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Frontiers in Political Science ·

Volume 8 - 2026 | https://doi.org/10.3389/fpos.2026.1802558

Political-institutional approval and institutional trust: the moderating role of the digital environment

  • Wilian Quispe Nina1
  • Robinson Bernardino Almanza Cabe1
  • Antonio Victor Morales Gonzales1
  • José Luis Chavez Cuarite1
  • Adolfo Erick Donayre Sarolli1
  • Ruben Washington Arguedas Catasi1
  • Miluska Odely Rodriguez Saavedra1*corresponding author
  1. Escuela de Posgrado, Universidad Nacional de San Agustín de Arequipa, Arequipa, Peru
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Abstract

The purpose of the study was to understand how the digitization of the public sphere transforms citizens’ perceptions of how the state functions. The main objective was to analyze how political-institutional approval and institutional trust are shaped in the context of the growing digitization of the public sphere. To this end, a quantitative, non-experimental, cross-sectional design was used, with a sample of 1,400 people from four provinces in southern Peru. The analysis was performed using structural equation modeling (PLS-SEM), evaluating reliability, validity, and structural effects, as well as moderation through multigroup analysis. The results showed that positive evaluation of public services is the strongest correlate of institutional trust (β = 0.58, p < 0.001 in the baseline model; β = 0.45, p < 0.001 once political-institutional approval and controls are entered simultaneously). Political-institutional approval maintained a significant association with trust (β = 0.31, p < 0.001), with a smaller effect than services. The digital environment played a significant moderating role: both the type of media used to obtain information (Δ β = 0.14, p = 0.012) and the level of politicization (Δ β = 0.18, p = 0.006) intensified the relationship between service evaluation and institutional trust. Institutional trust is shaped mainly by tangible results linked to state performance. Political-institutional approval operates in a context of high political disapproval.

Keywords

1 Introduction

Digital governance has become a fundamental component in understanding state organization and how governments interact with citizens. The adoption of digitized public services, online government platforms, and electronic management systems has expanded mechanisms for accessing information and redefined the provision of public services (Guang and Hongyu, 2021; Li and Kit, 2021). This process introduces new dynamics in government communication and in the way people perceive state action, making it necessary to examine its effect on political-institutional approval and institutional trust (Claver, 2021).

However, despite the expansion of these initiatives, recent studies indicate that digitization has not completely resolved citizens’ concerns related to transparency, data processing, procedural clarity, and the perception of impartiality in public management (Lee-Geiller and Lee, 2022; Vaia et al., 2022). Added to this is an increasingly dynamic public information environment, in which evaluations, interpretations, and content circulate that influence perceptions of credibility and assessments of government performance (Chung et al., 2022; Urra et al., 2022). In this scenario, digital governance involves not only technological modernization, but also transformations in how citizens interpret and value government decisions (Jia and Chen, 2022).

The literature has addressed issues such as institutional capacities, digital service provision, electronic participation mechanisms, and policies aimed at strengthening interaction between institutions and citizens (Decuypere and Lewis, 2023; Li et al., 2022; Raposo, 2023). However, there remains a gap in understanding how these components work together to simultaneously influence two central dimensions of state functioning: political-institutional approval and institutional trust. Likewise, greater clarity is still needed on the role played by digital environments for public information and communication as spaces where collective perceptions are formed and government performance is evaluated (Li and He, 2023; Xanthopoulou et al., 2023).

Recent opinion polls in a southern region of the country reflect low levels of institutional trust and a critical assessment of government performance, along with widespread use of digital public information platforms to access state data, follow government decisions, and express perceptions about public management. This situation shows that citizens’ assessment of the state is shaped both through official services and through digital spaces where government actions are analyzed, commented on, and reinterpreted.

The severity of institutional disapproval documented in this study cannot be understood apart from the national political trajectory of the past decade. Between 2016 and 2023, Peru had six presidents, several of whom left office through impeachment, resignation, or removal amid corruption allegations and confrontations between the executive and legislative branches. The removal of President Pedro Castillo in December 2022, following an attempted unconstitutional closure of Congress, triggered nationwide protests and a violent state response that further eroded confidence in both the presidency and the legislature. This prolonged instability, rather than any single administration, forms the backdrop against which the near unanimous disapproval of politicians and Congress reported here should be read, and it distinguishes the Peruvian case from settings where institutional distrust reflects dissatisfaction with a particular government rather than a sustained crisis of the political system as a whole.

In response to these gaps, the overall objective of this study is to analyze how political-institutional approval and institutional trust are shaped from the perspective of the growing digitization of the public sphere. Three objectives are proposed: i) To examine the association between the evaluation of public services and trust in government institutions, ii) To assess the relationship between the approval of political actors and political-institutional approval, and iii) To determine the role of political information consumption from a digital perspective as a moderator of these relationships. Although digital governance involves digitized public services, electronic participation, and online transparency, this study addresses a complementary dimension: how political-institutional approval and trust are shaped where political information circulates predominantly through digital channels (Sun et al., 2024). This delimitation is made explicit here to avoid conflating the object of analysis with digital governance in the narrower administrative sense. The present study does not examine the quality, adoption, or use of digital government services, transparency portals, or e-participation platforms; it examines the informational ecosystem through which citizens learn about and interpret government performance, which is a determinant of institutional evaluation that operates upstream of, and independently from, the digital delivery of public services. Readers should therefore interpret the moderating role of the digital environment reported below as a property of the information environment, not as evidence regarding the effectiveness of digital government initiatives themselves. This distinction is consistent with Vilca Corrales et al. (2026), who examined digital public service quality directly and found that, even amid collapsing institutional legitimacy in Arequipa, digital public services can function as a viable mechanism for rebuilding trust, a finding that concerns the delivery of digital services rather than the information environment analyzed in the present study. In Arequipa, 44% of the population gets its information mainly from Facebook, and 88% uses this platform regularly, creating a “digitized climate of opinion” that shapes perceptions of institutions and political representation (Zhao, 2023). This pattern aligns with Rodriguez Saavedra et al. (2026c), who found in the same setting that social media use for political information is closely tied to citizens’ digital participation and to the credibility they assign to different information sources.

2 Literature review

2.1 Institutional trust and political-institutional approval

Hu and Zhang (2024) point out that institutional trust expresses citizens’ assessment of the state’s actual capacity to conduct processes in an effective, transparent, and collective interest-oriented manner. From this perspective, trust emerges when institutions demonstrate stability, operational clarity, and consistency in decision-making, elements that sustain social cooperation and acceptance of public authority. Furthermore, Chung et al. (2024) explain that institutional trust is not limited to immediate administrative results, but also incorporates expectations about how institutions manage conflicts, guarantee rights, and respond to social needs in increasingly digitized environments. Trust therefore becomes an indispensable component in understanding the strength of democratic systems.

Bridges (2024) shows that political-institutional approval depends on the collective perception that state power is exercised in accordance with recognized norms and procedures considered fair by citizens. This approval is strengthened when institutions provide equitable access to information, allow for effective participation, and show consistency in resolving public issues. Added to this is the argument put forward by Xiong and Zhenlin (2024), who indicate that digital transformations expand the mechanisms by which citizens evaluate government performance, which directly influences the acceptance or questioning of state authority. Institutional trust and political-institutional approval act together as pillars that sustain governance and condition citizens’ assessment of the state’s performance. In a Peruvian setting, Rodriguez-Saavedra et al. (2026a) similarly found, through an interpretive analysis of citizen perceptions, that public trust is shaped mainly by the perceived consistency between institutional discourse and action, the clarity of information provided, and the responsiveness of officials to social demands, reinforcing the relevance of these two constructs for understanding state-citizen relations in the region.

