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Determinants of behavioral intention toward telemedicine services among Indonesian Gen-Z and Millenials: a PLS–SEM study on Alodokter application

Determinants of behavioral intention toward telemedicine services among Indonesian Gen-Z and... ekapramudita.w@gmail.com Telemedicine has become increasingly important in healthcare, especially Graduate School of Management, Faculty with the COVID-19 pandemic. Despite, Younger generations are more fluent in using of Economics and Business, technology, previous study shows that older generations (Gen-X) are more satisfied Universitas Pelita Harapan, South in using telemedicine compared to younger generations. This study aims to identify Jakarta 12930, Indonesia Health Center at Pattimura the factors influencing user satisfaction and behavioral intention toward Alodokter’s Air Force Base, Ambon 97326, telemedicine service application usage among Millennials and Gen-Z in Indonesia. Indonesia A quantitative cross-sectional study was conducted using a purposive sampling tech- nique. A total of 160 Millenials and Gen-Z respondents who had used the Alodokter telehealth application within the past year were chosen in this study. The data were collected by means of an online questionnaire that was distributed through widely used social media platforms. The questionnaire consisted of 30 questions that assessed variables, such as performance expectancy, effort expectancy, social influence, price value, customer satisfaction, and behavioral intention. Data were analyzed using Partial Least Square–Structural Equation Modeling (PLS–SEM) with SmartPLS software ver- sion 3.2.9. The findings reveal that customer satisfaction positively affects behavioral intention. Performance expectancy, effort expectancy, price value, and social influ- ence positively impact customer satisfaction. Price value was shown to have the most positive influence on behavioral intention. This study provides insights into the factors influencing user satisfaction and behavioral intention toward 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. Keywords: User satisfaction, Behavioral intention, Telemedicine, UTAUT2 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 © The Author(s) 2023. Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the mate- rial. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http:// creativecommons.org/licenses/by/4.0/. Pramudita et al. Journal of Innovation and Entrepreneurship (2023) 12:68 Page 2 of 21 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 & 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 tel - emedicine, 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 Asso- ciation, 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 health- care application among urban people after Halodoc. Halodoc is the most popular appli- cation, 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 Unified Theory of Acceptance and Use of Technology 2 (UTAUT2) technology acceptance model (Baudier et  al., 2021; Byun & Park, 2021; Martins et  al., 2021; Melinda & Setiawati, 2022; Suroso & Sukmoro, 2021). In research conducted by Baudier et al. (Baudier et al., 2021) and 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 & Park, 2021) found that the price value factor has a positive influence on technology acceptance. Further - more, research by Melinda and Setiawati (Melinda & Setiawati, 2022) included all seven UTAUT2 variables and found that factors influencing 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 toward a tech- nology. When users are satisfied with an information system, they tend to return the appropriate value to the information system service provider (Kim & Son, 2009). Pre- vious research has linked UTAUT predictor variables with satisfaction variables in the field of m-Commerce and m-Health use (Kalinić et al., 2019; 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. (2022) found that patients from Generation X—those born between 1960 and 1980—are the most satisfied with telemedicine, even though Millennials and Gen Z are generations that heavily rely on technology platforms and social media to communicate and fulfill 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 & Rina, 2020). Gen Z has also been found to face P ramudita et al. Journal of Innovation and Entrepreneurship (2023) 12:68 Page 3 of 21 financial challenges which may affect their decision-making in using commercially avail - able telemedicine services (Ozkan & Solmaz, 2015). This research aims to explore the factors influencing behavioral intention mediated by customer satisfaction among young adults (Gen Z and Millennials) in using Alodokter telemedicine services. This study contributes to existing knowledge by examining factors influencing young adults’ (Gen Z and Millennials) behavioral intention and satisfaction when using Alo- dokter telehealth services. While numerous studies have evaluated technology accept- ance using the Unified Theory of Acceptance and Use of Technology 2 (UTAUT2) model, little research has focused on user satisfaction within this framework. This study fills a gap in the literature and provides valuable insight into the adoption and use of telehealth services by examining the relationship between technology acceptance factors and user satisfaction. In addition, this study extends the applicability of the UTAUT2 model to the context of telemedicine and contributes to a more comprehensive under- standing of users’ attitudes and intentions in the area of health technology. To provide a coherent and informative presentation of the study, the overall structure of this paper follows a comprehensive framework in addition to the aforementioned sec- tions. This paper begins by exploring the theoretical and empirical foundations related to study. Following this, hypotheses and the research model are developed based on the insights gained from the previous literature. The next section presents the methodology, which includes the research design, sampling, data collection procedures, operational definitions of variables and the chosen data analysis technique. In the results section, the findings obtained through the analysis are presented, followed by a detailed discus - sion of the results. The article then concludes with a section summarizing the main con - clusions of the study and their implications for both theory and practice. In addition, this article critically discusses the limitations encountered during the research and offers recommendations for future research to address these limitations. 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 defined as the use of information and electronic communi - cation technology to provide and support healthcare when distance separates partici- pants. Many efforts have been made, ranging from communication through telephone to video conferences, enabling doctors to see, hear, examine, interview, and advise dis- tant 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 telemedi- cine services to address COVID-19 (Gandhawangi, 2021). One of the companies that has played a role in this is Alodokter. Pramudita et al. Journal of Innovation and Entrepreneurship (2023) 12:68 Page 4 of 21 Despite numerous studies on the adoption or acceptance of telemedicine using the UTAUT2 model, there are limited studies that explore user satisfaction with telemedi- cine (Lee et al., 2021; Tiara & Antonio, 2022; Wijaya & Wardani, 2022). In the two stud- ies, the original TAM model was used for analysis, while 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 unified 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 by employees in specific contexts, but it has since been successfully implemented in various studies on the adoption of ser- vices and specific applications by organizations and consumers (Sheikh et al., 2017). To adapt UTAUT to the context of consumer use, 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 model is considered compre- hensive and provides better explanations of technology acceptance compared to other technology adoption models (Macedo, 2017). This model has been successfully tested in the context of online shopping acceptance (Tandon et al., 2017), m-commerce (Chopdar et  al., 2018), internet banking (Alalwan et  al., 2018), mobile applications (Gupta et  al., 2018), mobile social networking games (Baabdullah, 2018), m-health (Dwivedi et  al., 2016), and telemedicine (Baudier et  al., 2021; Byun & Park, 2021; Martins et  al., 2021; Melinda & Setiawati, 2022; Suroso & Sukmoro, 2021). 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 et al., 2019). While TAM provides insight into the cognitive processes underlying technology accept- ance and customer satisfaction index (CSI) offers a comprehensive approach to meas - uring customer satisfaction, the UTAUT has the most comprehensive understanding and model, because it combines eight pre-existing theories and models of technology acceptance, including TAM (Lee & Kim, 2022; Venkatesh et  al., 2003, 2012). UTAUT is one of the most widely used models in the acceptance of technology or information systems (Dwivedi et al., 2020). UTAUT originally stated that four independent variables directly influence behavioral intention (Venkatesh et  al., 2003). However, it is argued that information system users’ cognitive and affective perceptions first form an attitude toward the information system and then affect behavioral intention based on the TPB in UTAUT(Ajzen, 2002; Lee & Kim, 2022). Thus, it can be reasonably concluded that the independent variables affect the attitude of information system users and influence behavioral intention (Lee & Kim, 2022). Several previous studies also confirm that there is an effect of attitude on behavioral intention, which contradicts the opinion of Ven - katesh et al. where the effect of attitude on behavioral intention is spurious (Jairak et al., 2009; Nassuora, 2013; Thomas et al., 2013). Satisfaction with the use of information sys - tems is one of the most commonly used attitude variables (Bhattacherjee, 2001; Lee & Kim, 2022). P ramudita et al. Journal of Innovation and Entrepreneurship (2023) 12:68 Page 5 of 21 In their study titled "Determinants Impacting User Behavior toward Emergency Use Intentions of m-Health Services in Taiwan," Lee et al. (2021) used the original UTAUT model adapted to the context of user satisfaction. Furthermore, Kalinic et  al. (2019) were the first to adopt UTAUT2 and adapt it to the context of customer satisfaction in m-commerce. The integration of the UTAUT2 adoption model with customer satisfac - tion 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 et  al. (2021) and Suroso and Sukmoro (2021) did not include hedonic motivation and price value (PV) variables in their studies, stating that they were not relevant to their research. However, Melinda and Setiawati (2022) and Byun and Park (2021) found that price value factor has a positive influence 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.com, 2022), 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 significantly influence or delay consumer’s use of telemedicine (Byun & 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) (Lee et al., 2021). Moreover, con- sidering 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 influ - ence their acceptance, which could be due to Gen Z’s tendency to learn everything inde- pendently through the internet (Alexandra et al., 2021; Rettig & Rina, 2020). Hypothesis development and research model This study aims to investigate the factors influencing user satisfaction and behavioral intention (BI) of telemedicine services among Gen-Z and Millennials user in Indonesia. Based on the review above, modified UTAUT2 model was selected as the basis concep - tualized framework by adding user satisfaction dimension to it. Therefore, four main fac - tors, namely, performance expectancy (PE), effort expectancy (EE), social influence (SI), and price value (PV), were selected to influence user satisfaction affecting BI. 