Consumer trust in telemedicine in Indonesia

Abstract

Objectives: Advances in technology have improved the lives of Indonesians. For example, the health sector. This is indicated by the emergence of telemedicine to facilitate health services. This study aims to test the effect of individual trust on the intention to use telemedicine applications. Therefore, the Technology Acceptance Model (TAM) and the Unified Theory of Acceptance and Use of Technology (UTAUT) are used as the basis for answering the questions posed by this research. Methods: Sampling was conducted online and involved 402 respondents who all came from the Indonesian island of Java. Validity, reliability, and hypothesis testing used Structural Equation Modeling (SEM) with Smart-PLS 4 tools. Result: The results show that effort expectancy, performance expectancy, and social influence have a positive influence on individual belief; intention to use is influenced by individual belief and behavioral intentions. The results of the hypothesis testing show that behavioral intention has the greatest influence on intention to use with a t value of 31.315 and a β value of 0.801. Conclusion: The novelty of this study is that it includes individual belief variables that are influenced by variables from UTAUT, namely effort expectancy, performance expectancy, social influence, and facilitating conditions.

Keywords

behavioral intention consumer trust Indonesia intention to use telemedicine

Introduction

Entering the era known as Industry 4.0 has manifested itself in the development of technology that is getting faster and that makes human life easier. Changes have occurred in various industrial sectors. One of these is the health sector which is experiencing changes in terms of the development of its services which are provided both offline (in person) and now online as well. These online services have given rise to facilities known as telemedicine. Telemedicine makes it easier for consumers to meet their health needs. This development accelerated during the COVID-19 pandemic when the paramount demand was for safe, fast, precise, and convenient health services. Telemedicine services existed before the pandemic, but they tended to be more complementary. Then, during the pandemic, telemedicine services became the primary way to reach many patients as quickly as possible. The Indonesian Ministry of Communication and Information Technology (Kominfo) noted that before the pandemic, there were four million users of various telemedicine applications, while, as of June 2020, there were 15 million users of such applications.¹ Telemedicine is the use of technology to provide and support health services remotely.² Generally, telemedicine is carried out online using communication and information technology-based devices. In Indonesia, telemedicine services are regulated in the Regulation of the Minister of Health No. 20 of 2019 concerning the Implementation of Telemedicine Services Between Health Service Facilities. Article 1 paragraph 1 states that the definition of telemedicine is a remote health service provided by health professionals using information and communication technology, including the exchange of diagnostic information, treatment, prevention of disease and injury, research and evaluation, and continuing the education of health service providers for the benefit of improving individual and community health.³ Telemedicine services in Indonesia are also called remote health services.⁴ In addition to digital health services, there has also been the development of digital pharmacy by paying attention to patient care.⁵

Research by Miner et al.⁶ shows that consumers are satisfied with the quality of service from the application providers when using telemedicine. The application providers pay attention to perceived ease of use which is the basis for influencing perceived usefulness and behavioral intention so that consumers are willing to use telemedicine and feel satisfaction when doing so.⁷ If the perceived usefulness felt by consumers is positive, it will create continuance intention according to Hadji and Degoulet.⁸ This is different from the research of Lu et al.⁹ where perceived usefulness does not affect continuance intention in using mobile health applications. According to Zobair et al.,¹⁰ consumers adopt telemedicine if they feel self-efficacy, telemedicine experience, enjoyment, and prior satisfaction. Meanwhile, research on telemedicine by Schmitz et al.¹¹ shows that performance expectancy has an influence on intention to use while effort expectancy, social influences, and facilitating conditions are not significant.

Factors that influence people to want to use telemedicine from a health perspective are perceived ease of use, self-efficacy, subjective norm, facilitating conditions, compatibility, attitude, perceived usefulness, perceived behavioral control, health culture, government policies, security, and reduced cost.¹² The results of research by Alexandra et al.¹³ show that facilitating conditions and social influences have no effect on behavioral intention to use telemedicine. This is different from the results of research by Cobelli et al.¹⁴ which show that facilitating conditions, performance expectancy, and effort expectancy have an influence on the intention to adopt telemedicine. Indria et al.¹⁵ show that patients are satisfied with telemedicine but service providers are still paying attention to improving infrastructure and performance.

