Underlying model

Much research has found that the technology acceptance model (TAM), which is dependable, may be utilized as a theoretical model to investigate the intention to use different kinds of information and communications technology (ICT) and intelligent systems [26]. The TAM was originally developed by Davis in 1989 and is widely used to characterize consumer acceptance of emerging concepts, ideas, and things, as well as the primary variables influencing acceptance. With the continuous progress of information technology, scholars from several disciplines have successively developed the theory of reasoned action (TRA) [27, 28], the theory of planned behavior (TPB) [29, 30], innovation diffusion theory (IDT) over 20 years [31, 32], uses and gratification (U&G) [33] and other theories have been combined with TAM to continuously improve the theoretical model of TAM. Currently, TAM has become one of the most well-liked and convincing strategies for gaining users’ willingness to embrace information technology, so TAM is chosen as the base model in this paper [34–36].

According to the TAM theory, users’ actual adoption or rejection of an appliance is primarily affected by two factors, namely perceived usefulness (PU) and perceived ease of use (PEOU). Among them, the improvement of perceived ease of use will further improve perceived usefulness, while both of them positively affect users’ willingness to use [37]. In this paper, perceived usefulness reflects the extent to which older users perceive an improvement in their work or life performance when using a smart home system, perceived ease of use reflects the extent to which older users perceive ease of use when using a smart home system, and willingness to use refers to the subjective likelihood that an older user plans to use a smart home [38]. According to the published literature, Choukou has investigated the acceptance and willingness to use smart assisted living technologies among older adults, and the results showed that despite some concerns about the adoption of the technology, older adults found such products useful and were willing to try them out [39]. Alaiad’s study also showed that customer desire to use is significantly positively impacted by perceived usefulness [40]. Furthermore, Bao, in extending the traditional TAM model, found that perceived usefulness, perceived ease of use, and perceived safe home environments had a direct positive correlation on smart home acceptance [41]. King’s study confirmed how perceived usefulness is influenced by perceived ease of use [42].

Therefore, the hypothesis can be formulated:

1. H1: Perceived usefulness (PU) of smart homes by older users favors the intention to usage intention (UI) of smart homes.
2. H2: Perceived ease of use (PEOU) of smart homes by older users favors the intention to usage intention (UI) of smart homes.
3. H3: Perceived ease of use (PEOU) of smart homes by older users favors perceived usefulness (PU) of smart homes.

Extended variables

In the field of geriatric research, Chen emphasizes that although the TAM is a practical and trustworthy framework, it is important to take into account the physiological and psychological changes experienced by older adults during the aging process when predicting their adoption behaviors toward smart homes [43, 44]. To more accurately explain technology adoption within older persons, Chen proposed the concept of the senior technology acceptance model (STAM) by combining the traditional structure of technology acceptance with age-related health and ability factors [45]. In addition, Venkatesh proposed the unified theory of acceptance and use of technology (UTAUT) based on technology acceptance modeling research, which can effectively access the feelings and experiences of older adults when using products, and thus has a high value of application in the process of evaluating the design of age-adapted products [46]. Finally, Nikou summarized the theoretical assessment criteria in the research literature review on influencing older persons’ adoption and use of new technologies, which are broadly categorized into three types: (i) broad acceptability standards, including technology, privacy risk, and economic factors; (ii) personal characteristics, including individual user characteristics, perceived usefulness, perceived ease of use, and use scenarios; and (iii) sociological criteria, including social inclusion and exclusion [37]. These theoretical assessment criteria are important guides for understanding and promoting the adoption and use of new technologies by older adults.

