Overcoming Data Barriers: Digital Inclusion and Equitable Public Services for People with Disabilities in Thailand

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Despite advancements in data governance frameworks, disparities in accessibility continue to exclude marginalized populations. While government-led digital initiatives f ocus on efficiency and compliance, their impact on vulnerable groups remains underexplored. This study examines the causal factors influencing audit data governance in Thailand’s public sector, with a particular focus on the contrasting perspectives between government officials, who are responsible for delivering public services, and people with disabilities, who rely on these services. Methods This research employs Multi-Group Structural Equation Modeling (MG-SEM) to analyze survey responses from 711 participants, including policymakers, policy implementers, auditors, and people with disabilities (specifically individuals with visual and physical impairments). The study utilizes a quantitative approach, ensuring that all analyses are based on structured survey data. Key constructs examined include public service delivery for people with disabilities (measured by perception of public service, enablers for access to data, citizen-oriented policy, and fair & efficient administrative procedures), data-driven culture, which ultimately influence audit data governance. Results The findings reveal significant disparities in how data governance is perceived. Government officials emphasize efficiency, compliance, and regulatory adherence, whereas people with disabilities prioritize accessibility, usability, and inclusivity in digital public services. These contrasting perspectives highlight systemic barriers that hinder the equitable implementation of digital governance frameworks, exacerbating the digital divide in public service accessibility. Conclusions To address these challenges, the study proposes targeted policy interventions, including the integration of AI-driven auditing mechanisms, the establishment of universal digital accessibility standards, and the implementation of participatory governance strategies. These recommendations provide valuable insights for policymakers, auditors, and public administrators aiming to foster transparency, trust, and inclusivity in digital public services. 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F1000Research 2025, 14 :473 ( https://doi.org/10.12688/f1000research.163271.1 ) NOTE: If applicable, it is important to ensure the information in square brackets after the title is included in all citations of this article. Close Copy Citation Details Export Export Citation Sciwheel EndNote Ref. Manager Bibtex ProCite Sente EXPORT Select a format first Track Share ▬ ✚ Research Article Overcoming Data Barriers: Digital Inclusion and Equitable Public Services for People with Disabilities in Thailand [version 1; peer review: awaiting peer review] Sitthisak Chaiyasuk https://orcid.org/0009-0009-5303-550X 1 , Krish Rugchatjaroen 2 , Somboon Sirisunhirun 2 , Nopraenue Sajjarax Dhirathiti 2 , Somsak Amornsiriphong 2 , Phut Ploywan 2 Sitthisak Chaiyasuk https://orcid.org/0009-0009-5303-550X 1 , Krish Rugchatjaroen 2 , [...] Somboon Sirisunhirun 2 , Nopraenue Sajjarax Dhirathiti 2 , Somsak Amornsiriphong 2 , Phut Ploywan 2 PUBLISHED 02 May 2025 Author details Author details 1 Doctor of Public Administration (Public Policy and Public Management), Faculty of Social Sciences and Humanities, Mahidol University, Salaya, Nakhon Pathom, 73170, Thailand 2 Faculty of Social Sciences and Humanities, Mahidol University, Salaya, Nakhon Pathom, 73170, Thailand Sitthisak Chaiyasuk Roles: Conceptualization, Data Curation, Formal Analysis, Investigation, Methodology, Project Administration, Resources, Software, Validation, Visualization, Writing – Original Draft Preparation Krish Rugchatjaroen Roles: Conceptualization, Methodology, Project Administration, Supervision, Writing – Review & Editing Somboon Sirisunhirun Roles: Conceptualization, Methodology, Supervision, Writing – Review & Editing Nopraenue Sajjarax Dhirathiti Roles: Conceptualization, Methodology, Supervision, Writing – Review & Editing Somsak Amornsiriphong Roles: Conceptualization, Methodology, Supervision, Writing – Review & Editing Phut Ploywan Roles: Conceptualization, Methodology, Supervision, Writing – Review & Editing OPEN PEER REVIEW REVIEWER STATUS AWAITING PEER REVIEW This article is included in the Health Services gateway. Abstract Background Ensuring equitable access to public services for people with disabilities remains a persistent challenge in digital governance. Despite advancements in data governance frameworks, disparities in accessibility continue to exclude marginalized populations. While government-led digital initiatives f ocus on efficiency and compliance, their impact on vulnerable groups remains underexplored. This study examines the causal factors influencing audit data governance in Thailand’s public sector, with a particular focus on the contrasting perspectives between government officials, who are responsible for delivering public services, and people with disabilities, who rely on these services. Methods This research employs Multi-Group Structural Equation Modeling (MG-SEM) to analyze survey responses from 711 participants, including policymakers, policy implementers, auditors, and people with disabilities (specifically individuals with visual and physical impairments). The study utilizes a quantitative approach, ensuring that all analyses are based on structured survey data. Key constructs examined include public service delivery for people with disabilities (measured by perception of public service, enablers for access to data, citizen-oriented policy, and fair & efficient administrative procedures), data-driven culture, which ultimately influence audit data governance. Results The findings reveal significant disparities in how data governance is perceived. Government officials emphasize efficiency, compliance, and