=Paper= {{Paper |id=Vol-2393/paper_394 |storemode=property |title=Influence of the Country’s Information Development on its Tourist Attractiveness |pdfUrl=https://ceur-ws.org/Vol-2393/paper_394.pdf |volume=Vol-2393 |authors=Yuliia Lola,Svitlana Prokopovych,Olena Akhmedova |dblpUrl=https://dblp.org/rec/conf/icteri/LolaPA19 }} ==Influence of the Country’s Information Development on its Tourist Attractiveness== https://ceur-ws.org/Vol-2393/paper_394.pdf

Influence of the Country’s Information Development on
Its Tourist Attractiveness

Yuliia Lola1, Svetlana Prokopovich1, Olena Akhmedova1
1
Simon Kuznets Kharkiv National University of Economics, 9-A, Ave.Nauki,
Kharkiv, 61166 Ukraine
yuliia.lola@hneu.net, prokopovichsv@gmail.com,
yelena.akhmedova@hneu.net

Abstract. A number of studies have researched the effects of tourism on trans-
portation system, hotel industry, economic efficiency and environment. This
paper examines the influence of the information and communication tech-
nologies development on the inbound tourism intensity. The correlation and
regression analysis has been used to identify the relationship between the Travel
and Tourism Competitiveness Index, the Information and Communication
Technology Development Index and International tourism arrivals. The results
demonstrate that there is a close link between the countries’ tourist attractive-
ness and the level of their information and communication development. How-
ever, it is not equal for different countries, which are grouped by the level of in-
tensity of tourism arrivals, the level of the country’s attractiveness and its in-
formation and communication technologies development. Besides, the coun-
try’s information and communication technologies development has little effect
on the inbound tourism intensity

Keywords: tourist attractiveness of a destination, information and communica-
tion development of a country, the travel and tourism competitiveness index, in-
ternational tourism arrivals.

1 Introduction

The contemporary unification of the world society into a single information and
communication network and the transformation of information technologies into the
generative force of socio-economic development contributes to the close interconnec-
tion between countries, regions and societies of different nations. Most countries are
actively using Internet space to shape the country’s image (including tourist image).
Mainly because it is one of the most important tools for creating an appropriate image
of the country as a tourist destination that can greatly increase the intensity of the
inbound tourism flow. In this regard, the study of the impact of the country’s informa-
tion and communication technologies development on the tourism and travel devel-
opment is relevant and requires further theoretical and practical research.
2 Theoretical development and hypotheses formulation

