MEASURING EFFICIENCY IN THE HOTEL SECTOR · 2013. 10. 11. · THE HOTEL SECTOR Carlos Pestana Barros Technical University of Lisbon, Portugal Abstract: This study discusses, by means

Annals of Tourism Research, Vol. 32, No. 2, pp. 456–477, 2005� 2005 Elsevier Ltd. All rights reserved.

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MEASURING EFFICIENCY INTHE HOTEL SECTOR

Carlos Pestana BarrosTechnical University of Lisbon, Portugal

Abstract: This study discusses, by means of data envelopment analysis, the efficiency of indi-vidual hotels belonging to the Portuguese state-owned chain, Pousadas de Portugal, which ismanaged by the enterprise, ENATUR. The use of this technique for the analysis of intrachaincomparative hotel efficiency can be of value in examining the competitiveness of the chain asa whole. By identifying the efficient hotels in a sample, the slacks in inputs and outputs of theinefficient hotels and the peer group of efficient hotels, the data envelopment analysis standsout as one of the most promising techniques to aid the improvement of efficiency. Manage-rial implications arising from this study are also considered. Keywords: hotel efficiency, DEA,Portugal. � 2005 Elsevier Ltd. All rights reserved.

Resume: Le mesurage du bon fonctionnement dans le secteur hotelier. Cette etude dis-cute, par moyen d’une analyse d’enveloppement de donnees, le bon fonctionnement de plu-sieurs hotels individuels qui appartiennent a la meme chaıne etatisee portugaise Pousadas dePortugal, qui est geree par l’entreprise ENATUR. L’utilisation de cette technique pour l’ana-lyse du rendement comparative des hotels d’une meme chaıne peut etre utile pour l’evalua-tion de la competitivite de la chaıne entiere. En identifiant les hotels efficaces dans unechantillon, les ralentissements dans les intrants et les rendements des hotels inefficaces etles hotels efficaces similaires, l’analyse d’enveloppement de donnees se distingue commeune des techniques les plus prometteuses pour promouvoir l’amelioration du fonctionne-ment. On examine aussi les implications pour la gestion qui proviennent de cette etude.Mots-cles: bon fonctionnement des hotels, AED, Portugal. � 2005 Elsevier Ltd. All rightsreserved.

INTRODUCTION

The competitiveness of a country derives from the performance of itsenterprises. At the national level, it is reflected in the performance ofthe economy, while at the operational level, it is viewed in terms of thesize of the market share secured by an enterprise (Begg 1999; Porter1998; Krugman 1996). In both cases, the importance of performanceis highlighted. Performance at company level, which is what motivatesthe present study, is measured either by productivity or efficiency.However, while identifying that efficiency is a key determinant of com-petitiveness, it should also be acknowledged that it is, by itself, an insuf-ficient determinant. Competitiveness often has more to do with

C.P. Barros is Auxiliary Professor of Economics at Instituto Superior de Economia eGestao, Technical University of Lisbon (Rua Miguel Lupi, 20.1249-078 Lisbon, Portugal.Email <[email protected]>). One of his principal research interests is tourism, with afocus on the performance of the hotel sector and demand analysis. He has published articlesin a number of leading academic journals.

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http://[email protected]

CARLOS PESTANA BARROS 457

‘‘pursuing the correct strategy’’ (its effectiveness in terms of its contri-bution to the Government’s stated goals). Operational efficiency is, onthe other hand, a management objective, since it relates to earningsand profits and thus is a vital factor in competitive markets such astourism.

Productivity is defined as the ratio of outputs over inputs, which ren-ders it a different concept from efficiency, being measured and de-fined in different metrics. This ratio yields a relative measurement ofperformance that may be applied to any factor of production. Theratio can be calculated for a single input and output or by aggregatingmultiple inputs and outputs. It is, however, more usually applied to asingle production factor, because of the aggregation problem posedwhen combining different factors. Since it is a relative measurement,there is the need to look to external benchmarks to interpret the pro-ductivity ratio. Moreover, there are many alternative productivity ratios,and choosing from among them is somewhat arbitrary. All of thesemeasurement limitations are overcome by the efficiency concept.

Economic efficiency relates to the concept of the production possi-bility frontier (Anderson, Lewis and Parker 1999). A production func-tion is widely used to define the relationship between inputs andoutputs by depicting graphically the maximum output obtainable fromthe given inputs consumed. Thus, it reflects the current status of tech-nology available to the industry. As the economic efficiency is a relativemeasurement with reference to the production function, a benchmarkis included in its definition, meaning the production frontier. Thisbeing the case, an external benchmark is not required. The technicalefficiency of a hotel is a comparative measure of how well it actuallyprocesses inputs to achieve its outputs, as compared to its maximumpotential for doing so, as represented by its production possibility fron-tier. A hotel can be technically inefficient if it operates below thefrontier.

The methodology applied in this article addresses the above issues indeveloping a framework for effective hotel evaluation and rationaliza-tion. The article utilizes DEA, or data envelopment analysis, which isa nonparametric, multifactor, productivity analysis tool, that considersmultiple input and output measurements in evaluating relative effi-ciencies. DEA does not require a priori assignments of financial perfor-mance dimensions utilized in the evaluation process. It allows for theidentification of appropriate benchmarks, and, above all, those hotelswhich are performing poorly.

The efficiency of each of the individual hotels is a key issue of ENA-TUR’s competitiveness, since the global profitability of any enterprisedepends on the profitability of its parts. For this reason, intrachaincomparative efficiency is of paramount importance. In spite of this,there is a paucity of research into this aspect of hotel management.Recent exceptions are Morey and Dittman (1995) and Anderson, Fish,Xia and Michello (1999).

