Technology acceptance, multidimensional experience and disability sensitivity: a critical review Open Access

According to the World Health Organization (WHO), roughly 15% of the world's population suffers from a full or partial disability, translating to more than 1 billion people worldwide (WHO, 2024). The WHO underlines that disability does not arise only from physical and cognitive impairments but also from the interaction between health conditions and contextual factors – including environmental, social, cultural and personal barriers – that limit personal participation and inclusion. This understanding highlights that the challenge of disability extends beyond the medical domain and calls for structural and societal transformation (Garrod and Fennell, 2023). Building on these necessities, it becomes essential for society, policymakers and the scientific community to acknowledge and address the physical, social and cultural obstacles that hinder the full inclusion of people with disabilities (PwDs), thereby safeguarding their right to a dignified, equitable and inclusive life (Bagnato et al., 2025b; Gharibi et al., 2022; Iftikhar et al., 2022; Millar et al., 2018; Rubio-Escuderos et al., 2021). In response to these challenges, scientific research across interdisciplinary fields has emphasized the vast potential of emerging technologies – e.g. mobile applications, artificial intelligence, extended/virtual reality – in promoting greater autonomy for PwDs and, more broadly, to all marginalized people, positioning them as key drivers of social inclusion and equity (Carmo Rodrigues Almeida et al., 2021; de Lurdes Calisto and Sarkar, 2024). Within this landscape, virtual reality (VR) has been recognized as a powerful tool that provides a multisensory and embodied experience tailored to users’ physical, cognitive and sensory needs. In VR, PwDs can now meet, interact and socialize without restrictions, as VR not only reduces physical barriers through immersive interaction and collaboration (Farah and Ramadan, 2024) but also offers customizable interfaces, thereby ensuring broader participation (Jiang et al., 2024).

However, the potential of VR – and inclusive technology more broadly – is still not examined sufficiently and comprehensively in the business and management literature (e.g. Bagnato et al., 2025a, b), especially concerning user acceptance and usability for PwDs. This shortfall is part of a theoretical limitation in technology acceptance research, which often relies on standardized frameworks such as the technology acceptance model (TAM) or the unified theory of acceptance and use of technology (UTAUT). These frameworks have been criticized for their limited applicability to disability contexts, as they frequently neglect the specific implications of sensory (e.g. blindness, deafness) and cognitive impairments, relying on abstract constructs and linguistically demanding items that assume normative perceptual and linguistic capabilities (Sheehan and Hassiotis, 2023; Theodorou and Meliones, 2019; Vereenooghe and Westermann, 2019). These studies have taken different paths. Some have examined inclusive digital interventions (Vereenooghe and Westermann, 2019). Others have offered critical insights into digital marginalization (Sheehan and Hassiotis, 2023). Research has also explored how acceptance models, such as TAM, apply to people with intellectual disabilities (Theodorou and Meliones, 2019). Finally, some scholars have proposed conceptual frameworks to guide technology development in specific sectors for specific disabilities (e.g. Ali et al., 2023a). Yet, no studies have translated into adaptive or inclusive theoretical frameworks. Moreover, studies often focus on a single disability category, thereby overlooking the complexity and challenges faced by diverse users in various digital environments (Ali et al., 2023a; Childers and Kaufman-Scarborough, 2009; Gharibi et al., 2022; Theodorou et al., 2024; Theodorou and Meliones, 2019).

Therefore, this study conducts a critical review of the development of technology acceptance frameworks to examine whether their limited inclusivity arises from structural issues within the frameworks themselves related to the implicit normative assumptions about user abilities embedded in the operationalization of key variables (Sheehan and Hassiotis, 2023; Theodorou and Meliones, 2019; Vereenooghe and Westermann, 2019). By deconstructing how variables – such as perceived ease of use, perceived usefulness and behavioral intention – are defined and measured, the study questions.

To do the aforementioned, VR is used as the representative technology to exemplify the adaptation of variables, given its potential to reduce barriers through immersive interaction (Farah and Ramadan, 2024) and to enable broader participation via customizable interfaces (Jiang et al., 2024).

Accordingly, this study proposes a multidimensional framework in which technology acceptance variables – adapted in their formulation to reflect different disability categories considered (physical, sensory and intellectual) – are structured in subcategories and reorganized into twelve distinct dimensions. Each dimension captures specific moments of PwDs' experiences with technology under the perspectives of acceptance, non-acceptance, usability and non-usability. More specifically and grounded on the meaning of acceptance and usability (Nadal et al., 2020; Thomas et al., 2023; Vlachogianni and Tselios, 2022), each dimension reflects distinct moments of this experience, in terms of positive or negative perceptions of technology use and in terms of satisfaction or dissatisfaction with achieving personal goals through technology use.

By advancing inclusive technology acceptance research, this study stands out as one of the first to offer a conceptual contribution (Corley and Gioia, 2011; Whetten, 1989) by redefining technology acceptance variables through a multidimensional, disability-sensitive lens framework. In doing so, it adds to ongoing academic conversations that challenge conventional TAMs, advocating instead for more inclusive and user-centered perspectives (e.g. Sheehan and Hassiotis, 2023; Theodorou and Meliones, 2019; Vereenooghe and Westermann, 2019). It moves beyond the prevailing one-size-fits-all perspective, typical of technology acceptance frameworks (Davis and Granić, 2024) and paves the way for inclusive and technology experience-aware variables that reflect the diversity of user needs and experiences, too often overlooked in the literature. Drawing on Jaccard and Jacoby (2020), the framework also serves as a generative and diagnostic lens, uncovering the implicit exclusions embedded in technology acceptance frameworks. It offers a foundation for inclusive research and design on technology and disability. Furthermore, the multidimensional framework clarifies how perception and satisfaction evolve across distinct stages, providing a view of PwDs' technology interactions over time. The framework supports the idea that the interaction between disability and technology needs to be experience-adaptive and context-sensitive. Conclusively, it may be situated as a middle-range theory (Gregor, 2006; Merton, 1968), as it is theoretically informed yet grounded in the specific context of technology and disability, offering practical insights for supporting inclusive intervention strategies by underscoring that supporting both technology acceptance and usability, while preventing non-acceptance and non-usability, requires more than post-hoc adjustments. Technology developers and policymakers should embed disability-specific considerations from the outset and adopt strategies that capture diverse technology experiences.

The remainder of the paper is structured as follows: the next section presents the methodology, theoretical background and the critical review. The study ends with the discussion, theoretical and managerial implications and suggestions for future research directions.

The research employs a critical review as a qualitative method to systematically examine the current scholarly debate on a specific topic (e.g. Truong and Papagiannidis, 2022; Gutuleac et al., 2025). This approach falls within the category of non-systematic literature reviews (non-SLR), defined as “reviews conducted without any systematic procedure or protocol (…) shaped by exposure, expertise, and experience (i.e. the ‘3 Es’ in judgments calls) of the author(s)” (Kraus et al., 2022, pp. 2581–2582). A non-SLR weaves together relevant literature through the authors' critical evaluations and (subjective) judgments, following a process of discovery and reflection. It is particularly effective for challenging established assumptions, exposing critical gaps and inaccuracies and fostering a forward-looking scientific dialogue, while laying the groundwork for building and refining theoretical frameworks beyond the limits of isolated empirical studies (Jaccard and Jacoby, 2020; Post et al., 2020; Seuring et al., 2020). This design suits our objective of deconstructing the conceptual and operational underpinnings of established technology acceptance frameworks and reframing them through a disability-sensitive lens.