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 satisfied with the service, which will influence their intention to use it. Research has found that PE significantly influences user satisfaction in the use of m-Health (Lee et  al., 2021) and m-commerce (Kalinić et al., 2019). Pramudita et al. Journal of Innovation and Entrepreneurship (2023) 12:68 Page 6 of 21 PE is often associated with perceived usefulness (PU) in the TAM and is a strong predictor of technology acceptance (Venkatesh et  al., 2003). An et  al. (2021) con- ducted a study on factors affecting the use of telehealth using the TAM model and found that perceived usefulness has a significant positive impact on attitudes toward telehealth. In this study, positive attitude includes satisfaction and high favourability. In addition, the significant positive impact of PU on customer satisfaction has been confirmed in cases of m-commerce (Marinkovic & Kalinic, 2017), mobile social appli- cations (Hsiao et  al., 2016), mobile services (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 influence user satisfaction in Generation Z and Millennials. Although some research has found that effort expectancy does not significantly affect user satisfaction (Kalinić et al., 2019; Lee et al., 2021), effort expectancy, which is often equated with perceived ease of use (PEU) in the TAM model, has been found to have a significant positive influence on customer satisfaction in telemedicine research (An et al., 2021; Yan et al., 2021). Furthermore, in other studies, it has been found that PEU significantly affects customer satisfaction in mobile application ser - vices (Lee et  al., 2015) and mobile websites (Zhou, 2011). Based on the descriptions provided, the following hypothesis can be formulated: Hypothesis 2 (H2). Effort expectancy positively influence user satisfaction Generation Z and Millennials. Social influence is one of the predictors commonly found in research on technology acceptance and use. Although social influence does not significantly affect user satis - faction in some studies on m-health (Lee et al., 2021) and m-commerce (Kalinić et al., 2019), several previous studies examining the influence of social environment on cus - tomer satisfaction have obtained significant 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 & Abareshi, 2018). Considering that reviews from others can also influence the intention to use an application, the following hypothesis can be formulated: Hypothesis 3 (H3). Social influence positively influence user satisfaction Generation Z and Millennials. 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. (2019) and Lin and Wang ( 2006) found that perceived value in m-commerce significantly affects customer satisfaction. Additionally, previous research has found that perceived value in the monetary context influences customer satisfaction in the P ramudita et al. Journal of Innovation and Entrepreneurship (2023) 12:68 Page 7 of 21 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 influence user satisfaction Generation Z and Millennials. Customer satisfaction greatly reflects the customer’s assessment of a particular ser - vice or product (Tandon et al., 2017). Customer satisfaction is usually a key driver in a customer’s attitude toward the continued use of a technology or system (Marinkovic & Kalinic, 2017). Lin and Wang (2006) found that customer satisfaction affects customer loyalty in m-commerce usage, while Kalinic et  al. (2019) found that customer satisfac- tion influences commitment to continued use in m-commerce. In the context of medical services and m-Health, Lee et al. (2021) and Barutçu et al. (2018) found that user satis- faction with m-Health has a positive influence on intention to use m-Health services. Based on the descriptions provided, the following hypothesis can be formulated: Hypothesis 5 (H5). User satisfactions positively influence behavioral intention Genera - tion Z and. Figure  1 visualizes the relationship between variables that make up research model. Each hypothesis is assigned to Fig. 1. Methods 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 influencing user satisfaction toward behavioral intention of Gen-Z and Millennials on the Alodokter tel- emedicine application. There are a total of 6 variables involved in this study, with the Fig. 1 Research model showing factors influencing behavioral intention mediated by customer satisfaction. H1 Hypothesis 1, H2 Hypothesis 2, H3 Hypothesis 3, H4 Hypothesis 4 Pramudita et al. Journal of Innovation and Entrepreneurship (2023) 12:68 Page 8 of 21 independent variables being performance expectancy, effort expectancy, social influ - ence, and price value. The dependent variable in this study is behavioral intention with user satisfaction as the mediating variable. Sampling and data collection While probability sampling is considered ideal in research, many studies in social science research actually rely on non-probability sampling (Rowley, 2014). Non-probability sam- pling involves purposive selection, chance, or expert judgment, where individuals’ chances of being selected are unknown (Burns et al., 2008). Non-probability sampling is more com- mon and appropriate in field research, especially studies involving human subjects (Bry - man & Bell, 2015). Carefully controlled non-probability sampling can produce valid and meaningful results (Schindler, 2011). The choice between probability and non-probability sampling does not determine the quality of the research (Memon et al., 2017). Probability sampling is ideal for generalizability sampling, but if the goal is rigorous theory testing, non- probability sampling is more appropriate (Calder et al., 1981; Hulland et al., 2018). In social science studies, it is extension of knowledge and generalization of theory that is impor- tant, not generalization of sampling (Memon et al., 2017). Probability sampling techniques require a sampling frame, which is a complete list of all subjects in the target population. However, obtaining a sampling frame and achieving a 100% response rate can be challeng- ing, especially in studies involving diverse and dispersed populations such as in Indonesia (It is easy to compromise the underlying assumptions of probability sampling by giving each subject an equal chance in a large geographic area with uneven Internet penetration.). Non- probability sampling is appropriate when the choice of sampling strategy is consistent with the research objectives, the goal is to generalize theory, and a complete sampling frame is not available (Hulland et al., 2018; Memon et al., 2017; Rowley, 2014). As this study is con- cerned with testing the theoretical framework from a predictive perspective and aims to extend existing theories or models, a purposive sampling method was selected. In this study, Millennial and Gen-Z consumers who have used telemedicine applica- tions in Indonesia within the past year were selected as the population. Sampling was done purposively during November 2022. Respondents remained anonymous and vol- untary, with their data confidentiality assured through consent. The researcher has con - ducted 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 meas- ure 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 field of health mar - keting 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 Lik- ert 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 sam - ple was excluded from the main study. The questionnaire was adapted from previous literature and studies (Byun & Park, 2021; Kalinić et al., 2019; Lee et al., 2021; Venkatesh et al., 2003, 2012) and modified for the purpose of novelty and understanding concepts. P ramudita et al. Journal of Innovation and Entrepreneurship (2023) 12:68 Page 9 of 21 Table 1 Demographic characteristics No Demographic characteristics Sample (n) Percentage (%) 1 Gender Male 84 52.5 Female 76 47.5 2 Age 17–25 years (Gen Z) 40 25 26–41 years (Millennials) 120 75 3 Current Residence DKI Jakarta 83 51.9 Java Island other than DKI Jakarta 48 30 Other areas 25 18.1 4 Income < Rp. 4.500.000 45 28.1 Rp. 4.500.000–Rp. 10.000.000 75 46.9 > Rp. 10.000.000 40 25 5 Education High School (SMP/SMA) 46 28.8 Diploma (D3) 5 3.1 Bachelor’s degree (S1) 91 56,9 Post-graduate (S2) 18 11.2 6 Occupation Private sector employee 89 55.6 Student 19 11.9 Civil servant 26 16.2 Self-employed 17 10.6 Labor 4 2.5 Others 5 3.2 An online questionnaire was created using Google Forms. It was then distributed through the most widely used social media platforms in Indonesia such as WhatsApp, Line, Facebook, and Instagram. Either groups or individuals belonging to the Gen-Z and Millennial age categories were used to distribute the questionnaire. Some of the eligibil- ity criteria for filler participants were: (1) aged over 17, (2) having used the Alodokter telehealth application, (3) being Indonesian citizens. The exclusion criteria were those who did not fill out the questionnaire in its entirety. A total of 317 individuals participated in this study. From these data, 160 respondents will be analyzed as they are the ones who have used the Alodokter telemedicine appli- cation within the past year. This sample size meets the minimum criteria for analysis using Partial Least Square-Structural Equation Modelling (PLS–SEM) (Hair et al., 2012; Memon et al., 2020). Table 1 shows demographic characteristics of the respondents. Operational definition of variables In this study, performance expectancy, effort expectancy, social influence, and price value