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Study reveals a universal travel pattern across four continents

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What explains how often people travel to a particular place? Your intuition might suggest that distance is a key factor, but empirical evidence can help urban studies researchers answer the question more definitively.

A new paper by an MIT team, drawing on global data, finds that people visit places more frequently when they have to travel shorter distances to get there.

“What we have found is that there is a very clear inverse relationship between how far you go and how frequently you go there,” says Paolo Santi, a research scientist at the Senseable City Lab at MIT and a co-author of the new paper. “You only seldom go to faraway places, and usually you tend to visit places close to you more often. It tells us how we organize our lives.”

By examining cellphone data on four continents, the researchers were able to arrive at a distinctive new finding in the urban studies literature.

“We might shop every day at a bakery a few hundred meters away, but we’ll only go once a month to the fancy boutique miles away from our neighborhood. This kind of intuitive notion had never been empirically tested. When we did it we found an incredibly regular and robust law — which we have called the visitation law,” says Carlo Ratti, a co-author of the paper and director of the Senseable City Lab, which led the research project.

The paper, “The universal visitation law of human mobility,” is published today in Nature .

The paper is co-authored by Markus Schläpfer, a scholar in the Urban Complexity Project at the ETH Future Cities Lab in Singapore; Lei Dong, a researcher at Peking University in Beijing; Kevin O’Keeffe, a postdoc at the MIT Senseable City Lab; Santi, a research director at Istituto di Informatica e Telematica, CNR (the National Research Council of Italy); Michael Szell, an associate professor in Data Science at IT University of Copenhagen; Hadrien Salat of the Future Cities Laboratory, Singapore-ETH Centre; Samuel Anklesaria, a researcher at the MIT Senseable City Lab; Mohammad Vazifeh, a senior postdoc at the MIT Senseable City Lab; Ratti; and Geoffrey West, a professor at and former president of the Santa Fe Institute. Schläpfer, Dong, Santi, and Szell are also former members of the Senseable City Lab.

To conduct the study, the researchers used anonymized cellphone data from large communications providers to track the movement of people in the metro areas of Abidjan, Ivory Coast; Boston; Braga, Lisbon, and Porto, Portugal; Dakar, Senegal; and Singapore.

Cellphone data are ideal for this kind of study because they establish both the residence area of people and the destinations they travel to. In some cases, the researchers defined areas visited by using grid spaces as small as 500 square meters. Overall, the researchers charted over 8 billion location-indicating pieces of data generated by over 4 million people, charting movement for a period of months in each location.

And, in each case, from city to city, the same “inverse law” of visitation held up, with the charted data following a similar pattern: The frequency of visits declined over longer distances, and higher-density areas were filled with people who had, on aggregate, taken shorter trips. To the extent that there was some variation from this pattern, the largest deviations involved sites with atypical functions, such as ports and theme parks.

The paper itself both measures the data and presents a model of movement, in which people seek out the closest locations that offer particular kinds of activity. Both of those buttress “central place theory,” an idea developed in the 1930s by German scholar Walter Christaller, which seeks to describe the location of cities and towns in terms of the functions they offer to people in a region.

The scholars note that the similarity in movement observed in very different urban areas helps reinforce the overall finding.

“This generalized behavior is not just something you observe in Boston,” Santi says. “From a scientific viewpoint, we are adding evidence about a generalized pattern of behavior.”

The researchers also hope the finding, and the methods behind it, can be usefully applied to urban planning. Santi suggests this type of study can help predict how substantial changes in the physical layout of a city will affect movement within it. The method also makes it possible to examine how changes in urban geography affect human movement over time.

“The visitation law could have many practical applications — from the design of new infrastructure to urban planning,” adds Ratti. “For instance, it could help implement the concept of the ‘Fifteen-Minute City,’ which aims to reorganize physical space around walkable neighborhoods and which has become very popular during the Covid-19 pandemic. Our law suggests that we can indeed capture a large fraction of all urban trips within a fifteen-minute radius, while leaving the rest — perhaps 10 percent — further away.”

Support for the research was provided by the National Science Foundation, the AT&T Foundation, the Singapore-MIT Alliance for Research and Technology (SMART), the MIT Center for Complex Engineering Systems, Audi Volkswagen, BBVA, Ericsson, Ferrovial, GE, the MIT Senseable City Lab Consortium, the John Templeton Foundation, the Eugene and Clare Thaw Charitable Trust, the U.S. Army Research Office Minerva program, the Singapore National Research Foundation, and the National Natural Science Foundation of China.

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Press mentions, financial times.

Writing for the Financial Times , Prof. Carlo Ratti explores the concept of the “15-minute city,” which is aimed at creating walkable neighborhoods. “The 15-minute city must be paired with investment in transport between neighborhoods,” writes Ratti, noting that investment is especially needed in public transportation to ensure that 15-minute cities do not contribute to greater segregation.

Motherboard

Researchers from the MIT Senseable City Lab have uncovered a new travel pattern in human mobility that remains consistent across four continents, reports Beck Ferreira for Motherboard . “The notion that distance and frequency of visitation are related is in accordance with intuition,” the researchers explain. “What is surprising is that the relationship between these two quantities can be described by a simple and clean mathematical law.” 

United Press International (UPI)

UPI reporter Brooks Hays writes that a new study by MIT researchers finds that people tend to follow a predictable travel pattern that remains consistent in countries around the world. The findings could help urban planners “better understand how populations interact with their surroundings, as well as assist city planners with zoning, infrastructure and other development decisions,” writes Hays.

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Trip chaining patterns of tourists: a real-world case study

• Published: 14 September 2023

Cite this article

• Cong Qi 1 ,
• Jonas De Vos 2 ,
• Tao Tao 3 ,
• Linxuan Shi 4 &
• Xiucheng Guo 1  

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Insights into tourist travel behaviours are crucial for easing traffic congestions and creating a sustainable tourism industry. However, a significant portion of the literature analysed tourist travel behaviour by predefined tourist trip chains which result in the loss of more representative classification. Using tourist travel survey data from Nanjing, China, this paper presents an innovative methodology that combines the tourist trip chain identification and the trip chain discrete choice model to comprehensively analyse the travel behaviour of tourists. The discretized trip chains of tourists are clustered using the ordering points to identify the clustering structure (OPTICS) clustering algorithm to identify typical tourist trip chains, which will then be considered as the dependent variable in the nested logit model to estimate the significant explanatory variables. The clustering results show that there are two main categories, namely single and multiple attraction trip chains, and seven subcategories, which were named according to the characteristics of trip chains. The clustering result is analysed and three main trip chain patterns are derived. Departure city, travel cost, travel time, and travel mode show significant influence on the choice between single and multiple attraction trip chains. The urban attraction trip chain is more favoured by tourists with children, and the typical trip chain shows stronger dependence on travel intention. Visiting Lishui for the first time only affects the choice of the multiple suburban attraction trip chain. These findings are valuable for optimising tourist public transport infrastructure, promoting travel by public transport and better tourism management.

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Smart tourism: foundations and developments.

Ride-hailing, travel behaviour and sustainable mobility: an international review

What is a walkable place? The walkability debate in urban design

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This research was funded by Postgraduate Research&Practice Innovation Program of Jiangsu Province, Grant Number KYCX23_0303.

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• Published: 10 February 2022

Analysis of spatial patterns and driving factors of provincial tourism demand in China

• Xuankai Ma 1 , 2 , 3 ,
• Zhaoping Yang 2 &
• Jianghua Zheng 1  

Scientific Reports volume  12 , Article number:  2260 ( 2022 ) Cite this article

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Modeling and forecasting tourism demand across destinations has become a priority in tourism research. Most tourism demand studies rely on annual statistics with small sample sizes and lack research on spatial heterogeneity and drivers of tourism demand. This study proposes a new framework for measuring inter-provincial tourism demand's spatiotemporal distribution using search engine indices based on a geographic perspective. A combination of spatial autocorrelation and Geodetector is utilized to recognize the spatiotemporal distribution patterns of tourism demand in 2011 and 2018 in 31 provinces of mainland China and detect its driving mechanisms. The results reveal that the spatial distribution of tourism demand manifests a vital stratification phenomenon with significant spatial aggregation in the southwest and northeast of China. Traffic conditions, social-economic development level, and physical conditions compose a constant and robust interaction network, which dominates the spatial distribution of tourism demand in different development stages through different interactions.

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Introduction.

Tourism is an essential driver of world economic development. The world was affected by the COVID-19 outbreak in 2019, and according to a report by the World Tourism Organization 1 , the world's top ten consumer countries showed continued growth in tourism consumption against the backdrop of a global economic slowdown. According to the Ministry of Culture and Tourism of the People’s Republic of China ( https://www.mct.gov.cn/ ), China's 2020 annual domestic tourism numbered 2.879 billion trips, down 52.1% from a year earlier. The first quarter of 2021 saw 11.872700 billion domestic trips organized by national travel agencies, increasing 138.50% year-on-year. As the contribution of tourism to the regional economy is improving more and more significantly, the study of tourism demand has become a popular research topic 2 . Many scholars have carried out tourism demand forecasting through qualitative analysis, time series models, econometric models with artificial intelligence, and the accuracy of forecasting has gradually improved. However, tourism and tourists are closely correlated in terms of spatial mobility, and if spatial effects are ignored, a model estimation can be biased and produce misleading coefficient estimates 3 , 4 , 5 . Deng and Athanasopoulos were the first to incorporate spatiotemporal dynamics into an Australian domestic tourism demand model study 6 , and Yang and Zhang showed that spatiotemporal models have a significantly enhanced effect on the performance of tourism demand forecasting between domestic provinces in China 7 . Liu et al. noted that demographic factors, climate, key transportation modes, economic level, and other aspects of tourism demand were not investigated 8 . Therefore, it is imperative to understand the spatiotemporal patterns and driving mechanisms of tourism demand.

From the tourism supply side, the geographical and spatial clustering of tourism-related services produces spatial dependence and scale effects at the macro level, thus providing tourists with more acceptable prices and convenient services to achieve regional tourism growth. From the tourism demand side, tourists from the same region have more similar social psychology and tourism demand 9 , their tourism demand patterns are similar in terms of spatial preferences, and travel patterns show a more consistent cyclicality in time. Domestic tourism is an undisputed driver of economic development and poverty alleviation in less developed regions than international tourism 10 . Pompili et al. argued that choosing the provincial level as the geographic unit to study tourism flows yields more valuable results 11 . The results of the detection of spatial effects within a region can provide a scientific and empirical reference to local governments, tourism planners and administrative units regarding resource allocation and infrastructure development.

Several studies have explored the factors affecting tourism demand. For example, Priego et al. explored the impact of climate change on domestic tourism flows in Spain 12 . Massidda and Etzo studied the contribution of road infrastructure to tourism demand in domestic tourism in Italy 9 . Priego et al. emphasize the importance of meteorological factors on domestic travel destinations in Spain 12 . Technological innovation 13 and knowledge spillovers 14 cannot be ignored in driving tourism productivity and making tourism demand grow. Alvarez‐Diaz et al. 15 , Marrocu and Paci 16 , Massidda and Etzo 9 confirmed that the size of the population is also one of the drivers of tourism demand. Despite the large number of studies exploring the factors affecting tourism demand, most researchers have focused more on the impact of single aspects of socio-economic or natural factors on tourism demand, and there are no studies based on a geographic perspective that integrate the various dominant factors into a comprehensive mechanism of impact on tourism demand.

We found from the early literature that the number of tourists and tourism income served as the main proxies for tourism demand modeling. With the development of the Internet, some researchers 8 , 17 found that tourists' search engines for tourism information retrieval are the starting point and an essential part of tourism decision and travel. Li et al. summarized relevant 2012–2019 in their latest review 18 . We know about search engine data primarily based on empirical studies investigating the eximious contribution to tourism demand observation and forecasting. With Google Trends being widely used for tourism demand forecasting at multiple spatial scales worldwide 19 , Baidu Index performs even better in the Greater China region 20 . Song et al. demonstrated that Internet data has a significant driving effect on tourism demand research, with search engine data being the most common Internet data source used by researchers 19 . It is now well established from various literature that analytical methods have been implemented to address the single driving mechanisms of tourism demand. In their paper, Marrocu and Paci indicated that the application of spatial autoregressive models gave the spatial dependence patterns of tourism flows access to be effectively presented 16 . Yang and Fik investigated tourism growth change in 342 cities in China using spatial growth regression models 21 . Deng et al. used a spatial econometric analysis framework to analyze the impact of air pollution on inbound tourism in China 22 . In general, tourism demand is not affected by any individual factor, and the interactions among the factors affect the distribution of tourism demand. Therefore, it is crucial to detect the interrelated effects of tourism demand drivers. However, most existing studies ignore the interaction among the drivers of tourism demand. In addition, most existing models used in the literature make assumptions about the data and fail to reveal the interaction among the factors.

To fill these gaps, this study aims to address the spatial heterogeneity and drivers of tourism demand by using 678,900 Origin–Destination flows (OD flows) of tourism demand data from 31 Chinese provinces at the years 2011 and 2018, which helps gain insight into the spatial heterogeneity of tourism demand exhibited in the period of rapid economic development. Second, to our knowledge, this might be the first attempt to present a theoretical framework for a multi-factor driving mechanism of tourism demand, which incorporates social-economic development, population size, urban ecological conditions, tourism resources, physical conditions, traffic conditions, and technological innovation. Third, from the perspective of spatially stratified heterogeneity, this study taps the influence of the main driving factors and the interaction between different potential factors on the spatial heterogeneity of tourism demand. In addition, the study of tourism demand should not only focus on the influence of local economic activities and the natural environment but also the influence of inter-regional spatial correlation. Therefore, this study uses spatial autocorrelation and geographic detector models to analyze the spatial variation of tourism demand and its drivers at the provincial level.

The rest of the study is organized as follows: “ Materials and methodology ” section provides an overview of the proxies affecting tourism demand and the data and methods used in this paper. “ Results ” section analyzes the drivers and spatial characteristics of tourism demand. Finally, “ Discussion ” and “ Conclusions ” sections are the discussion and conclusion of the findings, respectively.

Materials and methodology

Study areas.

After the world economic crisis in 2008, China started its economic recovery in 2011, followed by an average annual growth of 9.48% in GDP and 14.99% in total domestic tourism consumption until 2018 (Fig.  1 b,c). This paper investigates the factors driving the changes in the spatial distribution of tourism demand at a provincial level in China in 2011 and 2018 to provide a basis for planning decision-makers in developing countries and regions. This study regarded the provinces as the primary geographical unit, and 31 administrative provinces in mainland China were selected as the study area. Due to the unavailability of data, Hong Kong, Macao, and Taiwan are not included in the study area (Fig.  1 ).

An overview map of the study area: ( a ) 31 provinces in mainland China; ( b ) economic conditions in the study area from 2011–2018; ( c ) tourism in the study area from 2011 to 2018. Data on China's economy are from the National Bureau of Statistics of China ( http://www.stats.gov.cn/ ) and the Chinese Academy of Social Sciences ( http://english.cssn.cn/ ). Standard map services are provided by the Ministry of Natural Resources of China ( http://bzdt.ch.mnr.gov.cn/ ), GS (2020)4619.

