a study of tourism hotel industry in taiwan -...

A Study of Tourism Hotel Industry in Taiwan

NCCU

Students: TU, CHIA-YU

WU, ZONG-HAN

CHEN, YU-AN

CHEN, WEI-YU

KUO, WEN-SHAN


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Content

I. Introduction-----------------------------------------------------------------------------3

1.1 Motivation and Background-----------------------------------------------------3

1.2 Purpose of Study------------------------------------------------------------------4

II. Literature Review----------------------------------------------------------------------4

2.1 Internal factors of Hotel’s Revenue --------------------------------------------4

2.1.1 Hotel’s investment--------------------------------------------------------4

2.2 External factors of Hotel’s Revenue--------------------------------------------5

2.2.1 Government investment--------------------------------------------------5

2.2.2 Economic Indicator-------------------------------------------------------6

2.2.3 MICE-----------------------------------------------------------------------7

III. Methodology---------------------------------------------------------------------------8

3.1 Research Framework-------------------------------------------------------------8

3.2 Research Model-------------------------------------------------------------------8

3.3 Definition of the variables-------------------------------------------------------9

3.4 Data Analysis--------------------------------------------------------------------10

3.4.1 External Factors---------------------------------------------------------10

3.4.2 Internal Factors----------------------------------------------------------11

3.4.2.1 Taipei------------------------------------------------------------12

3.4.2.2 Kaohsiung-------------------------------------------------------13

3.4.2.3 Taichung--------------------------------------------------------14

3.4.2.4 Hualien----------------------------------------------------------16

3.4.2.5 Scenic areas----------------------------------------------------17

3.4.2.6 TCM-------------------------------------------------------------18

3.4.2.7 Others-----------------------------------------------------------20

3.4.3 External factors - Event analysis--------------------------------------21

3.4.3.1 Data Analysis--------------------------------------------21

3.4.3.2 China Hotel Visitors Analysis-------------------------------22

3.4.3.3 Crowding-Out Effect of China Hotel Visitors-------------23

IV. Conclusion----------------------------------------------------------------------------24

V. Data Sources--------------------------------------------------------------------------25

VI. Reference------------------------------------------------------------------------------26


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I. Introduction

1.1 Motivation and Background

Travel & Tourism (T&T) is one of the world’s largest industries. In 2011, the

World Travel & Tourism Council (WTTC) expected it to contribute US$6 trillion to

the global economy, or 9% of global gross domestic product (GDP) and supports 260

million jobs worldwide. Meanwhile, UNWTO’s Tourism 2020 Vision shows that East

Asia and the Pacific are forecasted to record growth at rates of over 5% year,

compared to the world average of 4.1%. T&T industry not only holds an impact on

economic-aspect, but runs deep into countries’ developing stage. As 2011 Travel

&Tourism mentioned, Europe T&T investment expansion has been much more

aligned to actual demand trends. From recent marketing trends in Asia, it shows that

Korea’s government involvement in tourism is boosting the number of their

international arrivals. So as in Taiwan, it took 13 years (1976-1989) to reach a million

of arrivals. In 2010, only in two decades, Taiwan has achieved the total of 5 million

only in one year.

According to the data from Tourism Bureau, the annually tourism revenue has

progressed to 5.14 billion in the last ten years. On the other hand, the tourists’ expense

survey has shown that inbound visitors tend to spend mostly on room tariff and

in-hotel expenditure. As the average of 2006~2010, they hold a proportion of 42%,

which could be concluded that hotel industry plays a big role in tourism industry.


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Besides, as tourism industry boosted, the number of hotel has gradually increased at

the same time.

With all the materials above, hotel industry brought Taiwan many profits. By

knowing this, it triggered our curiosity to find out what would contribute to hotel

revenue. We looked on the macroeconomic aspect and other internal factors i.e. how

does hotel its place differentiate the source of their income.

1.2 Purpose of Study

ü To learn the market trend of Taiwan hotel industry.

ü To discover hotel-related factor in Taiwan hotel industry.

ü Find out the impact of Chinese tourists on hotel’s industry in Taiwan.

II. Literature Review

2.1 Internal factors of Hotel’s Revenue

2.1.1 Hotel’s investment

To look on the aspect of hotel’s investment, there are various reasons influencing

the willingness of foreign tourists coming to visit Taiwan, and among those factors,

we found out the development of hotel industry is the main reason. (Hsiao-Hsing,

Huang, 2011). Furthermore, physical environment of the hotel, such as decorations,

has an obvious and positive influence on customer satisfaction and company identity.

