Bridging the Digital Disability Divide: Determinants of Internet Use among Visually Impaired Individuals in Thailand

Abstract

This study investigates the sociodemographic and contextual determinants influencing Internet usage among individuals with visual impairments in Thailand, contributing to the literature on the digital disability divide. Data from the “Disability Survey 2022” conducted by the National Statistical Office of Thailand were used. Descriptive statistics, chi-square tests, and logistic regression analysis were performed on data from 5621 visually impaired respondents. The findings indicate that approximately 26.88% of individuals with visual impairments use the Internet. The logistic regression analysis highlights several critical disparities. Males exhibit lower odds of Internet use compared with females (adjusted odds ratio [AOR] = 0.850, p = 0.034). Younger individuals are more likely to use the Internet; a decline in use was observed with increasing age (AOR for 60+ years = 0.052, p < 0.001). Regional disparities are evident. Individuals from the northeastern (AOR = 2.044, p < 0.001), central (AOR = 1.356, p < 0.008), and southern (AOR = 1.992, p < 0.001) regions showed higher odds of Internet use compared with those from the northern region. Higher income (AOR for 5000–9999 THB = 1.798, p = 0.001), educational attainment (AOR for bachelor’s degree = 14.915, p < 0.001), and wealth index (AOR for wealthy = 5.034, p < 0.001) increase the likelihood of Internet use. Employed individuals are more likely to use the Internet (AOR = 3.159, p < 0.001) compared with unemployed individuals. Additionally, the severity of the visual impairment is crucial, with those having low vision in both eyes more likely to engage online than those who are completely blind in both eyes (AOR = 5.935, p < 0.001). These findings highlight the need for comprehensive digital inclusion initiatives that address various factors, including age-inclusive digital literacy programs, targeted regional infrastructure development, economic support to improve digital access, and advancements in assistive technologies. This study provides valuable insights for policymakers in Thailand and other developing countries, enhancing the understanding of the digital disability divide and informing strategies to foster greater digital equity.

1. Introduction

In the digital age, Internet access has become integral to modern life, providing unprecedented opportunities for information retrieval, communication, and social participation [1,2]. For individuals with visual impairments, i.e., individuals with varying degrees of vision loss, from mild impairment to complete blindness [3], the Internet is not only a tool but also a vital resource that enables them to engage with the world in ways that would otherwise be challenging. Visually impaired individuals use the Internet for multiple purposes, including information search, communication, socialization, shopping, and education [4]. Nevertheless, the digital landscape presents unique challenges that must be addressed to ensure equitable access and meaningful participation of visually impaired individuals.

The significance of Internet use for individuals with visual impairments is underscored by its potential to enhance their quality of life. Studies have shown that the use of smartphones for communication can reduce negative emotions among visually impaired individuals, although certain activities, such as leisure browsing or information searches, may sometimes lead to increased feelings of depression and loneliness [5]. Social media platforms, such as Facebook, X (formerly Twitter), YouTube, WhatsApp, LinkedIn, Instagram, and Skype, are popular among this demographic, with Facebook being the most widely used [6]. However, the ability of visually impaired individuals to use these platforms effectively is often hampered by their insufficient digital skills; this highlights the need for targeted support to help them acquire higher-level competencies [7].

Accessibility and user experience are critical considerations for visually impaired Internet users. The design and functionality of websites and digital platforms must consider the specific needs of these users to ensure that they can access and use online content without unnecessary barriers. Accessible website design and the involvement of visually impaired individuals in the creation and implementation of accessibility policies and laws are necessary [8,9]. Studies have demonstrated that visually impaired students frequently possess strong digital literacy and are heavy Internet users who are able to use digital media to create content and tutorials [10]. For older visually impaired individuals, in addition to being a tool for creation, the Internet is a lifeline for performing daily tasks, coping with vision impairment, and maintaining social inclusion [11]. The importance of Internet access for individuals with visual impairments is further highlighted by international laws and agreements. Article 9 of the United Nations Convention on the Rights of Persons with Disabilities emphasizes the need for states to ensure that persons with disabilities have equal access to the physical environment, transportation, information, and communications, including information and communication technologies (ICTs) and systems. This access is crucial for the independence and full participation of disabled individuals in all aspects of life.

Globally, visual impairment remains a significant public health issue. According to Steinmetz et al. [12], nearly 600 million people worldwide have difficulty seeing well at a distance, with 43 million people classified as blind and 295 million experiencing moderate to severe vision impairment. An additional 258 million people have mild vision impairment. The International Agency for the Prevention of Blindness projects that the prevalence of visual impairment will continue to increase, which emphasizes the urgent need for effective interventions and support systems. Furthermore, gender disparities are evident in visual impairment prevalence, with women and girls accounting for 55% of those with vision loss [12]. Women are 12% more likely than men to experience vision loss, particularly moderate to severe vision impairment and near vision impairment. Moreover, regional disparities are considerable, with 64% of individuals with vision loss residing in South Asia, Southeast Asia, East Asia, and Oceania. These regions bear a disproportionately high burden of visual impairment relative to their population size, with lower-income regions experiencing higher rates of vision impairment compared with high-income regions. The World Health Organization [13] reported that at least 2.2 billion people globally have near or distance vision impairment; at least 1 billion of these cases are preventable or unaddressed. Conditions causing distance vision impairment or blindness include cataracts, refractive error, age-related macular degeneration, glaucoma, and diabetic retinopathy. The primary cause of near vision impairment is presbyopia. The prevalence of unaddressed vision impairments is significantly higher in low- and middle-income regions; the rates exceed 80% in some areas of sub-Saharan Africa, whereas they are lower than 10% in high-income regions.

In Thailand, visual impairment is a critical public health concern, affecting 658,934 individuals [14]. As previously defined, visually impaired individuals are classified into two main types, namely blind individuals and individuals with low vision. Blind individuals have such significant vision loss that they must rely on tactile and auditory media. The corrected visual acuity of their better eye does not exceed 6/60 (20/200), up to the point of being unable to perceive light. This is referred to as two-eyed blindness and, in Thailand, affects 40,638 individuals, which constitutes 6.17% of the visually impaired population.

Individuals with low vision have vision loss but can read large print with the assistance of devices or accessibility technology. The corrected visual acuity of their better eye does not exceed 6/18 (20/70). This category is further divided into two subtypes: individuals with two-eyed low vision (445,601 individuals or 67.62% of the visually impaired population) and individuals with one-eyed blindness and one-eyed low vision (172,695 individuals or 26.21% of the visually impaired population). Furthermore, the gender distribution among individuals with visual impairments in Thailand reveals a notable disparity, with 57% of those affected being female, highlighting a higher incidence rate compared to their male counterparts, who constitute 43% of the visually impaired population [14].

Visually impaired individuals face a range of challenges in Internet use, which vary depending on the severity of their impairment. First, blind individuals rely on assistive technologies such as screen readers and Braille displays; however, these tools can be costly and often encounter compatibility issues with websites that lack proper accessibility features [15,16]. Navigational difficulties are common, particularly with websites that use visual CAPTCHAs or have complex layouts [17]. Additionally, Voykinska et al. [18] highlighted specific challenges in navigating social media platforms, especially with image-heavy content and rapidly changing interfaces.

Second, those with moderate to severe vision impairment experience visual strain and fatigue, which are exacerbated by poor website designs that lack adequate color contrast and resizable text. These users often rely on screen magnifiers, which may not always suffice [19]. Recent research by Abraham et al. [19] demonstrated that although text and page modifications can improve reading rates, the benefits vary significantly between sighted and visually impaired individuals, underscoring the need for more adaptive solutions.

Third, individuals with one-eyed blindness and one-eyed low vision struggle with depth perception and focus issues, making interaction with website elements challenging. They frequently need to adjust screen settings for optimal viewing, and combining multiple assistive tools can be cumbersome [20]. Recent studies, such as that by Holzinger et al. [21], suggest that emerging technologies (e.g., virtual and augmented reality) present new opportunities and challenges for users with varying degrees of visual impairment. Barriers to accessing online educational content and participating in virtual classrooms have become increasingly prominent across all levels of visual impairment, particularly in the post-COVID-19 era [22]. Despite these challenges, concurrent advancements in artificial intelligence (AI) and assistive technologies offer new solutions to long-standing issues. Kawale et al. [23] developed smart blind sticks that integrate AI with advanced sensor technologies and data processing techniques. These devices provide precise obstacle detection and instantaneous guidance, significantly enhancing the mobility and independence of those with visual impairments. Furthermore, AI-based assistive technologies are transforming the ways visually impaired individuals interact with their environment. Joshi et al. [24] proposed the use of AI-based fully automatic assistive technology capable of recognizing different objects and providing real-time auditory inputs. The system achieved high accuracy in object detection and recognition, potentially revolutionizing the way visually impaired individuals navigate their surroundings. Caytuiro-Silva et al. [25] conducted a systematic review of technological advancements in assistive tools for the visually impaired, which underscored the rapid progress in this field and emphasized the need for ongoing research and development to address the multifaceted challenges faced by this community.

