Public demand and optimization of smart government service platform from the perspective of service life cycle based on the Kano model: A Chinese case study

Introduction

Smart government is an advanced product of the development of e-government at a certain stage [1]. It is a new service model that applies emerging technologies such as mobile internet, big data, cloud computing, blockchain, and artificial intelligence to the entire process of government services [2]. Smart government is an effective way to promote government innovation and public service under new circumstances [3,4]. It can effectively improve administrative efficiency, reduce operational costs, enhance transparency and accountability, and improve the quality of public services [5]. Currently, countries worldwide are vigorously developing smart government.

According to the UN E-Government Survey 2022, high EGDI (E-Government Development Index) scoring countries have increased from 69.36% to 73.38% over the past two years [6]. The United States ranks first in the Americas in terms of e-government index, focusing primarily on data openness, digital services, and smart city projects. Federal agencies like the United States Digital Service (USDS) and the 18F team promote the accessibility of public data through initiatives like Data.gov, supporting innovation and government transparency, and providing a better user experience [7,8]. In Singapore, the government is promoting digital transformation through the “Smart Nation 2025” plan, which includes smart city infrastructure, e-government services, and an artificial intelligence (AI) governance framework [9,10]. Japan ranks high among Asian countries in e-government index, primarily advancing government service digitization through the Digital Agency to improve the efficiency and convenience of public services [11]. Additionally, Japan emphasizes the application of artificial intelligence and big data technology in government decision-making to promote scientific and transparent policy-making [12]. Although China’s EGDI score lags behind that of developed countries, it is actively advancing smart government construction. By 2021, China had established a nationwide integrated government service platform, connecting all 31 provinces and cities, with real-name users exceeding 400 million. The online acceptance of provincial administrative licensing matters and the proportion of “only once” visits reached 82.13%, and the average commitment time for more than half of the national administrative licensing matters was reduced by over 40% [13]. By 2024, the national government service platform has accumulated over 890 million registered users, with over 10.7 billion identity authentication and verification services and over 10.8 billion electronic certificate sharing services provided by various regions and departments, and over 54 billion platform data sharing services have been facilitated [14]. The central government’s portal and its new media platforms cover more than 7.68 billion users in over 200 countries and regions. Meanwhile, in China, the “14th Five-Year Plan for Promoting National Government Informatization” points out that by 2025, the construction of smart government informatization will generally enter a new stage of government characterized by data empowerment, collaborative governance, smart decision-making, and high-quality services [15]. Currently, some provinces in China have achieved significant results in the construction of smart government platforms, such as “Zheliban” of Zhejiang, “Shanghai International Services”, and “Ehuiban” of Wuhan.

However, at the current stage, there is still a contradiction between the development of smart government service platforms and the alignment with public demands [16]. The construction of smart government service platforms in various countries generally follows a top-down, government-centered approach. This often leads to an inability to accurately grasp the public demands for smart government services, resulting in a mismatch between supply and demand [17]. To advance the construction of high-quality smart government service platforms, it is essential to approach it from the demand side, clarifying the public pain points and difficulties in each service process of smart government [18].

Therefore, this paper selects Beijing’s smart government service platform as the research object, focusing on four main issues:

1. How to construct a public demand analysis framework from the demand side?

2. Based on the constructed analysis framework, how to scientifically identify public demands for smart government service platforms?

3. Based on survey results, how to categorize and prioritize public demands?

4. Based on the analysis results, how to propose optimization measures for the smart government service platform?

Literature review and analytical framework

Literature review

At present, China has transitioned from an era of “government administrative management informatization” focused on e-government to an era of “leveraging big data to enhance the modernization of national governance” through digital government [19]. In 2012, the National Development and Reform Commission issued the “12th Five-Year Plan for the Construction of National E-Government Informatization Projects”, promoting initiatives such as “information benefiting the people” and “smart cities” nationwide. With the development of the Internet, especially mobile Internet, the communication gap between entities has shortened, improving information interaction and processing efficiency. Public demand for smart government services has genuinely extended from the physical realm into cyberspace. In 2021, the General Office of the State Council issued the “Guidelines for the Construction of Mobile Platforms for the National Integrated Government Service Platform”, emphasizing the need to address the public’s difficulties in handling affairs. It aimed to break the “last mile” of livelihood projects through services like “one-stop online processing” and “cross-region processing”. The goal at this stage is to provide a comprehensive and high-quality experience in handling matters, hoping that online and offline government service platforms can offer more personalized and humanized services [20].

The ultimate goal of building a smart government service platform to meet public demands is public satisfaction. It should be guided by public demand, meaning that the public should no longer be passive recipients of government services but should actively participate in the design and construction of these services alongside government departments [21]. Only by thoroughly identifying public demands and conducting targeted construction can the effort yield twice the results with half the effort.

However, demands are inherently the subjective perceptions of the objectively existing public, and different research perspectives can lead to varying analyses and measurements of these demands. Relevant studies have analyzed citizens’ demands for government services across various fields, such as education, healthcare, and finance [22,23]. These studies also emphasize that the government should assess and address public demands more openly, fairly, and transparently. Moreover, in constructing information systems, the government should ensure the security and privacy of personal sensitive data [24]. Other research identifies and analyzes demands from the perspectives of public behavior, personalization, and experience. For example, some studies explore factors influencing the quality of government electronic information services from the perspective of user information behavior [25], construct government information service models based on user personalized demands [26], and investigate intelligent search in government services from the user experience perspective [27]. However, these studies discuss the demands for government services that arise during user information behavior, focusing only on specific aspects of government services such as browsing, interaction, and querying. These studies are fragmented and lack a comprehensive examination of the entire process of government services.

Firstly, in terms of research methods for identifying demand, related studies either apply statistical methods to summarize and classify large-scale survey data or use mature interdisciplinary demand analysis theories to identify and analyze public demands [28,29]. Some scholars have used the ERG theory to categorize public e-government service needs into functional, relational, and growth needs and have constructed a theoretical analysis framework for public service demand research within the context of consumer decision-making and system success theories [30,31]. Some use information system success model and the technology acceptance model to examine user attitudes and satisfaction with E‐government system [32]. Others have applied the KANO model to classify public demand for online government information disclosure services into must-be qualities (M), One-dimensional qualities (O), Attractive qualities (A), and Indifferent qualities (I) [33]. Additionally, some scholars have employed lifecycle theory to study the demand identification issues of both citizen and corporate users [34] or used unsupervised statistical classification methods to analyze the demands of one million public service applications [35].

Secondly, in terms of research subjects, most existing research tends to focus on the demands arising from users’ use of online apps or government platforms. For example, some studies explore the functionality completeness of online government apps or analyze the demands and satisfaction related to government transparency on government websites [36,37], or identify barriers, successes in the digital government [38]. However, e-government should combine both online and offline components and cannot be separated.

Thirdly, most existing research targets e-government platforms, with insufficient examination of the specific characteristics of smart government service platforms. For instance, some research focuses on optimizing development approaches and governance solutions [39], or focus on personal information security and privacy [40], or investigating utilization of open government data [41]. Some research analyze the practical development logic of smart government platforms [42], explore new administrative approval models [43], and investigate comprehensive governance solutions for smart government services [44]. However, there is a lack of systematic analysis of the demands for smart government service platforms.

