As the biggest archipelago, Indonesia has more than 13,000 islands, resulting in the unequal distribution of medical professionals on each island. World Health Organization recommends one doctor per 1000 citizens, while Indonesia has a ratio of 1 doctor per 3333 citizens. This results in Indonesia having the lowest doctors’ ratio to Southeast Asia [
There are other challenges in accessing healthcare for Indonesians, such as the medical cost. The Center of National Statistics stated that in March 2019, 9.41% of Indonesians are in poverty, and families who are in poverty have 4.68 people in each house on average. Looking at the average spending of a poverty-stricken family, they spend Rp 425.250 per capita per month or equals roughly $26 per capita per month [
Other costs that plague healthcare issues in Indonesia include unaffordable hospital charges and medicine. As of 2005, the estimated cost per day for a primary hospital is around $30.36, while the estimated cost per outpatient visit is $9.25 for a primary hospital. These costs do not include drugs and diagnostic testings [
The rapid growth of changing technologies, especially in the digital innovation sector, has brought massive medical practice changes. Information and communication technologies (ICTs) have been implemented to assist, deliver healthcare services, or serve as a make-shift alternative for consultations during the COVID-19 pandemic [
Mobile health applications are defined as tools that assist in medicine and public health via mobile devices. Mobile communication devices, such as cellphones, tablet computers, personal digital assistants (PDAs), and wearable devices, such as smart watches, are widely used for health care, information, and data collection [
Intention to use a specific technology influences technology’s actual use [
Summary of studies with mHealth adoption models.
Author (year) | Theory | Dependent variable | Findings |
---|---|---|---|
Diño and de Guzman [ | UTAUT and HBM | Behavioural intention for telehealth use | UTAUT constructs (especially EE) are significant influences, while gender shows no moderating effect. |
Deng et al. [ | Extended TAM, trust and perceived risk | Adoption of mHealth services | Trust, PU, and PEOU positively correlate with adoption, while privacy and performance risks negatively correlate with trust and intention to adopt. |
Meng et al. [ | Trust transfer model | mHealth service use intention | Trust in mHealth services and trust in offline health services affect intention to use positively. |
Gong et al. [ | Extended valence and trust | Adoption of OHCS | Subjective norms, trust in providers, and perceived benefit have a positive effect, while offline habits negatively affect. |
Zhang et al. [ | UTAUT | Intention to use diabetes management applications | PE and social influence are the most important determinants. |
Ramírez-Correa et al. [ | TPB and TAM | Adoption of telemedicine during COVID-19 pandemic | TPB provides a significant explanatory power. |
TAM: technology acceptance model; OHCS: online health consultation service; UTAUT: unified theory of acceptance and use of technology; TPB: the theory of planned behaviour; HBM: health belief model; EE: effort expectancy; PU: perceived usefulness; PEOU: perceived ease of use.
The reason for conducting this study is a mixture of all of the reasons above. Poor healthcare access, coupled with increasing NCDs, might be alleviated by the use of ICTs. In the COVID-19 era, the utilization of ICTs is increasing, and an ever-increasing number of smartphone users helps this. However, despite all of these, the adoption of mHealth applications is still shallow. Although the government and Indonesian healthcare organizations are revamping their telemedicine policies, including mHealth applications due to the COVID-19 pandemic, this effort is still in the very early phase, with minimal progress desired. Therefore, this study is aimed at finding out why the adoption of mHealth is still low and gives suggestions to managers and mHealth developers on how to increase the use of mHealth.
A specific theoretical model needs to be developed and tested for different mHealth technologies in different user groups and settings to provide a better context-related understanding of technology adoption [
Venkatesh et al. [
However, UTAUT focuses more on employee technology acceptance at the individual level and might not be suitable for mHealth applications. The model is then extended to study acceptance and use of technology in a consumer-based context [
Rogers developed this theory in 1995. It became one of the popular theories to explain the adoption of information technology (IT) and better understand how IT innovation spreads with indicators that are more accurate in indicating consumer behavior within and between communities [
Beuker and Abbing [
Jarvenpaa et al. [
In the mHealth context, initial trust consists of two main components: initial trust in the doctor and initial trust in mHealth platform. Doctors are the primary care provider, while mHealth platform is the media where online healthcare services are implemented. Initial trust in doctor is associated with the doctor’s information quality and interaction quality, while initial trust in mHealth platform is associated with its services. Cao et al. [
Increased penetration of the internet has changed the way people search for their information, especially health-related issues. One survey done in Indonesia shows that mHealth users utilize their application to search for health-related information (51.06%), while only 14.05% use it to consult with health professionals [
As transactions occur digitally through mHealth, information security concerns the consumers’ perceptions about the platform’s inability to safeguard financial or other important data about the consumers [
Raymond A. Bauer was the first to introduce the concept of privacy risk. He believed that every single action that one takes might cause unwanted consequences. The undesirable or unexpected aspect of this variable is that the individual cannot control this risk and harm the individual, which is called the risk due to an individual’s actions [
Intention may be described as how hard people are willing to try and how much determination they want to behave. As a result, intention to adopt refers to a person’s subjective likelihood of engaging in a particular action [
Meanwhile, in the literature on mHealth applications, the intention to recommend variables has never been studied [
We chose DOI, UTAUT2, and internet customer trust model as the main framework as DOI and UTAUT2 have been extensively studied in the adoption and recommendation of information technology, including mHealth applications [
Innovativeness is defined as one’s willingness to try every new technology [ Innovativeness positively influences the intention to adopt mHealth applications Users with a higher level of innovativeness have a higher level of compatibility Users with a higher level of innovativeness have a higher level of performance expectancy Users with a higher level of innovativeness have a higher level of effort expectancy.