regulatory adherence, whereas people with disabilities prioritize accessibility, usability, and inclusivity in digital public services. These contrasting perspectives highlight systemic barriers that hinder the equitable implementation of digital governance frameworks, exacerbating the digital divide in public service accessibility. Conclusions To address these challenges, the study proposes targeted policy interventions, including the integration of AI-driven auditing mechanisms, the establishment of universal digital accessibility standards, and the implementation of participatory governance strategies. These recommendations provide valuable insights for policymakers, auditors, and public administrators aiming to foster transparency, trust, and inclusivity in digital public services. READ ALL READ LESS Keywords Audit Data Governance, Data-Driven Culture, Public Service Delivery, People with Disabilities, Multi-Group Structural Equation Modeling Corresponding Author(s) Krish Rugchatjaroen ( [email protected] ) Close Corresponding author: Krish Rugchatjaroen Competing interests: No competing interests were disclosed. Grant information: The author(s) declared that no grants were involved in supporting this work. Copyright: © 2025 Chaiyasuk S et al . This is an open access article distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. How to cite: Chaiyasuk S, Rugchatjaroen K, Sirisunhirun S et al. Overcoming Data Barriers: Digital Inclusion and Equitable Public Services for People with Disabilities in Thailand [version 1; peer review: awaiting peer review] . F1000Research 2025, 14 :473 ( https://doi.org/10.12688/f1000research.163271.1 ) First published: 02 May 2025, 14 :473 ( https://doi.org/10.12688/f1000research.163271.1 ) Latest published: 23 Mar 2026, 14 :473 ( https://doi.org/10.12688/f1000research.163271.2 )  There is a newer version of this article available. Suppress this message for one day. Introduction Digital technology increasingly shapes contemporary governance, particularly in public administration, by enhancing transparency, accountability, and efficiency in public service delivery. Emerging technologies such as artificial intelligence (AI), big data analytics, and integrated digital service platforms have transformed governance practices globally, facilitating better policy outcomes and service delivery ( OECD, 2019a ; United Nations, 2022 ). Several governments, notably those in Estonia, Denmark, and other OECD member countries, have successfully implemented comprehensive data governance frameworks that foster digital inclusion and equitable public service delivery for vulnerable populations ( European Commission, 2023 ; OECD, 2019b ). Despite these international advances, developing countries such as Thailand still encounter substantial disparities, especially concerning digital access for People with disabilities (People with disabilities). While the Thai government has introduced initiatives aimed at digital governance improvement, such as the establishment of the Digital Government Development Agency (DGA) and the enhancement of public service audit systems, the gap in equitable access to digital information remains pronounced ( DGA, 2023 ). This persistent divide underscores critical challenges in achieving inclusive public services. The concept of audit data governance, defined as structured management encompassing data collection, analysis, accessibility, and transparency in public sector audits ( Chen & Lee, 2017 ), is pivotal in addressing these disparities. International bodies, including the OECD and World Bank, emphasize robust data governance to enhance trust, accountability, and inclusive decision-making processes in public administration ( World Bank, 2023 ; OECD, 2019b ). Audit data governance serves as a mechanism to bridge the digital divide by ensuring equitable access to reliable and transparent public service information. Effective governance structures facilitate data-driven decision-making, enhance service inclusivity, and improve the accountability of public institutions ( World Bank, 2023 ). This study specifically addresses the digital divide between government officials who manage public services and people with disabilities who access these services by exploring the variations in data governance practices through MG-SEM. MG-SEM is employed to compare the structural relationships of audit data governance across groups, allowing for an examination of how government officials and people with disabilities perceive data governance differently. This approach provides a robust statistical framework to test measurement invariance and assess intergroup differences in a theoretically meaningful way ( Byrne, 2016 ). This study follows the STROBE (Strengthening the Reporting of Observational Studies in Epidemiology) guidelines to ensure transparency and reproducibility in reporting research findings ( von Elm et al., 2007 ). The objective is to provide evidence-based insights that inform policy development, enhance digital accessibility frameworks, and promote equitable public service delivery. The findings contribute to the broader discourse on digital governance in public administration, offering practical recommendations for policymakers, auditors, and administrators to design inclusive digital strategies that reduce disparities and foster a more accessible and transparent public sector in Thailand and beyond. Methods Study design This research is based on the conceptual framework of Audit Data Governance, which plays a crucial role in increasing transparency and reliability in public service delivery, particularly for vulnerable groups such as people with disabilities in Thailand. Effective data management in public service processes is a key factor that helps enhance service quality and build public trust. The researcher adopted a previously developed model of the factors influencing audit data governance of public service for people with disabilities in Thailand for the analysis, as shown in Figure 1 . Figure 