Building on existing research, this paper will start from the perspective of service life cycle, considering both online and offline scenarios to identify public demands within the smart government service platform, ensuring that the identification of demands is scientific, comprehensive, and systematic.

Analytical framework

Dimension 1: Life cycle of smart government service platform application

Smart government services aim to provide a more comfortable experience and higher satisfaction for the public. User experience includes not only feelings during processing period but also learning costs before processing and feedback after processing. To comprehensively identify public demands for smart government service platforms, this study draws on the life cycle to construct an analytical framework from a full-process of government services. The life cycle often regarded as a “Cradle-to-Grave” process and widely used in many fields such as economy, politics, and management [45].

For example, the Customer Relationship Life Cycle typically refers to the entire process from establishing a business relationship with a customer to completely terminating the relationship, generally divided into four stages: inspection period, formation period, stable period, and degradation period [46]. Based on the user activity level when handling government service matters, the life cycle of smart government service platform applications is divided into four periods: information query stage before handling business (guide period), consultation and communication stage before business pre-processing (interaction period), material submission stage during business processing (processing period), and evaluation feedback stage after business completion (evaluation period), forming a “Guide Period—Interaction Period—Processing Period—Evaluation Period” life cycle, as shown in Fig 1.

[Figure omitted. See PDF.]

Dimension 2: Scenarios of smart government service platforms application

The application scenarios of smart government service platforms are divided into online smart government services and offline smart government services. Online smart government services mainly refer to “the public accessing government service platforms through internet methods such as mobile applications and government official websites, without geographical restrictions, and handling related government matters anytime and anywhere [47]”. Offline smart government services mainly refer to “the public arriving at government service agencies at specific times, communicating face-to-face with staff, and handling related matters [48]”.

Offline smart government services are an extension of online smart government services, with both scenarios revolving around smart government service platforms and complementing each other. In the application of smart government service platforms, online smart government services are the main focus. With the evolution of digital and smart government services, the majority of government services can be handled online, and the public prefers simple, efficient, and convenient online smart government services. Offline smart government services complement online smart government services, addressing technical blind spots, special matters, and services for special groups, providing assistance to those unfamiliar with online operations.

Constructing the framework: Analysis framework of public demands for smartgovernment service platforms

From the perspective of the service life cycle, this paper divides the life cycle of a smart government service platform into four stages: the Guide Period, the Interaction Period, the Processing Period, and the Evaluation Period. Additionally, the application scenarios are categorized into two types: online and offline (as shown in Fig 2).

[Figure omitted. See PDF.]

During the guide period, finding the entrance to handle services quickly is public main demands. Online smart government service platforms meet the public demands for quick positioning to the processing interface through shared cloud resources, AI navigation, etc.; offline smart government services reduce the public running time through organizational integration. Online and offline smart government services complement each other to ensure accurate service entrance positioning.

During the interaction period, clearing blinding spots and obstacles before handling services is public main demands. Online smart government service platforms quickly solve personalized public issues through Artificial Intelligence Generated Content (AIGC), natural language processing (NLP), etc.; offline smart government services solve public difficult problems by enhancing staff quality. Online and offline smart government services organically combine to ensure full resolution of service difficulties.

During the processing period, ensuring the service quality is public main demands. Online smart government service platforms enhance platform security and data security through blockchain, hierarchical protection, etc., and efficiently handle processing matters through optical character recognition (OCR); offline smart government services optimize algorithms to reduce waiting time through IoT, real-time sensing of the hall status. Online and offline smart government services collaborate to ensure efficient processing of service matters.

During the evaluation period, providing objective and fair evaluation of completed matters is public main demands. Online smart government service platforms ensure objective and fair public evaluation through intelligent evaluation; offline smart government services ensure real-time tracking and processing of complaints through multi-level feedback mechanisms. Online and offline smart government services complement each other to ensure comprehensive feedback on service effects.

Identification of public demand for smart government service platforms

Smart government is an advanced product of e-government, a specialized form of e-government services. Public demand for smart government services is a refined derivative of e-government service demand, with characteristics such as intelligence, precision, personalization, digitalization, cloud resources, AI, privacy protection, reliable transmission, and information transparency [49].

At the early stage of the research, 24 public demands were identified through literature review. Later, five experts in the fields of e-government, information management, and digital government were invited to review, modify, and add to these demands, (as shown in Table 1) resulting in the final list of 29 public demands for the smart government service platform (as shown in Table 2).

[Figure omitted. See PDF.]

[Figure omitted. See PDF.]

Research design

Research approach

The research approach is shown in Fig 3.

[Figure omitted. See PDF.]

Method

In this paper, the Kano model is selected as the theoretical analysis method. The Kano model is a tool for categorizing and prioritizing user demands. It was proposed by Noriaki Kano in 1984 [63], who linked user demands with satisfaction levels regarding a product or service. Kano model identifies five types of factors that influence satisfaction: Must-be qualities (M), One-dimensional qualities (O), Attractive qualities (A), Indifferent qualities (I), and Reverse qualities (R), as shown in Fig 4. Must-be qualities are attributes or functions of a platform or service that the public considers “must-have”. In this paper, it means that if this need is not met, the public will be very dissatisfied; however, even if the need is met, it does not necessarily increase public satisfaction. One-dimensional qualities are attributes or functions that the public expect the platform or service to achieve. In this paper, it means that if this need is not met, the public will be very dissatisfied; but if the need is fulfilled, the public will feel satisfied. Attractive qualities are attributes or functions of a platform or service that the public consider to be “beyond expectations”. In this paper, it means that if this need is not met, the public will not be dissatisfied; however, if this need is met, it will pleasantly surprise the public and greatly enhance their satisfaction. Indifferent qualities are attributes or functions that the public consider to be “optional”. In this paper, it means that regardless of whether this need is met, the public will not care, nor will it affect their satisfaction or dissatisfaction. Reverse qualities are attributes or functions that users or the public consider the platform or service “should not provide”. In this paper, it means that this need is negatively correlated with public satisfaction, and providing it may decrease public satisfaction instead.

[Figure omitted. See PDF.]

Two reasons for selecting this model. First, compared to other demand analysis theories such as the ERG model, the Kano model considers user demands from both an objective perspective (i.e., the impact of the completeness of a service on user demands) and a subjective perspective (i.e., user satisfaction). Second, the Kano model is widely used in e-government demand research [64–66], as it not only highlights the different levels of public demands but also emphasizes high-level public demands. For these reasons, using the Kano model as the theoretical research method can thoroughly clarify and identify the pain points and difficult demands of the public throughout the service life cycle of government services. In addition, the research has got ethics approval application form of Beijing University of Posts and Telecommunications.

Research data

Questionnaire design

The questionnaire was designed strictly according to the Kano model methodology. Initially, 50 questionnaires were distributed in a small-scale pilot, and based on the results and feedback from the pre-survey, the questionnaire was revised and improved. The final questionnaire is divided into three parts: the first part contains the title and instructions, explaining the purpose of the survey, the content of the questionnaire, and the use of the data collected; the second part focuses on the respondents’ basic information, including gender, age, Physical condition, degree, occupation, and their purpose of smart government services; the third part addresses the main content of the questionnaire, where questions are based on the public demands identified in the four periods of the smart government services life cycle (as shown in Table 3). A total of 29 questions are set: 8 for the guide period, 8 for the interaction period, 7 for the processing period, and 6 for the evaluation period. The functional questions assume the service or function is provided, while the dysfunctional questions assume the service or function is not provided. Each question uses a five-point Likert scale for respondents to choose their answers: “Dislike”, “Live with”, “Neutral”, “Must-be”, and “Like ” (as shown in Table 4) [65,67].