Compatibility measures the extent to which an innovation is deemed in line with the value of the current consumer lifestyle and current and past experiences [ Compatibility positively influences the intention to adopt mHealth applications Users with a higher level of compatibility have a higher level of performance expectancy Users with a higher level of compatibility have a higher level of effort expectancy.
Performance expectancy refers to the degree of benefits obtained by the user in adopting new technology [ Performance expectancy positively influences the intention to adopt mHealth applications.
Effort expectancy refers to the degree of convenience associated with users’ technology [ Effort expectancy positively influences performance expectancy Effort expectancy positively influences the intention to adopt mHealth applications.
Dodds et al. [ Price value positively influences the intention to adopt mHealth applications.
In this study, facilitating condition is defined as the extent to which patients or users perceive that there is an adequate technical infrastructure to support the use of network-based healthcare application services and resources that offer the necessary knowledge using network-based healthcare application services [ Facilitating conditions positively influence the intention to adopt mHealth applications.
Information-seeking motive is defined as the purposive search for information due to the need to fulfill specific goals [ Information-seeking motive positively influences the intention to adopt mHealth applications.
Firm-generated content is any form of content (written, audio, visual, and combined) created by marketers on social media channels [ Firm-generated content positively influences the intention to adopt mHealth applications.
In the mHealth context, privacy risk refers to the possibility of information abuse due to mHealth services, such as information theft and leakage [ Perceived privacy risk negatively influences the intention to adopt mHealth applications.
Perceived security is defined as the level of trust in the internet to transmit sensitive information [ Perceived security positively influences the intention to adopt mHealth applications.
Initial trust is a vital acceptance influence factor. Previous researches have examined initial trust mechanisms to reduce uncertainty in technology acceptance and the context of use [ Initial trust in doctor positively influences the intention to adopt mHealth applications Initial trust in mHealth platform positively influences the intention to adopt mHealth applications.
Consumers with a greater intention to adopt new technology are more likely to become users and recommend the technology to others [ Intention to adopt positively influences the intention to recommend mHealth applications.
The 19 research hypotheses are summarized in the research model (Figure
The research model. DOI: diffusion of innovation; UTAUT2: an extended unified theory of acceptance and use of technology; SMBCT: social media brand communication theory.
All survey items were adopted from studies regarding health information technology except for firm-generated content with some minor changes. Perceived security in this study refers to the transaction process in the mHealth application. The questionnaire items (Table
Questionnaire items of each construct.
Constructs | Items | Source |
---|---|---|
Innovativeness | I1: If I heard about new information technology, I would look for ways to experiment with it. | [ |
I2: Among my peers, I am usually the first to try out new information technologies. | ||
I3: In general, I am hesitant to try out new information technologies. | ||
I4: I like to experiment with new information technologies. | ||
Compatibility | C1: Using mHealth application is compatible with all aspects of my lifestyle. | [ |
C2: Using mHealth application is completely compatible with my current situation. | ||
C3: I think that using mHealth application fits well with the way I like to manage my health | ||
Performance expectancy | PE1: mHealth application is useful to support critical aspects of my healthcare. | [ |
PE2: mHealth application will enhance my effectiveness in managing my healthcare. | ||
PE3: Using mHealth application will improve my productivity. | ||
PE4: Overall, mHealth application will be useful in managing my healthcare. | ||
Effort expectancy | EE1: Learning how to use mHealth application is easy for me. | [ |
EE2: My interaction with mHealth application is clear and understandable. | ||
EE3: I find mHealth application easy to use. | ||
EE4: It is easy for me to become skillful at using mHealth application. | ||
Firm generated content | FGC1: I am satisfied with the company’s social media communications for mHealth applications. | [ |
FGC2: The level of the company’s social media communications for mHealth applications meets my expectations. | ||
FGC3: The company’s social media communications for mHealth applications are very attractive. | ||
FGC4: This company’s social media communications for mHealth applications perform well when compared with social media communications of other companies. | ||
Price value | PV1: mHealth application is reasonably priced. | [ |
PV2: mHealth application is a good value for the money. | ||
PV3: At the current price, mHealth application provides a good value. | ||
Facilitating conditions | FC1: I have the resources necessary to use mHealth application. | [ |
FC2: I have the knowledge necessary to use mHealth application. | ||
FC3: mHealth application is compatible with other technologies I use. | ||
Information seeking motive | ISM1: I have a high intention to seek health information through mHealth application. | [ |
ISM2: I will seek health information through mHealth application in the near future. | ||
ISM3: I will recommend others to seek health information through mHealth application. | ||
