Effect of older adults willingness on telemedicine usage: an integrated approach based on technology acceptance and decomposed theory of planned behavior model

Abstract

Background

Telemedicine, as a novel method of health management system, has demonstrated to have a significant impact on health levels. However, a challenge persists in the form of low usage rates and acceptance among older adults in China. There are accumulating evidence that willingness will affect the telemedicine usage among older adults. This study investigates factors influencing older users’ trust in adopting telemedicine technology, thereby promoting actual use.

Methods

A questionnaire survey was conducted with 400 urban seniors aged 60 and above. Drawing from the Technology Acceptance Model (TAM) and the Decomposed Theory of Planned Behavior (DTPB), the author combines elements such as Perceived Usefulness, Perceived Ease of Use, Subjective Norms, Service Environment, Self-Efficacy, Behavioral Intention to Use, and Usage Behavior. The aim is to explore the interrelationships between these factors.

Results

Perceived Usefulness (PU) and Service Environment (SE) significantly and positively impact Behavioral Intention (BI) to use telemedicine, with Trust (TR) identified as a crucial mediating variable. Enhancing trust can substantially increase older adults’ intention to use telemedicine services. Furthermore, the study reveals a significant relationship between older adults’ trust in telemedicine and factors such as Perceived Usefulness (PU), Service Environment (SE), Subjective Norms (SR), as well as Emotional Risk (ER) and Cost Risk (CR), the latter two tending to decrease Trust(TR).

Conclusions

This paper constructs and validates a combined model based on TAM and DTPB, comprehensively exploring the potential factors influencing the older adults’ intention to use telemedicine. The findings suggest that telemedicine services for older adults should prioritize improving user perception and enhancing trust throughout the service process to effectively increase their willingness to use these services.

Background

The issue of global population ageing is becoming globally prevalent. According to the latest United Nations World Population Prospects for 2022, projections indicate that individuals aged 65 and older will constitute 10% of the global population. With this figure, this population is anticipated to increase by 16% by 2050 [1]. In China, the challenge of population aging is particularly pronounced. The nation’s economic growth and advancements in health care have led to an increase in average life expectancy, marking China’s transition into an aging society at an unprecedented pace. By 2020, the population of individuals aged 65 and is expected to reach 190.64 million [2]. A World Bank report suggests that by 2050 individuals over 65 will make up 26% of China’s population, with those over 80 comprising 5% [3]. Despite the global trend of increased life expectancy among older adults over the last three decades, their health status has not exhibited significant improvement [4]. The combination of declining physical functions and a rise in chronic ailments necessitates heightened health care and nursing support for older individuals [5]. However, with the growth of the population, the 2015 China Family Development Report reveals that nearly 10% of older adults live alone while 41.9% reside only with their partners. In other words, the older adults lack the necessary conditions for care. The number of empty-nested older adults was in China is on the rise [6], indicating that conventional model of aging at home may be unviable.

The American Telemedicine Association defines telemedicine as “an advanced medical diagnostic system that facilitating the exchange of patients’ medical information across different locations through electronic communication means, such as two-way video technology, emails, smart phones, and wireless tools”. The aim is to enhance the level of medical diagnostics for patients [7]. Telemedicine encompasses a range of services, including health information management and assessment, medical appointment reminders, health education, health testing, health surveys and data collection [8]. In the United States, telemedicine services flourished with approximately 200 telemedicine networks employing various technological modes. These networks connect more than 3,000 remote sites, benefiting more than 80,000 residents who use telemedicine and health monitoring services [7]. In a 2017 survey conducted by the American Telemedicine Association (ATA) Advisory Board, it was found that 77% of patients preferred online consultations [9].

However, in China, the development of telemedicine is currently ineffective in most regions, with an actual utilization rate of less than 30%. The utilization of telemedicine by older adults is even lower. A study involving African and Hispanic older adults revealed that 63% of them used the telemedicine services to acquire health-related information, surpassing all other activities, such bill payment or products orders [10]. Conversely a study examining telemedicine services use among older adults in a Chinese province found that only 3.1% of obtained disease-related information online [11].

