A Systematic Literature Review of Empirical Research on the Impact of E-Government Use on Citizens’ Trust in Government

Abstract

The use of e-government has transformed public service delivery, however, the impact of this digital shift on citizens’ trust in government remains unclear. For this purpose, a systematic review of 49 peer-reviewed studies on the relationship between e-government use and citizens’ trust in government, published between 2004 and 2024, was conducted. This review traces the evolution of research focus, theoretical frameworks, and empirical findings in this field. Findings demonstrate a steady growth of interest, particularly in countries with advanced digital infrastructure. Most studies focus on service quality, transparency, data security, and user satisfaction as primary drivers of trust. Methodologically, quantitative, cross-sectional survey designs dominate the field, with limited use of qualitative and mixed-method approaches. Theoretically, many studies rely on technology adoption models and traditional trust frameworks, with limited attention to emerging technology features such as explainability or algorithmic transparency. This review contributes to the literature by mapping these theoretical and methodological patterns, identifying key gaps, and proposing a research agenda centered on actual use and emerging AI-based government services. It provides valuable insights for both scholars and policymakers seeking to understand and strengthen public trust in an increasingly digital public sector.

Keywords

Digital governance impact of e-government use citizen’s trust in government user satisfaction artificial intelligence

Introduction

The use of e-government has changed the way public institutions operate and communicate with citizens. E-government refers to the strategic use of information and communication technologies (ICTs) to deliver government services, disseminate public information, and facilitate communication between public institutions and stakeholders. It encompasses a broad range of digital channels and tools, such as websites, mobile apps, online portals, and increasingly, Artificial Intelligence (AI)-powered systems, including chatbots, predictive analytics, and automated decision-making tools (Upadhyaya, 2024). More than technical innovation, e-government transforms public administration, shifting from traditional paper-based bureaucratic systems to citizen-centric digital governance (Gracia & Casaló Ariño, 2014; Liu & Zhou, 2010). It restructures how decisions are made, how public value is co-produced, and how institutions maintain legitimacy (Parent et al., 2005). As governments worldwide adopt these digital tools to enhance service delivery and citizen engagement, it becomes essential to understand how the use of these technologies shapes citizen’s trust.

Trust is a foundational element in the relationship between government institutions and citizens, particularly in the digital context. Trust has been defined as a psychological state comprising the intention to accept vulnerability based on positive expectations of another party’s intentions or behavior (Morgeson et al., 2011; Welch et al., 2005). In the digital context, trust in government institutions determines citizens’ willingness to engage with online platforms (Alzahrani et al., 2017). Within the context of e-government, where traditional face-to-face interactions are replaced by digital interfaces, perceived trust becomes even more crucial to reduce uncertainty and risk in interactions (Liu & Zhou, 2010). Research consistently shows that initial skepticism can hinder adoption of new technology (Beldad et al., 2012; Welch et al., 2005), yet when people do engage and find the systems usable, transparent, and responsive, trust often grows (Gracia & Casaló Ariño, 2014; Li & Shang, 2023; Porumbescu, 2016a,2016b,2016c). Conversely, negative experiences such as delays, security lapses, or a lack of transparency may cause trust to erode even after initial engagement (Beldad et al., 2012; Welch et al., 2005). Thus, the quality of digital service delivery, defined by responsiveness, interactivity, and data security, can either fortify or erode public trust (Gracia & Casaló Ariño, 2014; Welch et al., 2005). It can be concluded that successful digital interactions can reinforce institutional legitimacy and strengthen citizens’ trust in the public sector (Gracia & Casaló Ariño, 2014; Li & Shang, 2023). Hence, governments must consider trust as a dynamic and evolving construct, shaped by citizens’ digital experience (Beldad et al., 2012; Morgeson et al., 2011).

E-government research has significantly evolved over the past two decades, highlighting the shift in public administration literature. Prior studies were focused on technological implementation, infrastructure, and digital capacities, viewing e-government as a technical initiative aimed at enhancing operational efficiency. Gradually, scholars began investigating user behaviors, adoption factors, and citizen satisfaction with digital services. However, these studies mainly focused on factors influencing citizens’ willingness to adopt e-government services, such as perceived ease of use, security, and technological readiness (Liu & Zhou, 2010; Welch et al., 2005). As a result, trust was often treated as a predictor rather than an outcome (Morgeson et al., 2011). More recently, scholars have begun to treat trust not only as a predictor, but also as an important outcome of e-government use. This shift marks a growing interest in how digital interactions influence citizens’ perceptions of government. As citizens move from intention to actual use, it becomes essential to understand how these experiences affect their level of trust. Accordingly, this review is guided by the following research question: How does the use of e-government services affect citizens’ trust in government?

This study aims to systematically review empirical evidence from the past two decades by analyzing 49 peer-reviewed articles, primarily from top-tier journals such as Government Information Quarterly, Public Administration Review, Public Management Review, and the Journal of Public Administration Research and Theory. This review provides a coherent and comprehensive overview of the current state of research. It makes three contributions to the literature: first, it maps key theoretical and methodological patterns in empirical research on e-government use and citizens’ trust in government. Second, it synthesizes the main technological, experiential, and contextual factors that shape how e-government use affects trust. Third, it highlights important gaps in the literature and develops a structured agenda for future research and implications for digital governance practice. In the following sections, we first discuss the research background, followed by the research approach for this systematic literature review. We then present and analyze the findings derived from the reviewed literature on how e-government use impacts citizen trust. Building upon this synthesis, we propose a research agenda highlighting areas requiring further investigation. Finally, the paper concludes by summarizing key contributions, implications for policy and practice, and limitations of the current review.

Research Background

The Use of Information and Communication Technologies in Government: Implications for Public Governance

The integration of ICTs into government operations has transformed the way governments operate. By using tools such as the internet, mobile applications, and cloud computing, governments aim to enhance service delivery, improve efficiency, and promote transparency and accountability (Heeks & Bailur, 2007; Mergel, 2019). This digital transformation, commonly referred to as e-government, began taking shape in the late 1990s and early 2000s, marking a significant shift toward digitalization (Fountain, 2001). According to Fountain’s research, e-government has the potential to fundamentally alter administrative practices and restructure traditional bureaucratic hierarchies. Scholars define e-government as the use of ICTs to deliver both informational and transactional services to citizens, businesses, and other stakeholders.

The global shift toward e-government is reflected in significant investments and growing advancements in digital governance. Governments worldwide are reshaping how they operate by adopting e-government technologies to improve efficiency, reduce costs, and encourage more citizen participation in public affairs (Thompson et al., 2008). Indeed, global IT spending on government services reached over $548 billion in 2022 and was expected to grow to nearly $590 billion in 2023, reflecting governments’ continued commitment to digital transformation (Statista, 2024). This trend is further supported by global assessments. Data shows that over the past decade, a majority of the Organization for Economic Co-operation and Development (OECD, 2020) nations have made substantial progress in moving key government services online, thereby reinforcing their digital governance structures. Furthermore, the 2022 United Nations E-government Development Index highlights this progress, with countries such as Denmark, Finland, and South Korea leading the rankings, demonstrating high levels of digital public service maturity and effective implementation of digital government policies (UN E-Government Survey, 2022). The latest 2023 OECD Digital Government Index (EGDI) further confirms this progress, with South Korea achieving a top score of 0.94 out of 1, demonstrating a highly developed digital public service system and strong implementation of digital government policies (OECD, 2024). Similarly, countries like the United States and China have been at the forefront of these efforts, making large-scale investments in e-government platforms and services, aiming to modernize public administration and improve how governments engage with their citizens (Statista, 2024). In recent years, advancements in mobile government (m-government), cloud computing, and AI have further expanded governments’ digital capabilities, enabling real-time responsiveness, improved data sharing, and more personalized service delivery (Al-Sai & Abualigah, 2017; Kundra, 2011; Marri et al., 2019).

As countries worldwide invest in ICTs to improve their service delivery and promote transparency, a critical question remains: to what extent does the use of ICTs in government actually foster trust among citizens? While digital tools may improve service delivery, and promote transparency, their impact on public trust is not guaranteed. Understanding the relationship between e-government use and trust in government requires deeper insight into the nature of trust: what is it, how it forms, and what shapes it in the digital age.

Citizens’ Trust in Government

Trust in government is a multifaceted construct involving citizens’ beliefs about the government’s ability, integrity, benevolence, and responsiveness (S. Grimmelikhuijsen et al., 2013; Houston & Harding, 2013; Rousseau et al., 1998). It reflects both cognitive and emotional dimensions: it combines rational expectations about institutional performance and a psychological state of vulnerability (Easton, 1975; Levi & Stoker, 2000; Mayer et al., 1995). In this sense, trust in government reflects a dynamic combination of cognitive expectations and psychological willingness to accept the risks of being governed. Thus, trust plays a critical role in maintaining the legitimacy of public institutions, fostering voluntary compliance with laws, and contributing to social order (Tolbert & Mossberger, 2006).

It is important to note that trust is dynamic, shaped by ongoing interactions between citizens and governments, and is influenced by citizens’ perceptions of process fairness, transparency, and accountability (Donovan & Bowler, 2004; S. Grimmelikhuijsen et al., 2013). According to prior literature, trust in government can be distinguished into “general trust” and “specific trust.” General trust reflects citizens’ overall satisfaction with the political system, while specific trust relates to satisfaction with particular policies, services, or public actors (Craig, 1996; Easton, 1965, 1975). Trust in government is shaped by a combination of objective factors, such as government performance, transparency, and responsiveness, and subjective factors, including citizens’ perceptions of fairness, inclusiveness, and alignment with cultural norms (S. Grimmelikhuijsen et al., 2013; Levi & Stoker, 2000).

In today’s digital era, trust in government is increasingly influenced by how citizens experience online services and e-government platforms. While these technologies can enhance access to information and services, they also introduce new challenges. Citizens expect digital interactions to be fair, transparent, and ethical, and they hold governments accountable to these standards (Alzahrani et al., 2017; Bannister & Connolly, 2011). Ultimately, while these platforms can make the government more accessible, they must be well-designed. Meeting citizens’ expectations for transparency, accountability, and ethical conduct is essential for building and maintaining trust in the digital age (S. Grimmelikhuijsen et al., 2013; Zuiderwijk et al., 2021).

