Examining the factors influencing teleconsultation adoption during the pandemic using the TAM model

Ekaimi, San; Utomo, Prio; Gunawan, Djohan; Jimmy, So Yohanes; Christian, Michael Article Examining the factors influencing teleconsultation adoption during the pandemic using the TAM model Global Business & Finance Review (GBFR) Provided in Cooperation with: People & Global Business Association (P&GBA), Seoul Suggested Citation: Ekaimi, San; Utomo, Prio; Gunawan, Djohan; Jimmy, So Yohanes; Christian, Michael (2024) : Examining the factors influencing teleconsultation adoption during the pandemic using the TAM model, Global Business & Finance Review (GBFR), ISSN 2384-1648, People & Global Business Association (P&GBA), Seoul, Vol. 29, Iss. 3, pp. 149-160, https://doi.org/10.17549/gbfr.2024.29.3.149 This Version is available at: https://hdl.handle.net/10419/305976 Standard-Nutzungsbedingungen: Die Dokumente auf EconStor dürfen zu eigenen wissenschaftlichen Zwecken und zum Privatgebrauch gespeichert und kopiert werden. 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Introduction The COVID-19 pandemic has accelerated technology Received: Nov. 18, 2023; Revised: Dec. 10, 2023; Accepted: Dec. 27, 2023 † Corresponding author: Prio Utomo E-mail: [email protected] adoption by several years in all sectors and regions. Across all sectors, healthcare and pharmaceuticals have seen significant increase in consumer digital interaction compared with consumer packaged goods (CPG), while across regions in the Asia-Pacific regions technology adoption has accelerated for more than ten years (McKinsey, 2020; Wang et al., 2022). These accelerations cause significant challenges for ⓒ Copyright: The Author(s). This is an Open Access journal distributed under the terms of the Creative Commons Attribution Non-Commercial License (https://creativecommons.org/licenses/by-nc/4.0/) which permits unrestricted non-commercial use, distribution , and reproduction in any medium, provided the original work is properly cited. GLOBAL BUSINESS & FINANCE REVIEW, Volume. 29 Issue. 3 (APRIL 2024), 149-160 pISSN 1088-6931 / eISSN 2384-1648∣Https://doi.org/10.17549/gbfr.2024.29.3.149 ⓒ 2024 People and Global Business Association GLOBAL BUSINESS & FINANCE REVIEW www.gbfrjournal.org for financial sustainability and people-centered global business1) Examining the Factors Influencing Teleconsultation Adoption During the Pandemic Using the TAM Model San Ekaimia, Prio Utomoa†, Djohan Gunawana, So Yohanes Jimmya, Michael Christianb aDepartment of Technology Management Universitas Multimedia Nusantara Tangerang, Indonesia bDepartment of Management and Master of Management Universitas Bunda Mulia Jakarta, Indonesia A B S T R A C T Purpose: The purpose of this study is to understand patients' behavior in adopting teleconsultation services during the pandemic which has accelerated technology adoption in various sectors and regions in unnatural ways through experimentation in the wide-spread adoption of telemedicine specifically in teleconsultation. Design/methodology/approach: The study is cross-sectional quantitative research with purposive sampling that was analyzed using the partial least squares structural equation model (PLS-SEM) with data gathered from 100 patients who used tele-consultation services during the pandemic. The technology adoption model (TAM) was used to frame the phenomenon as previous study revealed that TAM is most suitable to understand technology adoption in the healthcare industry. Findings: The analysis revealed that trust, ease of use, and privacy are significant latent variables that influence the intention and actual use of the tele-consultation services, while the usefulness of the services insignificantly influences the intention to use. The study revealed that trust is the dominant factor despite the emergency and experimentation state of patients to get healthcare services during the pandemic. Self-preservation, and the patients accumulated experience they had with the healthcare services provider are the determinants of this behavior based on theory and confirmative interview. Research limitations/implications: The implication of this research opens interdisciplinary future research and practical action on the relationship between healthcare provider, patient experience, self-preservation, and technology adoption during the emergency and pandemic era. Research Limitation: Early system usage and adopter focused, limited system quality and reliability, and generalization are the limitation of this research. Originality/value: The study provides insight into factors that influence the adoption and usage of tele-consultation experimentation health services during a pandemic. Keywords: telemedicine, teleconsultation, healthcare, PLS-SEM, TAM, pandemic GLOBAL BUSINESS & FINANCE REVIEW, Volume. 29 Issue. 