key: cord-0782384-953lthxz
authors: Lebrun, Anne-Marie; Corbel, Roxane; Bouchet, Patrick
title: Impacts of Covid-19 on travel intention for summer 2020: a trend in proximity tourism mediated by an attitude towards Covid-19
date: 2021-07-04
journal: Serv Bus
DOI: 10.1007/s11628-021-00450-z
sha: 8a36d3bf7b4327163282791e6990d63667e6af6e
doc_id: 782384
cord_uid: 953lthxz

This exploratory study examines the impact of the Covid-19 pandemic on the travel intention of French people (n = 655) for holidays in summer 2020. Qualitative results show demographic and generation differences in the intention to go on vacation caused by a combination of personal uncertainties due to the pandemic, compulsory state measures and travel bans. Using PLS-SEM, quantitative results indicate that influence of perceived risks and attitude towards Covid-19 led tourists to seek out greater proximity when considering vacation travel. The findings may help tourism managers apply proximity marketing strategies using more local and digital services during global health crises. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1007/s11628-021-00450-z.

Since the advent of the Covid-19 health crisis, many professional and scientific studies have been carried out to measure its health, economic and social impacts at the worldwide, continental, national and local levels (Lew 2020; Cabello et al. 2020; Romagosa 2020) . From the beginning of the restrictions (in Europe, these generally began in March), airlines and tourist operators have sought to evaluate the unprecedented financial losses caused by the absence of foreign tourists. Between March polarized destination images through associations with the geographical distance between home and elsewhere (Jeuring and Haartsen 2017; Larsen and Guiver 2013) . A few months ago, domestic holidays might have been considered old-fashioned, but a revival now seems to be taking shape amidst the Covid-19 crisis.

In Europe, nearly half of domestic tourism expenditure is linked to same day visits, which can also be termed proximity tourism (TSA 2019). Since domestic tourists tend to travel shorter distances (Fennell 2008) , proximity tourists may come from neighbouring areas for a weekend or short stay (Canavan 2013; Jeuring 2017) , or an overnight or same day trip (at least four hours' duration; Wynen 2013) .

Recently, a definition of proximity tourism has been proposed that highlights accessing local destinations, travelling short distances and using lower-carbon modes of transport, as well as capturing 'the mundane exceptionality of the ordinary' (Rantala et al. 2020, p. 1) . The concept of proximity tourism (Diaz-Soria 2017; Jeuring and Haartsen 2017) revolves around the notion that in a hypermobile world where everybody has become a tourist and every place can be a destination (Franklin and Crang 2001) , touristic experiences of engaging with the 'other', negotiating between familiarity and unfamiliarity (Kastenholz 2010; Szytniewski et al. 2017) and the general purpose of tourism are all connected. Such experiences have become strongly embedded in everyday life and decoupled from the idea of travelling long physical distances, termed Distant Travel Destination (DTD; Jeuring and Haartsen 2016) . Accordingly, we propose the first research question: RQ1: Covid-19 influences summer vacations in 2020 with a greater contribution of Proximity Tourism (PT) in French tourists' travel intention.

Introduced by Karl Mannheim (1952) , the generational cohort theory was extended by Strauss and Howe (1991) in their work 'History of America's Future (1584 to 2069)'; it was then incorporated into marketing by Noble and Schewe (2003) and into tourism by Pennington-Gray and Spreng (2001) and Pennington-Gray et al. (2002 . Strauss and Howe (1991, p. 60 ) define a generation as 'a cohort group whose length approximates the span of a phase of life and whose boundaries are fixed by peer personalities'. A generational cohort refers to a group of people who were born in the same time period, who go through their lives together, and who have therefore experienced similar external events in their late adolescent and early adult years (Meredith and Schewe 1994; . Each generation is influenced by broad forces (i.e., parents, peers, media, critical economic and social events, and popular culture), which create common value systems distinguishing them from people who grew up in a different period. Every generational cohort shares a common cultural perspective and norms (Noble and Schewe 2003) and has a collective memory (Schuman and Scott 1989) . Pennington-Gray et al. (2003) categorize individuals into age cohorts based on generation, on the assumption that their behaviour would have been influenced by epochal events taking place during specific periods in their lives. Currently, four generations constitute the majority of the workforce, employees and tourists in America and Western Europe; Baby Boomers, Generation X, Generation Y and Generation Z (Bakendorff et al. 2010; Noble and Schewe 2003; Noble et al. 2004 Noble et al. , 2009 Seemiller et al. 2019) .

