data sharing in the fire industry€¦ · predictive policing is a similar, already existing...

MasterDegreeProjectinKnowledge-BasedEntrepreneurship

Datasharinginthefireindustry–

creatingbetterandproactivesafetyAqualitativecasestudy

Niels-MalteThorn

Supervisor:KarinBergMasterDegreeProjectNo.GraduateSchool

Datasharinginthefireindustry-creatingbetterandproactivesafety Niels-MalteThorn

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AbstractWeareallfamiliarwiththetraditionaljobofafireman:theyreceivenewsthatafireoccurred,theyrushintoafiretruck,analarmsoundsandtheyraceofftosaveandprotectlifeandproperty(Schilling,2014).Whilethisapproachwillstaymostlythesame,theaimofthisresearchistoanalyzefactorsthatarelikelytoaffectstakeholdersandtheirdecisiontosharefire-relateddataasitpresumablyenhancesproactivefiresafety.SandboxisthenameoftheprojectwhichwasinitiallyintroducedbytheSvenskBrandskydds-föreningen (SBF).Theideaistodevelopabusinessmodelwhichconnectsdifferentstakeholdersinthefire-relatedindustry.Subsequently,theirdatashouldbeaggregatedandanalyzedinordertodeducenewfindingswiththegoaltoenhanceproactivefiresafety.However,beforesomeonecanstarttodevelopabusinessmodel,itisimportanttounderstandtheviewpointsandconcernsofeachofthestakeholdersastheir data is a crucial variable, determining the feasibility of thewhole project. This thesis employs aqualitativeapproachinformofacasestudy.Therequireddatawascollectedthroughouttheconductionof semi-structured interviews, involving six different organizations that are currently engaged in thecollectionoffire-relateddata.Theresultsindicatethattheoverallwillingnesstosharefire-relateddataiswell existent, nevertheless the findings also highlight that there are a number of motivational anddiscouragingfactorsthatinfluencedataownersandtheirdecisiontoengageindatasharing.Thesefactorsmainlyrelatetotheorganizationitselfbutalsotoaspects,identifiedbyElinorOstromandherperspectiveon the collective action theory. Further, the results show that relatedbenefits and challengesof datasharinganddataanalyticsarelikelytoaffectdataownersandtheirdecisiontoengageindatasharing.Basedontheempiricalfindingsandreviewedtheory,anewmodelwasdevelopedwhichincorporatesthepreviously mentioned factors and concurrently summarizes the thesis. Further, it outlines theprerequisitesforfutureresearch,whichshouldaimtowardsthedevelopmentofabusinessmodelrelatedtotheSandboxidea.Keywords–datasharing,dataanalytics,firesafety,data-driveninnovation,datasilos,collectiveaction,Sandbox,Brandskyddsföreningen


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AcknowledgementsIwould like toexpressmyappreciation to theSvenskBrandskyddsföreningen (SBF) andFirst toKnowScandinaviaABforprovidingtheopportunitytoconductthisthesis.Writingthisthesiswouldnothavebeen possible without their great support and time spent as well as providing valuable insights andfeedback.MygratitudealsogoestoKarinBergforprovidingmenotonlyvaluablefeedbackandguidancebutalsopreciousinspiration.Moreover,Iwouldliketothankeachoftheintervieweeswhoparticipatedinthisstudyforsharingtheirtimeandinsights,makingthisstudypossible.

PersonalRefectionWriting this thesis was challenging and rewarding at the same time. As this research has a practicalbackground,itwasinparticularchallengingtomergethepiraticalsituationwiththeacademia.Alsobeinga single author does not leave room for fruitful and stimulating discussionswhich on the other handcreatesindependenceandflexibilityintheprocessofresearchingandwriting.HavingaclosecollaborationwithSBFandFirsttoKnowhelpedmetostayfocused,butalsoregularmeetingswithmysupervisorwhichoften resulted in inspiringdiscussions,helpedme toovercomethe flawsofbeinga singleauthor.Thechosenfieldofstudydidnotarisefrommyownpersonalinterest,ratheritwasanopportunitywhichIembarkedupon.Nonetheless,myinterestforthisfieldrapidlygrewthroughoutconductingthisresearchandhopefullyresultedinastudywhichnotonlyaddsvalueforSBFbutalsolaysthefoundationforfutureresearch.


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TableofContent

1. Introduction.....................................................................................................................71.1 Background...........................................................................................................................71.2 ProblemSetting....................................................................................................................81.3 ResearchQuestion................................................................................................................81.4 Disposition............................................................................................................................9

2. TheoreticalBackground...................................................................................................92.1 Settings.................................................................................................................................9

2.1.1 ResearchAreas........................................................................................................................92.1.2 ScopeoftheTheoreticalBackground....................................................................................10

2.2 Thelogicofcollectiveaction...............................................................................................102.2.1 ElinorOstromsapproach.......................................................................................................112.2.2 Ostromcollectiveactionframework.....................................................................................11

2.3 Bigdata&Dataanalytics....................................................................................................152.3.1 Thebenefitsofdatasharinganddataanalytics....................................................................162.3.2 Thechallengesofdatasharing&dataanalytics....................................................................18

2.4 Summaryofliteraturereview.............................................................................................20

3. Methodology.................................................................................................................203.1 ResearchStrategy...............................................................................................................203.2 ResearchDesign..................................................................................................................213.3 ResearchMethodsandDataCollection...............................................................................21

