Next Article in Journal
Thermoelectric Generator Applications in Buildings: A Review
Previous Article in Journal
Multi-Source Data-Based Investigation of Spatiotemporal Heterogeneity and Driving Mechanisms of Coupling and Coordination in Human Settlements in Urban Agglomeration in the Middle Reaches of the Yangtze River
 
 
Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
Journals
Sustainability
Volume 16
Issue 17
10.3390/su16177584
sustainability-logo
Submit to this Journal Review for this Journal Propose a Special Issue
► Article Menu

Article Menu

share Share announcement Help format_quote Cite question_answer Discuss in SciProfiles
first_page
settings
Order Article Reprints
Font Type:
Arial Georgia Verdana
Font Size:
Aa Aa Aa
Line Spacing:
Column Width:
Background:
Article

Sustainable Healthcare in China: Analysis of User Satisfaction, Reuse Intention, and Electronic Word-of-Mouth for Online Health Service Platforms

by
Jiexiang Jin
1 and
Mi Hyun Ryu
2,*
1
Department of International Business and Commerce, Graduate School, Konkuk University, 120 Neungdong-ro, Gwangjin-gu, Seoul 05029, Republic of Korea
2
Department of Global Business, Konkuk University, 120 Neungdong-ro, Gwangjin-gu, Seoul 05029, Republic of Korea
*
Author to whom correspondence should be addressed.
Sustainability 2024, 16(17), 7584; https://doi.org/10.3390/su16177584
Submission received: 30 July 2024 / Revised: 30 August 2024 / Accepted: 30 August 2024 / Published: 2 September 2024
Browse Figure
Versions Notes

Abstract

:
Online health service (OHS) platforms can provide sustainable healthcare services in China where healthcare demand continues to grow along with the scarcity of healthcare resources. This study investigated the levels of satisfaction of Chinese consumers’ experiences with OHS platforms and their reuse and electronic word-of-mouth (e-WOM) recommendation intentions. This study provides data for educating consumers on OHS platforms and for guiding strategic business planning for the OHS market. The theoretical model applied was the theory of planned behavior, augmented by integrating factors such as perceived service quality and subjective knowledge into its framework. Data were collected in April 2024 through an online survey of Chinese consumers who had used OHS platforms in the past year. The data were assessed using structural equation modeling and multiple group analysis. The findings indicate that various factors, including attitudes, perceived interaction quality, behavioral control, perceived system quality, perceived information quality, and subjective knowledge concerning OHS platforms, are significant enhancers of user satisfaction and reuse and e-WOM intentions. Health consciousness is a moderating variable in the dynamics between consumer satisfaction and their propensity to engage in e-WOM. Strategies targeting factors that influence satisfaction require development.

1. Introduction

Sustainable development goals (SDGs) aim to eliminate poverty, respond to climate change, and eliminate inequality by 2030 through consideration of economic, social, and environmental sustainability and include important content for human health and well-being [1]. Additionally, the need for sustainable healthcare services has become increasingly urgent due to the global health crisis, climate change, and the increasing burden of chronic disease [2].
With the rising demand for medical services and a shortage of healthcare service resources in China, the imbalance of medical resources and service usage has become a critical problem [3]. To address this issue, online health service (OHS) platforms that leverage Internet technology and increase public health consciousness have emerged as an effective solution to meet the growing need for medical services [4,5]. Additionally, OHS platforms provide telemedicine, e-prescriptions, remote monitoring, access to electronic health records, AI-based symptom checkers, health education resources, appointment scheduling, mental health support, pharmacy services, health insurance integration, and limited urgent care access [6], all of which can help achieve the SDGs and provide sustainable healthcare services [7,8].
The adoption of OHS platforms offers a variety of benefits for patients. Online platforms reduce transaction costs associated with geographic restrictions and time limitations inherent in offline services. Moreover, they are user-friendly and support patient’s self-health management by enabling easy access to health-related information and knowledge [9]. Additionally, OHS platforms enhance patient–doctor interactions and communication, addressing issues related to medical resource shortages and patient–doctor conflicts [10].
China’s online health service platform serves as an “internet hospital.” It aims to deliver comprehensive hospital services including diagnosis, prescriptions, and medicine supply, leveraging internet-based technology, electronic prescription services, and medicine e-commerce [11]. According to 2024 internet development data published by the China Internet Network Information Center, by December 2023, the number of subscribers to online health communities in China had increased by 51.39 million (14.2%) compared to 2022 [12]. As older adults face aging-related health declines, the healthcare services provided by the OHS platform become particularly important for supporting their well-being [8]. Additionally, young individuals have also become major users of the OHS platform owing to their familiarity with digital technology, fast-paced lifestyles, and high demand for convenient medical services [13]. Consequently, the OHS platform has gained widespread use across various age groups, from younger to older adults, with its influence expanding continuously. With the growing use of online health services, research is required to examine factors influencing consumer satisfaction on OHS platforms and their utilization by Chinese consumers.
Prevailing research on OHS platforms has primarily concentrated on their technical characteristics. Academic inquiries frequently employ established theoretical frameworks to examine how these platforms are perceived and utilized. Among these frameworks, the Technology Acceptance Model [14,15], along with the Unified Theory of Acceptance and Usage of Technology [16,17] and its enhanced version, the Unified Theory of Acceptance and Usage of Technology 2 [18,19], are particularly prominent. These models facilitate a deeper understanding of the determinants that influence users’ readiness to embrace and use digital health services effectively. The OHS platform delivers medical services to customers via Internet technology, enhancing user convenience. However, as patients do not receive services in the same way as in physical hospital environments, patient satisfaction has emerged as a critical challenge [11]. This illuminates the need for further investigation into patient satisfaction with online health services. Furthermore, retaining existing customers is more cost-effective and beneficial for businesses than attracting new ones [20]; thus, additional research on reuse intention regarding OHS platforms is required. The proliferation of social networking services has significantly enhanced the influence of electronic word-of-mouth (e-WOM) on how consumers make decisions about purchases or the use of products [21]. This warrants further analysis of e-WOM related to online healthcare. Therefore, research on satisfaction, reuse intention, and e-WOM OHS platform users, incorporating relevant theories and variables, is required.
The Theory of Planned Behavior (TPB) is an extension of the Theory of Reasoned Action, which has been used extensively to describe the attitude–behavior relationship of consumers. It is a useful framework to explain intentions as precursors of actions and is the foundation of decision-making models [22]. Furthermore, the TPB explains different social behaviors and has demonstrated validity and applicability in research predicting behavioral intentions and actual behavior [23]. The TPB, along with customer satisfaction, significantly influences customers’ behavioral intentions [24]. Hasan et al. [25] used the TPB model to investigate behavioral intention regarding travel destination choices, adding satisfaction as a mediator. Thus, this study used TPB components as explanatory variables to assess satisfaction with OHS platforms.
Perceived service quality is also a key factor influencing business performance, cost reduction, customer satisfaction, customer loyalty, and profitability [26], with previous studies reporting that service quality impacted patient satisfaction in hospital settings [27]. Moreover, it is a strong predictor of satisfaction with mobile health (mHealth) services [28]. Other studies have found that the high service quality of online health communities [29] and medical information platforms [30] correlates with improved consumer satisfaction and continuous use intention. This study examined the service quality of the OHS platform and emphasized the significant impact of interactive user experience on service quality and satisfaction.
An important consideration of this study is that in marketing, subjective knowledge is a key factor influencing consumer satisfaction, visit intention, motivations, and other perceptions and choices [31]. Subjective knowledge has high explanatory power for the acceptance of unfamiliar products and services or those with high uncertainty [32]. Thus, understanding the relationship between subjective knowledge and the use of OHS platforms is crucial. Additionally, the influence of health consciousness on consumer’s decision-making behavior should also be considered [33]. Health consciousness has been used as a moderator or variable in other research [34]; hence, this study examined its moderating effects on the relationship between satisfaction, reuse intention, and e-WOM for OHS platforms.
With regard to the use of the OHS platform, this study aimed to understand users, innovate through this understanding, and undergo experiential evolution to provide a better user experience, thereby becoming a differentiated study. The results of this study will serve as basic data for user education to help inform consumers about the benefits of OHS platforms. Moreover, the findings can benefit businesses engaged in strategic planning around OHS platform marketing by offering insight into attracting and retaining customer loyalty through a better understanding of customer behavior. Ultimately, this will assist people in living healthy lives.

