business analytics/business intelligence and it infrastructure

DOI: 10.4018/JOEUC.2020100107

Journal of Organizational and End User ComputingVolume 32 • Issue 4 • October-December 2020

Copyright©2020,IGIGlobal.CopyingordistributinginprintorelectronicformswithoutwrittenpermissionofIGIGlobalisprohibited.

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Business Analytics/Business Intelligence and IT Infrastructure:Impact on Organizational AgilityXiaoFeng Chen, Western Washington University, USA

Keng Siau, Missouri University of Science and Technology, USA

ABSTRACT

This is an empirical research investigating the impact of business analytics (BA) and businessintelligence(BI)use, IT infrastructureflexibility,and their interactionsonorganizationalagility.Synthesizingthesystemstheoryandawareness-motivation-capabilityframework,theauthorsproposethatBA-Use,ITinfrastructureflexibility,andtheirinteractionssignificantlyinfluenceorganizationalagility.TheresultsshowthesignificantassociationofBAuseandITinfrastructureflexibilitywithorganizationalagility.TheresultsalsosuggestthatBAusemaydemandcorporationstobuildamoreflexibleITinfrastructure.However,thedatadoesnotrevealtheproposedinteractionbetweenthetwodriversoforganizationalagility.

KEywORdSBusiness Analytics, Business Intelligence, IT Infrastructure Flexibility, Organizational Agility, Strategic Value

INTROdUCTION

Businessanalytics(BA)andbusinessintelligence(BI)havebeencreditedforbettermanagementdecisionsinbusiness.Businessintelligence(BI)becameapopularterminthe1990s(Chenetal.,2012).Inthe2000s,businessanalytics(BA)wasintroducedtoemphasizetheanalyticcomponentinBI(Davenport,2006;Adeborna&Siau,2014).BAhasaverysubtledistinctionfromBIintoday’sliterature.Sometime,theunifiedtermBI&Aisused(Chenetal.,2012).WeviewBAandBIastwosynonymousterms.Inthisstudy,weusethetermBAthroughoutthepaperbecauseitismorecommonlyusedinthecurrentliteratureanditreflectstheuniquecharacteristicoftheinformationsystems.Organizationshavespentmillions,ifnotbillions,ofdollarsandsometimesmadesignificantorganizationalchangestoimplementBA.BAispopularinpractice.However,empiricalstudiesonBAanditsbusinessvaluearerelativelyscarceinacademicresearchcomparedtostudiesonanalyticalandmathematicalmodeling,statisticalmethods,andminingalgorithms(Jourdanetal.,2008).Althoughthelast15yearshavewitnessedariseinthenumberofempiricalstudiesonthebusinessvalueofBA,only3%ofpriorBAstudiesempiricallyinvestigatedtheimpactofBA-Use(Trieu,2016).Inthisstudy,wetheorizethatBAusehasadirectimplicationonorganizationalperformancebasedonthesynthesisofsystemstheory,awareness-motivation-capability(AMC)framework,andextantISresearch.

Basedontheliteraturereview,wetheorizethattheorganizationalagility(OA)perspectiveisapromisinglensforstudyingthesignificanceofBAinorganizationalperformance(Chen&Siau,2012).OAwasdefinedastheorganizationalabilitythatcanbemeasuredbyfactorsfromtwodimensions:the

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dimensionofdetectingandthedimensionofrespondingtoopportunitiesandthreats(Nevo&Wade,2010;Sambamurthyetal.,2003).However,ourliteraturereviewalsoshowsthatthedefinitionwaspresentedwithouttheoreticaljustificationsonwhythetwodimensionspossessfactorsthataffectOA.ArethereotherfactorsfromotherdimensionsthatmayalsoaffectOA?WhileinvestigatingthebusinessvalueofBA-UsethroughthelensofOA,wealsowanttocloselyexaminethetheoreticalfoundationoftheOAdefinitionthathasbeenusedintheISfield.Theresearchquestionsofthisstudyinclude:(1)IsthereatheoreticalfoundationforthecurrentOAdefinition?(2)WhatistheroleofBA-UseinconvertingBAassetsintoBAimpacts?(3)HowBA-UseandITflexibilityinfluenceeachotherandinteractwitheachothertoinfluencetheOA?Inthisstudy,weusethesystemstheoryandAMCframeworktoelaborateonwhytheOAdefinitionshouldincludetheorganizationalabilitytodetectandrespondtoopportunitiesandthreats.WealsoproposethattheexistingOAdefinitionmayneedtobeexpandedtoincludethemotivationdimension.ChenandSiau(Chen&Siau,2012)proposed,andthisstudyfurtherelaborates,thatBA-UseandITinfrastructureflexibility(IIF)aretwoenablingcomponentsthatcanhelptoimproveOA.Inthisstudy,accordingtothesystemstheory,wetheorizethatBA-UsewillinteractwithIIFtoaffectanorganization’sagility.BasedontheAMCframework,wefurtherproposethatinthenewdata-drivenworld,BA-UserequiresflexibleITinfrastructurefororganizationstobeandremainagile.

TheresultsshowthatbothBA-UseandIIFhaveasignificantimpactonOA.TheresultsalsoshowthatBA-UseisadrivingfactorforimprovingIIF,andBA-UsehasasignificantinfluenceonorganizationalagilitythroughIIF.However,thedatadoesnotshowtheinteractioneffectbetweenBA-Use and IIF on OA. This study contributes to the academic research in a number of ways.First, although OA has been defined as “an organization’s detecting and responding ability toopportunitiesandthreatswithspeed”(Nevo&Wade,2010;Sambamurthyetal.,2003)andtestedintheISliterature,nopriorstudyhasprovidedatheoreticalexplanationonwhyOAshouldbedefinedbytheorganizationalabilitiesinthedetectingandrespondingdimensions.Thisstudyusesanewperspective,AMCframework,toprovidetheoreticalsupportforthetwoexistingdimensionsofOAdefinitionandproposesapossiblemodificationoftheOAdefinitiontoincludethethirddimension:motivation.Second,thisstudyprovidestheoreticalfoundationstoanswerthefundamentalquestionofwhyBAisimportantinthebusinesscontext.Third,itprovidestheoreticalsupportstoconnectIIFwithOAfromanewperspective:AMCframework.Andfourth,throughthelensoftheAMCframework,itconnectsBA-UsewithITinfrastructureandproposesandempiricallysuggeststhatinthenewdata-drivenenvironment,BA-UsedemandsamoreflexibleITinfrastructurethroughwhichBA-UseexertsastronginfluenceonOA.Practicalcontributionsofthestudyincludes(1)thattheresultsprovidesupportsforBAmanagerstojustifytheirrequestsforresourcestoimplementBAsystems;(2)strategicplannersneedtoconsiderBA-Useasacrucialassetinthestrategicblueprintforimprovingandenhancingorganizationalcompetitiveadvantage;and(3)inthenewdata-drivenenvironment,buildinga flexible IT infrastructure is crucial forOAbecauseBA-UsedemandsaflexibleITinfrastructureandbothBA-UseandIIFhavedirectinfluenceonOA.

Thepaperisorganizedasfollows.Section2presentsanddiscussesthetheoreticalbackground.Section 3 describes the research model. Section 4 presents the literature review and hypothesisdevelopment.Section5describes the researchmethod.Section6puts forward themeasurementanalysisresults.Section7presentsthestructuralmodelanalysisresultsanddiscussesthefindings.Section8highlightstheresearchcontributions.Section9discussesthelimitationsoftheresearchandsuggestspotentialfutureresearchdirections.


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THEORETICAL BACKGROUNd

Systems Theory and Organizational Agility (OA)Thesystemstheorydefinesthatsystemsarecompositethingsandthatsystemspossesspropertiesthatarederivedfromtheinteractionamongthecomposingcomponents(Ackoff,1971;Checkland,1981).Thesystemstheoryassertsthat(1)theworldismadeupofthings;(2)thingspossessproperties;and(3)eachpropertyisrepresentedbysomevalueatanypointintime(Nevo&Wade,2010,p.165).Extendingthesystemstheory,NevoandWade(2010)proposed“somesystempropertiesmaybepropertiesoftheircomponentsbutwithnewvalues”while“othersystempropertiesarenewinthesensethatnoindividualcomponentpossessestheminisolation.”(Nevo&Wade,2010,p.166).Thelattersystempropertiesarecalledemergentproperties(Nevo&Wade,2010).Thevalueofemergentpropertiesisdeterminedbyboththecomposingcomponentsandtheirrelationships(Nevo&Wade,2010).

MostOAdefinitionsintheinformationsystems(IS)literatureexplicitlyorimplicitlydefinesOAasanorganization’sabilitiestodetectandrespondtoopportunitiesandthreatswithspeed(e.g.,Lietal.,2008;Lu&Ramamurthy,2011;Sambamurthyetal.,2003;Tallon&Pinsonneault,2011).OneoftherepresentativeOAdefinitionsis“theabilitytodetectandrespondtoopportunitiesandthreatswithease,speed,anddexterity”(Tallon&Pinsonneault,2011,p.464).However,notheoreticaljustificationintheliteraturewasgivenonwhyOAisdefinedbythetwodimensions.InconjunctionwiththeintroductionoftheAMCframeworkbelow,weexplainwhyOAshouldincludethetwodimensionsofabilitiesandshouldanotherdimensionofabilitybeadded.SincethereisnosinglecomponentthatcandefineOA,weproposethatOAisanemergentproperty.Therefore,thevalueofOAcomesfromitscomposingcomponents.SinceOAisanemergentproperty,wecannotignoretheinteractioneffectofthecomposingcomponentsaccordingtothesystemstheory.Therefore,inthisstudy,wealsoexaminetheinteractioneffectoftheOA’scomposingcomponentsonOA.

Awareness-Motivation-Capability (AMC) FrameworkChen(1996)firstintroducedtheAMCframework.Competitivedynamicsresearchershaveusedittostudytheantecedentsofcompetitiveactions.TheAMCperspectivesuggeststhatthreebehavioraldriversinfluenceafirm’sdecisiontoact:awareness,motivation,andcapability(Chen,1996).ThedefinitionofOAandtheliteratureindicatethattheessenceofOAistorespondtoopportunitiesandthreatswithspeed.Respondingisanaction.Therefore,itisgoingtobeinfluencedbythethreebehavioral drivers as suggested by the AMC framework. The original AMC framework did notexplicitlydescribe thedynamicsof the threedrivers.ChenandMiller(2015)furtherrefinedtheAMCframework.Theyindicated thatforanorganization to takeaction, itmustbeawareof theopportunitiesandthreats,motivatedtoreact,andcapableofreacting.Itimpliesthatawarenessisthepremisetomotivation,thentocapabilitybuildingbeforetakingaction.BecauseOAisaboutanorganization’sabilitytotakeactionwithspeed,basedontheAMCframework,weproposethatOAshouldbedeterminedbytheawareness,motivation,andcapabilitydriversforaction.

Synthesizing the systems theoryandAMCframework,we theorize thatOA is anemergentsystempropertywhosevalueisdeterminedbyitscomposingcomponentsandtheirinteractions.Wefurtherproposethatdetectingabilityispartoftheawarenessdriverandrespondingabilityispartofthecapabilitydriver.Becauseawarenesscanhelpanorganizationtointentionallybuilditscapability,detectingabilitywillhaveadirect influenceonanorganization’scapabilitybuilding.Therefore,detectingabilitywillinfluenceanorganization’sabilitytotakeactionthroughcapability.ThecurrentOAdefinitiononlyemphasizesthefactorsfromthedetectingandrespondingdimensions.FromtheAMCperspective,anOAdefinitionshouldbebroadenedtoincludethecomponentfromthemotivationdriver.Therefore,weproposeaslightlymodifiedOAdefinitionas“theabilitytodetectandtobemotivatedtorightfullyrespondtoopportunitiesandthreatswithease,speed,anddexterity”.Becausebothawarenessandcapabilitydriversmeasureanorganization’sabilityandcanbebuiltbyITassets,


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thisstudyfocusesonhowfactorsinthesetwodriversinfluenceOA.Specifically,weproposethatBA-Useinanorganizationisoneofthefactorsthatinfluenceanorganization’sdetectingability.ITinfrastructureflexibilitycanimproveanorganization’scapabilitytorespond.WeproposethatBA-UseandIIFaretwodirectinfluentialfactorsonOA;BA-UsewillinfluenceIIFinthenewdata-drivenenvironment.BecauseBA-UseisoneofthedetectingfactorsofOAandIIFisoneoftherespondingfactorsofOA,theywillinteractwitheachothertoinfluenceOA.

