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In recent years, there has been a significant increase in the older adult population in China (1), which has resulted in new challenges for social retirement. It is essential to note that the majority of seniors desire to live independently for as long as possible to increase their satisfaction and prevent them from incurring costly institutional care. Smart-home services for the older adult focus on providing better aging-in-place services to promote a happier life.

Initially, smart home technologies were developed with a focus on security and energy efficiency (2). As time went by, the range of users gradually expanded to include vulnerable individuals such as the older adult, and people with chronic diseases (3, 4). Smart home services are home automation services based on Internet of Things (IoT) technologies that can be purchased, prefabricated, or installed at home (5). These services include security systems, keyless entry, body detection devices, smart lighting, smart water valves, and more. In the past decade, various smart home services have been utilized to address the unique needs of in-home aging. Numerous studies have shown that smart home services are beneficial to the well-being of the older adult (6). The benefits of smart home services include independent living, improved healthcare, social involvement, safety, cost reduction, and decision making (7). Therefore, smart home services can help maintain independence and improve the quality of life (8). Scholars have proposed various flexible smart-home service designs to address the specific needs of the older adult (9, 10).

Although technology advancements bring apparent benefits, promoting even the best smart home devices may face potential obstacles such as high expense, technological challenges, safety concerns, burden on others, difficulty in recalling functions, stigmatization, and lack of perceived need (6, 11, 12). Consequently, the older adult do not use them. In China, the adoption rate of smart homes among the older adult remains low (13).

We all know that modern seniors are not entirely resistant to new technologies. They can adopt new technology under the influence of various factors and may even actively try new products. Numerous scholars are investigating the positive attitudes of older adults toward smart-home services and the factors that influence these attitudes. Identifying these positive factors can effectively promote the use of more smart-home services among senior citizens, which can ultimately have a substantial impact on their well-being.

The Technology Acceptance Model (TAM) is inadequate for assessing the primary factors and barriers to the adoption of smart home services by older adult individuals. To address this issue, Chen and Chan (14) proposed the Senior Technology Acceptance Model (STAM) specifically for the older adult population in Hong Kong, while Pal et al. (15) developed the Older Adult Smart Home Technology Acceptance Model (ESHTAM), a comprehensive model for smart home technology acceptance among older adult individuals. Several studies have examined the factors that influence the acceptance of smart home services by older adults, including personal and environmental perception factors. Personal perception factors comprise perceived ease of use, perceived usefulness, perceived cost (16), facilitating conditions (7),technology anxiety (17), and security and privacy concerns (16–18). Environmental perception factors include social influence (7, 18), subjective norms (15), cultural influence (7), family management policies, and government policies (18).

A literature review conducted by Peek et al. (19) revealed a dearth of valid quantitative studies during the post-implementation phase of technology adoption by older adults. Tsertsidis et al. (20) discovered that the perceptions of older adults regarding technology changed between the pre-implementation and post-implementation stages in their investigation of smart home technology acceptance. They observed that some of the negative concerns expressed during the pre-implementation stage were viewed positively during the post-implementation stage. Similarly, Ghorayeb et al. (11) reported that older adult consumers’ approval of smart home monitoring equipment increased with use, in contrast to those who had not utilized monitoring technology. The factors influencing technology adoption also vary across different stages of use in old age. For instance, the longer an older adult individual uses technology, the greater the influence of social factors on their technology adoption behavior (21). Therefore, it is important to focus on changes in the factors affecting user acceptance attitudes after technology implementation when studying the intention to use smart homes.

Research on technology adoption by the older adult has been criticized for excessively focusing on models such as TAM and UTAUT (Unified Theory of Acceptance and Use of Technology) while ignoring other critical variables. For instance, Chen and Chan’s (22) literature review found that despite unique psychosocial factors of the older adult, such factors were often disregarded. Such psychosocial factors may arise from their changing intergenerational relationships.