While increasing attention has recently been devoted to transparency and replicability in review articles mapping large knowledge domains – such as the bibliometric-systematic literature reviews (B-SLR) protocol proposed by Marzi et al. (2025) –, their focus on exhaustive corpus identification, bibliometric mapping and statistical clustering make them less suitable for interrogating the assumptions embedded in foundational models, questioning their inclusivity and generating an adapted conceptual framework. In contrast, we conducted a purposive and critical synthesis: variables from acceptance models (e.g. perceived usefulness, ease of use, behavioral intention) were analyzed for implicit normative assumptions (e.g. standard sensory or intellectual abilities) and re-examined against disability-focused conceptual and empirical insights, identifying gaps and proposing reconceptualization through the authors’ 3 Es (Kraus et al., 2022, pp. 2581–2582).

Consistent with Kraus et al. (2022), this review adopts a deductive reasoning approach. First, we traced the sequential evolution of disability categorizations, considering biomedical, social and interaction models, as disability is the contextual lens guiding our analysis. We then examined studies on the use of VR by PwDs, adopting VR as a representative technology to exemplify the deconstruction of technology acceptance variables. Its potential to reduce barriers through immersive interaction and foster broader participation makes it a compelling exemplar (Farah and Ramadan, 2024; Jiang et al., 2024).

Given the limited number of studies in the business and management literature addressing inclusive technology acceptance (e.g. Bagnato et al., 2025a,b), we classified disabilities into three categories: physical, sensory and intellectual. We then critically analyzed how these categories have been considered across the chronological evolution of technology acceptance frameworks (see the supplementary file). These frameworks – e.g. TAM, UTAUT, technology readiness – served a dual purpose in the study. First, they provided the theoretical background for understanding user-technology acceptance; second, they enabled a critical evaluation of inclusivity by examining how key variables (e.g. perceived usefulness, ease of use, behavioral intention) are operationalized. This analysis revealed that the syntactic construction of item formulations often presumes normative physical, sensory or intellectual abilities, implicitly aligning with certain disability groups while marginalizing others.

From this assessment, through a deductive yet structured review process, variables are grouped into 12 experience-based dimensions across four overarching perspectives (i.e. acceptance, non-acceptance, usability and non-usability) offering a differentiated lens on PwDs' experiences with technologies: from variables reflecting dynamic changes in perception over time (e.g. evolving trust, familiarity), to rejection or disengagement (e.g. frustration, overload), satisfaction in achieving meaningful and relevant goals (e.g. completing tasks, delivering perceivable outcomes) and dissatisfaction when goals are not met (e.g. poor effort, lack of support).

The coding and interpretive process was iterative and multi-step. First, the underlying assumptions and constraints of each variable's operationalization were identified. Second, variables were aligned with disability categories and experiential perspectives based on conceptual fit and examples from VR literature. Third, assignments were refined by discussing and reconciling coding discrepancies among the authors to ensure consistency and validity. Analytical rigor was further enhanced through triangulation across foundational acceptance models, disability studies and VR empirical research, with a consistency check confirming that each variable's classification reflected theoretical and practical implications for different disability categories (detailed tables in the supplementary file show all items, the assigned variables and their source models).

The resulting 12 experience-based dimensions serve as both a classification tool and a theory-building instrument (Corley and Gioia, 2011; Whetten, 1989), contributing to the construction of a middle-range theory (Gregor, 2006; Merton, 1968). The framework operates dialectically by critically engaging with dominant models and offering a generative architecture for future theorizing, consistent with guidelines by Jaccard and Jacoby (2020). Furthermore, this approach aligns with Post et al. (2020)'s notion of a theory-generating avenue, whereby a critical review can consolidate established theoretical perspectives while exposing and advancing emerging ones. Dominant frameworks, such as widely used TAMs, often evolve incrementally but may lose alignment with contemporary realities, especially when societal and technological changes reshape the underlying phenomena. In the current study, the emerging perspective arises from recognizing the ableist underpinnings of existing models and generating an alternative, disability-sensitive conceptual framework. By foregrounding an inclusive, experience-based lens over classical acceptance constructs, the proposed framework offers a new theoretical perspective that bridges business and management literature with disability scholarship.

Early disability classifications focused on core impairments such as mobility, hearing, vision and speech (Reedy, 1993). Building on this foundation, later approaches expanded these categories by considering diagnostic-level distinctions between different types of disabilities (Hopkins, 1998). This change led to more comprehensive frameworks, such as the biopsychosocial model introduced by the International Classification of Functioning, Disability and Health (ICF), which defines disability as “a complex phenomenon that is both a problem at the level of a person's body and a complex and primarily social phenomenon” (ICF, 2002, p. 9). Drawing on this broader perspective, newer categorizations began reflecting people's lived experiences (Waldrop and Stern, 2003). For example, scholars grouped conditions such as blindness, deafness and vision and hearing impairments, distinguishing them from other major categories, including mobility disabilities, learning difficulties and self-care difficulties, particularly those affecting movement indoors, outdoors or at work. The scope of disability was further expanded, identifying six distinct types of disabilities, deepening the perspective beyond traditional impairments such as visual, hearing and speech impairments, as well as physical limitations, to include challenges like reading difficulties and manual dexterity issues (Childers and Kaufman-Scarborough, 2009). Other frameworks incorporated aspects to include diverse daily challenges beyond just physical impairments, such as color-blindness, severe asthma, severe obesity and attention-deficit disorder (Baker et al., 2007). Similarly, the WHO includes mobility, self-care, cognition, interpersonal activities and emotional functioning within its classification (WHO, 2024).

The evolving understanding of disability categorizations has significantly influenced management studies, particularly in the context of service delivery (Dickson et al., 2016). Scholars have emphasized the importance of recognizing the diverse categories of impairments, including physical and cognitive limitations, as well as psychiatric, neurological and sensory sensitivities (e.g. Dickson et al., 2016). This complexity necessitates resource allocation strategies tailored to meet the diverse needs of people with physical, sensory, cognitive and learning disabilities (Cerdan Chiscano and Darcy, 2021). An inclusive approach also requires extending the notion of disability to encompass all-embracing physical, sensory and intellectual disabilities, as well as the elderly, temporary disabilities, young children, pregnant women and people with multiple disabilities (Garrod and Fennell, 2023). Thus, the discussion of disability in business and management studies points to the critical need to recognize this diversity and prioritize inclusivity in both policy and practice (Kalargyrou et al., 2018; Liu et al., 2024).

Anchored in the broad disability categories identified across previous studies, VR has increasingly been explored as a versatile tool for addressing the needs of these groups (Almuaqel, 2023; Jiang et al., 2024). As defined by Guttentag (2010, p. 638), VR is described as “the use of a computer-generated 3D environment – called a ‘virtual environment’ (VE) – that one can navigate and possibly interact with, resulting in real-time simulation of one or more of the user's five senses. ‘Navigate’ refers to the ability to move around and explore the VE, and ‘interact’ refers to the ability to select and move objects within the VE.” Among the different applications of VR across different disability categories, the medical field offers significant examples. For individuals with physical disabilities, VR enables exploration of virtual environments, engages in realistic simulations and even undertakes interactive rehabilitation programs, such as fitness training and muscle rehabilitation, from the comfort of their homes. These opportunities open up new possibilities for physical activity and therapeutic support (Lotan et al., 2011). In learning disabilities, VR has enhanced educational delivery by providing interactive and tailored content that improves instructional outcomes and accessibility (Khasawneh, 2024). When it comes to mental health disabilities, the use of VR has shown clinical effectiveness in treating several conditions, including anxiety, depression, eating disorders and post-traumatic stress disorder (Anderson et al., 2013; Bissonnette et al., 2016). However, despite these promising developments, the acceptance and usability of VR among PwDs have remained underexplored (Iftikhar et al., 2022; Wang et al., 2024).