The literature suggests a complex and evolving relationship between E-government use and citizens’ trust in government. Therefore, a systematic literature review is essential to bring clarity to the field, identify knowledge gaps, and provide a stronger foundation for future research on how ICTs in government affect trust across different systems and cultures.

Research Approach: Systematic Literature Review

This section outlines the systematic literature review (SLR) approach employed to investigate the relationship between e-government use and citizens’ trust in government. This review adopts a configurative SLR design with narrative and thematic synthesis. This design is appropriate when the evidence base is conceptually rich but methodologically diverse, and when the aim is to build an integrated understanding of concepts, mechanisms, and contexts rather than to compute pooled effect sizes. In such reviews, findings from individual studies are systematically mapped and then interpreted across cases to develop frameworks and research agendas. Similar configurative SLR designs are well established in e-government and trust research. Mahmood et al. (2014) and Alzahrani et al. (2017) use structured database searches and screening procedures to synthesize how trust in e-government adoption has been studied, grouping antecedents into thematic categories and developing conceptual frameworks through qualitative rather than bibliometric or meta-analytic techniques. Similarly, Lai and Marques (2024) employ a systematic review to identify key dimensions of citizens’ trust in e-government and organize them into an integrative framework. Furthermore, comparable approaches are used for other digital-government outcomes. MacLean and Titah (2022) review 60 empirical studies on e-government impacts from a public value perspective, classifying impact types and value dimensions through qualitative synthesis. Similarly, Khan et al. (2021) thematically organize 63 studies on e-government and corruption and propose an integrated framework and research agenda, while Zuiderwijk et al. (2021) synthesize 26 studies on AI in public governance into themes of opportunities and risks and derive a research agenda. In the broader public administration field, Ritz et al. (2016) and Halling and Baekgaard (2024) use systematic searches combined with narrative and thematic synthesis to map concepts and relationships and to develop comprehensive models and research agendas. The present review follows this established configurative SLR paradigm in the specific context of e-government use and citizens’ trust in government. Following Kitchenham’s (2004) principles for systematic reviews, this section details the five steps that structured the review: study identification, study selection, relevance and quality assessment, data extraction, and data synthesis.

Step 1: Identification of Studies

The starting point of this review is the following research question: How does the use of e-government services affect citizens’ trust in government? In this first step, we determined the objectives that shaped the literature review in order to answer this question in a structured way: (1) To examine how citizens’ trust in government is conceptualized, defined, and measured in the context of e-government use, this first objective is addressed in Section “Conceptualization, Operationalization, and Measurement of Trust.” (2) To analyze the dominant methodological approaches used in studies on e-government and trust. The goal is to highlight methodological strengths and weaknesses across the literature. These findings are reported in Section “Methodology Adopted.” (3) To identify and categorize the key factors that influence citizens’ trust in e-government, including independent, mediating, and moderating variables. This objective is reflected in Section “Key Determinants of Citizens’ Trust in E-Government,” which outlines the main determinants of trust and their theoretical placement. (4) To identify gaps in the current literature, particularly concerning underexplored areas such as the role of AI in public service delivery and emerging trust challenges in digital governance. This objective is addressed in Section “Gaps in the Current Literature on E-Government Use and Citizens’ Trust,” which also informs the discussion on future research directions in Section “Discussion.” To support these objectives, we also provide general findings in Section “General Findings,” which presents publication trends, sources, geographic focus, research evolution over time, and the main theoretical foundations applied in the literature.

Step 2: Selection of Studies

In the second step, we defined the search terms, as well as the exclusion and inclusion criteria. Using the search terms outlined in Table 1, we limited the search results to journal articles and conference proceedings written in English and published between 2004 and 2024. The search was conducted across major academic databases, including Scopus, Web of Science, ScienceDirect, and Google Scholar, to ensure comprehensive coverage of high-quality, peer-reviewed literature in the fields of public administration and information systems. Major search terms involved combinations of: “e-government,” “digital government,” “citizen trust,” “trust in government,” and “impact.” Boolean operators “AND” and “OR” were employed to optimize coverage.

Table 1.

Search terms used for the literature review.

Databases	Search terms in the title/keywords
Scopus, Web of Science, Google Scholar	(“e-government use” AND “trust in government” AND (impact OR effect OR influence)) OR (“digital government” AND (“citizen trust” OR “public trust”) AND impact) OR (“online public services” AND “institutional trust” AND (influence OR effect)) OR (“e-government” AND “trustworthiness” AND influence) OR (“e-government use” AND “institutional trust” AND correlation) OR (“government website use” AND “public trust” AND impact)

Databases

Search terms in the title/keywords

Scopus, Web of Science, Google Scholar

(“e-government use” AND “trust in government” AND (impact OR effect OR influence)) OR (“digital government” AND (“citizen trust” OR “public trust”) AND impact) OR (“online public services” AND “institutional trust” AND (influence OR effect)) OR (“e-government” AND “trustworthiness” AND influence) OR (“e-government use” AND “institutional trust” AND correlation) OR (“government website use” AND “public trust” AND impact)

Note. The search terms used for the literature review were identical throughout all three databases (Scopus, Web of Science, and Google Scholar). Duplicate or overlapping terms were consolidated to optimize search efficiency while ensuring comprehensive coverage of relevant articles specifically addressing e-government and trust in government.

Restricting the search to English-language publications and to databases that mainly index international journals likely introduces geographical and cultural bias. This approach may underrepresent studies published in local or regional outlets and may contribute to the concentration of cases from digitally advanced countries. As a result, the findings of this review should be interpreted with caution when generalizing to contexts that are less represented in English-language and Scopus/Web of Science-indexed journals. Future reviews could address this limitation by incorporating non-English publications and regionally indexed databases.

Step 3: Study Relevance and Quality Assessment

The third step involved assessing the relevance and quality of the studies identified in the search. The initial search generated 2,057 records, including 1,881 from academic databases (Scopus, n = 1,692; Web of Science, n = 127; Google Scholar, n = 62) and 176 additional records identified through the top journals in public administration and information systems. After removing duplicates manually (n = 10) and via Covidence (n = 984), 1,063 unique records remained for screening. Then, we screened the titles and abstracts to check whether studies met three relevance criteria: (1) explicit focus on e-government or digital government, (2) inclusion of citizens’ trust in government or institutional trust, and (3) an explicit link between e-government use or digital interaction and trust. This stage excluded 977 records and resulted in 86 articles selected for full-text assessment.

In the second stage, the 86 full-text articles were assessed for both relevance and quality. Quality assessment drew on criteria derived from Batini et al. (2009) and Bano and Zowghi (2015), including clarity of research objectives, alignment between research design and objectives, methodological transparency, completeness of reporting, robustness of analysis, and timeliness. Particular attention was paid to studies published in reputable, peer-reviewed outlets, with a focus on journals ranked in the top three Scimago quartiles (Q1–Q3). Studies were excluded if they did not clearly model trust as an outcome, did not examine e-government use, or exhibited major methodological weaknesses. After this stage, 49 articles met all relevance and quality criteria and formed the final sample for synthesis. The selection process followed PRISMA guidelines to ensure transparency and replicability, as shown in Figure 1.

Figure 1.

Study selection, assessment, and inclusion (presented using the PRISMA flow diagram).

Step 4: Data Extraction

A structured data extraction protocol was developed to collect information consistently from each of the 49 included studies. For every study, we recorded bibliographic details (authors, year, outlet), contextual information (country or region, level of government, policy domain), methodological characteristics (research design, data collection methods, sample type and size, analytical techniques), and the theoretical framework used. We also coded the substantive variables relevant to the research objectives, including how trust in government was defined and measured, how e-government use was operationalized, the main explanatory factors (such as system quality and transparency), and any mediating or moderating variables (e.g., satisfaction, perceived ease of use, or cultural factors). All extracted information was entered into a structured spreadsheet to ensure consistency and traceability across studies.

Step 5: Data Synthesis

In the final step, we synthesized the extracted data to answer the guiding research question: How does the use of e-government services affect citizens’ trust in government? We applied narrative and thematic synthesis. Based on the extracted variables, we conducted thematic coding to organize the evidence into four analytical dimensions that reflect the aims of the review: (1) conceptualization and measurement of trust, (2) methodological approaches, (3) factors influencing trust in government, and (4) gaps and future directions. Within each dimension, we compared studies side by side, noting recurring patterns, points of divergence, and underexplored issues. This synthesis provides the foundation for our main findings and the subsequent research agenda.

Findings

In the following sections we report on the results emerging from the analysis of the 49 papers included in our review. The results are organized to provide a clear understanding of how research on e-government use and citizens’ trust in government has developed.

General Findings

Articles by Publication Source and Year

Research on the relationship between e-government use and citizens’ trust in government has grown steadily over the past two decades. In the early 2000s, few academic articles addressed this topic, largely because e-government itself was a relatively new concept. Interest increased after the OECD (2003) recognized e-government as a key part of governance and the UN launched its e-Government Surveys in 2003 (UN E-Government Survey, 2008). During the 2010s, publications rose as governments invested in digital services. The COVID-19 pandemic (2020–2023) triggered another wave of studies, as governments accelerated digital transformation to handle the crisis. Since 2023, interest has grown further with the rise of AI and efforts to improve digital inclusion, as emphasized in the latest UN e-Government Survey (UN E-Government Survey, 2024). Table 2 presents the 49 reviewed articles by publication source and year. Key journals like Public Management Review, Government Information Quarterly, and Public Administration Review frequently publish articles on e-government and trust, underlining the topic’s importance in both public administration and information systems research. Over time, research on e-government and trust has expanded from a handful of specialized journals to a much wider range of academic outlets, showing how relevant the topic has become across different disciplines.

Table 2.

Number of papers reviewed by publication source and year of publication.

