Factors Influencing International Digital Nomads’ Decision to Choose Bangkok as a Workation Destination

Abstract

This study examined the decision-making factors influencing foreign digital nomads in selecting Bangkok as a work-and-travel destination. The objectives were to 1) investigate the behavior of foreign digital nomads visiting Bangkok, 2) examine the motivational factors influencing their intention to return for work-and-travel purposes, and 3) analyze the decision-making factors involved in selecting Bangkok as a destination. This research employed a quantitative approach, with data collected from 400 respondents via questionnaires, using convenience sampling. Data were analyzed using a t-test, one-way ANOVA, and chi-square tests at the 0.05 significance level. The results indicated that the majority of respondents were male (54.75%), aged 30–49 years (53.00%), working in the technology sector (41.75%), and earning an average monthly income of USD 2,001–3,000 (40.75%). Most respondents were from the Americas or Europe. In terms of travel behavior, most had visited Bangkok more than three times (51.50%) and stayed for more than three months (59.50%). Preferred locations were business and lifestyle districts such as Thonglor, Ekkamai, Silom, and Sathorn (38.50%), with preferred accommodation types including hotels (37.50%) and apartments (36.50%). Key motivational factors for destination selection included LGBTQ+ friendliness (M = 4.44), country image (M = 4.42), and health and safety (M = 4.40), while nightlife (M = 4.43), acceptance of racial diversity (M = 4.31), and cost value (M = 4.26) were identified as primary drivers of revisit intention. However, air quality (M = 3.62), traffic safety (M = 3.68), and English language proficiency (M = 3.70) were identified as notable constraints. Chi-square analysis revealed that gender, age, income, and place of residence were significantly associated with selected motivational factors at the 0.05 significance level. Specifically, gender was associated with food and service factors, age with cultural adaptability, income with climate conditions, and place of residence with technological infrastructure and LGBTQ+ friendliness.