The Aging-Related Factors on Assistive Technology Acceptance Among the Pre-aging and Aging Population

The Aging-Related Factors on Assistive Technology Acceptance Among the Pre-aging and Aging Population | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article The Aging-Related Factors on Assistive Technology Acceptance Among the Pre-aging and Aging Population Mansoor Ali Mohamed Yusoof, Haris Abd Wahab, Kumarashwaran Vadevelu This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6799606/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Purpose Assistive technology was aimed at reducing the age based digital divide. However, there is a disconnection between the stakeholders and the aging users in terms of the capability and ability required to use the technology, which requires a better understanding of the intention to use AT based on the cognitive, physical, and social capabilities of older users. This research examines the aging-related factors on the acceptance of assistive technology among the pre-aging and the aging population. Methods This study is anchored in conceptual frameworks relating to intention to use assistive technological and the factors of cognitive, physical, and social changes caused by aging. Data was collected through a structured questionnaire derived and adopted from previous studies from pre-aging (45 to 60 years of age) and aging (60–75 years of age) users. This research examines the conceptual structural models by the constructs of Cognitive, Physical Capabilities, and Social Presence, as well as the mediating function of effort expectancy. Results It was found that cognitive and physical capabilities were significant factors that support effort expectancy, with the latter having a much stronger influence. The analysis shows that social presence has a weak and insignificant influence on effort expectancy. The findings also reveal that respondents in both groups feel that their current cognitive and physical capability is adequate when accepting and using assistive technology. Most aging respondents perceive themselves to be younger than their actual chronological age showing higher levels of self-confidence, self-respect, innovativeness, as well as willingness to try new innovations and accept change. Conclusion The findings offer practical implications for technology developers, designers, and policymakers aiming to improve the accessibility and usability of technology for aging individuals. Pre-aging and Aging Users Assistive Technology Cognitive Physical Social Figures Figure 1 Figure 2 Figure 3 Figure 4 1.0. INTRODUCTION Technology is the central force that shapes our lives and our evolution. Technology offers real benefits to society but also creates an environment where there is constant requirement to catch up when technology changes rapidly [1]. The need to keep up eventually leads to the technological divide, and in today’s information technology, it created the “digital divide”. While some of the digital divides are caused by lack of access or challenges in using technology, it also can occur with ageing [1]. Ageing is a natural biological process that leads to decline in health and physical conditions, ability to adapt, and reduced cognitive capabilities. Ageing can lead to the inability of an individual to use a technology, which may be challenging due to biological changes, and social structure changes [2]. In recent times, there is increasing focus on assistive technologies (AT) that provide support for users, especially older people and those with disabilities or long-term conditions, to compensate for their functional difficulty or decline [3]. Example of ATs are robotic nursing, ambient assisted living, and assistive robotics. With the increase for AT, there is a need for researchers and developers to understand the acceptance and usage of AT among ageing users, considering their deteriorating cognitive and physical capabilities of EIAT. Despite various theories explaining technology acceptance and use, there is limited research on the users’ capabilities that influence the perceived effort expectancy (EE) and acceptance of AT, particularly among the rapidly aging society in Malaysia. Thus, there is a critical need for research to explore how users’ capability such as cognitive, physical, and social factors impacts the acceptance and use of AT among ageing adults. Compaine [4] describe digital divide as the differences in accessing and usage of digital technologies by various social groups and communities. There is increasing concern about the age-related digital divide regarding access to technology, use, and capability. This research examines the aging-related factors on the acceptance of assistive technology. This study is anchored in conceptual frameworks relating to intention to use assistive technological and the factors of cognitive, physical, and social changes caused by aging. We examine the conceptual structural models by the constructs of Cognitive Presence (COG), Physical Capabilities (PHY), and Social Presence (SOCI), as well as the mediating function of effort expectancy. The rest of the paper is organized as follows: Section 2 provides the background, the underlying theories, and the conceptual framework of this study. Section 3 provided details of the methods adopted to achieve the aim of this research. Section 4 present the results of the data analysis that includes the demographic and SEM analysis, while section 5 provides detailed discussions of the results. Section 6 concludes the findings. 2.0. LITERATURE REVIEW AND HYPOTHESES DEVELOPMENT By 2030, 1 in 6 people around the world will be 60 years or over, and by 2050, the world’s population of people aged 60 years and older will double to 2.1 billion [3]. On the same note, the number of people aged 80 years or older is expected to reach 426 million by 2050. In Malaysia, the Department of Statistics Malaysia state that in 2023, 7.2% is aged 65 and above 1 and it is estimated that more than 15% of its population will be above the age of 65 by 2050 2 . The oxford dictionary defines ageing as “grow old or older or cause to appear old or older”. From the perspective of biology, age is defined as “The aging of an organism is a process resulting from the combination of mechanisms limiting its lifespan (“promotive”) and mechanisms modulating their effects (“protective”). Presently, chronological age has been studied as social construction, life transition, and an important age association [5, 6]. The use of chronological age may lead to better administration of human growth both physically, mentally, and spiritually [5,6], but it also creates the age stereotyping, which vary in terms of characteristics and qualities such as young vs. old dualism, where younger people are generally positivized, celebrated and aspired to, while old and ageing people are derogated, derided and rejected [7]. Individuals may reject the idea of chronological age, by having younger or older identity by focusing on socially acceptable and popular conceptualizations of age through appearance, interests, and life choices to be more mature [8] or youthful [9]. Such behavior can also exist in technology usage, where older people want to be technologically in par with the younger generation such as the involvement in social media [10]. Gilleard and Higgs [11] suggest that age is a measure of digital differences and the factors that cause the difference in technological engagement between age groups varies depending on the uses of technology [12], technology being studies [13], contextual factors [14], and stereotyping [15]. There are common alternative findings that defy the regular stereotyping among EIAT and technology, such as the use of mobiles apps show that EIAT are keen to adopt smartphones [16]. 2.1. Assistive Technology for Aging Individuals. de Almeida et al. [17], state AT as “any product which has the primary purpose to maintain or improve an individual’s functioning and independence, and thereby promote their well-being”, while Khasnabis et al. [18], consider AT as “An assistive product is any product (including devices, equipment, instruments, and software), either specially designed and produced or generally available, whose primary purpose is to maintain or improve an individual’s functioning and thereby pro-mote their wellbeing”. Abdi et al. [19] have identified the potential of new digital technology that can benefit both young and old users alike. These include the increasing presence and power of Artificial Intelligence (AI), new form of human-computer interaction (HCI) such as virtual reality, speech-based interactions, and facial or gesture recognition, as well as sensory and robotics [20]. 2.1.1. Human Activity Assistive Technology Model Human Activity Assistive Technology Model (HAAT) [21–23], relates to the AT and capability of users. Figure 1 depicts the HAAT model, which has four basic components: activity, human, assistive technology, and context. The human element focuses on user’s physical, cognitive, and sensory skills when participating in an activity, as well as their life roles in terms of social, experience, and motivation [21]. The Activity elements include task(s), task demands, and the contextual influencers of participation such as friends and family, while AT is described as device that enables activity performance, including technology complexities and the human-technology interface [21]. There are many activities that AT are benefiting the elder users of AT (EUAT), such as in health, safety, communication, social involvement, entertainment, leisure, and home living [24]. For the EIATs, the common forms of digital assistive technologies are robotics, and mobile or computer-based applications [19]. 2.2. Technology Acceptance Davis developed the Technology Acceptance Model (TAM) based on two pivotal pillars, which are perceived usefulness and perceived ease of use, which influence the behavioural intention to use technology [25]. Perceived ease of use is defined as “the perception of a low degree of effort during the use of the technology”, while perceived usefulness is the “perception that the use of the technology will increase performance” [26]. The perceived usefulness and perceived ease of use are widely acknowledged utilitarian variables and are strong predictors of intention to technology [27]. Perceived usefulness is a type of extrinsic motivation toward the intention to use technology, while perceived ease of use is intrinsic motivation [28]. Chan et al. [29] stated that users are intrinsically motivated when they believe they can perform well in a task, which confirms the argument in [28]. The Unified Theory of Acceptance and Use of Technology (UTAUT) [30] model has four basic constructs, which are performance expectancy, effort expectancy, social influence, and facilitating conditions that determine the usage intention and usage behaviour. The model also has four moderating variables of gender, age, experience, and voluntariness of use. Figure 2 depicts the UTAUT model. 2.3. Conceptual Framework Based on the HAAT model, EUAT cognitive, physical, and social influence are critical for using AT, which determines the acceptance and usage of AT. Ageing decreases, health and physical conditions, ability to adapt, reduced cognitive capabilities, and relations and social roles change [31,32]. Figure 3 shows the conceptual framework for this research that depicts the independent, mediating, and dependent variable. The framework is based on the UTAUT and HAAT model. This research examines cognitive, physical, and social as the key human elements for the development of AT based on HAAT, and EE from UTAUT. While UTAUT has two core variables of EE and performance expectancy (PE), only EE is used on this research as the focus of this research is related to the perceived capacity of the users based on their age. Cognitive Presence In traditional human–machine interaction, cognitive workload or mental workload was regarded as an important variable influencing human performance, safety, and efficiency, which may be disadvantageous for ageing users due to deteriorating physical and cognitive function [33]. Cognitively old elders refer to individuals that perceive themselves as old or older than their chronological age, exhibit higher cautiousness and risk aversion, have anxiety toward technology, as well as exhibiting low self-esteem and self-confidence [5,6]. Farivar et al. [34], states that individuals with older cognitive age are less efficient and process information more slowly, despite their chronological age. Lee et al. [35] found cognitively older adults tend to have negative attitude towards technology, considering the need to learn and use those technologies places a significant cognitive burden on them, reducing their willingness to explore how technology works. However, as older people experience increasing cognitive constrained, they can vary the cognitively demanding choice strategies, which are mostly based on experience, or heuristics [36]. On top of that, complexity of a technology demotivates the usage and acceptance of technology, especially for individuals with weaker memory or spatial ability [34]. Cognitive abilities, such as working memory and intelligence are important for technology usage and acceptance. It was stated that there was a reciprocal relationship between cognitive functioning and general use of information and communication technology [34]. It is hypothesized that: H1: Cognitive presence has a significant influence on the effort expectancy in using AT. Physical Capabilities The physical deterioration among ageing adults increases the challenges in accessing hardware features or digital content caused by limitations in mobility and decreased visual capacity [37]. As such, older individuals find it difficult to adopt and use technologies that require physical effort. Combined with waning physical and cognitive abilities such as hearing, vision, speech, locomotion, and memory capabilities, the ability to use technology decreases [38]. For example, users with poor hand-movement and coordination may find it difficult to use computer mouse or a touch screen [39]. Welch et al. [40], lists several intrinsic capabilities that are critical for AT of older adults, which includes mental, sensory functions, neuro-musculoskeletal function, voice and speech, among others. Physical and sensory limitations impair EUAT’s ability to seek and receive information [41]. Based on the work in [42], the physical ability considered in this research is motor neuron (hand movement), visual, and hearing. These physical abilities are needed when interacting with technology, particularly computers and mobile devices. Some of the physical functions that are critical for AT usage are sensory functions and neuro-musculoskeletal function, particularly the hand movement. Existing works signal that older users demonstrate poor performance in using devices such as touch screen compared to younger users [43]. Sensory functions, particularly visual functions, are important for many of the current technologies such as smartphones and tablets, with some offer assistive support for individuals with low or poor vision such as colour, contrast, and size adjustment for improving visibility and readability [44]. Hearing impairment affects about two-thirds of adults aged 70 years and above [45]. Hearing impairment can adversely affect physical functioning through reduced