Dominant factors and proxy variables of tourism demand

From a systemic perspective, the underlying driving mechanism of tourism demand is constituted by the tourist travel intention of the source and the destination's attractiveness. It is influenced by the resistance of temporal distance, spatial distance, and social distance 23 , 24 , 25 . Natural conditions and human factors determine tourism demand (Fig.  2 ). In this study, social-economic development (Z1), population (Z2), urban ecology (Z3), tourism resources (Z4), physical conditions (Z5), traffic conditions (Z6), and technology innovation (Z7) are used as factors that directly affect tourism demand. Considering the availability of data, GDP per capita, value-added of tertiary industry, and the average wage of employees is used to characterize the level of socio-economic development. The total population and nighttime light index measure the population scale. Urban ecological condition is indicated by Urban park green area. Museums and A-class scenic spot index represent the richness of tourism resources. The physical environmental conditions consist of altitude, average daily hours of sunshine, average daily temperature, green space coverage index. Transportation conditions are reflected by the urban road area, highway mileage, and railroad mileage. The number of enterprises in the high-tech industry represents the region's scientific and technological innovation capability. The search intensity of the Baidu index was employed to quantify tourism demand.

Determinants and their geographical proxy variables concerning the spatial distribution of tourism demand.

Tourism demand of inter-province

Search engine big data are one of the data sources that can accurately quantify tourism demand. Search engines collect records of Internet users retrieving information on the Internet to form search engine indices with high timeliness. Baidu index ( https://index.baidu.com/ ) has better accuracy in the Greater China region for measuring tourism demand, and keywords query it. The keyword database consisted of the combinations of destination provinces name + "tourism". The Baidu index of each keyword could be decomposed according to the region and time, to obtain the daily search intensity of internet users in province A for tourism information in province B. We constructed an origin–destination (OD) spatiotemporal matrix in 2011 and 2018, which contains the intensity of travel information retrieved by residents of one province on the Internet for another province for each day. The correlation matrix of tourism demand among provinces visualized in Fig.  3 was obtained by summing up the daily tourism demand flows by year and accumulated in terms of destination provinces to obtain the tourism retrieval index of each province for a year. It characterized the total tourism demand of that province. Additionally, the annual Baidu Index of province-A Internet users query for tourism information about province-B is defined as a tourism demand flow for this OD.

Correlation matrix of tourism demand between provinces: Based on the daily Baidu indexes accumulated throughout the year in ( a ) 2011; ( b ) 2018. The Y-axis is the origin, and the X-axis is the abbreviation that replaces the destination, the name of each province. The color of each grid represents the total annual amount of tourism demand from the source province to the destination province. The grid color From lighter to darker represents the strength of tourism demand flow.

Indicators for influencing factors

We collected statistical panel data released in 2012 and 2019 from the China City Statistical Yearbook ( http://www.stats.gov.cn/tjsj/tjcbw/ ), including official statistics on the economic development level, population, urban ecological conditions, transportation conditions, and science and technology innovation. A-class tourism resources lists were extracted from the summary of government documents published on each local government website, and the tourism resources were numerically mapped according to A-1, AA-2, AAA-3, AAAA-4, and AAAA-5 remapping. The tourism resource addresses were geocoded into spatial point data, and kriging interpolation was implemented for spatial interpolation to generate the A-class tourism resources index raster. In addition, we used remote sensing data as a geographic proxy variable for physical conditions and population distribution. The NPP-VIIRS-like NTL Data from Harvard Dataverse ( https://library.harvard.edu/services-tools/harvard-dataverse/ ), which represents the intensity of human activity at night, the elevation data is from the Resource and Environmental Science Data Center of the Chinese Academy of Sciences ( https://www.resdc.cn/ ), the climate data is from the National Meteorological Science Data Center of China ( http://data.cma.cn/ ), and the green space coverage index is from the USGS ( https://www.usgs.gov/ ). All proxy variables were free of being counted according to provincial administrative boundaries, and the raw data were in a GeoDetector model where sampling points would capture the values of different variables.

Methodology

Exploratory spatial data analysis.

Exploratory spatial data analysis is a series of spatial data statistical analyses applied to describe and visualize the spatial and temporal distribution patterns of tourism demand. Global spatial autocorrelation 26 is adopted to determine whether the spatial distribution pattern of tourism demand is clustered, dispersed, or random 27 . The local spatial autocorrelation 28 is practiced in identifying areas where spatial clustering and outliers occur to explore their spatial effects 29 . Considering the spatial data of provinces are polygons and checked by topology, Queen contiguity is utilized to indicate the spatial weight matrix between provinces 30 .

Spatial stratification heterogeneity analysis

GeoDetector is an advanced spatial statistical analysis model used to study factors' impact on diseases at a specific geographical area early 31 . Furthermore, it gradually developed into various research fields with spatial characteristics, such as ecological security, food production, urban land use, carbon emissions. It is a hypothesis that if the independent variable directly influences the dependent variable in space, then the spatial distribution of the dependent variable should converge with the spatial distribution of the independent variable 32 . The model detects the similarity of two variables in spatial distribution patterns from the perspective of spatially stratified heterogeneity 33 .

In this paper, GeoDetector was adopted to analyze the factors affecting the spatial distribution of tourism demand in 31 provinces of China (Fig.  4 ). Factor detection, ecological detection, and interactive detection submodules were applied to quantify their spatial heterogeneity and interactions between factors on the spatial distribution of tourism demand. The Q statistic measured and explained the influence of the independent variable X on the dependent variable Y on spatial heterogeneity. The expressions are as follows.

where: \(Q\) -statistic is a measure of the explanatory power of the influence of factor X on tourism demand Y; M represents the number of strata (subdivisions); \(N\) represents the number of provincial geographical units in the study area; \({N}_{j}\) represents the number of provinces in subdivision \(j\) ; \({\sigma }^{2}\) and \({\sigma }_{j}^{2}\) indicate the variance of tourism demand in the whole study area and the variance of tourism demand in each subdivision, respectively. The greater the value of Q, the stronger the influence of factor X on tourism demand Y.

Principle of geodetector.

Stratification of geographic proxy variables has a significant impact on the accuracy of factor detection. The optimization algorithm for stratifying geographic proxy variables parameters proposed by Song et al. offered optimizing spatial discretization 34 . The optimization algorithm assumes that each variable is stratified using different unsupervised discretization methods to form different stratification schemes. If one alternative scheme obtains an enormous Q-statistic based on factor detection calculations, this stratification scheme captures the most significant driving force between that variable and the observed variables.

The study area was spaced at 50 km intervals, and 3795 sampling points were generated to sample 16 continuous-type variables. Quantile method, natural break method, geometric break method, standard deviation break method, and equivalence breakpoint method were used as statistical stratification methods with intervals of 3–6, and the Q-statistic of proxy variables and observations under different stratification schemes were probed. Finally, the scheme with the enormous Q-statistic was selected as the stratification and interval parameters for this proxy variable.

Spatial patterns of tourism demand in provinces

Spatial distribution.

Figure  5 illustrates the spatial distribution of tourism demand and flows between provinces in 2011 and 2018. In 2011, tourism demand was mainly distributed in China's first-tier cities with Beijing and Shanghai and the border provinces from southwest to northwest, with Hainan, Yunnan, Tibet, and Xinjiang being the main tourism demand destinations 35.33% of tourism demand in the study area. In 2018, tourism demand showed a trend of migration to first-tier cities, with Yunnan, Tibet, Shanghai, Chongqing, and Guizhou accounting for 36.46% of the total tourism demand.

Spatial distribution of tourism demand and tourism demand flow: ( a ) tourism demand in 2011, and ( e ) is in 2018; ( b – d ) are the inter-provincial directional tourism demand flows in 2011, which are high-intensity flow, medium intensity flow, and low-intensity flow respectively, and ( f – h ) is in 2018.

From the spatial perspective of tourism flow, there are 930 tourism flows between provinces with a minimum Euclidean distance of 115 km, a maximum of 3600 km, and a median of 1286 km. Long-distance travel 35 is a characteristic of tourism between domestic provinces. In 2011 the tourists' origins were concentrated in eastern China, and there were two clusters of high-intensity tourism flows distributed from Beijing and the ring-Beijing area to Yunnan and Hainan; this phenomenon altered significantly in 2018, with high-intensity tourism flows concentrated on one cluster of tourism flows from eastern China to Yunnan Province. Medium-intensity tourism flows presented a complex network with a stochastic pattern in 2011; in 2018, the complexity of the tourism network decreased, with steady clusters of tourism flows originating from the eastern provinces to Xinjiang and Tibet, while the northeastern region became a complete exporter of tourists. Comparing the low-intensity tourism flows in 2011 and 2018, it can be attended that the overall origin and destination were practically completely connected, i.e., there were tourism flows from both border provinces and the central region. The central provinces also energetically export tourists to the peripheral provinces, diverting the overall tourism flow network to a more luxuriant state. In summary, we can catch that the domestic tourism network in China during the period of rapid economic development showed a remarkable complex pattern, with the origins of tourists consolidated in the densely populated and economically developed areas in the east and the destinations distributed in the first-tier cities, and remote areas in the central and western regions.

From 2011 to 2018, the northeastern provinces, Beijing-Tianjin-Hebei region (Beijing, Tianjin, Hebei), Yangtze River Delta region (Shanghai, Jiangsu, Zhejiang, Anhui), and Pearl River Delta region (Guangzhou) are stable tourist sources; the average value of tourism demand in each province rose from 642,515 to 1,529,387, an increase of 2.38 times (Fig.  6 ). Yunnan and Guizhou in the southwest and Gansu in the west grew at a much higher rate than the national average; Heilongjiang, Jilin, and Liaoning in the northeast grew at a much lower rate than the average; and Hainan in the south became the only province in the country where tourism demand decreased.

Spatial distribution of the ratio of tourism demand in 2018 to 2011.

Spatial dependency

The spatial distribution pattern of tourism demand shifted from medium to high clustering in 2011 and 2018, and the positive Moran’s I revealed the existence of high-value to high-value clustering or low-value to low-value clustering of tourism demand in the study area, and the spatial pattern and spatial dependence of tourism demand with evident clustering. The z-score increased by 177.58% during this period, and the p-value decreased by 88.96%. The probability of rejecting the null hypothesis increased from 90 to 95%. Thus, the spatial clustering trend of tourism demand strengthened.

The global Moran’s I identified the overall spatial dependence of tourism demand in each province within the study area, and Local spatial autocorrelation analysis was applied to uncover the local spatial association patterns. As can be seen from Fig.  7 , significant stratification of tourism demand in 2011 and 2018 on a local spatial basis in China (absolute value of Z-score > 2.56, p-value < 0.01), consisting mainly of high-high value clusters (H–H) and low-low value clusters (L-L). In 2011, the H-H cluster was in Yunnan Province in southwestern China, the high values surrounded by low values cluster (H-L) appeared in Beijing, and the L-L cluster was in Heilongjiang Province in northeastern China. In 2018, the H-H cluster was still in Yunnan province, and the L-L clusters were distributed in Heilongjiang, Jilin, and Liaoning province, covering the whole northeastern region. From the perspective of spatial and temporal distribution, tourism demand formed a growth pole in southwestern China centered on Yunnan from 2011 to 2018, and tourism demand in Guizhou, adjacent to Yunnan, grew significantly and showed a spatial diffusion effect the region (Fig.  7 ). While in northeast China, the number of L-L clusters increased, and the provincial growth rate of tourism demand in low-value agglomeration was much lower than the national average during the study period. The existence of the H-L cluster in Beijing in 2011 and the disappearance of this cluster in 2018 indicated that Beijing had strong competitiveness in the region in the early stage, and the weakening polarization effect and the increasing diffusion effect diminished in the later stage when tourism demand was gradually distributed in a balanced manner in the Beijing-ring region. It suggested that tourism demand was more stable in China spatially in high-value clustering, and low-value clustering had increased, forming an increasingly stable high-value area in the southwest and low-value area in the northeast. Therefore, tourism demand in one province largely influences tourism demand in the adjacent provinces.

Local indicators of spatial association (LISA) for tourism demand in ( a ) 2011 and ( b ) 2018. The numerical marks on the maps represent P values, ** 5% level of significance (P < 0.05); *** 1% level of significance (P < 0.01).

Driving forces of tourism demand

Influencing factors of tourism demand.

Figure  8 showed the explanatory power of the driving factors for tourism demand in 2011 vs. 2018. In 2011, The number of enterprises in the high-tech industry (0.5622) had the highest explanatory power, implying that GDP had a remarkably noticeable impact on tourism demand. Average daily hours of sunshine (0.4934), Urban park green space area (0.4928), Urban road area (0.4763), GDP per capita (0.4473), Railroad mileage (0.4229) had the same level of high explanatory power, which meant that these three drivers had the most noticeable impact on tourism demand. The number of museums (0.3932), value-added of tertiary industry (0.3892), Average wage of employees (0.3479), Total population (0.3429), Highway mileage (0.3024) were also significant drivers of tourism demand. Average daily temperature (0.2757) and altitude (0.2028) also influenced tourism demand. Green space coverage index (0.0616), A-class scenic spot index (0.0321). The nighttime light index (0.0062) had minimal explanatory power on tourism demand.

Power of determinant Q-statistic value for each driving factor in 2011 and 2018.

In 2018, the average daily hours of sunshine (0.5848) significantly affected tourism demand, expressing the strongest association with tourism demand: urban park green space area (0.4084), Average daily temperature (0.407). GDP per capita (0.4058) had a significant explanatory power on the spatial distribution of tourism demand. Urban road area (0.3924), Railroad mileage (0.3863), Average wage of employees (0.3753), The number of museums (0.37), Total population (0.3347), Highway mileage (0.3299), The number of enterprises in the high-tech industry (0.3191) were also significant factors influencing tourism demand. Tourism demand was limitedly influenced by the value-added of tertiary industry (0.2148), Altitude (0.1443). Green space coverage index (0.0198), A-class scenic spot index (0.0062). The nighttime light index (0.0054) had minimal effects on tourism demand.

Interaction of driving factors

One hundred twenty couple of interactions were generated yearly between the 16 factors in 2011 and 2018, the bulk of which had an enhancing effect on tourism demand, with the primary interaction type being nonlinear enhancement (55.83% in 2011 and 75.83% in 2018), followed by bi-variable enhancement (43.33% in 2011 and 23.33% in 2018). The explanatory power of the interaction on tourism demand was greater than that of the single factor with the maximum explanatory power.

As shown in Fig.  9 a,c, in 2011, Average wage of employees-Average daily hours of sunshine (0.9935), Average wage of employees-Urban road area, GDP per capita-Railroad mileage, were two-factor non-linearly enhanced interactions with Q-statistic for the interactions greater than 0.99, showing a tourism demand with extreme explanatory power. Urban park green space area-Highway mileage, Average daily hours of sunshine-Highway mileage, Average daily hours of sunshine-Railroad mileage, Average wage of employees-Highway mileage, The number of museums-Average daily hours of sunshine, Average wage of employees-Urban park green space area, Urban park green space area-The number of museums were two-factor none-linearly enhanced interactions with interaction Q-statistic values greater than 0.98, a significant increase in the influence of synergy on tourism demand.

GeoDetector results: Power of determinants in interaction in ( a ) 2011 and ( b ) 2018; the difference of the impacts between two explanatory variables in ( c ) 2011 and ( d ) 2018.

