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Article

Exploring Driving Factors of Digital Transformation among Local Governments: Foundations for Smart City Construction in China

by
Jianying Xiao
1,2,
Lixin Han
1 and
Hui Zhang
1,*
1
School of Public Policy & Management, Research Center of Digital Rural Services, China University of Mining and Technology, Xuzhou 221116, China
2
The Research Center for Transition Development and Rural Revitalization of Resource-Based Cities in China, China University of Mining and Technology, Xuzhou 221116, China
*
Author to whom correspondence should be addressed.
Sustainability 2022, 14(22), 14980; https://doi.org/10.3390/su142214980
Submission received: 26 August 2022 / Revised: 25 October 2022 / Accepted: 10 November 2022 / Published: 12 November 2022
(This article belongs to the Special Issue Future of Sustainable Smart Cities: User Experience and Policy Perspectives)
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Abstract

:
To achieve sustainable urban development, many countries around the world are exploring the path of smart cities. The progress and development of digital technology has paved a promising road. Digital transformation of the government is an important factor for smart cities in China. Scholars are increasingly interested in the digital transformation of government, but there is little empirical research on the driving factors of digital transformation among local governments. Based on the technology–organization–environment (TOE) framework, this study explores the driving factors of digital transformation among local governments in China. A questionnaire survey and the structural equation model were used. Technological readiness, organizational efficiency, public service delivery, citizens’ expectations and superior pressure are significant driving factors. Technological readiness is the first factor affecting the government’s digital transformation. Applying the identified drivers, the results of this research could be used as orientation towards, and provide concrete practical approaches for, successful digital transformation among local governments, which would lay a foundation for smart city construction.

1. Introduction

With the development of digital technologies, the digital age has arrived. Digital technologies make a difference to the success of the Fourth Industrial Revolution [1,2] and a series of profound changes have taken place in society and organizations [3,4]. Achieving sustainable urban development is an important topic of the United Nations 2030 Sustainable Development Goals, with smart cities as the main direction. Digital governance is an important way to achieve this goal. Digital transformation in government is the inevitable choice [5,6,7,8]. Thus, governments around the world are also launching a digital movement based on different development strategies [9,10,11]. Meanwhile, governments consider digital transformation as a strategy to improve public service, empower customers, streamline operations, and create new models of smart cities [12]. Digital transformation is, therefore, considered helpful for the government itself and citizens [13]. The challenges around the COVID-19 pandemic forced governments to urge the adoption and implementation of digital technologies, which accelerated the pace of digital transformation [14].
During this pandemic era, strategic and creative ideas had to be generated and acted upon [15] to continually promote digital transformation in government, especially in the new era of China. The 14th Five-Year Plan for the Development of a Digital Economy proposed optimizing resource allocation and building a new digital life, with shared wisdom, through the digitization of public services. Thus, this study is significant and urgent. To make cities smarter, safer and more convenient, local governments are adapting to the rapid development of digital technologies to carry out digital transformation in China. Compared with the intelligent development of big cities, the intelligent development of local governments is still in the foundation stage.
Digital transformation in government has attracted the attention of some scholars. Extensive research in the existing literature contributed to the understanding of the government’s digital transformation. For example, currently, primary research in construction is dedicated to the main pillars of sustainable digital and human transformation in construction [16,17,18]. However, the study of digital transformation in government is still in its initial stage and has some limitations. Interviews and case studies were used in previous research. However, there are few empirical studies on such issues to draw upon and, especially, empirical research on the drivers of local governments’ digital transformation in China is rare [19,20,21]. Thus, the main research questions of interest to this study were as follows: (1) Which driving factors push digital transformation the most? (2) What are the degrees of influence of driving factors in local governments in China? (3) How can the digital transformation of local governments be rapidly realized?
To address these questions, the technology–organization–environment (TOE) framework was drawn upon, from holistic and systemic perspectives, to propose a model with respect to the driving factors of digital transformation among local governments. Section 2 reviews relevant literature in relevant fields. Section 3 describes the TOE framework. The research model and hypotheses are described. Section 4 and Section 5 empirically estimate the relationships among the technological, environmental, and organizational contexts and the digital transformation intention, and summarizes the findings. Section 6 discusses the implications and makes a conclusion. The results of this study not only provide valuable information on the factors influencing governments’ digital transformation, but also provide suggestions for sustainable urban development, relevant not only to China.

