Expert Systems with Applications 38 (2011) 12974–12982
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Expert Systems with Applications journal homepage: www.elsevier.com/locate/eswa
Beyond Travel & Tourism competitiveness ranking using DEA, GST, ANN and Borda count Wei-Wen Wu International Trade Department, Ta Hwa Institute of Technology, 1, Ta Hwa Road, Chiung-Lin, Hsin-Chu 307, Taiwan
a r t i c l e
i n f o
Keywords: Ranking trustworthiness Data envelopment analysis Grey system theory Artificial neural network Borda count
a b s t r a c t Travel & Tourism competitiveness rankings are helpful when we wish to consider the issue of how to enrich the global competitiveness of tourism destinations. However, even if a ranking is obtained from a highly reputed institute, it is important to evaluate such a ranking’s trustworthiness, particularly with regard to the possibility that calculation errors and various forms of human bias may be embedded in the ranking result. It is especially a cause for concern that different ranking methods may generate dissimilar results. This paper therefore proposes a solution that involves applying a variety of objective weighting methods, including data envelopment analysis (DEA), grey system theory (GST), and artificial neural network (ANN), to produce sensible rankings, as well as employing Borda count methodology to merge these rankings. By using this method, policy makers and stakeholders can arrive at more prudent and informed decisions than would be the case when solely depending on the original rankings. Ó 2011 Elsevier Ltd. All rights reserved.
1. Introduction A successful tourism industry can contribute to regional economic development, as well as being a source of abundant foreign exchange earnings. Referring to the UNWTO Tourism Highlights 2008 Edition, tourism has experienced continued growth and diversification and has become one of the largest and fastest growing economic sectors in the world. In fact, modern tourism has acted as a key driver for socioeconomic progress. Because of the significance of the Travel & Tourism sector, many countries are currently striving to foster the global competitiveness of their own tourism destinations. When considering the issue of how to enrich the global competitiveness of tourism destinations for a country, a helpful source is the Travel & Tourism competitiveness ranking. This ranking is published by the Travel & Tourism Competitiveness Report, and was first published in 2007 by the World Economic Forum. The Travel & Tourism (T&T) competitiveness ranking is a global ranking based on the Travel & Tourism Competitiveness Index (TTCI). Referring to Blanke and Chiesa (2009), the TTCI aims to provide a comprehensive strategic tool for measuring the factors and policies that make it attractive to develop the T&T sector in different countries. Basically, the TTCI consists of three subindexes that assess T&T competitiveness. These three subindexes are (1) T&T regulatory framework, (2) T&T business environment and infrastructure, and (3) T&T human, cultural, and natural resources. Furthermore, rank order within the T&T competitiveness ranking is derived from an E-mail address:
[email protected] 0957-4174/$ - see front matter Ó 2011 Elsevier Ltd. All rights reserved. doi:10.1016/j.eswa.2011.04.096
overall score, arrived at by using the arithmetic mean method to aggregate the scores of these three subindexes. That is, the T&T competitiveness ranking considers these three subindexes to be equally important. In the real world, however, this is usually not true: in many cases, subindexes do not share the same importance. Hence, we need to employ objective weighting methods in order to arrive at more meaningful rankings. To obtain unbiased and objective rankings recommended approaches include: data envelopment analysis (DEA), grey system theory (GST), and artificial neural network (ANN). These approaches do not make use of any form of subjective weightings for generating rankings. In addition, we must be concerned about the previously noted fact that different methods may create diverse rankings. In order to integrate the diverse rankings, it is appropriate to make use of Borda count methodology. This method was first taken from the social theory of voting. Used in data fusion, it is a way to amalgamate two or more ranked lists into a single one (Nuray & Can, 2006). Even if a ranking is obtained from a highly reputed institute, it is important to evaluate such a ranking’s trustworthiness, particularly with regard to the possibility that calculation errors and various forms of human bias may be embedded in the ranking result. It is also a cause for concern that different ranking methods may generate dissimilar results. In undertaking to improve ranking trustworthiness, this paper proposes a solution that applies objective weighting methods, including DEA, GST, and ANN, to produce sensible rankings, as well as employing Borda count methodology to merge these rankings. In this way, policy makers and stakeholders can arrive at more prudent and informed decisions than would be the case if they relied solely on the original ranking.
