Factors influencing user perception and adoption of e-government services

Ilieva, Galina et al. Article Factors influencing user perception and adoption of egovernment services Administrative Sciences Provided in Cooperation with: MDPI – Multidisciplinary Digital Publishing Institute, Basel Suggested Citation: Ilieva, Galina et al. (2024) : Factors influencing user perception and adoption of egovernment services, Administrative Sciences, ISSN 2076-3387, MDPI, Basel, Vol. 14, Iss. 3, pp. 1-30, https://doi.org/10.3390/admsci14030054 This Version is available at: https://hdl.handle.net/10419/320877 Standard-Nutzungsbedingungen: Die Dokumente auf EconStor dürfen zu eigenen wissenschaftlichen Zwecken und zum Privatgebrauch gespeichert und kopiert werden. Sie dürfen die Dokumente nicht für öffentliche oder kommerzielle Zwecke vervielfältigen, öffentlich ausstellen, öffentlich zugänglich machen, vertreiben oder anderweitig nutzen. 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Factors Influencing User Perception and Adoption of E-Government Services. Administrative Sciences 14: 54. https://doi.org/10.3390/ admsci14030054 Received: 4 February 2024 Revised: 4 March 2024 Accepted: 6 March 2024 Published: 12 March 2024 Copyright: © 2024 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https:// creativecommons.org/licenses/by/ 4.0/). administrative sciences Article Factors Influencing User Perception and Adoption of E-Government Services Galina Ilieva 1,* , Tania Yankova 1, Margarita Ruseva 1, Yulia Dzhabarova 2, Veselina Zhekova 1, Stanislava Klisarova-Belcheva 1, Tanya Mollova 1and Angel Dimitrov 1 1Department of Management and Quantitative Methods in Economics, University of Plovdiv Paisii Hilendarski, 4000 Plovdiv, Bulgaria; [email protected] (T.Y.); [email protected] (M.R.); [email protected] (V.Z.); [email protected] (S.K.-B.); [email protected] (T.M.); [email protected] (A.D.) 2Department of Marketing and International Economic Relations, University of Plovdiv Paisii Hilendarski, 4000 Plovdiv, Bulgaria; [email protected] *Correspondence: [email protected] Abstract: The objective of this study is to investigate and determine factors influencing user perception and acceptance of electronic government services in the context of technological advancements. The research focuses on classifying the main features of e-administrative systems with an emphasis on user satisfaction by integrating both traditional and modern data analysis techniques. Structural Equation Modelling (SEM), machine learning (ML) techniques, and multi-criteria decision-making (MCDM) methods have been applied to survey data to uncover the interdependencies between variables from the perspective of online users. The developed models discover and explain the underlying relationships in user attitudes towards e-government services. As the perception of customer satisfaction is subjective and dynamic, stakeholders should conduct regular measurements and data analysis to ensure continuous improvement of e-public services. Keywords: electronic public services; electronic government services; technology adoption; customer satisfaction; behaviour intention; structural equation modelling; PLS-SEM; machine learning 1. Introduction Electronic administrative processes streamline traditional operations, reducing costs associated with outdated manual practices and contributing to the development of a more efficient, transparent, and customer-centric administrative ecosystem (Doran et al. 2023) . Additionally, digital government platforms enhance connectivity via interactions over distance through cost-effective communication systems. Digitalization in the public sector not only improves the quality and accessibility of public services, it reshapes citizen– government, businesses–government, and government–government relationships. Moreover, this transformation in public organizations supports the transition to cleaner energy sources by optimizing resource usage and minimizing the need for physical inputs (Firmandayu and Elfaki 2023;Gomez-Trujillo and Gonzalez-Perez 2021;Hochstetter et al. 2023). The digital evolution of public administration further facilitates the development of inclusive institutions and positively influences the overall progress of society (Tokovska et al. 2023). Electronic government (e-government) refers to the use of information and communication technologies (ICT) in delivering public services. E-government instruments, including digital platforms, IT systems, and software apps, ensure dynamic interactions remotely even in times of crisis, as demonstrated during the COVID-19 pandemic (Hodzic et al. 2021) . Furthermore, e-government tools have the potential to offer services for vulnerable social groups, such as individuals living in poverty, older adults, people with disabilities, immigrants, and youth by addressing their specific needs (Seljan et al. 2020). Adm. Sci. 2024,14, 54. https://doi.org/10.3390/admsci14030054 https://www.mdpi.com/journal/admsci Adm. Sci. 2024,14, 54 2 of 30 At the global level, international organizations such as United Nations (UN), Organization for Economic Co-operation and Development (OECD), and World Bank (WB) collaborate with their member countries to facilitate the spread of e-government technologies and practices. These cooperative efforts aim to enhance openness and citizen engagement in public administration activities. For example, the UN Sustainable Development Goals (SDGs), as outlined in their 2030 Agenda (UN General Assembly 2015), closely align with the concept of e-government, emphasizing connectivity and open data availability as fundamental to a more sustainable global community (Othman et al. 2020). As a result, an increasing number of countries are prioritizing the digitization of public services. For example, in the European Union (EU), the Digital Decade programme (2022) seeks to achieve 100% online provision of “key public services” by 2030, while the goal of the Interoperable Europe Act (2022) is to boost cross-border interoperability and co-operation in the public sector. In Denmark, India, the Netherlands, and other countries, electronic public services are even mandatory for public administration (Tangi et al. 2021). Electronic public services (e-public services, electronic administrative services, eadministrative services) encompass a wide range of activities at both central and territorial levels of government. These services include digital document submission, electronic tax filing and payment, online applications for licenses and permits, and online communication with organizational structures and entities. Their prevalence can be significantly increased, driven by technological innovations and the demand for efficient and convenient government interactions (Torres et al. 2005). The COVID-19 pandemic and subsequent social distancing measures accelerated the dissemination of e-government services. With restrictions on in-person contacts, individuals, and businesses turned to digital channels to fulfil their administrative needs remotely. This transition has led to elevated utilization of electronic platforms for various government-related transactions even among customers who previously had limited experience or engagement with electronic public services (Chatzopoulou et al. 2021). Unfortunately, as the health situation normalizes, there has been some decrease in requests for e-administrative services. For example, in Bulgaria, during the COVID-19 health crisis, the number of provided e-administrative services increased by 130% in 2021 compared to the previous year, while in the following year (2022) it decreased by 18% (Council of Ministers 2020;Council of Ministers 2021;Council of Ministers 2022). According to UN Electronic Government Evaluation Index (EGDI) reports, Europe has consistently held the highest average EGDI among the continents since the initiation of UN e-Government Survey in 2003. Furthermore, the development of e-government across Europe is