Technical Foundation,External Environment,and Government Internet Service Capability: Evidence From China

Abstract

This study examines the influencing factors of the local government’s Internet service capability construction in China. The research framework for this study was the “technology-organization-environment” (TOE) framework. A total of 277 local governments were collected from Chinese cities and used for the data analysis. The data analysis was conducted with Stata. The results of this research demonstrated that there is a significant positive correlation between information technology talents, peer pressure, leader attachment, sustainable competitiveness, data open platform, and the government’s Internet service capability. Surprisingly, there is a negative correlation between per capita budget expenditure and the government’s Internet service capability. The aforementioned conclusions hold even after the robustness test. Discussion is held regarding how these conclusions may affect the development of the government’s Internet service capability.

Keywords

local government Internet service capability e-government digital government TOE

Introduction

The modern social environment is constantly undergoing major changes as a result of technological innovation and the expansion of the Internet infrastructure. The complex idea of national economic development, people’s patterns of employment, models of government administration, and service delivery all witness the dissolution of old forms and the formation of new ones. “The door to difficult, face ugly and difficult to do,” endless signatures, certificates, and official seals were once the painful experiences the Chinese general public had when visiting government departments. The network has features such as convenience, speed, and efficiency to make the masses of China’s traditional service mode and service efficiency of the government at all levels put forward new requirements and higher expectations. The “China Government Work Report” published in 2016 for the first time explicitly proposed to vigorously promote the “Internet plus government services” to overcome departmental data barriers and increase the government’s service capability and efficiency online. This was done to improve the efficiency of government administration and release the effectiveness of “reform to streamline administration and delegate power, improve regulation, and upgrade service.” It is also suggested that China should further develop its “Internet plus government services,” fully integrating digital information technology into the administration and services of government organizations, and further enhancing the capabilities of the government’s integrated online service platform in China’s “14th Five-Year Plan” and “Long-Range Objectives Through the Year 2035.” In the meantime, the Chinese central government’s efforts to implement “Internet plus government services” have resulted in the formulation of several guidance documents and significant deployments, demonstrating the importance placed on the local government’s capability to provide Internet service as well as the government’s desire to transform its governance model and enhance its capacity for administration and governance.

With the expansion of the Internet, academics have thoroughly investigated how it interacts with the government. To actualize the intelligent service of government affairs and create novel means of public engagement, J. Lee and Lee (2014) and Algebri et al. (2017) advocated that the government ought to utilize Internet information technology to offer intelligent interactive services for the masses. This service architecture can effectively promote data exchange and transfer (Reforgiato et al., 2016), thus laying a solid foundation for the great improvement of government administrative efficiency and the provision of high-quality government services for the public (Mohammed & Ibrahim, 2015). Meanwhile, after researching the national capacity of e-government services in South Africa, according to Mpinganjira (2015), the public’s sense of trust and satisfaction with the government plays a key role in the construction of the government’s online service capacity. In a similar vein, Alshibly and Chiong (2015) suggested that increasing public empowerment can effectively enhance e-government service capacity and effectiveness. Good governance is the intrinsic pursuit of government, and corruption is considered to be a barrier to good governance. As a result of the government’s digital transformation, e-procurement is viewed as a tool for eradicating corruption. After studying the e-procurement of the Indonesian government, Hermansyah and Yusa (2019) found that e-procurement couldn’t eliminate government corruption, generally caused by the weakness of the system to control the suppliers to arrange the bidding with each other. But can be combated by straightening rules and code of conduct and punishment for anyone who involves their self in unjust practices. Making digital government transformation a better engine of good governance. In addition, the rapid development of the Internet has also promoted the building of smart cities. Several scholars focused on the construction of smart cities in developing countries and systematically elaborated on their security, privacy, risks (Ismagilova et al., 2022), challenges (Janssen et al., 2019), and corresponding policy suggestions (Chatterjee & Kar, 2015). On this basis, Kar et al. (2019) did a preliminary exploration of the relationship between the smart city and the digital country and pointed out that the construction of both smart city and digital country can lay the groundwork for the digital transformation of government. The above studies, whether on smart government services, smart city, digital country, or good governance, provide a substantive perspective for the study of the government’s internet service capability and a successful experience for the government’s internet service capability construction in reality. Focusing on the Chinese local government’s Internet service capability, according to Tang et al. (2019), the essence of Internet service capability is the process of integrating internal and external information from the government, providing higher quality “citizen-centered” public services, and achieving seamless government-citizen interaction. Using the balanced scorecard, Liu et al. (2019) systematically evaluated the Chinese government’s Internet service capability from the perspectives of user satisfaction, informatization base, public benefit, and organizational improvement, and suggested that it should be reasonable to design the evaluation system, spurred the development of the government’s Internet service capability. Deng and Wang (2020) through case studies found that the working ability of the government’s staff will have an important impact on the Internet service capability of the Chinese government. Currently, the Chinese government’s construction of Internet service capability faces poor interdepartmental communication of information (Xie, 2019), but this can be addressed by updating the technology and improving the institutional mechanism, as well as further improving the government’s Internet service capacity (Fei et al., 2016).

While the existing research has produced fruitful results and formed a mature research paradigm, there is still scope for further extensions. First, some of the studies have been conducted from a theoretical analysis perspective, which lacks some support from the data. At the same time, the single perspective is difficult to cover completely and accurately the complexity of realistic elements. The universality of the findings obtained from the case study also requires further study. As the Internet became more popular, and WEB2.0 and 5G’s perfection developed rapidly, online government affairs service platforms are slowly becoming the mainstream communication channels of the masses and government, and their serviceability is directly related to the public’s impression of and satisfaction with government and has become a key element in the success or failure of e-government construction to some degree. Given this, this research article takes the influential factors of the government’s Internet service capability as its breakthrough point and grounded the TOE framework to construct a new general interpretive model for the government’s Internet service capability. The novelty of this study, in comparison to traditional research on the government’s Internet service capability, is primarily reflected in the following aspects. Let’s start with an original research perspective. In this study, we focus on local governments in China, make a horizontal comparison of differences in the capacity construction of Internet services among local governments, and look for the mechanism of explaining differences from the “technology-organization-environment” dimensions, and may better present Chinese local governments’ actual model of operation. In addition, the novel object of research. Constructing the government’s capacity for Internet service is an important step for the government to build public service capacity, create a service-oriented government, and promote good governance within the current digital context. On the one hand, it can enhance government transparency, social participation, and promote scientific and democratic decision-making. On the other hand, it’s also a necessary means of improving the government’s administrative efficiency and social supervision. The choice of the government’s construction of Internet service capability, a new stage in the government’s digital transformation, as the research focus of this research can systematically reflect the frontier progress and future trend in China’s e-government construction. Moreover, a novel research sample. In this study, 277 prefecture-level cities in China are selected as the research sample, which can comprehensively and systematically reflect the state of construction of the Internet service capability of local governments in China, and the findings are more universal and expandable. The results of this study can help capture the developmental status and influencing factors of the local government’s Internet service capacity in China as a whole, to provide practical experience and policy suggestions for the subsequent construction of the “Internet plus government services.” The remainder of this research article is organized into the following sections: Connotation and research framework, research design, empirical analysis result, conclusion, discussion, limitation and prospect of the study.

Connotation and Research Framework

The Connotation of Government’s Internet Service Capability

To understand the connotation of the government’s Internet service capability, we can start from the perspectives of government public service capacity and e-government service capacity. Government public service capability belongs to the category of government capability, and academic circles usually use the definition of government capability in the “1997 World Development Report: Government in a Changing World,” that is, government capability is the government’s ability to provide public goods and services (World, 1997). Existing studies have also defined e-government service capability from different perspectives, such as the satisfaction of the public (Morgeson et al., 2011), the interaction and cooperation between the government and the public (Mergel & Greeves, 2012), the degree to which the needs of users are satisfied (Schedler & Summermatter, 2007). In this research, based on the views of the above researchers, the government’s Internet service capability is defined as: Through the use of advanced information technology, the government breaks data barriers, realizes resource integration and re-engineering government affairs processes, innovation of the public service model, optimization of the government service system, and realization of the government’s ability to operate efficiently and provide high-quality government services, which primarily includes the characteristics of transparency, refinement, and intelligence.

