Statistical Analysis of Tourism in Lugano, Switzerland: A Report

This report presents a statistical analysis conducted for the Lugano Tourist Board, focusing on understanding tourist behavior and destination choices in Lugano, Switzerland, and its competitors, Como and Milan. The study investigates the influence of age on destination choice, the impact of age on core destination motivations, and the effect of destination on the strength of attributes for tourists. Employing data collection through a survey of 1500 respondents and utilizing statistical tools like SPSS, including regression and ANOVA, the analysis examines relationships between variables such as age, destination preference, spending habits, and motivations. The findings reveal insights into market trends, segment motivations, and the strengths of Lugano compared to its rivals, offering strategic recommendations for the Tourist Board to enhance its appeal, address declining visitor numbers, and improve its communication strategies. The report also highlights the importance of providing affordable accommodation and effective marketing to attract tourists, providing a comprehensive overview of the challenges and opportunities in the Lugano tourism sector.

Statistics for Travel and Tourism

Table of Contents
INTRODUCTION......................................................................................................................2
1.1Client Description.............................................................................................................2
1.2 Research Questions..........................................................................................................2
1.3 Practical Importance.........................................................................................................3
Methods......................................................................................................................................4
2.1 Data Collection.................................................................................................................4
2.2 Data Analysis...................................................................................................................4
Findings......................................................................................................................................5

INTRODUCTION
1.1Client Description
Lugano Tourist Board are responsible for the development of Tourism in Swiss City
of Lugano in the Province of Ticino. After conducting research it has been analyzed that
Lugano has an unwarranted image as a sleepy destination and as a result, none of the tourist
comes or attracted towards it. Though it is famous of its Alpine lake that keep attracting
tourist and even tourist may easily visit Italy because it is close to it specially to the Como
and Milan which keep attract younger tourist toward it. It is so because visitor enjoy the
nightlife and these places are more cheaper than Lugano. The place is famous for old
buildings, museum and its natural sights which in turn leads to attract range of people but,
from many years, number of visitors are decreasing. The city is well known for its natural
insights but from last many years, the number of visitor are decreases and even data reveals
that the young generation people are also not attracted for their education purpose and this in
turn leads to affect the results in opposite manner. But with the help of current study, these
issues are easily identified and also describe the relationship between the variable by using
relevant tools under statistical data.
Through this report, it is easy to understand the challenges of Lugano Tourist Board
and also provide strategies in order to overcome the issue by using the real data. The entire
report is based upon the statistical analysis in which different tools and methods are used that
assist to answer the research question and also examine the solution in order to overcome the
issue.
1.2 Research Questions
 What is the influence of destination choice on age of tourist (participants) to Southern
Switzerland/ Northern Italy?
Under this, the independent variable are Lugano, Como or Milan, while dependent variable is
age.
 What is the influence of age category on importance of core destination choice
motivations to Southern Switzerland/ Northern Italy?
In this research question, independent variable are 18-30, 31-50, 51-70, 71+ and dependent
variable are relaxation, beauty, activities, cultural and social.

 What is the influence of destination on strength of attributes for tourist (participants)
to Southern Switzerland/ Northern Italy?
Under this, independent variable are Lugano, Como or Milan, and dependent variable are
relaxation natural beauty, outdoor activities, cultural and social.
1.3 Practical Importance
By referring the above questions, study will inform the position strategies for Lugano
with regards to their competitor i.e. Como and Milan such as:
Through the first research question, Lugano’s current market trend is identified with
regards to tourist age, by comparing its rivals. Thus, it provide the positioning strategy for the
company, while in second research questions the relationship is determine between age and
motivation to the chosen destination, thus it helps to analyze what are the different market
segments which motivate the destination choice.
As per the last research question, the perspective of tourist is identified and also the
strength of Lugano is determined as compared to their competitor and this describe the
strategy through which the issues is overcome up to some level. Hence, with the help of the
current strategy, Lugano reposition itself in order to attract the customers and make a market
viable for the tourist and also develop the bets communication strategy that helps to meet the
define aim and answer the research question in better manner.
Further, it is analyzed that with the help of natural beauty and attractive places,
visitors are automatically attracted towards a destination and this in turn leads to enhance the
brand image of the place towards other competitors. In the same way, the present study will
also help to analyze the ways through which the destination is attracted. As government keep
developing the best way through which the destination keep attracted visitors, some of them
are as mention below:
 There is a need to provide cheap accommodation facilities to the visitor which in turn
leads to attract the customers and they visit the place again and again.
 On the other side, tax rate should be decreased because it discourage the people to
visit other places because most of the amount has to be paid to government and as a
result, middle class people do not afford to visit the place again and again. By
implementing this strategy it is beneficial for the city to implement the same which in
turn leads to attract the visitors.

