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Procedia - Social and Behavioral Sciences 175 (2015) 58 – 65

Available online at www.sciencedirect.com

1877-0428 © 2015 The Authors. Published by Elsevier Ltd. This is an open access article under the CC BY-NC-ND license

(http://creativecommons.org/licenses/by-nc-nd/4.0/ ).

Peer-review under responsibility of I-DAS- Institute for the Dissemination of Arts and Science.

doi:10.1016/j.sbspro.2015.01.1174

ScienceDirect

International Conference on Strategic Innovative Marketing, IC-SIM 2014, September 1-4, 2014,Madrid, Spain

Obtaining the efficiency of Tourism Destination website based on

Data Envelopment Analysis

Aurkene Alzua-Sorzabala, Mikel Zurutuza

a, Fidel Rebón

a*, Jon Kepa Gerrikagoitia

a

aCICtourGUNE, Donostiako Parke Teknologikoa,Mikekeletegi Paselekua 71 – 3 Solairua. E-20009, Donostia-San Sebastián, Spain

Abstract

Internet is an important marketing channel for destinations as it makes a lot of information available to potential tourists; and at

the same time, it has allowed tourists and resident population to amplify their traditional channels of influence as opinion makers.

This fact has forced the destinations to invest many resources: time, effort and money; but very few of them have made a real

effort to quantify the efficiency of its communication channel. This article presents a novel system to verify the efficiency of the

channel, with this objective, the use of a nonparametric model is studied, Data Envelopment Analysis, based on a regional

destination and the study contains the comparative with the main cities of the region. For the correct operation of the system a

series of input and output variables are suggested in order to obtain the efficiency outcome. Finally, the result is presented in a

ranking list that allows the analysis of variables that should influence the destination with the aim of improving the

communication channel.© 2015 The Authors. Published by Elsevier Ltd.

Peer-review under responsibility of I-DAS- Institute for the Dissemination of Arts and Science.

Keywords: e-marketing, Tourism Destination Website, Data Envelopment Analysis, efficiency

1. Introduction

The real world is opening the way to a new and attractive digital and virtual world that is adapting quickly to the

needs of society (Amboage, 2010). We live in a society that responds to the phenomena of globalization and

* Corresponding author. Tel.: +34-943-010-885; fax: +34-943-010-846.

E-mail address: [email protected]

© 2015 The Authors. Published by Elsevier Ltd. This is an open access article under the CC BY-NC-ND license(http://creativecommons.org/licenses/by-nc-nd/4.0/ ).Peer-review under responsibility of I-DAS- Institute for the Dissemination of Arts and Science.

http://crossmark.crossref.org/dialog/?doi=10.1016/j.sbspro.2015.01.1174&domain=pdf

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59 Aurkene Alzua-Sorzabal et al. / Procedia - Social and Behavioral Sciences 175 (2015) 58 – 65

digitization and where the Information and Communication Technologies (ICT) represent one of the basic pillars in

the new knowledge economy (Castells, 2006).

This new paradigm facilitates the creation, the acquisition and the effective usage of the knowledge (codified and

tacit) by businesses, organizations, individuals and communities in order to achieve a further economic and social

development (Carl, Thomas, & others, 2001). Additionally, it has brought a new fast, direct and economic media of

communication (Pérez, Rodríguez, & Rubio, 2003) named Internet; in which the information exchange is

independent of their geographical location (Kahn et al., 1997).

It must be remarked that the digital media is getting a vast importance in the construction and dissemination of

the tourist image of a place due to the growth of the tourism market in Internet. One might add that the development

of mass communication media allows tourists to describe the image of a particular destination prior to their arrival,

implying that they visit touristic places in order to “reconfirm” the images they had in advance (Daniel, 1964). In

fact, a traveller never looks at the destination with a neutral perception of the territory, his/her perception is preceded

by a cluster of earlier images that determine the way a visitor relates to the visited place, providing at the same time,

the parameter of confrontation to assess the reality (Talavera, 2002).