TOE Framework

The construction of the government’s Internet service capability is not only a strategic choice for the government to voluntarily change its management model under the impact of the information technology revolution, but also the product of advanced information technology development such as big data, the Internet of Things, and cloud computing. It’s bound to be affected by the degree of regional technology development. With the emergence and rapid iteration and upgrading of information technology, a strong foundation has been laid for constructing the government’s Internet service capability, and with the continued development of Internet service capability, the fundamental role of information technology will be further highlighted. In addition, the government’s construction of Internet service capability also belongs to the government’s initiative to change the traditional model of service and the means of service, and the level of its construction capacity is also affected by the government’s factors. If there is no information technology and the government is unable to achieve efficient management and application, the government will not be able to effectively promote the construction of the government’s Internet service capability. This requires the government to have sufficient resources and to be proficient in advanced methods, and more importantly, how to improve its ability in balancing and coordinating resources and methods. The reality is that the existence and development of anything, including government, can’t exist in the absence of a certain social environment. It’s the continual input of information and requirements from the external environment that allows the entire government system to function and update itself, exporting information to the outside world, and responding to external government requirements, in this continuous “input-run-output” cycle, the government is constantly improving its capacity. In short, the government’s Internet service capability is the result of a combination of factors, and how integrating these factors into the research framework to construct a better interpretation model of the government’s Internet service capability is an urgent problem.

The Technology-Organization-Environment (TOE) framework provides the possibility for the construction of this model. The TOE framework was first proposed by Tornatzky and Fleischer (1990), initially, it was used to investigate the factors influencing organizational innovation adoption, they concluded that the adoption of organizational innovation is influenced not only by the technology of the innovation but also by organizational factors and the external environment of the organization (Tornatzky & Fleischer, 1990). After an extended period of development, the scope of application of the TOE framework has been expanded to other areas, from the original organizational innovation to e-business (Zhu et al., 2006), enterprise resource planning (Pan & Jang, 2008), customer relationship management (Hung et al., 2010), information system adoption (Li & Zuo, 2014). In recent years, the TOE framework has had a high rate of “appearance” in areas related to e-government, such as the e-government cloud (Liang et al., 2017), open innovation in the public sector (Zhang et al., 2017), the construction of government Web sites (Tan et al., 2019), and many other types of research. As can be seen, the TOE framework can have strong applicability and operability across different studies due to the consideration of both internal and external organizational factors and the addition of technical ones. In this research study, the TOE framework is used to analyze the influencing factors of the government’s Internet service capability and to make practical policy recommendations.

Research Design

Research Model

Figure 1 illustrates the research model for this study. The independent variables are designed to be Internet foundation, IT talent, government data open platform, innovation culture, leader attachment, sustainable competitiveness, peer pressure, and financial capacity. It’s anticipated that they will have a significant impact on the government’s Internet service capability.

Figure 1.

Research model.

Research Hypothesis

This article will attempt to explore the following research hypotheses based on the research model illustrated in Figure 1.

Technical Dimension

Internet Foundation

The Internet service capability of the government itself is a product of updates and iterations of the technology, and the degree of development and the level of technology is the basis of government provision of Internet services. In addition, the technological capacity possessed by the government will also critically affect its level of Internet services (Mergel & Greeves, 2012; Tolbert et al., 2008). Studies have shown that there is a significant positive correlation between the technical capability of the Internet and the level of e-government services (Sachan et al., 2018), and the proportion of households that use the Internet in the region will affect the government’s measure and means of governance (Hong & Lee, 2018). Other studies also showed that the rate of Internet penetration played a significant role in promoting e-government development and enhancing the government’s public service capability (Asogwa, 2013). In general terms, the better the Internet foundation, the more the government is inclined to change its style of governance and ways of improving its Internet service capability. Meanwhile, the better the Internet foundation, the more useful it is for improving the government’s Internet service capability. Based on this, H1 was therefore proposed:

Hypothesis 1 (H1): The Internet foundation significantly impacts the government’s Internet service capability.

IT Talent

To provide a high level of Internet service, in addition to the Internet foundation, the government needs to invest a large number of information technology talent with Internet expertise to provide professional guidance in the construction and operation of government websites. Mata and Barney (1995) proposed that professional talent in information technology can provide a significant level of performance for the organization. With the rapid development and popularity of big data, the Internet of Things, and 5G technology, the government has been forced to change its traditional services and promote the government to establish a new model of smart, intelligent, and integrated government online services. This process moves from technology innovation to platform construction and business process reengineering and daily maintenance, each stage requires the knowledge of high-end technical staff to provide professional guidance and to act as a think tank for the construction of the government’s Internet service capability. Ross and Beath (1996) pointed out that human resources are the core factor in the construction of e-government. In reality, the lack of professional information technology talent has led to the slow progress or even stagnation of the government’s Internet construction and service capability enhancement. On this basis, H2 was proposed:

Hypothesis 2 (H2): IT talent significantly impacts the government’s Internet service capability.

Government Data Open Platform

As the government generates and saves massive data and is closely linked to the daily production and life of the public (Zheng, 2015), the government needs to integrate data from a variety of departments to form a large database of government in the process of constructing Internet service capability. Database construction is a crucial problem to be solved in the process of government opening (Sayogo & Yuli, 2018), and big data is the basis of the government’s Internet service, while data open is the foundation of big data (John et al., 2014). One of the core features of the government’s Internet service capability is to achieve a high degree of integration of internal and external data resources of the government and to achieve such integration requires the government to establish a unified open data platform, and use this to realize the interconnection with the public (McBride et al., 2019), to enhance the government’s capacity and level of serving the public on the Internet. The transparency, efficiency, and convenience provided by data openness are in line with the requirements of the government’s Internet service, and the degree of openness of government data has become a significant influencing factor on the government’s Internet service capability (Maxat, 2018). On this basis, H3 was proposed:

Hypothesis 3 (H3): Government data open platform significantly impacts the government’s Internet service capability.

Organizational Dimension

Innovation Culture

Innovation is the primary force guiding development, innovation has become a prominent sign of a city’s competitiveness but also a major driving force for urban development (Barney, 1986; Barney & Clark, 2007). A city’s innovation culture supports the transformation and upgrading of the city as well as the iterative updating of governance means, the government’s Internet services as a form of government service model innovation, represent the future direction of government development and is also closely linked to the city’s innovation culture. In cities with a good innovation culture, the innovation environment tends to be relatively complete, with high innovation capacity, innovation efficiency, and conversion rate of innovation achievement (Jones & Davis, 2000), laying the foundation for the innovation of the government service model. Others have pointed out that the culture of innovation affects the government’s innovation consciousness to a great extent (Walker, 1969), governments with a strong sense of innovativeness will actively explore reform and dare to adopt new technologies and equipment to promote the networked and intelligent upgrading of their service models (Yuan & Pan, 2017). Accordingly, H4 was proposed:

Hypothesis 4 (H4): Innovation culture significantly impacts the government’s Internet service capability.

Leader Attachment

Government agencies are faced with a wide range of tasks within their jurisdiction, what tasks can “stand out” and attract high levels of attention from leaders, and what matters can be placed on the agenda and resolved quickly. In China’s hierarchical system, leadership attention is a scarce resource, and paying attention to lots of business is neither possible nor realistic, leaders will allocate only a limited amount of attention to a few crucial tasks (Akerlof & George, 1991), which can lead to something that places a premium on strengthening the longitudinal control of the subordinates, thereby assuring the superior policy to be implemented effectively, ensuring that the policy of “does not out of shape,” reducing the deviation from policy implementation (S. Chen et al., 2019; Larsen-Price, 2012). If the construction of the government’s Internet service capability is highly valued by the leaders, dozens of resources will be invested by the government to promote this implementation of the project and achieve the practical effect, thus improving the capacity of the government’s Internet service capability. Hence, H5 was proposed:

Hypothesis 5 (H5): Leader attachment significantly impacts the government’s Internet service capability.