( Yi-Shiuan, Huang, 2007)


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As the studies we learned, customer satisfaction is strongly related to revisit

intention, which contributes to hotel revenue. ( Mei-Lun, Liu, 2011)Because hotel’s

investment affects customer satisfaction, and customer satisfaction affects hotel

revenue, we consider hotel’s investment as one of the factors influencing hotel

revenue. ( Chiang-Miao, Lan, 2009)

2.2 External factors of Hotel’s Revenue

2.2.1 Government Investment

To discuss about the external factors of hotel’s revenue, we can first focus on the

role of government which plays a significant character in the hotel industry. We found

the book “Governments and tourism” studies tourism in general and the specific

contribution of central and local governments to its development. (Jeffries D, 2011)

Moreover, according to the literature we found, local government services, such as

infrastructure and general government administration expenditure, can influence the

share of tourism. (Wong JD, 1996) Since, we decide to discuss about some of the

government’s investment in the hotel industry.

Furthermore, we found that there are some difference between the urban and

other regions when it comes to the resource allocation by the government, so we also

want to discuss the government management and financial investment in different

area. Among the factors of urban cultural resources, urban environment condition and


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government management, income level and government financial investment have

specific effect on cultural tourism competitiveness in urban area. (Chen-Yun Huang,

2011)

But most of the literatures are talking about how the government’s investments

boots the tourism industry, so we want to focus more on the hotel industry which may

come to the same result.

2.2.2 Economic Indicator

To observe the hotel industry in broad eyesight, we can focus on the influence of

economic indicator. In the literature we found, it shows that macroeconomic

environment affect international travelling arrivals. (Tai-Min Han, 2007).

Aiming to evaluate tourism competitive, comprehensive index and quantification

data sources could be used to analyze the capability advantages and disadvantages in

each urban area in Taiwan and to offer policy makers vital information on tourism

development. (Chen-Yun, 2011). And according to the literature, the primary

influence factors are some economic indicators such as “monetary supply” and

“income of tourist hotel industry” that reach to conspicuous statistic correlation

(Sun-Chein Chi, 2008).

So people can use some macroeconomic variables such as host countries GDP to

run a model in order to point out the intimate relationship between the monetary


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supply and income of tourist hotel industry. All variables we infer to exist in a

long-term equilibrium, it could be indicated that macroeconomic variables is capable

to infer to inbound tourism arrivals. (Yu-Shan Wang, 2008).

2.2.3 MICE

Since the international meetings and exhibitions became more and more

important in this generation, we also focus on the MICE aspect which includes

meetings, incentives, conferences and exhibitions. According to the study, it revealed

international conventions, seminars and exhibitions have a positive impact on

international hotel demand of metropolitan area and tourism hotel efficiency. (Kim

Young Tae, 1998) Also, Empirical results show that the international conference and

exhibition have a significantly positive impact on tourism hotel efficiency, and show

days, times also has positive impact on tourism hotel efficiency. (Chin-Yi Fang, 2010)

However, on the other hand, the distance between hotel and the convention

center directly affects hotel’s benefit. In addition, it is found that domestic and foreign

exhibitors have different preferences for lodgings, and that deluxe hotels share most

of the benefits. (Qiu-ju Luo, Xiao-li Li, 2005)

But MICE is an emerging industry in Taiwan and not many studies discuss about

this part, so we are eager to find out its contribution to hotel revenue. Therefore,

meetings and exhibitions become one of the significant factors in our paper.


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III. Methodology

3.1 Research framework

3.2 Research model

We use multiple regression models to discuss the relationship among the

variables, with the significant level of 0.1.

For the external environment (t is for 2004 to 2010)

HRt = β0 +β1GBt +β2MTt +β3EHt +β4ERt + ε

For the internal environment (t is for 2001 to 2010)

HRt = β0 +β1HRt +β2RPt + ε

For event analysis (t is for 2004 to 2010)

HRt = β0 +β1GBt +β2MTt +β3EHt +β4ERt +β5CTt +ε

Hotel’s Revenue

Internal Factors

External Factors MICE Industry

Microeconomic Environment

Government Investment

Renting Price

Housing Rate


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Where:

HR = Hotel revenue GB = Central Government Budget

MT = Number of Meetings EH = Number of exhibitions

ER = Exchange rate HR = Housing rate

RP = Renting Price CT = Policy for Chinese Tourists

(dummy variable)

β0 is the inception and β1 to β5 are the coefficients of the variables. Through the

coefficients, we can realize the independent variables have positive or negative

effects to the dependent variables, and which of the variables influence more.