For this reason, Internet constitutes an important e-marketing channel for institutions and the business sector of

the destinations, providing a vast volume of information available to potential tourists (Stienmetz & Fesenmaier,

2013). The spread of Internet as intercom environment has allowed the tourists and the resident population to

amplify their traditional channels of influence such as prescribing updated and detailed information to their cultural

heritage and tourism concerns (Naval & Perosanz, 2012; Valdés, del Valle, & Sustacha, 2011; Buhalis & Law, 2008;

Garces, Gorgemans, Martínez Sánchez, & Pérez Pérez, 2004). Moreover, the traditional chain is replaced by

webpages that allow access to all the information (Abou-Shouk, Lim, & Megicks, 2012), becoming transparent and

dynamic. This fact provides agencies an excellent way to boost tourist activity which plays a crucial role in the

development of the regions (Wanhill, Baum, Mudambi, & others, 1998).

Once known this and focusing the attention about Destination Management Organizations (DMOs), it can be

observed that they invest many resources: time, effort and money to have presence in Internet is contemplated, but

very few of them carry out a further study on management threads, improvement and maintenance of their web

presence (Y. Wang & Fesenmaier, 2006). Although new disciplines have emerged in the last decade; none of them

has stood out in the measurement of the efficiency of the web as a DMO e-marketing channel. This is the case of

Web Mining able to facilitate the discovery and analysis of useful information from websites (Agarwal, Bharat

Bhushan Dhall, 2010) but it has not deepened on the analysis of the efficiency as a communication channel. Thisdiscipline involves areas and technologies related to the management and recovery of information, artificial

intelligence, machine learning, natural language, network analysis and integration of information processing (X.

Wang, Abraham, & Smith, 2005).

In this context, this paper presents a case study on the efficiency evaluation by means of Data Envelopment

Analysis (DEA) based on the website of three main cities of the Basque Country’s region and the region itself. For

this purpose, firstly some previous piece of works are analysed, afterwards, the methodology will be described.

Then, the results will be discussed, and finally, the work’s main conclusions will be shown.

2.

Related work

The evaluation of the efficiency is an issue that has raised a great interest within the scientific community. This

interest is driven by the fact of living together in a highly competitive environment in which the first proposal toimprove the profitability is to optimize the efficient use of resources (Tavares, 2002; Seiford, 1997).

The publications in this field focus mainly on two methodologies: a) parametric methods that use a statistical

language based on little sensitive to fluctuations in data units, which estimates best and allows the measurement of

errors and, b) non-parametric methods that provide more accurate estimates of the relative efficiency levels and

objectives, identifying the sources of inefficiency as it shows the resources being overused. Additionally, these

require less assumption on the efficiency frontier.

As it can be seen, both methods have their advantages and disadvantages (Mortimer, 2002) but DEA has emerged

as the most used tool, this is often motivated by the flexibility, easy handling and the ease when submitting results

(Martínez, Gallego, Cárceles, & García, 2012).

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DEA is a non-parametric technique for measuring the relative efficiency of organizational units or Decision

Making Units (DMUs), in situations where the shape of the production function does not need to be specified a

priori and there are multiple inputs and outputs that allow incorporating environmental variables (Golany & Roll,

1989). This non-parametric techniques require three conditions to be imposed on the chosen units: a) units under

analysis must fulfil the hypothesis of homogeneity, they have to be sufficiently homogeneous so that they can be

compared, but sufficiently heterogeneous so that some information can be extracted; b) besides, they are in charge

of the management for the production of resources and c) It should constitute a group large enough not able to limitthe discriminatory capacity of analysis (Torrico, 2000).

The performance of each unit is represented by a vector of variables that includes all relevant information for the

evaluation. The selection of input and output using variables is one of the most important steps involving the DEA

because it has a direct impact on the valuation of the efficiency (de Mello, Gomes, Angulo Meza, & Estellita Lins,

2004; Meza, Mello, Gomes, & Fernandes, 2007; Wagner & Shimshak, 2007; González-Araya, Hernández, &

Espejo, 2013).

The set of efficient DMUs constitutes the efficiency frontier, measuring the efficiency of each unit as the distance

to itself. In this way, the different DMUs can be classified according to its efficiency. This technique and its variants

of improvement have been used primarily to analyse the efficiency in non-profit organizations (Cooper, Seiford, &

Tone, 2007), where efforts to quantify the benefits are particularly difficult to calculate; but is being deployed

successfully in other areas such as the financial sector (Bonilla, Casasús, Medal, & Sala, 1998). Within web context

it includes the work on e-commerce platforms (Hahn & Kauffman, 2004; Zheng, Jianbiao, & Xiaoyi, 2009) and, in

particular, based on the tourism sphere (Tierney, 2000; Feng, Morrison, & Ismail, 2004) the DEA has been bound

together to comparative studies based on the websites of the tourism offices. These studies obey a system where the

inputs are subjected to a regression analysis to discover the most significant variables (Law, Qi, & Buhalis, 2010).

