The Role of Emotions in Destination Visitation Intentions: A Cross-Cultural Perspective
White, Christopher J., Scandale, Steve, Journal of Hospitality and Tourism Management
The rote of emotions in consumer attitudes and behaviour has become the focus of much recent research, and no doubt this trend will continue as new findings bring benefits. Within the tourism literature, emotions or affect have been investigated in conjunction with destination image, and a number of studies in the area have provided a direction for the present study. A sample of Italian and American individuals were surveyed to determine the role emotions played in influencing destination visitation intentions, the relationship between emotions and the physical characteristics of a destination, and to determine whether emotions van/across nationalities. The results indicated that emotions were the strongest predictor of visitation intention, that the tourism attraction of a destination stimulate the strongest emotional responses and that there are only minor differences in the intensity of emotional responses between nationalities.
A number of recent studies have focused on the concept of image in relation to tourist destination management and marketing (Baloglu & McCleary, 1999; Baloglu & Brinberg, 1997; Leisen, 2001; Ahmed, 1996; Echmer & Ritchie, 1993; Javalgi, Thomas, & Rao, 1992; Gallarza, Saura, & Garcia, 2002). While there appears to be much uncertainty as to exactly what image is, recent work has attempted to clarify the relationship between it and other constructs, such as perceptions and attitudes, so as to determine where similarities and differences exist. Much of the work that has been done on tourism destination image appears to be focusing more on 'attitudes' rather than image, in that the operationalisation of the image construct has usually involved some or all of the elements contained in the well known tri-component model of attitudes. Examples of such elements include affective, cognitive/perceptual and behavioural components.
The tri-component model of attitudes has been popular in the psychology literature since the 1940s (Breckler, 1984). The thesis of this model is that attitudes are evaluative statements formed through the interaction of cognitive, affective and behavioural components, where the cognitive component represents the beliefs and knowledge one holds about an object or person, the affective component represents one's feeling or emotions towards an object (sad, happy and so on) and the behavioural component is how one reacts towards the object. Numerous studies have supported this conceptualisation (Bootzin, Bower, Crocker, & Hall, 1991); although until recently much work in the area of consumer research has focused on the cognitive component leaving the affective component comparatively less understood.
Research that has focused on the relationship between the affective and cognitive components, particularly as they relate to decision-making, seems to have converged at a similar position (Epstein, 1993; Zajonc, 1980); namely that when confronted with a decision task, two processes are likely to occur. The first is a relatively automatic affective reaction that may be positive or negative, and that also varies in strength. The second is thought to be more controlled and deliberate, thus requiring higher-order cognitive processing that may strengthen or weaken the position established from the affective reaction.
The implications of this model, in terms of tourism destination selection, is that individuals are likely to be influenced by the lower-order affective reactions when little information or processing resources are available; however, when information such as political unrest, the outbreak of disease or heavy competition among resorts is on hand, individuals will most probably be influenced by cognitions evolving from the higher-order processes: decisions at this level will occur relatively more slowly than the more impulsive reactions arising from lower-level processes.
This interplay between the affective and the cognitive processes is likely to vary according to the kind of decision; for example, a decision that involved choosing between two similar brands of automobile brake parts may be influenced by cognitive elements such as price, reputation of the manufacturer or location of the retailer. On the other hand, a holiday destination choice is likely to involve more affective input due to the hedonistic nature of a holiday experience.
The notion that these two components worked together to influence behaviour was further supported in a paper by Russell and Snodgrass in 1987 (cited in Baloglu & Brinberg, 1997), who posited that individuals have an affective disposition towards a place before they enter it, while they are in it and after they leave, and that behaviour may be influenced more by an affective state than physical or objective characteristics. Interestingly, recent studies in the customer satisfaction and loyalty literatures, which have operationalised the elements of the tri-component attitude model have found the affective component to be a better predictor of consumers' behavioural intentions (Liljander & Strandvik, 1997; Yu & Dean, 2001).