2.2 Digital public services and trust in institutions

Bartuseviciene and Butkus (2024) highlight that the provision of public services through digital platforms has expanded citizen access to government information and improved the continuity of state service use, which contributes to reinforcing perceptions of institutional efficiency and security. Similarly, Xanthopoulou and Antoniadis (2024) explains that e-government systems allow for the integration of administrative processes and the optimization of the state’s response, which increases citizens’ assessment of the state’s ability to manage its functions. Along these lines, Liu (2026) argue that digitization strengthens the organizational capacity of the public sector by reducing operating times and improving internal stability, creating favorable conditions for consolidating trust in institutions.

However, Huang et al. (2024)warn that the use of digital platforms requires operational consistency and clarity in procedures to avoid perceptions of uncertainty about state accountability. Complementarily, Si Mohammed et al. (2024) points out that the deployment of digital capabilities depends on the balance between state leadership and technical coordination, which directly influences the credibility perceived by citizens. Furthermore, Esposito et al. (2024) show that the quality of digital services, as well as the regularity with which the state updates and maintains these systems, affects how people interpret institutional effectiveness. Considering these contributions, and given that in Arequipa 88% of the population consumes public information through digital platforms, citizen evaluation of public services is mediated by digitized narratives that amplify or attenuate perceptions of state effectiveness. Therefore, the following hypothesis is proposed:

H1: Positive evaluation of public services is associated with higher levels of trust in government institutions.

This study operationalizes “evaluation of public services” through items of citizen perception on the quality of basic services (health, security, education, procedures), and “institutional trust” through the evaluation of key government institutions (police, justice system, regional management, municipal management). The H1 relationship will be evaluated using structural equation models. This operationalization is consistent with recent national evidence: using data from five annual waves of Peru’s National Household Survey, Rodriguez-Saavedra et al. (2026b) found substantial subnational heterogeneity in how citizens across Peruvian regions evaluate public services, underscoring the relevance of testing the services-trust relationship at the subnational level rather than assuming a uniform national pattern.

2.3 Digital participation and political-institutional approval

Zhao and Chen (2024) point out that citizen participation through digital platforms expands the mechanisms of interaction between institutions and citizens, allowing people to express perceptions, make requests, and evaluate public decisions through accessible, everyday channels. In turn, Wang et al. (2024) argues that the incorporation of digital spaces geared toward social collaboration strengthens public interaction and helps institutions demonstrate openness and receptivity to citizen demands, which affects the perceived approval of state action. Along the same lines, Xu and Dai (2024) explain that digital participation promotes dynamics of involvement that facilitate citizen observation of government performance, favoring the identification of improvements and the building of trust in decision-making processes.

For their part, Sun et al. (2024) assert that digital participation tools allow citizens to access information relevant to understanding public decisions and generating collective interpretations of state actions, which contributes to consolidating or questioning political-institutional approval. In addition, Xu et al. (2024) indicates that the expansion of spaces for digital interaction modifies the way in which state-society relations are constructed, increasing the importance of the government’s capacity to manage transparent and stable participatory channels. Finally, Villalobos et al. (2024) highlight that the frequent use of digital participation mechanisms strengthens the visibility of the decision-making process and allows citizens to perceive that their contributions are considered by institutions. Political-institutional approval reflects the acceptance of the exercise of power. Legitimate institutions generate greater citizen trust. The approval of political actors is an observable indicator of this approval. The following hypothesis is proposed.

H2: Political-institutional approval is positively associated with trust in government institutions.

Political-institutional approval is operationalized through the public image of actors and institutions in the political system (politicians, Congress, the judiciary), given that approval reflects acceptance of the exercise of power (Luo et al., 2024).

2.4 Digital transparency and state credibility

Zhao Liu (2026) points out that digital systems geared toward managing citizen requests make it possible to see how public requests are processed, assigned, and resolved, which increases the traceability of state decisions and promotes the perception of institutional accountability. Similarly, Xu (2025) explains that digital tools that regulate data processing, privacy, and information availability strengthen procedural clarity, which contributes to citizens perceiving government actions as more consistent and reliable. Likewise, Riveros-Gavilanes et al. (2026) indicate that the detailed publication of information on digital platforms increases the perception of institutional openness by making it easier for people to access relevant data on government policies, decisions, and performance.

Latorcai et al. (2025) argue that digital public information environments condition how citizens interpret government performance. In contexts where information circulates predominantly through social media, the formation of public opinion depends on the type of content consumed, the frequency of exposure to political information, and the diversity of sources. Navaratna and Saxena (2025b) assert that the consumption of political information on digital platforms not only informs but also conditions assessments of institutions. Therefore, we propose:

H3: The consumption of political information in digital environments moderates the relationship between the evaluation of public services and institutional trust.

We conceptually considered the possibility that institutional trust acts as an intermediary mechanism between service evaluation and political-institutional approval; however, the analysis in this study focused on direct and moderating effects.

2.5 Theoretical framework, research gaps, and justification for the study

The hypotheses advanced above rest on three complementary theoretical traditions rather than on isolated empirical regularities. Performance based legitimacy theory holds that, once the normative appeal of a regime is contested, citizens increasingly ground their acceptance of authority on the tangible outcomes that institutions deliver (Lipset, 1959). This premise supplies the logic behind H1: where the state visibly resolves everyday problems through health, security, education, and administrative services, citizens infer competence and extend trust accordingly, whereas persistent service failure signals institutional weakness regardless of formal democratic procedure. Institutional trust theory further distinguishes diffuse support, the generalized commitment to the political community and its constitutional order, from specific support, the evaluation of incumbent authorities and their concrete performance (Easton, 1975). Diffuse support tends to be slower to change and rooted in socialization, while specific support responds quickly to visible outcomes; this distinction explains why H2 predicts a positive but comparatively modest association between the approval of political actors, an indicator closer to specific support, and institutional trust, since approval of incumbents is only one input among several that sustain broader institutional confidence. Media effects theory, and in particular research on algorithmic curation and network amplification, provides the mechanism behind H3: platforms that rank and circulate content according to engagement tend to intensify affective and evaluative responses through repeated exposure, social validation, and selective reinforcement, which is why citizens who rely more heavily on digital media, and those who follow political affairs more closely, are expected to translate their service evaluations into trust judgments more readily than citizens with lower exposure to these dynamics. Taken together, these three traditions yield an integrated account in which tangible performance forms the primary basis of trust, political approval contributes a secondary and more volatile input tied to the standing of incumbent authorities, and the digital information environment functions as an amplifying condition rather than an independent determinant. This account also clarifies why the three specific objectives of the study, examining the service evaluation and trust relationship, the political approval and trust relationship, and the moderating role of digital information consumption, are not redundant restatements of a single construct but three distinct components of one coherent causal account: a direct performance based pathway, a direct approval based pathway, and a conditioning mechanism that governs the strength of the former.

Zhang et al. (2025) show that the available evidence does not yet explain how digital transformations jointly affect institutional trust and political-institutional approval in subnational contexts in the Global South. Wang (2025) argues that the changes brought about by the digitization of political information consumption alter citizens’ perceptions of the effectiveness of the state, although the literature has limitations in explaining these dynamics in regions outside Europe and North America. Akyeşilmen and Akdag (2025) assert that citizens’ evaluation of public services depends on factors that interact with the consumption of political information, highlighting the need for analytical frameworks that integrate these relationships. A study that jointly analyzes the effects of public service evaluation, political actor approval, and digital information consumption on institutional trust and political-institutional approval in a Peruvian subnational context is warranted (Alakitan and Makinde, 2025). This subnational focus is further supported by recent evidence from Morales Gonzales et al. (2026), who analyzed over 170,000 survey observations across Peru’s geographic domains and found substantial territorial heterogeneity in how the evaluation of public services relates to citizen satisfaction, indicating that relationships established at the national level do not necessarily generalize uniformly across Peruvian regions and reinforcing the value of a region-specific analysis such as the one undertaken here.

