Exploring Factors Affecting User Satisfaction and Behavioral Intention towards Telemedicine Services among Gen-Z and Millennials in Indonesia: A PLS-SEM Study on Alodokter Application

Telemedicine has become increasingly important in healthcare, especially with the COVID-19 pandemic. Alodokter, one of the pioneers of health applications providing telemedicine services in Indonesia since 2014, has gained signi�cant attention. Younger generations–Millennials and Gen Z–are more �uent in using technology. However, previous study shows that older generations (Gen X) is more satis�ed in using telemedicine compared to younger generations. This study aims to identify the factors in�uencing user satisfaction and behavioral intention towards Alodokter's telemedicine service application usage among Millennials and Gen-Z in Indonesia. A survey was conducted on 160 respondents using a purposive sampling technique. The data was analyzed using the PLS-SEM based method with the SmartPLS MacOS application version 3.2.9. The study focused on examining the relationships between the variables of behavioral intention, customer satisfaction, and factors including performance expectancy, effort expectancy, price value, and social in�uence. The study �ndings indicate that customer satisfaction positively affects behavioral intention. Performance expectancy, effort expectancy, price value, and social in�uence positively impact customer satisfaction. Price value was shown to have the most positive in�uence on behavioral intention. The study provides insights into the factors in�uencing user satisfaction and behavioral intention towards telemedicine service application usage among younger generations in Indonesia. The results can be used to improve telemedicine services and enhance the experience of users, particularly Millennials and Gen-Z.


Introduction
In 2022, the number of global internet users reached 4.95 billion (penetration of 62.5%), an increase of 192 million from the previous year.This trend is also evident in Indonesia, where the penetration rate is 73.7%, with a total of 204.7 million users.Thanks to the internet, rapid technological advancements have played a crucial role in the development and growth of various aspects of life, including healthcare, which can enhance individual health status by improving the quality of healthcare services and management (Buntin et al. 2011;Eid 2011;Kemp 2022) In addition to the rapid technological advancements, the COVID-19 pandemic, which has caused drastic changes and resulted in the "contactless society" initiative worldwide, has made the term "telemedicine" increasingly popular among people (Byun and Park 2021; Wang et al. 2020).In a survey conducted by McKinsey in the United States in 2021, 46% of respondents switched to online consultations (telemedicine) compared to face-to-face consultations (Bestsennyy et al. 2021).The use of technology, including telemedicine, which has increased over the last few decades, is more preferred by younger generations (Millennials, Gen Z, and Gen X) than older ones (American Hospital Association 2021; Vogt et al. 2022).Alodokter, which is one of the pioneering telemedicine applications in Indonesia since 2014, still ranks second as the most widely used healthcare application among urban people after Halodoc.Halodoc is the most popular application, chosen by at least 45.3% of respondents, compared to Alodokter, which is chosen by 32.3% of respondents (Pusparisa 2019;Sari 2021).
Several studies have examined the intention to use or behavior in using telemedicine services using the Uni ed Theory of Acceptance and Use of Technology 2 (UTAUT2) technology acceptance model (Baudier et (Baudier et al. 2021) and Suroso and Sukmono (Suroso and Sukmoro 2021), both eliminated the hedonic motivation and price value variables because they were considered not suitable for the research.However, in their model, Byun and Park (Byun and Park 2021) found that the price value factor has a positive in uence on technology acceptance.Furthermore, research by Melinda and Setiawati (Melinda and Setiawati 2022) included all seven UTAUT2 variables and found that factors in uencing behavioral intention were price value, habit, Facilitating condition, and effort expectancy.
Despite many studies that have evaluated technology acceptance using the UTAUT2 model, studies exploring user or customer satisfaction using this model are still rare, and there is no standard model regarding satisfaction predictors (Kalinić et al. 2019).However, satisfaction variables are essential in determining user behavior towards a technology.When users are satis ed with an information system, they tend to return the appropriate value to the information system service provider (Kim and Son 2009).Previous research has linked UTAUT predictor variables with satisfaction variables in the eld of m-Commerce and m-Health use (Kalinić et al. 2019; W.-I. Lee et al. 2021).However, the model used in the acceptance of m-Health use only relates to predictor variables in UTAUT, not UTAUT2.
Considering the high number of telemedicine users from younger generations, Alhajri et al. (Alhajri et al. 2022) found that patients from Generation X -those born between 1960 and 1980 -are the most satis ed with telemedicine, even though Millennials and Gen Z are generations that heavily rely on technology platforms and social media to communicate and ful ll their needs (Alhajri et al. 2022;Ng et al. 2010).Furthermore, a study on the acceptance of telemedicine in Indonesia found that most Gen Z respondents were not affected by facilitating conditions, possibly because Gen Z is self-taught through the internet (Alexandra et al. 2021;Rettig and Rina 2020).Gen Z has also been found to face nancial challenges which may affect their decision-making in using commercially available telemedicine services (Ozkan and Solmaz 2015).This research aims to explore the factors in uencing behavioral intention mediated by customer satisfaction among young adults (Gen Z and Millennials) in using Alodokter telemedicine services.