2. Literature Review

The transformation of government caused by Information and Communication Technology (ICT) is a continuous process. The transformation of government pushed by information technology could be regarded as the first stage in the process. Following on from this, the concept of E-government gained more attention [22]. Then, governments strengthened the application of social media in different departments, which could be seen as the second stage of government transformation. At present, with the further development of digital technologies, government transformation has entered a new stage [23]. Digital technologies have changed the way people live. To satisfy the yearning of citizens for better lives and to construct and sustainably develop smart cities require further transformation of the government [14]. This study focuses on the transformation of local governments driven by digital technologies in China.
There are many definitions of digital transformation in public administration, each of which pays attention to a different aspect of digital transformation [8]. This study understands digital transformation from a digital innovation perspective. The reason for this is that digital transformation is about innovation [24], and the concept of digital transformation is often used interchangeably with the concept of digital innovation. Inspired by Luna-Reyes and Gil-Garcia [25], and given our interest in the key drivers to digital transformation in local governments, previous studies on digital transformation in government were divided into three categories. The first of these categories is technology-driven digital transformation. Scholars in this research stream emphasize that technology is the key driver in the transformation process [25]. Some researchers confirm that digital technology enables digital transformation in government [26]. Digital technology is used to respond to pressure and engage in various forms of digital governmental innovation in each stage of government evolution [27]. Previous studies found that cloud computing, big data and artificial intelligence played an important role in promoting digital government transformation [28,29].
The second category is socially-driven digital transformation. Scholars of this research stream examine how organizations use technology to respond to perceived environmental changes. They believe that organizational characteristics, contexts and institutions impact on technological applications [30]. The success of digital transformation requires fundamental changes in government processes, personnel digital literacy, policies, and leadership [19]. Previous studies found that organizational form, institutional pressures, organizational elements, managers knowing about the requisition of digital transformation and the co-production of different stakeholders significantly impacted digital transformation [20,31,32,33]. In addition, strategic digital culture, interoperability, digital skills of employees and technology procurement were success factors in digital transformation of local government [21].
The third category is social-technology driven digital transformation. Scholars of this research stream emphasize that digital transformation is driven by both social and technological factors [34]. Gong et al. studied government flexibility in approaching digital transformation and found that flexibility can be technology-enabled or policy-enabled, depending on the stage and related organizational elements [35]. Relevant suggestions for improving urban green innovation capabilities should be devised around the development of the digital economy, industrial transformation and upgrading [36]. There is also research that considers both internal and external factors affecting digital transformation. External drivers, such as technological change, environmental pressures and external legal obligations, have more significant impacts on digital transformation [19].
To sum up, previous studies on digital transformation in government could be mainly divided into three categories: (1) Socially-driven digital transformation in government; (2) Technology-driven digital transformation in government; (3) Social-technology driven digital transformation in government [31,34,37]. Research in these categories has contributed to the government’s digital transformation but there are still some limitations. There are few empirical studies on the government’s digital transformation and there is little research focused on social-technology factors. We focus on local governments’ transformation driven by social and technological factors, based on the TOE framework, exploring and studying specific driving factors.

3. Theoretical Foundation and Hypothesis

3.1. Theoretical Foundation

Tornatzky and Fleischer proposed the TOE framework to study the adoption of general technological innovations at the level of the firm. The TOE framework investigates how technological context, organizational context, and environmental context influence the process of adoption of technological innovations and their implementation in firms.
The technological context concerns both the internal and external technologies relevant to the organization. Such technologies involve those that exist within organizations and the pool of available external technologies in the market, not yet adopted. It mainly emphasizes how technological context influences the adoption process.
The organizational context is typically defined in terms of several descriptive measures, features and internal resources of the firm. This context mainly includes the organizational size and scope, managerial structure, strategies, organizational culture, and the process of communication among employees, quality of human resources and the amounts of slack resources in a firm.
The environmental context is described as the external arena where the organization conducts its business, as well as the ability to access resources offered by suppliers, and relations with the government and other firms. The environmental context includes three elements: competitive, legal and regulatory, which supply both constraints and opportunities for technological innovations [37].
With a solid theoretical basis and consistent empirical support, the TOE framework can be applied to other IS innovation domains. It was extended to innovative implementation research on public organization information systems, such as Open Government Data (OGD), in [38]. As for digital transformation, some research found specific driving factors from technological, organizational and environmental aspects [39]. On the basis of these achievements, we applied the TOE framework in an exploratory way to the field of government public administration.

