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Do Technology-Specific Factors Influence the Usage Intention for Online Healthcare Among Older Adults? Evidence from the Urban Areas of Chongqing in China

  † These authors contributed equally to this work.

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28 August 2026

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28 August 2026

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Abstract
Background: As online healthcare is to support aging for reducing health cost and offer sustainable solutions, it is important to develop fundamental knowledge on what causes the usage intention for online healthcare among older persons. However, information about the technology-specific predictors of the willingness to use online healthcare remains scare. The goal of this study is to better observe whether technology-based factors are associated with the intention of using online healthcare. Methods: In the current study, we conducted a cross-sectional survey in 2023 in Chongqing city of Western China, and 403 urban older adults aged 60 and above were enrolled. Results: We found that technology-specific factors including perceived risk (B = -0.100, p < 0.001), perceived benefit (B = 0.209, p < 0.001), relative advantage (B = 0.261, p < 0.001) and compatibility (B = 0.274, p < 0.001) were significantly linked to the usage intention for online healthcare. Additionally, financial strain status of perceived difficult (B = -1.024, p = 0.019) was negatively related to the usage intention for online healthcare. Conclusions: The results of this study identify the four technology-specific factors including perceived risk, perceived benefit, relative advantage and compatibility that have a strong influence on the usage intention for online healthcare. This influence implies that the technology-based factors are the key elements affecting older individuals’ perceptions, values, needs, preferences, experience and lifestyles in response to digital health technology. In addition, the results of this paper confirm that the attributes of relative advantage, compatibility and perceived benefit could act as facilitator and perceived risk as a barrier to the usage intention for online healthcare. Findings also enrich the applicable case of the diffusion of innovations theory and the benefit-risk analysis model in understanding the usage intention for online healthcare. Moreover, these findings and their implications here could provide specific evidence that the reinforcement of the usage intention for online healthcare in everyday life depends on technology-based factors in the Chinese context. The theoretical mechanisms underlying the link between the usage intention and technology-specific factors can also aid in developing and implementing sustainable technology-based solutions for online healthcare among older adults.
Keywords: 
usage intention
;  
technology-specific factors
;  
older Chinese adults
Subject: 
Social Sciences  -   Behavior Sciences

1. Introduction

Societies worldwide identify the two megatrends that reinforce each other: the emergence of aging populations and digital technologies, both of which are shaping the evolving future at local, national and global level. Due to the sweeping changes in the population aging, there has been an upsurge in the demands and expenditure [1] associated with healthcare [2], leading to a situation where the existing supply falls short of demand and cost cannot be covered for health service, and then older adults face problems accessing health services [3]. Particular attention is paid to technological aspects, especially for the advanced digital health technological solutions that contribute to sustainable improvements in healthcare service.
A type of digital health technologies called online healthcare is defined as internet-based healthcare service, which has emerged as a complement to traditional care [4], and is an extension of offline healthcare service. Internet-based healthcare services consist of telehealth and online healthcare-related appointments, inquiry and consultation [5]. Several studies have examined digital technologies have the potential to improve the allocation of scarce resources, enhance the efficiency and cost-effectiveness of the services [3], they can offer low cost, sustainable solutions for maintaining support among older adults. Hence, it is important to encourage older people to use digital health technologies to reduce health costs [6].
The current age witnesses the development of digital technology and how technology tools alter engagement of individuals in their daily tasks. The integration of digital technology and public services programs particularly benefits individuals in accessing medical services online. National investigations indicated that the number of users related to internet-based healthcare was about 411 million in China in 2025 [7], however, online healthcare is not yet ubiquitous, most of the elderly are a particularly disadvantaged population with lower rates of utilization for healthcare online. Hence, it is essential and urgent to take measures to enhance the usage intention and rates of online healthcare for older population in China.
Digital health advancements indeed offer numerous benefits, however, they present difficulties for many nations and individuals [8], especially those in the older population [9]. There is evidence that the elderly often experience significant difficulties integrating technological innovations into their daily routines [10]. A previous systematic review indicated that technology-specific factors were the top barriers to adopting telemedicine worldwide [11]. Technological challenges and changes are the two main sources of resistance to digital shift and engagement [12], particularly for the e-health services [8] among older adults. In other words, due to societies transition toward technology driven systems, many older individuals encounter barriers to adoption, leading to digital exclusion [9]. Moreover, most existing research emphasizes that the use of modern technologies in everyday activity depends on technological aspects [13,14,15], such as technical compatibility [16], relative advantage [17], the perceived benefits of technological support [18] and perceived risks (i.e., technology-induced concerns or fears) [8]. Therefore, it is important to explore and understand the technology-specific predictors including compatibility, relative advantage, perceived benefit and perceived risk associated with the intention of using online healthcare, and to establish ways and mechanism of successfully improving behavioral intention of online healthcare among older persons.