have been chosen as the main indicators influencing behavioral intention in the use of Alodokter telemedicine, with user satisfaction as a mediating variable. Table 2 presents operational definitions of these variables are presented. Pramudita et al. Journal of Innovation and Entrepreneurship (2023) 12:68 Page 10 of 21 Table 2 Operational definitions of construct variables Variable Operational definitions References Performance expectancy The level of perceived influence of using (Byun & Park, 2021; Kalinić et al., 2019; Lee Alodokter telemedicine services in et al., 2021; Venkatesh et al., 2003, 2012) achieving the expected goals Eor ff t expectancy The level of perceived ease of use of (Byun & Park, 2021; Kalinić et al., 2019; Lee Alodokter telemedicine services et al., 2021; Venkatesh et al., 2003, 2012) Social influence The level of perception of how much an (Byun & Park, 2021; Kalinić et al., 2019; Lee individual feels that others, whom they et al., 2021; Venkatesh et al., 2012) consider important, believe they should use Alodokter telemedicine services Price value The level of satisfaction in using Alodok- (Byun & Park, 2021; Kalinić et al., 2019; ter telemedicine services compared to Venkatesh et al., 2012) the price to be paid User satisfaction Feelings of pleasure or disappointment (Kalinić et al., 2019; Lee et al., 2021) regarding the comparison between expectations and performance outcomes of Alodokter telemedicine services Behavioral intention Intention or plan to use Alodokter (Byun & Park, 2021; Lee et al., 2021; Ven- telemedicine services currently or in the katesh et al., 2003, 2012) future Data analysis The analysis in this study uses PLS–SEM as it is suitable for explanatory research (Hair et  al., 2019). In addition to examining the behavioral intention of Gen-Z and Millennials toward telemedicine service, this study explores theoretical or knowledge extensions of existing established theories that are preferable for PLS–SEM analysis (Hair et al., 2019; Memon et al., 2017). Because PLS–SEM shows the direct and indi- rect effects of independent variables, it is considered superior to regression analy - sis, and it also provides less contradictory results in the detection of mediation effect (Ramli et  al., 2018). PLS–SEM facilitates both modes (regression and correlation weights) in the measurement model more efficiently (Hair et  al., 2019). PLS–SEM is able to deal with complex structural cause and effect models with a large number of constructs and indicators (Richter et  al., 2016; Rigdon, 2012). PLS–SEM eliminates some of the assumptions of ordinary least squares regression, like the data must have a normal multivariate distribution and the absence of multicollinearity prob- lem between exogenous variables (Ramayah et  al., 2017). Data characteristics, such as small sample size and non-normal data, are another reason for choosing PLS–SEM analysis in this study (Hair 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 meas - urement model, tests the reliability and validity of the indicators of the variable con- structs. 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 fulfilling the reliabil - ity and validity tests, the next step is to conduct the structural analysis or inner model analysis. P ramudita et al. Journal of Innovation and Entrepreneurship (2023) 12:68 Page 11 of 21 The inner model is the structural model that displays the relationships between the constructs and their influences 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 first 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 (Hair et al., 2019) If there are indicators with outer loadings below this thresh- old, it can be considered whether removing those indicators can improve the reliability and validity values (both convergent and discriminant). Next, reliability testing is conducted through Cronbach’s Alpha and composite reliabil- ity 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 Cron- bach’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 (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 indica- tors also have reliability values above 0.7 and do not exceed the upper limit of 0.95, indi- cating 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 Henseler et  al. (2015) showed that the For- nell–Larcker criterion performs poorly, especially when the indicator loadings on a con- struct are only slightly different. Instead, Henseler proposed the heterotrait–monotrait (HTMT) correlation ratio (Hair et  al., 2019). Accepted HTMT values are below 0.90, indicating that a construct has specific discriminated indicators (Hair et  al., 2019; Henseler et al., 2015). 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 multicol - linearity to determine the possibility of relationships between the independent variables within a model. This can be seen through the analysis of Variance Inflation 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 (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 influence, and price value. This indicates that both models have Pramudita et al. Journal of Innovation and Entrepreneurship (2023) 12:68 Page 12 of 21 Table 3 Evaluation of measurement model test results Variables Items Outer Loadings Cronbach’s α Composite Average Reliability Variance Extracted (AVE) Performance expectancy PE1 0.778 0.834 0.883 0.603 PE2 0.701 PE3 0.808 PE4 0.788 PE5 0.802 Eor ff t expectancy EE1 0.847 0.831 0.881 0.599 EE2 0.711 EE3 0.759 EE4 0.811 EE5 0.733 Social influence SI1 0.828 0.892 0.920 0.698 SI2 0.865 SI3 0.792 SI4 0.886 SI5 0.802 Price value PV1 0.728 0.814 0.869 0.571 PV2 0.782 PV3 0.792 PV4 0.718 PV5 0.754 User satisfaction SAT1 0.751 0.850 0.893 0.626 SAT2 0.816 SAT3 0.730 SAT4 0.797 SAT5 0.857 Behavioral intention BI1 0.715 0.827 0.878 0.590 BI2 0.745 BI3 0.848 BI4 0.808 BI5 0.715 PE Performance expectancy, EE Effort expectancy, SI Social influence, PV Price value, SAT User satisfaction, BI Behavioral intention Table 4 Discriminant validity BI EE PE PV SAT SI BI EE 0.649 PE 0.627 0.799 PV 0.696 0.555 0.553 SAT 0.870 0.819 0.803 0.781 SI 0.709 0.368 0.468 0,0.454 0.537 BI Behavioral intention, EE Effort expectancy, PE Performance expectancy, PV Price value, SAT User satisfaction, SI Social influence P ramudita et al. Journal of Innovation and Entrepreneurship (2023) 12:68 Page 13 of 21 Table 5 Hypothesis test result Hypothesis Path Standardized p-Values* t-Statistics Results path coefficient H1 Performance Expectancy → User Satisfac- 0.250 0.000 3.629 Supported tion H2 Eor ff t Expectancy → User Satisfaction 0.320 0.000 4.915 Supported H3 Social Influence → User Satisfaction 0.123 0.009 2.385 Supported H4 Price Value → User Satisfaction 0.346 0.000 4.611 Supported H5 User Satisfaction → Behavioral Intention 0.744 0.000 17.430 Supported *Sig. at p ≤ 0.05. H1 Hypothesis 1, H2 Hypothesis 2, H3 Hypothesis 3, H4 Hypothesis 4 Table 6 Specific indirect effect Path Standardized path p-values* Coefficient Performance Expectancy → User Satisfaction → Behavioral Intention 0.238 0.000 Eor ff t Expectancy → User Satisfaction → Behavioral Intention 0.186 0.000 Price Value → User Satisfaction → Behavioral Intention 0.258 0.000 Social Influence → User Satisfaction → Behavioral Intention 0.093 0.009 *Sig. at p ≤ 0.05 moderate strength of predictive accuracy. Another test, Q _Predict was also measured to know the predictive relevance on the variable. The Q _Predict value on user satis- faction (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 significant posi- tive influence between the variables being tested. This can be seen from all the posi- tive path coefficient 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 toward the dependent variable. Table  6 reflects that the four independent variables, namely, performance expectancy, effort expectancy, social influence, and price value, are mediated by the customer satisfaction variable in influencing the independent variable of behavioral intention, as they meet the sig- nificance criteria with a p value < 0.05 and t-statistic value < 1.645. From the hypothesis testing, it can be found that social influence has the small- est path coefficient (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 coefficients. From these results, it can be stated that the proposed model has the capability to depict the factors that influence telemedicine behavioral intention. Pramudita et al. Journal of Innovation and Entrepreneurship (2023) 12:68 Page 14 of 21 Fig. 2 Result model. Arrows toward the yellow box indicates outer loadings, while arrows pointing on the blue circle represent standardize coefficient effect. R was shown inside the blue circle Discussion This study discusses the factors that influence customer satisfaction as a mediat - ing 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 findings of Darrat et  al. (2021), which showed that females prefer virtual visits compared to males. Addition- ally, 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 telemedi- cine 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 influence on customer satisfaction with the Alodokter telemedicine service application. This is in line with previous studies (Hsiao et  al., 2016; Kalinić et  al., 2019; Lee et  al., 2021; Marinkovic & Kalinic, 2017) that found performance expectations to be positively correlated with customer satisfaction. This indicates that users have high expectations or expectations of Alodokter in provid- ing existing services, and users are satisfied 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. P ramudita et al. Journal of Innovation and Entrepreneurship (2023) 12:68 Page 15 of 21 Furthermore, a positive relationship was found between effort expectancy and the intention to use the telemedicine service application. This is consistent with sev - eral previous studies (An et  al., 2021; 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; 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 pur- poses. However, the current study was conducted in a developing country, Indonesia. The results of the analysis above have successfully proven that performance expec - tancy has a positive influence on user satisfaction with the Alodokter telemedicine service application. This is consistent with previous studies (Hsiao et al., 2016; Kalinić et  al., 2019; Lee et  al., 2021; Marinkovic & 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 satisfied 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 influence was also found in the relationship between social influence and user satisfaction. This is consistent with previous research (Beyari & 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; Lee et al., 2021), this may be due to, as explained earlier, users’ habits toward mobile services that allow indi- viduals to determine the benefits and uses regardless of their environment. However, in this study the social influence variable has the smallest effect on satisfaction com - pared to other variables. Price value was found to have the greatest positive influence on behavioral intention to use the telemedicine application. This finding is consistent with previous research (Kalinić et al., 2019; Kim et al., 2013; Kuo et al., 2009; Lin & Wang, 2006). 