologies within the bureaucratic framework, but also the dynamic recalibration of the relationships between government and society, as well as government and the market, catalyzed by the emergence of novel governance paradigms. Co-production theory, which underscores the collaborative involvement of both traditional service providers and consumers in the delivery of public services, offers valuable theoretical underpinnings for enhancing the digital transformation performance of local governments [35]. Under the guidance of this theory, the public assumes a dual role as both consumers and co-producers in the realm of government digital transformation. The advent of digital technologies such as the Internet, artificial intelligence, and cloud computing, among others, has engendered a novel ecosystem, mechanism, and platform for fostering interactions between government and citizens, creating an opportunity for more seamless government–citizen engagement [34]. Efficacious government–citizen interaction can facilitate a reciprocal exchange of information, catering to the public’s needs while concurrently enabling local governments to gain insights into public sentiments and preferences, thus refining the trajectory of government digital transformation. Consequently, this reciprocal process holds the potential to enhance the digital transformation performance of governments. In addition, the correlation between the public’s interaction behavior with government departments in terms of the digital transformation of the government and the performance of the transformation can also provide theoretical clues from the research on customer engagement and customer satisfaction. Early research on business management found that customers would inevitably be involved in service provision and that customer engagement behaviors are critical to service production, inevitably affecting customer satisfaction [36]. Based on the findings identified in previous research, this paper proposes the following hypothesis:
Hypothesis 1 (H1).
Government–citizen interaction exerts a statistically significant positive influence on the digital transformation performance of local governments.
In recent years, organizational reputation theory, as a new perspective in terms of observing and understanding organizational behavior, has become a cutting-edge research hotspot in the field of public administration and has been widely used in the analysis of policy formulation, organizational legitimacy, organizational performance, and other issues [37,38]. Although scholars such as Wilson discussed organizational reputation in their early works on public administration, they did not theorize it [39]. This was not outlined until Carpenter took the U.S. Food and Drug Administration as a case study, pioneering the discussion of the relationship between organizational reputation and power, regulation, and other factors [40], laying the foundation for the theory of public sector organizational reputation. With the rapid development and wide application of information technology such as the Internet, public access to information has become more convenient and diversified, and the government’s behavior has been placed under the magnifying glass of the Internet, which affects the public’s perception and comprehensive evaluation of the government. Image management has been highly valued by government departments. Therefore, how local governments build and maintain a good government image in the digital era is an important issue for public management to pay attention to and solve. The government’s image encompasses the comprehensive perception and evaluative judgments rendered by the public regarding the government’s performance and overall competence. In the context of local governance, the government’s image assumes a role of paramount importance: it serves as a fundamental political resource and an intangible asset. Furthermore, it represents a crucial wellspring of legitimacy and authority, ensuring the smooth execution of administrative functions [41]. Consequently, government image plays a pivotal role in improving the transformation performance. Carpenter’s research found that building and maintaining a favorable image with the public is important for the success of government departments [40]. A good government image represents a strategic competitive advantage that helps the public sector better interact with the public and garner public trust and support, diminish resistance to the digital transformation initiatives undertaken by local governments, reduce transformation costs, and ultimately enhance transformation performance. Consequently, the paper proposes a second hypothesis based on findings identified in previous research:
Hypothesis 2 (H2).
Government image exerts a statistically significant positive impact on the digital transformation performance of local governments.

