Research ArticleA Novel Evaluation Approach for Tourist Choice ofDestination Based on Grey Relation Analysis

Xudong Guo12 and Zilai Sun2

1Tourism and Historical Culture College Zhaoqing University Yingbin Avenue Zhaoqing 526061 China2Institute of Systems Engineering Dalian University of Technology No 2 Linggong Road Ganjingzi District Dalian 116024 China

Correspondence should be addressed to Zilai Sun sunzilaimaildluteducn

Received 16 June 2016 Revised 16 August 2016 Accepted 21 August 2016

Academic Editor Guo Chen

Copyright copy 2016 X Guo and Z Sun This is an open access article distributed under the Creative Commons Attribution Licensewhich permits unrestricted use distribution and reproduction in any medium provided the original work is properly cited

The decision-making process of choosing an ideal tourism destination is influenced by a number of psychological andnonpsychological variables Tourists need a method to quickly and easily select a suitable destination Driven by this practicaldecision issue a novel approach of tourist destination evaluation grey relation analysis (GRA) is developed and applied to theranking evaluation of Taiwan tourism destinations in China In the evaluating process we apply entropy to calculate the weight ofeach index which is a more objective method of calculating weights The results of the study indicate that although the same sizeis small and the distribution of data is unknown GRA can still be successfully used in evaluating tourist destinations In additionwe compare the GRA results with the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) and show that moreaccurate ranking results can be obtained

1 Introduction

With the development of social economy and the improve-ment of peoplersquos living standard more public holidaysfaster transportation (eg convenient air networks and high-speed railways) and greater openness toward the worldChinarsquos domestic and outbound demand for tourism havegrown unprecedentedly since the 2000s [1] According tothe National Bureau of Statistics of China the number ofdomestic tourists reached 3611 million in 2014 In additionthe number of overseas visitor arrivals was 1284983 millionand the number of Chinese outbound visitors was 1072755million in 2014 Along with the significant growth in tourismtourism expenditure and foreign exchange earnings in Chinahave also increased significantly In 2014 Chinarsquos earningsfrom domestic tourism reached RMB 303118 billion andthe foreign exchange earnings from international tourismreached USD 56913 million In tandem with the tremendousdevelopment of Chinarsquos tourism economy Chinarsquos govern-ment has committed to making tourism development amajor policy while China already has so much to offer to

international and domestic travelers its natural beauty a richculture great cuisine amazing technological advancementand friendly citizens According to the China NationalTourism Administration by 2014 China has 184 5A-classtourist scenic spots 862 5-star hotels and 600 travel agencies[2] as shown in Figure 1 Tourism has become one of thestrongest and largest industries and turned into the periodof buyerrsquos market in Chinarsquos process of development

One of the biggest issues facing the tourist today is howto evaluate and rank the tourism destination as well asthe need to understand what factors influence their choices[3] However the decision-making process of choosing anappropriate travel destination is a very complex process andchoosing a viable method for effective evaluation of tourismdestination is not an easy task There is a substantial bodyof literature discussing different research methods applied toevaluation and choice of tourism destinationThese methodsinclude (1)Multinomial LogitModel (MNL) [4ndash7] (2) BinaryRegression Model [8ndash11] (3) Multinomial Probit Model [12](4) Scobit Based Discrete-Continuous Choice Model [13](5) Structural Equation Model [14ndash19] (6) Meta-analysis

Hindawi Publishing CorporationScientific ProgrammingVolume 2016 Article ID 1812094 10 pageshttpdxdoiorg10115520161812094

2 Scientific Programming

Number of travel agencies (unit)

Number of star-rated hotels (unit)

Number of overseas visitor arrivals (10000 person-times)

Number of foreigners arrivals (10000 person-times)

Number of Chinese outbound visitors (10000 person-times)

2010 2011 2012 20132009147794644094

1140394163669421329942629294

Figure 1 Chinarsquos tourism development situation for nearly fiveyears

[20] (7) Technique for Order Preference by Similarity toIdeal Solution (TOPSIS) [21ndash24] (8) Principal ComponentAnalysis [25ndash30] and (9) Analytic Hierarchy Process (AHP)[31ndash37] Some of the methods may have already been knownto the public Other methods were simply borrowed from thedomain of industrial study and applied to tourism Some arestill in the embryonic stage Each of the above nine methodscan be independently applied to evaluate tourismdestinationHowever none of them is perfect Researchers can onlychoose a method to evaluate tourism destination that hasthe least amount of drawbacks for that studyrsquos particularsituation Some evaluation methods require the high qualitydata that is the data should obey a certain distribution or thedata volume should be large In addition the other evaluationmethods have strong subjectivity

Therefore a viable method for effective evaluation oftourism destination is aimed at providing solutions for issueswith multiple variables and targets However in studyingthe evaluation of tourism destination survey data are oftenincomplete or unclear and this paper therefore is boundby realistic limits confining itself to a situation where theamount of data is small and its significance indefinite Giventhese considerations grey relation analysis (GRA)may be thebest method for this study and will be used to explore practi-cal procedure of tourist destination evaluation and choice toidentify the various features that need to reflect preferencesof tourists for destinations in the context of 8 tourist spotsby utilizing the index system and data in literature [21] Onthe basis of such preliminary analysis this paper intends toachieve twofold aims The first applies entropy to find therelative weights of each index of the 8 tourist spots Thesecond is to rank the tourist spots by grey relation analysis(GRA) and compare the ranking results with literature [21]

The rest of this paper is organized as follows After a briefreview of relevant literature in Section 2 we present hierarchyof tourism destination selection and evaluation index systemin Section 3 The grey relation analysis evaluation modelfor tourism destinations selection is presented in Section 4

Section 5 illustrates the results obtained from the grey relationanalysis evaluation model and then we compare and analyzethese results with those obtained from the Technique forOrder Preference by Similarity to Ideal Solution (TOPSIS)evaluation method used by literature [21] Conclusions aredrawn in Section 6

2 Literature Review

Evaluation and choice of tourism destination is one of theimportant topics frequently investigated by scholars [38]These evaluation and choice studies have been linked tothe issues of decision rules decision-making processes andchoice factors [5 39ndash45] Hsu et al [21] proposed a 4-levelAHP model consisting of 22 attributes on the 4th level andtested it using data collected from tourists visiting Taiwanto establish the relative importance of preselected factors(criteria) In this study visiting friendsrelatives and personalsafety appear to be the 2 most important factors for inboundtourists to Taiwan price is the least important and Taipei 101is the first priority for travelers Huybers [46] applied discretechoice modeling technique to study the short-break destina-tion choices of prospective Sydney residentsThe study resultsshow that a destinationrsquos attractiveness to prospective short-break visitors from Sydney is enhanced to different degreesby lower prices being moderately busy having a moderatelevel of night life being visited during springsummer beingaccessible within two hoursrsquo travel time and offering amix of natural and culturalheritage attractions Prayag andRyan [14] used confirmatory factor analysis to explore therelationships among four constructs namely destinationimage place attachment personal involvement and visitorsrsquosatisfaction as antecedents of loyalty The research findingsindicate that destination image personal involvement andplace attachment are antecedents of visitorsrsquo loyalty but thisrelationship is mediated by satisfaction levels

Dellaert et al [47] pointed out that touristsrsquo choicesare complex multifaceted decisions where the choices fordifferent elements are interrelated and evolved in a decisionprocess over timeThe decision-making process is influencedby a number of psychological (internal) and nonpsycho-logical (external) variables Sirakaya and Woodside [48]provided a comprehensive qualitative review of the touristdecision-making literature and integrated the main con-ceptual and empirical work that has been reported in thetourism literature Lam andHsu [49] argued that the complexdecision-making process leading to the choice of a traveldestination had not been well researched Past studies relatedto destination choice mainly focus on identifying importantattributes affecting destination choice professional judgmentand factor analysis are the main methods [50ndash53] RichardsandWilson [54] in their work focused on independent youthand student travel and used factor analysis to help identifyfour main motivating factors including experience seekingrelaxation seeking and sociability and contributing to thedestination Tomis et al [55] studied the factors considered

Scientific Programming 3

The choice of the destination

Internal force External force

Psychologicalfactor

Physicalfactor

Socialinteraction

Seekingexploration

Tangiblefactor

Intangiblefactor

(i) Transportation facilities(ii) Friendliness of people

(iii) Quality amp variety of food(iv) Accommodation facilities(v) Personal safety

(vi) Price(vii) Culture amp historical

resources(viii) Good shopping

(ix) Environmental safetyamp quality

(i) Rest amprelaxation

(ii) Medicaltreatment

(iii) Health ampfitness

(i) Noveltyseeking

(ii) Cultureexploration

(iii) Adventureseeking

(iv) Enjoying nightlife amp shopping

(i) Visitingfriends andor relatives

(ii) Meetingnew people

(i) Destinationimage

(ii) Benefitsexpectations

(i) Escape(ii) Self-

actualization

Figure 2 The hierarchy of destination selection (source Hsu et al [21])

by young people when choosing a city destination in Europeand identified seven major factors partying and having funaccessibility to destination info easy and cheap travel orga-nization outdoor activities socializing with the local peoplegood shopping places and exploring the unknown Furtheranalysis showed that there are significant differences amongseveralmotivation factors when it comes toDanish and inter-national students Pike and Page [30] provided a comprehen-sive narrative analysis of the destinationmarketing literature

The above studies mainly applied qualitative and quanti-tative methods to analyze the factors influencing the choiceof tourism destinations They pointed out that the decision-making process leading to the final choice of a travel destina-tion is very complexHowever these studies failed to considerthe preferences of tourists in the decision-making processandutilizemuchmore complex decision-making approachesMotivated by this we develop a new evaluationmodel to eval-uate and rank the tourism destinations considering personalpreferences Thus we attempt to evaluate the preferencesof tourists for destinations and recognize the key aspectsinfluencing the touristsrsquo choice of destination Finally Guoet al [56] proposed a novel similarity-based algorithm forclassifying tourism scenic spots and this evaluation methodnot only has some similarities to our own but also hassome key differencesThey studied the problem of classifyingdifferent tourism scenic spots into different levels throughcalculating the relative similarity of each scenic spot to thebest and worst scenic spots but in our model we do not

consider the worst scenic spots which make the evaluationprocess simple and easy to conduct

3 The Hierarchy of TourismDestinations Selection

According to Hsu et al [21] the main factors affectingtourism destination choice are grouped into two categoriesnamely internal and external factors The push motivationrelates to internal forces which consist of 4 criteria (psycho-logical physical social interaction and seekingexploration)and are further divided into 11 subcriteria The pull moti-vation relates to external forces which consist of 2 criteria(tangible and intangible) with tangible criteria including 9subcriteria and intangible criteria including 2 subcriteria (seeFigure 2)

4 The Grey Relation Analysis Model ofTourism Destination Choice

Grey relation analysis (GRA) is a method that can be used indecision-making in situations where there are many criteriaby ordering them as to relational grade It is especially pre-ferred in ordering the alternatives in situations in which thesample is small and sample distribution is not known GRA ispart of the grey system theory [57] Gray system theory wasfirst introduced by Ju-Long [58] The fundamental definitionof ldquogreynessrdquo is information being incomplete or unknown

4 Scientific Programming

thus an element from an incomplete message is considered tobe of ldquogreyrdquo element A ldquogrey relationrdquo refers to the measure-ment of changing relations between two systems or elementsthat occur in a system over timeThe analysis method whichmeasures the relations between elements based on the degreeof similarity or difference of development trends among theseelements is called ldquogrey relation analysisrdquo More preciselyduring the process of system development should the trendof change between two elements be consistent it then enjoysa higher grade of synchronized change and can be consideredas having a greater grade of relation otherwise the gradeof relation is smaller [59] It has been widely applied in avariety of fields such as financial performance evaluation [60ndash64] motor vehicular energy consumption [65 66] and greensupplier selection [67] As far as we know GRA has not beenapplied in evaluation and choice of tourism destination Webelieve we are the first to utilize the GRA to evaluate and rankthe tourism destinations

Personal preferences like motivations may be bothintrinsic reflecting individual likes and dislikes and extrin-sic or socially conditioned Pearce [68] stated that prefer-ences are more specific than motivations and are revealed bywhere travelers go and what travelers do However touristsrsquopreferences are psychological behaviors which are not easyto accurately measure having the feature of grey and fuzzyChoosing a tourism destination is a grey system evaluationprocess therefore the grey relation analysis is a more suitablemethod to evaluate it The evaluation procedure of this studyconsists of several stepsThedetailed descriptions of each stepare given in the following subsections

41 Problem Definition Assume that there are 119899 tourismdestinations 119879

1 1198792 119879

119899 which are to be evaluated in

terms of 119898 evaluation criteria 1198621 1198622 119862

119898 and relative

weights are denoted by 120596119894119895

(sum119898

119895=1120596119894119895

= 1) The objective ofthe decision-making is to rank these tourism destinationsAccording to the assumption we can get the original indexdata matrix

119877 = (119903119894119895)119899times119898

=

[

[

[

[

[

[

[

11990311

11990312

sdot sdot sdot 1199031119898

11990321

11990322

sdot sdot sdot 1199032119898

1199031198991

1199031198992

sdot sdot sdot 119903119899119898

]

]

]

]

]

]

]

(1)

where 119903119894119895is the observed value of object 119879

119894in terms of criteria

119862119895

(119894 = 1 2 119899 119895 = 1 2 119898)

Definition 1 The best tourism destination is the referentialsequence made up of the best tourism destination indexesamong all the alternative tourism destinations that is

1198770

= (11990301

11990302

1199030119895

) (2)

where 1199030119895

= Optimum(119903119894119895) (119894 = 1 2 119899 119895 = 1 2 119898)

So we can construct initial matrix 1198771

1198771

= (119903119894119895)119899times119898

=

[

[

[

[

[

[

[

[

[

[

11990301

11990302

sdot sdot sdot 1199030119898

11990311

11990312

sdot sdot sdot 1199031119898

11990321

11990322

sdot sdot sdot 1199032119898

sdot sdot sdot

1199031198991

1199031198992

sdot sdot sdot 119903119899119898

]

]

]

]

]

]

]

]

]

]

(3)

42 Normalization of Index Values In order to make com-parison between the various indicators we normalized eachof the index values in accordancewith the following formulasFor larger-is-better transformation 119903

119894119895can be transformed to

119909119894119895 The formula is defined as

119883119894119895

=

119903119894119895

minus min119895119903119894119895

max119895119903119894119895

minus min119895119903119894119895

(4)

For smaller-is-better transformation the formula totransform 119903

119894119895to 119909119894119895is

119883119894119895

=

max119895119903119894119895

minus 119903119894119895

max119895119903119894119895

minus min119895119903119894119895

(5)

The normalized values form a new matrix as shown in

119883 = (119909119894119895)119899times119898

=

[

[

[

[

[

[

[

[

[

[

11990901

11990902

sdot sdot sdot 1199090119898

11990911

11990912

sdot sdot sdot 1199091119898

11990921

11990922

sdot sdot sdot 1199092119898

sdot sdot sdot

1199091198991

1199091198992

sdot sdot sdot 119909119899119898

]

]

]

]

]

]

]

]

]

]

(6)

43 Calculating Grey Relation Coefficient According to Ju-Long [69] let 119883

0= (11990901

11990902

1199090119898

) as referential sequenceand 119883

119894= (1199091198941

1199091198942

119909119894119898

) as comparative sequence Then1199090119895and 119909119894119895would be the values of 119909

0and 119909119894at optimum index

119895 If 120585(1199090119895

119909119894119895) are real numbers then they can be defined as

120585 (1199090 119909119894) =

1

119898

119898

sum

119895=1

120585 (1199090119895

119909119894119895) (7)

The mean of 120585(1199090119895

119909119894119895) needs to meet the four axioms of grey

relation

Axiom 1 (norm interval) Consider

0 lt 120585(1199090119895

119909119894119895) le 1

120585(1199090119895

119909119894119895) = 1 harr 119909

0119895= 119909119894119895(this is called complete

relation)120585(1199090119895

119909119894119895) = 0 harr 119909

0isin 120593 119909

119894isin 120593 (this is called

complete nonrelation) where 120593 is an empty set

Axiom 2 (duality symmetric) Consider

1199090 119909119894isin 119883

120585(1199090119895

119909119894119895) = 120585(119909

119894119895 1199090119895

) harr 119883 = (1199090 119909119894)

Scientific Programming 5

Axiom 3 (wholeness) Consider

119909119895 119909119894isin 119883

120585(1199090119895

119909119894119895) = 120585(119909

119894119895 1199090119895

) this case almost alwayshappened

Axiom 4 (approachability) Consider

120585(1199090119895

119909119894119895) get larger along with |119909

0119895minus 119909119894119895| getting

smaller

If the abovementioned four axioms could all be satisfied120585(1199090 119909119894) is designed as the grade of grey relation in 119909

119894corre-

spondence to1199090 and 120585(119909

0119895 119909119894119895) is said to be the grey relational

coefficient of 119909119894119895to 1199090119895at optimum index 119895 Professor Ju-Long

[58] proposes a mathematical equation that will satisfy thesefour axioms of grey relation which is as follows

120585 (1198830119895

119883119894119895)

=

min119894min119895

100381610038161003816100381610038161198830119895

minus 119883119894119895

10038161003816100381610038161003816+ 120588max

119894max119895

100381610038161003816100381610038161198830119895

minus 119883119894119895

10038161003816100381610038161003816

100381610038161003816100381610038161198830119895

minus 119883119894119895

10038161003816100381610038161003816+ 120588max

119894max119895

100381610038161003816100381610038161198830119895

minus 119883119894119895

10038161003816100381610038161003816

(8)

where 120588 isin [0 1] is distinguished coefficient the function ofwhich is to reduce its numerical value bymax

119894max119895|1198830119895

minus119883119894119895|

increasing so as to affect its loss-authenticity and to heightenthe significance of difference among relational coefficientsand 120588 = 05 is generally used Relational coefficients120585(1198830119895

119883119894119895) matrix can be obtained through formula (8) As

a matter of convenience we use 120585119894119895instead of 120585(119883

0119895 119883119894119895) in

the following pages

119864 = (120585119894119895)119899119898

=

[

[

[

[

[

[

[

12058511

12058512

sdot sdot sdot 1205851119898

12058521

12058522

sdot sdot sdot 1205852119898

1205851198991

1205851198992

sdot sdot sdot 120585119899119898

]

]

]

]

]

]

]

(119894 = 1 2 119899 119895 = 1 2 119898)

(9)

44 Determining the Index Weights Based on Entropy Mak-ing normalization processing for formula (9) 120585

119894119895can be

transformed to 120583119894119895 and a new matrix (9) can be obtained as

follows

1198640

= (120583119894119895)119899119898

=

[

[

[

[

[

[

[

12058311

12058312

sdot sdot sdot 1205831119898

12058321

12058322

sdot sdot sdot 1205832119898

1205831198991

1205831198992

sdot sdot sdot 120583119899119898

]

]

]

]

]

]

]

(119894 = 1 2 119899 119895 = 1 2 119898)

(10)

where 0 le 120583119894119895

le 1 and sum119899

119894=1120583119894119895

= 1 then the informationentropy is

119867 (119895) = minus

119899

sum

119894=1

120583119894119895

sdot log 120583119894119895 (11)

Let

]119894119895

= 1 +

sum119899

119894=1120583119894119895

sdot log 120583119894119895

log 119899

(12)

Then we obtain the weights of indexes in terms offormula (12)

120596119894119895

=

]119894119895

sum119898

119895=1]119894119895

(0 le 120596119894119895

le 1

119898

sum

119895=1

120596119894119895

= 1) (13)

45 Calculating Grey Relational Grade After obtaining thegrey relational coefficient we normally take the weightedvalue of the grey relational coefficientmultiplied byweightingvalue as the grey relational grade The grey relational grade isdefined as follows [70]

119861 = 120596119894sdot (120585119894119895)

119879

119899119898= 120596119894sdot

[

[

[

[

[

[

[

12058511

12058521

sdot sdot sdot 1205851198991

12058512

12058522

sdot sdot sdot 1205851198992

1205851119898

1205851198992

sdot sdot sdot 120585119899119898

]

]

]

]

]

]

]

= (1198871 1198872 119887

119899)

(14)

where 119887119899is grey relational grade of the tourism destination119879

119899

which indicates the degree of influence that the comparativesequence could exert over the referential sequenceThereforeif a particular comparative sequence is better than the othercomparative sequences to the referential sequence then thegrey relational grade for that comparative sequence andreferential sequence will be higher than other grey relationalgrades Finally all tourismdestinations are evaluated synthet-ically according to the value of 119887

119899 where when 119887

119899is larger the

tourism destination 119879119899is better

5 A Numerical Example

In this section we use the proposed model to evaluate 8tourism destinations in Taiwan Province of China (includingTaipei 101 National Museum Sun Moon Lake AlishanYushan National Park Taroko National Park Love River andKenting National Park) Twenty-two (22) indexes identifiedfrom literature [21] as shown in Table 1 are used in the study(ldquoardquo is the best preference out of the 8 destinations)

51 The Relational Grade to Ideal Destination According tothe data from Table 1 we identify the optimum column as(716 716 759 640 715 854 745 804 787 673 856 860695 885 781 720 736 605 847 804 794 767) and incombination with Table 1 an initial matrix is obtained asfollows

6 Scientific Programming

Table 1 Overall preference measures of destinations

Criteria Taipei 101 National Museum Sun Moon Lake Alishan Yushan Park Taroko Park Love River Kenting ParkEscape 623 542 716a 680 594 708 599 692Self-actualization 716a 689 630 609 522 641 566 645Restrelaxation 538 505 759a 706 613 705 636 730Medical treatment 640a 441 430 434 302 409 538 473Health and fitness 505 410 657 698 634 715a 517 657Visiting friendrelative 796 554 854a 701 588 684 686 742Meeting new people 745a 555 598 555 437 561 576 680Novelty seeking 804a 608 535 506 416 522 561 629Culture exploration 627 787a 654 641 542 628 566 645Adventure seeking 397 343 596 656 630 667 494 673a

Enjoying night life 856a 553 449 395 343 441 645 580Transportation facilities 860a 803 620 528 404 545 678 670Friendliness of people 648 652 668 684 595 693 633 695a

Qualityvariety of food 885a 641 570 558 428 580 716 688Accommodation 781a 726 667 572 431 663 736 752Environment safety 610 638 698 681 607 720a 624 673Personal safety 736a 729 580 502 455 538 580 659Price 439 512 555 564 588 605a 530 530Culture and historicalresources 611 847a 678 672 622 663 560 649

Good shopping 804a 599 489 479 363 540 613 611Destination image 778 727 794a 706 653 692 638 714Benefits expectations 767a 680 541 541 460 532 648 667

1198771

=

[

[

[

[

[

[

[

[

[

[

[

[

[

[

[

[

[

[

[

[

716 716 759 640 715 854 745 804 787 673 856 860 695 885 781 720 736 605 847 804 794 767

623 716 538 640 505 796 745 804 627 397 856 860 648 885 781 610 736 439 611 804 778 767

542 689 505 441 410 554 555 608 787 343 553 803 652 641 726 638 729 512 847 599 727 680

716 630 759 430 657 854 598 535 654 596 449 620 668 570 667 698 580 555 678 489 794 541

680 609 706 434 698 701 555 506 641 656 395 528 684 558 572 681 502 564 672 479 706 541

594 522 613 302 634 588 437 416 542 630 343 404 595 428 431 607 455 588 622 363 653 460

708 641 705 409 715 684 561 522 628 667 441 545 693 580 663 720 538 605 663 540 692 532

599 566 636 538 517 686 576 561 566 494 645 678 633 716 736 624 580 530 560 613 638 648

692 645 730 473 657 742 680 629 645 673 580 670 695 688 752 673 659 530 649 611 714 667

]

]

]

]

]

]

]

]

]

]

]

]

]

]

]

]

]

]

]

]

(15)

Each of the index values is normalized according toformulas (4) and (5) and then a new matrix is obtained asfollows

119883

=

[

[

[

[

[

[

[

[

[

[

[

[

[

[

[

[

[

[

[

[

100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100

047 100 013 100 031 081 100 100 035 016 100 100 053 100 100 003 100 000 018 100 090 100

000 086 000 041 000 000 038 049 100 000 041 088 057 047 084 027 098 044 100 054 057 072

100 056 100 038 081 100 052 031 046 077 021 047 073 031 067 081 044 070 041 029 100 026

079 045 079 039 094 049 038 023 040 095 010 027 089 028 040 065 017 075 039 026 044 026

030 000 043 000 073 011 000 000 000 087 000 000 000 000 000 000 000 090 022 000 010 000

095 061 079 032 100 043 040 027 035 098 019 031 098 033 066 100 030 100 036 040 035 023

033 023 052 070 035 044 045 037 010 046 059 060 038 063 087 015 044 055 000 057 000 061

086 063 089 051 081 063 079 055 042 100 046 058 100 057 092 058 073 055 031 056 049 067

]

]

]

]

]

]

]

]

]

]

]

]

]

]

]

]

]

]

]

]

(16)

Scientific Programming 7

According to formula (8) that is 120585119894119895

= (0 + 05 times

1)(|1198830119895

minus 119883119894119895| + 05 times 1) we obtain the matrix of grey

relational coefficients as follows

120585119894119895

=

[

[

[

[

[

[

[

[

[

[

[

[

[

[

[

[

[

[

048 100 036 100 042 072 100 100 043 037 100 100 052 100 100 034 100 033 038 100 083 100

033 078 033 046 033 033 045 050 100 033 046 080 054 048 076 041 095 047 100 052 054 064

100 053 100 045 072 100 051 042 048 068 039 049 065 042 061 072 047 062 046 041 100 040

071 048 071 045 090 050 045 039 046 091 036 041 082 041 046 059 038 067 045 040 047 040

042 033 047 033 065 036 033 033 033 079 033 033 033 033 033 033 033 083 039 033 036 033

092 056 070 042 100 047 046 041 044 096 038 042 096 043 060 100 042 100 044 046 043 040

043 039 051 062 044 047 048 044 036 048 055 056 045 057 080 037 047 053 033 054 033 056

078 058 081 050 072 057 070 053 046 100 048 055 100 054 086 055 065 053 042 053 049 061

]

]

]

]

]

]

]

]

]

]

]

]

]

]

]

]

]

]

(17)

According to formulas (10)sim(13) the index weightsvector is calculated as follows

120596

= (005 004 005 004 004 004 004 005 005 005 005 004 004 004 003 005 006 004 005 004 005 005)

(18)

Using formula (14) we obtain the grey relational gradesas follows

119861 = (073 057 061 053 040 060 048 063) (19)

52 Comparison and Analysis In this paper we focus ondeveloping a novel evaluation approach tourist choice of des-tination based on grey relation analysis in order to demon-strate the advantages of the developed evaluation methodsand comparing the evaluation results with the evaluationmethod of ldquoTOPSISrdquo used by literature [21] So after obtain-ing the grey relational grade to the ideal destination by usingour proposed method we compare and analyze these resultswith the evaluation results obtained from the Technique forOrder Preference by Similarity to Ideal Solution (TOPSIS)used by literature [21] The results are shown in Table 2

As can be seen from Table 2 we can make the followingcomparative analysis

(i) The ranking results obtained from literature [21] andthe results in this paper are not completely the same asshown in column 2 and column 4 of Table 2 which reflect theconsistency and difference of two methods In other wordsthe ranking results of the top three and the last one are thesame but the others are not the same Interestingly almost allrelational grades to ideal solution of our proposed approachare bigger than those of the similarity to ideal solution by theTechnique for Order Preference by Similarity to Ideal Solu-tion (TOPSIS) evaluationmethod which shows that our pro-posed approach has the advantage over the TOPSIS approach

(ii) Comparing the overall preference measures of desti-nations as shown in Table 1 of literature [21] it is observedthat the Taroko National Park has three best preferences

out of the 24 factors and the National Palace Museum hastwo best preferences Therefore we intuitively think that thedestination with more best preferences should be rankedhigher than the destination with less best preferences andthe actual evaluation results are consistent with our intuitionwhich show that our proposed evaluation approach is morecredible and more in line with the actual situation Thefundamental reason why our proposed evaluation approachis more in line with the actual situation lies in its owncharacteristics In other words the touristsrsquo preferences totourism destinations are grey and fuzzy which is in line withthe cognition rules of human being to the objective world

(iii) No matter what kind of evaluation approach Taipei101 is ranked the number one favorite local destination Thatis Taipei 101 is worthy of visiting as it is the highest buildingin the world located in the business center of Taipei cityand a Taipei landmark As shown in Table 1 Taipei 101 isconsidered the most desirable destination with respect toldquoself-actualizationrdquo ldquomeeting new friendsrdquo ldquomedical treat-mentrdquo ldquonovelty seekingrdquo ldquoenjoying night liferdquo ldquotransporta-tion facilitiesrdquo ldquoquality and variety of foodrdquo ldquoaccommodationfacilitiesrdquo ldquogood shoppingrdquo ldquopersonal safetyrdquo and ldquobenefitexpectationsrdquo which reflects the consistency of twomethods

6 Conclusions

From a methodological point of view we proposed a greyrelation analysis evaluation model for choosing tourism des-tinations and the findings demonstrate that the grey relationanalysis evaluation approach is a useful tool to help supporta decision in destination choice It integrates the opinion

8 Scientific Programming

Table 2 Final ranking of destinations

Destination

Results of literature[21] Results in this paper

RankSimilarity to

idealsolution

RankRelational

grade to idealsolution

Taipei 101 1 075 1 073KentingNational Park 2 063 2 063

Sun Moon Lake 3 055 3 061National PalaceMuseum 4 048 5 057

Love River 5 047 7 048TarokoNational Park 6 043 4 060

Alishan 7 037 6 053YushanNational Park 8 016 8 040

and evaluation of experts and makes the complex decision-making system into a simple grey relation analysis systemIn other words our proposed evaluation approach makes theevaluation process simple and easy to conductWhat is moreit is especially preferred in ordering the alternatives in situa-tions in which the sample is small and sample distribution isnot known In our future studies we will apply the approachto evaluating other tourism destinations in China and makea ranking for Chinese main tourism destinations

Competing Interests

The authors declare that there are no competing interestsregarding the publication of this paper

Acknowledgments

This study is supported by the National Natural ScienceFoundation of China under Grant no 41440045

References

[1] V S Lin R Mao and H Song ldquoTourism expenditure patternsin ChinardquoAnnals of Tourism Research vol 54 pp 100ndash117 2015

[2] Domestic Tourism National Bureau of Statistics of China 2014httpdatastatsgovcnenglisheasyqueryhtmcn=C01

[3] S C S Jang and S Ham ldquoA double-hurdle analysis oftravel expenditure baby boomer seniors versus older seniorsrdquoTourism Management vol 30 no 3 pp 372ndash380 2009

[4] T Huybers and J Bennett ldquoImpact of the environment on holi-day destination choices of prospective UK tourists implicationsfor Tropical North Queenslandrdquo Tourism Economics vol 6 no1 pp 21ndash46 2000

[5] H R Seddighi andA LTheocharous ldquoAmodel of tourism des-tination choice a theoretical and empirical analysisrdquo TourismManagement vol 23 no 5 pp 475ndash487 2002

[6] G Lee J T OrsquoLeary L Sunhee and A Morrison ldquoComparisonand contrast of push and pull motivational effects on trip

behavior an application of a multinomial logistic regressionmodelrdquo Tourism Analysis vol 7 no 2 pp 89ndash104 2001

[7] H Oppewal T Huybers and G I Crouch ldquoTourist destinationand experience choice a choice experimental analysis of deci-sion sequence effectsrdquo Tourism Management vol 48 pp 467ndash476 2015

[8] A Molina and A Esteban ldquoTourism brochures usefulness andimagerdquoAnnals of TourismResearch vol 33 no 4 pp 1036ndash10562006

[9] H Kim C-K Cheng and J T OrsquoLeary ldquoUnderstanding par-ticipation patterns and trends in tourism cultural attractionsrdquoTourism Management vol 28 no 5 pp 1366ndash1371 2007

[10] L Jin Y He andH Song ldquoService customization to upgrade orto downgrade An investigation of how option framing affectstouristsrsquo choice of package-tour servicesrdquoTourismManagementvol 33 no 2 pp 266ndash275 2012

[11] J Ashwell ldquoGoing bush Factors which influence internationaltouristsrsquo decisions to travel to remote Australian destinationsrdquoTourism Management vol 46 pp 80ndash83 2015

[12] V V Can ldquoEstimation of travel mode choice for domestictourists to Nha Trang using the multinomial probit modelrdquoTransportation Research Part A Policy and Practice vol 49 pp149ndash159 2013

[13] L Wu J Zhang and A Fujiwara ldquoTourism participation andexpenditure behaviour analysis using a scobit based discrete-continuous choice modelrdquo Annals of Tourism Research vol 40no 1 pp 1ndash17 2013

[14] G Prayag and C Ryan ldquoAntecedents of touristsrsquo loyalty tomauritius the role and influence of destination image placeattachment personal involvement and satisfactionrdquo Journal ofTravel Research vol 51 no 3 pp 342ndash356 2012

[15] N K Prebensen E Woo J S Chen and M Uysal ldquoMotivationand involvement as antecedents of the perceived value of thedestination experiencerdquo Journal of Travel Research vol 52 no2 pp 253ndash264 2013

[16] H Song R van der Veen G Li and J L Chen ldquoTheHongKongtourist satisfaction indexrdquo Annals of Tourism Research vol 39no 1 pp 459ndash479 2012

[17] J-Y Wong and C Yeh ldquoTourist hesitation in destinationdecision makingrdquo Annals of Tourism Research vol 36 no 1 pp6ndash23 2009

[18] B M Josiam D L Spears S Pookulangara K Dutta T RKinley and J L Duncan ldquoUsing structural equation modelingto understand the impact of Bollywood movies on destinationimage tourist activity and purchasing behavior of IndiansrdquoJournal of Vacation Marketing vol 21 no 3 pp 251ndash261 2015