[Figure omitted. See PDF.]

[Figure omitted. See PDF.]

Questionnaire distribution and collection

“Jingtong” was chosen as the research object for the following reasons: (1) Launched in October 2023, “Jingtong” integrates the services of “Beijing Tong” and “Beijing Health Treasure”, serving as the mobile gateway for urban public services in the construction of a smart city. (2) “Jingtong” covers a wide range of application areas, offering one-stop services from healthcare to cultural and tourism activities. It updates and iterates its services rapidly, having already added functions such as mobile payment for medical insurance and a big data platform for rural revitalization. (3) “Jingtong” actively utilizes cutting-edge technologies such as AI, big data, and cloud computing. It embodies the digitalization, cloud resource integration, and AI features that a smart government service platform should have, such as storing personal information on Beijing’s government cloud.

From December 1, 2023 to March 31, 2024, electronic questionnaires were distributed to the public using Beijing’s “Jingtong” smart government service platform. A random sampling method was adopted, with a total of 695 questionnaires distributed. By including a question asking, “Have you used the ‘Jingtong’ platform?” respondents were screened, and suspicious or invalid questionnaires were excluded. A total of 648 valid questionnaires were collected, resulting in an effective response rate of 93.23%.

[Figure omitted. See PDF.]

Sample characteristics

Respondents had balanced gender ratios, reasonable distributions of age, physical status, education, occupation, and purposes for using “Jingtong”, as shown in Table 5).

Samples with similar characteristics exhibit convergent demands for smart government service platforms [68]. Therefore, after conducting scientific sampling, a small sample survey still holds representativeness.

Questionnaire quality

The questionnaire’s quality allows for analysis. Cronbach’s alpha coefficients for functional and dysfunctional questions were 0.789 and 0.806, respectively, indicating good consistency and stability. KMO values for functional and dysfunctional questions were 0.867 and 0.873, indicating the questions effectively reflect public demand.

[Figure omitted. See PDF.]

Result

Classification of public demands for smart government service platforms

In the Kano model classification analysis, the option with the highest occurrence in the questionnaire is considered as the attribute of the demand. Based on this, the collected questionnaire data is subjected to the classification analysis as shown in the Table 6. According to these criteria, 10 attractive qualities (A), 9 one-dimensional qualities (O), 7 must-be qualities (M), 3 indifferent qualities (I), and 0 reverse quality (R) were identified for the smart government service platform.

Attribute correction of public demand for smart government service platformbased on hybrid class analysis

After the preliminary classification using traditional methods, situations with similar frequencies exist, making it difficult to clarify dominant demands. Lee et al. proposed the use of hybrid class analysis to determine the developmental trends of demand elements’ category attributes [69]. The TS (Total Strength) index reflects whether respondents would be satisfied with the demand element (as shown in Eq 1), while CS (Category Strength) reflects the degree of acceptance of the classification results(as shown in Eq 2).(1)(2)

When the total strength TS is equal or greater than 60% and the category strength CS is equal or smaller than 6%, it is classified as a hybrid category (H), based on the top two demand categories by frequency ranking. The attribute correction results are as follows:

1. H(M+I) includes the demands for comfortable environment, information sharing, and human customer service. This indicates the necessity of incorporating these functions into the smart government service platform. Continuous optimization of these functions is required to facilitate the transition to stable must-be quality.

2. H(O+I) includes the demand for intelligent navigation. This indicates that such demands are attractive to the public and require improvement in supply quality to prevent them from becoming indifferent qualities.

3. H(O+A) includes the demands for intelligent reminders, quality assessment, and organizational integration. This suggests that the public is sensitive to the high-quality supply of such services. They may be excited by the platform’s user-friendly operation, timely progress notifications, and abundant self-service facilities. Therefore, enhancing the public’s experience is necessary.

The types of demand elements will change over time [70], following the trend of I→A→O→M.

Classification presentation of public demand for smart government service platform based on Better-Worse coefficient analysis

The hybrid analysis lacks consideration of the distribution of other categories with slightly lower frequencies. Therefore, Berger C. et al. proposed the Satisfaction Index (Better-Worse coefficient) to analyze the impact of demand on public satisfaction [71]. The Better coefficient (satisfaction coefficient, SI) reflects the increase in public satisfaction when a demand is met. This value is usually greater than 0, and the closer to 1 indicates the higher influence of this demand element on public satisfaction, as shown in Eq 3. The Worse coefficient (dissatisfaction coefficient, DSI) reflects the decrease in public satisfaction when a demand is not met. This value is usually less than 0, and the closer to –1 indicates the higher influence of this demand element on public dissatisfaction, as shown in Eq 4.(3)(4)

A quadrant chart is plotted with the average absolute values of the Better-Worse coefficient as the origin (0.434, 0.487) (Fig 5). The presentation of results is as follows:

1. Emphasize one-dimensional qualities of the smart government service platform. Provide sufficient supply to enhance user experience and meet user expectations.

2. Highlight attractive qualities of the smart government service platform. Improve supply as much as possible to maintain happiness and enhance user stickiness.

3. Indifferent qualities of the smart government service platform can be considered as weaknesses. Promote their transformation into attractive quality, enhance the sense of acquisition, and stimulate the generation of new demands.

4. Focus on must-be qualities of the smart government service platform as the baseline. Ensure high-quality supply to provide a sense of security and guarantee user trust.

[Figure omitted. See PDF.]

Summary analysis

Among the three analytical methods, 27 demands show high consistency in their classification results, ultimately categorized as follows:

1. Attractive quality include intelligent scheduling, convenient location, intelligent customer service, intelligent guidance, intelligent assistant, comprehensive access, intelligent assessment, complaint tracking, categorized evaluation, and complaint follow-up.

2. One-dimensional quality include decision support, smart search, intelligent navigation, smart reminders, intelligent auditing, organizational integration, privacy protection, quality tracking, and quality assessment.

3. Must-be quality include information sharing, clear identification, comfortable environment, personalized service, data security, network security, and one-click navigation.

4. Only the quick response requirement falls under the category of no difference.

There are two requirements with inconsistent results among the three analytical methods: human customer service and resource integration. Human customer service is categorized as no difference in the traditional classification. After hybrid analysis, it appears as both no difference and must-be quality. According to the Better-Worse coefficient analysis, it appears as a must-be quality. This indicates that the functionality of human customer service does not affect the public’s use of the smart government service platform. However, for certain groups, such as the elderly, human customer service may impact satisfaction, suggesting the need for ongoing maintenance of the platform. Resource integration is categorized as no difference in both traditional and hybrid analyses. According to the Better-Worse coefficient analysis, it appears as a attractive quality. This suggests that resource integration enhances public satisfaction with the smart government service platform.