 Further, different marketing tools should be used which in turn help to attract visitor
and these tools must contain all the effective and related information which encourage
visitor to attract the range of people towards it. Though different tourist agency uses
this method in order to attract local and national visitors towards it and thus, there is
need to focus on the same.
Methods
2.1 Data Collection
With the help of data collection methods, it is easy to determine the challenges which
Lugano face and for that primary data collection methods has been used in which 1500
respondents are selected who visited Southern Switzerland or Northern Italy destination in
2016. With the help of these respondents, answer of research question is determined, but with
the help of secondary data it is analysed that there were around 30 million visitors visited the
place and that is why selecting only 1500 out of them is very small in relation to the large
population.
So, for the research question, use of destination choice is consider as an independent
variable and for this research these destinations are Lugano, Como and Milan. Thus, the
sample size is reduce up to 600 while on the other hand the ideal sample size for a population
is 18 million who are working with 95% of the confidence level, while 5% is consider as a
margin of error. Moreover, the data is collected with the help of simple random sampling
method under probability sampling in which all the respondents are selected randomly.
Further, the biggest disadvantage of using this strategy is such that it do not provide a chance
to all the respondents to take part in this and that is why, chooses some out of many. Hence,
in the current study, this is used by the scholar in order to generate the best outcomes.
2.2 Data Analysis
For this study, scholar uses thematic data analysis for the study in which proper charts
and tables are used which in turn leads to provide proper interpretation to all the views of
scholar. Further, statistical tool is also used such as SPSS in which regression analysis is
perform in order to determine the relationship between two variables. Further, using
statistical software, all the data are proper interpreted and also assist to determine the
relationship between each other. With the help of table, proper results are interpreted which
are collected by using primary research and this in turn leads to determine the results as well.

On the other side, descriptive analysis is also perform for the given data in order to
determine the range, mean, median and standard deviation. This in turn leads to analyze the
actual challenges face by Lugano which affect the visit of tourist in opposite manner.
Moreover, to answer all the three research question stated above, SPSS as a statistical tool is
used which in turn leads to generate accurate results and meet the define aim as well. But on
the other side, there is a limitation of using this tool is such that it do not help to analyze the
large data set and it also affect the results which are conducted while performing the internal
functionality. That is why, most of the scholar are not uses this model which in turn leads to
affect the results in negative way. In the present study, anova test is also applied which in
turn leads to determine the mean time spend between the destination and how it affect the
results in opposite manner.
Findings
It is necessary to interpret and find out outcomes so that relationship between dependent and
independent variable is analysed. Here, regression and annova test is done to find out whether
there is influence of time of stay on visited destination or not. The annova is applied to
determine mean time spend between destination is significant or not. Furthermore, another
factor identified is influence of money spend on visited destination by airport selection.
Descriptive Statistics
N Range Sum Mean Std. Deviation
Statistic Statistic Statistic Statistic Std.
Error
Statistic
Residence 44717 947 34125724 763.15 1.265 267.551
Countryvisit 27737 951 14892986 536.94 1.684 280.468
Airportcode 39206 995988 22188059313 565935.30 1332.457 263833.198
Ukleg 39205 11720 85711985 2186.25 9.831 1946.609
Bustick 6859 8 13495 1.97 .010 .823
Flightype 38577 7 80321 2.08 .003 .546
Ind 2310 1 2372 1.03 .003 .162
Stay 14475 999 191797 13.25 .580 69.805
Spend 14065 568297
7 23449441 1667.22 594.113 70459.339
Valid N (listwise) 1604
Descriptive Statistics
Variance
Statistic
Residence 71583.526
Countryvisit 78662.403
Airportcode 69607956230.535
Ukleg 3789286.974

Bustick .677
Flightype .299
Ind .026
Stay 4872.737
Spend 4964518479.406
Valid N (listwise)
Interpretation - from above data it is analysed that mean of residence is 763.6 and SD is
267.7.. also the range is 947. Also, it is stated that mean of country visit is 536 and SD is 280
similarly, mean of airport code is 585935 and SD is 263883. Likewise the mean of ukleg is
2186 and SD is 1946. Furthermore, average of bustick is 1.97 and SD is .823 and mean of
flight type is 2.08 and SD is .546 the average of ind is 1.03 and standard deviation is .162.
also, it has been analysed that the mean of stay is 13.25 and SD is 69. And at last average
of spend is 1667 and SD is 70459. So, these all data was obtained from descriptive analysis.
However, the variance of residence is 71583 and of country visit is 78662. Besides that,
airport code variance is 696907 and of ukleg is 3789. In addition, the busticck variance
is .677 and of flight type is .299. also, variance of ind is .026 and of stay is 4872. At last
variance of spending is 49645.
Oneway ANOVA
countryvisit
Sum of Squares df Mean
Square
F Sig.
Between Groups 47405095.426 134 353769.369 4.774 .000
Within Groups 536899692.777 7245 74106.238
Total 584304788.203 7379
Interpretation – by analyzing the above table it is found that significant value obtained is
P= .000 which is less than P= 0.05. this means that there is no relationship between country
visit groups. The people do visit countries mentioned in it that is France, Spain and
Germany. Also, the destinations in it are not related to each other. However, mean time spend
between these destination is not significantly static. So, all these destinations are significant
as there is no relationship between them.
Regression
Descriptive Statistics
Mean Std. Deviation N
airportcode 564221.69 262277.300 12107