2.2. Department Collaborative Capacity and Digital Transformation Performance of Local Governments

Dynamic capabilities theory began with resource-based theory, which systematically answered the question of how organizations can achieve transformational development and thus gain competitive advantage. Resource-based theory (RBT) suggests that the differences in the performance levels of organizations are due to the consistency of the resources they possess, but because the environment is in a constant state of development and change, RBT is unable to respond to the question of how organizations can transform and achieve better performance in a dynamic environment [42]. Against this background, Teece proposed the idea of dynamic capabilities, suggesting that an organization’s competitive advantage derives from the extent to which its ability to utilize organizational resources matches the external environment [43]. The dynamic capability of an organization can help the organization achieve organizational transformation and improve organizational performance by integrating internal resources and eliminating dependence on traditional paths according to changes in the external environment in a timely manner. Capability is an organizational trait, and the capability of the public sector refers to the activities of acquiring, integrating, configuring, and applying internal and external resources to achieve the established strategic goals [44]. Dynamic capability theory provides a scientific theoretical perspective for analyzing the influence mechanism of government digital transformation performance. Government digital transformation is a double-edged sword, which brings both opportunities and challenges and requires public sector capacity building to overcome all kinds of problems arising in practice. In government digital transformation, collaborative capacity building between departments has been a key topic in both practical and theoretical circles. The iterative evolution of modern information technology has provided the essential technical infrastructure for facilitating interconnection and cooperation among these government entities. At the same time, governments have implemented numerous institutional innovations that provide institutional safeguards for enhancing collaboration among departments. Given the nature of digital transformation in government as a form of institutional collective action, it necessitates effective collaboration among departments spanning diverse domains, administrative tiers, and operational systems [45]. In the face of the multifaceted governance environment, the digital transformation of local governments presents both opportunities for development and tangible challenges. Dynamic capability theory underscores the importance of adapting government capacity development to the external landscape. This adaptation requires a continuous adjustment of capabilities to surmount various practical obstacles, thereby enhancing the digital transformation performance. Furthermore, a case study rooted in the Chinese context confirmed the pivotal role of departmental collaboration in influencing the digital transformation of governments [46]. Consequently, the paper proposes a third hypothesis based on findings identified in previous research:
Hypothesis 3 (H3).
Department collaborative capacity exerts a statistically significant positive influence on the digital transformation performance of local governments.

2.3. Cross-Level Moderation of Department Collaborative Capacity

Collaboration, as the ability to access resources and promote innovation in practice, has become an important influencing factor in the digital transformation of government. The rising public demand for quality government services in the digital environment urgently requires the public sector to respond quickly to its needs, a goal that can be achieved almost exclusively through the building of collaborative capacity. Digital transformation is a holistic and systematic public management change in local governments that is subject to the comprehensive influence of multiple subjects, which puts forward higher requirements for the synergistic capacity of the public sector. Looking at the practice of government digital transformation worldwide, it can be found that the cross-sectoral collaborative capacity at the government operation level plays an important role in realizing the optimization and integration of the governance model, service process, and institutional mechanism [45], which can promote straightforward improvement in the transformation performance process. At the district level, performance in terms of department collaborative capacity exhibits variability across local governments. The perceptions of government digital transformation performance may differ among individual members of the public. Local governments equipped with robust department collaborative capacity are better positioned to facilitate public convenience by clarifying departmental responsibilities and streamlining collaboration and cooperation processes. This, in turn, imparts enduring momentum to the digital transformation of local governments while bolstering enthusiasm for government–citizen interaction [47]. Conversely, local government departments characterized by weak department collaborative capacity may grapple with issues such as conflicts of interest, disputes over responsibilities, and communication barriers [48]. These problems can lead to non-optimal interactions between the public and the government, thereby creating substantial hindrances to the enhancement of digital transformation performance. As a result, differentiated department collaborative capacity can yield varying effects on government–citizen interaction and the digital transformation performance of local governments. The paper proposes a fourth hypothesis based on the findings identified in previous research:
Hypothesis 4 (H4).
Departmental collaborative capacity exerts a moderating influence on the relationship between government–citizen interaction and the digital transformation performance of local governments.
Existing research focuses on analyzing the impact of exogenous variables represented by resources on the performance of government digital transformation and simplistically equates between technology application, resource possession, and transformation performance, ignoring the time difference due to different organizational capabilities, resulting in insufficient explanation of the time situation. As one of the classic theories in the field of organizational studies, dynamic capability theory emphasizes the key role of capability as an endogenous variable in organizational transformation and organizational performance improvement [44]. Guided by the dynamic capability theory, collaborative capacity building among government departments endeavors to surmount the genuine dilemma of fragmentation and achieve effective collective action through cooperative efforts among various departmental entities [49]. Local governments endowed with robust departmental collaborative capacity enjoy discernible advantages in transcending the conventional fragmented management model. They successfully foster information sharing and transmission among departments. This, in turn, contributes to an enhanced public perception of the government image, laying the organizational foundation for the sustainability and comprehensiveness of local government digital transformation. Conversely, local governments characterized by inadequate department collaborative capacity often exhibit pronounced disparities in readiness in terms of implementation and task completion among their departments. Such disparities can undermine a government’s image and hinder the overarching enhancement of the digital transformation performance of local governments [50]. In summation, divergent department collaborative capacities yield heterogeneous effects on the relationship between government image and the digital transformation performance of local governments. The paper proposes a fifth hypothesis based on findings identified in previous research:
Hypothesis 5 (H5).
Departmental collaborative capacity serves as a moderating factor in the relationship between government image and the digital transformation performance of local governments.
The digital transformation of local governments represents a multifaceted “governance change” initiative, where the successful enhancement of its performance is impacted by an array of factors operating at both macro and micro levels. Thus, referring to Desmidt et al.’s idea of constructing a theoretical framework [51,52], and under the guidance of cooperative production theory, organizational reputation theory and dynamic capabilities theory, combined with the conclusions of the established studies, we put forward the above research hypotheses and constructed the theoretical model shown in Figure 1.

