Next Article in Journal
Emojis Are Comprehended Better than Facial Expressions, by Male Participants
Previous Article in Journal
Battling Unawareness of One’s Test Performance: Do Practice, Self-Efficacy, and Emotional Intelligence Matter?
 
 
Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
Journals
Behavioral Sciences
Volume 13
Issue 3
10.3390/bs13030277
behavsci-logo
Submit to this Journal Review for this Journal Propose a Special Issue
► Article Menu

Article Menu

share Share announcement Help format_quote Cite question_answer Discuss in SciProfiles thumb_up
...
Endorse textsms
...
Comment
first_page
settings
Order Article Reprints
Font Type:
Arial Georgia Verdana
Font Size:
Aa Aa Aa
Line Spacing:
Column Width:
Background:
Article

Research on the Factors Affecting the Adoption of Smart Aged-Care Products by the Aged in China: Extension Based on UTAUT Model

by
Xiang Wang
1,2,*,
Chang-Franw Lee
1,*,
Jiabei Jiang
3,
Genlei Zhang
2 and
Zhong Wei
4
1
Graduate School of Design, National Yunlin University of Science and Technology, Yunlin 64002, Taiwan
2
Pujiang Institute, Nanjing Tech University, Nanjing 211200, China
3
The Future Laboratory, Tsinghua University, Beijing 100000, China
4
Faculty of Social Sciences and Liberal Arts, UCSI University, Kuala Lumpur 56000, Malaysia
*
Authors to whom correspondence should be addressed.
Behav. Sci. 2023, 13(3), 277; https://doi.org/10.3390/bs13030277
Submission received: 8 February 2023 / Revised: 16 March 2023 / Accepted: 17 March 2023 / Published: 21 March 2023
Browse Figures
Versions Notes

Abstract

:
With the rapid development of network technology and smart technology, smart aged-care products are becoming increasingly valued for their ability to help the aged actively cope with the challenges of aging. However, seniors face challenges in using smart aged-care products for many reasons, which reduces their willingness to adopt them. As a result, the sustainable development of smart aged-care products is constrained. This study combined the unified theory of technology acceptance and use, perceived risk theory and perceived cost theory, and reconstructed a research model that investigated the adoption of smart aged-care products by the elderly in China. Questionnaires were given to older Chinese adults in this study, and 386 valuable responses were received. The findings of the structural equation model (SEM) analysis are as follows: (1) performance expectancy, effort expectancy, and social influence were positively related to the behavioral intention of seniors to use smart aged-care products; (2) perceived cost and perceived risk were negatively related to the behavioral intention of seniors to use smart aged-care products; (3) perceived risk indirectly affected use behavior through behavioral intentions; (4) facilitating conditions did not have a significant impact on the use behavior of seniors in adopting smart aged-care products. Based on the empirical results, this study sought to improve the use behavior of the aged in relation to the adoption of smart aged-care products, and provided suggestions to improve the overall service quality and sustainability of those products.

1. Introduction

1.1. Research Background

China is currently facing the enormous challenge of rapid population aging on a large scale, with people becoming elderly without the necessary financial resources. A successful response to aging depends to a large extent on the ability to improve and maintain the health of the aged; that is, successful aging is healthy aging [1]. Smart aged-care service technology has the potential to help the aged in monitoring and maintaining their health, and in managing health conditions and diseases [2]. Additionally, the use of smart aged-care products is one of the solutions to the problem aged-care [3].
Based on data from the National Bureau of Statistics of China, the population of people over 65 years old is constantly rising, and comprises 12.64% of the total population of the Chinese mainland as of 2020, which growth is speeding up. According to the UN’s statistics, people over 65 years old in China comprise 7% of the total population, indicating the nation is becoming an aging society [4]. The proportion of those over 65 years old in China is well above 7%, indicating its nearness to becoming an advanced aging society [5]. As the aging of China’s population further intensifies, it will gradually become a deeply aged society, and the demand placed on the smart aged-care industry is continuing to rise. In 2019, the size of China’s smart aged-care industry was nearly CNY 3.2 trillion, with a compound growth rate of more than 18% over the past three years; the industry was expected to reach CNY 4 trillion by 2020 (as shown in Figure 1) [6]. Continuous breakthroughs will also drive the continuous development of the smart aged-care product industry.
It was found that the aged are worried about smart technology and discouraged from adopting it [7]. According to the 47th Statistical Report on the Development Status of the Internet in China, released by the China Internet Network Information Center (CNNIC), as of December 2020, the proportion of the netizen group in China aged 60 and above was only 11.2% [8]. These data partially reflect the low familiarity of the elderly with modern technology products, and suggest the majority may encounter barriers to using smart products. However, the effectiveness of smart elderly products has been demonstrated by many scholars. Pilotto et al. [9] studied 223 Alzheimer’s patients who used information technology, and showed that this technology was effective in improving their quality of life, quality of service, and safety. Chan et al. [10] pointed out that intelligent technologies can prevent behaviors that endanger health through health warnings. The results of the study by Faucounau et al. [11] show that intelligent robotics can play a useful role in social interaction, psychological guidance, educational learning, cognitive enhancement, and intelligent monitoring for the elderly.

1.2. Research Purpose

The popularity and adoption of smart aged-care products in China are not only related to the development of the technology and services but are closely related to the acceptance of these products by the elderly. In order to better understand and predict the adoption behavior of the aged towards smart aged-care products, it is important to explore the influencing factors. Currently, foreign research on smart elderly products is relatively mature, mainly focusing on research on product system development and smart homes, with relatively little research on the demand for smart elderly products [12]. Chinese research is still in its infancy, mostly focusing on theoretical constructs, and it does not extend to actual demand research [13].
This study combined the unified theory of acceptance and use of technology, the theory of perceived cos, and the theory of perceived risk and constructed a research model to analyze the factors influencing the adoption of smart aged-care products by the aged. It will fill a gap in the research on this issue in China and provide recommendations for the development of the smart aged-care products industry as well as theoretical guidance for government departments and service providers, while also providing suggestions to ensure the adoption of these products by the aged.

2. Literature Research

2.1. Smart Aged-Care Products

Lacking a rigorous definition in the academic world, and using the previous literature [14,15,16], we define smart aged-care products as products that incorporate advanced modern technology, as well as new smart hardware products and smart aged service information platforms, which are able to help the aged actively cope with aging. These technologies can fit into two main categories, namely, smart service platform systems for the aged, and smart service terminals for the aged. Among these, smart service platform systems for the aged refer to products that rely on information technology to perceive, transmit, publish, and integrate the service needs of the aged, and can facilitate the communication between medical care, services, families, and individuals to meet the diversified and multifaceted needs of the aged. Smart aged-care terminals are smart devices that incorporate advanced technology, such as Robot Doctors, smart nursing robots, companion robots, and other types of service robots.

2.2. Research Status of Smart Aged-Care Products

Recent studies on smart aged-care products mainly focus on two aspects: technical innovation and product development, such as Ref. [17], which concerns the application of smart material sensors and soft electronics in wearable healthcare devices, and Ref. [18], which concerns physiotherapy programs aided by virtual reality (VR) solutions; and service platforms and service modes, such as Ref. [19] on the improvement of countermeasures incorporated into digital health service platforms based on empirical research, and Ref. [20] on the construction of smart pension cloud platforms based on big data technology. To date, negligible advancements have been in China in assessing the willingness of the aged to use smart technology products and the corresponding influencing factors, and these achievements have mainly been made via qualitative research. Few scholars have performed quantitative analyses on this issue, and even fewer results have been derived specifically concerning the willingness of the aged to use smart technology products and its corresponding influencing factors. Some scholars have studied the factors influencing the preferences and behaviors of the aged via the unified theory of acceptance and use of technology and the diffusion of innovation theory. However, these fail to consider the influences of costs and risk factors on the use behaviors of the aged in China, based on their actual conditions. As China is a developing country, a great number of its aged citizens are of relatively low education and income level. In light of this and the actual conditions of the aged in China, this study specifically addresses the influences of perceived risk and perceived cost factors on the use behavior of the aged, and thus extends the UTAUT model.