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The remainder of this paper is organized as follows: in Section 2, a literature review related to DEA, GST, ANN, as well as Borda count is conducted; in Section 3, the proposed solution is discussed; and in Section 4, an empirical study is presented by way of illustration. Finally, based on the findings of this research, conclusions and implications for management are presented. 2. DEA, GST, ANN and Borda count 2.1. Data envelopment analysis Well-known as an efficiency measurement technique, the DEA is a mathematical method that measures the relative efficiency of decision making units (DMUs) with multiple inputs and outputs, without the need for predefined production functions or assumptions. The relative efficiency can be defined as the ratio of total weighted output to total weighted input. DEA is also useful in identifying the best performer as well as in providing actionable measures for improvement of performance (Donthu, Hershberger, & Osmonbekov, 2005). Moreover, Cook, Seiford, and Zhu (2004) remark that (1) DEA evaluates efficiency without the need to specify the relationships or trade-offs among the performance measures prior to the computation; (2) DEA identifies an efficient frontier along with efficiency scores for all DMUs; and (3) the type of efficiency evaluation using the DEA has been regarded as benchmarking. We may note, additionally, that DEA is a non-parametric technique for performance measurement and benchmarking, employing linear programming to determine the relative efficiencies of a set of homogeneous and comparable units. Referring to previous studies (Adler, Friedman, & S.Stern, 2002; Cook & Seiford, 2009; Golany & Roll, 1989), there are three popular DEA models, including the CCR model (Charnes, Cooper, & Rhodes, 1978), the BCC model (Banker, Charnes, & Cooper, 1984), and the super-efficiency model (Andersen & Petersen, 1993). The CCR model measures the overall efficiency for each unit, while the BCC model measures technical efficiency. Generally, CCR or BCC models produce an efficiency score (between zero and 1) for each unit. A unit with a score of 100% is relatively efficient, while any unit with a score of less than 100% is relatively inefficient. However, neither CCR nor BCC models allow for a ranking of the efficient units themselves (Golany & Roll, 1989). With regard to the issue of ranking efficient units, Andersen and Petersen (1993) first developed the super-efficiency model, a model which can, in fact, rank efficient units. This is because the super-efficiency model enables an extreme efficient unit to achieve an efficiency score greater than 100%. Hence, when conducting a ranking, the super-efficiency DEA model is suitable for use. 2.2. Grey system theory GST, developed by Deng (1982) in the 1980s, is a system which has the ability to effectively deal with incomplete and uncertain information (Lin & Yang, 2003). GST can be used to supplement the limitations of traditional statistic methods (Kung & Wen, 2007), and can deal with systems that have well-defined external boundaries but exhibit internal uncertainty or vagueness (Liu & Lin, 1998). Wen (2004) comments that GST focuses mainly on the model construction of grey relational analysis, for purposes of decision making under situations featuring no certainty, multidata input, discrete data, and insufficient data. The GST is thus an effective method for use in solving uncertainty problems having discrete data (Tseng, 2009), and it provides a multidisciplinary approach to analyzing and modeling problems for which the information is limited, incomplete and/or characterized by random uncertainty (Lu & Wevers, 2007).