Internet use presents challenges for visually impaired individuals, including the requirement for a high level of digital literacy to effectively use assistive technologies, socioeconomic barriers due to the high cost of accessible devices, and the necessity for educational support to develop effective Internet use skills. Internet accessibility for visually impaired individuals varies considerably between desktop and mobile environments. Desktop computers typically rely on third-party screen reading software such as JAWS or NVDA, which can be expensive, and the use of such software requires substantial training. Furthermore, complex website layouts and inconsistent adherence to web accessibility standards often present challenges to these programs. In contrast, modern mobile operating systems such as iOS and Android have revolutionized accessibility for visually impaired users by incorporating built-in screen reading capabilities. Features such as VoiceOver for iOS and TalkBack for Android provide comprehensive, system-wide screen reading at no additional cost [26,27].

Furthermore, mobile platforms offer additional accessibility features, including magnification gestures, adjustable text sizes, and high-contrast modes, which can be particularly beneficial for users with low vision. Recent advancements in mobile AI technologies have further enhanced accessibility. For instance, machine learning-powered features can now automatically describe images without the need for manually provided alternative text, a capability not typically available on desktop platforms [28]. Mobile devices often provide more intuitive touch-based interfaces, which some visually impaired users find easier to navigate than traditional keyboard and mouse setups.

However, both desktop and mobile environments continue to be crucial for Internet access among visually impaired individuals. Many professional and educational tasks continue to be primarily designed for desktop environments, whereas mobile devices offer unparalleled portability and increasingly sophisticated accessibility features. The choice between desktop and mobile use often depends on factors such as the specific task at hand, personal preference, and visual impairment level. In the context of this study, which examines Internet use among visually impaired individuals in Thailand, the potential influence of these different environments on the patterns of Internet access and use must be considered. Although our data do not explicitly differentiate between desktop and mobile use, understanding these distinctions provides important context for interpreting our findings and identifying areas for future research.

The digital inclusion of visually impaired individuals is not only a technological challenge but also a critical policy issue. As the Internet becomes increasingly central to education, employment, and social participation, policies play a crucial role in shaping the accessibility landscape. In developing countries such as Thailand, where resources may be limited and digital infrastructure is continuously evolving, effective policies can significantly affect the ability of visually impaired individuals to engage with digital technologies [29,30]. Policy decisions influence various aspects of digital accessibility, including the development of assistive technologies, the implementation of website accessibility standards, the provision of digital skills training, and the affordability of Internet access and devices [31,32]. Understanding the factors that influence Internet use among visually impaired individuals can inform these policy decisions, potentially leading to more targeted and effective interventions.

Moreover, as countries strive to achieve the United Nations Sustainable Development Goals, particularly Goals 10 (Reduced Inequalities) and 9 (Industry, Innovation, and Infrastructure), policies promoting digital inclusion for people with disabilities become increasingly important [33]. To contextualize our study within the efforts toward digital inclusion in Thailand, the existing policy landscape and initiatives aimed at enhancing digital accessibility for visually impaired individuals must be understood. The legal foundation for these efforts is established in the Constitution of the Kingdom of Thailand B.E. 2560 (2017) [34], which guarantees the rights of persons with disabilities. This is further reinforced by the Persons with Disabilities Empowerment Act B.E. 2550 (2007) [3] and its subsequent amendments, which specifically address the rights to access information, communication, and technology.

The Thai government has implemented several key initiatives to promote digital inclusion. The Thailand Digital Economy and Society Development Plan [35] outlines strategies for using digital technologies to enhance the quality of life for all citizens, including those with disabilities. In the education sector, Thailand has adopted policies promoting inclusive education and digital literacy for visually impaired students. These include provisions for assistive technology in schools and universities, although implementation varies across institutions. For employment support, programs focused on digital skills training for visually impaired job seekers and incentives for employers to provide accessible work environments have been established [36].

However, the effectiveness and reach of these programs require further research. Moreover, the government collaborates with the private sector to develop and provide assistive technologies. Telecommunication regulations require companies to offer accessible services, and some providers offer reduced rates for individuals with disabilities [37]. Nongovernmental organizations, particularly the Thailand Association of the Blind, are crucial in advocating for and implementing digital accessibility initiatives. Their efforts complement government programs and often provide targeted support to the visually impaired community [38]. Despite these policies and programs, challenges persist in achieving comprehensive digital inclusion for visually impaired individuals in Thailand. Our study aims to contribute to this ongoing effort by examining the factors influencing Internet use among this population, informing future policy directions and interventions.

Given the critical role of the Internet in the lives of visually impaired individuals and the significant challenges they face, the factors influencing Internet use among this population in Thailand must be explored. This study aims to address this by investigating the sociodemographic and contextual determinants that affect Internet use among individuals with visual impairments. Therefore, the following research question is specifically addressed:

RQ: Which sociodemographic and contextual factors influence Internet use among individuals with visual impairments in Thailand, and how can these insights inform policies to promote digital inclusion?

These factors must be understood to develop targeted interventions and policies that can bridge the digital divide and promote digital inclusivity. By examining the specific challenges and barriers faced by visually impaired individuals in accessing and using the Internet, this research contributes valuable insights that can enhance the quality of life and social participation of this marginalized group, especially in the context of developing countries.

2. Literature Review

2.1. Concept of the Digital Disability Divide

The digital disability divide refers to the disparities in access to, use of, and benefits from information and communication technologies (ICTs) between individuals with and without disabilities or within the disability group. This divide encompasses a range of issues, from having physical access to the Internet and devices to possessing the digital skills to use digital tools and resources effectively. Moreover, the digital disability divide involves a deeper analysis of how different aspects of digital technology are used and experienced by people with disabilities. Vicente and López [39] highlighted the importance of moving beyond the binary classification of “haves” and “have-nots.” Instead, they proposed examining the divide across various dimensions, including affordability, motivation and attitudes, skills, and usage patterns. This broader perspective is essential because the barriers faced by individuals with disabilities in accessing and using ICT are complex and multifaceted.

Affordability is a significant dimension of the digital disability divide. Individuals with disabilities often face higher costs associated with acquiring and using technology. In addition to the price of hardware and software, these costs include the expenses related to assistive technologies such as screen readers, Braille displays, and specialized input devices [40,41,42]. Moreover, a study by Vicente and López [39] found that the socioeconomic constraints under which many individuals with disabilities live pose a major barrier to accessing and using ICT. In many cases, these individuals have limited financial resources, making it challenging to afford the necessary technology and services.

Furthermore, motivation and attitudes toward technology are crucial in the digital disability divide. For individuals with disabilities, the perceived usefulness and ease of use of ICT can significantly influence their willingness to adopt and engage with digital tools [43,44]. Negative attitudes toward technology, often stemming from past negative experiences or a lack of confidence in using digital device can further widen the divide [45].

Skills are another critical factor in the digital disability divide. The ability to effectively use ICT depends on having the necessary digital skills. Individuals with disabilities often require specialized training to use assistive technologies and adapt to digital environments. However, access to such training is frequently limited, particularly in low-income regions [46,47]. Furthermore, several studies noted that a lack of digital skills can significantly hinder the ability of individuals with disabilities to benefit from the opportunities provided by the Internet and other digital platforms [39,48,49].

In addition, the usage patterns of individuals with disabilities significantly differ from those of individuals without disabilities. Dobransky and Hargittai [50] found that although people with disabilities face many barriers to accessing the online world, once they are online, they engage in a wide range of activities. These include searching for health information, communicating with others, and using government services. However, their use is often focused on addressing specific needs related to their disabilities. For instance, compared with individuals without disabilities, those with disabilities are more likely to look for information about health and government services, play games, and make phone calls online. This indicates that the Internet serves as a crucial resource for addressing the unique challenges faced by people with disabilities.

2.2. Factors Related to the Digital Disability Divide

Various factors contribute to the digital disability divide, including socioeconomic status, educational attainment, technological accessibility, and social and cultural barriers. These elements collectively create a complex landscape where individuals with disabilities face significant challenges in accessing and using digital technologies.

Socioeconomic constraints significantly impact the ability of individuals with disabilities to access and use ICT. Vicente and López [39] highlighted that many people with disabilities live under challenging economic conditions, which limit their ability to afford the necessary technologies. Similarly, Sachdeva et al. [51] noted that the financial situation is a crucial yet underexplored factor in the digital disability divide. Lower-income individuals are less likely to own computers or have Internet connections, exacerbating the divide. The costs associated with acquiring and maintaining digital devices, as well as the additional expenses for assistive technologies, such as screen readers, Braille displays, and specialized input devices, pose substantial financial burdens. As a result, individuals with disabilities in lower socioeconomic brackets face compounded disadvantages, making it more difficult for them to access digital tools and resources.

