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International Journal of Hospitality Management 42 (2014) 165–173

Contents lists available at ScienceDirect

International Journal of Hospitality Management

jo u r n al homep age: www.elsev ier .com/ locate / i jhosman

he impact of quality management on productivity in the hospitalityector

arlos Guillermo Benavides-Chicón ∗, Bienvenido Ortega1

epartment of Applied Economics (Economic Structure), University of Malaga, C/ Ejido, 6, 29071 Malaga, Spain

r t i c l e i n f o

EL classification:15, J24, M54

eywords:

a b s t r a c t

This paper attempts to determine the relationship between quality and productivity in the hospitalitysector. Although both variables are crucial to the competitive and value creation processes in hotels, fewempirical studies have explicitly analyzed the relationship between them. Thus, we estimated a produc-

ualityabour productivityotels

tion function for a hotel sample that shows how a set of determinants influences labour productivity.Among these determinants we included different quality variables, defined from the point of view oftotal quality. The estimation results show a direct and significant effect of quality on labour productivity.This suggests that the implementation of total quality management (TQM) systems, or the adoption ofthe TQM principles, have a positive impact on hotel labour productivity.

© 2014 Elsevier Ltd. All rights reserved.

. Introduction

Since the late 1980s, many authors — especially those associatedith the Nordic School of Services — have studied productivity in

he service sector and have addressed the need to define a con-ept of productivity that takes into account the distinctive featuresf this sector of economic activity. This approach was followedy Vuorinen et al., 1998, Ojasalo (1999), Parasuraman (2002), andore recently by Djellal and Gallouj (2013), indicating that this

opic is still under discussion. During this period, the first studiesn productivity in hotels appeared (Van der Hoeven and Thurik,984). Several focused on measuring hotel productivity, and manythers investigated its key drivers (Brown and Dev, 1999; Kilic andkumus, 2005; Marchante and Ortega, 2012).

The concept of quality in business has evolved over time, lead-ng to changes in the methods used for its management. Sincehe 1980s, the concept of Total Quality Management (TQM) haseen adopted together with a strategic approach to quality in anttempt to focus all the resources on achieving excellence. Variousuality management systems, international standards, and qual-

ty certificates are based on this approach. The implementation ofhis concept has brought out the difficulty of managing quality inhe service sector. However, compared to the number of studies

∗ Corresponding author. Tel.: +34 952 13 11 83; fax: +34 952 13 66 16.E-mail addresses: cgbch@uma.es (C.G. Benavides-Chicón), ortega@uma.es

B. Ortega).1 Tel.: +34 952 13 11 87; fax: +34 952 13 66 16.

ttp://dx.doi.org/10.1016/j.ijhm.2014.07.004278-4319/© 2014 Elsevier Ltd. All rights reserved.

on tangible goods, there are still few studies on this subject. Suchstudies are driven by two main schools of thought: the North Amer-ican school (Rosander, 1989; Horovitz, 1990; Zeithaml et al., 1990;Berry, 1995); and the Nordic school (Grönroos, 1984; Edvardssonand Gustavsson, 1988). Interest in quality in hotels arose in thelate 1980s — similar to the case of productivity — in response toincreasing competition in the hotel industry, which was followedby a recession in Western economies (Stewart and Johns, 1996). Thefirst studies focused on quality management from a theoretical per-spective, the implementation of specific management systems, andthe gaining of quality certification. Over time, two lines of researchemerged in the literature in this field (Hernández-Maestro et al.,2009): studies addressing quality from the customer’s perspective(Camisón et al., 1996; Al Khattab and Aldehayyat, 2011) and othersanalysing the advantages and disadvantages of the implementationof quality management practices and systems in hotels and theirrelationship to business performance (Claver-Cortés et al., 2008;Viada-Stenger et al., 2010).

Although the relationship between quality and productivityhas attracted some interest from a theoretical perspective, mostresearch has focused either on quality or productivity; studiesfocusing on the relationship between the two aspects remain few,especially empirical studies. However, in recent years, growinginterest in the study of productivity and quality in the servicesector has led to the emergence of studies that have more or less

explicitly investigated the relationship between the two variables,based on different perspectives and with different results. Thetraditional view is that quality and productivity are incompat-ible. However, many authors have questioned this relationship,

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66 C.G. Benavides-Chicón, B. Ortega / International J

roposing a modern vision that takes into account the customer’serspective. Thus, researchers consider the two variables as beingositively related, and assume that quality has a direct effectn productivity (Gummesson, 1992; Parasuraman, 2002, 2010;ontoghiorghes, 2003; Hope, 2007). In fact, an integrative or dualerspective is considered necessary (Hernández-Maestro et al.,009; Parasuraman, 2010), whereby perceived quality dependsn objective quality and consumer expectations. Consequently,rom the standpoint of managing both productivity and quality,raditionally addressed in isolation, integrated management islso considered a necessity (Vuorinen et al., 1998; Parasuraman,002; Kontoghiorghes, 2003). TQM is seen as the most usefulool in this approach, although some authors have proposed theirwn management systems that are based on similar philosophiesYoung and Colosi, 1993; Mohanty, 1998).

Thus, taking into account the foregoing, we use a strategicpproach based on the concept of TQM to investigate the relation-hip between quality and productivity. This analysis is also justifiedy the fact that various public administrative bodies have strongly

nvested efforts in promoting improvements in quality in hotelsn Spain. These activities have often been developed under pro-rammes designed to increase the competitiveness of the sector,nsure the survival of companies that form the sector, and main-ain or steadily increase the sector’s contribution to the economyver time.

