MEASURING EFFICIENCY IN THE HOTEL SECTOR

Transcription

1 doi: /j.annals Annals of Tourism Research, Vol. 32, No. 2, pp , 2005 Ó 2005 Elsevier Ltd. All rights reserved. Printed in Great Britain /$30.00 MEASURING EFFICIENCY IN THE HOTEL SECTOR Carlos Pestana Barros Technical University of Lisbon, Portugal Abstract: This study discusses, by means of data envelopment analysis, the efficiency of individual hotels belonging to the Portuguese state-owned chain, Pousadas de Portugal, which is managed by the enterprise, ENATUR. The use of this technique for the analysis of intrachain comparative hotel efficiency can be of value in examining the competitiveness of the chain as a whole. By identifying the efficient hotels in a sample, the slacks in inputs and outputs of the inefficient hotels and the peer group of efficient hotels, the data envelopment analysis stands out as one of the most promising techniques to aid the improvement of efficiency. Managerial implications arising from this study are also considered. Keywords: hotel efficiency, DEA, Portugal. Ó 2005 Elsevier Ltd. All rights reserved. Résumé: Le mesurage du bon fonctionnement dans le secteur hôtelier. Cette étude discute, par moyen d une analyse d enveloppement de données, le bon fonctionnement de plusieurs hôtels individuels qui appartiennent à lamême chaîne étatisée portugaise Pousadas de Portugal, qui est gérée par l entreprise ENATUR. L utilisation de cette technique pour l analyse du rendement comparative des hôtels d une même chaîne peut être utile pour l évaluation de la compétitivité de la chaîne entière. En identifiant les hôtels efficaces dans un échantillon, les ralentissements dans les intrants et les rendements des hôtels inefficaces et les hôtels efficaces similaires, l analyse d enveloppement de données se distingue comme une des techniques les plus prometteuses pour promouvoir l amélioration du fonctionnement. On examine aussi les implications pour la gestion qui proviennent de cette étude. Mots-clés: bon fonctionnement des hôtels, AED, Portugal. Ó 2005 Elsevier Ltd. All rights reserved. INTRODUCTION The competitiveness of a country derives from the performance of its enterprises. At the national level, it is reflected in the performance of the economy, while at the operational level, it is viewed in terms of the size of the market share secured by an enterprise (Begg 1999; Porter 1998; Krugman 1996). In both cases, the importance of performance is highlighted. Performance at company level, which is what motivates the present study, is measured either by productivity or efficiency. However, while identifying that efficiency is a key determinant of competitiveness, it should also be acknowledged that it is, by itself, an insufficient determinant. Competitiveness often has more to do with C.P. Barros is Auxiliary Professor of Economics at Instituto Superior de Economia e Gestao, Technical University of Lisbon (Rua Miguel Lupi, Lisbon, Portugal. < One of his principal research interests is tourism, with a focus on the performance of the hotel sector and demand analysis. He has published articles in a number of leading academic journals. 456

2 CARLOS PESTANA BARROS 457 pursuing the correct strategy (its effectiveness in terms of its contribution to the Government s stated goals). Operational efficiency is, on the other hand, a management objective, since it relates to earnings and profits and thus is a vital factor in competitive markets such as tourism. Productivity is defined as the ratio of outputs over inputs, which renders it a different concept from efficiency, being measured and defined in different metrics. This ratio yields a relative measurement of performance that may be applied to any factor of production. The ratio can be calculated for a single input and output or by aggregating multiple inputs and outputs. It is, however, more usually applied to a single production factor, because of the aggregation problem posed when combining different factors. Since it is a relative measurement, there is the need to look to external benchmarks to interpret the productivity ratio. Moreover, there are many alternative productivity ratios, and choosing from among them is somewhat arbitrary. All of these measurement limitations are overcome by the efficiency concept. Economic efficiency relates to the concept of the production possibility frontier (Anderson, Lewis and Parker 1999). A production function is widely used to define the relationship between inputs and outputs by depicting graphically the maximum output obtainable from the given inputs consumed. Thus, it reflects the current status of technology available to the industry. As the economic efficiency is a relative measurement with reference to the production function, a benchmark is included in its definition, meaning the production frontier. This being the case, an external benchmark is not required. The technical efficiency of a hotel is a comparative measure of how well it actually processes inputs to achieve its outputs, as compared to its maximum potential for doing so, as represented by its production possibility frontier. A hotel can be technically inefficient if it operates below the frontier. The methodology applied in this article addresses the above issues in developing a framework for effective hotel evaluation and rationalization. The article utilizes DEA, or data envelopment analysis, which is a nonparametric, multifactor, productivity analysis tool, that considers multiple input and output measurements in evaluating relative efficiencies. DEA does not require a priori assignments of financial performance dimensions utilized in the evaluation process. It allows for the identification of appropriate benchmarks, and, above all, those hotels which 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 enterprise depends on the profitability of its parts. For this reason, intrachain comparative 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 associated with the management of hotels, in addition to the advantage of applying DEA analysis to intrachain comparative efficiency. First,