Priority ranking of public demands for smart government service platform

The smart government service platform represents the further evolution of e-government service platform business scenarios and technological capabilities. To maximize public satisfaction, the following basic sorting principles will be followed: first, fully enhance the supply of one-dimensional quality, second, strive to improve the supply of attractive quality, third, ensure the supply of must-be quality, and finally, promote the transformation of indifferent quality. Therefore, according to the sequence of the first quadrant, second quadrant, fourth quadrant, and third quadrant in Fig 5, the Better-Worse values are calculated respectively and sorted from largest to smallest [72]. As shown in Table 7 for the sorting results.

[Figure omitted. See PDF.]

It is worth mentioning that the choice of the above ranking principle in this paper is based on the premise that questionnaire responses are somewhat subjective, and the analysis is relatively simple and intuitive. The Better-Worse ranking method allows for the quick completion of demand priority ranking without the need for complex methods such as AHP or TOPSIS. If further decomposition of the demand hierarchy and determination of weights are required, AHP can be chosen; if the relative advantages and disadvantages of the options need to be considered, TOPSIS can be selected.

Discussion

The optimization approach for smart government service platforms is as follows: First, priority should be given to ensuring personal privacy security, as this is the precondition for widespread use of smart government services. Existing studies [73] have analyzed the phenomenon of collection of personal information by platforms. The results of this analysis show that the public values personal privacy security the most. As the development of intelligent government systems requires more personal information to support business processes, the focus should be on designing robust privacy protection mechanisms for the platform. Subsequently, the platform should prioritize its development according to one-dimensional qualities, attractive qualities, and must-be qualities at different periods. Finally, efforts should be made to transform indifferent qualities into other types of qualities.

Guide period: Focus on enhancing smart push effects

The analysis results show that during the guidance period, the public’s expectations for smart reminders and intelligent navigation are particularly urgent. Existing research [74] has analyzed the public’s demand for aesthetically pleasing interfaces. However, the practical challenge is that as more government services become available online, it becomes difficult for the public to find the services they need through complicated interfaces. Government service platforms should focus on using new media technologies to enhance reminder efficiency. For example, they could implement reasonable functional partitions and hierarchical navigation in the interface design. Regarding offline government services, existing research [75] primarily emphasizes the integrated development of online and offline services. The analysis results suggest focusing on developing organizational integration of one-dimensional qualities,reducing the spatial distance and time consumption for the public to access services, and further integrating different departments and services within a unified physical area. For instance, emphasis could be placed on using technologies such as the Internet of Things and sensor networks to strengthen organizational integration capabilities, or deploying various types of self-service robots in government service halls.

Interaction period: Emphasize improving human-computer interactionefficiency

The analysis results indicate that one-dimensional quality such as decision support and smart search, as well as intelligent customer service, are particularly urgent. However, there is limited literature analyzing these demands. As the number of online services provided by government platforms increases, so do the requirements for processing these services. The reality is that even if the public finds the service they need, complex processing requirements often leave them feeling at a loss. Optimizing these demands can help the public save time better on inquiries and improve service efficiency. Government service platforms should further utilize technologies such as AI, big data analysis, and cloud computing to enhance human-machine interaction efficiency. For example, deep reinforcement learning algorithms, continuous training of semantic large models, and deploying OpenStack cloud computing models could all be used to improve question-answering efficiency.

Offline smart government service platforms should focus on developing attractive quality for intelligent guidance and intelligent assistants. Optimizing these demands can reduce the time spent by the public in handling affairs. For offline smart government service platforms, there should be a focus on developing intelligent guidance and intelligent assistant needs, which differs from the focus of existing research [76]. At this period, it is crucial to utilize various sensors to perceive the status of the public as a group and as individuals, or to apply natural language processing technology to intelligent assistant robots to enhance human-machine interaction efficiency.

Processing period: Focus on enhancing intelligent processing efficiency

The processing period is the most critical phase in determining the effectiveness of public service. Existing research [73] has examined the collection of personal information by platforms. This study further identifies that the public places significant emphasis on the expectations for privacy protection and intelligent auditing. In practice, it is often observed that, despite submitting application materials, the public faces prolonged manual review processes, and in some cases, their applications are returned for revisions before resubmission. Government service platforms should prioritize the application of technologies such as Optical Character Recognition (OCR) and Multi-Level Security Protection to enhance the efficiency of intelligent auditing and safeguard privacy. For instance, regular security assessments by professional institutions can ensure platform security, while the integration of convolutional neural networks and Hadoop-based big data analytics can significantly improve intelligent auditing processes.

For offline smart government service platforms, the focus should be on developing the attractive qualities for cross-regional and cross-departmental integrated service capabilities, thereby improving the efficiency of joint service delivery. This aligns with existing research findings [77]. During this period, it is essential to focus on utilizing virtualization technologies such as KVM and Docker. This ensures that government service applications can run smoothly on different operating systems such as Android, HarmonyOS, iOS, Windows, Linux, and Kirin, thereby achieving linkage and functionality upgrades across computer terminals, lobby terminals, mobile terminals, and self-service terminals.

Evaluation period: Ensure real-time adaptive dynamic feedback

The analysis results indicate that the public demand for attractive qualities such as intelligent assessment and one-dimensional quality like quality tracking is particularly urgent. Previous research [78] has analyzed the factors influencing satisfaction with smart government services. This study expands upon those factors, suggesting that quality tracking should also be considered as a key element. Government service platforms should focus on leveraging technologies such as 5G and big data analytics to enhance evaluation and tracking capabilities.For example, the service process can be broken down into detailed stages with scoring options to ensure comprehensive coverage of public evaluation needs. Additionally, AI and big data technologies can be used to analyze the efficiency and quality of service completion, providing recommendations for improvement.

For offline platforms, there should be a focus on the development of one-dimensional quality such as quality evaluation. Existing research [79] has primarily emphasized efficiency evaluation. This study, however, further identifies the importance of the public real-time sensory experience during service processes. For instance, it is possible to utilize IoT sensors to monitor indicators such as queue length, queue time, environmental temperature, and humidity in real time. Backend algorithms can then be employed to dynamically adjust the environmental conditions, ensuring the real-time experience of the public.

Conclusion

The innovations and theoretical contributions of this paper are as follows: First, it introduces an innovative framework for analyzing public demand on government service platforms across both online and offline scenarios from a service life cycle perspective, which has not been addressed in existing studies. The life cycle perspective ensures the completeness of process analysis, while considering both online and offline contexts ensures comprehensive scenario analysis. Future researchers can adopt this analytical framework to construct demand frameworks for more specific areas. Second, based on the current state of government service capabilities in China, the paper identifies 29 types of public demand attributes and their importance, reflecting actual public needs and providing a reference for evaluating the current level of government service capabilities in China. Finally, unlike previous studies that focus solely on either management or technology, this paper simultaneously proposes optimization strategies for smart government service platforms from both technical and managerial perspectives.

Limitations

This paper analyzes the actual public demand using Beijing’s government service platform as the research subject, without taking into account specific factors such as varying levels of economic development across different administrative regions or the age distribution of major population groups. Future research will focus on specific regions or demographic groups. Although this study employs hybrid class analysis and the Better-Worse coefficient analysis to rank various demands, for deeper analysis, future studies could consider combining a more systematic Analytical Hierarchy Process (AHP) and the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS), which allows for the ranking of priorities among specific options.

References

1. 1. Huang H. Digital government: Policies, characteristics and concepts. Gov Stud. 2020:36(03);6.