perception of auditory input that contributes to walking and body balance [45], that lead to faster decline in physical function over time [46]. It is hypothesized that: H2: Physical Capability has a significant influence on the effort expectancy in using AT. Social Presence Since it is not unusual for humans to engage with technology as if it were a social entity, it can be expected that this effect is exacerbated when technology takes the form of an embodied character and interacts in a social manner. In [47], they expect that the sense of presence increases if a system is perceived to have more social abilities, thus increasing the acceptance of technology. In terms of technology, especially digital technology, digital inclusion focuses on the ability and opportunity of individuals to access digital technology such as the Internet [48], though it was argued that digital inclusion does not necessarily directly translate into social inclusion, and that digital inclusion activities cannot follow a one-size-fits-all approach [48]. Social inclusion can be a strong motivator for users to accept and use technology [49]. A person who uses or non-uses of technology in later life is influenced by variety of agents within a social field and the power relations between those agents [50]. Technology acceptance in later life such as the Internet is supported by relatives and friends as well as the media discourses surrounding age and demographic change, the institutions [50]. Social Inclusion and social exclusion are one of the motivators for EIAT. Based on the above, it is hypothesized that: H3: Social Presence has a significant influence on the effort expectancy in using AT. Effort Expectancy A person who lacks confidence in his/her abilities and skills would not make the effort to accomplish a task, and she/he would show less persistence in overcoming any potential obstacles than those with high confidence in their abilities and skills [51]. When faced with challenges, those who have significant doubts about their abilities either slacken their efforts or give up entirely, whereas those who have a strong sense of efficacy put up greater effort to overcome the obstacles. For some, being old also affects the perception of self and the world changes such as the lowering of self-esteem, feeling of no longer being needed, as well as loss of meaning of life. Aging can lead to the inability of an individual to use technology, which is becoming more challenging due to biological changes, and social structure changes [51]. EE is one of the important predictors of intention to use technology [52,53] as it has a significant effect on behavioral intention. The direct effect of EE on behavioral intention has been supported [54,55] and rejected [56, 57]. EE concerning the ease of use of the technology and was derived from perceived ease of use [25], which was defined as ‘‘the degree of ease associated with the use of the system” [28]. EE was a predictor of attitude, especially in the beginning, and interestingly, it was also the strongest predictor of intention. While both PE and EE play a role in affecting technology acceptance among older adults [58], EE is a major challenge uniquely associated with aging because decline in physiological functions (e.g. vision, hearing, health) and cognitive functions (e.g. memory) can effectively reduce the ability to use technology. On top of that, EE was found to have a major role in the assessment of PE for older adults but not for younger adults [59]. According to Venkatesh et al. [28], increase in age may negatively affect the EE as it is associated with the difficulty in processing complex stimuli and allocating attention to information on the job, both of which may be necessary when using software systems. Age may affect the EE among older adults, due to reduced cognitive abilities to learn, have lower perception of self-efficacy and higher anxiety over technology use, and have limited experiences and “how-to” knowledge [60]. The increase of age weakens the relationship between EE and behavioural intention [61]. This is because older users perceive the complexity of technology more, while younger people perceive more the usefulness of technology. It means that older users perceived that they need to put more effort into using the technology as compared to younger users. As such it is hypothesized that; H4: effort expectancy has a significant influence on the intention to use AT. H5: Effort expectancy mediates the influence of Cognitive presence on the intention to use AT. H6: Effort expectancy mediates the influence of Physical Capability on the intention to use AT. H7: Effort expectancy mediates the influence of Social Presence on the intention to use AT. 3.0. METHODS This study examines the influence of aging-related factors on the acceptance of AT. The target respondents are Malaysian that are pre-ageing (40 to 55 years of age) and ageing (55–70 years of age). This form of classification was based on the agreement among the researchers that ageism has different effects for different ages [62, 63]. The location of the research will be the Klang Valley, home to more than eight million Malaysian. Klang Valley is an ideal place for conducting this kind of research as it is Malaysia’s most congested and developed central city [64]. It is the most suitable geographical location to study AT due to high penetration of the Internet [65], enabling the administration of the survey online. On top of that, research related to AT was performed in the Klang Valley, indicating the high degree of awareness of AT among the Klang Valley dwellers [66–68]. The research instrument and the data collection method has been reviewed and approved by the Universiti Malaya Research Ethics Committee (UMREC). The participants are not forced to be involved in the research and give their informed consent. In this research, the survey instruction prompts the potential respondent to say that if he or she has no prior experience with AT, they can opt not to continue with the survey. Similarly, during the face-to-face survey, the researcher obtains permission before conducting the survey. The participants are assured that the survey is not expected to cause any harm mentally, physically, and physiologically to the participant. Should the participant feel uncomfortable with the survey, he or she may withdraw from the study at any time. The researcher does not share the respondent’s information with third parties. The data are collected solely for statistical analysis and hypothesis testing. As the survey did not ask for personally identifiable data such as name, religion, race, or identity card number, there is potentially no risk on data privacy of the respondents. The survey was administered via online survey and by physical distribution for older respondents (age 55 and above). For the online survey, the link being shared via social media platforms. The online survey instruction prompts the potential respondent that, if he or she has no prior experience with AT, they can opt not to continue with the survey. At the end of the survey, the respondents are encouraged to forward the link of the survey to any of their family and friends who are above the age of 40. The physical distribution of the survey for older adults is mainly because many of them may be lacking or have difficulties in accessing online surveys [69]. It is also to ensure a balance between pre-aging and aging groups as most responses from the online survey are likely the pre-aging group. In this study, data analysis was conducted in two phases. The first one is the preliminary data analysis that was performed on the data collected from the pilot test to check the reliability of the data collection instrument, followed with meticulously evaluating the measurement and structural models, setting a benchmark for analytical precision. 4.0 RESULTS 4.1. Demographics Analysis A total of 391 respondents took part in the study, where 234 (59.8%) are from the 40–55 age range, while 157 (40.2%) from 56–70 age bracket. Table 1 depicts the demographic composition of the two age groups, indicating a balanced composition in term of gender, level of education and income. Table 1 The demographic composition of the two age groups Demographic Category Pre-Aging Aging Total Gender Male 119 (30.43%) 92 (23.53%) 234 (59.9%) Female 115 (29.41%) 65 (16.62%) 157 (40.1%) Level of education Diploma and lower 15 (3.8%) 35 (9.0) 50 (12.8%) Bachelor’s degree 64 (16.4%) 33 (8.4%) 97 (24.8% Master’s degree 135 (34.5%) 77 (19.70%) 212 (54.2%) Ph.D. 20 (5.1%) 12 (3.1%) 32 (8.2%) Level of income None 0 (0.0%) 1 (0.3%) 1 (0.3%) Less than RM 2000 22 (5.6%) 82 (21.0%) 104 (26.6%) RM 2,000 to 4,000 32 (8.2%) 15 (3.8%) 48 (12.0%) RM 4,000 to 6,0000 54 (13.8%) 29 (7.4%) 83 (21.2%) More than RM 6,000 126 (32.2%) 30 (7.7%) 156 (39.9%) 4.2. Measurement Model Analysis Reliability Statistics Table 2 outlines the measurement model statistics for the lower order constructs, which are Cognitive Age (AGE), EE, Intelligence (INT), Intention to Use AT (IUAT), Cognitive Load (LOAD), Hand Movement (PHM), Speech and Hearing Ability (PSH), Social Inclusion (SIN), Social Isolation (SIS), and Visual Ability (VIS). Table 2 also includes the outer loadings (OL), Variance Inflation Factor (VIF), Cronbach's alpha, composite reliability (both rho_a and rho_c), and the Average Variance Extracted (AVE) for each item within the constructs. The measurement model statistics for the higher order construct of COG, PHY, and SOCI are provided in Table 3 . The Cronbach's alpha and composite reliabilities are indicators of internal consistency, ensuring that the items within each construct reliably measure the concept. It becomes evident that all constructs display good reliability, with Cronbach's alpha and composite reliability values well above the acceptable threshold of 0.7, suggesting that the constructs are consistently measuring the intended underlying phenomena [70]. The AVE values are also above the commonly accepted level of 0.5, indicating satisfactory convergent validity [71]. Table 2 Measurement Model Statistics (Lower Order Construct) Construct Items OL VIF Cronbach's alpha Composite reliability (rho_a) Composite reliability (rho_c) Average variance extracted AGE AGE1 0.930 3.203 0.811 0.844 0.889 0.730 AGE2 0.902 2.851 AGE3 0.716 1.382 EE EE1 0.861 2.211 0.850 0.854 0.899 0.690 EE2 0.796 1.758 EE3 0.811 1.925 EE4 0.854 2.254 INT INT1 0.896 3.058 0.903 0.906 0.933 0.776 INT2 0.892 2.995 INT3 0.886 2.753 INT4 0.847 2.319 IUAT IUAT1 0.808 1.615 0.816 0.828 0.891 0.732 IUAT2 0.903 2.212 IUAT3 0.852 1.908 LOAD LOAD1 0.884 2.717 0.906 0.906 0.934 0.780 LOAD2 0.877 2.717 LOAD3 0.875 2.733 LOAD4 0.896 3.083 PHM PHM1 0.816 1.813 0.826 0.830 0.885 0.657 PHM2 0.858 2.127 PHM3 0.767 1.594 PHM4 0.799 1.711 PSH PSH1 0.882 2.088 0.844 0.846 0.906 0.762 PSH2 0.883 2.242 PSH3 0.854 1.840 SIN SIN1 0.814 1.942 0.892 0.894 0.925 0.756 SIN2 0.899 3.045 SIN3 0.894 3.056 SIN4 0.867 2.425 SIS SIS1 0.871 2.000 0.815 0.818 0.890 0.730 SIS2 0.805 1.555 SIS3 0.885 2.196 VIS VIS1 0.533 1.101 0.667 0.750 0.818 0.611 VIS2 0.892 1.832 VIS3 0.868 1.780 AGE→ Cognitive age, EE→ Effort Expectancy, INT→ Intelligence, IUAT→ Intention to Use AT, LOAD→ Cognitive load, PHM→ Hand Movement, PSH→ Speech and hearing ability, SIN→ Social Inclusion, SIS→ Social Isolation, VIS→ Visual ability. Table 3 Measurement Model Statistics (Higher Order Construct) Construct Items OL VIF Cronbach's alpha Composite reliability (rho_a) Composite reliability (rho_c) Average variance extracted COG AGE 0.922 3.384 0.899 0.904 0.937 0.832 INT 0.943 3.907 LOAD 0.915 2.896 PHY PHM 0.917 3.168 0.828 0.828 0.897 0.745 PSH 0.852 1.770 VIS 0.878 2.655 SOCI SIN 0.919 1.360 0.865 0.872 0.897 0.557 SIS 0.811 1.360 EE→ Effort Expectancy, IUAT→ Intention to Use AT, COG→ Cognitive, PHY→ Physical, SOCI→ Social. Discrimination Validity Table 4 presents the Heterotrait-Monotrait ratio (HTMT) matrix as a measure of discriminant validity for the constructs in the study. For discriminant validity to be established, the HTMT values should be significantly lower than 1. For discriminant validity to be considered satisfactory, HTMT values should ideally be below 0.85, a threshold indicating that the constructs are empirically distinct and not excessively overlapping in what they measure. Table 4 shows that most construct pairs maintain HTMT ratios below the threshold, implying adequate discriminant validity. Table 4 Discriminant Validity (Heterotrait-monotrait ratio) AGE EE INT IUAT LOAD PHM PSH SIN SIS VIS AGE EE 0.778 INT 0.847 0.837 IUAT 0.764 0.804 0.835 LOAD 0.810 0.745 0.843 0.793 PHM 0.790 0.781 0.777 0.634 0.611 PSH 0.773 0.767 0.825 0.850 0.827 0.704 SIN 0.714 0.763 0.732 0.718 0.751 0.608 0.768 SIS 0.617 0.435 0.610 0.606 0.655 0.427 0.678 0.555 VIS 0.818 0.805 0.825 0.688 0.681 0.847 0.671 0.655 0.511 AGE→ Cognitive age, EE→ Effort Expectancy, INT→ Intelligence, IUAT→ Intention to Use AT, LOAD→ Cognitive load, PHM→ Hand Movement, PSH→ Speech and hearing ability, SIN→ Social Inclusion, SIS→ Social Isolation, VIS→ Visual ability. 4.3. Structural Model Analysis 4.3.1. Model Fit Statistics Figures 4 visualize the structural model and its overall fit, respectively. For EE, an R-square of 0.618 suggests a significant explanatory power, with 61.8% of EE's variance accounted for by the model. Table 5 Model Fit Statistics R-square R-square adjusted Q²predict RMSE MAE EE 0.618 0.615 0.608 0.631 0.423 IUAT 0.455 0.454 0.509 0.706 0.513 EE→ Effort Expectancy, IUAT→ Intention to Use AT. From Table 5 , the R-square adjusted value of 0.615 confirms this explanatory power's robustness, adjusting for the number of predictors [72]. IUAT's R-square of 0.455, with an R-square adjusted of 0.538, demonstrates the model's substantial explanatory capacity for this construct as well. The results suggest that the proposed model not only fits the observed data well but also provides valuable insights into the factors influencing AT adoption. These findings are aligned with established SEM literature that underscores the importance of comprehensive model fit assessment in validating SEM analyses [70, 73]. The R 2 shows that the proposed independent variables explain 61.8% of EE, and the EE explain 45.5% of the intention to use assistive technology, which is not unusual since UTAUT has two basics independent variables of PE and EE. 4.3.2. Direct and Mediation Relationship Table 6 provides a detailed overview of the structural model statistics for the direct and mediation effects present in the study focused on the adoption and use of AT. Table 6 Structural Model Statistics Original sample (O) Sample mean (M) Standard deviation (STDEV) T statistics P values f-square Support H1 COG ->EE 0.341 0.334 0.084 4.062 0.000 0.078 Yes H2 PHY ->EE 0.674 0.675 0.037 18.427 0.000 0.835 Yes H3 SOCI ->EE 0.109 0.117 0.059 1.851 0.066 0.012 No H4 EE ->IUAT 0.395 0.395 0.076 5.211 0.000 0.134 Yes H5 COG ->EE ->IUAT 0.230 0.226 0.061 3.761 0.000 Yes H6 PHY ->EE ->IUAT 0.266 0.266 0.052 5.108 0.000 Yes H7 SOCI ->EE ->IUAT 0.073 0.078 0.039 1.866 0.062 No EE→ Effort Expectancy, IUAT→ Intention to Use AT, COG→ Cognitive, PHY→ Physical, SOCI→ Social. 