3 (APRIL 2024) 149-160 150 various stakeholders, including the government as pertaining to the regulatory and legal aspects (Choi, 2022), healthcare providers, who lack the necessary training or infrastructure to effectively utilize technology, and users who are hesitant due to virtual consultation and care compared with their previous habits of having in-person healthcare visits. These challenges push stakeholders to invest in technology despite future uncertainty (Bellman et al., 2021). The adoption of technology is hoped to provide an alternative to traditional face-to-face consultation to prevent COVID-19 viral transmission. The use of technology provided the opportunity for experimentation with the wide spread of telemedicine (Wanderås et al., 2023). E-healthcare has emerged and gained momentum (Hossain et al., 2019) as it is not an optional as it is necessary, safer and effective to provide healthcare services for chronic diseases or other (Shiferaw et al., 2020). It has played a critical role in patients treatment during the pandemic (Wang et al., 2022). The use of digital technology in healthcare is developing in specific healthcare services (Rouidi et al., 2022),. The World Health Organization (WHO) defines telemedicine as " the delivery of health-care services where distance is a critical factor, by all health-care professionals using information and communication technologies for the exchange of valid information for diagnosis, treatment, and prevention of disease and injuries, all in the interests of advancing the health of individuals and their communities " (Bento et al., 2022). One of the subsets of health services provided by telemedicine is tele-consultation, wherein it has crucial effects on the decision of the requesting physicians that can change the treatment plan, referral rate, evacuation rate, change of diagnostic, and educational effects (Deldar et al., 2016). This concept also arose largely because of the inability of specific areas to meet the number of health demands and special professional employees (de Souza Filho et al., 2022). Previous study on teleconsultation revealed seven factors that hinder the adoption and sustainability of teleconsultation (James et al., 2021). The first factor is the patient's clinical condition, where the complexity of the patient's condition will determine the suitability of patients for video consultation. For example, an investigation of a video consultation scale-up for spinal cord injury suggested that a common concern was the perceived limits in evaluating physical symptoms over video. It was used for mild symptoms examination (Wang et al., 2022). The second factor is technology, which comprises not only physical technology but also the knowledge required to use it. There is still an unclear result on which technology is best and in which health consultation case as most of the previous research only focused on the implementation process. The third factor is the value proposition, which determines the development and adoption of technological value propositions by clinicians, patients, and suppliers (Greenhalgh et al., 2017). Furthermore, the fourth factor is the adopter systems which are related to the staff, patients, and caregivers and their potential and desire to continue to use technology. At the very least, this can be related to the benefits of technology that can assist health personnel, whose roles are sometimes physically demanding (Christian, Yuniarto, et al., 2023). The last three factors are the organization's capacity to innovate, supporting policy and regulation, and adaptation over time, which become the next factors that influence the adoption of tele consultation. Despite the abundance of research on widespread implementation of most of the factors, there has been little in-depth consideration of the impact of patient and caregiver adoptions and how the work and acceptance of these groups may have influenced widespread and sustainable implementation (James et al., 2021). Given the lack of understanding of the compelling factors for teleconsultation adoption in the pandemic, this study will aim to understand patient behaviors by identifying and evaluating compelling factors that influenced the adoption and usage of teleconsultation during the pandemic whereby healthcare experimented with new health services. There were several limitations to this research: first, this research only focuses on studying early patient behavior in relation to the early adoption and usage of teleconsultation. Second, the teleconsultation San Ekaimi, Prio Utomo, Djohan Gunawan, So Yohanes Jimmy, Michael Christian 151 application was new, and the system quality and reliability were questioned. Third, generalization is not possible as data were gathered only from patients within one area due to the physical distancing policy during the pandemic. Lastly, this research is crosssectional, and to understand