Literature Review
For over decades, healthcare practitioners, health researchers, and others have been continuously searching for and innovating the use of cutting-edge telecommunications and computer technology to improve healthcare services.One result of these efforts is telemedicine, which is de ned as the use of information and electronic communication technology to provide and support healthcare when distance separates participants.Many efforts have been made, ranging from communication through telephone to video conferences, enabling doctors to see, hear, examine, interview, and advise distant patients for diagnostic and therapeutic purposes directly or in real-time (Institute of Medicine (US) Committee on Evaluating Clinical Applications of Telemedicine 1996).
Since the COVID-19 pandemic, the term telemedicine has become more popular among the public.This is due to the pandemic requiring people to implement social distancing or maintaining distance to reduce the transmission of the highly contagious COVID-19 virus through direct contact (Wang et al. 2020).In response to this, both the government and private companies in Indonesia have joined forces to develop telemedicine services to address COVID-19 (Gandhawangi 2021).One of the companies that has played a role in this is Alodokter.
Despite numerous studies on the adoption or acceptance of telemedicine using the UTAUT2 model, there are limited studies that explore user satisfaction with telemedicine (W.-I. Lee et al. 2021; Tiara and Antonio 2022; Wijaya and Wardani 2022).In the rst two studies, the original TAM model was used for analysis, while Lee (W.-I. Lee et al. 2021) employed the UTAUT model.On the other hand, technology acceptance theories have developed well, with many important theories and models presented, including the theory of planned behavior (TPB), the diffusion of innovation (DOI), the technology acceptance model (TAM), and the uni ed theory of acceptance and use of technology (UTAUT).Although initially developed for use in organizational contexts (Venkatesh et al. 2003), UTAUT is considered the most comprehensive theory of technology acceptance and use in various contexts.UTAUT initially emerged as a method to explain predictors of adoption and use of information and communication technology (ICT) by employees in speci c contexts, but it has since been successfully implemented in various studies on the adoption of services and speci c applications by organizations and consumers (Sheikh et al. 2017).To adapt UTAUT to the context of consumer use, Venkatesh et al. (Venkatesh et al. 2012) expanded it by adding three additional contextual variables, namely hedonic motivation, price value, and habit, thus creating the UTAUT2 model.UTAUT2 is considered comprehensive and provides better explanations compared to other technology adoption models (Macedo 2017).This model has been successfully tested in the context of online shopping acceptance (Tandon et  The integration of the UTAUT2 adoption model with customer satisfaction in the context of telemedicine is still lacking.However, the three additional variables in UTAUT2 provide a comprehensive understanding of a customer's use of information systems or technology. Based on previous research on UTAUT2 and telemedicine, Baudier (Baudier et al. 2021) and Suroso and Sukmoro (Suroso and Sukmoro 2021) did not include HM and PV variables in their studies, stating that they were not relevant to their research.However, Melinda and Setiawati (Melinda and Setiawati 2022) and Byun and Park (Byun and Park 2021) found that price value factor has a positive in uence on technology acceptance.This can be assumed due to the fact that during the COVID-19 pandemic, telemedicine services were provided for free by the government, but considering the current situation where telemedicine services are becoming paid and there are many complaints about pricing (play.google.com2022), it is important to include this variable in this study.On the other hand, hedonic motivation and habit variables were not included, as the individual level of technological support is not expected to signi cantly in uence or delay consumer's use of telemedicine (Byun and Park 2021).In the context of telemedicine services offered for healthcare, consumers' intention to use it is not routine but rather depends on the unique healthcare needs of each individual (depending on their health condition) (W.-I. Lee et al. 2021).Moreover, considering the critical characteristics of medical care directly related to human health and the situational characteristics of the commercialization stage of telemedicine, these two variables were not included.Furthermore, a study on telemedicine acceptance among Generation Z respondents in Indonesia found that facilitating conditions did not in uence their acceptance, which could be due to Gen Z's tendency to learn everything independently through the internet (Alexandra et al. 2021; Rettig and Rina 2020).

