Introduction

In December 2019, Wuhan in China reported an outbreak of a form of pneumonia of unknown cause (AlQershi, 2020). The outbreak spread rapidly across the world, as air and sea transport had already carried the virus to all continents, and by mid-March it had emerged in 146 countries (Kluge et al., 2020; Qu et al., 2020).

On 25 January 2020, Malaysia’s first case of the coronavirus, known as COVID-19, was confirmed (Rampal & Liew, 2021), involving three imported cases from individuals who had arrived in Johor Bharu via Singapore two days earlier. By the end of April, Malaysia was reporting 8,718 COVID-19 infection cases, with 122 deaths, from a total population of 32.8 million (Elengoe, 2020).

According to the UN’s World Tourism Organization, UNWTO (2020), COVID-19 pandemic-related travel restrictions in April 2020 involved almost all global destinations, and involved 97 countries. Meanwhile, 97 destinations (45%) had been totally or partially closed to tourists, including Malaysia (Umair, Waqas & Faheem, 2021).

In line with above scenario, the Malaysian travel industry attracts millions of tourists every year; the top ten nationalities visiting in 2018 were from Singapore (10,615,986), Indonesia (3,277,689), China (2,944,133), Thailand (1,914,692), Brunei (1,382,031), South Korea (616,783), India (600,311), the Philippines (396,062), Japan (394,540), and Taiwan (383,922) (Today, 2019).

The travel industry’s immediate contribution to Malaysia’s GDP in 2018 was 6.1%, with an estimated RM82.6 billion, compared to RM76.6 billion in 2016 (Today, 2019), and increasingly benefiting the financial development of the nation. However, the outbreak of COVID-19 had a devastating effect on the economy, particularly the travel and tourism sector, which recorded a loss of RM45 billion within the first two months of 2020 (Bernama, 2020) UNWTO predicted that in 2020, the international tourist arrivals point would decline by 58% to 78%, depending on the speed of containment and the duration of restrictions.

The most important predictor in understanding tourist behaviour is clearly motivation, which reflects the intention to revisit (Bayih & Singh, 2020). Although the link between motivation and intention is implied in the Theory of Planned Behaviour (TPB), action only follows motivation, and researchers’ conflicting results require further study (Carvache-Franco, Carvache-Franco & Hernández-Lara, 2020; Cengizci, Ba?er & Karasakal, 2020). Moreover, considering only a single dimension of motivation reduces the validity and reliability of the results (Albayrak, Caber & Cater, 2019). In order to fully confirm the significance of motivation the intention to revisit, investigating the moderating effect of perceived risk on the relationship between motivation and behavioural intention to revisit has been suggested (Trivedi, 2019).

Correspondingly, the association between perceived risk and the travel destination has been explored in previous studies (Caber, González-Rodríguez, Albayrak & Simonetti, 2020). However, the moderating effect of perceived risk, especially on the relationships among variables in the context of public health, has not yet been examined. Although the restriction on all tourist activities in order to break the chain of COVID-19 infection (Singh et al., 2021) is unprecedented, there is evidence that COVID-19 will be transformative for the tourism sector (Farzanegan, Gholipour, Feizi, Nunkoo & Andargoli, 2020).

Marketing managers have already made a real effort to understand the elements of travellers’ characteristics, especially in regard to risk, including health risk (Karl & Schmude, 2017). Some studies focus on the micro-issues related to tourist behavioural intention, attempting to define and describe tourists’ demographic characteristics as perceived influence. However, most of the studies considering DC have resulted in inconsistent findings in a variety of settings (Wang, Wong & Narayanan, 2020).

Wang et al., (2020), concluded that the role of demographics as a determinant of behavioural intention is still a matter of debate among researchers, especially in tourism. This research will therefore attempt to provide a rich view of the role of DC in classifying a social and psychological internal environment for predicting behavioural intention. Given the previous inconsistent findings, it will therefore bridge the research gap by investigating the effect of socio-demographics and its link with repeat visit intention.