There are three types of model construction, as follows: GM(1,1) model, GM(1,N) model, and GM(0,N) model. The GM(1,1) model is used for prediction, while the GM(1,N) model and the GM(0,N) model can be used for weighting. Referring to Xie, Yao, and Liu (2009), Hsu and Yan (2007), and Wen (2004), GM(1,N) model and GM(0,N) model can be defined as follows. The GM(1,N) model is a dynamic model that can illustrate the impacts of secondary factors on the main factor. It can be denoted as below: N X dx ð1Þ ð1Þ bi xi ðkÞ þ ax1 ¼ dt i¼2 ð1Þ
ð1Þ
where a and bi are determined coefficients; for the sequence ð0Þ ð0Þ ð0Þ ð0Þ ð0Þ xi ðkÞ; x1 ðkÞ is the major sequence, while x2 ðkÞ; x3 ðkÞ; x4 ðkÞ; . . . ; ð0Þ xN ðkÞ are the secondary sequences. The following are the analytical steps of the GM(1,N) model. a. Build the original sequences:
ð0Þ ð0Þ ð0Þ ð0Þ ð0Þ x1 ðkÞ ¼ x1 ð1Þ; x1 ð2Þ; x1 ð3Þ; . . . ; x1 ðnÞ 2 X; where k ¼ 1; 2; 3; . . . ; n
ð2Þ
b. Build the AGO sequences:
AGOxð0Þ ¼ xð1Þ " # 1 2 3 n X X X X ð0Þ ð0Þ ð0Þ ð0Þ ¼ x ðkÞ; x ðkÞ; x ðkÞ; . . . ; x ðkÞ k¼1
k¼1
k¼1
k¼1
ð3Þ
ð1Þ
ð1Þ
ð1Þ
ð1Þ
ð1Þ
xi ðkÞ ¼ xi ð1Þ; xi ð2Þ; xi ð3Þ; . . . ; xi ðnÞ 2 X; where k ¼ 1; 2; 3; . . . ; n c. Combine the AGO sequences with the major sequence: ð0Þ
ð1Þ
x1 ðkÞ þ az1 ðkÞ ¼
N X
ð1Þ
bi xi ðkÞ
ð4Þ
i¼2 ð1Þ
ð1Þ
ð1Þ
where az1 ðkÞ ¼ 0:5x1 ðkÞ þ 0:5x1 ðk 1Þ;
kP2
d. Transform the Eq. (4) into matrix form:
2
ð0Þ
x1 ð2Þ
3
2
ð1Þ
7 6 6 ð0Þ 6 x ð3Þ 7 6 zð1Þ ð3Þ 7 6 1 6 1 6 . 7 ¼ 6. 6 . 7 6. 4 . 5 4. ð0Þ ð1Þ x1 ðnÞ z1 ðnÞ
32
3 a 7 6b 7 ð1Þ ð1Þ x2 ð3Þ . . . xN ð3Þ 7 2 7 76 6 . 7 7 7 .. .. . 76 54 . 5 . . ð1Þ
ð1Þ
z1 ð2Þ x2 ð2Þ . . . xN ð2Þ
ð1Þ
ð1Þ
x2 ðnÞ . . . xN ðnÞ
ð5Þ
bN
By using the inverse and matrix method to find the values of bN, the relationship between the major sequence and the secondary sequences can be found (Wen, 2004). As for the GM(0,N) model, it is a special case of GM(1,N). Regarding the difference between GM(1,N) and GM(0,N), Wen (2004) remarks that GM(0,N) is based on the static state while the GM(1,N) model is based on the dynamic state. According to Hsu and Yan (2007), the GM(0,N) method is applicable to analysis of the static relationship between n variables and its analytical steps are the same as those of the GM(1,N) method; it can thus be denoted as below: ð1Þ
az1 ðkÞ ¼
N X
ð1Þ
ð1Þ
ð1Þ
ð1Þ
bj xj ðkÞ ¼ b2 x2 ðkÞ þ b3 x3 ðkÞ þ . . . þ bN xN ðkÞ
ð6Þ
j¼2
2.3. Artificial neural network The ANN is usually called the neural network (NN), and is a computational model that aims to emulate the biological neural
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system. Liu, Yuan, and Liao (2009) comment that (1) the ANN is a network with nodes, and is analogous to the network of biological neurons, where the nodes are interconnected to weighted links; (2) the performance of an ANN depends mainly on the weights of its connections; and (3) knowledge is represented and stored by the weights of the connections between the neurons. Moreover, Walczak (2001) remarks that (1) the NN is viewed as the universal approximator which can learn complex (non-linear) mappings between input and output variables; (2) many traditional statistical modeling techniques (e.g., linear and logistic regression or discriminant analysis) require specific distributions for valid applications, while NNs are non-parametric modeling techniques where the input data are not required to fit a specific distribution, and relationships between variables do not have to be pre-specified; and (3) NNs are able to handle noise in both the training set and testing sets. In addition, unlike the techniques of conventional serial computing systems, the ANN employs a parallel and distributed method of information processing so as to ensure robustness and fault tolerance (Eickhoff & Ruckert, 2007). The NN has been employed in many applications such as simulation, modeling, classification, and prediction. Two popular types of feed-forward networks are multilayer perceptron (MLP) and radial basis function (RBF) networks. As for the structure of a feedforward network, it consists of three kinds of layers: the input layer containing the predictors (independent