The SIJ Transactions on Industrial, Financial & Business Management (IFBM), Vol. 2, No. 3, May 2014
A Study on the Operating Efficiency of
Taiwan Tourist Hotels
Feng-sheng Julian Chien*
*Ph.D. Student, National Sun Yat-Sen University of Business Management, 70 Lienhai Rd., Kaohsiung 80424, TAIWAN, R.O.C.
E-Mail: Julian{at}hp-oa{dot}com
Abstract—Travel is an indispensable lifestyle that grows along with a person’s income. The tourism industry
helps many countries increase their foreign exchange, offer job opportunities, and protect the environment
through economic development. This study mainly investigates managerial efficiency in Taiwan tourist hotels
and compares their efficiency among different areas of the country The research herein employs Data
Envelopment Analysis (DEA) of the CCR and BCC models to explore tourist hotels’ operational any
difference between “managerial” and “operational” efficiency. The findings are as follows. First, the CCR
model shows the highest technical efficiency in 2009. The BCC model exhibits that 2008 and 2009 are
superior than in 2010. Data from 2008 present that the non-Taipei area is the highest in the CCR model.
However, the non-Taipei area and the scenic area are equivalent in the BCC model. Final, data from 2009 find
that the scenic area is the highest no matter for the CCR or BCC model. Results from 2010 are the same as
2009.
Keywords—Data Envelopment Analysis; Managerial Efficiency; Taiwan’s Tourist Hotel.
Abbreviations—Association for Relations Across the Taiwan Straits (ARATS); Data Envelopment Analysis
(DEA); Decision-Making Unit (DMU); Marginal Rate of Substitution (MRS); Straits Exchange Foundation
(SEF).
I.
W
INTRODUCTION
ITH developmental progress throughout time, an
increase in national income causes leisure and
tourism to become a greater indispensable part of
society. The tourism industry not only increases a country’s
foreign exchange earnings and improves employment
opportunities through its non-polluting nature, but also
protects the environment from disaster during economic
growth. Hence, promoting tourism has become the trend and
focus of economic development for many countries.
However, with internationalization, globalization, and the
influence from a rapidly changing business environment and
customer needs, even the tourism industry can encounter
problems. The end result could be that tourist-related
companies fail to respond to such dynamic changes and lose
their competitiveness or, even worse, get pushed out of the
market.
China’s statistics indicate that up to 4million China
people are willing to come to Taiwan for scenic sightseeing,
demonstrating that Chinese tourists have a great desire to
visit Taiwan [Shi-Chi Chen & Wei-Suei Tu, 2002]. Professor
Lung of the Law School of Chinese People’s University
noted that Sina.com’s sample survey found that up to 86% of
Chinese people prefer Taiwan to be their first choice when
travelling abroad. Their motives behind this are different
from most ordinary tourists around the world, as Chinese
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people are curious about Taiwan’s five decades of political
democracy, economic development, open society, and
citizens’ lifestyle. Thus, it is their hope to travel to Taiwan
once in their life [Yang, 2008]. In 2008 there was a great leap
in cross-strait relations when the Straits Exchange Foundation
(SEF) and the Association for Relations Across the Taiwan
Straits (ARATS) signed a document that included, but was
not limited to, “Agreement for Cross-strait Views of China
Residents’ Journey to Taiwan”. Since then, the number of
Chinese tourists coming over Taiwan has rapidly grown like
a mushroom. In April 2009, for the first time the number of
Chinese tourists to Taiwan surpassed Japanese tourists,
making them the largest and most important segment of
tourists for the island country.
This study suggests that Taiwan’s tourism situation may
have a big impact on the local hotel industry. Therefore, the
study examines the operating efficiency of Taiwan’s travel
and tourism industry, trying to make a comparison between
each travel agency’s operating efficiency and operating
efficiencies in various regions under this current competitive
market environment. The year 2008 is this study’s target
period, because that was when a large number of Chinese
tourists came over to Taiwan. This study has two main
purposes. 1) In what manner can many Chinese tourists to
Taiwan lead to better operating efficiencies for Taiwan’s
hotels? 2) In terms of geographical area, what difference arise
behind Taiwan hotels’ operating efficiency.
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The SIJ Transactions on Industrial, Financial & Business Management (IFBM), Vol. 2, No. 3, May 2014
II.
LITERATURE
2.1. Allowing Chinese Tourists to Taiwan
2.1.1. The Evolution of Chinese Tourists to Taiwan
Shao Qi-Wei divided the development of related policies and
China’s tourism development into various time periods. 1)
Starting from when New China was founded until the time
prior to its first reforms. 2) From the late 1970s up to the
early 1990s. 3) In the 1990s. 4) Four time periods in the new
twenty-first century. Taiwan scholars Wu-Chung Wu & ShiPing Fang (2004) divided the development into two periods:
1949 to 1977 - that is, the reception of foreign affairs; and
starting from China’s reforms in 1978 and up to the present.
The first period is further divided into 1949-1959, 1960-1965,
1966-1968, and 1969-1977. The second period is separated
into 1978-1988, 1989, 1990-2002, 2003, and after 2003.
