Narrow Results

Tourism route planning significantly enhances the tourist experience by curating efficient and engaging itineraries. This strategic approach not only minimizes travel time but also highlights attractive destinations, ensuring that visitors fully enjoy their journey. Traditional tourism route designs often fail to personalize and optimize routes based on individual tourist preferences and constraints, resulting in suboptimal experiences. The study’s objective was to maximize tourism route design by employing a clustering algorithm to group tourist destinations based on proximity and tourist preferences, ultimately creating efficient personalized itineraries. Initially, the study collected data from various sources, including location coordinates, tourist preferences, and travel distances. The study proposed an Intelligent Artificial Rabbit Algorithm-driven Density-based Spatial Clustering (IARA-DSC) method that computes the shortest and most efficient routes between clustered destinations by effectively grouping them based on proximity and tourist preferences, optimizing travel itineraries, and enhancing overall tourist satisfaction. The IARA-DSC method for tourism route design systems resulted in more personalized and efficient routes. The model reduced travel time, improved tourist satisfaction, and optimized travel paths based on individual preferences compared to traditional methods. This study provides significant benefits for both tourists and tourism operators by enhancing travel experiences and optimizing resource utilization.

Prior empirical studies have employed various econometric estimation techniques to study the environmental effect of tourism demand. Prominently, these econometric modeling techniques implicitly assume that the environmental effect of tourism is symmetrical, which could sometimes be problematic. This study, therefore, utilized two econometric estimation techniques, namely, the Pesaran et al. (2001). Bounds testing approaches to the analysis of level relationships. Journal of Applied Econometrics, 16(3), 289–326) symmetric autoregressive distributed lag (ARDL) and Shin et al. (2014). Modelling asymmetric cointegration and dynamic multipliers in a nonlinear ARDL framework. In Festschrift in Honor of Peter Schmidt, pp. 281–314. New York: Springer) nonlinear ARDL (NARDL) estimation technique to disentangle the effect of tourism demand on carbon emissions in Australia. The results from the symmetric ARDL model reveal that tourism demand significantly increases carbon emissions in the long run, indicating that a 1% increase in tourism demand contributes to a 0.155% increase in carbon emissions in the long run. Contrarily, the NARDL model shows that a positive shock (an increase) in tourism demand reduces carbon emissions while a negative shock (a decrease) in tourism demand increases carbon emissions in the long run. From the NARDL estimate, a 1% increase in tourism demand is associated with a 0.220% decline in carbon emissions, while a 1% decrease in tourism demand increases carbon emissions by 0.250%. Therefore, I argue that carbon emissions depend not only on the size of tourism demand but also on the pattern — thus the increase and decline — of tourism demand. The implications of these results for policy are discussed.

This paper aims to examine the relationship between tourism and economic growth in China’s eight central provinces through the annual data from 1995 to 2019 using quantile-on-quantile approaches. Results show a positive relationship between tourism and economic growth for China’s eight central provinces considered with substantial variations across provinces and quantiles within each province. The weakest relationship was noted for Shanxi, possibly because of the limited importance of the tourism sector relative to other major economic activities in this province. For Heilongjiang, Hubei, Hunan and Jilin, the most pronounced relationship between tourism activities and economic growth was observed only during the period of a deep economic upturn. The main reason for the economic downturn is that economic development during the periods of severe acute respiratory syndrome, avian influenza, Middle East respiratory syndrome coronavirus and coronavirus disease 2019 pandemics impacted tourist arrivals. Important provincial-specific policy implications may be drawn from these findings.