Hypothesis development and research model
This study aim to investigate the factors in uencing user satisfaction and behavioral intention of telemedicine services among Gen-Z and Millennials user in Indonesia.Based on the review above, modi ed UTAUT2 model was selected as the basis conceptualised framework by adding user satisfaction dimension to it.Therefore 4 main factors namely performance expectancy, effort expectancy, social in uence, and price value were selected to in uence user satisfaction affecting behavioral intention.The factor facilitating conditions, habit, and hedonic motivation were not included as mentioned before.
In the context of telemedicine, PE refers to the perceived effectiveness of telemedicine services among users.Therefore, if users perceive telemedicine services as effective in improving their healthcare experience, they are likely to be satis ed with the service, which will in uence their intention to use it.Research has found that PE signi cantly in uences user satisfaction in the use of m-Health (W.-I. Lee et al. 2021) and m-commerce (Kalinić et al. 2019).
PE is often associated with perceived usefulness in the Technology Acceptance Model (TAM) and is a strong predictor of technology acceptance (Venkatesh et al. 2003).An et al. (An et al. 2021) conducted a study on factors affecting the use of telehealth using the TAM model and found that perceived usefulness (PU) has a signi cant positive impact on attitudes towards telehealth.In this study, positive attitude includes satisfaction and high favourability.Additionally, the signi cant positive impact of PU on customer satisfaction has been con rmed in cases of m-commerce (Marinkovic and Kalinic 2017), mobile social applications (Hsiao et  In this study, considering that the current telemedicine service applications are paid, it can be assumed that the perceived value ratio of the telemedicine service in relation to the monetary cost incurred to use the service affects customer satisfaction.Kalinic et al. (Kalinić et al. 2019) and Lin and Wang (Lin and Wang 2006) found that perceived value in m-commerce signi cantly affects customer satisfaction.Additionally, previous research has found that perceived value in the monetary context in uences customer satisfaction in the use of mobile social tourism (Kim et al. 2013) and mobile services (Kuo et al. 2009).
Based on the descriptions provided, the following hypothesis can be formulated: Hypothesis 4 (H4).Price value positively in uence user satisfaction Generation Z and Millennials.
Customer satisfaction greatly re ects the customer's assessment of a particular service or product (Tandon et al. 2017).Customer satisfaction is usually a key driver in a customer's attitude towards the continued use of a technology or system (Marinkovic and Kalinic 2017).Lin and Wang ( Figure 1 visualises the relationship between variables that make up research model.Each hypothesis was assigned to Fig. 1.