In the tourism literature, generation has played an important role in determining an individual's preferences, behaviour and demand Prideaux 2004) ; travel behaviour (Bojanic 2011; Opperman 1995; You and O'Leary 2000) ; travel consumption (Gardiner et al. 2013; Glover and Prideaux 2008; Huang and Petrick 2009) and attitudes and intention Gardiner et al. (2013) . Some significant findings suggest that generational cohort analysis of preferences is useful in the tourism industry because different generations will be attracted to different activities at different times . Baby Boomers and Generations X, Y and Z, whose key dates vary according to author and country (Glover 2010) , represent a large part of the tourist population and are significant segments of opportunity for marketers. Ollivier and Tanguy (2017) along with Rochford (2016) have defined the key dates of these four generations in France. Rochford (2016) defines Baby Boomers in France as people born before 1960 (i.e., aged over 60 in 2020) and who lived through the Glorious Thirties, employment for all and May 1968. This generation is important because of its size and discretionary income (Huang and Petrick 2009 ). Strauss and Howe (1991) describe it as being made up of people born in an era of optimism and growth and characterized by a quest for self-realization. Baby Boomers were nonconformist rule breakers during their early years (Lyons et al. 2005) , but have become more preoccupied with material possessions and money as they have aged (Roberts and Manolis 2000) . They refuse to behave like stereotypical old people, but still look for memorable experiences (Patterson et al. 2017) and keep in touch with the latest travel reports and products (Hudson 2010) . Baby Boomers are affluent, healthy and active, better educated, IT literate and increasingly confident about using the Internet as an important source for planning their trips. Rochford (2016) portrays Generation X in France as those born between 1960 France as those born between and 1980 France as those born between (i.e., aged 40-60 in 2020 . The childhoods of members of Generation X were defined by both parents working full-time or by only one parent supporting them, due to the increased divorce rate. This is the TV generation and the first to use computers. Strauss and Howe (1991) describe Generation X as comprising people who are sceptical about their economic future and realize they will have to work harder to achieve the same standard of living as their parents. Whereas Baby Boomers live to work, Generation X works to live, and so the search for work/life balance is also a hallmark of this generation (Beutell and Wittig-Berman 2008) . This generation, which is the first living cohort to have faced mass unemployment, tends to set out to find balance between their private and professional lives rather than spending all their time at work (Bennett et al. 2012) . Members of Generation X grew up in a leisure-focused environment in which travel was recognized as important, and would therefore be more inclined to travel despite a busy work and family schedule. This group is known for including particularly active and free-spending leisure travellers (Laliberté 2005) . Rochford (2016) describes Generation Y in France as those born between 1981 France as those born between and 1995 France as those born between (i.e., aged 25-39 in 2020 . Compared to Baby Boomers and Generation X, members of Generation Y tend to prefer jobs that offer more vacation time (Cennamo and Gardner 2008) and have a greater desire for flexible work that will give them sufficient time to take part in leisure activities to achieve relaxation and happiness (Park and Gursoy 2012) . Generation Y members are open-minded, optimistic, goal-oriented and highly motivated to achieve success-as they perceive it. They are accordingly image-driven with a strong need for peer acceptance and connection, a sense of fitting in and social networking, which keeps them permanently connected (Dickey and Sullivan 2007; Himmel 2008) . This generation is regarded as highly consumption oriented, with sophisticated tastes and strategies for dealing with brands, advertising, pricing and decision-making, and complex shopping preferences (Marjanen et al. 2019) .

Finally, Generation Z is defined in France (Rochford 2016) as people born after 1995 (i.e., aged 24 or under in 2020). This will become the youngest and largest consumer group across all generations over the period 2017 through to 2030 (Euromonitor 2018) . In France, Generation Z has grown up in the presence of terrorism, both global and local (e.g., the 2015 Paris attacks), economic uncertainty, recession, the mortgage crisis (Gentina 2019 ) and now the Covid-19 outbreak. Its members are highly educated, individualistic, pragmatic, creative, innovative, open-minded and socially responsible (Euromonitor 2018) . They are committed to helping others and are accordingly more tolerant of LGBTQ (Lesbian, Gay, Bisexual, Transgender, Queer) rights; they are also environmentally conscious and more inclined to collective action, based on an awareness of their role in the world and their responsibility to help improve it (Merriman 2015) . The most significant characteristic of this generation is the omnipresent use of technology. This generation was the first to be born into an entirely digital world (Bernstein 2015) , making its members digital natives who are even more socially connected than previous generations (Ruangkanjanases and Wongprasopchai 2017) . Living in the era of social networks and mobile devices makes Generation Z more prone to engaging in social interaction and to co-creating experiences in the virtual world (Skinner et al. 2018) . They are influenced by new media and virtual friends, and are strongly engaged through social media (Yussof et al. 2018) . They broadcast, exchange and remix texts and photos on online platforms or in their own blogs. However, whilst this widespread use of technology has produced people who tend to be efficient, its members can also be impatient individuals who want everything quickly (Berkup 2014). On the basis of these analyses, we propose the second research question:

There is a generational difference (Baby Boomers, Generations X, Y or Z) in the impact of Covid-19 on French tourists' travel intention for summer 2020.