Furthermore, opportunities may exist for increasing productiv-ty levels in the hospitality sector in Spain. In this sense, Table 1resents data on labour productivity levels (measured as the aver-ge value added generated by each person employed) for EUember states in 2006. These states represent 85.7% of the total

mployment of EU-27 in that year. Labour productivity in Spainas D 20,000 for the entire hotel and restaurant sector. This figure

s very similar to the one reported for Italy (D 19,700) and very closeo the figure reported for the UK (D 21,600). However, there is anmportant gap in productivity between Spain and leading countriesuch as France and Austria in the hospitality industry. Moreover,abour productivity decreased in real terms in Spain up to 2008,

ainly due to a large increase in employment during the latter halff the 1990s (Ortega and Benavides Chicón, 2013).

In fact, the increase in productivity in Spain in 2009 is a conse-uence of job destruction and an increased unemployment rate inhe context of the current recession. Thus, these data suggest thathere is a high potential for improvement in productivity in theospitality sector in Spain if the output per worker is compared to

hat in leading countries in Europe.

This article is structured as follows: First, we present a reviewf the relevant literature on the subject. Then, the theoretical

able 1abour productivity level and growth in the hospitality sector in EU countries (valuedded divided by the number of persons employed).

Levelsa

(103 D /employee)Annual growth, in per centb

2006 2006 2007 2008 2009

France 31.2 0.5 −0.6 n/a n/aSweden 26.5 −2.5 1.4 1.6 −4.3Austria 26.3 0.4 −2.3 2.2 −0.8United Kingdom 21.6 5.2 2.2 0.5 n/aSpain 20.0 −1.7 −0.6 −0.3 1.4Italy 19.7 2.7 −0.5 1.5 −0.5Netherlands 19.2 0.1 −1.2 1.9 −6.9Germany 17.7 −0.7 3.9 0.5 −5.2Greece 11.4 −2.4 −3.6 2.7 8.6Poland 11.1 7.6 −2 −0.5 n/aCzech Republic 7.9 21 −2.5 3.1 n/a

ources: (a) Eurostat (2009). (b) OECD.

l of Hospitality Management 42 (2014) 165–173

framework used to investigate the relationship between qualityand productivity in services, and specifically in hotels, is pre-sented. Subsequently, we describe the database and constructionof the empirical variables used to estimate the proposed model.Following the econometric assessment of the model, we discussour findings and the limitations of the study.

2. Review of the literature

Few studies have specifically addressed the relationshipbetween quality and productivity in hotels. However, some rel-evant studies exist that address quality from a managerial pointof view or that analyze the influence of quality on business per-formance, which has sometimes been quantified using differentmeasures of productivity.

In the first case, a positive association between TQM and produc-tivity was identified in theoretical studies, such as those by Vrtiprahand Ban (2000) and Avelini Holjevac and Vrtodusic Hrgovic (2012).Both studies include measures of productivity in the Croatian hos-pitality industry in order to justify the relevance of productivitymanagement in hotels; they also analyze the factors which influ-ence productivity and suggest strategies for increasing it. Amongthese factors, product and service quality play a significant role, andthis association is best illustrated by the concept and goal of TQM(Avelini Holjevac and Vrtodusic Hrgovic, 2012). Thus, they concludethat quality management systems have a positive effect on produc-tivity, based on the results of a descriptive analysis that compareslabour productivity in ISO 9001 certified and non-certified Croatianhotels.

In the second case, a positive association was observed betweenquality and management aspects or performance variables fromboth the management’s and consumer’s points of view (Skalpeand Sandvik, 2002; Claver et al., 2006; Claver-Cortés et al., 2008;Tarí et al., 2010), although the conclusions presented by theseauthors differed. These studies include capital productivity meas-ures among the economic performance variables, using the numberof available rooms as a measure of input. Skalpe and Sandvik(2002) studied the economics of quality in the hospitality sectorand demonstrated that quality requires economic resources andhas positive effects on the economic performance of hotels. Theyassessed the consumer’s perception of quality based on qualityratings from 40,000 customers and concluded that quality has asignificant impact on revenues per room available. Claver et al.(2006) studied the reasons for the adoption and certification ofquality systems and their impact on performance, based on theperceptions of two hotel managers in Spain. The results show that,among the variables considered, certification has a relatively highimpact on productivity (5 out of 7 points). Claver-Cortés et al. (2008)analyzed the relationship between TQM, managerial factors, andperformance in a sample of Spanish hotels. Among the perfor-mance measures considered, they included gross operative profitper available room and day (GOPPAR). They concluded that hotelsthat show higher commitment to TQM reach higher levels of GOP-PAR. However, a more recent study (Tarí et al., 2010) did not find apositive relation between commitment to TQM and GOPPAR, eitherseparately or in combination with environmental commitment.

Finally, some studies have examined the factors that influenceproductivity in the hospitality sector. Some of them identified qual-ity as a relevant factor (Sasse and Harwood-Richardson, 1996;Brown and Dev, 2000; Kilic and Okumus, 2005). The studies bySasse and Harwood-Richardson (1996) and by Kilic and Okumus

(2005) were both based on interviews with hotel managers with theaim of identifying the factors influencing productivity. However,these studies were not based on a specific definition or measure-ment of productivity. According to Sasse and Harwood-Richardson

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1996), the HCTC study attempted to identify the factors that couldave the greatest impact on productivity. To do so, a list of factorsas drawn up, based on exploratory interviews with hotel man-

gers, academics, and industrialists, and a review of the literature.ilic and Okumus (2005) provided a ranking of the factors influenc-

ng productivity in hotels in Northern Cyprus. Their research wasased on a review of the literature in order to design a question-aire. This questionnaire was tested on several managers in order todd or eliminate factors influencing productivity. They affirm thatn improvement in quality would drive an immediate rise in pro-uctivity. However, they question causality between both variablesnd their relation with other determinant factors, such as trainingnd motivation.

