INTRODUCTION
1. Background
Korea has the fastest aging population worldwide and is projected to become the oldest country by 2045. The average number of chronic diseases per elderly is 2.1 [1], and the aging population and resulting increase in chronic diseases contribute to rapidly increasing healthcare costs. The elderly with chronic diseases have relatively high rates of healthcare visits and needs [2], and chronic diseases are often lifestyle-related and require ongoing management [3]. The global spread of COVID-19 has changed many aspects of lives and impacted healthcare use [4]. Compared to the pre-COVID-19 period, emergency and outpatient visits in Korea decreased by 22.8% and 8.3% in 2020, respectively, but increased among the elderly and those with chronic diseases [5]. Accordingly, elderly patients are exposed to infectious diseases more frequently because of frequent visits to medical institutions even during an infectious disease outbreak, and their reduced immunity owing to aging and chronic diseases acts as a factor that increases infection and fatality rates. As of February 2023, approximately 93% of COVID-19 deaths in Korea were among individuals aged ≥60 years [6].
Digital healthcare services are considered an effective way to minimize exposure to infectious agents and ensure access to healthcare during pandemics such as COVID-19 [7]. Additionally, digital healthcare services have been recommended as a solution to the burden of healthcare expenditures [8] and need for continuous management of chronic diseases [9]. Digital healthcare refers to “the convergence of healthcare, information and communication, and digital technology” [10], and the government is implementing public digital healthcare projects, such as mobile healthcare and senior citizen health management based on artificial intelligence (AI) and the Internet of Things (IoT), in response to the changes in the healthcare paradigm centered on prevention and management and the social demand for remote services after COVID-19 [11]. Among them, the AI-IoT-based elderly healthcare pilot project targeting older adults demonstrated high effectiveness in disease management, health behavior improvement, and cost reduction [12]. Based on these results, the ripple effect is expected to grow as more elderly individuals utilize digital healthcare services. However, according to the 2020 Digital Information Gap Survey, the elderly have the lowest digital literacy level among the information vulnerable groups (68.6%), and although they want to use the Internet, many of them do not know how to use it or cannot use it because of difficulties [13]. In the digital healthcare service pilot project, individuals who experienced difficulties using electronic communication devices discontinued the service within a month, and some were unable to use the service due to a lack of such devices or financial constraints [12]. Therefore, to establish and promote the use of digital healthcare services, it is essential to first identify factors related to the acceptance of digital healthcare services by elderly patients.
Previous research on digital healthcare services mostly focused on service providers [14,15], while there is limited research on patients. Choi and Kim’s [16] study confirmed the experience of digital healthcare service pilot project participants, but it targeted only low-income elderly individuals in a specific region, and other previous studies [17,18] only studied one group according to their experience with digital healthcare services. Therefore, it is necessary to understand the expectations and intentions of the inclusive elderly patients to use digital healthcare services as the service introduction is discussed. Digital healthcare services, due to their characteristics in healthcare and information communication technology, require a logical and systematic analysis of the acceptance of new technologies based on an understanding of medical technology. The Unified Theory of Acceptance and Use of Technology (UTAUT) is a behavioral prediction theory that integrates eight behavioral intention-related models to identify factors that influence behavioral intention to use a new technology and actual use behavior [19]. It has been used in many studies related to the acceptance of new technologies [20] and shown to be an excellent model for predicting intention to use, with 70% more explanatory power than traditional models [19]. In this theory, factors that influence behavioral intention comprise effort expectancy (an individual believes that using a technology will be easy), performance expectancy (an individual believes that the technology will be beneficial), and social influence (an individual perceives that others support the use of the technology). Moreover, behavioral intention affects use behavior.
Thus, this study aimed to provide a basis for intervention strategies to help elderly patients access and use digital healthcare services by identifying factors that influence their intention to use digital healthcare services based on UTAUT. Furthermore, we present academic and practical implications for considering the accessibility of vulnerable populations in digital healthcare service policies and related industries.
2. Purpose
This study aimed to examine the multifaceted factors influencing elderly patients’ intention to use digital healthcare services through the following objectives:
1) To describe the general characteristics of elderly patients and their levels of effort expectancy, performance expectancy, social influence, and intention to use digital healthcare services.
2) To analyze differences in effort expectancy, performance expectancy, social influence, and intention to use according to elderly patients’ general characteristics.