Despite the crucial nature of these issues, the tourist motivation literature has barely investigated the relationship between motivation and destination image as separate constructs. Therefore, this study attempts to investigate the impact of tourists’ motivation, demographics, and destination image on repeat visit intention, and the moderating role of perceived risk on the link between motivation and revisit intention, and destination image and revisit intention (see Figure 1). Moreover, the integration of these constructs will bring new insight on travellers to give destination managers a better understanding of new promotion strategies that will reduce the effect of the COVID-19 recovery plan in the tourism subsector (i.e. short-term occupancy, inbound tourists, airlines and restaurants).

The main aim of this research is thus to provide an integrated approach to understanding the intention of the destination revisit, and to analyse the theoretical and empirical evidence of the causal relationships between the variables, using structural equation modelling, to bridge the gap in the revisit intention literature.

Literature Review and Framework

Motivation and Revisit Intention

It is an internal force resulting from an unsatisfied need which encourages individuals to involve themselves in a particular behaviour (Hashemi et al., 2017). For tourists, it is a driving force that motivates them to seek to meet their various needs, and the primary reason for engaging in tourism activities (Wang & Leou, 2015). According to Suhartanto, Brien, Primiana, Wibisono & Triyuni (2020), motivation is one of the complex and crucial constructs that affects the attitude, beliefs, and emotions of tourists. Considerate customer motivations, destination preferences, service quality, and satisfaction can assist tourism marketers in forecasting future travel demand and achieving successful results (Fan et al., 2015).

According to Santoso (2019), motivation to travel is related to the social needs, achievement and self-realization categories in Maslow’s hierarchy of needs theory. Motivational factors play essential roles in affecting a person who feels a psychological imbalance that can be rectified by travelling (Fan et al., 2015). People may plan a trip to meet their natural (food, climate, and health) as well as psychological (adventure and relaxation) needs.

The motivation of each individual is different, reflecting the diversity in individual needs. The main motivations to visit a destination are to relax, take a vacation, get away from daily routine work, meet new people, visit relatives and friends, and visit historical sites. As suggested by Lee (2009), these motivations can be categorized into driving and pulling factors.

Thiumsak & Ruangkanjanases (2016) found that tourists’ motives (e.g. accommodation, shopping, restaurants & food, and attitude of Thai people towards visitors) influence intention to revisit Thailand. Zhang, Chen & Li, (2019) found that tourist motivation has a direct and significant impact on tourists’ intention to revisit Gankeng Hakka Town in China. However, a study by Li, Cai, Lehto & Huang (2010) found no significant relationship between motivation and revisit intention. As a result, based on the literature and empirical evidence discussed above, this study hypothesizes that:

H1: There is a significant association between a visitor’s motivation and revisit intentions.

Destination Image and Revisit Intention

While there are various definitions of destination image, in general it can be described as the individual perception or impression of a particular place that emotionally describes and portrays the destination. The image of a destination consists of the subjective interpretation of reality made by the tourist (Bigné, Sánchez & Sánchez, 2001). Schuster, Sullivan & Kuehn (2008) conceptualized destination image as consisting of cognitive images, which are the tangible and physical attributes of the destination; and affective images, which refer to the feelings or emotional experience that a tourist has on a destination. Several factors including destination attributes (e.g. beautiful landscape, shopping opportunities, cultural exchange, infrastructure, safety, and activities) also contribute to destination image (Kim, 2014).

Several benefits of understanding destination image have been suggested by Schuster, Sullivan, Kuehn (2008), including: (1) provision of information on opportunities and destination attributes to potential tourists; (2) repositioning the destination relative to the competitors; (3) reinforcing positive perceptions of the destination, and (4) identifying and segmenting the target market. Giao, et al., (2020) found that destination image attributes (infrastructure, variety seeking, accessibility, local food, atmosphere, environment, and price, leisure, and entertainment) influence domestic tourists’ revisit intentions.

Harun, et al., (2018) found that two dimensions of destination image (entertainment and events, and natural attraction) had positive relationships with revisit intention, while travel environment and infrastructure were not significant. Hashemi, et al., (2017) posited that destination image influenced the intention of international students to revisit Malaysia’s neighbouring countries. Campo-Martínez, Garau-Vadell & Martínez-Ruiz, (2010) indicated that destination image had a direct influence on the intention to revisit the Spanish island of Mallorca.