In 2018 (Fig.  9 b,d), Average daily temperature-Urban road area (0.9949), Urban park green space area-Average daily temperature, Average daily temperature-Highway mileage, Total population-Average daily temperature, GDP per capita-Highway mileage, Average wage of employees-Highway mileage, GDP per capita-Total population, The number of museums-Average daily temperature, were two-factor non-linearly augmented interaction patterns with interaction Q-statistic greater than 0.99, which almost wholly control the spatial distribution of tourism demand. Value-added of tertiary industry-Average daily temperature, GDP per capita-Average daily hours of sunshine, GDP per capita-Value added of tertiary industry, GDP per capita-Urban road area, GDP per capita-The number of museums, GDP per capita-Urban park green space area, were two-factor none-linearly enhanced and GDP per capita-Average daily hours of sunshine was two-factor enhanced with interaction Q-statistic values greater than 0.98.

The regional economic development and construction were the main drivers, followed by the size of the population and the base of tourism services, and again by the traffic conditions, with the influence of natural factors and tourism resources being minimal in 2011. Moreover, by 2018, the influence of tourism comfort factors began to rise, such as average daily hours of sunshine and average daily temperature, representing a significant increase. The level of social and personal economic development and transportation conditions also increased influence to 2011. It indicated that in the aftermath of the world economic crisis and during the economic recovery, the driving force affecting tourism demand is the city's economy and level of development. However, after high economic growth, tourists have more requirements for the comfort of the experience during tourism, and the economy is no longer the main driving force directly affecting the spatial distribution of tourism demand.

Interaction mechanism

We filtered the five combinations with the maximum Q-statistic values from each of the 120 interaction combinations for 2011 and 2018, respectively, with an average explanatory power greater than 0.99 and two-factor nonlinear enhancement. It indicates that these combinations play a decisive role in the spatial distribution of tourism demand. The dominant factors to which each proxy variable belongs are also shown in Table 1 . The ten combinations generated the interaction networks with the most explanatory power. The proxy variables and the determinants are mapped as nodes; the cumulative value of the Q statistics for the interactions between the node and other nodes determines the node's size. The interactions between the factors are edges, and the Q-statistic values for interaction measured the weight of the edges. Different hierarchical interaction networks are visualized in Fig.  10 revealed the interactive mechanism.

Diagram of Interactive Network: the interactive network of proxy variables in 2011 ( a ) and 2018 ( b ); interactive network of dominants in ( c ) 2011 and ( d ) 2018; ( e ) global interactive network based on proxy variables; ( f ) global interactive network based on dominants.

From the perspective of the interaction of proxy variables, a strong triangular network community was formed in 2011 by average wages of employees—average daily hours of sunshine-highway mileage. In 2018, the interaction network shaped a significant polarization with the average daily temperature at the center, and average daily temperature and highway mileage formed a chain community. From the perspective of the interaction of dominants, Fig.  10 c,d illustrate a substantial triangular network community formed by traffic conditions dominated by socio-economic development conditions and complemented by physical conditions in 2011. This community continued to persist in 2018, with the difference that the roles of physical and traffic conditions were switched.

Notably, the most significant interaction results in 2011 and 2018 were integrated to reveal the driving mechanisms impacting the distribution of tourism demand. Physical conditions existed at the core of the interaction network, mainly in the form of average daily temperature, which interacted extensively with other factors and was the central driver influencing the distribution of tourism demand, indicating that tourism comfort is the basis of the natural scenery tourism attraction. Socio-economic development followed closely behind in physical conditions, with the average wage of employees representing the general level of economic development in the region and the prosperity of the tertiary sector, characterizing the level of tourism services, as well as being the foundation for driving the city to be a tourist attraction. The importance of traffic conditions was uncovered in tourism accessibility and the compression of the time distance. The three formed a concrete network of interactions that influenced the spatial distribution of tourism demand.

Tourism is one of the critical engines of local economic development and serves as a regulatory tool for coordinated development within and between countries and regions. Tourism is extraordinarily vital and dynamic, and according to the latest report of the World Tourism Organization, tourism worldwide has shown a rapid recovery after the impact of COVID-19. This paper suggested a theoretical framework to explore the spatial distribution of driving tourism demand based on a spatially stratified heterogeneity perspective, obtained the dominant drivers to shape the spatial distribution of tourism demand, and discussed the interaction mechanisms among the drivers.

Internet Big data have derived a tremendous amount of Internet operation records of individual Internet users, which provide us with new means of observation. Early observations of tourism demand relied on management statistics of scenic spots and cities. After 2008 Internet search engines, big data were widely used as indicators to quantify tourism demand, and they proved to have good reliability at different geographic scales to accurately reflect the amount of tourism demand. For example, Yang et al. and Xin et al. predicted tourism demand in Hainan Province 36 , China, and Beijing Forbidden City 37 , Beijing, China. The results proved that the Baidu index could more accurately reflect tourism demand's spatial and temporal characteristics. This paper uncovered the spatial distribution of tourism demand and flow network patterns reflected by the Baidu index on a national scale (Figs.  3 , 6 ). It demonstrated that the Baidu index could characterize tourism demand dynamically and build tourism flow networks.

Notably, there were regional differences in the distribution of inter-provincial tourism demand in China. The study results showed that tourism demand increased significantly from 2011 to 2018, the spatial clustering pattern of tourism demand was not randomly distributed (Fig.  5 ); there were two types of spatial effects in regional tourism growth, namely spatial spillover and spatial heterogeneity 21 . The high tourism demand cluster was shaped in the southwest, and the low tourism demand cluster was rendered in the northeast (Fig.  7 ). The spatial competitive effect of high and low imbalance in the capital ring gradually vanishes, and the tourism demand in the central region tends to be homogeneous.

By investigating the spatial distribution pattern of domestic inter-provincial tourism demand in China, we recognized heterogeneity in the sensitivity of long-distance tourism flows to the distance in different intensities (Fig.  11 ). There was an explicit reversal at 1900 km for high-intensity tourism flows, i.e., the distance between origin and destination was shorter than 1900 km, and tourism demand positively corresponded with distance; conversely, whereas the was over 1900 km, tourism demand declined with increasing distance. Medium-intensity tourism flows were not sharp with distance. Low-intensity tourism flows obeyed the distance decay law.

Results of locally weighted regressions of distance and travel demand: ( a ) 2011 ( b ) 2018. 2011 and 2018 tourism demand flow intensities were normalized separately. Locally weighted regression 38 of distance and normalized tourism demand intensity was performed according to the stratification of tourism demand flows in Fig.  5 , where the Euclidean distances of flows were calculated in the Beijing_1954_3_Degree_GK_Zone_35 coordinate system.

The dominants that have a decisive influence on tourism demand were physical conditions, socio-economic development, and traffic conditions; the proxy variables are the average daily temperature, the average wage of employees, and highway mileage.

Physical conditions had high explanatory power for the spatial distribution of tourism demand, proving that natural scenery tourism was more prevalent in China than urban humanistic tourism. Tourist attraction and comfort of the natural scenery type were determined physical conditions; for instance, world natural heritage sites have a stronger role in promoting tourism 39 . Murphy et al. analyzed daily time-scale park visitation and weather data for Pinery Provincial Park, Canada, from 2000 to 2009, demonstrating the high sensitivity of tourism demand to average daily temperatures 40 .

While socio-economic development furnishes the foundation for breeding urban humanistic tourism, the average wage of employees is an efficient indicator of the region's economic development 41 , where high-quality tourist reception, available public information, travel safety, and diversified recreational convenience services are important factors attracting tourists. Meanwhile, efficient administrative supervision services in economically developed regions directly impact public information, recreational convenience, safety protection, and recreational convenience 42 .

Traffic conditions make it possible to connect tourists to tourist attractions. Wang et al. (2020) uncovered a well-coupled relationship between tourism efficiency and traffic accessibility in Hubei Province 43 , China, from 2011 to 2017. Highway mileage enhanced the coverage of inter-regional connections and compressed tourists' time costs to their destinations; on the other hand, it also increased the polarization of intra-regional connections, thus benefiting the central regions rather than the peripheral ones from the traffic 44 . This finding was consistent with the results that Southwest China forms a high tourism demand cluster, while Northeast China is a low tourism demand cluster in “ Spatial dependency ” section and Fig.  7 . Another recent study 45 has observed that less-developed central and western regions attract more visitors than developed eastern regions by improving transportation conditions in China.

Several aspects need to be considered in related follow-up studies. First, this study analyzed the drivers of tourism demand at the provincial level in China, with prominent medium- and long-distance tourism characteristics. In contrast, complete tourism demand occurs between prefecture-level cities, which should be considered the primary research unit in the future. However, writing a crawler program to request raw data from the Baidu index to obtain the daily tourism demand O-D flow has limitations. Therefore, moderately reducing the scope of the study may be helpful. Secondly, direct dominants in this study's theoretical framework of the driving mechanism are economic development level, population size, urban ecological conditions, tourism resources, natural environment, transportation conditions, and science and technology innovation. The results showed that the geographical variables represented by the tourism resources index, night lighting index, and green space coverage index have little impact on tourism demand, it might be caused by the difference between the scale of rasters and spatial panel data, these rasters may have more efficiently representation on the scale of urban. Therefore, more representative ones should be selected in future research as proxy variables. Finally, the COVID-19 global pandemic significant public safety event on tourism demand is also very impacting. The effects of severe public contingencies and the government's immediate response policies on tourism demand should be added to the tourism demand driving mechanism in the future.

Conclusions

This study adopted Baidu index data spatialized into flow space, and multi-source data to investigate domestic tourism demand's spatial pattern and drivers during China's rapid economic development (2011–2018). The results show that (1) China's domestic tourism demand has significantly increased, shaping a spatial pattern in which first-tier cities and western regions where the core tourism destinations and the tourism attractiveness of northeastern regions gradually disappeared. The tourism demand network is increasingly prosperous and gradually develops from disorderly to orderly, with eastern regions as the main source of tourists. (2) From the single driving factor, the factor with the strongest and increasing control over the spatial distribution of tourism demand is sunshine hours > the average wage of employees > highway mileage. (3) In terms of the composite factor interaction results, the interaction network formed by physical conditions-economic development level-transportation conditions steadily and strongly determines the spatial distribution pattern of tourism demand.

The novelty of this study is the flow-based spatialization of the search engine index (Baidu index), which efficiently mapped the spatial mode of tourism demand and unearthed the network formed by domestic tourism flows, domestic long-distance travel in China is positively correlated with distance in terms of travel demand between the source and destination within 1900 km and vice versa. Additionally, the factors affecting the spatial distribution of tourism demand were interpreted from spatial heterogeneity, and the significant impact of the interaction between factors on tourism demand was resolved and captured the complex network. The findings of this paper can provide a reference for regional tourism planning decision-makers. Simultaneously, it can also provide a systematic tourism demand driving mechanism for tourism demand forecasting researchers and promote modeling accuracy.

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Acknowledgements

The authors acknowledge the help of Dr.Yongze Song and reviewers on improving and commenting on the manuscript. This work was supported by The Second Tibetan Plateau Scientific Expedition and Research Program (STEP) [Grant No. 2019QZKK1004].

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Personalities shaping travel behaviors: post-COVID scenario

Journal of Tourism Futures

ISSN : 2055-5911

Article publication date: 31 August 2022

This study aims to offer insights into a sounder understanding of tourist behavior and travel patterns by systematically identifying psychological manifestations reflected in the basic human value system in the pandemic-induced environment.

Design/methodology/approach

A large random sample (49,519 respondents from 29 European countries), generated from the core module Round 9 of the European Social Survey, was used. A post-COVID-19 psychological travel behavior model was constructed by using 12 variables within two opposing value structures (openness to change versus conservatism), shaping specific personalities.

Four types of tourists were identified by using K-means cluster analysis (risk-sensitive, risk-indifferent, risk-tolerant and risk-resistant). The risk-sensibility varied across the groups and was influenced by socio-demographic characteristics, economic status and even differed geographically among nations and traveling cultures.

Research limitations/implications

First, data were collected before the pandemic and did not include information on tourism participation. Second, the model was fully driven by internal factors – motivation. Investigation of additional variables, especially those related to socialization aspects, and some external factors of influence on travel behaviors during and after the crisis, will provide more precise scientific reasoning.

Originality/value

The model was upgraded to some current constructs of salient short-term post-COVID-19 travel behavior embedded in the core principles of universal human values. By separating specific segments of tourists who appreciate personal safety and conformity, from those sharing the extensive need for self-direction and adventure, the suggested model presents a strong background for predicting flows in the post-COVID-19 era.

• Travel behavior
• Human values
• Personality types
• Risk perception
• Travel restriction and regulations

Terzić, A. , Petrevska, B. and Demirović Bajrami, D. (2022), "Personalities shaping travel behaviors: post-COVID scenario", Journal of Tourism Futures , Vol. ahead-of-print No. ahead-of-print. https://doi.org/10.1108/JTF-02-2022-0043

Emerald Publishing Limited

Copyright © 2022, Aleksandra Terzić, Biljana Petrevska and Dunja Demirović Bajrami

Published in Journal of Tourism Futures . Published by Emerald Publishing Limited. This article is published under the Creative Commons Attribution (CC BY 4.0) licence. Anyone may reproduce, distribute, translate and create derivative works of this article (for both commercial and non-commercial purposes), subject to full attribution to the original publication and authors. The full terms of this licence may be seen at http://creativecommons.org/licences/by/4.0/legalcode

Introduction

At specific points in time, various risks affect people’s lives and behaviors, causing changes in consumer habits in line with the new situation. Tourism is highly sensitive to risks (natural hazards, wars, pandemics, terrorism, politics, environmental risks, etc.) that influence sudden changes in the tourist market ( Lee et al. , 2021 ). Any severe risk that outbreaks promptly reduces the tourism flows due to the decision of tourists not to visit dangerous destinations, but also due to government restrictions that cause shifts in tourist demand and affect travelers’ choices and behaviors ( Fotiadis et al. , 2021 ). The recent global COVID-19 pandemic endangered people’s health and lives, disturbed everyday life, disrupted the economy and brought tourism to a standstill. The COVID-19 outbreak followed by lasting travel bans and strict regulations changed almost every aspect of tourism. The whole tourism system went through profound negotiations on multiple levels. Underpinned by reasonable concerns among tourists and governments on travel risks, each country defined its own regulatory measures and entrance rules. A whole two years of COVID-19 frightening and fighting, along with lasting travel bans, brought a reasonable question of what to expect in the forthcoming tourist seasons. When dealing with uncertainties, the existing differences in risk construction are potentially crucial for assessing existing risks and their implications for the travel industry ( Gossling et al. , 2020 ; Hall et al. , 2020 ). There were some indications that harsh restrictions even magnified travel intentions, particularly among those most severely affected by the pandemic ( Boto-Garcíaand Leoni, 2021 ). Aside from travel restrictions, different prohibitions and sanctions imposed as coercive measures for diplomatic and political reasons. Fluctuating mobility bans caused confusion among tourists, creating various perceptions of the ease of traveler access ( Seyfi et al. , 2020 ) and outlined radical behavioral changes.

Demand aspects are changing swiftly because of the growing uncertainties in the world. Vales and the psychological response to crises could provide insight into probable scenarios in pandemic and post-pandemic situations. In line with Li and Cai (2012) , this study investigated the effects of values on travel motivation and behavioral intentions of European tourists. It contributes to motivational and behavioral tourism research by improving comprehension of the psychological manifestations of tourist motivation and behavior by applying multiple methodologies and big data quantifications. A developed post-COVID-19 travel behavior model implied the existence of distinct tourist segments by distinguishing those who genuinely appreciate personal safety and conformity from those with intensified travel needs.

