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Article

The Influence of Content Presentation on Users’ Intention to Adopt mHealth Applications: Based on the S-O-R Theoretical Model

by
Yizhi Liu
1,
Xuan Lu
1,
Chengjiang Li
1,2,* and
Gang Zhao
2
1
School of Management, Guizhou University, Guiyang 550025, China
2
School of Engineering, University of Tasmania, Hobart, TAS 7005, Australia
*
Author to whom correspondence should be addressed.
Sustainability 2022, 14(16), 9900; https://doi.org/10.3390/su14169900
Submission received: 22 June 2022 / Revised: 8 August 2022 / Accepted: 9 August 2022 / Published: 10 August 2022
(This article belongs to the Special Issue The Dawn of mHealth Innovation)
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Abstract

:
The development of internet technology enables mobile medical health (mHealth) applications (Apps) to provide users with health services. The content presentation of apps is an important factor affecting users’ adoption of mHealth Apps. This study aims to examine the effect of the content presentation of mHealth Apps on users’ adoption intention, using the stimulus-organism-response (S-O-R) theory as the theoretical framework. A model of the effect of the content presentation of mHealth Apps on users’ intention to adopt, integrating perceived value and trust, was constructed. Furthermore, a quantitative study using a questionnaire survey was carried out to test the model. The analysis shows that platform information presentation, guidance information presentation, and relational information presentation indirectly have a significant positive effect on willingness toward participation and recommendation through the mediating effect of perceived value or trust. All three presentations have a significant positive effect on perceived value. Platform information presentation has a significant positive effect on trust in contrast to the other two presentations. In addition, the results reveal that perceived value and trust have a significant positive effect on willingness toward participation and recommendation, respectively. However, the effect of perceived value on trust is not significant. This work could provide measures and suggestions to improve users’ intention to adopt mHealth Apps as well as help researchers, developers, service providers, and app users to use and improve this modern mobile medical service.

