Abstract
This paper examines how tourist behaviour is conditioned by the COVID-19 pandemic, developing a theoretical framework that considers not only traditional variables such as image, satisfaction, and variety seeking, but also the risks perceived during a pandemic to better explain loyalty towards a tourist destination. Moreover, the paper explores whether the effects of these variables differ in loyalty formation if people who consider visiting a destination in their country again are compared to people who contemplate travelling again to an international destination. Empirical evidence from a survey sample comprising more than 1000 Spanish tourists shows that pandemic-related risks differently influence the tourist loyalty dimensions intention to revisit and recommend both a national destination (441 respondents) and an international destination (600 respondents). Finally, affective image and satisfaction are the main drivers of loyalty in both subsamples.
Keywords: Perceived risk, destination, image, satisfaction, variety seeking, loyalty
Introduction
The tourism industry, and tourist behaviour in particular, is extremely susceptible to disaster events, which can be divided into natural disasters, such as earthquakes, and manmade disasters for example, terrorist attacks (Ma et al., 2020). While pandemics such as COVID-19 can be considered natural disasters, they should be analysed as a specific typology due to their dramatic impact on tourism activity (Moya Calderón et al., 2021; Villacé -Molinero et al., 2021; Zenker et al., 2021), giving rise to many and relevant constraints in many countries, including mobility restrictions, social distancing, and limited access to services and infrastructures. Under these circumstances, this paper considers it necessary to revisit the study of a topic that has received great attention in tourism research: tourist loyalty formation (Kim, 2018). In particular, together with the antecedents most widely examined in literature, several pandemic-related risks are expected to play an important role in loyalty formation.
Loyalty, generally defined as the individual’s commitment to a product or brand (Oliver, 1997), is usually operationalized in tourism research as the intention to revisit a tourist destination in the future and the intention to recommend it to other people such as friends and relatives (Kim, 2018; Lee et al., 2011; Prayag and Ryan, 2012). In addition, some of the most relevant variables that have been proposed in tourism research to explain tourist loyalty formation are (a) destination image—that is, the set of associations attached to a tourist site; (b) satisfaction—that is, the tourist’s state derived from his/her experience at the destination; and (c) variety seeking—that is, the individual’s tendency to change the location of holidays (Hong and Desai, 2020; San Martín et al., 2019). However, during disaster events, risks perceived by people may play an important role in their evaluation of tourist destinations (Xie et al., 2020) and, consequently, in their loyalty behaviours. This research takes as a reference the main risk dimensions previously highlighted in tourism (Boksberger and Craig-Smith, 2006; Björk and Kauppinen-Räisänen, 2012) and reframes them to the specific context of the COVID-19 pandemic. Specifically, we analyse the period of time just after the lockdown, when international trips were again allowed but countries had access restrictions (e.g. test certificate requirements). Thus, this research analyses how tourists’ loyalty towards a destination is affected by the specific uncertainties and restrictions of a pandemic in terms of performance, physical, financial, time, social, and psychological risks.
With this in mind, this paper aims to develop a new theoretical framework where the formation of tourist loyalty is considered following a health pandemic, an extraordinarily complex process conditioned by both the physical and psychological constraints on travellers. Thus, the main objective of this paper is to examine how loyalty to a destination is influenced not only by the variables most widely studied in previous research on tourist behaviour (i.e. image, satisfaction and variety seeking), but also by the different risk perceptions that may operate in the tourist’s mind during the first stages after a health pandemic lockdown. In addition, since different levels of novelty and uncertainty associated with the visit to a destination may influence tourists’ behaviour (especially in post-pandemic times), the present study has as its second objective exploring the difference in destination loyalty formation between tourists considering visiting a national destination again and people contemplating travelling again to an international destination.
Theoretical background
The theoretical framework of this study includes two types of variables to understand how loyalty to a tourist destination is affected by a pandemic. On the one hand, the different risks expected to be perceived by tourists in a pandemic are considered: financial, performance, physical, psychological, social, and time risks. On the other hand, several behavioural variables that have received great attention in tourism research are also considered: destination image, tourist satisfaction, and variety seeking.
Risk dimensions in the study of tourist behaviour: A necessary adaptation in pandemic times
Since its introduction in the seminal works of the 1960s (Bauer, 1960; Cox, 1967), the concept of risk has been a recurrent topic of research in the fields of marketing and consumer behaviour (Conchar et al., 2004; Mitchell, 1999). Risk perception is considered to reflect the individual’s evaluation of both the magnitude of potential losses and the chance of them occurring (Weber and Milliman, 1997), although it is usually conceived as a subjective expectation of loss associated with a behaviour (Mitchell, 1999). Accordingly, the higher the risk associated with a behaviour by individuals, the lower the probability that they will get involved in it (Conchar et al., 2004). Past research on consumer behaviour and perceived risk can be classified into two main lines according to the level of analysis of this psychological construct. Some studies analyse the influence of perceived risk on consumer decision-making, conceiving risk as a unidimensional construct, while others adopt a multidimensional approach, identifying several dimensions of perceived risk and studying their effects on consumer behaviour (Herrero and Rodríguez, 2010). This multidimensional approach has led to the proposal of diverse dimensions or facets of perceived risk, adapted to different sectors and contexts under investigation (Giordano et al., 2017; Herrero and San Martín, 2012; San Martín et al., 2020).
In the specific field of tourist destinations, perceived risk represents the tourist’s subjective expectation of loss associated with the visit to a destination (Çetinsöz and Ege, 2013). In this regard, perceived risk affects the destination experience (Kozak et al., 2007; Simpson and Siguaw, 2008) but also has a direct influence on the information search and the evaluation of alternatives in the pre-visit stage (Björk and Kauppinen-Räisänen, 2012; Qi et al., 2009; Rittichainuwat and Chakraborty, 2009; Villacé -Molinero et al., 2021). Study of the effects of risk facets on destination choice and assessments has received great attention in the literature during the last two decades.