Technology adoption represents a major driver of societal change, redefining patterns of living, social interaction and engagement with the broader environment (Fox and Griffy-Brown, 2023). It has become a powerful engine for inclusion, opening doors for individuals of all abilities to participate more fully in social, educational and professional life (Gharibi et al., 2022; Iftikhar et al., 2022; Millar et al., 2018; Rubio-Escuderos et al., 2021). Yet the inclusivity of this process is far from guaranteed, and achieving inclusive technological environments depends not only on providing innovative tools but also on how users accept and use them. Acceptance refers to how users perceive and evaluate their interaction with a given technology over time (Nadal et al., 2020), while usability concerns the effectiveness, efficiency and satisfaction with which users achieve their goals using the technology (Vlachogianni and Tselios, 2022). Building on this understanding, scholarly attention has increasingly focused on modeling the factors that shape users' acceptance and use of technology, recognized as a prerequisite for its realization, leading to the development of numerous frameworks aimed at exploring the mechanisms behind technology acceptance (Momani and Jamous, 2017; Putra, 2018). However, a persistent challenge lies in the conceptual underpinnings of technology acceptance research, where acceptance is often conflated with usability. This confusion is not merely semantic but reflects a tendency of frameworks to privilege abstract intention over embodied experience, thereby overlooking how diverse users navigate technological environments (Thomas et al., 2023).

Early frameworks, starting with the TAM, introduced by Davis (1989), have been developed to predict and clarify user behaviors related to system use (Davis and Granić, 2024). However, as digital environments have become increasingly complex and socially embedded, scholars have highlighted certain limitations of the original TAM model, which assumes a rational, universal user and abstracts away from the variability of embodied capabilities. Its extensions – TAM2 (Venkatesh and Davis, 2000) and TAM3 (Venkatesh and Bala, 2008) – added variables such as social influence, self-efficacy or enjoyment, but did not challenge the assumption that intention is the primary driver of adoption. Similarly, the Perceived Characteristics of Innovation Model (PCIM, Moore and Benbasat, 1991) shifted attention to perceptions of innovation, while the Decomposed Theory of Planned Behavior (DTPB, Shih and Fang, 2004) and the Theory of Interpersonal Behavior (TIB, Triandis, 1977; Pee et al., 2008) expanded the lens to include beliefs, habits and emotions. The Technology Readiness (TR) model (Parasuraman, 2000) and subsequent integrations, such as UTAUT (Venkatesh et al., 2003) and UTAUT2 (Venkatesh et al., 2012), have consolidated these perspectives into broader explanatory frameworks, with UTAUT2 adapting the model for consumer contexts.

When taken together and viewed critically, these models mark an evolutionary layering of predictors: each introduces new factors, but few interrogate the underlying assumption of a normative, intention-driven user. As technology acceptance frameworks have evolved progressively (Pramanik and Jana, 2025), various studies have questioned their applicability to individuals with specific disabilities (Sheehan and Hassiotis, 2023; Theodorou and Meliones, 2019; Vereenooghe and Westermann, 2019). They assume users to be rational, cognitively homogeneous agents, whose motivations can be captured through intention-based constructs. Even when acknowledging social or affective factors, the models rarely interrogate how differences in physical, sensory or cognitive abilities shape technology interaction. As a result, disability remains peripheral to their development. For instance, TAM and its variants often fail to address the specific implications of blindness and deafness impairments, highlighting the need for model adaptations that better reflect the perceptual and relational barriers faced by these user groups (Theodorou and Meliones, 2019). Likewise, standard content derived from general acceptance frameworks is often too complex for people with cognitive and linguistic impairments, as traditional item formulations tend to assume a high language level that is not universally present, making such models inaccessible and inherently non-inclusive (Vereenooghe and Westermann, 2019). This point is further bolstered by the reflective editorial in psychiatry and digital mental health of Sheehan and Hassiotis (2023), who emphasize that evaluations of digital technologies should explicitly include usability measures tailored to people with cognitive impairments. These tensions suggest that the field has evolved by layering additional predictors of acceptance without questioning its foundational assumptions. By prioritizing intention-based models and treating usability as secondary, technology acceptance research risks reinforcing exclusionary logics: it explains adoption for the “average user” while rendering invisible the structural and experiential barriers faced by PwDs.

Building on the above studies, the next section introduces a tailored categorization of disabilities and critically deconstructs existing frameworks, using VR as a representative technology to expose how their assumptions marginalize alternative user experiences.

In line with key domains of activity and participation identified in the ICF's biopsychosocial model (ICF, 2002) and based on the evolving discourse around disability categorizations (e.g. Cerdan Chiscano and Darcy, 2021; Dickson et al., 2016; Garrod and Fennell, 2023), this study adopts three categories of disabilities – physical, sensory and intellectual – to critically review the technology acceptance frameworks.

Physical disabilities reflect challenges in mobility and body structure. Sensory disabilities refer to impairments in vision or hearing, while intellectual disabilities encompass cognitive delays, communication difficulties and developmental disorders.

To operationalize these categorizations within the context of the chronological evolution of technology acceptance frameworks, we examine how they accommodate, or fail to accommodate, different types of disabilities. We also explain why they suit specific disabilities and their technology experience continuum. The process begins with an analysis of the syntactic structure of the items – using VR as a representative technology – used to measure each variable, focusing on potential barriers such as linguistic complexity, conceptual abstraction or limited accessibility of meaning. We then assess whether these variables meaningfully align with the real-life decision-making processes that PwDs engage in when evaluating technology. All variables, structured in subcategories, are subsequently grouped into broader dimensions that reflect the relationship between disability and technology experience. Finally, by considering the meaning of acceptance and usability (Nadal et al., 2020; Vlachogianni and Tselios, 2022), these variables are classified under four perspectives: evolving perceptions (acceptance), negative perceptions (non-acceptance), satisfaction in achieving personal goals (usability) and dissatisfaction when such goals are not met (non-usability, see Figure 1).

Under acceptance, four central dimensions emerge. The first is accessibility and technological adaptability. This reflects how easily the technology can be reached, tested or personalized to meet PwDs’ needs. Its first subcategory is Trialability, which resonates with physical disabilities because hands-on evaluation reduces uncertainty and supports autonomy by confirming functional alignment before full adoption. Thus, it allows these users with this specific disability to test whether technology tools align with their motor capacities. Compatibility is the second subcategory, which also aligns with physical disabilities because it minimizes the need for bodily adaptations when integrating technology into daily life. If a tool fits naturally within existing physical routines and constraints, it reduces strain and promotes sustained, autonomous use. Technology needs to harmonize with existing routines and not disrupt physical comfort or mobility. Perceptions of External Control, identified as the third subcategory, indicate that for people with physical disabilities, autonomy depends on whether tools, resources and compatible technologies are accessible. Accordingly, it is suited for this disability, as their autonomy often depends on the availability and accessibility of assistive devices and supportive technologies. On the other hand, objective usability is the fourth subcategory and presents a key barrier for intellectual disabilities because difficulties in processing ambiguous information make them more vulnerable to disengagement when clarity and consistency are lacking. Comprehension breaks down when technology outputs are vague or inconsistent, leading to frustration or abandonment. Similarly, the last subcategory, complexity, proves crucial for people with intellectual disabilities, as excessive cognitive load can easily exceed their processing abilities, leading to disengagement and reduced likelihood of sustained interaction with the technology. If technology isn't cognitively simple, engagement falters.