Further support for the role of affect in predicting purchase intentions was provided in a study that involved over 23,000 responses to 240 advertising messages, which indicated that affect was much stronger (accounting for more than twice the variance) in predicting individuals' behavioural intentions than cognitive responses were (Morris, Woo, Geason, & Kim, 2002)
The view that affective processes involve or somehow include emotions is rarely disputed, although the relationship between affect, emotions and mood is not particularly clear. For some scholars moods are just different kinds of emotions, whereas others use the terms interchangeably, and while identifiable boundaries between these concepts are hard to find, the following descriptions are intended to define each element for the purposes of focusing the subsequent discussion. Affect will be viewed as a general and primitive state (Frijda, 1993) that encompasses moods and emotions. Moods differ from emotions in that they are less intense, of longer duration, and are a less specific response to the environment. That is, a mood is a feeling state that has no apparent cause or focus (Frijda, 1993), and in this sense, emotions can be viewed as being easier to identify and therefore measure, and as a consequence of more interest to consumer behaviour researchers.
Beginning with the assumption that environmental stimuli impact on an individual's emotional state, which in turn elicits an approach or avoidance response, Mehrabian and Russell (1974), building on previous research (Morris et al., 2002), identified three emotional states that purportedly mediated the relationship between the environment and human behaviour. Their model consisted of three independent bipolar dimensions--pleasure, arousal and dominance--and while earlier evidence suggested that these dimensions define all emotional states (Mehrabian & Russel, 1974), other work has found that dominance is not significantly related to behaviour (Sweeney & Wyber, 2002).
Baloglu and Brinberg (1997) discussed and operationalised a model, similar to that of Mehrabian and Russell (1974), which was designed to measure the affective quality of an environment. This model was also based on a pleasure-arousing two-dimensional bipolar conceptualisation but included two additional scales of exciting-gloomy and relaxing-distressing that purportedly enhanced the overall reliability of the scale. Their empirical study confirmed the convergent validity of the model, in that it met a priori expectations, leading the authors to conclude that destinations have distinctive positive and negative affective associations that could be used for positioning purposes, and that future research could examine the strength of affective and cognitive components in predicting destination visitation intention. Additionally, the authors suggested that the relationship between the affective and cognitive attributes could be examined to determine 'what destination attributes make people feel that a specific destination is exciting, relaxing ...' (p. 15).
In a follow-up study that included one of the authors discussed above (Baloglu & McCleary, 1999) the same affective emotion scale was employed, along with a scale to capture respondents' cognitive assessments, and the focus of this work was to determine whether affective and cognitive processes were influenced by prior visitation. The findings indicated that there were differences in affective and cognitive states between those who had and had not visited the destination; however, because it was not a before and after research design, it cannot be certain that visitation actually influenced the results. Moreover, no information was provided in this study that could further support the reliability and validity of the affective scale.
Evidence suggests that an individual's affective processes play an important part in influencing purchase intention behaviours across different industries and markets and it is clear that tourism researchers have made significant progress towards understanding the relationship between an individual's affective state and tourist destinations, and the resultant marketing implications. The present study aims to further these understandings by, first, addressing Baloglu and Brinberg (1997) recommendations to clarify whether emotions are stronger predictors of desire to visit a destination than the cognitive assessments; second, by examining the strength and direction of the relationship between cognitive and emotion components; and finally, by determining whether emotional responses differ according to nationality.
The scale used in this study to capture the emotions component was based on that employed in similar studies (Baloglu & Brinberg, 1997; Baloglu & McCleary, 1999); however, instead of using a bipolar measure with pleasant being 1 and unpleasant 7, respondents were requested to indicate on a 7-point scale how strongly they felt about each emotion when thinking of Hong Kong as a tourism destination. Therefore, each of the eight emotions was measured separately to allow for a more specific and individual analysis of each emotion.
