Chronic diseases impose substantial global healthcare burdens, particularly in aging populations like China [1]. These conditions demand long-term, continuous medical management, presenting significant challenges to healthcare systems [2].
Internet Medical Services (IMS), leveraging internet platforms to deliver healthcare, offer a promising approach to optimizing resource allocation and improving access to care, especially for chronic disease management [3]. Driven by policy support and catalyzed by events like the COVID-19 pandemic [4], IMS has seen rapid growth in China. By December 2022, IMS users reached 363 million (34% of Chinese internet users), with services available in 68.9% of tertiary hospitals and 53.0% of secondary hospitals nationwide [5].
Chronic disease patients, with their persistent healthcare needs, are a core beneficiary group of IMS. However, their acceptance and utilization rates vary considerably across different functional categories of IMS. Crucially, a paradoxical “high demand–low penetration” phenomenon persists among this population, reflecting underlying barriers rooted in the digital divide. This digital divide encompasses disparities in access, skills, and the effective use of technology [6], critically limiting the attainment of health equity through IMS [7].
To systematically analyze the mechanisms of this divide in the IMS adoption pathway, Liang’s seminal work conceptualizes it as a three-stage process: Awareness, Want, and Adoption, revealing that initial awareness disparities significantly constrain downstream utilization [8]. This staged perspective allows for more precise identification of barriers faced by patients at different decision points.
Theoretical framework and hypothesis development
The Technology Acceptance Model (TAM), introduced by Fred Davis in 1989 [9], provides a robust framework for explaining and predicting user acceptance of information technology systems. Rooted in the Theory of Reasoned Action (TRA) [10], it highlights perceived usefulness (PU) and perceived ease of use (PEOU) as key factors in technology adoption, capturing the core logic of technology promotion [11]. PU reflects users’ belief that technology enhances work efficiency or quality of life, whereas PEOU indicates users’ perception of a technology’s ease of use. These two key perceptions collectively shape the user’s Attitude towards adopting and using the technology, which can be understood as an overall positive or negative evaluation or affective response [12]. Attitude subsequently and directly influences the user’s Behavioral Intention (BI) regarding the technology, which is a primary determinant of Actual Use (AU) [13]. Briefly, users are more inclined to adopt technologies they perceive as helpful or easy to use; these perceptions, mediated by Attitude, ultimately drive adoption intention and shape AU decisions. Over the past few decades, TAM has been widely applied in information technology research, particularly in studies examining the promotion and usage of e-commerce platforms, social media, and IMS, where its validity has been widely confirmed [14,15,16].
Liang’s digital divide theory [8] conceptualizes technology adoption as a three-stage process (Awareness, Want, Adoption), suggesting that disparities in early awareness constrain subsequent utilization. Drawing on this, we subdivide AU in TAM into Awareness, Want, and Adoption to clarify stage-specific barriers.
eHealth literacy encompasses skills and knowledge essential for effectively interacting with technology-based health tools [17]. Empirical studies demonstrate that higher eHealth literacy considerably promotes patients’ IMS utilization [18, 19]. Technology anxiety, a critical psychological factor affecting the acceptance and use of new technologies, manifests as resistance stemming from patients’ fears that their technical inadequacies or operational errors may threaten their health. This anxiety-driven apprehension hinders IMS use [20]. Hence, this study integrated eHealth literacy and Technology anxiety as core variables into the extended TAM.
The following hypotheses are proposed:
Perceived usefulness (PU) and perceived ease of use (PEOU)
PU is one of the most central variables in models ranging from TAM to Unified Theory of Acceptance and Use of Technology 2 (UTAUT2) [21], reflecting users’ cognitive evaluation of technology’s functional value and operational convenience [11]. In chronic disease management, it manifests as patients’ assessment of IMS in controlling disease, preventing complications, and improving quality of life (e.g., accessing tailored treatment plans via online consultations) ) [22]. TAM suggests PU positively affects Attitude toward technology adoption [9]. For example, higher PU correlated with 66.7% higher Attitude toward healthcare apps among 264 cancer patients [23], and PU significantly promoted Attitude toward AI-based home care systems [24]. PU also directly influences adoption behavior, such as predicting information and communication technology use (β = 0.117, P = 0.02) and smartphone use (β = 0.158, P = 0.002) among older Asian Americans [25].