The motivation for this research stems from four critical issues asso-ciated with the management of hotels, in addition to the advantage ofapplying DEA analysis to intrachain comparative efficiency. First,

458 HOTEL EFFICIENCY

evaluation techniques developed by ENATUR’s Human Resources divi-sion for use by the individual hotel managers are presently based onlyon subjective evaluation. What is often difficult to clarify in an objectivemanner is the operational efficiency behind the subjective evaluation,since no view or analysis of the operational performance is included.This results from the conceptualization of the instrument to obtainthe data, in addition to the fact that for the hotel managers, this reportis merely one of many that they are obliged to compile. Furthermore,they are usually unable to observe behavior accurately, resulting eitherin subjective positive or negative discrimination, or central tendencyproblems, meaning all employees rated as average (Baker and Riley1994).

The operational activities are considered by management theory tobe a vital component of any strategy to achieve improvements. Thus,this neglect of operational activities is an obvious limitation of an eval-uation technique in which the final ranking of hotels is heavily depen-dent on the assignment of the involuntary, biased, reportedperformance alone (Anderson, Fish, Xia and Michello 1999). Second,in order for hotels to improve their decisionmaking effectiveness inrelation to their production activities by means of operational-processimprovement, the effective deployment of scarce or costly resources foroperational programs and restructuring of the operational base, thereis a need for an objective and comprehensive method which can beconsistently and universally applied to all hotels in the chain (Moreyand Ditman 1995). Third, the adoption of a ‘‘best-practice’’ approachto hotels requires ongoing monitoring of hotel management proce-dures and their influence on hotel performance, because the inputsand outputs that contribute to inefficiency are identified by the DEA(Bessent, Bessent, Charnes, Cooper and Thorogood 1983). Fourth,the evaluation techniques used by the central body are usually basedheavily on financial reports.

Seeking to overcome the above-mentioned limitation, this studycontributes to the methodology used to assess the efficiency of amulti-unit state-owned hotel chain. This approach could, however,equally benefit private-sector hotel chains. In addition, the study high-lights some of the challenges in attaining operating efficiency and pro-fessionalism in a public-sector operation that is overseen by a politicalentity. Finally, the study sheds some insight into the interestingapproach taken by Portugal to promote its historical and cultural tour-ism assets.

POUSADAS DE PORTUGAL

According to WTO (1999), Portugal was ranked in 19th place as adestination in 1990 and in 24th place in 1998, accounting for 1.1%of total tourism in 1998 (Financial Times 2002). This value compareswith a figure of 6.7% for its neighbor, Spain. At the international levelit is in sharp contrast to the importance of the tourism industry at thenational level (estimated to be 5% of GDP in 2000). To attempt to


account for this increase in arrivals, the study chose to focus on theoperational efficiency of one specific category of the Portugueseaccommodation market, a nationwide hotel chain known as the Pousa-das de Portugal.

The pousadas have various characteristics, which distinguish themfrom other chains, associations, or forms of accommodation availablein the Portuguese market. The primary distinction is that the chain,indeed the particular category of hotel, was created by Portugal andremains a state-owned and run enterprise. It is centrally run by ENA-TUR, an autonomous company within the Ministry of the Economy.The ENATUR management is appointed from among those public-sec-tor personnel specialized in tourism, on a political basis by the Secre-tary of State for Tourism, who is accountable to the Minister of theEconomy.

The concept behind the pousadas, the first of which opened in 1942,was to provide comfortable, rustic, genuinely Portuguese lodging inlocations of outstanding historic or scenic merit, while restoring andpreserving country’s cultural, historic, and architectural heritage.The cuisine at all of today’s 45 pousadas highlights traditional dishesand wines of the regions where they are situated. The pousadas arefound outside densely populated areas and are generally located awayfrom Portugal’s mass-tourism destinations. Each has its own uniquecharacteristics, ranging from the sober to the luxurious, with a limitednumber of rooms (ranging from 9 to 41). ENATUR has developed twobranches of the pousada chain: the historic and the regional. The for-mer are situated in carefully restored monuments, mainly castles, mon-asteries, and convents, and adapted to the needs of the modern hotelindustry. The latter are purpose-built, the architecture respectfullyblending into the local environment, in locations of great naturalbeauty or historic interest.

To summarize the pousadas’ distinction, they are not necessarily ona par with the best or the most comfortable hotels, or with those whichoffer a complete range of services. However, they possess their ownidentity, catering for discerning tourists seeking a memorable experi-ence and atmosphere far removed from what is offered by the largeinternational hotel chains (Santos 2004). According to the clientelebreakdown for the year 1999, 49% of the guests were Portuguese and51% no. Among the latter, the strongest representation (17.2%) wasfrom Germany, followed by the United States (15.7%), and the UnitedKingdom (10.4%).

Committed to the restoration and conservation of the Portugueseheritage, as well as the promotion of tourism, the public enterpriseENATUR is confronted with conflicting objectives. The pousadas aresmall hotels, with small scale economies, a high level of indebtedness(given the high cost of restoring and modernizing buildings whichare sometimes many hundreds of years old). Further, they are dis-persed throughout the country (Figure 1), in some cases situated inareas in which there is no other tourism infrastructure. These are char-acteristics which must be taken into account when analyzing factorsaffecting the performance of the chain.