3 458 HOTEL EFFICIENCY evaluation techniques developed by ENATUR s Human Resources division for use by the individual hotel managers are presently based only on subjective evaluation. What is often difficult to clarify in an objective manner 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 obtain the data, in addition to the fact that for the hotel managers, this report is merely one of many that they are obliged to compile. Furthermore, they are usually unable to observe behavior accurately, resulting either in subjective positive or negative discrimination, or central tendency problems, meaning all employees rated as average (Baker and Riley 1994). The operational activities are considered by management theory to be a vital component of any strategy to achieve improvements. Thus, this neglect of operational activities is an obvious limitation of an evaluation technique in which the final ranking of hotels is heavily dependent on the assignment of the involuntary, biased, reported performance alone (Anderson, Fish, Xia and Michello 1999). Second, in order for hotels to improve their decisionmaking effectiveness in relation to their production activities by means of operational-process improvement, the effective deployment of scarce or costly resources for operational programs and restructuring of the operational base, there is a need for an objective and comprehensive method which can be consistently and universally applied to all hotels in the chain (Morey and Ditman 1995). Third, the adoption of a best-practice approach to hotels requires ongoing monitoring of hotel management procedures and their influence on hotel performance, because the inputs and 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 based heavily on financial reports. Seeking to overcome the above-mentioned limitation, this study contributes to the methodology used to assess the efficiency of a multi-unit state-owned hotel chain. This approach could, however, equally benefit private-sector hotel chains. In addition, the study highlights some of the challenges in attaining operating efficiency and professionalism in a public-sector operation that is overseen by a political entity. Finally, the study sheds some insight into the interesting approach taken by Portugal to promote its historical and cultural tourism assets. POUSADAS DE PORTUGAL According to WTO (1999), Portugal was ranked in 19th place as a destination in 1990 and in 24th place in 1998, accounting for 1.1% of total tourism in 1998 (Financial Times 2002). This value compares with a figure of 6.7% for its neighbor, Spain. At the international level it is in sharp contrast to the importance of the tourism industry at the national level (estimated to be 5% of GDP in 2000). To attempt to

4 CARLOS PESTANA BARROS 459 account for this increase in arrivals, the study chose to focus on the operational efficiency of one specific category of the Portuguese accommodation market, a nationwide hotel chain known as the Pousadas de Portugal. The pousadas have various characteristics, which distinguish them from other chains, associations, or forms of accommodation available in the Portuguese market. The primary distinction is that the chain, indeed the particular category of hotel, was created by Portugal and remains 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-sector personnel specialized in tourism, on a political basis by the Secretary of State for Tourism, who is accountable to the Minister of the Economy. The concept behind the pousadas, the first of which opened in 1942, was to provide comfortable, rustic, genuinely Portuguese lodging in locations of outstanding historic or scenic merit, while restoring and preserving country s cultural, historic, and architectural heritage. The cuisine at all of today s 45 pousadas highlights traditional dishes and wines of the regions where they are situated. The pousadas are found outside densely populated areas and are generally located away from Portugal s mass-tourism destinations. Each has its own unique characteristics, ranging from the sober to the luxurious, with a limited number of rooms (ranging from 9 to 41). ENATUR has developed two branches of the pousada chain: the historic and the regional. The former are situated in carefully restored monuments, mainly castles, monasteries, and convents, and adapted to the needs of the modern hotel industry. The latter are purpose-built, the architecture respectfully blending into the local environment, in locations of great natural beauty or historic interest. To summarize the pousadas distinction, they are not necessarily on a par with the best or the most comfortable hotels, or with those which offer a complete range of services. However, they possess their own identity, catering for discerning tourists seeking a memorable experience and atmosphere far removed from what is offered by the large international hotel chains (Santos 2004). According to the clientele breakdown for the year 1999, 49% of the guests were Portuguese and 51% no. Among the latter, the strongest representation (17.2%) was from Germany, followed by the United States (15.7%), and the United Kingdom (10.4%). Committed to the restoration and conservation of the Portuguese heritage, as well as the promotion of tourism, the public enterprise ENATUR is confronted with conflicting objectives. The pousadas are small hotels, with small scale economies, a high level of indebtedness (given the high cost of restoring and modernizing buildings which are sometimes many hundreds of years old). Further, they are dispersed throughout the country (Figure 1), in some cases situated in areas in which there is no other tourism infrastructure. These are characteristics which must be taken into account when analyzing factors affecting the performance of the chain.

5 460 HOTEL EFFICIENCY Figure 1. Locations of the Pousadas de Portugal Related to the macroeconomic environment, the pousadas were the result of a government initiative for nonmass tourists in order to alleviate partially the austerity enforced by World War II (Portugal remained neutral). In the 40s, the nascent Portuguese tourism industry was naturally suspended until the war was over. Since the 60s, the industry was developed and expanded into a key sector of the economy, exploiting the wide range of possibilities that Portugal can offer to satisfy the demands and tastes of any type of tourist. Since 1942, ENATUR has contributed to the development of tourism by training managers who were later contracted by the private sector, by attracting tourism to remote regions, for the sake of regional development, and

