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Article

DigitAal Asset Adoption in Inheritance Planning: Evidence from Thailand

by
Tanpat Kraiwanit
,
Pongsakorn Limna
* and
Supakorn Suradinkura
International College, Pathumthani University, Pathum Thani 12000, Thailand
*
Author to whom correspondence should be addressed.
J. Risk Financial Manag. 2025, 18(6), 330; https://doi.org/10.3390/jrfm18060330
Submission received: 2 June 2025 / Revised: 16 June 2025 / Accepted: 16 June 2025 / Published: 17 June 2025
(This article belongs to the Section Financial Technology and Innovation)
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Abstract

:
This study investigates key factors influencing individuals’ intentions to incorporate digital assets into inheritance planning in Thailand. The research focuses on three primary determinants: demographic characteristics, knowledge of digital assets, and the perceived risks associated with their usage and transfer. Utilizing a quantitative research design, data were collected from 630 Thai respondents via a structured online questionnaire through convenience sampling. Binary logistic regression analysis was applied to identify statistically significant predictors. The results indicate that digital-asset knowledge, gender, age, income, saving behavior, and risk perception collectively account for a substantial variance in individuals’ intentions to use digital assets as part of their inheritance planning. Notably, knowledge and income positively influence adoption, suggesting that financial education and broader economic development may encourage increased usage. Conversely, factors such as age, gender, and perception of risks pose significant barriers, underscoring the need for targeted strategies to foster inclusivity. As digital assets transition from speculative tools to recognized financial instruments, their role in inheritance planning becomes increasingly relevant. This study contributes to a deeper understanding of this evolving financial landscape in the Thai context and offers insights applicable to other emerging markets undergoing similar digital transformations.

1. Introduction

The global rise of digital assets is fundamentally transforming financial landscapes by introducing new modes of transaction, investment, and economic engagement. Digital assets—including cryptocurrencies, stablecoins, and tokenized assets—are increasingly being incorporated into the mainstream financial ecosystem. This shift is largely propelled by the proliferation of blockchain technology, which offers decentralized, transparent, and secure transactions, appealing to both consumers and institutional stakeholders (Popescu, 2022; Intelligence Team, 2023a, 2023b; Suwannasichon, 2023). As digital assets gain popularity, a growing number of financial institutions, technology firms, and governments are formulating strategies to facilitate their secure and efficient integration into existing systems. The emergence of accessible platforms, such as digital wallets and cryptocurrency exchanges, has broadened participation, enabling individuals from diverse backgrounds to engage with digital finance. Additionally, digital assets have assumed a pivotal role in global transactions, portfolio diversification, and as hedges against inflation. These developments underscore the increasing relevance of digital assets in shaping contemporary economic behavior and financial planning, including their emerging role in inheritance and estate management (Gomber et al., 2018; Corbet et al., 2019; Richards, 2021; Voskobojnikov, 2021).
The adoption of digital assets in Thailand has experienced significant momentum, driven by regulatory advancements, technological innovation, and evolving consumer behavior. The Thai government has established a comprehensive legal framework to regulate the secure use of digital assets, including cryptocurrencies and blockchain-based technologies. This proactive stance has not only attracted local and international investors but also fostered a dynamic ecosystem of startups and established firms engaged in digital-asset trading, decentralized finance, and digital payment solutions. Notably, the Bank of Thailand’s initiative to explore the development of a Central Bank Digital Currency (CBDC) reflects the country’s commitment to integrating digital assets into its broader financial system. These developments, combined with growing digital literacy and expanded digital payment infrastructure, position Thailand as a leader in regional financial innovation and inclusion. The convergence of regulatory support, technological progress, and consumer engagement has strengthened Thailand’s standing in the global digital-asset landscape, with the potential to influence regional and international financial practices (Ariya, 2023; Intelligence Team, 2023a, 2023b). Parallel to the rise in digital-asset adoption is the growing importance of digital inheritance, which addresses the management and transfer of digital assets after an individual’s death. As Kharitonova (2021) notes, the increasing integration of digital elements—such as cryptocurrencies, social media, and digital intellectual property—into people’s lives has introduced new challenges in inheritance planning. Key concerns include securing legal access for heirs, protecting privacy, and navigating complex international legal frameworks. Although legal systems are beginning to adapt, jurisdictional inconsistencies often complicate the transfer of digital assets, particularly across borders. Without adequate planning, heirs may encounter significant obstacles in locating or accessing digital assets, especially in the absence of clear documentation or access credentials. To mitigate these risks, estate planning should incorporate digital elements by appointing a digital executor, leveraging password management tools, and explicitly addressing digital-asset transfer in wills or trusts (Sivabala & Vidyasri, 2024; Ugli, 2024).
In Thailand, the increasing prevalence of digital assets has amplified the urgency of addressing digital inheritance in both legal and personal contexts. As these assets become integral to financial portfolios, incorporating them into inheritance plans is essential for preserving wealth and honoring the intentions of the deceased. However, the integration of digital assets into inheritance planning is complicated by cultural norms and the relatively recent emergence of these technologies (Akramov et al., 2024; Expat Tax Thailand, 2024; Juhász, 2024). This evolving legal and financial landscape calls for a deeper understanding of how individuals in Thailand perceive and approach digital-asset inheritance. In response to this need, the present study seeks to investigate the key factors that influence Thai individuals’ decisions to incorporate digital assets into their inheritance strategies. The central research question is, what factors influence individuals’ intentions to use digital assets as inheritance in Thailand, focusing on demographic variables, digital-asset knowledge, and perceived risks? By examining variables such as demographic characteristics, knowledge of digital assets, and perceived risks, this research aims to provide insights into the motivations, concerns, and planning behaviors shaping this critical aspect of digital financial management.

2. Literature Review

The rapid evolution of digital technologies has significantly transformed the nature of personal assets, introducing new dimensions to wealth management, ownership, and inheritance. As digital assets become increasingly integrated into daily life—ranging from cryptocurrencies and digital wallets to online accounts and data—understanding how individuals perceive, manage, and plan for their transfer has become a crucial area of scholarly inquiry (Busari et al., 2023; Nasrul et al., 2023). This literature review aims to explore the existing body of knowledge related to demographic influences, digital-asset knowledge, and risk perception in the context of digital inheritance planning.

