An Index for Trustworthy Government in the Digital Age

Introduction

Societies depend on trustworthy government services, including essential utilities, education, healthcare, transportation and civil administration. Over the past three decades, digital technology has transformed the delivery of government services in three main stages:

• Web Presence (1990s-2000s): governments begin creating websites and portals, allowing individuals to access information online
• Web Engagement (2000s-2010s): social-media and interactive web platforms emerge, enabling two-way communication between government and public. Government service transactions move online, but systems are usually siloed between departments
• Digital-Native Government (2010s-Present): governments adopt holistic digital strategies, pursuing integration across departments, high-performance cloud infrastructure, mobile-first service delivery, and universal identification systems. Moves toward this stage received an impetus from the covid pandemic.

The Trustworthy Digital Government project aims to develop a global trust index for government services transitioning to G3 and beyond. The proposed indexing system will integrate expert evaluations across key dimensions of digital trust with user-experience data from government platforms.

The project will deliver an operational framework for benchmarking the trustworthiness of digital government services. It will devise systems for aggregating complex, dynamic data to evaluate and monitor government service delivery. It will produce indicators for digital-age institutional capacity and service quality, intended to guide transformation initiatives, inform sovereign assessments, and promote productivity-enhancing public investments in digital technology.

This paper will describe the motivations for this program, reviewing digital government maturity and trust metrics, the scope of digital government services, and the challenge of measuring government service productivity. It will consider the requirements of a system for dynamically measuring trust in government services, and some of the key elements that this program could integrate.

Digital Government Maturity and Trust Metrics

A range of metrics have been developed to capture elements of government effectiveness and institutional quality (see Appendix A). Some of these metrics are referenced by credit ratings agencies in sovereign risk assessments. Metrics for digital government maturity, evaluating the level of effectiveness in the adoption of digital technology by governments, are published by organisations including the United Nations, the World Bank, and the Organization of Economic Co-operation and Development (OECD). These metrics allow for international comparisons (Figures 1 and 2, Appendix B) but overlook significant variation at the subnational level. Digital government maturity metrics focus on infrastructure, technical capabilities, and service availability, but do not specifically consider outcomes in government service-delivery, nor user trust in service-providers.

A trust metric quantifies the degree of trust held by stakeholders in institutions. Trust measurement frameworks derive standardized scores from structured assessments, summarizing information over multiple trust dimensions such as competence and ethics (Edelman Trust Barometer) or effectiveness, ease and emotion (Forrester Customer Experience CX).

Trust metrics have been used to rate public trust in consumer brands, international corporations, digital platforms and public institutions (Table 1). These metrics have traditionally relied on surveys – e.g. customer experience or public opinion – which limit data to the responses of individuals to pre-defined questions. Advances in machine learning now enable trust-related sentiment to be tracked dynamically and automatically from unstructured digital communications, such as news articles, social media posts, and online forums. Boston Consulting Group's 2022 Trust Index processed real-time natural-language input from leading wire-services, newspapers, journals, and Twitter, deriving dynamic trust indicators for over one thousand companies. Numerous open-source and paid machine-learning tools are now available for identifying topical themes and monitoring sentiment in various forms of live communications.

Table 1. Prominent Trust Metrics

Digital Trust

Along with new ways to measure trust, digitisation introduces new dimensions to institutional trust-relationships. A World Economic Forum initiative defines digital trust as individuals' expectation that digital technologies and services – and the organizations providing them – will protect all stakeholders' interests and uphold societal expectations and values. These "societal expectations and values" include upholding digital systems' security and reliability, accountability and oversight, and inclusive, ethical, and responsible use (WEF 2022). Some elements of digital trustworthiness that are common across organisations can be measured using prospective, objective capability indicators. The WEF initiative identifies eight elements as dimensions of a proposed structured framework for objectively evaluating technical capabilities and governance processes: cybersecurity, safety, transparency, interoperability, auditability, redressability, fairness, and privacy (Table 2). Other elements of trustworthiness may be tracked using retrospective, subjective perception metrics that capture the experience of users interacting with digital systems. Collection of this type of metric relies on the quality of digital systems' feedback mechanisms. They may include user ratings for satisfaction and confidence, or user comprehension of system features and data flows.