notably more uniform compared to other continents (UN DESA 2022). The majority of surveyed European countries fall within the highest EGDI group, with eight of them ranking among the global leaders in e-government development. However, as per the EU e-Government Benchmark report (Capgemini et al. 2023), there are significant variations among European countries in key indicators such as transparency and cross-border services. Globally these differences are even more pronounced, with African countries facing the greatest lag in e-government development. Moreover, the deployment of new e-administrative services often entails a variety of challenges extending beyond software implementation issues, such as integration, user awareness, and training needs. These issues have a negative impact on perception of e-government services. However, there is no unified framework or methodology for assessment of e-public services adoption. Examining factors influencing these attitudes and predicting their impact on the utilization of electronic public services poses a complex challenge for the following three primary reasons: 1. Recent advancements in ICT technologies, including Artificial Intelligence (AI), blockchain, and the Internet of Things (IoT) can enhance the methods and channels of e-government (Ivi´c et al. 2022). 2. The dynamics, uncertainty, and complexity of the economic landscape influence users’ requirements, preferences, and habits. As technologies evolve, expectations Adm. Sci. 2024,14, 54 3 of 30 and demands of users for the channels delivering e-public services are increasingly shifting online (Solvak et al. 2019). 3. The existing methods for customer satisfaction research can be expanded through the incorporation of machine learning (ML) (AlHadid et al. 2022), fuzzy logic, big data, and other intelligent techniques or their combinations. This enrichment requires the exploration of new dependencies in understanding user satisfaction and preferences towards e-administrative services. The objective of this study is to examine the factors that influence user perception and intention to use e-public services. By establishing a comprehensive understanding of these factors, we aim to develop a theoretical framework and empirical models that can guide government agencies in designing and implementing effective e-administrative systems. Additionally, we investigate the impact of demographic and socioeconomic factors, such as gender, age, education level, residence area, and monthly income, on user acceptance and adoption of e-administrative services. The main tasks of this research are as follows: • Propose a methodological framework that facilitates the systematic analysis of customer data and can reveal hidden relationships between factors influencing the adoption of new information technologies (IT) in the public sector; • Collect and systemize a customer dataset about their experiences and preferences regarding online public services (gender, age, residential area, monthly income, attitudes and opinions); • Create and validate a Structural Equation Model (SEM) based on factors from the literature review and assess their influence on customer attitude toward e-administrative services; • Identify the key factors affecting customer use and intention to use e-administrative services according to the obtained model; • Create and evaluate alternative ML and MCDA models for prediction of user perception and adoption of e-administrative services. To explore customer adoption of e-government services, we divide satisfaction factors into seven main groups and employ the corresponding mathematical models for prediction. The obtained factors’ weights can be integrated into multi-criteria assessment systems for evaluation of e-administrative services. The main contribution of this paper is the development of a new complex methodology incorporating structural equation and ML models with MCDM for evaluation, comparison, and prediction of customer attitudes towards e-public services. The remainder of this paper is organised as follows: Section 2provides an overview of e-government services and the indicators for their assessment; Section 3reviews relevant literature on user perception and acceptance of e-public services; Section 4outlines the research objectives and methodology; Section 5presents the results obtained from the analysis of the collected dataset; finally, in Section 6we discuss the implications of the study, highlight its contributions, and provide future research directions in the field of e-administrative services. 2. State of the Art Review of Digital Administrative Services Digital public services revolutionize the way citizens and businesses interact with public administration, providing efficient ICT instruments for various transactions. These innovative services enable their users to conveniently perform everyday tasks such as document submissions, fee payments, and application processing through digital interactions. Simultaneously, they empower government agencies by transforming service delivery methods, offering added value and enhanced user experience through geographical boundaries. Additionally, these advancements foster improvements in administration-to-administration relationships, offering greater collaboration and efficiency in intergovernmental interactions. Adm. Sci. 2024,14, 54 4 of 30 However, a significant challenge arises, as individuals may not be fully acquainted with the capabilities of these cutting-edge administrative applications. This lack of knowledge often results in a discrepancy between the potential benefits of electronic administrative services and the needs of the citizens they serve. In this context, we explore the distinctive features of e-administrative services, emphasizing their transformative potential. 2.1. Key Features and Taxonomy of Electronic Public Services Digital public services enclose a wide range of online services provided by governmental bodies to citizens, businesses, and other stakeholders. Recently, emerging ICT such as AI, blockchain, and IoT (Internet of Things) have reshaped the landscape of delivering such public services (Ivi´c et al. 2022). Artificial Intelligence plays a significant role in automating routine administrative tasks, improving decision-making processes, and enhancing overall efficiency. AI algorithms analyse vast datasets to derive insights, enabling governments to make data-driven policy decisions and streamline service delivery. Chatbots powered by AI methods provide citizens with instant support, enhancing accessibility and responsiveness in public services (Al-Mushayt 2019). Blockchain technology, known for its decentralized and secure nature, has brought transparency and trust to public service transactions. It ensures the integrity and immutability of records, reducing fraud and corruption risks. In areas such as identity verification and document authentication, blockchain enhances security and minimizes the risk of data manipulation. This technology is particularly valuable in creating accountable and transparent systems for public records, such as land registries and financial transactions, fostering greater trust between the government and its stakeholders (Lykidis et al. 2021). The Internet of Things is another transformative technology that contributes to the delivery of public services. IoT devices ranging from smart sensors to connected infrastructure enable real-time monitoring and data collection. In public safety, for instance, IoT devices can enhance emergency response systems by providing accurate and timely information. Smart city initiatives leverage IoT to optimize resource management, improve transportation, and refine environmental monitoring (Bansal et al. 2022). Integrating innovative technologies such as AI, blockchain, and IoT in delivering public services creates a technological synergy that improves efficiency while fostering more citizen-centric, secure, and responsive governance. E-public