Previous research results show how important it is to change consumer attitudes so as to create confidence in adopting telemedicine. This suits the circumstances found in an archipelago like Indonesia as it makes it easier to provide health services to the community. Based on the background above, this study uses a different approach from previous studies. The focus of this study is to test the effect of the unified theory of acceptance and use of technology (UTAUT) on the technology acceptance model (TAM) through consumer trust. The UTAUT variables used are performance expectancy, effort expectancy, social influences, and facilitating conditions. As for the TAM variables, they are perceived usefulness (equivalent to performance expectancy) and perceived ease of use (equivalent to effort expectancy).¹⁶ The TAM and UTAUT theories emphasize intention to use without including beliefs. In research with health objects, the belief factor is important for patients. The belief factor needs to be developed by means of a relationship based on communication between the doctor and the patient. If the relationship is formed, it will lead to belief. This is the basis for individual belief to mediate between UTAUT and TAM theories.

Literature review

Telemedicine

Telemedicine is defined by the World Health Organization (WHO) as follows: “The provision of health care services, where distance is a significant consideration, by all health care professionals utilizing information and communication technologies for the exchange of accurate information pertaining to diagnosis, treatment, and disease and injury prevention, research and evaluation, as well as the ongoing education of health care providers, all aimed at enhancing the health of individuals and their communities.” (World Health Organization 2010). The WHO delineates several fundamental elements that telemedicine must encompass: providing clinical support, overcoming geographical barriers, employing information and communications technology, and finally enhancing patient outcomes (World Health Organization 2010).17

Telemedicine, a branch of information and communication technology (ICT), has emerged as a viable approach for delivering healthcare remotely. It surmounts geographical barriers, enhances medical outcomes, increases patient engagement, and reduces costs.^18–20 It can be employed to provide diverse clinical services, including consultations, diagnosis, treatment, monitoring, therapeutic processes, and the exchange of medical information via electronic communication tools such as video conferencing, telephone calls, or secure messaging.²¹

Numerous systematic literature evaluations have been performed regarding the application of telemedicine and e-consultation in healthcare. Most of this research focuses on the effectiveness, efficiency, and potential of telemedicine to improve healthcare services.²² One of the important things about telemedicine is that it demonstrates that geography is not an obstacle, which means it is suited to the archipelago of Indonesia; this means it is very helpful for the inhabitants of the islands in maintaining their health²³ because health services can reach the outermost areas of the country effectively and efficiently.

Unified theory of acceptance and use of technology (UTAUT)

UTAUT was first developed in 2003 by Venkatesh et al.²⁴ The theory’s variables consist of performance expectancy, effort expectancy, social influence, and facilitating conditions which form behavioral intention, thus causing use behavior. Research by Schmitz et al.¹¹ has modified UTAUT; the results of their study show that performance expectancy has an influence on the intention to use virtual doctor appointments. Meanwhile, effort expectancy, social influence, and facilitating conditions do not affect the intention to use.¹¹ The results of the study by Schmitz et al.¹¹ differ from the results of the study by Akinnuwesi et al.²⁵ who show that performance expectancy, social influence, and facilitating conditions have an influence on the intention to use digital protective tools for COVID-19, while effort expectancy does not affect the intention to use.

