Digital Asset Adoption in Inheritance Planning: Evidence from Thailand
Bibliographic record
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.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".