With the development of technology and the popularization of digitalization, intergenerational interaction and communication have become more widespread and frequent, which also affects intergenerational support (23). Intergenerational support is defined as “the process of economic reciprocity, mutual assistance, and emotional support between generations in a family, as well as the sharing of life experiences and resources” (24). It is a two-way exchange behavior between generations. Studies in developed Western countries have shown that intergenerational support flows from older adults to their offspring. However, research conducted in developing countries indicates that intergenerational support primarily flows from younger to older generations (25, 26). This difference may be due to the fact that in developing countries, older adult care is primarily funded by households rather than the government. Intergenerational support is a broad concept and can be classified into instrumental, financial, and emotional support (27, 28).

Intergenerational support has a significant impact on many aspects of older adults’ lives. In general, family support exchange, regardless of the type of support, has a positive effect on the lives of older adults and positively impacts their mental health, self-esteem, and well-being (29–31). Both emotional and financial support enhance physical and mental health (32). There have also been numerous studies on the effect of intergenerational support on technology acceptance among older adults. Seniors who lack daily exposure to new technology are more likely to develop mistrust and negative attitudes towards technological products and ultimately resist adopting new technologies (33). Family structure and communication provide an opportunity to overcome these attitudes barriers. Positive intergenerational interactions can encourage older adults to become interested in ICT (information and communications technology) and start learning about technology, ultimately having a positive external effect on their adoption of new technological items (34, 35). Some studies have shown that the number of minors in the home is positively associated with the willingness of older adults to use Internet technology, although there is no significant effect on the intensity of use (36). However, other studies have found that the presence of children does not clearly explain the adoption of Internet technology by their parents and may be due to the Internet demands associated with having children in the home (37). Further research is needed on the specific effects of intergenerational relationships in families.

Intergenerational support can affect older adults’ technology acceptance in several ways. The current focus is on intergenerational technical support provided by children (or grandchildren) to older adults. Grandchildren can encourage grandparents to adopt new technologies by demonstrating and explaining how to use various devices (38). Research has confirmed that the involvement of young “enthusiastic experts” can facilitate intergenerational knowledge transfer and ultimately promote the use of new technologies by older adult users (36, 39). “Enthusiastic experts” provide both intergenerational instrumental and emotional support. He and Huang (40) found that intergenerational technological support has a positive effect on seniors’ attitudes towards smartphone use and their well-being. However, assistance from family members (or intergenerational home care) may act as a substitute for technology, negatively affecting older adults’ acceptance of home technology (41, 42), indicating the need for further research. Several studies have examined seniors’ adoption of digital feedback (bottom-up technology transfer). Factors that facilitate the acceptance of digital feedback from younger generations by older adult individuals include lower age, higher literacy level, higher economic status, and good family communication practices (43).

Intergenerational support can have both positive and negative effects on older adults’ acceptance of smart home services. However, there is a lack of in-depth quantitative studies in this area. The attitudes of older adults towards the use of new technology devices and the type of intergenerational support they receive are unclear, and no research has examined the effect of specific types of intergenerational support on technology acceptance by older adults. There is also a lack of research on the impact of intergenerational financial support. These challenges suggest that intergenerational support should be incorporated into the concept of senior acceptance of smart home services. More quantitative research is needed to determine which factors are most influential. Therefore, this study aims to integrate the TAM model with intergenerational support theory and investigate the influence of three major factors on the willingness of older adults to use smart home services.

Davis et al. (44) proposed the Technology Acceptance Model (TAM), which has been extensively used as a theoretical model to investigate the intention to use different ICT technologies and intelligent systems. In this study, a TAM-based research model is proposed to investigate the effectiveness of smart home services for older adults.

We have developed an online survey tool to measure the perceptions and intergenerational support of the older adult for smart home services. While numerous models of smart home services exist, there is a lack of applicable theoretical frameworks for modeling smart home services for seniors. To address the specific needs of the older adult, we focused on the primary design guidelines found in past literature and extracted a smart home model that is more suitable for their well-being (10, 12, 15). Figure 2 illustrates the model’s five dimensions: Environment Monitoring (EM), Health Monitoring (HM), Community Management (CM), Amenity Improvement (AI), and Risk Management (RM). For the sake of user comprehension, the model omits the technical description of information collection and transmission.

