Tourism_report_FINAL

TOURIST EXPECTATIONS and

SATISFACTION

Visitor opinions of New Zealand’s main tourist

activities

Emily A. Lawrence

Department of Statistics

University of Otago, Dunedin, New Zealand

1

Contents

1.0 Executive summary………………………………..………… 2

1.1 Introduction……………………………………………………… 3

2.0 Visitor profiles…………………………………………..……… 3

2.1 Australia……………………………………………………...…… 3

2.2 Germany…………………………………………………………… 6

2.3 Japan………………………………………………………………… 10

2.4 All visitors………………………………………….……………… 14

3.0 Exploratory factor analysis……………….……………… 14

4.0 Cluster analysis………………………………………………… 21

5.0 Activity expectations and experiences………..…… 27

6.0 Logistic regression…………………………………….……… 29

References…………………………………………………..…… 34

Appendix…………………………………………………..……… 34

2

1.0 Executive summary

The majority of tourists of all studied nationalities reported feeling “extremely satisfied”

with their visit to New Zealand.

The average tourist to New Zealand was middle aged (Australian, �̅� = 49; German, �̅� =

42; Japanese, �̅� = 50), and with a university degree or in a skilled occupation.

Australian and German tourists showed a tendency to travel alone, and independence

was similarly favoured by Japanese travellers.

Australian and German tourists favoured travelling New Zealand by car, while Japanese

tourists used coaches.

Motel accommodation was preferred by Australian tourists, campervans were favoured

by Germans, and the majority of Japanese stayed in hotels.

Adventure activities showed frequent groupings, indicating that tourists interested in

one adventure activity are likely to have interest in others too.

Tourists from both Australia and Japan include individuals seeking culture-based

activities (e.g. Māori performances), or high end life-style activities (e.g. golf, casinos).

Offering more packages targeted at tourists with specific interests could be a

strategy to boost tourist numbers and their experience satisfaction.

Local cuisine fell below tourist expectations – while highly anticipated and frequently

experienced by tourists to New Zealand, it did not rank as a highlight.

Improving the tourist experience of local cuisine offers an opportunity to increase

visitor satisfaction and potentially attract more tourists.

Walking activities were popular across nationalities – highly anticipated, frequently

experienced and often a top highlight.

German tourists stood out among the nationalities analysed, seeking unique

combinations of activities. Young German tourists (≤44 years) considered day hikes and

marine life as highlights, while this was not the case for senior German tourists (≥65

years).

Investment in perennially popular activities will likely prove profitable; improving

accessibility may increase an activity’s appeal to broader demographics.

Japanese tourists found meeting locals an unexpected highlight of their New Zealand

visit.

Development of marketing to promote lesser-known tourists activities may

attract more visitors and diverse demographics.

3

1.1 Introduction

Background

New Zealand is globally recognized as a top travel destination, offering many diverse landscapes

and experiences within its compact area (Wanderlust, 2016). In the year ending August 2016,

over 3.3 million overseas visitors travelled to New Zealand (Statistics NZ, 2016). The tourism

industry directly contributed $10.6 billion to the national GDP in the last recorded year (year

ended March 2015) and directly employs more than 168,000 people (Statistics NZ 2015); in terms

of foreign exchange earnings, this sector is the nation’s second largest export industry (Tourism

New Zealand, 2016). Developing an understanding of New Zealand’s diverse visitor

demographics, prospective tourists’ perceptions of this country, and what visitors make of their

travel experience is crucial to sustain and grow the tourism market.

The following report aims to profile tourists of different nationalities and develop an

understanding of how this relates to their experiences of New Zealand as a tourist destination.

Data set and analyses

Survey respondents totaled 3664 individuals, and were nationals of Australia (1043 respondents),

Germany (1095) and Japan (1526). Respondents were categorized as either potential visitors,

actual visitors, or both (those planning a return trip). The survey polled respondents on their

opinions of 33 main tourist attractions offered in New Zealand. The individual expectations of

potential tourists and satisfaction of actual tourists regarding each activity were recorded, as was

whether or not a given activity was a highlight.

Analyses were carried out on subsets of data using IBM SPSS 22 and Microsoft Excel 2013

software.

2.0 Visitor profiles

Aim: to establish a profile of the average tourist of different nationalities who intend to travel, or

have travelled, to New Zealand.

2.1 Australia

A total of 1043 Australian tourists participated in the survey, comprising 227 individuals prior to

their New Zealand trip, 667 individuals following their travel, and 149 past New Zealand tourists,

prior to a return trip.

4

Of the total number of Australians surveyed, 40.8% were male and 55.8% were female, leaving

3.4% undisclosed. The average age of visitors was 49, and visitor ages ranged from 2-84 years

(Fig. 1).

Figure 1. Age distribution (%) of potential and actual Australian visitors to New

Zealand (n = 648).

Many of the potential and actual Australian visitors to New Zealand were either people with

established careers or that had retired, with the highest categories being “skilled” individuals

(26%) and “retired” (22%) (Fig. 2).

Figure 2. Percentage distribution of occupations of potential and actual Australian

visitors to New Zealand (n = 981).

0

5

10

15

20

25

30

35

40

0-9 10-19 20-29 30-39 40-49 50-59 60-69 70-79 80-89 90-99

Perc

en

tag

e

Age category

0

5

10

15

20

25

30

Perc

en

tag

e

5

The majority of actual Australian tourists travelled alone and by car, and chose to stay in motel

accommodation (Fig. 3).

Figure 3. Travel choices of actual Australian tourists: (a) Accommodation (n=1150), (b) Travel companions (n=1147), (c) Transport (n=1214).

22%

40%

12%

18%

8%

(a)Hotel

Motel

B&B

Campervan

Backpackers

60%12%

15%

8%

5%

(b) Alone

With family

With partner

With friends

With a tourgroup

58%11%

15%

8%

5% 3%

(c) Car

Campervan

Coach

Train

Plane

Other

6

The average length of (intended) stay in New Zealand for potential and actual Australian tourists

is 20 days, with few visitors staying for longer than 4 weeks (Fig. 4).

Figure 4. Percentage distribution of trip length (weeks) of potential and actual

Australian visitors to New Zealand (n = 969).

2.2 Germany

A total of 1090 German tourists participated in the survey, comprising 457 individuals prior to



0

5

10

15

20

25

30

35

40

45

0-1 1-2 2-3 3-4 4-5 5-6 >6

Perc

enta

ge

Weeks

7

Of the total number of Germans surveyed, 53.0% were male and 40.5% were female, leaving 6.5%

undisclosed. The average age of visitors was 42, and visitor ages ranged from 2-99 years (Fig. 5).

Figure 5. Age distribution (%) of potential and actual German visitors to New Zealand

(n = 937).

The majority of the potential and actual German visitors to New Zealand were “skilled” individuals

(33% skilled, 21% highly skilled) (Fig. 6).

Figure 6. Percentage distribution of occupations of potential and actual German

visitors to New Zealand (n = 892).

0

5

10

15

20

25

30

35

40

0-9 10-19 20-29 30-39 40-49 50-59 60-69 70-79 80-89 90-99

Perc

en

tag

e

Age category

0

5

10

15

20

25

30

35

Perc

en

tag

e

8

The majority of actual German tourists travelled alone and by car, and chose to stay in

campervans (Fig. 7).

Figure 7. Travel choices of actual German tourists: (a) Accommodation (n=1540), (b) Travel companions (n=1171), (c) Transport (n=1459).