Intergenerational technical support (ITS)

In modern societies, where age differences lead to increasing segregation, older adults face many challenges in technology use [47]. Many smart home products require a WiFi connection before setup and use, a step that is already quite difficult for many older adults. In addition, the various buttons and functions of smart homes are also more complicated for many older adults. Because of an absence of technical support and ignorance of the device parameters, the complexity of the operating commands tests the already declining memory, learning ability, and even Mandarin ability of older adults, which may lead them less confident in their capacity to understand how to operate them, thus creating technology anxiety [45, 48–50]. In addition, older adults may encounter challenges in learning to operate the smart home. They may not be able to fully understand the designer’s intent and the feedback provided by the device, making the learning process more complex and difficult, and they may experience the phenomenon of ‘teaching and forgetting’ [51, 52]. Liu’s study confirms that technology manipulation is a key issue in most complex smart home systems [53]. Xue found that older users may be prone to rejection of smart home technologies due to anxiety or nervousness, which in turn may lead to cognitive skepticism about the usefulness and ease of use of the smart home, with a direct negative impact on the intention to use [54]. Due to the richness and complexity of the information processing system, the difficulty of using it increases, which discourages many older people, and thus the issue of technical support for older people from their children has become a growing concern [55]. In the areas of technology use, new media acceptability, and new media expertise, younger generations outperform older individuals. Intergenerational technical help from younger generations to older generations has led to more communication topics and communication opportunities between parents and children. Several studies have shown that the phenomenon of intergenerational technical support in the family is very common in the digital age and significantly influences older individuals’ embrace of technology [56, 57].

Therefore, this study takes children’s intergenerational technical support for older adults’ smart home use as an important independent variable and further explores whether children’s technical guidance and assistance to their parents affects older adults’ experience and acceptance of technology use in the process of using smart homes. This study presents the following hypotheses:

1. H4(a): Intergenerational technical support (ITS) favors perceived usefulness (PU).
2. H4(b): Intergenerational technical support (ITS) favors perceived ease of use (PEOU).

Perceived value (PV)

According to market survey data, we found that the largest obstacle to the uptake of innovative technologies is the upfront cost, followed by privacy issues [58]. Purchasing home products is an important part of the household budget; however, for the majority of consumers, smart home products are of high value, and failure to purchase them can bring serious financial losses to consumers. Therefore, when consumers decide whether to purchase smart home products, the sensitivity to perceived value significantly affects their purchase intention [59]. Perceived value refers to the comprehensive evaluation of the utility of a product or service derived by consumers from the benefits received and the costs paid [60]. Factors such as product quality, price, category, and appearance affect consumers’ perceptions of the value of the product, which directly influences their willingness to buy. Consumers’ willingness to buy is facilitated if they can obtain a satisfactory cost-effectiveness of the product, at which point the cost of the product can be adjusted slightly upwards [61–64]. Generally speaking, the higher income and higher education level of the elderly group will have a more thorough comprehension of the concept of community residential care services, intelligent transformation of home products and their role, so that they are more concerned about the home environment and can provide the necessary financial support. In contrast, low-income or low-education people are at a relative disadvantage in the field of consumption. They tend to have more conservative consumer attitudes and pay less attention to smart products. This view is further supported by other academic studies. According to Jamal’s research, customers’ propensity to shop and perceived value are positively correlated [65]. Chang argues that consumers’ evaluation of product value has a major and direct influence on their purchasing decisions and that the process of making decisions is heavily influenced by perceived value [66].

This study presents the following hypotheses:

1. H5(a): Perceived value (PV) favors perceived usefulness (PU).
2. H5(b): Perceived value (PV) favors perceived ease of use (PEOU).

Perceived risk (PR)