3.3.1 SelectionofOrganizationsandRespondents........................................................................213.3.2 Practicalities...........................................................................................................................23

3.4 DataAnalysis......................................................................................................................243.5 Qualityofthestudy............................................................................................................25

3.5.1 Reliability...............................................................................................................................253.5.2 Validity...................................................................................................................................25

4. EmpiricalFindings..........................................................................................................264.1 Topicareas..........................................................................................................................26

4.1.1 Topicarea1:ChallengesandFuture.....................................................................................264.1.2 Topicarea2:Datacollectionpractices..................................................................................274.1.3 Topicarea3:Datasharing&dataanalytics..........................................................................294.1.4 Topicarea4:Benefits&challengesofdatasharinganddataanalytics................................304.1.5 Topicarea5:ProjectSandbox...............................................................................................31

4.2 Summaryoftheempiricalfindings......................................................................................33

5 Analysis.........................................................................................................................355.1Analysisofthetopicareas..................................................................................................35

5.1.1 Topicarea1:ChallengesandFuture.....................................................................................355.1.2 Topicarea2:Datacollectionpractices..................................................................................375.1.3 Topicarea3:Datasharinganddataanalytics.......................................................................385.1.4 Topicarea4:Benefitsandchallengesofdatasharinganddataanalytics............................395.1.5 Topicarea6:ProjectSandbox...............................................................................................42

5.2 Results................................................................................................................................44

6 Conclusion&FutureResearch........................................................................................466.1 Concludingdiscussion.........................................................................................................466.2 FutureResearch..................................................................................................................49


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7 References.....................................................................................................................50

8 Appendices....................................................................................................................54 Appendix1:InterviewGuide.....................................................................................................54


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ListofFiguresFigure1.1Outlineofthethesis………………………………………………………………………………..................................9Figure2.1Outlineofthetheoreticalbackground……………………………………………………………………………………9Figure2.2Thecorerelationshipsattheindividuallevelaffectingthelevelofcooperation…………………..13Figure2.3Ostromcollectiveactionframework…………………………………………………………………………………….14Figure2.4Activegrowthofglobaldata………………………………………………………………………………...................15Figure5.1Newdatasharingmodel……………………………………………………………………………….........................46

ListofTablesTable2.1Structuralvariablespredictedtoaffectthelikelihoodofcollectiveaction…………………………....12Table2.2Sevengreatwaysthatdatacanbenefitsociety……………………………………………………………………..17Table3.1Overviewoftheconductedinterviewsandorganizations……………………………………………………..22Table4.1Summaryoftheempiricalfindings………………………………………………………………………………………..34Table5.1Mentionedbenefitsofdatasharing&dataanalytics…………………………………………………………….39Table5.2Mentionedchallengesofdatasharing&dataanalytics…………………………………………………………41

ListofAbbreviationsCPR Common-PoolResourceDDI DataDrivenInnovationGDPR GeneralDataProtectionRegulationICT InformationandCommunicationTechnologyIoT InternetofThingsSBF SvenskBrandskyddsföreningenUN UnitedNations


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1. Introduction1.1 Background

In Sweden and throughout the rest of the world, increasing population numbers and resources areconcentratedaroundcities.Tothisday,morethan50percentoftheworldpopulationlivesinurbanareasand especially Sweden has a high degree of urbanization, which according to Statistiska centralbyrån(2018)was87percentin2017.TheUNforecasts,thattheurbanizationwillriseupto66percentbytheyear2050andthereforethepromotionofsafe,resilientandsustainableurbanenvironmentsisoneofthe17newUNsustainabilitygoals(Hedeklint,2016).Alongsidetheincreasinglevelofurbanization,theemergenceofdigitizationandbigdataaffectsourlives.Today, major advances in information and communication technologies (ICT), the increasing use ofelectronicdevicesandnetworksandthedigitalizationofprocessesmeanthatenormousamountsofdataaregenerated24/7bysocialandeconomicactivities.Thisso-calledbigdatacanbetransmitted,collected,aggregatedandanalyzedtoprovidevaluableinsightsintoprocessesandhumanbehaviors(Davies,2016).Itissaidthattheexplosionofdataenablesthecreationofnew,innovativeproducts,servicesandbusinessmodels, while also stimulating greater competitiveness and economic growth (Schalenkamp, 2014).AccordingtotheOECD(2015),thisso-calledData-DrivenInnovation(DDI)willbeakeypillarin21stcenturysourcesofgrowth.Inbusinesses,theexploitationofdatapromisesthecreationofadditionalvalueinavarietyofoperations,rangingfromtheoptimizationofvaluechainstoamoreefficientuseoflaborandimprovedcustomerrelationships(OECD,2015).Butalsothepublicsectorisakeyprofiteer,asitisboth,akeysourceanduserofdata,whichcreatestheopportunitytogeneratebenefitsacrosstheeconomy.Bytakingacloserlookatthesedevelopmentswecanidentifyanopportunitywhichrelatestofiresafety.Ononehand,theincreaseofurbanizationsdemandsforimprovedfiresafetyaslastyear’shappeninginWestLondonpointsout,where71peopledied ina24-storyhousingcomplexduetoa firewhichwasacceleratedbythebuilding’sexteriorcladdingandsignificantfiresafetyfailures(Bowcott,2018).Ontheother hand, advances in ICT create the possibility to collect and share data in an unprecedentedwayamongstdifferentstakeholders.Thesestakeholders,suchasinsurancecompanies,privatesafetyfirmsandgovernmentalagenciescollect fire-relateddataandbyaggregatingandanalyzingthisdata,predictionscanbemadewherethe likelihoodofafire is increased.Predictivepolicing isasimilar,alreadyexistingframework in law enforcement, which applies mathematical, predictive and analytical techniques toidentify potential criminal activity (Rienks, 2005). In Santa Cruz, California, the implementation ofpredictivepolicingforaperiodof6-monthsresultedina19percentdropinthenumberofhousebreakingsand theoverall situation consistently improved (Friend,2013). Theexampledemonstrates the currentstateoftechnologicaldevelopmentsandindicatesthepossibilitiesforfutureprojects.