Brown and Dev (2000), however, used an explicit theoreticalramework and a specific measure of productivity. They consid-red that this measure might reflect the ratio of a firm’s outputso its inputs: the more output a firm can produce from a given setf inputs, the more productive it is. From this perspective, theyttempted to gain insight into how managers could improve theroductivity of their hotels by examining productivity through these of an empirical production function and addressing the con-epts of economic outputs, economic inputs, and strategic andrganizational inputs. The output measure was a dollar measure ofhe value added by the hotel. Labour was measured in terms of thestablishment’s total number of employees (number of its full-timemployees plus half the number of its part-time employees). Cap-tal measures included the number of rooms available for sale andransaction-specific assets (i.e. intangible assets that are devoted to

specific exchange relationship), measured on six 7-point Likert-ype items. In their study, these are assets invested on behalf of theotel that are only applicable to the hotel’s relationship with thehain.

In summary, a review of the literature shows that there is a lackf empirical research on the relationship between quality and pro-uctivity in the hospitality sector. In this context, the aim of thisaper was to fill this gap by using a methodological approach sim-

lar to that employed by Brown and Dev (2000). With this aim, apecific quantitative measure of labour productivity was definednd a number of production functions were estimated to investi-ate the impact of quality measures on hotel labour productivity.iven the results of the above-mentioned studies, it can be hypoth-sized that there is a significant and positive effect of quality (TQM)n labour productivity.

. Methods

.1. Theoretical model

Our starting point in the analysis is that quality managementould be an expansive strategy that allows hotels to obtain an

ncrease in output greater than the output related to labour input,hus improving labour productivity. For this reason, to analyze theffect that quality has on hotel productivity, we specified a Cobb-ouglas production function in which we assume that technology

s common to all establishments in the sample. This can be repre-ented by the following expression:

= A · (K˛ · Lˇ) (1)

here Y is the amount of output, K is the capital input, L is the

abour input, and A is a technological parameter. The parameterso be estimated, and ˇ, represent the elasticities of output (Y) inelation to capital (K) and labour (L), respectively. This equation haso be linearized in order to estimate it using ordinary least squares

l of Hospitality Management 42 (2014) 165–173 167

(OLS), thus taking logarithms on both sides of the equation, thefollowing expression can be written:

ln Y = ln A + ln K + ln L (2)

However, to analyze the effect of quality on productivity, wehave to include the variable or variables related to quality in expres-sion (2). To do this, following the methodology proposed by Görget al. (2008), we assume that quality has a direct impact on out-put in hotels, leading to a change in the technological parameter(A). As mentioned, we approach quality from a strategic point ofview based on the concept of TQM. So, the impact of quality on thetechnological parameter is based on the hypothesis that TQM andcontinuous improvement help to optimize the production process,leading to greater efficiency. We thus assume that quality, theo-retically denoted as Q, leads to an expansion in the output of thehotels, which we represent by the following expression:

ln A(Q ) = � + ϕQ (3)

where � is a constant and ϕ is the coefficient of the variable Q.Substituting with (3) in (2), subtracting lnL on both sides of theequation, and adding a vector representing the other variables thatcan affect productivity (X) and a random disturbance term, weobtain the equation to be estimated in terms of labour productivity:

ln Y − ln L = � + ϕQ + ln K + ( − 1) ln L + �X + ε (4)

In Eq. (4), the dependent variable, ln Y − ln L = ln (Y/L), is the nat-ural logarithm of labour productivity, measured as the quotientbetween output (Y) and labour input (L). In addition to the measureof the hotel’s physical capital (K) and labour (L), based on previousstudies, we include an additional set of variables (X) to control fordifferences in organized activities and in the markets where thehotel operates. These variables relate to the age of the hotel, hotelcategory, location, and whether the hotel belongs to a chain.

Given the above equation, which in theory serves as the basis forthe estimation, the null hypothesis that we test is H0: ϕ > 0. That is, ifthe coefficient of the quality variable Q is significantly greater thanzero, then that variable has a positive effect on labour productivity.To obtain this estimate, we must first explain which variables areincluded and how they are constructed and measured.

3.2. Description of the database

The full database used in this study was created by the Quality,Productivity and Competitiveness in the Hospitality Industry forAndalusia project [PO7/SEJ-02889]. It includes representative datafor a sample of 232 hotels with three or more stars providing atotal of 64,036 hotel rooms. According to the Turespana (2008),there were a total of 818 hotel establishments with 3 or more starsin southern Spain in 2008. Therefore, the sample includes 28.36%of the establishments in the region, representing a confidence levelof 94.55%. The database containing information from the question-naires was created in 2010. The fieldwork required to complete thisdatabase was conducted in 2011. Secondary sources were also used,specifically, the SABI (Iberian Balance-Sheets Analysis System)database (http://www.informa.es/en/soluciones-financieras/sabi),the annual accounts filed by firms in the Mercantile Reg-istry (http://www.rmc.es/Home.aspx?lang=en), sectorialdata from the Spanish National Institute of Statistics (INE:http://www.ine.es/en/inebmenu/mnu hosteleria en.htm), and theTurespana Official Hotel Guide 2008. The establishments’ websiteswere also consulted.

