measuring hotel performance using data envelopment analysis

This article was downloaded by: [University of Limerick]On: 15 May 2013, At: 14:00Publisher: RoutledgeInforma Ltd Registered in England and Wales Registered Number: 1072954Registered office: Mortimer House, 37-41 Mortimer Street, London W1T 3JH,UK

Anatolia: An InternationalJournal of Tourism andHospitality ResearchPublication details, including instructions forauthors and subscription information:http://www.tandfonline.com/loi/rana20

Measuring Hotel PerformanceUsing Data EnvelopmentAnalysisNILSUN TUMER aa Department of Tourism Administration, BogaziciUniversity, Hisar Campus Bebek, Istanbul, Turkey E-mail:Published online: 26 Jul 2011.

To cite this article: NILSUN TUMER (2010): Measuring Hotel Performance Using DataEnvelopment Analysis, Anatolia: An International Journal of Tourism and HospitalityResearch, 21:2, 271-287

To link to this article: http://dx.doi.org/10.1080/13032917.2010.9687103

PLEASE SCROLL DOWN FOR ARTICLE

Full terms and conditions of use: http://www.tandfonline.com/page/terms-and-conditions

This article may be used for research, teaching, and private study purposes.Any substantial or systematic reproduction, redistribution, reselling, loan,sub-licensing, systematic supply, or distribution in any form to anyone isexpressly forbidden.

The publisher does not give any warranty express or implied or make anyrepresentation that the contents will be complete or accurate or up todate. The accuracy of any instructions, formulae, and drug doses should beindependently verified with primary sources. The publisher shall not be liable

http://www.tandfonline.com/loi/rana20

http://dx.doi.org/10.1080/13032917.2010.9687103

http://www.tandfonline.com/page/terms-and-conditions

http://www.tandfonline.com/page/terms-and-conditions

for any loss, actions, claims, proceedings, demand, or costs or damageswhatsoever or howsoever caused arising directly or indirectly in connectionwith or arising out of the use of this material.

Dow

nloa

ded

by [

Uni

vers

ity o

f L

imer

ick]

at 1

4:00

15

May

201

3

Anatolla: An lnternatlonal Journal of Tourism and Hospltallty Research Volume 21. Number 2. pp. 271-287,2010

Copyright Q 2010 anatolla Printed In Turkey. All rights reserved

1303-2917/10 $20.00 t 0.00

Measuring Hotel Performance Using Data Envelopment Analysis

NILSUN TUMER' 1 Department of Tnurirm Admmishahon, aOgazto Umwmty. Hisai Campus Bebek, Istanbul. Turkey. E-mail. n t h n Iurrr&wwdu.fr

A B S T R A C T K E Y W O R D S Measuring performance is a means for firms to improve their existing posi- Resort hotel industry tions in today's highly competitive environment. Resort hotel industry has Data envelopment analysis become a significant area of interest as holiday and leisure trips turned out Efficiency to be the main purpose of international travel in recent years. This article is based on a study which measures the performance of resort hotels by techru- cal efficiency. The dataset covers almost 15 percent of the total capacity of 4 and 5 star hotels located in the main coastal cities of Turkey. This study modifies revenue per available room (RevPAR) indicator to make it appli- cable for "all inclusive" resort hotels. The study, by means of data envelopment analysis (DEA), indicated that particularly the number of rooms, star Resubmitted ' '' December categories, and food &beverage cost per room sold were important factors in efficiency differences among resort hotels.

A R T I C L E H I S T O R Y Submitted : 28 September 2009

Resr'bmitted ' 28 '010 Accepted I 15 March 2010

INTRODUCTION Measuring performance is actually based on results (outputs) and the costs (inputs) of achieving them. Performance measures are popular both at country and at company levels. Efficiency is a common method to measure performance at company level (Barros and Mascarenhas 2005). Efficiency as proposed by Farrell (1957) has two components namely technical and allocative efficiency. Technical efficiency is eligible to define the relationship between multiple inputs and outputs by using a production function. Any point on production function represents the maximum output at each input level which indicates technically efficient points. Technical efficiency measures actual output as compared to potential output with actual inputs in use

Volume 21 0 Number 2 0 Winter2010 0 271

Dow

nloa

ded

by [

Uni

vers

ity o

f L

imer

ick]

at 1

4:00

15

May

201

3


and with the existing technology. Technical efficiency indicates the relative performance of companies with respect to production possibility frontier. Allocative efficiency on the other hand, measures the right combination of inputs and outputs with respect to their price levels. It can only be calculated by drawing isorevenue line only if price information is known (Coelli et al. 1998). Despite the difficulty of separating price and quantity information in hotel industry, most of the studies in the literature preferred to measure only technical efficiency levels of hotels.

This study employs data envelopment analysis (DEA), using the non parametric piecewise linear convex isoquant to estimate the efficiency frontier and to measure the relative technical efficiency of 28 resort hotels in Turkey. DEA does not impose any assumption on the functional form of efficiency frontier since efficiency frontier is estimated by the best performer units of the group. It also allows the use of multiple input and multiple output applications.

The analyses of this article contribute to the hotel efficiency literature by exam- ining the performance of resort hotel industry. The need to evaluate managerial efficiency performance has risen in resort hotel industry in Turkey, especially after the rapid expansion of hotel supply in the coastal cities of Turkey. In the last decade, the total number of bed capacity in the three main coastal cities of Turkey, namely Aydin, Antalya and Mugla increased from 300 thousands to 530 thousands which indicate a significant amount of investment (Ministry of Culture and Tourism - MCT 2007). Turkey managed to utilize this rapid investment growth by focusing on international tourists rather than on domestic market. In a short period of time, Turkey, being among the top twelve most visited countries in the world, became a competitive international player in the resort hotel industry with its new hotel supply and with its widespread “all inclusive” concept. In this context, the analysis of resort hotel efficiency in Turkey becomes an attractive field to study not only for Turkey but also for other Mediterranean countries that are competing with Turkey in the resort hotel industry.

