www.elsevier.com/locate/dsw
Past,present,andfutureofdecisionsupporttechnology$J.P.Shima,*,MerrillWarkentina,*,JamesF.Courtneyb,DanielJ.Powerc,
RameshShardad,ChristerCarlssoneMississippiStateUniversity,MississippiState,MS39762USAUniversityofCentralFlorida,Orlando,FL32816-1400USAcUniversityofNorthernIowa,CedarFalls,IA50614USAdOklahomaStateUniversity,Stillwater,OK74078USA
eIAMSR/AboAkademiUniversity,DataCityB6734,20520Abo,Finland
baAbstract
Sincetheearly1970s,decisionsupportsystems(DSS)technologyandapplicationshaveevolvedsignificantly.Manytechnologicalandorganizationaldevelopmentshaveexertedanimpactonthisevolution.DSSonceutilizedmorelimiteddatabase,modeling,anduserinterfacefunctionality,buttechnologicalinnovationshaveenabledfarmorepowerfulDSSfunctionality.DSSoncesupportedindividualdecision-makers,butlaterDSStechnologieswereappliedtoworkgroupsorteams,especiallyvirtualteams.TheadventoftheWebhasenabledinter-organizationaldecisionsupportsystems,andhasgivenrisetonumerousnewapplicationsofexistingtechnologyaswellasmanynewdecisionsupporttechnologiesthemselves.Itseemslikelythatmobiletools,mobilee-services,andwirelessInternetprotocolswillmarkthenextmajorsetofdevelopmentsinDSS.ThispaperdiscussestheevolutionofDSStechnologiesandissuesrelatedtoDSSdefinition,application,andimpact.Itthenpresentsfourpowerfuldecisionsupporttools,includingdatawarehouses,OLAP,datamining,andWeb-basedDSS.Issuesinthefieldofcollaborativesupportsystemsandvirtualteamsarepresented.Thispaperalsodescribesthestateoftheartofoptimization-baseddecisionsupportandactivedecisionsupportforthenextmillennium.Finally,someimplicationsforthefutureofthefieldarediscussed.D2002PublishedbyElsevierScienceB.V.
Keywords:Decisionsupporttechnology;DSSdevelopment;Collaborativesupportsystems;Virtualteams;Optimization-baseddecisionsupport
1.Introduction
Decisionsupportsystems(DSS)arecomputertechnologysolutionsthatcanbeusedtosupport
Thispaperisbasedonapaneldiscussionatthe30thDecisionSciencesInstituteAnnualMeetinginNewOrleans,LA.TheauthorswereinvitedpanelistsfortheDecisionSupportToolssession.
*Correspondingauthors.
E-mailaddresses:jshim@cobilan.msstate.edu(J.P.Shim),mwarkentin@acm.org(M.Warkentin),Jim.Courtney@bus.ucf.edu(J.F.Courtney),Daniel.Power@uni.edu(D.J.Power),sharda@okstate.edu(R.Sharda),christer.carlsson@abo.fi(C.Carlsson).
$complexdecisionmakingandproblemsolving.DSShaveevolvedfromtwomainareasofresearch—thetheoreticalstudiesoforganizationaldecisionmaking(Simon,Cyert,March,andothers)conductedattheCarnegieInstituteofTechnologyduringthelate1950sandearly1960sandthetechnicalwork(Gerrity,Ness,andothers)carriedoutatMITinthe1960s[32].ClassicDSStooldesigniscomprisedofcomponentsfor(i)sophisticateddatabasemanagementcapabilitieswithaccesstointernalandexternaldata,information,andknowledge,(ii)powerfulmodelingfunctionsaccessedbyamodelmanagementsystem,and(iii)powerful,yetsimpleuserinterfacedesignsthatenable
0167-9236/02/$-seefrontmatterD2002PublishedbyElsevierScienceB.V.PII:S0167-9236(01)00139-7
112J.P.Shimetal./DecisionSupportSystems33(2002)111–126
interactivequeries,reporting,andgraphingfunctions.Muchresearchandpracticaldesignefforthasbeenconductedineachofthesedomains.
DSShaveevolvedsignificantlysincetheirearlydevelopmentinthe1970s.Overthepastthreedeca-des,DSShavetakenonbothanarrowerorbroaderdefinition,whileothersystemshaveemergedtoassistspecifictypesofdecision-makersfacedwithspecifickindsofproblems.Researchinthisareahastypicallyfocusedonhowinformationtechnologycanimprovetheefficiencywithwhichausermakesadecision,andcanimprovetheeffectivenessofthatdecision[49].Theevolutionofinformationtechnologyinfrastruc-turesparallelthethreeerasofgrowthinthecomputerindustry—thedataprocessing(DP)era,themicro-computerera,andthenetworkera[44].Basedontheinfrastructures,DSStoolsstartedintheDOSandUNIXenvironmentsaroundthelate1970sandthenmovedtoWindowsintheearly1990s.TheadventoftheInternethasgivenrisetomanynewapplicationsofexistingtech-nology.ThetechnologybehindDSSiswellsuitedtotakeadvantageoftheopportunitiesthattheWorldWideWeb(Web)presents,especiallytherapiddisseminationofinformationtodecision-makers.TheWeb’simpactondecisionmakinghasbeentomaketheprocessmoreefficientandmorewidelyused.Thisisduelargelytothefactthatatypicalbrowserservesastheuserinter-facecomponentofthedecision-makingsystems,i.e.,makingthetechnologyeasytounderstandanduse.Theevolutionofthehuman–computerinterfaceistheevolutionofcomputing.Thegraphicaluserinter-face(GUI)thatwasrefinedatXerox,popularizedbyMacintosh,andlaterincorporatedintoWindows,andthenthePalm,aretypicalexamplesofhowsignificanttheGUIisintegratingtechnologyintodecision-mak-er’sand/oruser’sdailytasks.Inthefuture,decision-makerswillaccesselectronicservicesthroughtheirmobilephonesorotherwirelessdevicesasmuchasthroughtheirdesktopcomputers.Inthefuture,mobiletools,mobilee-services,andwirelessInternetproto-colswillmarkthenextmajorsetsofdevelopmentinDSS[15],therebyexpandingtheaccessibilityofthetoolstodecision-makerswherevertheymaybe.
Theprimarypurposeofthispaperistopresentthepast,present,andfutureofdecisionsupportsystems,includingthelatestadvancesindecisionsupporttools.ThepaperdiscussesanumberofimportanttopicsincludingdevelopmentoftheDSSconcept,dataware-
housing,on-lineanalyticalprocessing,datamining,Web-basedDSS,collaborativesupportsystems,virtualteams,knowledgemanagement,optimization-basedDSS,andactivedecisionsupportforthenextmillen-nium.Thispaperhassevenmainsections.ThenextsectiondiscussesdevelopmentoftheDSSconcept.Section3isadescriptionofdatawarehousing,on-lineanalyticalprocessing,anddatamining.Section4dis-cussescollaborativesupportsystems,virtualteams,andknowledgemanagement.Section5discussesopti-mization-basedDSS,andSection6discussesactivedecisionsupportforthenextmillennium.Thefinalsectionprovidessomeimplicationsforthefutureofdecisionsupporttechnology.
2.DevelopmentoftheDSSconcept
TheoriginalDSSconceptwasmostclearlydefinedbyGorryandScottMorton[23],whointegratedAnthony’s[2]categoriesofmanagementactivityandSimon’s[54]descriptionofdecisiontypes.Anthonydescribedmanagementactivitiesasconsistingofstra-tegicplanning(executivedecisionsregardingoverallmissionandgoals),managementcontrol(middleman-agementguidingtheorganizationtogoals),andopera-tionalcontrol(firstlinesupervisorsdirectingspecifictasks).Simondescribeddecisionproblemsasexistingonacontinuumfromprogrammed(routine,repetitive,wellstructured,easilysolved)tononprogrammed(new,novel,ill-structured,difficulttosolve).GorryandScottMortoncombinedAnthony’smanagementactivitiesandSimon’sdescriptionofdecisions,usingthetermsstructured,unstructured,andsemi-structured,ratherthanprogrammedandnonprogrammed.TheyalsousedSimon’sIntelligence,Design,andChoicedescriptionofthedecision-makingprocess.Inthisframework,intelligenceiscomprisedofthesearchforproblems,designinvolvesthedevelopmentofalter-natives,andchoiceconsistsofanalyzingthealterna-tivesandchoosingoneforimplementation.ADSSwasdefinedasacomputersystemthatdealtwithaproblemwhereatleastsomestagewassemi-structuredorun-structured.AcomputersystemcouldbedevelopedtodealwiththestructuredportionofaDSSproblem,butthejudgmentofthedecision-makerwasbroughttobearontheunstructuredpart,henceconstitutingahuman–machine,problem-solvingsystem.