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1.2 ProblemSetting

Althoughmanyscholarshavetoucheduponthebenefitsandchallengesofdatasharinganddataanalyticsingeneral,therehassofarbeenlittleresearchonsuitableareasofapplication.Especiallywhenturningthefocusonfiresafety,thereisalmostnon-exitingresearchwhichrelatestodatasharingandfiresafety.Thishoweverpresentsanimportantresearcharea,particularlywithrespecttoincreaseofurbanization,as it has the potential to save people’s lives but also to reduce fire-related damages. Every yearapproximately90personsdieindomesticfiresinSweden(Winberg,2016)andmajorinsurancecompaniesstatethattheexpensesfor fire insuranceclaimsbyfarexceedtheexpensesforanyother insured loss(Svensk Försäkring, 2017). Having this in mind, this study will help to highlight today’s advances intechnologybutalsothepotentialimpactofdatasharinganddataanalyticstoolsinregardstofiresafety.Further,itwillbeafirststeptogetintouchwithfire-relatedstakeholders,bringingthemclosertogetherinaframework,whichintheupcomingstudywillbereferredtoasSandbox.1.3 ResearchQuestion

AsCharlesDarwinoncesaid:“Inthelonghistoryofhumankind(andanimalkind,too),thosewholearnedtocollaborateandimprovisemosteffectivelyprevailed”(Clarke,2017).Andeventhough,thisstudydoesnot focuson thecompetitivecorporateworld, thebeforementionedquotemighthelpusexplainonemajorobstaclewithinthisresearch,namelythecircumstancethatdataissensitive.Intoday’sseaofdata,littlecanbedoneifdataexistsinseparate“silos”,causedbyreluctanceorthedataownersfearofsharingdata(Lin,2016).Therefore,themainobjectiveofthisstudyistoidentifyfactorsthatareinfluencingdataownersandtheirdecisiontoengage indatasharing,as it isadeterminingaspect to turntheSandboxmodelintopractice.ElinorOstrom,anAmericanpoliticaleconomist,whowontheNobelMemorialPrizeinEconomicSciencesforheranalysisofeconomicgovernance(Grandin,2010),developedanapplicableframework,whichisbasedonthelogicofcollectiveaction.Ostrom(2009)argues,thatindividualswhofaceasocialdilemma,choseinterdependentactionsthatmaximizetheirshort-termbenefits.However,abetteroptimaloutcomecouldhavebeenachievedifthoseinvolvedcooperated.Ostrom(2009)developedaframeworkwithasetofvariables,thatarepredictedtoaffectthelikelihoodofcollectiveactionandwillsubsequentlybeappliedthroughoutthisstudy.

Basedonthepreviousconsiderations,thefollowingresearchquestionandrelevantsubordinateresearchquestionwillguidethisstudy:

GuidingResearchQuestion:• Whatfactorsinfluencestakeholders,makingthemwillingtosharetheirdataforthemutualbenefit

intermsoffiresafetyinSweden?•

RelevantSub-question:• Whatarepotentialbenefitsandobstaclesthataffectdatasharinganddataanalytics?


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1.4 Disposition

This researchstudy is structuredas follows:The thesiswillproceedwithanexplanatory frameworkofcollectiveaction,rootedinliterature.Thiswillcoveradescriptionofrelevantvariablesthatarerelatedtotheconcept.Thereafter,thepaperwillcontinuewithanelaborationondatasharinganddataanalyticsincludingrelatedbenefitsandchallenges.Subsequentlytheappliedmethodologytoanswertheresearchquestionwillbeelucidated.Thefindingsarethenpresented,followedbyananalysis.Thestudyendsbypresentingtheconclusionsincludingrecommendationsandfutureresearch.Figure1.1belowsummarizestheoutlineandtherelevantcontentforeachofthesections.

Figure1.1OutlineoftheThesis

2. TheoreticalBackground2.1 Settings2.1.1 ResearchAreas

Inordertobeabletocreateatheoreticalframeworkforthisresearch,aliteraturereviewwasconducted.Theliteraturereviewwasbrokendownintotworesearchareas,namely:collectiveactiontheoryandbigdatasharinganddataanalytics.Thereviewofthesetworesearchareasresultedintheidentificationoftwomainblocks,whichshallsubsequentlyhelptoanswertheresearchquestion.

