Available online at www.HighTechJournal.org HighTech and Innovation Journal Vol. 6, No. 1, March, 2025 123 ISSN: 2723-9535 The Impact of Self-Efficacy on Telemedicine Adoption in Emerging Country Danupol Hoonsopon 1 , Chaninun Ketkaew 2, Wilert Puriwat 1 , Wattana Viriyasitavat 1 , Suchart Tripopsakul 3* 1 Chulalongkorn Business School, Chualongkorn University, 254 Phyathai Road, Pathumwan, Bangkok 10330, Thailand. 2 Chonburi Cancer Hospital, Chonburi, Thailand. 3 School of Entrepreneurship and Management, Bangkok University, Pathumthani 12120, Thailand. Received 15 November 2024; Revised 07 February 2025; Accepted 15 February 2025; Published 01 March 2025 Abstract Telemedicine has emerged as a vital innovation in healthcare, improving access to medical services by reducing the need for physical interactions. Previous studies revealed that self-efficacy influences individuals’ perceptions and behaviors toward adopting new technologies, especially telemedicine. However, these studies do not emphasize understanding how three sources of self-efficacy, namely, enactive mastery (EM), vicarious experience (VE), and verbal persuasion (VP), affect telemedicine adoption (TA) through perceptions of telemedicine technology. Based on the sample of 240 respondents, structural equation modeling (SEM) analysis was utilized to examine the proposed hypotheses. The results revealed that enactive mastery and vicarious experience positively influence perceived ease of use (PE), with vicarious experience also significantly impacting perceived usefulness (PU). Perceived ease of use significantly impacted perceived usefulness, strongly influencing telemedicine adoption. These findings confirm the impact of self-efficacy, especially enactive mastery, and vicarious experience components in sharping perceived ease of use and perceived usefulness. These are essential for driving telemedicine adoption and facilitating its adoption in an emerging country. The findings highlight the importance of these self-efficacy sources in telemedicine adoption strategies and suggest that enhancing individuals’ direct experiences and observational learning can foster telemedicine usage in emerging markets. Keywords: Telemedicine Adoption; Self-efficacy; Technology Acceptance Model; Emerging Countries. 1. Introduction Self-efficacy has influenced the behavior of individuals to do or not to do something [1, 2]. In the past decades, several studies have attempted to investigate the role of psychological factors in adopting technology in the healthcare industry [3, 4]. Prior studies agree that self-efficacy significantly influences individuals’ use of telemedicine [5-7]. Self- efficacy is defined as the expectations of individual efficacy that determine initiating behavior, how much effort will be dedicated, and how long he/she sustains the obstacle encountered and can be classified into three sources: enactive mastery, vicarious experience, and verbal persuasion [1, 8]. The COVID-19 pandemic has deeply reformed public health systems globally, demanding strategies that diminish the risk of direct exposure among people in society. Telemedicine technology emerges as one of the vital strategies to improve the quality of the public health system. Telemedicine is * Corresponding author: suchart.t@bu.ac.th http://dx.doi.org/10.28991/HIJ-2025-06-01-09  This is an open access article under the CC-BY license (https://creativecommons.org/licenses/by/4.0/). © Authors retain all copyrights. https://creativecommons.org/licenses/by/4.0/ https://orcid.org/0000-0001-6408-4790 https://orcid.org/0000-0001-8891-3637 https://orcid.org/0000-0001-7247-4596 https://orcid.org/0000-0002-8031-8056 HighTech and Innovation Journal Vol. 6, No. 1, March, 2025 124 medical experts using information and communication technology to provide public health services to remote populations. It involves the interchange of data related to the diagnosis, treatment, and prevention of diseases and the continual research and education of medical services [9]. Based on BIS Research, the worldwide telemedicine market was valued at approximately $22 billion in 2019. It is anticipated to reach $123 billion by 2030, with a compound annual growth rate of 17% from 2019 to 2030 [10]. Today, the Thai healthcare system faces difficulties meeting the growing demand for healthcare services. The estimated overall expenditure in Southeast Asia is expected to exceed USD 740 billion, with Thailand’s portion being substantial. Nevertheless, the physician-to-patient ratio in Thailand is only 0.47 per 1,000 patients. This discrepancy underscores the significant unmet medical needs in Thailand [11]. Telemedicine can be realized as a mechanism to enhance patients’ access to healthcare services at a lower cost but with more convenience since telemedicine does not require physical interaction between healthcare service providers (e.g., doctors, nurses, and pharmacists) and patients. However, telemedicine has been realized as an innovative healthcare service technology, and its adoption and continual usage, especially in emerging countries such as Thailand, face several challenges. The problems encompass infrastructure deficiencies, including unreliable internet access in rural regions, inadequate availability of telemedicine-compatible devices, and the absence of dependable technical support systems for healthcare practitioners and patients. Moreover, digital literacy constitutes a substantial obstacle, especially for older persons and rural communities, who may lack confidence in utilizing digital platforms and have difficulties with intricate user interfaces. Insufficient awareness and trust in telemedicine’s capabilities intensify these issues, as individuals may doubt their health information’s dependability, efficacy, and confidentiality. This deficiency in confidence is directly linked to self-efficacy, which affects individuals’ opinions of their capability to utilize telemedicine technologies proficiently. The challenges are infrastructural readiness, lack of adequacy