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Abstract

Objectives

This study propose an extended Technology Acceptance Model (TAM) to bridge the gap between technical feasibility and human-centric adoption by incorporating external variables such as regulatory compliance (RC), training and technical support (TTS), peer influence (PEI), information literacy (IL), and technology/digital literacy (TDL) to investigate the factors driving users’ intention to adopt metaverse telehealth services (MVTHS) in the Chinese healthcare context.

Methods

The study uses a cross-sectional survey-based quantitative research design to validate an extended TAM model. Valid data from 739 respondents were collected across China using an online research questionnaire between October 1 and December 31, 2024. Smart PLS Structural Equation Modeling was employed to test the proposed model.

Results

The results indicate that RC and PEI significantly influence perceived usefulness (PU), perceived ease of use (PEOU), and individual attitudes (ATT) toward the use of MVTHS. TTS demonstrated a significant positive influence on both PU and PEOU but did not influence ATT. While PU positively influenced ATT, PEOU did not significantly affect ATT toward adopting MVTHS. IL significantly influences metaverse telehealth adoption intention (MVTHAI) and moderates the relationship between PU and ATT, as well as between ATT and MVTHAI. Similarly, TDL significantly influences both ATT and MVTHAI. TDL also significantly moderates the relationship between PEOU and ATT, and between ATT and MVTHAI.

Conclusion

A clear regulatory framework is essential for healthcare providers and policymakers to leverage PEI through social channels to encourage users’ ATT to adopt MVTHS. While TTS improves PU and PEOU, PEOU does not directly shape ATT—suggesting that resources should be strategically allocated to RC, IL, and TDL to engage users to adopt MVTHS.

Keywords

digital health telehealth Metaverse-driven telehealth services technology acceptance model regulatory compliance information literacy technology literacy training and technical support

Introduction

Telehealth uses digital communications technologies to provide health care in distant places.^1–3 It is essential for enhancing healthcare accessibility, especially for rural and marginalized communities. It improves operational efficiency, reducing patient travel and wait times, facilitating immediate consultations between individuals receiving medical care and those delivering healthcare services, and enhancing healthcare quality without increasing costs.^4,5 In contrast, traditional telehealth services rely on two-dimensional interactions, limiting engagement and immersive experiences.

The rapid advancement of internet technology services has driven innovative developments within the metaverse, enabling users to access a wide range of services through enhanced virtual interactions and immersive digital environments.^6,7 The metaverse technology provides a foundation for the evolution of telehealth services, offering a highly interactive and engaging virtual space for advanced healthcare for virtual consultations and transformed patient engagement.^8,9 Integrated cutting-edge technologies such as artificial intelligence (AI), virtual reality (VR), augmented reality (AR), blockchain, and digital twins create a realistic, three-dimensional healthcare environment,^10,11 empowering the transformation and provision of Metaverse-driven telehealth (MVTH).

This approach enables patients and healthcare providers to participate in realistic virtual sessions, immersive medical training, AI-powered diagnostics, and blockchain-secured health data management. Unlike traditional telehealth, MVTH promotes a more engaging, community-driven, and persistent virtual healthcare environment, revolutionizing remote medical care, patient education, and rehabilitation through immersive, real-time experiences.^12–14 Though both MVTH and traditional telehealth incorporate VR or AR immersive tools to enhance healthcare delivery,^15,16 MVTH offers a more integrated interactive experience that boosts the efficacy of conventional telehealth solutions.^17,18 It establishes a persistent, collaborative virtual space that facilitates continuous engagement, education, and cooperation,^19,20 distinguishing it from the more transactional and isolated nature of traditional VR and AR telehealth applications. As the demand for digital healthcare solutions grows, the adoption of MVTH technologies is expected to increase significantly in the coming decade.²¹

The integration of metaverse technology within the healthcare sector remains in its nascent phase, even though it holds significant promise for revolutionizing healthcare delivery services.^22,23 This capacity revolutionizes the health care system for the benefit of patients. For instance, those patients who have the chance to undergo virtual examinations in an immersive environment feel as if they are engaging with one another in a familiar setting.²² MVTH also facilitates remote patients’ access to quality healthcare in isolated areas where health facilities and qualified medical professionals are limited. Moreover, the metaverse facilitates remote medical consultations, enhances access to health-related information and resources, and provides training opportunities.²⁴ This advancement benefits people to take greater control of their health via informed decision-making.

Furthermore, scholars such as Wang, Zhu²² and Chengoden, Victor²⁵ emphasized that the significant adoption of metaverse technologies in the healthcare field requires tackling obstacles related to interoperability issues, data privacy, public trust, information concerns, loss of personal touch, high cost of technology, adoption/acceptance of new technologies, and legal and regulatory compliance Wang, Zhu.²² Particularly, since the metaverse is still an emerging concept, it is crucial to engage in further promotional and educational activities to boost public awareness and foster acceptance.^22,26 While there is growing enthusiasm and hope regarding the metaverse’s potential, a substantial gap remains in understanding its operational mechanisms and real-world applications,²⁷ particularly in the context of MVTHS. Based on these backgrounds, this study addresses a critical gap in the literature, i.e., the limited research on the adoption of MVTHS from the user perspective. To achieve this, the study extends the TAM framework by combining information literacy, technology literacy, regulatory compliance, training and technical support, and peer influence to examine how these factors collectively explain individuals’ acceptance of MVTHS. Thus, this work bridges this gap by proposing an integrated model that offers a comprehensive framework context for analyzing user acceptance of telehealth services in the metaverse.

The selection and integration of these variables is grounded on well-established technology acceptance theories, digital literacy theory, social cognitive theory, and institutional framework. Thus the adoption of MVTHS will be driven or shaped by three foundational dimensions: First, their intrinsic individual capabilities (IL and TDL grounded in digital literacy theories); Second, the extrinsic social influence from their environment (social/peer influence grounded in social cognitive theory) and; Third, the institutional framework established by the state (RC and TTS grounded in institutional theory). The integrated and extended TAM addresses the complex nature of metaverse telehealth adoption.

Research objectives

This research aims to investigate the factors influencing individuals’ acceptance and utilization of MVTHS from the lens of an extended Technology Acceptance Model (TAM) in China. The specific research questions (objectives) are:

1. How do information literacy and technology literacy impact attitudes and adoption intention of MVTHS?

2. How do information literacy and technology literacy moderate the influence of attitudes on adoption intention and PU and PEOU on attitudes?

3. How do PU and PEOU drive people’s attitudes toward MVTHS?

4. How do regulatory compliance, peer influence, training, and technical support positively impact the PU and PEOU of MVTHS, respectively?

Addressing these research questions equips government officials and stakeholders with critical insights to improve the design of MVTHS by ensuring MVTHS are more accessible, manageable, and receptive to the needs and preferences of prospective users.

Theoretical framework

Technology acceptance model

The Technology Acceptance Model (TAM) Davis²⁸ has been tested in diverse contexts by numerous researchers to explain the espousal of information systems at the personal level. Although the TAM is based on the fundamental theoretical concept of the Theory of Reasoned Action (TRA), it substantially differs from the TRA in two ways: First, the TAM presents two novel constructs: (1) perceived usefulness (PU), which refers to the belief that utilizing an application will enhance an individual’s performance, and (2) perceived ease of use (PEOU), which signifies the belief that engaging with an application will require minimal effort. Within this model, both PU and PEOU serve as predictors of an individual’s attitude (ATT) toward the application’s usage.²⁸ Additionally, the TAM does not consider subjective norms as a factor influencing intention.²⁹

The popularity of TAM is evident in its diverse applications across various fields and contexts, whether through modifications, extensions, or the full utilization of the model. The extensive applications of TAM confirm TAM’s reliability and robustness to adequately account for the use of technology, i.e., demonstrating its robustness as a predictor of technology acceptance. Additionally, TAM is relatively straightforward, consisting of key constructs such as PU and PEOU. This simplicity makes it easier for researchers to operationalize and measure these concepts in studies. Furthermore, TAM versatility allows it to adapt to different technological environments—from information systems to mobile applications, public health, and more.

Thus, focusing on TAM, the study can build on existing literature and also incorporate the context-specific factors (information literacy, technology literacy, regulatory compliance, training and technical support, and peer influence) that influence user acceptance in this novel environment. This adaptability offers a balance between theoretical rigor and practical relevance. Those aforementioned reasons and advantages empowered its application in this paper to study the adoption intention of MVTHS.

Research model

The proposed research model and the summary of the research hypotheses, their paths, and relationships are illustrated in Figure 1 and Table 1.

Figure 1.

Research model.

Table 1.

Summary of proposed research hypotheses, paths, and relationships.

Hypotheses	Paths	Proposed relationship
H1	RC -> PU	RC has a positive impact on the PU of MVTHS.
H2	RC -> ATT	RC has a positive influence on ATT toward using MVTHS.
H3	RC -> PEOU	RC has a positive influence on the PEOU of MVTHS.
H4	TTS -> PU	TTS has a positive influence on the PU of MVTHS.
H5	TTS -> ATT	TTS has a positive influence on the ATT towards using MVTHS.
H6	TTS -> PEOU	TTS has a positive influence on the PEOU of MVTHS.
H7	PEI -> PU	PEI positively influences the PU of MVTHS.
H8	PEI -> ATT	PEI positively influences an individual’s attitude towards using MVTHS.
H9	PEI -> PEOU	PEI positively influences the PEOU of MVTHS.
H10	PU -> ATT	PU has a positive influence on attitudes toward using MVTHS.
H11	PEOU -> ATT	PEOU has a positive influence on attitudes toward using MVTHS.
H12	ATT -> MVTHAI	ATT toward using MVTHS has a positive influence on the adoption intention of MVTH.
H13	IL -> ATT	IL positively influences the individual’s attitude toward MVTHS.
H14	IL -> MVTHAI	IL positively influences the individual’s intention of adopting MVTHS.
H15	IL * PU -> ATT	IL positively moderates the PU influence on an individual’s attitudes toward using MVTHS.
H16	IL*ATT-> MVTHAI	IL positively moderates the influence of the individual’s attitude on the adoption intention of MVTHS.
H17	TDL -> ATT	TDL positively influences the individual’s attitude toward MVTHS.
H18	TDL -> MVTHAI	TDL positively influences the individual’s intention of adopting MVTH
H19	TDL*PEOU->ATT	TDL positively moderates the PEOU influence on an individual’s attitudes
H20	TDL*ATT->MVTHAI	TDL positively moderates the influence of the individual’s attitude towards using MVTHS on the adoption intention.

Note. ATT=Attitude Toward Using, IL =Information Literacy, MVTHAI = Metaverse Telehealth Adoption Intention, PEI =Peer Influence, PEOU =Perceived Ease of Use, PU =Perceived Usefulness, RC = Regulatory compliance, TDL =Technology/Digital Literacy, TTS =Training and Technical Support.

