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1. Introduction
Kim & Fesenmaier (2015) have stated that tourism is an experience-centric activity. Due to this, more and more focus is given to the experience part of tourism to make it a more sustainable process (Habibi & Rasoolimanesh, 2020). As tourism experience is a complex phenomenon formed at the destinations, there is a need to understand the experience in light of tourists’ thought processes (Tung & Ritchie, 2011). Chandralal et al. (2015) have pointed out that the tourism experience is an amalgamation of different psychological emotions a person feels while enjoying activities at the tourist destination. This leads to the importance of experience undergone by tourists, and understanding of tourism experiences is gaining importance in academia and with destination marketers as it can influence the future behavioural intentions of the tourists (Sharma & Nayak, 2019). However, not all experiences transformed into future behavioural intentions (Shuib et al., 2015). Sthapit et al. (2019) and Zhang et al. (2018) stated that mainly memorable tourism experiences transform into behavioural intentions like revisit intention.
The experiences that are enjoyed extensively or leave an image in mind (for certain reasons) of the tourists are stored in the memory for the long haul (Kim et al., 2010). These experiences, when recalled, are termed as memorable tourism experiences (Tung & Ritchie, 2011). Shuib et al. (2015) and Vada et al. (2019) stated that a memorable tourism experience (MTE) could be explained as recalling experiences while deciding on the next tourism destination or activity. Memorable tourism experiences are vital for the tourism industry as they are the predecessor to revisit intention (Rasoolimanesh et al., 2021). However, according to researchers, delivering memorable experiences to travellers is a complex and problematic process because of the personal and intimate nature of experiences and the different psychologies of travellers (Coelho et al., 2018). Therefore, a complete comprehension of what makes a travel industry experience memorable is essential to improve the travel industry to achieve sustainability in the tourism process (Chandralal et al., 2015). Many studies (Chandralal et al., 2015; Coudounaris & Sthapit, 2017; Kim et al., 2010) have tried to comprehend the MTE as a multidimensional construct. Chen et al. (2020), Shuib et al. (2015), and Wei et al. (2019) have classified antecedents of MTE in two broad categories: psychological factors (novelty, hedonism, meaningfulness, human emotions) and destination attributes (infrastructure, local culture, services at the destination). Nevertheless, very little focus is given to human emotions as an antecedent of the MTE in influencing the revisit intention of tourists (Bastiaansen et al., 2019; Chandralal & Valenzuela, 2015; Knobloch et al., 2017).
The concept of human emotions is fundamental to the memorable tourism experience (Bastiaansen et al., 2019). Chandralal et al. (2015) have stated that human emotions are the precursor to a memorable tourism experience. Human emotions are an essential component contributing to memory and behaviour (Servidio & Ruffolo, 2016; Skavronskaya et al., 2020). Tourism as an activity arose different emotions in tourists (Prayag et al., 2013). Human emotions are defined as psychological states that are created because of the appraisal of own thoughts or experiences (Hosany & Gilbert, 2009). Human emotions in tourism are explained as the feelings a tourist perceives while performing any tourist activity, influencing their behaviour according to those feelings (Le et al., 2020). These emotions influence tourists' overall experience and make them memorable (Knobloch et al., 2017). Human emotions are the complex structure of positive and negative constructs a person feels while experiencing something (Hosany & Gilbert, 2009). However, researchers stated that evaluating the emotions as positive and negative does not cover the whole picture in the tourism process (Hosany et al., 2020). Therefore, researchers have tried to evaluate emotions in tourism using different methods like Pleasure-Arousal-Dominance (PAD) and Destination Emotional Scale (DES) (Faullant et al., 2011; Hosany et al., 2014; Hosany & Gilbert, 2009; Russell, 1980). Nevertheless, according to both methods, there are some limitations. The PAD model does not evaluate emotions in the context of experience at the destinations (Faullant et al., 2011). The DES method is basically focused on the positive part of emotions (Hosany et al., 2014). Therefore this study will try to address these limitations. Despite human emotions' significance in tourism, studies are sparse and fragmented, and empirical studies are limited (Knobloch et al., 2017). Moreover, very few studies try to comprehend the relationship of the individual component with MTE in influencing the revisit intention of tourists (Chandralal & Valenzuela, 2015; Servidio & Ruffolo, 2016). So this research study will try to explore the human emotions as an individual antecedent of MTE and will try to assess its influence on MTE to encourage revisit intention in tourists. This study will help the destination managers assess the importance of human emotions in framing memorable tourism experiences, which can influence the revisit intention of tourists.
Also, tourism is one of the major sectors that is severely impacted by the COVID-19 pandemic (Rather, 2021). The perceived risk of COVID-19 impacts tourists' tourism-related decisions (Yu et al., 2021). The perceived risk of COVID-19 is the assumption about the negative consequences that might happen because of the pandemic (Sánchez-Cañizares et al., 2020). Matiza (2020) and Rather (2021) have stated that the perceived risk of COVID-19 influences the future behaviour of tourists. However, there is a lack of research about the health-related concerns that can influence the relationship between MTE and revisit intention (Lu, 2021). So in the second part of this study, we will try to examine the moderating effect of the perceived risk of COVID-19 on the relationship between MTE and revisit intention. This will help the destination managers understand the effect of the perceived risk of COVID-19 on tourists' decisions and help formulate more pandemic resilience policies for tourism destinations to contain the effect of heath-linked risks. The following section will discuss the literature review and hypothesis development.