3.3 Definition of the variables

1. GB: Central government budget represents government expenditures on

tourism, like spending on policies, budget on festivals and activities,

communication to the national scenic areas, promotion on local cultures, and

so on.

2. MT & EH: For MICE industry, we focus on the two common types of

MICE, meetings and exhibitions. The numbers are the meetings and

exhibitions held in Taiwan in one year.

3. ER: Exchange rate is the annual average value of NTD in form of USD.


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4. HR & RP: Housing rate is the proportion of occupation on average of one

year. Renting price is the cost for staying in hotel for one night on average of

one year.

5. CT: Policy for Chinese tourists is considered as a dummy variable. The

policy allows Chinese tourists to visit Taiwan without flight transferring.

3.4 Data Analysis

3.4.1 External Factors

Table.1 2004 – 2010 External Factors Data

Source: Statistic of Tourism Bureau MOTC, Central Bank, UFI, ICCA

First, we want to find out the aggregate side of Taiwan hotel’s industry. As

shown in the table above, we can see that central budgets, MICE industry and hotel’s

revenue are going upward while exchange rate is going downward. We use regression

model to figure out whether these factors have significant impact on hotel’s revenue

by collecting the data from 2004 to 2010.

Year HR = Hotel

Revenue (NTD) (NTD)

GB = Central Budget for Tourism

(NTD)

MT = Number of Meetings

EH = Number of Exhibitions

ER = Exchange

Rate (NTD/$)

2004 35,051,785,075 4,786,271,356 68 47 33.4220 2005 38,917,856,693 4,624,536,019 66 58 32.1670 2006 38,958,487,009 4,573,239,874 63 60 32.5310 2007 38,988,157,285 4,789,178,923 99 46 32.8420 2008 38,579,526,688 6,168,224,946 79 62 31.5170 2009 35,967,291,767 7,952,228,276 91 63 33.0490 2010 43,031,176,447 10,357,388,179 138 74 31.6420


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Table.2 Regression Result for External Factors

HRt = 28914349314 -2.3259GBt +160813113MTt +345208671EHt -312435357ERt

From the equation and table above, we can conclude that MICE industry does

have a strong positive impact on hotel’s revenue while central budget has the negative

influence on hotel’s revenue. As to the exchange rate, the result shows that it is

rejected in this model therefore will not be considered in the equation above. We get

the result of regression analysis- the modified R Square, which is 0.978. It states that

the increasing of meetings and exhibitions does help the hotel’s industry while

government’s tourism budget doesn’t have obvious effect for hotel’s performance.

3.4.2 Internal Factors

After discussing the aggregate side of the Taiwan hotel’s industry, we want to

study more about internal factors of Taiwan’s tourism hotels in different regions. The

government categorizes all the tourism hotels into seven sections based on their

regions. Those seven regions are Taipei, Taichung, Kaohsiung, Hualien, TCM, Scenic

Sign P-value

Central Budget(β1) Negative 0.051

Number of Meetings(β2) Positive 0.032

Number of Exhibitions(β3 ) Negative 0.070

Exchange Rate(β4) No 0.706

R-square = 97.8% F-significant level = 0.044


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areas, others. We’ll discuss them by studying the relationship between hotel’s revenue

and two internal factors, renting price and housing rate.

3.4.2.1 Taipei Region

Table.3 2001 – 2010 Taipei Year Average Data

Source: Statistic of Tourism Bureau MOTC

As shown in the table above, Taipei maintained a high housing rate with a

relative higher renting price throughout past ten years. This shows that the tourism

hotels in this area have a more obvious position and less variability in the visitors. We

look into whether housing rate or renting price affects the revenue by using regression

model.