Education factors play a pivotal role in digital literacy and the ability to navigate digital environments. Individuals with higher educational attainment generally possess better digital skills and confidence in using technology. However, people with disabilities often face barriers to education, which, in turn, affects their digital capabilities. Van der Geest et al. [7] emphasized that educational disparities contribute to the digital disability divide by limiting opportunities for individuals with disabilities to develop essential ICT skills. Access to quality education and specialized training programs is often inadequate, particularly in regions with limited resources [47,52]. Consequently, individuals with disabilities may lack the foundational knowledge needed to effectively use digital technologies, further widening the divide.

Technological barriers are among the most direct contributors to the digital disability divide. Inaccessible website designs, a lack of assistive technologies, and compatibility issues hinder the ability of people with disabilities to engage with digital content. Henkelmann and Fertig [8] emphasized the need for inclusive design practices and the involvement of disabled individuals in creating and implementing accessibility policies. Many digital platforms do not comply with accessibility standards, making it difficult for people with disabilities to use them effectively [46]. For instance, websites without proper labeling for screen readers, inadequate color contrast, and non-resizable text present significant challenges [53,54]. Moreover, the pace of technological advancements often exceeds that of the development and implementation of accessibility features, leaving individuals with disabilities struggling to keep up.

Social and cultural attitudes toward disability significantly impact digital inclusion. Stigmatization and discrimination may discourage individuals with disabilities from engaging with technology or seeking help to improve their digital skills. Shethia and Techatassanasoontorn [9] stated that policymakers and developers often lack awareness and understanding of the specific needs of disabled users. This lack of sensitivity and inclusion in the design and development processes leads to the creation of inaccessible technologies and services. Cultural perceptions of disability can also influence the motivation and confidence of individuals with disabilities to pursue digital literacy [55]. Negative stereotypes and low expectations can diminish their self-efficacy, further hindering their ability to engage with ICT [56,57,58].

Therefore, the digital disability divide is shaped by a multitude of interrelated factors that collectively hinder the ability of individuals with disabilities to participate fully in the digital society. Narrowing this divide requires a comprehensive understanding of the unique challenges posed by socioeconomic status, educational attainment, technological accessibility, and social and cultural barriers. Exploring these interconnected issues is crucial in developing effective strategies that promote digital inclusion and in ensuring equitable access to ICTs for individuals with disabilities.

2.3. Empirical Studies on the Digital Disability Divide

In recent decades, several empirical studies on the digital disability divide have significantly advanced the understanding of the barriers and challenges faced by individuals with disabilities in accessing and using ICTs. These studies can be grouped into several thematic areas, including analyses of the digital disability divide within the population with disabilities, specific disability groups, and visually impaired individuals.

Past studies provided a broad overview of the digital disability divide within the population with disabilities. Duplaga [59] conducted a comprehensive analysis of factors determining Internet usage among people with disabilities in Poland. Using data from the 2013 “Social Diagnosis” survey, the study found that predictors of Internet use among individuals with disabilities included the degree of disability, place of residence, education level, marital status, occupational status, net income, use of healthcare services, and use of cell phones. The findings indicate that people with disabilities face a significant digital divide, similar to the general population, with age and income being common predictors of online activities such as accessing the websites of public institutions, checking and sending emails, and publishing content online. This study underscored the multifaceted nature of the digital divide, which is influenced by both socioeconomic and individual factors. Johansson et al. [60] extended this line of inquiry by examining Internet use among people with disabilities in Sweden, one of the most digitalized countries. Their cross-sectional survey adapted nationwide questions for individuals with cognitive disabilities and involved 771 participants across 35 diagnoses or impairments. The study found that larger proportions of individuals with autism, ADHD, and bipolar disorder reported using the Internet compared with other disability groups. Notably, women with autism used the Internet most frequently, whereas women with aphasia used it the least. The study highlighted significant differences in digital inclusion between subgroups, emphasizing the need to investigate the digital disability divide by subgroups rather than as a homogeneous entity.

Several studies have focused on specific disability groups to uncover unique challenges and usage patterns. For example, Gell et al. [61] explored technology use among older adults with and without disabilities in the United States. Their study found that technology use was significantly associated with sociodemographic factors such as younger age, male sex, white race, higher education level, and being married. Importantly, technology use decreased with greater physical limitations and disability; vision impairment and memory limitations also contributed to a lower likelihood of technology use. Additionally, this research highlighted the intersection of aging and disability, pointing to specific barriers faced by older adults. Mengual-Andrés et al. [62] conducted a bibliometric analysis of Internet use by people with intellectual disabilities, revealing a growing academic interest in this area. The study found that recent publications have focused on usability, online activities, and the benefits and risks of Internet use for this group. Anrijs et al. [63] further examined within-group differences in Internet use among people with intellectual disabilities in Flanders. Their face-to-face survey revealed that, compared with older individuals, younger individuals with intellectual disabilities had more access, used the Internet for more diverse purposes, and reported higher levels of skills and support. The study emphasized the heterogeneity within disability groups and the need for tailored interventions.

Empirical studies on the digital disability divide among visually impaired individuals highlight a spectrum of challenges and potential solutions in accessing and using the Internet and digital technologies. For example, Oppenheim and Selby [64] emphasized the pivotal role of web design in either facilitating or impeding the access of blind and visually impaired users. Their investigation into how search engines present information to visually impaired users revealed that adherence to simple web design guidelines could significantly enhance accessibility. This foundational work underscores the importance of thoughtful design in mitigating access barriers. Then, Williamson et al. [65] explored the role of the Internet in information provision for blind and visually impaired individuals, contrasting it with traditional access methods like print. They found that traditional forms posed significant challenges, positioning the Internet as a crucial tool for overcoming these barriers. This highlights the transformative potential of digital technologies in providing accessible and comprehensive information to visually impaired users. Complementing these works, Shethia and Techatassanasoontorn [9] conducted a comprehensive literature review, identifying persistent challenges related to inaccessible content. They argued that web designers must implement proper guidelines, thereby addressing digital exclusion and fostering a more inclusive digital environment.

Hafiar et al. [66] explored the Internet use patterns of visually impaired students, demonstrating that assistance programs enable access to various online platforms, such as WhatsApp, YouTube, and Google. Despite these advancements, the study identified ongoing obstacles that require further technological innovation and robust institutional support. Fuentes et al. [67] extended this discussion by examining ICT usability among visually impaired individuals in Spain, revealing the vital role of ICTs in promoting autonomy and social inclusion. However, they also noted significant misinformation among users outside the support of organizations such as The National Organization of the Spanish Blind (ONCE) and emphasized the need for professional training to improve ICT literacy and usability.

The accessibility of health information remains a critical concern, as demonstrated by Hewitt and He [68]. Their evaluation of US state and territory COVID-19 websites revealed widespread inaccessibility, underscoring the need for improved web design to ensure equitable access to crucial health information. Similarly, Choi et al. [69] examined Internet and health information technology use among older adults with vision impairment and found that this group was less likely to engage with these technologies. Their work highlights the importance of targeted interventions to narrow the digital divide and improve health outcomes for older adults with visual impairments. A recent study by Pettersson et al. [70] focused on the accessibility of e-health services in Sweden, highlighting significant disparities in accessibility based on the type of impairment. Their study reveals an e-health disability digital divide, emphasizing the need for tailored e-health solutions that address the specific needs of different impairments. They call for more inclusive e-health strategies to ensure that visually impaired individuals can effectively access and benefit from digital health services.

Despite the significant progress, several gaps remain in empirical research on the digital disability divide. First, more context-specific studies are needed, particularly in low- and middle-income countries where socioeconomic constraints and infrastructural challenges are pronounced. Second, although many studies have highlighted the barriers faced by individuals with disabilities, research on the effectiveness of interventions aimed at bridging the digital divide is insufficient. Third, most studies focus on access and basic usage, with less attention given to the quality of digital engagement and the specific outcomes of Internet use for individuals with disabilities. This research aims to address some of these gaps by investigating the factors influencing Internet use among individuals with visual impairments in Thailand. By focusing on a specific disability group within a particular socioeconomic and cultural context, this study seeks to provide deeper insights into the digital disability divide and inform targeted interventions for the promotion of digital inclusion.