2.1. Digital Assets and Inheritance: An Overview

Digital assets are intangible entities that exist in electronic form and are managed through digital technologies, encompassing a wide spectrum such as cryptocurrencies (e.g., Bitcoin, Ethereum), blockchain-based tokens (security and utility tokens), non-fungible tokens (NFTs), digital records (e.g., contracts, intellectual property), and media files like music and e-books. These assets are typically stored in digital wallets and tracked through blockchain technology, ensuring security, transparency, and decentralized ownership (Ferro et al., 2023; Intelligence Team, 2023a; Paisanthanachot & Chainirun, 2023; Shoommuangpak & Wongta, 2023). The Thai digital-asset market has grown rapidly, especially during the COVID-19 pandemic, with approximately 2.9 million trading accounts reported in 2022. Despite this momentum, concerns persist about speculative risks and market volatility, especially following high-profile failures like FTX, Three Arrows Capital, and Genesis, which have cast uncertainty over investor confidence and market stability. In Thailand, digital assets are classified into centralized and decentralized types. Centralized assets include Central Bank Digital Currencies (CBDCs) and privately issued tokens, such as stablecoins and investment tokens, while decentralized assets—governed by Decentralized Autonomous Organizations (DAOs)—comprise cryptocurrencies, DeFi tokens, native coins, and NFTs. This taxonomy offers a clearer understanding of the digital-asset ecosystem and its governance models (Intelligence Team, 2023a, 2023b).
Digital inheritance refers to the transfer of digital assets upon the owner’s death. Unlike traditional inheritance, which is governed by well-established laws, digital inheritance lacks consistent legal recognition across jurisdictions. It involves assets such as digital wallets, social media accounts, and emails, which must be carefully documented in estate planning to ensure a smooth transfer. Privacy, access rights, valuation, and legal uncertainty pose significant challenges, particularly in countries like Thailand and India, where legislative clarity is still evolving. While platforms like Facebook and Google offer limited options for account management after death, they often fall short of granting full control to heirs. Some individuals have begun using digital inheritance services or password vaults to facilitate posthumous access, though these methods often require formal verification processes. Overall, digital inheritance represents a critical yet underdeveloped area within modern estate planning, calling for comprehensive legal reform and increased public awareness (McCarthy, 2015; Mikk & Sein, 2018; Mali & Prakash, 2019; Wata, 2016; Chaiyong, 2024; Crespo, 2024).
Thailand’s legal and cultural landscape significantly shapes digital-asset inheritance planning, presenting both established principles and notable gaps. While digital assets are generally considered part of a deceased person’s estate under Thai law, there is currently no specific legal framework dedicated to digital inheritance, leading to reliance on the traditional Civil and Commercial Code. A major challenge arises from the conflict between these inheritance rights and the Terms of Service (TOS) agreements of digital platforms, which often restrict access and transferability of accounts and data to heirs. Furthermore, Thai law explicitly does not recognize electronic wills, requiring traditional paper-based wills for legal validity. Culturally, a pervasive reluctance to openly discuss death and estate planning often leads to individuals not documenting their digital assets or providing access instructions, creating significant practical hurdles for executors in identifying, accessing, and distributing these intangible assets. This combination of legal voids and cultural norms often results in valuable or sentimental digital property being permanently lost or inaccessible to rightful heirs (Investments for Expats, 2025; Mahanakorn Partners, 2024; Wata, 2016; Yolanda et al., 2024).

2.2. Demographic Factors

Demography refers to the study of populations, focusing on elements such as size, density, fertility, mortality, growth, age distribution, migration, and other vital statistics, and examining how these interact with social and economic variables. It relies on data collected through vital statistics systems and specialized surveys to identify and analyze population trends over time (Tulchinsky & Varavikova, 2014). A subset of this field, economic demography, explores how demographic factors influence population dynamics and their economic implications. This includes phenomena such as birth and death rates, family formation, marriage and divorce, urbanization, migration, and population density. It also considers age distribution, gender composition, ethnic diversity, and overall population growth. By examining how these factors intersect with economic systems and social structures, economic demography seeks to explain the complex relationships between demographic change and economic outcomes (Kelley & Schmidt, 2008).
Demographic variables are also fundamental in marketing and consumer behavior research. They serve as essential tools for market segmentation, strategy development, and target audience identification. Commonly examined demographic characteristics include age, gender, education, occupation, income, religion, and ethnicity. These factors are considered primary criteria for segmentation due to their measurable nature and influence on consumption patterns (Patel & Bansal, 2018; Puška et al., 2018). Gender, for instance, can influence communication styles and decision-making processes. Age determines preferences and information-processing capabilities. Educational attainment affects individuals’ knowledge, attitudes, and behavior. Marital status provides insights into household composition and consumption habits. Income indicates purchasing power and consumer priorities, while occupation reflects lifestyle and interests. These demographic insights allow marketers and researchers to better understand population structures and tailor strategies that address specific needs within target groups (Aksorndee, 2017; Kraiwanit et al., 2023).
In the context of inheritance planning, demographic factors such as age, gender, education, and income significantly influence individuals’ engagement with digital assets. These characteristics shape familiarity with digital technologies, levels of risk tolerance, and preferences for managing and transferring wealth. Recognizing these demographic influences is essential for developing inheritance strategies that are responsive to the diverse needs and behaviors of individuals and families.