Figure 1. Locations of the Pousadas de Portugal


Related to the macroeconomic environment, the pousadas were theresult of a government initiative for nonmass tourists in order to alle-viate partially the austerity enforced by World War II (Portugal re-mained neutral). In the 40s, the nascent Portuguese tourism industrywas naturally suspended until the war was over. Since the 60s, theindustry was developed and expanded into a key sector of the econ-omy, exploiting the wide range of possibilities that Portugal can offerto satisfy the demands and tastes of any type of tourist. Since 1942,ENATUR has contributed to the development of tourism by trainingmanagers who were later contracted by the private sector, by attractingtourism to remote regions, for the sake of regional development, and


by preserving historical buildings, for the appreciation of Portugal’scultural heritage (Santos 2004).

The role of the government as a provider of tourism services restrictsthe expansion of the private sector. The planned privatization of ENA-TUR is a sound public policy, insofar as it maintains the historical andarchitectural heritage, while allowing the chain to be managed from amarket-oriented, commercial perspective. This planned privatizationreflects the drive away from nationalized industry on which the countryembarked some years ago, and owes much to European integration.The latter is based on the European Union’s Single Market Program,which was established in 1992 with the aim of facilitating the free move-ment of goods and services throughout the union. Hence the need tofoster economic policies leading to greater internal monetary stabilityin each member-state and favoring increased growth and the expan-sion of a strong market block. The Single Market Program is a vitalcomponent of the plan of convergence of EU national economies inprices and costs and its emphasis is on competitiveness. The introduc-tion of the euro has removed the possibility of national governmentsmonetarizing their public deficits, while the Maastricht Treaty placesrestrictions on the deficits in financing public services, includingstate-owned hotels. These policies oblige EU-member governments toprivatize industries.

Hotel Efficiency

The analysis of hotel efficiency is restricted to a small number ofstudies. Among the earliest, Baker and Riley (1994) propose the useof ratios to analyze the performance of the lodging industry. Wijeysin-ghe (1993) suggests the use of break-even analysis to discern the effec-tiveness of tourism management. Brotherton and Mooney (1992) andDonaghy, McMahon and McDowell (1995) apply yield management toanalyze the efficiency of hotel management. Table 1 presents the stud-ies on tourism frontier models, which are more in line with the presentstudy.

Eleven studies are clearly brief for such an important tourism issue inthe market context, particularly when compared with other field re-search, such as banking (Berger and Humphrey 1997). Moreover,Anderson’s (2003) is more a methodological exposition of the DEAthan an applied study. The present work intends to enlarge the eco-nomics of tourism in this specific respect and to call the attention ofother researchers to this neglected area. It departs from the previousstudies in that it uses intrachain cross-section data of ENATUR’s hotels.

Theoretical Framework

Following Farrell (1957), Charnes, Cooper and Rhodes (1978) firstintroduced the data envelopment analysis to describe what is a mathe-matical programming approach to the construction of productionfrontiers and the measurement of efficiency of developed frontiers.

Table 1. Literature Survey of Frontier Models on Tourism

Study Method Units Inputs Outputs Prices

Morey and

Dittman

(1995)

DEA 54 hotels (1) room division

expenditure; (2)

energy costs; (3)

Salaries; (4)

nonsalary

expenditure for

property; (5)

salaries and

related

expenditure for

advertising; (6)

nonsalary

expenses for

advertising; (7)

fixed marked

expenditure for

administrative

work.

(1) total

revenue; (2)

level of service

delivered; (3)

market share;

(4) rate of

growth.

–

Bell and

Morey

(1995)

DEA 31 units of

Corporate

Travel

Departments

(1) actual level of

travel

expenditure;

(2)nominal level

of other

expenditure;

(3) level of

environment

factors (ease of

negotiating

discounts,

percentage of

legs with

commuters,

flights required);

(4) actual level of

labor costs.

(1) level of

service

provided,

qualified as

excellent and

average.

–

Johns,

Howcroft,

and Drake,

L., (1997)

DEA 15 UK hotels

over a 12-month

period

(1) number of room

nights available;

(2) total labor

hours; (3) total

food and

beverage costs;

(4) total utilities

cost.

(1) number of

rooms nights

sold; (2) total

covers served;

(3) total

beverage

revenue.

–

Anderson,

Lewis and

Parker

(1999)

DEA and

Stochastic

Frontier

31 corporate

travel

departments

(1) total air

expenses; (2)

hotel expenses;

(3) car expenses;

(4) labor

expenses; (5)

hourly labor; (6)

part-time labor;

(7) fee expenses;

(8) technology

costs; (9)

building and

occupancy

expenses.

(1) number of

trips.

(1)price of

labor, estimated

by dividing the

labor expenses

by the number

of trips; (2)

price of travel,

obtained

dividing the

travel expenses

by the number

of trips; (3)

price of capital,

obtained by

dividing the

capital expense

by the number

of trips.


Table 1 (continued)


Anderson,

Fish, Xia

and

Michello

(1999)

Stochastic

Translog

Production

Frontier

48 hotels (1) number of full-

time equivalent

employees; (2)

number of

rooms; (3) total

gaming-related

expenditure; (4)

total food and

beverage expenses;

(5) other expenses.

(1) Total

revenue

(1) price of

labor proxied

by the hotel

revenue per

full-time

equivalent

employee; (2)

room price

proxied by

hotel revenue

by the product

of number of

rooms times the

occupancy rate

and day per-

year; (3) Price

of gaming,

food, beverage

and other

expenses

proxied as the

percentage of

total revenue.

Anderson,

Fok and

Scott

(2000)

DEA (Technical

and Allocative)

48 hotels (1) full-time

equivalent

employees; (2)

the number of

rooms; (3) total

gaming-related

expenses; (4)

total food and

leverage expenses;

(5) other expenses.

(1) total

revenue; (2)

other revenue.