The results obtained for the final sample available for theestimations (173 hotels) show that the mean level of labour pro-ductivity (calculated as the quotient between the GVA and numberof full-time equivalent employees) in 2008 was 33,554 Euros per

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mployee (see Table 3). According to the “Annual Services Sur-ey” produced by the National Institute of Statistics (INE), the GVAer worker in Spanish hotels with more than 5 employees in 2008as 35,811 Euros. Moreover, the “Hotel Occupancy Survey”, which

ncludes regional data produced by the Spanish National Institute oftatistics (INE), indicates that there was an average of 39.5 work-rs per establishment in the region and that the share of hotelsccording to their level of services was as follows: 3-star 51.6%,-star 43.0%, and 5-star 5.4%. According to the final sample, thereas an average of 46.5 workers per establishment (see Table 3) and

2.2% of hotels in the sample were classified as 3-star, 52.6% as 4-tar, and 5.2% as 5-star. Thus, these figures indicate that there is aeasonable degree of consistency between the sample employed inhis study and the official statistical sources available for the hotelector in Spain. Furthermore, the sub-sample of chain-hotels in thenal sample includes 106 establishments belonging to 42 differenthains or groups (around 21% of the chain-hotels in the region). Theajor chains operating in Spain are included in this sub-sample.

.3. Empirical variables used

The measure of output used to calculate hotel productivity wasross value added (GVA). This measure has a number of advan-ages over alternative measures, such as sales revenue, as it takesnto account the cost of the factors and the use of intermediatenputs. Moreover, the use of a monetary measure of output haseveral advantages, such as ease of use, as well as the potential touantify both the heterogeneity of the service and its intangiblelements (Ojasalo, 1999). It was calculated by adding the employ-es’ salaries and the gross operating surplus (net operating surplusinus depreciation) for each establishment.Regarding capital input, we used the size of the establishment,

easured by the number of available rooms (AVR), as a proxy forapital investment. It is reasonable to assume that the greater theumber of hotel rooms the greater the capital investment in termsf furniture and fittings, equipment, and infrastructure. Moreover,he use of this measure is widespread in the literature, as shownn the work of Brown and Dev (1999, 2000) or Claver-Cortés et al.2008). Labour input was measured by the total number of full-ime equivalent employees (FTEE) in 2008. This figure is calculatedssuming that a part-time worker is equivalent to half a full-timeorker.1 Therefore, the productivity data (LPROD) — that is, theependent variable of the model — is obtained by dividing the GVAy the FTEE. Labour productivity is a suitable measure of produc-ivity in the hotel sector as this is considered a labour intensivendustry, as shown in the specialist literature (Hu and Cai, 2004;ones and Siag, 2009; Marchante and Ortega, 2012). Thus, improv-ng labour productivity is key to output expansion strategies.

The equation to be estimated also includes a vector for theontrol variables that represents the effect of the main charac-

eristics of the establishment on labour productivity. Specifically,ollowing Marchante and Ortega (2012), three dummy variablesave been included to represent the effect of belonging to a chain

1 While recognizing that FTEE is not the best way to measure labour input, datavailability prevents us from employing other more accurate measures of labournput. It is also important to note that calculating labour volumes using this methods not uncommon in the hospitality literature; it has been used, for example, byrown and Dev (1999, 2000) and Hu and Cai (2004). In general, FTEE can be cal-ulated by dividing the total hours actually worked by the average annual hoursctually worked in a full-time job, considering a “normal” full-time working year oraking contractual hours as an indication. In our case, given that we do not have datan hours worked, we assumed that each part-time employee represented one-halff a full-time employee in order to address the problem of the non-equivalence ofull- and part-time employees. As Brown and Dev (1999) put it: “This is not unrea-onable, we believe, as some part-time employees work nearly as much as full-timemployees while others merely work a handful of hours per week”.

l of Hospitality Management 42 (2014) 165–173

of establishments (CHA), the location of the establishment (LOC,which takes the value one if it is located in the capital of a province),and its category (CATEG, which takes the value one if the hotel hasthree stars). A variable has also been included to control for theeffect of the age of the property (AGE), measured in years.

The quality variables included were constructed using the EFQMExcellence Model, designed by the European Foundation for Qual-ity Management (EFQM). This model provides organizations withthe tools needed to implement the TQM paradigm (the approachfollowed in this paper), is able to overcome the problems and short-comings encountered in other quality management systems, andcan integrate other management systems (environmental, occu-pational health and safety, etc.) that have emerged in recent years.These characteristics make it an ideal model for TQM. This has beenvalidated empirically by Bou-Llusar et al. (2009), who concludedthat the EFQM Excellence Model reproduces TQM and a firm couldimplement TQM by adopting the EFQM Framework. For this reason,the quality variables used in the estimates were constructed basedon its components (see EFQM, 2010), similar to the methodologyfollowed by Patiar et al. (2012).

Specifically, our aim was to establish to what extent theenablers of the EFQM model influence hotel productivity, as theseenablers determine whether excellence is achieved in organiza-tional performance. Following a multi-item approach, a variablewas constructed for each of the five elements or criteria thatform the enablers (leadership, people, strategy, partnerships andresources, and processes, products and services). That is, each ofthe variables created is composed of a set of 36 items containedin the questionnaire and formulated taking into account the sub-criteria into which the five enablers of the model are divided (seeTable A.1). Some of these items are dichotomous and others arebased on a seven-point Likert scale (where 1 represents the lowestlevel of achievement and 7 the highest). The values of the con-structs in the case of dummy variables are obtained by calculatingthe means of the scores of all the items that compose them. In thecase of Likert items, we first normalize the Likert scale in orderto express all the items on a scale from 0 to 1. This transforma-tion makes it easy to determine the mean value of the constructsand provides a single measure of the combined mean value of thedichotomous and Likert items on a scale from 0 to 1 for each estab-lishment.