Tourism development affects the development of a range of other areas of economic
activity. Inbound and outbound tourism has bidirectional causality with air transporta-
tion (Syed Abdul Rehman Khan and other, 2017). Air transport and tourism are
highly connected. Researches show that tourist-oriented airports may achieve higher
efficiency levels than non-touristic ones (Xosé Luis Fernández, Pablo Coto-Millán,
Benito Díaz-Medina, 2018).
The level of tourism development is estimated by the global index. The Travel &
Tourism Competitiveness Index has been the subject of some methodological criti-
cism, such as the arbitrary weighting of variables. There is an alternative methodol-
ogy for calculating this index based on two points of reference to propose a new stan-
dardization. А synthetic index that measures the state of the pillar in the worst posi-
tion, as well as other alternative indices, is calculated (Juan Ignacio Pulido-Fernández,
Beatriz Rodríguez-Díaz, 2019).
Depending on how the variables are included in the underlying technology specifi-
cation, the same tourism index can be oriented towards the assessment of either the
private or the public sector’s effectiveness (Walter Briec and other, 2018).
Trade openness, climate change and intensity of market competition increase tour-
ism efficiency in China. Tourism efficiency improvement in China was mainly driven
by technological improvement (Sami Chaabouni, 2019).
UNESCO’s World Heritage inscription is considered to positively influence tour-
ism demand. However, relevant econometric research has yielded inconsistent results.
A sub-group analysis identifies different factors in developing vs. developed countries
and cultural vs. natural WHS types. (Yang LanXuе, Thomas E.Jones, 2019).
The expansion of tourism translates into an environmental deterioration of the des-
tination (risk dimension) and, furthermore, it substantiates that there are specific vari-
ables connected to environmental sustainability (regulatory dimension) that contribute
to greater tourism growth, so that the relationship between tourism and environmental
sustainability is bidirectional (Juan Ignacio Pulido-Fernández, Pablo Juan Cárdenas-
García, Juan Antonio Espinosa-Pulido 2019).
The studies suggest that the effect of growth rate of total foreign tourist arrivals on
hotel equity return is asymmetric and state-dependent, conditional on the distributions
of hotel equity return. The study further identified that GTA has a significant influ-
ence only on equity returns of hotels with a small size (Ming-Hsiang Chen, 2016).
The shift of our view on information technology in tourism research from a primar-
ily a marketing-driven tool to a knowledge creation tool due to new technological
conditions such as the smartphone, drone, wearables, new connectivity and big data is
recognized. Some possible future research problems and challenges regarding our
existing views of the relationship between information technology and tourism are
studied (Zheng Xiang, 2018).
Not only ICTs empower consumers to identify, customise and purchase tourism
products but they also support the globalisation of the industry by providing effective
tools for suppliers to develop, manage, and distribute their offerings worldwide (Bu-
halis, 1998).
Buhalis (1998) stated that potential tourists have become moreindependent and so-
phisticated on using a wide range of tools to arrange for their trips (such as Expedia,
Googleand Kayak, visitbritain.com), web 2.0portals, wayn and tripadvisor, kelkoo).
Information Search is a signiﬁcant part of the purchase decision process and was
revolutionised as a result of the Internet. ICTs not only reduce uncertainty and per-
ceived risks but also enhance the quality of trips (Fodness &Murray, 1997).
The quality of the website, Digital Marketing, Social Networking, Multimedia,
Mobile Technologies and Intelligent Environments are the main keys factors of ICT
in Tourism (Elisabete Paulo Morais & other, 2016).
A Virtual Travel Community (VTC) makes it easier for people to obtain informa-
tion, maintain connections, develop relationships, and eventually make travel-related
decisions (Stepchenkova, Mills & Jiang, 2007).
Increasingly the impacts of ICTs are becoming clearer, as networking, dynamic in-
terfaces with consumers and partners and the ability to re-develop the tourism product
proactively and reactively are critical for the competitiveness of tourism organizations
(Buhalis, D., & Law R., 2008).
The analysis of the mentioned resources has allowed hypothesizing the following:
Hypothesis 1. Information development of the society contributes to the improve-
ment of the country’s tourist attractiveness.
Hypothesis 2. The development of information and communication technologies
in the countries across the globe positively influences the inbound tourism intensity.

3 Methods

The methods of multivariate statistical analysis, such as Descriptive Statistics, the
multiple regression, the cluster analysis were used to study the influence of informa-
tion and communication technologies on tourism. These statistical methods were im-
plemented with the StatSoft's software package Statistica. This package is well bal-
anced with the “power / convenience ratio”, has a wide range of functional data
analysis algorithms and has wide graphical capabilities for data visualization.
To carry out the research, the global indices and variables of tourism development
were selected:
The Travel and Tourism Competitiveness Index (TTCI), which reflects the level of
the country’s attractiveness for both tourists and also investors and representatives of
the tourism business. This index includes the characteristics of the following frame-
work: Enabling Environment, Travel and Tourism Policy and Enabling Conditions,
Tourism and Transport Infrastructure, Natural and Cultural Resources [12];
The Information and Communication Technologies Development Index (ICT) re-
flects the level of networked infrastructure and access to ICTs, the level of use of
ICTs in the society and more efficient and effective ICT use [11].
International tourism arrivals (ITA) is one of the main indicators that reflects the
effectiveness of all the measures adopted for the development of tourism in the coun-
try [3].
The objects of research are 80 countries of the world. The variables are the data for
2016. The countries without sufficient data were excluded from the database.