The available empirical evidence confirms that the perceived risk of visiting a destination has a negative effect on tourist behaviour, considering both global risk (Lepp and Gibson, 2008; Quintal et al., 2010) and specific risk dimensions (Hasan et al., 2017; Lebrun et al., 2022; Tapsall et al., 2022). However, there is no consensus in the academic literature about the risk dimensions to be analysed or their effects on tourist behaviour (Fuchs and Reichel, 2006), as these depend on the specific characteristics of the destination and contextual factors such as natural disasters, pandemics, or terrorist attacks (Björk and Kauppinen-Räisänen, 2012; Hasan et al., 2017; Zenker et al., 2021). See Hasan et al. (2017) for a detailed summary of the different risk dimensions identified in tourism research.
Among the diverse typologies of risk dimensions, the most widespread proposal in the study of consumer behaviour in general (Herrero and Rodríguez, 2010) and tourist behaviour in particular (Boksberger and Craig-Smith, 2006; Björk and Kauppinen-Räisänen, 2012; Khasawneh and Alfandi, 2019) is the one that distinguishes between performance risk, physical risk, financial risk, time risk, social risk, and psychological risk. Table 1 summarizes the theoretical definitions of each of these facets or dimensions of perceived risk, as well as previous studies that have considered them in tourism research. In general, it is widely accepted that all the risk dimensions associated with a tourist destination exert a negative influence on individuals’ intention to visit the tourist site (Çetinsöz and Ege, 2013; Chew and Jahari, 2014; Fuchs and Reichel, 2006; Kaushik and Chakrabarti, 2018; Khasawneh and Alfandi, 2019) and their subsequent behaviours (Rittichainuwat and Chakraborty, 2009). However, the results regarding these effects are heterogeneous and even contradictory (Hasan et al., 2017), given that risk perceptions may differ depending on the destination and contextual factors. Moreover, regarding the effect of risk dimensions on behavioural intentions, previous research has only considered tourists’ intention to visit or revisit a destination but not the influence of risk dimensions on the intention to recommend it to other people (see Hasan et al., 2017). Given the relevance of this variable in recent studies (Herrero et al., 2017; Martínez et al., 2018), this paper intends to fill this gap in the academic literature by examining the effects of perceived risk dimensions on the two expressions of tourist loyalty: the intention to revisit a destination and the intention to recommend it to other people.
Table 1.
Definition of the perceived risk dimensions in tourism.
In the specific context of a pandemic like COVID-19, it is reasonable to assume that tourists’ intentions to visit a destination and recommend it to other people are affected by the risk associated with the place because of the health crisis (Lebrun et al., 2022; Pan et al., 2022; Villacé -Molinero et al., 2021). Moreover, this risk may have different facets (Jeong et al., 2022; Jiang et al., 2022; Tapsall et al., 2022) such that people may perceive not only that there is a risk to their health (physical risk) but also that the tourism experience will not be the same as in a normal situation (performance risk), or that they may have economic losses due to the pandemic (financial risk). There are also other possible associated risks (Jeong et al., 2022; Tapsall et al., 2022), such as the possibility of having to cancel the trip or stay in a lockdown at the destination (time risk), stress during the pre-visit and visit stages (psychological risk), or the social loss linked to travelling in such an uncertain context (social risk). According to the evidence from previous research, and considering the dimensions of perceived risk of visiting a destination, as proposed by Boksberger and Craig-Smith (2006) and Björk and Kauppinen-Räisänen (2012), the following research hypotheses are established:
H1
The perceived financial risk of visiting the destination again negatively influences the intentions to revisit (H1a) and recommend (H1b) the destination.
H2
The perceived performance risk of visiting the destination again during a pandemic negatively influences the intentions to revisit (H2a) and recommend (H2b) the destination.
H3
The perceived physical risk of visiting the destination again during a pandemic negatively influences the intentions to revisit (H3a) and recommend (H3b) the destination.
H4
The perceived psychological risk of visiting the destination again during a pandemic negatively influences the intentions to revisit (H4a) and recommend (H4b) the destination.
H5
The perceived social risk of visiting the destination again during a pandemic negatively influences the intentions to revisit (H5a) and recommend (H5b) the destination.
H6
The perceived time risk of visiting the destination again during a pandemic negatively influences the intentions to revisit (H6a) and recommend (H6b) the destination.
Non-pandemic-related determinants of tourist loyalty
In addition to the potential risks perceived by tourists in pandemic times, the present paper considers several behavioural variables that have received great attention in the academic literature to explain tourists’ loyalty: destination image, tourist satisfaction, and variety seeking. Firstly, in line with San Martín et al. (2019), destination image can be considered as the set of associations attached to a tourist site. According to the nature of these associations, two types of image can be considered: cognitive and affective destination images. Cognitive image refers to the tourist’s beliefs about the resources and attractions of the place (Pike and Ryan, 2004), while affective image, which has received increasing attention in tourism research, is defined as the amalgam of emotions and feelings that a tourist associates with the place (San Martín et al., 2019). Additionally, it is necessary to distinguish between pre-visit and post-visit destination images. Before a trip, people form a certain image of the tourist site and they build their expectations of the tourist destination based on that image and other personal and contextual factors (San Martín and Del Bosque, 2008). Tourists often modify their image of the destination after the trip, mainly due to the experiences and feelings they had during their stay at the tourist site (Lee et al., 2014).
Considering the focus of the present study, the post-visit destination image—and particularly the affective component of this psychological construct—is considered in this theoretical framework. The choice of the post-visit affective image is based on several factors. Firstly, more effort to investigate the role of emotions in post-experience behaviours has recently been requested in tourism research (Prayag et al., 2017). Secondly, it should be emphasized that tourists feel strong emotions when they enjoy the different resources of the destination (Del Bosque and San Martín, 2008) and have multiple interactions with local people (Stylidis, 2020). In general, emotions leave affective memory traces, which are especially relevant in people’s subsequent evaluations (Cohen and Areni, 1991). The post-visit affective image can thus be considered a powerful variable in explaining tourists’ loyalty since this type of image is mainly based on the emotions and feelings experienced at the destination (Li et al., 2021). According to this theoretical approach, and some previous evidence confirming a positive relationship between destination image and tourist loyalty (e.g. Kim, 2018; Stavrianea and Kamenidou, 2021; Jiang et al., 2022), the following hypothesis is established:
H7
The affective destination image, formed by tourists after their stay at the place, positively influences the intentions to revisit (H7a) and recommend (H7b) the destination during a pandemic.