Research design
The research was conducted using a quantitative study method with a cross-sectional approach.The objective of this study is to test and analyze the factors in uencing user satisfaction towards behavioral intention of Gen-Z and Millennials on the Alodokter telemedicine application.There are a total of 6 variables involved in this study, with the independent variables being performance expectancy, effort expectancy, social in uence, and Price value.The dependent variable in this study is behavioral intention with User satisfaction as the mediating variable.

Sampling and data collection
In this study, Millennial and Gen-Z consumers who have used telemedicine applications in Indonesia within the past year were selected as the population.Sampling was done purposively during November 2022.Respondents remained anonymous and voluntary, with their data con dentiality assured through consent.The researcher has conducted peer reviews by experts and obtained approval from the Marketing Division, Department of Management, Universitas Pelita Harapan.
An online questionnaire was used to collect quantitative data, which aimed to measure the constructs in the previously outlined model.The questionnaire was developed based on indicators obtained from relevant journals, books, and other information.It was translated into Bahasa Indonesia and reviewed by experts in the eld of health marketing to ensure accuracy and comprehensibility.A total of 30 questions were obtained from various literature and rephrased.Each indicator was assessed using a 5-point Likert scale to indicate agreement, ranging from 1 (strongly disagree) to 5 (strongly agree).
Prior to the main study, the questionnaire underwent a pilot test among the public with feedback to improve question items and the overall questionnaire.The pretest sample was excluded from the main study.The questionnaire was adapted from previous literature and studies (Byun and  A total of 317 individuals participated in this study.From this data, 160 respondents will be analyzed as they are the ones who have used the Alodokter telemedicine application within the past year.This sample size meets the minimum criteria for analysis using Partial Least Square-Structural Equation Modelling (PLS-SEM) (Joe F Hair et al. 2012;Memon et al. 2020).Table 1 shows demographic characteristics of the respondents.

Operational de nition of variables
In this study, performance expectancy, effort expectancy, social in uence, and price value have been chosen as the main indicators in uencing behavioral intention in the use of Alodokter telemedicine, with user satisfaction as a mediating variable.The inner model is the structural model that displays the relationships between the constructs and their in uences on each other, in this case, testing the hypotheses of each relationship.Testing is done using the parameter value of p < 0.05 with a t-statistic value > 1.645.

Results
The rst result of data processing using SmartPLS software version 3.2.9 on MacOS is the outer model measurement result.Here, validity and reliability testing will be conducted.In assessing convergent validity, besides looking at the average variance extracted (AVE) value ≥ 0.50, the outer loadings should also be considered, which should be ≥ 0.708 Next, reliability testing is conducted through Cronbach's Alpha and composite reliability values.These values need to be evaluated if they are above 0.70 or not.The upper limit commonly used as a criterion is composite reliability, while the lower limit is Cronbach's Alpha.If both have values > 0.70, it can be said that the variables in this study are reliable with the assumption that the model is correct (Joseph F Hair et al. 2019).However, it should be noted that the values should not exceed 0.95 as it may cause redundancy.Table 3 enlists reliability and convergent validity analysis are presented.All constructs in the study have AVE values above 0.5, indicating that each construct can explain at least 50% of the variance of each item in the model.In addition, all indicators also have reliability values above 0.7 and do not exceed the upper limit of 0.95, indicating that the reliability of the constructs is acceptable (see Table 3).
Another step is to measure discriminant validity.Discriminant validity can be tested using the Fornell-Larcker criterion, but Table 4 shows how the model meets the criteria for discriminant validity testing.In this study, all values below 0.9 indicate that the model discriminates well in assessing each construct.The evaluation of the structural model is done by checking for multicollinearity to determine the possibility of relationships between the independent variables within a model.This can be seen through the analysis of Variance In ation Factor (VIF) values.The criteria for VIF values are below 5.0, but it is recommended to be below 3.0 to ensure there are no issues with multicollinearity (Joseph F Hair et al. 2019).In this model, all VIF values are below 3.0.The R-Square values for BI and SAT are 0.554 and 0.678, respectively, indicating that 55.4% of the variance in behavioral intention can be explained by user satisfaction, while 67.8% of the variance in user satisfaction can be explained by performance expectancy, effort expectancy, social in uence, and price value.
This indicates that both models have moderate strength of predictive accuracy.Another test, Q 2 _Predict was also measured to know the predictive relevance on the variable.The Q 2 _Predict value on user satisfaction (0.647) shows large predictive relevance, while on behavioral intention (0.469) shows medium predictive relevance.
Table 5 compiles the results of hypothesis testing using bootstrapping feature in SmartPLS, it informs that all hypotheses are supported, indicating a signi cant positive in uence between the variables being tested.This can be seen from all the positive path coe cient values, p-value < 0.05, and t-statistic values above 1.645.We can observe in Table 6 that the independent variables are mediated by the customer satisfaction variable towards the dependent variable.Table 6 re ects that the four independent variables, namely performance expectancy, effort expectancy, social in uence, and price value, are mediated by the customer satisfaction variable in in uencing the independent variable of behavioral intention, as they meet the signi cance criteria with a p-value < 0.05 and t-statistic value < 1.645.From the hypothesis testing, it can be found that social in uence has the smallest path coe cient (0.123), therefore SI has small effect on satisfaction compared to price value (0.346) which affect the most.Figure 2 displays the results of the PLS-SEM analysis with standardized path coe cients.From these results, it can be stated that the proposed model has the capability to depict the factors that in uence telemedicine behavioral intention.

