Influential Factors Affecting the Intention to Utilize Advance Care Plans (ACPs) in Thailand and Indonesia

Abstract

Demographic shifts resulting from population aging are evident globally, including in Southeast Asia, Thailand, and Indonesia. The relevance of advance care plans is becoming increasingly apparent as the worldwide demographic transforms due to aging. This study sought to investigate the factors influencing the use and implementation of advance care plans (ACPs) using the health belief model (HBM) and technology acceptance model (TAM). This study selected a sample of individuals aged 30–60 in Indonesia and Thailand based on established inclusion and exclusion criteria. The study utilized the purposive random sampling method, integrating aspects of purposive and random selection. A total of 532 questionnaires were distributed via an online form, and 472 were obtained after data cleaning. Most respondents to this survey came from Indonesia, comprising 238 out of 472 respondents (50.4%), and from Thailand, comprising 49.5%; most respondents were women who were between 36 and 40 years old, and most reported graduating with a bachelor’s degree. A significant construct influences the use of advance care plans, i.e., perceived barriers. The perceived barrier (PBA) construct included data security, accessibility, and language barriers. In summary, overcoming existing barriers can indirectly increase the benefits of advance care plans. The results show that perceived usefulness (β = 0.189, p < 0.001), perceived ease of use (β = 0.150, p < 0.01), perceived susceptibility (β = 0.153, p < 0.01), perceived severity (β = 0.105, p < 0.05), and perceived benefits (β = 0.241, p < 0.001) all had significant positive effects on behavioral intention. In contrast, health motivation (β = 0.073, p = 0.100) and perceived barriers (β = 0.034, p = 0.134) did not show significant relationships with behavioral intention in Indonesia and Thailand, offering insights into both countries’ development strategies and the promotion of advance care plans with media as the technology.

1. Introduction

The global phenomenon of aging populations has emerged as a prominent and impactful issue, reshaping social and economic landscapes. At present, this issue is significant and prevalent worldwide [1,2,3]. Given this significant global phenomenon, projections by the United Nations indicate a substantial increase in the number of individuals aged 60 years and older, which will nearly double from 970 million in 2020 to an estimated 1.5 billion by 2050 [3]. Experts predict a rise in the proportion of elderly individuals within the global population, with estimates indicating an increase from 12% to approximately 22% [4,56]. This demographic transition is occurring across diverse cultural contexts, including in East and Southeast Asia [7,8,9]. Extended life expectancy due to improved healthcare, nutrition, and sanitation requires societies to cater to a larger population living longer than ever before [10]. However, providing sustainable healthcare services for the aging population presents substantial challenges in terms of finances and resources, especially for public healthcare systems. These are responsible for meeting the expanding medical needs of elderly individuals, including specialized care for age-related chronic conditions, in a cost-effective and accessible manner [11]. Governments worldwide face significant challenges in developing creative and practical approaches to meet the needs of the aging population. Therefore, addressing this issue is of the utmost importance.

Demographic shifts resulting from population aging are also evident in Southeast Asia, particularly in Indonesia and Thailand. Projections by the United Nations estimate a significant increase of 30–50% in the proportion of elderly individuals by 2050 [12]. These demographic changes will place a considerable burden on the national healthcare systems of both countries [13,14]. Historically, these systems have been predominantly focused on acute care for acute illnesses [15]. However, the primary challenge lies in meeting the demand for chronic and continuous care for the rapidly growing elderly population [16]. Implementing innovative solutions in healthcare delivery will be crucial in ensuring that high-quality care is both accessible and affordable for all segments of the aging population.

The implementation of advance care plans becomes increasingly necessary as the worldwide demographic transforms due to aging [17]. With the increasing proportion of elderly individuals in all regions of the world, the prevalence of elderly individuals with chronic health issues is also increasing. Documenting end-of-life priorities, limitations, and care preferences through legal instruments such as advance directives ensures that the care received aligns with the values and desired standards of living for each individual [18,19,20,21]. The documentation process mitigates ethical dilemmas and family conflicts in cases in which an individual experiences pronounced suffering. It is critical to assess advance care plan (ACP) use, including self-service technologies, to uncover the factors that influence individuals’ intentions to use an advance care plan.

Previous research has found several factors that influence an individual’s intention to utilize and implement an advance care plan (ACP). Research in the United States by Sable-Smith et al. (2018) found that elderly individuals and women tended to use ACPs more often. Additionally, individuals with higher education tended to use ACPs more often [22]. Another study of elderly female caregivers in the US in 2019 found an association between older age and a higher prevalence of ACP documentation and discussion. For example, individuals aged 90 years and over had a prevalence rate of ACP documentation that was 18% and a prevalence rate of ACP documentation plus discussion that was 28% higher than those of individuals aged 65–69. Additionally, college degrees increase the prevalence of ACP documentation alone and ACP documentation plus education [23]. Another study in Taiwan by Tsai et al. (2022) showed that awareness and knowledge of ACPs were significantly associated with a higher willingness to participate in ACPs [24].