Therefore, we hypothesize the following:
H1a
PU directly promotes IMS utilization among chronic disease patients.
H1b
PU positively influences the Attitude of IMS utilization among chronic disease patients.
PEOU, a construct integral to models ranging from the TAM to UTAUT2, covers interface intuitiveness, operational fluency, and information accessibility [12, 21], reflecting users’ evaluation of effort needed to interact with technology. In chronic disease management, it refers to patients’ ability to navigate IMS, retrieve records, and complete self-management tasks effortlessly. TAM notes PEOU directly enhances Attitude and indirectly affects Attitude/BI via PU. A study showed healthcare professionals’ PEOU toward health information systems directly influenced Attitude (β = 0.339, P = 0.003) and indirectly improved Attitude/BI through PU (β = 0.745, P < 0.001) [26].
Therefore, we hypothesize the following:
H2a
PEOU positively affects the Attitude of IMS utilization among chronic disease patients.
H2b
PEOU positively affects PU of IMS utilization among chronic disease patients.
Attitude and Behavioral intention (BI)
Attitude, a core mediator in TAM [9], integrates affective commitment, value cognition, and behavioral propensity toward technology adoption [27]. It subsequently drives BI, representing the deliberate intent to engage with specific technological functions. Ultimately, BI translates into tangible usage behaviors, completing the technology adoption pathway [9].
Therefore, we hypothesize the following:
H3
Attitude positively impacts the BI of IMS utilization among chronic disease patients.
H4
BI positively impacts Awareness, Want, and Adoption of IMS utilization among chronic disease patients.
eHealth literacy
eHealth literacy, first proposed by Canadian scholar Norman in 2006, refers to individuals’ abilities to search for, understand, evaluate, process and apply mobile Internet health information to address health problems [17]. It includes skills such as using search engines and health websites to find relevant content, comprehending medical terminology with basic medical knowledge, assessing information credibility by checking sources and publishers, and applying knowledge to lifestyle adjustments or medical decisions [28].
Previous studies show that people with higher eHealth literacy have a clearer understanding of their health needs and are more proactive in health monitoring and information seeking [29]. They can efficiently extract accurate information from IMS with their information searching and filtering skills [30, 31], making them more likely to recognize IMS advantages and form active use intentions. In addition, they have strong learning abilities and can quickly master IMS operations through self-exploration or learning from others, thus effectively using IMS for health management [32].
This yields the following hypotheses:
H5a
eHealth literacy directly promotes the use of IMS among chronic disease patients.
H5b
eHealth literacy enhances IMS utilization among chronic disease patients by promoting BI.
Technology anxiety
Technology anxiety evolved from early Computer Anxiety into a generalized psychological barrier toward technological tools [33]. It refers to users’ anxiety and fear when facing technological systems, often with avoidant behavior. Chronic disease patients usually have higher Technology anxiety [34, 35], possibly due to worries that improper use (e.g., incorrect blood pressure input) might harm their health. Research indicates that such anxiety significantly undermines the usage intention toward mobile medical technology [20]. For chronic disease patients, it may become a psychological barrier, making them reject IMS and stick to traditional medical services despite IMS potential.
Additionally, a study on 1,369 South Korean middle-aged and elderly people found Technology anxiety significantly reduces eHealth literacy (β=-0.204, P < 0.01) [36]. This is likely because it affects how often and effectively patients use smart devices [37], making it harder for them to search, understand, and use online health information, thus limiting their use of eHealth literacy for health management.
This yields the following hypotheses:
H6a
Technology anxiety weakens BI toward IMS utilization among chronic disease patients.
H6b
Technology anxiety inhibits IMS utilization among chronic disease patients by diminishing eHealth literacy.
This study constructs a comprehensive conceptual framework by synthesizing these theoretical perspectives and hypothesized relationships, depicted in Fig. 1.
Proposed conceptual model
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