1. Introduction

The rapid development of internet technology has brought about the transformation of mHealth services, of which mHealth Apps are the most important part [1]. Different from traditional medical services, mHealth Apps, as the intelligent terminal system, can optimize the processes of making appointments and registration, paying online, and viewing testing reports, greatly reducing the time and frequency of onsite queues and effectively improving the user’s experience [2]. The mHealth Apps can also provide remote medical consultation, letting users conduct self-diagnosis through online consultation. For example, through mHealth Apps, such as Ping An Good Doctor, Good Doctor Online, and Chunyu Doctor, users can communicate with famous doctors through pictures, recordings, and videos and make self-diagnoses according to the results, which greatly alleviates “the difficulty of seeing a doctor” in China. mHealth Apps have changed traditional medical services and have been playing a very important role in the delivery of medical services, opening up a new way for users’ health management with unique features of ease of use, usefulness, and convenience [3,4,5].
The existing studies on the content presentation of mobile apps mainly concentrate on the fields of e-commerce [6], tourism [7], social networking sites [8], and online knowledge payment [9]. Studies show that effective content presentation significantly affects users’ intention to participate and recommend the apps [10]. Chi et al. (2021) discussed that commodity information presented by advertisements could facilitate consumers’ purchasing decisions and influence consumers’ participation behavior [11]. When online information contained images (e.g., a picture or video clip) as well as comments, consumers were more likely to adopt arguments in the social media context [12]. Information presented effectively could significantly alter the perceptions of information consumers [13]. Healthcare as a whole is a business and entity in which the developed apps must promote and explain their products through content presentation. The content presentation of mHealth Apps is an important carrier for realizing medical services and has a great influence on users’ attitudes, adoption intentions, and behavior [14,15,16]. Good content presentation makes medical service information easier for users to understand and recognize [17,18,19]. Therefore, how to ensure the effectiveness of the content presentation of mHealth Apps so as to improve users’ intention to adopt has attracted wide attention from experts and scholars.
However, many scholars have only discussed the simple impact of content presentation or its characteristics on user behavior [11,20] without any in-depth research on its specific impact mechanism. Less attention has been paid to the effect of the content presentation of mHealth Apps on users’ intention to adopt. Therefore, this study will explore the effects of content presentation of mHealth Apps on the users’ adoption intention, integrating perceived value and trust using the stimuli-organism-response framework.
Perceived value in this study is defined as the user’s trade-off comparison of the cost and the value gained from using mHealth services and is considered to be one of the key factors affecting users’ adoption intention [21]. Because perceived value runs through the whole process of the users’ experience of the mHealth Apps, the users’ perceived value of the mHealth services will directly affect their intention to adopt, and this has been verified in the mHealth service. mHealth Apps have the obvious advantages of breaking through time and space constraints, helping users to register, seek treatment, and inquire about medical information or services anytime and anywhere, providing users with more freedom and convenience, and at the same time effectively improving the efficiency of medical treatment. For example, Alam et al. (2020) confirmed that perceived value had a significant positive effect on mHealth users’ adoption intention [22]. When the mHealth services could provide users with cost-effective medical services, their satisfaction with mHealth services increased. Therefore, this study will examine the direct effect of perceived value on users’ intention to adopt mHealth Apps and the indirect effect of the content presentation of mHealth Apps on adoption intention through the mediating effect of perceived value.
Trust plays an important role in relationship marketing and significantly affects the maintenance of lasting relationships between users and platforms, triggering users’ positive attitudes and adoption behaviors [23]. Since mHealth Apps are closely related to users’ own health and security, users’ trust strongly affects their willingness to adopt apps. Alam et al. (2020) showed that trust had a positive influence on users’ behavioral intentions in Bangladesh [24]. Based on the TAM model, Octavius and Antonio (2021) also showed that initial trust in the mHealth platform had a statistically significant effect on the intention to adopt mHealth Apps [25]. However, only a few studies have explored the effect of trust on the content presentation of platform, guidance, and relations in mHealth Apps. In fact, the specificity of medical services makes trust a key role in the content presentation of mHealth Apps and users’ adoption behavior. Therefore, this study considers trust as a mediating factor affecting users’ adoption intention.
As an emerging form of mobile technology in healthcare, a variety of factors influence the adoption behavior of mHealth users. Scholars have constructed models of factors influencing mHealth user adoption behavior based on different theoretical frameworks, such as the technology acceptance model (TAM) [25], unified theory of acceptance and use of technology (UTAUT) [26], theory of reasoned action (TRA) [27], and privacy personalization paradox (PPP) (Figure 1). Based on the TAM model, Byrd et al. (2021) showed that users’ intention to adopt mHealth Apps was more likely to be improved by intensifying their perceived ease of use and perceived usefulness [28]. Semiz et al. (2021) also reported that the key variables in the UTAUT model were the determinants of mobile health application acceptance in their survey [29]. In addition, Wu et al. (2022) chose the UTAUT2 model as their theoretical framework to explore the factors that affected the users’ continuous usage intention of mHealth Apps, and the results showed that users’ effort expectancy, performance expectancy, social influence, facilitating conditions, price value, and habits could positively influence the users’ continuous usage behavior [30]. We found that most scholars have mainly explored the factors influencing mHealth adoption behavior related to technology features [31,32,33]. Although TAM, UTAUT, and their extended models are useful, many efforts are still needed to improve their explanatory power.
Stimulus-organism-response (S-O-R), first proposed by environmental psychologists Mehrabian and Russell in 1974, is a learning theory based on cognitive behavioral models [34]. By adding the cognitive and psychological responses of the recipients into the original Input-Output model, the current S-O-R model was developed and widely used to explain the behavior of individuals under the stimulation of internal and external factors [35,36]. According to this model, external environmental stimuli affect internal emotions through the cognition of users (organisms), which in turn promotes people’s internal psychological responses (emotion/cognition) [37]. The series of intrinsic psychological reactions further shapes users’ behavioral responses to stimuli [38]. Intrinsic psychological responses usually refer to the recipient’s emotions or attitudes, while behavioral responses are often referred to as approach/avoidance behavior [34].
In the past, the S-O-R model was often used to study the impact of the external environment on individuals [39,40,41,42] and sometimes was applied to reveal consumers’ online purchase behavior [43]. In recent years, the S-O-R model has been more and more widely used in investigating online users’ behavioral responses to information [44,45,46]. In the related existing literature, Tuncer (2021) explored the role of IT affordance, flow experience, and trust on social commerce intention using the S-O-R theory [47]. Cao et al. (2020) used the S-O-R model to study the resistance behavior of elderly users to mHealth Apps and found that the information overload and system function overload of mHealth Apps had a significant effect on the fatigue and technical stress of elderly users, which in turn aggravated their resistance behavior [48].
This paper aims to analyze the relationship between content presentation, users’ internal experience, and users’ adoption intentions and construct the effect model from the perspective of the S-O-R theoretical model. Specifically, this study investigates the effects of three aspects of content presentation—platform information presentation, guidance information presentation, and relational information presentation—on users’ perceived value and trust, further influencing users’ willingness toward participation and recommendation. In the proposed model, the content presentation of mHealth Apps is regarded as an external stimulus (S) of the user’s inner emotion; the user’s internal experience (perceived value and trust) is regarded as an organism with internal cognition (O), which is a psychological response to external information stimuli, and the users’ intention to adopt (willingness to participate and recommend) is the behavioral response (R) made under the psychological reaction. In the context of mHealth services, the content presentation (stimulus) of mHealth Apps strengthens the users’ perceived value and trust (psychological state) and triggers the user’s intention to adopt (behavioral response).

2. Theoretical Background

2.1. Content Presentation and User Adoption Behavior of mHealth Apps

The rapid development of internet technology has changed the traditional way of information dissemination and promoted the further upgrade of consumer behavior [49,50,51]. Compared with traditional information dissemination, content presentation on online platforms has the characteristics of diversity, portability, rapidness, and interestingness [52,53]. Content presentation is one of the important factors affecting consumers’ willingness to purchase [54,55,56]. On the one hand, content presentation on the online platform enables users to accept the products or services described in the content. On the other hand, it expands the influence of the platform and effectively maintains the brand relationship with users. For content presentation, some scholars divide it into two dimensions: the content presentation of information [57] and the format presentation of information [58,59,60]. Content presentation emphasizes the type and quantity of information, while format presentation emphasizes the methods and techniques of information dissemination, both of which have an impact on internet users’ information retrieval [61,62]. Gao (2002) showed that the content presentation of commercial websites mainly includes price/value information, functional information, and quality information [15]. Holbrook and Batra (1987) divided the content presentation of online commodities into factual information and evaluation information [63]. Compared with text descriptions of commodity information, digital descriptions, graphics, and symbols have a greater influence on consumer preferences [64,65,66].
Different scholars have different elaborations on content presentation, but the aim of all previous studies is to better meet the demands of users for obtaining information effectively [67]. For medical institutions, the information about the drug or medical service presented by the app platforms provides a decision-making reference for users’ adoption behaviors [68]. In this study, the content presentation is an approach to attract its users to the relative activities by the mHealth providers [69,70]. In the field of IT, information disclosures can be categorized as presentation format and information content. We focus on the effect of the type of information on users’ intention to adopt mHealth Apps. Therefore, we classify the content presentation as platform information presentation, guidance information presentation, and relational information presentation. As shown in Figure 2, platform information (from Quintero Johnson et al. (2017) and Veltri et al. (2020) [71,72]) includes the basic information about the platform and medical-related information that helps users understand the advantages or service features of the platform and medical services. Guidance information (from Wang et al. (2020) [73]) refers to the information that guides visitors to participate in the marketing activities of mHealth Apps. Relational information (from Heycke and Gawronski (2020) and Córdova et al. (2019) [74,75]) is mainly used for communication and the maintenance of relationships with users.

