emotions, gender and destination visitation intentions - CiteSeerX

EMOTIONS, GENDER AND DESTINATION VISITATION INTENTIONS

Christopher J White Glion Institute of Higher Education Rue de l' Ondine 20 1630 Bulle Switzerland Tel: +41 26 919 7838 Fax: +41 26 919 7839 [email protected]

Abstract The role emotions play in influencing consumer attitudes and behavior has become the focus of much recent research and no doubt this trend will continue as new findings reinforce the potential benefits of such a focus. Within the tourism literature, emotions or affect has been investigated in conjunction with destination image and a number of studies relating to this area have provided a direction for this present study. A sample of males and females were surveyed to determine the role emotions played in influencing their destination visitation intentions, and the relationship between their emotions and the physical characteristics of a destination. The results indicated that emotions are the strongest predictor of visitation intention for both gender and that different components of destination attributes influence both male and female feelings towards the destination.

Biography (Corresponding author) Christopher White is a Professor in Psycho-Sociology in the Hospitality Industry and Human Resource Management at Glion Institute of Higher Education, Switzerland. His research interests focus on the role of emotions in customer and employee satisfaction and loyalty, and the interaction between values and attitudes in cross cultural contexts.

Literature Review A number of recent studies have focused on the concept of image in relation to tourist destination (Baloglu & McCleary, 1999; Baloglu & Brinberg, 1997; Leisen, 2001 Ahmed, 1996; Echtner & Ritchie, 1993; Javalgi, Thomas & Rao, 1992; Gallarza, Saura, & Garcia, 2002) management and marketing and 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 the 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. Such elements include affective, cognitive / perceptual and a behavioral components 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). That being, 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 process 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 out break 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 slower than the more impulsive reactions arising from lower level processes. The 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 manufacture or the 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 behaviors 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 of a place before they enter it, while they are in it, and after they leave, and that behavior may be influenced more by an affective state than physical or objective characteristics. Interestingly, recent studies in the customer satisfaction and loyalty literatures, that have operationalised the tri-component attitude model, have found the affective component to be a better predictor of consumers’ behavioral intentions (Liljander & Strandvik, 1997; Yu & Dean, 2001). Further support for the role of affect in predicting purchasing intentions was provided in a study that involved over 23,000 responses to 240 advertising messages and the results indicated that affect was much stronger (accounting for more than twice the variance) in predicting individuals behavioral intentions than cognitive responses (Morris, Woo, Geason, & Kim, 2002). The awareness that affective processes involved or somehow included emotions is rarely disputed although the relationship between affect, emotions and mood are not that clear. For some scholar’s moods are just different kinds of emotions and 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 researchers. Starting with the assumption that environmental stimuli impact on an individual’s emotional state, which in turn elicit an approach or avoidance response, Mehrabian & Russell (1974), building on previous research (Morris et al., 2002), identified three emotional states that purportedly mediated the relationship between the environment and human behavior. Their model consisted of three independent bipolar dimensions, namely, pleasure, arousal and dominance and while earlier evidence suggested that these dimensions define all emotional states (Mehrabian & Russell 1974), more recent work has questioned the role of the dominance factor. Baloglu & Brinberg (1997) on the other hand, discussed and operationalize a model, similar to that of Mehrabian & Russell (1974), which was designed to measure the affective quality of an environment. This model was also based on a pleasant arousing two-dimensional bipolar conceptualization but included two additional scales of exciting – gloomy and relaxing – distressing, that purportedly enhanced the overall scale reliability. Their empirical study confirmed the convergent validity of the model, in that, it met a priori expectations, leading the authors to concluded 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. Evidence suggests that an individual’s affective processes are important influencers of purchase intention behaviors across different industries and markets, and it is clear that tourism researchers have made significant inroads in attempting to understand the relationship between an individual’s affective state and tourist destinations in general. This present study aims to further these understandings by firstly addressing, from a gender perspective, Baloglu & Brinberg (1997) recommendation’s: to clarify whether the affective component is a stronger predictor of desire to visit a destination than the cognitive component, and secondly, to determine whether the relationship between cognitive and affective emotion components differs according to gender.