J.P.Shimetal./DecisionSupportSystems33(2002)111–126113
GorryandScottMortonalsoarguedthatcharacter-isticsofbothinformationneedsandmodelsdifferinaDSSenvironment.Theill-definednatureofinforma-tionneedsinDSSsituationsleadstotherequirementfordifferentkindsofdatabasesystemsthanthoseforoperationalenvironments.Relationaldatabasesandflexiblequerylanguagesareneeded.Similarly,theill-structurednatureofthedecisionprocessimpliedtheneedforflexiblemodelingenvironments,suchasthoseinspreadsheetpackages.
Fig.1describeswhatprobablycametobeamorecustomarilyusedmodelofthedecision-makingproc-essinaDSSenvironment.Here,theemphasiscametobeonmodeldevelopmentandproblemanalysis.Oncetheproblemisrecognized,itisdefinedintermsthatfacilitatethecreationofmodels.Alternativesolutionsarecreated,andmodelsarethendevelopedtoanalyzethevariousalternatives.ThechoiceisthenmadeandimplementedconsistentwithSimon’sdescription.Ofcourse,nodecisionprocessisthisclear-cutinanill-structuredsituation.Typically,thephasesoverlapandblendtogether,withfrequentloopingbacktoearlierstagesasmoreislearnedabouttheproblem,assolutionsfail,andsoforth.
Overthelasttwodecadesorso,DSSresearchhasevolvedtoincludeseveraladditionalconceptsandviews.Beginninginabout1985,groupdecisionsup-portsystems(GDSS),orjustgroupsupportsystems(GSS),evolvedtoprovidebrainstorming,ideaevalua-tion,andcommunicationsfacilitiestosupportteamproblemsolving.Executiveinformationsystems(EIS)haveextendedthescopeofDSSfrompersonalorsmallgroupusetothecorporatelevel.Modelmanagementsystemsandknowledge-baseddecisionsupportsys-temshaveusedtechniquesfromartificialintelligenceandexpertsystemstoprovidesmartersupportforthedecision-maker[5,12].Thelatterbeganevolvingintotheconceptoforganizationalknowledgemanagement[47]aboutadecadeago,andisnowbeginningtoma-ture.
Inthe21stcentury,theInternet,theWeb,andtele-communicationstechnologycanbeexpectedtoresultinorganizationalenvironmentsthatwillbeincreasinglymoreglobal,complex,andconnected.Supplychainswillbeintegratedfromrawmaterialstoendconsumers,andmaybeexpectedtospantheplanet.Organizationswillinteractwithdiversecultural,political,social,economicandecologicalenvironments.MitroffandLinstone[43]arguethatradicallydifferentthinkingisrequiredbymanagersoforganizationsfacingsuchenvironments;thinkingthatmustincludeconsiderationofmuchbroadercultural,organizational,personal,ethicalandaestheticfactorsthanhasoftenbeenthecaseinthepast.Courtney[11],followingMitroffand
Fig.1.TheDSSdecision-makingprocess.
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Linstone,suggeststhatDSSresearchersshouldem-braceamuchmorecomprehensiveviewoforgani-zationaldecisionmaking(seeFig.2)anddevelopdecisionsupportsystemscapableofhandlingmuch‘‘softer’’informationandmuchbroaderconcernsthanthemathematicalmodelsandknowledge-basedsys-temshavebeencapableofhandlinginthecaseinthepast.Thisisanenormouschallenge,butisimperativethatwefaceifDSSistoremainavitalforceinthefuture.
TheprimarydifferencebetweenFig.2andtypicaldecisionmodelsinaDSScontextisthedevelopmentofmultipleandvariedperspectivesduringtheprob-lemformulationphase.MitroffandLinstone[43]suggestthatperspectivesbedevelopedfromorganiza-tional(O),personal(P)andtechnical(T)positions.Inaddition,ethicalandaestheticfactorsareconsideredaswell.Thementalmodelsofstakeholderswithvariousperspectiveslieattheheartofthedecisionprocess,fromdefiningwhatisaproblem,toanalysisoftheresultsoftryingtosolvetheproblem.
ThetechnicalperspectivehasdominatedDSSprob-lemformulationinthepast,andinvolvesthedevelop-mentofdatabasesandmodels.Theorganizationalandpersonalperspectivesaredevelopedbydiscussingtheproblemwithallaffectedstakeholders,atleastasre-sourcespermit,soastoensurethatallrelevantvaria-blesareeitherincludedinmodels,ortakenintoaccountduringtheanalysis,iftheycannotbequantified.Asmanyofthesefactorsmaybemorehumanisticandnonquantifiable,especiallyethicalandaestheticcon-cerns.Theneedforbroaderformsofanalysis,suchas
groupsessions,maybecomeevenmoreappropriateinthefuture.
TheremainderofthepaperdiscussesrecentandexpectedDSSdevelopmentsinmoredetail.First,re-centactivityindatawarehousing,onlineanalyticalprocessing(OLAP),dataminingandWeb-basedDSSisconsidered,followedbytreatmentofcollaborativesupportsystemsandoptimization-baseddecisionsup-port.
3.Datawarehouses,OLAP,datamining,andweb-basedDSS
Beginningintheearly1990s,fourpowerfultoolsemergedforbuildingDSS.Thefirstnewtoolfordecisionsupportwasthedatawarehouse.Thetwonewtoolsthatemergedfollowingtheintroductionofdatawarehouseswereon-lineanalyticalprocessing(OLAP)anddatamining.ThefourthnewtoolsetisthetechnologyassociatedwiththeWorldWideWeb.TheWebhasdrawnenormousinterestinthepastfewyearsanditcanhaveanevengreaterimpactintheyearsahead.Allofthesetoolsremain‘‘hot’’topicsincorporateandacademiccomputingpublications.Thissectionattemptstobrieflyexaminethepast,presentandfutureofthesefourdecisionsupporttechnologies.Therootsofbuildingadatawarehouselieinimproveddatabasetechnologies.Initially,Codd[8]proposedtherelationaldatamodelfordatabasesin1970.Thisconceptualdatabasemodelhashadalargeimpactonbothbusinesstransactionprocessingsys-
Fig.2.AnewdecisionparadigmforDSS.Source:Courtney[11].
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temsanddecisionsupportsystems.Morerecently,Codd’sspecification[9]ofon-lineanalyticalprocess-ing(OLAP)standardshashadanequallylargeimpactonthecreationofsophisticateddata-drivenDSS[50].Intheearly1990s,onlyafewcustom-builtdataware-housesexisted.TheworkofInmon[29],Devlin,andKimball[33]promotedadatawarehouseasasolutionforintegratingdatafromdiverseoperationaldatabasestosupportmanagementdecisionmaking.Adataware-houseisasubject-oriented,integrated,time-variant,nonvolatilecollectionofdata[29].Manycompanieshavebuiltdatawarehouses,buttherehasbeenanongoingdebateaboutusingrelationalormultidimen-sionaldatabasetechnologiesforon-lineanalyticalprocessing[55,59].Bothdatabasetechnologiesarecurrentlyusedandrelationalstructureslikethestarschemaarepreferredforverylargedatawarehouses.Buildingalargedatawarehouseoftenleadstoanincreasedinterestinanalyzingandusingtheaccumu-latedhistoricalDSSdata.Onesolutionistoanalyzethehistoricaldatainadatawarehouseusingon-lineanaly-ticalprocessingtools.‘‘On-lineanalyticalprocessing(OLAP)isacategoryofsoftwaretechnologythatenablesanalysts,managers,andexecutivestogaininsightintodatathroughfast,consistent,interactiveaccesstoawidevarietyofpossibleviewsofinforma-tionthathasbeentransformedfromrawdatatoreflecttherealdimensionalityoftheenterpriseasunderstoodbytheuser.’’[45]
OLAPtoolshavebecomemorepowerfulinrecentyears,butasetofartificialintelligenceandstatisticaltoolscollectivelycalleddataminingtools[16]hasbeenproposedformoresophisticateddataanalysis.Dataminingisalsooftencalleddatabaseexploration,orinformationandknowledgediscovery.Dataminingtoolsfindpatternsindataandinferrulesfromthem[50].Therapidlyexpandingvolumeofreal-timedata,resultingfromtheexplosioninactivityfromtheWebandelectroniccommerce,hasalsocontributedtothedemandforandprovisionofdataminingtools.Anewcategoryoffirms,termed‘‘infomediaries,’’willevenconductreal-timedatamininganalysisofso-called‘‘clickstreamdata’’onbehalfoftheircustomers,whoaretypicallyhighlyinteractivewebsitesthatgeneratealotofdatawheremanagerswishtograspthebuyingpatternsoftheirvisitors.