2.4. Hypothesis Development

The digital disability divide, as discussed in the literature, highlights the multifaceted challenges that visually impaired individuals face in accessing and using ICTs. This divide is influenced by a range of sociodemographic and contextual factors, including age, education, income, the severity of impairment, geographic location, and employment status. Drawing on the theoretical framework provided by the literature review, the following hypotheses are developed to explore the determinants of Internet use among visually impaired individuals in Thailand. These hypotheses are tested using statistical methods, including chi-square tests to assess the relationships between categorical variables and Internet use and logistic regression analysis to estimate the odds of Internet use based on the hypothesized determinants.

Gender is a well-documented factor influencing Internet use and digital inclusion, with research indicating that males and females often experience different levels of access, skill, and engagement with digital technologies. The literature on the digital divide has consistently shown that males are more likely to use the Internet than females across various contexts—a trend that extends to the population of individuals with disabilities [61,71]. For visually impaired individuals, these gender-based differences may be even more pronounced. Studies have shown that visually impaired males tend to have greater access to education and employment opportunities, which, in turn, enhance their digital literacy and familiarity with ICTs [72,73]. Therefore, we posit the following hypothesis (H1):

Hypothesis 1 (H1): 

Males are more likely to use the Internet compared to females among visually impaired individuals.

Age is a significant factor in determining Internet use, with younger individuals generally being more adept at using digital technologies. The literature indicates that younger people are more likely to engage with ICTs due to their higher levels of digital literacy and greater exposure to technology from an early age [74,75,76]. In the context of visually impaired individuals, younger people may also be more motivated to use the Internet to overcome barriers posed by their impairments, such as accessing information and maintaining social connections [59,61,63]. Therefore, we posit the following hypothesis (H2):

Hypothesis 2 (H2): 

Older visually impaired individuals are less likely to use the Internet compared to younger individuals.

Geographic location plays a crucial role in determining access to digital technologies, particularly in a country like Thailand, where regional disparities in infrastructure and socioeconomic development are pronounced. The literature suggests that individuals living in more developed regions with better infrastructure and resources are more likely to have access to the Internet. In contrast, those in rural or less developed regions may face significant barriers, including limited access to technology and lower availability of assistive services [37,77,78]. Therefore, we posit the following hypothesis (H3):

Hypothesis 3 (H3): 

There are significant regional disparities in Internet use among visually impaired individuals in Thailand.

Socioeconomic status is a critical determinant of access to digital technologies, with income and wealth playing distinct but complementary roles. Income refers to the flow of money received regularly from work, investments, or other sources. It directly affects an individual’s ability to afford necessary technologies and Internet services on an ongoing basis [79]. In contrast, wealth represents the total value of assets owned, such as property, characteristics of housing, water and sanitation facilities, and other attributes related to household wealth [14]. Wealth provides a financial cushion that can support significant expenditures, such as purchasing high-cost assistive technologies like screen readers and Braille displays [80,81,82]. The literature consistently shows that individuals with higher incomes are more likely to afford the necessary technologies and services required for Internet access [51,59]. For visually impaired individuals, the cost of assistive technologies can be prohibitive, making Internet use more accessible to those with greater financial resources, whether through higher income or accumulated wealth [40,41].

Hypothesis 4a (H4a): 

Higher income is positively associated with Internet use among visually impaired individuals.

Hypothesis 4b (H4b): 

Greater wealth is positively associated with Internet use among visually impaired individuals.

Education plays a pivotal role in equipping individuals with the digital skills necessary to navigate online environments effectively. The literature suggests that individuals with higher educational attainment are more likely to possess the confidence and ability to use digital technologies [7,47]. For visually impaired individuals, education is particularly crucial, as it often includes training in the use of assistive technologies that enable access to digital platforms [46]. As a result, we posit the following hypothesis (H5):

Hypothesis 5 (H5): 

Visually impaired individuals with higher levels of education are more likely to use the Internet compared to those with lower levels of education.

Employment status is another critical factor influencing Internet use. The literature indicates that employed individuals are more likely to use the Internet, both for work-related tasks and personal activities [83]. For visually impaired individuals, employment may also provide access to resources such as assistive technologies and training to facilitate Internet use [80,84]. Moreover, the need to remain connected for professional purposes may drive higher Internet usage among employed individuals. Therefore, we posit the following hypothesis (H5):

Hypothesis 6 (H6): 

Employed visually impaired individuals are more likely to use the Internet compared to unemployed individuals.

Residence is a crucial factor influencing Internet use, with significant disparities observed between urban and rural areas. Urban areas typically offer better infrastructure, including more reliable and faster Internet connections, greater availability of digital services, and easier access to technology and support services [30,85,86]. For visually impaired individuals, residing in an urban area may provide greater opportunities to engage with digital technologies due to the proximity to educational institutions, assistive technology providers, and community support networks. In contrast, rural areas often suffer from limited Internet access, fewer educational opportunities, and less availability of assistive technologies, all of which can hinder Internet use among visually impaired residents [87,88,89,90]. Therefore, we posit the following hypothesis (H7):

Hypothesis 7 (H7): 

Visually impaired individuals residing in urban areas are more likely to use the Internet compared to those in rural areas.

The severity of visual impairment significantly impacts an individual’s ability to use the Internet. The literature indicates that individuals with less severe impairments are better able to engage with digital technologies, as they may require fewer adaptations or assistive devices [91,92]. Those with more severe impairments, such as blindness, face greater challenges, including reliance on expensive and complex assistive technologies [80,81,93]. Consequently, we posit the following hypothesis:

Hypothesis 8 (H8): 

Individuals with less severe visual impairments are more likely to use the Internet compared to those with more severe impairments.

3. Materials and Methods

3.1. Data Source and Sample Selection

The primary data source for this study was the “Disability Survey 2022”, which was conducted by the National Statistical Office of Thailand. This survey, which has been conducted every five years since 2002, collected comprehensive information on various health and disability issues affecting both children and adults. The 2022 survey was the fifth iteration and included data on difficulties in self-care, the impact of health problems on daily life, educational attainment, employment status, disability registration, access to state assistance (such as disability allowances and assistive devices), computer use, and caregiver information.

This survey was conducted nationwide from October to December 2022. Prior to initiating the data collection process, the National Statistical Office held a briefing session for the field staff. The purpose of this session was to ensure that the data collectors were well-versed in the questionnaires, definitions, scope, and relevant observations. Additionally, it addressed issues and obstacles encountered in previous surveys, as well as potential challenges anticipated for this survey. Data collection involved interviewers conducting household interviews with the sample members and recording the information on medium-sized portable computers (tablets). Once the accuracy and completeness of the data were verified, the information was transmitted to the central office for further processing [14].

In the 2022 survey, 88,273 households were selected for data collection (excluding vacant houses, demolished properties, and those affected by fire); 82,184 households were eligible, and 79,615 households responded, resulting in a response rate of 96.9%. From the main dataset, we filtered data specifically for individuals with visual impairments, categorized into the following three groups: (1) completely blind in both eyes, (2) low vision in both eyes, and (3) blind in one eye and low vision in the other. The sample size for individuals with visual impairments totaled 5621 respondents.

3.2. Dependent Variable

The primary dependent variable in this study was Internet use. This binary variable indicated whether respondents used the Internet in the 12 months prior to the interview. The variable was derived from the following survey question: “During the 12 months prior to the interview, did [name] use the Internet?”. It was coded as 1 for respondents who reported using the Internet and 0 for those who did not. This variable provided a clear measure of general Internet engagement among individuals with visual impairments in Thailand. It is important to note that our data on Internet use potentially encompass both desktop and mobile use, without distinction. Respondents who answered affirmatively could have been referring to Internet access via desktop computers, laptops, smartphones, tablets, or any combination of these devices. While this approach provides a comprehensive view of Internet use among visually impaired individuals, capturing their full spectrum of digital engagement, it also presents certain limitations. We cannot draw conclusions about the prevalence or preferences for desktop versus mobile Internet use among our study population. Moreover, given the significant differences in accessibility features between desktop and mobile platforms, as discussed in the Introduction, the experiences of Internet users in our study may vary widely depending on their primary mode of access. While this broad approach to measuring Internet use allows us to capture overall digital engagement, it also highlights an area for potential future research. More granular data on the types of devices used for Internet access could provide valuable insights into the specific challenges and preferences of visually impaired Internet users in Thailand.

3.3. Independent Variables

The independent variables in this study are critical for understanding the factors influencing Internet use among individuals with visual impairments in Thailand. These variables included gender, age, region, income, education level, employment status, residence, level of visual impairment, and wealth index. Each variable was operationally defined to ensure precise measurement and analysis (

Table 1. Operational definitions of independent variables.