Sustainable Competitiveness

The importance of sustainable urban competitiveness is becoming increasingly prominent with economic development and social progress. Sustainability of competitiveness means that cities seek to optimize the system while developing their strengths (NPF, 2020), to meet the citizens’ continually increasing demand for social services. The government’s original intent in constructing Internet service capability is to meet citizens’ higher expectations of government services in the Internet era and to force the government to seek reform with the demands of the citizenry. The sustainable competitiveness of a city covers the developmental status of many aspects of society and has a significant impact on the construction of the government’s Internet service capability, the greater the sustainable competitiveness, the greater the vitality and system optimization of the city, which offers a good environment for constructing the government’s Internet service capability. Meanwhile, the higher the level of sustainable competitiveness, the more complex and advanced the citizens’ demand for social services, which may require the government to improve its Internet service capability to a certain extent. Consequently, H6 was proposed:

Hypothesis 6 (H6): Sustainable competitiveness significantly impacts the government’s Internet service capability.

Environmental Dimension

Peer Pressure

Beyond these factors, the government’s behavior is also influenced by the behavior of peer governments, which are filled with the competition due to opposing interests, and this type of competition has a large promotive effect on improving government behavior (C. P. Lee et al., 2011). To seize the initiative in the bitter competition for scarce resources and increase the “chip” for their promotion, governments will rapidly take actions similar to those of their competitors (Jun & Weare, 2011). Furthermore, to effectively reduce the cost of risk and improve the success rate of their actions, governments are prone to learn from the successful experience of other governments as they innovate and reform (Volden et al., 2008). In the construction process of the government’s Internet service capability, there is also a tendency to learn from the rich experienced, and high-level government, this peer pressure can help complement each other among the governments (Ling et al., 2018), improving the Internet service capability of the whole region. In addition, it has also been pointed out that, geographical proximity is an important consideration for the government when selecting targets from which to learn (Karch, 2007). Consequently, H7 was proposed:

Hypothesis 7 (H7): Peer pressure significantly impacts the government’s Internet service capability.

Financial Capacity

The economic base determines the superstructure, as the innovation and modernization of the government service model in the information age, to build the government’s Internet service capability, the amount of financial support is needed for the purchase of information equipment and the construction, maintenance, and operation of government websites. Existing studies have highlighted that there is a positive correlation between regional economic development and the capability of the government to provide Internet service (Ahn, 2011; Ahn & Bretschneider, 2011; Y. C. Chen & Thurmaier, 2008), and the degree of economic development at the regional is the major factor affecting the level of e-government development (Coursey & Norris, 2008). Accordingly, H8 was proposed:

Hypothesis 8 (H8): Financial capacity significantly impacts the government’s Internet service capability.

Research Data

Sample Selection

Prefecture-level governments (including municipalities directly under the central government) in China as the research sample of this article for the following reasons: Firstly, compared to the provincial-level Internet service capability as the object of research, it is easier to meet the needs of statistical analysis and reduce the error caused by covariance by having a larger sample size of studies at the prefecture level. Secondly, compared to county-level governments, data statistics for prefecture-level governments are more complete and precise, and most databases take cities at the prefecture level as statistical samples, making data more readily available and facilitating the study. Thirdly, the construction of the Internet service capability of prefecture-level governments is more comprehensive, which is convenient for comparison among them. In addition, prefecture-level governments are also central to the bureaucratic system, and building their Internet service capability acts as a bridge between the provinces and the counties. In the absence of statistical data, to ensure data integrity, continuity, and accuracy and reduce error, 277 prefecture-level cities are ultimately selected as the research sample for this study.

The Dependent Variable

For this study, the Chinese local government’s Internet service capability is chosen as the dependent variable, and the relevant data are taken from the “Report on the Development of Internet Service Capability of Chinese Local Governments (2021).” The report relied on the “Government Internet Service Capability Big Data Monitoring Platform” to gather the full amount of data on the “multiple networks and multiple ends” of municipal governments at the prefecture level to comprehensively assess the Internet service capability of Chinese local governments. There is a wide variation in the Internet service capability across the assessed samples: 10% of prefecture-level governments are in the lead development category (85.39–100 points), 54% are in the active development category (73.41–83.59 points), 32% are in the stable development category (63.79–73.41 points), and 4% are in the urgent development category (0–63.79 points).

The Independent Variable

To account for the causal mechanism, data for the independent variables in this study is drawn from the 2020 statistical values, and independent variables are broken down into three dimensions: Technology, organization, and environment.

The technical dimension includes Internet foundation, information technology talent, and open data platform. One of these is the Internet foundation, which is measured by the number of Internet broadband access users/the number of permanent residents at the end of the fiscal year, and the data is taken from the “China City Statistical Yearbook 2020.” IT talent is measured by the logarithm of the number of people involved in the transmission of information, computer services, and the software industry, and the data is obtained from the “China City Statistical Yearbook 2020.” The open platform for the data is measured by the “Chinese Local Government Data Open Report” issued jointly by Fudan University and the National Institute of Data and Chinese Research of the State Information Center, and the dummy variable is set, if the government has set up a unified open data platform, the value was assigned as “1,” otherwise, the value was assigned as “0.”

The organizational dimension includes the culture of innovation, leader attachment, and sustainable competitiveness of the city. To measure innovation culture, we use the log of the number of patents issued by China’s central government, the information is gained from the “China City Statistical Yearbook 2020 .” The standard of measurement for the degree of leader attachment is based on the following criteria: If the “first leader” or “second leader” of the city serves as the leader of the government’s “Internet plus government services” leader group, the value is assigned as “1,” otherwise, the value is assigned as “0,” and the messages are gained from the websites of all local governments. The sustainable competitiveness of cities is measured by the score of “Sustainable Competitiveness of Chinese Cities” in the “China Urban Competitiveness Report No.18” released by the Research Center for Cities and Competitiveness of the Chinese Academy of Social Sciences.

The environmental dimension includes peer pressure and financial capacity. Existing research suggests that peer pressure is measured by the mean value of Internet service capability of other cities in the province at the prefecture level (Ling et al., 2018), and the data is gathered from the “Report on the Development of Internet Service Capability of Chinese Local Governments (2021).” The specific formula for the calculation is:

peer_{capacity}_{- m}^{s} = \frac{\sum_{N^{s}} capacit y^{s} - {capacity}_{m}^{s}}{N^{s} - 1}

( $peer_{capacity}_{- m}^{s}$ denotes the mean value of the governmental Interment service capability of all cities in province s at the prefecture-level except for prefecture m. $\sum_{N^{s}} capacit y^{s}$ denotes the sum of the Internet service capability of all municipal governments at the prefecture-level within province s. ${capacity}_{m}^{s}$ denotes the Internet service capability of the prefecture-level government m within province s. $N^{s}$ denotes the number of all municipalities at the prefecture level in province s). It should be noted that due to the particular nature of municipalities directly under the Chinese central government, their peer pressure is measured in terms of their Internet service capability score. This research measures financial capacity as the log of general public budget spending per capita of cities. The specific method of calculation is: Urban public budget expenditure/urban resident population. The information is taken from the “China City Statistical Yearbook 2020.”

The Control Variable

To mitigate the bias caused by omitted variables, we make the city level a control variable in the statistical model based on existing research. The specific operation is as follows: If the city is a municipality directly under the Chinese central government, a capital city of the province, or a sub-provincial city, the value is given as “1,” the rest are given the value “0.” Details of the research variables are shown in Table 1.

Table 1.

Research Variable.