The second dimension, autonomy, control and personal capacity, explores how internal confidence and perceived competence of technology influence PwDs’ engagement. Voluntariness is the first subcategory, which means the freedom to decide when and how to use technology is critical. It fits best with physical disabilities because rigid or imposed usage conditions can conflict with users' physical limitations, reducing both comfort and effective engagement. The second subcategory, positive perceived consequences, also aligns with physical disabilities for the reason that they often assess technology based on its concrete ability to alleviate physical strain and enhance daily autonomy. It highlights tangible benefits like reduced effort or improved access, given the use of technology. Conversely, subcategories like computer self-efficacy, habit and optimism hold greater weight for those with intellectual disabilities due to the fact that they foster progressive familiarization and reduce reliance on abstract reasoning, which is often a barrier; by reinforcing self-belief, predictability and positive anticipation, they make digital engagement more cognitively sustainable. Here, confidence is often fragile; building it is key to overcoming hesitation and encouraging the repeated use of technology. Habit strengthens implicit learning of technology, reducing cognitive strain. Optimism counters self-doubt, fostering a willingness to explore technologies. Perceived behavioral control represents the last subcategory and spans all disability categories. However, it carries distinctive implications: for physical disabilities, it hinges on the availability of adaptive technologies; for sensory disabilities, on accessible technology cues; and for intellectual disabilities, on step-by-step clarity and reduced abstraction of technology. Thus, its relevance across all disability types lies in its capacity to adapt control perceptions to specific needs, enabling users to feel competent and autonomous despite diverse limitations.

The third dimension, emotional and motivational involvement, captures how affect and enjoyment shape PwDs' technology engagement. Computer playfulness is the first subcategory and resonates with intellectual disabilities, as creativity and fun promote curiosity and sustained technology use. It is suitable for this disability because playful, low-pressure environments help reduce cognitive overload, making it easier to engage with technology. The second subcategory, Hedonic Motivation, similarly encourages intrinsic interest in the technology, easing resistance and increasing immersion. It is also suitable for intellectual disabilities, as fostering intrinsic enjoyment helps bypass cognitive barriers and supports a more natural, sustained engagement with technology. Perceived enjoyment, portraying the third subcategory, makes technology feel less like a task and more like an experience, reducing psychological barriers. It is particularly fitting for intellectual disabilities, as reframing interaction as a pleasurable experience lowers resistance and supports sustained engagement without relying on complex cognitive processing. Affect, which represents the fourth subcategory, measures emotional tone to support technology engagement for intellectually disabled users. It is appropriate for this disability, as emotional responses often guide their engagement more strongly than abstract reasoning, making affective alignment essential to sustaining interaction. The fourth subcategory, named Attitude, too, plays a key function: when technology feels positive, it draws users in without requiring detailed rational analysis. This makes it especially suitable for people with intellectual disabilities, who may rely more on intuitive affective evaluations than on complex cognitive appraisals when deciding to engage with technology. Image, in contrast, represents the second-to-last subcategory and fits better with physical disabilities. For users who face stigma, technology may serve as a symbol of competence and inclusion, counteract perceptions of physical limitation, and reinforce their social identity through visible markers of capability. Finally, the last subcategory is Innovativeness, which reflects autonomy and confidence in exploring new technologies, an empowering factor for physically disabled users navigating digital barriers, as it supports their proactive engagement with tools that may enhance independence and reduce constraints.

The final acceptance-related dimension centers on social and intentional adoption of technology factors. Here, social context and perceived support shape technology adoption by PwDs. The subcategory Subjective Norm proves especially relevant for intellectual disabilities, where social cues and encouragement play an important role in technology decision-making, as people with this type of disability often rely on trusted social feedback to interpret situations and validate their choices. Result Demonstrability is the second subcategory and also fits with intellectual disabilities. If users can't understand or explain the benefits, the trust and motivation to use technology drop, since clear, observable outcomes are essential for building confidence and reinforcing purposeful engagement among individuals with limited abstract reasoning. Among those with physical disabilities, Social Factors are pivotal and represent the third subcategory. Encouragement from peers, colleagues or family can tip the scale toward technology adoption, as external validation and practical support often help overcome physical access barriers and build confidence in navigating new technologies. Job Relevance, the fourth subcategory, reinforces this: technology must clearly support work tasks and reduce physical strain. Thus, it is important for physically disabled users who rely on tools that minimize effort and enable sustained, independent performance in professional settings. To conclude, Behavioral Intention is identified as the last subcategory, which applies universally; across all disabilities, intention to use technology depends on whether the technology is accessible, functional and perceived as valuable, as these elements directly influence the willingness to engage with technologies that must accommodate physical, sensory or intellectual needs in meaningful ways.

Non-acceptance reveals three dimensions: uncertainty and cognitive challenge, discomfort in interaction and fear of negative effects. The first, uncertainty and cognitive challenge, erodes the confidence of PwDs in using technology. Insecurity, identified as the first subcategory, breeds hesitation when it stems from unclear cues or unpredictable technology. Sensory-disabled users may miss vital signals; intellectually disabled users may struggle with complexity and ambiguity, leading to anxiety and mistrust. This makes the variable particularly suited to both disabilities, as it captures how perceptual gaps and cognitive overload undermine users' sense of predictability and control. The second dimension, discomfort in interaction, highlights how poor design of technology causes PwDs’ frustration. In this dimension, Discomfort is included as the only subcategory, and it is important for all disabled users. When technology demands physical, sensory and intellectual effort, it doesn't accommodate, frustration rises and usage declines. Users with all types of disabilities are especially sensitive to effortful interactions that exceed their motor, sensory or cognitive thresholds, quickly discouraging sustained engagement. The third dimension, fear of negative effects, captures concerns about confusion, error or failure with technology by PwDs. Two key sub-categories shape the last dimension: Perceived Consequences (negative) and Computer Anxiety. Regarding Perceived Consequences (negative), for people with sensory disabilities, missing audio or visual cues in technology can lead to a fear of making mistakes or missing information. Intellectual disabilities often bring difficulty predicting outcomes, triggering anxiety over breaking the technology or doing something wrong. For those with physical disabilities, the fear centers on failing tasks due to inaccessible controls, especially in critical technology contexts like rehab or training. Computer Anxiety intensifies these feelings. Sensory impairments raise doubts when technology feedback is unclear. Intellectual overload makes complex interfaces stressful for intellectually disabled users. Meanwhile, those with physical disabilities may fear being unable to complete tasks without adaptive technologies. These two subcategories are suitable for these disabilities because they capture how sensory or cognitive limitations exacerbate the fear of errors or failure, which can significantly hinder trust in and engagement with the technology.

Turning to usability, four dimensions surface. Ease of use and interaction is the first dimension that reflects how intuitive and straightforward PwDs interact with the technology. Perceived Ease of Use and Ease of Use (positive) represent the first and the second subcategories and are important across all disabilities because they directly address the specific access and interaction needs – whether physical, sensory or intellectual – required to reduce barriers and enable effective, independent use. For physical disabilities, it means low-effort technology interfaces. For sensory disabilities, technology clarity and feedback. For intellectual disabilities, intuitive design and simplified steps of technology. Effort Expectancy, the last subcategory, while cross-cutting, speaks most to physical disabilities, where technology must be manageable and not taxing. Excessive physical demands in interaction can limit usability and discourage sustained engagement, especially when physical effort is compromised.