3.

Methodology

One of the most important issues when using DEA analysis is the selection of input and output variables; due to

that, a variable selection criterion will be presented. Next, selected variables’ data acquiring process will be

described, and to conclude, software that facilitates the application of the DEA for destination management

efficiency’s measurement will be shown.

3.1.

Criterions for the variable selection

The selection of variables to perform DEA efficiency analysis is supported by previous relevant piece of works

(lo Storto, 2014; Bauernfeind & Mitsche, 2008; Emrouznejad, Parker, & Tavares, 2008) and stakeholders’

knowledge about this field.

In the first place input oriented variables are considered; in terms of understanding the web content, the number

of available languages is very important. Basque country has two official languages: Basque and Spanish. However,

also other communication languages have to be taken into account due to different reasons: a language which is fully

spread around the world, official languages of geographically nearby countries, or languages of countries detected as

potential visitors.

Accessibility and positioning of a website is another relevant point. A potential visitor could decide to stop

preparing the travel if the destination information is hard to find. If the official web address (URL) is not known bythe user search engines are used to find the desired information. Thus, a good positioning strategy in well-known

search engines and web directories is necessary to avoid losing potential visitors.

Related to this, taking the search engines ranking systems into account, a periodically updated website is a way of

demonstrating that it is active and deserves to be at the top. Furthermore, even if the website is well positioned but

the potential visitor cannot find updated information about the destination, the consequence may be that he/she will

not be able to properly prepare the travel and therefore the physical visit will not be performed.

From the visitor’s point of view, the cognitive cost of navigation through the website plays also a crucial role.

The structure of a website itself contains parts according to a certain level of technical complexity in general terms.

One way of measuring this complexity is with the website’s level of depth on its link structure, in plain words, the

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number of consecutive clicks a visitor must perform in order to achieve certain information. According to this

indicator, the number of total pages existing at the structure of the website is also relevant to quantify the cognitive

navigation cost.

Previously described issues have an economical cost which involves an annual expenditure. In conjunction with

expenditure, with the purpose of quantifying human resources implied on this task, the number of web maintainers is

also considered as an input variable.

In view of displaying the inputs variables, it is proceeded to indicate the output variable named visitor’s demandon website. This variable is quantified using two indicators measured with DWM system: a) number of visits per

year and b) average time on site per visit. Both input and output oriented variables are shown in Table 1.

Table 1. Chosen of variables

Type of variables (input/output) Name of variables Description of variables

Input LanguageNumber Number of languages on the websiteInput GlobalSeoRank Global SEO Rank

Input SpainSeoRank Spain SEO RankInput StructureDepth Distance from the main page to furthest page (folders)

Input MaintenanceCost Cost of Maintenance on the website: personal, hardware, licences…

Input MaintainersNumber Number of people annually maintain wen content

Input WebContentUpdate Number of updates on the website content in a year

Output Visits Number of visits on the website in a yearOutput TimeOnSite Average time of the visits in a year

3.2. Consolidated process for obtaining variables

Input variables will be different on their typology, but they must be able to value DMO’s website aspects such as

design, navigability difficulty or maintenance cost. Relative to navigability and design variables, different techniques

will be used in order to get their corresponding information. For example, design related variables will be obtained

using Web Mining techniques (Ocaña, Martos, & Vicente, 2002;Piazzi, Baggio, Neidhardt, & Werthner, 2011).

Finally, stakeholders’ expertise will be taken into account in order to gather information about technical or human

resources.

Additionally, existing literature about diverse Web Mining techniques applied to tourism destinations reveals that

acquired information about visitors’ behaviour allows customizing new visitors’ navigation scheme according to previously defined behaviour patterns. Recent studies apply a script, to the detriment of analysis performed with

server logs on which the website is located (Arbelaitz et al., 2013).

To obtain selected variables’ related data values, the authors developed a system called Destination Web Monitor

(DWM), in which the previously described Web Mining techniques are applied.

