A Comparative Study of Elderly and Young Adults’ Needs of Smart Home Security Systems: Evidence from an Extended Technology Acceptance Model (TAM)

A Comparative Study of Elderly and Young Adults’ Needs of Smart Home Security Systems: Evidence from an Extended Technology Acceptance Model (TAM) | 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 A Comparative Study of Elderly and Young Adults’ Needs of Smart Home Security Systems: Evidence from an Extended Technology Acceptance Model (TAM) Jia Xin Xiao, Le Yan Xie, Chang Zhong, Ming Jun Luo, Tiansheng Xia This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8617018/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 6 You are reading this latest preprint version Abstract The process of population aging has accelerated significantly, and an increasing number of senior citizens have chosen to spend their later years at home. Consequently, home safety has become a growing social concern. To design and optimize more scientifically advanced home security systems in the context of an aging population, this study focused on exploring and comparing the Intention to use and acceptance of such systems among both senior citizens and young adults. Using a mixed-methods approach that combines qualitative and quantitative analyses, the study surveyed 220 senior citizen participants (aged 55 and above) and 215 young adults (aged 18–55) to collect data on their perceptions, attitudes, and behavioral intentions toward smart home security systems. Based on this data, extended structural equation models were constructed. The results indicated that governments should promote differentiated pricing policies for age-friendly products to reduce economic barriers. System design should emphasize one-click operation and voice interaction to enhance accessibility, and system functionality should integrate environmental sensing, automatic alarms, and health monitoring modules. Adopting transparent data protocol mechanisms could strengthen user trust, thereby improving their sense of security and alleviating technological anxiety. This study aimed to compare the differentiated needs and cognitive characteristics of senior citizens and young adults regarding the use of smart home security systems, providing empirical evidence and design recommendations for developing and promoting age-friendly smart home security systems. Smart home Smart home security systems Senior citizens Structural equation modeling Technology acceptance Figures Figure 1 Figure 2 1. Introduction With the continuous deepening of global population aging, demographic structures are undergoing profound changes (Huh and Seo, 2015). From 1974 to 2024, the global proportion of people aged 65 and above rose from 5.5% to 10.3%, and according to the United Nations Population Fund (2024), this proportion is projected to further increase to 20.7% by 2074, with the population aged 80 and above expected to at least double. In response to this demographic shift, Most Senior citizens prefer to continue living in a familiar family environment. ‘Home-based elderly care’ has gradually become an important social model for addressing population aging (Clark et al., 2024). Long-term living experiences enable individuals to develop a sense of attachment to their family space, which not only helps maintain close ties with family and friends but also has a positive impact on their physical and mental well-being (Ghorayeb et al., 2021; Golant, 2020). Surveys indicate that approximately 90.0% of individuals aged 60 and above spend most of their time at home (Ma, 2024). Previous studies have demonstrated that the physical environment significantly impacts the health and safety of senior citizens, with the home environment playing a key role in determining the quality of home-based care (Pettersson et al., 2021). Modern residents not only prioritize residential comfort but are also increasingly concerned with home safety and potential risk prevention, such as fire hazards, gas leakage, and burglary. This growing concern is driving the advancement of smart home technologies (Sarhan, 2020). Senior citizens, due to declining mobility, cognitive deterioration, and unstable health conditions, often face multiple challenges when operating complex technological systems. A lack of perceived safety not only directly diminishes their quality of life but also reinforces psychological resistance toward technological products (Mauritzson et al., 2023; Milberg et al., 2014). Therefore, designing Smart Home Security Systems (SHSS) tailored to the needs and characteristics of senior citizens is of great practical and social significance, as it can mitigate household risks and improve the user experience. Within the broader field of smart home technology, security systems have emerged as a central focus of both research and industrial development due to residents' increasing demand for safety (Yuan et al., 2023). A typical smart home security system consists of access control, video surveillance, and alarm modules. These systems continuously monitor the home environment, utilizing algorithms such as convolutional neural networks (CNNs) to detect abnormal intrusions and immediately send alert notifications to users (Taiwo et al., 2022). The system can actively respond to hazards, for example, activating sprinklers via smoke detectors to suppress fires or initiating ventilation to reduce flammable gas concentration. Even when users are away from home, the system can transmit real-time updates on environmental conditions. If necessary, trigger sound and light alarms to alert neighbors or relevant personnel, thus ensuring safety in various scenarios (Sarhan, 2020; Uppuluri and Lakshmeeswari, 2024). In recent years, several academic and engineering projects have made significant progress in the development of SHSS. For instance, Ouyang et al. (2023) proposed a high-security dual-lock system based on a triboelectric nanogenerator and a deep learning model, which enables dual authentication by collecting users’ respiratory signals and facial features, significantly enhancing protective performance. Dong et al. (2023) developed a multimodal neuromorphic sensing and processing system based on memristor circuits, capable of indoor human behavior recognition and intelligent surveillance. This system not only improves household security but also reduces implementation costs and power consumption. Abdusalomov et al. (2024) proposed a smart home fire and smoke detection model based on MII-DETR, effectively addressing the issue of low recognition accuracy of traditional methods in the case of flame overlap and occlusion, and enhancing the early warning and prevention capabilities of fires. Passive Infrared (PIR) sensors have been widely employed for intrusion detection (ShariqSuhail et al., 2016). Based on the Raspberry Pi 3 Home Server (RHS) and the Support Vector Machine (SVM) algorithm, Alam et al. (2024) designed a real-time monitoring system that integrates MQ2 sensors. It can automatically issue early warnings and respond to security anomalies. However, existing studies have largely concentrated on technological innovation and optimization, while research on the needs of the aging population is still relatively scarce. Senior citizens and young adults differ significantly in their functional needs, interaction preferences, and emotional experiences, largely due to differences in technological familiarity and proficiency. To address this research gap, this study compares the perceptions, attitudes, and adoption intentions of senior citizens and young adults regarding SHSS, examining the key factors that influence their intentions. The findings aim to provide theoretical support and practical guidance for the design and promotion of age-friendly SHSS, ultimately enhancing home safety, quality of life, and overall well-being for senior citizens. 2. Research model and hypotheses The technology acceptance model (TAM), first proposed by Davis (1989), is widely used to explain and predict the acceptance and use of information technologies by individuals and organizations. The model posits that perceived usefulness (PU) and perceived ease of use (PEOU) are two core determinants that influence users’ technology adoption behavior, and both remain relatively stable in the model structure. Although the TAM has been widely regarded as a reliable and robust theoretical framework, its structural validity may still be influenced by factors such as the type of technology under study, cultural context, and situational background (Johnson et al., 2014; Sharma et al., 2024; Xia et al., 2025). Numerous studies have demonstrated that extending TAM can effectively enhance its explanatory power (Al-Adwan et al., 2023; Lin and Yu, 2023; Rajak and Shaw, 2021). Moreover, the external variables of the model can be flexibly adjusted according to specific research contexts, including factors such as technology anxiety, perceived cost, self-fulfillment, and self-assessed health status (Venkatesh and Davis, 2000). For studies focusing on the senior citizens population, scholars have developed smart home technology acceptance models specifically tailored for senior consumers, incorporating factors such as personal characteristics, financial costs, technological complexity, privacy concerns and interaction difficulties (Chen and Chan, 2014; Wei et al., 2023; Zhou et al., 2024). These studies have validated the degree of acceptance of smart home technologies among senior citizens. However, most of these works focus on the broader category of smart homes and lack a targeted analysis of SHSS as a distinct subdomain. Given that security systems possess unique characteristics related to safety sensitivity, the present study extends the TAM framework by integrating the technological usage traits and psychological differences between senior citizens and young adults, aiming to construct and validate a model that explains their differential mechanisms in the acceptance process of SHSS. 2.1. Perceived ease of use, perceived usefulness, and intention to use In classical technology acceptance theory, Davis (1989) introduced two key constructs, PU and PEOU. PU refers to an individual’s subjective belief that a given technology enhances task performance, while PEOU reflects the user’s perception of the simplicity and cognitive effort required to operate the technology. PEOU and PU are widely regarded as the two primary constructs for predicting technology adoption behavior and behavioral intention (Davis, 1989; Guner and Acarturk, 2020; Lin et al., 2012). Additionally, intention to use (IU) measures an individual's willingness to adopt a smart home security system. In the context of SHSS, PU captures the system’s value in improving convenience and a sense of safety. PEOU represents the level of effort users perceive when operating system functions. The ease of use of a system can indirectly enhance users’ perceptions of its usefulness, and together, these two variables shape behavioral intention to use. Therefore, PU and PEOU are regarded as key predictors of users’ intention to adopt SHSS. Based on this theoretical foundation, the following hypotheses are proposed: H1a. PEOU has a significant positive effect on PU among senior citizens and young adults. H1b. PEOU has a significant positive effect on IU among elderly and young adults. H2. PU has a significant positive effect on IU among senior citizens and young adults. 2.2. Perceived cost Economic conditions, employment status, and life stage may all influence individuals’ willingness to adopt emerging technologies. SHSS are typically perceived as high-cost, long-term investments, where purchase price, installation, and maintenance costs serve as key decision factors (Balta-Ozkan et al., 2014; Hong et al., 2020; Lin and Chen, 2025). Research has shown that even when consumers recognize the potential benefits of new technologies, many remain reluctant to replace existing functional devices (Ji and Chan, 2019). Users tend to weigh financial affordability, cost-effectiveness and convenience when deciding whether to adopt a system. Therefore, this study defines perceived cost (PC) as users’ subjective evaluation of the economic and practical burden associated with SHSS, and posits it as an important determinant of behavioral intention. Therefore, the assumptions related to PC are as follows: H3a. PC has a significant effect on the IU of senior citizens and young adults. 2.3. Perceived privacy risk Although SHSS are designed to enhance safety, their interconnected and networked nature introduces potential risks to privacy and data security (Kraemer et al., 2023). While these systems offer features such as monitoring, access control and remote management, they can also expose users to cyberattacks, leading to data breaches, device intrusion or system malfunction (Hammi et al., 2023; Morgan et al., 2022). As all devices typically operate within the same network, a single point of vulnerability can compromise the security of the entire system (Sicato et al., 2019). Perceived privacy risk (PPR) threats may reduce users’ trust in the system's safety, negatively affecting both their experience and intention to adopt. Therefore, the following hypotheses are proposed: H4a. PPR has a significant negative effect on PEOU among senior citizens and young adults. H4b. PPR has a significant negative effect on PU among senior citizens and young adults. 2.4. Technology anxiety Technology Anxiety (TA) refers to feelings of tension, worry or fear experienced by individuals when learning or using new technologies (Valencia-Arias et al., 2023). When users lack confidence or experience in operating technology, their perceived effort increases, leading to a decrease in PEOU (Zhou et al., 2024). In an aging society, some senior citizens feel that they are too old to learn technological skills (Zhang, 2023). TA has become one of the main psychological barriers to adopting intelligent products. High levels of anxiety not only affect senior citizens but also diminish young adults’ confidence when interacting with complex systems, thereby potentially reducing their PU. Therefore, the relevant assumptions about TA are as follows: H5a. TA has a significant negative effect on PEOU among senior citizens and young adults. H5b. TA has a significant negative effect on PU among senior citizens and young adults. 2.5. Self-reported health conditions Users' health conditions are critical factors influencing their technology adoption and usage behaviors. Health status encompasses current physical conditions (e.g., hypertension, cardiovascular disease, diabetes) as well as sensory, mobility and cognitive abilities (Li et al., 2019). Previous studies have shown that health condition significantly affects technology acceptance and usage intention (Chen and Chan, 2014). In the context of SHSS, individuals with better health tend to exhibit higher confidence and adaptability in learning and operating system functions, which enhances both perceived ease of use and intention to use. For example, smart cameras, an essential component of SHSS, can monitor residents’ daily activities, health, and safety remotely through sensing and predictive algorithms, thereby reducing loneliness and improving safety for senior citizens (Kadylak and Cotten, 2020). Therefore, this study investigates how differences in Self-reported health conditions (SHC) between senior citizens and young adults affect their acceptance of SHSS and proposes the following hypotheses: H6a. SHC has a significant positive effect on PEOU among senior citizens and young adults. H6b. SHC has a significant positive effect on PU among senior citizens and young adults. H6c. SHC has a significant positive effect on IU among senior citizens and young adults. 2.6. Living environment This study defines living environment (LE) as users' overall perception of the livability of their residential community, including infrastructure quality and neighborhood support. A favorable living environment can strengthen residents’ sense of security and social connectedness, thereby increasing their willingness to adopt intelligent security technologies. The specific definition method will be elaborated in detail in the following text. Based on this conceptualization, the following hypotheses are proposed: H7a. LE has a significant positive effect on PEOU among senior citizens and young adults. H7b. LE has a significant positive effect on PU among senior citizens and young adults. H7c. LE has a significant positive effect on IU among senior citizens and young adults. 3. Methods The primary objective of this study is to construct an analytical model that systematically compares differences between senior citizens and young adults in the use and acceptance of SHSS. A mixed methods approach, combining both qualitative and quantitative research, was adopted to ensure the scientific rigor of model construction and the reliability of empirical findings. First, based on the classical TAM, a preliminary theoretical framework for the acceptance of SHSS was developed. As the core variables of the original TAM often require contextual adaptation and extension when applied to specific scenarios, this study conducted semi-structured interviews in the initial phase to collect qualitative data on the needs, perceptions, and attitudes of both senior citizens and young adult users toward SHSS. Subsequently, the collected interview data were analyzed using Grounded Theory, which involved systematic coding and conceptual categorization to identify key factors influencing users’ technology adoption behaviors. The results of this qualitative phase were then used to refine and supplement the external variables of the initial model, ultimately forming the TAM of SHSS. After the model was constructed, a unified structured questionnaire was developed to quantitatively test the proposed hypotheses and assess group differences between the two age cohorts. The questionnaire was administered to samples of both senior citizens and young adults. By comparing the structural equation modeling results between the two groups, the study identified the critical determinants influencing technology acceptance and behavioral intention across different age groups, thereby providing empirical evidence and design recommendations for the age-friendly development and promotion of SHSS. 3.1. Qualitative Research 3.1.1. Measurements To construct a more explanatory and comprehensive model of technology acceptance for senior citizens and young adults regarding SHSS, the qualitative phase of this study employed grounded theory, as proposed by Glaser and Strauss (1999). The research involved conducting semi-structured interviews with participants residing in various types of communities. All interviews were recorded and later transcribed verbatim to systematically capture participants’ genuine perceptions and usage experiences with SHSS. Following the procedures of grounded theory, the data were analyzed through three stages of coding: open coding, axial coding and selective coding. Through iterative categorization and abstraction, key factors influencing users’ system experience and design needs were identified, which were subsequently used to refine the model’s external variables. To ensure the specificity and validity of the interview content, a pre-interview outline was developed, encompassing questions about participants’ current usage status and expectations regarding SHSS. The interview process included the following steps: (1) The researcher explained the purpose of the study and presented the main functions and representative product images of SHSS. (2) Basic demographic information was collected, including housing type, living arrangement and age; (3) Open-ended questions were raised concerning product functions, interaction experiences, and simulated usage scenarios to explore participants’ needs, attitudes and perceptions regarding system operation. A total of eight open-ended questions were designed. During the semi-structured interviews, the interviewer adjusted the phrasing and sequence of questions to ensure contextual relevance and depth of information, based on participants' comprehension and responses. An overview of the main interview questions is presented in the appendix. 3.1.2. Participants Participants were recruited through offline random sampling, covering three representative types of residential environments: gated residential communities (with property management), old residential neighborhoods (without property management) and urban villages. The four-week interview process yielded valid data from 38 participants, whose ages ranged from 29 to 86 years. Among them, 18 were senior citizens and 20 were young adults. The diversity of the sample distribution ensured the representativeness of the findings. It provided a comprehensive understanding of the perceptual and behavioral differences between age groups in their use and acceptance of SHSS. 3.2. Quantitative Research 3.2.1. Objective and Method To enable an accurate comparison of the differences between senior citizens and young adults in their usage and acceptance of SHSS, this study employed a uniformly designed structured questionnaire to analyze key influencing factors across both groups quantitatively. The survey data were used to construct a structural equation model, which was employed to examine the relationships among variables and their effects on users’ behavioral intentions. Considering that some senior citizen respondents might experience visual impairment or limited literacy, the researcher supplemented the questionnaire administration with individual oral interviews. In these cases, researchers read the questions and explained their meaning to ensure full comprehension and enhance the validity and reliability of the collected data. 3.2.2. Measurement The questionnaire consisted of three sections: The first section aimed to understand participants’ current use and needs related to SHSS. It employed multiple-choice questions to capture information on the types of systems commonly used at home, frequency of use, and expected functions, thereby reflecting participants’ existing usage conditions and primary requirements. The second section comprised a Likert 7-point scale survey, where 1 represented “strongly disagree” and 7 represented “strongly agree”. All measurement items were adapted from established scales in domestic and international literature to ensure reliability and validity. To align with the context of an aging society, certain items were reworded to enhance comprehension among senior citizen respondents. The third section collected demographic information, including gender, age, education level, housing type and family structure, for subsequent descriptive analysis. Before the formal distribution of the questionnaire, a pre-experiment was conducted with 30 senior citizens and 30 young adults to evaluate the clarity and comprehensibility of the items. Based on feedback from the pre-experiment, revisions were made to question wording, logical order and semantic expression. The finalized questionnaire was then used for large-scale data collection. The detailed measurement items and their literature sources are presented in Table 1 . Table 1 Measurement constructs, items in this study. Constructs Items Contents Sources Intention to Use (IU) IU1 I believe that using smart home security systems is worthwhile. (Pan and Jordan-Marsh, 2010) IU2 I am interested in using smart home security systems. IU3 I think using smart home security systems is a good idea. IU4 I intend to use smart home security systems in the future. Perceived Ease of Use (PEOU) PEOU1 I find smart home security systems easy to use and operate. (Davis, 1989; Venkatesh et al., 2003) PEOU2 I can skillfully operate and use the functions of smart home security systems. Perceived Usefulness (PU) PU1 Using smart home security systems will improve my life efficiency. (Davis, 1989; Venkatesh et al., 2003) PU2 Using smart home security systems will make my life more convenient. PU3 I find smart home security systems very useful for me. Perceived Cost (PC) PC1 The cost of purchasing a smart home security system does not affect my daily living expenses. (Nikou, 2019) PC2 The daily expenses of smart home security systems (e.g., maintenance, electricity) should be economical. PC3 The daily expenses of smart home security systems (e.g., maintenance, electricity) should be economical. PC4 My financial situation is sufficient to cover the costs of using a smart home security system. Perceived Privacy Risk (PPR) PPR1 I believe that the security of smart home systems is stable. (Alam et al., 2012; Zhou et al., 2024) PPR2 I believe that smart home systems will not disclose my personal privacy. PPR3 I believe that smart home security systems only collect information within the necessary scope. PPR4 Without my explicit consent, I believe that smart home security systems will not leak my personal data. Technology Anxiety (TA) TA1 I feel somewhat worried about operating and using such a system. (Venkatesh et al., 2003) TA2 I hesitate to use this system because I am afraid of making mistakes (e.g., pressing the wrong button). TA3 If I make a mistake while using the system, I would not know how to fix it. Living Environment (LE) LE1 The size of my residential community and its convenient transportation affect my demand for such systems. (Verschuur, 2014) LE2 The quality of roads, signage, elevators, and access control systems in my community affects my demand for such systems. LE3 The level of safety in my community at night influences my demand for such systems. LE4 The level of social interaction and mutual support among residents in my community affects my demand for such systems. Self-Reported Health Condition (SHC) SHC1 My overall health condition is excellent. (Radda and Schensul, 2011) SHC2 Compared with my peers, my health condition is very good. SHC3 My hearing, vision, and mobility are all in good condition. 