Due to the tendency of seniors to rely on heuristics when making decisions (64), additional questions were included in the survey to trigger semantic processing. To facilitate the comprehension and recollection of smart home systems and devices used in their lives, we presented each of the five dimensions of the smart home model in the form of questions and asked respondents to rate the usefulness of five devices using a seven-point Likert scale. Previous studies have demonstrated the effectiveness of similar methods (65, 66). As most of these studies focus on users over the age of 55 (14), we also restricted our study to this age group. Before distributing the questionnaire to the target population, we consulted two independent experts in the field of user experience to check the validity and consistency of the developed questionnaire. We used a convenience sampling method, creating a link to the questionnaire on the website “Wen Juan Xing” and distributing it through several WeChat chat groups. Older Adult people in the groups completed the questionnaire directly, while younger members of the groups sent the link to their senior relatives to complete the survey.

As technological products become more prevalent in homes, an increasing number of senior citizens are adopting smart home technology. However, due to their inexperience with smart home technology, many older adult individuals are not able to fully utilize these systems. Furthermore, the stage of adoption can affect the level of technology acceptance and influencing factors among older adults who use smart home services (20, 47, 67). To ensure the accuracy of the data, we screened senior adults who had used smart home products using a series of questions designed to reduce hypothetical answer bias among participants who had never used a smart home. Specifically, we included a screening question: “Have you ever used smart home technology/services?” Response options included “often,” “occasionally,” “not sure if I have used it,” and “have not used.” Users who answered “not sure if I have used it” and “have not used” were filtered out and did not continue with the survey.

We measured intergenerational emotional support through three questions, adapted from the Intergenerational Solidarity Survey (68). Since intergenerational financial support includes gift-giving (62), we asked the respondents if they had received any monetary or gift support from their children in the 2 years prior to the survey in order to determine the level of intergenerational financial support. Considering the high cost of smart home technology and its prevalent use in urban areas, this study attempted to distribute questionnaires mainly among the older adult population residing in urban areas, in order to seek a larger pool of respondents who have used smart home technology. Responses to the questionnaire were rated on a five-point Likert scale ranging from 1 (strongly disagree) to 5 (strongly agree). Table 1 presents the final variable definitions and their corresponding sources.

A total of 298 valid questionnaires were collected in this study, with 128 (43%) males and 170 (57%) females responding. The respondents’ basic demographic information is presented in Table 2. SPSS20 and Smart PLS3.0 were used to analyze and statistically process the data, with the PLS-SEM technique utilized since it is optimal for exploratory investigations.

To examine the impact of controlling variables on the relationship between the independent variable and the dependent variable, we assessed the model both with and without controlling variables. The findings demonstrated that the difference in standardized coefficients of the independent variable between the two models was less than 0.1. As advised by Becker et al. (72), this suggests that the effects of controlling variables are negligible, and therefore, we presented only the results without controlling variables in the subsequent section.

The collected data were analyzed using Confirmatory Factor Analysis (CFA) and Structural Equation Modeling (SEM) methods. First, a CFA was performed on eight constructs (dimensions), as shown in Table 3. For all the constructs used, Cronbach’s alpha values obtained were more significant than 0.7 and synthetic reliability (CR) greater than 0.6, which indicates a high degree of internal consistency (73). For each construct, the Average Variance Extracted (AVE) is more significant than 0.5, indicating good convergent validity of the measurement model (74). According to Henseler et al. (75), all the values in the HTMT must be less than 0.90. As presented in Table 4. The results indicate that these constructs fulfill the discriminant validity.

To assess the absence of correlation between measurements, divergent validity was examined. Table 5 displays the obtained results. The diagonal element, which represents the square root of AVE, has a higher correlation level between any two specific factors. As a result, the vast majority of constructs in this study exhibit good discriminant validity (76).

Tenenhaus et al. (77) has presented an alternative method for determining the goodness of fit (GoF). The formula for the GoF value is as follows: GoF = AVE ¯ × R 2 ¯ = 0.783 × 0.426 = 0.578.

We obtain a GoF value of 0.578, which is greater than the suggested GoF value of 0.36 (77), proving the validity of the model.

The hypothesis testing was done in Smart PLS 3.0. We used the bootstrapping method (78). According to the criteria of Henseler et al. (79), SRMR <0.08 is acceptable, we derived an SRMR of 0.061.