Ryan et al., (2009) divided the development into periods such
as the economic reform period before 1978, 1978-1985,
1986-1991, 1992-2001, and 2002-present [Yu-Ching Liu,
2011].
In 1997 the PRC State Council approved the National
Tourism Administration and the Ministry of Public Security
to issue “Interim Measures for Chinese Citizens’ Travelling
Abroad at their Own Expenses”. In addition, Hong Kong and
Macau tourism, as well as boundary tourism, were included
into the scope of management [Wu-Chung Wu & Shi-Ping
Fang, 2004]. This allowed China citizens, who were
originally supposed to exit to Hong Kong and Macau for
relatives to have the right to travel and sightsee overseas.
With the advancement of “Administrative Measures for
Chinese Citizens’ Travelling Abroad” during 2001 and 2004,
China’s overseas tourism industry experience average annual
growth of 29.3%, with approximately 28.8 million residents
travelling abroad. The World Tourism Organization predicts
that China’s overseas tourism industry will hit 100 million
people in 2020. Furthermore, according to country
destination data provided for public research by China
National Tourism Administration, a total of 110 countries
have opened up their gates for Chinese citizens’ tourism.
China has thus become the world’s fastest growing exporter
of residents for tourism [Shi-Ping Fang, 2010].
In November 2001, Taiwan’s R.O.C. government
promoted “China People Coming over to Taiwan for Tourism
Initiatives” [The Mainland Affairs Council, Executive Yuan,
2009]. On the basis of this initiative, Taiwan identified three
categories for Chinese tourists to come to Taiwan. First
category: Chinese coming to Taiwan through Hong Kong and
Macao. Second category: Chinese travelling abroad or on
business trips who transferred to Taiwan for sightseeing
activities. Third category: Chinese who studied abroad or
lived abroad acquiring, permanent stay authority from
overseas [The Mainland Affairs Council, Executive Yuan,
2009]. The sequence of these categories that opened up
tourism and sightseeing was initially “third category” in
January 2002, then “second category” in May 2002, and
lastly “first category” in July 2008.
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Following in 2008, after nearly 10 years of delay due to
cross-strait negotiations, Chinese tourists, who failed to come
to Taiwan (first category), were permitted access to Taiwan
in July 16, 2008. On June 11, 2008, both countries entered
into “Agreement for Cross-strait Views of China Residents’
Journey to Taiwan” [Tourism Bureau, The Ministry of
Transportation, 2010A], which enabled Chinese citizens who
were classified into the “first category” and failed to come to
Taiwan for tourism to finally be able to visit Taiwan. This led
to a variance behind the number of cross-strait tourists,
showing asymmetry after individuals classified under “first
category” were allowed into Taiwan in July 2008.
Since July 2008, China’s citizens have been allowed to
travel to Taiwan, and as of July 2010, various provinces and
cities in China were in full liberalization to allow their
residents to come to Taiwan. An average of 1,661 Chinese
daily came to Taiwan in tour groups in 2009, rising to a daily
average of 3,440 in the latter half of 2010 [Tourism Bureau,
Ministry of Transportation, 2010B]. Compared to an average
of 230 people each day in 2007, the numbers have obviously
ballooned. In fact, statistics extracted from Taiwan’s
Immigration Department, Ministry of the Interior, and
Ministry of Tourism show that the number of Chinese tourists
leapfrogged over Japanese tourists for the first time in 2010
to become the number one exporter of tourist to Taiwan.
In order to make Chinese tourists have a smooth trip to
Taiwan, Taiwan’s authority has gradually stream lined the
application process and reduced restrictions. Taiwan has also
initiated regulations over the quality behind Chinese tourists
and sightseeing activities in Taiwan, including the protection
of passenger rights, assuring the integrity behind the travel
industry’s operations and services, and prohibiting zero or
negative fares in order to maintain tourism market order, thus
adequately protecting Chinese tourists’ interests and rights of
travel in Taiwan [Tourism Bureau, Ministry of
Transportation, 2009].
2.2. The Literature on Allowing Chinese Tourists to Visit
Taiwan
In recent years, Chinese tourists coming to Taiwan have
shown rapid growth, resulting in positive impacts to Taiwan’s
tourism sector, economy, and environment. The findings in
some studies come from surveys made on Chinese tourists’
socio economic backgrounds, indicating that the majority of
them are male, unmarried, and aged 25 to 49, have an average
monthly income of between RMB 4,501 to 6,000, have a
bachelor degree, and most are employees working for private
firms [Yang, 2008; Jin, 2010]. In addition, most Chinese
tourists were on their first trip to Taiwan and their trip
averaged 5-7 days. For tours, the number of group members
was about 7-16 [Chi-Yueh Lee, 2007; Yang, 2008; Jin,
2010].
Li-Chun Chen (2011) found that an improvement in
hotel room condition can be observed from occupancy rates.
Hotel staff felt an obvious booming effect from Chinese
tourists coming over to Taiwan. Such positive growth has
resulted in some studies indicating that most respondents
© 2014 | Published by The Standard International Journals (The SIJ)
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The SIJ Transactions on Industrial, Financial & Business Management (IFBM), Vol. 2, No. 3, May 2014
perceived positive effects were greater than negative ones.
Respondents also believed that the greatest positive effect
from tourism development would be on the local economy
[Yu-Ching Liu, 2011].