(1) wages

proxied by

the hotel

revenue per

full-time

employee;

(2) rooms

price

proxied by

hotel

revenue

divided by

the product

of rooms

times

occupancy

rate and day

per-year).

Brown, J. R.

and

Ragsdale,

C. T.

(2002)

DEA-CCR

model and

cluster analysis

46 US hotels

rated in

consumer

report

(1) median price;

(2) problems

(defined in a 4-

point scale); (3)

service; (4) upkeep;

(5) hotels and (6)

rooms.

(1) satisfaction

value (defined

on a 100-point

scale); (2) value

(defined in a 5-

point scale).

–

Reynolds, D.

(2003)

DEA CCR and

BCC model

38 restaurants (1) front-of-the-

house hours

worked per day

during lunchtime;

(2) front-of-the-

hours worked

during dinner

per day; (3)

average wages;

Uncontrollable

input (4) number of

competitors

within a two-mile

radius; (5)

seating capacity.

(1) sales; (2)

customer

satisfaction.

–

(continued on next page)


Table 1 (continued)


Hwang and

Chang

(2003)

CCR DEA

model; super

efficiency

model;

Malmquist

45 Taiwan

hotels

(1)number of full

time employees; (2)

number of guest

rooms;(3)total area

of meal

department; (4)

operating expenses.

(1) room

revenue; (2)

food and

beverage

revenue; (3)

other revenue.

–

Barros (2004) Cobb-Douglas

Cost Frontier

43 Enatur

hotels

(1) sales; (2) nights

occupied; (3) a

dummy (historical

vs. regional).

operational cost (1) price of

labor; (2) price

of capital; (3)

price of food.

Chiang, Tsai

and Wang

(2004)

DEA-CCR and

BCC model

25 Taipei hotels (1) Rooms; (2)

food; (3)

beverages; (4)

number of

employees; (5)

total cost.

(1) yielding

index; (2) food;

(3) beverage

revenue; (4)

miscellaneous

revenue.


The latter authors proposed a model (CCR, named after them) thathad an input orientation and assumed constant returns-to-scale(CRS). Later studies have considered alternative sets of assumptions.Banker, Charnes and Cooper (1984) first introduced the assumptionof variable returns-to-scale (VRS). This model is known in the literatureas the BCC model (named after them).

There are five other basic DEA models, less common in the litera-ture: the additive model (Charnes, Cooper, Gollany, Seiford and Stutz1985), the multiplicative model (Charnes, Cooper, Seiford and Stutz1982), the cone-ratio DEA model (Charnes, Cooper and Huang1990), the assurance region DEA model (Thompson, Langemeier,Lee and Thrall 1990; Thompson, Singleton, Thrall and Smith 1986),and the super-efficiency model (Anderson and Peterson 1993). Thecone-ratio and the assurance region models include a priori informa-tion (experts’ opinion, opportunity costs, rate of transformation, orrate of substitution) to restrict the results to the single best-performingdecisionmaking unit (assurance region DEA model), or linking it withmulticriteria analysis (cone-ratio DEA model). Other developments ofDEA include the disentangling of technical and allocative efficiency(Anderson, Fok and Scott 2000) and the Malmquist index (Malmquist1953).

Since the model is well established and extensively applied, only abrief description of the model is outlined (details on model develop-ment can be found in Charnes, Cooper, Lewinard Seiford, 1995; Coelli1996; Coelli, Prasada and Battese 1998; Cooper, Seiford and Tone2000; Fare, Grosskopf and Lovel 1994; and Thanassoulis 2001). Thetwo scientific methods used to analyze efficiency quantitatively, namely,the econometric frontier and DEA, have their advantages and draw-backs. Unlike the econometric stochastic frontier approach (Andersonet al. 1999), the DEA allows the use of multiple inputs and outputs(Bell and Morey 1995; Morey and Dittman 1995). Moreover, since itis estimated with a nonparametric methodology (DEA), there is no


need to impose any functional form on the data, or to make distribu-tional assumptions for the inefficiency term.

Both methods assume that the production function of the fully effi-cient decision unit is known. In practice, this is not the case and theefficient isoquant must be estimated from the sample data. In theseconditions, the frontier is relative to the sample considered in theanalysis.

DEA is applied to unit assessment of homogeneous units such as ho-tels. The unit of assessment is normally referred to as a decisionmakingunit (DMU). It converts inputs into outputs, and the identification ofthese in an assessment is as difficult as it is crucial. The literature re-view, the availability of data, and managers’ subjective opinions all playa role. Thus, in this study, these three procedures are followed to selectthe inputs and outputs used.

In the programming method, DEA ‘‘floats’’ a piece-wise linear sur-face to rest on the top of the observation (Seiford and Thrall 1990).The facets of the hyperplane define the efficiency frontiers, and the de-gree of inefficiency is quantified and partitioned by a series of metricsthat measure various distances from the hyperplane and its facets.

In order to solve the linear-programming problem, the user mustspecify three characteristics of the model: the input-output orientationsystem, the returns-to-scale, and the weights of the evaluation system.In relation to the first of these, the choice of input- or output-orientedDEA is based on the market conditions of the DMU. As a general ruleof thumb, in competitive markets, the DMUs are output-oriented, sinceit is assumed that inputs are under the control of the decisionmakingunit, which aims to maximize its output, subject to market demand,outside the control of the DMU. With exogenous inputs, the produc-tion function is the natural choice (Khumbhakar 1987). In monopolistmarkets, the DMUs are input-oriented, because output is endogenous,while input is exogenous and the cost function is the natural choice.The input-orientation system searches for a linear combination ofDMUs that maximizes the excess input usage of DMUi, subject to theinequality restraints presented below. With regard to the returns-to-scale, they may be either constant or variable. Both forms (CCR andBCC models) are calculated for comparative purposes. In relation tothe weights possibly placed on inputs and outputs in the objective func-tion, these are subject to the inequality constraints. Weights are endog-enous defined by the algorithm in the CCR and BCC models used andmeasure the distance between the DMU and the frontier.