Literature review

The devastating impacts of COVID-19 on tourism demand and future travel behavior have been widely discussed ( Brouder, 2020 ; Ivanova et al. , 2021 ; Aebli et al. , 2022 ). Tourists’ risk perception and protective behavior during or after crises (wars, terrorism, health, financial collapse, natural disasters, etc.) have been discussed repeatedly, while a multitude of studies have recently been published regarding COVID-19 effects on tourism ( Chua et al. , 2021 ; Matiza, 2022 ; Podra et al. , 2021 ; Ahmad et al. , 2021 ).

Motivation, opportunity and ability are lead factors influencing travel intentions, but the perception of travel risks is focal for making travel decisions ( Hasan et al. , 2017 ). Motivation isa principal psychological aspect that directs an individual’s behavior, activity, travel intentions, choices and behaviors ( Dann, 1981 ; Demirović et al. , 2019 ; Yoon and Uysal, 2005 ). Travel motivation and decision-making include gauging perceived benefits against perceived costs or risks ( Aebli et al. , 2022 ) and reaching extremes during COVID-19 circumstances. Restrictions occurring in pandemics inhibit continued traveling and lower the subjective perception of well-being ( Hwang and Lee, 2019 ; Brodeur et al. , 2021 ). Some studies strived to identify the behavioral patterns caused by fearful reactions to crises and uncertainties ( Kock et al. , 2020 ; Miao et al. , 2021 ), while others even proposed the existence of crisis-resistant tourists who follow their travel plans despite unexpected internal and external events ( Hajibaba et al. , 2015 ).

Personality and human values

Personality is strongly related to psychological processes, defined as an enduring disposition that causes characteristic behavioral patterns, shaped by values, being closely linked to motivation ( Parks and Guay, 2009 ). Psychological theories ( Locke, 2000 ; Rokeach, 1973 ; Roccas and Sagiv, 2010 ) indicated that needs, values and goals were arranged hierarchically, as needs influenced the development of value systems, while values influenced the decision to pursue various goals (e.g. to travel). The fulfillment of long-term goals leads to the attainment of values and satisfaction of needs ( Parks and Guay, 2009 ). Values stand for the manifestation of culture as the basis on which attitudes, cognition, emotions and behaviors evolve ( Hills, 2002 ; Li and Cai, 2012 ; Schwartz, 2012 ).

Schwartz’s theory ( Schwartz, 2012 ) is the most well-known and widely used value theory, which identified and described dynamic relationships between motivationally distinct value groups (power, achievement, hedonism, stimulation, self-direction, universalism, benevolence, conformity, tradition and security). This theory classified human values into four dimensions: openness to change, self-enhancement, conservation and self-transcendence. The basic human value sets determine motivation and can be employed to predict general travel behavior, destination choices, leisure activities, preferences, trip length, etc. ( Salim Saji et al. , 2015 ; Terzić et al. , 2021 ).

Aebli et al. (2022) use Herzberg’s theory of motivation to consider tourists’ needs and observed risks as opposing sets of psychological factors shaping travel willingness during and after the COVID-19 pandemic. People’s psychological reactions are embedded in the underlying psychological constructs – human values. According to Parks and Guay (2009) , values are more general than attitudes and are ordered by importance, as a person will tend to follow the more important value when two values are in conflict.

Risk perception, protection motivation, travel intentions and behaviors

Risk perception has a severe impact on travel intentions and destination choices ( Denda et al. , 2021 ), as people tend to carry out travel decisions in a way that mitigates threats, reflecting the basic psychological defense strategy of humans ( Miao et al. , 2021 ). Even though tourism demand declines as heath risk increases ( Yang et al. , 2020 ), there is also an opportunity to develop a sort of tourism resilience ( Gossling et al. , 2020 ). Perception of risk may vary depending on different cases and subjective risk assessment, dependent on individual psychological characteristics, socio-demographic factors, cultural background, previous travel experiences and the influence of various external factors such as media ( Pennington-Grey and Schroeder, 2013 ). When a tourist is confronted with a threat (health risk), the psychological response is to take protective action by performing a coping appraisal that influences changes in attitudes and travel intention ( Seow et al. , 2022 ). Common psychological responses related to COVID-19 were reflected in increased xenophobia, tourist ethnocentrism and negative crowding perception ( Kock et al. , 2020 ; Miao et al. , 2021 ). Taking into account the effects of aggressive media reports, Miao et al. (2021) use “terror management theory” and “posttraumatic growth” as theoretical grounding for understanding the psychological processes underlining overt human behaviors during COVID-19 pandemic. Miao et al. (2021) proposed proximal and distal travel behavior to represent short-term (travel abstinence, disruptive travel behavior, rational travel and compensative travel behavior) and long-term post-COVID-19 travel behavior (distal bounded travel behavior, voluntary de-crowding, mindful tourism and travel as a quest for meaning).

Using a large random sample generated by the European Social Survey (ESS), this study seeks to test whether the human value system can provide a basis for dividing Europeans into various personalities and behavioral types, using a large random sample generated by the ESS and if such segments can be empirically linked to crisis-resilient tourist behavior.

Research methodology

We engaged the protection motivation theory and used the post-COVID-19 travel behavior construct developed by Miao et al. (2021) to construct a post-COVID-19 travel behavior model based on human value systems. To do this, we have upgraded the salient proximal post-COVID-19 travel behavior construct by using the personality traits defined by universal human values ( Schwartz and Bardi, 2001 ) as principles governing all aspects of people’s lives ( Figure 1 ).

Data extracted from the core module Round 9 of the ESS ( ESS, 2018 ) consisted of a random sample of 49,519 respondents from 29 European countries. Over two-third of the Europeans participated in tourism in 2019, which provides some certainty that the data can be considered reliable for the examination of the travel intentions of Europeans ( Terzić et al. , 2021 ). The ESS database allows result generalization, while extant socio-demographical, political, economical and geographical data provided can be continuously added and tested using different samples and timeframes. The constructed model based on psychological constructs can be used for predicting post-pandemic travel to indicate expected tourist behaviors.

The original human values construct ( Schwartz, 2012 ) was reduced from the initial 21 to 12 variables directly related to general safety and conformity, openness to change, and hedonism. Variables considered influential to tourism decision-making were divided into two categories: (1) o penness to change and (2) c onservatism . Six variables were used to assess openness to change , including self-direction and stimulation values, reflected in independent thought and action (choosing, exploring, creativity, excitement, novelty and challenge in life) and certain aspects of hedonism (tourism satisfaction). The fundamental motive of exploration was a focal evolutionary driver of modern tourism activity. Conservatism (six variables), on the other hand, reflected greater general importance of issues of security, conformity and tradition. These characteristics were reflected in certain restraint of actions and violations of social norms and expectations, placing personal security and safety on a pedestal and indicating general anxiety avoidance (demotivation). For the study, we focused on opposing value sets – c onservation (security and conformity) as a demotivation factor, and o penness to change (hedonism and stimulation) as a motivator indicating intensified tourists’ needs. The proposed model divided people who are more conservative in terms of the role of personal safety from those whose travel needs and adventure-seeking blur their risk perception and stimulate more risky behavior. The latter were used as indicators of a person’s willingness to engage in traveling and transmitted into the post-pandemic behavioral construct.

Performing a K-means cluster analysis in SPSS 24.0 software was used for data processing. The K-means clustering represents one of the most commonly used quantitative analysis techniques in tourism for market segmentation ( Dolnicar et al. , 2014 ; Fuchs and Höpken, 2022 ). The main limitations of K-means lay in the potential of cluster overlapping, the need for pre-defining the number of clusters, dimensionality and unbalance in cluster sizes ( Fränti and Sieranoja, 2018 ). In this study, clusters were defined based on previously established models ( Miao et al. , 2021 ) and empirical findings ( Aebli et al. , 2022 ; Hajibaba et al. , 2015 ). The dimensionality problem was avoided by using a large random sample and the modest number of variables provided by the ESS database, as suggested by Dolnicar et al. (2014) . Despite the slight disproportion in cluster sizes, the size of each cluster was large enough to provide confidence in the presented results and to reflect a more realistic situation in life.

Sample profile

Among the sample, there was slight domination of female respondents (51.4%) compared to males (48.6%). Young adults up to 39 years made up 36.2% of the sample, middle-aged people (aged 40 to 65) made up 42.9% and seniors (aged 66 to 90) made up 20.9% of the total sample. Secondary education was the most common (58%), followed by advanced vocational and lower tertiary (BA) education (22%) and higher tertiary education (12.1%). Employed and self-employed persons (89.1%) were dominant respondents. The primary source of the household income was salary from employment (66%), pensions (24.1%) or unemployment or other social benefits (4.5%).

Defining clusters

Human values (12 variables) on the opposite sides of the human value system were manipulated to produce clusters reflecting the psychological types of potential tourists, particularly those with outlined travel needs and those with contrasting conservative characteristics. The first cluster analysis (containing two to six clusters) indicated that three- and four-cluster solutions provided the best cluster solution. The use of four clusters to indicate behavioral patterns was chosen in accordance with Miao et al. (2021) . Conduction of the K-means non-hierarchical cluster analysis classified 48,789 respondents (with valid responses) into four clusters. ANOVA found that all included variables were statistically different ( Table 1 and Figure 2 ). Means were calculated for each variable and compared for the whole sample. The final cluster solution was achieved due to no or small change in cluster centers, with the maximum absolute coordinate change for any center 0.000, with 84 iterations and minimal distance between initial centers 12.41.

The ANOVA analysis showed that the values reflected in the statements “Important to seek adventures and having an exciting life,” “Seek fun and things that give pleasure,” “Try new and different things in life” and “Having a good time” strongly affected the segmenting process. These statements were in direct relationship with tourism motivation. Other issues related to general safety and stability are of outmost importance in the crisis-related (pandemic) circumstances and give some insight into possible behavioral aspects of different groups considering defined and proposed safety measures at destinations.

Table 2 presents some patterns of the clusters' socio-demographic characteristics. There was evident slight gender difference across clusters indicating women were more risk-sensitive than men. Sharper age differences existed, marking older respondents risk-sensitive, while younger groups were predominantly risk resistant. Travel risk perception increases with age and decreases with travel experience. The influence of gender and age on risk perceptions was consistent with the results of previous research ( Floyd and Pennington-Gray, 2004 ; Lepp and Gibson, 2003 ).

According to Bernini and Carcolici (2015) , economic stability has the greatest impact on tourism participation and consumption. Differences in tourism participation reflect inequalities in living standards, as disposable income is an important determinant influencing tourist behavior, participation and spending ( Bernini and Fang, 2021 ). Risk-sensitive and risk-resistant personalities are dependent on subjective general health evaluation and current financial status assessment. Education and economic status were important aspects, as risk-sensitive personalities were predominant among less educated and economically disadvantaged groups.

Certain aspects of the overall trust in governmental decisions revealed that the risk-indifferent group displays a slightly lower confidence in national politics than other groups. Emotional attachment levels to country and Europe might indicate possible travel boundaries in post-pandemic circumstances. The risk-sensitive and risk-tolerant groups have an extremely high emotional attachment to their country of residence, and the risk-tolerant and risk-resistant groups are highly attached to Europe. Such attitudes might indicate the expected level of ethnocentrism that will most probably affect post-COVID-19 travel behaviors. The same is reflected in some moral obligations to support the domestic tourism economy by staying within national borders ( Kock et al. , 2020 ). Figures 3 and 4 present the uneven geographical distribution of certain tourist types in Europe with a clear dominance of risk-tolerant personalities.

In 2019, 64.9% of the EU population (aged 15 or over) made at least one travel for personal purposes, but tourism participation share ranged from 85% in The Netherlands to 28.6% in Romania ( Eurostat, 2020 ). Nearly half of the Europeans (45%) who did not participate in tourism reported financial reasons as one of the main reasons for not traveling, 25% mentioned no motivation to travel and another 24% of non-tourists outlined health problems ( Eurostat, 2020 ). The standard travel patterns of European nations before the COVID-19 pandemic are presented in Figure 5 . Travel habits are likely to be transmitted into post-COVID-19 travel patterns, as previous knowledge and experiences provide certain confidence for tourists.

To provide more precise conclusions, stratification filters were used to exclude those highly unlikely to engage in travel activities due to the justified risks of poverty or health-related disability. The effects on cluster groups were minimal, as socio-demographic characteristics of defined clusters were experiencing insignificant changes (0.2%–0.5%). The spatial distribution of clusters remains stable, experiencing changes up to 1%: in risk-resistant cluster (−0.1 to −0.7%), risk-tolerant group (−0.7%–0.3%), risk-indifferent group (−0.6 to 0.4%) and risk-sensitive group (−0.1 to 1.4%). The risk-resistant and risk-tolerant groups were most stable. However, the share of the risk-sensitive group was slightly enlarged, particularly on account of the economically unstable SEE countries: Slovakia, Estonia, Croatia, Bulgaria and Serbia (up to 1.4%).

Increased perceived security in a travel context does not necessarily motivate travel because motivation is stimulated by higher-level socio-psychological needs (self-achievement), whereas safety concern is an elementary need of avoiding unpleasant situations/death ( Aebli et al. , 2022 ). Tourists exhibit a “zone of tolerance” when faced with high motivation and high risks, appearing to be willing to modify their behavior if the overall perceived risk associated with travel does not exceed an individual threshold. In dealing with a crisis (COVID-19 pandemic) some tourists may still travel and apply risk reduction strategies, while others will desist from traveling ( Aebli et al. , 2022 ). Risk-related behavior can be determined by the risk category and perceived behavioral control, indicating potential differences in crisis resistance ( Hajibaba et al. , 2015 ). Neuburger and Egger (2021) identified segments of travelers with distinctive characteristics based on their perceived risk of traveling during the pandemic and changes in travel behavior: the anxious, the nervous and the reserved. Our study identified four different risk-related tourist segments based on the psychological constructs embedded in the human values and showed that there was a clear dominance of the risk-tolerant personalities (34%), followed by the risk-indifferent (22.7%) and risk-sensitive (23.2%) groups. The smallest share was present in risk-resistant one (20.1%). Furthermore, differences in socio-demographic characteristics of defined cluster groups were present.

Conservatism is the supreme factor influencing the behavior of risk-sensitive group (Cluster 1). This group is dominated by middle/aged and senior respondents (aged 55 plus), with a slight prevalence of women. Moreover, this group is characterized by modest education (secondary), low to medium household income coming from wages or pensions and slightly lower subjective health perception compared to other groups. They indicate the insignificant effect of “openness to change” in their value system. Travel abstinence behavior is expected, reflected in a significant decline in travel members of a risk-sensitive group in proximal post-pandemic circumstances. However, younger, healthier or more financially secure members of this cluster may continue to travel domestically, or in exceptional cases, regionally. Travels will most likely be organized strictly according to social norms and defined rules, with extreme caution toward the pandemic flow and outstanding prudence in cost–benefit evaluations.