The growing global competition compels Turkish resort hotel industry to become more efficient in order to keep and improve its current position in the international scene (details on global competition in hotel industry can be found in Tumer 2008). This article focuses on two issues: 1) the measurement of relative managerial performance of Turkish resort hotel industry based on a sample of hotels; 2) Analyzing the effects of different factors on managerial efficiency and identifying common characteristics of efficient hotels. It is believed that defining the reasons for top and weak performance for a cluster of resort hotels will provide insights that may improve the efficiency of the Turkish resort hotel industry.

LITERATURE REVIEW

Performance is a relative concept and the information derived from the assessment of performance depends on the aim and the method used. The tradition-

272 Anatolia: An International Journal of Tourism and Hospitality Research

Dow

nloa

ded

by [

Uni

vers

ity o

f L

imer

ick]

at 1

4:00

15

May

201

3

Nilsun Turner

a1 performance indicators such as ratio, break-even and yield management analysis in the hotel industry were more commonly used before the frontier methods (Barros 2005). Traditional performance indicators provide limited information about the performance of units since they do not capture how the multiple inputs affect simultaneously the multiple outputs. However, the introduction of frontier models took performance measurement to a new stage since they examine the production process entirely, providing more information about the firms potential for further improvement, its optimal scale size and also on role models to improve its performance (Thanassoulis 2001).

The four main frontier methods that are used to analyze efficiency are thick frontier approach (TFA), distribution free approach (DFA), stochastic frontier approach (SFA) and DEA. Each of these models differs from one another by their underlying assumptions about the shape of the efficiency frontier and the treatment of random error (Bauer et a1.1998). The major advantage of DEA over the other frontier methods is that since it is estimated by a nonparametric methodology, it does not impose a structure on the shape of the efficient frontier (Barros 2005). Research evidence indicates that among all frontier methods, DEA is the most extensively applied model in hotel industry (Morey and Dittman 1995; Hwang and Chang 2003; Tarim et al. 2000; Johns et al. 1997; Keh et al. 2006; Sigala et al. 2005; Barros 2005; Barros and Mascarenhas 2005; Onut and Soner 2006; Chen 2009). Despite the coherence of applying DEA to measure efficiency, different input-output combinations were used for each referred study.

Input and Output Selection

The selection of inputs and outputs depends on the approach to productivity definition, namely a partial or total approach as well as on the focus of the analysis (Sigala 2004). Productivity definition, under partial approach relate total output to a single measure of input (e.g. labor, capital) which does not necessarily reflect a proper overall productivity level because of the joint effects of other inputs. Therefore, total approach is more favored if data availability is not a constraint. On the other hand, the focus of the analysis may be the overall managerial efficiency of the hoteI or the departmental efficiency. In this case, relevant inputs and outputs are determined, depending on the focus of the analysis.

The complexity of defining inputs/outputs at service industries mainly result from the intangible features of service industries (Sigala et al. 2005) and which makes it difficult to standardize quality. As opposed to service industries, in manufacturing industries, quality is assumed to be constant since products are more homogenous and tangible (Gronross, qasalo 2004). Therefore, in manufacturing industry, customers usually evaluate a product only by its amount whereas in service industries a given service is often evaluated both by the quantity and quality aspects (Rutkauskas and Paulaviciene 2005).

At this point, some of the studies (Morey and Dittman 1995; Tarim et al. 2000) in the literature preferred to include customer satisfaction as an additional


Dow

nloa

ded

by [

Uni

vers

ity o

f L

imer

ick]

at 1

4:00

15

May

201

3


output to reflect the quality aspect of performance in their efficiency measures. Majority of the studies preferred to include either one or more of the tangible elements such as revenue, occupancy rate, number of guests and profit as proxies for output (Johns et al. 1997; Hwang and Chang 2003; Sigala et al. 2005; Barros 2005; Barros and Mascarenhas 2005; Keh et al. 2006; Onut and Soner 2006; Chen 2009). The argument supporting the use of only tangible elements is that comprehensive quantitative measures such as revenues and profits are also a reflection of customer satisfaction level (Sigala et al. 2005). However, price level being one of the crucial components of revenue and profit, does not reflect the quality level in cases where price regulations, government subsidized products or monopolistic markets exist (Gronross and qasalo 2004).

In terms of input selection, researchers employed various combinations of cost elements. Physical characteristics of hotels (number of rooms, total surface area of the hotel) could be considered under the initial investment cost of the hotels while salaries, room-division costs, food and beverage costs and utility costs could be categorized under operational expenses. Since all of these costs have an impact on the performance of the hotels, depending on the availability of the data, they can be included in hotel efficiency studies.

Causes of technical inefficiency could result from the factors that are either under or outside the control of the management (Barros 2005). Factors, which can influence the efficiency of a DMU, but at the same time are not under the control of the management, are usually described as uncontrollable environmental factors. Ownership differences, location characteristics, labor union power and government regulations are some examples for uncontrollable environmental factors. It is not common to include uncontrollable environmental factors directly either as an input or as an output in the DEA models since the direction of the influence of the uncontrollable variable is not usually known (Coelli et al. 1998).

Factors Affecting Performance

Inefficiencies caused by the management are easier to overcome while uncontrollable factors are more difficult if not impossible to change. Barros and Mascarenhas (2005) suggested that mergers and acquisitions are useful tools to overcome technical inefficiencies as a resuIt of size while closing down the hotel is the only way to overcome inefficiencies as a result of location. Nev- ertheless it is believed that all those uncontrollable factors causing technical inefficiencies can serve as a decision making tool for future investments.