Figure2.1OutlineoftheTheoreticalBackground

Introduction

Conclusion

Analysis

EmpiricalFindings

Methodology

TheoreticalFramework

• Background:Urbanization&Newtechnologies• ProblemSetting&ResearchQuestion

• Thelogicofcollectiveaction• Datasharing&dataanalytics–Benefits&Challenges

• ResearchStrategy&Design• ResearchMethods

• Presentationof5identifiedtopicareas• Summaryofmainfindings

• Analysisoftheempiricalfindings• Presentinganewdatasharingmodel

• Conclusion&FutureResearch

2.2. Thelogicofcollectiveaction

2.3.1 Thebenefits

2.3.2 Thechallenges

2.2.1 ElinorOstromsapproach

2.3 Bigdatasharing&dataanalytics

2.2.2 Ostromscollectiveactionframework


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2.1.2 ScopeoftheTheoreticalBackground

Thefirstresearcharearelatestotheconceptofcollectiveaction.ElinorOstromandherbookGoverningtheCommons:TheevolutionofInstitutionsforCollectiveAction(1990)wasanimportantcontributortothislogic(Little,2012).Ostromsbook(1990)presentsanewtheoreticalframeworkwhichdescribes,howhumancommunitiesaccomplishtohandlecommonpropertyresourceslikeforests,fishinggroundsandwatersupplies.Althoughthisresearchdoesnotfocusononeofthebeforementionedcommonpropertyresourcesperse,thesameframeworkshallbeappliedintheareaofdatasharing,asdataisavaluableresourcewhichbecomesevenmorepreciouswhen it is sharedandcombinedwithother sourcesandthereforerelatestothecollectiveactiontheory.Thus,thisapproachmightgiveconsiderablenewinsightsandshallserveasafundamentfortheSandboxmodel.Thesecondresearcharearelatestobigdatasharinganddataanalyticsasitiscentraltothewholecasestudy.Inparticular,thebenefitsandchallengesofdatasharingwillbehighlighted,whichwillmostlikelyaffectstakeholdersandtheirdecisiontoengageinadatasharingnetwork.

2.2 Thelogicofcollectiveaction

ThelogicofcollectiveactionwasinitiallyaddressedbyMancurOlsoninhissamenamebookwhichwaspublished in1965.Olsondescribeshowgroupsare formedandexplores theeconomic incentives anddisincentivesforgroupformation.Inconclusion,Olsonstatesthatindividualsaretemptedtoactintheirown interest which consequently restrains individuals to work towards a collective good (Congleton,2015).Thelogicofcollectiveactioncanbedefinedasaformalorganizationalalignment,whichinvolvesactions,thatarecarriedoutbyagroupofpeoplewhoaretryingtoobtainacommongood(Bennett&Segerberg,2012). Those actions often require a stronger commitment by the individual and result in a collectivestructure,whichisbasedonasetofvaluesthatrelatetothegroup(Lim,2013).Collectiveactiontypicallyevolves,whentwoormoreindividualsfaceasocialdilemma,whichisasituationinwhichtheinvolvedindividualsreceiveahigherpayoffforacompetitivechoicethanforacooperativechoice.However,allmemberswouldbebetteroff,ifthoseinvolvedcooperated(Komorita&Hilty,1991).Behaviorinasocialdilemmaisanimportanttopic,asitreflectsmanyreal-lifeproblems,thatwearefacinginsociety,suchasenvironmentalpollutionorresourcefading(Komorita&Hilty,1991).Networksthatreflectthislogic,aregenerally characterized by explicit groups that are continuously networking to bring committedparticipantsintoactionandkeepthemthere(Bennet&Segerberg,2012).Relatedtothelogicofcollectiveaction,thereisanoftendiscussedissue,namelytherational,self-interesthabitofindividuals.Inmanycases,individualswillnotacttoachieveacommongroupinterest,andrather“freeride”onthecontributionsofothers.Olson(1965)describesthisphenomenonbyusingtheexampleofcollectivebargaining.Factoryworkersusuallyhaveaninterestinunionizingtonegotiatehigherwagesandforcebetterworkingconditions.However, joiningtheunionrequirestheuseofresources.Ontheotherhand,non-joinerswouldbenefitfromthesameagreement.Consequently,eachindividualworkerwouldhaveaninterestinnotjoiningwhilestillobtainingthebenefitsofbeinga“freerider”.Asmostofthepeoplewouldattemptto“free-ride”,thenumberofjoiningmemberswouldn’tbesignificantenough


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toachievetheendgoal(Sabin,2003).Thisexampledescribesasocialdilemmawhichinvolvesaconflictbetweentheindividualrationalityandoptimaloutcomeforagroup.Buttheassumptionthathumancommunitiesarecontinuouslystuckinasocialdilemmahasincreasinglybeenreplacedwitharecognitionthatindividualsfacethepossibilitytoachieveresultsthatcircumventtheworstpossibleoutcome,andinsomecaseseventurnoutoptimal(Ostrom,2007).Today,thepredictionsofearliertheorieshavebeenreplacedbyfarmoreoptimisticones.AsopposedtoOlson’stheory,Ostrom(1999)argues,thathumancommunitiesvoluntaryorganizethemselvesandcontributewiththemindsetof gaining collective benefits, as the willingness to conduct is strongly correlating with the expectedbehaviorsofothers.Ostromsapproachwillbefurtherelaboratedinthefollowingpart.