2.2. Platform Information Presentation

In this study, platform information presentation refers to the descriptive information released to users, such as the development history of the platform, an overview of medical resources, social responsibility, corporate culture, technical strengths, and drugs or medical services. For mHealth Apps, because users cannot communicate with doctors face-to-face and can only rely on the presentation of information on the platform, different users have different perceptions of the content presentation. Some studies have pointed out that the platform information presentation is an important factor in attracting consumers to participate in interactive activities [56,76]. Kramer et al. (2007) showed that the way that the promotion information of online products was presented made significant differences in consumers’ perception of promotion strategies [77]. Users’ acceptance and processing of information about platforms, drugs, or medical services can facilitate their understanding of mHealth services, thereby stimulating users to form basic value perceptions. In addition, in the internet environment, providing information about products and services can improve the credibility of ecommerce [78]. Tuncer (2021) also showed that the platform information presentation of online stores significantly affected consumers’ online trust [47]. It is believed that the more accurate the information provided on the mHealth platform and the more extensive the topics covered, the more “real” and “friendly” the users feel in their hearts, and the easier it is to generate recognition and trust in obtaining the medical service. Therefore, this paper proposes the following hypotheses:
H1. 
The platform information presentation has a positive effect on perceived value.
H2. 
The platform information presentation has a positive effect on trust.

2.3. Guidance Information Presentation

Guidance information presentation refers to the information released to users about marketing, lottery activities, promotional activities, reviews on experiences, product promotion, online free clinics, etc. It is also an important carrier for mHealth App promotion, service support, and service innovation. Some studies have shown that the reviews and information exchange on the enterprise microblog platform have a significantly positive effect on user behavior [79,80]. Information about promotional activities can deliver preferential information on medical services to users, which is conducive to attracting users’ attention to medical products or services and promoting their perception of the usefulness of apps. At the same time, in mHealth services, potential users build trust in medical apps through online user ratings, reviews, and participation in interactions [81]. Reviews on experiences are users’ real perceptions of medical products or services, which can promote the trust between mHealth Apps and users, allowing users to learn more about medical knowledge and services [7]. When the guidance information presentation is effective, and the relationship and interaction between the platform and the users is strong, the users can gain a better experience from a good mHealth service, resulting in increasing the perceived value or trust in the mHealth Apps. Consequently, this paper proposes the following hypotheses:
H3. 
Guidance information presentation has a positive effect on perceived value.
H4. 
Guidance information presentation has a positive effect on trust.

2.4. Relational Information Presentation

Relational information presentation refers to relational information such as gratitude feedback, holiday greetings, caring services, and online interaction. In addition to searching for information about health and medical care on mHealth Apps, more and more users are actively participating in various online community activities that mainly focus on sharing health information. Zhang et al. (2018) confirmed that the usefulness and interestingness of the content on Weibo are important factors affecting users’ intention to participate in platform activities [12]. Through online interaction and real-time communication services, the mHealth Apps can solve the medical and health problems that users care about in time, which helps to give users a positive impression and has a positive effect on the users’ perceived value. Li et al. (2018) also showed that good relational information presentation had a significantly positive influence on consumers’ perceived value and trust [82]. With the relational information online, the mHealth Apps convey the importance of customer relationship management and increase users’ perceived value of medical services. Moreover, with the presentation of caring services, relational information can also improve users’ trust in mHealth Apps. It can be seen that relational information presentation can improve the relationship between the platform and users, stimulate users’ inner emotional responses, and increase the perceived value and trust in mHealth Apps. Considering these reasons, this paper proposes the following hypotheses:
H5. 
The relational information presentation has a positive effect on perceived value.
H6. 
The relational information presentation has a positive effect on trust.

2.5. Perceived Value and Trust

Perceived value is the user’s comprehensive evaluation of the mHealth Apps after weighing the gains and losses. It is the internal driving force for user trust, and also an important factor to maintain the sustainable relationship between users and the platform [83]. In online purchases, consumers’ high perceived value of products will positively affect consumers’ trust for the brand [84,85]. In recent years, empirical studies have also been conducted on the relationship between perceived value and trust in the field of mHealth [86,87]. The results show that the greater the value users perceive from medical products or services, the more positive emotions users have toward mHealth Apps, and thus the higher their satisfaction and trust. Therefore, this paper proposes the following hypotheses:
H7. 
Perceived value has a positive effect on trust.