3. Research Design

3.1. Sample Selection and Data Sources

This research delves into the digital transformation performance of local governments in Hubei province for several compelling reasons. Firstly, a substantial portion of the existing body of work primarily concentrates on nationwide or regional analyses of the digital transformation performance of local governments, employing a broad macroscopic lens [53,54]. At a high level, this emphasis often overlooks the nuanced examination of specific provinces, creating a gap in meso-level analysis. Secondly, Hubei province, a pivotal transportation hub and an economic epicenter in central China, has substantial practical significance in terms of conducting an in-depth exploration of the digital transformation performance of local governments. The design of the research questionnaire was a meticulous process, drawing insights from the established literature and a thorough interpretation of pertinent policies. Respondent selection was conducted using a thoughtfully structured combination of stratified step-by-step sampling and random sampling. The specific sampling procedure involved the random selection of 2–3 sub-districts within each city, followed by the random selection of 2–3 communities within each sampled street. Subsequently, 10 respondents were randomly chosen from each selected community to participate in the study. Between August and November 2020, members of the research group distributed a total of 1900 questionnaires. After eliminating invalid questionnaires such as “unclear,” “do not know,” and blank data, a total of 1446 valid questionnaires were collected. This yielded an impressive effective response rate of 76.11%. The survey comprehensively spanned all 13 prefectural-level administrative regions within Hubei province, ensuring the dataset’s robustness, representativeness, and reliability. Detailed sample characteristics are presented in Table 1.

3.2. Variable Measurement

3.2.1. Dependent Variable

The digital transformation performance of local governments (DTPs) is rooted in the people-centered philosophy of development, serving as its core value and conceptual foundation. A key criterion for assessing the digital transformation performance of local governments is public satisfaction. To measure the dependent variable, we employed public satisfaction with the online government service platform as an indicator. Precisely, the measurement of public satisfaction encompassed five dimensions, which included the comprehensiveness of service coverage, timeliness of information updates, the degree of realization of the entire online process, the level of intelligent service, and the extent of personalized service offered by the online government service platform. The respondents were provided with a scale ranging from 0 to 10, with 0 denoting “very dissatisfied” and 10 signifying “very satisfied.” Drawing on the approach of Li Haitao and Song Linlin [55], an exploratory factor analysis conducted on these five indicators yielded a KMO statistic value of 0.865. Factor orthogonal rotation, employing the principal component analysis method and the variance maximization principle, resulted in a cumulative variance contribution of 74.24%. Reliability analysis demonstrated a Cronbach’s alpha value of 0.912, signifying robust internal consistency among the five question items used to assess the digital transformation performance of local governments. Consequently, the scores from these five questions were averaged to derive the final score for the digital transformation performance of local governments.