However, the implementation of ACPs also faces numerous obstacles in various countries. In Thailand, although a palliative care policy was introduced in 2014, its implementation by doctors still requires improvement. The main limitation is the fact that Thai culture values the role of the family in end-of-life planning, often limiting individual participation in decision-making and care planning [25]. Meanwhile, in Indonesia, research involving 26 out of 31 participants aged under 60 years old showed that individuals tended to be more prepared to carry out advance care planning if it aligned with their cultural values and religious perspectives. Culture and religion are important contextual factors shaping the health of individuals in Indonesia. Therefore, a culture-based approach to advance care planning in Indonesia could facilitate open communication between individuals and families and consider various points of view in delivering information, communication about poor prognoses, and medical decision-making [26]. However, research on the use of advance care plans is limited, especially regarding their characteristics as a self-technology. Therefore, this study aims to study the factors that influence the use of advance care plans.

Based on the identified research gaps, this study aims to apply the health belief model (HBM) and technology acceptance model (TAM) to understand the factors that influence the intention to utilize advance care plans. Both models provide a strong theoretical basis for the study of user behavior and perceptions. The HBM framework considers how perceptions of threats (perceived susceptibility and severity), benefits, and barriers influence health-related actions. Meanwhile, the technology acceptance model (TAM) posits that perceived usefulness and ease of use drive the acceptance of new technologies. The present study aims to address the following research questions: (1) How do cultural, religious, and local healthcare system dimensions, as contextual factors, influence perceptions of advance care plans’ usefulness and ease of use? (2) If perceptions are shaped by these contextual factors, in what ways does this impact the formation of sustained adoption intentions over time based on perceived susceptibility, perceived severity, health motivation, benefits, and barriers?

Through these research questions, this work aims to understand the factors that influence the intention to use and adopt advance care plans (ACPs) among Indonesian and Thai individuals. This research employs the technology acceptance model (TAM) and health belief model (HBM), combining the core variables from both models, such as perceived usefulness, perceived ease of use, perceived susceptibility, perceived severity, perceived benefits, perceived barriers, and cues to action, with an additional variable from the HBM, i.e., health motivation [27,28]. This research incorporates contributions from key academic, practical, policy, and product development perspectives. From an academic perspective, it aims to advance the understanding of the global cultural, structural, and behavioral drivers influencing ACP adoption to strengthen health decision-making [29,30]. A practical perspective provides healthcare providers with user-centered recommendations for meaningful ACP services by offering deep insights into diverse experiences [31,32]. At a policy level, the findings can guide national medical frameworks to support local end-of-life values [33,34]. Finally, studies on influential factors in the use of ACP technology represent a holistic means of addressing knowledge gaps across the research, practice, policy, and technological innovation domains, promising to yield rich insights and drive valuable outcomes in respectful and human-centered ways.

2. Literature Review

Several studies have identified various factors that impact an individual’s intention to use and execute an advance care plan (ACP). Studies have indicated that the use of advance care plans (ACPs) encounters challenges in different countries [25]. This study delves into the factors that impact the intention to utilize and adopt an advance care plan, providing a detailed analysis of the theories and relevant literature that inform the development of the structural model and hypotheses in this research.

2.1. Advance Care Planning

The first publication on advance care planning emerged in 1976. In 1976, California became the pioneering state to legalize the advance care plan [35]. An advance care plan (ACP) is designed to help individuals of all ages and backgrounds to understand and communicate their values and preferences regarding future medical care [36,37,38]. The goal of an ACP is to ensure that people receive medical care that is consistent with their values, goals, and preferences during periods of serious or chronic illness [39]. An ACP has several benefits. It enables healthcare professionals to conduct structured, meaningful conversations with individuals about their wishes and preferences regarding their treatment goals, preferences, and location of care [40]. While several studies have demonstrated the benefits of advance care plans (ACPs), one study found that the evidence supporting ACPs’ benefits requires greater consistency [41]. Promising findings have emerged from several prior studies assessing digital advance care planning technologies. The research by Allsop (2022) and Lum (2019) demonstrated that these technologies can increase care plans’ completeness, clinicians’ involvement, and the availability of care across settings [42,43]. However, Neale (2021) acknowledges the need for additional evidence validating these technologies’ impacts within integrated care systems, actively seeking solutions to facilitate shared decision-making and access care preferences in real time [44]. A systematic evaluation of the variations in support in web-based advance care plans (ACPs) reinforced this perspective. Meanwhile, Dupont’s (2022) analysis uncovered various available technologies but highlighted the necessity of engaging users to ensure that their content reflects evidence-based best practices [45].

