Tourist Arrivals Research Articles

ANALYSIS OF THE IMPACT OF COVID-19 AND THE RUSSIAN-UKRAINIAN WAR ON THE DEVELOPMENT OF INTERNATIONAL TOURISM IN GERMANY

Germany is one of the leading tourism countries in the EU, where international tourism contributes significantly to economic development, job creation and infrastructure. Germany is a popular destination for tourists, attracting millions of visitors annually. Major cities such as Berlin, Munich and Frankfurt are significant hubs for cultural and business tourism. This article examines the influence of the COVID-19 pandemic and the Russian-Ukrainian conflict on the German tourism industry between 2000 and 2023. The global pandemic resulted in a precipitous decline in international tourist arrivals due to the imposition of travel restrictions. However, following the lifting of these restrictions, the tourism industry began to demonstrate signs of recovery. The impact of the war also resulted in a reduction in the number of tourists and a shift in the structure of demand, with a greater focus on domestic tourism and neighbouring countries. Concurrently, Germany remains a significant tourist destination for a considerable number of European and international tourists. The article presents recommendations for enhancing the sustainability of the industry, particularly through market diversification, the integration of digital technologies and the development of novel tourism forms. Furthermore, the paper addresses the necessity for the tourism industry to evolve in order to meet the demands of a changing global landscape.

Sustainable Cycling: Boosting Commuting and Tourism Opportunities in Istria

This study explores the evolution of cycling in Istria, a region located in parts of Slovenia and Croatia along the Northern Adriatic coast, recognised for its vibrant tourism sector. The research aims to evaluate the potential of cycling for both daily commuting and tourism while examining its economic, environmental, and social impacts. The study uses secondary data from 2010 to 2023, econometric methods, and interviews to assess how cycling contributes to economic growth, regional collaboration, employment rates, and greenhouse gas (GHG) emissions. In Slovenia, regression analysis indicates that bicycle imports positively correlate with tourist arrivals, which positively influences GDP. However, VAR analysis shows that employment has a negative impact on GHG emissions. In contrast, the increase in bicycles contributes to higher GHG emissions, suggesting the possibility of greenwashing or double pollution. In Croatia, regression coefficients were found to be insignificant, aligning with insights gained from interviews. Nonetheless, VAR analysis reveals that tourist arrivals and GDP positively reinforce each other, while GHG emissions negatively affect employment. Bicycles are shown to reduce GHG emissions, and tourist arrivals are also associated with lowered GHG emissions. The study concludes that cycling holds significant potential for fostering sustainable tourism and regional development; however, the differing impacts observed in Slovenia and Croatia necessitate tailored policy interventions. The hypothesis is partially confirmed in Slovenia, where bicycle imports positively impact tourist arrivals and GDP but are challenged by increased GHG emissions, while in Croatia, the hypothesis is not supported by direct economic gains from cycling. However, bicycles contribute to reducing GHG emissions.

Wings of progress: The economic and social impacts of low-cost carriers in the Philippines

Low-cost carriers (LCCs) have become a significant force in the Philippine aviation industry, providing affordable air travel and contributing to regional economic and social development. However, the full extent of their economic, social, environmental, and infrastructural impacts has not been comprehensively studied. This study aims to evaluate the economic and social implications of LCCs in the Philippines, focusing on tourism growth, job creation, business development, and the accessibility and mobility of the population. The study also explores environmental challenges and infrastructural strains caused by the expansion of LCC operations. A mixed-methods approach was employed, incorporating quantitative data analysis of tourism statistics, employment rates, business growth, and regional economic output. Qualitative data were collected through interviews and surveys with stakeholders, including passengers, local businesses, government officials, and environmental agencies. Statistical tools such as regression analysis and chi-square tests were used to determine significance levels in the findings. The results demonstrate that LCCs have contributed significantly to regional economic growth, with a 25% increase in tourist arrivals and a 30% rise in tourism revenue. Social impacts include enhanced travel accessibility, increased cultural exchange, and improved quality of life. However, environmental challenges, such as a 15% increase in carbon emissions, highlight the need for sustainable practices. The study recommends further investment in airport infrastructure, implementation of sustainable aviation practices, and ongoing government support for regional tourism. Future studies should focus on long-term environmental impacts and strategies for reducing carbon footprints.