5.0. DISCUSSION Hypothesis 1 (H1) posits a direct relationship between Cognitive Presence (COG) and Effort Expectancy (EE), which is supported at a significant P-value of 0.000. This suggest that COG significantly impact users' perceived EE of AT. It indicates that individuals with better cognitive capabilities tend to experience the ease of use when engaging to AT. It makes sense as the user’s current cognitive presence allows them to engage with the AT technology and can experience the ease of effort. When there is a decline in cognitive ability, users place more effort and may not be able to experience the ease in the use of effort derived from the AT, thus avoiding or reducing the use of technology. This finding supports the findings in [74], where they relate reduction in technology use as a sign on cognitive decline, and [58], that found EE was negatively affected by cognitive decline. The t-value indicates that cognitive presence has moderate effect on the EE. This is evidence from Mata et al, [36], stating that, when older people experience increasing cognitive constrained, they switch to less cognitive demanding solutions. On top of that, better familiarity with technology through memory also supports the perceived EE by EUAT [75]. Low t-value also indicates the similarity between COG among the different groups of respondents as indicated in [76], that argues that the decline of cognitive and physical abilities of EUAT of the same or similar age can be different due to differences in cognitive, physical, and social factors associated with lifestyle or life course. The responses from the different age groups were not significantly different, indicating that the aging group did not exhibit any comfort to the general stereotyping, as highlighted in [43], where some older users’ decision to decline the acceptance of technology is due to threat of stereotyping. Among the low-level predictor of COG, intelligence has the highest t-value, which suggest strong intelligence is insignificant for experiencing perceived EE. This is reflected in [77], where older adults in the age group of 65–70 have some prior use and knowledge of technology due to crystalized intelligence, thus support the perceived EE when using AT. On the other hand, cognitive load has the lowest t-value indicating the similarity in the responses between cognitive load and perceived EE, which echoes the finding in [60], where high cognitive load coupled with low experience impact their efficacy in term of learning how to use AT, thus affecting their perception on the EE and ultimately, the acceptance of the technology. While older users rely on their experience for better perceived EE, the complexity of AT requires more effort from them, thereby affecting the perceived EE [34]. The low t-value indicates that the cognitive load has a significant impact on the EE, but the effect is lower than the other low-level construct. This can be explained from two perspective; users perceived AT to reduce their effort despite their age group, and they were not able to realize the potential of AT to the fullest due to the cognitive load when using the ATs. The hypothesis (H2) state that PHY have significant influence on the EE in the use of technology and was found to have a strong significant relationship. The result indicates that the physical ability has a significant influence on the EE, where decreased physical ability reduces the effect of perceived EE, as users feels that they are using more effort to use the AT. PHY has stronger effect on perceived EE than COG, supporting [78], where older adults usually have sensory decline, especially auditory and visual decline, which can affect memory, information and cognition processing. In [43], it was found that the declines in motor control and coordination compromise how individuals physically interact with technology, which explains why there is a significant influence of PHY on perceived EE. Similarly, [79], found that the perception of a user’s physical condition has a significant influence on the EE. However, in [38], there was no significant relationship between declining PHY on perceived EE where senior citizens view aging as creative process of continuous adaptation, thus having a positive effect on perceived EE among some older users. The t-statistics of physical hand movement were the highest, while speech and hearing was the lowest. [39] found that users with poor hand-movement and coordination may find it difficult to use computer mouse or a touch screen, which is the core interaction medium of most current digital based ATs. [43] found that older users demonstrate poor performance in using devices such as touch screen due to deteriorating hand control and movement. In [80], while the current aging population has used digital technology, rapid advancement and declining ability will likely to a have a permanent lag in technology adoption as they aged. Hypothesis 3 (H3) posits that SOCI has significant influence on EE in the context of technology use, with empirical findings supporting a modest and insignificant effect. SOCI has been accepted as an important indicator of technology acceptance among the aging users [48–50]. While SOCI is an important indicator for acceptance of technology [8, 9], it did not support the perceived EE. In other words, the existence of SOCI has little role on the older user’s capability and the perceived EE from AT. This finding contradicts with some of the existing works relating to social influence. For example, [50] argues that technology acceptance in later life is supported by relatives and friends. Being socially included can be achieved through digital inclusion [48] and can be a strong motivator for users to accept and use technology [49]. Users are intrinsically motivated when they believe they can perform well in a task. Thus, support from others is not significant for perceived EE among the EIAT [29]. Social inclusion has a stronger influence with perceived EE than social exclusion. It can be concluded that a pleasant form of motivation has greater impact on perceived EE. Social inclusion promotes the intention to use technology among EIAT. However, the ties of friendship and communication were much stronger among older individuals seeking independence, autonomy, and good social relations with others [81] rather than creating a dependency on the younger generation. For Hypothesis 4 (H4), the notion that EE has a significant influence on the intention to use AT is supported at p < 0.05. This finding is similar with [82] that found perceived EE as a significant indicator for intention to use technology among mid-aged and older users of m-health system. They stated that there was no significant different in the responses between the mid-aged and older users of m-health system. Similarly, [83] found that EE is a significant indicator for continuous intention to use m-health system among respondents aged 60 and above. However, the result of this study differs to the findings in [61] that stated, increase of age weakens the relationship between EE and behavioural intention to use and technology. It means that older users perceived that they need to put more effort in using the technology as compared to younger users. [84] empirical findings did not support the hypothesised relationship between EE and intention to use mobile banking, which is because the EE required was a significant consideration when determining intention to use. As people aged, the cognitive performance declines and this adversely affect the effort required to use a system which could counteract any benefits derived from system use. [85] also found that EE did not significantly affect behavioural intention among the participants, citing two major causes. The first one is that users especially with disability, faces challenges in accessing the required assistive products, and the second in the lack of available technology suited to their needs becomes a barrier among older adults. Mediated Relationship The COG associated with new technology can deter its use, particularly among older adults who may already be experiencing a decline in cognitive functions [86]. This aligns with the findings of Hypothesis H5, where cognitive presence significantly impacts EE, thereby influencing IUAT. Similarly, PHY, as highlighted in [37,87] play a critical role in technology use among older adults. Physical deterioration can limit the ability to engage with technology, necessitating designs that accommodate physical limitations. The support for Hypothesis H6, with an effect size of 0.266 for PHY ->EE ->IUAT, suggests that addressing physical accessibility can significantly ease the EE, thereby fostering greater intention to use technology. Again, the importance of PHY on intention to use AT is highlighted. Just like H3, there is lack of support for Hypothesis H7, with a smaller effect size of 0.073 for SOCI ->EE ->IUAT, indicates that social factors modest yet insignificantly determine EE and, by extension, IUAT. While SOCI was critical in technology acceptance as indicated in precious work [88–90], the maturity in digital technology in recent years have created older users who are less dependent on the younger generations when using technology. Additional factors are at play in people’s technological socialization experiences such as self-motivation, social practices, academic and professional literacy which are intrinsic factors [49]. The empirical support for these hypotheses, grounded in the literature on cognitive and physical factors influencing technology adoption, highlights the integral role of EE as a mediator. It suggests that technology acceptance among older adults can be enhanced by addressing these key areas, thereby reducing the perceived EE, and facilitating a more inclusive digital landscape. Efforts to design and promote AT must consider these factors to ensure that such technologies are accessible, usable, and meaningful for ageing populations, ultimately bridging the digital divide, and promoting digital inclusion for all ages. 6.0. CONCLUSION This research contributes to the knowledge in technology acceptance in AT by focusing on the capability of the users to use AT. The knowledge gained from this research is valuable to many stakeholders of technology inclusions in society. The findings did shows that majority of the respondents are aware about the existence of AT and how important these technologies are among them. It shows that there are more that developers and policymakers can do to increase the availability of AT for the pre-ageing and ageing users. This research offers the perspective of respondents related to the AT that they perceived to be important for graceful ageing. The age-related capabilities of cognitive and physical were found to be significant indicators of intention to use AT. Among the identified factors, the role of cognitive especially the intelligence is an important indicator for developers need to consider when designing the AT in the future. Research Limitations This study has limitations that should be taken into consideration when interpreting its findings. The potential for socially desirable responses by participants may have influenced the results. The study was conducted in the context of pre-ageing and ageing user in Malaysia residing in Klang Valley. Respondents from Klang Valley have the highest adoption of technology by different age group, both young and old. The findings of this research thus will be limited on its relevance to other pre-ageing and ageing users. The target respondents are the pre-aging (40–55 years) and aging (55–70 years). The technology in questions is only limited to AT, and no other forms of generic technologies. As such, the findings of this results are limited to the acceptance of AT and may not be generalized to other forms of technology. Finally, the analysis of the possible advantages of the researched components may be constrained by the absence of a final variable assessing the effort expectancy and intention to use AT. The R 2 shows that the proposed independent variables explain 61.8% of EE, indicating that there are other variables that can influence the EE on the acceptance of AT. Identifying and including these variables can increase the effectiveness of the proposed model. Future Research Firstly, a longitudinal study could be conducted to understand the impact of ageing on the actual use of technology and the capability over time. Researchers might confirm the constructs' constancy through time and their linked linkages using such a study, as well as continuously gauge the impact of modifications. Furthermore, additional research studies should be conducted to further validate and reinforce the findings of this study. Secondly, future research could benefit from including non-users of digital technology in the study population. Additionally, a larger sample size would enable researchers to further validate the study findings and explore additional perspectives. To further understand the causes and consequences of user’s capability in various digital environments, it would be helpful to undertake comparable research in other countries may have different technology uptakes. Thirdly, future research could explore the application of the current antecedents, particularly cognitive, in the development of AT by developers. Given the potential association between the concept within the setting and behaviours, developing a measurement scale for cognitive capability could produce substantial results in AT development. Declarations Funding This research was not funded. Author Contribution All authors contributed to the study conception and design. Material preparation, data collection and analysis were performed by [Mansoor Ali Mohamed Yusoof]. The first draft of the manuscript was written by [Mansoor Ali Mohamed Yusoof] and all authors commented on previous versions of the manuscript. All authors read and approved the final manuscript Data Availability The data in this research is saved in a Microsoft Excel file by the researchers. References Chundur, S. (2020). Digital justice: Reflections on a community-based research project. The Journal of Community Informatics , 16 , 118–140. Özsungur, F. (2022). A research on the effects of successful aging on the acceptance and use of technology of the elderly. 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Educational and Psychological Measurement , 30, 607-610. Wildenbos, G. A., Jaspers, M. W., Schijven, M. P., & Dusseljee-Peute, L. W. (2019). Mobile health for older adult patients: Using an aging barriers framework to classify usability problems. International Journal of Medical Informatics , 124 , 68-77. Footnotes https://www.dosm.gov.my/uploads/release-content/file_20231013170239.pdf https://www.dosm.gov.my/uploads/content-downloads/file_20221215161042.pdf Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-6799606","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":482768567,"identity":"b1e69690-0494-41c7-b229-05534dae41eb","order_by":0,"name":"Mansoor Ali Mohamed Yusoof","email":"","orcid":"","institution":"University of Malaya","correspondingAuthor":false,"prefix":"","firstName":"Mansoor","middleName":"Ali Mohamed","lastName":"Yusoof","suffix":""},{"id":482768569,"identity":"038a2dcf-15ed-424f-807e-c69b2b32aa34","order_by":1,"name":"Haris Abd Wahab","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAq0lEQVRIiWNgGAWjYBACAyA+wFABZxOt5QypWhgY20jRYs5+9uHBn/MORzOwN2+TYMw5TFiLZU+6wWHebYdzG3iOlUkwbiNCi8GBNIbDjCAtEjlmRGo5/4zh4M85QC3yb4jVciON4QBvA8gWHqK1PGM4zHMsPbeNJ63YInFbOjEOS2P++KPGOref/fDGGx+3WRPWAgdsICKBoZkELVBQR7qWUTAKRsEoGPYAAEuOO7uDpL/+AAAAAElFTkSuQmCC","orcid":"","institution":"University of Malaya","correspondingAuthor":true,"prefix":"","firstName":"Haris","middleName":"Abd","lastName":"Wahab","suffix":""},{"id":482768570,"identity":"dbcd26e0-9f30-4740-8736-ec777a30c0a9","order_by":2,"name":"Kumarashwaran Vadevelu","email":"","orcid":"","institution":"University of Malaya","correspondingAuthor":false,"prefix":"","firstName":"Kumarashwaran","middleName":"","lastName":"Vadevelu","suffix":""}],"badges":[],"createdAt":"2025-06-02 07:08:31","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6799606/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6799606/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":86435545,"identity":"b44fc664-10fa-49d7-991b-41d66d44aa63","added_by":"auto","created_at":"2025-07-10 15:30:46","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":150409,"visible":true,"origin":"","legend":"\u003cp\u003eThe Human Activity Assistive Technology Model [21].