TheWebenvironmentisemergingasaveryimpor-tantDSSdevelopmentanddeliveryplatform.TheprimaryWebtoolsareWebserversusingHypertextTransferProtocol(HTTP)containingWebpagescre-atedwithHypertextMark-upLanguage(HTML)andJavaScriptaccessedbyclientmachinesrunningclientsoftwareknownasbrowsers.ThisenvironmenttracesitsrootstooriginalresearchbyTimBerners-Lee,whoin1990developedapoint-and-clickhypertexteditor,whichranonthe‘‘NeXT’’machine.Berners-Leere-leasedthiseditorandthefirstWebservertoanarrowtechnicalaudienceinthesummerof1991(cf.,http://www.w3.org/People/Berners-Lee/ShortHistory.html).Hisinnovationledtotheexcitingdevelopmentsine-businessande-commercebytheendofthe1990s.Atthebeginningofthe21stcentury,theWebisthecenterofactivityindevelopingDSS.WhenvendorsproposeaWeb-basedDSS,theyarereferringtoacomputerizedsystemthatdeliversdecisionsupportinformationordecisionsupporttoolstoamanagerorbusinessanalystusingaWebbrowsersuchasNetscapeNavigatororInternetExplorer[50].ThecomputerserverthatishostingtheDSSapplicationislinkedtotheuser’scomputerbyanetworkwiththeTCP/IPprotocol.MostWebdatawarehousessupportafour-tierarchitectureinwhichaWebbrowsersendsHTMLrequestsusingHTTPtoaWebserver.TheWebserverprocessestheserequestsusingaCommonGatewayInterface(CGI)script.ThescripthandlesStructuredQueryLanguage(SQL)generation,post-SQLprocess-ing,andHTMLformatting.Thisapplicationserverthensendsrequeststoadatabaseserver,whichgen-eratesthequeryresultsetandsendsitbackforviewingusingaWebbrowser.Manytechnologyimprovementsareoccurringthatarespeedingupqueryprocessingandimprovingthedisplayofresultsandtheinteractiveanalysisofdatasets.
Web-basedDSShavereducedtechnologicalbar-riersandmadeiteasierandlesscostlytomakede-cision-relevantinformationandmodel-drivenDSS[50]availabletomanagersandstaffusersingeo-graphicallydistributedlocations.BecauseoftheInter-netinfrastructure,enterprise-wideDSScannowbeimplementedingeographicallydispersedcompaniesandtogeographicallydispersedstakeholdersinclud-ingsuppliersandcustomersatarelativelylowcost.UsingWeb-basedDSS,organizationscanprovideDSScapabilitytomanagersoveraproprietaryintra-net,tocustomersandsuppliersoveranextranet,ortoanystakeholderovertheglobalInternet.TheWebhas
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increasedaccesstoDSSanditshouldincreasetheuseofawell-designedDSSinacompany.UsingaWebinfrastructureforbuildingDSSimprovestherapiddisseminationof‘‘bestpractices’’analysisanddeci-sion-makingframeworksanditshouldpromotemoreconsistentdecisionmakingonrepetitivetasks.
Web-basedDSSvendorsarerapidlyinnovatingandmergersbetweenvendorsarecommon.Anyanalysisofthefeaturesofdatawarehouse,OLAP,dataminingorotherWeb-basedDSSproductsisobsoletebeforeitiscompleted.AWebsitelikeTheDataWarehousingInformationCenter(http://www.dwinfocenter.org)hasanextensivelistoftoolsandtoolvendors.TheDSSRe-sources.COMVendorspageatURLhttp://www.dssre-sources.com/vendorlist/listsmorethan75companiesthatmarketDSSproducts.ManyofthesevendorshaveWeb-basedDSSproducts.AnumberofvendorshaveexamplesofproductsattheirWebsites.
BuildingDSSwiththesenewtoolsremainsacom-plexanalyticaltask.Someconsultantsuseindustryspecifictemplatesfordatawarehouses,othersusestructureddesignmethodologies.VendorspromoteWeb-enabledbusinessintelligencesoftwareandWebportalsoftwareasameanstospeedthedevelopmentofWeb-basedDSS.Insomesituations,anexistingdatawarehousecanbeWeb-enabledormadeavailableusingaWebbrowser,butthedatastoragesystemsmayhaveproblemsservinganincreasednumberofon-lineusers.Web-basedDSSwithdatawarehousesandOLAPareavailable7daysaweekand24hoursaday,sotheneedsofusershavechanged.Webdatabasearchitecturesmusthandlealargenumberofconcurrentrequests,whilemaintainingconsistentqueryresponsetimesasthenumberofusersandvolumeofdatachangesandwilllikelyincreaseovertime.
Inmostdataminingapplications,adatafileofqueryresultsiscreatedfromadatawarehouseandthenanalyzedbyaspecialistusingartificialintelligenceorstatisticaltools.ThisnewdatafilecouldbemadeavailablethroughanIntranettoabroadgroupofbusinessanalystsbyclient-servertechnologies.Inthe21stcentury,bothe-commerceandcustomerrelation-shipmanagement(CRM)willincreasethedemandformoreanalysisofcustomertransactiondata.Manysoftwarevendorsandpublications,suchasDatamation(http://www.datamation.com/dataw/),aresuggestingthatallknowledgeworkerswillbecomedataminersinthefuture.Thispotentialuseofthetechnologies
wouldlikelyleadtopoorlyconceivedend-useranaly-sesanddubiousresults.Inmanyacademicdisciplines,dataminingisvieweddisparaginglyas‘‘datadredg-ing.’’Knowledgeable,well-trainedbusinessusersneedtoworkwiththedataminingclassificationandcluster-ingtools.Makingtoolslikeneuralnetworks,decisiontrees,ruleinduction,anddatavisualizationwidelyavailabletonaı¨veusersusingWebtechnologieswillbeamistake.
SowheredoestheWebleadthetechnologiesofdatawarehousing,OLAP,dataminingandmodel-drivenDSS?TheuniversalTCP/IPprotocolorWebplatformleadstowidespreaduseandadoptionofdecisionsupportsystemsinorganizations.ManagerswhohavenotusedDSSwillfindthenewtoolspowerfulandconvenient.Newmanagers,salesstaffandotherswhowerenotexposedtoclient-servertoolsorotherDSStoolsofthe1980sand1990swillexpectDSStobeeasytouseandavailablefromtheiroffice,home,andclient/customerlocations.4.Collaborativesupportsystems1Oneofthemoresignificanttrendsoverthepast20yearshasbeentheevolutionfromindividualstand-alonecomputerstothehighlyinterconnectedtelecom-municationsnetworkenvironmentoftoday.Initially,computerswithinfirmswereconnectedvialocalareanetworks(LANs),allowingteamsandworkgroupstosharedecision-makinginformationmoreeasily.Then,firmsbegantoconnecttheirnetworksinwideareanetworkstofacilitatesharingofinformationacrossorganizationalboundaries.Finally,theInternetandWebcreatedanenvironmentwithalmostubiquitousaccesstoaworldofinformation.Atthesametime,manyorganizationaldecisionsmigratedfromindivid-ualdecisionstoonesmadebysmallteamstocomplexdecisionsmadebylargediversegroupsofindividualswithinafirmorevenfrommultiplefirms.Inthisenvironment,severalkeytechnologicaldevelopmentshaveoccurredintheareaofdecisionsupport.Varioustoolstosupportcollaborationandgroupprocesseshavebeendeveloped,implemented,evaluated,andrefined.