According to previous studies by (Bigné et al., 2001; Chen & Tsai, 2007) found that only the affective image of the destination influenced revisit intention. Therefore, it is thought that destinations with more positive images are more likely to be included and chosen in the decision-making process. Based on this literature and empirical evidence, this study hypothesizes that:

H2: There is a significant association between destination image and revisit intentions.

Demographic Characteristics and Revisit Intention

Tourists evaluate several attributes before choosing a destination. These attributes include cultural landscape, holiday satisfaction, transport alternatives, climatic conditions, and travelling expenses. Only if they are satisfied with their holiday experience will they have the intention to revisit the same destination. Since tourists come from various backgrounds, it is necessary to investigate the influence of their demographic profiles (e.g. age, gender, income) on their revisit intentions. Demographic characteristics can be classified by socio-demographic factors (Kara & Mkwizu, 2020).

Several demographic variables have been found to influence travel demands. Age is considered to be a crucial factor and travel demand can effectively be predicted through a visitor’s age (Kara & Mkwizu, 2020). According to Kim et al., (2015), a young tourist will have a higher probability of participating in an activity than the same individual as he/she grows older. Gender is another important influence on travel demand, often reflected in their purpose in travelling. Men tended to travel more than women for business and work-related reasons, in contrast to women who travelled more for holidays, visiting family, and for educational purposes (Kara & Mkwizu, 2020).

Gender also influences travel behaviour: Women prefer to travel shorter distances than men (Guntoro & Hui, 2013), and value most destination attributes more than men, particularly environmental scenery and recreational activities such as attending festivals/museums, visiting historical sites, sightseeing, and shopping. The level of income has also been recognized to influence revisit intention to tourism destinations, and customer classification by income is important for both the providers of tourism services in the customer’s place of residence, and for the destination of travel. Kim, et al., (2015) found that respondents at all income levels showed a greater intention to revisit travel destinations. However, income level has no significant influence on group travel. Based on the literature and empirical evidence, the study hypothesizes that:

H3: There is a significant relationship between demographic characteristics and revisit intentions.

Perceived Risk as a Moderator

Tourists increasingly consider risk and safety issues when choosing a destination (Hasan, Ismail & Islam, 2017). Risk is uncertainty about the severity of an event and the consequences or outcome of an activity (Satyarini et al., 2020). In general, risk in marketing is defined as a customer’s subjective feeling of uncertainty about whether the outcomes of a potential purchase will be favourable or unfavourable (Hasan et al., 2017).

Scholars currently define risk perception as consumers’ perceptions of uncertainty and the magnitude of potential negative consequences. According to Al-Gasawneh & Al-Adamat (2020), tourists will perceive some risks or degree of uncertainty and their potential impact during the purchasing process that involves destination and travel. These risks include financial and social losses as well as psychological and physical risks (Harun et al., 2018).

The literature highlights the negative association between tourists’ perceived risk and their revisit intention toward a destination (Artu?er, 2015). These risk perceptions usually arise from the uncertainty experienced while visiting a destination, such as natural disasters, food safety, financial crises, infrastructure problems, poor weather conditions, political conditions, and acts of terrorism (Schroeder, Pennington-Gray, Kaplanidou & Zhan, 2015). Hasan, et al., (2017) posited that each risk factor has a different effect on travel satisfaction and revisit intention. Considering risk is especially important in consumer decision making because it represents the uncertainty of potential outcomes of behaviour as well as the unpleasantness of those outcomes (Branca, 2008).

As the relationships between revisit intention and a destination’s image, and revisit intention and tourists’ motivation, are not always positive and that conclusions can thus be said to be inconsistent, it is proposed that any specific situation that weakens the effect of motivation on revisit intention or the effect of destination image on revisit intention is characterized by a high level of perceived risk. Perceived risk as a moderator variable can be described when such an effect on future behavioural intention occurs (Tavitiyaman & Qu, 2013). Campbell & Goodstein (2001) hypothesized that consumers become more conservative when they perceive high risk; conversely, when their perception of risk is low, they enjoy positive stimulation and evaluate products more favourably. If a risk is perceived to be high, consumers tend to postpone their purchasing decisions or completely abandon them (Artu?er, 2015).