3.2.3. Data Collection and Participants This study adopted a mixed online and offline data collection approach to broaden sample coverage and enhance representativeness. The online survey was distributed via social media platforms (such as WeChat groups and Questionnaire Star), primarily targeting young adult participants. The offline survey was conducted through on-site paper-based questionnaires distributed in urban parks, community activity centers, and residential areas of various types, including gated communities, old neighborhoods and urban villages. For the senior citizen group, researchers provided in-person explanations and assistance to ensure the accuracy and completeness of responses. All participants were informed of the research purpose and provided informed consent, with clear statements that all data would be used solely for academic research and that personal privacy would remain strictly confidential. In accordance with previous literature, participants aged 55 and above were classified as the senior citizen group, while those aged 18–55 were categorized as the young adult group (Ma et al., 2016). Through both online and offline methods, a total of 435 valid questionnaires were collected, including 220 senior citizens and 215 young adults. Table 2 provides detailed demographic data of the participants in the research questionnaire design. A total of 74 questionnaires were excluded as invalid due to the following reasons: (1) respondents exhibited signs of fatigue from the questionnaire’s length, leading to patterned or mechanical answering; (2) some respondents experienced comprehension difficulties, resulting in logically inconsistent or skipped responses. Among the senior citizen participants, 170 individuals (77.27%) reported prior use of SHSS, while 50 (22.73%) had never used them. Among young adults, 159 individuals (73.95%) had experience using such systems, whereas 56 (26.05%) had not. The survey collected data on the distribution of usage functions of the SHSS across these two groups. The data reveal that young adults are more inclined to use functions such as door and window intrusion alarms, 24-hour video surveillance, remote mobile control, and smoke detection, whereas senior citizen users primarily rely on basic security features, including 24-hour video monitoring, door and window intrusion alarms and fire detection. Additionally, the survey investigated concerns related to home safety in both age groups. senior citizen participants expressed greater concern about their ability to seek emergency assistance in the event of sudden incidents, which is significantly different from the young and middle-aged group. The specific data is shown in the appendix. Table 2 Demographics of the study participants Demographics Senior citizens ( n =220) Younger adults ( n =215) Frequency Percentage (%) Frequency Percentage (%) Gender Female 110 50 115 53.49 Male 110 50 100 51.16 Age (years) 18–22 - - 19 8.8 23–30 - - 41 19.1 31–40 - - 79 36.7 41–55 - - 76 35.3 56–60 79 36.7 - - 61–65 78 36.3 - - 66–70 34 15.8 - - > 70 29 13.5 - - Education level Junior High School and Below 93 42.3 5 2.3 High School / Vocational School 66 30.0 32 14.9 Junior College / Associate Degree 34 15.5 51 23.7 Bachelor’s Degree 22 10.0 112 52.1 Master’s Degree and Above 5 2.3 15 7.0 Work status Full-time work 60 27.3 187 87.0 Part-time work 13 5.9 5 2.3 Retired 146 66.4 3 1.4 Not applicable/never worked 1 0.4 20 9.3 Source of income Salary 84 38.2 188 87.44 Family/relative(s) support 49 22.27 24 11.16 Pension 129 58.6 3 1.40 Endowment insurance 19 8.6 - - Government support 4 1.8 - - Monthly income Below 3,000 RMB 74 16.2 42 19.5 3,001–5,000 RMB 85 18.6 66 30.7 5,001–10,000 RMB 57 12.5 67 31.2 Above 10,001 RMB 4 0.9 40 18.6 Living arrangement Living alone 22 4.8 25 11.6 With family member(s) 194 42.5 178 82.8 With family friend(s) 1 .2 7 3.3 Company / Student Dormitory 3 .7 5 2.3 Housing Ownership Owner-Occupied Housing 210 95.45 185 86.04 Rental Housing 10 4.55 30 13.95 Housing Type Commercial Residential Community (Gated Community with Property Management) 93 42.3 148 68.8 Old Residential Community (No Property Management) 24 10.9 26 12.1 Work Unit / Family Compound 21 9.5 7 3.3 Self-Built Housing in Urban Village 6 2.7 15 7.0 Detached Villa 1 0.5 1 0.5 Self-Built Rural Housing 75 34.1 18 8.4 4. Data analysis 4.1. Effects of demographic variables on IU Demographic characteristics are among the key factors influencing users' technology adoption behavior. To examine how different demographic variables affect users' intention toward SHSS, this study employed a Kruskal–Wallis non-parametric test to analyze the sample data. The results indicate that age (χ² = 26.389, df = 7, p < 0.001) and education level (χ² = 22.600, df = 4, p < 0.001) exert significant effects on users’ usage intention. As shown in Table 3 , participants with higher age and higher educational attainment demonstrated greater average willingness to use SHSS. In contrast, gender (χ² = 2.028, df = 1, p = 0.154), monthly income (χ² = 4.321, df = 3, p = 0.229), living status (χ² = 1.608, df = 3, p = 0.658), employment condition (χ² = 9.451, df = 3, p = 0.024), and housing type (χ² = 6.318, df = 5, p = 0.276) did not show statistically significant influences on users' behavioral intention to use the system. Table 3 Characteristics of participants ( n = 435). Items Freq. Percentage (%) Means (S.D.) of IU Gender Female 225 51.7 5.49(1.02) Male 210 48.3 5.32(1.11) Age 18 ~ 22 19 4.4 5.70(1.12) 23 ~ 30 41 9.4 5.64(1.00) 31 ~ 40 79 18.2 5.68(1.16) 41 ~ 55 76 17.5 5.65(1.06) 56 ~ 60 79 18.2 5.20(0.99) 61 ~ 65 78 17.9 5.16(1.00) 66 ~ 70 34 7.8 5.20(0.83) >70 29 6.7 5.04(1.15) Education Level Junior High School and Below 98 22.5 5.06(0.91) High School / Vocational School 98 22.5 5.34(1.08) Junior College / Associate Degree 85 19.5 5.50(1.13) Bachelor’s Degree 134 30.8 5.62(1.06) Master’s Degree and Above 20 4.6 5.73(1.07) Work Status Full-time work 247 56.8 5.50(1.12) Part-time work 18 4.1 5.60(0.92) Retired 149 34.3 5.22(0.96) Not applicable/never worked 21 4.8 5.58(1.02) Monthly income Below 3,000 RMB 116 26.7 5.35(1.06) 3,001–5,000 RMB 151 34.7 5.37(1.13) 5,001–10,000 RMB 124 28.5 5.42(1.03) Above 10,001 RMB 44 10.1 5.70(0.93) Living Arrangement Living alone 47 10.8 5.49(0.97) With family member(s) 372 85.5 5.41(1.10) With family friend(s) 8 1.8 5.56(0.85) Company / Student Dormitory 8 1.8 5.09(0.65) Housing Type Commercial Residential Community (Gated Community with Property Management) 241 55.4 5.50(1.06) Old Residential Community (No Property Management) 50 11.5 5.23(1.04) Work Unit / Family Compound 28 6.4 5.14(1.30) Self-Built Housing in Urban Village 21 4.8 5.01(1.32) Detached Villa 2 0.5 5.62(1.94) Self-Built Rural Housing 93 21.4 5.46(0.93) 4.2. User Interviews and Coding Analysis During the four-week qualitative research phase, a total of 40 semi-structured interviews were conducted, including 20 senior citizen participants and 20 young adults. All interviews were personally conducted and documented by the researchers. After each interview, the audio recordings were transcribed, proofread, and verified, resulting in complete verbatim transcripts. These transcripts were then analyzed using NVivo 14 qualitative analysis software for systematic coding and thematic categorization. In the initial stage, key information was extracted from the transcripts to generate preliminary concepts. Semantically similar concepts were then merged and standardized, yielding 39 subcategories. Based on these, the researchers performed open coding to consolidate further and refine ideas, ultimately identifying 12 main categories. Subsequently, through selective coding and theoretical abstraction, several core categories were identified and developed, including economic cost, privacy and security, technology anxiety, physical health and perceived living environment. Among them, the variable "perceived living environment" emerged as an additional category during the interview process, reflecting the potential influence of residential conditions on users' attitudes toward technology adoption. The analytical process and interrelationships among categories are illustrated in Table 4 . Table 4 Latent variable factor coding. Initial Concepts Sub-categories Initial Concepts Economic cost Purchase decision drivers Safety payment willingness Price sensitivity Perceived purchase necessity Cost structure considerations Initial purchase cost Installation cost Implicit cost Maintenance and upgrade cost Cost-Performance trade-off Feature comparison Functional value evaluation Cost-Effectiveness preference Privacy and security Data leakage risk Biometric data privacy concern Video privacy concern Data misuse concern Hacking risk Account security risk Biometric technology concerns Preference for traditional keys Face recognition concern Technology anxiety System reliability Fingerprint recognition failure Technology maturity concern Technical complexity Elderly usability barrier Set up complexity concern Preference for simple operation AI technology concerns AI misjudgment concern AI data theft concern Physical health Personal safety threat Family emergency concern Fire or gas risk Burglary concern Physical decline condition Decline in risk perception Mobility decline Living environment Impact of past experiences News incident influence Neighbor safety incident Past burglary experience Assessment of the current environment Aging circuit hazard Clutter hazard Home vacancy risk Aging appliance hazard Weak community management Stranger threat 4.3. Measurement model assessment Firstly, SPSS 26 was used to test the validity and reliability of each variable. The skewness and kurtosis indicators were calculated to assess the normality of the data distribution. It is generally accepted that when the skewness and kurtosis coefficients fall within the range of -1.5 to + 1.5, the data can be considered as following a univariate normal distribution. The results show that the skewness and kurtosis values for all variables in this study fall within this range, with detailed data presented in Table 5 . The measurement model analysis primarily examined convergent validity and discriminant validity to evaluate the reliability and construct validity of the proposed model. For convergent validity, this study employed Cronbach’s α coefficient to assess the internal consistency of the measurement scales. The results indicated that all constructs had α values greater than 0.7, demonstrating a high level of internal reliability. Composite Reliability (CR) and Average Variance Extracted (AVE) were calculated to further validate convergent validity. When CR exceeds 0.7 and AVE is higher than 0.5, the model is considered to exhibit satisfactory convergent validity (Gefen and Straub, 2005; Tabachnick and Fidell, 2014). Based on the analysis results obtained using the Partial Least Squares (PLS) algorithm (Table 6 ), all latent variables demonstrated factor loadings above 0.70, CR values greater than 0.7, and AVE values exceeding 0.5. These findings confirm that the TAM for SHSS constructed in this study possesses strong convergent validity. In terms of discriminant validity, the results showed that the square root of each construct’s AVE was significantly greater than its correlations with other constructs (Table 7 ), indicating satisfactory discriminant validity. Therefore, the measurement scales used in this study demonstrate high reliability and robust construct validity. Table 5 Skewness and kurtosis values for TAM constructs Construct and items Senior citizens Younger adults Skewness Kurtosis Standard deviation Skewness Kurtosis Standard deviation Perceived Cost (PC) 1.23762 -1.025 0.850 1.27451 -0.790 0.585 Perceived Privacy Risk (PPR) 1.15619 -0.613 0.262 1.42566 -0.211 -0.649 Technology Anxiety (TA) 1.27114 -1.121 1.160 1.37056 0.525 -0.444 Living Environment (LE) 1.34629 -0.951 0.314 1.46089 -0.697 0.273 Self-Reported Health Condition (SHC) 1.16192 -0.712 0.657 1.12189 -1.025 1.358 Perceived Ease of Use (PEOU) 1.08025 -0.419 0.170 1.16654 -0.693 0.089 Perceived Usefulness (PU) 1.15069 -0.361 -0.372 1.03353 -0.437 -0.410 Intention to Use (IU) 0.99026 -0.586 0.530 1.08834 -0.637 -0.123 Table 6 Measurement model analysis Construct and items Senior citizens Younger adults CA CR AVE CA CR AVE Perceived Cost (PC) 0.908 0.935 0.783 0.845 0.896 0.683 Perceived Privacy Risk (PPR) 0.893 0.926 0.757 0.894 0.927 0.759 Technology Anxiety (TA) 0.872 0.918 0.789 0.835 0.900 0.750 Living Environment (LE) 0.927 0.949 0.822 0.895 0.927 0.762 Self-Reported Health Condition (SHC) 0.909 0.943 0.846 0.900 0.938 0.833 Perceived Ease of Use (PEOU) 0.822 0.918 0.849 0.859 0.934 0.877 Perceived Usefulness (PU) 0.864 0.917 0.786 0.883 0.928 0.811 Intention to Use (IU) 0.860 0.905 0.705 0.902 0.932 0.774 CA cronbach alpha, CR composite reliability, AVE average variance extracted Table 7 Discriminant validity scores IU LE PC PEOU PPR PU SHC TA Senior citizens IU 0.839 LE 0.510 0.907 PC 0.421 0.244 0.885 PEOU 0.480 0.450 0.347 0.921 PPR 0.289 0.297 0.467 0.391 0.870 PU 0.371 0.204 0.261 0.405 0.421 0.886 SHC 0.470 0.454 0.153 0.414 0.351 0.323 0.920 TA -0.228 -0.026 -0.029 -0.287 -0.018 -0.102 -0.065 0.889 Younger adults IU 0.880 LE 0.390 0.873 PC 0.525 0.256 0.826 PEOU 0.623 0.289 0.511 0.936 PPR 0.491 0.269 0.427 0.433 0.871 PU 0.798 0.352 0.423 0.595 0.455 0.901 SHC 0.450 0.237 0.333 0.544 0.246 0.390 0.913 TA -0.246 -0.074 -0.273 -0.336 -0.147 -0.218 -0.172 0.866 4.4. Structural model testing The Structural Equation Model (SEM) is widely used in the research of testing TAM (Gefen et al., 2000), so this study uses the structural equation model to test the hypotheses (e.g., Al-Gahtani, 2016; Guner and Acarturk, 2020; Ma et al., 2016; Macedo, 2017). At the structural model analysis stage, this study aimed to verify the proposed hypotheses by examining the causal path relationships among latent variables. To achieve this, SmartPLS 4.0 software was employed, and the bootstrapping method was applied to conduct SEM on the extended TAM. During the analysis, the partial least squares structural equation modeling (PLS-SEM) algorithm was used to calculate the path coefficients among latent variables, thereby exploring both the direct and indirect effects between constructs. The bootstrapping technique was performed to repeatedly resample the data, obtaining corresponding t-values and p-values for each path coefficient in order to determine their statistical significance and assess whether each hypothesis was supported. Based on these procedures, the calculated results are presented in Table 8 . After path adjustments and parameter optimization, the final structural equation models for both the senior citizen group and the young adult group are illustrated in Fig. 1and Fig. 2 . Table 8 Results of path analysis and hypothesis testing. Hypotheses Path coefficient Sample mean (M) Standard deviation (STDEV) T- values P- values Result Senior citizens TA→PU -0.015 -0.016 0.089 0.171 0.864 Not significant TA→PEOU -0.264 -0.264 0.057 4.646 0.000*** Significant SHC→PU 0.143 0.144 0.082 1.739 0.082 Not significant SHC→PEOU 0.182 0.181 0.070 2.621 0.009* Significant SHC→IU 0.214 0.212 0.074 2.891 0.004* Significant PU→IU 0.130 0.131 0.060 2.177 0.030* Significant PPR→PU 0.289 0.292 0.085 3.392 0.001*** Significant PPR→PEOU 0.236 0.238 0.061 3.872 0.000*** Significant PEOU→PU 0.256 0.253 0.078 3.279 0.001*** Significant PEOU→IU 0.136 0.134 0.068 1.979 0.048* Significant PC→IU 0.242 0.247 0.077 3.158 0.002* Significant LE→PU -0.063 -0.062 0.074 0.851 0.395 Not significant LE→PEOU 0.290 0.289 0.063 4.567 0.000*** Significant LE→IU 0.266 0.263 0.060 4.448 0.000*** Significant Younger adults TA→PU -0.025 -0.026 0.052 0.477 0.633 Not significant TA→PEOU -0.218 -0.215 0.052 4.173 0.000*** Significant SHC→PU 0.074 0.076 0.071 1.031 0.303 Not significant SHC→PEOU 0.415 0.415 0.070 5.938 0.000*** Significant SHC→IU 0.079 0.078 0.052 1.534 0.125 Not significant PU→IU 0.599 0.606 0.076 7.858 0.000*** Significant PPR→PU 0.214 0.212 0.077 2.782 0.005* Significant PPR→PEOU 0.271 0.270 0.066 4.132 0.000*** Significant PEOU→PU 0.409 0.407 0.089 4.602 0.000*** Significant PEOU→IU 0.114 0.105 0.066 1.731 0.084 Not significant PC→IU 0.165 0.167 0.043 3.810 0.000*** Significant LE→PU 0.157 0.160 0.064 2.459 0.014* Significant LE→PEOU 0.102 0.102 0.067 1.524 0.128 Not significant LE→IU 0.085 0.082 0.039 2.203 0.028* Significant Note: *P ≤ 0.05, ***P ≤ 0.001 5. Discussions and conclusions 5.1. Demographic and attitudinal variables The results of this study indicate that individuals with higher age and higher educational attainment exhibit stronger intentions to use smart home security systems. This finding suggests that a higher level of education and richer life experience may enhance users’ understanding and trust in the functional value of home security technologies. Unlike the conclusions of Werner et al. (2011) and Li et al. (2019), this study found that economic level did not exert a significant effect on users’ usage intention. A possible explanation is that smart home security systems are primarily safety-oriented technologies and regardless of economic background, users generally regard household safety as a fundamental life priority. 5.1.1. Key finding This study adopted a mixed-methods approach, integrating qualitative and quantitative analyses, to develop an extended technology acceptance model. By systematically investigating the behaviors and attitudes of both senior citizens and young adult users toward smart home security systems, the research comprehensively identified the key factors influencing adoption intention and the differences between these two groups. The findings contribute not only to the theoretical enrichment of technology acceptance research in the domain of smart home security systems but also offer practical implications for product and system design. which helps designers and developers better understand and meet the actual needs of elderly users during the system design process. 5.1.2. Perceived usefulness and perceived ease of use Results derived from research hypothesis analysis reveal that, for the elderly group, both perceived usefulness and perceived ease of use have significant positive effects on usage intention. Additionally, perceived ease of use exerts a positive influence on perceived usefulness. This may be attributed to the fact that elderly users often perceive ease of use as the most critical criterion when evaluating whether to adopt smart home security systems. Due to their limited prior experience with smart devices, elderly users are generally more sensitive to the learning and operational costs associated with new technologies. Thus, system simplicity and usability become decisive factors in their adoption decision. Consequently, perceived ease of use directly influences their usage intention while also indirectly affecting adoption behavior through perceived usefulness. Unlike elderly users, young adults, who are more accustomed to operating smart devices and mobile applications, do not exhibit a significant positive relationship between perceived ease of use and usage intention. However, this does not imply that perceived usefulness is unimportant for young adults. Instead, its role becomes indirect, as systems become easier to operate, users are more likely to perceive their efficiency and convenience, thereby strengthening their recognition of the system’s overall value. 5.1.3. Perceived Cost Perceived cost refers to the overall cognitive evaluation formed by consumers after weighing the potential benefits of a technology or product against the costs required for its acquisition and use. The results of this study show that perceived cost has a significant influence on usage intention among senior citizen participants, whereas its effect is not significant among young adults. For senior citizens, the safety assurance and emergency assistance functions provided by smart home security systems are of particular importance. This finding is consistent with the concerns expressed by the majority of senior citizen participants in the questionnaire survey about ‘being unable to call for help in time when suddenly ill’. Compared with price and cost, most senior citizens pay more attention to safety. In China, senior citizens often co-reside with their adult children, with financial resources derived mainly from pensions and family support. As a result, they experience lower economic pressure and exhibit higher cost tolerance, showing greater willingness to invest in systems that enhance household safety. In contrast, young adult users tend to regard smart home security systems as value-added features that improve life quality. They are more sensitive to cost-benefit evaluations, focusing on product value and affordability. If they feel that the price is too high, their willingness to use such security systems will decrease, which is consistent with the research results of Liu et al. (2024). Consequently, cost tolerance does not significantly influence their intention to use such systems. 5.1.4. Perceived Privacy Risk Perceived privacy risk refers to users’ subjective concerns regarding data security and privacy protection when using smart home security systems. Path analysis of the structural equation models for both senior citizen and young adult groups revealed that perceived privacy risk exerted significant positive effects on perceived ease of use and perceived usefulness, a result contrary to the negative influence predicted by hypotheses H4a and H4b. This result is inconsistent with the research result of Zhou et al. (2024). The reason might be the user's psychological trade-off mechanism triggered by the special attributes of the smart home security system as a security product. Although users are aware of potential privacy vulnerabilities, when evaluating the overall utility of the system, they tend to prioritize household safety over privacy concerns, treating the latter as an acceptable cost. In other words, most users acknowledge potential data security risks but still perceive the tangible safety benefits of the system, such as prevention, alarm, and emergency-response functions, as outweighing those risks. 5.1.5. Technology Anxiety Technology anxiety describes the tension, worry, or apprehension users experience when interacting with smart home security systems. The model analysis results indicate that technology anxiety only has a significant negative effect on perceived ease of use among both senior citizen and young adult groups (negative path coefficients), thus supporting hypothesis H5a. The relationship between Technology Anxiety and perceived usefulness, hypothesis H5b, was not supported. Further questionnaire analysis revealed that the senior citizen exhibited higher levels of technology anxiety, whereas the young adult group reported lower anxiety levels. For senior citizen users, factors such as physiological decline and reduced cognitive ability weaken their technological familiarity and self-efficacy. When confronted with complex smart security systems, they are more prone to anxiety, which in turn lowers their perceived ease of use, making them believe the system is difficult to learn or operate. Young adults, by contrast, generally possess higher digital literacy and adaptability. As noted in interviews, even when they experienced mild anxiety, it stemmed mainly from doubts about technological reliability rather than concerns about their own competence. Therefore, such anxiety did not significantly affect their perception of ease of use. Therefore, young adults have a lower perception of technical anxiety, but instead, they have a higher evaluation of the ease of use of the system. Concerning perceived usefulness, the conclusions of both groups were not significant for technology anxiety, suggesting that users’ judgments of a system’s usefulness depend primarily on its actual functionality and performance rather than on their emotional experience during use. Compared with the study by Guner and Acarturk (2020), this study shows certain differences. Guner and Acarturk noted that for younger adults, technology anxiety not only has a significant negative influence on perceived ease of use but also undermines perceived usefulness. For senior citizens, technology anxiety significantly affects perceived ease of use, while its impact on perceived usefulness does not reach statistical significance but remains close to the threshold. The difference can be attributed to the broader scope of information and communication technologies examined in their study. In contrast, the present research focuses specifically on smart home security systems, whose functional value is highly concentrated on the explicit goal of household safety. Therefore, even when users experience anxiety during operation, such concerns do not substantially diminish their evaluation of the system’s perceived usefulness. 