Perceived privacy risk | PPR1: It would be risky to disclose my personal health information to mHealth application. | [ |
PPR2: There would be a high potential for loss associated with disclosing my personal health information to mHealth application. | ||
PPR3: There would be too much uncertainty associated with giving my personal health information to mHealth application. | ||
Perceived security | PS1: I would feel secure sending sensitive information across mobile payment for mHealth application. | [ |
PS2: Mobile payment via mHealth application is a secure means through which to send sensitive information. | ||
PS3: I would feel safe providing sensitive information about myself over mHealth application via mobile payment. | ||
Initial trust in doctor | ITD1: I believe the doctors in the mHealth application have medical qualifications. | [ |
ITD2: The consultation or diagnosis provided by the doctors in mHealth application is reliable. | ||
ITD3: In my opinion, the doctors in the mHealth application are trustworthy. | ||
Initial trust in mHealth platform | ITE1: This mHealth application can fulfill its tasks. | [ |
ITE2: This mHealth application will keep its promises. | ||
ITE3: This mHealth application will keep the customers’ best interests in mind. | ||
Intention to adopt | IA1: I intend to use mHealth application to consult health issues when needed in the future. | [ |
IA2: I predict that I will use mHealth application to consult health issues when needed in the future. | ||
IA3: I plan to use mHealth application to consult health issues when needed in the future. | ||
Intention to recommend | IR1: I would recommend this mHealth application to others. | ([ |
IR2: I will definitely tell others that this mHealth application is good. | ||
IR3: I am willing to tell others about the good aspects of the mHealth application. | ||
IR4: I will tell my friends and family about my good experiences using mHealth application. |
This study uses a cross-sectional study design. Our inclusion criteria include adult users’ target population (above 18 years old) who had used mHealth applications at least once in the past year. This study’s exclusion criteria are users who access the application via other gadgets (laptops or tablets). Due to the COVID-19 pandemic, data were collected using Google Forms from September 31 to October 15, 2020. We sent the survey link to healthcare providers, friends, and colleagues, who then shared the survey link through their contacts network (snowballing technique). At the beginning of the survey, we described the purpose of the questionnaire and explained mHealth applications’ definition, and informed consents were then obtained. After that, the respondents will fill up three questionnaires that serve as the determinant for them to be included or excluded in our studies. The questions concern their age, gadgets used to access the application, and previous use of mHealth applications. If respondents are below 18 years old, access the applications via any other gadgets except mobile phones, or had never used mHealth applications before, they are excluded from the study. The questionnaires are self-filled, and one email address could only complete the form once.
There are only two Indonesian mHealth applications used in this study, and those are Halodoc© and Alodokter©. These two applications are used for the following reasons: (1) They are the two most prominent mHealth applications in Indonesia currently, and hence, comparisons can be made [
The ethics committee approved the study of the Faculty of Medicine of the University of Pelita Harapan with an ethical clearance number of 154/K-LKJ/ETIK/VIII/2020.
Figure
Sampling procedure and results.
Demographic data of the respondents (
Demographic data | Frequency (%) |
---|---|
Sex | |
Male | 232 (29) |
Female | 555 (71) |
Age (years) | |
18-25 | 498 (63.3) |
26-35 | 169 (21.5) |
36-45 | 85 (10.8) |
56-65 | 32 (4.1) |
>65 | 3 (0.4) |
Education level | |
Diploma | 547 (70) |
Bachelor degree | 151 (19) |
Master’s degree | 69 (9) |
Doctoral degree | 19 (2) |
Last mHealth apps usage | |
<1 month ago | 386 (49) |
1-3 months ago | 266 (34) |
3-6 months ago | 87 (11) |
6-12 months ago | 48 (6) |
Monthly household spending | |
<Rp 3,000,000 (~$214) | 321 (41) |
Rp 3,000,000–Rp 6,000,000 (~$427) | 297 (38) |
Rp 6,000,000–Rp 10,000,000 (~$712) | 109 (14) |
>Rp 10,000,000 | 60 (7) |
Private insurance | |
Yes | 427 (54) |
No | 360 (46) |
mHealth application used | |
Halodoc© | 494 (63) |
Alodokter© | 293 (37) |
Increased mHealth apps use due to COVID-19 | |
Yes | 403 (51) |
No | 384 (49) |
Descriptive results of each item in every variable studied.
Indicator | Mean | Standard deviation |
---|---|---|
I1 | 3.849 | 0.908 |
I2 | 3.216 | 1.179 |
I4 | 3.67 | 0.959 |
C1 | 3.670 | 0.959 |
C2 | 4.022 | 0.889 |
C3 | 3.602 | 0.998 |
PE1 | 4.018 | 0.866 |
PE2 | 4.255 | 0.798 |
PE3 | 4.197 | 0.823 |
PE4 | 3.995 | 0.943 |
EE1 | 4.028 | 0.857 |
EE2 | 4.202 | 0.788 |
EE3 | 4.278 | 0.727 |
EE4 | 4.278 | 0.717 |
FGC1 | 3.784 | 0.910 |
FGC2 | 3.726 | 0.878 |
FGC3 | 3.813 | 0.874 |
FGC4 | 3.694 | 0.874 |
PV1 | 3.841 | 0.840 |
PV2 | 3.831 | 0.797 |
PV3 | 3.879 | 0.797 |
FC1 | 4.304 | 0.715 |
FC2 | 4.337 | 0.706 |
FC3 | 4.400 | 0.679 |
ISM1 | 4.149 | 0.958 |
ISM2 | 4.001 | 1.008 |
ISM3 | 3.893 | 1.048 |
PPR1 | 3.623 | 1.051 |
PPR2 | 3.618 | 1.074 |
PPR3 | 3.524 | 1.114 |
PS1 | 3.416 | 1.083 |
PS2 | 3.257 | 1.081 |
PS3 | 3.586 | 0.925 |
ITD1 | 4.094 | 0.820 |
ITD2 | 3.846 | 0.896 |
ITD3 | 3.983 | 0.834 |
ITE1 | 4.020 | 0.758 |
ITE2 | 4.004 | 0.780 |
ITE3 | 4.001 | 0.791 |
IA1 | 4.166 | 0.733 |
IA2 | 4.136 | 0.753 |
IA3 | 4.133 | 0.774 |
IR1 | 3.961 | 0.851 |
IR2 | 3.980 | 0.854 |
IR3 | 4.079 | 0.806 |
IR4 | 4.051 | 0.808 |
Services used by respondents. Note: Each respondent can select more than one service.