When UTAUT was used to test mobile payment services (MPSs), it demonstrated that effort expectancy had no effect on the intention to use mobile payment services, whereas performance expectancy and social influence did have an effect on the intention to use them.²⁶ The results of a study by Patil et al.²⁷ show that performance expectancy and effort expectancy influence attitude, while social influence and facilitating conditions influence behavioral intention. Meanwhile, research by Abu-Taieh et al.²⁸ shows that performance expectancy, effort expectancy, and social influence in addition to facilitating conditions influence behavioral intention. The results of previous studies have resulted in inconsistent results with regard to UTAUT variables; therefore, this study uses performance expectancy, effort expectancy, social influence, and facilitating conditions as variables. These are in accordance with the four core variables of UTAUT, namely performance expectancy, effort expectancy, social influence, and facilitating conditions.²⁹

Individual belief

Attitude to the use of telemedicine is influenced by positive and negative factors; if the positive influence is greater then there will be an intention to use telemedicine³⁰; in this way, individual beliefs regarding the use of telemedicine will be formed. Telemedicine has been shown to be beneficial, cost-effective, and satisfying for both patients and service providers in the treatment of various types of diseases, except when it is important that there is a physical examination.³¹ Overall, a good experience strengthens the ability of telemedicine to complement traditional health services³¹ making it easier for consumers to use those services. Consumers are comfortable consulting by telephone and are satisfied with the services received from health workers.³²

Consumer trust in telemedicine is influenced by performance expectancy and effort expectancy where the antecedent variables are information quality, system quality, and service quality.³³ Positive self-efficacy in adopting telemedicine services increasingly forms individual belief.¹⁰ Research by Velsen et al.³⁴ shows that trust in telemedicine services is formed by trust in care organizations, trust in treatment, trust in care professionals, and trust in technology which can ultimately form individual belief.

Technology acceptance model (TAM)

The development of technology changes human lives. TAM was initiated by Davis in 1986 and thereafter developed by many other studies. According to Davis,³⁵ there are two dimensions that form TAM, namely perceived usefulness and perceived ease of use. TAM has also been used in telemedicine research such as the studies on perceived ease of use and perceived usefulness by Kissi et al.,⁷ Lee et al.,³⁶ and Garavand et al.¹² using TAM. Perceived usefulness affects attitudes and intentions while perceived ease of use does not affect attitudes and perceived usefulness³⁷ on the object of Internet banking research. Meanwhile, telemedicine research shows that perceived usefulness has an effect while perceived ease of use does not affect the intention to use.³⁸

Perceived ease of use affects the intention to use through perceived usefulness in telemedicine.³⁰ In addition, perceived ease of use and perceived usefulness form behavioral intention.⁷

Intention to use

If the consumer’s experience in using the telemedicine application creates belief, then the intention to use will occur. This belief can be formed from effort expectancy, performance expectancy, social influence, and facilitating conditions. Individual belief will create behavioral intention toward the telemedicine application where behavioral intention is influenced by perceived usefulness and perceived ease of use in the telemedicine application. The usefulness of the application felt by consumers will form the intention to use.⁸ In addition, behavioral intention also has an influence on the actual use of consumers.¹³

In addition, the variables effort expectancy, performance expectancy, and facilitating conditions can have a direct effect on the intention to adopt telemedicine.¹⁴ Meanwhile, there is a basic theory developed by Venkatesh et al.²⁴ on behavioral intention toward use behavior, subsequently developed by many other studies. This study develops TAM and UTAUT as well as the variable individual belief in forming consumer intention to use telemedicine in Indonesia.

Based on the theoretical study above, the following research model was created (Figure 1).

Figure 1.

Research model.

Effort expectancy, performance expectancy, social influence, facilitating conditions, and individual belief

When using the application online, very practical facilities are also needed so that it can make it easy for consumers to use it; this means that facilitating conditions can form perceived usefulness.⁸ Meanwhile, when the telemedicine application is used it creates an experience that forms consumer expectations.¹⁰ The social influence that occurs can influence consumer beliefs, thus creating behavioral intentions, especially in perceived usefulness.⁸

Effort expectancy, social influence, and facilitating conditions apparently do not affect intention to use in telemedicine applications, while performance expectancy can only form intention to use.¹¹ Based on their research, the following hypotheses will be tested.