The intelligent home atmosphere is an important component that has to be highlighted since it affects the user’s life directly and has the potential to do major harm in risky circumstances. Hemil integrated the diffusion of innovations theory (IDT) into the TAM model, adding two new crucial components, perceived risk (PR) and trust. The model shows that perceived risk has a substantial impact on perceived usefulness [27]. Perceived risk refers to a customer’s subjective perception of the nature and degree of risk that a particular good may pose when considering its purchase [67]. Since smart homes are still emerging in the minds of most potential buyers, most of them are very cautious when deciding whether to buy a smart home or not. Before making a purchase, they usually deeply analyze and evaluate the usability and ease of use of the smart home, as well as the possible risks associated with the purchase, and make their purchase decision accordingly. Featherman and Sintonen reviewed the ethical issues related to the use of smart home devices by older persons over the past decade and concluded that privacy invasion and security are the main variables influencing older adults’ usage of smart homes [68, 69]. Seniors are usually reluctant to delegate all their uncertainties to machines due to misgivings about their technology [70]. On the one hand, smart homes involve high technologies that are not in the hands of the general public, and when hardware and software systems malfunction, the smart home can quickly go out of control and may cause distress or even harm to users [71]. On the other hand, the smart home system is a product of the era of high data and informatization, relying on the collection of user lifestyle data such as exercise, travel, energy use, etc. to provide appropriate services, the home security system may be an illegal invasion or data leakage and other issues, which makes the user concerned about the use of the information security of the smart home [72, 73]. Numerous studies have shown the significant influence perceived risk has on the adoption of new technology. Holak found that for consumer durables, the perceived risk rises significantly with increasing complexity, which reduces consumers’ purchase intention to a certain extent [71]. Yang’s study confirms that security risk and privacy risks hurt the adoption of smart home services by prospective customers [73]. It can be seen that the presence of perceived risk stimulates consumers’ self-protective behaviors, and the greater the perceived risk, the more detrimental it is to consumers’ positive willingness to use [74, 75].

Therefore, this study presents the following hypotheses:

1. H6(a): Perceived risk (PR) is detrimental to perceived usefulness (PU).
2. H6(b): Perceived risk (PR) is detrimental to perceived ease of use (PEOU).

Finally, Fig 1 displays the conceptual paradigm suggested in this study.

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Fig 1. Theoretical model for this study.

https://doi.org/10.1371/journal.pone.0300574.g001

Questionnaire design

As a scientific and effective means of modern information acquisition, questionnaire survey is widely used in information collection and analysis. In light of the micro viewpoint, this study utilizes the data obtained from the questionnaire survey to conduct an in-depth discussion on the older population’s desire for smart home devices. In existing studies, we are concerned that the individual variations in the desire for smart homes among the elderly are related to a variety of factors, including their physiological, psychological, cognitive, sociological, and family situations [76].

The questionnaire survey was designed in four parts. The first section mainly collected socio-demographic data about the elderly population, such as age, gender, health status, monthly income, and education level. Some older adults may have difficulty completing basic daily activities due to health conditions or chronic diseases, and smart home devices provide these older adults with the opportunity to continue living at home and preserve their quality of life [77]. The high technology and high investment in smart home devices make the use of smart homes closely related to the income and education level of elderly users and other information. The second part of the study focused on the home situation of the elderly, especially the family structure and living status. It was found that in the case of needing specialized care, the elderly preferred to stay at home rather than enter a healthcare facility, which undoubtedly increased the demand for family members [78]. According to relevant data, China has also been promoting the “9073 model” at the policy level, which means that 90% of the elderly rely on home care, 7% rely on community care, and 3% rely on institutional care. The third section asked respondents about their experience with smart home products. For respondents who have never used the products, we screened them out and terminated the survey. For respondents who had experience in using the products, we continued with the fourth part of the survey, which focused on their views and acceptance of smart home services. All items were assessed utilizing the five-point Likert scale method, which categorizes respondents’ agreement with the content of the question items into five levels: complete disagreement (1), disagreement (2), neutral (3), agreement (4), and complete agreement (5). During the questionnaire, in cases where respondents did not select any option or did not answer a question, the questionnaire was designed to allow for skipping that question and moving on to the next one.

Based on the reference to the relevant mature questionnaires, the fourth part of the questionnaire scale of this study was developed and designed in combination with the previous research, as shown in Table 1. Among them, perceived usefulness, perceived ease of use, and willingness to use mainly refer to the questionnaire of Davis [18]. Among them, perceived usefulness, perceived ease of use, and willingness to use mainly refer to the questionnaire of Davis [18]; perceived usefulness consists of three entries, such as “I think the smart home is a must-have system for life”; perceived ease of use consists of two entries, such as “I think the smart home is easy to learn how to use”; and willingness to use consists of two entries, such as “I will actively use it in the future”; and willingness to use consists of “I will actively use it in the future”. Perceived ease of use consisted of two entries, including “I think smart home is easy to learn how to use”, and willingness to use consisted of two entries, including “I will actively purchase smart home products in the future”. Intergenerational technology support was referenced from questionnaires by Czaja [79], Lie [80], and Slegers [81], and consisted of 2 entries such as “I think my children will guide me in using the smart home”; perceived value was referenced from questionnaires by Hsu [82], Mitzner [83], and consisted of 2 entries such as “I think smart home is easy to learn to use”; willingness to use consisted of 2 entries such as “I will actively purchase smart home products in the future”. “I think the cost of purchasing and using the smart home is acceptable” and 2 other entries; perceived risk refers to the questionnaire of Hemil [31], Alam [84], and Ostlund [85], and consists of, “I think the security performance of the smart home is stable.” and other 2 entries. Both of the 2 entries measuring perceived risk were reverse-scored.