2.6. Intention of Adoption

In this study, adoption intention is divided into the willingness to participate and the willingness to recommend. Willingness to participate refers to users’ personal subjective judgments about the possibility of seeking online medical care through mHealth Apps [88]. Willingness to recommend refers to the behavior of existing users to recommend mHealth Apps to new users. Chen and Tsai (2007) proposed a concept of intention to adopt that includes the willingness to recommend as “a customer’s judgment of the likelihood of purchasing the same product or recommending it to others” [89]. Perceived value is considered to be a key factor for users to make behavioral decisions and plays an important role in determining users’ intention to adopt, behavior intention, and satisfaction [90]. Many studies have shown that perceived value has a significant positive effect on user behavioral intention [91,92]. Gan (2017) argued that users’ decision to purchase a product or service was mainly related to the value of the product or service and confirmed that users’ perceived value had a significant effect on users’ purchase intention [93]. Jayashankar et al. (2018) explored the application of IoT in agriculture and found that perceived value had a significant positive influence on IoT adoption [94]. In the context of mHealth services, we predict that perceived value will be positively related to users’ willingness to participate and recommend. Consequently, this paper proposes the following hypotheses:
H8. 
Perceived value has a positive effect on willingness to participate.
H9. 
Perceived value has a positive effect on willingness to recommend.
Trust mainly refers to the users’ belief that the mHealth Apps can serve in the way they expect and their willingness to undertake the risks. It is embodied in the users’ positive evaluation of the mHealth Apps, which is reflected by users’ confidence and preference for mHealth, as well as the tendency towards seeking online medical treatment [95]. In the context of mHealth services, trust can reduce psychological uncertainty for users, and also reflects the users’ acceptance of products or services [96]. In addition, a patient’s trust in online mHealth services has a significant positive effect on the patient’s behavioral intention, which is also a manifestation of willingness toward participation and recommendation, in a sense [97,98,99]. In fact, the particularity of mHealth services makes trust play a very important role in users’ adoption behavior. When users trust mHealth products or services, their perceived risk level will decrease, and their willingness to participate and recommend will become stronger. Considering these reasons, this paper proposes the following hypotheses:
H10. 
Trust has a positive effect on willingness to participate.
H11. 
Trust has a positive effect on willingness to recommend.
Based on the S-O-R theoretical model, this paper explores the relationship between the content presentation, the user’s intrinsic experience, and the user’s intention to adopt mHealth Apps. In this study, different types of content presented by mHealth Apps are regarded as external stimuli. Such external stimuli significantly affect users’ internal emotional cognition (perceived value or trust) of mHealth Apps and can effectively stimulate users to make the decision to adopt mHealth Apps (willingness to participate and willingness to recommend). Based on this, the paper constructs a model of the effect of the content presentation of mHealth Apps on users’ adoption intention, as shown in Figure 3.

3. Methods

3.1. Questionnaire Design

The questionnaire was scored using a Likert 7-level scale, with 1 indicating “Complete disagreement” and 7 indicating “Complete agreement”. In addition, in the research model, the platform information presentation was adapted from the research variable of Negsah et al. (2003) [100], the guidance information presentation from Laugesen et al. (2015) [101], and the relational information presentation from Liu’s (2003) research scale [102]. The perceived value was from Kim et al. (2007) [103], and the trust was from Pavlou and David (2004) [104]. The willingness to participate came from Dodds et al. (1991) [105], and the willingness to recommend came from Sun et al. (2006) [106].

3.2. Sample Selection and Data Collection

First, a questionnaire on “The influence of content presentation of mHealth Apps on users’ adoption intention” was designed. To help the respondents better understand the connotation of mHealth Apps, the beginning of the questionnaire contains elaboration and examples about the connotations of mHealth Apps. At the same time, there is a screening question in the questionnaire: “Have you ever used an mHealth App?” If the answer is “No”, the questionnaire is invalid. After modifying the questionnaires based on the results of the pretest study, 316 questionnaires were collected, of which 230 were valid questionnaires, with an effective rate of 72.8%. We used the snowball sampling method to collect questionnaires randomly. First, the questionnaire was designed and formed a link or QR code on a professional questionnaire website (Questionnaire Star). Then, 30 respondents, known to the authors, including civil servants, medical workers, students, teachers, freelancers, and retirees, were randomly selected and invited to fill in the questionnaire. At the same time, they were asked to send the questionnaire link to their WeChat friends and invite their friends and relatives to fill in the questionnaire (Supplementary Materials).
In this study, statistical software SPSS25.0 and AMOS24.0 were used as analytical tools to test the reliability and validity of the sample data, while the Structural Equation Model (SEM) was used for hypothesis testing. SEM can accurately perform parameter estimation to test whether the hypothesis is valid, and there are two main calculation methods: Maximum Likelihood Estimation Method (MLE) and Partial Least Square (PLS). Given that PLS can maximize the prediction of the weights and factor loadings of the hypothesized relationships in the model, we used the PLS method for hypothesis testing in the SEM.

4. Empirical Analysis

Referring to the previous literature, this paper analyzes the data in two steps [107]. The first step is to conduct an exploratory factor analysis and confirmatory factor analysis on the measurement model to examine the reliability and validity of the measuring scale, and the second step is to analyze the structural model and test the model hypothesis.

