Introducing the municipal digital offering index for evaluating online services and addressing the digital divide

Conceptual evolution and impact of e-government

E-government refers to the use of technology to enhance access to and delivery of government services, benefiting citizens, businesses, and employees. It aims to create a modernized, integrated, and transparent public service model, fostering collaboration between governments and citizens (Silcock, 2001). Over time, e-government has evolved into digital government, maintaining the core principles of the original definition. As research progresses, the concepts become more diverse and perspectives more intricate. For instance, differences in the pace of this transformation between national/federal governments and local governments are noticeable (Manoharan & Ingrams, 2018). Additionally, studies explore the role of digital government in promoting transparency, trust in government, and combating corruption (Arayankalam, Khan & Krishnan, 2021; Tolbert & Mossberger, 2006), its impact on specific social groups (Hien, 2014), and the influence of social media in its management (Kahn et al., 2013).

Over the past 20 years, e-government studies have focused on assessing the extent and quality of services provided through ICTs (Mensah, Zeng & Luo, 2020). Researchers have also developed maturity models to aid in planning modernization strategies. Various obstacles have been identified, including the digital skills of citizens, the establishment of informative platforms and streamlined processes, and cybersecurity concerns (Das, Singh & Joseph, 2017; Davidson, Wagner & Ma, 2015). To analyze e-government, indicators can be categorized into four main areas: government-to-citizen (G2C), government-to-business (G2B), government-to-employee (G2E), and government-to-government (G2G). The latter involves communication between different government agencies, often utilizing extensive databases to promote interoperability (Alshehri & Drew, 2010).

At the national level, according to Reffat (2003), e-government represents a significant shift in how the government interacts with the public, beyond just setting up computers or websites; instead, it entails using technology to improve how government services are provided. The process of implementing e-government involves five stages: deciding to make changes in systems, which is influenced by both internal and external factors; spreading these changes, which depend on background and knowledge; institutionalizing e-government practices; and evaluating the extent to which these changes have been put into practice (Dias, 2020).

Ashaye & Irani (2019) introduced a series of developmental stages illustrating the gradual improvement in the interaction between government and citizens through digital tools. The first stage, transmission, focuses on providing government information through websites and other electronic means. Next is the interaction stage, where there is increased two-way communication, allowing citizens to ask questions and give feedback via digital platforms. The third stage is a transaction, which marks the point where electronic transactions like tax payments and online form submissions become possible. Finally, the integration stage, the most advanced phase, involves fully incorporating digital technologies to organize and manage various government services comprehensively and efficiently. These stages depict the progression of a government towards offering more advanced and efficient electronic services.

Nam (2014) identified several ways in which e-government is utilized. This encompasses transactional services, where electronic platforms facilitate government-related transactions like online payments. Citizens also utilize electronic resources to access general governmental information and obtain details on government policies through digital platforms. Furthermore, citizen participation involves engaging in online interaction with the government to take part in decision-making processes and offer feedback. Lastly, policy co-creation involves actively collaborating with the government in formulating and developing public policies.

In the same study, Nam (2014) identified five key factors that affect how citizens adopt and use e-government services. These factors are psychological predisposition, trust in the government, information channels, demographic conditions, and civic mindset. It is important for citizens to effectively utilize government electronic services. Additionally, Nam (2014) found that e-government users can be categorized into two main groups: policy researchers and citizens who use digital channels to engage with the government.

Malodia et al. (2021) indicate that the success of e-government is influenced by factors like citizen orientation, channel orientation, and technology. Effective e-government implementation brings about tangible benefits and improves the efficiency of government operations. Nevertheless, challenges such as the digital skills gap and economic inequality can weaken the relationship between e-government and citizens. Additionally, a perception of government transparency positively impacts the less tangible outcomes of e-government.

A relevant line of research focused on benchmarking e-government: Skargren (2020), Przeybilovicz, Cunha & Ribeiro (2023). This literature highlights the importance of continuously and critically examining how e-government development is defined and proposes an effective benchmark that must be sensitive to regional and local contexts, as well as aligned with the core purposes of public administration—namely, to deliver better services and generate public value. As such, any evaluation of e-government should move beyond standardized metrics and consider the diverse institutional, social, and cultural conditions in which digital governance is implemented.