Independent Variables	Categories/Operational Definitions
Gender	0: Female 1: Male
Age	1: 9–19 years (Children and Teenagers) 2: 20–29 years (Young Adults) 3: 30–39 years (Adults) 4: 40–49 years (Middle-Aged Adults) 5: 50–59 years (Pre-retirement Age) 6: 60+ years (Senior Citizens)
Region	1: Northern 2: Northeastern 3: Central 4: Southern
Income	0: Less than 5000 THB 1: 5000–9999 THB 2: 10,000–14,999 THB 3: 15,000–29,999 THB 4: 30,000–45,000 THB 5: More than 45,000 THB
Education Level	0: Below primary education 1: Lower primary education 2: Primary school 3: Lower secondary education 4: Upper secondary education 5: Vocational 6: Post-secondary education 7: Bachelor’s degree 8: Higher than a bachelor’s degree
Employment	0: Unemployed 1: Employed
Residence	0: Rural area 1: Urban area
Level of Visual Impairment	1: Completely blind in both eyes 2: Low vision in both eyes 3: Blind in one eye and low vision in the other
Wealth Index	1: Very Poor 2: Poor 3: Middle 4: Wealthy 5: Very Wealthy

).

Gender was categorized as a binary variable, with 0 representing females and 1 representing males. This classification allowed the study to explore any gender-based differences in Internet usage among the visually impaired. Age was segmented into the following six categories: 9–19 years (children and teenagers), 20–29 years (young adults), 30–39 years (adults), 40–49 years (middle-aged adults), 50–59 years (pre-retirement age), and 60+ years (senior citizens). These age groups facilitated the examination of age-related trends in Internet use.

Education level ranged from below primary education to higher than a bachelor’s degree, encompassing the following eight categories: below primary education, lower primary education, primary school, lower secondary education, upper secondary education, vocational, post-secondary education, bachelor’s degree, and higher than a bachelor’s degree. This variable was used to examine the role of educational attainment in Internet use. Employment was a binary variable, where 0 denoted unemployed individuals and 1 denoted employed individuals. This variable evaluated the influence of employment status on Internet usage patterns, providing insights into how occupational engagement affects digital behavior.

Residence was distinguished between rural and urban areas, coded as 0 for rural areas and 1 for urban areas. This distinction was vital for understanding the urban–rural divide in Internet accessibility and usage. Level of visual impairment was categorized into the following three groups: completely blind in both eyes, low vision in both eyes, and blind in one eye and low vision in the other. This classification helped investigate how different levels of visual impairment affect Internet use, offering a nuanced understanding of the challenges faced by each group. Finally, the wealth index was divided into the following five categories: very poor, poor, middle, wealthy, and very wealthy. This variable is essential for assessing the impact of economic resources on digital inclusion, indicating how financial capability influences access to and use of the Internet.

3.4. Statistical Analysis

The statistical analysis for this study followed a structured approach to thoroughly examine the factors influencing Internet use among individuals with visual impairments in Thailand. Initially, descriptive statistics were employed to detail the characteristics of the study population. Frequencies and percentages were calculated to illustrate the distribution of participants across various categories, including gender, age, region, income, education level, employment status, residence, level of visual impairment, and wealth index.

We initially employed the chi-square test of independence to assess the relationships of categorical variables such as gender, region, and employment status with Internet use (the dependent variable). This test was chosen to examine the associations outlined in the hypotheses (H1–H8). For example, the chi-square test was applied to analyze the relationships of gender (H1), age group (H2), and region (H3) with Internet use. This method allowed us to determine whether the observed differences in Internet usage across various sociodemographic groups were statistically significant.

Following this, we used logistic regression as the primary statistical method to explore the determinants of Internet use among visually impaired individuals. This analysis, aligned with our hypotheses (H1–H8), produced adjusted odds ratios (AORs), along with robust standard errors, 95% confidence intervals, and p values for each independent variable. Logistic regression was particularly suitable for this study, as it handles a binary dependent variable (Internet use vs. non-use) and estimates the probability of Internet use based on various predictors.

Results are presented with a focus on statistical significance, as denoted by p values; the standard thresholds for significance (p < 0.05, p < 0.01, and p < 0.001) were maintained. Significant findings are highlighted to underscore the strength and reliability of the associations between the independent variables and Internet use among visually impaired individuals.

4. Results

4.1. Characteristics of Individuals with Visual Impairments Based on Internet Use

Table 2. Characteristics of individuals with visual impairments in Thailand based on Internet use.

Characteristics	All n = 5621 (100%)	Used Internet n = 1511 (26.88%)	Did Not Use Internet n = 4110 (73.12%)	p Value (Pearson’s Chi-Squared Test)
Gender				<0.001
Male	2487 (44.25%)	730 (12.99%)	1757 (31.26%)
Female	3134 (55.75%)	781 (13.89%)	2353 (41.86%)
Age				<0.001
9–19 years (Children and Teenagers)	498 (8.85%)	491 (8.73%)	7 (0.12%)
20–29 years (Young Adults)	164 (2.92%)	159 (2.83%)	5 (0.09%)
30–39 years (Adults)	208 (3.70%)	203 (3.61%)	5 (0.09%)
40–49 years (Middle-Aged Adults)	346 (6.16%)	341 (6.07%)	5 (0.09%)
50–59 years (Pre-Retirement Age)	313 (5.57%)	308 (5.48%)	5 (0.09%)
60+ years (Senior Citizens)	4092 (72.80%)	3107 (55.28%)	985 (17.52%)
Region				<0.001
Northern	1267 (22.54%)	243 (4.32%)	1024 (18.22%)
Northeastern	2086 (37.11%)	572 (10.18%)	1514 (26.93%)
Central	1234 (21.95%)	369 (6.56%)	865 (15.39%)
Southern	1034 (18.40%)	327 (5.82%)	707 (12.58%)
Income				<0.001
Less than 5000 THB	5142 (91.48%)	1173 (20.87%)	3969 (70.61%)
5000–9999 THB	248 (4.41%)	161 (2.86%)	87 (1.55%)
10,000–14,999 THB	103 (1.83%)	77 (1.37%)	26 (0.46%)
15,000–29,999 THB	102 (1.82%)	78 (1.39%)	24 (0.43%)
30,000–45,000 THB	17 (0.30%)	14 (0.25%)	3 (0.05%)
More than 45,000 THB	9 (0.16%)	8 (0.14%)	1 (0.02%)
Education Level				<0.001
Below primary education	843 (15.00%)	94 (1.67%)	749 (13.33%)
Lower primary education	3602 (64.08%)	730 (12.99%)	2872 (51.09%)
Primary school	606 (10.78%)	312 (5.55%)	294 (5.23%)
Lower secondary education	210 (3.74%)	118 (2.10%)	92 (1.64%)
Upper secondary education	154 (2.74%)	106 (1.89%)	48 (0.85%)
Vocational	51 (0.91%)	32 (0.57%)	19 (0.34%)
Post-secondary education	37 (0.66%)	22 (0.39%)	15 (0.27%)
Bachelor’s degree	103 (1.83%)	84 (1.49%)	19 (0.34%)
Higher than bachelor’s degree	15 (0.27%)	13 (0.23%)	2 (0.04%)
Employment				<0.001
Unemployed	4475 (79.61%)	861 (15.32%)	3614 (64.29%)
Employed	1146 (20.39%)	650 (11.56%)	496 (8.83%)
Residence				<0.001
Rural	3112 (55.36%)	764 (13.59%)	2348 (41.77%)
Urban	2509 (44.64%)	747 (13.29%)	1762 (31.35%)
Levels of Visual Impairments				<0.001
Completely blind in both eyes	782 (13.91%)	69 (1.23%)	713 (12.68%)
Low vision in both eyes	3123 (55.56%)	919 (16.35%)	2204 (39.21%)
Blind in one eye and low vision in the other	1716 (30.53%)	523 (9.30%)	1193 (21.23%)
Wealth Index				<0.001
Very poor	2035 (36.20%)	248 (4.41%)	1787 (31.79%)
Poor	1288 (22.91%)	385 (6.85%)	903 (16.06%)
Middle	1138 (20.25%)	400 (7.12%)	738 (13.13%)
Wealthy	812 (14.45%)	308 (5.48%)	504 (8.97%)
Very wealthy	348 (6.19%)	170 (3.02%)	178 (3.17%)

provides a comprehensive overview of the characteristics of 5621 individuals with visual impairments in Thailand segmented by their Internet use. Among the participants, 1511 individuals (26.88%) reported using the Internet, whereas 4110 individuals (73.12%) did not.

Regarding gender, there was a significant difference in Internet use (p < 0.001). Of the total participants, 2487 (44.24%) were male, and 3134 (55.76%) were female. Among Internet users, 730 (12.99%) were male, and 781 (13.89%) were female, indicating a relatively balanced distribution between genders among Internet users. Age also showed a significant impact on Internet use (p < 0.001). The majority of Internet users were senior citizens aged 60+ years (72.80%), with 3107 (55.28%) of them using the Internet. Other age groups had the following lower usage rates: children and teenagers, 8.73%; middle-aged adults, 6.07%; pre-retirement-age individuals, 5.48%; adults, 3.61%; and young adults, 2.92%.