\u003c/p\u003e","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-6799606/v1/d0218316414749198257d8a6.png"},{"id":86434254,"identity":"c028590e-0edd-460d-814f-53c4debd57ad","added_by":"auto","created_at":"2025-07-10 15:14:46","extension":"jpeg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":43401,"visible":true,"origin":"","legend":"\u003cp\u003eUnified Theory of Acceptance and Use of Technology [28].\u003c/p\u003e","description":"","filename":"floatimage2.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-6799606/v1/11f7cf595b2f5acbba109e62.jpeg"},{"id":86434789,"identity":"ea37f276-3676-4d06-86c0-8fa99fe752a9","added_by":"auto","created_at":"2025-07-10 15:22:46","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":40556,"visible":true,"origin":"","legend":"\u003cp\u003eThe conceptual framework for this research depicts the independent, mediating, and dependent variable.\u003c/p\u003e","description":"","filename":"floatimage3.png","url":"https://assets-eu.researchsquare.com/files/rs-6799606/v1/a825173ec1545303d1ea76fc.png"},{"id":86434260,"identity":"44b6fc92-8c66-4338-97d8-85682b9f3ee8","added_by":"auto","created_at":"2025-07-10 15:14:46","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":458191,"visible":true,"origin":"","legend":"\u003cp\u003eStructural Model Illustration.\u003c/p\u003e","description":"","filename":"floatimage4.png","url":"https://assets-eu.researchsquare.com/files/rs-6799606/v1/a21d286f77f5c98b49abafb6.png"},{"id":96306668,"identity":"7eabb478-9ca2-44de-8282-78f0c329d188","added_by":"auto","created_at":"2025-11-19 15:24:02","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1830731,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6799606/v1/fc10ad52-cf21-4053-ad4a-1b335069bf85.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"\u003cp\u003eThe Aging-Related Factors on Assistive Technology Acceptance Among the Pre-aging and Aging Population\u003c/p\u003e","fulltext":[{"header":"1.0. INTRODUCTION","content":"\u003cp\u003eTechnology is the central force that shapes our lives and our evolution. Technology offers real benefits to society but also creates an environment where there is constant requirement to catch up when technology changes rapidly [1]. The need to keep up eventually leads to the technological divide, and in today\u0026rsquo;s information technology, it created the \u0026ldquo;digital divide\u0026rdquo;. While some of the digital divides are caused by lack of access or challenges in using technology, it also can occur with ageing [1].\u003c/p\u003e\u003cp\u003eAgeing is a natural biological process that leads to decline in health and physical conditions, ability to adapt, and reduced cognitive capabilities. Ageing can lead to the inability of an individual to use a technology, which may be challenging due to biological changes, and social structure changes [2].\u003c/p\u003e\u003cp\u003eIn recent times, there is increasing focus on assistive technologies (AT) that provide support for users, especially older people and those with disabilities or long-term conditions, to compensate for their functional difficulty or decline [3]. Example of ATs are robotic nursing, ambient assisted living, and assistive robotics. With the increase for AT, there is a need for researchers and developers to understand the acceptance and usage of AT among ageing users, considering their deteriorating cognitive and physical capabilities of EIAT. Despite various theories explaining technology acceptance and use, there is limited research on the users\u0026rsquo; capabilities that influence the perceived effort expectancy (EE) and acceptance of AT, particularly among the rapidly aging society in Malaysia. Thus, there is a critical need for research to explore how users\u0026rsquo; capability such as cognitive, physical, and social factors impacts the acceptance and use of AT among ageing adults.\u003c/p\u003e\u003cp\u003eCompaine [4] describe digital divide as the differences in accessing and usage of digital technologies by various social groups and communities. There is increasing concern about the age-related digital divide regarding access to technology, use, and capability. This research examines the aging-related factors on the acceptance of assistive technology. This study is anchored in conceptual frameworks relating to intention to use assistive technological and the factors of cognitive, physical, and social changes caused by aging. We examine the conceptual structural models by the constructs of Cognitive Presence (COG), Physical Capabilities (PHY), and Social Presence (SOCI), as well as the mediating function of effort expectancy.\u003c/p\u003e\u003cp\u003eThe rest of the paper is organized as follows: Section 2 provides the background, the underlying theories, and the conceptual framework of this study. Section 3 provided details of the methods adopted to achieve the aim of this research. Section 4 present the results of the data analysis that includes the demographic and SEM analysis, while section 5 provides detailed discussions of the results. Section 6 concludes the findings.\u003c/p\u003e"},{"header":"2.0. LITERATURE REVIEW AND HYPOTHESES DEVELOPMENT","content":"\u003cp\u003eBy 2030, 1 in 6 people around the world will be 60 years or over, and by 2050, the world\u0026rsquo;s population of people aged 60 years and older will double to 2.1\u0026nbsp;billion [3]. On the same note, the number of people aged 80 years or older is expected to reach 426\u0026nbsp;million by 2050. In Malaysia, the Department of Statistics Malaysia state that in 2023, 7.2% is aged 65 and above \u003csup\u003e1\u003c/sup\u003e and it is estimated that more than 15% of its population will be above the age of 65 by 2050\u003csup\u003e2\u003c/sup\u003e. The oxford dictionary defines ageing as \u0026ldquo;grow old or older or cause to appear old or older\u0026rdquo;. From the perspective of biology, age is defined as \u0026ldquo;The aging of an organism is a process resulting from the combination of mechanisms limiting its lifespan (\u0026ldquo;promotive\u0026rdquo;) and mechanisms modulating their effects (\u0026ldquo;protective\u0026rdquo;).\u003c/p\u003e\u003cp\u003ePresently, chronological age has been studied as social construction, life transition, and an important age association [5, 6]. The use of chronological age may lead to better administration of human growth both physically, mentally, and spiritually [5,6], but it also creates the age stereotyping, which vary in terms of characteristics and qualities such as young vs. old dualism, where younger people are generally positivized, celebrated and aspired to, while old and ageing people are derogated, derided and rejected [7].\u003c/p\u003e\u003cp\u003eIndividuals may reject the idea of chronological age, by having younger or older identity by focusing on socially acceptable and popular conceptualizations of age through appearance, interests, and life choices to be more mature [8] or youthful [9]. Such behavior can also exist in technology usage, where older people want to be technologically in par with the younger generation such as the involvement in social media [10].\u003c/p\u003e\u003cp\u003eGilleard and Higgs [11] suggest that age is a measure of digital differences and the factors that cause the difference in technological engagement between age groups varies depending on the uses of technology [12], technology being studies [13], contextual factors [14], and stereotyping [15]. There are common alternative findings that defy the regular stereotyping among EIAT and technology, such as the use of mobiles apps show that EIAT are keen to adopt smartphones [16].\u003c/p\u003e\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003e2.1. Assistive Technology for Aging Individuals.\u003c/h2\u003e\u003cp\u003ede Almeida et al. [17], state AT as \u0026ldquo;any product which has the primary purpose to maintain or improve an individual\u0026rsquo;s functioning and independence, and thereby promote their well-being\u0026rdquo;, while Khasnabis et al. [18], consider AT as \u0026ldquo;An assistive product is any product (including devices, equipment, instruments, and software), either specially designed and produced or generally available, whose primary purpose is to maintain or improve an individual\u0026rsquo;s functioning and thereby pro-mote their wellbeing\u0026rdquo;. Abdi et al. [19] have identified the potential of new digital technology that can benefit both young and old users alike. These include the increasing presence and power of Artificial Intelligence (AI), new form of human-computer interaction (HCI) such as virtual reality, speech-based interactions, and facial or gesture recognition, as well as sensory and robotics [20].\u003c/p\u003e\u003cdiv id=\"Sec4\" class=\"Section3\"\u003e\u003ch2\u003e2.1.1. Human Activity Assistive Technology Model\u003c/h2\u003e\u003cp\u003eHuman Activity Assistive Technology Model (HAAT) [21\u0026ndash;23], relates to the AT and capability of users. Figure\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e depicts the HAAT model, which has four basic components: activity, human, assistive technology, and context. The human element focuses on user\u0026rsquo;s physical, cognitive, and sensory skills when participating in an activity, as well as their life roles in terms of social, experience, and motivation [21]. The Activity elements include task(s), task demands, and the contextual influencers of participation such as friends and family, while AT is described as device that enables activity performance, including technology complexities and the human-technology interface [21].\u003c/p\u003e\u003cp\u003eThere are many activities that AT are benefiting the elder users of AT (EUAT), such as in health, safety, communication, social involvement, entertainment, leisure, and home living [24]. For the EIATs, the common forms of digital assistive technologies are robotics, and mobile or computer-based applications [19].\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv id=\"Sec5\" class=\"Section2\"\u003e\u003ch2\u003e2.2. Technology Acceptance\u003c/h2\u003e\u003cp\u003eDavis developed the Technology Acceptance Model (TAM) based on two pivotal pillars, which are perceived usefulness and perceived ease of use, which influence the behavioural intention to use technology [25]. Perceived ease of use is defined as \u0026ldquo;the perception of a low degree of effort during the use of the technology\u0026rdquo;, while perceived usefulness is the \u0026ldquo;perception that the use of the technology will increase performance\u0026rdquo; [26]. The perceived usefulness and perceived ease of use are widely acknowledged utilitarian variables and are strong predictors of intention to technology [27]. Perceived usefulness is a type of extrinsic motivation toward the intention to use technology, while perceived ease of use is intrinsic motivation [28]. Chan et al. [29] stated that users are intrinsically motivated when they believe they can perform well in a task, which confirms the argument in [28].\u003c/p\u003e\u003cp\u003eThe Unified Theory of Acceptance and Use of Technology (UTAUT) [30] model has four basic constructs, which are performance expectancy, effort expectancy, social influence, and facilitating conditions that determine the usage intention and usage behaviour. The model also has four moderating variables of gender, age, experience, and voluntariness of use. Figure\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e depicts the UTAUT model.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec6\" class=\"Section2\"\u003e\u003ch2\u003e2.3. Conceptual Framework\u003c/h2\u003e\u003cp\u003eBased on the HAAT model, EUAT cognitive, physical, and social influence are critical for using AT, which determines the acceptance and usage of AT. Ageing decreases, health and physical conditions, ability to adapt, reduced cognitive capabilities, and relations and social roles change [31,32]. Figure\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e shows the conceptual framework for this research that depicts the independent, mediating, and dependent variable. The framework is based on the UTAUT and HAAT model. This research examines cognitive, physical, and social as the key human elements for the development of AT based on HAAT, and EE from UTAUT. While UTAUT has two core variables of EE and performance expectancy (PE), only EE is used on this research as the focus of this research is related to the perceived capacity of the users based on their age.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003cem\u003eCognitive Presence\u003c/em\u003e\u003c/p\u003e\u003cp\u003eIn traditional human\u0026ndash;machine interaction, cognitive workload or mental workload was regarded as an important variable influencing human performance, safety, and efficiency, which may be disadvantageous for ageing users due to deteriorating physical and cognitive function [33]. Cognitively old elders refer to individuals that perceive themselves as old or older than their chronological age, exhibit higher cautiousness and risk aversion, have anxiety toward technology, as well as exhibiting low self-esteem and self-confidence [5,6]. Farivar et al. [34], states that individuals with older cognitive age are less efficient and process information more slowly, despite their chronological age. Lee et al. [35] found cognitively older adults tend to have negative attitude towards technology, considering the need to learn and use those technologies places a significant cognitive burden on them, reducing their willingness to explore how technology works. However, as older people experience increasing cognitive constrained, they can vary the cognitively demanding choice strategies, which are mostly based on experience, or heuristics [36]. On top of that, complexity of a technology demotivates the usage and acceptance of technology, especially for individuals with weaker memory or spatial ability [34]. Cognitive abilities, such as working memory and intelligence are important for technology usage and acceptance. It was stated that there was a reciprocal relationship between cognitive functioning and general use of information and communication technology [34].\u003c/p\u003e\u003cp\u003eIt is hypothesized that:\u003c/p\u003e\u003cp\u003eH1: Cognitive presence has a significant influence on the effort expectancy in using AT.