International Tourism Marketing: An Analysis on Xinjiang Uygur Autonomous Region (XUAR) of China

IOSR Journal of Business and Management (IOSR-JBM)
e-ISSN: 2278-487X, p-ISSN: 2319-7668. Volume 19, Issue 1. Ver. V (Jan. 2017), PP 20-26
www.iosrjournals.org
DOI: 10.9790/487X-1901052026 www.iosrjournals.org 20 | Page
International Tourism Marketing: An Analysis on Xinjiang
Uygur Autonomous Region (XUAR) of China
Dilidaer Mulati1
, İlkay Karaduman2
1
(Social Sciences Institute, Istanbul Aydin University, Turkey)
2
(Faculty of Economics and Administrative Sciences, Istanbul Aydin University, Turkey)
Abstract: As XUAR international tourism industry is improving gradually, we should see there are still serious
problems in its industrial structure performance. With shift-share method, Pearson Correlation method and
Principal component method, based on data of XUAR international tourism foreign exchange incomes from
2006 to 2015, this paper empirically analyzes each sector of XUAR international tourism and its industrial
structure performance, as well as problems in sectors of transportation, sightseeing, accommodation, cater,
commodity sales and entertainment. Meanwhile this paper also puts forward solutions to increase the benefit of
XUAR international tourism industry.
Keywords: International tourism, XUAR, Tourism Marketing
I. Introduction
International tourism is an important part of tourism industry and international service trade, which
plays a more important role in promoting global economic development, so the development of international
tourism has gained more attention around the world. Per the UNWTO Tourism Highlights by United Nations
World Tourism Organization [1]
, the number of international visitors in 2015 increased by 4.6% to 1.186 billion
compared with the number of 2014. In 2014, more than 52 million tourists (overnight visitors) traveled abroad.
The total revenue generated by international travelers for accommodation, catering, entertainment, shopping and
other services in 2015 was approximately $1.232 billion, increasing by 3.6% due to the foreign exchange rate
fluctuations and inflation. International tourist arrivals increased by 4.4 % or 1.184 million in 2015.In addition
to international tourism revenue (travel expenses of international balance of payments), international tourism
also generated $210 billion in international export transport services for non-residential visitors, bringing
tourism exports revenues of $1.4 trillion or an average daily income of $4 billion [2]
.
United Nations World Tourism Organization has released the UNWTO Tourism Highlights[3]
, which
shows World Tourism Organization is very concerned about the emerging destinations, and economies and
destinations are mentioned frequently. Chinese elements continue to be the focus of attention.Per Annual Report
on China 's Inbound Tourism Development 2016 from China Tourism Research Institute [4]
, in 2015 China
received 13,382.04 million inbound tourists, an increase of 4.14%. In 2005, foreign exchange revenue of
inbound tourism reached $113.65 billion, and tourism service trade surplus reached $9.15 billion. In 2015,
China receives 568.857 billion overnight visitors, an increase of 2.30%.The gross market size ranked fourth in
the world, following France, United States and Spain. As the concept of new Silk Road has been proposed,
XUAR has taken actions in their own specialties - the success organization of International Conference on
Tourism Development on the Silk Road, and Urumqi Declaration with 14 other countries along the Silk Road.
XUAR has set up six oversea sales centers in Kazakhstan, Russia, Dubai and other countries. We have cooperated
closely with Kazakhstan and other neighboring countries for tourism exchanges and more convenient visa policy -the policy
of visa free for tourism team of 3-50 persons will soon be implemented. However, compared with the rapid
development of the global tourism industry, growth of XUAR international tourism industry is left far behind.
Therefore, now, it is of great significance to grasp consumption structure of XUAR international market, to
establish marketing strategy and to enhance the international competitiveness of XUAR.
II. Analysis On Consumption Structure Of XUAR International Tourism
Simply, tourism can be defined as the practice of traveling for recreation and with a point of
management the definition can be such as the management process of tourists and tourism related businesses[5]
.
In the literature, tourism marketing has been researched in detail from its past to its present and future
[6][7][8][9][10][11][12][13]
.Dwyer, et al. found tourism has been commonly recognized that it is neither a single industry
nor a single market. In Tourism is a social industry with strong integrity and intrinsic relevance which composed
of elements of sightseeing, catering, accommodation, entertainment and Commodity sales[14]
.An analysis on
consumption structure of international tourist market is instructive and meaningful to take effective measures to
solve tough problems, optimize the structure of the tourism industry and improve economic level of tourism
industry.