1Note:CertainelementsfromthissectionareadaptedfromRef.[58].
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4.1.GroupprocessessupportingdecisionmakingIndividualsoftenmakedecisionsinsmallgroupsorinlargeorganizationalnetworks.AlaviandKeen[1]defineabusinessteamasa‘‘small,self-regulating,self-containedtask-orientedworkgroup’’that‘‘typicallyfocusonorganizationallyassignedtasks.’’Collabora-tionoccurswithinthecontextofcooperativeworkandisdefinedas‘‘multipleindividualsworkingtogetherinaplannedwayinthesameproductionprocessorindifferentbutconnectedproductionprocesses’’[60].Becauseindividualswhocooperateorperformtaskstogethershareonlypartiallyoverlappinggoals,indi-vidualgroupmembers’activitiesmustbecoordinatedtoensurethatthedisparateindividualscometosharethesamegoals.Coordinationinvolvesactorsworkingtogetherharmoniously[37,38]toaccomplishacollec-tivesetoftasks[56].Agroupdecisionresultsfrominterpersonalcommunicationamonggroupmembers[14].
4.2.Groupsupportsystems
Groupsupportsystems(GSS)orcollaborationsup-portsystemsenhancethecommunication-relatedactiv-itiesofteammembersengagedincomputer-supportedcooperativework.Thecommunicationandcoordina-tionactivitiesofteammembersarefacilitatedbytechnologiesthatcanbecharacterizedalongthethreecontinuaoftime,space,andlevelofgroupsupport[1,14,30].Teamscancommunicatesynchronouslyorasynchronously;theymaybelocatedtogetherorremotely;andthetechnologycanprovidetasksupportprimarilyfortheindividualteammemberorforthegroup’sactivities.Thesetechnologiesareutilizedtoovercomespaceandtimeconstraintsthatburdenface-to-facemeetings,toincreasetherangeanddepthofinformationaccess,andtoimprovegrouptaskperform-anceeffectiveness,especiallybyovercoming‘‘processlosses’’[41,42].Inshort,GSSfacilitatesmoreeffectivegroupinteraction,leadingtogreaterdecision-makingeffectivenessinmoderndistributedorganizations.[58]GSSandcomputer-mediatedcommunicationsys-tems(CMCS)providesupportforeithersynchronousorasynchronousmeetings.Synchronousmeetingsarespontaneouswhereideasareexchangedwithlittlestructure.Participantscommunicatewitheachotherinsuchawaythatitissometimesdifficulttoattribute
anideatooneparticipantorestablishthereasonbe-hindaparticulardecision.Itisestimatedthatman-agersspend60%oftheircommunicationtimeinsynchronousmeetings[46],whichincludeface-to-facemeetings,telephonecalls,desktopconferencing,certaingroupdecisionsupportsystems(GDSS),andWeb-based‘‘chatrooms.’’
Ontheotherhand,asynchronousmeetingsaremorestructuredthansynchronousmeetings.Thesemeetingsrelymoreondocumentsexchangedamongpartici-pants.Comparedtosynchronousmeetings,asynchro-nousmeetingparticipantshavelongertocomposetheirmessagesand,therefore,itiseasytoattributeanideatoitsoriginatorandestablishthereasonbehindapar-ticulardecision.However,asynchronousmeetingsrequiremoretimethansynchronousmeetingsbecauseinformationexchangetakeslonger.Asynchronousmeetingsarefrequentlyusedbygroupswhereatleastoneparticipantisinaremotelocation[34].Technolo-giesthatfacilitateasynchronousmeetingsincludee-mail,bulletinboardsystems,andInternetnews-groups.Computerconferencing,whichisa‘‘structuredformofelectronicmailinwhichmessagesareorgan-izedbytopicanddialoguesareoftenmediated’’[3,27],canbeasynchronous(suchasbulletinboardsystemsandInternetnewsgroups)orsynchronous(suchas‘‘chatrooms’’).
4.3.VirtualteamsandtheimpactoftechnologyAsdecisionmakingmovesfromanindividualact-ivitytowardagroupone,manyorganizationsareforming‘‘virtualteams’’ofgeographicallydistributedknowledgeworkerstocollaborateonavarietyofworkplacetasks.Theeffectsofthereduced‘‘commu-nicationmodalities’’onvirtualteammembersandthecircumstancesinwhichtheseeffectsoccurhasbeenthefocusofmuchoftheCMCSresearch[28,42].Al-thoughnotdefinitiveintermsofspecificeffects,theresearchinthisareasuggeststhatvirtualteamscom-municatedifferentlythanface-to-facegroups[6,25,42,58].Whilethereisaplethoraofresearchdescrib-ingvarioustechnologiesforcomputer-mediatedcom-munications,thereisalackofstudiesexamining‘‘sustained,project-orientedteamworkofthesortthatisimportantinmostreal-worldorganizations’’[20].AnanalysisofCMCScommunicationcharacteristicsiswarranted.
118J.P.Shimetal./DecisionSupportSystems33(2002)111–126
Collaborationsupportsystemsplayacentralroleinfacilitatingcommunicationamongmembersofvirtualteams.Thetechnologyimposesconstraintsoncommu-nicationthatarelikelytoaffectagroup’sperformance.Peoplerelyonmultiplemodesofcommunicationinface-to-faceconversation,suchasparaverbal(toneofvoice,inflection,voicevolume)andnonverbal(eyemovement,facialexpression,handgestures,andotherbodylanguage)cues.Thesecueshelpregulatetheflowofconversation,facilitateturntaking,providefeed-back,andconveysubtlemeanings.Asaresult,face-to-faceconversationisaremarkablyorderlyprocess.Innormalface-to-faceconversation,therearefewinter-ruptionsorlongpausesandthedistributionofpartic-ipationisconsistent,thoughskewedtowardhigherstatusmembers[36,40].Collaborationsupportsystemsprecludethesesecondarycommunicationmodes,thusalteringtheorderlinessandeffectivenessofinforma-tionexchange.Suchcommunicationmodalitiesareconstrainedtoavaryingextentdependingonthecharacteristicsofthetechnologicalsystem.Forexam-ple,electronicmailpreventsbothparaverbalandnon-verbalcues,telephoneconferencecallsallowtheuseofmostparaverbalcues(butnotnonverbalones),whilevideoconferencingenablesextensiveuseofbothpara-verbalandnonverbalcues.Thelackofthesecuesreducestherichnessoftheinformationtransmittedbyvirtualteammembers.DaftandLengel[13]definemediarichnessas‘‘theabilityofinformationtochangeunderstandingwithinatimeinterval.’’Richmediaallowmultipleinformationcues(thewordsspoken,toneofvoice,bodylanguage,etc.)andfeedback.Ittakesmoretimeandeffortbygroupmemberstoachievethesamelevelofmutualunderstandinginaleanmedium,suchasCMCS,thaninarichonesuchasface-to-facecommunication.Thiscommunicationcon-straintaffectsthegroup’sabilitytoreachaconsensusdecision.
Becausevirtualteamscommunicatelessefficientlythanface-to-facegroups[25,26,42],theytendtobemoretask-orientedandexchangelesssocial–emo-tionalinformation,slowingthedevelopmentofrela-tionallinks[6].Developmentofrelationallinksisimportantbecauseresearchershaveassociatedstrongrelationallinkswithmanypositiveoutcomesinclu-dingenhancedcreativityandmotivation,increasedmorale,fewerprocesslosses,andbetterdecisions[57,58].
4.4.Creatingeffectivevirtualteams
Face-to-faceteamsgenerallyreportgreatersatis-factionwiththegroupinteractionprocessthanvirtualteams[57,58].Therefore,sincevirtualteamsarebecominganecessarytool,organizationsmuststrivetobolsterthesatisfactionlevelofCMCS.Ifthiswereaccomplished,therewouldbenosignificantdrawbacktotheuseofvirtualteams,whichcanbemademoreacceptableandsatisfyinginseveralways.Zack[61]showedthatthehighlyinteractivenatureofface-to-facemeetingsmakesthismode‘‘appropriateforbuildingasharedinterpretivecontextamonggroupmembers,while[CMCS],beinglessinteractive,ismoreappropriateforcommunicatingwithinanestab-lishedcontext.’’Ongoinggroupshaveanestablishedcultureandsetofroutines,andmayhaveagreatercommitmenttoachievingeffectivecommunications.Further,Zacksuggestedthatwhile‘‘socialpresence’’(asenseofbelonging)isdiminishedinvirtualteams,itisthelackofinteractivitythatprimarilyconstrainscomputermediatedcommunication.