2.3. UTAUT Model

Venkatesh et al. [21], Davis [22], and others have proposed an integrated model of technology acceptance and use (UTAUT) that can more accurately depict the actual situation by thoroughly considering the theory of reasoned behavior, the technology acceptance model, the computer use model, the theory of planned behavior, innovation diffusion theory, social cognition theory, the TAM and TPB models, and the motivation model. Our in-depth study of the UTAUT model (see Figure 2) effectively integrates the strengths and characteristics of these eight models to yield an integrated model of technology acceptance and use (UTAUT) that can more accurately predict the actual situation. The UTAUT model integrates the main considerations of the eight models into four core variables—performance expectations, effort expectations, social influence, and contributing factors—and four moderating variables—gender, age, experience, and voluntariness-related constructs and explanations.
Performance expectation (PE) refers to the increase in performance that the user anticipates the technology or product will bring. This variable is used to measure the extent to which the technology and product will be helpful to the user. Effort expectation (EE) refers to the ease with which the user expects they will be able to accept the technology or product. This variable is used to measure the degree of effort required by the individual in accepting the technology or product. Social Influence (SI) is defined as the impact of the adoption of this technology or product on other people who are perceived by the user as having high social status or as being important. This variable is used to measure the extent to which the user is influenced by social groups. Facilitating conditions (FC) refers to the degree to which users believe that the existing technology or product will be supported by social groups and further technological developments. This variable mainly measures the perceived convenience of the available support for using the technology or product.
The UTAUT model has a wide range of applications in the field of smart aging [12,23,24,25,26,27]. Since smart aged-care products are a relatively new type of product, an integrated technology acceptance model can help us explore the key factors affecting the adoption of smart aged-care products by the aged. This paper is focused on the willingness to adopt, and therefore excludes the two control variables of experience of use and voluntariness from the model. Related studies have shown that there is no significant difference as regards the gender and age of seniors in adopting smart aged-care products. Therefore, these two control variables were not included in the model of this study.

2.4. Perceived Cost

Perceived cost is the sum of the expenses that customers feel they have had to pay during the actual consumption process, and compiles costs related to time, money, physical effort, energy, and psychological effort [28], not just the actual price paid by the customers. Perceived cost theory has been widely applied in the field of smart aged-care [29,30,31]. The high initial costs associated with any new technology, product, or service are often seen as a major barrier to its acceptance [32,33,34]. As an emerging type of technology and product, the adoption of smart aged-care products by the aged will necessitate more time spent in learning, more money spent in purchasing, more effort expended in use, etc. The excessive costs will reduce the behavioral intention of the aged to use smart aged-care products. Therefore, this study will use perceived cost theory to explore the effects of perceived costs on the adoption of smart aged-care products by the aged.

2.5. Perceived Risk

Perceived risk theory was first conceptualized by Bauer, a professor at Harvard University. Bauer [35] experimentally confirmed that people’s behavioral outcomes cannot be accurately predicted before they begin to act, and the outcomes resulting from people’s behavior can be either good or bad. However, people anticipate the riskiness of behavioral outcomes before the act begins, the product of which is referred to as perceived risk. Perceived risk is defined as the user’s ultimate expectation of an unpleasant outcome, or of an end result that is detrimental to the individual. Taylor [36] argued that users’ decisions regarding adoption are related to their perception of risk. Perceived risk theory has a wide range of applications in the field of smart aged-care products [37,38,39,40]. Perceived risk is related to the existence of uncertainty, and the aged often face many uncertainties when adopting smart aged-care products. This study will use perceived risk to explore its impact on the adoption of smart aged-care products by the aged.

3. Research Hypothesis and Methodology

3.1. Research Hypothesis

Based on a study of the literature, we propose several research hypotheses regarding the factors that influence the adoption of smart aged-care products by the aged.

3.1.1. Relationship between Performance Expectations and Behavioral Intentions

Performance expectations are a rich variable based on perceived usefulness, and their significant effect on user adoption has been confirmed by several studies [41,42,43,44,45,46]. For example, Prasetyo et al. [41] confirmed that performance expectations had a positive effect on behavioral intentions to receive medical-educational e-learning and found that performance expectations had the greatest effect on behavioral intentions; Oliveira et al. [43] confirmed in their empirical study that performance expectations had a positive relationship with behavioral intentions. Performance expectancy implies that seniors expect to benefit from the use of smart aged-care products. Therefore, Hypothesis 1 was proposed.
Hypothesis 1.
The performance expectation of the aged is positively related to their behavioral intention to adopt smart aged-care products.

3.1.2. Relationship between Effort Expectation and Behavioral Intention

Effort expectancy has a similar impact on perceived ease of use in the TAM model and refers to whether the user perceives the technology or product as easy to incorporate into use. Several empirical studies have demonstrated that ease of use metrics have a significant impact on users’ behavioral intention [23,27,47,48]. For example, Mao and Li [27] demonstrated through empirical methods that effort expectancy had a positive influence on behavioral intention. Cimperman, M et al. [23] confirmed that effort expectancy directly influenced behavioral intention to use home telehealth services. Therefore, Hypothesis 2 is proposed.
Hypothesis 2.
The effort expectation of the aged is positively related to their behavioral intention to adopt smart aged-care products.

3.1.3. Relationship between Social Influence and Behavioral Intention

Venkatesh et al. [21] stated that social influence positively affects individuals’ intention to adopt under the UTAUT. Studies by Hsu and Peng [49], Mabkhot [50], Nysveen et al. [51], and Hoque and Sorwar [12] have shown users influenced by their surroundings and social environment when using services, whose intention to use was enhanced when those in their immediate surroundings were already using, or when the social climate encouraged it. This study thus explores whether the intention to adopt among seniors is positively influenced by the surrounding environment. Therefore, Hypothesis 3 is proposed.
Hypothesis 3.
Social influence is positively related to behavioral intention.

3.1.4. Relationship between Facilitating Conditions and Use Behavior

Yang et al. [52] empirically demonstrated that facilitating conditions positively influenced the smartphone use behavior of older adults. Wang et al. [53] empirically demonstrated that facilitating conditions positively influenced consumers’ behavioral intention to use wearable healthcare devices. Cimperman, M et al. [23] empirically demonstrated that facilitating conditions directly influenced older users’ behavioral intention to use home telehealth services. In this study, facilitating conditions are described as the ability of the senior to use the support resources, and the conditions associated with using the smart aged-care product. When these necessary resources are available, seniors display use behavior related to smart aged-care products. Therefore, Hypothesis 4 is proposed.
Hypothesis 4.
Facilitating conditions are positively related to use behavior.

3.1.5. Relationship between Behavioral Intention and Use Behavior

Behavioral intention refers to a user’s tendency to adopt a certain behavior, and as theorized by UTAUT, behavioral intention has a positive impact on user behavior. Oliveira et al. [43] empirically confirmed that behavioral intention to use m-banking had a positive impact on user adoption. Prasetyo et al. [41] confirmed in their study that behavioral intention has a positive impact on the use of an e-learning platform. In the study by Zhang M. et al. [54], it was confirmed that behavioral intention positively influenced use behavior. This study explored the behavioral intention of the aged to adopt smart aged-care products, finding a positive impact on use behavior. Therefore, Hypothesis 5 is proposed.
Hypothesis 5.
Behavioral intention is positively related to use behavior.

3.1.6. Relationship between Perceived Cost and Behavioral Intention

Zainab et al. [55] found that perceived cost had a significant impact on adoption intention related to e-training. Gupta et al. [56] found that perceived cost had a negative impact on behavioral intention related to knowledge sharing. Zhang and Gao [31] concluded that perceived cost had a negative impact on older adults’ use of online health information services, in terms of the negative use behavioral intentions of avoidance and withdrawal. This study explored the effect of perceived cost on behavioral intention among seniors. Therefore, Hypothesis 6 is proposed.
Hypothesis 6.
Perceived cost is negatively related to behavioral intention.

3.1.7. Relationship between Perceived Risk and Behavioral Intention and Use Behavior

In a study by Wang [39], it was confirmed that the risks perceived by the aged regarding the use of smart aged-care services affect the intention to use such services. A study by Khan et al. [57] found that perceived risk negatively influences users’ adoption of new technology. A study by Ku and Hsieh [40] found that perceived risk was a key factor influencing the acceptance of cloud-based healthcare services among the elderly in Taiwan. In this study, we assume that seniors face many risks and challenges related to using smart aged-care products. For that reason, we will explore whether perceived risk affects adoption behavioral intention and use behavior. Therefore, we propose Hypothesis 7—that perceived risk is negatively related to behavioral intention—and Hypothesis 8—that perceived risk is negatively related to use behavior.

3.2. Research Structure

We constructed the research model of this paper by integrating the integrated technology acceptance model, perceived cost theory and risk perception theory, based on the previous research undertaken by its authors, as shown in Figure 3.

3.3. Definition and Measurement of Variables

We first constructed the research model and hypotheses (as indicated in Figure 3). Eight factors ultimately affecting behavioral intentions have been determined, including performance expectations, effort expectations, social influence, facilitating conditions, perceived costs, perceived risks, behavioral intentions, and use behaviors. To ensure the reliability and validity of the variables, on the basis of the relevant national and international literature, this study has established 33 questions on a rating scale; that is, one question corresponding to each dimension in the questionnaire (as shown in Table 1).