The second dimension, adaptability and personalization, addresses whether technology can meet PwDs' needs. The subcategories Perceived Usefulness, Relative Advantage and Performance Expectancy align with physical disabilities, where gains in efficiency, output, and independence measure the value technology provides. Accordingly, these variables are adequate for this disability, as technological tools that demonstrably reduce physical effort or enhance autonomy directly address the core functional limitations faced in daily tasks. However, Facilitating Conditions, the last subcategory, stand out for sensory disabilities because they often rely on assistive technologies and need consistent, integrated support systems to ensure seamless and independent use of technologies. Accessible platforms, technical support and cross-technology compatibility are essential for this group's technology experience.

The third dimension, output quality and detectability, looks at how clearly PwDs perceive and interpret technology results. The first subcategory, Output Quality, is central for intellectual disabilities because they often face challenges in interpreting ambiguous or inconsistent feedback, making clear and stable outputs essential for their comprehension and continued engagement. Consistency and clarity of technology reduce confusion and build trust. Visibility, as the second subcategory, by contrast, assumes the functioning of visual technology, making it more aligned with physical users, given that they typically retain full sensory capacity, so clear visual outputs enhance their ability to navigate interfaces efficiently and independently. The third subcategory is Price Value, which affects all disability categories because affordability becomes a gatekeeper to accessibility, and people from all three disability categories must assess whether the benefits of the technology justify the financial investment. For physically disabled users, high technology cost can block access. Sensory and intellectual disability users weigh technology cost against usefulness or clarity. Result Demonstrability, the last subcategory, again fits best with intellectual disabilities, where users need visible, explainable feedback to trust and adopt technology.

Finally, the overall use and functionality dimension reflects the real-world engagement of PwDs with technology. It includes one subcategory represented by Use, which cuts across all disability categories; technology adoption depends on inclusive design. Without it, users with physical, sensory and intellectual disabilities find the technology unusable, no matter how strong their intention. Thus, only an inclusive design can address the distinct access, interpretation and interaction needs across these three disability types, ensuring that intention translates into use.

Non-usability reveals one dimension: interactional breakdown. This captures PwDs’ frustration when technology is cognitively demanding, poorly adapted or emotionally alienating. It includes Discomfort as the first subcategory that emerges from inaccessible language, lack of support or overly complex features of technology, particularly affecting users with all disabilities, who may struggle with unclear instructions and feel overwhelmed. All disabilities face heightened vulnerability to frustration when faced with a design that disregards their motor, sensory, or cognitive limitations, making discomfort a key deterrent to use. Further, the last subcategory, Ease of Use (negative), amplifies frustration when technology requires high mental effort or lacks intuitive control. Intellectually disabled users are most impacted, facing barriers to autonomy and understanding. Cognitive overload and lack of intuitive pathways hinder their ability to process information, making interaction exhausting and discouraging sustained use.

Across the framework’s four perspectives – acceptance, non-acceptance, usability and non-usability – disability categories shape the applicability and meaning of the technology acceptance variables. Cowan et al. (2012) argue that the key to effectively obtaining technologies for people with physical impairments lies in seamless integration between users' residual abilities and assistive devices. This requires design solutions that reduce physical strain, ensure motor compatibility and support consistent, long-term use, making effort-related and functionality-based variables especially relevant. Given the critical reported aspects, in terms of acceptance, these users benefit most from Trialability, Compatibility and Perceptions of External Control, which allow them to test whether a technology aligns with their physical abilities. Variables such as Voluntariness, Perceived Consequences (positive), Perceived Behavioral Control, Image, Innovativeness, Social Factors, Job Relevance and Behavioral Intention also play a key role, reinforcing a sense of control and perceived usefulness. When it comes to non-acceptance, Discomfort, Perceived Consequences (negative) and Computer Anxiety become decisive, particularly when technologies are inaccessible or create uncertainty in use. From a usability standpoint, variables such as Perceived Ease of Use, Ease of Use, Effort Expectancy, Perceived Usefulness, Relative Advantage, Performance Expectancy, Output Quality, Visibility, Price Value and Use are essential, ensuring that interactions remain low-effort and transparent. When these conditions are lacking, non-usability manifests through Discomfort, as the technology interface becomes too physically demanding.

Sensory disabilities highlight the importance of technologies able to convert information related to a sense into a language understandable by another sense, thus emphasizing the importance of codified, precise and integrated technologies (Sorgini et al., 2018). Indeed, acceptance is supported by variables like Perceived Behavioral Control and Behavioral Intention, which help users engage with technology through clear signals and integrated support systems. Non-acceptance may stem from Insecurity, Discomfort, Perceived Consequences (negative) and Computer Anxiety, especially when missing visual or audio cues lead to confusion or fear of mistakes. On the usability front, variables such as Perceived Ease of Use, Ease of Use, Facilitating Conditions, Price Value and Use ensure that technology remains perceivable and interpretable. In the absence of these conditions, non-usability emerges as Discomfort, caused by the sensorially demanding nature of the interface.

Intellectual disabilities, in contrast, reflect technology acceptance variables that rely on low mental well-being challenges and predictability, highlighting the need for intuitive design – less reliant on digital literacy – social cues and emotional simplicity (Woodward et al., 2023). Acceptance is encouraged by variables such as Objective Usability, Complexity, Computer Self-Efficacy, Habit, Optimism, Perceived Behavioral Control, Computer Playfulness, Hedonic Motivation, Perceived Enjoyment, Affect, Attitude, Subjective Norm, Result Demonstrability and Behavioral Intention, which make it easier to trust the technology, feel emotionally involved and interact socially. Non-acceptance emerges when interaction feels ambiguous, overly abstract or anxiety-inducing, triggered by Insecurity, Discomfort, Perceived Consequences (negative) and Computer Anxiety. On the usability side, variables like Perceived Ease of Use, Ease of Use, Price Value, Result Demonstrability and Use are critical in reducing cognitive load and fostering understanding. In contrast, non-usability is tied to Discomfort and Ease of Use (negative), leading to disengagement or breakdown.

Further, Figures 2–4 illustrate the process diagram depicting the experiential continuum of technology use for all disability categories, revealing interdependencies between variables traditionally treated as separate. The journey begins with acceptance, where users form positive, evolving perceptions of the technology. Ideally, this paves the way to usability – a stage where users find satisfaction by achieving personal goals through accessible and adaptive technology interactions. But the path is rarely straightforward. Even when usability, a technology that fails to support satisfaction continuously, can quickly slip into non-usability, triggering dissatisfaction and possible disengagement. Similarly, what starts as acceptance can unravel. Ambiguous feedback, cognitive overload or unmet expectations may tip the balance toward non-acceptance, where negative perceptions of technology overshadow the initial engagement with it.