Venue	2004	2005	2006	2007	2008	2009	2010	2011	2012	2013	2014	2015	2016	2017	2018	2019	2020	2021	2022	2023	2024	Total
Digital Policy, Regulation and Governance	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	1	—	—	—	1
Economics and Islamic Governance in the Digital Era (EIGDE)	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	1	—	1
Government Information Quarterly	—	—	—	—	—	—	—	—	1	—	1	—	1	—	—	—	—	1	—	1	—	5
Hawaii International Conference on System Sciences (HICSS)	—	1	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	1
Heliyon	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	1	1
Information and Management	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	1	—	1
Information Systems Research	—	—	—	—	—	—	—	—	1	—	—	—	—	—	—	—	—	—	—	—	—	1
Information Technology for Development	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	1	—	—	—	—	—	1
Intelligent Systems, Business, and Innovation Research	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	1	1
Interdisciplinary Journal of Information, Knowledge, and Management	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	2	2
International Conference on Software Engineering and Service Sciences (ICSESS)	—	—	—	—	—	—	1	—	—	—	—	—	—	—	—	—	—	—	—	—	—	1
International Institute of Administrative Sciences (IIAS)	—	—	—	—	—	—	—	—	1	—	—	—	—	—	—	—	—	—	—	—	—	1
International Journal of Data and Network Science	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	1	1
International Journal of Disaster Risk Reduction	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	1	—	—	—	—	1
International Review of Management and Marketing	—	—	—	—	—	—	—	—	—	—	—	—	1	—	—	—	—	—	—	—	—	1
Journal of Business Research	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	1	—	1
Journal of Logistics, Informatics and Service Science	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	1	1
Journal of Policy Studies	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	1	1
Journal of Public Administration Research and Theory	—	1	—	—	—	—	—	1	—	—	—	—	—	—	—	—	—	—	—	1	—	3
Policy and Internet	—	—	—	—	—	—	1	—	—	—	—	—	—	—	—	—	—	—	—	—	—	1
Proceedings on Engineering Sciences (PES)	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	1	—	—	—	—	1
Public Administration Review	—	—	1	—	—	—	—	—	1	1	—	—	—	—	—	—	—	—	—	—	—	3
Public Management Review	—	—	—	—	—	—	—	—	—	1	1	—	3	—	—	—	—	—	—	—	—	5
Public Organization Review	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	1	—	—	—	—	1
Public Performance and Management Review	—	—	—	—	—	—	—	—	—	—	—	—	1	—	—	—	—	—	—	—	—	1
Public Relations Review	—	—	—	—	—	—	—	—	—	1	—	—	—	—	—	—	—	—	—	—	—	1
Records Management Journal	—	—	—	—	—	—	—	—	—	—	—	—	—	—	1	—	—	—	—	—	—	1
Research Journal of Applied Sciences, Engineering and Technology	—	—	—	—	—	—	—	—	1	—	—	—	—	—	—	—	—	—	—	—	—	1
Revista Española de Investigación de Marketing ESIC	—	—	—	—	—	—	—	—	—	—	—	1	—	—	—	—	—	—	—	—	—	1
Sustainability	—	—	—	—	—	—	—	—	—	—	1	—	—	—	—	1	—	—	—	—	—	2
Telematics and Informatics	—	—	—	—	—	—	—	—	—	—	—	1	—	—	—	—	—	—	—	—	—	1
Technology in Society	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	1	—	—	—	1
Transforming Government: People, Process and Policy	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	1	—	1	2
World Journal of Social Sciences and Humanities	—	—	—	—	—	—	—	—	—	—	—	—	—	1	—	—	—	—	—	—	—	1
Total	0	2	1	0	0	0	2	1	5	3	3	2	6	1	1	2	3	3	1	5	8	49

Note: Conferences and study workshops can be recognized from the presence of an acronym in brackets.

Geographic Distribution of the Studies

Table 3 shows that the 49 studies are unevenly distributed across countries. South Korea (10 articles), the United States (7), Indonesia (6), and China (5) account for almost half of the sample. Most of these studies are set in countries with relatively advanced or fast-developing digital government and stronger administrative capacity. In these contexts, e-government is often linked to efforts to improve service performance and transparency (Hartanto et al., 2021; Li & Shang, 2023; Parent et al., 2005; Pramuditha et al., 2024; Tolbert & Mossberger, 2006). When citizens experience digital services as convenient, reliable and easy to use, trust in government tends to rise. At the same time, some studies show mixed or even negative effects of certain forms of online interaction, for instance intensive use of information-oriented portals (Im et al., 2014; Porumbescu, 2016a,2016b,2016c). Only a small number of studies focus on local governments in lower-income or otherwise more constrained settings, such as Ethiopia, Pakistan, or rural local governments in developing countries (Beshi & Kaur, 2020; Ella, 2020; Mansoor, 2021). In these cases, basic governance problems and inequality are more prominent. The findings suggest that e-government can support trust where citizens also observe fair treatment, integrity, and responsive behavior, and where digital channels provide timely and accurate information. If these conditions are weak, digital services may do little to change distrust. Furthermore, cultural and institutional differences further shape these patterns. Cross-national and group-comparison studies show that the effects of transparency, internet use, and e-government use on trust vary across countries and social groups (S. Grimmelikhuijsen et al., 2013; Im et al., 2014; Lissitsa, 2021). In societies with stronger acceptance of hierarchy, citizens may place more emphasis on performance and reliability, while in settings with lower power distance expectations for openness and voice are more central to trust. Differences in administrative traditions and accountability arrangements further shape how responsiveness, fairness, and data protection are built into digital services (Beldad et al., 2012; Bertot et al., 2010; Myeong et al., 2014). In conclusion, these patterns indicate that the mechanisms identified in this review are most likely seen in digitally advanced and more stable public sectors and may work differently in other political and cultural contexts.

Table 3.

Countries where the studies were conducted.

Countries	Number of articles
South Korea	10
United States	7
Indonesia	6
China	5
Jordan	2
Netherlands	2
Pakistan	2
Canada	1
Canada and United States	1
Ethiopia	1
Global/multiple countries (meta-analysis, not single-country data)	1
India	1
Iran	1
Israel	1
Kuwait	1
Lebanon	1
Malaysia	1
Mexico	1
OECD member countries (36 countries included in the sample)	1
Singapore	1
Spain	1
The Netherlands and South Korea	1
Total	49

Research Trends

This section summarizes the main research trends on how e-government use affects citizens’ trust in government. The 5-year segmentation reflects the evolving e-government research, as outlined in Table 4. From 2004 to 2008, the focus was on the foundational aspects of e-government adoption, particularly government websites and online service accessibility, driven by the global rise in internet penetration, predominantly in countries like the U.S. and South Korea (OECD, 2020). 2009-2013 saw a shift to service quality and citizen satisfaction, with the 2008 financial crisis prompting governments to leverage e-government for greater efficiency and transparency (S. Grimmelikhuijsen et al., 2013). From 2014 to 2018, the focus expanded to direct communication via social media and digital feedback, as platforms like Twitter and Facebook became integral to government engagement, enhancing transparency and trust (Porumbescu, 2016a,2016b,2016c). The period 2019 to 2024 marked the rise of AI-driven services, accelerated by the COVID-19 pandemic, which highlighted the need for digital inclusivity and greater government responsiveness through advanced digital technologies (Kreps & Jakesch, 2023; J. Wang et al., 2024). This segmentation provides a clear view of how e-government research has shifted from basic adoption to AI-driven governance, aligned with technological advancements and societal changes. The evolution of e-government research reflects changing public expectations and the increasing complexity of government services.

Table 4.

Research trends on e-government use and trust in government over the years.

Years	Research trends	Key findings	References
2004–2008	• Initial focus on government websites, online service adoption, and basic digital engagement. • Examined how website accessibility, transparency, and ease of use shape trust and satisfaction in government.	• Website accessibility and transparency are strongly linked to increased trust. • Satisfaction with e-government services fosters greater public confidence.	Parent et al. (2005), Welch et al. (2005), Tolbert and Mossberger (2006)
2009–2013	• Research expanded to multi-dimensional models, including service quality, citizen satisfaction, and engagement. • Explored participatory e-government, the role of government responsiveness, and transparency.	• Service quality and satisfaction are key trust drivers. • Participation and responsiveness boost confidence in government. • Transparency in decision-making increases perceived trustworthiness.	S. G. Grimmelikhuijsen (2010), Liu and Zhou (2010), Lim et al. (2012), Choi and Kim (2012), S. K. Kim et al. (2013)
2014–2018	• Focus moved to transparency, fairness, and direct communication. • Increasing use of feedback, social media, and smart digital services to foster trust. • Highlighted the need for consistent trust measurement and the effects of participatory tools.	• Two-way communication and digital feedback channels enhance trust. • Participatory tools and procedural fairness foster openness and accountability. • Social media engagement by government has positive effects on trust.	Im et al. (2014), Park et al. (2015), Porumbescu (2016a,2016b,2016c), Gracia and Casaló Ariño (2014)
2019–2024	• Latest research addresses social trust, good governance, inclusiveness, and e-participation. • Explores advanced digital engagement, AI-driven public services, transparency, and community decision-making. • Examines digital divides and long-term trust sustainability.	• Transparency, inclusiveness, and fairness are essential to trust. • Good governance practices and digital participation support long-term citizen trust. • AI and advanced technologies present new opportunities and challenges for trust-building.	S. K. Kim et al. (2019), Kreps and Jakesch (2023), Jameel et al. (2019), Al-Omoush et al. (2023)

It can be concluded that e-government use can strengthen trust only when it improves accessibility, quality, and openness. In the early years, basic web access and transparent information increased satisfaction and modestly raised trust. As services developed, trust depended more on perceived service quality, responsiveness, and opportunities to participate online. With the spread of social media, two-way communication and procedural fairness through digital channels became central to building trust. Recently, AI-driven government services can support longer-term trust when they are inclusive, transparent, and exhibit good governance. However, they also create new risks related to lack of transparency, privacy issues, intensified surveillance, digital exclusion, and algorithmic bias that may disadvantage specific groups. Overall, the evidence suggests that e-government use trust through the quality, fairness, protection or rights, and inclusiveness of digital experiences, rather than through technology alone.

Theoretical Foundations

The analysis of the selected studies shows a variety of theoretical foundations. Among the 49 studies, only four did not explicitly reference or utilize any formal theoretical framework. In addition, several studies, while not entirely theory-free, were mainly informed by broad concepts or frameworks drawn from prior literature rather than by established theories. For instance, S. G. Grimmelikhuijsen (2010) adopted a multidimensional concept of trust; Choi and Kim (2012) relied on prior work in public administration; and Beldad et al. (2012) used prior literature on online trust as their foundation. The remaining studies employed a range of formal theories or models to investigate trust in e-government (see Table 5).