3) To investigate the relationships among effort expectancy, performance expectancy, social influence, and intention to use digital healthcare services.
4) To determine the impact of effort expectancy, performance expectancy, and social influence on intention to use digital healthcare services.
METHODS
Ethics statement: This study was approved by the Institutional Review Board (IRB) of Chungnam National University Sejong Hospital (IRB No. CNUSH 2023-04-002). Informed consent was obtained from the participants.
1. Study Design
This cross-sectional correlational study aimed to identify factors affecting effort expectancy, performance expectancy, social influence, and intention to use digital healthcare services using a questionnaire based on UTAUT described in accordance with the Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) reporting guidelines (https://www.strobe-statement.org).
2. Participants
The participants were elderly patients aged ≥65 years who visited a medical institution (general hospital, health sub-center, and health clinic) with health problems. Eligible participants were able to communicate, understand the purpose and procedures of the study, and voluntarily consented to participate. The age criterion for the elderly were set at ≥65 years, as defined by laws and regulations such as the Welfare of Senior Citizens Act. Since this study examined the intention to use digital healthcare services within the context of existing healthcare service delivery, participants with no experience in outpatient face-to-face medical services were excluded. The required sample size was calculated using the G*power 3.1.9.4 program (University of Dusseldorf), based on a significance level of .05, an effect size of .15, a power of .80, and seven predictors for multiple regression analysis. The sample size required was 103. The survey was conducted via in-person interviews, ensuring no missing data. A total of 103 questionnaires were distributed, and all responses were included in the analysis.
3. Study Tools
1) General Characteristics
The participants’ general characteristics included gender, age, experience with digital healthcare services, number of chronic diseases, ability to perform activities of daily living, and instrumental activities of daily living. Activities of daily living and instrumental activities of daily living were measured using the Korean Activities of Daily Living (K-ADL) and Korean Instrumental Activities of Daily Living (K-IADL) scales, respectively, which were developed by Won et al. [21]. The K-ADL comprised seven questions in a 3-point scale with a minimum score of 7 and maximum score of 21, with higher scores indicating poorer functioning. Regarding tool reliability, Cronbach’s α was .94 during development and .88 in our study. The K-IADL comprised 10 questions, measured on a 3- or 4-point scale with a minimum score of 10 and maximum score of 33. Higher scores indicate reduced functioning and greater dependence on others. Regarding tool reliability, Cronbach’s α was .94 during development and .91 in our study.
2) Effort Expectancy
Effort expectancy for digital healthcare services was measured using the effort expectancy measurement tool among the 12 constituent concepts in the questionnaire developed by Koo and Jin [17] based on UTAUT by Venkatesh et al. [19]. This tool comprised four questions about ease of learning how to use, clarity of the user instructions, ease of use, and ease of adaptation. Each statement is measured on a 4-point Likert scale (1=“strongly disagree” to 4=“strongly agree”), with a higher total score indicating a more positive effort expectancy to digital healthcare services. Cronbach’s α was .91 in the study by Koo and Jin [17] and .93 in our study.
3) Performance Expectancy
For the performance expectancy of digital healthcare services, the performance expectancy measurement tool was used among the 12 constituent concepts in the survey tool developed by Koo and Jin [17] based on UTAUT by Venkatesh et al. [19]. The tool comprised three questions on healthcare usefulness, prompt access to healthcare, and expectations for improved health. Each statement is measured on a 4-point Likert scale (1=“strongly disagree” to 4=“strongly agree”), with higher total scores indicating more positive performance expectancy for digital healthcare services. Cronbach’s α was .84 in the study by Koo and Jin [17] and .77 in our study.
4) Social Influence
The social influence of digital healthcare services was measured using the social influence measurement tool, a survey instrument developed by Koo and Jin [17] based on UTAUT of Venkatesh et al. [19]. The tool comprised three questions on perceived family preference for use, friends’ support for use, and advisers’ support for use. Each statement is measured on a 4-point Likert scale (1=“strongly disagree” to 4=“strongly agree”), with a higher total score indicating a higher social influence for digital healthcare services. Cronbach’s α was .89 in the study by Koo and Jin [17] and .91 in our study.
