Impact of Perceived Risk on Tourist Satisfaction and Future Travel Intentions: A Mediation

Abstract

This research study is conducted in the Kashmir Valley, which is rated heavenly for its natural beauty. However, it is also perceived as risky by the outsiders since 1989. Earlier, it was a very popular destination for tourists and for shooting of films. Kashmir still gets good number of tourists, but the puzzle of impact of perceived risks on their behaviour still remains blurred. This research study is an attempt to understand the behaviour of tourists visiting Kashmir in terms of satisfaction from tour and future travel-related intentions as an outcome of perceived risk. Tourist satisfaction as mediator and demographic variables as moderator are carried out to gain insight into the behaviour of these tourists. The results of the study show that tourist satisfaction and future travel intentions of tourists in the Kashmir Valley are not impacted by their perception of risks. The results are a valuable input for DMOs that suggests that risks at the destination cannot be taken as the default barrier to the promotion of tourism. The necessary supply-side interventions at the destination to mitigate identified perceived risks by tourists and tourism relationship strategies at the demand side to address tour satisfaction can reflect in rewarding future travel intentions like revisits and recommendations.

Keywords

Risk perception tourist satisfaction future travel intentions Kashmir Valley mediation–moderation

Introduction

Tourism is a big business that has seen a gradual increase to 1.4 billion international tourists in 2019 from 25 million in 1950 (UNWTO, 2019). It is a major contributor towards foreign exchange earnings and employment in many countries. Increased disposable income and budget travel have given rise to mass tourism in place of limited tourism of rich classes (Swarbrooke & Horner, 2007; Yang & Nair, 2014). The good going of tourism has hit the stop in the year 2020, and tourist arrivals have declined by 72% due to the Coronavirus Disease 2019 (COVID-19) (UNWTO, 2020). The pandemic of the year 2020 is an outlier of the century for tourism, but Seddighi et al. (2001) and Glaesser (2006) note the vulnerability of tourism to external forces. The major past events to impact global tourism are the terror attacks in Egypt, severe acute respiratory syndrome (SARS), 9/11, Tsunamis in Asia, Bali bombing, the Mumbai attacks (Aziz, 1995; Garg, 2015; Glaesser, 2006). Such events cause economic losses, damage the image and safety, and security of travellers (Liu & Pratt, 2017; Sönmez et al., 1999). The impact of perceived risk on destinations is extensively researched (Abraham & Poria, 2019; Cohen, 1988; Deng & Ritchie, 2018; Demos, 1992; George, 2010; Lepp & Gibson, 2003; Moutinho, 1987; Perpiña et al., 2020; Sönmez & Graefe, 1998b; Wantono & McKercher, 2020), and literature provides ample evidence that travel decisions are influenced by both perceived and real risks (Cossens & Gin, 1995; Karl, 2018; Sönmez & Graefe, 1998a). The close association between perceived risks of tourists and their travel behaviour is established by earlier literature, and this forms the backdrop of this study. This association is revisited in the Kashmir Valley of India, having divergent perception of ‘heavenly beautiful but risky’ destination.

The ensuing discussion on this study follows the sequence of literature review, research objectives, research methodology, analyses and conclusion of the study. The study concludes by presenting implications, limitations and directions for future research.

Literature Review and Research Questions

The literature on different dimensions of association between perceived risk and tourist behaviour is reviewed to draw relevant research questions for the study.

Perceived Risks and Tourist Behaviour

The concept of risk in marketing literature introduced by Raymond Bauer in 1960 is defined as expected damage or possible loss (Taylor, 1974). It is the risk and uncertainty perceived by the consumers for a purchase decision (Cox & Rich, 1964; Dowling & Staelin 1994; Glaesser, 2006). In tourism, risk is viewed as the possibility of perceived misfortunes in traveling to a destination (Tsaur et al., 1997). Perceived tourism risk is inherently a subjective evaluation of an adverse event (Liu et al., 2013). It is the probability of loss that influences attitude towards behaviour negatively (Quintal et al., 2010). It is also viewed as negative perceptions that may or may not exist but have the potential to amend travel decisions and something that affects the entire image of a state rather than a particular area or site (Pizam, 1999; Sönmez & Graefe, 1998b).

The perceived risk in tourism is multidimensional, and the dimensions of consumer behaviour risks were applied in the context of tourism (Cheron & Ritchie, 1982; Roehl & Fesenmaier, 1992). Later modification of these studies resulted in the addition of tourism destination–specific risks such as crime (Brunt et al., 2000; Demos, 1992; Mawby et al., 2000; Pizam 1999), health (Cohen, 1988; Cossens & Gin 1995; Leggat & Franklin, 2013), shopping (Yüksel & Yüksel, 2007), terrorism (Aziz, 1995; Liu and Pratt, 2017; Richter & Waugh, 1986; Sundararaman, 2012) and political instability (Seddighi et al., 2001). The dimensions of equipment, financial, physical, psychological, satisfaction, social and time risks identified in earlier studies (Cheron & Ritchie, 1982; Roehl & Fesenmaier, 1992) were expanded to 10 dimensions with the addition of heath, terrorism and political instability (Sönmez & Graefe, 1998a). The perceived risks are found to negatively impact destination business in terms of damage to infrastructure, reduction in tourist receipts and tour cancellations (Misrahi, 2016; Ural, 2016, Zhu & Deng, 2020). Tourists avoid destinations perceived risky because of higher safety concerns (Cossens & Gin, 1995; Demos, 1992; Pinhey & Iverson, 1994; Sönmez & Graefe, 1998). Researchers find that tourists with proper information and exposure to destination are less affected by risk perceptions (Lepp et al., 2011; Reisenwitz & Fowler, 2019). Studies find that past travel experiences and sources of information assist in balancing risk perceptions and destination choice (Pizam & Fleischer, 2002; Sönmez & Graefe, 1998a; Um & Crompton, 1990; Witt & Moutinho, 1989). Tourists accept certain risk, and the intention to visit a destination only starts to decrease only when the risk exceeds the threshold (Glaesser, 2006).