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Table 1. Measurement scales for variables.

https://doi.org/10.1371/journal.pone.0300574.t001

Participants

According to international regulations, senior citizens are defined as those aged 60 years or older, including young older adults (60 ∼ 74 years old), generally older adults (75 ∼ 90 years old), and long-lived older adults (over 90 years old) [42]. Older persons undergo substantial cognitive changes in comparison to younger adults, including a notable reduction in fundamental cognitive functions like memory and attention. In some cases, memory loss may also lead to a decline in logical reasoning, slower analysis of sensations, and longer reaction times to perform motor behaviors, resulting in a loss of ability to carry out daily tasks. Considering that this study aims to investigate the variables influencing senior users’ inclination to utilize smart home products and that the cognitive abilities involved in human-computer interaction are closely related to attentional management, memory retention, processing speed, executive capacity, and visuospatial ability, this study will focus on elderly people who have experience in using smart homes, are over 60 years old, and can complete human-computer interactions either independently or with the help of others as the main target [86].

Questionnaire collection

The questionnaire survey for this study was conducted mainly in the school community and nearby cities (i.e., the Yangtze River Delta region), which is easy to implement and less costly. In the survey process, a convenience sampling method was used, i.e., the investigator randomly selected respondents for question-and-answer interactions while encouraging their active participation. Compared with other methods, this method has the advantage of simplicity of operation, while the respondents have a certain degree of randomness. The interviewers used a uniform language of instruction and distributed the questionnaires in the communities where the schools were located and in the surrounding urban communities. For the interviewed elderly with writing ability, we asked them to fill in the questionnaire by themselves; for those who could not fill in the questionnaire by themselves, their primary caregivers or interviewers filled in the questionnaire on their behalf. The questionnaires were collected on-site after completion.

The Ethics Committee of Nanjing Forestry University (Science and Technology Department of Nanjing Forestry University) has evaluated and authorized this study. All interviewees who participated in this study were aware of its background, methodological process, results, and purpose, and had signed a written informed consent or verbally agreed to participate in the study. After completing the research questionnaire, we conducted specific user interviews on the options for the older population. Finally, we analyzed and summarized the research data and proposed feasible smart home product design strategies to increase the willingness of the elderly to use them.

Data processing

The study began on March 10, 2023, and ended on March 20, 2023, with a total of 236 questionnaires. At the termination of the survey, the collected questionnaires were reviewed and counted. The study was divided into two phases. In the first phase, the questionnaire data were entered into SPSS 26.0 software for preliminary processing. If the percentage of identical answers for an option was 70% or more, or if there was 70% missing data, the sample was considered invalid. After removing the invalid samples, data entry was performed again. In the end, the total number of valid questionnaires was 200, and the effective recovery rate reached 84.75%. For details of the questionnaire contents and responses, please refer to the S1 File. In the second stage, to verify the validity and reliability of the questionnaire, valid data were imported into SPSS 26.0 statistical software for reliability analysis. After this, we used AMOS 26.0 software to build a structural equation model (SEM) and factor analysis to explore the intrinsic connection between various factors affecting the adoption of smart homes by the senior group. We used α = 0.05 as the test threshold to guarantee the accuracy and consistency of the analysis findings.