RESEARCH MOdEL

Drawingonsystemstheory,AMCframework,andcurrentliteratureonBA,ITflexibility,OA,andcompetitiveperformance, this studyhasdevelopeda researchmodelas shown inFigure1.Thisresearchmodelisfurtherexplainedbelowintheliteraturereviewandhypothesisdevelopment.

LITERATURE REVIEw ANd HyPOTHESIS dEVELOPMENT

Organizational Agility (OA)OAhasbeendefinedintheinformationsystemsliteratureastheorganizational“abilitytodetectandrespondtoopportunitiesandthreatswithease,speed,anddexterity”(Tallon&Pinsonneault,2011,p.464).Theliteratureshowedthattwodimensionsdeterminedagility:theabilitytodetectopportunitiesandthreatsandtorespondwithspeed(Lietal.,2008;Lu&Ramamurthy,2011;Sambamurthyetal.,2003;Tallon&Pinsonneault,2011).Accordingtothesystemstheory,wetheorizethatOAisnotonlydeterminedby the twodimensionsbut also the interactionof the twodimensions.TheAMCframeworkindicatesthattotakeaction,anorganizationneedstobeawareofopportunities/threats,thenbemotivatedtotakeaction,andultimatelyhavethecapabilitytotakeaction.Therefore,capabilityisthepremisetorespond.

Sambamurthyetal.(Sambamurthyetal.,2003)theoreticallyarguedthatOAcomprisedthreeinterrelated capabilities: customer agility, partner agility, and operational agility. Furthermore,Sambamurthyetal.(Sambamurthyetal.,2003)definedcustomeragilityas“theco-optingofcustomers

Figure 1. Conceptual model


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intheexplorationandexploitationofopportunitiesforinnovationandcompetitiveactionmoves”(p.245).Theirdefinitionofcustomeragilitynarrowlyrelatestotheco-creationofnewideas,products,andservices.Weviewcustomeragilityinabroadersenseasanorganization’sabilitytosenseandrespondtocustomerchangesindemandforproductsandservices.BasedonVenkatraman&Henderson(1998)’sresearch,partneragilityisdefinedasthe“abilitytoleveragetheassets,knowledge,andcompetencies of suppliers, distributors, contract manufacturers, and logistics providers throughalliances,partnerships,andjointventures”(Sambamurthyetal.,2003,p.245).Operationalagilityconcernstheabilityofanorganization’soperationalprocessestoinnovateandcompetewithspeed,accuracy,andcost-effectiveness.TallonandPinsonneault(2011)devisedasetof indicatorswithwhichtomeasurethethreecapabilitiesofOA.Webasedourmeasurementindicatorson(Tallon&Pinsonneault,2011).

ResearchondynamiccapabilityfromthestrategicmanagementfieldprovidesthetheoreticalfoundationonwhyOA,whichhasbeenstudiedintheISfieldfordecades,isacriticalcompetitivefactorandasourceofcompetitiveadvantage(e.g.,Sambamurthyetal.,2003).ThesestudiesdemonstratetheroleofOAincreatingstrategicbusinessvalues.WechooseOAasthedependentvariableinthis study to illustrate the strategic values of the two studied IS/IT components by theoreticallyconnectingBA-UseandITinfrastructurewiththestrategicallyimportantorganizationalproperty:organizationalagility.

BA-Use and its Impact on OAInthisresearch,weadoptaninclusivedefinitionofBAasdefinedbyWatson(Watson,2009,p.491)“abroadcategoryofapplications,technologies,andprocessesforgathering,storing,accessing,andanalyzingdatatohelpbusinessusersmakebetterdecisions.”ThisdefinitionindicatesthatBAhasthreecomponents.Thefirstistheapplicationcomponent,whichanalyzesthedatatoproduceinsights/newknowledgesothatspecificbusinessprocessescanusetheinsights/newknowledgetoincreasebusinessvalue.Thesecondisthetechnologycomponent,whichhelpstocollect,store,anddeliverinformation.Thethirdisthehumanprocesscomponent,whichempowersuserstoeffectivelyandefficientlyretrievedataanddeliveritasinformation,thushelpingwiththegenerationofknowledgeandthemakingofbetterdecisions(Laursen&Thorlund,2010).Inthisstudy,BA-UsereferstotheextentusersinanorganizationemploythebroadBAapplicationandtechnologyforbetterdecisions.Thisincludestheuseofreportingandquerycapabilitiesforreports,applicationofstatisticalmethodsinanalyzingdata,andemploymentofdataminingcapabilityforidentifyingpatterns(Lee&Siau,2001).

ManypriorresearcheffortsonthebusinessvalueofBAhavefocusedprimarilyonexploratoryresearch:formaltheoryandliteraturereviews(Jourdanetal.,2008).Since2008,BAresearchhasblossomed. Chen et al. (2012) outlined the key emerging research areas. Although those areasarecritical inadvancingBA, they focuson theapplicationsand technologycomponentsofBA,includingtheareasof(big)dataanalytics,textanalytics,webanalytics,networkanalytics,andmobileanalytics.Sincethen,therehasbeenaconsiderableamountofresearchonspecificBAapplicationsand technologies. However, research from management and strategy perspectives, especially onthe business value of BA for organizational performance, is relatively underexplored. In the ITimpactliterature,findingsaremixedregardingtheimpactsofgeneralITonorganizationalagility.Forexample,Chakravartyetal.(2016)andLuandRamamurthy(2011)foundthatITcapabilitiespositivelyinfluenceorganizationalagility,whereasLiuetal.(2013)andSwaffordetal.(2008)foundthereisnodirectlinkbetweenITcapabilitiesandorganizationalagility.Ghasemaghaeietal.(2018)foundthepositiveimpactofdataanalyticscompetencyondecisionmakingperformance.Throughinterviewswiththestakeholdersintheminingindustry,Zeshanetal.(2019)foundthatBAbenefitstheindustrythroughimprovingefficiencyandproductivityamongothers.BAsystemshavebeenconsideredtobestrategicinformationtechnologiesinmanyorganizations.Nevertheless,howBAsystemscontribute toorganizationalcompetitivestrengthandhowBA-UseconvertsBAsystemsintoBAimpactsareunder-researchedsubjects in thecurrentBAliterature. Integrating the three


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mostpopularITbusinessvaluemodelsfrom(Schrven,2013;Soh&Markus,1995),Trieu(2016)proposedaBAbusinessvalueframeworkforclassifyingpriorBAresearch.Itwasaprocessmodelthatdescribedachainofoutcomesandtheirrelationships.ThechainincludesBAinvestment,BAassets,BAimpacts,andorganizationalperformance.Eachdownstreamchainelementwasavaluethatanupstreamchainelementcreatedthroughabusinessprocess.Forexample,BAinvestmentscreatedBAassetsthroughtheBAconversionprocess,andBAassetscreatedBAimpactsthroughtheBA-Useprocess(Trieu,2016).AccordingtoTrieu(2016),only3%ofpriorresearchinvestigatedBAimpactthroughBA-Use.ItfurtherdemonstratesBA-UsewasunderstudiedintheBAvalueliterature.

OurreviewofpriorstudiesrevealsthatmostofthepriorstudiesonBAvalueeitherwerenotempiricalstudiesorfocusedonBAapplicationsandtechnologies(e.g.,Abbasietal.,2012;Chaeetal.,2014;Huetal.,2012;Popovicetal.,2014).TherearesomerecentBAusestudiesthatinvestigateBAuseasadependentvariable(e.g.,Daradkeh,2019).FewofthepriorBAvaluestudiesempiricallyinvestigatedBAimpactsfromaBA-UseperspectiveandtreatedBA-Useasanindependentvariableforbusinessperformance(e.g.,Akteretal.,2016;Elbashiretal.,2008;Ghasemaghaeietal.,2017;Popovicetal.,2012;Sangari&Razmi,2015;Shollo&Galliers,2016).Elbashiretal.(2008)investigatedtheimpactofBA-Useonbusinessprocessperformanceandorganizationalperformance.ItwasoneofthepioneerstudiesthatempiricallyinvestigatedthevalueofBA-Use.However,Elbashiretal.(2008)measuredtheperceivedimpactofBA-Useonbusinessprocessesandorganizationalperformancewhenthesystemswerefirstimplemented.ItdidnotaddresstheimpactofhowtheBAsystemswereused.Ghasemaghaeietal.(2017)investigatedtheimpactoftheuseofdataanalyticsonOA.TheyfoundthatthelinkbetweentheuseofdataanalyticsandOAissignificantlymoderatedbythefitbetweentaskandtechnology,personandtechnology,anddataandtechnology.ThisfindingprovidesthebaseforustoinvestigateothermoderatingvariablesonthelinkbetweenBA-UseandOA.SomerecentempiricalstudiesthatinvestigatetheroleofBAsystemsfromthemanagerialperspectiveinclude(Popovicetal.,2012;Sangari&Razmi,2015;Shollo&Galliers,2016).However,thesestudiesdonotaddresshowcomplementaryresources,especiallyothertechnologicalresources,interactedwithBA-UseandthecontributionofBA-Usetoanorganization’scompetitiveperformance.ThelimitednumberofempiricalstudiesonBAbusinessvalue,especiallyonhowBA-UseconvertsBAsystemsintoBAimpacts,madeitchallengingformanagerstojustifytheirrequestsforresourcestoinitiateandimplementBAprojectsandtopromotetheuseofBAforbusinessoperationalefficiencyandorganizationalperformanceatthestrategiclevel.Therefore,althoughtherearenumerousBAstudies,thereisstillaneedforaconceptualfoundationthathelpstounderstandthevalueofBAsystems,toconnecttheoreticallyBA-Usewithbusinessprocessandorganizationalperformance,andtofindoutthefactorsthataffecttheimpactofBA-Useonorganizationalperformance(Ghasemaghaeietal.,2017).ThisisbecausepriorBAstudiesmainlyfocusedonwhatBAimpactswere,butdidnotaddressthequestionofhowBAsystemswereconvertedintoBAimpactsthroughBA-Use.Thisstudyisoneattempttobuildthefoundation.ItaimstoinvestigatethesignificanceofBAbytheoreticallyandempiricallydemonstratinghowandwhyBA-UsecanhelporganizationsandwhatotherorganizationalresourcesaffectthebenefitsofusingBAinorganizations.

ThepurposeofBAsystemsistogather,store,access,andanalyzedata,whichincludesdataaboutcustomers,partners,operations,andotherenvironmentchangeinformation.Fordecisionmaking,wearguethattheuseofBAinorganizationswillhelptoincreaseOAbyimprovinganorganization’sabilitytodetectthechangesreflectedinthedatainBAsystems.ThereisalsoempiricalevidenceofBA’scontributiontoOAincurrentISresearchonthetopic.Forexample,(Ghasemaghaeietal.,2017)directlyfoundthemoderatedimpactofBA-Useonorganizationalagility.Theconstructofinformationmanagementcapability(IMC)fromMithasetal.(2011)isencompassingandincludescapabilitiesthatBAprovides.Mithasetal.(2011)foundsignificantpositiveinfluencesofIMConthreeorganizationalcapabilities:performancemanagementcapability,customermanagementcapability,andprocessmanagementcapability,whichcanbemeasuredbyproposedOAofcustomer,partner,andoperation(Sambamurthyetal.,2003).


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StrategicITalignmentliteraturealsoprovidessupportforthepositiveeffectofBA-UseonOA.Forexample,studieshaveshownknowledgecreation,sharing,andusetobeenablersofstrategicITalignment(Preston&Karahanna,2009;Siauetal.2010,Nahetal.2005).DuetoknowledgesharingbetweenbusinessandITexecutives,anorganizationcanrespondquicklytochangesinmarketplacesand,thus,increaseanorganization’sagility.Knowledgecreation,sharing,andusearetheargumentsforthepositiveeffectofstrategicITalignmentonagility(Tallon&Pinsonneault,2011).SinceBA-Usecancreateanenvironmentforgenerating,sharing,andusingnewlyfound/createdknowledge,BA-UsecanhelpincreaseOA.Therefore,wepostulatethatBA-Usecanenhanceanorganization’sagilitytodetectchanges:

H1:BA-Usewillpositivelyinfluenceanorganization’sagility.

IIF and its Impact on OAFlexibilityisthedegreetowhichathingismalleable.Flexibilitywasviewedasoneofthefirm’scapabilitiesthatinfluenceafirm’sspeedtoactorrespond(e.g.,Tiwana&Konsvnski,2010).Studieshaveshownthatflexibilityisakeyantecedentofagilityinabusinesscontext(e.g.,Lietal.,2008;Tiwana&Konsvnski,2010).