The perceived risks in tourism are increasing over the years (Dioko & Harrill 2019; Ural, 2016), and the studies in the field explore different facets of risks such as association between destination image and perceived risk (Caber et al., 2020; Lepp et al., 2011; Liu et al., 2013; Perpiña et al., 2020), use of theory of planned behaviour to understand the impact of perceived risk on visit intentions (Quintal et al., 2010), and the relationship among natural, physical, political and performance risks with satisfaction and future intentions (An et al., 2010). Hasan et al. (2017) analyse perceived risk through critical literature review and find the gaps in the common dimensions of risk perception in tourism and their impact on tourist satisfaction and future behavioural intention.

The available literature on perceived risks in tourism finds the common theme of relationship between perceived risks of tourists and tourists’ satisfaction. However, the behavioural aspect of future tour intentions in relation to perceived risks is a less researched area.

In the case of the Kashmir Valley, the presence of tourists at the destination is important for the much-needed support to the hill economy, and it was considered apt to study the impact of perceived risks on tourist satisfaction and future travel intentions by framing the following hypotheses:

H₁: Perceived risks have a negative impact on tourist satisfaction.

H₂: Perceived risks have a negative impact on future travel intentions.

Tourist Satisfaction and Future Travel Intentions

Tourist behaviour is a complex subject, and researchers explore it at different stages of purchase process to gain an insight into its dynamics (Mathieson & Wall, 1982; Pearce, 2005; Woodside & Lysonski, 1989). Throughout the purchase process, tourist satisfaction remains an important determinant of behaviour. Consumer behaviour research provides evidence that satisfaction has a strong influence on future intentions towards a particular product or brand (Cronin & Taylor, 1992; Howard, 1977; Oliver, 1980, 1981), and same has been noted in tourist behaviour as well (Chen & Hsu, 2000; Moutinho, 1987).

Tourist expectations and experience at a destination lead to tourist satisfaction (Pizam et al., 1978) and are emotional responses of the traveller’s experience based on perceptions (An et al., 2010). Tourist satisfaction is linked to destination loyalty (Chen & Phou, 2013; Hui, et al., 2007; Ross & Iso-Ahola, 1991; Singh & Singh, 2019; Vigolo, 2015), and it also impacts future intentions of tourists (Chen & Chen 2010; Cho, 1998; Qu et al., 2011; Woodside & Dubelaar, 2002). Higher satisfaction levels are linked to increased good word and loyalty (Baker & Crompton, 2000). Although studies on the Kashmir Valley also find high satisfaction levels (Bhat & Qadir, 2013; Kumar & Dar, 2017), they do not address its effect on future travel behaviour. This gap is an important dimension of this study and a generic assumption is made that:

H₃: Tourist satisfaction has a positive impact on future travel intentions.

Tourist Satisfaction as Mediator

Various contemporary tourist behaviour studies identify the mediating role of satisfaction (Albaity & Melhem, 2017; Kumar, 2016; Reitsamer et al., 2016; Um et al., 2006), and it can be partial or full (Hair et al., 2010). In full meditation, direct effect is not significant, and in partial mediation, both direct and indirect impacts are significant (Hair et al., 2010). Studies find satisfaction as a partial mediator between destination attributes and future inclinations (Chi & Qu, 2008; Mazursky, 1989; Um et al., 2006), and as full mediator between destination attributes and tourists’ loyalty (Chaudhary & Islam, 2020; Chen & Chen, 2010; Reitsamer & Brunner-Sperdin, 2017; Reitsamer et al., 2016). Research studies identify the mediating impact of satisfaction on different attitudinal and behavioural outcomes, and this is explored for this study too by making an assumption that:

H₄: Tourist satisfaction mediates the impact of perceived risk on future travel intentions.

Moderating Role of Demographic Variables on Behaviour

Tourism literature notes variance in attitudes and behaviours based on nationality, culture, gender, age and past experience (Albaity & Melhem, 2017; Demos, 1992; Kuentzel, & McDonald, 1992; Mazursky, 1989; Suki, 2014; Vigolo, 2015; Zgolli & Zaiem, 2017). Some studies observe no variations in perceptions of risks across gender, age, past experience and purpose (An et al., 2010; Milman et al., 1999; Seabra et al., 2013), while others find significant differences across tourist’s role, gender, past experience and nationality (Pizam et al., 2004; Seabra et al., 2013). Male, domestic, young and experienced tourists were reported to be less affected by risk perceptions than their counterparts (George, 2010; Kim et al., 2016; Mawby et al., 2000). Um et al. (2006) argue that previous tourist behaviour studies ignore the systematic effect of moderators. For this study, select demographic variables are studied for their moderating impact and it assumes that:

H₅: Gender moderates the impact of perceived risk on tourist’s satisfaction and future travel intentions.

H₆: Past travel experiences moderate the impact of perceived risk on tourist’s satisfaction and future travel intentions.

H₇: A Tourist's origin moderates the impact of perceived risk on tourist’s satisfaction and future travel intentions.

Study Objectives

This study aims to explore the impact of perceived risk on tourist satisfaction and their future travel intentions. Further, it evaluates the mediating role of tourist satisfaction between perceived risk and future intentions of tourists. Moreover, the study intends to examine the influence of moderating role of gender, past experience and tourists’ place of origin between perceived risk and tourists’ behaviour.

Theoretical Framework

The study developed a hypothetical research model to better illustrate the relationships and the corresponding hypotheses. The model presented in Figure 1 provides the foundation for study objectives and hypotheses.

Figure 1.

Source: The authors.

The model assumes the causal relationship between perceived risk and future intentions of tourists. Further, it considers that a tourist’s satisfaction mediates the relationship between perceived risk and future travel intentions. The tourist demographics such as gender, past travel experience and origin of tourists are expected to moderate the relationship among perceived risk, tourist satisfaction and future travel intentions.