5.1.6. Living Environment Living environment refers to users’ overall perception of their residential space and community atmosphere. Its influence on technology acceptance pathways exhibited both similarities and notable differences between the senior citizen and young adult groups. For senior citizen users, prolonged home-based living increases dependence on the comfort, safety, and controllability of their residential environment. A favorable environment enhances psychological security and stability, thereby improving their willingness to adopt new technologies and increasing their perception that such systems are easy to learn and use, ultimately strengthening their intention to adopt them. However, senior citizens’ evaluation of a system’s usefulness is primarily determined by its practical functionality, for instance, emergency alarms or remote monitoring, rather than environmental comfort itself. Thus, while the living environment fosters emotional security, it exerts no significant impact on perceived usefulness. In contrast, young adults, who lead faster-paced lives and spend less time at home, place greater emphasis on quality of life and efficiency. They regard the living environment as an integral aspect of life quality, reinforcing their recognition of the functional value of smart home security systems, particularly whether the system provides tangible convenience and safety. Because most young adults already possess adequate competence in using smart devices, ease of use is not their main concern. Instead, they focus on whether the system genuinely delivers efficiency and protection. Therefore, for young adults, the living environment enhances perceived usefulness, which in turn promotes usage intention, but it has no direct effect on perceived ease of use. 5.1.7. Self-Reported Health Condition Self-reported health condition reflects users’ subjective assessment of their health status based on physical, psychological, and lifestyle factors. The results show that for senior citizen participants, Self-reported health condition has a significant positive effect on both perceived ease of use and usage intention, Consistent with previous findings (Chen and Chan, 2014), whereas among young adults, health condition only exerts a positive influence on perceived ease of use and shows no significant effect on usage intention. For senior citizen users, health is one of the most crucial concerns in daily life. A relatively high self-evaluation of health often corresponds with greater willingness for autonomous activity and higher self-efficacy, which strengthens their confidence in learning and operating new technologies, leading them to perceive the system as easier to use. Moreover, senior citizen users tend to associate their health status with safety needs, seeking to enhance household health and security through smart home systems. Therefore, they believe that their physical health condition will directly increase their willingness to use smart technologies. They perceive whether the system is useful primarily based on whether its functions meet their actual needs, rather than on their self-reported health condition. Therefore, health conditions do not directly influence their perception of the usefulness of technology. Young adults generally enjoy better physical and cognitive conditions, which enhances their perception of ease of use at the operational level. However, their intention to adopt SHSS and their perceived usefulness are more heavily influenced by factors such as economic cost, functional value, and lifestyle relevance, rather than by health status itself. Consequently, the impact of health conditions on their technology adoption behavior is limited, manifesting primarily through its modest positive effect on perceived ease of use. 5.2. Contributions and implications Amid the accelerating global trend of population aging, an increasing number of senior citizens are choosing to age in place, which has led to a growing demand for home safety. As a result, smart home security systems have attracted extensive attention. In China, products specifically designed and developed for senior citizens remain in their early developmental stage. In practice, senior citizen users frequently encounter challenges such as complex operations, unintuitive interaction designs, and insufficient system trustworthiness. By employing a mixed-method approach that integrates both qualitative and quantitative analyses, this study systematically explored and compared the key factors and attitudinal differences influencing senior citizen and young adult users’ adoption of SHSS. A total of 220 valid senior citizen samples and 215 valid young adult samples were collected. Based on the extended structural equation models and factor analysis, 13 hypotheses were proposed and tested, of which 9 were supported for the senior citizen group and 7 were validated for the young adult group. The findings reveal significant differences in technology acceptance pathways between senior citizens and young adult users. These differences primarily stem from variations in technological experience, functional needs, and psychological cognition between the two groups. For senior citizen users, perceived ease of use exerts a direct and significant effect on usage intention and also promotes adoption indirectly through perceived usefulness. Due to limited prior experience with smart products, senior citizen users are more concerned with the simplicity of operation and the educational difficulty. Perceived cost, self-reported health condition, and living environment significantly influence their usage intention, whereas perceived privacy risk does not act as a barrier, indicating that senior citizen users tend to prioritize safety needs over privacy concerns. For young adults, perceived ease of use does not directly affect usage intention but exerts an indirect influence through perceived usefulness. Given their proficiency in handling digital technologies, young adults focus more on the functional value and practical utility of the system. Perceived cost has no significant impact on their intention to use the system, while the living environment enhances usage intention indirectly by increasing perceived usefulness. Technology anxiety negatively affects perceived ease of use in both groups but through different mechanisms: for senior citizens, anxiety arises from limited operational experience, while for young adults, it stems mainly from concerns about technological reliability and advancement. In both cases, technology anxiety does not significantly affect perceived usefulness. At the theoretical level, this study extends and refines the technology acceptance model by incorporating empirical findings from the experiences of senior citizens and young adults, obtained through qualitative interviews and quantitative analysis. The expanded model provides a new perspective for examining technology acceptance mechanisms across different age groups in safety-oriented smart products, thereby deepening the understanding of behavioral differences between senior citizens and young adult users. By validating the extended technology acceptance model through empirical data analysis, this research enhances the credibility and applicability of the technology acceptance model in the context of aging-oriented technology design. At the practical level, the findings provide valuable insights for the design and promotion of SHSS in an aging society. Governments should encourage enterprises to implement differentiated pricing strategies for the elderly through policy guidance, reducing financial barriers, and improving technology accessibility for senior citizen users. In terms of privacy protection, although senior citizen users tend to prioritize household safety over data privacy, system designers should still establish transparent and trustworthy data management and authorization mechanisms to strengthen user trust. From a design perspective, smart home security systems should adhere to age-friendly interaction principles, incorporating simplified modes such as one-click operation and voice control, as well as remote assistance from family members and community-based technical support to mitigate operational difficulty and technology anxiety. The system should achieve deep integration of health monitoring and security management, utilizing environmental sensing and automatic alert technologies to enhance users’ sense of environmental control. This allows senior citizen users to intuitively perceive the system’s dual role in safeguarding both health and safety, thereby improving their willingness to adopt and overall satisfaction with smart home security systems. 5.3. Limitations This study has several limitations regarding sampling and data collection. The sample was primarily drawn from southern regions of China (e.g., Guangzhou, Wuhan, and other cities). Although economic background diversity was considered when selecting communities, the sampling process relied mainly on convenience sampling due to research constraints. Consequently, the communities and participants selected were geographically concentrated and easily accessible, which limits the representativeness of the sample in terms of regional and socioeconomic diversity. During data collection, due to the physiological and cognitive limitations of some senior citizen respondents, the research team adopted a one-on-one interview questionnaire method, whereas young adult participants primarily completed self-administered questionnaires. This methodological inconsistency may have introduced slight variation in measurement consistency across groups. Furthermore, the researchers acknowledge that subjective bias could not be eliminated during the design of interview questions and survey instruments, which may have had a minor impact on the objectivity and accuracy of the collected data. Future research will aim to expand the sampling scope to include a broader range of geographic regions and demographic profiles, thereby improving both sample representativeness and the generalizability of findings. Subsequent studies should also explore the potential moderating effects of factors such as gender, residential environment, and family structure, to provide more precise and personalized insights for user research and product design in the field of smart home security systems. Abbreviations IU : Intention to Use PEOU: Perceived Ease of Use PU: Perceived Usefulness PC: Perceived Cost PPR: Perceived Privacy Risk TA: Technology Anxiety LE: Living Environment SHC: Self-reported Health Condition SHSS : Smart Home Security Systems TAM : Technology Acceptance Model SEM : The Structural Equation Model Declarations Ethics statement Participants were first informed about the objectives of the study and were asked for their consent to participate, with the option to withdraw at any time. All demographic data concerning disabled people (based on official statistics) were anonymised and de-identified prior to analysis to ensure data privacy and confidentiality. The ethics approval number is XXX2023173. Declaration of interests No potential competing interest was reported by the authors. Funding: This work was supported by National Natural Science Foundation of China (Grant No. 52008114) and Social Sciences Fund of Guangdong Province (GD24CYS04). Acknowledgments The authors would like to acknowledge the National Natural Science Foundation of China (52008114) and Social Sciences Fund of Guangdong Province (GD24CYS04) for the data collection and the preparation of the paper. The authors thank Ministry of Housing and Urban-Rural Development, China Disabled Persons’ Federation, Office of Guangzhou Municipal Commission on Aging, China Association for the Blind, China Disabled Persons’ Federation, Guangzhou Disabled Persons’ Federation and Guangzhou Volunteer Association for providing support for the research. Author Details Author 1 Name: Jia Xin Xiao Department/University: School of Art and Design, Guangdong University of Technology, China Country: China Email: [email protected] Author 2 Name: Le Yan Xie Department/University: School of Art and Design, Guangdong University of Technology, China Country: China Email: [email protected] Author 3 Name: Chang Zhong Department/University: School of Art and Design, Guangdong University of Technology, China Country: China Email: [email protected] Author 4 Name: Ming Jun Luo Department/University: 1. School of Design, Guangdong Industry Polytechnic University, China; 2. City University of Macau Email: [email protected] Author 5 Name: Tiansheng Xia Department/University: School of Art and Design, Guangdong University of Technology, China Country: China Email: [email protected] Corresponding author: Chang Zhong Corresponding Author’s Email: [email protected] Corresponding Author’s address: School of Design, 729 Dongfeng East Rd., Yuexiu District, Guangdong University of Technology, Guangzhou 510300, China Biographical Details (if applicable): [Author 1 bio] Associate Professor of School of Art and Design, Guangdong University of Technology. Leader of Science and Technology for Humanity Design Laboratory. Her research focus is on inclusive design research and practice. She has been involved in several funded research projects related to inclusive design. She has received more than 20 international and national design awards, including Design for Asia Awards (DFA), HKDA Global Design Awards and Hong Kong Awards for industries. She has published several peer-reviewed journal paper, international conference papers and book chapters. [Author 2 bio] Postgraduate and research assistant of School of Art and Design, Guangdong University of Technology. Her current research interests are inclusive design and public design. [Author 3 bio] Associate Professor of School of Art and Design, Guangdong University of Technology. Associate Dean, Home Industry Design Research Institute. She has been involved in several funded research projects related to aging-friendly design and sustainable design. She published several papers in top tier journals and obtained over 30 international invention and design grant awards. [Author 4 bio] Associate Professor of School of Design, Guangdong Industry Polytechnic University. PhD Candidate of City University of Macau. He has actively involved in social design projects to promote inclusive public space through participatory action research approach. His research focuses on social design, inclusive design, participatory design and design education. He has been involved in a number of funded research and design projects related to public design and participatory design. He promotes action research and works closely with end users. [Author 5 bio] Professor of School of Art and Design, Guangdong University of Technology. He received his Ph.D. in Psychology from South China Normal University, China. His current research interests include experience design, user study, and multimodal learning analysis. He has published papers in Human Brain Mapping, Education and Information Technologies, Interactive learning Environment, and Child Abuse & Neglect. References Abdusalomov, A., Umirzakova, S., Safarov, F., Mirzakhalilov, S., Egamberdiev, N., Cho, Y.-I., 2024. A multi-scale approach to early fire detection in smart homes. 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Sci. 46(2), 186–204. https://doi.org/10.1287/mnsc.46.2.186.11926 Venkatesh, V., Morris, M.G., Davis, G.B., Davis, F.D., 2003. User acceptance of information technology: toward a unified view. MIS quarterly, 425–478. https://doi.org/10.2307/30036540 Wei, W., Gong, X., Li, J., Tian, K., Xing, K., 2023. A study on community older people’s willingness to use smart home-an extended technology acceptance model with intergenerational relationships. Front. Public Health 11, 1139667. https://doi.org/10.3389/fpubh.2023.1139667 Werner, R.M., Kolstad, J.T., Stuart, E.A., Polsky, D., 2011. The effect of pay-for-performance In hospitals: lessons for quality improvement. Health Aff. (Millwood) 30(4), 690–698. https://doi.org/10.1377/hlthaff.2010.1277 Wiles, J.L., Leibing, A., Guberman, N., Reeve, J., Allen, R.E.S., 2012. The meaning of “aging in place” to older people. Gerontologist 52(3), 357–366. https://doi.org/10.1093/geront/gnr098 Xia, T., Pan, X., Cao, M., & Guo, J. (2025). An investigation of college students' acceptance of AI-assisted reading tools: an expansion of the TAM and SDT. Education and Information Technologies. 30(13), 18031–18058. Yuan, B., Wan, J., Wu, Y.-H., Zou, D.-Q., Jin, H., 2023. On the security of smart home systems: a survey. J. Comput. Sci. Tech. 38(2)_, 228–247. https://doi.org/10.1007/s11390-023-2488-3 Zhang, M., 2023. Older people’s attitudes towards emerging technologies: a systematic literature review. Public Underst. Sci. 32(8), 948–968. https://doi.org/10.1177/09636625231171677 Zhou, C., Qian, Y., Kaner, J., 2024. A study on smart home use intention of elderly consumers based on technology acceptance models. PLOS One 19(3), e0300574. https://doi.org/10.1371/journal.pone.030057 Additional Declarations No competing interests reported. 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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-8617018","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":602909381,"identity":"c7c45eec-32f7-4e22-ace8-3f55bcc123fa","order_by":0,"name":"Jia Xin Xiao","email":"","orcid":"","institution":"Guangdong University of Technology","correspondingAuthor":false,"prefix":"","firstName":"Jia","middleName":"Xin","lastName":"Xiao","suffix":""},{"id":602909382,"identity":"2b839f23-0bca-4015-97b9-7f6600a3b789","order_by":1,"name":"Le Yan Xie","email":"","orcid":"","institution":"Guangdong University of Technology","correspondingAuthor":false,"prefix":"","firstName":"Le","middleName":"Yan","lastName":"Xie","suffix":""},{"id":602909383,"identity":"780e4e25-ad0f-4441-bb78-c5abfb4c87f1","order_by":2,"name":"Chang Zhong","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA+UlEQVRIiWNgGAWjYDACCTD5X46x4fABBh4wJ4EoLczGzI3HEoBaDIjXktjefMaAOC38s5uPfa5sYzPmbTvz+cXbtj8M/Ow5Bgw/d+Cx5M6x5Jln23jkJHvObrOc22bAINnzxoCx9wxuLQYSOcaMjW0SxoYzzm4DWmXAYHAjx4CZsQ2flvzPQC0Gifvvv3kG1mJPWEsOM1BLQmJjwxnmx2BbJAhokbiRZszYcO4AkDhmxjjnnDGPxJlnBQd78Wjhn5H8mLGh7AAoKh9/eFMmJ8ffnrzxwU88WsCAkQ1MsYHiCJwADhDQAAR/wCTzB8IqR8EoGAWjYCQCAPEYU9j69V9yAAAAAElFTkSuQmCC","orcid":"","institution":"Guangdong University of Technology","correspondingAuthor":true,"prefix":"","firstName":"Chang","middleName":"","lastName":"Zhong","suffix":""},{"id":602909384,"identity":"f9e838f9-162e-45d2-9251-97f394e19ce2","order_by":3,"name":"Ming Jun Luo","email":"","orcid":"","institution":"Guangdong Industry Polytechnic University","correspondingAuthor":false,"prefix":"","firstName":"Ming","middleName":"Jun","lastName":"Luo","suffix":""},{"id":602909385,"identity":"59959401-e19b-4df0-afe1-bba54c5b9bda","order_by":4,"name":"Tiansheng Xia","email":"","orcid":"","institution":"Guangdong University of Technology","correspondingAuthor":false,"prefix":"","firstName":"Tiansheng","middleName":"","lastName":"Xia","suffix":""}],"badges":[],"createdAt":"2026-01-16 10:45:35","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-8617018/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-8617018/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":104472101,"identity":"3648db27-b908-4b5d-b192-5b2e8db050f2","added_by":"auto","created_at":"2026-03-12 07:29:15","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":225389,"visible":true,"origin":"","legend":"\u003cp\u003eFinal research model for senior citizens\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-8617018/v1/5ada913c7c0790f62842b024.png"},{"id":104472100,"identity":"2ec7e7d4-9bd8-404a-8397-09493289bc37","added_by":"auto","created_at":"2026-03-12 07:29:15","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":218163,"visible":true,"origin":"","legend":"\u003cp\u003eFinal research model for younger adults\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-8617018/v1/ff3703ef767f8de87a68ecd8.png"},{"id":104834892,"identity":"2bad2b9e-d1f3-43e0-91ae-67a484fa3090","added_by":"auto","created_at":"2026-03-17 17:35:17","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2612486,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8617018/v1/3f1bb174-7d55-447a-a710-72235b511ca4.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"A Comparative Study of Elderly and Young Adults’ Needs of Smart Home Security Systems: Evidence from an Extended Technology Acceptance Model (TAM)","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eWith the continuous deepening of global population aging, demographic structures are undergoing profound changes (Huh and Seo, 2015). From 1974 to 2024, the global proportion of people aged 65 and above rose from 5.5% to 10.3%, and according to the United Nations Population Fund (2024), this proportion is projected to further increase to 20.7% by 2074, with the population aged 80 and above expected to at least double. In response to this demographic shift, Most Senior citizens prefer to continue living in a familiar family environment. \u0026lsquo;Home-based elderly care\u0026rsquo; has gradually become an important social model for addressing population aging (Clark et al., 2024). Long-term living experiences enable individuals to develop a sense of attachment to their family space, which not only helps maintain close ties with family and friends but also has a positive impact on their physical and mental well-being (Ghorayeb et al., 2021; Golant, 2020). Surveys indicate that approximately 90.0% of individuals aged 60 and above spend most of their time at home (Ma, 2024). Previous studies have demonstrated that the physical environment significantly impacts the health and safety of senior citizens, with the home environment playing a key role in determining the quality of home-based care (Pettersson et al., 2021). Modern residents not only prioritize residential comfort but are also increasingly concerned with home safety and potential risk prevention, such as fire hazards, gas leakage, and burglary. This growing concern is driving the advancement of smart home technologies (Sarhan, 2020).\u003c/p\u003e \u003cp\u003eSenior citizens, due to declining mobility, cognitive deterioration, and unstable health conditions, often face multiple challenges when operating complex technological systems. A lack of perceived safety not only directly diminishes their quality of life but also reinforces psychological resistance toward technological products (Mauritzson et al., 2023; Milberg et al., 2014). Therefore, designing Smart Home Security Systems (SHSS) tailored to the needs and characteristics of senior citizens is of great practical and social significance, as it can mitigate household risks and improve the user experience.\u003c/p\u003e \u003cp\u003eWithin the broader field of smart home technology, security systems have emerged as a central focus of both research and industrial development due to residents' increasing demand for safety (Yuan et al., 2023). A typical smart home security system consists of access control, video surveillance, and alarm modules. These systems continuously monitor the home environment, utilizing algorithms such as convolutional neural networks (CNNs) to detect abnormal intrusions and immediately send alert notifications to users (Taiwo et al., 2022). The system can actively respond to hazards, for example, activating sprinklers via smoke detectors to suppress fires or initiating ventilation to reduce flammable gas concentration. Even when users are away from home, the system can transmit real-time updates on environmental conditions. If necessary, trigger sound and light alarms to alert neighbors or relevant personnel, thus ensuring safety in various scenarios (Sarhan, 2020; Uppuluri and Lakshmeeswari, 2024).