Descriptive statistical analysis

Table 2 provides a thorough breakdown of the descriptive statistical analysis results. From the 200 valid samples, it is found that there are more females than males, accounting for 73.5% of the total, while the percentage of males is 26.5%. This is consistent with the characteristics of our national situation, i.e., the average life expectancy of the female population is higher than that of males, and the degree of population aging in females is higher than that of males. Regarding the age distribution, 66.0% of the participants were between 60 ∼ 74 years old, 32.5% were between 75 ∼ 90 years old, and only 1.5% were over 90 years old. Among the respondents, young seniors occupy a major proportion, which suggests that the use of smart homes may be related to age, cognition, mobility, and other aspects. In terms of education level, 53.0% of the participants had an education level of junior high school and above, which indicates that the respondents of this study have a good education level. This good educational background helps elderly users to better understand and operate smart home devices so that they can better take advantage of the convenience that comes with smart homes. The lack of user awareness is a key barrier to China’s present smart home development, which directly leads to a low household penetration rate, and the smart home application environment has not yet been fully diffused. In terms of capacity for self-care, most of the elderly can take care of themselves completely or need only partial help, and only 1.5% of the respondents rely completely on the help of others. As the aging process accelerates, there are more “empty nesters”. Smart home technology can be adapted to the decreasing ability of the elderly to do things, and effectively meet their living needs. In terms of family status, only 7.5% of the respondents live alone, 51.5% live with their spouses, 11% live with their children, and 29.5% live with their spouses and children. After retirement, the elderly have fewer social contacts and a stronger desire for affection, and they need more love and help from their families. Compared to elderly individuals who live alone or have no one to care for them, those who have children to care for them are more likely to be joyful, which in turn affects their attitude towards life. Maintaining good relationships and close ties with family members can reduce the cognitive and learning burden on older adults and encourage them to try new, age-friendly technologies and products. Regarding the experience of using smart home products, over half of the participants said they had familiarized themselves with the basic operating steps, but 21.5% of the respondents still had no knowledge of smart homes. indicated that they were already familiar with the basic operating steps, but 21.5% of respondents were still unaware of the smart home. Despite the growing maturity of AI in the smart home sector, it is still at the stage of promotion and popularization among this older age group.

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Table 2. Descriptive statistical analysis of demographic variables.

https://doi.org/10.1371/journal.pone.0300574.t002

Reliability and validity analysis

This data involves six dimensions, which are perceived usefulness, perceived ease of use, willingness to use, intergenerational technology support, perceived value, and perceived risk dimensions. The questionnaire data were entered into SPSS 26.0 software, and Cronbach’s α coefficients were used to conduct the reliability test, and the results showed that α was 0.907. The value of the reliability coefficients of the research data is higher than 0.8, which comprehensively indicates that the data reliability is high. The validity of the questionnaire was analyzed using the kaiser-meyer-olkin (KMO) test and bartlett’s test of sphericity, and the results showed that the KMO was 0.909, and the KMO value was higher than 0.7, which means that the research data can be used to extract information effectively. Meanwhile, bartlett’s test of sphericity (p <0.05) was passed, indicating that the questionnaire has good reliability and validity and that the research data is suitable for factor analysis. As shown in Table 3.

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Table 3. Scale reliability and validity coefficient tests.

https://doi.org/10.1371/journal.pone.0300574.t003

13 studied items and 6 factors in total underwent confirmatory factor analysis (CFA), which showed good significance as shown in Table 4. The absolute values of standardized loading coefficients (Std. Estimate) were all greater than 0.6 and showed significance, implying a good measurement relationship. The research data reliability coefficient values are all greater than 0.6, which clearly shows that statistical reliability is excellent and suitable for additional study. Meanwhile, the average variance extracted (AVE) value is greater than 0.36 and the component reliability (CR) value is also greater than 0.6, indicating that there is a good control relationship and degree of aggregation between the factors and items. Table 5 implies that it has good discriminant validity.