The growth of technology and social media websites has increased the potential to online explore different products and places around the globe. While online websites are primarily responsible for the generation of large amounts of data, this big data may be beneficial to other users provided the proper decision pattern can be analyzed. This work is focusing on the big data from social media to determine the travel destination preferences for Indian tourists. The analysis of online tourism reviews is beneficial to both tourists and businesses in tourist countries. Tourists can analyze all the required aspects prior to traveling and businesses in the destination country can enhance their products. The study aims to analyze the online tourist reviews using supervised machine learning methods (decision tree, k-nearest neighbor, back propagation neural networks and support vector machine) and ensemble learning in order to ascertain the travel preferences of Indian tourists visiting other countries. For the research experiments, significant travel data histories of tourists for the five destination places (Dubai, Indonesia, Malaysia, Thailand and Singapore) are extracted from TripAdvisor. TripAdvisor is a worldwide popular tourism website that provides access to consumers to share their travel experiences. From the selected five destination places, the preferences of Indian tourists are analyzed for the factors of travel & destination comfort, hotel facilities, food quality and attractions of the place. The analysis results of the proposed recommendation system indicate the determination of precise suggestions for Indian tourists traveling to other countries.

The paper empirically examines the nexus between tourism and peace. To do so, it uses in the analysis the recently developed composite Global Peace Index. The sample employed consists of 113 countries and covers the period 2008–2014. The methodology adopted includes PVAR Granger causality tests and impulse response functions. The sample was split into two income groups to allow for the possibility that the nexus differs between developed and developing countries. Findings reported herein indicate a temporary, very short-term adverse effect on tourism as a result of worsening levels of peacefulness only for the former group of countries. A peace promoting effect by tourism is established in the case of the latter group.

The purpose of this paper is threefold: to assess the performance of 55 international tourist hotels in Taiwan in 2001 in terms of managerial, occupancy, and catering efficiencies; to analyze hotel operating characteristics, which might explain the variation in managerial efficiencies across these hotels; and to measure productivity growth in the 34 international tourist hotels over the years 1990–2001. Empirical results indicate that (1) the marketing for lodging services was not operated efficiently in 2001; (2) the hotels operated poorly both at the levels of occupancy and catering efficiencies in 2001; (3) there is a weak tendency for a hotel with relative high catering efficiency to go with good occupancy efficiency; (4) differences in operating variables, such as the floor space of catering department, the number of guest rooms, the closeness of a hotel to CKS international airport, and the number of employees do have a significant influence upon hotel performance; and finally, (5) about 61.76% of hotels had annual productivity changes over time.

There is a class of complex problems where solutions must satisfy multiple subjective criteria, while meeting specific quantifiable constraints. Route planning for leisurely travel is an example of a problem in this class. Constraints including total available time, transit times, and one's budget and subjective interests determine whether a potential solution is acceptable to a prospective traveler.

In this paper we present a route planning (routing) interface that metaphorically leverages various elastic properties of a rubber band to allow for playful interaction with the relevant constraints. Each of these properties — attenuation, tension, and color — were integrated into an experimental system and then investigated in a series of task-based evaluations.

Our research shows this playful interaction enables potential travelers to explore the solution space in order to find a route that meets, not only the easily quantifiable constraints, but also their own subjective preferences.

e-Tourism is a tourist recommendation and planning application to assist users on the organization of a leisure and tourist agenda. First, a recommender system offers the user a list of the city places that are likely of interest to the user. This list takes into account the user demographic classification, the user likes in former trips and the preferences for the current visit. Second, a planning module schedules the list of recommended places according to their temporal characteristics as well as the user restrictions; that is the planning system determines how and when to realize the recommended activities. Having the list of recommended activities organized as an agenda (i.e. an executable plan), is a relevant characteristic that most recommender systems lack.

In this work, multifractal methods have been successfully used to characterize the temporal fluctuations of daily Jiuzhai Valley domestic and foreign tourists before and after Wenchuan earthquake in China. We used multifractal detrending moving average method (MF-DMA). It showed that Jiuzhai Valley tourism markets are characterized by long-term memory and multifractal nature in. Moreover, the major sources of multifractality are studied. Based on the concept of sliding window, the time evolutions of the multifractal behavior of domestic and foreign tourists were analyzed and the influence of Wenchuan earthquake on Jiuzhai Valley tourism system dynamics were evaluated quantitatively. The study indicates that the inherent dynamical mechanism of Jiuzhai Valley tourism system has not been fundamentally changed from long views, although Jiuzhai Valley tourism system was seriously affected by the Wenchuan earthquake. Jiuzhai Valley tourism system has the ability to restore to its previous state in the short term.

