Investigating the Behavioral Intention of Smart Home Systems among Older People in Linyin City

Abstract

Background: With an aging population and the continuous advancement of smart technology, the Chinese government is exploring smart elderly care models to address the challenges posed by aging. Although smart home systems are viewed as a promising solution, their adoption rate among older people remains low. Objectives: This study aimed to investigate the factors influencing the behavioral intention to use smart home systems among older people in Linyi City, Shandong Province, China. Methods: A literature review revealed a lack of quantitative research on older people’s behavioral intention toward smart home systems based on the Innovation Diffusion Theory. This study developed an extended model based on the Innovation Diffusion Theory, Technology Acceptance Model, and external variables, incorporating eight variables: intergenerational technical support, perceived cost, self-reported health conditions, compatibility, observability, trialability, perceived usefulness, perceived ease of use, and behavioral intention. Results: Analysis of 387 valid questionnaires showed that compatibility and trialability significantly and positively affect perceived ease of use, while self-reported health conditions, perceived ease of use, and perceived usefulness have significant effects on behavioral intention. In addition, perceived cost had a negative influence on behavioral intention. Contributions/Significance: These findings highlight the importance of considering these factors in the design of smart home systems to improve user experience and provide valuable practical guidance to smart home system developers, R&D institutions, and policymakers.

1. Introduction

By 2050, people aged 65 and older are expected to make up 20% of the global population, with 80% residing in low- and middle-income countries [1]. In China, the population aged 60 and older is projected to reach 280 million in 2022, comprising 19.8% of the total population [2]. Shandong Province has the largest elderly population in China, exceeding 20 million [3]. Linyi City alone had an elderly population of 2.163 million in 2020, making it one of the most aged cities in the province [4]. The aging population presents significant challenges for nations, societies, and families. It negatively impacts economic growth and innovation due to reduced labor supply, decreased capital accumulation, and diminished innovation capacity, all of which lower potential growth rates. Furthermore, aging intensifies pressure on social security and public services, leading to resource shortages and imbalances in the supply and demand for pension systems, healthcare, and elder care services [5]. In China, “aging in place” is still the dominant elderly care model, with the vast majority of older people spending their later years at home [6]. As people age, they increasingly face physical decline and health challenges [7]. Therefore, maintaining physical and cognitive function and delaying the onset of diseases and disabilities have become major challenges for older people. In their later years, the dependence on the younger generation has also increased significantly [8]. However, due to the busy work schedules and high mobility of young people, it is becoming increasingly difficult for older people to rely on their children for care, and the importance of independent living has become more prominent [9]. This has led to an increase in demand for information technology support [7]. In addition, older people experience a significant decrease in social activities as they age, especially after retirement. Isolation from the outdoor environment weakens older people’s positive interactions with nature, thereby damaging their connection to nature [10]. Studies show that biophilia can provide numerous physical and psychological benefits. On a physical level, biophilic experiences can help lower blood sugar, increase comfort and contentment, reduce disease symptoms, and improve health; on a psychological level, these experiences can help increase contentment and motivation and reduce stress and anxiety [11,12]. Biophilia refers to the evolutionary affinity for living forms and their characteristics [13], which is the inherent need for humans to interact and integrate with nature to achieve and maintain optimal health and well-being [14]. In summary, information technology [7] and biophilia [12,15,16,17] constitute common needs of older people in their later years and play an important role in improving the quality of life and promoting their physical and mental health.

In recent years, several Chinese government departments collaboratively issued the “Implementation Plan on Solving the Difficulties of Older People in Using Smart Technology”, “General Design Specifications for Internet Websites for Older People”, and “General Design Specifications for Mobile Internet Applications (APPs) for Older People”, along with launching the “Smart Elderly Care” program [18]. These initiatives leverage advanced information technologies, including the Internet, big data, and artificial intelligence, to foster the development of smart elderly care models and deliver real-time, efficient services to older people and related institutions. Regions across China are actively exploring the integration of smart cities, smart buildings, and smart home systems to optimize elderly care models [19]. The development of smart cities encompasses extensive data collection and analysis, intelligent infrastructure upgrades, and cross-departmental collaboration to ensure the synchronized operation of diverse technologies and systems [20]. As a critical component of smart cities, smart buildings are designed to provide users with efficient and comfortable living and working environments through the optimization of building structures, systems, and management [21]. In China, smart homes are viewed as an essential component of smart city development. As a third-level indicator of smart cities, smart home systems integrate smart sensors, security monitoring, and home appliances to enhance the comfort and safety of households. These systems typically include devices such as smart lighting, thermostats, home appliances, speakers, and curtains, which are interconnected to improve convenience, safety, and energy efficiency. Remote monitoring equipment, often installed near the ceiling, requires regular maintenance and updates to ensure its effectiveness and reliability [22]. Smart home systems allow users to seamlessly connect with a building’s overall intelligent management system through home automation controls. By incorporating advanced information, communication, and automation technologies, these systems not only enhance urban operational efficiency but also provide residents with a safer, more convenient, and energy-efficient living environment [23]. As the global population continues to age, smart home systems present a promising solution to help older people extend their independent living and enhance their quality of life through personalized health management, telemedicine services, and emergency assistance features [24,25,26,27,28]. Smart home systems offer numerous conveniences to older people, including those with diminished cognitive or physical abilities [29].

The living environment of older people has a profound impact on their physical and mental health [16]. An increasing number of studies on older people indicate that natural elements are closely related to their health [17]. Frequent contact with nature can effectively reduce stress, lower blood pressure, relieve pain, and improve recovery from illnesses [30]. Kellert [30] emphasizes the need for a deeper understanding of how human contact with nature is the basis for healthy, efficient, and successful modern cities. Biophilic design is not only about the creation of good habitats in the modern built environment but also represents a holistic approach that aims to reconnect humans with nature by optimizing the surrounding environment. Pandita and Choudhary [12] pointed out that biophilic design is a strategy to integrate natural elements into the built environment, aiming to enhance the connection between older people and nature, thereby improving their physical and mental health and quality of life. Smart home services based on biophilia play an important role in promoting active and independent living for older people [11,15]. For example, Lee and Park [11] analyzed smart home components and related research that can support the biophilic experience and the corresponding technology. The results suggest the types and content of smart home services that ensure a biophilic experience and indicate the configuration of supporting input and output devices based on a service framework. Subsequently, the smart home service framework proposed by Lee and Park [15] uses sensors and devices that support the biophilic experience and IoT-based smart devices as service resources. The framework monitors and controls the internal status information of the house through the home gateway, while the IoT-based devices are configured to be controlled through the IoT cloud. The smart home platform manages device information transmitted by various IoT clouds through smart gateways and converts and integrates the protocols of all devices to achieve smooth service provision and collaboration. In summary, smart homes and systems show great potential in creating biophilic living spaces for older people.