2. Literature review and hypothesis development
The cognitive appraisal theory guides this study for the hypotheses development. The cognitive appraisal theory (CAT) is used in marketing to examine emotions' role in affecting consumer behaviour (Hosany et al., 2020). It explains how the experiences/events affect emotions and how these emotions influence the decisions of consumers/tourists (Lim, 2014). CAT defined emotions as psychological states that result from an appraisal of experiences (Watson & Spence, 2007). According to CAT, the appraisal of any experience is dependent on its alignment with the personally relevant goals (Choi & Choi, 2018). According to this theory, the emotional responses of the tourists are the direct result of the appraisal of the experiences, and these emotional responses affect their future behavioural intentions (Tiwari et al., 2021). As a person appraise his/her experience at the destination, emotions are framed, which influence the memorability of the events and further affect the behavioural intention of tourists (Choi & Choi, 2018). Thus this theory can help us understand the effect of human emotions in framing MTE and influencing revisit intention in tourists (Tiwari et al., 2021).
As this study is divided into two parts, for the second part, protection motivation theory is used to examine the effect of the perceived risk of COVID-19 on the tourism experience. PMT theory was introduced by Rogers (1975). PMT is used in marketing studies as a cognitive behaviour model to predict health-related behaviour and intentions (Weston et al., 2020). Rather (2021) and Wang et al. (2019) have applied PMT in tourism to understand the effects of safety and risk perceptions on tourists' behaviour. Applying this theory in our research will help us comprehend the moderating effect of the perceived risk of COVID-19 on tourists' revisit intention (Kim et al., 2021; Rather, 2021; Rogers, 1975). In the next section, we have tried to review the literature on human emotions, memorable tourism experience (MTE), revisit intention, and perceived risk.
2.1 Human emotions (HE)
Human emotions research is gaining importance in tourism research related to tourism experience (Weston et al., 2020). Most of the research work in tourism is derived from the marketing field, focusing on customer satisfaction with a lack of research on understanding human emotions concerning experience and behavioural intentions (Güzel, 2014; Skavronskaya et al., 2020). Human emotions are an important part of the tourism process and play an essential and prominent role in framing memorable experiences (Li et al., 2015; Tung & Ritchie, 2011). Due to the complex nature of the concept, defining, measuring, and analysing human emotions is challenging and difficult (Volo, 2017). The word emotion has two Latin roots, ex and movere, referring to stir up or to disturb (Scuttari & Pechlaner, 2017). According to this, emotions are generally the phenomenon that stirs up/disturbs mental states (Knobloch et al., 2017). There is no widely accepted definition of human emotions. However, it can be explained as the psychological state of acceptance/readiness arising from the appraisal of one's own thoughts or the experiences he/her has gone through (Bagozzi et al., 1999). According to experiences and what they mean to the individual, these psychological states may result in some actions (appraisals or coping) (Lazarus & Folkman, 1984). Niedenthal and Brauer (2012) stated that human emotions are the mental states resulting from evaluating certain events/ experiences that are according to or different to a person's goals. In tourism, human emotions are arises when a person experiences something relevant/irrelevant to his/her goals (Brunner-Sperdin et al., 2012). The tourism experiences are fundamental determinants of emotional appraisals that leads to results like memory building, motivation, choice process, satisfaction and post-consumption behaviour like revisit intention (Hosany et al., 2020).
Drozdova (2014), Hosany et al. (2014), and Li et al. (2015) have stated that evaluating emotions elicited by the events/experiences in tourism is a challenging task. There are two basic approaches to measuring human emotions, dimensional and basic emotion approaches (Knobloch et al., 2017; Li et al., 2015). Like Pleasure-Arousal-Dominance (PAD), the dimensional approach differentiated the emotions based on common dimensions (Li et al., 2015). Basic emotion approaches use the emotions as discrete structures like in Destination Emotional Scale (DES) (Hosany et al., 2014). These approaches have some limitations. Dimensional approaches are derived from marketing studies, so they lack in comprehending tourists' experiences (Li et al., 2015). As a basic emotional approach, DES overcame this gap, but it uses three primary discreet emotions: joy, love, and positive surprise (Hosany et al., 2014).
Joy refers to feelings of pleasure, delight, enthusiasm, and cheerfulness tourist experiences the destination (Prayag et al., 2013). The feelings of caring, affection, tenderness and warm-hearted felt by tourists are part of the love dimension (Hosany & Gilbert, 2009). When tourists feel amazement, astonishment, fascination, and inspiration, he/ she feel positive surprise (Li et al., 2015; Prayag et al., 2013). These dimensions are positive emotions (Hosany et al., 2014; Hosany & Gilbert, 2009; Prayag et al., 2013). However, tourism is evolving nowadays, and activities like adventure tourism, fright tourism, and dark tourism are gaining importance (Bristow & Jenkins, 2019; Jamal et al., 2019; Ross, 2012). These new concepts become relevant as tourists seek scary opportunities for hedonic experiences (Light, 2017). Tourists often experience sinister pleasure when they feel scared, nervous, afraid, sad, and displeasure (Bristow & Jenkins, 2019). So our study will try to evaluate human emotions through the following dimensions, joy, love, and positive surprise, as used in previous studies, with fear and displeasure as the fourth dimension.