2.3. Knowledge and Awareness of Digital Assets

Awareness of digital inheritance has become increasingly important in the digital era, addressing complex issues related to managing and transferring digital assets after death (Ali et al., 2022). This emerging concept adapts traditional inheritance frameworks to accommodate digital assets. Berlee (2017) outlines a three-part model of digital inheritance comprising the original owner, the transfer of rights, and the new owner. In this framework, the original owner is the individual holding the digital assets. Vučković and Kanceljak (2019) elaborate on the second element—transferring digital rights—which encompasses the transmission of data, information, memories, and access credentials to designated heirs. The third element involves the new owner or beneficiary, responsible for managing these digital assets posthumously. Digital inheritance awareness, therefore, refers to an individual’s understanding of the importance and process of transferring digital rights to ensure appropriate asset management after death. This operationally includes awareness of how digital assets—ranging from data and digital memories to financial instruments and platform access—can be successfully passed on to successors. More broadly, knowledge and awareness of digital assets involve understanding various types of digital financial tools, such as cryptocurrencies, digital wallets, and blockchain technologies, along with their associated risks and opportunities. This includes the ability to distinguish between different digital-asset types, comprehend the underlying technologies, monitor regulatory developments, and apply security best practices. Security awareness—such as safeguarding private keys and using trusted platforms—is a vital component in protecting digital investments. Enhanced digital-asset literacy is essential for informed decision-making, risk management, and capitalizing on financial opportunities in a rapidly evolving landscape (Ali et al., 2022; Chaisiripaibool et al., 2025).
In this study, digital-asset knowledge refers to a comprehensive set of competencies required to navigate the emerging digital financial environment. These competencies include an understanding of blockchain mechanisms, the ability to differentiate between asset types, awareness of investment strategies, familiarity with legal and regulatory frameworks, and knowledge of robust security practices. When applied to inheritance planning, these competencies empower individuals to preserve, protect, and effectively transfer their digital wealth to future generations.

2.4. Risk Perception

Risk perception is the subjective evaluation individuals or groups make regarding the severity and probability of potential threats. It encompasses the consideration of various factors that might result in negative outcomes—such as financial loss, security breaches, or regulatory changes—especially in the context of digital-asset ownership. Influenced by personal experiences, knowledge, emotions, and cultural or societal contexts, risk perception plays a pivotal role in shaping behavior and decision-making. High perceived risk often results in greater caution, whereas underestimated risks may lead to insufficient preparation and vulnerability. Understanding how individuals perceive risk is essential for designing effective mitigation strategies and enhancing decision-making in high-uncertainty contexts, such as digital-asset investment and management (Paek & Hove, 2017; Siegrist & Árvai, 2020; Siegrist, 2021; Zamoras et al., 2024).
When applied to digital assets, risk perception reflects how individuals assess and react to specific threats. Volatility is a major concern, as prices of digital assets can fluctuate drastically, potentially resulting in large financial losses. Security vulnerabilities—such as hacking, phishing, and loss of private keys—pose significant risks. Regulatory uncertainty adds complexity, given that changes in law or government policies can affect the legality or value of digital assets. Inadequate understanding of these systems, concerns about market manipulation, liquidity constraints, and privacy issues further contribute to elevated risk perceptions. Historical cases of fraud, theft, and technological failures have amplified public skepticism and hesitancy in adopting digital assets (Abramova et al., 2021; Sagheer et al., 2022; Hacibedel & Perez-Saiz, 2023; Zamoras et al., 2024). Moreover, the introduction of digital inheritance adds another layer of complexity. The lack of clear legal procedures for transferring digital assets upon death raises concerns about accessibility and security for heirs. Without legal clarity, digital assets may become irretrievable, leading to substantial losses. Addressing these concerns requires multi-faceted solutions: strengthening cybersecurity protocols, expanding public education, establishing clear regulatory standards, and developing comprehensive digital inheritance frameworks (Wata, 2016; Kreiczer-Levy & Donyets-Kedar, 2019; Sarlet, 2022).
In this study, the perceived risks associated with digital assets—such as market volatility, legal uncertainty, and security vulnerabilities—are examined as key factors influencing inheritance planning decisions. Understanding and mitigating these risks is essential for safeguarding digital wealth and ensuring its secure and seamless transfer across generations.

3. Materials and Methods

This recent study conducted a quantitative research approach, characterized by its systematic and empirical examination of phenomena through measurable data and statistical analysis. This methodology involves the rigorous collection and analysis of numerical data, enabling researchers to draw conclusions, make predictions, and identify patterns or relationships within the data. Figure 1 presents a schematic diagram outlining the research methodology flow utilized in the study.
Figure 1 illustrates a structured yet iterative process of conducting quantitative research. It begins with defining the study’s goals, followed by formulating research objectives and determining specific research questions. Once the research questions are established, an appropriate quantitative research design is selected to guide the study. Data collection then takes place, supported by processes that ensure the legitimacy and reliability of the data. The model highlights the importance of re-evaluating research objectives and questions based on preliminary findings, enabling refinement and alignment throughout the process. Subsequent steps include data analysis and interpretation, leading to the final stage of writing the research report. The cyclical flow among data collection, legitimation, re-evaluation, and interpretation underscores the dynamic and reflective nature of rigorous quantitative research.