3.2.2. Independent Variables

Based on Ma Baojun et al.’s measurement [56] of government–citizen interaction (GCI), we quantified the frequency of public interaction with government departments through the online government service platform. Respondents were provided with a scale to assign values to their interaction frequency, ranging from 1, indicating “no interaction,” to 5, representing “frequent interaction.” The value chosen by respondents served as the ultimate score for assessing government–citizen interaction.
We applied Fan Bonai and Jin Jie’s framework to assess government image (GI) [57], focusing on three main aspects: the government’s innovation in service delivery, staff demeanor, and overall work quality. Respondents rated their satisfaction with these aspects on a scale of 0 to 10, with 0 indicating “very dissatisfied” and 10 signifying “very satisfied.” After conducting an exploratory factor analysis on these three indicators, we obtained a KMO statistic of 0.704, validating the suitability of the data for factor analysis. We employed factor orthogonal rotation using principal component analysis and variance maximization, revealing a cumulative variance contribution of 70.3%. Furthermore, the reliability analysis demonstrated strong internal consistency among the three questions assessing government image, with a Cronbach’s alpha value of 0.787. As a result, we computed the average scores for these three questions, representing the final measurement of government image.
The core objective of government digital transformation is to embed digital technology into public service and product provisioning, facilitating the enhancement and effectiveness of services through interdepartmental cooperation involving data, systems, and operations. Consequently, effective department collaboration capacity (DCC) emerges as the linchpin for the successful digital transformation of local governments. In accordance with the findings of Tang Zhiwei and Han Xiao [58], we adopted local governments’ digital service capability scores as a surrogate measure for department collaborative capacity. The data for this variable were sourced from the Report on the Development of Internet Service Capability of Local Government in China (2019) [59].

3.2.3. Control Variables

Digital infrastructure (DI) serves as the foundational resource underpinning the realization of local government digital transformation and facilitates the seamless flow of data and information across multiple entities through network ports. Substantial empirical evidence endorses the notion that the development of digital infrastructure significantly enhances the digital transformation performance of local governments [60]. Consequently, we incorporated digital infrastructure as a control variable. In alignment with the approach of Zeng Jingjing and Wen Yonglin [61], the number of subscribers with broadband access was selected as a surrogate metric for digital infrastructure. The data for this variable were drawn from the Hubei Statistical Yearbook (2019) [62].
The digital transformation of local governments constitutes a substantial and resource-intensive financial commitment, with prior research confirming a notable influence of local government financial input on digital transformation performance [63]. Therefore, it was imperative to incorporate financial input (FI) as a control variable. Leveraging the methodology employed by Gan, Xingqiong et al. [64], we selected the share of general public budget expenditure in GDP as an indicator of financial input. Data for this metric were sourced from the Hubei Provincial Statistical Yearbook (2019) [62].
In accordance with the extensive body of research on the digital transformation performance of governments, we selected gender (GENDER), age (AGE), career (CAR), and political status (POS) as micro-level control variables. Gender, career, and political status were all quantified numerically.
Table 2 presents variables, variable measurements, reliability, and validity indicators.

3.3. Analysis Method

The data in this paper consist of two components: individual-level questionnaire data and objective quantitative data at the regional level, creating a two-level “individual–district” data structure, which is a typical nested format. Each member of the public possesses unique individual characteristics and is situated within a specific regional context. Individuals across different regions are interconnected, challenging the assumption of observation independence in the ordinary least squares (OLSs) technique [65]. The hierarchical linear model (HLM) is adept at handling non-independent data with a nested structure, effectively merging micro-individual and macro-regional data and distinguishing individual impacts from regional effects [66]. Consequently, we employed the hierarchical linear model to scrutinize the impact of regional and individual factors on the digital transformation performance of local governments. This approach serves to mitigate issues related to endogeneity and common method bias that can arise from relying solely on questionnaire-based data.

4. Empirical Analysis and Results

4.1. Descriptive Statistics and Correlation Analysis

We performed initial analyses by conducting descriptive and correlation examinations of the core variables. We utilized SPSS 23.0 software for these analyses, serving as a preliminary assessment of our research hypotheses. Table 3 exposes the descriptive statistics of the research variables (including skewness and kurtosis). As depicted in Table 4, the results reveal a substantial positive association between government–citizen interaction and the digital transformation performance of local governments, as well as a significant relationship between government image and the digital transformation performance of local governments. These findings lay the groundwork for the subsequent testing of our research hypotheses.

4.2. Hierarchical Linear Model Analysis

To explore the mechanisms through which individual and district-level variables influence the digital transformation performance of local governments, we conducted empirical data analysis using HLM 6.08 software. As part of the hierarchical linear model analysis, we sequentially examined the null model, random coefficient model, intercept model, and full model in accordance with the necessary procedures.