\u003c/p\u003e\u003cp\u003e\u003cem\u003ePhysical Capabilities\u003c/em\u003e\u003c/p\u003e\u003cp\u003eThe physical deterioration among ageing adults increases the challenges in accessing hardware features or digital content caused by limitations in mobility and decreased visual capacity [37]. As such, older individuals find it difficult to adopt and use technologies that require physical effort. Combined with waning physical and cognitive abilities such as hearing, vision, speech, locomotion, and memory capabilities, the ability to use technology decreases [38]. For example, users with poor hand-movement and coordination may find it difficult to use computer mouse or a touch screen [39]. Welch et al. [40], lists several intrinsic capabilities that are critical for AT of older adults, which includes mental, sensory functions, neuro-musculoskeletal function, voice and speech, among others.\u003c/p\u003e\u003cp\u003ePhysical and sensory limitations impair EUAT\u0026rsquo;s ability to seek and receive information [41]. Based on the work in [42], the physical ability considered in this research is motor neuron (hand movement), visual, and hearing. These physical abilities are needed when interacting with technology, particularly computers and mobile devices.\u003c/p\u003e\u003cp\u003eSome of the physical functions that are critical for AT usage are sensory functions and neuro-musculoskeletal function, particularly the hand movement. Existing works signal that older users demonstrate poor performance in using devices such as touch screen compared to younger users [43]. Sensory functions, particularly visual functions, are important for many of the current technologies such as smartphones and tablets, with some offer assistive support for individuals with low or poor vision such as colour, contrast, and size adjustment for improving visibility and readability [44]. Hearing impairment affects about two-thirds of adults aged 70 years and above [45]. Hearing impairment can adversely affect physical functioning through reduced perception of auditory input that contributes to walking and body balance [45], that lead to faster decline in physical function over time [46].\u003c/p\u003e\u003cp\u003eIt is hypothesized that:\u003c/p\u003e\u003cp\u003eH2: Physical Capability has a significant influence on the effort expectancy in using AT.\u003c/p\u003e\u003cp\u003e\u003cem\u003eSocial Presence\u003c/em\u003e\u003c/p\u003e\u003cp\u003eSince it is not unusual for humans to engage with technology as if it were a social entity, it can be expected that this effect is exacerbated when technology takes the form of an embodied character and interacts in a social manner. In [47], they expect that the sense of presence increases if a system is perceived to have more social abilities, thus increasing the acceptance of technology.\u003c/p\u003e\u003cp\u003eIn terms of technology, especially digital technology, digital inclusion focuses on the ability and opportunity of individuals to access digital technology such as the Internet [48], though it was argued that digital inclusion does not necessarily directly translate into social inclusion, and that digital inclusion activities cannot follow a one-size-fits-all approach [48]. Social inclusion can be a strong motivator for users to accept and use technology [49].\u003c/p\u003e\u003cp\u003eA person who uses or non-uses of technology in later life is influenced by variety of agents within a social field and the power relations between those agents [50]. Technology acceptance in later life such as the Internet is supported by relatives and friends as well as the media discourses surrounding age and demographic change, the institutions [50].\u003c/p\u003e\u003cp\u003eSocial Inclusion and social exclusion are one of the motivators for EIAT. Based on the above, it is hypothesized that:\u003c/p\u003e\u003cp\u003eH3: Social Presence has a significant influence on the effort expectancy in using AT.\u003c/p\u003e\u003cp\u003e\u003cem\u003eEffort Expectancy\u003c/em\u003e\u003cdiv class=\"BlockQuote\"\u003e\u003cp\u003eA person who lacks confidence in his/her abilities and skills would not make the effort to accomplish a task, and she/he would show less persistence in overcoming any potential obstacles than those with high confidence in their abilities and skills [51]. When faced with challenges, those who have significant doubts about their abilities either slacken their efforts or give up entirely, whereas those who have a strong sense of efficacy put up greater effort to overcome the obstacles. For some, being old also affects the perception of self and the world changes such as the lowering of self-esteem, feeling of no longer being needed, as well as loss of meaning of life. Aging can lead to the inability of an individual to use technology, which is becoming more challenging due to biological changes, and social structure changes [51].\u003c/p\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eEE is one of the important predictors of intention to use technology [52,53] as it has a significant effect on behavioral intention. The direct effect of EE on behavioral intention has been supported [54,55] and rejected [56, 57]. EE concerning the ease of use of the technology and was derived from perceived ease of use [25], which was defined as \u0026lsquo;\u0026lsquo;the degree of ease associated with the use of the system\u0026rdquo; [28]. EE was a predictor of attitude, especially in the beginning, and interestingly, it was also the strongest predictor of intention.\u003c/p\u003e\u003cp\u003eWhile both PE and EE play a role in affecting technology acceptance among older adults [58], EE is a major challenge uniquely associated with aging because decline in physiological functions (e.g. vision, hearing, health) and cognitive functions (e.g. memory) can effectively reduce the ability to use technology. On top of that, EE was found to have a major role in the assessment of PE for older adults but not for younger adults [59].\u003c/p\u003e\u003cp\u003eAccording to Venkatesh et al. [28], increase in age may negatively affect the EE as it is associated with the difficulty in processing complex stimuli and allocating attention to information on the job, both of which may be necessary when using software systems. Age may affect the EE among older adults, due to reduced cognitive abilities to learn, have lower perception of self-efficacy and higher anxiety over technology use, and have limited experiences and \u0026ldquo;how-to\u0026rdquo; knowledge [60].\u003cdiv class=\"BlockQuote\"\u003e\u003cp\u003eThe increase of age weakens the relationship between EE and behavioural intention [61]. This is because older users perceive the complexity of technology more, while younger people perceive more the usefulness of technology. It means that older users perceived that they need to put more effort into using the technology as compared to younger users.\u003c/p\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eAs such it is hypothesized that;\u003c/p\u003e\u003cp\u003eH4: effort expectancy has a significant influence on the intention to use AT.\u003c/p\u003e\u003cp\u003eH5: Effort expectancy mediates the influence of Cognitive presence on the intention to use AT.\u003c/p\u003e\u003cp\u003eH6: Effort expectancy mediates the influence of Physical Capability on the intention to use AT.\u003c/p\u003e\u003cp\u003eH7: Effort expectancy mediates the influence of Social Presence on the intention to use AT.\u003c/p\u003e\u003c/div\u003e"},{"header":"3.0. METHODS","content":"\u003cp\u003eThis study examines the influence of aging-related factors on the acceptance of AT. The target respondents are Malaysian that are pre-ageing (40 to 55 years of age) and ageing (55\u0026ndash;70 years of age). This form of classification was based on the agreement among the researchers that ageism has different effects for different ages [62, 63]. The location of the research will be the Klang Valley, home to more than eight million Malaysian. Klang Valley is an ideal place for conducting this kind of research as it is Malaysia\u0026rsquo;s most congested and developed central city [64]. It is the most suitable geographical location to study AT due to high penetration of the Internet [65], enabling the administration of the survey online. On top of that, research related to AT was performed in the Klang Valley, indicating the high degree of awareness of AT among the Klang Valley dwellers [66\u0026ndash;68].\u003c/p\u003e\u003cp\u003e The research instrument and the data collection method has been reviewed and approved by the Universiti Malaya Research Ethics Committee (UMREC). The participants are not forced to be involved in the research and give their informed consent. In this research, the survey instruction prompts the potential respondent to say that if he or she has no prior experience with AT, they can opt not to continue with the survey. Similarly, during the face-to-face survey, the researcher obtains permission before conducting the survey. The participants are assured that the survey is not expected to cause any harm mentally, physically, and physiologically to the participant. Should the participant feel uncomfortable with the survey, he or she may withdraw from the study at any time. The researcher does not share the respondent\u0026rsquo;s information with third parties. The data are collected solely for statistical analysis and hypothesis testing. As the survey did not ask for personally identifiable data such as name, religion, race, or identity card number, there is potentially no risk on data privacy of the respondents.\u003c/p\u003e\u003cp\u003eThe survey was administered via online survey and by physical distribution for older respondents (age 55 and above). For the online survey, the link being shared via social media platforms. The online survey instruction prompts the potential respondent that, if he or she has no prior experience with AT, they can opt not to continue with the survey. At the end of the survey, the respondents are encouraged to forward the link of the survey to any of their family and friends who are above the age of 40. The physical distribution of the survey for older adults is mainly because many of them may be lacking or have difficulties in accessing online surveys [69]. It is also to ensure a balance between pre-aging and aging groups as most responses from the online survey are likely the pre-aging group.\u003c/p\u003e\u003cp\u003eIn this study, data analysis was conducted in two phases. The first one is the preliminary data analysis that was performed on the data collected from the pilot test to check the reliability of the data collection instrument, followed with meticulously evaluating the measurement and structural models, setting a benchmark for analytical precision.\u003c/p\u003e"},{"header":"4.0 RESULTS","content":"\u003cdiv id=\"Sec9\" class=\"Section2\"\u003e\u003ch2\u003e4.1. Demographics Analysis\u003c/h2\u003e\u003cp\u003eA total of 391 respondents took part in the study, where 234 (59.8%) are from the 40\u0026ndash;55 age range, while 157 (40.2%) from 56\u0026ndash;70 age bracket. Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e depicts the demographic composition of the two age groups, indicating a balanced composition in term of gender, level of education and income.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eThe demographic composition of the two age groups\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"5\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDemographic\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eCategory\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003ePre-Aging\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eAging\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eTotal\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eGender\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eMale\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e119 (30.43%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e92 (23.53%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e234 (59.9%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eFemale\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e115 (29.41%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e65 (16.62%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e157 (40.1%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"3\" rowspan=\"4\"\u003e\u003cp\u003eLevel of education\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eDiploma and lower\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e15 (3.8%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e35 (9.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e50 (12.8%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eBachelor\u0026rsquo;s degree\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e64 (16.4%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e33 (8.4%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e97 (24.8%\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eMaster\u0026rsquo;s degree\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e135 (34.5%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e77 (19.70%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e212 (54.2%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003ePh.D.\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e20 (5.1%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e12 (3.1%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e32 (8.2%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"4\" rowspan=\"5\"\u003e\u003cp\u003eLevel of income\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eNone\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0 (0.0%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e1 (0.3%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e1 (0.3%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eLess than RM 2000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e22 (5.6%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e82 (21.0%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e104 (26.6%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eRM 2,000 to 4,000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e32 (8.2%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e15 (3.8%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e48 (12.0%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eRM 4,000 to 6,0000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e54 (13.8%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e29 (7.4%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e83 (21.2%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eMore than RM 6,000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e126 (32.2%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e30 (7.7%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e156 (39.9%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec10\" class=\"Section2\"\u003e\u003ch2\u003e4.2. Measurement Model Analysis\u003c/h2\u003e\u003cp\u003e\u003cem\u003eReliability Statistics\u003c/em\u003e\u003c/p\u003e\u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e outlines the measurement model statistics for the lower order constructs, which are Cognitive Age (AGE), EE, Intelligence (INT), Intention to Use AT (IUAT), Cognitive Load (LOAD), Hand Movement (PHM), Speech and Hearing Ability (PSH), Social Inclusion (SIN), Social Isolation (SIS), and Visual Ability (VIS). Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e also includes the outer loadings (OL), Variance Inflation Factor (VIF), Cronbach's alpha, composite reliability (both rho_a and rho_c), and the Average Variance Extracted (AVE) for each item within the constructs.