4. Data and Methods

4.1. Questionnaire Survey

This study employed a questionnaire survey for the purpose of data collection, with 37 questions in total split across 3 parts. The first part concerned the definition and introduction of smart aged-care products. Products such as “Xiaomi Smart bracelet”, “Yuyue Smart blood glucose detector”, and “Folca Smart medicine box”, which are most commonly used in China, were listed in the questionnaire for illustrative purposes. The second part concerned the basic information of the aged, including gender, age, education level, monthly income, etc. (four questions in total). The third part sought to measure the willingness of the aged to use smart aged-care products with 33 questions in total; that is, 1 item corresponding to each dimension.
This study employed a questionnaire survey for data collection, and we performed pretesting to determine the questionnaire’s reliability prior to the formal commencement. A 7-point Likert scale was used in the pre-test questionnaire, which was conducted from 14 July to 23 July 2022 with 50 copies distributed to aged users of smart devices in nursing homes in Jiangning Disitrct and Gulou District in Nanjing. We analyzed the reliability values and the items of the pre-test questionnaire, and we eliminated undesirable items to ensure more accurate research results and enhance sample reliability and distinction capacity.
Formal questionnaires were distributed to smart aged-care products users over 60 years old in places with relatively high numbers of aged people, such as smart aged-care communities, and in the aged activity centers and nursing homes in Jiangsu Province and the surrounding areas, from 14 August to 10 September 2022. A total of 450 copies were distributed and 386 valid questionnaires were received, with a validity rate of 85.78%.
This study used the Cronbach’s α of statistical software Spss 22.0(IBM Corp., Armonk, NY, USA) to review the scale’s reliability. As shown in Table 2, the values of Cronbach’s α are 0.916, 0.909, 0.907, 0.906, 0.898, 0.895, 0.874, and 0.935 for performance expectation, effort expectation, social influence, facilitating conditions, behavioral intention, use behavior, perceived cost, and perceived risk, respectively, all of which are higher than 0.7, indicating good scale reliability (as shown in Table 2).

4.2. Sample and Data Collection

(1) As shown by Table 3, the proportion of females using smart aged-care products is 51.55%, which is essentially equal to the proportion of males—48.45%. This indirectly reflects that the use of smart aged-care products does not differ by gender. (2) The proportions of those who were 60–65 years old, 66–70 years old, 71–75 years old, 76–80 years old, and over 80 years old were 16.84%, 25.13%, 27.98%, 22.80%, and 7.25%, respectively; those over 80 years old were placed in the hyper-aged group, comprising 7.25% of the users—this was a smaller group than the other age groups, into which the users were relatively equally distributed. (3) In terms of educational background, the proportions of those with senior high school education and under, junior college education, undergraduate education, and Master’s education and above were 75.65%, 6.48%, 12.95%, and 4.92%, respectively; in relation to the specific conditions of China, those with senior high school education and under constituted 75.65% of the sample, meaning this variable could not be used to assess the effect of educational background on the use of smart aged-care products. (4) In terms of monthly income, the proportions of those earning below CNY 2000, CNY 2001–3500, CNY 3501–5000, and over CNY 5000 were 8.29%, 20.21%, 32.38%, and 39.12%, respectively. We can see that with higher monthly incomes, the proportion of people using smart aged-care products increases. This also verifies the impact of perceived cost on the behavioral willingness to adopt smart care products. The samples collected in this study were relatively evenly distributed across the range of demographic variables, which fulfilled our expectation.

4.3. Validity Analysis

4.3.1. KMO and Bartlett Tests

Validity generally refers to the accuracy of the test results and is normally measured by the difference between the test results and the test objective. The construct validity here was derived via SPSS factor analysis to verify whether the scale could meet the demands of the research objective. KMO and Bartlett’s test of sphericity were adopted to verify the relevant variables, and the results are shown in Table 4. As regards the KMO test, the values given should fall between 0 and 1; the closer the value is to 1, the stronger the correlation between the variables is. Normally, only statistics with values above 0.7 are taken as fit for factor analysis, while those below 0.5 are not. The KMO value of the results in the table is larger than 0.9, and the p value of the Bartlett test of sphericity is 0, indicating that the preliminary validity test yielded good results.

4.3.2. Exploratory Factor Analysis

Exploratory factor analysis was applied here to the 8 structures of the hypothesis model, which abstracts 8 factors with eigenvalues larger than 1. The factor loading of each item was larger than 0.6 and the cumulative percentage of variance explained was 77.993%, indicating the high validity of the abstracted common factors. As can be seen from Table 5, a total of 8 common factors were extracted, which showed consistent pre-dimensionality, further indicating good validity.

4.4. Measurement Model

4.4.1. Convergent Validity

For the structural equation model analysis, we used AMOS 17.0, a provably reliable modeling software for structural equations with wide application in a great number of studies. According to Anderson and Gerbing [64], data analysis can be divided into two stages. The first stage involves establishing the measurement model, and applying maximum likelihood estimation to the estimated parameters, including factor loading, reliability, convergent validity, and discriminative validity. The second stage involves the research conducted by Hair et al. [65], Nunnally and Bernstein [66], Fornell and Larcker [67], Chin [68], and Hooper et al. [69] on convergent validity. As shown in Table 6, the standardized factor loadings for the entire variable are larger than 0.7, indicating that each variable under observation will be significantly helpful in explaining its latent variable. It should be noted that the value of CR is larger than 0.8, and thus higher than the standard of 0.7. As such, the observed variables for each dimension can effectively explain the respective dimension. All the values of AVE derived here are above the standard 0.6, indicating the good convergent validity of the scale.

4.4.2. Discriminant Validity

The approach of Fornell and Larker [67] has been adopted for the study of the validity of the discriminant. The sample data used in the model show good discriminant validity if all the AVE square roots of each dimension are larger than the absolute values of the correlation coefficients between each variable. The results show that the values shown on the diagonal are larger than others. Consequently, all the structures here are interpreted to be valid (see Table 7), and each aspect of the study is of high discriminant validity.

4.5. Structural Model Analysis

4.5.1. Model Fit Criteria

The model fitting index is here used to test the existing models, inspect the fitting degree of the models to the collected variable data, and compare the degree of coincidence between the prediction results and the actual situation. Based on research by Jackson et al. [70], Kline [71], Whittaker [72], Hu and Bentler [73], and other scholars, we selected a number of indexes (MLχ2, DF, χ2/DF, RMSEA, SRMR, TLI, CFI, GFI, and IFI) to evaluate the structure model’s fit, and the results are shown in Table 8, which were derived after parameter measurement was applied to the model and the hypothesis. It can be seen from the data in the table that the actual measured data fit well. Therefore, no further modifications to the model were required, and path analysis could be conducted.

4.5.2. Path Analysis

As shown in Table 9, the performance expectation (R = 0.28, p < 0.001) has a positive correlation with the behavioral intention of the elderly to use smart aged-care products; that is, H1 is supported. Effort expectation (R = 0.27, p < 0.001) has a positive correlation with the behavioral intention of the elderly to use smart aged-care products; that is, H2 is supported. Social impact (SI) (R = 0.20, p < 0.001) has a positive correlation with the behavioral intention of the elderly to use smart aged-care products; that is, H3 is supported. Facilitation conditions (R = 0.018, p = 0.761) have no significant impact on the use behavior of smart aged-care products for the elderly; that is, H4 is not supported. Behavior intention (BI) (R = 0.86, p < 0.001) positively affects the use behavior; that is, H5 is supported. Perceived cost (R = −0.25, p < 0.001) has a negative correlation with the behavioral intention of the elderly to use smart aged-care products; that is, H6 is supported. Perceived risk (R = −0.25, p < 0.001) has a negative correlation with the behavioral intention of the elderly to use smart aged-care products; that is, H7 is supported. Perceived risk (R = 0.10, p = 0.222) has no significant impact on the use behavior of intelligent elderly care products for the elderly; that is, H8 is not supported.

4.6. Hypothesis Explanation

In Figure 4, the regression coefficients of the SEM model used in this study are shown. A higher coefficient means that the independent variable plays a more important role that the dependent variable. The assumptions of H1, H2, H3, H5, H6 and H7 are valid, while the assumptions of H4 and H8 are not.