Research hypotheses development

Regulatory compliance (RC)

Regulatory compliance by service providers is a fundamental element in guaranteeing users’ safety and confidence in any technology-driven healthcare system. Particularly, the strong legislation and regulation are vital for governance, accountability, and the assurance of safety and security for healthcare professionals and technologies.³⁰ Regulatory compliance measures to guide telemedicine services, such as remote physiologic monitoring, check-ins, therapeutic monitoring, and the disclosure of service location,³¹ can boost user adoption of telehealth technology systems. Gajarawala and Pelkowski³² argue that various regulatory, legal, and repayment issues have obstructed the broader usage of telehealth services. Therefore, MVTHS providers who ensure compliance with government regulations regarding state-specific physicians and practice measures will encourage users’ positive attitudes towards MVTHS. MVTHS providers should comply with applicable state and federal privacy laws, as well as strict regulations related to advertising and marketing of health-related products and services. It is essential to implement regulations and compliance measures for “remote clinical healthcare, health-related education for patients and professionals, public health, and health administration, utilizing electronic information and telecommunication technologies”,³³ along with metaverse technologies. Additionally, the nature of the government regulatory framework and the MVTH system design reflect on the perceived ease of use and usefulness of such a technology. Furthermore, government support has been confirmed to influence technology adoption.³⁴ Based on this, we proposed that RC towards MVTHS would influence the PU, attitudes, and PEOU toward MVTHS. Accordingly, H1, H2 and H3 were proposed.

RC has a positive impact on the PU of MVTHS.

RC has a positive influence on ATT toward using MVTHS.

RC has a positive influence on the PEOU of MVTHS.

Training and technical support (TTS)

Adequate training and technical support are vital for users to achieve maximum benefits from the use of technology-driven healthcare services. Better training and technical support have been the key enablers for efficient delivery of important telehealth services.^35,36 Particularly, the provision of adequate training and technical support through health coaching mechanisms (education, encouragement, feedback, etc.) can drive users’ behavior towards adoption of telehealth services.³⁷ Furthermore, some argue that providing adequate training and technical support can better address the barriers patients face in using telemedicine services, healthcare systems, and telemedicine portals, and at the policy level, thereby achieving equity in the field of telemedicine.³⁸ In the context of MVTHS, adequate training is required to understand metaverse healthcare services and how they differ from traditional telehealth services for their usage. Also, teaching users how to operate MVTH systems, such as movement, communication tools, and access to virtual health spots, boosts their confidence and attitude in metaverse health systems. Training that provides information regarding the essential hardware and software prerequisites, such as VR headsets, mobile devices, and computers, along with detailed instructions on their setup, would be helpful to users. This includes a comprehensive guide on utilizing MVTH applications to schedule appointments, engage with healthcare professionals, and obtain health-related resources. Moreover, a help desk that offers multiple communication channels, such as phone calls, chat, and email, will provide users with round-the-clock support for their technical difficulties. Additionally, developing an extensive online repository of information that encompasses commonly asked questions, troubleshooting manuals, and resource articles addressing prevalent issues and their resolutions would serve as an effective method of technical assistance for users. Studies have shown that technical support drives both PU and PEOU of technology.³⁹ Based on this, we assert that these training and technical support services provided to MVTH users would shape their perceptions of PU, attitudes, as well as PEOU associated with MVTHS. Consequently, H4, H5 and H6 were proposed.

TTS has a positive influence on the PU of MVTHS.

TTS has a positive influence on the ATT towards using MVTHS.

TTS has a positive influence on the PEOU of MVTHS.

Peer Influence (PEI)

Peer Influence (PEI) is one of the significant social factor that facilitate the diffusion and adoption of technology.^40,41 Individuals usually recognize and value the power of their peers to influence their actions and work-related activities due to the implicit trust mechanism it generates.⁴² With the ongoing expansion of the metaverse, which integrates virtual environments and interactions into sectors such as healthcare. It is essential to comprehend the influence of peer dynamics on the utilization of these services. The likelihood of individuals trusting MVTHS increases when they receive endorsements from their peers. Positive feedback and recommendations from friends or family can serve as social validation, prompting others to consider utilizing these services. People tend to adopt the behaviors of those around them in their social circles. When individuals witness their peers engaging with MVTHS, they may be more likely to participate as well. Various studies have confirmed that peer influence is a positive indicator of a positive attitude toward technology adoption.^43,44 Consequently, we propose that peers will influence individual knowledge regarding the PU, attitudes, and PEOU of MVTHS. Accordingly, H7, H8 and H9 were proposed.

PEI positively influences the PU of MVTHS.

PEI positively influences an individual’s attitude towards using MVTHS.

PEI positively influences the PEOU of MVTHS.

Perceived usefulness (PU)

The PU pertains to the degree to which an individual holds a belief that the application of a particular technology will enhance his/her performance.^28,45 Perceived usefulness encapsulates the expected benefits a technology provides to its users. Telehealth represents an auspicious approach to enhancing access to behavioral healthcare for both uninsured communities and the general population.⁴⁶ Metaverse technology facilitates patients’ access to healthcare conveniently via telehealth services without a physical visit. Access to healthcare in remote or deprived regions would not only encourage more users to use it but also drive their positive attitudes toward MVTHS. Consequently, MVTHS that provide tangible benefits for users to access health services would influence users’ attitudes toward them. Empirical studies have confirmed that the PU of technology, such as telehealth, positively drives attitudes toward it.^47,48 Accordingly, H10 was advanced.

H10

PU has a positive influence on attitudes toward using MVTHS.

Perceived ease of use (PEOU)

PEOU denotes the level of confidence a person has in the belief that a particular technology will require minimal effort and be straightforward to utilize.^28,49 The potential of telehealth technology to integrate with existing IT systems and user-friendly designs, accompanied by adequate technical support, is one of the issues concerning the user-friendliness of telehealth services.⁵⁰ The ease of use associated with telehealth services that make it easier for users to operate and learn is a top priority for users.⁵¹ Also, information and communication technology skills have been determined to be key antecedents of the perceived ease of telehealth service.⁵¹ PEOU in the context of MVTH is the extent to which individuals believe that utilizing telehealth services is free of effort. A straightforward, intuitive interface of MVTHS that enhances users’ comfort and reduces the cognitive load required to navigate the platform would increase users’ PEOU. Easy-to-use features like straightforward appointment scheduling, clear video calls, or accessible medical records can contribute positively to PEOU. Additional features like appointment reminders, simple billing processes, and integration with other health tools (like wearable health apps) can enhance the overall user experience towards MVTHS, making the platform feel easier to use. These easy-to-use features of MVTHS would significantly impact users’ attitudes towards it. Research indicates that the PEOU is positively correlated with users’ attitudes.^52,53 Subsequently, H11 was proposed.

H11

PEOU has a positive influence on attitudes toward using MVTHS.

Attitude (ATT)

Attitude is characterized as a predetermined mental state that reflects one’s views on the advantages of a system in optimizing job efficiency, improving effective management of time for carrying out tasks, and its role in elevating the quality of the work performed.⁵⁴ A notable segment of the population faces considerable healthcare demands and seeks enhanced healthcare access for themselves and their families, making extensive utilization of information and communication technology (ICT).⁴⁸ At the same time, there is a generally positive perception of telehealth, with socio-demographic variables, including age, location, education level, and family size, significantly impacting attitudes toward telehealth services.⁴⁸ The perspective (attitude) on the utilization of MVTH is impacted by a variety of issues, such as technological progress, cultural viewpoints, user interactions, and accessibility to healthcare services. MVTH can serve an essential function in increasing accessibility for individuals located in remote areas with inadequate services. For those who encounter difficulties in traveling to physical appointments, metaverse solutions may present a more convenient alternative, consequently promoting a positive attitude towards their utilization. However, access to the necessary technology, such as virtual reality headsets and high-speed internet, is not universally available. As a result, individuals who possess these essential systems are likely to exhibit a more favorable attitude towards utilizing MVTHS. A variety of research findings suggest that the attitudes held by users are a vital determinant in both the acceptance and the efficiency of technology implementation in real-world scenarios.^55–57 Studies have confirmed explicitly that attitudes positively influence the intent to use telehealth.⁵³ Based on the foregoing, H12 was proposed.

H12

ATT toward using MVTHS has a positive influence on the adoption intention of MVTH.

Information literacy (IL)

IL is defined as the skill to identify the need for information, undertake specific tasks or resolve issues, search for information in a cost-efficient manner, organize or reorganize information sources, interpret and analyze the required information, and evaluate the trustworthiness, reliability, and dependability of the information.⁵⁸ The concept of IL includes a range of abilities that equip individuals to proficiently search for and analyze different information sources, while also evaluating the authenticity and reliability of the information presented.⁵⁹ It entails a process of introspective inquiry, grasping, and valuing the formation and relevance of information, as well as its proficient use in generating novel insights.⁵⁹ To function effectively within society, an individual must prioritize the advancement of information literacy.⁶⁰ In the current era characterized by an abundance of information, individuals need to develop the skills necessary to assess the authenticity and reliability of the information they encounter,⁵⁹ particularly concerning digital health services such as telehealth. Individuals’ capacity to access health-related information is crucial to their effective management of well-being,⁵⁹ particularly when using MVTHS. Consequently, the degree to which individuals can obtain, interpret, and understand critical MVTH information and services is essential for making informed health decisions. These abilities may influence their perceptions of the PU, attitudes, and PEOU, which might lead to MVTH service use. Studies have confirmed that information literacy failed to influence the PU of technology directly but exhibited an indirect effect on the intention to adopt technology through positive attitude.⁵² Another study showed that the technology adoption behavior was moderated by information literacy.⁶¹ However, in this study, we assert that IL can directly influence both attitudes and adoption intention of MVTHS as well as moderate the impact of PU on attitudes and MVTH adoption intention. According, H12, H13, H14, H15 and H16 were advanced.

H13

IL positively influences the individual’s attitude toward MVTHS.

H14

IL positively influences the individual’s intention of adopting MVTHS.

H15

IL positively moderates the PU influence on an individual’s attitudes toward using MVTHS.

H16

IL positively moderates the influence of the individual’s attitude on the adoption intention of MVTHS.

Technology/Digital literacy (TDL)

The idea of technology or digital literacy is regarded as a crucial element in achieving digital transformation,⁶² particularly in the integration of information technology in healthcare delivery systems. Digital literacy encompasses the skills and attitudes individuals need to efficiently use digital technologies by enabling them to access, manage, evaluate, and integrate digital resources, as well as create and communicate digital content.⁶³ This concept is relevant to personal contexts and aims to foster positive social actions and self-reflection.⁶³ Digital literacy plays a crucial role in improving health literacy in contemporary society. The capacity to access, comprehend, and effectively use health information has relied on one’s digital skills and knowledge.^64,65 TDL encompasses the ability to navigate online health resources, interpret digital health records, and effectively use health-related applications⁶⁵ such as MVTH systems. Higher levels of digital literacy significantly influence health literacy. Therefore, inadequate literacy can directly impact individuals’ health by restricting their personal, social, and cultural growth, thereby obstructing the advancement of digital health literacy.⁶⁶ In this research, digital literacy and MVTHS refer to an individual’s ability to navigate virtual environments effectively and efficiently for healthcare purposes. Individuals’ poor technology literacy is identified as a barrier to access to telehealth care.⁶⁷ Researchers argue that digital literacy positively influences the ease of use of technology and, indirectly, the intention to use it, through one’s attitude toward its use.⁵² Digital literacy also showed a direct impact on effort expectancy, as well as indirectly affected intention to use via effort expectancy.⁶² In our study, we posit that technology literacy may influence individuals’ perceptions of MVTHS attitudes and adoption intentions. Additionally, we assert that the TDL moderates the influence of PEOU on attitudes as well as the influence of attitudes on the adoption intention of MVTHS. Consequently, H17, H18, H19 and H20 were advanced.

H17

TDL positively influences the individual’s attitude toward MVTHS.

H18

TDL positively influences the individual’s intention of adopting MVTHS.

H19

TDL positively moderates the PEOU influence on an individual’s attitudes toward using MVTHS.