In addition to the above perceptions held by Taiwanese
on Chinese tourists’, the quality of tourism service, overall
satisfaction for services and products, and protection of rights
and interests behind tourism are also worthy of attention. The
findings of Chun-Chi Chen (2008) indicate that Chinese
tourists’ variance of socio-economic backgrounds shows a
discrepancy behind their perception of satisfaction level from
their trip and their degree of willingness to revisit Taiwan
[*original sentence made no sense. Guessing here.*].
Furthermore, the image of tourist attractions, shops, product
attributes, attitudes by hotel staff, etc. have significant
correlations with Chinese tourists’ perception of travelling in
Taiwan [Yu-meng Chuang, 2009; Shun-Chin Lin, 2010; Ong,
2011].
2.3. Literature of Taiwan Tourism Businesses’ Operational
Efficiency
In the past, there have been many domestic and foreign
studies in the literature with themes on the operating
efficiency of tourism, wherein the research methods adopted
are as varied as the research purposes. They can be
summarized as data envelopment analysis, cost function
analysis, balanced score card, regression analysis, etc.
Moery & Dittman (1995) utilized 54 hotels throughout
the United of States as subjects, where their operating
performance was measured through DEA of the CCR model.
The number of rooms, occupancy rate, average room rate,
operating cost of the room service department, energy cost,
payroll cost, operating cost, payroll cost of advertisement,
other advertising costs, fixed marketing cost, payroll of
management cost, and other administrative costs were
selected as input variables, while total revenue, service
satisfaction index, and facility satisfaction index were
selected as output variables. The findings indicate that the
overall average operating efficiency value of a hotel was 0.89
(the lowest efficiency value among all hotels was 0.64).
Anderson et al., (2000) analyzed variable operating efficiency
in the hotel industry through the DEA method, where the
number of full-time employees, number of rooms, costs and
expenses as to casino operations, catering cost, and other
expenses were selected as input variables, while total
operating revenue was selected as the output variable. The
findings show an overall efficiency value of only 42%,
whereby the main reason was poor performance in efficiency
for technology and allocation. Hence, hotel managers should
think more about how to allocate resources instead of
managing resources. Hwang & Chang (2003) investigated the
changes made in operations among 45 Taiwan international
tourist hotels during 1994~1998 through the DEA method.
Due to the highly competitive situation in the hotel industry,
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differences behind customer resources and management
resulted in significant performance differences. Thus, many
local hotels were willing to introduce the management mode
of international hotels. The findings indicate significant
improvements displayed in operational efficiency, which has
become a trend for the hotel industry in Taiwan to follow in
the future. In addition, since the DEA method was considered
as the most effective method in assessing inputs/outputs,
many domestic research studies focusing on the operational
efficiency of hotels used it for in-depth discussions [WenMin Lu, 2006; Wei-Huang Hsieh, 2007; Wang, 2008; Yang,
2008; Wan-Yi Yang, 2009; Shu-Jing Yeh, 2011; Qiu, 2011].
In addition to employing DEA on the operating
performance of tourism, cost function analysis has also been
considered by studies. Chen & Soo (2007) analyzed 47
Taiwan international hotels, their cost structure, and their
production growth rate during 1997 to 2001 through a trans
log cost function module, where the cost function mainly
includes three input functions and three output functions. The
findings indicate that the production growth rate grew during
the three-year-period of 1997-1999, while it fell from 2000 to
2001 due to the lagging effects from the Asian financial
crisis. Chen (2007) analyzed the cost efficiency of Taiwan’s
international tourist hotels, where the cost function mainly
includes three inputs and one output (total revenue). The
findings indicate that on average Taiwan’s international
tourist hotels have efficient operations at 80%, in which chain
hotels exhibited better efficiency than that of hotels operated
independently.
Other studies have used two or more than two kinds of
analysis methods, such as the balanced scorecard being
selected first to choose the benchmark of each dimension, and
then the difference behind efficiency of international tourism
was assessed through the DEA method [Wang & Hsu-Hung,
2006]. The findings indicate that up to 77% of the
international hotels in Taipei and Kaohsiung are relatively
inefficient, because of poor performance in the output items
including but not limited to profitability of the room service
department and catering service department, customer
satisfaction, market share, ratio of service to revenue, and
employee productivity leading to poor performance in the
current period. The findings behind multivariate analysis of
differences suggest how a hotel can adjust its overall volume
of improvements in each variable so as to enhance its overall
operational efficiency. The operating efficiency of a hotel
varies significantly with geographical location and
management mode, and a hotel’s operating efficiency does
not display significance due to different sources of tourists
and scale of rooms. Yu Ching Wang (2009) placed 50
Taiwan international tourist hotels as the samples in his study
and employed DEA and the Malmquist model to assess the
root causes of the differences behind the relative operating
efficiencies of these hotels during 2002-2006 so as to achieve
strategic management goals.
© 2014 | Published by The Standard International Journals (The SIJ)
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III.