2. Theoretical Background and Hypothesis Development

2.1. Theory of Planned Behavior

Fishbein and Ajzen’s Theory of Reasoned Action is the precursor to the TPB, which incorporates key constructs such as consumer attitudes, subjective norms, and perceived behavioral control [22,35]. These elements build upon the foundational ideas of the earlier theory, enriching the understanding of how consumers make decisions. The TPB has been widely adopted in a variety of sectors, such as marketing, information systems, and health services [23,36]. It has also been applied to the utilization of online health services, including studies on user acceptance of mHealth [37,38] and the intention to use OHS platforms [39,40].
In TPB, attitudes refer to an individual’s evaluation of a behavior of interest rather than an object; the attitude toward behavior refers to an individual’s positive or negative evaluation of self-performance of that particular behavior [22]. In this study, attitudes were defined as a user’s positive or negative evaluations of the use of OHS platforms. Subjective norms refer to an individual’s perception of social normative pressures from significant others or influential groups [41]. In the present study, subjective norms were defined as the degree to which a user accepts the opinions of significant others on the use of OHS platforms. Another TPB construct—perceived behavior control—refers to an individual’s perceived ease or difficulty in performing a particular behavior or the perception of one’s self-efficacy in performing said behavior [37]. In this study, it was defined as an individual’s self-perception of the degree of ease of using OHS platforms.
Reuse intention is determined by satisfaction [42]. In Hsu and Chiu’s study on predicting electronic service (e-service) continuance, satisfaction was added as a variable to understand the relationship between e-service reuse intention and TPB components [24]. Including satisfaction in analyzing the relationship between TPB variables and reuse intention can provide meaningful insights into how user satisfaction impacts their continued use of OHS platforms.
Previous studies have explored the relationship between TPB variables (attitudes, subjective norms, perceived behavior control) and satisfaction. They demonstrated that attitude [25,43], subjective norms [25], and perceived behavioral control [44] can effectively account for user satisfaction. Building on these findings, the following hypotheses on OHS platforms in China were developed:
H1. 
TPB variables for OHS platforms positively impact user satisfaction with OHS platforms.
H1-1. 
Attitudes toward OHS platforms will positively impact user satisfaction with OHS platforms.
H1-2. 
Subjective norms on OHS platforms will positively impact user satisfaction with OHS platforms.
H1-3. 
Perceived behavior control for OHS platforms will positively impact user satisfaction with OHS platforms.

2.2. Perceived Service Quality

Service quality is a key theme in marketing-related research across various sectors [45]. In healthcare, service quality is generally considered a precursor to patient satisfaction and behavioral intention [46]. Parasuraman et al. defined perceived service quality as “the consumer’s judgment on the overall excellence or superiority of a particular service” [47]. They developed SERVQUAL—a 22-item scale for measuring consumer perceptions of service quality [48]. The model assesses service quality across five dimensions—tangibles, reliability, responsiveness, assurance, and empathy—and is commonly adopted for use in offline health services [49,50]. However, OHS platforms are based on information and communication technology, requiring a different quality assessment approach [51]. Therefore, perceived service quality for OHS platforms should be categorized and investigated by category.
Previous studies have investigated service quality dimensions and categorized them. Akter et al. analyzed the virtual landscape of mHealth services, delineating its dimensions as system quality, interaction quality, and information quality [28]. Furthermore, Motamarri et al. extended this framework by including outcome quality along with the previously mentioned dimensions [52]. Similarly, Pandesenda et al. employed these dimensions to evaluate online healthcare platforms [53]. Consequently, this study adopted the evaluation of perceived system quality, perceived interaction quality, and perceived information quality as critical metrics for assessing the perceived service quality of OHS platforms.
System quality is the comprehensive quality of an information system encompassing availability, reliability, response time, usability, and adaptability [54]. High system quality enhances user convenience and reduces system failures and errors, thereby improving user satisfaction and effectiveness [30]. Interaction quality refers to the quality of interpersonal relationships between customers and service employees during service [55]. Due to the intangibility and simultaneity of service production and consumption, interactions between frontline customer service employees and customers are integral to the overall service process [56]. Information quality is an essential factor in making useful decisions [52], and its key attributes include the reliability and usefulness of the information provided by the service [57,58].
Previous studies in the field of mHealth have shown that information quality, interaction quality, and system quality all have a positive effect on satisfaction [59,60,61]. Additionally, these quality factors are positively correlated with satisfaction with online healthcare platforms [53]. Building on these findings, the following hypotheses were developed:
H2. 
The perceived service quality of OHS platforms will positively impact user satisfaction with OHS platforms.
H2-1. 
The perceived system quality of OHS platforms will positively impact user satisfaction with OHS platforms.
H2-2. 
The perceived interaction quality of OHS platforms will positively impact user satisfaction with OHS platforms.
H2-3. 
The perceived information quality of OHS platforms will positively impact user satisfaction with OHS platforms.

2.3. Subjective Knowledge

Users’ subjective knowledge may influence decision-making through self-reflection on existing knowledge [62]. A patient’s existing knowledge of medical devices is an important factor for processing information and making decisions about their use [63]. Lee et al. found that users’ knowledge of mHealth impacted their emergency use intention [64]. Therefore, users’ familiarity with healthcare facilities or services significantly affects their behavior and information processing. In this study, subjective knowledge refers to users’ self-evaluated knowledge and familiarity with the OHS platform and its use. This knowledge significantly impacts consumer satisfaction during decision-making and information processing [65]. Thus, it is important to examine the role of knowledge in enhancing satisfaction with OHS platforms.
Previous studies have shown that knowledge about health foods, the use of medical devices, and e-health platforms positively impacts satisfaction, with higher levels of knowledge leading to increased satisfaction and usage intention [63,65,66].
Based on these findings that subjective knowledge is positively associated with satisfaction, this study applied the relationship to OHS platforms to establish the following hypothesis:
H3. 
Subjective knowledge of OHS platforms will positively impact user satisfaction with OHS platforms.

2.4. Satisfaction, Reuse Intention, and e-WOM

Consumer satisfaction hinges on the extent to which purchased products or services fulfill the needs and surpass the expectations of buyers. As such, satisfaction escalates when these offerings not only meet, but exceed the expectations set by customers [67]. Howard and Sheth described customer satisfaction as the buyer’s cognitive state of feeling adequately or inadequately rewarded for the price paid or sacrifice made [68]. Satisfaction also influences continued use intention [69] and the electronic word-of-mouth (e-WOM) intention of e-services [21]. Therefore, it is necessary to explore the relationship between satisfaction with OHS platforms, reuse intention, and e-WOM in an online environment. In the context of this research, satisfaction following the use of the OHS platform is characterized by the emotional responses, either positive or negative, that users experience.
Reuse intention is defined as an individual’s intention to continue using or repurchasing preferred products or services despite continuous marketing efforts for alternatives [70]. Continuous use by consumers is a key characteristic of business success and competitiveness in a rapidly changing market with intense brand competition [20,71]. This study defined reuse intention as the intention to reuse a service after using it via the OHS platform.
Word -of-mouth (WOM) is the non-commercial communication between consumers about a product, service, or brand [72]. E-WOM differs from traditional person-to-person WOM [73]. e-WOM refers to consumers’ sharing and exchanging information such as product information, user reviews, and recommendations via the Internet; it significantly impacts consumers’ behavioral and purchasing intentions [74,75]. In this study, e-WOM was defined as the intention to share a positive or negative evaluation of a service in an online environment after using the services provided by OHS platforms.
Previous studies have examined the relationship between satisfaction, reuse intention, and WOM. They found that satisfaction positively affects WOM and reuse intention in the healthcare services and mHealth fields [63,76]. Additionally, satisfaction has a positive effect on both reuse intention and e-WOM in mobile internet-based health services [77]. Based on the findings of these studies, the following hypotheses were developed:
H4. 
Satisfaction with an OHS platform’s service will positively impact the reuse intention of the service.
H5. 
Satisfaction with an OHS platform’s service will positively impact e-WOM for the service.