DEA optimizes each observation for the purpose of constructing theproduction frontier (Figure 2), which consists of a discrete curveformed exclusively by efficient DMUs, those that maximize outputs.The inefficient ones are below the frontier, since they do not maximizeoutput.

A Pareto-efficient or DEA-efficient DMU is defined in cases in whichthe DMU uses k P 1 inputs to secure m P 1 outputs in either an out-put orientation or an input orientation.

The general-purpose DEA developed by Charnes, Cooper andRhodes (1978) considers n DMUs (j = 1, . . . , n). using k inputs to

Input (x)

Inefficient area

Inefficient area

Efficient production frontier

Efficient DMUs Output (y)

Inefficient DMUs

Figure 2. Efficient Production Function


secure m outputs. Let xij, yij denote the observed level of the kth inputand mth output, respectively, at DMU j.

An efficiency score for the nth DMU can be obtained by maximizingthe ratio of total weighted output over total weighted input for allunits, subject to the constraint on all such ratios of the other DMUsin the sample being less than, or equal to, one. Mathematically, thiscan be written as:

maxu;v

u0yi

v0xi

s:t:u0yj

v 0xj� 1 6 0

u; v P 0

ð1Þ

where u is a vector of output weights and v are the input weights. Thesystem of equation 1 is a fractional programming model for computingtechnical efficiency and can be solved with nonlinear techniques. Oneproblem with this ratio is that it has an infinite number of solutions. Tosimplify computation, a transformation of the fractional model allowsthe system of equation 1 to be formulated as a linear programmingproblem. The multiplier form of the linear is presented in equation 2:

maxu;v

ðu0viÞ

s:t:

v0xi ¼ 1

u�yj � v0xj 6 0; j ¼ 1; 2; . . . ;N ;

u; v P 0

ð2Þ


Using duality, it is possible to derive an equivalent envelopment formwhich is the DEA CCR reference model.

minh;k

hi

s:t:

� yi þ Y k P 0

hxi � Xk P 0

k P 0

ð3Þ

where h is a scalar variable, measuring the level of efficiency and k is aN · 1 vector of constants. This envelopment form of the DEA modelinvolves fewer constants and is thus the favored form in order to solvethe model. The model works as follows. For a given set of feasible h val-ues, which are the efficient scores, the LHSs of the input- and output-related constraints specify a production point within the productionpossibility set. The model seeks a production possibility set point whichoffers at least the output levels of DMU j0 while using as low a propor-tion of its input levels as possible. With the superscript * denoting opti-mal values, the j0 DMU is DEA-efficient if, and only if, h�

0 ¼ 1. If h�0 6 1

the j0 DMU is DEA—inefficient. h�0 is a measurement of the radial DEA

efficiency of DMU j0. Note that the linear programming problem mustbe solved N times, once for each DMU in the sample. A value of h isthen obtained for each DMU.

The model assesses efficiency in a production context. Its dual doesthis in a value context. By virtue of duality, the primal and dual modelsyield the same efficiency ratings in respect to DMU j0. (Charnes,Cooper and Rhodes 1978).

Study Data and Results

To estimate the production frontier, the study uses cross-section dataon 43 pousada hotels for the year 2001, which are listed in Table 3. Thedata was obtained from ENATUR’s Financial Control Report and sup-plemented with additional data available from the company.

In order to choose the inputs of the DMUs, the distinction betweencontrollable and uncontrollable factors must be taken into account.However, only the former were available for this study. The inputsare measured by 7 indicators. Labor is measured by the number offull-time equivalent employees and by the cost of labor. Capital is mea-sured by the number of rooms, the surface area of the pousada insquare meters, the book value of the premises, and the operationalcosts and the external costs. The study measures output by 3 indicators:sales, the number of guests and the aggregated number of nightsspent.

The observations and the variables used ensure the DEA conventionthat the minimum number of DMUs is greater than three times thenumber of inputs plus output [48 P 3(3 + 7)] (Raab and Lichty2002). Table 2 presents the characteristics of the variables and verifies

Table 2. Characteristics of the Inputs and Outputs for Year 2001

Variables Units Range Mean Square Deviation

OutputsSales Value in Euro 236.211–2.300.592 850.699 491.143Number of guests Number 2452–13359 6100 2476,37Nights spent Number 3615–18149 9013 3808

InputsFull time workers Number 11–52 26 9Cost of labor Value in Euro 122.200–696.087 342.146 135.476Rooms Number 9-51 24 10Surface area of the hotel Square meters 344–3904 1613 979Book value of property Value in Euro 23.868–7.768.983 1.954.570 2.113.910Operational costs Value in Euro 984–426.536 158.874 95.476External costs Value in Euro 54.144–387.913 152.303 85.857,85


that the mean hotel has 24 rooms and 26 employees, signifying that thepousadas are small hotels.