The cognitive scale was based on findings from other studies that identified attributes found to be influential in shaping individuals perceptions' of tourism destination image (Echtner, 1993; Javalgi et al., 1992; Leisen, 2001; Baloglu & McCleary, 1999). Respondents from America and Italy were requested to indicate on a 7-point scale the extent to which they agreed or disagreed with each attribute as it related to Hong Kong. Both scales were translated into Italian from English by a person fluent in both languages and then sent for verification to another bilingual person. Changes were made on the basis of this process and the questionnaires were then distributed.
The internal consistency of the various scales used in this study was determined by computing Cronbach's alpha. In order to establish the dimensionality of the scales a principal components analysis (PCA) with varimax rotation was used. This was chosen because the researchers believed that only a small proportion of specific and error variance would be represented in the total variance, and the aim was to determine the minimum number of factors needed to explain the maximum amount of variance within the data set (Hair, Anderson, Tatham, & Black, 1998). Strengths and directions of the relationship between variables were determined by stepwise regression, which was chosen because this procedure adds the independent variables one at a time and excludes any that do not contribute reliably to the regression equation.
A nonprobability sampling procedure was employed and this was dictated by the time constraints on the project completion. A total of 400 questionnaires were sent to contacts known to one of the authors in the US and Italy, who then distributed them among work colleagues and associates. The characteristics of the sample may be viewed in Table 1. The convenience sampling technique and the method of data collection used in this study will limit the representativeness and generalisability of the findings; however, given that research in this area is still very much in the exploratory stage this work should be viewed as a path-clearing exercise that will hopefully contribute to clarifying existing knowledge and providing directions for future research.
Before proceeding with a presentation of the results a number of assumptions that underlie the application of the statistical procedures employed in this study were tested and the following discussion outlines the results of this screening process. The assumptions were sample size, outlying cases, factorability of the correlation matrix, linearity and normality (Coakes & Steed, 1997; Kinnear & Gray, 1997).
First, the sample size of 182 and 166 for the American and Italian samples respectively was deemed acceptable by exceeding the suggested five subjects per variable for both scales, and over 100 subjects in total (Coakes & Steed, 1997). Second, an examination of box-plot graphs generated by the SPSS application (these have not been included in this paper because of the considerable number required) detected no outlying cases within the data. Furthermore, the factorability of the correlation matrix was assumed because it contained a significant number of correlations in excess of 0.3, and because Bartlett's test of sphericity was large and significant and the Kaiser-Meyer-Olkin measure was greater than .6 in both cases.
Skewness and kurtosis statistics indicated that no extreme values were present in the data set, and scatter plots of the variables provided no evidence of a curvilinear effect between any combination of variables. Therefore the assumptions of linearity and normality have not been violated. Moreover, the alpha coefficient for the emotion and cognitive scales of .879 and .856 respectively suggest good internal consistency between the items, and comfortably exceed the .70 suggested by Nunnally (1967). As both the Italian and American responses were included in the computation of the alpha, it can be assumed that the translation process did not interfere with the reliability of the scales.
Findings and Discussion
In order to make the data analysis more manageable a PCA was used to identify underlying dimensions of both scales for each nationality, and these results for the cognitive scale can be found in Tables 2 and 3. An examination of scree plots suggested that a three-factor solution was appropriate for both samples, and these explained 56.7% of the overall variance for the American sample and 46.8% of the variance in the Italian sample. The first component for the American sample was labelled tourist attractions, and accounted for 22.1% of the total variance, the second facilities (18.1%) and the third access (17.3).
The components that formed the Italian solution are quite similar in composition but not in terms of the amount of variance explained by each. The first component for this sample was facilities, accounting for 22.1% of the variance, the second was tourist attractions (13.2%) and the third access (11.5%).
A PCA was performed on the data pertaining to the emotions scale and both nationalities, and a two-component positive and negative solution resulted. Because of this, a PCA that included data from both nationalities was performed and Table 4 displays the rotated component matrix output. It is evident that a clean two-factor solution was obtained that explained 65.2% of the total scale variance. Factor one has been labelled positive emotions, and this accounted for 33.2% of the variance, and factor 2 negative emotions (32.6%).
