Theoretical orientation
The UTAUT model developed by Venkatesh20 has been widely used to understand technology adoption. This model incorporates several prior theories: the theory of reasoned action, the technology acceptance model, the business motivation model, the theory of planned behavior, the Model of PC Utilization, the innovation diffusion theory, and the social cognitive theory. However, the UTAUT model exhibited superior predictive power compared to all other models25. The UTAUT identifies four key factors as critical determinants of technology acceptance: PE, EE, SI, and FC7. This model has demonstrated its utility in effectively elucidating substantial variance in technology usage and acceptance behavior across diverse contexts. Developed to delve into the primary drivers of an individual’s intention to adopt a particular technology, the UTAUT model empowers researchers to scrutinize the influence of moderating factors that can amplify or diminish the impact of those drivers.
The four fundamental constructs of the UTAUT model—PE, EE, SI, and FC—significantly affect an individual’s intention to use technology and their ongoing usage. According to this model, PE, EE, and SI determine an individual’s intention to use technology, whereas behavioral intention and FC play crucial roles in influencing technology use. The UTAUT, originally developed to investigate the integration of technologies in the workplace, was later expanded to include individual-level adoption factors for various technologies across different contexts. Researchers have used the UTAUT to investigate various aspects of healthcare technology. For example, Arfi, Nasr8 investigated the impact of trust on users’ intentions to utilize the IoT in eHealth. Wang, Tao25 examined consumer acceptance of wearable healthcare devices, whereas Magsamen-Conrad, Wang26 explored adults’ e-health literacy. However, the UTAUT is commonly extended with additional contextual constructs that integrate factors from a user’s perspective27. Although the UTAUT has been extensively validated and expanded in various contexts, its use in investigating the acceptance of IoT-enabled healthcare devices has been relatively limited25. The model fails to fully address the complexities of specific industries, such as healthcare, where the accuracy of technology and perceived value of products play crucial roles. For example, in the healthcare industry, users are concerned with more than just the ease of use or the expected performance of a device. They also prioritize the reliability and accuracy of technology, particularly when handling personal health data28. Moreover, the UTAUT model predominantly addresses cognitive and practical aspects, such as performance and effort expectancy, yet it inadequately captures more emotional and subjective factors7, such as perceived product value and data accuracy. In the realm of IoT healthcare devices, users typically evaluate technologies based on their perceived benefits (such as improvements in health, cost efficiency, or user-friendliness) and the accuracy of health data or insights generated by the device23. Therefore, this study incorporates PV and TA into the original UTAUT model to obtain a comprehensive understanding of the core influential factors affecting individuals’ attitudes toward and intentions to use IoT-enabled healthcare devices. PV alludes to customers’ favorable views of a product or service in contrast to a competitor’s offering, leading to the adoption or consumption of that product or service29. Instead, when customers have faith in a technology’s accuracy and trust its performance, they are inclined to adopt it30. Therefore, it is crucial to gain empirical evidence to support the applicability of this extended UTAUT model in the context of IoT-enabled healthcare devices and explore user acceptance to promote the development and implementation of these devices.
Hypothesis formulation
Performance expectancy, attitude, and intention
According to the UTAUT mode, PE refers to the extent to which individuals believe that utilizing the system will enhance their job performance16. In the context of this study, PE denotes the extent to which individuals believe that utilizing IoT-enabled devices facilitates accomplishing a particular task. PE has been established as a robust predictor of intention to use healthcare technology, as indicated by Arfi, Ben27 and Wang, Tao25. In the context of IoT-enabled healthcare devices, PE can be considered as the level at which the device facilitates users to monitor their physical well-being, create self-care plans, and decrease health risks. Consequently, an increase in individuals’ PE, such as a stronger belief in effective health management, improved access to healthcare services, and better quality of life, can substantially influence their attitudes and intentions to use IoT-enabled healthcare devices. Thus, we hypothesized the following:
H1a: PE positively influences attitude towards IoT-enabled healthcare devices.
H1b: PE positively influences intention to use IoT-enabled healthcare devices.