18%

20%

16%

27%

19%

(a)Hotel

Motel

B&B

Campervan

Backpackers

39%

23%

19%

8%

11%

(b) Alone

With family

With partner

With friends

With a tourgroup

36%

21%

17%

8%

10% 8%

(c) Car

Campervan

Coach

Train

Plane

Other

9

The average total household income before tax per year for potential and actual German tourists

to New Zealand is 50-60k DM (Fig. 8).

Figure 8. Percentage distribution of total household income before tax per year (DM)

of potential and actual German tourists to New Zealand (n = 886).

The average length of (intended) stay in New Zealand for potential and actual German tourists is

47 days, with few visitors staying for longer than 8 weeks (Fig. 9).


German visitors to New Zealand (n = 992).

0

2

4

6

8

10

12

14

Perc

en

tag

e

0

5

10

15

20

25

30

35

40

0-1 1-2 2-3 3-4 4-5 5-6 6-7 7-8 8-9 9-10 10-1111-12 >12

Perc

enta

ge

Weeks

10

2.3 Japan

A total of 1526 Japanese tourists participated in the survey, comprising 1009 individuals prior to



Of the total number of Germans surveyed, 56.4% were male and 39.0% were female, leaving 4.6%

undisclosed. The average age of visitors was 50, and visitor ages ranged from 13-85 years (Fig.

10).

Figure 10. Age distribution (%) of potential and actual Japanese visitors to New

Zealand (n = 1442).

0

5

10

15

20

25

30

35

40

0-9 10-19 20-29 30-39 40-49 50-59 60-69 70-79 80-89 90-99

Perc

en

tag

e

Age category

11

The majority of the potential and actual Japanese visitors to New Zealand had attained a

university degree (60%) (Fig. 11).

Figure 11. Percentage distribution of levels of education of potential and actual

Japanese visitors to New Zealand (n = 1449).

0

10

20

30

40

50

60

70

high school college/short uni university degree graduate school

Perc

en

tag

e

12

While the majority of actual Japanese tourists travelled independently, the popularity of both

packaged and semi-packaged tours accounts for travel by coach being selected by many

respondents. Hotel accommodation was clearly favoured by Japanese tourists (Fig. 12).

Figure 12. Travel choices of actual Japanese tourists: (a) Accommodation (n=1337), (b) Travel companions (n=1410), (c) Transport, (n=1735).

79%

11%

5%1%

4%(a)

Hotel

Motel

B&B

Campervan

Backpackers

30%

31%

36%

3%

(b)

Package tour

Semi-packaged

Free &independent

Other

1%

21%

39%

18%

18%

3%

(c)Car

Campervan

Coach

Train

Plane

Other

13

The average total household income before tax per year for potential and actual Japanese tourists

to New Zealand is 8-9000k JPY (Fig. 13).

Figure 13. Percentage distribution of total household income before tax per year (JPY)

of potential and actual Japanese tourists to New Zealand (n = 1391).

The average length of (intended) stay in New Zealand for potential and actual Japanese tourists

is 18 days, with few visitors staying for longer than 2 weeks (Fig. 14).


Japanese visitors to New Zealand (n = 1371).

0

5

10

15

20

25

Perc

en

tag

e

0

10

20

30

40

50

60

0-1 1-2 2-3 3-4 4-5 5-6 6-7 7-8 >8

Perc

enta

ge

Weeks

14

2.4 All visitors

The majority of actual tourist respondents of all nationalities reported feeling “extremely

satisfied” with their New Zealand trip (Fig. 15).

Figure 15. Overall satisfaction (%) of visitors to New Zealand following their trip

(Australian, n = 816; German, n = 633; Japanese, n = 517).

3.0 Exploratory factor analysis

Aim: to look for associations between activity preferences of potential tourists from different

countries.

Method

Participants

Analyses were carried out on a subset of data – that of potential tourists to New Zealand (both

first-time and returning visitors), surveyed while in their home country of Australia, Germany or

Japan. Data was collected from a total of 2003 potential tourists (376 Australian, 531 German,

1096 Japanese).

Materials

The survey listed main tourist activities in New Zealand, and potential tourists indicated the

strength of their intention to pursue particular activities (e.g. bungee-jumping, bird watching) via

a Likert-type scale, ranging from 1-7 (1 = definitely not, 4 = do that too, 7 = that’s why I’m going).

0

10

20

30

40

50

60

70

Absolutelydissatisfied

2 3 4 5 6 Extremelysatisfied

Perc

enta

ge

Australian German Japanese

15

Procedure

Analysis was carried out on the sample correlation matrix using the principal components

method, as no prior assumptions were made about potential relationships between factors. A

fixed number of factors (four) was set for extraction. The selected rotation method was varimax

rotation, and small coefficients (<0.4) were suppressed in the results. The Kaiser-Meyer-Olkin and

Bartlett’s measures were included, as were scree plots, to test assumptions of each analysis.

Results

Australia

The Kaiser-Meyer-Olkin measure of sampling adequacy was reported as 0.854, exceeding the

recommended value of 0.5, and Bartlett’s test of sphericity was significant (𝜒2(528) = 4691.451,

p < 0.5). Sampling adequacy measured by the diagonals of the anti-image correlation matrix also

gave evidence for the inclusion of all items in the factor analysis, with all values over 0.5 (see

Appendix). Considering these results, the inclusion of all 33 variables in the factor analysis

appeared appropriate.

Selecting components based on eigenvalues >1 suggested choosing 8 factors, though subsequent

examination of the scree plot (given the large sample size) indicated that 3 factors be used. The

amount of variance explained by each of the three factors was reallocated but essentially

unchanged following rotation, and the factor loading pattern was improved with almost all

activities having only a single large loading. The four factors explain 47.399% of the total variance

in the 33 variables (see Appendix).

The loadings in the resultant component matrix suggest that an Australian tourist with a high

score on rotated factor 1 are likely to have a preference for cultural activities, such as Māori

performances, historic places and meeting Māori. A tourist with a high score on rotated factor 2

seeks adventure sports activities on their New Zealand holiday such as rafting, jet-boating and

skiing. A tourist with a high score on rotated factor 3 is interested in viewing New Zealand’s

nature, with activities such as hiking/tramping, kayaking/canoeing and bird watching (Fig. 16).

16

Figure 16. Rotated factor loadings for tourist activities based on Australian visitor

expectations (n = 376).

17

Of the relationships between the four extracted factors and the demographics of Australian

tourists, the negative correlation between age and factor 2 is most clear – as age increases, the

score for factor 2 (adventure sports) decreases (Fig. 17).

Figure 17. Relationship between factor score (rotated factor 2) and age for Australian

tourists (n = 376).

Germany







Selecting components with eigenvalues >1 suggested choosing 9 factors, though subsequent


amount of variance explained by each of the four factors was reallocated but essentially

unchanged following rotation, though the factor loading pattern showed several activities to have

multiple large loadings. The four factors explain 39.534% of the total variance in the 32 variables

(see Appendix).

The loadings in the resultant component matrix suggest that a German tourist with a high score

on rotated factor 1 are likely to have a preference for nature walking, favouring day hikes, short

walks and bird watching. A tourist with a high score on rotated factor 2 seeks adventure sports

18

activities on their New Zealand holiday such as rafting, kayaking/canoeing and cycling/mountain

biking. A tourist with a high score on rotated factor 3 would like a relaxing New Zealand holiday,

with activities such as shopping, evening entertainment and sunbathing. A tourist with a high

score on rotated factor 4 may be an older person, with interests such as theatres & shows, casinos

and golf (Fig. 18).