As pointed out by Sheehan and Hassiotis (2023), Theodorou and Meliones (2019) and Vereenooghe and Westermann (2019), widely used technology acceptance frameworks, such as TAM and UTAUT, often rely on linguistically dense constructs designed for general populations. They presuppose standardized perceptual, cognitive and communicative abilities, implicitly designed around the needs of a generic user (Davis and Granić, 2024). Building on this critique, the present study proposes a multidimensional, disability-sensitive framework. It adapts technology acceptance variables to physical, sensory and intellectual disabilities, rather than assuming a universal user profile, and structures them into subcategories, which are then grouped into twelve distinct technology experience dimensions. This reorganization is necessary, as it addresses a persistent weakness in the technology acceptance debate: despite their contributions, models such as TAM, PCIM, DTPB, TIB, TRI and UTAUT2 remain anchored mainly in a pre-interaction technology logic. That is, they focus on how and why users intend to adopt a system, while paying little attention to what unfolds during and after the technology experience (Mogaji et al., 2024). Accordingly, there is a pressing need to move beyond models like TAM toward approaches that engage with effective technology experience (Bagozzi, 2007; Benbasat and Barki, 2007; Mogaji et al., 2024). Traditionally, technology adoption is explained using variables linked to internal personal traits and stable experiences, such as perceived usefulness, self-efficacy, subjective norms or psychological predispositions (Davis, 1989; Parasuraman, 2000; Venkatesh et al., 2003, 2012). Yet this approach flattens the diversity of user needs and overlooks interactions that fall outside standard use patterns, especially those requiring tailored support. In contrast, our framework breaks away from this static view. It captures technology experience as a sequence of stages, each shaped by the evolving interaction between the user with specific needs and the technology. These stages are framed under the perspectives of acceptance, non-acceptance, usability and non-usability, acknowledging that the relationship between disability and technology is rarely linear or uniform, but rather a process, marked by shifts, setbacks and adaptation. Thus, in line with recent calls in the literature (e.g. Thomas et al., 2023), this approach moves beyond the rigid separation between acceptance and usability, especially critical in vulnerable contexts. Here, usability is not seen as a static property of a system, but as a relational and dynamic construct that directly shapes, and is shaped by, acceptance. Also, by incorporating the opposite perspectives of acceptance and usability (i.e. non-acceptance and non-usability, Nadal et al., 2020; Vlachogianni and Tselios, 2022), the proposed multidimensional framework acknowledges that technology use is not always homogeneous or successful. Instead, it creates space for recognizing the negative perceptions and dissatisfaction that PwDs may experience in their interaction with technology.

In addressing RQ1, unlike TAM3 (Venkatesh and Bala, 2008), which expands on perceived usefulness and ease of use but remains focused on the conditions that lead to successful adoption, our framework introduces the perspectives of non-acceptance and non-usability. These perspectives allow for a critical analysis of the negative or disrupted aspects of PwDs' technology experience, aspects that intention-based models systematically overlook. By making these aspects visible, the model enables a deeper understanding of why certain users, especially those with disabilities, may disengage from technology despite initial motivation or access. Similarly, while valuable for foregrounding users' subjective perceptions, the PCIM (Moore and Benbasat, 1991) remains limited to an initial snapshot of technology adoption. Our framework goes further; it considers not only what users perceive but also what they fail to perceive, whether the interaction proves satisfactory, and when it fails to satisfy the user's specific needs. The same logic applies to DTPB (Taylor and Todd, 1995) and TIB (Pee et al., 2008; Triandis, 1977). Although both introduce emotional and habitual factors, they remain grounded in a predictive paradigm that links intention to behavior. Our framework departs from this path by investigating whether and how technology helps users achieve personally satisfying outcomes. It shifts the focus from intention to the quality and continuity of the user experience, catering to different needs. An even more pronounced theoretical gap emerges when compared to the TR (Blut et al., 2016; Blut and Wang, 2020; Parasuraman, 2000). While TR measures individuals' general predisposition toward technology, treating it as a fixed trait, our framework reverses the lens. It doesn't ask whether people are ready for technology; it asks whether technology is prepared for people affected by different impairments. This inversion highlights specific needs and environmental and relational factors that enable or prevent the technology experience. Similarly, UTAUT2 (Venkatesh et al., 2012) expands the behavioral predictors in consumer contexts, yet continues to overlook the diversity of social, environmental and functional conditions through which individuals interact with technology. Our model takes a fundamentally different approach. Rather than adjusting existing frameworks for subgroups, it rebuilds the theoretical architecture from the ground up, based on the specific technology experiences of people with different disability-related needs. In this view, technology adoption is not a uniform, linear process but a diverse, situated experience shaped by individual needs and contextual factors. Thus, in response to RQ2, diverging from previous theoretical models of technology acceptance, the proposed framework was first designed to reflect the diversity of users' experiences interacting with technology (Lee et al., 2025). By adopting the lenses of acceptance, non-acceptance, usability and non-usability, it reorganizes traditional acceptance variables into 12 experiential dimensions – each tied to a distinct phase in the interaction journey of PwDs. Within each dimension, we subsequently included the technology acceptance variables that, based on their theoretical definitions and corresponding items, are conceptually aligned with that specific dimension of the technology experience as lived by PwDs. Each variable (named subdimension) was then associated with one or more disability categories (physical, sensory or intellectual), depending on its relevance in reflecting the specific characteristics and needs of those user groups. Grounded in this premise, the framework moves beyond a singular or uniform encounter with technology. Instead, it views the experience as a multi-phase process, where specific challenges can also emerge – especially for users facing physical, sensory or intellectual barriers (Bagnato et al., 2025a; Cowan et al., 2012; Sorgini et al., 2018; Woodward et al., 2023). In doing so, it diverges from linear models and acknowledges the complex, often nonlinear nature of technology engagement (Mogaji et al., 2024).

Moreover, by mapping diverse technology experiences through acceptance, non-acceptance, usability and non-usability lenses, the proposed multidimensional framework captures an experiential continuum for all disability categories, as illustrated in Figures 2–4. Technology use is not merely a matter of acceptance or rejection; it is shaped by a positive perception that, in turn, can lead to use, which, in turn, fosters satisfaction as users achieve personal goals. Thus, in line with Kalargyrou et al. (2018) and Liu et al. (2024), we argue that the interaction between disability and technology is a dynamic process that needs to evolve by reflecting experiential complexity, addressing not only functional needs but also perception and satisfaction in technology use.

The findings discussed above also align with those of Lee et al. (2025), who underscore the urgent need to adapt established technology frameworks to individuals whose interaction with technology is shaped by non-standard learning profiles or complex environments. This perspective is echoed in the work of other scholars, such as Ali et al. (2023b), Theodorou et al. (2024) and Theodorou and Meliones (2019), who emphasize the need to revisit technology acceptance frameworks to better address the challenges faced by marginalized groups, including PwDs. Further, our work aligns with the conclusions of Iftikhar et al. (2022), reinforcing the idea that the diverse needs and capabilities of PwDs require theoretical frameworks that more accurately capture and reflect their unique experiences.

The managerial relevance of the presented findings lies in their ability to guide concrete and inclusive intervention strategies. Supporting technology acceptance and usability – while preventing non-acceptance and non-usability – cannot rely on after-the-fact fixes. Instead, it calls for proactive managerial approaches. From this perspective, technology developers, managers and policymakers are encouraged to incorporate disability-specific considerations from the earliest stages of design, development and implementation. At the same time, decision-making strategies should recognize and value the diverse technology experiences of PwDs. By translating the framework's 12 dimensions into design, testing, evaluation and policy practices, it helps move beyond generic inclusion efforts and provides a managerial roadmap for context-sensitive decisions.

This critical review gives rise to several theoretical implications. First, the study positions itself within the field of inclusive technology acceptance research, advocating a shift from fragmented adaptations of existing frameworks (e.g. Gharibi et al., 2022; Theodorou et al., 2024) toward a systemic reframing for PwDs' use of technology. It challenges the ableist assumptions embedded in the evolution of technology acceptance frameworks (Davis and Granić, 2024) by redefining their key variables through the lens of different disability categories. In line with previous critiques on the limited applicability of traditional models to users with specific cognitive or functional needs (Sheehan and Hassiotis, 2023; Theodorou and Meliones, 2019; Vereenooghe and Westermann, 2019), this study advances the field by proposing a structured adaptation. Here, acceptance variables, represented in the proposed framework as subcategories, are adapted in their formulation to reflect the characteristics of physical, sensory and intellectual disabilities in their experience of using technology. Rather than treating variables as universally valid, the framework anchors them in differentiated, disability-sensitive user profiles.