3.2. Research Model and Hypothesis

3.2.1. Research Model

Based on the TOE framework, the research model (as shown in Figure 1) was developed to explain the driving factors of digital transformation among local governments. This proposed conceptual model contains two parts (interdependent variables and independent variables) with six aspects. The five independent variables include technological readiness in the category of technological context, organizational efficiency and public service delivery in the category of organizational context, and citizens’ expectations and superior pressure in the category of environmental context. The dependent variable in the conceptual model is the intention to transform.
The TOE framework was applied in order to identify specific factors important for digital transformation in the existing literature, appropriate to the governmental environment.
Firstly, technological readiness is an important measuring aspect of technological context [40]. The degree of technological readiness makes a difference to the digital transformation process. For instance, some new infrastructures created through digital technology provide the basic ingredients for an organization’s digital transformation [9]. Secondly, modern governments seek to improve operational efficiency and digital transformation is a strategic imperative for governments to achieve the goal of improving public service efficiency [35,41]. Therefore, organizational efficiency is seen as a driver in the government’s digital transformation. Thirdly, the improvement of digital technology has changed public demands, forcing governments to provide more convenient public services through advanced technology. The COVID-19 pandemic also made the public and governments aware of the importance of online services. Hence, public service delivery began to be seen as a driver. Fourthly, meeting citizens’ expectations of public administrations providing high-value and real-time digital services is asserted to be an important external reason of government digital transformation in the literature [19]. So, citizens’ expectations were added in the environmental context. Last, but not least, pressure from superior departments significantly affects the process of digital transformation. For example, after the central government issued policy documents on digital transformation, local governments were under pressure and generally tended to act on the document. Therefore, superior pressure is considered one of the key factors that should be incorporated within the environmental context.

3.2.2. Hypotheses

(1)
Technological readiness
Technological readiness consists of technological infrastructures and IT human resources [40]. Technological readiness’s importance in the organizational context was demonstrated in [42]. Technological infrastructures provide hardware support for digital transformation. For example, government portals caused changing work manners and organizational structures [25]. IT human resources are those employees with the knowledge and skills to implement the infrastructures within the government, such as employees with computer skills and IT specialists [43]. Digital technology itself is crucial to the digital transformation process [44]. A public sector with more substantial technological readiness is more likely to implement digital transformation. The viewpoints expressed above led to the following hypothesis:
Hypothesis 1 (H1):
Technological readiness has a positive effect on digital transformation among local governments.
(2)
Organizational efficiency
Organizational efficiency refers to the relationship between inputs and outputs of a given activity. Government departments are required to improve work efficiency and also have the desire to do so [45]. Improving operational efficiency is embedded as a core ethos of modern governments [43]. Previous studies showed that improving government efficiency could be achieved by the use of technologies, coordination among departments, improvement of services and so on [28,46,47]. This can be perfectly implemented through digital transformation. So, digital transformation is becoming a strategic imperative for governments to achieve the goal of improving efficiency [35]. The pursuit of improved organizational efficiency drives the government’s digital transformation. Hence, the second research hypothesis was established as:
Hypothesis 2 (H2):
Organizational efficiency has a positive effect on digital transformation among local governments.
(3)
Public service delivery
Public service delivery refers to the process that products designed and produced by public policy makers and service professionals have to be delivered to users. It includes social work, health care, education, community development and regeneration and so on [48]. Local governments play a key role in public service delivery [49]. The focus of public sector management is provision of public services that better meet the needs of consumers, especially in the rapid development of smart cities [50]. The notion of serving the public promotes governmental digital transformation. Since the rapid development of ICT, digital technology has been necessary in public service delivery [19]. In addition, the COVID-19 pandemic brought to the fore public and government awareness of the importance of online services, which acted as an accelerator for digital transformation in public service delivery [51]. In order to provide more convenient public services, digital transformation of government is essential [20]. This led to the following hypothesis:
Hypothesis 3 (H3):
Public service delivery has a positive effect on digital transformation among local governments.
(4)
Citizens’ expectations
Citizens’ expectations refer to the expectations of citizens that the government is able to deliver high value digital services. Citizens expect governments to provide efficient and effective online services by using new technology. The online services that citizens expect include, but are not limited to, the following: SDGs, protection of human rights, legal regulations to maintain responsible use of digital technology and AI, human wellbeing, the common good, societal values, climate and environmental protection, and ensured full access to new knowledge on new technologies by means of adequate education in the digital era. Expectations play a very important role in the government’s digital transformation [19]. Previous research showed that citizens’ expectations positively influenced the transformation of government and that meeting citizens’ expectations is necessary to achieve a transformed government [52]. In addition, citizens’ expectations regarding the speed and convenience of government services are continuously increasing [53]. During the lockdown period of COVID-19, digital technologies made citizens’ lives easier [14]. Citizens have further experienced the convenience of government services provided through digital technologies, such as Contact Tracing Apps and Online permits. The pandemic has led citizens to have high expectations from their government [54]. Citizens want smarter cities. This condition drives government digital transformation and led to the following hypothesis:
Hypothesis 4 (H4):
Citizens’ expectations have a positive effect on digital transformation among local governments.
(5)
Superior Pressure
Superior pressure can be defined as the pressure stemming from superior organizations. The fact that superior pressure heavily influences the decisions of administrative organizations was demonstrated in [55]. Subsidiaries are strictly required to adhere to structures and practices consistent with the policies of superior organizations. Therefore, a superior government agency that adopted digital transformation would be likely to exert pressure on its subordinates to do likewise. Responding to political pressure drives the digital transformation process [27]. China’s central government clearly put forward the strategic goal of building the digital government in the 14th Five-Year Plan, and most local governments have started to build digital government and have achieved some good results. Pressure from the central government urges the local governments to adopt digital technology and implement digital transformation. This led to the following hypothesis:
Hypothesis 5 (H5):
Superior pressure has a positive effect on digital transformation among local governments.