Table.4 Taipei Area Regression Result

HRt = 7025189237 - 24935261HRt + 4741490RPt

Year HR = Hotel

Revenue (NTD) RR = Housing Rate

(%) PP = Renting Price

(NTD) 2001 23,272,704,560 70.21 3322 2002 21,699,780,184 69.25 3249 2003 18,294,244,464 57.06 3031 2004 20,579,462,828 69.64 3211 2005 22,720,228,072 77.00 3352 2006 22,631,065,034 75.17 3565 2007 22,624,787,674 75.54 3738 2008 21,985,879,157 72.2 3751 2009 19,960,447,583 70.88 3488 2010 23,079,732,759 75.31 3591

Coefficient/ P-value: housing rate

Coefficient/ P-value: Renting Price

R-sq F significant level

Taipei -24935261 0.266 4741490 0.051 45.1% 0.122


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The result showed that it might not be the renting price has more relation

comparing with housing rate. We get a relatively low R-Square of 0.457, and we

referred to the complicated environment in Taipei city therefore creating more

variables that can influence Taipei’s hotel revenue. Although the R-Square wasn’t

high enough to explain the whole Taipei tourism hotels, we can still conclude that the

hotel’s revenue in Taipei area is positive correlated with the renting price.

3.4.2.2 Kaohsiung Region

Table.5 2001 – 2010 Kaohsiung Year Average Data


As shown in the table above, Kaohsiung’s housing rate is around 60 to 70

percent while its renting price raised or the past 10 years. This showed that the

tourism hotels in this area has improved its overall standard and therefore bring more

visitors and revenues. We look into whether housing rate or renting price affects the

revenue by using regression model.

Year HR = Hotel


(%) RP = Renting Price

(NTD) 2001 3,456,359,838 56.39 2063 2002 3,367,834,645 53.71 2029 2003 3,371,859,348 56.86 2021 2004 3,744,529,997 62.64 2123 2005 4,515,269,805 71.66 2134 2006 4,619,545,999 68.92 2174 2007 4,744,131,913 70.75 2253 2008 4,759,440,404 68.91 2271 2009 4,215,592,530 64.52 2310 2010 4,646,661,517 68.79 2280


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Table.6 Kaohsiung Area Regression Result

HRt = -3370061374 + 69577596HRt + 1403316RPt

The result showed that both factors have strong impact on the hotel’s revenue

with a R-Square of 0.953. Both factors have positive sign towards hotel’s revenue,

which means the hotel’s revenue will be higher if Kaohsiung’s tourism hotels have a

higher renting price and housing rate. It implied that Kaohsiung need to put more

efforts on marketing when facing their hotels visitors in order to maintain a high

housing rate when they have higher renting price.

3.4.2.3 Taichung Region

Table.7 2001 – 2010 Taichung Year Average Data





Kaohsiung 69577596 0.000 1403316 0.077 95.3% 0.000

Year HR = Hotel



(NTD) 2001 1,918,119,070 53.57 2415 2002 1,882,736,354 54.39 2282 2003 1,846,802,165 58.12 2155 2004 2,223,347,861 68.1 2303 2005 2,494,351,813 83.56 2389 2006 2,521,553,491 75.81 2429 2007 2,355,138,629 65.78 2403 2008 2,356,877,006 63.95 2366 2009 2,094,512,804 59.71 2231 2010 2,502,978,933 69.84 2258


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As shown in the table above, from 2001 to 2010, Taichung maintained a steady

renting price around NT$ 2300 with an unstable housing rate. This show that the

tourism hotels in this area does not have enough room to raise their price, but on the

other hand, their revenue will be depends on their housing rate. For more evidence,

we look into whether housing rate or renting price affects the revenue by using

regression model.

Table.8 Taichung Area Regression Result

HRt = 204083556 + 22528590HRt + 533639RPt

The result showed that it might not be the housing rate has more relation

comparing with renting price with a R-Square of 0.789. We can indicate that the

competiveness of Taichung’s tourism hotels is relative low since their average renting

price nearly stays the same for the past ten years. In order to gain hotel’s revenue,

they should devote themselves to promoting their hotels rather than raising their

renting price.

Coefficient/ P-value: Housing rate



Taichung 22528590 0.004 533639 0.365 78.9% 0.004


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3.4.2.4 Hualien Region

Table.9 2001 – 2010 Hualien Year Average Data


As shown in the table above, housing rate and the renting price has both

increased throughout the past ten years in this area. This showed that the tourism

hotels in this area has improved its overall standard and therefore has more power to

bargain and higher housing rate. As to whether housing rate or renting price affects

the revenue, we will analyze by using regression model.