4 Results

In order to study the influence of the country’s information and communication de-
velopment on tourism development, the following algorithm of the research has been
proposed:
Stage 1. Selection of the initial variables.
Stage 2. Research of the basic statistical characteristics of the selected variables.
Stage 3. Verification of the first hypothesis on the basis of the correlation-
regression analysis methods.
Stage 4. Verification of the second hypothesis on the basis of the correlation-
regression and cluster analysis methods for the whole array of initial data and within
the scope of separate groups of countries, which are similar according to the level of
tourism activity.
For implementation of the first stage of the algorithm, the following variables were
selected: Travel and Tourism Competitiveness Index (TTCI), Information and Com-
munication Technologies Development Index (ICT) and International Tourism Arri-
vals (ITA).
The descriptive statistics was used to process, systematize and provide quantitative
description of the empirical data by means of the main statistical indicators. The im-
plementation of the second stage of the study presupposed the calculation of the fol-
lowing characteristics: Mean, Median, Mode, Frequency of Mode, Minimum, Maxi-
mum, Variance, Standard Deviation, Coefficient of Variation, Skewness, Kurtosis, as
well as histograming. The results of calculation are presented in Table.1.

Table 1. Descriptive Statistics
Descriptive Statistics
Variable

Variance
of Mode
Valid N

Median

Kurtos.
Skewn.
Mode
Mean

Coef.
Freq.

Dev.
Max

Var.
Min

Std.

TTCI 80 4,13 4,125 3,910 3 3,09 5,43 0,36 0,60 14,542 0,2079 -0,7614
ICT 80 6,52 6,875 – – 3,03 8,98 2,66 1,63 24,946 -0,4806 -0,7991
ITA 80 12684,2 5460,0 – – 121,0 82600,0 316498734 17790,4 140,256 2,3697 5,6854

The results of the histograming of distribution for each of the studied variables are
presented in Fig. 1 – 3.
Histogram: TTCI
K-S d=,06011, p> .20; Lilliefors p> .20
Shapiro-Wilk W=,97569, p=,13091
30

20
No. of obs.

0
2,5 3,0 3,5 4,0 4,5 5,0 5,5
X <= Category Boundary

Fig. 1. TTCI variable distribution histogram

Histogram: ICT
K-S d=,10402, p> .20; Lilliefors p<,05
Shapiro-Wilk W=,94517, p=,00184
22

14
No. of obs.

0
2 3 4 5 6 7 8 9
X <= Category Boundary

Fig. 2. ICT variable distribution histogram
Histogram: Arrivals
K-S d=,24004, p<,01 ; Lilliefors p<,01
Shapiro-Wilk W=,67618, p=,00000
60

40
No. of obs.

0
-10000 0 10000 20000 30000 40000 50000 60000 70000 80000 90000
X <= Category Boundary

Fig. 3. ITA variable Distribution Histogram

According to the results of the analysis of the obtained statistical characteristics
(see Table 1) and distribution histograms (see Figures 1-3), the following conclusions
can be made:
- the TTCI variable has a distribution close to normal. This is evidenced by the prox-
imity of the mean, mode and median, as well as small values of the skewness and
kurtosis. This variable has the least value of the coefficient of variation (14,52);
- the ICT variable also has a distribution rather close to normal. Its average value is
close to the median. But unlike the TTCI variable, it has a slightly larger range (from
3.03 to 8.98) and a larger coefficient of variation (24.95). It should be noted that al-
most half of the world’s countries (38 out of 80) fall into the last two intervals with
values of 7 to 8 and from 8 to 9. This means that a significant part of the countries in
the considered group has a high level of development of information and communica-
tion technologies (ICT);
- the ITA variable is significantly different from the previous two. First of all, it has a
completely different unit of measurement and dimension, therefore, during further
research with the simultaneous use of the TTCI and ICT variables, the calculations
will be made on the basis of the standardized data. Secondly, the distribution of this
variable is quite distant from normal. This is evidenced by the large difference be-
tween the mean and the median (12 684.2 and 5 460.0 thousand persons respectively),
as well as the statistical criteria of the Kolmohorov-Smirnov (K-S test), Shapiro-Wilk
test and Lillifors test. For the distribution of this variable the right-side bias is charac-
teristic (the skewness equals 2.37) and significant elevation (the kurtosis is equal to
5.69). In 2016, this variable was significant (from 121 to 82,600 people), more than
50 countries had the value of this variable up to 10,000 thousand people, in France
this value was more than 80,000, and in Spain and United States – it ranged from
70,000 to 80,000 thousand people.
The verification of the first hypothesis that the information development of the so-
ciety contributes to the improvement of the country’s tourist attractiveness was car-
ried out during the implementation of the third stage of the study. A pair correlation
coefficient between the TTCI and ICT variables was calculated according to the data
from all 80 countries. In 2016 it was equal to 0.711 that indicates a fairly close direct
linear relationship between these variables. The graphic representation of this connec-
tion is given in Fig. 4.