Methodology

Study Site

The study area is the Kashmir Valley, a popular tourist destination, in the northernmost border state of India, having boundaries with Pakistan and China. The area is commonly perceived as a visual delight but has gained a risky image for the past few decades. The destination makes a compelling case for the study of perceived risks on behaviour because of opposing forces of attractiveness and risks in the valley. The Kashmir Valley has great wealth of natural and cultural tourist resources (Dev, 1983; Dhar, 2005). Pleasing climate, lofty mountains, powdery snow, historical gardens, freshwater bodies and unique wildlife make the Kashmir Valley the best place for tourists in the Himalayas with hill stations such as Gulmarg, Pahalgam, Yousmarg, Kokernag and Sonamarg. Due to its glorious natural and cultural beauty, the valley has been praised by travellers, rulers and saints. A sixteenth-century European traveller—Bernier—described the Kashmir Valley as the paradise of the Indies (Lawrence, 1895). Rightly pointed by Emperor Jahangir, ‘if there is paradise on earth, it is this’ (Sundararaman, 2012). The location and natural beauty of Kashmir make it an ideal destination for tourists with a huge market for niche tourism (Chauhan, 2007). Tourism is a major contributor to the economy of the Kashmir Valley by generating employment and providing imputes to other allied sectors such as handicraft, handloom and transport (Economic Survey, 2017). The steady tourist arrivals are important for the valley, but they have fluctuated from the past few years. This is commonly attributed to the natural calamities such as floods and frequent lockdowns, boycotts and other disturbances in the valley due to the fluid political conditions. This has changed the narrative of Kashmir for the outsiders, and it is commonly seen as an unsafe place to visit. However, studies have not been conducted to understand the perception of tourists who visit Kashmir at such times, and this study tries to fill this gap.

The Kashmir Valley has remained a very popular tourist destination among domestic and foreign tourists, but things have taken a turn for the worse since 1989, with the rise of militancy in the region (Evans, 2000; Taylor, 1991). After 1989, the valley has seen periods of ups and downs in tourism, marked by the periods of frequent mass protests, terror activities and curfews. This has changed the whole narrative of the Kashmir Valley for visitors from beautiful Kashmir to dangerous and disturbed Kashmir. Consequently, travel advisories of different countries recommend and caution tourists to avoid all travel to Kashmir. Since August 2019, another wave of political change has swept the valley with the repeal of Article 370, altering the existing political discourse. The transition from an old to a new political system was carried out through complete shutdown and closure of the valley for outsiders, and all national and international tourists were asked to leave the valley, and things have not yet reverted back to the old normal. The negligible arrivals of tourists to the valley have adversely impacted the interests of all tourism stakeholders. The valley has seen long periods of lockdown, first, due to the repealing of Article 370 and, later, because of the COVID-19 pandemic. However, it is assumed that things will return back to normalcy, and findings of the study will hold good once normal tourism returns.

Study Constructs and Measures

The perceptions of tourists are captured through primary data collected with the help of the questionnaire. The parameters were selected, considering as influencing perceived risk on the basis of past studies of An et al. (2010), Demos (1992), Fuchs and Reichel (2006), Lepp et al. (2011), Law (2006), Mawby et al. (2000), Rittichainuwat and Chakraborty, (2009), Richter and Waugh (1986), Seabra et al. (2013), Tsaur et al. (1997), and Yüksel and Yüksel (2007). Tourist behaviour was measured over items adapted from Albaity and Melhem (2017), Kumar (2016) and Vigolo (2015). The items obtained from these studies were paraphrased as per the nature of study area. A 5-point scale was used with end-point descriptions very low (1) to very high (5) for perceived risk and strongly disagree (1) to strongly agree (5) for tourist behaviour. The questionnaire also included three demographic variables—gender, travel experience and origin of tourist to measure the socio-demographic character of the respondents.

Pilot Testing and Data Collection

The pilot testing of questionnaire was conducted on 50 tourists at Srinagar and Gulmarg in Kashmir. The process resulted in content modification of a few items. The final questionnaire consisted of 18 statement, 12 related to perceived risk, 3 related to tourist satisfaction and 3 related to future intentions. The revised questionnaire was used to collect data in 2018 and 2019 to avoid the impact of any non-routine event on tourist’s perceptions. The respondents were approached at Srinagar, Gulmarg, Pahalgam, Sonamarg and Srinagar international airport. Each respondent was approached personally by the researcher and was given a brief on the importance of the study and the value of feedback before getting the questionnaire filled to ensure effective answers.

The sample size was based on the number of items and should be 10 times the number of items in the questionnaire (Hair et al., 2010). Accordingly, 400 respondents were considered appropriate and data were collected with the permission obtained from the Ministry of Tourism, Government of Jammu and Kashmir, and Airport Authority of India. In all, 400 filled questionnaires were obtained to allow the margin for rejection of incomplete responses. A total of 30 questionnaires were eliminated due to incomplete responses, and the remaining 370 (92.5%) questionnaires were included in the study.

Data Analysis

The data analysis was performed using IBM SPSS v23 and AMOS v24 software. First, descriptive analysis of respondents was presented. Exploratory factor analysis (EFA) was applied to determine factorization of perceived risk. Further, confirmatory factor analysis (CFA) was used for validity and reliability test of measurement model. Finally, structural equation modelling (SEM) was performed to check the various relationships between the study constructs.

Analysis

Profile of Tourists

Descriptive analysis has been used to know the profile of 370 tourists and is presented in Table 1.

Table 1.

Tourists’ Profile.

Variables		Number	Percentage
Gender	Male	205	55.4
Gender	Female	165	44.6
Previous travel experience	First visit	231	62.4
Previous travel experience	Repeat	139	37.6
Origin	Domestic	201	54.3
Origin	Foreign	169	45.7

Source: The authors.

The percentage analysis of the tourists’ profile shows that majority of the tourists are males (55.6%) and on their first visit (62.4%). Domestic tourists are 54.3%, while 45.7% are foreign tourists. The sample has a good balance between males and females, and domestic and foreign tourists.

Exploratory Factor Analysis

The dimensionality of perceived risk was extracted by EFA. The results of this steps showed a Kaiser–Meyer–Olkin (KMO) of 0.738 and significant Bartlett’s test of sphericity (p < 0.001, χ² = 2226.85, df = 66). These values suggest that data are appropriate for further analysis.

The EFA reduced 12 items into 4 factors with eigenvalues above 1. The factor loadings showed that no item is required to delete since all the items have loadings above 0.50 (Hair et al., 2010; Smith 1987). The four factors explained 73.83% of the total variance. The factors were found reliable with Cronbach’s reliability value, ranging between 0.741 and 0.879. The factor loadings, eigenvalues, variance explained and reliability values are presented in Table 2.