2.2.1 ElinorOstromsapproach

IncontrasttoMancurOlsonswork(1965),Ostrompresentsanew,morepositivetheoreticalframework,in which human communities can handle common property resources. In her book Governing theCommons: The evolution of Institutions for collective action (1990),Ostromdemonstrates that humancommunitieshaveactuallycreatedanumberofinformalagreementsthroughwhichacommunityofusersisabletomanageresourcescollectivelyandcontrolviolators(free-riders)insuchawaythattheresourceispreservedovertime.Although Ostroms (1990) work mainly focuses on individuals and common property regimes in theagriculturalsector,herwayofframingproblemsleavessubstantialroomforthestudyofsocialsystems,suchasthebehaviorofpeopleasindividualsbutalsoasactorsinamarketsettingorinapubliceconomy(Laerhoven,2011).Acommon-propertyregimecanforinstancealsobethoughtofasasettingoffirmsinwhichrepresentativesagreetoenteralong-termcontracttoeconomizeoncertaintransactioncosts,andthereforeengageintheinterestsofothersinthejointuseofcommon-poolresources(Bromley,1993).Thesameapproachappliestothisresearchasthisstudydoesnotfocusonthebehaviorofindividualsbutratherdifferentorganizationsthatcollectfire-relateddata.

2.2.2 Ostromcollectiveactionframework

Throughouttheyearsagrowingandextensivetheoreticalliteratureproposedthatanumberofstructuralvariablesarepresumedtoaffectthelikelihoodofparticipantstoachievecollectiveactionandovercomesocialdilemmas.Inherworkcollectiveactionandlocaldevelopmentprocess,Ostrom(2007)presentsanumberofstructuralvariablesandwillbefurtherexemplifiedinthefollowingtable2.1.StructuralVariable Effectoncollectiveaction

1. Thenumberofparticipantinvolved

Thereisatwosidedopinionregardingthegroupsizeanditseffectoncollectiveaction.InhisbookThelogicofcollectiveaction,MancurOlson(1965)arguesthatan increasinggroupsizenegativelyaffects theprobabilityofachievingapublicgood,ashearguesthatanincreasedgroupsizeleadstoanincreaseofthe“freerider”effectandthusnegativelyaffectsthelikelihoodthatthecommongoodwillbeachieved.Ontheotherhand,otherscholarssuchasBatesandShepsle(1995)developed theoppositeprediction, saying that theprovisionofpublicgoods ispositivelycorrelatedwiththegroupsize.


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2. Whetherbenefitsaresubtractiveorfullyshared

Sharing or subtracting benefits, relates to the problem of “free-riding”. Publicgoodsgenerallyhavethecharacteristicofnon-subtractability,whereascommon-poolresources(CPRs)aresubtractable.AccordingtoOstrom,WalkerandGardner(1992), inaCPRenvironment,an increase in thenumberofparticipants,whileholdingothervariablesconstant,isgenerallynegativelyrelatedtoachievingsocialbenefits.

3. Theheterogeneityofparticipants

Olson (1965) argues that a number of individuals with a strong interest inachievingapublicgood,hasan increasedprobability toachieveapublicgood.Other scholars such as Hardin (1982) however speculate that heterogeneity isnegatively related to gaining cooperation, as for instance heterogeneity ininformation increases the conflict that would exist over the distribution ofbenefits.

4. Face-to-facecommunication

Communicationisusedforconvictionandbybeingabletolookothersdirectlyintheeyewhilediscussingissues.Theeffectivenessofcommunicationishigherthanrelyingonwrittencommunication(FrohlichandOppenheimer,1998)inOstrom(2007).

5. Theshapeoftheproductionfunction

In order to solve a social dilemma, it requires individuals to take actions thatproducebenefitsforothersandthemselvesatacosttheymustbearthemselves.It can be argued, that when the shape of a production function is step (highinvolvementofindividuals),solvingasocialdilemmaisfacilitated.

6. Informationaboutpastaction

Knowingaboutearlieractionsofotherscanhaveasubstantialimpactontheindividualschosenstrategyandishighlyrelatedtotheparticipant’sreputation.Howeveritrequiresarepetitionofinteractions.

7. Howindividualsarelinked

Havingdirectlinksbetweenbetweenindividualse.g.actorsAcontributesresourcestoactorBincreasesthelikelihoodthatindividualscontributetoeachother’swelfare,ratherthaneveryone’scontributiongoestoageneralizedpool.

8. Whetherindividualscanenterorexitvoluntarily

HaukandNagel(2001)arguethatwhenindividualshaveachoicewhethertocooperatewithothersinasituationofasocialdilemmaandtheycanidentifytheindividualswithwhomtheyhavecooperatedbefore,individualswillchoosepartnerssoastoincreasethefrequencywithwhichcooperativeoutcomesareachieved.

Table2.1StructuralvariablespredictedtoaffectthelikelihoodofcollectiveactionInadditionto theabove listedstructuralvariables,Ostrom(2007) furtherhighlights the importanceofthreecorerelationshipsthatarepresumedtoaffectthelevelofcooperationwhenfacingasocialdilemma,namely-trust,reputationandreciprocity.TrustTrustisthecentraltheoreticalvariablewithinOstromscollectiveactiontheory,asitisacornerstoneofcollaboration. Cooperative behavior requires leaving one’s own self-interest in order to advance theinterestofthegroup.Thishowevercarriestheriskthatotherswillnotcooperate,leavingthecooperatorpaying all the costs of cooperationwithout receiving benefits (Henry & Dietz, 2011). Thus, onemustassumethatsomedegreeoftrustexistsbetweenoneandtheotherstoestablishalevelofcooperation.Theabilitytoworkcollaborativelyisacorecompetencyforalearningorganization,andbuildingtrustlaysthefoundationforcollaborativepractices(Hattori&Lapidus,2007).Trustdevelopsthroughrepeatedand


