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Hindawi Publishing CorporationThe Scientific World JournalVolume 2013 Article ID 751728 18 pageshttpdxdoiorg1011552013751728

Research ArticleEvolving MCDM Applications Using Hybrid Expert-Based ISMand DEMATEL Models An Example of Sustainable Ecotourism

Huan-Ming Chuang1 Chien-Ku Lin1 Da-Ren Chen2 and You-Shyang Chen3

1 Department of Information Management National Yunlin University of Science and Technology No 123University Road Section 3 Douliou Yunlin 640 Taiwan

2Department of Information Management National Taichung University of Science and Technology No 129Section 3 Sanmin Road North District Taichung 404 Taiwan

3Department of Information Management Hwa Hsia Institute of Technology No 111 Gong Jhuan RoadChung Ho District New Taipei 235 Taiwan

Correspondence should be addressed to You-Shyang Chen ys chencchwhedutw

Received 30 July 2013 Accepted 10 October 2013

Academic Editors B Niu and J Pavon

Copyright copy 2013 Huan-Ming Chuang et al This is an open access article distributed under the Creative Commons AttributionLicense which permits unrestricted use distribution and reproduction in any medium provided the original work is properlycited

Ecological degradation is an escalating global threat Increasingly people are expressing awareness and priority for concernsabout environmental problems surrounding them Environmental protection issues are highlighted An appropriate informationtechnology tool the growing popular social network system (virtual community VC) facilitates public education and engagementwith applications for existent problems effectively Particularly the exploration of related involvement behavior of VC memberengagement is an interesting topic Nevertheless member engagement processes comprise interrelated sub-processes that reflectan interactive experience within VCs as well as the value co-creation model To address the top-focused ecotourism VCs thisstudy presents an application of a hybrid expert-based ISMmodel and DEMATEL model based on multi-criteria decision makingtools to investigate the complex multidimensional and dynamic nature of member engagement Our research findings provideinsightful managerial implications and suggest that the viral marketing of ecotourism protection is concerned with practitionersand academicians alike

1 Introduction

This section includes the engagement behavior for a virtualcommunity the importance of environmental concerns andour research objectives and problems

As information technology (IT) advances the wordsldquocommunityrdquo and ldquonetworkrdquo are commonly referred to asldquovirtual community VCrdquo VC sites formed by the internetoffer users the ability to interact with others and exchangeinformation and knowledge of the environment Varioussocial networking sites are emerging to attract a large numberof users to participate in discussions on current issues andtrends Previous studies that indicate the factors that willaffect user interest to use social networking sites are mainlyfocused on their initial beliefs (perceived usefulness and easeof use) as well as the attitudes to explore ideas for their use

Chen [1] claimed that the concept of community networkoriginated from Barnes [2] an anthropologist discoveredthe concept from an investigation of the social structureof a Norwegian fishing village One cannot clearly explainthe actual operations of a fishing village by looking at theformal role of social structure such as identity or statusInstead one can explain the interactions in a fishing villagefrom looking at the informal role of social structure suchas friends and relatives and how they organize and impactan internal social network Mitchell [3] argued that socialnetworks show the interrelationship among individuals of aparticular society Pattison [4] believed that social networksare a social organization that collectively gather from theinterrelationship among individuals and organizations Chanet al [5] claimed that individual behavior and attitudesdemonstrate that a particular environment was influenced

2 The Scientific World Journal

by the interconnections with other individuals in thatsocial environment The network connects nodes and nodestogether form the structure The nodes can be individualsteams or organizations [6] and its overall structure describessocial behavior

In recent years development of VC websites has grownrapidly The use of social networking and their fast growthhave become an important trend According to the BusinessNext [7] survey of the top 100 Taiwanwebsites 25 communitysites were selected among to represent 25 of the overall listAfter the financial crises of 2008 theVCwebsite developmentseems to be the most promising and popular business modelor trend This trend is buttressed by social networking sitesand microblogs such as Facebook Twitter and Plurk UsersfromUS President Barack Obama former GE chief executiveJack Welch to Dell Computer and Starbucks Coffee areutilizing this trend [8] According to a survey by ComScore[9] the number of VCwebsites has grown at an amazing rate

How will social networking sites grow to replace othertypes of sites such as shopping sites and search enginewebsites as users actively become more involved with theseactivities Here are some important reasons (1) Social net-working sites bring interpersonal relationships into virtualsituations (2) they create more opportunities for humaninteraction and real-time sharing of personal informationsuch as pictures videos and interactive games and (3) userscan create their own program to share many features thatwill meet their social leisure and entertainment needsThussocial networking sites successfully win user interest whichmakes it a popular site type They attract many users inVCs to share information and emotional communicationThis free open yet with hidden features communicationplatform has led various ldquointernet communitiesrdquo to spread[10] Ecotourism and environmental concern communitieshave sprung up as a result

Increasingly the ecological degradation problems arehighlighted and environmental protection issues haveemerged from social networking sites to express awarenessand priority concerns One of the greatest challenges forsustainable ecotourism is to encourage visitors to act inways that minimize environmental and experiential impactThis is the case for protected areas where the environmentis often fragile and mindful and engaging experiences aresought Previous research shows that all types of recreationalactivities can cause environmental damage even at low uselevels [11 12] and visitor behavior and density levels influencethe quality of visitor experiences [13]

Protected area managers employ a suite of strategies toaddress visitor problems though for reasons of expediencycost and efficiency education is the preferred and dominantvisitor management worldwide [14ndash16] As Ham and Weiler[17] pointed out interpretation is ultimately aimed at com-municating messages about a place and in some instancesthe persuasion of people to behave in ways that are consistentwith the protected values Therefore a better understandingfor how visitors think and what factors determine theirbehavior could contribute to better achieve managerial goalsGiven these reasons we explore the behaviors that are

amenable to persuasive influence and how to tap into therelevant thoughts held by visitors about particular actions

This study is aimed at themost popular social networkingsites related to ecotourism issues (ecotourism VCs) Weuse the technology acceptance model (TAM) proposed byDavis et al [18] to study IT user intention and behavior toinvestigate the participation of members of a virtual environ-mental protection community Simultaneously the qualityof information and system from the virtual sites to achieveuser information and system satisfaction are explored aswell as the correlation between user attitude and willingnessIn summary information and system quality satisfactionperceived usefulness and ease of use are discussedThe afore-mentioned factors affect VC member attitudes intentionsand participation results The four research objectives of thisstudy are as follows (1) integrate two major IT successesrelated research streams namely technology acceptance anduser satisfaction (2) explore the dynamics among object-oriented beliefs and attitudes behavioral beliefs and attitudesas well as engagement behavior (3) investigate the interactiveand co-creation value behavior of environmental protectionVC and (4) suggest effective strategies for the promotion ofenvironmental protection issues

This study determines themotivations and objectivesTheremainder of this paper is organized as follows Section 2looks at related literature on VC marketing of ecotourismconcerns Section 3 looks at techniques used and establishesour study framework and decisions for the appropriateresearch methodology Section 4 looks at our online ques-tionnaires for an empirical case study thatwas distributed andanalyzed with statistical software and a series of techniquesSection 5 shows our findings and the managerial implica-tions are described Section 6 is our conclusions and futureresearch directions offered

2 Literature Review

This section describes the relevant literature including sus-tainable ecotourism social network sites andVCs and relatedtechnology acceptance

The first mention of ecotourism in the English-languageacademic literature was by Romeril [19] In the 1990s thetourism industry realized the profit potential of ecotourismand the suffix ldquoecordquo was used by travel agents for mar-keting Eventually ecotourism organizations such as theInternational Ecotourism Society (TIES) and the EcotourismSociety (TES) were formed [20] While some may usethe term ldquoecotourismrdquo as being synonymous with ldquonaturetourismrdquo ldquoadventure tourismrdquo ldquoresponsible tourismrdquo ldquoeth-ical tourismrdquo and ldquogreen tourismrdquo ecotourism does havespecific characteristics that differentiate it from these othersegments in the tourism industry A subset of sustainabletourism (tourism that actively reduces the negative impactof tourism) ecotourism is defined by TIES as ldquoresponsibletravel to natural areas that conserves the environment andimproves the welfare of local peoplerdquo [21 22] Additionallyecotourism should build a constituency to promote conserva-tion and provide an impetus for private conservation efforts

The Scientific World Journal 3

Under these circumstances conservation benefits can extendbeyond the immediate experience of an ecotourism ventureas ecotourists become active advocates for conservation in thearea visited as well as at home [23]

Ecotourismhas been identified as a growing nichemarketfor years According to the World Resources Institute whiletourism grew by 4 in the early 1990s ldquonature travelrdquo grew ata rate between 10 and 30 [24]World TourismOrganizationestimates showglobal spending on themore narrowly definedecotourismmarket increasing at a rate of 20 per year aboutfive times the rate for tourism [24] However more strikingthan this growth is the identification of ecotourism by itsadvocates as part of an ethical lifestyle (a form of life politics)Particularly the idea ecological degradation is an escalatingglobal threat Increasingly the worldwide awareness and con-cern expressed regarding the environmental problems thatsurround themThe concern for environmental issues focuseson education as to improve environmental behavior [25] andto study behavior as it relates to remediation of environmentalissues Thus one way experts can promote environmentalimprovement through mediators is to examine the linkbetween educational intervention and responsible behavioralchange [25] It is believed that environmental education islinked to environmental behavior From this environmentaleducation leads to greater awareness and attitude change thatimproves environmental protection behavior Such behaviorwas successful to change pro-environmental intention andactions that presented as credible environmental informationas well as to become actively involved participants in sus-tainable ecotourism Sustainable ecotourism has been widelyresearched in various fields such as the related literature [26ndash32]

Some of the latest research on ecotourism such asanalyses on the relationships among tourism [33] the mea-surement of sustainable tourism development indicators anddeveloping standards associated [34] and issues regardingzoos as a morally acceptable form of ecotourism [35] Ourstudy explores related ecotourism involvement behavior forVC members

Social networking sites have gained important and vigor-ous development most notably Facebook MySpace Plurkand Twitter The formation of virtual communities fromthe related literature [36] is an important research areaThere were prior studies done regarding social networksFirst House et al [37] had pointed out that social networkscan be divided into two parts structure and process Asfor the structure they may include social integration andnetwork structure Yeh and Luo [38] indicated that onlinedating patterns were similar to the real world Existent realsocial networks influenced the formation and developmentof online communities Boyd and Ellison [39] claimed thatsocial network sites provided the following functional char-acteristics (1) construct a public or semipublic profile withina bounded system (2) articulated a list of other users withwhom they share a connection and (3) view and traversetheir list of connections and those made by others withinthe system Xia and Bu [40] described the topic in socialnetwork analysis community detection can help us discoverthe network properties shared by its members The VC was

widely used for purposes of collaborative recommendation[41] knowledge sharing [42ndash44] and online market research[45 46] Although the nature and nomenclature of theseconnections may vary from site to site professional VCsprovide spaces that allow domain experts to interact to assistin the creation and to share tacit knowledge with the goal ofbecoming an intelligent enterprise [47ndash49]