The concept of Perceived Risk was first introduced into marketing, and more specifically into consumer behaviour, by Bauer (1960) and then expanded within the stream of tourism research by Roehl and Fesenmaier (1992) . It has been defined by Mansfeld (2006) as a consumer's perception of the overall negativity of an action that might affect travel behaviour if experienced beyond an acceptable level. According to Roehl and Fesenmaier (1992) tourists make travel decisions based on perceptions rather than reality. Perceived Risk has been studied by many researchers, who have further classified it according to different types of risk, such as performance, financial, physical, psychological, satisfaction, social, political instability, war, disease (including epidemics) and time risks (Floyd et al. 2003; Khan et al. 2019 ). According to Noh and Vogt (2013) , the Perceived Risks of vacationing at a given destination have become especially influential on tourists' decision-making in recent years, given the international awareness of tragic or high-risk events around the globe. Indeed, with the increase in the frequency of natural disasters and global epidemics, researchers have begun to integrate risk studies measured across different dimensions. Fuchs and Reichel (2006) look at the risk perception of international tourists going to a risky destination, Dolnicar (2005) examines the risk perception of natural disasters and landslides and Moreira (2007) focuses on stealth and catastrophic risks. It has been consistently shown that risk perception has a significant effect on travel intention (Floyd et al. 2003; Sönmez and Graefe 1998a, 1998b) , particularly after incidents that are perceived as dangerous have taken place (Floyd et al. 2003; McKercher and Chon 2004; Rittichainuwat 2006; Rittichainuwat and Chakraborty 2009) . This is reinforced when risks such as an epidemic, natural or manmade disasters and terrorism draw the focus of travellers to their safety and security (Rittichainuwat and Chakraborty 2009; Fuchs and Reichel 2006) . Perceived Risk could therefore affect travel behaviour (Reichel et al. 2007 ) and the intention to travel (Floyd et al. 2003) . This is expressly the case for disease, which is strongly related to changes in travel plans (Kozak et al. 2007) . A few years ago, international travel in the Asia-Pacific region was harmed by severe and tragic events such as the SARS outbreak. McKercher and Chon (2004) show that the perceived risk of SARS had a more devastating effect on tourists' behaviour than the disease itself, and that this effect was observed not only in countries that had SARS cases but also in bordering nations with no reported infections. Cahyanto et al. (2016) point out that Perceived Risk is the strongest predictor explaining propensity to avoid travel. Actually, tourists base their decisions on these (biased) risk perceptions (Roehl and Fesenmaier 1992) , which might not represent the actual situation at destination (Fuchs and Reichel 2006) . Floyd et al. (2003) developed a four-dimension scale describing risk perceptions. The first dimension is related to travel risk. This Travel Perceived Risk is important in tourism because travel is one of the activities most vulnerable to global risk factors (Ritchie 2004) . The second dimension, Perceived Safety Risk, is linked to safety concerns, which strongly influence tourists' decision-making processes (Seabra et al. 2013) . The third dimension, Distance Perception Comparative Risk, compared international versus domestic travel. This geographic impact will encourage post-crisis tourists to choose destinations closer to where they live (Lew 2020) . The last dimension, Perceived Destination Risk, is related to the destination. Seabra et al. (2013) consider that travellers select destinations that have the lowest possible risks and consequently, destinations perceived as being safer may be preferred, and those perceived as risky or unsafe may be rejected (Sönmez 1998 ). According to Floyd et al. (2003) , these four factors could be used to measure Per- (2003), we can formulate the following hypotheses: H1: Perceived Risk will be positively related to Proximity Tourism (PT).

Research on Attitude has received considerable attention in social psychology, marketing and tourism. Attitude towards a destination describes the psychological tendencies that tourists express through their positive or negative evaluations of a destination experience (Lee 2009 ). It refers to a learned predisposition of tourists to respond in a consistently favourable or unfavourable manner to a given object (Ajzen and Fishbein 2000) . Attitude towards an object (e.g., the Covid-19 pandemic) is therefore a function of the belief about the object and its implicit evaluative responses. Tourist attitude is an effective predictor of tourists' choice behaviour (Mohsin 2005; Lee 2009 ) and tourist participation and satisfaction (Ragheb and Tate 1993) . Research exploring individual, latent destination attractiveness factors as antecedents of tourists' cognitive evaluations within a destination context remains rather limited. Um and Crompton (1990) , for instance, demonstrated that attitude was found to be influential in determining a potential travel destination choice. Surprisingly little effort has been made to investigate the impact of a perceived risk of sanitary crisis on attitude towards this crisis and on travel intention.

Recently, Nazneen et al. (2020) have demonstrated that the Covid-19 outbreak has affected tourists' attitude towards travel intention in terms of distance (proximity tourism/distant tourism destination). Tourists' perception of risk has a significant negative effect on travel decision-making by creating anxiety around the globe (Floyd et al. 2003) . In particular, plans to travel to large and crowded cities may be affected by this, because people consider that this type of travel is becoming unsafe. Attitude towards virus outbreak received some interest from researchers. Wen et al. (2005) developed a scale about the impact of SARS on tourist behaviours. One of the dimensions of this scale (Travelling Attitude) defines the attitude towards travel, which includes tourism inclination and mode of tours. Jittrapirom and Tanaksaranond (2020), adapted from the work of Sadique et al. (2007) , developed a measure of precautionary attitude towards travel, including Sanitary Attitude and Material Risk. According to Wen et al. (2005) and Jittrapirom and Tanaksaranond (2020) , Attitude Towards Covid-19 could be described by Travelling Attitude (TA) and precautionary measures such as Material Risk (MR) and Sanitary Attitude (SA). This reflects the fact that tourists are sensitive to world crises events and that their attitude towards Covid-19 will have affected their intention to travel (Nazneen et al. 2020) . As identified by Romagosa (2020) and Cabello et al. (2020) , one of the consequences of the Covid-19 crisis is likely to be the boosting of proximity tourism in terms of travel intention. Therefore, we hypothesise the following: H2: Perceived Risk will be positively related to Attitude Towards Covid-19;

H3: Attitude Towards Covid-19 will be positively related to Proximity Tourism.