[19] R Pinky Pawaskar and M Goel ldquoImproving the efficacy ofdestination marketing strategies a structural equation modelfor leisure travelrdquo Indian Journal of Science and Technology vol9 no 15 pp 1ndash11 2016

[20] H Zhang X Fu L A Cai and L Lu ldquoDestination image andtourist loyalty a meta-analysisrdquo Tourism Management vol 40pp 213ndash223 2014

[21] T-K Hsu Y-F Tsai andH-HWu ldquoThe preference analysis fortourist choice of destination a case study of Taiwanrdquo TourismManagement vol 30 no 2 pp 288ndash297 2009

[22] J-H Huang and K-H Peng ldquoFuzzy Rasch model in TOPSISa new approach for generating fuzzy numbers to assess thecompetitiveness of the tourism industries in Asian countriesrdquoTourism Management vol 33 no 2 pp 456ndash465 2012

Scientific Programming 9

[23] M H N Salleh R Othman and T Sarmidi ldquoTourist satis-faction of the environmental service quality for Tioman IslandMarine Parkrdquo Indian Journal of Geo-Marine Sciences vol 41 no2 pp 173ndash179 2012

[24] D Mohamad and R M Jamil ldquoA Preference analysis model forselecting tourist destinations based on motivational factors aCase Study in Kedah Malaysiardquo ProcediamdashSocial and Behav-ioral Sciences vol 65 pp 20ndash25 2012

[25] M Cucculelli and G Goffi ldquoDoes sustainability enhancetourism destination competitiveness Evidence from Italiandestinations of excellencerdquo Journal of Cleaner Production vol111 pp 370ndash382 2016

[26] J K S Jacobsen and A M Munar ldquoTourist information searchand destination choice in a digital agerdquo Tourism ManagementPerspectives vol 1 no 1 pp 39ndash47 2012

[27] A H N Mak M Lumbers A Eves and R C Y ChangldquoAn application of the repertory grid method and generalisedProcrustes analysis to investigate the motivational factors oftourist food consumptionrdquo International Journal of HospitalityManagement vol 35 pp 327ndash338 2013

[28] G Fuchs N Uriely A Reichel and D Maoz ldquoVacationingin a terror-stricken destination touristsrsquo risk perceptions andrationalizationsrdquo Journal of Travel Research vol 52 no 2 pp182ndash191 2013

[29] W Elias and Y Shiftan ldquoThe influence of individualrsquos riskperception and attitudes on travel behaviorrdquo TransportationResearch Part A Policy and Practice vol 46 no 8 pp 1241ndash12512012

[30] S Pike and S J Page ldquoDestination marketing organizationsand destinationmarketing anarrative analysis of the literaturerdquoTourism Management vol 41 pp 202ndash227 2014

[31] G I Crouch and J R B Ritchie ldquoApplication of the analytichierarchy process to tourism choice and decision making areview and illustration applied to destination competitivenessrdquoTourism Analysis vol 10 no 1 pp 17ndash25 2005

[32] D Das and K Mukherjee ldquoDevelopment of an AHP-QFDframework for designing a tourism productrdquo InternationalJournal of Services and Operations Management vol 4 no 3pp 321ndash344 2008

[33] HN Ali A A I Sabri andMMNNoor ldquoRating and rankingcriteria for selected Islands using fuzzy analytic hierarchyprocess (FAHP)rdquo International Journal of Applied Mathematicsand Informatics vol 1 no 6 pp 57ndash65 2012

[34] G Fan E D Goodman and Z Liu ldquoAHP (analytic hierarchyprocess) and computer analysis software used in tourism safetyrdquoJournal of Software vol 8 no 12 pp 3114ndash3119 2013

[35] K J PoolM Amani andAHaroni ldquoAssessment of alternativesfor tourism destinations advertising using AIDA model andAHP approachrdquo Asian Journal of Research in Marketing vol 3no 3 pp 88ndash97 2014

[36] Y Zhou K Maumbe J Deng and S W Selin ldquoResource-baseddestination competitiveness evaluation using a hybrid analytichierarchy process (AHP) the case study of West VirginiardquoTourism Management Perspectives vol 15 pp 72ndash80 2015

[37] X Wang X R Li F Zhen and J Zhang ldquoHow smart isyour tourist attraction measuring tourist preferences of smarttourism attractions via a FCEM-AHP and IPA approachrdquoTourism Management vol 54 pp 309ndash320 2016

[38] J S Chen and D Gursoy ldquoAn investigation of touristsrsquodestination loyalty and preferencesrdquo International Journal ofContemporary HospitalityManagement vol 13 no 2 pp 79ndash852001

[39] M Kozak ldquoComparative analysis of tourist motivations bynationality and destinationsrdquo TourismManagement vol 23 no3 pp 221ndash232 2002

[40] J De Vos P L Mokhtarian T Schwanen V Van Ackerand F Witlox ldquoTravel mode choice and travel satisfactionbridging the gap between decision utility and experiencedutilityrdquo Transportation vol 43 no 5 pp 771ndash796 2016

[41] U Probstl-Haider W Haider V Wirth and B BeardmoreldquoWill climate change increase the attractiveness of summerdestinations in the European Alps A survey of Germantouristsrdquo Journal of Outdoor Recreation and Tourism vol 11 pp44ndash57 2015

[42] A Garg ldquoTravel risks vs tourist decision making a touristperspectiverdquo International Journal of Hospitality amp TourismSystems vol 8 no 1 pp 1ndash9 2015

[43] S Amaro and P Duarte ldquoAn integrative model of consumersrsquointentions to purchase travel onlinerdquo TourismManagement vol46 pp 64ndash79 2015

[44] Z Tang ldquoAn integrated approach to evaluating the couplingcoordination between tourism and the environmentrdquo TourismManagement vol 46 pp 11ndash19 2015

[45] N Paker and C A Vural ldquoCustomer segmentation for marinasevaluating marinas as destinationsrdquo Tourism Management vol56 pp 156ndash171 2016

[46] T Huybers ldquoModelling short-break holiday destinationchoicesrdquo Tourism Economics vol 9 no 4 pp 389ndash405 2003

[47] B G C Dellaert D F Ettema and C Lindh ldquoMulti-facetedtourist travel decisions a constraint-based conceptual frame-work to describe touristsrsquo sequential choices of travel compo-nentsrdquo Tourism Management vol 19 no 4 pp 313ndash320 1998

[48] E Sirakaya and A G Woodside ldquoBuilding and testing theoriesof decisionmaking by travellersrdquo TourismManagement vol 26no 6 pp 815ndash832 2005

[49] T Lam and C H C Hsu ldquoPredicting behavioral intention ofchoosing a travel destinationrdquoTourismManagement vol 27 no4 pp 589ndash599 2006

[50] C Goossens ldquoTourism information and pleasure motivationrdquoAnnals of Tourism Research vol 27 no 2 pp 301ndash321 2000

[51] V C S Heung H Qu and R Chu ldquoThe relationship betweenvacation factors and socio-demographic and travelling char-acteristics the case of Japanese leisure travellersrdquo TourismManagement vol 22 no 3 pp 259ndash269 2001

[52] S S Kim and B Prideaux ldquoMarketing implications arisingfrom a comparative study of international pleasure touristmotivations and other travel-related characteristics of visitors toKoreardquo Tourism Management vol 26 no 3 pp 347ndash357 2005

[53] M Kozak ldquoComparative analysis of tourist motivations bynationality and destinationsrdquo TourismManagement vol 23 no3 pp 221ndash232 2002

[54] G Richards and J Wilson ldquoTodayrsquos youth travellers Tomor-rowrsquos global nomads New horizons in independent youthand student travelrdquo International Student Travel Confederation(ISTC) 2003

[55] N Tomis B Kovatevis N Berber and N Milis ldquoFactors influ-encing the motivation of young people when choosing a citydestination in Europemdasha case study from Esbjerg (Denmark)rdquoEuropean Researcher vol 69 no 2 pp 414ndash428 2014

[56] X D Guo H L Zhang and J H Ruan ldquoA novel similarity-based algorithm for classifying tourism scenic spotsrdquo ICICExpress Letters Part B Applications vol 6 no 2 pp 503ndash5092015

10 Scientific Programming

[57] J Moran E Granada J L Mıguez and J Porteiro ldquoUse of greyrelational analysis to assess and optimize small biomass boilersrdquoFuel Processing Technology vol 87 no 2 pp 123ndash127 2006

[58] D Ju-Long ldquoControl problems of grey systemsrdquo Systems ampControl Letters vol 1 no 5 pp 288ndash294 1982

[59] R-T Wang C-T B Ho and K Oh ldquoMeasuring productionand marketing efficiency using grey relation analysis anddata envelopment analysisrdquo International Journal of ProductionResearch vol 48 no 1 pp 183ndash199 2010

[60] C-T Ho ldquoMeasuring bank operations performance anapproach based on Grey Relation Analysisrdquo Journal of theOperational Research Society vol 57 no 4 pp 337ndash349 2006

[61] C-Y Kung and K-L Wen ldquoApplying Grey Relational AnalysisandGreyDecision-Making to evaluate the relationship betweencompany attributes and its financial performancemdasha case studyof venture capital enterprises in Taiwanrdquo Decision SupportSystems vol 43 no 3 pp 842ndash852 2007

[62] Y-J Wang ldquoCombining grey relation analysis with FMCGDMto evaluate financial performance of Taiwan container linesrdquoExpert Systems with Applications vol 36 no 2 pp 2424ndash24322009

[63] P T-W Lee C-W Lin and S-H Shin ldquoA comparative study onfinancial positions of shipping companies in Taiwan and Koreausing entropy and grey relation analysisrdquo Expert Systems withApplications vol 39 no 5 pp 5649ndash5657 2012

[64] F Ecer and A Boyukaslan ldquoMeasuring performances of foot-ball clubs using financial ratios the gray relational analysisapproachrdquo American Journal of Economics vol 4 no 1 pp 62ndash71 2014

[65] S J Lin I J Lu and C Lewis ldquoGrey relation performancecorrelations among economics energy use and carbon dioxideemission in Taiwanrdquo Energy Policy vol 35 no 3 pp 1948ndash19552007

[66] I J Lu S J Lin and C Lewis ldquoGrey relation analysis of motorvehicular energy consumption inTaiwanrdquoEnergy Policy vol 36no 7 pp 2556ndash2561 2008

[67] S H Hashemi A Karimi and M Tavana ldquoAn integratedgreen supplier selection approachwith analytic network processand improved Grey relational analysisrdquo International Journal ofProduction Economics vol 159 pp 178ndash191 2015

[68] DG Pearce ldquoTourist time-budgetrdquoAnnals of TourismResearchvol 15 no 1 pp 106ndash121 1988

[69] D Ju-Long ldquoIntroduction to grey system theoryrdquoThe Journal ofGrey System vol 1 no 1 pp 1ndash24 1989

[70] U Caydas and A Hascalık ldquoUse of the grey relational analysisto determine optimum laser cutting parameters with multi-performance characteristicsrdquo Optics amp Laser Technology vol40 no 7 pp 987ndash994 2008

Submit your manuscripts athttpwwwhindawicom

Computer Games Technology

International Journal of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Distributed Sensor Networks

International Journal of

Advances in

FuzzySystems

Hindawi Publishing Corporationhttpwwwhindawicom

Volume 2014

International Journal of

ReconfigurableComputing

Hindawi Publishing Corporation httpwwwhindawicom Volume 2014

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Applied Computational Intelligence and Soft Computing

thinspAdvancesthinspinthinsp

Artificial Intelligence

HindawithinspPublishingthinspCorporationhttpwwwhindawicom Volumethinsp2014

Advances inSoftware EngineeringHindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Electrical and Computer Engineering

Journal of

Journal of

Computer Networks and Communications

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Hindawi Publishing Corporation

httpwwwhindawicom Volume 2014

Advances in

Multimedia

International Journal of

Biomedical Imaging

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

ArtificialNeural Systems

Advances in

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

RoboticsJournal of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Computational Intelligence and Neuroscience

Industrial EngineeringJournal of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Modelling amp Simulation in EngineeringHindawi Publishing Corporation httpwwwhindawicom Volume 2014

The Scientific World JournalHindawi Publishing Corporation httpwwwhindawicom Volume 2014

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Human-ComputerInteraction

Advances in

Computer EngineeringAdvances in

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

2 Scientific Programming

Number of travel agencies (unit)

Number of star-rated hotels (unit)

Number of overseas visitor arrivals (10000 person-times)

Number of foreigners arrivals (10000 person-times)

Number of Chinese outbound visitors (10000 person-times)

2010 2011 2012 20132009147794644094

1140394163669421329942629294

Figure 1 Chinarsquos tourism development situation for nearly fiveyears

[20] (7) Technique for Order Preference by Similarity toIdeal Solution (TOPSIS) [21ndash24] (8) Principal ComponentAnalysis [25ndash30] and (9) Analytic Hierarchy Process (AHP)[31ndash37] Some of the methods may have already been knownto the public Other methods were simply borrowed from thedomain of industrial study and applied to tourism Some arestill in the embryonic stage Each of the above nine methodscan be independently applied to evaluate tourismdestinationHowever none of them is perfect Researchers can onlychoose a method to evaluate tourism destination that hasthe least amount of drawbacks for that studyrsquos particularsituation Some evaluation methods require the high qualitydata that is the data should obey a certain distribution or thedata volume should be large In addition the other evaluationmethods have strong subjectivity

Therefore a viable method for effective evaluation oftourism destination is aimed at providing solutions for issueswith multiple variables and targets However in studyingthe evaluation of tourism destination survey data are oftenincomplete or unclear and this paper therefore is boundby realistic limits confining itself to a situation where theamount of data is small and its significance indefinite Giventhese considerations grey relation analysis (GRA)may be thebest method for this study and will be used to explore practi-cal procedure of tourist destination evaluation and choice toidentify the various features that need to reflect preferencesof tourists for destinations in the context of 8 tourist spotsby utilizing the index system and data in literature [21] Onthe basis of such preliminary analysis this paper intends toachieve twofold aims The first applies entropy to find therelative weights of each index of the 8 tourist spots Thesecond is to rank the tourist spots by grey relation analysis(GRA) and compare the ranking results with literature [21]

The rest of this paper is organized as follows After a briefreview of relevant literature in Section 2 we present hierarchyof tourism destination selection and evaluation index systemin Section 3 The grey relation analysis evaluation modelfor tourism destinations selection is presented in Section 4

Section 5 illustrates the results obtained from the grey relationanalysis evaluation model and then we compare and analyzethese results with those obtained from the Technique forOrder Preference by Similarity to Ideal Solution (TOPSIS)evaluation method used by literature [21] Conclusions aredrawn in Section 6

2 Literature Review

Evaluation and choice of tourism destination is one of theimportant topics frequently investigated by scholars [38]These evaluation and choice studies have been linked tothe issues of decision rules decision-making processes andchoice factors [5 39ndash45] Hsu et al [21] proposed a 4-levelAHP model consisting of 22 attributes on the 4th level andtested it using data collected from tourists visiting Taiwanto establish the relative importance of preselected factors(criteria) In this study visiting friendsrelatives and personalsafety appear to be the 2 most important factors for inboundtourists to Taiwan price is the least important and Taipei 101is the first priority for travelers Huybers [46] applied discretechoice modeling technique to study the short-break destina-tion choices of prospective Sydney residentsThe study resultsshow that a destinationrsquos attractiveness to prospective short-break visitors from Sydney is enhanced to different degreesby lower prices being moderately busy having a moderatelevel of night life being visited during springsummer beingaccessible within two hoursrsquo travel time and offering amix of natural and culturalheritage attractions Prayag andRyan [14] used confirmatory factor analysis to explore therelationships among four constructs namely destinationimage place attachment personal involvement and visitorsrsquosatisfaction as antecedents of loyalty The research findingsindicate that destination image personal involvement andplace attachment are antecedents of visitorsrsquo loyalty but thisrelationship is mediated by satisfaction levels

Dellaert et al [47] pointed out that touristsrsquo choicesare complex multifaceted decisions where the choices fordifferent elements are interrelated and evolved in a decisionprocess over timeThe decision-making process is influencedby a number of psychological (internal) and nonpsycho-logical (external) variables Sirakaya and Woodside [48]provided a comprehensive qualitative review of the touristdecision-making literature and integrated the main con-ceptual and empirical work that has been reported in thetourism literature Lam andHsu [49] argued that the complexdecision-making process leading to the choice of a traveldestination had not been well researched Past studies relatedto destination choice mainly focus on identifying importantattributes affecting destination choice professional judgmentand factor analysis are the main methods [50ndash53] RichardsandWilson [54] in their work focused on independent youthand student travel and used factor analysis to help identifyfour main motivating factors including experience seekingrelaxation seeking and sociability and contributing to thedestination Tomis et al [55] studied the factors considered

Scientific Programming 3

The choice of the destination

Internal force External force

Psychologicalfactor

Physicalfactor

Socialinteraction

Seekingexploration

Tangiblefactor

Intangiblefactor

(i) Transportation facilities(ii) Friendliness of people

(iii) Quality amp variety of food(iv) Accommodation facilities(v) Personal safety

(vi) Price(vii) Culture amp historical

resources(viii) Good shopping

(ix) Environmental safetyamp quality

(i) Rest amprelaxation

(ii) Medicaltreatment

(iii) Health ampfitness

(i) Noveltyseeking

(ii) Cultureexploration

(iii) Adventureseeking

(iv) Enjoying nightlife amp shopping

(i) Visitingfriends andor relatives

(ii) Meetingnew people

(i) Destinationimage

(ii) Benefitsexpectations

(i) Escape(ii) Self-

actualization

Figure 2 The hierarchy of destination selection (source Hsu et al [21])

by young people when choosing a city destination in Europeand identified seven major factors partying and having funaccessibility to destination info easy and cheap travel orga-nization outdoor activities socializing with the local peoplegood shopping places and exploring the unknown Furtheranalysis showed that there are significant differences amongseveralmotivation factors when it comes toDanish and inter-national students Pike and Page [30] provided a comprehen-sive narrative analysis of the destinationmarketing literature

The above studies mainly applied qualitative and quanti-tative methods to analyze the factors influencing the choiceof tourism destinations They pointed out that the decision-making process leading to the final choice of a travel destina-tion is very complexHowever these studies failed to considerthe preferences of tourists in the decision-making processandutilizemuchmore complex decision-making approachesMotivated by this we develop a new evaluationmodel to eval-uate and rank the tourism destinations considering personalpreferences Thus we attempt to evaluate the preferencesof tourists for destinations and recognize the key aspectsinfluencing the touristsrsquo choice of destination Finally Guoet al [56] proposed a novel similarity-based algorithm forclassifying tourism scenic spots and this evaluation methodnot only has some similarities to our own but also hassome key differencesThey studied the problem of classifyingdifferent tourism scenic spots into different levels throughcalculating the relative similarity of each scenic spot to thebest and worst scenic spots but in our model we do not

consider the worst scenic spots which make the evaluationprocess simple and easy to conduct

3 The Hierarchy of TourismDestinations Selection

According to Hsu et al [21] the main factors affectingtourism destination choice are grouped into two categoriesnamely internal and external factors The push motivationrelates to internal forces which consist of 4 criteria (psycho-logical physical social interaction and seekingexploration)and are further divided into 11 subcriteria The pull moti-vation relates to external forces which consist of 2 criteria(tangible and intangible) with tangible criteria including 9subcriteria and intangible criteria including 2 subcriteria (seeFigure 2)

4 The Grey Relation Analysis Model ofTourism Destination Choice

Grey relation analysis (GRA) is a method that can be used indecision-making in situations where there are many criteriaby ordering them as to relational grade It is especially pre-ferred in ordering the alternatives in situations in which thesample is small and sample distribution is not known GRA ispart of the grey system theory [57] Gray system theory wasfirst introduced by Ju-Long [58] The fundamental definitionof ldquogreynessrdquo is information being incomplete or unknown

4 Scientific Programming

thus an element from an incomplete message is considered tobe of ldquogreyrdquo element A ldquogrey relationrdquo refers to the measure-ment of changing relations between two systems or elementsthat occur in a system over timeThe analysis method whichmeasures the relations between elements based on the degreeof similarity or difference of development trends among theseelements is called ldquogrey relation analysisrdquo More preciselyduring the process of system development should the trendof change between two elements be consistent it then enjoysa higher grade of synchronized change and can be consideredas having a greater grade of relation otherwise the gradeof relation is smaller [59] It has been widely applied in avariety of fields such as financial performance evaluation [60ndash64] motor vehicular energy consumption [65 66] and greensupplier selection [67] As far as we know GRA has not beenapplied in evaluation and choice of tourism destination Webelieve we are the first to utilize the GRA to evaluate and rankthe tourism destinations

Personal preferences like motivations may be bothintrinsic reflecting individual likes and dislikes and extrin-sic or socially conditioned Pearce [68] stated that prefer-ences are more specific than motivations and are revealed bywhere travelers go and what travelers do However touristsrsquopreferences are psychological behaviors which are not easyto accurately measure having the feature of grey and fuzzyChoosing a tourism destination is a grey system evaluationprocess therefore the grey relation analysis is a more suitablemethod to evaluate it The evaluation procedure of this studyconsists of several stepsThedetailed descriptions of each stepare given in the following subsections

41 Problem Definition Assume that there are 119899 tourismdestinations 119879

1 1198792 119879

119899 which are to be evaluated in

terms of 119898 evaluation criteria 1198621 1198622 119862

119898 and relative

weights are denoted by 120596119894119895

(sum119898

119895=1120596119894119895

= 1) The objective ofthe decision-making is to rank these tourism destinationsAccording to the assumption we can get the original indexdata matrix

119877 = (119903119894119895)119899times119898

=

[

[

[

[

[

[

[

11990311

11990312

sdot sdot sdot 1199031119898

11990321

11990322

sdot sdot sdot 1199032119898

1199031198991

1199031198992

sdot sdot sdot 119903119899119898

]

]

]

]

]

]

]

(1)

where 119903119894119895is the observed value of object 119879

119894in terms of criteria

119862119895

(119894 = 1 2 119899 119895 = 1 2 119898)

Definition 1 The best tourism destination is the referentialsequence made up of the best tourism destination indexesamong all the alternative tourism destinations that is

1198770

= (11990301

11990302

1199030119895

) (2)

where 1199030119895

= Optimum(119903119894119895) (119894 = 1 2 119899 119895 = 1 2 119898)

So we can construct initial matrix 1198771

1198771

= (119903119894119895)119899times119898

=

[

[

[

[

[

[

[

[

[

[

11990301

11990302

sdot sdot sdot 1199030119898

11990311

11990312

sdot sdot sdot 1199031119898

11990321

11990322

sdot sdot sdot 1199032119898

sdot sdot sdot

1199031198991

1199031198992

sdot sdot sdot 119903119899119898

]

]

]

]

]

]

]

]

]

]

(3)

42 Normalization of Index Values In order to make com-parison between the various indicators we normalized eachof the index values in accordancewith the following formulasFor larger-is-better transformation 119903

119894119895can be transformed to

119909119894119895 The formula is defined as

119883119894119895

=

119903119894119895

minus min119895119903119894119895

max119895119903119894119895

minus min119895119903119894119895

(4)

For smaller-is-better transformation the formula totransform 119903

119894119895to 119909119894119895is

119883119894119895

=

max119895119903119894119895

minus 119903119894119895

max119895119903119894119895

minus min119895119903119894119895

(5)

The normalized values form a new matrix as shown in

119883 = (119909119894119895)119899times119898

=

[

[

[

[

[

[

[

[

[

[

11990901

11990902

sdot sdot sdot 1199090119898

11990911

11990912

sdot sdot sdot 1199091119898

11990921

11990922

sdot sdot sdot 1199092119898

sdot sdot sdot

1199091198991

1199091198992

sdot sdot sdot 119909119899119898

]

]

]

]

]

]

]

]

]

]

(6)

43 Calculating Grey Relation Coefficient According to Ju-Long [69] let 119883

0= (11990901

11990902

1199090119898

) as referential sequenceand 119883

119894= (1199091198941

1199091198942

119909119894119898

) as comparative sequence Then1199090119895and 119909119894119895would be the values of 119909

0and 119909119894at optimum index

119895 If 120585(1199090119895

119909119894119895) are real numbers then they can be defined as

120585 (1199090 119909119894) =

1

119898

119898

sum

119895=1

120585 (1199090119895

119909119894119895) (7)

The mean of 120585(1199090119895

119909119894119895) needs to meet the four axioms of grey

relation

Axiom 1 (norm interval) Consider

0 lt 120585(1199090119895

119909119894119895) le 1

120585(1199090119895

119909119894119895) = 1 harr 119909

0119895= 119909119894119895(this is called complete

relation)120585(1199090119895

119909119894119895) = 0 harr 119909

0isin 120593 119909

119894isin 120593 (this is called

complete nonrelation) where 120593 is an empty set

Axiom 2 (duality symmetric) Consider

1199090 119909119894isin 119883

120585(1199090119895

119909119894119895) = 120585(119909

119894119895 1199090119895

) harr 119883 = (1199090 119909119894)

Scientific Programming 5

Axiom 3 (wholeness) Consider

119909119895 119909119894isin 119883

120585(1199090119895

119909119894119895) = 120585(119909

119894119895 1199090119895

) this case almost alwayshappened

Axiom 4 (approachability) Consider

120585(1199090119895

119909119894119895) get larger along with |119909

0119895minus 119909119894119895| getting

smaller

If the abovementioned four axioms could all be satisfied120585(1199090 119909119894) is designed as the grade of grey relation in 119909

119894corre-

spondence to1199090 and 120585(119909

0119895 119909119894119895) is said to be the grey relational

coefficient of 119909119894119895to 1199090119895at optimum index 119895 Professor Ju-Long

[58] proposes a mathematical equation that will satisfy thesefour axioms of grey relation which is as follows

120585 (1198830119895

119883119894119895)

=

min119894min119895

100381610038161003816100381610038161198830119895

minus 119883119894119895

10038161003816100381610038161003816+ 120588max

119894max119895

100381610038161003816100381610038161198830119895

minus 119883119894119895

10038161003816100381610038161003816

100381610038161003816100381610038161198830119895

minus 119883119894119895

10038161003816100381610038161003816+ 120588max

119894max119895

100381610038161003816100381610038161198830119895

minus 119883119894119895

10038161003816100381610038161003816

(8)

where 120588 isin [0 1] is distinguished coefficient the function ofwhich is to reduce its numerical value bymax

119894max119895|1198830119895

minus119883119894119895|

increasing so as to affect its loss-authenticity and to heightenthe significance of difference among relational coefficientsand 120588 = 05 is generally used Relational coefficients120585(1198830119895

119883119894119895) matrix can be obtained through formula (8) As

a matter of convenience we use 120585119894119895instead of 120585(119883

0119895 119883119894119895) in

the following pages

119864 = (120585119894119895)119899119898

=

[

[

[

[

[

[

[

12058511

12058512

sdot sdot sdot 1205851119898

12058521

12058522

sdot sdot sdot 1205852119898

1205851198991

1205851198992

sdot sdot sdot 120585119899119898

]

]

]

]

]

]

]

(119894 = 1 2 119899 119895 = 1 2 119898)

(9)

44 Determining the Index Weights Based on Entropy Mak-ing normalization processing for formula (9) 120585

119894119895can be

transformed to 120583119894119895 and a new matrix (9) can be obtained as

follows

1198640

= (120583119894119895)119899119898

=

[

[

[

[

[

[

[

12058311

12058312

sdot sdot sdot 1205831119898

12058321

12058322

sdot sdot sdot 1205832119898

1205831198991

1205831198992

sdot sdot sdot 120583119899119898

]

]

]

]

]

]

]

(119894 = 1 2 119899 119895 = 1 2 119898)

(10)

where 0 le 120583119894119895

le 1 and sum119899

119894=1120583119894119895

= 1 then the informationentropy is

119867 (119895) = minus

119899

sum

119894=1

120583119894119895

sdot log 120583119894119895 (11)

Let

]119894119895

= 1 +

sum119899

119894=1120583119894119895

sdot log 120583119894119895

log 119899

(12)

Then we obtain the weights of indexes in terms offormula (12)

120596119894119895

=

]119894119895

sum119898

119895=1]119894119895

(0 le 120596119894119895

le 1

119898

sum

119895=1

120596119894119895

= 1) (13)

45 Calculating Grey Relational Grade After obtaining thegrey relational coefficient we normally take the weightedvalue of the grey relational coefficientmultiplied byweightingvalue as the grey relational grade The grey relational grade isdefined as follows [70]

119861 = 120596119894sdot (120585119894119895)

119879

119899119898= 120596119894sdot

[

[

[

[

[

[

[

12058511

12058521

sdot sdot sdot 1205851198991

12058512

12058522

sdot sdot sdot 1205851198992

1205851119898

1205851198992

sdot sdot sdot 120585119899119898

]

]

]

]

]

]

]

= (1198871 1198872 119887

119899)

(14)

where 119887119899is grey relational grade of the tourism destination119879

119899

which indicates the degree of influence that the comparativesequence could exert over the referential sequenceThereforeif a particular comparative sequence is better than the othercomparative sequences to the referential sequence then thegrey relational grade for that comparative sequence andreferential sequence will be higher than other grey relationalgrades Finally all tourismdestinations are evaluated synthet-ically according to the value of 119887

119899 where when 119887

119899is larger the

tourism destination 119879119899is better

5 A Numerical Example

In this section we use the proposed model to evaluate 8tourism destinations in Taiwan Province of China (includingTaipei 101 National Museum Sun Moon Lake AlishanYushan National Park Taroko National Park Love River andKenting National Park) Twenty-two (22) indexes identifiedfrom literature [21] as shown in Table 1 are used in the study(ldquoardquo is the best preference out of the 8 destinations)

51 The Relational Grade to Ideal Destination According tothe data from Table 1 we identify the optimum column as(716 716 759 640 715 854 745 804 787 673 856 860695 885 781 720 736 605 847 804 794 767) and incombination with Table 1 an initial matrix is obtained asfollows

6 Scientific Programming

Table 1 Overall preference measures of destinations

Criteria Taipei 101 National Museum Sun Moon Lake Alishan Yushan Park Taroko Park Love River Kenting ParkEscape 623 542 716a 680 594 708 599 692Self-actualization 716a 689 630 609 522 641 566 645Restrelaxation 538 505 759a 706 613 705 636 730Medical treatment 640a 441 430 434 302 409 538 473Health and fitness 505 410 657 698 634 715a 517 657Visiting friendrelative 796 554 854a 701 588 684 686 742Meeting new people 745a 555 598 555 437 561 576 680Novelty seeking 804a 608 535 506 416 522 561 629Culture exploration 627 787a 654 641 542 628 566 645Adventure seeking 397 343 596 656 630 667 494 673a

Enjoying night life 856a 553 449 395 343 441 645 580Transportation facilities 860a 803 620 528 404 545 678 670Friendliness of people 648 652 668 684 595 693 633 695a

Qualityvariety of food 885a 641 570 558 428 580 716 688Accommodation 781a 726 667 572 431 663 736 752Environment safety 610 638 698 681 607 720a 624 673Personal safety 736a 729 580 502 455 538 580 659Price 439 512 555 564 588 605a 530 530Culture and historicalresources 611 847a 678 672 622 663 560 649

Good shopping 804a 599 489 479 363 540 613 611Destination image 778 727 794a 706 653 692 638 714Benefits expectations 767a 680 541 541 460 532 648 667

1198771

=

[

[

[

[

[

[

[

[

[

[

[

[

[

[

[

[

[

[

[

[

716 716 759 640 715 854 745 804 787 673 856 860 695 885 781 720 736 605 847 804 794 767

623 716 538 640 505 796 745 804 627 397 856 860 648 885 781 610 736 439 611 804 778 767

542 689 505 441 410 554 555 608 787 343 553 803 652 641 726 638 729 512 847 599 727 680

716 630 759 430 657 854 598 535 654 596 449 620 668 570 667 698 580 555 678 489 794 541

680 609 706 434 698 701 555 506 641 656 395 528 684 558 572 681 502 564 672 479 706 541

594 522 613 302 634 588 437 416 542 630 343 404 595 428 431 607 455 588 622 363 653 460

708 641 705 409 715 684 561 522 628 667 441 545 693 580 663 720 538 605 663 540 692 532

599 566 636 538 517 686 576 561 566 494 645 678 633 716 736 624 580 530 560 613 638 648

692 645 730 473 657 742 680 629 645 673 580 670 695 688 752 673 659 530 649 611 714 667

]

]

]

]

]

]

]

]

]

]

]

]

]

]

]

]

]

]

]

]

(15)

Each of the index values is normalized according toformulas (4) and (5) and then a new matrix is obtained asfollows

119883

=

[

[

[

[

[

[

[

[

[

[

[

[

[

[

[

[

[

[

[

[

100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100

047 100 013 100 031 081 100 100 035 016 100 100 053 100 100 003 100 000 018 100 090 100

000 086 000 041 000 000 038 049 100 000 041 088 057 047 084 027 098 044 100 054 057 072

100 056 100 038 081 100 052 031 046 077 021 047 073 031 067 081 044 070 041 029 100 026

079 045 079 039 094 049 038 023 040 095 010 027 089 028 040 065 017 075 039 026 044 026

030 000 043 000 073 011 000 000 000 087 000 000 000 000 000 000 000 090 022 000 010 000

095 061 079 032 100 043 040 027 035 098 019 031 098 033 066 100 030 100 036 040 035 023

033 023 052 070 035 044 045 037 010 046 059 060 038 063 087 015 044 055 000 057 000 061

086 063 089 051 081 063 079 055 042 100 046 058 100 057 092 058 073 055 031 056 049 067

]

]

]

]

]

]

]

]

]

]

]

]

]

]

]

]

]

]

]

]

(16)