2.5. Moderating Effect of Health Consciousness

Health consciousness is a preference or consumer trait that influences an individual’s behavior regarding health-related issues [78]. Health-conscious consumers frequently monitor their health and undertake the necessary actions to improve or maintain it [79]. That is, an individual’s determination or motivation toward healthy behavior influences changes in their health-related behavior [80]. In this study, health consciousness is defined as the degree to which users attend to their own health and how actively they maintain and improve it. As a user’s health consciousness affects their intention to use OHS platforms [33], evaluating the moderating effect of health consciousness concerning OHS platform usage can provide valuable insights into user engagement and platform effectiveness.
Previous research indicates that health consciousness positively influences WOM intention in the food and mHealth fields [81,82]. In studies related to health and fitness applications, health consciousness demonstrated a moderating effect on the relationships between satisfaction, reuse intention, and e-WOM [34]. In light of these findings, a set of hypotheses was systematically developed to further explore these relationships:
H6. 
Health consciousness will moderate the relationship between satisfaction with OHS platforms and reuse intention.
H7. 
Health consciousness will moderate the relationship between satisfaction with OHS platforms and e-WOM.

2.6. Research Model

To understand Chinese consumers’ satisfaction, reuse intention, and e-WOM concerning OHS platforms usage, we proposed a model using TPB variables, perceived service quality, and subjective knowledge as independent variables. Health consciousness is introduced as a moderator in the relationship between satisfaction, reuse intention, and e-WOM in the research model shown in Figure 1. This study aimed to demonstrate the importance of these factors on consumer behavior on OHS platforms and to provide insight on how to improve consumer satisfaction, reuse intention, and e-WOM for these platforms.

3. Research Method

3.1. Data Collection

Study participants were Chinese consumers with experience using OHS platforms within the last 12 months. Data were collected through an online survey platform in China called WJX (https://www.wjx.cn/, accessed on 10–25 April 2024). To ensure the respondents understood what an OHS platform was in the context of the survey, the questionnaire included explanations and relevant examples. From a total of 698 collected responses, those with missing or incomplete data were discarded, leaving 593 valid responses for the final analysis. This reflects an effective response rate of 85%. It was anticipated that some respondents may have been reluctant to answer the monthly income question that was included in the survey but would have been comfortable completing the rest of the questions. The demographic and other relevant characteristics of the respondents are detailed in Table 1.

3.2. Questionnaire Design

The questionnaire used to evaluate OHS platforms in this study is presented in Table 2. The questions were adapted from existing measures whose reliability and validity have been verified in previous studies. This questionnaire was originally developed in English and then translated into Chinese. A professional translator reviewed the questionnaire to ensure the accuracy of the translation. Questions related to TPB variables were based on Ajzen and Wang et al., while perceived service quality questions were derived from Akter et al., Motamarri et al., and Dagger et al. [22,28,37,52,55]. Questions concerning subjective knowledge referenced Keikhosrokiani et al., and those regarding satisfaction, reuse intention, e-WOM, and health consciousness were based on Elsotouhy et al., Birkmeyer et al., Oppong et al., and Gu et al. [34,43,60,61,77]. Respondents’ demographic variables included gender, age, educational level, and monthly income. These variables were measured using a nominal scale, while other variables were measured using a 5-point Likert scale (1 = strongly disagree to 5 = strongly agree).

3.3. Data Analytic Procedure

SPSS 26.0 and AMOS 24.0 were used for data analysis. First, frequency analysis was conducted to identify participant’s socio-demographic characteristics. Second, the reliability of each item in the scale was evaluated using Cronbach’s α. Third, confirmatory factor analysis and correlation analysis were performed to evaluate the convergent and discriminant validities of variables related to OHS platforms. Fourth, to examine the relationships among study variables, the hypotheses were tested using structural equation modeling (SEM) and multigroup analysis.

4. Data Analysis and Results

4.1. Reliability and Validity Testing

Before testing the hypotheses, the measurement model’s reliability and validity were assessed. All Cronbach’s α values were greater than 0.7, indicating good reliability and internal consistency [83]. Convergent validity was assessed using standardized factor loadings and confirmatory factor analysis to compare construct reliability and average variance extracted (AVE). The goodness-of-fit criteria for the model were χ2/df < 2, AGFI > 0.8, TLI > 0.95, CFI > 0.9, and RMSEA < 0.05 [84]. The model fit satisfied all goodness-of-fit criteria, with standardized factor loadings for each variable greater than 0.6, AVE between 0.534 and 0.623 (all above 0.5), and construct reliability values between 0.774 and 0.878 (all above 0.7). These results confirm the measures’ convergent validity [85]. Details of the above analyses are presented in Table 3.
Table 4 shows the correlation coefficients between all variables and the square root of the AVE values (numbers along the diagonal line). The square roots of the AVE values were larger than the correlation coefficients between constructs, indicating that the criteria for discriminant validity were met [86].

4.2. Structural Model and Hypotheses Testing

SEM was performed for hypotheses testing. The model’s goodness-of-fit values were x2 = 1103.613, df = 672, p < 0.001, x2/df = 1.642, AGFI = 0.897, TLI = 0.956, CFI = 0.960, and RMSEA = 0.033, all meeting the criteria for the model fit. Table 5 summarizes the hypotheses testing results. Attitudes (β = 0.177, p < 0.001) and perceived behavior control (β = 0.157, p < 0.001) had positive and significant effects on satisfaction, supporting H1-1 and H1-3. Subjective norms (β = 0.055, p < 0.001) did not have a significant effect on satisfaction, rejecting H1-2. This indicates that positive user opinions and higher determination and perceived ease of use of OHS platforms positively impact satisfaction.
However, the opinions of significant others did not impact user satisfaction with the OHS platform, as they showed no correlation with the user’s evaluation. Perceived system quality (β = 0.136, p < 0.01), perceived interaction quality (β = 0.234, p < 0.001), and perceived information quality (β = 0.281, p < 0.001) had significant positive effects on satisfaction, supporting H2-1, H2-2, and H2-3. These results indicate that the high perceived service quality of OHS platforms increases user satisfaction. Subjective knowledge (β = 0.211, p < 0.001) also had a significant positive impact on satisfaction, supporting H3. This shows that greater user knowledge about the OHS platform correlates with increased satisfaction. Additionally, satisfaction had a significant positive impact (β = 0.632, p < 0.001) on reuse intention and also on e-WOM (β = 0.668, p < 0.001), supporting H4-1 and H4-2. Therefore, higher satisfaction with the OHS platform correlates with increases in reuse intention and e-WOM.

4.3. Moderation Analysis

Cheng et al.’s methodology was used in this study to examine the moderating effect of health consciousness in the relationship between satisfaction and both reuse intention and e-WOM on OHS platforms [87]. Health consciousness was divided into high and low groups using an average value of 3.986, and differences were tested using the χ2 test. Table 6 shows the results of testing the moderating effect of health consciousness. The Δχ2 value in the relationship between satisfaction and e-WOM for OHS platforms was 6.663, indicating a significant difference between the two models. This suggests that in the high health consciousness group, satisfaction had a greater impact on e-WOM compared to the low health consciousness group. However, when testing the moderating effect of health consciousness on the relationship between satisfaction and reuse intention, the Δχ2 value was 2.637, which is below 3.84. This indicates no significant difference between the two models. Therefore, health consciousness did not have a moderating effect on the relationship between satisfaction and reuse intention for the OHS platform.