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Table 4. Reliability and validity tests of extended TAM questionnaire.

https://doi.org/10.1371/journal.pone.0300574.t004

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Table 5. AVE square root and correlation matrix.

https://doi.org/10.1371/journal.pone.0300574.t005

Model fitness test

The questionnaire data were imported into the AMOS 26.0 software for constructing and testing the measurement model, and path analysis was performed using great likelihood estimation (ML). The indexes and results of the model fit test are shown in Table 6. In the AMOS analysis results, when the chi-square degrees of freedom ratio (CMIN/DF) is 1 ∼ 3, it indicates that the model fit is ideal. The absolute fit index: root mean square error of approximation (RMSEA) value <0.05 indicates acceptable model fit. 3 value-added fit indexes: comparative fit index (CFI), incremental fit index (IFI), and corrective fit index (CFI). Incremental fit index (IFI), and tucker-lewis fit in-Dex (TLI), when their values are in the range of 0 ∼ 1, the closer they are to 1 indicates a higher degree of model fit, and when they are >0.9, they indicate a good model fit. The two parsimony fitness fit indices: parsimony goodness of fit index (PGFI), and parsimony normed fit index (PNFI) are both >0.5, indicating a good model fit. The fitness indicators are shown in Table 6, and in terms of the model fitness test, the CMIN/DF ratio was 1.223, the RMSEA value was 0.033, the CFI value was 0.991, the IFI value was 0.991, the TLI value was 0.987, the PGFI value was 0.546, and the PNFI value was 0.636, and the indicator values were all within the acceptable range. Fig 2 displays the standardized path coefficients.

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Table 6. Model fitting indexes.

https://doi.org/10.1371/journal.pone.0300574.t006

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Fig 2. Normalised path coefficient diagram.

https://doi.org/10.1371/journal.pone.0300574.g002

Hypothesis testing analysis

The model normalized path coefficients reflect the importance of the factors affecting the smart home demand of elderly users, and the path coefficients are normalized and can be regarded as the weighting coefficients of the relevant variables. The SEM path coefficients are shown in Table 7, and the p-value of all the paths is <0.5, which indicates that all the nine hypothetical paths are significant.

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Table 7. Extended TAM related variables statistic.

https://doi.org/10.1371/journal.pone.0300574.t007

Table 7 displays the impact analysis results on the factors, H1 (PU → UI, β = 0.723, p <0.001), H2 (PEOU → UI, β = 0.151, p = 0.118), H3 (PEOU → PU, β = 1.000), H4(b) (ITS → PEOU, β = 0.750, p <0.001), H5(a) (PV → PU, β = 3.737, p = 0.089), H5(b) (PV → PEOU, β = 0.904, p = 0.161). The six regression coefficients are positive, indicating that there is a positive correlation between all six hypotheses and that an improvement in the status of each variable improves the intention to use the smart home, thus hypothesis 1, hypothesis 2, hypothesis 3, hypothesis H4(b), hypothesis 5(a), hypothesis 5(b) is confirmed. The three regression coefficients of H4(a) (ITS → PU, β = -1.056, p = 0.033), H6(a) (PR → PU, β = -3.563, p = 0.133), and H6(b) (PR → PEOU, β = -0.755, p = 0.277) are negative, which indicates that these three variables have negative effects on the intention to use smart home. There is a negative effect, thus assuming that hypothesis 6(a) and hypothesis 6(b) are confirmed. The results show that all hypotheses H1, H2, and H3 introduced in the base TAM regarding the variables PEOU, PU, and UI are supported, and for the extended variables ITS, PV, and PR, all hypotheses except H4(a) are supported.

Conclusions

Against the backdrop of a deepening trend of population aging, the trend of smart homes for aging is becoming more and more obvious. Although smart home appliances have a chance to significantly enhance the lives of the elderly, their popularity is still in its infancy in China, especially among the elderly. Smart home products developed, produced, and designed for the elderly are extremely scarce, and the elderly still face many difficulties in using smart homes. Therefore, we need a deeper comprehension of the various aspects that influence elderly users’ acceptance and use of smart homes.

To explore and validate the aspects influencing older consumers’ use of smart homes and to determine their level of importance, this study employs the technology acceptance model with additional variables. The study used convenience sampling to distribute 236 questionnaires among older adults in the Yangtze River Delta region who are over 60 years old, who have experience in using smart homes, and who can complete human-computer interactions independently or with the help of others. In addition, user interviews were conducted to understand the barriers and specific needs of this population when using smart home services in terms of four aspects: user characteristics, family situation, experience of use, and willingness to use. 200 relevant samples were examined to summarize the study’s findings using structural equation modeling (SEM) and factor analysis.