Table 2. Digital Trust Dimensions and Objective Capability Indicators (World Economic Forum framework)

Perception indicators can be complemented by behavioural metrics that reveal how users engage with digital services, including adoption rates, engagement patterns, support requests, retention over time, and willingness to promote the service to others. While prospective metrics on trustworthiness may be suited to expert evaluation of an organisation's capabilities, it is these retrospective perception and behavioural measures that capture the actual progress of an organisation toward trusted relationships with users.

Digitisation of government services introduces novel capabilities for gauging user experience and tracking service quality. Data from digital government platforms, including structured or unstructured user feedback, can be used in the construction public service performance indicators. The digital trust measurement framework can be adapted to the modern public sector by combining these indicators with objective capability evaluations of responsible technology use and organisational governance. Through enabling performance benchmarking and the identification of trust-gaps, an indexing system for government services based on this framework can help establish digital trustworthiness as a concrete policy objective.

The Scope of Digital Government Services

The public sector produces a significant share of total services consumed by individuals in developed economies, making public service quality an important element of living standards globally (Figure 3). Comparative evaluations of the quality of individual public services are complicated by the variety of determining factors – for example, user-experience, time, satisfaction, accessibility, and outcomes – and by the absence of standardised units or methods for their measurement. Measuring the general quality of service provision by government is further complicated by the vast heterogeneity of services that governments provide, and complex divisions of responsibility between various government bodies at different levels (central, regional, and local).

Government services include activities relating to core state functions, such as law enforcement, tax collection, currency issuance, pension payments, licensing and regulations, and foreign affairs. Governments also typically provide education services – public schools and universities – as well as healthcare and social work services – public hospitals, disease-control programs, insurance systems, and care-services for children, the elderly, and persons with disabilities. Modes of provision vary between and within countries, with governments both providing these services directly and supporting private provision through accreditation, funding, and regulatory mechanisms. Health and education services account for significant components of general government expenditure in all developed economies (Figure 4) and have been priority areas for public digitisation initiatives internationally.

Government services extend to transportation and utilities: public transit systems, road networks, ports and airports, postal services, telecommunications, water supply, sewerage, waste management, power generation, and the transmission and distribution of gas and electricity. Due to scale and network economies, services in these sectors tend to be provided either by public entities or by private entities operating under substantial public regulation and oversight. Governments also provide (or support the provision of) many other areas of service-provision potentially suitable for inclusion in a service trust index (see Appendix C). The collective value of 'non-market' services provided directly by government, or in service sectors where statutory regimes are prevalent, represents a major component of economic activity (around one-quarter by gross value-added) across developed countries (Figure 5). Digitisation offers the potential to significantly improve service quality by enabling the large-scale collection and analysis of data that directly reflects the perceptions and behaviour of service users. A key objective of trustworthy digital government is to leverage this data responsibly to transform the design and delivery of public services, boosting productivity growth across key economic sectors.

Productivity, Service Quality, and Public Feedback

Measuring productivity in the public sector is a known methodological challenge (see Atkinson, 2005). Productivity estimates normally involve comparing market-values of inputs and outputs, which is problematic for public services that produce outputs without market-based valuations. National accounting conventions, which are used for calculating economic indicators like Gross Domestic Product, traditionally assume that the value of non-market outputs is equivalent to the value of their inputs (i.e. their costs of production, including labour costs), regardless of the value that the public might place on them. This convention overlooks the public sector's potential for productivity improvements, as changes to the volume of non-market output for a fixed set of inputs are not registered as a change in productivity.

To address this problem, a number of countries have experimented with 'output-based' measurements for public sector output volume, which adopt quantity indicators that 'count' public service outputs in the same manner as physical units (Schreyer, 2010), for example:

• Education: number of students enrolled, number of hours of teaching received by students ('pupil hours'); number of degrees or credits obtained
• Healthcare: number of patients treated, number of diagnostic procedures or surgeries performed, number of hospital or care-facility occupant-days
• Public Safety and Justice: number of crimes resolved, number of emergency call responses, number of inspections conducted, number of court hearings held.

These volume measurements do not account for output quality, so can be misleading indicators of productivity if used by themselves. To reflect service productivity in terms of outcomes rather than just outputs, statistical agencies that construct public service productivity indices typically adjust output volumes using quality indicators. Due to the diversity and complexity of relevant outcomes – which are often influenced by factors outside the control of the service-provider – there is no single standard approach to making these adjustments. Quality indicators used by statistical agencies include:

• Education: standardised test-score performance, teacher qualifications, prevalence of bullying, graduation rates, graduate employability and incomes
• Healthcare: hospital readmission rates, surgical complication rates, survival rates, waiting times for surgeries, procedures avoided due to early prevention, prevalence of health conditions in the community
• Public Safety and Justice: seriousness of crimes prevented, rates of recidivism, court processing timelines, number of cases dismissed, number of verdicts appealed or overturned on appeal.