in digital literacy, and healthcare regulatory issues that slow the telemedicine adoption rate. Furthermore, investigating these factors is still unclear despite attempts to understand what factors act as antecedents to drive individuals’ perceptions and behaviors to adopt and continue using telemedicine. More investigation is needed, especially into psychological factors. One of the psychological factors that has been paid attention to in the telemedicine context is self-efficacy. Self- efficacy refers to individuals’ belief in their capability to perform behaviors essential to yield specific performance accomplishments [1]. Previous literature argues that individual beliefs such as self-efficacy cause individual action [12]. Also, these studies mainly focus on enacting mastery, which is one source of self-efficacy, especially [6, 7]. However, to our knowledge, other sources of self-efficacy, vicarious experience, and verbal persuasion have been paid little attention to in previous literature. Also, developed and emerging countries’ cultures are different [13]. Cultural differences provide dissimilar reasons for adopting new products and services [14, 15]. The role of self-efficacy in adopting telemedicine may not be the same in developed and emerging countries. Earlier literature examines factors that affect telemedicine adoption in developed or Western countries [6]. However, few studies have investigated the impact of self-efficacy on telemedicine adoption in emerging countries [7]. To enhance the understanding of self-efficacy in emerging countries, this study investigates the effect of elements (enactive mastery, vicarious experience, and verbal persuasion) of self-efficacy on the adoption of telemedicine. The rest of this study contains section two, formulating a theoretical foundation and a conceptual framework; section three, proposing research methodology; section four, presenting data analysis and research findings; section five, discussing theoretical contributions and managerial implications; section six, concluding; and section seven, revealing limitations and directions for future research. 2. Literature Review 2.1. Self-Efficacy Theory Self-efficacy theory is proposed by Bandura (1977), defined as the expectations of individual efficacy that determine initiating behavior, how much effort will be dedicated, and how long he/she will sustain the obstacle encountered [1]. There are three sources of self-efficacy: enactive mastery, vicarious experience, and verbal persuasion [6]. Enactive mastery refers to the degree of recognition of individuals and their ability to succeed on tasks [8]. Vicarious experience is defined as individuals perceiving the behavior of others (e.g., friends, family, influence, and role models), observing what they can do, evaluating the outcome of their behavior, and using this information as a guideline for doing something [2]. Lastly, verbal persuasion is when individuals are convinced by people who succeed in a specific task [16]. This theory is applied to various areas, and one application area is the adoption of telemedicine [7, 17]. Social cognitive factors such as self-efficacy are strongly related to healthcare app adoption [5]. Because individuals have high self- efficacy, they tend to recognize their ability to use telemedicine, observe how to use telemedicine from others, and follow suggestions from influencing people. 2.2. Technology Acceptance Model (TAM) and Cultural Differences in Technology Adoption The concept of the Technology Acceptance Model (TAM), derived from Davis (1989), relates to telemedicine adoption [18]. Individuals will accept new technology or products when they perceive ease of use and usefulness toward new technology or products [19]. Prior research confirms that TAM can be applied in telemedicine contexts such as HighTech and Innovation Journal Vol. 6, No. 1, March, 2025 125 contactless payment technology, mobile healthcare, and telemedicine for physicians [7, 17, 20]. Cultural differences provide different norms, beliefs, attitudes, and behaviors of individuals in each society, such as individualism vs. collectivism, uncertainty avoidance, and long-term vs. short-term orientation [13, 21]. Existing literature investigates the impact of self-efficacy on telemedicine adoption in a Western context. However, the norms of Western and Eastern countries are not the same. Different norms reflect individuals’ attitudes and behavior, which may lead to the rate of technology adoption. In collectivism, low uncertainty avoidance, and long-term orientation, the impact of imitation among individuals in society dominates technology adoption [22]. 2.3. The Impact of Self-Efficacy on Telemedicine Adoption Although much literature explains the impact of self-efficacy on telemedicine adoption, these studies focus on the multi-dimensional concept of self-efficacy. Apart from enactive mastery, other dimensions of self-efficacy, which are vicarious experience and verbal persuasion, lack examination in the telemedicine context. This study proposes the impact of three dimensions of self-efficacy on telemedicine adoption. A conceptual framework of this study is shown in Figure 1. Figure 1. A conceptual framework Enactive mastery plays a crucial role in telemedicine adoption. Individuals who believe in their ability to use telemedicine technology find it easier [7]. This confidence also enhances their perception of its usefulness in daily life. Enactive mastery also builds confidence through successful experiences, making telemedicine technology seem more straightforward and beneficial. This confidence and positive perception are crucial for the widespread adoption of telemedicine. For these reasons, we can hypothesize that. H1: Enactive mastery positively impacts (a) perceived ease of use and (b) perceived usefulness. Vicarious experience increases the likelihood that individuals will perceive new technology, such as telemedicine, as easy to use. Observing others teaches individuals how to perform tasks without