After the comprehensive data processing and model validation analysis, we derived the six dimensions of the model of influencing factors on the use of smart home products by the elderly and the weight sizes of their corresponding 13 influencing factors. The study’s findings indicate that older users’ propensity to adopt smart homes is greatly affected by their perceived usefulness and perceived ease of use. Meanwhile, there is a significant improvement in the perceived ease of use of smart homes on the perceived usefulness of smart homes by older users. In addition, intergenerational technical support has a significant negative effect on the perceived usefulness of smart homes and a significant positive effect on the perceived ease of use of smart homes. Furthermore, there is a significant positive effect of perceived value on perceived usefulness and perceived ease of use of smart homes. Finally, there is a significant negative impact of perceived risk on perceived usefulness and perceived ease of use of smart homes by older users.

The results of this study provide in-depth insights at the theoretical level for a comprehensive understanding of smart home acceptance willingness among the elderly population. By introducing and expanding the Technology Acceptance Model (TAM), this study not only enriches the existing theoretical framework but also provides new perspectives for research in other related fields by constructing a more comprehensive and detailed model. This contributes to a more in-depth understanding of the acceptance of smart homes by older users and their influencing factors and provides a valuable reference for future research. In addition, this study adopts an empirical analysis method to verify the validity of the extended TAM model by collecting and analyzing data. This not only enhances the credibility and applicability of the theory but also provides empirical support for subsequent theoretical studies.

At the practical level, this study is a significant guide for the progress and promotion of the smart home industry. Our findings are not only significant for the elderly population in the Yangtze River Delta region but also provide valuable lessons for other regions and countries facing similar demographic issues. Moreover, the primary kind of aged care provided by the “9073” design is home care, which means that the elderly spend a relatively long time at home. Given that the physical functions of the elderly are declining, including mobility, cognition, vision, and ability to cope with their surroundings, they are less capable of controlling and managing various home devices. Therefore, we strongly recommend that all relevant stakeholders pay attention to the smart home demands of seniors and take proactive measures. The government should step up policy support to guide more social forces to participate in the legislative support and provide shopping allowances to encourage the elderly to try out smart home devices. Service providers and designers should fully understand the characteristics and actual needs of target users and emphasize the functionality, operability, and user experience of the products to develop easy-to-use smart home solutions. Children or caregivers should always pay attention to the psychological state of the elderly, actively guide the use of smart homes, and teach them patiently in the process of using them, to assist them in realizing their self-worth.

Limitations

In terms of the limitations of this research, it is crucial to remember that while extensive literature references and summary screening of social phenomena were conducted in determining the evaluation indicators, the assessment of quantification is still highly subjective. Therefore, future research will aim to improve the subjectivity of the assessment and further explore it using more objective methods and experimental data.

In addition, this research has a rather small sample size, only older adults living in the Yangtze River Delta region who are over 60 years old and have experience in using smart homes and can complete human-computer interactions independently or with the help of others are taken as the main target. In the choice of sampling method, convenience sampling was used, which means that interviewers may tend to select respondents who are closer to them or easily accessible, thus having an impact on the precision of sampling. Meanwhile, this is only a typical case and only explores the factors influencing this target group’s willingness to use smart homes for senior citizens within a specific range, limiting the statistical power and generalization of the results. Phenomena such as different economic and cultural development, social inclusion, and different concepts of old age result in diverse strengths strengths, and breadths in the implementation of smart products for the elderly, resulting in different differences among the elderly thus affecting the reasonableness of the experimental data. In future research, the study can be improved through offline and online open-ended questionnaires synchronized collection, by comparing the social environment in the region where the elderly are located, such as the sense of belonging to a social class, social trust, social activities, the government system support and other aspects of the degree of influence of factors to consider.

In addition, the complete home space using the crowd should include infants, youth, middle-aged, elderly, and other people of all ages. Varies age groups have various needs for the use of intelligent home products. When researching intelligent interactive home products, we should consider their generality as a universal product, which needs to meet the common demands of family members, regardless of age, gender, and other distinctions, but also provide precise and personalized services for users of different age groups.