The related technology acceptance literature includes thetheory of reasoned action (TRA) theory of planned behavior(TPB) and the technology acceptance model (TAM) TRAwas improved by Fishbein and Ajzen [50] and was based onsocial psychology theory that was widely and successfullyapplied in various disciplines TRA states that behavioralintentions to perform a specific behavior predict explainor influence actual performance of the said behavior TPBpredicts deliberate behavior based on the assumption thatbehavior is deliberative and planned [51] TPB suggests thatbehavior is determined by intention to perform the behaviorand that this intention is in turn a function of attitudetoward the behavior and subjective norm TAM proposed byDavis et al [18] studied the idea of IT user intention andbehavior This model is one of the most acceptable modelsin the investigation of IT related behaviors It encompassedWeb 20 technology focused subjects [52] and recognized twoprimary principles ldquoPerceived Usefulnessrdquo and ldquoPerceivedEase of Userdquo which are major predictors of user attitude andcomplete emotional reaction for usage

3 Methods and Materials

31 Information System Success Model In the system man-agement area a crucial topic is to successfully implementan information system (IS) into an organization An IS wasused to enhance a business to create competitive advantage[53] however IS of IT will result in failure if it not acceptedby its users Therefore the evaluation of success of an ISsuccess model (ISSM) has been an important subject toorganizations

DeLone andMcLean [54] proposed a successful IS modelthat indicated the factors that influenced successful IS iscomprised of six parts (1) system quality (2) informationquality (3) use (4) user satisfaction (5) individual impactand (6) organizational impact They showed that system andinformation quality affects the level of use and user satisfac-tion as well as the level of use to positively or negatively affectuser satisfaction In additional use level and user satisfactionwill affect individuals and organizations Pitt et al [55] hasadded another factor ldquoservice qualityrdquo in addition to the sixlisted above They demonstrate that service quality alongwith system quality and information quality together affectedthe use and user satisfaction levels DeLone and McLean[56] later augmented this revised successful IS model Therevised model additionally added service quality as well asincluding system quality information quality service qualityuser intention user satisfaction and net benefit

The literature on user satisfaction indicates that IS char-acters were core factors such as system quality informationquality and service quality in a successful IS model as well as


higher order and overall expectancy disconfirmation in thepost-acceptance model (PAM) [54 57] Bhattacherjee [57]proposed that PAM should be combined with expectancydisconfirmation theory (EDT) and TAM to better investigateuser intentions for continual use IS Furthermore usersatisfaction was regarded as the attitude toward a particularIS whereas it should be considered as an object-orientedattitude [58 59]This opinion is indicated by various satisfac-tion correspondencemeasurements that have adopted systemcharacter-oriented measures [60ndash62]

System characters affect the beliefs and attitudes towardthe system itself Along with behavior beliefs and attitudes asmedia theymanipulated final use behavior of the system Forexample the perceived reliability of an e-commerce websitecannot directly control the use of that website Neverthelessit can affect hisher attitude (satisfaction) toward that websiteand then buttress the beliefs (ie ease of use) and attitudeto use it and eventually the use behavior Wixom andTodd [59] effectively partitioned and empirically supportedthe relationship of object-oriented beliefs object-orientedattitude behavior beliefs and behavior attitude based on theexpectancy-value theory and the correspondence principle

32 The Delphi Method The Delphi method is a researchtechnique that is used to address complex problems byusing a structured communication process of a panel ofexperts [63] to forecast make decisions and solve complexproblems With objective application of the Delphi methodwe explore creative ideas and produce valuable informationKnowledge collected during the Delphi study is synthesizedand distilled from the use of a series of questionnairesResponses to questionnaires were collected on site and werereviewed directly [64] A few of the features of the Delphimethod include (1) rapid consensus (2) participants canreside anywhere (3) coverage of wide range of expertise and(4) avoid groupthink The limitations of the Delphi methodinclude (1) cross impact neglected in the original form (2)does not copewell with paradigm shifts and (3) success of themethod depends on the quality of the participants Delphi hasbeen applied to various issues such as to forecast a specificand single-dimension future issue consensus building andavoidance of groupthink and to generate creative ideas [63]The features of the Delphi method provide comprehensiveexpert opinion and much needed objective consensus

The latest research on ecotourism uses the Delphimethod For example they analyze the relationships amongtourism [33] and developed a point evaluation system forecotourism destinations [65]This study thus uses the Delphimethod to analyze VC for eco-travel expert consensus

In the standard Delphi method several experts areconsulted for estimations of a project or to prognosticate itThe process of Delphi method is described as follows

(1) A project manager prepares a description of theproject in that the individual partial products arelisted and prepared on a job form

(2) The project manager presents the goals of the overallproject and distributes copies of the job form to each

expert however it does not take place of a discussionof the estimations

(3) Each expert estimates the work packages contained inthe job form there is no cooperation between experts

(4) All job forms are collected and evaluated by theproject manager

(5) If serious discrepancies result then these are com-mented on by the project manager uniformly on alljob forms as regard to the deviation Each job form isthen returned to its original editor

(6) The experts consider their estimations as a functionof the comments

(7) The described loop repeats itself until the estimationsindependently (in a range of tolerance) consent toadjustment

(8) The average values are calculated and presented by allestimations as the final estimation

33 The ISM Method Warfield [66 67] first proposedinterpretive structural modeling (ISM) to analyze complexsocioeconomic systems It is a process that helps individualsor groups to structure domain knowledge into a modelof interrelationships to enhance the understanding of itscomplexity The result of the ISM process is representedby a graph that shows the directed relationships as wellas hierarchical levels of elements within the system underconsideration A few features of the ISM method include (1)incorporating the subjective judgments and the knowledgebase of experts systematically (2) to provide ample opportu-nity for revision of judgments and (3) computational effortsinvolved are far less for criteria ranging from 10 to 15 numbersas well as used as a handy tool for real-life applications[68] The limitations of the ISM method include (1) thecontextual relation among the variables always depends onuser knowledge and familiarity with the firm its operationsand its industry (2) the bias of the judgment variablesinfluence the final result (3) ISM acts as a tool to imposeorder and direction on the complexity of relationships amongthe variables and (4) there is no weight associated with thevariables [69] ISM has been widely applied in various fieldssuch as supply chains [70] balanced scorecard [71] successfactors [72] product design [73] and risk analysis [74] TheISMmethod can transform nebulous thoughts and ideas intoan intuitive model of structural relationships to understandthe relationship between the variables

The computational processes in the ISM method aredescribed in the following steps

(1) Identification of elements through research (egliterature review) or expert opinion (eg Delphi orbrainstorming)

(2) Specification of contextual relationship depends onthe objective and nature of the case

(3) Construction of a structural self-interaction matrix(SSIM) in four types of possible relationships betweenthe elements (a amp b)


Identification of elements

Check transitivity of the RM

Develop the initial reachability matrix

(RM)

Expert opinion

Literature review

Develop a structural self-interaction matrix (SSIM)

Establish contextual relationships between

elements

Develop digraph and model of ISM

Figure 1 Flow diagram for implementing ISM

(4) Transformation of SSIM into an initial reachabilitymatrix (RM) in the rules for the substitution of 1s and0s

(5) Checking the initial RM for transitivity(6) Partitioning levels of the final RM(7) Building ISM digraph and model

The ISM is generated by replacing all element numberswith the actual element description Finally the ISM givesa clear picture of the relationships among the system ofelements Conclusively Figure 1 shows the above steps of theISMmethod Furthermore the details of ISMmethod can bereferred to the Warfield [66 67] for the limited space

34 The DEMATEL Method The decision making trail andevaluation laboratory (DEMATEL)method is amathematicalprocedure originated from theGenevaResearchCentre of theBattelle Memorial Institute designed to deal with importantissues of world societies [75 76] The DEMATEL possessessome excellent features For example it is based on matricesthat represent the contextual relation as well as strength ofinfluence of the elements for the target system It converts thecause-effect relationship of elements into visible structuralmodels With its practical benefits the DEMATEL has beenwidely applied in various fields such as marketing [77 78]education [79 80] investment [81] supply chain manage-ment [82 83] smart phone [84] and influential factors [85]The DEMATEL method has advantages that help researchersbetter understand the nature of the problem

Mathematically the procedures of DEMATEL are nar-rated step-by-step as follows

Step 1 Generate the initial direct-relation matrix Acquirethe assessments about direct affect between each pair ofelements from experts The pair-wise comparison designatedby four levels 0 1 2 and 3 to represent ldquoNo influencerdquoldquoLow influencerdquo ldquoHigh influencerdquo and ldquoVery high influencerdquorespectively The initial direct-relation matrix 119860 is a 119899 times 119899matrix in which 119886

119894119895is denoted as the degree to which the

element 119894 affects the element 119895 is formatted as 119860 = [119886119894119895]119899times119899

Step 2 Normalize the initial direct-relation matrix Thenormalized direct-relation matrix 119883 = [119909

119894119895] can be obtained

from (1) and (2)

119904 = max [

[

max1le119894le119899

119899

sum

119895=1

119886119894119895max1le119895le119899

119899

sum

119894=1

119886119894119895]

]

119894 119895 isin 1 2 119899

(1)

119883 =1

119904119860 (2)

where (1) represents the maximum values of the sums of allthe rows and the sums of all the columns and (2) representsthe normalized initial direct-relation matrix All elements inmatrix 119883 comply with 0 le 119909

119894119895le 1 and all principal diagonal

elements are equal to 0

Step 3 Compute the total relation matrix After Step 2 thetotal relation matrix T is obtained by using the followingnumerical calculation

119879 = 119883 + 1198832+ sdot sdot sdot + 119883

119901= 119883 times (119868 minus 119883)

minus1

= [119909119894119895]119899times119899119901 997888rarr infin

(3)

where 119901 represents the power Hence when 119901 tends toinfinity the matrix 119883 will converge Furthermore 119868 is theidentity matrix

Step 4 Calculate the sum of rows and columns of matrix119879 The sum of rows and the sum of columns are separatelydenoted as vector119863 and vector 119877 as follows

119879 = [119905119894119895]119899times119899 119894 119895 = 1 2 119899

119863 = [

[

119899

sum

119895=1

119905119894119895]

]119899times1

= [119905119894119895]119899times1

119877 = [

119899

sum

119894=1

119905119894119895]

1times119899

= [119905119894119895]1times119899

(4)