5. Discussion and Conclusion

5.1. Summary of Research Results

This study investigated the levels of satisfaction of Chinese consumers’ experiences with OHS platforms and their reuse and e-WOM recommendation intentions with the aim of providing sustainable healthcare services for healthy living. Based on the TPB framework, with perceived service quality and subjective knowledge as added variables, analyses were performed on how these variables influence Chinese consumers’ satisfaction, reuse intention, and e-WOM using OHS platforms. The moderating effect of health consciousness in the satisfaction–reuse intention and satisfaction–e-WOM relationships was also evaluated. Among the TPB variables related to OHS platform usage, attitudes and perceived behavior control had significant positive effects on satisfaction. In terms of perceived service quality dimensions, perceived system quality, perceived interaction quality, and perceived information quality had significant positive effects on satisfaction. Subjective knowledge had a significant positive impact on satisfaction. Additionally, satisfaction with the OHS platform positively influenced reuse intention and e-WOM. Lastly, health consciousness had a moderating effect on the relationship between satisfaction and e-WOM.

5.2. Theoretical and Managerial Implications

Considering the academic implications of this study, previous studies typically used the TPB model to analyze behavioral intentions through attitudes, subjective norms, and perceived behavior control. The present study added satisfaction to the relationship between the TPB and reuse intention, emphasizing its importance. The proposed research model analyzing the relationship between TPB variables and satisfaction provides a theoretical basis for follow-up studies.
The present study went beyond analyzing the relationships between TPB variables and satisfaction and analyzing the relationships between its dimensions and satisfaction. The service quality perceived by consumers was categorized into system quality, interaction quality, and information quality. It was confirmed that satisfaction with OHS platforms improved with an increase in these dimensions. The empirical analyses confirmed that system quality, interaction quality, and information quality perceived by users are important factors influencing satisfaction with OHS platforms. This finding provides a meaningful theoretical basis for perceived service quality types in future OHS platforms.
Moreover, this study investigated and verified the relationship between subjective knowledge and satisfaction, as well as the impact of satisfaction with the OHS platform on reuse intention and e-WOM. Additionally, the moderating effect of health consciousness was analyzed, which has implications for future studies. The SEM analysis included several variables, enhancing the understanding of how Chinese consumers use OHS platforms and providing essential data for future studies on satisfaction, reuse intention, and e-WOM.
This study also has implications from managerial/business perspectives. First, among TPB variables, attitudes significantly influenced satisfaction. This supports Hasan et al., who found a positive association between attitudes and satisfaction for users of mHealth services [21]. Attitudes are primarily formed based on an individual’s perceived beliefs, and these perceived beliefs are influenced by external variables [33]; hence, companies should promote the benefits of OHS platforms to encourage their use by consumers for health management and improvement.
Perceived behavior control also had a positive effect on user satisfaction with the OHS platform, which is consistent with Zhang et al. [40]. They found that perceived behavior control for a service was positively associated with satisfaction. Higher perceived ease of use of OHS platforms leads to greater satisfaction. Therefore, companies should create user environments that provide simple and user-friendly experiences for OHS platforms. As perceptions of the ease of use of OHS platforms can vary depending on user age, companies should implement user-friendly and simplified designs for the content, functions, user interface, and communication with doctors.
Notably, subjective norms had no significant effect on satisfaction. Despite the importance of subjective norms, consumers evaluate their satisfaction based on their actual health rather than on the opinions of significant others, thereby reducing the impact of subjective norms on OHS platform satisfaction.
Second, perceived system quality, perceived interaction quality, and perceived information quality were identified as key factors influencing satisfaction with OHS platforms. The positive impacts of perceived system quality, perceived interaction quality, and perceived information quality on user satisfaction align with the findings of studies on mHealth services by Keikhosrokiani et al., Oppong et al., and Pratama et al., respectively [55,56,57]. Therefore, OHS platform companies must maintain and improve these perceived quality factors to enhance user satisfaction with OHS platforms.
Regarding perceived system quality, Akter et al. found that system reliability and efficiency are important for satisfaction with mHealth services [24]. Hence, continuous technological development and innovation are essential to ensure perceived system quality for OHS platforms, providing efficient and error-free user experiences. Furthermore, Oppong et al.’s research on mHealth service noted that service employees’ attitudes and behaviors could positively or negatively affect users’ perceptions of mHealth service quality [56]. Therefore, OHS platform companies should enhance perceived interaction quality by encouraging excellent customer service attitudes through regular training and incentive schemes for health service providers.
Peng et al. reported on the importance of accurate, credible, and useful information in online communities [54]. Hence, to secure the perceived information quality on OHS platforms, all medical content should be reviewed by healthcare professionals for accuracy. Moreover, the information must be sourced from credible healthcare organizations. Another important factor to consider on OHS platforms is the timely provision of medical information. Timeliness is particularly important for updates on infectious disease prevention and vaccinations. Securing and improving perceived system quality, perceived interaction quality, and perceived information quality will enhance user satisfaction on OHS platforms.
Third, subjective knowledge about OHS platforms had a positive impact on user satisfaction, which is consistent with the results of a previous study on e-health mobile applications [62]. When individuals understand OHS platforms, they better perceive their benefits [60]. Building a knowledge base about healthcare, medical services, and OHS platform terms could reduce user complaints and encourage the rational utilization of the platforms. Therefore, consumers should actively acquire relevant knowledge to maximize their use of OHS platforms.
Businesses should actively promote OHS platforms, providing relevant information and user education to increase consumer knowledge on OHS platforms and on healthcare and medical services, thereby improving customer satisfaction. Furthermore, user satisfaction can be improved by enhancing consumers’ subjective and objective knowledge, benefiting both consumers and companies.
Fourth, satisfaction with the OHS platform positively affected reuse intention and e-WOM, consistent with a previous study on mHealth services [39]. Therefore, continuous efforts need to be made to increase user satisfaction to increase reuse intention and e-WOM for OHS platform users. As suggested by Gu et al., satisfaction must be considered in terms of both the actual medical services and the overall user engagement experience [73]. Therefore, maintaining or improving the perceived service quality of OHS platforms and promptly addressing and resolving user inquiries or complaints raised through Voice-of-the-Customer channels is crucial. Additionally, OHS platforms should provide various benefits to users, such as complimentary health checkups to enhance their experiences and satisfaction, ultimately increasing reuse intention and e-WOM.
Lastly, the moderating effect of health consciousness was tested, revealing that user satisfaction had a greater impact on e-WOM in the high health consciousness group, consistent with the findings of a previous study on health and fitness apps [30]. Therefore, OHS platforms should target consumers with high health consciousness and provide them with accurate and useful health information. These users will value the platform’s professional expertise and further enhance their own health consciousness. Additionally, high health consciousness users are more likely to provide positive feedback about the platform on social networks, thus promoting the platform to others.

5.3. Limitations and Future Research

This study has the following limitations: First, the types of medical services that can be provided on the OHS platforms vary widely, including online health consultations and medical examinations, appointment scheduling and hospital registrations, online medication purchasing, health management, consultation on checkup results, dissemination of health knowledge, and general healthcare-related consumption (e.g., oral checkup, physical examination, etc.). This variation means that satisfaction, reuse intention, and e-WOM may differ based on the type of service received. Therefore, future research should include comparative studies on these factors across different types of medical services.
Second, in addition to service type, adding rurality information and access to care information to the questionnaire could also improve our understanding of whether the OHS platform improves the equitable accessibility of healthcare in China.
Third, the participants of this study were consumers who had experience using OHS platforms. Consequently, the reasons for not using the OHS platform among non-users could not be investigated. Therefore, future research should explore the reasons for the non-utilization of OHS platforms by surveying participants with no experience using platforms.
Finally, China’s large and diverse OHS platform user base (or online health communities) leads to considerable variations in OHS platform responses and evaluations. This study’s relatively small sample size may not fully reflect the behavior and feedback of all OHS platform users. Therefore, future research should include a larger sample size and more user data to improve the generalization and further validate this study’s findings.

Author Contributions

Conceptualization, J.J. and M.H.R.; Methodology, J.J. and M.H.R.; Formal analysis, J.J.; Investigation, J.J.; Data curation, J.J.; Writing—original draft, J.J. and M.H.R.; Writing—review and editing, M.H.R.; Supervision, M.H.R. All authors have read and agreed to the published version of the manuscript.

Funding

This research received no external funding.

