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

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.).