Table 5.

Findings of theoretical foundations.

Theories/models	Theory/model description	References
Agency theory	Explains relationships where one party (the agent) acts on behalf of another (the principal), focusing on trust and control.	Im et al. (2014), Q. Wang and Guan (2023)
Channel expansion theory	Explains how users’ experience with a channel, message, and partners increases perceived richness and effective use of that channel.	Kim et al. (2019)
Cognitive dissonance theory	Explains how inconsistency between people’s beliefs and behaviors creates discomfort, pushing them to adjust their attitudes or perceptions.	Beshi and Kaur (2020), Porumbescu (2016a,2016b,2016c)
Coleman’s trust framework	Explores how trust forms in social systems and networks, focusing on social structure and rational choice.	Song and Lee (2016)
Customer relationship management	Emphasizes managing ongoing interactions with users to better understand their needs and improve satisfaction and loyalty.	Liu and Zhou (2010)
Diffusion of innovation theory	Explains how new ideas or technologies spread through a population over time, from early adopters to late users.	Alaaraj and Hassan (2016)
Dual process theory	Proposes that people use both fast, intuitive thinking and slow, deliberate thinking when forming judgments.	Beshi and Kaur (2020)
E-S-QUAL model	Measures the quality of online services, focusing on efficiency, system availability, and privacy.	Gracia and Casaló Ariño (2014)
Expectancy-disconfirmation model	Explains satisfaction as the result of comparing expectations with actual performance, leading to confirmation or disconfirmation.	Li and Shang (2023), Pramuditha et al. (2024)
General incentives theory of participation	Suggests people participate in activities based on the benefits or rewards they expect.	Warren et al. (2014)
Good governance theory	Emphasizes transparency, accountability, participation, and rule of law as key principles of effective public governance.	Hartanto et al. (2021), Mansoor (2021)
Hofstede’s national culture framework	Explains how cultural values shape behaviors in organizations and society.	S. Grimmelikhuijsen et al. (2013)
Holzer and Melitski (2003) model	A framework for public service improvement focused on performance management.	Jalali and Khorasani (2012)
Information system success model	Links system, information, and service quality to system use, user satisfaction, and overall benefits.	Ella (2020), Fadrial et al. (2024), Kala et al. (2024), J. Wang et al. (2024)
Institutional trust theory	Focuses on how trust is built and maintained in institutions like governments.	Alaaraj and Hassan (2016), Fadrial et al. (2024), Li and Shang (2023), Lim et al. (2012), Tolbert and Mossberger (2006); Welch et al. (2005)
Justice theory	Examines fairness in organizations and its impact on trust.	Q. Wang and Guan (2023)
Media richness theory	Explains how different communication channels vary in their ability to carry rich information and reduce uncertainty.	Kim et al. (2019)
New public service theory	Views citizens as partners rather than customers and stresses participation, dialogue, and the public interest.	Pramuditha et al. (2024)
Organizational trust theory	Explains how perceptions of an organization’s ability, integrity, and care shape trust in that organization.	Choi and Kim (2012)
Political efficacy theory	Focuses on citizens’ belief that they can influence politics and that government will respond to their actions.	Parent et al. (2005)
Political trust theory	Explores how citizens develop trust or distrust in political systems and authorities.	Parent et al. (2005)
Process-based trust theory	Argues trust is formed based on repeated interactions or experiences with services.	Evans et al. (2018), Jameel et al. (2019), Li and Shang (2023), Tolbert and Mossberger (2006)
Psychological distance	Explains how feeling closer or farther away in time, space, or social terms affects people’s judgments and trust.	Porumbescu (2015)
Public value theory	Evaluates public services based on the value they deliver to society as a whole.	Alsaad et al. (2024)
Self-efficacy theory	States that people’s beliefs in their own abilities strongly influence their choices, effort, and persistence.	Kim et al. (2017)
SERVQUAL/E-SERVQUAL models	Assesses service quality using five dimensions (like reliability and empathy), including for online services.	Alsarraf et al. (2022), Myeong et al. (2014), Parent et al. (2005)
Signaling theory	Explains how actors send visible signals to reduce information gaps and show reliability or quality.	Gracia and Casaló Ariño (2014)
Social capital theory	Emphasizes how social networks, norms, and trust help people cooperate and solve collective problems.	Fadrial et al. (2024)
Social cognitive theory	Explains behavior as shaped by the two-way influence of personal factors, behavior, and the environment.	S. K. Kim et al. (2013)
Task-technology fit	Argues that technology works best when its features match the tasks users need to perform.	S. K. Kim et al. (2019)
Technology acceptance model	Explains how users accept and use technology, focusing on perceived usefulness and ease of use.	Ella (2020), Kala et al. (2024), Liu and Zhou (2010), Parent et al. (2005)
Theory of participation	Explains why and how citizens take part in political or civic activities and how this shapes governance.	Warren et al. (2014)
Theory of reasoned action	Predicts behavior based on attitudes and social norms, with intention as the main driver.	S. K. Kim et al. (2013)
Trust expansion theory	Explains how trust developed in one actor or service can spread to related actors or services.	Park et al. (2015)
Trustworthiness theory	Focuses on ability, benevolence, and integrity as the main dimensions of a trustee’s perceived trustworthiness.	Porumbescu (2015, 2016a,2016b,2016c)
Zucker’s trust theory	Explains that trust develops through shared institutions and societal routines.	Park et al. (2015)

A small set of frameworks appears repeatedly. Institutional trust theory, the technology acceptance model, the information system success model, process-based trust theory, and public value theory are the most common bases for modeling how e-government use relates to trust. Across the sample, these theories are mainly used to structure research models and select variables rather than to extend, integrate, or challenge existing explanations of trust. In this sense, the theoretical landscape is broad but fragmented, with limited attempts to develop new trust frameworks for digital and AI-enabled government services.

Conceptualization, Operationalization, and Measurement of Trust

Definitions of Trust

A review of the selected studies reveals that only a limited number of papers provide an explicit definition of trust. When defined, trust is typically described as a belief, expectation, or evaluation of the integrity, reliability, or responsibility of institutions or systems. Some studies frame trust as an attitude or assessment of whether public authorities fulfill expected responsibilities according to normative standards (Tolbert & Mossberger, 2006). Others define trust as a willingness to rely on institutions based on perceived competence and integrity, or as confidence in expected behavior under uncertainty (Im et al., 2014; S. Kim & Lee, 2012; Parent et al., 2005). These definitions portray trust as both relational and evaluative, involving vulnerability and risk. In contrast, most studies do not directly define trust but treat it as a multidimensional concept or discuss it in relation to its role in e-government or digital governance. In these cases, trust is generally conceptualized as citizens’ confidence or positive expectations toward government agencies or digital platforms (Beldad et al., 2012; Choi & Kim, 2012; S. G. Grimmelikhuijsen, 2010; Lim et al., 2012).

For this review, it is important to recognize both the explicit definitions provided in some studies and the more implicit, conceptual approaches that characterize the majority of the literature. This diversity reflects both the complexity of trust and its central importance in digital governance research. Building on these insights, this study proposes the following definitions of trust and trust in government. Trust is the willingness to rely on institutions, systems, or individuals, based on positive expectations of their competence, integrity, and ability to act fairly and transparently. It involves accepting vulnerability, where the trustor believes the other party will act in their best interest and perform their tasks reliably and satisfactorily. Thus, trust is not only confidence in another’s actions but also the assurance that the trustee will act in ways that align with the trustor’s needs. Trust in government is citizens’ willingness to accept vulnerability and rely on public institutions, officials, and their digital systems. It reflects positive expectations that government will use its power and technological resources responsibly, fairly, and transparently. It is reflected in citizens’ confidence and satisfaction with how government acts effectively and responsively. It also extends to the belief that governmental decisions contribute to the broader welfare of society and address citizens’ needs.

Trust Measurement

Trust is operationalized as an outcome variable across the selected studies (Parent et al., 2005; Welch et al., 2005). The most widely used way to measure trust is through surveys. Researchers typically ask respondents to rate their experiences with government websites, online portals, and digital interactions using scales or sets of questions (Liu & Zhou, 2010; Welch et al., 2005). These questions often ask about perceptions of transparency, how easy digital services are to use, satisfaction with online interactions, and how secure citizens feel about sharing information (Beldad et al., 2012; S. Kim & Lee, 2012; Porumbescu, 2016a,2016b,2016c). Most surveys draw from established question sets or adapt items from previous research to ensure comparability across studies (S. K. Kim et al., 2013; Parent et al., 2005). Beyond surveys, a smaller number of studies employ experiments or scenario-based methods to investigate trust in controlled settings (S. G. Grimmelikhuijsen, 2010; S. Grimmelikhuijsen et al., 2013). These methods test how variations in transparency or service features affect participants’ trust. In addition, some research utilizes large national or cross-country datasets to analyze trust at a broader level (Alswalmh et al., 2024; Q. Wang & Guan, 2023). While survey-based measurement remains the dominant approach, the use of experiments and large-scale data adds diversity and depth to the field (S. G. Grimmelikhuijsen, 2010; Lee-Geiller, 2024).

In summary, most research uses clear, structured questions in surveys to measure trust, though other methods like experiments and large-scale data analysis are also used. The trend toward standardized tools helps researchers compare results and strengthens understanding of what shapes citizens’ trust in the digital era (S. Grimmelikhuijsen et al., 2013; Parent et al., 2005; Porumbescu, 2016a,2016b,2016c).

Methodology Adopted

In this section we examined the methodological approaches of the selected studies.

Methodological approaches: The review shows that most studies on e-government and citizens’ trust use a quantitative approach. As shown in Figure 2, out of 48 empirical studies, 43 used quantitative methods. These studies often used surveys to collect data and then applied statistical techniques and quantitative models to examine people’s trust in online government services. Only three studies used qualitative methods, such as interviews or case studies, to explore people’s views in more detail. There were also two studies that used both methods (mixed methods), combining both quantitative and qualitative data to achieve more comprehensive findings. It is important to note that one study (Choi & Kim, 2012) was a theoretical or conceptual paper. This means it did not collect new data but instead developed a model to explain trust in government. Because it is not based on original empirical data, this paper was not included in the main chart. Even though it is not empirical, this kind of study is still helpful as it gives insights and frameworks that other researchers can use to design future studies.

