tourism demand
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2022 ◽  
Vol 90 ◽  
pp. 104490
Author(s):  
Mingming Hu ◽  
Hengyun Li ◽  
Haiyan Song ◽  
Xin Li ◽  
Rob Law

2022 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Dinda Thalia Andariesta ◽  
Meditya Wasesa

PurposeThis research presents machine learning models for predicting international tourist arrivals in Indonesia during the COVID-19 pandemic using multisource Internet data.Design/methodology/approachTo develop the prediction models, this research utilizes multisource Internet data from TripAdvisor travel forum and Google Trends. Temporal factors, posts and comments, search queries index and previous tourist arrivals records are set as predictors. Four sets of predictors and three distinct data compositions were utilized for training the machine learning models, namely artificial neural networks (ANNs), support vector regression (SVR) and random forest (RF). To evaluate the models, this research uses three accuracy metrics, namely root mean square error (RMSE), mean absolute error (MAE) and mean absolute percentage error (MAPE).FindingsPrediction models trained using multisource Internet data predictors have better accuracy than those trained using single-source Internet data or other predictors. In addition, using more training sets that cover the phenomenon of interest, such as COVID-19, will enhance the prediction model's learning process and accuracy. The experiments show that the RF models have better prediction accuracy than the ANN and SVR models.Originality/valueFirst, this study pioneers the practice of a multisource Internet data approach in predicting tourist arrivals amid the unprecedented COVID-19 pandemic. Second, the use of multisource Internet data to improve prediction performance is validated with real empirical data. Finally, this is one of the few papers to provide perspectives on the current dynamics of Indonesia's tourism demand.


2022 ◽  
Author(s):  
Selcuk Cankurt ◽  
Abdulhamit Subasi

AbstractOver the last decades, several soft computing techniques have been applied to tourism demand forecasting. Among these techniques, a neuro-fuzzy model of ANFIS (adaptive neuro-fuzzy inference system) has started to emerge. A conventional ANFIS model cannot deal with the large dimension of a dataset, and cannot work with our dataset, which is composed of a 62 time-series, as well. This study attempts to develop an ensemble model by incorporating neural networks with ANFIS to deal with a large number of input variables for multivariate forecasting. Our proposed approach is a collaboration of two base learners, which are types of the neural network models and a meta-learner of ANFIS in the framework of the stacking ensemble. The results show that the stacking ensemble of ANFIS (meta-learner) and ANN models (base learners) outperforms its stand-alone counterparts of base learners. Numerical results indicate that the proposed ensemble model achieved a MAPE of 7.26% compared to its single-instance ANN models with MAPEs of 8.50 and 9.18%, respectively. Finally, this study which is a novel application of the ensemble systems in the context of tourism demand forecasting has shown better results compared to those of the single expert systems based on the artificial neural networks.


2022 ◽  
Vol 30 (1) ◽  
pp. 781-800
Author(s):  
Rehana Parvin

The nonlinear interaction of oil prices, inflation, the exchange rate, institutional quality, and trade balance on tourist arrivals in Bangladesh is scrutinized in this study. The technique utilized in this study, Nonlinear Autoregressive Distributed Lag (NARDL), is a novel co-integrating strategy. The yearly time series data used in this study spanned 1995 to 2019. The NARDL bound test is performed to assess if variables like oil prices, inflation, the exchange rate, institutional quality, and trade balance on tourist arrivals are co-integrated. Oil prices and exchange rates, according to the findings, have a long-run negative and significant impact on tourism demand, whereas improvements in institutional quality are positively associated with tourist arrivals. Moreover, the study’s findings revealed a nonlinear kinship between the trade balance, inflation, and tourism demand across time. The asymmetric results obtained could enable Bangladeshi policymakers to make more precise decisions.


2022 ◽  
pp. 230-248
Author(s):  
Iveta Hamarneh

The COVID-19 pandemic has not only a significant impact on public health but also severely affected the tourism sector, one of the drivers of the global economy. Although this situation crisis makes tourism highly vulnerable, the sector is also in a unique position to contribute to broader and just effective recovery plans and actions. This chapter considers the major significant impacts, behaviours, and experiences that four major tourism stakeholders are experiencing during the COVID-19 period. Research on (1) tourism demand, (2) tourism supply, (3) destination management organizations, and (4) policy makers will identify the main challenges and opportunities in tourism sector in the post-COVID-19 period.