Scientific Programming 7

According to formula (8) that is 120585119894119895

= (0 + 05 times

1)(|1198830119895

minus 119883119894119895| + 05 times 1) we obtain the matrix of grey

relational coefficients as follows

120585119894119895

=

[

[

[

[

[

[

[

[

[

[

[

[

[

[

[

[

[

[

048 100 036 100 042 072 100 100 043 037 100 100 052 100 100 034 100 033 038 100 083 100

033 078 033 046 033 033 045 050 100 033 046 080 054 048 076 041 095 047 100 052 054 064

100 053 100 045 072 100 051 042 048 068 039 049 065 042 061 072 047 062 046 041 100 040

071 048 071 045 090 050 045 039 046 091 036 041 082 041 046 059 038 067 045 040 047 040

042 033 047 033 065 036 033 033 033 079 033 033 033 033 033 033 033 083 039 033 036 033

092 056 070 042 100 047 046 041 044 096 038 042 096 043 060 100 042 100 044 046 043 040

043 039 051 062 044 047 048 044 036 048 055 056 045 057 080 037 047 053 033 054 033 056

078 058 081 050 072 057 070 053 046 100 048 055 100 054 086 055 065 053 042 053 049 061

]

]

]

]

]

]

]

]

]

]

]

]

]

]

]

]

]

]

(17)

According to formulas (10)sim(13) the index weightsvector is calculated as follows

120596

= (005 004 005 004 004 004 004 005 005 005 005 004 004 004 003 005 006 004 005 004 005 005)

(18)

Using formula (14) we obtain the grey relational gradesas follows

119861 = (073 057 061 053 040 060 048 063) (19)

52 Comparison and Analysis In this paper we focus ondeveloping a novel evaluation approach tourist choice of des-tination based on grey relation analysis in order to demon-strate the advantages of the developed evaluation methodsand comparing the evaluation results with the evaluationmethod of ldquoTOPSISrdquo used by literature [21] So after obtain-ing the grey relational grade to the ideal destination by usingour proposed method we compare and analyze these resultswith the evaluation results obtained from the Technique forOrder Preference by Similarity to Ideal Solution (TOPSIS)used by literature [21] The results are shown in Table 2

As can be seen from Table 2 we can make the followingcomparative analysis

(i) The ranking results obtained from literature [21] andthe results in this paper are not completely the same asshown in column 2 and column 4 of Table 2 which reflect theconsistency and difference of two methods In other wordsthe ranking results of the top three and the last one are thesame but the others are not the same Interestingly almost allrelational grades to ideal solution of our proposed approachare bigger than those of the similarity to ideal solution by theTechnique for Order Preference by Similarity to Ideal Solu-tion (TOPSIS) evaluationmethod which shows that our pro-posed approach has the advantage over the TOPSIS approach

(ii) Comparing the overall preference measures of desti-nations as shown in Table 1 of literature [21] it is observedthat the Taroko National Park has three best preferences

out of the 24 factors and the National Palace Museum hastwo best preferences Therefore we intuitively think that thedestination with more best preferences should be rankedhigher than the destination with less best preferences andthe actual evaluation results are consistent with our intuitionwhich show that our proposed evaluation approach is morecredible and more in line with the actual situation Thefundamental reason why our proposed evaluation approachis more in line with the actual situation lies in its owncharacteristics In other words the touristsrsquo preferences totourism destinations are grey and fuzzy which is in line withthe cognition rules of human being to the objective world

(iii) No matter what kind of evaluation approach Taipei101 is ranked the number one favorite local destination Thatis Taipei 101 is worthy of visiting as it is the highest buildingin the world located in the business center of Taipei cityand a Taipei landmark As shown in Table 1 Taipei 101 isconsidered the most desirable destination with respect toldquoself-actualizationrdquo ldquomeeting new friendsrdquo ldquomedical treat-mentrdquo ldquonovelty seekingrdquo ldquoenjoying night liferdquo ldquotransporta-tion facilitiesrdquo ldquoquality and variety of foodrdquo ldquoaccommodationfacilitiesrdquo ldquogood shoppingrdquo ldquopersonal safetyrdquo and ldquobenefitexpectationsrdquo which reflects the consistency of twomethods

6 Conclusions

From a methodological point of view we proposed a greyrelation analysis evaluation model for choosing tourism des-tinations and the findings demonstrate that the grey relationanalysis evaluation approach is a useful tool to help supporta decision in destination choice It integrates the opinion

8 Scientific Programming

Table 2 Final ranking of destinations

Destination

Results of literature[21] Results in this paper

RankSimilarity to

idealsolution

RankRelational

grade to idealsolution

Taipei 101 1 075 1 073KentingNational Park 2 063 2 063

Sun Moon Lake 3 055 3 061National PalaceMuseum 4 048 5 057

Love River 5 047 7 048TarokoNational Park 6 043 4 060

Alishan 7 037 6 053YushanNational Park 8 016 8 040

and evaluation of experts and makes the complex decision-making system into a simple grey relation analysis systemIn other words our proposed evaluation approach makes theevaluation process simple and easy to conductWhat is moreit is especially preferred in ordering the alternatives in situa-tions in which the sample is small and sample distribution isnot known In our future studies we will apply the approachto evaluating other tourism destinations in China and makea ranking for Chinese main tourism destinations

Competing Interests

The authors declare that there are no competing interestsregarding the publication of this paper

Acknowledgments

This study is supported by the National Natural ScienceFoundation of China under Grant no 41440045

References

[1] V S Lin R Mao and H Song ldquoTourism expenditure patternsin ChinardquoAnnals of Tourism Research vol 54 pp 100ndash117 2015

[2] Domestic Tourism National Bureau of Statistics of China 2014httpdatastatsgovcnenglisheasyqueryhtmcn=C01

[3] S C S Jang and S Ham ldquoA double-hurdle analysis oftravel expenditure baby boomer seniors versus older seniorsrdquoTourism Management vol 30 no 3 pp 372ndash380 2009

[4] T Huybers and J Bennett ldquoImpact of the environment on holi-day destination choices of prospective UK tourists implicationsfor Tropical North Queenslandrdquo Tourism Economics vol 6 no1 pp 21ndash46 2000

[5] H R Seddighi andA LTheocharous ldquoAmodel of tourism des-tination choice a theoretical and empirical analysisrdquo TourismManagement vol 23 no 5 pp 475ndash487 2002

[6] G Lee J T OrsquoLeary L Sunhee and A Morrison ldquoComparisonand contrast of push and pull motivational effects on trip

behavior an application of a multinomial logistic regressionmodelrdquo Tourism Analysis vol 7 no 2 pp 89ndash104 2001

[7] H Oppewal T Huybers and G I Crouch ldquoTourist destinationand experience choice a choice experimental analysis of deci-sion sequence effectsrdquo Tourism Management vol 48 pp 467ndash476 2015

[8] A Molina and A Esteban ldquoTourism brochures usefulness andimagerdquoAnnals of TourismResearch vol 33 no 4 pp 1036ndash10562006

[9] H Kim C-K Cheng and J T OrsquoLeary ldquoUnderstanding par-ticipation patterns and trends in tourism cultural attractionsrdquoTourism Management vol 28 no 5 pp 1366ndash1371 2007

[10] L Jin Y He andH Song ldquoService customization to upgrade orto downgrade An investigation of how option framing affectstouristsrsquo choice of package-tour servicesrdquoTourismManagementvol 33 no 2 pp 266ndash275 2012

[11] J Ashwell ldquoGoing bush Factors which influence internationaltouristsrsquo decisions to travel to remote Australian destinationsrdquoTourism Management vol 46 pp 80ndash83 2015

[12] V V Can ldquoEstimation of travel mode choice for domestictourists to Nha Trang using the multinomial probit modelrdquoTransportation Research Part A Policy and Practice vol 49 pp149ndash159 2013

[13] L Wu J Zhang and A Fujiwara ldquoTourism participation andexpenditure behaviour analysis using a scobit based discrete-continuous choice modelrdquo Annals of Tourism Research vol 40no 1 pp 1ndash17 2013

[14] G Prayag and C Ryan ldquoAntecedents of touristsrsquo loyalty tomauritius the role and influence of destination image placeattachment personal involvement and satisfactionrdquo Journal ofTravel Research vol 51 no 3 pp 342ndash356 2012

[15] N K Prebensen E Woo J S Chen and M Uysal ldquoMotivationand involvement as antecedents of the perceived value of thedestination experiencerdquo Journal of Travel Research vol 52 no2 pp 253ndash264 2013

[16] H Song R van der Veen G Li and J L Chen ldquoTheHongKongtourist satisfaction indexrdquo Annals of Tourism Research vol 39no 1 pp 459ndash479 2012

[17] J-Y Wong and C Yeh ldquoTourist hesitation in destinationdecision makingrdquo Annals of Tourism Research vol 36 no 1 pp6ndash23 2009

[18] B M Josiam D L Spears S Pookulangara K Dutta T RKinley and J L Duncan ldquoUsing structural equation modelingto understand the impact of Bollywood movies on destinationimage tourist activity and purchasing behavior of IndiansrdquoJournal of Vacation Marketing vol 21 no 3 pp 251ndash261 2015

[19] R Pinky Pawaskar and M Goel ldquoImproving the efficacy ofdestination marketing strategies a structural equation modelfor leisure travelrdquo Indian Journal of Science and Technology vol9 no 15 pp 1ndash11 2016

[20] H Zhang X Fu L A Cai and L Lu ldquoDestination image andtourist loyalty a meta-analysisrdquo Tourism Management vol 40pp 213ndash223 2014

[21] T-K Hsu Y-F Tsai andH-HWu ldquoThe preference analysis fortourist choice of destination a case study of Taiwanrdquo TourismManagement vol 30 no 2 pp 288ndash297 2009

[22] J-H Huang and K-H Peng ldquoFuzzy Rasch model in TOPSISa new approach for generating fuzzy numbers to assess thecompetitiveness of the tourism industries in Asian countriesrdquoTourism Management vol 33 no 2 pp 456ndash465 2012

Scientific Programming 9

[23] M H N Salleh R Othman and T Sarmidi ldquoTourist satis-faction of the environmental service quality for Tioman IslandMarine Parkrdquo Indian Journal of Geo-Marine Sciences vol 41 no2 pp 173ndash179 2012

[24] D Mohamad and R M Jamil ldquoA Preference analysis model forselecting tourist destinations based on motivational factors aCase Study in Kedah Malaysiardquo ProcediamdashSocial and Behav-ioral Sciences vol 65 pp 20ndash25 2012

[25] M Cucculelli and G Goffi ldquoDoes sustainability enhancetourism destination competitiveness Evidence from Italiandestinations of excellencerdquo Journal of Cleaner Production vol111 pp 370ndash382 2016

[26] J K S Jacobsen and A M Munar ldquoTourist information searchand destination choice in a digital agerdquo Tourism ManagementPerspectives vol 1 no 1 pp 39ndash47 2012

[27] A H N Mak M Lumbers A Eves and R C Y ChangldquoAn application of the repertory grid method and generalisedProcrustes analysis to investigate the motivational factors oftourist food consumptionrdquo International Journal of HospitalityManagement vol 35 pp 327ndash338 2013

[28] G Fuchs N Uriely A Reichel and D Maoz ldquoVacationingin a terror-stricken destination touristsrsquo risk perceptions andrationalizationsrdquo Journal of Travel Research vol 52 no 2 pp182ndash191 2013

[29] W Elias and Y Shiftan ldquoThe influence of individualrsquos riskperception and attitudes on travel behaviorrdquo TransportationResearch Part A Policy and Practice vol 46 no 8 pp 1241ndash12512012

[30] S Pike and S J Page ldquoDestination marketing organizationsand destinationmarketing anarrative analysis of the literaturerdquoTourism Management vol 41 pp 202ndash227 2014

[31] G I Crouch and J R B Ritchie ldquoApplication of the analytichierarchy process to tourism choice and decision making areview and illustration applied to destination competitivenessrdquoTourism Analysis vol 10 no 1 pp 17ndash25 2005

[32] D Das and K Mukherjee ldquoDevelopment of an AHP-QFDframework for designing a tourism productrdquo InternationalJournal of Services and Operations Management vol 4 no 3pp 321ndash344 2008

[33] HN Ali A A I Sabri andMMNNoor ldquoRating and rankingcriteria for selected Islands using fuzzy analytic hierarchyprocess (FAHP)rdquo International Journal of Applied Mathematicsand Informatics vol 1 no 6 pp 57ndash65 2012

[34] G Fan E D Goodman and Z Liu ldquoAHP (analytic hierarchyprocess) and computer analysis software used in tourism safetyrdquoJournal of Software vol 8 no 12 pp 3114ndash3119 2013

[35] K J PoolM Amani andAHaroni ldquoAssessment of alternativesfor tourism destinations advertising using AIDA model andAHP approachrdquo Asian Journal of Research in Marketing vol 3no 3 pp 88ndash97 2014

[36] Y Zhou K Maumbe J Deng and S W Selin ldquoResource-baseddestination competitiveness evaluation using a hybrid analytichierarchy process (AHP) the case study of West VirginiardquoTourism Management Perspectives vol 15 pp 72ndash80 2015

[37] X Wang X R Li F Zhen and J Zhang ldquoHow smart isyour tourist attraction measuring tourist preferences of smarttourism attractions via a FCEM-AHP and IPA approachrdquoTourism Management vol 54 pp 309ndash320 2016

[38] J S Chen and D Gursoy ldquoAn investigation of touristsrsquodestination loyalty and preferencesrdquo International Journal ofContemporary HospitalityManagement vol 13 no 2 pp 79ndash852001

[39] M Kozak ldquoComparative analysis of tourist motivations bynationality and destinationsrdquo TourismManagement vol 23 no3 pp 221ndash232 2002

[40] J De Vos P L Mokhtarian T Schwanen V Van Ackerand F Witlox ldquoTravel mode choice and travel satisfactionbridging the gap between decision utility and experiencedutilityrdquo Transportation vol 43 no 5 pp 771ndash796 2016

[41] U Probstl-Haider W Haider V Wirth and B BeardmoreldquoWill climate change increase the attractiveness of summerdestinations in the European Alps A survey of Germantouristsrdquo Journal of Outdoor Recreation and Tourism vol 11 pp44ndash57 2015

[42] A Garg ldquoTravel risks vs tourist decision making a touristperspectiverdquo International Journal of Hospitality amp TourismSystems vol 8 no 1 pp 1ndash9 2015

[43] S Amaro and P Duarte ldquoAn integrative model of consumersrsquointentions to purchase travel onlinerdquo TourismManagement vol46 pp 64ndash79 2015

[44] Z Tang ldquoAn integrated approach to evaluating the couplingcoordination between tourism and the environmentrdquo TourismManagement vol 46 pp 11ndash19 2015

[45] N Paker and C A Vural ldquoCustomer segmentation for marinasevaluating marinas as destinationsrdquo Tourism Management vol56 pp 156ndash171 2016

[46] T Huybers ldquoModelling short-break holiday destinationchoicesrdquo Tourism Economics vol 9 no 4 pp 389ndash405 2003

[47] B G C Dellaert D F Ettema and C Lindh ldquoMulti-facetedtourist travel decisions a constraint-based conceptual frame-work to describe touristsrsquo sequential choices of travel compo-nentsrdquo Tourism Management vol 19 no 4 pp 313ndash320 1998

[48] E Sirakaya and A G Woodside ldquoBuilding and testing theoriesof decisionmaking by travellersrdquo TourismManagement vol 26no 6 pp 815ndash832 2005

[49] T Lam and C H C Hsu ldquoPredicting behavioral intention ofchoosing a travel destinationrdquoTourismManagement vol 27 no4 pp 589ndash599 2006

[50] C Goossens ldquoTourism information and pleasure motivationrdquoAnnals of Tourism Research vol 27 no 2 pp 301ndash321 2000

[51] V C S Heung H Qu and R Chu ldquoThe relationship betweenvacation factors and socio-demographic and travelling char-acteristics the case of Japanese leisure travellersrdquo TourismManagement vol 22 no 3 pp 259ndash269 2001

[52] S S Kim and B Prideaux ldquoMarketing implications arisingfrom a comparative study of international pleasure touristmotivations and other travel-related characteristics of visitors toKoreardquo Tourism Management vol 26 no 3 pp 347ndash357 2005

[53] M Kozak ldquoComparative analysis of tourist motivations bynationality and destinationsrdquo TourismManagement vol 23 no3 pp 221ndash232 2002

[54] G Richards and J Wilson ldquoTodayrsquos youth travellers Tomor-rowrsquos global nomads New horizons in independent youthand student travelrdquo International Student Travel Confederation(ISTC) 2003

[55] N Tomis B Kovatevis N Berber and N Milis ldquoFactors influ-encing the motivation of young people when choosing a citydestination in Europemdasha case study from Esbjerg (Denmark)rdquoEuropean Researcher vol 69 no 2 pp 414ndash428 2014

[56] X D Guo H L Zhang and J H Ruan ldquoA novel similarity-based algorithm for classifying tourism scenic spotsrdquo ICICExpress Letters Part B Applications vol 6 no 2 pp 503ndash5092015

10 Scientific Programming

[57] J Moran E Granada J L Mıguez and J Porteiro ldquoUse of greyrelational analysis to assess and optimize small biomass boilersrdquoFuel Processing Technology vol 87 no 2 pp 123ndash127 2006

[58] D Ju-Long ldquoControl problems of grey systemsrdquo Systems ampControl Letters vol 1 no 5 pp 288ndash294 1982

[59] R-T Wang C-T B Ho and K Oh ldquoMeasuring productionand marketing efficiency using grey relation analysis anddata envelopment analysisrdquo International Journal of ProductionResearch vol 48 no 1 pp 183ndash199 2010

[60] C-T Ho ldquoMeasuring bank operations performance anapproach based on Grey Relation Analysisrdquo Journal of theOperational Research Society vol 57 no 4 pp 337ndash349 2006

[61] C-Y Kung and K-L Wen ldquoApplying Grey Relational AnalysisandGreyDecision-Making to evaluate the relationship betweencompany attributes and its financial performancemdasha case studyof venture capital enterprises in Taiwanrdquo Decision SupportSystems vol 43 no 3 pp 842ndash852 2007

[62] Y-J Wang ldquoCombining grey relation analysis with FMCGDMto evaluate financial performance of Taiwan container linesrdquoExpert Systems with Applications vol 36 no 2 pp 2424ndash24322009

[63] P T-W Lee C-W Lin and S-H Shin ldquoA comparative study onfinancial positions of shipping companies in Taiwan and Koreausing entropy and grey relation analysisrdquo Expert Systems withApplications vol 39 no 5 pp 5649ndash5657 2012

[64] F Ecer and A Boyukaslan ldquoMeasuring performances of foot-ball clubs using financial ratios the gray relational analysisapproachrdquo American Journal of Economics vol 4 no 1 pp 62ndash71 2014

[65] S J Lin I J Lu and C Lewis ldquoGrey relation performancecorrelations among economics energy use and carbon dioxideemission in Taiwanrdquo Energy Policy vol 35 no 3 pp 1948ndash19552007

[66] I J Lu S J Lin and C Lewis ldquoGrey relation analysis of motorvehicular energy consumption inTaiwanrdquoEnergy Policy vol 36no 7 pp 2556ndash2561 2008

[67] S H Hashemi A Karimi and M Tavana ldquoAn integratedgreen supplier selection approachwith analytic network processand improved Grey relational analysisrdquo International Journal ofProduction Economics vol 159 pp 178ndash191 2015

[68] DG Pearce ldquoTourist time-budgetrdquoAnnals of TourismResearchvol 15 no 1 pp 106ndash121 1988

[69] D Ju-Long ldquoIntroduction to grey system theoryrdquoThe Journal ofGrey System vol 1 no 1 pp 1ndash24 1989

[70] U Caydas and A Hascalık ldquoUse of the grey relational analysisto determine optimum laser cutting parameters with multi-performance characteristicsrdquo Optics amp Laser Technology vol40 no 7 pp 987ndash994 2008

Submit your manuscripts athttpwwwhindawicom

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Distributed Sensor Networks

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International Journal of

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Applied Computational Intelligence and Soft Computing

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Artificial Intelligence

HindawithinspPublishingthinspCorporationhttpwwwhindawicom Volumethinsp2014

Advances inSoftware EngineeringHindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Electrical and Computer Engineering

Journal of

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Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

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Advances in

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International Journal of

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Advances in

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RoboticsJournal of

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Modelling amp Simulation in EngineeringHindawi Publishing Corporation httpwwwhindawicom Volume 2014

The Scientific World JournalHindawi Publishing Corporation httpwwwhindawicom Volume 2014

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Human-ComputerInteraction

Advances in

Computer EngineeringAdvances in

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Scientific Programming 3

The choice of the destination

Internal force External force

Psychologicalfactor

Physicalfactor

Socialinteraction

Seekingexploration

Tangiblefactor

Intangiblefactor

(i) Transportation facilities(ii) Friendliness of people

(iii) Quality amp variety of food(iv) Accommodation facilities(v) Personal safety

(vi) Price(vii) Culture amp historical

resources(viii) Good shopping

(ix) Environmental safetyamp quality

(i) Rest amprelaxation

(ii) Medicaltreatment

(iii) Health ampfitness

(i) Noveltyseeking

(ii) Cultureexploration

(iii) Adventureseeking

(iv) Enjoying nightlife amp shopping

(i) Visitingfriends andor relatives

(ii) Meetingnew people

(i) Destinationimage

(ii) Benefitsexpectations

(i) Escape(ii) Self-

actualization

Figure 2 The hierarchy of destination selection (source Hsu et al [21])

by young people when choosing a city destination in Europeand identified seven major factors partying and having funaccessibility to destination info easy and cheap travel orga-nization outdoor activities socializing with the local peoplegood shopping places and exploring the unknown Furtheranalysis showed that there are significant differences amongseveralmotivation factors when it comes toDanish and inter-national students Pike and Page [30] provided a comprehen-sive narrative analysis of the destinationmarketing literature

The above studies mainly applied qualitative and quanti-tative methods to analyze the factors influencing the choiceof tourism destinations They pointed out that the decision-making process leading to the final choice of a travel destina-tion is very complexHowever these studies failed to considerthe preferences of tourists in the decision-making processandutilizemuchmore complex decision-making approachesMotivated by this we develop a new evaluationmodel to eval-uate and rank the tourism destinations considering personalpreferences Thus we attempt to evaluate the preferencesof tourists for destinations and recognize the key aspectsinfluencing the touristsrsquo choice of destination Finally Guoet al [56] proposed a novel similarity-based algorithm forclassifying tourism scenic spots and this evaluation methodnot only has some similarities to our own but also hassome key differencesThey studied the problem of classifyingdifferent tourism scenic spots into different levels throughcalculating the relative similarity of each scenic spot to thebest and worst scenic spots but in our model we do not

consider the worst scenic spots which make the evaluationprocess simple and easy to conduct

3 The Hierarchy of TourismDestinations Selection

According to Hsu et al [21] the main factors affectingtourism destination choice are grouped into two categoriesnamely internal and external factors The push motivationrelates to internal forces which consist of 4 criteria (psycho-logical physical social interaction and seekingexploration)and are further divided into 11 subcriteria The pull moti-vation relates to external forces which consist of 2 criteria(tangible and intangible) with tangible criteria including 9subcriteria and intangible criteria including 2 subcriteria (seeFigure 2)

4 The Grey Relation Analysis Model ofTourism Destination Choice

Grey relation analysis (GRA) is a method that can be used indecision-making in situations where there are many criteriaby ordering them as to relational grade It is especially pre-ferred in ordering the alternatives in situations in which thesample is small and sample distribution is not known GRA ispart of the grey system theory [57] Gray system theory wasfirst introduced by Ju-Long [58] The fundamental definitionof ldquogreynessrdquo is information being incomplete or unknown

4 Scientific Programming

thus an element from an incomplete message is considered tobe of ldquogreyrdquo element A ldquogrey relationrdquo refers to the measure-ment of changing relations between two systems or elementsthat occur in a system over timeThe analysis method whichmeasures the relations between elements based on the degreeof similarity or difference of development trends among theseelements is called ldquogrey relation analysisrdquo More preciselyduring the process of system development should the trendof change between two elements be consistent it then enjoysa higher grade of synchronized change and can be consideredas having a greater grade of relation otherwise the gradeof relation is smaller [59] It has been widely applied in avariety of fields such as financial performance evaluation [60ndash64] motor vehicular energy consumption [65 66] and greensupplier selection [67] As far as we know GRA has not beenapplied in evaluation and choice of tourism destination Webelieve we are the first to utilize the GRA to evaluate and rankthe tourism destinations

Personal preferences like motivations may be bothintrinsic reflecting individual likes and dislikes and extrin-sic or socially conditioned Pearce [68] stated that prefer-ences are more specific than motivations and are revealed bywhere travelers go and what travelers do However touristsrsquopreferences are psychological behaviors which are not easyto accurately measure having the feature of grey and fuzzyChoosing a tourism destination is a grey system evaluationprocess therefore the grey relation analysis is a more suitablemethod to evaluate it The evaluation procedure of this studyconsists of several stepsThedetailed descriptions of each stepare given in the following subsections

41 Problem Definition Assume that there are 119899 tourismdestinations 119879

1 1198792 119879

119899 which are to be evaluated in

terms of 119898 evaluation criteria 1198621 1198622 119862

119898 and relative

weights are denoted by 120596119894119895

(sum119898

119895=1120596119894119895

= 1) The objective ofthe decision-making is to rank these tourism destinationsAccording to the assumption we can get the original indexdata matrix

119877 = (119903119894119895)119899times119898

=

[

[

[

[

[

[

[

11990311

11990312

sdot sdot sdot 1199031119898

11990321

11990322

sdot sdot sdot 1199032119898

1199031198991

1199031198992

sdot sdot sdot 119903119899119898

]

]

]

]

]

]

]

(1)

where 119903119894119895is the observed value of object 119879

119894in terms of criteria

119862119895

(119894 = 1 2 119899 119895 = 1 2 119898)

Definition 1 The best tourism destination is the referentialsequence made up of the best tourism destination indexesamong all the alternative tourism destinations that is

1198770

= (11990301

11990302

1199030119895

) (2)

where 1199030119895

= Optimum(119903119894119895) (119894 = 1 2 119899 119895 = 1 2 119898)

So we can construct initial matrix 1198771

1198771

= (119903119894119895)119899times119898

=

[

[

[

[

[

[

[

[

[

[

11990301

11990302

sdot sdot sdot 1199030119898

11990311

11990312

sdot sdot sdot 1199031119898

11990321

11990322

sdot sdot sdot 1199032119898

sdot sdot sdot

1199031198991

1199031198992

sdot sdot sdot 119903119899119898

]

]

]

]

]

]

]

]

]

]

(3)

42 Normalization of Index Values In order to make com-parison between the various indicators we normalized eachof the index values in accordancewith the following formulasFor larger-is-better transformation 119903

119894119895can be transformed to

119909119894119895 The formula is defined as

119883119894119895

=

119903119894119895

minus min119895119903119894119895

max119895119903119894119895

minus min119895119903119894119895

(4)

For smaller-is-better transformation the formula totransform 119903

119894119895to 119909119894119895is

119883119894119895

=

max119895119903119894119895

minus 119903119894119895

max119895119903119894119895

minus min119895119903119894119895

(5)

The normalized values form a new matrix as shown in

119883 = (119909119894119895)119899times119898

=

[

[

[

[

[

[

[

[

[

[

11990901

11990902

sdot sdot sdot 1199090119898

11990911

11990912

sdot sdot sdot 1199091119898

11990921

11990922

sdot sdot sdot 1199092119898

sdot sdot sdot

1199091198991

1199091198992

sdot sdot sdot 119909119899119898

]

]

]

]

]

]

]

]

]

]

(6)

43 Calculating Grey Relation Coefficient According to Ju-Long [69] let 119883

0= (11990901

11990902

1199090119898

) as referential sequenceand 119883

119894= (1199091198941

1199091198942

119909119894119898

) as comparative sequence Then1199090119895and 119909119894119895would be the values of 119909

0and 119909119894at optimum index

119895 If 120585(1199090119895

119909119894119895) are real numbers then they can be defined as

120585 (1199090 119909119894) =

1

119898

119898

sum

119895=1

120585 (1199090119895

119909119894119895) (7)

The mean of 120585(1199090119895

119909119894119895) needs to meet the four axioms of grey

relation

Axiom 1 (norm interval) Consider

0 lt 120585(1199090119895

119909119894119895) le 1

120585(1199090119895

119909119894119895) = 1 harr 119909

0119895= 119909119894119895(this is called complete

relation)120585(1199090119895

119909119894119895) = 0 harr 119909

0isin 120593 119909

119894isin 120593 (this is called

complete nonrelation) where 120593 is an empty set

Axiom 2 (duality symmetric) Consider

1199090 119909119894isin 119883

120585(1199090119895

119909119894119895) = 120585(119909

119894119895 1199090119895

) harr 119883 = (1199090 119909119894)

Scientific Programming 5

Axiom 3 (wholeness) Consider

119909119895 119909119894isin 119883

120585(1199090119895

119909119894119895) = 120585(119909

119894119895 1199090119895

) this case almost alwayshappened

Axiom 4 (approachability) Consider

120585(1199090119895

119909119894119895) get larger along with |119909

0119895minus 119909119894119895| getting

smaller

If the abovementioned four axioms could all be satisfied120585(1199090 119909119894) is designed as the grade of grey relation in 119909

119894corre-

spondence to1199090 and 120585(119909

0119895 119909119894119895) is said to be the grey relational

coefficient of 119909119894119895to 1199090119895at optimum index 119895 Professor Ju-Long

[58] proposes a mathematical equation that will satisfy thesefour axioms of grey relation which is as follows

120585 (1198830119895

119883119894119895)

=

min119894min119895

100381610038161003816100381610038161198830119895

minus 119883119894119895

10038161003816100381610038161003816+ 120588max

119894max119895

100381610038161003816100381610038161198830119895

minus 119883119894119895

10038161003816100381610038161003816

100381610038161003816100381610038161198830119895

minus 119883119894119895

10038161003816100381610038161003816+ 120588max

119894max119895

100381610038161003816100381610038161198830119895

minus 119883119894119895

10038161003816100381610038161003816

(8)

where 120588 isin [0 1] is distinguished coefficient the function ofwhich is to reduce its numerical value bymax

119894max119895|1198830119895

minus119883119894119895|

increasing so as to affect its loss-authenticity and to heightenthe significance of difference among relational coefficientsand 120588 = 05 is generally used Relational coefficients120585(1198830119895

119883119894119895) matrix can be obtained through formula (8) As

a matter of convenience we use 120585119894119895instead of 120585(119883

0119895 119883119894119895) in

the following pages

119864 = (120585119894119895)119899119898

=

[

[

[

[

[

[

[

12058511

12058512

sdot sdot sdot 1205851119898

12058521

12058522

sdot sdot sdot 1205852119898

1205851198991

1205851198992

sdot sdot sdot 120585119899119898

]

]

]

]

]

]

]

(119894 = 1 2 119899 119895 = 1 2 119898)

(9)

44 Determining the Index Weights Based on Entropy Mak-ing normalization processing for formula (9) 120585

119894119895can be

transformed to 120583119894119895 and a new matrix (9) can be obtained as

follows

1198640

= (120583119894119895)119899119898

=

[

[

[

[

[

[

[

12058311

12058312

sdot sdot sdot 1205831119898

12058321

12058322

sdot sdot sdot 1205832119898

1205831198991

1205831198992

sdot sdot sdot 120583119899119898

]

]

]

]

]

]

]

(119894 = 1 2 119899 119895 = 1 2 119898)

(10)

where 0 le 120583119894119895

le 1 and sum119899

119894=1120583119894119895

= 1 then the informationentropy is

119867 (119895) = minus

119899

sum

119894=1

120583119894119895

sdot log 120583119894119895 (11)

Let

]119894119895

= 1 +

sum119899

119894=1120583119894119895

sdot log 120583119894119895

log 119899

(12)

Then we obtain the weights of indexes in terms offormula (12)

120596119894119895

=

]119894119895

sum119898

119895=1]119894119895

(0 le 120596119894119895

le 1

119898

sum

119895=1

120596119894119895

= 1) (13)

45 Calculating Grey Relational Grade After obtaining thegrey relational coefficient we normally take the weightedvalue of the grey relational coefficientmultiplied byweightingvalue as the grey relational grade The grey relational grade isdefined as follows [70]

119861 = 120596119894sdot (120585119894119895)

119879

119899119898= 120596119894sdot

[

[

[

[

[

[

[

12058511

12058521

sdot sdot sdot 1205851198991

12058512

12058522

sdot sdot sdot 1205851198992

1205851119898

1205851198992

sdot sdot sdot 120585119899119898

]

]

]

]

]

]

]

= (1198871 1198872 119887

119899)

(14)

where 119887119899is grey relational grade of the tourism destination119879

119899

which indicates the degree of influence that the comparativesequence could exert over the referential sequenceThereforeif a particular comparative sequence is better than the othercomparative sequences to the referential sequence then thegrey relational grade for that comparative sequence andreferential sequence will be higher than other grey relationalgrades Finally all tourismdestinations are evaluated synthet-ically according to the value of 119887

119899 where when 119887

119899is larger the

tourism destination 119879119899is better

5 A Numerical Example

In this section we use the proposed model to evaluate 8tourism destinations in Taiwan Province of China (includingTaipei 101 National Museum Sun Moon Lake AlishanYushan National Park Taroko National Park Love River andKenting National Park) Twenty-two (22) indexes identifiedfrom literature [21] as shown in Table 1 are used in the study(ldquoardquo is the best preference out of the 8 destinations)

51 The Relational Grade to Ideal Destination According tothe data from Table 1 we identify the optimum column as(716 716 759 640 715 854 745 804 787 673 856 860695 885 781 720 736 605 847 804 794 767) and incombination with Table 1 an initial matrix is obtained asfollows