Step 5 Construct a cause-effect diagram The cause-effectdiagram is drawn bymapping the data set of the (119863+119877119863minus119877)The horizontal axis vector (119863 + 119877) named ldquoprominencerdquo ismade by adding 119863 to 119877 which shows the importance of theelement Similarly the vertical axis (119863minus119877) named ldquorelationrdquo ismade by subtracting119863 from 119877 When (119863minus119877) is positive theelement belongs to the cause group otherwise the elementbelongs to the effect group [86 87]

After calculating the means of (119863 + 119877) and (119863 minus 119877) thecausal-effect diagram is divided into four quadrants I to IVElements in quadrant I have high prominence and relationwhich indicates the highest interaction influence level withother elements Thus they are identified as driving factorselements in quadrant II have low prominence but highrelation are identified as voluntariness elements in quadrantIII have low prominence and relation They are relativelydisconnected from the system The elements in quadrant IV


have high prominence and low relation which indicates theirimportance as impacted by other elements [88] From thisdiagram the complex interrelationship among elements isvisualized to provide valuable insight for decision makingEspecially the DEMATEL method needs experts to decideon a threshold value to concentrate onmost important effectsfrom consideration in matrix 119879

35 Research Methodology This section introduces the studyprocedures including research framework research designand data collection

351 Research Framework Based on the above related lit-erature review this study organizes ISSM TAM TPB andTRA models to propose a research framework for exploringmember engagement behavior as shown in Figure 2 with 12major dimensions In this framework information qualitysystem quality information satisfaction and system satisfac-tion are based on ISSM usefulness ease of use and attitudeare related to TAM subjective norms perceived behaviorcontrol environmentally conscious behavior are constructedfrom TRA and engagement behavior as well as engagementconsequences are involved in TRA These dimension will bemeasured by expert opinion through Delphi technique asdescribed later

352 Research Design Since members of environmentalprotectionVCs exert amulti-function dynamic complex andvalue co-creation behavior multi-criteria decision making(MCDM) is employed to gain insights when concerned withthe relationships This study presents ISM and DEMATELmethods to solve real-life applications of MCDM problemswhich provide a complete understanding of the proceduresof MCDM tools Figure 3 shows a flowchart of our proposedmethod of study

353 Data Collection This subsection briefly elucidatesthe computational processes using an empirical case studyincluding case introduction research subject definition andinstrument development

(1) Case Introduction Two most popular ecotourism VCsin Taiwan EZTravel and LulalaTravel are committed toecological and environmental protection

First for a constructed web site httpeztravelcom wasestablished in January 2000 to provide a full range serviceof online booking and online payments It has total capitalof NT$218 million with 460 employees and more than 22million served members (many might be tourists) It hasbeen the leader among domestic online travel agents inTaiwan Itmaintained sustainable rapid growth in the revenueand has been ranked a top operating performer amongdomestic tourism websites httpeztravelcom has aimedto aggressively develop differentiated fashionable productsto satisfy various demands from consumers It enhancedits core competitiveness to create profitability EZTravel hasoriginated the following specialized tours environmentalprotection tourism luxury tours on trains million dollars

around the world tours of most of the world internationaltravelers and a variety of tourswith local themes Particularlyenvironmental protection tourism has been welcomed andfocused on

Second LulalaTravel Company was established onDecember 2005 The company has a mission to serveyounger travelers which has originated from the ChinaYouth Corp It has been expanding domestic tourist activitieslegally and professionally In April 2006 the companyestablished eight branch offices Keelung City New TaipeiCity Taichung City Changhua County Yunlin CountyChiayi County Tainan City and Yilang County LulalaTravelhas 32 professional operators who handle all the businessrelated to tourism as well as offers courses to educateprofessional tour guides military instructors and universitystaff Thus they created sequential training courses for thecontinual education of tourism professionals LulalaTravelemphasizes ecotourism based on a mission from the ChinaYouth Corp to provide a variety of services They haveoriginated many activities such as educational group athleticactivities mountain training casual weekends holiday tourspotential development upstream canoeing and survivalgames During summer and winter breaks LulalaTraveloffered various educational tasks to perform ecologicalleisure activities that had incorporated ecological themes forTaiwan

(2) Research Subjects Twelve professionally active membersof ecotourist VCs were invited to engage in this study mainlyby contributing their opinions regarding the relationshipsamong all research variables (Figure 2) through the Delphiprocess to find group consensus as input to all later relatedanalyses

(3) Instrument Development Table 1 shows the instrumentsof this study as constituted by the components and theirrelated elements

4 Experiment and Data Analysis

We decomposed our study framework into three modelsnamely models 1ndash3 (Figures 4 5 and 6) to thoroughlyinvestigate the dynamics of the variables identified in ourresearch model Afterwards we describe the profile of theexperts that we have interviewed and then conduct ISM andDEMATEL analyses for the three models

41 Expert Profiles We conducted a questionnaire to gatherthe opinions of experts A total of 12 eco-travel expertsparticipated in our questionnaire using the Delphi methodThus 12 surveys were received The survey lasted from April2012 until May 2012 The first round of surveys was receivedon April 16 2012 We compiled and summarized differentopinions from the experts and then sent them a second roundof questionnaires After five rounds of opinion consolidationwe received a final consensus from the experts on May25 2012 Table 2 summarizes the demographic profile of 12experts


Information quality

CompletenessAccuracyFormat

Currency

System quality

ReliabilityFlexibilityIntegration

AccessibilityTimeliness

Informationsatisfaction

Systemsatisfaction

Usefulness

Ease of use

Attitude

Object-based attitudeBehavioral attitude

Engagement behavior

SharingLearning

Co-developmentAdvocatingSocialising

Engagement consequences

Loyalty and satisfactionEmpowerment

Connection and emotional bonds

Trust and commitment

Environmentally conscious behavior

Environmental concernPerceived customer

effectiveness

Subjective norms

Perceived behavioral control

Figure 2 Research framework of this study

Obtain consensus of domain experts

Conduct ISM and DEMATEL analysis

Draw conclusions and implications

Build cause effect model ISM diagrammingDEMATEL diagramming

Comparison and analysis

Delphi method

ISM technique

DEMATEL technique

Figure 3 Flowchart of the proposed method of the study

Information quality


Currency

System quality



Information satisfaction

System satisfaction

Perceived usefulness

Perceived ease of use

Attitude

Attitude toward websiteAttitude toward website usage

Figure 4 The structure of proposed model 1


Table 1 System component definitions

Components Definitions Elements Reference

C1 engagement consequences The desired results of engagement behavior

E11 loyalty and satisfactionE12 empowermentE13 connection and emotionalbondsE14 trust and commitment

[89]

C2 engagement behavior The interaction and value co-creation behaviorof community members

E21 sharingE22 learningE23 co-developmentE24 advocatingE25 socializing

[89]

C3 perceived behavioralcontrol

An individualrsquos perception on the ease ordifficulty of conducting the behavior mdash [51 90]

C4 attitude A favorable or unfavorable evaluation ofsomething

E41 object-based attitudeE42 behavioral attitude [59]

C5 environmentallyConscious Behavior

Recognizing the serious of environmentalproblems people proactively engaged inrecycling saving electricity and water and soforth

E51 environmental concernE52 perceived customereffectiveness

[91]

C6 subjective normsAn individualrsquos estimate of the social pressureon himher to engage or not engage in thetarget behavior

mdash [51]

C7 perceived usefulness Beliefs concerning instrumental outcomesassociated with technology use mdash [92]

C8 perceived ease of use Beliefs that technology use will be relativelyfree of cognitive burden mdash [92]

C9 information satisfaction The degree of favorableness with respect to theinformation produced by the system mdash [59]

Ca system satisfaction The degree of favorableness with respect to thesystem and the mechanics of interaction mdash [59]

Cb information quality Desired features of the information producedby the system

Cb1 completenessCb2 accuracyCb3 formatCb4 currency

[59]

Cc system quality Desired features of the system and themechanism of interaction

Cc1 reliabilityCc2 flexibilityCc3 integrationCc4 accessibilityCc5 timeliness

[59]

42 ISM Analysis In ISM analysis a four-stage approachwas used to systematically analyze They are as follows(1) construct structural self-interaction matrix (2) generatereachability matrix (3) partition the levels and (4) buildingthe ISM model

421 Construct Structural Self-Interaction Matrix The firststep of ISM analysis was to perform analysis on the contextualrelationship of variables Based on the consensus from theexpert panel we captured the relationships in structural self-interaction matrixes (SSIM) for models 1ndash3

422 Generate Reachability Matrix Next the SSIM is trans-formed into a binary matrix called initial reachability matrixby substituting the arrows by related 1 and 0

423 Partition the Levels From the final reachability matrixthe reachability set and antecedent set for each variable wereobtained The reachability set includes variables themselvesand others that help while the antecedent set consists ofvariables and the other variables that help Consequentlythe intersection of these sets was derived for all variablesThe variable for which its reachability was set to equal itsintersection set is identified as the top-level variable in theISMhierarchyOne important feature of the top-level variablein the hierarchy is that it does not help achieve any othervariable above its own level Therefore once the top-levelvariable is identified it is separated from the other variablesThe same process is repeated to find out the next level untilthe level of each variable was found Tables 3 4 and 5 formodels 1ndash3 respectively show and summarize the results forthe iteration process Particularly models 1 2 and 3 showlevels of 4 3 and 2 respectively


Attitude

Attitude toward websiteAttitude toward website

usage

Engagement behavior

SharingLearning





effectiveness

Subjective norms







Satisfaction and loyalty Empowerment

Connection and emotional bond


Sharing

SocializingAdvocating

Co-developmentLearning

Engagement behavior


424 Building the ISMModel Based on the final reachabilitymatrix the structural model of the proposed three modelscan be generated If there is a relationship between variable 119894and 119895 then an arrow is drawn to connect the two pointsThisgraph is called a directed graph or digraph After removingthe transitivity the digraph is finally transformed into theISM-based model (Figures 7 8 and 9) for model(s) 1ndash3respectively

43 DEMATEL Analysis As for DEMATEL analysis thefive-stage approach was analyzed in detail and includes (1)obtain averagematrix from experts (2) normalize the averagematrix to get initial direct-relation matrix (3) compute thetotal relation matrix (4) calculate the sum of rows andcolumns of total relation matrix and (5) construct the cause-effect diagram

431 Obtain Average Matrix from Experts First the assess-ments of the direct affect between each pair of variables

Attitude toward website usage


Attitude toward website


Information satisfaction System satisfaction

Information quality System quality

Figure 7 Four levels of structural model of model 1

designated by the four levels 0ndash4 are summarized as anaverage matrix of models 1ndash3 (Tables 6 7 and 8)


Subjective norm Engagement consequencesEngagement behavior



Perceived behavior control

Environmental concerns

Perceived personal effectiveness

Figure 8 Three levels of structural model of model 2

Satisfaction and loyalty


Connection and emotional bondEmpowerment

Sharing Learning Co-development Advocating Socializing

Figure 9 Two levels of structural model of model 3

432 Normalize the Average Matrix to Get Initial Direct-Relation Matrix The average matrix is normalized by divid-ing all elements by the maximum values from the sums of allrows as well as the sums of all columns For the limited spaceonly Table 9 shows the above results on the initial direct-relation matrix of model 1