The risk-indifferent group (Cluster 2) maintains slight domination of middle-aged and older men, living as couples or families. Average education (secondary or tertiary), medium to high household incomes and good to fair subjective general health are present. This group reflects the more modes to influence of both “openness to change” and “conformity” factors. However, they demonstrate greater affinity for “making own decisions and being free” while opposing “proper behavior and following the defined rules.” Such group characteristics are in line with “invincible me” as proximal travel behavior ( Miao et al. , 2021 ), whose behavioral patterns reject the existence of “potential risk” and follow societal rules, strongly opposed to changing their travel habits. Despite being relatively less interested in travel, this type of personality is likely to avoid sustainable destinations and seek hedonic destinations that do not have bans and rigorous regulations. This personality trait alarms for potentially problematic behavior and avoidance of defined rules, especially concerning social distancing.

Risk-tolerant and risk-resistant groups are those whose travel needs have the potential to overpower the risk perception in proximate post-COVID-19 travels. The dominant risk-tolerant group (Cluster 3) displays equal distribution of age and gender. There is a domination of couples and family living, with the prevalence of secondary education, medium to high-income levels and excellent health condition. Exhibiting a high level of “openness to change” and equally important values related to personal safety and security, their behavior resembles Miao et al. (2021) “corona light” rational travel behavior. Such personalities are likely to make careful choices keeping up to the well-known domestic or regional destinations to ensure safety and confidence, excluding all potential risks (overcrowding, swift changes in prices and regulations).

The humblest presence has the risk-resistant group (Cluster 4) whose willingness to travel is dominated by the “openness to change” values. The risk-resistant group is significantly younger than other groups, made of economically independent (mostly employed) and/or financially supported youth, with better financial status, very healthy and predominantly living in a household composition of three or more. Highly educated juvenile males dominate in this cluster. This kind of personality likes to try new and different things, have a good time, make their own decisions and seek adventures, pleasure, fun and excitement. Personal safety and security are of lesser importance, indicating extremely intense travel needs that suppress risk-related factors, even in times of pandemics (in line with findings of Hajibaba et al. , 2015 ). It stands for “binge” travel behavior ( Miao et al. , 2021 ) when personalities try to compensate for a previously experienced loss. Apart from great willingness to travel, this group is directed to more sustainable travel choices, far-away destinations providing more stimulation and experiences than standardized tourist products, but mostly staying within the European continent. Despite frequent findings that crisis-resistant tourists are often single (family-free), Hajibaba et al. (2015) found that internal crisis-resistant tourists are more frequently traveling with their partner and children (30.5%) and less alone (13.3%). According to Kim et al. (2021) , the compensatory tourism consumption may be expected after the pandemic crisis, as COVID-19 will likely increase the latent travel demand. Exotic destinations with low infection numbers and mild governmental bans will become extremely popular among higher-income segments of risk-resistant and risk-indifferent groups.

The geographical distribution of cluster groups within countries show the dominance of risk-resistant groups in Iceland, The Netherlands, Switzerland and Sweden (> 30%), whose tourist stereotypes indicate very active and adventure-seeking personalities ( Terzić et al. , 2021 ). Risk-tolerant groups dominate in Slovenia (>60%), Cyprus (>50%), Belgium, Austria, Spain, Denmark, Croatia, Czechia (>40%), Finland and Germany (>30%). The latter marked as countries with stable economies, high quality of life index and sustainable tourism performance ( Băndoi et al. , 2020 ) reflect in well-established travel habits (with over 80% travel-active populations).

The results revealed the high presence of risk-sensitive groups in Poland, Italy, Serbia, Bulgaria and Croatia. These countries have a lower quality of life index and a modest share of active tourist populations ( Băndoi et al. , 2020 ), characterized by a predominance of domestic travel ( Figure 5 ). Such behavior is also in line with common traveling patterns of popular destination countries like France, Croatia, Greece, Italy, Montenegro and Spain, whose domestic tourism is traditionally higher than outbound ( Figure 5 ).

Relatively passive tourists, placed under the risk-indifferent group, can be found in the most significant numbers in Lithuania (40%), Portugal and Iceland (>30%), Bulgaria, Estonia and Norway (>25%). The determining psychological trait for this group is strong opposition to “following rules and proper behavior” and lack of travel motivation. This cluster represents personalities (stereotypes) that are considered rather passive and domestically oriented, such as people in Bulgaria and Portugal, but are also present in countries with predominantly active tourist populations, such as Norway, Iceland and Estonia. Such personalities indicate a lack of need for exploration and unwillingness to exit a personal comfort zone. Strong opposition to confine to defined behavioral rules and avoidance of restrictive measures in times of pandemic will inevitably lead to the favorization of domestic travels within this group.

Conclusions

Travel bans were lifted to a large extent by March 2022, announcing pandemic relief. Nevertheless, a new crisis in the form of the Russian–Ukrainian war ensued, bringing concerns about the possible escalation of the conflict in Europe. The latter have swiftly shifted media attention and public worries from COVID-19 to another topic. Yet, the tourism recovery process has already started, still strongly dependent on people’s travel motivations and overall risk perceptions. The new circumstances brought a reasonable question of what to expect in the forthcoming period. Mindful observations indicated that two crucial factors are likely to determine the future of tourism: politics and personalities. Politics and regulatory policies immensely impact travel intentions and decisions, in line with the slow international tourism recovery process. General risk perception in times of pandemic went beyond health-related issues, spilling over economic and regional political tensions, while controversial travel bans and sanctions affected not only targeted countries but perceptions of travel risks in general. The enforcement of quarantine on international travelers has immense implications for the relative attractiveness of countries to tourists after lockdowns have been eased. The safe resumption of outbound tourism will continue to highly depend on a coordinated response among countries considering travel restrictions, harmonized security and hygiene protocols, as well as effective communication to help restore consumer confidence and trust.

Risk perceptions and behavioral responses differ significantly between individuals, groups and even nations. In general, European tourists proved to be crisis-tolerant as their travel intentions remained strong despite pandemic concerns and all existing obstacles to travel. As people’s travel needs were suppressed and significantly magnified due to the previous long-term restrictions, psychological constructs will likely prevail on the demand side. Along with various socio-economic aspects, evaluation of personal health status, reliability of governmental decisions and available health services in destination countries will be very important for the decision-making process. Tourists will undoubtedly seek the most flexible destination countries, providing some confidence in risk mitigation and the lowest impact on satisfaction aspects.

Theoretical and practical implications

This study has important implications for theory and practice, extending the body of knowledge on consumer behavior by investigating the effects of perceived risks on travel motivation and behavioral intention in the context of European tourists projected to post-pandemic circumstances. In a theoretical context, the use of psychological constructs for explaining travel motivation and behavior supports predicting tourist flows and related economic effects in the tourism recovery process. Risk perception and travel needs act in opposing directions when it comes to travel motivation and related behaviors. Furthermore, the proposed model provides evidence that values can affect travel motivation and behaviors to a great extent. Also, other internal and external factors influence travel motivation as well. The findings of this study indicated that despite the fact that perceived risks of COVID-19 negatively affected travel motivation, it has increased significantly travel needs and changed travel preferences in the post-pandemic period. Tourism experiences act as sort of stress relievers, as leisure travels provide opportunities for relaxation, physical and mental recovery (“escapism” and “discovery”). Two years of constant COVID-19-related stress, produced by various reasons, increased people’s need for recovery, a need for movement, a need for travel. However, those needs are not of same importance and strength for all. The given construct provides some indication that various nations differ significantly in these aspects, which influence their travel behavior. Thus, outlining the existence of specific tourist stereotypes is based on their demographic and cultural background. Due to predicted changes in tourist behavior, the tourism industry could benefit from observing psychological characteristics and demands of specific market segments (clusters), and in line with this, re-evaluate tourist product development. The proposed model illustrates the importance of combining different methodologies and data sources to produce tourism forecasts. Empirical studies providing reliable quantifications and predictions in tourism were lacking, and the comparability and replicability of such models were often questionable. In this line, the ESS database provided valuable source of information with a potential to indicate travel motivation (psychological constructs), socio-demographic, economic and health-related indicators for various European countries. Those were utilized in the construct and transformed to a tourism-prediction model. The developed model is applicable to various types of risks that might have direct effects on tourism flows. Contrary to the pandemic that had global and rather uniform characteristics, the effects of other types of risks are more localized, reflected in most negative effects on the tourism sector within affected and nearby countries.

As the research indicates potential demand markets across Europe, practical implications lay in given insights into basic behavioral information for developing post-pandemic tourism recovery strategies. Although coordinating tourism recovery strategies across Europe may seem politically difficult, insight into psychological constructs (motivational aspects), tourism trends and related behavioral patterns of Europeans may provide solutions to dealing with potential risks. Various measures defined by tourism policy regulators on the national level to control the pandemics strongly affect tourist decision-making, obstructing travel intentions more severely than health concerns. Providing some expectations on behavioral aspects among tourists, appropriate and timely responses in the risk management process can be provided. The effects of the COVID-19 pandemic produced increased demand for personal safety and security grants during the travel planning process. Despite expectations of fast tourism recovery, the effects of pandemic on tourism industry, caused by deep psychological distress, will last for long time and will be less predictable. Destination policymakers are especially interested in ensuring destination safety, which can be accomplished by communicating destination trust to reduce fear and uncertainty among tourists. Addressing the most vulnerable groups and encouraging their travel participation by increasing destination trust, while identifying and managing potentially risky/unwanted behaviors, is regarded as beneficial in shaping future travel intentions.

Limitations and recommendations for future research

The research has several limitations. First, ESS data were collected before the pandemic and do not include information on tourism participation nor risk perception measured during the pandemic. Therefore, it provides limited tourism-related predictions, while geographical distribution of clusters (potential tourism markets) must be taken into consideration with caution and with support of other more recent of future empirical findings. Second, the model is fully driven by internal factors – motivation. Investigation of additional variables, especially those related to social and economic aspects, and some external factors of influence on travel behaviors, can provide more precise scientific reasoning. Effects of political stability and confidence are also of great importance in this particular timeframe and current crises. The proposed basic model can be upgraded to create more complex theoretical construct with higher predictability potential and continuously replicated and tested. The next ESS Round 10 (2020/2021) will include COVID-19-related questions that open new research possibilities for testing the presented results. Exploring similarities and differences between European nations in terms of travel needs and cultures, as well as the formation of tourist stereotypes, are of future research interest. Comparing results to similar research in different cultural and geographical settings is also a challenge.

A post-COVID-19 travel behavior model

Visualization of the impact of risk on travel behavior of specific personalities (cluster groups)

Geographical distribution of risk-related personalities within Europe

Spatial distribution of dominant types on regional (NUTS 3) level

Common travel patterns of European nations

Clusters reflecting psychological types (personalities) of the potential tourists

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Where Americans Are Traveling in 2024: By the Numbers

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Americans are traveling abroad in droves.

The number of U.S. citizens flying to international destinations reached nearly 6.5 million passengers in March, according to the International Trade Administration. That’s the highest March total in over five years and shows that the post-pandemic “revenge travel” trend is the new normal.

It wasn’t just March, which usually sees a spike in international departures for spring break. In every month of 2024 so far, more Americans left the country than last year and 2019. These trends point to a blockbuster summer for overseas travel.

Nearly half of Americans (45%) plan to travel by air and/or stay in a hotel this summer and expect to spend $3,594 on average, on these expenses, according to a survey of 2,000 U.S. adults, conducted online by The Harris Poll and commissioned by NerdWallet.

That's despite rising travel prices that have caused some hesitancy among would-be travelers. About 22% of those choosing not to travel this summer cite inflation making travel too expensive as a reason for staying home, according to the poll.

So where are traveling Americans going? And what does it mean for those looking to avoid crowds of tourists and higher travel prices?

New travel patterns

Nearly every region in the world saw an increase in U.S. visitors in March 2024 compared with March 2023, according to International Trade Administration data. Only the Middle East saw a decline of 9%. Yet not every region saw the same year-over-year bump. U.S. visitors to Asia saw a 33% jump, while Oceania and Central America each saw a 30% increase.

Comparing 2024 with 2023 only tells part of the story, however. The new patterns really emerge when comparing international travel trends to 2019. For example, Central America received 50% more U.S. visitors in March 2024 compared with March 2019. Nearly 1.5 million Americans visited Mexico, up 39% compared with before the pandemic. That’s almost as many visitors as the entire continent of Europe, which has seen a more modest 10% increase since 2019.

Only Canada and Oceania saw fewer visitors in March 2024 than in 2019, suggesting that interest in these locations has not rebounded. Indeed, the trends indicate a kind of tourism inertia from COVID-19 pandemic-era lockdowns: Those destinations that were more open to U.S. visitors during the pandemic, such as Mexico, have remained popular, while those that were closed, such as Australia, have fallen off travelers’ radars.

Price pressures

How these trends play out throughout the rest of the year will depend on a host of factors. Yet, none will likely prove more important than affordability. After months of steadiness, the cost of travel, including airfare, hotels and rental cars, has begun to sneak up again.

About 45% of U.S. travelers say cost is their main consideration when planning their summer vacation, according to a survey of 2,000 Americans by the travel booking platform Skyscanner.

That’s likely to weigh further on U.S. travelers’ appetite for visiting expensive destinations such as Europe, while encouraging travel to budget-friendly countries. It could also depress overall international travel as well, yet so far, Americans seem to be traveling more.

For those looking to avoid crowds while maintaining a budget, Skyscanner travel trends expert Laura Lindsay offered a recommendation many of us might need help finding on a map.

“Albania has been on the radar of travelers looking for something different,” Lindsay said. "Most people have yet to discover it, but flights and tourism infrastructure are in place, and there are fewer crowds in comparison to trending European destinations like Italy, Greece, or Portugal.”

On the flip side, American travelers looking to avoid crowds of compatriots would do well to avoid Japan, which has seen a staggering 50% increase in U.S. tourists between March 2019 and 2024.

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Toward a ‘New Normal’? Tourist Preferences Impact on Hospitality Industry Competitiveness

Maria teresa cuomo.

1 Department of Economics and Statistics, University of Salerno, via Giovanni Paolo II, 132, 84084 Fisciano, SA Italy

Debora Tortora

2 Department of Business and Law, University of Milan ‘Bicocca’, Milano, Italy

Alessandro Danovi

3 Department of Business, University of Bergamo, Bergamo, Italy

Giuseppe Festa

Gerardino metallo.

The recent outbreak of novel coronavirus (Covid-19) has led to a global panic due to its fatal nature which has harshly impacted the tourist sector and on the place reputation in general. This study aims to compare the factors that develop tourist preferences in terms of (i) what drives the favorability of tourist preferences? (ii) what relationship exists between tourist expectations, proximity, and favorable reputation? and (iii) what are the main influences of tourist preferences on hospitality system competitiveness pre and post Covid-19? By employing structural equation modeling, this study advances knowledge into the research variables’ relationships and advances reputation and marketing performance and practices in the hospitality industry.