services are also important in attracting foreign investments by establishing an accessible and transparent business environment. Countries with well-developed digital platforms for public services create a positive business climate, offering streamlined processes for regulatory compliance, licensing, and other administrative procedures. Consequently, e-public service infrastructure not only enhances the overall attractiveness of a country for foreign investments but reflects its commitment to modernization and effective governance (Al-Sadiq 2021). Given the increasing dependence on digital business-to-consumer interactions in various aspects of everyday life, citizens expect the public sector to meet elevated standards similar to those set by business software. As technologies evolve, citizens expect e-government to provide intuitive interfaces, timely services, and a user-centric approach, aligning with the benchmarks established by successful online business applications (Holzer et al. 2019). For instance, the integrated services of the Chinese super-application WeChat, including utility payments and notifications, can be personalized to some extent based on user preferences and location (Pan 2020). Starting in the 1990s, numerous public administration entities have implemented technological innovations by offering online alternatives to traditional service delivery methods. Electronic public services can be categorized in various ways by different criteria. Service model: E-public services can be split by their recipients (users) and classified into three main categories: government-to-citizen (G2C), government-to-business (G2B), and government-to-government (G2G). Adm. Sci. 2024,14, 54 5 of 30 Service type: This criterion categorizes online public services based on their primary function, distinguishing between core government functions, citizen-centric services, and those catering to businesses and industries. Government level: According to their administrative level, public services can be divided into three groups—central, regional, or local government offerings. Interactivity level: Differentiates between informational, transactional, and interactive services, reflecting the extent of user engagement and functionality. Delivery channel: E-public services can be classified according to the medium through which they are accessed—online platforms, systems, or mobile applications. Service maturity: According to the stage model (Layne and Lee 2001), different stages represent the evolutionary progression in the implementation of electronic public services. A common stage model for e-government includes the following stages: emerging, enhanced, transactional, integrated, networked, and ubiquitous services (Lemke et al. 2020). The widespread use of digital public services by citizens, businesses, and public administration relies on many factors, including the presence of suitable electronic infrastructure, internet availability, and the proficiency to design, implement, manage, and utilize e-government systems. 2.2. Assessing Electronic Public Services When evaluating electronic government development, a range of assessment tools can be employed to evaluate the quality, security, and overall effectiveness of public services. These can be categorized in five main areas: (1) indices; (2) standard specifications; (3) frameworks; (4) theoretical models; and (5) metrics (Table 1). These benchmarks can complement each other in enhancing the understanding and improvement of the adoption and maturity of e-government initiatives. Among the most widely used indices for e-government evaluation are the following: EGDI: Published by the UN, EGDI is a composite index that measures the state of e-government development in countries based on three dimensions: online services, telecommunications infrastructure, and human capital. It ranks countries according to their e-government readiness (Hernández et al. 2024). e-Government Benchmark: This benchmark of the European Commission assesses the digital maturity of European countries in providing online public services. It evaluates different dimensions, such as online services, online cross-border services, electronic IDentification (eID), e-documents, and pre-filled forms (Majo 2023). Digital Government Index: The OECD defines a set of indicators to assess the maturity of digital government across its member countries. It covers aspects such as digital-bydesign, data-driven public sector, government as a platform, open by default, user-driven, and proactiveness (Ubaldi and Okubo 2020). GovTech Maturity Index: WB’s index assesses the readiness of countries to participate in the digital economy. It considers factors such as the regulatory environment, technology infrastructure, and digital literacy (Dener et al. 2021). These indices vary in their methodologies, focus areas, and the dimensions that they consider. Governments, policymakers, and researchers often use a combination of these indices to gain a comprehensive understanding of the state of e-government development globally and regionally. Adm. Sci. 2024,14, 54 6 of 30 Table 1. Comparison of the most widely used measurement tools for evaluating digital government services. Assessment Tool Measurement Goal(s) Appraisal Dimensions Evaluation Scope EGDI (UN DESA 2022) E-government development EGDI evaluates online services, telecommunication infrastructure, human capital Global and regional e-Government Benchmark (EC 2023) E-government development The index consists of assessments of online services, online cross-border services, eID, e-documents, pre-filled forms European Digital Government Index (Ubaldi and Okubo 2020) E-government development This index comprises six e-government measures: digital-by-design, data-driven, platform-based, open, user-driven, and proactive Global and regional GovTech Maturity Index (Dener et al. 2021) E-government maturity GTMI includes four components: core government systems, public service delivery, digital citizen engagement, and GovTech enablers Global and regional ISO 20000 (ISO/IEC 20000-1:2018 2018) Performance, Quality Management Standard for IT service management, focusing on efficiency and performance Organizational ISO 27001 (ISO/IEC 27001:2022 2022) Security Standard for information security management systems, ensuring confidentiality, integrity, and availability of information Organizational EN 301549 (EN 301549:2018 2018)Accessibility This standard contains detailed requirements for websites, web-delivered documents, and mobile applications European organizational CAF (Prorok 2020) Organizational Performance Assessment The framework has nine criteria: leadership, personnel, partnerships, budget, knowledge, IT, processes, citizens and customers, social responsibility, and key performance European organizational SERVQUAL (Parasuraman et al. 1991) Service Quality This framework assesses service quality based on five factors: tangibles, reliability, responsiveness, assurance, and empathy map Organizational TAM (Davis 1989) User Acceptance Model evaluating user acceptance of technology, focusing on factors influencing users’ willingness to adopt and use e-services User-centric UTAUT (Venkatesh et al. 2003)User Behaviour Model integrating various factors to predict user acceptance and behaviour toward e-public services User-centric UX Evaluation * Usability, Satisfaction Metric assessing overall user experience, encompassing usability, accessibility, and satisfaction with e-administrative services User-centric Digital Accessibility Evaluation * Accessibility Metric evaluating the accessibility of e-administrative services to ensure usability for individuals with disabilities User-centric Efficiency and Performance Metrics * Performance Metrics Metrics assessing the efficiency and performance of e-administrative services, including response time, throughput, server uptime, and resource utilization User-centric Citizen-Centric Evaluation * User Satisfaction, Expectations Metric