Since we are using multi-item scales, their reliability and valid-ity have to be measured. The internal consistency of a set of two ormore indicators of a construct is a widely used measure of reliabilityand is justified in that the individual items or indicators of the scaleshould be measuring the same constructs and therefore should befully correlated (Hair et al., 1998). The measure most often used tomeasure internal consistency is Cronbach’s alpha coefficient, whichtakes values between 0 and 1. In general, researchers set the lowestacceptable limit of the coefficient at 0.7 (Hair et al., 1998). Observa-tions of the inter-element correlation matrixes showed that therewere two items with particularly low correlations. These were lessthan 0.30 (the minimum acceptable value according to Hair et al.,1998) for all their values. After eliminating these two items, Cron-bach’s alpha values increased to within acceptable limits for bothconstructs, similar to the remaining constructs. Table 2 shows theresults of the reliability analysis as measured by Cronbach’s alpha.

Finally, an additional variable was constructed that included allof the enablers in order to analyze their effect on labour produc-tivity in hotels. This variable, global quality (Q), was obtained bycalculating the mean value of the five constructs described — whichcorrespond to each of the five criteria that form the enablers of the

EFQM Model, whose weights are the same in the 2010 version ofthe Model.

Table 3 shows the descriptive statistics for the variables used inthe estimation of the production function. Starting with the initial

C.G. Benavides-Chicón, B. Ortega / International Journa

Table 2Reliability of the items of the construct.

Construct Cronbach’sAlpha

Items No. Hotelsa

Leadership (LEAD) 0.700 5 155People (PEOP) 0.706 3 171Strategy (STR) 0.703 5 159Partnerships and resources

(P&R)0.702 4 168

Processes, products andservices (PRO)

0.878 19 100

a Number of hotels that gave a full response to all the items in the construct.

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atabase, which consisted of 232 hotel establishments, we elim-nated 59 hotels for which no data were available for any of theariables included in the estimates or outliers of the dependentariable. Following this procedure, the sample used for the esti-ation consisted of 173 hotels.2 However, the composition of the

ample used for estimation is very similar to the original sample,ccording to the location and the main characteristics of the hotels,nd is consistent with the main official data available for the sector,s shown in Section 3.2.

As mentioned, the replies to the items used to construct theuality variables are expressed on a scale from 0 to 1. That is, thestablishment that has the highest score on a particular item isiven a value of 1, indicating the highest level of excellence withinhe sample. Similarly, the establishment with the lowest score on aarticular item is given a value of 0, indicating the lowest level ofxcellence within the sample. The values of the constructs are inter-reted in a similar way, and thus the mean values of the items

ndicate the degree to which excellence or the objectives haveeen achieved. Thus, a maximum value of a construct (1) indi-ates that there is at least one establishment that has achievedhe highest level of excellence in all items that make up the vari-ble, whereas a minimum value of 0 indicates that there is ateast one establishment with a score equal to 0 in all cases. Inable 3, we can observe that, regarding Q (the constructed globaluality variable), the establishment with the lowest mean scoreeached 0.29 of the objectives in terms of excellence, whereas thestablishment with the best score (0.97) would need to improvets performance in some items by just 0.03 points in order toeach the highest level in excellence. In general, the lowest per-ormance in reaching these objectives was found for the variablesomposing Leadership (LEAD; 0.59), whereas the highest perfor-ance was found for the variables composing People (PEOP; 0.74)

nd Partnerships and Resources (P&R; 0.74). In relation to Q, thestablishments analyzed reached a mean degree of excellence of.69.

. Estimates

.1. Econometric model

Having described the construction and measurement of the vari-bles used in the estimates, we can specify the empirical Eq. (5),

2 This statement seems to be inconsistent with the information shown in Table 2.his table also shows the number of hotels that gave a full response to all the itemsncluded in the questionnaire for each of the quality variables used in the esti-

ates. However, in order to avoid a greater loss of observations, a minimum level ofesponse was considered for each set of items (enabler) to include the hotel in theample. This level was set at two-thirds of the answers for all the items. Overall, allhe hotels included in the sample answered 29 or more items out of the 36 items inhe questionnaire.

l of Hospitality Management 42 (2014) 165–173 169

which, considering expression (4), also includes the four controlvariables described above and the five quality constructs:

ln LPROD = � + ln AVR + (b − 1) ln FTEE + �1CHA + �2LOC

+ �3CATEG + �4AGE + ϕ1LEAD + ϕ2PEOP + ϕ3STR

+ ϕ4P&R + ϕ5PRO + u (5)

The results of the estimation are shown in Table 4. However,we considered that there may exist a problem of multicollinear-ity between the different quality constructs included together asindependent variables in this equation. To explore this possibility,we calculated the Variance Inflation Factor (VIF) for each coeffi-cient. The high values of VIF obtained in the estimation of Eq. (5)confirmed the existence of multicollinearity and thus we estimatedfive different equations (Eqs. (1)–(5) shown in Table 5), includinga single construct or quality variable in each of them. A final equa-tion was also estimated (Eq. (6) in Table 5) that included the overalleffect of quality by including the values obtained for the variable Qin the model.

4.2. Results

By examining the results of the estimations of the equationscorresponding to each of the five constructs of quality and of theequation that included the variable global quality, we can seewhich variables have the greatest influence on labour productiv-ity.

First, the results of estimating the coefficients for labour andcapital are in line with those obtained in the literature (Brownand Dev, 2000; Marchante and Ortega, 2012). In relation to theinfluence of the characteristics of the hotels, there is a positive asso-ciation between labour productivity and belonging to a chain, beinglocated in a provincial capital, and the age of the establishment, anda negative association if the establishment has three stars (the low-est category analyzed); these results are also in line with Marchanteand Ortega (2012).