H20

TDL positively moderates the influence of the individual’s attitude towards using MVTHS on the adoption intention.

Methods

Research design and questionnaire development

The study uses a cross-sectional survey design based on a quantitative approach and developed a comprehensive research questionnaire for data collection to analyze the proposed model depicted in Figure 1. The questionnaire instrument was based on items adopted from past literature works, but was adjusted to the context and content of this study. The variable items were adapted as follows: PU,⁶⁸ PEOU,⁶⁸ attitudes,^69,70 PEI,^71,72 adoption intention,^73,74 RC,⁷⁵ TTS,⁷⁶ IL,⁵² and TDL.⁷⁷ The first component of the questionnaire consists of these main variable items, which were measured on five (5) points Likert scales ranging from 1 = strongly disagree to 5 = strongly agree. The second component consists of basic demographic information about respondents, such as age, gender, education, and professional type.

Study settings

We hired two Chinese teachers who were both fluent in English and Chinese and understood the subject matter (experts in public health) to translate the English questionnaire into Chinese. This approach was implemented to ensure that Chinese respondents (Chinese-speaking audience) could relate to the questionnaire, thus promoting active engagement and high-quality responses. Back-translation (from Chinese into English) was conducted to verify the accuracy of the earlier version. It also helped assess the reliability and validity of the translated survey instruments. We ensured that the questionnaire was free from any ambiguity and did not pose any challenge to respondents to get quality responses. Therefore, the questionnaire was pre-tested and piloted among 60 respondents. This was an important step to ensure the effectiveness and validity of the instrument before a full-scale data collection was undertaken.^78,79 The pre-testing and piloting study improved the overall clarity of the questions and thus enhanced the likelihood of obtaining high-quality responses and data.

Sampling strategy and data collection

We employed convenience sampling to collect the data. The convenience sampling technique provides researchers with straightforward access to readily available respondents, decreasing the costs associated with data collection by lessening the need for extensive planning or travel.^80,81 Moreover, it enables the rapid collection of data, ensuring broad coverage. The questionnaire was hosted on the WJX online survey platform (https://www.wjx.cn/) between October 1 and December 31, 2024. The questionnaire link, including a QR Code, was then shared with respondents residing in Wuhan, Hubei Province, China, via social media platforms such as WeChat (a popular social media platform in China) to collect data. People were also requested to share the questionnaire link/QR code with their friends, family, and others within their communities, work units, and organizations. This method allows us to disseminate the questionnaire quickly and enables participants to complete the survey at their convenience. Additionally, using WeChat offers the advantage of reaching a large audience given its extensive user base. This method can significantly reduce the biases that may arise from collecting data exclusively through convenience sampling.

A total of 900 questionnaires were collected between October 1 and December 31, 2024. After careful screening, 161 invalid responses were deleted based on inclusion/exclusion criteria. The remaining 739 valid responses were retained and utilized for the analysis. The primary criterion for determining the inclusion or exclusion of participants was the stipulation that they must be at least 18 years old. Additionally, the identification of response patterns, where participants select the same response option across multiple items, served as a basis for including or excluding specific responses. Following data collection, a screening process was implemented to assess the dataset for missing values, outliers, and response patterns. Other measures, such as Cronbach’s alpha and factor loadings, were also employed to identify and eliminate responses that did not meet the established thresholds.

The adequacy of the sample was determined using a common rule of thumb that recommends that the sample size should be at least 10 times the number of items.⁸² This is commonly used in the context of structural equation modeling (SEM) or other multivariate analyses to ensure adequate statistical power. The current study has 37 construct items, implying that the minimum required sample should be 370 (37*10). Our sample size exceeds the recommended minimum sample size, indicating the robustness of the analysis and the sample’s representativeness. The data were analyzed using SPSS and SmartPLS, employing structural equation modeling. Smart PLS is an effective tool designed for researchers interested in applying PLS-SEM to analyze path models.^83,84

Ethical statement and informed consent

This study was granted ethical approval (No. NTU-IRB24-SML05) by the Institutional Review Board (IRB) of NingboTech University, Ningbo, China. Additionally, written informed consent was obtained from all the participants. The participants were informed of their right to withdraw or voluntarily participate in this study at any point in time. They were assured that the information collected through this questionnaire would be used only for scientific research and would not be shared with any third party. Additionally, we strictly follow the standardized academic and institutional guidelines along with the 2024 Declaration of Helsinki Ethical standards to ensure data privacy, anonymity, and safety during the data processing and analysis stage.

We used the STROBE reporting guideline to draft this manuscript, and the STROBE reporting checklist is included as supplement material A.

Data analysis and results

Demographic characteristics of the respondents

The demographic details indicate that 417 males (56.43%) and 322 females (43.57%) participated in this study. The detailed basic information of the respondents is illustrated in Table 2.

Table 2.

Sample basic information.

Category	Items	Frequency	Percentage (%)
Gender	Male	417	56.43%
Gender	Female	322	43.57%
Age	18-25	65	8.80%
	26-30	189	25.58%
	31-40	269	36.40%
	41-50	144	19.49%
	51+	72	9.74%
Education	Bachelor degree	363	49.12%
	master	280	37.89%
	PhD	31	4.19%
	Others	65	8.80%
Profession Type	Student	67	9.07%
	Academic Staff	134	18.13%
	Non-teaching staff	108	14.61%
	Professional medical staff	60	8.12%
	Public and civil servants	170	23.00%
	Private sector workers	146	19.76%
	Others	54	7.31%

Common method bias (CMB) test

We used Harman’s single-factor analysis to test CMB in our data. Results indicate that a single factor explained 25.617% of the total variance, which is well below the recommended threshold of 50%. Consequently, CMB is not a threat in our study.

Measurement model analysis

The evaluation of the measurement model includes examinations of both reliability and validity. The reliability assessment measures the consistency and trustworthiness of the utilized scale. The test indicators are Cronbach’s Alpha and Composite Reliability (CR). A Cronbach’s Alpha and Composite Reliability (CR) value exceeding 0.7 indicates that the scale demonstrates a strong internal consistency.⁸⁵ Table 3 demonstrates that the Cronbach’s Alpha values for the nine scales examined in this study vary between 0.818 and 0.868, while the composite reliability (CR) values range from 0.89 to 0.904. All these values exceed 0.7, demonstrating that each scale possesses strong reliability.

Table 3.

Reliability and convergent validity test.

Variables	Items	Factor loading	Cronbach’s alpha	CR	AVE
ATT	ATT1	0.793	0.865	0.903	0.650
	ATT2	0.815
	ATT3	0.801
	ATT4	0.818
	ATT5	0.803
IL	IL1	0.835	0.849	0.898	0.687
	IL2	0.821
	IL3	0.842
	IL4	0.818
MVTHAI	MVTHAI1	0.842	0.818	0.892	0.733
	MVTHAI2	0.856
	MVTHAI3	0.870
PEI	PEI1	0.836	0.835	0.890	0.669
	PEI2	0.800
	PEI3	0.825
	PEI4	0.810
PEOU	PEOU1	0.815	0.840	0.893	0.675
	PEOU2	0.827
	PEOU3	0.824
	PEOU4	0.821
PU	PU1	0.829	0.844	0.895	0.681
	PU2	0.814
	PU3	0.827
	PU4	0.831
RC	RC1	0.818	0.863	0.901	0.646
	RC2	0.796
	RC3	0.782
	RC4	0.806
	RC5	0.815
TDL	TDL1	0.834	0.858	0.904	0.702
	TDL2	0.848
	TDL3	0.844
	TDL4	0.824
TTS	TTS1	0.824	0.868	0.904	0.655
	TTS2	0.831
	TTS3	0.785
	TTS4	0.802
	TTS5	0.802

Validity testing encompasses both convergent validity and discriminant validity assessments. Convergent validity pertains to the degree of similarity in measurement outcomes when various methods are employed to evaluate a particular scale. According to Fornell,⁸⁶ the convergent validity of variables can be determined by the factor loading, composite reliability (CR), and average variance extracted (AVE) of the measurement items. Among them, the factor loading of the measurement items must exceed the minimum limit of 0.6, the composite reliability CR value of the scale must exceed the minimum limit of 0.7, and the average variance extracted AVE value must be greater than the minimum limit of 0.5. As shown in Table 3, the factor loading of the measurement items of the nine scales ranges from 0.782 to 0.870, all greater than 0.6; the composite reliability (CR) of the nine scales ranges from 0.890 to 0.904, all greater than 0.7; the average variance extracted (AVE) ranges from 0.646 to 0.733, all greater than 0.5. It can therefore be concluded that the convergent validity of the seven scales in this study is good.

Furthermore, an examination of discriminant validity was performed to determine the distinction among different constructs, utilizing the Fornell-Larcker validation criteria alongside the HTMT test.⁸⁷ First, according to Fornell-Larcker validation criteria, we can determine the degree of discrimination between two constructs by comparing the square root of the average variance extracted (AVE) of the constructs and the correlation coefficient between the constructs.⁸⁸ Table 4 illustrates that the square root values of the AVE exceed the correlation coefficients among these constructs, thereby demonstrating strong discriminant validity. Secondly, when the heterotrait-monotrait correlation ratio (HTMT) value between two constructs is less than 0.85, it is considered that the constructs show good discriminant validity.⁸⁹ As shown in Table 5, the maximum HTMT value between the two constructs in this study is 0.515 (TDL and MVTHAI), which is less than 0.85. Hence, our scales show strong discriminant validity.

Table 4.

Fornell-Larcker criterion.

	ATT	IL	MVTHAI	PEI	PEOU	PU	RC	TDL	TTS
ATT	0.806
IL	0.200	0.829
MVTHAI	0.223	0.341	0.856
PEI	0.320	0.229	0.295	0.818
PEOU	0.218	0.253	0.298	0.250	0.822
PU	0.294	0.251	0.286	0.273	0.229	0.825
RC	0.329	0.272	0.413	0.373	0.296	0.332	0.804
TDL	0.299	0.358	0.433	0.349	0.267	0.266	0.357	0.838
TTS	0.291	0.258	0.345	0.315	0.268	0.265	0.397	0.314	0.809

Table 5.

Heterotrait-monotrait ratio (HTMT).

	ATT	IL	MVTHAI	PEI	PEOU	PU	RC	TDL	TTS
ATT	-
IL	0.232	-
MVTHAI	0.262	0.407	-
PEI	0.374	0.274	0.355	-
PEOU	0.254	0.301	0.360	0.294	-
PU	0.344	0.298	0.345	0.323	0.271	-
RC	0.379	0.320	0.489	0.440	0.347	0.386	-
TDL	0.345	0.418	0.515	0.409	0.313	0.312	0.413	-
TTS	0.332	0.303	0.408	0.371	0.311	0.308	0.458	0.363	-

Note. ATT=Attitude Toward Using, IL =Information Literacy, MVTHAI = Metaverse Telehealth Adoption Intention, PEI = Peer Influence, PEOU =Perceived Ease of Use, PU =Perceived Usefulness, RC = Regulatory compliance, TDL =Technology/Digital Literacy, TTS =Training and Technical Support.