4. Methods and Materials

4.1. Empirical Approach

To address the objectives of this study and provide data for evaluating the research model, a questionnaire was developed and a survey was conducted. Public servants in China’s local governments were the subjects of investigation. The method of the structural equation model (SEM) was used to test the conceptual model and the associated proposed hypotheses. Details of the research methods used in this article are provided in the following sections.

4.2. Construct Operationalization

In order to ensure the reliability and validity of the variables, the measurement index of each hypothetical variable proposed in this study was developed based on existing relevant research literature. The construct operationalization in these studies was used for reference. Since this study focused on digital transformation among local governments in China, this study also took into account the actual situation in China to modify and supplement. The measurement items of the questionnaire were measured using the Likert 5-grade scale (1 = strongly disagree, 5 = strongly agree).
After the design of the questionnaire was complete, experts and scholars were invited to review the questionnaire, and the questionnaire was modified and improved according to the review suggestions. After that, a small-scale pre-survey was carried out among civil servants, and the questionnaire was modified again according to the feedback results of the respondents. Then, the valid questionnaire was determined. The final questionnaire contained 6 factors and 18 measurement items (Table 1).

4.3. Data Collection

The data was collected by questionnaire survey. Questionnaires were distributed to public servants, working in local governments, through the professional questionnaire website “So jump”. The online questionnaire survey lasted for one month and 311 electronic questionnaires were collected in total. According to the screening criteria of invalid questionnaires defined by incomplete questionnaire filling, 267 valid questionnaires were finally determined after carefully review and cleaning.

5. Results

5.1. Scale Validation

The fundamental purpose of confirmatory factor analysis (CFA) is to examine a given theory. CFA is a kind of Structural Equation Modelling (SEM). Multitrait–multimethod and CFA make fewer assumptions and provide more diagnostic information about reliability and validity [64]. CFA was conducted in this study using AMOS 22 to evaluate the adequacy of the measurement model. Scale validation involved testing its reliability, convergent validity and discriminant validity [65].