4.2.1. Results of Zero Model Analysis

To assess the suitability of the hierarchical linear model, a null model was constructed without any independent variables to explore the within- and between-group variances in the digital transformation performance of local governments. The evaluation of the data’s appropriateness was based on the calculation of the intra-group correlation coefficient (ICC). The results of this null model analysis are presented in Table 5. Notably, the ICC(1) value, which stands at 0.075, surpasses the critical threshold of 0.059, indicating a lack of independence between the individual-level and district-level data. Furthermore, the ICC(2) value, amounting to 0.882, exceeds the critical threshold of 0.7, signifying a high level of model confidence. Given the fulfillment of these two conditions, the data in this paper align with the criteria for statistical soundness and the necessity for conducting a hierarchical linear model analysis.

4.2.2. Results of Random Coefficient Model Analysis

Incorporating individual-level and control variables into the random coefficient model, which encompassed government–citizen interaction, government image, gender, age, career, and political status, we present the findings of the random coefficient model analysis in Table 6. Government–citizen interaction exhibits a notable positive effect on the digital transformation performance of local governments (β = 0.069, p < 0.1). This implies that increased government–citizen interaction is associated with enhanced digital transformation performance of local governments, thereby corroborating hypothesis H1. Furthermore, government image exerts a substantial positive impact on the digital transformation performance of local governments (β = 0.688, p < 0.01), underscoring that a more favorable government image corresponds to heightened digital transformation performance of local governments and thus substantiates hypothesis H2. On the contrary, political status demonstrates a significant negative influence on the digital transformation performance of local governments (β = −0.111, p < 0.05). However, gender (β = −0.065, p > 0.1), age (β = −0.003, p > 0.1), and career (β = 0.011, p > 0.1) do not manifest any statistically significant impacts on the digital transformation performance of local governments.

4.2.3. Results of Intercept Model Analysis

We extended the intercept model to incorporate district-level and control variables, which encompassed department collaborative capacity, digital infrastructure, financial input, gender, age, career, and political status. The analysis results for the intercept model are presented in Table 4. Department collaborative capacity exhibits a significant positive influence on the digital transformation performance of local governments (β = 0.130, p < 0.01). This implies that stronger department collaborative capacity corresponds to better digital transformation performance of local governments, thereby supporting hypothesis H3. Conversely, digital infrastructure is found to have a significant negative impact on the digital transformation performance of local governments (β = −3.358, p < 0.05). Financial input exerts a significant positive effect on the digital transformation performance of local governments (β = 1.104, p < 0.01). Gender (β = −0.128, p < 0.1), age (β = −0.005, p < 0.1), political status (β = −0.110, p < 0.05), and career (β = 0.035, p < 0.01) each exert a statistically significant impact on the digital transformation performance of local governments.

4.2.4. Results of the Full Model Analysis

The full model, incorporating both individual- and district-level variables, was developed for analysis. The outcomes of the full model analysis are illustrated in Table 4. Notably, government–citizen interaction (β = 0.064, p < 0.05), government image (β = 0.674, p < 0.01), and department collaborative capacity (β = 0.131, p < 0.01) all exhibit substantial positive impacts on the digital transformation performance of local governments. These findings reaffirm the validity of hypotheses H1, H2, and H3. Conversely, the interaction term of department collaborative capacity and government–citizen interaction does not significantly influence the digital transformation performance of local governments (β = −0.012, p > 0.1). This outcome suggests the absence of a negative moderating effect of department collaborative capacity between government–citizen interaction and digital transformation performance of local governments, thus failing to support hypothesis H4. However, the interaction term between department collaborative capacity and government image has a noteworthy and positive effect on the digital transformation performance of local governments (β = 0.114, p < 0.01). This implies that department collaborative capacity serves as a positive moderator between government–citizen interaction and the digital transformation performance of local governments, thus lending support to hypothesis H5.
An examination of the control variables indicates that all district-level control variables exert significant influences on the digital transformation performance of local governments. Similarly, political status at the individual level significantly affects the digital transformation performance of local governments, with the direction of this effect aligning with the preceding analysis. A detailed depiction of the influence mechanism is presented in Figure 2.