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meaningfulinteraction,wheretheinvolvedlearntofeelcomfortableandopeninsharingtheirindividualinsightsandconcerns(Holton,2001). Ithelpsyoutounderstandtheotherpartiespositionandsensingwhether there isa truthfulopportunity forgive-and-take (Cisco,2007).Especially in today’sdigitalizedeconomywithagrowingtrendindatasharing,themeaningoftrustbecomesevenmoreimportantasdatabreachesand identitytheftarecommonincidents.Therefore,anetworkoftrusteddata,thatprovidessecureandsafeaccesstoeveryoneinvolved,mustbeestablished(Hardjonoet.al.,2016).Theroleoftrustisanimportantvariableamongstthosethatemergedata-sharingpractices(Merson&Phong,2015)andthereforearelevantfactorinthisstudy.Reputation“Reputationistheopinionthatpeopleingeneralhaveaboutsomeoneorsomething,orhowmuchrespector admiration someone or something receives, based on past behavior or character” (CambridgeDictionary).Therehasbeenalotofresearchonreputationanditsimpactoncooperativebehaviorandrecentexperimentswithhumansubjectsrevealed,thatitrequiresknowledgeofthepartners’reputationinordertoworkasacooperationdriver.Individualsdonotonlybasetheirdecisionsbasedonpayoffsbutbehaveconditionallyonthenumberofcooperativeactstheyreceive,aswellasontheirownpreviousactions(Cuesta,et.al.,2015).Reciprocity“Reciprocityisthebehaviorinwhichtwopeopleorgroupsofpeoplegiveeachotherhelpandadvantages”(CambridgeDictionary).AccordingtoGouldner(1960),reciprocityisthebasisofstablerelationshipsandexplainstheoriginsoftrustandtrustworthybehavior.Thenormprescribes,thatpeopleshouldhelpthosewhohavehelpedthem.Concurrentlythenormprescribesthatpeopleshouldcounterthosewhoviolatetheinterestsandthatexploitationofcooperationshouldnotbetolerated(Komorotia&Hilty,1991).Illustration2.2belowprojectstherelationbetweenreputation,trustandreciprocityandhighlightsthatagoodreputation,ahighleveloftrustandreciprocityarepositivelyreinforcingthemselves.

Figure2.2Thecorerelationshipsattheindividuallevelaffectinglevelofcooperation(Ostrom2007)

Ostrom(2007)states,thatwhenindividualsinitiatecooperationinarepeatedsituation,otherslearntotrustthemandaremorewillingtoadoptreciprocitythemselves,leadingtohigherlevelofcooperation.Followingthis,Ostrom(2007) linksthebeforementionedexternalstructuralvariablestotheindividual


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corevariables– reputation,trustandreciprocity,which inturnaffectthe levelofcooperationandnetbenefitsachieved.

Figure2.3Ostroms(2007)collectiveactionframework

Figure2.3illustratestheoverallframeworkwhichlinkstheexternalstructuralvariablesandtheinnercorevariables.However,itshouldbenoted,thatitisnotpossibletolinkalltheidentifiedstructuralvariablesinonedefinitivecausalmodel,duetothelargenumberofvariablesandthatmanyofthemaredependedonthevalueofothervariables(Ostrom,2009).Also,thefigureabovedoesnotrepresentthewholesetofstructural variables, that are likely to affect collective action. Other scholars such as Agrawal (2000)identifiedover30additionalvariablesthatarepositedtoaffectcollectiveaction.However,animportantnextstep istoexplorehowthestructuralvariables interactwithoneother.Onecannotarguethatforinstance, the number of participants alonemakes a difference, rather it is a combination ofmultiplevariablesthatevokenormsandhelptobuildtrust,reputationandreciprocity(Ostrom,2009).Ostrom(2009)furtherstatesthatresearchoncollectiveactionisachallengebothintermsofacquiringconsistentandaccuratedatabutalsobecauseofthelargenumberofvariablesthatmightaffectcollectiveaction.Shesuggeststhatinsteadoflookingatallofthepotentialvariables,oneshouldfocusonadistinctandprecisechainofrelationships.Inregardstothisresearch,thefocuswillthereforebeontheinnercorevariable–, trust,as trust isacentralelementwithinOstromscollectiveaction frameworkand lays thefoundation for collaborative behavior but also because itwas emphasized upon by previous scholars,throughouttheemergenceofdata-sharingpractices.Besidestheimportanceoftrust,thisresearchwillfocusontheexternalvariables;numberofparticipantsandheterogeneityofparticipants.Thetwoexternalvariableswerechosenbecausethisresearchconstitutesfirstadvanceswithrelevantstakeholders.AstheinterviewedstakeholdershadnoorlittlepreviousknowledgeabouttheSandboxideaingeneral,itseemedreasonablenot to focusonotherstructuralvariables suchashow individuals/organizationsare linked,whichundercertaincircumstancesrequirespreviousin-depthknowledge.Byratherfocusingonvariablesthatarestraightforwardtoanswer,abetterqualityofreceivedanswerswasassured.