Fig. 4. Dispersion field (correlation field) between factors

The dispersion field proves a linear relationship between TTCI and ICT, therefore,
we can accept the hypothesis 1 that the greater the information development of the
country is, the better the tourism and travel sector is developed in this country. This
allows putting forward the second hypothesis that the development of information and
communication technologies in the countries of the world positively influences the
intensity of inbound tourism.
Thus, the implementation of the fourth stage involves verification of the second
hypothesis that involves the following sequence of steps:
Step 1. Determination of the pair correlation coefficients between the resulting ITA
variable and factor variables of TTCI and ICT.
Step 2. Construction of the multiple regression based on the standardized data of
the type:
where a1 та a2 are unknown parameters that are evaluated by the least squares
method.
Step 3. Distribution of countries into homogeneous groups according to the ITA,
TTCI and ICT variables on the bassis of the cluster analysis methods.
Step 4. Construction of the multiple regression (1) for each of the clusters.
Step 5. Making conclusions as to the hypothesis acceptance or rejection.
In the result of implementation of the first step the following values of the pair cor-
relation coefficients have been received: ; . These
values mean that there is a direct linear link of the moderate level between ITA and
TTCI, but between ITA and ICT there is a direct but weak link.
The obtained values are not sufficient for accepting or rejecting the hypothesis 2.
Therefore, during the second step, the following equation of multiple regression was
constructed:

This regression equation is statistically significant in terms of Fisher’s criterion (F
= 40.32), and separate parameters according to Student’s criterion ( ,
). The coefficients of the multiple correlation (R = 0.713), the determina-
tion ( )) and the corrected determination coefficient ( ) indi-
cate a sufficient quality of the model. There is no autocorrelation of the errors in this
model (the statistics of Darbine-Watson are approximately equal to 2, and the cyclic
coefficient of autocorrelation is close to 0). Thus, this model can be used for analysis
and forecasting.
We have analysed the problem under study according to this model. As can be seen
from the obtained regression equation, compared with the pair correlation coeffi-
cients, there is a significant increase of the influence of TTCI on ITA (from 0.6574 to
0.9293), the simultaneous change of direction and the increase of the influence of ICT
on ITA (from + 0.2734 to -0.3915). To answer the question whether these changes are
only due to the multicollinearity that are present in the model, or in fact there is an
inverse relationship between ICT and ITA, partial correlation coefficients have been
calculated and their statistical significance checked. The results of calculations are
given in Table 2.

Table 2. Results of correlation coefficients calculations
Variables currently in the Equation; DV: ITA
Partial Semipart
b* in Tolerance R-square t(77) p-value
Variable Cor. Cor.
TTCI 0,936420 0,684528 0,658439 0,494413 0,505587 8,23980 0,000000
ICT -0,392394 -0,366155 -0,275910 0,494413 0,505587 -3,45277 0,000905

As can be seen from Table 3, the value of the partial coefficients is:
; and they are statistically significant according to the
Student’s criterion.
During the next, third step, using the cluster analysis methods, we obtain homoge-
neous groups of countries. The grouping of countries is based on the hierarchical
method of full communication, which allows clearly divide the countries into two,
three, or four clusters. The division into 2 clusters is not informative. If we divide
countries into 4 clusters, then the last cluster will consist of only three countries (28,
69 and 77). Therefore, it is rational to divide countries into three clusters, which cor-
responds to the logical distribution of countries with high, medium and low intensity
of foreign tourists’ arrivals.
Based on the iterative method of clustering k-means, the following cluster results
have been obtained. The first cluster includes 30 countries with the low inbound tour-
ism activity (Cluster contains 30 cases). These countries are listed in Table 3.