In the context of online shopping (for holidays), perceived risk has a negative impact on purchase intention by increasing the fear of information disclosure, which raises awareness of the danger of unauthorized use of personal data (Ozyer, Kocoglu & Gozukara, 2014). Thus, on this basis, the following hypotheses are proposed:

H4a: Perceived risk moderates the relationship between motivation and revisit intention.

H4b: Perceived risk moderates the relationship between destination image and revisit intention.

H4c: Perceived risk moderates the relationship between demographic characteristics and revisit intention.

Based on the synthesis of past studies, the conceptual model of the current study was designed. The study focuses on the predictors of tourists’ intentions to revisit Kinabalu Mountain National Park in Sabah, Malaysia. Figure 1 illustrates the research model developed, showing the hypothesized associations between the independent variables (demographic characteristics, motivation, destination image), the dependent variable (revisit intention), and the moderating variable (perceived risk)(Figure 1).

Methodology

Sampling and Procedure

Data Collection and Sampling

Kinabalu Mountain National Park in Sabah, Malaysia is the background of this research. The population and the unit of analysis were the individual local and international tourists who had visited the park. The study was conducted under the pandemic Movement Control Order (MCO) between April and June 2020, when both the Park Department and the Sabah Tourism Board were shut down. The most practical (and also the most cost effective) method to reach the potential respondents was therefore online (Y?ld?r?m & Güler, 2020). Messages containing the link to a questionnaire, constructed in Google Forms, were distributed online via social media platforms including Instagram, WhatsApp and the Facebook page of the Kinabalu Park’s fan club members.

A cross-sectional method was used to request the questionnaire data from 300 respondents (Jin, Lee & Lee, 2013) selected from the above sources through convenience sampling. The formula of G-power was used to estimate the sample size; based on the predictors of the current model of the study, 107 was estimated as the minimum sample size, and a total of 262 respondents was therefore seen as sufficient for conclusive investigation. A datasheet containing information about the survey was sent to each respondent with the questionnaire.

Demographic Profile

The details are presented in Table 1. Just over half the respondents (51.1%) were female. 44.3% were in the 20-25-year age group, with 28.2% and 27.5% aged 26-30 years and 31 years and above, respectively. In terms of education level, 91.9% of the respondents were educated and professional. Approximately half (49.2%) were in the medium income category. Regarding travel parties to Kinabalu Mountain National Park, most of the respondents visited with friends/relatives (38.5%) or family/spouse (37.0%), with only 6.1% visiting as individuals and 18.3% with tour agents. A majority of the visitors were international (58.8%), and 41.2% local.

Table 1 Profile of Respondents
Measure	Item	Frequency
Gender	Male	128
	Female	134
Education	Foundation	34
	Diploma	41
	Degree	108
	Master	28
	PhD	32
	Others	19
Age	20-25 years	116
	26-30 years	74
	31 years and above	72
Travel Party	Alone	16
	With friends/relatives	101
	Family/spouse	97
	With tour	48
Sources of Information	Social Media	147
	Travel agent/Tour operator	98
	Travel office abroad	5
	Others	12
Types of Tourist	Local	108
	International	154
Marital status	Single	171
	Married	85
	Others	6
Occupation	Self-employed	28
	Private firm employed	117
	Civil servant	5
	Student	100
	Others	12
Types of Tour	Conducted	179
	Not conducted	83
Frequency of visit	First time	67
	1-2 times	140
	3-4 times	33
	5-6 times	22
Sources: Researcher

Measurements

In order to ensure the content validity of the scales, most of the items in this study were adopted from well-established authors. Motivation adapted from (Dayour & Adongo, 2015); destination image modified from (Zain et al., 2015); perceived risk adopted from (Artu?er, 2015); revisit intention from (Gani, Mahdzar & Anuar 2019); and the demographic adopted from (Wang et al., 2020). All the tested constructs were measured on 7-point Likert-type scales ranging from 1 (strongly disagree) to 7 (strongly agree). The questionnaire was prepared in English.