Chief medical complaints about using mHealth applications. Note: Each respondent can select more than one problem.
.
Our model consisted of compatibility and innovativeness (DOI), performance expectancy, effort expectancy, price value, facilitating conditions (UTAUT2), information-seeking motive, firm-generated content, perceived privacy risk, perceived security, initial trust in doctor, initial trust in mHealth platform, and their effects towards intention to adopt and ultimately intention to recommend. A more detailed method of data analysis is provided in Appendices
A bootstrap with 5000 iterations of resampling was done to obtain the maximum consistency possible in the results for structural model path significance [
Formative indicators’ quality criteria.
Construct | Item | VIF | |||
---|---|---|---|---|---|
Innovativeness | I1 | 2.253 | N/A | N/A | 0.345 |
I2 | 1.605 | ||||
I4 | 2.019 | ||||
Compatibility | C1 | 1.637 | 0.417 | 0.416 | 0.286 |
C2 | 1.523 | ||||
C3 | 1.634 | ||||
Performance expectancy | PE1 | 1.791 | 0.486 | 0.484 | 0.345 |
PE2 | 2.312 | ||||
PE3 | 2.669 | ||||
PE4 | 1..977 | ||||
Effort expectancy | EE1 | 1.508 | 0.264 | 0.262 | 0.186 |
EE2 | 2.961 | ||||
EE3 | 3.948 | ||||
EE4 | 3.149 | ||||
Firm generated content | FGC1 | 2.689 | N/A | N/A | N/A |
FGC2 | 3.397 | ||||
FGC3 | 3.029 | ||||
FGC4 | 2.386 | ||||
Price value | PV1 | 3.328 | N/A | N/A | N/A |
PV2 | 3.159 | ||||
PV3 | 2.391 | ||||
Facilitating conditions | FC1 | 2.122 | N/A | N/A | N/A |
FC2 | 2.327 | ||||
FC3 | 2.369 | ||||
Information seeking motive | ISM1 | 2.442 | N/A | N/A | N/A |
ISM2 | 2.849 | ||||
ISM3 | 2.300 | ||||
Perceived privacy risk | PPR1 | 2.300 | N/A | N/A | N/A |
PPR2 | 2.979 | ||||
PPR3 | 2.169 | ||||
Perceived security | PS1 | 1.819 | N/A | N/A | N/A |
PS2 | 2.006 | ||||
PS3 | 1.974 | ||||
Initial trust in doctor | ITD1 | 2.132 | N/A | N/A | N/A |
ITD2 | 2.304 | ||||
ITD3 | 2.763 | ||||
Initial trust in mHealth platform | ITM1 | 2.778 | N/A | N/A | N/A |
ITM2 | 2.847 | ||||
ITM3 | 2.407 | ||||
Intention to adopt | IA1 | 2.235 | 0.532 | 0.525 | 0.417 |
IA2 | 2.453 | ||||
IA3 | 2.515 | ||||
Intention to recommend | IR1 | 3.274 | 0.483 | 0.482 | 0.396 |
IR2 | 3.704 | ||||
IR3 | 3.609 | ||||
IR4 | 3.550 |
C: compatibility; EE: effort expectancy; FC: facilitating conditions; FGC: firm-generated content; ISM: information-seeking motive; ITD: initial trust in doctor; ITM: initial trust in mHealth; I: innovativeness; IA: intention to adopt; IR: intention to recommend; PPR: perceived privacy risks; PS: perceived security; PE: performance expectancy; PV: price value; N/A: not available.
Hypotheses results.
Hypothesis | Path | Beta | Results | |
---|---|---|---|---|
H1 | Innovativeness ➔ intention to adopt | 0.007 | 0.169 | Not supported |
H2 | Innovativeness ➔ compatibility | 0.646 | 25.418 | Supported |
H3 | Innovativeness ➔ performance expectancy | 0.019 | 0.520 | Not supported |
H4 | Innovativeness ➔ effort expectancy | 0.111 | 2.873 | Supported |
H5 | Compatibility ➔ intention to adopt | 0.067 | 2.129 | Supported |
H6 | Compatibility ➔ performance expectancy | 0.432 | 10.502 | Supported |
H7 | Compatibility ➔ effort expectancy | 0.435 | 11.247 | Supported |
H8 | Effort expectancy ➔ performance expectancy | 0.357 | 9.697 | Supported |
H9 | Performance expectancy ➔ intention to adopt | 0.099 | 2.285 | Supported |
H10 | Effort expectancy ➔ intention to adopt | 0.067 | 1.604 | Not supported |
H11 | Price value ➔ intention to adopt | -0.023 | 0.524 | Not supported |
H12 | Facilitating conditions ➔ intention to adopt | 0.131 | 3.109 | Supported |
H13 | Information seeking motive ➔ intention to adopt | 0.097 | 2.862 | Supported |
H14 | Firm generated content ➔ intention to adopt | -0.034 | 0.950 | Not supported |
H15 | Perceived privacy risk ➔ intention to adopt | -0.001 | 0.0032 | Not supported |
H16 | Perceived security ➔ intention to adopt | 0.023 | 0.726 | Not supported |
H17 | Initial Trust in Doctor ➔ intention to adopt | 0.094 | 2.039 | Supported |
H18 | Initial trust in mHealth platform ➔ intention to adopt | 0.373 | 6.856 | Supported |
H19 | Intention to adopt ➔ intention to recommend | 0.695 | 26.083 | Supported |
In contrast, effort expectancy has little relevance, with a
Values of
Path | Effect | ||
---|---|---|---|
Compatibility ➔ effort expectancy | 0.1500 | Medium | 4.7500 |
Compatibility ➔ performance expectancy | 0.1837 | Medium | 4.8682 |
Effort expectancy ➔ performance expectancy | 0.1820 | Medium | 4.4027 |
Initial trust in mHealth platform ➔ intention to adopt | 0.0794 | Small | 2.9645 |
Innovativeness ➔ compatibility | 0.7155 | Large | 7.3928 |
Intention to adopt ➔ intention to recommend | 0.9336 | Large | 6,6805 |
Overall, compatibility, performance expectancy, facilitating conditions, information-seeking motive, initial trust in the doctor, and initial trust in mHealth platform explain 53.2% of intention to adopt mHealth. In comparison, the intention to adopt explains 48.3% of the intention to recommend mHealth applications. Initial trust in mHealth platform (
Structural model results.