directly engaging with them. They watch others use the technology, learn from their experiences, and evaluate the outcomes. These observations help individuals find telemedicine easy to use and recognize its usefulness. Hence, H2: Vicarious experience positively impacts (a) perceived ease of use and (b) perceived usefulness. Verbal persuasion can enhance the perceived ease of use and usefulness of telemedicine for individuals who are not familiar with it. When people are persuaded by those with experience or expertise in telemedicine, they are likelier to find it easy to use. By consulting with and following the guidance of these experts, individuals can more easily recognize the benefits of telemedicine. As such, we can hypothesize that, H3: Verbal persuasion positively impacts (a) perceived ease of use and (b) perceived usefulness. When individuals perceive telemedicine as easy to use for improving their health, they recognize its benefits, such as the convenience of meeting with a doctor or nurse and cost savings in terms of time and transportation. As they perceive the ease of use and usefulness of telemedicine technology, they are more likely to accept new technologies that enhance their quality of life, such as telemedicine, instead of visiting a doctor at a hospital or clinic. Therefore, it can be hypothesized that H4: Perceived ease of use has a positive impact on perceived usefulness. H5: Perceived ease of use has a positive impact on telemedicine adoption. H6: Perceived usefulness has a positive impact on telemedicine adoption. Enactive mastery (EM) H5 H1a Vicarious experience (VE) Verbal persuasion (VP) Perceived ease of use (PE) Perceived usefulness (PU) Telemedicine adoption (TA) H6 H4 H1b H2a H2b H3a H3b HighTech and Innovation Journal Vol. 6, No. 1, March, 2025 126 3. Research Methodology This quantitative study conducts survey research in Thailand. Thailand can be a good proxy for an emerging country in which the healthcare industry heavily invests in developing telemedicine systems, and many hospitals provide telemedicine services [23]. However, several studies examine self-efficacy’s impact on telemedicine adoption in a Western context. Our findings can contribute to expanding the boundary of knowledge in adopting telemedicine in the Eastern context. The empirical data was collected using the quota sampling method to ensure the representation of respondents of different strata, such as sex and age [24]. The quota sampling method has advantages and disadvantages. In terms of strengths of the quota sampling method, this method improves the representation of the sample related to the population of the study, provides prediction as good as the probability sampling method, enhances the response rate, and increases the efficiency of cost and time than probability sampling [25, 26]. However, there are weaknesses in the quota sampling method, such as the variation of respondents from different characteristics, leading to the potential bias of research findings [27]. For these reasons, we find the advantages outweigh the disadvantages of the method. The respondents were selected using a quota sampling method to ensure representation across age and gender groups, minimizing bias. However, this non-probability approach may still carry some risk of selection bias due to its reliance on pre-defined quotas. For the Thai population, the number of populations aged between 36 and 55 is highest compared with other groups [28]. In this study, quotas are set into three groups according to generation: 16-35 (Generation Z), 36-55 (Generation X), and 56+ (Baby Boomer). For the gender of the Thai population, the ratio of males and females is approximately equal [29]. Another criterion is that a respondent must have experienced telemedicine service at least once. This approach ensures that the sample reflects the diversity of the population while focusing on individuals with prior experience using telemedicine, which enhances the reliability of the findings. A self-administered questionnaire with a cover letter to explain the research objective is used in this study. To increase the credibility of research findings and reduce the bias of respondents, all respondents are anonymous by not asking for their contact information, such as name, telephone number, and e-mail address, in the questionnaire. We adopt the measures from previous studies to develop the measures of six constructs in this study (enactive mastery, vicarious experience, verbal persuasion, perceived ease of use, perceived usefulness, and telemedicine adoption). All constructs in this study are measured by a five-point Likert scale (1 = strongly disagree to 5 = strongly agree). The three sources of self-efficacy, which are active mastery (3 items), vicarious experience (3 items), and verbal persuasion (6 items), were measured by the items modified from Wangwongwiroj & Yasri (2021) [30]. To evaluate perceived ease of use, we use a four-item measure and perceived usefulness, and a three-item measure is adapted from Zhang et al. (2017) [7]. For telemedicine adoption, a three-item measure is modified from Dam et al. (2018) and Zhang et al. (2017) [5, 7]. The constructs were measured using validated scales adapted from previous studies, and reliability was ensured by conducting a pilot test and confirming internal consistency through Cronbach’s alpha values above the acceptable threshold of 0.7. The detailed constructs and measurement items are shown in the Appendix I. For data analysis, structural equation modeling (SEM), using a two-step approach, is used to investigate the interrelationship among constructs in the conceptual framework. AMOS version 24 was used for data analysis. To calculate the sample size (n) for data analysis, Hair et al. (2010) suggest that a ratio of sample size to the number of observed variables should be greater than 10 [31]. In this study, the number of observed variables is 21. Hence, the sample size of this study should be greater than 210. 