ITinfrastructurewasconsistentlydefinedinliteratureasasetofsharedITresourcesthatareafoundationforenablingcommunicationacrossanorganizationandenablingpresentandfuturebusinessapplications(Byrd&Turner,2001;Duncan,1995).ITinfrastructuremayinclude(1)platformtechnology(e.g.,hardwareandoperatingsystems);(2)networkandtelecommunicationtechnologies;and(3)dataanddatamanagementsystems(Duncan,1995).IIFreferstothedegreetowhichthefirm’sITresourcesaremalleable(Duncan,1995).ThedefinitionofIIFfromByrd&Turner(2001)emphasizesITinfrastructure’sabilitytoeasilyandreadilysupportawidevarietyofhardware,software,andcommunicationtechnologies,todistributeinformationtoanywhereinsideanorganizationandbeyond,andtosupportthedesign,development,andimplementationofaheterogeneityofbusinessapplications.TherearefourkeycomponentsofIIF:connectivity,compatibility,modularity,andITpersonnelcompetency(Byrd&Turner,2001;Duncan,1995).Mostcommonlyacceptedtechnical(static)dimensionsofIIFareconnectivity,compatibility,andmodularity.

BecausecontemporaryorganizationsaremostlyIT-enabled,organizationalcapabilitiesareofteninseparablefromIT(Pavlou&ElSawy,2010).Today,organizationalactionsarerarelyexecutedwithoutinformationtechnology.Sambamurthyetal.(Sambamurthyetal.,2003)theoreticallyarguedthat thereshouldbeapositive relationshipbetween IIFandOA.TheAMCframeworksuggeststhattotakeaction,anorganizationmusthavethecapabilitytodoit inadditiontotheawarenessof opportunity and threat, and motivation to take action. Flexible IT infrastructure can help anorganizationquicklyandeconomicallyintegrateandreconfigureinternalandexternalIT-enabledresourcestorespondtoever-changingrequestsfromfunctionallinesofbusiness.Priorresearch(ElSawy&Pavlou,2008;Raietal.2006)alsoempiricallysupportstheviewofthepositiveroleofIIFinintegratingandreconfiguringinternalandexternalresourcestorespondtochanges.Therefore,wecanviewaflexibleITinfrastructureaspartoftheorganizationalcapabilitytorespondtochangesintheIT-enabledbusinessenvironment.Becausecapabilityisthedriverfortakingactionwithspeed,whichisOA’srespondingdimension,aflexibleITinfrastructurecanbenaturallyconnectedwiththerespondingdimensionofOA.

ScholarsinISfieldhavestudiedIIFasanindependentvariableaswellasamoderator(Byrd&Turner,2001;Tallon&Pinsonneault,2011;Tiwana&Konsvnski,2010).However,directinvestigationoftherelationshipbetweenIIFandOAisscarcealthoughthereisadirectlinkbetweenflexibilityandagilityingeneral(e.g.,Tiwana&Konsvnski,2010).WeonlyfoundafewinvestigationsofthiskindinISfield(Bhattetal.,2010;Tallon&Pinsonneault,2011).TheothersstudiedvariousaspectsofITinfrastructure,butnottheIIFonOA(e.g.,Leeetal.2015).Kumar(2004)proposedthatthereal


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valuesofIIFlieintheflexibleinteractionbetweenanITinfrastructureanditsorganizationalcontext.ThisstudyextendsthatpropositionbyspecifyingtheorganizationalcapabilitythatIIFcontributes.ThisstudyemphasizesthatIIFisoneoftwocontributingfactorsthatcanimproveOA.Basedonacasestudy,Richardsonetal.(Richardsonetal.,2014)proposedthatanITplatformbasedonopensourcetoolscouldleadtohigherlevelsofcustomer,operational,andpartneragility.TheopensourceITplatformimpliedbetterconnectivityandmorecompatibilityamongsubsystems.Therefore,thestudyof(Richardsonetal.,2014)canbeviewedassupportingthepositiveimpactofIIFonOA.

Synthesizingthesystemtheory,AMCframework,andpriorISresearch,weproposethatIIFisacriticalcontributortoanorganization’sagilitytorespondtochangesincompetitiveenvironmentswhetherthechangesoriginatefromcustomers,partners,oroperations.Therefore,theoreticalargumentsandISliteraturesuggestthefollowinghypothesis:

H2:ITinfrastructureflexibilitywillpositivelyinfluenceorganizationalagility.

The Relationship Between IIF and BA-UseTheAMCframework suggests that three forcesdriveanorganization to takeaction: awareness,motivation,andcapability.Itfurthertheorizesthattotakeanaction,anorganizationmustfirstbeawareofthechangesoftheenvironmentandwithinitsownoperationtobemotivatedtotakeaction,andthenitmusthaveorbuildtherequiredcapabilitybeforetakingaction(Chen,1996;Chen&Miller,2015).WediscussedabovethatBA-Usecancontributetoanorganization’sabilitytodetectchanges.Therefore,itcanhelpincreaseanorganization’sawareness.TheabovediscussionalsoshowsthatIIFisoneofthekeyorganizationalcapabilitiesintheIT-enabledbusinessenvironmentfororganizationstoactquicklyandeconomically.

Inaddition,BAisaninformationsystemthatisbuiltonanITinfrastructure.Thus,wecanconnectBA-UsefromtheawarenessforcewithIIFfromthecapabilityforcefororganization’sactions.Also,BA-Userequiresuserstobeabletoaccessavarietyofdatasourcesanddistributedatatodifferentusersanddatainterfaces,suchaswebbrowsersondesktopcomputers,smallscreensonmobiledevices,orsenddataproducedintheBAsystemstootherinformationsystems.Thereby,BA-UsedemandsaflexibleITinfrastructuretofacilitatetheintegrationofexistingandnewdatasourcesanddistributingthegeneratedinformationtouserswheneverandwhereveritisneeded.AflexibleITinfrastructurecanprovidegoodconnectionsandcompatibleresourcesamongsubsystems(Bharadwaj&Bendoly,2007).Insummary,increasedBA-UsewilldriveorganizationstobuildmoreflexibleIT.Therefore,weproposethatBA-UseinanorganizationdemandshigherIIF:

H3:BA-UsewillpositivelyinfluenceITinfrastructureflexibility.

Thesystemstheorysuggeststhatthevalueofanemergentpropertyofasystemisdeterminedbythevaluesofcomposingcomponentsandtheinteractionsofthecomposingcomponents.OAisanemergentpropertyofanorganization.Itsvalueisdeterminedbythevaluesofthecomposingcomponents from two dimensions: detecting and responding as well as the interactions of thesourcecomponents.WepostulatethatBA-Use,oneofthedetectingcomponents,andIIF,oneoftherespondingcomponents,willinteractwitheachothertoinfluencethevalueofOA.

FromtheAMCperspective,wehavetheorizedthatbothBA-UseandIIFmayhaveadirectpositiveinfluenceonOA.BasedontheaboveargumentregardingtherelationshipbetweenBA-UseandIIF,wedemonstratehowBA-UsemayalsopositivelyinfluenceITinfrastructureflexibility.Asaresult,wecantheoreticallyproposethattheeffectofBA-UseonOAmaybefacilitatedthroughIIF.Thus,wealsoproposethatIIFmediatestheeffectofBA-UseonOA.Therefore,ourfourthhypothesisis:

H4:BA-UseanditsimpactonOAismediatedbyITinfrastructureflexibility.


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Moderatedmediationstudiesshowthatthereisapossibilitythatanindependentvariablemayinteractwithitsmediatortoinfluencethedependentvariable(Preacheretal.,2007).Inthecurrentstudy,weproposedthatIIFmaybeamediatorfortheindependentvariableBA-UseandthedependentvariableOA.Therefore,fromthemoderatedmediationperspective,thereisapossibilitythatBA-UsemayinteractwithIIFtoinfluenceOA.Fromtheperspectiveofthesystemtheory,wealsoargueabovethatOAisanemergentpropertyofanorganization.ThevalueoftheemergentsystempropertyOAisdeterminedbythevaluesofthecomposingdimensionsandtheirinteractions.Therefore,thefactorsfromthetwocomposingdimensionsmayinteractwitheachothertoinfluencethevalueofOA.Whenstudyingtheimpactofbusinessanalyticsonorganizationalagility,fromthedynamiccapabilityperspective,Ghasemaghaeietal.(2017)arguedthatbothOAandbusinessanalyticsuseareafirm’sdynamiccapabilities.OAisahigherorderofdynamiccapabilityandbusinessanalyticsuseisalowerorderofdynamiccapability.TheyproposedthatbusinessanalyticsusewouldincreaseOA.Thefitperspectivesuggeststhatorganizationaloutcomesaretheresultsoffitbetweentwoormorefactors(Goodhue&Thompson,1995).Fromthefitperspective,theyalsotheorizedthatthefitbetweenanalyticstoolsanddata,tasks,andpeoplemayinfluencethedirectrelationshipbetweenbusinessanalyticsuseandOA.Therefore,thefitvariablemaymoderatetherelationshipbetweenbusinessanalyticsandOA.TheirdatasupportthemoderationeffectofthefitvariablesontherelationshipbetweenbusinessanalyticsuseandOA.WearguethatIIFisoneoftheorganization’sITresources,whichhasacloserelationshipwithBA-Use,whichisataskperformedbybusinessusers.Thus,thefitbetweenthesetwowillhaveaconsequenceonOA.Therefore,combiningthethreeperspectivesofmoderatedmediation,systemtheory,andfit,wepostulatethattheBA-UseandIIFmayinteractwitheachothertoinfluencetheoutcomeofOA.Basedontheargumentsabove,weproposethefollowinghypothesisfortheinteractiontermbetweenBA-UseandIIFonOA:

H5:ITinfrastructureflexibilitymoderatestherelationshipbetweenBA-UseandOrganizationalAgility.

RESEARCH METHOd

Survey Instrument developmentAcross-sectionalsurveystudywasemployedtotesttheresearchmodel.Thisstudyusestheexistingsurveyinstrumentswheneveritispossible.Weexaminedtheexistingmeasurementscalesaccordingtowellrecognizedandstandardscaledevelopmentprocedure,suchastheprocedureproposedby(Churchill, 1979). The instrument for BA-Use was developed in this study following the sameprocedureproposedby(Churchill,1979).Allotherinstrumentsusedinthisstudyareadaptedfromexistingmeasures.AppendixAliststhesurveyinstrumentsinthisstudy.

Scale for BA-UseBurton-JonesandStraub(2006)proposedthatsystemusageisanactivitythatinvolvesthreeelements:auser,asystem,andatask.Theyrecommendedconceptualizingsystemusagewithtwostages.Thefirstisthedefinitionstageandthesecondistheselectstage.AccordingtoBurton-JonesandStraub(Burton-Jones&Straub,2006),thesystemusagecanbecapturedbytwosub-constructs:cognitiveabsorption that represents the extent to which a user is absorbed, and deep structure usage thatrepresentstheextenttowhichfeaturesinthesystemthatrelatetothecoreaspectsofthetaskareused(Burton-Jones&Straub,2006,p.236).Theysuggestthattheselectstagesshouldincludetwosteps:Step1: Selecttheelementsofusagethataremostrelevanttotheresearchmodelandcontext.Step2: Selectmeasuresforthechosenelementsthataretiedtootherconstructsinthenomologicalnetwork.

Webelievethatcognitiveabsorptionismorerelatedtoindividualtaskperformancethantheextenttowhichasystemisusedbyauser.WeareinterestedinwhichBAsystemcomponentsa


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userisusingsothatwecangaugetheextenttowhichaBAsystemisusedinanorganization.Thus,ourmeasurementindicatorsforBA-Usearemostlyinthedeepstructureusagecategory.WefirstselectedfeaturesofBAinformationsystemsthatwouldbeusedbyusers.ThenwecombinedtheselectedfeatureswithcorrespondingtaskstomeasuretheextentofBA-Useinanorganization.Theselectedfeatureswerebasedonthecommonfunctionalitiesoutlinedbyindustrialgroups,suchasthefunctionalitiespresentedinthereportprovidedbyThe Data Warehousing Institute(Eckerson,2009).WefurtherrefinedthefeaturesandtheinstrumentwithhelpfromseveralacademicresearcherswhohaddonevariousstudiesonBAandtaughtBAclassesinuniversities.Aconveniencesampleof22businessandITstaff fromvarious industriesand threeacademicISresearcherspre-testedthequestionnaire.Then,wefurtherrefinedthemeasurementscalebasedontheirsuggestions.Wealsousedpilotstudytoensurethesurveywebsitewasfunctioningasexpected.ThemeasurementreliabilityandvalidityofthisinstrumentarediscussedintheMeasurementAnalysisResultssection.