spend 675.20 1220.368 12107
Correlations
airportcode spend
Pearson
Correlation
airportcode 1.000 .008
spend .008 1.000
Sig. (1-tailed) airportcode . .202
spend .202 .
N airportcode 12107 12107
spend 12107 12107
Model Summaryb
Model R R Square Adjusted R
Square
Std. Error of the
Estimate
Change Statistics
R Square
Change
F Change df1
1 .008a .000 .000 262280.564 .000 .699 1
Model Summaryb
Model Change Statistics Durbin-Watson
df2 Sig. F Change
1 12105a .403 1.709
a. Predictors: (Constant), spend
b. Dependent Variable:
airportcode
ANOVAa
Model Sum of Squares df Mean Square F Sig.
1
Regression 48058623729.774 1 48058623729.774 .69
9 .403b
Residual 832716198169647.400 12105 68791094437.806
Total 832764256793377.100 12106
a. Dependent Variable:
airportcode
b. Predictors: (Constant), spend
Coefficientsa
Model Unstandardized Coefficients Standardized Coefficients t Sig.
B Std. Error Beta
1 (Constant) 563119.330 2724.219 206.709 .000
Spend 1.633 1.953 .008 .836 .403
a. Dependent Variable:
airportcode

Residuals Statisticsa
Minimum Maximum Mean Std. Deviation N
Predicted Value 563119.31 625516.13 564221.6
9 1992.440 12107
Residual -558373.625 436856.813 .000 262269.731 12107
Std. Predicted Value -.553 30.764 .000 1.000 12107
Std. Residual -2.129 1.666 .000 1.000 12107
a. Dependent Variable:
airportcode
Interpretation: Through the above table, it has been interpreted that the p value of anova
tables reflected that it is higher than 0.05 which means that null hypothesis is accepted. So it
can be stated that there is a relationship between the destination and spending. Such that
people have high amount, then they are able to spend high amount to the destination and this
is clearly reflected that high income people are able to visit expensive destination and this in
turn leads to explore the places in better manner.
Regression
Descriptive Statistics
Mean Std. Deviation N
airportcode 563718.98 262415.370 12451
stay 13.29 66.776 12451
Correlations
airportcode stay
Pearson
Correlation
airportcode 1.000 -.020
stay -.020 1.000
Sig. (1-tailed) airportcode . .014
stay .014 .
N airportcode 12451 12451
stay 12451 12451
Model Summaryb
Model R R Square Adjusted R
Square
Std. Error of the
Estimate
Change Statistics
R Square
Change
F Change df1
1 .020a .000 .000 262374.855 .000 4.845 1

Model Summaryb
Model Change Statistics Durbin-Watson
df2 Sig. F Change
1 12449a .028 1.708
a. Predictors: (Constant), stay
b. Dependent Variable:
airportcode
ANOVAa
Model Sum of Squares df Mean Square F Sig.
1
Regression 333554321304.020 1 333554321304.02
0 4.845 .028b
Residual 856996185063519.000 1244
9 68840564307.456
Total 857329739384823.000 1245
0
a. Dependent Variable:
airportcode
b. Predictors: (Constant), stay
Coefficientsa
Model Unstandardized Coefficients Standardized Coefficients t Sig.
B Std. Error Beta
1 (Constant) 564749.188 2397.490 235.558 .000
stay -77.514 35.214 -.020 -2.201 .028
a. Dependent Variable:
airportcode
Residuals Statisticsa
Minimum Maximum Mean Std. Deviation N
Predicted Value 487312.81 564749.19 563718.9
8 5176.052 12451
Residual -556583.188 439823.125 .000 262364.317 12451
Std. Predicted Value -14.761 .199 .000 1.000 12451
Std. Residual -2.121 1.676 .000 1.000 12451
a. Dependent Variable:
airportcode
Interpretation: Through the above table, it is interpreted that the significance value is 0.28
which is higher than standard value and this is clearly reflected that the null hypothesis is
accepted and that is why, there is a relationship between the destination and stay. Such as if a
tourist stay in the place for some specific days, then they are able to explore the things in

more better manner. Like, Lugano is famous for natural sights and building and that is why,
there is a need to stay for more days in order to visit entire city and this in turn leads to
creates positive impression.
Oneway
ANOVA
Countryvisit
Sum of Squares df Mean
Square
F Sig.
Between Groups 155950936.092 1441 108224.106 1.523 .000
Within Groups 411010577.912 5784 71059.920
Total 566961514.004 7225
Interpretation – by analyzing the above table it is found that significant value obtained is
P= .000 which is less than P= 0.05. this means that there is no relationship between country
visit these three destination. So, it shows that there is no influence of money on the
destination and the mean time spend in them is not significantly static. Therefore, no
destination is significant from one another and all are same.