Figure 18. Rotated factor loadings for tourist activities based on German visitor


19

Japan







Selecting components with eigenvalues >1 suggested choosing 8 factors, though subsequent


amount of variance explained by each of the three factors was reallocated but essentially

unchanged following rotation, and improved the factor loading pattern with few activities having

multiple large loadings. The three factors explain 40.042% of the total variance in the 33 variables

(see Appendix).

The loadings in the resultant component matrix suggest that a Japanese tourist with a high score

on rotated factor 1 hopes to do thrill-seeking activities in New Zealand, such as rafting, jet-boating

and parachuting. A tourist with a high score on rotated factor 2 is interested in viewing New

Zealand’s nature, such as marine life, glaciers and bird watching. A tourist with a high score on

rotated factor 3 is interested in the culture of the New Zealand people, and looks forward to

activities such as meeting locals, meeting Māori and Māori performances (Fig. 19).

20

Figure 19. Rotated factor loadings for tourist activities based on Japanese visitor


21

Conclusions

Each nationality had a factor relating to adventure sports (e.g. rafting), and being out in

nature (e.g. bird watching)

Both Australia and Japan had a factor relating to culture-based activities.

German tourists differed most from the other two nationalities, with unique factors

relating to relaxing activities (e.g. sunbathing, evening entertainment) and older persons

activities (e.g. golf, casinos).

4.0 Cluster analysis

Aim: to identify groups of activities that were ranked similarly by survey respondents with regards

to activity “expectation”.

Method

Participants

Analyses were carried out on subsets of data – that of tourists in their home country of Australia,

Germany or Japan prior to their visit to New Zealand. Data used was from a total of 1998 tourists

(373 Australian, 537 German, 1088 Japanese).

Materials

The survey listed main tourist activities in New Zealand, and tourists indicated the likelihood of

their participation in certain activities via a Likert-type scale, ranging from 1-7 (1 = “definitely

not”, 4 = “will do that too”, 7 = “that’s why I’m going”).

Procedure

The hierarchical clustering method was used, and the 33 main tourist activities were clustered by

variables. The number of clusters was restricted to 4. Ward’s method of clustering was applied,

with Squared Euclidean distance as the interval measure. A dendrogram was produced for each

analysis.

22

Results

Table 1. Activity clusters for potential Australian, German and Japanese tourists to New Zealand.

Australian (n = 373) German (n = 537) Japanese (n = 1088)

Cluster Activity Cluster Activity Cluster Activity

1 Rafting Jet-boating Bungee-jumping Parachuting etc. Skiing Winter sports

1 Rafting Bungee-jumping Jet-boating Parachuting etc. Fishing & hunting Golf Casinos Winter sports

1 Jet-boating Bungee-jumping Parachuting etc.

2 Swimming with dolphins Swimming Kayaking/canoeing Short walks Hiking/tramping Scenic flights Boat tours Bird watching Glaciers Marine life Farmstay Sunbathing

2 Swimming with dolphins Kayaking/canoeing Theatre & shows Hiking/tramping Sunbathing Scenic flights Cycling, mountain biking Boat tours Farmstay

2 Swimming with dolphins Swimming Kayaking/canoeing Special events Theatre & shows Farmstay Sunbathing

3

Museums & galleries Māori performances Special events Theatre & shows Meeting Māori Sight-seeing tours Shopping Meeting locals Local cuisine Evening entertainment Botanic gardens Historic places

3

Swimming Short walks Day hikes Sight-seeing tours Local cuisine Glaciers Marine life Historic places

3

Museums & galleries Māori performances Short walks Hiking/tramping Meeting Māori Sight-seeing tours Shopping Scenic flights Boat tours Local cuisine Meeting locals Evening entertainment Botanic gardens Bird watching

23

3 (cont.)

3 (cont.)

3 (cont.)

Glaciers Marine life Historic places

4 Fishing & hunting Golf Casinos

4 Museums & galleries Māori performances Special events Shopping Evening entertainment Botanic gardens Bird watching

4 Fishing & hunting Skiing Golf Casinos Winter sports

The clusters based on the expectations of potential Australian tourists suggest that respondents

showed preferences for either extreme sports activities (cluster 1), nature/outdoors activities

(cluster 2), cultural activities (cluster 3) or high-end lifestyle activities (cluster 4) (Table 1; Fig. 20).

24

Figure 20. Dendrogram with activity clusters based on expectations of potential

Australian tourists to New Zealand.

25

The clusters based on the expectations of potential German tourists suggest that respondents

showed preferences for either thrill-seeking activities (cluster 1), experiential activities (cluster 3)

or sedate activities (cluster 4) (cluster 2 not clearly definable) (Table 1; Fig. 21).


German tourists to New Zealand.

26

The clusters based on the expectations of potential Japanese tourists suggest that respondents

showed preferences for either extreme sports activities (cluster 1) or high-end lifestyle activities

(cluster 4) (clusters 2 and 3 not clearly definable) (Table 1; Fig. 22).


Japanese tourists to New Zealand.

27

Conclusions

Each nationality shows a cluster relating to thrill-seeking activities (e.g. jet-boating), i.e.

respondents who highly anticipate New Zealand’s thrill-seeking activities are a common

type of international tourist

Both Australia and Japan have a cluster relating to high-end lifestyle activities (e.g.

casinos, golf)

German tourists stand out as having unique clusters (defined as “experiential activities”

and “sedate activities”) – this suggests that tourists of this nationality have different

intentions for their visit to New Zealand than people of other nationalities.

5.0 Activity expectations and experiences

Aim: to identify the activities most highly anticipated by potential tourists to New Zealand and

compare them to the activities done most frequently and rated most highly by past visitors to

New Zealand.

Method

Participants

Analyses were carried out on subsets of data – potential or actual Australian, German and

Japanese tourists to New Zealand, totalling 3682 individuals (1055 Australians, 1114 Germans,

1513 Japanese).

Procedure

For each main tourist activity, respondents gave a binary response as to whether activity was a

highlight (= 1) or not (= 0). The frequency of “highlight” response for each activity was calculated,

and then converted into a percentage value. Activities were ranked from highest to lowest

highlight percentage in order to identify the top activities. The same procedure was used to

determine the most highly anticipated activities (based on the number of potential tourists

whose expectation response for given activity was “that’s why I am going [to New Zealand]”) and

most frequently participated in activities (based on the number of actual tourists whose

satisfaction response for a given activity was “very frequent [participation]”).

Results

Local cuisine was the most highly anticipated activity for both Australian and Japanese potential

tourists, but did not feature in the top highlights of those who have already visited New Zealand

– even though it was a most frequent activity of Australian (and German) tourists (local cuisine

28

was a highlight for 12.8% and 5.8% of tourist from these countries respectively (Table 2); highlight

for 10.7% of Germans, with 36.8% anticipation (Appendix)).

Day hikes were the most highly anticipated activity for German potential tourists, and also the

top highlight of German tourists following their New Zealand trip (Table 2).

Short walks and Hiking/Tramping were highly anticipated by Japanese potential tourists, and

also the top two activities of Japanese tourists following their travel to New Zealand (Table 2).

Glaciers were a highly anticipated activity for Australian potential tourists, and also the top

highlight of those Australians surveyed who had already visited New Zealand (Table 2).