Tourist satisfaction, which can be defined as a cognitive-affective state derived from the experience at the destination (San Martín et al., 2019: p.1994), is a crucial variable in the study of tourist behaviour. On the one hand, most previous studies have focused on the influence of the image of the destination, formed by people before or during their stay at the place, on their satisfaction with the experience (e.g. Bigné et al., 2001; Chi and Qu, 2008; Kim, 2018; Stavrianea and Kamenidou, 2021). However, considering the context under investigation, this paper adopts another approach and posits that the image that tourists build of a destination is affected by their previous satisfaction with that place. Satisfaction is a strong predictor of destination image since tourists place more trust in primary information from their experiences than in secondary information provided, for example, by mass media (Li et al., 2021). On the other hand, tourist satisfaction is considered one of the most relevant variables influencing loyalty behaviours. Many studies have demonstrated, in different geographical contexts, that individuals’ intentions to revisit a tourist destination and recommend it to other people are positively influenced by their satisfaction with the destination (e.g. Bigné et al., 2001; Prayag and Ryan, 2012; San Martín et al., 2013; Hernández-Mogollón et al., 2020; Huwae et al., 2020). With all this in mind, the following hypotheses are established:
H8
The tourist’s satisfaction with the destination positively influences the affective destination image.
H9
The tourist’s satisfaction with the destination positively influences the intentions to revisit (H9a) and recommend (H9b) the destination during a pandemic.
Nevertheless, satisfaction is not always an excellent indicator of loyalty behaviours. In this regard, it is necessary to highlight the concept of variety seeking, which is the individual’s tendency to change provider or brand due to the mere desire to obtain sensorial pleasure from the change (Zuckerman, 1994) and not to achieve a higher utility or functional value from a market alternative (Barroso et al., 2007). In tourism literature, it is widely recognized that people are prone to seek variety for their holidays (Legohérel et al., 2015; Sánchez-Garcia et al., 2012). More concretely, Hong and Desai (2020) emphasized that people perceive greater novelty when they think about distant destinations (compared to nearby places) or when they evaluate, before the trip, tourist destinations with a greater diversity of activities and attractions. Variety seeking implies that tourists’ intention to visit a destination (or the probability of visiting it) diminishes when they have visited the place on a previous holiday (Nicolau, 2010). Based on these theoretical arguments and some empirical evidence from previous research in tourism (e.g. Jang and Feng, 2007; Bigné et al., 2009; Giao et al., 2020), the next hypothesis suggests linking variety seeking and tourist loyalty:
H10
Variety seeking negatively influences tourists’ intention to revisit a destination during a pandemic.
Figure 1 illustrates the theoretical model and summarizes the hypotheses postulated in this study.
Finally, many previous studies have highlighted the complex nature of the loyalty formation process since it is conditioned by different contextual and personal factors (Fernandes and Esteves, 2016; Pan et al., 2012; Smith, 2020). On one hand, the present study considers that cultural distance—the degree to which the culture of origin of an individual is different to the culture of the host (Ahn and McKercher, 2015)—is a variable that is expected to influence the tourist loyalty formation. In their study, Liu et al. (2020) established that the behaviours of individuals with a higher cultural distance from a destination are more influenced by the emotions they associate with that place, while those of people with a smaller cultural distance from a tourist site are more guided by their cognitions about the place. In addition, perceived risks may be higher among tourists with a higher cultural distance from the destination since, in general, risk perceptions rise with the level of novelty associated with the place (Elsrud, 2001). On the other hand, this study establishes that, in a pandemic context, tourists may perceive a higher level of risk when they are thinking about a holiday abroad (Moya Calderón et al., 2021; Zenker et al., 2021). This is due to the special uncertainty provoked among tourists by potential restrictions in a foreign country regarding mobility, access to health services, or the availability of tourist attractions, among other critical issues (Lebrun et al., 2022). Under these circumstances, this study establishes the following research question:
Figure 1.
Theoretical model of loyalty towards a destination in pandemic time.
RQ: Is loyalty formation different depending on the type of destination (i.e. national versus international destinations) tourists are considering visiting again during a pandemic?
Methodology
Measurement instrument
Data were collected using online surveys, which have experienced a remarkable growth in recent years in academic research (Guinalíu and Rada, 2020). Specifically, the online questionnaire included four blocks of questions: (1) respondents’ sociodemographic characteristics; (2) general tourist behaviour; (3) tourists’ assessments of the last destination visited before COVID-19; and (4) the risks perceived by tourists regarding the last destination visited but considering the new COVID-19 context. The variables of the theoretical model were all measured using seven-point Likert scales adapted from previous studies to assure their content validity (see the Appendix for the measurement scales and reference sources).
Special care was taken in the design of the questionnaire to avoid or minimize common method variance (CMV), a concern in previous research based on self-report questionnaires (Podsakoff et al., 2003). Specifically, and regarding ex-ante strategies (Podsakoff et al., 2003), the complexity of measures, the way in which scales are presented to respondents, and the context in which the items are placed were considered as potential sources of CMV. Thus, the construction of items was systematically examined to avoid ambiguous, vague, and unfamiliar terms (Chang et al., 2010), and measurements of the predictor variables and the criterion variables were separated in the questionnaire (Podsakoff et al., 2003). Regarding ex-post strategies, and before the analysis of results, the variance bias of the common method was analysed by applying Harman’s single-factor test (Podsakoff et al., 2003). The results showed that the main factor explained 27.1% of the variance, lower than the 50% threshold, thus confirming that the bias is acceptable for the data analysis.