\u003c/p\u003e \u003cp\u003eIn recent years, several academic and engineering projects have made significant progress in the development of SHSS. For instance, Ouyang et al. (2023) proposed a high-security dual-lock system based on a triboelectric nanogenerator and a deep learning model, which enables dual authentication by collecting users\u0026rsquo; respiratory signals and facial features, significantly enhancing protective performance. Dong et al. (2023) developed a multimodal neuromorphic sensing and processing system based on memristor circuits, capable of indoor human behavior recognition and intelligent surveillance. This system not only improves household security but also reduces implementation costs and power consumption. Abdusalomov et al. (2024) proposed a smart home fire and smoke detection model based on MII-DETR, effectively addressing the issue of low recognition accuracy of traditional methods in the case of flame overlap and occlusion, and enhancing the early warning and prevention capabilities of fires. Passive Infrared (PIR) sensors have been widely employed for intrusion detection (ShariqSuhail et al., 2016). Based on the Raspberry Pi 3 Home Server (RHS) and the Support Vector Machine (SVM) algorithm, Alam et al. (2024) designed a real-time monitoring system that integrates MQ2 sensors. It can automatically issue early warnings and respond to security anomalies. However, existing studies have largely concentrated on technological innovation and optimization, while research on the needs of the aging population is still relatively scarce. Senior citizens and young adults differ significantly in their functional needs, interaction preferences, and emotional experiences, largely due to differences in technological familiarity and proficiency. To address this research gap, this study compares the perceptions, attitudes, and adoption intentions of senior citizens and young adults regarding SHSS, examining the key factors that influence their intentions. The findings aim to provide theoretical support and practical guidance for the design and promotion of age-friendly SHSS, ultimately enhancing home safety, quality of life, and overall well-being for senior citizens.\u003c/p\u003e"},{"header":"2. Research model and hypotheses","content":"\u003cp\u003eThe technology acceptance model (TAM), first proposed by Davis (1989), is widely used to explain and predict the acceptance and use of information technologies by individuals and organizations. The model posits that perceived usefulness (PU) and perceived ease of use (PEOU) are two core determinants that influence users\u0026rsquo; technology adoption behavior, and both remain relatively stable in the model structure.\u003c/p\u003e \u003cp\u003eAlthough the TAM has been widely regarded as a reliable and robust theoretical framework, its structural validity may still be influenced by factors such as the type of technology under study, cultural context, and situational background (Johnson et al., 2014; Sharma et al., 2024; Xia et al., 2025). Numerous studies have demonstrated that extending TAM can effectively enhance its explanatory power (Al-Adwan et al., 2023; Lin and Yu, 2023; Rajak and Shaw, 2021). Moreover, the external variables of the model can be flexibly adjusted according to specific research contexts, including factors such as technology anxiety, perceived cost, self-fulfillment, and self-assessed health status (Venkatesh and Davis, 2000).\u003c/p\u003e \u003cp\u003eFor studies focusing on the senior citizens population, scholars have developed smart home technology acceptance models specifically tailored for senior consumers, incorporating factors such as personal characteristics, financial costs, technological complexity, privacy concerns and interaction difficulties (Chen and Chan, 2014; Wei et al., 2023; Zhou et al., 2024). These studies have validated the degree of acceptance of smart home technologies among senior citizens. However, most of these works focus on the broader category of smart homes and lack a targeted analysis of SHSS as a distinct subdomain. Given that security systems possess unique characteristics related to safety sensitivity, the present study extends the TAM framework by integrating the technological usage traits and psychological differences between senior citizens and young adults, aiming to construct and validate a model that explains their differential mechanisms in the acceptance process of SHSS.\u003c/p\u003e \u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1. Perceived ease of use, perceived usefulness, and intention to use\u003c/h2\u003e \u003cp\u003eIn classical technology acceptance theory, Davis (1989) introduced two key constructs, PU and PEOU. PU refers to an individual\u0026rsquo;s subjective belief that a given technology enhances task performance, while PEOU reflects the user\u0026rsquo;s perception of the simplicity and cognitive effort required to operate the technology. PEOU and PU are widely regarded as the two primary constructs for predicting technology adoption behavior and behavioral intention (Davis, 1989; Guner and Acarturk, 2020; Lin et al., 2012). Additionally, intention to use (IU) measures an individual's willingness to adopt a smart home security system. In the context of SHSS, PU captures the system\u0026rsquo;s value in improving convenience and a sense of safety. PEOU represents the level of effort users perceive when operating system functions. The ease of use of a system can indirectly enhance users\u0026rsquo; perceptions of its usefulness, and together, these two variables shape behavioral intention to use. Therefore, PU and PEOU are regarded as key predictors of users\u0026rsquo; intention to adopt SHSS. Based on this theoretical foundation, the following hypotheses are proposed:\u003c/p\u003e \u003cp\u003e \u003cb\u003eH1a.\u003c/b\u003e PEOU has a significant positive effect on PU among senior citizens and young adults.\u003c/p\u003e \u003cp\u003e \u003cb\u003eH1b.\u003c/b\u003e PEOU has a significant positive effect on IU among elderly and young adults.\u003c/p\u003e \u003cp\u003e \u003cb\u003eH2.\u003c/b\u003e PU has a significant positive effect on IU among senior citizens and young adults.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2. Perceived cost\u003c/h2\u003e \u003cp\u003eEconomic conditions, employment status, and life stage may all influence individuals\u0026rsquo; willingness to adopt emerging technologies. SHSS are typically perceived as high-cost, long-term investments, where purchase price, installation, and maintenance costs serve as key decision factors (Balta-Ozkan et al., 2014; Hong et al., 2020; Lin and Chen, 2025). Research has shown that even when consumers recognize the potential benefits of new technologies, many remain reluctant to replace existing functional devices (Ji and Chan, 2019). Users tend to weigh financial affordability, cost-effectiveness and convenience when deciding whether to adopt a system. Therefore, this study defines perceived cost (PC) as users\u0026rsquo; subjective evaluation of the economic and practical burden associated with SHSS, and posits it as an important determinant of behavioral intention. Therefore, the assumptions related to PC are as follows:\u003c/p\u003e \u003cp\u003e \u003cb\u003eH3a.\u003c/b\u003e PC has a significant effect on the IU of senior citizens and young adults.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e\u003cem\u003e2.3. Perceived privacy risk\u003c/em\u003e\u003c/h2\u003e \u003cp\u003eAlthough SHSS are designed to enhance safety, their interconnected and networked nature introduces potential risks to privacy and data security (Kraemer et al., 2023). While these systems offer features such as monitoring, access control and remote management, they can also expose users to cyberattacks, leading to data breaches, device intrusion or system malfunction (Hammi et al., 2023; Morgan et al., 2022). As all devices typically operate within the same network, a single point of vulnerability can compromise the security of the entire system (Sicato et al., 2019). Perceived privacy risk (PPR) threats may reduce users\u0026rsquo; trust in the system's safety, negatively affecting both their experience and intention to adopt. Therefore, the following hypotheses are proposed:\u003c/p\u003e \u003cp\u003e \u003cb\u003eH4a.\u003c/b\u003e PPR has a significant negative effect on PEOU among senior citizens and young adults.\u003c/p\u003e \u003cp\u003e \u003cb\u003eH4b.\u003c/b\u003e PPR has a significant negative effect on PU among senior citizens and young adults.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003e\u003cem\u003e2.4. Technology anxiety\u003c/em\u003e\u003c/h2\u003e \u003cp\u003eTechnology Anxiety (TA) refers to feelings of tension, worry or fear experienced by individuals when learning or using new technologies (Valencia-Arias et al., 2023). When users lack confidence or experience in operating technology, their perceived effort increases, leading to a decrease in PEOU (Zhou et al., 2024). In an aging society, some senior citizens feel that they are too old to learn technological skills (Zhang, 2023). TA has become one of the main psychological barriers to adopting intelligent products. High levels of anxiety not only affect senior citizens but also diminish young adults\u0026rsquo; confidence when interacting with complex systems, thereby potentially reducing their PU. Therefore, the relevant assumptions about TA are as follows:\u003c/p\u003e \u003cp\u003e \u003cb\u003eH5a.\u003c/b\u003e TA has a significant negative effect on PEOU among senior citizens and young adults.\u003c/p\u003e \u003cp\u003e \u003cb\u003eH5b.\u003c/b\u003e TA has a significant negative effect on PU among senior citizens and young adults.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003e\u003cem\u003e2.5. Self-reported health conditions\u003c/em\u003e\u003c/h2\u003e \u003cp\u003eUsers' health conditions are critical factors influencing their technology adoption and usage behaviors. Health status encompasses current physical conditions (e.g., hypertension, cardiovascular disease, diabetes) as well as sensory, mobility and cognitive abilities (Li et al., 2019). Previous studies have shown that health condition significantly affects technology acceptance and usage intention (Chen and Chan, 2014). In the context of SHSS, individuals with better health tend to exhibit higher confidence and adaptability in learning and operating system functions, which enhances both perceived ease of use and intention to use. For example, smart cameras, an essential component of SHSS, can monitor residents\u0026rsquo; daily activities, health, and safety remotely through sensing and predictive algorithms, thereby reducing loneliness and improving safety for senior citizens (Kadylak and Cotten, 2020). Therefore, this study investigates how differences in Self-reported health conditions (SHC) between senior citizens and young adults affect their acceptance of SHSS and proposes the following hypotheses:\u003c/p\u003e \u003cp\u003e \u003cb\u003eH6a.\u003c/b\u003e SHC has a significant positive effect on PEOU among senior citizens and young adults.\u003c/p\u003e \u003cp\u003e \u003cb\u003eH6b.\u003c/b\u003e SHC has a significant positive effect on PU among senior citizens and young adults.\u003c/p\u003e \u003cp\u003e \u003cb\u003eH6c.\u003c/b\u003e SHC has a significant positive effect on IU among senior citizens and young adults.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003e\u003cem\u003e2.6. Living environment\u003c/em\u003e\u003c/h2\u003e \u003cp\u003eThis study defines living environment (LE) as users' overall perception of the livability of their residential community, including infrastructure quality and neighborhood support. A favorable living environment can strengthen residents\u0026rsquo; sense of security and social connectedness, thereby increasing their willingness to adopt intelligent security technologies. The specific definition method will be elaborated in detail in the following text. Based on this conceptualization, the following hypotheses are proposed:\u003c/p\u003e \u003cp\u003e \u003cb\u003eH7a.\u003c/b\u003e LE has a significant positive effect on PEOU among senior citizens and young adults.\u003c/p\u003e \u003cp\u003e \u003cb\u003eH7b.\u003c/b\u003e LE has a significant positive effect on PU among senior citizens and young adults.\u003c/p\u003e \u003cp\u003e \u003cb\u003eH7c.\u003c/b\u003e LE has a significant positive effect on IU among senior citizens and young adults.\u003c/p\u003e \u003c/div\u003e"},{"header":"3. Methods","content":"\u003cp\u003eThe primary objective of this study is to construct an analytical model that systematically compares differences between senior citizens and young adults in the use and acceptance of SHSS. A mixed methods approach, combining both qualitative and quantitative research, was adopted to ensure the scientific rigor of model construction and the reliability of empirical findings. First, based on the classical TAM, a preliminary theoretical framework for the acceptance of SHSS was developed. As the core variables of the original TAM often require contextual adaptation and extension when applied to specific scenarios, this study conducted semi-structured interviews in the initial phase to collect qualitative data on the needs, perceptions, and attitudes of both senior citizens and young adult users toward SHSS. Subsequently, the collected interview data were analyzed using Grounded Theory, which involved systematic coding and conceptual categorization to identify key factors influencing users\u0026rsquo; technology adoption behaviors. The results of this qualitative phase were then used to refine and supplement the external variables of the initial model, ultimately forming the TAM of SHSS. After the model was constructed, a unified structured questionnaire was developed to quantitatively test the proposed hypotheses and assess group differences between the two age cohorts. The questionnaire was administered to samples of both senior citizens and young adults. By comparing the structural equation modeling results between the two groups, the study identified the critical determinants influencing technology acceptance and behavioral intention across different age groups, thereby providing empirical evidence and design recommendations for the age-friendly development and promotion of SHSS.\u003c/p\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003e\u003cem\u003e3.1. Qualitative Research\u003c/em\u003e\u003c/h2\u003e \u003cdiv id=\"Sec11\" class=\"Section3\"\u003e \u003ch2\u003e\u003cem\u003e3.1.1. Measurements\u003c/em\u003e\u003c/h2\u003e \u003cp\u003eTo construct a more explanatory and comprehensive model of technology acceptance for senior citizens and young adults regarding SHSS, the qualitative phase of this study employed grounded theory, as proposed by Glaser and Strauss (1999). The research involved conducting semi-structured interviews with participants residing in various types of communities. All interviews were recorded and later transcribed verbatim to systematically capture participants\u0026rsquo; genuine perceptions and usage experiences with SHSS. Following the procedures of grounded theory, the data were analyzed through three stages of coding: open coding, axial coding and selective coding. Through iterative categorization and abstraction, key factors influencing users\u0026rsquo; system experience and design needs were identified, which were subsequently used to refine the model\u0026rsquo;s external variables.\u003c/p\u003e \u003cp\u003e To ensure the specificity and validity of the interview content, a pre-interview outline was developed, encompassing questions about participants\u0026rsquo; current usage status and expectations regarding SHSS. The interview process included the following steps: (1) The researcher explained the purpose of the study and presented the main functions and representative product images of SHSS. (2) Basic demographic information was collected, including housing type, living arrangement and age; (3) Open-ended questions were raised concerning product functions, interaction experiences, and simulated usage scenarios to explore participants\u0026rsquo; needs, attitudes and perceptions regarding system operation. A total of eight open-ended questions were designed. During the semi-structured interviews, the interviewer adjusted the phrasing and sequence of questions to ensure contextual relevance and depth of information, based on participants' comprehension and responses. An overview of the main interview questions is presented in the appendix.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section3\"\u003e \u003ch2\u003e\u003cem\u003e3.1.2. Participants\u003c/em\u003e\u003c/h2\u003e \u003cp\u003eParticipants were recruited through offline random sampling, covering three representative types of residential environments: gated residential communities (with property management), old residential neighborhoods (without property management) and urban villages. The four-week interview process yielded valid data from 38 participants, whose ages ranged from 29 to 86 years. Among them, 18 were senior citizens and 20 were young adults. The diversity of the sample distribution ensured the representativeness of the findings. It provided a comprehensive understanding of the perceptual and behavioral differences between age groups in their use and acceptance of SHSS.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003e3.2. Quantitative Research\u003c/h2\u003e \u003cdiv id=\"Sec14\" class=\"Section3\"\u003e \u003ch2\u003e\u003cem\u003e3.2.1. Objective and Method\u003c/em\u003e\u003c/h2\u003e \u003cp\u003eTo enable an accurate comparison of the differences between senior citizens and young adults in their usage and acceptance of SHSS, this study employed a uniformly designed structured questionnaire to analyze key influencing factors across both groups quantitatively. The survey data were used to construct a structural equation model, which was employed to examine the relationships among variables and their effects on users\u0026rsquo; behavioral intentions. Considering that some senior citizen respondents might experience visual impairment or limited literacy, the researcher supplemented the questionnaire administration with individual oral interviews. In these cases, researchers read the questions and explained their meaning to ensure full comprehension and enhance the validity and reliability of the collected data.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section3\"\u003e \u003ch2\u003e\u003cem\u003e3.2.2. Measurement\u003c/em\u003e\u003c/h2\u003e \u003cp\u003eThe questionnaire consisted of three sections: The first section aimed to understand participants\u0026rsquo; current use and needs related to SHSS. It employed multiple-choice questions to capture information on the types of systems commonly used at home, frequency of use, and expected functions, thereby reflecting participants\u0026rsquo; existing usage conditions and primary requirements. The second section comprised a Likert 7-point scale survey, where 1 represented \u0026ldquo;strongly disagree\u0026rdquo; and 7 represented \u0026ldquo;strongly agree\u0026rdquo;. All measurement items were adapted from established scales in domestic and international literature to ensure reliability and validity. To align with the context of an aging society, certain items were reworded to enhance comprehension among senior citizen respondents. The third section collected demographic information, including gender, age, education level, housing type and family structure, for subsequent descriptive analysis.\u003c/p\u003e \u003cp\u003eBefore the formal distribution of the questionnaire, a pre-experiment was conducted with 30 senior citizens and 30 young adults to evaluate the clarity and comprehensibility of the items. Based on feedback from the pre-experiment, revisions were made to question wording, logical order and semantic expression. The finalized questionnaire was then used for large-scale data collection. The detailed measurement items and their literature sources are presented in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\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\u003eMeasurement constructs, items in this study.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\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=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eConstructs\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\u003eContents\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eSources\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003e\u003cb\u003eIntention to Use (IU)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eIU1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eI believe that using smart home security systems is worthwhile.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003e(Pan and Jordan-Marsh, 2010)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eIU2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eI am interested in using smart home security systems.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eIU3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eI think using smart home security systems is a good idea.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eIU4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eI intend to use smart home security systems in the future.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e\u003cb\u003ePerceived Ease of Use (PEOU)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePEOU1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eI find smart home security systems easy to use and operate.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e(Davis, 1989; Venkatesh et al., 2003)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePEOU2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eI can skillfully operate and use the functions of smart home security systems.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e\u003cb\u003ePerceived Usefulness (PU)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePU1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eUsing smart home security systems will improve my life efficiency.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e(Davis, 1989; Venkatesh et al., 2003)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePU2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eUsing smart home security systems will make my life more convenient.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePU3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eI find smart home security systems very useful for me.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003e\u003cb\u003ePerceived Cost (PC)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePC1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eThe cost of purchasing a smart home security system does not affect my daily living expenses.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003e(Nikou, 2019)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePC2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eThe daily expenses of smart home security systems (e.g., maintenance, electricity) should be economical.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePC3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eThe daily expenses of smart home security systems (e.g., maintenance, electricity) should be economical.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePC4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMy financial situation is sufficient to cover the costs of using a smart home security system.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003e\u003cb\u003ePerceived Privacy Risk (PPR)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePPR1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eI believe that the security of smart home systems is stable.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003e(Alam et al., 2012; Zhou et al., 2024)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePPR2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eI believe that smart home systems will not disclose my personal privacy.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePPR3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eI believe that smart home security systems only collect information within the necessary scope.