Recent researches have delved into the intentions of older adults to use telemedicine. A study explores the attitudes of women over 50 towards adopting intelligent health services, and identified significant impacts of perceived usefulness, perceived ease of use, and subjective norms on the adoption intentions of older women [12]. Zhang’s study examined users’ adoption intentions focusing on wearable medical technology focusing on technological attributes (Perceived Convenience, Perceived Irreplaceability, Perceived Trustworthiness, and Perceived Usefulness) [13]. Another study [14] found that the Physician Service Environment and Subjective Norms positively influence patients’ adoption intention of online medical services. Trust has emerged as crucial factor in the study of older adults’ intentions to use telemedicine services. Meng’s study [15] analyzed elderly users’ Behavioral Intention to use telemedicine based on a trust transfer model from the users’ trust in telemedicine service platforms. Mun Yi [16] studied the initial trust of online health information, and using trust as a mediating variable between perceived information quality and perceived risk. Additional studies by Egea and González [17] indicated that perceived risk significantly affects trust.

However, the relationship between user trust, technical attributes of telemedicine services and older adults’ Behavioral Intention to use telemedicine services remains unclear. The specific influential role of user trust as a mediating variable between the technological attributes of telemedicine services and older adults’ Behavioral Intention to use them has not been fully discussed. Particularly in China, there is a lack of attention to the older adults, who are most in need of medical resources, in studies on the willingness to use telemedicine services.

The purpose of this study is to investigate how trust affects the willingness of older adults to use telemedicine services and to identify the factors influencing their trust in these services. Understanding these aspects is crucial for addressing the medical challenges faced by older adults.

Literature review and hypotheses

The Technology Acceptance Model (TAM) proposed by Davis et al. in 1989 stands as one of the most influential theories in the realm of information technology adoption research [18]. It primarily investigates how two key determinants, perceived usefulness, and perceived ease of use, collectively influence behavioral intentions. Despite TAM’s widespread applicability and logical foundations supported by numerous empirical studies, some research argues that the dimensions within TAM are overly simplistic and lack practicality [19].

However, exploration into information technology adoption extends beyond TAM alone. Both the Theory of Planned Behavior (TPB) [19,20,21], which shares the same origin as TAM, and the Innovation Diffusion Theory (IDT) [22] from the field of communication studies, have contributed to the theoretical advancement of this field from different perspective.

Behavioral theories rooted in the social cognitive model have gained widespread use, taking a forefront role in predicting and explaining health behaviors [23], social marketing [24], and lifestyle studies [25]. According to Dahl [24] and others classify these Behavioral theories as the Theory of Reasoned Action (TRA), the Theory of Planned Behavior (TPB), the Protection Motivation Theory (PMT), the Health Belief Model (HBM), and the Stages of Change Model. Social cognitive models emphasize assessing people’s behaviors and beliefs within a social context [23]. While each model has a distinct focus, they share similar ideas about how people act.

The Theory of Planned Behavior (TPB), an extension of the Theory of Reasoned Action (TRA), was developed by Ajzen in 1990. It is one of the most widely used social cognitive theories for understanding the relationship between intentions and behaviors [26]. According to TPB, Attitudes towards the Behavior, Subjective Norms, and Perceived Behavioral Control (PBC) determine intentions, which subsequently predict behavior [27]. TPB has proven to be one of the most concise models in predicting intentions and various behavioral outcomes [28]. However, it is inherently subjective and lacks universal applicability [29]. To address this, Taylor and Todd proposed the DTPB model, an extension of TPB, which exhibits stronger explanatory abilities compared to the traditional TPB model and finds utility in various fields [30]. The DTPB model breaks down Subjective Norms into social influences from peers and superiors.

In academic circles, research confirms that while these models individually possess explanatory capabilities when applied individually, they also have significant limitations, necessitating integration for enhanced explanatory power [20]. Therefore, by integrating TAM with the DTPB model, we amalgamate the elements such as Perceived Usefulness, Perceived Ease of Use, Subjective Norms, Service Environment, Self Efficacy, Behavioral Intention to Use, and Usage Behavior from both models and investigate their influential relationships.

Within the framework of the Technology Acceptance Model (TAM), the key determinants influencing a user’s decision to adopt a technology are Perceived Usefulness and Perceived Ease of Use. Perceived Usefulness is defined by Holden and Karsh [31] as an individual’s subjective perception of the extent to which using technology enhances their job performance. In the context of telemedicine, Perceived Usefulness refers to the extent to which users perceive telemedicine services as beneficial for treating diseases. Ahmed MH’s [32] study found that the higher older adults’ perceive the value and effectiveness of telemedicine platforms, the higher their Behavioral Intention to use telemedicine. Kim’s [33] study demonstrated that perceived usefulness positively and significantly influences older users’ intention to use telemedicine services. In TAM theory, Perceived Usefulness can directly impact users’ Behavioral Intention of use.