Table.10 Hualien Area Regression Result

HRt = -1551313161 + 15342110HRt + 791159RPt

Year HR = Hotel



(NTD) 2001 763,216,896 45.2 2091 2002 853,224,231 47.84 2,110 2003 1,552,397,093 63.39 2701 2004 1,593,111,840 57.05 2896 2005 1,505,096,708 58.14 2737 2006 1,399,782,542 55.94 2659 2007 1,322,153,970 52.51 2603 2008 1,334,542,695 54.96 2475 2009 1,322,362,961 62.02 2438 2010 1,425,371,635 64.46 2531

Coefficient/ P-value: Housing rate



Hualien 15342110 0.000 791159 0.000 98.7% 0.000


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The result showed that both factors have strong impact on the hotel’s revenue

with a R-Square of 0.987. Both factors have positive sign towards hotel’s revenue,

which means the hotel’s revenue will be higher if Hualien’s tourism hotels have a

higher renting price and housing rate. It implied that Hualien need to have more

promotion or policy when facing their hotels visitors in order to maintain a high

housing rate when they have higher renting price.

3.4.2.5 Scenic Areas

Table.11 2001 – 2010 Scenic Area Year Average Data


As shown in the table above, Taipei maintained a steady housing rate with a

relative higher renting price throughout past ten years. This showed that the tourism

hotels in this area have its own advantage comparing with others. Since Scenic Areas

has the absolute advantage on their view and nature resource, they’ll have more room

to bargain throughout the years. However, we use regression model to see whether

housing rate or renting price affects the revenue for more evidence.

Year HR = Hotel



(NTD) 2001 2,154,453,480 54.07 3126 2002 2,380,365,593 58.42 3320 2003 2,432,015,011 58.62 3349 2004 2,494,302,592 58.18 3450 2005 2,775,746,520 56.6 3732 2006 3,079,193,039 55.44 3980 2007 3,137,487,173 53.72 4056 2008 3,433,132,778 53.59 4121 2009 3,223,724,757 56.91 3832 2010 3,743,803,950 59.71 4051


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Table.12 Scenic Area Regression Result

HRt = -5072808147 + 48195477HRt + 1413927RPt

The result showed that it might not be the renting price has more relation

comparing with housing rate with a R-Square of 0.921. It implied that tourism hotels

in this area could generate their revenue by raising their renting price, because their

housing rate will not fluctuate very often due to their scenic areas. Therefore, we can

conclude that they don’t have to spend much money on promotion towards the

general hotel’s visitors comparing with other areas, but they have to put more effort

on cooperating with the travel agency.

3.4.2.6 TCM Region

Table.13 2001 – 2010 TCM Year Average Data





Scenic areas 48195477 0.101 1413927 0.000 92.1% 0.000

Year HR = Hotel



(NTD) 2001 1,847,799,450 51.73 2371 2002 2,066,032,182 54.54 2515 2003 1,951,493,290 48.03 2418 2004 2,300,275,295 64.36 2536 2005 2,547,518,982 74.96 2388 2006 2,358,559,437 73.34 2434 2007 2,427,663,181 64.18 2527 2008 2,400,187,182 55.76 2542 2009 2,504,932,135 49.78 2271 2010 3,760,322,182 65.24 2254


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From 2001 to 2010, the table showed that tourism hotels in TCM area have an

unstable housing rate and renting price. Moreover, renting price has reach to a

relatively low level for the past two years. Since TCM has got the highest china

visitors for the past two years, we find it might be one possible reason for the decrease

in the renting price. Therefore, for further information, we look into whether housing

rate or renting price affects the revenue by using regression model for the past 10

years.

Table.14 TCM Area Regression Result

HRt = 7501991586 + 25342577HRt - 2725484RPt

The result showed that it might not be the renting price has a negative relation

comparing with housing rate with a R-Square of 0.475. We inferred the lower

R-Square to the range of hotels is very large in this area. However, we can imply that

the price elasticity is relatively low. In other words, when the price decreases a bit, the

revenue will increase more than the amount it decrease. Therefore, we can conclude

that tourism hotels in TCM can generate their revenue by lowering their room price.




TCM 25342577 0.137 -2725484 0.086 47.5% 0.105


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3.4.2.7 Other Region

Table.15 2001 – 2010 Others Area Year Average Data


For others region, it includes more than 5 cities so the data from 2001 to 2010

may vary a lot. Since it represents one third of Taiwan’s cities, we still can analyze

the impact of housing rate and renting price on hotel’s revenue.