4.1. Reliability and Validity Test

Before conducting the exploratory factor analysis, the KMO value should be calculated and the Bartlett sphere test should be carried out. The KMO value of the data in this paper is 0.875, and the Bartlett test value is significant at the level of 0.001, indicating that the data is suitable for exploratory factor analysis. Through the empirical test, the KMO value of this data is 0.875, and the Bartlett test value is significant at the level of 0.001, which indicates that the data is suitable for exploratory factor analysis. As shown in Table 1, seven factors are extracted from the sample data according to the standard that the characteristic value is greater than 0.9, and the sample data explain 71.035% variance.
From the demographical data of the collected questionnaires, it is found that most of the mHealth App users are young and well-educated, with 156 under the age of 40, accounting for 67.8%, and 162 with a bachelor’s degree or above, accounting for 70.4%, which shows that users with higher education have a friendly attitude towards mHealth Apps and are more likely to seek online treatment. This also indicates that the participants have the ability to effectively fill in the questionnaire, which ensures the effectiveness of data collection to a certain extent.
The reliability and validity of the observed variables were tested. Table 2 shows the standardized factor loading coefficient, Cronbach’s alpha coefficient, CR value, and AVE value of each variable. Using AMOS 24.0 to conduct a confirmatory factor analysis on 24 items, it can be seen from Table 1 that the standardized factor loadings reached the significance level of 0.05. Except for the factor loading coefficient of the intention to participate (0.59), the factor loadings of other variables were all above 0.6, satisfying the validity requirements. In Table 1, Cronbach’s alpha coefficient was 0.687~0.803. Except for the combined reliability of the intention to participate (0.688), the CR values of other variables were all greater than 0.700, which met the internal consistency requirements of each factor, indicating that the questionnaire had good reliability.
Table 3 presents the correlation coefficient between variables as well as the square root of the AVE value (the number marked in black on the diagonal). The results show that the relationship between variables was significantly positive (r = 0.293~0.553, p < 0.05), and the square root of each variable AVE was between 0.652 and 0.875, all higher than 0.600, indicating that the discriminant validity of the variables was good.
In order to test the fit degree between the empirical data and the research model, the fit index of the model is given in Table 4. The results show that the model fit index was: x2/df = 1.763, GFI = 0.874, AGFI = 0.841, CFI = 0.932, IFI = 0.933, RMSEA = 0.058. It can be seen that the important fit indicators are all within the acceptable recommended value range, indicating that the model fit is good.

4.2. Hypothesis Test

After testing the reliability and validity of the scale, AMOS 24.0 was used to test the structural equation model to verify the relationship between content presentation, intrinsic experience, and adoption intention.
It can be seen from Table 5 that three of the eleven hypothetical paths in the structural equation model fail the significance test, that is, the guidance information presentation, the relational information presentation, and perceived value have no significant positive effect on trust. The platform information presentation has a significant positive effect on both perceived value and trust (β = 0.49, t = 4.387; β = 0.48, t = 3.481), reaching a significance level of 0.001. Hypotheses 1 and 2 are verified. Guidance information presentation has a significant positive effect on perceived value (β = 0.20, t = 1.987) but not on trust (β = 0.10, t = 0.945). Hypothesis 3 is supported, but hypothesis 4 fails. At the same time, the relational information presentation also has a significant positive effect on perceived value (β = 0.19, t = 2.559), but has no significant effect on trust (β = 0.13, t = 1.699). Hypothesis 5 passes the verification, but Hypothesis 6 fails. Perceived value and trust have a significant positive effect on the intention to adopt and recommend, respectively, with the influence coefficients of 0.59 and 0.21 and 0.23 and 0.52, respectively, all reaching a significance level of 0.01. Hypothesis 8, 9, 10, and 11 are supported. Furthermore, perceived value has no significant positive effect on trust (β = 0.08, t = 0.654), and Hypothesis 7 is not supported.
As shown in Figure 4, content presentation has a significant positive effect on perceived value, among which the platform information presentation has the greatest influence on perceived value (path coefficient is 0.49), followed by the guidance information presentation (path coefficient is 0.20) and relational information presentation (path coefficient is 0.19), which indicates that content presentation can deepen users’ understanding of mHealth Apps, reduce users’ perceived risks, improve users’ perceived value and trust in Apps, and ultimately facilitate users’ intention to participate and recommend. At the same time, among the dimensions of content presentation, only the platform information presentation has a significant positive effect on trust (the path coefficient is 0.48), while the guidance information presentation and the relational information presentation have no significant effect on trust. This shows that the platform information presentation is the basis for users to generate trust in the process of experiencing the mHealth Apps. The more effective the platform information presentation, the higher the user’s trust in the platform. This paper also finds that perceived value has no significant effect on trust. The reason may be that mHealth Apps are special products, and the medical treatment is carried out in a virtual network environment. Even if users do have a certain value awareness of apps, it is still difficult for them to generate trust in them. In addition, platform information, guidance information, and relational information have a significant positive effect on the intention to participate and recommend through the mediating role of perceived value and trust. Among them, perceived value has a greater effect on participation intention (path coefficient 0.59), while trust has a greater effect on recommendation intention (path coefficient is 0.52). We calculated R2 (Squared Multiple Correlations) values, which are 58%, 38%, 54%, and 42%, indicating that the explanatory degree of the variables and models is good.

4.3. Mediating Effect Test

According to Baron and Kenny’s (1986) [108] judgment method of mediating variables, to test whether the mediating effect exists, it must first judge the significance of the path relationship between the independent variable and the outcome variable. Then, test whether the independent variable and the outcome variable are significantly related. On the premise that the independent variable and the outcome variable are significantly related when the direct effect and the indirect effect exist at the same time, the mediating variable plays a part of a mediating role. If the indirect effect exists, the direct effect does not exist, and the mediating variable plays a complete mediating role.
Accordingly, the intermediary role of perceived value and trust is tested, and the test results are shown in Table 6. Specifically, perceived value plays a partial mediating role between the presentation of platform information content and participation intention. Trust plays a partial mediating role between the presentation of platform information content and recommendation intention. Perceived value plays a partial mediating role between the presentation of guiding content information and participation intention. Perceived value plays a partial mediating role between the presentation of related information content and participation intention.

