Introduction
As Internet technology advances, “Internet Plus” is widely integrated with various industries. Online health communities (OHCs) are developed with the application of Internet technology in the field of medical and health care [
1,
2]. They connect patients, physicians, hospitals, and other medical ecosystems through the internet and specifically provide an information exchange platform for patients and physicians [
3].
Encouraging patients to use online health communities (OHCs) in conjunction with offline treatments is necessary. OHCs provide patients with the convenience of seeking help and advice online, regardless of location and time [
4,
5], and improve the possibility of timely diagnoses. Besides, during the period of the COVID-19 pandemic, OHCs can effectively reduce patients’ visits to hospitals, thus minimizing patient-to-patient and patient-to-physician physical contact and ensuring the safety and well-being of both patients and physicians. In addition, since the interactions between patients and physicians are one-to-one and not face-to-face, patients’ privacy can be protected [
6,
7]. OHCs are also proven to play an important role in helping physicians optimize their time utilization [
4,
8,
9], reduce healthcare costs [
10], and alleviate issues related to hospital congestion and uneven distribution of healthcare resources [
11]. Currently, many countries recognize intelligent medical services as an important project of public health development. For example, the Chinese government encourages strengthening the capacity of Internet medical services [
12], illustrating the development of OHCs in line with current national strategies. Given the benefits of OHCs and the recent inevitable development trend, patients’ widespread use of OHCs is critical.
A comprehensive understanding of the factors determining patients’ acceptance of OHCs is a prerequisite for facilitating patients’ acceptance of OHCs [
13], which is therefore necessary. Currently, OHCs are still not widely used [
14,
15]. The lack of patient engagement can limit the success and sustainability of OHCs, undermine the development policies of many countries, and weaken health care’s modernization and rapid development [
12]. It raises questions about the factors that influence patients’ decisions to use OHCs. Previous works mainly adopted or extended several famous technology adoption models based on the literature to examine predictors of OHC adoption [
7,
13,
16‐
19]. Within these models, the Unified Theory of Acceptance and Use of Technology (UTAUT) model integrates the eight most widely used models [
20] to measure the use intention of information technology [
21,
22]. It can explain 70% of the variance of behavioral intention and better explain the influencing factors of behavioral intention than the eight models mentioned before [
23,
24]. Given the advantages of the UTAUT, researchers often adopted the UTAUT model or used it as the basic theoretical framework to develop their research models to better reflect the characteristics of OHCs [
25‐
27]. Although these studies enhanced the understanding of the adoption of OHCs, the variables do not fully explain the factors influencing patients’ adoption of OHCs. There are two reasons. First, for studies using UTAUT as the basic model, the variables included in UTAUT cannot fully explain patients ‘usage intention towards OHCs. Venkatesh et al. [
20] and Alam et al. [
13] have criticized UTAUT’s predictive power in healthcare technology adoption as unclear and insufficient, particularly in developing countries, and suggested the inclusion of context-specific determinants to improve its predictability [
13,
28]. Second, for studies that extend the UTAUT model, factors other than the basic UTAUT model were selected from subjective judgments or developed theories, resulting in a lack of comprehensive coverage of all factors that impact patients’ acceptance of OHCs. For instance, Hoque and Sorwar [
25] extended UTAUT with systematic variables (i.e., technology anxiety and resistance to change) based on their knowledge and literature. Sun et al. [
27] developed the research model integrating UTAUT, credibility online health information, and perceived risk based on the literature. These previous studies ignored the impact of patients’ ability and relationships with the platforms, which would impact patients’ acceptance of OHCs. The limited perspective of the determinants of OHC adoption restricts our understanding of patient behavior and hinders the development of effective strategies to promote OHC uptake. Addressing this gap requires objective data to identify patients’ actual needs and extend the UTAUT model to fully explain the factors that determine patients’ intentions and behaviors in using OHCs.
The availability of health information through OHCs can significantly affect individuals’ health management and perception of using these platforms [
29‐
31]. Consequently, an analysis of patients’ information demands in OHCs can reveal factors that influence patients’ adoption of OHCs. This study aims to explore patients’ online information demands in OHCs and to identify the critical factors that impact patients’ behavioral intentions and adoption of OHCs. Three research questions are proposed as follows:
RQ1. What are the information demands of patients in OHCs?
RQ2. Based on the information demands of patients in OHCs, what potential factors can be identified that influence patients’ adoption of OHCs?
RQ3. What factors determine patients’ adoption of OHCs?