Table.16 Others Area Regression Result

HRt = 9280449103 - 57928924HRt - 1441886RPt

According to the result above, both factors does not have significant impact on

hotel’s revenue. We inferred to the complexity of this areas. Therefore, we cannot

give any supportive evidence according to our regression model.

Year HR = Hotel



(NTD) 2001 754,407,255 61.62 2951 2002 1,451,101,498 61.3 2907 2003 1,545,679,783 56.43 2763 2004 2,116,754,662 60.7 2401 2005 2,359,644,793 62.01 2541 2006 2,348,787,467 55.37 2605 2007 2,376,794,745 51.79 2564 2008 2,309,467,466 52.91 2512 2009 2,645,718,997 49.41 2458 2010 3,872,305,471 56.13 2749




Others -57928924 0.401 -1441886 0.385 29.9% 0.288


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3.4.3 External factors - Event analysis

3.4.3.1 Data Analysis

Table.17 2004 – 2010 External Factors Data with Dummy Variable

Source: Statistic of Tourism Bureau MOTC, Central Bank, UFI, ICCA

Table.18 Regression Result of Dummy Variable

HRt = 64597727245 -1.1821GBt +114653330MTt +234566796EHt

-1280634421ERt -2021849987CTt

Year HR

(NTD) GB

(NTD) MT EH

ER (NTD/$)

CT = Policy for Chinese Tourists

(as a dummy variable)

2004 35,051,785,075 4,786,271,356 68 47 33.4220 0 2005 38,917,856,693 4,624,536,019 66 58 32.1670 0 2006 38,958,487,009 4,573,239,874 63 60 32.5310 0 2007 38,988,157,285 4,789,178,923 99 46 32.8420 0 2008 38,579,526,688 6,168,224,946 79 62 31.5170 1 2009 35,967,291,767 7,952,228,276 91 63 33.0490 1 2010 43,031,176,447 10,357,388,179 138 74 31.6420 1

coefficient P-value

Central Budget(β1) -1.1821 0.217

Number of Meetings(β2) 114653330 0.103

Number of Exhibitions(β3 ) 234566796 0.141

Exchange Rate(β4) -1280634421 0.203

Policy(β5) -2021849987 0.190

R-square = 99.8% F-significant level = 0.075


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To focus on the policy for Chinese tourists, we put in the dummy variable see the

change in our regression model. The R-square raised from 97.8% to 99.8%, but the

p-value become higher than before. It is because we use dummy variable and the

number of data is not enough, or there may be other factors, such as financial crisis.

However, it seems that policy does not have a positive effect on hotel’s revenue.

3.4.3.2 China Hotel Visitors Analysis

Since we want to check whether the result is reliable or not, we come to discuss

about the regional again. The figure below shows hotel’s visitors of seven areas in the

last three years. The table below the figure shows the changes of the inbound visitors

and hotel’s visitor among three years. From 2010 to 2011, although the inbound

visitors are increasing, the hotel’s visitors are decreasing.

Figure.1 China Hotel Visitors by Region



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Table.19 2009-2011 China Inbound Visitors & Hotel Visitors


For figuring out the reason, we do more research on the tourism hotels. We

calculate the proportion of Chinese visitors staying in tourism hotels comparing

with general hotels. We find out the proportion in general hotels is increasing in

the last three years, which can explain the decrease of the tourism hotels’

visitors.

Figure.2 2009-2011 China Hotel Visitors by General and Tourism Hotels


3.4.3.3 Crowding-Out Effect of China Hotel Visitors

Finally, we want to see if the increase of hotel’s Chinese visitors will have

crowding-out effect toward other foreign visitors. However, in our data analysis, the

Figure.3 below shows that the crowding-out effect is not obvious.

Inbound Visitors Hotel Visitors 2009 - 2010 + 658612 (68%) 523711 (39%) 2010 - 2011 + 153450 (9%) -174942 (-10%)


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Figure.3 2007-2011 Hotel Visitors by Nation


IV. Conclusion

With all the research above, Tourism hotels’ revenue has been rising the last ten

years, though declined in 2003 and 2009 due to SARS and financial crisis. In some

places, the number of tourist has increased a lot due to their location and the country

tourists from and low renting price.