3.1. Questionnaire Design and Instrument Validation

In this quantitative study, a structured questionnaire was meticulously designed and implemented to ensure methodological rigor and alignment with the study’s central objective: to examine the factors influencing individuals’ intentions to use digital assets as inheritance in Thailand. The instrument comprised several sections, including demographic characteristics (e.g., gender, age, marital status, education level, student status, income, and savings), knowledge of digital assets (score), and perceived risks associated with their use and transfer. The “score” referenced in this study reflects participants’ knowledge of digital assets. This was measured through a cognitive test integrated into the questionnaire, comprising 10 multiple-choice questions designed to assess understanding of fundamental digital-asset concepts. Each correct response was awarded 1 point, while incorrect answers received 0 points, resulting in a possible score range from 0 to 10. Higher scores indicated a stronger foundation in digital-asset literacy, which is essential for navigating digital-asset environments. The scores were categorized into three levels: low knowledge (0–3 points), moderate knowledge (4–6 points), and high knowledge (7–10 points). This scoring system allowed researchers to examine how varying levels of digital-asset knowledge may influence individuals’ intentions to adopt and incorporate digital assets into their inheritance planning.
The questionnaire items were developed based on the established literature, ensuring alignment with validated theoretical frameworks and empirical findings relevant to digital-asset adoption. Specifically, items were adapted from Chaisiripaibool et al. (2025), Duangsin et al. (2023), and Wahab et al. (2024), with modifications made to reflect the unique characteristics of the Thai digital context. The adaptation process followed a structured approach, beginning with a comprehensive content review to evaluate the relevance and clarity of the original items in relation to the study’s aims. Revisions were then made to enhance cultural and contextual appropriateness while preserving the integrity of the original constructs.
To ensure content validity, the questionnaire was reviewed by three academic experts who assessed its appropriateness for measuring individuals’ intentions regarding digital-asset inheritance. Following expert review, a pilot test was conducted with a sample of 30 participants to evaluate item clarity and identify any ambiguities. As recommended by Aithal and Aithal (2020), such preliminary testing is essential for assessing instrument effectiveness prior to full-scale deployment. Insights from the pilot test led to several refinements: technical terminology was simplified, response options were expanded to better capture participant perspectives, and the sequence of questions was adjusted to improve logical flow and reduce cognitive load. Redundant items were removed to streamline the instrument, ensuring that it remained both concise and comprehensive. These revisions enhanced the overall validity and reliability of the questionnaire, thereby strengthening the study’s methodological foundation.

3.2. Sample Selection

The sample selection process for this study was carefully designed to ensure an accurate representation of the target population: Thai residents aged 18 years and older who possess experience with or an interest in digital assets. The minimum required sample size was calculated using Cochran’s formula, based on a 95% confidence level and a 0.05 margin of error. Following the recommendation of Uakarn et al. (2021), this yielded a minimum sample size of 384 participants. To enhance the robustness of the study and mitigate the impact of potential non-responses or incomplete submissions, the final sample was expanded, resulting in 630 valid and completed responses.
Participants were selected through convenience sampling, a non-probability sampling technique that enables efficient access to individuals who are readily available and meet the study’s inclusion criteria. This approach was deemed suitable due to the digital nature of the research topic and the need for timely data collection from a diverse demographic. All participants were required to meet specific eligibility conditions: being at least 18 years old, residing in Thailand, and having prior experience with or interest in digital assets. This sampling strategy ensured that the dataset was relevant and reflective of the broader population engaged in digital-asset usage.

3.3. Data Collection

The data collection phase was systematically conducted through the LINE platform, a widely used communication tool in Thailand known for its extensive reach and popularity among the general population. The mobile-friendly survey was distributed during March and April 2025, leveraging LINE’s broad user base to maximize engagement and response rates. The timing and duration of the data collection were carefully planned to capture diverse experiences and behaviors related to digital-asset ownership and inheritance intentions. Using LINE facilitated timely participation and allowed for real-time support, helping maintain a high level of response quality. This approach enabled the study to gather data that accurately reflect current trends in digital-asset engagement and the emerging importance of digital inheritance planning in Thailand.
After the data collection phase, all responses were carefully screened for completeness and relevance to ensure that only valid and consistent data concerning digital assets and inheritance were included in the final dataset. Particular attention was given to excluding incomplete responses and verifying that participants met the eligibility criteria—being 18 years of age or older and having experience or interest in digital assets. This quality-control process enhanced the reliability and accuracy of the findings. The study also strictly adhered to ethical research standards. Participants were provided with a clear and comprehensive informed consent form outlining the purpose of the study, the voluntary nature of participation, the assurance of confidentiality, and the right to withdraw at any time without penalty. Only fully completed questionnaires were retained for analysis, thereby ensuring the integrity of the dataset and supporting robust statistical examination of the factors influencing individuals’ intentions to include digital assets in their inheritance planning.

3.4. Data Analysis

In the data analysis phase, both descriptive and inferential statistical techniques were applied using the statistical software Jamovi (version 2.16.17.0) to examine relationships among key variables and identify factors influencing individuals’ intentions to include digital assets in their inheritance plans. Descriptive statistics were used to present an overview of participant demographics, levels of digital-asset knowledge, and perceived risks. These summaries provided a foundational understanding of the sample’s characteristics. To analyze the impact of multiple independent variables—including demographic factors, knowledge, and risk perceptions—on a binary dependent variable (intention to use digital assets for inheritance, coded as 1 = Yes and 0 = No), the study employed binary logistic regression analysis. As emphasized by Chatla and Shmueli (2017), logistic regression is an appropriate and widely used method when the outcome variable is dichotomous. This technique estimates the probability that an individual intends to incorporate digital assets into their inheritance strategy based on the values of the predictor variables. By modeling this binary outcome, the analysis provided a clearer understanding of the variables that significantly shape individual decisions regarding digital-asset usage in the context of inheritance. Logistic regression also produced odds ratios, which helped interpret the extent to which each factor increased or decreased the likelihood of intention. This approach yielded valuable insights into how digital-asset planning is currently approached within Thailand’s evolving financial and technological environment.