UsersofCMCSmustexerciseleadershipandinflu-encewithlittlemeansofsocialcontrol,andsomemembersmaybecome‘‘lostincyberspace’’andmay‘‘dropout’’ofvirtualteamsintheabsenceoffamiliarcommunicationspatterns.Caremustbeexercisedtodevelopandfosterfamiliarityandproficiencywiththesenewtoolsandtechniquesofsocialinteraction.ThemostimportantgoalofCMCSistofosterinter-action,inclusionandparticipation[39],whichareallrelatedtothefeelingof‘‘beingthere’’orsocialpres-ence[61].Socialpresencedefinestheextenttowhichacommunicationsmediumallowsparticipantstoexpe-rienceeachotherasbeingpsychologicallycloseorpresent[19].Face-to-facecommunication,forexam-ple,ischaracterizedbysocialcuessuchasnonverbalandparaverbalcommunicationschannelsandcontin-uousfeedback[52].Thesuccessofgroupsupportsystemsliesinpartontheirabilitytoprovidetheparticipantswithsocioemotionalcontentsharing.Clearly,videoconferencingoffersagreateropportunityforsharingthesesocialcuesthantext-basedcommu-nicationsmodes,yetthelatterdonotentirelylacksuchcues[51,57].DesignersofGSSshouldexplicitlyworktoincorporateinnovativemethodsandchannelsforsharingvariouscuesbetweenparticipants,suchas‘‘emoticons’’(alsoknownas‘‘smileys’’)toincrease
J.P.Shimetal./DecisionSupportSystems33(2002)111–126119
themediarichnessoftheircommunications.Whereasmanyfirst-timeusersofCMCSsuchase-mailmightwriteformalmessagesthatreadlikebusinessletters,themessagesofhigh-volumeusersusuallyevolveintoafarmorefamiliartonewithpersonalcommentsandcommontermsandabbreviationsthatcancreateagreatersenseofactuallyspeakingwithsomeone.
Krautetal.[35]suggestthatwhereasformalcom-municationischaracterizedbypresetagendasbetweenarrangedparticipantsscheduledinadvancewith‘‘im-poverishedcontent,’’informalcommunicationoftenoccursspontaneouslywithnoarrangedagendabet-weenrandomparticipantswithrichercontent.Further,theyshowthatinformalencounterscreateacommoncontextandperspectivethatsupportplanningandcoordinationofgroupwork.Withoutinformalex-changes,‘‘collaborationislesslikelytostartandlessproductiveifitdoesoccur’’[35].Participantsinpurelycomputer-mediatedsystemswhohavenevermetandexchangedinformalconversationhaveexhibitedastrongdesiretodosowhengiventheopportunity—GSSdevelopersshouldfacilitateinformalface-to-facecontactwhereverpossible.
Inthefuture,organizationsintroducingthesedeci-sionsupporttechnologiesintotheworkplacemustleveragethebeneficialdifferencesinherentincom-puter-mediatedcommunicationsandmitigatetheneg-ativedifferences.Managersmustbecomefamiliarwiththestrengthsandlimitationsoftherelevanttechnolo-gies.Theuseofcollaborativesupportsystemswillin-creaseastheWebenablesmorestrategicalliancesandasintranetsbecomeawidespreadplatformforgroupdecisionmaking.
5.Optimization-baseddecisionsupportmodelsThissectiondescribesthestateoftheartofopti-mization-orienteddecisionsupport,andspeculatesonthefutureofsuchsystems.Model-baseddecisionsupportcanbedividedintothreestages:formulation,solution,andanalysis.Formulationreferstothegen-erationofamodelintheformacceptabletoamodelsolver.Thesolutionstagereferstothealgorithmicsolutionofthemodel.Theanalysisstagereferstothe‘what-if’analysesandinterpretationofamodelsol-utionorasetofsolutions.ThedevelopmentofDSStoolstosupportthesethreestageshasoccurredat
differentrates.Researchinoptimizationtraditionallyfocusedongeneratingabettersolutionalgorithm;asthetechnologieshaveevolved,moreprogresshasbeenmadeintheformulationandanalysisfunctionsofDSSsupport.5.1.Formulation
Convertingadecision-maker’sspecificationofadecisionproblemintoanalgebraicformandthenintoaformunderstandablebyanalgorithmisakeystepintheuseofamodel.WehavecomealongwayfromthedaysofrequiringanoptimizationproblemtobeinputinthecommonlyusedMathematicalProgram-mingSystem(MPS)format.Severalalgebraicmodel-inglanguageprocessorsystems(AMLPS)havebeendevelopedthatmakeitconvenienttoinputthemod-eler’sformofanoptimizationproblemdirectlyintoasolver.TheseAMLPSalsocanreadandwritedatafilesfrom/tomanydiversedatabases,enablingatrulyintegratedmodelgeneration.SomeofthesesAMLPSsupportODBCcallsandthusnowcanbeusedfordevelopmentofamodelthatdependsuponmanydatasourceslocatedacrossanenterprise.Indeed,thegrowthinthesesystemsisnowleadingtothedevel-opmentofaModelingEnvironment(ME)wherethesolvertakesasupportrole.TheMEservesasthemodeltranslatorandmanagerofallinput/outputandinteractionwiththeuser.Thesesystemsareextensiblethroughalinktoanyothersolver.
Thenextgenerationofformulationsupportisdisplayedinfurtherintegrationofthemodelspecifi-cationinhostcomputingplatforms.ModelingEnvi-ronmentsarebecomingavailableasAPIssothatthesecanbecalleddirectlyintoanend-userapplication.Theformulationsupportisalsoextendedthroughthegrowthofenterpriseresourceplanning(ERP)move-ment.Optimization-basedDSSwillplayakeyroleinthenextwaveofERPsoftware,andthemodelinglanguageswillmakeithappen.5.2.Solution
Historically,mostoftheresearcheffortinoper-ationsresearch(OR)hasbeenconcentratedondevel-opmentofnewalgorithmstosolveproblemsfaster.Thegoodnewsisthatdecisionsupportsoftwarede-velopersappeartoincorporateadvancesinthesolu-
120J.P.Shimetal./DecisionSupportSystems33(2002)111–126
tionalgorithmsquitequicklytolettheuserbenefitfromtheseenhancements.Somemajortrendsarehigh-lightedbelow.
Thetraditionallinearprogrammingsoftwarecon-tinuestoberefinedinbothsimplexmethodandinteriorpointalgorithms.Theemphasisisontakingadvantageofproblemcharacteristicstoreducetheproblemsizeortospeedupaspecificalgorithmicstep.Theresultistheabilitytosolvereallylargeproblems.Ithasalsoena-bledthemodelerstoconsideruncertaintyinthedeci-sionsituationthroughstochasticprogrammingwithrecoursetypeapproaches.
Perhapsthebiggestgainsinthesolutionalgorithmsareevidentinthemixed-integerprogramming(MIP)arena.Withtheincorporationofvarioustricks,sol-utionsofmuchlargerMIPproblemsarenowpossible.Amajordevelopmentisthesolutionofintegerpro-grammingproblemsistheuseofconstraintlogicprogramming[17,18].Thisapproachemploysthetreesearchphilosophyofbranchandbound,butdoesnotrequiresolutionofLPproblems.
Thenextmajortrendinthesolutionsoftwareisthegrowthofmetaheuristicstosolvecombinatorialprob-lems[21,22].Thetechniquesemployedincludetabusearch,geneticalgorithms,simulatedannealing,neuralnetworks,andseveralothers.Forexample,Evolverisacommerciallyavailabletool(fromPalisadesSoftware)thatsolvesMIPproblemsusinggeneticalgorithms.Thecombinationoftechniquesfromartificialintelli-genceandoperationresearchtoattackmuchlargerproblemsisgoingtobenefittheDSSmovementinthenextfewdecades.