As to external factor, we found out that government’s tourism budget has no

significant impact on tourism hotels revenue and MICE industry has positive effect

towards hotel’s revenue. About Internal factors revenue is affected by renting price

and housing rate, people’s extravagant consumption can be seen in Taipei since

higher renting price brings higher revenue, and TCM has a higher price elasticity

which will bring higher revenue by lowering the renting price.

The macroeconomic aspect, we infer that Chinese tourist honeymoon period is


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over, and the China hotel visitors have started to decline since 2010, and Customers of

tourism hotels shifted to general hotels that went directly with government’s

expectations on the tourism hotels performance. There is no evidence showing

crowding-out effect since the distribution of foreign visitors nearly remains the same

after the China tourists arrive.

At last, we conclude all the findings into two parts. First- Managerial

Implications- which is to differentiate position in the market when facing the China

tourists, and have more cooperation with MICE industry, especially in holding

international conferences. Second-Implications for Policy Making- Amend the law to

encourage the travel agency to arrange the china tourists in the star rated hotel, and

stimulate the MICE industry in Taiwan and make efforts to get the right to host

meetings or exhibitions.

With all of the materials mentioned above, we know that the government has

been recently devoting in MICE industry, hope we can give the best guide for our

government to build up a better tourism hotels industry.

V. Data Sources

ü Statistic of Tourism Bureau MOTC:

• Hotel revenue

• Central government budget for tourism


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• Housing rate

• Renting price

ü Central Bank

• Average exchange rate

ü UFI and ICCA

• The number of meetings

• The number of exhibitions

VI. References

6.1 Literatures

1. Hsiao-Hsing, Huang. 2011. “An Empirical Study on the effect of Hotel

Location, Service Quality, Product Price and Customer Value”

2. Mei-Lun, Liu. 2011. “The Relationships among Service Quality, Perceived

Value, Customer Satisfaction and Revisit Intention : A Case Study of

Hot-Spring Hotels in Sz-Chung-Shi Area”

3. Yu-Shan Wang. 2008. “The Impact of Crisis Event and Macroeconomics on

Taiwan’s International Inbound Tourism Demand.” Tourism Management,

30(1):75-82


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4. Jing-Wen Chang. 2011. “The Study on the Factors Affecting the Business

Performance of International Hotel – Application of Panel Threshold

Regression Model”

5. Chih-Chiang Weng. 2008. “Cost Structure and Market Power of International

Tourist Hotels in Taiwan”

6. Tai-Min, Han. 2007.”A Study of International Traveling Arrivals Affected by

Government Expenditure and Macroeconomic Factors”

7. 留美萍. 2002.”1973~2000年東南亞經濟體國際觀光競爭力之研究”

8. Chien-Chiang Lee and Chun-ping Chung. 2008. ”Tourism Development and

Economic Growth: A Closer Look at Panels.” Tourism Management,

29(1):180-192

9. Hyun-Jeong Kim, Ming-Hsiang Chen, and Soo-Cheong Jang. 2006.“Tourism

Expansion and Economic Development: The Case of Taiwan.” Tourism

Management, 27(5):925-933

10. Wong, JD. 1996. “The Impact of Tourism on Local Government

Expenditures”

11. Kim YoungTae, 1998. “Time-dependent analysis for international hotel

demand in Seoul.” Tourism Economics 1998 Vol. 4 No. 3 pp. 253-263


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12. Chin-Y, Fang. 2010.“The Study of Convention & Exhibition Activities

Influenced on Operating Efficiency of the Tourism Hotels in Taiwan”

13. Sun-Chein, Chi. 2008. “A Study On the Economic Indicators For Tourist

Hotel Industry”

14. Chen-Yun, Huang. 2011.“Assessment of Cultural Tourism Competitiveness

Factors in Taiwan”

15. Qiu-ju Luo, Xiao-li Li, 2005 “A Study on the Impacts of MICE on Profit and

Spatial Layout of Hotels: A Case Study of Chinese Export Commodities Fair”

16. Yi-Shiuan, Huang. 2007. “The Influences of Service Encounter Factors on

Customer-Company Identification－Taking the Customers of the Hotel in

Hualien Country for Example”

6.2 Websites

1. Taiwan Tourism Bureau http://admin.taiwan.net.tw/

2. UFI (Union of International Fairs) http://www.ufi.org/

3. ICCA (International Congress and Convention Association)

http://iccaworld.com/

4. Taiwan Directorate-General of Budget, Accounting, Statistics

www.dgbas.gov.tw

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