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePPR4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eWithout my explicit consent, I believe that smart home security systems will not leak my personal data.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e\u003cb\u003eTechnology Anxiety (TA)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTA1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eI feel somewhat worried about operating and using such a system.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e(Venkatesh et al., 2003)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTA2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eI hesitate to use this system because I am afraid of making mistakes (e.g., pressing the wrong button).\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTA3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eIf I make a mistake while using the system, I would not know how to fix it.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003e\u003cb\u003eLiving Environment (LE)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLE1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eThe size of my residential community and its convenient transportation affect my demand for such systems.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003e(Verschuur, 2014)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLE2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eThe quality of roads, signage, elevators, and access control systems in my community affects my demand for such systems.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLE3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eThe level of safety in my community at night influences my demand for such systems.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLE4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eThe level of social interaction and mutual support among residents in my community affects my demand for such systems.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e\u003cb\u003eSelf-Reported Health Condition (SHC)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSHC1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMy overall health condition is excellent.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e(Radda and Schensul, 2011)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSHC2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCompared with my peers, my health condition is very good.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSHC3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMy hearing, vision, and mobility are all in good condition.\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=\"Sec16\" class=\"Section3\"\u003e \u003ch2\u003e\u003cem\u003e3.2.3. Data Collection and Participants\u003c/em\u003e\u003c/h2\u003e \u003cp\u003eThis study adopted a mixed online and offline data collection approach to broaden sample coverage and enhance representativeness. The online survey was distributed via social media platforms (such as WeChat groups and Questionnaire Star), primarily targeting young adult participants. The offline survey was conducted through on-site paper-based questionnaires distributed in urban parks, community activity centers, and residential areas of various types, including gated communities, old neighborhoods and urban villages. For the senior citizen group, researchers provided in-person explanations and assistance to ensure the accuracy and completeness of responses. All participants were informed of the research purpose and provided informed consent, with clear statements that all data would be used solely for academic research and that personal privacy would remain strictly confidential.\u003c/p\u003e \u003cp\u003eIn accordance with previous literature, participants aged 55 and above were classified as the senior citizen group, while those aged 18\u0026ndash;55 were categorized as the young adult group (Ma et al., 2016). Through both online and offline methods, a total of 435 valid questionnaires were collected, including 220 senior citizens and 215 young adults. Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e provides detailed demographic data of the participants in the research questionnaire design. A total of 74 questionnaires were excluded as invalid due to the following reasons: (1) respondents exhibited signs of fatigue from the questionnaire\u0026rsquo;s length, leading to patterned or mechanical answering; (2) some respondents experienced comprehension difficulties, resulting in logically inconsistent or skipped responses.\u003c/p\u003e \u003cp\u003eAmong the senior citizen participants, 170 individuals (77.27%) reported prior use of SHSS, while 50 (22.73%) had never used them. Among young adults, 159 individuals (73.95%) had experience using such systems, whereas 56 (26.05%) had not. The survey collected data on the distribution of usage functions of the SHSS across these two groups. The data reveal that young adults are more inclined to use functions such as door and window intrusion alarms, 24-hour video surveillance, remote mobile control, and smoke detection, whereas senior citizen users primarily rely on basic security features, including 24-hour video monitoring, door and window intrusion alarms and fire detection. Additionally, the survey investigated concerns related to home safety in both age groups. senior citizen participants expressed greater concern about their ability to seek emergency assistance in the event of sudden incidents, which is significantly different from the young and middle-aged group. The specific data is shown in the appendix.\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\u003eDemographics of the study participants\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\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=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eDemographics\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eSenior citizens (\u003cem\u003en\u003c/em\u003e=220)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c6\" namest=\"c4\"\u003e \u003cp\u003eYounger adults (\u003cem\u003en\u003c/em\u003e=215)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"1\" nameend=\"c7\" namest=\"c7\"\u003e\u0026nbsp;\u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFrequency\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePercentage (%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eFrequency\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003ePercentage (%)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"6\" nameend=\"c6\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eGender\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c7\" namest=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFemale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e110\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e115\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e53.49\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e110\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e100\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e51.16\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"6\" nameend=\"c6\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eAge (years)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c7\" namest=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e18\u0026ndash;22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e8.8\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e23\u0026ndash;30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e41\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e19.1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e31\u0026ndash;40\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e79\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e36.7\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e41\u0026ndash;55\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e76\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e35.3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e56\u0026ndash;60\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e79\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e36.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e61\u0026ndash;65\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e78\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e36.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e66\u0026ndash;70\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e34\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e15.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;70\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e13.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"6\" nameend=\"c6\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eEducation level\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c7\" namest=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eJunior High School and Below\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e93\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e42.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e2.3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHigh School / Vocational School\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e66\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e30.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e32\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e14.9\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eJunior College / Associate Degree\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e34\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e15.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e51\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e23.7\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBachelor\u0026rsquo;s Degree\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e10.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e112\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e52.1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMaster\u0026rsquo;s Degree and Above\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e7.0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"6\" nameend=\"c6\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eWork status\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c7\" namest=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFull-time work\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e60\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e27.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e187\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e87.0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePart-time work\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e2.3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRetired\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e146\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e66.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e1.4\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNot applicable/never worked\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e9.3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"6\" nameend=\"c6\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eSource of income\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c7\" namest=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSalary\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e84\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e38.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e188\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e87.44\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFamily/relative(s) support\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e49\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e22.27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e11.16\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePension\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e129\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e58.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e1.40\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEndowment insurance\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e8.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGovernment support\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"6\" nameend=\"c6\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eMonthly income\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c7\" namest=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBelow 3,000 RMB\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e74\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e16.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e42\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e19.5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e3,001\u0026ndash;5,000 RMB\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e85\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e18.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e66\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e30.7\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e5,001\u0026ndash;10,000 RMB\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e57\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e12.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e67\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e31.2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAbove 10,001 RMB\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e40\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e18.6\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"6\" nameend=\"c6\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eLiving arrangement\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c7\" namest=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLiving alone\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e11.6\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWith family member(s)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e194\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e42.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e178\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e82.8\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWith family friend(s)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e3.3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCompany / Student Dormitory\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e2.3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"6\" nameend=\"c6\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eHousing Ownership\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c7\" namest=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOwner-Occupied Housing\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e210\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e95.45\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e185\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e86.04\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRental Housing\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4.55\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e13.95\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"6\" nameend=\"c6\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eHousing Type\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c7\" namest=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCommercial Residential Community (Gated Community with Property Management)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e93\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e42.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e148\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e68.8\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOld Residential Community (No Property Management)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e10.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e12.1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWork Unit / Family Compound\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e9.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e3.3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSelf-Built Housing in Urban Village\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e7.0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDetached Villa\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e0.5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSelf-Built Rural Housing\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e75\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e34.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e8.4\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 \u003c/div\u003e"},{"header":"4. Data analysis","content":"\u003cdiv id=\"Sec18\" class=\"Section2\"\u003e \u003ch2\u003e\u003cem\u003e4.1. Effects of demographic variables on IU\u003c/em\u003e\u003c/h2\u003e \u003cp\u003eDemographic characteristics are among the key factors influencing users' technology adoption behavior. To examine how different demographic variables affect users' intention toward SHSS, this study employed a Kruskal\u0026ndash;Wallis non-parametric test to analyze the sample data. The results indicate that age (χ\u0026sup2; = 26.389, df\u0026thinsp;=\u0026thinsp;7, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) and education level (χ\u0026sup2; = 22.600, df\u0026thinsp;=\u0026thinsp;4, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) exert significant effects on users\u0026rsquo; usage intention. As shown in Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e, participants with higher age and higher educational attainment demonstrated greater average willingness to use SHSS. In contrast, gender (χ\u0026sup2; = 2.028, df\u0026thinsp;=\u0026thinsp;1, p\u0026thinsp;=\u0026thinsp;0.154), monthly income (χ\u0026sup2; = 4.321, df\u0026thinsp;=\u0026thinsp;3, p\u0026thinsp;=\u0026thinsp;0.229), living status (χ\u0026sup2; = 1.608, df\u0026thinsp;=\u0026thinsp;3, p\u0026thinsp;=\u0026thinsp;0.658), employment condition (χ\u0026sup2; = 9.451, df\u0026thinsp;=\u0026thinsp;3, p\u0026thinsp;=\u0026thinsp;0.024), and housing type (χ\u0026sup2; = 6.318, df\u0026thinsp;=\u0026thinsp;5, p\u0026thinsp;=\u0026thinsp;0.276) did not show statistically significant influences on users' behavioral intention to use the system.\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\u003eCharacteristics of participants (\u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;435).\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\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=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eItems\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFreq.\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePercentage (%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eMeans (S.D.) of IU\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colspan=\"4\" nameend=\"c4\" namest=\"c1\"\u003e \u003cp\u003e\u003cem\u003eGender\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFemale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e225\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e51.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e5.49(1.02)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e210\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e48.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e5.32(1.11)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"4\" nameend=\"c4\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eAge\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e18\u0026thinsp;~\u0026thinsp;22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e5.70(1.12)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e23\u0026thinsp;~\u0026thinsp;30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e41\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e9.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e5.64(1.00)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e31\u0026thinsp;~\u0026thinsp;40\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e79\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e18.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e5.68(1.16)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e41\u0026thinsp;~\u0026thinsp;55\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e76\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e17.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e5.65(1.06)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e56\u0026thinsp;~\u0026thinsp;60\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e79\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e18.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e5.20(0.99)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e61\u0026thinsp;~\u0026thinsp;65\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e78\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e17.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e5.16(1.00)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e66\u0026thinsp;~\u0026thinsp;70\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e34\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e7.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e5.20(0.83)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026gt;70\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e5.04(1.15)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"4\" nameend=\"c4\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eEducation Level\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eJunior High School and Below\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e98\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e22.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e5.06(0.91)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHigh School / Vocational School\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e98\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e22.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e5.34(1.08)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eJunior College / Associate Degree\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e85\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e19.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e5.50(1.13)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBachelor\u0026rsquo;s Degree\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e134\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e30.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e5.62(1.06)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMaster\u0026rsquo;s Degree and Above\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e5.73(1.07)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"4\" nameend=\"c4\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eWork Status\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFull-time work\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e247\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e56.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e5.50(1.12)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePart-time work\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e5.60(0.92)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRetired\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e149\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e34.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e5.22(0.96)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNot applicable/never worked\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e5.58(1.02)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eMonthly income\u003c/b\u003e\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 \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBelow 3,000 RMB\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e116\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e26.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e5.35(1.06)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e3,001\u0026ndash;5,000 RMB\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e151\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e34.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e5.37(1.13)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e5,001\u0026ndash;10,000 RMB\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e124\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e28.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e5.42(1.03)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAbove 10,001 RMB\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e44\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e10.