Method 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 were 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 that were found to be influential in shaping individuals perceptions of tourism destination image. Respondents were requested to indicate on a 7-point scale the extent they agreed or disagreed with each attribute as it related to Hong Kong. The internal consistency of the various scales used in this study was determined by computing Cronbach’s coefficient alpha. In order to establish the dimensionality of the scales a principle components analysis with variamax rotation was used and this was chosen because the researcher believes that only a small proportion of specific and error variance will be represented in the total variance, and the aim of this exercise is to determine the minimum number factors needed to explain the maximum amount of variance within the data set. Linear regression will be used to determine the predictive ability of both cognitive and emotional scales A non-probability sampling procedure was employed and this was dictated by the time constraints on the project completion. A total of 400 questionnaires were distributed in the US and Italy, and the characteristics of the sample can be viewed in Table 1. The convenient nature of the sampling design and data collection methods will limit the representativness 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 on the topic and provide directions for future research.

Table 1: Demographic characteristics of sample

GENDER Male Female Total

AGE 13-25 76 91 167

Total 26-35 52 55 107

36+ 42 32 74

170 178 348

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). Firstly the sample size of 170 and 178 for the male and female samples was deemed acceptable by exceeding the suggested 5 subjects per variable for both scales, and over 100 subjects in total (Coakes & Steed, 1997). Secondly, an examination of boxplot graphs generated by the SPSS application (these have not been attached 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 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 combinations 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 comfortable exceed the .70 suggested by Nunnally (1967).

Results Table 2: Rotated component solution for cognitive scale

Comfort Attractions Facilities Good Room Quality .877 Good Hotel Staff Service Quality .863 Plenty of Quality Hotels .836 Availability of Tourist Information .459 People are Friendly, Polite, and Helpful .452 Scenic Beauty .818 Pleasant Climate .706 Unique travel Destination .655 Many Interesting Places to Visit .649 Adventurous Atmosphere .616 Wonderful Cultural Traditions .410 Easy Online Shopping .810 Shopping Paradise .781 Good Entertainment Facilities .640 Good Recreational and Health Facilities .580 Reasonable Distance and Travel Time Similar Lifestyles No Language Barrier Safe Place Easy to get Travel Visa Extraction Method: Principal Component Analysis. Rotation Method: Varimax with Kaiser Normalization.

Access

.743 .664 .647 .485 .430

Table 2 displays the results of a principle components analysis in which four components explained a total of 56.61% of the overall scale variance. The four components labeled comfort, attractions, facilities and access contributed 31.15%, 10.95%, 8.43% and 6.07% respectively. Separate analysis was conducted for both male and female respondents and because of the similarities between the two analyses a combined male / female solution was generated. A similar strategy was deemed appropriate for the emotions scale reported in Table 3. The component scores from both analyses will be used to achieve the objectives of this study. Table 3: Rotated component solution for the emotions scale Negative emotions 1 .849 .838 .790 .600

Positive emotions

2 Gloomy Unpleasant Sleepy Distressing Exciting .847 Arousing .810 Pleasant .752 Relaxing .555 Extraction Method: Principal Component Analysis. Rotation Method: Varimax with Kaiser Normalization.

Table 2 displays the results of a second components analysis with two components, negative emotions and positive emotions accounting for 64.0% of the total scale variance. Individually they contributed 47.55% and 16.40% respectively. Table 4: Linear regression for the cognitive and emotion components in predicting visitation intentions. GENDER Model R R Square 1 .707 .500 1 .654 .428

Male Female

Adjusted R Square .479 .406

Std. Error of the Estimate .9582 1.0241

Predictors: (Constant), Access, Comfort, Attraction, Facilities, Negative emotions, Positive emotions