433 Compute the Total Relation Matrix The direct relationmatrix has further raised its power to gain a convergent totalrelationmatrix For the limited space only Table 10 shows theabove results on the total relation matrix of model 1

434 Calculate the Sumof Rows andColumns of Total RelationMatrix Let vectors119863 and 119877 denote the sum of rows and thesum of columns from total relation matrix respectively andthen the values were obtained formodels 1ndash3 Particularly theaverage values of (119863+119877) and (119863minus119877) were taken as the axialcross of 119884 (119863 minus 119877) and119883 (119863 + 119877) in the next stage

435 Construct the Cause-Effect Diagram By mapping thedata set of (119863+119877119863minus119877) a casual diagram was drawn where119883 of (119863 + 119877 prominence) was made by adding 119863 to 119877 and119884 of (119863 minus 119877 relation) was made by subtracting 119863 from 119877

Based on the means of (119863 + 119877) and (119863 minus 119877) four quadrantswere identified with their respective natures Quadrants Iand IV are defined as strong ldquocauserdquo and ldquoeffectrdquo factors ofdesired outcome respectively In contrast Quadrants II andIII are defined as weak ldquocauserdquo and ldquoeffectrdquo factors for desiredoutcomes respectively Thus the cause-effect diagrams forthe Models 1ndash3 can be drawn as Figures 10 11 and 12

5 Results and Discussion

We explored the analytical results to mine hidden informa-tion from our empirical case study for the research modelsThe following implied study findings and implications thatsuggest the management of engagement behavior that stemfrom the VC are compiledThey are of value to the academicsand practitionerswho focus on ecotourism community fields

51 Findings

(1) Ecotourism Virtual Community Acceptance Given itshuge investments and great impact on businesses the evalua-tion of information system success has gained wide attention


Table 2 The demographic variables of 12 experts

Demographic variables Number(119873 = 12) Percentage

GenderMale 5 417Female 7 583

AgeBelow 30 years old 3 25031sim40 3 25041sim50 4 33351 years old and above 2 167

EducationalCertificate 2 167Bachelor 7 583Master 2 167Doctoral 1 83

Experience in online tourismLess than 5 years 2 1675ndash10 3 25011ndash15 1 8316ndash20 4 33321 years and above 2 167

Ecotourism virtual communities(most frequently engaged)

EZTravel 6 500LulalaTravel 6 500

and involvement from academia Traditionally this area hasbeen investigated within two primary research streams theuser satisfaction literature of ISSM (eg DeLone amp McLean[54]) and the technology acceptance literature of TAM (egDavis et al [18]) These two approaches have been developedin parallel and have not been reconciled or integrated [59]To bridge this gap Wixom and Todd [59] have proposed anintegrated research model that distinguishes between object-based beliefs and attitudes toward ldquothe systemrdquo as well asbehavioral beliefs and attitudes toward ldquousing the systemrdquoand successfully links two dominant approaches They alsoemphasized the correspondence principle for accurate pre-dictions beliefs and attitudes must be specified in a mannerthat is consistent in time target and contextwith the behaviorof interest [50] Namely to predict IS acceptance behaviorbehavioral beliefs and attitudes performed better than object-based beliefs and attitudes In our model 1 ISM model allthese propositions were supported well Wixom and Todd[59] validated our study findings

(2) Ecotourism Virtual Community Engagement Conse-quence One important track of this study is customerengagement (CE) According to Brodie et al [89] in thehighly dynamic and interactive business environment CEplays a role in co-creating customer experiences that valuesand receives increasing attention from business practitioners

and academics alike As salient evidence for this devel-opment Marketing Science Institute (MSI) chose CE as akey research priority for 2010ndash2012 Even though most CEresearch is business-oriented this study assumed the conceptalso applied to ecotourism VC context The results of model3 justified this point Table 11 offers justification of the 5fundamental propositions (FP) of CE summarized by Brodieet al [89] as well as evidence from this study

(3) Ecotourism Virtual Community Engagement BehaviorKim and Han [91] have tested and modified the TPB byadding two important concepts environmental concernsand perceived customer effectiveness which contribute toenvironmentally conscious behavior that helps to criticallypredict eco-friendly consumer behaviors This study followsthis approach in Model 2 and verifies these two constructsexert critical driving power to impact usersrsquo attitude towardthe website as well as its usage Besides they affect perceivedbehavioral control directly then engagement behavior andengagement consequences Compared with TPB model 2revealed an interesting finding that subjective norms play therole of an effect instead of a cause variableThis phenomenonindicates attitudes toward ecotourism VCs and perceivedbehavioral control work together to shape a social pressurethat will encourage engagement

(4) Summarization of Cause and Effect Factors Salimifard etal [93] has suggested a driving power-dependence diagramto help classify various decision factors into four clustersThe cluster in quadrant I include ldquolinkagerdquo elements thathave strong driving power and dependence The implicationis that all the factors above this level are affected by themwhile these elements are also dependent on lower level factorsfor the ISM model The cluster in quadrant II consists ofdependent factors that have weak driving power but strongdependencies Factors in this cluster are the most importantand influential ones The cluster in quadrant III includesldquoautonomousrdquo factors that have weak driving power andweak dependence These factors are relatively disconnectedfrom the system Finally the cluster in quadrant IV includesldquodependentrdquo factors that have weak driving power but strongdependence These factors are representative of a desiredsystem of outcomes Figures 13 14 and 15 show the drivingpower and dependence diagrams of this study

(5) Overlapping Extension of Cause and Effect Factors After-wards we compared cause and effect variables in models1ndash3 identified by different ISM and DEMATEL algorithmsTables 12 and 13 show the results Obviously great overlappingbetween them which are highlighted in black-frame wasfound This phenomenon revealed that the performance oftwo intelligent methods provides similar analytical resultstoward an exploration of engagement behavior of ecotourismVC members to imply that the study results can be trusted

Finally the two methods are desirable for the followingtwo reasons

Reasons on Selected Used Techniques First ISM and DEMA-TEL under the MCDM condition are two major method-ologies with the capability to clarify complex relationships


Table 3 Levels of model 1 elements

Element Reachability set Antecedent set Intersection set LevelA1 A1A3A5A7A8 A1 A1 4A2 A2A3A4A5A6A7A8 A2 A2 4A3 A3A5A7A8 A1A2A3 A3 3A4 A4A6A7A8 A2A4 A4 3A5 A5A7A8 A1A2A3A5 A5 2A6 A6A7A8 A2A4A6 A6 2A7 A7A8 A1A2A3A4A5A6A7A8 A7A8 1A8 A7A8 A1A2A3A4A5A6A7A8 A7A8 1


Element Reachability set Antecedent set Intersection set LevelB1 B1B2B4B7B8 B1B2B5B6 B1B2 2B2 B1B2B4B7B8 B1B2B5B6 B1B2 2B3 B3B4B7B8 B3B5B6 B3 2B4 B4B7B8 B1B2B3B4B5B6B7B8 B4B7B8 1B5 B1B2B3B4B5B6B7B8 B5B6 B5B6 3B6 B1B2B3B4B5B6B7B8 B5B6 B5B6 3B7 B4B7B8 B1B2B3B4B5B6B7B8 B4B7B8 1B8 B4B7B8 B1B2B3B4B5B6B7B8 B4B7B8 1

0 1 2 3 4 5 6

A6

A1 A2A5 A8

A4A3

A7minus08

minus1

minus06

minus04

minus02

002040608

112 Dminus R

D+ R

Figure 10 Cause-effect diagram of model 1

B6

B5

B3B4

B8

B1 B2B7

0

05

1

15

2

0 1 2 3 4 5 6

minus1

minus05

D minus R

D + R


between the elements involved in complex decision makingThere are several similarities between them such as theyemphasize a cause-effect relationship among several decision

0

05

1

15

2

minus1

minus2

minus05

minus15

minus25

D minus R

D + R

2475 248 2485 249 2495 25 2505 251 2515 252 2525

C1 C2 C3 C4 C5

C6 C7 C8 C9


Driv

ing

pow

er

8 Independent Linkage7 A265 A14 A4 A33 A6 A52 A7 A81 Autonomous Dependent

1 2 3 4 5 6 7 8Dependence

Figure 13 Driving power-dependence diagram of model 1

elements (eg the driving power and dependence in ISMand the prominence and relation in DEMATEL) as wellas present the relationships in easily understood diagrams



Element Reachability set Antecedent set Intersection set LevelC1 C1C2C3C4C5C6C7C8C9 C1C2C3C4C5 C1C2C3C4C5 2C2 C1C2C3C4C5C6C7C8C9 C1C2C3C4C5 C1C2C3C4C5 2C3 C1C2C3C4C5C6C7C8C9 C1C2C3C4C5 C1C2C3C4C5 2C4 C1C2C3C4C5C6C7C8C9 C1C2C3C4C5 C1C2C3C4C5 2C5 C1C2C3C4C5C6C7C8C9 C1C2C3C4C5 C1C2C3C4C5 2C6 C6C7C8C9 C1C2C3C4C5C6C7C8C9 C6C7C8C9 1C7 C6C7C8C9 C1C2C3C4C5C6C7C8C9 C6C7C8C9 1C8 C6C7C8C9 C1C2C3C4C5C6C7C8C9 C6C7C8C9 1C9 C6C7C8C9 C1C2C3C4C5C6C7C8C9 C6C7C8C9 1

Table 6 The average matrix of model 1

A1 A2 A3 A4 A5 A6 A7 A8 Row total

A1 0 1 4 1 3 1 2 2 14A2 1 0 1 4 1 3 2 2 14A3 3 1 0 1 4 1 3 3 16A4 1 3 1 0 1 4 3 3 16A5 2 1 3 1 0 1 4 4 16A6 1 2 1 3 1 0 4 4 16A7 1 1 2 2 3 3 0 4 16A8 1 1 2 2 3 3 4 0 16Columntotal 10 10 14 14 16 16 22 22 22


B1 B2 B3 B4 B5 B6 B7 B8 Row total

B1 0 4 1 1 3 3 4 3 19B2 4 0 1 1 3 3 4 3 19B3 1 1 0 1 1 1 4 3 12B4 1 1 1 0 1 1 4 3 12B5 4 4 1 1 0 4 4 3 21B6 4 4 1 1 4 0 4 3 21B7 3 3 3 3 3 3 0 4 22B8 2 2 2 2 2 2 2 0 14Columntotal 19 19 10 10 17 17 26 22 26