METHOD
3.1. Sample
This study selects a large number of Chinese tourists coming
over Taiwan during 2008 to 2010 as the sample for
discussing the operating efficiency of tourist hotels. The data
source selected is from statistics of the Tourism Bureau,
Ministry of Transportation. From the original case hotels in
2008, 2009, and 2010, 75, 77, and 85 hotels were respectively
selected. For ease of analysis, the study herein also divides
the country into Taipei region, non-Taipei region (included
Kaohsiung, Taichung, Hualien, and Taoyuan / Hsinzhu /
Miaoli), and scenic region (statistics of the Tourism Bureau,
Ministry of Transportation), with 31 hotels categorized into
the Taipei region, 28 into the non-Taipei region, and 16 into
the scenic region in 2008, 31 hotels into the Taipei region, 31
into the non-Taipei region, and 15 into the scenic region in
2009, and 35 hotels into the Taipei region, 34 into the nonTaipei region, and 16 into the scenic region in 2010.
Differences are displayed in the number of hotels for each
year, caused by new tourist hotels opening up and the
shutting down of some tourist hotels.
3.2. Measurement
3.2.1. Evaluation of Operational Efficiency - DEA
The previous chapter presents many ways utilized by studies
of the operational efficiency of the tourism industry, such as
data envelopment analysis, regression analysis, cost function
analysis, etc., with various kinds of methods applicable for
whatever reasons. On the basis of the research purpose
herein, this study employs the DEA method.
Data Envelopment Analysis (DEA) was first proposed by
Farrell (1957), who used a multi-input and multi-output
analysis model. Farrell utilized the concept of the frontier
production function to measure the level of production
efficiency. DEA is a non-parametric frontier in efficiency
measurement that does not employ a default function type,
relying instead on a mathematical programming technique to
identify the enveloping dimension of efficiency instead of
presetting the allocation between the production function and
interference items for benchmarking each decision-making
unit used in the measurement of efficiency. The analysis
method applies inputs and outputs to the desired DecisionMaking Unit (DMU) for assessment in the module, seeking
the efficiency calculated from each DMU and then all
efficiency values through a linear connection to form an
envelopment, which is also known as the efficiency frontier.
Productivity is divided into technical efficiency and
allocative efficiency. Technical efficiency refers to a
manufacturer’s ability behind the maximum output from the
effective use of input elements given a certain level of
technology. Allocative efficiency refers to a manufacturer’s
ability behind the minimum cost from the optimized
allocation of the ratio of the production factor to the inputs
given a certain level of technology and factor prices. Suppose
that the manufacturer’s Marginal Rate of Substitution (MRS)
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of both production factors equals the ratio of both input
prices, i.e. MRS = W1/W2. It is then claimed that the said
manufacturer has allocative efficiency in its production
behavior. On the contrary, suppose that MRS of both factors
of production is not equivalent to that of both input prices. It
is then claimed that the manufacturer shall have nonallocative efficiency in its production behavior.
Continuing from the assessment of efficiency affirmed
through the non-parameter method suggested by Farrell
(1957), Charnes et al., (1978) improved upon it further to
acquire the production frontier for the evaluation unit via
linear programming techniques and to calculate the relative
efficiency of the subject. Any one subject whose relative
efficiency lies on the production frontier (i.e. production
boundary) will be considered as the unit with the optimized
efficiency when its performance indicator is 1, while the unit
whose relative efficiency failed to be on the production
frontier shall be referred to as being inefficient, whereby the
relative performance indicator is able to be acquired from the
efficiency point distanced with the envelope curve. This
method is known as the CCR model, which assumes that
constant scale of returns is generated from mass production that is, when an input has an equally proportional increase, so
does the output, whereas an input decline in equal proportion
does the same for the output. However, mass production may
also be classified under increased/decreased scale of returns,
where its inefficiency displayed in the DMU may be caused
by operating under variable scale of returns. Hence, the state
of scale of returns through an understanding of an individual
DMU can be a reference for managers to improve.
Given that not every DMU has its mass production under
constant scale of returns, Banker et al., (1984) derived the
BCC model where pure technical efficiency and scale
efficiency can be measured from a set of four axioms and
Shephard’s distance function, which might be collected from
mass production. The BCC model removes the assumption of
an unchanged scale of returns in the CCR model and replaces
it with a change in scale of returns. Thus, pure technical
efficiency and scale efficiency can be separated. The
approach is to divide the efficiency value of the CCR model
by that of the BCC model - that is, the scale efficiency of a
DMU -so as to measure if mass production is at an optimal
scale under changeable production technology of the subject.
If scale efficiency shows a constant scale of returns, then
it represents that the DMU is at a scale with ultimate
productivity, and the DMU’s efficiency value in the CCR and
BCC models is. If scale efficiency is expected to be
increased, then it means that scale of the DMU is smaller than
the optimal one, which thus needs to be expanded; otherwise,
it means that the DMU is larger than the optimal one and
needs to be scaled down. Hence, it can be a reference for a
DMU manager in the decision-making phase.
3.2.2. Input / Output Variables
Based on the historical literature for tourist hotels and
referring to the availability of accessing the information, this
study sets up output and input variables as follows.
© 2014 | Published by The Standard International Journals (The SIJ)
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The SIJ Transactions on Industrial, Financial & Business Management (IFBM), Vol. 2, No. 3, May 2014
1.