Data indicate that, between 2019 and 2020, the proportion of Americans aged 50 and over who owned smart home technology increased from 10% to 19% [31]. According to Statista, in 2022, the countries with the highest proportions of smart home elderly users were the United Kingdom (15%), Germany (15%), the United States (13%), and France (12%). In contrast, the proportion of elderly users in China was 9%, lower than in these developed countries [32]. Technology-driven solutions for older people’s care have become a global trend [33]. As a key component of smart cities, smart home systems hold significant potential in addressing the challenges of aging populations [29]. Therefore, exploring the factors that affect older people’s behavioral intention toward these systems is essential.

In the field of smart homes, most research on older people’s behavioral intention primarily focuse on smart home technologies. For example, Arthanat et al. [34] investigated the factors affecting the adoption of information, communication, and smart home automation technologies among older people in the United States. Arar et al. [35] investigated older people’s intention to use smart home technology in Dubai, UAE. Similarly, Maswadi et al. [36] examined older people’s behavioral intention toward smart home technologies in Saudi Arabia. However, some researchers have noted that older people’s behavioral intention can be affected by negative factors. For example, Alzahrani et al. [37] from New Zealand analyzed the obstacles to using smart home technology among older people. Pal et al. [38] developed a model of negative perceptions to explore the factors hindering the adoption of smart home technology. In addition, a study conducted in the UK found that users were concerned about the costs associated with various aspects of smart home technology [39]. As smart homes evolve from individual devices to integrated systems [40], researchers are increasingly focused on improving the quality of life for older people through technology. For instance, Tan et al. [41] developed the Alexa Eldercare system to enhance the convenience and safety of older people’s living environments. Moreover, some scholars have proposed an information fusion model based on ubiquitous computing, designed to create more age-appropriate living conditions for older people [42]. However, most studies on smart home systems for older people remain centered on technical aspects, with only Jo et al.’s [43] qualitative study and Yan and Lee‘s [44] quantitative study examining older people’s behavioral intention toward smart home systems.

While smart home systems have significant potential in addressing the challenges of aging, research on older people’s behavioral intention toward these systems remains limited. Most existing studies focus primarily on the technical aspects of smart home systems [41,42], with insufficient empirical research on older people’s behavioral intentions. Furthermore, as one of the most rapidly aging regions in China, Shandong Province deserves particular attention in this context. To address this gap, this study aims to develop an extended model to identify the key factors affecting older people’s behavioral intention toward smart home systems. The findings of this study not only uncover the complex mechanisms underlying older people’s behavioral intention but also offer practical guidance to smart home system developers, R&D institutions, and policymakers.

2. Literature Review

2.1. Older People’s Behavioral Intention Toward Smart Homes and Systems

With the growing aging population and the advancement of smart elderly care policies, extensive research has been conducted on the factors affecting older people’s behavioral intention toward smart home devices and technologies. Among the variables examined in the literature, perceived usefulness and perceived ease of use are consistently recognized as central determinants of behavioral intention. For instance, studies by Wei et al. [45] and Song et al. [46] have highlighted the significant influence of these factors on older adults’ behavioral intention. The study by Zhou et al. [7] further confirmed this. In addition, Li et al. [47] pointed out that compatibility, facilitating conditions, and social influence are factors that affect older people’s intention to use smart wearables [47]. As individuals age, declining physical abilities and self-reported health conditions increasingly affect their decisions regarding technology adoption [47]. However, existing research predominantly emphasizes positive factors, which may lead to an incomplete understanding of the behavioral intention of older people. Specifically, fewer studies have explored the negative factors that hinder the acceptance of smart home technologies among older people. In fact, older people often experience technology anxiety when confronted with new innovations [48]. Compared to younger generations, they are more vulnerable to the technological divide [49], which can impede their adoption of new technologies. Scholars have noted that intergenerational technical support can help mitigate these barriers, enhance perceived usefulness and perceived ease of use, and thereby foster behavioral intention [7,45]. Moreover, cost remains a significant concern for older people, with perceived cost recognized as a crucial factor that inhibits their behavioral intention [37].

As smart homes evolve from individual devices to integrated smart home systems [40], increasing research attention has been directed toward improving the living environments of older people through technological innovations. For instance, Oliveira et al. [50] developed a method based on a multi-agent system, demonstrating its effectiveness in enhancing the user experience for older people. Similarly, Tan et al. [41] introduced the Alexa Eldercare system, which was specifically designed to enhance both convenience and safety for older residents. Yun et al. [42] took a different approach by proposing an information fusion model within the context of ubiquitous computing, which aimed to create age-appropriate home environments. The system also included predictive capabilities to anticipate the behavior of older people. Although significant progress has been made in smart home technologies, most current research remains focused on technological development, with limited exploration from the perspective of older people. Notably, only two empirical studies have explored older people’s behavioral intention toward smart home systems. For example, Yan and Lee’s [34] quantitative research investigated older people’s behavioral intention toward the smart home healthcare system. Moreover, the qualitative study by Jo et al. [43] examined the views of older Koreans on smart home systems. The findings revealed that older people initially held negative attitudes toward these systems due to their complexity and perceived interference with daily life. However, as they gained a better understanding of the actual benefits, their willingness to use smart home technologies increased. This shift in attitude indicates that perceived usefulness is key to shaping older people’s behavioral intention, and involving them in participatory processes offers valuable opportunities for observation and trial.

In summary, existing literature primarily focuses on the technological development of smart home systems [41,42,50], while empirical research on older people’s behavioral intention remains limited. This research gap highlights the need to shift attention toward understanding the needs of older people. A deeper understanding of older people’s behavioral intention is essential to providing effective technical support for them. Furthermore, such insights can inform the design, development, and promotion efforts of smart home system developers, R&D institutions, and policymakers, ultimately contributing to the advancement of smart elderly care.

2.2. Theoretical Background

2.2.1. Technology Acceptance Model (TAM)

Current research on the acceptance of smart home technology by older people primarily relies on theoretical models such as the Technology Acceptance Model (TAM) [7,44,45], the Unified Theory of Acceptance and Use of Technology (UTAUT) [36,51], the Theory of Reasoned Action (TRA) [52], and the Theory of Planned Behavior (TPB) [52]. Among these, TAM is the most widely used model and is considered to have strong explanatory power.