The DEA index can be calculated in several ways. An output-ori-ented, technically efficient DEA index is estimated in this study(Khumbhakar 1987). The output-oriented technical efficiency definesa production frontier and the measurement addresses the question:‘‘By how much can output quantities be proportionally increased with-out changing the input quantities used?’’. The variable return-to-scale(VRS) hypothesis was chosen because scale size is controllable by thecentral management of ENATUR. The CRS scores measure pure tech-nical efficiency only. However, for comparative purposes, This mea-surement index is also presented. The VRS index is composed of anonadditive combination of pure technical and scales efficiencies. Aratio of the overall efficiency scores to pure technical efficiency scoresprovides a scale measurement. The reason for including this ratio tomeasure scale efficiencies stems from the fact that VRS is due to scaleeffects, while CRS is due to the absence of the latter. Therefore, a ratiobetween the two captures the scale effect, when this is present in thedata. The relative efficiency of the pousadas is presented below inTable 3.

The rankings are ordered from the most efficient to the least effi-cient, according to the VRS hypothesis. It is verified that the DEA indexis equal to 1 for the majority of the hotels when the overall level of effi-ciency is assumed (CRS scores), while a large number of pousadas,including all the CRS-efficient pousadas, are only efficient when VRSis assumed, signifying that the dominant source of inefficiency is dueto scale economies. The average efficiency score under CRS is equalto 0.909. Including all sources of inefficiency, pousadas could operate,on average, at 90.9% of their current output level and maintain the in-put value. However, the efficiency score assuming VRS is equalto 0.945. Given the scale of operation, a majority of pousadas are effi-cient in managing their resources, the mean loss amounting to

Table 3. DEA Technical Efficiency Scores for ENATUR Pousadas (2001)

Location Designation Technically

efficient,

Constant

Return-to-Scale

index (CCR Model)

Technically

Efficient,

Variable

Return-to-Scale

index (BCC model)

Technically

Efficient

Scale

index

Evora Pousada dos Loios 1.000 1.000 1.000

Guimaraes Pousada de Santa Marinha 1.000 1.000 1.000

Obidos Pousada do Castelo 1.000 1.000 1.000

Queluz Pousada de Dona Maria I 1.000 1.000 1.000

Batalha Pousada do Mestre A. Domingues 1.000 1.000 1.000

Braganca Pousada de Sao Bartolomeu 1.000 1.000 1.000

Condeixa Pousada de Santa Cristina 1.000 1.000 1.000

Geres Pousada de Sao Bento 1.000 1.000 1.000

Guimaraes Pousada de Nossa Senhora da Oliveira 1.000 1.000 1.000

Manteigas Pousada de Sao Lourenco 1.000 1.000 1.000

Marvao Pousadade Santa Maria 1.000 1.000 1.000

Miranda do Douro Pousada de Santa Catarina 1.000 1.000 1.000

Monsanto Pousada Monsanto 1.000 1.000 1.000

Murtosa Pousada Ria 1.000 1.000 1.000

Sao Bras de Alportel Pousada de Sao Bras 1.000 1.000 1.000

Sagres Pousada do Infante 1.000 1.000 1.000

Santiago do Cacem Pousada Quinta da Ortiga 1.000 1.000 1.000

Viana do Castelo Pousada do Monte de Santa Luzia 1.000 1.000 1.000

Santiago do Cacem Pousada de Sao Tiago 0.984 1.000 0.984

Povoa das Quartas Pousada de Santa Barbara 0.956 1.000 0.956

Estremoz Pousada de Santa Isabel 0.897 1.000 0.897

Marao Pousada de Sao Goncalo 0.864 0.997 0.867

Crato Pousada Flor da Rosa 0.924 0.944 0.978

Caramulo Pousada de Sao Jeronimo 0.726 0.990 0.733

Arraiolos Pousada de Nossa Senhora da Assuncao 0.969 0.984 0.985

Amares Pousada de Santa Marta do Bouro 0.879 0.954 0.921

Palmela Pousada de Palmela 0.799 0.951 0.840

Alijo Pousada do Barao de Forrester 0.905 0.937 0.966

Sousel Pousada de Sao Miguel 0.919 0.921 0.997

Torrao Pousada do Vale do Gaio 0.830 0.912 0.910

Serpa Pousada de Sao Gens 0.901 0.905 0.995

Alcacer do Sal Pousada D. Afonso II 0.901 0.902 1.000

Vila Vicosa Pousada D. Joao IV 0.824 0.856 0.963

Santa Clara a Velha Pousada de Santa Clara 0.840 0.849 0.989

Setubal Pousada de Sao Filipe 0.802 0.845 0.948

Alvito Pousada do Castelo do Alvito 0.835 0.840 0.994

Ourem Pousada Conde de Ourem 0.827 0.839 0.986

Beja Pousada de Sao Francisco 0.817 0.828 0.987

Vila Nova de Cerveira Pousada D. Diniz 0.774 0.812 0.954

Castelo de Bode Pousada de Sao Pedro 0.805 0.806 1.000

Valencia do Minho Pousada de Sao Teotonio 0.803 0.806 0.996

Elvas Pousada de Santa Luzia 0.709 0.745 0.952

Almeida Pousada da Senhora das Neves 0.592 0.599 0.987

Mean 0.909 0.945 0.972


1 � 0.945 = 5.5%. A hotel is output-oriented Pareto-efficient if it is notpossible to raise any of its output levels without lowering at least one ofits other output levels and/or without increasing at least one of itsinput levels. The technical output efficiency of a hotel is the inverseof the maximum factor by which its output levels could be jointlyexpanded while its input levels do not rise.