Maritime Vocabularies Learning In English For Children And Teenagers In The Geopark Area Of The Bajo-Tilamuta Ethnic Village

Boalemo Regency selected the Bajo-Tilamuta Ethnic Village as a Cultural Diversity site for the Gorontalo Geopark due to the uniqueness of this ethnic group's way of life. The Bajo-Tilamuta tribe, one of the Bajo tribes, can be found on the coast of Gorontalo Province, specifically in Boalemo Regency. Despite residing on the coast, the Bajo-Tilamuta tribe remains isolated and distant from the broader population's settlements. However, the intensity of meetings between the Bajo and Gorontalo tribes often occurs through activities as fishermen and marriages between the Bajo and Gorontalo tribes. The Bajo tribe is known as a nomadic tribe at sea. Because of its spread beyond national borders, this tribe is known as a free border crosser. The Bajo tribe's reliance on the sea for shelter has led to their spread to various parts of the world, particularly in Southeast Asia. In the past, the Bajo tribe was known for exclusively living at sea, meaning they were born, lived, and died on boats. The Bajo people's way of life has now changed. Although the Bajo people still live on the sea's edge, their lives tend to settle in one place, leading many of their children to attend school and become graduates. The potential of the Bajo village as an ethnic geopark village hinges on the availability of human resources capable of supporting the village's designation as a geopark tourism village. Therefore, children and teenagers in the Bajo-Tilamuta Village require foreign language training, such as English. We take this action to prepare for the potential arrival of foreign tourists in the future. Through community service integrated with KKN (Community Service Program), students use the English teaching method for young learners by introducing maritime English vocabulary whose objects are in the daily lives of the Bajo Community, namely the sea and its environment.

Attracting tourists to Indonesia: how SEZs and visa-free policies influence international arrivals

ABSTRACT The tourism sector drives international connectivity, prompting countries to adopt policies to attract global visitors. This study examines factors influencing tourist arrivals to Indonesia through an augmented gravity model incorporating economic, geographic, and policy variables. Key determinants include Indonesia's GDP, origin countries’ GDP, geographic distance, visa-free entry, Special Economic Zones (SEZ), and the impact of COVID-19. Data from 37 countries (2006–2023) reveal that Indonesia's and origin countries’ GDP positively impact arrivals, while distance deters tourism. SEZ policies show a modest positive influence, whereas visa-free policies unexpectedly exhibit a negative effect, likely due to challenges in managing visitor quality and maintaining destination appeal. COVID-19 significantly reduced arrivals, underscoring the sector's vulnerability to global disruptions. These insights support refining visa and SEZ policies to foster sustainable tourism aligned with Indonesia's long-term goals.

Post Covid-19 tourism based on environment conservation and community empowerment

Purpose: This research aims to analyse the tourism sector through the lens of conservation and community empowerment, with Indonesia as an example that illustrates the impact of the COVID-19 pandemic on achieving these goals. Design/Methodology/Approach: The research employs descriptive and bibliometric analysis to address the research objectives. Data from the Central Statistics Agency (BPS) of the Republic of Indonesia (bps.go.id) are utilised for a simple descriptive analysis of the development of the tourism sector. The bibliographic analysis leverages Scopus. com data and processes it with the VOS viewer application to identify trends in ecotourism topics based on time and country factors. Findings: In line with the global impact of the COVID-19 pandemic, Indonesia experienced a decline in tourist arrivals at its outset, particularly in 2020. This trend coincided with a global rise in ecotourism development studies, a field where Indonesia emerged as one of the dominant countries. Originality/Value: This research indicates that ecotourism, a form of global tourism involving active community participation, offers a potential solution to tourism crises. Ecotourism can enhance social cohesion and capacity by empowering communities to face critical situations like the COVID-19 pandemic. Although the analysis is limited to a specific period during the pandemic, this study provides insights into tourism dynamics when confronted with crises that lead to mobility restrictions and policies significantly impacting the industry. Consequently, there is an urgency to integrate Sustainable Development Goals (SDGs) into tourism planning and development to increase the sector’s resilience, sustainability, and responsiveness to future global challenges. Keywords: Tourism; Ecotourism; Environment Conservation; Community; Empowerment; COVID-19.