Barros (2005) claimed that location of the hotels influence efficiency since demand of clients increase in more populated areas, leading to higher efficiency. His empirical findings on Portuguese hotels confirmed that city center hotels had higher technical efficiency scores than hotels located in remote areas. He also observed that physical features of the hotels such as having limited number of rooms or historic buildings converted to hotels are less efficient than hotels with more rooms and purpose-built properties. His data set


Dow

nloa

ded

by [

Uni

vers

ity o

f L

imer

ick]

at 1

4:00

15

May

201

3

Nilsun Ttimer

composed of 43 state-owned single chain properties having limited number of rooms ranging from 9 to 41.

Barros and Mascarenhas (2005) utilized the same hotel set with Barros (2005) by changing the input combination and adding allocative efficiency aspect to the above mentioned study. A number of similar and different points emerged when two studies are compared. Barros and Mascarenhas (2005) observed similar findings with Barros (2005) that hotels with more rooms and in city centers are more efficient. The contradictory finding between two studies is that the former study indicated that hstoric buildings converted to hotels are less efficient than purpose-built hotels, while the latter study stressed that there is no relationship to efficiency either hotels are historic or purpose-built. A study on 93 UK hotels verified the findings of Barros (2005) and revealed that purpose built hotels have higher efficiency levels than historic ones (Si- gala et al. 2005). However, contrary to the findings of earlier studies (Barros 2005; Barros and Mascarenhas 2005), the study on UK hotels, with room num- bers ranging from 90 to 350, did not find any significant efficiency difference based on number of rooms.

In a similar study, Hwang and Chang (2003) found out that operating expense, number of employees and number of visitors are the reasons for relatively poor efficiency performance of Taiwan hotels. Furthermore, significant efficiency differences recognized among hotels due to market conditions, sources of customers and management style. Hotels targeting leisure markets, hosting foreign customers and those operating under international chains, have higher levels of efficiency than hotels that are independently managed, targeting business markets and hosting local customers. Furthermore, similar findings were observed for the UK; Sigala et al. (2005) provide evidence that hotels operating under chains achieve higher efficiency scores than independently managed hotels. This might be a result of chain hotels’ having wider ac- cess to international reservation and distribution systems (Sigala et al. 2005).

METHODOLOGY As noted earlier, DEA is the most preferred method used for measuring efficiency. This study evaluated technical efficiency of resort hotels in Turkey by using output oriented DEA which is a non-parametric and multi factor method. DEA forms the efficiency frontier by the best performing units in the group and rest of the unit efficiencies are calculated accordingly. That is, efficiency measures are relative with reference to efficiency frontier. DEA is preferred for being suitable for multi input and multi output applications (Morey and Dittman 1995). It calculates technical efficiency by the ratio between the actual outputs to the potential outputs that a company can produce with its set of inputs and existing technology. Additionally, conducting both constant returns to scale (CRS) and variable returns to scale (VRS) version of DEA de- composes technical efficiency (TE) as “pure” and ”scale” in order to differen- tiate the sources of inefficiencies. These concepts can be expressed as:

TECRS = TEVRS X Scale Eficiency


Dow

nloa

ded

by [

Uni

vers

ity o

f L

imer

ick]

at 1

4:00

15

May

201

3


If technical efficiency for a DMU is measured under CRS assumption, this means that the firm is operating at an optimal scale. At optimal scale, the firm is capable of increasing its input by the same proportion as its output. Thus, scale efficiency is 100 percent and technical efficiency under CRS only indicates pure technical efficiency. In this case, technical efficiency illustrates the amount of additional output to be increased to reach the CRS production possibility frontier without requiring extra input.

Technical efficiencies assessed under VRS are called "pure" to indicate that they are net of any scale effects (Thanassoulis 2001). However, in reality technical efficiency is impacted by both pure and scale efficiencies. Scale efficiency measures the impact of size which affects technical efficiency since a change in input level does not always change output level by the same proportion. In certain cases, raising input levels by a small percentage can lead to the expansion of output levels by an even larger percentage (increasing returns to scale, IRS) or the rise in output levels will be lower than that of input levels (decreasing returns to scale, DRS). The ideal scale size is to operate where CRS hold. DMUs that are not at the optimum scale size, can not reach technical efficiency even though they achieve pure technical efficiency. Therefore, conducting both CRS and VRS version of DEA will help to indicate the underlying reasons for technical inefficiency as "pure" and "scale".

After differentiating the reasons for technical inefficiency, a peer group among the efficient peers is allocated to each inefficient hotel to guide that DMU to reach pure technical efficiency. The projected point on the efficiency frontier for each inefficient DMU is the linear combination of points that represent efficient peers for that DMU. The information on the weights of peers in this linear combination also provides potential output value with respect to its actual output levels while keeping its set of inputs unchanged.

The origins of DEA go back to the non parametric efficiency approach of Far- re11 (1957). All measures are based on the assumption that efficient production function is known which is not the case in practice. Therefore, Farrell (1957) suggested to use either a parametric function or a non parametric piecewise linear convex isoquant to estimate efficiency frontier. The former is the basis for SFA, DFA and TFA methods while the latter is the origins of DEA.

DEA, which uses the non parametric piecewise linear frontier to measure efficiency, was introduced in 1978 as an input oriented constant returns to scale (CRS) model (Norman and Stoker 1991). The unit of evaluation is usually referred as a decision making unit (DMU). CRS assumption is only appropriate if all DMUs are operating at optimal scale which in practice is not the case. Therefore, an alternative model with variable returns to scale (VRS) assumption was introduced in 1984 (Coelli et al. 1998). The CRS linear programming problem can be modified to VRS by adding a constant in the numerator.