Effort expectancy, attitude, and intention
EE is related to the ease of technological use. This reflects the perception that adopting and operating a particular system or technology does not require substantial effort or difficulty31. In the existing research, EE is considered a significant factor in determining individuals’ intentions to adopt healthcare technologies25,27. Walle, Jemere31 investigated chronically ill patients’ intention to adopt wearable health devices and found a significant association between EE and intention to adopt these devices. In the context of IoT-enabled healthcare devices, EE is linked to the enhanced perception that these devices are beneficial and useful. Consequently, a higher EE indicates that less energy is required to manage and utilize these devices. When individuals perceive that using IoT-enabled healthcare devices is straightforward and hassle-free, they are likely to anticipate improved health management and are more inclined to embrace and use these devices. Accordingly, we develop the following hypotheses:
H2a: EE positively influences attitude towards IoT-enabled healthcare devices.
H2b: EE positively influences intention to use IoT-enabled healthcare devices.
Social influence, attitude, and intention
An individual’s perception of how important others agree with their specific behavior is referred to as SI. In the context of IoT-enabled healthcare devices, SI, including encouragement from family members and close friends, plays a significant role in enhancing individuals’ intentions to use IoT-enabled healthcare devices. SI may occur because people wish to reinforce their connections with important others by adopting their perspectives on a particular behavior27. Investigating the intention of patients’ willingness to adopt wearable healthcare devices, Walle, Jemere31 proved that social influence has a substantial influence on the adoption of these devices. Accordingly, people are more inclined to embrace and use IoT-enabled healthcare devices when their significant others encourage their use. Therefore, we hypothesize the following:
H3a: SI positively influences attitude towards IoT-enabled healthcare devices.
H3b: SI positively influences intention to use IoT-enabled healthcare devices.
Perceived product value, attitude, and intention
The PV of a product is the overall mental evaluation that consumers formulate, which encompasses their assessment of the product’s quality and satisfaction as well as the costs incurred, such as money and time29. Considering a fundamental concept that prioritizes both customers and users, PV has been established as a crucial and consistent predictor of purchasing behavior for products and services. Therefore, the customer perception of a product is widely acknowledged as a vital factor in service research32. In the context of IoT-based healthcare devices, consumers evaluate these devices based on innovation, quality, and other relevant factors, resulting in a higher PV than that of alternative options. This higher PV is attributed to the ability of these devices to offer secure and real-time remote monitoring and health management, which enhances the quality of life of users. Consequently, a positive perception is created for both the devices and users, leading to increased adoption. Thus, the associated hypotheses were as follows:
H4a: PV positively influences attitude towards IoT-enabled healthcare devices.
H4b: PV positively influences intention to use IoT-enabled healthcare devices.
Perceived technology accuracy, attitude, and intention
TA is an individual’s belief that technology will function seamlessly and without any issues30. In the context of IoT-enabled healthcare devices, this perception is particularly crucial as it can influence the adoption of these devices. This is because the accuracy of users’ health data is essential for assessing their health status and the effectiveness of any exercise they may perform33. Thapa, Bello34 highlighted that users’ trust in the accuracy of health data provided by wearable Internet of Medical Things is crucial for their acceptance and continued use. Kim, Zhong35 demonstrated how the benefits of IoT healthcare devices are evident, but their widespread adoption depends on user acceptance, which is influenced by factors related to perceived risk, trust in technology, and concerns about data privacy and security. Unfortunately, there is a scarcity of studies in the existing healthcare literature examining the influence of TA on users’ decisions to adopt these devices. In their systematic literature review, Stavropoulos, Papastergiou36 highlighted TA as a significant factor influencing the adoption of IoT-enabled wearable sensors and devices for elderly care. The authors recommend that empirical data be used to investigate this issue further. Therefore, it is imperative to explore the role of TA in users’ intentions to adopt and utilize IoT-enabled healthcare devices to manage their health and well-being. Accordingly, the following hypotheses are proposed:
H5a: TA positively influences attitude towards IoT-enabled healthcare devices.
H5b: TA positively influences intention to use IoT-enabled healthcare devices.
Facilitating conditions, attitude, and intention
FC is a means by which an individual evaluates the level of support offered by an organization and the technical infrastructure for the execution of a system or technological solution. In the context of healthcare technologies, these conditions involve skills, knowledge, resources, or any other favorable circumstances that facilitate the use of IoT-based healthcare devices25. These conditions may include knowing how to connect these devices to mobile phones and how to transfer data to cloud servers. Previous research has demonstrated that an increase in FC, such as access to technical support, has a direct impact on users’ behavioral intentions to accept technology8,27. Unlike the original UTAUT model, which links FC directly to use behavior, previous studies have shown that enhancing FC leads to improved user behavior toward technology adoption25. Additionally, a higher level of FC can help reduce the effort required to use technology. When users possess adequate resources to support their use of technology, such as possessing the necessary knowledge or receiving guidance from experts, they are more likely to believe that utilizing technology is straightforward; thus, they are more likely to use it. Accordingly, we develop the following hypotheses:
H6a: FC positively influences attitude towards IoT-enabled healthcare devices.