* View Article

* Google Scholar

2. 2. Meng T. Elements, mechanisms and approaches towards digital transformation of government: The dual drivers from technical empowerment to the state and society. Gov Stud. 2021:37(01);5.

* View Article

* Google Scholar

3. 3. Li W, Zhang L. Influencing factors and realization paths for smart community construction in China. PLoS One 2024;19(5):e0303687. pmid:38805438

* View Article

* PubMed/NCBI

* Google Scholar

4. 4. Dai Y. Big precise construction of public services driven by big data—Based on the perspective of citizens’ sense of gain. Serv Sci Manag. 2023;12:129.

* View Article

* Google Scholar

5. 5. Huang X. The development of china’s digital government service industry and the solution to lts problems-an investigation centered on administrative intelligent decision-making. J Jishou Univ (Social Sciences). 2023:44(06);72.

* View Article

* Google Scholar

6. 6. Department of Economic and Social Affairs UN. UN E-Government Survey 2022. 2022.

* View Article

* Google Scholar

7. 7. Huang R, Chen G. Study on the sharing cooperation mode of U.S. open government data. Libr Inf Serv. 2016;60(19):6–14.

* View Article

* Google Scholar

8. 8. Lee J. Big data strategies for government, society and policy-making. J Asian Finance Econ Bus. 2020;7(07):475–87.

* View Article

* Google Scholar

9. 9. Wei J, Wang Z. Concept expression and characteristic analysis of smart public culture service. Inf Documentation Serv. 2020;41(04):12–21.

* View Article

* Google Scholar

10. 10. Chang F, Das D. Smart nation Singapore: Developing policies for a citizen-oriented smart city initiative. In: Developing national urban policies: ways forward to green and smart cities. New York: Springer; 2020. p. 425–440.

11. 11. Zeng Y, Zhang Z, Ye Z, Li L. Regional innovation effect of smart city construction in China. PLoS One 2023;18(2):e0281862. pmid:36795669

* View Article

* PubMed/NCBI

* Google Scholar

12. 12. Holroyd C. Technological innovation and building a “super smart” society: Japan’s vision of society 5.0. J Asian Public Policy. 2022;15(01):18–31.

* View Article

* Google Scholar

13. 13. National Development and Reform Commission, China. The report shows that the integrated government service capacity has been significantly improved, driving market entities and the public to handle their affairs more conveniently and efficiently. 2022. https://www.gov.cn/xinwen/2022-09/08/content_5708968

* View Article

* Google Scholar

14. 14. National Development and Reform Commission, China. Over 890 million users in 5 years! The national government service “One Network” benefits you and me. 2024. https://www.gov.cn/yaowen/liebiao/202405/content_6954752

* View Article

* Google Scholar

15. 15. National Development and Reform Commission, China. Notice of the national development and reform commission on issuing the “14th five year plan for promoting national government informatization”. 2022.

* View Article

* Google Scholar

16. 16. Meijer A. Datapolis: A public governance perspective on “Smart Cities”. Perspect Public Manag Gov 2017;1(3):195–206.

* View Article

* Google Scholar

17. 17. Chen Q. Investigating the current approach to developing data governance in the Canadian smart city. Waterloo: University of Waterloo; 2023.

18. 18. Nikiforova A, Lnenicka M. A multi-perspective knowledge-driven approach for analysis of the demand side of the open government data portal. Gov Inf Q. 2021:38(04);101622.

* View Article

* Google Scholar

19. 19. Qi Z. Construction process, practical logic and historical experience of China’s e-government. J Shenzhen Univ (Humanities & Social Sciences). 2022;39(02):13–23.

* View Article

* Google Scholar

20. 20. Wang D. Literature review and prospects of e-government user value. Adv Soc Sci. 2024:13(2);1216.

* View Article

* Google Scholar

21. 21. Heaton J, Parlikad AK. A conceptual framework for the alignment of infrastructure assets to citizen requirements within a Smart Cities framework. Cities. 2019;90:32–41.

* View Article

* Google Scholar

22. 22. Sebo M, Bel G. E-government and provision of public services: economic, social, and political determinants of citizen complaints. Int Public Manag J. 2024;27(4):659–79.

* View Article

* Google Scholar

23. 23. Afiyah S. The impact of e-government services, citizen participation, and transparency on public trust in government. Global Int J Innov Res. 2024;2(6):1246–61.

* View Article

* Google Scholar

24. 24. Djuric A. Challenges, citizens’ trust and privacy protection models in e-government systems: Security and privacy perspective. In: International Symposium INFOTEH-JAHORINA. 2024. p. 1–6.

* View Article

* Google Scholar

25. 25. Noori M. The effect of e-service quality on user satisfaction and loyalty in accessing e-government information. Int J Data Netw Sci. 2022;6(03):945–52.

* View Article

* Google Scholar

26. 26. Xia L, Zhai S, Li G. User demand-oriented E-Government information service model. Libr Inf Serv. 2010:54(08);21.

* View Article

* Google Scholar

27. 27. Meng Q, Wang Y, Tian H. Intelligent search in government services: characteristics, application scenarios, and operating mechanisms. E-Government. 2020;17(02):21–33.

* View Article

* Google Scholar

28. 28. Liu L, Miller HJ, Scheff J. The impacts of COVID-19 pandemic on public transit demand in the United States. PLoS One 2020;15(11):e0242476. pmid:33206721

* View Article

* PubMed/NCBI

* Google Scholar

29. 29. Lin Y, Hu Z, Zhao Q, Alias H, Danaee M, Wong LP. Understanding COVID-19 vaccine demand and hesitancy: A nationwide online survey in China. PLoS Negl Trop Dis 2020;14(12):e0008961. pmid:33332359

* View Article

* PubMed/NCBI

* Google Scholar

30. 30. Wang T. The matching between citizen demand and government supply in smart government. 2022.

* View Article

* Google Scholar

31. 31. Huang G, Li D, Ng ST. A methodology for assessing supply-demand matching of smart government services from citizens’ perspective: A case study in Nanjing, China. Habitat Int. 2023;138:102880.

* View Article

* Google Scholar

32. 32. Al-Rahmi W, Uddin M, Alkhalaf S. Validation of an integrated IS success model in the study of E-Government. Mob Inf Syst. 2022;(01):8909724.

* View Article

* Google Scholar

33. 33. Kermanshachi S, Nipa TJ, Nadiri H. Service quality assessment and enhancement using Kano model. PLoS One 2022;17(2):e0264423. pmid:35213604

* View Article

* PubMed/NCBI

* Google Scholar

34. 34. Ye Q, Wei D, Huang J. Research on performance evaluation of government informatization projects throughout their lifecycle: A case study of Wenzhou city. China Inform. 2024;(08):49–51.

* View Article

* Google Scholar

35. 35. Kang Y, Zhou L. RubE: Rule-based methods for extracting product features from online consumer reviews. Inf Manag 2017;54(2):166–76.

* View Article

* Google Scholar

36. 36. Chan VHY, Chiu DKW, Ho KKW. Mediating effects on the relationship between perceived service quality and public library app loyalty during the COVID-19 era. J Retail Consum Serv. 2022;67:102960.