Institutional Review Board Statement

Not applicable.

Informed Consent Statement

Informed consent was obtained from all participants involved in the study. By completing the online survey, the participants consented to participate in this study.

Data Availability Statement

The raw data supporting the conclusions of this article will be made available by the authors on request.

Conflicts of Interest

The authors declare no conflicts of interest.

References

  1. Lee, B.X.; Kjaerulf, F.; Turner, S.; Cohen, L.; Donnelly, P.D.; Muggah, R.; Davis, R.; Realini, A.; Kieselbach, B.; MacGregor, L.S.; et al. Transforming Our World: Implementing the 2030 Agenda Through Sustainable Development Goal Indicators. J. Public Health Policy 2016, 37 (Suppl. S1), 13–31. [Google Scholar] [CrossRef] [PubMed]
  2. Zanobini, P.; Del Riccio, M.; Lorini, C.; Bonaccorsi, G. Empowering Sustainable Healthcare: The Role of Health Literacy. Sustainability 2024, 16, 3964. [Google Scholar] [CrossRef]
  3. Shen, J.; An, B.; Xu, M.; Gan, D.; Pan, T. Internal or external Word-of-Mouth (WOM), why do patients choose doctors on online medical services (OHSs) single platform in China? Int. J. Environ. Res. Public Health 2022, 19, 13293. [Google Scholar] [CrossRef] [PubMed]
  4. Ye, P.; Liu, L. Factors affecting user intention to pay via online medical service platform: Role of misdiagnosis risk and timeliness of response. Int. J. Healthc. Inf. Syst. Inform. 2022, 16, 1–26. [Google Scholar] [CrossRef]
  5. Liu, S.; Zhang, Y. Designing a doctor evaluation index system for an online medical platform based on the information system success model in China. Front. Public Health 2023, 11, 1185036. [Google Scholar] [CrossRef]
  6. Yang, Y.; Zhang, X.; Lee, P.K.C. Improving the effectiveness of online healthcare platforms: An empirical study with multi-period patient–doctor consultation data. Int. J. Prod. Econ. 2019, 207, 70–80. [Google Scholar] [CrossRef]
  7. Addotey-Delove, M.; Scott, R.E.; Mars, M. Healthcare Workers’ Perspectives of mHealth Adoption Factors in the Developing World: Scoping Review. Int. J. Environ. Res. Public Health 2023, 20, 1244. [Google Scholar] [CrossRef]
  8. Xu, F.; Hu, J.; Liu, D.; Zhou, C. Towards Sustainable Healthcare: Exploring Factors Influencing Use of Mobile Applications for Medical Escort Services. Sustainability 2024, 16, 6058. [Google Scholar] [CrossRef]
  9. Wu, H.; Lu, N. Service provision, pricing, and patient satisfaction in online health communities. Int. J. Med. Inform. 2018, 110, 77–89. [Google Scholar] [CrossRef]
  10. Zhang, L.; Lv, D.; Li, W.; Xing, Z. Promotion strategy for online healthcare platform during the COVID-19 pandemic: Evidence from spring rain doctor in China. Front. Psychol. 2022, 13, 960752. [Google Scholar] [CrossRef]
  11. Wu, D.C.; Zhao, X.; Wu, J. Online physician–patient interaction and patient satisfaction: Empirical study of the Internet hospital service. J. Med. Internet Res. 2023, 25, e39089. [Google Scholar] [CrossRef]
  12. The 53rd Statistical Report on Internet Development in China. Available online: https://www.cnnic.cn/n4/2024/0322/c88-10964.html (accessed on 10 May 2024).
  13. Cao, J.; Feng, H.; Lim, Y.; Kodama, K.; Zhang, S. How Social Influence Promotes the Adoption of Mobile Health among Young Adults in China: A Systematic Analysis of Trust, Health Consciousness, and User Experience. Behav. Sci. 2024, 14, 498. [Google Scholar] [CrossRef]
  14. Islam, S.; Hoque, M.R.; Jamil, M.A.A. Predictors of users’ preferences for online health services. J. Consum. Mark. 2020, 37, 215–225. [Google Scholar] [CrossRef]
  15. Yan, M.; Zhang, M.; Kwok, A.P.K.; Zeng, H.; Li, Y. The roles of trust and its antecedent variables in healthcare consumers’ acceptance of online medical consultation during the COVID-19 pandemic in China. Healthcare 2023, 11, 1232. [Google Scholar] [CrossRef] [PubMed]
  16. Gu, D.; Khan, S.; Khan, I.U.; Khan, S.U.; Xie, Y.; Li, X.; Zhang, G. Assessing the adoption of e-health technology in a developing country: An extension of the UTAUT model. SAGE Open 2021, 11, 21582440211027565. [Google Scholar] [CrossRef]
  17. Zhang, Q.; Zhang, R.; Lu, X.; Zhang, X. What drives the adoption of online health communities? An empirical study from patient-centric perspective. BMC Health Serv. Res. 2023, 23, 524. [Google Scholar] [CrossRef]
  18. Byun, H.; Park, J. A study on the intention to use Korean telemedicine services: Focusing on the UTAUT2 model. In Data Science and Digital Transformation in the Fourth Industrial Revolution; Springer: Berlin/Heidelberg, Germany, 2021; pp. 1–12. [Google Scholar]
  19. Schmitz, A.; Díaz-Martín, A.M.; Yagüe Guillén, M.J. Modifying UTAUT2 for a cross-country comparison of telemedicine adoption. Comput. Hum. Behav. 2022, 130, 107183. [Google Scholar] [CrossRef]
  20. Wibowo, A.; Chen, S.-C.; Wiangin, U.; Ma, Y.; Ruangkanjanases, A. Customer behavior as an outcome of social media marketing: The role of social media marketing activity and customer experience. Sustainability 2021, 13, 189. [Google Scholar] [CrossRef]
  21. Duarte, P.; Costa e Silva, S.; Ferreira, M.B. How convenient is it? Delivering online shopping convenience to enhance customer satisfaction and encourage e-WOM. J. Retail. Consum. Serv. 2018, 44, 161–169. [Google Scholar] [CrossRef]
  22. Ajzen, I. The theory of planned behavior. Organ. Behav. Hum. Decis. Process. 1991, 50, 179–211. [Google Scholar] [CrossRef]
  23. Arkorful, V.E.; Shuliang, Z.; Lugu, B.K.; Jianxun, C. Consumers’ mobile health adoption intention prediction utilizing an extended version of the theory of planned behavior: The moderating role of Internet bandwidth. ACM SIGMIS Database 2022, 53, 96–114. [Google Scholar] [CrossRef]
  24. Hsu, M.-H.; Chiu, C.-M. Predicting electronic service continuance with a decomposed theory of planned behaviour. Behav. Inf. Technol. 2004, 23, 359–373. [Google Scholar] [CrossRef]
  25. Hasan, A.A.-T.; Biswas, C.; Roy, M.; Akter, S.; Kuri, B.C. The applicability of theory of planned behaviour to predict domestic tourist behavioural intention: The case of Bangladesh. GeoJ. Tour. Geosites 2020, 31, 1019–1026. [Google Scholar] [CrossRef]
  26. Seth, N.; Deshmukh, S.G.; Vrat, P. Service quality models: A review. Int. J. Qual. Reliab. Manag. 2005, 22, 913–949. [Google Scholar] [CrossRef]
  27. Kalaja, R.; Krasniqi, M. Patient satisfaction with quality of care in public hospitals in Albania. Front. Public Health 2022, 10, 925681. [Google Scholar] [CrossRef] [PubMed]
  28. Akter, S.; D’Ambra, J.; Ray, P.; Hani, U. Modelling the impact of mHealth service quality on satisfaction, continuance and quality of life. Behav. Inf. Technol. 2013, 32, 1225–1241. [Google Scholar] [CrossRef]
  29. Wu, B. Patient continued use of online health care communities: Web mining of patient–doctor communication. J. Med. Internet Res. 2018, 20, e126. [Google Scholar] [CrossRef]
  30. Myung, J.; Kim, B. The effects of service quality of medical Information O2O Platform on Continuous Use Intention: Case of South Korea. Information 2022, 13, 486. [Google Scholar] [CrossRef]
  31. Kaya, B.; Behravesh, E.; Abubakar, A.M.; Kaya, O.S.; Orús, C. The moderating role of Website familiarity in the relationships between E-service quality, e-satisfaction and e-loyalty. J. Internet Commer. 2019, 18, 369–394. [Google Scholar] [CrossRef]
  32. Pieniak, Z.; Aertsens, J.; Verbeke, W. Subjective and objective knowledge as determinants of organic vegetables consumption. Food Qual. Prefer. 2010, 21, 581–588. [Google Scholar] [CrossRef]
  33. Lu, W.; Wei, D.; Li, C.; Gao, P.; Ma, R.; Zhai, Y.; Wang, C. How to promote telemedicine patient adoption behavior for greener healthcare? J. Clean. Prod. 2024, 434, 139884. [Google Scholar] [CrossRef]
  34. Elsotouhy, M.M.; Ghonim, M.A.; Alasker, T.H.; Khashan, M.A. Investigating health and fitness app users’ stickiness, WOM, and continuance intention using S-O-R Model: The moderating role of health consciousness. Int. J. Hum. Comput. Interact. 2024, 40, 1235–1250. [Google Scholar] [CrossRef]
  35. Fishbein, M.; Ajzen, I. Belief, Attitude, Intention and Behavior: An Introduction to Theory and Research. Philos. Rhetor. 1977, 10, 130–132. [Google Scholar]
  36. Schuster, L.; Tossan, V.; Drennan, J. Consumer acceptance of mHealth services: A comparison of behavioral intention models. Serv. Mark. Q. 2017, 38, 115–128. [Google Scholar] [CrossRef]
  37. Wang, H.; Zhang, J.; Luximon, Y.; Qin, M.; Geng, P.; Tao, D. The determinants of user acceptance of mobile medical platforms: An investigation integrating the TPB, TAM, and patient-centered factors. Int. J. Environ. Res. Public Health 2022, 19, 10758. [Google Scholar] [CrossRef]