Figure 2.

Types of methodological approaches in reviewed studies.

Given that most of the research relies on quantitative methods, there is a clear gap in the field. Quantitative methods can indeed measure trends across large populations effectively. However, they cannot capture the complexity of trust. Trust is shaped by many factors that quantitative approaches may overlook, these include emotional, social, and cultural influences that affect how citizens perceive government services. Qualitative research offers a way to explore these factors in greater depth. Interviews or case studies can provide insights into the reasons behind citizens’ attitudes; this kind of research can reveal the personal and contextual elements that influence trust. This methodological imbalance suggests opportunities for future research to adopt a mixed-method approach. By incorporating both qualitative and quantitative methods, future studies could capture the complexity of citizens’ trust in government services and the underlying factors that influence their perceptions and behaviors.

Sampling techniques: Most of the studies (33 out of 49) used convenience sampling. This method is common in online surveys and reflects the ease of reaching people who already use digital government services (e.g., S. Kim & Lee, 2012; Liu & Zhou, 2010). While it helps collect data quickly and with fewer costs, it limits how well the results can represent the broader population. People who choose to respond to online surveys may already be more digitally skilled or more trusting than those who do not. Random sampling, on the other hand, appeared in 11 studies. These usually used national datasets or large-scale surveys, like those from the Pew Internet Project or the American Customer Satisfaction Index (e.g., Morgeson et al., 2011; Tolbert & Mossberger, 2006). Random sampling helps ensure that different types of people are included, making findings more generalizable. A few studies used purposive sampling, focusing on specific groups such as users of e-filing systems or participants in e-consultation platforms (e.g., Lim et al., 2012). These cases aimed to collect data from people with direct experience of e-government. However, results from these groups may not reflect the views of the general public. The widespread use of convenience sampling shows a practical approach in the field but also points to a gap in methodological rigor. Few studies used more advanced designs, like stratified or quota sampling, which could help capture differences across regions, ages, or digital skill levels.

Target populations: Most studies targeted adult citizens who had used e-government services. These included online users, city residents, or university students. Some studies focused on more specific groups such as public employees or e-participation users (e.g., Porumbescu, 2016a,2016b,2016c). These populations are often easy to reach but may not reflect the views of people who are less connected to digital services, such as rural residents or older adults. This focus on digitally active groups limits our understanding of trust among those who may be excluded from e-government. These groups are important, especially when considering digital inequality and inclusiveness in public service delivery.

Sample sizes: Most surveys included between 300 and 800 respondents, which is common for statistical analysis such as regression or structural equation modeling (e.g., Alzahrani et al., 2017; Park et al., 2015). A few large-scale studies had more than 1,000 participants, especially when aiming for national coverage (e.g., Li & Shang, 2023). Some qualitative or experimental studies used smaller samples, sometimes under 50 people (e.g., S. G. Grimmelikhuijsen, 2010), focusing more on depth than breadth.

Key Determinants of Citizens’ Trust in E-Government

This section identifies and groups the most common variables examined across the selected studies. These factors help explain why citizens choose to trust or distrust digital government services.

Factors Influencing Citizens’ Trust

A review of the 49 studies reveals that citizens’ trust in government is shaped by multiple interrelated factors. Citizen satisfaction is the most frequently cited determinant; trust rises when users find digital services effective, user-friendly, and responsive. Contextual factors, such as age, gender, education, and internet experience, also matter, with higher digital literacy linked to greater trust. Governance practices like transparency, accountability, and openness are repeatedly highlighted. When governments communicate clearly and act ethically online, legitimacy and trust are reinforced. Privacy and data security remain critical; citizens are more trusting when they believe their personal data is protected. Other key drivers observed across the selected studies include the perceived value and efficiency of digital services, service quality (timely responses, reliability), and system quality (ease of use, information reliability). Recent research points to the positive impact of AI-powered systems and mobile apps on interactivity and efficiency, further enhancing trust. Finally, user engagement, through e-participation and feedback mechanisms, helps citizens feel heard, further building trust. Taken together, these findings underscore that building trust in government depends on delivering secure, high-quality, transparent, and inclusive digital services. These insights are summarized in Table 6, which presents the factor categories and their associated influencing factors.

Table 6.

Factor categories and key influencing factors.

Factor category	Key influencing factors
Citizen satisfaction and behavioral responses	Overall satisfaction with e-government services, continuance intention, compliance behavior
Contextual controls	Age, gender, education, internet experience, country context
Governance practices	Transparency, accountability, openness
Citizen attributes	Trust propensity, digital literacy, political efficacy, citizen expectations
Privacy and security	Data privacy, perceived security
Public value and outcome perception	Perceived value, goal alignment, citizen-centricity
Service quality	Responsiveness, efficiency, service reliability
System and information	System quality, information quality, perceived ease of use
Technology and AI factors	Adoption channel, AI-mediated communication, group incentives
User engagement	E-participation, social media use, interactivity

Visual Summary of Variables

This section presents a visual summary of the key variables identified across the selected studies. The summary groups variables that influence citizens’ trust in government through e-government use. The most common independent variables include system-related factors such as perceived usefulness, ease of use, service quality, information quality, and responsiveness, all highlighting the importance of the user experience. The dependent variable is typically trust in government, usually measured as citizens’ belief in government competence, integrity, and benevolence. Mediating variables, such as satisfaction, perceived performance, and transparency, help explain how e-government use leads to changes in trust. Moderating variables like digital literacy, political ideology, and social media exposure influence the strength or direction of these effects. Control variables (age, gender, education, and other socio-demographic factors) help account for variation across user groups. Figure 3 provides a visual summary of these relationships as found in the 49 reviewed studies.

Figure 3.

Conceptual framework of variables influencing citizens’ trust in government through E-government use.

In constructing Figure 3, variables were assigned to the roles of independent, mediating, moderating, or control factors based on two criteria. First, we coded how each construct was specified in the original empirical models and placed it in the category in which it most frequently appeared across the 49 studies. Second, we considered the theoretical position of the construct in dominant frameworks (e.g., service and system quality as antecedent conditions; satisfaction and perceived performance as process variables linking use and trust). We acknowledge that some variables are more fluid: for instance, transparency or satisfaction are treated as direct predictors of trust in some studies and as mediators or moderators in others. The framework should therefore be read as an integrative heuristic that summarizes typical roles in the literature, rather than as a fixed causal model in which each variable can only occupy a single position. This approach allows us to reflect the diversity of operationalizations while still providing a coherent overview of how e-government use is theorized to affect trust in government. To conclude, the review finds that trust in government is built step by step. It is shaped by many different factors, and this structure clarifies how various elements interact to shape trust through e-government use.

Gaps in the Current Literature on E-Government Use and Citizens’ Trust

The analysis of 49 empirical studies shows clear progress in understanding the relationship between e-government use and citizens’ trust in government. However, several important gaps remain.

A. Underexplored Impact of Actual Use and Emerging Technologies

Most studies still focus on citizens’ intentions or willingness to use e-government services, rather than examining the effects of actual use. Only a small number of articles analyze how real interactions with e-government platforms shape trust over time (Li & Shang, 2023; Tolbert & Mossberger, 2006; Welch et al., 2005; Xia, 2017). In addition, there is limited research on new technologies, such as AI-driven public services and mobile application, even as these tools become more common in digital government. Few studies address how features like automation, algorithmic decision-making, or chatbots affect trust in government systems.

B. Methodological Limitations

The majority of reviewed studies use quantitative, cross-sectional surveys and often rely on convenience sampling (S. Kim & Lee, 2012; Liu & Zhou, 2010; Morgeson et al., 2011; Tolbert & Mossberger, 2006). This approach limits the generalizability of findings and makes it difficult to capture changes in trust over time. There are few qualitative studies that explore citizens’ lived experiences, and longitudinal or experimental research is almost nonexistent.

C. Weak Theoretical Development and Integration

Many studies rely on established theories, such as the TAM or Institutional Trust Theory, but seldom attempt to extend or adapt these models to the changing realities of e-government (Liu & Zhou, 2010; Morgeson et al., 2011). There is a lack of theoretical innovation, especially concerning the integration of new frameworks for digital, algorithmic, or AI-mediated trust. The field would benefit from testing and developing theories that reflect the complexity of today’s digital government services (S. Grimmelikhuijsen, 2012; Parent et al., 2005; Welch et al., 2005).

D. Underexplored Interpretive and Emotional Dimensions

Few studies address how citizens interpret or feel about digital governance, especially with new technologies like AI and automated decision-making. There is a lack of research into how people react to algorithm-driven services, and what emotional or psychological responses these generate (Mansoor, 2021; Porumbescu, 2016a,2016b,2016c). This gap is important as technology continues to shape the relationship between government and citizens.

E. Inconsistent Measurement and Operationalization of Trust

Trust is defined and measured in different ways across studies, making comparisons and synthesis difficult (Alswalmh et al., 2024; Fadrial et al., 2024; Kala et al., 2024). Many studies use survey questions or indices that are not always explained in detail. There is a need for more consistent and transparent operationalization of trust, as well as better reporting of measurement tools, to strengthen the reliability of future research.

F. Limited Contextual Diversity and Inclusion

The reviewed literature is dominated by studies from developed or digitally advanced countries, often focusing on urban, highly literate, or younger populations (Porumbescu, 2016a,2016b,2016c; Tolbert & Mossberger, 2006; West, 2004). Rural areas, older adults, marginalized groups, and people with lower digital skills are rarely studied. This means that existing findings mainly reflect the experiences of already-connected users and tell us little about those at the margins of the digital state. The link between e-government use and trust is therefore likely to differ across political systems, cultural settings, and administrative traditions, but current evidence cannot capture this variation.