DWM is defined as “a system to measure, analyse, and model the behaviour of visitors in different virtual areas

in which a destination is promoted and with the objective of providing benchmarking ratios that facilitate strategic

surveillance and intelligent marketing policies” (Alzua-Sorzabal, Gerrikagoitia, & Rebón, 2014: 64).

Inside this monitor’s research lines is the development of a model which enables analysing the efficiency of

different virtual spaces of DMOs, such as marketing channels and contributes to obtaining an holistic view of

destination, since it facilitates extracting explicit and tacit knowledge from a network system that different websites

form on two fronts: relations between them and visitor’s interaction at each one of them.

To end, a survey to DMOs has provided some of resource related variable, such as maintaining expenditure ornumber of total maintainers of a website.

3.3. Selection of DEA software

Previous works have attempted to perform DEA analysis using different algorithms and software but main

studies use R program in order to implement DEA models (Pessanha, Marinho, da Costa Laurencel, & do Amaral,

2013). Pessanha’s study is related to data from the Brazilian electric power distribution utilities and shows the

flexibility R provides when manipulating data. The use of R related to DEA in the tourism field, and specifically to

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measure destination websites’ efficiency, has not been applied yet but there are tourism related studies that make use

of R like Roman’s work (Roman, Ibarguren, Gerrikagoitia, & Torres-Manzanera, 2012).

This paper aims to use the distribution approach with DEA models in R used in the Brazilian electric power

distribution example and apply it to tourism websites’ efficiency calculation. Efficiency measures selected have

been Constant Returns to Scale (CRS) and Variable Returns to Scale (VRS). CRS aims to reflect that outputs will

change by the same proportion when inputs are changed. By contrast, VRS reflects that outputs may increase,

decrease or keep constant when inputs are changed.Related to scale assumptions, it is also important to state that efficiency calculation can be performed in different

ways. DEA models’ development usually fall into two categories: input-oriented or output-oriented. First one, input-

oriented DEA determines how much the input variables of a website could contract if managed efficiently in order

to achieve the same output level, and vice-versa with output-oriented model.

3.4. Implementing DEA in R

Before executing DEA algorithm in R, some considerations have to be taken into account when assigning values

to variables: a) the value of the variables whose composition is inversely proportional to its efficiency will be

inversed to hold to this relation. The variables that fulfil this condition are: GlobalSeoRank, SpainSeoRank,

StructureDepth, MainteinanceCost and MainteinersNumber; b) it is not necessary a normalization or scaling for

input and output variables; DEA algorithm itself takes this into account; c) if there are VRS values equal to zero, asit is the case in the current experiment, implies that DMU’s amount isn’t enough to different study cases. As a

consequence, SE value (CRS/VRS) tends to infinite, and they cannot be plotted; d) regional and capital city level

websites’ names will be anonymized to preserve confidentiality of related DMOs’ information; e) Pessanha’s

algorithm works correctly only when one input variable and several output variables are used. Consequently, this

paper firstly introduces each input variable’s impact on output variables. Then, all the input variables are grouped to

see the overall impact on the efficiency and f) note that this work considers 4 different DMOs’ websites, so there are

4 DMUs in DEA analysis process.

4. Results and discussion

The analysis performed first has focused on checking efficiency of each input with respect to two outputs: Visits

and TimeOnSite.Figure 1 shows how LanguageNumber input is determinant in the efficiency of Visits variable for Capital1 (See

Fig 1a). Instead, in section b of the same figure (See Fig. 1b), GlobalSeoRank determines the maximum efficiency

for Capital1 and Capital2 according to Visits output. Furthermore, regarding to TimeOnSite, it is visible that this is

also determinant for Capital3. SpainSeoRank variable’s behaviour which is similar compared to GlobalSeoRank; but

it does not admit the same use for Capital1 (See Fig 1c).

Fig. 1 Efficacy based on Visits and TimeOnSite outputs according to (a) LanguageNumber; (b) GlobalSeoRank and (c) SpainSeoRank inputs

by website

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Once some input’s influence is analysed, LanguageNumber, GlobalSeoRank and SpainSeoRank; other variables

are studied: StructureDepth, MaintenanceCost y MaintainersNumber. Figure 2 it highlights their determination with

respect to the configuration of variable Visits for Region (See Fig. 2).