4. Results

The general characteristics of the survey participants were carefully examined using data obtained from online questionnaires. This demographic assessment served as a vital foundation for interpreting the study’s overall results. Table 1 presents the cleaned demographic data of the respondents, reflecting the final dataset used in the analysis.
Table 1 summarizes the demographic characteristics of the 630 survey respondents. The sample shows a balanced gender distribution (56.0% male, 44.0% female), predominantly young, with 42.9% of participants under 35 years old. Most participants were single (53.3%) and held a bachelor’s degree (75.1%). Respondents indicated that they were mainly students (37.1%), government officers (33.8%), and private employees (19.9%). Income was concentrated between THB 25,001 and 50,000, and the majority saved THB 1000–10,000 monthly. These insights form a foundational context for the study’s analysis.
Table 2 highlights respondents’ perceptions regarding the use of digital assets as inheritance, offering valuable insight into the evolving landscape of digital finance in legacy planning. A significant majority (77.9%) of the 630 participants agreed that digital assets are viable for inheritance, indicating growing public acceptance and trust in digital wealth. In contrast, 22.1% expressed disagreement, reflecting lingering concerns about regulation, technology, and security. These findings underscore a societal shift toward embracing digital assets in long-term financial planning, with important implications for legal, financial, and policy frameworks.
The chi-square test is employed to examine the association between categorical variables by comparing the observed frequencies with those expected under the assumption of independence. A result is deemed statistically significant when the p-value is less than or equal to 0.05, suggesting that the observed differences are unlikely to have occurred by chance. In this study, the chi-square statistic of 318.658 with 9 degrees of freedom, as presented in Table 3, indicates a statistically significant association between the dependent variable and at least one of the independent variables.
The Cox and Snell R-squared and Nagelkerke R-squared are pseudo-R2 statistics commonly used in binary logistic regression to evaluate the goodness-of-fit of a model. These measures estimate the proportion of variation in the dependent variable that is accounted for by the independent variables. However, the Cox and Snell R2 tends to underestimate this proportion, as it cannot reach a maximum value of 1. In contrast, the Nagelkerke R2 adjusts for this limitation by rescaling the value to span from 0 to 1, allowing for more intuitive interpretation. As presented in Table 4, the model produces a Nagelkerke R2 value of 0.609, indicating that approximately 60.9% of the variance in individuals’ intentions to incorporate digital assets into their inheritance plans in Thailand can be explained by the included predictors: digital-asset knowledge (score), gender, age, marital status, education level, student status, income, savings, and risk perception.
A classification table is a diagnostic tool used to assess the performance of classification models by comparing the model’s predicted outcomes with the actual observed outcomes. It provides insight into the model’s predictive accuracy by detailing the number of correct and incorrect classifications. Back-testing, which involves applying the model to historical data, is commonly used to evaluate its robustness and reliability over time. As illustrated in Table 5, the classification table assesses the model’s capability to predict individuals’ intentions to incorporate digital assets into their inheritance plans in Thailand, using all independent variables. Applying a 50% cut-off threshold, the model correctly predicted approximately 89.7% of the outcomes. This high level of accuracy demonstrates the model’s effectiveness in identifying key factors influencing individuals’ intentions regarding digital-asset inheritance planning in the Thai context.
The predictive regression equation of Model (1) using the coefficients from Table 6 can be described by the following equation:
P = 1 1 + e − z
where P is an individual’s intent to incorporate digital assets into their inheritance plans within Thailand, and Z = 1.613 + 0.502(score) + 1.485(gender) − 1.481(age) + 0.945(income) + 0.588(saving) − 0.464(risk).
The logistic regression analysis in Table 6 examines factors influencing individuals’ intentions to incorporate digital assets into inheritance plans in Thailand. The model yields several statistically significant predictors that warrant detailed examination. Gender emerges as the most powerful predictor (B = 1.485, p < 0.001) with an odds ratio of 4.416, indicating that individuals of one gender (being males) are more than four times as likely to plan for digital-asset inheritance compared to the reference gender. The score, which serves as an indicator of digital-asset knowledge (B = 0.502, p < 0.001, OR = 1.652), suggests that with each unit increase in the score, the likelihood of planning for digital-asset inheritance rises by 65.2%. Income level shows a strong positive relationship (B = 0.945, p < 0.001, OR = 2.572), suggesting that higher-income individuals are significantly more likely to consider digital assets in their inheritance planning, with each unit increase in income more than doubling the odds. Similarly, saving positively influences this intention (B = 0.588, p = 0.001, OR = 1.800), indicating that those with more substantial savings are 80% more likely to incorporate digital assets into their inheritance plans. Conversely, age demonstrates a significant negative association (B = −1.481, p < 0.001, OR = 0.227), revealing that older individuals are markedly less inclined to include digital assets in inheritance planning, with each unit increase in age reducing the odds by approximately 77.3%. Risk perception also shows a significant negative relationship (B = −0.464, p = 0.024, OR = 0.629), suggesting that individuals with higher risk awareness are approximately 37.1% less likely to plan for digital-asset inheritance. Interestingly, three factors—relationship status (being single), education level, and student status—failed to achieve statistical significance in the model, although student status approaches significance (p = 0.064). The model’s constant term (B = 1.613, p = 0.154) is not statistically significant, indicating that when all predictors are at zero, there is no reliable baseline tendency toward or against digital-asset inheritance planning. The derived predictive equation provides researchers and practitioners with a tool to estimate the probability of individuals incorporating digital assets into their inheritance planning based on these key demographic and behavioral factors within the Thai context.
Following the initial data analysis, only statistically significant independent variables were retained for further investigation. This selective approach aimed to identify variables with a meaningful impact on predicting the factors influencing individuals’ intention to incorporate digital assets into their inheritance plans within Thailand. By focusing on these key predictors, the model’s accuracy and efficiency were enhanced, reducing unnecessary complexity and mitigating the risk of distortion from irrelevant factors. This streamlined analysis also facilitated a clearer interpretation of the results, strengthening the study’s reliability and supporting the development of more targeted and actionable recommendations.
Table 7 shows a chi-square value of 310.455 (df = 6), significant at the 0.05 level, indicating robust predictive capability. Each independent variable shows statistical significance at p < 0.001, confirming its meaningful contribution to the prediction model.
As shown in Table 8, the model reports an R-squared value of 0.597, indicating that approximately 59.7% of the variance in the dependent variable—individuals’ intentions to incorporate digital assets into their inheritance plans—is explained by the refined set of independent variables: digital-asset knowledge (score), gender, age, income, savings, and risk perception. This suggests that the model effectively captures a substantial proportion of the observed variation in individuals’ intentions to incorporate digital assets into their inheritance plans, highlighting the relevance of these key predictors within the Thai context.
Table 9 presents the evaluation of the predictive model through a classification table based solely on the statistically significant independent variables. The model is designed to predict individuals’ intentions to incorporate digital assets into their inheritance plans and to identify the key determinants influencing this decision within the Thai context. Using a 50% cut-off threshold, the model successfully classified approximately 90.3% of the cases in the back-testing dataset. This high classification rate highlights the model’s strong predictive performance and its effectiveness in capturing behavioral patterns relevant to digital-asset inheritance planning in Thailand.
The predictive regression equation of Model (2) using the coefficients from Table 10 can be described by the following equation:
P = 1 1 + e − z
where P is an individual’s intent to incorporate digital assets into their inheritance plans within Thailand, and Z = 0.873 + 0.496(score) + 1.490(gender) − 1.004(age) + 1.202(income) + 0.652(saving) − 0.697(risk).
Table 10 presents the results of a binary logistic regression model examining factors that influence individuals’ intention to incorporate digital assets into their inheritance plans in Thailand. Gender emerges as the strongest predictor (B = 1.490), with an odds ratio of 4.437, suggesting that males are over four times more likely than females to include digital assets in their inheritance planning. This substantial gender disparity warrants further investigation into potential sociocultural factors influencing this difference. Income level follows as the second strongest positive predictor (B = 1.202, Exp(B) = 3.325), indicating that for each unit increase in income category, the odds of planning digital-asset inheritance more than triple, highlighting the role of financial capacity in digital inheritance decisions. Furthermore, digital-asset knowledge (represented by “Score”) demonstrates a positive relationship (B = 0.496, Exp(B) = 1.643), suggesting that each unit increase in digital-asset knowledge increases the likelihood of digital-asset inheritance planning by approximately 64%. Similarly, saving behavior shows a positive association (B = 0.652, Exp(B) = 1.920), indicating that individuals with disciplined saving habits are nearly twice as likely to consider digital assets in their inheritance plans. Age exhibits a strong negative relationship (B = −1.004, Exp(B) = 0.366), revealing that older individuals are significantly less likely to incorporate digital assets into inheritance planning. This age-related resistance might reflect generational differences in technological adoption and digital-asset familiarity. Risk perception also shows a negative association (B = −0.697, Exp(B) = 0.498), indicating that individuals with higher risk perception are approximately half as likely to include digital assets in inheritance plans, likely due to perceived volatility or security concerns associated with digital assets.