E-government comparative rankings

In e-government, transparency plays a pivotal role in fostering accountability, trust, and effective governance (Arayankalam, Khan & Krishnan, 2021; Tolbert & Mossberger, 2006). Transparent practices ensure that citizens have access to information about government actions, decisions, and processes, thus empowering them to hold their leaders accountable and participate meaningfully in democratic processes. Moreover, transparency enhances the efficiency and effectiveness of e-government initiatives by promoting openness and accountability in the delivery of public services (Bayona & Morales, 2017; Ndou, 2004).

To gauge the progress and effectiveness of e-government implementations, various studies conduct rankings that assess the level of development and performance of digital government initiatives worldwide. These rankings serve as tools for policymakers and researchers, offering insights into the strengths, weaknesses, and overall development of e-government systems across different countries.

The Institute of Digital Government has been producing the International Digital Government Ranking since 2002. In the 2024 ranking, Singapore (94.69); the UK (94.49) and Denmark (93.29) lead the index, while Chile holds the 41st position (67.44) (WASEDA-IAC, 2024; International Academy of CIO (IAC), 2022).

Conversely, the OECD Digital Government Index 2023 (OECD, 2024) assesses how well countries implement data and digital technologies in various aspects, such as digital design, government operations as a platform, data-driven decision-making, transparency, user-centric approach, and proactivity. According to the Digital Government Index (DGI) findings, many OECD countries faced challenges due to insufficiently trained personnel with digital expertise. In this context, Korea, Denmark, and the United Kingdom lead this ranking with scores exceeding 0.778, while Chile ranks 32^nd with an index of 0.398 as one of the last places.

Additionally, the United Nations E-Government Development Index (EGDI) (United Nations, 2022, 2024) provides a global benchmark for assessing the extent of digitalization in national governments. The index combines the Online Service Index, the Telecommunication Infrastructure Index, and the Human Capital Index. While 2022 results highlighted advancements in global e-government adoption, with more countries enhancing their EGDI scores, the last results for 2024 show a significant narrowing of the digital divide: the proportion of the world population lagging in digital government development fell from 45.0% to 22.4%. This progress is largely driven by gains in Asia, notably the rise of India and Bangladesh above the global average. The Americas also demonstrated steady advancement, with a greater number of countries joining the very high EGDI group. While Africa and Oceania have made some progress, they continue to lag behind the global average. These results reflect a global trend toward more inclusive digital governance, though regional disparities persist.

The comparison between studies reveals differences in their emphasis: For example, the DGI focuses on aspects of government digitalization, whereas the EGDI takes a broader view, considering both human capacities and telecommunication infrastructure. However, due to different measurement methods and country samples, e-Government indicators remain inconsistent across rankings. This discrepancy poses challenges in generating coherent impressions and highlights the need for standardized methods to facilitate research in this field.

Dias (2020) suggests that socioeconomic factors and local political aspects impact how e-government is implemented in municipalities. Evaluation frameworks, such as the one proposed by Tejedo-Romero et al. (2022), consider important factors like online presence, access to urban information, interaction, transactions, and e-democracy to gauge the progress of digital services at the municipal level. Transparency is identified as a vital element for the effective management of resources in local governments, and citizen involvement is closely tied to electronic transparency, which relies on the availability of information to encourage active participation.

The United Nations regularly assesses local e-government development through the Local Online Services Index (LOSI), a tool designed to evaluate municipal digital portals worldwide. These evaluations are increasingly relevant as urban populations grow and internet access becomes more widespread. In 2024, LOSI underwent a significant methodological update, expanding from 86 to 95 indicators and from five to six dimensions. This revision responds to the need for more inclusive and context-sensitive assessments of digital government, in line with global trends in public engagement and digital inclusion.

The six dimensions in the 2024 LOSI are: (1) institutional framework (five indicators), covering municipal digital strategies, governance structures, legal frameworks on access and privacy, and open data policies; (2) content provision (30 indicators), assessing the availability of essential public information online; (3) services provision (30 indicators), evaluating the accessibility and delivery of government services; (4) participation and engagement (10 indicators), measuring citizen interaction and participatory mechanisms; (5) technology (18 indicators), focusing on portal accessibility, usability, and technical standards; and (6) e-government literacy (10 indicators), a newly introduced dimension measuring the population’s ability—especially among vulnerable groups—to effectively use digital public services.

This updated version aligns LOSI more closely with the national-level Online Service Index (OSI), offering a more coherent view of e-government performance across levels of administration. Notably, the 2024 results show Santiago de Chile ranked 74th globally, while Bogotá leads Latin America in 16th place. European cities such as Madrid and Tallinn also score among the highest, reflecting mature local digital governance ecosystems.