2. Literature Review

2.1. Theoretical Mechanisms Underlying the Relationship Between Technology-Specific Factors and the Usage Intention for Online Healthcare

Rogers identified two main viewpoints in the diffusion of innovations theory [19], one emphasizes the five key elements of innovation such as relative advantage, compatibility, complexity, observability and trialability, and the other argues that relative advantage, along with compatibility, have been shown to influence the rate of technology adoption. By applying the two constructs and characteristics of innovation, our present study elicits a research model that explains how technology-specific factors are associated with individuals’ intention of using online healthcare.
In addition, benefit-risk analysis model has been a mainstream theory for detecting consumption-related behaviors [20]. This model represents two important factors including perceived risk and perceived benefit, which have a large influence on an individual’s willingness to use new technology [21]. Based on the two factors, our current study employs a research design that understand technology-specific predictors of the usage intention for online healthcare.
In together, our current study allows for an integration of the diffusion of innovations theory and benefit-risk analysis model. This integration is conducted to interpret older adults’ diverse feelings and reactions, especially intention to using online healthcare and their technology-related reasons.

2.2. Technology-Specific Factors Associated with the Usage Intention for Online Healthcare

Previous research have illustrated the critical role of perceived risk in influencing older people’s attitudes and actions regarding e-health services [8]. Another two previous studies found that perceived risk harms the behavioral intention to use new things [22]. Li et al. discovered that perceived risk negatively impacts the behavioral intention to use remote health management [23].
Perceived benefit was initially defined as knowing the concrete benefits of purchasing and/or using of an object [24]. Put another way, it measures the degree of belief a person has in their ability to use technology [17] in the context of this research. Past studies have investigated the link between perceived benefit and the adoption and use of digital technology [3,25]. For instance, perceptions of the benefits of online health consultation services, positively affect the intention of adopting [26].
Rogers developed the Diffusion of Innovations Theory, which defines relative advantage as the degree that determines how a new technology is considered better than its precursor [27,28], indicating that the participants prefer to adopt (or already adopted) technologies that showed better accessibility, safety, and usability (i.e., efficiency, effectiveness, and satisfaction) as compared to conventional devices, methods, and tools [29]. A comprehensive meta-analysis illustrated that relative advantage is consistently linked to the adoption of innovation [30]. Previously published studies noted the impact of relative advantage on the use intention of technology [31], for instance, the importance of relative advantage has been proved in the context of the consumerization of information technology [32]. Nezamdoust et al. stated that relative advantage has a direct and significant effect on nurses’ use of practical health related mobile applications [33].
According to Rogers’ Diffusion of Innovations Theory, compatibility—second only to relative advantage—is the key innovation element that explains different rates of adoption among individuals [28,34]. Rogers defined compatibility as “the degree to which an innovation complies with the existing values, past experiences, and needs of potential adopters” [28,35]. It indicates that innovation compatibility with the technical functionalities of other existing products and with the values, norms, needs, experience and lifestyles [36] of an individual would significantly affect their intention of using a new technology. Compatibility is commonly added to the technology acceptance model for enhancing the technology acceptance model’s explanatory power for the acceptance of various technologies including mobile learning [37], Web 2.0 services [38], wearable health monitoring technologies [16], self-driving vehicles [39], online mapping technology [40] and self-service technologies [41]. Compatibility is identified to significantly and positively influence individuals’ intention to use the technology [42]. Similar findings have been shown in previous studies conducted in a number of countries, including China [43,44], Japan and Spain [45], Iran [33], the Netherlands [46].