Shmueli et al. [
PLSpredict assessment of manifest variables.
Item | PLS-SEM | LM | PLS-SEM – LM RMSE | |
---|---|---|---|---|
RMSE | RMSE | |||
IA1 | 0.5464 | 0.4462 | 0.5446 | 0.0018 |
IA2 | 0.6042 | 0.3581 | 0.6088 | -0.0267 |
IA3 | 0.6193 | 0.3609 | 0.6312 | -0.0119 |
IA: intention to adopt; RMSE: root mean square error; LM: linear model; PLS-SEM: partial least square structural equation modeling.
Lastly, importance-performance map analysis (IPMA) was done to compare the structural model’s total effects on a specific target construct with its predecessors’ average latent variable scores [
Importance-performance map (intention to adopt) for each construct.
Importance-performance map (intention to adopt) for each indicator.
Compatibility, performance expectancy, facilitating conditions, information-seeking motive, initial trust in the doctor, and initial trust in the mHealth platform explained 53.2% of the variance of intention to adopt with a medium predictive power
The first hypothesis (H1) is not supported as innovativeness does not significantly affect the intention to adopt. Harst et al. [
Another rejected hypothesis is the third hypothesis (H3), where innovativeness does not significantly affect performance expectancy. This finding is similar to results from earlier studies [
All hypotheses regarding compatibility are statistically significant, from compatibility to intention to adopt (H5), compatibility to performance expectancy (H6), and compatibility to effort expectancy (H7). This finding’s implication is that performance expectancy, effort expectancy, and intention to adopt are higher when the customer regards mHealth as compatible with their lives, especially in the health sector. These results are also found in previous studies [
In our model, performance expectancy has a statistically significant effect on the intention to adopt, which suggests that users care about the advantages of using mHealth applications in their lives, supporting the ninth hypothesis (H9). Effort expectancy does not have a statistically significant effect on the intention to adopt (H10). Other studies also found this finding which evaluated medical technology adoptions [
Price value does not have a statistically significant effect on the intention to adopt has a statistically significant effect on the intention to adopt, and H11 was rejected. Similar results were also found by Tavares and Oliveira [
In our study, facilitating conditions had a statistically significant effect on the intention to adopt, and H12 was supported. Despite the increasingly prevalent use of smartphones in Indonesia, the internet and broadband reach can still be limited in rural areas. Therefore, mHealth manufacturers should still provide customer care and assistance services to support mHealth use for users’ benefit. Hypothesis 13 (H13) was supported as information-seeking motive had a statistically significant effect on the intention to adopt. This finding is supported by Alwi and Murad [
Hypothesis 14 (H14) was rejected as firm-generated content does not significantly affect the intention to adopt. One possible explanation was that consumers could assume that firm-generated content that tries to give a positive image to the company can provide an image that is too ambitious, subjective, and overwhelming for consumers. This supports previous researches that the positive tone in firm-generated content is ineffective following these findings, although these studies are not done on health technologies [
In our model, perceived security does not have a statistically significant effect on the intention to adopt, and therefore, H16 was rejected. In contrast with previous research findings from Johnson et al. [
Initial trust in doctor (H17) and initial trust in mHealth platform (H18) had a statistically significant effect on the intention to adopt, and they are both supported. A study shows that new service that is not commonly known and involves a large group can cause uncertainty or potential risk. Users usually decide whether to adopt this service based on trust evaluation. So, the initial trust in a health service application, including doctors and platforms, is an essential factor for users to decide whether to use such a platform [
Furthermore, Cao et al. [
This study considers broader and more practical antecedents that may influence mHealth application adoption and its intention to recommend mHealth application. Trust in mHealth platform is a significant adoption driver of mHealth application and the most prominent contribution amongst other constructs. This construct was also the most critical construct with a high-performance score in our IPMA analysis. One of the main reasons for not adopting mHealth in Indonesia is that consumers give low trust to mHealth applications [
Facilitating conditions is found to be the second most crucial factor in affecting the intention to adopt mHealth. Even though there is an increasing number of smartphone users in Indonesia, internet penetration can still be shallow in rural areas [
Another essential factor to note is initial trust in doctor as it has a significant effect on the intention to adopt. Application managers need to ensure the strict criteria for doctors who can work in their company by doing a more thorough profile check, competency testing, and completeness of national-based medical doctor certifications. Another route that can be taken to increase the initial trust in doctor is to recruit senior doctors or respectable doctors in their respective fields so that patients, especially those with chronic diseases, are familiar with them. Thus, initial trust in doctors will increase.