The contributions of this study are as follows. First, this study presents a comprehensive conceptual model that fills the gaps in the literature on OHCs by rigorously clarifying the factors influencing patients’ adoption of OHCs and their impact. This study extended the UTAUT model based on the analysis results of patients’ information demands of OHCs. Patients’ information demands of OHCs can reflect the factors that patients in OHCs concern about, thus providing references for the selection of influencing factors of patients’ intention to adopt OHCs. Introducing influencing factors selected based on patients’ information demands in OHCs to the research model can expand the coverage of influencing factors on behavioral intention to use OHCs, strengthen the understanding of patients’ acceptance of using OHCs, and improve the explanatory capacity of the research model. Second, this study expands on the impact of relation quality and eHealth literacy in patients’ use of OHCs, enriches the literature on relation quality and eHealth literacy, and reveals the effects of patients’ competencies and relationships with the platform on the behavioral intention of OHCs. Third, this study identifies the key factors influencing patients’ behavioral intention of using OHCs and their impact mechanism. It validates the research model using data from patients in OHCs in China. These findings can inform decision-makers to facilitate the adoption of OHCs in China and other developing countries similar to China in terms of digital health technology development.
Discussion
Main findings
First of all, the results indicate that patients’ behavioral intention has a significant positive impact on usage behavior, and the degree of this influence is large, exceeding 50.00% (path coefficients = 0.60). This finding is in accordance with previous studies that elucidated the significant positive relationship between behavioral intention and usage behavior in IT (Information Technology) adoption contexts [
27,
55,
109]. Additionally, facilitating conditions also have a significant positive effect on usage behavior, which is supported by previous studies clarifying that facilitating conditions significantly contribute to usage behavior [
110,
111]. Thus, we believe that patients’ attitudes toward OHCs and ease of use are significant factors influencing their adoption of OHCs.
Second, performance expectancy, effort expectancy, and social influence have significant positive impacts on patients’ behavioral intention of using OHCs. The UTAUT model suggests that the above three variables are important determinants of behavioral intention. Therefore, these findings are consistent with the conventional findings of the UTAUT model [
13,
20,
25]. In particular, performance expectancy had the greatest effect on behavioral intention (path coefficients = 0.16), indicating that when deciding on using OHCs, patients are most concerned about whether they can get the information and services they want to receive. The findings demonstrate that patients’ behavioral intention to use OHCs largely depends on their perception of the help that OHCs can bring to them and the difficulty of using OHCs. If patients think that OHCs can significantly improve the efficiency of getting diagnosis and increase the speed of information acquisition, or they believe that technical problems can be solved in time during the use of OHCs, they will be willing to use OHCs. In addition, patients are likely to be influenced by their friends and relatives. If others have positive comments on OHCs, patients will trust on OHCs, thereby improving their intention to use OHCs.
Third, the results reveal that perceived risk has an insignificant impact on patients’ behavioral intention of using OHCs. Interestingly, our finding differs from previous studies’ results as these studies found a statistically significant effect of perceived risk on behavioral intention [
40,
62,
112]. The reasons are as follows. First of all, patients mainly use the free modules of OHCs, such as the problem posts and health information modules. Therefore, they may not be sensitive to the economy and privacy. In addition, for patients, the information obtained from OHCs is considered a reference rather than psychological dependence. Finally, patients may be inured to the existence of risk in the context of the Internet environment.
Fourth, price value has a significant positive influence on patients’ behavioral intention of using OHCs, which means that price value plays an important role in influencing patients’ intention to use OHCs. This result is consistent with the findings of previous studies [
13,
113]. The finding indicated that patients would weigh whether their expenditures on OHCs are worth it. Setting a reasonable price is a sufficient condition for OHCs to attract users.
Fifth, consistent with the conclusions inferred from the previous literature [
74,
76,
114], eHealth literacy has a significant positive effect on behavioral intention. More importantly, eHealth literacy is the most important determinant of patients’ behavioral intention to use OHCs. eHealth literacy reflects the ability of users to access and process health information. Patients with strong abilities in information reception, information processing, information understanding, and information discrimination are more adept at utilizing the information provided on OHCs to help themselves, thereby increasing their willingness and behavior to use OHCs. The results suggest that patients’ own perceptions and ability to adopt health information can have a significant impact on their intention to use OHCs. Therefore, enhancing patients’ positive attitudes toward OHCs can also start from the perspective of patients’ understanding and abilities of medical and health knowledge.
Last, the empirical result indicates that relation quality has a significant positive effect on patients’ behavioral intention of using OHCs. This finding is consistent with prior studies indicating that users with high relation quality are more willing to use the corresponding information technology [
44]. This finding suggests the importance of patients’ trust and satisfaction with the platform has been highlighted. The relationship between patients and the platform needs to be paid attention to.
Theoretical implications
This study contributes to OHCs literature by constructing a comprehensive model for explaining patients’ adoption behavior of OHCs based on the UTAUT model and the analysis results of patients’ information demands in OHCs. First, this study enriches the literature on the adoption of OHCs. Previous literature lacked an understanding of the role of patients’ abilities to identify and process online health information as well as patient-platform relationships. Our findings enrich the knowledge of the impact of eHealth literacy and relation quality with OHCs and provide new insights from the perspective of patient’s intrinsic motivation to enhance the understanding of the factors to be considered in technology adoption models in the context of OHCs.