This model is signed to evaluate the relative performance of DMUs, based on observed performance of m = 1,. . .,n.

soutputsdenotedbyyj, j = 1, ..., s Y inputs denoted by xi, i = 1,. . .,Y

276 . Anatolia: An International Journal of Tourism and Hospitality Research

Dow

nloa

ded

by [

Uni

vers

ity o

f L

imer

ick]

at 1

4:00

15

May

201

3

Nilsiln Turner

The efficiency measurement of a single DMU for output oriented VRS model, o is

subject to

Ci=l..r V, xim - ca

2 1 m = I , ...., n

Cj=l,.,s wj yjm

wj 2 0 , j = 1, ..., s

v , > O . i = I , ..., r

In cases, when the ratio for unit o is less than 1, the subset of units whose ratio is equal to 1 is the peer group for unit 0. The weights are denoted by w and v for outputs and inputs respectively. These weights are unknown and are determined by solving linear programming problem. The problem can be expressed as the following linear programming (LP):

rnin el,= &i..r vi xi0 - CO

(2) subject to

C i ~ l ~ . , v i x i ~ - ~ ~ ~ l , . , s w j y , ~ . c o ~ O m = 1 ,...., n

xj=l,..s wj y jo= 1 wj 2 0 , j = I , ..., s

v , z O , i = 1 ,..., r

The above form is known as the primal form of DEA linear programming problem. All linear programming has both primal and dual formulations. The objective of the dual model is to derive an equivalent solution.

Volume 21 0 Number 2 0 Winter 2010 0 277

Dow

nloa

ded

by [

Uni

vers

ity o

f L

imer

ick]

at 1

4:00

15

May

201

3

Measuring Hotel Perfomance Using Data Envelopment Analysis

max f

(3) subject to

Cm=l, .n L'om yjm - fo yjo > O j = I ,...., s

This dual form involves fewer constraints than the primal form so it is usually more preferred to solve (Coelli et al. 1998). Lom being the dual weights, fo is a measure of how much all of the inputs of unit o can be increased in the same proportion to produce a performance in line with the weighted combination. For each DMU, the dual problem will be solved.

Input oriented models aim to minimize input without causing any reduc- tion in output, while output oriented models aim to maximize output without causing any increases in inputs. The decision to choose input or output oriented approach is based on the aim of the study as well as on the factors that managers control. For instance, industries having particular orders to fill, such as electricity generation prefers to use input oriented models to minimize input usage while DMUs having fixed quantity of resources prefer to use output oriented models to maximize output (Coelli et al. 1998). Input and output oriented technical efficiency results are same under CRS assumption.

Research Instrument Design

The main objective of this study is to measure the relative managerial efficiency of resort hotels in Turkey. Input and output factors of this study are determined by focusing on the dynamics of resort hotel industry in Turkey and the availability of data. Although previous studies were also useful in the selection of factors, this study used its own unique set of inputs and outputs. Table 1 provides an overview of the past studies.

Key indicators of performance in a hotel are occupancy rate, average room rate (ARR) and restaurant & banquet revenues. The former two are the main indicators of room revenues while the latter is the main component of food and beverage (F&B) revenue. Generally, room revenues and F&B revenues constitute more than 90 percent of total revenues of hotels. Although resort hotels are not any exception of this, the dynamics of "all inclusive" concept in the Turkish resort hotel industry makes it difficult to separate room and F&B revenue.

Among hotel performance indicators, revenue per available room (RevPAR) is the most widely used one (Brown and Dev 1999). Although RevPAR is de- fined as the ratio of total room revenue over yearly room capacity, this study modified RevPAR definition based on "all inclusive" concept and used ac-


Dow

nloa

ded

by [

Uni

vers

ity o

f L

imer

ick]

at 1

4:00

15

May

201

3

Nilson Tumer

Tabte 1. Efficiency Studies in the Hotel Industry outputs - Study Yeem DMU Methad inputs

Morey. R. and 1993. USA 54 DEA (CRS-input 'total expenses under 10 'total mom revenue, *level of guest Dman . D. oriented) subtitles

(1995) satisfadion

Anderson. R , 1994, USA 48 SFA (input onenled) *cost of personnel 'totai revenue Fish. M., Xia, Y. and Michello. F. (1999) 'f8b expenses

_____.

'number of moms *total gaming related expense

'olher expenses

'number of personnel *rwm revenue

'other revenue 'total area fw restaurants *all operating expenses

Hwang. S And 1994.1998. 45 DEA (CRS) and Chang. T (2003) Taman Malmqutst Index 'number of r m s 'f8b revenue

Tam. S.. Dener. H. I. and Tarim. A (2000)

Johns. N.. Howcroft. 8. and Drake, L. (1997)

Keh. H.. Chu,S. and Xu.J. (2006)

1998. 21 DEA(CRS-mlput 'number of personnel *repeat w s t m r ratio Turkey onented) *investment wst 'occupancy rate

'total expense excl. personnel 'profit wst

UK 15 DEA (VRS-output *num of r m s available 'number of room nights __ ~

oriented) *to181 personnel hours sold, 'total covers

'total utility expenses beverage revenue total f&b expenses W N e d . *total

1999-2000, 49 DEA (VRS-output 'total expenses 'marketing exp Asia PasMc oriented) 'number of rwms (intermediate)

.rwm revenues 'markekebng expenses (intermediate input) 'f8b revenue

- Sigala. M , 1999. UK 93 StepmseDEA 'front offlca salary 'average rwm rate Jones, P (CRS-output onenled) *other salary ARR Lodovood, A 'number of rwms 'demand *nights spent and Airey. D variability 'adminislraLlon 'non mom revenues (2005) expenses 'other expenses

Earros (2005) 2001. 43 Portugal

-- 'number of personnel *sales *wst of personnel 'number of rwms 'surface area of hotel 'book value 'operational oost 'external cost

'number of guests 'number of nights spent

Earrm. C. and 2001. 43 DEA (VRS-output 'number of personnel *sales Mascarenhas. M. Portugal onenled) Technical 'number of rwms 'number of guests (2005)

Onut. S and 2004 32 DEA (CRS-input * number of personnel 'ouxrpancy rate Soner. S. (2006) Turkey oriented) 'electricity consumption .annuai total reventm.