2.2 Memorable tourism experience (MTE)
Pine & Gilmore's (1998) landmark work in management had noted the transition from a service economy to an experience economy. Accordingly, tourism is fundamentally based on the immersive experience of tourists at the destination; it becomes part of the experience economy (Tung & Ritchie, 2011). Therefore, academicians and destination managers nowadays focus more on tourism experience than destination attractions (Kim & Fesenmaier, 2015). In previous studies (Sharma & Nayak, 2019; Zhang et al., 2018), tourism experience is mainly associated with analysing tourists' satisfaction levels, but recently memorable tourism experiences are receiving attention. Sthapit et al. (2019) and Wei et al. (2019) stated that a memorable tourism experience is an essential antecedent to behavioural choices of tourists like revisit intention for making the destinations more competitive. Memorable tourism experience is defined as the unforgettable and significant experiences recalled and remembered by the tourists after their tourism destinations experience (Chandralal et al., 2015). Several attempts have been made to explore its dimensions and scale MTE (Sthapit et al., 2019; Tiwari et al., 2021). These dimensions include novelty, human emotions, hedonism, meaningfulness, involvement, refreshment, local culture, infrastructure, services, and environment (Coelho et al., 2018; Shuib et al., 2015).
Moreover, very little focus is given to comprehensively analysing the effect of a single dimension on MTE (Tiwari et al., 2021). So this study will try to understand the effect of human emotions on MTE to influence the revisit intention of tourists. It is argued that instead of human emotions' importance in understanding MTE, the studies pertaining to this are limited (Knobloch et al., 2017). Human emotions are the mental states induced during the events relevant to a person's goals (Skavronskaya et al., 2020). Several studies (Bastiaansen et al., 2019; Chandralal & Valenzuela, 2015; Servidio & Ruffolo, 2016) have tried to establish the relationships between human emotions, tourism experience, and memorability. It is found that the emotions that arise because of tourism experiences decide the evaluation and memorability of those experiences (Coelho et al., 2018). Indicating human emotions is one of the most important factors of tourism experiences, making them memorable (Bastiaansen et al., 2019). Thus based on these arguments and CAT, the hypothesis is suggested as follows:
H1: Human emotions (HE) significantly influence memorable tourism experience (MTE).
2.3 Revisit intention (RI)
Post consumption behaviour is becoming an essential part of today’s marketing practices (Yu et al., 2019). This is because the cost of retaining the existing customer is far lesser than inviting the new one (Tiwari et al., 2021). In tourism, this post-consumption behaviour is linked to the revisit intention of tourists (Chen et al., 2020). The revisit intention of tourists makes the tourism destination more competitive and sustainable (Kim, 2018). Revisit intention is defined as the eagerness of the tourists to revisit the destination. Revisit intention is fundamental to tourists' post outing behaviours (Gohary et al., 2020). Destination managers are now focusing on the factors that would encourage revisit intention as it is an essential element for destination competitiveness (Zhang et al., 2018). Revisit intention is an outcome of a memorable tourism experience as tourists generally want to relive the memories they had during their previous visit (Tiwari et al., 2021). Researchers have stated that if the tourists perceive a memorable tourism experience at the destination, they tend to revisit there (Yu et al., 2019). This leads to the formulation of the following hypothesis:
H2: MTE has a significant influence on revisit intention (RI).
Previous studies (Rasoolimanesh et al., 2021; Sthapit et al., 2019) have stated that MTE is crucial in tourists’ future behavioural choices like revisit intention. To establish the importance of MTE, researchers have tried to explore its mediation effect on the behavioural intention of tourists (Gohary et al., 2020). Tiwari et al. (2021) have stated that antecedents of MTE first influence MTE, which is later reflected in the revisit intention of tourists. Studies by Sthapit et al. (2019) and Mittal et al. (2021) also explored the mediating effect of MTE on the relationship between its antecedents and revisit intention. Despite this, studies evaluating the mediating effect of MTE on the relationship between human emotions and revisit intention are few (Chandralal & Valenzuela, 2015). As human emotions are the important antecedent of MTE and can influence the MTE to encourage the revisit intention of the tourists later, this study proposes:
H3: Memorable tourism experience (MTE) mediates the relationship between human emotions (HE) and revisit intention (RI).