GENDER Model 1 Regression Residual Total 1 Regression Female Residual Total

Male

Sum of Squares 131.972 132.213 264.185 123.251 164.651 287.902

df Mean Square 6 21.995 144 .918 150 6 20.542 157 1.049 163

F Sig. 23.956 .000

19.587 .000

Dependent Variable: I would like to go to Hong Kong for a holiday

GENDER Model 1 Male

Female

1

(Constant) Negative emotions Positive emotions Comfort Attraction Facilities Access (Constant) Negative emotions Positive emotions Comfort Attraction Facilities Access

Unstandardized Coefficients B Std. Error 3.116 .080 -.487 .085 .694 .105 .145 .087 -.105 .097 1.275E-02 .079 .184 .076 3.056 .081 -.561 .092 .671 .115 5.606E-03 .095 -1.592E-02 .107 .251 .089 2.348E-02 .088

Standardized Coefficients Beta -.372 .510 .114 -.077 .010 .146 -.394 .508 .004 -.012 .179 .016

t

Sig.

38.760 -5.753 6.608 1.670 -1.077 .161 2.422 37.636 -6.087 5.842 .059 -.149 2.840 .267

.000 .000 .000 .097 .283 .872 .017 .000 .000 .000 .953 .881 .005 .789

Table 4: Linear regression for the cognitive scale components in predicting visitation intentions GENDER Model R R Square Adjusted R Square Std. Error of the Estimate 1 .472 .223 .204 1.1666 1 .496 .246 .228 1.1766

Male Female

GENDER Model Sum of Squares 1 Regression 64.728 Residual 225.903 Total 290.632 1 Regression 77.046 Female Residual 236.749 Total 313.795

Male

df Mean Square F Sig. 4 16.182 11.891 .000 166 1.361 170 4 19.262 13.912 .000 171 1.384 175

Predictors: (Constant), Access, Facilities, Attraction and Comfort Dependent Variable: I Would like to go to Hong Kong for a holiday

The results displayed in Table 3 indicate that the linear combination of the cognitive and emotion components were significantly related to visitation intentions for both male and female respondents, and together explained 50.0% and 42.8% of the variance in the dependent variable. The significant standardized beta values (these give the number of standard deviations change on the dependent variable, that have been produced by a change of one standard deviation on the independent variables) suggest that positive emotions are the best predictor of visitation intentions for both gender, followed by the negatively signed negative emotions. The negative sign implies that for every increment of negative emotions, visitation intentions declines by 0.37 for males and 0.39 for females. Intuitively, these 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 investigate the emotions / behavior relationship across various industries (Baloglu & Brinberg, 1997; Mehrabian & Russell 1974; Liljander & Strandvik, 1997; Yu & Dean, 2001). In order to substantiate the finding that the inclusion of an emotions scale improves the predictive power of an instrument, a separate regression was performed with visitation intentions as the dependent variable and the four cognitive components as predictors, and these results can be viewed in Table 4. It is clear that the considerably higher R-squared values for the combined model attest to the importance of using an emotions and a cognitive scale to fully explain an individuals visitation intentions. It is also interesting to note that the significant cognitive components differed according to gender, with access variables being more influential for males, and facility variables more important to females in predicting visitations intentions. This finding indicates that marketers need to consider gender differences when devising communication strategies, and the following analyses examines in more detail the relationship between specific cognitive destination attributes and emotions.

Table 5: Regression results for cognitive components in predicting positive emotions.

GENDER Model R R Square 1 .584 .341 1 .709 .502

Male Female


Male


Sum of Squares 53.751 104.037 157.788 89.425 88.703 178.129

Std. Error of the Estimate .8063695 .7223464

df Mean Square 4 13.438 160 .650 164 4 22.356 170 .522 174

F Sig. 20.666 .000

42.846 .000

Predictors: (Constant), Access, Facilities, Attraction and Comfort Dependent Variable: Positive emotions

GENDER Model 1 Male

Female

1

(Constant) Comfort Attraction Facilities Access (Constant) Comfort Attraction Facilities Access

Un-standardized Coefficients B -2.641 .415 .283 7.787E-02 5.105E-03 -3.381 .145 .697 .175 -7.274E-02

Std. Error .333 .097 .062 .100 .108 .291 .097 .075 .094 .109

Standardized Coefficients Beta .343 .319 .068 .004 .111 .587 .146 -.048

t

Sig.