variables), the hidden layer containing unobservable nodes or units, and the output layer containing the responses (target variables). Within a feed-forward network, information moves from the input layer to the output layer without any feedback loops. Referring to Bovis, Singh, Fieldsend, and Pinder (2000), there are some dissimilarities between the MLP and the RBF network, such as: (1) the MLP network can have one or more hidden layers, while the RBF network has only one hidden layer; (2) in the MLP network both the hidden and the output layer are nonlinear, while in the RBF network the hidden layer is nonlinear and the output layer is linear; and (3) the MLP network constructs global approximations, while the RBF network constructs local approximations. More importantly, the tasks of selecting a proper architecture and tweaking parameters are imperative for neural network analysis. Hu, Lee, Yen, and Tsai (2009) emphasize that the performance of the NN is greatly affected by parameters. Without appropriate tweaking parameters, the resulting performance may not necessarily prove to be favorable. In this regard, the SPSS statistical software provides ‘‘automatic architecture selection’’ with default parameter values in order to select the best architecture or parameter automatically. When the ‘‘automatic architecture selection’’ is used, the MLP network is built with one hidden layer, using the hyperbolic tangent function for the hidden layer, and using the softmax function for the output layer. As for the RBF network, the normalized radial basis function is used for the hidden layer while the identity function is used for the output layer. More details are provided in the documentation ‘‘SPSS Neural Networks’’.
Fig. 1. The Borda count matrix.
from a set of individual rankings to a combined ranking leading to the most relevant decision (Lumini & Nanni, 2006).Generally, Borda count method assigns zero points to a voter’s least preferred option, 1 point for the next option, and (n 1) points for the most preferred (where n is the number of alternatives). The Borda ranking is then determined by ordering the Borda scores (Bassett Jr. & Persky, 1999). Specifically, let the Borda count matrix B ¼ ½bij nn (Fig. 1) represent the election with a set of alternatives A = {Ai|i = 1, 2, . . . , n}, in which the the rows and columns of the matrix are labeled with the alternatives’ names; and the entry bij in the row labeled i and the column labeled j is the number of a result that is derived from ‘‘number of voters’’ times ‘‘the point value’’, and is acquired through comparing alternative Ai with alternative Aj by the voters. The row sum represents the Borda scores S = {Si|i = 1, 2, . . . , n} of alternatives, and then the Borda ranking is performed by ordering the Borda scores. 3. The proposed solution In order to enhance ranking trustworthiness, this paper suggests a hybrid solution based on multiple ranking methods. As shown in Fig 2, the proposed procedure is divided into four main phases. Phase 1: Selecting evaluation variables. Numerous groups have created various kinds of rankings. A ranking is a ranked list resulting from the comparisons of objects using specific evaluation methods with one criterion or multiple criteria. From the
Selecting evaluation variables
Clustering the DMUs
Multiple ranking methods
2.4. Borda count During the decision-making process, voting methods can be applied to facilitate decision-making (Laukkanen, Palander, & Kangas, 2004) by ranking and selecting alternatives. Borda count can be used as a way to order the alternatives according to their ranking sums (Lamboray, 2007). This is a simple summing of expressed voter preferences to achieve a social ranking. As a voting method, the Borda count was proposed by Jean-Charles de Borda (Borda, 1784), and represents an important step in the development of modern electoral systems (Reilly, 2002). Additionally, Borda count has been used as a data fusion technique for combining ranked lists (Kim, Min, & Han, 2006), and is defined as a mapping
DEA
GST
Incorporating ranked lists
Fig. 2. The proposed solution procedure.