1. Data governance factors for auditing public services for people with disabilities in Thailand model. As shown in Figure 1 , the parameter values of the model used in this study are presented in Table 1 . These values illustrate the factors influencing audit data governance of public service for people with disabilities in Thailand. Table 1. Factors influencing audit data governance in public services for people with disabilities. Dimension Influence factors λ S.E. R 2 p -Value Public Service Delivery Perception of Public Services 0.972 0 0.944 s23 General service accessibility satisfaction 0.890 0 0.792 s24 Digital service accessibility satisfaction 0.871 0.040 0.758 *** s25 Time and cost accessibility satisfaction 0.842 0.043 0.709 *** s05 Service quality satisfaction 0.795 0.046 0.633 *** Enablers for Access to Data 0.948 0.056 0.898 *** s15 Service management tools 0.855 0.057 0.731 *** s21 Government website accessibility 0.840 0.056 0.705 *** s18 Interoperable data exchange framework 0.828 0.056 0.686 *** s22 WCAG compliance testing 0.809 0.059 0.655 *** s16 Consultation services for People with disabilities 0.799 0.056 0.638 *** s19 One-stop service for People with disabilities 0.770 0 0.592 s20 Public disclosure of disability statistics 0.764 0.062 0.583 *** s17 Digital disability registry 0.751 0.063 0.564 *** s14 Use of electronic or digital signatures 0.676 0.065 0.457 *** Fair & Efficient Administrative Procedures 0.942 0.049 0.887 *** s10 Comprehensive public service legislation 0.852 0.054 0.725 *** s09 Efficiency and resource optimization 0.820 0.055 0.672 *** s11 Service quality audit against resource usage 0.812 0.045 0.659 *** s12 Value-for-money reporting 0.809 0 0.655 s13 Compliance with international service standards 0.769 0.062 0.592 *** s07 Service process efficiency 0.742 0.055 0.550 *** s08 Legal framework for service delivery 0.731 0.054 0.535 *** Citizen-Oriented Policy 0.848 0.052 0.718 *** s04 Budget allocation and monitoring for ICT services 0.831 0.052 0.690 *** s02 Budget allocation and monitoring for public services 0.827 0 0.684 s06 Provincial-level coordination 0.760 0.064 0.577 *** s03 Information Technology Policy 0.745 0.053 0.555 *** s01 Joint planning and responsibility assignment 0.721 0.055 0.520 *** Audit Data Governance 0.150 0.028 0.653 *** Data-Driven Organization c08 Open, Trusting Culture 0.783 0.065 0.624 *** c06 Inquisitive, Questioning Culture 0.777 0.065 0.618 *** c10 Data Handling Ethics 0.759 0.061 0.617 *** c09 Data analysis training and team communication skills 0.756 0.063 0.610 *** c07 Goals-First Culture 0.752 0.066 0.596 *** c0304 Anti-HiPPO Culture 0.745 0.066 0.586 *** c01 Data Leadership 0.692 0.068 0.582 *** c02 Data leadership career paths and incentives 0.681 0 0.566 c05 Iterative, Learning Culture 0.674 0.059 0.540 *** Public Service Delivery 0.320 0.093 0.103 *** Audit Data Governance 0.748 0.068 0.653 *** Audit Data Governance a07 Data sharing and collaboration 0.790 0.064 0.536 *** a12 Metadata management 0.786 0.068 0.515 *** a15 Data quality 0.785 0.061 0.498 *** a14 Promotion of equitable data benefits 0.781 0.067 0.467 *** a08 Big data management 0.772 0.070 0.449 *** a13 Data ownership and access rights 0.765 0.066 0.430 *** a06 Data model design and development/Data Architecture 0.763 0.063 0.613 *** a09 In-depth data analysis/Data Science 0.752 0 0.604 a10 Application of data to emerging digital technologies 0.735 0.066 0.575 *** a11 Innovation creation 0.732 0.069 0.571 *** a05 Master and reference data management 0.717 0.060 0.565 *** a02 Trust-building in data systems 0.705 0.063 0.556 *** a04 Data system and service enhancement 0.683 0.069 0.478 *** a01 Protection of individuals or communities 0.670 0.069 0.464 *** a03 Ensure data security 0.656 0.073 0.454 *** Fit Indices : χ 2 =1983.192; df=1060; p-value=.000; Relative χ 2 =1.871; RMSEA=.051; SRMR=.033; CFI=.930; TLI=.922 Table 2. Number and percentage classified by characteristics of the sample group. Category Public service providers (Government officials) Public service recipients (People with disabilities) Attribute n = 340 Percentage Attribute n = 371 Percentage Gender Male 111 32.60 Male 211 56.90 Female 227 66.80 Female 158 42.60 Not specified 2 0.60 Not specified 2 0.50 Age range 18-25 years 13 3.80 18-25 years 15 4.00 26-33 years 60 17.60 26-33 years 29 7.80 34-41 years 91 26.80 34-41 years 64 17.30 42-49 years 109 32.10 42-49 years 89 24.00 above 50 years 67 19.70 above 50 years 174 46.90 Province Chiang Mai 45 13.20 Chiang Mai 53 14.30 Phitsanulok 41 12.10 Phitsanulok 40 10.80 Khon Kaen 41 12.10 Khon Kaen 61 16.40 Nakhon Ratchasima 48 14.10 Nakhon Ratchasima 93 25.10 Nonthaburi 38 11.20 Nonthaburi 28 7.50 Chonburi 49 14.40 Chonburi 27 7.30 Surat Thani 31 9.10 Surat Thani 31 9.10 Songkhla 47 13.80 Songkhla 47 13.80 Experience 1-5 years 167 49.10 1-3 months ago 270 72.80 6-10 years 80 23.50 more than 3 months ago 54 14.60 more than 10 years 93 27.40 more than 1 year ago 47 12.70 Group Government officials at the policy formation level (Provincial Disability Service Center) 46 13.50 Persons with physical or mobility impairments 301 81.10 Government officials at the policy implementation level (Local Admin. Organization) 209 61.50 Persons with visual impairments 70 18.90 Government officials at the auditing level (State Audit Office) 85 25.00 Categories of Public Service Disability registration 341 19.80 Education 146 8.50 Employment 168 9.70 Voting 317 18.40 Government websites or applications 257 14.90 Living conditions (e.g., housing, sidewalks, sports facilities, public transportation, green and safe public spaces) 194 11.30 Government welfare (e.g., disability allowance, sign language interpreter services, necessary tools or equipment, medical or vocational rehabilitation) 301 17.50 Based on the theoretical framework and previous empirical findings, this study formulates the following hypothesis: Hypothesis (H1): There are variations in the causal factors influencing audit data governance for public service