Hypothesis 1

(H1): Perceived Usefulness (PU) significantly positively influences on older adults’ Behavioral Intention (BI) to use telemedicine.

Furthermore, another variable commonly employed in TAM theory is Perceived Ease of Use, defined as an individual’s perception of the required when using a specific technology [31]. In the context of telemedicine, Perceived Ease of Use represents the level of difficulty users perceive when utilizing or learning about telemedicine. It pertains to whether patients find that using telemedicine easy to learn. In TAM theory, Perceived Ease of Use can directly impact both Perceived Usefulness and User Behavioral Intention of Use [34].

Hypothesis 2

(H2): Perceived Ease of Use (PE) significantly positively influences on older adults’ Behavioral Intention (BI) to use telemedicine.

Service Environment, as proposed by Ronnie Jia [35], refers to customers’ perception of the organizational support they receive when employees provide services at work. Parasuraman [36] defines Service Environment as users’ overall assessment of the service received, encompassing perceived quality and objective quality. Schneider [37] considers Service Environment as an organizational context that reflects the employees’ behaviors and attitudes toward service recipients influencing users’ Behavioral Intention.

Hypothesis 3

(H3): Service Environment (SE) significantly positively influences on older adults’ Behavioral Intention (BI) to use telemedicine.

Deng [38] posits that Subjective Norms primarily indicate the influence of the social environment on individual behavior. According to DTPB model’s categorization of Subjective Norms, for older adults, these norms mainly encompass social influence factors such as family members, children, and doctors. Research by Lu [39] and others has found that Subjective Norms have a positively impact on users’ intentions to use information technology. Ernst’s study [40] suggests that both Subjective Norms and Self Efficacy can influence users’ self-usage intentions.

Hypothesis 4

(H4): Subjective Norms (SR) significantly positively influences on older adults’ Behavioral Intention (BI) to use telemedicine.

Self Efficacy is an individual’s self-assessment of their ability to perform a task [41]. Research by Thomas [42] and others indicates that Self Efficacy has a significant positive impact on individual behavior. Studies by Choi [43] and colleagues found that Self Efficacy significantly influences the intention to accept intelligent medical services. Lim [44] and others have shown that Self Efficacy significantly affects women’s intentions to adopt smart health services.

Hypothesis 5

(H5): Self Efficacy (SV) significantly positively influences on Behavioral Intention (BI) of older adults to use telemedicine.

The study of trust issues has always consistently attracted attention across multiple disciplines such as sociology, philosophy, psychology, management, and marketing [45]. Trust serves as a crucial bond between social systems and individuals, especially in the healthcare field, where it is a vital factor in determining the quality of doctor-patient relationships. Zarolia [46] argue that trust is the belief that the other partner will perform behaviors that benefit their partner and will not engage in unintended behaviors to the detriment of the transactional partner. Kautish [47] and Yang [48] define trust (TRU) as users’ willingness to act on and perform the information and advice received through an telemedicine service, along with the expectation that the platform will fulfill its responsibilities. In service adoption research, trust is widely regarded as a strong mediating factor influencing service adoption. Akter & D’Ambra [49] consider trust to play an important mediating role between credibility and usage behavior. Studies by Akter & Ray [50] show that user trust has a significant positive impact on continuous usage intentions. Kampmeijer R’s study [51] demonstrates that older adults’ trust in healthcare and telemedicine is influenced by various factors, including Subjective Norms, Education, Health Level, Gender, Age, Self Efficacy, Service Environment, and more. Yang’s research [48] shows that Subjective Norms have a positive impact on Trust.

Hypothesis 6

(H6): Trust (TR) significantly positively influences older adults’ Behavioral Intention (BI) to use telemedicine.

Hypothesis 7

(H7): Perceived Usefulness (PU) significantly positively influences trust (TR) telemedicine services among older adults.

Hypothesis 8

(H8): Perceived Ease of Use (PE) significantly positively influences older adults’ trust (TR) in telemedicine services.

Hypothesis 9

(H9): Service Environment (SE) significantly positively influences older adults’ trust (TR) in telemedicine services.

Hypothesis 10

(H10): Subjective Norms (SR) significantly positively influences older adults’ trust (TR) telemedicine services.

Hypothesis 11

(H11): Self Efficacy (SV) significantly positively influences older adults’ trust (TR) telemedicine services.