Table 2.

EFA Results.

Factor	Loadings	Eigen value	% of Variance Explained	Alpha
PR1: Human induced risk		3.706	30.886	0.808
X1—Terrorism	0.850
X2—Unrest	0.843
X3—Crime	0.807
X4—Shopping	0.621
PR2: Personal safety		2.429	20.238	0.879
X5—Food	0.946
X6—Personal health	0.944
X7—Adventure	0.796
PR3: Natural risk		1.533	12.778	0.822
X8—Sickness	0.899
X9—Hygiene and cleanliness	0.871
X10—Natural calamities	0.722
PR4: Cultural risk		1.192	9.931	0.741
X11—Language	0.869
X12—Clothing	0.858

Source: The authors.

Measurement Model

CFA under maximum likelihood (ML) estimation was applied to check the construct’s validity and reliability (Anderson & Gerbing, 1988). The goodness of fit indices were used to determine model adequacy. Model fit values such as χ² = 230.111, df = 120, χ²/df = 1.918, RMR = 0.038 GFI = 0.953, AGFI = 0.908, CFI = 0.969 and RMSEA = 0.050, all exceeded their acceptable levels and indicated fitness of data in the measurement model (Hair et al., 2010; Kline, 2015).

Reliability and Validity

Reliability and validity are assessed through composite reliability (CR), average variance extracted (AVE) and discriminant validity (Hair et al., 2010). The validity of constructs is presented in Table 3.

Table 3.

CFA Results.

Constructs	SRW	t-Values	CR	AVE
PR1			0.825	0.551
X1	0.85
X2	0.84	17.161*
X3	0.74	15.165*
X4	0.48	9.023*
PR2			0.894	0.745
X5	0.96
X6	0.96	29.468*
X7	0.62	14.301*
PR3			0.839	0.641
X8	0.94
X9	0.82	16.517*
X10	0.61	12.248*
PR4			0.747	0.598
X11	0.77
X12	0.77	7.125*
TS: Tourist satisfaction			0.868	0.691
TS1—Satisfied	0.97
TS2—Pleased	0.78	19.868*
TS3—Good money value	0.72	17.214*
FI: Future intentions			0.821	0.612
FI1—Revisit	0.71
FI2—Recommend	0.97	15.293*
FI3—Positive WOM	0.63	11.761*

Source: The authors.

Notes: * = p < 0.001.

The standard regression weight (SRW) and t-values of the items were above 0.50 and 1.96, respectively, indicating that item defines each construct well (Hair et al., 2010). The weighting for one item was slightly low (0.48) from the optimal values (0.50) because it was still acceptable according to Hair et al. (2010) and its significance for the study context. The regression weights varied between 0.48 and 0.97, with the maximum above 0.70. Further, CR and AVE were above 0.70 and 0.50, respectively. Therefore, these results indicated good convergent validity of the measurement model.

Discriminant validity of the model was checked to compare correlations between constructs and square root of AVE. The square root of AVE is presented in Table 4 in diagonals, and the values of diagonals represent intercorrelations among constructs.

Table 4.

Discriminant validity.

Construct	TR1	TR2	TR3	TR4	TS	FI
TR1	0.743
TR2	0.077	0.863
TR3	0.390	0.005	0.800
TR4	0.335	0.078	0.417	0.773
TS	−0.319	−0.118	−0.143	−0.129	0.831
FI	−0.338	−0.157	−0.189	−0.181	−0.771	0.782

Source: The authors.

The intercorrelations among constructs were less than the square root of AVE, thereby demonstrating that constructs are significantly distinct from one another (Fornell & Larcker, 1981). The convergent and discriminant validity tests indicate that the model is valid and reliable and hence appropriate for testing structural relationships.

Hypotheses Testing

The hypothetical study model was tested through SEM. First, the direct impact of perceived risk on future travel intentions was evaluated (see Figure 2). Model fit indices for this relationship were as follows: χ² = 136.792, df = 86, χ²/df = 1.591, RMR = 0.053, GFI = 0.953, AGFI = 0.934, CFI = 0.981 and RMSEA = 0.040. The goodness of fit indices indicate good fit for the present relationship.

Figure 2.

Source: The authors.

Figure 3.

Source: The authors.

The path from perceived risk to future travel intentions has beta value at −0.39 with critical ratio of −4.575, which is significant at 0.001. The relationship is negative and weak in magnitude. This shows that as the perception of risk increases, favourable intentions towards a destination decreases. Therefore, Hypothesis 2 is supported.

The whole structural model was validated using SEM under maximum likelihood estimation with direct and indirect effect under 2,000 sub-samples with 95% bootstrap confidence intervals (see Figure 3). The direct and indirect impacts were preformed through standardized regression weights (SRW), critical ratio and significance level. The model fit indices revealed good fit for whole model and were as follows: χ² = 249.974, df = 129, χ²/df = 1.938, RMR = 0.052, GFI = 0.930, AGFI = 0.908, CFI = 0.966, RMSEA = 0.050 and PCLOSE 0.458. These values indicated that data fits in the full casual model. The results of full model are discussed in Table 5.

Table 5.

Structural Model Results.

Hypotheses	Direct estimate	Indirect Estimate	t-Value	Result
PR → TS	−0.35**	–	−4.097	H₁ supported
TS → FI	0.71**	–	11.481	H₃ supported
PR → TS → FI	−0.155*	−0.251**	–	H₄ partial meditation

Source: The authors.

Notes: * p = < 0.01; **p = <0.001; PR = perceived risk; TS = tourist satisfaction; FI = future intentions.

The result of full structural model explains that all paths are significant. The path from perceived risk to tourist satisfaction has a beta value of −0.35 with critical ratio of −4.469, which is significant at 0.001. The impact is negative and weak in strength, providing evidence to support Hypothesis 1. Further, the path from tourist satisfaction to future travel intentions has a beta value of 0.71 with a t-value of 11.481, which is significant at 0.001, explaining that there is a strong positive impact of tourist satisfaction on their future intentions. The path is positively strong in magnitude, indicating that satisfaction increases and so does the future intentions. Hence, Hypothesis 3 is supported.