Discussion
This discusses the factors that in uence customer satisfaction as a mediating variable for the intention to use the Alodokter telemedicine service application.Based on the demographic data presented in the results section, although the number of male Alodokter users is higher than female users, when it comes to telemedicine usage, females outnumber males.This is consistent with the ndings of Darrat et al. ( 2021), which showed that females prefer virtual visits compared to males.Additionally, they found that older patients, patients with low income, and patients with low education are less likely to engage in virtual visits, including telehealth or telemedicine that utilizes remote communication instead of face-to-face consultations.This is in line with the demographic data in this study, where the majority of telemedicine users have at least a bachelor's degree and a middle to high income.
The results of the above analysis have successfully demonstrated that performance expectancy has a positive in uence on customer satisfaction with the Alodokter telemedicine service application.This is in line with previous studies (Hsiao et  ).This implies that the monetary value or price offered by the Alodokter telemedicine service has an impact on customer satisfaction.Although the average income is middle to high, this may be due to concerns about the nancial ability or issues of the young adult population, especially Gen-Z that usually feel anxious about their nancial (Ozkan and Solmaz 2015).A study conducted among the Gen Z population found that nancial attitude has a signi cant impact on nancial happiness, indicating that they need to have a positive nancial attitude in order to effectively address nancial di culties.This speaks to how they manage their nances, including healthcare spending nabila (Nabila et al. 2023).
Lastly, it was found that customer satisfaction has a positive in uence on behavioral intention to use.This is in line with previous research (Barutçu et  The model used in this research has shown good predictive accuracy and predictive relevance, allowing for accurate prediction of customer satisfaction and behavioral intention to use telemedicine applications.The study also found that price value has the greatest in uence on young adult users, who may still experience nancial instability.This may explain why Gen-X users are more satis ed with using telemedicine services.
Further research with a larger sample size is recommended to be conduct considering the limitations of the number respondents obtained.Since this study mainly focused on variables in the UTAUT2 model, we also suggest to include other indicators that may affect both customer satisfaction and/or behavioral intention such as trust and perceived risk.Given that price value has the most signi cant impact on behavioral intention, future studies exploring in uence of nancial attitude in telemedicine usage behavior among Gen Z may be conducted.The ndings on this research can also be applied to the telemedicine in general.