4. Results 4.1. Sample Profile The majority of the sample is male (50.4%). Most respondents are between 16 and 35 years old (45.8%). Regarding education, the largest group holds a bachelor’s degree (42.5%), followed by those with a master’s degree or higher (26.7%). The income distribution shows that most respondents earn between 20,001-30,000 Baht per month (32.9%), and the largest group occupation is private sector employees (40.8%). Most respondents use hospital or healthcare services 2-3 times a year (57.9%), and most spend the average medical expenses per visit between 1,001-3,000 Baht (52.1%) when visiting hospitals or medical facilities. Most usually go with family (50.4%), followed by those who go alone (42.9%). The sample profile is shown in Table 1. HighTech and Innovation Journal Vol. 6, No. 1, March, 2025 127 Table 1. Sample profile N % Gender Male 121 50.4 Female 119 49.6 Age 16-35 years old 110 45.8 36-55 years old 90 37.5 above 56 years old 40 16.7 Education attainment Primary School 1 0.4 Secondary School 19 7.9 Vocational Certificate / Diploma 54 22.5 Bachelor’s Degree 102 42.5 Master’s degree or higher 64 26.7 Monthly Income (Baht) Less than 10,000 Baht 49 20.4 10,001-20,000 Baht 37 15.4 20,001-30,000 Baht 79 32.9 30,001-40,000 Baht 25 10.4 More than 40,000 Baht 50 20.8 Average times using the services at a hospital or healthcare facility Less than once a year 56 23.3 2-3 times a year 139 57.9 More than 3 times a year 45 18.8 Average medical expenses per visit Less than 1,000 Baht 76 31.7 1,001-3,000 Baht 125 52.1 More than 3,000 Baht 39 16.3 Occupation Student 52 21.7 Private sector employee 98 40.8 Government official / State enterprise employee 50 20.8 Business owner 35 14.6 Homemaker 5 2.1 Who do you usually visit the hospital or medical facility with? Alone 103 42.9 Family 121 50.4 Friend 14 5.8 4.2. Hypothesis Testing Structural equation modeling (SEM) was employed to examine the interrelationships among constructs in the conceptual framework using a two-step approach. First, confirmatory factor analysis (CFA) was conducted to validate the measurement items. Convergent validity refers to the extent to which two measures of the same trait agree, and discriminant validity, which ensures that constructs are distinct, was assessed to confirm the model’s goodness of fit. Convergent validity is satisfied when factor loadings exceed 0.7, and the squared multiple correlations are greater than 0.5 [32, 33]. Discriminant validity is established when the square root of a construct’s AVE (Average Variance Extracted) is greater than its correlations with other constructs in the model. Following the suggested modification indices, the measurement model was revised, and item VP6 was removed as its factor loading did not meet the recommended threshold. The final measurement model demonstrated an acceptable fit to the data (Chi-square = 318.933, df = 170, CMIN/df = 1.876, GFI = 0.888, RMSEA = 0.061; CFI = 0.945; NFI = 0.891). Table 2 presents the confirmatory factor analysis results, confirming that convergent and discriminant validity were achieved. HighTech and Innovation Journal Vol. 6, No. 1, March, 2025 128 Table 2. Measurement Model Results Constructs and items Loading t-value SE Alpha Composite Reliability AVE Enactive Mastery 0.678 0.821 0.339 EM1 0.435 - - EM2 0.629 7.617 0.183 EM3 0.595 5.929 0.242 Vicarious Experience 0.709 0.910 0.453 VE1 0.692 - - VE 2 0.651 8.624 0.130 VE 3 0.675 8.896 0.112 Verbal persuasion 0.805 0.982 0.458 VP1 0.754 - - VP2 0.701 10.258 0.100 VP3 0.623 9.096 0.086 VP4 0.659 9.640 0.096 VP5 0.637 9.312 0.092 Perceived ease of use 0.875 0.986 0.636 PE1 0.738 - - PE2 0.774 11.804 0.099 PE3 0.785 11.869 0.093 PE4 0.886 13.265 0.094 Perceived usefulness 0.835 0.972 0.652 PU1 0.872 - - PU2 0.645 11.214 0.073 PU3 0.882 17.448 0.061 Telemedicine adoption 0.893 0.983 0.739 TA1 0.893 - - TA2 0.792 15.529 0.066 TA3 0.892 18.996 0.056 Based on the data presented in Table 3, the measurement model demonstrates acceptable convergent and discriminant validity. Most factor loadings exceed 0.6, Composite Reliability (CR) values surpass the 0.7 threshold for all constructs, and Average Variance Extracted (AVE) values are close to the recommended 0.5 level for most constructs. Discriminant validity is confirmed as the AVE values are generally higher than the squared inter-construct correlations. While the AVE value for the Enactive Mastery construct falls slightly below the 0.5 benchmark, its high CR and Cronbach’s Alpha indicate strong internal consistency. For most constructs, the square root of the AVE is greater than its correlations with other constructs, affirming discriminant validity. These confirmatory factor analysis results validate the measurement model, allowing further path analysis. Table 3. Discriminate Validity Construct EM VE VP PE PU TA Enactive Mastery 0.582 Vicarious Experience 0.947 0.673 Verbal persuasion 0.914 0.810 0.677 Perceived ease of use 0.592 0.468 0.340 0.797 Perceived usefulness 0.455 0.577 0.459 0.657 0.807 Telemedicine adoption 0.413 0.520 0.398 0.596 0.858 0.860 Note: The square root of AVE of each construct is shown in bold on the diagonal Following the validation of the measurement model, the proposed hypotheses were tested by analyzing the structural model and path coefficients linking independent constructs to their respective dependent constructs, as defined by the research hypotheses. The structural model achieved acceptable model fit statistics, indicating a good fit with the data. The results of the hypothesis testing are summarized in Figure 2 and Table 4. HighTech and Innovation Journal Vol. 6, No. 1, March, 2025 129 Notes: n.s = not significant; Chi-square = 270.987; df = 155; CMIN/df = 1.748; RMSEA = 0.056; CFI = 0.957; NFI = 0.907 Figure 2. The Path Coefficient for all hypotheses of interest in the study Table 4. Hypotheses testing Hypothesis Loading t-value Result H1a: Enactive mastery has a positive impact on perceived ease of use. 0.393** 2.033 Supported H1b: Enactive mastery has a positive impact on perceived usefulness. -0.201 -1.367 Not Supported H2a: Vicarious experience has a positive impact on perceived ease of use. 0.397** 2.554 Supported H2b: Vicarious experience has a positive impact on perceived usefulness. 