Sampling procedure and sample description
To test the hypotheses, empirical research based on the online surveys was conducted in Spain. The target population consisted of Spanish tourists over 16 years of age who had travelled at least once for leisure or holidays during the last year. The sample was selected using a non-probabilistic sampling procedure. Specifically, a quota sampling method was used, considering the characteristics of the target population in terms of age, gender, and the size of the region (quotas were calculated based on the official statistics provided by the Spanish Statistical Institute).
Online surveys were carried out between May and June 2020 with the collaboration of Netquest, an international company specializing in online research. A questionnaire was distributed through the online platform Nicequest to panellists that fulfilled the requirements related to the target population and sampling. It is necessary to emphasize that data collection was performed according to the ISO 26362:2009 standard in order to ensure the quality of the empirical data. For example, the respondents’ IP addresses were verified to ensure that each respondent only submitted the online questionnaire once; in addition, several control questions were included in different sections of the questionnaire to ensure the reliability of responses.
Finally, 1011 valid responses were obtained, demonstrating very high correspondence with the Spanish population in terms of gender and age (Table 2) and place of residence. Of the overall sample of respondents, 411 were people whose last visited destination was national (subsample 1), while 600 had visited an international destination (subsample 2).
Table 2.
Demographic characteristics of respondents.
| Variable | % | Variable | % |
|---|---|---|---|
| Gender | Age | ||
| Male | 48.6 | 18–29 years old | 20.6 |
| Female | 51.4 | 30–44 years old | 28.8 |
| 45–64 years old | 37.7 | ||
| 65+ years old | 12.9 | ||
This research, and specifically the potential issues related to human participants, was supervised and authorized by the Research Ethics Commission of the University of Cantabria (Spain).
Results
A covariance-based structural equation modelling approach was applied to test the research hypotheses using EQS 6.1 software. Following the two-step approach proposed by Anderson and Gerbing (1988), this study assessed the adequacy of measurements using confirmatory factor analysis (measurement model) and tested the hypothesized relationships by estimating the structural model. Prior to this, descriptive analysis was performed to check the unidimensionality of scales. The values of skewness and kurtosis were, in most cases, within the threshold of ±2 (George and Mallery, 2010). In order to control potential problems related to the non-normality of data, the measurement and structural models were estimated following a robust maximum likelihood procedure, which provides robust chi-square statistic and robust standard errors outputs, corrected for non-normality (Byrne, 2006). Moreover, the sample (411 individuals) implies more than five observations to estimate in the measurement and structural model for each of the parameters (70), therefore achieving the recommendations proposed by Byrne (2006) and Hair et al. (2010).
Estimation of the measurement model
Confirmatory factor analysis was performed to test the psychometric properties of the measurement scales, considering the two subsamples separately. The results confirmed the reliability and convergent validity of the measurement scales in both subsamples (Tables 3 and 4). Cronbach’s alpha and composite reliability in all cases were above the required minimum values of 0.7, and AVE coefficients were above 0.5 (Hair et al., 2010). All items were significantly associated with their hypothesized factors at a confidence level of 95% and their standardized lambda coefficients were higher than 0.5 (Steenkamp and Van Trijp, 1991), confirming the convergent validity. Finally, goodness-of-fit indices met the criteria for an acceptable model fit (Normed χ2 = 1.53, NFI = 0.92, NNFI = 0.96, CFI = 0.97, IFI = 0.97, and RMSEA = 0.04 for subsample 1; Normed χ2 = 1.67, NFI = 0.95, NNFI = 0.98, CFI = 0.98, IFI = 0.98, and RMSEA = 0.03 for subsample 2).
Table 3.
Measurement model for subsample 1 (national destinations).
| Factor | Variable | Stand. Coef. | R2 | Cronbach’s alpha | Composite reliability | AVE | Goodness of fit indices |
|---|---|---|---|---|---|---|---|
| Financial risk | F_R1 | 0.76 | 0.58 | 0.85 | 0.85 | 0.66 | Normed χ2 = 1.53 BBNFI = 0.92 BBNNFI = 0.96 CFI = 0.97 IFI = 0.97 RMSEA = 0.04 |
| F_R2 | 0.83 | 0.68 | |||||
| F_R3 | 0.84 | 0.71 | |||||
| Performance risk | P_R1 | 0.82 | 0.67 | 0.86 | 0.86 | 0.67 | |
| P_R2 | 0.77 | 0.59 | |||||
| P_R3 | 0.87 | 0.75 | |||||
| Physical risk | PH_R1 | 0.94 | 0.88 | 0.93 | 0.93 | 0.82 | |
| PH_R2 | 0.88 | 0.77 | |||||
| PH_R3 | 0.90 | 0.81 | |||||
| Psychological risk | PS_R1 | 0.95 | 0.91 | 0.96 | 0.96 | 0.88 | |
| PS_R2 | 0.93 | 0.87 | |||||
| PS_R3 | 0.94 | 0.88 | |||||
| Social risk | S_R1 | 0.82 | 0.66 | 0.78 | 0.80 | 0.58 | |
| S_R2 | 0.85 | 0.72 | |||||
| S_R3 | 0.59 | 0.35 | |||||
| Time risk | T_R1 | 0.92 | 0.85 | 0.87 | 0.87 | 0.70 | |
| T_R2 | 0.88 | 0.78 | |||||
| T_R3 | 0.69 | 0.48 | |||||
| Revisit intention | RI1 | 0.96 | 0.93 | 0.88 | 0.90 | 0.81 | |
| RI2 | 0.84 | 0.71 | |||||
| Recommendation intention | WOM1 | 0.95 | 0.90 | 0.92 | 0.93 | 0.86 | |
| WOM2 | 0.91 | 0.82 | |||||
| Destination image | DI2 | 0.73 | 0.54 | 0.84 | 0.85 | 0.65 | |
| DI3 | 0.83 | 0.69 | |||||
| DI4 | 0.85 | 0.72 | |||||
| Satisfaction with the destination | SAT1 | 0.95 | 0.90 | 0.96 | 0.96 | 0.88 | |
| SAT2 | 0.96 | 0.93 | |||||
| SAT3 | 0.91 | 0.83 | |||||
| Variety seeking | VS1 | 0.84 | 0.70 | 0.91 | 0.91 | 0.77 | |
| VS3 | 0.91 | 0.83 | |||||
| VS4 | 0.89 | 0.80 |
Table 4.
