Unveiling the Path to Mobile Payment Adoption: Insights from Thai Consumers
Bibliographic record
Abstract
Mobile payment, replacing traditional methods like cash and cards, offers users convenience and accessibility, benefiting individuals, businesses, and governments. However, most research on mobile payment adoption has primarily focused on developed countries, leaving a gap in understanding the adoption factors in developing nations. This study addresses this gap by investigating the determinants of mobile payment adoption in Thailand, an emerging economy experiencing significant smartphone adoption and e-commerce growth. Through a quantitative approach and a survey of 475 Thai consumers, this research applies an extended Unified Theory of Acceptance and Use of Technology (UTAUT) model as a theoretical foundation to examine Thai consumers’ mobile payment adoption. Data analysis using SPSS 28.0 and AMOS 28.0 identifies key factors influencing Thai consumers to adopt mobile payment. By offering a comprehensive research model and considering evolving smartphone technology, this study aims to guide policymakers and stakeholders in promoting mobile payment adoption, ultimately enhancing Thailand’s economic development and tourism industry.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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".