6 Scientific Programming

Table 1 Overall preference measures of destinations

Criteria Taipei 101 National Museum Sun Moon Lake Alishan Yushan Park Taroko Park Love River Kenting ParkEscape 623 542 716a 680 594 708 599 692Self-actualization 716a 689 630 609 522 641 566 645Restrelaxation 538 505 759a 706 613 705 636 730Medical treatment 640a 441 430 434 302 409 538 473Health and fitness 505 410 657 698 634 715a 517 657Visiting friendrelative 796 554 854a 701 588 684 686 742Meeting new people 745a 555 598 555 437 561 576 680Novelty seeking 804a 608 535 506 416 522 561 629Culture exploration 627 787a 654 641 542 628 566 645Adventure seeking 397 343 596 656 630 667 494 673a

Enjoying night life 856a 553 449 395 343 441 645 580Transportation facilities 860a 803 620 528 404 545 678 670Friendliness of people 648 652 668 684 595 693 633 695a

Qualityvariety of food 885a 641 570 558 428 580 716 688Accommodation 781a 726 667 572 431 663 736 752Environment safety 610 638 698 681 607 720a 624 673Personal safety 736a 729 580 502 455 538 580 659Price 439 512 555 564 588 605a 530 530Culture and historicalresources 611 847a 678 672 622 663 560 649

Good shopping 804a 599 489 479 363 540 613 611Destination image 778 727 794a 706 653 692 638 714Benefits expectations 767a 680 541 541 460 532 648 667

1198771

=

[

[

[

[

[

[

[

[

[

[

[

[

[

[

[

[

[

[

[

[

716 716 759 640 715 854 745 804 787 673 856 860 695 885 781 720 736 605 847 804 794 767

623 716 538 640 505 796 745 804 627 397 856 860 648 885 781 610 736 439 611 804 778 767

542 689 505 441 410 554 555 608 787 343 553 803 652 641 726 638 729 512 847 599 727 680

716 630 759 430 657 854 598 535 654 596 449 620 668 570 667 698 580 555 678 489 794 541

680 609 706 434 698 701 555 506 641 656 395 528 684 558 572 681 502 564 672 479 706 541

594 522 613 302 634 588 437 416 542 630 343 404 595 428 431 607 455 588 622 363 653 460

708 641 705 409 715 684 561 522 628 667 441 545 693 580 663 720 538 605 663 540 692 532

599 566 636 538 517 686 576 561 566 494 645 678 633 716 736 624 580 530 560 613 638 648

692 645 730 473 657 742 680 629 645 673 580 670 695 688 752 673 659 530 649 611 714 667

]

]

]

]

]

]

]

]

]

]

]

]

]

]

]

]

]

]

]

]

(15)

Each of the index values is normalized according toformulas (4) and (5) and then a new matrix is obtained asfollows

119883

=

[

[

[

[

[

[

[

[

[

[

[

[

[

[

[

[

[

[

[

[

100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100

047 100 013 100 031 081 100 100 035 016 100 100 053 100 100 003 100 000 018 100 090 100

000 086 000 041 000 000 038 049 100 000 041 088 057 047 084 027 098 044 100 054 057 072

100 056 100 038 081 100 052 031 046 077 021 047 073 031 067 081 044 070 041 029 100 026

079 045 079 039 094 049 038 023 040 095 010 027 089 028 040 065 017 075 039 026 044 026

030 000 043 000 073 011 000 000 000 087 000 000 000 000 000 000 000 090 022 000 010 000

095 061 079 032 100 043 040 027 035 098 019 031 098 033 066 100 030 100 036 040 035 023

033 023 052 070 035 044 045 037 010 046 059 060 038 063 087 015 044 055 000 057 000 061

086 063 089 051 081 063 079 055 042 100 046 058 100 057 092 058 073 055 031 056 049 067

]

]

]

]

]

]

]

]

]

]

]

]

]

]

]

]

]

]

]

]

(16)

Scientific Programming 7

According to formula (8) that is 120585119894119895

= (0 + 05 times

1)(|1198830119895

minus 119883119894119895| + 05 times 1) we obtain the matrix of grey

relational coefficients as follows

120585119894119895

=

[

[

[

[

[

[

[

[

[

[

[

[

[

[

[

[

[

[

048 100 036 100 042 072 100 100 043 037 100 100 052 100 100 034 100 033 038 100 083 100

033 078 033 046 033 033 045 050 100 033 046 080 054 048 076 041 095 047 100 052 054 064

100 053 100 045 072 100 051 042 048 068 039 049 065 042 061 072 047 062 046 041 100 040

071 048 071 045 090 050 045 039 046 091 036 041 082 041 046 059 038 067 045 040 047 040

042 033 047 033 065 036 033 033 033 079 033 033 033 033 033 033 033 083 039 033 036 033

092 056 070 042 100 047 046 041 044 096 038 042 096 043 060 100 042 100 044 046 043 040

043 039 051 062 044 047 048 044 036 048 055 056 045 057 080 037 047 053 033 054 033 056

078 058 081 050 072 057 070 053 046 100 048 055 100 054 086 055 065 053 042 053 049 061

]

]

]

]

]

]

]

]

]

]

]

]

]

]

]

]

]

]

(17)

According to formulas (10)sim(13) the index weightsvector is calculated as follows

120596

= (005 004 005 004 004 004 004 005 005 005 005 004 004 004 003 005 006 004 005 004 005 005)

(18)

Using formula (14) we obtain the grey relational gradesas follows

119861 = (073 057 061 053 040 060 048 063) (19)

52 Comparison and Analysis In this paper we focus ondeveloping a novel evaluation approach tourist choice of des-tination based on grey relation analysis in order to demon-strate the advantages of the developed evaluation methodsand comparing the evaluation results with the evaluationmethod of ldquoTOPSISrdquo used by literature [21] So after obtain-ing the grey relational grade to the ideal destination by usingour proposed method we compare and analyze these resultswith the evaluation results obtained from the Technique forOrder Preference by Similarity to Ideal Solution (TOPSIS)used by literature [21] The results are shown in Table 2

As can be seen from Table 2 we can make the followingcomparative analysis

(i) The ranking results obtained from literature [21] andthe results in this paper are not completely the same asshown in column 2 and column 4 of Table 2 which reflect theconsistency and difference of two methods In other wordsthe ranking results of the top three and the last one are thesame but the others are not the same Interestingly almost allrelational grades to ideal solution of our proposed approachare bigger than those of the similarity to ideal solution by theTechnique for Order Preference by Similarity to Ideal Solu-tion (TOPSIS) evaluationmethod which shows that our pro-posed approach has the advantage over the TOPSIS approach

(ii) Comparing the overall preference measures of desti-nations as shown in Table 1 of literature [21] it is observedthat the Taroko National Park has three best preferences

out of the 24 factors and the National Palace Museum hastwo best preferences Therefore we intuitively think that thedestination with more best preferences should be rankedhigher than the destination with less best preferences andthe actual evaluation results are consistent with our intuitionwhich show that our proposed evaluation approach is morecredible and more in line with the actual situation Thefundamental reason why our proposed evaluation approachis more in line with the actual situation lies in its owncharacteristics In other words the touristsrsquo preferences totourism destinations are grey and fuzzy which is in line withthe cognition rules of human being to the objective world

(iii) No matter what kind of evaluation approach Taipei101 is ranked the number one favorite local destination Thatis Taipei 101 is worthy of visiting as it is the highest buildingin the world located in the business center of Taipei cityand a Taipei landmark As shown in Table 1 Taipei 101 isconsidered the most desirable destination with respect toldquoself-actualizationrdquo ldquomeeting new friendsrdquo ldquomedical treat-mentrdquo ldquonovelty seekingrdquo ldquoenjoying night liferdquo ldquotransporta-tion facilitiesrdquo ldquoquality and variety of foodrdquo ldquoaccommodationfacilitiesrdquo ldquogood shoppingrdquo ldquopersonal safetyrdquo and ldquobenefitexpectationsrdquo which reflects the consistency of twomethods

6 Conclusions

From a methodological point of view we proposed a greyrelation analysis evaluation model for choosing tourism des-tinations and the findings demonstrate that the grey relationanalysis evaluation approach is a useful tool to help supporta decision in destination choice It integrates the opinion

8 Scientific Programming

Table 2 Final ranking of destinations

Destination

Results of literature[21] Results in this paper

RankSimilarity to

idealsolution

RankRelational

grade to idealsolution

Taipei 101 1 075 1 073KentingNational Park 2 063 2 063

Sun Moon Lake 3 055 3 061National PalaceMuseum 4 048 5 057

Love River 5 047 7 048TarokoNational Park 6 043 4 060

Alishan 7 037 6 053YushanNational Park 8 016 8 040

and evaluation of experts and makes the complex decision-making system into a simple grey relation analysis systemIn other words our proposed evaluation approach makes theevaluation process simple and easy to conductWhat is moreit is especially preferred in ordering the alternatives in situa-tions in which the sample is small and sample distribution isnot known In our future studies we will apply the approachto evaluating other tourism destinations in China and makea ranking for Chinese main tourism destinations

Competing Interests

The authors declare that there are no competing interestsregarding the publication of this paper

Acknowledgments

This study is supported by the National Natural ScienceFoundation of China under Grant no 41440045

References

[1] V S Lin R Mao and H Song ldquoTourism expenditure patternsin ChinardquoAnnals of Tourism Research vol 54 pp 100ndash117 2015

[2] Domestic Tourism National Bureau of Statistics of China 2014httpdatastatsgovcnenglisheasyqueryhtmcn=C01

[3] S C S Jang and S Ham ldquoA double-hurdle analysis oftravel expenditure baby boomer seniors versus older seniorsrdquoTourism Management vol 30 no 3 pp 372ndash380 2009

[4] T Huybers and J Bennett ldquoImpact of the environment on holi-day destination choices of prospective UK tourists implicationsfor Tropical North Queenslandrdquo Tourism Economics vol 6 no1 pp 21ndash46 2000

[5] H R Seddighi andA LTheocharous ldquoAmodel of tourism des-tination choice a theoretical and empirical analysisrdquo TourismManagement vol 23 no 5 pp 475ndash487 2002

[6] G Lee J T OrsquoLeary L Sunhee and A Morrison ldquoComparisonand contrast of push and pull motivational effects on trip

behavior an application of a multinomial logistic regressionmodelrdquo Tourism Analysis vol 7 no 2 pp 89ndash104 2001

[7] H Oppewal T Huybers and G I Crouch ldquoTourist destinationand experience choice a choice experimental analysis of deci-sion sequence effectsrdquo Tourism Management vol 48 pp 467ndash476 2015

[8] A Molina and A Esteban ldquoTourism brochures usefulness andimagerdquoAnnals of TourismResearch vol 33 no 4 pp 1036ndash10562006

[9] H Kim C-K Cheng and J T OrsquoLeary ldquoUnderstanding par-ticipation patterns and trends in tourism cultural attractionsrdquoTourism Management vol 28 no 5 pp 1366ndash1371 2007

[10] L Jin Y He andH Song ldquoService customization to upgrade orto downgrade An investigation of how option framing affectstouristsrsquo choice of package-tour servicesrdquoTourismManagementvol 33 no 2 pp 266ndash275 2012

[11] J Ashwell ldquoGoing bush Factors which influence internationaltouristsrsquo decisions to travel to remote Australian destinationsrdquoTourism Management vol 46 pp 80ndash83 2015

[12] V V Can ldquoEstimation of travel mode choice for domestictourists to Nha Trang using the multinomial probit modelrdquoTransportation Research Part A Policy and Practice vol 49 pp149ndash159 2013

[13] L Wu J Zhang and A Fujiwara ldquoTourism participation andexpenditure behaviour analysis using a scobit based discrete-continuous choice modelrdquo Annals of Tourism Research vol 40no 1 pp 1ndash17 2013

[14] G Prayag and C Ryan ldquoAntecedents of touristsrsquo loyalty tomauritius the role and influence of destination image placeattachment personal involvement and satisfactionrdquo Journal ofTravel Research vol 51 no 3 pp 342ndash356 2012

[15] N K Prebensen E Woo J S Chen and M Uysal ldquoMotivationand involvement as antecedents of the perceived value of thedestination experiencerdquo Journal of Travel Research vol 52 no2 pp 253ndash264 2013

[16] H Song R van der Veen G Li and J L Chen ldquoTheHongKongtourist satisfaction indexrdquo Annals of Tourism Research vol 39no 1 pp 459ndash479 2012

[17] J-Y Wong and C Yeh ldquoTourist hesitation in destinationdecision makingrdquo Annals of Tourism Research vol 36 no 1 pp6ndash23 2009

[18] B M Josiam D L Spears S Pookulangara K Dutta T RKinley and J L Duncan ldquoUsing structural equation modelingto understand the impact of Bollywood movies on destinationimage tourist activity and purchasing behavior of IndiansrdquoJournal of Vacation Marketing vol 21 no 3 pp 251ndash261 2015

[19] R Pinky Pawaskar and M Goel ldquoImproving the efficacy ofdestination marketing strategies a structural equation modelfor leisure travelrdquo Indian Journal of Science and Technology vol9 no 15 pp 1ndash11 2016

[20] H Zhang X Fu L A Cai and L Lu ldquoDestination image andtourist loyalty a meta-analysisrdquo Tourism Management vol 40pp 213ndash223 2014

[21] T-K Hsu Y-F Tsai andH-HWu ldquoThe preference analysis fortourist choice of destination a case study of Taiwanrdquo TourismManagement vol 30 no 2 pp 288ndash297 2009

[22] J-H Huang and K-H Peng ldquoFuzzy Rasch model in TOPSISa new approach for generating fuzzy numbers to assess thecompetitiveness of the tourism industries in Asian countriesrdquoTourism Management vol 33 no 2 pp 456ndash465 2012

Scientific Programming 9

[23] M H N Salleh R Othman and T Sarmidi ldquoTourist satis-faction of the environmental service quality for Tioman IslandMarine Parkrdquo Indian Journal of Geo-Marine Sciences vol 41 no2 pp 173ndash179 2012

[24] D Mohamad and R M Jamil ldquoA Preference analysis model forselecting tourist destinations based on motivational factors aCase Study in Kedah Malaysiardquo ProcediamdashSocial and Behav-ioral Sciences vol 65 pp 20ndash25 2012

[25] M Cucculelli and G Goffi ldquoDoes sustainability enhancetourism destination competitiveness Evidence from Italiandestinations of excellencerdquo Journal of Cleaner Production vol111 pp 370ndash382 2016

[26] J K S Jacobsen and A M Munar ldquoTourist information searchand destination choice in a digital agerdquo Tourism ManagementPerspectives vol 1 no 1 pp 39ndash47 2012

[27] A H N Mak M Lumbers A Eves and R C Y ChangldquoAn application of the repertory grid method and generalisedProcrustes analysis to investigate the motivational factors oftourist food consumptionrdquo International Journal of HospitalityManagement vol 35 pp 327ndash338 2013

[28] G Fuchs N Uriely A Reichel and D Maoz ldquoVacationingin a terror-stricken destination touristsrsquo risk perceptions andrationalizationsrdquo Journal of Travel Research vol 52 no 2 pp182ndash191 2013

[29] W Elias and Y Shiftan ldquoThe influence of individualrsquos riskperception and attitudes on travel behaviorrdquo TransportationResearch Part A Policy and Practice vol 46 no 8 pp 1241ndash12512012

[30] S Pike and S J Page ldquoDestination marketing organizationsand destinationmarketing anarrative analysis of the literaturerdquoTourism Management vol 41 pp 202ndash227 2014

[31] G I Crouch and J R B Ritchie ldquoApplication of the analytichierarchy process to tourism choice and decision making areview and illustration applied to destination competitivenessrdquoTourism Analysis vol 10 no 1 pp 17ndash25 2005

[32] D Das and K Mukherjee ldquoDevelopment of an AHP-QFDframework for designing a tourism productrdquo InternationalJournal of Services and Operations Management vol 4 no 3pp 321ndash344 2008

[33] HN Ali A A I Sabri andMMNNoor ldquoRating and rankingcriteria for selected Islands using fuzzy analytic hierarchyprocess (FAHP)rdquo International Journal of Applied Mathematicsand Informatics vol 1 no 6 pp 57ndash65 2012

[34] G Fan E D Goodman and Z Liu ldquoAHP (analytic hierarchyprocess) and computer analysis software used in tourism safetyrdquoJournal of Software vol 8 no 12 pp 3114ndash3119 2013

[35] K J PoolM Amani andAHaroni ldquoAssessment of alternativesfor tourism destinations advertising using AIDA model andAHP approachrdquo Asian Journal of Research in Marketing vol 3no 3 pp 88ndash97 2014

[36] Y Zhou K Maumbe J Deng and S W Selin ldquoResource-baseddestination competitiveness evaluation using a hybrid analytichierarchy process (AHP) the case study of West VirginiardquoTourism Management Perspectives vol 15 pp 72ndash80 2015

[37] X Wang X R Li F Zhen and J Zhang ldquoHow smart isyour tourist attraction measuring tourist preferences of smarttourism attractions via a FCEM-AHP and IPA approachrdquoTourism Management vol 54 pp 309ndash320 2016

[38] J S Chen and D Gursoy ldquoAn investigation of touristsrsquodestination loyalty and preferencesrdquo International Journal ofContemporary HospitalityManagement vol 13 no 2 pp 79ndash852001

[39] M Kozak ldquoComparative analysis of tourist motivations bynationality and destinationsrdquo TourismManagement vol 23 no3 pp 221ndash232 2002

[40] J De Vos P L Mokhtarian T Schwanen V Van Ackerand F Witlox ldquoTravel mode choice and travel satisfactionbridging the gap between decision utility and experiencedutilityrdquo Transportation vol 43 no 5 pp 771ndash796 2016

[41] U Probstl-Haider W Haider V Wirth and B BeardmoreldquoWill climate change increase the attractiveness of summerdestinations in the European Alps A survey of Germantouristsrdquo Journal of Outdoor Recreation and Tourism vol 11 pp44ndash57 2015

[42] A Garg ldquoTravel risks vs tourist decision making a touristperspectiverdquo International Journal of Hospitality amp TourismSystems vol 8 no 1 pp 1ndash9 2015

[43] S Amaro and P Duarte ldquoAn integrative model of consumersrsquointentions to purchase travel onlinerdquo TourismManagement vol46 pp 64ndash79 2015

[44] Z Tang ldquoAn integrated approach to evaluating the couplingcoordination between tourism and the environmentrdquo TourismManagement vol 46 pp 11ndash19 2015

[45] N Paker and C A Vural ldquoCustomer segmentation for marinasevaluating marinas as destinationsrdquo Tourism Management vol56 pp 156ndash171 2016

[46] T Huybers ldquoModelling short-break holiday destinationchoicesrdquo Tourism Economics vol 9 no 4 pp 389ndash405 2003

[47] B G C Dellaert D F Ettema and C Lindh ldquoMulti-facetedtourist travel decisions a constraint-based conceptual frame-work to describe touristsrsquo sequential choices of travel compo-nentsrdquo Tourism Management vol 19 no 4 pp 313ndash320 1998

[48] E Sirakaya and A G Woodside ldquoBuilding and testing theoriesof decisionmaking by travellersrdquo TourismManagement vol 26no 6 pp 815ndash832 2005

[49] T Lam and C H C Hsu ldquoPredicting behavioral intention ofchoosing a travel destinationrdquoTourismManagement vol 27 no4 pp 589ndash599 2006

[50] C Goossens ldquoTourism information and pleasure motivationrdquoAnnals of Tourism Research vol 27 no 2 pp 301ndash321 2000

[51] V C S Heung H Qu and R Chu ldquoThe relationship betweenvacation factors and socio-demographic and travelling char-acteristics the case of Japanese leisure travellersrdquo TourismManagement vol 22 no 3 pp 259ndash269 2001

[52] S S Kim and B Prideaux ldquoMarketing implications arisingfrom a comparative study of international pleasure touristmotivations and other travel-related characteristics of visitors toKoreardquo Tourism Management vol 26 no 3 pp 347ndash357 2005

[53] M Kozak ldquoComparative analysis of tourist motivations bynationality and destinationsrdquo TourismManagement vol 23 no3 pp 221ndash232 2002

[54] G Richards and J Wilson ldquoTodayrsquos youth travellers Tomor-rowrsquos global nomads New horizons in independent youthand student travelrdquo International Student Travel Confederation(ISTC) 2003

[55] N Tomis B Kovatevis N Berber and N Milis ldquoFactors influ-encing the motivation of young people when choosing a citydestination in Europemdasha case study from Esbjerg (Denmark)rdquoEuropean Researcher vol 69 no 2 pp 414ndash428 2014

[56] X D Guo H L Zhang and J H Ruan ldquoA novel similarity-based algorithm for classifying tourism scenic spotsrdquo ICICExpress Letters Part B Applications vol 6 no 2 pp 503ndash5092015

10 Scientific Programming

[57] J Moran E Granada J L Mıguez and J Porteiro ldquoUse of greyrelational analysis to assess and optimize small biomass boilersrdquoFuel Processing Technology vol 87 no 2 pp 123ndash127 2006

[58] D Ju-Long ldquoControl problems of grey systemsrdquo Systems ampControl Letters vol 1 no 5 pp 288ndash294 1982

[59] R-T Wang C-T B Ho and K Oh ldquoMeasuring productionand marketing efficiency using grey relation analysis anddata envelopment analysisrdquo International Journal of ProductionResearch vol 48 no 1 pp 183ndash199 2010

[60] C-T Ho ldquoMeasuring bank operations performance anapproach based on Grey Relation Analysisrdquo Journal of theOperational Research Society vol 57 no 4 pp 337ndash349 2006

[61] C-Y Kung and K-L Wen ldquoApplying Grey Relational AnalysisandGreyDecision-Making to evaluate the relationship betweencompany attributes and its financial performancemdasha case studyof venture capital enterprises in Taiwanrdquo Decision SupportSystems vol 43 no 3 pp 842ndash852 2007

[62] Y-J Wang ldquoCombining grey relation analysis with FMCGDMto evaluate financial performance of Taiwan container linesrdquoExpert Systems with Applications vol 36 no 2 pp 2424ndash24322009

[63] P T-W Lee C-W Lin and S-H Shin ldquoA comparative study onfinancial positions of shipping companies in Taiwan and Koreausing entropy and grey relation analysisrdquo Expert Systems withApplications vol 39 no 5 pp 5649ndash5657 2012

[64] F Ecer and A Boyukaslan ldquoMeasuring performances of foot-ball clubs using financial ratios the gray relational analysisapproachrdquo American Journal of Economics vol 4 no 1 pp 62ndash71 2014

[65] S J Lin I J Lu and C Lewis ldquoGrey relation performancecorrelations among economics energy use and carbon dioxideemission in Taiwanrdquo Energy Policy vol 35 no 3 pp 1948ndash19552007

[66] I J Lu S J Lin and C Lewis ldquoGrey relation analysis of motorvehicular energy consumption inTaiwanrdquoEnergy Policy vol 36no 7 pp 2556ndash2561 2008

[67] S H Hashemi A Karimi and M Tavana ldquoAn integratedgreen supplier selection approachwith analytic network processand improved Grey relational analysisrdquo International Journal ofProduction Economics vol 159 pp 178ndash191 2015

[68] DG Pearce ldquoTourist time-budgetrdquoAnnals of TourismResearchvol 15 no 1 pp 106ndash121 1988

[69] D Ju-Long ldquoIntroduction to grey system theoryrdquoThe Journal ofGrey System vol 1 no 1 pp 1ndash24 1989

[70] U Caydas and A Hascalık ldquoUse of the grey relational analysisto determine optimum laser cutting parameters with multi-performance characteristicsrdquo Optics amp Laser Technology vol40 no 7 pp 987ndash994 2008

Submit your manuscripts athttpwwwhindawicom

Computer Games Technology

International Journal of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Distributed Sensor Networks

International Journal of

Advances in

FuzzySystems

Hindawi Publishing Corporationhttpwwwhindawicom

Volume 2014

International Journal of

ReconfigurableComputing

Hindawi Publishing Corporation httpwwwhindawicom Volume 2014

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Applied Computational Intelligence and Soft Computing

thinspAdvancesthinspinthinsp

Artificial Intelligence

HindawithinspPublishingthinspCorporationhttpwwwhindawicom Volumethinsp2014

Advances inSoftware EngineeringHindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Electrical and Computer Engineering

Journal of

Journal of

Computer Networks and Communications

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Hindawi Publishing Corporation

httpwwwhindawicom Volume 2014

Advances in

Multimedia

International Journal of

Biomedical Imaging

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

ArtificialNeural Systems

Advances in

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

RoboticsJournal of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Computational Intelligence and Neuroscience

Industrial EngineeringJournal of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Modelling amp Simulation in EngineeringHindawi Publishing Corporation httpwwwhindawicom Volume 2014

The Scientific World JournalHindawi Publishing Corporation httpwwwhindawicom Volume 2014

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Human-ComputerInteraction

Advances in

Computer EngineeringAdvances in

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

4 Scientific Programming

thus an element from an incomplete message is considered tobe of ldquogreyrdquo element A ldquogrey relationrdquo refers to the measure-ment of changing relations between two systems or elementsthat occur in a system over timeThe analysis method whichmeasures the relations between elements based on the degreeof similarity or difference of development trends among theseelements is called ldquogrey relation analysisrdquo More preciselyduring the process of system development should the trendof change between two elements be consistent it then enjoysa higher grade of synchronized change and can be consideredas having a greater grade of relation otherwise the gradeof relation is smaller [59] It has been widely applied in avariety of fields such as financial performance evaluation [60ndash64] motor vehicular energy consumption [65 66] and greensupplier selection [67] As far as we know GRA has not beenapplied in evaluation and choice of tourism destination Webelieve we are the first to utilize the GRA to evaluate and rankthe tourism destinations

Personal preferences like motivations may be bothintrinsic reflecting individual likes and dislikes and extrin-sic or socially conditioned Pearce [68] stated that prefer-ences are more specific than motivations and are revealed bywhere travelers go and what travelers do However touristsrsquopreferences are psychological behaviors which are not easyto accurately measure having the feature of grey and fuzzyChoosing a tourism destination is a grey system evaluationprocess therefore the grey relation analysis is a more suitablemethod to evaluate it The evaluation procedure of this studyconsists of several stepsThedetailed descriptions of each stepare given in the following subsections

41 Problem Definition Assume that there are 119899 tourismdestinations 119879

1 1198792 119879

119899 which are to be evaluated in

terms of 119898 evaluation criteria 1198621 1198622 119862

119898 and relative

weights are denoted by 120596119894119895

(sum119898

119895=1120596119894119895

= 1) The objective ofthe decision-making is to rank these tourism destinationsAccording to the assumption we can get the original indexdata matrix

119877 = (119903119894119895)119899times119898

=

[

[

[

[

[

[

[

11990311

11990312

sdot sdot sdot 1199031119898

11990321

11990322

sdot sdot sdot 1199032119898

1199031198991

1199031198992

sdot sdot sdot 119903119899119898

]

]

]

]

]

]

]

(1)

where 119903119894119895is the observed value of object 119879

119894in terms of criteria

119862119895

(119894 = 1 2 119899 119895 = 1 2 119898)

Definition 1 The best tourism destination is the referentialsequence made up of the best tourism destination indexesamong all the alternative tourism destinations that is

1198770

= (11990301

11990302

1199030119895

) (2)

where 1199030119895

= Optimum(119903119894119895) (119894 = 1 2 119899 119895 = 1 2 119898)

So we can construct initial matrix 1198771

1198771

= (119903119894119895)119899times119898

=

[

[

[

[

[

[

[

[

[

[

11990301

11990302

sdot sdot sdot 1199030119898

11990311

11990312

sdot sdot sdot 1199031119898

11990321

11990322

sdot sdot sdot 1199032119898

sdot sdot sdot

1199031198991

1199031198992

sdot sdot sdot 119903119899119898

]

]

]

]

]

]

]

]

]

]

(3)

42 Normalization of Index Values In order to make com-parison between the various indicators we normalized eachof the index values in accordancewith the following formulasFor larger-is-better transformation 119903

119894119895can be transformed to

119909119894119895 The formula is defined as

119883119894119895

=

119903119894119895

minus min119895119903119894119895

max119895119903119894119895

minus min119895119903119894119895

(4)

For smaller-is-better transformation the formula totransform 119903

119894119895to 119909119894119895is

119883119894119895

=

max119895119903119894119895

minus 119903119894119895

max119895119903119894119895

minus min119895119903119894119895

(5)

The normalized values form a new matrix as shown in

119883 = (119909119894119895)119899times119898

=

[

[

[

[

[

[

[

[

[

[

11990901

11990902

sdot sdot sdot 1199090119898

11990911

11990912

sdot sdot sdot 1199091119898

11990921

11990922

sdot sdot sdot 1199092119898

sdot sdot sdot

1199091198991

1199091198992

sdot sdot sdot 119909119899119898

]

]

]

]

]

]

]

]

]

]

(6)

43 Calculating Grey Relation Coefficient According to Ju-Long [69] let 119883

0= (11990901

11990902

1199090119898

) as referential sequenceand 119883

119894= (1199091198941

1199091198942

119909119894119898

) as comparative sequence Then1199090119895and 119909119894119895would be the values of 119909

0and 119909119894at optimum index

119895 If 120585(1199090119895

119909119894119895) are real numbers then they can be defined as

120585 (1199090 119909119894) =

1

119898

119898

sum

119895=1

120585 (1199090119895

119909119894119895) (7)

The mean of 120585(1199090119895

119909119894119895) needs to meet the four axioms of grey

relation

Axiom 1 (norm interval) Consider

0 lt 120585(1199090119895

119909119894119895) le 1

120585(1199090119895

119909119894119895) = 1 harr 119909

0119895= 119909119894119895(this is called complete

relation)120585(1199090119895

119909119894119895) = 0 harr 119909

0isin 120593 119909

119894isin 120593 (this is called

complete nonrelation) where 120593 is an empty set

Axiom 2 (duality symmetric) Consider

1199090 119909119894isin 119883

120585(1199090119895

119909119894119895) = 120585(119909

119894119895 1199090119895

) harr 119883 = (1199090 119909119894)

Scientific Programming 5

Axiom 3 (wholeness) Consider

119909119895 119909119894isin 119883

120585(1199090119895

119909119894119895) = 120585(119909

119894119895 1199090119895

) this case almost alwayshappened

Axiom 4 (approachability) Consider

120585(1199090119895

119909119894119895) get larger along with |119909

0119895minus 119909119894119895| getting

smaller

If the abovementioned four axioms could all be satisfied120585(1199090 119909119894) is designed as the grade of grey relation in 119909

119894corre-

spondence to1199090 and 120585(119909

0119895 119909119894119895) is said to be the grey relational

coefficient of 119909119894119895to 1199090119895at optimum index 119895 Professor Ju-Long

[58] proposes a mathematical equation that will satisfy thesefour axioms of grey relation which is as follows

120585 (1198830119895

119883119894119895)

=

min119894min119895

100381610038161003816100381610038161198830119895

minus 119883119894119895

10038161003816100381610038161003816+ 120588max

119894max119895

100381610038161003816100381610038161198830119895

minus 119883119894119895

10038161003816100381610038161003816

100381610038161003816100381610038161198830119895

minus 119883119894119895

10038161003816100381610038161003816+ 120588max

119894max119895

100381610038161003816100381610038161198830119895

minus 119883119894119895

10038161003816100381610038161003816

(8)

where 120588 isin [0 1] is distinguished coefficient the function ofwhich is to reduce its numerical value bymax

119894max119895|1198830119895

minus119883119894119895|

increasing so as to affect its loss-authenticity and to heightenthe significance of difference among relational coefficientsand 120588 = 05 is generally used Relational coefficients120585(1198830119895

119883119894119895) matrix can be obtained through formula (8) As

a matter of convenience we use 120585119894119895instead of 120585(119883

0119895 119883119894119895) in

the following pages

119864 = (120585119894119895)119899119898

=

[

[

[

[

[

[

[

12058511

12058512

sdot sdot sdot 1205851119898

12058521

12058522

sdot sdot sdot 1205852119898

1205851198991

1205851198992

sdot sdot sdot 120585119899119898

]

]

]

]

]

]

]

(119894 = 1 2 119899 119895 = 1 2 119898)

(9)

44 Determining the Index Weights Based on Entropy Mak-ing normalization processing for formula (9) 120585

119894119895can be

transformed to 120583119894119895 and a new matrix (9) can be obtained as

follows

1198640

= (120583119894119895)119899119898

=

[

[

[

[

[

[

[

12058311

12058312

sdot sdot sdot 1205831119898

12058321

12058322

sdot sdot sdot 1205832119898

1205831198991

1205831198992

sdot sdot sdot 120583119899119898

]

]

]

]

]

]

]

(119894 = 1 2 119899 119895 = 1 2 119898)

(10)

where 0 le 120583119894119895

le 1 and sum119899

119894=1120583119894119895

= 1 then the informationentropy is

119867 (119895) = minus

119899

sum

119894=1

120583119894119895

sdot log 120583119894119895 (11)

Let

]119894119895

= 1 +

sum119899

119894=1120583119894119895

sdot log 120583119894119895

log 119899

(12)

Then we obtain the weights of indexes in terms offormula (12)

120596119894119895

=

]119894119895

sum119898

119895=1]119894119895

(0 le 120596119894119895

le 1

119898

sum

119895=1

120596119894119895

= 1) (13)

45 Calculating Grey Relational Grade After obtaining thegrey relational coefficient we normally take the weightedvalue of the grey relational coefficientmultiplied byweightingvalue as the grey relational grade The grey relational grade isdefined as follows [70]

119861 = 120596119894sdot (120585119894119895)

119879

119899119898= 120596119894sdot

[

[

[

[

[

[

[

12058511

12058521

sdot sdot sdot 1205851198991

12058512

12058522

sdot sdot sdot 1205851198992

1205851119898

1205851198992

sdot sdot sdot 120585119899119898

]

]

]

]

]

]

]