evaluating the extent to which e-administrative services are citizen-centric and meet user needs and expectations User-centric Digital Inclusion Assessment * Inclusiveness Metric evaluating the inclusiveness of e-administrative services, ensuring accessibility to diverse user groups based on factors such as language diversity and outreach efforts User-centric Note: * denotes that the concept continually evolves, integrating diverse guidelines and methodologies developed by various organizations and expert groups. The ISO/IEC standards contribute to the assessment of e-government development by offering common guidelines and measures, facilitating consistency, transparency and Adm. Sci. 2024,14, 54 7 of 30 interoperability across different systems for public services. They help governments to align their digital strategies with international best practices in a more efficient e-government structure. The ISO/IEC 20000 standard (ISO/IEC 20000-1:2018 2018) outlines guidelines for service management, including the planning, delivery, and improvement of IT services. Therefore, this standard can be applied to assess the quality of electronic administrative services (Sarwar et al. 2023). The ISO/IEC 27001 standard (ISO/IEC 27001:2022 2022) focuses on information security management systems and is applicable for evaluating the security aspects of electronic administrative services, ensuring the confidentiality, integrity, and availability of information. Although originally designed for quality management in general, ISO/IES 9001 can be applied to assess the quality of IT services, including electronic administrative services. It emphasizes customer satisfaction and continuous improvement. EN 301549 (EN 301549:2018 2018) is a European standard providing accessibility requirements for ICT products and services, ensuring that electronic administrative services are accessible to all users, including those with disabilities. While ISO/IEC 20000 guides IT service management, ISO/IEC 27001 focuses on information security, ISO/IES 9001 emphasizes quality, and EN 301549 sets European accessibility requirements, these standards collectively provide a comprehensive framework to assess and enhance electronic administrative services. A diverse range of frameworks serves to provide suitable tools for benchmarking e-public services, offering structured rules to optimize IT service management and improve the overall efficiency of digital service delivery. Information Technology Infrastructure Library (ITIL) is a set of practices for IT service management. It offers a framework for delivering high quality IT services, aligning them with the needs of the business. ITIL can enhance the management of electronic administrative services (Batmetan et al. 2022). In recent years, the Common Assessment Framework (CAF) (Prorok 2020) has been prioritized as a quality management system in the administrations of EU member states. It serves as a common framework for evaluating the performance of public sector organizations. Another instrument for e-public services assessment is the European Foundation for Quality Management (EFQM). EFQM is a framework for organizational management and quality improvement known as the EFQM Excellence Model. It is a holistic approach to assessing and improving organizational performance, focusing on leadership, strategy, people, partnerships, resources, processes, products/services, customer results, people results, society results, and key performance results (Rahmati and Jalilvand 2023). The Open Data Readiness Assessment evaluates the readiness of e-administrative services to publish and use open data. This framework provides a structured approach for assessing open data readiness (Kawashita et al. 2020). In the assessment of e-public services, various theoretical models encompass aspects such as quality, maturity, user acceptance, and broader technology adoption in the public sector. SERVQUAL compares expectations with perceptions on different service quality aspects. Later, this model was identified with five dimensions of service quality: Tangibles, Reliability, Responsiveness, Assurance, and Empathy (Parasuraman et al. 1991). Maturity models, such as the Capability Maturity Model Integration (CMMI), can help organizations to assess and improve their processes, capabilities, and overall maturity in delivering services or solutions. They offer a systematic approach to organizations in moving from lower maturity levels to higher ones while incorporating best practices and continuous improvement. Maturity models are often applied at the organizational level to evaluate and enhance the maturity of e-governance processes, service delivery, and overall capabilities (Kawashita et al. 2020;Hujran et al. 2023). The Technology Acceptance Model (TAM) (Davis 1989) and Unified Theory of Acceptance and Use of Technology (UTAUT) (Stefanovic et al. 2021) are user-centric models that focus on understanding and predicting user acceptance and adoption of technology, including e-government services. TAM emphasizes Perceived Ease of Use and Perceived Usefulness, while UTAUT incorporates additional factors such as Performance Expectancy, Effort Expectancy, Social Influence, and Facilitating Conditions (Pedrosa et al. 2020;Zeebaree et al. 2022). Adm. Sci. 2024,14, 54 8 of 30 The abovementioned models for e-public service evaluation contribute distinct insights: SERVQUAL emphasizes service quality, maturity models assess evolutionary stages, TAM focuses on user acceptance, and UTAUT provides a holistic understanding of user behaviour. Metrics for e-public services cover various aspects of user experience evaluation, ensuring usability, accessibility, and satisfaction; digital accessibility evaluation for individuals with disabilities; efficiency and performance metrics measuring response time, throughput, server uptime, and resource utilization; citizen-centric evaluation gauging user satisfaction and expectations; and digital inclusion assessment considering language diversity, accessibility features, and outreach efforts (Holzer et al. 2019). Depending on their main features, the most widely used measurement tools for evaluating digital government services, as outlined in Table 1, can be classified based on several criteria such as assessment type, focus, methodology and application scope. Type of Assessment: Standards such as ISO/IEC 20000 and frameworks such as CAF define structured guidelines for quality management and performance assessment, while models such as SERVQUAL and metrics such as User Experience (UX) Evaluation cover diverse aspects of gauging electronic public service. Focus of Assessment: User-centric approaches are addressed by models such as TAM and UTAUT and metrics such as UX Evaluation and Citizen-Centric Evaluation, standards such as ISO/IEC 27001 emphasize security, and EN 301549 and the Digital Accessibility Evaluation focus on accessibility. Methodology: Quantitative methodologies are employed in standards such as ISO/IEC 20000 and metrics such as Efficiency and Performance, Digital Accessibility Evaluation, and Digital Inclusion Assessment, while qualitative methods are applied in frameworks such as CAF and models such as SERVQUAL and UX Evaluation. Qualitative methods involve approaches such as user surveys, focus groups, in-depth interviews, and usability studies of user perceptions and overall experiences with electronic services. Scope: Global standards such as ISO/IEC 20000 and ISO/IEC 27001 have a worldwide focus, European standards such as EN 301549 cater to a regional context, organizational assessments are addressed by frameworks such as CAF and models such as SERVQUAL, and user-centric evaluations are covered by models such as TAM and UTAUT and metrics such as UX Evaluation and Citizen-Centric Evaluation. The multitude of measurement tools for the assessment of e-public services provides a multifaceted approach, from user experience and efficiency to security and accessibility, contributing to continuous improvement and informed decision-making in digital governance. When evaluating electronic administrative services, organizations often combine relevant measurement tools based on their specific goals, regulatory requirements, and the nature of the services provided. 