Personality: Katherine

Of the 244 participants, 240 fully completed the section of the survey regarding the venturesomeness concept; however, as these only represented 1.6%, the missing data was replaced with the mean. Two analysis techniques were used to segment the participants by personality traits. First, a PCA analysis was conducted to assess the number of factors that the scale was measuring, and then a cluster analysis was used to segment the visitors. Prior to performing the PCA, the suitability of the data for this type of analysis was assessed. Inspection of the correlation matrix revealed the presence of many coefficients of .3 and above. The Kaiser- Meyer-Olkin value was .76, exceeding the recommended value of .6 (Kaiser 1970, 1974 as cited in Pallant, 2011) and Bartlett’s Test of Sphericity (Bartlett, 1954 as cited in Pallant, 2011) reached statistical significance, supporting the factorability of the correlation matrix.

The PCA revealed the presence of three components with eigenvalues exceeding 1, explaining 30.1%, 13.1% and 11.1% of the variance respectively. An inspection of the scree plot revealed a clear break at the second component. It was decided to retain two components for further investigation. The two-component solution explained a total of 43.2% of the variance, with Component 1 contributing 30.1% and Component 2 contributing 13.1%. Oblimin rotation was performed to assist in the interpretation of the components. Both components showed a number of strong loadings and all items (except one) loading substantially on only one. The item that did not load substantially on any of the components was expectation of services, which loaded .397 on Component 1 and .309 on Component 2. This item was removed from the analysis. The results show a weak correlation between the two factors (r=292). Therefore, the results of the analysis argue the use of the Weaver’s (2012) scale as an instrument to measure only one personality trait. It appears that there are two components within the scale (adventure and mental stimulation). Based on these results, two-cluster analysis procedures were conducted to segment the visitors based on the two personality traits identified. It is necessary to be aware that the terms “venturer”, “near-venturer” “centric” and “dependable” which are used to describe the different groups within the “venturesomeness” personality trait are used within the “adventure” trait as this part of the trait is the closest one related to the concept of “venturesomeness”. See the pattern and structure matrix for PCA with oblimin rotation of two-factor solution of the venturesomeness items in Table 5-3.

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Table 5-3 Pattern and Structure Matrix for PCA of the venturesomeness items - Katherine

Pattern Matrix Structure Matrix

Item Component 1 Component 2 Component 1 Component 2

1. I am willing to inconvenience myself physically to see something that interests me when I travel

.728 -.049 .712 .189

2. When I travel, I tend to be open to unplanned

or spontaneous experiences .604 .160 .657 .358

3. I often travel to out-of-the-way places to

observe rare or unusual attractions .771 -.008 .768 .244 4. It is important to me to learn as much as

possible about the places I visit .052 .627 .258 .645

5. I like to be physically active when I travel .678 .064 .698 .285 6. I prefer to make all of my travel arrangements

myself -.043 .622 .161 .608

7. Mental stimulation is an important reason why I

travel -.055 .740 .187 .722

8. I prefer to visit places that I have never visited before

.065 .545 .243 .566

9. I don’t expect a lot of services when I travel .331 .201 .397 .309 10. I like to experience an element of risk when I

travel .698 -.159 .646 .069

Table 5-4 (on the following page) shows the results of the cluster analysis for the “adventure” trait. The two- cluster solution differentiated only between the venturer and centric dimensions, while the four-cluster solution divided the centric cluster into two similar clusters. Hence, a three-cluster solution was accepted that allocated the sample to statistically well-differentiated groups. A one-way between-groups analysis of variance was conducted to explore whether the cluster groups were significantly different from each other. The results suggest there was a statistically significant difference at the p < .001 level on the items’ scores for the three groups: (1) willing to inconvenience myself F (2, 244) = 62.390 p < .001; (2) open to unplanned or spontaneous experiences F (2, 244) = 61.324 p < .001; (3) travel to out-of-the-way places F (2, 244) = 147.641 p < .001; (4) physically active F (2, 244) = 42.833 p < .001; and (5) element of risk F (2, 244) = 50.281 p < .001. Post hoc comparisons using the Tukey HSD test indicated that the mean score for all groups differed significantly from each other on all the items.

From Table 5-4, it is clear that of the 244 participants, 43 (18%) were allocated to the venturer cluster, 128 (52%) to the centric and only 73 (29%) to the dependable.

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Table 5-4 Overall and cluster means on the adventure trait included in cluster analysis - Katherine

Item* Overall n = 244 Venturer n = 43 Centric n = 128 Dependable n = 73 Gap**

1. I am willing to inconvenience myself physically

to see something that interests me when I travel 4.05 4.84 4.19 3.33 (1.51) 2. When I travel, I tend to be open to unplanned

or spontaneous experiences 4.26 4.95 4.34 3.73 (1.22)

3. I often travel to out-of-the-way places to

observe rare or unusual attractions 3.84 4.93 4.03 2.85 (2.08)

5. I like to be physically active when I travel 4.03 4.60 4.13 3.52 (1.08) 10. I like to experience an element of risk when I

travel 3.05 4.14 3.02 2.47 (1.67)

*Item number and description as administered in the survey **Difference in mean between the venturer and dependable clusters

Cronbach alpha = .734. If any of the items were removed from the scale, the Cronbach alpha would decrease. A one-way between-groups analysis of variance suggests that the mean of the various items is significantly different from each other.