Output variables: contain RENT (rental income) and
FOOD (food and beverage income); both are the
major income source for each tourist hotel and are
considered as the variables for output measurement;
unit is NTD (New Taiwan Dollar).
2. Input variables: a total of six variables; ROOM refers
to the total number of rooms available in a tourist
hotel; OCCUPY (occupancy rate) refers to average
room utilization rate; PRICE (average price) refers to
the rental income of each room where the unit is NTD;
LOCAL refers to the number of local tourists a tourist
hotel has entertained throughout the year; GUEST
refers to the total number of tourists the tourist hotel
has entertained throughout the year; EMPLOYEE
refers to the total number of employees the tourist
hotel has employed.
This study uses the DEA model that mainly contains the
CCR model and BCC model to analyze tourist hotels’
operating efficiency. The CCR model expands a single-output
model of efficiency measurement into a multiple-output
model that assumes that a constant scale of return is
generated from mass production, plus input and output shall
make changes in equal proportion, while the BCC model
cancels the limits set for such constant scale of returns. The
CCR model’s efficiency value is called technical efficiency,
while that of the BCC model is called pure technical
efficiency. Scale efficiency is generated from technical
efficiency divided by pure technical efficiency. If this scale
efficiency equals 0, it refers to a constant scale of returns; if
scale efficiency is greater than0, then it refers to an increase
in the scale of returns; if scale efficiency is less than0, then it
indicates a decrease in the scale of returns.
IV.
RESULTS
4.1. Descriptive Statistics
Table 1 compiles the descriptive statistics containing all
variables for tourist hotels. In terms of the output variables,
average RENT is NT$192 million with a standard deviation
of NT$200 million. Average FOOD is NT$207 million with a
standard deviation of NT$264 million. As for the input
variables, the averages of ROOM, OCCUPY, PRICE,
LOCAL, and GUEST in 2009 are all lower compared to
those of the previous year. Up to 2010, there were signs of
recovery, where LOCAL has more fluctuations during these
three years and EMPLOYEE conversely exhibits a
decreasing average number.
Table 1: Overall Descriptive Statistics for 2008-2010
2008 (N=75)
Means
Standard Deviation
Minimum
Maximum
RENT
Variable
200,000,000
214,000,000
5,415,453
1,240,000,000
FOOD
212,000,000
261,000,000
957,940
1,130,000,000
ROOM
3,016
1,902
420
10,272
OCCUPY
PRICE
0.62
3,076
0.11
1,182
0.90
9,966
Minimum
Maximum
6,766,356
828,124
1,060,000,000
1,200,000,000
Means
0.18
1,506
2009 (N=77)
Standard Deviation
RENT
FOOD
180,000,000
191,000,000
188,000,000
241,000,000
ROOM
2,974
1,887
420
10,272
OCCUPY
0.61
0.14
0.19
0.87
Variable
PRICE
2,882
1,405
1,135
9,658
LOCAL
15,724
14,853
85
70,861
GUEST
EMPLOYEE
96,654
2,854
68,886
2,418
8,255
252
300,254
10,164
2010 (N=85)
RENT
FOOD
Variable
Means
192,000,000
207,000,000
Standard Deviation
200,000,000
264,000,000
Minimum
3,932,181
28,145
Maximum
1,180,000,000
1,370,000,000
ROOM
2,888
1,875
39
10,272
OCCUPY
PRICE
0.67
3,095
0.16
1,702
0.13
1,071
0.93
10,795
LOCAL
35,770
44,715
825
239,935
GUEST
EMPLOYEE
100,292
2,782
71,836
2,359
1,829
91
327,045
10,363
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The SIJ Transactions on Industrial, Financial & Business Management (IFBM), Vol. 2, No. 3, May 2014
Table 2 lists the descriptive statistics for all variables by
regional tourist hotel. In terms of the output variables, Taipei
region’s RENT and FOOD during these 3 years are much
higher than those of non-Taipei regions, while the lowest
ones are in the scenic region. For the input variables, Taipei
region’s average numbers for ROOM, OCCUPY, GUEST,
and EMPLOYEE are all higher than those of the non-Taipei
regions during the 3 years, where the lowest numbers are in
the scenic region. The scenic region has the highest PRICE
during the 3 years, followed by the Taipei region, and then
the non-Taipei region. For LOCAL, Taipei region, nonTaipei region, and scenic illustrate considerably large
fluctuations during 2008-2010, where the scenic region has
the highest number of person in 2008, and the lowest one is
displayed in the Taipei region, which has a number far lower
than the other two regions. The number of domestic tourists
visiting the scenic region in 2009 drop significantly to the
lowest among the three regions, while the Taipei region
increases slightly and the non-Taipei region decreases
substantially. In 2010, the number of domestic tourists to the
scenery region and non-Taipei region exhibit another
dramatic increase once more, while that of the Taipei region
slightly decreases.