First, our study was based on a web-based survey using Google Forms. We could not administer the questions directly or clarify some points when filling up the questionnaire. Our respondents were also young adults and highly educated with low to middle monthly spending; thus, prevention of diseases and healthcare awareness will be priorities. Previous studies have also shown that mHealth users are relatively young and with higher education backgrounds [
Initial trust in mHealth applications is the most critical determinant of the patient’s intention to adopt mHealth applications, followed by facilitating conditions and performance expectancy. Therefore, managers and developers need to pay special attention to maintaining and increasing users’ perceptions of how credible mHealth applications are. Building supporting facilities such as customer centers and increasing the application’s effectiveness should also be done to promote the applications. Our study supports the use of UTAUT2, DOI, and the internet customer trust model in explaining patients’ intention to adopt mHealth applications. Besides, other context-related determinants such as habit and social influence should be examined to understand better patients’ intention to adopt.
Descriptive statistics analyzed the demographic characteristics of respondents. Testing of the research model will be carried out by SmartPLS 3.3 [
The results of the measurement model can be seen in Table
Measurement models and factor loadings.
Construct | Item | Factor loading | AVE | Composite reliability | Cronbach’s alpha | |
---|---|---|---|---|---|---|
Innovativeness | I1 | 0.892 | 110.04 | 0.738 | 0.894 | 0.821 |
I2 | 0.810 | 52.23 | ||||
I4 | 0.872 | 91.31 | ||||
Compatibility | C1 | 0.830 | 55.98 | 0.689 | 0.869 | 0.775 |
C2 | 0.815 | 51.29 | ||||
C3 | 0.845 | 76.93 | ||||
Performance expectancy | PE1 | 0.815 | 57.94 | 0.717 | 0.910 | 0.868 |
PE2 | 0.858 | 66.97 | ||||
PE3 | 0.885 | 91.21 | ||||
PE4 | 0.826 | 57.59 | ||||
Effort expectancy | EE1 | 0.772 | 43.10 | 0.738 | 0.918 | 0.881 |
EE2 | 0.891 | 89.46 | ||||
EE3 | 0.900 | 104.21 | ||||
EE4 | 0.868 | 64.44 | ||||
Firm-generated content | FGC1 | 0.882 | 81.84 | 0.787 | 0.936 | 0.909 |
FGC2 | 0.916 | 126.88 | ||||
FGC3 | 0.895 | 94.11 | ||||
FGC4 | 0.854 | 60.81 | ||||
Price value | PV1 | 0.919 | 113.18 | 0.834 | 0.938 | 0.901 |
PV2 | 0.917 | 93.47 | ||||
PV3 | 0.904 | 87.15 | ||||
Facilitating conditions | FC1 | 0.872 | 64.94 | 0.790 | 0.919 | 0.867 |
FC2 | 0.896 | 84.64 | ||||
FC3 | 0.898 | 96.29 | ||||
Information-seeking motive | ISM1 | 0.894 | 81.81 | 0.809 | 0.927 | 0.882 |
ISM2 | 0.917 | 104.53 | ||||
ISM3 | 0.888 | 78.45 | ||||
Perceived privacy risk | PPR1 | 0.915 | 15.99 | 0.792 | 0.919 | 0.873 |
PPR2 | 0.921 | 16.85 | ||||
PPR3 | 0.831 | 10.45 | ||||
Perceived security | PS1 | 0.831 | 36.27 | 0.747 | 0.899 | 0.833 |
PS2 | 0.861 | 45.89 | ||||
PS3 | 0.899 | 102.72 | ||||
Initial trust in doctor | ITD1 | 0.873 | 79.74 | 0.796 | 0.921 | 0.871 |
ITD2 | 0.885 | 77.50 | ||||
ITD3 | 0.918 | 122.88 | ||||
Initial trust in mHealth platform | ITM1 | 0.912 | 107.74 | 0.823 | 0.933 | 0.892 |
ITM2 | 0.913 | 106.80 | ||||
ITM3 | 0.897 | 117.64 | ||||
Intention to adopt | IA1 | 0.893 | 71.01 | 0.802 | 0.924 | 0.877 |
IA2 | 0.894 | 65.50 | ||||
IA3 | 0.900 | 70.96 | ||||
Intention to recommend | IR1 | 0.904 | 113.30 | 0.828 | 0.949 | 0.931 |
IR2 | 0.917 | 121.36 | ||||
IR3 | 0.912 | 93.15 | ||||
IR4 | 0.908 | 93.62 |
Fornell-Larcker criterion: matrix of correlation constructs and the square root of AVE (in italics).