These indicators are all quantitative values which can be measured independently, or 'objective' indicators. Some quality indicators may rely on user-reporting, for example Patient-Reported Outcome Measures (PROMS), which are responses by patients to standardised questionnaires on health outcomes post-treatment, used to evaluate clinical effectiveness and widely used in healthcare service-volume quality-adjustments.

Another class of quality indicator is the 'subjective' kind: feedback given directly by users of the service. Patient-Reported Experience Measures (PREMs) use standardised questionnaires to record patients' feelings and opinions on various aspects of the care services they have received (e.g. waiting times, facilities, patient-clinician interaction). Subjective, experienced-based (i.e. user-satisfaction) measures are not widely used in official productivity statistics, but several national statistical agencies use extensive experience-based data to construct parallel quality indicators for public service performance benchmarking, complementing objective performance metrics (Productivity Commission 2022, EIPA 2024).

Subjective experience metrics can be collected at the point of service (online, by phone or at in-person at service-centres), when users are prompted after an interaction to rate their experience. This may involve a scale (e.g. very satisfied to very dissatisfied) or a simple two-option (Thumbs-Up, Thumbs-Down) or three-option (Happy, Neutral, Sad) sentiment indicator. Simple forms of post-transaction feedback require little time or effort, encouraging high response rates. They can provide governments with real-time data that serves as a parsimonious high-level, user-focused performance indicator, suitable for aggregation across different service functions.

More detailed experience surveys can deliver actionable insights for performance improvements, but when collected at the point of service they may be subject to lower response rates and attribution problems. Detailed post-transaction user-experience surveys usually aim to collect user-profile information (to construct representative samples and investigate differences in experience between groups) and query multiple dimensions of user-experience – for example, ease, satisfaction, and trust. In addition to surveys that specifically gather post-transaction feedback, many countries undertake 'central' or 'citizen' surveys that periodically gather feedback on perceptions of government service quality from the general population, often with a focus on life events. Some of these surveys employ a panel of respondents (retaining the same respondents for multiple survey periods), allowing for more robust comparisons over time. Data on subjective user experience are used in performance measurement systems as a complement to objective process and outcome metrics, to which they are generally correlated (Baredes 2022).

Several countries have developed integrated national platforms for government services with feedback mechanisms that permit free text (or speech) along with post-transaction user-experience ratings. These systems process complex natural language data to track service quality and identify issues at a granular level. Advanced public service digitisation opens channels for continuous unstructured feedback and detailed real-time population data, in contrast to the structured sample data provided by periodic central surveys. Table 3 gives examples of both continuous feedback systems and periodic central surveys that are used internationally.

Table 3. Central Surveys and Feedback Systems

Results from service feedback systems and central surveys are not well-suited for making cross-country comparisons, due to the variety of structures used, and the tendency of service quality to be perceived relative to expectations – which may vary widely between populations – rather than consistent standards. To compare perceptions of government and government services across countries, the OECD recently (in 2021 and 2023) undertook an international Survey on the Drivers of Trust in Public Institutions, part of the Organisation's Reinforcing Democracy Initiative. The survey questions respondents on attitudes toward national (Figure 6), regional, and local government and law and order institutions; as well as satisfaction with healthcare and education systems and administrative services, and trust in the use of personal data by public agencies (Figure 7).

While finding wide variation between countries and demographics, the OECD Trust Survey found that a consistently strong driver of trust in service providers is their perceived responsiveness, both to evidence and to public feedback. The importance of the responsiveness dimension to public evaluations of government trustworthiness underscores the value of the new channels for policy input opened by digital transformation.

Indexing Trust in Digital Government Services

The transition from G2 to G3 describes a shift from fragmented, siloed data environments to cross-departmental digital systems based on sophisticated national infrastructures. A leading example is Estonia's X-Road data exchange platform, which enables secure information sharing between hundreds of public and private sector organisations across thousands of digital services. Another example is South Korea's Government 24, which integrates thousands of services at all levels of government under one 24/7 online portal, with case-tracking and mechanisms for user feedback, linked to the government's real-time performance management and evaluation system. A similar digital 'front-door' for unified digital services is Singapore's government app LifeSG., which organises services around life events and offers personalised recommendations based on user profiles and behavioural data.