4.7. Results and Discussion

This study constructed an impact model of the willingness of the aged to adopt smart aged-care products, based on the unified theory of acceptance and use of technology, perceived cost theory, and perceived risk theory. The results of the empirical analysis of the UTAUT extended model show that performance expectancy, effort expectancy, social influence, behavioral intention, perceived cost, and perceived risk influenced the acceptance of smart aged-care products by seniors. As regards impact effects, that of behavioral intention was the largest at 0.86, while that of performance expectation was 0.28, the effort expectation impact effect was 0.27, the perceived cost impact effect was 0.25 (negative correlation), the social impact effect was 0.20, and the perceived risk impact effect was 0.15 (negative correlation).
(1)
Performance expectations have a positive effect on behavioral intentions, which result is consistent with previous findings [32,33,34,35,36]. The performance expectation factor has the largest effect other than behavioral intention, and Sun et al. [74] showed that the higher the performance expectation, the stronger the willingness to use, which verifies the UTAUT model. This indicates that seniors will be willing to adopt smart aged-care products when they think they can improve the services available to them. Therefore, considering seniors’ demands, the service providers should introduce personalized services in addition to the basic functions, targeted to improve the use expectations of the aged [39].
(2)
Effort expectancy has a positive effect on behavioral intention, which finding is consistent with those of previous studies [23,27,47,48], indicating that the aged are more willing to adopt smart aged-care products when they are perceived as easy to use. It can be seen that the ease of use of smart aged-care products is a key concern for service providers, who should provide services that are as convenient and easy-to-use as possible. In view of the problem that the aged do not have sufficient skills to use smart aged-care products and that they are not easy to use, it is necessary to increase the diversification of smart aged-care products and services, and thus develop smart aged-care products that are more suitable for use.
(3)
Social influence has a positive impact on behavioral intention, which result is consistent with previous findings [12,21,50,51], suggesting that when the aged individual perceives a higher level of acceptance of smart aged-care products among those around them, their intention to adopt will rise. At present, the aged generally have a low level of understanding of smart aged-care products, and few have actually used and experienced them [75]. Chen et al. [76] argued that there are few channels for smart aged-care product use, leading to a relatively low degree of willingness to use and a lower frequency of use behavior amongst the aged. Society has not yet developed sufficient motivation to encourage aged groups to accept and use advanced technology. This means that service providers should pay more attention to their own approaches to publicity, as an increase in social recognition can enhance the trust of users and thus improve their willingness to use.
(4)
There is no significant effect of facilitating conditions on the use behavior of smart aged-care products among the elderly. This result is inconsistent with those of previous studies [23,52,53,77], and indicates that China has not yet developed the necessary resources to support the use of smart aged-care products amongst the elderly. (1) The current Chinese government and other concerned organizations seek to assist the aged mainly via economic benefits and donations, which do not contribute to their use of smart aged-care products; (2) instead of individually targeted services, the care-related actions of family members, communities and volunteers are more focused on short-period companionship and care, and thus fail to develop the necessary recourses that will support the systematic use of smart aged-care products; (3) instead of developing follow-up support services, such enterprises prefer to focus on sales in relation to short-term interests. As they age, people undergo physiological and psychological changes, which cause their learning ability and attention to gradually decline [78]. At the same time, the aged perceive internet technologies as high-tech products that require a whole new body of knowledge to navigate. Therefore, technophobia is common [79]. This requires the government, market, community, and family members to develop better resources, enhance technical support and usage knowledge, and develop more inclusive products. This will help in offering the timely assistance required when aged people use smart aged-care products. In relation to this, the government could encourage family members to bear more responsibilities in relation to the smart education of the aged. Both communities and public service organization should provide the aged with more targeted services related to education on the use of smart aged-care products; the service organizations concerned may even prepare some accessible literature instructing on the use of smart aged-care products, thus making information resources and assistance accessible when necessary. Furthermore, enterprises must establish complete service systems for smart aged-care products, and provide the aged users with training, maintenance, and product-upgrading services, thus enhancing the healthcare services that use smart aged-care products.
(5)
Behavioral intention has the greatest positive influence on use behavior, which is consistent with previous findings [32,44]. It has been indicated that positive behavioral intention can promote use behavior among the aged. Gamma et al. [80] concluded that behavioral intention and use behavior are highly correlated. Behavioral intention is constructed from the senior user’s own experience, cognitive abilities, and needs to be met by the care services. Therefore, in order to improve the use behavior of seniors, it is necessary to improve their behavioral intention, increase their relevant experience, improve their cognitive abilities in relation to the use of smart aged-care products, and meet their care needs in a targeted manner in order to help develop a positive attitude toward smart aged-care products.
(6)
Perceived cost has a negative impact on behavioral intention, which result is consistent with previous findings [31,34,55,56]. It has been indicated that factors such as excessive monetary, learning and time costs significantly reduce the behavioral intention of the aged to use smart aged-care products. Smart aged-care products, as information technology-based products, are more expensive than typical products, and policy and financial support from the government is required in order to reduce the associated costs. At the same time, service providers are required to improve the user-friendliness of their smart aged-care products in order to reduce the associated learning and time costs.
(7)
Perceived risk is negatively related to the behavioral intention of the aged to adopt smart aged-care products, which result is consistent with previous findings [36,39]. However, perceived risk is not significantly related to use behavior, which is inconsistent with previous findings [40,57]. It has been indicated that the risks perceived by the aged have a negative impact on their behavioral intention to adopt smart aged-care products, and also indirectly influence use behavior. This can be explained with reference to the dimension of use behavior and the measurement items selected in this study. Related to the rapid growth and application of internet technology, the use of smart aged-care products is emerging as a trend that is making later life much safer, more convenient, and more comfortable. However, the aged tend to be concerned about risks such as the security of use and privacy breaches when using smart aged-care products, and these risks influence their related behavioral intentions. The strengthening of security assurances related to smart aged-care products will help to establish a sense of trust and safety amongst the aged in relation to smart aged-care products, thus enhancing their use behavior. The government is duty-bound to set up security systems concerning at smart aged-care products, form security supervision mechanisms in collaboration with judicial and public safety departments for the joint consideration of network information security issues and ensure both the personal and financial security of the aged during their use of smart aged-care products.

5. Conclusions

5.1. Theoretical Contribution

Research on this topic in China is still in its infancy, and previous studies on smart aged-care products have mostly focused on the design, development, and application of specific models of smart products and their effects, with little focus on user behavior. This study enhances the research on user behavior in relation to smart aged-care products in China and enriches our understanding of smart aged-care and adoption intention. This study focuses on the elderly in China. The demand for healthy aged-care is strong among the elderly, but the adoption rate of smart aged-care products is low due technology acceptance, cost, and risk factors. Therefore, this study has adopted a new perspective focusing on the influence of these factors and has constructed a research model addressing the adoption of smart aged-care products by Chinese seniors based on the UTAUT model combined with perceived risk and perceived cost theories. The model provides a comprehensive means of understanding the adoption of smart aged-care products.
This study has derived some valuable conclusions through its empirical research analysis: (1) performance expectation, effort expectation, and social influence are positively related to the behavioral intention of the aged to adopt smart aged-care products; (2) behavioral intention is positively related to use behavior and shows the greatest effect of all factors studied; (3) we found no significant effect of facilitating conditions on seniors’ use of smart aged-care products, which yields a more accurate perception of the resources that must be developed to ensure smart aged-care product support; (4) perceived cost is negatively related to behavioral intention; in this study, perceived cost is defined as the time, money, and effort required in order for the aged to adopt smart aged-care products; these factors significantly affect willingness to adopt since China is still a developing country with low income amongst the aged; (5) many studies have shown that perceived risk is an inhibiting factor that hinders individuals’ intention to adopt. This study confirms that perceived risk has a negative effect on the behavioral intention of the aged to adopt smart aged-care products, and this indirectly (but not directly) affects use behavior. These findings have important theoretical implications and will help in proposing solid recommendations.

5.2. Practical Contribution

As the issue of the aging population in China is becoming increasingly serious, smart aged-care products can provide timely, efficient, and convenient care services for the aged, which will help to significantly improve and maintain the health of the aged. They thus represent one of the main paths to achieving healthy aging. If the user behavior of smart aged-care products amongst the aged is to be improved, service providers must strengthen the positive influencing factors and address the negative influencing factors. For example, they must (1) improve the performance expectation, effort expectation, social impact, and behavioral intention of smart aged-care products, and pay attention to the usefulness, ease of use, and public promotion of smart aged-care products; (2) reduce the learning time, purchasing, and use effort costs related to smart aged-care products; (3) address concerns about the risks related to using smart aged-care products, and enhance the trust of the aged. The results of this study have important practical implications that will help providers improve the overall quality of smart aged-care products and services.
The practical value of this study is in the fact that it offers an objective view of the actual needs of the aged in China in terms of smart aged-care products and the roles they play. It also provides a reference for other similar studies on the use behavior related to smart aged-care products, which will be conducive to improving the adoption level and health literacy of the aged.

6. Limitations and Future Prospects

Firstly, the data collected in this study are limited to the aged in Jiangsu Province and the surrounding areas. Due to the differences in economic level and other corresponding factors between provinces, the use of smart service products by the aged will differ in different places. We will perform further studies on aged groups in other provinces in the future.
Secondly, due to the various types of smart service products for the aged that are available, there may be some differences in the degree of use amongst the aged. In the future, research and data collection should be carried out in relation to different types of smart service products in order to build corresponding theoretical research models.
Lastly, aged users may face challenges of physical and cognitive decline, which may further affect their use of information technology. Therefore, future research should aim to explore the differences among aged groups in relation to physical condition and cognitive competence, and their impacts on the use of smart aged-care products.