The subsamples were randomly selected from the original data set, and this process was repeated multiple times to create a large number of random subsamples. The results indicate that all hypotheses are supported, except for H7. Table 6 presents the significant results of each path coefficient, and the final theoretical framework is illustrated in Figure 3.

The findings of this study suggest that many of the explanatory variables are highly significant, and even after using technology, the Technology Acceptance Model (TAM) still has explanatory power regarding older adults’ acceptance of smart homes. PEOU has a significant positive effect on both PU and BI. Although PU also profoundly influenced BI, PEOU (standardized path coefficient of 0.260) had a greater effect on BI than PU (standardized path coefficient of 0.198). This supports previous research indicating a strong link between perceived ease of use and technology acceptance among older adults (80). These findings emphasize the importance of making smart home technology simple and easy to use to meet the requirements of senior citizens, especially by designing appropriate voice interface styles, interface navigation, swipe layout, and button size based on the cognitive behavior of older users (81, 82).

However, Pal et al. (15) found the opposite result in their study of smart home use by older adults, concluding that the influence of usefulness on behavioral intentions is more significant. One possible explanation for this discrepancy could be gender differences; Venkatesh and Morris (83) suggest that the effect of perceived usefulness on intention should be stronger for older adult men than for older adult women. In Pal’s study, men comprised 65.7% of the participants, while in this study, men only comprised 42.95%.

Consistent with Peek’s earlier findings (19), our study reveals that although older adults are aware of the advantages of smart homes in terms of increased independence and safety, this awareness does not necessarily translate into a willingness to use them. This may be because many older adult people believe that smart home technology is mainly aimed at older adults who are in poorer health conditions, rather than themselves. Additionally, other barriers may significantly hinder the perceived benefits of using smart homes.

Smart home technology is considered an effective means of supporting in-home aging for seniors, as it can significantly improve their health and independence. However, despite increasing attention, smart homes are not widely adopted among the older adult. Therefore, when developing and delivering smart home technology for older adults, it is important to understand the social supports that influence their use and decision-making, as well as to fully comprehend their needs and provide solutions that are easier to use.

The influence from their family can overcome the fear and rejection of older adults towards smart home technology. This paper contributes to a better understanding of the factors influencing in the initial acceptance of smart home technology by older adults. When seniors live with their children, they have access to a wide variety of intergenerational support, which can have a positive external effect on them. In Chinese households, which tend to consist of extended families, older adults’ acceptance of new technologies may be significantly enhanced. Public policies that incentivize intergenerational interactions can help achieve this goal.

Future research can continue to explore related directions, such as how family relationships affect the number of smart home devices used by the older adult, how to increase their interest in using them, and how to improve their skills. However, the technical maturity of smart home services is currently low and most studies have focused on health monitoring technologies, limiting the possibility of studying the acceptance factors of smart homes after full use.

There are several limitations to this study. First, although the participants were randomly selected, some selection bias may have occurred. For example, many of our questionnaires are delivered to older adult people through their children, so older adult people with high emotional cohesion with their children are more likely to receive this questionnaire that we have sent out. Additionally, the age range of the participants was relatively young, which may not fully represent the intergenerational support and acceptance of smart home services among the older adult population. Hence, the results need to be interpreted with caution. Second, this study provides a general understanding of smart home acceptance. However, there are various categories of smart homes with different levels of usability, ease of use, and purpose, which may influence older adults’ attitudes towards them. Therefore, future research should segment the study based on different types of smart homes. Third, the study does not consider the impact of brand influence on older adults’ attitudes towards smart homes, as different brands of smart home products have different interfaces and designs. Therefore, future research should take brand influence into account. In conclusion, more research is needed to capture the complexity of the acceptance process of different types of smart home services by older adults in the community to better leverage technology for their aging-in-place.

The original contributions presented in the study are included in the article/supplementary material, further inquiries can be directed to the corresponding author.

WW proposed the idea of this research, wrote the paper, and acquired the funding. XG wrote a part of the paper. KT, JL, and KX collected the data and revised the paper. All authors contributed to the article and approved the submitted version.

This work was supported by the key project of the Beijing Social Science Foundation funded by the Beijing Federation of Social Sciences (18YTA001).

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.