Discussion

Synthesis of the Main Findings

This systematic review examined 49 empirical studies to answer the question: How does the use of e-government services affect citizens’ trust in government? Overall, the evidence shows that e-government use can strengthen trust when digital interactions signal competence, integrity, and fairness. Trust in Government tends to increase when citizens experience services as useful, easy to use, reliable, and responsive, and when digital channels make government actions more transparent. Across the studies, citizen satisfaction is the central pathway linking e-government use to citizen’s trust in government. Positive experiences with online services lead to higher satisfaction, which in turn improves perceptions of government performance and reliability. Perceived service quality, system quality, transparency, and responsiveness are the most consistent drivers of this process. When citizens believe that their data are handled securely and that privacy is protected, they are also more willing to trust digital government systems. Furthermore, the effect of e-government use on citizens’ trust in government is not uniform. Contextual factors such as age, education, digital literacy, and prior internet experience shape how different groups interpret their online interactions with government. Studies from countries with advanced digital infrastructure show stronger positive links between use and trust, whereas findings from less resourced settings are more mixed. Recent work on AI and mobile platforms suggests that new technologies can further enhance trust when they improve efficiency, interactivity, and access, but research on these developments remains limited.

We can conclude that these 49 studies indicate that e-government use enhances citizens’ trust in government when services are user-centered, transparent, secure, and responsive, and when citizens feel that digital channels give them a fair and reliable way to interact with public institutions. However, as discussed in Section “Gaps in the Current Literature on E-Government Use and Citizens’ Trust,” important gaps remain regarding emerging technologies, excluded groups, and the longer-term dynamics of trust.

Theoretical Implications

This systematic review highlights how research on e-government and trust draws from a range of established theories, such as the TAM, Institutional Trust Theory, Public Value Theory, and Process-Based Trust Theory (see Table 5). Most studies use these frameworks to structure their research questions and interpret findings, rather than to develop new theories. As a result, theory development in this field remains limited. Many studies do not fully integrate or extend these theories to capture the unique features of digital and AI-enabled government services. Furthermore, while TAM remains popular for explaining adoption and use, it often overlooks the complexity of trust as an outcome. Similarly, Institutional Trust Theory and Public Value Theory provide strong foundations for understanding relationships between citizens and institutions but overlook critical aspects like algorithmic governance and automated decision-making processes. Indeed, most reviewed studies rely on traditional models without adapting them for the evolving digital context. Another theoretical limitation is the lack of models that connect the technical, social, and emotional dimensions of trust in digital governance. Some studies suggest that trust in government is shaped by user satisfaction, perceived fairness, and data security. However, these factors are not always clearly linked in the conceptual models. There is a need for frameworks that explain how citizens’ digital experiences shape their expectations, perceptions, and willingness to trust government services. The review also notes that newer models, especially those incorporating AI, human–machine interaction, and algorithmic transparency, are largely absent from current research. To conclude, the field would benefit from more integrated and updated theoretical models (See Section “A Research Agenda on the Effects of Technology Use in Government Services on Citizens’ Trust in Government”).

Practical Implications

The findings have practical implications for central government policymakers, local public service managers, and system designers or IT providers. For central policymakers, the results underline the need for clear standards on service quality, transparency, and data protection. Governments should adopt national guidelines on usability and accessibility for all public-facing digital platforms, including plain language, mobile-friendly design, and minimum response time targets. They should also set mandatory rules for privacy and security, such as default data minimization, clear consent procedures, and regular independent audits of digital and AI-based systems. Public reporting of performance indicators (e.g., uptime, response times, complaint resolution) can signal reliability and strengthen trust at the system level. For local managers and frontline agencies, trust-building depends on how digital channels work in day-to-day interactions. Managers should ensure that online requests and complaints receive timely, substantive responses, and that citizens can track the status of their cases. E-participation tools and feedback forms should not only collect comments but also feed into visible changes, for example by publishing “you said, we did” reports. Local agencies should also provide assisted digital options, such as help desks, call centers, or in-person support, so that older adults, rural residents, and people with low digital skills are not excluded from online services. For system designers and IT providers, the evidence points to the importance of user-centered and trustworthy design. Interfaces should be tested with diverse user groups, including those with limited literacy or unstable internet access, to ensure that key tasks (such as applying, paying, or requesting information) are simple and reliable. Privacy and security safeguards should be made visible in the interface, for example through clear explanations of what data are collected and how they are used. Where AI or automated decision-making is involved, systems should provide simple explanations of the main decision criteria, offer human review or appeal channels, and log errors or complaints for continuous improvement. These design features can reduce perceptions of arbitrariness and help sustain trust in digital government. Finally, all three actor groups need to address digital inequality explicitly. Policies and designs should be tested against the needs of groups who are less connected or less trusting, not only frequent online users. This includes monitoring differential take-up and satisfaction across age, income, education, and location, and adjusting outreach and support strategies accordingly.

A research Agenda on the Effects of Technology Use in Government Services on Citizens’ Trust in Government

This section proposes a research agenda aiming to move beyond general e-government use and to develop theory about how concrete AI-enabled public services shape citizens’ trust in government.

Recommendation 1: Study specific AI-based government services as objects of trust. Most existing studies examine “e-government use” in general, often focusing on websites or mobile portals. Future research should analyze concrete AI services, such as chatbots, automated eligibility checks, or decision-support systems, and examine how their use affects trust in government. This implies describing clearly what type of AI is used, in which policy domain, and at which level of government, rather than using “AI” as a broad label. Researchers should model AI-specific features such as explainability, perceived fairness, and human oversight, instead of treating AI as just another delivery channel.

Recommendation 2: Test whether satisfaction still mediates the link between AI government services use and trust in government. Previous work suggests a “use → satisfaction → trust” pathway for traditional e-government. Future studies should test whether this mediation still holds for AI-mediated services. Researchers can examine whether interactions with AI systems increase trust mainly by raising satisfaction with service performance, or whether AI also changes deeper perceptions of government competence and benevolence. Comparative models, with and without satisfaction as a mediator, would clarify whether this long-standing mechanism remains valid in an AI context.

Recommendation 3: Examine algorithm literacy as a moderator between AI government services use and trust in government. The review shows that digital skills and experience shape how citizens react to e-government use. In the AI context, an important extension is algorithm literacy, that is, citizens’ basic understanding of how algorithmic systems work and what their limits are. Future research should test whether algorithm literacy moderates the relationship between using AI-based services and trust in government, for example by comparing high- and low-literacy groups or by designing interventions that raise algorithm literacy and measuring the effects on trust.

Recommendation 4: Use Mayer et al.’s (1995) cognitive trustworthiness framework and complement it with emotional responses. Many variables already used in the literature map onto Mayer et al.’s three dimensions of trustworthiness: ability (competence, efficiency), benevolence (public interest, care), and integrity (fairness, honesty, privacy protection). Future models should make this structure explicit and examine how AI-based government services use influence each dimension. At the same time, interactions with AI systems also trigger emotional reactions, such as anxiety, fear of surveillance, or feelings of reassurance when processes appear fair and predictable. Building on Mayer et al.’s cognitive framework and linking it with work on affective trust would allow researchers to analyze how beliefs about ability, benevolence, and integrity, together with feelings of safety or worry, jointly shape citizens’ trust and distrust in AI-enabled government services.

Recommendation 5: Apply Zucker’s (1986) institutional trust theory and move beyond adoption models. Most studies still rely on technology adoption theories (e.g., TAM), even when they analyze actual use rather than intention. Future research should instead draw on Zucker’s distinction between process-based, characteristic-based, and institutional-based trust. This would shift attention to how repeated AI-mediated interactions (process-based), social and group positions (characteristic-based), and formal guarantees such as laws, standards, and oversight bodies (institutional-based) jointly sustain or erode trust. Such an approach aligns better with a mature e-government context, where the question is less “will citizens adopt?” and more “does continued use consolidate or undermine institutional trust?”

Recommendation 6: Develop multilevel, context-sensitive models of effects of AI-enabled government services use on trust in government. Findings from this review suggest that the same digital features may have different effects in different political and cultural settings. Future studies should therefore use multi-level designs that combine individual-level data (e.g., use, satisfaction, perceptions of AI and fairness) with contextual indicators (e.g., governance quality, rule of law, corruption, digital inclusion, cultural values) and specify the policy domains in which AI is used (such as welfare, taxation, policing, or licensing). This would allow researchers to test whether AI-enabled government services build trust more strongly in contexts with strong rights protections and accountability, and whether effects are weaker or even negative where institutions are perceived as abusive, biased, or unresponsive.

Recommendation 7: Use mixed-method and explanatory designs to show how and why emerging technology use shapes trust in government. This review shows a strong dominance of quantitative, cross-sectional survey studies and very few qualitative or mixed-method designs. Future research should use mixed methods to connect broad patterns with citizens lived experiences. For instance, researchers could combine surveys or experiments on AI-based services with follow-up interviews or focus groups to explore how people interpret automated decisions, transparency messages, or error handling. Mixed-method designs would help clarify not only whether technology use affects trust, but also how and why these effects arise in different groups and contexts.

Limitations of this Review

This review, like all systematic reviews, has several limitations. First, the search was limited to selected academic databases and key terms related to “e-government” and “trust.” This may have led in the exclusion of relevant articles found in other databases or using different keywords. As a result, there may be studies on related topics that were not included in the analysis. Second, the review focused only on peer-reviewed journal articles published in English. Grey literature and non-English publications were therefore not considered, so some valuable insights from these sources may have been missed. Third, the review did not conduct a meta-analysis or quantitative synthesis because the included studies used diverse research designs, theoretical models, and outcome measures. As a result, it was not possible to provide a statistical estimate of the effects of e-government use on trust in government. Future systematic reviews could broaden the search to include additional databases, keywords, and languages. Including grey literature may also add depth and variety to the analysis. Meta-analyses or mixed-methods syntheses could also be used to provide more robust quantitative estimates.