Second, this study identifies 12 technology experience dimensions, each corresponding to a distinct stage of interaction between PwDs and technology. These dimensions serve as the backbone of the proposed framework, grouping adapted acceptance variables based on how PwDs with different needs experience the technology. Moving away from the notion that technology experience is a single event (Mogaji et al., 2024), the framework treats it as a process composed of distinct stages. This structure also identifies where difficulties emerge, especially for users who may encounter physical, sensory or intellectual barriers throughout the technology interaction (Bagnato et al., 2025a, b). In doing so, it laid the groundwork for building an inclusive acceptance framework capable of engaging more directly with the realities of technology experience (Bagozzi, 2007; Benbasat and Barki, 2007; Mogaji et al., 2024) and, in finer detail, reflecting the diversity of user experiences with technology (Lee et al., 2025).

Third, through the multidimensional framework, this study introduces a continuum-based perspective on technology experience for all disability categories (Figures 2–4), moving beyond the binary view of acceptance or rejection that has traditionally shaped TAMs. Rather than isolating conceptual elements, the proposed framework brings together perspectives often treated separately in the literature (Thomas et al., 2023), offering an account of how experiences with technology are formed – or disrupted – over time for all disability categories. Reflecting the above, this study supports previous studies (e.g. Kalargyrou et al., 2018; Liu et al., 2024), echoing the view that the interaction between disability and technology needs to be experience-adaptive and context-sensitive. In this sense, the framework also engages with ongoing debates on post-adoption behavior, technology non-use and the limitations of intentions-based models. Traditional approaches, such as TAM or UTAUT, primarily predict intention and assume linear progression toward adoption. Our framework, instead, captures discontinuities, cycles of engagement and disengagement, and ambivalence in technology use, which is particularly relevant for PwDs. This shift moves the epistemological focus from static prediction to dynamic, experience-based technology use theorization (Mogaji et al., 2024).

Fourth, this study stands out for applying a critical review methodology (Kraus et al., 2022; Seuring et al., 2020; Truong and Papagiannidis, 2022), a still underused approach in the analysis of technology acceptance frameworks focused on PwDs. This method allowed for further development of the discourse on the implicit biases embedded in traditional technology acceptance frameworks. It reinforces the idea that the interaction between disability and technology cannot be understood as the same for everyone; instead, it requires adaptation to specific physical, sensory and intellectual needs, rather than conforming to standard expectations of technology use. Further, this interaction is not uniform but unfolds across multiple stages of experience. The proposed framework makes it possible to trace this process, from perception to non-perception and from satisfaction to dissatisfaction, offering a more detailed and inclusive understanding of what technology experience means for diverse users. By explicitly theorizing acceptance and usability together, and equally non-acceptance and non-usability, the framework advances acceptance frameworks beyond its current boundaries, showing how design failures, frustration or overload can transform adoption into disengagement. At the same time, inclusive interventions can enable re-engagement (Pramanik and Jana, 2025). In addition, the findings of this study inform a set of practical recommendations, supported by policy guidance, to help technology developers better address the diverse requirements of PwDs.

Fifth, the proposed framework should be understood as a conceptual theoretical contribution (Corley and Gioia, 2011; Whetten, 1989) with a generative and diagnostic lens (Jaccard and Jacoby, 2020). As a conceptual contribution, it offers an inclusive reinterpretation of established acceptance variables of technology acceptance frameworks through a disability-sensitive lens. Specifically, in line with Whetten's (1989) criteria for theoretical contributions, the framework clearly articulates the “what” (the adapted variables), the “how” (their reconfiguration into technology experience-based dimensions) and the “why” (to account for disability-specific technology experiences that are overlooked). Extending these existing frameworks risks preserving their built-in structural flaws – especially the tacit assumption that all users are fully able-bodied (Sheehan and Hassiotis, 2023; Theodorou and Meliones, 2019; Vereenooghe and Westermann, 2019). On the other hand, based on the study by Jaccard and Jacoby (2020), the framework adopts both diagnostic and generative lenses: diagnostic, as it uncovers the implicit exclusions embedded in technology acceptance frameworks; and generative, in that it lays the foundation for future inclusive technology experience design and empirical inquiry. Finally, the framework can be situated as a middle-range theory. In line with Merton (1968), it is theoretically informed yet grounded in a specific context – technology and disability – offering transferable insights without claiming universal generalizability. Moreover, following Gregor (2006), the proposed framework fulfills both explanatory and design-oriented functions: it clarifies how and why exclusion occurs in existing TAMs, and it supports inclusive intervention strategies by underscoring that technology developers and policymakers should embed disability-specific considerations from the outset and adopt strategies that capture diverse technology experiences-technology acceptance and usability, while preventing non-acceptance and non-usability. In this respect, the framework reframes the boundaries of acceptance frameworks by considering ambivalence, discontinuity and situated experience as core constructs. This opens space for future research questions concerning the “where next” for inclusive acceptance research (e.g. Farah and Ramadan, 2024).

Finally, the categorization of disabilities introduced in this study offers an approach that can serve as a reference for future research in inclusive technology acceptance research. In line with key domains of activity and participation identified in the ICF's biopsychosocial model (ICF, 2002) and based on the evolving discourse around disability categorizations (e.g. Cerdan Chiscano and Darcy, 2021; Dickson et al., 2016; Garrod and Fennell, 2023), this study proposes three disability categories: physical, sensory and intellectual. This categorization aims to provide a structured foundation for future research to design investigations that capture the diverse needs and experiences of different disability groups.

In the modern setting, technologies progressively transform obstacles into bridges that create new paths toward a more inclusive society. However, turning this promise into reality requires all hands on deck and coordinated efforts between legislators and technical experts, going beyond technology innovation alone. Their role is key to reshaping a future where diversity is not seen as a stumbling block but embraced as a powerful asset. Unlocking technology's full impact, particularly for PwDs, depends on addressing acceptance and usability in an integrated way, while preventing non-acceptance and non-usability. To foster long-term inclusivity, technology developers and policymakers should conform to the specific experiences of PwDs in using technology tools.

In terms of acceptance, to ensure accessibility and technological adaptability, technology developers should pay attention to technologies that need to be easy to access, test and personalize, meeting individual needs in practical ways. To guarantee autonomy, control and personal capacity, technology developers should introduce intuitive interfaces that enhance users' confidence and sense of control. At the same time, policymakers should invest in accessible training to strengthen digital skills. Also, to improve emotional and motivational involvement, technology developers should support technological tools that are enjoyable, motivating and rewarding. Public policies should, therefore, support solutions that foster well-being and active participation. Lastly, for social and intentional adoption, technology designers should account for social dynamics, peer influence and the perceived value of technology use. Here, policymakers can create inclusive environments, run awareness campaigns, and support practices that enhance technology adoption. To illustrate, consider a hypothetical case of a visually impaired user engaging with VR training software. In such a scenario, the 12 dimensions of our framework highlight possible breakdown. For example, a lack of detectability in feedback may reduce trust (non-acceptance), or limited adaptability of the interface may hinder personalized use (non-usability). By mapping these issues against the framework, developers and policymakers can better anticipate where interventions are needed, such as implementing adaptive feedback mechanisms, voice-guided navigation or context-sensitive help features.

On the other hand, avoiding non-acceptance of technology among PwDs means preventing design and policy decisions that undermine their experience. To reduce uncertainty and cognitive challenge, technology developers should avoid complex or unclear interfaces, while policymakers should ensure that regulations prioritize clarity and simple information. To minimize discomfort in interaction, technology developers should not overlook inclusive design principles, and public institutions should avoid promoting solutions that exclude all user co-design. Finally, technology developers and policymakers should ensure transparency in the functioning of systems to mitigate the fear of negative effects.