5) Intention to Use Digital Healthcare Services
Intention to use digital healthcare services (i.e., behavioral intention) was measured using the behavioral intention measurement tool, a subscale of the survey instrument developed by Koo and Jin [17] based on UTAUT of Venkatesh et al. [19]. The tool comprised three questions on inclination to use, willingness to use, and enthusiasm to use. Each statement is measured on a 4-point Likert scale (1=“strongly disagree” to 4=“strongly agree”), with a higher total score indicating a more positive intention to use digital health services. Cronbach’s α was .91 in the study by Koo and Jin [17] and .87 in our study.
4. Data Collection
Data were collected from one general hospital, one health sub-center, and one health clinic in City Sejong from April 25, 2023 to July 29, 2023. Before data collection, permission to recruit participants was obtained from the director of each institution by letter and telephone, and data were collected by the researcher in-person at the outpatient and inpatient departments of the institution on the date of permission. The questionnaire was administered face-to-face for approximately 30 minutes, with each participant being read the questionnaire individually only after they have been fully informed of the purpose and procedures of the study and voluntarily agreed to participate in the study.
5. Ethical Considerations
This study was approved by the Institutional Review Board (IRB) of Chungnam National University Sejong Hospital (IRB No. CNUSH 2023-04-002). Before data collection, the participants were informed of the purpose, objectives, and confidentiality of the study and explained in easy-to-understand terms, considering the physiological characteristics of the elderly. The participants were informed that participation in the study was voluntary and that they could stop participating at any time without any disadvantages. Thereafter, the survey was conducted only on participants who understood the purpose and procedures of the study and voluntarily filled out the consent form. Participants who completed the survey were given a small gift as a token of appreciation.
6. Data Analysis
The data collected were analyzed using the SPSS/WIN 26.0 program (IBM Corp.).
1) The participants’ general characteristics, effort expectancy, performance expectancy, social influence, and intention to use digital healthcare services were analyzed using descriptive statistics.
2) Differences in effort expectancy, performance expectancy, social influence, and intention to use digital healthcare services according to the participants’ general characteristics were analyzed using independent t-tests and one-way ANOVA. Post-hoc analyses were conducted using the Scheffé test, and Dunnett’s T3 test was used when equal variances were not assumed.
3) Pearson’s correlation coefficient was used to analyze the correlations among effort expectancy, performance expectancy, social influence, and intention to use digital healthcare services.
4) The factors affecting the participants’ intention to use digital healthcare services were analyzed using hierarchical multiple regression.
RESULTS
1. Participants’ General Characteristics
The participants were mostly women (75 women [72.8%] and 28 men [27.2%]). The mean age was 75.86±6.17 years, with the 75~79 years age group being the most prevalent, comprising 30 participants (29.1%). The number of chronic diseases suffered by respondents was highest in 53 (51.5%) with ≥4, followed by 20 (19.4%) with three, 17 (16.5%) with ≤1, and 13 (12.6%) with two. Most participants had no experience with digital healthcare services, with 83 (80.6%) reporting no experience. The elderly patients had a mean of 7.57±1.75 points for activities of daily living and 11.24±3.16 for instrumental activities of daily living (Table 1).
2. Effort Expectancy, Performance Expectancy, Social Influence, and Intention to Use
The participants’ mean effort expectancy was 2.20±0.87 points, with sub-items of clarity of the user instruction (EE2) at 2.37±0.93, ease of learning how to use (EE1) at 2.16±0.97, ease of use (EE3) at 2.16±0.98, and ease of adaptation (EE4) at 2.14±0.98. Performance expectancy had a mean of 2.69±0.65, with sub-items of healthcare usefulness (PE1) at 2.94±0.67, expectations for improved health (PE3) at 2.73±0.76, and prompt access to healthcare (PE2) at 2.41±0.92. Social influence had a mean of 2.80±0.72, with sub-items including advisers’ support for use (SI3) at 2.83±0.77, perceived family preference for use (SI1) at 2.81±0.76, and friends’ support for use (SI2) at 2.78±0.82. The mean score for the dependent variable, intention to use, was 2.69±0.66, with the sub-items of inclination to use (BI1) 2.79±0.72, willingness to use (BI2) 2.78±0.73, and enthusiasm to use (BI3) 2.50±0.79 (Table 2).