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 financial ability or issues of the young adult population, especially Gen-Z that usually feel anxious about their financial (Ozkan & Solmaz, 2015). A study conducted among the Gen Z population found that financial attitude has a significant impact on financial happiness, indicating that they need to have a positive financial attitude in order to effectively address financial difficulties. This speaks to how they manage their finances, including healthcare spending nabila (Nabila et al., 2023). Lastly, it was found that customer satisfaction has a positive influence on behavioral intention to use. This is in line with previous research (Barutçu et  al., 2018; Kalinić et  al., 2019; Lee et  al., 2021). Moreover, customer satisfaction has a potential effect on usage behavioral intention with a path coefficient 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. The model used in this research has shown good predictive accuracy and predic - tive relevance, allowing for accurate prediction of customer satisfaction and behavioral Pramudita et al. Journal of Innovation and Entrepreneurship (2023) 12:68 Page 16 of 21 intention to use telemedicine applications. The study also found that price value has the greatest influence on young adult users, who may still experience financial instability. This may explain why Gen-X users are more satisfied with using telemedicine services. Theoretical implications The theoretical implications of this study lie in the confirmation and extension of the Unified Theory of Acceptance and Use of Technology 2 (UTAUT2) model. By examin - ing the factors that influence young adults’ behavioral intention to use the Alodokter telemedicine service, this study validates the positive effects of performance expectancy, effort expectancy, social influence, and price value on behavioral intention, with user satisfaction serving as the mediating variable. These findings contribute to the existing literature by providing empirical evidence in the context of telemedicine applications. Managerial implications Managerial implications of this study highlight the importance of understanding and addressing the factors that influence customer satisfaction and behavioral intention in the telemedicine industry, particularly among young adults. First, service providers should focus on increasing performance expectancy by meeting users’ high expectations and providing optimal telemedicine services. Effort expectancy should also be empha - sized by ensuring that the application is user-friendly, intuitive, and easily accessible. Social influence, while having a smaller impact on satisfaction, can still be leveraged by encouraging positive word-of-mouth and testimonials from satisfied users. The most important managerial implication, however, is to recognize the influence of price value on behavioral intention. Providers should consider implementing pricing strategies that align with the financial concerns and expectations of the target demographic, particu - larly Gen-Z and Millennials. By addressing these theoretical and managerial implica- tions, telehealth service providers can improve customer satisfaction, enhance the user experience, and increase usage and adoption among young adults. Limitations, recommendations, and future research This study was conducted at a single point in time, while users’ perceptions of using tele - medicine service may change over time as new experiences are gained and the pandemic situation changes. Future studies could use a longitudinal design to obtain more accurate results from a specific group. In addition, this study only collected data in one coun - try. Cross-cultural research would allow attitudes from different nations to be compared. 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 suggest that future research could explore other variables and the use of other models to provide additional perspectives on user satisfac- tion in the context of telehealth applications, such as TAM and CSI that encompasses multiple dimensions and factors beyond the scope of UTAUT2. By applying these alter- native frameworks, researchers can further the understanding of telemedicine service’s user satisfaction and identify unique predictors and determinants, ultimately improving overall user experience and engagement. Given that price value has the most significant impact on behavioral intention, future studies exploring influence of financial attitude in P ramudita et al. Journal of Innovation and Entrepreneurship (2023) 12:68 Page 17 of 21 telemedicine usage behavior among Millennials and Gen Z may be conducted. The find - ings on this research can also be applied to the telemedicine in general. Conclusions This study investigates factors influencing behaviotal intention with user satisfaction as mediating factor. The model focuses on factors in UTAUT2 model that exceptionally influence the variables. We conclude that: • The positive influence of performance expectancy, effort expectancy, social influ - ence, and price value on Gen-Z and Millennials’ behavioral intention in using Alo- dokter Telemedicine was found to be mediated by user satisfaction in this study. • The enhancement of customer satisfaction through performance expectancy, effort expectancy, social influence, 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 satisfied with the Alodokter telemedi - cine service. • Price value shows to have the most positive influence on Gen-Z and Millennials user satisfaction. This may be explored in further research since the commercialization of telemedicine usage is rising. We foresee those suggested findings from this study might aid the improvement of commercialization telemedicine usage among younger adults. Appendix 1. Variables and measurements Performance expectancy Response options 1 2 3 4 5 1 Using Alodokter telemedicine service application is useful in my daily life 2 Using Alodokter telemedicine service application makes me get health services faster 3 Using Alodokter telemedicine service application increases the opportunity to achieve things that are very important to me 4 Using Alodokter telemedicine service application improves my ability to man- age my daily health 5 Using Alodokter telemedicine service application improves my health Eor ff t expectancy 1 Easy for me to operate the Alodokter telemedicine service application 2 Learning how to use the Alodokter telemedicine service application is easy for me 3 My interaction of using the Alodokter telemedicine service application is clear 4 My interaction of using the Alodokter telemedicine service application is easy to understand 5 It is easy for me to get the skill to use the Alodokter telemedicine service application Pramudita et al. Journal of Innovation and Entrepreneurship (2023) 12:68 Page 18 of 21 Performance expectancy Response options 1 2 3 4 5 Social influence 1 People who are important to me think that I should use the Alodokter tel- emedicine service application 2 People around me who use the Alodokter telemedicine service application look more prestigious than those who do not 3 According to my colleagues, I must use the Alodokter telemedicine service application 4 People whose opinions that I value prefer that I use the Alodokter telemedi- cine service application 5 Most people around me use Alodokter telemedicine service application Price value 1 The Alodokter’s telemedicine service has a reasonable price 2 The Alodokter’s telemedicine services is a good value for money 3 At the current price, Alodokter’s telemedicine services provides a good value for money 4 The price of Alodokter’s telemedicine service suits me 5 Regardless of the price offered, Alodokter’s telemedicine services is always good User satisfaction 1 The Alodokter telemedicine service application fulfills my expectations 2 I am satisfied with the Alodokter telemedicine service application’s user interface 3 I am satisfied with the Alodokter telemedicine service application’s service quality 4 I am satisfied with the Alodokter telemedicine service application’s efficiency 5 Overall, I am satisfied with the services provided by Alodokter Telemedicine Service Application Behavioral intention 1 I intend to use Alodokter Telemedicine Service Application in the future 2 I will always try to use Alodokter Telemedicine Service Application when I need health services in my daily life 3 I plan to continue using Alodokter Telemedicine Service application frequently 4 I prefer to use Alodokter Telemedicine Service application when I need health services in the future 5 Alodokter Telemedicine Service Application is my first choice when I need health services in the future Abbreviations UTAUT Unified Theory of Acceptance and Use of Technology TPB Theory of planned behavior TAM T echnology acceptance model DOI Diffusion of innovation CSI Customer satisfaction index PE Performance expectancy EE Eorfft expectancy SI Social influence PV Price value BI Behavioral intention SAT User satisfaction PU Perceived usefulness PEU P erceived ease of use PLS–SEM Partial Least Square–Structural Equation Modelling AVE Average variance extracted VIF Variance Inflation Factor Acknowledgements Authors would like to thank Universitas Pelita Harapan, family, and friends whom were involved in data collections. Thanks to Dr. F. Hakiki Soemarsono for the help and review before submission. This work was completed as part of master degree graduate requirement. P ramudita et al. Journal of Innovation and Entrepreneurship (2023) 12:68 Page 19 of 21 Author contributions EP: conceptualize, data collection, analysis and writing original draft. HA: validation, review, and editing writing. HN: revised parts of the manuscript throughout drafting process. All authors read and approved the final manuscript. All authors have read and agreed to the published version of the manuscript. Funding This research received no external funding. Availability of data and materials The data that support the findings of this study are available on request from the corresponding author on reasonable request. Declarations Competing interests The authors declare no competing interests. Received: 22 April 2023 Accepted: 17 September 2023 References Ajzen, I. (2002). 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Determinants of behavioral intention toward telemedicine services among Indonesian Gen-Z and Millenials: a PLS–SEM study on Alodokter application