8 AlloCatNe 'book value 'number of nights spent _______-

'water wnsumption 'liquid petroleum gas

*total number of guests

cordingly. The modified form of RevPAR is the ratio of total room and F&B revenues over yearly room capacity. The traditional form of RevPAR is commonly used in hotels that can separate their room and F&B revenues. This kind of pricing strategy is usually employed by city hotels and it is impossible to calculate traditional RevPAR for all inclusive hotels which do not separate their revenue as accommodation and F&B. Therefore, the modified form of RevPAR is designed to make it possible to evaluate resort hotels' efficiencies.

Along with RevPAR, other revenue per room sold is also used among output factors. Other revenues, including all kinds of revenues except from room and F&B, do not exceed 10 percent of total revenues of resort hotels unless the hotel has professional golf facility and extensive conference center.


Dow

nloa

ded

by [

Uni

vers

ity o

f L

imer

ick]

at 1

4:00

15

May

201

3


Hotel industry has two main input components; initial investment cost and operational costs. Initial investment cost, being the cost of construction, fixture and furniture of the hotel, is a significant cost item when total annual revenue of the hotel is considered. In this study, room capacity is employed as the single indicator of initial investment cost as most of the studies did (Johns et al. 1997; Anderson et al. 1999; Sigala et al. 2005; Keh et al. 2006). However, using number of rooms has an important assumption indicating that investment cost per room is almost the same for each hotel in the data set. In fact, the assumption is rather strong in most of the cases especially when hotels with various star categories are compared. However, this study limited its data set with only 4 and 5 star hotels in an effort to have a homogeneous investment cost structure to the extent possible.

On the other hand, operating expenses are used to represent cost of all kinds of inputs to run the operation of the hotel. The study covered all operational expenses under four main headings such as personnel costs, F&B costs, energy costs and other costs. Personnel costs include salaries and all kinds of fringe benefits payable to departmental and administrative staff. Therefore, operational expenses such as F&B costs and energy costs used in the study are net of any personnel costs. Other costs include all cost aside from the above three main costs such as marketing, auxiliary materials, transportation and main- tenance. Thus, two outputs and five inputs shown below were chosen for the output maximization analysis which reflects the key industry indicators:

outputs 9 Modified RevPAR 9 Other revenue per room sold

Room capacity Personnel cost Energy cost F&B cost Other cost

In the literature, three different applications for the minimum number of DMU were recognized. Similar to the study of Johns et al. (1997) and Tarim et al. (2000), also preferred to use the number of DMUs greater than twice the number of inputs and outputs, which results in a minimum of 15 DMUs (15? 2(2+5)) for the case of this study in order to reach acceptable findings. On the contrary, Raab, R. and Lichty, R. (2002) indicated that the outcome would be meaningful if the minimum number of hotels is greater than three times the number of inputs and outputs. Based on the second approach, this study should have at least 21 DMUs (21? 3(2+5)) in its data set. The last approach recently used by Sigala et al. (2005) suggests that the number of DMUs should be greater than number of inputs times number of outputs. Under these cir- cumstances, this study should have had at least 10 (2*5) DMUs.

Inputs


Dow

nloa

ded

by [

Uni

vers

ity o

f L

imer

ick]

at 1

4:00

15

May

201

3

Nilsun Turner

Based on the 5 input factors, 2 output factors and 28 DMUs used for this study all three approaches listed above for the minimum number of DMUs with regard to employed factors were satisfied.

Data Collection

For the purpose of this study, the hotel managers of resort hotels in the three main coastal cities of Turkey, namely Antalya, Mugla and Aydin, were contacted and questionnaires were mailed in 2006. The questionnaire aimed at obtaining data on operational activities as well as physical features of the property and the management style. The hotel managers were selected using convenience sampling. In order to set up a relatively homogenous group of data, only 4 and 5 star resort hotels are included in the study. Primarily, the customer profile of 4 and 5 star resort hotels are dominated by international tourists, which account for 77 percent and 79 percent of the total accomm 1-

dation in 4 and 5 star resort hotels respectively (MCT 2007). Secondly, 4 and 5 star hotels have more professional reporting and business structure compared to family-owned 1 to 3 star hotels.

The number of hotels contacted was 53 and 41 questionnaires were received back. Among these 41 hotels, 13 of them were eliminated because of the receipt of incomplete and inconsistent questionnaires. The questionnaires were also overviewed with some of the hotel managers to ensure that there is a mu- tual understanding between the researcher and the hotel management. As a result, there were a total of 28 resort hotels left for the study which constituted 15 percent of all 4 and 5 star hotels in the study area. It was not possible to include all 4 and 5 star resort hotels in the coastal line of Turkey therefore the study has been limited with Antalya, Mugla and Aydin which are most visited by international tourists. These three cities account for about 80 percent of the international nights spent in the country (MCT 2007).

DISCUSSION OF FINDINGS

During the study which was based on output oriented VRS model, the technical efficiency of resort hotels in Turkey was conducted for year 2005. Output oriented technical efficiency defines a production frontier and aims to increase output of each inefficient DMU with respect to frontier without changing the input quantities or combinations used. Table 2 gives information about the characteristics of the variables in detail.

Eventhough the data set is composed of either 4 or 5 star hotels, RevPAR changes in a wide range between 15.07€ and 104.7361. When RevPAR is examined in more detail, it is realized that hotels having higher room prices also achieved higher occupancy rate leading to higher RevPAR. Likewise, hotels having lower room prices also struggle with low occupancy rates. This result shows the importance of marketing strategies, that is simply sacrificing from room prices does not guarantee an increased occupancy if the facilities and services are not meeting the customer expectations.