Although few countries have reached a similar stage of G3, digital transformation initiatives worldwide are increasingly shifting responsibility for government-wide service-delivery monitoring and improvement to central digital government units, recognising the strategic role of integrated data resources. The United States Digital Service (USDS) plays a central role in improving federal online services and operates from the Executive Office of the President. A 2021 Executive Order directs high-impact agencies to prioritise 'customer experience' and service design centred on life events. In 2025 the U.K. launched a new digital centre of government (Government Digital Service) with a comprehensive strategy for "wholesale reshaping" of the public sector. In Australia, the Digital Transformation Agency (DTA) has been made responsible for driving cross-departmental data-driven service improvement with a life-event approach and a strong focus on digital trust. These initiatives reflect the increasing importance of digital performance to the credibility of public institutions, a development that may become increasingly relevant for sovereign assessments.

Integration of digital systems demands institutional commitment and coordination at different levels of government, requiring substantial investments in technology and infrastructure alongside the development of legislative and regulatory frameworks and governance schemes to ensure digital trustworthiness. The benefits include more efficient and accessible services, reductions in 'time taxes' imposed on the public by fragmented administrative processes, and the expansion of digital resources supporting responsive data-driven decision-making by governments, improving resource allocation and guiding policy adjustments. Interoperable information systems support reporting tools including public dashboards and enable the development of real-time performance indicators that combine objective and subjective metrics collected from various agency systems to account for multiple dimensions of government service provision.

An index is a synthetic statistic that condenses information from many indicators (usually as a weighted average), which can be monitored over time or compared across divisions. A general government trust index would be a composite of numerous subindices for particular service areas or functions (such as education or healthcare), which will themselves be composites of indices for numerous aspects of service quality and trustworthiness. The indexing system will involve selected 'baskets' of indicators forming the component elements of each subindex. These components – objective or subjective metrics that capture aspects of interest – must be standardised to common scales and frequencies, then aggregated using weights that reflect each indicators' relative importance. The selection of appropriate indicators and weighting formulae for indexing specific government functions will be an ongoing process involving consultation with experts and stakeholders, the objective being meaningful comparisons to support sovereign assessments. The index will aim to reflect the element of trust that capital markets can have in national, regional, and municipal governments' creditworthiness according to the institutional virtues revealed in those governments' provision of services.

With digital capabilities increasingly critical for institutional legitimacy, a trust index for any area of government service-provision should place appropriate weight on objective capability indicators for digital trust dimensions such as those identified in the WEF's trust-measurement framework (Table 2). Independently evaluated capability indicators for these dimensions could be combined as index components with indicators for other relevant dimensions of public governance. The remaining index components would include a combination of objective (process and outcome) and subjective indicators reflecting standards of service provision, potentially constructed according to a framework identifying key aspects such as:

• Access – the opportunity to obtain an appropriate service in case of need: affordability (including indirect costs); geographic proximity to point of service (or digital channel coverage); accessibility of information (including for marginalised or 'low-tech' groups)
• Responsiveness – how well (and how quickly) services take into account the needs, preferences, perspective and dignity of individuals who use them: courtesy and treatment; match to special needs (e.g. adaptability of service delivery for people with disabilities); timeliness
• Quality – the degree to which services increase the likelihood of desired outcomes and are consistent with current professional knowledge: effective delivery and outcomes; consistency (including in outcomes across all population segments); security and safety (including exposure to physical or digital risks in the service-delivery process).

Weights chosen for different component indicators of a subindex may reflect relative importance as determined through consultation with stakeholders, using heuristics such as service-user numbers and budget item allocations, or with evidence of statistical significance for overall trust levels. Trust indices for particular government service areas (e.g. health, education, transport) can be aggregated into an overall general measure using weighting schemes that reflect their importance at different levels of government; for example, a subindex for trust in transportation services would weigh more heavily in an overall trust index for municipal government than for national government due to the municipal level's direct role in providing these services, although a trust index for central government would still include some weighting on the quality of municipal service provision to reflect its general responsibility over national service standards. High levels of public trust in transportation services registered by this index would be an indicator of the institutional competence of municipal authorities; a potential factor for consideration in credit assessments.