Table 3. Members of Cluster Number 1 and Distances from Respective

Cluster Center
Саse Coun- Case Dis- Case Dis-
Distance Country Country
No. try No. tance No. tance
Alba- Nicara-
C_1 0,2130 C_29 Georgia 0,370 C_56
nia gua 0,569
Arme- Guate- 0,533 Para-
C_3 0,3328 C_32 C_58
nia mala 6 guay 0,448
Azer- Hondu- 0,562
C_6 0,5073 C_33 C_59 Peru
baijan ras 3 0,479
Philip-
C_9 Bhutan 0,4266 C_36 India 0,961 C_60
pines 0,119
Bosnia
Iran,
and Roma-
C_10 0,4561 C_37 Islamic 0,287 C_63
Herze- nia
Rep,
govina 0,626
Cam-
C_13 0,5963 C_41 Jamaica 0,166 C_65 Serbia
bodia 0,649
Co- Kyrgyz Sri
C_17 0,3328 C_44 0,455 C_70
lombia Republic Lanka 0,421
Do-
mini-
C_23 can 0,1642 C_51 Moldova 0,723 C_75 Ukraine
Repub-
lic 0,428
Vene-
Ecua- Mongo-
C_24 0,3597 C_52 0,244 C_79 zuela,
dor lia
RB 0,288
El
Monte-
C_25 Salva- 0,4453 C_53 0,585 C_80 Vietnam
negro
dor 0,348
Besides, these countries have a very low level of tourism potential (TTCI), infor-
mation, and communication technologies development (ICTs). Under the current
conditions of certain instability, Ukraine is referred to this group.
The second cluster includes 38 countries with an average level of inbound tourism
activity (Cluster contains 38 cases) and is presented in Table 4.

Table 4. Members of Cluster Number 2 and Distances from Respective

Cluster Center
Саse Dis- Саse Dis- Саse Dis-
Country Country Country
No. tance No. tance No. tance
Argen- 0,42326 C_2 0,29058 C_5 Nether- 0,47531
C_2 Estonia
tina 0 6 9 4 lands 0
Austra- 0,75429 C_2 0,22647 C_5 New Zea- 0,44557
C_4 Finland
lia 2 7 6 5 land 0
Barba- 0,51231 C_3 0,55392 C_5 0,42966
C_7 Greece Norway
dos 5 1 3 7 1
0,20128 C_3 0,42158 C_6 0,44142
C_8 Belgium Hungary Poland
8 4 9 1 8
0,54964 C_3 0,55850 C_6 0,41838
C_11 Brazil Iceland Portugal
9 5 0 2 3
0,33395 C_3 0,22727 C_6 Russian 0,57500
C_12 Bulgaria Ireland
7 8 4 4 Federation 4
0,69699 C_3 0,53658 C_6 0,52116
C_14 Canada Israel Singapore
3 9 2 6 2
0,47380 C_4 Korea, 0,55880 C_6 Slovak 0,48333
C_15 Chile
4 3 Rep, 7 7 Republic 7
Costa 0,47433 C_4 0,44958 C_6 0,26416
C_18 Latvia Slovenia
Rica 4 5 2 8 8
0,21570 C_4 0,49697 C_7 0,34754
C_19 Croatia Lithuania Sweden
6 6 7 1 2
0,37245 C_4 Luxem- 0,42012 C_7 Switzer- 0,69462
C_20 Cyprus
7 7 bourg 3 2 land 1
Czech
0,22669 C_4 0,73836
C_21 Repub- Malaysia
7 8 2 C_7 0,75310
lic Uruguay
8 3
Den- 0,40213 C_4 0,26146
C_22 Malta
mark 6 9 8

Countries in the cluster 2 are characterized by the highest level of tourism potential
(TTCI) and the average level of ICT development.
The third cluster includes 12 countries with the highest level of inbound tourism
activity (Cluster contains 12 cases). This claster is presented in Table 5.