Traditionallysoldoptimizationsoftwareisbecom-ingafoundationintheDSSplatform.AcasuallookatarecentissueofORMSTodaywouldshowadvertise-mentsfromcompaniessuchasMaximalSoftwareofferingtheirsolverinApplicationProgrammingInter-face(API)formtoXAofferingtheirproductforfullintegrationinABAP/4,SAP’sprogramminglanguage.5.3.Analysis
Onlyrecentlyhavevendorsofoptimizationsoft-warebeguntofocusonthefinalstageofthemodelingprocess—analysis.Thisstageincludesdeliveryofmodelsolutioninausableformtoenhancetheabilitytoanalyzeandunderstandtheproblemandthesolution.Reportgeneratingfunctionalityisnowacommon
featureusedtopresenttheresultstotheuserinausableformthatcanbeintegratedintodatabases.Solutionscanalsobestoredinpopularspreadsheetformatsforsimplegraphicalanalysesorreportgeneration.Somemodelingenvironmentsoffertheirowngraphicaldis-playtoolstodisplayresultsineasytouseformat.ItislikelythatthegrowthofnewvisualizationtoolswillbenefittheprocessofsolutiondeliveryinORmodelsaswell.Itwouldbepossibletoincorporatemultimediainhighlightingsolutionsorespeciallyexceptionstothenormorsignalinfeasibilities.
Theanalysisstagehasalsobenefitedfromincorpo-rationofdeductivetechniquessuchasIIS[7]todiagnosethecauseofinfeasibilitiesorANALYZE[24]toperformpostsolutionanalysisbeyondtheclas-sicsensitivityanalysis.Anewtrendistheabilitytostoreandanalyzemultiplesolutionscenarios.TheScenarioManagertoolwithinMicrosoftExcelpopu-larizedtheconceptofsavingmultiplesolutionsandunderstandsanyunderlyingpatterns.Someresearchers[53]haveproposedtheuseofinductiveanalysistech-niquestofurthergenerateinsightintotheproblembystudyingmultiplesolutions.Theconceptofgeneratingmultiple‘what-if’scenariosandsolutionsisnowavail-ableincommercialsoftwaresuchasRiskOptimizerfromPalisadeSoftware.
Wehaveseenmanydevelopmentsinanalyticalmodels,optimizationandmodel-basedDSS,butthepossibilitiesforgreaterexploitationofmodelsinde-cisionmakingareenormous.Inthenextsection,weexaminesomebroaderissuesinactivelysupportedmanagementdecisionmaking.
6.ActivedecisionsupportforthenextmillenniumTheneedforactivedecisionsupportwasassertedbyKeen[31]whenheoutlined‘‘thenextdecadeofDSS’’in1987.HisfirstpointisthattheDSStechnol-ogyitselfisnotimportant—itisthesupportweintendtoprovidewhichisthekeyelement.KeengaveDSSresearchthefollowingbroadagenda:(i)itshouldlookforareaswheretheprovenskillsofDSSbuilderscanbeappliedinnew,emergentoroverlookedareas;(ii)itshouldmakeanexplicitefforttoapplyanalyticmodelsandmethods;itshouldembodyafarmoreprescriptiveviewofhowdecisionscanbemademoreeffectively;(iii)itshouldexploittheemergingsoftwaretoolsand
J.P.Shimetal./DecisionSupportSystems33(2002)111–126121
experiencebaseofAItobuildsemi-expertsystems,and(iv)itshouldre-emphasisethespecialvalueofDSSpractitionersasbeingtheircombinationofexpertiseinunderstandingdecisionmakingandknowinghowtotakeadvantageofdevelopmentsincomputer-relatedfields.
WewilluseKeen’sagendafor‘‘thenextdecadeofDSS’’,butwewillupdateitfrom1987to1997,andlookaheadtotheyear2007.Managersandknowl-edgeworkersinthelate1980sand1990saredifferentfromearlierDSSusers,andwillbequitedifferentfromthoseof2007.Technologicalproficiencylevelsofalluserscontinuetoincrease.ThecompromiseswemadewithsystemdesignsinordertofacilitatetheuseofDSSbyinexperiencedusersinthelate1980swillnotbenecessaryfortheusersofthe2007.Ontheotherhand,thisnewgenerationoftechnologicallyadvanceduserswillalsoexpectmorefunctionalityinDSStechnology.TheDSStechnologyofthefuturewillbeenhancedbymobiletools,mobilee-services,andwirelessprotocolssuchasWirelessApplicationsProtocol(WAP),WirelessMarkupLanguage(WML),andiMode,therebyleadingtoubiquitousaccesstoinformationanddecisionsupporttools.Greatercol-laborationfunctionswillbeenabled,facilitatingmoreinteractivedecisionprocesses.
Inthelastfewyears,wehaveseenasteadyinflowofmodelsandtoolsformultiple-criteriadecisionmakinginDSSapplications(Keen’ssecondpoint),anditappearsthatthiswillcontinueasdevelopersincorpo-ratemoreadvancedmathematicalprogrammingsoft-wareintegratedwith(forinstance)MSExcel.Theuseofartificialintelligence(AI),asadvocatedinKeen’sthirdpoint,isbeingreplacedwithintelligentsystemsandsoftcomputing,whichareemergingnewtechno-logicalplatforms.Infact,ratherthanstand-aloneAImodules,intelligentlogicisnowusuallyinherentintheprocessingofalldecisionsupporttools.
Becausemoreseniorexecutivesarecomfortablewithinformationtechnology(IT),theroadblocksofthe1980sand1990sforusingITinexecutivedecisionmakingarebeingremoved.Infact,ITisnowviewedasastrategictoolthatiscentraltothepursuitofcom-petitiveadvantage.Therefore,variousDSStechnolo-gieswillbemoreacceptedthroughouttheenterprise,fromoperationalsupporttoexecutiveboardrooms.Further,moderncorporationsandtheirstrategicbusi-nessunitswillcontinuetolosetheirhierarchical
organizationalstructures.Companiesseektocreatebusinessentitiesthatareleaner,moreflexibleandmoreresponsivetoarapidlychangingbusinessenvi-ronment.Withreductionsinstaffandmiddlemanage-mentpersonnel,seniormanagersandexecutivesgetmoredirectlyinvolvedwithproblemsolving,decisionmakingandplanningthantheywereinthe1980s.Agileandflexibleorganizationsalsoasktheirmanag-ersandstafftofrequentlychangetheirfocus.There-fore,decisionsupporttoolswillplayamorecentralroleinthisrapidlychangingenvironment.
Thefirsttargetforintelligentsystemstechnologyshouldbetheoverwhelmingflowofdata,informationandknowledgeproducedforexecutivesbyanincreas-ingnumberofsources.Expertsystemstechnology,whichwasafocalareaforventurecapitalin1985–1990,isnowbeingreplacedbyintelligentsystems,whicharebuilttofulfilltwokeyfunctions:(i)thescreening,siftingandfilteringofagrowingoverflowofdata,informationandknowledge(describedabove),and(ii)thesupportofaneffectiveandproductiveuseoftheExecutiveInformationSystems(EIS),whichquiteoftenistailoredtotheneedsandthepersonalityoftheuser.Intelligentsystems,whichcanbeimplementedforthesepurposes,rangefromself-organizingmapstosmartadd-onmodulestomaketheuseofstandardsoft-waremoreeffectiveandproductivefortheusers.Intel-ligentdataminingwillalsoplayasignificantroleinhelpingorganizationstransformhugevolumesofdataintovaluablecorporateknowledgeandintelligence.Softwareagents(alsocalledintelligentagents)havealsobeendesignedandimplementedtoaddressthisprocessofdatascreeningandfiltering.TheseJava-basedcomponentscanbedesignedandimple-mentedtosearchfordatasourceswithuser-definedsearchprofiles,toidentifyandaccessrelevantdata,tocopythedata,andtoorganizeandstoreitinadatawarehouse.Otheragentsofthesame‘‘family’’canthenbeusedtoretrievethedata,insertitinreportsandtodistributeitovere-mailaccordingtotopic-specificdistributionprofiles.