2.4 Perceived risk of COVID-19 (PR)
In the second part of this study, we will explore the perceived risk of COVID-19. Tourism is one of the most affected sectors because of the global pandemic of COVID-19 (Yu et al., 2021). Following World Health Organisation (WHO) guidelines, many countries have restricted travel, thus affecting the tourism industry (Sánchez-Cañizares et al., 2020). Rather (2021) and Weston et al. (2020) have highlighted that health-linked risks like the perceived risk of COVID-19 can influence the behavioural intention of tourists. Perceived risk can be defined as the assumption that something bad can happen while performing some activity (Dryhurst et al., 2020). In the current scenario, it is assumed that COVID-19 may have some adverse effects on tourists' health, leading to the perceived risk of COVID-19 in the tourism industry (Yıldırım et al., 2020). As the travelling activity requires that the tourist leave his natural habitat and visit different places, the perceived risk of what might happen at a new place plays a vital role in influencing their revisit intention (Sánchez-Cañizares et al., 2020). Although few studies have tried to understand the effect of the perceived risk of COVID-19 on revisit intention, research pertaining to the moderating effect of the perceived risk of COVID-19 on the relationship between MTE and revisit intention is relatively scarce (Chen et al., 2020; Rather, 2021). Thus, the following hypothesis is suggested:
H4: Perceived risk of COVID-19 (PR) moderates the relationship between MTE and revisit intention.
3. Methodology
Our study aims to examine human emotions that influence MTE in tourists to invoke revisit intention in them. In this section, we will discuss the study setting, measurement instrument, data analysis tools, and results of the data analysis.
3.1 Study setting, measurement instruments, and data analysis tools
Tourism as a process of experiences induces behavioural choices in tourism (Ramires et al., 2018). To enjoy these wholesome experiences, tourists often visit tourism destinations (Barnes et al., 2016). In the context of the present study, central India is taken as a target area. Central India is a dynamic amalgamation of different tourist destinations catering to heritage, religious, cultural, wildlife, dark, and adventure tourism (Gupta & Singh, 2015; Kaur et al., 2016; Rejikumar et al., 2021; Sanjeev & Birdie, 2019). These tourism destinations attract domestic and international tourists throughout the year, making central India an apt place for this study (Tiwari et al., 2021). The questionnaire is provided to the tourists visiting the destinations in central India at the hotels, exit gates of tourism attractions, bus stands, railway stations, and airports using the convenience sampling technique to collect the data (Lim, 2014).
As this study tries to comprehend human emotions, the literature is thoroughly reviewed to measure them. Positive emotions like joy, love, and positive surprise items are adapted through the Destination Emotional Scale (Hosany et al., 2014; Hosany & Gilbert, 2009). To answer the limitations of DES, this study also includes negative human emotions like fear and displeasure to answer the needs of dark and adventure tourism (Faullant et al., 2011; Nawijn & Biran, 2019; Prayag & Ryan, 2012). For measuring fear and displeasure, items from the studies of Prayag et al. (2013), Prayag and Ryan (2012), Ross (2012), and Yang and Nair (2014) are adapted. Scales of MTE and revisit intention are extensively used in previous studies (Chandralal & Valenzuela, 2013; Chen et al., 2021; Chen et al., 2020; Sthapit & Coudounaris, 2018; Vada et al., 2019; Yu et al., 2019). For measuring MTE four-item scale of Vada et al. (2019) is operationalised. In this scale, MTE is measured through 'I had a once in a lifetime experience'; 'I had a unique experience'; 'My trip was different from my previous trip,' and 'I have experienced something interesting and new' (Vada et al., 2019). Revisit intention is represented through three statements 'If I could, I would come to this place again'; 'I will always consider this place as my first destination,' and 'I have a strong intention to visit this place again' adapted from the study of Chen et al. (2020). As we are trying to evaluate the moderating effect of the perceived risk of COVID-19 in the second part of this study, we have used the three items scale of Rather (2021). Here perceived risk is represented as 'In the current situation, I prefer to avoid travelling to prominent cities/destinations'; 'I feel more averse to travelling due to the risk from the Covid19 epidemic' and 'In the current situation, I prefer to shorten the duration of my potential trips' (Rather, 2021). Data is collected on 5 points Likert-type scales in which "1" represents strongly disagree, and "5" represents strongly agree. According to Dawes (2008), the five-point Likert scale makes reading out the complete list of descriptors simpler. Buttle (1996) has stated that the five-point Likert scale should be used to increase response quality and response rate by reducing the frustration level of respondents. With the five-point Likert scale, it became possible to compare the reliability coefficients with other research (Saleh & Ryan, 2006). Around 1230 questionnaires are provided to respondents, and 1120 responses are received with a 91% response rate. The description of respondents is represented in Table 1.
Table 1 Description of respondents
| No. | Demographic Criteria | Categories | Percentage |
|---|---|---|---|
| 1 | Gender | Male | 55.3 |
| Female | 44.7 | ||
| >2 | >Age (in years) | Between 18-35 years | 38.2 |
| Between 35-60 years | 44.6 | ||
| Above 60 years | 17.2 | ||
| 3 | Education level | Undergraduate | 43.9 |
| Graduate | 45.2 | ||
| Postgraduate and above | 10.9 | ||
| >4 | >Income group | Low income group | 20.6 |
| Middle-income group | 47.2 | ||
| High-income group | 32.2 | ||
| >5 | >Revisits | One time | 25.6 |
| Two time | 40.3 | ||
| Three-time and more | 34.1 |
The total sample of 1120 is divided into two random subsamples to perform exploratory factor analysis (EFA) and confirmatory factor analysis-structural equation modelling (CFA-SEM) (Hair et al., 2018). The first subsample of 350 is subjected to EFA using SPSS version 25 (Tiwari et al., 2021). The second subsample of 770 questionnaires is used to analyse the validity, model fit indices, and hypotheses by structural model through CFA-SEM using AMOS version 23 (Hair et al., 2018). SPSS is also used to measure the normality of data through skewness and kurtosis (Hair et al., 2018). It suggested no violation of data normality as all the values of scale items are under the recommended value of 3 and 8 for skewness and kurtosis, respectively (Kline, 2016).