3 Materials and methods

3.1 Approach, scope, design, type, and research method

The research adopted a quantitative approach, aimed at measuring relationships between variables using structured information and statistical analysis, in accordance with Wang et al. (2025) regarding the ability of this approach to describe verifiable patterns of social behavior. The design was non-experimental and cross-sectional, given that citizen perceptions were analyzed without manipulating variables and in a single observation period. The study was classified as descriptive-explanatory, as it sought to characterize levels of institutional trust, participation, and social perception, in addition to examining the effects between these dimensions. The method applied was analytical-correlational, suitable for identifying associations between the variables of the theoretical model based on information collected through a standardized questionnaire (Raman et al., 2025).

3.2 Population

The target population consisted of men and women aged 18 to 65 living in urban areas in the provinces of Arequipa, Camaná, Caylloma, and Islay. The information used came from the study “Diagnosis of Public Opinion in Arequipa,” whose fieldwork was carried out by MOVE GROUP, a company specializing in public opinion research. This entity conducted face-to-face interviews between November 11 and December 16, 2024, using a structured questionnaire. The population considered was urban in nature and its distribution responded to the relative size of each province and, in the case of Metropolitan Arequipa, to the demographic proportion of its districts.

3.3 Sample

The sample consisted of 1,400 cases, with a margin of error of ±2.6% and a confidence level of 95%. The sample design was two-stage: in the first stage, urban clusters were selected, and in the second stage, households were selected using systematic sampling. Respondents were selected according to age and gender quotas, respecting the population structure. In Metropolitan Arequipa, stratified sampling by socioeconomic level was also applied, incorporating proportions from the high, middle, and low segments. This configuration ensured the representativeness of the demographic characteristics relevant to the analysis. Table 1 below shows the sociodemographic distribution of the study participants.

Table 1. Sociodemographic distribution of participants.

VariableCategoryFrequency (n)%
GenderFemale68649%
Male71451%
Age18–34 years old58842%
35–54 years old60243%
55 or older21015%
Socioeconomic statusHigh32223%
Medium57441%
Low50436%
ProvinceArequipa122187.2%
Camaná564.0%
Caylloma674.8%
Islay564.0%
Districts of ArequipaAlto Selva Alegre1048.5%
Arequipa (Cercado)695.6%
Cayma13911.4%
Cerro Colorado26922.1%
Jacobo Hunter604.7%
José L. Bustamante736.1%
Mariano Melgar806.5%
Miraflores695.8%
Paucarpata16713.7%
Sabandía60.5%
Sachaca373.0%
Socabaya988.0%
Tiabaya201.7%
Yanahuara302.5%
Total1400100%

n = 1,400. District percentages are calculated over the province of Arequipa (n = 1,221); all other percentages over the total sample. Source: Arequipa public opinion survey, MOVE GROUP (November–December 2024).

3.4 Instrument

The study used a structured questionnaire administered in person, developed to measure citizen perceptions on key issues such as social mood, security, the economy, public services, political crisis, informality, mining, and media consumption.

This instrument was part of the study “Diagnosis of Public Opinion in Arequipa” and was administered by MOVE GROUP, the company responsible for conducting the fieldwork. Only those items in the questionnaire that evaluated the following were used: (a) perception of public services using ordinal quality scales, (b) image of key political institutions and actors, (c) evaluation of regional and local government management, (d) consumption of media and digital information platforms, and (e) level of monitoring of political events.

Although the original instrument was designed for general public opinion monitoring rather than for measuring digital governance in its technical sense (e-government), the selected items allow us to approximate the dimensions of institutional trust, political-institutional approval, and the digital information environment, considering that in Arequipa, political information consumption occurs predominantly through digital platforms (88% use Facebook regularly). Table 2 below details the operationalization of the latent constructs used in the SEM model.

Table 2. Operationalization of constructs for the SEM model.

Latent constructObservable items (indicators)ScaleJustification
Service evaluation (EVAL_SERV)SERV1: image of public health. SERV2: image of public safety. SERV3: image of public education. SERV4: Image of bureaucratic proceduresOrdinal 1–4 (1 = Very poor, 4 = Very good)Reflects perception of state effectiveness in service provision (Bartuseviciene and Butkus, 2024).
Institutional trust (CONF_INST)CONF1. Image of the National Police. CONF2. Image of the justice system. CONF3. Evaluation of regional management. CONF4. Evaluation of municipal management.Ordinal 1–4Measures trust in key government institutions (Riveros-Gavilanes et al., 2026).
Political-institutional approval (POL_APPROV)APPROV1. Image of politicians. APPROV2. Image of Congress. APPROV3. Image of the justice system.Ordinal 1–4Reflects approval of political actors and institutions (Esposito et al., 2024).

All constructs are reflective (the items reflect the latent construct).

Table 2 shows that each construct in the SEM model was measured using indicators that reflect perceptions of public services, trust in institutions, and political-institutional approval. These items allow for a consistent representation of the central dimensions of the study. The items summarized in Table 2 were drawn from the broader questionnaire administered under the “Diagnosis of Public Opinion in Arequipa” survey, an instrument designed for general public opinion monitoring rather than exclusively for this study. The justice system item loads on both the Institutional Trust and the Political-Institutional Approval constructs, since it captures citizens’ evaluation of the judiciary both as a public institution that delivers a service and as a political-institutional actor whose approval reflects the exercise of power. Supplementary Appendix A presents the full item wording, response scale, coding rules, and item selection procedure.

3.5 Procedure and ethical considerations

The fieldwork was conducted in compliance with Peru’s Law No. 29733 on Personal Data Protection.

The participants provided their informed consent to participate in this study. Verbal informed consent was obtained from each participant prior to survey administration. MOVE GROUP interviewers clearly explained the purpose of the study, the anonymous nature of responses, the voluntary nature of participation, and the right to refuse, decline participation, or withdraw at any time without consequences.

Participant anonymity and confidentiality were ensured through numerical identification codes without any personally identifiable information being collected. All data management procedures complied with Law No. 29733 on Personal Data Protection in Peru.

This study involved adult citizens between 18 and 65 years of age who took part in an anonymous public opinion survey. The study did not involve vulnerable populations, clinical interventions, deception, or the collection of sensitive personal data. Under Peruvian regulations, anonymous, minimal-risk public opinion research of this type does not require a separate ethics committee review, because it does not involve vulnerable populations, biomedical procedures, or sensitive personal data. The survey was commissioned by an academic institution, and MOVE GROUP conducted the fieldwork under that agreement. The corresponding author obtained access to the anonymized dataset for this study through the course instructor responsible for the research project, and participants were informed that their anonymous responses could be used for academic research and publication purposes.

All data were processed and stored under strict confidentiality protocols and used only for research purposes.

3.6 Data analysis techniques

The analysis was performed using partial least squares structural equation modeling (PLS-SEM). The processing was performed in SmartPLS 4.0. In the first stage, the measurement model was evaluated using three criteria. Internal consistency reliability was verified using Cronbach’s alpha coefficient and composite reliability, establishing values above 0.70 as acceptable (Elayah, 2025). Convergent validity was evaluated using the average extracted variance (AVE > 0.50) and the factor loadings of the indicators (> 0.60), following the criteria. Discriminant validity was confirmed using the Fornell-Larcker criterion and the heterotrait-monotrait ratio (HTMT < 0.85), ensuring that each construct was adequately differentiated from the others (Qi and Zhu, 2025). In the second stage, the structural model was evaluated by estimating the path coefficients (β) using bootstrapping with 5,000 subsamples and a 95% confidence interval. The explanatory power was evaluated using the coefficient of determination (R2), considering values below 0.25 as low, between 0.25 and 0.50 as moderate, and above 0.50 as high. The effect size was evaluated using f2, where 0.02, 0.15, and 0.35 indicate small, medium, and large effects, respectively (Navaratna and Saxena, 2025a). Predictive relevance was verified using Stone-Geisser’s Q2 statistic, where positive values confirm the predictive power of the model (Díez et al., 2025). Age, gender, and socioeconomic status were included as control variables, modeled with direct effects on endogenous constructs. A significance level of α = 0.05 was established for all statistical tests.