A person’s preference to select or reject certain meals is influenced by several aspects, including colour. In this paper, we study the relevance of food colour for such preferences. To this end, a set of images of meals is processed by an automatic method that associates mood adjectives that capture such meal preferences. These adjectives are obtained by analyzing the colour palettes in the image, using a method based in Kobayashi’s model of harmonic colour combinations. The paper also validates that the colour palettes calculated for each image are harmonic by developing a rating model to predict how much a user would like the colour palettes obtained. This rating is computed using a regression model based on the COLOURlovers dataset implemented to learn users’ preferences. Finally, the adjectives associated automatically with images of dishes are validated by a survey which was responded by 178 people and demonstrates that the labels are adequate. The results obtained in this paper have applications in tourism marketing, to help in the design of marketing multimedia material, especially for promoting restaurants and gastronomic destinations.

The paper presents an electronic tourist guide, relying on an evolutionary optimizer, able to plan personalized multiple-day itineraries by considering several contrasting objectives. Since the itinerary planning can be modeled as an extension of the NP-complete team orienteering problem with time windows, a multiobjective evolutionary optimizer is proposed to find in reasonable times near-optimal solutions to such an extension. This optimizer automatically designs the itinerary by aiming at maximizing the tourists’ satisfaction as a function of their personal preferences and environmental constraints, such as operating hours, visiting times and accessibility of the points of interests, and weather forecasting. Experimental evaluations have demonstrated that the proposed optimizer is effective in different simulated operating conditions.

In contemporary Tourism industry, DMOs are necessary to adopt and offer innovative experience in order to attract contemporary tourists. Gamification, combined with Augmented Reality and all the relevant to this technology innovations are examined in this paper. Efforts should rely on the demands and needs of generation Z tourists as up and coming generation. However, there are specific implications such as the use of augmented reality smart glasses, incentives and personal data protection that occur. This paper contributes in understanding the new Generation called “Z”, under the light of tourism and the effective use of augmented reality for tourism purposes, combined in one travel-product experience.

The paper’s goals are to comprehend strategies for tourism destinations after the coronavirus. The research question (RQ) is if the digital platform experience of a leading country in wine tourism can help to overcome COVID-19 and to turn the sector. The general topics related to destinations, specifically Italian wine tourism destinations, have been elaborated on in the introduction. Starting with a literature review relating to the destination tourism crisis in the tourism sector, the paper highlights the potential and basic lessons for coping with the current crisis. The method, which is connected to a wine tourism destination in Italy, emphasizes both qualitative and quantitative approaches. The results demonstrate the best practices as well as the bottlenecks. The discussions would result in the creation of strategic alternatives specific to the sector and its destinations. The study limits, considering bottlenecks that might arise in the future. The findings emphasize that the sector requires substantial funding from information and communication technology (ICT) to build smart destinations.

In recent years, markets have been hit by various crises, both economically and naturally. These shocks have highlighted how important it is for companies to be able to adapt with resilient behaviors. Entrepreneurs and their resilience capacity will play a very important role in the recovery process. Resilience is closely linked to other abilities such as risk intelligence; that is, the ability to look at uncertainty as an opportunity rather than a disadvantage or danger. The study assesses the relationship between these two constructs using a sample of entrepreneurs in the tourism sector within Italy. Analysis confirmed a relationship between subjective risk intelligence and resilience. These relations are explained by taking into consideration four aspects (imaginative capacity, problem-solving self-efficacy, attitude toward uncertainty and emotional stress management) included in risk intelligence, highlighting which of them contributed to explaining the three dimensions of the resilience construct (hardiness, resourcefulness and optimism).