Structural model analysis

Common test indicators of structural models include multicollinearity diagnosis (VIF), coefficient of determination (R²)^, predictive relevance (Q²), and effect size (f²). The test results are shown in Table 6. (1) When VIF is less than 5, the multicollinearity problem does not occur.⁹⁰ The PLS Algorithm program results can present the VIF value. As shown in Table 6, the maximum VIF value of the constructs in this study is 1.522, which is less than 5, indicating that there is no collinearity problem in the structural model of this study; (2) the explanatory power is measured by the coefficient of determination (R²). According to the research,⁹¹ when R² is close to 0.19, it means that the explanatory power of the model is weak; when R² is close to 0.33, it means that the explanatory power of the model is at a medium level. When R² is close to 0.67, it means that the explanatory power of the model is strong. As shown in Table 6, the R² of the endogenous latent variables Attitude toward Using, Adoption Intention, PEOU, and PU in this study are 0.223, 0.256, 0.129, and 0.149, respectively, which shows that the model has a certain explanatory power; (3) the predictive ability of the model is usually evaluated by predictive relevance (Q²)^. The evaluation criterion for the predictive relevance of an endogenous construct is when the Q² value of the construct is greater than 0, which is an indication that the model has predictive relevance for the latent variable.⁹² As shown in Table 6, the Q² values of the endogenous latent variables Attitude Toward Using, Adoption Intention, PEOU, and PU in this study are 0.138, 0.181, 0.084, and 0.098, respectively, all greater than 0, indicating that the exogenous constructs in this research model have a good predictive correlation with the endogenous constructs. (4) The f² effect size measures the contribution of the exogenous latent variables to the R² value of the endogenous latent variables. The critical values of the f² effect size of 0.02, 0.15, and 0.35, respectively, indicate that the effect of the exogenous construct on the endogenous latent construct is small, medium, and large.⁹³ As shown in Table 6, the f² values of most paths in this study are greater than 0.02, indicating a substantial effect size.

Table 6.

Structural model test.

	VIF				R²	Q²	f²
	ATT	MVTHAI	PEOU	PU	R²	Q²	ATT	MVTHAI	PEOU	PU
ATT	-	1.115	-	-	0.223	0.138	-	0.010	-	-
IL	1.261	1.187	-	-	-	-	0.001	0.038	-	-
MVTHAI	-	-	-	-	0.256	0.181	-	-	-	-
PEI	1.299	-	1.208	1.208	-	-	0.022	-	0.017	0.021
PEOU	1.233	-	-	-	0.129	0.084	0.002	-	-	-
PU	1.249	-	-	-	0.149	0.098	0.020	-	-	-
RC	1.429	-	1.292	1.292	-	-	0.015	-	0.031	0.046
TDL	1.366	1.251	-	-	-	-	0.011	0.121	-	-
TTS	1.331	-	1.234	1.234	-	-	0.007	-	0.022	0.016

Note. ATT=Attitude Toward Using, IL =Information Literacy, MVTHAI =Adoption Intention, PEI = Peer Influence, PEOU =Perceived Ease of Use, PU =Perceived Usefulness, RC = Regulatory compliance, TDL =Technology/Digital Literacy, TTS =Training and Technical Support.

Hypotheses testing

The analysis utilized Smart PLS4.0 to evaluate the model’s path parameters by performing Bootstrapping sampling 5000 times, yielding the path coefficient test results presented in Table 7 and graphically depicted in Figure 2. The results indicate that RC (β= 0.127, t = 3.1, p < 0.01), PEI (β= 0.147, t = 3.541, p < 0.001), PU (β= 0.14, t= 3.604, p < 0.001), TDL (β= 0.107, t= 2.457, p<0.05) have a significant positive impact on ATT toward using MVTHS. These findings indicate that RC, PEI, PU, and TDL significantly influence the attitude toward the usage of MVTSHS in China and validate H2, H8, H10, and H17 of the study. However, TTS (β= 0.085, t = 1.940, p > 0.05), PEOU (β= 0.043, t= 1.107, p>0.05), and IL (β= 0.023, t= 0.578, p>0.05) had no significant impact on ATT toward using MVTHS. These findings suggest that TTS, PEOU, and IL fail to significantly influence the individual’s attitude toward using MVTHS in China and reject the H5, H11, and H13 of the study.

Table 7.

Hypotheses testing.

Hypotheses	Paths	β	Standard deviation (STDEV)	T statistics	P values	Result
H1	RC -> PU	0.225	0.041	5.443	0.000	Supported
H2	RC -> ATT	0.127	0.041	3.100	0.002	Supported
H3	RC -> PEOU	0.187	0.042	4.435	0.000	Supported
H4	TTS -> PU	0.129	0.038	3.374	0.001	Supported
H5	TTS -> ATT	0.085	0.044	1.940	0.052	Not Supported
H6	TTS -> PEOU	0.152	0.041	3.694	0.000	Supported
H7	PEI -> PU	0.148	0.037	4.011	0.000	Supported
H8	PEI -> ATT	0.147	0.042	3.541	0.000	Supported
H9	PEI -> PEOU	0.132	0.040	3.320	0.001	Supported
H10	PU -> ATT	0.140	0.039	3.604	0.000	Supported
H11	PEOU -> ATT	0.043	0.039	1.107	0.268	Not Supported
H12	ATT -> MVTHAI	0.093	0.036	2.598	0.009	Supported
H13	IL -> ATT	0.023	0.040	0.578	0.563	Not Supported
H14	IL -> MVTHAI	0.184	0.037	4.944	0.000	Supported
H15	IL * PU -> ATT	0.093	0.035	2.651	0.008	Supported
H16	IL * ATT -> MVTHAI	0.041	0.039	1.046	0.295	Not Supported
H17	TDL -> ATT	0.107	0.044	2.457	0.014	Supported
H18	TDL -> MVTHAI	0.336	0.040	8.467	0.000	Supported
H19	TDL * PEOU -> ATT	0.074	0.037	1.976	0.048	Supported
H20	TDL * ATT -> MVTHAI	0.141	0.037	3.813	0.000	Supported

Note. ATT=Attitude Toward Using, IL =Information Literacy, MVTHAI = Metaverse Telehealth Adoption Intention, PEI =Peer Influence, PEOU =Perceived Ease of Use, PU =Perceived Usefulness, RC =Regulatory compliance, TDL =Technology/Digital Literacy, TTS =Training and Technical Support.

Figure 2.

Validated structural MVTH model diagram.

The study also finds that attitude toward using MVTHS (β=0.093, t=2.598, p<0.01), IL (β=0.184, t=4.944, p<0.001), and TDL (β=0.336, t=8.467, p<0.001) have a significant positive impact on the Adoption intention of MVTHS. This means that the positive attitude toward MVTHS and digital literacy can play an important role in the adoption of MVTHS in China, validating H12, H14, and H18 of the study. Results also indicate that RC (β= 0.225, t= 5.443, p<0.001), TTS (β= 0.129, t= 3.374, p<0.01), and PEI (β= 0.148, t= 4.011, p<0.001) have a significant positive impact on PU. Findings indicate that RC, TTS, and PEI play an important role in the PU of MVTHS in China. Thus, confirm H1, H4, and H7 according.

Moreover, our analysis revealed that RC (β = 0.187, t = 4.435, p<0.001), TTS (β = 0.152, t = 3.694, p<0.001), and PEI (β = 0.132, t = 3.32, p<0.01) have a significant positive impact on PEOU of MVTHS in China. The findings consequently provide support for H3, H6, and H12. Furthermore, the interaction term between IL and PU (β = 0.093, t = 2.651, p < 0.01) had a significant positive impact on ATT toward using MVTHS. This is an indication that IL plays an important moderating role, i.e., IL has a positive moderating effect on the relationship between PU and ATT toward using MVTHS. This result provides support for H15. Also, the interaction term between TDL and PEOU (β= 0.074, t= 1.976, p < 0.05) has a positive moderating effect on ATT toward using MVTHS. This is also an indication that TDL has a significant positive moderating impact on the relationship between PEOU and ATT toward using MVTHS. This result provides support for H19 of the study.

Additionally, the interaction term between IL and ATT (β = 0.041, t = 1.046, p > 0.05) on the adoption intention of MVTHS was not significant, indicating that IL did not play a significant moderating role in the relationship between ATT and the adoption intention of MVTHS. This finding fails to provide support for H16. Also, the interaction term between TDL and ATT (β= 0.141, t= 3.813, p < 0.001) had a significant effect on the adoption intention of MVTHS. This is an indication that TDL has a positive moderating effect on the relationship between ATT and the adoption intention of MVTHS. Consequently, provide support for H20.

Discussions

This paper explores the factors influencing the adoption of MVTHS from the lens of an extended Technology Acceptance Model (TAM). Based on the results analysis, we have demonstrated that RC positively influences ATT, suggesting that both individuals and healthcare providers are more inclined to feel secure and assured in their engagement with these services when the right legal regulatory frameworks are in place and complied with. Telehealth services within the metaverse, guided by regulatory frameworks and compliance standards,^94,95 have the potential to enhance utilization rates, advance patient outcomes, and contribute to a more resilient healthcare system that seamlessly incorporates digital health technologies. Moreover, the positive influence of regulatory compliance on attitudes toward metaverse-telehealth services reflects China’s robust legal framework governing health data. China has established comprehensive regulations, including the Cybersecurity Law, Data Security Law, and Personal Information Protection Law, which collectively govern internet healthcare provision.⁹⁶ The National Health Commission further mandates that medical institutions implement strict data security protocols, including access controls and traceability mechanisms for electronic medical records.⁹⁷ For metaverse healthcare applications specifically, scholars emphasize that proactive development of legal frameworks is imperative for sustainable development.⁹⁸ This regulatory environment demonstrates to users that their sensitive health data receives legal protection, thereby fostering positive attitudes toward adopting these innovative services within China’s uniquely structured digital health landscape. It has also been noted that legal concerns prevent the application of the metaverse in the health sector,⁹⁹ limiting its transformative role in the healthcare ecosystem. This transformation can be realized in the form of efficient healthcare delivery, improved patient outcomes, and reduced healthcare costs.⁹⁸ Maximizing the value of the medical metaverse requires proactively shaping technical and legal standards and continuously monitoring their performance to ensure sustainable development.⁹⁸ This finding cannot be compared to any current or past studies due to novel testing.