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2.3 Bigdata&Dataanalytics

Intoday’sdigitaleconomydatahasbecomeincreasinglyvaluable.Notonlytobusinessesbutalsotothepublicsector,asitrealizesenormouspotential,thatcanbeunlockedbydatasharinganddataanalytics(Schalenkamp,2014).Itisarguedthatbigdatastimulatesinnovation,productivityandgrowth,improveclinicalmedicine,policingbutalsotorevolutionizescience(Hand,2016).Inthesphereofbigdata,theory,whichisbasedonassumptionsbecomeslessimportant,aswecansimplylookatwhatthedatasays(Hand,2016).ThedriverbehindthisisacombinationofspectacularadvancesinICTs,coupledwitharoutineofdatacollection.Accordingtosomeestimates,theamountofdataproducedworldwideisdoublingeverytwoyearsandthesourcesforthisenormousamountofdataareamongstothersinteractionsontheweb,socialmedia,mobileapps,biometricwearablesandsensorsinobjectsthatarelinkedtotheinternetofthings(IoT)(Davies,2016).

Figure2.4ActiveGrowthofGlobalData(Schalenkamp,2014)

Thedramaticgrowthofdataisinducedbyavarietyoffactors,startingwithnewandcheapersolutionstostore,manageandprocessdata(e.g.cloudsolutions),enormousadvancesincomputingpower,butalsoduetoadeclineofrelatedcostsandtheomnipresenceoftheinternetandthesprawlofonlinedevices(Silverberg,2016).Theseadvancescreatedthepossibilitytostore,transmitandprocessalargeamountofdatamuchquickerandeffectivelyandatthesametimemuchcheaperthanbefore(Davies,2016).Theglobalbig-datatechnologyandservicesmarketisexpectedtoincreaseatacompoundannualgrowthrateofapproximately23%between2014and2019,whiletheworldwiderevenueforbigdataandbusinessanalyticsisexpectedtoincreasemorethan50%fromalmostUS$122billionin2015tomorethanUS$187billion in 2019 (Davies, 2016). The largest sectors within thismarket includemanufacturing, banking,insurance,government,professionalservices,telecommunications,health,transportandretail (Davies,2016).Sharingandlinkingdataholdstremendouspromiseandthereisawidevarietyofpotentialusesforbigdata anddata analytics.However, it also raises crucial questionswhether our legal, ethical and socialnormsaresufficienttoprotectprivacyandothervaluesinabigdataworld(ExecutiveOfficeoftheUSPresident,2014).Bigdata,createsthepossibilitytopromoteinnovationwhilealsoimprovingthequality


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ofourlife,neverthelessmostofthesecapabilitiesarenotvisibleoravailabletotheaverageconsumerandmight also create an imbalance of power between thosewho hold the data and thosewho supply it(ExecutiveOfficeoftheUSPresident,2014).Therefore,it’squitenaturallythattherearealsobigconcernsrelatedtotheeraofbigdataanddatasharing.Therelatedbenefitsandchallengeswillbefurtherdiscussedinthesubsequentpart.

2.3.1 Thebenefitsofdatasharinganddataanalytics

Thereisawidevarietyofstimulatingopportunitiesconnectedtoaneverincreasingcapacitytocollect,store and analyze data. Even though there hasn’t been a big bang moment at which entire sectorscompletely transformeddue to the increased use of data, there is a trend that businesses undergo asignificantandgradualtransitiontowardsamoredata-drivenlandscape(Schroeder,2016).Thefollowingpartwillhighlightanumberofidentifiedbenefitsofdatasharinganddataanalytics.

IncreasedproductivityStudiessuggestthatcompaniesthatadoptbigdatapracticescanincreaseproductivityby5%-10%morethancompaniesthatdonot,andthatbigdatapracticesinEuropecouldadd1.9%totheGDPbetween2014and2020.Today’slargeamountofdata,eitheronitsownorincombinationwithdatafromothersourcescanbeusedtoidentifypatternsandmeaningfulrelationships.Thesegainedinsightscanthenbeusedtodesignnewproductsandservices,improveproductionprocesses,optimizemarketingorenablebetterdecisionmaking(Davies,2016).

NewfactorofproductionAccordingtoastudywhichwasconductedamongstglobalbusinessleadersin2012,datahasbecomeanewfactorofproduction,astheanalysisoflargequantitiesofdatacanleadtonewinsightsandthereforeactasacompetitiveadvantageamongst firms.Data is thereforementionedasa fundamentalasset tobusinesses besides physical assets, labor or capital (Davies, 2016).Worlds famous economist,MichaelPorterevenstated,thatdataismostlikelybecomingacoreassetformanybusinessesinthefuture(Porter,2014).

LargerscaleanalyticsBeingabletoanalyzeenormousamountsofdatafromdifferentsourcesareespeciallyvaluableforscience.Inmedicine,forinstance,researchersanalyzedterabytesofbrainimagedata,whichwascollectedoverthirty yearsby several institutions.Due to the large scaleof data, the researcherswere able tomakeprogress in understanding the Alzheimer disease, by being able tomap five critical factors in distinctregionsofthebrain(Davies,2016).Brieflyspeaking,themoredatawehave,themorewewillunderstand.

EncouragingcollaborationAspreviouslyimplied,bigdataencouragesthelevelofcollaborationamongstdifferentstakeholders.Oneexample of a fruitful collaboration in the Pharma industry and big data includes the French companySanofi,whoposteditsprostatecancertrialsonawebsite,wherecompaniescansharedatawiththeaimtodevelopcancercures.Bypromotingcooperationwithinthe industrytheyhopetogetbreakthroughtreatmenttothepatientmorequickly(TotalBiopharma,2014).