The validity of the questionnaire items had been tested for content and face validity by several experts, academics and professionals. In this current study, a group of experts comprising four academics and three from tourism agencies that deal with National Park visitors were approached. Items with redundant and ambiguous meanings were eliminated, and some missing items were added to improve the quality of the measurement. The content validity of the survey instrument was thus deemed adequate.

Empirical Results

Mean and Standard Deviation

The descriptive statistics are presented in Table 2 for the five variables: Demographic Characteristics (DC) (mean=2.74, standard deviation=0.71); Motivation (MV) (2.85, 0.67); Perceived Risk (PR) (2.61, 0.79); Destination Image (DI) (2.60, 0.87); and Revisit Intention (RI) (3.31, 0.63).

Table 2 Descriptive Statistics for Latent Variables
	N	Minimum	Maximum	Mean	Std. Deviation
MV	262	1	7	2.8514	0.6704
DC	262	1	7	2.7481	0.7173
RI	262	1	7	2.3185	0.639
DI	262	1	7	2.6082	0.8739
PR	262	1	7	2.6145	0.7947
Valid N (listwise)	262

Common Method Variance Test

Common method bias is frequent in self-reported surveys (Jarvis, MacKenzie & Podsakoff, 2003). According to (Burton-jones, 2009; Reio, 2010), common method bias results in a measurement error that may influence the validity of the data. Since the data in the current study was collected using only a single instrument, in order to reduce measurement error, Harman’s single factor test was performed. The researcher tested all the variables in the instrument, which were loaded on a single factor with no rotation, and Exploratory Factor Analysis (EFA) was executed. The total variance extracted was 15.227%, below the recommended threshold of 50% of the total variance extracted for a single factor. A two-stage data analysis was then applied, EFA followed by Confirmatory Factor Analysis (CFA). The CFA focused on confirming the scales used, not in developing them (Gerbing & Anderson, 1988).

To conduct CFA, the SmartPLS, Partial Least Squares (PLS) approach was employed. According to (Hair et al., 2016; AlQersji et al., 2021), SmartPLS is a component-based method. In comparison to other methods such as covariance-based structural equation modelling, it is a structure latent variable method, like LISREL. SmartPLS is suitable for predictive application and theory building, and does not require normally distributed data. In the measurement model this study therefore used CFA to verify the reliability, convergent and discriminant validity of all the items used, following the suggestion of (Fornell & Lacker 1981). The structural model was assessed by the bootstrapping procedure. The following shows the results of the measurement and structural models.

Measurement Model

Table 3 shows the result of the convergent validity, internal consistency and reliability of each first-order construct. Both reliability and validity met the rule of thumb and all indicator loadings were more than 0.5, with a CR value above 0.7 (Henseler et al., 2014; AlQershi et al., 2019).

Table 3 Loadings, Composite Reliability and Average Variance Extracted
	Convergent Validity		Construct Reliability
Construct	Loading	AVE	a	CR
Revisit intention	0.52-0.814	0.531	0.774	0.847
Motivation	0.64-0.782	0.508	0.918	0.936
Destination image	0.57-0.869	0.573	0.851	0.863
Demographic	0.59-0.893	0.527	0.842	0.811
Perceived risk	0.62-0.871	0.523	0.802	0.813

The second stage, evaluating the discriminant validity, compared the squares of the relationship coefficients and AVE (Fornell & Larcker, 1981); see Table 4. This investigation additionally used an ongoing technique proposed by Henseler & colleges (2014), Heterotrait-Monotrait proportion (HTMT). Using this strategy, the necessity of discriminant validity is satisfied as all HTMT values are below 0.9. Similarly, the reliability requirement was fulfilled as all the Cronbach’s alpha values are above the suggested level of 0.7 (Hair et al., 2016).

Table 4 Fornell-Larcker Criterion
	MO	DC	RI	DI	PR
MT	0.719
DC	0.201	0.748
RI	0.395	0.377	0.729
DI	0.279	0.381	0.421	0.793
PR	0.371	0.468	0.542	0.051	0.764

Structural Model

The structure model examines the relationship between latent variables. The structural coefficients from all directions were inspected by bootstrapping with 500 subsamples. Table 5 summarizes the estimates for each direct effect in the structural model along with standards errors, bootstrapping confidence intervals, and two-tailed p-value.