2.3. Other Factors Associated with the Usage Intention

A previous study discovered that gender is a predictor of the technology adoption behavior of older individuals [47]. Research has demonstrated that gender is related to the intention to use digital health service, with women reporting a higher intention [48]. Age seems to influence people’s experiences and could act as a barrier or facilitator for using digital health [49]. Yap et al. stated that age is a significant factor associated with technology adoption [47]. Additionally, old people are less willing and have less experience and interest in using eHealth compared to younger people [50]. Many studies indicated that education does make an effect on the use of eHealth in chronic disease [51,52]. Those who have a higher level of education attainment are more likely to use the mhealth [53]. The impact of economic characteristics of the population may influence the uptake of digital technology [54]. Digital health is less accessible for people with lower financial status [55].
The groups with low socio-economic position struggle to adopt ehealth programs [56], they lag behind in the ehealth adoption [57]. It appears that low socio-economic position populations have more difficulty accessing mhealth [46]. Economic constraint is a barrier to the adoption and effective use of digital health tools [58].
It seems that healthier older people are far more likely to use computers than their unhealthy coevals [59]. Isabelle et al. confirmed the significant impact of self-reported health status on the intention to use smart wearable systems among older users, many people with better health status are more inclined to adopt such technologies [42]. Older adults with worse age-related health status are more inclined to adopt smart wearable systems to ensure continuing surveillance of their physical signs [16].
The literature reviews above indicate that none of these previous researches specifically addressed technological aspects that could contribute to improving the usage intention for online healthcare in older adults. These disparities highlight importance of in-depth examination of the relationship between technology-specific factors and the usage intention for online healthcare in China. In the current study, we present evidence pertaining to the relationship among older people from the urban areas of Chongqing in China.

3. Research Method

3.1. Data

After receiving approval for the study from the Ethics Review Board of the School of Journalism and Communication, Chongqing University, a cross-sectional questionnaire with close-ended questions was recruited from October to December in 2023 in urban areas of Chongqing in China. Using a stratified multistage and cluster sampling, we enrolled 403 urban older adults aged 60 and above who lived in Shapingba district of Chongqing municipality.
The process of survey sampling included four stages. In the first stage, we selected four urban blocks from Shapingba district, namely Huxi, Zengjiazhen, Shapingba and Yubeilu. In the second stage, we selected two urban residential communities including Huxi garden and Huama in Huxi block, Longqiao community in Zengjiazhen block, two communities including Shazhengjie and Yinshuicun in Shapingba block, two communities including Shuangxiangzi and Wusicun in Yubeilu block. In the third stage, we selected a total of 1, 2, 1, 1,1 and 1 residential apartment buildings from the seven residential communities in Huxi garden, Huama, Longqiao, Shazhengjie, Yinshuicun, Shuangxiangzi and Wusicun, respectively. In the fourth stage, households were selected from the selected residential apartment buildings, resulting in data for 403 urban households, including 110, 90, 90 and 135 households in Huxi block, Zengjiazhen block, Shapingba block and Yubeilu block, respectively. Within the selected households, in the case of households with more than one person aged 60 and above, one individual was selected at random using the Kish table.
In addition, informed written consents were obtained prior to formal interview. A paper-assisted and in-person structured interview was conducted to promote effective communication with respondents and to ensure the adequacy and success of questionnaire completion. A total of 403 questionnaires were collected, and the response rate was 94.82% (403 of 425).

3.2. Variables

In our study, the dependent variable was the usage intention for online healthcare. The independent variables were perceived risk, perceived benefit, relative advantage, compatibility.
The controlled attributes included demographic variable (gender, age), socio-economic information (educational level, financial strain), and health status (self-rated health, suffering from illness in the past two weeks).

3.3. Measurement Instruments

The dependent variable (the usage intention for online healthcare) and the independent variables (perceived risk, perceived benefit, relative advantage and compatibility) in the questionnaire were measured by a 5-point Likert scale ranging from 1(strongly disagree) to 5(strongly agree).

3.3.1. The Usage Intention for Online Healthcare

The usage intention for online healthcare was measured using the 3-item scale drawn from Venkatesh et al. [60]. The three specific items were: “I intend to continue using online healthcare in the future”, “I will always try to use online healthcare in my daily life” and “I plan to continue to use online healthcare frequently”. In current sample, Cronbach’s alpha for the 3 items was 0.899. The 3 items were summed to create an overall score with a range of 3-15. Higher scores indicated a stronger usage intention for online healthcare.