As the main focus of this paper relates to emotions, little time will be devoted to the interpretation of the cognitive scale. Suffice to say that a reliable three-factor solution produced similar results for both nationalities. For the US and Italian sample, the emotion scale formed two dimensions that resembled the distinct positive and negative conceptualisation produced in other studies that have used this particular instrument. This finding, while offering nothing new, is still important for researchers in that it continues to confirm the stability of the positive/negative conceptualisation of the emotions construct, and provides further support for the cohesiveness of the items as indicated by the alpha coefficient. Researchers that are interested in pursuing the role of emotions as they relate to tourism destinations should feel confident about adopting this scale for their study.
In order to determine the strengths of each component in predicting visitation intentions, stepwise regressions were performed for both nationalities. The relative strengths of each variable in predicting the dependent variable was determined by examining the standardised beta weights. These indicate that the number of standard deviations change on the dependent variable produced by a change of one standard deviation on the independent variables concerned. Table 6 displays the results for the American sample and it is evident that the best predictor of visitations intentions was positive emotions (.497) followed by negative emotions (-.353), and then to a lesser, albeit a significant extent, the cognitive access (.137) and facilities (.092) components. The negative sign implies that for every increment of negative emotions, visitation intention declines by 0.35.
Similar analysis was conducted for the Italian sample and Table 7 displays these results. It is evident that in this instance three components were entered into the equation and again, positive emotions were the strongest predictor of visitation intention (.491) followed by negative emotions (-.348) and the cognitive facilities component (.127).
For the first time in a tourism context, Tables 6 and 7 provide substantial support for the view that emotions are stronger predictors of a desire to visit a destination than their cognitive counterparts, and moreover there appears to be consensus across nationalities. This finding raises some very interesting possibilities for destination planners and marketers who are concerned with consumer travel intentions. For example, lengthy questionnaires that capture physical characteristics of a destination could be replaced with a more economical scale consisting of eight emotions that would reduce the possibility of respondent fatigue, encourage more responses and reduce data input time.
Intuitively, this finding makes sense, in that stronger positive feelings towards a destination are likely to stimulate a positive intention to visit while negative feelings will have an adverse impact. Thus the way one feels about a destination will affect their visitation intentions and these findings converge with a number of other studies discussed above that have postulated upon and investigated the emotions/behaviour relationship across various industries (Baloglu & Brinberg, 1997; Liljander & Strandvik, 1997; Mehrabian & Russell, 1974; Yu & Dean, 2001).
It should also be recognised that some cognitive components played a small but significant role in influencing visitation intentions and that these components differed according to nationality. For example, access variables, such as similar lifestyles, no language barriers and safety, are important to Americans, while for Italians the quality of hotel facilities and service and shopping opportunities have a higher priority. On the surface this finding suggests that destinations should focus on developing all aspects of the holiday experience in order to appeal to a range of cultural preferences, and design communication strategies that conspicuously promote destination characteristics that are significantly related to visitation intentions in different cultural contexts.
In order to examine the relationship between emotions and cognitive components stepwise regression was again used. Given that positive emotions were the strongest predictor of visitations intentions, this variable was chosen as the dependent variable and the predictors were the three cognitive components. Table 7 displays the results for the US sample and it is evident that the best predictor of positive emotions was the attraction component with a beta value of .600, followed by facilities (.386) and access (.150)
Table 8 displays the results of the same analysis for the Italian sample and a similar pattern has emerged, where the attraction component was the strongest predictor, followed by facilities and access. Understanding the relationship between destination attributes and positive emotions is vitally important for destination marketers and planners, particularly as positive emotions have been recognised as effective predictors of destination visitation intentions. The message from these findings is that for Americans, and to a lesser extent Italians, attraction variables, such as uniqueness, adventure and interesting places explain most of the variance in positive emotions.