The positive impact of PEI on ATT indicates that individuals are more inclined to embrace and utilize MVTHS when they witness their peers positively interacting and advocating for them. The positive experiences shared by friends, family, or colleagues¹⁰⁰ regarding MVTH can play a significant role in alleviating skepticism and fostering greater acceptance of these services among others. This contributes to building trust in MVTH platforms, thereby encouraging users to feel more at ease when seeking care in a virtual setting. Healthcare organizations can create online communities where individuals can share their experiences with MVTH. These platforms can provide peer support, thereby increasing comfort levels and encouraging more individuals to utilize telehealth services. Our findings contradict a study that demonstrated that social influence failed to influence intention to use innovative healthcare technologies.¹⁰¹

Furthermore, the positive effect of PU on ATT towards the use of MVTHS means that individuals are more likely to have a positive outlook on these services if they perceive that engaging with metaverse technology leads to better health outcomes or provides considerable benefits in comparison to traditional telehealth or in-person appointments. This positive perception can foster greater acceptance and utilization of telehealth solutions within the metaverse, as individuals acknowledge their ability to provide timely, effective, and personalized healthcare. This discovery aligns with research findings that PU drives the users’ attitudes toward telehealth technology.⁴⁷ It also supports a study that performance expectancy is positively related to attitude towards AI-based healthcare technology.¹⁰¹

The positive influence of TDL on ATT demonstrates that with a higher level of digital literacy, patients can confidently navigate virtual environments, effectively engage with healthcare providers, and smoothly access online resources that enhance their understanding of health issues. Those who are adept with technology tend to regard MVTHS as valid and beneficial, which helps to alleviate skepticism and opposition. Users equipped with significant digital skills are more prone to engage proactively in their health management, contributing to greater adherence to treatment strategies and health guidelines, including health care behaviors.¹⁰² Our finding is in line with a study that e-health literacy has a positive influence on attitude towards internet health information.¹⁰³ It also supports a study in a non-metaverse context that digital literacy is positively associated with attitude using information technology.¹⁰⁴

Furthermore, the positive influence of ATT on the adoption intention of MVTHS means that individuals who hold a favorable view and positive emotions regarding these services are more likely to express an intention to utilize them. This further implies that if users perceive MVTHS as advantageous, efficient, and user-friendly, they are more likely to embrace and interact with MVTH technologies. If users become aware of the benefits, like enhanced access to healthcare, personalized interactions, and improved relationships with healthcare providers, they are more likely to adopt a positive attitude. Positive attitudes toward digital health solutions promote the exploration, use, and trust of eHealth tools, facilitating their integration into routine healthcare.¹⁰⁵ Our findings validate previous studies that attitude drives the adoption intention of telehealth technology.^106,107

The positive influence of IL on the adoption intention of MVTH means that individuals who possess higher levels of IL are more likely to embrace and utilize these innovative healthcare solutions. Because IL enhances individuals’ capacity to efficiently locate, assess, and utilize information,¹⁰⁸ which is essential for successfully navigating the intricacies of telehealth services within a metaverse context. Individuals who sharpen their information literacy skills are more capable of understanding the features, advantages, and potential hazards related to MVTHS. This level of understanding can help alleviate worries and foster greater confidence in engaging with MVTHS. This discovery aligns with previous research that IL, in the form of e-health literacy, drives technology adoption.¹⁰⁷

The positive influence of TDL on the adoption of MVTHS suggests that individuals with higher levels of technological understanding and skills are more likely to embrace and use telehealth services in the metaverse environment. Technologically literate personnel are more aware of the benefits and potential hazards linked to MVTHS, allowing them to make more prudent decisions about their healthcare options. Low digital literacy is a significant barrier to e-health services adoption.¹⁰⁹ Higher technology literacy can reduce anxiety and stress while using new technologies and boost confidence in one’s own ability to use those technologies.¹¹⁰ Research confirms that increasing digital skills through training programs encourages adoption of telemedicine.¹¹¹ Our findings align with a study that technology literacy (i.e., digital competence) drives the adoption of telehealth.¹¹²

Additionally, the positive influence of RC on both PU and PEOU of MVTHS indicates that when there are clear regulatory frameworks and compliance standards in place, users are more likely to view these services as beneficial. To comply with regulations, providers should be required to embrace best practices and technologies that prioritize user-friendliness and improved overall user experience (i.e., alleviating ambiguity and uncertainty). Moreover, these findings established a trustworthy operational foundation within China’s strict legal system. Feng, Niwa¹¹³ found that regional regulations in East Asia significantly shape users’ expectations of mHealth, with Chinese users exhibiting heightened sensitivity to compliance signals that enhance perceived utility. Jiang and Zheng¹¹⁴ demonstrated that internet hospital apps achieving higher compliance scores provide clearer data processing protocols, reducing cognitive burden and thereby improving PEOU. Furthermore, Akiogbe, Feng¹¹⁵ identified legal supervision as an indirect but significant factor shaping healthcare professionals’ technology perceptions, suggesting compliance frameworks facilitate acceptance by legitimizing system functionalities. Thus, regulatory compliance will push users to use these services and, consequently, will enhance both PU and PEOU. These results, though, could not be compared with any past or current studies on MVTHS. However, these findings are supported by a non-metaverse study that government regulation drives PU and PEOU of a technology.¹¹⁶

The positive influence of training and technical support on PU and PEOU of MVTHS suggests that effective training and technical support empower users to perceive telehealth services as useful and easy to use, leading to a higher degree of engagement with MVTHS. The provision of training and technical support reduces the frustrations that arise from technical challenges.¹¹⁷ Therefore, providing necessary training and support is essential for keeping users informed about new features and best practices in the use of MVTHS. This approach helps sustain high levels of PU and PEOU over time, which is vital for the long-term viability of telehealth services within the metaverse. A lack of adequate technical support has been identified as a significant barrier to the adoption of video-based telehealth services.¹¹⁸ Our findings are similar to a study, though from a non-MVTH field, that training and technical skills have a direct influence on both PU and PEOU.^39,119

Moreover, the non-significant influence of TTS on the ATT toward the use of MVTHS indicates that while there may be a slight tendency for increased training and support to improve users’ attitudes, this effect is not strong enough to be statistically significant. It further implies that users may not view TTS as a crucial component influencing their attitudes, suggesting that other factors, such as service quality, accessibility, or individual experiences, may have a more substantial impact. This finding contrasts with a study from a non-MVTH area that indicated technical support directly drives the attitudes toward the use of technology.³⁹ It also contradicts a study that prior training in computer use, along with the frequency of computer usage, significantly shapes attitudes toward ICT.¹⁰⁴

Furthermore, PEI showed a significant positive impact on both PU and PEU of MVTHS, suggesting that PEI plays a crucial role in shaping individuals’ perceptions and acceptance of MVTHS. Additionally, it implies that if peers advocate positively for these services based on positive experience and outcomes, individuals are more likely to regard them as beneficial.¹⁰⁵ This highlights the importance of peer-driven initiatives and community engagement¹²⁰ in the diffusion of health technologies such as MVTHS. This further indicates that constructive peer influence may lead individuals to consider telehealth services as more accessible, due to common experiences or recommendations from their peers. In line with our findings, social influence, similar to peer influence, positively drives PU and PEOU of telemedicine.¹²¹

However, the lack of significant influence of PEOU on attitudes toward MVTHS indicates that ease of use alone does not lead to a positive attitude. In the Chinese context, the non-significant PEOU→Attitude path can be robustly explained by the nature of the user sample and technology type. Tang, Yang¹²² study of Chinese online medical websites found PEOU influenced behavioral intention only indirectly through attitude and usefulness, suggesting that for health-related technologies in China, ease of use may function as a basic expectation rather than an attitude-shaping factor.¹²² This aligns with meta-analytic evidence showing that user type (students vs. professionals) and technology characteristics moderate TAM relationships.¹²³ For Chinese users with prior digital health experience, PEOU becomes a hygiene factor—necessary but insufficient to generate positive attitudes when metaverse telehealth promises transformative rather than merely convenient healthcare solutions. Furthermore, the non-significant impact of PEOU on attitude aligns with cross-cultural meta-analyses demonstrating that TAM relationships function differently across contexts. Li, Zhu¹²⁴ found that while PEOU has stronger effects on PU and attitude in Eastern cultures compared to Western cultures, this relationship attenuates when technology complexity exceeds users’ existing competency schemas. Guo, Zhu¹²⁵ further revealed that effect sizes among TAM constructs are greater for supervisory employees or in Eastern cultures, yet PEOU’s direct influence diminishes when pragmatic utility—specifically, regulatory compliance and peer influence in collectivist contexts—supersedes usability concerns. Similarly, the insignificant impact of information literacy on attitudes suggests that simply being informed on how to use the technology does not improve users’ views, indicating that there are more complex challenges involved beyond just understanding the technology or its information. These findings contradict studies that have shown PEOU has a positive impact on attitudes towards telemedicine¹⁰⁶ and other studies that eHealth literacy (information literacy) has a positive and significant impact on attitudes towards telehealth-medicine.⁹⁵ Also, given that neither perceived ease of use nor information literacy had a significant impact on attitudes, it may be advantageous for policymakers to support a more comprehensive strategy for telehealth that incorporates metaverse solutions alongside conventional healthcare services. This approach could entail the promotion of hybrid models that merge virtual and in-person interactions, effectively addressing potential user apprehensions regarding technology while simultaneously improving access to care.

In terms of moderating role results, the positive moderating effect of IL on the relationship between PU and adoption intention indicates that individuals with advanced IL skills possess a greater capacity to comprehend and value the advantages offered by telehealth services in the metaverse environment. This enhanced understanding can strengthen the favorable correlation between the perceived utility of these services and their willingness to embrace them. Additionally, the non-significant moderating influence of information literacy on the relationship between attitude and adoption intention means that information literacy does not have a statistically meaningful effect on how an individual’s attitude toward MVTHS influences their intention to adopt those services. The research findings imply that an individual’s level of information literacy does not influence their attitude toward MVTHS, which remains a reliable predictor of their intention to adopt such services. Consequently, this suggests that merely increasing information literacy may not enhance adoption rates if individuals’ attitudes toward the services are not positively oriented.

Furthermore, the positive moderating influence of TDL on the relationship between PEOU and ATT means that individuals with greater TDL experience may have a more pronounced positive effect of PEOU on their ATT regarding the use of MVTHS. People with higher levels of technological proficiency are more inclined to perceive MVTHS as user-friendly, resulting in a more favorable disposition toward the adoption of these services.

Finally, the positive moderating effect of TDL on the relationship between ATT and adoption intention of MVTHS suggests that individuals with advanced TDL tend to exhibit more positive attitudes towards these services (metaverse telehealth services), thereby enhancing their likelihood of adopting them. Developers of MVTH solutions ought to take into account the diverse levels of technological proficiency among prospective users. The findings on the moderating outcomes cannot be compared with any prior or contemporary literature, particularly regarding telehealth services in the metaverse, as these interactions were never tested before. However, our findings are similar to those of a non-metacosmic study of e-pharmacies, which showed that technological literacy plays a significant moderating role between users’ willingness to use e-pharmacies platforms and their actual use of those platforms.¹²⁶

Theoretical implications

This study extends the technology acceptance model (TAM) by integrating regulatory compliance, training, and technical support, and peer influence as antecedents of PU, PEOU, and ATT towards the use of MVTHS. The study introduces IL and TDL as direct influencers on ATT and adoption intention, as well as moderators between ATT and the adoption intention of MVTHS. These integrations highlight the importance of contextual and social factors in technology acceptance. The study found that individual understanding of PU, PEOU, and ATT toward technology is not formed in isolation but is influenced by external factors, which could lead to the development of a more comprehensive framework for understanding technology adoption, such as MVTHS. Our findings have established that RC, TTS, PEI, IL, and TDL jointly accounted for 14.9%, 12.9%, and 22.3%, respectively, towards PU, PEOU, and attitudes. Additionally, attitude, along with information and technology literacies, explained about 25.6% of the variance in the adoption intention of MVTHS. These findings provide a theoretical foundation for studies to further validate TAM in the context of metaverse integration into healthcare delivery systems.

Limitations

While this study offers valuable insights into the factors influencing the adoption of MVTHS, several limitations should be acknowledged when interpreting the findings.

Sample generalizability

First, the data for this study were collected in a specific geographical and cultural context (Chinese). The advancement and application of metaverse technology in healthcare may vary significantly across countries due to differences in technological infrastructure, regulatory environments, cultural attitudes toward digital health, and the robustness of the healthcare system. As a result, careful interpretation is necessary when extending these findings to broader or more diverse populations.

Scope of investigated variables

Second, this research did not investigate all potential elements that drive the adoption of metaverse telehealth services. While the study focused on key constructs derived from established technology acceptance theories (e.g., perceived usefulness, perceived ease of use, peer influence), it is impractical for a single study to address every potential determinant.