3.2 Data analysis and interpretation
In this section, the authors try to address the research objectives by analysing the results of exploratory factor analysis, confirmatory factor analysis, and structural equation modelling.
3.2.1 Exploratory factor analysis (EFA)
In the present study, the principal factor analysis with varimax rotation is used to identify the underlying components and confirm the unidimensionality of constructs. Also, the items measuring human emotions are applied to different locations to test the relevance in the Indian context of EFA. The Kaiser-Meyer-Olkin (KMO) measure of sampling adequacy is found to be 0.828, which is higher than the suggested value of 0.5 (Bajpai, 2017). The Bartlett's Test of Sphericity came out significant, showing p= 0.000 and approx χ2 =6395.861. These measures have suggested that EFA can be applied to the available dataset (Hair et al., 2018). Four factors with an eigenvalue more significant than one are extracted through EFA (Hair et al., 2018). All the factor loadings are found to be above the 0.5 level, see Table 2. The factor structure is represented as joy (4 items), love (4 items), positive surprise (4 items), and fear and displeasure (5 items). These four factors represent human emotions in our study (Bastiaansen et al., 2019; Hosany et al., 2014; Hosany & Gilbert, 2009). Cronbach's alpha values are calculated to analyse the reliability of items (Bajpai, 2017). All the constructs have a value greater than the suggested value of 0.7 (see Table 2) (Nunnaly & Bernstein, 1994). This suggests the excellent reliability of the items measuring human emotions. These factors represent 85.311% of the total variance, higher than the desired value of 50%, suggesting a good model (Emerson, 2017). The reliability of MTE (Vada et al., 2019), revisit intention (Chen et al., 2020), and perceived risk (Rather, 2021) scales are assessed by calculating respective Cronbach's alpha values. They are found to be 0.960, 0.983, and 0.950, respectively, above the recommended value of 0.7 (Nunnaly & Bernstein, 1994). Furthermore, common method variance (CMV) is examined through Harman's single factor test (Podsakoff et al., 2003; Tiwari et al., 2021). According to this method, all the items are analysed through principal component analysis without rotation. It is found that the single factor explains 20.409 % variance, which is less than the suggested value of 50% (Podsakoff et al., 2003). This explains CMV is under control in this study.
Table 2 Results of the EFA for human emotions
| No. | Factor name and related statements | Communalities | Factor loadings | Cronbach's alpha (reliability) |
|---|---|---|---|---|
| 1 | Joy(J) | 0.928 | ||
| J1- I felt cheerful during my experience | 0.915 | 0.948 | ||
| J2- I felt pleasure and joy during my experience | 0.807 | 0.896 | ||
| J3- I felt delighted during my stay | 0.791 | 0.878 | ||
| J4- I felt enthusiasm | 0.803 | 0.889 | ||
| 2 | Love (L) | 0.938 | ||
| L1- I felt care during my experience | 0.931 | 0.961 | ||
| L2- I felt warm-hearted during my stay | 0.837 | 0.907 | ||
| L3- I felt affection and love during my stay | 0.826 | 0.803 | ||
| L4- I felt tenderness during my experience | 0.793 | 0.885 | ||
| 3 | Positive surprise (PS) | 0.944 | ||
| PS1- I felt surprised during my stay | 0.956 | 0.975 | ||
| PS2- I felt amazement during my experience | 0.809 | 0.895 | ||
| PS3- I felt inspired during my stay | 0.818 | 0.897 | ||
| PS4- I felt astonishment and fascination during my stay | 0.861 | 0.925 | ||
| 4 | Fear and displeasure (FD) | 0.962 | ||
| FD1- I felt fear during my stay | 0.961 | 0.979 | ||
| FD2- I felt nervous and scared during my experience | 0.847 | 0.917 | ||
| FD3- I felt jittery during my experience | 0.860 | 0.924 | ||
| FD4- I felt sadness and regret during my experience | 0.838 | 0.910 | ||
| FD5- I felt afraid during my experience | 0.848 | 0.920 |
3.2.2 Confirmatory factor analysis (CFA)
CFA is considered one of the best statistical tools to analyse the measurement model (Byrne, 2001). In the present study, CFA is applied to analyse the significance of data for model fitness, confirm factor structure, and test the validation of the measurement model. This model has four latent constructs, human emotions, memorable tourism experience (MTE), revisit intention (RI), and perceived risk of COVID-109 (PR). According to the measurement model, human emotions is the second-order construct represented by joy, love, positive surprise, and fear & displeasure. Maximum likelihood estimation is used to analyse the measurement model of 27 items across eight dimensions. The results show the factors are adequately explained by their statements, as weights for all the items are found to be significant at p<〖0.001〗^(***) (Byrne, 2001). The model is found to be adequately fit as the calculated ratio of chi-square to the degree of freedom, i.e., CMIN/df (1.847), is well below the cutoff value of <3 (Kline, 2016). The adequacy of the measurement model is also analysed through goodness-of-fit and badness-of-fit indices. It is found that for goodness-of-fit indices the calculated values of GFI (0.946), AGFI (0.937), NFI (0.981) , RFI (0.979), IFI (0.991), TLI (0.990) and CFI (0.991) are greater than the suggested value of 0.9 (Hair et al., 2018). To find the badness-of-fit indices for the model, values of RMSEA (0.033) and RMR (0.040) are calculated. It is found that the calculated values are within the suggested value of 0.08 (Bajpai, 2017; Hair et al., 2018). The results of goodness-of-fit and badness-of-fit indices suggest the model is adequately fit. The unidimensionality of the constructs is established by analysing the standard regression weights and inter-construct correlations, as suggested by Kline (2016). The measurement model results suggested that the standardised regression weights are above the cutoff value of 0.5 (Hair et al., 2018). The results have also suggested that the inter-construct correlations are below the cutoff value of 0.85 (Kline, 2016). Table 3 represents the standardised loadings, and Table 4 represents the descriptive analysis and correlation matrix. Figure 1 represents the results of standardised regression weights of observed and unobserved variables and inter-construct correlations.