In addition, the following can be stated regarding the qualityvariables:

- A positive but nonsignificant association was found betweenlabour productivity and the variables Leadership, and People.Given the results obtained in items LEAD4 and PEOP1 shownin Table A.1 (0.267 and 0.551, respectively), the lack of signif-icance of the impact of Leadership and People on productivitycould be related to the fact that, in general, there may be lit-tle awareness among hotel managers of the importance of staffmotivation and ongoing training as drivers of quality and pro-ductivity. Moreover, it should be taken into account that, in thecontext of TQM, the management’s commitment to quality isshown through increased employee motivation (LEAD4) and sys-tematic training programmes (PEOP1), which are the key factorsin attaining superior final service quality (Powell, 1995). More-over, both items are related given that employee empowermentand motivation is not effective unless employees have receivedformal training in quality management (Ahire et al., 1996). Forthese reasons, low scores on the two key items LEAD4 and PEOP1,despite being compensated for by high scores on other itemswithin the Leadership and People constructs (LEAD and PEOP),may indicate that hotels are failing to implement fully effective

quality-oriented management which, in turn, may explain its lackof effect on performance. On this point, in-depth case studieswould be useful to analyze to what extent the lack of awarenessamong managers of the importance of these key TQM elements

170 C.G. Benavides-Chicón, B. Ortega / International Journal of Hospitality Management 42 (2014) 165–173

Table 3Descriptive statistics of the variables used in the estimations.

Variables Units Minimum Maximum Mean S.D.

GVA Euros (2008) 29,687 9,609,194 1,657,213.72 1,792,686.6Labour productivity (GVA/total number of full-time equivalent employees) Euros (2008) 9,895.67 104,102.24 33,553.55 13,070.34Total number of full-time equivalent employees Number 3 237 46.53 45.58Number of available rooms Number 7 1200 141.87 149.53Dummy = 1 if the hotel belongs to a chain % establ. – – 61.27 48.9Dummy = 1 if hotel located in the capital of a province % establ. – – 42.77 49.6Dummy = 1 if the hotel has 3 stars % establ. – – 42.2 49.5Agea Years 0 79 13.22 14.06Leadershipb 0 1 0.59 0.23Peopleb 0.21 1 0.74 0.19Strategyb 0 1 0.67 0.25Partnerships and resourcesb 0.17 1 0.74 0.16Processes, products and servicesb 0.40 1 0.74 0.12Global qualityb 0.29 0.97 0.69 0.15

a The minimum value, set at 0, corresponds to hotels that began to operate in 2008.b Values of the items that compose each construct, expressed on a scale from 0 to 1.

Table 4Regression including all the quality variables in the model.

Variables Coefficients Vif

Number of rooms (ln) 0.208* (0.560) 3.933Total number of full-time equivalent employees (ln) −0.154* (−0.392) 4.336Dummy = 1 if the hotel belongs to a chain 0.095 (0.124) 2.177Dummy = 1 if hotel located in the capital of a province 0.145* (0.190) 1.310Dummy = 1 if the hotel has 3 stars −0.153* (−0.201) 1.665Age 0.003* (0.123) 1.169Leadership −0.066 (−0.039) 2.219People −0.025 (−0.013) 1.862Strategy 0.289** (0.190) 2.632Partnerships and resources −0.266 (−0.114) 2.365Processes, products and services 0.495* (0.160) 1.910

Adjusted R2 0.290F statistic (p value) 7.377 (0.000)Jarque–Bera test of normality in residuals (p value) 0.264 (0.876)White heteroscedasticity test (p value) 1.226 (0.171)Ramsey RESET test (p value) 1.142 (0.287)Number of observations used 173

Note: The table shows the values of the OLS estimated coefficients of the variables and their corresponding standardized coefficients in brackets.* The coefficients of the corresponding variables are significant at 5%.

** The coefficients of the corresponding variables are significant at 10%.The dependent variable in all of them is the logarithm of labour productivity, measured as the ratio between the GVA and the total number of full-time equivalent employees.Standard errors and covariance are White heteroscedasticity-consistent.

Table 5Regressions using the different quality variables.

Variables Eq. (1) Eq. (2) Eq. (3) Eq. (4) Eq. (5) Eq. (6)

Number of rooms (ln) 0.185* (0.497) 0.180* (0.483) 0.196* (0.527) 0.180* (0.483) 0.193* (0.520) 0.188* (0.505)Total number of full-time equivalent

employees (ln)−0.140* (−0.357) −0.134* (−0.340) −0.144* (−0.365) −0.129* (−0.329) −0.151* (−0.383) −0.145* (−0.368)

Dummy = 1 if the hotel belongs to a chain 0.127* (0.164) 0.139* (0.181) 0.073 (0.095) 0.143* (0.185) 0.135* (0.175) 0.109** (0.141)Dummy = 1 if hotel located in the capital of

a province0.135* (0.178) 0.136* (0.178) 0.142* (0.187) 0.139* (0.184) 0.137* (0.181) 0.135* (0.178)

Dummy = 1 if the hotel has 3 stars −0.174* (−0.229) −0.173* (−0.227) −0.153* (−0.201) −0.169* (−0.222) −0.170* (−0.223) −0.169* (−0.222)Age 0.002** (0.091) 0.002** (0.095) 0.002** (0.087) 0.002** (0.094) 0.003* (0.118) 0.002** (0.092)Leadership 0.138 (0.083) – – – – –People – 0.153 (0.081) – – – –Strategy – – 0.294* (0.193) – – –Partnerships and resources – – – 0.003 (0.001) – –Processes, products and services – – – – 0.523* (0.170) –Global quality – – – – – 0.364* (0.142)