5. Discussion

The study’s findings on key factors influencing individuals’ intentions to incorporate digital assets into inheritance planning in Thailand identified six significant factors—digital-asset knowledge, gender, age, income, saving behavior, and risk perception—that underscore the complex interplay among individual characteristics, evolving digital literacy, and the current legal vacuum. Together, these factors collectively influence individuals’ intentions—or lack thereof—to include digital assets in their inheritance plans. They explain approximately 59.7% of the variance in inheritance intentions, providing a robust predictive model.
Knowledge about digital assets emerged as a significant positive predictor (B = 0.496, Exp(B) = 1.643), indicating that each unit increase in knowledge increased the likelihood of digital-asset inheritance planning by approximately 64.3%. In line with this perspective, Steen et al. (2023) affirmed that digital-asset planning literacy—defined as an individual’s awareness of the management and disposition of their digital assets in the event of death or incapacity—is a critical component of contemporary financial and estate planning. Moreover, the relationship between knowledge and adoption intention supports previous research by Duangsin et al. (2023), who found that understanding NFTs significantly predicted adoption intentions. This result underscores the importance of educational initiatives to enhance public understanding of digital assets, particularly regarding their potential for wealth transfer and inheritance planning.
Gender emerged as the strongest predictor of digital-asset inheritance intentions (B = 1.490, Exp(B) = 4.437), with males being over four times more likely than females to consider digital assets for inheritance. This significant gap may stem from differences in risk tolerance, financial literacy, and technological confidence. Almenberg and Dreber (2015) suggest that gender disparities in financial literacy partly explain gaps in investment behavior, a trend likely relevant to digital assets. Similarly, Alonso et al. (2023) found that females were more skeptical about the security of cryptocurrencies and less willing to invest, while males showed greater understanding of blockchain technology and cryptocurrency transactions. These findings underscore the importance of gender-inclusive financial education and support in Thailand’s digital economy.
Age demonstrated a strong negative relationship with digital-asset inheritance intentions (B = −1.004, Exp(B) = 0.366), suggesting that older individuals are significantly less likely to incorporate digital assets into inheritance planning. This age-related resistance aligns with the technology adoption literature, which consistently identifies age as a barrier to new technology adoption (Venkatesh et al., 2003). The age effect may reflect generational differences in technological familiarity, risk perception, or trust in traditional financial institutions versus digital alternatives. Similar age effects have been documented in studies of cryptocurrency adoption (Bohr & Bashir, 2014; Stix, 2021), suggesting that this pattern extends across cultural contexts. As the digital-asset ecosystem matures, longitudinal research will be valuable to determine whether this represents a cohort effect that will diminish over time or a persistent age-related pattern.
Income level was the second strongest positive predictor (B = 1.202, Exp(B) = 3.325), indicating that higher-income individuals are substantially more likely to consider digital assets for inheritance. This pattern is consistent with traditional models of innovation diffusion (Rogers, 2003), which suggest that innovations often begin with higher socioeconomic groups before diffusing more broadly. In addition, the strong income effect may reflect several mechanisms: greater disposable income for experimental investments, better access to information and advisory services, or reduced sensitivity to potential losses. These findings align with research by Aiello et al. (2023) and Kasemrat and Kraiwanit (2023), who found that cryptocurrency adoption is positively associated with income and wealth. The implication is that, as digital assets become more accessible and understood, adoption for inheritance purposes may expand beyond the higher-income segments.
Saving showed a positive association with digital-asset inheritance intentions (B = 0.652, Exp(B) = 1.920), suggesting that individuals with disciplined saving habits are nearly twice as likely to consider digital assets in inheritance planning. This finding connects digital-asset adoption to broader patterns of financial behavior and planning. The relationship between saving discipline and digital-asset inheritance planning may reflect a general orientation toward future planning, as suggested by research on time preferences and financial decision-making. Individuals who regularly save demonstrate a concern for future financial outcomes, which may extend to an interest in innovative inheritance planning tools (Brounen et al., 2016; Lewis & Messy, 2012; Webley & Nyhus, 2006; Xie et al., 2023). This finding suggests potential synergies between traditional financial literacy programs focusing on saving behavior and emerging digital-asset education initiatives.
Risk perception showed a negative association with digital-asset inheritance intentions (B = −0.697, Exp(B) = 0.498), indicating that individuals with higher risk perception are approximately half as likely to include digital assets in inheritance plans. This finding aligns with behavioral finance theories that emphasize the role of risk perception in investment decisions. In line with Chaisiripaibool et al. (2025), Loukil et al. (2025), and Nafiu et al. (2025), the negative impact of risk perception may reflect awareness of digital-asset volatility, regulatory uncertainty, or security vulnerabilities. Similar concerns have been documented in studies of cryptocurrency adoption barriers (Qi et al., 2025). As the digital-asset regulatory environment matures and security measures improve, this barrier may diminish, but the findings suggest that addressing risk perceptions should be a priority for stakeholders promoting digital-asset adoption.