Table 2. Most highly anticipated activities for potential tourists to New Zealand, and most

frequently participated in and top highlight activities for actual tourists.

Most anticipated % Most frequent % Top highlights %

Australian Local cuisine 38.2 Local cuisine 46.9 Glaciers 23.1

Short walks 36.8

Short walks 43.1 Sight-seeing

tours

19.1

Glaciers 36.4 Meeting locals 30.1 Boat tours 18.5

German Day hikes 45.0 Short walks 55.5 Day hikes 31.3

Marine life 44.4 Day hikes 46.1 Marine life 31.1

Sight-seeing tours 39.4 Local cuisine 29.1 Glaciers 28.9

Japanese Local cuisine 48.3 Short walks 37.7 Short walks 17.9

Short walks 46.2 Shopping 30.9 Hiking/Tramping 17.0

Hiking/Tramping 40.3 Meeting locals 21.5 Meeting locals 14.9

Conclusions

Expectations of New Zealand’s local cuisine were not met, despite frequent tourist

participation in local cuisine activities.

Walking activities (short walks, day hikes, hiking/tramping) were universally popular –

often highly anticipated, frequently participated in and top highlights for all nationalities.

Meeting locals was an unexpectedly important aspect of the New Zealand experience for

Japanese tourists.

29

6.0 Logistic regression

Aim: to identify the variables which impact on the classification of tourist activities as a highlight

by German tourists to New Zealand

Method

Participants

A subset of 1032 individuals were included in the analysis – those respondents who had returned

to their home country following their New Zealand visit. Data included the gender and age of

each respondent, with ages categorised into young (≤44 years), middle-aged (45-64 years) or

senior (≥65 years).

Materials

The predictor and outcome variables are defined as:

Age: D1 = young, 0 otherwise

D2 = middle-aged, 0 otherwise

Gender: 1 = male, 2 = female

Outcome variables: 1 = highlight, 0 otherwise

Procedure

A logistic regression analysis was carried out to predict the selection of a given activity as a tourist

highlight. Analyses were carried out on select activities, chosen for their ranking as top highlights

for German tourists – day hikes, marine life, and glaciers (see Table 2, Section 5.0). The binary

logistic regression was carried out with predictor variables as categorical covariates.

Having run the analysis, the activity “glaciers” was found to have no significant associations with

any age categories or gender, and has therefore been omitted from the results.

Results

Day hikes

Overall, 68.4% of the highlight choices have been correctly classified (see Appendix).

For predicting the probability of choosing day hikes as a highlight, the age groups young and

senior are significant variables at the 0.05 level.

30

There is evidence that age group has an effect on the probability of a German respondent

choosing day hikes as a highlight of their New Zealand holiday. It appears that young tourists are

more likely to consider day hikes a highlight (p = 0.001), while senior tourists are less likely to do

so (p < 0.001) (Table 3). A negative relationship between age and the predicted probability of

highlight classification for “day hikes” is evident (Fig. 23).

Table 3. Variables in the equation for highlight classification of “day hikes” by German

tourists to New Zealand.

Variables in the Equation

B S.E. Wald df Sig. Exp(B)

Step 1a Age_category 19.027 2 .000

Age_category(1) 1.600 .491 10.607 1 .001 4.952

Age_category(2) .920 .503 3.342 1 .068 2.510

b19(1) -.277 .185 2.237 1 .135 .758

Constant -1.913 .498 14.736 1 .000 .148

a. Variable(s) entered on step 1: Age_category, b19.

Equation of the fitted regression:

ln (𝜋

1−𝜋) = −1.913 + 1.600𝐷1 + 0.920𝐷2 − 0.277𝑔𝑒𝑛𝑑𝑒𝑟

𝜋 =exp(−1.913+1.600𝐷1+0.920𝐷2−0.277𝑔𝑒𝑛𝑑𝑒𝑟)

1−exp(−1.913+1.600𝐷1+0.920𝐷2−0.277𝑔𝑒𝑛𝑑𝑒𝑟)

31

Figure 23. Relationship between age and predicted probability of “day hikes” being a

highlight activity for German tourists to New Zealand (n = 1032).

Marine life

Overall, 69.2% of the highlight choices have been correctly classified (see Appendix).

For predicting the probability of choosing marine life as a highlight, the age groups young and

senior are significant at the 0.05 level.

There is evidence that age group has an effect on the probability of a German respondent

choosing marine life as a highlight of their New Zealand holiday. It appears that young tourists

are more likely to consider marine life a highlight (p = 0.008), while senior tourists are less likely

to do so (p < 0.001) (Table 4). A negative relationship between age and the predicted probability

of highlight classification for “marine life” is evident (Fig.24).

32

Table 4. Variables in the equation for highlight classification of “marine life” by German

tourists to New Zealand.




Age_category(1) 1.088 .410 7.028 1 .008 2.967

Age_category(2) .345 .426 .658 1 .417 1.412

b19(1) -.050 .187 .070 1 .791 .952

Constant -1.566 .420 13.891 1 .000 .209


Equation of the fitted regression:

ln (𝜋

1−𝜋) = −1.566 + 1.088𝐷1 + 0.345𝐷2 − 0.050𝑔𝑒𝑛𝑑𝑒𝑟

𝜋 =exp(−1.566+1.088𝐷1+0.345𝐷2−0.050𝑔𝑒𝑛𝑑𝑒𝑟)

1−exp(−1.566+1.088𝐷1+0.345𝐷2−0.050𝑔𝑒𝑛𝑑𝑒𝑟)

33

Figure 24. Relationship between age and predicted probability of “marine life” being

a highlight activity for German tourists to New Zealand (n = 1032).

Conclusions

For German tourists, the two activities with the highest highlight classification were day

hikes and marine life (Table 1, Section 4). There is evidence that for young tourists (≤44

years) for both day hikes and marine life were a highlight, whereas it is indicated that both

activities were not a highlight for senior tourists (≥65 years).

34

REFERENCES

Statistics NZ. (2015) Tourism Satellite Account: 2015. Statistics New Zealand.

Statistics NZ. (2016) International Visitor Arrivals to New Zealand: August 2016. Statistics New

Zealand

Tourism New Zealand. (2016) About the industry. [Online] Available from:

http://www.tourismnewzealand.com/about/about-the-industry/ [Accessed 8th October,

2016].

Wanderlust. (2016) Wanderlust Readers’ Travel Awards 2016: THE WINNERS. [Online] Available

from: http://www.wanderlust.co.uk/magazine/news/wanderlust-travel-awards-2016-the-

winners [Accessed 8th October 2016].