To further clarify the originality and positioning of this study within the existing literature, Table 1 summarizes key contributions, methodological approaches, and gaps identified across the main strands reviewed. The table also highlights how MDOI responds to these gaps by offering a context-sensitive, operational tool tailored to the Chilean municipal landscape.

Legal and regulatory framework of e-government in Chile

A legal framework is crucial for examining how e-government is implemented and operates within municipalities, as well as for assessing their adherence to transparency and information access standards. In Chile, the Law on Digital Transformation of the State (Law No. 21,180: Government of Chile, 2019) was published in November 2019 and came into force in 2021. This modifies the basis of the administrative procedures for its transformation and digitalization, promoting digital requests with the support of interoperability, for better integration between public institutions.

Chile’s territory is presently divided into 16 regions for the State’s governance and internal management. Each region is further divided into 56 provinces. These provinces are then subdivided into 345 communes for local administration purposes. The legal and regulatory framework within Chilean municipalities operates under Law No. 18,695, also referred to as the Organic Constitutional Law of Municipalities.

Regarding transparency and public information accessibility, the Transparency Law, (Law No. 20.285: Government of Chile, 2008), approved in 2008, acknowledges everyone’s right to access public information and functions through two primary aspects: First, active transparency entails public entities’ responsibility to provide pertinent and up-to-date information regularly. Secondly, passive transparency (or the right of access to information) mandates that public bodies respond to any individual’s information inquiries.

According to Article 7, municipal entities are required to provide the public with essential background information on their websites, updating it at least once every month, covering details on the organizational structure, responsibilities, functions, and authorities of each unit or internal body, the municipal regulatory framework, staff information, employment practices, public fund transfers, service access procedures and requirements, subsidy initiatives, budget allocation, outcomes of internal audits, and other pertinent data.

Data gathering

The research design employs a probabilistic, descriptive, empirical, cross-sectional, and quantitative approach. The unit under study is the websites of Chilean municipalities, encompassing the entire population of 344 municipalities. As a research instrument, a content analysis guideline was constructed to quantitatively record (Kerlinger, 1975; Espín, 2002) the digital services offered by municipalities (see ‘Research Instrument’ in Supplemental Material). This instrument enables a systematic and reproducible collection of information and facilitates the inference of content, aiding in the establishment of indicators. It consists of a custom-designed tool to evaluate all the websites of Chilean municipalities based on 163 dichotomous variables that address information on the web portals in compliance with the Chilean transparency law. Based on the findings of the literature review, these variables are classified into six main dimensions and 30 sub-dimensions. Dichotomous variables can only have two results: “YES”, indicating the presence of the required information, or “NO”, indicating its absence. The complete instrument can be found in Table A1, and a summary of the instrument’s dimensions is schematized in Fig. 1. The research instrument was applied in 2022 by researchers from Universidad Diego Portales in Chile.

The information collected from websites is complemented with additional data gathered from official governmental sources like Electoral Registry of Mexico (INE) (2017), CASEN (2020), and División Observatorio Social MDSF-CEPAL (2022). Variables included are Region, Community Development Index (CDI) 2020, Area of the commune in km² (Size), Population Quantity Poverty Rate %, and Income (from Municipal Patents and Circulation Permits, in Million $).

Exploratory factor analysis

The exploratory factor analysis (EFA) aims to use the data collected on the municipalities’ websites to construct an index able to measure the MDOI. The EFA model operates under the assumption that observable dichotomous variables are linearly connected by unobservable factors. The purpose of EFA is to recognize shared factors that account for the intercorrelations among the observed items, in the sense that when the factors are held fixed, the observed items would be uncorrelated (Harman, 1960; Lloret-Segura et al., 2014; MacCallum et al., 2001). This allows information from numerous variables to be condensed into smaller factors. Within the EFA framework, the number of factors is unknown, and a criterion to decide on is required. Parallel analysis (Horn, 1965), jointly with the scree plot derived from it, is employed to decide the number of factors in this study.