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Abstract

ekapramudita.w@gmail.com Telemedicine has become increasingly important in healthcare, especially Graduate School of Management, Faculty with the COVID-19 pandemic. Despite, Younger generations are more fluent in using of Economics and Business, technology, previous study shows that older generations (Gen-X) are more satisfied Universitas Pelita Harapan, South in using telemedicine compared to younger generations. This study aims to identify Jakarta 12930, Indonesia Health Center at Pattimura the factors influencing user satisfaction and behavioral intention toward Alodokter’s Air Force Base, Ambon 97326, telemedicine service application usage among Millennials and Gen-Z in Indonesia. Indonesia A quantitative cross-sectional study was conducted using a purposive sampling tech- nique. A total of 160 Millenials and Gen-Z respondents who had used the Alodokter telehealth application within the past year were chosen in this study. The data were collected by means of an online questionnaire that was distributed through widely used social media platforms. The questionnaire consisted of 30 questions that assessed variables, such as performance expectancy, effort expectancy, social influence, price value, customer satisfaction, and behavioral intention. Data were analyzed using Partial Least Square–Structural Equation Modeling (PLS–SEM) with SmartPLS software ver- sion 3.2.9. The findings reveal that customer satisfaction positively affects behavioral intention. Performance expectancy, effort expectancy, price value, and social influ- ence positively impact customer satisfaction. Price value was shown to have the most positive influence on behavioral intention. This study provides insights into the factors influencing user satisfaction and behavioral intention toward 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. Keywords: User satisfaction, Behavioral intention, Telemedicine, UTAUT2 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 © The Author(s) 2023. Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the mate- rial. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http:// creativecommons.org/licenses/by/4.0/. Pramudita et al. Journal of Innovation and Entrepreneurship (2023) 12:68 Page 2 of 21 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 & 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 tel - emedicine, 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 Asso- ciation, 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 health- care application among urban people after Halodoc. Halodoc is the most popular appli- cation, 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 Unified Theory of Acceptance and Use of Technology 2 (UTAUT2) technology acceptance model (Baudier et  al., 2021; Byun & Park, 2021; Martins et  al., 2021; Melinda & Setiawati, 2022; Suroso & Sukmoro, 2021). In research conducted by Baudier et al. (Baudier et al., 2021) and 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 & Park, 2021) found that the price value factor has a positive influence on technology acceptance. Further - more, research by Melinda and Setiawati (Melinda & Setiawati, 2022) included all seven UTAUT2 variables and found that factors influencing 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 toward a tech- nology. When users are satisfied with an information system, they tend to return the appropriate value to the information system service provider (Kim & Son, 2009). Pre- vious research has linked UTAUT predictor variables with satisfaction variables in the field of m-Commerce and m-Health use (Kalinić et al., 2019; 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. (2022) found that patients from Generation X—those born between 1960 and 1980—are the most satisfied with telemedicine, even though Millennials and Gen Z are generations that heavily rely on technology platforms and social media to communicate and fulfill 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 & Rina, 2020). Gen Z has also been found to face P ramudita et al. Journal of Innovation and Entrepreneurship (2023) 12:68 Page 3 of 21 financial challenges which may affect their decision-making in using commercially avail - able telemedicine services (Ozkan & Solmaz, 2015). This research aims to explore the factors influencing behavioral intention mediated by customer satisfaction among young adults (Gen Z and Millennials) in using Alodokter telemedicine services. This study contributes to existing knowledge by examining factors influencing young adults’ (Gen Z and Millennials) behavioral intention and satisfaction when using Alo- dokter telehealth services. While numerous studies have evaluated technology accept- ance using the Unified Theory of Acceptance and Use of Technology 2 (UTAUT2) model, little research has focused on user satisfaction within this framework. This study fills a gap in the literature and provides valuable insight into the adoption and use of telehealth services by examining the relationship between technology acceptance factors and user satisfaction. In addition, this study extends the applicability of the UTAUT2 model to the context of telemedicine and contributes to a more comprehensive under- standing of users’ attitudes and intentions in the area of health technology. To provide a coherent and informative presentation of the study, the overall structure of this paper follows a comprehensive framework in addition to the aforementioned sec- tions. This paper begins by exploring the theoretical and empirical foundations related to study. Following this, hypotheses and the research model are developed based on the insights gained from the previous literature. The next section presents the methodology, which includes the research design, sampling, data collection procedures, operational definitions of variables and the chosen data analysis technique. In the results section, the findings obtained through the analysis are presented, followed by a detailed discus - sion of the results. The article then concludes with a section summarizing the main con - clusions of the study and their implications for both theory and practice. In addition, this article critically discusses the limitations encountered during the research and offers recommendations for future research to address these limitations. 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 defined as the use of information and electronic communi - cation technology to provide and support healthcare when distance separates partici- pants. Many efforts have been made, ranging from communication through telephone to video conferences, enabling doctors to see, hear, examine, interview, and advise dis- tant 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 telemedi- cine services to address COVID-19 (Gandhawangi, 2021). One of the companies that has played a role in this is Alodokter. Pramudita et al. Journal of Innovation and Entrepreneurship (2023) 12:68 Page 4 of 21 Despite numerous studies on the adoption or acceptance of telemedicine using the UTAUT2 model, there are limited studies that explore user satisfaction with telemedi- cine (Lee et al., 2021; Tiara & Antonio, 2022; Wijaya & Wardani, 2022). In the two stud- ies, the original TAM model was used for analysis, while 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 unified 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 by employees in specific contexts, but it has since been successfully implemented in various studies on the adoption of ser- vices and specific applications by organizations and consumers (Sheikh et al., 2017). To adapt UTAUT to the context of consumer use, 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 model is considered compre- hensive and provides better explanations of technology acceptance compared to other technology adoption models (Macedo, 2017). This model has been successfully tested in the context of online shopping acceptance (Tandon et al., 2017), m-commerce (Chopdar et  al., 2018), internet banking (Alalwan et  al., 2018), mobile applications (Gupta et  al., 2018), mobile social networking games (Baabdullah, 2018), m-health (Dwivedi et  al., 2016), and telemedicine (Baudier et  al., 2021; Byun & Park, 2021; Martins et  al., 2021; Melinda & Setiawati, 2022; Suroso & Sukmoro, 2021). 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 et al., 2019). While TAM provides insight into the cognitive processes underlying technology accept- ance and customer satisfaction index (CSI) offers a comprehensive approach to meas - uring customer satisfaction, the UTAUT has the most comprehensive understanding and model, because it combines eight pre-existing theories and models of technology acceptance, including TAM (Lee & Kim, 2022; Venkatesh et  al., 2003, 2012). UTAUT is one of the most widely used models in the acceptance of technology or information systems (Dwivedi et al., 2020). UTAUT originally stated that four independent variables directly influence behavioral intention (Venkatesh et  al., 2003). However, it is argued that information system users’ cognitive and affective perceptions first form an attitude toward the information system and then affect behavioral intention based on the TPB in UTAUT(Ajzen, 2002; Lee & Kim, 2022). Thus, it can be reasonably concluded that the independent variables affect the attitude of information system users and influence behavioral intention (Lee & Kim, 2022). Several previous studies also confirm that there is an effect of attitude on behavioral intention, which contradicts the opinion of Ven - katesh et al. where the effect of attitude on behavioral intention is spurious (Jairak et al., 2009; Nassuora, 2013; Thomas et al., 2013). Satisfaction with the use of information sys - tems is one of the most commonly used attitude variables (Bhattacherjee, 2001; Lee & Kim, 2022). P ramudita et al. Journal of Innovation and Entrepreneurship (2023) 12:68 Page 5 of 21 In their study titled "Determinants Impacting User Behavior toward Emergency Use Intentions of m-Health Services in Taiwan," Lee et al. (2021) used the original UTAUT model adapted to the context of user satisfaction. Furthermore, Kalinic et  al. (2019) were the first to adopt UTAUT2 and adapt it to the context of customer satisfaction in m-commerce. The integration of the UTAUT2 adoption model with customer satisfac - tion 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 et  al. (2021) and Suroso and Sukmoro (2021) did not include hedonic motivation and price value (PV) variables in their studies, stating that they were not relevant to their research. However, Melinda and Setiawati (2022) and Byun and Park (2021) found that price value factor has a positive influence 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.com, 2022), 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 significantly influence or delay consumer’s use of telemedicine (Byun & 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) (Lee et al., 2021). Moreover, con- sidering 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 influ - ence their acceptance, which could be due to Gen Z’s tendency to learn everything inde- pendently through the internet (Alexandra et al., 2021; Rettig & Rina, 2020). Hypothesis development and research model This study aims to investigate the factors influencing user satisfaction and behavioral intention (BI) of telemedicine services among Gen-Z and Millennials user in Indonesia. Based on the review above, modified UTAUT2 model was selected as the basis concep - tualized framework by adding user satisfaction dimension to it. Therefore, four main fac - tors, namely, performance expectancy (PE), effort expectancy (EE), social influence (SI), and price value (PV), were selected to influence user satisfaction affecting BI. 