Equally important is addressing usability. Improving satisfaction with technology requires close attention to how PwDs interact with several technology tools. Ease of use and interaction calls to technology developers for natural and straightforward interfaces. At the same time, public policy should encourage universal design standards. Adaptability and personalization necessitate that technology developers adapt scalable solutions, while regulations support the development of customizable technologies. Output quality and detectability demand clear feedback. Technology designers should avoid ambiguity, and policymakers should promote guidelines that ensure clarity and transparency. Finally, the overall use and functionality experience highlights the need to support adoption. Technologies should be sustainable and accessible on a daily basis, backed by policies that ensure continuous and equitable access.

To prevent non-usability, developers and policymakers should avoid solutions leading to interactional breakdown. Complex interfaces obstruct effective use. Technology designers should prioritize simplicity and inclusive testing with PwDs as a prerequisite for technology implementation.

Notably, the practical implications of this study derive directly from the 12 dimensions identified in the framework, which offer concrete directions for design, testing and policy interventions. For example, dimensions such as evolving trust and familiarity point to the need for adaptive feedback systems, gradual tutorials and accessible onboarding procedures that progressively build user confidence. Conversely, frustration and overload highlight the importance of iterative usability testing with PwDs to prevent complex, redundant or cognitively demanding interfaces. Task completion and outcome relevance emphasize that technologies must allow PwDs to achieve meaningful and perceivable results, aligning functionalities with everyday goals. Similarly, satisfaction and dissatisfaction suggest that designers should integrate motivational elements while guaranteeing rapid support mechanisms to mitigate negative experiences. Perceived ease of use and perceived usefulness underscore the necessity of intuitive, universally designed and customizable solutions, coupled with clear communication of tangible benefits. Finally, the dimensions of behavioral intention and disengagement draw attention to the social context of adoption. At the same time, peer support and community engagement can strengthen intention to use, and policies and design strategies should also provide re-engagement opportunities to prevent exclusion when barriers arise. By explicitly translating each of these dimensions into design, testing and policy practices, the framework can help move beyond generic inclusive design principles and suggest a roadmap to guide more targeted managerial decisions.

Through a critical review, this study introduces a multidimensional framework in which technology acceptance variables, adapted to reflect the characteristics of physical, sensory and intellectual disabilities, are structured in subcategories and reorganized into distinct dimensions. These capture specific phases of the technology experience under the perspectives of acceptance, non-acceptance, usability and non-usability. This multidimensional, disability-sensitive framework advances inclusive technology acceptance research by tailoring variables to disability categories and technology experiences. It identifies 12 technology experience dimensions, each linked to a specific stage of interaction between PwDs and technology, laying the groundwork for an inclusive acceptance framework that captures user diversity and reveals where technology experience barriers emerge. Moreover, it reflects technology interaction as a dynamic continuum from perception to satisfaction among PwDs. Ultimately, this research guides technology professionals and policymakers on fostering acceptance and usability while preventing non-acceptance and non-usability, making disability inclusion a core component of their strategies.

Although this research moves the inclusive technology acceptance conversation forward, certain limitations should be acknowledged. First, following the recommendations of Carnwell and Daly (2001), future research could apply the results of this critical review to an illustrative case study. Specifically, a case study could apply the proposed framework in real-world settings, offering a testing ground for future research to assess its ability to track how technology perceptions and PwDs' satisfaction evolve across different disability categories. In addition, future studies might zoom in on the key acceptance variables along the technology experience continuum, from perception to satisfaction, to sharpen the framework's capacity to reflect each stage of inclusive technology use.

Second, given the proposed conceptual framework, future research could further refine and extend it through complementary methodologies. Qualitative approaches could offer insights into the lived experiences of PwDs. Ethnographic research (Hammersley and Atkinson, 2019) or longitudinal designs (Saldaña, 2003) relying on in-depth interviews and prolonged observational fieldwork to explore how the interaction between PwDs and specific technologies evolves. Additionally, focus groups (Krueger, 2014), organized by type of disability, could collect comparative narratives of technology experiences, revealing common ground and contrasting realities in users’ experiences. Also, future research could complement our critical review that problematizes the assumptions underlying the technology acceptance frameworks with systematic evidence mapping. In particular, the recently proposed B-SLR protocols (Marzi et al., 2025), which emphasize transparency, replicability and bibliometric mapping, offer a promising avenue for situating the proposed multidimensional framework within broader knowledge domains. By combining critical synthesis with the exhaustive identification and statistical clustering of B-SLR methods, future studies could empirically trace how disability-sensitive acceptance constructs are distributed across disciplines, identify blind spots in the literature and uncover latent connections between fragmented research streams. In parallel, quantitative studies could enhance replicability and empirically validate the framework across different technology contexts. It will be important to assess the structural robustness of the framework through exploratory and confirmatory factor analyses (Brown, 2015; Goretzko et al., 2021). This could be followed by using structural equation modeling (SEM or PLS-SEM, Sarstedt et al., 2017) to examine causal relationships among the variables along the experiential continuum. Furthermore, multi-group analysis could test the framework (Ringle et al., 2015) to compare acceptance/non-acceptance and usability/non-usability trajectories across PwDs. Finally, it would be beneficial to incorporate co-design and participatory design approaches to enhance the practical relevance of the framework in inclusive policy development and user-centered design (Sanders and Stappers, 2008). Building on the proposed methodological directions, several empirical questions may emerge. For instance: What factors facilitate – or hinder – the transition from acceptance to usability among PwDs? Under what conditions does an initially positive perception of technology shift into non-acceptance, and what leads usability to break down into non-usability? Do these transitions vary across different types of disability? Do age, gender, socioeconomic status or prior experience with technology mediate or moderate the relationship between acceptance and usability for PwDs? Does the technology experience journey differ across disability types – physical, sensory or intellectual?

Third, the proposed framework could be further expanded to include the affective and motivational dimensions of the technology experience of PwDs. In particular, it may be valuable to integrate the Cognitive Affective Theory of Learning with Media (Moreno, 2006; Moreno and Mayer, 2007), which extends the traditional Cognitive Theory of Multimedia Learning by incorporating affective and motivational elements into cognitive processes. Additionally, future research could incorporate intersectional characteristics (e.g. age, gender, socioeconomic status) and experiential variables, such as the number of years of experience with the specific technology being analyzed.

Fourth, while this study employs a multidimensional framework to reframe technology acceptance variables, capturing distinct moments of technology experience for different disability categories, future research could advance this work by identifying which variables act as stimuli and which act as responses, referring to the stimulus–organism–response model (Mehrabian and Russell, 1974). Distinguishing these roles would clarify which variables serve as stimuli for technology use by people with different disabilities.

Fifth, future research could explore the cross-contextual application of the proposed framework. Specifically, the model could be adapted to diverse technological domains – such as generative artificial intelligence, virtual or augmented reality or assistive robotics – to investigate how the continuum of technology experience unfolds and shifts for PwDs depending on the nature and demands of each technology. Moreover, applying the framework across varied use settings – such as education, employment, healthcare and leisure – could help assess its validity and adaptability in real-world scenarios. Future studies may also test the theoretical robustness of the model across different sociocultural environments (e.g. Western vs Asian countries) and institutional contexts (e.g. public vs private education systems, universal healthcare vs insurance-based models, salaried vs self-employment).

Finally, based on the scientific literature, we categorized disabilities into three categories. Future research could adopt a more detailed approach, drawing on professional expertise and established studies to explore alternative or more refined classifications where appropriate.

The supplementary material for this article can be found online.