Regional differences in Internet use were significant (p < 0.001). The northeastern region had the highest proportion of individuals with visual impairments (37.11%), followed by the northern (22.54%), central (21.95%), and southern (18.40%) regions. Notably, the northeastern region had 572 Internet users (10.18%), followed by the central (6.56%), southern (5.82%), and northern (4.32%) regions. Income level was another factor with significant differences in Internet use (p < 0.001). The majority of individuals with visual impairments had an income of less than 5,000 THB (91.48%), with 1173 Internet users (20.87%) in this bracket. Other income brackets showed the following lower usage rates: 5,000–9,999 THB, 2.86%; 10,000–14,999 THB, 1.37%; 15,000–29,999 THB, 1.39%; 30,000–45,000 THB, 0.25%; and more than 45,000 THB, 0.14%.

The education level of participants also showed significant differences in Internet use (p < 0.001). Most Internet users had lower primary education (12.99%), followed by primary school (5.55%), lower secondary education (2.10%), upper secondary education (1.89%), below primary education (1.67%), a bachelor’s degree (1.49%), vocational education (0.57%), post-secondary education (0.39%), and higher than a bachelor’s degree (0.23%). Employment status exhibited significant differences in Internet use among individuals with visual impairments (p < 0.001). Of the total participants, 4475 (79.61%) were unemployed, whereas 1146 (20.39%) were employed. Among those who used the Internet, 861 individuals (15.32%) were unemployed, and 650 individuals (11.56%) were employed, indicating that employment status plays a critical role in Internet use.

Residence also showed significant differences in Internet use between rural and urban areas (p < 0.001). Of the total participants, 3112 individuals (55.36%) resided in rural areas, whereas 2509 individuals (44.64%) lived in urban areas. Among those who used the Internet, 764 individuals (13.59%) were from rural areas, and 747 individuals (13.29%) were from urban areas. The level of visual impairment significantly influenced Internet use in our sample (p < 0.001). Individuals with low vision in both eyes (16.35%) were the largest group of Internet users, followed by those with one-eye blindness and one-eye low vision (9.30%) and those blind in both eyes (1.23%).

The wealth index revealed significant differences in Internet use among individuals with visual impairments (p < 0.001). The majority of Internet users were from the middle (7.12%) and poor (6.85%) categories, with fewer users in the wealthy (5.48%), very poor (4.41%), and very wealthy (3.02%) categories. These findings highlight the various sociodemographic factors that influence Internet use among individuals with visual impairments in Thailand, providing insights into the digital divide within this population.

4.2. Factors Influencing Internet Use among Individuals with Visual Impairments in Thailand

Table 3. Factors influencing Internet use among individuals with visual impairments in Thailand (results of logistic regression analysis).

	Adjusted Odds Ratio (Robust SE)	95% CI	p Value	Sig.
Gender
Female (ref.)	1.00	1.00
Male	0.850 (0.065)	0.731–0.987	0.034	*
Age
9–19 years (Children and Teenagers) (ref.)	1.00	1.00
20–29 years (Young Adults)	0.380 (0.187)	0.145–0.995	0.049	*
30–39 years (Adults)	0.220 (0.100)	0.090–0.535	0.001	**
40–49 years (Middle-Aged Adults)	0.155 (0.067)	0.066–0.360	<0.001	***
50–59 years (Pre-Retirement Age)	0.153 (0.064)	0.068–0.346	<0.001	***
60+ years (Senior Citizens)	0.052 (0.021)	0.023–0.115	<0.001	***
Region
Northern (ref.)	1.00	1.00
Northeastern	2.044 (0.213)	1.665–2.508	<0.001	***
Central	1.356 (0.155)	1.084–1.696	0.008	**
Southern	1.992 (0.233)	1.583–2.506	<0.001	***
Income
Less than 5000 THB (ref.)	1.00	1.00
5000–9999 THB	1.798 (0.318)	1.270–2.544	0.001	**
10,000–14,999 THB	1.926 (0.537)	1.115–3.327	0.019	*
15,000–29,999 THB	1.673 (0.457)	0.979–2.858	0.060
30,000–45,000 THB	1.801 (1.292)	0.442–7.349	0.412
More than 45,000 THB	2.186 (2.120)	0.327–14.631	0.420
Education Level
Below primary education (ref.)	1.00	1.00
Lower primary education	1.590 (0.212)	1.225–2.064	<0.001	***
Primary school	2.963 (0.481)	2.157–4.072	<0.001	***
Lower secondary education	3.601 (0.757)	2.385–5.437	<0.001	***
Upper secondary education	7.732 (1.762)	4.947–12.086	<0.001	***
Vocational	6.865 (2.262)	3.599–13.096	<0.001	***
Post-secondary education	5.219 (2.058)	2.409–11.304	<0.001	***
Bachelor’s degree	14.915 (4.366)	8.404–26.471	<0.001	***
Higher than a bachelor’s degree	24.926 (17.637)	6.228–99.760	<0.001	***
Employment
Unemployed (ref.)	1.00	1.00
Employed	3.159 (0.329)	2.575–3.876	<0.001	***
Residence
Rural (ref.)	1.00	1.00
Urban	1.034 (0.078)	0.893–1.198	0.654
Level of Visual Impairment
Completely blind in both eyes (ref.)	1.00	1.00
Low vision in both eyes	5.935 (0.977)	4.298–8.195	<0.001	***
Blind in one eye and low vision in the other	4.944 (0.830)	3.557–6.870	<0.001	***
Wealth Index
Very poor (ref.)	1.00	1.00
Poor	3.975 (0.427)	3.220–4.907	<0.001	***
Middle	4.672 (0.561)	3.693–5.911	<0.001	***
Wealthy	5.034 (0.771)	3.729–6.796	<0.001	***
Very wealthy	2.938 (0.312)	2.386–3.619	<0.001	***

Note: Robust standard errors in parentheses; *** p < 0.001, ** p < 0.01, and * p < 0.05.

presents the results of a logistic regression analysis conducted to identify factors influencing Internet use among individuals with visual impairments in Thailand. The analysis reveals a complex interplay of demographic, socioeconomic, and health-related variables that significantly impact Internet use within this population.

The findings show a notable gender disparity in Internet use, with males exhibiting significantly lower odds of using the Internet compared with females (AOR = 0.850, p = 0.034), confirming H1, i.e., that gender is a significant factor influencing digital engagement within this population. It is important to note that this finding should be interpreted with caution. The observed difference may be influenced by other factors, such as age, the distribution and duration of vision impairment within our sample.

Age emerged as a crucial determinant, with younger age groups more likely to use the Internet. Specifically, the odds of Internet use decreased significantly with age, consistent with H2. Compared with children and teenagers (9–19 years), young adults (20–29 years) had markedly lower odds of using the Internet (AOR = 0.380, p = 0.049). This likelihood further decreased in adults (30–39 years) (AOR = 0.220, p = 0.001), middle-aged adults (40–49 years) (AOR = 0.155, p < 0.001), pre-retirement-age individuals (50–59 years) (AOR = 0.153, p < 0.001), and senior citizens (60+ years) (AOR = 0.052, p < 0.001). These findings align with H2, highlighting a clear trend that indicates that younger individuals with visual impairments are significantly more inclined to use the Internet compared with their older counterparts.

Furthermore, the results reveal significant regional disparities in Internet use, confirming H3. Individuals with visual impairments from the northeastern, central, and southern regions are more likely to use the Internet compared with those from the northern region. The AORs were 2.044 (p < 0.001) for the northeastern region, 1.356 (p = 0.008) for the central region, and 1.992 (p < 0.001) for the southern region. These findings suggest that regional factors significantly influence Internet use, with those in the northeastern and southern regions being approximately twice as likely to use the Internet compared with those in the northern region. In addition, the central region showed a higher likelihood of Internet use, although to a lesser extent than the northeastern and southern regions. This confirms H3, underscoring the importance of considering regional context when addressing digital inclusion for individuals with visual impairments.

Income level partially influenced Internet use among individuals with visual impairments in Thailand, supporting H4a. Compared to those earning less than 5,000 THB, individuals earning 5,000–9,999 THB had significantly higher odds of using the Internet (AOR = 1.798, p = 0.001), as did those earning 10,000–14,999 THB (AOR = 1.926, p = 0.019). No statistically significant differences were found for other income brackets.