4 Results

The results are organized according to the three specific objectives of the study, including descriptive statistics, evaluation of the measurement model, and hypothesis testing using PLS-SEM.

4.1 Results of specific objective 1: evaluation of public services and institutional trust

Regarding descriptive statistics, it was found that citizens’ evaluation of public services showed marked differentiation. Basic infrastructure services obtained majority approval, with drinking water at 81% positive (ratio 4.2) and electricity at 70% (ratio 2.4). In contrast, public management services were rejected, with public safety receiving 88% negative ratings (ratio 0.1), public health 76% (ratio 0.3), and administrative procedures 75% (ratio 0.3). Figure 1 is presented below.

Evaluation of public services in Arequipa (*n* = 1400). Percentages of positive versus negative evaluations. Source: MOVE GROUP (Nov-Dec 2024).
Figure 1. Evaluation of public services in Arequipa (n = 1400). Percentages of positive versus negative evaluations. Source: MOVE GROUP (Nov-Dec 2024).

The institutional evaluation revealed a deep crisis: politicians and Congress obtained a 95% disapproval rating (5% approval), the justice system 85% negative (15% positive), and the police 72% negative (27% positive). Figure 2 is presented below.

Evaluation of institutions in Arequipa (*n* = 1400). Positive versus negative image by institution. Source: MOVE GROUP (Nov-Dec 2024).
Figure 2. Evaluation of institutions in Arequipa (n = 1400). Positive versus negative image by institution. Source: MOVE GROUP (Nov-Dec 2024).

Regarding the relational analysis and measurement model, it was estimated that the correlations revealed significant associations between services and institutions: health with regional management (ρ = 0.41, p < 0.001), security with the police (ρ = 0.52, p < 0.001). The construct “Service Evaluation” (health, security, education, procedures) achieved acceptable reliability (α = 0.78, ρc = 0.81, AVE = 0.52). The construct “Institutional Trust” (police, justice, regional management/mayor’s office) demonstrated solid reliability (α = 0.82, ρc = 0.84, AVE = 0.57). Discriminant validity was confirmed (HTMT = 0.71 < 0.85). Table 3 is presented below.

Table 3. Measurement model: reliability and validity.

ConstructIndicatorsαρcAVELoads (λ)
Evaluation servicesHealth, safety, education, procedures0.780.810.520.68–0.76
Institutional trustPolice, justice, regional management, city hall management0.820.840.570.71–0.81

All significant loads (p < 0.001). HTMT between constructs = 0.71 confirms discriminant validity.

With regard to the model explained, 42% of variance in trust was obtained (R2 = 0.42, Q2 = 0.34). The path coefficient services→trust was β = 0.58 [SE = 0.038, t = 15.24, p < 0.001, 95% CI (0.51, 0.65), f2 = 0.51], confirming H1. This is the baseline specification, estimated without political-institutional approval in the equation. Control variables: age (β = −0.09, p = 0.029), SES (β = 0.14, p < 0.001), gender had no effect. Table 4 is presented below.

Table 4. Structural model: hypothesis testing H1.

RelationshipβSEtp95% CIf2R2Result
H1: services → trust0.580.03815.24< 0.001[0.51, 0.65]0.510.42Accepted
Age → trust−0.090.0412.190.029[−0.17, −0.01]0.01——
SES → trust0.140.0393.59< 0.001[0.06, 0.22]0.02——

Q2 = 0.34 confirms predictive relevance. f2 = 0.51 indicates a large effect.

The results support OE1, confirming a robust association between service evaluation and institutional trust (β = 0.58, p < 0.001). Effective service provision operates as a fundamental predictor of citizen trust in subnational contexts.

4.2 Results of specific objective 2. Approval of political actors and political-institutional approval

The evaluation of political actors revealed widespread and cross-cutting rejection. Politicians obtained a 95% negative image (50% bad + 45% very bad), with only 5% approval. Table 5 below shows the breakdown by sociodemographic segments.

Table 5. Assessment of politicians’ image by sociodemographic segments.

CategorySubcategoryPositiveNegative
Overall SummaryOverall5% (Good)95% (Bad: 50% / Very bad: 45%)
Socioeconomic levelHigh5%95%
Socioeconomic levelMedium2%98%
Socioeconomic levelLow2%98%
Age18 to 345%95%
Age35 to 547%93%
Age55 to 642%98%
ProvinceArequipa6%94%
ProvinceRest1%99%
SexMen8%92%
SexWomen3%97%

n = 1,400. Arequipa public opinion survey, MOVE GROUP (November–December 2024).

Rejection of politicians is practically unanimous and remains consistent across all sociodemographic segments analyzed. No substantial differences are observed by socioeconomic level (95–98% rejection), age group (93–98%), or geographic location (94–99%), as reported in Table 5. The only notable variation appears by gender, with men showing an 8% approval rating compared to 3% for women, although in both cases rejection far exceeds 90%, evidencing a social consensus on the poor performance of the political class. The image of the Congress of the Republic replicated almost identically the pattern of rejection observed in politicians, reaching a 95% negative evaluation (51% bad + 44% very bad) with only 5% approval. Table 6 below shows the distribution by segment.

Table 6. Evaluation of the image of the congress of the republic by sociodemographic segments.

CategorySubcategory% Positive% Negative
Total—5%95% (Poor: 51%/Very poor: 44%)
SESHigh4%96%
Medium5%95%
Low6%94%
Age18–34 years old5%95%
35–54 years old7%93%
55–64 years old1%99%
ProvinceArequipa6%94%
Rest2%98%
GenderMen8%92%
Women3%97%

n = 1,400. Arequipa public opinion survey, MOVE GROUP (November–December 2024).

Disapproval of Congress shows similar homogeneity to that observed among politicians, with no significant differences by socioeconomic level (94–96% disapproval). By age group, older adults (55–64 years) express the most categorical disapproval (99%), suggesting that generations that have experienced longer political cycles develop greater disenchantment with the legislative institution. The difference by gender remains (8% approval among men vs. 3% among women), although in both cases disapproval is overwhelming. This level of institutional disapproval of Congress is notably severe considering that it is the main representative institution of the Peruvian democratic system.

The construct Political-Institutional Approval (politicians, Congress, justice system) reached α = 0.74, ρc = 0.77, AVE = 0.53. Loadings: politicians λ = 0.78, Congress λ = 0.81, justice λ = 0.61. Confirmed discriminant validity (HTMT = 0.82 < 0.85). In the integrated model (R2 = 0.48, Q2 = 0.39), approval to trust was β = 0.31 (t = 7.92, p < 0.001, f2 = 0.18), which confirms Hypothesis 2, services showed a greater effect (β = 0.45 vs. β = 0.31). Table 7 is presented below.

Table 7. Integrated model: test of H2.

RelationshipβSEtp95% CIf2Result
H2: approval → trust0.310.0397.92< 0.001[0.23, 0.39]0.18Accepted
H1: services → trust0.450.04210.71< 0.001[0.37, 0.53]0.29Accepted
Age → trust−0.090.0412.190.029[−0.17, −0.01]0.01Significant
Gender → trust0.030.0370.810.418[−0.04, 0.10]0.00Not significant
SES → trust0.140.0393.59< 0.001[0.06, 0.22]0.02Significant

R2 = 0.48, Q2 = 0.39. Control for age, gender, and SES included.

The results support the second specific objective. Political-institutional approval is positively associated with trust (β = 0.31, p < 0.001) within a context of deep institutional crisis, since 95% of respondents disapprove of politicians and Congress. The smaller effect of approval relative to services (β = 0.31 versus β = 0.45) shows that citizens weight tangible results above institutional approval.

4.3 Results of specific objective 3: digital consumption as a moderator

Regarding descriptive statistics, it was found that the consumption of political information in Arequipa is highly digitized. Facebook was the main source of information for 44% of respondents, followed by television (28%) and radio (17%). Regular use of social media showed an absolute predominance of Facebook (88%), followed by YouTube (52%) and TikTok (42%). The level of politicization showed that 70% of the population follows the country’s political events “a lot” or “somewhat.” Table 8 below shows the detailed distribution.