The concept of Perceived Risk, originally rooted in psychology, was extended to behavioral science by Bauer [52]. Numerous studies [53,54,55] hypothesized that six dimensions social, temporal, financial, physical, functional, and psychological risks could comprehensively explain the overall perceived risk. Hassan [56] categorized perceived risks into eight types: financial, functional, temporal, social, psychological, physical, source, and privacy. These risks are further divided into three categories based on their characteristics. Technical Risk [57] pertains to the possibility that the service obtained by the user after using telemedicine services does not achieve the expected effect. Emotional risk [58] involves the potential theft, leakage, or inappropriate use of user’s personal information, along with that the user’s personal information will be stolen, leaked, or used inappropriately as well as the possibility of psychological or mental stress when using telemedicine services. Cost risk [59] refers the potential loss of time and money for users when using telemedicine services. Research on the telemedicine user adoption model indicates that perceived remote medical risk significantly influences trust, which, in turn, significantly impacts the intention to use telemedicine. In other words, the higher perceived risk of telemedicine leads to lower trust and reduced willingness to use it for medical consultations. Empirical results from Yang [48] suggests that reducing early perceived risk can rapidly establish consumer trust and usage intentions. Studies by Keith [60] and Kim [61] indicate that trust can reduce the uncertainty and risk individuals experience when using new information technology.

Hypothesis 12

(H12): Perceived technological risk (TER) significantly negatively influences older adults’ trust (TR) in telemedicine services.

Hypothesis 13

(H13): Perceived emotional risk (ER) significantly negatively influences older adults’ trust in telemedicine services (TR).

Hypothesis 14

(H14): Perceived cost risk (CR) significantly negatively influences older adults’ trust in telemedicine services (TR).

Behavioral intention refers to an individual’s subjective will to perform a specific action and plays a pivotal role in predicting whether the individual will engage in the target behavior. Holden [62] and others define behavioral intention as the user’s subjective willingness to adopt telemedicine services. Research by Teo [63] and others suggests that strong behavioral intentions can drive actual usage behavior. The Theory of Planned Behavior (TPB) posits that the most critical determinant of individual behavior is behavioral intention [64].

Hypothesis 15

(H15): Behavioral Intention (BI) significantly positively influences on older adults’ actual usage behavior (UB) of telemedicine services.

To further gain insights into the mechanisms influencing older adults’ willingness to use telemedicine services and trust, this study empirically investigated the factors that influence older users’ trust in telemedicine and how this, in turn, affects older adults’ willingness to use telemedicine services. More specifically, we constructed a relationship model based on Technology Acceptance Theory (TAM) and Deconstructive Theory of Planned Behavior (DTPB) among Perceived Usefulness (PU), Perceived Ease of Use (PE), Service Environment (SE), Subjective Norms (SR), Self Efficacy (SV), Trust (TR), Technological Risk (TER), Emotional Risk (ER), Cost Risk (CR), and Behavioral Intentions of Use (BI) with Use Behavior (UB). In this relational model, there are a total of 15 hypotheses, with 12 of them categorized into three main groups. Hypotheses H1 to H5 represent a set that has a direct effect on BI. Hypotheses H7 to H11 potentially have an indirect effect through TR and BI. Hypotheses H12 to H14 are a set that may have a negative effect on TR.

This study extends the research on trust in telemedicine services among older adults and unveils the mediating mechanism of older adults’ Behavioral Intention to use telemedicine services based on TAM and DTPB. Building upon on the above hypotheses and analysis, we propose a conceptual model (see Fig. 1.).

Fig. 1

Conceptual model

Methods and measures

Aim and participants

This cross-sectional study aimed to explore the relationship between the intention to use and trust in telemedicine services among an older adult population in Shanghai, China, spanning from May 2023 to July 2023, utilizing a questionnaire sample. This study explores the influence of trust on older adults’ use of telemedicine services and identifies the factors that affect their trust in these services. Data were gathered through a self-reported questionnaire adapted from prior studies and refined based on heuristic study findings. The questionnaire was developed for this study (see Additional file 2). The questionnaire data were collected from a Tertiary Hospital in Shanghai, China. The survey was distributed through an online platform called Wenjuanxing. Each invited participant received a link to complete the questionnaire, and only those invited could participate. A total of 548 older adults in Shanghai were surveyed using TAM and DTPB modeling research methods to understand their intentions and behaviors related to telemedicine usage. The questionnaire, distributed from May-July 2023, underwent initial small-scale testing before wider application among the elderly population.