Conclusions
This study investigates factors in uencing behaviotal intention with user satisfaction as mediating factor.The model focuses on factors in UTAUT2 model that exceptionally in uence the variables.We conclude that: The positive in uence of performance expectancy, effort expectancy, social in uence, and price value on Gen-Z and Millennials' behavioral intention in using Alodokter Telemedicine was found to be mediated by user satisfaction in this study.
The enhancement of customer satisfaction through performance expectancy, effort expectancy, social in uence, and price value is crucial in young adults' behavior of using Alodokter telemedicine, considering the increase demand of telemedicine usage since the COVID-19 pandemic.
The results indicate that most respondents are satis ed with the Alodokter telemedicine service.
Price value shows to have the most positive in uence on Gen-Z and Millennials user satisfaction.This may be explored in further research since the commercialization of telemedicine usage is rising.

2 ( 3 (
al. 2016), mobile services (C.-Y. Lee et al. 2015), m-banking (Susanto et al. 2016), and mobile websites (Zhou 2011).Based on the description above, the following hypotheses can be proposed: Hypothesis 1 (H1).Performance expectancy positively in uence user satisfaction in Generation Z and Millennials.expectancy,which is often equated with perceived ease of use (PEU) in the TAM model, has been found to have a signi cant positive in uence on customer satisfaction in telemedicine research(An et al. 2021;Yan et al. 2021).Furthermore, in other studies, it has been found that PEU signi cantly affects customer satisfaction in mobile application services (C.-Y.Lee et al. 2015) and mobile websites(Zhou 2011).Based on the descriptions provided, the following hypothesis can be formulated: Hypothesis H2).Effort expectancy positively in uence user satisfaction Generation Z and Millennials.Social in uence is one of the predictors commonly found in research on technology acceptance and use.Although social in uence does not signi cantly affect user satisfaction in some studies on m-health (W.-I.Lee et al. 2021) and m-commerce(Kalinić et al. 2019), several previous studies examining the in uence of social environment on customer satisfaction have obtained signi cant results, such as in the use of mobile social apps(Hsiao et al. 2016), online life insurance purchase(Viswanathan et al. 2020), and social commerce websites(Beyari and Abareshi 2018).Considering that reviews from others can also in uence the intention to use an application, the following hypothesis can be formulated: Hypothesis H3).Social in uence positively in uence user satisfaction Generation Z and Millennials.

5 (
Lin and Wang 2006) found that customer satisfaction affects customer loyalty in m-commerce usage, while Kalinic et al. (Kalinić et al. 2019) found that customer satisfaction in uences commitment to continued use in m-commerce.In the context of medical services and m-Health, Lee et al. (W.-I. Lee et al. 2021) and Barutçu et al. (Barutçu et al. 2018) found that user satisfaction with m-Health has a positive in uence on intention to use m-Health services.Based on the descriptions provided, the following hypothesis can be formulated: Hypothesis H5).User satisfactions positively in uence behavioral intention Generation Z and.
Park 2021; Kalinić et al. 2019; W.-I. Lee et al. 2021; Venkatesh et al. 2003, 2012) and modi ed for the purpose of novelty and understanding concepts.
Joseph F Hair et al. 2019) If there are indicators with outer loadings below this threshold, it can be considered whether removing those indicators can improve the reliability and validity values (both convergent and discriminant).
Henseler et al. (2015) showed that the Fornell-Larcker criterion performs poorly, especially when the indicator loadings on a construct are only slightly different.Instead, Henseler proposed the Heterotrait-Monotrait (HTMT) correlation ratio (Joseph F Hair et al. 2019).Accepted HTMT values are below 0.90, indicating that a construct has speci c discriminated indicators (Joseph F Hair et al. 2019; Henseler et al. 2015).
al. 2018; Kalinić et al. 2019; W.-I. Lee et al. 2021).Moreover, customer satisfaction has a potential effect on usage behavioral intention with a path coe cient of 0.745.Therefore, it is crucial to improve customer satisfaction among the young adult population to enhance the intention to use the Alodokter telemedicine application.