Fred Davis proposed the conceptual model of technology acceptance in 1985 [53]. He posited that the actual use of a system could be explained or predicted by user motivation, which is directly influenced by external stimuli, including the characteristics and capabilities of the system [54]. Subsequently, he refined the conceptual model and introduced the Technology Acceptance Model (TAM) [53]. In TAM, user motivation is determined by three factors: perceived ease of use, perceived usefulness, and attitude toward use. Later, Davis and his colleagues found that attitude did not fully mediate the effects of perceived usefulness and perceived ease of use [54]. Based on these findings, the attitude construct was removed from TAM [55]. As TAM evolved, behavioral intention was introduced as a new variable, directly influenced by perceived usefulness [56]. TAM gradually became the primary model for explaining and predicting system use. Consistent research findings have shown that perceived usefulness is the main determinant of behavioral intention [55,56]. Consequently, Venkatesh and Davis [57] proposed an extended model called TAM 2. In this model, perceived ease of use positively affects perceived usefulness, and both perceived ease of use and perceived usefulness positively influence behavioral intention [57].

Several studies have applied TAM to examine the acceptance of smart home technologies among older people [34,47,58,59,60]. For example, Yan and Lee [44] explored the acceptance of smart home healthcare systems among older people, finding that factors such as perceived usefulness and perceived ease of use significantly influence acceptance. Similarly, Wei et al. [45] found that perceived usefulness, perceived ease of use, and intergenerational support significantly affect the acceptance of smart home technologies among older people. Zhou et al. [7] investigated the intention to use smart home technologies among older consumers based on TAM. While substantial research has been conducted on the acceptance of smart home technologies among older people, most studies have focused on a single theoretical model. Furthermore, there is still a lack of comprehensive research on the acceptance of smart home systems, specifically among older people. Given the relevance of TAM in this field, this study uses perceived usefulness, perceived ease of use, and behavioral intention as research variables.

2.2.2. Innovation Diffusion Theory (IDT)

The Diffusion of Innovations Theory (IDT) was proposed by communication scholar and sociologist Everett M. Rogers (1931–2004) [61]. IDT explains the acceptance and dissemination of innovations, products, or technologies within a social system over time [62]. Rogers [63] argues that the acceptance and decision-making process for an innovation is closely linked to its attributes or characteristics, which can be measured through five dimensions: relative advantage, compatibility, complexity, trialability, and observability. He noted that an individual’s perception of these characteristics can predict the rate of adoption of an innovation. As one of the foundational theories of innovation acceptance and dissemination, IDT has been widely applied since the 1960s to examine innovations across various fields [63,64]. According to this theory, the inherent characteristics of an innovation affect the speed of its adoption within a specific environment. Therefore, the way these characteristics are perceived directly impacts the rate at which the innovation is adopted [65].

Researchers have used the IDT to explain consumers’ behavioral intentions. Sanguinetti et al. [66] applied the IDT to identify barriers to adopting smart homes. Hubert et al. [67] developed a comprehensive model to evaluate the acceptance of smart home technology. Similarly, Nikou [65] created a model to explore factors influencing the adoption of smart home technologies. Wang et al. [68] proposed a model integrating IDT and switching cost theory to examine how innovation characteristics and switching costs impact potential users’ intention to switch to smart home technology. Hubert et al. [67] and Nikou [65] demonstrated that perceived usefulness and perceived ease of use mediate the relationship between IDT variables and behavioral intention. Scholars have emphasized that IDT is particularly effective for understanding consumers’ behavioral intention toward smart homes, as it accounts for both the product and user interaction [68]. In addition, researchers noted that relative advantage in IDT aligns with perceived usefulness in TAM, and complexity in IDT is similar to perceived ease of use in TAM [68]. Therefore, this study excludes relative advantage and complexity.

In summary, although some studies have applied IDT to examine user acceptance of smart homes, there is a limited body of research that specifically addresses older people’s behavioral intention toward smart home systems. Therefore, this study integrates IDT into the theoretical framework.

2.3. External Variables

Current research on older people’s behavioral intention toward smart homes rarely considers their health conditions. Previous studies have demonstrated that self-reported health conditions significantly affect older people’s behavioral intention [47]. In addition, while many qualitative studies have indicated that the cost of smart homes had a negative effect on older people’s behavioral intention [26,37,69], quantitative studies in this area remain limited. In the Chinese context, intergenerational support is a key factor affecting older people’s behavioral intention [7,45]. Due to the influence of Confucian culture and traditional values of filial piety, Chinese older people often receive intergenerational support from their children [70,71]. Since older people frequently experience technological anxiety when confronted with new technologies [72], intergenerational technical support can help alleviate this anxiety and enhance older people’s behavioral intention [7].

To comprehensively understand older people’s behavioral intention toward smart home systems, this study incorporated self-reported health conditions, perceived cost, and intergenerational technical support as external variables. By including these factors, this study provides a more thorough analysis of how they affect older people’s behavioral intention toward smart home systems.

2.4. Research Hypotheses and Model Construction

2.4.1. Research Hypotheses

Intergenerational technical support, perceived ease of use, and perceived usefulness

In recent years, it has become increasingly common for young people to assist their parents in using digital technology, a phenomenon known as intergenerational technical support [73]. Perceived ease of use is defined as the extent to which users believe that a technology or system is easy to use [55]. Perceived usefulness refers to the extent to which users believe that a technology or system enhances their performance [55]. Older people often encounter challenges with smart home systems, such as technical complexity, learning difficulties, and the resulting technological anxiety [74,75,76]. Although smart home systems offer valuable functions, such as remote control and health monitoring, their complex operating procedures and technical settings can negatively affect how older people perceive the utility and value of these systems. Research indicates that intergenerational technical support can effectively reduce technological anxiety among older people, helping them adapt to and use new technologies more effectively [77]. For example, Zhou et al. [7] found that intergenerational technical support positively affects the perceived ease of use and perceived usefulness of smart homes for older people. Similarly, Wei et al. [45] noted that intergenerational support has a positive effect on perceived usefulness. Through guidance and assistance from younger generations, older people can master the basic operations of smart homes more quickly, reducing difficulties in use and enhancing their perceived ease of use and usefulness. Based on this discussion, the following hypotheses are proposed:

H1: 

Intergenerational technical support has a positive effect on perceived ease of use.

H2: 

Intergenerational technical support has a positive effect on perceived usefulness.

Compatibility, perceived ease of use, and perceived usefulness.