5. Discussion

Based on the S-O-R theoretical model, according to the different types of content presentation, this paper divides the content presentation of the mHealth Apps into platform information presentation, guidance information presentation, and relational information presentation. The relationship between content presentation, users’ intrinsic experience (perceived value and trust), and adoption intention of adoption was investigated, leading to the following main conclusions.
First, in the context of mHealth Apps, the platform information presentation, the guidance information presentation, and the relational information presentation are important factors that affect users’ perceived value. Among them, the platform information presentation has the greatest effect on perceived value. On the one hand, it shows that the content presentation plays an important role in users’ perceived value and users’ intention to adopt. On the other hand, it shows that users’ perceived value mainly comes from the platform information presentation. Other studies have also shown that users are more likely to persistently use the apps and form positive perceived value toward mHealth Apps when medical information or services are highly related [109,110].
Second, the effect of perceived value on trust is insignificant. mHealth Apps are very different from traditional medical channels and other types of apps. They have the unique features of high professionalism, high risk of use, and high privacy of personal health information. On mHealth Apps, medical resources need to be certified, because they are directly related to the user’s life, health, and property safety. In addition, online medical treatment is carried out in a virtual network environment, and users do not have face-to-face contact with doctors in the hospital, so it is difficult for them to trust the apps just based on the perceived value of the information provided. Therefore, users’ trust in mHealth Apps often occurs after a successful medical treatment experience, rather than the perceived value of information before medical treatment.
Third, guidance information and relational information have no significant effect on trust, and only the platform information presentation has a significant positive effect on trust. The more effectively the platform information is presented, the stronger users have trust in the platform, and the stronger users have the intention to obtain medical services through the mHealth Apps. However, guidance information and relational information only help increase users’ understanding of products and services but do not result in an increase in the users’ trust toward the mHealth Apps [111]. They often have the attribute of “strategy” in the user’s subconsciousness and are often regarded as a marketing method for merchants, so users will not have a sense of trust in the apps. At the same time, although trust has a significant positive effect on both users’ intention to participate and recommend, it has a greater effect on the willingness to recommend. This aligns with previous findings by Sullivan and Kim (2018) [112] and Alshurideh et al. (2019) [113]. This shows that once the user has a sense of trust in the mHealth Apps, the user will not only adopt apps by himself but will also be more motivated to recommend them to relatives and friends.
Finally, the presentation of information content will significantly affect users’ adoption intention through the intermediary of perceived value and trust. It can be seen that perceived value and trust play a very important role in the willingness of users to adopt mobile medical apps. Compared with users searching for medical information through offline hospitals, the presentation of mobile medical app information is conducted in a virtual network environment, with uneven information quality, and users cannot truly feel and judge the reliability and effectiveness of medical information. As medical information is different from other types of product information, false or exaggerated medical information will damage users’ property and endanger users’ life and health. Under such circumstances, users will have a cautious or skeptical attitude towards medical information, which further highlights the importance of users’ value perception and trust in the information contained in the adoption intention of mobile medical apps. This will significantly affect users’ participation intention and recommendation intention.

5.1. Theoretical Contributions

Compared with the existing studies, this paper makes the following main theoretical contributions: First, based on the S-O-R framework, it conducts an in-depth discussion and empirical test on the relationship between the content presentation and users’ intention to adopt mHealth Apps, and proposes and confirms the effect of platform information presentation, guidance information presentation, and relational information presentation of mHealth Apps on users’ adoption intention, comprehensively predicting and explaining users’ adoption behavior toward mHealth services. Second, by introducing two mediating variables, perceived value and trust, into the framework, a theoretical model of the effect of the content presentation of mHealth Apps on users’ adoption intention is creatively constructed, which is conducive to revealing the effect mechanism of content presentation on adoption intention. Third, while most of the existing studies focused on many factors such as the external characteristics, system design, technical environment, and usage context of mobile health care [32,114], this paper deeply analyzes the influence of content presentation of mHealth Apps on the intention to participate and recommend, broadening the research scope of mHealth users’ adoption behavior.

5.2. Management Suggestions

First of all, the management of platform information presentation in mHealth Apps should be strengthened. Among the three dimensions of content presentation, only the platform information presentation can significantly affect users’ perceived value and trust at the same time. Therefore, managers need to enhance the content presentation of mHealth Apps, especially the platform information presentation. On the one hand, the mHealth Apps not only need to present basic information, such as platform development history, technical strengths, medical services, and medical resource certification, so that users can witness the development of the medical platform in real-time and can also inquire about relevant drug or medical service information. On the other hand, managers should also display symbolic information such as the cultural connotation, brand story, and social responsibility of the medical platform. All in all, the information presented by the platform must not only be authentic and reliable but also demonstrate the strengths and responsibility of the platform so as to facilitate users’ perceived value and trust in the platform.
Second, managers should enrich the guidance information presentation and relational information presentation of mHealth Apps. The findings of this study show that the guidance information presentation and the relational information presentation significantly affect users’ perceived value. In terms of guidance information presentation, users’ perceived value of mHealth Apps can be comprehensively enhanced through marketing campaigns, lottery activities, promotional activities, personal reviews, and product promotion, triggering users’ intention to participate in and recommend mHealth Apps. Regarding the relational information presentation, thanksgiving gifts, holiday greetings, caring services, and online interaction can effectively enhance users’ attachment to the use of mHealth Apps, strengthen users’ use of the various functions of the platform, and promote users’ understanding and recognition of mHealth Apps.
Finally, managers should focus on improving users’ perceived value and trust in mHealth Apps. The user’s intrinsic experience (perceived value and trust) helps stimulate users’ adoption intention. Specifically, users with high perceived value are more willing to adopt mHealth Apps, and users with high trust are more willing to recommend the apps. Therefore, managers should regularly evaluate users’ perceived value and trust and strengthen the positive effect of users’ internal experience (perceived value and trust) on users’ intention of adoption. In addition, the managers should strengthen the effective management of the content presentation by clarifying the factors that affect the user’s perceived value and trust and identify the weight of each factor in the content presentation, ultimately triggering users’ intention to participate and recommend mHealth Apps.