= (1198871 1198872 119887

119899)

(14)

where 119887119899is grey relational grade of the tourism destination119879

119899

which indicates the degree of influence that the comparativesequence could exert over the referential sequenceThereforeif a particular comparative sequence is better than the othercomparative sequences to the referential sequence then thegrey relational grade for that comparative sequence andreferential sequence will be higher than other grey relationalgrades Finally all tourismdestinations are evaluated synthet-ically according to the value of 119887

119899 where when 119887

119899is larger the

tourism destination 119879119899is better

5 A Numerical Example

In this section we use the proposed model to evaluate 8tourism destinations in Taiwan Province of China (includingTaipei 101 National Museum Sun Moon Lake AlishanYushan National Park Taroko National Park Love River andKenting National Park) Twenty-two (22) indexes identifiedfrom literature [21] as shown in Table 1 are used in the study(ldquoardquo is the best preference out of the 8 destinations)

51 The Relational Grade to Ideal Destination According tothe data from Table 1 we identify the optimum column as(716 716 759 640 715 854 745 804 787 673 856 860695 885 781 720 736 605 847 804 794 767) and incombination with Table 1 an initial matrix is obtained asfollows

6 Scientific Programming

Table 1 Overall preference measures of destinations

Criteria Taipei 101 National Museum Sun Moon Lake Alishan Yushan Park Taroko Park Love River Kenting ParkEscape 623 542 716a 680 594 708 599 692Self-actualization 716a 689 630 609 522 641 566 645Restrelaxation 538 505 759a 706 613 705 636 730Medical treatment 640a 441 430 434 302 409 538 473Health and fitness 505 410 657 698 634 715a 517 657Visiting friendrelative 796 554 854a 701 588 684 686 742Meeting new people 745a 555 598 555 437 561 576 680Novelty seeking 804a 608 535 506 416 522 561 629Culture exploration 627 787a 654 641 542 628 566 645Adventure seeking 397 343 596 656 630 667 494 673a

Enjoying night life 856a 553 449 395 343 441 645 580Transportation facilities 860a 803 620 528 404 545 678 670Friendliness of people 648 652 668 684 595 693 633 695a

Qualityvariety of food 885a 641 570 558 428 580 716 688Accommodation 781a 726 667 572 431 663 736 752Environment safety 610 638 698 681 607 720a 624 673Personal safety 736a 729 580 502 455 538 580 659Price 439 512 555 564 588 605a 530 530Culture and historicalresources 611 847a 678 672 622 663 560 649

Good shopping 804a 599 489 479 363 540 613 611Destination image 778 727 794a 706 653 692 638 714Benefits expectations 767a 680 541 541 460 532 648 667

1198771

=

[

[

[

[

[

[

[

[

[

[

[

[

[

[

[

[

[

[

[

[

716 716 759 640 715 854 745 804 787 673 856 860 695 885 781 720 736 605 847 804 794 767

623 716 538 640 505 796 745 804 627 397 856 860 648 885 781 610 736 439 611 804 778 767

542 689 505 441 410 554 555 608 787 343 553 803 652 641 726 638 729 512 847 599 727 680

716 630 759 430 657 854 598 535 654 596 449 620 668 570 667 698 580 555 678 489 794 541

680 609 706 434 698 701 555 506 641 656 395 528 684 558 572 681 502 564 672 479 706 541

594 522 613 302 634 588 437 416 542 630 343 404 595 428 431 607 455 588 622 363 653 460

708 641 705 409 715 684 561 522 628 667 441 545 693 580 663 720 538 605 663 540 692 532

599 566 636 538 517 686 576 561 566 494 645 678 633 716 736 624 580 530 560 613 638 648

692 645 730 473 657 742 680 629 645 673 580 670 695 688 752 673 659 530 649 611 714 667

]

]

]

]

]

]

]

]

]

]

]

]

]

]

]

]

]

]

]

]

(15)

Each of the index values is normalized according toformulas (4) and (5) and then a new matrix is obtained asfollows

119883

=

[

[

[

[

[

[

[

[

[

[

[

[

[

[

[

[

[

[

[

[

100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100

047 100 013 100 031 081 100 100 035 016 100 100 053 100 100 003 100 000 018 100 090 100

000 086 000 041 000 000 038 049 100 000 041 088 057 047 084 027 098 044 100 054 057 072

100 056 100 038 081 100 052 031 046 077 021 047 073 031 067 081 044 070 041 029 100 026

079 045 079 039 094 049 038 023 040 095 010 027 089 028 040 065 017 075 039 026 044 026

030 000 043 000 073 011 000 000 000 087 000 000 000 000 000 000 000 090 022 000 010 000

095 061 079 032 100 043 040 027 035 098 019 031 098 033 066 100 030 100 036 040 035 023

033 023 052 070 035 044 045 037 010 046 059 060 038 063 087 015 044 055 000 057 000 061

086 063 089 051 081 063 079 055 042 100 046 058 100 057 092 058 073 055 031 056 049 067

]

]

]

]

]

]

]

]

]

]

]

]

]

]

]

]

]

]

]

]

(16)

Scientific Programming 7

According to formula (8) that is 120585119894119895

= (0 + 05 times

1)(|1198830119895

minus 119883119894119895| + 05 times 1) we obtain the matrix of grey

relational coefficients as follows

120585119894119895

=

[

[

[

[

[

[

[

[

[

[

[

[

[

[

[

[

[

[

048 100 036 100 042 072 100 100 043 037 100 100 052 100 100 034 100 033 038 100 083 100

033 078 033 046 033 033 045 050 100 033 046 080 054 048 076 041 095 047 100 052 054 064

100 053 100 045 072 100 051 042 048 068 039 049 065 042 061 072 047 062 046 041 100 040

071 048 071 045 090 050 045 039 046 091 036 041 082 041 046 059 038 067 045 040 047 040

042 033 047 033 065 036 033 033 033 079 033 033 033 033 033 033 033 083 039 033 036 033

092 056 070 042 100 047 046 041 044 096 038 042 096 043 060 100 042 100 044 046 043 040

043 039 051 062 044 047 048 044 036 048 055 056 045 057 080 037 047 053 033 054 033 056

078 058 081 050 072 057 070 053 046 100 048 055 100 054 086 055 065 053 042 053 049 061

]

]

]

]

]

]

]

]

]

]

]

]

]

]

]

]

]

]

(17)

According to formulas (10)sim(13) the index weightsvector is calculated as follows

120596

= (005 004 005 004 004 004 004 005 005 005 005 004 004 004 003 005 006 004 005 004 005 005)

(18)

Using formula (14) we obtain the grey relational gradesas follows

119861 = (073 057 061 053 040 060 048 063) (19)

52 Comparison and Analysis In this paper we focus ondeveloping a novel evaluation approach tourist choice of des-tination based on grey relation analysis in order to demon-strate the advantages of the developed evaluation methodsand comparing the evaluation results with the evaluationmethod of ldquoTOPSISrdquo used by literature [21] So after obtain-ing the grey relational grade to the ideal destination by usingour proposed method we compare and analyze these resultswith the evaluation results obtained from the Technique forOrder Preference by Similarity to Ideal Solution (TOPSIS)used by literature [21] The results are shown in Table 2

As can be seen from Table 2 we can make the followingcomparative analysis

(i) The ranking results obtained from literature [21] andthe results in this paper are not completely the same asshown in column 2 and column 4 of Table 2 which reflect theconsistency and difference of two methods In other wordsthe ranking results of the top three and the last one are thesame but the others are not the same Interestingly almost allrelational grades to ideal solution of our proposed approachare bigger than those of the similarity to ideal solution by theTechnique for Order Preference by Similarity to Ideal Solu-tion (TOPSIS) evaluationmethod which shows that our pro-posed approach has the advantage over the TOPSIS approach

(ii) Comparing the overall preference measures of desti-nations as shown in Table 1 of literature [21] it is observedthat the Taroko National Park has three best preferences

out of the 24 factors and the National Palace Museum hastwo best preferences Therefore we intuitively think that thedestination with more best preferences should be rankedhigher than the destination with less best preferences andthe actual evaluation results are consistent with our intuitionwhich show that our proposed evaluation approach is morecredible and more in line with the actual situation Thefundamental reason why our proposed evaluation approachis more in line with the actual situation lies in its owncharacteristics In other words the touristsrsquo preferences totourism destinations are grey and fuzzy which is in line withthe cognition rules of human being to the objective world

(iii) No matter what kind of evaluation approach Taipei101 is ranked the number one favorite local destination Thatis Taipei 101 is worthy of visiting as it is the highest buildingin the world located in the business center of Taipei cityand a Taipei landmark As shown in Table 1 Taipei 101 isconsidered the most desirable destination with respect toldquoself-actualizationrdquo ldquomeeting new friendsrdquo ldquomedical treat-mentrdquo ldquonovelty seekingrdquo ldquoenjoying night liferdquo ldquotransporta-tion facilitiesrdquo ldquoquality and variety of foodrdquo ldquoaccommodationfacilitiesrdquo ldquogood shoppingrdquo ldquopersonal safetyrdquo and ldquobenefitexpectationsrdquo which reflects the consistency of twomethods

6 Conclusions

From a methodological point of view we proposed a greyrelation analysis evaluation model for choosing tourism des-tinations and the findings demonstrate that the grey relationanalysis evaluation approach is a useful tool to help supporta decision in destination choice It integrates the opinion

8 Scientific Programming

Table 2 Final ranking of destinations

Destination

Results of literature[21] Results in this paper

RankSimilarity to

idealsolution

RankRelational

grade to idealsolution

Taipei 101 1 075 1 073KentingNational Park 2 063 2 063

Sun Moon Lake 3 055 3 061National PalaceMuseum 4 048 5 057

Love River 5 047 7 048TarokoNational Park 6 043 4 060

Alishan 7 037 6 053YushanNational Park 8 016 8 040

and evaluation of experts and makes the complex decision-making system into a simple grey relation analysis systemIn other words our proposed evaluation approach makes theevaluation process simple and easy to conductWhat is moreit is especially preferred in ordering the alternatives in situa-tions in which the sample is small and sample distribution isnot known In our future studies we will apply the approachto evaluating other tourism destinations in China and makea ranking for Chinese main tourism destinations

Competing Interests

The authors declare that there are no competing interestsregarding the publication of this paper

Acknowledgments

This study is supported by the National Natural ScienceFoundation of China under Grant no 41440045

References

[1] V S Lin R Mao and H Song ldquoTourism expenditure patternsin ChinardquoAnnals of Tourism Research vol 54 pp 100ndash117 2015

[2] Domestic Tourism National Bureau of Statistics of China 2014httpdatastatsgovcnenglisheasyqueryhtmcn=C01

[3] S C S Jang and S Ham ldquoA double-hurdle analysis oftravel expenditure baby boomer seniors versus older seniorsrdquoTourism Management vol 30 no 3 pp 372ndash380 2009

[4] T Huybers and J Bennett ldquoImpact of the environment on holi-day destination choices of prospective UK tourists implicationsfor Tropical North Queenslandrdquo Tourism Economics vol 6 no1 pp 21ndash46 2000

[5] H R Seddighi andA LTheocharous ldquoAmodel of tourism des-tination choice a theoretical and empirical analysisrdquo TourismManagement vol 23 no 5 pp 475ndash487 2002

[6] G Lee J T OrsquoLeary L Sunhee and A Morrison ldquoComparisonand contrast of push and pull motivational effects on trip

behavior an application of a multinomial logistic regressionmodelrdquo Tourism Analysis vol 7 no 2 pp 89ndash104 2001

[7] H Oppewal T Huybers and G I Crouch ldquoTourist destinationand experience choice a choice experimental analysis of deci-sion sequence effectsrdquo Tourism Management vol 48 pp 467ndash476 2015

[8] A Molina and A Esteban ldquoTourism brochures usefulness andimagerdquoAnnals of TourismResearch vol 33 no 4 pp 1036ndash10562006

[9] H Kim C-K Cheng and J T OrsquoLeary ldquoUnderstanding par-ticipation patterns and trends in tourism cultural attractionsrdquoTourism Management vol 28 no 5 pp 1366ndash1371 2007

[10] L Jin Y He andH Song ldquoService customization to upgrade orto downgrade An investigation of how option framing affectstouristsrsquo choice of package-tour servicesrdquoTourismManagementvol 33 no 2 pp 266ndash275 2012

[11] J Ashwell ldquoGoing bush Factors which influence internationaltouristsrsquo decisions to travel to remote Australian destinationsrdquoTourism Management vol 46 pp 80ndash83 2015

[12] V V Can ldquoEstimation of travel mode choice for domestictourists to Nha Trang using the multinomial probit modelrdquoTransportation Research Part A Policy and Practice vol 49 pp149ndash159 2013

[13] L Wu J Zhang and A Fujiwara ldquoTourism participation andexpenditure behaviour analysis using a scobit based discrete-continuous choice modelrdquo Annals of Tourism Research vol 40no 1 pp 1ndash17 2013

[14] G Prayag and C Ryan ldquoAntecedents of touristsrsquo loyalty tomauritius the role and influence of destination image placeattachment personal involvement and satisfactionrdquo Journal ofTravel Research vol 51 no 3 pp 342ndash356 2012

[15] N K Prebensen E Woo J S Chen and M Uysal ldquoMotivationand involvement as antecedents of the perceived value of thedestination experiencerdquo Journal of Travel Research vol 52 no2 pp 253ndash264 2013

[16] H Song R van der Veen G Li and J L Chen ldquoTheHongKongtourist satisfaction indexrdquo Annals of Tourism Research vol 39no 1 pp 459ndash479 2012

[17] J-Y Wong and C Yeh ldquoTourist hesitation in destinationdecision makingrdquo Annals of Tourism Research vol 36 no 1 pp6ndash23 2009

[18] B M Josiam D L Spears S Pookulangara K Dutta T RKinley and J L Duncan ldquoUsing structural equation modelingto understand the impact of Bollywood movies on destinationimage tourist activity and purchasing behavior of IndiansrdquoJournal of Vacation Marketing vol 21 no 3 pp 251ndash261 2015

[19] R Pinky Pawaskar and M Goel ldquoImproving the efficacy ofdestination marketing strategies a structural equation modelfor leisure travelrdquo Indian Journal of Science and Technology vol9 no 15 pp 1ndash11 2016

[20] H Zhang X Fu L A Cai and L Lu ldquoDestination image andtourist loyalty a meta-analysisrdquo Tourism Management vol 40pp 213ndash223 2014

[21] T-K Hsu Y-F Tsai andH-HWu ldquoThe preference analysis fortourist choice of destination a case study of Taiwanrdquo TourismManagement vol 30 no 2 pp 288ndash297 2009

[22] J-H Huang and K-H Peng ldquoFuzzy Rasch model in TOPSISa new approach for generating fuzzy numbers to assess thecompetitiveness of the tourism industries in Asian countriesrdquoTourism Management vol 33 no 2 pp 456ndash465 2012

Scientific Programming 9

[23] M H N Salleh R Othman and T Sarmidi ldquoTourist satis-faction of the environmental service quality for Tioman IslandMarine Parkrdquo Indian Journal of Geo-Marine Sciences vol 41 no2 pp 173ndash179 2012

[24] D Mohamad and R M Jamil ldquoA Preference analysis model forselecting tourist destinations based on motivational factors aCase Study in Kedah Malaysiardquo ProcediamdashSocial and Behav-ioral Sciences vol 65 pp 20ndash25 2012

[25] M Cucculelli and G Goffi ldquoDoes sustainability enhancetourism destination competitiveness Evidence from Italiandestinations of excellencerdquo Journal of Cleaner Production vol111 pp 370ndash382 2016

[26] J K S Jacobsen and A M Munar ldquoTourist information searchand destination choice in a digital agerdquo Tourism ManagementPerspectives vol 1 no 1 pp 39ndash47 2012

[27] A H N Mak M Lumbers A Eves and R C Y ChangldquoAn application of the repertory grid method and generalisedProcrustes analysis to investigate the motivational factors oftourist food consumptionrdquo International Journal of HospitalityManagement vol 35 pp 327ndash338 2013

[28] G Fuchs N Uriely A Reichel and D Maoz ldquoVacationingin a terror-stricken destination touristsrsquo risk perceptions andrationalizationsrdquo Journal of Travel Research vol 52 no 2 pp182ndash191 2013

[29] W Elias and Y Shiftan ldquoThe influence of individualrsquos riskperception and attitudes on travel behaviorrdquo TransportationResearch Part A Policy and Practice vol 46 no 8 pp 1241ndash12512012

[30] S Pike and S J Page ldquoDestination marketing organizationsand destinationmarketing anarrative analysis of the literaturerdquoTourism Management vol 41 pp 202ndash227 2014

[31] G I Crouch and J R B Ritchie ldquoApplication of the analytichierarchy process to tourism choice and decision making areview and illustration applied to destination competitivenessrdquoTourism Analysis vol 10 no 1 pp 17ndash25 2005

[32] D Das and K Mukherjee ldquoDevelopment of an AHP-QFDframework for designing a tourism productrdquo InternationalJournal of Services and Operations Management vol 4 no 3pp 321ndash344 2008

[33] HN Ali A A I Sabri andMMNNoor ldquoRating and rankingcriteria for selected Islands using fuzzy analytic hierarchyprocess (FAHP)rdquo International Journal of Applied Mathematicsand Informatics vol 1 no 6 pp 57ndash65 2012

[34] G Fan E D Goodman and Z Liu ldquoAHP (analytic hierarchyprocess) and computer analysis software used in tourism safetyrdquoJournal of Software vol 8 no 12 pp 3114ndash3119 2013

[35] K J PoolM Amani andAHaroni ldquoAssessment of alternativesfor tourism destinations advertising using AIDA model andAHP approachrdquo Asian Journal of Research in Marketing vol 3no 3 pp 88ndash97 2014

[36] Y Zhou K Maumbe J Deng and S W Selin ldquoResource-baseddestination competitiveness evaluation using a hybrid analytichierarchy process (AHP) the case study of West VirginiardquoTourism Management Perspectives vol 15 pp 72ndash80 2015

[37] X Wang X R Li F Zhen and J Zhang ldquoHow smart isyour tourist attraction measuring tourist preferences of smarttourism attractions via a FCEM-AHP and IPA approachrdquoTourism Management vol 54 pp 309ndash320 2016

[38] J S Chen and D Gursoy ldquoAn investigation of touristsrsquodestination loyalty and preferencesrdquo International Journal ofContemporary HospitalityManagement vol 13 no 2 pp 79ndash852001

[39] M Kozak ldquoComparative analysis of tourist motivations bynationality and destinationsrdquo TourismManagement vol 23 no3 pp 221ndash232 2002

[40] J De Vos P L Mokhtarian T Schwanen V Van Ackerand F Witlox ldquoTravel mode choice and travel satisfactionbridging the gap between decision utility and experiencedutilityrdquo Transportation vol 43 no 5 pp 771ndash796 2016

[41] U Probstl-Haider W Haider V Wirth and B BeardmoreldquoWill climate change increase the attractiveness of summerdestinations in the European Alps A survey of Germantouristsrdquo Journal of Outdoor Recreation and Tourism vol 11 pp44ndash57 2015

[42] A Garg ldquoTravel risks vs tourist decision making a touristperspectiverdquo International Journal of Hospitality amp TourismSystems vol 8 no 1 pp 1ndash9 2015

[43] S Amaro and P Duarte ldquoAn integrative model of consumersrsquointentions to purchase travel onlinerdquo TourismManagement vol46 pp 64ndash79 2015

[44] Z Tang ldquoAn integrated approach to evaluating the couplingcoordination between tourism and the environmentrdquo TourismManagement vol 46 pp 11ndash19 2015

[45] N Paker and C A Vural ldquoCustomer segmentation for marinasevaluating marinas as destinationsrdquo Tourism Management vol56 pp 156ndash171 2016

[46] T Huybers ldquoModelling short-break holiday destinationchoicesrdquo Tourism Economics vol 9 no 4 pp 389ndash405 2003

[47] B G C Dellaert D F Ettema and C Lindh ldquoMulti-facetedtourist travel decisions a constraint-based conceptual frame-work to describe touristsrsquo sequential choices of travel compo-nentsrdquo Tourism Management vol 19 no 4 pp 313ndash320 1998

[48] E Sirakaya and A G Woodside ldquoBuilding and testing theoriesof decisionmaking by travellersrdquo TourismManagement vol 26no 6 pp 815ndash832 2005

[49] T Lam and C H C Hsu ldquoPredicting behavioral intention ofchoosing a travel destinationrdquoTourismManagement vol 27 no4 pp 589ndash599 2006

[50] C Goossens ldquoTourism information and pleasure motivationrdquoAnnals of Tourism Research vol 27 no 2 pp 301ndash321 2000

[51] V C S Heung H Qu and R Chu ldquoThe relationship betweenvacation factors and socio-demographic and travelling char-acteristics the case of Japanese leisure travellersrdquo TourismManagement vol 22 no 3 pp 259ndash269 2001

[52] S S Kim and B Prideaux ldquoMarketing implications arisingfrom a comparative study of international pleasure touristmotivations and other travel-related characteristics of visitors toKoreardquo Tourism Management vol 26 no 3 pp 347ndash357 2005

[53] M Kozak ldquoComparative analysis of tourist motivations bynationality and destinationsrdquo TourismManagement vol 23 no3 pp 221ndash232 2002

[54] G Richards and J Wilson ldquoTodayrsquos youth travellers Tomor-rowrsquos global nomads New horizons in independent youthand student travelrdquo International Student Travel Confederation(ISTC) 2003

[55] N Tomis B Kovatevis N Berber and N Milis ldquoFactors influ-encing the motivation of young people when choosing a citydestination in Europemdasha case study from Esbjerg (Denmark)rdquoEuropean Researcher vol 69 no 2 pp 414ndash428 2014

[56] X D Guo H L Zhang and J H Ruan ldquoA novel similarity-based algorithm for classifying tourism scenic spotsrdquo ICICExpress Letters Part B Applications vol 6 no 2 pp 503ndash5092015

10 Scientific Programming

[57] J Moran E Granada J L Mıguez and J Porteiro ldquoUse of greyrelational analysis to assess and optimize small biomass boilersrdquoFuel Processing Technology vol 87 no 2 pp 123ndash127 2006

[58] D Ju-Long ldquoControl problems of grey systemsrdquo Systems ampControl Letters vol 1 no 5 pp 288ndash294 1982

[59] R-T Wang C-T B Ho and K Oh ldquoMeasuring productionand marketing efficiency using grey relation analysis anddata envelopment analysisrdquo International Journal of ProductionResearch vol 48 no 1 pp 183ndash199 2010

[60] C-T Ho ldquoMeasuring bank operations performance anapproach based on Grey Relation Analysisrdquo Journal of theOperational Research Society vol 57 no 4 pp 337ndash349 2006

[61] C-Y Kung and K-L Wen ldquoApplying Grey Relational AnalysisandGreyDecision-Making to evaluate the relationship betweencompany attributes and its financial performancemdasha case studyof venture capital enterprises in Taiwanrdquo Decision SupportSystems vol 43 no 3 pp 842ndash852 2007

[62] Y-J Wang ldquoCombining grey relation analysis with FMCGDMto evaluate financial performance of Taiwan container linesrdquoExpert Systems with Applications vol 36 no 2 pp 2424ndash24322009

[63] P T-W Lee C-W Lin and S-H Shin ldquoA comparative study onfinancial positions of shipping companies in Taiwan and Koreausing entropy and grey relation analysisrdquo Expert Systems withApplications vol 39 no 5 pp 5649ndash5657 2012

[64] F Ecer and A Boyukaslan ldquoMeasuring performances of foot-ball clubs using financial ratios the gray relational analysisapproachrdquo American Journal of Economics vol 4 no 1 pp 62ndash71 2014

[65] S J Lin I J Lu and C Lewis ldquoGrey relation performancecorrelations among economics energy use and carbon dioxideemission in Taiwanrdquo Energy Policy vol 35 no 3 pp 1948ndash19552007

[66] I J Lu S J Lin and C Lewis ldquoGrey relation analysis of motorvehicular energy consumption inTaiwanrdquoEnergy Policy vol 36no 7 pp 2556ndash2561 2008

[67] S H Hashemi A Karimi and M Tavana ldquoAn integratedgreen supplier selection approachwith analytic network processand improved Grey relational analysisrdquo International Journal ofProduction Economics vol 159 pp 178ndash191 2015

[68] DG Pearce ldquoTourist time-budgetrdquoAnnals of TourismResearchvol 15 no 1 pp 106ndash121 1988

[69] D Ju-Long ldquoIntroduction to grey system theoryrdquoThe Journal ofGrey System vol 1 no 1 pp 1ndash24 1989

[70] U Caydas and A Hascalık ldquoUse of the grey relational analysisto determine optimum laser cutting parameters with multi-performance characteristicsrdquo Optics amp Laser Technology vol40 no 7 pp 987ndash994 2008

Submit your manuscripts athttpwwwhindawicom

Computer Games Technology

International Journal of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Distributed Sensor Networks

International Journal of

Advances in

FuzzySystems

Hindawi Publishing Corporationhttpwwwhindawicom

Volume 2014

International Journal of

ReconfigurableComputing

Hindawi Publishing Corporation httpwwwhindawicom Volume 2014

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Applied Computational Intelligence and Soft Computing

thinspAdvancesthinspinthinsp

Artificial Intelligence

HindawithinspPublishingthinspCorporationhttpwwwhindawicom Volumethinsp2014

Advances inSoftware EngineeringHindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Electrical and Computer Engineering

Journal of

Journal of

Computer Networks and Communications

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Hindawi Publishing Corporation

httpwwwhindawicom Volume 2014

Advances in

Multimedia

International Journal of

Biomedical Imaging

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

ArtificialNeural Systems

Advances in

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

RoboticsJournal of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Computational Intelligence and Neuroscience

Industrial EngineeringJournal of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Modelling amp Simulation in EngineeringHindawi Publishing Corporation httpwwwhindawicom Volume 2014

The Scientific World JournalHindawi Publishing Corporation httpwwwhindawicom Volume 2014

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Human-ComputerInteraction

Advances in

Computer EngineeringAdvances in

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Scientific Programming 5

Axiom 3 (wholeness) Consider

119909119895 119909119894isin 119883

120585(1199090119895

119909119894119895) = 120585(119909

119894119895 1199090119895

) this case almost alwayshappened

Axiom 4 (approachability) Consider

120585(1199090119895

119909119894119895) get larger along with |119909

0119895minus 119909119894119895| getting

smaller

If the abovementioned four axioms could all be satisfied120585(1199090 119909119894) is designed as the grade of grey relation in 119909

119894corre-

spondence to1199090 and 120585(119909

0119895 119909119894119895) is said to be the grey relational

coefficient of 119909119894119895to 1199090119895at optimum index 119895 Professor Ju-Long

[58] proposes a mathematical equation that will satisfy thesefour axioms of grey relation which is as follows

120585 (1198830119895

119883119894119895)

=

min119894min119895

100381610038161003816100381610038161198830119895

minus 119883119894119895

10038161003816100381610038161003816+ 120588max

119894max119895

100381610038161003816100381610038161198830119895

minus 119883119894119895

10038161003816100381610038161003816

100381610038161003816100381610038161198830119895

minus 119883119894119895

10038161003816100381610038161003816+ 120588max

119894max119895

100381610038161003816100381610038161198830119895

minus 119883119894119895

10038161003816100381610038161003816

(8)

where 120588 isin [0 1] is distinguished coefficient the function ofwhich is to reduce its numerical value bymax

119894max119895|1198830119895

minus119883119894119895|

increasing so as to affect its loss-authenticity and to heightenthe significance of difference among relational coefficientsand 120588 = 05 is generally used Relational coefficients120585(1198830119895

119883119894119895) matrix can be obtained through formula (8) As

a matter of convenience we use 120585119894119895instead of 120585(119883

0119895 119883119894119895) in

the following pages

119864 = (120585119894119895)119899119898

=

[

[

[

[

[

[

[

12058511

12058512

sdot sdot sdot 1205851119898

12058521

12058522

sdot sdot sdot 1205852119898

1205851198991

1205851198992

sdot sdot sdot 120585119899119898

]

]

]

]

]

]

]

(119894 = 1 2 119899 119895 = 1 2 119898)

(9)

44 Determining the Index Weights Based on Entropy Mak-ing normalization processing for formula (9) 120585

119894119895can be

transformed to 120583119894119895 and a new matrix (9) can be obtained as

follows

1198640

= (120583119894119895)119899119898

=

[

[

[

[

[

[

[

12058311

12058312

sdot sdot sdot 1205831119898

12058321

12058322

sdot sdot sdot 1205832119898

1205831198991

1205831198992

sdot sdot sdot 120583119899119898

]

]

]

]

]

]

]

(119894 = 1 2 119899 119895 = 1 2 119898)

(10)

where 0 le 120583119894119895

le 1 and sum119899

119894=1120583119894119895

= 1 then the informationentropy is

119867 (119895) = minus

119899

sum

119894=1

120583119894119895

sdot log 120583119894119895 (11)

Let

]119894119895

= 1 +

sum119899

119894=1120583119894119895

sdot log 120583119894119895

log 119899

(12)

Then we obtain the weights of indexes in terms offormula (12)

120596119894119895

=

]119894119895

sum119898

119895=1]119894119895

(0 le 120596119894119895

le 1

119898

sum

119895=1

120596119894119895

= 1) (13)

45 Calculating Grey Relational Grade After obtaining thegrey relational coefficient we normally take the weightedvalue of the grey relational coefficientmultiplied byweightingvalue as the grey relational grade The grey relational grade isdefined as follows [70]

119861 = 120596119894sdot (120585119894119895)

119879

119899119898= 120596119894sdot

[

[

[

[

[

[

[

12058511

12058521

sdot sdot sdot 1205851198991

12058512

12058522

sdot sdot sdot 1205851198992

1205851119898

1205851198992

sdot sdot sdot 120585119899119898

]

]

]

]

]

]

]

= (1198871 1198872 119887

119899)

(14)

where 119887119899is grey relational grade of the tourism destination119879

119899

which indicates the degree of influence that the comparativesequence could exert over the referential sequenceThereforeif a particular comparative sequence is better than the othercomparative sequences to the referential sequence then thegrey relational grade for that comparative sequence andreferential sequence will be higher than other grey relationalgrades Finally all tourismdestinations are evaluated synthet-ically according to the value of 119887

119899 where when 119887

119899is larger the

tourism destination 119879119899is better

5 A Numerical Example

In this section we use the proposed model to evaluate 8tourism destinations in Taiwan Province of China (includingTaipei 101 National Museum Sun Moon Lake AlishanYushan National Park Taroko National Park Love River andKenting National Park) Twenty-two (22) indexes identifiedfrom literature [21] as shown in Table 1 are used in the study(ldquoardquo is the best preference out of the 8 destinations)

51 The Relational Grade to Ideal Destination According tothe data from Table 1 we identify the optimum column as(716 716 759 640 715 854 745 804 787 673 856 860695 885 781 720 736 605 847 804 794 767) and incombination with Table 1 an initial matrix is obtained asfollows

6 Scientific Programming

Table 1 Overall preference measures of destinations

Criteria Taipei 101 National Museum Sun Moon Lake Alishan Yushan Park Taroko Park Love River Kenting ParkEscape 623 542 716a 680 594 708 599 692Self-actualization 716a 689 630 609 522 641 566 645Restrelaxation 538 505 759a 706 613 705 636 730Medical treatment 640a 441 430 434 302 409 538 473Health and fitness 505 410 657 698 634 715a 517 657Visiting friendrelative 796 554 854a 701 588 684 686 742Meeting new people 745a 555 598 555 437 561 576 680Novelty seeking 804a 608 535 506 416 522 561 629Culture exploration 627 787a 654 641 542 628 566 645Adventure seeking 397 343 596 656 630 667 494 673a

Enjoying night life 856a 553 449 395 343 441 645 580Transportation facilities 860a 803 620 528 404 545 678 670Friendliness of people 648 652 668 684 595 693 633 695a

Qualityvariety of food 885a 641 570 558 428 580 716 688Accommodation 781a 726 667 572 431 663 736 752Environment safety 610 638 698 681 607 720a 624 673Personal safety 736a 729 580 502 455 538 580 659Price 439 512 555 564 588 605a 530 530Culture and historicalresources 611 847a 678 672 622 663 560 649

Good shopping 804a 599 489 479 363 540 613 611Destination image 778 727 794a 706 653 692 638 714Benefits expectations 767a 680 541 541 460 532 648 667

1198771

=

[

[

[

[

[

[

[

[

[

[

[

[

[

[

[

[

[

[

[

[

716 716 759 640 715 854 745 804 787 673 856 860 695 885 781 720 736 605 847 804 794 767

623 716 538 640 505 796 745 804 627 397 856 860 648 885 781 610 736 439 611 804 778 767

542 689 505 441 410 554 555 608 787 343 553 803 652 641 726 638 729 512 847 599 727 680

716 630 759 430 657 854 598 535 654 596 449 620 668 570 667 698 580 555 678 489 794 541

680 609 706 434 698 701 555 506 641 656 395 528 684 558 572 681 502 564 672 479 706 541

594 522 613 302 634 588 437 416 542 630 343 404 595 428 431 607 455 588 622 363 653 460

708 641 705 409 715 684 561 522 628 667 441 545 693 580 663 720 538 605 663 540 692 532

599 566 636 538 517 686 576 561 566 494 645 678 633 716 736 624 580 530 560 613 638 648

692 645 730 473 657 742 680 629 645 673 580 670 695 688 752 673 659 530 649 611 714 667

]

]

]

]

]

]

]

]

]

]

]

]

]

]

]

]

]

]

]

]

(15)

Each of the index values is normalized according toformulas (4) and (5) and then a new matrix is obtained asfollows

119883

=

[

[

[

[

[

[

[

[

[

[

[

[

[

[

[

[

[

[

[

[

100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100

047 100 013 100 031 081 100 100 035 016 100 100 053 100 100 003 100 000 018 100 090 100