\u003c/p\u003e\u003cp\u003eThe measurement model statistics for the higher order construct of COG, PHY, and SOCI are provided in Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e. The Cronbach's alpha and composite reliabilities are indicators of internal consistency, ensuring that the items within each construct reliably measure the concept. It becomes evident that all constructs display good reliability, with Cronbach's alpha and composite reliability values well above the acceptable threshold of 0.7, suggesting that the constructs are consistently measuring the intended underlying phenomena [70]. The AVE values are also above the commonly accepted level of 0.5, indicating satisfactory convergent validity [71].\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eMeasurement Model Statistics (Lower Order Construct)\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"8\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eConstruct\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eItems\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eOL\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eVIF\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eCronbach's alpha\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003eComposite reliability (rho_a)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e\u003cp\u003eComposite reliability (rho_c)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c8\"\u003e\u003cp\u003eAverage variance extracted\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAGE\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eAGE1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.930\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e3.203\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.811\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.844\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.889\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e0.730\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eAGE2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.902\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e2.851\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eAGE3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.716\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e1.382\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eEE\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eEE1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.861\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e2.211\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.850\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.854\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.899\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e0.690\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eEE2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.796\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e1.758\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eEE3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.811\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e1.925\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eEE4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.854\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e2.254\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eINT\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eINT1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.896\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e3.058\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.903\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.906\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.933\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e0.776\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eINT2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.892\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e2.995\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eINT3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.886\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e2.753\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eINT4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.847\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e2.319\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eIUAT\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eIUAT1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.808\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e1.615\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.816\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.828\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.891\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e0.732\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eIUAT2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.903\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e2.212\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eIUAT3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.852\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e1.908\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLOAD\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eLOAD1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.884\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e2.717\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.906\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.906\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.934\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e0.780\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eLOAD2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.877\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e2.717\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eLOAD3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.875\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e2.733\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eLOAD4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.896\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e3.083\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePHM\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003ePHM1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.816\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e1.813\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.826\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.830\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.885\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e0.657\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003ePHM2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.858\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e2.127\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003ePHM3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.767\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e1.594\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003ePHM4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.799\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e1.711\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePSH\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003ePSH1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.882\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e2.088\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.844\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.846\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.906\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e0.762\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003ePSH2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.883\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e2.242\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003ePSH3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.854\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e1.840\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSIN\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eSIN1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.814\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e1.942\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.892\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.894\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.925\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e0.756\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eSIN2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.899\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e3.045\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eSIN3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.894\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e3.056\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eSIN4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.867\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e2.425\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSIS\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eSIS1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.871\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e2.000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.815\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.818\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.890\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e0.730\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eSIS2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.805\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e1.555\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eSIS3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.885\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e2.196\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eVIS\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eVIS1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.533\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e1.101\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.667\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.750\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.818\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e0.611\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eVIS2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.892\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e1.832\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eVIS3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.868\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e1.780\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eAGE\u0026rarr; Cognitive age, EE\u0026rarr; Effort Expectancy, INT\u0026rarr; Intelligence, IUAT\u0026rarr; Intention to Use AT, LOAD\u0026rarr; Cognitive load, PHM\u0026rarr; Hand Movement, PSH\u0026rarr; Speech and hearing ability, SIN\u0026rarr; Social Inclusion, SIS\u0026rarr; Social Isolation, VIS\u0026rarr; Visual ability.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eMeasurement Model Statistics (Higher Order Construct)\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"8\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eConstruct\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eItems\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eOL\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eVIF\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eCronbach's alpha\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003eComposite reliability (rho_a)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e\u003cp\u003eComposite reliability (rho_c)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c8\"\u003e\u003cp\u003eAverage variance extracted\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCOG\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eAGE\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.922\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e3.384\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.899\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.904\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.937\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e0.832\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eINT\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.943\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e3.907\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eLOAD\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.915\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e2.896\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePHY\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003ePHM\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.917\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e3.168\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.828\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.828\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.897\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e0.745\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003ePSH\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.852\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e1.770\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eVIS\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.878\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e2.655\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSOCI\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eSIN\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.919\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e1.360\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.865\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.872\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.897\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e0.557\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eSIS\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.811\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e1.360\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eEE\u0026rarr; Effort Expectancy, IUAT\u0026rarr; Intention to Use AT, COG\u0026rarr; Cognitive, PHY\u0026rarr; Physical, SOCI\u0026rarr; Social.\u003c/p\u003e\u003cp\u003e\u003cem\u003eDiscrimination Validity\u003c/em\u003e\u003c/p\u003e\u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e presents the Heterotrait-Monotrait ratio (HTMT) matrix as a measure of discriminant validity for the constructs in the study. For discriminant validity to be established, the HTMT values should be significantly lower than 1. For discriminant validity to be considered satisfactory, HTMT values should ideally be below 0.85, a threshold indicating that the constructs are empirically distinct and not excessively overlapping in what they measure. Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e shows that most construct pairs maintain HTMT ratios below the threshold, implying adequate discriminant validity.