APPENDIX

3.0 Exploratory factor analysis (pg. 14)

Australia

KMO and Bartlett's Test

Kaiser-Meyer-Olkin Measure of Sampling Adequacy. .854

Bartlett's Test of Sphericity Approx. Chi-Square 4691.451

df 528

Sig. .000

Total Variance Explained

Component

Initial Eigenvalues

Extraction Sums of Squared

Loadings

Rotation Sums of Squared

Loadings

Total

% of

Variance

Cumulative

% Total

% of

Variance

Cumulative

% Total

% of

Variance

Cumulative

%

1 7.758 23.510 23.510 7.758 23.510 23.510 6.671 20.214 20.214

2 4.469 13.542 37.052 4.469 13.542 37.052 4.062 12.310 32.523

3 1.865 5.652 42.704 1.865 5.652 42.704 2.671 8.094 40.617

4 1.549 4.695 47.399 1.549 4.695 47.399 2.238 6.782 47.399

5 1.431 4.337 51.736

6 1.351 4.094 55.830

7 1.243 3.767 59.597

8 1.014 3.072 62.669

9 .954 2.890 65.559

35

10 .918 2.783 68.342

11 .827 2.506 70.848

12 .769 2.329 73.177

13 .739 2.239 75.416

14 .703 2.131 77.547

15 .670 2.031 79.578

16 .612 1.856 81.434

17 .567 1.717 83.151

18 .531 1.608 84.759

19 .503 1.524 86.284

20 .475 1.440 87.723

21 .456 1.381 89.104

22 .422 1.279 90.384

23 .412 1.249 91.633

24 .373 1.131 92.763

25 .354 1.071 93.835

26 .347 1.052 94.886

27 .308 .934 95.820

28 .295 .894 96.714

29 .284 .859 97.573

30 .251 .760 98.333

31 .205 .620 98.953

32 .184 .557 99.510

33 .162 .490 100.000

Extraction Method: Principal Component Analysis.

36

37

Rotated Component Matrixa

Component

1 2 3 4

Rafting .816

Jetboating .762

Bungy-jumping .552

Swimming with dolphins .569

Parachuting etc .612

Swimming .480

Kayaking/canoeing .584 .517

Museums & galleries .577

Maori performances .752

Special events (sport/culture) .625

Theatre & shows .584

Short walks .493

Hiking/Tramping .714

Meeting Maoris .710

Fishing & hunting .620

Skiing .624

Golf .481

Sight seeing tours .630

Shopping .608

Scenic flights .434

Boat tours .572

Meeting Locals .709

Local cuisine .674

Evening entertainment .645

Botanic gardens .625

Bird watching .523

Casinos .608

Glaciers .408

Marine life .496

Historic places .733

Winter sports .587 .450

Farmstay

Sunbathing .503


Rotation Method: Varimax with Kaiser Normalization.

a. Rotation converged in 7 iterations.