-7.942 4.299 4.549 .776 .047 -11.629 1.491 9.313 1.870 -.670

.000 .000 .000 .439 .962 .000 .138 .000 .063 .504

Again, the relative strengths of the four cognitive components in predicting positive emotions has been determined by examining the standardized beta weights. Table 5 displays the results of a linear regression where the four cognitive components were regressed against the dependent variable, positive emotions. The standardized beta values indicate that the variables comfort and attraction make a unique and significant contribution to positive emotions for the males, while for females, the attraction component stands alone. The relationship between destination attributes and positive emotions is vitally important to destination marketers and planners particularly as positive emotions have been recognized as strong predictors of destination visitation intentions. The message from these findings is that, for females, and to a lesser extent males, attraction variables, such as uniqueness, adventure and interesting places need to be

conspicuously emphasized and reinforced through communication strategies. Moreover, for males, attributes related to hotel service quality and other comfort variables should also be considered.

Table 6: Regression results for cognitive components in predicting negative emotions. GENDER Model R R Square 1 .344 .118 1 .238 .057

Male Female


Male


Sum of Squares 20.757 154.559 175.316 9.082 150.774 159.856

Std. Error of the Estimate .9828513 .9417580

df Mean Square 4 5.189 160 .966 164 4 2.270 170 .887 174

F Sig. 5.372 .000

2.560 .040

Predictors: (Constant), Access, Facilities, Attraction and Comfort Dependent Variable: Negative emotions

GENDER Model 1 Male

Female

1

(Constant) Comfort Attraction Facilities Access (Constant) Comfort Attraction Facilities Access

Un-standardized Coefficients B 1.517 -.230 -9.172E-02 -.245 .206 .678 -.369 2.963E-02 .102 2.056E-02

Std. Error .405 .118 .076 .122 .132 .379 .127 .098 .122 .142

Standardized Coefficients Beta -.180 -.098 -.204 .137 -.298 .026 .090 .014

t

Sig.

3.742 -1.957 -1.211 -2.005 1.566 1.789 -2.912 .303 .834 .145

.000 .052 .228 .047 .119 .075 .004 .762 .405 .885

Table 6 displays the relative strengths of the four cognitive components in predicting negative emotions and it is evident the variable facilities for the male sample and comfort for the females both make a unique and significant contribution to the dependent variable. These findings imply that for every increment of facilities and comfort, negative emotions decline by .20 and .29 respectively. The low R-squared values, while significant at the 95% confidence level, suggests that this model does not explain much of the variance in negative emotions and this should be considered before attempting to interpret these results. From these findings, it appears that the relationship between various destination attributes for males and females and positive and negative emotions are different; intuitively, one may have expected that the attraction component would have had a significant negative affect on the dependent

variable for females’ as it was, for them, a strong and significant predictor of positive emotions. On the basis of what is presented here negative emotions can be reduced for women and men by emphasizing comfort and facility attributes respectively.

Conclusion This study has provided some interesting 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 has also been established for the emotion scale, in that the component structure obtained in this study was in line with previous research findings. Researchers that are interested in pursuing the role of emotions as they relate to tourism destinations should feel quite confident in adopting this particular scale for their study. The importance of capturing an emotional component has been confirmed here and while the emotional differences between male and female respondents related to variations in intensity rather than major shifts in direction, important differences were noted in the way that cognitive attributes interacted with positive and negative emotions. Understanding these differences can assist destination marketers in maximizing the returns on promotional expenses, and from a destination management perspective, these attributes can be developed to enhance positive emotional states. Future research could determine whether emotions are influenced by other variables such as age or nationality, and it would be also interesting to observe the stability of emotions from the initial vacation idea through to the actual purchase. The role of emotions in predicting or influencing satisfaction / dissatisfaction evaluations of a destination would provide another interesting research possibility.

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