Nevertheless ISM considered four possible relationshipswhile DEMATEL investigated the relationships deeper witha more sophisticated evaluation (from 0 to 4) as well asallowing different degrees of mutual influences ThereforeISM is more macro-oriented and DEMATEL more micro-oriented They can complement each other to exert synergicbenefits As far as our limited knowledge no research hasadopted this approach We also proved the adequacy of thisapproach

52 Managerial Implications The management implicationsin this study are based on the ISM and DEMATEL methods


C1 C2 C3 C4 C5 C6 C7 C8 C9 Row total

C1 0 4 4 4 4 4 4 4 4 32C2 4 0 4 4 4 4 4 4 4 32C3 4 4 0 4 4 4 4 4 4 32C4 4 4 4 0 4 4 4 4 4 32C5 4 4 4 4 0 4 4 4 4 32C6 3 3 3 3 3 0 4 4 4 27C7 3 3 3 3 3 4 0 4 4 27C8 3 3 3 3 3 4 4 0 4 27C9 3 3 3 3 3 4 4 4 0 27Columntotal 28 28 28 28 28 32 32 32 32 32

Table 9 Initial direct-relation matrix of model 1

A1 A2 A3 A4 A5 A6 A7 A8A1 0000 0045 0182 0045 0136 0045 0091 0091A2 0045 0000 0045 0182 0045 0136 0091 0091A3 0136 0045 0000 0045 0182 0045 0136 0136A4 0045 0136 0045 0000 0045 0182 0136 0136A5 0091 0045 0136 0045 0000 0045 0182 0182A6 0045 0091 0045 0136 0045 0000 0182 0182A7 0045 0045 0091 0091 0136 0136 0000 0182A8 0045 0045 0091 0091 0136 0136 0182 0000

521 ISM Method

(1) Model 1 System and information quality are importantfactors in IS Good quality systems bring community mem-bers a better site experience and good information qualitymakes them willing participants in ecotourism VCs forgetting the latest ecotourism information

(2) Model 2 Environmental concerns affect the attitude ofthe community members and ease of design of the systeminterface in the use of IS Furthermore the power of the


Table 10 Total relation matrix of model 1


8 B5 B6765 B1 B24 B33 B4 B7 B821

1 2 3 4 5 6 7 8


community is a driving force to learn and share engagementbehavior to promote environmental awareness Ecotourismexperiences share common topics of the natural environmentthrough social networking sites(3) Model 3 The engagement on the public discussion thathas enough emotion to form a network of human relation-shipswith certain characteristics of social organization for thepromotion of ecotourism VC issues is a positive relationshipover time Thus common ecotourism virtual issues weredeveloped through learning and sharing among the com-munity members Particularly member loyalty enhanced thecommunity and resulted in the real value of the communitysites

522 DEMATEL Method (1) Model 1 System quality is amajor driving power for other system components Besidessystem satisfaction and information satisfaction greatly influ-ence system beliefs and attitude Consequently enhancingsystem infrastructure such as hardware and software as wellas encouraging information sharing will be beneficial forcommunity membersrsquo active and better engagement

(2) Model 2 Environmental concerns and perceived personaleffectiveness have strong influencing power on other systemcomponentsThe emphasis on environmental protection wasenhanced through the understanding of the importance ofenvironmental issues More importantly the use of VCs tostrengthen the basic concepts of international conservationand sustainable development are interesting issues that com-bine the power of the community members on the internet

9 C1 C2 C3 C4 C5 87654 C6 C7 C8 C9 321

1 2 3 4 5 6 7 8 9


and the awareness of environmental protection to everycorner of the world

(3) Model 3 Sharing learning co-development advocatingand socializing have a strong relationship with other systemcomponents Community members that share their ideasprovide mutual understanding as well as contribute to thedevelopment of the community to achieve a vested emotionThe relationship and the sharing of member demands are tofoster a sense of trust and commitment The operation of thecommunity website provides the members a centripetal forceto achieve the maximum benefits of the community network

6 Conclusions

The MCDM is a sub-disciplinet of operations research thatexplicitly considersmultiple criteria in decision-making envi-ronments However there typically exist multiple conflictingcriteria that need to be evaluated formaking decisionsThere-fore structuring complex problems well and consideringmultiple criteria explicitly lead to more informed and betterdecisions They are developed methods that transform suchcomplex problem into essentially single criterion problemsThey are used to solve MCDM problems by constructingvalue functions Perhaps a well-known method includesISM and DEMATEL Particularly the two methods haveseldom been seen in hybrid use to solve MCDM problemsin ecotourism VCs

Given the above reasons this study focused on fill-ing these knowledge gaps and conducting an intelligenthybrid model to solve a real life application problem thatinvolves MCDM toward sustainable ecotourism This studyhas proposed hybrid expert-based ISM and DEMATELmodels that are suitable for those members of environmentalprotection VCs who intend to use intelligent systems Thisstudy performs well and provides useful insight into the keycharacteristics that exploresmember engagement behavior inthe VC industry and is critical with respect to respondingto the rapidly changing environment under VC membersthat exert the multi-functions of dynamic complex andvalue co-creation behaviorThe analytical results have impor-tant implications that are worthwhile for practitioners andacademics that focus on environmental protection in VCsMoreover future research can be done in three directions as


Table 11 The justification of research results on FP of CE

Fundamental propositions Justification

FP1CE reflects a psychological state thatoccurs by virtue of interactive customerexperiences with a focal agentobjectwithin specific service relationships

(i) The focal agentobject a customer interacts with may be a brand product ororganization (ie the ecotourism VC)(ii) Focal CE behaviors that have a brand- or firm-focus extend beyondtransactionspurchase (the environmental concern in ecotourism VC transcendsover transactions)(iii) Two-way interactions generating CE may occur within a broader network ofcustomers stakeholders and other actors in specific service relationships (there aredifferent participating roles in ecotourism VC)

FP2CE states occur within a dynamiciterative process of a service relationshipthat co-creates value

CE processes may range from short-term to long-term relatively stable to highlyvariable processes typified by CE levels varying in complexity over time (there isdifferent degrees of involvement in ecotourism VC)

FP3CE plays a central role within anomological network of servicerelationships

(i) There is antecedent as well as consequence factors of ecotourism virtualcommunity engagement behavior as shown in our research framework(ii) The mutual-influence and iterative nature of engagement behaviors imply thatspecific CE consequence may extend to be an antecedent in the next process

FP4CE is a multidimensional concept subjectto a context andor stakeholder-specificexpression of relevant cognitiveemotional and behavioral dimensions

(i) Variables in this research framework include cognitive (eg information qualityand system quality) emotional (eg attitude toward website itself and websiteusage) and behavioral (eg engagement behavior) dimensions in nature(ii) Different situational conditions might generate distinct CE complexity levels

FP5CE occurs within a specific set ofsituational conditions generatingdiffering CE levels

Specific interaction between a customer and a focal agentobject and other actorswithin specific focal relationships may generate different levels of cognitiveemotional andor behavioral CE intensity depending on specific CE stakeholderand contextual contingencies driving particular CE levels (the casual modelacquired by ISM or DEMATEL analyses show this dynamic well)

Table 12 Cause and effect factors identified by ISM model

Model Dependent Independent

1 A7 (attitude toward website) A1 (information quality)A8 (Attitude toward websiteusage) A2 (system quality)

2

B4 (subjective norm) B1 (attitude towardwebsite)

B7 (engagement behavior) B2 (attitude towardwebsite usage)

B8 (engagementconsequences)

B5 (environmentalconcerns)B6 (perceived personaleffectiveness)

3

C6 (satisfaction and loyalty) C1 (sharing)C7 (empowerment) C2 (learning)C8 (connection andemotional bond) C3 (co-development)

C9 (trust and commitment) C4 (advocating)C5 (socializing)

follows (1) To screen and organize a domain expert panelswith excellent experience in ecotourism virtual communitiesand apply procedures suggested by Delphi method and focusgroups to find consensus efficiently and effectively from theuse of larger samples to conduct structural equationmodeling(SEM) to cross-verify the study results (2) to investigatethe dynamic nature decision elements more insightfullyanalytical network process (ANP) can be applied especially

Table 13 Cause and effect factors identified by DEMATEL model

Model Strong dependent variables Strong independentvariables

1A5 (perceived usefulness) A4 (system satisfaction)A7 (attitude toward website)A8 (attitude toward websiteusage)

2

B1 (attitude toward website) B2 (attitude towardwebsite usage)

B7 (engagement behavior) B5 (environmentalconcerns)

B8 (engagement consequences)

3

C6 (satisfaction and loyalty) C1 (sharing)C7 (empowerment) C2 (learning)C8 (connection and emotionalbond) C3 (co-development)


for the engagement related variables and (3) other qualitativeresearch methods such as interactive qualitative analysis(IQA) and means-ends chain (MEC) by laddering analysiscan also be applied to gain more detailed responses fromdomain experts

Conflict of Interests

The authors declare that there is no conflict of interestsregarding the publication of this paper


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[59] B H Wixom and P A Todd ldquoA theoretical integration of usersatisfaction and technology acceptancerdquo Information SystemsResearch vol 16 no 1 pp 85ndash102 2005

[60] J E Bailey and S W Pearson ldquoDevelopment of a tool for mea-suring and analyzing computer user satisfactionrdquoManagementScience vol 29 no 5 pp 530ndash545 1983

[61] J J Baroudi andW J Orlikowski ldquoA short-formmeasure of userinformation satisfaction a psychometric evaluation and noteson userdquo Journal of Management Information Systems vol 4 no4 pp 44ndash59 1988

[62] B IvesMHOlson and J J Baroudi ldquoThemeasurement of userinformation satisfactionrdquo Communications of the ACM vol 26no 10 pp 785ndash793 1983

[63] M Mattingley-Scott ldquoDelphi Methodrdquo 2006 httpwww12managecommethods helmer helmer delphi methodhtml

[64] M Adler and E Ziglio Gazing into the Oracle The DelphiMethod and Its Application to Social Policy and Public HealthJessica Kingsley London UK 1996

[65] B Garrod ldquoApplying the Delphi method in an ecotourismcontext a response to Deng et alrsquos lsquo development of apoint evaluation system for ecotourism destinations a Delphimethodrsquordquo Journal of Ecotourism vol 11 no 3 pp 219ndash223 2012

[66] J WarfieldAn Assault on Complexity Battelle Office of Corpo-rate Communications Columbus Ohio USA 1973

[67] J Warfield Societal Systems Planning Policy and ComplexityJohn Wiley amp Sons New York NY USA 1976

[68] JThakkar S G Deshmuk A D Gupta and R Shankar ldquoSelec-tion of third-party logistics (3PL) a hybrid approach usinginterpretive structural modeling (ISM) and analytic networkprocess (ANP)rdquo Supply Chain Forum vol 6 no 1 pp 32ndash462005

[69] V Ravi R Shankar and M K Tiwari ldquoProductivity improve-ment of a computer hardware supply chainrdquo InternationalJournal of Productivity and Performance Management vol 54no 4 pp 239ndash255 2005