The coefficient revealed that tourists’ MV has a critical and significant direct impact on RI, which supports H1. DI also had a significant direct impact on RI, supporting H2, as did DC, supporting H3. Table5 also provides the R2 value of endogenous latent factors, showing 35.6% of the explanatory power of RI (Chin, 1998). According to Chin (1998), these rates indicate that the free factors clarification effect on RI was moderate. The results of the procedure (Table 5)showed that DC significantly affects R-0.203, t=2.778, p<0.04); MV, as expected, has a significant d5.294 p<04.477, p< 0.05).

Table 5 Direct Relationships
Relationships	Std. Beta	Std. Error	t-values	p-values	Decision	R2
DC -> RI	0.203	0.048	2.778	0.004	Supported	35.60%
MOT -> RI	0.263	0.058	5.294	0.001	Supported
DI -> RI	0.237	0.071	4.477	0.005	Supported
Note: significant *p< 0.05, **p<0.01

Moderating Effect

For hypotheses H4a, H4b and H4c, the moderating effect of PR on the pathways among MV, DC, DI and RI was examined, using the product indicator approach for creating interaction terms in regression base analysis in PLS-SEM (262). The interaction between constructs is shown in Table 6: PR moderates the relationship between MV, DI and RI (β=0.251, t=3.638, p<0.000), (β=0.194, t=2.371, p<0.002) respectively, which means that H4a and H4b are supported, but not with DC (β=0.083, t=1.095, p<0.086) which means that H4c is rejected.

Table 6 Indirect
Relationships	Std. Beta	Std. Error	t-values	p-values	Decision
PR*MV -> RI	0.251	0.054	3.638	0	Supported
PR*DI   -> RI	0.194	0.049	2.371	0.002	Supported
PR*DC  -> RI	0.083	0.058	1.095	0.086	Not Supported

In moderation analysis, the R² change becomes an important issue. The result of the previous R² shown for the main effect model is 0.356, and for the current interaction effect model, 0.426. This change of 0.07 indicates that with the additional interaction of (PR*DI*MV*DEM), the R2 change is about 7% (additional variance) both intercepts. Based on the f2 of 0.001, it can be concluded that there is no effect size of (PR*DI), as suggested by. In addition,` for (PR*MV), the f2 of 0.022 indicates a medium effect size (Kenny, 2016).

To illustrate the moderating effects, the predicted revisit intention was plotted against higher or lower destination images as a moderator variable. However, Figures 2 and 3 illustrate that the moderating effect of perceived risk on the relationship between destination image and revisit intention is not significant, and that perceived risk was significant in moderating the relationship between travel motivation and revisit intention.

Discussion

This study contributes to the understanding of the integrated model of revisit intention and examines the role of the moderating effect of perceived risk on the relationship between motivation, demographic characteristics, destination image and revisit intention under the COVID-19 post-recovery phase. It also contributes to the existing body of tourism knowledge in terms of predictors of revisit intentions.

The main aim of the study was to examine the relationships between motivation, socio-demographics and destination image on revisit intentions, and the moderating role of perceived risk. The results indicate the significance of motivation, demographics and destination image as predictors of visitors’ intention to revisit tourist destinations (Al-jazzazi & Sultan, 2017; Dayour & Adongo, 2015).

Findings from the current model revealed that motivation is an underlying factor in visiting National Park. The National Park is a place that brings together people from different backgrounds, which leads to making new friends and networks which contribute to future revisit intention. This cultural experience contributes to the motivation for repeat visits. Similarly, Ooi & Laing (2010) found that social interaction contributed to repeat revisits.

The literature also explained that some tourists visit a specific place to join adventure activities and expand their personal horizons (Mansfeld, 1992). The findings indicate that repeat visit adventure activities take priority over destination attractiveness. Repeat visitors, including international travellers, enjoy discovering something new, and close to nature, encouraged by a feeling of escapism. This is in contrast to culture, which rarely triggers tourists’ repeat revisits to the National Park. Overall, repeat visits by tourists indicate that they have gained a positive experience from the previous visit.