		Variable	Measuring way
Dependent Variable		Government Internet service capability	Government Internet service capability score
Independent Variable	Technological Dimension	Internet foundation	Internet broadband access users / resident population
		IT talent	The logarithm of the number of staff engaged in the information transmission, computer services, and software industry
		Government data open platform	A unified open data platform assigns"1″, otherwise assigns ”0″
	Organizational Dimension	Innovation culture	The logarithm of the number of city patents
		Leader attachment	The “No.1” or “No.2” as the “Internet plus government service” group leader is assigned “1,” otherwise assigned “0”
		Sustainable competitiveness	Urban sustainable competitiveness score
	Environmental Dimension	Peer pressure	The average value of the Internet service capability of other prefecture-level cities in the province
	Environmental Dimension	Financial capacity	The logarithm of ( public budget expenditure /resident population )
Control Variable		City level	Municipalities directly under the Chinese central government, provincial capitals, and sub-provincial cities are assigned “1,” others are assigned “0.”

Empirical Analysis Result

Econometric Model

Based on the above theoretical analysis and the proposed research hypothesis, this research constructs the following econometric model to explore the relevant factors affecting the Internet service capability of Chinese local governments on the basis of the relevant data collected from 277 cities at the prefecture level. And then offers practical policy suggestions for improving the local government’s Internet service capability.

\begin{matrix} capabilit y_{m} = β_{0} + β_{1} foundatio n_{m} + β_{2} labo r_{m} \\ + β_{3} ope n_{m} + β_{4} innovatio n_{m} + β_{5} leade r_{m} + \\ β_{6} sustainabl e_{m} + β_{7} pee r_{-} capabilit y_{- m} + β_{8} financ e_{m} \\ + β_{9} leve l_{m} + ε_{m} \end{matrix}

(Where m stands for the city at the prefecture level. The $capabilit y_{m}$ represents the Internet service capability of city m at the prefecture level. The foundation, labor, and open respectively represent the Internet foundation, talents in information technology, and the construction of open data platform. The innovation, leader, and sustainable represent the culture of innovation, leader attachment, and sustainable competitiveness of cities at the prefecture level. The $pee r_{-} capabilit y_{- m}$ denotes the mean value of Internet service capability of the other prefecture-level governments in the province except for m. The finance refers to the financial capacity of the region. The level indicates the city level. The ε denotes the random perturbation term.)

Descriptive Statistics and Correlation Analysis

Descriptive Statistics

According to the results of the descriptive statistics (see Table 2), the average score of the government’s Internet service capability of 277 cities in China at the prefecture level is 76.4, with a maximum score of 89.71, a minimum score of 55.65, and a standard deviation of 6.543. These results indicate that there are clear disparities in Internet service capability across governments and extremely imbalanced regional development. Globally, the Internet service capability of China’s local governments still has a significant amount of development space. At the same time, the difference between statistical samples indicates that the observed data have some degree of scatter, which is observationally convenient and amenable to the following statistical analysis.

Table 2.

Descriptive Statistics.

Variable	Obs	Mean	Std. Dev	Min	Max
Capability	277	76.401	6.543	55.65	89.71
Foundation	277	0.334	0.116	0.139	1.242
Labor	277	8.418	1.171	6.151	13.664
Open	277	0.415	0.494	0	1
Innovation	273	7.859	1.568	2.303	12.023
Leader	277	0.069	0.253	0	1
Sustainable	277	0.251	0.144	0	0.901
Peer	277	76.349	5.012	58.033	89.71
Finance	277	9.357	0.301	8.743	10.429
Level	277	0.123	0.329	0	1

Correlation Analysis

As shown in the correlation analysis (see Table 3), the correlation between the government’s Internet service capability and two independent variables of Internet foundation and financial capacity, the relationship doesn’t reach the significance level, which is a preliminary indication that the relevant hypothesis may not hold, then further regression analysis is required to verify this. The remaining independent variables are all significant at the 1% level, which is appropriate for regression analysis. Specifically, the correlation coefficients for innovation culture (r = .502, p < .01), sustainable competitiveness (r = .542, p < .01), and peer pressure (r = .689, p < .01) were all found to be greater than .5, indicating that there is a strong positive correlation between government’s Internet service capability and these variables, however, the degree of influence on the dependent variable requires further testing subsequently. Meantime, to avoid errors caused by multicollinearity in the regression results, the test for multicollinearity was carried out further in this study. In academic circles, multicollinearity is commonly referred to as the diagnostic criteria proposed by Hair et al. (1995). That is, when the Variance Inflation Factor (VIF) value of the independent variable is less than 10 and the Tolerance value is greater than 0.1, then it can be considered that there is no problem of collinearity in the data. As can be seen from the results (see Table 4), the VIF values for the independent variables in this study are all less than 10, and the values of Tolerance are all greater than 0.1, indicating that multicollinearity does not exist among the independent variables in this study and subsequent regression can be carried out.

Table 3.

Correlation Analysis.

Variables	1	2	3	4	5	6	7	8	9	10
Capability	1.000
Foundation	0.027	1.000
Labor	0.459***	0.105*	1.000
Open	0.446***	0.125**	0.255***	1.000
Innovation	0.502***	0.175***	0.745***	0.411***	1.000
Leader	0.235***	−0.035	0.174***	0.032	0.162***	1.000
Sustainable	0.542***	0.200***	0.838***	0.384***	0.857***	0.150**	1.000
Peer	0.689***	0.029	0.277***	0.485***	0.471***	0.239***	0.430***	1.000
Finance	−0.089	0.192***	0.170***	0.041	−0.037	−0.007	0.146**	−0.129**	1.000
Level	0.325***	0.187***	0.761***	0.131**	0.472***	0.073	0.603***	0.051	0.245***	1.000

p<.1. **p<.05. ***p<.01.

Table 4.

Collinearity Test.

Variables	Foundation	Labor	Open	Innovation	Leader	Sustainable	Peer	Finance	Level
VIF	1.140	5.770	1.430	4.760	1.100	6.360	1.670	1.270	2.720
Tolerance	0.877	0.173	0.699	0.210	0.912	0.157	0.597	0.790	0.367

Regression Analysis

The results of the regression analysis are shown in Table 5. Specifically, Model 1 regresses three variables of the technical dimension. Model 2 includes the organizational dimension variables. Model 3 is the regression results of the environmental dimension variables. Model 4 is a combination of the variables of the technical dimension and organizational dimension. Model 5 includes Model 1 and Model 3. Model 6 consists of Model 2 and Model 3. All variables are regressed in Model 7. Based on the regression results (Table 5), all models passed significance testing (p < .01), and the R² for the total regression model rose to .574 after adjustment, which is greater than the value of any of the sub-regression models, and was able to explain 57.4% of the variation in the data, indicating that the goodness of fit of the model is great, reflects that the research model constructed in this research has strong explanatory power for the government’s Internet service capability. As well as reflecting the side that the government’s Internet service capability is a result of the overall effect of multiple factors. With respect to each variable, each regressor will appear four times in all seven regression models, to avoid errors in the final results caused by factors such as spurious regression and contingency in the data, and to strengthen the robustness of the interpretation of the results, based on existing research (Han & Wu, 2019), the explanatory variable in this article can only be considered to have a significant effect on the dependent variable if it passes the test of significance two or more times in all four models.

Table 5.

Regression Results.