5.1.1. Validity and Reliability

First, factor analysis was employed in aspects of improvement, and assessment of tests, and scales. Using this analysis method, we could ensure the scales’ validity. Through principal components analysis with varimax rotation, a six-factor solution was performed. Factors altogether explained 74.873% of the total variance, each factor loading ranging from 0.692 to 0.904 (Table 2), which showed the significant results of factor analysis. Meanwhile, we verified the reliability of the six factors. Reliability was defined as the degree to which measures were free from error. It yielded consistent results. Cronbach’s alpha is usually used to assess reliability [66]. As shown in Table 2, six constructs in the model showed good reliability with Cronbach’s alphas, the values of which were greater than 0.70.

5.1.2. Convergent Validity

Convergent validity refers to the extent to which various construct measurement approaches yield the same results. From Table 3, items in this research had a factor loading greater than 0.50 indicating sufficient convergent validity. The construct reliability (CR) for six factors exceeded 0.8 respectively, and satisfied with the threshold. This suggested that significant convergent validity was maintained in the research model [67].

5.1.3. Discriminant Validity

Discriminant validity is the degree to which measures of different concepts are distinct. From Table 4, the square root values of average variance extracted (AVE) were above the threshold of 0.5, and higher than the correlation value between the underlying variable and other variables in the model. So, we concluded that the test for discriminant validity was satisfied. The above results were assessed using criteria suggested by Fornell and Larcker [67].

5.2. Model Test

The conceptual model was tested using structural equation modeling (SEM) as performed in AMOS 22. Related data is summarized in Table 5. The value of CMIN/DF was 1.449, which was less than 3.0. The value of RMSEA was 0.041, which was less than 0.1. The value of GFI was 0.936, which was more than 0.9. Other results of model-fit were also satisfied within the recommended values. That is, all the indices suggested a very good fit. The goodness of fit was 0.46.
The hypotheses were tested collectively by examining the significance of the relationships in the SEM model. Table 6 presents a summary of the hypotheses testing results, which shows that all hypothesized causal paths in the research model were significant.
As is shown in Table 6, all the paths were significant. Technological readiness (0.483, p = 0.000 < 0.01), organizational efficiency (0.178, p = 0.013 < 0.05), public service delivery (0.144, p = 0.036 < 0.05), citizens’ expectations (0.153, p = 0.01) and superior pressure (0.130, p = 0.03 < 0.05), respectively, had significant effects on the variable of intention to transform. All hypotheses in the research model were supported. Figure 2 shows the computational results of the research model.

6. Discussion and Conclusions

6.1. Drivers of Digital Transformation among Local Governments

The results of the study showed that technological readiness, organizational efficiency, public service delivery, citizens’ expectations, and superior pressure actively affected the intention of local governments towards digital transformation. That meant that the above factors were important drivers of the digital transformation among local governments.
Within the technological context, technological readiness was proven to be the first driver of digital government transformation. In recent years, digital technologies have developed further in the social arena. Cloud computing, blockchains, artificial intelligence, 5G and other technologies are being widely used in the private sector, as well as in the public sector. These new digital technologies have provided infrastructures and skills for government departments to implement digital transformation, affecting public sector applications, processes, culture, and structure, and civil servants’ responsibilities and tasks [18]. The digital infrastructures are easily reconfigurable and accessible [72], providing digital spaces for organizations [73]. The higher the level of technical readiness, the easier it is to implement digital transformation. In addition, investment in infrastructure and the acceleration of digital transformation are conducive to solving the social crisis resulting from public crisis events, such as COVID-19. In China, some local governments in Guangdong and Zhejiang provinces carried out digital transformation and achieved good performance.
Within the organizational context, organizational efficiency and public service delivery were both proven to be significant factors driving the digital transformation of the government. The study showed that organizational efficiency had a positive effect on digital transformation among local governments. Improving organizational efficiency is the goal that modern government pursues. Traditional governments often suffer from lack of information flow between departments, high administrative costs, and low office efficiency, which reduces citizens’ trust and is not conducive to the maintenance of government legitimacy. Digital technologies break barriers between different departments, save costs, strengthen communication and co-ordination within and between organizations, expand citizens’ participation and increase government accountability, all of which are beneficial to the improvement of business efficiency and departmental performance [74]. Digital technology meets the governments’ need to enhance organizational efficiency, which could promote digital transformation.
Public service delivery is a basic function of local governments. The development of digital technology makes it possible to provide online public services. The establishment of government websites, applications and other digital tools provide convenience for citizens in accessing public services. Furthermore, COVID-19 has made the public and governments aware of the importance of online services, accelerating digital transformation in public service delivery [51]. Public service delivery is one of the driving forces of the governments’ digital transformation. Meanwhile, digital transformation is also conducive to the governments’ provision of more convenient public services. For example, the government shares information and data in many fields with the public through websites, which is conducive to the public finding the information they need anytime and anywhere. This reflects well on the openness and transparency of the government. Quality and accurate services for the public can be provided. More public value can be created. Therefore, this is a process of interaction, to some extent.
As for the environment, the results confirmed the hypothesis that citizens’ expectations and superior pressure are driving factors of digital government transformation. With digital technologies, the needs and expectations of the public are increasingly diversified and complicated. Digital transformation outside the public sector has led to higher demands from the public, who expect governments to deliver more smart services with ICT. In addition, COVID-19 led citizens to have high expectations from their government because people deeply felt the convenience brought by digital technology during the lockdown. Citizens’ expectations drive digital government transformation. [54] The positive relationship between citizens’ expectations and digital transformation is consistent with the viewpoints of previous research [52].
Superior pressure proved to be a driver of digital transformation. Superior authorities heavily affect the decisions of subordinate administrative organizations in China. Lower governments are often required to produce documents or conduct actions implemented by superior governments. A superior government that has adopted digital transformation is likely to exert pressure on its subordinates. Responding to superior pressure drives the process of digital government transformation.