3. Differences in Effort Expectancy, Performance Expectancy, Social Influence, and Intention to Use Based on General Characteristics
Participants were classified as independent if their activities of daily living and instrumental activities of daily living scores met the baseline thresholds of 7 and 10, respectively, and as dependent if their scores exceeded these thresholds. Effort expectancy was significantly different by age (F=4.65, p=.002), number of chronic diseases (F=4.32, p=.010), experience with digital healthcare services (t=4.04, p<.001), and instrumental activities of daily living (t=2.35, p=.021). Post-hoc analyses showed that effort expectancy was higher among patients aged 65~69 and 70~74 years than those aged ≥85 years, in those with two chronic diseases than those with ≥4 chronic diseases, in those with experience in digital healthcare services than those without, and in those who were independent in instrumental activities of daily living than those who were dependent. Performance expectancy were significantly different by age (F=2.69, p=.047) and number of chronic diseases (F=5.71, p=.003). Patients aged 65~69 years had higher performance expectancy than those aged ≥85 years, but the difference was not significant in post-hoc analyses. Post-hoc analyses of the number of chronic diseases showed that patients with two chronic diseases had higher performance expectancy than those with three and ≥4 chronic diseases. Social influence was only significantly different with or without experience with digital healthcare services (t=2.84, p=.006), with those with experience reporting higher social influence than those without. Intention to use was significantly different by age (F=4.04, p=.008), number of chronic diseases (F=6.21, p=.001), previous experience with digital healthcare services (t=2.66, p=.010), and instrumental activities of daily living (t=2.02, p=.046). Patients aged 65~69 years showed a higher intention to use than elderly patients aged ≥85 years, with no significant difference in the post-hoc analyses. Post-hoc analyses showed that the intention to use digital healthcare services was higher among patients with two chronic diseases than among patients with ≥4 chronic diseases, among experienced participants than among those without experience, and among independent groups than among dependent groups regarding instrumental activities of daily living (Table 3).
4. Correlation of Study Variables
In this study, effort expectancy was positively correlated with performance expectancy (r=.58, p<.001), social influence (r=.35, p<.001), and intention to use (r=.54, p<.001). Performance expectancy was significantly and positively correlated with social influence (r=.66, p<.001) and intention to use (r=.65, p<.001). Social influence and intention to use (r=.78, p<.001) were positively correlated. These findings indicate that higher effort expectancy, performance expectancy, and social influence scores were associated with higher scores for intention to use (Table 4).
5. Factors Influencing Intention to Use Digital Healthcare Services
To examine the factors influencing the intention to use digital healthcare services, hierarchical regression analysis was performed. Among the general characteristics, experience with digital healthcare services was treated as a dummy variable, coded as 1 for no experience and 0 for experience. The Durbin–Watson statistic was 1.88, indicating no autocorrelation as it is close to 2. Tolerance was ≥0.1, and the Variance Inflation Factor was <10, indicating no multicollinearity issues.
Model 1 explained 17% of the variance in intention to use (F=6.29, p<.001) after controlling for variables with significant differences among general characteristics, such as age, number of chronic diseases, experience with digital healthcare services, and instrumental activities of daily living. Age (t=-4.22, p<.001) was found to be a significant explanatory variable (β=-.39).
Model 2 examined whether the addition of effort expectancy, performance expectancy, and social influence would affect intention to use even after controlling for exogenous variables. The regression analysis showed that effort expectancy (t=3.30, p=.001) and social influence (t=7.96, p<.001) explained 68% of the variance in intention to use digital healthcare services (ΔF=51.80, p<.001), whereas performance expectancy was not a statistically significant explanatory variable (t=0.64, p=.523). Therefore, effort expectancy and social influence are significant explanatory variables for elderly patients’ intention to use digital healthcare services, with social influence (β=.62) having a larger effect than effort expectancy (β=.24) (Table 5).
DISCUSSION
This study aimed to identify factors that influence the intention to use digital healthcare services among elderly patients based on UTAUT and provide a basis for developing intervention strategies to provide digital healthcare services that meet the needs of the elderly.