0.444*** 3.156 Supported H3a: Verbal persuasion has a positive impact on perceived ease of use. -0.297 -1.561 Not Supported H3b: Verbal persuasion has a positive impact on perceived usefulness. 0.321* 1.709 Partial Supported H4: Perceived ease of use has a positive impact on perceived usefulness. 0.582*** 6.552 Supported H5: Perceived ease of use has a positive impact on telemedicine adoption 0.024 0.367 Not Supported H6: Perceived usefulness has a positive impact on telemedicine adoption 0.848*** 11.888 Supported Notes: *p <0.1; **p <0.05; ***p < 0.01 The influence of the three sources of self-efficacy—enactive mastery, vicarious experience, and verbal persuasion— on telemedicine adoption is examined through two key perceptions of telemedicine technology: perceived ease of use and perceived usefulness. The findings indicate that enactive mastery significantly enhances perceived ease of use (β = 0.393; p < 0.05; supporting H1a) but does not exhibit a significant positive impact on perceived usefulness (β = -0.201; p > 0.05; not supporting H1b). Vicarious experience positively affects both perceived ease of use (β = 0.397; p < 0.05; supporting H2a) and perceived usefulness (β = 0.444; p < 0.01; supporting H2b), emerging as the strongest contributor to self-efficacy influencing perceptions of telemedicine. Verbal persuasion does not significantly impact perceived ease of use (β = -0.297; p > 0.05; not supporting H3a) but shows a partial positive effect on perceived usefulness (β = 0.321; p < 0.1; partially supporting H3b). Additionally, the results confirm a strong relationship between perceived ease of use and perceived usefulness (β = 0.582; p < 0.01; supporting H4). However, perceived ease of use does not significantly influence telemedicine adoption (β = 0.024; p > 0.05; not supporting H5), while perceived usefulness has a significant and positive impact on telemedicine adoption (β = 0.848; p < 0.001; supporting H6). These findings suggest that strategies to promote telemedicine adoption should prioritize enhancing perceived usefulness, particularly by leveraging vicarious experiences to shape users’ perceptions of telemedicine technology’s benefits positively. 5. Discussion The study aims to investigate the impact of three sources of self-efficacy: enactive mastery, vicarious experience, and verbal persuasion on telemedicine adoption through perceptions of ease of use and usefulness in the emerging country – Thailand. The finding shows that self-efficacy is an antecedent that enhances individuals’ adoption of new technology. Enactive mastery and vicarious experience show a positive effect on the perceived ease of use of telemedicine. Vicarious experience also strongly impacts telemedicine’s perceived ease of use and usefulness. Nevertheless, enactive mastery does not significantly impact perceived usefulness. This implies that direct experience may not always translate to usefulness perception, especially in emerging countries with telemedicine adoption. Verbal persuasion did not significantly influence perceived ease of use and was found partially significant in perceived usefulness in our study. The finding aligns with the study of Lunenburg (2011), which indicates that verbal persuasion can enrich self-efficacy and subsequent perceptions and behaviors [16]. In telemedicine adoption in emerging countries, verbal persuasion alone may have a limited effect on changing perceptions of the technology’s ease of use and usefulness. Unlike prior studies, this study provides a novel perspective on how self-efficacy shapes willingness to adopt telemedicine. Whereas earlier studies mainly focused on enactive mastery as the key source of self-efficacy, this study EM PU PE TA VE VP 0.393 0.321 0.397 0.024 (n.s) 0.848 -0.201 (n.s) 0.444 -0.297 (n.s) 0.582 HighTech and Innovation Journal Vol. 6, No. 1, March, 2025 130 is one of the pioneering works to include and explore the roles of vicarious experience and verbal persuasion in telemedicine adoption. By treating self-efficacy as a multi-dimensional construct, our study offers a more comprehensive viewpoint on how self-efficacy influences technology adoption behaviors that can help telemedicine practitioners and policymakers design more effective strategies for enhancing telemedicine adoption, especially in the emerging market context. While existing literature has mainly studied this in the context of developed or innovation-driven countries, this study extends the understanding of self-efficacy and technology adoption within emerging markets. This study highlights the unique challenges and opportunities in emerging markets like Thailand, providing valuable insights and theoretical and practical contributions. The findings emphasize the significance of considering local cultural and infrastructural factors, providing a broader insight into how self-efficacy impacts technology adoption in various contexts. The findings of this study underscore the nuanced relationship between self-efficacy and telemedicine adoption, particularly in emerging markets like Thailand. In such contexts, cultural norms, technological literacy, and infrastructural disparities are pivotal in shaping individual perceptions of technology. For instance, the collectivist culture in many emerging economies fosters a firm reliance on observational learning and peer influence, making vicarious experience a critical driver of telemedicine adoption. Similarly, challenges such as limited exposure to advanced technologies and lower digital proficiency may explain why verbal persuasion alone does not significantly influence adoption behavior [34-36]. Recent studies emphasize that telemedicine adoption strategies must consider socio-cultural contexts to overcome adoption barriers effectively [37, 38]. These insights highlight the importance of tailoring