000 086 000 041 000 000 038 049 100 000 041 088 057 047 084 027 098 044 100 054 057 072

100 056 100 038 081 100 052 031 046 077 021 047 073 031 067 081 044 070 041 029 100 026

079 045 079 039 094 049 038 023 040 095 010 027 089 028 040 065 017 075 039 026 044 026

030 000 043 000 073 011 000 000 000 087 000 000 000 000 000 000 000 090 022 000 010 000

095 061 079 032 100 043 040 027 035 098 019 031 098 033 066 100 030 100 036 040 035 023

033 023 052 070 035 044 045 037 010 046 059 060 038 063 087 015 044 055 000 057 000 061

086 063 089 051 081 063 079 055 042 100 046 058 100 057 092 058 073 055 031 056 049 067

]

]

]

]

]

]

]

]

]

]

]

]

]

]

]

]

]

]

]

]

(16)

Scientific Programming 7

According to formula (8) that is 120585119894119895

= (0 + 05 times

1)(|1198830119895

minus 119883119894119895| + 05 times 1) we obtain the matrix of grey

relational coefficients as follows

120585119894119895

=

[

[

[

[

[

[

[

[

[

[

[

[

[

[

[

[

[

[

048 100 036 100 042 072 100 100 043 037 100 100 052 100 100 034 100 033 038 100 083 100

033 078 033 046 033 033 045 050 100 033 046 080 054 048 076 041 095 047 100 052 054 064

100 053 100 045 072 100 051 042 048 068 039 049 065 042 061 072 047 062 046 041 100 040

071 048 071 045 090 050 045 039 046 091 036 041 082 041 046 059 038 067 045 040 047 040

042 033 047 033 065 036 033 033 033 079 033 033 033 033 033 033 033 083 039 033 036 033

092 056 070 042 100 047 046 041 044 096 038 042 096 043 060 100 042 100 044 046 043 040

043 039 051 062 044 047 048 044 036 048 055 056 045 057 080 037 047 053 033 054 033 056

078 058 081 050 072 057 070 053 046 100 048 055 100 054 086 055 065 053 042 053 049 061

]

]

]

]

]

]

]

]

]

]

]

]

]

]

]

]

]

]

(17)

According to formulas (10)sim(13) the index weightsvector is calculated as follows

120596

= (005 004 005 004 004 004 004 005 005 005 005 004 004 004 003 005 006 004 005 004 005 005)

(18)

Using formula (14) we obtain the grey relational gradesas follows

119861 = (073 057 061 053 040 060 048 063) (19)

52 Comparison and Analysis In this paper we focus ondeveloping a novel evaluation approach tourist choice of des-tination based on grey relation analysis in order to demon-strate the advantages of the developed evaluation methodsand comparing the evaluation results with the evaluationmethod of ldquoTOPSISrdquo used by literature [21] So after obtain-ing the grey relational grade to the ideal destination by usingour proposed method we compare and analyze these resultswith the evaluation results obtained from the Technique forOrder Preference by Similarity to Ideal Solution (TOPSIS)used by literature [21] The results are shown in Table 2

As can be seen from Table 2 we can make the followingcomparative analysis

(i) The ranking results obtained from literature [21] andthe results in this paper are not completely the same asshown in column 2 and column 4 of Table 2 which reflect theconsistency and difference of two methods In other wordsthe ranking results of the top three and the last one are thesame but the others are not the same Interestingly almost allrelational grades to ideal solution of our proposed approachare bigger than those of the similarity to ideal solution by theTechnique for Order Preference by Similarity to Ideal Solu-tion (TOPSIS) evaluationmethod which shows that our pro-posed approach has the advantage over the TOPSIS approach

(ii) Comparing the overall preference measures of desti-nations as shown in Table 1 of literature [21] it is observedthat the Taroko National Park has three best preferences

out of the 24 factors and the National Palace Museum hastwo best preferences Therefore we intuitively think that thedestination with more best preferences should be rankedhigher than the destination with less best preferences andthe actual evaluation results are consistent with our intuitionwhich show that our proposed evaluation approach is morecredible and more in line with the actual situation Thefundamental reason why our proposed evaluation approachis more in line with the actual situation lies in its owncharacteristics In other words the touristsrsquo preferences totourism destinations are grey and fuzzy which is in line withthe cognition rules of human being to the objective world

(iii) No matter what kind of evaluation approach Taipei101 is ranked the number one favorite local destination Thatis Taipei 101 is worthy of visiting as it is the highest buildingin the world located in the business center of Taipei cityand a Taipei landmark As shown in Table 1 Taipei 101 isconsidered the most desirable destination with respect toldquoself-actualizationrdquo ldquomeeting new friendsrdquo ldquomedical treat-mentrdquo ldquonovelty seekingrdquo ldquoenjoying night liferdquo ldquotransporta-tion facilitiesrdquo ldquoquality and variety of foodrdquo ldquoaccommodationfacilitiesrdquo ldquogood shoppingrdquo ldquopersonal safetyrdquo and ldquobenefitexpectationsrdquo which reflects the consistency of twomethods

6 Conclusions

From a methodological point of view we proposed a greyrelation analysis evaluation model for choosing tourism des-tinations and the findings demonstrate that the grey relationanalysis evaluation approach is a useful tool to help supporta decision in destination choice It integrates the opinion

8 Scientific Programming

Table 2 Final ranking of destinations

Destination

Results of literature[21] Results in this paper

RankSimilarity to

idealsolution

RankRelational

grade to idealsolution

Taipei 101 1 075 1 073KentingNational Park 2 063 2 063

Sun Moon Lake 3 055 3 061National PalaceMuseum 4 048 5 057

Love River 5 047 7 048TarokoNational Park 6 043 4 060

Alishan 7 037 6 053YushanNational Park 8 016 8 040

and evaluation of experts and makes the complex decision-making system into a simple grey relation analysis systemIn other words our proposed evaluation approach makes theevaluation process simple and easy to conductWhat is moreit is especially preferred in ordering the alternatives in situa-tions in which the sample is small and sample distribution isnot known In our future studies we will apply the approachto evaluating other tourism destinations in China and makea ranking for Chinese main tourism destinations

Competing Interests

The authors declare that there are no competing interestsregarding the publication of this paper

Acknowledgments

This study is supported by the National Natural ScienceFoundation of China under Grant no 41440045

References

[1] V S Lin R Mao and H Song ldquoTourism expenditure patternsin ChinardquoAnnals of Tourism Research vol 54 pp 100ndash117 2015

[2] Domestic Tourism National Bureau of Statistics of China 2014httpdatastatsgovcnenglisheasyqueryhtmcn=C01

[3] S C S Jang and S Ham ldquoA double-hurdle analysis oftravel expenditure baby boomer seniors versus older seniorsrdquoTourism Management vol 30 no 3 pp 372ndash380 2009

[4] T Huybers and J Bennett ldquoImpact of the environment on holi-day destination choices of prospective UK tourists implicationsfor Tropical North Queenslandrdquo Tourism Economics vol 6 no1 pp 21ndash46 2000

[5] H R Seddighi andA LTheocharous ldquoAmodel of tourism des-tination choice a theoretical and empirical analysisrdquo TourismManagement vol 23 no 5 pp 475ndash487 2002

[6] G Lee J T OrsquoLeary L Sunhee and A Morrison ldquoComparisonand contrast of push and pull motivational effects on trip

behavior an application of a multinomial logistic regressionmodelrdquo Tourism Analysis vol 7 no 2 pp 89ndash104 2001

[7] H Oppewal T Huybers and G I Crouch ldquoTourist destinationand experience choice a choice experimental analysis of deci-sion sequence effectsrdquo Tourism Management vol 48 pp 467ndash476 2015

[8] A Molina and A Esteban ldquoTourism brochures usefulness andimagerdquoAnnals of TourismResearch vol 33 no 4 pp 1036ndash10562006

[9] H Kim C-K Cheng and J T OrsquoLeary ldquoUnderstanding par-ticipation patterns and trends in tourism cultural attractionsrdquoTourism Management vol 28 no 5 pp 1366ndash1371 2007

[10] L Jin Y He andH Song ldquoService customization to upgrade orto downgrade An investigation of how option framing affectstouristsrsquo choice of package-tour servicesrdquoTourismManagementvol 33 no 2 pp 266ndash275 2012

[11] J Ashwell ldquoGoing bush Factors which influence internationaltouristsrsquo decisions to travel to remote Australian destinationsrdquoTourism Management vol 46 pp 80ndash83 2015

[12] V V Can ldquoEstimation of travel mode choice for domestictourists to Nha Trang using the multinomial probit modelrdquoTransportation Research Part A Policy and Practice vol 49 pp149ndash159 2013

[13] L Wu J Zhang and A Fujiwara ldquoTourism participation andexpenditure behaviour analysis using a scobit based discrete-continuous choice modelrdquo Annals of Tourism Research vol 40no 1 pp 1ndash17 2013

[14] G Prayag and C Ryan ldquoAntecedents of touristsrsquo loyalty tomauritius the role and influence of destination image placeattachment personal involvement and satisfactionrdquo Journal ofTravel Research vol 51 no 3 pp 342ndash356 2012

[15] N K Prebensen E Woo J S Chen and M Uysal ldquoMotivationand involvement as antecedents of the perceived value of thedestination experiencerdquo Journal of Travel Research vol 52 no2 pp 253ndash264 2013

[16] H Song R van der Veen G Li and J L Chen ldquoTheHongKongtourist satisfaction indexrdquo Annals of Tourism Research vol 39no 1 pp 459ndash479 2012

[17] J-Y Wong and C Yeh ldquoTourist hesitation in destinationdecision makingrdquo Annals of Tourism Research vol 36 no 1 pp6ndash23 2009

[18] B M Josiam D L Spears S Pookulangara K Dutta T RKinley and J L Duncan ldquoUsing structural equation modelingto understand the impact of Bollywood movies on destinationimage tourist activity and purchasing behavior of IndiansrdquoJournal of Vacation Marketing vol 21 no 3 pp 251ndash261 2015

[19] R Pinky Pawaskar and M Goel ldquoImproving the efficacy ofdestination marketing strategies a structural equation modelfor leisure travelrdquo Indian Journal of Science and Technology vol9 no 15 pp 1ndash11 2016

[20] H Zhang X Fu L A Cai and L Lu ldquoDestination image andtourist loyalty a meta-analysisrdquo Tourism Management vol 40pp 213ndash223 2014

[21] T-K Hsu Y-F Tsai andH-HWu ldquoThe preference analysis fortourist choice of destination a case study of Taiwanrdquo TourismManagement vol 30 no 2 pp 288ndash297 2009

[22] J-H Huang and K-H Peng ldquoFuzzy Rasch model in TOPSISa new approach for generating fuzzy numbers to assess thecompetitiveness of the tourism industries in Asian countriesrdquoTourism Management vol 33 no 2 pp 456ndash465 2012

Scientific Programming 9

[23] M H N Salleh R Othman and T Sarmidi ldquoTourist satis-faction of the environmental service quality for Tioman IslandMarine Parkrdquo Indian Journal of Geo-Marine Sciences vol 41 no2 pp 173ndash179 2012

[24] D Mohamad and R M Jamil ldquoA Preference analysis model forselecting tourist destinations based on motivational factors aCase Study in Kedah Malaysiardquo ProcediamdashSocial and Behav-ioral Sciences vol 65 pp 20ndash25 2012

[25] M Cucculelli and G Goffi ldquoDoes sustainability enhancetourism destination competitiveness Evidence from Italiandestinations of excellencerdquo Journal of Cleaner Production vol111 pp 370ndash382 2016

[26] J K S Jacobsen and A M Munar ldquoTourist information searchand destination choice in a digital agerdquo Tourism ManagementPerspectives vol 1 no 1 pp 39ndash47 2012

[27] A H N Mak M Lumbers A Eves and R C Y ChangldquoAn application of the repertory grid method and generalisedProcrustes analysis to investigate the motivational factors oftourist food consumptionrdquo International Journal of HospitalityManagement vol 35 pp 327ndash338 2013

[28] G Fuchs N Uriely A Reichel and D Maoz ldquoVacationingin a terror-stricken destination touristsrsquo risk perceptions andrationalizationsrdquo Journal of Travel Research vol 52 no 2 pp182ndash191 2013

[29] W Elias and Y Shiftan ldquoThe influence of individualrsquos riskperception and attitudes on travel behaviorrdquo TransportationResearch Part A Policy and Practice vol 46 no 8 pp 1241ndash12512012

[30] S Pike and S J Page ldquoDestination marketing organizationsand destinationmarketing anarrative analysis of the literaturerdquoTourism Management vol 41 pp 202ndash227 2014

[31] G I Crouch and J R B Ritchie ldquoApplication of the analytichierarchy process to tourism choice and decision making areview and illustration applied to destination competitivenessrdquoTourism Analysis vol 10 no 1 pp 17ndash25 2005

[32] D Das and K Mukherjee ldquoDevelopment of an AHP-QFDframework for designing a tourism productrdquo InternationalJournal of Services and Operations Management vol 4 no 3pp 321ndash344 2008

[33] HN Ali A A I Sabri andMMNNoor ldquoRating and rankingcriteria for selected Islands using fuzzy analytic hierarchyprocess (FAHP)rdquo International Journal of Applied Mathematicsand Informatics vol 1 no 6 pp 57ndash65 2012

[34] G Fan E D Goodman and Z Liu ldquoAHP (analytic hierarchyprocess) and computer analysis software used in tourism safetyrdquoJournal of Software vol 8 no 12 pp 3114ndash3119 2013

[35] K J PoolM Amani andAHaroni ldquoAssessment of alternativesfor tourism destinations advertising using AIDA model andAHP approachrdquo Asian Journal of Research in Marketing vol 3no 3 pp 88ndash97 2014

[36] Y Zhou K Maumbe J Deng and S W Selin ldquoResource-baseddestination competitiveness evaluation using a hybrid analytichierarchy process (AHP) the case study of West VirginiardquoTourism Management Perspectives vol 15 pp 72ndash80 2015

[37] X Wang X R Li F Zhen and J Zhang ldquoHow smart isyour tourist attraction measuring tourist preferences of smarttourism attractions via a FCEM-AHP and IPA approachrdquoTourism Management vol 54 pp 309ndash320 2016

[38] J S Chen and D Gursoy ldquoAn investigation of touristsrsquodestination loyalty and preferencesrdquo International Journal ofContemporary HospitalityManagement vol 13 no 2 pp 79ndash852001

[39] M Kozak ldquoComparative analysis of tourist motivations bynationality and destinationsrdquo TourismManagement vol 23 no3 pp 221ndash232 2002

[40] J De Vos P L Mokhtarian T Schwanen V Van Ackerand F Witlox ldquoTravel mode choice and travel satisfactionbridging the gap between decision utility and experiencedutilityrdquo Transportation vol 43 no 5 pp 771ndash796 2016

[41] U Probstl-Haider W Haider V Wirth and B BeardmoreldquoWill climate change increase the attractiveness of summerdestinations in the European Alps A survey of Germantouristsrdquo Journal of Outdoor Recreation and Tourism vol 11 pp44ndash57 2015

[42] A Garg ldquoTravel risks vs tourist decision making a touristperspectiverdquo International Journal of Hospitality amp TourismSystems vol 8 no 1 pp 1ndash9 2015

[43] S Amaro and P Duarte ldquoAn integrative model of consumersrsquointentions to purchase travel onlinerdquo TourismManagement vol46 pp 64ndash79 2015

[44] Z Tang ldquoAn integrated approach to evaluating the couplingcoordination between tourism and the environmentrdquo TourismManagement vol 46 pp 11ndash19 2015

[45] N Paker and C A Vural ldquoCustomer segmentation for marinasevaluating marinas as destinationsrdquo Tourism Management vol56 pp 156ndash171 2016

[46] T Huybers ldquoModelling short-break holiday destinationchoicesrdquo Tourism Economics vol 9 no 4 pp 389ndash405 2003

[47] B G C Dellaert D F Ettema and C Lindh ldquoMulti-facetedtourist travel decisions a constraint-based conceptual frame-work to describe touristsrsquo sequential choices of travel compo-nentsrdquo Tourism Management vol 19 no 4 pp 313ndash320 1998

[48] E Sirakaya and A G Woodside ldquoBuilding and testing theoriesof decisionmaking by travellersrdquo TourismManagement vol 26no 6 pp 815ndash832 2005

[49] T Lam and C H C Hsu ldquoPredicting behavioral intention ofchoosing a travel destinationrdquoTourismManagement vol 27 no4 pp 589ndash599 2006

[50] C Goossens ldquoTourism information and pleasure motivationrdquoAnnals of Tourism Research vol 27 no 2 pp 301ndash321 2000

[51] V C S Heung H Qu and R Chu ldquoThe relationship betweenvacation factors and socio-demographic and travelling char-acteristics the case of Japanese leisure travellersrdquo TourismManagement vol 22 no 3 pp 259ndash269 2001

[52] S S Kim and B Prideaux ldquoMarketing implications arisingfrom a comparative study of international pleasure touristmotivations and other travel-related characteristics of visitors toKoreardquo Tourism Management vol 26 no 3 pp 347ndash357 2005

[53] M Kozak ldquoComparative analysis of tourist motivations bynationality and destinationsrdquo TourismManagement vol 23 no3 pp 221ndash232 2002

[54] G Richards and J Wilson ldquoTodayrsquos youth travellers Tomor-rowrsquos global nomads New horizons in independent youthand student travelrdquo International Student Travel Confederation(ISTC) 2003

[55] N Tomis B Kovatevis N Berber and N Milis ldquoFactors influ-encing the motivation of young people when choosing a citydestination in Europemdasha case study from Esbjerg (Denmark)rdquoEuropean Researcher vol 69 no 2 pp 414ndash428 2014

[56] X D Guo H L Zhang and J H Ruan ldquoA novel similarity-based algorithm for classifying tourism scenic spotsrdquo ICICExpress Letters Part B Applications vol 6 no 2 pp 503ndash5092015

10 Scientific Programming

[57] J Moran E Granada J L Mıguez and J Porteiro ldquoUse of greyrelational analysis to assess and optimize small biomass boilersrdquoFuel Processing Technology vol 87 no 2 pp 123ndash127 2006

[58] D Ju-Long ldquoControl problems of grey systemsrdquo Systems ampControl Letters vol 1 no 5 pp 288ndash294 1982

[59] R-T Wang C-T B Ho and K Oh ldquoMeasuring productionand marketing efficiency using grey relation analysis anddata envelopment analysisrdquo International Journal of ProductionResearch vol 48 no 1 pp 183ndash199 2010

[60] C-T Ho ldquoMeasuring bank operations performance anapproach based on Grey Relation Analysisrdquo Journal of theOperational Research Society vol 57 no 4 pp 337ndash349 2006

[61] C-Y Kung and K-L Wen ldquoApplying Grey Relational AnalysisandGreyDecision-Making to evaluate the relationship betweencompany attributes and its financial performancemdasha case studyof venture capital enterprises in Taiwanrdquo Decision SupportSystems vol 43 no 3 pp 842ndash852 2007

[62] Y-J Wang ldquoCombining grey relation analysis with FMCGDMto evaluate financial performance of Taiwan container linesrdquoExpert Systems with Applications vol 36 no 2 pp 2424ndash24322009

[63] P T-W Lee C-W Lin and S-H Shin ldquoA comparative study onfinancial positions of shipping companies in Taiwan and Koreausing entropy and grey relation analysisrdquo Expert Systems withApplications vol 39 no 5 pp 5649ndash5657 2012

[64] F Ecer and A Boyukaslan ldquoMeasuring performances of foot-ball clubs using financial ratios the gray relational analysisapproachrdquo American Journal of Economics vol 4 no 1 pp 62ndash71 2014

[65] S J Lin I J Lu and C Lewis ldquoGrey relation performancecorrelations among economics energy use and carbon dioxideemission in Taiwanrdquo Energy Policy vol 35 no 3 pp 1948ndash19552007

[66] I J Lu S J Lin and C Lewis ldquoGrey relation analysis of motorvehicular energy consumption inTaiwanrdquoEnergy Policy vol 36no 7 pp 2556ndash2561 2008

[67] S H Hashemi A Karimi and M Tavana ldquoAn integratedgreen supplier selection approachwith analytic network processand improved Grey relational analysisrdquo International Journal ofProduction Economics vol 159 pp 178ndash191 2015

[68] DG Pearce ldquoTourist time-budgetrdquoAnnals of TourismResearchvol 15 no 1 pp 106ndash121 1988

[69] D Ju-Long ldquoIntroduction to grey system theoryrdquoThe Journal ofGrey System vol 1 no 1 pp 1ndash24 1989

[70] U Caydas and A Hascalık ldquoUse of the grey relational analysisto determine optimum laser cutting parameters with multi-performance characteristicsrdquo Optics amp Laser Technology vol40 no 7 pp 987ndash994 2008

Submit your manuscripts athttpwwwhindawicom

Computer Games Technology

International Journal of

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Distributed Sensor Networks

International Journal of

Advances in

FuzzySystems

Hindawi Publishing Corporationhttpwwwhindawicom

Volume 2014

International Journal of

ReconfigurableComputing

Hindawi Publishing Corporation httpwwwhindawicom Volume 2014

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Applied Computational Intelligence and Soft Computing

thinspAdvancesthinspinthinsp

Artificial Intelligence

HindawithinspPublishingthinspCorporationhttpwwwhindawicom Volumethinsp2014

Advances inSoftware EngineeringHindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Electrical and Computer Engineering

Journal of

Journal of

Computer Networks and Communications

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Hindawi Publishing Corporation

httpwwwhindawicom Volume 2014

Advances in

Multimedia

International Journal of

Biomedical Imaging

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

ArtificialNeural Systems

Advances in

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

RoboticsJournal of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Computational Intelligence and Neuroscience

Industrial EngineeringJournal of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Modelling amp Simulation in EngineeringHindawi Publishing Corporation httpwwwhindawicom Volume 2014

The Scientific World JournalHindawi Publishing Corporation httpwwwhindawicom Volume 2014

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Human-ComputerInteraction

Advances in

Computer EngineeringAdvances in

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

6 Scientific Programming

Table 1 Overall preference measures of destinations

Criteria Taipei 101 National Museum Sun Moon Lake Alishan Yushan Park Taroko Park Love River Kenting ParkEscape 623 542 716a 680 594 708 599 692Self-actualization 716a 689 630 609 522 641 566 645Restrelaxation 538 505 759a 706 613 705 636 730Medical treatment 640a 441 430 434 302 409 538 473Health and fitness 505 410 657 698 634 715a 517 657Visiting friendrelative 796 554 854a 701 588 684 686 742Meeting new people 745a 555 598 555 437 561 576 680Novelty seeking 804a 608 535 506 416 522 561 629Culture exploration 627 787a 654 641 542 628 566 645Adventure seeking 397 343 596 656 630 667 494 673a

Enjoying night life 856a 553 449 395 343 441 645 580Transportation facilities 860a 803 620 528 404 545 678 670Friendliness of people 648 652 668 684 595 693 633 695a

Qualityvariety of food 885a 641 570 558 428 580 716 688Accommodation 781a 726 667 572 431 663 736 752Environment safety 610 638 698 681 607 720a 624 673Personal safety 736a 729 580 502 455 538 580 659Price 439 512 555 564 588 605a 530 530Culture and historicalresources 611 847a 678 672 622 663 560 649

Good shopping 804a 599 489 479 363 540 613 611Destination image 778 727 794a 706 653 692 638 714Benefits expectations 767a 680 541 541 460 532 648 667

1198771

=

[

[

[

[

[

[

[

[

[

[

[

[

[

[

[

[

[

[

[

[

716 716 759 640 715 854 745 804 787 673 856 860 695 885 781 720 736 605 847 804 794 767

623 716 538 640 505 796 745 804 627 397 856 860 648 885 781 610 736 439 611 804 778 767

542 689 505 441 410 554 555 608 787 343 553 803 652 641 726 638 729 512 847 599 727 680

716 630 759 430 657 854 598 535 654 596 449 620 668 570 667 698 580 555 678 489 794 541

680 609 706 434 698 701 555 506 641 656 395 528 684 558 572 681 502 564 672 479 706 541

594 522 613 302 634 588 437 416 542 630 343 404 595 428 431 607 455 588 622 363 653 460

708 641 705 409 715 684 561 522 628 667 441 545 693 580 663 720 538 605 663 540 692 532

599 566 636 538 517 686 576 561 566 494 645 678 633 716 736 624 580 530 560 613 638 648

692 645 730 473 657 742 680 629 645 673 580 670 695 688 752 673 659 530 649 611 714 667

]

]

]

]

]

]

]

]

]

]

]

]

]

]

]

]

]

]

]

]

(15)

Each of the index values is normalized according toformulas (4) and (5) and then a new matrix is obtained asfollows

119883

=

[

[

[

[

[

[

[

[

[

[

[

[

[

[

[

[

[

[

[

[

100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100

047 100 013 100 031 081 100 100 035 016 100 100 053 100 100 003 100 000 018 100 090 100

000 086 000 041 000 000 038 049 100 000 041 088 057 047 084 027 098 044 100 054 057 072

100 056 100 038 081 100 052 031 046 077 021 047 073 031 067 081 044 070 041 029 100 026

079 045 079 039 094 049 038 023 040 095 010 027 089 028 040 065 017 075 039 026 044 026

030 000 043 000 073 011 000 000 000 087 000 000 000 000 000 000 000 090 022 000 010 000

095 061 079 032 100 043 040 027 035 098 019 031 098 033 066 100 030 100 036 040 035 023

033 023 052 070 035 044 045 037 010 046 059 060 038 063 087 015 044 055 000 057 000 061

086 063 089 051 081 063 079 055 042 100 046 058 100 057 092 058 073 055 031 056 049 067

]

]

]

]

]

]

]

]

]

]

]

]

]

]

]

]

]

]

]

]

(16)

Scientific Programming 7

According to formula (8) that is 120585119894119895

= (0 + 05 times

1)(|1198830119895

minus 119883119894119895| + 05 times 1) we obtain the matrix of grey

relational coefficients as follows

120585119894119895

=

[

[

[

[

[

[

[

[

[

[

[

[

[

[

[

[

[

[

048 100 036 100 042 072 100 100 043 037 100 100 052 100 100 034 100 033 038 100 083 100

033 078 033 046 033 033 045 050 100 033 046 080 054 048 076 041 095 047 100 052 054 064

100 053 100 045 072 100 051 042 048 068 039 049 065 042 061 072 047 062 046 041 100 040

071 048 071 045 090 050 045 039 046 091 036 041 082 041 046 059 038 067 045 040 047 040

042 033 047 033 065 036 033 033 033 079 033 033 033 033 033 033 033 083 039 033 036 033

092 056 070 042 100 047 046 041 044 096 038 042 096 043 060 100 042 100 044 046 043 040

043 039 051 062 044 047 048 044 036 048 055 056 045 057 080 037 047 053 033 054 033 056

078 058 081 050 072 057 070 053 046 100 048 055 100 054 086 055 065 053 042 053 049 061

]

]

]

]

]

]

]

]

]

]

]

]

]

]

]

]

]

]

(17)

According to formulas (10)sim(13) the index weightsvector is calculated as follows

120596

= (005 004 005 004 004 004 004 005 005 005 005 004 004 004 003 005 006 004 005 004 005 005)

(18)

Using formula (14) we obtain the grey relational gradesas follows

119861 = (073 057 061 053 040 060 048 063) (19)

52 Comparison and Analysis In this paper we focus ondeveloping a novel evaluation approach tourist choice of des-tination based on grey relation analysis in order to demon-strate the advantages of the developed evaluation methodsand comparing the evaluation results with the evaluationmethod of ldquoTOPSISrdquo used by literature [21] So after obtain-ing the grey relational grade to the ideal destination by usingour proposed method we compare and analyze these resultswith the evaluation results obtained from the Technique forOrder Preference by Similarity to Ideal Solution (TOPSIS)used by literature [21] The results are shown in Table 2

As can be seen from Table 2 we can make the followingcomparative analysis

(i) The ranking results obtained from literature [21] andthe results in this paper are not completely the same asshown in column 2 and column 4 of Table 2 which reflect theconsistency and difference of two methods In other wordsthe ranking results of the top three and the last one are thesame but the others are not the same Interestingly almost allrelational grades to ideal solution of our proposed approachare bigger than those of the similarity to ideal solution by theTechnique for Order Preference by Similarity to Ideal Solu-tion (TOPSIS) evaluationmethod which shows that our pro-posed approach has the advantage over the TOPSIS approach

(ii) Comparing the overall preference measures of desti-nations as shown in Table 1 of literature [21] it is observedthat the Taroko National Park has three best preferences

out of the 24 factors and the National Palace Museum hastwo best preferences Therefore we intuitively think that thedestination with more best preferences should be rankedhigher than the destination with less best preferences andthe actual evaluation results are consistent with our intuitionwhich show that our proposed evaluation approach is morecredible and more in line with the actual situation Thefundamental reason why our proposed evaluation approachis more in line with the actual situation lies in its owncharacteristics In other words the touristsrsquo preferences totourism destinations are grey and fuzzy which is in line withthe cognition rules of human being to the objective world

(iii) No matter what kind of evaluation approach Taipei101 is ranked the number one favorite local destination Thatis Taipei 101 is worthy of visiting as it is the highest buildingin the world located in the business center of Taipei cityand a Taipei landmark As shown in Table 1 Taipei 101 isconsidered the most desirable destination with respect toldquoself-actualizationrdquo ldquomeeting new friendsrdquo ldquomedical treat-mentrdquo ldquonovelty seekingrdquo ldquoenjoying night liferdquo ldquotransporta-tion facilitiesrdquo ldquoquality and variety of foodrdquo ldquoaccommodationfacilitiesrdquo ldquogood shoppingrdquo ldquopersonal safetyrdquo and ldquobenefitexpectationsrdquo which reflects the consistency of twomethods

6 Conclusions

From a methodological point of view we proposed a greyrelation analysis evaluation model for choosing tourism des-tinations and the findings demonstrate that the grey relationanalysis evaluation approach is a useful tool to help supporta decision in destination choice It integrates the opinion

8 Scientific Programming

Table 2 Final ranking of destinations

Destination

Results of literature[21] Results in this paper

RankSimilarity to

idealsolution

RankRelational

grade to idealsolution

Taipei 101 1 075 1 073KentingNational Park 2 063 2 063

Sun Moon Lake 3 055 3 061National PalaceMuseum 4 048 5 057

Love River 5 047 7 048TarokoNational Park 6 043 4 060

Alishan 7 037 6 053YushanNational Park 8 016 8 040

and evaluation of experts and makes the complex decision-making system into a simple grey relation analysis systemIn other words our proposed evaluation approach makes theevaluation process simple and easy to conductWhat is moreit is especially preferred in ordering the alternatives in situa-tions in which the sample is small and sample distribution isnot known In our future studies we will apply the approachto evaluating other tourism destinations in China and makea ranking for Chinese main tourism destinations

Competing Interests

The authors declare that there are no competing interestsregarding the publication of this paper

Acknowledgments

This study is supported by the National Natural ScienceFoundation of China under Grant no 41440045

References

[1] V S Lin R Mao and H Song ldquoTourism expenditure patternsin ChinardquoAnnals of Tourism Research vol 54 pp 100ndash117 2015

[2] Domestic Tourism National Bureau of Statistics of China 2014httpdatastatsgovcnenglisheasyqueryhtmcn=C01

[3] S C S Jang and S Ham ldquoA double-hurdle analysis oftravel expenditure baby boomer seniors versus older seniorsrdquoTourism Management vol 30 no 3 pp 372ndash380 2009

[4] T Huybers and J Bennett ldquoImpact of the environment on holi-day destination choices of prospective UK tourists implicationsfor Tropical North Queenslandrdquo Tourism Economics vol 6 no1 pp 21ndash46 2000

[5] H R Seddighi andA LTheocharous ldquoAmodel of tourism des-tination choice a theoretical and empirical analysisrdquo TourismManagement vol 23 no 5 pp 475ndash487 2002

[6] G Lee J T OrsquoLeary L Sunhee and A Morrison ldquoComparisonand contrast of push and pull motivational effects on trip

behavior an application of a multinomial logistic regressionmodelrdquo Tourism Analysis vol 7 no 2 pp 89ndash104 2001

[7] H Oppewal T Huybers and G I Crouch ldquoTourist destinationand experience choice a choice experimental analysis of deci-sion sequence effectsrdquo Tourism Management vol 48 pp 467ndash476 2015

[8] A Molina and A Esteban ldquoTourism brochures usefulness andimagerdquoAnnals of TourismResearch vol 33 no 4 pp 1036ndash10562006

[9] H Kim C-K Cheng and J T OrsquoLeary ldquoUnderstanding par-ticipation patterns and trends in tourism cultural attractionsrdquoTourism Management vol 28 no 5 pp 1366ndash1371 2007

[10] L Jin Y He andH Song ldquoService customization to upgrade orto downgrade An investigation of how option framing affectstouristsrsquo choice of package-tour servicesrdquoTourismManagementvol 33 no 2 pp 266ndash275 2012

[11] J Ashwell ldquoGoing bush Factors which influence internationaltouristsrsquo decisions to travel to remote Australian destinationsrdquoTourism Management vol 46 pp 80ndash83 2015

[12] V V Can ldquoEstimation of travel mode choice for domestictourists to Nha Trang using the multinomial probit modelrdquoTransportation Research Part A Policy and Practice vol 49 pp149ndash159 2013

[13] L Wu J Zhang and A Fujiwara ldquoTourism participation andexpenditure behaviour analysis using a scobit based discrete-continuous choice modelrdquo Annals of Tourism Research vol 40no 1 pp 1ndash17 2013

[14] G Prayag and C Ryan ldquoAntecedents of touristsrsquo loyalty tomauritius the role and influence of destination image placeattachment personal involvement and satisfactionrdquo Journal ofTravel Research vol 51 no 3 pp 342ndash356 2012