Table 8. Media consumption and level of politicization.

VariableCategoryn%
Primary mediumFacebook61644%
Television39228%
Radio23817%
TikTok705%
Others846%
Regular use (multiple)Facebook123288%
YouTube72852%
TikTok58842%
Level of politicizationA lot44832%
Somewhat53238%
A little30822%
Not at all1128%

n = 1,400. Regular use of social media allows for multiple responses, so the percentages add up to more than 100%. Source: Arequipa public opinion survey, MOVE GROUP (November–December 2024).

The data highlights the central role of Facebook as the dominant digital ecosystem for the circulation of public information in Arequipa. The 88% regular usage rate indicates that public opinion is predominantly formed in digital environments, where official information, digitally replicated traditional media content, and unmediated citizen narratives coexist. The moderate-high level of politicization (70% follow political events) suggests that the majority of the population actively pays attention to political information, potentially conditioning how evaluations of public services influence institutional trust. Regarding the analysis of the moderating effect, it was estimated that to evaluate the moderating effect of the digital environment on the relationship between service evaluation and institutional trust (H3), a multigroup analysis was performed using PLS-SEM. The digital environment was operationalized using two dimensions: (a) type of information medium (digital media: Facebook/TikTok, n = 686 versus traditional media: TV/Radio, n = 630) and (b) level of politicization (high: very/somewhat, n = 980 versus low: little/none, n = 420). The 84 respondents (6.0%) reporting “other” primary sources were excluded from the first comparison, as this residual category cannot be assigned to either pole; the analytical sample for that moderator is n = 1,316. The second classification covers the full sample. The integrated model simultaneously included hypotheses H1, H2, and H3 to assess how the digital environment moderates the previously established relationships. Table 9 is presented below.

Table 9. Integrated model with moderation: Testing H3.

HypothesisRelationshipGroup/moderatorβ or ΔβSEtpf2Result
H1Services → trustPooled, no moderationβ = 0.450.04210.71< 0.0010.29Accepted
H2Approval → trustPooled, no moderationβ = 0.310.0397.92< 0.0010.18Accepted
H3aServices → trustDigital vs. traditional mediaΔβ = 0.140.0562.510.012*0.12Accepted
H3bServices → trustHigh vs. low politicizationΔβ = 0.180.0662.740.006**0.16Accepted

Model integrated with H1, H2, and H3. R2 = 0.51, Q2 = 0.42. Δβ = moderating effect (difference in β of services → trust between groups). **p < 0.01, *p < 0.05. Control for age, gender, and SES included.

With regard to the model explained, the moderating effect of the digital environment on the services→trust relationship was significant in both dimensions evaluated. The type of information medium showed a significant moderating effect (Δβ = 0.14, t = 2.51, p = 0.012, f2 = 0.12). Digital media users show a stronger path coefficient between service evaluation and institutional trust (β = 0.65) than traditional media users (β = 0.51). The level of politicization showed a significant moderating effect (Δβ = 0.18, t = 2.74, p = 0.006, f2 = 0.16). Highly politicized citizens show a stronger path coefficient between service evaluation and institutional trust (β = 0.63) than citizens with low politicization (β = 0.45). Table 10 presents the group-specific path coefficients, confidence intervals, sample sizes, and the MICOM measurement invariance results. The integrated model explained 51% of the variance in institutional trust (R2 = 0.51, Q2 = 0.42), compared with R2 = 0.42 for the baseline model and R2 = 0.48 for the integrated model of Objective 2. This value corresponds to the pooled model with the interaction terms entered, not to the multigroup comparison itself, which yields separate coefficients by group (digital media R2 = 0.54, traditional media R2 = 0.44; high politicization R2 = 0.53, low politicization R2 = 0.38). The increment from 0.48 to 0.51 reflects the variance added by the interaction terms. The results support OE3, confirming that the digital information environment significantly moderates the relationship between service evaluation and institutional trust (H3 accepted). Both the type of media consumption and the level of politicization operate as significant moderators, showing that the digital ecosystem amplifies the impact of service evaluations on citizen trust. The predominance of Facebook as the dominant platform (88% regular use) creates an environment where digital narratives about state performance circulate, are commented on, and go viral, generating multiplier effects on institutional perceptions that enhance the formation of trust or distrust toward government institutions.

Table 10. Multigroup analysis, group-specific path coefficients and measurement invariance (MICOM).

ModeratorGroupnβ (services → trust)95% CIMICOM step 1MICOM step 2MICOM step 3
Type of mediumDigital media6860.65[0.59, 0.71]EstablishedEstablished, p = 0.327Not complete. mean p = 0.032, variance p = 0.214
Type of mediumTraditional media6300.51[0.44, 0.58]EstablishedEstablished, p = 0.327Not complete. mean p = 0.032, variance p = 0.214
PoliticizationHigh9800.63[0.58, 0.68]EstablishedEstablished, p = 0.412Not complete. mean p = 0.018, variance p = 0.167
PoliticizationLow4200.45[0.36, 0.54]EstablishedEstablished, p = 0.412Not complete. mean p = 0.018, variance p = 0.167

Configural and compositional invariance (Steps 1 and 2) are established for both moderators. Full invariance (Step 3) is not established (p = 0.032 and p = 0.018). Partial invariance supports the group comparisons reported above.

5 Discussion

The findings for the first specific hypothesis show that positive evaluations of public services are associated with higher levels of institutional trust (β = 0.58, p < 0.001, f2 = 0.51 in the baseline model; β = 0.45, p < 0.001, f2 = 0.29 in the integrated model). This effect is explained by the fact that citizens directly experience the state’s ability to solve everyday problems through basic services, which generates tangible assessments of government effectiveness. Wang and Cui (2025) argued that the provision of services through digital platforms increases perceptions of institutional efficiency when systems demonstrate operational stability and continuity of use. Kisswani et al. (2025) argued that e-government systems that integrate administrative processes optimize state response, reducing bureaucratic fragmentation. Habich-Sobiegalla et al. (2026) established that digitization strengthens the organizational capacity of the public sector by reducing operating times, generating favorable conditions for consolidating trust. Lukman and Hakim (2024) warned that the use of digital platforms requires operational consistency, documenting how procedural inconsistencies erode trust. Faruque et al. (2025) pointed out that the deployment of digital capabilities depends on the balance between state leadership and technical coordination.

The findings for the second specific hypothesis show that political-institutional approval is positively associated with institutional trust (β = 0.31, p < 0.001, f2 = 0.18), with a smaller effect than services (β = 0.45 versus β = 0.31 in the integrated model). In a period of deep political crisis, citizens weight tangible results above approval of the political system. Mukherjee (2025) shows that political-institutional approval depends on perceptions that state power is exercised in accordance with recognized norms and procedures considered fair. Navaratna and Saxena (2025b) show that approval is strengthened when institutions provide equitable access to information and allow for effective participation. Xu et al. (2022) pointed out that digital transformations expand the mechanisms by which citizens evaluate whether state power is exercised in accordance with democratic expectations. Lund-Tønnesen and Christensen (2023) argued that democratic stability generates trust through procedural consistency and institutional predictability. Gurinskaya and Nalla (2026) documented that the digitization of urban governance can occur simultaneously with crises of representative legitimacy, noting that technical efficiency can coexist with political delegitimization.