Compatibility refers to the extent to which products or technology are perceived as consistent with users‘ existing values, beliefs, habits, and previous experiences [78]. Previous studies have shown that compatibility positively affects both perceived ease of use and perceived usefulness. For instance, research on e-learning systems has demonstrated that compatibility positively influences both perceived ease of use and perceived usefulness [79,80]. For older people, a smart home that fits seamlessly into their daily routines and meets their needs without requiring significant changes is more likely to be perceived as useful and easy to use. In the context of smartwatches, compatibility has been shown to positively impact perceived usefulness [81]. Therefore, the following hypotheses are proposed:

H3: 

Compatibility has a positive effect on perceived ease of use.

H4: 

Compatibility has a positive effect on perceived usefulness.

Compatibility and behavioral intention.

Compatibility is a key factor influencing behavioral intention. For example, Hubert et al. [67] found that compatibility significantly and positively affects older people’s behavioral intention toward smart home technologies. This finding was further supported by Nikou [65], who also demonstrated a strong positive relationship between compatibility and behavioral intention. When smart home systems align well with older people’s lifestyle, existing electronics, and needs, they are more likely to be accepted. Based on these findings, the following hypothesis is proposed:

H5: 

Compatibility has a positive effect on behavioral intention.

Trialability, perceived ease of use, and perceived usefulness.

Trialability refers to the ability to experiment with new technology before adoption. Potential adopters who can experiment with an innovation are more likely to feel comfortable with it and subsequently adopt it [62,82]. This is particularly important for older people who may be hesitant to adopt new technology without firsthand experience. Studies on smartwatches confirm that trialability positively impacts perceived ease of use [81]. Hubert et al. [67] found that trialability significantly influences the perceived usefulness of smart homes for older people. Other studies have also confirmed that trialability positively influences both perceived usefulness and perceived ease of use [79,80]. For smart home systems, providing opportunities for older people to try out the technology in a supportive environment can enhance their perceived ease of use and perceived usefulness. Based on these findings, the following hypotheses are proposed:

H6: 

Trialability has a positive effect on perceived ease of use.

H7: 

Trialability has a positive effect on perceived usefulness.

Observability and perceived usefulness.

Observability refers to the extent to which an innovation is visible to members of a social system, and its benefits can be easily observed and communicated [62]. When the positive outcomes of using a new technology are visible, they can encourage others to adopt it. This visibility can come from seeing the technology in use by peers or through demonstrations. In a study of e-learning systems, scholars confirmed that observability positively impacts perceived usefulness [79]. This suggests that when older people observe the benefits of smart home systems, such as improved safety and convenience, their perceived usefulness of these systems increases. Therefore, the following hypothesis is proposed:

H8: 

Observability has a positive effect on perceived usefulness.

Perceived cost and behavioral intention.

Perceived cost refers to the extent to which an individual believes that using a product or technology is expensive [83,84]. In this study, it specifically refers to the costs associated with using smart home systems for older people. Dermody et al. [26] highlighted that cost is a major barrier to the adoption of smart home technologies among older people in communities. Concerns include potential additional costs for smart monitoring technologies. Similarly, Alzahrani et al. [37] noted that cost negatively affects the use of smart home technology by older people. Rattanaburi and Vongurai [83] also found perceived cost to be a significant factor affecting the behavioral intention toward mobile shopping applications. Based on the above discussion, the following hypothesis is proposed:

H9: 

Perceived cost has a negative effect on behavioral intention.

Self-reported health conditions, perceived ease of use, and perceived usefulness

Self-reported health conditions encompass respondents’ current health conditions, including diseases such as hypertension, cardiovascular disease, and diabetes, as well as biophysical characteristics such as vision, hearing, mobility, and cognitive abilities [47]. Research indicates that health conditions significantly influence perceived usefulness and behavioral intention regarding technology use. This study focuses on older people, whose health may impact their views on the utility of smart home systems. Chen and Chan [85] pointed out that health conditions affect older people’s behavioral intention. Li et al. [47] further confirmed that self-reported health conditions positively affect older people’s perceived usefulness and behavioral intention toward smart wearable devices. Therefore, the following hypotheses are proposed:

H10: 

Self-reported health conditions have a positive effect on perceived usefulness.

H11: 

Self-reported health conditions have a positive effect on behavioral intention.

Perceived ease of use, perceived usefulness, and behavioral intention.

Behavioral intention is defined as ”a measure of the strength of one’s intention to perform a specified behavior” [56]. In the context of smart home technology, behavioral intention reflects the likelihood that older people will adopt and use the technology. Research on older people’s behavioral intention toward smart home technology consistently emphasizes the relationships among perceived usefulness, perceived ease of use, and behavioral intention [45,46,86]. Pal et al. [86] demonstrated that perceived ease of use positively influences perceived usefulness, and both perceived usefulness and perceived ease of use positively affect behavioral intention in the context of smart home services. These findings have been further corroborated by Song et al. [46] and Wei et al. [45], confirming the significant positive influence of perceived ease of use on perceived usefulness and behavioral intention. These findings indicate that the perceived usefulness and perceived ease of use of smart homes among older people positively affect their behavioral intention. Therefore, the following hypotheses are proposed:

H12: 

Perceived ease of use has a positive effect on perceived usefulness.

H13: 

Perceived ease of use has a positive effect on behavioral intention.

H14: 

Perceived usefulness has a positive effect on behavioral intention.

2.4.2. Research Model

Considering the significance of the TAM variables—perceived usefulness, perceived ease of use, and behavioral intention—in explaining older people’s behavioral intention toward smart home technologies, this study integrated these variables into the theoretical framework. Moreover, most existing research relies on single-theory models, and there is a lack of studies examining older people’s behavioral intention toward smart home systems from the perspective of the IDT. A comprehensive review of the literature indicates that perceived usefulness and perceived ease of use moderate the relationship between IDT variables and behavioral intention. To thoroughly investigate the complex mechanisms underlying the behavioral intention of older people, this study combines the TAM and IDT models. Furthermore, this study incorporates external variables, including self-reported health conditions, perceived cost, and intergenerational technical support, based on the unique circumstances and requirements of older people, thereby extending the existing theoretical model. The research model not only helps in gaining a deeper understanding of the behavioral intention of older people toward smart home systems but also enhances older people’s independent living ability and biophilic experiences by improving the compatibility and trialability of the system, thereby contributing to their physical and mental health. In addition, intergenerational technical support can help older people better adapt to and become proficient in using smart home systems, thereby enhancing their behavioral intention. Drawing from the 14 hypotheses discussed above, this study developed an extended model to investigate older people’s behavioral intention toward smart home systems, as illustrated in Figure 1.

3. Research Methodology

This study employed a quantitative research method to ensure data objectivity and broad applicability of the findings. Data were collected through questionnaires to gather relevant quantitative information on the variables. Structural equation modeling (SEM) was used in the data analysis to examine the relationships between various factors and their influence on the participants’ behavioral intention.