Effort expectancy has an influence on individual belief

Performance expectancy has an influence on individual belief

Social influence has an influence on individual belief

Facilitating conditions have an influence on individual belief

Individual belief and behavioral intention

Health information technology (HIT), seen through a combination of TAM and UTAUT, can influence individual beliefs and consumer attitudes.³⁹ Consumer attitudes are also formed by effort expectancy, performance expectancy, personal innovativeness, anxiety, and trust while behavioral intention is influenced by social influence and facilitating conditions which ultimately form use behavior.²⁷ Patil et al.’s research in 2020 used UTAUT which was developed from TAM.

Their research explored the effect of technological changes on consumer beliefs. Technology makes it easier for consumers to adopt products or services. Therefore, they developed TAM into UTAUT. Although the emphasis of their research is different, this study emphasizes the influence of individual belief on behavioral intention, where individual belief is influenced by effort expectancy, performance expectancy, social influence, and facilitating conditions. If individual belief has been formed in the consumer, it will cause behavioral intention. Individual belief places more emphasis on consumer trust in using telemedicine applications, while attitude places more emphasis on the emotional influence of consumers in using them. Therefore, attitudes can also be formed from effort expectancy, performance expectancy, social influence, and facilitating conditions.⁴⁰ This study places more emphasis on individual belief. Based on this, the following hypothesis will be tested.

Individual belief has an influence on behavioral intention.

Behavioral intention and intention to use

Behavioral intention will occur if what is offered by the company providing the application is attractive to consumers. Attraction can be realized by creating consumer trust or individual belief. There are things that must be considered in forming consumer behavioral intention. Therefore, telemedicine applications need to offer usefulness and ease of use. This accords with the TAM theory which posits that perceived usefulness/performance expectancy and perceived ease of use/effort expectancy influence behavioral intention¹⁶ which will ultimately lead to the intention to use.

Intention to use is also formed by effort expectancy, performance expectancy, and facilitating conditions.⁴¹ However, according to the TAM theory, behavioral intention forms the intention to use or actual use.¹³ For this reason, this study emphasizes the influence of behavioral intention on the intention to use telemedicine services in Indonesia. This is the basis for testing hypothesis 6.

Behavioral intention has an influence on intention to use.

Methodology

Data analysis

This research uses a quantitative approach, so hypothesis testing is carried out to answer the problem under study. The hypothesis testing used structural equation modeling (SEM) with smart-partial least square (Smart-PLS 4). This analysis tool was used in accordance with the research model consisting of exogenous, mediating, and endogenous variables. In this study, there are four exogenous variables, two mediating variables, and one endogenous variable.

Variables

The variables used refer to those used in previous research, with the novelty being the instruments used in this study. Effort expectancy, performance expectancy, social influence, and facilitating conditions refer to research by Schmitz et al.,¹¹ Akunnuwesi et al.,²⁵ and Patil et al.²⁷ The individual belief instrument refers to trust in care professionals.³⁴ Meanwhile, the instruments of behavioral intention and the intention to use have been developed from those used in previous research.^7,12,42 The instruments used in this study were tested to check their validity and reliability. The test used structural equation modeling (SEM) with Smart-PLS 4. An even scale with a range of 1 to 6 was used. The purpose of using an even scale is so that there are no neutral answers.⁴³ The use of an even scale creates a balanced distribution of data that is more proportional. The indicators used in the research are in (Table 1).

Table 1.

The indicators of the research variables.