Research design and causality

Third, this study employed a cross-sectional design, collecting data at a single point in time. While this approach is appropriate for examining relationships between variables, it limits the ability to establish causal direction or assess long-term behavioral patterns. For instance, it is possible that actual use of MVTHS could, over time, influence perceptions of usefulness and ease of use, creating reciprocal relationships that cannot be captured in a cross-sectional design. Furthermore, adoption intentions measured at one point may not reliably predict actual long-term adoption behavior.

Explained variance

Fourth, the relatively low amount of explained variance (R²) observed in this study for certain dependent variables warrants consideration. This suggests that while the model includes significant predictors of MVTHS adoption, there might be additional factors that can influence users’ adoption intentions. The low R² values may be attributed to the complexity of human decision-making in emerging technology contexts, where uncertainty and lack of prior experience may introduce variability that is difficult to explain with traditional technology acceptance models.

Measurement considerations

Finally, this study relied on self-reported measures for all constructs, which may be subject to common method bias and social desirability effects. Although statistical precautions were taken to minimize these concerns, the reliance on perceptual data means that the findings may not fully align with objective behavioral patterns.

Despite these limitations, this study provides a meaningful foundation for understanding the adoption of MVTHS and offers practical guidance for stakeholders seeking to implement these technologies.

Conclusions and practical implications

This study offers several actionable implications for policymakers, healthcare managers, educators, and future researchers. By understanding the factors that drive the adoption of MVTHS, stakeholders can make informed decisions to facilitate the successful integration of this technology into healthcare systems.

Implications for policy making

Policymakers play a foundational role in creating an environment where MVTHS can thrive safely and equitably. First, it is essential to establish robust regulatory frameworks that ensure user safety, data privacy, and protection against fraud, as these are prerequisites for building trust in new digital health services. Second, to prevent a digital divide in healthcare access, policymakers should advance and fund digital education initiatives at the community level. These programs should specifically target less tech-savvy and vulnerable populations to ensure that all citizens can acquire the necessary skills to benefit from MVTHS.

Implications for healthcare management

For healthcare administrators and managers, the findings underscore the need to address both the technical and social dimensions of MVTHS adoption. First, marketing strategies should focus on enhancing perceived value and trust. This can be achieved by disseminating success stories and peer testimonials that demonstrate the tangible benefits and positive patient outcomes associated with MVTHS. Second, managers must prioritize the development of comprehensive training programs. These programs should be tailored to the needs of different user groups, particularly patients with lower digital literacy, to build their proficiency and confidence in using the platforms effectively.

Implications for education

Educators and healthcare trainers should integrate modules on digital health literacy into their curricula for both healthcare professionals and patients. For medical and nursing students, training should include hands-on experience with MVTH platforms to prepare them for the future of healthcare delivery. For patient education, healthcare providers should develop accessible learning materials and workshops that demystify the technology, emphasizing its ease of use and the improved access to care it provides.

Implications for future research

While this study provides valuable insights, several avenues for future investigation remain. Researchers are encouraged to conduct longitudinal studies to examine how user perceptions and adoption intentions evolve as they gain more experience with MVTHS. Additionally, comparative studies across different cultural and socioeconomic contexts would help determine whether the drivers of adoption identified here are universal or context-dependent. Also, future research could explore the potential negative outcomes of MVTH adoption, such as technology fatigue or the depersonalization of patient-provider interactions, to provide a more holistic understanding of its impact. It is further recommended that future research disaggregate specific metaverse features (e.g., VR immersion, avatar interaction, haptic feedback) to examine their differential effects on user acceptance. We finally suggest that subsequent studies test whether attitude partially or fully mediates relationships between RC, TTS, PEI, and adoption intention.

Supplemental material

Supplemental material -Metaverse-driven telehealth services adoption in China: The role of regulatory compliance, training and technical support, peer influence and literacy determinants in an extended technology acceptance model

Supplemental material -Metaverse-driven telehealth services adoption in China: The role of regulatory compliance, training and technical support, peer influence and literacy determinants in an extended technology acceptance model by Isaac Kofi Mensah, Deborah Simon Mwakapesa and Muhammad Khalil Khan in DIGITAL HEALTH.

Footnotes

ORCID iDs

Isaac Kofi Mensah

Deborah Simon Mwakapesa

Muhammad Khalil Khan

Ethical considerations

This study follows the institutional guidelines and the 2024 Declaration of Helsinki Ethical standards. Ethical approval (No. NTU-IRB24-SML05) was granted by the Institutional Review Board (IRB) of NingboTech University, China.

Consent to participate

Written informed consent was obtained from all the participants.

Author contributions

IKM: Writing - review & editing, Writing - original draft, Resources, Project administration, Investigation, Funding acquisition, Formal analysis, Conceptualization.

DSM: Writing - original draft, Writing - review & editing, Investigation.

MKK: Writing - original draft, Writing - review & editing, Investigation, Formal analysis.

Funding

The authors disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This research was supported by the High-Level Talent Introduction Program and Hubei Private Enterprise Innovation and Development Research Center (HPEIDRC) of Wuhan College.

Declaration of conflicting interests

The authors declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Data Availability Statement

The raw data will be available to the corresponding author on reasonable request.*

Supplemental material

Supplemental material for this article is available online.

References

Tuckson

Edmunds

Hodgkins

. Telehealth. New England Journal of Medicine 2017; 377(16): 1585–1592. https://doi.org/10.1056/NEJMsr1503323

Rutledge

Kott

Schweickert

, et al. Telehealth and eHealth in nurse practitioner training: current perspectives. Advances in medical education and practice 2017; 8: 399–409. https://doi.org/10.2147/AMEP.S116071

Balestra

. Telehealth and legal implications for nurse practitioners. The Journal for Nurse Practitioners 2018; 14(1): 33–39. https://doi.org/10.1016/j.nurpra.2017.10.003

Monaghesh

Hajizadeh

. The role of telehealth during COVID-19 outbreak: a systematic review based on current evidence. BMC public health 2020; 20: 1–9. https://doi.org/10.1186/s12889-020-09301-4

Lillicrap

Hunter

Goldswain

. Improving geriatric care and reducing hospitalisations in regional and remote areas: The benefits of telehealth. Journal of telemedicine and telecare 2021; 27(7): 397–408. https://doi.org/10.1177/1357633X19881588

Lee

. Application of metaverse service to healthcare industry: a strategic perspective. International Journal of Environmental Research and Public Health 2022; 19(20): 13038. https://doi.org/10.3390/ijerph192013038

Murala

Panda

. The role of immersive reality (AR/VR/MR/XR) in metaverse. In: Metaverse and Immersive Technologies: An Introduction to Industrial, Business and Social Applications. 2023, pp. 159–189.

Pachauri

Kumar

. Chapter 13 - An Ethical and privacy considerations for metaverse in healthcare. In: Kannan H, Ganie SH, Rodriguez V, Rege M, Munuswamy SB (Eds) , Metaverse in the Healthcare Industry. Elsevier, 2026, pp. 267–285.

Senapati

Panda

Mishra

. Chapter 6 - Virtual health clinics and patient engagement. In: Kannan H, Ganie SH, Rodriguez V, Rege M, Munuswamy SB (Eds) , Metaverse in the Healthcare Industry. Elsevier, 2026, pp. 91–122.

10.

Sharma

Kaur

Sharma

. Metaverse Unveiled: A Comprehensive Analysis of Taxonomy, Applications, and Future Opportunities. In: Archives of Computational Methods in Engineering. Springer, 2024, pp. 1–23.

11.

Laato

Spors

, et al. Making sense of reality: a mapping of terminology related to virtual reality, augmented reality, mixed reality, XR and the metaverse. In: Annual Hawaii International Conference on System Sciences, Hilton Hawaiian Village Waikiki Beach Resort, Hawaii, USA, 2024.

12.

Tom Dieck

Han

D-ID

Jung

. When XR meets the Metaverse: Advancing new realities in an evolving space. Elsevier, 2024, p. 108481.

13.

Gunasekeran

RaviChandran

, et al. The next generation of healthcare ecosystem in the metaverse. Biomedical Journal 2024; 47(3): 100679. https://doi.org/10.1016/j.bj.2023.100679

14.

Tan

Lim

, et al. Metaverse and virtual health care in ophthalmology: Opportunities and challenges. Asia-Pacific Journal of Ophthalmology 2022; 11(3): 237–246. https://doi.org/10.1097/APO.0000000000000537

15.

Chaddad

Jiang

. Integrating Technologies in the Metaverse for Enhanced Healthcare and Medical Education. IEEE Transactions on Learning Technologies 2025; 18: 216–229.

16.

Sujatha

, et al. Integration of metaverse and AoT into the healthcare system. In: Leveraging Metaverse and Analytics of Things (AoT) in Medical Systems. Elsevier, 2025, pp. 15–39.

17.

Tang

Chen

, et al. How COVID-19 impacts telehealth: an empirical study of telehealth services, users and the use of metaverse. Connection science 2024; 36(1): 2282942. https://doi.org/10.1080/09540091.2023.2282942

18.

Zoccali

Mallamaci

. Exploring the metaverse: opportunities for nephrology in patient care and education. Nephrology Dialysis Transplantation 2024; 40(5): gfae281.

19.

Bansal

Bhattacharya

. Metaverse in healthcare: Potential applications and challenges. Exploring the Metaverse 2025; 31–43.

20.

Khan

Mir

. The metaverse beyond the hype: Interdisciplinary perspectives on applications, tools, techniques, opportunities, and challenges of the metaverse. In: Exploring the Metaverse. Elsevier Inc, 2025, pp. 213–223.

21.

Girijakumari Sreekantan Nair

Chandrasekaran

. MetaHealth: unlocking metaverse technologies in digital healthcare. Expert Review of Medical Devices 2024; 21(8): 727–739. https://doi.org/10.1080/17434440.2024.2382233

22.

Wang

Zhu

Chen

, et al. The application of metaverse in healthcare. Frontiers in Public Health 2024; 12: 1420367. https://doi.org/10.3389/fpubh.2024.1420367

23.

Yang

. Implications of immersive technologies in healthcare sector and its built environment. Frontiers in Medical Technology 2023; 5: 1184925. https://doi.org/10.3389/fmedt.2023.1184925

24.

Ullah

Manickam

Obaidat

, et al. Exploring the potential of metaverse technology in healthcare: Applications, challenges, and future directions. IEEE Access 2023; 11: 69686–69707. https://doi.org/10.1109/access.2023.3286696

25.

Chengoden

Victor

Huynh-The

, et al. Metaverse for healthcare: a survey on potential applications, challenges and future directions. IEEE Access 2023; 11: 12765–12795. https://doi.org/10.1109/access.2023.3241628

26.

Koohang

Nord

Ooi

, et al. Shaping the metaverse into reality: a holistic multidisciplinary understanding of opportunities, challenges, and avenues for future investigation. Journal of Computer Information Systems 2023; 63(3): 735–765. https://doi.org/10.1080/08874417.2023.2165197

27.

Richter

. What is novel about the Metaverse? International Journal of Information Management 2023; 73: 102684. https://doi.org/10.1016/j.ijinfomgt.2023.102684

28.

Davis

. Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS quarterly 1989; 13: 319–340. https://doi.org/10.2307/249008

29.

Chen

S-C

Shing-Han

Chien-Yi

. Recent related research in technology acceptance model: A literature review. Australian journal of business and management research 2011; 1(9): 124.

30.

Al-Alawy

Moonesar

. Perspective: Telehealth–beyond legislation and regulation. Sage Open Medicine 2023; 11: 20503121221143223. https://doi.org/10.1177/20503121221143223

31.