5. Conclusions and Outlook

5.1. Conclusions and Discussion

The digital transformation of local governments in China has evolved from government informatization, which was primarily based on internal office systems and one-way service provision, to open, inclusive, and diversified smart governance on a broader scale. With the backdrop of national-level policy advancements and the practical initiatives of local governments, China’s digital transformation efforts are entering a new historical phase. To some extent, this can be seen as a microcosm of government digital transformation efforts around the world. Enhancing the digital transformation performance of local governments has also garnered significant attention from both practical and theoretical perspectives. As Scott notes, a single set of factors does not explain organizational performance very well. Therefore, analysis of the digital transformation performance of local governments should not overlook the synergistic impact of macro and micro-level factors, as well as environmental and organizational factors. In light of this context, this paper devised a cross-level analytical framework that incorporates government–citizen interaction, government image, and department collaborative capacity, which makes an important contribution to the field of public administration. Furthermore, it employs a hierarchical linear model to investigate the underlying mechanisms through which individual and district-level determinants influence the digital transformation performance of local governments.
Firstly, at the individual level, this study highlights the coexisting positive impacts of government–citizen interaction and government image as components of the external environment on the digital transformation performance of local governments. It validates the causal relationship between government–citizen interaction and the digital transformation performance of local governments, signifying that more frequent interactions between government and citizens have a greater impact on enhancing local government digital transformation performance. This finding confirms Holgersson et al.’s research [67] and refutes the negative perceptions of Williams et al. [14] and Alford and Yates [15] about the behavior of political–civilian interactions, which not only deepens our comprehension of the co-production effect’s underlying mechanisms but also contributes to offering the possibility of bridging the theoretical divide over the relationship between the government–citizen interaction and government performance. Furthermore, this paper elucidates the role of government image in fostering the digital transformation performance of local governments. This view is consistent with the findings of Lee et al. on the relationship between government reputation and organizational performance [41]. Government image represents the comprehensive assessment of government behavior and capability by the public and serves as a valuable intangible asset for local governments. A positive government image garners higher public recognition and support, thus facilitating the seamless execution of digital transformation initiatives by local governments. In line with relevant research findings, providing an accessible and efficient government–citizen interaction platform for the public while consistently enhancing the government image emerges as a crucial avenue for elevating the digital transformation performance of local governments.
Secondly, at the district level, it becomes evident that a department’s collaborative capacity as an organizational element plays a pivotal role in enhancing the digital transformation performance of local governments. This paper affirms the substantial influence of local government departments’ collaborative capacity on digital transformation performance. In essence, the stronger the collaborative capacity among departments, the more significant the improvement in the digital transformation performance of local governments. This confirms previous related research by Tangi et al. [68]. A lack of coordination between departments can create obstacles to government digital transformation. This finding suggests that the dynamic capabilities of an organization can indeed help to adapt to changes in the external environment and that the adjustment of its own capabilities, especially department capacities, provides strong support for improvements in the performance of a government’s digital transformation, and that this study also expands the context in which the dynamic capabilities theory can be applied. Drawing from the insights derived from these research findings, it is evident that enhancing collaborative capacity among local government departments represents a key avenue for improving digital transformation performance.
Thirdly, at the cross-level interaction, it is evident that department collaborative capacity positively moderates the relationship between government image and the digital transformation performance of local governments. This interaction implies that local governments characterized by stronger department collaborative capacity and a positive government image will exhibit a higher level of digital transformation performance. This conclusion confirms the correctness of applying dynamic capability theory to discuss government digital transformation and further clarifies the multiple logics of local government digital transformation performance generation, suggesting that strengthening inter-governmental data sharing and enhancing department capacity is a feasible way to address the structural barriers to government digital transformation, thus facilitating the straightforward advancement of digital transformation.
The results of this study affirm that the enhanced digital transformation performance of local governments is an outcome influenced by a combination of factors operating at both the district and individual levels. This means that the success of government digital transformation is driven by both environmental and organizational factors; therefore, breaking through the limitations of environmental determinism and organizational idiosyncrasy and improving the integration and utilization of internal and external resources can be seen as an important path to enhance the effectiveness of digital transformation in local governments. Consequently, local governments, as they focus on capacity building, also need to actively engage their citizenry, recognizing the vital role played by the public in driving improvements in digital transformation performance.