Scale for IIFIIFhasbeenmeasuredasasecondordervariableintheliterature.TherearethreedimensionsinIIF.ThethreedimensionsofIIFareconnectivity,hardwarecompatibility,andITmodularity.TheIIF instrumentwas adapted fromDuncan (1995),Tallon&Pinsonneault (2011), andTiwana&Konsvnski(2010).

Scale for OASambamurthyetal.(2003)firsttheoreticallyproposedthattherearethreedimensionsofOA.TallonandPinsonneault(2011)devisedasetofeightindicatorstoassesstheOA.TheOAinstrumentwasadaptedfromTallon&Pinsonneault(2011)’ssurveyinstrument.

Participants and data CollectionThepopulationofinterestforthisstudyisbusinessleaderswhosecompaniesareusingBA.18,000seniorbusinessleaderswereselectedfromU.S.companiesthathadatleast20milliondollarsinannualrevenue.TheseseniorbusinessleadersincludeCEOs,CFOs,CTOs,CIOs,VPsforbusinessfunctions,andseniorbusinessdirectorsormanagers.Wereceivedemailaddressesalongwithotherinformation, suchas the titleofacontact inacompany, thecompanyname,and thecompany’sannualrevenuethroughacommercialdirectmarketingcompany,ConsumerBase,LLC.AllemailsfromConsumerBaseare100%opt-inand100%guaranteeddeliverablewithin30daysofpurchase.

Qualtrics.comwasusedtohostoursurvey,whichisaleadingweb-basedmarketingresearchprovider.Theinitialinvitationemailandseveralroundsofremindingemailsweresentouttotheselectedexecutives.Thedatacollectionperiodwasonemonth.

Tables1and2displaytheindustryandcompanyinformationoftheparticipantsinthisstudy.

Sample Size and data Analysis TechniqueAtotalof237completedentrieswerecollectedduringthefourweekdatacollectionperiod.Twenty-onecaseswereeliminatedfromthesamplebecausetheywereeitherincompleteresponsesortheorganizationswerenotusinganyBAsystem.Onesubjectselected”1”andanothersubjectselected”7”forallthequestions.Thesetwosubjectswereremovedfromthesampletoo.Ourfinalsamplesizeinthisstudyis214.

PartialLeastSquares(PLS)wasemployedinthisstudytoassessthemeasurementmodelandthestructuralmodel.PLSisappropriateforthisstudybecauseitisvariance-basedandplacesminimalrestrictionsonmeasurementscales,samplesizes,andresidualdistributions(Chinetal.,2003).WechosePLStechniqueoverthecovariance-basedSEMtechniquebecauseofthesample-sizerequirementforeachtechnique.Kline(2005)suggestedthatthedesiredsamplesizewouldbe20timesthenumberoffreeparametersforcovariance-basedSEManalysis.Thesamplesizerequiredforourmodelcouldreachmorethan700(7firstorderconstructs,2secondorderconstructs,andatotalof35indicators)


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ifthecovariance-basedSEMtechniquewasused.TheruleofthumbforminimumsamplesizeforthePLStechniqueis10timesthelargestnumberofstructuralpathsdirectedataparticularconstructinthestructuremodel(Hairetal.,2013).Thelargestnumberofstructuralpathsintheresearchmodelis3.Therefore,theminimumsamplesizeforthisstudyis30usingPLS.Followingthesuggestionsfrom(Faulelal.,2007),aprioripoweranalysiswasconductedtodeterminethesamplesizeforthisstudywiththefollowingcriteria:effectsizeof0.30(medium),alphaof0.05,andpowerof0.95.Thecalculatedsamplesizeis134fortwo-tailt-tests.Thesamplefromoursurveyis214andissufficientforthePLSanalysis.SmartPLS,aPLSsoftwaredevelopedby(Ringleetal.,2005),wasusedforthePLSanalysis.

Table 1. Industries represented by participants

Industry Number of Subjects

Percentage

Basicmaterial(basicresources,chemicals) 5 2.3%

Consumergoods(auto&parts,foodbeverage,personal&householdgoods) 22 10.3%

Consumerservices(media,retail,travel&leisure) 7 3.3%

Education(K-12andhighereducation) 4 1.9%

Financials(banks,financialservices,insurances) 31 14.5%

Government(federalandlocalgovernments) 7 3.3%

Healthcare 39 18.2%

Industrials(construction&materials,industrialgoods&services) 22 10.3%

Oil&Gas 4 1.9%

Technology(software&computerservices,technologyhardware&equipment) 46 21.5%

Telecommunications 3 1.4%

Utilities 1 0.5%

Professionalservices 18 8.4%

Manufacturing 5 2.3%

Total 214 100%

Table 2. Number of employees in participants’ companies

Number of employees Number of Subjects Percentage

Missingdata 2 0.9%

1–49 26 12.1%

50–499 53 24.8%

500orMore 133 62.1%

Total 214 100%


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MEASUREMENT ANALySIS RESULTS

Common Method Bias and Multicollinearity CheckCommonmethodbiasisapotentialprobleminresearch,especiallyinsurveyresearch(Podsakoffetal.2003).Severalposthocstatisticalanalysescanhelptodetermineifthereisanexceedinglycommonmethodvarianceindata.Partialcorrelationmethodisamethodtocheckcommonmethodbiasindata(Podsakoffetal.2003).FollowingthepracticeofPavlou&EISawy(2006)forthismethod,weaddedthehighestfactorfromtheprincipalcomponentanalysistothePLSmodelasacontrolvariableondependentvariables.Thisfactordidnotproduceasignificantchangeinexplainedvarianceinthedependentvariablesinthemodel.Correlationanalysiscanalsohelptodetermineifthereisanexcessivecommonmethodvarianceindata(Bagozzietal.,1991).Table3presentsthecorrelationmatrixofthemainconstructsaswellastheaveragevarianceextracted(AVE)inthisstudy.Bagozzietal.(1991)suggestedthatacorrelationthatis>0.9wouldindicateevidenceofcommonmethodbias.Thehighestcorrelationamongthesecondorderconstructsis0.67andthatisbetweenoperationalagilityandcustomeragility,whichislessthanthethresholdssuggestedbyBagozzietal.(1991).Thisanalysisalsosuggestsnoexcessivecommonmethodbiasinthedata.ThethirdmethodweemployedtotestifthereisacommonmethodbiasinthedataisHarman’sone-factortest.WeperformedHarman’sone-factortestinSPSS.Thetotalvarianceexplainedbythesinglefactorfromthetestis30%.Itindicatesthereisnoexcessivecommonmethodbiasexistinginthedata.

Multicollinearitymayalsobeanissuewhenasurveymethodisusedforaresearch.Weobtainedthevarianceinflatedfactor(VIF)forBA-UseandIIFfromSmartPLS3.TheVIFvalueforBA-Useis1.15andIIFis1.16,indicatingverylowmulticollinearityissue.

Measurement Reliability and ValidityWeusedSmartPLS(Ringleetal.,2005)toperformtheconfirmatoryfactoranalysis.Basedontheloadingscoresoftheindicators,wedroppedseveralindicatorsfromtheIIFscale.TheCronbach’sAlphavaluesforallconstructs,exceptforPartnerAgility,areabovetherecommendedthresholdvalueof0.7(Kline,2005;Nunnally&Berbstein,1994).TheCronbach’sAlphaforPartnerAgilityis0.61,whichisstillacceptable(Robinsonetal.,1991).Furthermore,theconstruct,aswellasallotherconstructs,hasacompositereliabilityscoreabove0.83.Thecompositereliabilityscoresare

Table 3. Constructs’ Average Variance Extracted (AVE) and correlation

AVE SQRT of AVE

Construct Correlation

BA-Use C-agility O-Agility P-Agility IT Connectivity

IT Hardware Compatibility

IT Modularity

0.61 0.78 BA-Use 1.00

0.66 0.81 C-Agility 0.28 1.00

0.66 0.81 O-Agility 0.24 0.67 1.00

0.72 0.85 P-Agility 0.27 0.58 0.65 1.00

0.61 0.78 ITConnectivity 0.26 0.43 0.31 0.32 1.00

0.65 0.80 ITHardwarecompatibility

0.28 0.40 0.36 0.37 0.72 1.00

0.71 0.84 ITModularity 0.37 0.28 0.28 0.29 0.58 0.52 1.00

C-Agility=Customer AgilityO-Agility=Operational AgilityP-Agility=Partner Agility


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abovetherecommendedthresholdvalueof0.7(Nunnally&Berbstein,1994).Thesescoresindicatethatmeasurementscalesarereliable.

Allthekeptindicatorshaveloadingscoresof0.6orhigherontheircorrespondingconstructsandhaveatleastonemagnitudelowerscoresonotherconstructs.Table3presentstheAVEvaluesandconstructscorrelationsforallthefirstorderconstructsintheproposedmodelafterwedroppedtheindicatorsthatcausediscriminantandconvergentvalidityissuesfortheirtheoreticalconstructs.

FromTable3,wecanseethatthesquarerootsofallconstructs’AVEarelargerthananycorrelationamonganypairoflatentconstructs.

Fromtheanalysisabove,wecanassumethatthemodifiedmeasurementscaleforallfirst-orderconstructsinthisstudyhasdiscriminantandconvergentvalidity.Therefore,wecancontinuewithourmodelassessments.

STRUCTURAL MOdEL ANALySIS RESULTS ANd dISCUSSIONS

ThefullstructuralmodelwasassessedusingSmartPLS(Ringleetal.,2005).Themodelwasestimatedwithcompanysizeandindustryascontrolvariables.Companysizewasoperationalizedbyannualrevenueandemployeenumber.WefirstperformedPLSAlgorithmtestsinSmartPLStoobtainthepathcoefficients.Then,thesignificanceofdirectandindirectimpactsofvariablesinthemodelweretestedusingbootstrappingwith5000resamplesanda95%confidenceintervalassuggestedbyVanceetal.(Vanceetal.,2015)and“bias-correctedbootstrapthathavetheleastbiasedconfidenceintervals,greatestpowertodetectnonzeroeffectsandcontrasts,andthemostaccurateoverallTypeIerror”assuggestedbyChinetal.(Chin,2010).HenselerandChin(2010)recommendedatwo-stageapproachtoinvestigatetheinteractioneffectofamodel.Wefollowedtherecommendationtoinvestigatetheinteractioninthisresearch.TheresultedpathcoefficientsandsignificanceswithcontrolledcompanysizeandindustryaredisplayedinFigure2andTable4.Thenumberintheendogenousvariables,suchasITInfrastructureFlexibilityandOrganizationalAgilityinFigure2,istheR2value.

BA-Use and OAFromthesystemstheoryperspective,wetheorizethatOAisanemergentsystemproperty.OAmeasureshowquicklyanorganizationcanactinabusinessenvironment.AccordingtotheAMCframework,thethreedriversofactionareawareness,motivation,andcapability.Therefore,theantecedentsofOA,measuringtheabilityofanorganizationtoquicklyact,shouldcomefromthreedimensions:(1)detectingchangesinbusinessenvironment,(2)respondingtothechanges,and(3)beingmotivatedtotakeactions.BA-Usehelpsdecisionmakersgainmoreinsightsaboutchangesintheiroperationsandexternalenvironments.Thiscontributestotheawarenessbehaviordriverthathelpsdetermineanorganization’sactions,whichmatcheswellwiththedetectingchangesdimensionofOA.Therefore,thisstudyproposesthatBA-UseisoneofthecontributingcomponentsonthedetectingdimensionofOA.ThePLSresultsinFigure2showthatthepathcoefficientforthedirectimpactofBA-UseonOAis0.175andthatissignificantatthe0.05significantlevel(p = .017).Hypothesis1(H1)issupportedinthisstudy.

