Attribute perceptions as factors explaining Mobile Internet acceptance of cellular customers in GermWord格式.docx

Attribute perceptions as factors explaining Mobile Internet acceptance of cellular customers in GermWord格式.docx

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227

TheUKtransportcarbonmodel:

Anintegratedlifecycleapproachtoexplorelowcarbonfutures 

EnergyPolicy,InPress,CorrectedProof,Availableonline9September2010

ChristianBrand,MartinoTran,JillianAnable

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AbstractAbstract|Figures/TablesFigures/Tables|ReferencesReferences

Abstract

Currentdebatefocusesontheneedforthetransportsectortocontributetomoreambitiouscarbonemissionreductiontargets.IntheUK,variousmacro-economicandenergysystemwide,top-downmodelsareusedtoexplorethepotentialforenergydemandandcarbonemissionsreductioninthetransportsector.Thesemodelscanlackthebottom-up,sectoraldetailneededtosimulatetheeffectsofintegrateddemandandsupply-sidepolicystrategiestoreduceemissions.Bridgingthegapbetweenshort-termforecastingandlong-termscenario“models”,thispaperintroducesanewlydevelopedstrategictransport,energy,emissionsandenvironmentalimpactsmodel,theUKTransportCarbonModel（UKTCM）.TheUKTCMcoverstherangeoftransport–energy–environmentissuesfromsocio-economicandpolicyinfluencesonenergydemandreductionthroughtolifecyclecarbonemissionsandexternalcosts.Themodelisdemonstratedinthispaperbypresentingtheresultsofthreesinglepoliciesandonepolicypackagescenario.Limitationsofthemodelarealsodiscussed.DevelopedundertheauspicesoftheUKEnergyResearchCentre（UKERC）theUKTCMcanbeusedtodeveloptransportpolicyscenariosthatexplorethefullrangeoftechnological,fiscal,regulatoryandbehaviouralchangepolicyinterventionstomeetUKclimatechangeandenergysecuritygoals.

ArticleOutline

1.Introduction

2.Background

2.1.Carbonpathways

2.2.Strategicmodellingofthetransport–energy–environmentsystem

3.Modeldesignandmethods

3.1.Modeloverviewandarchitecture

3.2.Transportdemandmodel

3.2.1.Approach

3.2.2.Overviewofmodelspecification

3.2.3.Demandmodelcalibration

3.3.Vehiclestockmodel（VSM）

3.3.1.Approach

3.3.2.Modelspecification:

focusonvehicletechnologychoice

3.4.Directenergyandemissionsmodel（DEEM）

3.4.1.Approach

3.4.2.Modelspecification,datasourcesandcalibration

3.5.Lifecycleandenvironmentalimpactsmodel（LCEIM）

3.5.1.Approach

3.5.2.Modelspecificationanddatasources

4.Areferencescenarioforcomparison

4.1.Approach

4.2.Keydatasourcesandassumptions

4.3.Referencescenarioresults

5.Alternativescenarios

5.1.Policydescriptionandassumptions

5.2.Mainresults

6.Conclusions

Acknowledgements

References

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Researchhighlights

►Newstrategictransport,energy,emissionsandenvironmentalimpactsmodel.►Tooltodevelopandanalysefullconsequencesofmultiplescenariosoftransportpolicypackages.►Novelapproachtomodellingdemandfornewvehiclesbymarketandtechnology.►Modelavailableforusebyresearchcommunityviahttp:

//www.ukerc.ac.uk/support/tiki-index.php?

page=UK+Transport+Carbon+Model.

228

KnowledgetransferinMNCs:

Examininghowintrinsicmotivationsandknowledgesourcingimpactindividualcentralityandperformance 

JournalofInternationalManagement,Volume15,Issue1,March2009,Pages15-31

RobinTeigland,MollyWasko

Studiesinvestigatingindividualperformanceinknowledge-intensiveworkarefindingthatindividualperformanceisaresult,tosomedegree,ofobtainingtherightinformationtosolvenovel,challengingproblems.Yetweknowlittleabouttherolethattheindividualplaysintheknowledgeprocessesinmultinationalsandtherelationshipbetweenindividualknowledgesourcingactivitiesandperformance.Ourexpectationisthatsuccessfulknowledgetransferwillbebasedonanindividual'

sintrinsicmotivation,accesstoknowledgefromexplicitortacitknowledgesourceswithinandacrossfirmboundaries,andpositionintheoveralladvicenetworkofthemultinationalcorporation.Usingsurveyandsocialnetworkdatacollectedinonemultinationalconsultingfirm,weinvestigatedtheknowledgesourcingactivitiesandinformaladvicenetworksfor1439oftheentire1698individualsinonemultinationalspreadacross28offices（84.7%responserate）.Throughhierarchicalregressionanalysiswefindthat1）intrinsicmotivationsarestronglyrelatedtocreativityandefficiencyand2）MNCsshouldsupportindividuallevelactivitiesthatincludenotonlytheuseofinternalknowledgesourcesbutalsotheuseofexternalknowledgesources.Researchresultsalsosuggestthat3）therearedifferentpatternsofknowledgesourcingactivitiesbasedonwhetherefficientorcreativeperformanceisthegoaland4）thesediffersignificantlydependinguponthefunctionalgrouptowhichtheindividualbelongs.Implicationsfortheoriesoftheknowledge-basedviewofthemultinationalcorporationandpracticalimplicationsformanagersarediscussed.