Managerial Implications

A number of points emerge from the present study. First, the best-practice calculations indicate that many pousadas under the VRS


hypothesis (51.1%) operated at a high level of pure technical efficiencyin 2001. However, almost half of the pousadas were technically ineffi-cient, with different slacks in different inputs and outputs. Second,all technically efficient constant return-to-scale pousadas are also tech-nically efficient at variable return-to-scale, signifying the dominantsource of efficiency is scale. Third, inefficiency is more prevalentamong the historic pousadas (66%) than among the regional pousadas(46%). Fourth, the location appears to be an explanatory factor of effi-ciency, with pousadas in, or near, the cities more efficient than those inmore remote locations. A rationale for this result is that demand playsa role in organizational efficiency, with the hotels near more populatedzones attracting more clients. This higher demand enables greater effi-ciency. Hence, assuming that there are two hotels with the same man-agerial expertise, the one with more demand tends to be moreefficient. Fifth, although DEA identifies the inefficient hotels in thesample, it does not reveal the cause of the inefficiency. DEA suggeststhe slacks for the inefficient hotels and gives to each a reference set(peer group) which allows for specific recommendations to improveefficiency. Adjustments for the inefficient hotels can be identified foroutputs and inputs in order for them to join the efficient frontier.

Technical inefficiency is a consequence of one or more factors. One,factors of substructural rigidities associated with the pattern of owner-ship may induce the principal-agent relationship (Jensen and Meckling1976). The difficulty of controlling those empowered as managers toact on behalf of the owner (the State) is a prevalent issue in publicenterprises. The job tenure of the ENATUR managers may encouragethe development of principal-agent problems, since the managers arealways connected to (and often dependent on) influential friends inthe governing political party. Two, structural rigidities associated withthe labor market (Ingram and Baum 1997) give rise to the collective-action problem (Olson 1965) in which workers can free-ride on themanagement’s own efforts to improve performance. This situation oc-curs when job tenure is not linked to performance, which is frequentlythe case in the public sector.

Three, organizational factors associated with X-inefficiency (Leiben-stein 1966) relate to the fact that the production function is not com-pletely specified or known, the contracts for labor are incomplete, andnot all inputs are marketed on equal terms to all buyers. Inefficienciesassociated with incomplete markets exist everywhere, but are particu-larly prevalent in the public domain. In this situation, the managersmay be unable to adopt the correct strategy, since they do not knowwhat it should be. Four, other factors outside the control of the man-agement are the contextual causes of technical inefficiency, such asscale and scope economies, economies of scale (Chung and Kalnins2000), and location and agglomeration effects. All of these factorsmay play a role, as multiple causes active in the market interact to con-tribute to the level of technical inefficiency. With the politically-ap-pointed senior management liable to be replaced after every changeof the elected government, there is scant tenure in their activity,and the stakeholders may exercise an inadequate control of the


management procedures. Against such a background, incentives areabsent for management to adopt a resource-based strategy. This dy-namic gives rise to a depletion of variable critical resources, such ascommitted, high-quality managers.

Due to any, some, or all of these factors, ENATUR’s pousadas mayproduce at a level below their potential, the maximum possible output,in the production environment specific to tourism. As an example ofadjustment based on slacks, in Table 4 shows, the adjustments pro-posed for the Pousada do Castelo do Alvito, in central-southern Portu-gal. It is verified that there are slacks in the aggregated nights spentand this output should be increased for the projected value. In relationto inputs, there are slacks in the surface area, the book value of theproperty, and the number of employees, signifying that these inputsare used inefficiently by the pousada. There is a margin to decreasethose inputs and to increase the outputs with slacks, for the unit tocatch up with the frontier.

A peer group for the above-mentioned pousada consists of Sagres,Viana, Braganca, S. Tiago do Cacem, Miranda, and Evora. This peergroup of efficient hotels is defined only for the Pousada do Alvito, be-cause their data characteristics render them more similar to the ineffi-cient Alvito. Thus, notably, not all the efficient pousadas are includedin the peer group for the inefficient ones, only those defined by DEAas eligible for this purpose (other slacks for the other inefficient hotelsare not shown, but are available on request from the author).

DEA does not identify the factors that cause inefficiency and only di-rects attention to the units where inefficiency exists. Nonetheless, thisis valid information, since the inputs and outputs that contribute tothis inefficiency are also identified (Bessent et al. 1983). However, con-cerns have been raised as to the robustness of DEA models on thegrounds that since DEA only determines relative efficiency, it cannotidentify all of the inefficient units, because all of the units in the sam-ple may in fact be inefficient. Nevertheless, the exercise is still valid, be-cause it ranks the units under analysis according to a benchmark. Evenin a completely inefficient sample, some units will be more inefficient

Table 4. DEA Results for the Pousada do Castelo do Alvito

Outputs and Inputs Original Value Radial Movement Slack Projected Value

Number of guests 5143 977 0 6120Nights spent 7277 1.383 0 8660Sales 568.769 108.114 47.105 724.016Rooms 20 0 0 20Surface area 1.809 0 �524,97 1.284Property 2.986.690 0 �1.997.438 989.251Operational cost 108.376 0 0 108.376Number of employees 23 0 �1.43 21.57Cost of labor 270.491 0 0 270.491External cost 117.967 0 0 117.967


than others and can profit from improving their operational activities.Despite the advantages of DEA, a further qualitative analysis on acase-by-case basis is usually necessary, to determine the true sourceof their inefficiencies and the appropriate corrective actions to betaken.

The DEA approach has several managerial advantages: the DEAscore is a surrogate for the ‘‘overall competence and capability’’ ofthe pousadas, which cannot be easily and cost-efficiently discernedthrough the company’s audited accounts. Using audits is an expensive,time-consuming means of gathering, analyzing and evaluating. Themethodology proposed in this study overcomes some of these difficul-ties, allowing ENATUR to gather useful data cost-efficiently and swiftly.Further, since multiple dimensions are simultaneously considered inevaluating the overall operational competence of the pousada, it ismore robust and comprehensive than any of the typical productivityratios commonly used in financial analysis.