In summarising these findings, it has been established that emotions are stronger predictors of visitation intentions than cognitive assessments for both nationalities. Some cognitive components did, however, make a unique and significant contribution to explaining the variance in visitation intentions and these differed according to nationality. When the three cognitive components were regressed against the positive emotion variable, all three were significant and the attraction component accounted for the most variance. This particular component did not make a unique and significant contribution to the prediction of visitation intentions and Figure 1 displays this information diagrammatically.
From a cross-cultural perspective the differences between the nationalities occur at the middle section of Figure 1 and relate to cognitive components, which are largely overshadowed by the emotion components, in terms of explaining the variance in visitations intentions. The influence of these variables should be carefully monitored, however, particularly as the consumer moves towards the actual point of purchase, when extraneous variables such as price or vacation duration may diminish the intensity of emotional responses as higher-order cognitive processing occurs.
The fact that there is a strong relationship between attraction variables and positive emotions is not surprising, given that interesting places, beauty, adventure and uniqueness can be arousing and exciting. The attraction component could be viewed, for pleasure travellers, as the core benefit of a vacation experience, while the other more functional components, access and facilities, are important yet peripheral in terms of explaining the variance in positive emotions. If one were to extract the components that were both strongly related and common to both nationalities and model the relationship between them, it would appear to be linear, and resemble that displayed in Figure 2.
From a theoretical perspective, this linear relationship questions the tri-component attitude model described above, in that the attraction component, while distinct, does not correlate with visitation intentions. This suggests that a one-dimensional conceptualisation of the emotion/cognitive/behaviour relationship may be more appropriate. From a practitioner's perspective, this relationship suggests that promotional communication strategies should embellish attraction attributes, as these are more likely to induce positive emotions, which in turn are more likely to influence visitation intentions.
This study has provided some insights into the role of emotions in influencing destination visitation intentions, and the interaction between emotions and cognitive destination attributes. Both scales used in this study have demonstrated good reliability, and content and convergent validity was also established for the emotion scale, in that the component structure obtained in this study was in line with previous research findings. Moreover, the realisation that emotion components are more powerful predictors of visitation intentions than traditional cognitive attributes, and that they are stable across nationalities, opens up interesting possibilities for researchers and practitioners.
One of the most significant findings of this study is the identification of a linear relationship between a core cognitive component, positive emotions and visitation intentions and while the emotion/ intentions link is important, the realisation that certain cognitive factors play a significant role in influencing emotions emphasises the importance of understanding the emotion/cognitive relationship.
In terms of future research, it would be interesting to determine whether similar patterns of emotions and cognitions occur within a broader range of nationalities and even different market segments, such as group leisure travel and conventions and incentives. Moreover, monitoring the relationship between affective and cognitive components before, during and after visitation could provide important insights into the way these two components interact throughout the entire experience.
It should again be mentioned that this present study requested participants to respond to a hypothetical situation, and that cognitive processes may become more influential when a decision needs to be made and factors such as price and special benefit offers congest the decision process. Finally, controlling for 'mood' effects would also assist in understanding the role of this particular variable in influencing the emotion, cognitive and behavioural intentions relationship.
The authors would like to thank the Glion Centre for Cross-Cultural Hospitality Management Research for assisting in the development of this article.