Data collection

A total of 463 questionnaires were collected. Among them, 38 responses were from individuals under the age of 60 and thus did not meet the World Health Organization’s definition of older adults. Additionally, 18 responses were excluded due to having over 70% repeated answers, missing values, or extreme values, and 7 questionnaires were excluded because the completion time was less than 60 s. The final data set comprised of 400 effective questionnaires yielding an effective recovery rate of 86.39%. Demographic characteristics are presented in Table 1, with 223 females (55.8%) and 177 males (44.2%) The majority fell within the 60–69 age group (232 or 58.0%), followed by 117 or 29.3% were the 70–79 age group 51 or 12.7% over 80 years. Over 62.8% of participants had a college or higher education level, while 6.7% had a primary education or below. Questions covered age, gender, education, telemedicine use behavior, intention to use telemedicine, perceived risk, perceived usefulness, perceived ease of use, Service Environment, Self Efficacy, and subjective norms. To safeguard participant’s privacy the questionnaire did not request names and addresses.

Table 1 Demographic characteristics

Measure

To ensure the reliability and validity of variables, metrics for each hypothesized variable in this study were derived from measurement items commonly used as theoretical bases in existing literature. These items were then modified and supplemented in alignment with the theoretical foundations relevant to the telemedicine field, and further adjusted to suit the actual usage situations of the older adults. The final questionnaire underwent evaluation by doctors at a Grade 3 A hospital in China. Utilizing a 5-point Likert scale response format (1 = completely disagree, 2 = disagree, 3 = neutral, 4 = agree, 5 = completely agree), the questionnaire comprised 41 measurement curarized into 12 factors: Demographics, Perceived Usefulness (PU), Perceived Ease of Use (PEU), Service Environment (SE), Subjective Norms (SR), Self Efficacy (SV), Behavioral Intention to Use (BI), Usage Behavior (UB), Trust (TR), Technology Risk (TER), Emotional Risk (ER), and Cost Risk (CR).

Statistical analysis

Structural Equation Modeling (SEMs) was employed to test the hypotheses based on the theoretical background Initially, descriptive demographic analysis of the sample was conducted using IBM SPSS Statistic 23.0 software package (IBM Corp., Armonk, NY, USA) to analyze the general characteristics. Factor loading and structural equation analysis were then performed using SPSS 23.0 and AMOS 24.0 to validate the measurement model’s reasonableness. Subsequently, AMOS 24.0 was used to test each hypothesis proposed in the article, setting the significance level for the structural model at a p-value of less than 0.05.

Whether the data follow a normal distribution is crucial for subsequent analyses. According to Kline (1998), when the absolute value of skewness is less than 3 and the absolute value of kurtosis is less than 10, it indicates that the sample essentially follows a normal distribution. As shown in Table 2 (see Additional file 1), the results for the formal sample demonstrate that the absolute values of skewness for all items are less than 3, and the absolute values of kurtosis are less than 10. Both skewness and kurtosis meet the criteria for a normal distribution, indicating that all items conform to a normal distribution. Therefore, the collected questionnaire data can be directly used for subsequent statistical analyses, such as reliability and validity testing.

Table 2 Descriptive statistics

From Table 3, it can be observed that the Variance Inflation Factor (VIF) values are all below 3, indicating that there is no collinearity among the variables.

Table 3 Collinearity statistics

Results

Statistical analysis

The conceptual model underwent confirmatory factor analysis. As presented in Table 4 (see Additional file 1), all items exhibited standardized factor loadings greater than 0.5, with significant and positive residuals. The Composite Reliability (CR) values surpassed 0.7, Average Variance Extracted (AVE) values exceeded 0.5, and Cronbach’s Alpha values were greater than 0.7. Consequently, all items met the standards for convergent validity and reliability.