2.3. Challenges in Evaluating Electronic Public Services When assessing electronic public services through different frameworks, models, and indices, several challenges may arise. First, different frameworks and models employ varied attributes, making it difficult to compare and standardize evaluations across indices. Second, the rapid evolution of technology requires constant updates. For example, UTAUT continues to progress and be enriched. Lastly, contextual variations are an issue, as the effectiveness of evaluation frameworks may vary based on the goals and characteristics of each government or region. To overcome these obstacles, continuous refinement and adaptation of evaluation measures are important to ensure alignment with the changing landscape of electronic public services. Considering the problem of selecting the optimal approach, we propose a combination of assessment methods for e-public services to ensure comprehensive and reliable estimates. The TAM and UTAUT models are among the most widely employed for understanding and predicting customer satisfaction in technology adoption. Their simplicity and clarity make them accessible and applicable across diverse technological Adm. Sci. 2024,14, 54 15 of 30 data on model constructs (from Question #10 to Question #20) showed that there were eight duplicates of two dataset rows, as follows: (#40, #81, #139, #148, #150, #160), (#119, #124, #128, #156) (see Figure 1). Adm. Sci. 2024, 14, x FOR PEER REVIEW 15 of 31 opinions. Intelligent ML methods can uncover unknown patterns and relationships between variables that may not be apparent when using classical statistical methods. 5. Results The methodology outlined in Section 4 was employed step-by-step to address the research tasks. 5.1. Customer Data Collection We shared a link to the online survey through our institutional websites, social media (Facebook groups), and emails. The survey targeted Bulgarian users of online public services and was completed on a voluntary basis. Created using Google Forms, the survey consisted of 21 questions designed to measure customers’ perceptions toward e-public services (Ilieva et al. 2024). The respondents’ data were collected from 18 January 2023 to 22 May 2023. A total of 258 participants completed the questionnaire, of whom 64 indicated that they do not use online administrative services (Question #7). A duplicate check was performed, and there were no identical values found in the dataset rows. However, the data on model constructs (from Question #10 to Question #20) showed that there were eight duplicates of two dataset rows, as follows: (#40, #81, #139, #148, #150, #160), (#119, #124, #128, #156) (see Figure 1). Figure 1 illustrates the degree of similarity between the respondents’ answers, with closer distances indicating smaller differences. The degree of similarity is represented by different colours, ranging from full coincidence (0—white) to maximum difference (20— light green). Because the dataset did not contain identical records, all observations are included in the analysis. Figure 1. The matrix of distances (ordered dissimilarity matrix) between respondents’ answers. Data storage The questionnaire and respondents’ dataset are available online (Ilieva et al. 2024). Data encoding Figure 1. The matrix of distances (ordered dissimilarity matrix) between respondents’ answers. Figure 1illustrates the degree of similarity between the respondents’ answers, with closer distances indicating smaller differences. The degree of similarity is represented by different colours, ranging from full coincidence (0—white) to maximum difference (20—light green). Because the dataset did not contain identical records, all observations are included in the analysis. Data storage The questionnaire and respondents’ dataset are available online (Ilieva et al. 2024). Data encoding The rules for coding and coded data are also available online. Out of all 21 responses, 19 have been coded (Ilieva et al. 2024). The remaining two open-text answers (municipality and opinions) have been additionally processed. Data preprocessing Preprocessing was carried out and the dataset quality was examined for accuracy and consistency. 5.2. Statistical Analysis To clarify the profile of the participants in the survey, a classical statistical analysis (percentage distribution of responses, descriptive statistics, and correlation analysis) was performed. Main Characteristics of the Sample Table 3illustrates the demographics of the questionnaire respondents. A significant majority of the participants were female, accounting for 74% of the total number of partici- Adm. Sci. 2024,14, 54 16 of 30 pants (Question #1). More than three quarters (77%) of the respondents were under the age of 40 (Question #2). The sample was dominated by individuals with at most a high school degree, comprising 59.3% of the participants (Question #6). The survey was primarily conducted in urban areas, with 95.7% of the respondents residing in such locations (Question #3). Table 3. User profile of the sample (n= 258). Variables of the Sample No. of Respondents Percentage (%) 1. Gender Male 66 25.6 Female 192 74.4 2. Age Under 20 77 29.8 Between 21 and 30 87 33.7 Between 31 and 40 35 13.6 Between 41 and 50 43 16.7 Over 50 16 6.2 3. Place of residence City 161 62.4 Town 86 33.3 Village 11 4.3 4. Municipality/Province - - - 5. Monthly income per household member Less than BGN 1320 140 54.3 More than BGN 1320 118 45.7 6. Education High school 153 59.3 Bachelor 59 22.9 Master 42 16.3 PhD 4 1.6 7. Do you use electronic administrative services? No 64 24.8 Yes 194 75.2 The geospatial distribution of participants (Question #4) reveals that the majority were from the Plovdiv district (71.6% of the survey participants). The second highest proportion of respondents were from the Pazardzhik district (6.2% of the total), followed by the Sofia city district with 5.2% of sample size. The survey primarily targeted the Southern Central region, comprising 83.5% of participants, followed by the Southwestern and Southeastern regions, each representing 6.2%. For 75% of the participants, online administrative services were the preferred form of communication with public authorities (Question #7). This percentage is relatively close to the adoption rate of digital public services as reported in a national survey conducted at the beginning of 2023 (NCPS 2023). The most widely utilized e-public services at the territorial level encompass those associated with local taxes and fees and civil status, representing 40.4% and 26.0%, respectively (Question #8). At the national level, the most commonly used e-services include social security for employees, corporate taxes, and value-added tax, constituting 23.3%, 22.0%, and 19.8%, respectively (Question #9). Feature Selection The colour depth of the heat maps in Figures 2and 3represents standardized values, ranging from a minimum of − 2.92 (white) to a maximum of 1.95 (green). The dendrogram at the top of Figure 2illustrates the grouping of respondents based on similarities in their e-government attitudes. Additionally, the variable structure in Figure 3(right) underscores their similarities. Both heat maps visually display clusters of observations and variables sharing similar characteristics with no unusual or unexpected patterns. Adm. Sci. 2024,14, 54 17 of 30 Adm. Sci. 2024, 14, x FOR PEER REVIEW 17 of 31 to the adoption rate of digital public services as reported in a national survey conducted at the beginning of 2023 (NCPS 2023). The most widely utilized e-public services at the territorial level encompass those associated with local taxes and fees and civil status, representing 40.4% and 26.0%, respectively (Question #8). At the national level, the most commonly used e-services include social security for employees, corporate taxes, and value-added tax, constituting 23.3%, 22.0%, and 19.8%, respectively (Question #9). Feature Selection The colour depth of the heat maps in Figures 2 and 3 represents standardized values, ranging from a minimum of −2.92 (white) to a maximum of 1.95 (green). The dendrogram at the top of Figure 2 illustrates the grouping of respondents based on similarities in their e-government attitudes. Additionally, the variable structure in Figure 3 (right) underscores their similarities. Both heat maps visually display clusters of observations and variables sharing similar characteristics with no unusual or unexpected patterns. Figure 2. Hierarchical group heat map by respondents. Figure 2. Hierarchical group heat map by respondents. Clustering To identify the groups of users with similar characteristics and the variables that have a comparable effect on attitudes toward e-government, we employed the k-means method for cluster analysis. The optimal number of clusters was determined using the Elbow and Silhouette methods, with the results indicating that this number is 2 (Figure 4). The two clusters consisted of 62 and 132 respondents, respectively. The smaller cluster (Cluster #1) comprises dissatisfied e-government users with a less positive attitude towards e-public service adoption, as reflected by lower ratings in Attitude (Question #14), Intention to Use (Question #15), and Intention to Recommend (Question #16) e-government services (Table 4). The indicators with the most significant influence on overall dissatisfaction are Trust (Question #17), Service Quality (Question #19–Question #20), and Perceived Usefulness (Question #10). In contrast, the second cluster, consisting of the majority of users, demonstrates a higher level of satisfaction with digital public services. Perceived Ease of Use (Question #11) and Perceived Risk (Question #18) are the most significant factors contributing to the positive attitude of this second group of users. Table 4presents the average values of the indicators for the two clusters along with the differences between these estimates. Adm. Sci. 2024,14, 54 18 of 30 Adm. Sci. 2024, 14, x FOR PEER REVIEW 18 of 31 Figure 3. Hierarchical group heat map by factors. Clustering To identify the groups of users with similar characteristics and the variables that have a comparable effect on attitudes toward e-government, we employed the k-means method for cluster analysis. The optimal number of clusters was determined using the Elbow and Silhouette methods, with the results indicating that this number is 2 (Figure 4). The two clusters consisted of 62 and 132 respondents, respectively. Figure 3. Hierarchical group heat map by factors. Sentiment Analysis The open-ended question (Question #21) received 54 text replies. After preprocessing and filtering, 35 responses concerning e-public services were kept. The average sentiment scores for responses were as follows: positive—22 (63%, average: 0.712), neutral—5 (14%, average: 0.519), and negative—8 (23%, average: 0.218). These results indicate that respondents generally support e-government services as a convenient means of interacting with public administration, highlighting some of the advantages. Those expressing a negative attitude primarily had concerns about complex website navigation and potential security issues. Neutral opinions supported the usage of e-public services but highlighted weaknesses in online data processing. Adm. Sci. 2024,14, 54 19 of 30 Adm. Sci. 2024, 14, x FOR PEER REVIEW 19 of 31 Figure 4. Users’ clusters by k-means (k = 2, 3, 4, 5) using 34 input indicators. The smaller cluster (Cluster #1) comprises dissatisfied e-government users with a less positive attitude towards e-public service adoption, as reflected by lower ratings in Attitude (Question #14), Intention to Use (Question #15), and Intention to Recommend (Question #16) e-government services (Table 4). The indicators with the most significant influence on overall dissatisfaction are Trust (Question #17), Service Quality (Question #19– Question #20), and Perceived Usefulness (Question #10). In contrast, the second cluster, consisting of the majority of users, demonstrates a higher level of satisfaction with digital public services. Perceived Ease of Use (Question #11) and Perceived Risk (Question #18) are the most significant factors contributing to the positive attitude of this second group of users. Table 4 presents the average values of the indicators for the two clusters along with the differences between these estimates. Figure 4. Users’ clusters by k-means (k= 2, 3, 4, 5) using 34 input indicators. The respondents proposed the following recommendations for e-service improvements: • Online systems could provide technical support to users and response to user queries in real time; •The structure and navigation system of websites could be optimised; • The citizens’ easy access to e-services could be ensured without additional requirements, such as electronic signatures or training in accounting; •Cybersecurity measures and data protection could be strengthened. The recommended actions aim to facilitate users’ acceptance and utilization of egovernment services. Enhancing the benefits of online systems and improving responsiveness aligns with the principle of Ease of Use by promoting a more user-friendly experience. Improving website organization and navigation corresponds to Effort Expectancy by simplifying user interactions. The suggestion to ensure easy accessibility without complex Adm. Sci. 2024,14, 54 20 of 30 prerequisites aligns with Facilitating Conditions, specifically reducing barriers to entry. Strengthening cybersecurity addresses concerns about Perceived Risk, mitigating users’ apprehensions about potential security issues. Collectively, these recommendations contribute to a positive user experience and acceptance of e-government services based on UTAUT principles. Table 4. Average values by cluster and absolute differences between clusters by indicators. Q10.1 Q10.2 Q10.3 Q11.1 Q11.2 Q11.3 Q11.4 Q12.1 Q12.2 Cluster #1 2.968 2.903 2.371 2.661 2.629 2.823 3.097 2.274 2.145 Cluster #2 4.144 4.114 3.568 3.424 3.614 3.985 4.136 3.402 3.364 Difference −1.176 −1.211 −1.197 −0.763 −0.985 −1.162 −1.039 −1.128 −1.219 Q12.3 Q13.1 Q13.2 Q13.3 Q14.1 Q14.2 Q14.3 Q14.4 Q14.5 Cluster #1 2.355 3.387 3.387 3.129 2.903 2.919 2.968 2.935 2.274 Cluster #2 3.333 4.576 4.417 4.220 3.947 4.205 4.129 4.212 3.530 Difference −0.978 −1.189 −1.030 −1.091 −1.044 −1.286 −1.161 −1.277 −1.256 Q15.1 Q15.2 Q15.3 Q16.1 Q16.2 Q16.3 Q17.1 Q17.2 Q17.3 Cluster #1 3.081 2.613 2.710 2.500 2.500 2.177 2.371 2.500 2.371 Cluster #2 3.947 3.848 3.955 4.076 4.091 3.432 3.788 3.879 3.735 Difference −0.866 −1.235 −1.245 −1.576 −1.591 −1.255 −1.417 −1.379 −1.364 Q18.1 Q18.2 Q19.1 Q19.2 Q19.3 Q20.1 Q20.2 Cluster #1 3.097 2.839 2.823 2.565 2.419 2.629 2.452 Cluster #2 3.114 3.068 3.977 3.856 3.742 3.697 3.765 Difference −0.017 −0.229 −1.154 −1.291 −1.323 −1.068 −1.313 5.3. SEM Model of Customer Attitudes towards E-Government Services The assessment of consumer attitudes towards adoption of e-public services lacks consensus on defining inputs and outputs, as highlighted in the review of previous similar research (Section 3). To address this issue, we iteratively employed the SEM method using SmartPLS 3 software (Ringe et al. 2015). The algorithm used for structural regression modelling involved the following five steps: 1. Formulate hypotheses about constructs and their interrelationships. 2. Identify indicators for each construct. 3. Execute the modelling procedure and assess the model fit. 4. Evaluate the quality of the model; if satisfactory, proceed to Step 5, otherwise return to Step 3 to enhance the model. 