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eDiscriminant Validity (Heterotrait-monotrait ratio)\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"11\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c11\" colnum=\"11\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eAGE\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eEE\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eINT\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eIUAT\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003eLOAD\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e\u003cp\u003ePHM\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c8\"\u003e\u003cp\u003ePSH\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c9\"\u003e\u003cp\u003eSIN\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c10\"\u003e\u003cp\u003eSIS\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c11\"\u003e\u003cp\u003eVIS\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAGE\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eEE\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.778\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eINT\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.847\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.837\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eIUAT\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.764\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.804\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.835\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLOAD\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.810\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.745\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.843\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.793\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePHM\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.790\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.781\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.777\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.634\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.611\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePSH\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.773\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.767\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.825\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.850\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.827\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.704\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSIN\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.714\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.763\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.732\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.718\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.751\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.608\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e0.768\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSIS\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.617\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.435\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.610\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.606\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.655\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.427\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e0.678\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e0.555\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eVIS\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.818\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.805\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.825\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.688\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.681\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.847\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e0.671\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e0.655\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e\u003cp\u003e0.511\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eAGE\u0026rarr; Cognitive age, EE\u0026rarr; Effort Expectancy, INT\u0026rarr; Intelligence, IUAT\u0026rarr; Intention to Use AT, LOAD\u0026rarr; Cognitive load, PHM\u0026rarr; Hand Movement, PSH\u0026rarr; Speech and hearing ability, SIN\u0026rarr; Social Inclusion, SIS\u0026rarr; Social Isolation, VIS\u0026rarr; Visual ability.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e\u003ch2\u003e4.3. Structural Model Analysis\u003c/h2\u003e\u003cdiv id=\"Sec12\" class=\"Section3\"\u003e\u003ch2\u003e4.3.1. Model Fit Statistics\u003c/h2\u003e\u003cp\u003eFigures \u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e visualize the structural model and its overall fit, respectively. For EE, an R-square of 0.618 suggests a significant explanatory power, with 61.8% of EE's variance accounted for by the model.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab5\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eModel Fit Statistics\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"6\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eR-square\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eR-square adjusted\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eQ\u0026sup2;predict\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eRMSE\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003eMAE\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eEE\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.618\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.615\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.608\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.631\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.423\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eIUAT\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.455\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.454\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.509\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.706\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.513\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eEE\u0026rarr; Effort Expectancy, IUAT\u0026rarr; Intention to Use AT.\u003c/p\u003e\u003cp\u003eFrom Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e, the R-square adjusted value of 0.615 confirms this explanatory power's robustness, adjusting for the number of predictors [72]. IUAT's R-square of 0.455, with an R-square adjusted of 0.538, demonstrates the model's substantial explanatory capacity for this construct as well. The results suggest that the proposed model not only fits the observed data well but also provides valuable insights into the factors influencing AT adoption. These findings are aligned with established SEM literature that underscores the importance of comprehensive model fit assessment in validating SEM analyses [70, 73]. The R\u003csup\u003e2\u003c/sup\u003e shows that the proposed independent variables explain 61.8% of EE, and the EE explain 45.5% of the intention to use assistive technology, which is not unusual since UTAUT has two basics independent variables of PE and EE.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec13\" class=\"Section3\"\u003e\u003ch2\u003e4.3.2. Direct and Mediation Relationship\u003c/h2\u003e\u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab6\" class=\"InternalRef\"\u003e6\u003c/span\u003e provides a detailed overview of the structural model statistics for the direct and mediation effects present in the study focused on the adoption and use of AT.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab6\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 6\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eStructural Model Statistics\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"9\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eOriginal sample (O)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eSample mean (M)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eStandard deviation (STDEV)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003eT statistics\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e\u003cp\u003eP values\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c8\"\u003e\u003cp\u003ef-square\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c9\"\u003e\u003cp\u003eSupport\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eH1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eCOG -\u0026gt;EE\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.341\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.334\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.084\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e4.062\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e0.078\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eH2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003ePHY -\u0026gt;EE\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.674\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.675\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.037\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e18.427\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e0.835\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eH3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eSOCI -\u0026gt;EE\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.109\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.117\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.059\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e1.851\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.066\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e0.012\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003eNo\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eH4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eEE -\u0026gt;IUAT\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.395\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.395\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.076\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e5.211\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e0.134\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eH5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eCOG -\u0026gt;EE -\u0026gt;IUAT\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.230\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.226\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.061\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e3.761\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eH6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003ePHY -\u0026gt;EE -\u0026gt;IUAT\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.266\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.266\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.052\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e5.108\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eH7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eSOCI -\u0026gt;EE -\u0026gt;IUAT\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.073\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.078\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.039\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e1.866\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.062\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003eNo\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eEE\u0026rarr; Effort Expectancy, IUAT\u0026rarr; Intention to Use AT, COG\u0026rarr; Cognitive, PHY\u0026rarr; Physical, SOCI\u0026rarr; Social.\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e"},{"header":"5.0. DISCUSSION","content":"\u003cp\u003e\u003cstrong\u003eHypothesis 1\u003c/strong\u003e (H1) posits a direct relationship between Cognitive Presence (COG) and Effort Expectancy (EE), which is supported at a significant P-value of 0.000. This suggest that COG significantly impact users' perceived EE of AT. It indicates that individuals with better cognitive capabilities tend to experience the ease of use when engaging to AT. It makes sense as the user\u0026rsquo;s current cognitive presence allows them to engage with the AT technology and can experience the ease of effort. When there is a decline in cognitive ability, users place more effort and may not be able to experience the ease in the use of effort derived from the AT, thus avoiding or reducing the use of technology. This finding supports the findings in [74], where they relate reduction in technology use as a sign on cognitive decline, and [58], that found EE was negatively affected by cognitive decline.\u003c/p\u003e\u003c/p\u003e\u003cp\u003eThe t-value indicates that cognitive presence has moderate effect on the EE. This is evidence from Mata et al, [36], stating that, when older people experience increasing cognitive constrained, they switch to less cognitive demanding solutions. On top of that, better familiarity with technology through memory also supports the perceived EE by EUAT [75]. Low t-value also indicates the similarity between COG among the different groups of respondents as indicated in [76], that argues that the decline of cognitive and physical abilities of EUAT of the same or similar age can be different due to differences in cognitive, physical, and social factors associated with lifestyle or life course. The responses from the different age groups were not significantly different, indicating that the aging group did not exhibit any comfort to the general stereotyping, as highlighted in [43], where some older users\u0026rsquo; decision to decline the acceptance of technology is due to threat of stereotyping.\u003c/p\u003e\u003cp\u003eAmong the low-level predictor of COG, intelligence has the highest t-value, which suggest strong intelligence is insignificant for experiencing perceived EE. This is reflected in [77], where older adults in the age group of 65\u0026ndash;70 have some prior use and knowledge of technology due to crystalized intelligence, thus support the perceived EE when using AT.\u003c/p\u003e\u003cp\u003eOn the other hand, cognitive load has the lowest t-value indicating the similarity in the responses between cognitive load and perceived EE, which echoes the finding in [60], where high cognitive load coupled with low experience impact their efficacy in term of learning how to use AT, thus affecting their perception on the EE and ultimately, the acceptance of the technology. While older users rely on their experience for better perceived EE, the complexity of AT requires more effort from them, thereby affecting the perceived EE [34]. The low t-value indicates that the cognitive load has a significant impact on the EE, but the effect is lower than the other low-level construct. This can be explained from two perspective; users perceived AT to reduce their effort despite their age group, and they were not able to realize the potential of AT to the fullest due to the cognitive load when using the ATs.\u003c/p\u003e\u003cp\u003eThe hypothesis (H2) state that PHY have significant influence on the EE in the use of technology and was found to have a strong significant relationship. The result indicates that the physical ability has a significant influence on the EE, where decreased physical ability reduces the effect of perceived EE, as users feels that they are using more effort to use the AT. PHY has stronger effect on perceived EE than COG, supporting [78], where older adults usually have sensory decline, especially auditory and visual decline, which can affect memory, information and cognition processing.