Table 2: Descriptive Statistics for 2008-2010 by Region
Region
Variable
Taipei Region(N=31)
Non-Taipei Region(N=28)
Means
Standard Deviation
Means
RENT
288,000,000
288,000,000
FOOD
316,000,000
349,000,000
3,424
0.70
3,332
LOCAL
GUEST
3,814
ROOM
OCCUPY
PRICE
EMPLOYEE
Scenic Region(N=16)
Standard Deviation
Means
Standard Deviation
148,000,000
99,400,000
123,000,000
123,000,000
180,000,000
153,000,000
68,400,000
60,300,000
2,292
0.13
1,349
3,247
0.60
2,412
1,491
0.17
708
1,820
0.50
3,743
1,171
0.20
2,283
16,754
15,436
51,456
44,563
68,782
66,054
108,622
76,483
97,595
62,281
75,458
69,788
3,200
2,853
1,903
1,723
1,272
2009
Region
Variable
Taipei Region(N=31)
Non-Taipei Region(N=31)
Means
Standard Deviation
Means
RENT
259,000,000
253,000,000
FOOD
ROOM
293,000,000
3,422
331,000,000
2,290
0.69
0.12
OCCUPY
Scenic Region(N=15)
Standard Deviation
Means
Standard Deviation
129,000,000
91,400,000
123,000,000
118,000,000
149,000,000
3,069
127,000,000
1,519
68,300,000
1,851
57,300,000
1,152
0.57
0.12
0.53
0.16
2,382
PRICE
3,077
1,114
2,309
650
3,666
LOCAL
17,557
14,670
17,690
16,701
7,876
7,534
GUEST
110,457
76,934
91,405
60,387
78,977
66,670
3,661
3,075
2,559
1,821
1,799
1,244
EMPLOYEE
2010
Region
Variable
RENT
FOOD
ROOM
OCCUPY
PRICE
LOCAL
GUEST
EMPLOYEE
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Taipei Region(N=35)
Non-Taipei Region(N=34)
Scenery Region(N=16)
Means
Standard Deviation
Means
Standard Deviation
Means
Standard Deviation
264,000,000
304,000,000
3,191
0.74
272,000,000
364,000,000
2,278
0.11
146,000,000
170,000,000
3,093
0.65
88,900,000
141,000,000
1,506
0.14
132,000,000
76,200,000
1,788
0.54
134,000,000
69,100,000
1,159
0.19
3,289
15,712
106,703
1,292
14,200
78,761
2,375
41,261
102,449
709
38,671
60,848
4,197
67,977
81,683
2,962
73,050
78,846
3,437
3,035
2,587
1,735
1,761
1,216
© 2014 | Published by The Standard International Journals (The SIJ)
67
The SIJ Transactions on Industrial, Financial & Business Management (IFBM), Vol. 2, No. 3, May 2014
4.2. Empirical Analysis
This study employs the CCR model and BCC model to
compute technical efficiency, pure technical efficiency, scale
efficiency, and scale of returns from 2008-2010. It then
subdivides each year into data for the Taipei region, nonTaipei region, and scenic region (Table 3).
Among the whole efficiency analysis for 2008, average
technical efficiency is 0.791 and average pure technical
efficiency is 0.924. In the CCR model, 30 tourist hotels have
a relative efficiency value of 1, accounting for 40% of the
total, while 45 tourist hotels have a relative efficiency of 1 in
the BCC model, accounting for 60% of the total. For the scale
of returns, if that of the tourist hotel belongs to IRS, then its
size enables it to increase gradually, along with average
productivity; on the contrary, if the scale of returns of the
tourist hotel belongs to DRS, then its size enables it to
decrease gradually, along with average productivity. If it has
CRS, then its average productivity is not affected by size. In
2008, 38 tourist hotels display IRS, accounting for 50.67% of
and meaning that half of them were able to increase their size
gradually, along with average productivity. Seven 7 tourist
hotels display DRS, accounting for 9.33% of all, while those
belonging to CRS made up 40% of the total. The efficiency
analysis for the Taipei region indicates an average technical
efficiency of 0.913 and an average pure technical efficiency
of 0.964, both of which are much higher than the national
average number.
In the CCR model, 22 tourist hotels display a relative
efficiency value of 1, accounting for 70.97% of those in the
Taipei region, while 24 tourist hotels display a relative
efficiency of 1 in the BCC model, accounting for 77.42% of
those in the Taipei region and showing that Taipei’s tourist
hotels have high operating efficiency. For the scale of returns,
7 tourist hotels display IRS in the Taipei region in 2008,
accounting for 22.58% of those in the Taipei region and
suggesting that only about a quarter of tourist hotels in Taipei
are able to increase their size gradually, along with average
productivity. Two of them belong to DRS, representing
6.45% of those in the Taipei region, while70.97% of those
belong to CRS.
In the efficiency analysis for the non-Taipei region, the
average technical efficiency is 0.946, while the average pure
technical efficiency is 0.980, both of which are much higher
than the national average number and that of the Taipei
region. In the CCR model, 22 tourist hotels display a relative
efficiency value of 1, accounting for 78.57% of those in the
non-Taipei region, while 25 tourist hotels display a relative
efficiency of 1 in the BCC model, accounting for 89.29% of
the non-Taipei region hotels and also showing that hotels in
the non-Taipei region have high operating efficiency.
Moreover, for the scale of returns, 6 non-Taipei tourist hotels
display data belonging to IRS, accounting for 21.43% of
those non-Taipei ones, while no hotel belongs to DRS and
78.57% belong to CRS.