5. Discuss the obtained results. In Step 1, hypotheses about model constructs and their interrelationships are formulated, guided by the comparison of existing models for user attitudes towards e-public services (Table 2) (AlHadid et al. 2022). H 1 :There is a significant impact of Perceived Usefulness on Attitude towards e-government services. H 2 :There is a significant impact of Perceived Ease of Use on Attitude towards e-government services. H3:There is a significant impact of Social Influence on Attitude towards e-government services. H 4 :There is a significant impact of Facilitating Conditions on Attitude towards e-government services. H5:There is a significant impact of Perceived Trust on Attitude towards e-government services. H6:There is a significant impact of Perceived Risk on Attitude towards e-government services. Adm. Sci. 2024,14, 54 21 of 30 H7:There is a significant impact of Service Quality on Attitude towards e-government services. H 8 :Demographic characteristics have a statistically significant impact on customer satisfaction with e-government services (AlHadid et al. 2022). The demographic characteristics include Gender, Age, Residence, Income and Education level.) Step 2. Identify indicators for each construct. The indicators of latent variables (8 constructs and 28 variables) were derived from the survey questionnaire (Ilieva et al. 2024). The measurement model consisted of 23 input indicators: PU1, PU2, and PU3 from the Perceived Usefulness (PU); PE1, PE2, PE3, and PE4 from Perceived Ease of Use (PE); SI1, SI2, and SI3 from Social Influence (SI); FC1, FC2, and FC3 from Facilitating Conditions (FC); PT1, PT2, and PT3 from Perceived Trust (PT); PR1 and PR2 from Perceived Risk (PR); SQT1, SQT2, SQT2, SQR1, SQR2, and SQR3 from Service Quality (SQ); and five output indicators, ATT1, ATT2, ATT3, ATT4, and ATT5, from the output construct Attitude towards e-Government Services (ATT), represented in Figure 5. Adm. Sci. 2024, 14, x FOR PEER REVIEW 22 of 31 for the new model are acceptable, and the model examination continues to establish the validity and reliability of the constructs (Step 4). Construct Validity and Reliability The initial phase of the validity check requires assessing both the measurement model and the structural model. The measurement model evaluates the validity and reliability of the constructs; this evaluation encompasses evaluating the reliability of the constructs and indicators along with the convergent validity and discriminant validity of the latent variables. The structural model is essential for determining the significance of the proposed hypotheses. Figure 5. Measurement model with six latent variables along with their path coefficients and pvalues. Factor Loadings Factor loadings measure the extent to which each item in the correlation matrix is linked to the specified principal component. In our model, all items demonstrate factor loadings surpassing the recommended threshold of 0.5 suggested by Hair et al. (Hair et al. 2014). Figure 6 and Table 5 display the model’s factor loadings. Figure 6. SEM procedure results, showing the regression coefficient for each construct and the coefficient of determination. Figure 5. Measurement model with six latent variables along with their path coefficients and p-values. Step 3. Execute the modelling procedure and assess the model fit. The PLS algorithm has been employed and model parameters have been obtained. Step 4. Evaluate the quality of the model. If satisfactory, proceed to Step 5; otherwise, return to Step 3 to enhance the model. Based on evaluation of the path coefficients, the model does not align well with the dataset. This discrepancy arises from the p-values of Perceived Usefulness, Perceived Ease of Use, and Social Influence (0.437, 0.680, and 0.251, respectively), which exceed the acceptable threshold (Figure 5). Consequently, the process returns to Step 3 and modifies the model settings by eliminating certain factors. Now, the p-values of the path coefficients for the new model are acceptable, and the model examination continues to establish the validity and reliability of the constructs (Step 4). Construct Validity and Reliability The initial phase of the validity check requires assessing both the measurement model and the structural model. The measurement model evaluates the validity and reliability of the constructs; this evaluation encompasses evaluating the reliability of the constructs and indicators along with the convergent validity and discriminant validity of the latent Adm. Sci. 2024,14, 54 22 of 30 variables. The structural model is essential for determining the significance of the proposed hypotheses. Factor Loadings Factor loadings measure the extent to which each item in the correlation matrix is linked to the specified principal component. In our model, all items demonstrate factor loadings surpassing the recommended threshold of 0.5 suggested by Hair et al. (Hair et al. 2014). Figure 6and Table 5display the model’s factor loadings. Adm. Sci. 2024, 14, x FOR PEER REVIEW 22 of 31 for the new model are acceptable, and the model examination continues to establish the validity and reliability of the constructs (Step 4). Construct Validity and Reliability The initial phase of the validity check requires assessing both the measurement model and the structural model. The measurement model evaluates the validity and reliability of the constructs; this evaluation encompasses evaluating the reliability of the constructs and indicators along with the convergent validity and discriminant validity of the latent variables. The structural model is essential for determining the significance of the proposed hypotheses. Figure 5. Measurement model with six latent variables along with their path coefficients and pvalues. Factor Loadings Factor loadings measure the extent to which each item in the correlation matrix is linked to the specified principal component. In our model, all items demonstrate factor loadings surpassing the recommended threshold of 0.5 suggested by Hair et al. (Hair et al. 2014). Figure 6 and Table 5 display the model’s factor loadings. Figure 6. SEM procedure results, showing the regression coefficient for each construct and the coefficient of determination. Figure 6. SEM procedure results, showing the regression coefficient for each construct and the coefficient of determination. Table 5. Factor loadings for indicators. Indicator Variable Factor Loading Indicator Variable Factor Loading Indicator Variable Factor Loading Indicator Variable Factor Loading ATT1 0.834 FC1 0.926 PT1 0.946 SQT1 0.852 ATT2 0.861 FC2 0.965 PT2 0.956 SQT2 0.827 ATT3 0.831 FC3 0.893 PT3 0.96 SQT3 0.814 ATT4 0.914 PR1 0.959 SQR1 0.854 ATT5 0.740 PR2 0.962 SQR2 0.879 Indicator Multicollinearity To assess the multicollinearity among indicators, the Variance Inflation Factor (VIF) statistic was calculated. A VIF value below five is considered acceptable, indicating acceptable multicollinearity (Fornell and Bookstein 1982). Table 6presents the VIF values, demonstrating that each indicator has a VIF below the recommended threshold. Table 6. Construct reliability (DG rho and CR), convergent validity (AVE), and multicollinearity (VIF). Factor DG rho CR AVE VIF Perceived Risk 0.917 0.960 0.922 1.079 Perceived Trust 0.951 0.968 0.910 2.391 Facilitating Conditions 0.924 0.949 0.862 1.341 Service Quality 0.914 0.926 0.715 2.217 Attitude 0.898 0.921 0.702 DG rho: Dillon–Goldstein’s rho (>0.7), CR: Composite Reliability (>0.6); AVE: Average Variance Extracted (>0.5); VIF: Variance Inflation Factors (<5). Reliability Analysis To assess construct reliability, two main methods—Dillon–Goldstein’s rho (DG rho) and Composite Reliability (CR)—were applied for measuring repeatability. To ensure adequate reliability, both the DG rho and CR values should surpass 0.7 (Fornell and Bookstein 1982). The DG rho values ranged from 0.898 to 0.951, while the CR values Adm. Sci. 2024,14, 54 23 of 30 ranged from 0.921 to 0.968 (Table 6). Consequently, the DG rho and CR values for all latent variables in the model are acceptable, indicating reliable coefficients for all constructs. Construct