Hsinchu, known as the Silicon Valley of Taiwan and the 'Windy City', presents a paradox: a globally significant technology hub that remains largely undiscovered by international tourists compared to Taipei and Kaohsiung. This study examines two dominant barriers that impede international tourists from independently exploring Hsinchu's iconic attractions—namely, the complexity of public transportation systems and the pervasive language constraints arising from Chinese-language dominance in public signage, ticketing systems, and service encounters. Employing an interpretive qualitative methodology, this research conducted in-depth semi-structured interviews with 24 international tourists from 14 countries who visited Hsinchu between August and November 2024. Data were analysed using Braun and Clarke's (2006) six-phase reflexive thematic analysis, yielding four overarching themes: (1) navigational disorientation in multi-modal transit systems; (2) communicative exclusion at transport interfaces; (3) cumulative anxiety and destination avoidance; and (4) adaptive coping strategies and technological mediation. The findings reveal that language barriers and transportation complexity function not as isolated inconveniences but as mutually reinforcing constraints that produce compounded tourist anxiety and significantly reduce destination reach, particularly for non-Chinese-speaking independent travellers. Theoretical and practical implications are discussed, with particular attention to destination marketing, smart tourism infrastructure, and multilingual accessibility frameworks. The study contributes to growing scholarship on under-researched secondary cities in East Asia and advances understanding of inclusive tourism development in language-complex destinations.
The development of digital technology encourages changes in learning methods in vocational education, especially in the field of tourism and hospitality which focuses on practical competencies. However, pre-practicum learning is still dominated by conventional methods so that it is not able to provide a learning experience that resembles real working conditions. This research aims to design and develop a Virtual Reality (VR)-based Smart Tourism and Hospitality learning system as an immersive learning medium to increase student readiness before participating in practicum activities. The research uses a Research and Development (R&D) approach through the Software Development Life Cycle (SDLC) model with the Waterfall method which includes the stages of needs analysis, system design, implementation, testing, and implementation. Needs analysis was carried out through the dissemination of Questioners, interviews, and discussions with lecturers and laboratories of the Hospitality and Travel Study Program. The results of the study show that students need learning media that is able to realistically simulate the work environment of the Front Office Hotel and tour guide activities. The prototype developed integrates scenario-based simulation and decision-based learning approaches so that students gain learning experience before carrying out the practicum directly. The development of this system supports the implementation of the Smart Laboratory concept while strengthening digital transformation in tourism vocational education through increasing learning effectiveness, practicum efficiency, and student competency readiness.
This study aims to analyze the effect of Generative Artificial Intelligence (AI) utilization on middle manager performance in the digital creative industry of Malang City using the Technology-to-Performance Chain (TPC) approach. Specifically, this study examines how Task Characteristics and Technology Characteristics influence Task-Technology Fit (TTF), and their subsequent impact on manager performance through Utilization as a mediating variable. The research method employed is quantitative cross-sectional. Data were collected via questionnaires from 182 middle managers and analyzed using Partial Least Squares Structural Equation Modeling (PLS-SEM). The results indicate that complex task characteristics and reliable AI technology characteristics significantly influence the formation of Task-Technology Fit. Furthermore, TTF is proven to have a positive and significant effect on middle manager performance, both directly and indirectly through utilization as a mediator. These findings indicate that the fit between Generative AI capabilities and managerial task requirements drives more intensive technology utilization, which ultimately enhances efficiency and decision-making quality.
This study focuses on the digital literacy of Tamansari tourism village business actors in building and developing community-based tourism villages. This research was conducted qualitatively by conducting focus group discussions (FGD) with 14 people consisting of representatives of the sub-district/village government and tourism village actors in Tamansari, Pasir Eurih, and Sukajadi. The results of the study show that digital literacy and fluency in tourism village marketing communication practices are divided into four categories. The first category is filled by tourism village actors who have digital fluency at the level of being able to utilize social media, especially Facebook, to promote tourism villages simply because it is still done manually. The second category is filled by tourism village actors who have digital fluency at the level of learning content editing practices. The third category is filled by tourism village actors who have digital fluency at the level of being able to create photo uploads that have been equipped with information about price descriptions and business addresses of products and/or services related to tourism village. The fourth category is filled by village tourism actors who possess digital fluency, capable of using multiple social media platforms simultaneously, implementing strategies for managing upload times and coordinating online with other village tourism business actors.
This study aims to analyze the influence of workload, motivation, and time management on employee performance with job satisfaction as a mediating variable in the modern retail industry. This research employed a quantitative explanatory approach using survey data collected through the distribution of structured questionnaires with a five-point Likert scale to employees in modern retail companies. The data were analyzed using Structural Equation Modeling (SEM) to examine the direct and indirect relationships among variables. The results indicate that workload and time management have a positive and significant effect on job satisfaction, while motivation does not significantly affect job satisfaction. Furthermore, time management and job satisfaction have a positive and significant effect on employee performance, whereas workload and motivation do not have a direct effect on employee performance. Job satisfaction was found to fully mediate the relationship between workload and employee performance and partially mediate the relationship between time management and employee performance. The findings highlight the importance of workload management and improving employees’ time management skills in enhancing employee performance in the modern retail industry.