Dow

nloa

ded

by [

Uni

vers

ity o

f L

imer

ick]

at 1

4:00

15

May

201

3


Table 2. Main Characteristics of the Outputs and Inputs

Variables Units Range Mean

outputs 2005 RevPAR Value in E 15.07 - 104.73 49.24

Other Revenue per room sold Value in E 0.42 - 34.05 6.51 Inputs

Room Capacity Number 179 - 888 390 Personnel Cost Value in (0001 E 305 - 6.1 oa 1 .a39 FBB Value in (000) E 204 - 4,918 1,579 Energy cost Value in (000) E 74 - 1,224 51 9 Other Cost Value in (OOOl E 346 - 7.941 1.563

Other revenue per room sold also changes drastically among DMUs from 0.4261 to 34.056. In “all inclusive” resort hotels, it is very common that other revenue is low since all F&B revenues are included in RevPAR. When data set is examined in more detail, it was found out that hotels having high levels of other revenues have golf and extensive conference facilities. Although the mean of the other revenue per room sold is 6.51€, its median is 3.6861 which is a more reasonable indicator in case of extremes.

This study preferred to limit its data set with hotels at the coastline of Turkey since their pricing, customer profile and investment costs are similar to each other when compared to city hotels. The sample consists of 28 resort hotels located in the main coastal cities of Turkey, namely Aydin, Antalya and Mug- la. Of the data set, 70 percent of the total number of rooms (7,657 rooms) are located in Antalya, 20 percent (2,155 rooms) in Mugla and 10 percent (1,111) in Aydin. The data set is a good sample of Turkey since the distribution of number of rooms with respect to locations is quite similar to Turkish average. Although the data set may seem limited with 28 resort hotels, it covers 15 percent of the total capacity in the selected cities which can be considered as a significant coverage in capacity terms.

Concerning hotel size, respondents’ room capacity varies from 179 to 888 (average 390) and room size ranges from 20 to 40 square meters (average 29.5). Regarding the number of employees, total number of employees ranges from 52 to 504 (average 241). As far as management style is concerned, 82 percent (23 hotels) of respondents are operated by a hotel chain and the remaining operated individually. Among the chain hotels; only one of them is operated by an international chain and rest of them are operated by local chains.

Table 3 presents technical efficiency (CRS), pure technical (VRS), scale efficiency of the hotels. Frequency column illustrates how many times a particular DMU was chosen as a peer, while peer group column indicates DMUs on the efficiency frontier that are benchmarked by other hotels. The study aimed to find the sources of technical inefficiency therefore the technical efficiency


Dow

nloa

ded

by [

Uni

vers

ity o

f L

imer

ick]

at 1

4:00

15

May

201

3

Nilsun Turner

score presented in the second column of Table 3 is decomposed into scale and pure technical efficiency. Under VRS model, which is employed in this study, technical efficiency is referred as "pure" to signal that it is net of any scale effects. In other words, the third column represents the pure technical efficiency score of DMUs. The fourth column illustrates the scale efficiency scores of DMUs a technical efficiency score of a DMU which is calculated by multiply- ing its scale and pure technical efficiency scores.

Tuble 3. DEA Technical Efficiency for Resort Hotels

Resort CRS VRS Scale Peer Group Frequency Rank Efficiency Efficiency Efficiency

5 1.000 1.000 1.000 R5 2 1

12 1.000 1.000 1.000 R12 14 2

15 1.000 1.000 1.000 R15 14 3

20 1.000 1.000 1 .ooo R20 3 4

22 1 .ooo 1 .ooo 1.000 R22 11 5 24 1.000 1.000 1.000 R24 4 6

27 1 .ooo 1 .ooo 1.000 R27 1 7

4 0.996 1 .ooo 0.996 R4 0 8

21 0.930 1.000 0.930 R2 1 3 9

10 0.926 0.940 0.985 R20, R22, R24 0 10

23 0.877 1 .ooo 0.877 R23 0 11

1 0.875 0.991 0.883 R15. R12. R22 0 12

3 0.741 0.852 0.869 R12. R22 0 13

8 0.675 0.771 0.876 R15. R24. R5 0 14

13 0.634 0.645 0.982 R27. R12. R15 0 15

11 0.589 0.756 0.779 R15. R21.RI2 0 16

17 0.587 0.655 0.895 R15. R22, R24 0 17

26 0.549 0.642 0.854 R22, R12, R15 0 18

7 0.537 0.636 0.844 R15, R12. R21 0 19

14 0.536 0.790 0.678 R12 0 20 9 0.532 0.590 0.902 R15,R5.R24,R22 0 21

6 0.500 0.679 0.736 R15, R12 0 22

2 0.482 0.637 0.757 R12. R22 0 23

18 0.462 0.563 0.820 R22. R15. R12. R20 0 24

16 0.430 0.536 0.803 R15. R12. R22 0 25 R20. R12, R15. R22 0 26 28 0.426 0.488 0.872

19 0.385 0.561 0.687 R15. R21.RI2 0 27

25 0.329 0.379 0.869 R15, R22,R12 0 28

Mean 0.714 0.790 0.889 ~ ~~~


Dow

nloa

ded

by [

Uni

vers

ity o

f L

imer

ick]

at 1

4:00

15

May

201

3


In this study, CRS assumption is not preferred since assuming that all DMUs are at optimal scale is not realistic. However, it should be kept in mind that at optimal scale, pure technical efficiency scores presented in the third column will be exactly the same as the technical efficiency scores in the second column. The study verified that, ten hotels were purely technically efficient. From the scale efficiency point of view, only seven hotels were efficient. Ef- ficient hotels are indicated with the value of 1 and rest of the data set was ranked accordingly. The picture changes when total technical efficiency is considered.

The average technical efficiency equals to 71.4 percent which indicates that resorts on average can improve their outputs by 28.6 percent without increasing their input usage. It is also realized that the average pure technical efficiency has a draw down effect on the average technical efficiency with a score of 79 percent with respect to 88.9 percent scale efficiency. The contrary is valid for Resort 4, Resort 21 and Resort 23. Despite being pure technically efficient, they are not technically efficient because of their scale inefficiencies.

