International Journal of Tourism Research最新文献

The present study aims to determine how tourism-related mobile games generate tourism visit intention. Drawing upon the technology acceptance model (TAM), the study seeks to find out the context-specific considerations that exert a greater influence on the intention to visit destinations associated with the mobile game. Using a quantitative approach, data from 263 respondents were collected in Malaysia via an online survey. Partial least-squares structural equation modelling was used to perform latent variable analysis. The findings show the significant effects of perceived ease of play and motivation to play on attitude and intention towards visiting the game destination. In particular, motivation to play is found to exert a greater effect on visit intention. Contradictory results about perceived enjoyment and the importance of motivation are also highlighted. This study adds to the literature that calls for the expansion of the TAM to include context-specific considerations to articulate tourism gamification behaviour and the (un)intended effect on tourism visit intention. Specifically, the importance of context to TAM is unveiled through the motivation to play games related to tourism on a mobile phone. In contrast, this study points to the negligible influence of perceived enjoyment of games on intention to visit.

Despite numerous studies on digital tools in tourism and hospitality, few quantitative investigations use an integrated set of these tools to enhance business success. This study examines the impact of digital tools on hospitality business performance, using data from 234 SMEs in Portugal's accommodation sector. Findings show that integrating tourist accommodation into business groups positively influences digital tool adoption. Higher investment in business digitalisation also positively affects tool adoption and overall performance. The most valued digital tools by Portuguese SMEs include updated websites, social networks, online store/reservation systems, and digital marketing. This study provides valuable insights for tourism marketers and business managers to enhance SMEs' competitiveness in the hospitality sector.

The accuracy of tourism demand (TD) prediction, essential for managing available resources in the tourism industry, still needs to be improved with the unreliability of traditional algorithms. This research proposes a deep learning methodology that combines the convolution neural network (CNN) and gated recurrent unit (GRU), efficiently predicting Vietnam's tourism demand. The Pearson correlation coefficients are performed to nominate the most appropriate feature inputs. The proposed algorithm is analyzed and evaluated with other benchmark approaches, comprising the recurrent neural network (RNN), the long short-term memory (LSTM), the GRU, and the CNN. The experiments prove that the developed hybrid algorithm could outperform previous methodologies in predicting TD in some of Vietnam's provinces. The proposed algorithm could provide satisfactory predictions for tourism demand with a supreme enhancement of 77.1% MSE, 37.4% validating MSE, 46.0% MAE, 20.6% validating MAE, 76.6% MAPE, and 90.3% validating MAPE comparing across deep learning benchmarks.

This study argues that the eSports live streaming viewers have a triple identity. It aims to investigate how eSports live streaming can turn streamers into consumers and potential eSports tourists from the aspect of viewer psychology and behavioral intentions. Based on 776 valid online questionnaires from eSports live streaming viewers, we use Stimulus-Organism-Response (SOR) theory to identify how their watching experience is influenced and what their behavior towards gifting streamers and purchasing tickets for offline eSports is. Using SmartPLS 4.0 to analyze causal relationships among variables, the research affirms that four specific live streaming stimuli positively affect telepresence and immersion, leading to increased gifting and purchase intentions. This article thus elucidates the psychological and behavioral mechanisms of eSports live streaming viewers, shedding light on their transition process into consumers and potential eSports tourists. The results provide valuable insights for eSports live streaming operators and demonstrate their contribution to eSports tourism.

Virtual tourism experiences (VTEs) have grown rapidly after COVID and are expected to grow further. Customer attitudes toward VTEs are therefore of interest. Attitude, in general, is known to have three components: cognitive, affective, and behavioral. Thematic details of attitudes toward VTEs have not yet been explored, motivating the current study. We use a mixed-method approach to explore latent themes within online reviews for VTEs. Latent Dirichlet allocation, used for topic modeling, is applied to 58,675 reviews from Airbnb online experiences. Next, we analyze 70 online reviews from Viator using thematic analysis. The primary themes that emerge are host appreciation, recommendation, hedonic experience, interactive aspects, social connections, and culture and learning. We discuss in detail how these themes relate to the tri-component model of attitude. Our study of post-purchase consumer attitudes provides new insights into the design, delivery, and marketing of VTEs which has academic as well as managerial implications.

This study introduces a novel approach to trail accessibility and difficulty rating tailored specifically for individuals, including those with mobility impairments, aiming to promote accessible trail tourism. Grounded in theoretical frameworks of inclusive outdoor recreation, equality, and complex adaptive systems, the proposed rating system incorporates criteria addressing physical access, terrain challenges, and amenities to accommodate diverse user needs. This study employed a sensor technology, called wheeled instrumentation sensor package (WISP) within the framework of the high-efficiency trail assessment process (HETAP) to develop a new quantitatively based trail classification scheme. A case study trail and two hypothetical synthetic data trails were used to demonstrate both the trail wheelchair accessibility binary classification approach and the proposed trail difficulty classification approach. This research pioneers trail accessibility and difficulty rating and underscores the importance of integrating accessibility considerations into trail management practices to create truly inclusive outdoor recreational experiences.

Hiking plays a significant role in experiencing linear heritage, and gaining insights into the spatiotemporal distribution of hikers' behavior is imperative for effectively utilizing heritage resources. This study employed GPS trajectory data to examine the spatiotemporal patterns of hiking behavior on the Great Wall in Beijing and investigate the utilization patterns of heritage resources in hiking tourism. The results revealed that: there were notable variations in trajectory quantity among different months with extended activity durations, early finishing times, and an absence of nighttime activities. Spatially, the trajectories exhibited dispersion and uneven distribution, leading to the underutilization of numerous heritage resources. The distribution of starting and ending points of trajectories demonstrated a substantial correlation with neighboring natural villages. Consequently, this study offers valuable insights for informing decision-making processes related to the development, construction, and optimization of the scenic area along the Great Wall and the planning and design of tourist routes.