3.3.2. Technology-Specific Factors

  • Perceived risk
Based on the work of Peter et al. [61] and Deng [62], perceived risk was assessed using the 5-item scale. The five specific items were: “I am concerned over any financial loss due to the improper use of online healthcare”, “I am concerned for the time lost due to the improper use of online healthcare”, “I am concerned for the physical injury due to the improper use of online healthcare”, “I am concerned for the psychological stress due to the improper use of online healthcare” and “I am afraid that personal health information would be disclosed or abused due to the improper use of online healthcare”. In our sample, Cronbach’s alpha for the 5 items was 0.903. An overall score of perceived risk was calculated, with a range of 5-25. Higher cumulative scores indicated higher levels of perceived risk.
2.
Perceived benefit
Gong [26] identified that perceived benefit was measured by six specific items, which were “Using online healthcare can be of benefit to me in managing my health”, “ Using online healthcare can increase my knowledge of my own health conditions”, “Using online healthcare can help to relieve my stresses draw from the symptoms or worries about symptoms”, “Using online healthcare can be contributed to my health”, “Save time by converting going to the hospital to using online healthcare” and “Save money by converting going to the hospital to using online healthcare”. In our sample, Cronbach’s alpha for the 6 items was 0.899. The scores from the six items with equal weighting were summed up to create an overall score, and the range of scores for perceived benefit was 6-30. Higher cumulative score indicated greater perceived benefit.
3.
Relative advantage
Choudhury [27] emphasized that relative advantage was measured by three items: “I would find it more convenient to educate myself about health-related management by reading from online healthcare than by asking questions of an offline healthcare provider”, “I would learn and understand health-related information offered by online healthcare more and better than by talking to a traditional healthcare provider”, and “I would find it more convenient to learn myself about health-related status by reading from online healthcare than by asking questions of an offline healthcare provider”. In our sample, Cronbach’s alpha for the 3 items was 0.917. The rang of the total score was 3-15, which was computed by adding scores of each item with equal weighting. Higher cumulative score indicated greater relative advantage.
4.
Compatibility
In the light of Zhao’s research finding [43], compatibility was measured by three items as stated: “Using online healthcare is completely compatible with my current health status and demand”, “I think that using online healthcare fits well with my value and philosophy of life”, and “Using online healthcare fits into my experience, habits and the way I live”. In our sample, Cronbach’s alpha for the 3 items was 0.913. Following common practice for relative advantage above, the scores for compatibility ranged from 3 to 15. Higher cumulative score indicated better compatibility.

3.3.3. Other Variables

Demographic variables included gender (0 = male, 1 = female) and age. For age, those who were between 60 and 69 years old were coded as 1, those between 70 and 79 years old were coded as 2, and those aged 80 and above were coded as 3.
Socio-economic status included educational attainment and financial strain. Educational attainment was a 4-response categorial variable (1 = illiterate or primary school, 2 = junior high school, 3 = polytechnic school or senior high school, 4=college and above). For financial strain, it was measured by a single question: “How do you report your financial strain condition now?”. Respondents were asked to rate this item on a five-point Likert scale (1 = more than sufficient, 2 = good sufficient, 3 = approximately sufficient, 4 = somewhat difficult, 5 = very difficult). Due to the severe skewness of the distribution of percent in our current research, we collapsed this variable into three categories: sufficient (more than sufficient and good sufficient), approximately and difficult (somewhat difficult and difficult).
Health status comprised self-rated health, suffering from illness in the past two weeks. For self-rated health, participants were asked a single item: “How do you assess your health situation now?” Then, their responses were scored on a 5-point Likert scale (1 = very bad, 2 = bad, 3 = fair, 4 = good, 5 = very good). Due to the severe skewness of the distribution of percent in the present study, we collapsed this variable into three categories: bad (very bad and bad), fair and good (good and very good).
We used a single item to measure suffering from illness in the past two weeks, and this item included a single question: “Did you suffer from any illness in the past two weeks? (0 = no, 1 = yes)”.

4. Results

4.1. Characteristics of the Sample

As shown in Table 1, the mean of the usage intention was 10.33. In addition, the average scores for the four technology-specific factors were perceived risk (18.19), perceived benefit (21.57), relative advantage (10.67), compatibility (10.18), respectively. In addition, significant differences in the usage intention existed in the present sample in terms of age, education attainment, financial strain, self-rated health and suffering from illness in the past two weeks. We also found a significant correlation between the usage intention and the three continuous variables, namely perceived benefit, relative advantage, compatibility.