Although some research has examined specific antecedents and consequences of pandemics, the majority of studies have assumed a direct relationship between the perception of sanitary risks and travel intention (e.g., George 2010; Reichel et al. 2007 ). For example, previous research has shown that perceived risk determines attitude, which in turn influences behavioural intention (Ajzen 1985; Quintal et al. 2010 ) and travel intention (Bae and Chang 2020) . This study supposes that attitude towards Covid-19 as primarily depending on mental representations that include destination self cognitions, thoughts, and memories of tourists' experience and destination attractiveness (Reitsamer et al. 2016) or visit intention (Doosti et al. 2016 ). Perceiving, storing and retrieving mental images of a perceived risk associated to Covid-19 are thus regarded as an important precondition for travel intention on proximity. Drawing on the preceding arguments, we propose: H4: Attitude Towards Covid-19 will mediate the relationship between Perceived Risk and Proximity Tourism. Schroeder et al. (2013) show that tourists from different age groups perceive destination risks differently. Wen (2005) indicates that the impact of SARS on older people was much more serious than on younger or middle-aged people. These two studies focus on age rather than generation, but they suggest that there may be differences due to cohorts or circumstances; this is precisely what generational analysis seeks to explore. Nonetheless, few studies have focused on generation as a specific variable, especially in terms of Covid-19. Clements (2020) shows that Baby Boomers have the highest awareness of Covid-related risks. Wise et al. (2020) find that the impact of perceived Covid-related risk is related to protective behaviours. We show that the more concerned people felt, the higher their perception of risk, and the more they engaged in protective behaviours. Unfortunately, the median age of participants in this study was around 30, so this does not fully cover older generations with a higher Covid-19 risk. Breakwell (2014) confirms that anxiety influences risk perception; such anxiety might rise with the media focus on the elderly (including Baby Boomers) in the coverage of the pandemic. Indeed, there has been a strong focus on the lethality of the disease amongst older people, who are seen as more at risk than younger generations (Remuzzi and Remuzzi 2020) .

It may therefore be expected that this pattern may increase concern for oneself and death-related anxiety amongst Baby Boomers, leading to an increase in selfprotective behaviours. One might therefore expect to see an increase in such risk attitudes across chronologically defined generations, due to a shift in the concern for the self that justifies such biases in risk perception. In other words, the older people are, the more worried they are likely to be about the risk posed by Covid-19, and the more likely it is that this risk perception will affect their travel intentions and lead to an increase in PT. Nonetheless, because of the media focus on the elderly, it is proposed that the attitudes and travel intention of Baby Boomers will be significantly different from the other generations. Therefore, we hypothesize the following: 

Between March 3 and July 10, 2020, the French government 1 took containment measures to prevent the spread of Covid-19 and limit the number of cases and deaths on national territory (Online Appendix 1). During this short and unprecedented period, many French people found themselves in a paradoxical situation where they wanted to go on vacation (especially those who were living in apartments and with their families) but the health crisis limited travel between countries and contact between people. This was especially the case since they had been accustomed for many weeks to limiting their trips for leisure or short stays to less than 100 km and/or within their country. At the same time, they had little or no purchasing power to consider a long stay in France or abroad in a manner that would ensure optimal health security. In this particular and highly uncertain context, in this study we asked a series of questions to French people about their intention to go on a trip during the summer vacation period in 2020.

Due to the need for social distancing in the actual pandemic crisis scenario, all the necessary information was collected online. The target population of this study was domestic tourists from different French regions. An online survey using LimeSurvey was administered to over 1057 randomly selected adults in these regions during the second half of June and first half of July 2020. This period was chosen because France and some European countries had reopened their borders on June 15. Respondents took an average of 10 min to complete the survey. A total of 655 usable responses were obtained. The profile of the respondents is summarized in Tables 1 and 2.

The survey questionnaire was composed of four sections: (a) geographical travel intention, (b) perceived risk, (c) attitude towards Covid-19 and (d) demographic information (Online Appendix 2). In the four sections, responses were collected using seven-point Likert-type scales. In the first section, the construct of Geographical Travel Intention was estimated using five items adapted from previous research (Floyd et al. 2003; Cahyanto et al. 2016 ). The measure focused on geographical aspects of intention in a general travel intention rather than an intention to travel in a specific destination. The second section of the survey captured Perceived Risk. Previous studies have conceptualized Perceived Risk as multidimensional, in order to investigate its relationship with visit intention (Khan et al. 2019 ).

The construct of Perceived Risk in this survey was evaluated using eight items based on previous research (Floyd et al. 2003; Cahyanto et al. 2016) . The third section of the survey measured Attitude Towards Covid-19 in terms of travel. The constructs used in this scale are based on previous research (Wen et al. 2005; Jittrapirom and Tanaksaranond 2020) . The survey instruments used in sections two and three were developed by considering valid and reliable scales used in previous research. The mobilized constructs and related items are set out in Online Appendix 2. The fourth section collected demographic information including gender, generation, location, marital status, employment status and educational background.

Firstly, a descriptive analysis of the intention to go on holiday in the next three months was carried out, using SPSS 21.0. The 655 respondents were almost evenly divided between those who did (54%, n = 355) and did not (46%, n = 300) intend to do so. 