Given that our observable variables are dichotomous, the assumption of multivariate normality is not appropriate. We employ the three-stage estimation method (Jöreskog, 1990; Muthén, 1984) to deal with it. The first step of this method consists of estimating thresholds of underlying response variables, which give the origin of the dichotomous variables. In the second step, given the thresholds previously obtained, the tetrachoric correlation matrix of the data, a dichotomous version of the Pearson correlation matrix, is estimated (Jöreskog, 1994). Finally, the resulting tetrachoric correlation is used to estimate the EFA employing the method of least squares extraction (Harman, 1960; Madrigal-Delgado et al., 2018). It is well known that the solution for the parameter’s estimation procedure is identified up to an orthogonal transformation of the factor matrix. In that sense, a rotation method that facilitates factor interpretation must be chosen. In this study, we employ an oblivion rotation procedure. The criteria for assigning variables to factors consisted of considering factor loadings bigger than 0.4, following the guidelines set by Lloret-Segura et al. (2014) and MacCallum et al. (2001). Finally, we use the KMO measure to confirm the suitability of the data for factorization, and Cronbach’s alpha coefficient to assess the reliability of the instrument. The R Psych package conducted the EFA model’s computational implementation (Revelle, 2024).

The factors obtained from the EFA were conceptually analyzed to identify the implicit abstract concept captured by the variables linked to each factor.

Index creation

The primary aim of this study is to develop a standardized tool called the MDOI. This index serves as a comparative measure for evaluating the digital services offered across various municipalities and facilitates their comparison. To construct the MDOI, we utilize the factors identified in “Exploratory Factor Analysis”. First, we estimated the Mean Index from Factor Loadings (MIFL) as defined in Eq. (1)

(1) $$MIF{L}_{jk}=\left({\displaystyle \frac{{\sum }_{i=1}^{D_j}{X}_{ijk}\ast F{L}_{ij}}{SF{L}_j}}\right)\mathrm{\forall }k\in \left\{1,2,\dots ,344\right\}$$In this equation, $X_{ijk}$ represents the result of a dichotomous variable i related to fact or j, for municipality k. $F{L}_{ij}$ represents the factor loading linked to the dichotomous variable i within factor j. D_j denotes the total number of dichotomous variables in factor j. Finally, SFL_j is the sum of factor loadings for factor j. This index enables the comparison of municipal portals based on factors identified in the factor analysis. A MIFL value of 1 signifies that the municipal portal analyzed contains all the required information, whereas a value of 0 indicates the absence of information related to that factor.

Subsequently, utilizing MIFL along with the explained variance of each factor ( $E{V}_j$), we formulate MDOI as per Eq. (2), facilitating a comprehensive comparison among the various municipal portals nationwide. This method ensures that each factor’s importance is proportional to its contribution to the overall explained variance, resulting in a fair and comprehensive measurement. The index formula considers both the factor loadings ( $F{L}_{ij}$) and the statistical relevance of each factor in explaining the presence of municipal digital services. An MDOI value of 1 indicates that the municipal portal provides information for all necessary aspects, whereas an MDOI value of 0 signifies that the municipal portal does not offer any required information.

(2) $$MDO{I}_k=\sum _{j=1}^{15}MIFLjk\ast E{V}_j\mathrm{\forall }k\in \left\{1,2,\dots ,344\right\}$$Finally, a multiple linear regression (MLR) model is implemented with the purpose of predicting the value of the MDOI through various independent variables of interest.

Factor analysis

The reliability of the instrument was assessed with Cronbach’s alpha coefficient, which exceeded the standard of excellence with a value above 0.90, reflecting a high internal consistency of the items and providing confidence in the consistency of the measurements. The suitability of the data for factorization was confirmed through the KMO measure, which was found to exceed the recommended threshold of 0.80, in line with guidelines established by Lloret-Segura et al. (2014), thereby ensuring the relevance of factor analysis for the dataset in question.

The least squares residuals methodology, used for factor extraction, contributed to an optimized data structure by minimizing discrepancies between observed and modeled correlations. This led to the elimination of 55 variables, thus focusing on those variables that present a stronger and more representative relationship with the identified factors. EFA effectively summarizes the original 88 variables into 15 factors that explain 79% of the data variability, a percentage that far exceeds the commonly recommended threshold of 60% for cumulative explained variances. This demonstrates an effective data reduction while maintaining considerable informational richness. Table 2 summarizes the selected factors, eigenvalues, and the percentage of variation explained.

Factor Load for each variable can be seen in Table A2. The factors were further clarified based on the elements they consist of. Municipal Unit and Official Communication (Factor 1) gathers variables that offer insights into councilors, as well as details from the Community Development Department (DIDECO) in charge of fostering growth and improvement within the community, such as addresses for municipal works, cabinet, and community development offices, among others. Integration Efforts (Factor 2) is about details regarding integration programs for women and people with disabilities on the municipal website. Transparency Measures (Factor 3) pertains to municipal transparency concerning the accessibility of information on the web portal regarding internal matters. Variables are linked to details found on the active transparency portal, as well as other variables indirectly linked to transparency, like information on the involvement of other entities in local projects.