auditing between government officials (service providers) and people with disabilities (service recipients). Data collection Data were collected from a total of 711 participants categorized into two primary groups: government officials involved in policy formulation, implementation, and auditing, and people with disabilities holding disability identification cards. Government officials participated through an online questionnaire, while data from people with disabilities, including individuals with visual and physical impairments, were collected via face-to-face field surveys conducted across eight representative provinces in Thailand: Chiang Mai, Phitsanulok (Northern region); Khon Kaen, Nakhon Ratchasima (Northeastern region); Nonthaburi (Central region); Chonburi (Eastern region); Surat Thani and Songkhla (Southern region). The structured questionnaire was designed based on established practices in evaluating public service and data governance factors. The questionnaire included four sections: • Section 1: Demographic characteristics of participants. • Section 2: Evaluation of relationships between Public Service Delivery factors and Audit Data Governance factors. • Section 3: Evaluating the importance of each factor that may affect the Audit Data Governance Factors of Public Services delivery for people with disabilities. • Section 4: Evaluating the importance of the positive relationship between Data-Driven Organizational Factors and Audit Data Governance Factors of Public Services Delivery for people with disabilities. A 5-point Likert scale was employed to gauge participant responses, ranging from 1 (not very important) to 5 (very important). The reliability of the questionnaire was assessed through pilot testing conducted separately for two groups, with 30 participants in each group. The results indicated a high level of internal consistency, with a Cronbach’s alpha of 0.95 for both groups, exceeding the acceptable threshold of 0.70 ( Nunnally & Bernstein, 1994 ). Data analysis This study employed Multi-Group Structural Equation Modeling (MG-SEM) using IBM SPSS AMOS 24.0 ( IBM Corp., 2016 ; https://www.ibm.com/products/structural-equation-modeling-sem ), a proprietary statistical software for which a valid copyright license has been obtained. IBM SPSS 25.0 ( IBM Corp., 2017 ; https://www.ibm.com/products/spss-statistics ) was used for data analysis. MG-SEM is a robust analytical method suitable for assessing relationships among latent variables and comparing such relationships across different groups ( Byrne, 2010 ; Hair et al., 2019 ). The Multi-group SEM analysis was conducted sequentially through six models: 1. Unconstrained Model : Baseline model without any constraints. 2. Measurement Weights Model : Factor loadings constrained to be equal across groups. 3. Structural Weights Model : Structural paths constrained equally across groups. 4. Structural Covariances Model : Covariances among latent variables constrained. 5. Structural Residuals Model : Residual variances of latent variables constrained to equality. 6. Measurement Residuals Model : Measurement errors constrained to be equal across groups. The model fit evaluation was conducted based on the criteria outlined in Table 3 , ensuring that the assessment adhered to widely accepted statistical benchmarks. These criteria were applied to examine the overall model fit and to determine measurement invariance across groups. The evaluation process incorporated both absolute and incremental fit indices, as well as model comparison indices, to comprehensively assess the consistency and robustness of the measurement structure. Table 3. Standards and thresholds for MG-SEM. Measure Criteria Source Relative Chi-Square or χ 2 /df 0.92 RMSEA < 0.07 SRMR 0.05 (Model fit = Yes) p -value < 0.05 (Model fit = No) Kline (2015) ΔCFI ≤ 0.01 (Model fit = Yes) Cheung and Rensvold (2002) Results Measurement model The confirmatory factor analysis (CFA) results indicate an acceptable fit of the measurement model, meeting the standard thresholds recommended by Hair et al. (2019) and Kline (2015) . Specifically, the Relative Chi-Square (χ 2 /df ), Comparative Fit Index (CFI), Tucker-Lewis Index (TLI), Root Mean Square Error of Approximation (RMSEA), and Standardized Root Mean Square Residual (SRMR) met the established criteria (see Table 4 ). Table 4. Regression weights for SEM model by public service provider and public service recipient. Dimension Influence factors Unstandardized regression weights Government officials People with disabilities Standardized regression weights p -Value Standardized regression weights p -Value Public Service Delivery Perception of Public Services s23 1.000 0.886 0.849 s24 0.991 0.881 *** 0.892 *** s25 0.951 0.837 *** 0.837 *** s05 0.872 0.782 *** 0.782 *** Enablers for Access to Data s17 1.000 0.772 0.811 s15 0.986 0.854 *** 0.826 *** s18 0.983 0.840 *** 0.832 *** s22 0.952 0.803 *** 0.776 *** s19 0.933 0.739 *** 0.696 *** s16 0.930 0.811 *** 0.822 *** s21 0.923 0.827 *** 0.753 *** s20 0.917 0.749 *** 0.780 *** s14 0.912 0.694 *** 0.647 *** Fair & Efficient Admin. Procedures s13 1.000 0.771 0.752 s11 0.990 0.815 *** 0.841 *** s10 0.985 0.850 *** 0.855 *** s09 0.959 0.818 *** 0.810 *** s12 0.956 0.793 *** 0.823 *** s07 0.869 0.738 *** 0.744 *** s08 0.856 0.747 *** 0.804 *** Citizen-Oriented Policy s02 1.000 0.838 0.759 s04 0.885 0.836 *** 0.759 *** s01 0.783 0.731 *** 0.783 *** s06 0.776 0.738 *** 0.752 *** s03 0.754 0.728 *** 0.720 *** Data-Driven Organization c08 1.000 0.796 0.808 c06 0.988 0.780 *** 0.775 *** c09 0.966 0.758 *** 0.757 *** c10 0.909 0.752 *** 0.706 *** c01 0.904 0.703 *** 0.792 *** c05 0.899 0.667 *** 0.723 *** c07 0.893 0.745 *** 0.751 *** c0304 0.887 0.750 *** 0.685 *** c02 0.822 0.663 *** 0.717 *** Audit Data Governance a08 1.000 0.769 0.862 a09 0.996 0.738 *** 0.894 *** a12 0.992 0.778 *** 0.891 *** a06 0.992 0.758 *** 0.859 *** a15 0.983 0.840 *** 0.835 *** a14 0.972 0.770 *** 0.850 *** a07 0.970 0.779 *** 0.888 *** a05 0.966 0.726 *** 0.859 *** a11 0.945 0.752 *** 0.874 *** a13 0.943 0.802 *** 0.796 *** a10 0.942 0.737 *** 0.823 *** a03 0.913 0.689 *** 0.825 *** a02 0.851 0.708 *** 0.802 *** a01 0.843 0.677 *** 0.807 *** a04 0.842 0.670 *** 0.794 *** Factor loadings for all observed variables exceeded 0.50, indicating sufficient convergent validity. The Cronbach’s alpha coefficients ranged from 0.75 to 0.95, confirming good internal consistency reliability of the constructs. Multi-Group SEM analysis The MG-SEM analysis was conducted to examine the differences between government officials and people with disabilities regarding factors influencing audit data governance. The results demonstrate significant differences between these groups in several structural paths. The unconstrained baseline model indicated a good fit to the data (see Table 5 ). Table 5. Comparison of path coefficients and model fit for structural weights in MG-SEM. Path coefficient Group Path coefficients β Δχ 2 test ( p -value) Latent exogenous variables Latent endogenous variables Standardized Unstandardized Public Service Delivery Perception of Public Services 1 0.973 1.000 - 2 0.827 Enablers for Access to Data 1 0.950 0.912 0.103 2 0.941 Fair & Efficient Admin. Procedures 1 0.955 0.906 0.084 2 0.930 Citizen-Oriented Policy 1 0.874 0.878 0.100 2 0.838 Audit Data Governance 1 0.147 0.098 0.098 2 0.112 Data-Driven Organization Public Service Delivery 1 0.481 0.819 0.002 2 0.646 Audit Data Governance 1 0.759 0.859 0.023 2 0.776 Further testing of structural invariance using model comparison indices (Δχ 2 and ΔCFI) confirmed significant differences in certain relationships between the two groups. Specifically, differences were notable in relationships involving data-driven organizational culture and audit data governance factors. Hypothesis testing Hypothesis H1, stating that there are significant variations in the causal factors influencing audit data governance between government officials and people with disabilities, was supported. Detailed standardized path coefficients and significance levels are presented in Table 6 . Table 6. Model comparison. Model χ 2 df χ 2 /df p -value CFI ΔCFI RMSEA SRMR TLI Model fit Unconstrained Model 4238.237 2130 1.990 0.000 0.930 - 0.037 0.030 0.923 Yes Measurement Weights Model 4293.986 2173 1.976 0.000 0.930 0.000 0.037 0.033 0.924 Yes Structural Weights Model 4329.663 2179 1.987 0.000 0.929 -0.001 0.037 0.062 0.923 Yes Structural Covariances Model 4333.230 2180 1.988 0.000 0.929 0.000 0.037 0.052 0.923 Yes Structural Residuals Model 4455.838 2186 2.038 0.000 0.925 -0.004 0.038 0.065 0.919 Yes Measurement Residuals Model 6227.815 2289 2.721 0.000 0.870 -0.055 0.049 0.068 0.866 No Discussion Multi-group variation of causal factor models influencing the audit data governance of public service for people with disabilities in Thailand This study found significant variation between the Government Officials and People with Disabilities groups. This variation reflects differences in the social context and data-driven organizational culture between service providers and service recipients, particularly among People with disabilities, who often face limitations in accessing information. The Measurement Weights Model, which compares the differences in the model between the two groups, is shown in Figure 2 . Figure 2. Multi-group causal models influencing audit data governance in public service delivery. Based on the multi-group path coefficient invariance testing, it was found that the path relationships between Data-Driven Organization and Public Service Delivery, as well as Audit Data Governance, vary between the group of government officials, who provide services, and the group of People with disabilities, who receive public services. This reflects that a Data-Driven Organization can enhance decision-making and data management more effectively within the Government Officials group, which has sufficient resources and access to information. Meanwhile, the People with disabilities group often faces challenges in accessing information and participating in policy decision-making processes ( Hofstede, 2010 ). This results in variations along these two paths, aligning with the study by Chen & Lee (2017) , which indicates that People with disabilities faces barriers in accessing the information and services necessary to improve their quality of life. These variations highlight the lack of government support in bridging the information access gap for vulnerable groups. However, although the service recipients may not have in-depth knowledge of organizational culture or data-driven decision-making processes, their perceptions are shaped by their experiences with public service delivery, particularly in terms of service quality and access to information. High-quality and efficient public service delivery can lead to greater satisfaction among People with disabilities, even though they may not have the same level of information access as government officials ( Nunnally & Bernstein, 1994 ; Parasuraman et al., 1988 ). When testing for group invariance by comparing the constrained model with the unconstrained model to assess whether the relationships between variables are equal or differ between groups, it was found that the Measurement Residuals Model did not maintain a significant fit. This clearly indicates measurement variance or error between groups, possibly reflecting technical issues or environmental factors that cause inconsistencies in variable measurement between groups. For instance, limited access to public services may contribute to a lower quality of life for People with disabilities, consistent with research by the World Bank (2019) , which highlights the connection between disability and poverty in developing countries. The report indicates that People with disabilities often has fewer opportunities for education or employment, and the lack of health resources and public service support in rural areas may create barriers to traveling to urban centers. Access to basic healthcare services for People with disabilities