Conclusion

This review examined the main empirical research on how e-government use affects citizens’ trust in government. Across 49 empirical studies, the evidence shows that e-government use can enhance citizens’ trust in government, but only under specific conditions. Trust in government tends to rise when digital interactions signal competent service delivery, fair treatment, and respect for privacy and rights, and when citizens experience digital channels as a reliable and legitimate way to deal with public institutions. When services are difficult to use, unreliable, hard to access for some groups, or raise fears about data misuse and monitoring, increased use does not automatically build trust and may reinforce doubt. This review makes three main contributions. First, it systematically maps how empirical research on e-government use and trust in government has developed over the past two decades. It highlights where and how studies have been conducted, which methods and theories dominate, and which contexts and populations remain under-represented. Second, it synthesizes the main mechanisms through which e-government use shapes trust. It shows that satisfaction, perceived performance, transparency, privacy, and user engagement are recurrent links between digital experience and institutional trust. Third, it identifies key gaps in the current literature and turns them into a focused research agenda for the AI era. This agenda points to concrete constructs, theories, and designs that future studies can use to examine AI-based services, the role of satisfaction and algorithm literacy, and trust as an outcome of continued use rather than only of initial adoption. To conclude, the findings suggest that e-government services use affects trust in government through the quality, fairness, and inclusiveness of digital experiences, rather than through digitalization itself. As governments expand AI-based and data-driven services, future research needs to address the gaps and recommendations identified in this review, as it will be crucial for understanding when digital government strengthens institutional trust, when it leaves it unchanged, and when it risks undermining it.

Footnotes

Acknowledgements

The author wishes to thank the School of Public Administration at Huazhong University of Science and Technology for academic support.

ORCID iD

Razanakoto Mino Fifaliana

Funding

The authors received no financial support for the research, authorship, and/or publication of this article.

Declaration of Conflicting Interests

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

References

Alaaraj

Hassan

(2016). Does good governance mediate relationship between e-government and public trust in Lebanon? DergiPark (Istanbul University). https://dergipark.org.tr/tr/pub/irmm/issue/32093/355373

Al-Omoush

K. S.

Garrido

Cañero

(2023). The impact of government use of social media and social media contradictions on trust in government and citizens’ attitudes in times of crisis. Journal of Business Research, 159, Article 113748. https://doi.org/10.1016/j.jbusres.2023.113748

Alsaad

Alkhawaldeh

Elrehail

Almomani

(2024). Linking e-government development and quality of governance to trust in government: Evidence from OECD member countries. Transforming Government People Process and Policy, 18(4), 575–593. https://doi.org/10.1108/tg-03-2024-0060

Al-Sai

Z. A.

Abualigah

L. M.

(2017, May 17–18). Big data and E-government: A review [Conference session]. 2017 8th International Conference on Information Technology (ICIT), Amman, Jordan, (pp. 580–587). https://doi.org/10.1109/ICITECH.2017.8080062

Alsarraf

H. A.

Aljazzaf

Ashkanani

A. M.

(2022). Do you see my effort? An investigation of the relationship between e-government service quality and trust in government. Transforming Government People Process and Policy, 17(1), 116–133. https://doi.org/10.1108/tg-05-2022-0066

Alswalmh

A. S.

Fawareh

Kanan

(2024). The E-government systems services and its impact on the public Jordanians trust from the software quality factors perspective. In Khoury

R. E.

Nasrallah

(Eds.), Studies in systems, decision and control (pp. 3–16). Springer. https://doi.org/10.1007/978-3-031-36895-0_1

Alzahrani

Al-Karaghouli

Weerakkody

. (2017). Analysing the critical factors influencing trust in e-government adoption from citizens’ perspective: A systematic review and a conceptual framework. International Business Review, 26(1), 164–175. https://doi.org/10.1016/j.ibusrev.2016.06.004

Bannister

Connolly

(2011). Trust and transformational government: A proposed framework for research. Government Information Quarterly, 28(2), 137–147. https://doi.org/10.1016/j.giq.2010.06.010

Bano

Zowghi

(2015). A systematic review on the relationship between userinvolvement and system success. Information and Software Technology, 58, 148–169. https://doi.org/10.1016/j.infsof.2014.06.011.

10.

Batini

Cappiello

Francalanci

Maurino

(2009). Methodologies for data quality assessment and improvement. ACM Computing Surveys, 41(3), 1–52. https://doi.org/10.1145/1541880.1541883.

11.

Beldad

van der Geest

de Jong

Steehouder

. (2012). A cue or two and I’ll trust you: Determinants of trust in government organizations in terms of their processing and usage of citizens’ personal information disclosed online. Government Information Quarterly, 29(1), 41–49. https://doi.org/10.1016/j.giq.2011.05.003

12.

Bertot

J. C.

Jaeger

P. T.

Grimes

J. M.

(2010). Using ICTs to create a culture of transparency: E-government and social media as openness and anti-corruption tools for societies. Government Information Quarterly, 27(3), 264–271. https://doi.org/10.1016/j.giq.2010.03.001

13.

Beshi

T. D.

Kaur

(2020). Public trust in local government: Explaining the role of good governance practices. Public Organization Review, 20(2), 337–350. https://doi.org/10.1007/s11115-019-00444-6

14.

Choi

S. O.

Kim

(2012). An exploratory model of antecedents and consequences of public trust in government [Conference presentation]. IIAS Study Group Workshop on Trust in Public Administration and Citizen Attitudes, Seoul National University, Seoul, South Korea. https://www.semanticscholar.org/paper/1-An-Exploratory-Model-of-Antecedents-and-of-Public-Choi/c2e1b95d7db675efd64afa7ddd7410476be7b49b#citing-papers

15.

Craig

S. C.

(1996). Broken contract? Routledge.

16.

Donovan

Bowler

(2004). Reforming the republic. Prentice Hall.

17.

Easton

(1965). A systems analysis of political life. University Of Chicago Press.

18.

Easton

(1975). A re-assessment of the concept of political support. British Journal of Political Science, 5(4), 435–457. https://www.jstor.org/stable/193437

19.

The e-Government imperative. (2003). In OECD e-government studies. https://doi.org/10.1787/9789264101197-en

20.

Ella

(2020). Assessing correlation on e-government and trust at local level in developing countries. Proceedings on Engineering Sciences, 2(3), 237–246. https://doi.org/10.24874/pes02.03.003

21.

Evans

Franks

Chen

H. M.

(2018). Voices in the cloud: Social media and trust in Canadian and US local governments. Records Management Journal, 28(1), 18–46. https://doi.org/10.1108/rmj-11-2016-0041

22.

Fadrial

Sujianto

Simanjuntak

H. T. R. F.

Wirman

Wibowo

W. S.

(2024). Fostering trust through bytes: Unravelling the impact of E-Government on public trust in Indonesian local government. Interdisciplinary Journal of Information Knowledge and Management, 19, 15. https://doi.org/10.28945/5317

23.

Fountain

J. E.

(2001). Building the virtual state. Brookings Institution Press.

24.

Gracia

D. B.

Casaló Ariño

L. V.

(2014). Rebuilding public trust in government administrations through e-government actions. Revista Española de Investigación de Marketing ESIC, 19(1), 1–11. https://doi.org/10.1016/j.reimke.2014.07.001

25.

Grimmelikhuijsen

(2012). Linking transparency, knowledge and citizen trust in government: An experiment. International Review of Administrative Sciences, 78(1), 50–73. https://doi.org/10.1177/0020852311429667

26.

Grimmelikhuijsen

S. G.

(2010). Transparency of public decision-making: Towards trust in local government? Policy & Internet, 2(1), 5–35. https://doi.org/10.2202/1944-2866.1024

27.

Grimmelikhuijsen

Porumbescu

Hong

(2013). The effect of transparency on trust in government: A cross-national comparative experiment. Public Administration Review, 73(4), 575–586. https://doi.org/10.1111/puar.12047

28.

Halling

Baekgaard

(2024). Administrative burden in citizen–state interactions: A systematic literature review. Journal of Public Administration Research and Theory, 34(2), 180–195. https://doi.org/10.1093/jopart/muad023

29.

Hartanto

Dalle

Akrim

Anisah

H. U.

(2021). Perceived effectiveness of e-governance as an underlying mechanism between good governance and public trust: A case of Indonesia. Digital Policy Regulation and Governance, 23(6), 598–616. https://doi.org/10.1108/dprg-03-2021-0046

30.

Heeks

Bailur

(2007). Analyzing e-government research: Perspectives, philosophies, theories, methods, and practice. Government Information Quarterly, 24(2), 243–265. https://doi.org/10.1016/j.giq.2006.06.005

31.

Holzer

Melitski

(2003). A comparative e-government analysis of New Jersey’s 10 largest municipalities. Faculty of Arts and Sciences Rutgers University Newark Campus Cornwall Center Publication Series.

32.

Houston

D. J.

Harding

L. H.

(2013). Public trust in government administrators. Public Integrity, 16(1), 53–76. https://doi.org/10.2753/pin1099-9922160103

33.

Cho

Porumbescu

Park

(2014). Internet, trust in government, and citizen compliance. Journal of Public Administration Research and Theory, 24(3), 741–763. https://doi.org/10.1093/jopart/mus037

34.

Jalali

Khorasani

(2012). The Relationship between E-Government and the public trust among the citizens in district 5, Tehran. Research Journal of Applied Sciences Engineering and Technology, 4(23), 5261–5267. https://www.airitilibrary.com/Publication/alDetailedMesh?DocID=20407467-201212-201512080013-201512080013-5261-5267

35.

Jameel

Asif

Hussain

Hwang

Sahito

Bukhari

M. H.

(2019). Assessing the moderating effect of corruption on the E-Government and trust relationship: An evidence of an emerging economy. Sustainability, 11(23), 6540. https://doi.org/10.3390/su11236540

36.

Kala

Chaubey

D. S.

Meet

R. K.

Al-Adwan

A. S.

(2024). Impact of user satisfaction with E-Government services on continuance use intention and citizen trust using TAM-ISSM framework. Interdisciplinary Journal of Information Knowledge and Management, 19, 1. https://doi.org/10.28945/5248

37.

Khan

Krishnan

Dhir

(2021). Electronic government and corruption: Systematic literature review, framework, and agenda for future research. Technological Forecasting and Social Change, 167, Article 120737. https://doi.org/10.1016/j.techfore.2021.120737

38.

Kim

S. K.

Park

M. J.

Rho

J. J.

(2013). Effect of the government’s use of social media on the reliability of the government: Focus on Twitter. Public Management Review, 17(3), 328–355. https://doi.org/10.1080/14719037.2013.822530

39.