[15] N K Prebensen E Woo J S Chen and M Uysal ldquoMotivationand involvement as antecedents of the perceived value of thedestination experiencerdquo Journal of Travel Research vol 52 no2 pp 253ndash264 2013

[16] H Song R van der Veen G Li and J L Chen ldquoTheHongKongtourist satisfaction indexrdquo Annals of Tourism Research vol 39no 1 pp 459ndash479 2012

[17] J-Y Wong and C Yeh ldquoTourist hesitation in destinationdecision makingrdquo Annals of Tourism Research vol 36 no 1 pp6ndash23 2009

[18] B M Josiam D L Spears S Pookulangara K Dutta T RKinley and J L Duncan ldquoUsing structural equation modelingto understand the impact of Bollywood movies on destinationimage tourist activity and purchasing behavior of IndiansrdquoJournal of Vacation Marketing vol 21 no 3 pp 251ndash261 2015

[19] R Pinky Pawaskar and M Goel ldquoImproving the efficacy ofdestination marketing strategies a structural equation modelfor leisure travelrdquo Indian Journal of Science and Technology vol9 no 15 pp 1ndash11 2016

[20] H Zhang X Fu L A Cai and L Lu ldquoDestination image andtourist loyalty a meta-analysisrdquo Tourism Management vol 40pp 213ndash223 2014

[21] T-K Hsu Y-F Tsai andH-HWu ldquoThe preference analysis fortourist choice of destination a case study of Taiwanrdquo TourismManagement vol 30 no 2 pp 288ndash297 2009

[22] J-H Huang and K-H Peng ldquoFuzzy Rasch model in TOPSISa new approach for generating fuzzy numbers to assess thecompetitiveness of the tourism industries in Asian countriesrdquoTourism Management vol 33 no 2 pp 456ndash465 2012

Scientific Programming 9

[23] M H N Salleh R Othman and T Sarmidi ldquoTourist satis-faction of the environmental service quality for Tioman IslandMarine Parkrdquo Indian Journal of Geo-Marine Sciences vol 41 no2 pp 173ndash179 2012

[24] D Mohamad and R M Jamil ldquoA Preference analysis model forselecting tourist destinations based on motivational factors aCase Study in Kedah Malaysiardquo ProcediamdashSocial and Behav-ioral Sciences vol 65 pp 20ndash25 2012

[25] M Cucculelli and G Goffi ldquoDoes sustainability enhancetourism destination competitiveness Evidence from Italiandestinations of excellencerdquo Journal of Cleaner Production vol111 pp 370ndash382 2016

[26] J K S Jacobsen and A M Munar ldquoTourist information searchand destination choice in a digital agerdquo Tourism ManagementPerspectives vol 1 no 1 pp 39ndash47 2012

[27] A H N Mak M Lumbers A Eves and R C Y ChangldquoAn application of the repertory grid method and generalisedProcrustes analysis to investigate the motivational factors oftourist food consumptionrdquo International Journal of HospitalityManagement vol 35 pp 327ndash338 2013

[28] G Fuchs N Uriely A Reichel and D Maoz ldquoVacationingin a terror-stricken destination touristsrsquo risk perceptions andrationalizationsrdquo Journal of Travel Research vol 52 no 2 pp182ndash191 2013

[29] W Elias and Y Shiftan ldquoThe influence of individualrsquos riskperception and attitudes on travel behaviorrdquo TransportationResearch Part A Policy and Practice vol 46 no 8 pp 1241ndash12512012

[30] S Pike and S J Page ldquoDestination marketing organizationsand destinationmarketing anarrative analysis of the literaturerdquoTourism Management vol 41 pp 202ndash227 2014

[31] G I Crouch and J R B Ritchie ldquoApplication of the analytichierarchy process to tourism choice and decision making areview and illustration applied to destination competitivenessrdquoTourism Analysis vol 10 no 1 pp 17ndash25 2005

[32] D Das and K Mukherjee ldquoDevelopment of an AHP-QFDframework for designing a tourism productrdquo InternationalJournal of Services and Operations Management vol 4 no 3pp 321ndash344 2008

[33] HN Ali A A I Sabri andMMNNoor ldquoRating and rankingcriteria for selected Islands using fuzzy analytic hierarchyprocess (FAHP)rdquo International Journal of Applied Mathematicsand Informatics vol 1 no 6 pp 57ndash65 2012

[34] G Fan E D Goodman and Z Liu ldquoAHP (analytic hierarchyprocess) and computer analysis software used in tourism safetyrdquoJournal of Software vol 8 no 12 pp 3114ndash3119 2013

[35] K J PoolM Amani andAHaroni ldquoAssessment of alternativesfor tourism destinations advertising using AIDA model andAHP approachrdquo Asian Journal of Research in Marketing vol 3no 3 pp 88ndash97 2014

[36] Y Zhou K Maumbe J Deng and S W Selin ldquoResource-baseddestination competitiveness evaluation using a hybrid analytichierarchy process (AHP) the case study of West VirginiardquoTourism Management Perspectives vol 15 pp 72ndash80 2015

[37] X Wang X R Li F Zhen and J Zhang ldquoHow smart isyour tourist attraction measuring tourist preferences of smarttourism attractions via a FCEM-AHP and IPA approachrdquoTourism Management vol 54 pp 309ndash320 2016

[38] J S Chen and D Gursoy ldquoAn investigation of touristsrsquodestination loyalty and preferencesrdquo International Journal ofContemporary HospitalityManagement vol 13 no 2 pp 79ndash852001

[39] M Kozak ldquoComparative analysis of tourist motivations bynationality and destinationsrdquo TourismManagement vol 23 no3 pp 221ndash232 2002

[40] J De Vos P L Mokhtarian T Schwanen V Van Ackerand F Witlox ldquoTravel mode choice and travel satisfactionbridging the gap between decision utility and experiencedutilityrdquo Transportation vol 43 no 5 pp 771ndash796 2016

[41] U Probstl-Haider W Haider V Wirth and B BeardmoreldquoWill climate change increase the attractiveness of summerdestinations in the European Alps A survey of Germantouristsrdquo Journal of Outdoor Recreation and Tourism vol 11 pp44ndash57 2015

[42] A Garg ldquoTravel risks vs tourist decision making a touristperspectiverdquo International Journal of Hospitality amp TourismSystems vol 8 no 1 pp 1ndash9 2015

[43] S Amaro and P Duarte ldquoAn integrative model of consumersrsquointentions to purchase travel onlinerdquo TourismManagement vol46 pp 64ndash79 2015

[44] Z Tang ldquoAn integrated approach to evaluating the couplingcoordination between tourism and the environmentrdquo TourismManagement vol 46 pp 11ndash19 2015

[45] N Paker and C A Vural ldquoCustomer segmentation for marinasevaluating marinas as destinationsrdquo Tourism Management vol56 pp 156ndash171 2016

[46] T Huybers ldquoModelling short-break holiday destinationchoicesrdquo Tourism Economics vol 9 no 4 pp 389ndash405 2003

[47] B G C Dellaert D F Ettema and C Lindh ldquoMulti-facetedtourist travel decisions a constraint-based conceptual frame-work to describe touristsrsquo sequential choices of travel compo-nentsrdquo Tourism Management vol 19 no 4 pp 313ndash320 1998

[48] E Sirakaya and A G Woodside ldquoBuilding and testing theoriesof decisionmaking by travellersrdquo TourismManagement vol 26no 6 pp 815ndash832 2005

[49] T Lam and C H C Hsu ldquoPredicting behavioral intention ofchoosing a travel destinationrdquoTourismManagement vol 27 no4 pp 589ndash599 2006

[50] C Goossens ldquoTourism information and pleasure motivationrdquoAnnals of Tourism Research vol 27 no 2 pp 301ndash321 2000

[51] V C S Heung H Qu and R Chu ldquoThe relationship betweenvacation factors and socio-demographic and travelling char-acteristics the case of Japanese leisure travellersrdquo TourismManagement vol 22 no 3 pp 259ndash269 2001

[52] S S Kim and B Prideaux ldquoMarketing implications arisingfrom a comparative study of international pleasure touristmotivations and other travel-related characteristics of visitors toKoreardquo Tourism Management vol 26 no 3 pp 347ndash357 2005

[53] M Kozak ldquoComparative analysis of tourist motivations bynationality and destinationsrdquo TourismManagement vol 23 no3 pp 221ndash232 2002

[54] G Richards and J Wilson ldquoTodayrsquos youth travellers Tomor-rowrsquos global nomads New horizons in independent youthand student travelrdquo International Student Travel Confederation(ISTC) 2003

[55] N Tomis B Kovatevis N Berber and N Milis ldquoFactors influ-encing the motivation of young people when choosing a citydestination in Europemdasha case study from Esbjerg (Denmark)rdquoEuropean Researcher vol 69 no 2 pp 414ndash428 2014

[56] X D Guo H L Zhang and J H Ruan ldquoA novel similarity-based algorithm for classifying tourism scenic spotsrdquo ICICExpress Letters Part B Applications vol 6 no 2 pp 503ndash5092015

10 Scientific Programming

[57] J Moran E Granada J L Mıguez and J Porteiro ldquoUse of greyrelational analysis to assess and optimize small biomass boilersrdquoFuel Processing Technology vol 87 no 2 pp 123ndash127 2006

[58] D Ju-Long ldquoControl problems of grey systemsrdquo Systems ampControl Letters vol 1 no 5 pp 288ndash294 1982

[59] R-T Wang C-T B Ho and K Oh ldquoMeasuring productionand marketing efficiency using grey relation analysis anddata envelopment analysisrdquo International Journal of ProductionResearch vol 48 no 1 pp 183ndash199 2010

[60] C-T Ho ldquoMeasuring bank operations performance anapproach based on Grey Relation Analysisrdquo Journal of theOperational Research Society vol 57 no 4 pp 337ndash349 2006

[61] C-Y Kung and K-L Wen ldquoApplying Grey Relational AnalysisandGreyDecision-Making to evaluate the relationship betweencompany attributes and its financial performancemdasha case studyof venture capital enterprises in Taiwanrdquo Decision SupportSystems vol 43 no 3 pp 842ndash852 2007

[62] Y-J Wang ldquoCombining grey relation analysis with FMCGDMto evaluate financial performance of Taiwan container linesrdquoExpert Systems with Applications vol 36 no 2 pp 2424ndash24322009

[63] P T-W Lee C-W Lin and S-H Shin ldquoA comparative study onfinancial positions of shipping companies in Taiwan and Koreausing entropy and grey relation analysisrdquo Expert Systems withApplications vol 39 no 5 pp 5649ndash5657 2012

[64] F Ecer and A Boyukaslan ldquoMeasuring performances of foot-ball clubs using financial ratios the gray relational analysisapproachrdquo American Journal of Economics vol 4 no 1 pp 62ndash71 2014

[65] S J Lin I J Lu and C Lewis ldquoGrey relation performancecorrelations among economics energy use and carbon dioxideemission in Taiwanrdquo Energy Policy vol 35 no 3 pp 1948ndash19552007

[66] I J Lu S J Lin and C Lewis ldquoGrey relation analysis of motorvehicular energy consumption inTaiwanrdquoEnergy Policy vol 36no 7 pp 2556ndash2561 2008

[67] S H Hashemi A Karimi and M Tavana ldquoAn integratedgreen supplier selection approachwith analytic network processand improved Grey relational analysisrdquo International Journal ofProduction Economics vol 159 pp 178ndash191 2015

[68] DG Pearce ldquoTourist time-budgetrdquoAnnals of TourismResearchvol 15 no 1 pp 106ndash121 1988

[69] D Ju-Long ldquoIntroduction to grey system theoryrdquoThe Journal ofGrey System vol 1 no 1 pp 1ndash24 1989

[70] U Caydas and A Hascalık ldquoUse of the grey relational analysisto determine optimum laser cutting parameters with multi-performance characteristicsrdquo Optics amp Laser Technology vol40 no 7 pp 987ndash994 2008

Submit your manuscripts athttpwwwhindawicom

Computer Games Technology

International Journal of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Distributed Sensor Networks

International Journal of

Advances in

FuzzySystems

Hindawi Publishing Corporationhttpwwwhindawicom

Volume 2014

International Journal of

ReconfigurableComputing

Hindawi Publishing Corporation httpwwwhindawicom Volume 2014

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Applied Computational Intelligence and Soft Computing

thinspAdvancesthinspinthinsp

Artificial Intelligence

HindawithinspPublishingthinspCorporationhttpwwwhindawicom Volumethinsp2014

Advances inSoftware EngineeringHindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Electrical and Computer Engineering

Journal of

Journal of

Computer Networks and Communications

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Hindawi Publishing Corporation

httpwwwhindawicom Volume 2014

Advances in

Multimedia

International Journal of

Biomedical Imaging

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

ArtificialNeural Systems

Advances in

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

RoboticsJournal of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Computational Intelligence and Neuroscience

Industrial EngineeringJournal of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Modelling amp Simulation in EngineeringHindawi Publishing Corporation httpwwwhindawicom Volume 2014

The Scientific World JournalHindawi Publishing Corporation httpwwwhindawicom Volume 2014

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Human-ComputerInteraction

Advances in

Computer EngineeringAdvances in

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Scientific Programming 7

According to formula (8) that is 120585119894119895

= (0 + 05 times

1)(|1198830119895

minus 119883119894119895| + 05 times 1) we obtain the matrix of grey

relational coefficients as follows

120585119894119895

=

[

[

[

[

[

[

[

[

[

[

[

[

[

[

[

[

[

[

048 100 036 100 042 072 100 100 043 037 100 100 052 100 100 034 100 033 038 100 083 100

033 078 033 046 033 033 045 050 100 033 046 080 054 048 076 041 095 047 100 052 054 064

100 053 100 045 072 100 051 042 048 068 039 049 065 042 061 072 047 062 046 041 100 040

071 048 071 045 090 050 045 039 046 091 036 041 082 041 046 059 038 067 045 040 047 040

042 033 047 033 065 036 033 033 033 079 033 033 033 033 033 033 033 083 039 033 036 033

092 056 070 042 100 047 046 041 044 096 038 042 096 043 060 100 042 100 044 046 043 040

043 039 051 062 044 047 048 044 036 048 055 056 045 057 080 037 047 053 033 054 033 056

078 058 081 050 072 057 070 053 046 100 048 055 100 054 086 055 065 053 042 053 049 061

]

]

]

]

]

]

]

]

]

]

]

]

]

]

]

]

]

]

(17)

According to formulas (10)sim(13) the index weightsvector is calculated as follows

120596

= (005 004 005 004 004 004 004 005 005 005 005 004 004 004 003 005 006 004 005 004 005 005)

(18)

Using formula (14) we obtain the grey relational gradesas follows

119861 = (073 057 061 053 040 060 048 063) (19)

52 Comparison and Analysis In this paper we focus ondeveloping a novel evaluation approach tourist choice of des-tination based on grey relation analysis in order to demon-strate the advantages of the developed evaluation methodsand comparing the evaluation results with the evaluationmethod of ldquoTOPSISrdquo used by literature [21] So after obtain-ing the grey relational grade to the ideal destination by usingour proposed method we compare and analyze these resultswith the evaluation results obtained from the Technique forOrder Preference by Similarity to Ideal Solution (TOPSIS)used by literature [21] The results are shown in Table 2

As can be seen from Table 2 we can make the followingcomparative analysis

(i) The ranking results obtained from literature [21] andthe results in this paper are not completely the same asshown in column 2 and column 4 of Table 2 which reflect theconsistency and difference of two methods In other wordsthe ranking results of the top three and the last one are thesame but the others are not the same Interestingly almost allrelational grades to ideal solution of our proposed approachare bigger than those of the similarity to ideal solution by theTechnique for Order Preference by Similarity to Ideal Solu-tion (TOPSIS) evaluationmethod which shows that our pro-posed approach has the advantage over the TOPSIS approach

(ii) Comparing the overall preference measures of desti-nations as shown in Table 1 of literature [21] it is observedthat the Taroko National Park has three best preferences

out of the 24 factors and the National Palace Museum hastwo best preferences Therefore we intuitively think that thedestination with more best preferences should be rankedhigher than the destination with less best preferences andthe actual evaluation results are consistent with our intuitionwhich show that our proposed evaluation approach is morecredible and more in line with the actual situation Thefundamental reason why our proposed evaluation approachis more in line with the actual situation lies in its owncharacteristics In other words the touristsrsquo preferences totourism destinations are grey and fuzzy which is in line withthe cognition rules of human being to the objective world

(iii) No matter what kind of evaluation approach Taipei101 is ranked the number one favorite local destination Thatis Taipei 101 is worthy of visiting as it is the highest buildingin the world located in the business center of Taipei cityand a Taipei landmark As shown in Table 1 Taipei 101 isconsidered the most desirable destination with respect toldquoself-actualizationrdquo ldquomeeting new friendsrdquo ldquomedical treat-mentrdquo ldquonovelty seekingrdquo ldquoenjoying night liferdquo ldquotransporta-tion facilitiesrdquo ldquoquality and variety of foodrdquo ldquoaccommodationfacilitiesrdquo ldquogood shoppingrdquo ldquopersonal safetyrdquo and ldquobenefitexpectationsrdquo which reflects the consistency of twomethods

6 Conclusions

From a methodological point of view we proposed a greyrelation analysis evaluation model for choosing tourism des-tinations and the findings demonstrate that the grey relationanalysis evaluation approach is a useful tool to help supporta decision in destination choice It integrates the opinion

8 Scientific Programming

Table 2 Final ranking of destinations

Destination

Results of literature[21] Results in this paper

RankSimilarity to

idealsolution

RankRelational

grade to idealsolution

Taipei 101 1 075 1 073KentingNational Park 2 063 2 063

Sun Moon Lake 3 055 3 061National PalaceMuseum 4 048 5 057

Love River 5 047 7 048TarokoNational Park 6 043 4 060

Alishan 7 037 6 053YushanNational Park 8 016 8 040

and evaluation of experts and makes the complex decision-making system into a simple grey relation analysis systemIn other words our proposed evaluation approach makes theevaluation process simple and easy to conductWhat is moreit is especially preferred in ordering the alternatives in situa-tions in which the sample is small and sample distribution isnot known In our future studies we will apply the approachto evaluating other tourism destinations in China and makea ranking for Chinese main tourism destinations

Competing Interests

The authors declare that there are no competing interestsregarding the publication of this paper

Acknowledgments

This study is supported by the National Natural ScienceFoundation of China under Grant no 41440045

References

[1] V S Lin R Mao and H Song ldquoTourism expenditure patternsin ChinardquoAnnals of Tourism Research vol 54 pp 100ndash117 2015

[2] Domestic Tourism National Bureau of Statistics of China 2014httpdatastatsgovcnenglisheasyqueryhtmcn=C01

[3] S C S Jang and S Ham ldquoA double-hurdle analysis oftravel expenditure baby boomer seniors versus older seniorsrdquoTourism Management vol 30 no 3 pp 372ndash380 2009

[4] T Huybers and J Bennett ldquoImpact of the environment on holi-day destination choices of prospective UK tourists implicationsfor Tropical North Queenslandrdquo Tourism Economics vol 6 no1 pp 21ndash46 2000

[5] H R Seddighi andA LTheocharous ldquoAmodel of tourism des-tination choice a theoretical and empirical analysisrdquo TourismManagement vol 23 no 5 pp 475ndash487 2002

[6] G Lee J T OrsquoLeary L Sunhee and A Morrison ldquoComparisonand contrast of push and pull motivational effects on trip

behavior an application of a multinomial logistic regressionmodelrdquo Tourism Analysis vol 7 no 2 pp 89ndash104 2001

[7] H Oppewal T Huybers and G I Crouch ldquoTourist destinationand experience choice a choice experimental analysis of deci-sion sequence effectsrdquo Tourism Management vol 48 pp 467ndash476 2015

[8] A Molina and A Esteban ldquoTourism brochures usefulness andimagerdquoAnnals of TourismResearch vol 33 no 4 pp 1036ndash10562006

[9] H Kim C-K Cheng and J T OrsquoLeary ldquoUnderstanding par-ticipation patterns and trends in tourism cultural attractionsrdquoTourism Management vol 28 no 5 pp 1366ndash1371 2007

[10] L Jin Y He andH Song ldquoService customization to upgrade orto downgrade An investigation of how option framing affectstouristsrsquo choice of package-tour servicesrdquoTourismManagementvol 33 no 2 pp 266ndash275 2012

[11] J Ashwell ldquoGoing bush Factors which influence internationaltouristsrsquo decisions to travel to remote Australian destinationsrdquoTourism Management vol 46 pp 80ndash83 2015

[12] V V Can ldquoEstimation of travel mode choice for domestictourists to Nha Trang using the multinomial probit modelrdquoTransportation Research Part A Policy and Practice vol 49 pp149ndash159 2013

[13] L Wu J Zhang and A Fujiwara ldquoTourism participation andexpenditure behaviour analysis using a scobit based discrete-continuous choice modelrdquo Annals of Tourism Research vol 40no 1 pp 1ndash17 2013

[14] G Prayag and C Ryan ldquoAntecedents of touristsrsquo loyalty tomauritius the role and influence of destination image placeattachment personal involvement and satisfactionrdquo Journal ofTravel Research vol 51 no 3 pp 342ndash356 2012

[15] N K Prebensen E Woo J S Chen and M Uysal ldquoMotivationand involvement as antecedents of the perceived value of thedestination experiencerdquo Journal of Travel Research vol 52 no2 pp 253ndash264 2013

[16] H Song R van der Veen G Li and J L Chen ldquoTheHongKongtourist satisfaction indexrdquo Annals of Tourism Research vol 39no 1 pp 459ndash479 2012

[17] J-Y Wong and C Yeh ldquoTourist hesitation in destinationdecision makingrdquo Annals of Tourism Research vol 36 no 1 pp6ndash23 2009

[18] B M Josiam D L Spears S Pookulangara K Dutta T RKinley and J L Duncan ldquoUsing structural equation modelingto understand the impact of Bollywood movies on destinationimage tourist activity and purchasing behavior of IndiansrdquoJournal of Vacation Marketing vol 21 no 3 pp 251ndash261 2015

[19] R Pinky Pawaskar and M Goel ldquoImproving the efficacy ofdestination marketing strategies a structural equation modelfor leisure travelrdquo Indian Journal of Science and Technology vol9 no 15 pp 1ndash11 2016

[20] H Zhang X Fu L A Cai and L Lu ldquoDestination image andtourist loyalty a meta-analysisrdquo Tourism Management vol 40pp 213ndash223 2014

[21] T-K Hsu Y-F Tsai andH-HWu ldquoThe preference analysis fortourist choice of destination a case study of Taiwanrdquo TourismManagement vol 30 no 2 pp 288ndash297 2009

[22] J-H Huang and K-H Peng ldquoFuzzy Rasch model in TOPSISa new approach for generating fuzzy numbers to assess thecompetitiveness of the tourism industries in Asian countriesrdquoTourism Management vol 33 no 2 pp 456ndash465 2012

Scientific Programming 9

[23] M H N Salleh R Othman and T Sarmidi ldquoTourist satis-faction of the environmental service quality for Tioman IslandMarine Parkrdquo Indian Journal of Geo-Marine Sciences vol 41 no2 pp 173ndash179 2012

[24] D Mohamad and R M Jamil ldquoA Preference analysis model forselecting tourist destinations based on motivational factors aCase Study in Kedah Malaysiardquo ProcediamdashSocial and Behav-ioral Sciences vol 65 pp 20ndash25 2012

[25] M Cucculelli and G Goffi ldquoDoes sustainability enhancetourism destination competitiveness Evidence from Italiandestinations of excellencerdquo Journal of Cleaner Production vol111 pp 370ndash382 2016

[26] J K S Jacobsen and A M Munar ldquoTourist information searchand destination choice in a digital agerdquo Tourism ManagementPerspectives vol 1 no 1 pp 39ndash47 2012

[27] A H N Mak M Lumbers A Eves and R C Y ChangldquoAn application of the repertory grid method and generalisedProcrustes analysis to investigate the motivational factors oftourist food consumptionrdquo International Journal of HospitalityManagement vol 35 pp 327ndash338 2013

[28] G Fuchs N Uriely A Reichel and D Maoz ldquoVacationingin a terror-stricken destination touristsrsquo risk perceptions andrationalizationsrdquo Journal of Travel Research vol 52 no 2 pp182ndash191 2013

[29] W Elias and Y Shiftan ldquoThe influence of individualrsquos riskperception and attitudes on travel behaviorrdquo TransportationResearch Part A Policy and Practice vol 46 no 8 pp 1241ndash12512012

[30] S Pike and S J Page ldquoDestination marketing organizationsand destinationmarketing anarrative analysis of the literaturerdquoTourism Management vol 41 pp 202ndash227 2014

[31] G I Crouch and J R B Ritchie ldquoApplication of the analytichierarchy process to tourism choice and decision making areview and illustration applied to destination competitivenessrdquoTourism Analysis vol 10 no 1 pp 17ndash25 2005

[32] D Das and K Mukherjee ldquoDevelopment of an AHP-QFDframework for designing a tourism productrdquo InternationalJournal of Services and Operations Management vol 4 no 3pp 321ndash344 2008

[33] HN Ali A A I Sabri andMMNNoor ldquoRating and rankingcriteria for selected Islands using fuzzy analytic hierarchyprocess (FAHP)rdquo International Journal of Applied Mathematicsand Informatics vol 1 no 6 pp 57ndash65 2012

[34] G Fan E D Goodman and Z Liu ldquoAHP (analytic hierarchyprocess) and computer analysis software used in tourism safetyrdquoJournal of Software vol 8 no 12 pp 3114ndash3119 2013

[35] K J PoolM Amani andAHaroni ldquoAssessment of alternativesfor tourism destinations advertising using AIDA model andAHP approachrdquo Asian Journal of Research in Marketing vol 3no 3 pp 88ndash97 2014

[36] Y Zhou K Maumbe J Deng and S W Selin ldquoResource-baseddestination competitiveness evaluation using a hybrid analytichierarchy process (AHP) the case study of West VirginiardquoTourism Management Perspectives vol 15 pp 72ndash80 2015

[37] X Wang X R Li F Zhen and J Zhang ldquoHow smart isyour tourist attraction measuring tourist preferences of smarttourism attractions via a FCEM-AHP and IPA approachrdquoTourism Management vol 54 pp 309ndash320 2016

[38] J S Chen and D Gursoy ldquoAn investigation of touristsrsquodestination loyalty and preferencesrdquo International Journal ofContemporary HospitalityManagement vol 13 no 2 pp 79ndash852001

[39] M Kozak ldquoComparative analysis of tourist motivations bynationality and destinationsrdquo TourismManagement vol 23 no3 pp 221ndash232 2002

[40] J De Vos P L Mokhtarian T Schwanen V Van Ackerand F Witlox ldquoTravel mode choice and travel satisfactionbridging the gap between decision utility and experiencedutilityrdquo Transportation vol 43 no 5 pp 771ndash796 2016

[41] U Probstl-Haider W Haider V Wirth and B BeardmoreldquoWill climate change increase the attractiveness of summerdestinations in the European Alps A survey of Germantouristsrdquo Journal of Outdoor Recreation and Tourism vol 11 pp44ndash57 2015

[42] A Garg ldquoTravel risks vs tourist decision making a touristperspectiverdquo International Journal of Hospitality amp TourismSystems vol 8 no 1 pp 1ndash9 2015

[43] S Amaro and P Duarte ldquoAn integrative model of consumersrsquointentions to purchase travel onlinerdquo TourismManagement vol46 pp 64ndash79 2015

[44] Z Tang ldquoAn integrated approach to evaluating the couplingcoordination between tourism and the environmentrdquo TourismManagement vol 46 pp 11ndash19 2015

[45] N Paker and C A Vural ldquoCustomer segmentation for marinasevaluating marinas as destinationsrdquo Tourism Management vol56 pp 156ndash171 2016

[46] T Huybers ldquoModelling short-break holiday destinationchoicesrdquo Tourism Economics vol 9 no 4 pp 389ndash405 2003

[47] B G C Dellaert D F Ettema and C Lindh ldquoMulti-facetedtourist travel decisions a constraint-based conceptual frame-work to describe touristsrsquo sequential choices of travel compo-nentsrdquo Tourism Management vol 19 no 4 pp 313ndash320 1998

[48] E Sirakaya and A G Woodside ldquoBuilding and testing theoriesof decisionmaking by travellersrdquo TourismManagement vol 26no 6 pp 815ndash832 2005

[49] T Lam and C H C Hsu ldquoPredicting behavioral intention ofchoosing a travel destinationrdquoTourismManagement vol 27 no4 pp 589ndash599 2006

[50] C Goossens ldquoTourism information and pleasure motivationrdquoAnnals of Tourism Research vol 27 no 2 pp 301ndash321 2000

[51] V C S Heung H Qu and R Chu ldquoThe relationship betweenvacation factors and socio-demographic and travelling char-acteristics the case of Japanese leisure travellersrdquo TourismManagement vol 22 no 3 pp 259ndash269 2001

[52] S S Kim and B Prideaux ldquoMarketing implications arisingfrom a comparative study of international pleasure touristmotivations and other travel-related characteristics of visitors toKoreardquo Tourism Management vol 26 no 3 pp 347ndash357 2005

[53] M Kozak ldquoComparative analysis of tourist motivations bynationality and destinationsrdquo TourismManagement vol 23 no3 pp 221ndash232 2002

[54] G Richards and J Wilson ldquoTodayrsquos youth travellers Tomor-rowrsquos global nomads New horizons in independent youthand student travelrdquo International Student Travel Confederation(ISTC) 2003

[55] N Tomis B Kovatevis N Berber and N Milis ldquoFactors influ-encing the motivation of young people when choosing a citydestination in Europemdasha case study from Esbjerg (Denmark)rdquoEuropean Researcher vol 69 no 2 pp 414ndash428 2014

[56] X D Guo H L Zhang and J H Ruan ldquoA novel similarity-based algorithm for classifying tourism scenic spotsrdquo ICICExpress Letters Part B Applications vol 6 no 2 pp 503ndash5092015

10 Scientific Programming

[57] J Moran E Granada J L Mıguez and J Porteiro ldquoUse of greyrelational analysis to assess and optimize small biomass boilersrdquoFuel Processing Technology vol 87 no 2 pp 123ndash127 2006

[58] D Ju-Long ldquoControl problems of grey systemsrdquo Systems ampControl Letters vol 1 no 5 pp 288ndash294 1982

[59] R-T Wang C-T B Ho and K Oh ldquoMeasuring productionand marketing efficiency using grey relation analysis anddata envelopment analysisrdquo International Journal of ProductionResearch vol 48 no 1 pp 183ndash199 2010

[60] C-T Ho ldquoMeasuring bank operations performance anapproach based on Grey Relation Analysisrdquo Journal of theOperational Research Society vol 57 no 4 pp 337ndash349 2006

[61] C-Y Kung and K-L Wen ldquoApplying Grey Relational AnalysisandGreyDecision-Making to evaluate the relationship betweencompany attributes and its financial performancemdasha case studyof venture capital enterprises in Taiwanrdquo Decision SupportSystems vol 43 no 3 pp 842ndash852 2007

[62] Y-J Wang ldquoCombining grey relation analysis with FMCGDMto evaluate financial performance of Taiwan container linesrdquoExpert Systems with Applications vol 36 no 2 pp 2424ndash24322009

[63] P T-W Lee C-W Lin and S-H Shin ldquoA comparative study onfinancial positions of shipping companies in Taiwan and Koreausing entropy and grey relation analysisrdquo Expert Systems withApplications vol 39 no 5 pp 5649ndash5657 2012

[64] F Ecer and A Boyukaslan ldquoMeasuring performances of foot-ball clubs using financial ratios the gray relational analysisapproachrdquo American Journal of Economics vol 4 no 1 pp 62ndash71 2014

[65] S J Lin I J Lu and C Lewis ldquoGrey relation performancecorrelations among economics energy use and carbon dioxideemission in Taiwanrdquo Energy Policy vol 35 no 3 pp 1948ndash19552007

[66] I J Lu S J Lin and C Lewis ldquoGrey relation analysis of motorvehicular energy consumption inTaiwanrdquoEnergy Policy vol 36no 7 pp 2556ndash2561 2008

[67] S H Hashemi A Karimi and M Tavana ldquoAn integratedgreen supplier selection approachwith analytic network processand improved Grey relational analysisrdquo International Journal ofProduction Economics vol 159 pp 178ndash191 2015

[68] DG Pearce ldquoTourist time-budgetrdquoAnnals of TourismResearchvol 15 no 1 pp 106ndash121 1988

[69] D Ju-Long ldquoIntroduction to grey system theoryrdquoThe Journal ofGrey System vol 1 no 1 pp 1ndash24 1989

[70] U Caydas and A Hascalık ldquoUse of the grey relational analysisto determine optimum laser cutting parameters with multi-performance characteristicsrdquo Optics amp Laser Technology vol40 no 7 pp 987ndash994 2008

Submit your manuscripts athttpwwwhindawicom

Computer Games Technology

International Journal of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Distributed Sensor Networks

International Journal of

Advances in

FuzzySystems

Hindawi Publishing Corporationhttpwwwhindawicom

Volume 2014

International Journal of

ReconfigurableComputing

Hindawi Publishing Corporation httpwwwhindawicom Volume 2014

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Applied Computational Intelligence and Soft Computing

thinspAdvancesthinspinthinsp

Artificial Intelligence

HindawithinspPublishingthinspCorporationhttpwwwhindawicom Volumethinsp2014

Advances inSoftware EngineeringHindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Electrical and Computer Engineering

Journal of

Journal of

Computer Networks and Communications

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Hindawi Publishing Corporation

httpwwwhindawicom Volume 2014

Advances in

Multimedia

International Journal of

Biomedical Imaging

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

ArtificialNeural Systems

Advances in

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

RoboticsJournal of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Computational Intelligence and Neuroscience