Another advantage of this approach is in identifying strategicallyimportant hotels. The performance-output-based evaluation methodsare based on evaluating ‘‘point-in-time’’ data, in that the data are snap-shots of the hotel’s performance at a particular time. In evaluating ho-tels from a strategic perspective, it can be argued that evaluations basedon inherent competence and capabilities are likely to be more compre-hensive. That is, pousadas with high efficiency scores are likely to sus-tain a high level of capabilities and thus are better candidates forinclusion in an environment in which they are performing as best-practice role models in the organization.

Considering the results, several managerial implications can be pro-posed. First, the central management of ENATUR must upgrade its fol-low-up inspection procedure of pousadas’ activities, in order to providemore explicitly binding incentives for increasing productive efficiency,while constructing the procedure so as to prevent manipulations byinternal parties. Second, the central management must expand thescope of the data obtained in the follow-up inspection to include con-textual factors beyond managerial control, since it is not clear if differ-ent pousadas have the same operating environment. Socioeconomicand environmental factors can be used as indicators of the quality ofcustomer service provided by each pousada. Third, a benchmark anal-ysis should be carried out, in order to enforce an efficient adjustmentof the least-performing pousadas. Fourth, a better analysis of the effi-ciency of the pousadas for the future of ENATUR, in terms of marketstrategy, would be welcome. The implications of those locations anddimensions which attract more guests and earn financial profits forthe historic pousadas are not matched by efficient procedures at thelatter. Consequently, this branch of the pousadas presents a lower pro-portion of efficiency.

Fifth, the regional pousadas of smaller dimensions and in more re-mote areas of the country are disadvantaged in terms of their compet-itiveness. Sixth, better analyses are required to establish the precisesignificance to ENATUR of each involved element, whether they besuppliers, partners, guests, or employees. Too often, companies assume


that as long as they meet their contractual obligations to each of theseelements, then everything is fine. In other words, complacency easilysets in. Recalling that ENATUR is a state-owned creation, the worldmoves on with time and the organization needs to align its interestswith those of the stakeholders. For example, it needs to communicateto the employees how and why they personally stand to benefit fromthe successful growth of the enterprise. Finally, the planned privatiza-tion in a competitive environment is felt to be a wise policy, since itis known that privatization coupled with competition tends to improveefficiency (Jones, Tandon and Vogelsang 1990).

Limitations and Extensions of this Study

With reference to the data set, the homogeneity of the pousadasused in the analysis is questionable, since it has compared pousadaswith different dimensions, production characteristics, and locations.These may face different restrictions and thus might not be considereddirectly comparable. However, it can always be claimed that the unitsare not comparable and the traditional ratio analysis (Vogel 2001)equally could not be carried out. Yet the fact that the pousadas areunder a common administration and seem to follow similar strategiesgives sufficient justification for analyzing them as a unit.

Since this research is an exploratory study, the intent is not to obtaindefinitive results for the direct use of the central management. Rather,it calls the attention of ENATUR to the value of benchmarking itspousadas in order to measure their performance (serving as a manage-ment tool). Moreover, since the data set is short, the conclusions arelimited. In order to generalize, a larger panel data set would be neces-sary. Reducing the number of observations in DEA variables increasesthe likelihood that a given observation will be judged relatively efficient(Banker 1993).

A variety of extensions to this study can be undertaken. One, in thisanalysis, the DEA model allowed for complete weight flexibility. In sit-uations in which some of the measurements are likely to be moreimportant than others, DEA allows for restricting factor weightsthrough linear constraints. These linear constraints represent rangesfor relative preferences among factors based on managerial input.Such analysis enables effective incorporation of managerial input intothe DEA evaluations. Two, the input and output dimensions consid-ered are context-specific. More comprehensive input and outputmeasurements, (allowing for nondiscretionary factors such as environ-mental, socioeconomic, and quality inputs and outputs) need to betaken into consideration. The influence of discretionary variablesbeing excluded from the analysis, amounts to an assumption that thesefactors are constant across the sample.

Three, allocative efficiency can be estimated, as well as the total-pro-ductivity Malmquist index, provided that there are more years of obser-vation. Four, nonparametric or parametric free-disposal hull analysiscan be used to assess the efficiency scores. However, previous research


has shown that the DEA scores are inferior in value to econometricscores, but the ranking is preserved (Bauer, Berger, Ferrier and Hum-phrey 1998). Finally, the hypothesis of the homogeneity of the pousa-das under analysis is based on their nature, on the fact that theycompete in the same market and that they all have the same stakehold-ers. However, nondiscretionary factors can render the pousadasnonhomogenous.

CONCLUSION

This article has proposed a simple framework for the evaluation ofhotels and the rationalization of their operational activities in the tour-ism industry. The analysis is based on a DEA model that allows for theincorporation of multiple inputs and outputs in determining relativeefficiencies. Benchmarks are provided for improving the operationsof poorly-performing hotels. Several interesting and useful managerialinsights and implications from the study are discussed. The generalconclusion is that the majority of the ENATUR Pousadas de Portugalare efficient, although this leaves a proportion which are inefficient.For the inefficient pousadas, a peer group was identified among theefficient operators, in addition to the slacks that they should adjustin order to reach the efficient frontier. The findings suggest that scaleeconomies and location are major issues in determining a unit’s effi-ciency in Portugal or elsewhere.

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MEASURING EFFICIENCY IN THE HOTEL SECTOR · 2013. 10. 11. · THE HOTEL SECTOR Carlos Pestana Barros Technical University of Lisbon, Portugal Abstract: This study discusses, by means