2.2. Proposed Conceptual Framework

There are some models used to evaluate the impact of the utilization and integration of technology into healthcare. The models most frequently used in healthcare to examine the adoption of new technologies include the technology acceptance model (TAM) [46], the unified theory of acceptance and use of technology (UTAUT) [47], and the diffusion of innovations theory [48]. A study conducted by Alhakami and Slovic (1994) explored the relationship between perceived risk assessments and perceived benefits in the context of technology in health. It studied the complexity of technology and its impact on individuals’ perceptions. The findings of the study highlighted a noteworthy and contrasting correlation between the two variables, suggesting that individuals frequently misunderstand the risks and benefits in their cognitive processes [49]. In a study conducted by Weinstein (2000), two crucial aspects of health hazards were examined: perceived probability and perceived severity. The findings revealed a strong connection between these two factors, indicating that they play a significant role in influencing individual actions and behavior [50]. In a recent study, Elkhalifa et al. (2022) examined alterations in individual behavior in relation to health conditions [51]. Nevertheless, these theories consist of standard variables used for assessment, and further clarification of the complexities regarding the use of technology in healthcare is required. This research aims to combine the TAM and HBM frameworks and expand their variables to examine the factors that impact the utilization of advance care plans. It integrates the primary variables of the TAM, such as perceived usefulness and perceived ease of use, with the variables of the HBM, such as perceived susceptibility, perceived severity, perceived benefits, and perceived barriers, and includes additional variables, such as health motivation. Consequently, the hypotheses are formulated as follows.

2.2.1. Perceived Usefulness

Perceived usefulness is one of the primary variables in the TAM. The technology acceptance model (TAM), proposed by Davis et al. in 1989, is a widely recognized model in the field of information systems and explains how users come to accept and use a technology [46]. The TAM is a valuable model in healthcare, especially regarding medical devices and clinical information technologies [52]. It helps us to understand individuals’ acceptance of healthcare technologies, such as advance care plans [53]. Over time, new technologies have led to the extension of the TAM with the addition of predictors such as complexity, enjoyment, and image [54,55]. These additions enrich our understanding of new, unique contexts and provide a more comprehensive view of technology acceptance. For instance, perceived enjoyment, reflecting the level of joy and fun that can be obtained from using a specific system or technology, significantly influences users’ intentions to adopt hedonic systems or technologies [54]. Perceived usefulness is the extent to which an individual believes that utilizing a specific system would improve their job performance. The studies by Wang et al. have already demonstrated the positive impact of perceived usefulness on behavioral intention, indicating that individuals are more likely to engage in a particular behavior when they perceive it as beneficial and valuable [56]. This perception positively correlates with attitudes towards use and actual system use, forming intentions that drive adoption behavior [46,57]. Consequently, the following hypothesis is proposed in this study:

Hypothesis H1.

The perceived usefulness of an advance care plan has a positive influence on utilization behavior.

2.2.2. Perceived Ease of Use

The perceived ease of use is a primary variable in the TAM. It refers to the extent to which an individual believes that using a particular system would require minimal effort [52,58]. In a study conducted by Xu et al. (2022) among nursing homes, it was noted that only a small number of the participants were familiar with advance care plans. This research suggests that the user-friendliness of an advance care plan could impact its adoption [59]. Another study by Ho et al. (2022) revealed that higher levels of perceived knowledge and positive attitudes towards end-of-life care were linked to advocating for an advance care plan, indicating that ease of use may influence the acceptance and adoption of a plan [60]. The study theorized that perceiving an advance care plan as easy to use can positively affect individuals’ willingness to utilize it. The following hypothesis is put forward:

Hypothesis H2.

The perceived ease of use of an advance care plan has a positive influence on utilization behavior.

2.2.3. Perceived Susceptibility

Perceived susceptibility, perceived severity, perceived benefits, perceived barriers, and cues to action are the core variables of the HBM [61]. The HBM has been widely used in health behavior and intervention research. It has been applied to understand and predict a variety of health-related behaviors, such as preventive health behaviors, screening behaviors, and the control of health conditions [61]. The model has also been utilized in the design of health behavior interventions, providing a framework for an understanding of the factors that influence individuals’ engagement in health-promoting behaviors [62]. Perceived susceptibility is defined as an individual’s belief about their personal risk or vulnerability to a health condition or threat [63]. Based on De Paoli et al.’s study, this belief can motivate individuals to discuss their future in advance care plans. Using the HBM, some studies have highlighted the experiences and attitudes of individuals and healthcare professionals regarding advance care plans, revealing insights into their perceived susceptibility to end-of-life care and its impact on decision-making. Studies [64,65] have underscored the significance of perceived susceptibility in driving health-related behaviors, focusing on the influence of individuals’ intentions and actions on their advance care plans. Consequently, the following hypothesis is proposed:

Hypothesis H3.

The perceived susceptibility of a person to need an advance care plan has a positive influence on their utilization behavior.

2.2.4. Perceived Severity

Perceived severity is defined as an individual’s perception of the seriousness and potential impact of a health condition or threat. This perception can influence an individual’s perspectives and behaviors when they make decisions about their future healthcare. Studies by Bennett et al. (2022) and George et al. (2019) report the influence of perceived severity on advance care planning and shed light on individuals’ perceptions of the potential outcomes of their healthcare decisions [66,67]. Another study by Ma et al. (2021) reports the relationship between the severity of a situation and how individuals perceive and act on an advance care plan, emphasizing its impact on their decision-making and choices [68]. In this situation, it can influence how individuals think and participate in conversations and decision-making regarding advance care planning. Therefore, the following hypothesis is put forward:

Hypothesis H4.

The perceived severity of a health event has a positive influence on a person’s utilization behavior regarding an advance care plan.