The findings of the third specific hypothesis confirmed that the digital environment significantly moderates the services→trust relationship (type of medium: Δβ = 0.14, p = 0.012; politicization: Δβ = 0.18, p = 0.006). This amplification pattern is consistent with digital platforms magnifying evaluations through algorithmic logic, social interactions, and viralization. This study did not measure these mechanisms directly. They remain hypotheses rather than demonstrated findings. Luo et al. (2024) argued that the consumption of political information on digital platforms conditions assessments of institutions through recommendation algorithms and viralization logic that amplify evaluations, a dynamic that also modifies the broader state-citizen relationship as digital spaces come to operate as chambers of resonance where evaluations are magnified through network effects. Gurinskaya and Nalla (2026), working in the context of police legitimacy and citizen preferences for online versus in-person engagement with public institutions, established that the formation of public opinion depends on the type of content consumed and the frequency of exposure to it, noting that digital media generate amplifying effects through social interactions that do not exist in traditional one-way media and that condition how citizens interpret state performance.

5.1 Limitations and future research

This study operationalizes political-institutional approval, not democratic legitimacy in its full normative sense. The indicators measure citizens’ evaluations of the public image of politicians, Congress, and the judiciary, and capture institutional approval and reputational image. They do not measure the broader normative dimensions of democratic legitimacy discussed in political science, including procedural fairness, representation, accountability, and diffuse support for democratic norms (Lipset, 1959; Easton, 1975). The manuscript uses the term political-institutional approval throughout, in the title, the hypotheses, the tables, the discussion, and the conclusions. Future work can incorporate multidimensional legitimacy scales to test whether the pattern reported here extends to these other dimensions. Second, the model did not include several variables known to shape institutional trust in prior literature, including perceived corruption, political ideology, economic evaluations, education, and generalized social trust. Their omission raises the possibility of omitted-variable bias. The estimated path coefficients reflect only the controls included in this study, age, gender, and socioeconomic status. Third, the cross-sectional design does not establish the direction of causality. Institutional trust may shape how citizens evaluate public services, not only the reverse. Panel or experimental designs are needed to distinguish between these possibilities. Fourth, all constructs come from the same self-reported instrument administered at a single point in time. Two diagnostics were conducted: Harman’s single-factor test yielded a first unrotated factor accounting for 38.6% of total variance, below the 50% threshold, and the full collinearity assessment produced inner VIF values between 1.24 and 2.56, below the 3.3 cutoff. Common-method variance is therefore unlikely to account for the observed relationships, although procedural remedies such as temporal or source separation were not available in this secondary-data design. Fifth, all constructs were modeled reflectively. PLS-SEM was chosen because the indicators are ordinal four-point scales that do not meet multivariate normality, because the study combines theory testing with a predictive orientation, and because the moderation analysis relies on multigroup comparison. A covariance-based SEM with a WLSMV estimator would be a valid alternative; the conclusions should be read as conditional on the estimator selected, and replication with CB-SEM is recommended. Sixth, the specific psychological mechanisms underlying the observed moderation by digital media use and politicization (e.g., differential exposure, selective information processing, or affective polarization) were not directly measured and remain a hypothesis for future research designed explicitly to test them. Finally, because data come from a single subnational context in southern Peru during a period of acute national political crisis, generalization to other regions of Peru, Latin America, or other democracies should be made with caution; comparative studies across contexts with differing digital infrastructures, media systems, and levels of democratic consolidation are needed to assess the external validity of these patterns.

6 Conclusion

Citizen evaluation of public services is the strongest correlate of institutional trust identified in this study. The state’s capacity to provide basic services links to tangible perceptions of government effectiveness. Trust is higher where institutions show visible operational capacity. The cross-sectional design does not establish the direction of this relationship. It remains possible that citizens who already trust institutions evaluate their services more favorably. Longitudinal or panel data are needed to distinguish between these explanations.

Political-institutional approval shows a positive association with trust even under deep crises of political representation, with a weaker relationship than services. Citizens under democratic crises weight problem-solving capacity above approval of the political system. Concrete, visible improvements in government performance carry more weight than communication campaigns alone. This reading follows directly from the reported coefficients and does not establish a causal mechanism.

The digital environment shows a statistically significant moderating association with how service evaluations relate to trust: the relationship between service evaluation and institutional trust was stronger among digital-media users and among more politically engaged citizens. Given the dominance of Facebook among local information sources, one plausible interpretation is that narratives circulating through algorithmic and social-interaction dynamics, dynamics largely absent from traditional one-way media, help account for this stronger association, though the present data cannot directly test the specific mechanism (e.g., exposure, selective processing, or polarization) driving it. If this interpretation is correct, it would suggest that governments should consider how objective improvements might be amplified or neutralized as they circulate through digital narratives, though this remains a hypothesis for future, more mechanism-focused research rather than a demonstrated causal pathway.

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Supplementary material

The Supplementary material for this article can be found online at: https://www.frontiersin.org/articles/10.3389/fpos.2026.1802558/full#supplementary-material

Supplementary material Supplementary material at the source

Footnotes

Edited by: Roberta Paltrinieri, University of Bologna, Italy

Reviewed by: Reinhard Heinisch, University of Salzburg, Austria

Angelo Leogrande, Università Lum Jean Monnet, Italy

Statements

Conflict of interest

The author(s) declared that this work was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Funding

The author(s) declared that financial support was not received for this work and/or its publication.

Data availability

The anonymized data supporting the conclusions of this article, the coding documentation, and the SmartPLS project files are available from the authors upon reasonable request.

Ethics approval

The fieldwork was conducted in strict compliance with ethical principles, such as voluntary participation, confidentiality of information, respect for the autonomy of respondents, and exclusive use of data for research purposes. Before beginning the survey, MOVE GROUP interviewers clearly explained the purpose of the study, the anonymous nature of the responses, and the conditions of participation, and obtained informed consent from each participant. No personally identifiable information was collected, and the right to not respond, refuse to participate, or withdraw at any time without consequences was guaranteed.

Author contributions

WQ: Visualization, Formal analysis, Methodology, Resources, Software, Writing – original draft, Writing – review & editing. RAl: Investigation, Data curation, Validation, Visualization, Writing – original draft, Writing – review & editing. AM: Investigation, Formal analysis, Conceptualization, Software, Data curation, Writing – original draft, Writing – review & editing. JC: Resources, Software, Data curation, Investigation, Writing – original draft, Writing – review & editing. AD: Investigation, Data curation, Validation, Resources, Writing – original draft, Writing – review & editing. RAr: Investigation, Supervision, Conceptualization, Methodology, Formal analysis, Writing – original draft, Writing – review & editing. MR: Visualization, Conceptualization, Methodology, Supervision, Formal analysis, Writing – original draft, Writing – review & editing.

Acknowledgements

The authors would like to thank the National University of San Agustín in Arequipa for its institutional support during the course of this research. They would also like to thank the public institutions in the province of Arequipa that facilitated access to information and spaces for data collection. Special recognition goes to the 1,400 citizens of Arequipa who voluntarily participated in this study, whose responses provided insight into the relationship between digital public services and governance in the regional context. Finally, we would like to thank the team of surveyors for their commitment during the fieldwork carried out between November and December 2024.