Effort expectancy had the lowest score among the four study variables with a mean of 2.20±0.87 out of 4, indicating that elderly patients face challenges in using electronic communication devices for accessing digital healthcare services. This aligns with previous research indicating that the elderly had the least level of information literacy among the four groups of individuals who are most vulnerable to information technology (the disabled, low-income, farmers and fishermen, and elderly), and majority of the elderly either did not know how to use the Internet or found it difficult to use; thus, could not use the Internet although they wanted to [13]. Additionally, it was consistent with previous research that only a small number of the elderly could perform advanced operations, such as searching for and installing applications through electronic communication devices [22]. In this study, elderly individuals aged ≥85 years had lower effort expectancy than those aged ≤74 years, which is consistent with previous research [22], in which those aged ≥75 years had lower competence in using information and communication technologies. Moreover, the elderly with two co-morbid chronic diseases had higher effort expectancy than the elderly with ≥4 chronic diseases, which supports Um et al.’s [23] study of information technology literacy among the Korean elderly, finding that higher information technology literacy was associated with fewer chronic diseases. Elderly individuals often experience multimorbidity along with age-related issues such as sensory and cognitive decline. These health challenges not only increase their need for healthcare services but also act as barriers to accessing them. Therefore, nurses should develop and implement tailored information literacy programs that consider both the developmental and health-related characteristics of older adults. Previously [16], elderly individuals who had learned to use electronic communication devices easily as they first learned how to use them, but experienced difficulties in trying to use them by themselves and preferred repeated training and proactive contact with medical staff. This is consistent with the result of this study that ease of adaptation, a sub-item of effort expectancy, has the lowest score. This suggests that ongoing training and monitoring is required, not just a one-time training.
Performance expectancy scored 2.69±0.65 out of 4, with significant differences by age and number of chronic diseases. Among the three sub-items, healthcare usefulness was measured as the highest among all the items of the research variables at 2.94±0.67. This is consistent with a previous study reporting that the benefits of digital healthcare for elderly individuals include the regular management of chronic diseases and aging-related conditions, and AI-driven healthcare and telemedicine have been reported as useful for improving health status [24]. Conversely, prompt access to healthcare scored 2.41±0.92, which was lower than the other items. These findings suggest that although individuals perceive that healthcare delivered via electronic communication devices can help them manage their health, their expectations may be diminished by other barriers. Particularly, it is crucial to efficiently design the processes of digital healthcare services to improve performance expectancy by minimizing steps and waiting times, and to be accompanied by interventions that can improve the ability to use unfamiliar devices and information technology that hinder prompt access to healthcare. The elderly with ≥4 chronic diseases had significantly lower performance expectancy than the elderly with two chronic diseases, which may be attributed to their relatively low technology literacy limiting their ability to access healthcare promptly [23].
Social influence was the highest among the study variables, with a mean score of 2.80±0.72 out of 4, and was higher among those with experience in digital healthcare services than those without. Previous studies [13,25,26] have found that elderly individuals often rely on their social networks to solve problems related to the use of electronic communication devices, and family members have a significant influence on their decision-making. This suggests that the degree of social support for the elderly is an important factor in the use of electronic communication devices, and a lack of social support can result in difficulties in problem-solving and discontinuation of use. Thus, if there is a desire for the elderly to use digital healthcare services, interventions should be provided to ensure that they have a well-established social support network and to support its sustainability.
Intention to use digital healthcare services had a mean score of 2.69±0.66 out of 4. Intention to use digital healthcare services was higher among younger individuals, those with fewer chronic diseases, and those with higher instrumental activities of daily living. This may be due to concerns about managing multimorbidity with limited information compared to face-to-face visits and differences in expectations regarding the use of electronic communication devices. According to UTAUT, experience has a moderating effect on intention to use, and this study found that those with experience in digital healthcare services had a higher intention to use than those without experience. This finding is similar to previous research [17], in which the elderly with computer or cellphone experience were less likely to drop out of u-Health services. However, only 19.4% of the study participants had experience, whereas 56.3% expressed willingness to use the service. This is likely related to changes in the attitudes of the elderly toward information and communication technologies after COVID-19. The 2020 Digital Information Gap Survey [13] reported that a majority of elderly respondents agreed with the statements that the importance of internet and mobile technology has increased in daily life after COVID-19, that there is a growing need for more educational opportunities on related technologies, and that lack of technology proficiency can lead to social exclusion. Thus, the COVID-19 pandemic has heightened the importance of information and communication technologies among elderly individuals; however, 43.7% of the study participants reported a lack of intention to use them. In South Korea, residences are in close proximity to primary healthcare centers, reducing the perceived need for digital healthcare services. However, if physical access to a medical institution is impossible because of unavoidable reasons, such as an outbreak of a new infectious disease, bioterrorism, immobility due to physical problems, or natural disasters, elderly individuals should be free to consume digital healthcare services by choice. Particularly, it is necessary to build an easy and simple access method and infrastructure so that the elderly can use the digital healthcare service on their own during an emergency although they do not normally use the service. For example, the elderly may have cognitive decline and worn fingerprints; thus, methods, such as facial recognition or iris recognition, may be easier than passwords or fingerprints. Since increased accessibility may heighten the risk of data breaches, appropriate countermeasures must be implemented.