2.Theoreticalfoundation

2.1.Intrinsicmotivations,centralityandperformance

2.2.Internalexplicitknowledgetransferandperformance

2.3.Externalexplicitknowledgetransferandperformance

2.4.Internaltacitknowledgeintegrationandperformance

2.5.Externaltacitknowledgeintegrationandperformance

2.6.Centralityandperformance

2.7.Differencesacrossfunctions

3.Methods

3.1.Researchsettinganddatacollection

3.2.Surveyitemsandmeasures

3.2.1.Intrinsicmotivations

3.2.2.CentralityintheMNC'

sadvicenetwork

3.2.3.Individualperformance

3.2.4.Businessfunction

3.2.5.Controlvariables

4.Analysesandresults

4.1.Assessmentofthemeasurementmodel

4.2.Correlationsanddescriptivestatistics

4.3.Analyses

5.Results

6.Discussionandimplications

6.1.Practicalimplications

6.1.1.Knowledge-basedviewofthemultinationalfirm

6.1.2.Knowledge“leakage”andfirmperformance

7.Concludingremarks

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229

Identifyingstrengthsandweaknessesoflandscapevisualisationforeffectivecommunicationoffuturealternatives 

LandscapeandUrbanPlanning,Volume100,Issue3,15April2011,Pages231-241

ChristopherJ.Pettit,ChristopherM.Raymond,BrettA.Bryan,HaydenLewis

Visualisationtechnologiesarepowerfultoolsforcommunicatingthescientificoutcomesofenvironmentalplanningmodels.Thisstudyspecificallyexploresthestrengthsandweaknessesoflandscapevisualisationtechniquesinenvironmentalplanningbydrawinguponinsightsfromthemulti-agencyLowerMurrayLandscapeFutures（LMLF）integratedmodellingandanalysisprojectinsouth-easternAustralia.Theprojectexploredspatiallyexplicitpolicyoptionsandscenariosforassessingtheimpactsofachievingregionalnaturalresourcemanagementtargets.Alternativelandscapefutureswerecommunicatedusingarangeofvisualisationtechniquesavailableonline.Weassessedthestrengthsandweaknessesofthesuiteofvisualisationproductsdeployedforcommunicatinglandscapefuturestotwoaudiences（i）currentuserscomprisingenvironmentalmanagersandplannersworkingwithinthestudyareaand（ii）futureuserscomprisinguniversitystudentscurrentlystudyingspatialinformationsciencesaspartoftheirenvironmentalmanagementprogram.Semi-structuredinterviewsandwebsiteevaluationsurveyswereundertakenwithcurrentusersandfutureusers,respectively.Resultsindicatethatbothaudiencesappreciatedtheenvironmentalplanningandinvestmentdecisionsupportstrengthsofthevisualisationproducts.Thecurrentusershadconcernsaroundthetargetaudienceandthenatureandextentofcommunicationsandstakeholderengagementinproductdevelopment.Thefutureusergroupweremoreinterestedinthevisualclarityofthevisualisationproducts.Enduserfeedbackprovidedanumberofusefulrecommendationsinrelationtohowthevisualisationproductscouldberefinedtoimprovethecommunicationeffectivenessandenduserexperience.Weconcludebypresentinganumberoffuturedirectionsforlandscapevisualisationscienceandcommunicationofenvironmentalmodellingoutcomes.

2.Methodology

2.1.Landscapemodelling

2.2.Visualisationmethods

2.3.Evaluationmethods

2.3.1.Currentusers

2.3.2.Futureusers

3.Results

3.1.Currentusers

3.1.1.Strengthsandweaknesses

3.1.2.Futuredirections

3.2.Futureusers

3.2.1.Strengthsandweaknesses

3.2.2.Futuredirections

4.Discussion

4.1.Visualisationforcommunicatingenvironmentalplanning

4.2.Embeddingvisualisationintotheenvironmentaldecision-makingprocess

4.3.Limitations

4.3.1.Landscapevisualisationapproach

4.3.2.Evaluation

5.Conclusions

Vitae

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►Resultsfromourenduserevaluationsuggestthatvisualisationtoolshaveanimportantroleinraisingknowledgeandawarenessoffuturelandscapescenario