PSYCHOLOGICAL MECHANISMS OF DIGITAL TOURISM TRAINING: MOTIVATION, COGNITIVE LOAD, AND MENTAL HEALTH IN TALENT DEVELOPMENT

Abstract Objective This study investigates the integration of psychological principles—namely Self-Determination Theory (SDT) and Cognitive Load Theory (CLT)—within digital training frameworks aimed at tourism talent development. It focuses on how such integration enhances learning motivation, optimizes cognitive load regulation, and mitigates mental health issues including chronic stress and decision fatigue among students. Subjects and Methods A mixed-methods design was adopted, incorporating quantitative and qualitative approaches. The quantitative strand involved surveying 386 vocational students enrolled in digital tourism programs using validated instruments—SDT-derived motivation scales and the NASA-TLX for cognitive load—along with measures for stress and fatigue. In-depth qualitative interviews were conducted with 20 industry and educational experts to gain contextual insights into cognitive and emotional challenges in digital learning environments. Results Analysis revealed that training designs stimulating intrinsic motivation led to a 32% increase in task efficiency and significantly reduced cognitive overload (β = –0.41, p<.01). Autonomy-supportive methods were found to mediate decision fatigue effectively (mediation effect: 58%, F = 12.73, p<.001). In contrast, environments dominated by extrinsic pressure raised cognitive strain, resulting in a 24% decrease in task retention. Furthermore, gamified mindfulness interventions substantially alleviated anxiety by 19% and improved knowledge retention by 37%. Conclusions These findings underscore the importance of embedding psychological principles into digital pedagogy to support both cognitive performance and mental well-being. Effective balancing of motivational elements and cognitive load is crucial for sustaining mental health and enhancing educational outcomes in tourism training. Further cross-cultural validation is recommended to generalize the applicability of these results.