Structural change towards environmentally benign service industries is widely considered a key strategy for sustainable development. However the environmental impact of service industries is not negligible. This paper analyses the environmental impact of Japanese leisure and tourism. The methodological problems and pitfalls are discussed and a new methodological framework is elaborated. The results suggest that leisure and tourism are responsible for 17% of the national greenhouse gas emissions, and 13% of the national primary energy use. Also a considerable part of the national land use is affected by leisure and tourism. The impact on biodiversity is hard to quantify because of inadequate monitoring systems, reference definition problems and interacting causes of environmental pressure. The environmental impact diverge widely for different types of leisure and tourism activities. Given these results, the sustainability of economic growth based on leisure and tourism should be analysed more carefully.

The purpose of this study was to determine the feasibility of using quality of life assessments (QLA) to evaluate social sustainability and impacts of a hypothetical tourism development modelled after the currently-proposed Jumbo Glacier Resort. Results of this study indicated that there was a significant difference between the pre- and post-development scenarios on respondents' perceived quality of life: respondents perceived that their quality of life would be lower after the development of the resort. Also, respondents' general attitudes towards tourism development, and the specific Jumbo Glacier Resort project, had a statistically significant impact on their expected quality of life, and their interpretation of how tourism impacts their quality of life. The study suggests that quality of life assessment can make valuable contributions to the fields of social impact assessment and social sustainability analysis, and the results of such assessments can make valuable contributions to the fields of sustainable community development.

Beaches are multidimensional environments, and their management must include the ecological, sociocultural and economic aspects. The continuous occupation of this ecosystem combined with the scarcity of adequate management plans has reduced the quality of coastal sceneries. Therefore, strategies are needed to ensure the perpetuity of resources and delivery of ecosystem services. The objective of this study was to assess the coastal scenery quality at three sandy beaches of Rio de Janeiro, Brazil. A quali-quantitative approach was applied using measurable aspects, considering eight categories as accessibility, water quality, scenic quality, infrastructure, safety and environmental education, based on 67 indicators. The beaches commonly presented higher values for water quality, while environmental education had the lowest ranks, indicating that this category should be prioritised in management strategies. In addition to the low cost of this effective tool for beach management, it is quick to apply, easy to analyse and represents an advancement in important issues about the use of integrative indicators to evaluate coastal sceneries, providing a scientific base that can offer evidence about the main management priorities in areas where coastal tourism has a significant role.

Seas, marine, and coastal regions are integral and essential parts of our ecosystem. Many scientific approaches have been taken to ensure the sustainable use of marine resources. Artificial intelligence (AI) plays a vital role in harvesting resources so that the system regenerates itself for the long term. This paper develops a two-input and two-output fuzzy logic-based model to predict the fisheries’ remaining biomass after harvesting and maintaining a high revenue level in the Bangladesh Sundarbans region. Fishing & tourism are taken as input parameters, and revenue & biomass are taken as output parameters. A total of 20 rules (IF-THEN type) have been generated in the fuzzy rule editor of Fuzzy Inference System (FIS), considering all possible combinations between input–output parameters. The data which we obtained from the real ecosystem exactly corresponds to the results that we got from our proposed model. Our fuzzy logic model yields valid predictions of the remaining biomass level without compromising profit, only by controlling the harvesting and tourist entry.

This study examines the effect of weather on outdoor recreation and specifically leisure farms in Taiwan. We find that temperature has a complex effect on visits, revenues, and occupancy rates. Cool temperatures are the least desirable, very cold temperatures and warm temperatures are the most beneficial, and hot temperatures are less beneficial. Sites designed for winter activities benefit the most from cold temperatures whereas sites designed for walking and boating do best in warm temperatures. Global warming is likely to harm the sites suited for winter activities but should prolong the outdoor season for most leisure farms in Taiwan. But leisure farms may want to find ways to cope with hot temperatures.