3.1. Ethical Review

This study was reviewed and approved by the Anhui University Biomedical Ethics Committee and was conducted in accordance with relevant guidelines and regulations. Informed consent was obtained from all participants and/or their legal guardians, adhering to the principles outlined in the Declaration of Helsinki.

3.2. Questionnaire Design

The questionnaire for this study was meticulously designed and refined based on prior relevant research. It consisted of two main sections: the first section collected the demographic information from the participants, while the second section contained the measurement items, as illustrated in

Table 1. Measurement items.

Constructs	Items	Contents (7-Point Likert Scale)	References
Intergenerational Technical Support	ITS 1	My children will encourage me to use smart home systems.	[7,45]
	ITS 2	I believe my children will guide me in using smart home systems.
	ITS 3	I believe I can easily operate smart home systems with my children’s guidance.
Compatibility	COM1	Using smart home systems is compatible with my existing electronics (such as the smartphone and other devices).	[47,65,81,87]
	COM2	Using smart home systems fits into all aspects of my life.
	COM3	Using smart home systems is compatible with my day-to-day needs.
Trialability	TR 1	Being able to try out and experiment with smart home systems before purchasing them is very important to me.	[65,81,87]
	TR 2	It is important to ask questions about smart home systems before buying and installing them.
	TR 3	I do need to see how smart home systems work before I buy and install them.
Observability	OB 1	It is important for me to see the benefits of others using smart home systems.	[65,81,87]
	OB 2	Observing other smart home system users before installing and using smart home appliances is necessary.
	OB 3	I can see the effects of using smart home systems.
Perceived Cost	PC 1	I fear that the cost of smart home systems may be well beyond my budget.	[65]
	PC 2	I consider costs carefully before I install smart home systems.
	PC 3	Given the current economic situation, I would carefully assess the cost of smart home systems.
Self-reported Health Conditions	SHC 1	My health is very good.	[47]
	SHC 2	My health is very good compared to that of my peers.
	SHC 3	My hearing, vision, and mobility are all very good.
Perceived Ease of Use	PEOU 1	Overall, using smart home systems is easy.	[46,65]
	PEOU 2	Using smart home systems does not require much effort.
	PEOU 3	It is not difficult to learn how to use smart home systems.
Perceived Usefulness	PU 1	Using smart home systems is useful in my daily life.	[46,65]
	PU 2	Using smart home systems increases my productivity.
	PU 3	Using smart home systems allows me to accomplish tasks more quickly.
Behavioral Intention	BI 1	Using a smart home system service is a good idea.	[46,65]
	BI 2	I expect to use smart home systems in my house.
	BI 3	I would recommend using smart home systems to others.

. The questionnaire employed a 7-point Likert scale to quantify participants’ responses, ranging from 1 (strongly disagree) to 7 (strongly agree). After completing the initial draft, a pilot study was conducted with 45 participants, and feedback was sought from three experts in the relevant field. Based on their feedback, detailed explanations for relevant concepts were incorporated, and language adjustments were made to enhance the precision and clarity of the questionnaire.

3.3. Sample and Data Collection

3.3.1. Participants

Consistent with previous research selecting participants aged 55 and older [38,85,88,89], this study focuses on older people in Linyi City, which has the largest elderly population in Shandong Province [4]. To better understand the questionnaire information, participants were required to have a certain level of understanding of smart homes or systems, similar to previous studies [44].

3.3.2. Sampling Procedure

A combination of convenience sampling and purposive sampling methods was used. Data were collected from locations frequently visited by older people, such as senior universities, parks, and squares. These sites were chosen because participants at senior universities are primarily retirees with a certain level of education and willingness to participate, providing reliable data. Public parks and squares are common gathering places for older people, allowing access to individuals from diverse backgrounds and socioeconomic statuses, ensuring the sample’s diversity and representativeness.

3.3.3. Sampling Size

Based on the seventh national census, the elderly population in Linyi City, Shandong Province, is 2.163 million [4]. The sample size for this study was determined using “Cochran’s Modified Formula for Finite Populations”, tailored for known population sizes [90]. The following formula ensures an optimal sample size with a set precision level:

$$n_{55}={\displaystyle \frac{n_0}{1+\frac{n_0-1}{N}}}$$

where:

• n₅₅ is the final sample size.
• n₀ is the ideal sample size calculated by Cochran’s formula.
• N is the total population size.

By applying this formula, this study requires at least 385 valid questionnaires.

$$n_{55}={\displaystyle \frac{385}{1+\frac{385-1}{\mathrm{2,160,000}}}}\approx 385$$

3.3.4. Data Collection

During data collection, three professionally trained interviewers distributed paper questionnaires at the entrances of universities for older people, as well as in public spaces like parks and squares. They directly engaged with the participants, explaining questionnaire items and assisting them in completing them. This face-to-face approach helped address any difficulties older people might encounter in understanding and filling out the questionnaire. As a token of appreciation, each participant received a small gift.

Data collection was conducted from 9 August 2024 to 28 August 2024. A total of 455 participants aged 55 and above participated in this study. After the survey, researchers cleaned the data by removing responses with low-quality input, such as incomplete questionnaires or those with uniform answers throughout. As a result, the final sample included 387 participants, resulting in a questionnaire validity rate of approximately 85%. The sample size needed to be more than 10 times the size of the measurement items (27 items) [91], which met the requirements of structural equation modeling for an adequate sample size. For additional data and supporting information, please refer to the Supplementary Materials available at (https://www.mdpi.com/article/10.3390/buildings14103145/s1).

Table 2. Demographic characteristics of the participants.

Sample	Category	Number	Percentage (%)
Age	55~60	97	25.1
	61~65	116	30.0
	66~70	98	25.3
	>70	76	19.6
Gender	Male	183	47.3
	Female	203	52.5
Education level	Junior high school and below	176	45.5
	High school and above	211	54.5
Primary means of living	Salary/Retirement pensions	215	55.6
	Family support	128	33.1
	Government subsidies	44	11.4
Occupation	Enterprises	260	67.2
	Government personnel	40	10.3
	Public institutions	67	17.3
	Freelancers	20	5.2

presents the demographic characteristics of the participants. The largest proportion of participants was in the 55–65 age group, accounting for 55.1%. The gender distribution was balanced, with a slight female majority. Most participants had at least a high school education. The primary sources of income were salaries or retirement pensions, followed by family support and government subsidies. Over half of the participants were employed in enterprises, with the remainder working in government, public institutions, or as freelancers.