Pandya

Waller

Portnoy

. The regulatory environment of telemedicine after COVID-19. The Journal of Allergy and Clinical Immunology: In Practice 2022; 10(10): 2500–2505. https://doi.org/10.1016/j.jaip.2022.07.001

32.

Gajarawala

Pelkowski

. Telehealth benefits and barriers. The Journal for Nurse Practitioners 2021; 17(2): 218–221. https://doi.org/10.1016/j.nurpra.2020.09.013

33.

Nicol Turner Lee

Roberts

. Removing regulatory barriers to telehealth before and after COVID-19. Brookings Institution, 2020.

34.

Chatterjee

Bhattacharjee

Tsai

, et al. Impact of peer influence and government support for successful adoption of technology for vocational education: A quantitative study using PLS-SEM technique. Quality & Quantity 2021; 55: 1–24. https://doi.org/10.1007/s11135-021-01100-2

35.

Traube

Cederbaum

Taylor

, et al. Telehealth training and provider experience of delivering behavioral health services. The Journal of Behavioral Health Services & Research 2021; 48: 93–102. https://doi.org/10.1007/s11414-020-09718-0

36.

Cottrell

Burns

Jones

, et al. Sustaining allied health telehealth services beyond the rapid response to COVID-19: learning from patient and staff experiences at a large quaternary hospital. Journal of Telemedicine and Telecare 2021; 27(10): 615–624. https://doi.org/10.1177/1357633X211041517

37.

Markert

Sasangohar

Mortazavi

, et al. The Use of Telehealth Technology to Support Health Coaching for Older Adults: Literature Review. The use of telehealth technology to support health coaching for older adults: literature review. JMIR Hum Factors 2021; 8(1): e23796. https://doi.org/10.2196/23796

38.

Dixit

Van Sebille

Crawford

, et al.

Disparities in telehealth use: How should the supportive care community respond? How should the supportive care community respond?

Supportive Care in Cancer 2021; 30: 1007–1010. https://doi.org/10.1007/s00520-021-06629-4

39.

Caratıquıt

. Influence of technical support on technology acceptance model to examine the project PAIR e-learning system in distance learning modality. Participatory Educational Research 2022; 9(5): 467–485. https://doi.org/10.17275/per.22.124.9.5

40.

Grossman

Rodden

Izama

, et al.

Peer effects and externalities in technology adoption: Evidence from community reporting in Uganda

2018: 3107855. https://ssrn.com/abstract

41.

Ferrali

Grossman

Platas

, et al. It takes a village: Peer effects and externalities in technology adoption. American Journal of Political Science 2020; 64(3): 536–553. https://doi.org/10.1111/ajps.12471

42.

Greszczuk

Mughal

Mathew

, et al. Peer influence as a driver of technological innovation in the UK National Health Service: a qualitative study of clinicians’ experiences and attitudes. BMJ Innovations 2018; 4(2): 68–74. https://doi.org/10.1136/bmjinnov-2017-000208

43.

Liu

, et al. Peer Influence in the Adoption of Video Games. International Journal of E-Business Research (IJEBR) 2022; 18(1): 1–16. https://doi.org/10.4018/ijebr.309399

44.

Niu

Chen

Gao

, et al. Peer effects, attention allocation and farmers’ adoption of cleaner production technology: taking green control techniques as an example. Journal of Cleaner Production 2022; 339: 130700. https://doi.org/10.1016/j.jclepro.2022.130700

45.

Davis

Bagozzi

Warshaw

. Extrinsic and intrinsic motivation to use computers in the workplace 1. Journal of applied social psychology 1992; 22(14): 1111–1132. https://doi.org/10.1111/j.1559-1816.1992.tb00945.x

46.

Fleddermann

Chwastiak

Fortier

, et al. Levels of Telehealth Use, Perceived Usefulness, and Ease of Use in Behavioral Healthcare Organizations After the COVID-19 Pandemic. The Journal of Behavioral Health Services & Research 2024; 52(1): 1–16.

47.

Hsieh

H-L

Lai

J-M

Chuang

B-K

, et al. Determinants of telehealth continuance intention: A multi-perspective framework. In: Healthcare. MDPI, 2022. 1–17.

48.

Bhatia

. Telehealth and COVID-19: Using technology to accelerate the curve on access and quality healthcare for citizens in India. Technology in society 2021; 64: 101465. https://doi.org/10.1016/j.techsoc.2020.101465

49.

Venkatesh

Morris

Davis

, et al. User acceptance of information technology: Toward a unified view. MIS quarterly 2003; 27: 425–478. https://doi.org/10.2307/30036540

50.

Nguyen

Fujioka

Wentlandt

, et al. Using the technology acceptance model to explore health provider and administrator perceptions of the usefulness and ease of using technology in palliative care. BMC palliative care 2020; 19: 1–9. https://doi.org/10.1186/s12904-020-00644-8

51.

Nissinen

Pesonen

Toivio

, et al. Occupational health professionals’ experiences with telehealth services: usage, perceived usefulness and ease of use. Health and Technology 2023; 13(5): 811–821. https://doi.org/10.1007/s12553-023-00776-w

52.

Nikou

De Reuver

Mahboob Kanafi

. Workplace literacy skills—how information and digital literacy affect adoption of digital technology. Journal of Documentation 2022; 78(7): 371–391. https://doi.org/10.1108/jd-12-2021-0241

53.

Kalayou

Endehabtu

Tilahun

. The applicability of the modified technology acceptance model (TAM) on the sustainable adoption of eHealth systems in resource-limited settings. Journal of multidisciplinary healthcare 2020; 13: 1827–1837. https://doi.org/10.2147/JMDH.S284973

54.

Rahimi

Nadri

Lotfnezhad Afshar

, et al. A systematic review of the technology acceptance model in health informatics. Applied clinical informatics 2018; 9(03): 604–634. https://doi.org/10.1055/s-0038-1668091

55.

Simpao

Ahumada

Gálvez

, et al. A review of analytics and clinical informatics in health care. Journal of medical systems 2014; 38: 1–7. https://doi.org/10.1007/s10916-014-0045-x

56.

Pullyblank

. A scoping literature review of rural beliefs and attitudes toward telehealth utilization. Western Journal of Nursing Research 2023; 45(4): 375–384. https://doi.org/10.1177/01939459221134374

57.

Connolly

Miller

Lindsay

, et al. A systematic review of providers’ attitudes toward telemental health via videoconferencing. Clinical Psychology: Science and Practice 2020; 27(2): e12311. https://doi.org/10.1111/cpsp.12311

58.

IFLA . The International Federation of Library Associations and Institutions 2006. [cited 2024 2024/11/07]; Available from: https://www.ifla.org/files/assets/information-literacy/publications/ifla-guidelinesen.pdf

59.

Lund

Agbaji

Teel

. Information literacy, data literacy, privacy literacy, and ChatGPT: Technology literacies align with perspectives on emerging technology adoption within communities. Human Technology 2023; 19(2): 163–177. https://doi.org/10.14254/1795-6889.2023.19-2.2

60.

Durodolu

. Technology Acceptance Model as a predictor of using information system'to acquire information literacy skills. Library Philosophy & Practice 2016.

61.

T-K

Lin

M-L

Liao

Y-K

. Understanding factors influencing information communication technology adoption behavior: The moderators of information literacy and digital skills. Computers in human behavior 2017; 71: 196–208. https://doi.org/10.1016/j.chb.2017.02.005

62.

Kabakus

Bahcekapili

Ayaz

. The effect of digital literacy on technology acceptance: An evaluation on administrative staff in higher education. Journal of Information Science 2023; 01655515231160028.

63.

Martin

. DigEuLit–a European framework for digital literacy: a progress report. Journal of eLiteracy 2005; 2(2): 130–136.

64.

Wang

. A comprehensive analysis of e-health literacy research focuses and trends. In: Healthcare. MDPI, 2021.

65.

Levin-Zamir

Bertschi

. Media health literacy, eHealth literacy, and the role of the social environment in context. International journal of environmental research and public health 2018; 15(8): 1643. https://doi.org/10.3390/ijerph15081643

66.

Rivera-Romero

Gabarron

Miron-Shatz

, et al. Social media, digital health literacy, and digital ethics in the light of health equity. Yearbook of Medical Informatics 2022; 31(1): 082–087. https://doi.org/10.1055/s-0042-1742503

67.

Terry

Buntoro

. Perceived usefulness of telehealth among rural medical providers: barriers to use and associations with provider confidence. Journal of technology in behavioral science 2021; 6(4): 567–571. https://doi.org/10.1007/s41347-021-00215-5

68.

Wade

Cartwright

Shaw

. Factors relating to home telehealth acceptance and usage compliance. Risk Management and Healthcare Policy 2012; 5: 25–33. https://doi.org/10.2147/RMHP.S30204

69.

Stewart

. Discovering the Factors That Influence a Patient’s Attitude Towards Telehealth In The Us. Florida International University, 2024.

70.

Biruk

Abetu

. Knowledge and attitude of health professionals toward telemedicine in resource‐limited settings: a cross‐sectional study in North West Ethiopia. Journal of healthcare engineering 2018; 2018(1): 2389268. https://doi.org/10.1155/2018/2389268

71.

Alam

Hoque

, et al. Factors influencing the adoption of mHealth services in a developing country: A patient-centric study. International journal of information management 2020; 50: 128–143. https://doi.org/10.1016/j.ijinfomgt.2019.04.016

72.

Napitupulu

Yacub

Perdana Kusuma Putra

. Factor Influencing of Telehealth Acceptance During COVID-19 Outbreak: Extending UTAUT Model. International Journal of Intelligent Engineering & Systems 2021; 14(3): 267–281. https://doi.org/10.22266/ijies2021.0630.23

73.

Zobair

Sanzogni

Houghton

, et al. Health seekers’ acceptance and adoption determinants of telemedicine in emerging economies. Australasian Journal of Information Systems 2021; 25: 1–30. https://doi.org/10.3127/ajis.v25i0.3071

74.

Hossain

Amin

Masud

, et al. What drives people’s behavioral intention toward telemedicine? an emerging economy perspective. SAGE Open 2023; 13(3): 21582440231181394. https://doi.org/10.1177/21582440231181394

75.

Zhou

Thieret

Watzlaf

, et al. A telehealth privacy and security self-assessment questionnaire for telehealth providers: development and validation. International journal of telerehabilitation 2019; 11(1): 3–14. https://doi.org/10.5195/ijt.2019.6276

76.

Sharifi

Michaelis

Chattopadhyay

. Development and evaluation of the mobile tech support questionnaire for older adults. In: Proceedings of the 26th International ACM SIGACCESS Conference on Computers and Accessibility, 2024.

77.

Nelson

Pennings

Sommer

, et al. A 3-item measure of digital health care literacy: development and validation study. JMIR Formative Research 2022; 6(4): e36043. https://doi.org/10.2196/36043

78.

Zainal

Masrek

. Measuring learning enablers in the Malaysian Armed Forces: pre-testing and pilot testing of the research. Journal of Information and Knowledge Management (JIKM) 2023; 2: 412–420.

79.

Arundel

. Questionnaire testing. In: How to Design, Implement, and Analyse a Survey. Edward Elgar Publishing, 2023, pp. 56–77.

80.

Golzar

Noor

Tajik

. Convenience sampling. International Journal of Education & Language Studies 2022; 1(2): 72–77.

81.

Taherdoost

. Sampling methods in research methodology; how to choose a sampling technique for research. International journal of academic research in management (IJARM) 2016; 5: 1–10.

82.