In future research and development, we need to analyze and study these factors more deeply to serve senior users’ demands and promote the popularization and development of smart homes.

Prospects

At present, the aging smart home market remains in its infancy, with great development potential and broad market prospects, however, it faces many challenges and problems in practical application.

First of all, since the number of senior people who utilize the Internet is comparatively low, the environment in which they live often has imperfect infrastructure problems, resulting in certain difficulties for the elderly to access the Internet at home. Moreover, the cost of Internet access is generally high, and not all elderly people can afford it, so there is a certain threshold for the use of smart homes for seniors. In addition, the supporting services of the smart home are not yet perfect, hardware and software have not yet been fully integrated into their daily life, and the living environment has not been effectively connected.

Secondly, the complexity of the aging smart home standard is one of the major challenges facing the industry. At present, various smart home brand manufacturers have established their ecosystems and follow different standards, resulting in the inability to realize interoperability between products, and the user experience is affected, which is detrimental to the industry’s overall development.

In addition, addressing the characteristics and requirements of older persons, age-appropriate smart homes have emerged to build a friendly home environment. However, the aging-friendly smart home is not cut off from the smart home but is friendly to all groups of people, which can meet the needs of young people and also take care of older persons’ requirements. However, the vast majority of smart home products on the market are not yet able to do this.

In addition, product market awareness is low and household penetration is not high. At present, the market awareness and acceptance of aging-appropriate smart products in China is low, mainly due to the relatively limited literacy of the elderly, insufficient cognition of smart products and experience in using them, as well as the fact that some of the so-called technological aging-appropriate products do not take into account the learning costs of the elderly, which makes the impression of the elderly consumers poorer. There is also an important reason, most of the families living in foreign countries, mainly in single-family houses, for home security, energy control, and other scenes have a stronger demand, while the country has not yet tapped into the immediate need for smart home use of the scene, many families tend to adopt smart homes because of the need to improve their quality of life, which also leads to the user’s awareness of the product is relatively limited.

Therefore, we strongly urge all relevant stakeholders to pay attention to the needs of the elderly population in terms of smart home technology and take proactive measures. Therefore, we strongly urge all relevant stakeholders to take notice of the needs of the senior population in terms of smart home technology and take proactive measures.

From the government’s perspective, policy support should be increased. On the one hand, smart home is an emerging industry, and more social forces should be guided to join in the aging smart home industry through various means, such as increasing the initial investment and financial subsidies; on the other hand, a certain amount of shopping allowances or preferential purchase rights should be provided to guide the elderly population to test buy and try out the smart home equipment.

From the industry’s point of view, it should actively participate in the stipulation of industry standards and try its best to guarantee the interconnection of products of different brands and manufacturers to promote the growth of the whole industry. The elderly smart home in smart home products meet the premise of industry regulations but also need to take into consideration the unique qualities of the elderly. At present, the standard of intelligent products for the elderly is still unclear, which requires the development of elderly smart home evaluation work, and improving the standard of elderly smart home products in continuous debugging. The design of smart home service terminals should be based on our national standards or an industry standard, there is no standardization of product quality is difficult to ensure the quality of its products, and its reliability is bound to be unable to guaranteed.

From the enterprise point of view should dig deep into the actual needs of the elderly, focusing on the flexibility and customizability of the product, the development of easy-to-use smart home solutions for elderly users, to enhance the experience of elderly users, and pay practical attention to the needs of the elderly users of the product to survive in the aging market.

The users involved in aging smart home products include both the elderly and their children, caregivers, etc. The elderly’s learning ability and memory capacity have declined, so the relevant staff should provide clear usage guidelines and specialized training courses to help the elderly and their loved ones and caregivers learn, stimulate the interest of potential users, and increase the utilization rate.

We must also note that the ethical issues brought about by smart home technology, such as privacy protection, data security, and informed consent, are key factors that need to be carefully considered. While promoting the popularization of smart homes, we must ensure that the rights of users are fully protected. We look forward to more research focusing on this area in the future and further exploring the specific issues and solutions involved.