Variables	Model 1	Model 2	Model 3	Model 4	Model 5	Model 6	Model 7
Foundation	−3.205 (2.814)			−4.946* (2.746)	−1.149 (2.311)		−2.57 (2.766)
Labor	2.087*** (0.287)			0.256 0.499	1.636*** (0.241)		0.152 (0.496)
Open	4.749*** (0.683)			3.737*** (0.701)	1.492** (0.619)		1.569** (0.659)
Innovation		0.592 (0.406)		0.191 0.396		−0.52 (0.357)	−0.632* (0.354)
Leader		3.892*** (1.303)		3.967*** (1.247)		1.618 (1.079)	1.678** (0.759)
Sustainable		17.618*** (4.414)		15.522*** (5.272)		18.929*** (3.885)	11.796** (4.585)
Peer			0.9*** (0.058)		0.711*** (0.062)	0.708*** (0.063)	0.711*** (0.068)
Finance			0.008 (0.96)		−1.496 (0.916)	−1.739* (0.967)	−2.396** (1.008)
Level							4.101*** (1.064)
_cons	57.932*** (2.485)	67.137*** (2.328)	7.652 (10.502)	68.732*** (3.965)	22.088** (10.128)	37.843*** (11.135)	44.887*** (11.417)
N	277	273	277	273	277	273	273
F	44.74***	41.44***	123.83***	28.07***	70.54***	65.43***	54.91***
Adj.R²	.322	.309	.471	.384	.558	.542	.574

p<.1. **p<.05. ***p<.01.

Technical Dimension

The Internet foundation under the technology dimension only passed the 10% significance test in Model 4, indicating that the impact of the Internet foundation on the government’s Internet service capability is not significant, that is, H1 did not pass. In Model 1 and Model 5, the significance of talent in information technology reached the level of 1%, indicating that the greater the number of information technology talent in the city, the more conducive to constructing the government’s Internet service capability, and H2 was verified to pass. The data open platform passed the significance test at the 1% level in Model 1, and Model 4, and passed the significance test at the 5% level in Model 5 and Model 7, indicating that the construction of the government’s data open platform has a significant impact on the government’s Internet service capability, so H3 passed.

Organizational Dimension

The innovation culture under the organizational dimension only passed the 10% test of significance in Model 7, indicating that the city’s innovation culture doesn’t have a significant impact on the government’s Internet service capability, so H4 didn’t pass. Leader attachment passed the 1% significance test in Model 2 and Model 4 and passed the 5% significance test in Model 7, indicating that leader attachment has a significant impact on the construction of the government’s Internet service capability, namely, H5 passed. In terms of the sustainable competitiveness of the city, it passed the 1% significance test in Model 2, Model4, and Model 6, and passed the 5% significance test in Model 7, which indicated that the sustainable competitiveness of the city has a significant impact on the construction of the government’s Internet service capability, so H6 passed.

Environmental Dimension

Under the environmental dimension, peer pressure achieves significance at the 1% level in Models 3, 5, 6, and 7, indicating that competitive pressure between local governments may foster the development of the government’s Internet service capability, hence, H7 passed. The financial capacity passes the 5% significance test in Model 7, and the 10% significance test in Model 6, but it should be noted that the regression coefficient is negative, contrary to the hypothesis, so H8 doesn’t hold. Possible explanations, we suggest, include primarily: First, the existence of sparsely populated prefecture-level cities, but the budget expenditure per capita is comparable to that of densely populated cities, such as Ordos, Karamay, Ulanqab, and other cities comparable the spending on the headcount of Beijing, Shanghai, Shenzhen, but the Internet service capability of these governments is far from one another. Second, a large budget expenditure ≠ of a large proportion of the investment in constructing the Internet service capability, government’s budget spending should be comprehensively considered and planned, and it could end up with less capital investment in the construction of the government’s Internet service capability.

The control variable city level passes the 1% significance test. In summary, the regression test shows that hypothesis 2, hypothesis 3, hypothesis 5, hypothesis 6, and hypothesis 7 pass, while hypothesis 1, hypothesis 4, and hypothesis 8 fail.

Robustness Test

As a further check on the reliability of the regression results in the present research, the robustness test was conducted on the original basis. Specifically, the operation consists of excluding four municipalities directly under the central government of China, namely Beijing, Shanghai, Tianjin, and Chongqing, from the initial regression samples. The reason is that, due to its peculiarity, municipalities directly under the central government of China have natural advantages concerning policy inclination and resource allocation, and inevitably take the leading position in building the government’s Internet service capability, and occupy a dominant horizontal position relative to other prefecture-level cities in China. Putting them in the regression samples for research may affect the overall results to some degree, thereby leading to deviations between research findings and the actual situation. Given this, to ensure consistency between research findings and the actual situation, as a robustness check, we excluded data from 4 municipalities that were directly under China’s central government in the robustness testing process.

The results of the robustness test show that (see Table 6), R² is adjusted from .574 to .578, the remaining results are in line with the original regression results, and there are no substantial changes, namely, hypothesis 2, hypothesis 3, hypothesis 5, hypothesis 6, and hypothesis 7 pass, while hypothesis 1, hypothesis 4, and hypothesis 8 fail. Thus, the findings of this paper can be considered robust.

Table 6.

Robustness Test.

Variables	Model 1	Model 2	Model 3	Model 4	Model 5	Model 6	Model 7
Foundation	−3.307 (2.837)			−5.109* (2.762)	−1.425 (2.331)		−2.808 (2.704)
Labor			2.044*** (0.316)	0.288 (0.516)	1.757*** (0.256)		0.315 (0.509)
Open	4.797*** (0.693)			3.811*** (0.712)	1.355** (0.635)		1.44** (0.684)
Innovation		0.558 (0.413)		0.115 (0.404)		−0.53 (0.361)	−0.64* (0.364)
Leader		4.046*** (1.372)		4.271*** (1.308)		1.99* (1.129)	2.066*** (0.778)
Sustainable		18.412*** (4.586)		16.287*** (5.343)		19.563*** (3.938)	11.571** (4.689)
Peer			0.891*** (0.061)		0.736*** (0.065)	0.719*** (0.064)	0.736*** (0.071)
Finance			−0.167 (1.048)		−0.983 (0.987)	−1.288 (1.034)	−1.781* (1.074)
Level							4.078*** (1.081)
_cons	58.286*** (2.693)	67.218*** (2.356)	9.919 (11.77)	68.888*** (4.077)	14.604 (11.435)	32.699*** (11.926)	36.116*** (12.67)
N	273	269	273	269	273	269	269
F	39.73***	36.72***	113.18***	25.55***	66.12***	61.12***	55.33***
Adj.R²	.299	.294	.456	.369	.553	.537	.578

p<.1. **p<.05. ***p<.01.

Conclusion

Research Findings

In this research, 277 prefecture-level cities across China are used as the research sample, and the TOE framework is constructed to look for influential factors of local government’s Internet service capability within the dimensions of technology, organization, and environment. The main research findings include: The first is that the environmental dimension has the strongest explanation for the government’s Internet service capability, with the technical dimension being the second strongest, and the explanation of the organizational dimension being minimal. Second, under the technical dimension, information technology talent and unified open data platforms have a great role to play in promoting the construction of the government’s Internet service capability, open data platforms are more influential, and there is no significant relationship between the foundation of the Internet and the government’s Internet service capability. Third, under the organizational dimension, leader attachment and sustainable competitiveness of the city positively influence the government’s capability to provide Internet service, although there is no significant influence of the city’s innovation culture on the ability of the government to provide Internet service. Fourth, peer pressure in the environmental dimension has a positive impact on the government’s Internet service capability. It is surprising that there is a negative relationship between financial capacity and the government’s Internet service capability. In addition, the city level is a major driving force in improving the government’s Internet service capability.

Policy Recommendation

This study advances the following policy recommendations based on the research findings. First and foremost, there’s a need to increase investment in information technology talent. Constructing the government’s Internet service capability is expected to rely heavily on advanced information technologies such as big data, the Internet of Things, and blockchain, which are necessary for professional talent in information technology to be manipulated. Increasingly, high-tech talent is becoming a significant influence on the vitality and competitiveness of cities, talent in information technology can use cutting-edge technology in the process of constructing the government’s Internet service capability, as a think tank for daily operation, bug fixing, updating, and iteration of government websites. Governments should improve the salaries of information technology talent to improve the attractiveness of jobs and provide sufficient labor guarantee for the construction of the government’s Internet service capability. The governments should improve the salary of information technology talent to improve job attractiveness and provide sufficient manpower guarantee for the construction of the government’s Internet service capability.