To conclude the impact analysis of the input’s efficiency, last input is studied: WebContentUpdate. Figure 3 (See

Fig. 3a) shows how Capital1’s efficiency is empowered by WebContentUpdate related to Visits output.

Table 2 summarizes the results of each input with respect to the output.

Table 2. Matrix based on website, input and output to the efficiency

WebSite Input Output WebSite Input Output

Capital1 LanguageNumber Visits Capital 3 GlobalSeoRank TimeOnSite

Capital1 GlobalSeoRank Visits Capital 3 SpainSeoRank TimeOnSite

Capital1 WebContentUpdate Visits Region StructureDepth Visits

Capital2 GlobalSeoRank Visits Region MaintenanceCost Visits

Capital2 SpainSeoRank Visits Region MaintainersNumber Visits

Next step is to analyse the overall efficiency behaviour of all the inputs together with respect to the outputs:Visits and TimeOnSite. Figure 3b shows which two websites are proficient: Capital1 and Region; both of them

conform the efficiency border, and Capital2 and Capital3 present sub-efficiency (See Fig. 3b). This fact is shown in

Table 3, where principal efficiency values of websites are shown.

Table 3. Summary of the efficiency values

Capital1 Capital2 Capital3 Region Min 1st Qu Median 3

er Qu. Max.

1.0000 0.1549136 0.3153876 1.0000 0.1549 0.2753 0.6176 1.0000 1.0000

Fig. 3. (a) Efficacy based on Visits and TimeOnSite outputs according to WebContentUpdate input by website; (b) Graph according to theefficiency border

Fig. 2. Efficacy based on Visits and TimeOnSite outputs according to (a) StructureDepth; (b) MaintenanceCost and (c) MaintainersNumber

inputs by website

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5.

Conclusions and future work

In this paper, different websites’ efficiency has been compared, based on DEA method according to study of

three main cities of the Basque Country’ region and the region itself. Additionally, for each website, two strong

variables have been highlighted, those which make output variables empower. Specifically, Region and Capital1

websites configure the efficiency border. First one is strongly related to following input variables: StructureDepth,

MaintenanceCost y MaintainersNumber; and second one, to these variables: LanguageNumber, GlobalSeoRank y

WebContentUpdate. As the strongest input variables are known, complementarily are given those input variables

which need to be improved in order to increase efficiency by means of the other input variables or the other output

variable. This because the DEA technique assumes a causal relationship between input and output, but it does not

perform any test about the existence of such a relationship. In short, it allows focusing efforts on those resources or

inputting variables of the system which are sub efficient.

Note that DEA method rapidly and easily allows evaluating a DMO’s web communication channel’s efficiency.

Such knowledge facilitates the improvement of e-marketing channel of institutions and business sector of

destinations. In the same way, it provides adequate strategic decisions that can strengthen communication channels,

and also, in the short run, it improves the competitiveness of the DMO with respect to their nearby competitors.

Furthermore, if DEA is regularly applied, impact of improvements applied to management of DMOs can be

analysed with the maximum expertise.

In future works with DEA, whose applicability are DMOs’ websites, more complex input variables will need to

be incorporated, such as: number of visitors related to certain virtual visitor profile or product or services conversion

rate. Both indicators might be significant and could establish a new way of approaching efficiency; as at the present

time the number of DMOs that market experiences on its web has increased. Also more output variables should be

added, such as: optimum navigation time, for the purpose of making visitors access to the web in certain time of the

day. Once these new indicators are obtained, the efficiency can be analysed on a certain timeframe. Later, this

efficiency could be compared to the profits made with product sales or services in the given timeframe. A valid

method would be lineal regression, and it would also allow forecasting efficiency and possible on the inputs

Moreover, in order to enrich the analysis, the study must be extended to other locations. This would allow

performing clustering actions with the obtained results. That would make another efficiency measure available; as

inputs’ behaviour could be compared between different geographical locations. As well, new algorithms should be

included or existing ones could be improved and developed with R software. For example, there are some

approaches using neural networks with public sector indicators, that seem to be very efficient technique. Even more,it could be very interesting to calculate efficiency using different techniques and then compare results between them

to increase the credibility of them and the correspondent decision make process DEA analysis arouses a great

interest in academic world, so it is usual to discover new publications in this field.