6. Conclusions

This research offers valuable insights into the factors influencing individuals’ intentions to incorporate digital assets into inheritance planning in Thailand. Key determinants such as gender, income, age, saving behavior, risk perception, and digital-asset knowledge significantly shape these intentions. The model’s strong predictive power underscores the relevance of these factors in understanding the shift toward digital inheritance. While over three-quarters of respondents acknowledge digital assets as viable tools for wealth transfer, the continued preference for traditional investments and widespread risk aversion suggest that the transition is still in its early stages. To support this evolving trend, targeted financial literacy programs are essential, particularly for underrepresented groups such as women and the elderly, to improve understanding of digital-asset opportunities, security, and associated risks. Simultaneously, clear and comprehensive legal frameworks are needed to define the status of digital assets; standardize procedures for documentation, transfer, and taxation; and reduce legal uncertainty. Nationwide financial literacy campaigns should be launched via television, radio, and digital platforms, focusing on digital-asset fundamentals, best security practices, and estate planning procedures. In addition, partnerships with educational institutions should be established to incorporate digital finance, asset management, and inheritance planning into secondary and tertiary curricula, particularly within business and law programs. Legal ambiguity remains a significant barrier to integrating digital assets into estate planning in Thailand. To address this, the government should enact or amend inheritance laws to explicitly recognize digital assets—such as cryptocurrencies, NFTs, and digital wallets—as part of an individual’s estate. These laws should include clear provisions for documentation, authentication, and transfer procedures. Additionally, regulatory bodies should collaborate with legal and technology experts to develop standardized and secure protocols for transferring digital assets to heirs. This may involve implementing digital wills and leveraging blockchain-based verification tools to ensure transparency, efficiency, and legal certainty. Financial institutions can use these insights to design tailored products such as secure storage solutions and inheritance-ready digital accounts, positioning themselves as leaders in the emerging digital inheritance space. Cross-sector collaboration among policymakers, educators, and financial service providers is critical to fostering trust, inclusivity, and equitable access to digital inheritance mechanisms. Public–private partnerships should be encouraged to drive innovation through secure digital platforms, including digital wills and blockchain-based verification systems. Lastly, continuous policy evaluation and coordinated efforts among regulatory, legal, and consumer protection agencies will be vital to building a responsive, secure, and future-ready inheritance ecosystem aligned with Thailand’s evolving digital economy.
While this study offers meaningful insights into the factors shaping individuals’ intentions to use digital assets for inheritance in Thailand, several limitations must be acknowledged. First of all, the use of a cross-sectional design restricts the ability to observe changes in attitudes or behaviors over time; future research should consider longitudinal approaches to assess how intentions evolve into actual practices. Additionally, the reliance on self-reported data may introduce social desirability bias or recall inaccuracies, suggesting the value of incorporating qualitative methods—such as interviews or case studies—to validate and enrich the findings. Moreover, the use of convenience sampling via LINE limits the representativeness of the sample, thereby constraining the generalizability of the results. Future studies would benefit from stratified random sampling or other probabilistic sampling techniques to enhance validity. The geographic focus on Thailand further limits the applicability of the findings to other contexts with differing regulatory systems, cultural norms, and levels of digital literacy. Comparative research across countries could offer broader insights into global digital inheritance trends. Lastly, future research should delve into psychological and socio-cultural dimensions—such as trust in digital technology, generational financial behaviors, and legal and inheritance-related cultural norms—to better understand the complexities influencing digital-asset integration into inheritance planning and to inform more inclusive and context-sensitive policies.

Author Contributions

Conceptualization, T.K. and P.L.; methodology, T.K. and P.L.; software, T.K. P.L. and S.S.; validation, T.K., P.L. and S.S.; formal analysis, T.K. and P.L.; investigation, T.K., P.L. and S.S.; resources, T.K., P.L. and S.S.; data curation, T.K. and P.L.; writing—original draft preparation, T.K., P.L. and S.S.; writing—review and editing, P.L.; visualization, P.L.; supervision, T.K. and P.L.; project administration, T.K. and P.L.; funding acquisition, T.K., P.L. and S.S. All authors have read and agreed to the published version of the manuscript.

Funding

This research was funded by the Research and Innovation Center of Pathumthani University, Thailand, grant number RIC68032.

Institutional Review Board Statement

The study was conducted in accordance with the International Conference on Harmonization—Good Clinical Practice (ICH-GCP) guidelines and received ethical approval from the Ethics Committee of Pathumthani University. Ethical clearance was granted under document number 007/2568 with the assigned project number 003/2568.

Informed Consent Statement

Informed consent was obtained from all subjects involved in the study.

Data Availability Statement

The data supporting the findings of this study are available from the first or corresponding author upon reasonable request.

Acknowledgments

The authors express their sincere appreciation to Pathumthani University and the Research and Innovation Center for their invaluable support and encouragement throughout the course of this research. The university’s resources, academic environment, and steadfast commitment to research excellence were instrumental in the successful completion of this study. During the preparation of this manuscript, the authors utilized the GPT-4o model to assist with language refinement and optimization of select sections. All content was subsequently reviewed and revised by the authors, who assume full responsibility for the final version of this publication.