Introduction

The magnitude and severity of the Covid-19 pandemic have dealt a heavy blow to the world travel and tourism sector, with profound economic and social repercussions (Mathieson and Wall 1982 ; Sigala 2020 ) on the entire supply chain and on the hospitality system, in particular. Just to have a benchmark, the Covid-19 outbreak impact on the American travel industry in 2020 was about nine times of that from 9/11. Hotel room revenue was cut in half, from $167 billion to $85 billion. Hotels were running at about 44% occupancy in 2020, down from 66% in 2019 (Kwok 2021 ). Furthermore, the impact of Covid-19 on business travel has varied (from April to December 2020) by region with huge contractions: in North America it declined by 79%; Western Europe 77%; Latin America 59%; Eastern Europe 63%; and Asia Pacific and Middle East and Africa 52% (Stimson 2021 ). Thus, very deep wounds will probably mark a change of direction in the way the tourism offer is provided (Hall et al. 2020 ; Gössling et al. 2020 ). With an overall rethinking, the tourist industry will have to show an unprecedented capacity to serve the changing needs of the tourist, so as to preserve the sector reputation, while at the same time trying to bring out alternative tourist needs (Nientied and Shutina 2020 ; Wachyuni and Kusumaningrum 2020 ). Ability to reorganize and reactivate the offer, together with an effective interpretation of the demand (Sigala 2020 ), will be the new keywords to remain competitive. Will the hospitality industry be able to capitalize in the moment?

Accordingly, the aim of the study is to provide insights that will help hospitality system to understand and interpret new tourist preferences that can build new normality, based on alternative formulas to capture tourists in line with emerging market sensitivities. Considering these arguments and this new context, the current research aims to provide responses to the following queries: (i) Which factors develop tourist preferences? (ii) What drives the favorability of tourist preferences? (iii) Is there a relationship between tourist expectations, proximity, and favorable reputation? and (iv) What are the main influences of tourist preferences on hospitality system competitiveness?

To answer to the abovementioned questions, a conceptual model based on these relationships is developed. To address these relationships, we will use the theory of needs and the theory of demand with variable consumer preferences. Then, the research seeks to examine preferences of tourist about factors that potentially explain expectations, proximity, and reputation and to study whether and how the tourist preferences may influence the hospitality system competitiveness in pre-Covid-19 and during and post-Covid-19 pandemic, using empirical testing of data collected on a sample of 441 tourists in Italy.

The tourism sector represents a perfect scenario for the analysis, due to a sad record: it was the first sector to face the catastrophic and devastating effects of the viral emergency, with evident and current difficulties both in terms of the sector's capacity to maintain and traces of recovery on the outlet markets (Fiavet 2020 ). It should also be remembered that most of the tourism activities are related to hospitality and, therefore, require contact often—direct and physical—with potential users. That makes difficult to respect the necessary and inevitable ‘social distancing’ practices in the management of the relationship with the virus (Wen et al. 2020 ) and often brings international visitors to the decision to abandon the trip (for 1 out of 4 tourists, UNWTO 2020 ). This prerogative of tourism production systems, in this historical phase, is supposed to be a high critical factor, imposing a radical revision of the internal organization and business models for the benefit of workers and tourists (Sigala 2020 ).

It is always difficult to venture predictions and less than ever in such a picture of uncertainty. Indeed, the countless numbers of forecasts announced in last months by the experts and the press shared a common view: a paralysis of the sector. The most credited Italian estimates, in fact, foresee overall decreases in turnover of almost 30 billion, with an equally significant decrease for the incoming tourism, reduced by 260 million admissions (− 43,4% in 2020 compared to 2019, Cst 2020 ), with a drop in the connected tourist expenditure of around 4.5 billion (Demoskopika 2020 ). Depending on the duration of the outbreak, then, the companies in the travel and tourism chain could even double their loss.

In this light, the paper is structured as follows: it starts with an explanation of the conceptual model and presenting a series of hypotheses. Next, the paper sets out the research method. A large-scale field survey investigation is undertaken to examine the results of the research hypotheses. Finally, discussion, implications, and conclusions are presented.

Theoretical Background and Conceptual Framework

Far beyond analyzing the appropriateness of the interventions—public and private—put in place so far for the support of the tourism sector (which perhaps deserves further study), it is worthwhile to focus the discussion on the responsiveness of the players of the segment to the changes that have occurred (Cillo et al. 2021 ). It is not yet clear if and when it will be possible to restore the status quo ante . However, the tourist offer should adopt a step-by-step approach. Therefore, after the initial moment of the health emergency, to be addressed by trying to resist and limit damages, in the current period of coexistence with the virus, it will be necessary to first manage the emerging needs required by the tourist (i.e., a need for security, Nientied and Shutina 2020 ). Appropriate reassurance actions will make it possible to recover the trust relationship with the target audience, sometimes limited by crisis information systems and communication (Yu et al. 2020 ). Only after having stimulating and reorganizing the production of tourist services, it will be possible to proceed with initiatives to stimulate the demand in terms of expectations, preferences, proximity, reputation, and impacts on hospitality system competitiveness (Sukumar et al. 2020 ). Moving toward the return to normality, it will be necessary to strengthen the tourist offer with renewed sense contents, obviating the age-old problem of overcrowding from mass tourism.

The conceptual model applied in this study is based on two theories. The first one is the theory of needs (Maslow’s hierarchy of needs), while the second is the theory of demand with variable consumer preferences (Basmann 1956 ). The well-known Maslow theory of needs is considered to define the quality of service as a definition of customers’ needs. This is particularly true in the tourism sector, whereas tourist expectations may be very consistent in the definition of the attributes of the supply (Bi et al. 2020 ) and in the following definition of preferences.

The theory of demand with variable consumer preferences is based on the fact that individual consumers have no unique ordinal utility index function, that is conversely replaced by a family of ordinal utility functions to be maximized, thus defining advertising elasticity of demand to be satisfied (Chen 2015 ). In this research, tourist preferences may be considered as a second-order factor, based on inter-correlations among several first-order factors (i.e., tourist expectations, proximity and place/destination reputation). We employed and extended tourist preferences patterns to develop the conceptual model that considers preferences directly affecting vacationer choices in terms of hospitality. The results can be useful to enable managers of the hospitality industry to better understand the competitive positioning of their organizations in the marketplace (Hsu et al. 2009 ) and to define the strategies and actions able to enhance the competitiveness of the entire system.

Hence, Fig.  1 presents the conceptual model applied in this study.

Conceptual framework

Tourist Expectations and Preferences

According to the literature, expectations refer to the aspects, both tangibles and intangibles, that tourists wish or are expected to find in the supply. In that sense, they identify a benchmark to determine customer satisfaction (Pleger Bebko 2000 ; Tripathi and Siddiqui 2010 ). Always in line with previous studies (Banerjee and Chua 2016 ; Dube and Renaghan 1999 ; Parasuraman et al. 1991 ; Radojevic et al. 2018 ), the factors included in the present analysis are retrieved by service quality measurements described by the Servqual scale (Parasuraman et al. 1985 ; Zeithaml et al. 1990 , 1993 ), and are confirmed as a stable tool for measuring service expectations—and perceptions—across service industries (e.g., hospitality services). According to the Servqual scale, the items to be considered are tangibles, reliability, empathy, assurance, and responsiveness (Parasuraman et al. 1991 ). Moreover, our analysis will focus exclusively on expectations because the aim of the research is to measure the system of preferences of the hospitality systems, thus overlooking the next post-positivistic model that takes into account three stages of consumer decision processes: pre-purchase influences and decision- making, post-purchase evaluation, and future decision-making (Chen and Gursoy 2001 ; Moutinho 1987 ; Mazursky 1989 ).

Therefore, the firm’s ability to collect and use information about customer needs, called market-sensing capabilities (Likoum et al. 2018 ), has a positive influence on tourist services planning and need to be constantly increased, in order to intercept future requirements and desires of the demand. This ability to sense and react to the changes of consumer needs and desires, especially linked to crisis events, updating and increasing the value offering, represents a critical factor for maintaining and increasing competitiveness of the hospitality system and the corporate reputation as well (Chun 2005 ; Kircova and Esen 2018 ; Pritchard and Wilson 2018 ).

Based on these considerations, the first hypothesis is as follows:

The World Tourism Organization (UNWTO)

Proximity and Tourist Preferences

Carrying on the analysis, the study suggests that tourist preferences are affected by proximity in terms of cultural and physical distance. The first dimension—cultural distance (Boschma 2005 ; Hofstede 1983 , 2001 ; Rodríguez-Pose 2011 ; Rutten and Boekema 2012 ; Torre 2008 )—is expressed furthermore as traditions, history, food, etc. of the country of destination; it is very relevant in the assessment of tourism services (Ahn and McKercher 2015 ; de Carlos et al. 2019 ; McKercher and Du Cros 2003 ). The latter—physical distance—, refers to the perceived attractiveness of the destination/accommodation, influenced by a barycentric location, according to tourists planned tours. On this stance, distance does not only represent a physical parameter, but it is related to a psychological and subjective understanding of the tourists’ appreciation of places, perceived as attractive to visit (Jeuring and Haartsen 2017 ) and accommodations adequate to their standards and desires. Therefore, many tourists consider places near home too familiar and ordinary to satisfy their needs of escape, sense of discovery, searching for exciting experiences associated with being on holiday (Nicolau 2008 ). In addition, instead of the objectively measured spatial separation, the relational aspects between objects—attractions—across space and their contextualization become meaningful (Larsen and Guiver 2013 ; Larsen 2015 ). Thus, the second hypothesis is the following:

Proximity – in terms of physical and cultural distance – has a positive impact on tourist preferences.

Reputation and Tourist Preferences

Among numerous definitions of reputation (Fombrun and Shanley 1990 ; Fombrun 1996 ; Wagner and Peters 2009 ; Urde and Greyser 2015 ), we focus on the tourists' viewpoint. Hence, according to the tourist perception, it can be taken into consideration his/her overall evaluation of a firm, based on his/her reactions to the firm’s products, services, communication activities, interactions (Walsh and Beatty 2007 ). Then, adapting the concept to a place/destination assessment, we considered its celebrity and offer in terms of attractions to visit or arranged for entertainment, which may influence the price/quality ratio. Moreover, promotional activities dynamically contribute to generating the tourist idea about destination. Consequently, a favorable reputation protects an area and its economic operators/stakeholders against the adverse event, as in health crisis, reassuring vacationers on the engagement of the whole system in making all the proper actions to contrast negative phenomenon (Coafee and Rogers 2008 ; Cillo et al. 2021 ). Tourists, on their hand, have a propensity for according a greater trust on such operators compared to destinations with a lower reputation (Foroudi et al. 2016 ). Hence, investing in a place/destination reputation constitutes a strategy that both public and private partners need to reinforce, as confirmed by the Covid-19 pandemic event. Thus, the third hypothesis is as follows:

Place reputation has a favorable impact on tourist preferences.

Tourist Preferences and Hospitality System Competitiveness

Then, we investigated the influence of tourist preferences on hospitality system competitiveness in terms of infrastructures, technology and innovation, history and culture, and macro-environment (Kim et al. 2019 ). Numerous and well managed public infrastructures (Bahar and Kozak 2007 ; Bordas 1994 ; Crouch and Ritchie 1999 ; Dwyer and Kim 2003 ; Enright and Newton 2005 ; Gooroochurn and Sugiyarto 2005 ; Kozak and Rimmington 1999 ), make the tourist experience easier, permitting the host to concentrate on the valuable aspects of the vacation. In addition, well-developed technology and innovation have a relevant impact on the tourist experience (Bordas 1994 ; Chon and Mayer 1995 ; Gooroochurn and Sugiyarto 2005 ; Heath 2003 ). Many studies underlined the unavoidable impact of the development of ICT (Ciampi et al. 2021 ) on the growing attractiveness of destinations and accommodations, increasingly characterized by intensive information sharing and value co-creation (Akehurst 2009 ; Porter and Heppelmann 2014 ; Da Costa Liberato et al. 2018 ; Stamboulis and Skayannis 2003 ). Therefore, the culture of sharing and its participatory implications are becoming more and more part of the travel experience for experts and scholars in the sector. Finally, numbers and variety of cultural attractions and places to visit—macro-environment—increase the hospitality system competitiveness (Bordas 1994 ; Chon and Mayer 1995 ; Crouch and Ritchie 1999 ; Dwyer and Kim 2003 ; Enright and Newton 2005 ; Sukumar et al. 2020 ), diversifying the offering in response to the tourist requests and satisfaction (Hong et al. 2020 ). Hence, we formulated the last hypothesis:

Tourist preferences have a positive effect on hospitality system competitiveness.

Data Collection

To afford our research questions, we collected data regarding tourists’ perception before and during and post the pandemic crisis. The reason why to choose Italy is the importance of the tourism and hospitality sector, which is one of the key economic drivers of the Country (telegraph.co.uk 2020 ). However, due to Covid-19, the sector had to face issues globally. The research illustrated that Covid-19 pandemic has significant influences on revenues of the sector by diminution over 40 billion euros, compared to the same period of the earlier year (Statista.com 2020 ).

To analyze the effects of Covid-19 pandemic on the hospitality system, this study concentrated on the demand for accommodation services based on two main reasons.

• (i) The accommodation facilities, initially and still today with great difficulties, had to respond to the changing needs of the tourist, both in that they are not really ready but above all because they had to wait to receive regulatory guidelines and address regarding the methods of providing the services and the time of reopening. This has been confirmed by a sample of hotel structures and territorial tourism development actors who have confirmed the difficulty in responding to potential changes without prior government indications. In this regard, the opening and service protocols have been issued only recently (05-11-2020) connected to the impossibility of moving among Italian regions (06-03-2020). In any case, the analysis of the offer could hardly have made explicit the changes in the expectations of the demand and in the new tourist behaviors (during and post-Covid). On the other hand, the analysis of the demand conducted in the paper has allowed and allows better to bring out the changing needs of the demand in terms of tourist preferences.
• (ii) The analysis directly observed the change of attitude of tourists who represent the real actors on which the changes are brought by the pandemic, only as a consequence reversed on the hospitality structures.

We distributed a questionnaire among social media and tourism association in Italy between April and June 2020. We got 473 answers, 441 of which were considered usable. Table ​ Table1 1 illustrates that the sample was composed by a slight majority of female (52.4%) young (born between 1991 to 2000 42.4%); elevated: graduated at secondary school (47.8%), and postgraduate (40.6%). 68.9% of the participants had traveled for vacation around three times during last year (16.1%). 50.6% of the applicants were interested in visiting.

Participant characteristics

We built the research item measurements according to the literature review and earlier researches. We used six items to measure expectations via five constructs: tangibles, reliability, responsiveness, assurance, and empathy (Banerjee and Chua 2016 ; Dube and Renaghan 1999 ; Parasuraman et al. 1991 ; Radojevic et al. 2018 ). Proximity was assessed by cultural distance (Boschma 2005 ; Hofstede 1983 , 2001 ; Rodríguez-Pose 2011 ; Rutten and Boekema 2012 ; Torre 2008 ) and physical distance (Ahn and McKercher 2015 ; de Carlos et al. 2019 ; McKercher and Du Cros 2003 ). The measurement items for reputation were assessed with four items: image, communication, price/quality relation, and attractions (Fombrun and Shanley 1990 ; Fombrun 1996 ; Wagner and Peters 2009 ; Urde and Greyser 2015 ). Tourist preferences were tested as a single item (Lockyer 2005 ). In addition, hospitality system competitiveness was expressed with four items: Infrastructure (Bahar and Kozak 2007 ; Bordas 1994 ; Crouch and Ritchie 1999 ; Dwyer and Kim 2003 ; Enright and Newton 2005 ; Gooroochurn and Sugiyarto 2005 ; Kozak and Rimmington 1999 ), Technology and Innovation (Bordas 1994 ; Chon and Mayer 1995 ; Gooroochurn and Sugiyarto 2005 ; Heath 2003 ), History and culture (Bahar and Kozak 2007 ; Crouch and Ritchie 1999 ; Draper et al. 2011 ; Dwyer and Kim 2003 ; Enright and Newton 2005 ; Go and Govers 2000 ; Heath 2003 ; Kozak and Rimmington 1999 ; Mazanec et al. 2007 ), and Macro-environment (Bordas 1994 ; Chon and Mayer 1995 ; Crouch and Ritchie 1999 ; Dwyer and Kim 2003 ; Enright and Newton 2005 ). Table ​ Table2 2 illustrates the item measurements and references, while the full questionnaire is included in Table ​ Table2. 2 . We used a seven-point Likert scale(1 = min importance, 7 = max importance).