6 CARLOS PESTANA BARROS 461 by preserving historical buildings, for the appreciation of Portugal s cultural heritage (Santos 2004). The role of the government as a provider of tourism services restricts the expansion of the private sector. The planned privatization of ENA- TUR is a sound public policy, insofar as it maintains the historical and architectural heritage, while allowing the chain to be managed from a market-oriented, commercial perspective. This planned privatization reflects the drive away from nationalized industry on which the country embarked 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 movement of goods and services throughout the union. Hence the need to foster economic policies leading to greater internal monetary stability in each member-state and favoring increased growth and the expansion of a strong market block. The Single Market Program is a vital component of the plan of convergence of EU national economies in prices and costs and its emphasis is on competitiveness. The introduction of the euro has removed the possibility of national governments monetarizing their public deficits, while the Maastricht Treaty places restrictions on the deficits in financing public services, including state-owned hotels. These policies oblige EU-member governments to privatize industries. Hotel Efficiency The analysis of hotel efficiency is restricted to a small number of studies. Among the earliest, Baker and Riley (1994) propose the use of ratios to analyze the performance of the lodging industry. Wijeysinghe (1993) suggests the use of break-even analysis to discern the effectiveness of tourism management. Brotherton and Mooney (1992) and Donaghy, McMahon and McDowell (1995) apply yield management to analyze the efficiency of hotel management. Table 1 presents the studies on tourism frontier models, which are more in line with the present study. Eleven studies are clearly brief for such an important tourism issue in the market context, particularly when compared with other field research, such as banking (Berger and Humphrey 1997). Moreover, Anderson s (2003) is more a methodological exposition of the DEA than an applied study. The present work intends to enlarge the economics of tourism in this specific respect and to call the attention of other researchers to this neglected area. It departs from the previous studies in that it uses intrachain cross-section data of ENATUR s hotels. Theoretical Framework Following Farrell (1957), Charnes, Cooper and Rhodes (1978) first introduced the data envelopment analysis to describe what is a mathematical programming approach to the construction of production frontiers and the measurement of efficiency of developed frontiers.

7 462 HOTEL EFFICIENCY Table 1. Literature Survey of Frontier Models on Tourism Study Method Units Inputs Outputs Prices Morey and Dittman (1995) Bell and Morey (1995) Johns, Howcroft, and Drake, L., (1997) Anderson, Lewis and Parker (1999) 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. DEA DEA DEA and Stochastic Frontier 31 units of Corporate Travel Departments 15 UK hotels over a 12-month period 31 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) number of room nights available; (2) total labor hours; (3) total food and beverage costs; (4) total utilities cost. (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) total revenue; (2) level of service delivered; (3) market share; (4) rate of growth. (1) level of service provided, qualified as excellent and average. (1) number of rooms nights sold; (2) total covers served; (3) total beverage revenue. (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.

8 CARLOS PESTANA BARROS 463 Table 1 (continued) Study Method Units Inputs Outputs Prices Anderson, Fish, Xia and Michello (1999) Anderson, Fok and Scott (2000) Brown, J. R. and Ragsdale, C. T. (2002) Reynolds, D. (2003) Stochastic Translog Production Frontier DEA (Technical and Allocative) DEA-CCR model and cluster analysis DEA CCR and BCC model 48 hotels (1) number of fulltime equivalent employees; (2) number of rooms; (3) total gaming-related expenditure; (4) total food and beverage expenses; (5) other expenses. 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. 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. 38 restaurants (1) front-of-thehouse hours worked per day during lunchtime; (2) front-of-thehours worked during dinner per day; (3) average wages; Uncontrollable input (4) number of competitors within a two-mile radius; (5) seating capacity. (1) Total revenue (1) total revenue; (2) other revenue. (1) satisfaction value (defined on a 100-point scale); (2) value (defined in a 5- point scale). (1) sales; (2) customer satisfaction. (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 peryear; (3) Price of gaming, food, beverage and other expenses proxied as the percentage of total 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). (continued on next page)

9 464 HOTEL EFFICIENCY Table 1 (continued) Study Method Units Inputs Outputs Prices Hwang and Chang (2003) Barros (2004) Chiang, Tsai and Wang (2004) CCR DEA model; super efficiency model; Malmquist Cobb-Douglas Cost Frontier DEA-CCR and BCC model 45 Taiwan hotels 43 Enatur hotels (1)number of full time employees; (2) number of guest rooms;(3)total area of meal department; (4) operating expenses. (1) sales; (2) nights occupied; (3) a dummy (historical vs. regional). 25 Taipei hotels (1) Rooms; (2) food; (3) beverages; (4) number of employees; (5) total cost. (1) room revenue; (2) food and beverage revenue; (3) other revenue. operational cost (1) yielding index; (2) food; (3) beverage revenue; (4) miscellaneous revenue. (1) price of labor; (2) price of capital; (3) price of food. The latter authors proposed a model (CCR, named after them) that had 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 assumption of variable returns-to-scale (VRS). This model is known in the literature as the BCC model (named after them). There are five other basic DEA models, less common in the literature: the additive model (Charnes, Cooper, Gollany, Seiford and Stutz 1985), the multiplicative model (Charnes, Cooper, Seiford and Stutz 1982), the cone-ratio DEA model (Charnes, Cooper and Huang 1990), 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). The cone-ratio and the assurance region models include a priori information (experts opinion, opportunity costs, rate of transformation, or rate of substitution) to restrict the results to the single best-performing decisionmaking unit (assurance region DEA model), or linking it with multicriteria analysis (cone-ratio DEA model). Other developments of DEA include the disentangling of technical and allocative efficiency (Anderson, Fok and Scott 2000) and the Malmquist index (Malmquist 1953). Since the model is well established and extensively applied, only a brief description of the model is outlined (details on model development can be found in Charnes, Cooper, Lewinard Seiford, 1995; Coelli 1996; Coelli, Prasada and Battese 1998; Cooper, Seiford and Tone 2000; Fare, Grosskopf and Lovel 1994; and Thanassoulis 2001). The two scientific methods used to analyze efficiency quantitatively, namely, the econometric frontier and DEA, have their advantages and drawbacks. Unlike the econometric stochastic frontier approach (Anderson et al. 1999), the DEA allows the use of multiple inputs and outputs (Bell and Morey 1995; Morey and Dittman 1995). Moreover, since it is estimated with a nonparametric methodology (DEA), there is no