Inputs into the design of aggregate index weighting schemes could include the relative sizes of budgetary allocations; these are shown in Figure 8 for Australia's national, state, and local governments. However, budget allocations do not reflect the relative importance of particular areas service-provision areas either in terms of economic activity or individual consumption. An alternative approach would be to organise index construction around key life events, in line with the user-centric approach that typically guides modern digital initiatives for service improvement. Weighting schemes for indexing systems that effectively capturing the complex and evolving interplay of factors determining overall government trustworthiness will need to be continuously refined and standardised as part of an ongoing program of policy research.

^{Source: Australian Bureau of Statistics. Data for 2023.
Total local government expenditures are about 5% of commonwealth expenditures.
Total state government expenditure are about 41% of commonwealth expenditures.}

From G3 to G4

Digitally mature governments are beginning to explore the application of emerging technologies that represent a distinct stage in the evolution of digital government beyond G3. This is most clearly defined by the generative artificial intelligence capabilities of modern Large Language Models (LLMs) but includes modern machine-learning and big-data analytics in general, along with other '4IR' digital technologies; robotics, the Internet of Things (IoT), distributed ledgers and 3D printing. The most fundamental change is the delivery of services using digital systems that can learn, reason, and make decisions. G4 services are predictive, proactive, and personalised. G4 represents 'intelligent government' that uses AI to anticipate user and community needs before they arise and customises delivery and engagement for individual circumstances and preferences.

LLMs were developed following the invention of Generative Pretrained Transformer neural networks in the late 2010s and came to public attention with the launch of OpenAI's ChatGPT web platform in 2022. The unprecedented human-like language abilities of GPT3.5 and GPT4 models, trained over long periods on enormous volumes of digital text data, represented a breakthrough in the sophistication of artificial intelligence, leading to rapid adoption and soon followed by the emergence of commercial competitors and (later) open-source alternatives. Over a short period, the technology underpinning LLMs was adapted for multimodal models using image, voice, and video as well as text, along with 'reasoning models' designed to handle complex tasks using chains of step-by-step problem solving. Like other organisations, government agencies and public-service-providers around the world have used LLMs to drive efficiencies in their internal operations, but a few countries with advanced digital strategies have moved to leverage the potential of LLM technology for integrated public service delivery. An exemplary case is Estonia's Bürokratt, a single, whole-of-government virtual assistant based on an interoperable network of chatbots that allows voice-based interaction.

A particularly transformative element of LLM technology is its potential to eliminate the traditional trade-off between 'breadth' and 'depth' in citizen engagement. Due to the labour involved in 'manually' interpreting and analysing unstructured information, governments seeking public feedback have previously faced a choice between simple structured forms of feedback from many users, or nuanced insights from small samples (e.g. focus groups). AI-powered systems have the potential to transcend this limitation, enabling mass participation in consultative processes by enabling large volumes of unstructured (and multimodal) natural language data – provided by large numbers of individuals – to be processed, summarised, and analysed for sentiment and insights.

Governments are often unable to utilise commercial AI platforms (such as ChatGPT), due to terms of service that conflict with privacy and security requirements. The data and computational requirements of LLMs are significant and involve specialised hardware, with leading commercial models based primarily in the United States and (more recently) China. Recognising the strategic importance of LLM technology, some countries have moved to develop a sovereign AI capability, building high-performance models that do not require sharing data with an foreign platform. For example, Singapore's state-backed LLM Sea-Lion, built with a focus on Southeast Asian languages capabilities, is now basis for the country's SENSE LLM chatbot supporting public sector workers with data and policy analysis. AI sovereignty refers to a government's ability to integrate LLM capabilities while maintaining full control over data, applications, and digital infrastructure.

With G4 still in the early stages, the transformative impact of AI-driven "intelligent government" will be gradually realised over the coming decade, along with public sector applications of other 4IR technologies. Few countries currently possess the advanced data infrastructures and robust digital governance structures required to explore the potential of AI-driven "intelligent government". However, for many countries, significant technical and institutional obstacles remain before even the G3 stage can be reached. A key challenge is encouraging the broad adoption of secure, interoperable national digital ID systems, which are essential for reliable user-authentication, data integrity and personalised service delivery. Digital ID is critical element of the two-way trust relationship between the public and the government necessary for realising the social benefits of digital technology.

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