validity requires two types of validity assessment: convergent validity and discriminant validity. Convergent Validity Convergent validity refers to the level of consistency among multiple measures of the same concept. To assess the convergent validity of the construct, the average variance extracted (AVE) was calculated, with a minimum threshold of 0.5 (Fornell and Bookstein 1982). The AVE scores for all constructs were statistically significant, indicating the strong convergent validity of our model (Table 6). Discriminant Validity Discriminant validity refers to the degree to which measures of distinct concepts can be distinguished from each other. Heterotrait–Monotrait Ratio (HTMT) To assess discriminant validity, the HTMT (Heterotrait–Monotrait) ratio calculates the correlation between constructs. The threshold for HTMT varies in the literature, typically falling between 0.85 and 0.9. The results for our model, outlined in Table 7, show that the HTMT ratios for the constructs are below the specified threshold of 0.85 and are statistically significant. Table 7. Discriminant validity—HTMT. Factor ATT FC PR PT SQ Attitude Facilitating Conditions 0.669 Perceived Risk 0.193 0.033 Perceived Trust 0.623 0.520 0.145 Service Quality 0.67 0.473 0.082 0.774 Assessment of Structural Model The p-values of the model constructs indicate a significant impact on user attitudes towards e-public services, with values below 1% for Facilitating Conditions, Perceived Risk, and Service Quality and below 5% for Perceived Trust, as shown in Figure 7and Table 8. These findings align with hypotheses H 4 –H 7 and previous similar research. The regression coefficients for all input factors are positive. Adm. Sci. 2024, 14, x FOR PEER REVIEW 24 of 31 Table 7. Discriminant validity—HTMT. Factor ATT FC PR PT SQ Attitude Facilitating Conditions 0.669 Perceived Risk 0.193 0.033 Perceived Trust 0.623 0.520 0.145 Service Quality 0.67 0.473 0.082 0.774 Assessment of Structural Model The p-values of the model constructs indicate a significant impact on user attitudes towards e-public services, with values below 1% for Facilitating Conditions, Perceived Risk, and Service Quality and below 5% for Perceived Trust, as shown in Figure 7 and Table 8. These findings align with hypotheses H4–H7 and previous similar research. The regression coefficients for all input factors are positive. Figure 7. Path coefficients and p-values—inner and outer model. Table 8. The path coefficient of the relationship between latent variables. Hypothesis 𝜷 Sample Mean Mean SD t Statistics p Values R2 f 2 Q 2 H4 Facilitating Conditions → Attitude 0.389 0.389 0.387 0.078 4.972 0.000 0.559 0.256 0.385 H5 Perceived Risk → Attitude 0.185 0.185 0.182 0.051 3.628 0.000 0.072 H6 Perceived Trust → Attitude 0.210 0.21 0.204 0.085 2.461 0.014 0.042 H7 Service Quality → Attitude 0.276 0.276 0.284 0.094 2.921 0.004 0.078 Regarding the obtained structural model, the pathways FC → ATT and SQ → ATT express relatively large effects, while the PT → ATT and PR → ATT relationships demonstrate weak influences. Step 5. Discuss the obtained results. The reasons for rejecting the effects of Perceived Usefulness (H1), Perceived Ease of Use (H2), and Social Influence (H3) on User Attitude towards e-government services (Figure 5) can be explained based on the specific context of our study. The new portal of the NRA has been developed using the latest IT technologies, which has changed supposed user perceptions. Users do not consider Perceived Usefulness as a critical factor due to the high level of maturity of the new portal. Additionally, the electronic equivalents of NRA public services have been available since 2012 and are well-established, leading to the rejection of Perceived Ease of Use as an influence on User Attitude. Furthermore, as users have accumulated sufficient experience with the portal, the influence of social networks Figure 7. Path coefficients and p-values—inner and outer model. Adm. Sci. 2024,14, 54 24 of 30 Table 8. The path coefficient of the relationship between latent variables. Hypothesis βSample Mean Mean SD t Statistics pValues R2f2Q2 H4Facilitating Conditions →Attitude 0.389 0.389 0.387 0.078 4.972 0.000 0.559 0.256 0.385 H5Perceived Risk →Attitude 0.185 0.185 0.182 0.051 3.628 0.000 0.072 H6Perceived Trust →Attitude 0.210 0.21 0.204 0.085 2.461 0.014 0.042 H7Service Quality →Attitude 0.276 0.276 0.284 0.094 2.921 0.004 0.078 Regarding the obtained structural model, the pathways FC → ATT and SQ → ATT express relatively large effects, while the PT → ATT and PR → ATT relationships demonstrate weak influences. Step 5. Discuss the obtained results. The reasons for rejecting the effects of Perceived Usefulness (H 1 ), Perceived Ease of Use (H 2 ), and Social Influence (H 3 ) on User Attitude towards e-government services (Figure 5) can be explained based on the specific context of our study. The new portal of the NRA has been developed using the latest IT technologies, which has changed supposed user perceptions. Users do not consider Perceived Usefulness as a critical factor due to the high level of maturity of the new portal. Additionally, the electronic equivalents of NRA public services have been available since 2012 and are well-established, leading to the rejection of Perceived Ease of Use as an influence on User Attitude. Furthermore, as users have accumulated sufficient experience with the portal, the influence of social networks has decreased. Consequently, H 3 , which pertains to Social Influence, is not supported in our findings. Our results are consistent with those obtained by Mensah et al. (2020) in their study on the adoption of Chinese e-government services. Both studies reject the significance of the same three factors—Perceived Usefulness, Perceived Ease of Use, and Social Influence (Table 2). One possible reason for this this alignment is the fact that both Bulgaria and China have developed e-government systems, ranked in the VHEGDI group according to the last UN survey (UN DESA 2022). However, results from a previous study by Xie et al. (Xie et al. 2017), conducted in China five years earlier, show a different outcome, with these three factors significantly influencing user attitudes towards e-government services. This difference supports our assumption regarding the impact of IT maturity. Additionally, Table 2indicates that in some countries less developed in information technologies these three factors only partially influence user attitudes towards e-government services (ElKheshin and Saleeb 2020;Nugroho et al. 2022). In the fitted SEM model, Facilitating Conditions (H 4 ) demonstrated the strongest positive relationship (Figure 7, β = 0.389, p< 0.001) with attitude towards e-public services. For citizens, Facilitating Conditions encompass access to technological resources along with the provision of technical support, aiding them during transactions with e-government. The robust impact of Facilitating Conditions indicates that available telecommunication services, software quality, a mobile-friendly interface, and accessible support systems can enhance customer satisfaction. This finding aligns with the results of previous similar studies by Kurdafi et al., Camilleri, AlHadid et al., Nugroho et al., and Garcia-Rio et al. (Kurfali et al. 2017;Camilleri 2020;AlHadid et al. 2022;Nugroho et al. 2022;Garcia-Rio et al. 2023). The result of testing H 5 , the effect of Perceived Trust, shows that confidence measures for e-public services can increase the intention of users to adopt new e-government platforms ( β = 0.210, p ≤ 0.05). Citizens need to trust that the information available on these platforms is accurate and up to date in order to rely on and use these e-services. When citizens trust these online platforms, they are more likely to actively participate, provide feedback, and engage in various government initiatives, leading to a more interactive and responsive governance model. This outcome aligns with research that identifies this vari-