2.2.5. Health Motivation

Health motivation is an additional variable in the HBM and is defined as an individual’s determination, desire, or willingness to participate in actions related to their health or decisions [69]. It determines individuals’ attitudes and intentions with regard to discussing and making decisions regarding their future healthcare preferences. The studies by [64] highlight the function of health motivation in advance care planning, providing insights into how the motivations and attitudes of individuals can shape their involvement in the planning process. A further study by [68] illustrates the relationship between health motivation and individuals’ attitudes and behavior towards ACPs. It emphasizes its role in shaping their decision-making and preferences. Subsequently, the following hypothesis is presented:

Hypothesis H5.

Health motivation has a positive influence on utilization behavior.

2.2.6. Perceived Benefits

Perceived benefits are defined as beliefs about the potential positive aspects of a health action [70]. This is one of the variables in the HBM and can influence individuals’ attitudes and motivations regarding participating in discussions and making decisions about their future healthcare. The studies by [64] investigated the perceived benefits of advance care plans, shedding light on individuals’ perspectives regarding the potential advantages of preparing for their future care. Furthermore, in the studies by [71], the connection between perceived benefits and individuals’ attitudes and behaviors towards advance care plans has been emphasized, underscoring their impact on decision-making and preferences. Thus, the following hypothesis is proposed:

Hypothesis H6.

The perceived benefits of advance care plans have a positive influence on utilization behavior.

2.2.7. Perceived Barriers

Perceived barriers are defined as beliefs about the potential negative aspects of a specific health action [72]. This variable of the HBM is related to the aspects that individuals perceive as hindrances to engaging in a particular health-related behavior or decision. The studies by [73] reported the perceived barriers to advance care planning, highlighting some challenges that individuals may face in the planning process. Additionally, the studies by [67] identified an association between perceived barriers and individuals’ attitudes and behaviors towards advance care planning, underscoring their role in shaping their decision-making and preferences. Thus, the following hypothesis is proposed in Figure 1 below:

Hypothesis H7.

Perceived barriers to advance care plans have a positive influence on utilization behavior.

3. Materials and Methods

3.1. Design

This research study employed a survey approach to investigate the factors that influence the use of advance care plans based in technology, using the health belief model (HBM) and technology acceptance model (TAM) as frameworks.

3.2. Participants

3.2.1. Recruitment

According to the latest information from Worldmeter, which uses the latest data from the United Nations, Thailand’s current population comprises approximately 71,856,127 individuals, equivalent to 0.89% of the world’s population. Meanwhile, the population of Indonesia currently amounts to approximately 278,998,177 individuals, which is the equivalent of 3.45% of the total world population [74]. This study utilized purposive random sampling to enlist Indonesian and Thai individuals after obtaining authorization from the Centre for Ethics in Human Research Committee at Khon Kaen University (HE673015). This approach does not involve random selection. When selecting participants, a particular approach is used that focuses on specific criteria instead of relying on chance [75,76]. This method is beneficial in situations where there is a need to quickly obtain a particular sample and ensuring equal representation is not the primary concern. This sampling method does not adhere to specific mathematical rules; instead, it targets individuals who possess the necessary characteristics or level of comprehension. The study focused on 450 participants to ensure a sufficiently large sample size that reflected the population’s diversity and considered the potential for participants to drop out or not respond. This study used this method to select individuals for the survey, based on criteria directly related to the research objectives. The study’s inclusion criteria were individuals aged between 30 and 60 years old, who were legal residents of Indonesia or Thailand, who showed an interest or potential interest in using advance care planning technology, and who were from one of 10 specific provinces in each country. These provinces included Bangkok, Changwat Nakhon Ratchasima, Changwat Ubon Ratchathani, Changwat Khon Kaen, Chiang Mai Province, Changwat Udon Thani, Changwat Nakhon Si Thammarat, Changwat Chanthaburi, Changwat Chon Buri, and Changwat Buri Ram in Thailand, as well as DKI Jakarta, East Java, West Java, North Sumatra, South Sulawesi, Central Java, South Sumatra, Bali, North Sulawesi, and West Kalimantan in Indonesia. The questionnaires were distributed to the targeted participants through direct messages or social media pages. The participants who completed the questionnaire were asked to share the link.

3.2.2. Sample Size and Characteristics

The target demographic for this study was individuals aged 30 years and over because this age range is believed to have a high level of awareness regarding advance care planning (ACP). In this study, 338 participants from Indonesia and 234 from Thailand were contacted online. Of all the participants contacted, 572 agreed and were willing to participate, resulting in a response rate of 100%. The adequacy of the sample was evaluated following the statistical criteria recommended by Sarstedt et al. (2021) [77], suggesting a minimum sample size of approximately 400 participants. Before the data cleaning process, 572 respondents were enrolled in this study. After the cleaning process, the data from 234 individuals from Thailand and 238 individuals from Indonesia were kept for assessment.

The demographic criteria that were assessed included items such as occupation and educational background, as the aim was to comprehend how these factors affected their perceptions of ACPs. Furthermore, external factors such as the planning system, the respondent’s current health status, the effectiveness of technology for health monitoring, and their confidence in technology were investigated and also the research constructs, as can be seen in the Appendix A.