Acknowledgements

The authors would like to thank the managers of Basque Tourism Agency (BASQUETOUR) and Computer

Society of Basque Government (EJIE) for their excellent cooperation and Basque Government for its ETORTEK

program.

References

Abou-Shouk, M., Lim, W. M., & Megicks, P. (2012). Internet Adoption by Travel Agents: a Case of Egypt. International Journal of Tourism

Research. Wiley Online Library.Agarwal, Bharat Bhushan Dhall, S. (2010). Web Mining: Information and pattern discovery on the World Wide Web. International Journal of

Science, Technology & Management , (December), 8. Retrieved from http://ijstm.co.in/archive/bharat_1003.pdfAlzua-Sorzabal, A., Gerrikagoitia, J. K., & Rebón, F. (2014). Using MWD: A Business Intelligence System for Tourism Destination Web.

Management , 2(1), 62–72.

Amboage, E. S. (2010). Promoción de un destino turístico a través de las redes sociales: el caso de A Coruña en Facebook. Redmarka: Revista

Académica de Marketing Aplicado, (5), 11–52.Arbelaitz, O., Gurrutxaga, I., Lojo, A., Muguerza, J., Pérez, J. M., & Perona, I. (2013). Web usage and content mining to extract knowledge for

modelling the users of the Bidasoa Turismo website and to adapt it. Expert Systems with Applications, 40(18), 7478–7491.

8/17/2019 1-s2.0-S1877042815012343-main

8/8


Bauernfeind, U., & Mitsche, N. (2008). The application of the data envelopment analysis for tourism website evaluation. Information Technology& Tourism, 10(3), 245–257.

Bonilla, M., Casasús, T., Medal, A., & Sala, R. (1998). Un Análisis de la Eficiencia de los Puertos Españoles. VI Jornada de ASEPUMA, Ud

Valencia, Departamento de Economía Financiera Y Matemática: Santiago de Compostela, 9.

Buhalis, D., & Law, R. (2008). Progress in information technology and tourism management: 20 years on and 10 years after the Internet. Thestate of eTourism research. Tourism Management , 29(4), 609–623.

Carl, D., Thomas, A., & others. (2001). Korea and the Knowledge-based Economy Making the Transition: Making the Transition. OECDPublishing.

Castells, M. (2006). La sociedad red: una visión global . Alianza Editorial.Cooper, W. W., Seiford, L. M., & Tone, K. (2007). Data envelopment analysis: a comprehensive text with models, applications, references and

DEA-solver software. Springer.Daniel, B. (1964). The Image: A guide to pseudo-events in America. New York: Atheneum.

De Mello, J., Gomes, E. G., Angulo Meza, L., & Estellita Lins, M. P. (2004). Selección de variables para el incremento del poder dediscriminación de los modelos DEA. Revista de La Escuela de Perfeccionamiento En Investigación Operativa, 24, 40–52.

Emrouznejad, A., Parker, B. R., & Tavares, G. (2008). Evaluation of research in efficiency and productivity: A survey and analysis of the first 30

years of scholarly literature in DEA. Socio-Economic Planning Sciences, 42(3), 151–157.

Feng, R., Morrison, A. M., & Ismail, J. A. (2004). East versus West: A comparison of online destination marketing in China and the USA. Journal of Vacation Marketing , 10(1), 43–56.

Garces, S. A., Gorgemans, S., Martínez Sánchez, A., & Pérez Pérez, M. (2004). Implications of the Internet an analysis of the Aragonesehospitality industry, 2002. Tourism Management , 25(5), 603–613.

González-Araya, M. C., Hernández, G. C., & Espejo, L. G. A. (2013). Selección de variables en modelos DEA usando series de tiempo. In

Simpósio Brasileiro de Pesquisa Operacional A pesquisa Operacional na busca de eficiência nos serviços públicos e/ou privados (p. 10).

Retrieved from http://ws2.din.uem.br/~ademir/sbpo/sbpo2013/pdf/arq0384.pdf

Hahn, J., & Kauffman, R. J. (2004). A methodology for business value-driven website evaluation: a data envelopment analysis approach.University of Minnesota, Minneapolis, MN Working Paper , 4–25.