Kim

S. K.

Park

M. J.

Rho

J. J.

(2019). Does public service delivery through new channels promote citizen trust in government? The case of smart devices. Information Technology for Development, 25(3), 604–624. https://doi.org/10.1080/02681102.2017.141229

40.

Kim

Lee

(2012). E-Participation, transparency, and trust in local government. Public Administration Review, 72(6), 819–828. https://doi.org/10.1111/j.1540-6210.2012.02593.x

41.

Kitchenham

B. A.

(2004). Procedures for performing systematic reviews. Keele University.

42.

Kreps

Jakesch

(2023). Can AI communication tools increase legislative responsiveness and trust in democratic institutions? Government Information Quarterly, 40(3), Article 101829. https://doi.org/10.1016/j.giq.2023.101829

43.

Kundra

(2011). Federal cloud computing strategy. Office of Management and Budget, Office of E-Government and Information Technology.

44.

Lai

Marques

A. J. L.

(2024). Aspects that constitute citizens’ trust in e-government - A review and framework development. Multidisciplinary Reviews, 7(2), Article 2024023. https://doi.org/10.31893/multirev.2024023

45.

Lee-Geiller

(2024). The moderating effect of digital literacy on the link between e-government effectiveness and trust in government. Journal of Policy Studies, 39(4), 77–101. https://doi.org/10.52372/jps.e672

46.

Levi

Stoker

(2000). Political trust and trustworthiness. Annual Review of Political Science, 3(1), 475–507. https://doi.org/10.1146/annurev.polisci.3.1.475

47.

Shang

(2023). How does e-government use affect citizens’ trust in government? Empirical evidence from China. Information & Management, 60(7), Article 103844. https://doi.org/10.1016/j.im.2023.103844

48.

Lim

E. T. K.

Tan

Cyr

Pan

S. L.

Xiao

(2012). Advancing public trust relationships in Electronic Government: The Singapore E-Filing Journey. Information Systems Research, 23(4), 1110–1130. https://doi.org/10.1287/isre.1110.0386

49.

Lissitsa

(2021). Effects of digital use on trust in political institutions among ethnic minority and hegemonic group – A case study. Technology in Society, 66, Article 101633. https://doi.org/10.1016/j.techsoc.2021.101633

50.

Liu

Zhou

(2010, July 16–18). A citizen trust model for e-government [Conference session]. 2010 IEEE International Conference on Software Engineering and Service Sciences, Beijing, China. https://doi.org/10.1109/icsess.2010.5552260

51.

MacLean

Titah

(2022). A systematic literature review of empirical research on the impacts of e-Government: A public value perspective. Public Administration Review, 82(1), 23–38. https://doi.org/10.1111/puar.13413

52.

Mahmood

Osmani

Sivarajah

(2014). The role of trust in E-Government adoption: A systematic literature review. Research Repository (Kingston University London). http://bura.brunel.ac.uk/handle/2438/9993

53.

Mansoor

(2021). Citizens’ trust in government as a function of good governance and government agency’s provision of quality information on social media during COVID-19. Government Information Quarterly, 38(4), Article 101597. https://doi.org/10.1016/j.giq.2021.101597

54.

Marri

A. A.

Albloosh

Moussa

Elmessiry

(2019, November 18–19). Study on the impact of artificial intelligence on government E-service in Dubai [Conference session]. 2019 International Conference on Digitization (ICD), Sharjah, United Arab Emirates. https://doi.org/10.1109/icd47981.2019.9105866

55.

Mayer

R. C.

Davis

J. H.

Schoorman

F. D.

(1995). An integrative model of organizational trust. The Academy of Management Review, 20(3), 709–734. https://doi.org/10.2307/258792

56.

Mergel

(2019). Digital service teams in government. Government Information Quarterly, 36(4), Article 101389. https://doi.org/10.1016/j.giq.2019.07.001

57.

Morgeson

F. V.

VanAmburg

Mithas

(2011). Misplaced trust? Exploring the structure of the E-government-citizen trust relationship. Journal of Public Administration Research and Theory, 21(2), 257–283. https://doi.org/10.1093/jopart/muq006

58.

Myeong

Kwon

Seo

(2014). Sustainable E-governance: The relationship among trust, digital divide, and E-Government. Sustainability, 6(9), 6049–6069. https://doi.org/10.3390/su6096049

59.

Organization for Economic Co-operation and Development. (2020). Digital Government Index. https://www.oecd.org/en/publications/digital-government-index_4de9f5bb-en.html

60.

Organization for Economic Co-operation and Development. (2024, January 29). 2023 OECD Digital Government Index. https://www.oecd.org/en/publications/2023-oecd-digital-government-index_1a89ed5e-en.html

61.

Parent

Vandebeek

C. A.

Gemino

A. C.

(2005). Building citizen trust through E-government. Government Information Quarterly, 22(4), 720–736. https://doi.org/10.1016/j.giq.2005.10.001

62.

Park

M. J.

Choi

Kim

S. K.

Rho

J. J.

(2015). Trust in government’s social media service and citizen’s patronage behavior. Telematics and Informatics, 32(4), 629–641. https://doi.org/10.1016/j.tele.2015.02.006

63.

Porumbescu

G. A.

(2015). Using transparency to enhance responsiveness and trust in local government: Can it work? State & Local Government Review, 47(3), 205–213. http://www.jstor.org/stable/24639237

64.

Porumbescu

G. A.

(2016a). Comparing the effects of E-government and social media use on trust in government: Evidence from Seoul, South Korea. Public Management Review, 18(9), 1308–1334. https://doi.org/10.1080/14719037.2015.1100751

65.

Porumbescu

G. A.

(2016b). Linking public sector social media and e-government website use to trust in government. Government Information Quarterly, 33(2), 291–304. https://doi.org/10.1016/j.giq.2016.04.006

66.

Porumbescu

G. A.

(2016c). Placing the effect? Gleaning insights into the relationship between citizens’ use of E-government and trust in government. Public Management Review, 18(10), 1504–1535. https://doi.org/10.1080/14719037.2015.1122827

67.

Pramuditha

Muhafidin

Sumaryana

Susanti

(2024). Exploring the impacts of e-government service quality on citizen satisfaction and trust: Evidence from population administration services. Journal of Logistics Informatics and Service Science, 11, 283–302. https://doi.org/10.33168/jliss.2024.0817

68.

Ritz

Brewer

G. A.

Neumann

(2016). Public service motivation: A systematic literature review and outlook. Public Administration Review, 76(3), 414–426. https://doi.org/10.1111/puar.12505

69.

Rousseau

D. M.

Sitkin

S. B.

Burt

R. S.

Camerer

(1998). Not so different after all: A cross-discipline view of trust. Academy of Management Review, 23(3), 393–404. https://doi.org/10.5465/AMR.1998.926617

70.

Statista. (2024). Government IT spending by segment 2019-2023. https://www.statista.com/statistics/1154210/worldwide-government-it-spending-forecast-by-segment/

71.

Song

Lee

(2016). Citizens’ use of social media in government, perceived transparency, and trust in government. Public Performance & Management Review, 39(2), 430–453. https://doi.org/10.1080/15309576.2015.1108798

72.

Thompson

Teo

Srivastava

Jiang

(2008). Trust and electronic government success: An empirical study. International Journal of Information & Communication Technology Education, and Encyclopedia of Information Science and Technology, 25(3), 99–131. https://doi.org/10.2753/MIS0742-1222250303

73.

Tolbert

C. J.

Mossberger

(2006). The effects of E-government on trust and confidence in government. Public Administration Review, 66(3), 354–369. https://doi.org/10.1111/j.1540-6210.2006.00594.x

74.

UN E-Government Survey. (2008). From E-government to connected governance. https://publicadministration.un.org/egovkb/Portals/egovkb/Documents/un/2008-Survey/unpan028607.pdf

75.

UN E-Government Survey. (2022). The future of digital government. https://publicadministration.un.org/egovkb/en-us/Reports/UN-E-Government-Survey-2022

76.

UN E-Government Survey. (2024). Accelerating digital transformation for sustainable development. https://publicadministration.un.org/egovkb/en-us/Reports/UN-E-Government-Survey-2024

77.

Upadhyaya

(2024). Artificial intelligence in web development: Enhancing automation, personalization, and decision-making. International Journal of Advanced Research in Science, Communication and Technology, 4, 534–540. https://doi.org/10.48175/ijarsct-19367

78.

Wang

Zhang

Jiang

Zhao

Evans

(2024). The role of mGovernment applications in building trust during public crises: Evidence from the COVID-19 epidemic. Heliyon, 10(12), e32476. https://doi.org/10.1016/j.heliyon.2024.e32476

79.

Wang

Guan

(2023). Can sunlight disperse mistrust? A meta-analysis of the effect of transparency on citizens’ trust in government. Journal of Public Administration Research and Theory, 33(3), 453–467. https://doi.org/10.1093/jopart/muac040

80.

Warren

A. M.

Sulaiman

Jaafar

N. I.

(2014). Social media effects on fostering online civic engagement and building citizen trust and trust in institutions. Government Information Quarterly, 31(2), 291–301. https://doi.org/10.1016/j.giq.2013.11.007

81.

Welch

E. W.

Hinnant

C. C.

Jae Moon

(2005). Linking citizen satisfaction with E-government and trust in government. Journal of Public Administration Research and Theory, 15(3), 371–391. https://doi.org/10.1093/jopart/mui021

82.

West

D. M.

(2004). E-government and the transformation of service delivery and citizen attitudes. Public Administration Review, 64(1), 15–27. https://doi.org/10.1111/j.1540-6210.2004.00343.x

83.

Xia

(2017). Does E-governance matter for the people’s trust in the government? Evidences from Shanghai, China. Journal of Political Sciences & Public Affairs, 4(4), 224. https://doi.org/10.4172/2332-0761.1000224

84.

Zucker

L. G.

(1986). Production of trust: Institutional sources of economic structure, 1840–1920. Research in Organizational Behavior. https://psycnet.apa.org/record/1988-10420-001

85.

Zuiderwijk

Chen

Y.-C.

Salem

(2021). Implications of the use of artificial intelligence in public governance: A systematic literature review and a research agenda. Government Information Quarterly, 38(3), Article 101577. https://doi.org/10.1016/j.giq.2021.101577