4. Data Analysis and Results

This study used Partial Least Squares Structural Equation Modeling (PLS-SEM) for data analysis, a method widely used in the social and behavioral sciences. PLS-SEM is advantageous for analyzing complex models, especially when the data do not follow a normal distribution and the model structure involves multiple variables and relationships [92]. It is especially effective in handling direct, indirect, and total effects among variables and allows for latent variables, offering stable estimates even in the presence of measurement errors [93]. The analysis consisted of two parts: evaluating the measurement model for reliability and validity and assessing the structural model to test hypotheses and relationships among latent variables.

4.1. Measurement Model Analysis

4.1.1. Common Method Bias (CMB)

Survey research frequently faces the risk of common method bias (CMB) [94], which necessitates careful management. Experts suggest utilizing the variance inflation factor (VIF) to identify full collinearity, with a generally accepted cut-off value of 3.33 [36,94]. To verify the integrity of the data, a VIF analysis was performed. As presented in

Table 3. Full collinearity.

Construct	BI	COM	ITS	OB	PC	PEOU	PU	SRH	TR
Behavioral Intention
Compatibility	1.254					1.251	1.356
Intergenerational Technical Support						1.200	1.358
Observability							1.389
Perceived Cost	1.049
Perceived Ease of Use	1.261						1.319
Perceived Usefulness	1.273
Self-reported Health Conditions	1.219						1.243
Trialability						1.317	1.466

, the VIF values for this study are below the established limit [95], indicating that the survey data are free from CMB concerns.

4.1.2. Reliability and Validity Test

The reliability and validity of the data were evaluated using Smart PLS 4.0 software. As presented in

Table 4. Standardized factor loadings, Cronbach’s alphas, CRs, and AVEs.

Construct	Item	Factor Loading	Cronbach’s Alpha	rho_A	Composite Reliability	AVE
Behavioral Intention	BI1	0.838	0.809	0.809	0.887	0.723
	BI2	0.855
	BI3	0.858
Compatibility	COM1	0.867	0.840	0.851	0.903	0.757
	COM2	0.845
	COM3	0.897
Intergenerational Technical Support	ITS1	0.847	0.828	0.833	0.897	0.744
	ITS2	0.879
	ITS3	0.861
Observability	OB1	0.832	0.819	0.823	0.892	0.734
	OB2	0.872
	OB3	0.865
Perceived Cost	PC1	0.812	0.830	0.900	0.894	0.739
	PC2	0.902
	PC3	0.862
Perceived Ease of Use	PEOU1	0.869	0.834	0.836	0.900	0.751
	PEOU2	0.872
	PEOU3	0.858
Perceived Usefulness	PU1	0.884	0.849	0.849	0.908	0.768
	PU2	0.879
	PU3	0.866
Self-reported Health Conditions	SRH1	0.835	0.813	0.825	0.888	0.726
	SRH2	0.872
	SRH3	0.849
Trialability	TR1	0.879	0.847	0.850	0.907	0.766
	TR2	0.887
	TR3	0.860

, all constructs exhibit Cronbach’s alpha coefficients above 0.7, signifying high reliability of the questionnaire and supporting further analysis [96]. In addition, the Average Variance Extracted (AVE) for each construct surpasses 0.5, indicating strong convergent validity within the measurement model [97].

Based on previous research, it is essential that all values within the heterotrait–monotrait (HTMT) ratio remain below 0.90 [45,98]. As shown in

Table 5. Heterotrait–monotrait (HTMT) ratio.

Construct	BI	COM	ITS	OB	PC	PEOU	PU	SRH	TR
Behavioral Intention
Compatibility	0.343
Intergenerational Technical Support	0.411	0.372
Observability	0.393	0.440	0.504
Perceived Cost	0.059	0.224	0.188	0.180
Perceived Ease of Use	0.447	0.429	0.349	0.380	0.128
Perceived Usefulness	0.451	0.379	0.444	0.434	0.137	0.404
Self-reported Health Conditions	0.420	0.326	0.404	0.379	0.199	0.362	0.421
Trialability	0.514	0.497	0.444	0.458	0.137	0.477	0.473	0.385

, the results confirm that these constructs meet the benchmarks for discriminant validity.

According to Fornell and Larcker’s criteria [93], the square root of each factor’s Average Variance Extracted (AVE) value should surpass the correlation coefficients of other factors. Our analysis confirms that this criterion is met, indicating robust discriminant validity of the scale as illustrated in

Table 6. Fornell–Larcker criteria.

Construct	BI	COM	ITS	OB	PC	PEOU	PU	SRH	TR
Behavioral Intention	0.850
Compatibility	0.286	0.870
Intergenerational Technical Support	0.337	0.306	0.862
Observability	0.321	0.365	0.414	0.857
Perceived Cost	−0.050	0.185	0.154	0.148	0.860
Perceived Ease of Use	0.368	0.361	0.293	0.313	0.104	0.867
Perceived Usefulness	0.374	0.323	0.372	0.364	0.115	0.341	0.876
Self-reported Health Conditions	0.346	0.269	0.334	0.312	0.153	0.295	0.352	0.852
Trialability	0.426	0.418	0.373	0.380	0.115	0.402	0.402	0.322	0.875

.

4.2. Structural Model Analysis

Smart PLS 4.0 was employed to test the hypotheses and validate the model. An R² value of at least 0.26 is deemed reasonable for explaining variance in the dependent variable, while for exploratory studies, an R² above 0.20 is considered acceptable [92]. The results of this study show that the R² values for behavioral intention, perceived usefulness, and perceived ease of use all exceed 0.20, indicating that the model provides an adequate explanation for these variables. A Q² value above zero confirms the model’s predictive relevance [92,95]. The Q² values obtained in this study meet these criteria, verifying the model’s predictive relevance. Detailed model fit indices are provided in

Table 7. Model fit indices.

Construct	R2	Q2
Behavioral Intention	0.257	0.186
Perceived Ease of Use	0.215	0.161
Perceived Usefulness	0.277	0.212

.

The hypothesis was evaluated using Smart PLS 4.0 and the bootstrapping method [99]. An SRMR value less than 0.08 is considered acceptable, and this analysis produced an SRMR of 0.044 [45]. The effects of the pathways within this model are detailed in

Table 8. Model path analysis results.