Variables	Indicators	References
Effort expectancy	I feel happy using the telemedicine service application.	Akinnuwesi et al.²⁵ Patil et al.²⁷ Schmitz et al.¹¹
	My interaction with the telemedicine app was clear.
	The use of telemedicine applications is easy to learn.
	In my opinion, telemedicine applications are flexible to use.
	I can optimally use the telemedicine application as needed.
Performance expectancy	Telemedicine application services suit my needs.
	It is like we are meeting directly with medical personnel in the telemedicine application service.
	I find it easier to use the telemedicine service application.
	Overall, using the telemedicine service application improved my health.
Social influence	People I know use telemedicine service applications.
	My family members use telemedicine service applications.
	My close friend uses a telemedicine service application.
Facilitating conditions	I have stable internet network access.
	I have a mobile phone that supports the use of telemedicine service applications.
	I understand how to access and use the telemedicine service application.
	I can interact in using the telemedicine service application.
Individual beliefs	I trust the doctor’s diagnosis of my health condition.	Velsen et al.³⁴
	I trust that doctors know my health needs.
	I trust that the doctor will tell me if there is an error in my health diagnosis.
	I am confident that the telemedicine service application can maintain and protect my personal data.
Behavior intention	Having access to a telemedicine application would mean I’d plan to use it.	Garavand et al.¹² Kissi et al.⁷ Venkatesh et al.⁴²
	Telemedicine applications being available in the long term would mean I’d plan to use them.
	I plan to use a telemedicine application in the future.
Intention to use	Telemedicine applications are my first choice when I need health services.
	I am willing to use telemedicine applications in the long term.
	I will continue to use the telemedicine service regardless of any change in the consultation rates.

Sample and data collection

The research respondents were specifically from the Indonesian island of Java which was selected due to its large population, which uses telemedicine at a relatively higher rate than people on other islands in Indonesia. The questionnaire was distributed online, with the exclusion criteria for respondents being only those who had used telemedicine. Sampling used the purposive sampling method. The respondents involved in this study expressed their agreement by continuing to fill out the entire questionnaire. The number of samples taken was 402 respondents. The population in this study is unknown, so the calculation of the number of samples taken used the guideline that says that the number of indicators and variables multiplied by a maximum of ten and a minimum of five (Joseph F. Hair et al., 2006).⁴⁴ This study has 41 indicators and variables, so the maximum number of respondents is 410, and the minimum is 205 respondents. This study did not examine a disease, but only focused on the desire to use telemedicine applications for respondents when they needed health consultations. The respondents’ data are presented in Table 2.

Table 2.

Characteristics of respondents.

No.	Characteristics	Number	Percentage (%)
1.	Gender:
	Male	168	42
	Female	234	58
2.	Age:
	17–25	344	86
	26–35	26	6
	36–45	20	5
	46–55	8	2
	56–65	4	1
3.	Education:
	SLTA (senior high school)	131	32.6
	Diploma	12	3
	S1 (undergraduate)	223	55.5
	S2 (master’s)	29	7.2
	S3 (doctorate)	7	1.7
4.	Employment:
	Students	284	70.6
	Private sector employee	60	14.9
	Civil servant	14	3.5
	Self-employed	251	6.2
	Profesional	19	4.7

Table 2 shows that the respondents in the sample were mostly women and mostly in the age range of 17–25 years. This shows that most application users are young people. The predominant level of education was S1 (undergraduate) and most work as students or are self-employed.

Statistical analysis

The instruments in this research were used first tested to establish their validity and reliability. After the instruments were deemed valid and reliable, the model construct test was carried out.

The instruments used in this study were tested for validity and reliability.

The indicators and research variables can be used if they meet the requirements for validity and reliability. For the outer loading value of at least 0.7.^45,46 An AVE value of >0.5 indicates convergent validity.^46,47 For the Cronbach alpha, the value must be at least 0.7 and the composite reliability value must be at least 0.8.⁴⁶ Table 3 shows that the loading factor value, Cronbach alpha, composite reliability, and AVE meet the requirements as indicators and variables so they can be used in this research. The overall test results show validity and reliability. After these tests have been carried out, the variable construct test is carried out. The discriminant validity test used the Fornell-Larcker criterion. This test uses discriminant validity as shown in Table 4.

Table 3.

Loadings, CA, CR and AVE.