  38. Ku, W.-T.; Hsieh, P.-J. Understanding the acceptance of health management mobile services: Integrating theory of planned behavior and health belief model. In Posters’ Extended Abstracts, Proceedings of the International Conference on Human-Computer Interaction; Stephanidis, C., Ed.; Springer International Publishing: New York, NY, USA, 2018; pp. 247–252. [Google Scholar]
  39. Li, D.; Hu, Y.; Pfaff, H.; Wang, L.; Deng, L.; Lu, C.; Xia, S.; Cheng, S.; Zhu, X.; Wu, X. Determinants of patients’ intention to use the online inquiry services provided by Internet hospitals: Empirical evidence from China. J. Med. Internet Res. 2020, 22, e22716. [Google Scholar] [CrossRef]
  40. Bondzie-Micah, V.; Qigui, S.; Arkorful, V.E.; Lugu, B.K.; Bentum-Micah, G.; Ayi-Bonte, A.N.A. Predicting consumer intention to use electronic health service: An empirical structural equation modeling approach. J. Public Aff. 2022, 22, e2677. [Google Scholar] [CrossRef]
  41. Cao, Z.; Zheng, J.; Liu, R. Factors affecting users’ continuous usage in online health communities: An integrated framework of SCT and TPB. Healthcare 2023, 11, 1238. [Google Scholar] [CrossRef]
  42. Hossain, M.A.; Quaddus, M. Expectation–confirmation theory in information system research: A review and analysis. In Information Systems Theory: Explaining and Predicting Our Digital Society; Dwivedi, Y.K., Wade, M.R., Schneberger, S.L., Eds.; Springer: New York, NY, USA, 2012; Volume 1, pp. 441–469. [Google Scholar]
  43. Birkmeyer, S.; Wirtz, B.W.; Langer, P.F. Determinants of mHealth success: An empirical investigation of the user perspective. Int. J. Inf. Manag. 2021, 59, 102351. [Google Scholar] [CrossRef]
  44. Zhang, X.; Liu, D.; Wang, Y.; Du, H. Behavioral intentions of urban rail transit passengers during the COVID-19 pandemic in Tianjin, China: A model integrating the theory of planned behavior and customer satisfaction theory. J. Adv. Transp. 2021, 2021, 8793101. [Google Scholar] [CrossRef]
  45. Arcand, M.; PromTep, S.; Brun, I.; Rajaobelina, L. Mobile banking service quality and customer relationships. Int. J. Bank Mark. 2017, 35, 1068–1089. [Google Scholar] [CrossRef]
  46. Senić, V.; Marinković, V. Patient care, Satisfaction and service quality in health care. Int. J. Consum. Stud. 2013, 37, 312–319. [Google Scholar] [CrossRef]
  47. Parasuraman, A.; Zeithaml, V.A.; Berry, L.L. Servqual: A multiple-item scale for measuring consumer Perc. J. Retail. 1988, 64, 12. [Google Scholar]
  48. Caruana, A.; Money, A.H.; Berthon, P.R. Service quality and satisfaction—The moderating role of value. Eur. J. Mark. 2000, 34, 1338–1353. [Google Scholar] [CrossRef]
  49. Umoke, M.; Umoke, P.C.I.; Nwimo, I.O.; Nwalieji, C.A.; Onwe, R.N.; Emmanuel Ifeanyi, N.; Samson Olaoluwa, A. Patients’ satisfaction with quality of care in general hospitals in Ebonyi State, Nigeria, using SERVQUAL theory. SAGE Open Med. 2020, 8, 2050312120945129. [Google Scholar] [CrossRef] [PubMed]
  50. Kitsios, F.; Stefanakakis, S.; Kamariotou, M.; Dermentzoglou, L. Digital service platform and innovation in healthcare: Measuring users’ satisfaction and implications. Electronics 2023, 12, 662. [Google Scholar] [CrossRef]
  51. Parasuraman, A.; Zeithaml, V.A.; Malhotra, A.A. E-S-QUAL: A multiple-item scale for assessing electronic service quality. J. Serv. Res. 2005, 7, 213–233. [Google Scholar] [CrossRef]
  52. Motamarri, S.; Akter, S.; Ray, P.; Tseng, C.-L. Distinguishing ‘mHealth’ from other healthcare services in a developing country: A study from the service quality perspective. Commun. Assoc. Inf. Syst. 2014, 34, 34. [Google Scholar] [CrossRef]
  53. Pandesenda, A.I.; Yana, R.R.; Sukma, E.A.; Yahya, A.; Widharto, P.; Hidayanto, A.N. Sentiment analysis of service quality of online healthcare platform using fast large-margin. In Proceedings of the International Conference on Informatics, Multimedia, Cyber and Information System (ICIMCIS), Jakarta, Indonesia, 19–20 November 2020; Volume 2020, pp. 121–125. [Google Scholar]
  54. Delone, W.H.; McLean, E.R. The DeLone and McLean model of information systems success: A ten-year update. J. Manag. Inf. Syst. 2003, 19, 9–30. [Google Scholar]
  55. Dagger, T.S.; Sweeney, J.C.; Johnson, L.W. A hierarchical model of health service quality: Scale development and investigation of an integrated model. J. Serv. Res. 2007, 10, 123–142. [Google Scholar] [CrossRef]
  56. Wu, Y.-C.; Tsai, C.-S.; Hsiung, H.-W.; Chen, K.-Y. Linkage between frontline employee service competence scale and customer perceptions of service quality. J. Serv. Mark. 2015, 29, 224–234. [Google Scholar] [CrossRef]
  57. Urbach, N.; Müller, B. The updated DeLone and McLean model of information systems success. In Information Systems Theory: Explaining and Predicting Our Digital Society; Springer: New York, NY, USA, 2012; Volume 1, pp. 1–18. [Google Scholar]
  58. Peng, Y.; Yin, P.; Deng, Z.; Wang, R. Patient–physician interaction and trust in online health community: The role of perceived usefulness of health information and services. Int. J. Environ. Res. Public Health 2020, 17, 139. [Google Scholar] [CrossRef]
  59. Pratama, A.; Ridwandono, D.; Suryanto, T.L.M.; Safitri, E.M.; Khusna, A. Service quality analysis of m-health application satisfaction and continual usage. In Proceedings of the 7th Information Technology International Seminar (ITIS), Surabaya, Indonesia, 6–8 October 2021; IEEE Publications: Piscataway, NJ, USA; Volume 2021, pp. 1–5. [Google Scholar]
  60. Oppong, E.; Hinson, R.E.; Adeola, O.; Muritala, O.; Kosiba, J.P. The effect of mobile health service quality on user satisfaction and continual usage. Total Qual. Manag. Bus. Excell. 2021, 32, 177–198. [Google Scholar] [CrossRef]
  61. Keikhosrokiani, P.; Mustaffa, N.; Zakaria, N.; Abdullah, R. Assessment of a medical information system: The mediating role of use and user satisfaction on the success of human interaction with the mobile healthcare system (iHeart). Cogn. Technol. Work 2020, 22, 281–305. [Google Scholar] [CrossRef]
  62. Brucks, M. The effects of product class knowledge on information search behavior. J. Consum. Res. 1985, 12, 1–16. [Google Scholar] [CrossRef]
  63. Chaniaud, N.; Métayer, N.; Megalakaki, O.; Loup-Escande, E. Effect of prior health knowledge on the usability of two home medical devices: Usability study. JMIR mHealth uHealth 2020, 8, e17983. [Google Scholar] [CrossRef]
  64. Lee, W.-I.; Fu, H.-P.; Mendoza, N.; Liu, T.-Y. Determinants impacting user behavior towards emergency use intentions of m-health services in Taiwan. Healthcare 2021, 9, 535. [Google Scholar] [CrossRef]
  65. Lee, S.-M. Influence of informational clues on subjective knowledge, concern, and satisfaction and behavioral intention toward healthy foods in full-service restaurants. Culin. Sci. Hosp. Res. 2016, 22, 78–86. [Google Scholar]
  66. Aldhahir, A.M.; Alqahtani, J.S.; Althobiani, M.A.; Alghamdi, S.M.; Alanazi, A.F.; Alnaim, N.; Alqarni, A.A.; Alwafi, H. Current knowledge, satisfaction, and use of e-health mobile application (Seha) among the general population of Saudi Arabia: A cross-sectional study. J. Multidiscip. Healthc. 2022, 15, 667–678. [Google Scholar] [CrossRef]
  67. Churchill, G.A.; Surprenant, C. An investigation into the determinants of customer satisfaction. J. Mark. Res. 1982, 19, 491–504. [Google Scholar] [CrossRef]
  68. Howard, J.A.; Sheth, J.N. The Theory of Buyer Behavior; John Willey & Sons: New York, NY, USA, 1969; Volume 63, p. 145. [Google Scholar]
  69. Liao, C.; Chen, J.-L.; Yen, D.C. Theory of planning behavior (TPB) and customer satisfaction in the continued use of e-service: An integrated model. Comput. Hum. Behav. 2007, 23, 2804–2822. [Google Scholar] [CrossRef]
  70. Oliver, R.L. A cognitive model of the antecedents and consequences of satisfaction decisions. J. Mark. Res. 1980, 17, 460–469. [Google Scholar] [CrossRef]
  71. Garbarino, E.; Johnson, M.S. The different roles of satisfaction, trust, and commitment in customer relationships. J. Mark. 1999, 63, 70–87. [Google Scholar] [CrossRef]
  72. Harrison-Walker, L.J. The measurement of word-of-mouth communication and an investigation of service quality and customer commitment as potential antecedents. J. Serv. Res. 2001, 4, 60–75. [Google Scholar] [CrossRef]
  73. Barreto, A.M. The word-of-mouth phenomenon in the social media era. Int. J. Mark. Res. 2014, 56, 631–654. [Google Scholar] [CrossRef]
  74. Hennig-Thurau, T.; Gwinner, K.P.; Walsh, G.; Gremler, D.D. Electronic word-of-mouth via consumer-opinion platforms: What motivates consumers to articulate themselves on the Internet? J. Interact. Mark. 2004, 18, 38–52. [Google Scholar] [CrossRef]
  75. Gruen, T.W.; Osmonbekov, T.; Czaplewski, A.J. eWOM: The impact of customer-to-customer online know-how exchange on customer value and loyalty. J. Bus. Res. 2006, 59, 449–456. [Google Scholar] [CrossRef]