Hypothesis	Path	Standardized Coefficient (β)	t-Statistics	p-Value	Hypothesis Status
H1	ITS→PEOU	0.130	2.700	0.007	Supported
H2	ITS→PU	0.148	2.781	0.005	Supported
H3	COM→PEOU	0.210	4.092	0.000	Supported
H4	COM→PU	0.077	1.506	0.132	Not Supported
H5	COM→BI	0.114	2.185	0.029	Supported
H6	TR→PEOU	0.266	5.316	0.000	Supported
H7	TR→PU	0.170	3.007	0.003	Supported
H8	OB→PU	0.125	2.271	0.023	Supported
H9	PC→BI	−0.148	2.289	0.022	Supported
H10	SRH→PU	0.154	3.384	0.001	Supported
H11	SRH→BI	0.201	4.187	0.000	Supported
H12	PEOU→PU	0.117	2.240	0.025	Supported
H13	PEOU→BI	0.211	4.539	0.000	Supported
H14	PU→BI	0.212	4.374	0.000	Supported

and depicted in Figure 2. Apart from H4, all other pathways in the model exhibited statistical significance. This highlights the model’s substantial contribution to understanding the factors that affect older people’s behavioral intention toward smart home systems.

5. Discussion

Of the 14 hypotheses tested in this study, only one hypothesis (H4) was not supported. Among the 10 supported hypotheses, only H9 showed a negative correlation. The remaining 12 hypotheses displayed positive correlations, with H3, H6, H11, H13, and H14 being particularly significant.

5.1. Hypotheses with Positive Correlation

H1 and H2: Intergenerational technical support has a positive effect on perceived ease of use and perceived usefulness, with path coefficients of 0.007 and 0.005, respectively. These findings are consistent with the studies by Zhou et al. [7] and Wei et al. [45]. In China, where filial piety is highly regarded, parent–child relationships play a crucial role in influencing older people. The intergenerational technical support provided by younger family members can alleviate the anxiety and barriers associated with technology use among older people, thereby increasing their perceived ease of use and perceived usefulness.

H3: Compatibility has a positive effect on perceived ease of use, with a path coefficient of 0.000. This result is consistent with previous studies [79,80,81]. When a smart home system is compatible with the habits of older people and existing home devices, they find it easier and more intuitive to operate. Such compatibility reduces the learning curve and cognitive load, thereby enhancing older people’s perceived ease of use.

H5: Compatibility has a positive impact on older people’s behavioral intention, with a path coefficient of 0.029. This finding aligns with the results of Hubert et al. [67]. When smart home systems are well-matched with the lifestyles, needs, and technological experience of older people, they can reduce barriers to use and increase older people’s behavioral intention.

H6: The findings indicate that trialability has the most substantial positive influence on older people’s perceived ease of use of smart home systems, with a path coefficient of 0.000. This outcome underscores the central role of trialability as proposed in the IDT. The result is consistent with Choe and Noh [81], confirming that trialability positively impacts perceived ease of use. This is particularly relevant for older people, who may have reservations about new technologies. The opportunity to trial a system can alleviate fears and build confidence, making it a critical factor in the adoption process. Providing older people with hands-on experiences through demonstrations or limited-use trials can significantly enhance their perceived ease of use.

H7: Trialability also has a positive effect on perceived usefulness, with a path coefficient of 0.003. This is consistent with previous findings [79,81], suggesting that trialability enables users to experience the technology’s benefits firsthand, reducing uncertainty and increasing confidence in its effectiveness. By allowing older people to interact with and understand the technology before full commitment, trialability effectively demonstrates the potential benefits, thereby increasing perceived usefulness.

H8: Observability has a positive influence on perceived usefulness, with a path coefficient of 0.023, consistent with Al-Rahmi et al. [79]. In the context of smart home systems for older people, observability refers to the ability to directly witness or experience how the system enhances their daily lives. The significance of observability underscores the importance of tangible demonstrations and visible benefits in shaping older people’s perceptions of these systems. When older people can observe the benefits of a smart home system in their daily lives, the perceived usefulness of the system is likely to increase.

H10 and H11: Self-reported health conditions have a positive effect on perceived usefulness and behavioral intention, with a path coefficient of 0.001 and 0.000, respectively. These results are consistent with the findings of Chen and Chan [85]. Furthermore, the study by Li et al. [47] indicated that the self-reported health conditions of older people positively affect their perceived usefulness and behavioral intention toward smart wearable devices. This study further confirms that self-reported health conditions positively affect older people’s perceived usefulness and behavioral intention toward smart home systems.

H12: Perceived ease of use has a positive effect on perceived usefulness, with a path coefficient of 0.025. This reaffirms the central role of perceived ease of use and perceived usefulness as proposed in the TAM. The association suggests that when older people perceive a technology as easy to use, they are more likely to believe it can significantly enhance daily life, such as by improving convenience, safety, or efficiency within the home environment. This alignment between perceived ease of use and perceived usefulness is critical for fostering the widespread acceptance and adoption of smart home systems among older people, as it increases their readiness to interact with and integrate technology into their everyday activities. Furthermore, this discovery echoes the conclusions drawn by Pal et al. [89], underscoring the reliability and consistency of this association across diverse studies and contexts within the domain of technology acceptance among older people.

H13: Perceived ease of use has a positive impact on behavioral intention, with a path coefficient of 0.000. This result is consistent with previous studies [45,46,86]. As a key concept of the TAM, perceived ease of use has been widely examined across various fields. Pal et al. [86] demonstrated that perceived ease of use is a crucial determinant of behavioral intention. This finding further confirms the significant role of perceived ease of use in shaping the behavioral intention of older people toward smart home systems, underscoring its importance.

H14: Perceived usefulness has a significant influence on behavioral intention, with a path coefficient of 0.000. This finding reinforces the central roles of perceived usefulness and behavioral intention as proposed in TAM, consistent with Pal et al. [86]. As people age, their needs related to security, independence, and quality of life become more pronounced. Smart home systems can address these concerns by offering health monitoring and assistance with daily activities. When older people perceive the usefulness of smart home systems, their behavioral intention will increase.

5.2. Hypothesis with Negative Correlation

H9: Perceived cost has a negative effect on behavioral intention, with a path coefficient of 0.022. This result aligns with the findings of Alzahrani et al. [37] and Rattanaburi and Vongurai [83]. When deciding whether to adopt smart home systems, older people often consider the cost. If they perceive the price to be too high, their intention to use these systems declines. Therefore, during the development and promotion of smart home systems, developers and policymakers should consider the impact of perceived cost.

5.3. The Invalid Hypothesis

H4: The finding suggests that compatibility does not affect perceived usefulness. This result is in contrast to the findings of Choe and Noh [81] on smartwatches. Several factors might explain this discrepancy.