2021 ◽  
Vol 18 (3) ◽  
pp. 153-169

Lejja Natural Tourism Park (Lejja-NTP) is a conservation area managed by the South Sulawesi Natural Resources Conservation Agency. Lejja NTP is a natural tourist attractions located in Marioriawa Sub-District, Soppeng Regency, South Sulawesi Province. The natural potential of Lejja NTP’s among others, as a hot springs, waterfall, flora-fauna, and the beauty of the natural panorama. The purpose of this study are (1) to determine the characteristics of visitors to Lejja NTP, (2)to identify the factors that influence the tourism demand, and (3) to calculate the economic value of environmental service-based tourism. Individual Travel Cost Method (ITCM) was used to estimate the potential economic value of tourism activity, and linear regression analysis was used to determine the influence factors of tourism demand. Sampling method was carried out by using a purposive convenience by interviewing visitors who came to the Lejja NTP. The results showed that the variable of travel costs, and distance of the residence from Lejja NTP had a significant effect on the level of tourist visits. The value of Lejja NTP for each visitors per year was Rp..464.476.00 and the total benefits derived by were Rp.838.232.00. The economic value of Lejja NTP for visitors in year 2013 of at least Rp.92.582.825.754.00. The value of economic benefits generated from Lejja NTP is expected to be considered by relevant stakeholder to participate in preserving the area, so it is necessary to coordinate and collaborate with stakeholders in managing of ecotourism in Lejja NTP


2021 ◽  
Vol 04 (04) ◽  
pp. 42-58
Author(s):  
Cosmin Nicolae Mirea ◽  
◽  
Puiu Nistoreanu ◽  

Practice has shown that tourism is an activity with a global spread, and sustainable development being a concept with global applicability, the intersection of the two elements is considered inevitable. Both elements are commensurable, which makes it possible to study them and analyze the relationships that arise from cohabitation in the economic and social environment. The purpose of this study is to find out to what extent the variation of tourism demand is influenced by the variation of some indicators of sustainable development. A multifactorial regression model was used, in which the number of tourists represents the dependent variable, and the number of unemployed, the natural increase of the population and the existing accommodation capacity are independent variables. For data processing, the Eviews statistical software was used. The greatest impact on the number of tourists is manifested by the existing accommodation capacity, and overall, the variation of the dependent variable is explained in proportion of 83% by the variation of the independent variables.


2021 ◽  
Vol 9 ◽  
pp. 102-107
Author(s):  
Maja Pervan ◽  
Ena Jurić

Due to its significant contribution to the prosperity and growth of economies, tourism industry has always been the one that attracted the attention of many practitioners and researchers who have tried in different ways and from different aspects to identify the key variables that determine tourism demand. The importance of tourism is especially evident in Croatia for which the contribution of travel and tourism industry to GDP and total economy employment amounts 25% and 25.1% respectively. Having in mind the importance and the role that tourism has, the main objective of this research is to examine the influence of different factors on tourism demand for Croatia. The analysis is conducted on the sample of 16 countries of origin and 9 competitor countries during the period 2012-2019 with the application of dynamic panel data model. All variables encompassed in the model i.e., price, income, corruption, terrorism and investments, show statistically significant influence on tourist arrivals in Croatia.


Tourism ◽  
2021 ◽  
Vol 70 (1) ◽  
pp. 28-42
Author(s):  
Nataša Erjavec ◽  
Kristina Devčić

This paper investigates the determinants of international tourism demand in Croatia, a country whose economy is heavily dependent on tourism. A particular focus is placed on the role of accommodation capacity and trade openness, two demand drivers that have been rarely examined in combination. Using the difference GMM estimator, a dynamic panel model of international tourism demand in Croatia is estimated, employing annual data for 16 tourism generating countries from 2007 to 2019. The results show that the lagged dependent variable, income, accommodation capacity, and exchange rate have a positive effect on international tourism demand, while the impact of relative prices and trade openness prove to be irrelevant in the Croatian context.


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