Educational and Health Resources (Factor 4) relates to information about educational and health facilities located within the municipality. Lobbying Regulations (Factor 5) check if the local platform meets all the requirements stated by the law, regarding paid activities or efforts conducted by Chilean or foreign individuals or entities to support, defend, or represent specific interests or influence decisions made by authorities. At the local level, regional councilors, mayors, councilors, executive secretaries of regional councils, construction directors, and municipal secretaries are open to lobbying activities. Communal Council for Civil Society Organizations (COSOC) (Factor 6) pertains to details about the COSOC, which serves as a platform to participate in governmental management, policies, programs, and plans, acknowledging their right to be involved.

Benefits, Responsibilities, and Fees (Factor 7) concerns details about the benefits available to residents of the municipality, like the neighbor card, vaccination schedules, and social housing registration. It also involves responsibilities such as waste collection fees, and fines payments. Informative Content Distribution (Factor 8) consists of details about emergency activities and involvement in the community. Community Project Initiatives (Factor 9) offers details and descriptions of projects initiated by the municipality for the welfare of the community. Online Transaction Processes (Factor 10) relate to the municipality’s readiness to conduct digital procedures, like applying for certificates, patents, circulation permits, and making inquiries.

Ethical standards (Factor 11) consist of three variables indicating whether the website includes the commune’s mission, vision, and code of integrity. Citizen Engagement and Consultation (Factor 12) consists of indicators that assess whether the municipality provides tools to address and communicate information to its users. Orientation Services (Factor 13) includes variables related to the tools available on the website, aiming to assist users with accessing the Chile Atiende portal and obtaining essential circumstantial information. Environmental Initiatives (Factor 14) comprises variables that assess if the website contains information regarding environmental care within the community. Security and Reliability Standards (Factor 15) measure whether the municipality provides information about various mechanisms employed to enhance security in different areas, including privacy, citizen safety, emergencies, and accidents.

Municipal Digital Offering Index

Aiming to answer the research question on the development level of the municipal digital offering in Chile (RQ1), we created the MDOI, enabling the creation of a systematic ranking that classifies Chilean municipalities according to their performance in digital offerings through their website. This classification allows for the distinction between municipalities with high levels of digital development, making information and interaction fully available for citizens through their website, and those that still have room for improvement.

Index scores range from 0–1, where 0 indicates the absence of a certain factor, and one indicates the complete presence of the elements that compose that factor.

Figure 2 shows that the MDOI index in Chilean municipalities does not surpass 0.6 and it mainly falls within the range of 0.25 to 0.4, suggesting a general low level of digital development. Three municipalities in the Metropolitan region have the highest index: Lo Barnechea (0.59), Las Condes (0.55), and Santiago (0.53), revealing an important gap and improvement opportunities for local e-government in Chile. The first two municipalities mentioned are categorized as high-income, while the third one is the central area of the city, which encompasses the historic center, the original neighborhoods of the city, and the primary government institutions.

Figure 3 illustrates the regional disparities in digital development across different areas. La Araucanía and Valparaíso regions stand out for their superior performance. The Metropolitan Region shows extreme cases, with three municipalities having the highest scores, while Calera de Tango (0.01), a rural municipality, is at the lowest end of the digital development spectrum. The figure also highlights the uneven variability of the index within regions. For example, the Antofagasta and Metropolitan regions exhibit greater dispersion in digital development compared to the more uniform Magallanes and Atacama regions.

A thorough examination of factors uncovers both strengths and weaknesses in the execution of digital services. Table 3 provides a statistical overview of the MIFL. The final column indicates the percentage of municipal portals with an MIFL < 0.3, indicating subpar performance regarding the respective factor.

Areas such as Lobby Regulations, Transparency Measures, and Municipal Unit and Official Communication present better performance with MIFL media over 0.47, while other aspects like Citizen Engagement and Consultation, Security and Reliability standards, Community Project Initiatives, Orientation services, Online Transaction Processes, Benefits, Responsibilities, and Fees (MIFL < 0.11) need significant improvements. These poorly developed aspects are also highly frequent, given that over 85% of municipalities have an index below 0.3.

Emphasis should be placed on Citizen Engagement and Consultation, as none of the municipalities offer avenues for participation. Furthermore, 98% of municipalities exhibit inadequate Online Transaction Processes, raising concerns about digital citizenship progress (Ribble, Bailey & Ross, 2004; Pade-Khene, 2018). Despite increased online activity during the pandemic, it is plausible to speculate that most interactions with municipalities still occur in person.