First, smart home systems are generally more complex than smartwatches, requiring the integration of multiple devices, platforms, and functions. This complexity can make it challenging to align these systems with users’ existing devices and experiences, potentially reducing their perceived usefulness even if the systems are compatible. In contrast, smartwatches are typically designed to work seamlessly with a limited range of devices, making their benefits more immediately apparent to users.

Second, although both studies focus on older people, the context in which these technologies are used may differ. For older people, smart home systems are often valued for enhancing safety, comfort, and overall functionality, which might reduce the perceived importance of compatibility. Conversely, smartwatches offer immediate benefits such as health monitoring and communication, which can be more directly enhanced by their compatibility with existing devices, thereby having a more significant impact on perceived usefulness.

In addition, regional and cultural differences might contribute to these varying outcomes. The adoption of smart home systems and smartwatches among older people can vary widely depending on factors like local infrastructure, familiarity with technology, and socioeconomic conditions. These differences may influence how users perceive the usefulness of a technology based on its compatibility. Therefore, future research should consider these factors to better understand how compatibility affects perceived usefulness across different types of technology and user groups in the elderly population.

6. Conclusions

This study developed an extended model based on the IDT, TAM, and external variables, providing a comprehensive view of the factors affecting older people’s intention to use smart home systems. The model enhances theoretical understanding in this area and offers valuable insights for smart home system developers, R&D institutions, and policymakers, helping them better address the needs of older people.

6.1. Theoretical Contributions

This study makes theoretical contributions to the field of smart home systems for older people. First, by leveraging the IDT, TAM, and external variables, this study developed an extended model to analyze older people’s behavioral intention toward smart home systems. The extended model expands the existing framework for understanding older people’s behavioral intention. Second, the findings confirm the pivotal roles of compatibility, trialability, self-reported health conditions, perceived ease of use, and perceived usefulness as core influencing factors on older people’s behavioral intention. These findings indicate that the development and design of smart home systems for older people should comprehensively consider these factors.

6.2. Practical Contributions

The results indicate that compatibility and trialability have the strongest positive effects on perceived ease of use. Moreover, self-reported health conditions, perceived ease of use, and perceived usefulness significantly affect behavioral intention. By thoroughly discussing and analyzing the findings, the practical contributions of this study are examined from the perspectives of various stakeholders, including older people, smart home system developers, R&D institutions, and policymakers.

For Older People:

The findings highlight the primary factors that affect older people’s behavioral intention toward smart home systems, with a focus on compatibility, trialability, self-reported health conditions, perceived ease of use, and perceived usefulness. These insights are vital for understanding the preferences of older people and provide a strong foundation for designing smart home systems tailored to their needs.

For Smart Home System Developers and R&D Institutions:

Enhance System Compatibility: Smart home system developers and R&D institutions should prioritize ensuring that smart home systems are highly compatible with existing home devices, the habits and needs of older people, and especially their biophilic needs. Smart home system developers should continuously update technologies and optimize the configuration of sensors and devices to enhance the biophilic experience and improve the compatibility of the system to better assist older people in their later years.

Provide Trial Opportunities: Smart home system developers and R&D institutions should provide short-term trial services for older people to help them better understand and adapt to smart home systems. This can not only improve older people’s perceived ease of use and usefulness of the system but also allow them to personally experience the improvement in their quality of life through smart home systems via practical biophilic experiences.

Offer Customized Services Based on Health Conditions: Smart home system developers and R&D institutions should integrate health monitoring functions to provide personalized settings and services based on the health status and biophilic needs of older people. By optimizing the configuration of biophilic sensors and smart devices based on the Internet of Things, real-time monitoring of environmental changes can be achieved, and adjustments to light and air quality can be made based on health data, or an immersive natural experience can be provided through virtual reality technology, thereby enhancing their behavioral intention.

For Policymakers:

Standardize Technical Specifications: Policymakers should establish standardized technical specifications to ensure compatibility across different brands and devices. This will lower barriers to use, simplify operation, and facilitate the broader adoption of smart home systems among older people.

Promote Community Pilot Projects: Policymakers can launch pilot projects of smart home systems in communities, allowing older people to experience the conveniences that smart home systems bring to life in a controlled environment, such as voice control, natural light adjustment, and air quality monitoring. These functions can meet the needs of older people for independence and biophilia, thereby improving their quality of life. Through these pilot projects, older people’s perceived usefulness and behavioral intention toward smart home systems can be effectively enhanced.

6.3. Limitations and Future Research Suggestions

6.3.1. Limitations

Geographical Limitation: This study is geographically limited to Linyi City, Shandong Province. The findings may not be generalizable to other regions with different socioeconomic, cultural, or technological contexts. Older people in different areas might have varying experiences and attitudes toward smart home systems. Therefore, the applicability of this study’s conclusions in other regions and contexts requires further verification.

Cross-sectional Study Design: This study used a cross-sectional design, which does not capture changes in variables over time. As a result, older people’s behavioral intention toward smart home systems may evolve with technological advancements or other external factors, but these potential changes are not reflected in this study.

Quantitative Method: This study used a quantitative approach, which offers valuable statistical insights. However, quantitative analysis alone cannot fully capture older people’s subjective experiences with smart home systems, potentially limiting the depth of understanding regarding their specific needs. Furthermore, this study does not account for moderating factors.

Self-reported Data: This research included self-reported data. Although efforts were made to mitigate social desirability bias through well-designed questionnaires and anonymous surveys, some bias may still arise due to the subjective nature of the responses. Such bias could affect the external validity of the findings, and the limitations of self-reported data should be carefully considered in future studies.

6.3.2. Future Research Suggestions

Exploring Different Geographical Areas: To enhance the generalizability and external validity of the findings, future research should include diverse geographical regions. Studying different socioeconomic, cultural, and technological contexts will provide a broader perspective and more tailored recommendations for the design, development, and promotion of smart home systems.

Conducting Longitudinal Research: Future studies should adopt a longitudinal design to monitor changes in older people’s attitudes and behavioral intention toward smart home systems over time. This approach will capture the dynamic nature of technology acceptance and yield deeper insights and more reliable results.

Combining Qualitative Methods and Moderating Effects: Future research could integrate qualitative methods, such as interviews or focus groups, to capture the subjective experiences of older people, thereby complementing quantitative analysis and deepening the understanding of the factors affecting their behavioral intention. In addition, the moderating effects of variables such as socioeconomic status, education level, and cultural background should be explored to better comprehend the complex mechanisms driving older people’s behavioral intention.

Incorporating Objective Data: To further strengthen the external validity of the findings, future studies should incorporate objective data (e.g., smart home system usage records and health monitoring data) to supplement self-reported data, making the research conclusions more broadly applicable.