ANN
W.-W. Wu / Expert Systems with Applications 38 (2011) 12974–12982
perspective of DEA, the ranking criteria can be viewed as the input variables while the overall score can be treated as the output variable. Moreover, when conducting a ranking over objects, these objects are regarded as DMUs. Phase 2: Clustering the DMUs. DEA is a useful nonparametric method that can not only measure the relative efficiency of a DMU but also indicate a reference target for an inefficient DMU (Estrada, Song, Kim, Namn, & Kang, 2009). Moreover, DEA evaluates the performance of DMUs through a transformation process of multiple inputs and outputs, employing a technique based on linear programming and without a need to introduce any subjective or economic parameters (Bouyssou, 1999). Especially noteworthy is the fact that DEA can be used as a clustering technique that divides DMUs into two classes: efficient or inefficient. Hence, the DEA can catalog ranked objects into two groups according to the overall score. Use of the super-efficiency DEA model is especially recommended because it allows an efficient DMU to achieve an efficiency score greater than 100%. Phase 3: Multiple ranking methods. This paper suggests using several ranking methods such as: the super-efficiency DEA model, the GM(0,N) model, and two popular ANN models (MLP and RBF). The super-efficiency DEA model can be used in ranking the performance of DMUs (Chen, 2004; Li, Jahanshahloo, & Khodabakhshi, 2007). Moreover, the GM(0,N) model has been used in applications such as the following: developing a business failure prediction model (Lin et al., 2009); examining policyholder’s characteristics and purchase decisions (Hsu & Yan, 2007); and evaluating the relationship between company attributes and its financial performance (Kung & Wen, 2007). MLP and RBF networks are two popular ANN models, and they are employed to handle such issues as the following: differentiating between chronic obstructive pulmonary and congestive heart failure diseases (Mehrabi, Maghsoudloo, Arabalibeik, Noormand, & Nozari, 2009); performing the critical heat flux prediction (Vaziri, Hojabri, Erfani, Monsefi, & Nilforooshan, 2007); and identifying the masses in digital mammograms (Bovis et al., 2000). Phase 4: Incorporating ranked lists. A number of ranked lists are created by the above ranking methods. However, different ranking methods may produce diverse ranked lists. In such cases we need to employ an effective technique for combining these ranked lists. This paper suggests using the Borda count. This is because the Borda count is a simple and easily comprehensible method applied widely in voting and data fusion (Nuray & Can, 2006). 4. Empirical study 4.1. Application of the proposed solution In this section, an empirical study based on the 2009 T&T competitiveness ranking is presented as an illustration of the application of the proposed solution. Phase 1 requires selecting evaluation variables. The three subindexes were used as the input variables while the overall score was treated as the output variable. In phase 2, the super-efficiency DEA model was used to divide DMUs into two classes: the efficient DMU (value = 1) or the inefficient DMU (value = 0), as shown in Table 2. Table 1 Normalized importance.
T&T regulatory framework T&T business environment and infrastructure T&T human, cultural, and natural resources
GM(0,N) model (%)
MLP network model (%)
RBF network model (%)
48.27 39.88
46.32 39.93
34.35 32.09
11.85
13.76
33.56
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In phase 3, multiple ranking methods were implemented. The data analysis of the super-efficiency DEA model was performed with the help of software called EMS (Efficiency Measurement System). As for the calculation tasks of the GM(0,N) model and the two ANN models (MLP and RBF), the Matlab Toolbox for Grey System Theory (Wen, Changchien, Ye, Won, & Lin, 2007) and the SPSS statistical software (with default parameter values) were implemented respectively. As a result, as exhibited in Table 1, the weights of three subindexes were calculated. Apart from the super-efficiency DEA model, which can directly compute the overall scores of DMUs, other ranking methods need to sum up the score of the subindex, multiplying the weight (normalized importance) of the subindex. Finally, phase 4 requires incorporating ranked lists. As shown in Table 2, the Top 5 were certainly not alike because different ranking methods generate dissimilar results. For example, the rank order of Top 5 was as follows: (1) United States, Tanzania, Brazil, Georgia, and Taiwan, according the super-efficiency DEA model; (2) Switzerland, Austria, Germany, Singapore, and France, using the GM(0,N) model; (3) Switzerland, Austria, Germany, France, and Singapore, employing the MLP network model; and (4) Switzerland, Austria, Germany, France, and Canada, applying the RBF network model. More importantly, the analytical result of using the Borda count indicates that the Top 5 were United States, Germany, Hong Kong, Austria, and Singapore. 