7.Conclusions
Thedevelopmentsinthelastdecadewillguideusinunderstandingthecomingevolutionofdecisionsup-porttechnologies.Changeswilloccurintechnologies
122J.P.Shimetal./DecisionSupportSystems33(2002)111–126
andintheimplementationenvironment—usersarebecomingmoresophisticatedandmoredemanding,organizationsarebecomingmorecomplexyetmoreagileandflexible,andglobalregulatoryandcompet-itivefactorsrapidlychange,affectingthedesignanduseofthesetools.Thefuturewilloffersurprises,tobesure,butcertaintrendscanbeobserved.
OnesuchtrendisthemeteoricriseoftheWebasacommonplatformfromwhichtoextendthecapabil-itiesofDSStoaverylargenumberofusers.ThefactthatastandardWebbrowsercanbeusedastheuserinterface/dialogmeansthatcompaniescanintroducenewDSStechnologiesattheirsitesatrelativelylowcostwhencomparedtoclient-basedDSS.AWebbrowseruserinterfaceallowstheimplementationofDSStechnologywithverylittleusertraining.Thepotentialexistsforweb-basedDSStoincreasepro-ductivityandprofitability,andspeedthedecisionmakingprocesswithoutregardtogeographiclimita-tions[48].Throughincreaseddecisionmakingability,reducedcosts,andreducedsupportneeds,Web-basedDSScansignificantlyimprovecompanies’useoftheirexistinginfrastructures.Moreexecutivesandmanagerscanhaveaccesstotechnologythatincreasesoverallorganizationalefficiencyandeffectiveness.TheWebalsodramaticallyincreasestheusabilityfactorsforDSS.StandardinterfacedesignfactorsmeanthatuserscanmorequicklyadoptnewDSSwithlesstrainingandwithmoreconfidence.However,whilestandardsareadvantageousfromthatperspec-tive,wealsorecommendthatpersonalizationoftheDSSuserinterfaceisafutureareathatshouldbeaddressedbydevelopersandresearchers.Theprocess-ingpoweroftoday’splatformsenablesthedesignofhighlyconfigurableinterfacesthatidentifytheusagepatternsofindividualusersandmodifythemselves(byreducingmenuchoices,forexample)inordertoprovidehigherusabilityforeachDSSuser.
Anothertrendistheincreasingsophisticationofmodel-basedDSSsoftware.Forexample,model-basedDSSsoftwareisstandardizingonWebtechnologiesasthefundamentaltechnologyforinterfacedesign.MostmajorDSSsoftwaredevelopersnowhavewebsitesandofferdownloadingtrialsoftwareforfurtherexplora-tion.EvenmoreexcitingisthetrendtowardusingtheApplicationServiceProvider(ASP)modelfordeliveryofDSSfunctionality.DSSsoftwarecustomersnolongerneedtopurchaseandinstallthesoftwareontheirownservers;theymayjustrentitonaper-usebasisfromanASPwhohoststhedecisionsupportapplicationandprovidessecureaccessovertheInter-net.Thisisespeciallyusefulforsolversoftwaresothatamodelercanemploythebestsolversoftwareappro-priateforaspecificsituationwithouthavingtobuyeverysingleprogram.ExamplesofthisapproachincludeIBM’sOSLsite(http://www.research.ibm.com/osl/bench.html)andtheNEOSServer(http://www.mcs.anl.gov/otc/Server/).Bhargavaetal.[4]havebeendevelopingDecisionNet(http://www.ini.cmu.edu/emarket/)asaportaltoenablethemodelertorentaspecificprogramonaperusebasis.
AmajortrendishowtheWebissupportingmoreinteractivityandcollaborationinDSS.Organizationsarebuildingnotonlyvirtualteamstructures,butalsoentirevirtualorganizations,basedonthistechnolog-icalplatform.Withtheapplicationofintranetsandenterpriseresourceplanning(ERP)systems,entireorganizationsroutinelyinteractviatechnologywithlittleornoface-to-faceinteraction.Suchvirtualorgan-izationshaveseeminglyovercomeallbarriersoftimeandspace,andhavecreatedentirefirmswithremotebusinesspartners.Afinaltrendinthisdomainisthedevelopmentofubiquitouscomputingbasedonsecurewirelessbandwidthandnew‘‘thinclient’’devicessuchasWeb-enableddigitalphonesanddigitalassis-tants.Inthisenvironment,virtualteammatescantrulycollaborateanywhereandanytime.Withouttheneedtophysicallybeatacomputertiedtoawirednetwork,individualsarefreetocollaboratemorenaturallyandnearlyallthetime.Thisensuresevengreaterconnec-tivitytomembersofworkgroupsandvirtualteams,withgreateraccessandmorerobustdecisionsupport.AnotherbenefitofthiswirelessinteractivityistheenhancementoftheabilityofknowledgeworkerstocollectmultipleperspectivesondecisionproblemsassuggestedinFig.2.UsingthemultipleperspectivesapproachtoproblemformulationshouldhelpleadustowardsKeen’sgoaloffindingareaswheretoolscanbedevelopedforturningqualitativeinsightsanduncertainandincompletedataintousefulknowledge.Ultimately,thisnewenvironmentallowsindividualsandorganizationstomakemoreinformed,morecol-laborativedecisionsthatwillachievetheorganiza-tion’sgoalsmoreeffectively.
Thoughinformationtechnologyisadvancingtheform,style,andcontentofdecisionsupport,webe-
J.P.Shimetal./DecisionSupportSystems33(2002)111–126123
lievethedevelopmentofmodel-basedDSSisstillatanearlystage,andfinallypoisedtoemergeasapowerfultoolformanagerialsupport.Oneofthechallengesinemployingmodelsfordecisionsupporthasbeentheavailabilityofdatafromacrossvariousdatawarehouseswithinanorganization.Theclientservermodeloftheweballowsmoretransparentaccesstothisdata,makingitpossibletorunmodelsbasedonactualdata.Inarecentpaper,Cohenetal.[10]describeseveralimplementationsofoptimization-basedDSSthatintegratedatafromseveralsources.Manyoptimizationsoftwareprovidersandprofessio-nalserviceorganizationsarebuildingspecificinter-facestobringallthedatatogethertomaketheseapplicationspossible.Theextraordinarygrowthofi2Technologiesandmanyothercompaniesthatemployoptimizationmodelstoenhancethesupplychainisagoodexample.GrowthoftheInternetenablessmallerorganizationstoalsoemploysomeofthesametools.Thisopportunitywillgrowsubstantiallyandresultinthenextgenerationofcheaper,faster,andbetterDSStoolsforamuchlargerclientbasethanwehaveseenbefore.
ByextendingKeen’sagendaforDSSresearchtotheyear2007,wecanreformulateitwiththepotentialsupportofthenewtechnologies.DSSresearchersanddevelopersshould(i)identifyareaswheretoolsareneededtotransformuncertainandincompletedata,alongwithqualitativeinsights,intousefulknowledge;(ii)bemoreprescriptiveabouteffectivedecisionmak-ingbyusingintelligentsystemsandmethods;(iii)exploitadvancingsoftwaretoolstoimprovethepro-ductivityofworkinganddecisionmakingtime,and(iv)assistandguideDSSpractitionersinimprovingtheircoreknowledgeofeffectivedecisionsupport.Thisprocesswillbeenhancedbycontinueddevelop-mentsinWeb-enabledtools,wirelessprotocols,andgroupsupportsystems,whichwillexpandtheinter-activityandpervasivenessofdecisionsupporttechnol-ogies.
Acknowledgements
TheauthorsgratefullyacknowledgeProfessorsEfraimTurban,PirkkoWalden,GeorgeMarakas,andthepanelaudiencefortheirhelpfulcommentsatthepanelsession.