SohandMarkus(1995)definedtheITimpactasastateanorganizationachieves.DuetotheIT asset, new products/services are created, business processes have been redesigned for betterperformance,anddecision-makingandcoordinationandflexibilityhavebeenimproved.WearguethatalthoughSohandMarkus(1995)’sdefinitionofITimpactiscomprehensive,animportantaspectofoutcomes,agility,shouldbeincluded.AsdiscussedintheliteraturereviewsectionofIIF,agilityisdifferentfromflexibility.Flexibilitydoesnotnecessarilyleadtoagility(Lietal.,2008;Tiwana&Konsvnski,2010).ThepurposeofcoordinationandflexibilityistoachieveOA.Therefore,wedefineITimpactas“astateanorganizationachieves,inwhichduetotheITasset,newproducts/servicesarecreated,businessprocesseshavebeenredesignedforbetterperformance,decision-makingandagilityhavebeenimproved”.Aswediscussedintheliteraturereviewsection,althoughthereare


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priorstudiesthatempiricallyinvestigatedtheBAimpacts(e.g.,Elbashiretal.,2008),Trieu(Trieu,2016)andourownliteraturereviewfoundonlyoneempiricalstudy(Ghasemaghaeietal.,2017)thatexaminedtherelationshipbetweenBAinvestmentandOAthroughBA-Use,andnoempiricalstudyonBAenabledOAandorganizationalperformancethroughacompetitiveprocess.ThisstudyprovidesthesecondempiricalsupportthatBAassetcanhelpbuildOAthroughBA-Use.

AlthoughTrieu(Trieu,2016)indicatedthatthereisnopriorempiricalstudythatexaminedtherelationshipbetweenBAimpactsandimprovedorganizationalperformancethroughacompetitiveprocess,therearesomestudiesthatbrieflymentionedtherelationship(e.g.,Lauetal.,2012).WespeculatethatoneofthereasonsforthelackofsuchstudiesintheliteraturemaybeduetothelackofempiricalstudiesthatshowBA-UseenhancedOA(i.e.,aBAimpact).Sambamurthyetal.(2003)theoreticallyarguedthatOAisacriticalcontributortobusinesscompetitiveadvantage.ThestrategicandITbusinessvalueliteraturehastheoreticallyjustifiedandempiricallydemonstratedthedirectrelationshipbetweencompetitiveadvantageandorganizationalperformance(e.g.,Vannoy&Salam,2010).Fromthisdiscussion,thereisaclearconnectionfromBA-UseenhancedOAtocompetitive

Figure 2. Test Results of Full Structural Model NOTE: The result for H4 regarding the mediation effect of IIF is not shown in this figure.

Table 4. Summarizes the results of hypothesis testing

Hypothesis Independent Variable

Mediator Moderator Dependent Variable

Path Coefficient

Hypothesis Supported?

H1 BA-Use N/A N/A OA 0.175* Yes

H2 IIF N/A N/A OA 0.368** Yes

H3 BA-Use N/A N/A IIF 0.357** Yes

H4 BA-Use IIF N/A OA 0.132(a)** Yes

H5 BA-Use N/A IIF OA 0.004(b)NS NO

N/A=Not ApplicableNS=Not Significanta=Total Indirect Path Coefficientb=Moderation Path Coefficient


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advantage,thentoorganizationalperformance.ThelackofstudiesexaminingtherelationshipbetweenBAimpactsandimprovedorganizationalperformancethroughacompetitiveprocessispartiallyduetothelackofstudiesinvestigatingBA-Useenhancedcompetitiveimpacts,suchasBA-UseenhancedOA.ThisstudyhelpstoinitiatetheresearchthatinvestigatestheBAvaluethroughthecompetitiveprocess.

With the theoretical justificationandempirical support from this study,wedemonstrate thestrategic value of BA-Use and argue that BA-Use should be treated as a critical component ofan organization because of its contribution to OA, a key contributor to competitive advantage(Sambamurthyetal.,2003;Tallon&Pinsonneault,2011).

IIF and OAIIFhasbeenextensivelystudiedinISresearchasanindependentvariable(Kumar,2004;Sambamurthyetal.,2003;Tiwana&Konsvnski,2010)aswellasamoderator(Tallon&Pinsonneault,2011).Bhattetal.(2010)isoneofthefewstudiesthatinvestigatedtheIIFandorganizationalresponsiveness,whichweviewasoneantecedentdimensionofOA.TheAMCframeworkshowsthattotakeactions,organizationsnotonlyneedtobeawareofopportunitiesandthreatsbutalsoneedthecapabilityforquickactions.TheoriesandempiricalstudiessuggestthatIIFcanhelpincreasetheorganizationalcapabilitytotakecompetitiveactionsbyenablingorganizationstoquicklyintegrateandreconfigureits internalandexternal resources(e.g.,EISawy&Pavlou,2008;Pavlou&ElSawy,2010;Raietal.2006;Sambamurthyetal.,2003).ThistheoreticallysuggeststhatIIFisacriticalpartofanorganization’scapabilityintoday’sIT-enabledorganizations.Thisstatementisempiricallysupportedbythedatainthisstudy.ThePLStestresultsinFigure2showthatthereisasignificantimpactofIIFonOA:thepathcoefficientis0.368,whichissignificantatthe0.01significantlevel(p<0.01).

Together,withthefindingontherelationshipbetweenBA-UseandOA,thisstudylendssupporttotheclaimthatITstillmatters(Kumar,2004).AlthoughsomeITcomponentsmaybecommoditizedandnotscarceanymore,theIIFisnotjustasimplecombinationofthosecomponents.ITinfrastructureisnotjustablackbox.ThedimensionsofIIFclearlyshowthatmanycharacteristicsofIIFcannotbebought.TheyneedtobecarefullycultivatedsothatotherorganizationalcapabilitiescanbenefitfromaflexibleITinfrastructure.ThissuggeststhataflexibleITinfrastructureisastrategicresourcethatcanhelpimproveanorganization’sbusinessperformancebyenhancingitsOA.

BA-UseandIIFcanexplain22%ofthevarianceinOA.AlthoughtheexplainedvarianceofOAisnotveryhigh,theimpactofBA-UseandIIFissignificantonOA.ThesefindingsareinlinewiththeexpectationthatOAisacomplexemergentsystempropertywhosevalueisinfluencedbymany factors from the twodimensions,detectingand responding,which include factors suchasIT,organizational,management,businessprocess,andothers.BA-Useisoneofthefactorsinthesensing/directingdimensionandIIFisoneofthefactorsintherespondingdimension.ThefindingsofthisstudyshowtheimportantrolesofBAandITinfrastructureinenablinganagileorganization.

Relationships Between BA-Use and IIF—Mediation and Moderation AnalysesBAsystemsareIT-enabledinformationsystems.OneofthekeysuccessfactorsofBAsystemsisitsabilitytoaccessandintegrateavarietyofdatasources,thenanalyzingtheintegrateddatatoproduceinsightsthatcanhelpdecisionmakersformbetterdecisions.Therefore,themoreBAisusedinanorganization,themoreitdemandsaflexibleITinfrastructuretohelptoquicklyintegrateheterogeneousdatasources.AnotherkeyfactorforthesuccessofBAsystemsisitsabilitytoshareresultswithwhoeverneedsthemanddeliverresultstowherevertheyareneeded.So,BA-UsedemandsamoreflexibleITinfrastructuretobebuilttofacilitatetheaccuratedeliveryofinformationtodecisionmakerswhereitisneededandwhenitisneeded.ThesetwodemandsfromBA-UsemayinfluenceanITdepartment’sfocusonbuildingamoreflexibleITinfrastructure,andtheresultsofthisstudysupportthisproposition.ThedirecteffectofBA-UseonIIFissignificant:thecoefficientis0.357,whichissignificantat0.01level(p<0.01).Thehypothesis3(H3)inthisstudyisthefirstpropositioninthe


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literaturethatconnectsIIFwithBA-Use.Althoughonly13%ofvarianceinIIFcanbeexplainedbyBA-Use,theimpactofBA-UseonIIFissignificant.ThesmallexplainedvarianceofIIFbyBA-UseisunderstandablesinceIIFisaffectedbymanyotherfactorsbecauseanorganization’sIIFisnotjustresponsivetoBA-Use.ThisfindingshowsthatflexibleITinfrastructureisnotonlyakeycontributorforOAbutalsoanimportanthelperforBA-Use.ChenandSiau(Chen&Siau,2012)testedtheimpactofIIFonBA-Use.TheresultsofthatstudyalsoshowthesignificantimpactofIIFonBA-Use.TheseresultsdemonstrateamutualinfluencebetweenIIFandBA-Use.ItisimpossibletodeterminethenatureofthereciprocalrelationshipbetweenIIFandBA-Usewiththecross-sectionaldatafromthisstudy.AlongitudinalstudywillbeneededtofindhowIIFandBA-Useinfluenceeachother.Butthemediatingeffectmayshedsomelightonthenature,whichisdiscussedbelow.

OurresearchmodelimpliesthatthereisamediationeffectofIIFontherelationshipbetweenBA-UseandOA.WealsoprovideatheoreticalexplanationonhowIIFmaymediatetheBA-UseandOArelationship.ThebootstrapmethoddevelopedbyPreacherandHayes(2008)wasimplementedinSmartPLS3.0fortestingmediation.BecausethedirecteffectofBA-UseonOAwassignificant,weaddedthemediatingvariableIIFandtestedthemodelusingthebootstrappingmethodinSmartPLS3.0toobtaintheindirecteffectofBA-UseonOAthroughIIF.ThesummaryresultsofmediationtestsarepresentedinTable5.ThereisasignificantindirecteffectofBA-UseonOAthroughIIF:theindirectpathcoefficientis0.13,whichissignificant(p<0.01).AccordingtoHairetal.(2014),iftheindirecteffectissignificant,theVarianceAccountedFor(VAF)shouldbecalculatedtodeterminethestrengthofthemediation.VAFiscalculatedusingthisformula:VAF=indirecteffect/totaleffect*100(Hairetal.,2014).Accordingto(Hairetal.,2014),aVAFvalueofgreaterthan80%isfullmediation,avaluebetween20%and80%ispartialmediation,andavaluelessthan20%indicatesnomediation.TheVAFforthemediationeffectofIIFonBA-UseandOAis43%.TheresultindicatesthatIIFonlypartiallymediatestheeffectofBA-UseonOA.Aspartofapost-testprocess,wealsoexaminedthemediationeffectofBA-UseonIIFandOA.TheindirecteffectofIIFonOAthroughBA-Useisinsignificant:theindirectpathcoefficientis0.061,p=0.058.ItindicatesthereisnomediatingeffectthroughBA-Use.ThecalculatedVAF,14.2%,alsoindicatesthereisnomediatingeffectofBA-UseonIIFandOA.BA-UseisoneoftheOA’sdetectingfactorsandIIFisoneoftherespondingfactors.ThesetestresultsindicatethatthetwodimensionsofOAmayexertmajorinfluencesonOAontheirown,butnotnecessarilythrougheachother.However,thepartialmediatingeffectofIIFontherelationshipbetweenBA-UseandOAindicatesthatBA-Use’seffectonOAmaybepartlyduetotheimprovedIIF.ThismayindicatethatBA-UseencouragesorganizationstoimprovetheirIIF.ThisresultalsosupportstheawarenessdriverisapremisetothecapabilitydriverintheAMCframeworkandprovidesthefirstempiricallinksamongtheactionenablersoftheAMCframework.

Fromtheperspectivesofmoderatedmediation,fittheory,andsystemtheory,wetheorizedthattheinteractiontermbetweenBA-UseandIIFmayinfluencetheorganizationaloutcomeOA.TheproposalsuggestsapossiblemoderationeffectofIIFontherelationshipbetweenBA-UseandOA.WetestedtheinteractionrelationshipinSmartPLS3.0.ThesummaryresultsarepresentedinTable6.

Following the recommendations from (Henseler & Chin, 2010), we tested the moderationeffectusingatwo-stagecalculationmethod,whichfirstcalculatesthelatentvariablescoresofthelatentpredictor(IIF)andlatentmoderatorvariable(BA-Use)fromthemaineffectmodelwithoutthe interaction variable. Then, these latent variable scores are used to calculate the interactionvariablebymultiplyingthescoresofthepredictorandmoderator.Then,theeffectsonOAoftheinteractionvariable,thepredictor,and,themoderatorareanalyzed(Hairetal.,2014;Ringleetal.,2005).Althoughtheoretically,thereisapossiblemoderatingeffectofIIFontheBA-UseandOArelationship,thedatafromthisstudydoesnotsupporttheclaimwiththetwo-stagemethod.OnepossibleexplanationmaybethatoursurveyinstrumentonlyincludedthreestaticcomponentsofIIF:connectivity,compatibility,andmodularity.ThedynamiccomponentofIIF:ITpersonnelcompetency(Byrd&Turner,2001;Duncan,1995)wasnotincluded.TheBA-Use’simpactonOAmaybemoresensitivetoITpersonnelcompetencysinceBA-UseinvolveswithnewITresourcesandpotentially


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newprocedures.ITpersonnelcompetencyhaspotentialtoimproveusers’effectivenessandefficiencytouseBAforobtainingnewdata,analyzingthedatatoproduceusefulinformation,andhelpingthemmakebetterdecisionsregardinghowtorespondtoopportunitiesandthreats.Futurestudiesmayfurthertestthisassumptionwhendataisavailable.