5.3. Limitations and Future Research

This study also has some limitations. First, only the adoption intentions (intention to participate and recommend) were studied, but adoption intention is not equal to adoption behavior [115]. In the future, new variables will be further introduced to explore the user’s adoption behavior and continuous use. Secondly, the respondents of the questionnaire survey were mainly the young and middle-aged groups of 18–40 years old, which cannot fully reflect users’ behaviors in different age groups. In future research, the participants of the questionnaire survey should be expanded, and a stratified sampling survey should be conducted in different age groups. The method of “scenario experiment + questionnaire random sampling survey” can be further combined to investigate the effect of the content presentation of mHealth Apps on the adoption intention of different user groups. Finally, information presentation includes two dimensions: content and format. This study mainly considers the presentation of content in mHealth Apps, but how the way that the content is presented affects users’ adoption behavior needs to be discussed in future research.

6. Conclusions

This study creates a comprehensive model to explain the influence of content presentation of mHealth Apps on users’ intention to adopt and recommend in terms of cognitive behavior. In this novel model, the content presentation of mHealth Apps is divided into platform information presentation, guidance information presentation, and relational information presentation. The findings show that the improved S-O-R model can explain the mechanism of the content presentation of mHealth Apps on users’ adoption intention. Results also show that platform information, guidance information, and relational information indirectly have a significant positive effect on the intention to participate and recommend through the mediating effect of perceived value or trust. However, the effect of perceived value on trust is insignificant. This work could provide valuable information and suggestions to boost users’ intentions to adopt mHealth Apps.

Supplementary Materials

The following supporting information can be downloaded at: https://www.mdpi.com/article/10.3390/su14169900/s1, Table S1: Questionnaire on the influence of content presentation on users’ intention to adopt mHealth Apps.

Author Contributions

Y.L. contributed to the study design and wrote the manuscript drafts. C.L. supervised the study and provided suggestions for the revision of the manuscript drafts. X.L. contributed to the analysis of the data. G.Z. provided some reviews on the manuscript. All authors contributed to the article and approved the submitted version. All authors have read and agreed to the published version of the manuscript.

Funding

This research was supported by the research on risk cognition and management strategy of the acquirement of online healthcare information under Grant Number 71673245.

Acknowledgments

The authors wish to appreciate Qian Chen for her guidance during the English writing of the manuscript.

Conflicts of Interest

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest. Copyright © 2022 Liu, Lu, Zhao, Li. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution, or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution, or reproduction is permitted that does not comply with these terms.