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 satisfied with the service, which will influence their intention to use it. Research has found that PE significantly influences user satisfaction in the use of m-Health (Lee et  al., 2021) and m-commerce (Kalinić et al., 2019). Pramudita et al. Journal of Innovation and Entrepreneurship (2023) 12:68 Page 6 of 21 PE is often associated with perceived usefulness (PU) in the TAM and is a strong predictor of technology acceptance (Venkatesh et  al., 2003). An et  al. (2021) con- ducted a study on factors affecting the use of telehealth using the TAM model and found that perceived usefulness has a significant positive impact on attitudes toward telehealth. In this study, positive attitude includes satisfaction and high favourability. In addition, the significant positive impact of PU on customer satisfaction has been confirmed in cases of m-commerce (Marinkovic & Kalinic, 2017), mobile social appli- cations (Hsiao et  al., 2016), mobile services (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 influence user satisfaction in Generation Z and Millennials. Although some research has found that effort expectancy does not significantly affect user satisfaction (Kalinić et al., 2019; Lee et al., 2021), effort expectancy, which is often equated with perceived ease of use (PEU) in the TAM model, has been found to have a significant positive influence on customer satisfaction in telemedicine research (An et al., 2021; Yan et al., 2021). Furthermore, in other studies, it has been found that PEU significantly affects customer satisfaction in mobile application ser - vices (Lee et  al., 2015) and mobile websites (Zhou, 2011). Based on the descriptions provided, the following hypothesis can be formulated: Hypothesis 2 (H2). Effort expectancy positively influence user satisfaction Generation Z and Millennials. Social influence is one of the predictors commonly found in research on technology acceptance and use. Although social influence does not significantly affect user satis - faction in some studies on m-health (Lee et al., 2021) and m-commerce (Kalinić et al., 2019), several previous studies examining the influence of social environment on cus - tomer satisfaction have obtained significant 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 & Abareshi, 2018). Considering that reviews from others can also influence the intention to use an application, the following hypothesis can be formulated: Hypothesis 3 (H3). Social influence positively influence user satisfaction Generation Z and Millennials. 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. (2019) and Lin and Wang ( 2006) found that perceived value in m-commerce significantly affects customer satisfaction. Additionally, previous research has found that perceived value in the monetary context influences customer satisfaction in the P ramudita et al. Journal of Innovation and Entrepreneurship (2023) 12:68 Page 7 of 21 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 influence user satisfaction Generation Z and Millennials. Customer satisfaction greatly reflects the customer’s assessment of a particular ser - vice or product (Tandon et al., 2017). Customer satisfaction is usually a key driver in a customer’s attitude toward the continued use of a technology or system (Marinkovic & Kalinic, 2017). Lin and Wang (2006) found that customer satisfaction affects customer loyalty in m-commerce usage, while Kalinic et  al. (2019) found that customer satisfac- tion influences commitment to continued use in m-commerce. In the context of medical services and m-Health, Lee et al. (2021) and Barutçu et al. (2018) found that user satis- faction with m-Health has a positive influence on intention to use m-Health services. Based on the descriptions provided, the following hypothesis can be formulated: Hypothesis 5 (H5). User satisfactions positively influence behavioral intention Genera - tion Z and. Figure  1 visualizes the relationship between variables that make up research model. Each hypothesis is assigned to Fig. 1. Methods 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 influencing user satisfaction toward behavioral intention of Gen-Z and Millennials on the Alodokter tel- emedicine application. There are a total of 6 variables involved in this study, with the Fig. 1 Research model showing factors influencing behavioral intention mediated by customer satisfaction. H1 Hypothesis 1, H2 Hypothesis 2, H3 Hypothesis 3, H4 Hypothesis 4 Pramudita et al. Journal of Innovation and Entrepreneurship (2023) 12:68 Page 8 of 21 independent variables being performance expectancy, effort expectancy, social influ - ence, and price value. The dependent variable in this study is behavioral intention with user satisfaction as the mediating variable. Sampling and data collection While probability sampling is considered ideal in research, many studies in social science research actually rely on non-probability sampling (Rowley, 2014). Non-probability sam- pling involves purposive selection, chance, or expert judgment, where individuals’ chances of being selected are unknown (Burns et al., 2008). Non-probability sampling is more com- mon and appropriate in field research, especially studies involving human subjects (Bry - man & Bell, 2015). Carefully controlled non-probability sampling can produce valid and meaningful results (Schindler, 2011). The choice between probability and non-probability sampling does not determine the quality of the research (Memon et al., 2017). Probability sampling is ideal for generalizability sampling, but if the goal is rigorous theory testing, non- probability sampling is more appropriate (Calder et al., 1981; Hulland et al., 2018). In social science studies, it is extension of knowledge and generalization of theory that is impor- tant, not generalization of sampling (Memon et al., 2017). Probability sampling techniques require a sampling frame, which is a complete list of all subjects in the target population. However, obtaining a sampling frame and achieving a 100% response rate can be challeng- ing, especially in studies involving diverse and dispersed populations such as in Indonesia (It is easy to compromise the underlying assumptions of probability sampling by giving each subject an equal chance in a large geographic area with uneven Internet penetration.). Non- probability sampling is appropriate when the choice of sampling strategy is consistent with the research objectives, the goal is to generalize theory, and a complete sampling frame is not available (Hulland et al., 2018; Memon et al., 2017; Rowley, 2014). As this study is con- cerned with testing the theoretical framework from a predictive perspective and aims to extend existing theories or models, a purposive sampling method was selected. In this study, Millennial and Gen-Z consumers who have used telemedicine applica- tions in Indonesia within the past year were selected as the population. Sampling was done purposively during November 2022. Respondents remained anonymous and vol- untary, with their data confidentiality assured through consent. The researcher has con - ducted 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 meas- ure 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 field of health mar - keting 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 Lik- ert 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 sam - ple was excluded from the main study. The questionnaire was adapted from previous literature and studies (Byun & Park, 2021; Kalinić et al., 2019; Lee et al., 2021; Venkatesh et al., 2003, 2012) and modified for the purpose of novelty and understanding concepts. P ramudita et al. Journal of Innovation and Entrepreneurship (2023) 12:68 Page 9 of 21 Table 1 Demographic characteristics No Demographic characteristics Sample (n) Percentage (%) 1 Gender Male 84 52.5 Female 76 47.5 2 Age 17–25 years (Gen Z) 40 25 26–41 years (Millennials) 120 75 3 Current Residence DKI Jakarta 83 51.9 Java Island other than DKI Jakarta 48 30 Other areas 25 18.1 4 Income < Rp. 4.500.000 45 28.1 Rp. 4.500.000–Rp. 10.000.000 75 46.9 > Rp. 10.000.000 40 25 5 Education High School (SMP/SMA) 46 28.8 Diploma (D3) 5 3.1 Bachelor’s degree (S1) 91 56,9 Post-graduate (S2) 18 11.2 6 Occupation Private sector employee 89 55.6 Student 19 11.9 Civil servant 26 16.2 Self-employed 17 10.6 Labor 4 2.5 Others 5 3.2 An online questionnaire was created using Google Forms. It was then distributed through the most widely used social media platforms in Indonesia such as WhatsApp, Line, Facebook, and Instagram. Either groups or individuals belonging to the Gen-Z and Millennial age categories were used to distribute the questionnaire. Some of the eligibil- ity criteria for filler participants were: (1) aged over 17, (2) having used the Alodokter telehealth application, (3) being Indonesian citizens. The exclusion criteria were those who did not fill out the questionnaire in its entirety. A total of 317 individuals participated in this study. From these data, 160 respondents will be analyzed as they are the ones who have used the Alodokter telemedicine appli- cation within the past year. This sample size meets the minimum criteria for analysis using Partial Least Square-Structural Equation Modelling (PLS–SEM) (Hair et al., 2012; Memon et al., 2020). Table 1 shows demographic characteristics of the respondents. Operational definition of variables In this study, performance expectancy, effort expectancy, social influence, and price value have been chosen as the main indicators influencing behavioral intention in the use of Alodokter telemedicine, with user satisfaction as a mediating