3.2.3. Measures

Likert scaling was used to code the survey data. The used Likert scale ranged from 1 to 5 (1 = strongly agree, 2 = agree, 3 = neutral, 4 = disagree, and 5 = strongly disagree) [78]. A path analysis using SmartPLS v.4.1.0.5 was used to evaluate the statistical significance for hypothesis testing [79]. The measurement process for this research begins with outer loading to describe the strength of the relationships between the indicators and the latent variables being measured. Validity and reliability testing measure the extent to which the indicators are used to represent the construct intended in the analysis and their consistency. Discriminant validity measures the extent to which the construct deviates from other constructs according to its standards. Next is collinearity statistics, known as the variance inflation factor (VIF), used in this study to identify the degree of multicollinearity between variables in a measurement or structural model. Finally, there is hypothesis testing. Hypothesis testing in this study was carried out using path coefficients to determine the magnitude and direction of the influence of an independent variable on the dependent variable in this study.

3.2.4. Procedures

The online questionnaire method was selected in this study due to several advantages. The first is that the method provides convenience, allowing the respondents to complete the survey in their preferred location. The second is that these methods are cost-effective, eliminate printing and distribution costs, and reach a wider geographic range of respondents, which is especially beneficial when targeting specific demographics, such as ten provinces in two countries, i.e., Thailand and Indonesia [80,81]. Another advantage is that the efficiency of the online survey method automates the data collection and organization process, thereby saving time and resources [82]. Moreover, with this method, the participants may feel more comfortable in providing honest feedback regarding sensitive topics due to the anonymity of online surveys. Additionally, the online questionnaire method reduces the number of errors associated with manual data entry, ensuring the accuracy of the data for analysis while also contributing to environmental sustainability by minimizing the use of paper [83,84]. Furthermore, this method aligned with the research objective of examining the factors influencing the use and adoption of advance care planning technology, collecting insights from individuals about their use and adoption of ACP technology and its contribution to improvements in end-of-life care and decision-making. This study was conducted from January to February 2024. The data collection phase was carried out from January to February 2024, making it possible to examine the research questions thoroughly and inclusively within the specified time. The distribution of the questionnaire was carried out through Facebook groups, allowing all regions in the two countries studied, Indonesia and Thailand, to be reached. This strategy was used to ensure a broad and diverse group of participants, reflecting the cultural and geographic diversity of the target countries. The participants categorized themselves based on their age, gender, education, occupation, and health conditions and shared their viewpoints in response to the questions presented. The respondents completed the questionnaire by accessing it through a provided link or scanning a QR code. They confirmed their comprehension of the information provided, that they were aged 30 or above, and that they agreed to participate in the research [85]. Participation in the research was anonymous and required approximately 15 to 30 min. The participants were informed that they could ‘skip’ a question if they wished not to respond to it and that closing the browser before completion would automatically save and send the completed data. The participants were advised that withdrawal was not possible once the responses were submitted [86].

4. Results

4.1. Demographic Characteristics

The participants in this study consisted of Indonesian and Thai individuals living in ten provinces in each country. Purposive random sampling was used to select individuals willing to participate in the research. A total of 532 questionnaires were distributed, and 472 questionnaires were obtained after data cleaning.

As seen in

Table 1. Demographic characteristics of participants (N = 472).

Characteristics	N	%
Country
Indonesia	238	50.4
Thailand	234	49.5
Age (Years)
30–35	112	25.9
36–40	156	35.9
41–45	101	23.3
46–50	52	12.0
51–55	35	8.1
56–60	16	3.7
Gender
Male	176	37.3
Female	296	62.7
Education Level
Non-Educated	12	2.5
Higher Education/College	77	16.3
Diploma	123	26.1
Bachelor’s Degree	199	42.2
Master’s Degree	54	11.4
Doctoral	7	1.5
Occupation
No Occupation	6	1.3
Private Sector Employee	113	23.9
Government Employee	143	30.3
Entrepreneur/Self-Employed	158	33.5
Student	31	4.63
Healthcare Professional	21	4.4

, most respondents came from Indonesia, comprising 238 out of 472 respondents (50.4%). In contrast, those from Thailand comprised 49.5%. Most respondents in this study were women aged between 40 and 36 years old, and most of them reported graduating with a bachelor’s degree. The study found that most respondents were entrepreneurs (33.5%), 143 were civil servants, and some were students. Individuals who work in public offices or private companies are covered by health insurance. For example, in Indonesia, the government has imposed regulations regarding health insurance coverage for workers through the Presidential Decree of the Republic of Indonesia Number 64 of 2020 [87]. In addition, this study found that most respondents had previous experience with ACP (75.8%) and most felt quite well (not well, but not sick; 44.7%). These findings show that respondents were aware of their health status. Regarding technology use, most respondents reported using technology to manage their health (41.7%), claiming that they had very strong confidence in their use of technology (46.4%).

4.2. Validity and Reliability

This study examined construct validity and reliability to evaluate the practicality of utilizing all variables in the study’s model.

Table 2. Validity, reliability, and collinearity tests.