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e5.70(0.93)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"4\" nameend=\"c4\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eLiving Arrangement\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLiving alone\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e47\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e10.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e5.49(0.97)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWith family member(s)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e372\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e85.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e5.41(1.10)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWith family friend(s)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e5.56(0.85)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCompany / Student Dormitory\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e5.09(0.65)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"4\" nameend=\"c4\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eHousing Type\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCommercial Residential Community (Gated Community with Property Management)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e241\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e55.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e5.50(1.06)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOld Residential Community (No Property Management)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e11.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e5.23(1.04)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWork Unit / Family Compound\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e5.14(1.30)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSelf-Built Housing in Urban Village\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e5.01(1.32)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDetached Villa\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e5.62(1.94)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSelf-Built Rural Housing\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e93\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e21.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e5.46(0.93)\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=\"Sec19\" class=\"Section2\"\u003e \u003ch2\u003e\u003cem\u003e4.2. User Interviews and Coding Analysis\u003c/em\u003e\u003c/h2\u003e \u003cp\u003eDuring the four-week qualitative research phase, a total of 40 semi-structured interviews were conducted, including 20 senior citizen participants and 20 young adults. All interviews were personally conducted and documented by the researchers. After each interview, the audio recordings were transcribed, proofread, and verified, resulting in complete verbatim transcripts. These transcripts were then analyzed using NVivo 14 qualitative analysis software for systematic coding and thematic categorization. In the initial stage, key information was extracted from the transcripts to generate preliminary concepts. Semantically similar concepts were then merged and standardized, yielding 39 subcategories. Based on these, the researchers performed open coding to consolidate further and refine ideas, ultimately identifying 12 main categories. Subsequently, through selective coding and theoretical abstraction, several core categories were identified and developed, including economic cost, privacy and security, technology anxiety, physical health and perceived living environment. Among them, the variable \"perceived living environment\" emerged as an additional category during the interview process, reflecting the potential influence of residential conditions on users' attitudes toward technology adoption. The analytical process and interrelationships among categories are illustrated in Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e.\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\u003eLatent variable factor coding.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\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=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eInitial Concepts\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSub-categories\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eInitial Concepts\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"9\" rowspan=\"10\"\u003e \u003cp\u003e\u003cb\u003eEconomic cost\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003ePurchase decision drivers\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSafety payment willingness\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePrice sensitivity\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePerceived purchase necessity\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003eCost structure considerations\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eInitial purchase cost\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eInstallation cost\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eImplicit cost\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMaintenance and upgrade cost\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eCost-Performance trade-off\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eFeature comparison\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eFunctional value evaluation\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCost-Effectiveness preference\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"6\" rowspan=\"7\"\u003e \u003cp\u003e\u003cb\u003ePrivacy and security\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\" morerows=\"4\" rowspan=\"5\"\u003e \u003cp\u003eData leakage risk\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eBiometric data privacy concern\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eVideo privacy concern\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eData misuse concern\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eHacking risk\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAccount security risk\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eBiometric technology concerns\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePreference for traditional keys\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eFace recognition concern\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"6\" rowspan=\"7\"\u003e \u003cp\u003e\u003cb\u003eTechnology anxiety\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eSystem reliability\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eFingerprint recognition failure\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eTechnology maturity concern\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eTechnical complexity\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eElderly usability barrier\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSet up complexity concern\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePreference for simple operation\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eAI technology concerns\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAI misjudgment concern\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAI data theft concern\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"4\" rowspan=\"5\"\u003e \u003cp\u003e\u003cb\u003ePhysical health\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003ePersonal safety threat\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eFamily emergency concern\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eFire or gas risk\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eBurglary concern\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003ePhysical decline condition\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eDecline in risk perception\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMobility decline\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"8\" rowspan=\"9\"\u003e \u003cp\u003e\u003cb\u003eLiving environment\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eImpact of past experiences\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNews incident influence\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNeighbor safety incident\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePast burglary experience\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\" morerows=\"5\" rowspan=\"6\"\u003e \u003cp\u003eAssessment of the current environment\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAging circuit hazard\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eClutter hazard\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eHome vacancy risk\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAging appliance hazard\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eWeak community management\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eStranger threat\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=\"Sec20\" class=\"Section2\"\u003e \u003ch2\u003e\u003cem\u003e4.3. Measurement model assessment\u003c/em\u003e\u003c/h2\u003e \u003cp\u003eFirstly, SPSS 26 was used to test the validity and reliability of each variable. The skewness and kurtosis indicators were calculated to assess the normality of the data distribution. It is generally accepted that when the skewness and kurtosis coefficients fall within the range of -1.5 to +\u0026thinsp;1.5, the data can be considered as following a univariate normal distribution. The results show that the skewness and kurtosis values for all variables in this study fall within this range, with detailed data presented in Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e.\u003c/p\u003e \u003cp\u003eThe measurement model analysis primarily examined convergent validity and discriminant validity to evaluate the reliability and construct validity of the proposed model. For convergent validity, this study employed Cronbach\u0026rsquo;s α coefficient to assess the internal consistency of the measurement scales. The results indicated that all constructs had α values greater than 0.7, demonstrating a high level of internal reliability. Composite Reliability (CR) and Average Variance Extracted (AVE) were calculated to further validate convergent validity. When CR exceeds 0.7 and AVE is higher than 0.5, the model is considered to exhibit satisfactory convergent validity (Gefen and Straub, 2005; Tabachnick and Fidell, 2014). Based on the analysis results obtained using the Partial Least Squares (PLS) algorithm (Table\u0026nbsp;\u003cspan refid=\"Tab6\" class=\"InternalRef\"\u003e6\u003c/span\u003e), all latent variables demonstrated factor loadings above 0.70, CR values greater than 0.7, and AVE values exceeding 0.5. These findings confirm that the TAM for SHSS constructed in this study possesses strong convergent validity. In terms of discriminant validity, the results showed that the square root of each construct\u0026rsquo;s AVE was significantly greater than its correlations with other constructs (Table\u0026nbsp;\u003cspan refid=\"Tab7\" class=\"InternalRef\"\u003e7\u003c/span\u003e), indicating satisfactory discriminant validity. Therefore, the measurement scales used in this study demonstrate high reliability and robust construct validity.\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\u003eSkewness and kurtosis values for TAM constructs\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=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eConstruct and items\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e \u003cp\u003eSenior citizens\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c8\" namest=\"c6\"\u003e \u003cp\u003eYounger adults\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSkewness\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eKurtosis\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eStandard deviation\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eSkewness\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eKurtosis\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eStandard deviation\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePerceived Cost (PC)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.23762\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-1.025\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.850\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.27451\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-0.790\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.585\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePerceived Privacy Risk (PPR)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.15619\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.613\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.262\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.42566\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-0.211\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-0.649\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTechnology Anxiety (TA)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.27114\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-1.121\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.160\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.37056\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.525\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-0.444\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLiving Environment (LE)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.34629\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.951\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.314\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.46089\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-0.697\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.273\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSelf-Reported Health Condition (SHC)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.16192\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.712\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.657\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.12189\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-1.025\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.358\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePerceived Ease of Use (PEOU)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.08025\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.419\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.170\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.16654\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-0.693\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.089\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePerceived Usefulness (PU)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.15069\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.361\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.372\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.03353\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-0.437\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-0.410\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIntention to Use (IU)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.99026\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.586\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.530\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.08834\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-0.637\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-0.123\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\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\u003eMeasurement model analysis\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=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eConstruct and items\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e \u003cp\u003eSenior citizens\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c8\" namest=\"c6\"\u003e \u003cp\u003eYounger adults\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCA\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCR\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eAVE\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eCA\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eCR\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eAVE\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePerceived Cost (PC)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.908\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.935\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.783\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.845\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.896\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.683\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePerceived Privacy Risk (PPR)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.893\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.926\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.757\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.894\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.927\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.759\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTechnology Anxiety (TA)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.872\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.918\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.789\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.835\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.900\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.750\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLiving Environment (LE)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.927\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.949\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.822\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.895\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.927\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.762\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSelf-Reported Health Condition (SHC)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.909\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.943\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.846\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.900\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.938\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.833\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePerceived Ease of Use (PEOU)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.822\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.918\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.849\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.859\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.934\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.877\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePerceived Usefulness (PU)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.864\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.917\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.786\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.883\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.928\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.811\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIntention to Use (IU)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.860\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.905\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.705\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.902\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.932\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.774\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\u003e \u003cem\u003eCA\u003c/em\u003e cronbach alpha, \u003cem\u003eCR\u003c/em\u003e composite reliability, \u003cem\u003eAVE\u003c/em\u003e average variance extracted\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab7\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 7\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eDiscriminant validity scores\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"10\"\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=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\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\u003eIU\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eLE\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003ePC\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003ePEOU\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003ePPR\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003ePU\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e \u003cp\u003eSHC\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c10\"\u003e \u003cp\u003eTA\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"9\" nameend=\"c9\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eSenior citizens\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIU\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e0.839\u003c/b\u003e\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\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLE\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.510\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e0.907\u003c/b\u003e\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\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.421\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.244\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e0.885\u003c/b\u003e\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\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePEOU\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.480\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.450\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.347\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e0.921\u003c/b\u003e\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\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePPR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.289\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.297\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.467\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.391\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e0.870\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePU\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.371\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.204\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.261\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.405\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.421\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e0.886\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSHC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.470\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.454\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.153\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.414\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.351\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.323\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e \u003cp\u003e\u003cb\u003e0.920\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.228\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.026\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.029\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.287\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-0.018\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-0.102\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e \u003cp\u003e-0.065\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e\u003cb\u003e0.889\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"10\" nameend=\"c10\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eYounger adults\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIU\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e0.880\u003c/b\u003e\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\" colspan=\"2\" nameend=\"c10\" namest=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLE\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.390\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e0.873\u003c/b\u003e\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\" colspan=\"2\" nameend=\"c10\" namest=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.525\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.256\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e0.826\u003c/b\u003e\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\" colspan=\"2\" nameend=\"c10\" namest=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePEOU\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.623\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.289\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.511\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e0.936\u003c/b\u003e\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\" colspan=\"2\" nameend=\"c10\" namest=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePPR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.491\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.269\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.427\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.433\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e0.871\u003c/b\u003e\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\" colspan=\"2\" nameend=\"c10\" namest=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePU\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.798\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.352\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.423\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.595\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.455\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e0.901\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c10\" namest=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSHC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.450\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.237\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.333\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.544\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.246\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.390\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003cb\u003e0.913\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c10\" namest=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.246\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.074\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.273\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.336\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-0.147\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-0.218\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-0.172\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c10\" namest=\"c9\"\u003e \u003cp\u003e\u003cb\u003e0.866\u003c/b\u003e\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=\"Sec21\" class=\"Section2\"\u003e \u003ch2\u003e\u003cem\u003e4.4. Structural model testing\u003c/em\u003e\u003c/h2\u003e \u003cp\u003eThe Structural Equation Model (SEM) is widely used in the research of testing TAM (Gefen et al., 2000), so this study uses the structural equation model to test the hypotheses (e.g., Al-Gahtani, 2016; Guner and Acarturk, 2020; Ma et al., 2016; Macedo, 2017). At the structural model analysis stage, this study aimed to verify the proposed hypotheses by examining the causal path relationships among latent variables. To achieve this, SmartPLS 4.0 software was employed, and the bootstrapping method was applied to conduct SEM on the extended TAM. During the analysis, the partial least squares structural equation modeling (PLS-SEM) algorithm was used to calculate the path coefficients among latent variables, thereby exploring both the direct and indirect effects between constructs. The bootstrapping technique was performed to repeatedly resample the data, obtaining corresponding t-values and p-values for each path coefficient in order to determine their statistical significance and assess whether each hypothesis was supported. Based on these procedures, the calculated results are presented in Table\u0026nbsp;\u003cspan refid=\"Tab8\" class=\"InternalRef\"\u003e8\u003c/span\u003e. After path adjustments and parameter optimization, the final structural equation models for both the senior citizen group and the young adult group are illustrated in Fig.