Second, this study extends UTAUT in OHCs and specific users (patients), enriching the literature on UTAUT application scenarios. This study proposes a more comprehensive and appropriate model to understand patients’ motivations to use OHCs by extending the UTAUT model based on the actual information demands of patients in OHCs. The extended UTAUT model proposed in this study enhances the understanding of the adoption of OHCs. The findings of the main hypotheses presented in the research model of this study are found to be consistent with the UTAUT model results, providing further support for the application of UTAUT to OHCs. In addition, the findings of this study enrich the literature of UTAUT by explaining the factors specific to the context of this study that influence the adoption of OHCs, and the context-based insights are well regarded as complementary to the existing knowledge in the field of technology adoption.
Third, our study extends the scope of application of the findings. Given that most related studies have examined OHCs in developed countries, the absence of study in other regions of the world, where the majority of the population resides, makes concluding other countries problematic. Our research model is validated by the data collected in China, which currently has the largest population. This study contributes to the literature on the development of OHCs in China and other developing countries with similar digital health technology development to China.
Practical implications
First of all, the findings showed that patients’ behavioral intention and facilitating conditions have significant positive impacts on usage behavior. Therefore, policymakers can promote the popularity of OHCs by improving patients’ intention to use OHCs, making OHCs compatible, and setting low usage conditions for the patients. For example, it may be beneficial for policymakers to collaborate with healthcare providers and other stakeholders to develop educational campaigns that promote the benefits of using OHCs, and to provide training and support for patients.
Second, the results indicated that high performance expectancy, effort expectancy, and social influence could significantly improve patients’ positive attitudes toward OHCs. The findings inform the operators of OHCs that they need to focus on information quality, friendly interfaces, obvious tags, and web navigation of OHCs to enhance the interaction between OHCs and the users. In addition, the government and hospitals can increase the promotion and publicity of OHCs and pay attention to patients’ evaluations to gain a good word-of-mouth reputation in order to attract more users.
Third, price value has a positive impact on patients’ behavioral intention of using OHCs. Therefore, the operators of OHCs should set a price based on market research that matches the service and information quality provided by OHCs. They also need to understand the actual attitude of patients towards information quality so as to try to narrow the gap between perceived information quality and actual information quality.
Fourth, eHealth literacy is an important factor influencing patients’ usage intention of OHCs. Therefore, it is a good choice for the government to strengthen the promotion of basic medical and health knowledge, healthy lifestyles and behaviors, and basic skills in obtaining information and services online. Besides, the hospitals can try to encourage the public to strengthen relevant learning in order to improve their ability to search, understand, evaluate, and use the information. Physicians would better explain health-related knowledge to patients actively, correct patients’ misconceptions, and guide patients to identify and use the information correctly.
Finally, the results show that relation quality has a considerable favorable influence on patients’ behavioral intention to use OHCs. Therefore, the operators of OHCs should make efforts to ensure the authority and professionalism of the hospitals and physicians in OHCs and enhance the quality of information and services in it. They can cooperate with reliable, authoritative, and professional hospitals and physicians, supervise the information released in OHCs, and strictly handle patients’ complaints.
Limitations and future research
It should be noted that this study has some limitations. First, this study used a cross-sectional investigation without dynamically studying the changes in participants’ attitudes toward all variables. In the subsequent research, longitudinal study methods will be adopted to test research models. Besides, the statistics on the patients of OHCs may change with the rapid growth of OHCs. Most of the patients surveyed are young and highly educated groups. Other groups may be expanded in the future, and we will collect the data again to verify the model at that time. Last, there are differences in the development of digital health technologies in developing countries. Future research will be conducted in more developing countries with different levels of digital health development to form a comprehensive study that takes into account the impact of technology developments specific to other countries.
Conclusion
In this study, we introduce a comprehensive research model by extending the UTAUT model based on patients’ information demands of OHCs. Alongside the impact of the constructs of UTAUT (i.e., performance expectancy, effort expectancy, and social influence have significant positive impacts on the behavioral intention of using OHCs, while behavioral intention and facilitating conditions have significant positive impacts on the usage behavior), this study identifies the significant positive effects of price value, eHealth literacy, and relation quality on behavioral intention of using OHCs. These findings suggest that the operators of OHCs need to focus on improving the construction of the platform, including compatibility and information content. Also, the operators of OHCs should increase the publicity of OHCs, set reasonable prices, publicize health knowledge, and protect patients’ property and information security. The hospitals and physicians can also provide patients with courses that teach them how to judge, understand and use the information in OHCs appropriately. The findings expand the understanding of the usage of OHCs and contribute to both the theories of adopting information technology and practical applications that promote the spread of OHCs.
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