telemedicine strategies to local socio-cultural and economic conditions, ensuring that interventions address the specific barriers and facilitators relevant to each market. By recognizing these contextual elements, healthcare stakeholders can develop targeted approaches that resonate with emerging country populations’ unique needs and expectations. This study provides specific practical contributions for practitioners such as healthcare providers and policymakers to enhance telemedicine adoption in emerging countries. Recognizing the significance of vicarious experience, governments and healthcare practitioners can capitalize on this by endorsing telemedicine via public demonstration events, webinars, and live case studies that illustrate practical applications. For instance, facilitating interactive sessions where individuals can witness physicians or colleagues utilizing telemedicine technologies can enhance confidence and trust. Moreover, video lessons or testimonials from reputable community members and influencers can effectively promote vicarious learning. These projects will illustrate the simplicity of telemedicine and emphasize its advantages in practical environments. Firstly, the strong impact of enactive mastery and vicarious experience on perceived ease of use and perceived usefulness imply that healthcare providers should focus on prioritizing practical learning and observational opportunities of telemedicine adoption and usage by implementing training programs or workshops that permit prospective users to participate and engage with telemedicine technology and services to enhance their familiarity and confidence with telemedicine technology. Moreover, presenting successful use cases can empower the influence of vicarious experience, subsequently developing the perceived value of telemedicine usage. Secondly, the limitations of verbal persuasion in changing perceptions of telemedicine found in this study imply that it might not be enough to encourage telemedicine verbally. Alternatively, a more practical approach with real-world examples and success stories could work better. This realization guides policymakers in creating immersive experiences that foster self-efficacy through direct involvement and observation rather than concentrating on conventional awareness efforts. Thirdly, perceived usefulness is critical to telemedicine technology adoption in emerging country contexts. Therefore, telemedicine platforms should be advantageous and beneficial. To establish telemedicine platforms and services that satisfy these requirements and promote wider acceptance, usability testing and iterative design processes taking user feedback into account can be valuable. 6. Conclusion The goal of this study is to investigate the role of self-efficacy in the adoption of telemedicine in an emerging country, Thailand. Although previous research confirms that self-efficacy acts as an antecedent affecting the likelihood of adopting the new technology, our study is one of the few empirical studies scrutinizing the multi-dimensional sources of self-efficacy impacting the adoption of healthcare-related technology, telemedicine. Using a Structural Equation Modelling (SEM) technique with 240 samples, the results highlight the imperative of the three sources of self-efficacy: enactive mastery, vicarious experience, and verbal persuasion, on perceived ease of use, perceived usefulness, and, eventually, telemedicine adoption. The outcomes represent those two sources of self-efficacy: enactive mastery and vicarious experience, which act as vital factors affecting individuals’ perceptions of telemedicine. Enactive mastery positively affects perceived ease of use, indicating that individuals who acquire direct familiarity with telemedicine technology tend to find it more user-friendly. The vicarious experience demonstrates both improved perceived ease of use and perceived usefulness. This emphasizes the importance of observational learning in technology adoption. While the study focuses on Thailand as a representative emerging country, the findings offer valuable insights that can be generalized to other emerging markets facing similar challenges in healthcare infrastructure, digital literacy, and technology adoption. Nevertheless, verbal persuasion shows no significant impact on either perceived ease of use or usefulness. It can be implied that in the context of telemedicine adoption in emerging countries, only verbal persuasion may not be adequate to change an individual’s perception and behavior to adopt telemedicine usage. HighTech and Innovation Journal Vol. 6, No. 1, March, 2025 131 6.1. Limitations and Directions of Future Research Although our study provides several theoretical and practical contributions, some limitations must be considered. Firstly, the data in this study was collected in Thailand to represent the adoption of telemedicine in emerging countries. The results should be carefully generalized to other countries with different cultural contexts. In the future, investigation in other emerging countries can increase the generalizability of self-efficacy theory in adopting telemedicine in emerging countries. Secondly, while a quantitative survey method was selected for this study, conducting in-depth interviews could offer additional insights into the adoption of telemedicine. Furthermore, future research could study other technology acceptant models, such as the unified theory of acceptance and use of technology (UTAUT) model and expectation Confirmation Model (ECM), to enhance understanding of telemedicine adoption. Lastly, other psychological factors that may hinder the acceptance of telemedicine technology, such as technophobia and e-health readiness, should be explored. We hope that our findings will increase the interest of this topic in the research domain of telemedicine. 