variable. Table 2 presents operational definitions of these variables are presented. Pramudita et al. Journal of Innovation and Entrepreneurship (2023) 12:68 Page 10 of 21 Table 2 Operational definitions of construct variables Variable Operational definitions References Performance expectancy The level of perceived influence of using (Byun & Park, 2021; Kalinić et al., 2019; Lee Alodokter telemedicine services in et al., 2021; Venkatesh et al., 2003, 2012) achieving the expected goals Eor ff t expectancy The level of perceived ease of use of (Byun & Park, 2021; Kalinić et al., 2019; Lee Alodokter telemedicine services et al., 2021; Venkatesh et al., 2003, 2012) Social influence The level of perception of how much an (Byun & Park, 2021; Kalinić et al., 2019; Lee individual feels that others, whom they et al., 2021; Venkatesh et al., 2012) consider important, believe they should use Alodokter telemedicine services Price value The level of satisfaction in using Alodok- (Byun & Park, 2021; Kalinić et al., 2019; ter telemedicine services compared to Venkatesh et al., 2012) the price to be paid User satisfaction Feelings of pleasure or disappointment (Kalinić et al., 2019; Lee et al., 2021) regarding the comparison between expectations and performance outcomes of Alodokter telemedicine services Behavioral intention Intention or plan to use Alodokter (Byun & Park, 2021; Lee et al., 2021; Ven- telemedicine services currently or in the katesh et al., 2003, 2012) future Data analysis The analysis in this study uses PLS–SEM as it is suitable for explanatory research (Hair et  al., 2019). In addition to examining the behavioral intention of Gen-Z and Millennials toward telemedicine service, this study explores theoretical or knowledge extensions of existing established theories that are preferable for PLS–SEM analysis (Hair et al., 2019; Memon et al., 2017). Because PLS–SEM shows the direct and indi- rect effects of independent variables, it is considered superior to regression analy - sis, and it also provides less contradictory results in the detection of mediation effect (Ramli et  al., 2018). PLS–SEM facilitates both modes (regression and correlation weights) in the measurement model more efficiently (Hair et  al., 2019). PLS–SEM is able to deal with complex structural cause and effect models with a large number of constructs and indicators (Richter et  al., 2016; Rigdon, 2012). PLS–SEM eliminates some of the assumptions of ordinary least squares regression, like the data must have a normal multivariate distribution and the absence of multicollinearity prob- lem between exogenous variables (Ramayah et  al., 2017). Data characteristics, such as small sample size and non-normal data, are another reason for choosing PLS–SEM analysis in this study (Hair 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 meas - urement model, tests the reliability and validity of the indicators of the variable con- structs. 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 fulfilling the reliabil - ity and validity tests, the next step is to conduct the structural analysis or inner model analysis. P ramudita et al. Journal of Innovation and Entrepreneurship (2023) 12:68 Page 11 of 21 The inner model is the structural model that displays the relationships between the constructs and their influences 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 first 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 (Hair et al., 2019) If there are indicators with outer loadings below this thresh- old, it can be considered whether removing those indicators can improve the reliability and validity values (both convergent and discriminant). Next, reliability testing is conducted through Cronbach’s Alpha and composite reliabil- ity 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 Cron- bach’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 (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 indica- tors also have reliability values above 0.7 and do not exceed the upper limit of 0.95, indi- cating 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 Henseler et  al. (2015) showed that the For- nell–Larcker criterion performs poorly, especially when the indicator loadings on a con- struct are only slightly different. Instead, Henseler proposed the heterotrait–monotrait (HTMT) correlation ratio (Hair et  al., 2019). Accepted HTMT values are below 0.90, indicating that a construct has specific discriminated indicators (Hair et  al., 2019; Henseler et al., 2015). 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 multicol - linearity to determine the possibility of relationships between the independent variables within a model. This can be seen through the analysis of Variance Inflation 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 (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 influence, and price value. This indicates that both models have Pramudita et al. Journal of Innovation and Entrepreneurship (2023) 12:68 Page 12 of 21 Table 3 Evaluation of measurement model test results Variables Items Outer Loadings Cronbach’s α Composite Average Reliability Variance Extracted (AVE) Performance expectancy PE1 0.778 0.834 0.883 0.603 PE2 0.701 PE3 0.808 PE4 0.788 PE5 0.802 Eor ff t expectancy EE1 0.847 0.831 0.881 0.599 EE2 0.711 EE3 0.759 EE4 0.811 EE5 0.733 Social influence SI1 0.828 0.892 0.920 0.698 SI2 0.865 SI3 0.792 SI4 0.886 SI5 0.802 Price value PV1 0.728 0.814 0.869 0.571 PV2 0.782 PV3 0.792 PV4 0.718 PV5 0.754 User satisfaction SAT1 0.751 0.850 0.893 0.626 SAT2 0.816 SAT3 0.730 SAT4 0.797 SAT5 0.857 Behavioral intention BI1 0.715 0.827 0.878 0.590 BI2 0.745 BI3 0.848 BI4 0.808 BI5 0.715 PE Performance expectancy, EE Effort expectancy, SI Social influence, PV Price value, SAT User satisfaction, BI Behavioral intention Table 4 Discriminant validity BI EE PE PV SAT SI BI EE 0.649 PE 0.627 0.799 PV 0.696 0.555 0.553 SAT 0.870 0.819 0.803 0.781 SI 0.709 0.368 0.468 0,0.454 0.537 BI Behavioral intention, EE Effort expectancy, PE Performance expectancy, PV Price value, SAT User satisfaction, SI Social influence P ramudita et al. Journal of Innovation and Entrepreneurship (2023) 12:68 Page 13 of 21 Table 5 Hypothesis test result Hypothesis Path Standardized p-Values* t-Statistics Results path coefficient H1 Performance Expectancy → User Satisfac- 0.250 0.000 3.629 Supported tion H2 Eor ff t Expectancy → User Satisfaction 0.320 0.000 4.915 Supported H3 Social Influence → User Satisfaction 0.123 0.009 2.385 Supported H4 Price Value → User Satisfaction 0.346 0.000 4.611 Supported H5 User Satisfaction → Behavioral Intention 0.744 0.000 17.430 Supported *Sig. at p ≤ 0.05. H1 Hypothesis 1, H2 Hypothesis 2, H3 Hypothesis 3, H4 Hypothesis 4 Table 6 Specific indirect effect Path Standardized path p-values* Coefficient Performance Expectancy → User Satisfaction → Behavioral Intention 0.238 0.000 Eor ff t Expectancy → User Satisfaction → Behavioral Intention 0.186 0.000 Price Value → User Satisfaction → Behavioral Intention 0.258 0.000 Social Influence → User Satisfaction → Behavioral Intention 0.093 0.009 *Sig. at p ≤ 0.05 moderate strength of predictive accuracy. Another test, Q _Predict was also measured to know the predictive relevance on the variable. The Q _Predict value on user satis- faction (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 significant posi- tive influence between the variables being tested. This can be seen from all the posi- tive path coefficient 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 toward the dependent variable. Table  6 reflects that the four independent variables, namely, performance expectancy, effort expectancy, social influence, and price value, are mediated by the customer satisfaction variable in influencing the independent variable of behavioral intention, as they meet the sig- nificance criteria with a p value < 0.05 and t-statistic value < 1.645. From the hypothesis testing, it can be found that social influence has the small- est path coefficient (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 coefficients. From these results, it can be stated that the proposed model has the capability to depict the factors that influence telemedicine behavioral intention. Pramudita et al. Journal of Innovation and Entrepreneurship (2023) 12:68 Page 14 of 21 Fig. 2 Result model. Arrows toward the yellow box indicates outer loadings, while arrows pointing on the blue circle represent standardize coefficient effect. R was shown inside the blue circle Discussion This study discusses the factors that influence customer satisfaction as a mediat - ing 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 findings of Darrat et  al. (2021), which showed that females prefer virtual visits compared to males. Addition- ally, 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 telemedi- cine 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 influence on customer satisfaction with the Alodokter telemedicine service application. This is in line with previous studies (Hsiao et  al., 2016; Kalinić et  al., 2019; Lee et  al., 2021; Marinkovic & Kalinic, 2017) that found performance expectations to be positively correlated with customer satisfaction. This indicates that users have high expectations or expectations of Alodokter in provid- ing existing services, and users are satisfied 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. P ramudita et al. Journal of Innovation and Entrepreneurship (2023) 12:68 Page 15 of 21 Furthermore, a positive relationship was found between effort expectancy and the intention to use the telemedicine service application. This is consistent with sev - eral previous studies (An et  al., 2021; 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; 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 pur- poses. However, the current study was conducted in a developing country, Indonesia. The results of the analysis above have successfully proven that performance expec - tancy has a positive influence on user satisfaction with the Alodokter telemedicine service application. This is consistent with previous studies (Hsiao et al., 2016; Kalinić et  al., 2019; Lee et  al., 2021; Marinkovic & 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 satisfied 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 influence was also found in the relationship between social influence and user satisfaction. This is consistent with previous research (Beyari & 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; Lee et al., 2021), this may be due to, as explained earlier, users’ habits toward mobile services that allow indi- viduals to determine the benefits and uses regardless of their environment. However, in this study the social influence variable has the smallest effect on satisfaction com - pared to other variables. Price value was found to have the greatest positive influence on behavioral intention to use the telemedicine application. This finding is consistent with previous research (Kalinić et al., 2019; Kim et al., 2013; Kuo et al., 2009; Lin & Wang, 2006). 