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Christopher J. White, Coordinator Hospitality & Tourism, Higher Education, Charles Darwin University, University Avenue, Casuarina, Northern Territory 0909, Australia. E-mail: Christopher.firstname.lastname@example.org
Table 1 Demographic Characteristics of the Sample Gender Total Nationality male female American Age 13-25 58 78 136 26-35 8 13 21 36+ 13 12 25 Total 79 103 182 Italian Age 13-25 18 13 31 26-35 44 42 86 36+ 29 20 49 Total 91 75 166 Table 2 American Sample Cognitive Components: Rotated Component Matrix Component 1 2 3 Wonderful cultural traditions .793 Scenic beauty .780 Unique travel destination .778 Many interesting places to visit .720 Pleasant climate .646 Adventurous atmosphere .639 Availability of tourist information .460 Plenty of quality hotels .857 Good room quality .853 Good hotel staff service quality .846 Good recreational and health facilities .545 Good entertainment facilities .446 .492 No language barrier .794 Reasonable distance and travel time .793 Similar lifestyles .754 People are friendly, polite and helpful .480 .510 Safe place .487 Shopping paradise .469 Easy online shopping .444 Easy to get travel visa .438 Note: Extraction method: Principal Component Analysis. Rotation method: Varimax with Kaiser normalisation. Rotation converged in five iterations. Table 3 Italian Sample Cognitive Components: Rotated Component Matrix Component 1 2 3 Plenty of quality hotels .782 Good entertainment facilities .770 Good hotel staff service quality .767 Shopping paradise .761 Good room quality .739 Good recreational and health facilities .664 Easy online shopping .610 Scenic beauty .792 Many interesting places to visit .621 Pleasant climate .591 Adventurous atmosphere .555 People are friendly, polite and helpful .486 Wonderful cultural traditions .414 Reasonable distance and travel time .668 Safe place .571 Availability of tourist information .403 .523 Easy to get travel visa .506 Similar lifestyles .499 No language barrier .421 Unique travel destination .416 Note: Extraction method: Principal Component Analysis. Rotation method: Varimax with Kaiser normalisation. Rotation converged in seven iterations. Table 4 Emotion Scale Components for Combined US and Italian Sample 1 2 Gloomy .849 Unpleasant .838 Sleepy .790 Distressing .600 Exciting .847 Arousing .810 Pleasant .752 Relaxing .555 Note: Extraction method: Principal Component Analysis. Rotation method: Varimax with Kaiser normalisation. Table 5 Stepwise Regression Output for the Cognitive and Affective Component Scores (US) Standardised t Sig. coefficients Model SE Beta (Constant) .058 52.196 .000 Positive emotions .058 .497 11.383 .000 Negative emotions .058 -.353 -8.262 .000 US access component .062 .137 3.236 .001 Note: Dependent variable. I would like to go to Hong Kong for a holiday. Adjusted [R.sup.2] .446. Table 6 Stepwise Regression Results for the Cognitive and Affective Component (Italian) Standardised t Sig. coefficients Model SE Beta (Constant) .058 53.648 .000 Positive emotions .061 .491 10.805 .000 Negative emotions .059 -.348 -8.027 .000 Italian facilities component .070 .127 2.738 .007 Note: Dependent variable: I would like to go to Hong Kong for a holiday. Adjusted [R.sup.2] .434. Table 7 The Relationship Between Positive Emotions and Cognitive Components (US) Standardised coefficients t Sig. Model 3 SE Beta (Constant) .047 2.093 .037 US attraction component .037 .600 13.303 .000 US facilities component .037 .386 8.591 .000 US access component .048 .150 3.411 .001 Note: Dependent variable: Positive emotions. Adjusted [R.sup.2] .402. Table 8 The Relationship Between Positive Emotions and Cognitive Components (Italian) Standardised coefficients t Sig. Model SE Beta (Constant) .045 -1.601 .110 Italian attraction component .043 .517 12.055 .000 Italian facilities component .049 .294 6.832 .000 Italian access component .041 .182 4.229 .000 Note: Dependent variable. Positive emotions. Adjusted [R.sup.2] .425.…
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Publication information: Article title: The Role of Emotions in Destination Visitation Intentions: A Cross-Cultural Perspective. Contributors: White, Christopher J. - Author, Scandale, Steve - Author. Journal title: Journal of Hospitality and Tourism Management. Volume: 12. Issue: 2 Publication date: August 2005. Page number: 168+. © 2008 Australian Academic Press Pty. Ltd. COPYRIGHT 2005 Gale Group.