4.2. Linear Regression Model

As described in Table 2, two main indices were used to assess the multicollinearity in linear regression model I, which showed an accepted multicollinearity as indicated by all the values of tolerance (>0.1) and VIF (<5). In other words, there is no multicollinearity among all the independent variables.
Furthermore, the regression model presented the value of R square up to 0.55 indicated a good fit, and demonstrated significant relationships between various constructs. Perceived risk (B = -0.100, p < 0.001) significantly and negatively influenced the usage intention for online healthcare. While perceived benefit (B = 0.218, p < 0.001), relative advantage (B = 0.285, p < 0.001) and compatibility (B = 0.265, p < 0.001) significantly and positively demonstrated the intention to use.
As presented in Table 3, all the values of tolerance (>0.1) and VIF (<5) suggested that no multicollinearity remained among all the independent variables. The incremental predictive value (R2 = 0.553) provided evidence for the predictive accuracy in linear regression model II, which could further and better demonstrate the impact of perceived risk, perceived benefit, relative advantage and compatibility on the intention to use.
Among the participants, perceived risk (B = -0.100, p < 0.001) was still negatively linked to the intention to use; perceived benefit (B = 0.209, p < 0.001), relative advantage (B = 0.261, p < 0.001) and compatibility (B = 0.274, p < 0.001) were positively related to the intention to use; whereas those who reported a higher financial strain status (B = -1.024, p = 0.019) had less intention to use (Table 3). Specifically, participants who obtained higher levels of perceived risk had lower intention to use; participants with higher levels of perceived benefit, relative advantage and compatibility had higher intention to use; participants who reported a financial strain status of “difficult” had lower intention to use