Indicator	Questions	Outer Loading	Cronbach’s Alpha (alpha)	Composite Reliability (rhoC)	AVE	VIF	Note
PU	Perceived usefulness		0.653	0.812	0.591
PU1	Using the health care application improved my everyday life quality	0.771				1.288	Valid
PU2	Using the health care application enhanced my effectiveness on taking care of myself and my family	0.772				1.283	Valid
PU3	I found the health care application useful in my everyday life	0.762				1.253	Valid
PEOU	Perceived ease of use		0.699	0.832	0.624
PEOU1	Learning to use the health care application is easy for me	0.806				1.391	Valid
PEOU2	My interaction with the health care application has been understandable	0.815				1.408	Valid
PEOU3	It is easy to become skillful at using the health care application	0.746				1.303	Valid
PSU	Perceived susceptibility		0.666	0.817	0.599
PSU1	It is likely that I will get a chance to have a palliative care giver.	0.822				1.345	Valid
PSU2	I feel knowledgeable about my risk of getting a chance to have a palliative care giver	0.801				1.382	Valid
PSU3	Perceived chances of contracting with some serious disease?	0.692				1.217	Valid
PSE	Perceived severity		0.733	0.849	0.652
PSE1	I feel that without this advance care plan application I won’t be able to return to my normal life because of the worry.	0.807				1.452	Valid
PSE2	I feel that if I do not use this application, I will have anything to precaution.	0.828				1.530	Valid
PSE3	I will have to pay more for medical bills, if I have nothing to warn me with health condition.	0.786				1.389	Valid
HM	Health motivation		0.73	0.844	0.645
HM1	Regularly the healthy behaviors have become the fundamental of my habits.	0.863				1.450	Valid
HM2	I believe it’s a good thing I can do to feel better about myself in general.	0.778				1.425	Valid
HM3	I think there are more important things to do than staying healthy.	0.765				1.440	Valid
PBE	Perceived benefits		0.709	0.837	0.632
PBE1	The ACP application makes me feel safe and secure of health condition.	0.839				1.443	Valid
PBE2	This service will facilitate the society.	0.773				1.366	Valid
PBE3	This service will reduce the severity of health conditions.	0.771				1.361	Valid
PBA	Perceived barriers		0.828	0.896	0.742
PBA1	This service will be difficult for me to use if available only on smartphones/tablets.	0.896				2.074	Valid
PBA2	This service will be difficult for me to access if offered exclusively in English.	0.885				1.991	Valid
PBA3	I feel unsecure to disclose my personal data.	0.799				1.707	Valid
BI	Behavioral intention or cues to action		0.734	0.850	0.653
BI1	I am interested and expect to use this ACP application in the future.	0.801				1.416	Valid
BI2	I plan to use this ACP application in the future.	0.815				1.467	Valid
BI3	I predict I will use this ACP application in the future.	0.808				1.477	Valid

(processed using SmartPLS).

shows each variable’s outcomes for the Cronbach’s Alpha, composite reliability, and average variance extracted (AVE) tests. The data reported indicate that every factor has a Cronbach’s Alpha value higher than 0.7, thus meeting the standards of dependability recommended by Garson (2016) [88]. Particularly in exploratory studies [89,90], certain variables show Cronbach’s Alpha values ranging from 0.5 to 0.6, which can be judged consistent and reliable in measurement. Furthermore, the composite reliability test revealed values above 0.7 for all variables, indicating a strong link among the indicators and meeting the composite reliability criteria [91]. The results of the average variance extracted (AVE) test show that every factor has values over 0.5, implying a lack of any problems concerning convergent dependability [92].

Moreover, promising findings were obtained by the discriminant validity analysis conducted using the Fornell–Larcker Criterion, Heterotrait–Monotrait (HTMT), and cross-loading methods; these are reported in Appendix B. The variance inflation factor (VIF) values in Table 2 also help guarantee no multicollinearity issues among the variables in this study’s measurement model. Thus, based on the results of the validity and reliability tests and multicollinearity, all constructs in this study can be used for assessment.

4.3. Testing Hypothesis

Based on

Table 3. Hypothesis testing results.

Hypothesis	Original Sample (O)	Sample Mean (M)	Standard Deviation (STDEV)	T Statistics (|O/STDEV|)	p Values	Code	Result
Perceived usefulness -> Behavioral Intention	0.189	0.181	0.053	3.584	0.000 ***	H1	Significant
Perceived ease of use -> Behavioral Intention	0.150	0.154	0.064	2.319	0.010 **	H2	Significant
Perceived susceptibility -> Behavioral Intention	0.153	0.152	0.056	2.737	0.003 **	H3	Significant
Perceived severity -> Behavioral Intention	0.105	0.105	0.054	1.931	0.027 *	H4	Significant
Health Motivation -> Behavioral Intention	0.073	0.075	0.057	1.283	0.100	H5	Not Significant
Perceived Benefits -> Behavioral Intention	0.241	0.240	0.062	3.866	0.000 ***	H6	Significant
Perceived Barriers -> Behavioral Intention	0.034	0.036	0.031	1.107	0.134	H7	Not Significant

Note: p values less than 0.05 are significant (*), less than 0.01 are highly significant (**), and less than 0.001 are highly significant (***).