7. Declarations 7.1. Author Contributions Conceptualization, D.H.; methodology, D.H. and S.T.; software, D.H. and S.T.; validation, D.H., C.K., W.P., W.V., and S.T.; formal analysis, D.H. and S.T.; investigation, D.H., C.K., W.P., W.V., and S.T.; resources, D.H.; data curation, D.H., C.K., W.P., W.V., and S.T.; writing—original draft preparation, D.H. and S.T.; writing—review and editing, D.H., C.K., W.P., W.V., and S.T.; visualization, D.H., C.K., W.P., W.V., and S.T.; supervision, D.H.; project administration, D.H.; funding acquisition, D.H. All authors have read and agreed to the published version of the manuscript. 7.2. Data Availability Statement The datasets generated during and/or analyzed during the current study are not publicly available due to IRB stipulations but are available from the corresponding author upon reasonable request. 7.3. Funding This project is partly funded by National Research Council of Thailand (NRCT), Project no. N42A660902. 7.4. Institutional Review Board Statement This study was reviewed and approved by the Ethics Committee of Chonburi Cancer Hospital, under Protocol No. 008/2023. 7.5. Informed Consent Statement Not applicable. 7.6. Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. 8. References [1] Bandura, A. (1977). Self-efficacy: Toward a unifying theory of behavioral change. Psychological Review, 84(2), 191–215. doi:10.1037/0033-295X.84.2.191. [2] Maddux, J. E. (1995). Self-efficacy theory: An introduction. In Self-efficacy, adaptation, and adjustment: Theory, research, and application. Springer, Boston, United States. doi:10.1007/978-1-4419-6868-5_1. [3] Chinn, D., & McCarthy, C. (2013). All Aspects of Health Literacy Scale (AAHLS): Developing a tool to measure functional, communicative and critical health literacy in primary healthcare settings. Patient Education and Counseling, 90(2), 247–253. doi:10.1016/j.pec.2012.10.019. [4] Del Giudice, M., Soto-Acosta, P., Carayannis, E., & Scuotto, V. (2018). Emerging perspectives on business process management (BPM): IT-based processes and ambidextrous organizations, theory and practice. Business Process Management Journal, 24(5), 1070–1076. doi:10.1108/BPMJ-09-2018-336. [5] Dam, L., Roy, D., Atkin, D. J., & Rogers, D. (2018). Applying an Integrative Technology Adoption Paradigm to Health App Adoption and Use. Journal of Broadcasting and Electronic Media, 62(4), 654–672. doi:10.1080/08838151.2018.1519568. [6] Scott Kruse, C., Karem, P., Shifflett, K., Vegi, L., Ravi, K., & Brooks, M. (2018). Evaluating barriers to adopting telemedicine worldwide: A systematic review. Journal of Telemedicine and Telecare, 24(1), 4–12. doi:10.1177/1357633X16674087. HighTech and Innovation Journal Vol. 6, No. 1, March, 2025 132 [7] Zhang, X., Han, X., Dang, Y., Meng, F., Guo, X., & Lin, J. (2017). User acceptance of mobile health services from users’ perspectives: The role of self-efficacy and response-efficacy in technology acceptance. Informatics for Health and Social Care, 42(2), 194–206. doi:10.1080/17538157.2016.1200053. [8] Margolis, H., & Mccabe, P. P. (2006). Improving self-efficacy and motivation: What to do, what to say. Intervention in School and Clinic, 41(4), 218–227. doi:10.1177/10534512060410040401. [9] WHO. (2010). Telemedicine: Opportunities and developments in member states. World Health Organization, Geneva, Switzerland. Available online: https://iris.who.int/handle/10665/44497 (accessed on February 2025). [10] BIS Research. (2023). Telehealth and telemedicine: Making treatment possible anytime, anywhere. BIS Research Inc. Noida, India. Available online: https://bisresearch.com/insights/telehealth-and-telemedicine-making-treatment-possible-anytime- anywhere (accessed on February 2025). [11] Sriyananda, V. (2024). Accessing telemedicine in Thailand: Benefits and effective tips for expats. Pacific Prime Thailand, Bangkok, Thailand. Available online: https://www.pacificprime.co.th/blog/accessing-telemedicine-in-thailand-benefits-and- effective-tips-for-expats/ (accessed on February 2025). [12] Abraham, C., & Sheeran, P. (2014). The health belief model. Cambridge Handbook of Psychology, Health and Medicine. Cambridge University Press, Cambridge, United Kingdom. doi:10.1017/CBO9780511543579.022. [13] Hofstede, G. (1984). Culture’s consequences: International differences in work-related values (Volume 5). Sage, New York, United States. [14] Yuen, Y. Y., Yeow, P. H. P., Lim, N., & Saylani, N. (2010). Internet banking adoption: Comparing developed and developing countries. Journal of Computer Information Systems, 51(1), 52–61. [15] Hoonsopon, D. (2016). Accelerating Adoption of New Products of Thai Consumers: The Moderating Roles of Self-Brand Concept and Reference Group. Journal of Asia-Pacific Business, 17(2), 151–172. doi:10.1080/10599231.2016.1166023. [16] Lunenburg, F. C. (2011). Self-efficacy in the workplace: Implications for motivation and performance. International journal of management, business, and administration, 14(1), 1-6. [17] Rho, M. J., Choi, I. young, & Lee, J. (2014). Predictive factors of telemedicine service acceptance and behavioral intention of physicians. International Journal of Medical Informatics, 83(8), 559–571. doi:10.1016/j.ijmedinf.2014.05.005. [18] Davis, F. D. (1989). Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS Quarterly: Management Information Systems, 13(3), 319–339. doi:10.2307/249008. [19] Davis, F. D., Bagozzi, R. P., & Warshaw, P. R. (1989). User Acceptance of Computer Technology: A Comparison of Two Theoretical Models. Management Science, 35(8), 982–1003. doi:10.1287/mnsc.35.8.982. [20] Puriwat, W., & Tripopsakul, S. (2021). Explaining an adoption and continuance intention to use contactless payment technologies: During the covid-19 pandemic. Emerging Science Journal, 5(1), 85–95. doi:10.28991/esj-2021-01260. [21] Hofstede, G. (2011). Dimensionalizing Cultures: The Hofstede Model in Context. Online Readings in Psychology and Culture, 2(1), 8. doi:10.9707/2307-0919.1014. [22] Lee, S. G., Trimi, S., & Kim, C. (2013). The impact of cultural differences on technology adoption. Journal of World Business, 48(1), 20–29. doi:10.1016/j.jwb.2012.06.003. [23] Donchalermpak, P. (2020). Telemedicine for Thailand. BOI e-Journal, 