Secondly, two analyses focused on the geographical intention of travel for respondents who did intend to go on vacation (n = 355) and may explain the trend of proximity tourism. A one-way ANOVA was used to compare the geographical intention of the respondents: intra-regional, close to region, France, Europe and continental. Then, in an analytical step, Exploratory Factor Analysis (EFA) was carried out on the items of the geographical intention to travel to measure and verify their inner structure with the sample.

Lastly, a partial least squares structural equation model (PLS-SEM) was constructed using SmartPLS3.2.8 to validate the measurement model and test the hypotheses for respondents who did intend to go on vacation (n = 355). PLS-SEM was chosen because of the exploratory nature of the work, especially with the Geographical Travel Intention. Moreover, some variables didn't fit the normal distribution either which leads also to PLS-SEM. The process was done using a repeated measure approach. The first-order model was run in a reflective way according to the initial scales previous validation. We followed Hair et al. (2019a, b) recommendations as for the first reflective order and as for the 2nd formative order, and a two-step approach was chosen. We then used GPower software to estimate the sample minimal size to make between-groups comparisons. Multi Group Analysis (MGA) was done following Matthews' (2017) statements using the permutation test to assess invariance and eventual differences between paths amongst generations.

Respondents who did not intend to go on vacation (46%, n = 300) can be further subdivided into those who had made a definite decision not to travel (65%, n = 196) and those who had not yet made a final choice (35%, n = 104) ( Table 2 ). The main reasons cited by those not intending to go on holiday were mainly the Covid-19 outbreak (46%), job-related issues (24%) or financial problems (11%). The main reasons for not having yet made a final decision were the outbreak (43%), job-related financial problems (23%), cancellations (15%) or other job-related issues (11%). Table 3 compares the subgroups reporting intention to travel (54%, n = 355) and no intention (46%, n = 300) in terms of the other variables. The results indicate no significant difference in terms of gender or employment status. However, some important and significant differences can be seen in terms of marital status, educational background and location. Married respondents (57%) were more likely to intend going on vacation than single people (48%). Respondents with postgraduate-level education (61%) were more likely to travel than those with undergraduate qualifications (51%) and urban dwellers (57%) were more inclined to go on Table 3 Percentage of the respondents (n = 655) who will (or not) travel according to their demographic information Note two different letters on the same column indicate a significant difference at the threshold p vacation than those living in rural areas (48%). There were also significant differences between generations. Members of Generation X (66%) were the most keen to go on vacation, followed by Generation Y (55%) and Baby Boomers (50%), with Generation Z (44%) the least willing. Most people who would not be going on vacation, or who had not yet made a decision, planned to stay at home and make same day visits around their neighbourhood. They answered No to the travel intention question because they did not plan to stay overnight, and so they were not included in the dataset analysed to construct the main model. However, it can be seen that these results support RQ2. Table 4 compares the plans, in terms of mean geographical distance, of those reporting they intended to travel (n = 355). A one-way ANOVA showed a significant difference between the variables (F = 161.1342; df = 351; p < 0.001). The Wilcoxon test showed significant differences between all the localization means (all p-values < 0.001; see Online Appendix 3) except for the comparison between intra-regional and close to region (p = 0.134). Three patterns emerged, that can be described as regional, national and continental. Regional intention denotes a response close to the middle of the scale, with a high response indicating national travel intention and a very low score denoting the continental response pattern. These results confirmed the hypotheses set out in terms of RQ1.

Regarding the geographical travel intention scale, an EFA was then carried out to validate the distance patterns concerning travel intention identified through the ANOVA. This was done before the PLS-SEM to understand how the items loaded on factors (see Online Appendix 4 for more details). Parallel analysis and principal axis factoring were chosen because of the exploratory nature of the scale but also because of the misidentification of the number of factors found with other methods (Gaskin and Happell 2014) . The inner correlation of the constructs showed a reflective structure as the correlation between factors was under 0.32 (Tabachnick and Fidell 2007, p. 646) .

A significant result for Bartlett's test of sphericity appeared (χ 2 = 237, df = 10, p < 0.001). The Keyer-Meyer-Olkin (KMO = 56.7) sample adequacy measure (Kaiser 1974) could be seen as weak, but the cutoff of 0.50 was seen as correct (Hair et al. 2019a, b) . Proximity Tourism (PT) explains 19.2% of the variance and Distant Travel Destination (DTD) 22.3%. It is interesting to note that the middledistance item 'France' loaded on the DTD dimension, but with a negative coefficient (− 0.83). This may be explained by the item being indeterminate, respondent error (Schmitt and Suits 1985), or the formulation of the item. The descriptive statistics clearly showed this omission in terms of the opposition of the means between the item 'France' and the Continental/Europe items, which could be seen as a misformulation of the item leading to different understandings amongst participants.