technology adoption, longitudinal research is required. The research has several sections, where, in the introduction, the research background and objectives are also discussed described, and some limitations of the study are also discussed. In the literature review, the theoretical and hypothesis development are conducted; the research method section describes the research design; and the result and discussion sections discuss the findings. II. Literature Review The adoption of technology was accelerated during the pandemic, as there was physical distancing that limited the movement of people to complete their usual activities prior to the pandemic. Technology adoption refers to the processes for the spread of a new idea, technology, or innovation over time (Straub, 2009). Technology adoption is related to the use of technology at an individual level. There are several theories that explain technology adoption: the technology acceptance model (TAM), innovation diffusion theory (IDT), theory of reasoned action (TRA), theory of planned behavior (TPB), and technology-organization-environment (TOE). Of these theories, IDT, UTAUT, and TAM are mostly used in services in many sectors (Lee et al., 2022; Tran & Vu, 2019; Yang et al., 2021) . In healthcare informatics, UTAUT and TAM are mostly used, where TAM predicts a significant part of technology adoption and is considered more suitable for theoretical and empirical tests in e-services application including healthcare services (Holden & Karsh, 2010; Lee et al., 2022; Urus et al., 2022; Yang et al., 2021). TAM is a theory introduced by Davis (1985) and is one of the many models most widely used to explain user acceptance behavior, and which is used in this research. The research by Khan et al. (2019) developed a model to study the adoption of technology in the health sector using perceived usefulness, perceived ease of use, trust, privacy, intention to use, and actual use. Perceived usefulness (PU), the perception when individuals have confidence that using an information system will improve performance and can provide benefits and new experiences for its use. Perceived ease of use (PEU) is the presumption of getting something easily in using technology, the level of confidence of an individual in his experience using a particular technology or system, and when using it, and it felt ease to use. A trust is a trust obtained by a party from another party in conducting certain transactions where, from these transactions, there is a belief that both are mutually beneficial and in accordance with what is expected by each party. Privacy (PP) is a special service provided by a company to its customers or consumers personally. Intention to use (ITU) can be defined as the desire, intention, and behavior of an individual to use a given technology. In using a technology, the level of a person's desire to use the technology can be seen and assessed from his daily attitude in dealing with the technology, for example, the desire to increase followers by becoming an influencer, as well as his motivation in continuing to use it and his desire to motivate other users. Actual use (AU) is a decision made by everyone in deciding to use a particular system that will eventually give rise to new habits of use, which is called actual system usage. Actual system usage is defined as an assessment of when to decide to use technology. Someone will have a sense of satisfaction when using a system if they are confident that it increases productivity, is easy to use, and is used repeatedly. Based on the above theory there are five hypotheses below, as depicted in Figure 1: H1: Perceived Usefulness (PU) has a positive relationship with Intention to Use (ITU) H2: Perceived Ease of Use (PEU) has a positive GLOBAL BUSINESS & FINANCE REVIEW, Volume. 29 Issue. 3 (APRIL 2024) 149-160 152 relationship with Intention to Use (ITU) H3: Trust (TRUST) has a positive relationship with Intention to Use (ITU) H4: Perceived Privacy (PP) has a positive relationship with Intention to Use (ITU) H5: Intention to Use (ITU) has a positive relationship with Actual Use (AU) III. Research Method This research is quantitative cross-sectional research with a purposive sampling method where data are gathered using an onsite questionnaire for patients that used teleconsultation services in one of the private hospitals in Tangerang, Indonesia, during the pandemic between 2020 and 2021. The private hospital was selected as they were among the first institution that introduce the teleconsultation services. There were two analyses conducted to reveal the result: the descriptive analysis and the inference analysis. The descriptive analysis was conducted to understand the profile of the respondent and the context of the analysis, while the inference analysis used the partial least squares structural equations model (PLS-SEM) as it can