Author Contributions

Conceptualization, X.W.; data curation, X.W. and J.J.; formal analysis, X.W.; supervision, C.-F.L.; writing—original draft, X.W.; writing—review and editing, X.W., C.-F.L., J.J., G.Z. and Z.W. All authors have read and agreed to the published version of the manuscript.

Funding

This research was supported by “Jiangsu Qinglan Project” (Funders: Jiangsu Provincial Department of Education and Pujiang Institute, Nanjing University of Technology); “First-class professional construction project” (Number: 2022XYL05; Funders: Pujiang Institute, Nanjing University of Technology).

Institutional Review Board Statement

The study was conducted in accordance with the Declaration of Helsinki and approved by the Ethics Committee of Pujiang College, Nanjing University of Technology (PJLL2022111702, 17 November 2022).

Informed Consent Statement

Informed consent was obtained from all the subjects involved in the study.

Data Availability Statement

Not applicable.

Conflicts of Interest

The authors declare no conflict of interest.

References

  1. Ge, Y.F.; Wang, L.J.; Feng, W.M.; Zhang, B.Z.; Liu, S.L.; Ke, Y.H. Challenges and strategic choices of healthy aging in China. J. Manag. World 2020, 36, 86–96. [Google Scholar]
  2. Devlin, A.S. Wayfinding in Healthcare Facilities: Contributions from Environmental Psychology. Behav. Sci. 2014, 4, 423–436. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  3. Yang, Q.H.; Feng, Y. Study on seniors’ willingness to use smart eldercare devices: Based on the UTAUT model. Sci. Res. Aging 2020, 8, 18–31. [Google Scholar]
  4. Liu, H.L. Trends of Population Aging in China and the World as a Whole. Sci. Res. Aging 2021, 9, 1–16. [Google Scholar]
  5. Liu, J.H. Environmental analysis and path thinking on the development of regional elderly care industry: A case study of Nantong. China J. Commer. 2018, 1, 134–138. [Google Scholar]
  6. Wu, X. The development trend, practical difficulties and optimization path of smart elderly care industry. East. China Econ. Manag. 2021, 35, 1–9. [Google Scholar]
  7. Jeng, M.-Y.; Pai, F.-Y.; Yeh, T.-M. Antecedents for Older Adults’ Intention to Use Smart Health Wearable Devices-Technology Anxiety as a Moderator. Behav. Sci. 2022, 12, 114. [Google Scholar] [CrossRef]
  8. China Internet Network Information Center. China Statistical Report on Internet Development. Available online: http://www.gov.cn/xinwen/2021-02/03/content_5584518.htm (accessed on 13 October 2022).
  9. Pilotto, A.; D’Onofrio, G.; Benelli, E.; Zanesco, A.; Cabello, A.; Margelí, M.C.; Wanche-Politis, S.; Seferis, K.; Sancarlo, D.; Kilias, D. Information and communication technology systems to improve quality of life and safety of Alzheimer’s disease patients: A multicenter international survey. J. Alzheimer’s Dis. 2011, 23, 131–141. [Google Scholar] [CrossRef]
  10. Chan, M.; Campo, E.; Estève, D.; Fourniols, J.Y. Smart homes—Current features and future perspectives. Maturitas 2009, 64, 90–97. [Google Scholar] [CrossRef]
  11. Faucounau, V.; Wu, Y.H.; Boulay, M.; Maestrutti, M.; Rigaud, A.S. Caregivers’ requirements for in-home robotic agent for supporting community-living elderly subjects with cognitive impairment. Technol. Health Care 2009, 17, 33–40. [Google Scholar] [CrossRef]
  12. Hoque, R.; Sorwar, G. Understanding factors influencing the adoption of mHealth by the elderly: An extension of the UTAUT model. Int. J. Med. Inf. 2017, 101, 75–84. [Google Scholar] [CrossRef]
  13. Wei, M. The positioning, shortcomings and development countermeasures of the smart elderly care industry of China. Theory J. 2021, 3, 143–149. [Google Scholar] [CrossRef]
  14. Meyer, G.G.; Främling, K.; Holmström, J. Intelligent products: A survey. Comput. Ind. 2009, 60, 137–148. [Google Scholar] [CrossRef] [Green Version]
  15. Zhang, Q.; Li, M.; Wu, Y. Smart home for elderly care: Development and challenges in China. BMC Geriatr. 2020, 20, 318. [Google Scholar] [CrossRef]
  16. Yu, M.; Rhuma, A.; Naqvi, S.M.; Wang, L.; Chambers, J. A posture recognition-based fall detection system for monitoring an elderly person in a smart home environment. IEEE Trans. Inf. Technol. Biomed. 2012, 16, 1274–1286. [Google Scholar]
  17. Ghosh, A.; Nag, S.; Gomes, A.; Gosavi, A.; Ghule, G.; Kundu, A.; Purohit, B.; Srivastava, R. Applications of Smart Material Sensors and Soft Electronics in Healthcare Wearables for Better User Compliance. Micromachines 2023, 14, 121. [Google Scholar] [CrossRef]
  18. Zak, M.; Sikorski, T.; Krupnik, S.; Wasik, M.; Grzanka, K.; Courteix, D.; Dutheil, F.; Brola, W. Physiotherapy Programmes Aided by VR Solutions Applied to the Seniors Affected by Functional Capacity Impairment: Randomised Controlled Trial. Int. J. Environ. Res. Public. Health 2022, 19, 6018. [Google Scholar] [CrossRef]
  19. Jia, M.X. Study on Improvement Countermeasures of HJ Company in Digital Health Service Platform Based on Smart Senior Care. Master’s Thesis, Zhejiang Sci-Tech University, Hangzhou, China, 2022. [Google Scholar] [CrossRef]
  20. Wang, S.J.; Li, H.; Wang, S.J. Study on Construction of Smart Pension Cloud Platform Based on Big Data Technology. Co-Oper. Econ. Sci. 2020, 636, 188–190. [Google Scholar] [CrossRef]
  21. Venkatesh, V.; Morris, M.G.; Davis, G.B.; Davis, F.D. User acceptance of information technology: Toward a unified view. MIS Q. 2003, 27, 425–478. [Google Scholar] [CrossRef] [Green Version]
  22. Davis, F.D. Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS Q. 1989, 13, 319–340. [Google Scholar] [CrossRef] [Green Version]
  23. Cimperman, M.; Brenčič, M.M.; Trkman, P. Analyzing older users’ home telehealth services acceptance behavior—Applying an Extended UTAUT model. Int. J. Med. Inform. 2016, 90, 22–31. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  24. Tian, X.-F.; Wu, R.-Z. Determinants of the Mobile Health Continuance Intention of Elders with Chronic Diseases: An Integrated Framework of ECM-ISC and UTAUT. Int. J. Environ. Res. Public. Health 2022, 19, 9980. [Google Scholar] [CrossRef] [PubMed]
  25. Boontarig, W.; Chutimaskul, W.; Chongsuphajaisiddhi, V.; Papasratorn, B. Factors influencing the Thai elderly intention to use smartphone for e-Health services. In Proceedings of the 2012 IEEE Symposium on Humanities, Science and Engineering Research, Kuala Lumpur, Malaysia, 24–27 June 2012; pp. 479–483. [Google Scholar] [CrossRef]
  26. Chen, Y. Research on the Influencing Factors of Health Education Behavior of the Elderly Receiving Mobile Intelligent Terminals. Master’s Thesis, Hangzhou Normal University, Hangzhou, China, 2018. [Google Scholar]
  27. Mao, Y.; Li, D.L. A study on the influencing factors of smart elderly care users’ use behavior based on UTAUT model: A case study on “E-Touch” of Wuhan. E-Gov. 2015, 11, 99–106. [Google Scholar] [CrossRef]
  28. Lu, H.P.; Lin, K.Y. Factors influencing online auction sellers’intention to pay: An empirical study integrating network externalities with perceived value. J. Electron. Commer. Res. 2012, 13, 238. [Google Scholar]
  29. Pal, D.; Funilkul, S.; Charoenkitkarn, N.; Kanthamanon, P. Internet-of-things and smart homes for elderly healthcare: An end user perspective. IEEE Access 2018, 6, 10483–10496. [Google Scholar] [CrossRef]
  30. Pal, D.; Papasratorn, B.; Chutimaskul, W.; Funilkul, S. Embracing the smart-home revolution in Asia by the elderly: An end-user negative perception modeling. IEEE Access 2019, 7, 38535–38549. [Google Scholar] [CrossRef]