As presented in the second column, only seven hotels are technically efficient (having efficiency score of 1). This means that only these seven hotels are both pure technical and scale efficient. The average number of rooms for these seven resorts is 223 while the average number of rooms for the remaining 21 inefficient resorts is 446. This finding indicates that the lower the number of rooms, the higher the efficiency scores of the resorts. The advantage of being small in service industries could be explained as increased flexibility and faster adaptation to changes.

With regard to star categories of resorts, 4 star resort hotels have higher average efficiency scores than 5 star resorts. This finding is parallel to the findings of Tarim et al. (2000), that also found out similar results for 21 resort hotels in Antalya. The analysis of data published by the Turkish Ministry of Culture and Tourism shows evidence that during the recent years, Turkey faces a de- cline in its receipt per international tourist arrival which is a result of the marketing strategy of Turkey. The Turkish hotel industry focuses on mid-level customer demand which actually is the right customer profile for 4 star hotels. Based on this, and noting that 5 star hotels are found to be less efficient than 4 star hotels in this study, it could be derived that compared to 4 star hotels. there is a mismatch between the cost and revenue structure of 5 star resort hotels in Turkey. That is, while these hotels provide superior accommodations and service, their associated revenues do not justify the increased costs of such superior facilities as mid income level tourists become the customers of these luxurious resorts. The cost and revenue structure does seem to be better aligned in the case of 4 star hotels as these provide the proper quality of service with controlled costs compared to their revenues. Consequently, it could be concluded that if the current customer profile of Turkish tourism industry does not change through time, the investors should consider investing in 4 star hotels rather than 5 star hotels.

When the efficiencies are compared on district basis, the findings are more difficult to evaluate since some districts such as Alanya-Antalya and Bod-

284 . Anatolia: An International Journal of Tourism and Hospitality Research

Dow

nloa

ded

by [

Uni

vers

ity o

f L

imer

ick]

at 1

4:00

15

May

201

3

Nilsiin Turner

rum-Mugla are represented by a single resort hotel. Despite this drawback, Kemer-Antalya and Belek-Antalya are still comparable since they are represented by almost equivalent and sufficient number of resort hotels. It is found out that resort hotels in Belek-Antalya are technically more efficient with respect to Kemer-Antalya. Belek is a relatively new destination and closer to the airport than Kemer which could be the main reasons behind this efficiency difference. Interestingly, management style is not found to have a significant impact on efficiency. That is, whether the resort is part of a hotel chain or operated individually did not affect the efficiency level significantly. A rationale for this result is that the individual hotel owners are equally successful as the managements of chain hotels in terms of markethg strategies and they pos- sess similar managerial expertise for running a hotel business., Alternatively, it might also be suggested that they make similar mistakes in operations with chain operated hotel managements.

There are significant differences among hotels in terms of their operating costs, namely personnel, F&B, energy and other costs. The dissimilarity oc- curs as a result of size differences among hotels. For instance, the personnel cost of a 179 room hotel can not be directly compared to a hotel having 888 rooms. As the room size is the main driver of costs, by dividing all costs to number of room in each DMU, the costs are indicated as “per room” basis so that the comparison of operating costs would give more appropriate results. The study points out that the efficient hotels have neither the highest not the lowest personnel cost per available room. This finding suggests that a bal- anced approach is more appropriate than focusing on personnel cost savings or aiming to maximize service quality at the expense of increased personnel cost. The study also verifies that hotels with low F&B cost per room have higher level of efficiency scores. This finding is not surprising since F&B cost accounts for 25 to 30 percent of the total operational costs of a hotel. However, it should also be noted that solely having low level of F&B cost per room sold does not guarantee the efficiency if a hotel can not generate reasonable level of RevPAR in the first place. The energy cost in resort hotels is found to be very standardized. The average energy cost per room sold is 6.54 € and almost all hotels are capable of achieving these levels. Only hotels at full efficiency have slightly less energy costs and hotels with additional features such as a golf course or a large conference facility have significantly higher energy costs per room sold.

The study also indicates that inefficient resort hotels have to improve their RevPAR around 50 percent on average in order to reach efficient hotels. It has been verified that low RevPAR resulted from both occupancy rate and ARR for one third of the inefficient hotels. The remaining two third is suffering from the problems with a single component, either occupancy rate or ARR.

CONCLUSION AND IMPLICATIONS

This study measured the technical efficiency of resort hotels in Turkey by using DEA model which allows the use of multiple inputs and outputs. For each in-

285

Dow

nloa

ded

by [

Uni

vers

ity o

f L

imer

ick]

at 1

4:00

15

May

201

3


efficient resort hotel, a peer group has been identified among efficient hotels, for improving performance of inefficient ones.

The results of the study provide some important insights not only for managers but also for future investors. In terms of managerial understanding, the study results revealed that high F&B cost per room sold is one of the vulner- able points of inefficient resorts since examined hotels with low F&B cost per room have higher level of efficiency scores. However, cutting on F&B cost per room sold does not guarantee efficiency since the reasonable level of RevPAR is the main determinant of efficiency level. That is, hotels are required to decrease their F&B cost to improve their efficiency levels but maintain or improve their RevPAR as well. Training of F&B personnel, reexamining the procurement process and implementing regular audits on the system might be useful to decrease F&B costs without sacrificing from the quality of foods and beverages served.

The analysis also showed that inefficient resort hotels have to improve their RevPAR around 50 percent on average in order to reach efficient hotels where RevPAR is a result of occupancy and ARR. Apart from improving operations such as the abovementioned F&B facilities, RevPAR levels of the inefficient hotels might also be improved by focusing more on marketing strategies to build brand image which would ultimately increase ARR and creating additional sales channels by attending more international fairs or concluding con- tracts with more reputable travel agencies to expand their customer variety in an effort to enhance occupancy.