analyse complex models (Utomo & Kurniasari, 2023). There are two steps required to evaluate PLS-SEM results: measurement and structural model evaluation. The measurement model assessment measures the relationships between the indicators and the construct (measurement models) as well as between the constructs (structural model), while the structural model evaluation examines the model's predictive capability and the relationship between the constructs. The reflective measurement model assessment was conducted by examining convergent validity, internal consistency reliability, and discriminator validity. The convergent validity examination comprises factor loading with a recommended threshold above 0.708, indicator reliability, and average variance extracted (AVE) with both thresholds above 0.5. The internal consistency reliability examination comprises composite reliability and Cronbach's alpha with a recommended threshold of between 0.6 and 0.9. Discriminant validity: use heterotrait-monotrait (HTMT) with a confidence interval not including one. In the structural model assessment, the significance and relationship of the structural model relationship (with a one-tailed test and a significant level of 5%) used the developed hypothesis, the coefficient of determination (R 2 ) and the f 2 effect size. The sample size minimum of Source: Khan et al. (2019) Figure 1. Research model San Ekaimi, Prio Utomo, Djohan Gunawan, So Yohanes Jimmy, Michael Christian 153 forty-one respondents are used as target threshold based on Power Calculation (statistical power 80%, significant level 5% and minimum R 2 of 0.25, four independent variable pointing to dependent variable). IV. Result and Discussion A. Descriptive Analysis There are one hundred patients who responded to the questionnaire. The description of the analysis shows that most of the respondents were female, with an age between 20 and 40 years with a bachelor's degree of education background. Many respondents have a monthly average income below 15 million, as depicted in Table 1. The descriptive analysis reveals that most of the respondents are a younger that are fluent using technology (Bakri et al., 2023; Christian, Gularso, et al., 2023), respondents were female, productive patients with an early education level. The difference between respondent's demography such as educational background, occupation type, salary, and computer literacy are statistically significant factors associated with e-health literacy where the respondents might not benefit mostly from e-health services (Shiferaw et al., 2020). B. Inference Analysis The measurement model analysis (Table 2) shows that all five latent variables have valid indicators where all the outer loading and indicator reliability are within the determined threshold (factor loading > 0.7 and indicator reliability above 0.5). AU1, ITU3, PE1, PP2, PU3, and TRUST2 are the indicators with the highest outer loading and indicator reliability, while AU3, ITU2, PE4, PP3, PU4, and TRUST1 are the least reliable. The convergent validity assessment based on the AVE values shows that all latent variables are also sufficiently valid, as the AVE values of all latent variables are above 0.5. Despite the sufficient value of convergent validity, the internal consistency reliability shows values above 0.9 for both composite reliability and Cronbach's alpha which indicate that all the indicator variables are measuring the same phenomena and are therefore not likely to be a valid measure of a construct. The discriminant validity analysis also shows that the internal HTMT confidence does not include one, which means that each latent variable can be differentiated from each other. The structural model analysis shows that the coefficient determinant of actual use is 0.340 and intention to use is 0.733 with respective f-square effect (Table 3 and Table 4). 1. The relationship between perceived 1Usefulness (PU) and Intention to Use (ITU) Based on the results of the inference analysis, the relationship between PU and ITU has path coefficients of 0.125, a t-statistic 1.098, and a p-value of 0.136, where the hypothesis is rejected (Table Char Class Freq Perc Age of respondents 20-40% 48 48% 41-50% 28 28% > 50% 24 24% Gender of Respondents Male 38 38% Female 62 62% Education Background K12 30 30% Bachelor's degree 56 56% Master's degree 12 12% Doctoral 2 2% Monthly Income (IDR) <15 million 48 48% 16-25 million 21 21% 26-35 million 18 18% >35 million 13 13% Char = Character; Class = Classification; Freq = Frequency; Perc = percentage. Table 1. Respondent profiles GLOBAL BUSINESS & FINANCE REVIEW, Volume. 29 Issue. 