is therefore limited compared to the general population ( GHRP, 2017 ). Additionally, statistics from this research indicate that 46.90% of People with disabilities are aged 50 or older, reinforcing the relevance of age-related issues. For example, a case study in Malawi found that elderly individuals with multiple chronic illnesses often prefer public hospitals due to the reliability of services, but still face geographical and social barriers that hinder access ( BMC Public Health, 2022 ). This similarity highlights the lack of public services in rural areas. Although technology has been introduced to public service delivery to reduce geographical barriers, challenges in accessing information through such technology remain, particularly for the visually impaired. This is reflected in the research by Henriquez-Camacho et al. (2014) , which discusses the use of eHealth and mHealth technologies to assist the elderly in accessing healthcare information in rural areas. It was found that elderly individuals with higher education and those using technology to search for medical information had better access to services compared to those with lower education or who did not use technology. Research limitations While this study provides valuable insights into audit data governance and its impact on public service delivery for people with disabilities, there are several opportunities for future research to build upon and expand these findings. First, the use of survey data allows for broad coverage and statistical robustness, capturing perceptions from a diverse sample. However, future research could further enhance these insights by incorporating mixed-method approaches, such as interviews or case studies, to provide deeper qualitative perspectives. Additionally, integrating objective behavioral data, such as digital service usage logs or administrative records, could offer more comprehensive and actionable insights into data governance effectiveness. Second, by focusing on Thailand’s public sector, this study contributes to a deeper understanding of digital governance challenges in a developing country context. Future research could expand on these findings by conducting comparative studies across multiple countries or regions. Such studies could provide cross-cultural perspectives on the adoption of digital accessibility frameworks, policy effectiveness, and technological infrastructure development, offering insights into globally adaptable best practices. Lastly, this study applies MG-SEM to examine group-based differences in audit data governance, providing a robust analytical foundation. Future research could further enhance methodological rigor by employing longitudinal modeling to track evolving trends over time or experimental designs to assess the real-world impact of digital governance interventions. These approaches would help policymakers and practitioners refine strategies for ensuring inclusive, transparent, and efficient digital governance frameworks. By addressing these areas, future research can play a crucial role in advancing digital public administration, optimizing data governance strategies, and ensuring equitable service delivery for all citizens. Practical implications The findings of this study reveal important distinctions between government officials and people with disabilities regarding the perceived importance of factors influencing audit data governance. Government officials emphasized data quality and organizational culture as essential drivers for effective data governance, reflecting their role in ensuring accurate and reliable data management systems. Conversely, people with disabilities highlighted data accessibility as a primary concern, underscoring the need for improved inclusivity in digital public service delivery. These results are consistent with international evidence that highlights the necessity of inclusive data practices within public governance frameworks. The OECD (2024) emphasizes that data-driven practices in public sectors must prioritize both quality and accessibility to foster public trust and ensure equitable service delivery. Similarly, the European Commission’s eGovernment Benchmark ( European Commission, 2023 ) illustrates successful practices across European governments, such as Estonia and Denmark, where comprehensive data governance and inclusive digital platforms have effectively narrowed the digital accessibility gap for vulnerable populations, including people with disabilities. Policy recommendations This study aligns with global recommendations from the United Nations (2022) , suggesting that inclusive and accessible public digital services significantly enhance trust and participation among vulnerable communities. The observed differences between government officials and people with disabilities highlight gaps that policymakers must address. Specifically, officials prioritize data-driven organizational culture and service quality, whereas people with disabilities emphasize accessibility and ease of data use. Practically, these insights advocate for tailored policy frameworks to bridge gaps in perceptions and practices. For instance, governments can leverage strategies such as those implemented in European Union countries, which successfully employ comprehensive accessibility guidelines in digital service frameworks ( European Commission, 2023 ). Thailand could benefit from adopting similar inclusive digital governance practices, potentially enhancing the transparency, accessibility, and quality of public audit services. Moreover, developing comprehensive guidelines that explicitly integrate accessibility considerations into digital audits can contribute significantly to closing the accessibility gap for people with disabilities. These guidelines should encompass clear mandates on data collection processes, analytical transparency, and effective dissemination practices aligned with international best practices ( OECD, 2019b ). Table 7 summarizes practical implications and policy recommendations, clearly