5.2. Limitations and Outlook

This study employed a hierarchical linear modeling approach to dissect the impact of government–public interaction, government image, and department collaborative capacity on the digital transformation performance of local governments. This methodology confers distinct advantages for validating the interplay between government entities and the citizenry within the ambit of enhancing government performance. However, this paper is limited in three aspects, which call for future research.
As the vanguard of digital transformation performance enhancement, government departments emerge as preeminent agents. While the public is acknowledged as a critical collaborator in this cooperative production, empirical evidence from public management practices and theoretical studies has underscored the necessity of acknowledging a diverse array of actors, including market forces and societal institutions [69]. This is particularly salient in the digital transformation efforts of local governments, where the influence of enterprises and civil society organizations is burgeoning in significance. Nevertheless, this study’s scope is limited by data access, preventing a full evaluation that considers corporate engagement and social organizations’ involvement. Future research can further explore the contribution of multiple forces in improving the digital transformation performance of local governments.
This study integrated subjective questionnaires with objective cross-sectional data, which limits the capacity to draw causal conclusions. The digital transformation process of local governments is inherently a complex undertaking. The causal interplay between individual-level and district-level factors and the effectiveness of transformation initiatives is also intricate. It is recommended that future researchers utilize panel data or engage in experimental methodologies to surmount this limitation and fortify the validity of causal deductions.
This paper examined how the digital transformation performance of local governments is affected, using data from Hubei province. Nonetheless, the effectiveness of digital transformation can vary significantly between different regions and provinces, and factors at the individual and regional levels may also differ. To gain a more comprehensive understanding, future research should expand to include additional provinces, investigating the diverse outcomes and driving forces behind the digital transformation of local governments across a broader geographic area.

Author Contributions

Conceptualization, W.Z. and S.C.; methodology, W.Z.; software, W.Z.; validation, Z.L. and S.C.; formal analysis, Z.L.; investigation, W.Z. and Z.L.; data curation, W.Z.; writing—original draft preparation, W.Z. and Z.L.; writing—review and editing, W.Z., Z.L. and S.C.; supervision, S.C.; project administration, S.C. All authors have read and agreed to the published version of the manuscript.

Funding

This research was funded by the key art project of the National Social Science Foundation of China, grant number 15AH007; Provincial Teaching Research Program of Hubei Universities, grant number 2022014; and the project of Hubei think tank in 2020, grant number 202008024.

Data Availability Statement

The original contributions proposed in the study are included in the article, and further inquiries can be directly addressed to the corresponding author.

Conflicts of Interest

The authors declare no conflicts of interest.