Table 5. Members of Cluster Number 3and Distances from Respective

Cluster Center
Саse No. Country Distance Саse No. Country Distance
C_5 Austria 0,701654 C_50 Mexico 0,941073
C_16 China 0,727240 C_69 Spain 1,028845
C_28 France 1,230744 C_73 Thailand 0,926716
C_30 Germany 0,654982 C_74 Turkey 0,916710
C_40 Italy 0,171680 C_76 United Kingdom 0,678250
C_42 Japan 0,932930 C_77 United States 0,968470

Countries of the latter cluster are characterized by the highest level of tourism po-
tential development (TTCI) and rather high level of ICT.
It should be noted that the received country grouping by the level of tourism activ-
ity is sustainable, since the hierarchical method of complete dependence and the k-
medium method yielded identical results, except for the country number 78 (Uruguay)
which, according to the first method, was reffered to the low-income countries devel-
opment, and, according to the second method, it was reffered to countries with an
average level of development. As the final result we accept the one that gives the k-
medium method, since this method minimizes intragroup variance and maximizes the
intergroup, thus providing higher-quality clusterization.
The average means of the variables, according to which the clusterization was car-
ried out, are presented in Fig. 6

Fig. 6. Average means of TTCI, ICT and ITA variables by clusters

Analysis of the means given in Fig. 6, allows to draw the following conclusions.
First, the cluster number 1 is formed by the countries with the lowest values of the
TTCI, ICT and ITA variables. The second cluster consists of the countries with an
average level of TTCI and ITA, but with the highest level of ICT. The third cluster
includes the countries with the highest levels of TTCI and ITA with high (but not the
highest) level of ICT. In addition, the countries of the second and third clusters are
characterised by the inverse dependence between the ITA and ICT variables. This is
also confirmed by the calculation of the pair correlation coefficients between the vari-
ables for each cluster separately. The results of calculations are presented in the Table
6.

Table 6. Matrices of the pair correlation coefficients for each cluster

Claster 1 Claster 2
Variable TTCI ICT ITA Variable TTCI ICT ITA
TTCI 1 -0,0763 0,4156 TTCI 1 0,4883 0,3444
-
ICT
-0,0763 1 -0,1822 ICT 0,4883 1 0,1534
ITA 0,4156 -0,1822 1 ITA 0,3444 -0,1534 1
Claster 3
Variable TTCI ICT ITA
TTCI 1 0,8328 0,3931

ICT 0,8328 1 0,1444

ITA 0,3931 0,1444 1

Let us analyze the means of the obtained coefficients in more detail.
Thus, cluster 1, in comparison with other clusters, is characterised by the strongest
direct dependence between TTCI and ITA (0.4156) and the inverse dependence be-
tween ICT and ITA (-0.1822). Besides, the countries within this cluster have almost
no dependence between TTCI and ICT (-0,0763), that is, for countries of this group
we reject the hypothesis 1.
The absence of multicollinearity between the TTCI and IST factors enables to con-
struct a two-factor regression model (1):