Table 4 Validation factor analysis results

Following the determination of dimension structure and the corresponding question items through validity and reliability analyses, the dimension scores were computed by averaging the scores of individual question items within each dimension. Subentry correlation analysis was conducted to explore the relationships between dimensions. The correlation coefficient values ranging from − 1 to 1, were scrutinized, where the larger absolute value indicated a stronger the correlation between variables. Chiu Haozheng [65] proposed a detailed categorization of correlation coefficients, |r| = 1, perfect correlation; 0.70 ≤ |r| < 0.99, high correlation; 0.40 ≤ |r| < 0.69, moderate correlation; 0.10 ≤ |r| < 0.39, low correlation; |r| < 0.10, weak or no correlation. Distinct validity analysis was employed to verify whether correlation between different constructs were statistically different. It was crucial to ensure that items in different constructs were not highly correlated, and if they were (0.85 or more), it suggested that these items were measuring the same underlying concept. This typically occurs when construct definitions overlap excessively. The present study utilizes the more rigorous Average Variance Extracted (AVE) method to assess differential validity [66]. For each factor the open root sign of the AVE for each factor had to be greater than the correlation coefficient of each paired variable indicating differential validity between factors. The diagonal line represents the standardized correlation coefficient for each factor, with the AVE open root sign greater than the off-diagonal line, affirming differential validity. The diagonal downward triangle displays the correlation coefficient (refer to Table 5 for details). Discriminant validity analysis is used to verify whether there is a statistical difference between the correlations of different constructs. Items from different constructs should not be highly correlated; if they are (with a correlation above 0.85), it indicates that these items are measuring the same thing, which usually occurs when the definitions of constructs excessively overlap. This study employs the more rigorous Average Variance Extracted (AVE) method to assess discriminant validity. According to Fornell and Larcker (1981), the square root of the AVE for each factor must be greater than the correlation coefficients of each pair of variables, indicating that the factors have discriminant validity.

In this study, the square roots of the AVE values (shown on the diagonal) are greater than the standardized correlation coefficients outside the diagonal, demonstrating discriminant validity. The correlation coefficients, which are below the diagonal, further support this. Detailed results are shown in the table below. All square roots of AVE are greater than 0.7, and all correlation coefficients are lower than 0.6, indicating good discriminant validity.

Table 5 Pearson correlation analysis and discriminant validity

In conducting confirmatory factor analysis, several key goodness-of-fit indices are used, including:

Chi-Square Test (χ²): The chi-square index is the most fundamental test for model fit. However, because the chi-square value is sensitive to sample size, the χ²/df ratio is used. A smaller value indicates a better model fit.

Goodness-of-Fit Index (GFI): GFI is similar to R-squared in regression analysis, with values ranging from 0 to 1. A value greater than 0.9 is considered ideal. The Adjusted Goodness-of-Fit Index (AGFI) adjusts GFI for degrees of freedom.

Root Mean Square Residual (RMR) and Root Mean Square Error of Approximation (RMSEA): RMR is the square root of the average of the squared differences between the observed and estimated variances and covariances. RMSEA, introduced by Steiger & Lind in 1980, is less sensitive to sample size and more sensitive to model misspecification, making it a preferred fit index. The closer the RMSEA value is to 0, the better the model fit.

Normed Fit Index (NFI), Incremental Fit Index (IFI), and Comparative Fit Index (CFI): NFI is a relative fit index that represents the proportion by which the theoretical model reduces the chi-square value. IFI is an adjustment of NFI that reduces dependency on sample size. CFI addresses the deficiencies of NFI in nested models by comparing the fit of a target model to an independent model, providing a robust estimate of model fit.

Together, these indices provide a comprehensive assessment of model fit, each contributing unique insights into the quality and accuracy of the model.

The model fit results, as presented in Table 6, indicate favorable fit indices. The CMIN/DF is 2.009, while the GFI, AGFI, NFI, TLI, IFI, and CFI all surpassed the standard threshold of 0.9. Additionally, RMSEA is less than 0.08. These fitting indices align with general standards for Structural Equation Modeling (SEM) studies, suggesting the model exhibits a strong fit.

Table 6 Structural model fit

Results of hypothesis testing

Main effects test

Examining the structural equation model results, depicted in Fig. 2 and detailed Table 7. It is evident that Perceived Usefulness (PU) and Trust (TR) exert a significantly positive impact on Behavioral Intention (BI) to use, with effect sizes of 0.279 and 0.329, respectively. Conversely the influences of Perceived Ease of Use (PE), Service Environment (SE), Subjective Norms (SR), and Self Efficacy (SV) on Behavioral Intention (BI) are not significant. This implies that among hypotheses H1-H6, only H1 and H6 are supported.