* View Article

* Google Scholar

37. 37. Ramirez-Madrid JP, Escobar-Sierra M, Lans-Vargas I, Montes Hincapie JM. Government influence on e-government adoption by citizens in Colombia: Empirical evidence in a Latin American context. PLoS One 2022;17(2):e0264495. pmid:35213659

* View Article

* PubMed/NCBI

* Google Scholar

38. 38. Nkgapele SM. The usability of e-government as a mechanism to enhance public service delivery in the South African government. DPS 2024;3(1):116–32.

* View Article

* Google Scholar

39. 39. Kulal A, Rahiman HU, Suvarna H, Abhishek N, Dinesh S. Enhancing public service delivery efficiency: Exploring the impact of AI. J Open Innov Technol Mark Complex 2024;10(3):100329.

* View Article

* Google Scholar

40. 40. Gupta P, Hooda A, Jeyaraj A. Trust, risk, privacy and security in e-Government use: Insights from a MASEM analysis. Inf Syst Front. 2024:1–17.

* View Article

* Google Scholar

41. 41. Wang D, Richards D, Bilgin AA, et al. Citizens’ utilization of open government data portals in China: a comparative case study of supply vs demand. Aslib J Inf Manag. 2024;76(03):522–551.

* View Article

* Google Scholar

42. 42. Pansera M, Marsh A, Owen R. Exploring citizen participation in smart city development in Mexico City: An institutional logics approach. Organ Stud. 2023;44(10):1679–701.

* View Article

* Google Scholar

43. 43. Li Y, Zhang L, Huang B. Research on the new model of “Internet plus” government information service: taking administrative approval as an example. E-Government. 2018;15(06):89–98.

* View Article

* Google Scholar

44. 44. Guimarães D, Severo E, Júnior L, et al. Governance and quality of life in smart cities: Towards sustainable development goals. J Clean Prod. 2020;253:119926.

* View Article

* Google Scholar

45. 45. Li J, Wang M. Research on full life cycle government services centered on service objects. J Chengdu Adm Inst. 2018;35(06):4–8.

* View Article

* Google Scholar

46. 46. Wang F. Research on enterprise customer relationship management capabilities based on customer life cycle. China Manag Inform. 2016:19(17);113.

* View Article

* Google Scholar

47. 47. Wang Z. Analysis of the optimization path of “Internet plus government services” from the perspective of digital governance. China Manag Inform. 2023;26(10):178–81.

* View Article

* Google Scholar

48. 48. Ye X, Dong L, Song Y. Research on construction and service strategies of “Internet + Government Services” cloud platform based on big data and knowledge. J Intell. 2018;37(02):154–60.

* View Article

* Google Scholar

49. 49. Yu G, Chen W, Wang Q. The analysis of E-government mode and smarter government structure. Chinese Publ Adm. 2016;02:22–6.

* View Article

* Google Scholar

50. 50. Xiao D, Zhou Q, Liu H. Making elderly-oriented design more “heartfelt”: The formation mechanism and optimization of elderly user experience of government service APPs. Library Tribune. 2023; p. 1–9.

* View Article

* Google Scholar

51. 51. Harun D, Priyanto S, Suharti L. Farmer cards: model, database, accuracy, and improvement in government quality service. Conscientia Beam. 2021;(3):14–33.

* View Article

* Google Scholar

52. 52. Špaček D, Špačková Z. Issues of e-government services quality in the digital-by-default era – the case of the national e-procurement platform in Czechia. JOPP 2022;23(1):1–34.

* View Article

* Google Scholar

53. 53. Di L. Studies on demand factors of cross-border collaborative service of regional smart government. Governance Modernization Studies. 2023;29(03):43–51.

* View Article

* Google Scholar

54. 54. National Academy of Governance, China. Annual report on China’s E-Government development; 2023. Annual report on China’s E-Government development. 2023.

* View Article

* Google Scholar

55. 55. Mohamed A, Najafabadi MK, Wah YB, Zaman EAK, Maskat R. The state of the art and taxonomy of big data analytics: view from new big data framework. Artif Intell Rev 2019;53(2):989–1037.

* View Article

* Google Scholar

56. 56. Shammar EA, Zahary AT. The Internet of Things (IoT): a survey of techniques, operating systems, and trends. LHT 2019;38(1):5–66.

* View Article

* Google Scholar

57. 57. China Academy of Information and Communications Technology China. White paper on the development and application of government data cloud. 2023.

* View Article

* Google Scholar

58. 58. Briere D, Hurley P. Smart homes for dummies. New Jersey: xJohn Wiley & Sons. 2011.

59. 59. Yang Y, Chen L. Exploration, achievement and optimization of intelligent government service reform in Shenzhen. Special Zone Econ. 2023;41(02):16–9.

* View Article

* Google Scholar

60. 60. Tsinghua University, China. Report on the Development of Government Data Governance in China. 2021.

* View Article

* Google Scholar

61. 61. Adams WC. Conducting semi‐structured interviews. Handbook of practical program evaluation. 2015; p. 492–505.

* View Article

* Google Scholar

62. 62. National Academy of Governance, China. China Digital Government Construction Report; 2021.

* View Article

* Google Scholar

63. 63. Kano N, Seraku N, Takahashi F, Tsuji S. Attractive quality and must-be quality. J Jpn Soc Qual Control. 1984;14(02):147–56.

* View Article

* Google Scholar

64. 64. Lu M, Lu H, Chen C. Exploring the key priority development projects of smart transportation for sustainability: Using Kano model. Sustainability. 2022:14(15);9319.

* View Article

* Google Scholar

65. 65. Li J, Wang Q, Xuan Y, Zhou H. User demands analysis of Eco-city based on the Kano model-An application to China case study. PLoS One 2021;16(3):e0248187. pmid:33690633

* View Article

* PubMed/NCBI

* Google Scholar

66. 66. Li P, Yang Q, Liu X, et al. Exploring the functional quality attributes of smart home for the elderly based on qualitative research and Kano model. Maturitas. 2024.

* View Article

* Google Scholar

67. 67. Liu X. Research on function optimization of electric vehicle charging stations based on user demand analysis: An empirical study using the Kano model and AHP method. Sustainability. 2024:16(23);10783.

* View Article

* Google Scholar

68. 68. Xu B. Research on the establishment and development of farmers professional cooperatives from the perspective of social support networks. Prod Res. 2018;(04):59–64.

* View Article

* Google Scholar

69. 69. Lin F-H, Tsai S-B, Lee Y-C, Hsiao C-F, Zhou J, Wang J, et al. Empirical research on Kano’s model and customer satisfaction. PLoS One 2017;12(9):e0183888. pmid:28873418

* View Article

* PubMed/NCBI

* Google Scholar

70. 70. Noriaki K. Life cycle and creation of attractive quality. In: Proceedings of the 4th QMOD Conference. 2001; p. 12–14.

* View Article

* Google Scholar

71. 71. Berger C, Blauth R, Boger D. Kano’s methods for understanding customer-defined quality. CQM J. 1993;2(04):3–36.

* View Article

* Google Scholar

72. 72. Mikulic J, Prebezac D. A critical review of techniques for classifying quality attributes in the Kano model. J Serv Theory Prac. 2011;21(01):46–66.

* View Article

* Google Scholar

73. 73. Chen Q. Research on user behavior characteristics and personalized information service strategy of mobile government. Nanchang University. 2023.

* View Article

* Google Scholar

74. 74. Wang Y. Evaluation of smart government service quality based on Servqual. Huazhong University of Science & Technology. 2019.