International Tourism Marketing: An Analysis on Xinjiang Uygur Autonomous Region (XUAR) of
2.1 Deviation of Structural Benefit of International Tourism Industry Shift- Share Analysis
Shift-share analysis is a relatively simple and widely used technique for describing regional economic
growth and measuring policy effects over time [15]
.According to international tourism income composition of
XUAR in the period of 2006-2014 (table 1), using the shift share analysis of the international tourism industry
(based on figures of 2013). Transportation, sightseeing, accommodation, catering, commodity sales and
entertainment are considered as the influencing factors of foreign exchange earnings of international tourism. [16]
:
Table 1: The Composition of XUAR International Tourism Revenue in the Period of 2006-2014 (%)
Year Transportation Sightseeing Accommodation Catering Commodity Sales Entertainment
2006 24.3 3.6 9.1 5 40 6.7
2007 15.5 2 6.8 5.5 52.5 1.6
2008 23.1 3.2 7.8 5.6 47.4 1
2009 22.1 5.3 8 6.1 46.2 1.6
2010 25.9 4.8 8.1 5.5 36.3 1.4
2011 24.3 3.2 7.7 4.3 38 0
2012 38.3 5.4 12.2 5 20 7.1
2013 35.9 6.4 12.3 6.1 19.8 6.2
2014 39.4 7 11.9 7.3 15.7 6.4
According to the shift-share method, the equation of XUAR tourism economic growth rate is shown as
follows: Yio is foreign exchange income of XUAR tourism industry (i) in base-period;Yit is foreign exchange
income of XUAR tourism industry (i) in a certain period (t); Cio is the cumulative foreign exchange earnings of
the whole tourism industry (i) from the base period; Cit is foreign exchange income of XUAR tourism industry
(i) in period (t);Co is tourism foreign exchange earnings of the entire Chinese tourism industry in base period; Ct
is foreign exchange income of Chinese tourism industry in a certain period (t); so Ri,foreign exchange earnings
growth rate of XUAR tourism industry (i) is shown as follow:
Ri=(Yit-Yio)/Yio=Ui+Vi+Wi
Ui=(Ct-Co)/Co*100%
Vi=(Cit/Cio-Ct/Co)/*100%
Wi=(Yit/Yio-Cit/Cio)/*100%
In those equations: Ui reflects the tourism industry growth rate; Vi reflects the efficiency in the tourism
industry (i): when Vi>0, the tourism industry sector is growing faster than the average growth rate of the whole
Chinese tourism industry, and the efficiency of the sector is advantageous; Wi is the advantage of XUAR
tourism industry (i) compared with the sector (i) nationwide. When Wi>0, the greater the value of XUAR
tourism industry the more advantages it has; when Wi<0, the smaller the value is the less advantages it has. Take
year 2013 as the base period and based on Xinjiang Statistical Yearbook (2013 and 2014), the values of Ui, Vi,
Wi of the international tourism industry in 2014 can be calculated (table 2) [17]
.
Table 2: Structural Benefit Deviation of International Tourism Industry-- Shift-Share Analysis (Base Period
2013)
According to data in table 2, it can be concluded that in tourism industry of China, sectors with better
revenue include catering, accommodation and transportation. However, there is no advantages of the tourism
sector in XUAR compared with the whole industry of China.
2.2 Pearson Correlation Analysis
In order to evaluate the rationality of the effect of each sector to the international tourism foreign
exchange income, here we have done the Pearson correlation analysis on international tourism industry and
sectors that affect international tourism industry[18]
. Using Pearson correlation analysis method of SPSS, the
results are as follows:

Table 3: Correlations Among Sections of Tourism Industry
Sectors Pearson Correlation Sig. (2-tailed)
Transportation .958** 0
Sightseeing .927** 0
Accommodation .955** 0
Catering .937** 0
Commodity Sales .558 0.118
Entertainment .748* 0.02
** Correlation is significant at the 0.01 level (2-tailed).
* Correlation is significant at the 0.05 level (2-tailed).
Generally, the four-level classification method is used for judging the relationship between variables:
the absolute value of correlation below 0.3 means weak correlation; 0.3-0.5 means low linear correlation; 0.5-
0.8 means significant correlation; number more than 0.8 means high correlation. It can be seen from table 5 that
the coefficient of transportation and international tourism industry is 0.958, the coefficient of sightseeing and the
international tourism industry is 0.927, accommodation 0.955, catering 0.937, entertainment 0.748 and sales of
goods 0.558, which means that each sector has significant positive correlation with the international tourism
industry. The values of transportation, sightseeing, accommodation and catering are more than 0.8, indicating
that these four factors are the most important factor in determining the number of foreign exchange earnings of
international tourism.
2.3 Principal Component Analysis
2.3.1 Basic Principle of Principal Component Analysis
Principal component analysis (PCA) is an algorithm to simplify the original data set by reducing the
dimension of data sets. The main idea of the algorithm is to repeatedly observe an object and do comprehensive
process of several correlated random variables, and then the obtained one or several comprehensive indexes can
replace the original variables. The indexes can represent characteristics of the object independently and, at the
same time, can retain the integrity of the original information. And principal components analysis is classified
among the descriptive methods analyzing interdependencies between variables. Hence, there are no dependent
variables and independent variables, it is so important to combine the interdependence between the variables [19]
.
The fundamental transformation can be shown as follow:












































pmpm
p
p
m
X
X
X
aaa
aaa
aaa
PC
PC
PC
..................
2
1
m21
22212
11211
2
1
(1)

















mpm
p
p
aaa
aaa
aaa
............
m21
22212
11211
is a weighted coefficient matrix, and
pm  . PCi means the the order of principal component of the original
eigenvector. According to equation (1), the principal component matrix is a linear combination of original
feature vectors. In addition, parameters in equation (1) should satisfy the following conditions:
(a)
;,...,3,2,12... 22
2
2
1 miaaa ipii  ，
(b)
,,...,2,1,,,0),( mjiijPCPCCov ji 
That is, principal component are orthogonal with each other.
2.3.2 Calculation Steps of PCA
To calculate relation matrix








pppp
p
p
N



...
......
...
...
21
22221
11211
(2)
In equation (2),( i,j=1,2,...,p) is correlation index of original variables xi and xj, the equation is as follow:

 

 




l
m
n
k
jkjiki
l
m
jkjiki
ij
xxxx
xxxx
r
1 1
22
1
)()(
))((
(3)
Because R is a real symmetric matrix (i.e. rij=rji), so we only need to calculate its upper triangular element or
lower triangular element.
To calculate eigenvalues and eigenvectors
To solve eigenvalue equation 0||  NI for eigenvalue i (i=1,2,...,p),and put them in order of magnitude
as
0...21  p
; and then to get the eigenvector ie
(i=1,2,...,p)corresponding to the eigenvalue i .
To calculate principal component contribution rate and cumulative contribution rate Contribution rate of
principal component iS
:
),...,3,2,1(
1
pi
r
s q
k
k
i
i 


(4)
Cumulative contribution rate:




q
k
k
m
k
k
1
1


(5)
Generally take eigenvalues of main components corresponding to the cumulative contribution rate between 85-
95% and put them in order - first, second,... , m(
pm  ).
To calculate principal component load
),...,2,1,(),( pkiexsp kikik  
(6)
The principal component score can be further calculated:








nmnn
m
m
sss
sss
sss
S
...
........
...
...
21
22221
11211
(7)
According to the previous standardized data, we put figure of each sample into the equation and get
new data of principal component for each sample respectively, or so-called principal component scores. And
then the preliminary analysis on information of original variables can be obtained through the analysis of the
principal component scores.
2.3.3 Results of Principal Component Analysis
When doing principal component analysis, we must first carry out KMO test and Bartlett test, which is
the applicable conditions of the principal component analysis. KMO is the indicator to check the sample
adequacy, which should be generally greater than 0.5. SPSS is used here for data processing, and KMO and
Bartlett test results are shown in the following table.
Table 4 KMO and Bartlett's Test
Kaiser-Meyer-Olkin Measure of Sampling Adequacy. 0.690
Bartlett's Test of Sphericity Approx. Chi-Square 84.789
df 15
Sig. 0.000
The spherical Bartlett test is to test whether the correlation matrix is a unit matrix, which indicates
whether the factor model is appropriate. Through the Bartlett test, it is showed that Bartlett value is 84.798 and
P is smaller than 0.0001, which means the correlation matrix is not a unit matrix, so factor analysis should be
considered. The value of KMO is 0.690 which is smaller than 0.5, indicating that factor analysis results are
acceptable based on the statistical test.Correlation coefficient matrix eigenvalue and variance contribution rate
of each factor. To input the data into SPSS, and after rotation, the figures of factors are as follow:

Table 5: Total Variance Explained
In Table-5 cumulative contribution rate of the first two common factors has reached 97.888%, so it can
represent the change of the correlation variables of the whole sample, and the cumulative contribution rate of the
first principal component has reached 80.116%.
Table 6: Communalities
Initial Extraction
Transportation 1 0.988
Visiting 1 0.98
Accommodation 1 0.989
Catering 1 0.954
Merchandise Sales 1 0.993
Entertainment 1 0.968
Extraction Method: Principal Component Analysis.
According to Table-6, the common variance of each index variable is greater than 0.9, which means that these
two common factors can practically reflect the most information of the original index variables.
Table 7: Component Matrix
Component
1 2
Transportation 0.994 0.012
Visiting 0.99 -0.013
Accommodation 0.995 0.011
Catering 0.975 0.069
Commodity Sales 0.288 0.954
Entertainment 0.904 -0.389
2 components extracted.
Figure1: Principal Component Plot
Component Initial Eigenvalues Extraction Sums of Squared Loading
Total % of Variance Cumulative % Total % of Variance Cumulative %
1 4.807 80.116 80.116 4.807 80.116 80.116
2 1.066 17.772 97.888 1.066 17.772 97.888
3 0.105 1.752 99.64
4 0.016 0.26 99.899
5 0.003 0.058 99.957
6 0.003 0.043 100

According to Table-7 and Figure-1, the first principal component has a greater positive load in traffic,
sightseeing, accommodation, catering and entertainment, which is positively correlated to the foreign exchange
earnings of international tourism industry. The second principal component has a greater positive load in
commodity sales, mainly reflecting the condition of tourism consumption.Calculation of the scores of each
principal component and the comprehensive score. Each principal component is a linear combination of the
original index, so the scores of the two principal components can be calculated according to index of each
original coefficient provided by coefficient matrix. Z1 and Z2 represent scores of the two-principal component.
Principal component weight:


2
1i
iiiW 
. The result of calculation is shown in table 8:
Table 8: Weight of Principal Factors
To calculate the comprehensive score of each region (Z) according to the linear weighted summation of weight
of each principal factor:
2182.01818.0 ZZZ 
Table 9: Evaluation of foreign exchange income of XUAR international tourism industry
Sector The first principal
component
The second principal
component
Comprehensive
result
Ranking
Accommodation 0.995 0.011 0.816 1
Transportation 0.994 0.012 0.816 2
Catering 0.975 0.069 0.811 3
Sightseeing 0.99 -0.013 0.808 4
Entertainment 0.904 -0.389 0.669 5
Commodity Sales 0.288 0.954 0.409 6
According to Table-9, it can be seen that during the period of 2006-2014 accommodation,
transportation, sightseeing and catering accounted for a larger proportion in XUAR tourism foreign exchange
earnings while entertainment and merchandise sales accounted for a relatively small proportion, which shows
that the structure of local tourism products is sole without outstanding features that can attract visitors to buy
products. The whole product line is relatively backward while the entertainment industry lags. To improve the
service and quality of these two sectors is a problem that we must pay attention to.
III. Results, Conclusion And Recommendations
After all this research and analysis, we should optimize the industrial structure and improve foreign
exchange income. Per the correlation between the various departments of the tourism industry analysis, we can
conclude that accommodation > traffic > catering> sightseeing > entertainment > shopping, which means we
need to support more for sectors of accommodation, transportation, catering and sightseeing. However,
entertainment and shopping does not contribute much to XUAR international tourism, indicating that these
departments should be the future focuses industry structure adjustment. Therefore, we should focus product
development on entertainment and commodity shopping to improve the tourism market structure, so that the
tourism market can gain tremendous development.
The industry cluster district should be built in areas with rich cultural background of XUAR, such as
cultures of the Silk Road and ethnic minority groups. Each sector of tourism industry should be developed
equally and the dominant sectors such as transportation, accommodation, catering and sightseeing should
provide stimulating power to commodity sales and entertainment. Moreover, we should build scenic spots with
unique features and enhance attractiveness of them, and therefore scenic spots with rich cultural content,
beautiful scenery and nationwide fame will be built in industry cluster district and infrastructure will be
improved there to attract more tourists.
The key profitable destinations should be built in areas with good infrastructure, unique features and
reputation at home and abroad, such as Kanas, Nalat grassland and old town of Kashgar. We can perform
intensive management to these scenic spots and invest more in construction to create a more standardized and
complete tourism management mechanism, and then expand to the surrounding clusters of tourism resources to
achieve the radiation effect and eventually a large-scale tourism industry area will be built.
Principal factor(Z) Eigenvalue
(λ)
Contribution
rate(%)
Cumulative
contribution(%)
Weight of principal
factor(W)
First factor 4.807 80.116 80.116 0.818
Second factor 1.066 17.772 97.888 0.182