References

  1. Akyeşilmen N., Akdag Y. (2025). Towards global digital governance: norms, institutions, and challenges of multilateral cooperation. Insight Turkey 27, 313 – 336. doi:, doi:10.25253/99.2025272.17
  2. Alakitan M. T., Makinde E. (2025). Where are the ethical guidelines? Examining the governance of digital technologies and AI in Nigeria. Policy Internet 17: 416. doi: doi:10.1002/poi3.416
  3. Bartuseviciene I., Butkus M. (2024). The effect of digital governance to stimulate the antifragile capabilities of public sector organizations. Econ. Soc. 17, 41 – 61. doi: doi:10.14254/2071-789X.2024/17-3/3
  4. Bridges L. E. (2024). Competing digital capacities: between state-led digital governance and local data center tradeoffs. Inf. Commun. Soc. 27, 1906 – 1923. doi: doi:10.1080/1369118X.2024.2331765
  5. Chung C., Choi H., Cho Y. (2022). Analysis of digital governance transition in South Korea: focusing on the leadership of the president for government innovation. J. Open Innov. Technol. Mark. Complex. 8: 2. doi: doi:10.3390/joitmc8010002
  6. Chung C., Wang K., Zhong Y. (2024). Policy review towards the transitions of regional development of digital governance in China: the case of Guangdong-Hong Kong-Macao Greater Bay (2013–2023). J. Infrastruct. Policy Dev. 8: 3080. doi: doi:10.24294/jipd.v8i3.3080
  7. Claver A. (2021). The Dutch “Digi commissioner” (2014-2018) an abortive attempt at national digital governance. Halduskultuur 21, 4 – 22. doi: doi:10.32994/HK.V21I2.245
  8. Decuypere M., Lewis S. (2023). Topological genealogy: a methodology to research transnational digital governance in/through/as change. J. Educ. Policy 38, 23 – 45. doi: doi:10.1080/02680939.2021.1995629
  9. Díez M. S., Fernández-Rodríguez E., Pradena-García Y. P. (2025). Algorithmic technopolitics and cosmo-diversity: a case study of digital governance and sociotechnical imaginaries in Education; Tecnopolíticas algorítmicas y cosmo-diversidad: un estudio de caso de la gobernanza digital y de los imaginarios sociotécnicos en Educación. Rev. Esp. Educ. Comp. 48, 331 – 353. doi: doi:10.5944/reec.48.2025.45329
  10. Easton D. (1975). A re-assessment of the concept of political support. Br. J. Polit. Sci. 5, 435 – 457. doi: doi:10.1017/s0007123400008309
  11. Elayah M. A. (2025). Transformative pathways in Qatar’s E-government: integrating societal engagement and NGO partnerships for sustainable digital governance. Public Adm. Dev. 45, 228 – 241. doi: doi:10.1002/pad.2102
  12. Esposito G., Terlizzi A., Guarino M., Crutzen N. (2024). Interpreting digital governance at the municipal level: evidence from smart city projects in Belgium. Int. Rev. Adm. Sci. 90, 301 – 317. doi: doi:10.1177/00208523231167538
  13. Faruque M. D. G., Al-Amin, Sharmin M., Rahman M. M. (2025). Fostering sustainable digital governance: citizen acceptance of web-portal-based integrated platforms in Bangladesh through a TAM-TPB approach. J. Risk Analysis and Crisis Res 15, 79 – 97. doi: doi:10.54560/jracr.v15i1.551
  14. Guang Y., Hongyu T. (2021). A study of the European Union’s path for constructing digital governance rules and the logical implications of the path. Contemporary Social Sciences 6, 36 – 52. doi: doi:10.19873/j.cnki.2096-0212.2021.06.003
  15. Gurinskaya A., Nalla M. K. (2026). Exploring the digital shift in crime reporting: a study of in-person and online preferences in the context of police legitimacy and trust in e-governance and technology. Int. Crim. Justice Rev. 36, 200 – 224. doi: doi:10.1177/10575677251372919
  16. Habich-Sobiegalla S., Zheng H., Pluemmer F. (2026). Temporal governance and accountability costs of Beijing’s digital citizen request system. Regul. Gov. 20, 18 – 34. doi: doi:10.1111/rego.12612
  17. Hu J., Zhang X. (2024). Digital governance in China: dispute settlement and stability maintenance in the digital age. J. Contemp. China 33, 561 – 577. doi: doi:10.1080/10670564.2023.2261877
  18. Huang C., Liu W., Iqbal W., Shah S. A. R. (2024). Does digital governance matter for environmental sustainability? The key challenges and opportunities under the prism of natural resource management. Resour. Policy 91: 104812. doi: doi:10.1016/j.resourpol.2024.104812
  19. Jia K., Chen S. (2022). Global digital governance: paradigm shift and an analytical framework. Glob. Public Policy Gov. 2, 283 – 305. doi: doi:10.1007/s43508-022-00047-w
  20. Kisswani K., Al-Khasawneh J. A., Khalid N., Kazmi S. H. R., Syed Q. R. (2025). Exploring new drivers of mineral resource rents in BRICS countries: the role of digital governance and political stability. J. Open Innov. Technol. Mark. Complex. 11: 100659. doi: doi:10.1016/j.joitmc.2025.100659
  21. Latorcai C. J., Strausz P., Csedő Z. (2025). Impact of institutional change programs on digital and sustainable public sector governance. Int. J. Public Sect. Manag. 38:610–626. doi: doi:10.1108/IJPSM-02-2024-0044
  22. Lee-Geiller S., Lee T. (2022). How does digital governance contribute to effective crisis management? A case study of Korea’s response to COVID-19. Public Perform. Manag. Rev. 45, 860 – 893. doi: doi:10.1080/15309576.2022.2054434
  23. Li Y., He Q. (2023). Techno-biopolitics under a tentative “state of exception”: the institutional logic of the digital governance of inbound travelers during the COVID-19 pandemic in China. Chin. J. Commun. 16, 444 – 463. doi: doi:10.1080/17544750.2023.2242976
  24. Li S., Kit C. (2021). Legislative discourse of digital governance: a corpus-driven comparative study of laws in the European Union and China. Int. J. Legal Discourse 6, 349 – 379. doi: doi:10.1515/ijld-2021-2059
  25. Li J., Zhan G., Dai X., Qi M., Liu B. (2022). Innovation and optimization logic of grassroots digital governance in China under digital empowerment and digital sustainability. Sustainability 14: 470. doi: doi:10.3390/su142416470
  26. Lipset S. M. (1959). Some social requisites of democracy: economic development and political legitimacy. Am. Polit. Sci. Rev. 53, 69 – 105. doi: doi:10.2307/1951731
  27. Liu K. Z. (2026). Localizing the digital: implementation frictions and digital governance in inland China. J. Inf. Technol. Polit. 23, 202 – 216. doi: doi:10.1080/19331681.2024.2445725
  28. Lukman S., Hakim A. (2024). Agile governance, digital transformation, and citizen satisfaction moderated by political stability in Indonesia’s socio-political landscape. J. Ethn. Cult. Stud. 11, 210 – 228. doi: doi:10.29333/EJECS/2001
  29. Lund-Tønnesen J., Christensen T. (2023). The dynamics of governance capacity and legitimacy: the case of a digital tracing technology during the COVID-19 pandemic. Int. Public Manag. J. 26, 126 – 144. doi: doi:10.1080/10967494.2022.2112328
  30. Luo C., Hasan N. A. M., Zamri Bin Ahmad A. M. (2024). Exploring satisfaction and trust as key drivers of e-government continuance intention: evidence from China for sustainable digital governance. Sustainability 16: 68. doi: doi:10.3390/su162411068
  31. Morales Gonzales A. V., Zapata Manrique C. R., Gamero Juarez L. R., Arguedas-Catasi R. W., Rodriguez-Saavedra M. O. (2026). Normalization of corruption and evaluation of public services: adaptive mechanisms and subnational heterogeneity in Peru. Front. Polit. Sci. 8: 1806986. doi: doi:10.3389/fpos.2026.1806986
  32. Mukherjee F. (2025). New urban spaces under Modi’s digital India—digitization of urban governance and state-citizen relationship. Environ. Plan. C Polit. Space 43, 95 – 112. doi: doi:10.1177/23996544241259316
  33. Navaratna A. R., Saxena D. K. (2025a). Digital governance through self-regulation a user-developer perspective of AI chatbots. J. Telecommun. Digit. Econ. 13, 1 – 29. doi: doi:10.18080/jtde.v13n3.1077