3(6), 12–17. Available online: https://www.boi.go.th/upload/content/DR.raksa.pdf (accessed on February 2025). [24] Munnukka, J., & Järvi, P. (2011). The value drivers of high-tech consumer products. Journal of Marketing Management, 27(5– 6), 582–601. doi:10.1080/02672571003737783. [25] Eisend, M., & Kuss, A. (2019). Research Methodology in Marketing: Theory Development, Empirical Approaches and Philosophy of Science Considerations. Research Methodology in Marketing: Theory Development, Empirical Approaches and Philosophy of Science Considerations. Springer, Cham, Switzerland. doi:10.1007/978-3-030-10794-9. [26] Gaganpreet, S. (2017). Pros and cons of different sampling techniques. International Journal of Applied Research, 3(7), 749– 752. [27] Yang, K., & Banamah, A. (2014). Quota sampling as an alternative to probability sampling? An experimental study. Sociological Research Online, 19(1), 56–66. doi:10.5153/sro.3199. [28] Statista. (2024). Resident population in Thailand as of March 2024, by age group. Statista. Available online: https://www.statista.com/statistics/1283627/thailand-resident-population-by-age-group/ (accessed on February 2025). https://bisresearch.com/insights/telehealth-and-telemedicine-making-treatment-possible-anytime-anywhere https://bisresearch.com/insights/telehealth-and-telemedicine-making-treatment-possible-anytime-anywhere https://www.pacificprime.co.th/blog/accessing-telemedicine-in-thailand-benefits-and-effective-tips-for-expats/ https://www.pacificprime.co.th/blog/accessing-telemedicine-in-thailand-benefits-and-effective-tips-for-expats/ https://www.boi.go.th/upload/content/DR.raksa.pdf https://www.statista.com/statistics/1283627/thailand-resident-population-by-age-group/ HighTech and Innovation Journal Vol. 6, No. 1, March, 2025 133 [29] Statista. (2023). Population Share of the ASEAN Countries in 2022. Statista. Available online: https://www.statista.com/statistics/1095818/asean-population-by-gender/ (accessed on February 2025). [30] Wangwongwiroj, T., & Yasri, P. (2021). The Comparison of Students’ Perceived Levels of Self-Efficacy in Live, Online and Live Online Courses. International Journal on Research in STEM Education, 3(1), 47–54. doi:10.31098/ijrse.v3i1.505. [31] Hair, J. F., Black, W. C., Babin, B. J., & Anderson, R. E. (2010). Multivariate data analysis: Pearson College Division. Person, London, United Kingdom. [32] Cheung, G. W., Cooper-Thomas, H. D., Lau, R. S., & Wang, L. C. (2024). Reporting reliability, convergent and discriminant validity with structural equation modeling: A review and best-practice recommendations. Asia Pacific Journal of Management, 41(2), 745–783. doi:10.1007/s10490-023-09871-y. [33] Awang, Z. (2015). SEM Made Simple: A Gentle Approach to Learning Structural Equation Modelling. In MPWS Rich Publication (Vol. 1, Issue 3). MPWS Rich Publication. [34] Booth, N., McConnell, T., Tully, M., Hamill, R., & Best, P. (2024). Exploring behaviour-intention gaps to explain low retention in a community-based video-conferencing intervention for depression: lessons for researchers using technology as interventions. Mental Health and Digital Technologies, 1(1), 5–23. doi:10.1108/mhdt-11-2023-0002. [35] Thangavel, P., & Chandra, B. (2024). Digital Immigrants versus Digital Natives: Decoding Their E-commerce Adoption Behavior. SAGE Open, 14(4), 21582440241282436. doi:10.1177/21582440241282437. [36] Muellmann, S., Wiersing, R., & Brand, T. (2024). Digital health literacy of adults with low reading and writing skills: A mixed- methods study. European Journal of Public Health, 34(Supplement_3), ckae144-374. doi:10.1093/eurpub/ckae144. [37] Amin, S., Mustofa, M., Rahman, A., & Barman, H. An exploratory cross-sectional study of awareness and barriers to adoption of telemedicine services among patients in Bangladesh. Future Health, 2(2), 107–113. doi:10.1016/j.fhth.2024.03.007. [38] Arora, S., Huda, R. K., Verma, S., Khetan, M., & Sangwan, R. K. (2024). Challenges, Barriers, and Facilitators in Telemedicine Implementation in India: A Scoping Review. Cureus, 16(8), 67388. doi:10.7759/cureus.67388. https://www.statista.com/statistics/1095818/asean-population-by-gender/ HighTech and Innovation Journal Vol. 6, No. 1, March, 2025 134 Appendix I Table A1. The constructs and measurement items of this study Constructs Measurement items Enactive Mastery (EM) I become confident in my ability when I complete a specific task. I become confident in my ability when I gain direct experience from a specific task. I become confident in my ability when I have a chance to do a specific task, no matter what the result would be. Vicarious Experience (VE) I become confident in my ability when someone demonstrates a specific task beforehand. I become confident in my ability when I see someone with a similar skill set as me accomplishing a specific task. I become confident in my ability when I see someone with a similar level of competency as me doing a specific task. Verbal Persuasion (VP) I become confident in my ability when other people tell me I am good at my work. I become confident in my ability when other people tell me to improve on something. I become confident in my ability when others compliment me on my learning performance. I become confident in my ability when others tell me I can overcome challenges by working hard. I become confident in my ability when others tell me I am perfect. I become confident in my ability when others tell me that I have done my best, even though the result is undesirable. Perceived Ease of Use (PE) Learning to operate telemedicine will be easy for me. I can quickly become skillful at using telemedicine. I can get the telemedicine to do what I want. Telemedicine is easy to use for me. Perceived Usefulness (PU) Using telemedicine will improve my quality of life. Using telemedicine will mean more healthcare conveniences. I find telemedicine to be helpful in my life. Telemedicine Adoption (TA) I intend to use telemedicine. I am considering using telemedicine. I would recommend telemedicine to others.