Moreover, Perceived Usefulness (PU), Service Environment (SE), and Subjective Norms (SR) exhibit significant positive influences on Trust (TR), with effect sizes of 0.156, 0.173, and 0.118, respectively. Emotional Risk (ER) and Cost Risk (CR) demonstrate significant negative impacts on Trust (TR), with effect sizes of -0.237 and − 0.282, respectively. However, the effects of Perceived Ease of Use (PE), Self Efficacy (SV), and Technical Risk (TER) on Trust (TR) are not significant. Therefore, hypotheses H7, H9, H10, H13, and H14 are supported, while H8, H11, and H12 are not. Furthermore, Behavioral Intention (BI) exhibits a significant positive effect on Usage Behavior (UB), with an effect size of 0.454, confirming hypothesis H15.

We found that most factors did not have a direct impact on older adults’ willingness to use telemedicine. This could be due to two main reasons. First, the current level of telemedicine adoption in China is insufficient. Most users do not have adequate awareness of telemedicine services, which prevents the formation of a direct willingness to use them. Second, the older adult population tends to be skeptical of new technologies such as telemedicine. The results might differ if the study were conducted with a younger demographic.

Table 7 Structural equation modeling path coefficient

Fig. 2

Structural equation modeling path coefficients

Mediation effect test

To investigate the mediating role of Trust (TR), this study employed a Bootstrap mediation effect test to determine the significance of the mediation effect. The validated data finding are presented in Table 8.

In the pathway from Perceived Usefulness (PU) to Behavioral Intention (BI), both the total effect and the indirect effect show confidence intervals that do not include zero, with the Z-values greater than 1.96. This indicates the presence of a mediating effect in the PU to BI pathway. Furthermore, the confidence interval od the direct effect do not include zero, and the Z-value is greater than 1.96, signifying partial mediation with a mediation effect size of 0.051. Therefore, suggests that the positive impact of PU on BI is partially realized through the enhancement of trust (TR).

In the pathway from Service Environment (SE) to Behavioral Intention (BI), both the total effect and the indirect effect exhibit confidence intervals that do not include zero, with Z-values surpassing 1.96, indicating the existence of a mediating effect. The confidence intervals of the direct effect encompassing zero and the Z-value is less than 1.96, rendering the direct effect nonsignificant. This points to full mediation with a mediation effect size of 0.057, suggesting that the positive impact of SE on BI is entirely mediated by enhancing trust (TR).

In the pathway from Subjective Norms (SR) to Behavioral Intention (BI), the confidence intervals of the total effect include zero, and the Z-value is less than 1.96. This suggests an absence of a mediating effect, and the direct effect is nonsignificant indicating no significant relationship between SR and BI.

The results of the mediation effect are shown in Table 9.

Table 8 Mediating effect test

Table 9 Summary of BOOTSTRAP mediation test results

Discussion

This study, grounded in a survey of older adults’ data and guided by the Technology Acceptance theory, delved into factors influencing older adults’ Behavioral Intention to use telemedicine. By amalgamating TAM and DTPB models and employing a mean variable model to scrutinize the path relationships, we discovered that Perceived Usefulness had a significantly positively affected Behavioral Intention to Use. And Service Environment ignificantly and positively affects Behavioral Intention through the mediating variable of Trust. Notably, Trust emerged as a crucial mediating variable in this relationship, emphasizing the pivotal role of trust in influencing older adults’ Behavioral Intention to use telemedicine services.

Perceived Usefulness (PU): Perceived Usefulness, a pivotal variable in the Technology Acceptance Model (TAM), serves as vital indicator for studying users’ acceptance of information technology services. Consistent with prior research [32, 33], PU demonstrated a positive influence on older adults’ intention to use telemedicine, highlighting its importance, especially among older demographics. Given potential hesitancy towards new technologies [67], tailoring telemedicine services to address the unique needs of older adults, emphasizing convenience, accessibility, and time savings, can enhance their understanding and enhance their understanding and appreciation. Clear communication of telemedicine’s functionality and advantages can foster a perception of usefulness [68], increasing the likelihood of acceptance among older adults.

Trust as a mediator

Our findings indicate that both Perceived Usefulness and Service Environment influence older adults’ Behavioral Intention to use telemedicine through the mediating role of trust. While PU affects intention through trust mediation, SE exerts its impact on Behavioral Intention through the full trust mediation. This underscores the importance for telemedicine service providers to concentrate on bolstering trust among older adults to enhance their willingness to engage with such services. Trust emerges as a pivotal mediator, aligning with existing research [49, 50, 57]. Telemedicine, being a potentially unfamiliar concept for older adults, may trigger concerns about its effectiveness, safety, and privacy [69]. Trust plays a critical role in allaying uncertainties and instilling confidence in telemedicine’s feasibility, effectiveness, and safety, thereby reducing doubts increasing older adults’ Behavioral Intention to use telemedicine.