  31. Zhang, N.; Gao, B. Research on the formation and influencing factors of the negative use of online health information services for the elderly under the CAC paradigm. Libr. Inf. Work. 2021, 65, 96–104. [Google Scholar] [CrossRef]
  32. Kim, S.H. Moderating effects of job relevance and experience on mobile wireless technology acceptance: Adoption of a smartphone by individuals. Inf. Manag. 2008, 45, 387–393. [Google Scholar] [CrossRef]
  33. Kim, D.; Ammeter, T. Predicting personal information system adoption using an integrated diffusion model. Inf. Manag. 2014, 51, 451–464. [Google Scholar] [CrossRef]
  34. Chen, J.; Wang, T.; Fang, Z.; Wang, H. Research on elderly users’ intentions to accept wearable devices based on the improved UTAUT model. Front. Public Health 2022, 10, 1035398. [Google Scholar] [CrossRef]
  35. Bauer, R.A. Consumer behavior as risk taking. In Dynamic Marketing for a Changing World; Hancock, R.S., Ed.; American Marketing Association: Chicago, IL, USA, 1960; pp. 389–398. [Google Scholar]
  36. Taylor, J.W. The role of risk in consumer behavior: A comprehensive and operational theory of risk taking in consumer behavior. J. Mark. 1974, 38, 54–60. [Google Scholar] [CrossRef]
  37. Klaver, N.S.; Van de Klundert, J.; Askari, M. Relationship between perceived risks of using mHealth applications and the intention to use them among older adults in the Netherlands: Cross-sectional study. JMIR Mhealth Uhealth 2021, 9, e26845. [Google Scholar] [CrossRef]
  38. Huang, F. Research on the Influence Mechanism of Wearable Medical Device Adoption Behavior of the Elderly in China. Master’s Thesis, Zhejiang University of Technology and Industry, Hangzhou, China, 2017. [Google Scholar]
  39. Wang, H.J. A Study on the User’s Continuance Intention and Optimization of Smart Elderly Care Services in China. Master’s Thesis, Harbin Normal University, Harbin, China, 2021. [Google Scholar]
  40. Ku, W.T.; Hsieh, P.J. Acceptance of cloud-based healthcare services by elderly Taiwanese people. In Human Aspects of IT for the Aged Population. In Proceedings of the Design for Aging: Second International Conference, ITAP 2016, Toronto, ON, Canada, 17–22 July 2016; Proceedings, Part I 2; Springer International Publishing: Berlin/Heidelberg, Germany, 2016; pp. 186–195. [Google Scholar]
  41. Prasetyo, Y.T.; Roque, R.A.C.; Chuenyindee, T.; Young, M.N.; Diaz, J.F.T.; Persada, S.F.; Miraja, B.A.; Perwira Redi, A.A.N. Determining Factors Affecting the Acceptance of Medical Education eLearning Platforms during the COVID-19 Pandemic in the Philippines: UTAUT2 Approach. Healthcare 2021, 9, 780. [Google Scholar] [CrossRef]
  42. Sivathanu, B. Adoption of Digital Payment Systems in the Era of Demonetization in India. J. Sci. Technol. Policy Manag. 2019, 10, 143–171. [Google Scholar] [CrossRef]
  43. Oliveira, T.; Faria, M.; Thomas, M.A.; Popovič, A. Extending the understanding of mobile banking adoption: When UTAUT meets TTF and ITM. Int. J. Inf. Manag. 2014, 34, 689–703. [Google Scholar] [CrossRef]
  44. Tan, C.; Zhang, J.; Zeng, Y. Research on influencing factors of consumer online shopping based on UTAUT model. Manag. Mod. 2014, 34, 28–30. [Google Scholar]
  45. Zhou, T.; Lu, Y.; Wang, B. Integrating TTF and UTAUT to Explain Mobile Banking User Adoption. Comput. Hum. Behav. 2010, 26, 760–767. [Google Scholar] [CrossRef]
  46. Yeoh, S.Y.; Chin, P.N. Exploring home health-care robots adoption in Malaysia: Extending the UTAUT model. Int. J. Pharm. Healthc. Mark. 2022, 16, 392–411. [Google Scholar] [CrossRef]
  47. Koksal, M.H. The Intentions of Lebanese Consumers to Adopt Mobile Banking. Int. J. Bank. Mark. 2016, 34, 327–346. [Google Scholar] [CrossRef]
  48. De Sena Abrahão, R.; Moriguchi, S.N.; Andrade, D.F. Intention of Adoption of Mobile Payment: An Analysis in the Light of the Unified Theory of Acceptance and Use of Technology (UTAUT). RAI Rev. Adm. Inovação 2016, 13, 221–230. [Google Scholar] [CrossRef] [Green Version]
  49. Hsu, C.W.; Peng, C.C. What drives older adults’ use of mobile registration apps in Taiwan? An investigation using the extended UTAUT model. Inform. Health Soc. Care 2022, 47, 258–273. [Google Scholar] [CrossRef] [PubMed]
  50. Mabkhot, H.; Isa, N.M.; Mabkhot, A. The Influence of the Credibility of Social Media Influencers SMIs on the Consumers’ Purchase Intentions: Evidence from Saudi Arabia. Sustainability 2022, 14, 12323. [Google Scholar] [CrossRef]
  51. Nysveen, H.; Pedersen, P.E.; Thorbjørnsen, H.; Berthon, P. Mobilizing the brand: The effects of mobile services on brand relationships and main channel use. J. Serv. Res. 2005, 7, 257–276. [Google Scholar] [CrossRef]
  52. Yang, C.-C.; Li, C.-L.; Yeh, T.-F.; Chang, Y.-C. Assessing Older Adults’ Intentions to Use a Smartphone: Using the Meta–Unified Theory of the Acceptance and Use of Technology. Int. J. Environ. Res. Public. Health 2022, 19, 5403. [Google Scholar] [CrossRef]
  53. Wang, H.; Tao, D.; Yu, N.; Qu, X. Understanding consumer acceptance of healthcare wearable devices: An integrated model of UTAUT and TTF. Int. J. Med. Inform. 2020, 139, 104156. [Google Scholar] [CrossRef]
  54. Zhang, M.; Hassan, H.; Migin, M.W. Exploring the Consumers’ Purchase Intention on Online Community Group Buying Platform during Pandemic. Sustainability 2023, 15, 2433. [Google Scholar] [CrossRef]
  55. Zainab, B.; Awais Bhatti, M.; Alshagawi, M. Factors affecting e-training adoption: An examination of perceived cost, computer self-efficacy and the technology acceptance model. Behav. Inf. Technol. 2017, 36, 1261–1273. [Google Scholar] [CrossRef]
  56. Gupta, B.; Joshi, S.; Agarwal, M. The effect of expected benefit and perceived cost on employees’ knowledge sharing behavior: A study of IT employees in India. Organ. Mark. Emerg. Econ. 2012, 3, 8–19. [Google Scholar] [CrossRef] [Green Version]
  57. Khan, D.; Fujiwara, A.; Shiftan, Y.; Chikaraishi, M.; Tenenboim, E.; Nguyen, T.A.H. Risk Perceptions and Public Acceptance of Autonomous Vehicles: A Comparative Study in Japan and Israel. Sustainability 2022, 14, 10508. [Google Scholar] [CrossRef]
  58. Walczuch, R.; Lemmink, J.; Streukens, S. The effect of service employees’ technology readiness on technology acceptance. Inf. Manag. 2007, 44, 206–215. [Google Scholar] [CrossRef]
  59. Kim, H.W.; Chan, H.C.; Gupta, S. Value-based adoption of mobile internet: An empirical investigation. Decis. Support Syst. 2007, 43, 111–126. [Google Scholar] [CrossRef]
  60. Zeithaml, V.A. Consumer perceptions of price, quality, and value: A means-end model and synthesis of evidence. J. Mark. 1988, 52, 2–22. [Google Scholar] [CrossRef]
  61. Stone, R.N.; Gr Nhaug, K. Perceived risk: Further considerations for the marketing discipline. Eur. J. Mark. 2013, 27, 39–50. [Google Scholar] [CrossRef]
  62. Wu, J.H.; Wang, S.C. What drives mobile commerce? An empirical evaluation of the revised technology acceptance model. Inf. Manag. 2005, 42, 719–729. [Google Scholar] [CrossRef]
  63. Zhao, Y.; Ni, Q.; Zhou, R. What factors influence the mobile health service adoption? A meta-analysis and the moderating role of age. Int. J. Inf. Manag. 2018, 43, 342–350. [Google Scholar] [CrossRef]
  64. Anderson, J.C.; Gerbing, D.W. Structural equation modeling in practice: A review and recommended two-step approach. Psychol. Bull. 1988, 103, 411–423. [Google Scholar] [CrossRef]