Lethal attraction: crime as tourism externality- evidence from the Indian states

ABSTRACT Tourism thrives upon the benefits of providing safer and risk-free systems, yet empirical evidence in developing economies still needs to be explored. Our study examines how crime and terrorist activities impact tourist demand in Indian states. We use panel data for 27 Indian states and 2 Union Territories from 1993 to 2022. Our findings confirm the differential impact of violent and non-violent crimes on tourism demand in India. We find that a 10% increase in violent and non-violent crimes leads to a decline in tourist arrival by 5.2% and 1.6%, respectively. Further, the result shows that terror mortality exhibits an inverse relation with tourist inflows, whereas improvements in hotel beds attract tourist inflows. In addition, estimates confirm that more urban expenditures enhance tourist inflows, although the effects remain marginal. Our results are consistent in different econometrics specifications.

TIME SERIES FORECASTING FOR TOURISM INDUSTRY IN MALAYSIA

This study is conducted to forecast the future tourism demand in Malaysia by applying Box-Jenkins modelling. The time series data of tourist arrivals volume in Malaysia before MCO retrieved from MOTAC Malaysia database is implemented in this study. The forecast evaluation methods used to validate the best Box-Jenkins model before proceeding to forecasting stage are MAPE and RMSE, and the analysis was performed by using Python. The findings show that SARIMA was considered as highly accurate forecasting model based on its least error produced.

Predicting Tourist Arrivals in Greece With a Novel Tourism-Specific Business Leading Indicator

We introduce a novel tourism-specific business expectations sentiment index and explore whether it can operate as a leading indicator for international tourist arrivals in Greece. Using monthly data spanning 2002‐2021 and employing a VAR model, we document that this newly introduced tourism-specific business expectations index serves as a leading indicator, whose higher levels foreshadow increased demand for international travel. We also find that its inclusion in a tourism-oriented model increases forecasting accuracy, which can be utilized by travel agent businesses, local government officials, and policymakers in their efforts to predict tourist arrivals in Greece.

Effect of Political Stability on International Tourism in Kenya

Purpose: The aim of the study was to analyze the effect of political stability on international tourism in Kenya. Methodology: This study adopted a desk methodology. A desk study research design is commonly known as secondary data collection. This is basically collecting data from existing resources preferably because of its low cost advantage as compared to a field research. Our current study looked into already published studies and reports as the data was easily accessed through online journals and libraries. Findings: Political stability is essential for international tourism in Kenya, as stability fosters a sense of safety that attracts foreign visitors. During peaceful periods, Kenya sees growth in tourist arrivals and revenue, while political unrest, such as the post-election violence in 2007-2008, results in sharp declines as safety concerns deter tourists. Tourism in Kenya is highly sensitive to political fluctuations, with even minor instability affecting tourist perceptions. To sustain and grow its tourism sector, Kenya must prioritize political stability through effective governance, peaceful elections, and strong conflict resolution strategies, which will help build a resilient tourism industry. Unique Contribution to Theory, Practice and Policy: Social conflict theory, political economy theory & the institutional theory may be used to anchor future studies on the effect of political stability on international tourism in Kenya. Implementing robust governance practices, such as transparency, accountability, and anti-corruption measures, will enhance the overall tourist experience and foster trust among potential visitors. Formulating policies that promote good governance and strengthen institutions will create a conducive environment for political stability.