In the efficiency analysis for the scenic region, the
average technical efficiency value is 0.875, which is lower
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than that in the Taipei region and non-Taipei region, while
the average pure technical efficiency is 0.981, which is quite
equal to that of the non-Taipei region. In the CCR model, 11
tourist hotels display a relative efficiency value of 1,
accounting for 68.75% of those in the scenic region, while 13
tourist hotels display a relative efficiency of 1 in the BCC
model, accounting for 81.25% of those in the scenic regions
and also showing that tourist hotels there have high operating
efficiency. In addition, for the scale of returns, 5 tourist hotels
in the scenic region showing data belonging to IRS,
accounting for 31.25% of those in the scenic region; zero
tourist hotels show data belonging to DRS, while 68.75%
show data belonging to CRS.
For the whole efficiency analysis of 2009, the average
technical efficiency value is 0.842, while the average pure
technical efficiency value is 0.912. In the CCR model, 45
tourist hotels show a relative efficiency value of 1,
accounting for 58.44% of the total and higher than that in
2008. A total of 50 tourist hotels display a relative efficiency
of 1 in the BCC model, accounting for 64.95% of all and also
higher than that in 2008. For the scale of returns, 28 tourist
hotels display data belonging to IRS in 2009, accounting for
36.36% of all and lower than that in 2008. It means that those
tourist hotels were able to increase their size gradually more
so than under the same situation in 2008, along with average
productivity. A total of 4 tourist hotels display data belonging
to DRS, accounting for 5.19% of all and lower than that in
2008, while 58.45% of all tourist hotels have data belonging
to CRS, which is higher than that in 2008.
In the efficiency analysis for the Taipei region, the
average technical efficiency is 0.932, while the average pure
technical efficiency is0.985, both of which are higher than the
national average number. In the CCR model, 23 tourist hotels
display a relative efficiency value of 1, accounting for
74.19% of Taipei’s tourist hotels, while 27 tourist hotels
display a relative efficiency value of 1 in the BCC model,
accounting for 87.10% of Taipei’s tourist hotels. Both
numbers are higher than that in 2008. For the scale of returns,
8 Taipei tourist hotels display data belonging to IRS in 2009,
accounting for 25.81% of those in Taipei and much the same
as that in 2008. No hotel has data belonging to DRS, while
74.19% of them have data belonging to CRS. In the
efficiency analysis for the non-Taipei region, the average
technical efficiency value is0.919, while the average pure
technical efficiency is 0.941, both of which are slightly lower
than that in the Taipei region. In the CCR model, 20 tourist
hotels display a relative efficiency value of 1, accounting for
64.52% of those in the non-Taipei region and lower than that
in 2008. Twenty-two tourist hotels display a relative
efficiency of1 in the BCC mode, accounting for 70.97% of
those in the non-Taipei region and also lower than that in
2008. For the scale of returns, 9 non-Taipei tourist hotels
display data belonging to IRS, accounting for 29.03% of the
non-Taipei ones and showing nearly the same as that in 2008.
Two of them have data belonging to DRS, while 64.52% of
them have data belonging to CRS. Finally, in the efficiency
analysis for the scenic region, the average technical
© 2014 | Published by The Standard International Journals (The SIJ)
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The SIJ Transactions on Industrial, Financial & Business Management (IFBM), Vol. 2, No. 3, May 2014
efficiency is 1, while the average pure technical efficiency is
also 1, showing that the relative efficiency of all tourist hotels
in this region is high. In terms of the scale of returns, no hotel
in the scenic region has data belonging to IRS in 2009, while
100% of them have data belonging to CRS.
For the whole efficiency analysis of 2010, the average
technical efficiency value is 0.766, while the average pure
technical efficiency is 0.855, both of which are lower than
those in 2008 and 2009, respectively. In the CCR model, 33
tourist hotels display a relative efficiency value of 1,
accounting for 38.82% of all hotels and lower than those in
2008 and 2009, respectively. Forty-nine tourist hotels display
a relative efficiency of 1 in the BCC model, accounting for
57.64% of all and also lower than those in 2008 and 2009,
respectively. A possible reason could be the number of tourist
hotels established in 2010. For the scale of returns, 39 tourist
hotels display data belonging to IRS in 2010, accounting for
45.88% of all and higher than that in 2008 and 2009,
respectively, and showing that the tourist hotels could
increase their size gradually along with average productivity.
Twelve tourist hotels display data belonging to DRS,
accounting for 14.12% of all and higher than that in 2008 and
2009, respectively. Twelve tourist hotels display data
belonging to CRS, accounting for 40% and lower than that in
2008 and 2009, respectively.