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[61]M.H.Zack,Interactivityandcommunicationmodechoicein
ongoingmanagementgroups,InformationSystemsResearch4(3)(1993)207–239.J.P.ShimisaProfessorofMISatMis-sissippiStateUniversity.HereceivedhisPhDfromtheUniversityofNebraskaandcompletedHarvardBusinessSchool’sExecutiveEducationinInformationSys-tems.HetaughtatGeorgiaStateUniver-sity,NewYorkUniversity,andChineseUniversityofHongKongasvisitingpro-fessorwhileonleave.Hehasco-authoredfourtextbooksandservesasdepartmentaleditorforDataBaseandoneditorialboard
forfivejournals.HisresearchhasbeenpublishedinCommunicationsoftheACM,Interfaces,JournalofManagementInformationSys-tems,JournalofStrategicInformationSystems,DecisionSupportSystems,ComputersandOperationsResearch,CommunicationsofAIS,JournaloftheOperationalResearchSociety,Omega,Informa-tionandManagement,MultimediaComputing,LongRangePlan-ning,ICIS,Socio-EconomicPlanningSciences,JournalofMulti-CriteriaDecisionAnalysis,andHumanRelations.HehasreceivednumerousgrantsfromMicrosoft,MississippiInstitutionsofHigherLearning,NSF,UniversityofWisconsinSystem,MSU,NYU,andPritsker’sSystem.Heisasix-timerecipientofOutstandingFacultyAward,ResearchAward,ServiceAward,andJohnGrishamFacultyExcellenceAwardatMSU.Dr.ShimhasworkedasaconsultantforseveralfirmsincludingBooz-Allen.Histeachingandresearchinterestsareintheareasofe-business,DSS,videostreamingintelecommunications,andmultimedia.
MerrillWarkentinisanAssociatePro-fessorofMISintheCollegeofBusinessandIndustryatMississippiStateUniver-sity.Hehasauthoredover100articles,chapters,andbooks.Hisresearch,primar-ilyine-commerce,virtualteams,andknowledgeengineering,hasappearedinsuchjournalsasMISQuarterly,DecisionSciences,InformationSystemsJournal,JournalofKnowledgeEngineeringandTechnology,CommunicationsoftheAIS,
JournalofElectronicCommerceResearch,LogisticsInformationManagement,ACMAppliedComputingReview,ExpertSystems,InformationSystemsManagementandJournalofComputerInfor-mationSystems.ProfessorWarkentinisaco-authorofElectronicCommerce2002:AManagerialPerspective(2e)(PrenticeHall,2002)andEditorofBusiness-to-BusinessElectronicCommerce:ChallengesandSolutions(IdeaGroupPublishing,2002).HeistheAssociateEditorofInformationResourcesManagementJournal.Dr.Warkentinhasservedasaconsultanttonumerouscompaniesandorganizations,andhasbeenafeaturedspeakeratover100industryassociationmeetings,executivedevelopmentseminars,andacademicconferences.HehasbeenaLecturerattheArmyLogisticsManagementCollege,andsince1996,hasservedasNationalDistinguishedLecturerfortheAssociationforComputingMachi-nery(ACM).ProfessorWarkentinholdsBA,MA,andPhDdegreesfromtheUniversityofNebraska-Lincoln.
JimF.CourtneyisaProfessorofMan-agementInformationSystemsattheUni-versityofCentralFloridainOrlando.HereceivedhisPhDinBusinessAdministra-tion(ManagementScience)fromtheUni-versityofTexasatAustinin1974.HewasformerlyTennecoProfessorofBusinessAdministrationintheInformationandOperationsManagementDepartmentatTexasA&MUniversityandhasalsoheldfacultypositionsatGeorgiaTech,Texas
Tech,LincolnUniversityinNewZealandandtheStateUniversityofNewYorkatBuffalo.OtherexperiencesincludepositionsasDatabaseAnalystatMRISystemsCorporationandVisitingResearchScientistattheNASAJohnsonSpaceCenter.Hispapershaveappearedinseveraljournals,includingManagementScience,CommunicationsoftheACM,IEEETransactionsonSystems,ManandCybernetics,MISQuarterly,DecisionSciences,DecisionSup-portSystems,theJournalofManagementInformationSystems,Database,Interfaces,theJournalofAppliedSystemsAnalysis,andtheJournalofExperientialLearningandSimulation.Heistheco-developeroftheSystemsLaboratoryforInformationManagement(BusinessPublications,1981),asoftwarepackagetosupportresearchandeducationindecisionsupportsystems,co-authorofDatabaseSystemsforManagement(2ndedn.,IrwinPublishing,1992),andDecisionSupportModelsandExpertSystems(MacMil-lanPublishing,1992).Hispresentresearchinterestsareknowledge-baseddecisionsupportsystems,intelligentorganizationalsystemsandinquiringorganizations.
126J.P.Shimetal./DecisionSupportSystems33(2002)111–126
DanielJ.PowerisaProfessorofInfor-mationSystemsandManagementattheCollegeofBusinessAdministrationatUniversityofNorthernIowa,CedarFalls,IA.Hisresearchinterestsincludethede-signanddevelopmentofdecisionsupportsystemsandhowDSSimpactindividualandorganizationaldecisionbehavior.Dr.Powerhaspublishedmorethan40articles,bookchaptersandproceedingspapers.HisarticleshaveappearedinMISQuarterly,
DecisionSciences,JournalofDecisionSystems,AcademyofMan-agementReview,andInformationandManagement.Heisalsoco-authorofatextbooktitledStrategicManagementSkillsandiscom-pletingatextbookonDecisionSupportSystems.ProfessorPoweristheeditoroftheWorldWideWebsiteDSSResources.COMatURLhttp://www.DSSResources.COM.In1982,ProfessorPowerre-ceivedaPhDinBusinessAdministrationfromtheUniversityofWisconsin-Madison.HewasonthefacultyattheUniversityofMaryland-CollegeParkfrom1982to1989.HeservedastheHeadoftheManagementDepartmentatUNIfromAugust1989toJanuary1996.HeservedasActingDeanoftheUNICollegeofBusinessAdministrationfromJanuary1996toJuly31,1996.Also,hehasbeenavisitinglectureratuniversitiesinChina,Denmark,Ireland,Israel,andRussia.
RameshShardaisaConoco/DuPontPro-fessorofManagementofTechnologyandaRegentsProfessorofManagementScienceandInformationSystemsintheCollegeofBusinessAdministrationattheOklahomaStateUniversity.HereceivedhisB.Eng.degreefromUniversityofUdaipur,MSfromTheOhioStateUniversityandanMBAandPhDfromtheUniversityofWis-consin-Madison.Oneofhismajoractiv-itiesinthelastfewyearshasbeentostart
theMSinTelecommunicationsManagementProgramatOkla-homaState.Now,heisestablishingamajorinterdisciplinaryre-searchcenterininformationandtelecommunicationstechnologiesatOSU.HeisthefoundingeditorofInteractiveTransactionsofOR/MS,anINFORMSelectronicjournal.HeisalsoanassociateeditoroftheINFORMSJournalonComputing.Hisresearchinte-restsareinoptimizationapplicationsondesktopcomputers,infor-mationsystemssupportfornewproductdevelopment,neuralnetworks,businessusesoftheInternet,andknowledgenetworks.Heandhiscolleaguesareworkingonusingtheinformationtech-nologytofacilitateelectroniccommercebetweentheUSgovern-mentandsmallbusiness.Heisalsoacofounderofacompanythatproducesvirtualtradefairs,iTradeFair.com.
ChristerCarlssonisDirectoroftheInstituteofAdvancedManage-mentA
˚SystemsResearchandaProfessorofmanagementscienceatboAkademiUniversity.ProfessorCarlssonisamemberoftheSteeringCommitteeofERUDIT,anESPRITNetworkofExcel-lence,andchairmanoftheBISC-SIGonSoftDecisionAnalysis.Hehasorganizedandmanagedseveralresearchprogramsinindustryinhisspecificresearchareas:knowledgebasedsystems,decisionsupportsystemsandexpertsystems,andhascarriedouttheoreticalresearchworkalsoinmultiplecriteriaoptimizationanddecisionmaking,fuzzysetsandfuzzylogic,andcyberneticsandsystemsresearch.SomerecentprogramsincludeSmarter(reducingfrag-mentationofworkingtimewithmoderninformationtechnology),EM-SBullwhip(eliminatingdemandfluctuationsinthesupplychainwithfuzzylogic),Waeno(improvingtheproductivityofcapitalingiga-investmentsusinghyperknowledge)andImagine21(foresightofnewtelecomservicesusingagenttechnology).HeisontheeditorialboardofseveraljournalsincludingtheEJOR,FuzzySetsandSystems,ITOR,CyberneticsandSystems,andIntelligentSystemsinAccounting,FinanceandBusiness.Heistheauthorof3books,andaneditororco-editorof5specialissuesofinternationaljournalsand11books,andhaspublishedmorethan200papers.
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