The results show partial mediation exists between perceived risk and future travel intentions because direct impact of perceived risk on future travel intentions is still significant. Before mediation, the direct effect was high, but after mediation, the direct effect considerably reduced. The standardized direct effect of −0.155 (p < 0.01) and standardized indirect effect of −0.251(p < 0.001) indicate partial mediation between perceived risk and future travel intentions. Hence, Hypothesis 4 is supported.

Moderation effects of gender, previous experience and origin were checked through multigroup analysis. Chi-square difference test was applied to study the difference in groups (Gaskin & Lim, 2018). The results of multigroup analysis presented in Table 6 indicated two significantly different paths across groups.

Table 6.

Moderator Effect.

Moderator	Category	PR → TS	TS → FI	PR → FI
Gender	Male beta	−0.347****	0.692****	−0.164**
	Female beta	−0.379****	0.736****	−0.141*
	p-value for difference	0.697	0.708	0.559
	Interpretation	No difference	No difference	No difference
Previous experience	First	−0.354****	0.716****	−0.177**
	Repeat	−0.316***	0.726****	−0.096
	p-Value for difference	0.508	0.358	0.667
	Interpretation	No difference	No difference	Relationship only significant for first
Origin	Domestic	−0.304****	0.755****	−0.138**
	Foreigner	−0.441***	0.688****	−0.196**
	p-Value for difference	0.290	0.015	0.796
	Interpretation	No difference	Relationship stronger for domestic	No difference

Source: The authors.

Note: * = p < 0.10; ** = p < 0.05; *** p = <0.01; **** = p < 0.001.

The chi-square test for gender shows no difference in individual’s paths, explaining that being male or female does not affect any relationships. Hence, Hypothesis 5 is not supported. Previous experience as moderator shows one path significant. The beta value of −0.177 (p = <0.05) suggests that negative relationship between path perceived risk and future intentions is only significant and meaningful for first-time visitors. Hence, overall, Hypothesis 6 is accepted for this path only. Furthermore, the origin of tourists moderated the impact of tourist satisfaction on future travel intentions. The beta of 0.755 and 0.688 (p < 0.01) indicates that the positive relationship between tourist satisfaction and future travel intentions is stronger for domestic tourists. Therefore, overall, Hypothesis 7 is only supported for path tourist satisfaction and future travel intentions.

Conclusion

This study suggests an inverse relationship between risk perceptions by tourists and their satisfaction, revisit and recommendation intents. The study finds low risk perception by tourists for the study area—the Kashmir Valley—that is commonly perceived as risky. The impact of risk perception by tourists on the combined variables of tourist satisfaction and future travel intentions is found to be low. The study finds strong positive impact of tourist satisfaction on future travel intentions. The findings get echoed in other studies that find association of lesser risk perception with greater satisfaction (Brunt et al., 2000; George, 2010; Yüksel & Yüksel, 2007).

The study finds tourist satisfaction mediating the impact of perceived risk on future travel intentions. The magnitude of impact changes with the mediator that was high in the absence of mediator and reduced in the presence of mediator.

The analysis of moderating role of gender, past travel experience and tourists’ origin on association between perceived risk and tourist satisfaction, between tourist satisfaction and future travel intentions and between perceived risk and future travel intentions shows that past travel experiences and origin moderate the impact on some paths, while gender is found to have no impact. Earlier studies also find no variations in perceived risk across males and females (An et al., 2010; George, 2010; Lepp & Gibson, 2003; Sönmez & Graefe, 1998b). In this study, negative impact of perceived risk on future travel intentions was higher among first-time visitors. Studies conducted in the past also found that level of risk perceptions declined as individuals became familiar with a destination (Deng & Ritchie, 2018; Gray et al., 2011;). The origin of tourists identified as domestic versus foreign moderated impact of tourist satisfaction on future travel intentions with higher estimate for domestic and lower estimate for foreigners. Researchers found that tourists’ behaviour varied across nationalities (Mayo & Jarvis, 1981). The difference between domestic versus foreign tourists regarding tourist satisfaction and future travel intentions could be because of variance in the social and cultural norms that shape the behavioural and emotional attachment to the destination.

Theoretical Implications

The model for the study is built upon existing studies with two basic differences:

The model looks at different relationships between risk perceptions and future travel intentions in totality.

It has been empirically studied in the Kashmir Valley that is generally perceived as risky.

The findings of the study validate the existing assumptions in completely different contexts and also establish the assumption that tourists may not accept common perceptions at face value; rather, they would use a different lens to assess risks and to gain tour experiences. Past travel experiences and tourists’ place of origin form this tourist lens. The findings of this study form the theoretical base to be used for further studies and explore moderating role of more factors in developing the tourists’ lens.

Managerial Implications

The study has special relevance for the Kashmir Valley and for similar destinations. Destination managers can take a cue from this study to promote and manage their destinations by using the constructs of destination risk perception to develop marketing strategies to minimize perceived risks of tourists. Enhanced tourist satisfaction shall be the goal of all the strategies to get revisits and positive retelling. The constructs identified in this study relate to various stakeholders and services and facilities offered by them and a proactive inclusion strategy for these can support confidence-building measures for tourists and alleviate any fears of risks.

Limitations and Future Research

This research is limited in the generalization of findings on account of select objectives and specific study area of Kashmir. The possibility of new findings with the consideration of more relationships and variables is not ruled out. However, the findings are in line with many previous studies, giving enough leverage for carrying out similar studies at other destinations albeit keeping in mind caveats of this study.

A similar study can be conducted at the destinations that are commonly perceived as risky due to political issues that are larger than the destinations or the concerned nations. A larger study may change the common perceptions of risky destinations as ‘not risky for tourism’ or ‘risky with conditions attached’. This may change the approach to destination management.

The same study can be replicated in the Kashmir Valley where political milieu has changed after data collection for this study was complete. Abrogation of Article 370 and status of Union Territory to the valley have changed the political landscape of the state. The spread of COVID-19 has further brought a number of other changes, impressing the need for study, with the inclusion of many new variables arising out of these changes.

Footnotes

Acknowledgement

The authors are grateful to the anonymous referees of the journal for their extremely useful suggestions to improve the quality of the article. Usual disclaimers apply.

Declaration of Conflicting Interests

The authors declared no potential conflicts of interest with respect to the research, authorship and/or publication of this article.