The major finding of this study guiding future investors is that inefficiency is more prevalent among 5 star resort hotels than 4 star resort hotels. A rationale for this result is, although the operational costs of 5 star resort hotels go beyond the operational costs of 4 star resort hotels, 5 star resort hotels in general do not have sufficient RevPAR to overcome these costs. The customer profile in Turkish resort industry is generally mid-income 'tourists who create siz- able amount of accommodation but not adequate levels of ARR for the costs of 5 star resort hotels. This leads to two conclusions; either new investments should be planned according to the mid-income tourists or wealthier customer profiles should be targeted for 5 star resort hotels whereas with the current structure of Turkish tourism industry the latter seems harder to achieve. In- vestors should not forget that the worst outcome for their investment is build- ing a resort hotel and incurring high levels of initial and operational costs for wealthier customer profile but ending up with mid-income tourists.

This study can be extended in several directions. The first one is to apply DEA to city hotels in Turkey. This kind of future research may illustrate whether same conclusions can be replicated in different hotel segments. Also, this study argued that financial outputs such as revenue and RevPAR will capture the qualitative dimension of the industry. Future research could try to use customer satisfaction as a proxy for quality dimension. However, the presen- tation of customer satisfaction requires caution since reliability and accessibil- ity of this kind of data is always debatable.


Dow

nloa

ded

by [

Uni

vers

ity o

f L

imer

ick]

at 1

4:00

15

May

201

3

REFERENCES Anderson, R. I., Fish, M., Xia, Y. and Michello, F. (1999). Measuring Efficiency in the Hotel Indus-

try: A Stochastic Frontier Approach, Hospitality Management, 18: 45-57. Barros, C. (2005). Measuring Efficiency in the Hotel Sector, Annals of Tourism Research, 32 (2): 456-477. Barros, C. and Mascarenhas, M. (2005). Technical and Allocative Efficiency in a Chain of Small

Hotels, Hospitality Manngement, 24: 415-436. Bauer, P., Berger, A.N., Ferrier, G.D. and Humphrey, D.B., (1998). Consistency Conditions for

Regulatory Analysis of Financial Institutions: A Comparison of Frontier Efficiency Meth- ods, lournal of Economics and Business, 50: 85-1 14.

Brown, J. and Dev, C. S. (1999). Looking Beyond RevPAR, Cornell Hotel and Restaurant Administra- tion Quarterly, 40(2): 23-33.

Chen, Tien-Hui. (2009). Performance Measurement of an Enterprise and Business Units with an Application to a Taiwanese HoteI Chain, Internatioizal Journal of Hospitality Management, 28: 415-422.

Coelli, T., Rao, D.S.P. and Battase, G.E., (1998). A n Introduction to Effiriency and Productivity Analy- sis. Norwell, Massachusetts: Kluwer Academic Publishers.

Farrell, M.J., (1957). The Measurement of Productive Efficiency, Journal of Royal Statistical Society Series A, 120(3): 253-290

Gronross, C. and Olasalo, K. (2004). Service Productivity towards a Conceptualization of the Transformation of Inputs into Economic Results in Services, Iournal of Business Research, 57: 414-423.

Hwang, S. and Chang, T. (2003). Using Data Envelopment Analysis to Measure Hotel Managerial Efficiency Change in Taiwan, Tourism Management, 24: 357-369

Johns, N., Howcroft, B. and Drake, L. (1997). The Use of Data Envelopment Analysis to Monitor Hotel Productivity, Progress in Tourism and Hospitality Research, 3: 119-127

Keh, H., Chu, S. and Xu, J. (2006). Efficiency, Effectiveness and Productivity of Marketing in Serv- ices European, Journal of Operational Research, 170: 265-276.

Ministry of Culture and Tourism (2007). http:llwm.ku1t1lr.gov.tr Morey, R. and Dittman D. (1995). Evaluating a Hotel GM's Performance, Cornell Hotel and Restau-

rant Administration Quarterly, 36: 30-35. Norman, M. and Stoker, B. (1991). Data Envelopment Analysis The Assessment of Performance. West

Sussex, England: John Wiley. (jniit, S. and Soner, S. (2006). Energy Efficiency Assessment for the Antalya Region Hotels in Tur-

key, Energy and Buildings, 38(8): 964-971. Raab, R. and Lichty, R. (2002). Identifying Sub-areas that Comprise a Greater Metropolitan Area:

The Criterion of Country Relative Efficiency, Iournal of Regional Science, 42: 579-594. Rutkauskas, J. and Paulaviciene, E. (2005). Concept of Productivity in Service Sector, Engineering

Economics, 43(3): 29-34. Sigala, M. (2004) Using Data Envelopment Analysis for Measuring and Benchmarking Productiv-

ity in the Hotel Sector, lournal of Travel and Tourism Marketing, 16(3) : 39-60. Sigala, M., Jones, P., Lockwood, A. and Airey, D. (2005). Productivity in Hotels: A Stepwise Data

Envelopment Analysis of Hotels' Room Division Processes, 77le Service Industries lournal, 25: 61-81.

Tarim, $., Dener, H. I. and Tarim, A. (2000). Otel Endiistrisinde Performans 0l*mii: Ciktl Faktor K~s~t l i DEA Uygularnasi, Anatolia: A n Intonational lournal of Tourism and Hospitality Re- search, 11 (2): 25-35.

Thanassoulis, E. (2001). lntroduction to the Theory and Application of Data Envelopment Analysis. Massachusetts: Kluwer Academic Publishers.

Tumer, N. (2008). Measuring Efficiency of Hotel Industry in Turkey Using Data Envelopment Analysis. Unpublished PhD Dissertation, Marmara University Istanbul, Turkey.

Volume 21 Number 2 Winter2010 287

Dow

nloa

ded

by [

Uni

vers

ity o

f L

imer

ick]

at 1

4:00

15

May

201

3

measuring hotel performance using data envelopment analysis

Documents