Make the best of correlation effect among each sectors of tourism to achieve the transformation from
ticket profit mode to comprehensive profit mode. The clear majority of XUAR tourism companies are small
businesses and the market is really small with low efficiency and insufficient abilities of product innovation and
market competition risk resistance. Therefore, the most urgent problem now is how to change the development
mode, encourage small companies to perform joint management and achieve tourism economy increase with
government lead. At the same time, we also need to make full use of related industries and integrate tourism
resources to change the traditional income mode of ticket selling to a comprehensive profit system of catering,
accommodation, transportation, sightseeing, commodity sales and entertainment. With the coordinated growth
of each sector, a profitable industrial chain will be formed eventually.
References
[1] United Nations World Tourism Organization 2016, Tourism Highlights 2016 Edition..
[2] United Nations World Tourism Organization 2016, World Tourism Barometer 2016, Vol.14.
[3] United Nations World Tourism Organization 2015, Tourism Highlights 2015 Edition..
[4] China Tourism Academy 2016, China inbound tourism development annual report 2016.
[5] Merriam Webster Dictionary, 2017.
[6] Towner, J., & Wall, G. (1991). History and tourism. Annals of Tourism Research, 18(1), 71-84.
[7] Walton, J. K. (2009). Prospects in tourism history: Evolution, state of play and future developments. Tourism Management, 30(6),
783-793.
[8] Cooper, C., & Hall, C. M. (2008). Contemporary Tourism. Contemporary Tourism, 347-373.
[9] Dolnicar, S., & Ring, A. (2014). Tourism marketing research: Past, present and future. Annals of Tourism Research, 47, 31-47.
[10] Volo, S. (2014). Contemporary tourism an international approach: 2nd Edn. Annals of Tourism Research, 45, 190-191.
[11] Battour, M., & Ismail, M. N. (2016). Halal tourism: Concepts, practises, challenges and future. Tourism Management Perspectives.
[12] Dwyer, L., Edwards, D., Mistilis, N., Roman, C., & Scott, N. (2009). Destination and enterprise management for a tourism future.
Tourism Management, 30(1), 63-74.
[13] Tsiotsou, R., & Ratten, V. (2010). Future research directions in tourism marketing. Marketing Intelligence & Planning, 28(4), 533-
544.
[14] Dwyer, L., Forsyth, P. & Dwyer, W. 2010. Tourism Economics and Policy. Bristol: Channel View Publications.
[15] Sirakaya,E.,Uysal,M.& Toepper,L.1995.Measuring the performance of South Carolina`s tourist industry from shift-share analysis:a
case study,Journal of travel Research,Vol 1,No 2,pp 55-62 .
[16] Statistic Bureau of Xinjiang Uygur Autonomous Region 2006-15, Xinjiang Statistical Yearbook 2006-2015.
[17] China National Tourism Administration 2006-15, The year book of China tourism statistic2006-2015.
[18] John E.Hanke &Dean Wichern 2014, Business Forecasting..Great Britain.Pearson New International Edition.
[19] Davis,J.C.2003, Statistics and Data Analysis in Geology,New York,John Wiley and Sons.

International Tourism Marketing: An Analysis on Xinjiang Uygur Autonomous Region (XUAR) of China

More Related Content

What's hot (18)

Similar to International Tourism Marketing: An Analysis on Xinjiang Uygur Autonomous Region (XUAR) of China (20)

More from IOSRJBM (20)

Recently uploaded (20)

International Tourism Marketing: An Analysis on Xinjiang Uygur Autonomous Region (XUAR) of China