The Impact of a Local Well-Being Philosophy on Revisit Intentions to Bali Among Digital Nomads

The level of success in tourism is gauged by several metrics; however, the most widely used is the level of tourist arrivals. However, this research answered the call for greater investigation of the impacts of qualitative factors and intangible cultural–heritage assets on destination performance. The primary research purpose was to analyze the effect of implementing a local well-being philosophy (Tri Hita Karana) on tourist revisit intentions for Bali and the mediation of destination quality and destination image. A research model was developed to examine the relationships among local wisdom (TKH), destination quality, destination image, and revisit intentions. Data were collected via a survey of 520 digital nomadic tourists and analyzed using SmartPLS 4. The results indicated that the implementation of THK positively and significantly affected revisit intentions, destination image, and destination quality. Destination image and destination quality had positive and significant effects on revisit intentions, and destination image and destination quality also significantly mediated the effect of THK implementation on revisit intentions. The findings suggested that implementing local wisdom values such as THK in the management of a destination makes visitors feel more favorably about the quality and image of the destination and they have the intention to revisit.

MODELING TOURIST ARRIVALS IN THE ISLAND GARDEN CITY OF SAMAL: IMPLICATIONS TO BUSINESS OPERATIONS OF TOURISM-RELATED INDUSTRY

<p>This study aimed to model the tourist arrivals in the Island Garden City of Samal (IGaCoS) by utilizing the autoregressive integrated moving average (ARIMA) method. Specifically, this paper sought to determine whether there was a statistically appropriate forecasting model using the ARIMA approach, based on which it would be possible to reasonably forecast the number of tourist arrivals in IGaCoS. Quantitative secondary data were analyzed by employing the Box-Jenkins ARIMA. The time series plot was characterized by an apparent increasing trend combined with intermittent sinusoidal oscillations. The time series revealed a steady, gradual increase up until 2020. Next, a sharp decline was observed midway through 2020, indicative of the ill effects of the COVID-19 pandemic. A gradual increase was noted once more, starting in 2021, with some fluctuations. The Augmented Dickey-Fuller (ADF) test revealed that the time series was non-stationary at level— but was made stationary after differenced twice. The ARIMA (1,2,1) model was found to be statistically significant. The fitted model initially showed some notable deviations from the original data, but, with time, converged closely to the actual values, proving it is a reliable way to represent underlying patterns. The ARIMA (1,2,1) model, selected for having the lowest AIC and BIC values and a MAPE within the acceptable range for reasonable forecasting, was used to predict tourist arrivals in IGaCoS over the next six months. The forecasted values suggested a mix of fluctuations and stability. There was a noticeable upward trajectory at the beginning, which was followed by a slight decline at the end that yet remained stable. The forecasted results from the ARIMA (1,2,1) model may provide businesses with a roadmap for comprehensive planning in terms of operational and strategic concerns. This study helped address Sustainable Development Goal (SDG) No. 8, Decent Work and Economic Growth.</p><p><strong>JEL:</strong> L83, R11, C53</p><p> </p><p><strong> Article visualizations:</strong></p><p><img src="/-counters-/soc/0776/a.php" alt="Hit counter" /></p>

Tourism’s Influence on Economic Growth and Environment in Saudi: Present and Future