[70] S Jharkharia and R Shanka ldquoIT enablement of supply chainsmodeling the enablersrdquo International Journal of Productivityand PerformanceManagement vol 53 no 8 pp 700ndash712 2004

[71] J Thakkar S G Deshmukh A D Gupta and R ShankarldquoDevelopment of a balanced scorecard an integrated approachof Interpretive Structural Modeling (ISM) and Analytic Net-work Process (ANP)rdquo International Journal of Productivity andPerformance Management vol 56 no 1 pp 25ndash59 2007

[72] R K Singh S K Garg S G Deshmukh and M KumarldquoModelling of critical success factors for implementation ofAMTsrdquo Journal of Modelling in Management vol 2 no 3 pp232ndash250 2007

[73] C Chen ldquoThe application of interpretive structural model-ing method to develop verity design solution of case hostpreference-based products a case study of Razorrdquo Journal ofTheoretical and Applied Information Technology vol 35 no 1pp 92ndash99 2012

[74] Y Li and C Zi ldquoAnalysis of load factors based on interpretivestructural modelrdquo Journal of Computers vol 7 no 7 pp 1704ndash1711 2012

[75] A Gabus and E Fontela World Problems An Invitation toFurther Thought within the Framework of DEMATEL BattelleGeneva Research Centre Geneva Switzerland 1972


[76] A Gabus and E Fontela ldquoPerceptions of the world problema-tique communication procedure communicating with thosebearing collective responsibilityrdquo DEMATEL Report 1 BattelleGeneva Research Centre Geneva Switzerland 1973

[77] J-I Shieh H-H Wu and K-K Huang ldquoA DEMATEL methodin identifying key success factors of hospital service qualityrdquoKnowledge-Based Systems vol 23 no 3 pp 277ndash282 2010

[78] M-L Tseng ldquoA causal and effect decision making modelof service quality expectation using grey-fuzzy DEMATELapproachrdquo Expert Systems with Applications vol 36 no 4 pp7738ndash7748 2009

[79] J-K Chen and I-S Chen ldquoUsing a novel conjunctive MCDMapproach based on DEMATEL fuzzy ANP and TOPSIS as aninnovation support system for Taiwanese higher educationrdquoExpert Systems with Applications vol 37 no 3 pp 1981ndash19902010

[80] G-H Tzeng C-H Chiang and C-W Li ldquoEvaluating inter-twined effects in e-learning programs a novel hybrid MCDMmodel based on factor analysis and DEMATELrdquo Expert Systemswith Applications vol 32 no 4 pp 1028ndash1044 2007

[81] W-R Jerry Ho C-L Tsai G-H Tzeng and S-K FangldquoCombined DEMATEL technique with a novel MCDM modelfor exploring portfolio selection based on CAPMrdquo ExpertSystems with Applications vol 38 no 1 pp 16ndash25 2011

[82] B Chang C-W Chang and C-H Wu ldquoFuzzy DEMATELmethod for developing supplier selection criteriardquo Expert Sys-tems with Applications vol 38 no 3 pp 1850ndash1858 2011

[83] S K Patil and R Kant ldquoA fuzzy DEMATEL method to identifycritical success factors of knowledge management adoption insupply chainrdquo Journal of Information amp Knowledge Manage-ment vol 12 no 3 pp 1ndash15 2013

[84] S K Hu Y C Chuang Y F Yeh and G H Tzeng ldquoCombininghybrid MADM with fuzzy integral for exploring the smartphone improvement in M-Generationrdquo International Journal ofFuzzy Systems vol 14 no 2 pp 204ndash214 2012

[85] W T Yang W H Liu H H Liu and Lanasari ldquoEvaluatinginfluential factors in event quality using DEMATEL methodrdquoInternational Journal of Trade Economics and Finance vol 4no 3 pp 92ndash97 2013

[86] W-W Wu ldquoChoosing knowledge management strategies byusing a combined ANP and DEMATEL approachrdquo ExpertSystems with Applications vol 35 no 3 pp 828ndash835 2008

[87] W-W Wu and Y-T Lee ldquoDeveloping global managersrsquo com-petencies using the fuzzy DEMATEL methodrdquo Expert Systemswith Applications vol 32 no 2 pp 499ndash507 2007

[88] Y-C Lee M-L Li T-M Yen and T-H Huang ldquoAnalysis ofadopting an integrated decision making trial and evaluationlaboratory on a technology acceptance modelrdquo Expert Systemswith Applications vol 37 no 2 pp 1745ndash1754 2010

[89] R J Brodie L D Hollebeek B Juric and A Ilic ldquoCustomerengagement conceptual domain fundamental propositionsand implications for researchrdquo Journal of Service Research vol14 no 3 pp 252ndash271 2011

[90] K Huchting A Lac and J W LaBrie ldquoAn application of theTheory of Planned Behavior to sorority alcohol consumptionrdquoAddictive Behaviors vol 33 no 4 pp 538ndash551 2008

[91] Y Kim and H Han ldquoIntention to pay conventional-hotel pricesat a green hotelmdasha modification of the theory of plannedbehaviorrdquo Journal of Sustainable Tourism vol 18 no 8 pp 997ndash1014 2010

[92] W Lewis R Agarwal and V Sambamurthy ldquoSources of influ-ence on beliefs about information technology use an empiricalstudy of knowledge workersrdquo MIS Quarterly vol 27 no 4 pp657ndash678 2003

[93] K Salimifard M A Abbaszadeh and A Ghorbanpur ldquoInter-pretive structural modeling of critical success factors in bank-ing process re-engineeringrdquo International Review of BusinessResearch Papers vol 6 no 2 pp 95ndash103 2010

Submit your manuscripts athttpwwwhindawicom

Computer Games Technology

International Journal of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014


Distributed Sensor Networks


Advances in

FuzzySystems

Hindawi Publishing Corporationhttpwwwhindawicom

Volume 2014


ReconfigurableComputing

Hindawi Publishing Corporation httpwwwhindawicom Volume 2014


Applied Computational Intelligence and Soft Computing

thinspAdvancesthinspinthinsp

Artificial Intelligence

HindawithinspPublishingthinspCorporationhttpwwwhindawicom Volumethinsp2014

Advances inSoftware EngineeringHindawi Publishing Corporationhttpwwwhindawicom Volume 2014


Electrical and Computer Engineering

Journal of

Journal of

Computer Networks and Communications


Hindawi Publishing Corporation

httpwwwhindawicom Volume 2014

Advances in

Multimedia


Biomedical Imaging


ArtificialNeural Systems

Advances in


RoboticsJournal of



Computational Intelligence and Neuroscience

Industrial EngineeringJournal of


Modelling amp Simulation in EngineeringHindawi Publishing Corporation httpwwwhindawicom Volume 2014

The Scientific World JournalHindawi Publishing Corporation httpwwwhindawicom Volume 2014


Human-ComputerInteraction

Advances in

Computer EngineeringAdvances in



by the interconnections with other individuals in thatsocial environment The network connects nodes and nodestogether form the structure The nodes can be individualsteams or organizations [6] and its overall structure describessocial behavior

In recent years development of VC websites has grownrapidly The use of social networking and their fast growthhave become an important trend According to the BusinessNext [7] survey of the top 100 Taiwanwebsites 25 communitysites were selected among to represent 25 of the overall listAfter the financial crises of 2008 theVCwebsite developmentseems to be the most promising and popular business modelor trend This trend is buttressed by social networking sitesand microblogs such as Facebook Twitter and Plurk UsersfromUS President Barack Obama former GE chief executiveJack Welch to Dell Computer and Starbucks Coffee areutilizing this trend [8] According to a survey by ComScore[9] the number of VCwebsites has grown at an amazing rate

How will social networking sites grow to replace othertypes of sites such as shopping sites and search enginewebsites as users actively become more involved with theseactivities Here are some important reasons (1) Social net-working sites bring interpersonal relationships into virtualsituations (2) they create more opportunities for humaninteraction and real-time sharing of personal informationsuch as pictures videos and interactive games and (3) userscan create their own program to share many features thatwill meet their social leisure and entertainment needsThussocial networking sites successfully win user interest whichmakes it a popular site type They attract many users inVCs to share information and emotional communicationThis free open yet with hidden features communicationplatform has led various ldquointernet communitiesrdquo to spread[10] Ecotourism and environmental concern communitieshave sprung up as a result

Increasingly the ecological degradation problems arehighlighted and environmental protection issues haveemerged from social networking sites to express awarenessand priority concerns One of the greatest challenges forsustainable ecotourism is to encourage visitors to act inways that minimize environmental and experiential impactThis is the case for protected areas where the environmentis often fragile and mindful and engaging experiences aresought Previous research shows that all types of recreationalactivities can cause environmental damage even at low uselevels [11 12] and visitor behavior and density levels influencethe quality of visitor experiences [13]

Protected area managers employ a suite of strategies toaddress visitor problems though for reasons of expediencycost and efficiency education is the preferred and dominantvisitor management worldwide [14ndash16] As Ham and Weiler[17] pointed out interpretation is ultimately aimed at com-municating messages about a place and in some instancesthe persuasion of people to behave in ways that are consistentwith the protected values Therefore a better understandingfor how visitors think and what factors determine theirbehavior could contribute to better achieve managerial goalsGiven these reasons we explore the behaviors that are

amenable to persuasive influence and how to tap into therelevant thoughts held by visitors about particular actions

This study is aimed at themost popular social networkingsites related to ecotourism issues (ecotourism VCs) Weuse the technology acceptance model (TAM) proposed byDavis et al [18] to study IT user intention and behavior toinvestigate the participation of members of a virtual environ-mental protection community Simultaneously the qualityof information and system from the virtual sites to achieveuser information and system satisfaction are explored aswell as the correlation between user attitude and willingnessIn summary information and system quality satisfactionperceived usefulness and ease of use are discussedThe afore-mentioned factors affect VC member attitudes intentionsand participation results The four research objectives of thisstudy are as follows (1) integrate two major IT successesrelated research streams namely technology acceptance anduser satisfaction (2) explore the dynamics among object-oriented beliefs and attitudes behavioral beliefs and attitudesas well as engagement behavior (3) investigate the interactiveand co-creation value behavior of environmental protectionVC and (4) suggest effective strategies for the promotion ofenvironmental protection issues

This study determines themotivations and objectivesTheremainder of this paper is organized as follows Section 2looks at related literature on VC marketing of ecotourismconcerns Section 3 looks at techniques used and establishesour study framework and decisions for the appropriateresearch methodology Section 4 looks at our online ques-tionnaires for an empirical case study thatwas distributed andanalyzed with statistical software and a series of techniquesSection 5 shows our findings and the managerial implica-tions are described Section 6 is our conclusions and futureresearch directions offered