\u0026nbsp;1and Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab8\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 8\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eResults of path analysis and hypothesis testing.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\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=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHypotheses\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePath coefficient\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSample mean (M)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eStandard deviation (STDEV)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003eT-\u003c/em\u003evalues\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cem\u003eP-\u003c/em\u003evalues\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eResult\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"7\" nameend=\"c7\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eSenior citizens\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTA\u0026rarr;PU\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.015\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.016\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.089\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.171\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.864\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eNot significant\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTA\u0026rarr;PEOU\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.264\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.264\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.057\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e4.646\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.000***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eSignificant\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSHC\u0026rarr;PU\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.143\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.144\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.082\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.739\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.082\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eNot significant\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSHC\u0026rarr;PEOU\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.182\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.181\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.070\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2.621\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.009*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eSignificant\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSHC\u0026rarr;IU\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.214\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.212\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.074\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2.891\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.004*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eSignificant\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePU\u0026rarr;IU\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.130\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.131\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.060\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2.177\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.030*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eSignificant\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePPR\u0026rarr;PU\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.289\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.292\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.085\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3.392\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.001***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eSignificant\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePPR\u0026rarr;PEOU\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.236\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.238\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.061\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3.872\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.000***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eSignificant\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePEOU\u0026rarr;PU\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.256\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.253\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.078\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3.279\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.001***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eSignificant\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePEOU\u0026rarr;IU\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.136\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.134\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.068\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.979\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.048*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eSignificant\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePC\u0026rarr;IU\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.242\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.247\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.077\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3.158\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.002*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eSignificant\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLE\u0026rarr;PU\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.063\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.062\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.074\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.851\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.395\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eNot significant\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLE\u0026rarr;PEOU\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.290\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.289\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.063\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e4.567\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.000***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eSignificant\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLE\u0026rarr;IU\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.266\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.263\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.060\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e4.448\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.000***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eSignificant\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"7\" nameend=\"c7\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eYounger adults\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTA\u0026rarr;PU\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.025\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.026\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.052\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.477\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.633\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eNot significant\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTA\u0026rarr;PEOU\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.218\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.215\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.052\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e4.173\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.000***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eSignificant\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSHC\u0026rarr;PU\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.074\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.076\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.071\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.031\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.303\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eNot significant\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSHC\u0026rarr;PEOU\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.415\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.415\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.070\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e5.938\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.000***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eSignificant\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSHC\u0026rarr;IU\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.079\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.078\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.052\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.534\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.125\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eNot significant\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePU\u0026rarr;IU\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.599\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.606\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.076\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e7.858\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.000***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eSignificant\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePPR\u0026rarr;PU\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.214\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.212\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.077\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2.782\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.005*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eSignificant\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePPR\u0026rarr;PEOU\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.271\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.270\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.066\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e4.132\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.000***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eSignificant\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePEOU\u0026rarr;PU\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.409\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.407\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.089\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e4.602\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.000***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eSignificant\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePEOU\u0026rarr;IU\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.114\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.105\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.066\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.731\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.084\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eNot significant\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePC\u0026rarr;IU\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.165\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.167\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.043\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3.810\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.000***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eSignificant\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLE\u0026rarr;PU\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.157\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.160\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.064\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2.459\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.014*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eSignificant\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLE\u0026rarr;PEOU\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.102\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.102\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.067\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.524\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.128\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eNot significant\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLE\u0026rarr;IU\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.085\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.082\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.039\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2.203\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.028*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eSignificant\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"7\"\u003eNote: *P\u0026thinsp;\u0026le;\u0026thinsp;0.05, ***P\u0026thinsp;\u0026le;\u0026thinsp;0.001\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"5. Discussions and conclusions","content":"\u003cdiv id=\"Sec23\" class=\"Section2\"\u003e \u003ch2\u003e\u003cem\u003e5.1. Demographic and attitudinal variables\u003c/em\u003e\u003c/h2\u003e \u003cp\u003eThe results of this study indicate that individuals with higher age and higher educational attainment exhibit stronger intentions to use smart home security systems. This finding suggests that a higher level of education and richer life experience may enhance users\u0026rsquo; understanding and trust in the functional value of home security technologies. Unlike the conclusions of Werner et al. (2011) and Li et al. (2019), this study found that economic level did not exert a significant effect on users\u0026rsquo; usage intention. A possible explanation is that smart home security systems are primarily safety-oriented technologies and regardless of economic background, users generally regard household safety as a fundamental life priority.\u003c/p\u003e \u003cdiv id=\"Sec24\" class=\"Section3\"\u003e \u003ch2\u003e5.1.1. Key finding\u003c/h2\u003e \u003cp\u003eThis study adopted a mixed-methods approach, integrating qualitative and quantitative analyses, to develop an extended technology acceptance model. By systematically investigating the behaviors and attitudes of both senior citizens and young adult users toward smart home security systems, the research comprehensively identified the key factors influencing adoption intention and the differences between these two groups. The findings contribute not only to the theoretical enrichment of technology acceptance research in the domain of smart home security systems but also offer practical implications for product and system design. which helps designers and developers better understand and meet the actual needs of elderly users during the system design process.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec25\" class=\"Section3\"\u003e \u003ch2\u003e\u003cem\u003e5.1.2. Perceived usefulness and perceived ease of use\u003c/em\u003e\u003c/h2\u003e \u003cp\u003eResults derived from research hypothesis analysis reveal that, for the elderly group, both perceived usefulness and perceived ease of use have significant positive effects on usage intention. Additionally, perceived ease of use exerts a positive influence on perceived usefulness. This may be attributed to the fact that elderly users often perceive ease of use as the most critical criterion when evaluating whether to adopt smart home security systems. Due to their limited prior experience with smart devices, elderly users are generally more sensitive to the learning and operational costs associated with new technologies. Thus, system simplicity and usability become decisive factors in their adoption decision. Consequently, perceived ease of use directly influences their usage intention while also indirectly affecting adoption behavior through perceived usefulness.\u003c/p\u003e \u003cp\u003eUnlike elderly users, young adults, who are more accustomed to operating smart devices and mobile applications, do not exhibit a significant positive relationship between perceived ease of use and usage intention. However, this does not imply that perceived usefulness is unimportant for young adults. Instead, its role becomes indirect, as systems become easier to operate, users are more likely to perceive their efficiency and convenience, thereby strengthening their recognition of the system\u0026rsquo;s overall value.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec26\" class=\"Section3\"\u003e \u003ch2\u003e\u003cem\u003e5.1.3. Perceived Cost\u003c/em\u003e\u003c/h2\u003e \u003cp\u003ePerceived cost refers to the overall cognitive evaluation formed by consumers after weighing the potential benefits of a technology or product against the costs required for its acquisition and use. The results of this study show that perceived cost has a significant influence on usage intention among senior citizen participants, whereas its effect is not significant among young adults. For senior citizens, the safety assurance and emergency assistance functions provided by smart home security systems are of particular importance. This finding is consistent with the concerns expressed by the majority of senior citizen participants in the questionnaire survey about \u0026lsquo;being unable to call for help in time when suddenly ill\u0026rsquo;. Compared with price and cost, most senior citizens pay more attention to safety. In China, senior citizens often co-reside with their adult children, with financial resources derived mainly from pensions and family support. As a result, they experience lower economic pressure and exhibit higher cost tolerance, showing greater willingness to invest in systems that enhance household safety.\u003c/p\u003e \u003cp\u003eIn contrast, young adult users tend to regard smart home security systems as value-added features that improve life quality. They are more sensitive to cost-benefit evaluations, focusing on product value and affordability. If they feel that the price is too high, their willingness to use such security systems will decrease, which is consistent with the research results of Liu et al. (2024). Consequently, cost tolerance does not significantly influence their intention to use such systems.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec27\" class=\"Section3\"\u003e \u003ch2\u003e5.1.4. Perceived Privacy Risk\u003c/h2\u003e \u003cp\u003ePerceived privacy risk refers to users\u0026rsquo; subjective concerns regarding data security and privacy protection when using smart home security systems. Path analysis of the structural equation models for both senior citizen and young adult groups revealed that perceived privacy risk exerted significant positive effects on perceived ease of use and perceived usefulness, a result contrary to the negative influence predicted by hypotheses H4a and H4b. This result is inconsistent with the research result of Zhou et al. (2024). The reason might be the user's psychological trade-off mechanism triggered by the special attributes of the smart home security system as a security product. Although users are aware of potential privacy vulnerabilities, when evaluating the overall utility of the system, they tend to prioritize household safety over privacy concerns, treating the latter as an acceptable cost. In other words, most users acknowledge potential data security risks but still perceive the tangible safety benefits of the system, such as prevention, alarm, and emergency-response functions, as outweighing those risks.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec28\" class=\"Section3\"\u003e \u003ch2\u003e5.1.5. Technology Anxiety\u003c/h2\u003e \u003cp\u003eTechnology anxiety describes the tension, worry, or apprehension users experience when interacting with smart home security systems. The model analysis results indicate that technology anxiety only has a significant negative effect on perceived ease of use among both senior citizen and young adult groups (negative path coefficients), thus supporting hypothesis H5a. The relationship between Technology Anxiety and perceived usefulness, hypothesis H5b, was not supported. Further questionnaire analysis revealed that the senior citizen exhibited higher levels of technology anxiety, whereas the young adult group reported lower anxiety levels. For senior citizen users, factors such as physiological decline and reduced cognitive ability weaken their technological familiarity and self-efficacy. When confronted with complex smart security systems, they are more prone to anxiety, which in turn lowers their perceived ease of use, making them believe the system is difficult to learn or operate. Young adults, by contrast, generally possess higher digital literacy and adaptability. As noted in interviews, even when they experienced mild anxiety, it stemmed mainly from doubts about technological reliability rather than concerns about their own competence. Therefore, such anxiety did not significantly affect their perception of ease of use. Therefore, young adults have a lower perception of technical anxiety, but instead, they have a higher evaluation of the ease of use of the system. Concerning perceived usefulness, the conclusions of both groups were not significant for technology anxiety, suggesting that users\u0026rsquo; judgments of a system\u0026rsquo;s usefulness depend primarily on its actual functionality and performance rather than on their emotional experience during use.