Table 3 Standardised loadings
| Loadings | |||
| Joy | <--- | HE | 0.657 |
| Love | <--- | HE | 0.638 |
| PS | <--- | HE | 0.617 |
| FD | <--- | HE | 0.578 |
| JOY4 | <--- | Joy | 0.890 |
| JOY3 | <--- | Joy | 0.878 |
| JOY2 | <--- | Joy | 0.889 |
| JOY1 | <--- | Joy | 0.982 |
| LOVE4 | <--- | Love | 0.891 |
| LOVE3 | <--- | Love | 0.915 |
| LOVE2 | <--- | Love | 0.913 |
| LOVE1 | <--- | Love | 0.982 |
| POSSP4 | <--- | PS | 0.935 |
| POSSP3 | <--- | PS | 0.896 |
| POSSP2 | <--- | PS | 0.904 |
| POSSP1 | <--- | PS | 0.987 |
| FEAR5 | <--- | FD | 0.921 |
| FEAR4 | <--- | FD | 0.921 |
| FEAR3 | <--- | FD | 0.938 |
| FEAR2 | <--- | FD | 0.924 |
| FEAR1 | <--- | FD | 0.994 |
| MTE1 | <--- | MTE | 0.991 |
| MTE2 | <--- | MTE | 0.933 |
| MTE3 | <--- | MTE | 0.937 |
| MTE4 | <--- | MTE | 0.945 |
| RI1 | <--- | RI | 0.997 |
| RI2 | <--- | RI | 0.985 |
| RI3 | <--- | RI | 0.990 |
| PR3 | <--- | PR | 0.909 |
| PR2 | <--- | PR | 0.922 |
| PR1 | <--- | PR | 0.995 |
Table 4 Descriptive analysis and correlations matrix
| Mean | Standard deviation | Joy | Love | PS | FD | MTE | RP | RI | |
|---|---|---|---|---|---|---|---|---|---|
| Joy | 4.1269 | 1.04134 | |||||||
| Love | 4.1194 | 1.01534 | 0.452 | ||||||
| PS | 4.1631 | 1.01294 | 0.439 | 0.370 | |||||
| FD | 4.0968 | 1.08151 | 0.350 | 0.350 | 0.362 | ||||
| MTE | 4.0646 | 1.05429 | 0.337 | 0.398 | 0.318 | 0.333 | |||
| RP | 4.0854 | 0.99897 | 0.411 | 0.366 | 0.362 | 0.340 | 0.440 | ||
| RI | 3.6951 | 1.66163 | 0.385 | 0.413 | 0.411 | 0.435 | 0.545 | 0.563 |
After establishing the model fit of the measurement model, convergent and discriminant validity is analysed. For this, the values of composite reliabilities (CR), average variance extracted (AVE), and maximum shared variance (MSV) are calculated. It was found that all the values of CR are above the cutoff value of 0.5, and all the values for AVE are above the suggested value of 0.7 (Bajpai, 2017; Hair et al., 2018). This established the convergent validity of the model (Tiwari et al., 2021). As all the values of MSV are less than AVE, discriminant validity is established (Hair et al., 2018). The CR, AVE, and MSV results are represented in Table 5. All these results show the good model fit and acceptability of the measurement model.