  76. Rahman, M.K.; Zainol, N.R.; Nawi, N.C.; Patwary, A.K.; Zulkifli, W.F.W.; Haque, M.M. Halal healthcare services: Patients’ satisfaction and word of mouth lesson from Islamic-friendly hospitals. Sustainability 2023, 15, 1493. [Google Scholar] [CrossRef]
  77. Gu, D.; Yang, X.; Li, X.; Jain, H.K.; Liang, C. Understanding the role of mobile Internet-based health services on patient satisfaction and word-of-mouth. Int. J. Environ. Res. Public Health 2018, 15, 1972. [Google Scholar] [CrossRef]
  78. Dellande, S.; Gilly, M.C.; Graham, J.L. Gaining compliance and losing weight: The role of the service provider in health care services. J. Mark. 2004, 68, 78–91. [Google Scholar] [CrossRef]
  79. Michaelidou, N.; Hassan, L.M. The role of health consciousness, food safety concern and ethical identity on attitudes and intentions towards organic food. Int. J. Consum. Stud. 2008, 32, 163–170. [Google Scholar] [CrossRef]
  80. Amit Kumar, G. Framing a model for green buying behavior of Indian consumers: From the lenses of the theory of planned behavior. J. Clean. Prod. 2021, 295, 126487. [Google Scholar] [CrossRef]
  81. DiPietro, R.B.; Remar, D.; Parsa, H.G. Health consciousness, menu information, and consumers’ purchase intentions: An empirical investigation. J. Foodserv. Bus. Res. 2016, 19, 497–513. [Google Scholar] [CrossRef]
  82. To, W.-M.; Lee, P.K.C.; Lu, J.; Wang, J.; Yang, Y.; Yu, Q. What motivates Chinese young adults to use mHealth? Healthcare 2019, 7, 156. [Google Scholar] [CrossRef]
  83. Schmitt, N. Uses and abuses of coefficient alpha. Psychol. Assess. 1996, 8, 350–353. [Google Scholar] [CrossRef]
  84. Mia, M.; Majri, D.Y.; Abdul Rahman, I.K. Covariance based-structural equation modeling (CB-SEM) using AMOS in management research. IOSR J. Bus. Manag. 2019, 21, 56–61. [Google Scholar]
  85. Bollen, K.A. Structural Equations with Latent Variables; John Wiley & Sons: Oxford, UK, 1989; Volume 210. [Google Scholar]
  86. Hair, J.F.; Anderson, R.E.; Tatham, R.L.; Black, W.C. Multivariate Data Analysis with Readings; Pearson Prentice Hall: New York, NY, USA, 2006; Volume 6. [Google Scholar]
  87. Cheng, L.K.; Huang, H.-L.; Lai, C.-C. Continuance intention in running apps: The moderating effect of relationship norms. Int. J. Sports Mark. Spons. 2022, 23, 132–154. [Google Scholar] [CrossRef]
Sustainability 16 07584 g001
Figure 1. Research model.
Figure 1. Research model.
Sustainability 16 07584 g001
Table 1. General characteristics of survey participants.
Table 1. General characteristics of survey participants.
ClassificationIndicatorsFrequency%
GenderMale30050.6
Female29349.4
Age20–2915325.8
30–3914825.0
40–4914524.4
Over 50 (including 50 years old) 14724.8
Education levelHigh school or below12721.4
University or college graduate 30351.1
Postgraduate or above16327.5
Monthly incomeLess than 851 USD
(excluding 851 USD)		20334.2
851 USD–1277 USD
(excluding 1277 USD)		18130.5
More than 1277 USD20935.3
Table 2. Scale construction.
Table 2. Scale construction.
ConstructsItemsItem Source
Attitudes
(ATT)		I think using the OHS platform is a wise idea.[22,37]
I think using the OHS platform is a good idea.
I think the OHS platform is useful for me.
Subjective
Norms
(SN)		My significant others understand my use of OHS platforms.
My significant others are supportive of my use of OHS platforms.
My significant others think that my using OHS platforms is a good idea.
My significant others will recommend a good OHS platform to me.
Perceived
Behavior
Control
(PBC)		I can access the OHS platform whenever I need to.
I can make my own decisions on whether to use the OHS platform.
Searching for health information using OHS platforms is something that I can do with confidence.
I do not find using the OHS platform difficult.
Perceived System
Quality
(PSQ)		I am provided with the health information I need through the OHS platform whenever needed.[28,52,55]
I am provided with the health information I need through the OHS platform wherever needed.
It is easy to search for health information using the OHS platform.
It is convenient to schedule a hospital appointment or receive a health consultation using the OHS platform.
The OHS platform is run accurately and consistently without errors.
Perceived Interaction
Quality
(PITQ)		The OHS platform gives me prompt feedback when I ask about hospital appointments or health-related questions.
The OHS platform understands my specific needs when I ask about hospital appointments or health-related questions.
The OHS platform responds courteously when I ask about hospital appointments or health-related questions.
The OHS platform provides me with personal attention in areas such as disease prevention.
Perceived Information
Quality
(PIFQ)		Health-related information obtained through the OHS platform is valuable to me.
I can obtain the latest health information using the OHS platform.
Reviews from other users posted on the OHS platform are useful.
I can get up-to-date information from the OHS platform since the information is continuously updated.
Subjective Knowledge
(SK)		I know the service characteristics (information on the service provision) of OHS platforms well.[61]
I know how to use OHS platforms well.
I have substantial knowledge about OHS platforms.
Satisfaction
(SAT)		I am satisfied with the disease prevention and health management process through the OHS platform.[60]
I am satisfied with my decision to use OHS platforms.
I am satisfied with the overall use of OHS platforms.
I am satisfied with the overall services provided by OHS platforms.
I think OHS platforms have more pros than cons.
Reuse
Intention
(RI)		I intend to continue using the OHS platform in the future.[43]
I will use the OHS platform frequently.
I will regularly use the OHS platform, where possible.
I will gradually increase the frequency of using the OHS platform in the future.
Electronic Word-of-Mouth
(e-WOM)		I am willing to recommend the OHS platform to others.[77]
I will frequently mention the service of OHS platforms to others.
I will discuss the OHS platform’s good services/positive points with other people online.
Health
Consciousness
(HC)		Living a life with the best health I can have is very important to me.[34]
A suitable diet, exercise, and prophylactic treatment will help me stay healthy for my lifetime.
My health depends on how well I manage it.
I make active efforts to prevent disease.
I will do my best to stay healthy.
Table 3. Confirmatory factor analysis and reliability analysis.
Table 3. Confirmatory factor analysis and reliability analysis.
VariableItemsStandardization EstimateCronbach’s AlphaAVECR
ATTATT10.7210.7740.5340.774
ATT20.734
ATT30.736
SNSN10.7440.8450.5780.846
SN20.770
SN30.778
SN40.749
PBCPBC10.7320.8310.5550.833
PBC20.742
PBC30.724
PBC40.779
PSQPSQ10.7920.8500.5350.852
PSQ20.741
PSQ30.722
PSQ40.678
PSQ50.721
PITQPITQ10.7360.8260.5470.828
PITQ20.739
PITQ30.658
PITQ40.764
PIFQPIFQ10.7740.8370.5640.838
PIFQ20.758
PIFQ30.704
PIFQ40.766
SKSK10.7730.7910.5650.795
SK20.792
SK30.685
SATSAT10.7730.8770.5910.878
SAT20.751
SAT30.802
SAT40.722
SAT50.794
RIRI10.8220.8680.6230.869
RI20.779
RI30.757
RI40.798
e-WOMe-WOM10.8370.8280.6190.829
e-WOM20.771
e-WOM30.750
HCHC10.7940.8640.5640.866
HC20.739
HC30.660
HC40.742
HC50.811
Goodness-of-fit: x² = 1312.653, df = 847, p < 0.001, x2/df = 1.55, AGFI = 0.892, TLI = 0.958, CFI = 0.962, RMSEA = 0.030.
Table 4. Results of discriminant validity.
Table 4. Results of discriminant validity.
ATTSNPBCPSQPITQPIFQSKSATRIe-WOWHC
ATT0.731
SN0.1190.760
PBC0.1680.2840.745
PSQ0.2600.3670.4600.740
PITQ0.1400.3540.3550.4270.751
PIFQ0.1350.3540.4010.4210.4190.752
SK0.1860.2160.4900.5010.3500.3600.752
SAT0.3650.3920.5730.6070.5780.6130.5770.769
RI0.2070.2600.2960.3370.3890.4240.3760.6130.789
e-WOW0.1740.3020.3540.3610.4100.4190.4310.6490.5820.787
HC0.1120.0450.1710.2060.1630.2200.0840.2670.3580.4250.751
The bold values along the diagonal line represent the square root of AVE.
Table 5. Results of SEM analysis.
Table 5. Results of SEM analysis.
Hypothesis βSE CRp-Value
H1-1. ATT → SAT0.1770.0394.9820.000 ***
H1-2. SN → SAT0.0550.0381.5070.132
H1-3. PBC → SAT0.1570.0393.7580.000 ***
H2-1. PSQ → SAT0.1360.0403.0970.002 **
H2-2. PITQ → SAT0.2340.0415.7430.000 ***
H2-3. PIFQ → SAT0.2810.0366.7590.000 ***
H3. SK → SAT0.2110.0354.8110.000 ***
H4-1. SAT → RI0.6320.05813.4310.000 ***
H4-2. SAT → e-WOW0.6680.06314.0390.000 ***
Goodness-of-fit: x2 = 1103.613, df = 672, p < 0.001, x2/df = 1.642, AGFI = 0.897, TLI = 0.956, CFI = 0.960, RMSEA = 0.033; ** p < 0.01, *** p < 0.001.
Table 6. Results of moderation analysis.
Table 6. Results of moderation analysis.
Hypothesis ∆χ2, ∆dfLOW HC
(N = 214)		HIGH HC
(N = 379)		Conclusion
βC.R.βC.R.
H5 SAT → RI∆χ2(1) = 2.6370.483 ***6.5610.698 ***10.191Reject
H6 SAT → e-WOW∆χ2(1) = 6.663 **0.477 ***6.2580.787 ***11.474Accept
** p < 0.01, *** p < 0.001.
Disclaimer/Publisher’s Note: The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content.