6.2. Implications for the Practice of Digital Transformation among Local Governments

The results showed that technological readiness is the most important factor driving digital transformation among local governments. Based on this finding, government departments should make full use of digital technologies to empower digital transformation. The application of new emerging technologies is the starting point but should not be considered in isolation from other intervening factors, and their possible combinations and specific characteristics [75]. Lots of practical steps are necessary. For example, providing solid technical support for digital transformation, equipping government agencies with relevant infrastructure, training civil servants’ knowledge and skills, so as to promote better implementation of digital transformation, are all necessary.
The need for functional implementation from local governments themselves is fundamental, and factors such as internal organizational efficiency and external public service delivery force local government digital transformation. Continuously improving service awareness and service performance incentives could increase the momentum of digital transformation. Interactions with other stakeholders in the environment facilitate digital transformation of government. Both increasing public digital literacy and central government formulation and implementation of digital and smart strategy could improve the digitalization process of local governments.
The proposed research model could be used to test the drivers of digital transformation in other public sectors, as well as private sectors, which would be beneficial for decision makers and practitioners in identifying specific factors and then formulating implementation strategies and guidance in digital transformation in different fields and departments.

6.3. Implications for theory about Digital Transformation among Local Governments

The digital transformation of local governments is becoming more and more obvious, but there is little research focus on the topic, especially exploring digital transformation drivers. We took the local governments’ digital transformation as the research object, reviewed the existing literature systematically and proposed five drivers scientifically. This contributes to the study of digital transformation, enriches the literature in this field and contributes to a better understanding of digital transformation logic among local governments.
Through literature review, we categorized the current research on drivers into three streams to enrich understanding of the theory: socially-driven, technology-driven and socio-technology driven digital transformation. We also provided empirical support for the digital government transformation drivers. The developed and tested research model provides the technological and social drivers of digital transformation among local governments, which is helpful in understanding digital government transformation more comprehensively from the perspective of technology, organization, and environment. This empirical study extends existing results of digital transformation among local governments and contributes to further research in this domain.