In the efficiency analysis for the Taipei region, the
average technical efficiency value is 0.904, while the average
pure technical efficiency value is 0.976, both of which are
higher than the national average averages. In the CCR model,
18 tourist hotels display a relative efficiency value of 1,
accounting for 52.94% and lower than those in 2008 and
2009, respectively. Twenty-three tourist hotels display a
relative efficiency value of 1 in the BCC model, accounting
for 67.65% of those in the non-Taipei region and also lower
than that in 2008 and 2009, respectively. For the scale of
returns, 14 tourist hotels display data belonging to IRS among
those in the non-Taipei region in 2010, accounting for
41.18% of those in that region and higher than that in 2008
and 2009, respectively. Two tourist hotels display data
belonging to DRS and 52.94% of all have data belonging to
CRS. In the efficiency analysis for the scenic region, the
technical efficiency average is 0.977, while the pure technical
efficiency average is 1, meaning the relative efficiency of all
tourist hotels in the scenic region is high. In the CCR model,
13 tourist hotels display a relative efficiency value of 1,
accounting for 81.25% of those in the scenic region. Sixteen
tourist hotels display a relative efficiency value of 1 in the
BCC model, accounting for 100% of those in the scenic
region. From the findings in 2009 and 2010, the operating
efficiency appears mature in the scenic region. For the scale
of returns, 2 tourist hotels display data belonging to IRS
among those in the scenic region in 2010, while 1 tourist
hotel displays data belonging to DRS and 81.25% of all there
have data belonging to CRS.
Table 3: Efficiency Analysis
Year
Region
Number of
Number of
Pure
Number of
Technical Tourist Hotels
Tourist Hotels
Percentage Technical
Percentage Tourist Hotels Percentage
Efficiency with Efficiency
with Efficiency
Efficiency
Showing IRS
of 1
of 1
All
0.791
30
40%
0.924
45
60%
38
51%
Taipei Region
0.913
22
71%
0.964
24
77%
7
23%
Non-Taipei Region
0.946
22
79%
0.980
25
89%
6
21%
Scenic Region
0.875
11
69%
0.981
13
81%
5
31%
All
0.842
45
58%
0.912
50
65%
28
36%
Taipei Region
0.932
23
74%
0.985
27
87%
8
26%
Non-Taipei Region
0.919
20
65%
0.941
22
71%
9
29%
Scenic Region
1.000
15
100%
1.000
15
100%
0
0%
All
0.766
33
39%
0.855
49
58%
39
46%
Taipei Region
0.904
22
63%
0.976
29
83%
12
34%
Non-Taipei Region
0.886
18
53%
0.918
23
68%
14
41%
Scenic Region
0.977
13
81%
1.000
16
100%
2
13%
2008
2009
2010
ISSN: 2321-242X
© 2014 | Published by The Standard International Journals (The SIJ)
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The SIJ Transactions on Industrial, Financial & Business Management (IFBM), Vol. 2, No. 3, May 2014
V.
DISCUSSION
The tourism industry not only produces solid effects such as
foreign exchange gains, income, and employment
opportunities, but also guides local citizenship in diplomacy
and beneficial international friendship and improves the
national image. Taking environmental protection and
economic growth into concurrent account, tourism helps
avoid a wide range of environmental pollution and disasters
resulting from industrial developments and also promotes
economic effects including natural resources and cultural
assets and improves public health. The promotion of tourism
has become a trend toward economic development and
valued by all developed countries.
The purpose of this study is to explore the operational
efficiency of tourist hotels in Taiwan and to compare them
among various regions. The findings are as follows. 1) The
operating efficiency of all tourist hotels in 2009 is the best in
the CCR model, which might be caused by more tourist
hotels opening up after the 2008 financial crisis in 2010,
while the operating efficiency of the hotels is nearly the same
under the BCC model in 2008 and 2009 and worse in 2010.
2) The non-Taipei region performs the best in terms of region
under the CCR model in 2008, followed by the Taipei region
and then the scenic region. The operating efficiency is nearly
the same under the BCC model between the non-Taipei
region and the scenic region, while the Taipei region
performs the worse. 3) The scenic region performs the best
under the CCR model in 2009 in terms of region, followed by
the Taipei region and then the non-Taipei region. The
operating efficiency of the scenic region performs the best
under the BCC model, followed by the Taipei region and then
the non-Taipei region. 4) The scenic region performs the best
under the CCR model in 2010 in terms of region, followed by
the Taipei region and then the non-Taipei region. The
operating efficiency of the scenic region continues to perform
the best under the BCC model, followed by the Taipei region
and then the non-Taipei region. The findings are the same as
those indicated in 2009.
This study set limitations and directions for future
studies as follows. 1) This study only discusses the operating
efficiency of tourist hotels, while exploring the current state
of Taiwan’s tourism industry. In the future, people can
examine the operating efficiency of other tourism-related
industries, including but not limited to recreational zones,
travel agencies, and passenger bus transportation. 2) The
period in this study is limited to the operating efficiency of
each tourist hotel after a change in rules governing Chinese
tourists, who exhibit a large discrepancy before and after the
time the rules changed. Hence, studies in the future may look
at any impact Chinese tourists have had on Taiwan’s tourism
industry before and after the changes were made. 3) The
input/output variables used herein exclude figures indicated
in hotels’ financial statements. Studies in the future may want
to analyze such financial figures and create a relevant
regression model to view the operational performance of each
tourist hotel. 4) Limited by the DEA research model, the size
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of sample had to be twice the sum of the input and output
variables, and thus national data were categorized into Taipei,
non-Taipei, and scenic regions. Further subjects for
efficiency ratings could include more information as to the
number of tourist hotels in the northern, central, southern, and
eastern regions of Taiwan or tourist hotels throughout the
various is cities and counties.
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