5. Discussion

The findings of the present study indicated the influence of the four technology-specific factors such as perceived risk, perceived benefit, relative advantage and compatibility on the willingness to use online healthcare among older adults living in urban environments in Chongqing, China. It suggested that older participants’ diverse reactions as to why they have or do not have the willingness to use online healthcare were generally well explained based on these four factors, which emerged as important elements influencing older individuals’ perceptions and emotions in response to new technology. Moreover, the discussion here could provide specific evidence that the reinforcement of the usage intention for online healthcare in everyday life depended on technology-based factors in the Chinese context.
Perceived risk was an inhibitive predictor of the usage intention for online healthcare; the greater the perceived risk, the more detrimental it is to old adults’ positive willingness to use. This is consistent with previous studies, for instant, some studies found that a risk perception was a significant deterrent to intention to use the technology including the mobile ICT for health promotion [63], a mobile chronic disease management system [64], and information service in the online health community [65]. Why did perceived risk inhibit the usage intention for online healthcare among older individuals?
In our current research, perceived risk referred to certain types of health risk, financial risk, privacy risk and psychological risk when an individual used online healthcare. Perceived risk hindered the usage intention across the four dimensions.
Perceived health risk meant that individuals’ concerns about the wrong diagnosis or delayed treatment. Due to the uncertainties of healthcare quality, the potential perceived risks of online healthcare far outweighed the benefits of having it. Lack of face-to-face consultations and physical examinations would restrict the accuracy and depth of online communication, and then lead to wrong diagnosis or delayed treatment, thereby cause harm to older persons’ health. When older people perceived these health risks, they tended to avoid using online healthcare services.
Perceived financial risk pertained to fears of financial loss or harm from using unfamiliar technologies [8]. An unclear fee and untransparent price provided through online healthcare platform would increase older individuals concerns about the burdens of treatment, and then result in the reduction of their willingness to use such services. Similar findings illustrated that the risk of money loss was an influential factor to older people’s adoption of mhealth services in UK [66].
Perceived privacy risk implied the anxiety about the security of personal health information. If older persons wanted to use online healthcare, they would satisfy the entrance requirement for online healthcare platforms, and register with real name and personal information such as gender, age, and past illnesses and chronic conditions, which might cause the concerns about the security and privacy of personal health information. To put it another way, older adults might live in constant fear of personal privacy information being leaked or illegally stolen and used. Due to the age, experience, and roles of older adults in society, they were more conservative and cautious about the privacy risks existing in online healthcare platforms, and their perception of privacy risks would reduce their willingness to use it for online healthcare.
Perceived psychological risk signified psychological burden from using unfamiliar innovations. The digital divide made older adults feel confused and anxious when exposed to online healthcare, thereby increased their perceived risk of psychological stress. Especially, the lower capacity to acquire and discern digital information would further amplify the perceived risk. Moreover, the pervasive problem of information overload, especially for health misinformation made older persons often struggle to distinguish which ones are credible. Such perceived risk affected decision-making and imposed a psychological burden for elderly people, leading to their reluctance to use online healthcare services.
Consistent with previous research conducted in Belgium [67], Saudi Arabia [68] and China [69], the current study illustrated that perceived benefit exerted a significant positive impact on the usage intention. This finding implied that older people were more likely to have the usage intention when they perceived obvious benefits from online healthcare services. Furthermore, it also extended and enriched the applicability of Rogers’ diffusion of innovations theory, which stated that fewer older people use new technologies if they do not perceive clear benefits [28]. Why did perceived benefit enhance the willingness of using online healthcare?
Perceived benefit covered three aspects of health promotion and quality improvement, access and use of health information, and time-saving. These three dimensions could provide a better examination of the link between the usage intention and perceived benefit. Perceived benefits of health promotion and quality improvement could be provided through online healthcare platforms. There were adequate resources for health information, sufficient quantity and quality of health tips, which could contribute to better health management and preventing potential health crisis, and then establish their adoption intention among older individuals. Perceived benefits of access and use of health information could be offered by online healthcare. The digital technology provider met the need for an online healthcare platform that operated “any place, any time,” challenging the space-time limitations of traditional healthcare. The digital transformation and advancement of healthcare platforms have ushered in a new era of accessibility, making health information and services widely available, and then boosted older adults’ usage intention for online healthcare. Perceived benefits of time-saving could be dispensed on the online healthcare platforms. Based on online healthcare service such as online appointment registration and payment, the efficiency and patient experience can be improved in medical clinics, wait times to see a doctor can be agonizingly reduced. These perceived benefits could play a certain and critical role in facilitating the use intention of online healthcare.
Current study pointed out that relative advantage had a significant positive impact on the usage intention for online healthcare services. The impact was confirmed by other studies [29,33], indicated that online healthcare could be accepted and adopted by older adults only if it was superior to traditional healthcare. In this case, it can be concluded that older adults were willing to adopt online healthcare due to the relative advantage of the technology. As employed in Diffusion of Innovations Theory [28], the successful diffusion of innovative products depended on the widespread acknowledgment of their relative advantages.
By definition, relative advantage meant the degree to which using an innovation was perceived as being better than using its precursor. Compared to the traditional healthcare, online healthcare showed distinct and relative strength. The conventional process in physical institutions tended to be more intricate, necessitating patients to frequently visit hospitals for follow-up consultations to secure essential prescriptions and medications. Conversely, online healthcare has shifted a portion of the follow-up procedures to digital platforms, thereby streamlining, clarifying, and visualizing the process of seeing a doctor. On the basis of online appointment, payment, and electronic reporting, patients could benefit from the “on-the-go” management of healthcare services. As a result, older adults could communicate with their attending physicians regarding disease progression and obtain electronic follow-up prescriptions via video or text communication, all without leaving their homes or navigating through hospitals. This approach alleviated the transportation-related stress and minimized the inconveniences during the medical treatment journey. If the elderly recognized these positive attributes of online healthcare, their inclination to utilize it would significantly increase.
The finding of the present study also demonstrated the importance of compatibility to the willingness to use online healthcare. Similar evidence had been found in China [43], the Dutch [46] and Thailand [33]. Why did compatibility enhance the usage intention? Compatibility was defined as the degree to which technology met individuals’ needs and complied with their past experience and existing lifestyles [38]. The diffusion of innovations theory [28] highlighted that when an innovation was compatible with current needs and values, the innovation might have a greater likelihood of being adopted by the individual. For older adults, technology compatibility with their needs, values, habits, preferences, experience and lifestyles was posited to significantly affect the usage intention for online healthcare as well. In other words, the higher the degree of compatibility, the more beneficial it was to older individuals’ positive intention to use.
Apart from the linkage between technology-specific factors and the usage intention, we could also confirm the influence of financial difficulty, which was a controlling covariate. An increase in financial difficulty would lead to a decrease in the usage intention for online healthcare. Why did financial difficulty exert a deleterious influence on the intention to use? In addition to technological factors, the use of modern technologies in everyday life also depended on economic factors [70]. Due to financial strain, there were digital risks, challenges and inequity in accessing and using digital health through digital platforms among older adults [71,72]. Specifically, older adults with economic constraints tended to have less access, resources and chances to obtain digital health, which would bring a barrier to the usage intention for online healthcare.