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Sustainability 14 09900 g001 550
Figure 1. Technology acceptance-based models and their constructs.
Figure 1. Technology acceptance-based models and their constructs.
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Figure 2. Classification of content presentation.
Figure 2. Classification of content presentation.
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Figure 3. Research Model.
Figure 3. Research Model.
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Figure 4. Structural equation test results (standardized path coefficient model). Note: The dashed line indicates that the path relationship does not pass the hypothesis test and the bold number is the explained variance of each factor.
Figure 4. Structural equation test results (standardized path coefficient model). Note: The dashed line indicates that the path relationship does not pass the hypothesis test and the bold number is the explained variance of each factor.
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Table
Table 1. Factor load matrix.
Table 1. Factor load matrix.
FactorRelational Information PresentationTrustWillingness to RecommendPerceived ValueGuidance Information PresentationPlatform Information PresentationWillingness to Participate
GIP10.135−0.0330.2310.561 0.552 0.002−0.056
GIP20.1030.0450.2110.537 0.486 0.092−0.105
GIP30.1420.1880.1060.119 0.758 0.1190.052
GIP40.1210.1800.054−0.018 0.792 0.1620.119
WTR10.1020.235 0.838 0.1090.1290.2040.147
WTR20.1330.225 0.807 0.0720.1900.2260.129
WTR30.0740.294 0.808 0.1520.0490.1370.102
T10.196 0.749 0.1960.1160.0770.0420.052
T20.078 0.749 0.0660.2110.1290.1270.104
T30.150 0.745 0.2930.1830.1190.1250.032
T40.015 0.754 0.1930.0000.1170.1390.030
RIP1 0.840 0.1540.1280.0700.1740.1250.104
RIP2 0.829 0.1910.1270.0790.1390.1120.107
RIP3 0.835 0.0820.0460.1280.0710.0310.140
RIP4 0.753 0.0020.0140.2010.0370.076−0.077
PV10.1740.2250.129 0.742 0.0500.1290.229
PV20.2240.2560.011 0.691 0.0560.1780.274
PV30.1570.1650.077 0.655 0.0210.4040.236
WTP1−0.0310.0550.0290.1830.0890.207 0.805
WTP20.1760.0410.2260.1210.023−0.057 0.814
WTP30.3410.3490.2250.194−0.0090.191 0.449
PIP10.0770.2370.3230.2070.185 0.562 0.196
PIP20.2590.1870.2310.2290.011 0.682 0.038
PIP30.0610.0950.1540.1200.225 0.803 0.063
Table
Table 2. Item loadings, AVE, composite reliabilities, and alpha.
Table 2. Item loadings, AVE, composite reliabilities, and alpha.
VariableItemLoadingCronbach’s αCRAVE
Platform Information Presentation30.74
0.71
0.64		0.6870.7400.487
Guidance Information Presentation40.69
0.66
0.65
0.61		0.7120.7480.427
Relational Information Presentation40.90
0.90
0.77
0.61		0.7950.8770.646
Perceived Value30.80
0.81
0.76		0.7690.8330.625
Trust40.72
0.71
0.84
0.68		0.7980.8280.548
Willingness to Participate30.59
0.64
0.72		0.8030.6880.425
Willingness to Recommend30.94
0.87
0.81		0.7190.9070.766
Table
Table 3. Correlations for latent variables and the square root of AVE.
Table 3. Correlations for latent variables and the square root of AVE.
Variable1234567
1. Platform Information Presentation 0.698
2. Guidance Information Presentation 0.463 0.653
3. Relational Information Presentation 0.3600.362 0.804
4. Perceived Value 0.5340.4570.412 0.791
5. Trust 0.4730.3840.3300.441 0.740
6. Willingness to Participate0.4410.2680.3640.5300.386 0.652
7. Willingness to Recommend0.5530.4090.2930.3930.5400.432 0.875
Table
Table 4. Recommended and actual values of the model fit index.
Table 4. Recommended and actual values of the model fit index.
Fit Indexx2/dfGFIAGFICFIIFIRMSEA
Recommended Value<2>0.90>0.80>0.90>0.90<0.08
Actual value1.7630.8740.8410.9320.9330.058
Table
Table 5. Structural equation model test results.
Table 5. Structural equation model test results.
HypothesisStandardized Path CoefficientStandard ErrorT ValueConclusion
H1: Platform Information
Presentation→Perceived Value 		0.49 ***0.1464.387Support
H2: Platform Information Presentation→Trust 0.48 ***0.1593.481Support
H3: Guidance Information Presentation→Perceived Value 0.20 *0.1041.987Support
H4: Guidance Information
Presentation→Trust 		0.100.0960.945Fail
H5: Relational Information
Presentation→Perceived Value 		0.19 *0.0612.559Support
H6: Relational Information
Presentation→Trust 		0.130.0571.699Fail
H7: Perceived Value→Trust 0.080.1010.654Fail
H8: Perceived Value→Willingness to
Participate 		0.59 ***0.0755.377Support
H9: Perceived Value→Willingness to
Recommend 		0.21 **0.0762.763Support
H10: Trust→Willingness to Participate 0.23 **0.0712.512Support
H11: Trust→Willingness to Recommend 0.52 ***0.0976.065Support
Note: * p < 0.05, ** p < 0.01, *** p < 0.001.
Table
Table 6. Mediating variables effects test results.
Table 6. Mediating variables effects test results.
IVMVDVIV→DVIV + MV→DVTest Results
IV→MVIV→DVMV→DV
Platform Information PresentationPerceived ValueWillingness to Participate0.441 **0.595 ***0.314 *0.276 ***Partial mediating
Platform Information PresentationPerceived ValueWillingness to Recommend0.553 **0.595 ***0.695 ***0.104Not significant
Platform Information PresentationTrustWillingness to Participate0.441 **0.489**0.314 *0.095Not significant
Platform Information PresentationTrustWillingness to Recommend0.553 **0.489 **0.695 ***0.345 ***Partial mediating
Guidance Information PresentationPerceived ValueWillingness to Participate0.268 **0.245 *0.171 *0.276 ***Partial mediating
Guidance Information PresentationPerceived ValueWillingness to Recommend0.409 **0.245 *0.0630.104Not significant
Guidance Information PresentationTrustWillingness to Participate0.268 **0.0770.171 *0.095Not significant
Guidance Information PresentationTrustWillingness to Recommend0.409 **0.0770.0630.345 ***Not significant
Relational Information PresentationPerceived ValueWillingness to Participate0.364 **0.148 *0.099 *0.276 ***Partial mediating
Relational Information PresentationPerceived ValueWillingness to Recommend0.293 **0.148 *0.0130.104Not significant
Relational Information PresentationTrustWillingness to Participate0.364 **0.0910.099 *0.095Not significant
Relational Information PresentationTrustWillingness to Recommend0.293 **0.0910.0130.345 ***Not significant
Note: IV stands for independent variable, MV stands for mediating variable, DV stands for depedent variable; *** p < 0.001, ** p < 0.01, * p < 0.05.
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Liu, Y.; Lu, X.; Li, C.; Zhao, G. The Influence of Content Presentation on Users’ Intention to Adopt mHealth Applications: Based on the S-O-R Theoretical Model. Sustainability 2022, 14, 9900. https://doi.org/10.3390/su14169900

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Liu Y, Lu X, Li C, Zhao G. The Influence of Content Presentation on Users’ Intention to Adopt mHealth Applications: Based on the S-O-R Theoretical Model. Sustainability. 2022; 14(16):9900. https://doi.org/10.3390/su14169900

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Liu, Yizhi, Xuan Lu, Chengjiang Li, and Gang Zhao. 2022. "The Influence of Content Presentation on Users’ Intention to Adopt mHealth Applications: Based on the S-O-R Theoretical Model" Sustainability 14, no. 16: 9900. https://doi.org/10.3390/su14169900

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