10 CARLOS PESTANA BARROS 465 need to impose any functional form on the data, or to make distributional assumptions for the inefficiency term. Both methods assume that the production function of the fully efficient decision unit is known. In practice, this is not the case and the efficient isoquant must be estimated from the sample data. In these conditions, the frontier is relative to the sample considered in the analysis. DEA is applied to unit assessment of homogeneous units such as hotels. The unit of assessment is normally referred to as a decisionmaking unit (DMU). It converts inputs into outputs, and the identification of these in an assessment is as difficult as it is crucial. The literature review, the availability of data, and managers subjective opinions all play a role. Thus, in this study, these three procedures are followed to select the inputs and outputs used. In the programming method, DEA floats a piece-wise linear surface to rest on the top of the observation (Seiford and Thrall 1990). The facets of the hyperplane define the efficiency frontiers, and the degree of inefficiency is quantified and partitioned by a series of metrics that measure various distances from the hyperplane and its facets. In order to solve the linear-programming problem, the user must specify three characteristics of the model: the input-output orientation system, the returns-to-scale, and the weights of the evaluation system. In relation to the first of these, the choice of input- or output-oriented DEA is based on the market conditions of the DMU. As a general rule of thumb, in competitive markets, the DMUs are output-oriented, since it is assumed that inputs are under the control of the decisionmaking unit, which aims to maximize its output, subject to market demand, outside the control of the DMU. With exogenous inputs, the production function is the natural choice (Khumbhakar 1987). In monopolist markets, 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 of DMUs that maximizes the excess input usage of DMUi, subject to the inequality restraints presented below. With regard to the returns-toscale, they may be either constant or variable. Both forms (CCR and BCC models) are calculated for comparative purposes. In relation to the weights possibly placed on inputs and outputs in the objective function, these are subject to the inequality constraints. Weights are endogenous defined by the algorithm in the CCR and BCC models used and measure the distance between the DMU and the frontier. DEA optimizes each observation for the purpose of constructing the production frontier (Figure 2), which consists of a discrete curve formed exclusively by efficient DMUs, those that maximize outputs. The inefficient ones are below the frontier, since they do not maximize output. A Pareto-efficient or DEA-efficient DMU is defined in cases in which the DMU uses k P 1 inputs to secure m P 1 outputs in either an output orientation or an input orientation. The general-purpose DEA developed by Charnes, Cooper and Rhodes (1978) considers n DMUs (j =1,..., n). using k inputs to

11 466 HOTEL EFFICIENCY Output (y) Efficient DMUs Efficient production frontier Inefficient area Inefficient DMUs Inefficient area Input (x) Figure 2. Efficient Production Function secure m outputs. Let x ij, y ij denote the observed level of the kth input and mth output, respectively, at DMU j. An efficiency score for the nth DMU can be obtained by maximizing the ratio of total weighted output over total weighted input for all units, subject to the constraint on all such ratios of the other DMUs in the sample being less than, or equal to, one. Mathematically, this can be written as: max u;v u 0 y i v 0 x i u 0 y j s:t: v 0 xj u; v P 0 where u is a vector of output weights and v are the input weights. The system of equation 1 is a fractional programming model for computing technical efficiency and can be solved with nonlinear techniques. One problem with this ratio is that it has an infinite number of solutions. To simplify computation, a transformation of the fractional model allows the system of equation 1 to be formulated as a linear programming problem. The multiplier form of the linear is presented in equation 2: max u;v ðu0 v i Þ s:t: v 0 x i ¼ 1 uy j v 0 x j 6 0; j ¼ 1; 2;...; N ; u; v P 0 ð1þ ð2þ

12 Using duality, it is possible to derive an equivalent envelopment form which is the DEA CCR reference model. min h i h;k s:t: y i þ Y k P 0 hx i X k P 0 k P 0 CARLOS PESTANA BARROS 467 where h is a scalar variable, measuring the level of efficiency and k is a N 1 vector of constants. This envelopment form of the DEA model involves fewer constants and is thus the favored form in order to solve the model. The model works as follows. For a given set of feasible h values, which are the efficient scores, the LHSs of the input- and outputrelated constraints specify a production point within the production possibility set. The model seeks a production possibility set point which offers at least the output levels of DMU j 0 while using as low a proportion of its input levels as possible. With the superscript * denoting optimal values, the j 0 DMU is DEA-efficient if, and only if, h 0 ¼ 1. If h the j 0 DMU is DEA inefficient. h 0 is a measurement of the radial DEA efficiency of DMU j 0. Note that the linear programming problem must be solved N times, once for each DMU in the sample. A value of h is then obtained for each DMU. The model assesses efficiency in a production context. Its dual does this in a value context. By virtue of duality, the primal and dual models yield the same efficiency ratings in respect to DMU j 0. (Charnes, Cooper and Rhodes 1978). ð3þ Study Data and Results To estimate the production frontier, the study uses cross-section data on 43 pousada hotels for the year 2001, which are listed in Table 3. The data was obtained from ENATUR s Financial Control Report and supplemented with additional data available from the company. In order to choose the inputs of the DMUs, the distinction between controllable and uncontrollable factors must be taken into account. However, only the former were available for this study. The inputs are measured by 7 indicators. Labor is measured by the number of full-time equivalent employees and by the cost of labor. Capital is measured by the number of rooms, the surface area of the pousada in square meters, the book value of the premises, and the operational costs and the external costs. The study measures output by 3 indicators: sales, the number of guests and the aggregated number of nights spent. The observations and the variables used ensure the DEA convention that the minimum number of DMUs is greater than three times the number of inputs plus output [48 P 3(3 + 7)] (Raab and Lichty 2002). Table 2 presents the characteristics of the variables and verifies