\u003c/p\u003e \u003cp\u003eCompared with the study by Guner and Acarturk (2020), this study shows certain differences. Guner and Acarturk noted that for younger adults, technology anxiety not only has a significant negative influence on perceived ease of use but also undermines perceived usefulness. For senior citizens, technology anxiety significantly affects perceived ease of use, while its impact on perceived usefulness does not reach statistical significance but remains close to the threshold. The difference can be attributed to the broader scope of information and communication technologies examined in their study. In contrast, the present research focuses specifically on smart home security systems, whose functional value is highly concentrated on the explicit goal of household safety. Therefore, even when users experience anxiety during operation, such concerns do not substantially diminish their evaluation of the system\u0026rsquo;s perceived usefulness.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec29\" class=\"Section3\"\u003e \u003ch2\u003e\u003cem\u003e5.1.6. Living Environment\u003c/em\u003e\u003c/h2\u003e \u003cp\u003eLiving environment refers to users\u0026rsquo; overall perception of their residential space and community atmosphere. Its influence on technology acceptance pathways exhibited both similarities and notable differences between the senior citizen and young adult groups. For senior citizen users, prolonged home-based living increases dependence on the comfort, safety, and controllability of their residential environment. A favorable environment enhances psychological security and stability, thereby improving their willingness to adopt new technologies and increasing their perception that such systems are easy to learn and use, ultimately strengthening their intention to adopt them. However, senior citizens\u0026rsquo; evaluation of a system\u0026rsquo;s usefulness is primarily determined by its practical functionality, for instance, emergency alarms or remote monitoring, rather than environmental comfort itself. Thus, while the living environment fosters emotional security, it exerts no significant impact on perceived usefulness.\u003c/p\u003e \u003cp\u003eIn contrast, young adults, who lead faster-paced lives and spend less time at home, place greater emphasis on quality of life and efficiency. They regard the living environment as an integral aspect of life quality, reinforcing their recognition of the functional value of smart home security systems, particularly whether the system provides tangible convenience and safety. Because most young adults already possess adequate competence in using smart devices, ease of use is not their main concern. Instead, they focus on whether the system genuinely delivers efficiency and protection. Therefore, for young adults, the living environment enhances perceived usefulness, which in turn promotes usage intention, but it has no direct effect on perceived ease of use.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec30\" class=\"Section3\"\u003e \u003ch2\u003e\u003cem\u003e5.1.7. Self-Reported Health Condition\u003c/em\u003e\u003c/h2\u003e \u003cp\u003eSelf-reported health condition reflects users\u0026rsquo; subjective assessment of their health status based on physical, psychological, and lifestyle factors. The results show that for senior citizen participants, Self-reported health condition has a significant positive effect on both perceived ease of use and usage intention, Consistent with previous findings (Chen and Chan, 2014), whereas among young adults, health condition only exerts a positive influence on perceived ease of use and shows no significant effect on usage intention. For senior citizen users, health is one of the most crucial concerns in daily life. A relatively high self-evaluation of health often corresponds with greater willingness for autonomous activity and higher self-efficacy, which strengthens their confidence in learning and operating new technologies, leading them to perceive the system as easier to use. Moreover, senior citizen users tend to associate their health status with safety needs, seeking to enhance household health and security through smart home systems. Therefore, they believe that their physical health condition will directly increase their willingness to use smart technologies. They perceive whether the system is useful primarily based on whether its functions meet their actual needs, rather than on their self-reported health condition. Therefore, health conditions do not directly influence their perception of the usefulness of technology.\u003c/p\u003e \u003cp\u003eYoung adults generally enjoy better physical and cognitive conditions, which enhances their perception of ease of use at the operational level. However, their intention to adopt SHSS and their perceived usefulness are more heavily influenced by factors such as economic cost, functional value, and lifestyle relevance, rather than by health status itself. Consequently, the impact of health conditions on their technology adoption behavior is limited, manifesting primarily through its modest positive effect on perceived ease of use.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec31\" class=\"Section2\"\u003e \u003ch2\u003e\u003cem\u003e5.2. Contributions and implications\u003c/em\u003e\u003c/h2\u003e \u003cp\u003eAmid the accelerating global trend of population aging, an increasing number of senior citizens are choosing to age in place, which has led to a growing demand for home safety. As a result, smart home security systems have attracted extensive attention. In China, products specifically designed and developed for senior citizens remain in their early developmental stage. In practice, senior citizen users frequently encounter challenges such as complex operations, unintuitive interaction designs, and insufficient system trustworthiness. By employing a mixed-method approach that integrates both qualitative and quantitative analyses, this study systematically explored and compared the key factors and attitudinal differences influencing senior citizen and young adult users\u0026rsquo; adoption of SHSS. A total of 220 valid senior citizen samples and 215 valid young adult samples were collected. Based on the extended structural equation models and factor analysis, 13 hypotheses were proposed and tested, of which 9 were supported for the senior citizen group and 7 were validated for the young adult group.\u003c/p\u003e \u003cp\u003eThe findings reveal significant differences in technology acceptance pathways between senior citizens and young adult users. These differences primarily stem from variations in technological experience, functional needs, and psychological cognition between the two groups. For senior citizen users, perceived ease of use exerts a direct and significant effect on usage intention and also promotes adoption indirectly through perceived usefulness. Due to limited prior experience with smart products, senior citizen users are more concerned with the simplicity of operation and the educational difficulty. Perceived cost, self-reported health condition, and living environment significantly influence their usage intention, whereas perceived privacy risk does not act as a barrier, indicating that senior citizen users tend to prioritize safety needs over privacy concerns.\u003c/p\u003e \u003cp\u003eFor young adults, perceived ease of use does not directly affect usage intention but exerts an indirect influence through perceived usefulness. Given their proficiency in handling digital technologies, young adults focus more on the functional value and practical utility of the system. Perceived cost has no significant impact on their intention to use the system, while the living environment enhances usage intention indirectly by increasing perceived usefulness. Technology anxiety negatively affects perceived ease of use in both groups but through different mechanisms: for senior citizens, anxiety arises from limited operational experience, while for young adults, it stems mainly from concerns about technological reliability and advancement. In both cases, technology anxiety does not significantly affect perceived usefulness.\u003c/p\u003e \u003cp\u003eAt the theoretical level, this study extends and refines the technology acceptance model by incorporating empirical findings from the experiences of senior citizens and young adults, obtained through qualitative interviews and quantitative analysis. The expanded model provides a new perspective for examining technology acceptance mechanisms across different age groups in safety-oriented smart products, thereby deepening the understanding of behavioral differences between senior citizens and young adult users. By validating the extended technology acceptance model through empirical data analysis, this research enhances the credibility and applicability of the technology acceptance model in the context of aging-oriented technology design.\u003c/p\u003e \u003cp\u003eAt the practical level, the findings provide valuable insights for the design and promotion of SHSS in an aging society. Governments should encourage enterprises to implement differentiated pricing strategies for the elderly through policy guidance, reducing financial barriers, and improving technology accessibility for senior citizen users. In terms of privacy protection, although senior citizen users tend to prioritize household safety over data privacy, system designers should still establish transparent and trustworthy data management and authorization mechanisms to strengthen user trust. From a design perspective, smart home security systems should adhere to age-friendly interaction principles, incorporating simplified modes such as one-click operation and voice control, as well as remote assistance from family members and community-based technical support to mitigate operational difficulty and technology anxiety. The system should achieve deep integration of health monitoring and security management, utilizing environmental sensing and automatic alert technologies to enhance users\u0026rsquo; sense of environmental control. This allows senior citizen users to intuitively perceive the system\u0026rsquo;s dual role in safeguarding both health and safety, thereby improving their willingness to adopt and overall satisfaction with smart home security systems.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec32\" class=\"Section2\"\u003e \u003ch2\u003e\u003cem\u003e5.3.\u003c/em\u003e Limitations\u003c/h2\u003e \u003cp\u003eThis study has several limitations regarding sampling and data collection. The sample was primarily drawn from southern regions of China (e.g., Guangzhou, Wuhan, and other cities). Although economic background diversity was considered when selecting communities, the sampling process relied mainly on convenience sampling due to research constraints. Consequently, the communities and participants selected were geographically concentrated and easily accessible, which limits the representativeness of the sample in terms of regional and socioeconomic diversity. During data collection, due to the physiological and cognitive limitations of some senior citizen respondents, the research team adopted a one-on-one interview questionnaire method, whereas young adult participants primarily completed self-administered questionnaires. This methodological inconsistency may have introduced slight variation in measurement consistency across groups. Furthermore, the researchers acknowledge that subjective bias could not be eliminated during the design of interview questions and survey instruments, which may have had a minor impact on the objectivity and accuracy of the collected data.\u003c/p\u003e \u003cp\u003eFuture research will aim to expand the sampling scope to include a broader range of geographic regions and demographic profiles, thereby improving both sample representativeness and the generalizability of findings. Subsequent studies should also explore the potential moderating effects of factors such as gender, residential environment, and family structure, to provide more precise and personalized insights for user research and product design in the field of smart home security systems.\u003c/p\u003e \u003c/div\u003e"},{"header":"Abbreviations","content":"\u003cp\u003e\u003cstrong\u003eIU\u003c/strong\u003e\u003cstrong\u003e:\u003c/strong\u003e Intention to Use\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ePEOU:\u0026nbsp;\u003c/strong\u003ePerceived Ease of Use\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ePU:\u0026nbsp;\u003c/strong\u003ePerceived Usefulness\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ePC:\u0026nbsp;\u003c/strong\u003ePerceived Cost\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ePPR:\u003c/strong\u003e Perceived Privacy Risk\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTA:\u003c/strong\u003e Technology Anxiety\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eLE:\u003c/strong\u003e Living Environment\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSHC:\u003c/strong\u003e Self-reported Health Condition\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSHSS\u003c/strong\u003e\u003cstrong\u003e:\u003c/strong\u003e Smart Home Security Systems\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTAM\u003c/strong\u003e\u003cstrong\u003e:\u0026nbsp;\u003c/strong\u003eTechnology Acceptance Model\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSEM\u003c/strong\u003e\u003cstrong\u003e:\u0026nbsp;\u003c/strong\u003eThe Structural Equation Model\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eParticipants were first informed about the objectives of the study and were asked for their consent to participate, with the option to withdraw at any time. All demographic data concerning disabled people (based on official statistics) were anonymised and de-identified prior to analysis to ensure data privacy and confidentiality. The ethics approval number is XXX2023173.\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003cstrong\u003eDeclaration of interests\u003c/strong\u003e\u003cbr\u003e\u0026nbsp;No potential competing interest was reported by the authors.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding:\u0026nbsp;\u003c/strong\u003eThis work was supported by National Natural Science Foundation of China (Grant No. 52008114) and Social Sciences Fund of Guangdong Province (GD24CYS04).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgments\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors would like to acknowledge the National Natural Science Foundation of China (52008114)\u0026nbsp;and Social Sciences Fund of Guangdong Province\u0026nbsp;(GD24CYS04)\u0026nbsp;for the data collection and the preparation of the paper.\u0026nbsp;The authors thank Ministry of Housing and Urban-Rural Development, China Disabled Persons\u0026rsquo; Federation,\u0026nbsp;Office of Guangzhou Municipal Commission on Aging,\u0026nbsp;China Association for the Blind, China Disabled Persons\u0026rsquo; Federation,\u0026nbsp;Guangzhou\u0026nbsp;Disabled Persons\u0026rsquo; Federation\u0026nbsp;and\u0026nbsp;Guangzhou Volunteer Association for providing support for the research.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor Details \u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAuthor 1 Name: Jia Xin Xiao\u003c/p\u003e\n\u003cp\u003eDepartment/University: School of Art and Design, Guangdong University of Technology, China\u003c/p\u003e\n\u003cp\u003eCountry: China \u003c/p\u003e\n\u003cp\u003eEmail: [email protected]\u003c/p\u003e\n\n\u003cp\u003eAuthor 2 Name: Le Yan Xie\u003c/p\u003e\n\u003cp\u003eDepartment/University: School of Art and Design, Guangdong University of Technology, China\u003c/p\u003e\n\u003cp\u003eCountry: China\u003c/p\u003e\n\u003cp\u003eEmail: [email protected]\u003c/p\u003e\n\n\u003cp\u003eAuthor 3 Name: Chang Zhong \u003c/p\u003e\n\u003cp\u003eDepartment/University: School of Art and Design, Guangdong University of Technology, China\u003c/p\u003e\n\u003cp\u003eCountry: China\u003c/p\u003e\n\u003cp\u003eEmail: [email protected]\u003c/p\u003e\n\n\u003cp\u003eAuthor 4 Name: Ming Jun Luo\u003c/p\u003e\n\u003cp\u003eDepartment/University: 1. School of Design, Guangdong Industry Polytechnic University, China; 2. City University of Macau\u003c/p\u003e\n\u003cp\u003eEmail: [email protected]\u003c/p\u003e\n\n\u003cp\u003eAuthor 5 Name: Tiansheng Xia\u003c/p\u003e\n\u003cp\u003eDepartment/University: School of Art and Design, Guangdong University of Technology, China\u003c/p\u003e\n\u003cp\u003eCountry: China\u003c/p\u003e\n\u003cp\u003eEmail: [email protected]\u003c/p\u003e\n\n\u003cp\u003e\u003cstrong\u003eCorresponding author: \u003c/strong\u003eChang Zhong\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCorresponding Author\u0026rsquo;s Email:\u003c/strong\u003e [email protected]\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCorresponding Author\u0026rsquo;s address: \u003c/strong\u003eSchool of Design, 729 Dongfeng East Rd., Yuexiu District, Guangdong University of Technology, Guangzhou 510300, China\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eBiographical Details (if applicable): \u003c/strong\u003e\u003c/p\u003e\n\n\u003cp\u003e[Author 1 bio] Associate Professor of School of Art and Design, Guangdong University of Technology. Leader of Science and Technology for Humanity Design Laboratory. Her research focus is on inclusive design research and practice. She has been involved in several funded research projects related to inclusive design. She has received more than 20 international and national design awards, including Design for Asia Awards (DFA), HKDA Global Design Awards and Hong Kong Awards for industries. She has published several peer-reviewed journal paper, international conference papers and book chapters. \u003c/p\u003e\n\u003cp\u003e[Author 2 bio] Postgraduate and research assistant of School of Art and Design, Guangdong University of Technology. Her current research interests are inclusive design and public design.\u003c/p\u003e\n\u003cp\u003e[Author 3 bio] Associate Professor of School of Art and Design, Guangdong University of Technology. Associate Dean, Home Industry Design Research Institute. She has been involved in several funded research projects related to aging-friendly design and sustainable design. She published several papers in top tier journals and obtained over 30 international invention and design grant awards. \u003c/p\u003e\n\u003cp\u003e[Author 4 bio] Associate Professor of School of Design, Guangdong Industry Polytechnic University. PhD Candidate of City University of Macau. He has actively involved in social design projects to promote inclusive public space through participatory action research approach. His research focuses on social design, inclusive design, participatory design and design education. He has been involved in a number of funded research and design projects related to public design and participatory design. He promotes action research and works closely with end users.\u003c/p\u003e\n\u003cp\u003e[Author 5 bio] Professor of School of Art and Design, Guangdong University of Technology. He received his Ph.D. in Psychology from South China Normal University, China. His current research interests include experience design, user study, and multimodal learning analysis. He has published papers in Human Brain Mapping, Education and Information Technologies, Interactive learning Environment, and Child Abuse \u0026amp; Neglect.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eAbdusalomov, A., Umirzakova, S., Safarov, F., Mirzakhalilov, S., Egamberdiev, N., Cho, Y.-I., 2024. A multi-scale approach to early fire detection in smart homes. Electronics 13(22), 4354. https://doi.org/10.3390/electronics13224354\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAl-Adwan, A.S., Li, N., Al-Adwan, A., Abbasi, G.A., Albelbis, N.A., Habibi, A., 2023. extending the technology acceptance model (TAM) to predict university students\u0026rsquo; intentions to use metaverse-based learning platforms. Educ. Inf. 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PLOS One 19(3), e0300574. https://doi.org/10.1371/journal.pone.030057\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"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":"universal-access-in-the-information-society","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"uais","sideBox":"Learn more about [Universal Access in the Information Society](http://link.springer.com/journal/10209)","snPcode":"10209","submissionUrl":"https://submission.nature.com/new-submission/10209/3","title":"Universal Access in the Information Society","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"Smart home, Smart home security systems, Senior citizens, Structural equation modeling, Technology acceptance","lastPublishedDoi":"10.21203/rs.3.rs-8617018/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8617018/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThe process of population aging has accelerated significantly, and an increasing number of senior citizens have chosen to spend their later years at home. Consequently, home safety has become a growing social concern. To design and optimize more scientifically advanced home security systems in the context of an aging population, this study focused on exploring and comparing the Intention to use and acceptance of such systems among both senior citizens and young adults. Using a mixed-methods approach that combines qualitative and quantitative analyses, the study surveyed 220 senior citizen participants (aged 55 and above) and 215 young adults (aged 18\u0026ndash;55) to collect data on their perceptions, attitudes, and behavioral intentions toward smart home security systems. Based on this data, extended structural equation models were constructed. The results indicated that governments should promote differentiated pricing policies for age-friendly products to reduce economic barriers. System design should emphasize one-click operation and voice interaction to enhance accessibility, and system functionality should integrate environmental sensing, automatic alarms, and health monitoring modules. Adopting transparent data protocol mechanisms could strengthen user trust, thereby improving their sense of security and alleviating technological anxiety. This study aimed to compare the differentiated needs and cognitive characteristics of senior citizens and young adults regarding the use of smart home security systems, providing empirical evidence and design recommendations for developing and promoting age-friendly smart home security systems.\u003c/p\u003e","manuscriptTitle":"A Comparative Study of Elderly and Young Adults’ Needs of Smart Home Security Systems: Evidence from an Extended Technology Acceptance Model (TAM)","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-03-12 07:28:57","doi":"10.21203/rs.3.rs-8617018/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"editorInvitedReview","content":"","date":"2026-04-01T09:34:31+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"72310536507612103448479337019806336641","date":"2026-03-09T07:34:53+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2026-03-06T10:39:59+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-03-06T10:29:29+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2026-01-22T03:45:42+00:00","index":"","fulltext":""},{"type":"submitted","content":"Universal Access in the Information Society","date":"2026-01-16T07:51:36+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"universal-access-in-the-information-society","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"uais","sideBox":"Learn more about [Universal Access in the Information Society](http://link.springer.com/journal/10209)","snPcode":"10209","submissionUrl":"https://submission.nature.com/new-submission/10209/3","title":"Universal Access in the Information Society","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"75a0b39d-2005-428a-89b2-9e646ef5a579","owner":[],"postedDate":"March 12th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2026-03-12T07:28:57+00:00","versionOfRecord":[],"versionCreatedAt":"2026-03-12 07:28:57","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8617018","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8617018","identity":"rs-8617018","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}