Education level showed a strong positive correlation with Internet use, confirming H5. Compared with those with below primary education, individuals with higher educational attainment were significantly more likely to use the Internet. Specifically, the AORs demonstrated this trend clearly; those with lower primary education had an AOR of 1.590 (p < 0.001), those with primary school education had an AOR of 2.963 (p < 0.001), and those with lower secondary education had an AOR of 3.601 (p < 0.001). The odds continued to rise with higher education levels as follows: upper secondary education: AOR = 7.732, p < 0.001; vocational education: AOR = 6.865, p < 0.001; and post-secondary education: AOR = 5.219, p < 0.001. The highest odds were observed among individuals with a bachelor’s degree (AOR = 14.915, p < 0.001) and higher than a bachelor’s degree (AOR = 24.926, p < 0.001). These findings strongly support H5, underscoring the critical role of educational attainment in enhancing digital inclusion and access among visually impaired individuals, highlighting education as a key factor in bridging the digital disability divide.

Employment status is a significant predictor of Internet use among individuals with visual impairments in Thailand, supporting H6. Employed individuals had substantially higher odds of using the Internet compared with their unemployed counterparts. Specifically, the AOR was 3.159 (p < 0.001). This suggests that employed individuals were more than three times as likely to use the Internet compared with unemployed individuals. This highlights the importance of employment in promoting digital inclusion among visually impaired individual

Surprisingly, our results show no significant difference in Internet use between rural and urban residents among individuals with visual impairments in Thailand (AOR= 1.034, p = 0.654), which does not support H7. This suggests that the likelihood of using the Internet is approximately the same for individuals living in rural areas as it is for those living in urban areas, indicating that residence location does not play a significant role in influencing Internet use within this population.

The severity of visual impairments significantly influenced Internet use, confirming H8. Individuals with less severe visual impairments, such as those with low vision in both eyes or one eye blind and one eye with low vision, were significantly more likely to use the Internet compared with those who were completely blind in both eyes. The AOR for individuals with low vision in both eyes was 5.935 (p < 0.001). This supports H8, indicating that individuals with low vision in both eyes were approximately six times more likely to use the Internet compared with individuals who were completely blind in both eyes. Similarly, the AOR for individuals with one eye blind and one eye with low vision was 4.944 (p < 0.001). This suggests that individuals with one blind eye and one eye with low vision were nearly five times more likely to use the Internet compared with those who were completely blind in both eyes. These findings suggest that the degree of visual impairment plays a crucial role in Internet usage, with those experiencing less severe impairments having greater odds of being able to use the Internet. This could be due to the relatively greater visual functionality that allows for better interaction with digital devices and online platforms.

Finally, our results indicate that the wealth index was a significant predictor of Internet use among individuals with visual impairments, supporting H4b. Individuals in higher wealth categories had markedly higher odds of using the Internet compared with those classified as very poor. For individuals categorized as poor, the AOR was 3.975 (p < 0.001). This indicates that individuals in the poor category were nearly four times more likely to use the Internet compared with those in the very poor category. Those in the middle wealth category had an AOR of 4.672 (p < 0.001), suggesting they were nearly five times more likely to use the Internet than the very poor. Individuals classified as wealthy had an AOR of 5.034 (p < 0.001), indicating that they were approximately five times more likely to use the Internet compared with the very poor. Interestingly, individuals in the very wealthy category, with an AOR of 2.938 (p < 0.001), were less likely than those in the wealthy category but still significantly more likely to use the Internet than the very poor—almost three times as likely. These findings highlight the strong positive correlation between wealth and Internet use. As wealth increased, the likelihood of Internet use increased significantly. This might have been due to the greater financial resources available to wealthier individuals, allowing them to afford Internet access, necessary devices, and assistive technologies that facilitate Internet use despite their visual impairments.

In summary, these results show that gender (H1), age (H2), region (H3), income (H4a), wealth (H4b), education (H5), employment status (H6), and severity of visual impairment (H8) significantly influence Internet use among visually impaired individuals in Thailand. However, no significant difference was observed between rural and urban residents (H7), suggesting that location does not play a major role in Internet access. Overall, most hypotheses were supported, except for H7.

5. Discussion

5.1. Summary of the Findings

This study investigated the sociodemographic and contextual determinants that affect Internet use among individuals with visual impairments in Thailand. The findings underscore the multifaceted nature of Internet use among the target population, revealing significant demographic, socioeconomic, and health-related factors that influence digital engagement.

Although our analysis initially suggested a gender disparity in Internet use among visually impaired individuals in Thailand, with males showing slightly lower odds of usage, further consideration reveals that this finding may be influenced by other factors not fully accounted for in our model. The age distribution within our sample, the duration of vision impairment, and other socioeconomic factors could all potentially contribute to this observed difference. It is worth noting that our finding contrasts with some previous studies in the general population [61,71,72,73], which have typically found higher Internet use among males. This discrepancy underscores the complex interplay of factors affecting Internet use among visually impaired individuals and highlights the need for more nuanced, multifaceted analyses in future research. However, the lower Internet use among males with visual impairments in Thailand could be attributed to several cultural and social factors. In many societies, females may be more proactive in seeking online support and resources related to health and well-being, which could explain their higher Internet engagement [94]. Targeted awareness and education programs may be necessary to address this gender gap and encourage higher Internet use among males with visual impairments.

Age emerged as a critical factor in Internet use, with younger individuals being significantly more likely to access the Internet than older individuals. This trend is consistent with global patterns, where younger generations are generally more technologically savvy and inclined toward digital engagement [74,75,76]. The stark decrease in Internet use among older age groups, particularly those aged 60 and above, highlights a critical area for intervention. The lower usage rates among older individuals can be attributed to several factors, including lower digital literacy, lack of access to digital devices, and a possible preference for traditional methods of communication and information retrieval [95,96]. Digital literacy programs tailored for older adults, along with improved accessibility of devices and user-friendly interfaces, are essential to bridge this age-related digital divide.

Furthermore, the study revealed significant regional disparities, with those from the northeastern, central, and southern regions more likely to use the Internet compared with their counterparts in the northern region. These disparities could be influenced by the varying levels of infrastructure development, economic opportunities, and educational resources across different regions [30,46,47,77,78]. The higher likelihood of Internet use in the northeastern and southern regions may reflect better infrastructure and more robust support systems for individuals with disabilities. Conversely, the lower Internet use rates in the northern region indicate a need for enhanced digital infrastructure and targeted programs to support digital inclusion. Policymakers should consider regional contexts when designing interventions to ensure equitable access to digital resources across all regions.

Income level was a partial predictor of Internet use, with certain higher income brackets associated with increased odds of Internet use. This finding underscores the importance of economic resources in facilitating digital access and use for some income groups [39,51,59,79]. These results suggest that individuals in these slightly higher income brackets may be better able to afford the necessary devices, Internet connections, and assistive technologies required for digital engagement. Conversely, those in the lowest income bracket face greater challenges in engaging with digital tools and resources [97]. Given these findings, there may be a need for targeted subsidized programs that provide affordable access to digital devices and Internet services for individuals with visual impairments, particularly those in the lowest income bracket. Additionally, financial support for the purchase of assistive technologies could potentially enhance digital inclusion for this population, especially for those earning less than 10,000 THB per month. However, it is important to note that the relationship between income and Internet use was not uniform across all income levels, as higher income brackets did not show statistically significant differences. This suggests that factors beyond income may also play crucial roles in determining Internet use among visually impaired individuals in Thailand.

Meanwhile, educational attainment showed a strong positive correlation with Internet use, with individuals with higher educational levels significantly more likely to use the Internet. This trend reflects the critical role of education in enhancing digital literacy and confidence in using technology [7,47]. Higher educational attainment equips individuals with the skills and knowledge necessary to navigate digital environments effectively [46]. The findings emphasize the need for inclusive education policies that ensure individuals with visual impairments have access to quality education and specialized training programs. By improving educational opportunities, we can empower individuals with the skills required to engage with digital technologies and bridge the digital disability divide.

Employment status was another significant predictor of Internet use, with employed individuals exhibiting substantially higher odds of using the Internet compared with their unemployed counterparts. This finding highlights the role of employment in promoting digital inclusion. Employment provides financial resources and opportunities for digital engagement through work-related activities such as assistive technologies and training that facilitate Internet use [80,84]. This suggests that job training programs for individuals with visual impairments should include digital literacy components to enhance their employability and digital engagement [98]. Additionally, employers should be encouraged to provide accessible digital tools and resources to support the participation of visually impaired employees in the digital workspace.

Interestingly, the study found no significant difference in Internet use between rural and urban residents among individuals with visual impairments in Thailand. This finding suggests that residence location does not play a significant role in influencing Internet use within this population. Therefore, efforts to promote digital inclusion should focus on addressing other barriers rather than solely targeting rural–urban disparities. However, it is essential to ensure that digital infrastructure and services in rural areas are on par with those in urban areas to prevent potential future disparities.