* View Article

* Google Scholar

75. 75. Xiang Y, Feng Y. Dual-layer learning interaction, local digital government service innovation process and generation mechanism: Analysis based on the experience of N city. Urban Problems. 2023;(10):14–23.

* View Article

* Google Scholar

76. 76. Zheng Y. Research on optimization strategy of smart city construction in Z city. Dalian Maritime University. 2023.

* View Article

* Google Scholar

77. 77. Liu Q. From convenient service to value-added service: government service system reshaping and governance reform. J Party Sch Tianjin Comm CPC. 2024;26(03):63–75.

* View Article

* Google Scholar

78. 78. Wang Z. Research on satisfaction with and evaluation of intelligent government service in Xiangfang district. Harbin Engineering University. 2023.

* View Article

* Google Scholar

79. 79. Ji C, Ma L. Mobile government performance and the driving forces based on user reviews. Gov. Stud. 2023;39(03):88–105.

* View Article

* Google Scholar

Citation: Zhu Y, Peng Y, Xin L, Zhang X (2025) Public demand and optimization of smart government service platform from the perspective of service life cycle based on the Kano model: A Chinese case study. PLoS ONE 20(4): e0319707. https://doi.org/10.1371/journal.pone.0319707

About the Authors:

Yang Zhu

Roles: Conceptualization, Formal analysis, Methodology, Project administration

E-mail: [email protected]

Affiliation: School of Economics and Management, Beijing University of Posts and Telecommunications, Beijing, China

ORICD: https://orcid.org/0000-0001-8345-5884

Yifan Peng

Roles: Data curation, Formal analysis, Resources, Writing – original draft

Affiliation: School of Economics and Management, Beijing University of Posts and Telecommunications, Beijing, China

Lingling Xin

Roles: Resources, Supervision

Affiliation: School of Economics and Management, Beijing University of Posts and Telecommunications, Beijing, China

Xiao Zhang

Roles: Validation

Affiliation: China Internet Network Information Center, Beijing, China

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[/RAW_REF_TEXT]

[/RAW_REF_TEXT]

References

1. Huang H. Digital government: Policies, characteristics and concepts. Gov Stud. 2020:36(03);6.

2. Meng T. Elements, mechanisms and approaches towards digital transformation of government: The dual drivers from technical empowerment to the state and society. Gov Stud. 2021:37(01);5.

3. Li W, Zhang L. Influencing factors and realization paths for smart community construction in China. PLoS One 2024;19(5):e0303687. pmid:38805438

4. Dai Y. Big precise construction of public services driven by big data—Based on the perspective of citizens’ sense of gain. Serv Sci Manag. 2023;12:129.

5. Huang X. The development of china’s digital government service industry and the solution to lts problems-an investigation centered on administrative intelligent decision-making. J Jishou Univ (Social Sciences). 2023:44(06);72.

6. Department of Economic and Social Affairs UN. UN E-Government Survey 2022. 2022.

7. Huang R, Chen G. Study on the sharing cooperation mode of U.S. open government data. Libr Inf Serv. 2016;60(19):6–14.

8. Lee J. Big data strategies for government, society and policy-making. J Asian Finance Econ Bus. 2020;7(07):475–87.

9. Wei J, Wang Z. Concept expression and characteristic analysis of smart public culture service. Inf Documentation Serv. 2020;41(04):12–21.

10. Chang F, Das D. Smart nation Singapore: Developing policies for a citizen-oriented smart city initiative. In: Developing national urban policies: ways forward to green and smart cities. New York: Springer; 2020. p. 425–440.

11. Zeng Y, Zhang Z, Ye Z, Li L. Regional innovation effect of smart city construction in China. PLoS One 2023;18(2):e0281862. pmid:36795669

12. Holroyd C. Technological innovation and building a “super smart” society: Japan’s vision of society 5.0. J Asian Public Policy. 2022;15(01):18–31.

13. National Development and Reform Commission, China. The report shows that the integrated government service capacity has been significantly improved, driving market entities and the public to handle their affairs more conveniently and efficiently. 2022. https://www.gov.cn/xinwen/2022-09/08/content_5708968

14. National Development and Reform Commission, China. Over 890 million users in 5 years! The national government service “One Network” benefits you and me. 2024. https://www.gov.cn/yaowen/liebiao/202405/content_6954752

15. National Development and Reform Commission, China. Notice of the national development and reform commission on issuing the “14th five year plan for promoting national government informatization”. 2022.

16. Meijer A. Datapolis: A public governance perspective on “Smart Cities”. Perspect Public Manag Gov 2017;1(3):195–206.

17. Chen Q. Investigating the current approach to developing data governance in the Canadian smart city. Waterloo: University of Waterloo; 2023.

18. Nikiforova A, Lnenicka M. A multi-perspective knowledge-driven approach for analysis of the demand side of the open government data portal. Gov Inf Q. 2021:38(04);101622.

19. Qi Z. Construction process, practical logic and historical experience of China’s e-government. J Shenzhen Univ (Humanities & Social Sciences). 2022;39(02):13–23.

20. Wang D. Literature review and prospects of e-government user value. Adv Soc Sci. 2024:13(2);1216.

21. Heaton J, Parlikad AK. A conceptual framework for the alignment of infrastructure assets to citizen requirements within a Smart Cities framework. Cities. 2019;90:32–41.

22. Sebo M, Bel G. E-government and provision of public services: economic, social, and political determinants of citizen complaints. Int Public Manag J. 2024;27(4):659–79.

23. Afiyah S. The impact of e-government services, citizen participation, and transparency on public trust in government. Global Int J Innov Res. 2024;2(6):1246–61.

24. Djuric A. Challenges, citizens’ trust and privacy protection models in e-government systems: Security and privacy perspective. In: International Symposium INFOTEH-JAHORINA. 2024. p. 1–6.

25. Noori M. The effect of e-service quality on user satisfaction and loyalty in accessing e-government information. Int J Data Netw Sci. 2022;6(03):945–52.

26. Xia L, Zhai S, Li G. User demand-oriented E-Government information service model. Libr Inf Serv. 2010:54(08);21.

27. Meng Q, Wang Y, Tian H. Intelligent search in government services: characteristics, application scenarios, and operating mechanisms. E-Government. 2020;17(02):21–33.

28. Liu L, Miller HJ, Scheff J. The impacts of COVID-19 pandemic on public transit demand in the United States. PLoS One 2020;15(11):e0242476. pmid:33206721

29. Lin Y, Hu Z, Zhao Q, Alias H, Danaee M, Wong LP. Understanding COVID-19 vaccine demand and hesitancy: A nationwide online survey in China. PLoS Negl Trop Dis 2020;14(12):e0008961. pmid:33332359

30. Wang T. The matching between citizen demand and government supply in smart government. 2022.

31. Huang G, Li D, Ng ST. A methodology for assessing supply-demand matching of smart government services from citizens’ perspective: A case study in Nanjing, China. Habitat Int. 2023;138:102880.

32. Al-Rahmi W, Uddin M, Alkhalaf S. Validation of an integrated IS success model in the study of E-Government. Mob Inf Syst. 2022;(01):8909724.