Table 5 Convergent and discriminant validity
| Factors | Composite reliabilities (CR) | Average variance extracted (AVE) | Maximum shared variance (MSV) |
|---|---|---|---|
| Joy | 0.951 | 0.831 | 0.204 |
| Love | 0.961 | 0.860 | 0.204 |
| Positive surprise | 0.964 | 0.869 | 0.193 |
| Fear & displeasure | 0.975 | 0.885 | 0.189 |
| Memorable tourism experience | 0.975 | 0.907 | 0.297 |
| Revisit intention | 0.994 | 0.981 | 0.317 |
| Perceived risk of COVID-19 | 0.960 | 0.889 | 0.317 |
3.2.3 Structural equation modelling
After the measurement model analysis, to test the hypotheses structural model is constructed. SEM is applied to test this structural model, analysing the relationships between human emotions, memorable tourism experience, and revisit intention. Mediation analysis is done to verify the nomological validity of the model. In the second part of this study, moderation of perceived risk of COVID -19 is also added to examine its impact on the relationship between MTE and RI. In the proposed model, human emotion is a second-order construct, represented by joy, love, positive surprise, and fear and displeasure. For the second-order model, CMIN/df (2.325) is smaller than 3, suggesting a good model fit. Also, goodness-of-fit and badness-of-fit indices are analysed. According to the results for the second-order model GFI (0.943), CFI (0.988), and RMSEA (0.42) are significant as they are within their suggested values. The structural analysis and hypotheses testing results are represented in Table 6 and Figure 2. As H1 suggested, human emotions influence MTE (β=0.594, p <0.001), suggesting a strong effect. This means human emotions as a second-order construct of joy, love, positive surprise, and fear & displeasure have a significant relationship with MTE. As H2 proposed, MTE significantly encourages revisit intention (β=0.612, p <0.001), suggesting a significant relationship between MTE and RI. Moreover, the coefficient of determination (R^2) is analysed to examine the explanatory power of the model. The R^2 values above 0.25 are considered to have near good explanatory power (Cohen, 1992). This suggests near good explanatory power of variables as human emotions explained 35.5% variance in MTE and MTE explained 37.5% variance in revisit intention, as they have R^2 values, 0.355 and 0.375 respectively.
Table 6 Results of SEM
| Hypothesis | Path coefficient | Significance | R2 | Decision |
|---|---|---|---|---|
| H1 Human emotions(MTE | 0.594 | 0.000 | 0.355 | Supported |
| H2 MTE(RI | 0.612 | 0.000 | 0.375 | Supported |
3.2.4 Mediation analysis
Mediation analysis established the nomological validity of the model (Ali et al., 2016). In the present study, according to H3, MTE is proposed to mediate the relationship between human emotions and revisit intention. To analyse the mediation of MTE in this proposed model, indirect effects are evaluated. It is found that the indirect effects are significant for all the connections in the proposed model. This signifies there is the mediation of the MTE. Results of standardised indirect effects and their significance are represented in Tables 7 and 8.
After analysing the significance of indirect effects, direct effects are examined to determine whether full or partial mediation exists between the variables (Tiwari et al., 2021). The significance of the direct effect is presented in Table 9.
Results show the direct effect between the variables suggesting partial mediation in our model supporting the proposed H3. This represents that human emotion first influences tourists' memorable tourism experience, which encourages revisit intention in them. Results are represented in Table 10.
Table 10 Results of mediation analysis
| Hypothesis | Mediating effect | Remarks | Decision |
|---|---|---|---|
| H3 HE(MTE(RI | Partial | The indirect effect is significant. However, the direct effect (HE(RI) is still significant. | Supported |
3.2.5 Moderation analysis
This study is divided into two parts; in the second part, the moderation of perceived risk of COVID-19 on the relationship between MTE and revisit intention is analysed. For this, the method proposed by Ping (1996) is used to analyse the moderating effect. According to this method, first, MTE and moderator perceived risk values are standardised. The standardised values of MTE (Xp) and perceived risk (Z) are used to calculate the interaction term (Xp*Z), representing the interaction effect (Ping, 1996). After this, the structural model is tested by adding the interaction term as moderating variable. The results show that the interaction coefficient is found to be significant (β= -0.152, p< 0.001). Results are represented in table 11. This confirms our hypothesis that H4 representing the perceived risk of COVID-19 negatively moderates the relationship between MTE and revisit intention.
4. Discussion and implications
This study aims to explore human emotions in the context of comprehensive tourism experience and evaluate its relationship with memorable tourism experience to influence revisit intention in tourists. Measuring human emotions in the tourism experience is essential for destination managers as it is an important antecedent of MTE. The factors affecting human emotions in tourism experience are explored through previous literature (Hosany & Gilbert, 2009; Krogh et al., 2011; Malone et al., 2014; Nawijn & Biran, 2019; Prayag et al., 2013; Santos et al., 2021). Consistent with previous studies, the present study confirms that human emotions are a multidimensional second-order construct, represented by joy, love, positive surprise, and fear and displeasure (Trang & Gyehee, 2018). Based on this, it can be inferred that joy, love, positive surprise, and fear & displeasure are responsible for developing human emotions in the tourism experience. After analysing factors of human emotions, a research model is proposed to comprehend the relationships between human emotions, memorable tourism experience, and revisit intention. Results suggested that the measurement model is found to be reliable and valid. This is important because a reliable and valid scale is provided, which can be used to human emotions in the context of MTE and revisit intention. Results also suggested the acceptance of all the hypotheses. It is found that human emotions significantly influence MTE in tourists (Hypothesis 1). It can be inferred that a higher perception of human emotions is responsible for making a memorable tourism experience for tourists aligning with the theoretical works of Chandralal et al. (2015) and Coelho et al. (2018). This study also confirms the relationship between MTE and revisit intention, supporting previous studies by Gohary et al. (2020) and Tiwari et al. (2021). According to this, when the tourists perceive a memorable tourism experience at the destination, it influences their behavioural choices like revisit intention supporting hypothesis 2.