Kahn, R., Leiner, B. M., Cerf, V. G., Clark, D. D., Kleinrock, L., Lynch, D. C., … Wolff, S. (1997). The evolution of the Internet as a global

information system. International Information and Library Review, 29(2), 129–151.

Law, R., Qi, S., & Buhalis, D. (2010). Progress in tourism management: A review of website evaluation in tourism research. Tourism

Management , 31(3), 297–313.Lo Storto, C. (2014). Measuring Performance in the Public Administration Sector: An Analysis of Websites Efficiency. Advanced Science

Letters, 20(1), 272–275.Meza, L. A., Mello, J. C. C. B. S. de, Gomes, E. G., & Fernandes, A. J. S. (2007). Selecção de variáveis em DEA aplicada a uma análise do

mercado de energia eléctrica. Investigação Operacional , 27 (1), 21–36.

Mortimer, D. (2002). Competing methods for efficiency measurement (No. 136) (p. 27). Retrieved from

http://www.buseco.monash.edu.au/centres/che/pubs/wp136.pdf Naval, V. P., & Perosanz, J. J. I. (2012). La difusión del patrimonio a través de internet. El caso de Castilla y León. Cuadernos de Turismo, 30,

191–217.Pérez, E., Rodríguez, E., & Rubio, F. (2003). Turismo en la sociedad de la información: modelos emergentes. El entorno general. Artículo

accesible en el portal de la UOC, http://www. uoc. edu/dt/20352/20352_resum. html.

Pessanha, J. F. M., Marinho, A., da Costa Laurencel, L., & do Amaral, M. R. dos S. (2013). Implementing DEA models in the R program. In

iDAs. 11Th International Conference on Data Envelopment Analysis (p. 28). Retrieved from http://professorjf.webs.com/DEA 2013.pdfRoman, I., Ibarguren, I., Gerrikagoitia, J. K., & Torres-Manzanera, E. (2012). Representación de las Dinámicas de Precios Hoteleros mediante R.

In IV Jornadas de Usuarios de R.

Seiford, L. M. (1997). A bibliography for data envelopment analysis (1978-1996). Annals of Operations Research, 73, 393–438.

Stienmetz, J. L., & Fesenmaier, D. R. (2013). Online Channel Use and Destination Advertising Response. In Information and CommunicationTechnologies in Tourism 2013 (pp. 530–542). Springer.

Talavera, A. S. (2002). Desarrollos y conflictos en torno al turismo rural: claves y dilemas desde la antropología social. Turismo Rural:Tendéncias E Sustentabilidade. Santa Cruz Do Sul: EDUNISC , 117–140.

Tavares, G. (2002). A bibliography of data envelopment analysis (1978-2001). Rutcor Res. Report , 1–2.

Tierney, P. (2000). Internet-based evaluation of tourism web site effectiveness: Methodological issues and survey results. Journal of Travel

Research, 39(2), 212–219.Torrico, A. (2000). Técnicas cuantitativas para un análisis microeconómico de la eficiencia y la financiación dentro de un sistema público de

educación superior. Una aplicación para la toma de decisiones de la Universidad de Málaga. Tesis Doctoral. Universidad de Málaga.Valdés, L., del Valle, E., & Sustacha, I. (2011). El conocimiento del turismo en el ámbito regional. Cuadernos de Turismo, 27 (27), 931–952.

Wagner, J. M., & Shimshak, D. G. (2007). Stepwise selection of variables in data envelopment analysis: Procedures and managerial perspectives. European Journal of Operational Research, 180(1), 57–67.

Wang, X., Abraham, A., & Smith, K. A. (2005). Intelligent web traffic mining and analysis. Journal of Network and Computer Applications,28(2), 147–165.

Wang, Y., & Fesenmaier, D. R. (2006). Identifying the success factors of web-based marketing strategy: An investigation of convention andvisitors bureaus in the United States. Journal of Travel Research, 44(3), 239–249.

Wanhill, S., Baum, T., Mudambi, R., & others. (1998). The economic aspects of location marketing. Economic and Management Methods for

Tourism and Hospitality Research., 159–195.

Zheng, L., Jianbiao, L., & Xiaoyi, L. (2009). A study on website operation efficiency evaluation: Based on DEA model. In Management andService Science, 2009. MASS’09. International Conference on (pp. 1–6).

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