, Hypothesis 1 (H1) is accepted because there exists a significant influence between perceived usefulness and behavioral intention to use an advance care plan (ACP) [mean = 0.181; STD = 0.053; t = 3.584], which has a very positive influence on behavioral intention and a p-value less than 0.001. The perceived ease of use has a significant influence on behavioral intention to use an advance care plan (ACP) because the p value is 0.010 ** [mean = 0.154; STD = 0.064; t = 2.319]. It can be concluded that Hypothesis 2 (H2) is accepted. Furthermore, perceived susceptibility has a significant influence on behavioral intention to use an advance care plan (ACP) because its p value is 0.003 *** [mean = 0.152; STD = 0.056; t = 2.737]. Hypothesis 3 (H3) is accepted. Furthermore, perceived severity has a significant influence on behavioral intention to use an advance care plan (ACP) because its p value is 0.027 * [mean = 0.105; STD = 0.054; t = 1.931]. This shows that Hypothesis 4 (H4) is accepted. On the other hand, health motivation has no significant effect on the intention to use an ACP. This means H5 is rejected, because Hypothesis 5 (H5) has a p value of 0.100 [average = 0.075; STD = 0.057; t = 1.283]. Meanwhile, perceived benefits have a significant influence on the intention to use an ACP because the p value is 0.000 *** [mean = 0.240; STD = 0.062; t = 3.866]. It can be said that Hypothesis 6 (H6) is accepted. Lastly, perceived barriers do not have a significant influence on the intention to use ACP because their p value is 0.134 [mean = 0.036; STD = 0.031; t = 1.107]. Moreover, it can be concluded that Hypothesis 7 (H7) is rejected.

5. Discussion

The increasing number of aging individuals is a global phenomenon that could have complex consequences if not addressed appropriately. As the number of aging individuals increases, the population dependency rate will also increase, presenting serious challenges in every country—not only in developed countries but also in developing countries. Population dependency is a condition in which several individuals require care and support from other individuals, such as in the case of healthcare. The challenges of population aging encompass almost every aspect of life, including the healthcare system [93]. Based on this phenomenon, the need for an advance care plan is highlighted, as this will ensure that individuals’ preferences for their future medical care are known and respected. The recent literature demonstrates the importance of integrating technology into managing and implementing advance care plans to increase awareness, accessibility, and sustainability within the healthcare system [17,94].

Several studies have highlighted various factors and strategies that can contribute to the increased the use of advance care plans by individuals [95]. However, most factors influencing their intention to use advance care planning based in technology have yet to be explored. Among the various models often used to study the use of technology are the TAM and HBM. Therefore, this research aimed to investigate the factors influencing the use of advance care plans (ACPs) by using the health belief model (HBM) and technology acceptance model (TAM). Although the combination of the TAM and HBMs has previously been applied to explain the use of technology in the healthcare system, there is limited research applying both models to the study of advance care planning. Therefore, a potential contribution of this research is the identification of relevant variables that influence the use of technology in advance care planning and reduce the barriers to the future adoption of advance care plans (ACPs). In this way, this research aims to provide a better theoretical understanding of the factors that influence the utilization of advance care planning and provide valuable insights enabling health practitioners to understand individuals’ preferences and attitudes towards advance care planning.

Overall, the results do not support all the proposed hypotheses. A SEM analysis found three latent variables in the HBM that were shown to have a significant influence on the use of advance care plans based in technology. This condition is different from TAM, where overall latent variables were proven to influence the use of advance care planning. The latent variables from the HBM that have an influence are perceived susceptibility (PSU), perceived severity (PSE), and perceived usefulness (PBE). The variables from the TAM are perceived usefulness (PU) and perceived ease of use (PEOU). Thus, only five of the seven research hypotheses can be accepted, namely H1, H2, H3, H4, and H6.

Our research findings from the combined analysis of respondents from both countries in this study support our first (H1) and second (H2) hypotheses. By using early care planning, behavioral intention (BI) is positively influenced by perceived usefulness (PU) and perceived ease of use (PEOU). These findings confirm the technology acceptance model (TAM)’s role and support research showing how perceived usefulness (PU) and perceived ease of use (PEOU) increase the subjective use of technology in medical settings. According to the TAM, perceived usefulness (PU) and perceived ease of use (PEOU) describe an individual’s behavioral intention to utilize a system [96,97]. In healthcare environments, this approach has been extensively applied in many contexts to understand user acceptance of technology. Research studies regularly show that PU and PEOU mostly define consumers’ inclination to embrace and use technology [96,97]. PU reflects the opinion that using a system will improve performance, while PEOU shows the assumption that using a system will be easy [96,97]. In advance care planning, the findings of this study are in accordance with those of the TAM, which implies that PU and PEOU have a positive influence on behavioral intentions [97]. This validates research that emphasizes the important influence of PU and PEOU in the user acceptance of technology as a medium for health services [96]. Promoting the acceptability of advance care planning treatments among individuals relies on their awareness of the value of a treatment and the simplicity of its use, which ultimately helps provide better healthcare outcomes.