Multivariate linear regression (MLR)

To answer research question 2 on the demographic, social, and economic factors related to the municipal digital offering level (RQ2), we developed a multiple linear regression analysis incorporating the following explanatory variables:

• -

  Log (CDI): The natural logarithm of the CDI for the year 2020. The CDI aggregates various community-level variables into three dimensions: Health and Social well-being, Economy and Resources, and Education. More details on this index can be found in Hernández Bonivento et al. (2020).

• -

  Log (Size): The natural logarithm of the area of the commune measured in square kilometers (km²).

• -

  Log (No schooling): The natural logarithm of the percentage of residents in the commune who do not have formal schooling.

• -

  Log (Income): The natural logarithm of the total income of the commune.

• -

  Region: Categorical variables indicating the region where the municipality is located.

The variable ‘Size’ was selected for its strong correlation with population size, density, and urban population percentage. A logarithmic transformation was employed for both, the dependent variable (MDOI index) and the explanatory continuous variables, to improve the model fit. This transformation served to mitigate extreme values and diminish the magnitude of fluctuations. The categorical variable ‘Region’ is also included to provide a comparison of the MDOI index between regions of Chile, considering the Metropolitan area of Santiago as the reference region. The resulting estimated model and its outcomes are presented in Table 4.

The regression outcomes underscored that the model explains 46% of the index’s variability. To consider the potential variance heterogeneity across regions (as shown in Fig. 3), the White robust standard errors (White, 1980) are employed. The parameters for the explanatory variables have the expected signs and show satisfactory statistical significance at the 5% level, except ‘log(Size)’, significant at the 10% level.

The ANOVA analysis divides the total sum of squares into percentages for each explanatory variable, showing that ‘Region’ and ‘CDI’ contribute significantly to explaining the variability of the MDOI index, accounting for 34.01% and 9.18% of the total variation. The analysis revealed that communes with a higher CDI present more advanced municipal portals, and larger ones tend to invest more in web development. The proportion of the population without education is negatively associated with the development of municipal portals. Additionally, municipal revenues correlate positively with the digitalization of web services.

Regarding regional coefficients, Arica y Parinacota, Antofagasta, Atacama, Coquimbo, and Ñuble exhibited negative coefficients, signaling lesser municipal digital advancement in comparison to the Metropolitan Region. Conversely, Valparaíso and La Araucanía show superior digital service provision. Regional differences raise questions about the political and administrative factors contributing to these disparities, which are not captured by the research instrument.

Contributions, limitations, and future research directions

This study contributes to the understanding of digital development in Chilean local governments by introducing the Municipal Digital Offering Index (MDOI), a tool specifically designed to assess the digital service provision of municipalities across the country. In contrast to standardized international measures, the MDOI offers a context-sensitive approach aligned with national policy frameworks and institutional realities. Recent scholarship on e-government benchmarking (Skargren, 2020; Przeybilovicz, Cunha & Ribeiro, 2023) emphasizes the need for evaluation tools that reflect local conditions and are oriented toward enhancing public value. In line with this perspective, the MDOI captures multiple dimensions of digital public service delivery as implemented in Chile’s diverse municipal settings. Using this index, the study presents a nationwide assessment based on 2022 data, encompassing all Chilean municipalities and identifying key factors associated with varying levels of digital development—such as geographic region, municipal revenues, and education levels.

While the dataset provides a valuable snapshot of Chile’s municipal digital landscape, it reflects conditions as of 2022. Although no significant institutional reforms have occurred since then, some municipalities may have made further progress. As such, the results should be interpreted as a baseline rather than a current assessment. Additionally, the study does not include information on the specific digital needs of local communities, which limits the ability to fully align service provision with demand.

Future research could address these limitations by updating the dataset and incorporating political and administrative variables at the regional level. One promising avenue is to apply the MDOI in a longitudinal design, particularly before and after municipal elections, to explore how changes in political leadership—such as shifts in mayoral administrations—influence digital development. This approach could help assess whether political orientation or specific administrative agendas correlate with improvements or stagnation in digital service provision. Additionally, future studies could examine the actual use (or non-use) of these digital tools by citizens, thereby complementing the supply-side analysis with insights into demand-side dynamics. Such extensions would enrich our understanding of how to design more responsive, contextually grounded, and equitable digital policies for local governments.