The severity of visual impairments emerged as a significant determinant of Internet use, with individuals experiencing less severe impairments demonstrating a higher propensity to engage with digital technologies compared with those who are completely blind. This underscores the pivotal role of residual visual functionality in facilitating digital engagement. Individuals possessing partial vision can more effectively navigate digital devices and online platforms, even when employing assistive technologies [91,92,99].

This disparity suggests that although current assistive technologies are beneficial, they may not be adequate for those with complete blindness. Therefore, continued innovation in this field is needed. Enhancing the design and functionality of assistive tools to be more intuitive and user-friendly could enable those with severe visual impairments to achieve a comparable level of digital engagement. By focusing on the development of advanced assistive technologies that specifically cater to the needs of the completely blind, significant strides can be achieved in reducing the digital disability divide and promoting greater inclusivity. This approach would facilitate practical digital interactions and empower individuals with severe visual impairments to participate more fully in the digital world, enhancing their quality of life and access to information.

The wealth index emerged as a significant predictor of Internet use, with individuals in higher wealth categories exhibiting markedly higher odds of using the Internet compared with those classified as very poor. This finding aligns with the broader trend of economic resources being a critical enabler of digital access. Wealthier individuals have greater financial means to afford Internet access, digital devices, and assistive technologies [40,41]. Therefore, policies and programs that address economic barriers to digital inclusion are required [100]. Providing financial assistance or subsidies for digital access can help mitigate the impact of economic disparities on Internet use among individuals with visual impairments.

This study highlights significant disparities in Internet use. It suggests that the digital disability divide is not merely a matter of physical access but is influenced by underlying social inequalities that disproportionately affect visually impaired individuals. For instance, the higher likelihood of Internet use among women may reflect differences in social and cultural expectations regarding online engagement. Similarly, age-related barriers in digital literacy, particularly for older visually impaired individuals, highlight the need for targeted digital literacy programs to bridge this divide.

In summary, this study significantly contributes to the literature and debates on the digital disability divide by analyzing the sociodemographic and contextual factors influencing Internet usage among visually impaired individuals in Thailand. It provides a comprehensive understanding of how gender, age, regional disparities, income levels, educational attainment, employment status, the severity of visual impairment, and wealth index impact digital engagement within this population.

5.2. Policy Implications

Based on the findings and discussions presented in this study, several policy implications are recommended to address the digital disability divide among visually impaired individuals in Thailand and other developing countries.

• Enhancing Inclusive Digital Initiatives: While the Thailand Digital Economy and Society Development Plan [35] aims to improve quality of life for all citizens, including those with disabilities, our study reveals that gender disparities in Internet use among visually impaired individuals may be influenced by additional factors, such as age and the duration of visual impairment. Rather than focusing solely on gender-specific programs, a broader and more nuanced strategy is needed. Policies should address the intersection of gender with other factors, such as age, education, and the severity and duration of visual impairment. For instance, digital literacy programs could be designed to target older individuals or those with longer-term impairments while still considering gender-specific needs. Collaboration with the Thailand Association of the Blind could support the development of digital literacy workshops that address the unique cultural or social barriers faced by certain groups, particularly males, within the visually impaired community. This approach would ensure that digital inclusion strategies are more comprehensive and effective, promoting engagement across different demographic groups.
• Strengthening Age-Inclusive Digital Literacy Programs: While Thailand has policies promoting inclusive education and digital literacy for visually impaired students, our study identified a significant decline in Internet use among older age groups. To mitigate this, existing educational policies should be expanded to include lifelong learning initiatives for older adults with visual impairments. The government could implement tailored digital literacy programs that provide user-friendly devices, continuous support, and training to enhance digital skills among older individuals. Establishing a specialized branch within the inclusive education framework focused on ongoing digital skills development for older visually impaired individuals would be beneficial. Collaborating with universities that have provisions for assistive technology could help develop and implement these initiatives.
• Addressing Regional Disparities: Our study revealed significant regional disparities in Internet use, with the northern region, in particular, lagging behind. To address this, the Thailand Digital Economy and Society Development Plan [35] should be more robustly implemented in underserved regions, especially in the north. Policymakers should prioritize enhancing digital infrastructure in these areas, which includes expanding broadband access, improving the availability of digital devices, and establishing regional support systems for visually impaired individuals. Partnering with local branches of the Thailand Association of the Blind could help establish regional digital access hubs that provide accessible devices, training, and support tailored to the specific needs of each region.
• Expanding Economic Support for Digital Access: While some telecommunication providers offer reduced rates for individuals with disabilities, our study found a partial correlation between higher income levels and increased Internet use. To address this, it is important to build on existing reduced-rate programs by developing a comprehensive subsidy scheme for Internet access and assistive technologies. Integrating this scheme into the ongoing collaboration between the government and the private sector for the development of assistive technologies will ensure that these technologies are not only developed but also made affordable and accessible to visually impaired individuals across all economic backgrounds.
• Advancing the Development of Assistive Technologies: The disparity in Internet use based on the severity of visual impairment highlights the need for more advanced assistive technologies. Policymakers should prioritize the development and integration of AI into assistive technologies to create more personalized and adaptive user experiences. AI-powered tools, such as advanced screen readers and voice assistants, have the potential to significantly enhance the accessibility and usability of digital platforms for individuals with severe visual impairments. Establishing a dedicated research and development initiative within the existing government–private sector collaboration framework, focused on AI-powered assistive technologies, would be a strategic move. This initiative should prioritize the development of advanced screen readers and voice assistants tailored to the Thai language and context, ensuring that these technologies meet the specific needs of the Thai visually impaired community.

5.3. Limitations and Future Research

Despite the valuable insights provided by this study, several limitations must be acknowledged. First, the cross-sectional nature of the data limits our ability to establish causality between the identified sociodemographic factors and Internet use among visually impaired individuals. Longitudinal studies would be beneficial in examining how these relationships evolve. Second, this study focuses solely on Thailand, which may limit the generalizability of the findings to other contexts with different socioeconomic, cultural, and technological landscapes. Future research should consider comparative studies across multiple countries to provide a more global perspective on the digital disability divide. Thirdly, although this study highlights significant demographic and socioeconomic factors, it does not extensively explore psychological and behavioral aspects, such as motivation, self-efficacy, and attitudes toward technology, which could also influence Internet use. Fourthly, this study lacks a distinction between desktop and mobile Internet use among visually impaired individuals in Thailand. The survey did not specify device types, overlooking significant differences in accessibility features between platforms. This impacts the interpretation of the results, as we cannot determine if reported experiences relate more to desktop or mobile use, affecting our understanding of accessibility challenges. It is also possible that the gender differences observed in this study may be skewed by age-related factors or the duration of visual impairment, which could disproportionately affect certain groups within the sample. Future research should further investigate these interactions to clarify the role of gender in digital inclusion for visually impaired individuals. As technology continues to evolve rapidly, future research should also consider the impact of emerging technologies, such as virtual reality and augmented reality, on digital inclusion for visually impaired individuals. Finally, explicit examinations of differences in Internet use between desktop and mobile environments could offer nuanced insights into how device-specific accessibility features affect digital engagement. This understanding could inform targeted interventions and technology development, ultimately enhancing digital accessibility across various platforms for visually impaired users.

6. Conclusions

This study investigated the factors influencing Internet use among visually impaired individuals in Thailand, aiming to inform digital inclusion policies. Our findings reveal a complex interplay of determinants. Younger age, higher education, employment, wealth level, and less severe visual impairment were associated with increased Internet use. Significant regional disparities were observed, with northeastern and southern regions showing higher odds of use. Economic resources also played a crucial role in determining digital engagement, with certain income brackets linked to higher usage. However, the relationship between income and Internet use was not uniform, suggesting that the digital disability divide is shaped by more than just financial barriers—it is also influenced by educational access, employment opportunities, and the severity of impairment. This highlights the multifaceted nature of the digital disability divide, where intersecting factors contribute to unequal access to digital technologies. These results suggest that policies promoting digital inclusion should adopt a multifaceted approach, addressing educational, economic, and regional disparities while considering varying needs across age groups and impairment levels. However, limitations such as the cross-sectional nature of our data and the broad definition of Internet use should be noted. Future research directions include longitudinal studies, qualitative explorations of user experiences, cross-country comparisons in Southeast Asia, and investigations into the effectiveness of specific assistive technologies. As digital technologies continue to advance, ongoing research and policy attention are crucial to ensuring visually impaired individuals are not left behind in the digital age. This study provides a foundation for understanding and addressing the digital disability divide in Thailand, contributing to the broader goal of inclusive digital access.