4.2. Discussion The T&T competitiveness ranking is based on the TTCI, which consists of three subindexes, including 14 pillars of T&T competitiveness, such as policy rules and regulations, environmental sustainability, safety and security, and so on. The T&T competitiveness ranking provides valuable information for policy makers and stakeholders in their efforts to consider how to assess and enrich the global competitiveness of tourism destinations. This is because the T&T competitiveness ranking can be utilized not only as guidance for benchmarking analysis but also as a thermometer for reputation management. From the standpoint of benchmarking analysis, the T&T competitiveness ranking can be viewed as the result of global tourism competitiveness benchmarking. Two aspects of benchmarking may be considered: (1) conceptually, it is a management tool for achieving better performance and competitive advantage through comparison and emulation of the best practices (Anand & Kodali, 2008; Cook et al., 2004; Francis, Humphreys, & Fry, 2002); and (2) operationally, it is a process of continuous learning, achieved by comparing business processes and performance metrics, such as those related to productivity or quality, to the best practices, in order to gain improved performance (Camp, 1989; Fernandez, McCarthy, & Rakotobe-Joel, 2001). Benchmarking includes three basic steps: identifying the best performer; setting benchmarking goals; and implementation (Donthu et al., 2005). Identifying the best performer through performance comparison is the foremost part of the process of a benchmarking analysis (Watson, 1993). Thus, policy makers should identify and learn from the best performer, i.e. the one ranked as No. 1 in the T&T competitiveness ranking. From the perspective of reputation management, the T&T competitiveness ranking can be regarded as the indicator of a country’s reputation. In other words, a country is like a virtual tourism destination within the T&T competitiveness ranking. Once a country is ranked prominently, it may become famous and receive a great deal of attention from various policy makers or stakeholders. Barnett, Jermier, and Lafferty (2006) emphasize that reputation can be accumulated and transformed into reputational capital, and this is a valuable intangible asset that enhances an organization’s
Table 2 Travel & Tourism competitiveness ranking.
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competitive standing relative to others in its field. Moreover, reputation has such effects as: reducing stakeholder uncertainty about future organizational performance, strengthening competitive advantage, contributing to public confidence, and creating value by maximizing an organization’s ability to receive a premium for products or services (Vidaver-Cohen, 2007). Hence, policy makers should strive to improve the rank order for purposes of advancing a country’s reputation; achieving a better rank order stands for gaining reputation. However, although a ranking result is an important factor for benchmarking analysis and reputation management, we also need to be careful about the trustworthiness of a ranking. This is, as mentioned earlier, because ranking results can be affected by calculating mistakes, human bias, and the use of a specific ranking method. Thus, this paper suggests using multiple ranking methods based on objective weighting. The analytical results of the empirical study reveal some useful implications as below. Firstly, Table 2 exhibits the fact that different ranking methods generated interestingly dissimilar results. For example, it is obvious that there were 18 efficient DMUs among 133 countries according to the super-efficiency DEA model. Among these 18 efficient DMUs, United States was the most efficient, followed by Tanzania. However, we may notice some discrepancies resulting from the use of different ranking methods, such as: (1) Switzerland was ranked as No. 1 by the original score while it was assessed as an inefficient DMU by the DEA score; and (2) Taiwan was ranked as No. 5 by the DEA score while was ranked as No. 43 by the original score. These discrepancies are meaningful clues for those seeking to enhance T&T competitiveness. In the case of Switzerland, the discrepancy implies that Switzerland needs to improve its efficiency based on the productivity metric. In the case of Taiwan, the discrepancy means that Taiwan needs to advance effectiveness based on the quality metric. Secondly, it is important and beneficial to achieve a better rank order in the T&T competitiveness ranking by making efforts to increase both efficiency and effectiveness. Technically, it is essential to be proficient, not only in using multiple ranking methods, but also in understanding the details of the 14 pillars of T&T competitiveness. By so doing, policy makers may become more capable of interpreting their own performance, as well as augmenting their potential to leverage the rank order. Thirdly, each ranking method has its own advantage or merit. In order to merge the results reached by using multiple ranking methods, the Borda count is needed. The analytical result of using the Borda count indicates that the Top 5 were United States, Germany, Hong Kong, Austria, and Singapore. However, among the Top 5, only United States also excelled in DEA efficiency. This means that United States was really the best performer. Fourthly, comparing the ranking results using four ranking methods, we can see that (1) the super-efficiency DEA model produced a ranked list that was extremely dissimilar to those produced by other ranking methods; and (2) the GM(0,N) model and the two ANN models (MLP and RBF) generated diverse results but with only slight differences. This suggests that the super-efficiency DEA model is more relevant than others, when conducting a supplementary analysis related to the T&T competitiveness ranking. Additionally, for policy makers, the DEA approach can be further employed to improve efficiency, while the ANN approach can be utilized to predict performance related to T&T competitiveness.
