The improper disposal of litter by tourists poses a significant threat to tourism destinations worldwide, including in Indonesia. To mitigate marine litter, promoting eco-friendly behavior (EFB) among tourists is essential. This study applies the extended Theory of Planned Behavior (TPB), which posits that an individual’s behavior is driven by their attitudes, subjective norms, and perceived behavioral control, to better understand the factors influencing eco-friendly behavioral intentions. In this research, ecological consciousness and ecological knowledge were added to the traditional TPB framework to gain deeper insights into tourist behavior. Data were collected through a structured questionnaire from 876 visitors to Lake Singkarak, Indonesia. The findings demonstrate that the inclusion of ecological consciousness and ecological knowledge significantly enhances the predictive power of the TPB model in explaining eco-friendly behavioral intentions. Based on these results, raising public awareness, improving government management, and enhancing the quality of lake attractions are recommended to encourage responsible tourism. These measures can reduce litter and conserve lake habitats, ultimately contributing to the sustainability of tourism in the region.

This study aims to analyze connectivity or accessibility between regions in Wakatobi islands, both within and between islands, to understand the available transportation network. Based on an understanding of the dynamics of connectivity, it is expected to provide a solid foundation for the development of more efficient and sustainable transportation infrastructure in the future. A combination of qualitative and quantitative approaches is used to explore data more comprehensively and accurately. The two primary airports and several ports are still insufficient in enhancing connectivity for both the residents and tourists within the archipelago. Improving road, sea, and air transportation networks is a necessity and expectation to improve connectivity between regions. An analysis of accessibility potential provides an overview of transportation costs and expensive and long travel fares. There are several needs that need to be met in the form of the revitalization of local ports, the development of the concept of Air Buses between crossing ports, optimizing routes between airports, and the implementation of Bus/BRT (Bus Rapid Transit) on each island with feeder lines. Furthermore, the development of connectivity in Wakatobi must consider various alternative modes of transportation, increasing service frequencies, and developing supporting infrastructure. This conclusion is the basis for the preparation of a holistic and sustainable connectivity development plan in the Wakatobi archipelago.

The coronavirus pandemic has reinforced the need for sustainable, smart tourism and local travel, with rural destinations gaining in their popularity and leading to increased potential of smart rural tourism. However, these processes need adjustments to the current trends, incorporating new transformative business concepts and marketing approaches. In this paper we provide real life examples of new marketing approaches, together with new business models within the context of the use of new digital technologies. Via hermeneutic research approach, consisting of the secondary analysis of the addressed subject of smart rural tourism in adversity of the COVID-19 and 6 semi-structured interviews, the importance of technology is underscored in transforming rural tourism to smart rural tourist destinations. The respondents in the interview section were chosen based on their direct involvement in the presented examples and geographical location, i.e. France, Slovenia and Spain, where presented research examples were developed, concretely within European programmes, i.e. Interreg, Horizon and Rural Development Programme (RDP). Interviews were taking place between 2022 and 2023 in person, email or via Zoom. This two-phased study demonstrates that technology is important in transforming rural tourism to smart tourist destinations and that it ushers new approaches that seem particularly useful in applying to rural areas, creating a rural digital innovation ecosystem, which acts as s heuristic rural tourist model that fosters new types of tourism, i.e. smart rural tourism.

Night tourism, increasingly recognized as integral to the travel experience, has gained attention for its impact on overall tourist satisfaction. This article offers a comprehensive analysis of night tourism development in Vietnam’s coastal cities, focusing on Nha Trang and Quang Ngai, as representative cases of mature and emerging destinations, respectively. Utilizing the Importance-Performance Analysis (IPA) tool, the study aims to provide practical insights for sustainable night tourism. Surveys with 524 domestic tourists were conducted to evaluate perceptions and satisfaction levels. Nha Trang emphasizes accessibility and vibrant nightlife, with a focus on the night market and outdoor shows. Conversely, Quang Ngai highlights its night landscape, dining options, and shopping areas. Recommendations for both destinations include enhancing entertainment offerings and reassessing priorities based on tourist preferences. The study underscores the need for tailored strategies to foster sustainable night tourism development that aligns with evolving tourist demands in coastal cities like Nha Trang and Quang Ngai.

Rural tourism plays a crucial role in rural development in Indonesia by providing employment opportunities, livelihood, infrastructure, cultural preservation, and environmental preservation. However, it is prone to external shocks such as natural disasters, public health events, and volatility in the national and global economy. This study measures the resilience of rural tourism to external shocks caused by the COVID-19 pandemic in 24 rural tourism destinations in Indonesia covering four years from 2019 to 2022. A synthetic composite index of the Adjusted Mazziotta-Pareto index (AMPI) is used to measure rural tourism resilience followed by clustering analysis to determine the typology of the resilience. The AMPI measure is also compared with the conventional Mazziotta-Pareto index (MPI) method. The resilience index is composed of capacity and performance components related to resilience. The results show that in the first year of COVID-19, most tourism villages in Indonesia were severely affected by the pandemic, yet they were able to recover afterward, as indicated by positive differences in the AMPI index before and after COVID-19. Thus, rural tourism villages in Indonesia have a strong capacity and performance to recover from pandemic shock. Lessons learned from this analysis can be applied to policies related to rural tourism resilience in developing countries.

Introduction: With the adoption of the rural rehabilitation strategy in recent years, China’s rural tourist industry has entered a golden age of growth. Due to the lack of management and decision-support systems, many rural tourist attractions in China experience a “tourist overload” problem during minor holidays or Golden Week, an extended vacation of seven or more consecutive days in mainland China formed by transferring holidays during a specific holiday period. This poses a severe challenge to tourist attractions and relevant management departments. Objective: This study aims to summarize the elements influencing passenger flow by examining the features of rural tourist attractions outside China’s largest cities. Additionally, the study will investigate the variations in the flow of tourists. Method: Grey Model (1,1) is a first-order, single-variable differential equation model used for forecasting trends in data with exponential growth or decline, particularly when dealing with small and incomplete datasets. Four prediction algorithms—the conventional GM(1,1) model, residual time series GM(1,1) model, single-element input BP neural network model, and multi-element input BP network model—were used to anticipate and assess the passenger flow of scenic sites. Result: The multi-input BP neural network model and residual time series GM(1,1) model have significantly higher prediction accuracy than the conventional GM(1,1) model and unit-input BP neural network model. A multi-input BP neural network model and the residual time series GM(1,1) model were used in tandem to develop a short-term passenger flow warning model for rural tourism in China’s outskirts. Conclusion: This model can guide tourists to staggered trips and alleviate the problem of uneven allocation of tourism resources.