Adjusted R2 0.262 0.263 0.285 0.256 0.285 0.274F statistic (p value) 9.743 (0.000) 9.762 (0.000) 10.786 (0.000) 9.466 (0.000) 10.775 (0.000) 10.261 (0.000)Jarque–Bera test of normality in residuals

(p value)0.624 (0.732) 0.585 (0.746) 0.396 (0.820) 0.727 (0.695) 0.416 (0.812) 0.420 (0.811)

White heteroscedasticity test (p value) 0.80 (0.766) 0.782 (0.788) 0.836 (0.717) 0.977 (0.510) 0.890 (0.639) 0.764 (0.811)Ramsey RESET test (p value) 1.811 (0.180) 0.799 (0.372) 2.626 (0.107) 0.633 (0.427) 0.991 (0.321) 2.039 (0.155)Number of observations used 173 173 173 173 173 173

Note: The table shows the values of the OLS estimated coefficients of the variables and their corresponding standardized coefficients in brackets.* The coefficients of the corresponding variables are significant at 5%.

** The coefficients of the corresponding variables are significant at 10%.The dependent variable in all of them is the logarithm of labour productivity, measured as the ratio between the GVA and the total number of full-time equivalent employees.Standard errors and covariance are White heteroscedasticity-consistent.

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could be related to the lack of significance in the estimationsdetected in this paper.

However, we found a positive and significant association betweenproductivity and the variables Strategy and Processes, Products andServices, and the variable Global Quality.

The variable Strategy had the largest effect of the three vari-ables. Given the complexity of interpreting the scale used tomeasure the quality variables, we considered that the best optionto determine their effects on productivity is to use their standard-ized coefficients or beta coefficients (see Table 5). Specifically, anincrease of one standard deviation in this variable leads to anincrease of 0.193 standard deviations in labour productivity. Theimportance of this criterion in the EFQM model is that its com-ponent sub-criteria (related to the needs and expectations of thestakeholders and business capacity, among others), are fully inline with the first fundamental concept of excellence: achievingbalanced results. Therefore, it can be stated that the relationshipbetween strategy and desired outcomes (including value creationand productivity, among others) is high.

The variable Processes, Products, and Services is essential to under-standing the importance of the EFQM model since, in the case ofthis enabler, the higher achievement of the objectives ensuresbetter performance results (see EFQM, 2010). The positive coef-ficient obtained for this variable (specifically, an increase ofone standard deviation in this variable leads to an increaseof 0.170 standard deviations in labour productivity) matchesexpectations. In fact, the reliability of this construct was alsothe highest. The items that compose it encompass importantaspects, such as the degree of implementation of business pro-cess management (crucial to the philosophy of TQM), the needsand expectations of the clients in order to introduce new services,and the degree of quality achieved in the main processes of thehotels.

The positive association between the variable Global Qual-ity and labour productivity is the most relevant finding ofthis study, as it confirms the validity of the EFQM ExcellenceModel regarding its positive effect on productivity in hotels.Specifically, when the variable Global Quality increases by onestandard deviation, productivity increases by 0.142 standarddeviations. This result implies that the adoption of TQM mod-els, or at least, the implementation of the principles underlyingthe concept of total quality, is an expansive strategy that hasa direct effect on the value added of hotels and their labourproductivity.

. Conclusions

The aim of this article was to determine the relationshipetween productivity and quality. Both variables are crucial to anyusiness and particularly to companies in the hotel sector, givenhe contextual and structural characteristics of the sector. As notedbove, given that there are very few empirical studies explicitlyddressing the relationship between the two variables, this paperttempts to fill this gap in the literature.

This study represents a contribution as it explores quality from strategic perspective (TQM) taking into account the EFQM Excel-ence Model as a foundation. The main finding of interest to

anagement is that some but not all dimensions of TQM practiceave a significant impact on hotel labour productivity levels. It isssumed that, as Samson and Terziovski (1999) pointed out, “TQMs substantially composed of ‘the right stuff’ for management”.

owever, the results obtained in this paper suggest that estab-

ishments should mainly concentrate on Strategy, and Processes,roducts and Services to improve hotel productivity. Not surpris-ngly, these constructs include most of the added-value generating

l of Hospitality Management 42 (2014) 165–173 171

activities within firms. This is not to say that the other enablersshould be ignored (authors like Bou-Llusar et al. (2009) considerthat it is highly recommended that managers take into account allits components as they are closely linked), but rather that furtherresearch is needed to analyze why Leadership and People in par-ticular — although they show the expected positive relationshipwith productivity — do not significantly explain the differencesin labour productivity across establishments. As noted above, thisfinding could be related to the fact that there may be little aware-ness among hotel managers of the importance of staff motivation,recognition programmes, and ongoing training as quality compo-nents. If so, managers would generally focus on excelling in someitems within these enablers, but would fail to pay enough atten-tion to other key factors such as those mentioned above, whichhave been identified for the hospitality industry by several authors(Sasse and Harwood-Richardson, 1996; Kilic and Okumus, 2005;Marchante and Ortega, 2012). Otherwise, this result may also bedue the fact that other items that are highly valued by man-agers within these constructs have low predictive power regardinghotel productivity. On this point, in-depth case studies would beuseful to analyze to what extent the lack of awareness amongmanagers of the importance of these TQM dimensions could berelated to the lack of significance in the estimations detected inthis paper.