5. Implications and conclusions In the face of the growing significance of the Travel & Tourism sector, many countries are currently motivated to cultivate the global competitiveness of their tourism destinations. In this regard,
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it is beneficial to learn from the T&T competitiveness ranking from the viewpoints of benchmarking analysis as well as reputation management. Nevertheless, we must not ignore the problem of ranking trustworthiness. Even if a ranked list is produced by a well-known or celebrated institution, we also need to be aware that a ranking result is easily affected by factors such as the following: calculating mistakes, subjective weighting, and manipulation of a specific ranking method. In this regard, the use of multiple ranking methods with objective weighting is called for. One feasible way to handle the issue of ranking trustworthiness is to make complementary analyses which go beyond the original ranking. This paper thus proposes a solution that applies objective weighting methods, including the super-efficiency DEA model, the GM(0,N) model, and the two ANN models (MLP and RBF) to create reasonable rankings, and finally employing the Borda count to merge these rankings. An empirical study based on the 2009 T&T competitiveness ranking was conducted as the application of the proposed solution. The results of the study indicate that (1) different ranking methods generate dissimilar results; and (2) the United States was really the best performer, because it excelled not only in the DEA efficiency but also in the Borda score. Moreover, several implications can be derived from the findings of the study, such as: (1) if a country had a good ranked order yet was assessed as an inefficient DMU, it may require some improvement in efficiency; (2) if a country did not have a good ranked order, yet was evaluated as an efficient DMU, it might need to advance its effectiveness; and (3) when performing an additional analysis related to the T&T competitiveness ranking, the super-efficiency DEA model is more relevant than other ranking methods, because it generates the most disparate ranked list. This paper is, we believe, successful in that it accomplishes the task of dealing with the issue of ranking trustworthiness, as well as demonstrating the usefulness of the proposed solution, a solution that enables policy makers and stakeholders to arrive at more prudent and informed decisions than would be possible relying merely on the original ranking. It contributes, as well, to the extension of practical applications, combining DEA, GST, ANN, and Borda count in the field of the Travel & Tourism competitiveness. The proposed solution is comprehensive and applicable to all policy makers facing problems of how to identify the best performer, as well as how to learn from a ranked list. However, this study has some inherent limitations. For instance, it is still unknown which ranking method is most robust or significant among GM(0,N) model, MLP model, and RBF model. This is an important issue calling for further research. References Adler, N., Friedman, L., & S.Stern, Z. (2002). Review of ranking methods in the data envelopment analysis context. European Journal of Operational Research, 140(2), 249–265. Anand, G., & Kodali, R. (2008). Benchmarking the benchmarking models. Benchmarking: An International Journal, 15(3), 257–291. Andersen, P., & Petersen, N. C. (1993). A procedure for Ranking Efficient Units in Data Envelopment Analysis. Management Science, 39(10), 1261–1264. Banker, R. D., Charnes, A., & Cooper, W. W. (1984). Some models for estimating technical and scale inefficiencies in data envelopment analysis. Management Science, 30(9), 1078–1092. Barnett, M., Jermier, J., & Lafferty, B. (2006). Corporate reputation: The definitional landscape. Corporate Reputation Review, 9(1), 26–38. Bassett, G. W., Jr., & Persky, J. (1999). Robust voting. Public Choice, 99(3–4), 299–310. Blanke, J., & Chiesa, T. (2009). The Travel & Tourism Competitiveness Report 2009. Geneva, Switzerland: World Economic Forum. Borda, J. C. (1784). Memoire sur les Elections au Scrutin. Paris: Histoire de I’ Academie Royale des Sciences. Bouyssou, D. (1999). Using DEA as a tool for MCDM: some remarks. Journal of the Operational Research Society, 50(9), 974–978. Bovis, K., Singh, S., Fieldsend, J., & Pinder, C. (2000). Identification of masses in digital mammograms with MLP and RBFnets. In Proceedings of the IEEE-INNSENNS International Joint Conference on Neural Networks, IJCNN 2000 1(1), pp. 342 – 347.
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