The assessment of the measurement model comprised an examination of reliability, convergent validity and discriminant validity. Reliability was checked using Cronbach's alpha, the value of which should exceed the threshold of 0.6 for exploratory research and Composite Reliability (CR), which should exceed 0.7 (De Vellis 2016; Hair et al. 2014; Matsunaga 2010 ). Discriminant validity was tested using the Het-eroTrait-MonoTrait ratio of correlations (HTMT) inference criterion (Online Appendix 5) instead of the Fornell-Larcker criterion; Fornell and Lacker (1981) ; Henseler et al. (2015) have demonstrated, by means of a Monte Carlo simulation study, that this approach performs better. According to Henseler et al. (2015) , the HTMT values must be above 0.85 as the constructs are different. Finally, as a check on convergent validity, the average variance extracted (AVE) must have a value of up to 0.50. The variables in this study satisfy this test of convergent validity. Online Appendix 2 shows the original questionnaire whereas Online Appendix 6 summarizes the results of the model assessment, with items falling below the abovementioned statistical thresholds excluded. The material risk dimension of Attitude Towards Covid-19 did not pass the tests; neither did the DTD dimension of Geographical Travel Intention, nor did some items of the kept Low Order Constructs (LOCs) and the PDR (Perceived Distance Risk dimension) (Fig. 1) .

index (Floyd et al. 2003) contains four Independent Variables (IVs) which can be seen as LOCs: TPR (Travel Perceived Risk), PSR (Perceived Safety Risk), DPCR (Distance Perceived Comparative Risk) and PDR (Perceived Distance Risk).

The HOC 'Attitude Towards Covid-19' includes three LOCs, namely Travel Attitude (TA), Sanitary Attitude (SA) and Material Risk (MR). As found by the previous EFA, the HOC 'Geographical travel intention' is formed by two LOCs Proximity Tourism (PT) and Distant Travel Destination (DTD). The first step was to assess the 1st order as reflective. Those that did not fit the criteria were not selected, like DTD, MR and PDR. The conceptual model is the following (Fig. 2) .

In the final reflective measurement model assessment, all items related to its LOC had loadings above 0.708 and significance levels of p < 0.001. MR was a factor for which the Cronbach's alpha is below 0.60 and PDR had an alpha of 0.619, so these LOCs were removed, as items that did not fit the 0.708 threshold of loading.

All items loaded significantly to their belonging LOC with p < 0.001. The variance inflation factors (VIF) were less than 3 (Online Appendix 7), which confirmed there are no collinearity issues in the model. The cutoff of 0.50 was respected for Average Variance Extracted (AVE) and meant that LOCs were explained by the variance of their respective indicators. The composite reliability values scored between 0.864 and 0.935, assessing the construct validity. HTMT criterion scored close or higher than 1 for LOCs on HOCs, which was normal in the repeated approach due to the presence of the same indicators but was below 0.90 for all the in-between LOC comparisons (Online Appendix 5). The interval confidence in the 5000 bootstrap procedure showed no score including 1 or above for the LOCs comparisons 2 (Franke and Sarstedt 2019). Thus, there was no issue of discriminant validity. We then used latent variable scores of the LOCs and computed them in the data to use them as manifest variables of their belonging HOCs. The further need was the 2nd order outer model assessment. Once this assessment was done, the inner path model could be run under Ali et al.'s recommendations (2018) .

The outer VIF assesses the lack of collinearity and always remained below 3; the outer weight and t-values were used to assess the formative constructs. The assessment of the high order outer model fit the criterion and is presented in Tables 5 and 6.

The model fit was correct according to Bentler (1998, 1999) because it was below 0.08, and the whole model fit can be seen in Online Appendix 8. Garson (2016, p. 68 ) explains that SRMR denotes the comparison between the observed and the model-implied correlation matrices; so the lower the SRMR, the better the fit. In our model, SRMR was 0.051 and the χ 2 was 35.137. The R 2 values were moderate for Attitude Towards Covid-19 (R 2 = 0.465) and weak for PT (R 2 = 0.038).

We hypothesized that Perceived Risk would positively influence Proximity Tourism (H1) but surprisingly it was not the case. Indeed, a non-sig- Table 6 Reflective proximity tourism assessment ***p < 0.001 As the construct finally keeps only one item and is the only formative measure in the model, neither the HTMT nor the cross-loadings are possible being calculated and the Fornell-Larcker criteria is 1 Concept (2nd order) Table 7 and Fig. 3) , and the direct effect of Perceived Risk on Proximity Tourism was not significant (β = -0.135; p > 0.05), confirming a full mediation effect of Attitude Towards Covid-19 between Attitude Towards Covid-19 and Proximity Tourism.

We used GPower software (Faul et al. 2009 ) to estimate the required minimum sample size (Lee and Hallak 2018; Melián-González et al. 2021; Rasoolimanesh et al. 2021; Ringle et al. 2015) . Selected thresholds (Cohen 1992; Hair et al. 2014 ) for linear multiple regression (selected fixed model, R 2 and deviation from zero) were the following: Effect size (f 2 ) at 0.20, type I error (α) at p < 0.05 and type II error at 1-β = 0.80 for 2 predictors (Perceived Risk and Attitude Towards Covid-19). The indicated minimum sample size was n = 52. Consequently, Baby Boomers (n = 21) was not a sample for which MGA seemed possible and was removed from further analysis. The three following generation groups were Generation X (n = 109), Generation Y (n = 145) and Generation Z (n = 80). A permutation test (n = 5000) 2 by 2 was done firstly to verify the invariance in the outer model, as the between-groups hypothesis was two-tailed (Matthews 2017) . Permutation tests can be seen in Online Appendix 10. Measurement Invariance of Composite Models (MICOM) procedure assessed the differences between the compared groups. Permutation tests did not show any difference between groups in the configural invariance nor in indicators, criteria and data treatment amongst the groups (step 1), nor in the compositional invariance (step 2) as original correlation is greater than or equal to the 5% quantile. However, partial invariance appeared in step 3 of the MICOM, which meant that composite mean values and variances were not equal, but allowed group comparison as step 1 and 2 were correct (Henseler et al. 2016) . H5 was rejected: there was no difference between generations amongst the effect of Perceived Risk on Proximity Tourism, nor on Attitude towards Covid-19 (Cf . Table 8 and Online Appendices 9 and 10 for details). Attitude towards Covid-19 was not different in its impact on Proximity Tourism across generations, as there was no different mediation of Attitude towards Covid-19 on the link between Perceived Risk on Proximity Tourism according to generation.