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 financial ability or issues of the young adult population, especially Gen-Z that usually feel anxious about their financial (Ozkan & Solmaz, 2015). A study conducted among the Gen Z population found that financial attitude has a significant impact on financial happiness, indicating that they need to have a positive financial attitude in order to effectively address financial difficulties. This speaks to how they manage their finances, including healthcare spending nabila (Nabila et al., 2023). Lastly, it was found that customer satisfaction has a positive influence on behavioral intention to use. This is in line with previous research (Barutçu et  al., 2018; Kalinić et  al., 2019; Lee et  al., 2021). Moreover, customer satisfaction has a potential effect on usage behavioral intention with a path coefficient 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. The model used in this research has shown good predictive accuracy and predic - tive relevance, allowing for accurate prediction of customer satisfaction and behavioral Pramudita et al. Journal of Innovation and Entrepreneurship (2023) 12:68 Page 16 of 21 intention to use telemedicine applications. The study also found that price value has the greatest influence on young adult users, who may still experience financial instability. This may explain why Gen-X users are more satisfied with using telemedicine services. Theoretical implications The theoretical implications of this study lie in the confirmation and extension of the Unified Theory of Acceptance and Use of Technology 2 (UTAUT2) model. By examin - ing the factors that influence young adults’ behavioral intention to use the Alodokter telemedicine service, this study validates the positive effects of performance expectancy, effort expectancy, social influence, and price value on behavioral intention, with user satisfaction serving as the mediating variable. These findings contribute to the existing literature by providing empirical evidence in the context of telemedicine applications. Managerial implications Managerial implications of this study highlight the importance of understanding and addressing the factors that influence customer satisfaction and behavioral intention in the telemedicine industry, particularly among young adults. First, service providers should focus on increasing performance expectancy by meeting users’ high expectations and providing optimal telemedicine services. Effort expectancy should also be empha - sized by ensuring that the application is user-friendly, intuitive, and easily accessible. Social influence, while having a smaller impact on satisfaction, can still be leveraged by encouraging positive word-of-mouth and testimonials from satisfied users. The most important managerial implication, however, is to recognize the influence of price value on behavioral intention. Providers should consider implementing pricing strategies that align with the financial concerns and expectations of the target demographic, particu - larly Gen-Z and Millennials. By addressing these theoretical and managerial implica- tions, telehealth service providers can improve customer satisfaction, enhance the user experience, and increase usage and adoption among young adults. Limitations, recommendations, and future research This study was conducted at a single point in time, while users’ perceptions of using tele - medicine service may change over time as new experiences are gained and the pandemic situation changes. Future studies could use a longitudinal design to obtain more accurate results from a specific group. In addition, this study only collected data in one coun - try. Cross-cultural research would allow attitudes from different nations to be compared. 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 suggest that future research could explore other variables and the use of other models to provide additional perspectives on user satisfac- tion in the context of telehealth applications, such as TAM and CSI that encompasses multiple dimensions and factors beyond the scope of UTAUT2. By applying these alter- native frameworks, researchers can further the understanding of telemedicine service’s user satisfaction and identify unique predictors and determinants, ultimately improving overall user experience and engagement. Given that price value has the most significant impact on behavioral intention, future studies exploring influence of financial attitude in P ramudita et al. Journal of Innovation and Entrepreneurship (2023) 12:68 Page 17 of 21 telemedicine usage behavior among Millennials and Gen Z may be conducted. The find - ings on this research can also be applied to the telemedicine in general. Conclusions This study investigates factors influencing behaviotal intention with user satisfaction as mediating factor. The model focuses on factors in UTAUT2 model that exceptionally influence the variables. We conclude that: • The positive influence of performance expectancy, effort expectancy, social influ - ence, and price value on Gen-Z and Millennials’ behavioral intention in using Alo- dokter Telemedicine was found to be mediated by user satisfaction in this study. • The enhancement of customer satisfaction through performance expectancy, effort expectancy, social influence, 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 satisfied with the Alodokter telemedi - cine service. • Price value shows to have the most positive influence on Gen-Z and Millennials user satisfaction. This may be explored in further research since the commercialization of telemedicine usage is rising. We foresee those suggested findings from this study might aid the improvement of commercialization telemedicine usage among younger adults. Appendix 1. Variables and measurements Performance expectancy Response options 1 2 3 4 5 1 Using Alodokter telemedicine service application is useful in my daily life 2 Using Alodokter telemedicine service application makes me get health services faster 3 Using Alodokter telemedicine service application increases the opportunity to achieve things that are very important to me 4 Using Alodokter telemedicine service application improves my ability to man- age my daily health 5 Using Alodokter telemedicine service application improves my health Eor ff t expectancy 1 Easy for me to operate the Alodokter telemedicine service application 2 Learning how to use the Alodokter telemedicine service application is easy for me 3 My interaction of using the Alodokter telemedicine service application is clear 4 My interaction of using the Alodokter telemedicine service application is easy to understand 5 It is easy for me to get the skill to use the Alodokter telemedicine service application Pramudita et al. Journal of Innovation and Entrepreneurship (2023) 12:68 Page 18 of 21 Performance expectancy Response options 1 2 3 4 5 Social influence 1 People who are important to me think that I should use the Alodokter tel- emedicine service application 2 People around me who use the Alodokter telemedicine service application look more prestigious than those who do not 3 According to my colleagues, I must use the Alodokter telemedicine service application 4 People whose opinions that I value prefer that I use the Alodokter telemedi- cine service application 5 Most people around me use Alodokter telemedicine service application Price value 1 The Alodokter’s telemedicine service has a reasonable price 2 The Alodokter’s telemedicine services is a good value for money 3 At the current price, Alodokter’s telemedicine services provides a good value for money 4 The price of Alodokter’s telemedicine service suits me 5 Regardless of the price offered, Alodokter’s telemedicine services is always good User satisfaction 1 The Alodokter telemedicine service application fulfills my expectations 2 I am satisfied with the Alodokter telemedicine service application’s user interface 3 I am satisfied with the Alodokter telemedicine service application’s service quality 4 I am satisfied with the Alodokter telemedicine service application’s efficiency 5 Overall, I am satisfied with the services provided by Alodokter Telemedicine Service Application Behavioral intention 1 I intend to use Alodokter Telemedicine Service Application in the future 2 I will always try to use Alodokter Telemedicine Service Application when I need health services in my daily life 3 I plan to continue using Alodokter Telemedicine Service application frequently 4 I prefer to use Alodokter Telemedicine Service application when I need health services in the future 5 Alodokter Telemedicine Service Application is my first choice when I need health services in the future Abbreviations UTAUT Unified Theory of Acceptance and Use of Technology TPB Theory of planned behavior TAM T echnology acceptance model DOI Diffusion of innovation CSI Customer satisfaction index PE Performance expectancy EE Eorfft expectancy SI Social influence PV Price value BI Behavioral intention SAT User satisfaction PU Perceived usefulness PEU P erceived ease of use PLS–SEM Partial Least Square–Structural Equation Modelling AVE Average variance extracted VIF Variance Inflation Factor Acknowledgements Authors would like to thank Universitas Pelita Harapan, family, and friends whom were involved in data collections. Thanks to Dr. F. Hakiki Soemarsono for the help and review before submission. This work was completed as part of master degree graduate requirement. P ramudita et al. Journal of Innovation and Entrepreneurship (2023) 12:68 Page 19 of 21 Author contributions EP: conceptualize, data collection, analysis and writing original draft. HA: validation, review, and editing writing. HN: revised parts of the manuscript throughout drafting process. All authors read and approved the final manuscript. All authors have read and agreed to the published version of the manuscript. Funding This research received no external funding. Availability of data and materials The data that support the findings of this study are available on request from the corresponding author on reasonable request. Declarations Competing interests The authors declare no competing interests. Received: 22 April 2023 Accepted: 17 September 2023 References Ajzen, I. (2002). 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Journal of Innovation and EntrepreneurshipSpringer Journals

Published: Oct 10, 2023

Keywords: User satisfaction; Behavioral intention; Telemedicine; UTAUT2

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