Second, the construction of a unified open data platform should be accelerated to achieve departmental linkage and data integration, so that a practical, fast, efficient, and transparent open data platform lays a solid foundation for enhancing the government’s Internet service capability. In addition, we should also place a high value on the role of organizational leaders, in terms of the regions where the government’s capacity for Internet service lags, “first hand” should be an “immersive” view of this work as the work of the center, increasing the proportion of budget spending, establishing a long-term resource input and evaluation feedback mechanism, to promote the construction of the government’s Internet service capability made great strides. Besides, the governments should pay attention to the city’s sustainable competitiveness, and make ongoing efforts in various areas related to the sustainable competitiveness of the city, providing a good organizational environment for constructing the government’s Internet service capability. Finally, the results of the research show that peer pressure is a key factor affecting the government’s Internet service capability, therefore prefecture-level governments should learn more from the successful experience of peer-level governments, increasing the success rate and reducing the cost of trial-and-error, to achieve the goal of improving the Internet service capability of the entire provincial governments while developing the Internet service capability of the municipal governments.

Discussion

This research article has examined the influence of Internet foundation, information technology talent, data open platform, innovation culture, leader attachment, sustainable competitiveness, peer pressure, and financial capacity on the government’s Internet service capability. The results have demonstrated that the Internet foundation had no significant effect on the government’s Internet service capability. This finding seems surprising and is inconsistent with the result findings that suggest that Internet foundation was a significant influence factor on the government’s Internet service capability (Brown et al., 2017; Y. Chen & Hsieh, 2009; Forero, 2013; Katsonis & Botros, 2015; Mensah, 2019; Savoldelli et al., 2014). Such a finding might mean that, in the context of the study sample, the Internet foundation may not be an influential factor in the government’s capability to provide Internet service. This may be due to two factors: Firstly, the Internet foundation in this study reflects more on the level of informatization of society and family, although the government’s capability to provide Internet service requires the level of informatization of the government. The informatization level of society and family can represent the informatization level of government to some extent, but they can’t be completely equivalent. Secondly, it may be that Internet foundation is measured by the number of users /year-end resident population of broadband Internet access, leading to some areas having less broadband Internet access but with a smaller resident population, which gives the impression that the Internet foundation in these areas is on par with that in places with high levels of Internet broadband access, but it’s a far cry from that. Overall, as the Internet continues to be popularized, the influence of broadband Internet access to the home on the government’s Internet service capability is gradually decreasing. To further improve their Internet service capabilities, governments should constantly upgrade their Internet infrastructure, accelerate the pace of upgrading information equipment, reform traditional models of governance, and improve the capabilities of integrated online services, rather than increasing the number of users of broadband Internet access.

Furthermore, IT talent and government data open platform were found to significantly influence the government’s Internet service capability. These findings were consistent with the existing findings which suggest that It talent and data open platforms both have a positive influence on the government’s capability to provide Internet service (Luk, 2009; McDaniel, 2005; Prybutok et al., 2008; Teresa & Djoko, 2014; Weerakkody et al., 2017; Wirtz et al., 2016; Yang & Wu, 2016). The government’s digital transformation is primarily based on the extensive application of information technology within the government and the open sharing of government data. Compared with the traditional service model, the government’s online Internet service model will generate massive data. To further enhance the government’s Internet service capability, it’s necessary to open up the massive data mastered by the government departments to society, so as to promote the interaction between the government and the masses. The process of opening government data, building an open platform, protecting significant privacy, and daily operation and maintenance, all require professional IT talent to provide intelligence and technical support, guarantee continuous data openness, and provide support for the construction of the government’s Internet service capability. It’s impossible to separate the construction of open government data platforms from the information technology talent’s professional orientation, and the positive interaction between them promotes the government’s capability to provide Internet service at a higher level.

Additionally, the results have illustrated that both leader attachment and peer pressure have a significant positive influence on the government’s Internet service capability. In the process of Internet service capability construction by governments, leader attachment can able to mobilize human, material, and technical resources quickly in a short period, and promote progress in constructing the government’s Internet service capability construction with strong political oppression (Lauwereynsj, 2010; Mortensen & Green-Pedersen, 2015). If, instead, leaders treat it only as a routine administrative business in this process, full cooperation among various departments in China’s bureaucratic system is often difficult to achieve, and there is the limited mobilization of various resources. Resulting in the government’s construction of Internet service capability being shelved. Peer pressure can also force leaders to place a high value on constructing the government’s Internet service capability, taking actions similar to or even more complete than those of governments of the same level, resulting in a policy convergence effect, to win the promotion competition (C. P. Lee et al., 2011). Besides, the greater the peer pressure on local government’s construction of Internet service capability, the more attention government leaders pay to compete for critical resources like capital and technology. Meantime, the more successful experiences can be used as a reference, the lower the risk and error rate, and the more conducive to constructing the government’s Internet service capability.

It is interesting to note that the results showed that there is a negative correlation between per capita budget expenditure and the government’s Internet service capability, which is inconsistent with other studies which indicated that financial capacity has a significant positive influence on the government’s capability to provide Internet service (Ahn, 2011; Coursey & Norris, 2008). One possible explanation is that China has a large land area and a very patchy population distribution, and areas with high fiscal outlays may have lower per capita fiscal outlays than areas with low fiscal outlays due to large populations. On the other hand, the government’s Internet service capability for cities with large budget outlays far outstrips that of cities with small budget outlays, which results in a negative correlation between budget spending per capita and the government’s Internet service capability. This also suggests, however, that governments simply increasing budget spending may have little effect on the construction of Internet service capability. Increasing the proportion of spending on constructing the Internet service capability is more important. Only in this way can the government’s Internet service capability be effectively improved.

Theoretical Implication

The theoretical implications of the results of this research are as follows: First, Internet foundation, innovation culture, and financial capacity do not have a significant positive influence on the government’s Internet service capability. Second, this research finds for the first time that per capita budgetary expenditure will have a negative impact on the government’s Internet service capability, which enriches the existing research findings and broadens the research field of government’s digital transformation. Beyond this, the findings have shown that the current research model can account for approximately 57.4% of the reason affecting the construction of the government’s Internet service capability. This was an improvement of the original “socialization-technology-government” model that accounted for approximately 25.4% of the influencing factors (Han & Wu, 2019).

Practical Implications

It is found that the government’s Internet service capability is affected by various factors, which suggests that the local governments should focus on the key task of constructing Internet service capability and coordinate and make good use of the resources that are most critical to them. Construction of the government’s Internet service capability, as a system project about the big picture, involves all sorts of resources, but different resources play different roles in that process. In the era of big data, for example, local governments need to become deeply aware of the key role that government data openness plays in improving online service capacity, to promote the optimal allocation and use of value-added data resources along technical, organizational, and environmental dimensions, and to improve the level of government’s Internet service.

Furthermore, the research findings illustrated that compared to technical and organizational factors, rather, environmental factors are important for enhancing the local government’s Internet service capability. This suggests that local governments should establish a “big picture,” take coordination, integration, and accountability as the governance mechanism, integrate diverse external environmental resources, and give play to the synergistic effect of resources. The essential attribute of resources is not to own, but to use (Amit & Schoemaker, 1993), which means that, on the one hand, local governments should have global planning of external governmental resources and stipulate how resources should be allocated at the institutional level. On the other hand, special management agencies should play a coordinating role to ensure that all sorts of resources can be sensibly combined and distributed scientifically, to lay a resource base for constructing the government’s Internet service capability.

Another implication is that leader attachment is a significant positive influencing factor on the local government’s Internet service capability. This means that even if the government’s Internet service capability is relatively low and its level of financial input and technology development is relatively backward, the government’s Internet service capability can be improved by strengthening the leadership, improving the organizational structure, and perfecting the institutional arrangement. The descriptive statistics reflect that there are significant differences in local government’s Internet service capabilities, and for China’s relatively backward regions, it’s not feasible to narrow the gap with developed regions utilizing information technology investment and talent introduction. Leaders in this context can promote the construction of the government’s Internet service capability by attaching importance to the high pressure formed within the bureaucracy, and even realize the “curve overtaking.”