This regression equation is statistically significant in general according to Fisher’s
criterion (F = 3.395, Significance F = 0.048). According to Student’s criterion, only
the influence of the TTCI factor ( ) is statistically signifi-
cant, whereas the influence of ICT is not statistically significant ( ). The
coefficients of the multiple correlation (R = 0.442), the determination ( )
and the corrected determination coefficient ( ) indicate insufficient qual-
ity of the model. Thus, on the basis of the above stated, for the countries with the low
level of tourism activity the hypothesis 2 is rejected.
Cluster 2 is characterised by a weak dependence between TTCI and ICT (0.4883),
as well as between TTCI and ITA (0.3444). There is a weak inverse dependence be-
tween the ICT and ITA values (-0.1534), that is, for countries of this group we accept
the hypothesis 1.
The lack of multicollinearity between the TTCI and ICT factors within the cluster
1 allows to construct a two-factor regression model (1):
This regression equation is statistically significant in terms of Fisher’s criterion (F
= 6.143, Significance F = 0.005). According to Student’s criterion, the influence of
both factors is statistically significant: і ТТСІ ( ), і ІСТ
( ). The coefficients of the multiple correlation (R =
0.504), the determination ( ) and the corrected determination coefficient
( ) indicate insufficient quality of the model. Thus, on the basis of the
above stated, the hypothesis 2 for the countries with the average level of tourism ac-
tivity is rejected.
Cluster 3 is characterised by a weak dependence between TTCI and ITA (0.33931),
as well as by a very weak dependence between ICT and ITA (0.1444). There is a
strong direct dependence between TTCI and ICT (0.8328), that is, for the countries of
this group we accept the hypothesis 1.
The presence of strong multicollinearity between the factors of TTCI and ICT does
not allow to construct a two-factor regression model (1).
Thus, on the basis of the aforementioned, the hypothesis 2 for the countries with
the high level of tourism activity is rejected.
Let us analyze the means of the obtained coefficients in more detail.
Thus, cluster 1, in comparison with other clusters, is characterised by the strongest
direct dependence between TTCI and ITA (0.4156) and the inverse dependence be-
tween ICT and ITA (-0.1822). Besides, the countries within this cluster have almost
no dependence between TTCI and ICT (-0,0763), that is, for countries of this group
we reject the hypothesis 1.
The absence of multicollinearity between the TTCI and IST factors enables to con-
struct a two-factor regression model (1):

This regression equation is statistically significant in terms of Fisher’s criterion (F
= 6.143, Significance F = 0.005). According to Student’s criterion, the influence of
both factors is statistically significant: і ТТСІ ( ), і ІСТ
( ). The coefficients of the multiple correlation (R =
0.504), the determination ( ) and the corrected determination coefficient
( ) indicate insufficient quality of the model. Thus, on the basis of the
above stated, the hypothesis 2 for the countries with the average level of tourism ac-
tivity is rejected.
Cluster 3 is characterised by a weak dependence between TTCI and ITA (0.33931),
as well as by a very weak dependence between ICT and ITA (0.1444). There is a
strong direct dependence between TTCI and ICT (0.8328), that is, for the countries of
this group we accept the hypothesis 1.
The presence of strong multicollinearity between the factors of TTCI and ICT does
not allow to construct a two-factor regression model (1).
Thus, on the basis of the aforementioned, the hypothesis 2 for the countries with
the high level of tourism activity is rejected.

5 Conclusion

Thus,
1) the tourist attractiveness of the country increases if there are developed informa-
tion and communication technologies, because the quality of tourist information re-
sources (including Internet resources), formation of the comfortable information envi-
ronment, the mass use of platforms for travel services searching and comparing of
their prices, the development of e-commerce in tourism in general contributes to the
improvement of the tourism infrastructure;
2) there is no dependence of this type between tourism attractiveness and the de-
velopment of information and communication technologies in the first group of coun-
tries, which is characterized by low TTCI, ICT and low intensity of tourist arrivals.
There is a significant dependence in the 2nd group of countries. There is a strong
direct dependence in the third group, characterised by the highest level of tourist arri-
vals and the highest level of tourist attractiveness. That is, the more the country is
attractive for tourism, the stronger is the interdependence between the indicators of
TTCI and ICT;
3) there is a weak inverse dependence between the countries’ information and
communication technologies development and the intensity of tourist arrivals. More-
over, this connection is not observed in any of the 3 distinct groups of countries. That
is, it can be argued that the development of information and communication technolo-
gies almost does not affect the intensity of inbound tourism.

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