6. Implications and Limitations

6.1. Implications

Our current findings have significant theoretical contributions. First, it provides a model for exploring the technology-specific predictors of the usage intention for online healthcare using the diffusion of innovations theory and benefit-risk analysis model. It is important to note that our findings are aligned with aspects of the two theories. The results corroborate the theoretical links between the usage intention and the four technology-specific factors such as perceived risk, perceived benefit, relative advantage and compatibility. These findings broaden the applicability of the diffusion of innovations theory and benefit-risk analysis model to the particular situation of older adults in China and prove their significance in understanding the usage intention in the context of digital health.
The present research’s conclusions shed light on the detailed characteristics that would ultimately contribute to designing, developing, and implementing online healthcare that meet the needs of the aging populations. First, in designing, developing, evaluating and implementing new digital health platforms for online healthcare, it should comprehensively consider and address the technology-based issues such as perceived risk, perceived benefit, relative advantage and compatibility. Doing so will effectively contribute to the achievement of the sustainability in a digital healthcare ecosystem. Secondly, to increase the usage intention for online healthcare, the results of this study suggest raising awareness of its benefits, risk, compatibility and relative advantage, especially, our current finding shows the need for interventions that reduce risk perceptions in online healthcare programs, emphasizing the significance of addressing perceived risk as a major hurdle. In the design stage, it should comprehensively consider safety issues including health protection, financial protection, psychological protection and privacy protection based on perceived risk. At the same time, the choice of online healthcare should take into account its compatibility with older adults’ needs, experience and lifestyles. Additionally, providing financial support and improving individuals’ socio-economic status can boost the intention.

6.2. Limitations

The current study has several limitations. First, as described in Table 1, our sample was selective with regarded to higher education level as well as the fact that respondents were recruited via events that related to online healthcare. Second, due to the present cross-sectional data, the causal relationship cannot be drawn from the results. Longitudinal research would be contributed to addressing these issues. Future studies are encouraged to conduct follow-up surveys with older persons in order to shed light on the longitudinal effects of relative advantages, compatibility, perceived benefit and perceived risk on the usage intention. Third, the relative advantage of online healthcare has been shown during the period of the COVID-19 outbreak in China as this also increases the convenience of online healthcare for necessity medicines and drugs. However, we failed to consider the effect of the past experience related to the epidemic on the usage intention in our current data and study. Consequently, future studies are encouraged to investigate the benefits of using online healthcare during previous public health crises.

7. Conclusions

The results of this study identify the four technology-specific factors including perceived risk, perceived benefit, relative advantage and compatibility that have a strong influence on the usage intention for online healthcare. This influence implies that the technology-based factors are the key elements affecting older individuals’ perceptions, values, needs, preferences, experience and lifestyles in response to digital health technology. In addition, the results of this paper confirm that the attributes of relative advantage, compatibility and perceived benefit could act as facilitator and perceived risk as a barrier to the usage intention for online healthcare. Findings also enrich the applicable case of the diffusion of innovations theory and the benefit-risk analysis model in understanding the usage intention for online healthcare. Moreover, these findings and their implications here could provide specific evidence that the reinforcement of the usage intention for online healthcare in everyday life depends on technology-based factors in the Chinese context. The theoretical mechanisms underlying the link between the usage intention and technology-specific factors can also aid in developing and implementing sustainable technology-based solutions for online healthcare among older adults.

Author Contributions

Conceptualization, C.L.; formal analysis, C.L.; funding acquisition, C.L.; investigation, H.L., C.L.; project administration, C.L., Y.Z, H.L., L.L. and K.L.; resources, C.L.; software, C.L., Y.Z, H.L., L.L. and K.L.; writing—original draft, C.L., Y.Z. and H.L.; writing—review and editing, C.L., Y.Z. All authors have read and agreed to the published version of the manuscript.

Institutional Review Board Statement

The study was conducted according to the guidelines of the Declaration of Helsinki, and approved by the Academic Board of the School of Journalism and Communication, Chongqing University (CQUSJC2026017 of approval).

Data Availability Statement

The data presented in this study are available upon request from the corresponding author. The data are not publicly available due to privacy restrictions.

Conflicts of Interest

The authors declare no conflicts of interest.