Measurement model evaluation for constructs

Analysis and Model Testing

We examined the research model by using the partial least squares structural equation modeling (PLS-SEM). Based on the number of items together with sample size, PLS-SEM is the better software, as it avoids the constraints of AMOS (Hair et al. 2014 ). In this study, we employed the measurement and structural models.

Measurement Model

To examine the reliability and validity, the measurement model was used as a preliminary inspection of the construct’s performance within the entire sample. Cronbach’s α and composite reliability were assessed for internal consistency reliability and the items are satisfactory (an α and CR above 0.80) (Nunally and Bernstein 1994 ). Discriminant validity and convergent validity (AVE) were tested for each variable. Table ​ Table2 2 shows that the results of AVEs for variables are above 0.50 (Field 2013 ). In addition, the indicators’ outer loadings on a construct signifying the discriminant validity is attained (Chin 1998 ). The results confirmed the respectable reliability of all measures. Table ​ Table3 3 demonstrates the correlations between the research constructs.

Correlations between constructs

**The correlation is significant at p > 0.01

Structural Model Assessment

We assessed the structural model results after confirming the construct measures. The collinearity between the constructs was tested before examining the path coefficient assessment. By examining each set of predictors in the structural model for collinearity, each predictor shows the Variance inflation factors (VIF) value was lower than 0.5. Then, we evaluated the significance of path coefficients to explore the hypothesized relationships proposed by the research conceptual model. As Table ​ Table4 4 demonstrates, the importance of the research path coefficients was tested by employing 5000 bootstrapping to create t -statistics.

Path coefficients

The statistics demonstrated that H1, the impact of tourist expectations on tourist preferences (pre-Covid: β  = 0.600; post-Covid: β  = 0.776, p  < 0.001) was significant from both samples. H2, the impact of proximity on tourist preferences was supported refering to within/pre-COVID ( β  = 0.217, p  < 0.001); however, the relationships were insignificant refering to post-Covid ( β  = 0.034, p > 0.001). H3 was supported (pre-Covid: β  = 0.337; post-Covid: β  = 0.245, p  < 0.001) and it shows a positive impact of place/destination reputation on tourist preferences. H4 is also supported (Pre-Covid: β  = 0.626; post-Covid: β  = 0.626, p  < 0.001) showing the strong impact of tourist preferences on hospitality system competitiveness.

Lastly, we estimated R 2 values in the path model for the endogenous variables. The R 2 values of our model demonstrated some degree of relationships and clarified over 0.928% of the variances of tourist preferences. To improve the predictive accuracy, we employed Stone-Geisser’s Q 2 value by employing the blindfolding technique for an omission distance of D  = 7. Hair et al. ( 2014 ) stated that the model could be trusted when the predictive relevance of Q 2 is larger than 0. Based on the results illustrated in Table ​ Table5, 5 , there is a support for the model’s predictive relevance (Chin 1998 ).

Results of R 2 and Q 2 values

Discussions and Implications

Based on the aim of the paper and to minimize the gaps previously underlined, we employed and extended tourist preferences patterns in order to develop our conceptual model (Fig.  1 ) that considers preferences directly affecting travelers’ decisions in terms of hospitality. The results can be helpful to enable operators of the tourism industry to better interpret the new needs of the marketplace (Hsu et al. 2009 ) improving the competitiveness of the entire hospitality system.

On this stream, the analysis carried out on pre-Covid and during and post-Covid pandemic is suitable in underlining that when a tourist defines his/her criteria to choose toward lodging, food and drink services, transports, events (Chiang et al. 2019 ) to attend, and attractions to visit, the first-order factors identified are very consistent and relevant. It is clear the strong tie between tourist expectations and tourist preferences, as demonstrated in H1. In fact, both in pre-Covid and during and post-Covid measurements, the impact of expectations on tourist preferences is observed, indicating that they are scarcely affected by external adverse conditions, e.g., the pandemic event. Hence, the outcomes highlight in terms of theoretical implications that the firm’s ability to collect and act on information about tourist desires has a positive influence on tourist services planning and need to be constantly increased, intercepting future requirements and aspirations of the demand (as widely demonstrated in previous studies: Banerjee and Chua 2016 ; Dube and Renaghan 1999 ; Parasuraman et al. 1991 ; Radojevic et al. 2018 ). Practically speaking, tourism operators need to really engage in the dialog with customers; social media, for instance, may constitute very interesting tools to directly connect with them (Cuomo et al. 2021 ).

Moreover, the study hints that tourist preferences are affected by proximity, expressed as a cultural and physical distance in H2. Employing this perspective, we may interpret the results of this research. They show that the impact of proximity on tourist preferences was supported with reference to pre-Covid time. However, the relationships were non-significant when referred to during and post-Covid. This likely expresses a theoretical implication, whereas pandemic outbreak actually has modified the Maslow's hierarchy of needs, ratifying the renewal of safety requirements in terms of personal security and relocating the relevance of proximity—conceived as similarity/closeness instead of distance (Diaz-Soria 2017 )—in tourist preferences. However, deeply analyzing the results, it is evident that in the during and post-Covid, the proximity dimension—both in terms of cultural affinity (Hofstede 1983 , 2001 ) and physical closeness of the destination (Ahn and McKercher 2015 ; McKercher and Du Cros 2003 )—can better satisfy the safety needs aforementioned, encouraging tourists to prefer less exotic or faraway destinations (Ahn and McKercher 2015 ). In this sense, local, regional, or national destinations have been preferred by 72,3% of the sample as a goal of their next vacation, while 12.2% declare they will not go on holidays in 2020. From a practical point of view, this outcome means that closer destinations communicate to the travelers a major sense of control and security, due to a better and easier knowledge toward national procedures and regulations adopted for the progressive resumption of tourism services and for health protocols in Italian hospitality establishments. So, proximity can be considered a ‘new commodity’ and the appreciation of the home region/nation as an appealing form of a tourism destination. In economic and managerial terms, while dramatically changing travel patterns on industry and destinations, Covid-19 crisis creates opportunities for sustainable and proximity tourism (Jeuring and Haartsen 2017 ; Higgins-Desbiolles et al. 2019 ; Romagosa 2020 ). As a matter of fact, home trips may also support a different type of tourism, more respectful of nature and of the visited communities, avoiding mass tourism destinations, where the health danger remains more uncertain (Jamal and Budke 2020 ). If accurately planned and incentivized, with both public and private support, this contingent variance on the tourism pattern may represent a durable response to the over-tourism phenomenon (Goodwin 2017 ; Koens et al. 2018 ; Milano et al. 2018 ), affecting many Italian cities (the case of Venice, Seraphin et al. 2018 ), while in the meantime less famous or popular destinations may be proposed as safer places, enjoyable and sustainable from an economic, social, and environmental viewpoint.

These considerations have an impact on the relevance of place/destination reputation (Fombrun 1996 ; Wagner and Peters 2009 ; Urde and Greyser 2015 ) and respect tourist preferences both in pre-Covid and during and post-Covid. Hence, the tourist perception is completely confirmed in H3. Overall evaluation of a firm is based on the reactions to the firm’s goods, services, communication activities, interactions with its representatives, and/or known corporate activities (Walsh and Beatty 2007 ) in terms of price/quality relations, image, attractions, and communication.

Lastly, we investigated the influence of tourist’s preferences on hospitality system competitiveness, as confirmed in H4 for both pre-Covid and during and post-Covid pandemics. More specifically, innovation, infrastructures, history and culture, and macro-environment improve tourist experience, and they define the most valuable aspects of vacation as described and confirmed by the literature (Gooroochurn and Sugiyarto 2005 ; Akehurst 2009 ; Porter and Heppelmann 2014 ; Da Costa Liberato et al. 2018 ; Cillo et al. 2021 ). The theory is confirmed in this case, but the managerial impact needs to understand how it is important to take into account the different variables that impact on tourist’s preferences in order to build strong competitive advantages in the hospitality system.

Limitations, Future Perspectives of Research, and Conclusions

Despite the interesting results presented above, the study has some limitations. The main limit regards the geographic area of the research process, since the country of origin of the participants under investigation significantly influences the characteristics of the sample, in such case formed by Italian tourists. National culture, system of offering, level of income, etc., deeply affect tourist perceptions and are reflected in the outcomes of the analysis. Thus, to overcome this limit, it would be useful to extend the test to an international sample. Future research, indeed, might compare different clusters of national tourists to evaluate contrasting preferences. The actual sample is also composed mainly by Millennials, in search of unique and authentic experiences, even in the hospitality sector. This generation has less availability of money, but is digital addicted and sensitive toward sustainable issues in the tourism sector, showing greater attention to local communities. However, it would be very compelling to compare the results enlarging the sample to Baby Boomers and Generation X—Covid-19 Generation (Zwanka and Buff 2020 ). Future studies might analyze the consequences of the hospitality system competitiveness. Furthermore, following studies on possible post-Covid-19 scenarios are essential to help tourism stakeholders profile the offer well, but more accurate data collected on more representative groups are needed.

Finally, the specific period of the analysis needs to be considered. The hospitality industry, and the tourism sector more in general, is facing immense challenges at present, strictly stressed by the global health crisis provoked by the novel Coronavirus–caused respiratory disease Covid-19 (Strielkowski 2020 ). Even though travel and tourism have been the first economic victims of that situation, at the same time, they have been the principal defendant ‘to sit at the dock’. Since nowadays people move mostly for for tourism reasons, some ascribed to leisure/business movements due to the dissemination all over the world of the Corona outbreak, developed in China last year.

Thus, even though this opinion cannot be shared, the hospitality and travel operators are due to suddenly recover the failure of trust from tourists and local communities. The key lies in the ability to satisfy the surfacing of emergent needs—or perhaps the renewal of old ones on the base of the Maslow Pyramid—linked to safety above all, that have an influence on the effective accessibility and pleasantness of the vacation, affecting the actual touristic demand of hospitality. From now on and waiting for international voyagers come back, the hospitality actors and the public agents need to transform this weakness into an opportunity (Sigala 2020 ), by investing in the under-tourism and tourism of proximity phenomena—strictly connected to local development (Diaz-Soria 2017 )— as the most feasible solutions to answer, in the middle term, the dramatic freeze of the global hospitality offer. For these reasons, it could be interesting investigating on the following topics for the future: no-touch technology anywhere, free cancelations up to 48 h, proximity of high-level hospital facilities, and their impacts on the tourist preferences.

Hence, all stakeholders, including tourists, have a great responsibility, in terms of redirecting tourism, from both supply and demand side, toward a truly sustainable and resilient system, able to answer to future challenges in a more balanced manner, from an economic, social, and environmental viewpoint. This new normal may actually represent a process toward the comprehensive transformation of touristic territories, while always balancing the arrangement of attractive systems of offering, local quality of living, and sustainable development of an area, in terms of favorable repercussions for all the players involved (Uriely et al. 2002 ). By this way, tourism can be considered as a form of deep civic engagement—more than a simple consumption—favoring the development of a new ethos of sustainable tourism.

Biographies

is an Associate Professor of Business Economics at the University of Salerno where she teaches “Management and Innovation” and “Management”. She teaches also at the Business School of the ‘Bicocca’ University in Milan. She is Member of several Editorial Committees of national and international journals. She has published in top international journals about identity and reputation, digital transformation, consumer behaviour, corporate, and investment assessment. She has presented papers and research outcomes at numerous Conferences all around the world. She carries out research, consultancy, and training to various organizations (both public and private) on finance and performance, investment assessment, market research, and marketing.

is currently researcher in “Business Management” at the University “Bicocca” of Milan (Italy) where he teaches “Management”. Her main subjects of interest concern Augmented reality and marketing, Consumer Behaviour, Retail and experience, and Brand and Corporate Reputation. She has published in several journals, both national and international; she has also presented papers and research outcomes at numerous international conferences. According to ASN (National Scientific Habilitation), she received the habilitation for the Associate Professorship. Actually she serves as a member of the editorial board on the Journals: Esperienze d’Impresa and Global Journal of commerce & Management Perspective.

is an Associate Professor of Business Economics at the University of Bergamo, where he teaches and “Management” and “Marketing”. He teaches also at the Business School of the ‘Bocconi’ University in Milan. He is Member of several Editorial Committees of national and international journals. He has published in top international journals about corporate finance, business crises, corporate, and investment assessment. He has presented papers and research outcomes at numerous Conferences all around the world. He carries out research, consultancy, and training to various organizations (both public and private) on finance, restructuring and turnaround, and investment assessment.

is an Associate Professor of Management at the Department of Economics and Statistics of the University of Salerno, Italy, EU. He holds a PhD in Economics and Management of Public Organizations from the University of Salerno, where he is the Scientific Director of the Postgraduate course in Wine Business and the Past Vice-Director of the Second Level Master’s in Management of Healthcare Organisations – Daosan. He is also the Chairman of the Euromed Research Interest Committee on Wine Business. His research interests focus mainly on wine business, corporate venture capital, information systems, and healthcare management.

is a Full Professor of Business Economics at the University of Salerno, where he teaches “Business Plan and development”. He is Member of several Editorial Committees of national and international journals. He has published in top national and international journals about corporate finance, digital transformation, and corporate performance. He carries out research, consultancy, and training to various organizations (both public and private) on finance and performance, investment assessment, and M&A.

Open access funding provided by Università degli Studi di Salerno within the CRUI-CARE Agreement.

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China’s Golden Week Holiday: What’s Driving Outbound Travel

Peden Doma Bhutia , Skift

April 24th, 2024 at 3:30 AM EDT

Understanding what Chinese travelers want is crucial for stakeholders in the tourism industry to tailor offerings and experiences that match these changing preferences.

Peden Doma Bhutia

Destinations in the Middle East are emerging as favorites for China’s upcoming May Day holiday, commonly known as Golden Week.

Overall outbound travel from China during this year’s holiday is still lagging behind 2019 levels by 13%. But travel to the United Arab Emirates (UAE) has surged by 66%, and Turkey has experienced a significant growth of 56%, according to ticket sales data from ForwardKeys.

Increased seat capacity from Africa and the Middle East, which is set to expand by 75% in the second quarter, has led to this surge in travel.

Corresponding increases in seat capacity to China have backed the travel growth rates of 56% to Turkey, 19% to Italy, and 12% to the UK.

Last month, Trip.com, China’s biggest online travel agency, announced record bookings in Abu Dhabi, exceeding 57,000 room nights over the past 12 months. From 2022 to 2023, outbound travel orders across all products from Chinese travelers to the UAE surged by more than threefold.

Chinese outbound travel to Middle East had already reached pre-pandemic levels during the Chinese New Year period in February.