33. Kermanshachi S, Nipa TJ, Nadiri H. Service quality assessment and enhancement using Kano model. PLoS One 2022;17(2):e0264423. pmid:35213604

34. Ye Q, Wei D, Huang J. Research on performance evaluation of government informatization projects throughout their lifecycle: A case study of Wenzhou city. China Inform. 2024;(08):49–51.

35. Kang Y, Zhou L. RubE: Rule-based methods for extracting product features from online consumer reviews. Inf Manag 2017;54(2):166–76.

36. Chan VHY, Chiu DKW, Ho KKW. Mediating effects on the relationship between perceived service quality and public library app loyalty during the COVID-19 era. J Retail Consum Serv. 2022;67:102960.

37. Ramirez-Madrid JP, Escobar-Sierra M, Lans-Vargas I, Montes Hincapie JM. Government influence on e-government adoption by citizens in Colombia: Empirical evidence in a Latin American context. PLoS One 2022;17(2):e0264495. pmid:35213659

38. Nkgapele SM. The usability of e-government as a mechanism to enhance public service delivery in the South African government. DPS 2024;3(1):116–32.

39. Kulal A, Rahiman HU, Suvarna H, Abhishek N, Dinesh S. Enhancing public service delivery efficiency: Exploring the impact of AI. J Open Innov Technol Mark Complex 2024;10(3):100329.

40. Gupta P, Hooda A, Jeyaraj A. Trust, risk, privacy and security in e-Government use: Insights from a MASEM analysis. Inf Syst Front. 2024:1–17.

41. Wang D, Richards D, Bilgin AA, et al. Citizens’ utilization of open government data portals in China: a comparative case study of supply vs demand. Aslib J Inf Manag. 2024;76(03):522–551.

42. Pansera M, Marsh A, Owen R. Exploring citizen participation in smart city development in Mexico City: An institutional logics approach. Organ Stud. 2023;44(10):1679–701.

43. Li Y, Zhang L, Huang B. Research on the new model of “Internet plus” government information service: taking administrative approval as an example. E-Government. 2018;15(06):89–98.

44. Guimarães D, Severo E, Júnior L, et al. Governance and quality of life in smart cities: Towards sustainable development goals. J Clean Prod. 2020;253:119926.

45. Li J, Wang M. Research on full life cycle government services centered on service objects. J Chengdu Adm Inst. 2018;35(06):4–8.

46. Wang F. Research on enterprise customer relationship management capabilities based on customer life cycle. China Manag Inform. 2016:19(17);113.

47. Wang Z. Analysis of the optimization path of “Internet plus government services” from the perspective of digital governance. China Manag Inform. 2023;26(10):178–81.

48. Ye X, Dong L, Song Y. Research on construction and service strategies of “Internet + Government Services” cloud platform based on big data and knowledge. J Intell. 2018;37(02):154–60.

49. Yu G, Chen W, Wang Q. The analysis of E-government mode and smarter government structure. Chinese Publ Adm. 2016;02:22–6.

50. Xiao D, Zhou Q, Liu H. Making elderly-oriented design more “heartfelt”: The formation mechanism and optimization of elderly user experience of government service APPs. Library Tribune. 2023; p. 1–9.

51. Harun D, Priyanto S, Suharti L. Farmer cards: model, database, accuracy, and improvement in government quality service. Conscientia Beam. 2021;(3):14–33.

52. Špaček D, Špačková Z. Issues of e-government services quality in the digital-by-default era – the case of the national e-procurement platform in Czechia. JOPP 2022;23(1):1–34.

53. Di L. Studies on demand factors of cross-border collaborative service of regional smart government. Governance Modernization Studies. 2023;29(03):43–51.

54. National Academy of Governance, China. Annual report on China’s E-Government development; 2023. Annual report on China’s E-Government development. 2023.

55. Mohamed A, Najafabadi MK, Wah YB, Zaman EAK, Maskat R. The state of the art and taxonomy of big data analytics: view from new big data framework. Artif Intell Rev 2019;53(2):989–1037.

56. Shammar EA, Zahary AT. The Internet of Things (IoT): a survey of techniques, operating systems, and trends. LHT 2019;38(1):5–66.

57. China Academy of Information and Communications Technology China. White paper on the development and application of government data cloud. 2023.

58. Briere D, Hurley P. Smart homes for dummies. New Jersey: xJohn Wiley & Sons. 2011.

59. Yang Y, Chen L. Exploration, achievement and optimization of intelligent government service reform in Shenzhen. Special Zone Econ. 2023;41(02):16–9.

60. Tsinghua University, China. Report on the Development of Government Data Governance in China. 2021.

61. Adams WC. Conducting semi‐structured interviews. Handbook of practical program evaluation. 2015; p. 492–505.

62. National Academy of Governance, China. China Digital Government Construction Report; 2021.

63. Kano N, Seraku N, Takahashi F, Tsuji S. Attractive quality and must-be quality. J Jpn Soc Qual Control. 1984;14(02):147–56.

64. Lu M, Lu H, Chen C. Exploring the key priority development projects of smart transportation for sustainability: Using Kano model. Sustainability. 2022:14(15);9319.

65. Li J, Wang Q, Xuan Y, Zhou H. User demands analysis of Eco-city based on the Kano model-An application to China case study. PLoS One 2021;16(3):e0248187. pmid:33690633

66. Li P, Yang Q, Liu X, et al. Exploring the functional quality attributes of smart home for the elderly based on qualitative research and Kano model. Maturitas. 2024.

67. Liu X. Research on function optimization of electric vehicle charging stations based on user demand analysis: An empirical study using the Kano model and AHP method. Sustainability. 2024:16(23);10783.

68. Xu B. Research on the establishment and development of farmers professional cooperatives from the perspective of social support networks. Prod Res. 2018;(04):59–64.

69. Lin F-H, Tsai S-B, Lee Y-C, Hsiao C-F, Zhou J, Wang J, et al. Empirical research on Kano’s model and customer satisfaction. PLoS One 2017;12(9):e0183888. pmid:28873418

70. Noriaki K. Life cycle and creation of attractive quality. In: Proceedings of the 4th QMOD Conference. 2001; p. 12–14.

71. Berger C, Blauth R, Boger D. Kano’s methods for understanding customer-defined quality. CQM J. 1993;2(04):3–36.

72. Mikulic J, Prebezac D. A critical review of techniques for classifying quality attributes in the Kano model. J Serv Theory Prac. 2011;21(01):46–66.

73. Chen Q. Research on user behavior characteristics and personalized information service strategy of mobile government. Nanchang University. 2023.

74. Wang Y. Evaluation of smart government service quality based on Servqual. Huazhong University of Science & Technology. 2019.

75. Xiang Y, Feng Y. Dual-layer learning interaction, local digital government service innovation process and generation mechanism: Analysis based on the experience of N city. Urban Problems. 2023;(10):14–23.

76. Zheng Y. Research on optimization strategy of smart city construction in Z city. Dalian Maritime University. 2023.

77. Liu Q. From convenient service to value-added service: government service system reshaping and governance reform. J Party Sch Tianjin Comm CPC. 2024;26(03):63–75.

78. Wang Z. Research on satisfaction with and evaluation of intelligent government service in Xiangfang district. Harbin Engineering University. 2023.

79. Ji C, Ma L. Mobile government performance and the driving forces based on user reviews. Gov. Stud. 2023;39(03):88–105.