Reports from the World Tourism Organization indicate that tourism activity has been increasingly booming; this sector is essential for economic growth and may affect the environment. Tourism is one of the key strategic sectors for planned growth in Saudi Arabia’s Vision 2030. This study is designed to evaluate the long-termning association between tourist arrivals, growth domestic product (GDP), and CO2 emissions in the Kingdom of Saudi Arabia (KSA). The data related to these variables were assessed for the period 2010 to 2020. The autoregressive distributed lag (ARDL) bounds results revealed that there are long-established relations between tourist arrivals and growth domestic product and tourist arrivals and CO2 emissions. The dynamic ordinary least squares (DOLS) and fully modified ordinary least squares (FMOLS) model outcomes were compatible with the outcomes of the ARDL model. In reference to the Granger causality test, tourist arrivals cause (affect) the GDP. Such a result confirms the conception that tourism encourages economic growth. No causality runs from tourist arrivals towards CO2 accumulation. This result may reflect the governmental effort to reduce CO2 accumulation and/or to perform tourism activities in a sustainable way. The results predicted that the growth rate of tourist arrivals, GDP, and CO2 accumulation equal 0.0023, 0.048, and 0.0169, respectively, during the forecast period (2021–2030), which appeared to be increasing for tourist arrivals and GDP and decreasing for CO2 accumulation. The study recommended that, to increase economic growth, tourist arrivals should be increased alongside performing tourism activities in a sustainable way. These findings point to the benefits of governance in ensuring effective policies to decarbonize the environment, and policy proposals are put forward accordingly.

The Dynamics of Tourism, Economic Growth, and CO2 Emissions in Uzbekistan: An ARDL Approach

In this study, we examined the impact of key macroeconomic variables on the sustainability of tourism in Uzbekistan using open data from the World Bank from 2000 to 2023. The selected variables for the study include the number of international tourists, GDP, CO2 emissions, and exchange rates. Considering the importance of climate issues in ensuring sustainable tourism, the study combines these factors using the autoregressive distributed lag (ARDL) model to assess their long-term relationships. We chose this model due to its flexibility in dealing with variables of different integration orders. The results of the bounds testing procedure confirm the existence of a long-term cointegration relationship between the dependent variable (international tourist arrivals) and the selected macroeconomic indicators. Specifically, the study results indicate that GDP growth, exchange rate fluctuations, and CO2 emissions positively affect tourist arrivals to Uzbekistan. The findings of this study underscore the importance of infrastructure development and macroeconomic stability in attracting international tourists while emphasizing the need for sustainable international practices to mitigate environmental impacts. This study contributes to understanding the complex interrelationship between tourism development and macroeconomic variables in the context of developing countries and aids decision-making in this area.

A Novel Approach for Forecasting Tourist Arrivals Using Web Search Data and Artificial Intelligence

The development of economic activity has been matched by growth in the tourism industry. According to information, the tourism industry is growing and both the number of domestic and international tourists visiting each year is expanding. Because of this quick expansion, there are now critical complications with the management of tourism, such as predicting the arrivals for travel, particularly when a lot of people are visiting appealing locations for particular periods. The proposed Artificial Fish Swarm Optimized Dynamic Gated Recurrent Unit (AFSO-DGRU) approach transforms the forecasting of demand for tourism by utilizing intelligence from swarms to improve forecasts and strategically adapting to fluctuating visitor structures. It ensures accurate and dynamic responses even during times of uncertainty when demand is high. The study used Google Trends to collect data from searches on the web and examine trends in tourist’s interest and demand for travel. By combining innovative artificial intelligence (AI) algorithms with realtime online search data, this study presents a novel way to improve the accuracy of visitor arrival predictions. The proposed method performs better than the existing methods to utilize the parameters such as mean absolute deviation called MAE (42.01), mean square error denoted by MSE (3059.85), mean absolute percentage error defined MAPE (1.34), and RMSPE or root mean square percentage error (1.43). This research utilizes web search data and AI to improve the accuracy of forecasting tourist arrivals, offering valuable insights for understanding tourism trends.

Unveiling fluctuation patterns of tourist volume: Insights from 56 major attractions in China

Understanding the spatiotemporal dynamics of tourist volume is crucial for effective tourism management and planning. However, existing tourism data analysis methods often fail to capture the complex, continuous fluctuations and temporal variations in tourist behavior. To address this challenge, we apply functional data analysis (FDA) in the tourism industry to provide a more nuanced understanding of tourist volume dynamics. Specifically, we perform FDA on real-time tourist volume data from 56 major attractions in Beijing, China, revealing intrinsic fluctuation patterns, key factors driving tourist arrivals, and the dynamic characteristics of attractions across temporal scales. Our findings enhance the ability to optimize attraction management, marketing strategies, and policy-making, while also advancing tourism data science by integrating FDA. This approach fills the methodological gap and offers a comprehensive framework for exploring the spatiotemporal complexities of tourism data.

