Narrow Results

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.

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.

This chapter examines the existing studies of the relationship between inbound tourism and economic growth. After a brief discussion of general economic growth theory and the reasons why a positive causal relationship may exist between export sectors and economic growth, it then delves into the theoretical basis for an inbound tourism-led growth relationship. Following this, the relatively new empirical literature on the causal relationship between tourism and growth is examined in detail. Both theoretical and empirical evidence to date suggest that there is good reason to believe that inbound tourism promotion can lead to increased economic growth, but the literature is still relatively narrow. Deeper research into the specific mechanisms behind this relationship is needed to inform our understanding of the true growth impacts of export tourism expansion.

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 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 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.

The environmental problem has become a worldwide issue that must be frequently examined. Thus, this study investigates the relation of tourism, natural resources, globalization, and foreign direct investment on the ecological footprint of the USA, the UK, Pakistan, China, and India by using annual data from 1995 to 2020. The study applied Zivot and Andrews (1992) test to demonstrate the stationarity properties of the series, and an autoregressive distributed lag (ARDL) approach was employed to estimate the long-run and short-run dynamics. The long-run findings disclose that tourism has a significantly positive effect on China and Pakistan, but a significantly negative impact on India. The outcomes further suggest that foreign direct investment increases environmental degradation in India and China in both long and short periods, but contributes to improving the UK’s ecosystem. Besides, globalization in China tends to save its environment. Natural resources in the long run also harm the ecosystems of Pakistan, China, and the USA. The error correction term is negatively significant for all countries. The reliability of the model is investigated through diagnostic tests. The selected model is stable as the critical value of CUSUM and CUSUMSQ lie within the 5% significance level.

Tourism as an economic activity in Goa hardly existed before the 1960s. There were limited tourist facilities, either in the form of accommodation or other amenities. After its independence, the government initiated a programme for rapid expansion of accommodation facilities and related tourist services, and beach tourism was adopted as a key sector for Goa's development. As a result, Goa became a major international tourist destination. The tourism industry has contributed significantly to the economic development of the territory, but also to the transformation of Goa's coastal areas. This chapter discusses the development of Goa as a tourist destination, examining the economic, socio-cultural and environmental impacts of such tourism development.

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.

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.

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.

Drawing on expenditure and survey data from the Gold and Sunshine Coasts in Queensland, Australia, this chapter compares expenditures on beaches relative to their recreational benefits. Beaches are found to be exceptional investments. The comparison of the two councils also provides insights into their relative capacity to adapt to the adverse impacts of climate change. The Gold Coast can rely to some extent on historical large investments in infrastructure to defend itself against change. In contrast, the Sunshine Coast has more options which may lower the cost of adaptation e.g., it can rely more heavily on retreating from change in certain locations because of historical investment in dunal buffer zones. However, historical investment patterns impact in different ways on the environmental quality of beaches and the benefits provided to users and non-users. Limitations and areas of future research are also outlined.

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.

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 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).

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.

This study examines the impact of growing political unrest or internal conflict on inbound tourism in the Republic of the Philippines during the period 1994 to 2011. From 2003 onwards, despite formal renunciation of terrorism by one group, the separatist MILF group, acts of violence have continued with increasing political unrest and internal conflict. At the same time an interesting trend of increasing numbers in tourist arrivals was observed from 2003 onwards. The study employs a state space model to test the factors driving tourism during the period 1994 to 2011. The results imply that despite the negative impact of internal conflict and rising inflation, the impact of past income and the inertial effect representing past experience and connectivity to the tourist destination have been sufficient to drive inbound tourism to the Philippines.

As a result of gathering information from multiple consumer centers, big data (BD) assists in analyzing traveler patterns and developing a unique marketing plan tailored to the target demographic. BD tourism forecasting is a relatively new academic field because of the challenges in capturing, gathering, and modeling this sort of data due to its inherent privacy and economic importance. The growth rate of cruise tourists has slowed down after years of rapid expansion. Investing in homeports, cruise ships, and promotional activities carries a growing danger of financial loss. To make investment decisions and prepare for the future, it is necessary to predict tourism demand. We present the least-squares vector regression (LSVR) model with the gravitational search method for forecasting demand for cruise tourism (FCT) based on BD to improve forecasting performance. As a part of the proposed model forecasting demand for cruise tourism based on big data (FDCT-BD), hyper-parameters of the LSVR model are improved using an algorithm and by comparing these models with various configuration combinations. This paper forecasts tourist arrivals based on internet BD from a search engine and online review platforms and the comparative advantage of multi-platform forecasting over single-platform forecasting based on online review data. However, the results show that the methodology’s recommended framework is successful and that BD may estimate cruise tourist demand with enhanced performance and accuracy 93.8% and 97.9%, respectively.