Funding

The authors received no financial support for the research, authorship and/or publication of this article.

References

Abraham

, & Poria

(2019). Political identification, animosity, and consequences on tourist attitudes and behaviours. Current Issues in Tourism , 23, 1–18. https://doi.org/10.1080/13683500.2019.1679095

Albaity

, & &Melhem

S. B.

(2017). Novelty seeking, image, and loyalty—The mediating role of satisfaction and moderating role of length of stay: International tourists’ perspective. Tourism Management Perspectives , 23, 30–37.

, Lee

, & Noh

(2010). Risk factors at the travel destination: Their impact on air travel satisfaction and repurchase intention. Service Business , 4(2), 155–166.

Anderson

J. C.

, & Gerbing

D. W.

(1988). Structural equation modeling in practice: A review and recommended two-step approach. Psychological Bulletin , 103(3), 411–423.

Aziz

(1995). Understanding attacks on tourists in Egypt. Tourism Management , 16(2), 91–95.

Baker

D. A.

, & Crompton

J. L.

(2000). Quality, satisfaction and behavioral intentions. Annals of Tourism Research , 27(3), 785–804.

Bhat

M. A.

, & Qadir

(2013). Tourist satisfaction in Kashmir: An empirical assessment. Journal of Business Theory and Practice , 1(1), 152–166.

Brunt

, Mawby

, & Hambly

(2000). Tourist victimisation and the fear of crime on holiday. Tourism Management , 21(4), 417–424.

Caber

, González-Rodríguez

M. R.

, Albayrak

, & Simonetti

(2020). Does perceived risk really matter in travel behaviour? Journal of Vacation Marketing , 26(3), 1–20.

10.

Chauhan

(2007). Safety and security perceptions of tourists visiting Kashmir, India. Advances in Hospitality and Leisure , 3, 3–17.

11.

Chaudhary

, & Islam

N. U.

(2020). Influence of destination attractiveness on tourist satisfaction and future travel intentions: A study of Kashmir valley. Journal of Tourism , 21(1), 13.

12.

Chen

J. S.

, & Hsu

C. H.

(2000). Measurement of Korean tourists’ perceived images of overseas destinations. Journal of Travel Research , 38(4), 411–416.

13.

Chen

C. F.

, & Chen

F. S.

(2010). Experience quality, perceived value, satisfaction and behavioral intentions for heritage tourists. Tourism Management , 31(1), 29–35.

14.

Chen

C. F.

, & Phou

(2013). A closer look at destination: Image, personality, relationship and loyalty. Tourism Management , 36, 269–278.

15.

Cheron

E. J.

, & Ritchie

J. B.

(1982). Leisure activities and perceived risk. Journal of Leisure Research , 14(2), 139–154.

16.

Chi

C. G. Q.

, & Qu

(2008). Examining the structural relationships of destination image, tourist satisfaction and destination loyalty: An integrated approach. Tourism Management , 29(4), 624–636.

17.

Cho

B. H.

(1998). Assessing tourist satisfaction: An exploratory study of Korean youth tourists in Australia. Tourism Recreation Research , 23(1), 47–54.

18.

Cossens

, & Gin

(1995). Tourism and AIDS: The perceived risk of HIV infection on destination choice. Journal of Travel and Tourism Marketing , 3(4), 1–20.

19.

Cox

D. F.

, & Rich

S. U.

(1964). Perceived risk and consumer decision-making—The case of telephone shopping. Journal of Marketing Research , 1(4), 32–39.

20.

Cronin

J. J.

Jr , & Taylor

S. A.

(1992). Measuring service quality: A reexamination and extension. Journal of Marketing , 56(3), 55–68.

21.

Demos

(1992). Concern for safety: A potential problem in the tourist industry. Journal of Travel and Tourism Marketing , 1(1), 81–88.

22.

Deng

, & Ritchie

B. W.

(2018). International university students’ travel risk perceptions: An exploratory study. Current Issues in Tourism , 21(4), 455–476.

23.

Dev

J. S.

(1983). Natural calamities in Jammu and Kashmir . Ariana Publishing House.

24.

Dhar

D. N.

(2005). Kashmir, a kaleidoscopic view . Kanishka Publishers.

25.

Dioko

L. D. A.

, & Harrill

(2019). Killed while traveling–Trends in tourism-related mortality, injuries, and leading causes of tourist deaths from published English news reports, 2000–2017 (1H). Tourism Management , 70, 103–123.

26.

Dowling

G. R.

, & Staelin

(1994). A model of perceived risk and intended risk-handling activity. Journal of Consumer Research , 21(1), 119–134.

27.

Economic Survey. (2017). Directorate of economic and statistics, Jammu and Kashmir . Economic Survey Annual Report.

28.

Evans

(2000). The Kashmir insurgency: As bad as it gets. Small Wars and Insurgencies , 11(1), 69–81.

29.

Fornell

, & Larcker

D. F.

(1981). Structural equation models with unobservable variables and measurement error: Algebra and statistics. Journal of Marketing Research , 18(3), 382–388.

30.

Fuchs

, & Reichel

(2006). Tourist destination risk perception: The case of Israel. Journal of Hospitality & Leisure Marketing , 14(2), 83–108.

31.

Garg

(2015). Travel risks vs tourist decision making: A tourist perspective. International Journal of Hospitality and Tourism Systems , 8(1), 1–9.

32.

Gaskin

, & Lim

(2018). Multigroup analysis, AMOS plugin . Gaskination’s StatWiki.

33.

George

(2010). Visitor perceptions of crime-safety and attitudes towards risk: The case of table mountain National Park, Cape Town. Tourism Management , 31(6), 806–815.

34.

Glaesser

(2006) Crisis management in the tourism industry . Elsevier Butterworth–Heinemann.

35.

Gray

, Schroeder

, & Kaplanidou

K. K.

(2011). Examining the influence of past travel experience, general web searching behaviors, and risk perceptions on future travel intentions. IJSSTH , 1(1), 64–89.

36.

Hair

J. F.

, Black

W. C.

, Babin

B. J.

, & Anderson

R. E.

(2010) Multivariate data analysis . Pearson Education.

37.