Measurement model for subsample 2 (international destinations).
| Factor | Variable | Stand. Coef. | R2 | Cronbach’s alpha | Composite reliability | AVE | Goodness of fit indices |
|---|---|---|---|---|---|---|---|
| Financial risk | F_R1 | 0.70 | 0.4 | 0.84 | 0.84 | 0.63 | Normed χ2 = 1.67 BBNFI = 0.95 BBNNFI = 0.98 CFI = 0.98 IFI = 0.98 RMSEA = 0.03 |
| F_R2 | 0.84 | 0.71 | |||||
| F_R3 | 0.84 | 0.71 | |||||
| Performance risk | P_R1 | 0.88 | 0.77 | 0.88 | 0.89 | 0.72 | |
| P_R2 | 0.83 | 0.69 | |||||
| P_R3 | 0.84 | 0.70 | |||||
| Physical risk | PH_R1 | 0.94 | 0.88 | 0.94 | 0.94 | 0.83 | |
| PH_R2 | 0.91 | 0.82 | |||||
| PH_R3 | 0.89 | 0.80 | |||||
| Psychological risk | PS_R1 | 0.96 | 0.91 | 0.96 | 0.97 | 0.91 | |
| PS_R2 | 0.95 | 0.90 | |||||
| PS_R3 | 0.95 | 0.90 | |||||
| Social risk | S_R1 | 0.80 | 0.64 | 0.79 | 0.82 | 0.58 | |
| S_R2 | 0.85 | 0.73 | |||||
| S_R3 | 0.62 | 0.39 | |||||
| Time risk | T_R1 | 0.92 | 0.85 | 0.82 | 0.85 | 0.66 | |
| T_R2 | 0.92 | 0.84 | |||||
| T_R3 | 0.55 | 0.31 | |||||
| Revisit intention | RI1 | 0.93 | 0.86 | 0.89 | 0.89 | 0.81 | |
| RI2 | 0.87 | 0.75 | |||||
| Recommendation intention | WOM1 | 0.93 | 0.87 | 0.92 | 0.92 | 0.86 | |
| WOM2 | 0.92 | 0.84 | |||||
| Destination image | DI2 | 0.73 | 0.54 | 0.84 | 0.84 | 0.64 | |
| DI3 | 0.85 | 0.72 | |||||
| DI4 | 0.82 | 0.68 | |||||
| Satisfaction with the destination | SAT1 | 0.93 | 0.87 | 0.95 | 0.95 | 0.85 | |
| SAT2 | 0.91 | 0.83 | |||||
| SAT3 | 0.93 | 0.87 | |||||
| Variety seeking | VS1 | 0.83 | 0.69 | 0.88 | 0.88 | 0.72 | |
| VS3 | 0.82 | 0.68 | |||||
| VS4 | 0.89 | 0.80 |
The discriminant validity of the measurement scales was tested following the procedure proposed by Fornell and Larcker (1981), which compares the average variance extracted (AVE) coefficient for each pair of constructs with the squared correlation estimated between these two constructs. In all cases, the AVE for each construct is greater than the squared correlation between them, which supports the discriminant validity of the instruments in each subsample (Tables 5 and 6).
Table 5.
Results for Fornell and Larcker’s criterion for discriminant validity (all constructs): subsample 1 (national destinations).
| F_R | P_R | PH_R | PS_R | S_R | T_R | RI | WOM | DI | SAT | VS | |
|---|---|---|---|---|---|---|---|---|---|---|---|
| F_R | 0.66a | ||||||||||
| P_R | 0.30 | 0.67a | |||||||||
| PH_R | 0.28 | 0.35 | 0.82a | ||||||||
| PS_R | 0.28 | 0.34 | 0.53 | 0.88a | |||||||
| S_R | 0.31 | 0.35 | 0.46 | 0.46 | 0.58a | ||||||
| T_R | 0.36 | 0.26 | 0.38 | 0.40 | 0.49 | 0.70a | |||||
| RI | 0.00 | 0.01 | 0.03 | 0.02 | 0.01 | 0.06 | 0.81a | ||||
| WOM | 0.03 | 0.00 | 0.04 | 0.04 | 0.02 | 0.10 | 0.29 | 0.86a | |||
| DI | 0.00 | 0.00 | 0.03 | 0.02 | 0.01 | 0.05 | 0.30 | 0.58 | 0.65a | ||
| SAT | 0.03 | 0.00 | 0.04 | 0.07 | 0.03 | 0.08 | 0.27 | 0.69 | 0.50 | 0.88a | |
| VS | 0.00 | 0.03 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.08 | 0.10 | 0.10 | 0.77a |
aSquare root of AVE coefficient of the construct
Table 6.