highlighting the target audience, international alignment, and expected outcomes. Table 7. Policy Recommendations and Practical Guidelines. Policy Recommendation Practical Guidelines Target Audience Best Practices & International Alignment Expected Outcomes Inclusive Public Service Delivery Policy 1. Integrate all relevant agencies in driving policies for equality in public service delivery. - Establish a national integration committee consisting of representatives from relevant ministries. - Jointly design policies and develop long-term plans for service delivery. Policymakers, Government Agencies OECD Inclusive Governance Framework ( OECD, 2019a ), UNDP Inclusive Policy Guidelines ( UNDP, 2016 ) Enhanced inter-agency collaboration, improved service equality 2. Decentralization of Decision-Making: Delegate decision-making authority to local levels. - Develop policies to decentralize power from the central government to the provincial and district levels. - Support local development through collaboration with community-level agencies. Local Government, Policy Planners Estonia Local Government Reform ( European Commission, 2023 ), EU Regional Governance Model ( OECD, 2019a ) Greater efficiency in localized service management 3. Establish Provincial Integration Committees. - Appoint diverse integration committees in each province. - Hold regular meetings to plan and monitor policy implementation. Provincial Administrators, Public Sector Leaders World Bank Public Administration Decentralization Model ( World Bank, 2019 ) Stronger multi-agency coordination at regional levels Data-Driven Public Sector Reform Policy 1. Develop Digital Platforms for Data Integration. - Develop a centralized digital data system such as Digital ID to enable agencies to access information quickly. - Promote the use of digital platforms for data exchange. System Developers, Public Administrators Estonia e-Government Model ( European Commission, 2023 ; OECD, 2019b ), EU Digital Strategy ( OECD, 2019b ) Efficient data sharing, improved inter-agency coordination 2. Upskilling Digital Literacy for Government Officials. - Implement digital skills training programs for government officials, focusing on new technologies in service delivery such as AI. Government Officials, IT Specialists UNDP Digital Literacy Program ( UNDP, 2024 ), OECD Public Sector IT Strategy ( OECD, 2024 ) Increased digital competency among public officials Transparent and Accessible Audit Data Governance Policy 1. Transparent and Accessible Public Service Audit Data. - Develop a digital platform that allows citizens to easily access audit data. - Ensure accessibility for people with disabilities, such as incorporating voice and language translation features. Auditors, Technology Developers, Public Administrators WCAG Standards ( World Wide Web Consortium, 2018 ), Denmark Digital Public Audit Systems ( European Commission, 2021 ) Improved accessibility, enhanced transparency and public trust 2. Developing Technology for Accessible Audit Processes. - Utilize AI and Big Data in auditing to enhance accuracy and efficiency. - Develop a digital system that enhances speed, transparency, and inclusivity in audit processes. Government Auditors, Data Analysts World Bank AI in Public Finance Initiative ( World Bank, 2020 ), OECD Big Data Governance ( OECD, 2019b ) Increased accuracy in audits, better decision-making for public sector finance Ethical considerations This study was approved by the Committee for Research Ethics (Social Sciences), Faculty of Social Sciences and Humanities, Mahidol University (MUSSIRB No. 2024/007) on 24 March 2024. The study commenced on 11 April 2024. Ethical compliance was ensured in accordance with the Declaration of Helsinki, the Belmont Report, and CIOMS Guidelines. Given the nature of the study and the anonymization process, the ethics committee approved a waiver of signed informed consent, while ensuring that participants were still adequately informed prior to participation. Participants were provided with comprehensive information regarding the study and their rights; voluntary participation was thus considered as implied consent. To protect participant confidentiality, all personally identifiable data (e.g., gender, age, occupation category, type of disability, and experience level) were removed prior to data analysis and publication. Additionally, province-level data were aggregated into regional classifications to prevent potential re-identification. The de-identified dataset has been made publicly available in Zenodo under a CC-BY 4.0 license. Licensing Data are available under the terms of the Creative Commons Attribution 4.0 International (CC-BY 4.0) license ( https://creativecommons.org/licenses/by/4.0/ ), permitting unrestricted use, distribution, and reproduction, provided the original authorship is properly cited. Data availability Underlying data The dataset supporting the findings of this study is available in Zenodo: https://doi.org/10.5281/zenodo.15075695 ( Chaiyasuk, 2025 ). This repository contains the following underlying data: • De-identified dataset (Dataset_Study.csv) – Raw dataset containing survey responses, anonymized according to Safe Harbor guidelines. • AMOS model file (AMOS_Model.amw) – Structural equation model used in the analysis. • README file (README.txt) – Documentation explaining dataset structure and variable definitions. Extended data The repository also contains the following extended data used in the study: • Survey Questionnaire (Survey_TH.pdf and Survey_EN.pdf ) – This document contains the original Thai version of the survey questionnaire used in the study and an English translation for reference. • Figures ○ Figure 1: Data Governance Factors for Auditing Public Services for People with Disabilities in Thailand Model (Figure 1.TIFF) ○ Figure 2: Multi-Group Causal Models Influencing Audit Data Governance in Public Service Delivery (Figure 2.TIFF) • Tables ○ Table 1: Factors Influencing Audit Data Governance in Public Services for People with