Leveraging large language models for daily tourist demand forecasting

ABSTRACT Large Language Models have attracted the attention of tourism researchers, and many discussions have been published in leading tourism journals. However, little research has been conducted on how to use Large Language Models in tourism research. In this paper, we propose a new tourist arrival forecasting model based on the Large Language Model. The Large Language Model is used to extract and produce the satisfaction scores from the review comments by the tourists. The generated satisfaction scores are incorporated into the tourist arrival forecasting model to take full advantage of the extracted information from the review comments. We have applied the Large Language Model based forecasting model to predict the tourist arrival in Macao. Experiment results show that the proposed model has produced forecasts with improved forecasting accuracy.

The role of economic freedom in the development of international tourism in Asian countries

Purpose ― This paper aims to investigate the influence of economic freedom and its components, namely business freedom and trade freedom, on international tourist arrivals in Asian countries. Additionally, it examines the effect of important macroeconomic factors, such as foreign direct investment, exchange rates, political stability, GDP per capita, and inflation on international tourist arrivals in Asian countries.Methods ― The GMM two-step estimation system is used to analyze data from 25 Asian countries from 1995 to 2020.Findings ― The results show that economic and trade freedom positively influence tourism, while business freedom has a less distinct impact. Inflation positively contributes to tourist arrivals. Exchange rates and political stability show inconclusive effects.Implications ― The study recommends that governments prioritize expanding economic freedom to boost international tourism.Originality ― This is the first study on the impact of economic freedom on developing international tourism in Asian countries.

The Influence of World Heritage Sites on Tourism Dynamics in the EU 27 Nations

Culture and tourism are crucial for economic growth and sustainable development, working together symbiotically. Culture boosts tourism by supporting heritage, production, and creativity, while cultural heritage, reflecting national identity and traditions, strengthens local economies. This study uses a panel data analysis to examine the impact of cultural factors on international tourist arrivals in 27 EU nations. The Driscoll–Kraay method reveals that UNESCO World Heritage Sites, government cultural expenditures, household recreation and cultural expenditures, real GDP per job, and bed capacity positively influence international tourist numbers. Regarding cultural factors, World Heritage Sites have the most significant influence, with each additional UNESCO site leading to a 0.22% rise in tourist numbers. The most crucial determinant of tourist arrivals is bed capacity, with a one percent increase leading to a 0.66% increase in tourist arrivals. Conversely, healthcare expenditures and political civil liberties have a negative impact on tourist arrivals. This study discusses managerial implications and offers recommendations for future research.

Comparison of Holt Winter's and SARIMA Methods on the data of the Number of Foreign Tourist Visits in Bali Province

The tourism sector is a critical component of a country’s economy, including in Indonesia, where its impact is felt both nationally and regionally, such as in provinces and cities. This research focuses on Bali Province and aims to conduct a comparative analysis of the Holt-Winter’s and Seasonal Auto-regressive Integrated Moving Average (SARIMA) methods for forecasting foreign tourist arrivals. The analysis centers on two primary entry points: Ngurah-Rai Airport and the seaport. The primary objective is to forecast the number of foreign tourist arrivals from February 2024 to January 2025. The results indicate that the Holt-Winter’s model has a Mean Absolute Percentage Error (MAPE) of 5.2631%, which is lower than the MAPE of 5.8920% for the SARIMA model. Additionally, the Mean Absolute Error (MAE) for the Holt-Winter’s model is 19,149.18, compared to 20,883.20 for the SARIMA model. Consequently, this study concludes that the Holt-Winter’s model provides more accurate predictions and is closer to the actual values than the SARIMA model. Bali, Holt-Winter’s, forecasting, SARIMA, tourism.