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Table 1. Characteristics and level of the usage intention by different characteristics among the participants (N = 403).
Table 1. Characteristics and level of the usage intention by different characteristics among the participants (N = 403).
Categorical Variables N (%) Level of the intention
Mean (SD) F/t p
Gender
Male 195(48.4) 10.31(2.24) -0.2a 0.842
Female 208(51.6) 10.36(2.58)
Age
60–69 277 (68.7) 10.59(2.42) 5.133 b 0.006
70-79 111 (27.5) 9.83(2.35)
80+ 15 (3.7) 9.40(2.29)
Education attainment
Illiterate or primary school 50 (12.4) 9.46(2.89) 3.577 b 0.014
Junior high school 87 (21.6) 10.35(2.11)
Polytechnic school or senior high school 118 (29.3) 10.21(2.20)
College and above 148 (36.7) 10.72(2.52)
Financial strain
Enough 87(21.6) 10.87(2.39) 12.068 b <0.001
Approximately enough 292(72.5) 10.35(2.33)
Difficult 24(6.0) 8.21(2.54)
Self-rated health
Bad 108 (26.8) 10.94(2.21) 5.649 b 0.004
Fair 250 (62.0) 10.18(2.42)
Good 45 (11.2) 9.69(2.62)
Suffering from illness in the past two weeks
No 256 (63.5) 10.60(2.42) 2.932 a 0.004
Yes 147 (36.5) 9.87(2.35)
Continuous Variables N Mean (SD) Correlation with the usage intention p
the usage intention (range: 3–15) 403 (100.0) 10.33 (2.42) - -
Perceived risk (range: 5–25) 403 (100.0) 18.19 (3.59) 0.065 0.191
Perceived benefit (range: 6–30) 403 (100.0) 21.57 (3.78) 0.639 <0.001
Relative advantage (range: 3–15) 403 (100.0) 10.67 (2.19) 0.671 <0.001
Compatibility (range: 3–15) 403 (100.0) 10.18 (2.38) 0.666 <0.001
Abbreviation: a represented for t value; b represented for F value.
Table 2. Linear regression model I regarding perceived risk, perceived benefit, relative advantage and compatibility linked to the usage intention for online healthcare.
Table 2. Linear regression model I regarding perceived risk, perceived benefit, relative advantage and compatibility linked to the usage intention for online healthcare.
Predictors B (95% CI) β p Value Tolerance VIF
Perceived risk −0.100(-0.147,-0.053) −0.149 <0.001 0.887 1.127
Perceived benefit 0.218(0.157,0.280) 0.341 <0.001 0.470 2.127
Relative advantage 0.285(0.151,0.419) 0.258 <0.001 0.293 3.410
Compatibility 0.265(0.148,0.383) 0.261 <0.001 0.324 3.087
Constant 1.713(0.626,2.800) 0.002
Adjusted R Square 0.55
F 123.939
p <.001
Table 3. Linear regression model II regarding perceived risk, perceived benefit, relative advantage and compatibility linked to the usage intention for online healthcare.
Table 3. Linear regression model II regarding perceived risk, perceived benefit, relative advantage and compatibility linked to the usage intention for online healthcare.
Predictors B (95% CI) β p Value Tolerance VIF
Perceived risk −0.100(-0.148,-0.051) −0.148 <0.001 0.837 1.194
Perceived benefit 0.209(0.147,0.272) 0.327 <0.001 0.450 2.224
Relative advantage 0.261(0.124,0.399) 0.237 <0.001 0.279 3.589
Compatibility 0.274(0.154,0.394) 0.270 <0.001 0.308 3.242
Gender
Male(reference)
Female -0.031(-0.356,0.293) -0.006 0.850 0.954 1.048
Age
60-69 (reference)
70-79 -0.339(-0.712,0.034) -0.063 0.075 0.904 1.106
80+ -0.183(-1.065,0.700) -0.014 0.684 0.900 1.111
Education level
Illiterate or primary school (reference)
Junior high school 0.401(-0.175,0.976) 0.068 0.172 0.447 2.235
Polytechnic school or senior high school -0.089(-0.650,0.472) -0.017 0.755 0.385 2.594
College and above 0.015(-0.545,0.576) 0.003 0.957 0.344 2.910
Financial strain
Enough (reference)
Approximately enough -0.193(-0.598,0.213) -0.036 0.351 0.764 1.308
Difficult -1.024(-1.879,-0.170) -0.100 0.019 0.614 1.630
Self-rated health
Bad (reference)
Fair -0.283(-0.853,0.286) -0.057 0.328 0.329 3.042
Good -0.105(-0.748,0.537) -0.019 0.747 0.310 3.223
The illness condition in the past two weeks
No (reference)
Yes 0.071(-0.278,0.421) 0.014 0.689 0.887 1.128
Constant 2.473(1.056,3.889) <0.001 -
Adjusted R Square 0.553
F 34.116
p <.001
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