Variables/Instruments	Loadings	Cronbach’s alpha (CA)	Composite reliability (CR)	Average variance extracted (AVE)
Effort expectancy (EE)	—	0.871	0.906	0.659
EE1	0.839
EE2	0.795
EE3	0.796
EE4	0.803
EE5	0.825
Performance expectancy (PE)		0.867	0.909	0.715
PE1	0.800
PE2	0.861
PE3	0.840
PE4	0.880
Social influence (SI)		0.858	0.913	0.778
SI1	0.876
SI2	0.878
SI3	0.892
Facilitating conditions (FC)		0.807	0.873	0.633
FC1	0.776
FC2	0.804
FC3	0.801
FC4	0.801
Individual belief (IB)		0.811	0.876	0.639
IB1	0.827
IB2	0.798
IB3	0.815
IB4	0.754
Behavioral intention (BI)		0.842	0.905	0.761
BI1	0.860
BI2	0.894
BI3	0.862
Intention to use (ITU)		0.847	0.908	0.766
ITU1	0.866
ITU2	0.889
ITU3	0.870

Table 4.

Discriminant validity Fornell-Larcker criterion.

	BI	EE	FC	IB	ITU	PE	SI
BI	0.872
EE	0.828	0.812
FC	0.709	0.765	0.795
IB	0.707	0.772	0.654	0.799
ITU	0.801	0.775	0.640	0.681	0.875
PE	0.746	0.834	0.672	0.774	0.783	0.846
SI	0.669	0.749	0.620	0.661	0.737	0.767	0.882

The discriminant validity test results show that there are invalid results with high loadings factor values between the constructs. For this reason, testing is carried out again by eliminating indicators that have high values on these constructs. The omitted indicators are PE2 = 0.861, and PE4 = 0.880. The results of the discriminant validity retest are presented in Table 5.

Table 5.

Final results of discriminant validity test using Fornell-Larcker criterion.

	BI	EE	FC	IB	ITU	PE	SI
BI	0.872
EE	0.828	0.812
FC	0.709	0.765	0.795
IB	0.707	0.772	0.654	0.799
ITU	0.801	0.775	0.640	0.681	0.875
PE	0.721	0.800	0.670	0.697	0.734	0.884
SI	0.669	0.749	0.620	0.661	0.737	0.707	0.882

After the discriminant validity test was carried out using the Fornell-Lacker criterion, the findings were supported by cross loadings. The results of the cross loadings are presented in Table 6.

Table 6.

Discriminant validity cross loadings.

	Behavioral intention	Effort expectancy	Facilitating conditions	Individual belief	Intention to use	Performance expectancy	Social influence
BI1	0.860	0.737	0.648	0.635	0.644	0.628	0.571
BI2	0.894	0.750	0.627	0.657	0.719	0.636	0.599
BI3	0.862	0.678	0.581	0.556	0.733	0.623	0.580
ITU1	0.681	0.650	0.531	0.560	0.866	0.615	0.621
ITU2	0.735	0.704	0.595	0.600	0.889	0.647	0.632
ITU3	0.686	0.679	0.553	0.629	0.870	0.667	0.682
EE1	0.712	0.839	0.646	0.677	0.698	0.733	0.653
EE2	0.628	0.795	0.512	0.628	0.642	0.641	0.639
EE3	0.649	0.796	0.632	0.570	0.572	0.569	0.566
EE4	0.667	0.803	0.668	0.597	0.555	0.626	0.539
EE5	0.701	0.826	0.650	0.651	0.664	0.666	0.633
FC1	0.477	0.540	0.776	0.522	0.403	0.490	0.422
FC2	0.556	0.609	0.804	0.495	0.462	0.533	0.382
FC3	0.570	0.599	0.801	0.487	0.563	0.515	0.551
FC4	0.645	0.679	0.801	0.568	0.602	0.586	0.604
IB2	0.550	0.625	0.556	0.800	0.556	0.581	0.554
IB3	0.611	0.648	0.504	0.814	0.571	0.583	0.534
IB4	0.516	0.599	0.481	0.753	0.502	0.524	0.527
IBI1	0.579	0.593	0.549	0.828	0.545	0.538	0.497
PE1	0.627	0.700	0.594	0.578	0.638	0.869	0.618
PE3	0.648	0.715	0.591	0.651	0.660	0.899	0.632
SI1	0.590	0.667	0.567	0.625	0.643	0.602	0.876
SI2	0.570	0.634	0.505	0.566	0.651	0.642	0.878
SI3	0.611	0.679	0.566	0.551	0.655	0.630	0.892