C | EE | FC | FGC | ISM | ITD | ITE | I | IA | IR | PPR | PS | PE | PV | |
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
C | ||||||||||||||
EE | 0.507 | |||||||||||||
FC | 0.346 | 0.637 | ||||||||||||
FGC | 0.550 | 0.539 | 0.349 | |||||||||||
ISM | 0.497 | 0.400 | 0.352 | 0.478 | ||||||||||
ITD | 0.456 | 0.449 | 0.406 | 0.485 | 0.431 | |||||||||
ITM | 0.504 | 0.539 | 0.507 | 0.542 | 0.476 | 0.799 | ||||||||
I | 0.646 | 0.392 | 0.238 | 0.468 | 0.475 | 0.338 | 0.374 | |||||||
IA | 0.489 | 0.514 | 0.493 | 0.442 | 0.461 | 0.591 | 0.677 | 0.363 | ||||||
IR | 0.553 | 0.506 | 0.426 | 0.516 | 0.518 | 0.589 | 0.672 | 0.483 | 0.695 | |||||
PPR | 0.120 | 0.105 | 0.085 | 0.181 | 0.219 | 0.219 | 0.115 | 0.195 | 0.116 | 0.116 | ||||
PS | 0.434 | 0.357 | 0.308 | 0.375 | 0.263 | 0.263 | 0.437 | 0.309 | 0.393 | 0.409 | 0.070 | |||
PE | 0.625 | 0.583 | 0.440 | 0.546 | 0.463 | 0.464 | 0.513 | 0.438 | 0.536 | 0.577 | 0.139 | 0.379 | ||
PV | 0.501 | 0.506 | 0.514 | 0.533 | 0.327 | 0.327 | 0.524 | 0.341 | 0.479 | 0.533 | 0.094 | 0.458 | 0.518 |
C: compatibility; EE: effort expectancy; FC: facilitating conditions; FGC: firm-generated content; ISM: information-seeking motive; ITD: initial trust in doctor; ITM: initial trust in mHealth; I: innovativeness; IA: intention to adopt; IR: intention to recommend; PPR: perceived privacy risks; PS: perceived security; PE: performance expectancy; PV: price value.
Heterotrait-monotrait ratio results for discriminant validity with average HTMT computed from 5000 bootstrap samples.
Original sample | Sample mean | Bias | 5.0% | 95.0% | |
---|---|---|---|---|---|
EE ➔ C | 0.606 | 0.606 | 0.000 | 0.551 | 0.661 |
FC ➔ C | 0.423 | 0.424 | 0.001 | 0.360 | 0.481 |
FC ➔ EE | 0.729 | 0.730 | 0.001 | 0.680 | 0.772 |
FGC ➔ C | 0.655 | 0.657 | 0.002 | 0.599 | 0.702 |
FGC ➔ EE | 0.595 | 0.593 | -0.002 | 0.552 | 0.643 |
FGC ➔ FC | 0.390 | 0.389 | -0.001 | 0.327 | 0.440 |
ISM ➔ C | 0.599 | 0.599 | 0.000 | 0.538 | 0.645 |
ISM ➔ EE | 0.448 | 0.448 | 0.000 | 0.379 | 0.518 |
ISM ➔ FC | 0.401 | 0.401 | 0.000 | 0.325 | 0.466 |
ISM ➔ FGC | 0.534 | 0.533 | 0.000 | 0.489 | 0.586 |
ITD ➔ C | 0.554 | 0.558 | 0.004 | 0.497 | 0.607 |
ITD ➔ EE | 0.507 | 0.508 | 0.000 | 0.450 | 0.569 |
ITD ➔ FC | 0.467 | 0.466 | -0.001 | 0.402 | 0.533 |
ITD ➔ FGC | 0.544 | 0.544 | 0.000 | 0.498 | 0.606 |
ITD ➔ ISM | 0.492 | 0.493 | 0.001 | 0.430 | 0.548 |
ITM ➔ C | 0.605 | 0.608 | 0.003 | 0.543 | 0.654 |
ITM ➔ EE | 0.602 | 0.603 | 0.001 | 0.552 | 0.652 |
ITM ➔ FC | 0.575 | 0.575 | 0.000 | 0.518 | 0.624 |
ITM ➔ FGC | 0.600 | 0.601 | 0.001 | 0.548 | 0.649 |
ITM ➔ ISM | 0.536 | 0.536 | 0.000 | 0.474 | 0.587 |
ITM ➔ ITD | 0.906 | 0.907 | 0.001 | 0.868 | 0.932 |
I ➔ C | 0.812 | 0.812 | 0.000 | 0.759 | 0.858 |
I ➔ EE | 0.454 | 0.454 | -0.001 | 0.396 | 0.509 |
I ➔ FC | 0.279 | 0.278 | 0.000 | 0.211 | 0.332 |
I ➔ FGC | 0.544 | 0.545 | 0.001 | 0.481 | 0.597 |
I ➔ ISM | 0.559 | 0.559 | 0.000 | 0.504 | 0.614 |
I ➔ ITD | 0.400 | 0.400 | 0.000 | 0.334 | 0.466 |
I ➔ ITM | 0.437 | 0.435 | -0.001 | 0.378 | 0.499 |
IA ➔ C | 0.591 | 0.594 | 0.002 | 0.519 | 0.644 |
IA ➔ EE | 0.579 | 0.579 | 0.000 | 0.522 | 0.629 |
IA ➔ FC | 0.564 | 0.564 | 0.000 | 0.512 | 0.629 |
IA ➔ FGC | 0.492 | 0.492 | 0.000 | 0.432 | 0.546 |
IA ➔ ISM | 0.523 | 0.522 | -0.001 | 0.466 | 0.581 |
IA ➔ ITD | 0.674 | 0.674 | -0.001 | 0.624 | 0.721 |