38

German




df 496

Sig. .000


Component

Initial Eigenvalues


Loadings


Loadings

Total

% of

Variance

Cumulative

% Total

% of

Variance

Cumulative

% Total

% of

Variance

Cumulative

%

1 5.397 16.866 16.866 5.397 16.866 16.866 4.134 12.918 12.918

2 3.356 10.487 27.354 3.356 10.487 27.354 3.440 10.749 23.667

3 2.234 6.982 34.336 2.234 6.982 34.336 2.560 8.001 31.668

4 1.663 5.198 39.534 1.663 5.198 39.534 2.517 7.866 39.534

5 1.618 5.058 44.592

6 1.370 4.282 48.874

7 1.338 4.181 53.055

8 1.169 3.654 56.709

9 1.014 3.168 59.876

10 .955 2.983 62.860

11 .868 2.713 65.572

12 .836 2.611 68.184

13 .828 2.586 70.770

14 .764 2.387 73.157

15 .709 2.215 75.372

16 .665 2.079 77.451

17 .656 2.049 79.500

18 .617 1.927 81.427

19 .589 1.841 83.268

20 .562 1.758 85.025

21 .528 1.650 86.676

22 .510 1.594 88.269

23 .503 1.571 89.841

24 .457 1.429 91.269

25 .437 1.366 92.636

39

26 .410 1.280 93.916

27 .408 1.274 95.190

28 .378 1.181 96.371

29 .336 1.051 97.422

30 .317 .992 98.414

31 .296 .925 99.339

32 .212 .661 100.000


40


Component

1 2 3 4

Rafting .689

Bungy-jumping .441

Swimming with dolphins .431 .423

Jetboot fahren .469 .443


Swimming .431

Kayaking/canoeing .667

Museums & galleries .486


Special events (sport/culture) .465

Theatre & shows .680

Short walks .631

Day hikes .656


Fishing & hunting

Sunbathing .536

Golf .576

Sight seeing tours .420 .416

Shopping .567

Scenic flights

Cycling, mtb .589

Boat tours

Local cuisine

Evening entertainment .548


Bird watching .634

Casinos .610

Glaciers .479

Marine life .478



Farmstay




41

Japan




df 528

Sig. .000


Component

Initial Eigenvalues


Loadings


Loadings

Total

% of

Variance

Cumulative

% Total

% of

Variance

Cumulative

% Total

% of

Variance

Cumulative

%

1 7.335 22.227 22.227 7.335 22.227 22.227 4.986 15.109 15.109

2 4.058 12.297 34.524 4.058 12.297 34.524 3.732 11.311 26.419

3 1.821 5.518 40.042 1.821 5.518 40.042 3.605 10.925 37.344

4 1.715 5.197 45.239 1.715 5.197 45.239 2.605 7.894 45.239

5 1.586 4.807 50.045

6 1.330 4.031 54.076

7 1.069 3.240 57.316

8 1.036 3.138 60.454

9 .973 2.947 63.402

10 .927 2.810 66.212

11 .838 2.538 68.750

12 .796 2.411 71.161

13 .774 2.346 73.507

14 .706 2.139 75.646

15 .671 2.032 77.678

16 .653 1.978 79.656

17 .604 1.829 81.486

18 .593 1.797 83.283

19 .569 1.725 85.008

20 .539 1.633 86.641

21 .470 1.425 88.065

22 .450 1.365 89.430

23 .427 1.293 90.723

24 .409 1.239 91.962

25 .397 1.203 93.166

26 .376 1.138 94.304

27 .356 1.079 95.384

42

28 .319 .965 96.349

29 .307 .930 97.279

30 .276 .835 98.114

31 .248 .751 98.865

32 .237 .717 99.582

33 .138 .418 100.000


43


Component

1 2 3 4

Rafting .763

Jet-boating .740

Bungy-jumping .600

Swimming with dolphins .674


Swimming .503

Kayaking/canoeing .720

Museums and galleries .447 .411


Special events (sport/cultural) .432 .448

Theatre and shows .413

Short walks .435


Meeting Maoris .625

Fishing and hunting .466

Skiing .582

Golf .595

Sight seeing tours(other than in your package) .459

Shopping .482

Scenic flights .471

Boat tours .571

Local cuisine (fruit, lamb, veggies, seafood etc.) .515

Meeting locals .704

Evening entertainment (pubs, live music, restaurants) .452 .487


Bird watching .609

Casinos .596

Glaciers .698

Marine life .725



Farmstay .453

Sunbathing




44

4.0 Cluster analysis (pg. 21)

Australia

Case Processing Summarya

Cases

Valid Missing Total

N Percent N Percent N Percent

325 87.1% 48 12.9% 373 100.0%

a. Squared Euclidean Distance used

Ward Linkage

Agglomeration Schedule

Stage

Cluster Combined

Coefficients

Stage Cluster First Appears

Next Stage Cluster 1 Cluster 2 Cluster 1 Cluster 2

1 9 14 269.500 0 0 17

2 12 23 626.500 0 0 4

3 16 31 991.500 0 0 27

4 12 30 1364.500 2 0 5

5 12 22 1787.500 4 0 13

6 8 25 2211.000 0 0 15

7 3 5 2636.000 0 0 25

8 28 29 3086.000 0 0 20

9 1 2 3570.500 0 0 27

10 19 24 4084.000 0 0 21

11 10 11 4650.000 0 0 17

12 20 21 5231.000 0 0 20

13 12 18 5882.800 5 0 15

14 4 7 6558.800 0 0 16

15 8 12 7302.786 6 13 21

16 4 6 8074.119 14 0 24

17 9 10 8885.869 1 11 26

18 17 27 9786.869 0 0 23

19 32 33 10703.369 0 0 22

20 20 28 11626.869 12 8 28

21 8 19 12557.750 15 10 26

22 26 32 13549.250 0 19 29

23 15 17 14546.250 0 18 25

24 4 13 15737.167 16 0 29

25 3 15 16991.767 7 23 31

45

26 8 9 18315.542 21 17 28

27 1 16 19761.792 9 3 30

28 8 20 21259.129 26 20 32

29 4 26 22855.451 24 22 30

30 1 4 25174.584 27 29 31

31 1 3 28417.967 30 25 32

32 1 8 42736.000 31 28 0

German


Cases

Valid Missing Total


456 84.9% 81 15.1% 537 100.0%


Ward Linkage


Stage

Cluster Combined

Coefficients



1 12 13 200.500 0 0 14

2 2 5 687.000 0 0 16

3 18 30 1176.000 0 0 6

4 25 26 1717.500 0 0 24

5 17 27 2262.500 0 0 11

6 18 29 2864.833 3 0 8

7 8 9 3505.833 0 0 13

8 18 23 4156.750 6 0 9

9 6 18 4984.100 0 8 14

10 1 4 5833.600 0 0 26

11 17 31 6741.933 5 0 16

12 19 24 7661.933 0 0 18

13 8 10 8590.933 7 0 18

14 6 12 9533.690 9 1 20

15 7 22 10548.190 0 0 19

16 2 17 11572.357 2 11 17

17 2 15 12723.690 16 0 26

46

18 8 19 13883.290 13 12 24

19 7 21 15044.124 15 0 25

20 6 28 16311.767 14 0 29

21 11 32 17651.267 0 0 28

22 3 16 18995.267 0 0 23

23 3 20 20461.933 22 0 25

24 8 25 21971.976 18 4 29

25 3 7 23526.476 23 19 27

26 1 2 25305.768 10 17 31

27 3 14 27236.982 25 0 28

28 3 11 29327.768 27 21 30

29 6 8 31778.125 20 24 30

30 3 6 38010.125 28 29 31

31 1 3 61422.594 26 30 0

Japanese


Cases

Valid Missing Total


897 82.4% 191 17.6% 1088 100.0%


Ward Linkage


Stage

Cluster Combined

Coefficients



1 16 31 579.000 0 0 28

2 12 13 1213.000 0 0 10

3 9 14 1957.500 0 0 21

4 8 25 2784.500 0 0 14

5 29 30 3691.000 0 0 8

6 1 2 4686.500 0 0 26

7 22 23 5706.500 0 0 10

8 28 29 6833.333 0 5 17

9 18 21 8032.333 0 0 12

10 12 22 9407.833 2 7 24

11 10 11 10786.333 0 0 23

47

12 18 19 12205.333 9 0 19

13 6 7 13641.833 0 0 15

14 8 26 15105.500 4 0 17

15 4 6 16747.000 0 13 26

16 3 5 18544.500 0 0 29

17 8 28 20455.000 14 8 21

18 32 33 22380.500 0 0 23

19 18 24 24410.750 12 0 20

20 18 20 26666.500 19 0 27

21 8 9 28951.750 17 3 24

22 15 27 31468.250 0 0 25

23 10 32 33991.250 11 18 30

24 8 12 36578.500 21 10 27

25 15 17 39228.000 22 0 28

26 1 4 42106.900 6 15 30

27 8 18 45279.782 24 20 32

28 15 16 48863.182 25 1 29

29 3 15 53068.711 16 28 31

30 1 10 57607.311 26 23 31

31 1 3 64909.820 30 29 32

32 1 8 92570.545 31 27 0

48

5.0 Activity expectations and experiences (pg. 27)

Australia

Activity Highlight % Never Once More than once Very frequently

Glaciers 23.1 28.2 29.4 30.9 4.4

Sight-seeing tours 19.1 16.4 11.9 40.0 19.3

Boat tours 18.5 22.8 29.9 35.2 3.4

Short walks 17.9 2.8 8.6 36.4 43.1

Jetboating 14.5 52.3 34.5 5.8 0.4

Historic places 14.2 7.5 12.0 50.6 20.9

Scenic flights 13.9 54.7 23.3 10.1 1.6

Maori performances 13.1 32.6 33.7 23.9 1.6

Hiking/Tramping 13.1 47.6 11.3 16.6 9.6

Local cuisine 12.8 2.7 5.4 35.7 46.9

Museums & galleries 11.5 12.3 18.9 46.7 14.8

Others 11.0 5.2 4.5 2.5 2.5

Meeting Locals 10.7 7.6 9.1 42.5 30.1

Botanic gardens 8.5 19.3 26.9 33.7 10.9

Evening entertainment 8.3 16.0 8.8 39.8 23.9

Marine life 5.9 38.2 21.6 24.5 4.4

Skiing 5.6 75.7 3.8 4.4 3.6

Meeting Maoris 5.6 29.6 20.1 31.3 7.4

Shopping 4.8 4.0 10.8 51.0 27.2

Rafting 4.5 76.2 11.8 2.2 0

Special events (sport/culture) 3.9 51.5 15.1 13.1 3.2

Bird watching 3.6 54.5 11.0 14.5 6.4

Fishing & hunting 3.4 74.6 5.9 4.2 3.2

Winter sports 3.3 74.8 4.9 3.8 3.3

Bungy-jumping 2.6 84.0 3.9 0.7 -

Kayaking/canoeing 2.6 75.2 7.6 3.3 0.5

Farmstay 2.6 74.4 8.6 3.4 2.2

Swimming with dolphins 2.5 80.5 6.6 0.4 -

Swimming 1.8 50.1 14.5 18.8 6.5

Golf 1.6 79.4 3.4 2.9 1.8

Theatre & shows 1.6 62.1 12.6 9.2 2.0

Parachuting etc 1.2 82.7 3.8 0.6 0.1

Sunbathing 0.6 75.0 3.7 5.8 2.3

Casinos 0.5 72.5 9.8 5.6 0.1

Activity Definitely

not 2 3

Do that too

5 6 That

Local cuisine (fruit, lamb, veggies, seafood etc.) 0.4 1.8 3.1 19.3 13.6 21.5 38.2

Short walks 0.0 0.4 1.3 15.8 18.0 25.0 36.8

Glaciers 6.1 3.5 4.8 12.3 11.0 23.7 36.4

Sight-seeing tours(other than in your package) 4.4 2.2 2.2 14.5 18.0 19.3 32.0

Historic places 0.4 2.2 4.8 17.1 20.2 24.1 28.9

49

Evening entertainment (pubs, live music, restaurants)