Table 6 shows that the result of the discriminant validity test gives a value above 0.7, meaning that the variable construct used in the structure of the model is valid.⁴⁸ Overall, the results of the validity, reliability, and construct tests, the variables met the requirements meaning that the variables and instruments can be used in this study.

Ethical considerations

This was how we obtained written informed consent from all the subjects prior to our study’s initiation, the requirement for which was supervised and approved by the institutional review board/ethics committee to which we were responsible. There was no need for legally authorized representatives to be involved because our subjects were all at least 17 years old, and in Indonesia, 17-year-olds have already obtained an ID card, so they can decide for themselves whether or not to participate in research. “Consumer Trust in Telemedicine in Indonesia” has adhered to ethical protocols by securing an approval certificate, confirming that this research does not violate any ethical standards.

The questionnaire’s cover page featured an opportunity for responders to give their informed written consent. The questionnaire explicitly delineated the study’s objectives, methodologies, potential risks and benefits, anonymity, confidentiality, and the voluntary nature of participation for each respondent. If the respondent consented, he or she would then proceed to complete the questionnaire. By proceeding, participants in the study indicated their consent. Consent was also expressed in the questionnaire with the following statement: “I have read and comprehended the consent form above. I affirm that I am 17 years of age or older. By selecting the ‘Next’ button to commence the survey, I am expressing my voluntary consent to participate in this research.”

Result

When viewed in terms of the R-squared values obtained from the variable testing, it shows that the one with the largest value is the behavioral intention variable (0.662), meaning that behavioral intention can be 62.2% influenced by perceived usefulness and perceived ease of use; while the remaining 37.8% of the influence is from variables that are not being studied. As for individual belief, it can be formed by effort expectancy, performance expectancy, social influence, and facilitating conditions which have an R-squared value of 0.627 or 62.7%, meaning that the remaining 37.3% of influence is from variables that are not being studied. Meanwhile, intention to use has an R-squared value of 0.642 or 64.2% which is formed from individual belief and behavioral intention, meaning the remaining 35.8% of influence is from variables that are not included in this study. The confidence interval value used is 97.5%. The 97.5% confidence interval indicates that the resulting interval contains the true population value of 97.5%. The hypothesis testing uses SEM with the Smart-PLS tool. The results of the testing are shown in Table 7.

Table 7.

Hypothesis test results.

	Hypothesis path	β	t-value	Result
H1	Effort expectancy -> individual belief	0.451	6.280	Supported
H2	Performance expectancy -> individual belief	0.162	2.379	Supported
H3	Social influence -> individual belief	0.137	2.087	Supported
H4	Facilitating condition -> individual belief	0.116	1.929	Not supported
H5	Individual belief -> behavioral intention	0.707	18.137	Supported
H6	Behavioral intention -> intention to use	0.801	31.315	Supported

Table 7 shows that the greatest influence on consumers’ use of telemedicine is when the attitude that forms behavioral intention has emerged. Attitudes are formed from the beliefs of individual consumers. Effort expectancy, social influence, and performance expectancy influence individuals’ beliefs. This study demonstrates that facilitating condition has no influence on consumers using telemedicine.

Table 7 shows that, although social influence have an effect on individual beliefs, their influence is very small at 0.137. Social influence shows little influence on patients because the use of telemedicine emphasizes the performance of service providers rather than social influence. This is in accordance with the results of hypothesis testing where effort expectancy is shown to have the greatest influence. The variable that most forms belief is effort expectancy with a value of 0.451; this shows that consumers will indeed have belief if the effort of telemedicine is good and in accordance with their expectations.