Hasan

M. K.

, Ismail

A. R.

, & Islam

M. F.

(2017). Tourist risk perceptions and revisit intention: A critical review of literature. Cogent Business and Management , 4(1), 1412874.

38.

Howard

J. A.

(1977). Consumer behavior: Application of theory (Vol. 325). McGraw-Hill.

39.

Hui

T. K.

, Wan

, & Ho

(2007). Tourists’ satisfaction, recommendation and revisiting Singapore. Tourism Management , 28(4), 965–975.

40.

Karl

(2018). Risk and uncertainty in travel decision-making: Tourist and destination perspective. Journal of Travel Research , 57(1), 129–146.

41.

Kim

, Schroeder

, & Pennington-Gray

(2016). Does culture influence risk perceptions? Tourism Review International , 20(1), 11–28.

42.

Kline

R. B.

(2015). Principles and practice of structural equation modeling . Guilford Publications.

43.

Kuentzel

W. F.

, & McDonald

C. D.

(1992). Differential effects of past experience, commitment, and lifestyle dimensions on river use specialization. Journal of Leisure Research , 24(3), 269–287.

44.

Kumar

(2016). Examining the role of destination personality and self-congruity in predicting tourist behavior. Tourism Management Perspectives , 20, 217–227.

45.

Kumar

R. B.

, & Dar

(2017). What makes domestic tourists satisfied: An empirical study of Srinagar (Jammu & Kashmir). International Journal of Tourism and Travel , 10(1), 40–48.

46.

Lawrence

W. R.

(1895). The valley of Kashmir . Oxford University Press.

47.

Leggat

P. A.

, & Franklin

(2013). Risk perception and travelers. Journal of Travel Medicine , 20, 1–2

48.

Lepp

, & Gibson

(2003). Tourist roles, perceived risk and international tourism. Annals of Tourism Research , 30(3), 606–624.

49.

Lepp

, Gibson

, & Lane

(2011). Image and perceived risk: A study of Uganda and its official tourism website. Tourism Management , 32(3), 675–684.

50.

Liu

, & Pratt

(2017). Tourism’s vulnerability and resilience to terrorism. Tourism Management , 60, 404–417.

51.

Liu

, Pennington-Gray

, & Schroeder

(2013). Images of safe tourism destinations in the United States held by African Americans. PASOS. Revista de Turismo y Patrimonio Cultural , 11(3), 105–121.

52.

Mathieson

, & Wall

(1982). Tourism, economic, physical and social impacts . Longman.

53.

Mawby

R. I.

, Brunt

, & Hambly

(2000). Fear of crime among British holidaymakers. British Journal of Criminology , 40(3), 468–479.

54.

Mayo

E. J.

, & Jarvis

L. P.

(1981). The psychology of leisure travel. Effective marketing and selling of travel services . CBI Publishing Company.

55.

Mazursky

(1989). Past experience and future tourism decisions. Annals of Tourism Research , 16(3), 333–344.

56.

Milman

, Jones

, & Bach

(1999). The impact of security devices on tourists’ perceived safety: The central Florida example. Journal of Hospitality and Tourism Research , 23(4), 371–386.

57.

Misrahi

(2016, March 23). How destinations can bounce back after terrorist attacks . https://www.weforum.org/agenda/2016/03/how-destinations-can-bounce-back-after-terrorist-attacks/

58.

Moutinho

(1987). Consumer behaviour in tourism. European Journal of Marketing , 21(10), 5–44. https://doi.org/10.1108/EUM0000000004718

59.

Oliver

R. L.

(1980). A cognitive model of the antecedents and consequences of satisfaction decisions. Journal of Marketing Research , 17(4), 460–469.

60.

Oliver

R. L.

(1981). Measurement and evaluation of satisfaction processes in retail settings. Journal of Retailing , 57(3), 25–48.

61.

Pearce

P. L.

(2005). Tourist behaviour: Themes and conceptual schemes . Channel View Publications.

62.

Perpiña

, Prats

, & Camprubí

(2020). Image and risk perceptions: An integrated approach. Current Issues in Tourism, 1–18. https://doi.org/10.1080/13683500.2020.1715355

63.

Pinhey

T. K.

, & Iverson

T. J.

(1994). Safety concerns of Japanese visitors to Guam. Journal of Travel and Tourism Marketing , 3(2), 87–94.

64.

Pizam

(1999). A comprehensive approach to classifying acts of crime and violence at tourism destinations. Journal of Travel Research , 38(1), 5–12.

65.

Pizam

, & Fleischer

(2002). Severity versus frequency of acts of terrorism: Which has a larger impact on tourism demand? Journal of Travel Research , 40(3), 337–339.

66.

Pizam

, Jeong

G. H.

, Reichel

, van Boemmel

, Lusson

J. M.

, Steynberg

, ... & Montmany

(2004). The relationship between risk-taking, sensation-seeking, and the tourist behavior of young adults: A cross-cultural study. Journal of Travel Research , 42(3), 251–260.

67.

Pizam

, Neumann

, & Reichel

(1978). Dimentions of tourist satisfaction with a destination area. Annals of Tourism Research , 5(3), 314–322.

68.

, Kim

L. H.

, & Im

H. H.

(2011). A model of destination branding: Integrating the concepts of the branding and destination image. Tourism Management , 32(3), 465–476.

69.

Quintal

V. A.

, Lee

J. A.

, & Soutar

G. N.

(2010). Risk, uncertainty and the theory of planned behavior: A tourism example. Tourism Management , 31(6), 797–805.

70.

Reisenwitz

T. H.

, & Fowler

J. G.

(2019). Information sources and the tourism decision-making process: An examination of Generation X and Generation Y consumers. Global Business Review , 20(6), 1372–1392.

71.

Reitsamer

B. F.

, & Brunner-Sperdin

(2017). Tourist destination perception and well-being: What makes a destination attractive? Journal of Vacation Marketing , 23(1), 55–72. https://doi.org/10.1177/1356766715615914

72.

Reitsamer

B. F.

, Brunner-Sperdin

, & Stokburger-Sauer

N. E.

(2016). Destination attractiveness and destination attachment: The mediating role of tourists’ attitude. Tourism Management Perspectives , 19, 93–101.

73.