In this section, we highlight the main theoretical contributions of this research paper and discuss the managerial implications of the results. We also consider the limitations of the study and make suggestions for future research.

In this exploratory study, we set out to understand the impact of Covid-19 on the geographical travel intention of French people in summer 2020. The main reasons cited by those not intending to go on holiday were mainly the Covid-19 outbreak (46%), job-related issues (24%) or financial problems (11%). The main reasons for not having yet made a final decision were the outbreak (43%), job-related financial problems (23%), cancellations (15%) or other job-related issues (11%). Most people who were not going on vacation or who had not decided, planned to stay at home and make same day visits around their neighbourhood.

For those who want (n = 355) or don't want to travel (n = 300), demographic results show important differences in the intention to go on vacation in terms of marital status, educational background and place of residence, but no significant differences in terms of gender and employment status. There are also significant differences between generations because there is a hierarchy of travel intentions: 66% amongst Generation X, 55% amongst Generation Y, 50% amongst the Baby Boomers and 44% amongst Generation Z. Finally, these results support RQ2 because there (Wen et al. 2005) . These authors indicated that the impact of SARS in China was more serious for older people than it was for younger and middle-aged people, for urban rather than rural tourists and for tourists with high levels of education compared with those with lower levels.

Finally, these comparative results show demographic and generation differences in the intention to go on vacation in summer 2020 caused by a combination of personal uncertainties due to the pandemic (job, financial situation, being afraid to travel…) as well as compulsory state measures (confinement, social distancing, restriction of movement…) and travel bans (cancellations, closed borders, imposed quarantine…) These findings are consistent with Wen et al. (2005) that indicated SARS has greatly affected Chinese people's life, work and travelling in terms of inclination to travel, preference of leisure trips and concern for public hygiene; people tend to be more interested in outdoor activities and ecotourism, and city residents prefer to travel to the suburbs and countryside during the pandemic. Generally speaking, although Covid-19 has aroused panic in a short period of time, leisure or proximity tourism seems to be a highly substitutive activity as people may reduce or postpone travel intention to avoid risk.

In the second part of the analysis, a PLS-SEM model analyses the influence of Perceived Risk and Attitude towards Covid-19 on Proximity Tourism for respondents who planned to travel (n = 355). Results show first that there is no influence of Perceived Risk on Proximity Tourism and no moderate effect of Generation on the model. Although only tendentially significant, Perceived Risk is negatively predictive of participants' intention to go in a proximity destination. One can only speculate that perceptions of risk may influence the decision of tourists to go on vacation or not, whether to a proximity or distant destination, due to the uncertainty of the impact of the pandemic in the summer of 2020. This result is consistent with former research on sport mega events such as the Olympic Games (Qi et al. 2009 ) or the FIFA World Cup (Toohey et al. 2003; Kim and Chalip 2004) . In contrast, findings indicate that the different generations do not moderate the relationship between Perceived Risk and Attitude Towards Covid-19 and Proximity Tourism. The lack of moderation of generation could be explained by the weak number of each generation in the sample due certainly to the online process during the outbreak, specifically for the Baby Boomers. Indeed, this result is divergent from the comparative data from the demographic approach that validated RQ2 and is not consistent with research on the impacts of the SARS pandemic in China (Wen et al. 2005) .

Thus, PLS-SEM results show (a) positive influence of Perceived Risk on Attitude Towards Covid-19 and of Attitude Towards Covid-19 on Proximity Tourism; (b) that Attitude towards Covid-19 fully mediates the relationship between Perceived Risk and Proximity Tourism (Cf. Table 8 ). These results confirm that Covid-19 has encouraged proximity tourism and support the relationship proposed in RQ1. They reinforce the findings of Romagosa (2020) and Nazneen et al. (2020) about the effects of the outbreak on changes in mobility. More specifically, Attitude towards Covid-19 has a double influence. On the one hand, this attitude explains the geographical travel intention in favour of Proximity Tourism (or the absence of travel altogether). The Covid-19 pandemic has created anxiety and reduced the distant travel plans of French people for summer 2020. On the other hand, Perceived Risk is experienced as a constraint on travel and therefore may explain the tendency to want to stay somewhere already known, with people who are already familiar, in a region or country without sanitary constraints. This result certainly also explains that Perceived Risk does not positively influence Proximity Tourism because all French people necessarily have to travel in France due to border closures or/and because of the quarantines imposed by other countries, travel bans and cancellations (flights, accommodation, etc.), regardless of their level of perception of the health risk related to the pandemic.