Table 5-5 below shows the results of the cluster analysis for the “mental stimulation” trait. The three-cluster solution was accepted that allocated the sample to statistically well-differentiated groups. The two-cluster and the four-cluster solutions were not accepted. The two-cluster solution was rejected as it differentiated only between the high mental stimulation and medium mental stimulation dimensions. The four-cluster solution was rejected as the post hoc comparisons using the Tukey HSD test indicated that the mean score for all groups did not differ significantly from each other on the four items. The results for the three-cluster solution suggest there was a statistically significant difference at the p < .001 level on the items’ scores for the three groups: (1) learn as much as possible about the places I visit F (2, 244) = 26.768 p < .001; (2) prefer to make all of my travel arrangements myself F (2, 244) = 82.855 p < .001; (3) mental stimulation is an important reason why I travel F (2, 244) = 59.536 p < .001; and (4) prefer to visit places I have never visited F (2, 244) = 43.487 p < .001. Despite reaching statistical significance, post hoc comparisons using the Turkey HSD test indicates that the mean score for some groups on certain items did not differ significantly from each other. For “learning” and “mental stimulation” the mean score for the high and medium clusters did not differ significantly. Table 5-5 illustrates that of the 244 participants, 116 (48%) were allocated to the high mental stimulation cluster; 66 (27%) to the medium mental stimulation cluster and 62 (25%) to the low mental stimulation cluster.

Table 5-5 Overall and cluster means on the mental stimulation trait included in cluster analysis - Katherine

Item* Overall

n = 244 n = 116 High Medium n = 66 n = 62 Low Gap**

4. It is important to me to learn as much as

possible about the places I visit 3.86 4.08 3.98 3.34 (0.84)

6. I prefer to make all of my travel arrangements myself

3.91 4.50 3.09 3.68 (0.82)

7. Mental stimulation is an important reason why I travel

3.77 4.03 4.02 3.00 (1.03)

8. I prefer to visit places that I have never visited

before 4.16 4.57 3.58 4.00 (0.57)

*Item number and description as administered in the survey **Difference in mean between the venturer and dependable clusters

Cronbach alpha = .521. If any of the items were removed from the scale, the Cronbach alpha would decrease. A one-way between-groups analysis of variance suggests that the mean of the various items is significantly different from each other.

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Table 5-6 (on the following page) shows the relationship between demographic characteristics and the two personality traits (adventure and mental stimulation). The results show a strong relationship between the “adventure” trait and age, household status, travelling party and estimated expenditure in tourism activities. It appears that venturers are more likely to be within the younger groups, single, travelling alone, and planning to spend less than visitors within the centric and dependable groups. Regarding the “mental stimulation” trait, the results show a strong relationship between this trait and gender, type of visitor, household status, employment status, travelling party, and estimated expenditure on tourism activities. It appears that visitors with a high and medium mental stimulation trait are more likely to be females than males in comparison with the low mental stimulation group. The percentage of domestic visitors within the medium and low groups is higher than within the high mental stimulation group. The percentage of visitors belonging to the “parent with children at home” household status and “not working/retired” employment status is higher within the high mental stimulation group than within the other two groups. The percentage of visitors travelling with a companion is higher within the low mental stimulation group than within the other groups. Finally, visitors within the high mental stimulation trait are less likely to spend $200+ on tourism activities than visitors within the other two groups.

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Table 5-6 Demographic characteristics of the personality traits in Katherine

Adventure trait Mental stimulation trait

Item Variables Venturer

n= 43 Centric n=128 Dependable n = 73 n = 116 High Medium n = 66 n = 62 Low

Gender* Female 56% 60% 58% 63% 65% 45% Male 44% 40% 41% 37% 35% 55% P < .05; Cramer’s V = .165 Age* 15-29 42% 24% 12% 31% 15% 19% 30-44 19% 15% 10% 15% 12% 15% 45-64 30% 45% 44% 33% 53% 47% 65+ 9% 16% 34% 22% 20% 19% P < .001; Cramer’s V = .221

Type visitor* Domestic 54% 69% 71% 59% 77% 71%

International 46% 31% 29% 41% 23% 29%

P < .05; Cramer’s V = .172

Culture Born in Australia 44% 60% 56% 51% 64% 58%

Born overseas 56% 40% 44% 49% 36% 42%

Household* Single 42% 21% 12% 26% 15% 23%

Young/midlife couple no

children or not @ home 42% 63% 63% 62% 59% 53%

Parent with children @

home 16% 16% 25% 48% 27% 24%

P < .05; Cramer’s V = .177 P < .10; Cramer’s V = .129

Employment

status* Not working/retired _{Part time/casual} 51% _26% 49% _18% 55% _20% 60% _22% 44% _18% 42% _19%

Full time 23% 33% 25% 18% 38% 39%

P < .05; Cramer’s V = .160

Education level

Less than undergraduate 47% 52% 56% 51% 49% 60%

Undergraduate 23% 23% 32% 28% 27% 19% Postgraduate 30% 24% 12% 21% 24% 21% Travelling party* Alone 28% 9% 7% 14% 15% 3% With others 72% 91% 93% 86% 85% 97% P < .001; Cramer’s V = .240 P < .10; Cramer’s V = .152

Time spent Day trip 21% 10% 7% 12% 14% 7%

1 night 19% 16% 16% 19% 12% 16% 2-3 nights 54% 63% 63% 61% 61% 63% 4+ nights 7% 11% 14% 8% 14% 15% Estimated expenditure in tourism activities* $0-49 65% 38% 29% 47% 35% 34% $50-199 26% 47% 41% 46% 36% 39% $200+ 9% 15% 30% 8% 29% 27% P < .001; Cramer’s V = .207 P < .05; Cramer’s V = .187

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