3.5 6.6 7.5 20.2 14.5 17.5 26.8

Meeting locals 0.9 3.1 9.2 23.2 17.1 19.3 25.0

Maori performances 3.9 3.1 6.6 21.9 17.1 19.7 24.6

Hiking/Tramping 18.9 6.1 6.1 15.4 12.3 14.0 23.7

Botanic gardens 2.6 3.5 9.6 28.9 15.8 14.5 22.8

Shopping 4.4 6.1 8.3 28.1 15.8 12.3 22.4

Marine life 3.9 3.5 4.8 23.7 21.1 19.7 20.6

Boat tours 5.7 2.6 5.3 25.0 19.3 21.9 18.0

Museums and galleries 3.1 3.5 10.1 22.4 18.4 21.9 17.5

Meeting Maoris 3.5 3.9 8.3 31.1 18.0 14.0 17.5

Special events (sport/cultural) 8.3 4.8 8.3 26.3 19.7 11.0 15.9

Scenic flights 12.3 8.3 10.5 23.2 16.2 13.2 14.0

Swimming 13.2 4.4 7.5 28.5 15.8 13.2 13.6

Theatre and shows 14.0 9.6 15.8 21.5 15.8 8.3 11.4

Swimming with dolphins 21.5 8.8 10.5 22.4 11.4 10.1 11.0

Skiing 45.6 6.1 6.6 11.0 4.8 11.4 11.0

Winter sports 32.9 11.8 8.8 15.8 10.1 7.0 11.0

Jet-boating 31.1 5.7 9.2 19.3 11.0 11.0 9.6

Fishing and hunting 45.2 8.3 8.8 11.8 8.3 5.3 8.8

Kayaking/canoeing 30.3 6.6 12.3 18.0 13.6 7.5 7.9

Bird watching 24.1 14.0 15.4 22.4 9.2 5.3 7.5

Farmstay 21.9 14.0 13.2 24.1 11.8 5.3 7.5

Rafting 33.8 8.8 14.0 14.5 13.2 5.3 7.0

Casinos 54.4 10.1 6.6 11.4 6.1 1.8 7.0

Golf 55.7 8.3 4.8 14.5 2.6 5.3 4.8

Sunbathing 37.7 13.2 11.8 21.9 5.3 4.4 3.1

Bungy-jumping 72.8 6.6 7.0 3.5 1.3 2.6 2.6

Parachuting etc 61.4 9.2 7.9 9.2 6.1 1.8 0.9

German

Activity Highlight % Never Once More than once Very frequently

Day hikes 31.3 4.6 3.2 39.5 46.1

Marine life 31.1 15.2 30.2 43.6 4.3

Glaciers 28.9 12.2 39.5 40.4 1.9

Scenic flights 21.3 47.9 31.9 12.8 1.4

Hiking/Tramping 16.9 65.7 10.3 7.3 6.3

Boat tours 16.4 19.7 27.5 44.5 3.5

Sight-seeing tours 15.8 12.3 7.4 48.3 21.3


Short walks 13.1 3.6 1.3 33.0 55.5

Local cuisine 10.7 6.2 8.2 52.3 29.1

Bird watching 10.1 20.5 14.1 46.6 13.3

Jetboating 9.2 - - - -

Historic places 8.8 5.1 12.3 68.6 10.0


Museums & galleries 7.0 10.3 17.5 56.6 10.4

50

Botanic gardens 6.8 19.4 27.0 42.3 6.5

Rafting 6.6 80.1 12.2 0.9 0

Bungy-jumping 6.2 85.5 7.1 1.1 0

Swimming with dolphins 6.2 77.7 14.1 1.6 0

Farmstay 5.7 63.3 20.5 8.2 1.7

Evening entertainment 5.4 17.1 12.3 51.8 14.4

Parachuting etc 4.6 87.0 5.5 0.3 0.2

Swimming 3.8 25.8 12.0 44.2 13.1

Cycling, mtb 3.0 75.7 7.3 5.8 3.8

Special events (sport/culture) 2.5 46.0 18.8 17.7 1.7

Fishing & hunting 2.4 83.6 4.6 4.3 0.9

Sunbathing 1.9 38.2 10.6 38.7 5.7

Shopping 1.7 5.7 9.0 63.3 17.5

Theatre & shows 0.8 76.9 8.2 3.6 0.8

Golf 0.6 88.0 2.7 2.1 0.6

Casinos 0.3 88.5 3.0 1.1 0.2

Winter sports 0.2 90.5 1.7 0.2 0

Activity Definitely

not 2 3

Do that too

5 6 That’s why I’m

going

Day hikes 0.6 0.9 1.7 13.6 6.9 28.8 45.0

Marine life 2.2 0.9 1.5 13.2 10.0 25.3 44.4

Sight-seeing tours(other than in your package)

1.7 0.9 1.5 18.2 9.5 22.3 39.4

Historic places 0.4 1.9 2.8 16.9 13.9 23.2 38.3

Short walks 1.3 0.9 1.9 15.8 11.3 28.1 37.0

Local cuisine (fruit, lamb, veggies, seafood etc.)

0.9 0.9 1.1 18.2 10.6 28.6 36.8

Glaciers 8.9 1.9 5.0 14.5 12.6 22.3 32.5

Swimming 2.8 0.9 1.5 25.5 13.2 23.8 29.4


Hiking/Tramping 19.3 8.4 8.4 12.1 10.8 14.5 22.5


4.1 5.0 5.8 19.3 16.2 25.1 21.9

Botanic gardens 3.5 2.8 7.8 23.8 18.0 20.3 21.2

Bird watching 6.7 6.5 10.4 19.5 15.6 19.5 19.9


Boat tours 6.5 3.5 5.8 27.9 17.3 22.3 13.9

Shopping 5.0 8.4 8.7 31.0 18.4 13.2 12.8

Sunbathing 13.0 6.9 8.4 28.8 13.9 15.2 11.7


Cycling, mtb 18.8 10.0 8.2 22.5 12.3 15.2 10.4

Scenic flights 13.9 7.6 11.5 26.4 14.9 13.2 10.0

Farmstay 18.4 8.9 8.2 27.3 13.6 10.0 9.3




Jet-boating 41.3 10.2 10.6 16.9 7.4 7.1 3.9

51


Winter sports 59.3 10.4 5.8 11.9 3.5 2.6 2.8

Golf 74.0 8.0 5.2 6.3 0.4 0.2 2.6

Rafting 42.6 7.8 12.1 21.9 6.9 4.5 1.5

Parachuting etc 65.2 8.4 6.1 9.5 5.2 1.5 1.1

Casinos 64.1 15.6 6.3 7.8 1.5 0.4 1.1

Bungy-jumping 76.0 7.4 4.1 5.0 2.6 1.5 0.6

Japanese

Activity Highlight

% Never Once

More than once

Very frequently

Short walks 17.9 2.6 16.8 34.5 37.7

Hiking/Tramping 17.0 28.5 21.5 23.2 14.0

Meeting locals 14.9 14.5 16.2 34.3 21.5

Glaciers 13.2 40.9 28.7 14.0 2.3

Scenic flights 13.1 53.8 21.1 11.1 1.9

Boat tours 12.5 27.0 36.8 21.1 3.6

Sight-seeing tours (other than in your package) 10.4 25.8 23.6 27.7 9.4


Museums and galleries 9.1 15.3 33.2 31.1 8.3

Jet-boating 8.5 58.7 22.1 3.8 0.4

Farmstay 7.2 64.9 13.8 2.6 1.7

Meeting Maoris 6.0 30.8 27.7 21.5 4.3

Shopping 5.8 2.6 14.0 46.4 30.9

Local cuisine (fruit, lamb, veggies, seafood etc.) 5.8 11.1 24.3 42.6 12.3

Botanic gardens 5.8 25.1 29.2 26.2 5.1

Bungy-jumping 5.7 76.4 3.0 1.1 0

Rafting 5.5 70.0 10.2 1.7 0.4

Marine life 4.2 51.1 20.0 9.2 1.5

Skiing 3.6 74.2 2.5 3.0 2.3

Special events (sport/cultural) 3.4 56.2 12.5 11.3 1.7

Golf 3.0 66.4 7.4 4.5 2.3

Swimming with dolphins 2.6 76.4 3.6 0.8 0


2.5 48.7 14.3 13.8 4.3

Historic places 2.5 43.4 19.2 16.4 2.5

Sunbathing 2.1 48.7 10.2 12.6 7.5

Bird watching 2.1 52.1 13.2 13.6 2.5

Parachuting etc 2.1 77.5 3.0 0.2 0


Theatre and shows 1.3 64.2 5.8 6.4 3.0

Fishing and hunting 1.3 72.3 3.4 2.8 1.5

Swimming 1.1 60.8 8.7 7.9 4.3

Casinos 1.1 70.4 4.0 5.1 1.9

Winter sports 1.1 73.8 2.3 2.6 1.5

52

Activity Definitely

not 2 3 4 5 6

That’s why I’m going

Local cuisine (fruit, lamb, veggies, seafood etc.)