3 (APRIL 2024) 149-160 154 5). The view of the perceived usefulness variable means that someone's response stating that something is useful will affect their intention to use or their desire to use a product that is in accordance with Latent Variable Indicator Convergent Validity Internal Consistency Reliability Discriminant Validity Factor Loading Indicator Reliability AVE Composite Reliability Cronbach's Alfa HTMT confidence interval does not include 1 >0.7 >0.50 >0.50 0.60-0.90 0.60-0.90 Actual Use (AU) AU1 0.913 0.834 0.676 0.861 0.771 YesAU2 0.790 0.624 AU3 0.755 0.569 Intention to Use (ITU) ITU1 0.956 0.914 0.915 0.970 0.954 YesITU2 0.945 0.893 ITU3 0.968 0.938 Perceived Ease of Use (PEU) PE1 0.950 0.903 0.840 0.955 0.936 Yes PE2 0.949 0.900 PE3 0.903 0.816 PE4 0.861 0.742 Perceived Privacy (PP) PP1 0.940 0.884 0.929 0.975 0.962 YesPP2 0.980 0.961 PP3 0.970 0.941 Perceived Usefulness (PU) PU1 0.886 0.786 0.825 0.950 0.930 Yes PU2 0.918 0.843 PU3 0.929 0.864 PU4 0.899 0.809 Trust TRUST1 0.956 0.915 0.914 0.970 0.953 YesTRUST2 0.968 0.937 TRUST3 0.944 0.890 Table 2. Measurement model evaluation Latent Variable f-square Intention to Use (ITU) to Actual use (AU) 0.528 Perceived Ease of Use (PEU) > Intention to Use (ITU) 0.162 Perceived Privacy (PP) > Intention to use (ITU) 0.182 Perceived Usefulness (PU) > Intention to Use (ITU) 0.016 Trust (TRUST) > Intention to Use (ITU) 0.816 Table 3. Table effect size (f-square) Latent Variable R-square R-square adjusted Actual Use (AU) 0.346 0.340 Intention to Use (ITU) 0.742 0.733 Table 4. Table R-square San Ekaimi, Prio Utomo, Djohan Gunawan, So Yohanes Jimmy, Michael Christian 155 the user's estimates of function and benefits. But in fact, based on the results of the analysis, patients feel that the function and benefits factors do not affect their desire to use this teleconsultation service. With this view, it can be concluded that functions and benefits are not factors driving patients to have a desire to use the application. This is not in line with the results of previous research conducted by Khan et al., which states that perceived usefulness has a positive and significant effect on the intention to use the teleconsultation service used. In line with the results of research conducted by Khan et al. (2019), the majority of e-health technology users in China are dominated by women because women need intensive services while undergoing pregnancy. In this study, most the teleconsultation service users were also dominated by women. The results of this study are also supported by research conducted by Susanto et al. (2021), which states that perceived usefulness does not have a positive and significant effect on the use that is suspected in this study and that respondents have not been able to feel the benefits of the system. This makes the intensity of use of the system decrease, or there is no use at all, as given information that is not appropriate and sufficient as needed by the respondent, so the respondent must look for more accurate and adequate information elsewhere. When viewed from the results of this study, it is suspected that they are similar to research conducted by Susanto et al. (2021) because the teleconsultation service provided does not provide complete and adequate information on what services are provided to the patient. This teleconsultation service will make the patient look for other information elsewhere, such as through the website or the call center, to provide an explanation of the services to be provided at the teleconsultation. Patients do not understand the main function of this teleconsultation service, so they still have the notion that this online consultation must be able to fulfill the overall function of face-to-face consultations and in the end feel disappointed because they feel that the function of this online consultation does not meet their expectations so that it does not encourage them to use this teleconsultation service. 2. The Relationship between perceived ease of use (PEU) with intention to use (ITU) The relationship between PEU and ITU has a path value of 0.422, a t-statistic of 3.754, and a p-value of 0.000, wherein the study accepted Hypothesis 2 (Table 5). The view of the perceived ease of the variable means that someone's response states that the teleconsultation application provided will be easy to use in terms of operation. With the response or perception that this teleconsultation service will be easy to use, the patient will have the intention to use the application in consultation with doctors or in carrying out other services provided. With the patient's view that the teleconsultation application is quite easy to use, the patient also wants to be directly involved in using the application. This happens because of the perceived ease of providing effectiveness and efficiency for patients, so that patients feel it does not take long to adapt to the application to obtain services, so that not much time is wasted. In practice, the simplicity of use of teleconsultation services gives prompt support not only to adult users but also to younger users (Peralta & Taveras, 2020). H Structural Path βt-statistics p value Result H1 Perceived Usefulness (PU) > Intention to Use (ITU) 0.125 1.098 0.136 Reject H2 Perceived Ease of Use (PEU) > Intention to Use (ITU) 0.422 3.754 0.000 Accepted H3 Trust (TRUST) > Intention to Use (ITU) 0.917 6.864 0.000 Accepted H4 Perceived Privacy (PP) > Intention to use (ITU) 0.263 2.518 0.006 Accepted H5 Intention to Use (ITU) to Actual use (AU) 0.588 8.279 0.000 Accepted Table 5. Structural model evaluation GLOBAL BUSINESS & FINANCE REVIEW, Volume. 