2 Literature Review

This section describes the relevant literature including sus-tainable ecotourism social network sites andVCs and relatedtechnology acceptance

The first mention of ecotourism in the English-languageacademic literature was by Romeril [19] In the 1990s thetourism industry realized the profit potential of ecotourismand the suffix ldquoecordquo was used by travel agents for mar-keting Eventually ecotourism organizations such as theInternational Ecotourism Society (TIES) and the EcotourismSociety (TES) were formed [20] While some may usethe term ldquoecotourismrdquo as being synonymous with ldquonaturetourismrdquo ldquoadventure tourismrdquo ldquoresponsible tourismrdquo ldquoeth-ical tourismrdquo and ldquogreen tourismrdquo ecotourism does havespecific characteristics that differentiate it from these othersegments in the tourism industry A subset of sustainabletourism (tourism that actively reduces the negative impactof tourism) ecotourism is defined by TIES as ldquoresponsibletravel to natural areas that conserves the environment andimproves the welfare of local peoplerdquo [21 22] Additionallyecotourism should build a constituency to promote conserva-tion and provide an impetus for private conservation efforts




































(RM)

Expert opinion

Literature review



elements













from (1) and (2)

119904 = max [

[

max1le119894le119899

119899

sum

119895=1

119886119894119895max1le119895le119899

119899

sum

119894=1

119886119894119895]

]

119894 119895 isin 1 2 119899

(1)

119883 =1

119904119860 (2)





119879 = 119883 + 1198832+ sdot sdot sdot + 119883

119901= 119883 times (119868 minus 119883)

minus1

= [119909119894119895]119899times119899119901 997888rarr infin

(3)



119879 = [119905119894119895]119899times119899 119894 119895 = 1 2 119899

119863 = [

[

119899

sum

119895=1

119905119894119895]

]119899times1

= [119905119894119895]119899times1

119877 = [

119899

sum

119894=1

119905119894119895]

1times119899

= [119905119894119895]1times119899

(4)



















Information quality


Currency

System quality




Systemsatisfaction

Usefulness

Ease of use

Attitude


Engagement behavior

SharingLearning








effectiveness

Subjective norms








Delphi method

ISM technique

DEMATEL technique


Information quality


Currency

System quality




System satisfaction



Attitude








[89]



[89]








[91]


mdash [51]







[59]



[59]






Attitude


usage

Engagement behavior

SharingLearning





effectiveness

Subjective norms










Sharing



Engagement behavior

































51 Findings
























0 1 2 3 4 5 6

A6

A1 A2A5 A8

A4A3

A7minus08

minus1

minus06

minus04

minus02

002040608

112 Dminus R

D+ R


B6

B5

B3B4

B8

B1 B2B7

0

05

1

15

2

0 1 2 3 4 5 6

minus1

minus05

D minus R

D + R



0

05

1

15

2

minus1

minus2

minus05

minus15

minus25

D minus R

D + R

2475 248 2485 249 2495 25 2505 251 2515 252 2525

C1 C2 C3 C4 C5

C6 C7 C8 C9


Driv

ing

pow

er





















521 ISM Method






8 B5 B6765 B1 B24 B33 B4 B7 B821

1 2 3 4 5 6 7 8





9 C1 C2 C3 C4 C5 87654 C6 C7 C8 C9 321

1 2 3 4 5 6 7 8 9




6 Conclusions



















2





3







2




3







References







































































































Advances in

FuzzySystems


Volume 2014












Journal of

Journal of





Advances in

Multimedia


Biomedical Imaging



Advances in


RoboticsJournal of










Advances in






































(RM)

Expert opinion

Literature review



elements













from (1) and (2)

119904 = max [

[

max1le119894le119899

119899

sum

119895=1

119886119894119895max1le119895le119899

119899

sum

119894=1

119886119894119895]

]

119894 119895 isin 1 2 119899

(1)

119883 =1

119904119860 (2)





119879 = 119883 + 1198832+ sdot sdot sdot + 119883

119901= 119883 times (119868 minus 119883)

minus1

= [119909119894119895]119899times119899119901 997888rarr infin

(3)



119879 = [119905119894119895]119899times119899 119894 119895 = 1 2 119899

119863 = [

[

119899

sum

119895=1

119905119894119895]

]119899times1

= [119905119894119895]119899times1

119877 = [

119899

sum

119894=1

119905119894119895]

1times119899

= [119905119894119895]1times119899

(4)



















Information quality


Currency

System quality




Systemsatisfaction

Usefulness

Ease of use

Attitude


Engagement behavior

SharingLearning








effectiveness

Subjective norms








Delphi method

ISM technique

DEMATEL technique


Information quality


Currency

System quality




System satisfaction



Attitude








[89]



[89]








[91]


mdash [51]







[59]



[59]






Attitude


usage

Engagement behavior

SharingLearning





effectiveness

Subjective norms










Sharing



Engagement behavior

































51 Findings
























0 1 2 3 4 5 6

A6

A1 A2A5 A8

A4A3

A7minus08

minus1

minus06

minus04

minus02

002040608

112 Dminus R

D+ R


B6

B5

B3B4

B8

B1 B2B7

0

05

1

15

2

0 1 2 3 4 5 6

minus1

minus05

D minus R

D + R



0

05

1

15

2

minus1

minus2

minus05

minus15

minus25

D minus R

D + R

2475 248 2485 249 2495 25 2505 251 2515 252 2525

C1 C2 C3 C4 C5

C6 C7 C8 C9


Driv

ing

pow

er





















521 ISM Method






8 B5 B6765 B1 B24 B33 B4 B7 B821

1 2 3 4 5 6 7 8





9 C1 C2 C3 C4 C5 87654 C6 C7 C8 C9 321

1 2 3 4 5 6 7 8 9




6 Conclusions



















2





3







2




3







References







































































































Advances in

FuzzySystems


Volume 2014












Journal of

Journal of





Advances in

Multimedia


Biomedical Imaging



Advances in


RoboticsJournal of










Advances in



























(RM)

Expert opinion

Literature review



elements













from (1) and (2)

119904 = max [

[

max1le119894le119899

119899

sum

119895=1

119886119894119895max1le119895le119899

119899

sum

119894=1

119886119894119895]

]

119894 119895 isin 1 2 119899

(1)

119883 =1

119904119860 (2)





119879 = 119883 + 1198832+ sdot sdot sdot + 119883

119901= 119883 times (119868 minus 119883)

minus1

= [119909119894119895]119899times119899119901 997888rarr infin

(3)



119879 = [119905119894119895]119899times119899 119894 119895 = 1 2 119899

119863 = [

[

119899

sum

119895=1

119905119894119895]

]119899times1

= [119905119894119895]119899times1

119877 = [

119899

sum

119894=1

119905119894119895]

1times119899

= [119905119894119895]1times119899

(4)



















Information quality


Currency

System quality




Systemsatisfaction

Usefulness

Ease of use

Attitude


Engagement behavior

SharingLearning








effectiveness

Subjective norms








Delphi method

ISM technique

DEMATEL technique


Information quality


Currency

System quality




System satisfaction



Attitude








[89]



[89]








[91]


mdash [51]







[59]



[59]






Attitude


usage

Engagement behavior

SharingLearning





effectiveness

Subjective norms










Sharing



Engagement behavior

































51 Findings
























0 1 2 3 4 5 6

A6

A1 A2A5 A8

A4A3

A7minus08

minus1

minus06

minus04

minus02

002040608

112 Dminus R

D+ R


B6

B5

B3B4

B8

B1 B2B7

0

05

1

15

2

0 1 2 3 4 5 6

minus1

minus05

D minus R

D + R



0

05

1

15

2

minus1

minus2

minus05

minus15

minus25

D minus R

D + R

2475 248 2485 249 2495 25 2505 251 2515 252 2525

C1 C2 C3 C4 C5

C6 C7 C8 C9


Driv

ing

pow

er





















521 ISM Method






8 B5 B6765 B1 B24 B33 B4 B7 B821

1 2 3 4 5 6 7 8





9 C1 C2 C3 C4 C5 87654 C6 C7 C8 C9 321

1 2 3 4 5 6 7 8 9




6 Conclusions



















2





3







2




3







References







































































































Advances in

FuzzySystems


Volume 2014












Journal of

Journal of





Advances in

Multimedia


Biomedical Imaging



Advances in


RoboticsJournal of










Advances in







(RM)

Expert opinion

Literature review



elements













from (1) and (2)

119904 = max [

[

max1le119894le119899

119899

sum

119895=1

119886119894119895max1le119895le119899

119899

sum

119894=1

119886119894119895]

]

119894 119895 isin 1 2 119899

(1)

119883 =1

119904119860 (2)





119879 = 119883 + 1198832+ sdot sdot sdot + 119883

119901= 119883 times (119868 minus 119883)

minus1

= [119909119894119895]119899times119899119901 997888rarr infin

(3)



119879 = [119905119894119895]119899times119899 119894 119895 = 1 2 119899

119863 = [

[

119899

sum

119895=1

119905119894119895]

]119899times1

= [119905119894119895]119899times1

119877 = [

119899

sum

119894=1

119905119894119895]

1times119899

= [119905119894119895]1times119899

(4)



















Information quality


Currency

System quality




Systemsatisfaction

Usefulness

Ease of use

Attitude


Engagement behavior

SharingLearning








effectiveness

Subjective norms








Delphi method

ISM technique

DEMATEL technique


Information quality


Currency

System quality




System satisfaction



Attitude








[89]



[89]








[91]


mdash [51]







[59]



[59]






Attitude


usage

Engagement behavior

SharingLearning





effectiveness

Subjective norms










Sharing



Engagement behavior

































51 Findings
























0 1 2 3 4 5 6

A6

A1 A2A5 A8

A4A3

A7minus08

minus1

minus06

minus04

minus02

002040608

112 Dminus R

D+ R


B6

B5

B3B4

B8

B1 B2B7

0

05

1

15

2

0 1 2 3 4 5 6

minus1

minus05

D minus R

D + R



0

05

1

15

2

minus1

minus2

minus05

minus15

minus25

D minus R

D + R

2475 248 2485 249 2495 25 2505 251 2515 252 2525

C1 C2 C3 C4 C5

C6 C7 C8 C9


Driv

ing

pow

er





















521 ISM Method






8 B5 B6765 B1 B24 B33 B4 B7 B821

1 2 3 4 5 6 7 8





9 C1 C2 C3 C4 C5 87654 C6 C7 C8 C9 321

1 2 3 4 5 6 7 8 9




6 Conclusions



















2





3







2




3







References







































































































Advances in

FuzzySystems


Volume 2014












Journal of

Journal of





Advances in

Multimedia


Biomedical Imaging



Advances in


RoboticsJournal of










Advances in



















Information quality


Currency

System quality




Systemsatisfaction

Usefulness

Ease of use

Attitude


Engagement behavior

SharingLearning








effectiveness

Subjective norms








Delphi method

ISM technique

DEMATEL technique


Information quality


Currency

System quality




System satisfaction



Attitude








[89]