Bootstrapping results show that all the effects are above the 0.05 significance level. The results concluded that human emotions have a significant indirect effect on the revisit intention of tourists due to the significance of MTE’s mediation, thus supporting hypothesis H3. All these results reprented the importance of human emotions in framing memorable tourism experience in tourists. When the human emotions were evoked in tourists in tourists destinantions, their experiences became memorable. These memorable tourism experiences then influence their revisit intention making tourism destinantions more sustainable. These results are also in line with the previous studies of Gohary et al. (2020) and Mittal et al. (2021). Most of the studies (Chen et al., 2021; Rather, 2020; Sthapit et al., 2019) explored the direct relation of MTE with revisit intention, but very few studies explored the moderation of health linked components in this relationship.
Furthermore, this is one of the first studies exploring the moderation effect of the perceived risk of COVID-19 on the relationship between MTE and revisit intention of tourists. It is found that the perceived risk of COVID-19 negatively moderates the relationship confirming hypothesis 4. This represents that when the tourists perceive the risk of COVID-19 at the destinations, it negatively influences the effect of MTE on revisit intention. All these results implied the significance of human emotions in framing the memorable tourism experience of tourists in encouraging revisit intention. According to the results, when tourists perceive the health-related risk of COVID-19 at the destinations, their revisit intention is negatively impacted. Moderation of perceived risk of COVID-19 helped understand tourists' health-linked behaviour. This will help gain necessary information in understanding tourists' psyche and their behavioural intentions for enriching the overall experience of tourists, and thus will help destination managers in making tourism a more sustainable process. Various academic and practical implications can be drawn from the results achieved during this study.
4.1 Theoretical implications and managerial implications
Most of the studies (Chen et al., 2021; Coudounaris & Sthapit, 2017; Sthapit et al., 2019) in tourism literature talk about the MTE and its relationship with behavioural intentions. This study is one of few studies that discuss the significance of an individual psychological antecedent (human emotions) through its underlying factor in influencing MTE to encourage revisit intention in tourists. This will help the academicians and researchers in understanding human emotions and its underlying factors in light of a memorable tourism experience. This is also one of the first studies that used cognitive appraisal theory and protection motivation theory for the theoretical framework. This will help understand the use of CAT and PMT in tourism research. This study contributes to the literature on human emotions and the perceived risk of COVID-19 in the tourism experience. Academicians and researchers can use these findings to advance the literature on tourism experience and tourists' behavioural intentions.
Based on this research, there are some managerial implications. First, it provided a better understanding of the importance of human emotions in making a memorable tourism experience. Human emotion is a second-order construct represented by four first-order constructs: joy, love, positive surprise, and fear and displeasure. Destination managers can focus on these elements to enhance joy, love, positive surprise, and fear and displeasure in the tourism experience. This will help in framing more emotionally indulging experiences, enhancing tourists' memorable tourism experience. The results of this study suggested that the MTE significantly influences the revisit intention of tourists.
Destination managers and tourism practitioners should focus their strategies on enriching the memorable tourism experience of tourists. This memorable tourism experience will transform into revisit intention of tourists, thus making tourism destinations more sustainable as the cost of retaining customers is lower than attracting new ones. It will increase the economic benefits to all the stakeholders in tourism. Also, it is found that the perceived risk of COVID-19 negatively impacted the revisit intention of tourists. Destination managers and stakeholders can work on making strategies to make tourism destinations safer from health-related risks to influence behavioural intention of tourists.
4.2 Limitations and future scope of the research
A few limitations to this research can contribute to future studies. As the tourism sector continuously evolves, concepts like rural tourism and eco-tourism are gaining popularity. These travelers look for relief, balance, and harmony in the tourism experience. Further enhancement of the scale of emotions can include these dimensions in future studies (Carver, 2001; Kastenholz et al., 2018). This study has provided focused on the psychological antecedent of MTE.
For further research, this model can be extended to include both psychological, and destination attributes to provide a more comprehensive picture of MTE in invoking different behavioural outcomes like destination loyalty and satisfaction. Another limitation is that this study examined the moderating effect of the perceived risk of COVID-19 on the relationship between MTE and revisit intention. Future research can examine the moderating effect of well-being, perceived satisfaction, perceived safety, and demographic variables. The other limitation that could be an opportunity for future research is that this study is focused on tourists travelling to central India. Future studies can apply this measurement model to different geographical regions. Future studies can also work on segmenting the tourists as domestic, international, cultural, rural, religious, wildlife, adventure, and flight tourists. This will help in catering to the needs of segmented tourists. All this can help the destination managers and destination management companies devise strategies to make the tourism experience more indulgent and memorable to influence behavioural intentions and make destinations more risk-averse and sustainable.