Disabilities (Table 1.xlsx) ○ Table 2: Number and Percentage Classified by Characteristics of the Sample Group (Table 2.xlsx) ○ Table 3: Standards and Thresholds for Multi-group MG-SEM (Table 3.xlsx) ○ Table 4: Regression Weights for SEM Model by Public Service Provider and Public Service Recipient (Table 4.xlsx) ○ Table 5: Comparison of Path Coefficients and Model Fit for Structural Weights in MG-SEM (Table 5.xlsx) ○ Table 6: Model Comparison (Table 6.xlsx) ○ Table 7: Policy Recommendations and Practical Guidelines (Table 7.xlsx) Reporting guidelines This study follows the STROBE (Strengthening the Reporting of Observational Studies in Epidemiology) ( von Elm et al., 2007 ) guidelines for reporting observational research. • The STROBE checklist is available at Zenodo: https://doi.org/10.5281/zenodo.15075695 Software availability The MG-SEM analysis in this study was conducted using IBM SPSS AMOS (Version 24.0; IBM Corp., 2016 ; https://www.ibm.com/products/structural-equation-modeling-sem ), while data processing and preparation were performed using IBM SPSS Statistics (Version 25.0; IBM Corp., 2017 ; https://www.ibm.com/products/spss-statistics ). The AMOS model files (.amw) are available in Zenodo at https://doi.org/10.5281/zenodo.15075695 , which require IBM SPSS AMOS to be opened. Due to the proprietary nature of AMOS, these files can only be accessed using the licensed software. Most universities and research institutions provide licensed access to IBM SPSS AMOS. However, for users without access, an open-source alternative is available: Jamovi (Version 2.6.44; https://www.jamovi.org ), which can be used to conduct MG-SEM analysis. The jSEM module in Jamovi, based on the lavaan package in R ( Rosseel, 2012 ), provides an equivalent method for structural equation modeling. The dataset (.csv) used for the analysis is publicly available in Zenodo at https://doi.org/10.5281/zenodo.15075695 ( Chaiyasuk, 2025 ). However, this study does not provide AMOS output reports (.spv) or a lavaan script for replication. Researchers interested in conducting an equivalent analysis using Jamovi or R may refer to the documentation of the jSEM module and lavaan package for guidance ( Rosseel, 2012 ). Acknowledgements We extend our gratitude to the five experts who assessed the questionnaire’s content validity, and to Associate Professor Dr. Poonpong Suksawang for his assistance with the MG-SEM analysis. Our heartfelt thanks also go to all participants, especially People with disabilities, for their time and invaluable contributions to this study. Additionally, OpenAI’s ChatGPT-4 assisted with language refinement, while all intellectual work was solely by the authors. References BMC Public Health: Primary health care challenges: Insights from older people with multimorbidity in Malawi – a qualitative study. BMC Public Health. 2022. Reference Source Byrne BM: Structural equation modeling with AMOS: Basic concepts, applications, and programming. 2nd ed.Routledge; 2010. 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Reference Source Comments on this article Comments (0) Version 2 VERSION 2 PUBLISHED 02 May 2025 ADD YOUR COMMENT Comment Author details Author details 1 Doctor of Public Administration (Public Policy and Public Management), Faculty of Social Sciences and Humanities, Mahidol University, Salaya, Nakhon Pathom, 73170, Thailand 2 Faculty of Social Sciences and Humanities, Mahidol University, Salaya, Nakhon Pathom, 73170, Thailand Sitthisak Chaiyasuk Roles: Conceptualization, Data Curation, Formal Analysis, Investigation, Methodology, Project Administration, Resources, Software, Validation, Visualization, Writing – Original Draft Preparation Krish Rugchatjaroen Roles: Conceptualization, Methodology, Project Administration, Supervision, Writing – Review & Editing Somboon Sirisunhirun Roles: Conceptualization, Methodology, Supervision, Writing – Review & Editing Nopraenue Sajjarax Dhirathiti Roles: Conceptualization, Methodology, Supervision, Writing – Review & Editing Somsak Amornsiriphong Roles: Conceptualization, Methodology, Supervision, Writing – Review & Editing Phut Ploywan Roles: Conceptualization, Methodology, Supervision, Writing – Review & Editing Competing interests No competing interests were disclosed. Grant information The author(s) declared that no grants were involved in supporting this work. Article Versions (2) version 2 Revised Published: 23 Mar 2026, 14:473 https://doi.org/10.12688/f1000research.163271.2 version 1 Published: 02 May 2025, 14:473 https://doi.org/10.12688/f1000research.163271.1 Copyright © 2025 Chaiyasuk S et al . This is an open access article distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Download Export To Sciwheel Bibtex EndNote ProCite Ref. Manager (RIS) Sente metrics Views Downloads F1000Research - - PubMed Central info_outline Data from PMC are received and updated monthly. - - Citations open_in_new 0 open_in_new 0 open_in_new SEE MORE DETAILS CITE how to cite this article Chaiyasuk S, Rugchatjaroen K, Sirisunhirun S et al. Overcoming Data Barriers: Digital Inclusion and Equitable Public Services for People with Disabilities in Thailand [version 1; peer review: awaiting peer review] . F1000Research 2025, 14 :473 ( https://doi.org/10.12688/f1000research.163271.1 ) NOTE: If applicable, it is important to ensure the information in square brackets after the title is included in all citations of this article. COPY CITATION DETAILS track receive updates on this article Track an article to receive email alerts on any updates to this article. TRACK THIS ARTICLE Share Open Peer Review Current Reviewer Status: AWAITING PEER REVIEW AWAITING PEER REVIEW ? Key to Reviewer Statuses VIEW HIDE Approved The paper is scientifically sound in its current form and only minor, if any, improvements are suggested Approved with reservations A number of small changes, sometimes more significant revisions are required to address specific details and improve the papers academic merit. 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