6.4. Conclusions

Digital technology is an important evolution of ICT which makes life and work more convenient for city dwellers. Digital technology and public demand have combined to promote smart cities development. Every aspect of cities is undergoing digital transformation. As an important implementer of the national strategy in China, the local government should be at the forefront of digital transformation. In order to identify the drivers of digital transformation among local governments, to support the digital transformation, we developed a conceptual model based on the TOE framework, which is more comprehensive and holistic, compared with previous research. This helps us to see the digital transformation of government from a more systematic perspective, including the influencing factors of the transformation and the impact of the transformation on other aspects, such as economy and society. In all, the research expands the application field of TOE theory.
This research considers technological readiness, organizational efficiency, public service delivery, citizens’ expectations and superior pressure as being the significant drivers of digital transformation among local governments. Different from other studies that focus on one aspect, this study reminds policymakers to recognize technological, organizational and environmental contexts that influence digital transformation in government, as a whole. Relevant measures should be guided by the systemic view, promoted in a coordinated manner, and the linkage between different factors should be considered. This would ensure sustainable urban development.
Despite these innovations, our study is still limited, mainly in terms of the methods. Although the drivers identified in this paper are infinitely close to the current situation, there is still room for improvement. Adding more variables, such as protection of human personal rights/privacy, protection of data and societal values, need to be taken into account. Typical case studies are also necessary. Exemplary experience of smart city construction in China, such as that in Shenzhen, as a role model for other smart city development around the globe, should be studied in detail. It is expected that these issues will be topics of ongoing research in the future.

Author Contributions

Conceptualization, J.X.; literature review, L.H. and H.Z.; methodology, H.Z.; Software, L.H. and H.Z.; validation, L.H. and J.X.; formal analysis, J.X., L.H. and H.Z.; investigation, J.X., L.H. and H.Z.; resources, L.H.; data curation, H.Z.; writing—original draft preparation, L.H., L.H. and H.Z.; writing—review and editing, J.X. and H.Z.; visualization, J.X.; funding acquisition, J.X. All authors have read and agreed to the published version of the manuscript.

Funding

This research was funded by the National Social Science Foundation of China (20BJY119) and the Fundamental Research Funds of the Central Universities (2022SK07).

Institutional Review Board Statement

Not applicable.

Informed Consent Statement

Informed consent was obtained from all questionnaire respondents involved in the study.

Data Availability Statement

Data that support the findings of this study are available from the corresponding author upon reasonable request.

Conflicts of Interest

The authors declare no conflict of interest.