13 468 HOTEL EFFICIENCY Table 2. Characteristics of the Inputs and Outputs for Year 2001 Variables Units Range Mean Square Deviation Outputs Sales Value in Euro Number of guests Number ,37 Nights spent Number Inputs Full time workers Number Cost of labor Value in Euro Rooms Number Surface area of the hotel Square meters Book value of property Value in Euro Operational costs Value in Euro External costs Value in Euro ,85 that the mean hotel has 24 rooms and 26 employees, signifying that the pousadas are small hotels. The DEA index can be calculated in several ways. An output-oriented, technically efficient DEA index is estimated in this study (Khumbhakar 1987). The output-oriented technical efficiency defines a production frontier and the measurement addresses the question: By how much can output quantities be proportionally increased without changing the input quantities used?. The variable return-to-scale (VRS) hypothesis was chosen because scale size is controllable by the central management of ENATUR. The CRS scores measure pure technical efficiency only. However, for comparative purposes, This measurement index is also presented. The VRS index is composed of a nonadditive combination of pure technical and scales efficiencies. A ratio of the overall efficiency scores to pure technical efficiency scores provides a scale measurement. The reason for including this ratio to measure scale efficiencies stems from the fact that VRS is due to scale effects, while CRS is due to the absence of the latter. Therefore, a ratio between the two captures the scale effect, when this is present in the data. The relative efficiency of the pousadas is presented below in Table 3. The rankings are ordered from the most efficient to the least efficient, according to the VRS hypothesis. It is verified that the DEA index is equal to 1 for the majority of the hotels when the overall level of efficiency is assumed (CRS scores), while a large number of pousadas, including all the CRS-efficient pousadas, are only efficient when VRS is assumed, signifying that the dominant source of inefficiency is due to scale economies. The average efficiency score under CRS is equal to Including all sources of inefficiency, pousadas could operate, on average, at 90.9% of their current output level and maintain the input value. However, the efficiency score assuming VRS is equal to Given the scale of operation, a majority of pousadas are efficient in managing their resources, the mean loss amounting to

14 CARLOS PESTANA BARROS 469 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 Évora Pousada dos Loios Guimarães Pousada de Santa Marinha Óbidos Pousada do Castelo Queluz Pousada de Dona Maria I Batalha Pousada do Mestre A. Domingues Bragança Pousada de São Bartolomeu Condeixa Pousada de Santa Cristina Gerês Pousada de São Bento Guimarães Pousada de Nossa Senhora da Oliveira Manteigas Pousada de São Lourenço Marvão Pousadade Santa Maria Miranda do Douro Pousada de Santa Catarina Monsanto Pousada Monsanto Murtosa Pousada Ria São Brás de Alportel Pousada de São Brás Sagres Pousada do Infante Santiago do Cacém Pousada Quinta da Ortiga Viana do Castelo Pousada do Monte de Santa Luzia Santiago do Cacém Pousada de São Tiago Póvoa das Quartas Pousada de Santa Bárbara Estremoz Pousada de Santa Isabel Marão Pousada de São Gonçalo Crato Pousada Flor da Rosa Caramulo Pousada de São Jerónimo Arraiolos Pousada de Nossa Senhora da Assunção Amares Pousada de Santa Marta do Bouro Palmela Pousada de Palmela Alijó Pousada do Barão de Forrester Sousel Pousada de São Miguel Torrão Pousada do Vale do Gaio Serpa Pousada de São Gens Alcácer do Sal Pousada D. Afonso II Vila Viçosa Pousada D. João IV Santa Clara a Velha Pousada de Santa Clara Setúbal Pousada de São Filipe Alvito Pousada do Castelo do Alvito Ourém Pousada Conde de Ourém Beja Pousada de São Francisco Vila Nova de Cerveira Pousada D. Diniz Castelo de Bode Pousada de São Pedro Valência do Minho Pousada de São Teotónio Elvas Pousada de Santa Luzia Almeida Pousada da Senhora das Neves Mean = 5.5%. A hotel is output-oriented Pareto-efficient if it is not possible to raise any of its output levels without lowering at least one of its other output levels and/or without increasing at least one of its input levels. The technical output efficiency of a hotel is the inverse of the maximum factor by which its output levels could be jointly expanded while its input levels do not rise. Managerial Implications A number of points emerge from the present study. First, the bestpractice calculations indicate that many pousadas under the VRS