  34. Navaratna A. R., Saxena D. K. (2025b). From policy to practice in digital governance: a three-level analysis of citizen-centric applications. Ejournal EDemocracy Open Gov. 17, 33 – 64. doi: doi:10.29379/jedem.v17i1.906
  35. Qi Z., Zhu C. (2025). Academic members’ shared experiences of virtual internationalization in digital governance contexts: a qualitative exploration through actor-network theory. Educ. Sci. 15: 1252. doi: doi:10.3390/educsci15091252
  36. Raman R., Nair V. K., Dinesh K. G. S., Acharyulu R. (2025). Comparative analysis of ChatGPT and bard in digital governance: accuracy, adaptability, and readability insights. Digit. Gov. Res. Pract. 6, 1 – 22. doi: doi:10.1145/3674846
  37. Raposo V. L. ú. (2023). Health, privacy and liberty: a call for digital governance during (and after) the pandemic. Int. J. Hum. Rights 27, 529 – 551. doi: doi:10.1080/13642987.2022.2132234
  38. Riveros-Gavilanes J. M., Riveros-Gavilanes J., Amaya-Mejía R., Guevara-Castro Y., Cabrera-Gonzales L. M., Roa-Yaguara B. L. (2026). The triad of digital governance: performance, participation, and digital implementation. Int. Rev. Public Adm. 31, 23 – 46. doi: doi:10.1080/12294659.2025.2546190
  39. Rodriguez Saavedra M. O., Morales Gonzales A. V., Cuentas Galindo I., Campos Ascuña L. M., Mamani Lopez J. W., Caira Mamani R. N.. (2026c). Political communication and digital participation in Arequipa, Peru: social media consumption and media credibility. Stud. Media Commun. 14, 284 – 298. doi: doi:10.11114/smc.v14i2.8332
  40. Rodriguez-Saavedra M. O., Cuentas Galindo I., Campos Ascuña L. M., Morales Gonzales A. V., Chavez Cuarite J. L., Donayre Sarolli A. E.. (2026b). Citizen satisfaction with public services in Peru: regional gaps in citizen perceptions of public service provision. PLoS One 21: e0349632. doi:, doi:10.1371/journal.pone.0349632
  41. Rodriguez-Saavedra M. O., Grundy López R. E., Paredes-Velazco R., Morales Gonzales A. V., Donayre Prado E. A., Apaza O. A.. (2026a). Political science and governance: citizen participation and rebuilding trust in the state. Soc. Sci. 15: 1. doi: doi:10.3390/socsci15010001
  42. Si Mohammed K., Abddel-Jalil Sallam O. A., Abdelkader S. B., Radulescu M. (2024). Dynamic effects of digital governance and government interventions on natural resources management: fresh findings from Chinese provinces. Resour. Policy 92: 105004. doi: doi:10.1016/j.resourpol.2024.105004
  43. Sun C., Jiang G., Zhang J. (2024). An analysis of hotspots, subject structure, and emerging trends in digital governance research. SAGE Open 14: 756. doi: doi:10.1177/21582440241268756
  44. Urra C. V., Rodriguez-Pastene F., Castro S. O. (2022). Electronic governance and social inclusion of the elderly through digital and information literacy strategies in Placilla, Chile; Gobernanza electrónica e inclusión digital de personas mayores mediante estrategias de alfabetización digital e informacional en la localidad de Placilla, Valparaíso, Chile. Palabra Clave (La Plata) 12: 168. doi: doi:10.24215/18539912e168
  45. Vaia G., Arkhipova D., DeLone W. H. (2022). Digital governance mechanisms and principles that enable agile responses in dynamic competitive environments. Eur. J. Inf. Syst. 31, 662 – 680. doi: doi:10.1080/0960085X.2022.2078743
  46. Vilca Corrales M. P., Caira-Mamani R. N., Jara-Ortega C. E., Aroquipa Apaza O., Rodriguez-Saavedra M. O. (2026). Digital public service quality and results-oriented governance in Peru. Front. Polit. Sci. 8: 1788065. doi: doi:10.3389/fpos.2026.1788065
  47. Villalobos C., Parcerisa L., Castillo K., Olguín T. (2024). EdTech companies and contemporary digital educational governance. Analyzing the expansion and role of the EdTech sector in the Chilean educational system. Int. J. Educ. Dev. 110: 103141. doi: doi:10.1016/j.ijedudev.2024.103141
  48. Wang Y. (2025). The role of Yueshengshi in advancing smart city development: a quantitative case study of Guangdong’s digital governance. Int. J. Sustain. Dev. Plann. 20, 1143 – 1154. doi: doi:10.18280/ijsdp.200320
  49. Wang H., Cui W. (2025). Network evolution of digital technology transfers and implications for urban digital innovation governance: evidence from Chinese patent transactions. Sustainability 17: 584. doi: doi:10.3390/su17219584
  50. Wang Z., Gao Y., Zhou Y., Xue L. (2025). Digital governance and AI: boosting OFDI’S impact on regional innovation performance. Sci. Public Policy 52, 438 – 449. doi: doi:10.1093/scipol/scae087
  51. Wang T., Wang D., Zeng Z. (2024). Research on the construction and measurement of digital governance level system of county rural areas in China—empirical analysis based on entropy weight TOPSIS model. Sustainability 16: 374. doi: doi:10.3390/su16114374
  52. Xanthopoulou P. I., Antoniadis I. I. (2024). Navigating the digital frontier: a systematic review of digital governance’s determinants in public administration. Int. J. Electron. Gov. 16, 274 – 302. doi: doi:10.1504/IJEG.2024.142398
  53. Xanthopoulou P. I., Antoniadis I. I., Avlogiaris G. (2023). Unveiling the drivers of digital governance adoption in public administration. Probl. Perspect. Manag. 21, 454 – 467. doi: doi:10.21511/PPM.21(4).2023.35
  54. Xiong Y., Zhenlin L. (2024). Digital transformation and the frontier of China’s state governance. Soc. Sci. China 45, 70 – 91. doi: doi:10.1080/02529203.2024.2367313
  55. Xu R. (2025). Making higher education market through open data platform: digital educational governance and struggling universities. High. Educ. Policy. doi: doi:10.1057/s41307-025-00405-3
  56. Xu X., Dai M. (2024). Evaluation of local government digital governance ability and sustainable development: a case study of Hunan Province. Sustainability 16: 84. doi: doi:10.3390/su16146084
  57. Xu J., Gong Q., Yin W. (2022). Maintaining ideological security and legitimacy in digital China: governance of cyber historical nihilism. Media Int. Aust. 185, 26 – 40. doi: doi:10.1177/1329878X221111826
  58. Xu A., Li P., Xin X. (2024). The impact of digital governance on entrepreneurial activity in relatively poor areas: evidence from Tibet, China. Sustainability 16: 247. doi: doi:10.3390/su16177247
  59. Zhang B., Xiong W., Yin J., Zhang P., Liu B. (2025). How to form the rural digital governance platform—under the framework of mixed-scanning–multiple streams—based on an empirical investigation of the platform of “JuHaoban” in Julu County, Hebei Province. Sustainability 17: 273. doi: doi:10.3390/su17062517
  60. Zhao Y. (2023). Digital governance with smart sensors: exploring grid administration in Zhejiang’s “future community.”. J. Comput. Mediat. Commun. 28: 16. doi: doi:10.1093/jcmc/zmad016
  61. Zhao Y., Chen H. (2024). Enhancing access to digital justice: digital governance of dispute resolution and dispute prevention in online commercial activities. J. Int. Dispute Settlement 15, 273 – 304. doi: doi:10.1093/jnlids/idae001
  62. Zhao Liu J. (2026). Digital governance, dataveillance, and participatory panopticon: public health surveillance in China from 2020 to 2022. J. Contemp. China 35, 1320 – 1336. doi: doi:10.1080/10670564.2025.2513409

Citation

Nina, W. Q., et al. (2026). Political-institutional approval and institutional trust: the moderating role of the digital environment. Frontiers in Political Science, Volume 8. https://doi.org/
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8 - 2026

Copyright

Copyright © 2026 Quispe Nina, Almanza Cabe, Morales Gonzales, Chavez Cuarite, Donayre Sarolli, Arguedas Catasi and Rodriguez Saavedra.

License: https://creativecommons.org/licenses/by/4.0/

*Correspondence: Miluska Odely Rodriguez Saavedra

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