Analysis of travel mobility under Covid-19: application of network science

Park, Sang Won; Kim, Yoo Ri; Ho, Ching Sum Theresa

doi:10.1080/10548408.2022.2089954

Cited by 21 publications

(4 citation statements)

References 64 publications

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“…Our research results confirm the results of Park et al, [53] on big data regarding tourism, which concluded that the COVID-19 pandemic substantially reshaped the tourism and hospitality industries, but studies on the changes in travel behavior in response to the pandemic are limited [53].…”

Section: Discussionsupporting

confidence: 90%

“…For future research, the authors' intent is to apply other quantitative methods, such as grey relational [56][57][58], or neural network [53] analysis, for example, and to include other important available dimensions for the tourism industry, such as a tourism competitiveness index or other tourism indices, as well as details regarding the impact of the COVID-19 pandemic on the most-visited Romanian counties and regions, such as Bras , ov, Prahova, Sibiu, Maramures , , Bucovina, the Danube Delta, and the Black Sea beach.…”

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confidence: 99%

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Google Mobility Data as a Predictor for Tourism in Romania during the COVID-19 Pandemic—A Structural Equation Modeling Approach for Big Data

2022

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Our exploratory research focuses on the possible relations between tourism and the mobility of people, using short longitudinal data for mobility dimensions during the COVID-19 pandemic. One of these is real-time, exhaustive type data, published by Google, about the mobility of people in six different dimensions, (retail, parks, residential, workplace, grocery, and transit). The aim is to analyze the directional, intensity, causal, and complex interplay between the statistical data of tourism and mobility data for Romanian counties. The main objective is to determine if real-world big data can be linked with tourism arrivals in the first 14 months of the pandemic. We have found, using correlations, factorial analysis (PCA), regression models, and SEM, that there are strong and/or medium relationships between retail and parks and overnights, and weak or no relations between other mobility dimensions (workplace, transit). By applying factorial analysis (PCA), we have regrouped the six Google Mobility dimensions into two new factors that are good predictors for Romanian tourism at the county location. These findings can help provide a better understanding of the relationship between the real movement of people in different urban areas and the tourism phenomenon: the GM parks dimension best predicts tourism indicators (overnights), the GM residential dimension correlates inversely with the tourism indicator, and the rest of the GM indices are generally weak predictors for tourism. A more complex analysis could signal the potential and the character of tourism in different destinations, by territorially and chronologically determining the GM indices that are better linked with the tourism statistical indicators. Further research is required to establish forecasting models using Google Mobility data.

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Section: Discussionsupporting

confidence: 90%

mentioning

confidence: 99%

Google Mobility Data as a Predictor for Tourism in Romania during the COVID-19 Pandemic—A Structural Equation Modeling Approach for Big Data

2022

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“…Similar trends have been observed during the COVID-19 pandemic (Derks et al, 2020;Craig & Karabas, 2021;Shartaj et al, 2022). The tourists prefer to stay in less popular destinations because of the lower risk of virus transmission (Park et al, 2022). Camping tourism is form of outdoor and relatively secluded recreation and as such, is considered as a relatively safe and social-distancing friendly form of tourism (Shartaj et al, 2022).…”

Section: From Camping To Glampingmentioning

confidence: 60%

Camping Tourism in Bulgaria: A time-series analysis of tourist demand

Naydenov,

Naumov,

Varadzhakova

2023

Geogr. Pol.

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Camping tourism has become very popular since the beginning of COVID-19 as tourists started to look for socially distanced and yet experiential activities. However, the academic scholarship is relatively thin, particularly in the context of Eastern Europe – a geographical location with rich traditions in outdoor camping andtourism. Drawing on a quantitative methodology based on ARIMA and SARIMA, this study addresses this gap by forecasting the changes in camping tourism development in the short term in Bulgaria. The results indicate a decline of inbound tourism and international visittors but also suggest a positive trend of developmentfor the domestic market.

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“…Tourism is among the industries hit hardest by COVID‐19 in terms of economic opportunities, public services, and livelihoods (Park et al, 2022). Prolonged travel restrictions due to COVID‐19 have impacted exchanges between individuals and countries.…”

Section: Introductionmentioning

confidence: 99%

Impact of travelers' consumer ethnocentrism on purchase and behavior intention: moderating effect of implicit prejudice

Kim,

Hyun

2024

Journal of Tourism Research

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This study investigated the effects of rigid thinking, ideology, and explicit and implicit prejudices on consumer ethnocentrism. Explicit and implicit prejudices significantly affected consumer ethnocentrism, but not travel behavioral intention. Among the three variances—Asian–European American, skin tone, and Arab‐Muslim Implicit Association Test (IAT)—used as implicit prejudice control variables, only implicit prejudice regarding Arab‐Muslim names significantly affected travel product purchase intention and travel behavior. The results revealed that consumers with strong consumer ethnocentrism are likely to avoid purchasing overseas travel products. These findings suggest the introduction of customized tourism marketing methods in countries with strong ethnocentrism.

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Analysis of travel mobility under Covid-19: application of network science

Cited by 21 publications

References 64 publications

Google Mobility Data as a Predictor for Tourism in Romania during the COVID-19 Pandemic—A Structural Equation Modeling Approach for Big Data

Google Mobility Data as a Predictor for Tourism in Romania during the COVID-19 Pandemic—A Structural Equation Modeling Approach for Big Data

Camping Tourism in Bulgaria: A time-series analysis of tourist demand

Impact of travelers' consumer ethnocentrism on purchase and behavior intention: moderating effect of implicit prejudice

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