[89]








[91]


mdash [51]







[59]



[59]






Attitude


usage

Engagement behavior

SharingLearning





effectiveness

Subjective norms










Sharing



Engagement behavior

































51 Findings
























0 1 2 3 4 5 6

A6

A1 A2A5 A8

A4A3

A7minus08

minus1

minus06

minus04

minus02

002040608

112 Dminus R

D+ R


B6

B5

B3B4

B8

B1 B2B7

0

05

1

15

2

0 1 2 3 4 5 6

minus1

minus05

D minus R

D + R



0

05

1

15

2

minus1

minus2

minus05

minus15

minus25

D minus R

D + R

2475 248 2485 249 2495 25 2505 251 2515 252 2525

C1 C2 C3 C4 C5

C6 C7 C8 C9


Driv

ing

pow

er





















521 ISM Method






8 B5 B6765 B1 B24 B33 B4 B7 B821

1 2 3 4 5 6 7 8





9 C1 C2 C3 C4 C5 87654 C6 C7 C8 C9 321

1 2 3 4 5 6 7 8 9




6 Conclusions



















2





3







2




3







References







































































































Advances in

FuzzySystems


Volume 2014












Journal of

Journal of





Advances in

Multimedia


Biomedical Imaging



Advances in


RoboticsJournal of










Advances in




Information quality


Currency

System quality




Systemsatisfaction

Usefulness

Ease of use

Attitude


Engagement behavior

SharingLearning








effectiveness

Subjective norms








Delphi method

ISM technique

DEMATEL technique


Information quality


Currency

System quality




System satisfaction



Attitude








[89]



[89]








[91]


mdash [51]







[59]



[59]






Attitude


usage

Engagement behavior

SharingLearning





effectiveness

Subjective norms










Sharing



Engagement behavior

































51 Findings
























0 1 2 3 4 5 6

A6

A1 A2A5 A8

A4A3

A7minus08

minus1

minus06

minus04

minus02

002040608

112 Dminus R

D+ R


B6

B5

B3B4

B8

B1 B2B7

0

05

1

15

2

0 1 2 3 4 5 6

minus1

minus05

D minus R

D + R



0

05

1

15

2

minus1

minus2

minus05

minus15

minus25

D minus R

D + R

2475 248 2485 249 2495 25 2505 251 2515 252 2525

C1 C2 C3 C4 C5

C6 C7 C8 C9


Driv

ing

pow

er





















521 ISM Method






8 B5 B6765 B1 B24 B33 B4 B7 B821

1 2 3 4 5 6 7 8





9 C1 C2 C3 C4 C5 87654 C6 C7 C8 C9 321

1 2 3 4 5 6 7 8 9




6 Conclusions



















2





3







2




3







References







































































































Advances in

FuzzySystems


Volume 2014












Journal of

Journal of





Advances in

Multimedia


Biomedical Imaging



Advances in


RoboticsJournal of










Advances in








[89]



[89]








[91]


mdash [51]







[59]



[59]






Attitude


usage

Engagement behavior

SharingLearning





effectiveness

Subjective norms










Sharing



Engagement behavior

































51 Findings
























0 1 2 3 4 5 6

A6

A1 A2A5 A8

A4A3

A7minus08

minus1

minus06

minus04

minus02

002040608

112 Dminus R

D+ R


B6

B5

B3B4

B8

B1 B2B7

0

05

1

15

2

0 1 2 3 4 5 6

minus1

minus05

D minus R

D + R



0

05

1

15

2

minus1

minus2

minus05

minus15

minus25

D minus R

D + R

2475 248 2485 249 2495 25 2505 251 2515 252 2525

C1 C2 C3 C4 C5

C6 C7 C8 C9


Driv

ing

pow

er





















521 ISM Method






8 B5 B6765 B1 B24 B33 B4 B7 B821

1 2 3 4 5 6 7 8





9 C1 C2 C3 C4 C5 87654 C6 C7 C8 C9 321

1 2 3 4 5 6 7 8 9




6 Conclusions



















2





3







2




3







References







































































































Advances in

FuzzySystems


Volume 2014












Journal of

Journal of





Advances in

Multimedia


Biomedical Imaging



Advances in


RoboticsJournal of










Advances in




Attitude


usage

Engagement behavior

SharingLearning





effectiveness

Subjective norms










Sharing



Engagement behavior

































51 Findings
























0 1 2 3 4 5 6

A6

A1 A2A5 A8

A4A3

A7minus08

minus1

minus06

minus04

minus02

002040608

112 Dminus R

D+ R


B6

B5

B3B4

B8

B1 B2B7

0

05

1

15

2

0 1 2 3 4 5 6

minus1

minus05

D minus R

D + R



0

05

1

15

2

minus1

minus2

minus05

minus15

minus25

D minus R

D + R

2475 248 2485 249 2495 25 2505 251 2515 252 2525

C1 C2 C3 C4 C5

C6 C7 C8 C9


Driv

ing

pow

er





















521 ISM Method






8 B5 B6765 B1 B24 B33 B4 B7 B821

1 2 3 4 5 6 7 8





9 C1 C2 C3 C4 C5 87654 C6 C7 C8 C9 321

1 2 3 4 5 6 7 8 9




6 Conclusions



















2





3







2




3







References







































































































Advances in

FuzzySystems


Volume 2014












Journal of

Journal of





Advances in

Multimedia


Biomedical Imaging



Advances in


RoboticsJournal of










Advances in























51 Findings
























0 1 2 3 4 5 6

A6

A1 A2A5 A8

A4A3

A7minus08

minus1

minus06

minus04

minus02

002040608

112 Dminus R

D+ R


B6

B5

B3B4

B8

B1 B2B7

0

05

1

15

2

0 1 2 3 4 5 6

minus1

minus05

D minus R

D + R



0

05

1

15

2

minus1

minus2

minus05

minus15

minus25

D minus R

D + R

2475 248 2485 249 2495 25 2505 251 2515 252 2525

C1 C2 C3 C4 C5

C6 C7 C8 C9


Driv

ing

pow

er





















521 ISM Method






8 B5 B6765 B1 B24 B33 B4 B7 B821

1 2 3 4 5 6 7 8





9 C1 C2 C3 C4 C5 87654 C6 C7 C8 C9 321

1 2 3 4 5 6 7 8 9




6 Conclusions



















2





3







2




3







References







































































































Advances in

FuzzySystems


Volume 2014












Journal of

Journal of





Advances in

Multimedia


Biomedical Imaging



Advances in


RoboticsJournal of










Advances in

























0 1 2 3 4 5 6

A6

A1 A2A5 A8

A4A3

A7minus08

minus1

minus06

minus04

minus02

002040608

112 Dminus R

D+ R


B6

B5

B3B4

B8

B1 B2B7

0

05

1

15

2

0 1 2 3 4 5 6

minus1

minus05

D minus R

D + R



0

05

1

15

2

minus1

minus2

minus05

minus15

minus25

D minus R

D + R

2475 248 2485 249 2495 25 2505 251 2515 252 2525

C1 C2 C3 C4 C5

C6 C7 C8 C9


Driv

ing

pow

er





















521 ISM Method






8 B5 B6765 B1 B24 B33 B4 B7 B821

1 2 3 4 5 6 7 8





9 C1 C2 C3 C4 C5 87654 C6 C7 C8 C9 321

1 2 3 4 5 6 7 8 9




6 Conclusions



















2





3







2




3







References







































































































Advances in

FuzzySystems


Volume 2014












Journal of

Journal of





Advances in

Multimedia


Biomedical Imaging



Advances in


RoboticsJournal of










Advances in








0 1 2 3 4 5 6

A6

A1 A2A5 A8

A4A3

A7minus08

minus1

minus06

minus04

minus02

002040608

112 Dminus R

D+ R


B6

B5

B3B4

B8

B1 B2B7

0

05

1

15

2

0 1 2 3 4 5 6

minus1

minus05

D minus R

D + R



0

05

1

15

2

minus1

minus2

minus05

minus15

minus25

D minus R

D + R

2475 248 2485 249 2495 25 2505 251 2515 252 2525

C1 C2 C3 C4 C5

C6 C7 C8 C9


Driv

ing

pow

er





















521 ISM Method






8 B5 B6765 B1 B24 B33 B4 B7 B821

1 2 3 4 5 6 7 8





9 C1 C2 C3 C4 C5 87654 C6 C7 C8 C9 321

1 2 3 4 5 6 7 8 9




6 Conclusions



















2





3







2




3







References







































































































Advances in

FuzzySystems


Volume 2014












Journal of

Journal of





Advances in

Multimedia


Biomedical Imaging



Advances in


RoboticsJournal of










Advances in



















521 ISM Method






8 B5 B6765 B1 B24 B33 B4 B7 B821

1 2 3 4 5 6 7 8





9 C1 C2 C3 C4 C5 87654 C6 C7 C8 C9 321

1 2 3 4 5 6 7 8 9




6 Conclusions



















2





3







2




3







References







































































































Advances in

FuzzySystems


Volume 2014












Journal of

Journal of





Advances in

Multimedia


Biomedical Imaging



Advances in


RoboticsJournal of










Advances in






8 B5 B6765 B1 B24 B33 B4 B7 B821

1 2 3 4 5 6 7 8





9 C1 C2 C3 C4 C5 87654 C6 C7 C8 C9 321

1 2 3 4 5 6 7 8 9




6 Conclusions



















2





3







2




3







References







































































































Advances in

FuzzySystems


Volume 2014












Journal of

Journal of





Advances in

Multimedia


Biomedical Imaging



Advances in


RoboticsJournal of










Advances in



















2





3







2




3







References







































































































Advances in

FuzzySystems


Volume 2014












Journal of

Journal of





Advances in

Multimedia


Biomedical Imaging



Advances in


RoboticsJournal of










Advances in




References







































































































Advances in

FuzzySystems


Volume 2014












Journal of

Journal of





Advances in

Multimedia


Biomedical Imaging



Advances in


RoboticsJournal of










Advances in


































































Advances in

FuzzySystems


Volume 2014












Journal of

Journal of





Advances in

Multimedia


Biomedical Imaging



Advances in


RoboticsJournal of










Advances in





























Advances in

FuzzySystems


Volume 2014












Journal of

Journal of





Advances in

Multimedia


Biomedical Imaging



Advances in


RoboticsJournal of










Advances in










Advances in

FuzzySystems


Volume 2014












Journal of

Journal of





Advances in

Multimedia


Biomedical Imaging



Advances in


RoboticsJournal of










Advances in



Documents

Research Article Evolving MCDM Applications Using Hybrid ...downloads.hindawi.com/journals/tswj/2013/751728.pdf · Research Article Evolving MCDM Applications Using Hybrid Expert-Based