0.5 0.6 0.5 9.3 11.9 28.3 48.3

Short walks 0.5 0.6 0.5 7.7 13.4 30.0 46.2

Hiking/Tramping 1.2 0.9 1.9 11.6 14.9 27.8 40.3

Glaciers 2.2 1.2 2.2 10.0 12.4 30.8 39.2


Meeting locals 0.5 1.1 2.4 23.4 17.7 22.2 29.9

Sight-seeing tours(other than in your package)

2.4 1.9 2.4 15.6 17.3 29.0 29.4

Botanic gardens 0.8 3.2 3.0 18.8 16.8 26.8 29.3

Marine life 3.2 2.8 3.3 20.3 17.3 24.3 25.7

Shopping 1.8 2.4 4.7 23.1 18.9 21.5 25.2

Historic places 2.1 2.7 4.5 21.1 17.5 24.4 24.7

Boat tours 2.1 1.8 2.2 18.8 19.4 28.9 23.2


Bird watching 3.8 3.8 4.8 22.9 20.0 22.2 20.1


Scenic flights 8.3 5.6 5.8 22.5 16.6 19.0 18.8


4.7 5.5 6.2 26.8 19.0 17.8 17.9

Meeting Maoris 2.0 2.3 5.0 32.0 18.6 18.8 17.7

Swimming 10.4 5.8 6.4 30.3 10.9 16.4 15.9

Sunbathing 10.6 5.5 6.4 30.1 14.0 14.4 14.0


Farmstay 9.1 7.5 6.3 28.8 15.8 15.5 12.9

Golf 36.0 9.7 6.2 15.5 6.7 10.3 11.6



Rafting 17.3 9.6 7.1 26.4 12.1 11.4 9.6

Skiing 30.7 11.5 7.0 22.0 7.2 8.9 8.9

Winter sports 22.4 12.9 9.8 26.7 9.1 6.9 8.2


Jet-boating 19.8 10.7 9.0 25.1 12.9 10.7 6.5

Casinos 27.1 13.6 8.9 23.6 10.2 7.3 5.9

Parachuting etc 31.7 10.0 7.6 19.6 12.3 9.6 4.7

Bungy-jumping 52.0 12.4 6.1 12.5 4.4 3.8 3.8

53

6.0 Logistic regression (pg. 29)

Day hikes

Case Processing Summary

Unweighted Casesa N Percent

Selected Cases Included in Analysis 588 99.0

Missing Cases 6 1.0

Total 594 100.0

Unselected Cases 0 .0

Total 594 100.0

a. If weight is in effect, see classification table for the total number of cases.

Dependent Variable Encoding

Original Value Internal Value

0 0

hightlight 1

Categorical Variables Codings

Frequency

Parameter coding

(1) (2)

Age_category young 342 1.000 .000

middle-aged 198 .000 1.000

senior 48 .000 .000

gender male 329 1.000

female 259 .000

Block 0: Beginning Block

Classification Tablea,b

Observed

Predicted

highlight: Day hikes

Percentage Correct 0 hightlight

Step 0 highlight: Day hikes 0 402 0 100.0

hightlight 186 0 .0

Overall Percentage 68.4

a. Constant is included in the model.

54

b. The cut value is .500



Step 0 Constant -.771 .089 75.533 1 .000 .463

Variables not in the Equation

Score df Sig.

Step 0 Variables Age_category 24.709 2 .000

Age_category(1) 21.539 1 .000

Age_category(2) 8.605 1 .003

b19(1) 7.248 1 .007

Overall Statistics 26.923 3 .000

Block 1: Method = Enter

Omnibus Tests of Model Coefficients

Chi-square df Sig.

Step 1 Step 29.068 3 .000

Block 29.068 3 .000

Model 29.068 3 .000

Model Summary

Step -2 Log likelihood Cox & Snell R Square Nagelkerke R Square

1 704.837a .048 .068

a. Estimation terminated at iteration number 5 because parameter estimates changed by less than .001.

Classification Tablea

Observed

Predicted

highlight: Day hikes


Step 1 highlight: Day hikes 0 402 0 100.0

hightlight 186 0 .0


a. The cut value is .500

55




Age_category(1) 1.600 .491 10.607 1 .001 4.952

Age_category(2) .920 .503 3.342 1 .068 2.510

b19(1) -.277 .185 2.237 1 .135 .758

Constant -1.913 .498 14.736 1 .000 .148


56

Marine life

Case Processing Summary

Unweighted Casesa N Percent

Selected Cases Included in Analysis 588 99.0

Missing Cases 6 1.0

Total 594 100.0

Unselected Cases 0 .0

Total 594 100.0

a. If weight is in effect, see classification table for the total number of cases.

Dependent Variable Encoding

Original Value Internal Value

0 0

hightlight 1

Categorical Variables Codings

Frequency

Parameter coding

(1) (2)

Age_category young 342 1.000 .000

middle-aged 198 .000 1.000

senior 48 .000 .000

gender male 329 1.000

female 259 .000

Block 0: Beginning Block

Classification Tablea,b

Observed

Predicted

highlight: Marine life


Step 0 highlight: Marine life 0 407 0 100.0

hightlight 181 0 .0


a. Constant is included in the model.

57

b. The cut value is .500



Step 0 Constant -.810 .089 82.263 1 .000 .445

Variables not in the Equation

Score df Sig.

Step 0 Variables Age_category 19.022 2 .000

Age_category(1) 18.463 1 .000

Age_category(2) 10.266 1 .001

b19(1) 1.714 1 .191

Overall Statistics 19.092 3 .000

Block 1: Method = Enter

Omnibus Tests of Model Coefficients

Chi-square df Sig.

Step 1 Step 19.785 3 .000

Block 19.785 3 .000

Model 19.785 3 .000

Model Summary

Step -2 Log likelihood Cox & Snell R Square Nagelkerke R Square

1 706.216a .033 .047

a. Estimation terminated at iteration number 4 because parameter estimates changed by less than .001.

Classification Tablea

Observed

Predicted

highlight: Marine life


Step 1 highlight: Marine life 0 407 0 100.0

hightlight 181 0 .0


a. The cut value is .500

58




Age_category(1) 1.088 .410 7.028 1 .008 2.967

Age_category(2) .345 .426 .658 1 .417 1.412

b19(1) -.050 .187 .070 1 .791 .952

Constant -1.566 .420 13.891 1 .000 .209


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