Share and Cite

MDPI and ACS Style

Jin, J.; Ryu, M.H. Sustainable Healthcare in China: Analysis of User Satisfaction, Reuse Intention, and Electronic Word-of-Mouth for Online Health Service Platforms. Sustainability 2024, 16, 7584. https://doi.org/10.3390/su16177584

AMA Style

Jin J, Ryu MH. Sustainable Healthcare in China: Analysis of User Satisfaction, Reuse Intention, and Electronic Word-of-Mouth for Online Health Service Platforms. Sustainability. 2024; 16(17):7584. https://doi.org/10.3390/su16177584

Chicago/Turabian Style

Jin, Jiexiang, and Mi Hyun Ryu. 2024. "Sustainable Healthcare in China: Analysis of User Satisfaction, Reuse Intention, and Electronic Word-of-Mouth for Online Health Service Platforms" Sustainability 16, no. 17: 7584. https://doi.org/10.3390/su16177584

APA Style

Jin, J., & Ryu, M. H. (2024). Sustainable Healthcare in China: Analysis of User Satisfaction, Reuse Intention, and Electronic Word-of-Mouth for Online Health Service Platforms. Sustainability, 16(17), 7584. https://doi.org/10.3390/su16177584

Note that from the first issue of 2016, this journal uses article numbers instead of page numbers. See further details here.

Article Metrics

Back to TopTop