In this study, effort expectancy and social influence were found to have a significant impact on intention to use digital healthcare services. These results are similar to those of Noh et al.’s study [27], which concluded that higher effort expectancy has a positive impact on the intention to use and a positive social influence has a positive impact on the use of telemedicine. Palas et al. [18] found social influence to be the most influential factor on the elderly in intention to use health-related mobile services, and a study on the self-directed learning of media education among the elderly [25] found that the elderly were relationship-dependent in their motivation and problem-solving in media utilization. In Confucian cultures, the family of the elderly can make decisions on their behalf if they use telemedicine services [26]. Previous research in China, a Confucian culture, showed that social influence has the greatest impact on the elderly [28]. In this study, the social influence was found to have a higher impact than other factors, which may be due to the difficulty of learning new skills with age and these cultural factors. Therefore, it would be effective to use simple terms and images to help elderly patients who need digital healthcare services understand them and let them experience them through pilot training. It is believed that if guardians are educated about the need and usage, it could improve their intention to use it and lead to use behavior. However, the elderly tend to have fewer social connections owing to retirement, children’s independence, death of a spouse or friends, and mobility limitations; thus, nurses should explore and connect them to community resources. Moreover, the government should create an environment and develop human resources for the use of digital healthcare services in places that are highly accessible to the elderly, such as community health centers, health clinics, and senior welfare centers, and the relevant industry should improve the simplicity, clarity, visibility, and accessibility of the application user interface.
Performance expectancy had a significant positive correlation with intention to use digital healthcare services but was not a significant predictor of intention to use. Baek et al. [29] found that social influence had the strongest impact on intention to use, with performance expectancy being a more valid factor among young adults. In some studies, performance expectancy was a significant factor [17,30], which may be because of the younger age of the participants in those studies, with a higher proportion in their 50s or 60s. These results suggest that although elderly patients perceive a new technology to be useful, barriers, such as difficulty in acquiring the skills and the absence of a support individual, may not influence their intention to use it. Digital technologies are still new to the current elderly generation; however, as generational shifts occur, continuous research on influencing factors will be necessary.
This study is significant in that it identifies the factors influencing the intention to use digital healthcare services among elderly patients in Korea from a nursing perspective. By applying the Technology Acceptance Model, this study systematically and logically analyzed the phenomenon of technology adoption, ultimately confirming that effort expectancy and social influence significantly influence the intention to use. The findings of this study can serve as foundational data for developing intervention strategies to facilitate elderly patients’ access to and use of digital healthcare services. Furthermore, the academic and practical implications presented in this study are expected to inform digital healthcare service policies and related industries, ensuring that the accessibility of vulnerable populations is adequately considered.
It should be noted that this study is limited by the fact that digital healthcare services are still unfamiliar to the general public, and elderly participants have even more restricted access to these services. As a result, the number of individuals who have experienced digital healthcare services is limited. Therefore, the study only assessed the intention to use the service, without considering the participants’ actual usage data, which limits the ability to extend the interpretation of the findings to actual service use behavior. Although this study focused on measuring the intention to use digital healthcare services, further research is needed to examine the factors that directly influence actual use behavior, incorporating actual usage data.
CONCLUSION
Based on UTAUT, this study examined the levels of effort expectancy, performance expectancy, social influence, and intention to use digital healthcare services among elderly patients aged ≥65 and identified the factors influencing their intention to use these services.
The study’s findings revealed that effort expectancy and social influence significantly explained the intention to use digital healthcare services, even after controlling for age, number of chronic diseases, experience with digital healthcare services, and instrumental activities of daily living. Higher levels of effort expectancy and social influence were associated with higher intention to use digital healthcare services. Therefore, interventions designed to improve effort expectancy and social influence are crucial for improving elderly patients’ intention to use digital healthcare services.
According to the study findings, the following suggestions are made: First, a follow-up study applying the expanded UTAUT, incorporating price value, habit, and usage variables, should be conducted following the introduction of digital healthcare services. Second, a study is needed to develop and validate a standardized intervention strategy to help elderly patients use digital healthcare services, that reflects their actual needs as identified in the study results.
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