Figures
Figures

Figure 2 Result
Figure 2 Result model.Arrows toward the yellow box indicates outer loadings, while arrows pointing on the blue circle represent standardize coe cient effect.R 2 was shown inside the blue circle.
Although there have been numerous studies that evaluate technology acceptance using the UTAUT2 model, studies that explore user satisfaction using this model are still limited, and there is no standard model for predicting satisfaction(Kalinić etal.2019).However, in their study titled "Determinants Impacting User Behavior towards Emergency Use Intentions of m-Health Services in Taiwan," Lee et al. (W.-I. Lee et al. 2021) used the original UTAUT model adapted to the context of user satisfaction.Furthermore, Kalinic et al. (Kalinić et al. 2019) were the rst to adopt UTAUT2 and adapt it to the context of customer satisfaction in m-commerce.

Table 2
(Ringle et al. 2015) study uses PLS-SEM as it is suitable for explanatory research(Joseph FHair et al. 2019).PLS-SEM analysis is conducted using SmartPLS software version 3.2.9 on MacOS(Ringle et al. 2015).From the results of the PLS-SEM testing, two models are obtained, namely the outer model and the inner model.The outer model, or measurement model, tests the reliability and validity of the indicators of the variable constructs.Reliability testing is done through indicator assessment (outer loading), and construct reliability is assessed using Cronbach's alpha and composite reliability.Validity testing is done through construct validity (average variance extracted) and discriminant validity through heterotrait/monotrait ratio.After ful lling the reliability and validity tests, the next step is to conduct the structural analysis or inner model analysis.

Table 3
Evaluation of measurement model test results.

Table 5
Hypothesis test result.

Table 6
Speci c indirect effect.
Lee et al. 2021ić et al. 2019; W.-I. Lee et al. 2021; Marinkovic andKalinic 2017) that found performance expectations to be positively correlated with customer satisfaction.This indicates that users have high expectations or expectations of Alodokter in providing existing services, and users are satis ed with it.This may be due to users who need medical treatment when they are unable to visit health facilities and are able to receive optimal treatment from Alodokter.Furthermore, a positive relationship was found between effort expectancy and the intention to use the telemedicine service application.This is consistent with several previous studies(An etal.2021;C.-Y. Lee et al. 2015; Yan et al. 2021; Zhou 2011).Although there are studies that found no positive effect of effort expectancy on behavioral intention (Kalinić et al. 2019; W.-I. Lee et al. 2021), this may be due to the fact that the studies were conducted in developed countries where mobile applications are no longer seen as innovative services but rather a part of daily life, where business and payments are all done using mobile applications, not just for entertainment purposes.However, the current study was conducted in a developing country, Indonesia.The results of the analysis above have successfully proven that performance expectancy has a positive in uence on user satisfaction with the Alodokter telemedicine service application.This is consistent with previous studies(Hsiao etal.2016;Kalinićetal.2019;W.-I. Lee et al. 2021; Marinkovic and Kalinic 2017) that found performance expectancy to have an impact on customer satisfaction.This indicates that users have high expectations of Alodokter telemedicine in providing the existing services and users are satis ed with it.This may be due to users who need medical treatment when they cannot visit health facilities and receive optimal treatment from Alodokter.Positive in uence was also found in the relationship between social in uence and user satisfaction.This is consistent with previous research(Beyari and Abareshi 2018; Hsiao et al. 2016; Viswanathan et al. 2020).Although there are studies that found no positive relationship between the two (Kalinić et al. 2019;W.-I.Lee et al. 2021), this may be due to, as explained earlier, users' habits towards mobile services that allow individuals to determine the bene ts and uses regardless of their environment.However, in this study the social in uence variable has the smallest effect on satisfaction compared to other variables.
Price value was found to have the greatest positive in uence on behavioral intention to use the telemedicine application.This nding is consistent with previous research(Kalinić etal.2019; Kim et al. 2013; Kuo et al. 2009; Lin and Wang 2006