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Sustainability 14 14980 g001 550
Figure 1. Research model.
Figure 1. Research model.
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Sustainability 14 14980 g002 550
Figure 2. Results of the research model. Abbreviations: TR, Technology readiness; OE, Organizational efficiency; PSD, Public service delivery; CE, Citizens’ expectation; SP, Superior pressure; INT, Intention to transform.
Figure 2. Results of the research model. Abbreviations: TR, Technology readiness; OE, Organizational efficiency; PSD, Public service delivery; CE, Citizens’ expectation; SP, Superior pressure; INT, Intention to transform.
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Table
Table 1. Variable measurement items.
Table 1. Variable measurement items.
VariablesMeasurement ItemsResources
Technology readiness
(TR)		TR1 The office system used by your working department can apply digital technology.Lokuge et al., 2019 [56]
Lai et al., 2016 [57]
TR2 Some colleagues in your working department have expertise in digital technology.
TR3 Some digital technology has been used in your working department.
Organizational Efficiency
(OE)		OE1 The use of digital technology can enable governments to reduce the time taken to conduct business.Chang et al., 2010 [58]
Chen et al., 2010 [59]
OE2 The use of digital technology enables governments to lower the cost of doing business.
OE3 The application of digital technology can enhance the flow of data between departments.
Public Service Delivery
(PSD)		PSD1 Using digital technology can meet the needs of the public better.Mergel et al., 2019 [19]
PSD2 The use of digital technology can increase the interaction between government and the public.
PSD3 The public can be served better by using digital technology.
Citizens’ Expectation
(CE)		CE1 The public expects government departments to use digital technology.Carlo et al., 2012 [60]
Wang et al., 2009 [61]
CE2 Meeting public expectations is one of the important factors in the use of digital technology by government departments.
CE3 The public expects government departments to respond to their demands with digital technology.
Superior Pressure
(SP)		SP1 Superior government departments require your departments to apply digital technology.Teo et al., 2003 [62]
SP2 Superior government departments require your departments to keep in touch with them through digital technology.
SP3 Superior government authorities asked your department to cooperate with them in using digital technology.
Intention to transform
(INT)		INT1 It is important for your department to achieve digital transformation as soon as possibleLiu et al., 2008 [63]
INT2 The department is implementing/planning digital transformation.
INT3 The department plans to achieve digital transformation in the future.
Table
Table 2. Results of validity and reliability.
Table 2. Results of validity and reliability.
OEINTPSDCESPTRCronbach’s Alpha
TR10.0830.237−0.0530.0380.1540.7450.720
TR20.0840.1000.1580.0110.0200.836
TR30.1530.267−0.054−0.0820.0280.773
OE10.8410.0890.208−0.0820.0380.1940.878
OE20.9040.1060.1250.0280.0410.060
OE30.8270.2620.1930.0550.0160.091
PSD10.1530.0910.8050.0530.081−0.0060.831
PSD20.2090.1190.8280.0820.0290.068
PSD30.1120.0800.8760.0510.0890.003
CE10.0090.0640.0050.888−0.0270.0010.793
CE2−0.0440.0740.1610.743−0.0170.068
CE30.0420.0250.0040.8870.036−0.096
SP10.0640.0850.082−0.0170.8670.0550.777
SP2−0.0180.1200.0620.0300.6920.093
SP30.0450.0650.038−0.0280.8990.021
INT10.0940.8090.0710.0690.1200.2800.875
INT20.1690.8510.1360.1200.1150.205
INT30.1930.8410.1320.0240.1140.166
Table
Table 3. Confirmatory factor analysis.
Table 3. Confirmatory factor analysis.
Latent VariablesObserved VariablesStandard Factor LoadingsCRAVE
Technology readiness
(TR)		TR10.6780.7620.517
TR20.696
TR30.779
Organizational efficiency
(OE)		OE10.8250.8790.709
OE20.861
OE30.839
Public service delivery
(PSD)		PSD10.7110.8350.628
PSD20.818
PSD30.843
Citizens’ expectation
(CE)		CE10.8530.8120.597
CE20.576
CE30.855
Superior pressure
(SP)		SP10.8270.7990.581
SP20.518
SP30.889
Intention to transform
(INT)		INT10.7830.8780.706
INT20.901
INT30.833
Table
Table 4. Discriminant validity.
Table 4. Discriminant validity.
AVETROEPSCESP
TR0.5170.719
OE0.7090.3520.842
PSD0.6280.120.4370.792
CE0.597−0.0430.0310.1340.773
SP0.5810.1820.1270.178−0.0150.762
Abbreviations: TR, Technology readiness; OE, Organizational efficiency; PSD, Public service delivery; CE, Citizens’ expectation; SP, Superior pressure; INT, Intention to transform; Diagonal elements are the square roots of AVE.
Table
Table 5. Overall model-fit indices for the research model.
Table 5. Overall model-fit indices for the research model.
Model-Fit IndicesResultsRecommended ValueReferences (e.g.,)
χ2/df1.419≤3.0Anderson and Gerbing (1988), Bentler (1990), Browne and Cudeck (1993), Gefen et al. (2000) [68,69,70,71]
RMSEA0.040≤0.08
GFI0.937≥0.9
AGFI0.911≥0.8
NFI0.926≥0.9
IFI0.977≥0.9
CFI0.976≥0.9
Table
Table 6. Summary of hypotheses tests.
Table 6. Summary of hypotheses tests.
HypothesesPath CoefficientsSupport for Hypotheses
H1: TR→INT0.483 ***Supported
H2: OE→INT0.178 **Supported
H3: PSD→INT0.144 **Supported
H4: CE→INT0.153 ***Supported
H5: SP→INT0.130 **Supported
** p < 0.01; *** p < 0.001. Abbreviations: TR, Technology readiness; OE, Organizational efficiency; PSD, Public service delivery; CE, Citizens’ expectation; SP, Superior pressure; INT, Intention to transform.
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Xiao, J.; Han, L.; Zhang, H. Exploring Driving Factors of Digital Transformation among Local Governments: Foundations for Smart City Construction in China. Sustainability 2022, 14, 14980. https://doi.org/10.3390/su142214980

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Xiao J, Han L, Zhang H. Exploring Driving Factors of Digital Transformation among Local Governments: Foundations for Smart City Construction in China. Sustainability. 2022; 14(22):14980. https://doi.org/10.3390/su142214980

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Xiao, Jianying, Lixin Han, and Hui Zhang. 2022. "Exploring Driving Factors of Digital Transformation among Local Governments: Foundations for Smart City Construction in China" Sustainability 14, no. 22: 14980. https://doi.org/10.3390/su142214980

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