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.

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.

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 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.

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).

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.

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.

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).

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.

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).

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 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.

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.

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.

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.

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.

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.

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.