Pre-Pandemic Travel Patterns

ForwardKeys anticipates that the May holiday will witness notable travel peaks around April 27 and May 1, closely resembling pre-pandemic travel patterns.

In the Asian region, Malaysia has emerged as the top-performing destination for Chinese travelers, with flight bookings currently 42% ahead of 2019 levels. Because of more lenient visa policies, including visa-free travel to Malaysia and Singapore, and streamlined procedures for South Korea, travel to these destinations is expected to surpass 2019 levels in May.

These findings align with insights from Dragon Trail’s Chinese Traveler Sentiment Report , releasing later on Wednesday. According to the report, visa-free policies, direct flights, and simplified application procedures are also making outbound travel more appealing and accessible.

According to ForwardKeys, a notable shift in passenger profiles is the decrease in group travellers, which has dropped by 53% compared to 2019 levels. In contrast, solo travellers are showing a strong interest in exploring Asian destinations, with a 9% increase.

The Dragon Trail report also highlights growing preference for independent travel among Chinese tourists. Travelers also prefer, semi-self-guided travel and boutique groups of 6-10 people, which offer flexibility and convenience.

Value-Oriented Approach

Along with flexibility and convenience, Chinese travelers are also seeking relaxation and comfort in their trips.

Amidst the challenges posed by the Covid-19 pandemic and China’s economic downturn in 2023, Chinese travelers are prioritizing relaxation and comfort in their trips.

The economic pressures have led to a value-oriented approach to travel planning, with a majority carefully considering their spending to maximize value and opting for affordable travel options.

Around 20% of respondents who said they would not travel outbound in 2024 cite limited income as a barrier. Only a small percentage (11%) are willing to pay a premium for superior products and services.

Most travelers allocate less than 20% of their income for travel, with budgets typically ranging from RMB10,000 to RMB30,000 for upcoming trips.

Despite budget-consciousness, shopping remains an integral part of outbound travel, with more than two-thirds of travelers spending a minimum of RMB2,000 (US$276) per trip. Local foods and souvenirs are the primary shopping categories, followed by cosmetics, clothing, shoes and bags.

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Tags: asia monthly , budget travel , china , china outbound , golden week , middle east , shopping , turkey , uae

Photo credit: Overall outbound travel from China during this year's May holiday is 13% behind 2019. Florence Lo / Reuters

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Economics > General Economics

Title: long-term forecasts of statewide travel demand patterns using large-scale mobile phone gps data: a case study of indiana.

Abstract: The growth in availability of large-scale GPS mobility data from mobile devices has the potential to aid traditional travel demand models (TDMs) such as the four-step planning model, but those processing methods are not commonly used in practice. In this study, we show the application of trip generation and trip distribution modeling using GPS data from smartphones in the state of Indiana. This involves extracting trip segments from the data and inferring the phone users' home locations, adjusting for data representativeness, and using a data-driven travel time-based cost function for the trip distribution model. The trip generation and interchange patterns in the state are modeled for 2025, 2035, and 2045. Employment sectors like industry and retail are observed to influence trip making behavior more than other sectors. The travel growth is predicted to be mostly concentrated in the suburban regions, with a small decline in the urban cores. Further, although the majority of the growth in trip flows over the years is expected to come from the corridors between the major urban centers of the state, relative interzonal trip flow growth will likely be uniformly spread throughout the state. We also validate our results with the forecasts of two travel demand models, finding a difference of 5-15% in overall trip counts. Our GPS data-based demand model will contribute towards augmenting the conventional statewide travel demand model developed by the state and regional planning agencies.

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The 10 Best Travel Umbrellas, Tested & Reviewed

By Claire Volkman

All products featured on Condé Nast Traveler are independently selected by our editors. However, when you buy something through our retail links, we may earn an affiliate commission.

The one thing that I absolutely never forget to pack: a travel umbrella. No matter where I’m going, except for the Wadi Rum or the Sahara Desert maybe, there’s bound to be some inclement weather. Rain, sleet, thunderstorms; no destination is without them. Having an umbrella on hand allows you to keep your travel plans intact, and even see the sights in some places without all the crowds.

However, the task of picking an umbrella is about as daunting as picking a suitcase . With about a million options to choose from and all of them claiming to be “the best travel umbrella,” it’s no wonder so many just pick the cheapest option and go. However, not all umbrellas are created equally—as anyone who has found themselves with one completely inverted during a torrential downpour will tell you. Below, we’ve rounded up the best of the best travel umbrellas, keeping features like durability, wind-resistance, and size in mind.

This article has been updated with new information since its original publish date.

Discover the best travel umbrellas:

• Best overall: Weatherman travel umbrella
• Most compact: Davek The Davek mini
• Best for cities: Amy's Automatic umbrella
• Most affordable: Repel windproof travel umbrella
• Most durable: Blunt Metro umbrella
• Best splurge: Pasotti Tropical umbrella
• Most lightweight: Totes mini manual umbrella
• Best patterns: Rifle Paper Company umbrella
• Most wind-resistant: Davek Elite umbrella
• Most sustainable: Totes recycled canopy umbrella

Best overall travel umbrella

Why we love it : Sturdy, wind-resistant, lightweight, and incredibly compact, the Weatherman travel umbrella is undeniably the best on the market. This umbrella has a serious fanbase, with hundreds of nearly perfect reviews on Amazon. It is surprisingly lightweight, and can easily fit in a carry-on or tote bag . It has an automatic open and close feature, a reinforced fiberglass base, and a water-resistant, Teflon-coated fabric. Plus, it shields winds up to 45 MPH and is tough enough to withstand even the heaviest of rainfall.

Worth noting : There aren’t many things to complain about this umbrella, however, it is a bit pricey coming in at $69. However, with a lifetime warranty, it’s well worth the splurge.

Dimensions:  Open diameter: 38"; Closed length: 12" Weight: 0.85 oz. Carrying sleeve included: Yes

Most compact

Why we love it: When they say mini, they mean mini. The Davek Mini is so small and compact that it fits in the palm of your hand. Measuring less than 7 inches when closed, you can easily stash this umbrella in your carry-on, backpack , or even a jacket pocket. Plus, it weighs less than a pound, making it almost unnoticeable when not in use. Made from reinforced fiberglass, it also comes in 10 bright colors so you can coordinate your umbrella with the rest of your outfit.

Worth noting: Because of its size, the canopy doesn’t provide overwhelming coverage. Additionally, it’s not built for extreme storms and functions best in light showers.

Dimensions : Open diameter: 38"; Closed length: 7"

Weight : 0.8 oz.

Carrying sleeve included : Yes

Best for cities

Why we love it : With a sleek and stylish small wooden or plastic handle, this lightweight umbrella features a wind-defying 8-rib canopy that’s also UPF 50+ certified, meaning it provides adequate sun protection, too. Small enough to walk down the busy streets of Chicago during a rainstorm without the awkward “bump and sideswipe,” it fits easily in a work tote, briefcase, or backpack. Plus, it features an automatic open and close, and comes in over a dozen bright and bold patterns and colorways, making it easy to stand out in a sea of black and blue canopies. It also comes with a five-year global warranty and is pretty affordable at only $40.

Worth noting: It’s small enough to fit in your hand, which means the canopy doesn’t offer premium protection in heavy rainstorms.

Dimensions : Open diameter: 38.1"; Closed length: 21.6"

Weight : 0.76 oz.

Most affordable

Why we love it : You can’t go wrong with this incredibly durable, lightweight, and affordable Repel travel umbrella. With over 36,000 4.5-star Amazon reviews, this is an overwhelming crowd favorite among travelers, worker bees, families, and everyone in between. I tested the durability during a heavy summer rainstorm in Chicago, and it stood up surprisingly well thanks to its 9-rib canopy, heavy-duty Teflon-coated fabric, and non-slip rubber grip. Plus, the automatic open and close feature makes it super easy to go in and out of buildings and restaurants without getting soaked or stuck. Another thing to note was how well it stood up to Chicago’s infamous winds—not bending, flipping, or even flapping during big gusts. The best part? It’s only $27 on Amazon and comes in 10 colors.

Worth noting: We didn’t find any problems with the umbrella, but some reviewers found that it didn’t stand up to heavier winds and isn't as lightweight as other comparable brands.

Dimensions : Open diameter: 42"; Closed length: 11.5"

Weight : 0.93 oz.

Carrying sleeve included : No

Most durable

Why we love it : This heavy-duty umbrella stands up to even the strongest rainstorms, and offers supreme coverage thanks to its patented wind-tip rounded edges, which work like mini umbrellas. It’s also built with a 360-degree spinning canopy which prevents it from breaking when dropped or knocked over. It’s extremely wind-resistant and has been tested to withstand the winds and rains of a category one hurricane. It’s also made from rip-resistant pongee fabric that is also super quick to dry, so you can easily go back indoors without needing a plastic cover.

Worth noting: This umbrella only features six ribs, which means it's smaller than Blunt’s other umbrellas.

Dimensions : Open diameter: 39"; Closed length: 15"

Weight : 0.85 oz.

Best splurge

Why we love it : If looking chic is just as important as staying dry, this is the umbrella for you. Handcrafted by Italian umbrella maker Pasotti, the canopy will stop people in their tracks—with a beautiful tropical pattern in sage, gold, and dark green. When I was walking in downtown Chicago, I had four people stop to ask me about this umbrella (and then a few ask to buy it from me). Although I originally gravitated to this umbrella for its looks, the functionality is why it’s my absolute go-to. The canopy extends 102 centimeters and has a 93 centimeter shaft, meaning I’m not going to get pelted by rain when it’s windy (which is everyday in Chicago).

Worth noting: At $275, this is by far the most expensive on the list.

Dimensions : Open diameter: 40"

Most lightweight

Why we love it : They weren’t kidding when they said this is one of the most lightweight umbrellas on the market, weighing only 8 ounces. Small enough to fit into most purses , carry-ons, totes, and computer bags, this umbrella doesn’t take up any more space than it needs too. One drawback with the size is that you need to activate the canopy manually (no quick release button). However, the handle is sturdy and the canopy provides ample coverage for most light-to-moderate rain storms. I love the fact that it comes in multiple colors and patterns, and the price makes it easy to stock up on a few (only $25 at Amazon).

Worth noting: Due to the size, it’s not super wind-resistant and wouldn’t be a great pick if you’re facing a heavy downpour or storm.

Dimensions : Open diameter: 43"; Closed length: 11.2"

Weight : 7.8 ounces

Best patterns

Why we love it : If you’re someone who buys a bottle of wine based on the label, these umbrellas are for you. Known for its bold, whimsical, and delicate patterns and floral designs, Rifle Paper Company’s umbrellas are true works of art. Each umbrella also features a sleek wooden handle and an automatic open/close feature.

Worth noting: This is not the umbrella to buy if you’re looking for durability, extreme wind resistance, or more bells and whistles. This basic umbrella provides decent coverage in light rain, but isn’t meant for heavy winds or downpours.

Dimensions : Open diameter: 43"; Closed length: 11"

Most wind-resistant

Why we love it : There’s a reason 81 people have given this umbrella a nearly perfect rating on Amazon—it actually holds up. Sure, it may be a splurge at $159, but it is well worth the price tag when you see how it holds up. Surprisingly compact, the canopy extends 50 inches, giving you extreme protection against even heavy rains. Plus, it’s small enough to fit in a carry-on, backpack, or large tote bag. The fabric is 201-thread count, making it luxurious to touch but also extremely powerful against rain. The best feature is the wind-tension frame system, which can withstand heavy winds and prevent inversion. We tested this against a very blustery 55 MPH wind day in Chicago, and there was barely any flapping or movement at all from the tough canopy.

Worth noting: It’s expensive, but comes with a lifetime warranty and replacement guarantee.

Dimensions : Open diameter: 50"; Closed length: 35"

Weight : 1 lb.

Most sustainable

Why we love it : Shopping sustainably is more important now than ever, and that extends to umbrellas, too. Tote’s recycled umbrella is made from 100 percent Recycled PET plastics, equating to about 7.5 recycled water bottles per umbrella. The handle and strap are also made from all renewable resources, like bamboo and hemp, and the production process uses less water overall. In terms of coverage, the Pet umbrella features Tote’s patented NeverWet invisible coating allowing the rain to drip off the umbrella 4 percent faster, leaving you with a drier umbrella once indoors.

Worth noting: Its compact design makes it easy to travel with, however, doesn’t provide a ton of additional coverage beyond your person.

Dimensions : Open diameter: 43"; Closed length: 11.5"

Weight : 1.15 lbs.

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Mastercard exec pitches potential tourism markets for Jamaica

Uses data to map consumer income and spending patterns.

J amaican-born director at the Mastercard Economics Institute Roiana Reid said that she would readily recommend potential tourism markets that the Jamaica Tourist Board should target based on data gathered about the spending habits and the income levels of credit and debit card users across several jurisdictions across the world.

Reid made this bold statement during her recent presentation, “Unlocking Growth: Understanding the Global Consumer and Navigating Travel Trends”, at Mastercard Day Jamaica, during which Mastercard highlighted its capacity to track tourism spend across the world and go deeper into identifying the spending power of people in specific regions.

Speaking at the event, Reid pointed out that unemployment is at its lowest in the US, Canada, the United Kingdom, and Germany — some of Jamaica’s largest tourism markets. Comparing the growth of wages in these jurisdictions to inflation, she then highlighted the spending power of those who travel.

“Broadly speaking, across these economies wage growth is outpacing inflation. That means that consumers have more real purchasing power, giving them opportunities to spend more on discretionary goods and services which, of course, includes travel and entertainment,” she shared.

Drilling down on Jamaica’s leading tourism market, the United States, Reid noted that states such as Montana, Nevada, Arizona, Texas, Florida, Georgia, and the Carolinas have experienced strong growth in salaries and consumer spending over the last five years. This she attributed to migration patterns that favour people moving to the warmer southern states.

“So, if I were on the Jamaica Tourist Board, I’d recommend that we market more in these states because there are real opportunities for growth to capture these consumers which are doing really, really well,” she asserted.

Texas, Reid indicated, was one of the states to watch since most sectors experienced employment and income growth higher than the US average.

Last year in the US, spending on air travel, cruises and accommodations rose by 19.8 per cent when compared to pre-COVID levels. In terms of discretionary spending, travel-related expenses ranked as third-highest in payments, just behind ticket purchases for movies and/or live events and paid sporting events.

Moreover, in a survey of consumers, 22 per cent of respondents indicated plans to take a foreign trip in the next six months.

“Before the pandemic, we never had this much share and we’re literally at 45-year highs in terms of the share of responders who plan to travel abroad within the next six months,” Reid highlighted.

What’s more, as Jamaica continues to rank among the top-five destination for American and Canadian tourists, inbound cross-border spend last year rose by 20 per cent on account of an increase in spend on experiences. The top-three payment categories among travellers from the US and UK were: accommodation, food and vehicle rentals.

Payments for hotel services alone rose 48 per cent in 2023.

While dissecting purchases using corporate cards as against consumer cards, Reid shared that she believes there is space for tourism stakeholders to boost corporate card spend through business conferences, retreats and hosting remote employees.

In Jamaica, parishes seeing the largest share of card spending were: St James, Kingston and St Andrew, St Ann, Westmoreland, Trelawny, and Hanover.

Prior to COVID, tourists spent on average six days at destinations compared to 10 days during the pandemic due to quarantines. Since 2022, travellers from Jamaica’s main tourism markets spend on average eight days.

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