In the HBM, there are several hypotheses, namely H3, H4, and H6, that have been proven. Perceived usefulness (PU), perceived susceptibility (PSU), perceived severity (PSE), and perceived benefits (PBE) are shown to have a positive effect on behavioral intention (BI), which has a positive effect on the use of advance care planning. These findings are in line with a previous study and support the hypothesis that perceived susceptibility (PSU), perceived severity (PSE), and perceived benefits (PBE) all have a positive influence on behavioral intention (BI). Previous research found that perceived benefits mediate the effect of perceived susceptibility on health behavior, suggesting that perceived susceptibility influences behavior through its impact on more immediate determinants [98]. Additionally, other research suggests that increasing perceived susceptibility can enhance the effects of increasing perceived severity on intentions and behavior [99]. In healthcare, research identified perceived severity and susceptibility as factors influencing the intention to comply with health-related policies during the COVID-19 pandemic [100]. This aligns with the HBM, which considers perceptions of severity and susceptibility as critical components influencing health-related decisions [61]. Furthermore, other studies observed the moderating effect of perceived susceptibility on intentions related to health behaviors such as smoking cessation [101].

Integrating the technology acceptance and health belief model is highly relevant to advance care planning. The TAM helps individuals understand the importance of having an awareness of technologies’ benefits and effectiveness in improving healthcare and quality of life. Individuals are more likely to use advance care planning due to an increased perceived ease of use and perceived usefulness. Compared with the HBM, the TAM underlines the need for education and training to enhance individuals’ understanding of the value of technology as the medium of healthcare to improve their outcomes. Organizations and stakeholders can invest in user experience and design user-friendly applications to reach individuals with different backgrounds, considering digital skills and age variables. By creating innovative strategies, organizations can ensure that users regularly engage with these applications and derive positive outcomes for their healthcare.

In practical terms, integrating the TAM and HBM can guide the development of effective advance care planning and its integration with technology. These can improve the quality of healthcare experiences and promote a culture within organizations that values proactive healthcare management and well-being. Companies can aid individual employees and overall organizational health by integrating these technologies into advance care plans within broader healthcare initiatives.

6. Limitations and Future Research

Although this study reported good results, several limitations must be considered when interpreting them. First, the research sample was recruited from only a few provinces in Thailand and Indonesia, so these results cannot be generalized. Second, comparative studies between countries in ASEAN could be carried out to understand the cultural differences in each country, their health systems, and other factors that influence the adoption of ACPs. Involving more countries in this research area will provide deeper insights into the variations in ACP use across the ASEAN region. Third, qualitative research can be conducted to better understand individuals’ perceptions, attitudes, and motivations regarding using ACPs. This qualitative approach will be beneficial in exploring the reasons behind individual choices and experiences regarding the use of ACPs. Due to the limited information obtained by the current study, which used an online survey, we could not capture the respondents’ emotions regarding the benefits of an advance care plan. Fourth, the scope of this research could be expanded to cover wider-ranging age groups and involve more varied populations, including ethnic minority groups or populations living in rural areas. This method will help us understand how social, cultural, and residential contextual factors may influence the use of ACPs.

Additionally, longitudinal research could be conducted to track changes in ACP-related attitudes and behavior over time. Future follow-up studies could be conducted using this research design to better understand the factors influencing these changes and the long-term impacts of ACP interventions. Furthermore, research could explore other factors influencing the use of ACPs, such as social, cultural, religious, and health policy factors, in each country. A more comprehensive and in-depth understanding of these factors will help design more effective strategies to drive ACP use. The development and testing of interventions may also be a focus of future research, as technological ACP interventions in Asia still need to be improved. By developing interventions designed to increase the awareness, understanding, and use of ACPs, future studies could evaluate the effectiveness of these interventions and enable improved ACP implementation efforts by ultimately deepening our understanding of the factors influencing the use of ACPs, improving advance care planning and improving the quality of healthcare not only in Thailand and Indonesia but in other ASEAN countries as well.

7. Conclusions

This research analysis combined the TAM and HBM to understand the factors influencing the use and application of advance care planning technology. It focused on the primary constructs of the two models, namely perceived usefulness (PU), perceived ease of use (PEOU), perceived susceptibility (PSU), perceived severity (PSE), and perceived benefits (PBE). It perceived barriers (PBAs), with the addition of another construct, namely health motivation (HM). In this research, a construct was found that greatly influenced the perceived barriers, namely limitations (PBAs), which include data security (security), accessibility, and language differences (linguistic barriers). This construct may be of greater importance because individuals are more concerned about the privacy and confidentiality of their personal information related to prior treatment plans.

Additionally, most individuals expressed concerns about frequent data misuse or unauthorized access to their sensitive information. In addition, many individuals need assistance accessing technology for an advanced care plan (ACP) due to limited device access or limited connectivity, so using an advanced care plan (ACP) based in technology may be less effective. Lastly, language differences also pose an obstacle in using and applying advanced care plans (ACPs) because most technological developments need to provide a variety of languages that various populations can understand. These barriers make it difficult for individuals to access information and communicate regarding advance care plans (ACPs). Because technological developments generally have positive impacts and could lead to improvements in the health service system, it would be beneficial to overcome these obstacles in the future so that individuals can use advance care plans (ACPs) more effectively. They could experience the benefits of implementing initial treatment plans, thus improving the health service and their quality of life. Overcoming these existing barriers will indirectly increase the benefits of advance care planning.