  65. Hair, J.F.; Black, W.C.; Babin, B.J.; Anderson, R.E.; Tatham, R.L. Multivariate Data Analysis; Prentice Hall: Upper Saddle River, NJ, USA, 1998. [Google Scholar]
  66. Nunnally, J.C. Psychometric Theory; Tata McGraw-Hill Education: New York, NY, USA, 1994. [Google Scholar]
  67. Fornell, C.; Larcker, D.F. Evaluating structural equation models with unobservable variables and measurement error. J. Mark. Res. 1981, 18, 39–50. [Google Scholar] [CrossRef]
  68. Chin, W.W. Commentary: Issues and opinion on structural equation modeling. MIS Q. 1998, 22, vii–xvi. [Google Scholar]
  69. Hooper, D.; Coughlan, J.; Mullen, M.R. Structural equation modelling: Guidelines for determining model fit. Electron. J. Bus. Res. Methods 2008, 6, 53–60. [Google Scholar]
  70. Jackson, D.L.; Gillaspy, J.A.; Purc-Stephenson, R. Reporting practices in confirmatory factor analysis: An overview and some recommendations. Psychol. Methods 2009, 14, 6–23. [Google Scholar] [CrossRef]
  71. Kline, R.B. Principles and Practice of Structural Equation Modeling; Guilford Publications: New York, NY, USA, 2015. [Google Scholar]
  72. Whittaker, T.A. A Beginner’s Guide to Structural Equation Modeling; Taylor & Francis: Abingdon, UK, 2011. [Google Scholar]
  73. Hu, L.T.; Bentler, P.M. Cutoff criteria for fit indexes in covariance structure analysis: Conventional criteria versus new alternatives. Struct. Equ. Model. 1999, 6, 1–55. [Google Scholar] [CrossRef]
  74. Sun, Y.; Wang, N.; Guo, X.; Peng, Z. Understanding the acceptance of mobile health services: A comparison and integration of alternative models. J. Electron. Commer. Res. 2013, 14, 183. [Google Scholar]
  75. Wang, L.J.; Jin, L. Willingness or intention: A study on the attitude of disabled elderly to use smart care products. J. Northwest Univ. 2021, 51, 89–97. [Google Scholar] [CrossRef]
  76. Chen, W.Z.; Qi, Y.; Wu, X.Y. Chronic disease-related science and technology for endowment: Latest development, assessment, and prospect. Chin. J. Public. Heal. 2018, 34, 1055–1060. [Google Scholar]
  77. Kang, H.-J.; Han, J.; Kwon, G.H. The Acceptance Behavior of Smart Home Health Care Services in South Korea: An Integrated Model of UTAUT and TTF. Int. J. Environ. Res. Public. Health 2022, 19, 13279. [Google Scholar] [CrossRef]
  78. Chou, W.H.; Lai, Y.T.; Liu, K.H. User requirements of social media for the elderly: A case study in Taiwan. Behav. Inf. Technol. 2013, 32, 920–937. [Google Scholar] [CrossRef]
  79. Sayago, S.; Blat, J. Telling the story of older people e-mailing: An ethnographical study. Int. J. Hum. Comput. Stud. 2010, 68, 105–120. [Google Scholar] [CrossRef]
  80. Gamma, A.E.; Slekiene, J.; von Medeazza, G.; Asplund, F.; Cardoso, P.; Mosler, H.J. Contextual and psychosocial factors predicting Ebola prevention behaviours using the RANAS approach to behaviour change in Guinea-Bissau. BMC Public. Health 2017, 17, 446. [Google Scholar] [CrossRef] [Green Version]
Behavsci 13 00277 g001 550
Figure 1. Market scale of smart aged-care industry in China during 2014–2020 [6].
Figure 1. Market scale of smart aged-care industry in China during 2014–2020 [6].
Behavsci 13 00277 g001
Behavsci 13 00277 g002 550
Figure 2. UTAUT model.
Figure 2. UTAUT model.
Behavsci 13 00277 g002
Behavsci 13 00277 g003 550
Figure 3. Research model.
Figure 3. Research model.
Behavsci 13 00277 g003
Behavsci 13 00277 g004 550
Figure 4. Research Model Diagram.
Figure 4. Research Model Diagram.
Behavsci 13 00277 g004
Table
Table 1. Definitions of variable’s operability and the reference scales.
Table 1. Definitions of variable’s operability and the reference scales.
CategoryResearch VariablesDefinition of OperabilityCodeMeasurement ItemSources
UTAUT model Performance expectationsThe elderly believe that the use of smart aged-care products can help them acquire better services PE1I think smart aged-care products are easy to use and can promote the effect of elderly care[21,58]
PE2The use of smart aged-care products can help me better enjoy elderly care service
PE3I think the use of smart aged-care products can save time, and is convenient and fast
PE4I think the use of innovative smart aged-care products and technology can promote the effect of elderly care
PE5I think the use of smart aged-care products can promote the convenience of elderly care services
Effort expectancyThe ease of use of smart aged-care products considered by the elderlyEE1I can easily learn to use smart aged-care products without spending too much time[21,58]
EE2For me, the operation process of smart aged-care products is simple and easy
EE3I fully understand and understand how to use smart aged-care products
EE4The innovative application of smart aged-care products is not a challenge to me
Social influenceThe extent to which the elderly are aware of whether others think they should use smart aged-care productsSI1People around me influence my decision to use smart aged-care products[21,58]
SI2People who use smart aged-care products look more capable than those who do not
SI3The use of smart aged-care products is a trend. I want to keep up with the pace of the times, and I will use them
SI4The use of smart aged-care products can improve personal image in society
Facilitating conditionsThe extent to which the elderly believe that the existing supporting resources can support the use of smart aged-care productsFC1I have the resources needed to use smart aged-care products[21,58]
FC2I think smart aged-care products can match other technologies used
FC3I have the necessary skills required to use smart aged-care products
FC4When I encounter difficulties in using smart aged-care products, I can ask friends for help
Behavioral intentionThe behavioral tendency of the elderly to use smart productsBI1It’s a good idea to use smart aged-care products[21,58]
BI2I think using smart aged-care products can promote my health
BI3I think smart aged-care products are very valuable
BI4I will use smart aged-care products in the future
Use behaviorThe elderly use smart aged-care productsUB1I am very willing to use smart aged-care products for health management[21,58]
UB2I learn how to use smart aged-care products
UB3Compared with other health care products, I prefer smart aged-care products
UB4I will continue to use smart aged-care products
Perceived cost theoryPerceived costThe elderly think that using smart aged-care products involves more costsPC1Smart aged-care products are much more expensive than non-intelligent pension products[31,59,60]
PC2I need to spend more time and energy on learning to use smart aged-care products
PC3It takes more energy to use smart aged-care products
Perceived risk theoryPerceived riskThe elderly think it is risky to use smart aged-care productsPR1The use of smart aged-care products may cause financial losses[61,62,63]
PR2The use of smart aged-care products may not meet my original expectations
PR3The use of smart aged-care products makes me nervous or anxious
PR4Use of smart aged-care products may cause harm to the body
PR5The use of smart aged-care products may cause my information to be leaked
Table
Table 2. Questionnaire reliability analysis.
Table 2. Questionnaire reliability analysis.
Variables Item Corrected Item–Total Correlation (CITC)Cronbach’s α If Item DeletedCronbach’s α