29 Issue. 3 (APRIL 2024) 149-160 156 This is in line with the results of a study by Khan et al., (2019), which found that African citizens who are citizens of developing countries residing in China also felt that perceptions of convenience had a positive effect on their intention to use. The finding might be explained by, that most of the research participants were African students studying in China, and 60% of respondents had more than 7 years' experience in using information technology. Then students are also more likely to easily adopt new technologies, so they don't find it difficult to adapt to using tele-consultation services in China. Thus, the perceived benefits of teleconsultation will be more important to them than perceived convenience. The results of this study are also in accordance with those of Khan et al. (2019), that the respondents of this study were patients, the majority of whom were aged from 20 to 40 years, namely 48% of the total respondents, and had an undergraduate education level. So that at their age and level of education, respondents can easily use the technology provided and the perception of convenience is something that is provided by teleconsultation services, which will create effective and efficient teleconsultation services. This result indicates that the younger generation is more adaptable to new technology, including in healthcare services (Christian, Gularso, et al., 2023). 3. The relationship between trust (TRUST) with Intention to use (ITU) The relationship between PEU and ITU has a path value of 0.917, a t-statistic of 6.864, and a p-value of 0.000, where the study accepted Hypothesis 3 (Table 5). The variable view of trust means that patients give trust to the application in the sense that patients believe that the teleconsultation application is a positive application to improve services from the hospital to patients, so that it will affect their level of intention to use the hospital. In launching this teleconsultation application, the trust factor from patients is indeed an important thing that must be obtained first. Patients must believe that by using this teleconsultation application, it does not reduce the quality that should be obtained when carrying out direct services. In addition, all service activities in the hospital related to medical record data or personal data are confidential, so these data may not be leaked or spread to irresponsible parties. By gaining trust, the patient will have the desire to use the teleconsultation application provided by the hospital. This is in line with the results of previous research, which states that Africans living in China believe that the e-health application can indeed provide services and convenience to patients. From the results of data processing, it can be concluded that the trust factor in the hospital supports the patient's interest in conducting online consultations at the hospital (Khan et al., 2019). The research findings of Seguí et al. (2020) note that to improve capacity and maximize use, the public must first be convinced that teleconsultation may be trustworthy and transparent. This is not in line with the results of previous research from Khan et al. (2019), who stated that Africans living in China stated that trust did not affect their intention to use the e-health application. The findings indicated that African students who are in China are not more concerned with trusting and disclosing all information to doctors for certain reasons. It is suspected that African student patients are willing to share information with doctors but not with others or even with their families. However, this is in contrast to the results of research from Abbas et al. (2018), which states that trust reflects a belief system that results in behavior change, especially in the use of technology applications related to health. The e-health application will build trust in the mind of the individual if it can perform the expected function and bring the results expected by the individual. So according to Abbas et al. (2018), trust is something that can make behavior to use e-health applications that can carry out a positive evaluation of the application. The more positive and trusting the individual's intention to use the e-health application, the higher the possibility that he will perform behaviors that will use the e-health application.