15 470 HOTEL EFFICIENCY hypothesis (51.1%) operated at a high level of pure technical efficiency in However, almost half of the pousadas were technically inefficient, with different slacks in different inputs and outputs. Second, all technically efficient constant return-to-scale pousadas are also technically efficient at variable return-to-scale, signifying the dominant source of efficiency is scale. Third, inefficiency is more prevalent among the historic pousadas (66%) than among the regional pousadas (46%). Fourth, the location appears to be an explanatory factor of efficiency, with pousadas in, or near, the cities more efficient than those in more remote locations. A rationale for this result is that demand plays a role in organizational efficiency, with the hotels near more populated zones attracting more clients. This higher demand enables greater efficiency. Hence, assuming that there are two hotels with the same managerial expertise, the one with more demand tends to be more efficient. Fifth, although DEA identifies the inefficient hotels in the sample, it does not reveal the cause of the inefficiency. DEA suggests the slacks for the inefficient hotels and gives to each a reference set (peer group) which allows for specific recommendations to improve efficiency. Adjustments for the inefficient hotels can be identified for outputs 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 ownership may induce the principal-agent relationship (Jensen and Meckling 1976). The difficulty of controlling those empowered as managers to act on behalf of the owner (the State) is a prevalent issue in public enterprises. The job tenure of the ENATUR managers may encourage the development of principal-agent problems, since the managers are always connected to (and often dependent on) influential friends in the governing political party. Two, structural rigidities associated with the labor market (Ingram and Baum 1997) give rise to the collectiveaction problem (Olson 1965) in which workers can free-ride on the management s own efforts to improve performance. This situation occurs when job tenure is not linked to performance, which is frequently the case in the public sector. Three, organizational factors associated with X-inefficiency (Leibenstein 1966) relate to the fact that the production function is not completely specified or known, the contracts for labor are incomplete, and not all inputs are marketed on equal terms to all buyers. Inefficiencies associated with incomplete markets exist everywhere, but are particularly prevalent in the public domain. In this situation, the managers may be unable to adopt the correct strategy, since they do not know what it should be. Four, other factors outside the control of the management are the contextual causes of technical inefficiency, such as scale and scope economies, economies of scale (Chung and Kalnins 2000), and location and agglomeration effects. All of these factors may play a role, as multiple causes active in the market interact to contribute to the level of technical inefficiency. With the politically-appointed senior management liable to be replaced after every change of the elected government, there is scant tenure in their activity, and the stakeholders may exercise an inadequate control of the

16 CARLOS PESTANA BARROS 471 management procedures. Against such a background, incentives are absent for management to adopt a resource-based strategy. This dynamic gives rise to a depletion of variable critical resources, such as committed, high-quality managers. Due to any, some, or all of these factors, ENATUR s pousadas may produce at a level below their potential, the maximum possible output, in the production environment specific to tourism. As an example of adjustment based on slacks, in Table 4 shows, the adjustments proposed for the Pousada do Castelo do Alvito, in central-southern Portugal. It is verified that there are slacks in the aggregated nights spent and this output should be increased for the projected value. In relation to inputs, there are slacks in the surface area, the book value of the property, and the number of employees, signifying that these inputs are used inefficiently by the pousada. There is a margin to decrease those inputs and to increase the outputs with slacks, for the unit to catch up with the frontier. A peer group for the above-mentioned pousada consists of Sagres, Viana, Bragança, S. Tiago do Cacém, Miranda, and Évora. This peer group of efficient hotels is defined only for the Pousada do Alvito, because their data characteristics render them more similar to the inefficient Alvito. Thus, notably, not all the efficient pousadas are included in the peer group for the inefficient ones, only those defined by DEA as eligible for this purpose (other slacks for the other inefficient hotels are not shown, but are available on request from the author). DEA does not identify the factors that cause inefficiency and only directs attention to the units where inefficiency exists. Nonetheless, this is valid information, since the inputs and outputs that contribute to this inefficiency are also identified (Bessent et al. 1983). However, concerns have been raised as to the robustness of DEA models on the grounds that since DEA only determines relative efficiency, it cannot identify all of the inefficient units, because all of the units in the sample may in fact be inefficient. Nevertheless, the exercise is still valid, because it ranks the units under analysis according to a benchmark. Even in 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 Nights spent Sales Rooms Surface area , Property Operational cost Number of employees Cost of labor External cost

17 472 HOTEL EFFICIENCY than others and can profit from improving their operational activities. Despite the advantages of DEA, a further qualitative analysis on a case-by-case basis is usually necessary, to determine the true source of their inefficiencies and the appropriate corrective actions to be taken. The DEA approach has several managerial advantages: the DEA score is a surrogate for the overall competence and capability of the pousadas, which cannot be easily and cost-efficiently discerned through the company s audited accounts. Using audits is an expensive, time-consuming means of gathering, analyzing and evaluating. The methodology proposed in this study overcomes some of these difficulties, allowing ENATUR to gather useful data cost-efficiently and swiftly. Further, since multiple dimensions are simultaneously considered in evaluating the overall operational competence of the pousada, it is more robust and comprehensive than any of the typical productivity ratios commonly used in financial analysis. Another advantage of this approach is in identifying strategically important hotels. The performance-output-based evaluation methods are based on evaluating point-in-time data, in that the data are snapshots of the hotel s performance at a particular time. In evaluating hotels from a strategic perspective, it can be argued that evaluations based on inherent competence and capabilities are likely to be more comprehensive. That is, pousadas with high efficiency scores are likely to sustain a high level of capabilities and thus are better candidates for inclusion in an environment in which they are performing as bestpractice role models in the organization. Considering the results, several managerial implications can be proposed. First, the central management of ENATUR must upgrade its follow-up inspection procedure of pousadas activities, in order to provide more explicitly binding incentives for increasing productive efficiency, while constructing the procedure so as to prevent manipulations by internal parties. Second, the central management must expand the scope of the data obtained in the follow-up inspection to include contextual factors beyond managerial control, since it is not clear if different pousadas have the same operating environment. Socioeconomic and environmental factors can be used as indicators of the quality of customer service provided by each pousada. Third, a benchmark analysis should be carried out, in order to enforce an efficient adjustment of the least-performing pousadas. Fourth, a better analysis of the efficiency of the pousadas for the future of ENATUR, in terms of market strategy, would be welcome. The implications of those locations and dimensions which attract more guests and earn financial profits for the historic pousadas are not matched by efficient procedures at the latter. Consequently, this branch of the pousadas presents a lower proportion of efficiency. Fifth, the regional pousadas of smaller dimensions and in more remote areas of the country are disadvantaged in terms of their competitiveness. Sixth, better analyses are required to establish the precise significance to ENATUR of each involved element, whether they be suppliers, partners, guests, or employees. Too often, companies assume

