In the realm of international trade, the utilization of letters of credit (L/Cs) has historically served as a pivotal mechanism for payment settlement. By incorporating a third party-a bank-L/Cs facilitate the establishment of trust and mitigate transactional risks, thereby alleviating apprehensions concerning payment settlements. However, the prevalence of L/C based transactions has been on a decline, attributed to inherent limitations associated with the L/C system itself. Despite this trend, the dynamic nature of international trade continuously introduces new parties lacking established credit, thereby sustaining a persistent demand for L/C transactions. The onset of the Fourth Industrial Revolution marks a significant milestone, with blockchain technology emerging as a key innovation. Its potential to revolutionize L/C transactions is grounded in its distinctive features such as decentralization, enhanced security, transactional transparency, expedited processing, and cost efficiency. These attributes are poised to address longstanding challenges inherent in traditional L/C transactions. Nonetheless, existing literature reveals a gap in understanding the specific factors that influence the integration of blockchain technology into L/C transactions. Research has predominantly focused on broader applications of blockchain in various facets of international trade and its implications within legal and institutional frameworks, often overlooking the intricacies of blockchain adoption in L/C contexts. This study aims to bridge this gap by hypothesizing a research model based on the extension of the Unified Theory of Acceptance and Use of Technology (UTAUT). It posits Performance Expectancy(PE), Effort Expectancy(EE), Social Influence(SI), Facilitating Conditions(FC), and Cost Value(CV) analysis as independent variables, with the Adoption Intention(AI) of blockchain in L/C transactions as the dependent variable. Furthermore, the model introduces Organizational Attitude(OA) as a mediating factor. The validation of this model was conducted through empirical research, utilizing data collected from various import-export enterprises. Employing Partial Least Squares Structural Equation Modeling (PLS-SEM) for data analysis, the study unearthed insightful correlations. PE, SI, and CV demonstrated statistically significant positive impacts on OA. Conversely, while FC exhibited a significant positive correlation with the AI, EE did not show a notable correlation with OA. These empirical findings not only enrich the academic discourse on technology adoption in international trade but also provide practical insights for industry practitioners and policy-makers. This comprehensive approach underscores the nuanced interplay of various factors influencing the adoption of innovative technologies like blockchain in the complex landscape of international trade finance.