Industrial EngineeringJournal of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Modelling amp Simulation in EngineeringHindawi Publishing Corporation httpwwwhindawicom Volume 2014

The Scientific World JournalHindawi Publishing Corporation httpwwwhindawicom Volume 2014

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Human-ComputerInteraction

Advances in

Computer EngineeringAdvances in

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

8 Scientific Programming

Table 2 Final ranking of destinations

Destination

Results of literature[21] Results in this paper

RankSimilarity to

idealsolution

RankRelational

grade to idealsolution

Taipei 101 1 075 1 073KentingNational Park 2 063 2 063

Sun Moon Lake 3 055 3 061National PalaceMuseum 4 048 5 057

Love River 5 047 7 048TarokoNational Park 6 043 4 060

Alishan 7 037 6 053YushanNational Park 8 016 8 040

and evaluation of experts and makes the complex decision-making system into a simple grey relation analysis systemIn other words our proposed evaluation approach makes theevaluation process simple and easy to conductWhat is moreit is especially preferred in ordering the alternatives in situa-tions in which the sample is small and sample distribution isnot known In our future studies we will apply the approachto evaluating other tourism destinations in China and makea ranking for Chinese main tourism destinations

Competing Interests

The authors declare that there are no competing interestsregarding the publication of this paper

Acknowledgments

This study is supported by the National Natural ScienceFoundation of China under Grant no 41440045

References

[1] V S Lin R Mao and H Song ldquoTourism expenditure patternsin ChinardquoAnnals of Tourism Research vol 54 pp 100ndash117 2015

[2] Domestic Tourism National Bureau of Statistics of China 2014httpdatastatsgovcnenglisheasyqueryhtmcn=C01

[3] S C S Jang and S Ham ldquoA double-hurdle analysis oftravel expenditure baby boomer seniors versus older seniorsrdquoTourism Management vol 30 no 3 pp 372ndash380 2009

[4] T Huybers and J Bennett ldquoImpact of the environment on holi-day destination choices of prospective UK tourists implicationsfor Tropical North Queenslandrdquo Tourism Economics vol 6 no1 pp 21ndash46 2000

[5] H R Seddighi andA LTheocharous ldquoAmodel of tourism des-tination choice a theoretical and empirical analysisrdquo TourismManagement vol 23 no 5 pp 475ndash487 2002

[6] G Lee J T OrsquoLeary L Sunhee and A Morrison ldquoComparisonand contrast of push and pull motivational effects on trip

behavior an application of a multinomial logistic regressionmodelrdquo Tourism Analysis vol 7 no 2 pp 89ndash104 2001

[7] H Oppewal T Huybers and G I Crouch ldquoTourist destinationand experience choice a choice experimental analysis of deci-sion sequence effectsrdquo Tourism Management vol 48 pp 467ndash476 2015

[8] A Molina and A Esteban ldquoTourism brochures usefulness andimagerdquoAnnals of TourismResearch vol 33 no 4 pp 1036ndash10562006

[9] H Kim C-K Cheng and J T OrsquoLeary ldquoUnderstanding par-ticipation patterns and trends in tourism cultural attractionsrdquoTourism Management vol 28 no 5 pp 1366ndash1371 2007

[10] L Jin Y He andH Song ldquoService customization to upgrade orto downgrade An investigation of how option framing affectstouristsrsquo choice of package-tour servicesrdquoTourismManagementvol 33 no 2 pp 266ndash275 2012

[11] J Ashwell ldquoGoing bush Factors which influence internationaltouristsrsquo decisions to travel to remote Australian destinationsrdquoTourism Management vol 46 pp 80ndash83 2015

[12] V V Can ldquoEstimation of travel mode choice for domestictourists to Nha Trang using the multinomial probit modelrdquoTransportation Research Part A Policy and Practice vol 49 pp149ndash159 2013

[13] L Wu J Zhang and A Fujiwara ldquoTourism participation andexpenditure behaviour analysis using a scobit based discrete-continuous choice modelrdquo Annals of Tourism Research vol 40no 1 pp 1ndash17 2013

[14] G Prayag and C Ryan ldquoAntecedents of touristsrsquo loyalty tomauritius the role and influence of destination image placeattachment personal involvement and satisfactionrdquo Journal ofTravel Research vol 51 no 3 pp 342ndash356 2012

[15] N K Prebensen E Woo J S Chen and M Uysal ldquoMotivationand involvement as antecedents of the perceived value of thedestination experiencerdquo Journal of Travel Research vol 52 no2 pp 253ndash264 2013

[16] H Song R van der Veen G Li and J L Chen ldquoTheHongKongtourist satisfaction indexrdquo Annals of Tourism Research vol 39no 1 pp 459ndash479 2012

[17] J-Y Wong and C Yeh ldquoTourist hesitation in destinationdecision makingrdquo Annals of Tourism Research vol 36 no 1 pp6ndash23 2009

[18] B M Josiam D L Spears S Pookulangara K Dutta T RKinley and J L Duncan ldquoUsing structural equation modelingto understand the impact of Bollywood movies on destinationimage tourist activity and purchasing behavior of IndiansrdquoJournal of Vacation Marketing vol 21 no 3 pp 251ndash261 2015

[19] R Pinky Pawaskar and M Goel ldquoImproving the efficacy ofdestination marketing strategies a structural equation modelfor leisure travelrdquo Indian Journal of Science and Technology vol9 no 15 pp 1ndash11 2016

[20] H Zhang X Fu L A Cai and L Lu ldquoDestination image andtourist loyalty a meta-analysisrdquo Tourism Management vol 40pp 213ndash223 2014

[21] T-K Hsu Y-F Tsai andH-HWu ldquoThe preference analysis fortourist choice of destination a case study of Taiwanrdquo TourismManagement vol 30 no 2 pp 288ndash297 2009

[22] J-H Huang and K-H Peng ldquoFuzzy Rasch model in TOPSISa new approach for generating fuzzy numbers to assess thecompetitiveness of the tourism industries in Asian countriesrdquoTourism Management vol 33 no 2 pp 456ndash465 2012

Scientific Programming 9

[23] M H N Salleh R Othman and T Sarmidi ldquoTourist satis-faction of the environmental service quality for Tioman IslandMarine Parkrdquo Indian Journal of Geo-Marine Sciences vol 41 no2 pp 173ndash179 2012

[24] D Mohamad and R M Jamil ldquoA Preference analysis model forselecting tourist destinations based on motivational factors aCase Study in Kedah Malaysiardquo ProcediamdashSocial and Behav-ioral Sciences vol 65 pp 20ndash25 2012

[25] M Cucculelli and G Goffi ldquoDoes sustainability enhancetourism destination competitiveness Evidence from Italiandestinations of excellencerdquo Journal of Cleaner Production vol111 pp 370ndash382 2016

[26] J K S Jacobsen and A M Munar ldquoTourist information searchand destination choice in a digital agerdquo Tourism ManagementPerspectives vol 1 no 1 pp 39ndash47 2012

[27] A H N Mak M Lumbers A Eves and R C Y ChangldquoAn application of the repertory grid method and generalisedProcrustes analysis to investigate the motivational factors oftourist food consumptionrdquo International Journal of HospitalityManagement vol 35 pp 327ndash338 2013

[28] G Fuchs N Uriely A Reichel and D Maoz ldquoVacationingin a terror-stricken destination touristsrsquo risk perceptions andrationalizationsrdquo Journal of Travel Research vol 52 no 2 pp182ndash191 2013

[29] W Elias and Y Shiftan ldquoThe influence of individualrsquos riskperception and attitudes on travel behaviorrdquo TransportationResearch Part A Policy and Practice vol 46 no 8 pp 1241ndash12512012

[30] S Pike and S J Page ldquoDestination marketing organizationsand destinationmarketing anarrative analysis of the literaturerdquoTourism Management vol 41 pp 202ndash227 2014

[31] G I Crouch and J R B Ritchie ldquoApplication of the analytichierarchy process to tourism choice and decision making areview and illustration applied to destination competitivenessrdquoTourism Analysis vol 10 no 1 pp 17ndash25 2005

[32] D Das and K Mukherjee ldquoDevelopment of an AHP-QFDframework for designing a tourism productrdquo InternationalJournal of Services and Operations Management vol 4 no 3pp 321ndash344 2008

[33] HN Ali A A I Sabri andMMNNoor ldquoRating and rankingcriteria for selected Islands using fuzzy analytic hierarchyprocess (FAHP)rdquo International Journal of Applied Mathematicsand Informatics vol 1 no 6 pp 57ndash65 2012

[34] G Fan E D Goodman and Z Liu ldquoAHP (analytic hierarchyprocess) and computer analysis software used in tourism safetyrdquoJournal of Software vol 8 no 12 pp 3114ndash3119 2013

[35] K J PoolM Amani andAHaroni ldquoAssessment of alternativesfor tourism destinations advertising using AIDA model andAHP approachrdquo Asian Journal of Research in Marketing vol 3no 3 pp 88ndash97 2014

[36] Y Zhou K Maumbe J Deng and S W Selin ldquoResource-baseddestination competitiveness evaluation using a hybrid analytichierarchy process (AHP) the case study of West VirginiardquoTourism Management Perspectives vol 15 pp 72ndash80 2015

[37] X Wang X R Li F Zhen and J Zhang ldquoHow smart isyour tourist attraction measuring tourist preferences of smarttourism attractions via a FCEM-AHP and IPA approachrdquoTourism Management vol 54 pp 309ndash320 2016

[38] J S Chen and D Gursoy ldquoAn investigation of touristsrsquodestination loyalty and preferencesrdquo International Journal ofContemporary HospitalityManagement vol 13 no 2 pp 79ndash852001

[39] M Kozak ldquoComparative analysis of tourist motivations bynationality and destinationsrdquo TourismManagement vol 23 no3 pp 221ndash232 2002

[40] J De Vos P L Mokhtarian T Schwanen V Van Ackerand F Witlox ldquoTravel mode choice and travel satisfactionbridging the gap between decision utility and experiencedutilityrdquo Transportation vol 43 no 5 pp 771ndash796 2016

[41] U Probstl-Haider W Haider V Wirth and B BeardmoreldquoWill climate change increase the attractiveness of summerdestinations in the European Alps A survey of Germantouristsrdquo Journal of Outdoor Recreation and Tourism vol 11 pp44ndash57 2015

[42] A Garg ldquoTravel risks vs tourist decision making a touristperspectiverdquo International Journal of Hospitality amp TourismSystems vol 8 no 1 pp 1ndash9 2015

[43] S Amaro and P Duarte ldquoAn integrative model of consumersrsquointentions to purchase travel onlinerdquo TourismManagement vol46 pp 64ndash79 2015

[44] Z Tang ldquoAn integrated approach to evaluating the couplingcoordination between tourism and the environmentrdquo TourismManagement vol 46 pp 11ndash19 2015

[45] N Paker and C A Vural ldquoCustomer segmentation for marinasevaluating marinas as destinationsrdquo Tourism Management vol56 pp 156ndash171 2016

[46] T Huybers ldquoModelling short-break holiday destinationchoicesrdquo Tourism Economics vol 9 no 4 pp 389ndash405 2003

[47] B G C Dellaert D F Ettema and C Lindh ldquoMulti-facetedtourist travel decisions a constraint-based conceptual frame-work to describe touristsrsquo sequential choices of travel compo-nentsrdquo Tourism Management vol 19 no 4 pp 313ndash320 1998

[48] E Sirakaya and A G Woodside ldquoBuilding and testing theoriesof decisionmaking by travellersrdquo TourismManagement vol 26no 6 pp 815ndash832 2005

[49] T Lam and C H C Hsu ldquoPredicting behavioral intention ofchoosing a travel destinationrdquoTourismManagement vol 27 no4 pp 589ndash599 2006

[50] C Goossens ldquoTourism information and pleasure motivationrdquoAnnals of Tourism Research vol 27 no 2 pp 301ndash321 2000

[51] V C S Heung H Qu and R Chu ldquoThe relationship betweenvacation factors and socio-demographic and travelling char-acteristics the case of Japanese leisure travellersrdquo TourismManagement vol 22 no 3 pp 259ndash269 2001

[52] S S Kim and B Prideaux ldquoMarketing implications arisingfrom a comparative study of international pleasure touristmotivations and other travel-related characteristics of visitors toKoreardquo Tourism Management vol 26 no 3 pp 347ndash357 2005

[53] M Kozak ldquoComparative analysis of tourist motivations bynationality and destinationsrdquo TourismManagement vol 23 no3 pp 221ndash232 2002

[54] G Richards and J Wilson ldquoTodayrsquos youth travellers Tomor-rowrsquos global nomads New horizons in independent youthand student travelrdquo International Student Travel Confederation(ISTC) 2003

[55] N Tomis B Kovatevis N Berber and N Milis ldquoFactors influ-encing the motivation of young people when choosing a citydestination in Europemdasha case study from Esbjerg (Denmark)rdquoEuropean Researcher vol 69 no 2 pp 414ndash428 2014

[56] X D Guo H L Zhang and J H Ruan ldquoA novel similarity-based algorithm for classifying tourism scenic spotsrdquo ICICExpress Letters Part B Applications vol 6 no 2 pp 503ndash5092015

10 Scientific Programming

[57] J Moran E Granada J L Mıguez and J Porteiro ldquoUse of greyrelational analysis to assess and optimize small biomass boilersrdquoFuel Processing Technology vol 87 no 2 pp 123ndash127 2006

[58] D Ju-Long ldquoControl problems of grey systemsrdquo Systems ampControl Letters vol 1 no 5 pp 288ndash294 1982

[59] R-T Wang C-T B Ho and K Oh ldquoMeasuring productionand marketing efficiency using grey relation analysis anddata envelopment analysisrdquo International Journal of ProductionResearch vol 48 no 1 pp 183ndash199 2010

[60] C-T Ho ldquoMeasuring bank operations performance anapproach based on Grey Relation Analysisrdquo Journal of theOperational Research Society vol 57 no 4 pp 337ndash349 2006

[61] C-Y Kung and K-L Wen ldquoApplying Grey Relational AnalysisandGreyDecision-Making to evaluate the relationship betweencompany attributes and its financial performancemdasha case studyof venture capital enterprises in Taiwanrdquo Decision SupportSystems vol 43 no 3 pp 842ndash852 2007

[62] Y-J Wang ldquoCombining grey relation analysis with FMCGDMto evaluate financial performance of Taiwan container linesrdquoExpert Systems with Applications vol 36 no 2 pp 2424ndash24322009

[63] P T-W Lee C-W Lin and S-H Shin ldquoA comparative study onfinancial positions of shipping companies in Taiwan and Koreausing entropy and grey relation analysisrdquo Expert Systems withApplications vol 39 no 5 pp 5649ndash5657 2012

[64] F Ecer and A Boyukaslan ldquoMeasuring performances of foot-ball clubs using financial ratios the gray relational analysisapproachrdquo American Journal of Economics vol 4 no 1 pp 62ndash71 2014

[65] S J Lin I J Lu and C Lewis ldquoGrey relation performancecorrelations among economics energy use and carbon dioxideemission in Taiwanrdquo Energy Policy vol 35 no 3 pp 1948ndash19552007

[66] I J Lu S J Lin and C Lewis ldquoGrey relation analysis of motorvehicular energy consumption inTaiwanrdquoEnergy Policy vol 36no 7 pp 2556ndash2561 2008

[67] S H Hashemi A Karimi and M Tavana ldquoAn integratedgreen supplier selection approachwith analytic network processand improved Grey relational analysisrdquo International Journal ofProduction Economics vol 159 pp 178ndash191 2015

[68] DG Pearce ldquoTourist time-budgetrdquoAnnals of TourismResearchvol 15 no 1 pp 106ndash121 1988

[69] D Ju-Long ldquoIntroduction to grey system theoryrdquoThe Journal ofGrey System vol 1 no 1 pp 1ndash24 1989

[70] U Caydas and A Hascalık ldquoUse of the grey relational analysisto determine optimum laser cutting parameters with multi-performance characteristicsrdquo Optics amp Laser Technology vol40 no 7 pp 987ndash994 2008

Submit your manuscripts athttpwwwhindawicom

Computer Games Technology

International Journal of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Distributed Sensor Networks

International Journal of

Advances in

FuzzySystems

Hindawi Publishing Corporationhttpwwwhindawicom

Volume 2014

International Journal of

ReconfigurableComputing

Hindawi Publishing Corporation httpwwwhindawicom Volume 2014

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Applied Computational Intelligence and Soft Computing

thinspAdvancesthinspinthinsp

Artificial Intelligence

HindawithinspPublishingthinspCorporationhttpwwwhindawicom Volumethinsp2014

Advances inSoftware EngineeringHindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Electrical and Computer Engineering

Journal of

Journal of

Computer Networks and Communications

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Hindawi Publishing Corporation

httpwwwhindawicom Volume 2014

Advances in

Multimedia

International Journal of

Biomedical Imaging

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

ArtificialNeural Systems

Advances in

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

RoboticsJournal of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Computational Intelligence and Neuroscience

Industrial EngineeringJournal of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Modelling amp Simulation in EngineeringHindawi Publishing Corporation httpwwwhindawicom Volume 2014

The Scientific World JournalHindawi Publishing Corporation httpwwwhindawicom Volume 2014

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Human-ComputerInteraction

Advances in

Computer EngineeringAdvances in

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Scientific Programming 9

[23] M H N Salleh R Othman and T Sarmidi ldquoTourist satis-faction of the environmental service quality for Tioman IslandMarine Parkrdquo Indian Journal of Geo-Marine Sciences vol 41 no2 pp 173ndash179 2012

[24] D Mohamad and R M Jamil ldquoA Preference analysis model forselecting tourist destinations based on motivational factors aCase Study in Kedah Malaysiardquo ProcediamdashSocial and Behav-ioral Sciences vol 65 pp 20ndash25 2012

[25] M Cucculelli and G Goffi ldquoDoes sustainability enhancetourism destination competitiveness Evidence from Italiandestinations of excellencerdquo Journal of Cleaner Production vol111 pp 370ndash382 2016

[26] J K S Jacobsen and A M Munar ldquoTourist information searchand destination choice in a digital agerdquo Tourism ManagementPerspectives vol 1 no 1 pp 39ndash47 2012

[27] A H N Mak M Lumbers A Eves and R C Y ChangldquoAn application of the repertory grid method and generalisedProcrustes analysis to investigate the motivational factors oftourist food consumptionrdquo International Journal of HospitalityManagement vol 35 pp 327ndash338 2013

[28] G Fuchs N Uriely A Reichel and D Maoz ldquoVacationingin a terror-stricken destination touristsrsquo risk perceptions andrationalizationsrdquo Journal of Travel Research vol 52 no 2 pp182ndash191 2013

[29] W Elias and Y Shiftan ldquoThe influence of individualrsquos riskperception and attitudes on travel behaviorrdquo TransportationResearch Part A Policy and Practice vol 46 no 8 pp 1241ndash12512012

[30] S Pike and S J Page ldquoDestination marketing organizationsand destinationmarketing anarrative analysis of the literaturerdquoTourism Management vol 41 pp 202ndash227 2014

[31] G I Crouch and J R B Ritchie ldquoApplication of the analytichierarchy process to tourism choice and decision making areview and illustration applied to destination competitivenessrdquoTourism Analysis vol 10 no 1 pp 17ndash25 2005

[32] D Das and K Mukherjee ldquoDevelopment of an AHP-QFDframework for designing a tourism productrdquo InternationalJournal of Services and Operations Management vol 4 no 3pp 321ndash344 2008

[33] HN Ali A A I Sabri andMMNNoor ldquoRating and rankingcriteria for selected Islands using fuzzy analytic hierarchyprocess (FAHP)rdquo International Journal of Applied Mathematicsand Informatics vol 1 no 6 pp 57ndash65 2012

[34] G Fan E D Goodman and Z Liu ldquoAHP (analytic hierarchyprocess) and computer analysis software used in tourism safetyrdquoJournal of Software vol 8 no 12 pp 3114ndash3119 2013

[35] K J PoolM Amani andAHaroni ldquoAssessment of alternativesfor tourism destinations advertising using AIDA model andAHP approachrdquo Asian Journal of Research in Marketing vol 3no 3 pp 88ndash97 2014

[36] Y Zhou K Maumbe J Deng and S W Selin ldquoResource-baseddestination competitiveness evaluation using a hybrid analytichierarchy process (AHP) the case study of West VirginiardquoTourism Management Perspectives vol 15 pp 72ndash80 2015

[37] X Wang X R Li F Zhen and J Zhang ldquoHow smart isyour tourist attraction measuring tourist preferences of smarttourism attractions via a FCEM-AHP and IPA approachrdquoTourism Management vol 54 pp 309ndash320 2016

[38] J S Chen and D Gursoy ldquoAn investigation of touristsrsquodestination loyalty and preferencesrdquo International Journal ofContemporary HospitalityManagement vol 13 no 2 pp 79ndash852001

[39] M Kozak ldquoComparative analysis of tourist motivations bynationality and destinationsrdquo TourismManagement vol 23 no3 pp 221ndash232 2002

[40] J De Vos P L Mokhtarian T Schwanen V Van Ackerand F Witlox ldquoTravel mode choice and travel satisfactionbridging the gap between decision utility and experiencedutilityrdquo Transportation vol 43 no 5 pp 771ndash796 2016

[41] U Probstl-Haider W Haider V Wirth and B BeardmoreldquoWill climate change increase the attractiveness of summerdestinations in the European Alps A survey of Germantouristsrdquo Journal of Outdoor Recreation and Tourism vol 11 pp44ndash57 2015

[42] A Garg ldquoTravel risks vs tourist decision making a touristperspectiverdquo International Journal of Hospitality amp TourismSystems vol 8 no 1 pp 1ndash9 2015

[43] S Amaro and P Duarte ldquoAn integrative model of consumersrsquointentions to purchase travel onlinerdquo TourismManagement vol46 pp 64ndash79 2015

[44] Z Tang ldquoAn integrated approach to evaluating the couplingcoordination between tourism and the environmentrdquo TourismManagement vol 46 pp 11ndash19 2015

[45] N Paker and C A Vural ldquoCustomer segmentation for marinasevaluating marinas as destinationsrdquo Tourism Management vol56 pp 156ndash171 2016

[46] T Huybers ldquoModelling short-break holiday destinationchoicesrdquo Tourism Economics vol 9 no 4 pp 389ndash405 2003

[47] B G C Dellaert D F Ettema and C Lindh ldquoMulti-facetedtourist travel decisions a constraint-based conceptual frame-work to describe touristsrsquo sequential choices of travel compo-nentsrdquo Tourism Management vol 19 no 4 pp 313ndash320 1998

[48] E Sirakaya and A G Woodside ldquoBuilding and testing theoriesof decisionmaking by travellersrdquo TourismManagement vol 26no 6 pp 815ndash832 2005

[49] T Lam and C H C Hsu ldquoPredicting behavioral intention ofchoosing a travel destinationrdquoTourismManagement vol 27 no4 pp 589ndash599 2006

[50] C Goossens ldquoTourism information and pleasure motivationrdquoAnnals of Tourism Research vol 27 no 2 pp 301ndash321 2000

[51] V C S Heung H Qu and R Chu ldquoThe relationship betweenvacation factors and socio-demographic and travelling char-acteristics the case of Japanese leisure travellersrdquo TourismManagement vol 22 no 3 pp 259ndash269 2001

[52] S S Kim and B Prideaux ldquoMarketing implications arisingfrom a comparative study of international pleasure touristmotivations and other travel-related characteristics of visitors toKoreardquo Tourism Management vol 26 no 3 pp 347ndash357 2005

[53] M Kozak ldquoComparative analysis of tourist motivations bynationality and destinationsrdquo TourismManagement vol 23 no3 pp 221ndash232 2002

[54] G Richards and J Wilson ldquoTodayrsquos youth travellers Tomor-rowrsquos global nomads New horizons in independent youthand student travelrdquo International Student Travel Confederation(ISTC) 2003

[55] N Tomis B Kovatevis N Berber and N Milis ldquoFactors influ-encing the motivation of young people when choosing a citydestination in Europemdasha case study from Esbjerg (Denmark)rdquoEuropean Researcher vol 69 no 2 pp 414ndash428 2014

[56] X D Guo H L Zhang and J H Ruan ldquoA novel similarity-based algorithm for classifying tourism scenic spotsrdquo ICICExpress Letters Part B Applications vol 6 no 2 pp 503ndash5092015

10 Scientific Programming

[57] J Moran E Granada J L Mıguez and J Porteiro ldquoUse of greyrelational analysis to assess and optimize small biomass boilersrdquoFuel Processing Technology vol 87 no 2 pp 123ndash127 2006

[58] D Ju-Long ldquoControl problems of grey systemsrdquo Systems ampControl Letters vol 1 no 5 pp 288ndash294 1982

[59] R-T Wang C-T B Ho and K Oh ldquoMeasuring productionand marketing efficiency using grey relation analysis anddata envelopment analysisrdquo International Journal of ProductionResearch vol 48 no 1 pp 183ndash199 2010

[60] C-T Ho ldquoMeasuring bank operations performance anapproach based on Grey Relation Analysisrdquo Journal of theOperational Research Society vol 57 no 4 pp 337ndash349 2006

[61] C-Y Kung and K-L Wen ldquoApplying Grey Relational AnalysisandGreyDecision-Making to evaluate the relationship betweencompany attributes and its financial performancemdasha case studyof venture capital enterprises in Taiwanrdquo Decision SupportSystems vol 43 no 3 pp 842ndash852 2007

[62] Y-J Wang ldquoCombining grey relation analysis with FMCGDMto evaluate financial performance of Taiwan container linesrdquoExpert Systems with Applications vol 36 no 2 pp 2424ndash24322009

[63] P T-W Lee C-W Lin and S-H Shin ldquoA comparative study onfinancial positions of shipping companies in Taiwan and Koreausing entropy and grey relation analysisrdquo Expert Systems withApplications vol 39 no 5 pp 5649ndash5657 2012

[64] F Ecer and A Boyukaslan ldquoMeasuring performances of foot-ball clubs using financial ratios the gray relational analysisapproachrdquo American Journal of Economics vol 4 no 1 pp 62ndash71 2014

[65] S J Lin I J Lu and C Lewis ldquoGrey relation performancecorrelations among economics energy use and carbon dioxideemission in Taiwanrdquo Energy Policy vol 35 no 3 pp 1948ndash19552007

[66] I J Lu S J Lin and C Lewis ldquoGrey relation analysis of motorvehicular energy consumption inTaiwanrdquoEnergy Policy vol 36no 7 pp 2556ndash2561 2008

[67] S H Hashemi A Karimi and M Tavana ldquoAn integratedgreen supplier selection approachwith analytic network processand improved Grey relational analysisrdquo International Journal ofProduction Economics vol 159 pp 178ndash191 2015

[68] DG Pearce ldquoTourist time-budgetrdquoAnnals of TourismResearchvol 15 no 1 pp 106ndash121 1988

[69] D Ju-Long ldquoIntroduction to grey system theoryrdquoThe Journal ofGrey System vol 1 no 1 pp 1ndash24 1989

[70] U Caydas and A Hascalık ldquoUse of the grey relational analysisto determine optimum laser cutting parameters with multi-performance characteristicsrdquo Optics amp Laser Technology vol40 no 7 pp 987ndash994 2008

Submit your manuscripts athttpwwwhindawicom

Computer Games Technology

International Journal of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Distributed Sensor Networks

International Journal of

Advances in

FuzzySystems

Hindawi Publishing Corporationhttpwwwhindawicom

Volume 2014

International Journal of

ReconfigurableComputing

Hindawi Publishing Corporation httpwwwhindawicom Volume 2014

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Applied Computational Intelligence and Soft Computing

thinspAdvancesthinspinthinsp

Artificial Intelligence

HindawithinspPublishingthinspCorporationhttpwwwhindawicom Volumethinsp2014

Advances inSoftware EngineeringHindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Electrical and Computer Engineering

Journal of

Journal of

Computer Networks and Communications

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Hindawi Publishing Corporation

httpwwwhindawicom Volume 2014

Advances in

Multimedia

International Journal of

Biomedical Imaging

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

ArtificialNeural Systems

Advances in

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

RoboticsJournal of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Computational Intelligence and Neuroscience

Industrial EngineeringJournal of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Modelling amp Simulation in EngineeringHindawi Publishing Corporation httpwwwhindawicom Volume 2014

The Scientific World JournalHindawi Publishing Corporation httpwwwhindawicom Volume 2014

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Human-ComputerInteraction

Advances in

Computer EngineeringAdvances in

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

10 Scientific Programming

[57] J Moran E Granada J L Mıguez and J Porteiro ldquoUse of greyrelational analysis to assess and optimize small biomass boilersrdquoFuel Processing Technology vol 87 no 2 pp 123ndash127 2006

[58] D Ju-Long ldquoControl problems of grey systemsrdquo Systems ampControl Letters vol 1 no 5 pp 288ndash294 1982

[59] R-T Wang C-T B Ho and K Oh ldquoMeasuring productionand marketing efficiency using grey relation analysis anddata envelopment analysisrdquo International Journal of ProductionResearch vol 48 no 1 pp 183ndash199 2010

[60] C-T Ho ldquoMeasuring bank operations performance anapproach based on Grey Relation Analysisrdquo Journal of theOperational Research Society vol 57 no 4 pp 337ndash349 2006

[61] C-Y Kung and K-L Wen ldquoApplying Grey Relational AnalysisandGreyDecision-Making to evaluate the relationship betweencompany attributes and its financial performancemdasha case studyof venture capital enterprises in Taiwanrdquo Decision SupportSystems vol 43 no 3 pp 842ndash852 2007

[62] Y-J Wang ldquoCombining grey relation analysis with FMCGDMto evaluate financial performance of Taiwan container linesrdquoExpert Systems with Applications vol 36 no 2 pp 2424ndash24322009

[63] P T-W Lee C-W Lin and S-H Shin ldquoA comparative study onfinancial positions of shipping companies in Taiwan and Koreausing entropy and grey relation analysisrdquo Expert Systems withApplications vol 39 no 5 pp 5649ndash5657 2012

[64] F Ecer and A Boyukaslan ldquoMeasuring performances of foot-ball clubs using financial ratios the gray relational analysisapproachrdquo American Journal of Economics vol 4 no 1 pp 62ndash71 2014

[65] S J Lin I J Lu and C Lewis ldquoGrey relation performancecorrelations among economics energy use and carbon dioxideemission in Taiwanrdquo Energy Policy vol 35 no 3 pp 1948ndash19552007

[66] I J Lu S J Lin and C Lewis ldquoGrey relation analysis of motorvehicular energy consumption inTaiwanrdquoEnergy Policy vol 36no 7 pp 2556ndash2561 2008

[67] S H Hashemi A Karimi and M Tavana ldquoAn integratedgreen supplier selection approachwith analytic network processand improved Grey relational analysisrdquo International Journal ofProduction Economics vol 159 pp 178ndash191 2015

[68] DG Pearce ldquoTourist time-budgetrdquoAnnals of TourismResearchvol 15 no 1 pp 106ndash121 1988

[69] D Ju-Long ldquoIntroduction to grey system theoryrdquoThe Journal ofGrey System vol 1 no 1 pp 1ndash24 1989

[70] U Caydas and A Hascalık ldquoUse of the grey relational analysisto determine optimum laser cutting parameters with multi-performance characteristicsrdquo Optics amp Laser Technology vol40 no 7 pp 987ndash994 2008

Submit your manuscripts athttpwwwhindawicom

Computer Games Technology

International Journal of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Distributed Sensor Networks

International Journal of

Advances in

FuzzySystems

Hindawi Publishing Corporationhttpwwwhindawicom

Volume 2014

International Journal of

ReconfigurableComputing

Hindawi Publishing Corporation httpwwwhindawicom Volume 2014

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Applied Computational Intelligence and Soft Computing

thinspAdvancesthinspinthinsp

Artificial Intelligence

HindawithinspPublishingthinspCorporationhttpwwwhindawicom Volumethinsp2014

Advances inSoftware EngineeringHindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Electrical and Computer Engineering

Journal of

Journal of

Computer Networks and Communications

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Hindawi Publishing Corporation

httpwwwhindawicom Volume 2014

Advances in

Multimedia

International Journal of

Biomedical Imaging

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

ArtificialNeural Systems

Advances in

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

RoboticsJournal of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Computational Intelligence and Neuroscience

Industrial EngineeringJournal of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Modelling amp Simulation in EngineeringHindawi Publishing Corporation httpwwwhindawicom Volume 2014

The Scientific World JournalHindawi Publishing Corporation httpwwwhindawicom Volume 2014

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Human-ComputerInteraction

Advances in

Computer EngineeringAdvances in

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Submit your manuscripts athttpwwwhindawicom

Computer Games Technology

International Journal of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Distributed Sensor Networks

International Journal of

Advances in

FuzzySystems

Hindawi Publishing Corporationhttpwwwhindawicom

Volume 2014

International Journal of

ReconfigurableComputing

Hindawi Publishing Corporation httpwwwhindawicom Volume 2014

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Applied Computational Intelligence and Soft Computing

thinspAdvancesthinspinthinsp

Artificial Intelligence

HindawithinspPublishingthinspCorporationhttpwwwhindawicom Volumethinsp2014

Advances inSoftware EngineeringHindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Electrical and Computer Engineering

Journal of

Journal of

Computer Networks and Communications

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Hindawi Publishing Corporation

httpwwwhindawicom Volume 2014

Advances in

Multimedia

International Journal of

Biomedical Imaging

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

ArtificialNeural Systems

Advances in

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

RoboticsJournal of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Computational Intelligence and Neuroscience

Industrial EngineeringJournal of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Modelling amp Simulation in EngineeringHindawi Publishing Corporation httpwwwhindawicom Volume 2014

The Scientific World JournalHindawi Publishing Corporation httpwwwhindawicom Volume 2014

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Human-ComputerInteraction

Advances in

Computer EngineeringAdvances in

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014