H6b: FC positively influences intention to use IoT-enabled healthcare devices.
Attitude and intention to use IoT-enabled healthcare devices
Attitude is a term used to describe an individual’s positive or negative evaluation of a particular behavior or trend37. According to this perspective, an individual’s positive attitude towards technology can be fostered if the technology is both useful and user-friendly. Behavioral intention represents the effort that individuals aim to achieve through specific behaviors. TAM posits that an individual’s behavioral intention influences the actual usage of technology. People are more likely to use a particular technology if they intend to38. In the context of healthcare technology, individuals with a favorable view of IoT-enabled healthcare devices and those who engage in behaviors facilitated by such devices are more likely to use them. Thus, older adults who are aware of the use of these devices for self-management and well-being are likely to have a higher intention to use them6. Consequently, we propose the following hypothesis:
H7: Individuals’ intention positively influences attitudes toward IoT-enabled healthcare devices.
Moderating effects of age and gender
Although age and gender are important factors that can provide valuable insights into their effects on technology adoption27, researchers caution that these moderators have not been thoroughly examined39. Furthermore, it is widely recognized that age and gender serve as critical moderators in the context of information technology acceptance, adoption, and intention to use20. Individuals belonging to a particular age group often possess similar religious beliefs, moral principles, and behavioral patterns because they were raised during the same historical period40. Arfi, Ben27 argue that individuals over a certain age are more inclined to rely on automatic information processing, which makes it difficult for them to process new information even with the aid of advanced technologies. This phenomenon can be attributed to the increasing cognitive and physical limitations that older individuals experience, which causes them to depend more on external factors than younger individuals to determine their intention to adopt IoT-enabled healthcare devices. Moreover, men and women differ in their focus on determining their behavioral intentions, as Venkatesh, Morris20 and Venkatesh, Thong41 found. While men are highly task-oriented and rely mainly on PE to guide their behavioral intentions, women consider a broader range of factors, such as EE, SI, and FC. Additionally, women tend to be less task-oriented than men and are more influenced by others’ opinions. Women also place greater importance on external support27. Thus, to investigate this further, we propose the following assumptions:
HM1−6: Age has significant moderating effects on the relationships between PE, EE, SI, PV, TA, FC, and intention to use IoT-enabled healthcare devices.
HM7: Age has significant moderating effects on the relationship between attitude and intention towards IoT-enabled healthcare devices.
HM8−13: Gender has significant moderating effects on the relationships among PE, EE, SI, PV, TA, FC, and intention to use IoT-enabled healthcare devices.
HM14: Gender has significant moderating effects on the relationship between attitude toward IoT-enabled healthcare devices and intention to use them.
Mediating role of attitude towards IoT-enabled healthcare devices
According to Arfi, Ben27, PE is a robust predictor of intention to use healthcare technology. When individuals believe that IoT-enabled devices are efficient for health management and result in better living standards, they are likely to develop a favorable attitude towards and use these devices. Wang, Tao25 revealed that EE is linked to an enhanced perception of IoT devices as both beneficial and practical. When individuals observe that these devices require minimal effort to operate, they tend to have positive intentions to use them. Arfi, Ben27 stated that the use of IoT-enabled devices by family members, friends, or peers could result in a positive attitude towards these devices, encouraging individuals to use them. Laukkanen and Tura32 asserted that when individuals perceive a product as valuable and affordable, they are more likely to purchase and use it. Stavropoulos, Papastergiou36 emphasized the importance of TA in influencing the adoption of IoT-enabled wearable sensors and impacting individuals’ intention to use such sensors. Wang, Tao25 demonstrated that FC positively impacts users’ attitudes and behaviors toward adopting technology, ultimately influencing their intention to use technology. Based on the explanation provided above, this study proposes the attitude towards IoT-enabled devices as a mediator in the relationships between influential factors (PE, EE, SI, PV, TA, PV, TA, and FC) and the intention to use such devices.
HM15−20: Attitude toward IoT-enabled healthcare devices mediate the relationships between performance expectancy, effort expectancy, social influence, perceived product value, perceived technology accuracy, facilitating conditions, and intention to use such devices.
The proposed assumptions are illustrated in Fig. 1 as follows,
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