Richter

L. K.

Jr , & Waugh

W. L.

(1986). Terrorism and tourism as logical companions. Tourism Management , 7(4), 230–238.

74.

Rittichainuwat

B. N.

, & Chakraborty

(2009). Perceived travel risks regarding terrorism and disease: The case of Thailand. Tourism Management , 30(3), 410–418.

75.

Roehl

W. S.

, & Fesenmaier

D. R.

(1992). Risk perceptions and pleasure travel: An exploratory analysis. Journal of Travel Research , 30(4), 17–26.

76.

Ross

E. L. D.

, & Iso-Ahola

S. E.

(1991). Sightseeing tourists’ motivation and satisfaction. Annals of Tourism Research , 18(2), 226–237.

77.

Seabra

, Dolnicar

, Abrantes

J. L.

, & Kastenholz

(2013). Heterogeneity in risk and safety perceptions of international tourists. Tourism Management , 36, 502–510.

78.

Seddighi

H. R.

, Nuttall

M. W.

, & Theocharous

A. L.

(2001). Does cultural background of tourists influence the destination choice? An empirical study with special reference to political instability. Tourism Management , 22(2), 181–191.

79.

Singh

, & Singh

(2019). Destination attributes to measure tourist revisit intention: A scale development. Global Business Review , 20(2), 549–572.

80.

Smith

S. L.

(1987). Regional analysis of tourism resources. Annals of Tourism Research , 14(2), 254–273.

81.

Sönmez

S. F.

, & Graefe

A. R.

(1998a). Determining future travel behavior from past travel experience and perceptions of risk and safety. Journal of Travel Research , 37(2), 171–177.

82.

Sönmez

S. F.

, & Graefe

A. R.

(1998b). Influence of terrorism risk on foreign tourism decisions. Annals of Tourism Research , 25(1), 112–144.

83.

Sönmez

S. F.

, Apostolopoulos

, & Tarlow

(1999). Tourism in crisis: Managing the effects of terrorism. Journal of Travel Research , 38(1), 13–18.

84.

Suki

N. M.

(2014). Moderating role of gender in the relationship between hotel service quality dimensions and tourist satisfaction. Journal of Quality Assurance in Hospitality and Tourism , 15(1), 44–62.

85.

Sundararaman

(2012). Impact of terrorism on Kashmir valley tourism (Doctoral dissertation). Pondicherry University, Shodhganga Inflibnet. https://shodhganga.inflibnet.ac.in/handle/10603/5285

86.

Swarbrooke

, & Horner

(2007). Consumer behaviour in tourism . Routledge.

87.

Taylor

(1991). The Kashmir crisis. Asian Affairs , 22(3), 303–313.

88.

Taylor

J. W.

(1974). The role of risk in consumer behavior: A comprehensive and operational theory of risk taking in consumer behavior. Journal of Marketing , 38(2), 54–60.

89.

Tsaur

S. H.

, Tzeng

G. H.

, & Wang

G. C.

(1997). Evaluating tourist risks from fuzzy perspectives. Annals of Tourism Research , 24(4), 796–812.

90.

, & Crompton

J. L.

(1990). Attitude determinants in tourism destination choice. Annals of Tourism Research , 17(3), 432–448.

91.

, Chon

, & Ro

(2006). Antecedents of revisit intention. Annals of Tourism Research , 33(4), 1141–1158.

92.

UNWTO (2019). UNWTO tourism highlights (2019 ed.). UNWTO.

93.

UNWTO (2020, December). World tourism barometer 2020 (2020 ed.). UNWTO.

94.

Ural

(2016). Risk management for sustainable tourism. European Journal of Tourism, Hospitality and Recreation , 7(1), 63–71.

95.

Vigolo

(2015). Investigating the attractiveness of an emerging long-haul destination: Implications for loyalty. International Journal of Tourism Research , 17(6), 564–576.

96.

Wantono

, & McKercher

(2020). Backpacking and risk perception: The case of solo Asian women. Tourism Recreation Research , 45(1), 19–29.

97.

Witt

S. F.

, & Moutinho

(1989). Tourism marketing and management handbook . Prentice Hall.

98.

Woodside

A. G.

, & Lysonski

(1989). A general model of traveler destination choice. Journal of Travel Research , 27(4), 8–14.

99.

Woodside

A. G.

, & &Dubelaar

(2002). A general theory of tourism consumption systems: A conceptual framework and an empirical exploration. Journal of Travel Research , 41(2), 120–132.

100.

Yang

E. C. L.

, & Nair

(2014). Tourism at risk: A review of risk and perceived risk in tourism. Asia-Pacific Journal of Innovation in Hospitality and Tourism (APJIHT) , 3(2), 1–21.

101.

Yüksel

, & Yüksel

(2007). Shopping risk perceptions: Effects on tourists’ emotions, satisfaction and expressed loyalty intentions. Tourism Management , 28(3), 703–713.

102.

Zgolli

, & Zaiem

(2017). Customer-to-customer interaction in tourism experience: Moderating role of nationality. Arab Economic and Business Journal , 12(1), 44–56.

103.

Zhu

, & Deng

(2020). How to influence rural tourism intention by risk knowledge during COVID-19 containment in China: mediating role of risk perception and attitude. International Journal of Environmental Research and Public Health , 17(10), 3514.

Impact of Perceived Risk on Tourist Satisfaction and Future Travel Intentions: A Mediation–Moderation Analysis

Abstract

Keywords

Introduction

Literature Review and Research Questions

Perceived Risks and Tourist Behaviour

Tourist Satisfaction and Future Travel Intentions

Tourist Satisfaction as Mediator

Moderating Role of Demographic Variables on Behaviour

Study Objectives

Theoretical Framework

Methodology

Study Site

Study Constructs and Measures

Pilot Testing and Data Collection

Data Analysis

Analysis

Profile of Tourists

Tourists’ Profile.

Exploratory Factor Analysis

EFA Results.

Measurement Model

Reliability and Validity

CFA Results.

Discriminant validity.

Hypotheses Testing

Structural Model Results.

Moderator Effect.

Conclusion

Theoretical Implications

Managerial Implications

Limitations and Future Research

Footnotes

Acknowledgement

Declaration of Conflicting Interests

Funding

References