The respondents’ socio-demographic characteristics are significantly related to revisit intention, in parallel with the findings of previous studies of consumer behaviour (Galley & Clifton, 2004; Wang et al., 2020). This study discovered that age, gender and income are also consistent with the subjects of previous studies (Galley & Clifton, 2004; Wang et al., 2020). In this case, visitors were largely from generation Z, that is in their 20s and 30s, and highly educated. This is consistent with previous studies (Kwan, Eagles & Gebhardt, 2008; Park & Reisinger, 2010).

The results also indicated the preference of international visitors for ecotourism, being aware of the natural environments outside their own communities, and interested to discover and enjoy natural settings in international destinations. This group of travellers accept the risk of travel, while taking precautions, even though they face health risks and natural disaster during their expedition. It is also important to note that their first-time visit may lead to increased perceived risk perception because they were not originally familiar with the destination (Lohiniva, Sane, Sibenberg, Puumalainen & Salminen, 2020).

In contrast, repeat visitors were more familiar with their surroundings and destination, which may reduce their anxiety level. Notably, over 56.1% of respondents found their destination information mainly through social media, one of the most effective platforms to promote and share information about tour packages (Martín, Cutter & Li, 2020). However, first-time visitors mostly preferred to obtain information from social media rather than travel agents and tour operators. These findings also provide valuable information about the perceptions of travellers in making the decision to travel.

The results confirmed the view that perceived risk is an important construct (Karl & Schmude, 2017). They highlighted the view of risk factors as including financial risk as compared to the benefits achieved from travel products or services (Fuchs & Reichel, 2011). In the same vein, the current study revealed socio-psychological risk factors as being the most crucial in tourist decision making. However, time risk, performance risk, and physical risk were the least important. Another primary purpose of this study was to examine the moderating effect of perceived risk on the link between motivation and revisit intention. Tourists with a low perceived risk of COVID-19 had a tendency for greater behavioural intention. In contrast, a low perceived risk level did not influence the destination image on revisit intention (Chew & Jahari, 2014).

Theoretical and Practical Implications

Post-pandemic COVID-19 Malaysia is currently in the Recovery Movement Order (RMCO) phase. It is at the stage of recovery from the pandemic that has affected economies throughout the world, especially the tourism industry as travel patterns have change due to restriction on movement and closure of borders to break the chain of the COVID-19 infection.

The findings clearly indicate that Malaysia has many valuable resources that have been promoted to local and international tourists through social media. Tourism products such as eco-parks, theme parks and ecotourism have caught the attention of tourists and been given priority. The majority of the tourists in this study are repeat visitors.

In this regard, although our work is unique the results may be applicable to other countries which depend on tourism, with several theoretical contributions. First, this study found that DC, MV and DI are significantly related to RI. Second, this is the first study worldwide to explore RI as a moderator in two of these relationship, with MV and DC, but not DI.

In terms of practical and managerial implications, the empirical results provide valuable information for nature-based tourism destinations as revisit intention is a precious strategy that must be tailored to meet the needs and motivation of travellers. Destination managers or business operators should create a variety of different leisure activities, destination products and services to offer a package of benefit to National Park tourists.

In particular, the package of the National Park must vary according to the socio-demographics and psychological characteristics of the visitors. Since international travellers are the highest repeat visitors, travel agent/business operators must consider the best price of the package to be offered. In a similar vein, offering different packages such as mountain biking trails, introducing local foods and local products will create new experiences. Festivals or events can provide nature-based tourism with a stimulating destination experience and leisure- and adventure-related activities. On the other hand, the success of the destination in attracting visitors largely depends on images formed in the minds of potential travellers and requires effective management to provide valuable images of the destination.

Therefore, destination managers need to take advantage of social media platforms to share positive experiences and feedback from visitors to attract future tourists. Besides the health risk, destination managers should provide safe travel arrangements, and with transparency of information can potentially reduce risk as well as increase the motivation to travel.

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