Conflicts of Interest

The authors declare no conflicts of interest.

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Jrfm 18 00330 g001
Figure 1. The research flow diagram (source: authors).
Figure 1. The research flow diagram (source: authors).
Jrfm 18 00330 g001
Table 1. General data characteristics of the respondents.
Table 1. General data characteristics of the respondents.
General InformationFrequencyPercentage
GenderMale
Female		353
277 		56.0
44.0
AgeLess than 35 years old
35–44 years old
45–55 years old
Over 55 years old		270
143
196
21		42.9
22.7
31.1
3.3
StatusSingle
Married (with children)
Married (without children)
Divorced
Other		336
182
89
2
21		53.3
29.0
14.1
0.3
3.3
EducationLower than bachelor’s degree
Bachelor’s degree
Higher than bachelor’s degree		96
473
61		15.2
75.1
9.7
OccupationFreelancer
Government officer
Private company employee
Business owner
Student		15
213
125
43
234		2.4
33.8
19.9
6.8
37.1
IncomeLess than THB 15,000
THB 15,000–25,000
THB 25,001–35,000
THB 35,001–45,000
More than THB 45,000		144
102
151
158
75		22.9
16.2
24.0
25.0
11.9
SavingsTHB 1000–5000
THB 5001–10,000
THB 10,001–15,000
THB 15,001–20,000
More than THB 20,000		291
269
21
4
45		46.3
42.7
3.3
0.6
7.1
Total630100.0
Table 2. Digital assets as inheritance.
Table 2. Digital assets as inheritance.
Digital Assets as InheritanceFrequencyPercentage
No13922.1
Yes49177.9
Total630100.0
Table 3. Omnibus test of the model’s performance (all variables).
Table 3. Omnibus test of the model’s performance (all variables).
Chi-SquaredfSig.
Step 1Step318.65890.000
Block318.65890.000
Model318.65890.000
Table 4. The model summary (all variables).
Table 4. The model summary (all variables).
Step−2 Log LikelihoodCox and Snell R-SquaredNagelkerke R-Squared
1346.257 a0.3970.609
a. Estimation terminated at iteration number 6 because parameter estimates changed by less than 0.001.
Table 5. Classification table for back-testing (all variables).
Table 5. Classification table for back-testing (all variables).
Observed Predicted
Digital Assets as InheritancePercentage Correct
NoYes
Step 1Digital Assets as InheritanceNo885163.3
Yes1447797.1
Overall percentage 89.7
Note: The cut-off value is 0.500.
Table 6. Variables in the model (all variables).
Table 6. Variables in the model (all variables).
BS.E.WalddfSig.Exp(B)
Step 1 aScore0.5020.08633.87110.0001.652
Gender1.4850.38015.31010.0004.416
Age−1.4810.26032.45310.0000.227
Being single−0.6250.5581.25510.2630.535
Education0.4500.3581.57710.2091.568
Being a student−1.1350.6143.41810.0640.322
Income0.9450.19523.57610.0002.572
Savings0.5880.17511.31710.0011.800
Risk−0.4640.2065.07810.0240.629
Constance1.6131.1312.03610.1545.019
a. Variable(s) entered in step 1: score, gender, age, being single, education, being a student, income, savings, risk.
Table 7. Omnibus test of the model’s performance (only significant variables).
Table 7. Omnibus test of the model’s performance (only significant variables).
Chi-SquaredfSig.
Step 1Step310.45560.000
Block310.45560.000
Model310.45560.000
Table 8. The model summary (only significant variables).
Table 8. The model summary (only significant variables).
Step−2 Log LikelihoodCox and Snell R-SquaredNagelkerke R-Squared
1354.462 a0.3890.597
a. Estimation terminated at iteration number 6 because parameter estimates changed by less than 0.001.
Table 9. Classification table for back-testing (only significant variables).
Table 9. Classification table for back-testing (only significant variables).
Observed Predicted
Digital Assets as InheritancePercentage Correct
NoYes
Step 1Digital Assets as InheritanceNo885163.3
Yes1048198.0
Overall percentage 90.3
Note: The cut-off value is 0.500.
Table 10. Variables in the model (only significant variables).
Table 10. Variables in the model (only significant variables).
BS.E.WalddfSig.Exp(B)
Step 1 aScore0.4960.07939.22110.0001.643
Gender1.4900.34518.62810.0004.437
Age−1.0040.16935.40510.0000.366
Income1.2020.15559.99510.0003.325
Savings0.6520.15916.80910.0001.920
Risk−0.6970.16617.58210.0000.498
Constance0.8730.7311.42810.2322.394
a. Variable(s) entered in step 1: score, gender, age, income, savings, risk.
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MDPI and ACS Style

Kraiwanit, T.; Limna, P.; Suradinkura, S. DigitAal Asset Adoption in Inheritance Planning: Evidence from Thailand. J. Risk Financial Manag. 2025, 18, 330. https://doi.org/10.3390/jrfm18060330

AMA Style

Kraiwanit T, Limna P, Suradinkura S. DigitAal Asset Adoption in Inheritance Planning: Evidence from Thailand. Journal of Risk and Financial Management. 2025; 18(6):330. https://doi.org/10.3390/jrfm18060330

Chicago/Turabian Style

Kraiwanit, Tanpat, Pongsakorn Limna, and Supakorn Suradinkura. 2025. "DigitAal Asset Adoption in Inheritance Planning: Evidence from Thailand" Journal of Risk and Financial Management 18, no. 6: 330. https://doi.org/10.3390/jrfm18060330

APA Style

Kraiwanit, T., Limna, P., & Suradinkura, S. (2025). DigitAal Asset Adoption in Inheritance Planning: Evidence from Thailand. Journal of Risk and Financial Management, 18(6), 330. https://doi.org/10.3390/jrfm18060330

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