Building on existing research, we conducted a more in-depth study of the factors and pathways that influence trust. Factors Influencing Trust: Perceived Usefulness, Service Environment and Subjective Norms were identified as significant contributors to older adults’ trust in telemedicine. Trust in telemedicine was bolstered when older adults perceived its utility in addressing health issues, experienced positive and caring attitudes, and received high-quality services and support. Moreover, positive community and authority endorsements, coupled with supportive subjective norms, were instrumental in enhancing older adults’ trust in telemedicine. Shaping Subjective Norms through ensuring telemedicine’s utility, providing positive service experiences and fostering a conductive Service Environment are imperative for cultivating trust among older adults in the realm of telemedicine. This may provide some guidance for the development of future standards and policies for telemedicine in China.

Additionally, Emotional Risk and Cost Risk demonstrated significant associations with trust with heightened Emotional Risk and Cost Risk inversely correlated with older adults’ trust in telemedicine [70]. The absence face-to-face communication and physical contact in telemedicine may elevate Emotional Risk, leading to emotions like anxiety, restlessness, and worry among older adults. Moreover, the technical requirements and potential expenses associated with telemedicine, such as software and subscription fees, could pose challenges, particularly for older adults with limited financial resources. Effectively addressing Emotional and Cost Risks emerges as a crucial measure for enhancing user trust. This offers a new perspective for the development of telemedicine service software in China. Based on the findings of this study, providers of telemedicine services should prioritize strategies that minimize older adults’ Emotional and Cost Risks during the design phase of services. This could potentially enhance the efficiency of building telemedicine service practices targeted at older adults in China.

Finally, our study highlights the significant impact of older adults’ Behavioral Intention to use telemedicine on their actual Usage Behavior. The absence of a positive Behavioral Intention may deter older adults from attempting or persisting in the use of telemedicine. Consequently, understanding and fostering a positive Behavioral Intention to use telemedicine among older adults becomes a critical aspect of successful telemedicine implementation.

Limitations

This study, utilized a cross-sectional survey approach, collecting data from 400 questionnaires in China. However, the subjective nature of the variables measured in the questionnaires inevitably introduced a certain level. Future research endeavors aim to enhance objectivity by incorporating experiments that observe objective variables, thus obtaining more accurate and unbiased data.

Furthermore, the scope of this study is constrained by the relatively modest size of the survey and limited by the research sample. The investigation focused on a specific hospital in the Shanghai area, introducing some limitations to the generalizability of the findings. Subsequent research should seek broader data sets to validate the factors influencing older adults’ Behavioral Intention to use telemedicine services.

Finally, the majority of the influencing factor variables considered in this paper are drawn from existing studies, with a gap in research on variables not addressed in previous literature. Recognizing this, ongoing research initiatives are underway to delve deeper into the influencing factors of older adults’ Behavioral Intention to use telemedicine. This involves an exploration of additional levels and a comprehensive analysis of factors that have not been extensively studied in previous research.

Conclusions

This study developed a comprehensive model of older adults’ Behavioral Intention (BI) to use telemedicine services by integrating the TAM and DTPB models. The aim was to explore the factors influencing older adults’ intention to use telemedicine. The results underscored that Perceived Usefulness (PU) and Service Environment (SE) significantly and positively influenced Behavioral Intention (BI) to use, with Perceived Usefulness (PU) emerging as the most critical factor shaping older adults’ intention to use telemedicine services. Furthermore, Trust (TR) emerged as a pivotal factor influencing older adults’ Behavioral Intention (BI) to use telemedicine services, exerting a positive effect on their intention to use these services.

Additionally, the study identified significant relationships between it was shown that Perceived Usefulness (PU), Service Environment (SE), Subjective Norms (SR), Emotional Risk (ER), and Cost Risk (CR) with older adults’ Trust (TR) in telemedicine, Emotional Risk (ER) and Cost Risk (CR) were found to reduce Trust (TR).

Consequently, it is imperative for telemedicine services aimed at older adults to focus on enhancing user perception of trust throughout the service process. This involves addressing factors such as Perceived Usefulness, Service Environment, Subjective Norms, Emotional Risks, and Cost Risks in the pre-use stage as well as emphasizing the importance of as Service Environment in the mid-use stage of the telemedicine service process. Such considerations will contribute to a more robust enhancement of older adults’ Behavioral Intention to use telemedicine services.