For these reasons we consider that this analysis contributes toa deeper understanding of the business value and strategic roleof each of the enablers of the EFQM Model in the hotel indus-try, helping managers to allocate resources to those categoriesthat may have the greatest effect on performance. The analysisalso suggests areas for improvement that may in fact be impedingimprovements in productivity due to the managers being unawareof them. This may be the case of Leadership and People. Neverthe-less, further empirical research is also needed to investigate andaccount for the possible lag between the implementation of TQMand changes in productivity in establishments. It is possible thatin many firms the quality system may yet to have an impact onlabour productivity because it may have only been implementedfor a short time. In this sense, Schmidt and Finnigan (1992) consid-ered that TQM cannot produce consistent benefits until after thethird year of implementation. Unfortunately we have no data tocontrol this feature in the sample. However, given the wide arrayof hotels with different characteristics and presumably at differentstages of TQM practice in the sample, our results provide robustevidence on the impact of EFQM Model enablers on hotel labourproductivity.

Finally, we discuss some of the limitations of this work. The mainlimitation concerns the type of data used, since cross-sectional datado not allow us to study how productivity evolves over time inhotels. Furthermore, although the data used come from varioussources, in the case of the quality variable, the primary data usedcame from the questionnaire. In this sense, the accuracy of thedata depends on the respondents and the information to whichthey had access. Another limitation concerns the problem of mea-suring outputs and inputs and the suitability of using one type ofmeasure or another. First, the measurement of FTEE has certain lim-itations, as described in Section 3.3. In addition, the use of a partialproductivity measure, such as labour productivity, precludes theconsideration of other relevant factors. However, its use is fullyjustified in the case of the hotel industry, as explained in Section3.3.

Appendix.

Table A.1 shows the quality variables items included in the ques-tionnaire.

172 C.G. Benavides-Chicón, B. Ortega / International Journal of Hospitality Management 42 (2014) 165–173

Table A.1Quality variables items (the answers have been normalized in order to express all the items on a scale from 0 to 1).

Items N Mean Standard deviation

LeadershipLEAD1A Is there a Quality Committee composed of the Director, Deputy

Director (if any) and Area Managers?164 0.51 0.501

LEAD1B Is there a Quality or Improvement Plan coordinator? 162 0.72 0.452LEAD2 What is the degree of personal commitment of the director to

ensure continuous improvement of the establishment’smanagement system?

171 0.897 0.171

LEAD3 To what extent do the director and area managers participatein professional associations, conferences, and seminars, andgive and receive training courses dedicated to the issue ofquality and excellence?

173 0.562 0.29

LEAD4 To what extent is continuous improvement promoted byprizes, employee of the month awards, or other staffrecognition programmes?

173 0.267 0.342

PeoplePEOP1 How often do the staff receive quality training and how often

are they trained to match their skills to the needs of theestablishment?

173 0.551 0.30

PEOP2 To what extent are staff committed to and assumeresponsibility for quality, for example, by participating inimprovement teams, contributing ideas and suggestions inevents and meetings?

172 0.678 0.246

PEOP3 Does the management promote awareness of hygiene, safety,the environment, and social responsibility through meetingsor other media?

172 0.833 0.207

StrategySTR1 Has the establishment or chain defined their Strategic Plan? 171 0.74 0.438STR2A Is there a Corporate Social Responsibility policy? 168 0.75 0.434STR2B Does the company have a master plan that describes these

policies?160 0.39 0.490

STR3 To what extent is all the information that may affect its futuresystematically taken into account?

173 0.753 0.267

STR4 How often does the establishment or chain develop, revise andupdate its policy and strategy?

172 0.69 0.275

Partnerships and resourcesP&R1 How often does the establishment develop partnerships and

cooperate with other companies and organizations? Howdiverse are they?

173 0.723 0.258

P&R2 To what degree have economic and financial managementtools, such as a Balanced Scorecard, a wide range of indicators,formal accounting, risk management, investmentmanagement, and so on been developed?

170 0.705 0.268

P&R3 How effective is the use of resources and the conservation ofbuildings and infrastructure? Are criteria of sustainability andenvironmental protection applied?

171 0.734 0.229

P&R4 To what extent do the information and communicationsystems allow people from different areas to accessappropriate and accurate information to do their jobs?

172 0.796 0.181

Processes, products and servicesPRO1 To what degree is Process Management implemented? 172 0.727 0.299PRO2A Indicate how often you develop innovations in management,

change processes in management, staff policies, organizationalstructure, information technology, etc.

172 0.499 0.258

PRO2B Indicate how often you introduce new services based oncustomer needs and expectations

172 0.673 0.252

PRO3A Degree of Quality: Complete customer service process 172 0.727 0.246PRO3B Degree of Quality: Comfort 173 0.718 0.217PRO3C Degree of Quality: Functionality 173 0.76 0.236PRO3D Degree of Quality: Cleaning of rooms and common spaces 173 0.754 0.308PRO3E Degree of Quality: Purchases 172 0.744 0.224PRO3F Degree of Quality: Maintenance of facilities and equipment 173 0.772 0.19PRO3G Degree of Quality: Modernity 173 0.755 0.201PRO3H Degree of Quality: Food and drink 159 0.839 0.157PRO3I Degree of Quality: Leisure activities 120 0.701 0.241PRO3J Degree of Quality: Organization of events 137 0.79 0.219PRO3K Degree of Quality: Customers and employee safety 173 0.74 0.273PRO3L Degree of Quality: Location 170 0.892 0.172PRO3M Degree of Quality: Human Resources Management 171 0.792 0.166PRO3N Degree of Quality: Promotion, Marketing 172 0.654 0.225PRO3O Degree of Quality: Strategic Planning 170 0.732 0.178PRO3P Degree of Quality: Administration 172 0.756 0.202

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