Results for Fornell and Larcker’s criterion for discriminant validity (all constructs): subsample 2 (international destinations).
| F_R | P_R | PH_R | PS_R | S_R | T_R | RI | WOM | DI | SAT | VS | |
|---|---|---|---|---|---|---|---|---|---|---|---|
| F_R | 0.63a | ||||||||||
| P_R | 0.31 | 0.72a | |||||||||
| PH_R | 0.22 | 0.48 | 0.83a | ||||||||
| PS_R | 0.26 | 0.44 | 0.66 | 0.91a | |||||||
| S_R | 0.31 | 0.41 | 0.52 | 0.49 | 0.58a | ||||||
| T_R | 0.27 | 0.24 | 0.29 | 0.30 | 0.30 | 0.66a | |||||
| RI | 0.01 | 0.03 | 0.04 | 0.03 | 0.04 | 0.07 | 0.81a | ||||
| WOM | 0.00 | 0.00 | 0.01 | 0.00 | 0.00 | 0.05 | 0.11 | 0.86a | |||
| DI | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.03 | 0.10 | 0.61 | 0.64a | ||
| SAT | 0.02 | 0.00 | 0.00 | 0.01 | 0.00 | 0.07 | 0.07 | 0.64 | 0.52 | 0.85a | |
| VS | 0.00 | 0.02 | 0.00 | 0.00 | 0.01 | 0.00 | 0.01 | 0.05 | 0.07 | 0.04 | 0.72a |
aSquare root of AVE coefficient of the construct
Estimation of structural model
Table 7 summarizes the results derived from the estimation of the proposed research model in the two subsamples, indicating the standardized coefficients and significance level (p-value) for each relationship. In general, the goodness-of-fit indices suggest a satisfactory fit of the data to the model for both subsamples (Normed χ2 = 1.72, NFI = 0.91, NNFI = 0.95, CFI = 0.96, IFI = 0.96, and RMSEA = 0.04 for subsample 1; and Normed χ2 = 1.82, NFI = 0.95, NNFI = 0.97, CFI = 0.97, IFI = 0.97, and RMSEA = 0.04 for subsample 2).
Table 7.
Final model estimated.
| Hypothesis | S. coefficients Sub-sample 1 (N = 441) |
S. coefficients Sub-sample 2 (N = 600) |
|---|---|---|
| H1a: Financial risk > revisit intention | 0.19*** | 0.04 (n.s.) |
| H1b: Financial risk > recommendation intention | −0.04 (n.s.) | 0.05 (n.s.) |
| H2a: Performance risk > revisit intention | −0.11 (n.s.) | −0.06 (n.s.) |
| H2b: Performance risk > recommendation intention | 0.00 (n.s.) | 0.06 (n.s.) |
| H3a: Physical risk > revisit intention | −0.10 (n.s.) | −0.11 (n.s.) |
| H3b: Physical risk > recommendation intention | 0.00 (n.s.) | −0.14** |
| H4a: Psychological risk > revisit intention | 0.06 (n.s.) | 0.07 (n.s.) |
| H4b: Psychological risk > recommendation intention | 0.05 (n.s.) | 0.05 (n.s.) |
| H5a: Social risk > revisit intention | 0.08 (n.s.) | −0.06 (n.s.) |
| H5b: Social risk > recommendation intention | 0.07 (n.s.) | 0.00 (n.s.) |
| H6a: Time risk > revisit intention | −0.17* | −0.12* |
| H6b: Time risk > recommendation intention | −0.13** | −0.04 (n.s.) |
| H7a: Destination image > revisit intention | 0.36*** | 0.30*** |
| H7b: Destination image > recommendation intention | 0.34*** | 0.42*** |
| H8a: Satisfaction with the destination > revisit intention | 0.27*** | 0.04 (n.s.) |
| H8b: Satisfaction with the destination > recommendation intention | 0.58*** | 0.50*** |
| H9: Satisfaction with the destination > destination image | 0.71*** | 0.72*** |
| H10: Variety seeking > revisit intention | −0.19*** | −0.15*** |
| Goodness of fit | Normed χ2 = 1.72 BBNFI = 0.91 BBNNFI = 0.95 CFI = 0.96 IFI = 0.96 RMSEA = 0.04 |
Normed χ2 = 1.82 BBNFI = 0.95 BBNNFI = 0.97 CFI = 0.97 IFI = 0.97 RMSEA = 0.04 |
Following the order of the hypotheses, the results estimating the effect of risk dimensions on behavioural intentions are explained first. Financial risk has a positive impact (the opposite result to the effect proposed) on the intention to revisit a national destination (B = 0.19; p < 0.01), while it has no effect for international destinations. This implies that, in the context of a pandemic, tourists may prefer to revisit a national destination, even accepting the economic risks associated with the visit. In addition, no evidence is found for an effect on intention to recommend the destination. These results do not support hypotheses H1a and H1b. Contrary to H3a, physical risk has no effect on intention to revisit, while it only has a significant negative impact on intention to recommend an international destination (B = 0.27; p < .01), confirming partial support for H3b. With regard to time risk, this dimension exerts a negative influence on intention to revisit, as proposed in H6a (B = −0.17 and B = −0.12; p < .10), but its effect on intention to recommend gives only partial support for H6b in the case of national destinations (B = −0.13; p < .05). Finally, no evidence is found for the effect of performance, psychological, and social risks on intentions to revisit and recommend; thus hypotheses H2a, H2b, H4a, H4b, H5a, and H5b are rejected for both subsamples.
Destination image has a significant influence on both intention to revisit (B = 0.36 and B = 0.30; p < .01) and intention to recommend (B = 0.34 and B = 0.42; p < .01), supporting H7a and H7b for both subsamples. In contrast, the effect of satisfaction with the destination on intention to revisit has only partial support for national destinations (B = 0.27; p < .01), while it is fully supported in the case of intention to recommend (B = 0.58 and B = 0.50; p < .01). Finally, satisfaction has a positive effect on destination image (B = 0.71 and B = 0.72; p < .01), while variety seeking has a significant negative effect on intention to revisit for both subsamples (B = −0.19 and B = −0.15; p < .01). Based on these results, H9 and H10 are supported.
Conclusions
Theoretical implications
The results obtained in this research have some relevant theoretical implications because they confirm the important role in tourist loyalty formation of several risks perceived in revisiting a destination during the first stages after a health pandemic lockdown, as well as the variables of affective image, satisfaction, and variety seeking. We adopted a multidimensional approach to risk (Giordano et al., 2017; Herrero and San Martín, 2012; San Martín et al., 2020), and we analysed the specific effect of each dimension in two variables to explain: a) the intention to visit or revisit a destination; and b) the intention to recommend it to other people. This is a contribution to the literature (Hasan et al., 2017). Furthermore, most previous studies were focused on perceived risk in international tourism in general (Lepp and Gibson, 2003) or the risks perceived by international tourists visiting a destination (Çetinsöz and Ege, 2013; Karamustafa et al., 2013; Fuchs and Reichel, 2006; Khasawneh and Alfandi, 2019). However, cultural distance (Ahn and McKercher, 2015; Liu et al., 2020) and type of destination (i.e. national versus international destinations) (Lebrun et al., 2022; Moya Calderón et al., 2021; Zenker et al., 2021) can condition the influence of perceived risk on tourists’ loyalty formation. This paper supports the effect of these factors.