RESEARCH CONTRIBUTIONS, LIMITATIONS, ANd FUTURE RESEARCH

Theoretical and Conceptual ImplicationsThis study is one of the few empirical studies that investigate the importance of BA from theperspectiveofBA-Use.ItemployssoundtheoreticallensestopositthatITandIScomponentscanbekey contributors toOA.Moreover, it uses empirical tests tovalidate thosepropositions.Thetheoreticalcontributionsofthisresearcharemanifold.

First,itisthefirststudythatutilizetheAMCperspectivetotheoreticallyjustifytheproposalthattheessentialdimensionsofOAincludedetectingandresponding.WealsoproposethepotentialmodificationoftheOAdefinitiontoincludethemotivationcomponent.FuturestudiesmaytesttheinfluenceonOAwithfactorsfromthemotivationdriverofanaction.ThroughthelensofOA,wealsotheoreticallyproposedandempiricallytestedtheeffectofBA-UseonOA.ThisisalsooneoftheveryfirstempiricalstudiestoinvestigatetheimpactofBA-UseonOA.ItprovidesatheoreticalfoundationfortheadvancementofBA’simportance.ThistheoreticalfoundationshowswhyorganizationsneedtobeBA-based.ThisresearchencouragesmoreempiricalresearchonthesignificanceofBA,especiallythestrategicimpactsofBA.TheoriginaldefinitionofITandBAimpactsincludetheoutcomesof(1)newproducts/services,(2)redesignedbusinessprocesses,(3)betterdecision-making,and(4)improvedcoordinationandflexibility(Soh&Markus,1995).WeproposethatthedefinitionofITimpactmayberedefinedbychangingthefourthelement:improvedcoordinationandflexibilitytoIT-enabledOA.Thereasonsfortheproposalinclude(1)OAistheultimategoalofcoordinationandflexibility.Therefore,OAreflectsoncoordinationandflexibility;(2)theothercomponentsofthe

Table 5. Mediation test results

Effects Path Path coefficient Mediator p-value VAF Mediating

effect

Direct BA-Use→OA 0.175* N/A .017 N/A N/A

Indirect BA-Use→IIF→OA 0.132** IIF <.01 43% Partial

Direct IIF→OA 0.368** N/A <.01 N/A N/A

Indirect IIF→BA-Use→OA 0.061NS BA-Use .058 14% No

N/A=Not ApplicableNS=Not Significant**Significant at p < .01*Significant at p < .05

Table 6. Moderation test results

Independent Variable

Dependent Variable Moderator Moderating

Path Coefficient p-value Moderating effect

BA-Use OA IIF 0.004NS 0.918 NO


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definitionconceptuallydefinehigherlevelsofbusinessgoals.Therefore,itisappropriatetousethemorestrategicconceptinthedefinition.

Second,followingtheguidelinesfordevelopingameasurementforsystemusage,wedevelopeda survey instrument for BA-Use that measured the user’s activity with different features of BAsystems.Weselectedthefeaturesaccordingtotherecommendationsofindustryexpertsandacademicresearchers.Thefinalinstrumentisvalid,reliable,andreadyforuseinfutureresearch.

Third, by theorizing that IIF couldhelp the respondingdimensionofOA,we suggested analternativewayofviewingITinfrastructureasastrategiccomponentfororganizations.ThroughthelensesofOAandtheAMCframework,weassertedthatflexibleITinfrastructurewasanessentialpartofanorganization’srespondingcapability.Fromthisstudy,itisclearthatIIFisamajorantecedenttoOAintoday’sIT-enabledorganizations.SinceOAhasadirectimpactonorganizationalperformance(Sambamurthyetal.,2003),itcanbearguedthatIIFhasstrategicimplicationsfororganizations.

Fourth, thefindingsfromthisstudysuggested thatBA-Usemayencourageorganizations toimprovetheirIIFbecauseBA-UsehasasignificantimpactonIIFandBA-Use’simpactonOAmaybepartiallyduetoIIF.TheresultsfurthershowthatthereisamutualinfluenceeffectbetweenBA-UseandIIFalthough,withthecross-sectionalsurveydata,wecannotmakeanyconclusionregardingthe nature of the reciprocal relationship between BA-Use and IIF and how they influence eachother.ThemediatingeffectofIIFalsoshowstheimportanceofIIFintoday’sIT-enabledoperationenvironment.ThenewlyfoundrelationshipcouldhelprenewtheinterestinstudyingtheinfluencesofITinfrastructureonbusinessorganizationsandnewtechnologicalphenomena.ItseemsthatMISresearchershaveignoredthetopicofITinfrastructureinthelastdecadealthoughtheperiodhaswitnessedrapidtechnologicaladvancementsandchanges.

Fifth,althoughthedatadoesnotsupportourmoderatingeffectsofIIFontheBA-UseandOArelationship,itpresentsaninterestingquestionforfuturestudies.Webelievethatthereisatheoreticalfoundationforthishypothesis.Itisnotsupportedbythedata,mostlikelybecauseIIFdatadoesnotincludetheinformationaboutthedynamiccomponentofIIF.However,thiscanbetestedinfuturestudies.

Finally,weextendedtheexistingresearchonITvaluebyprovidinginsightsonhowBAandIIFcouldbeconnectedwithOAandwhytheyshouldbetreatedasstrategicassets.Thisstudyanswersthecalltopromotestudiesonspecificinformationsystemsandtheiridiosyncraticeffects(Mukhopadhyayetal.,1995).Italsoanswersthecallforstudies“tounlockthemysteriesofanincreasinglyimportant,butcomplex,setofrelationshipsbetweenITinvestmentsandfirmperformance”(Sambamurthyetal.,2003,p.256).WebelievethatITvalueresearchisatopicthatrequirestheconstantattentionofISresearchersforMIStostayrelevant.

Practical ImplicationsInadditiontoresearch,thisstudyhasimplicationsforpractice.First,itprovidesinsightsintohowBAinteractswithotherorganizationalresourcestoenhanceOA.BAcancreatevalueswiththerightconditions.Asaninformationsystem,thevaluesofBAarebuiltonITinfrastructure.ThisstudyshowsthattoeffectivelyuseandpromoteBA,organizationsshouldpaymoreattentiontobuildaflexibleITinfrastructuresothattheirorganizationalagilitycanbeoptimallyimproved.AsforthesignificantimpactofBA-UseonOA,itshowsthatBAneedstobeviewedasapartoftheorganization’sstrategicassets.WithoutBA-Use,anorganization’sagilitymaysuffer.

Second,thefindingsremindorganizationalexecutivesthatITinfrastructureisnotonlyavaluableplatformthathelpstoenablecommunicationinternallyandexternallyandtoenablepresentandfuturebusinessapplications,butITinfrastructureisalsoastrategiccomponentbecauseitcancontributetoOA.AttentionshouldbeallocatedtovariousareasofITinfrastructure,suchasIIF,tofullytakeadvantageofITtoenhanceanorganization’sagility.

Third,althoughpriorresearchshowedinconsistentresultsofimplementingBAsystems,thisstudysuggestedthatBAhadstrategicvaluesbecauseofitscontributiontoOAthroughIIF.Some


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companiesmaynothavegarneredthefruitsfromtheirinvestmentsinBAbecausetheyhavenotcreatedtherightconditionsforimplementingandusingBAsystems.BusinessleadersneedtocontinuallyinvestigatethefactorsaffectingtheperformanceoftheirBAsystemsandprovideresourcestotackletheproblemsandissuesthathinderthesuccessfulimplementationofBAsystems.

Limitations of this Study and Future ResearchThisstudyusedcross-sectionaldatafromonepointintime.Thisdoesnotprovidehistoricalinformationonhowtheindependentvariables(IIFandBA-Use)impactthedependentvariable(OA)overtime.ThereisareciprocalrelationshipbetweenIIFandBA-UsefromthedataasreflectedbytheresultsofthisstudyandChenandSiau(2012).Thecross-sectionalsurveydatacannotanswerthequestionofhowIIFandBA-Useinfluenceeachother.Futurestudiesmaycollectdataatdifferentpointsintimetohelpustounderstandthenatureofthereciprocalrelationship.ThisstudysupportstheclaimthatIIFandBAhavestrategicvaluesbecausetheyaremajorantecedentstoOAthatdirectlyimpactstrategiccapabilities.ButtoanswerthequestionofwhetherIIFandBAcanhelpOAinalongrun,alongitudinalstudyisrequiredtocomparetheimpactsofIIFandBA-UseonOAovertime.Thiscross-sectionaldesignalsomakesitnecessarytotreattheresultswithcautionbecausecausalitycannotbeinferredfromcross-sectionaldata.Butasolidcross-sectionalstudyprovidesastrongfoundationforfuturelongitudinalstudies.

Asinglesubjectfilledupaquestionnaire.Thismaysuggest that theresultsaresubjectedtocommonmethodbias.Weusedstatisticaltests,suchasHarman’sOne-Factortest,partialcorrectionanalysis,andcorrelationanalysis,astheposthocteststocheckforcommonmethodbias.Theresultsshowthatcommonmethodbiasinthisstudyisminimal.Futurestudiescoulduseamatched-pairdesignthatusestwoinformantsfromeachorganization.Forexample,onecanbeatechnicalexecutiveandtheothercanbeabusinessfunctionexecutivetofurtheralleviatecommonmethodbias.

Themodelinthisstudyisgeneralanditisnotrestrictedtoaspecificbusinessactivity.Therefore,webelievethatthismodelisgeneralizabletovariousaspectsoforganizationalactivities,butitwillbebettertohaveotherindependentstudiestoverifytheclaim.Futureresearchcanlookatspecificbusinesscontexts.Forexample,futureresearchcanstudythemodelinoperationorcustomrelationshipmanagementtoverifyorfalsifythemodelinaspecificbusinesscontext.

Duetothespacelimitation,thisstudyonlypresentstherelationshipbetweenBA-UseandOAasawhole.FuturestudiesmayinvestigatespecificaspectsofBAsystems,suchasdatavisualization,predictivedatamining,andprescriptivedatamining,andtheirinfluencesondifferentaspectsofOA,suchascustomeragility,operationalagility,andoperationalagility.

ThisstudysuggeststhatIIFmayplayacriticalroleinenablingOAandmediatingeffectontheBA-UseandOArelationship.WerecommendthatfutureresearchinvestigateapproachestoandwaysofbuildingflexibleITinfrastructure.ThisincludesinvestigatinghowtocreateITinfrastructurethatfacilitatesconnectionandincreasescompatibilityamongsubsystems,andhowtomakesubsystemsmoremodularsothattheycanswapinoroutquickly.

ThenewfoundrelationshipbetweenIIFandBA-Usealsosuggeststhatitisnecessarytoconductmore research on how organizations should build their IT infrastructure with the technologicaladvancementsofthelastdecade.Theseadvancementsincludecloudcomputing,socialnetworking,ubiquitouscomputingthroughmobiledevicesandsensors(Siau&Zhen,2002,2006),distributedandBigDatadatabases,theInternetofThings,artificialintelligence,andaugmentedreality(Wang&Siau,2019;Hyderetal.2019;Siau2018;Siauetal.2018;Siau&Wang2018).TheliteraturereviewshowsthatveryfewstudiesinthelastdecadehavefocusedontheeffectsofthesetechnologicaladvancementsonITinfrastructureorviceversa.WehavenotfoundastudythatinvestigateswhetherthesetechnologicaladvancementswarrantnewITinfrastructureorifexistingITinfrastructureissufficienttosupportthem.Wesuggestthattimeisripetore-examinetheITinfrastructureanditsrelationshipwithnewtechnologyadvancementsandnewbusinessrequirements.


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REFERENCES

Abbasi,A.,Albrecht,C.,Vance,A.,&Hansen,J.(2012).MetaFraud:Ameta-learningframeworkfordetectingfinancialfraud.Management Information Systems Quarterly,36(4),1293–1327.doi:10.2307/41703508

Ackoff,R.L.(1971).Towardasystemofsystemsconcepts.Management Science,17(11),661–671.doi:10.1287/mnsc.17.11.661

Adeborna,E.,&Siau,K.(2014).AnApproachtoSentimentAnalysis–TheCaseofAirlineQualityRating.InProceedings of thePacific Asia Conference on Information Systems (PACIS 2014).AcademicPress.