\u003c/p\u003e\u003cp\u003eIn [43], it was found that the declines in motor control and coordination compromise how individuals physically interact with technology, which explains why there is a significant influence of PHY on perceived EE. Similarly, [79], found that the perception of a user\u0026rsquo;s physical condition has a significant influence on the EE. However, in [38], there was no significant relationship between declining PHY on perceived EE where senior citizens view aging as creative process of continuous adaptation, thus having a positive effect on perceived EE among some older users.\u003c/p\u003e\u003cp\u003eThe t-statistics of physical hand movement were the highest, while speech and hearing was the lowest. [39] found that users with poor hand-movement and coordination may find it difficult to use computer mouse or a touch screen, which is the core interaction medium of most current digital based ATs. [43] found that older users demonstrate poor performance in using devices such as touch screen due to deteriorating hand control and movement. In [80], while the current aging population has used digital technology, rapid advancement and declining ability will likely to a have a permanent lag in technology adoption as they aged.\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eHypothesis 3\u003c/strong\u003e (H3) posits that SOCI has significant influence on EE in the context of technology use, with empirical findings supporting a modest and insignificant effect. SOCI has been accepted as an important indicator of technology acceptance among the aging users [48\u0026ndash;50]. While SOCI is an important indicator for acceptance of technology [8, 9], it did not support the perceived EE. In other words, the existence of SOCI has little role on the older user\u0026rsquo;s capability and the perceived EE from AT. This finding contradicts with some of the existing works relating to social influence. For example, [50] argues that technology acceptance in later life is supported by relatives and friends. Being socially included can be achieved through digital inclusion [48] and can be a strong motivator for users to accept and use technology [49].\u003c/p\u003e\u003c/p\u003e\u003cp\u003eUsers are intrinsically motivated when they believe they can perform well in a task. Thus, support from others is not significant for perceived EE among the EIAT [29]. Social inclusion has a stronger influence with perceived EE than social exclusion. It can be concluded that a pleasant form of motivation has greater impact on perceived EE. Social inclusion promotes the intention to use technology among EIAT. However, the ties of friendship and communication were much stronger among older individuals seeking independence, autonomy, and good social relations with others [81] rather than creating a dependency on the younger generation.\u003c/p\u003e\u003cp\u003eFor Hypothesis 4 (H4), the notion that EE has a significant influence on the intention to use AT is supported at p\u0026thinsp;\u0026lt;\u0026thinsp;0.05. This finding is similar with [82] that found perceived EE as a significant indicator for intention to use technology among mid-aged and older users of m-health system. They stated that there was no significant different in the responses between the mid-aged and older users of m-health system. Similarly, [83] found that EE is a significant indicator for continuous intention to use m-health system among respondents aged 60 and above.\u003c/p\u003e\u003cp\u003eHowever, the result of this study differs to the findings in [61] that stated, increase of age weakens the relationship between EE and behavioural intention to use and technology. It means that older users perceived that they need to put more effort in using the technology as compared to younger users. [84] empirical findings did not support the hypothesised relationship between EE and intention to use mobile banking, which is because the EE required was a significant consideration when determining intention to use. As people aged, the cognitive performance declines and this adversely affect the effort required to use a system which could counteract any benefits derived from system use.\u003c/p\u003e\u003cp\u003e[85] also found that EE did not significantly affect behavioural intention among the participants, citing two major causes. The first one is that users especially with disability, faces challenges in accessing the required assistive products, and the second in the lack of available technology suited to their needs becomes a barrier among older adults.\u003c/p\u003e\u003cp\u003e\u003cem\u003eMediated Relationship\u003c/em\u003e\u003c/p\u003e\u003cp\u003eThe COG associated with new technology can deter its use, particularly among older adults who may already be experiencing a decline in cognitive functions [86]. This aligns with the findings of Hypothesis H5, where cognitive presence significantly impacts EE, thereby influencing IUAT. Similarly, PHY, as highlighted in [37,87] play a critical role in technology use among older adults. Physical deterioration can limit the ability to engage with technology, necessitating designs that accommodate physical limitations. The support for Hypothesis H6, with an effect size of 0.266 for PHY -\u0026gt;EE -\u0026gt;IUAT, suggests that addressing physical accessibility can significantly ease the EE, thereby fostering greater intention to use technology. Again, the importance of PHY on intention to use AT is highlighted.\u003c/p\u003e\u003cp\u003eJust like H3, there is lack of support for Hypothesis H7, with a smaller effect size of 0.073 for SOCI -\u0026gt;EE -\u0026gt;IUAT, indicates that social factors modest yet insignificantly determine EE and, by extension, IUAT. While SOCI was critical in technology acceptance as indicated in precious work [88\u0026ndash;90], the maturity in digital technology in recent years have created older users who are less dependent on the younger generations when using technology. Additional factors are at play in people\u0026rsquo;s technological socialization experiences such as self-motivation, social practices, academic and professional literacy which are intrinsic factors [49].\u003c/p\u003e\u003cp\u003eThe empirical support for these hypotheses, grounded in the literature on cognitive and physical factors influencing technology adoption, highlights the integral role of EE as a mediator. It suggests that technology acceptance among older adults can be enhanced by addressing these key areas, thereby reducing the perceived EE, and facilitating a more inclusive digital landscape. Efforts to design and promote AT must consider these factors to ensure that such technologies are accessible, usable, and meaningful for ageing populations, ultimately bridging the digital divide, and promoting digital inclusion for all ages.\u003c/p\u003e"},{"header":"6.0. CONCLUSION","content":"\u003cp\u003eThis research contributes to the knowledge in technology acceptance in AT by focusing on the capability of the users to use AT. The knowledge gained from this research is valuable to many stakeholders of technology inclusions in society. The findings did shows that majority of the respondents are aware about the existence of AT and how important these technologies are among them. It shows that there are more that developers and policymakers can do to increase the availability of AT for the pre-ageing and ageing users. This research offers the perspective of respondents related to the AT that they perceived to be important for graceful ageing. The age-related capabilities of cognitive and physical were found to be significant indicators of intention to use AT. Among the identified factors, the role of cognitive especially the intelligence is an important indicator for developers need to consider when designing the AT in the future.\u003c/p\u003e\u003cp\u003e\u003cem\u003eResearch Limitations\u003c/em\u003e\u003c/p\u003e\u003cp\u003eThis study has limitations that should be taken into consideration when interpreting its findings. The potential for socially desirable responses by participants may have influenced the results. The study was conducted in the context of pre-ageing and ageing user in Malaysia residing in Klang Valley. Respondents from Klang Valley have the highest adoption of technology by different age group, both young and old. The findings of this research thus will be limited on its relevance to other pre-ageing and ageing users. The target respondents are the pre-aging (40\u0026ndash;55 years) and aging (55\u0026ndash;70 years).\u003c/p\u003e\u003cp\u003eThe technology in questions is only limited to AT, and no other forms of generic technologies. As such, the findings of this results are limited to the acceptance of AT and may not be generalized to other forms of technology. Finally, the analysis of the possible advantages of the researched components may be constrained by the absence of a final variable assessing the effort expectancy and intention to use AT. The R\u003csup\u003e2\u003c/sup\u003e shows that the proposed independent variables explain 61.8% of EE, indicating that there are other variables that can influence the EE on the acceptance of AT. Identifying and including these variables can increase the effectiveness of the proposed model.\u003c/p\u003e\u003cp\u003e\u003cem\u003eFuture Research\u003c/em\u003e\u003c/p\u003e\u003cp\u003eFirstly, a longitudinal study could be conducted to understand the impact of ageing on the actual use of technology and the capability over time. Researchers might confirm the constructs' constancy through time and their linked linkages using such a study, as well as continuously gauge the impact of modifications. Furthermore, additional research studies should be conducted to further validate and reinforce the findings of this study.\u003c/p\u003e\u003cp\u003eSecondly, future research could benefit from including non-users of digital technology in the study population. Additionally, a larger sample size would enable researchers to further validate the study findings and explore additional perspectives. To further understand the causes and consequences of user\u0026rsquo;s capability in various digital environments, it would be helpful to undertake comparable research in other countries may have different technology uptakes.\u003c/p\u003e\u003cp\u003eThirdly, future research could explore the application of the current antecedents, particularly cognitive, in the development of AT by developers. Given the potential association between the concept within the setting and behaviours, developing a measurement scale for cognitive capability could produce substantial results in AT development.\u003c/p\u003e"},{"header":"Declarations","content":"\u003ch2\u003eFunding\u003c/h2\u003e\u003cp\u003eThis research was not funded.\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eAll authors contributed to the study conception and design. Material preparation, data collection and analysis were performed by [Mansoor Ali Mohamed Yusoof]. The first draft of the manuscript was written by [Mansoor Ali Mohamed Yusoof] and all authors commented on previous versions of the manuscript. All authors read and approved the final manuscript\u003c/p\u003e\u003ch2\u003eData Availability\u003c/h2\u003e\u003cp\u003eThe data in this research is saved in a Microsoft Excel file by the researchers.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n \u003cli\u003e\u003cspan\u003eChundur, S. (2020). Digital justice: Reflections on a community-based research project. \u003cem\u003eThe Journal of Community Informatics\u003c/em\u003e, \u003cem\u003e16\u003c/em\u003e, 118\u0026ndash;140.\u003c/span\u003e\u003c/li\u003e\n \u003cli\u003e\u003cspan\u003e\u0026Ouml;zsungur, F. (2022). 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Mobile health for older adult patients: Using an aging barriers framework to classify usability problems. \u003cem\u003eInternational Journal of Medical Informatics\u003c/em\u003e, \u003cem\u003e124\u003c/em\u003e, 68-77.\u003c/li\u003e\n\u003c/ol\u003e"},{"header":"Footnotes","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003e \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.dosm.gov.my/uploads/release-content/file_20231013170239.pdf\u003c/span\u003e\u003cspan address=\"https://www.dosm.gov.my/uploads/release-content/file_20231013170239.pdf\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003e \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.dosm.gov.my/uploads/content-downloads/file_20221215161042.pdf\u003c/span\u003e\u003cspan address=\"https://www.dosm.gov.my/uploads/content-downloads/file_20221215161042.pdf\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Pre-aging and Aging Users, Assistive Technology, Cognitive, Physical, Social","lastPublishedDoi":"10.21203/rs.3.rs-6799606/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6799606/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003ePurpose\u003c/h2\u003e\u003cp\u003eAssistive technology was aimed at reducing the age based digital divide. However, there is a disconnection between the stakeholders and the aging users in terms of the capability and ability required to use the technology, which requires a better understanding of the intention to use AT based on the cognitive, physical, and social capabilities of older users. This research examines the aging-related factors on the acceptance of assistive technology among the pre-aging and the aging population.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e\u003cp\u003eThis study is anchored in conceptual frameworks relating to intention to use assistive technological and the factors of cognitive, physical, and social changes caused by aging. Data was collected through a structured questionnaire derived and adopted from previous studies from pre-aging (45 to 60 years of age) and aging (60\u0026ndash;75 years of age) users. This research examines the conceptual structural models by the constructs of Cognitive, Physical Capabilities, and Social Presence, as well as the mediating function of effort expectancy.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e\u003cp\u003eIt was found that cognitive and physical capabilities were significant factors that support effort expectancy, with the latter having a much stronger influence. The analysis shows that social presence has a weak and insignificant influence on effort expectancy. The findings also reveal that respondents in both groups feel that their current cognitive and physical capability is adequate when accepting and using assistive technology. Most aging respondents perceive themselves to be younger than their actual chronological age showing higher levels of self-confidence, self-respect, innovativeness, as well as willingness to try new innovations and accept change.\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e\u003cp\u003eThe findings offer practical implications for technology developers, designers, and policymakers aiming to improve the accessibility and usability of technology for aging individuals.\u003c/p\u003e","manuscriptTitle":"The Aging-Related Factors on Assistive Technology Acceptance Among the Pre-aging and Aging Population","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-07-10 15:14:41","doi":"10.21203/rs.3.rs-6799606/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"d0009846-e012-4e97-a7f9-81b8f2f51185","owner":[],"postedDate":"July 10th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-11-19T15:23:41+00:00","versionOfRecord":[],"versionCreatedAt":"2025-07-10 15:14:41","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-6799606","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6799606","identity":"rs-6799606","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}