Specifically, and considering the dimensions of risk proposed by Boksberger and Craig-Smith (2006) and Björk and Kauppinen-Räisänen (2012), we find that the intention to revisit a tourist destination just after the COVID-19 pandemic is negatively influenced by the time risk associated with the visit for both national and international destinations: the possibility of wasting time preparing the trip or during the visit seems to inhibit revisiting a destination in both cases. However, the intention to recommend a destination to other people is negatively influenced by the time risk only for national destinations and by the physical risk only for international destinations. That is to say, tourists prefer not to recommend a national destination to other people if there is a risk of wasting their time, while recommendation of international destinations is avoided when tourists consider that there is a risk to personal health (Pan et al., 2022). Similarly, financial risk has a positive effect on the intention to revisit a national destination (contrary to the expected effect), which suggests that people intending to travel under the restrictions imposed by COVID-19 may be willing to come back to well-known local destinations, even assuming that they will risk economic losses. These results are coherent with the fact that the impact of risk perceptions on tourists’ behaviour may vary depending on the cultural distance between tourists and destinations (Elsrud, 2001; Karamustafa et al., 2013) and, specifically during a pandemic, depending on the restrictions perceived by tourists when they consider taking their holidays overseas (Lebrun et al., 2022).
Regarding the influences of affective image, satisfaction, and variety seeking on tourists’ loyalty, it is necessary to highlight that while these variables have been widely studied in tourism research (Bigné et al., 2001; Huwae et al., 2020; Kim, 2018; San Martín et al., 2019), this study provides a new perspective in the specific context of a pandemic like COVID-19. Specifically, our results show that affective destination image positively influences the intentions to revisit and recommend a tourist site at the beginning of the post-pandemic period for both national and international tourist destinations. In addition, satisfaction has a positive effect on recommendation intention for both types of destination, but revisiting intention is only determined by satisfaction in the case of national destinations. According to these results, tourists are not prone to revisiting international destinations under the COVID-19 pandemic restrictions even if they had satisfactory experiences during their previous visits. In general, this evidence is coherent with the findings of those studies confirming that satisfaction may not lead to intention to revisit in the case of more distant or exotic destinations. Finally, in line with previous studies (Bigné et al., 2009; Giao et al., 2020), our results support the proposal that variety seeking has a negative effect on the intention to revisit both national and international destinations.
Managerial implications
The present study also has practical implications for destination marketing organizations, both in general terms and in the specific context of a pandemic. From a general perspective, managers must be aware that the perceived risk of visiting a destination is multidimensional and may diverge between national and international tourists. Accordingly, it is necessary to identify the specific factors that may be critical for a destination. For example, security may be a key dimension for some destinations, while health or the time cost to get to the destination (or move within it) may be more important in other places. Moreover, it is important to understand that the risk dimensions, and their effects on tourists’ loyalty, may differ between national and international tourists. Once this strategic diagnosis is made, destination managers should implement policies to improve those aspects that may be more concerning for tourists and communicate these policies so that risk perceptions are minimized. That is to say, destination managers must work not only to reduce the real risks associated with the destination, but also to implement communication actions to improve tourists’ perceptions of them.
Moreover, it is important to consider that loyalty to a destination in terms of intention to revisit the place and to spread positive information by word-of-mouth (recommendation) is also influenced by affective image, satisfaction, and variety seeking. Accordingly, destination marketing organizations should focus on developing affective links with tourists during the visit and post-visit stages, establishing an uninterrupted interaction based mainly on emotional cues. This implies adopting a relational marketing approach and focusing the marketing messages on affective attributes of the destination and the emotions experienced by visitors. This emotional communication can be boosted by interactive technologies such as social media, which make it possible to establish a conversation with tourists and to foster both positive word-of-mouth communication and future visits. Additionally, managers should try to develop communication strategies taking into consideration the potential differences between national and international tourists.
This research also has important managerial implications in the specific context of a pandemic like COVID-19. A pandemic introduces new uncertainties for tourists or amplifies risk perceptions, an issue that should be taken into consideration by destination managers. For example, the waste of time associated with preparation for the trip in this context or the possibility of having to cancel it (with the corresponding economic and time losses) may inhibit positive word-of-mouth communication about the destination and the intention to visit it. Similarly, it is reasonable to expect that perceived health risk at the destination due to the pandemic may inhibit tourists’ loyalty behaviours. Therefore, destination managers must implement measures to reduce risk perceptions in the present context of COVID-19 and design contingency plans in case of future pandemics. In this regard, during the last months, destinations have tried to position themselves as COVID free and to develop and obtain certified seals to project or communicate a safe visit to potential tourists.
Limitations and future research
This study has some limitations that should be considered, some of which can also signal future lines of research. Firstly, the use of intentions (i.e. intention to revisit and intention to recommend the destination) as the dependent variables instead of actual behaviours can be considered a methodological weakness, although this approach is widespread in previous research about the impact of perceived risk on tourists’ behaviour (Hasan et al., 2017). In this regard, and given the difficulty of measuring pre-visit perceptions and post-visit behaviours for the same respondents, it would be interesting to develop experimental research to simulate a real decision-making scenario reflecting perceived risk dimensions and destination choice. Loyalty to the last destination visited by the tourist was examined in this study, so the same tourist site for all the respondents was not considered. This could also be considered a limitation, as each respondent refers to a different place, with different risks associated with the visit to the tourist site. However, use of the last destination visited as a reference ensures that people assess a destination already visited and that their affective image of the destination and satisfaction with it are more recent and salient.