Akter,S.,Bhattacharyya,M.,Fosso,S.,&Aditya,S.(2016,July).Howdoessocialmediaanalyticscreatevalue?Journal of Organizational and End User Computing,28(3),1–9.doi:10.4018/JOEUC.2016070101

Bagozzi,R.,Yi,Y.,&Phillips,L.(1991).Assessingconstructvalidityinorganizationalresearch.Administrative Science Quarterly,36(3),421–458.doi:10.2307/2393203

Bharadwaj,A.,Bharadwaj,S.,&Bendoly,E.(2007).Theperformanceeffectsofcomplementaritiesbetweeninformationsystems,marketing,manufacturing,andsupplychainprocesses.Information Systems Research,18(4),437–453.doi:10.1287/isre.1070.0148

Bhatt,G.D.,Emdad,A.,Roberts,N.,&Grover,V.(2010).Buildingandleveraginginformationindynamicenvironments:TheroleofITinfrastructureflexibilityasenableroforganizationalresponsivenessandcompetitiveadvantage.Information & Management,47(7-8),341–349.doi:10.1016/j.im.2010.08.001

Burton-Jones,A.,&Straub,D.W.Jr.(2006).Reconceptualizingsystemusage:Anapproachandempiricaltest.Information Systems Research,17(3),228–246.doi:10.1287/isre.1060.0096

Byrd, T. A., & Turner, D. E. (2001). An exploratory examination of the relationship between flexible ITinfrastructure and competitive advantage. Information & Management, 39(1), 41–52. doi:10.1016/S0378-7206(01)00078-7

Chae,B.,Yang,C.,Olson,D.,&Sheu,C.(2014).TheImpactofadvancedanalyticsanddataaccuracyonoperationalperformance:Acontingentresourcebasedtheory(RBT)perspective.Decision Support Systems,59,119–126.doi:10.1016/j.dss.2013.10.012

Chakravarty,A.,Grewal,R.,&Sambamurthy,V.(2016).Informationtechnologycompetencies,organizationalagility,andfirmperformance:Enablingandfacilitatingroles.Information Systems Research,24(4),976–997.doi:10.1287/isre.2013.0500

Checkland,P.(1981).Systems thinking, systems practice.NewYork:Wiley.

Chen,H.,Chiang,R.H.L.,&Storey,V.C.(2012).Businessintelligenceandanalytics:Frombigdatatobigimpact.Management Information Systems Quarterly,36(4),1165–1188.doi:10.2307/41703503

Chen,M.J.(1996).Competitoranalysisandinterfirmrivalry:Towardatheoreticalintegration.Academy of Management Review,21(1),100–134.doi:10.5465/amr.1996.9602161567

Chen,M.J.,&Miller,D.(2015).Reconceptualizingcompetitivedynamics:Amultidimensionalframework.Strategic Management Journal,36(4),758–775.doi:10.1002/smj.2245

Chen,X.,&Siau,K.(2012).EffectofbusinessintelligenceandITinfrastructureflexibilityonorganizationalagility.InProceedings of International Conference on Information Systems.AcademicPress.

Chin,W.W.(2010).HowtowriteupandreportPLSanalyses.InE.V.Esposito,W.W.Chin,J.Henseler,&H.Wang(Eds.),Handbook of Partial Least Squares.Berlin:Springer.doi:10.1007/978-3-540-32827-8_29

Chin,W.W.,Marcolin,B.L.,&Newsted,P.R.(2003).Apartialleastsquareslatentvariablemodelingapproachformeasuringinteractioneffects:ResultsfromaMonteCarlosimulationstudyandanelectronic-mailemotion/adoptionstudy.Information Systems Research,14(2),189–217.doi:10.1287/isre.14.2.189.16018

Churchill,G.A.Jr.(1979).Aparadigmfordevelopingbettermeasuresofmarketingconstructs.JMR, Journal of Marketing Research,16(1),64–73.doi:10.1177/002224377901600110

http://dx.doi.org/10.2307/41703508

http://dx.doi.org/10.1287/mnsc.17.11.661

http://dx.doi.org/10.1287/mnsc.17.11.661

http://dx.doi.org/10.4018/JOEUC.2016070101

http://dx.doi.org/10.2307/2393203

http://dx.doi.org/10.1287/isre.1070.0148

http://dx.doi.org/10.1016/j.im.2010.08.001


http://dx.doi.org/10.1016/S0378-7206(01)00078-7

http://dx.doi.org/10.1016/S0378-7206(01)00078-7

http://dx.doi.org/10.1016/j.dss.2013.10.012


http://dx.doi.org/10.2307/41703503

http://dx.doi.org/10.5465/amr.1996.9602161567

http://dx.doi.org/10.1002/smj.2245

http://dx.doi.org/10.1007/978-3-540-32827-8_29

http://dx.doi.org/10.1287/isre.14.2.189.16018

http://dx.doi.org/10.1177/002224377901600110


158

Daradkeh,M.(2019).Determinantsofself-serviceanalyticsadoptionintention:Theeffectsoftask-technologyfit,compatibility,anduserempowerment.Journal of Organizational and End User Computing,31(4),19–45.doi:10.4018/JOEUC.2019100102

Davenport,T.H.(2006).CompetingonAnalytics.Harvard Business Review,84(1),98–107.PMID:16447373

Duncan, N. (1995). Capturing flexibility of information technology infrastructure: A study of resourcecharacteristicsandtheirmeasure.Journal of Management Information Systems,12(2),37–57.doi:10.1080/07421222.1995.11518080

Eckerson, W. W. (2009) Beyond reporting: delivering insights with next-generation analytics. TDWI BestPracticesReport.Retrievedfromhttp://tdwi.org/webcasts/2010/06/beyond-reporting-delivering-insights-with-next-generation-analytics.aspx

El Sawy, O. A., & Pavlou, P. A. (2008). IT-enabled business capabilities for turbulent environments. MIS Quarterly Executive,7(3),139–150.

Elbashir,M.Z.,Collier,P.A.,&Sutton,S.G.(2008).Theroleoforganizationalabsorptivecapacityinthestrategicuseofbusinessintelligencetosupportintegratedmanagementcontrolsystems.The Accounting Review,86(1),155–184.doi:10.2308/accr.00000010

Faul,F.,Erdfelder,E.,Lang,A.G.,&Buchner,A.(2007).G*Power3:Aflexiblestatisticalpoweranalysisprogram for the social, behavioral, and biomedical sciences. Behavior Research Methods, 39(2), 175–191.doi:10.3758/BF03193146PMID:17695343

Ghasemaghaei, M., Ebrahimi, S., & Hassanein, K. (2018). Data analytics competency for improving firmdecision-makingperformance.The Journal of Strategic Information Systems,27(1),101–113.doi:10.1016/j.jsis.2017.10.001

Ghasemaghaei,M.,Hassanein,K.,&Turel,O.(2017).Increasingfirmagilitythroughtheuseofdataanalytics:Theroleoffit.Decision Support Systems,101,95–105.doi:10.1016/j.dss.2017.06.004

Goodhue,D.L.,&Thompson,R.L.(1995).Task-technologyfitandindividualperformance.Management Information Systems Quarterly,19(2),213–236.doi:10.2307/249689

Hair,J.F.,Hult,G.T.M.,Ringle,C.M.,&Sarstedt,M.(2014).A primer on Partial Least Squares structural equation modeling.ThousandOaks,CA:Sage.

Hair,J.F.Jr,Ringle,C.M.,&Sarstedt,M.(2013).Editorial-partialleastsquaresstructuralequationmodeling:Rigorousapplications,betterresultsandhigheracceptance.Long Range Planning,46(1-2),1–12.doi:10.1016/j.lrp.2013.01.001

Henseler,J.&Chin,W.W.(2010).Acomparisonofapproachesfortheanalysisofinteractioneffectsbetweenlatentvariablesusingpartialleastsquarespathmodeling.Structure Equation Modeling: A Multidisciplinary Journal, 17(1),82-109.

Hu,D.,Zhao,J.L.,Hua,Z.,&Wong,M.C.S.(2012).Network-basedmodelingandanalysisofsystemicriskinbankingsystems.Management Information Systems Quarterly,36(4),1269–1291.doi:10.2307/41703507

Hyder,Z.,Siau,K.,&Nah,F.(2019).ArtificialIntelligence,MachineLearning,andAutonomousTechnologiesinMiningIndustry.Journal of Database Management,30(2),67–79.doi:10.4018/JDM.2019040104

Jourdan, Z., Rainer, R. K., & Marshal, T. E. (2008). Business Intelligence: An Analysis of the Literature.Information Systems Management,25(2),121–131.doi:10.1080/10580530801941512

Kline,R.B.(2005).Principles and practice of structural equation modeling(2nded.).NewYork:TheGuilfordPress.

Kumar,R.(2004).Aframeworkforassessing thebusinessvalueof information technologyinfrastructures.Journal of Management Information Systems,21(2),11–32.doi:10.1080/07421222.2004.11045801

Lau,R.Y.,Liao,S.S.,Wong,K.-F.,&Chiu,D.K.(2012).Web2.0environmentalscanningandadaptivedecisionsupportforbusinessmergersandacquisitions.Management Information Systems Quarterly,36(4),1239–1268.doi:10.2307/41703506

http://dx.doi.org/10.4018/JOEUC.2019100102

http://www.ncbi.nlm.nih.gov/pubmed/16447373

http://dx.doi.org/10.1080/07421222.1995.11518080

http://dx.doi.org/10.1080/07421222.1995.11518080

http://dx.doi.org/10.2308/accr.00000010

http://dx.doi.org/10.3758/BF03193146


http://dx.doi.org/10.1016/j.jsis.2017.10.001



http://dx.doi.org/10.2307/249689

http://dx.doi.org/10.1016/j.lrp.2013.01.001

http://dx.doi.org/10.1016/j.lrp.2013.01.001

http://dx.doi.org/10.2307/41703507

http://dx.doi.org/10.4018/JDM.2019040104

http://dx.doi.org/10.1080/10580530801941512

http://dx.doi.org/10.1080/07421222.2004.11045801

http://dx.doi.org/10.2307/41703506


159

Laursen,G.H.N.,&Thorlund,J.(2010).Business analytics for managers: taking business intelligence beyond reporting.Wiley.

Lee,O.K.,Sambamurthy,V.,Lim,K.H.,&Wei,K.K.(2015).HowdoesITambidexterityimpactorganizationalagility?Information Systems Research,26(2),398–417.doi:10.1287/isre.2015.0577

Lee,S.,&Siau,K.(2001).AReviewofDataMiningTechniques.Industrial Management & Data Systems,101(1),41–46.doi:10.1108/02635570110365989

Li,X.,Chen,C.,Goldsby,T.J.,&Hosapple,C.W.(2008).Aunifiedmodelofsupplychainagility:Thework-designperspective.International Journal of Logistics Management,19(3),408–435.doi:10.1108/09574090810919224

Liu,H.,Ke,W.,Wei,L.L.,&Hua,Z.(2013).TheimpactofITcapabilitiesonfirmperformance:Themediatingrolesofabsorptivecapacityandsupplychainagility.Decision Support Systems,54(3),1452–1462.doi:10.1016/j.dss.2012.12.016

Lu, Y., & Ramamurthy, K. (2011). Understanding the link between information technology capability andorganizationalagility:Anempiricalexamination.Management Information Systems Quarterly,35(4),931–954.doi:10.2307/41409967

Mithas,S.,Ramasubbu,N.,&Sambamurthy,V.(2011).Howinformationmanagementcapabilityinfluencesfirmperformance.Management Information Systems Quarterly,35(1),237–256.doi:10.2307/23043496

Mukhopadhyay,T.,Kekre,S.,&Kalathur,S.(1995).Businessvalueofinformationtechnology:Astudyofelectronicdatainterchange.Management Information Systems Quarterly,19(2),137–156.doi:10.2307/249685

Nah, F., Siau, K., & Tian, Y. (2005). Knowledge Management Mechanisms of Financial Service Sites.Communications of the ACM,48(6),117–123.doi:10.1145/1064830.1064836

Nevo,S.,&Wade,M.R.(2010).TheformationandvalueofIT-enabledresources:Antecedentsandconsequencesofsynergisticrelationships.Management Information Systems Quarterly,34(1),163–183.doi:10.2307/20721419

Nunnally,J.C.,&Berbstein,I.(1994).Psychometric Theory.NewYork:McGraw-Hill.