These findings are consistent with a study on tourism in France after the summer vacations (Atout France 2020), which shows that 53% (71% in 2019) of French people went travelling in June-August 2020, and further confirms an increased number of visitors from neighbouring territories or regions. Moreover, almost all the respondents in this study who intended to travel were going to do so within France (89%), which is again consistent with this work (Atout France 2020), showing that 94% of those who had travelled had stayed in France. More precisely, amongst those who said they intended to travel during their summer 2020 vacation (n = 355), their order of preference in terms of destination was firstly within France, followed by within the region and finally outside France. 3 The PLS-SEM results also show that Attitude Towards Covid-19 fully mediated the relationship between Perceived Risk and Proximity Tourism. This mediation is an original and an important result because the Attitude Towards Covid-19 could be regarded as an important precondition for geographical travel intentions on a proximity destination rather than on a distant destination. Moreover, the mediating role of attitude could be included in future models that are used to study relationships between the perception of sanitary crisis (or health risks) and travel intention (Bae and Chang 2020), behavioural intention (Ajzen 1985; Quintal et al. 2010) , destination attractiveness (Reitsamer et al. 2016) or visit intention (Doosti et al. 2016) .

This study could help managers apply proximity marketing strategies for the implementation of tourism services in the context of a global health crisis. In most tourist locations, the development of domestic travel during summer 2020 will probably lead the way to more proximity tourism next year, especially if countries continue to impose strict health measures and limit international travel. Beyond the usual visits to friends or family nearby, it would be interesting for destinations to promote more authentic and/or more specific local holidays with services which develop nature or outdoor activities and ecotourism (Wen et al. 2005) . Such a strategy might include offering more original stays to enable domestic tourists to (re)discover the richness of their local territories (nature, culture, leisure, heritage, gastronomy…) with guides or local greeters capable of providing information and experiences that are more authentic and more adapted to the needs of domestic holidaymakers because they can help them optimize these enjoyable and memorable experiences (Hosany and Witham 2010; Su et al. 2018; Wong and Lee 2012; Wong and Wang 2009; Yim et al. 2012) .

For instance, as in the case of rural areas (Campos et al. 2018) , managers can promote services with low arousal and more familiar experiences that can provide tourists with creative and repetitive pleasure through the discovery of their own limits, new sensations, new playgrounds… They must also enable more active visitor participation by emphasizing the educational and escapist elements underlying local tourist services. They can create specific activities such as playful visits or areas such as event spaces in which tourists can more easily co-produce and co-create experiences based on their own intellectual and physical resources (Kim and Fesenmaier 2015; Volo 2009 ). For example, and despite the maintenance of strict sanitary conditions, it would be possible to renew the offer of gastronomy or wine and beer tourism with visits or tastings in vineyards or hop fields. This could take place in small or even intimate groups with knowledge closely linked to local people and culture. A more local and sustainable tourism, in which encounters, culture and leisure activities will have a prominent place, would allow local visitors to travel less far, less often, but better and safer.

In view of the health constraints imposed by the pandemic, the establishment of an effective communication system and digital services will contribute to the recovery of the domestic tourist market. A diversified communication strategy must be deployed by targeting the main social networks (Facebook, Snapchat, YouTube and Instagram) and identifying social media influencers as endorsers to promote local tourism destinations and their services (Belleau et al. 2007; Yussof et al. 2018) . Lima et al. (2020) identify social media and destination image as influencing visitors' expectations, and that this image arises out of word-of-mouth communication and tourists' past experiences.

But with the Covid-19 crisis and its more or less long-term consequences, new innovations and digital services should be proposed in tourist destinations (Atout France 2020): health reinsurance of tourist sites (accommodation, equipment, cultural sites, transport infrastructure, etc.) with the implementation of new visitor relationship protocols; new individual or collective protection equipment enabling compliance with health regulations whilst facilitating exchanges, visits and experiences; new management of public traffic flows taking into account the imperatives of social distancing and health constraints both during travel to the destination and on site; creation of innovative solutions for digitizing events, monuments, museums and sites with virtual or augmented reality services; digital, geolocalized, verified promotion of tourists, leisure and cultural services; creation of autonomous augmented reality type services to accompany visits, including on smartphones.

The current Covid-19 pandemic has forced many companies, especially those in the travel and food industries, to quickly develop contactless service solutions using robots or digital interfaces (O'Neill 2020). They are used to reduce in-person interaction (employee-customer, employee-employee), serve food and drinks, provide no-touch surface solutions (e.g., using apps instead of printed menus, using contactless payment systems such as Apple Pay instead of credit cards, using smart phones for opening hotel door rooms, controlling room temperature, adjusting lights, operating TV, etc.), provide virtual tours, etc. But for widespread use, consumer inputs and co-creation are necessary to ensure that digital services provide what consumers want with quality and security (Baisch et al. 2017; Čaić et al. 2018; Chiang and Trimi 2020) , especially in tourism.

This study has some limitations, leading to directions for future research. New studies should be conducted on larger groups of tourists and in other countries to provide external validity of these results. Furthermore, it would be interesting to test the impact of Covid-19 on proximity tourism with other outcomes like transport, accommodation or activities, and to explore other potential moderating variables like gender and place of residence (urban/rural).

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