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Systems 12 00030 g001
Figure 1. Theoretical framework.
Figure 1. Theoretical framework.
Systems 12 00030 g001
Systems 12 00030 g002
Figure 2. Mechanism of action.
Figure 2. Mechanism of action.
Systems 12 00030 g002
Table 1. Characteristics of the samples.
Table 1. Characteristics of the samples.
CharacteristicsCategoryFrequencyPercent (%)CharacteristicsCategoryFrequencyPercent (%)
GenderMale72650.21Age25 years old and below18512.79
Female72049.7926–35 years old63944.19
CareerStaff of government departments and institutions44430.7136–45 years old36024.9
Employees of state-owned enterprises17412.0346–55 years old21314.73
Private company employees43830.2956 years old and above493.39
Farmers745.12Political StatusThe masses73951.11
Military614.22Communist Youth League member24917.22
Students594.08Chinese Communist Party member42429.32
Retirees543.73Member of a democratic party60.41
No unit/self-employed1429.82Non-party member281.94
Table 2. Variable measurement.
Table 2. Variable measurement.
VariableVariable MeasurementReliability and Validity
DTPThe comprehensiveness of service coverage [0–10]KMO: 0.865
Cronbach’s Alpha: 0.912
Timeliness of information updates [0–10]
The degree of realization of the entire online process [0–10]
The level of intelligent service [0–10]
The extent of personalized service offered [0–10]
GCIThe frequency of public interaction with government departments [1–5]
GIThe government’s innovation in service delivery [0–10]KMO: 0.704
Cronbach’s Alpha: 0.787
The staff demeanor [0–10]
The overall work quality [0–10]
DCCLocal governments’ digital service capability score
DIThe number of subscribers with broadband access
FIThe share of the general public budget expenditure in GDP
GENDERFemale = 0; Male = 1
AGERespondent’s age
CARStaff of government departments and institutions = 1; employees of state-owned enterprises = 2; private company employees = 3; farmers = 4; military = 5; students = 6; retirees = 7; no unit/self-employed = 8
POSThe masses = 1; Communist Youth League member = 2; Chinese Communist Party member = 3; member of a democratic party = 4; non-party member = 5
Table 3. Descriptive statistics.
Table 3. Descriptive statistics.
VariableNMinMaxMeanStd. DeviationSkewnessKurtosis
DTP14460.0010.007.586 1.610 −0.498 0.077
GCI14461.005.002.060 1.081 0.138 −0.447
GI14460.0010.007.672 1.614 −0.627 0.485
GENDER14460.001.000.500 0.500 0.008 −2.003
AGE144616.0077.0035.700 9.532 0.658 0.043
CAR14461.0010.003.710 2.770 1.101 0.341
POS14461.005.001.850 0.986 0.818 −0.056
DCC1321.2731.8727.079 2.866 −0.348 0.074
DI130.190.470.315 0.070 0.693 1.356
FI130.070.670.214 0.158 2.360 6.067
Table 4. Correlation analysis.
Table 4. Correlation analysis.
Individual VariablesGENDERAGECARPOSIBGIDTP
GENDER1
AGE0.089 ***1
CAR−0.0290.135 ***1
POS0.077 ***−0.075 ***−0.310 ***1
GCI−0.021−0.057 **−0.0370.011
GI−0.019−0.0070.044 *−0.0410.0191
DTP−0.055 **−0.0270.084 ***−0.105 ***0.057 **0.664 ***1
Regional VariablesDIFIDCC
DI1
FI−0.089 ***1
DCC0.457 ***−0.514 ***1
Note: * significant at p < 0.10; ** significant at p < 0.05; and *** significant at p < 0.01.
Table 5. Results of the zero model analysis.
Table 5. Results of the zero model analysis.
Variablesd.f.τ00σ2ICC(1)ICC(2)
DTP122.4120.1960.0750.882
Table 6. Multilayer linear regression results of digital transformation performance of government.
Table 6. Multilayer linear regression results of digital transformation performance of government.
VariablesThe Random Coefficient ModelThe Intercept ModelThe Complete Model
INTRCPT22.383 **
(0.836)		5.245 ***
(0.605)		8.528 ***
(0.374)
IB0.069 *
(0.038)		 0.064 **
(0.032)
GI0.688 ***
(0.112)		 0.674 ***
(0.058)
GENDER−0.065
(0.074)		−0.128 *
(0.071)		−0.075
(0.046)
AGE−0.003
(0.002)		−0.005 *
(0.003)		−0.001
(0.001)
CAR0.011
(0.008)		0.035 ***
(0.010)		0.003
(0.006)
POS−0.057 **
(0.027)		−0.110 **
(0.047)		−0.042 *
(0.024)
DCC 0.130 ***
(0.029)		0.131 ***
(0.030)
DI −3.358 **
(1.352)		−3.238 **
(1.358)
FI 1.014 **
(0.428)		1.072 **
(0.433)
PPC × DCC −0.012
(0.015)
DI × DCC 0.114 ***
(0.019)
Within-group variance1.2712.3881.035
Between-group variance0.1700.1310.154
Note: Values in parentheses are robust standard errors; * significant at p < 0.10; ** significant at p < 0.05; and *** significant at p < 0.01.
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Zhou, W.; Lyu, Z.; Chen, S. Mechanisms Influencing the Digital Transformation Performance of Local Governments: Evidence from China. Systems 2024, 12, 30. https://doi.org/10.3390/systems12010030

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Zhou W, Lyu Z, Chen S. Mechanisms Influencing the Digital Transformation Performance of Local Governments: Evidence from China. Systems. 2024; 12(1):30. https://doi.org/10.3390/systems12010030

Chicago/Turabian Style

Zhou, Wei, Zhijie Lyu, and Shixiang Chen. 2024. "Mechanisms Influencing the Digital Transformation Performance of Local Governments: Evidence from China" Systems 12, no. 1: 30. https://doi.org/10.3390/systems12010030

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