Author Biographies
Ángel Herrero-Crespo is Full Professor of Marketing at the University of Cantabria. His main areas of work are marketing, consumer behavior and new technologies adoption. His research has led to the publication of about 50 scientific papers in renowned journals such us Tourism Management, Journal of Travel Research, Journal of Destination Marketing and Management, Journal of Sustainable Tourism, International Journal of Hospitality Management, International Journal of Contemporary Hospitality Management, Current Issues in Tourism, International Journal of Advertising, Computers Human Behavior, International Marketing Review and Journal of Business Ethics. He is author of 10 chapters published in collective books.
Héctor San-Martín-Gutiérrez is an Associate Professor of Marketing at Cantabria University. His main lines of work are tourism marketing, consumer behavior and new technologies in tourism. His research so far has led to the publication of over 20 scientific papers in renowned international journals such as Tourism Management, Journal of Travel Research, Annals of Tourism Research, Journal of Destination Marketing and Management, International Journal of Contemporary Hospitality Management, International Journal of Hospitality Management, Current Issues in Tourism, Computers in Human Behavior and International Marketing Review.
Jesús Collado-Agudo is Associate Professor in Marketing at the University of Cantabria (Spain). Its main lines of work are relationship marketing, change management and tourism marketing. His research activity has led to the publication of 18 scientific articles published in international and national journals of recognized prestige, between which are International Business Review, Industrial Marketing Management, Journal of Hospitality and Tourism Management and Current Issues in Tourism.
María-del-Mar García-de-los-Salmones is Associate Professor (accredited as Full Professor) teaching in the field of marketing. Her research interests focus on consumer behaviour, corporate social responsibility, social marketing and brand management. He has published more than thirty articles in academic journals and he has participated in numerous research projects with public institutions and private corporations. She has held several research stays at the Ehrenberg Center of Research in Marketing (South Bank University, Londres), Tecnológico de Monterrey (México), Center for Business Ethics (Bentley University, Boston), Universidad Nacional de Córdoba (Argentina) and University of Otago (New Zealand). Finally, she was director of communication of the University of Cantabria (2008-2014), director of the International University Menendez Pelayo headquarters in Santander (2019-2020), and Vicechancellor for Institutional Relationships and Cultural Activities of the Universidad Internacional Menéndez Pelayo (UIMP) (2020-2021).
APPENDIX. Measurement scales
Financial Risk (Karamustafa et al., 2013; Kaushik and Chakrabarti, 2018)
RFIN1. It is highly possible that visiting the destination again will be more expensive.
RFIN 2. It is very likely that I will lose money if I visit the destination again.
RFIN 3. I may incur additional expenses if I visit the destination again.
Performance Risk (Çetinsöz and Ege, 2013; Kaushik and Chakrabarti, 2018; Sharifpour et al., 2014)
RPER1. There is a high chance that I will not be able to enjoy the destination as I did during my previous stay.
RPER2. It is likely that the tourism services of the destination will not be working as they were during my previous stay.
RPER3. It is possible that my experience at the destination will be negatively affected if I visit it again.
Physical Risk (Çetinsöz and Ege, 2013; Kaushik and Chakrabarti, 2018)
RPHY1. My family’s health would be in danger if I were to decide to visit the destination again.
RPHY2. It is likely that my family or I would be infected if we were to visit the destination again.
RPHY3. The physical safety of my family would be in danger if we were to visit the destination again.
Psychological Risk (Çetinsöz and Ege, 2013; Kaushik and Chakrabarti, 2018)
RPSY1. Visiting the destination again would make me stressed.
RPSY2. I would feel anxiety if I were to visit the destination again.
RPSY3. I would be nervous if I were to visit the destination again.
Social Risk (Çetinsöz and Ege, 2013; Karamustafa et al., 2013)
RSOC1.Visiting the destination again would negatively affect the opinion that my friends and family have of me.
RSOC2. People whose opinions I value would disapprove of me visiting the destination again.
RSOC3. It is possible that local people are hostile to tourists, due to the Covid-19 situation.
Time Risk (Çetinsöz and Ege, 2013; Karamustafa et al., 2013; Kaushik and Chakrabarti, 2018)
RTIM1. Visiting the destination again would be a waste of time.
RTIM2. Visiting the destination again would mean wasting my vacation time.
RTIM3. Planning and preparing a revisit to the destination would take a lot of time.
Intention to revisit (Herrero et al., 2017; Martínez et al., 2018)
IRV1. I would like to visit the destination again in the near future.
IRV2. I intend to visit the destination again in the near future.
Intention to recommend (Herrero et al., 2017; Martínez et al., 2018)
IRE1. I would speak positively of destination to my friends and acquaintances.
IRE2. I would recommend visiting the destination to other people around me.
IRE3. I would speak positively of the destination on platforms and social networks.
IRE4. I would recommend visiting the destination on platforms and social networks.
Affective Destination Image (San Martín et al., 2019)
The last destination I visited for a holiday or leisure was …
ADI1. … is a pleasant destination.
ADI 2. … is a fun destination.
ADI 3. … is an incredible destination.
ADI 4. … is a stimulating destination.
Satisfaction with the Destination (San Martín et al., 2019)
SAT1. I am satisfied with my previous experience at the destination.
SAT2. I enjoyed my previous stay at the destination.
SAT3. My previous visit to the destination was positive.
Variety Seeking (Udunuwara et al., 2019)
VS1. I want to have new experiences at other tourist destinations.
VS2. I don´t like visiting the same tourist destinations.
VS3. I am curious to know other tourist destinations.
VS4. I like to experience other tourist destinations.
Footnotes
The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.
Funding: The author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This research was funded by the program “Projects to promote the creation and consolidation of emerging or pre-competitive research groups”, developed by SODERCAN, S.A.
ORCID iD
Angel Herrero-Crespo https://orcid.org/0000-0001-8103-9174
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