IA ➔ ITM | 0.764 | 0.765 | 0.002 | 0.713 | 0.798 |
IA ➔ I | 0.424 | 0.424 | 0.000 | 0.349 | 0.484 |
IR ➔ C | 0.650 | 0.650 | 0.000 | 0.597 | 0.698 |
IR ➔ EE | 0.552 | 0.551 | -0.001 | 0.498 | 0.601 |
IR ➔ FC | 0.474 | 0.472 | -0.002 | 0.411 | 0.529 |
IR ➔ FGC | 0.561 | 0.562 | 0.001 | 0.503 | 0.610 |
IR ➔ ISM | 0.572 | 0.572 | 0.000 | 0.518 | 0.621 |
IR ➔ ITD | 0.654 | 0.654 | 0.000 | 0.599 | 0.698 |
IR ➔ ITM | 0.737 | 0.738 | 0.001 | 0.690 | 0.778 |
IR ➔ I | 0.552 | 0.552 | 0.000 | 0.486 | 0.599 |
IR ➔ IA | 0.768 | 0.768 | 0.000 | 0.715 | 0.809 |
PPR ➔ C | 0.139 | 0.139 | 0.000 | 0.066 | 0.216 |
PPR ➔ EE | 0.117 | 0.116 | -0.001 | 0.055 | 0.175 |
PPR ➔ FC | 0.099 | 0.100 | 0.001 | 0.046 | 0.168 |
PPR ➔ FGC | 0.202 | 0.201 | -0.001 | 0.123 | 0.269 |
PPR ➔ ISM | 0.237 | 0.235 | -0.002 | 0.168 | 0.311 |
PPR ➔ ITD | 0.124 | 0.125 | 0.001 | 0.063 | 0.195 |
PPR ➔ ITM | 0.134 | 0.135 | 0.001 | 0.068 | 0.204 |
PPR ➔ I | 0.225 | 0.224 | -0.001 | 0.137 | 0.292 |
PPR ➔ IA | 0.127 | 0.127 | 0.000 | 0.069 | 0.202 |
PPR ➔ IR | 0.123 | 0.123 | 0.000 | 0.057 | 0.188 |
PS ➔ C | 0.535 | 0.535 | 0.000 | 0.470 | 0.594 |
PS ➔ EE | 0.407 | 0.406 | -0.001 | 0.349 | 0.464 |
PS ➔ FC | 0.355 | 0.355 | 0.000 | 0.293 | 0.416 |
PS ➔ FGC | 0.428 | 0.428 | 0.001 | 0.364 | 0.489 |
PS ➔ ISM | 0.309 | 0.308 | -0.002 | 0.238 | 0.379 |
PS ➔ ITD | 0.504 | 0.505 | 0.001 | 0.438 | 0.558 |
PS ➔ ITM | 0.567 | 0.568 | 0.001 | 0.509 | 0.622 |
PS ➔ I | 0.374 | 0.371 | -0.003 | 0.307 | 0.441 |
PS ➔ IA | 0.449 | 0.450 | 0.001 | 0.381 | 0.501 |
PS ➔ IR | 0.458 | 0.457 | -0.001 | 0.396 | 0.518 |
PS ➔ PPR | 0.079 | 0.086 | 0.007 | 0.034 | 0.155 |
PE ➔ C | 0.758 | 0.760 | 0.002 | 0.705 | 0.799 |
PE ➔ EE | 0.659 | 0.655 | -0.004 | 0.608 | 0.708 |
PE ➔ FC | 0.505 | 0.502 | -0.003 | 0.456 | 0.584 |
PE ➔ FGC | 0.614 | 0.612 | -0.001 | 0.568 | 0.665 |
PE ➔ ISM | 0.530 | 0.530 | 0.001 | 0.454 | 0.586 |
PE ➔ ITD | 0.589 | 0.593 | 0.003 | 0.533 | 0.635 |
PE ➔ ITM | 0.647 | 0.651 | 0.003 | 0.588 | 0.689 |
PE ➔ I | 0.517 | 0.518 | 0.000 | 0.457 | 0.575 |
PE ➔ IA | 0.612 | 0.614 | 0.002 | 0.553 | 0.663 |
PE ➔ IR | 0.642 | 0.643 | 0.001 | 0.582 | 0.689 |
PE ➔ PPR | 0.155 | 0.156 | 0.001 | 0.082 | 0.216 |
PE ➔ PS | 0.441 | 0.441 | 0.000 | 0.378 | 0.497 |
PV ➔ C | 0.597 | 0.597 | 0.000 | 0.533 | 0.656 |
PV ➔ EE | 0.561 | 0.564 | 0.002 | 0.498 | 0.617 |
PV ➔ FC | 0.581 | 0.581 | 0.001 | 0.523 | 0.631 |
PV ➔ FGC | 0.586 | 0.588 | 0.001 | 0.537 | 0.633 |
PV ➔ ISM | 0.364 | 0.364 | 0.000 | 0.301 | 0.430 |
PC ➔ ITD | 0.588 | 0.589 | 0.000 | 0.536 | 0.640 |
PV ➔ ITM | 0.688 | 0.688 | 0.000 | 0.636 | 0.725 |
PV ➔ I | 0.394 | 0.392 | -0.002 | 0.330 | 0.465 |
PV ➔ IA | 0.535 | 0.536 | 0.001 | 0.474 | 0.588 |
PV ➔ IR | 0.580 | 0.579 | -0.001 | 0.522 | 0.625 |
PV ➔ PPR | 0.099 | 0.101 | 0.002 | 0.037 | 0.157 |
PV ➔ PS | 0.525 | 0.524 | -0.001 | 0.463 | 0.581 |
PV ➔ PE | 0.582 | 0.583 | 0.001 | 0.520 | 0.633 |
The measurement model results indicate that the construct reliability, indicator reliability, convergent validity, and discriminant validity of the constructs are satisfactory. A structural model can be applied to this model in the next steps of evaluation.
The original data used to support the findings of this study are available from the corresponding author upon reasonable request.
The authors declare that they have no conflicts of interest.