Roscoe

. Fundamental research statistics for the behavioral scinces. J. Am. Stat. Assoc 1969; 66(333).

83.

Memon

Ramayah

Cheah

, et al. PLS-SEM statistical programs: a review. Journal of Applied Structural Equation Modeling 2021; 5(1): 1–14. https://doi.org/10.47263/jasem.5(1)06

84.

Hair

Alamer

. Partial Least Squares Structural Equation Modeling (PLS-SEM) in second language and education research: Guidelines using an applied example. Research Methods in Applied Linguistics 2022; 1(3): 100027. https://doi.org/10.1016/j.rmal.2022.100027

85.

Dijkstra

Henseler

. Consistent partial least squares path modeling. MIS quarterly 2015; 39(2): 297–316. https://doi.org/10.25300/misq/2015/39.2.02

86.

Fornell

. Structural equation models with unobservable variables and measurement error: Algebra and statistics. Publications Sage, 1981.

87.

Bagozzi

. Evaluating structural equation models with unobservable variables and measurement error: a comment. Sage Publications Sage, 1981.

88.

Fornell

Larcker

. Evaluating structural equation models with unobservable variables and measurement error. Journal of marketing research 1981; 18(1): 39–50. https://doi.org/10.2307/3151312

89.

Hair

Risher

Sarstedt

, et al. When to use and how to report the results of PLS-SEM. European business review 2019; 31(1): 2–24. https://doi.org/10.1108/ebr-11-2018-0203

90.

Hair

Ringle

Sarstedt

. PLS-SEM: Indeed a silver bullet. Journal of Marketing theory and Practice 2011; 19(2): 139–152. https://doi.org/10.2753/mtp1069-6679190202

91.

Chin

. The partial least squares approach to structural equation modeling. In: Modern methods for business research. Lawrence Erlbaum Associates, 1998.

92.

Henseler

Ringle

Sinkovics

. The use of partial least squares path modeling in international marketing. In: New challenges to international marketing. Emerald Group Publishing Limited, 2009, pp. 277–319.

93.

Cohen

. Statistical power analysis for the behavioral sciences. Routledge, 2013.

94.

Maddu

Kushal

KKG

Alagarsamy

. Case studies of successful metaverse implementations in healthcare. In: Metaverse in the Healthcare Industry. Elsevier, 2026, pp. 249–266.

95.

Awotunde

. Data Privacy in Virtual Healthcare Environments and Ethical Guidelines for Metaverse Health Applications. HealthTech Horizons: AI-Infused Metaverse Solutions for Smart Healthcare Systems 2025; 1224: 543–576.

96.

Wang

Shen

. China’s new legislation on personal information protection in light of the COVID-19 pandemic. J. Int’l & Comp. L 2022; 9: 109.

97.

Gong

. China Cybersecurity and Data Protection: Monthly Update - July 2025 Issue. 2025. [cited 2026 2026-3-07]; Available from: https://www.twobirds.com/en/insights/2025/china/china-cybersecurity-and-data-protection-monthly-update-july-2025-issue

98.

Xing

Song

, et al. The medical metaverse in China: Current applications and future prospects. BioScience Trends 2025; 19(5): 557–565. https://doi.org/10.5582/bst.2025.01209

99.

Ramtiyal

Shehzad

Jabeen

, et al. Barriers, sustainability and governance perspectives on Metaverse technology adoption in healthcare enterprise. Journal of Enterprise Information Management 2025; 19(5):1–29. https://doi.org/10.1108/jeim-10-2024-0586

100.

Ikpeazu

. Subscribers’ social factors and adoption of self-service platforms in Delta State. Southeast Asian Journal of Service Management 2025; 2(3): 159–173. https://doi.org/10.24123/seajsm.v223

101.

Kwak

Seo

Ahn

J-W

. Nursing students’ intent to use AI-based healthcare technology: Path analysis using the unified theory of acceptance and use of technology. Nurse Education Today 2022; 119: 105541. https://doi.org/10.1016/j.nedt.2022.105541

102.

Lin

, et al. A systematic review of strategies in digital technologies for motivating adherence to chronic illness self-care. npj Health Systems 2025; 2(1): 13. https://doi.org/10.1038/s44401-025-00017-4

103.

Lee

Shim

. Effects of diabetes knowledge and attitudes toward internet health information on e-health literacy in middle-aged patients with diabetes. In: Healthcare. MDPI, 2025.

104.

Jan

. Investigating the Relationship between Students’ Digital Literacy and Their Attitude towards Using ICT. International Journal of Educational Technology 2018; 5(2): 26–34.

105.

Sampa

Abdul Aziz

Rahman

, et al. Factors influencing the adoption and acceptance of eHealth in Malaysia: a systematic review. Critical Public Health 2025; 35(1): 2519780. https://doi.org/10.1080/09581596.2025.2519780

106.

Abdool

Abdallah

Akhlaq

, et al. User acceptance level of and attitudes towards telemedicine in the United Arab Emirates: a quantitative study. Sultan Qaboos University Medical Journal 2021; 21(2): e203–e209. https://doi.org/10.18295/squmj.2021.21.02.008

107.

Aldekhyyel

Alshuaibi

Alsaaid

, et al. Exploring behavioral intention to use telemedicine services post COVID-19: a cross sectional study in Saudi Arabia. Frontiers in Public Health 2024; 12: 1385713. https://doi.org/10.3389/fpubh.2024.1385713

108.

Saadia

Naveed

. Effect of information literacy on lifelong learning, creativity, and work performance among journalists. Online Information Review 2024; 48(2): 257–276. https://doi.org/10.1108/oir-06-2022-0345

109.

Aslan

Mold

van Marwijk

, et al. What are the determinants of older people adopting communicative e-health services: a meta-ethnography. BMC Health Services Research 2024; 24(1): 60. https://doi.org/10.1186/s12913-023-10372-3

110.

Sayfutdinovna Abdullaeva

Abdullaev

Djuraeva

. Decoding the significance of digital literacies among EFL learners: A probe into the consequences on technostress, foreign language anxiety, academic enjoyment, and language achievement. Applied Research on English Language 2024; 13(4): 101–128.

111.

Hoonsopon

Ketkaew

Puriwat

, et al. How Digital Health Literacy Affects Willingness to Adopt Telemedicine in an Emerging Country: The Case of Thailand. SAGE Open 2025; 15(3): 21582440251378068. https://doi.org/10.1177/21582440251378068

112.

Galperin

Traube

. The impact of digital competence on telehealth utilization. Health Policy and Technology 2023; 12(1): 100724. https://doi.org/10.1016/j.hlpt.2023.100724

113.

Feng

Niwa

Cao

, et al. Promoting the social implementation of digital and mobile health: effects of regulation on user and non-user behavior in East Asia. Drug Discovery Today 2024; 29(10): 104136. https://doi.org/10.1016/j.drudis.2024.104136

114.

Jiang

Zheng

. Medical information protection in internet hospital apps in China: scale development and content analysis. JMIR mHealth and uHealth 2024; 12(1): e55061. https://doi.org/10.2196/55061

115.

Akiogbe

Feng

Kurata

, et al. Cross-Analysis of mHealth Social Acceptance Among Youth: A Comparative Study Between Japan and China. Behavioral Sciences 2025; 15(2): 213. https://doi.org/10.3390/bs15020213

116.

Abate

Ukpabi

Karjaluoto

. Investigating digital marketing readiness among tourism firms: an emerging economy perspective. Current Issues in Tourism 2024; 28: 1–20. https://doi.org/10.1080/13683500.2024.2358047

117.

Nafus

Aslan

Foster

. It’s Still Frustrating! Human-Centered Approaches to Data in Enterprise PC Maintenance. In: International Conference on Human-Computer Interaction. Cham, June 26 – July 1, 2022, Virtual Event. Springer.

118.

Garvin

Greenan

Edelman

, et al. Increasing use of video telehealth among veterans experiencing homelessness with substance use disorder: Design of a peer-led intervention. Journal of technology in behavioral science 2023; 8(3): 234–245. https://doi.org/10.1007/s41347-022-00290-2

119.

Kapoor

Sohi

. Teachers’ acceptance of technology-based simulation games as teaching pedagogy in management education: an extended technology acceptance model. Innoeduca: international journal of technology and educational innovation 2024; 10(2): 93–112. https://doi.org/10.24310/ijtei.102.2024.18122

120.

Malarvizhi

CAN

Al Mamun

Reza

MNH

, et al. Ageing well with tech: Exploring the determinants of e-healthcare services adoption in an emerging economy. Digital Health 2024; 10: 20552076241272577. https://doi.org/10.1177/20552076241272577

121.

Wang

Cao

. Factors influencing continuous intention to use telemedicine after the COVID-19 pandemic in China: an extended technology acceptance model. Open Journal of Social Sciences 2022; 10(12): 344–359. https://doi.org/10.4236/jss.2022.1012023

122.

Tang

Yang

Y-T

Shao

Y-F

. Acceptance of online medical websites: An empirical study in China. International journal of environmental research and public health 2019; 16(6): 943. https://doi.org/10.3390/ijerph16060943

123.

Schepers

Wetzels

. A meta-analysis of the technology acceptance model: Investigating subjective norm and moderation effects. Information & management 2007; 44(1): 90–103. https://doi.org/10.1016/j.im.2006.10.007

124.

Zhu

Lin

MTB

, et al. The technology acceptance model and hospitality and tourism consumers’ intention to use mobile technologies: Meta-analysis and structural equation modeling. Cornell Hospitality Quarterly 2024; 65(4): 461–477. https://doi.org/10.1177/19389655241226558

125.

Guo

Zhu

Lin

MTB

, et al. Hospitality employees’ technology adoption at the workplace: evidence from a meta-analysis. International Journal of Contemporary Hospitality Management 2023; 35(7): 2437–2464. https://doi.org/10.1108/ijchm-06-2022-0701

126.

Ezeudoka

Fan

. Determinants of behavioral intentions to use an E-Pharmacy service: Insights from TAM theory and the moderating influence of technological literacy. Research in Social and Administrative Pharmacy 2024; 20(7): 605–617. https://doi.org/10.1016/j.sapharm.2024.03.007

Metaverse-driven telehealth services adoption in China: The role of regulatory compliance,training and technical support,peer influence and literacy determinants in an extended technology acceptance model

Abstract

Objectives

Methods

Results

Conclusion

Keywords

Introduction

Research objectives

Theoretical framework

Technology acceptance model

Research model

Research hypotheses development

Regulatory compliance (RC)

Training and technical support (TTS)

Peer Influence (PEI)

Perceived usefulness (PU)

Perceived ease of use (PEOU)

Attitude (ATT)

Information literacy (IL)

Technology/Digital literacy (TDL)

Methods

Research design and questionnaire development

Study settings

Sampling strategy and data collection

Ethical statement and informed consent

Data analysis and results

Demographic characteristics of the respondents

Common method bias (CMB) test

Measurement model analysis

Structural model analysis

Hypotheses testing

Discussions

Theoretical implications

Limitations

Sample generalizability

Scope of investigated variables

Research design and causality

Explained variance

Measurement considerations

Conclusions and practical implications

Implications for policy making

Implications for healthcare management

Implications for education

Implications for future research

Supplemental material

Supplemental material -Metaverse-driven telehealth services adoption in China: The role of regulatory compliance, training and technical support, peer influence and literacy determinants in an extended technology acceptance model

Footnotes

ORCID iDs

Ethical considerations

Consent to participate

Author contributions

Funding

Declaration of conflicting interests

Data Availability Statement

Supplemental material

References