Table 8 shows the indirect and direct effects. As for facilitating conditions, in direct and indirect tests they also showed no significance. This means that facilities in telemedicine applications are not the main thing for people to adopt telemedicine applications. The main factors behind consumers adopting telemedicine applications are effort expectancy and performance expectancy with research objects in Indonesia. The results of this study are different from the results of previous studies, especially the research of Siripipatthanakul et al.⁴¹

The above results of the testing of the hypotheses show that the greatest influence on the intention to use is where there is behavioral intention in consumers. Based on this, companies that provide telemedicine need to pay attention to consumer behavior in using their applications. The largest shaper of behavioral intention is perceived ease of use, meaning that companies need to pay attention to the ease and comfort that consumers experience while they are using telemedicine applications.

Discussion

Consumer behavior in the use of digitization shows differences in the services that are adopted. In telemedicine, where the services adopted are directly related to consumer health, consumers are cautious in the adoption process. They want telemedicine effort expectancy that can provide solutions for their health. Consumer trust is more formed from effort expectancy, performance expectancy, and social influence on telemedicine. Consumers’ behavioral intention is influenced by perceived usefulness, individual beliefs, and perceived ease of use of telemedicine services in Indonesia.

For telemedicine service providers, they can pay attention to the same service performance as offline services. All service factors are a focus for service providers. Factors such as the performance of doctors, nurses, administration, and pharmacy are of concern to patients. Therefore, their performance needs to be improved so that consumers are willing to adopt telemedicine. The advantages of telemedicine can reach the territory of Indonesia without experiencing problems if a good internet network is available for all regions across the archipelago of Indonesian.

Conclusion

In people’s everyday lives, technology is making everything easier to do. One example of this is in the health sector where technology has given rise to health applications. Telemedicine applications are one alternative means of conducting health consultations. Overall, consumers are willing to use telemedicine applications if they feel confident in the services offered by the providers. The manifestation of this belief can be seen in effort expectancy, performance expectancy, and social influence which have a positive influence on individual belief. The emergence of individual belief in consumers further strengthens trust in telemedicine applications which causes changes in consumer behavior. In addition, behavioral intention is also formed from perceived usefulness and perceived ease of use. The results of this study show consumer trust in telemedicine in Indonesia.

For telemedicine companies, they can improve the performance of those involved in providing the services. Improved performance would be in terms of doctors, nurses, delivery of medicines, and the system. The ease and convenience of patients in using telemedicine is a priority for telemedicine companies. Ease and comfort are a form of performance and effort expectancy.

The limitation of this study is that it did not differentiate between the generations that exist. Therefore, the results of this study are based on the perception of consumers broadly regarding the adoption of telemedicine applications without distinguishing between them based on age. Future research could be developed to examine the moderating role of generations in the use of telemedicine applications.

Footnotes

Acknowledgments

We would like to thank the respondents of survey.

ORCID iD

Yasintha Soelasih

Ethical statement

Author contributions

The contributions of each author are as follows: Yasintha Soelasih, first and corresponding author (email: yasintha.soelasih@atmajaya.ac.id): research leader contributed to concept, design, analysis, interpretation of results, preparation of drafts and article manuscripts for submission. Sumani (email: sumani@atmajaya.ac.id): data collection and preparation under the supervision of Soelasih. Efendi (email: efendi@atmajaya.ac.id): running test analysis and discussion of results. All authors agree to be accountable for all aspects of the work.

Funding

The authors disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: The research was supported by a grant from DRTPM Kemendikbudristek in Indonesia. Mandatory research grant Number: 0375.19/III/LPPM-PM.10.03.01/06/2024.

Declaration of conflicting interests

The authors declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Data Availability Statement

Data available on request from the authors.*

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