18 CARLOS PESTANA BARROS 473 that as long as they meet their contractual obligations to each of these elements, then everything is fine. In other words, complacency easily sets in. Recalling that ENATUR is a state-owned creation, the world moves on with time and the organization needs to align its interests with those of the stakeholders. For example, it needs to communicate to the employees how and why they personally stand to benefit from the successful growth of the enterprise. Finally, the planned privatization in a competitive environment is felt to be a wise policy, since it is known that privatization coupled with competition tends to improve efficiency (Jones, Tandon and Vogelsang 1990). Limitations and Extensions of this Study With reference to the data set, the homogeneity of the pousadas used in the analysis is questionable, since it has compared pousadas with different dimensions, production characteristics, and locations. These may face different restrictions and thus might not be considered directly comparable. However, it can always be claimed that the units are not comparable and the traditional ratio analysis (Vogel 2001) equally could not be carried out. Yet the fact that the pousadas are under a common administration and seem to follow similar strategies gives sufficient justification for analyzing them as a unit. Since this research is an exploratory study, the intent is not to obtain definitive results for the direct use of the central management. Rather, it calls the attention of ENATUR to the value of benchmarking its pousadas in order to measure their performance (serving as a management tool). Moreover, since the data set is short, the conclusions are limited. In order to generalize, a larger panel data set would be necessary. Reducing the number of observations in DEA variables increases the likelihood that a given observation will be judged relatively efficient (Banker 1993). A variety of extensions to this study can be undertaken. One, in this analysis, the DEA model allowed for complete weight flexibility. In situations in which some of the measurements are likely to be more important than others, DEA allows for restricting factor weights through linear constraints. These linear constraints represent ranges for relative preferences among factors based on managerial input. Such analysis enables effective incorporation of managerial input into the DEA evaluations. Two, the input and output dimensions considered are context-specific. More comprehensive input and output measurements, (allowing for nondiscretionary factors such as environmental, socioeconomic, and quality inputs and outputs) need to be taken into consideration. The influence of discretionary variables being excluded from the analysis, amounts to an assumption that these factors are constant across the sample. Three, allocative efficiency can be estimated, as well as the total-productivity Malmquist index, provided that there are more years of observation. Four, nonparametric or parametric free-disposal hull analysis can be used to assess the efficiency scores. However, previous research

19 474 HOTEL EFFICIENCY has shown that the DEA scores are inferior in value to econometric scores, but the ranking is preserved (Bauer, Berger, Ferrier and Humphrey 1998). Finally, the hypothesis of the homogeneity of the pousadas under analysis is based on their nature, on the fact that they compete in the same market and that they all have the same stakeholders. However, nondiscretionary factors can render the pousadas nonhomogenous. CONCLUSION This article has proposed a simple framework for the evaluation of hotels and the rationalization of their operational activities in the tourism industry. The analysis is based on a DEA model that allows for the incorporation of multiple inputs and outputs in determining relative efficiencies. Benchmarks are provided for improving the operations of poorly-performing hotels. Several interesting and useful managerial insights and implications from the study are discussed. The general conclusion is that the majority of the ENATUR Pousadas de Portugal are efficient, although this leaves a proportion which are inefficient. For the inefficient pousadas, a peer group was identified among the efficient operators, in addition to the slacks that they should adjust in order to reach the efficient frontier. The findings suggest that scale economies and location are major issues in determining a unit s efficiency in Portugal or elsewhere. REFERENCES Anderson, P., and N. Peterson 1993 A Procedure for Ranking Efficient Units in Data Envelopment Analysis. Management Science 39: Anderson, R., M. Fish, Y. Xia, and E. Michello 1999 Measuring Efficiency in the Hotel Industry: A Stochastic Frontier Approach. International Journal of Hospitality Management 18: Anderson, R., D. Lewis, and M. Parker 1999 Another Look at the Efficiency of Corporate Travel Management Departments. Journal of Travel Research 37: Anderson, R., R. Fok, and J. Scott 2000 Hotel Industry Efficiency: An Advanced Linear Programming Examination. American Business Review 18(1): Baker, M., and M. Riley 1994 New Perspectives on Productivity in Hotels: Some Advances and New Directions. International Journal of Hospitality Management 13: Banker, R Maximum Likelihood, Consistency and Data Envelopment Analysis. Management Science 39: Banker, R., A. Charnes, and W. Cooper 1984 Some Models for Estimating Technical and Scale Inefficiencies in Data Envelopment Analysis. Management Science 30: Barros, C A Stochastic Cost Frontier in the Portuguese Hotel Industry. Tourism Economics 10:

20 CARLOS PESTANA BARROS 475 Bauer, P., A. Berger, G. Ferrier, and D. Humphrey 1998 Consistency Conditions for Regulatory Analysis of Financial Institutions: A Comparison of Frontier Efficiency Methods. Journal of Economics and Business 50: Begg, J Cities and Competitiveness. Urban Studies 36: Bell, R., and R. Morey 1995 Increasing the Efficiency of Corporate Travel Management through Macro Benchmarking. Journal of Travel Research 33(3): Berger, A., and D. Humphrey 1997 Efficiency of Financial Institutions: International Survey and Directions for Future Research. European Journal of Operational Research 98: Bessent, A., E. Bessent, A. Charnes, W. Cooper, and N. Thorogood 1983 Evaluation of Educational Program Proposals by means of DEA. Education and Administrative Quarterly 19(2): Brotherton, B., and S. Mooney 1992 Yield Management Progress and Prospects. International Journal of Hospitality Management 11: Brown, J., and C. Ragsdale 2002 The Competitive Market Efficiency of Hotel Brands: An Application of Data Envelopment Analysis. Journal of Hospitality and Tourism Research 26: Charnes, A., W. Cooper, A. Lewin, and L. Seiford 1995 Data Envelopment Analysis: Theory, Methodology and Applications. Dordrecht: Kluwer Academic Publishers. Charnes, A., W. Cooper, and W. Huang 1990 Polyhedral Cone-Ratio DEA with an Illustrative Application to Large Commercial Banks. Journal of Econometrics 46: Charnes, A., W. Cooper, B. Gollany, L. Seiford, and J. Stutz 1985 Foundations of Data Envelopment Analysis for Pareto-koopmans Efficient Empirical Productions Functions. Journal of Econometrics 30(1/ 2): Charnes, A., W. Cooper, L. Seiford, and J. Stutz 1982 A Multiplicative Model of Efficiency Analysis. Socio-Economic Planning Sciences 16: Charnes, A., W. Cooper, and E. Rhodes 1978 Measuring the Efficiency of Decision-making Units. European Journal of Operations Research 2: Chiang, W., H. Tsai, and L. Wang 2004 A DEA Evaluation of Taipei Hotels. Annals of Tourism Research 31: Chung, W., and A. Kalnins 2000 Agglomeration Effects and Performance: A Test of the Texas Lodging Industry. Strategic Management Journal 22: Coelli, T A Guide to DEAP version 2.1: A Data Envelopment Analysis (Computer) Program. Working Study no 8/96, Centre for Efficiency and Productivity Analysis. University of New England. Armidale, Australia. Coelli, T., R. Prasada, and G. Battese 1998 An Introduction to Efficiency and Productivity Analysis. Dordrecht: Kluwer Academic Press. Cooper, W., L. Seiford, and K. Tone 2000 Data Envelopment Analysis. Boston: Kluwer. Donaghy, K., U. McMahon, and D. McDowell 1995 Yield Management: An Overview. International Journal of Hospitality Management 14: Fare, R., S. Grosskopf, and C. Lovell 1994 Production Frontiers. Cambridge University Press. Farrell, M The Measurement of Productive Efficiency. Journal of the Royal Statistical Society, Series A 120(3):

21 476 HOTEL EFFICIENCY Financial Times 2002 Financial Times Survey on Portugal (October 21):1. Hwang, S., and T. Chang 2003 Using Data Envelopment Analysis to Measure Hotel Managerial Efficiency Change in Taiwan. Tourism Management 24: Ingram, P., and J. Baum 1997 Chain Affiliation and Failure of Manhattan Hotels, Administration Science Quarterly 42: Jensen, M., and W. Meckling 1976 Theory of the Firm: Managerial Behaviour, Agency Costs and Capital Structure. Journal of Financial Economics 3: Johns, N., B. Howcroft, and L. Drake 1997 The Use of Data Envelopment Analysis to Monitor Hotel Productivity. Progress in Tourism and Hospitality Research 3: Jones, L., P. Tandon, and I. Vogelsang 1990 Selling Public Enterprises: A Cost-Benefit Methodology. Cambridge: MIT Press. Khumbhakar, S Production Frontiers and Panel Data: An Application to US Class 1 Railroads. Journal of Business and Economics Statistics 5: Krugman, P Making Sense of the Competitiveness Debate. Oxford Review of Economics and Policy 12: Leibenstein, H Allocative Efficiency vs. X-efficiency. American Economic Review 56: Malmquist, S Index Numbers and Indifference Surfaces. Trabajos de Estadística 4: Morey, R., and D. Dittman 1995 Evaluating a Hotel GM s Performance: A Case Study in Benchmarking. Cornell Hotel Restaurant and Administration Quarterly 36(5): Olson, M Logic of Collective Action. Cambridge: Harvard University Press. Porter, M The Competitive Advantage of Nations. London: Macmillan. Raab, R., and R. Lichty 2002 Identifying Sub-areas that Comprise a Greater Metropolitain Area: The Criterion of County Relative Efficiency. Journal of Regional Science 42: Reynolds, D Hospitality Productivity Assessment using Data Envelopment Analysis. Cornell Hotel and Restaurant Administration Quarterly 44(2): Santos, C Framing Portugal: Representational dynamics. Annals of Tourism Research 31: Seiford, L., and R. Thrall 1990 Recent Developments in DEA: The Mathematical Programming Approach to Frontier Analysis. Journal of Econometrics 46:7 38. Thanassoulis, E Introduction to the Theory and Application of Data Envelopment Analysis: A Foundation Text with Integrated Software. Dordrecht: Kluwer Academic Publishers. Thompson, R., L. Langemeier, C. Lee, and R. Thrall 1990 The Role of Multiplier Bounds in Efficiency Analysis with Application to Kansas Farming. Journal of Econometrics 46: Thompson, R., F. Singleton, R. Thrall, and B. Smith 1986 Comparative Site Evaluation for Locating a High-Energy Physics Lab in Texas. Interfaces 16(6):35 49.

22 CARLOS PESTANA BARROS 477 Wijeysinghe, B Breakeven Occupancy for Hotel Operation. Management Accounting 712: Vogel, H Travel Industry Economics: A Guide to Financial Analysis. London: Cambridge University Press. Submitted 23 November Resubmitted 9 March Resubmitted 12 July Accepted 28 July Final version 13 October Refereed anonymously. Coordinating Editor: Peter U.C. Dieke