Finally, this study selects the government’s Internet service capability construction under the background of digitization as the entry point, an in-depth analysis of the factors influencing the government’s Internet service capability construction, and a comparison of the causes of the differences in Internet service capability across governments. This can not only provide a practical reference for the improvement of the government’s Internet service capability but also lay the groundwork for the government’s digital transformation. With the advent of the digital age, the government’s capacity for public service is greatly challenged, and the construction of the government’s Internet service capability is not only the breakthrough path for addressing the challenge but also the active choice of the government itself for integration into the digital era. The findings of this research study can provide a practical reference and subsequent influence for the construction of e-government and digital government in reality.

Limitation and Prospect

First, many factors in real life may affect the capability of the government’s Internet service, and due to the limitation of space and variable measurement in this study, there may be variables that are missing from the design process that has some influence on the government’s Internet service capability but have not been incorporated in the research model. Second, due to the data limitation, this study uses cross-sectional data, which fails to take time series factors into account. In the future, hence, panel data can be considered to explore the influential factors of the government’s Internet service capability, and we will continue to explore other factors such as population density on the construction of the government’s Internet service capability.

Footnotes

Author Note

Tian Jin, PhD, graduated from Wuhan University and is a Professor and Associate Dean at the School of Public Administration, Zhongnan University of Economics and Law.

Cheng Dong, is a postgraduate student of the School of Public Administration, Zhongnan University of Economics and Law.

Declaration of Conflicting Interests

The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Funding

The author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This is an outcome of the project “Research on Social Mentality Analysis and Coping Strategies in Internet Public Events”(19BZZ030)supported by the National Social Science Foundation of China; Supported by “the Fundamental Research Funds for the Central Universities,” Zhongnan University of Economics and Law (202351215).

ORCID iD

Cheng Dong

References

Ahn

M. J.

(2011). Adoption of E-Communication applications in U.S. Municipalities: The role of political environment, bureaucratic structure and the nature of applications. The American Review of Public Administration, 41(4), 428–452.

Ahn

M. J.

Bretschneider

(2011). Politics of e-government: E-government and the political control of bureaucracy. Public Administration Review, 71(3), 414–424.

Akerlof George

. (1991). Procrastination and Obedience. The American Economic Review, 81(2), 1–19.

Algebri

H. K.

Husin

Abdulhussin

A. M.

Yaakob

(2017). Why Move toward the Smart Government? [Symposium] 2017 International Symposium on Computer Science and Intelligent Controls (ISCSIC) (pp. 167–171).

Alshibly

Chiong

(2015). Customer empowerment: Does it influence electronic government success? A citizen-centric perspective. Electronic Commerce Research and Applications, 14(6), 393–404.

Amit

Schoemaker

P. J. H.

(1993). Strategic Assets and Organizational Rent. Strategic Management Journal, 14(1), 33–46.

Asogwa Asogwa Asogwa

B. E.

(2013). Electronic government as a paradigm shift for efficient public services: Opportunities and challenges for Nigerian government. Library Hi Tech, 31(1), 141–159.

Barney

J. B.

(1986). Organizational culture: Can it be a source of sustained competitive advantage? The Academy of Management Review, 11(3), 656–665.

Barney

J. B.

Clark

D. N.

(2007). Resouce-based theory: Creating and sustaining competitive advantage. Oxford University Press.

10.

Brown

Fishenden

Thompson

Venters

(2017). Appraising the impact and role of platform models and government as a platform (GaaP) in UK government public service reform: Towards a Platform Assessment Framework (PAF). Government Information Quarterly, 34(2), 167–182.

11.

Chatterjee

Kar

A. K.

(2015). Smart Cities in developing economies: A literature review and policy insights [Conference session]. 2015 International Conference on Advances in Computing, Communications and Informatics (ICACCI) (pp. 2335–2340).

12.

Chen

Christensen

(2019). Competing for father’s love? The politics of central government agency termination in China. Governance, 32(4), 761–777.

13.

Chen

Hsieh

(2009). Advancing E-Governance: Comparing Taiwan and the United States. Public Administration Review, 69(s1), 151–158.

14.

Chen

Y. C.

Thurmaier

(2008). Advancing e-government: Financing challenges and opportunities. Public Administration Review, 68(3), 537–548.

15.

Coursey

Norris

D. F.

(2008). Models of E-Government: Are They correct? An empirical assessment. Public Administration Review, 68(3), 523–536.

16.

Deng

Wang

Z. Y.

(2020). The combination of administrative capacity: How Public Administrators shape the "Internet plus Government Service. Journal of Gansu Administration Institute, 3, 52–62.

17.

Fei

Jia

H. Z.

Wang

R. R.

(2016). Strategic research on the thinking and path of "Internet plus Government Affairs" under the background of modernization of national governance. E-Government, 8, 111–118.

18.

Forero

M. D. B.

(2013). Mobile communication networks and internet technologies as drivers of technical efficiency improvement. Information Economics and Policy, 25(3), 126–141.

19.

Hair

J. F.

Anderson

R. E.

Tatham

R. L.

Black

W. C.

(1995). Multivariate data analysis (4th ed., p. 757) Prentice-Hall Inc.,

20.

Han

J. P.

(2019). Governing Need, government capacity, and Government Internet service capabilities: With empirical evidences from local governments in China. Journal of Intelligence, 38(3), 173–179.

21.

Hermansyah

Yusa

(2019). Does the E-Procurement eradicate the corruption? (A lesson from E-procurement practices in Indonesia). International Journal of Advanced Science and Technology, 28(14), 476–482.

22.

Hong

Lee

(2018). Adaptive governance, status quo bias, and political competition: Why the sharing economy is welcome in some cities but not in others. Government Information Quarterly, 35(2), 283–290.

23.

Hung

S. Y.

Hung

W. H.

Tsai

C. A.

Jiang

S. C.

(2010). Critical factors of hospital adoption on CRM system: Organizational and information system Perspectives. Decision Support Systems, 48(4), 592–603.

24.

Ismagilova

Hughes

Rana

N. P.

Dwivedi

Y. K.

(2022). Security, privacy and risks within smart cities: Literature review and development of a smart city interaction framework. Information Systems Frontiers, 24(2), 393–414.

25.

Janssen

Luthra

Mangla

Rana

N. P.

Dwivedi

Y. K.

(2019). Challenges for adopting and implementing IoT in smart cities: An integrated MICMAC-ISM approach. Internet Research, 29(6), 1589–1616.

26.

John

C. B.

Zheng

H. N.

Bao

L. D.

(2014). Policy frameworks for Big Data and open data: Issues, policies, and recommendations. E-Government, 1, 6–14.

27.

Jones

G. K.

Davis

H. J.

(2000). National Culture and Innovation: Implications for locating Global R&D operations. Management International Review, 40(1), 11–39.

28.

Jun

K. N.

Weare

(2011). Institutional motivations in the adoption of innovations: The case of e-government. Journal of Public Administration Research and Theory, 21(3), 495–519.

29.

Kar

A. K.

Ilavarasan

Gupta

M. P.

Janssen

Kothari

(2019). Moving beyond smart cities: Digital nations for social innovation & sustainability. Information Systems Frontiers, 21(3), 495–501.

30.

Karch

(2007). Emerging issues and future directions in State Policy Diffusion Research. State Politics & Policy Quarterly, 7(1), 54–80.

31.

Katsonis

Botros

(2015). Digital government: A primer and professional perspectives. Australian Journal of Public Administration, 74(1), 42–52.

32.

Larsen-Price

H. A.

(2012). The right tool for the job: The Canalization of presidential policy attention by policy instrument. Policy Studies Journal: the Journal of the Policy Studies Organization, 40(1), 147–168.

33.