Pavlou,P.A.,&ElSawy,O.A.(2006).FromITleveragingcompetencetocompetitiveadvantageinturbulentenvironments:Thecaseofnewproductdevelopment.Information Systems Research,17(3),198–227.doi:10.1287/isre.1060.0094

Pavlou,P.A.,&ElSawy,O.A. (2010).The ‘thirdhand’: IT-enabledcompetitiveadvantage in turbulencethroughimprovisationalcapabilities.Information Systems Research,21(3),442–471.doi:10.1287/isre.1100.0280

Podsakoff,P.M.,Mackenzie,S.B.,Podsakoff,N.P.,&Lee,J.Y.(2003).Commonmethodbiasesinbehavioralresearch:Acriticalreviewoftheliteratureandrecommendedremedies.The Journal of Applied Psychology,88(5),879–903.doi:10.1037/0021-9010.88.5.879PMID:14516251

Popovic,A.,Hackney,R.,Coelho,P.S.,&Jaklic,J.(2012).Towardsbusinessintelligencesystemssuccess:Effects of maturity and culture on analytical decision making. Decision Support Systems, 54(1), 729–739.doi:10.1016/j.dss.2012.08.017

Popovic,A.,Hackney,R.,Coelho,P.S.,&Jaklic,J.(2014).Howinformation-sharingvaluesinfluencetheuseofinformationsystems:Aninvestigationinthebusinessintelligencesystemscontext.The Journal of Strategic Information Systems,23(4),270–283.doi:10.1016/j.jsis.2014.08.003

Preacher,K.J.,&Hayes,A.F.(2008).Asymptoticandresamplingstrategiesforassessingandcomparingindirecteffectsinmultiplemediatormodels.Behavior Research Methods,40(3),879–891.doi:10.3758/BRM.40.3.879PMID:18697684

Preacher,K.J.,Rucker,D.D.,&Hayes,A.F.(2007).Addressingmoderatedmediationhypotheses:Theory,methods,andprescriptions.Multivariate Behavioral Research,42(1),185–227.doi:10.1080/00273170701341316PMID:26821081

Preston, D. S., & Karahanna, E. (2009). Antecedents of IS strategic alignment: A nomological network.Information Systems Research,20(2),159–179.doi:10.1287/isre.1070.0159


http://dx.doi.org/10.1108/02635570110365989

http://dx.doi.org/10.1108/09574090810919224



http://dx.doi.org/10.2307/41409967

http://dx.doi.org/10.2307/23043496

http://dx.doi.org/10.2307/249685

http://dx.doi.org/10.1145/1064830.1064836

http://dx.doi.org/10.2307/20721419




http://dx.doi.org/10.1037/0021-9010.88.5.879




http://dx.doi.org/10.3758/BRM.40.3.879


http://dx.doi.org/10.1080/00273170701341316




160

Rai, A., Patnayakuni, R., & Seth, N. (2006). Firm performance impacts of digitally enabled supply chainintegrationcapabilities.Management Information Systems Quarterly,30(2),225–246.doi:10.2307/25148729

Richardson,S.,Banks,M.S.,Kettinger,W.J.,&Quintana,Y.(2014).ITandagilityinthesocialenterprise:AcasestudyofStJudeChildren’sresearchhospital’s‘Cure4Kids’IT-platformforinternationaloutreach.Journal of the Association for Information Systems,15(1),1–32.doi:10.17705/1jais.00351

RingleC.M.WendeS.WillA.(2005).SmartPLS.Retrievedfromhttp://www.smartpls.de

Robinson,J.P.,Shaver,P.R.,&Wrightsman,L.S.(1991).Criteriaforscaleselectionandevaluation.InJ.P.Robinson,P.R.Shaver,&L.S.Wrightsman(Eds.),Measures of personality and social psychological attitudes(pp.1–15).SanDiego,CA:Academic.doi:10.1016/B978-0-12-590241-0.50005-8

Sambamurthy,V.,Bharadwaj,A.,&Grover,V.(2003).Shapingagilitythroughdigitaloptions:Reconceptualizingthe role of IT in contemporary firms. Management Information Systems Quarterly, 27(2), 237–263.doi:10.2307/30036530

Sangari,M.S.,&Razmi,J.(2015).Businessintelligencecompetence,agilecapabilities,andagileperformanceinsupplychain.International Journal of Logistics Management,26(2),356–380.doi:10.1108/IJLM-01-2013-0012

Schryen,G.(2013).RevisitingISbusinessvalueresearch:Whatwealreadyknow,whatwestillneedtoknow,andhowwecangetthere.European Journal of Information Systems,22(2),139–169.doi:10.1057/ejis.2012.45

Shollo,A.,&Galliers,R.D.(2016).Towardsanunderstandingoftheroleofbusinessintelligencesystemsinorganizationalknowing.Information Systems Journal,26(4),339–367.doi:10.1111/isj.12071

Siau,K.(2018).EducationintheAgeofArtificialIntelligence:HowwillTechnologyShapeLearning?The Global Analyst,7(3),22–24.

Siau, K., Erickson, J., & Nah, F. (2010). Effect of National Culture on Types of Knowledge Sharing inVirtual Communities. IEEE Transactions on Professional Communication, 53(3), 278–292. doi:10.1109/TPC.2010.2052842

Siau,K.,Hilgers,M.,Chen,L.,Liu,S.,Nah,F.,Hall,R.,&Flachsbart,B.(2018).FinTechEmpowerment:DataScience,ArtificialIntelligence,andMachineLearning.Cutter Business Technology Journal,31(11/12),12–18.

Siau,K.,&Shen,Z.(2002).MobileCommerceApplicationsinSupplyChainManagement.Journal of Internet Commerce,1(3),3–14.doi:10.1300/J179v01n03_02

Siau,K.,&Shen,Z.(2006).MobileHealthcareInformatics.Medical Informatics and the Internet in Medicine,31(2),89–99.doi:10.1080/14639230500095651PMID:16777784

Siau,K.,&Wang,W.(2018).BuildingTrustinArtificialIntelligence,MachineLearning,andRobotics.Cutter Business Technology Journal,31(2),47–53.

Soh,C.,&Markus,M.L.(1995).HowITcreatesbusinessvalue:aprocesstheorysynthesis.InProceedings of the International Conference on Information Systems(pp.29-41).AcademicPress.

Swafford,P.M.,Ghosh,S.,&Murthy,N.(2008).AchievingsupplychainagilitythroughITintegrationandflexibility.International Journal of Production Economics,116(2),288–297.doi:10.1016/j.ijpe.2008.09.002

Tallon,P.P.,&Pinsonneault,A. (2011).Competingperspectiveson the linkbetweenstrategic informationtechnologyalignmentandorganizationalagility:Insightsfromamediationmodel.Management Information Systems Quarterly,35(2),463–486.doi:10.2307/23044052

Tiwana,A.,&Konsynski,B.(2010).ComplementaritiesbetweenorganizationalITarchitectureandgovernancestructure.Information Systems Research,21(2),280–304.doi:10.1287/isre.1080.0206

Trieu,V.H.(2016).GettingvaluefromBusinessIntelligencesystems:Areviewandresearchagenda.Decision Support Systems.doi:10.1016/j.dss.2016.09.019

Vance,A.,Lowry,P.B.,&Eggett,D.(2015).Increasingaccountabilitythroughuser-interfacedesignartifacts:A new approach to addressing the problem of access-policy violations. Management Information Systems Quarterly,39(2),345–366.doi:10.25300/MISQ/2015/39.2.04

http://dx.doi.org/10.2307/25148729

http://dx.doi.org/10.17705/1jais.00351

http://dx.doi.org/10.1016/B978-0-12-590241-0.50005-8

http://dx.doi.org/10.2307/30036530

http://dx.doi.org/10.1108/IJLM-01-2013-0012

http://dx.doi.org/10.1057/ejis.2012.45

http://dx.doi.org/10.1111/isj.12071

http://dx.doi.org/10.1109/TPC.2010.2052842

http://dx.doi.org/10.1109/TPC.2010.2052842

http://dx.doi.org/10.1300/J179v01n03_02

http://dx.doi.org/10.1080/14639230500095651


http://dx.doi.org/10.1016/j.ijpe.2008.09.002

http://dx.doi.org/10.2307/23044052



http://dx.doi.org/10.25300/MISQ/2015/39.2.04


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Xiaofeng Chen is an Associate Professor of MIS in the Department of Decision Sciences at the College of Business and Economics at Western Washington University. He earned his MS in Computer Sciences and PhD in Business Administration (Concentration in Management Information Systems) from the University of Nebraska-Lincoln (UNL). His research interests include IS security, business intelligence and analytics, design science research, and IT business value. He has publications in Information & Management, Journal of Database Management, Journal of Computer Information Systems, International Conference on Information Systems (ICIS) and Americas Conference on Information Systems (AMCIS) conference proceedings.

Keng Siau is Chair of the Department of Business and Information Technology (BIT) at the Missouri University of Science and Technology. Professor Siau received his Ph.D. in Business Administration from the University of British Columbia in 1996. His M.S. and B.S. (honors) degrees are in Computer and Information Sciences from the National University of Singapore. The University of British Columbia and the National University of Singapore are consistently ranked as two of the top 50 universities in the world (e.g., QS World University Rankings, Times Higher Education World University Rankings). Prior to joining Missouri University of Science and Technology in June 2012, he was Edwin J. Faulkner Chair Professor and Full Professor of Management at the University of Nebraska-Lincoln (UNL). He was also Director of the UNL-IBM Global Innovation Hub. Professor Siau is Editor-in-Chief of the Journal of Database Management and served as the North America Regional Editor of the Requirements Engineering journal (2010-2016). He also served as the Vice President of Education for the Association for Information Systems (AIS has over 4000 members in more than 100 countries) and served as the AIS representative on the Board of Partnership for Advancing Computing Education (PACE) from July 2011-June 2014 (i.e., 3-year term). PACE charter members are Association for Computing Machinery (ACM), Association for Information Systems (AIS), Computer Science Teachers Association (CSTA), IEEE Computer Society (IEEE), National Center for Women and Information Technology (NCWIT), and Computing Research Association. Professor Siau has more than 250 academic publications. According to Google Scholar, he has more than 11,000 citation counts. His h-index and i10-index, according to Google Scholar, are 52 and 128 respectively. Professor Siau is consistently ranked as one of the top information systems researchers in the world based on h-index and productivity rate. In 2006, he was ranked as one of the top ten e-commerce researchers in the world (Arithmetic Rank of 7, Geometric Rank of 3). In 2006, the citation count for his paper “Building Customer Trust in Mobile Commerce” is ranked in the top 1% in the field as reported by Essential Science Indicators. His research has been funded by NSF, IBM, and other IT organizations. Professor Siau has received numerous teaching, research, service, and leadership awards. He received the University of Nebraska-Lincoln Distinguished Teaching Award and the College of Business Administration Distinguished Teaching Award in 2001. He was awarded the Organizational Leadership Award in 2000 and Outstanding Leader Award in 2004 by the Information Resources Management Association. He received the University of Nebraska-Lincoln College of Business Administration Research Award in 2005. He is a recipient of the prestigious International Federation for Information Processing (IFIP) Outstanding Service Award in 2006, IBM Faculty Awards in 2006 and 2008, and IBM Faculty Innovation Award in 2010.

Vannoy,S.A.,&Salam,A.F.(2010).Managerialinterpretationsoftheroleofinformationsystemsincompetitiveactionsandfirmperformance:Agroundedtheoryinvestigation.Information Systems Research,21(3),496–515.doi:10.1287/isre.1100.0301

Venkatraman,N.,&Henderson,J.C.(1998).Realstrategiesforvirtualorganizing.Sloan Management Review,40,33–48.

Wang,W.,&Siau,K.(2019).ArtificialIntelligence,MachineLearning,Automation,Robotics,FutureofWork,andFutureofHumanity–AReviewandResearchAgenda.Journal of Database Management,30(1),61–79.doi:10.4018/JDM.2019010104

Watson,H.J.(2009).Tutorial:Businessintelligence-past,present,andfuture.Communications of the Association for Information Systems,25,487–510.doi:10.17705/1CAIS.02539

Zeshan,H.,Siau,K.,&Nah,F.(2019).Artificialintelligence,machinelearning,andautonomoustechnologiesinminingindustry.Journal of Database Management,30(2),67–79.doi:10.4018/JDM.2019040104



http://dx.doi.org/10.17705/1CAIS.02539


business analytics/business intelligence and it infrastructure

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