Performance expectationPE10.8130.9260.916
PE20.8480.92
PE30.8330.923
PE40.8350.922
PE50.830.923
Effort expectationEE10.8220.9160.909
EE20.8560.905
EE30.830.913
EE40.8480.908
Social influenceSI10.7120.8720.907
SI20.7510.857
SI30.7580.855
SI40.7970.839
Facilitating conditionsFC10.780.8510.906
FC20.7740.853
FC30.7610.859
FC40.7220.873
Behavioral intentionBI10.8570.9080.898
BI20.8360.915
BI30.8470.911
BI40.830.917
Use behaviorUB10.7560.8590.895
UB20.8020.842
UB30.8070.84
UB40.6730.891
Perceived costPC10.7510.8290.874
PC20.7680.814
PC30.7560.825
Perceived riskPR10.8850.9520.935
PR20.8970.95
PR30.8840.952
PR40.8820.953
PR50.8980.95
Table
Table 3. Basic information of respondents.
Table 3. Basic information of respondents.
Item Option FrequencyPercentage (%)Cumulative Percentage (%)
Gender Male 18748.4548.45
Female 19951.55100
Age 60–65 years old6516.8416.84
66–70 years old9725.13 41.97
71–75 years old10827.98 69.95
76–80 years old88 22.8092.75
Over 80 years old287.25 100
Education backgroundSenior high school and under29275.6575.65
Junior college256.4882.12
Undergraduate5012.9595.08
Master’s and above194.92100
Disposable monthly incomeBelow CNY 2000328.298.29
CNY 2001–35007820.2128.50
CNY 3501–500012532.3860.88
Over CNY 500015139.12100
Total 386100100
Table
Table 4. KMO and Bartlett’s test.
Table 4. KMO and Bartlett’s test.
KMO Value0.931
Bartlett test of sphericityChi-square approximation☐9339.6
df528
p-value0
Table
Table 5. Factor loadings (rotated).
Table 5. Factor loadings (rotated).
Name Factor LoadingCommonality (Common Factor Variance)
Factor 1Factor 2Factor 3Factor 4Factor 5Factor 6Factor 7Factor 8
PE1−0.1050.8220.1290.1350.0830.1270.12−0.1010.769
PE2−0.0710.8000.1570.1250.1190.1460.146−0.1020.753
PE3−0.0990.8020.0950.1110.1360.1800.128−0.1290.757
PE4−0.1000.7900.1620.1730.1060.0780.162−0.0890.742
PE5−0.0920.8110.0970.0770.1220.180.107−0.0640.745
EE1−0.1140.1470.1770.1790.7940.1910.126−0.0930.789
EE2−0.1070.1360.1020.1530.8000.2040.126−0.070.767
EE3−0.1550.1550.1210.1210.8280.1000.131−0.0890.799
EE4−0.1120.1110.1890.1760.8020.2030.15−0.0540.801
SI1−0.1270.1730.0960.8220.1430.1350.197−0.0470.811
SI2−0.0570.1150.1220.8240.1440.1450.102−0.0910.77
SI3−0.0690.1520.0980.8250.1540.1110.097−0.1230.779
SI4−0.1350.1370.1380.8150.1470.1360.113−0.0780.78
FC1−0.0500.1310.8530.1090.1230.0490.109−0.0410.791
FC2−0.0250.1400.8600.1000.1120.0970.103−0.0320.804
FC3−0.0040.1480.8420.0930.0870.040.074−0.0520.757
FC4−0.0060.1190.8420.1110.1720.060.085−0.0520.778
BI1−0.2150.1540.1370.1350.1590.2370.776−0.0460.793
BI2−0.1860.1810.0770.1170.2020.1920.766−0.1330.769
BI3−0.2410.2160.1500.1830.1150.1980.710−0.1620.744
BI4−0.2110.2010.1400.1770.1260.2290.752−0.0890.778
UB1−0.0880.150.0940.1250.1630.7660.25−0.1580.755
UB2−0.0930.2150.0830.1450.1770.8080.118−0.1610.807
UB3−0.0860.1990.0310.1640.2590.7170.279−0.1350.753
UB4−0.0850.2250.0880.1940.190.7400.211−0.1520.755
PC10.192−0.107−0.059−0.075−0.106−0.138−0.1550.8310.803
PC20.191−0.147−0.064−0.139−0.058−0.187−0.0880.8250.809
PC30.210−0.166−0.055−0.104−0.102−0.16−0.0790.8160.793
PR10.846−0.1060.021−0.066−0.107−0.059−0.1980.0880.793
PR20.875−0.069−0.015−0.064−0.086−0.071−0.120.1250.818
PR30.852−0.117−0.014−0.094−0.092−0.068−0.1020.1440.793
PR40.851−0.091−0.072−0.116−0.118−0.025−0.1350.1100.795
PR50.853−0.059−0.021−0.052−0.067−0.101−0.130.1470.788
Cumulative percentage of variance explained %77.993
Note: Bold numbers are those for which factor loading is greater than 0.6.
Table
Table 6. Summary table of confirmatory factor analysis.
Table 6. Summary table of confirmatory factor analysis.
Item EstimateS.E.C.R.pStdCRAVE
PE1<---PE1 0.8390.9160.686
PE2<---PE1.0220.05219.818***0.835
PE3<---PE1.0290.05219.685***0.837
PE4<---PE1.0030.05219.19***0.819
PE5<---PE1.0050.05219.195***0.812
EE1<---EE1 0.8550.9090.713
EE2<---EE0.9530.04819.686***0.821
EE3<---EE0.9730.04720.519***0.84
EE4<---EE1.0280.04920.934***0.862
SI1<---SI1 0.8670.9070.708
SI2<---SI0.9440.04720.058***0.822
SI3<---SI0.9390.04620.36***0.834
SI4<---SI0.9860.04820.719***0.843
FC1<---FC1 0.8510.9070.708
FC2<---FC1.0970.05220.918***0.866
FC3<---FC0.9660.05119.062***0.807
FC4<---FC1.0520.05220.092***0.842
BI1<---BI0.9990.0519.941***0.8450.8980.688
BI2<---BI0.9660.05118.921***0.816
BI3<---BI0.9550.05118.869***0.817
BI4<---BI1 0.84
UB1<---UB0.980.05418.093***0.8080.8740.699
UB2<---UB1.0630.05519.284***0.832
UB3<---UB1.0360.05518.862***0.831
UB4<---UB1 0.831
PC1<---PC1.0030.05617.887***0.8230.8950.681
PC2<---PC1.0690.05818.418***0.85
PC3<---PC1 0.835
PR1<---PR1 0.8570.9350.742
PR2<---PR1.0210.04522.831***0.879
PR3<---PR0.9980.04521.947***0.859
PR4<---PR1.0130.04622.098***0.858
PR5<---PR0.9720.04521.752***0.853
Note: *** p < 0.001.
Table
Table 7. Discriminant validity of the measurement model.
Table 7. Discriminant validity of the measurement model.
AVEPEEESIFCPRBIUBPC
PE0.6860.828
EE0.7130.430 ***0.845
SI0.7080.436 ***0.483 ***0.842
FC0.7080.388 ***0.409 ***0.345 ***0.842
PR0.742−0.299 ***−0.338 ***−0.296 ***−0.124 *0.861
BI0.6810.541 ***0.582 ***0.496 ***0.297 ***−0.312 ***0.825
UB0.6880.527 ***0.516 ***0.495 ***0.373 ***−0.498 ***0.667 ***0.830
PC0.699−0.405 ***−0.346 ***−0.357 ***−0.215 ***0.451 ***−0.513 ***−0.440 ***0.836
Note: The square root of AVE (shown as bold at diagonal).* p < 0.05, *** p < 0.001.
Table
Table 8. Model fit index.
Table 8. Model fit index.
Model FitCriteriaModel Fit of Research ModelJudgment
ML chi-square (χ2)The smaller the better519.53
Degrees of Freedom (df)The larger the better472
Normed Chi-square (χ2/df)<31.101Yes
Root Mean Square Error Approximation (RMSEA)<0.080.037Yes
Standardized Root Mean Square Residual (SRMR)<0.080.0461Yes
Tucker–Lewis Index (TLI)>0.90.946Yes
Comparative Fit Index (CFI)>0.90.951Yes
Goodness of Fit Index (GFI)>0.90.918Yes
Incremental Fit Index (IFI)>0.90.954Yes
Table
Table 9. Verification results of hypotheses.
Table 9. Verification results of hypotheses.
HypothesisRouteEstimateS.E.C.R.pSTDResults
H1BI<---PE0.2740.0515.416***0.277Support
H2BI<---EE0.270.0564.805***0.272Support
H3BI<---SI0.1880.0493.845***0.199Support
H4UB<---FC0.0170.0550.3040.7610.018Nonsupport
H5UB<---BI0.8230.0998.29***0.860Support
H6BI<---PC−0.2540.054−4.69***−0.251Support
H7BI<---PR−0.1350.058−2.3340.02−0.152Support
H8UB<---PR0.0820.0671.2210.2220.096Nonsupport
Note: *** p < 0.001.
Disclaimer/Publisher’s Note: The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content.

Share and Cite

MDPI and ACS Style

Wang, X.; Lee, C.-F.; Jiang, J.; Zhang, G.; Wei, Z. Research on the Factors Affecting the Adoption of Smart Aged-Care Products by the Aged in China: Extension Based on UTAUT Model. Behav. Sci. 2023, 13, 277. https://doi.org/10.3390/bs13030277

AMA Style

Wang X, Lee C-F, Jiang J, Zhang G, Wei Z. Research on the Factors Affecting the Adoption of Smart Aged-Care Products by the Aged in China: Extension Based on UTAUT Model. Behavioral Sciences. 2023; 13(3):277. https://doi.org/10.3390/bs13030277

Chicago/Turabian Style

Wang, Xiang, Chang-Franw Lee, Jiabei Jiang, Genlei Zhang, and Zhong Wei. 2023. "Research on the Factors Affecting the Adoption of Smart Aged-Care Products by the Aged in China: Extension Based on UTAUT Model" Behavioral Sciences 13, no. 3: 277. https://doi.org/10.3390/bs13030277

Note that from the first issue of 2016, this journal uses article numbers instead of page numbers. See further details here.

Article Metrics

Back to TopTop