Lauwereynsj. (2010). The anatomy of bias: How neural circuits weigh the options. MIT Press.

34.

Lee

C. P.

Chang

Berry

F. S.

(2011). Testing the development and diffusion of E-government and E-democracy: A global perspective. Public Administration Review, 71(3), 444–454.

35.

Lee

(2014). Developing and validating a citizen-centric typology for smart city services. Government Information Quarterly, 31(jun. suppl), S93–S105.

36.

Liang

Wei

Chen

(2017). Exploring the determinant and influence mechanism of e-Government cloud adoption in government agencies in China. Government Information Quarterly, 34(3), 481–495.

37.

Ling

Zhang

Zhen

(2018). Peer effects in consumption among Chinese rural households. Emerging Markets Finance and Trade, 54(10), 2333–2347.

38.

Q. D.

Zuo

M. Y.

(2014). How to promote the adoption of information systems in pension institutions? – A conceptual model based on TOE. Lanzhou Academic Journal, 2, 103–111.

39.

Liu

X. T.

Song

H. S.

Liang

S. X.

(2019). Design of E-government Performance Evaluation Indicators Systems based on Balanced Score card. Journal of Qiqihar University (Philosophy & Social Science Edition), 5, 60–63.

40.

Luk

S. C. Y.

(2009). The impact of leadership and stakeholders on the success/failure of e-government service: Using the case study of e-stamping service in Hong Kong. Government Information Quarterly, 26(4), 594–604.

41.

Mata

F. J.

Barney

J. B.

(1995). Information Technology and sustained competitive advantage: A resource-based analysis. MIS Quarterly, 19(4), 487–505.

42.

Maxat

(2018). Adopting and managing open data: Stakeholder perspectives, challenges and policy recommendations. Aslib Journal of Information Management, 70(5), 518–537.

43.

McBride

Aavik

Toots

Kalvet

Krimmer

(2019). How does open government data driven co-creation occur? Six factors and a ‘perfect storm’; insights from Chicago’s food inspection forecasting model. Government Information Quarterly, 36(1), 88–97.

44.

McDaniel

E. A.

(2005). Facilitating cross-boundary leadership in emerging e-government leaders. Electronic Government an International Journal, 2(1), 1–10.

45.

Mensah

I. K.

(2019). Factors influencing the intention of university students to adopt and use E-Government services: An Empirical Evidence in China. Sage Open, 9(2), 2158244019855823.

46.

Mergel

Greeves

(2012). Social media in the public sector field guide (p. 33). John Wiley & Sons Inc.

47.

Mohammed

Ibrahim

O. B.

(2015). Drivers of Cloud Computing Adoption for E-Government Services Implementation. International Journal of Distributed Systems and Technologies, 6(1), 1–14.

48.

Morgeson

F. V.

VanAmburg

Mithas

(2011). Misplaced Trust? Exploring the structure of the E-government-citizen trust relationship. Journal of Public Administration Research and Theory, 21(2), 257–283.

49.

Mortensen

P. B.

Green-Pedersen

(2015). Institutional effects of changes in political attention: Explaining organizational changes in the top bureaucracy. Journal of Public Administration Research and Theory, 25(1), 165–189.

50.

Mpinganjira

(2015). Use of e-government services: The role of trust. International Journal of Emerging Markets, 10(4), 622–633.

51.

NPF. (2020). China City Competitiveness Report NO.18. http://gucp.cssn.cn/yjcg/zcjy/202010/t20201027_5202025.shtml.

52.

Pan

M. J.

Jang

W. Y.

(2008). Determinants of the adoption of enterprise resource planning within the technology-organization-environment framework: Taiwan’s communications. Journal of Computer Information Systems, 48(3), 94–102.

53.

Prybutok

V. R.

Zhang

Ryan

S. D.

(2008). Evaluating leadership, IT quality, and net benefits in an e-government environment. Information Management, 45(3), 143–152.

54.

Reforgiato

D. R.

Castronovo

Consoli

Costanzo

Gangemi

Grasso

Lodi

Merendino

Mongiovì

Presutti

Rapisarda

S. D.

Rosa

Spampinato

(2016). An innovative, open, Interoperable Citizen Engagement Cloud Platform for Smart Government and users’ Interaction. Journal of the Knowledge Economy, 7(2), 388–412.

55.

Ross

Beath

(1996). Develop long-term competitiveness through IT assets. Sloan Management Review, 38(1), 31–42.

56.

Sachan

Kumar

(2018). Examining the impact of e-government service process on user satisfaction. Journal of Global Operations and Strategic Sourcing, 11(3), 321–336.

57.

Savoldelli

Codagnone

Misuraca

(2014). Understanding the e-government paradox: Learning from literature and practice on barriers to adoption. Government Information Quarterly, 31(S1), S63–S71.

58.

Sayogo

D. S.

Yuli

S. B. C.

(2018). Critical success factors of open government and open data at local government level in Indonesia. International Journal of Electronic Government Research, 14(2), 28–43.

59.

Schedler

Summermatter

(2007). Customer orientation in electronic government: Motives and effects. Government Information Quarterly, 24(2), 291–311.

60.

Tang

Z. W.

Zhang

L. P.

Zhang

H. P.

(2019). Research on the Internet service capability of local governments and its influencing factors: Based on the survey and analysis of 334 prefecture-level cities in China. E-Government, 7, 79–92.

61.

Tan

H. B.

Fan

Z. T.

Y. Z

. (2019). Technical Management ability, attention distribution and local government website construction: A configuration analysis based on TOE framework. World Journal of Management, 35(9), 81–94.

62.

Teresa, H., & Djoko, S. (2014). Transparency, participation, and accountability practices in open government: A comparative study. Government Information Quarterly, 31(4), 513–525.

63.

Tolbert

C. J.

Mossberger

McNeal

(2008). Institutions, Policy Innovation, and E-Government in the American States. Public Administration Review, 68(3), 549–563.

64.

Tornatzky

L. G.

Fleischer

(1990). The processes of Technological Innovation. Lexington Books.

65.

Volden

Ting

M. M.

Carpenter

D. P.

(2008). A formal model of learning and policy diffusion. The American Political Science Review, 102(3), 319–332.

66.

Walker

J. L.

(1969). The diffusion of innovations among the American States. The American Political Science Review, 63(3), 880–899.

67.

Weerakkody

Irani

Kapoor

Sivarajah

Dwivedi

Y. K.

(2017). Open data and its usability: an empirical view from the Citizen’s perspective. Information Systems Frontiers, 19(2), 285–300.

68.

Wirtz

B. W.

Piehler

Thomas

M. J.

Daiser

(2016). Resistance of public personnel to open government: A cognitive theory view of implementation barriers towards open government data. Public Management Review, 18(9), 1335–1364.

69.

World

B. A. N. K

. (1997). World Development Report 1997: Government in a Changing Worl. China Finance and Economics Press.

70.

Xie

X. Q.

(2019). Internet plus government services: Achievements, difficulties and suggestions. E-Government, 6, 62–72.

71.

Yang

T. M.

Y. J.

(2016). Examining the socio-technical determinants influencing government agencies’ open data publication: A study in Taiwan. Government Information Quarterly, 33(3), 378–392.

72.

Yuan

Pan

(2017). Analysis of the influencing factors of the total development of government WeChat from the perspective of innovation diffusion: Based on an empirical study of prefecture-level cities in China. Hubei Social Sciences, 8, 47–53.

73.

Zhang

Zhao

Zhang

Meng

Tan

(2017). What factors drive open innovation in China’s public sector? A case study of official document exchange via micro-blogging (ODEM) in Haining. Government Information Quarterly, 34(1), 126–133.

74.

Zheng

(2015). Study on Open Government Data: Definitions, factors and interactions. Chinese Public Administration, 11, 13–18.

75.

Zhu

Kraemer

K. L.

(2006). The process of innovation assimilation by firms in different countries: A technology diffusion perspective on E-business. Management Science, 52(10), 1557–1576.