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PromoteinnovationBigdataanddataanalyticsdoesnotonlyimproveproductivity,itisalsoreferredtoData-DrivenInnovation(DDI),whichentailstheexploitationofanykindofdataintheinnovationprocesstocreatevalue(Stone&Wang,2014).Themanufacturingindustrycanbementionedasagoodexample,asitisundergoingradicalchangeswiththeintroductionofITtechnologyonalargescale.Withtheupspringof“Industry4.0”sensorsandconnectivitybecomesmoreimportant.Smartmachinerysuchastruckenginescanbenefitfromthisdevelopment, as data-basedpredictivemaintenance is applied,where sensors are usedwithmachinelearning algorithms to avoid unnecessary maintenance jobs and to schedule protective repairs whenfailures are predicted (Zillner, et. al., 2016). According to the OECD report (2015) on data-driveninnovation,governmentsmustencouragemoreoninvestmentsinbigdataanddatasharingascountriescouldgetmuchmoreoutofdataanalyticsintermofsocialandeconomicgains.

BenefitsforsocietyBigdataanddataanalyticsdoesnotonlybenefittheindustry;onalargescaleitalsopositivelyaffectsthesociety. The U.S. Chamber of Commerce Foundation (2016) highlights seven areas, where properlyaccesseddatacanpayoffinsocialbenefits:

1. Publichealth Understandinganddefeatingdiseasesandinjury2. Publicsafety Anticipatingandpreventingcrime3. Nationalsecurity Preventinginstabilitythroughgraterknowledgeaboutitsforerunners

anddynamics4. Developmentand

povertyreductionDevelopingempirically-proventechniquesandtechnologiesforfosteringhumandevelopmentandpovertyreduction

5. Governance Puttingknowledgeaboutthedynamicsofsocialandeconomicproblemsinthehandsoflawmakers,alongwithoptionsandlikelyconsequencesofpolicyactions

6. Education Improvingpedagogicalartsandsciencestoenhancestudentperformance

7. Environmentalprotection

Protectournaturalheritageandsustainlife-criticalnaturalsystemsandresources

Table2.27greatwaysthatdatacanbenefitsociety(Raidt,2016)Thelistofabovementionedbenefitsofdatasharinganddataanalyticsisjustthethinendofthewedgeofseveralotheradvantagesanditwillmostlikelytakeseveralmoreyearsuntilabroaderadoptionofbigdatapracticesisapplied.However,thefocuswillnowbeturnedtoarisingchallengesthatarerelatedtodatasharinganddataanalytics.


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2.3.2 Thechallengesofdatasharing&dataanalytics

Despiteaconsiderableamountofadvantages,datasharingandanalyticpracticesalsoholdanumberofarisingchallenges,thatare inparticularrelatedto legal,ethicalandsocialnorms.Yet, it isagrayarea,containingamongstothersunclearandvaryingregulationsbutalsothegreaterextentof its impactonbusinessesandsocietyisnotyetvisible.Thefollowingpartwillhighlightanumberofidentifiedchallenges,thatarerelatedtodatasharinganddataanalytics.

DatasecurityandprivacyOneofthemainissuesrelatedtodatasharinganddataanalyticsareprivacyandpersonaldataprotection.Eventhough,alargeproportionofbigdataisnotpersonal(e.g.weatherinformationorsatelliteimaging),partsofbigdatapotentiallyincludeelementsthataredirectlylinkedtoaperson(e.g.name,address)andhencethisdataisconsideredtobepersonaldata(Davies,2016).Therearetechniquestopseudonymizeandremoveexplicitidentifiers,howeveritistechnicallyalsopossibletore-identifythisdataandthereforethe danger exists, that personal data can lead to unwanted disclosure of private information (Davies,2016). To increase privacy and personal data protection, the EU General Data Protection Regulation(GDPR)hasreinforcedEUdataprotectionstandards,whichareconsideredtobethehighestintheworld.(Davies, 2016). TheEuropeanCommissionargues, that thesehigh standardswill act as a competitiveadvantage,as they foster trustandwill consequently lead toan increasedwillingnessof sharingdata.OtheropinionshoweverbelievethatEuropeancompaniesarelosingoutintheapplicationofbigdataasstricterregulationsmayinfactpreventtherealizationofthepotentialbenefitsfrombigdata,ascostswilloutweighefficiencygains(Ciriani,2015).InSweden,variouslawsgoverntheuseofpersonaldata.Thoselawsapplytothedatacontrollers,whichisdefinedasthepersonorlegalentitywhoalone,ortogetherwithothersdecidesonthepurposeandmeansofpersonaldataprocessing(Svensson,2018).

DataownershipAnotherconcernrelatestodataownership.Generally,datadoesnotonlyhaveoneownerbuttypicallycomeswithacomplexsetofrights,associatedwithdifferentstakeholders.Tomentionanexample;smartcarstypicallygeneratealargequantityoftechnicaldata,butthenthequestionariseswhatrightstothatdataareassignedtotheownerofthecar,thedriver,thedealerwhosoldthecarorthecarmanufacturer?Asstatedpreviously,providingguidelinescouldincreaselegalcertaintybutontheotherhand,complexregulationsmightonceagainhinderdataexchange(Davies,2016).

DatacaptureandcleaningIngeneral,datasharingpracticesinvolvedifferentstakeholdersthatarecontributingtheirdatainordertogeneratevaluableoutput.However,oneoftenrelatedissueisthecompatibilityofthegathereddata.Thecaptureddataneedstobeclean,complete,accurate,consistentandformattedcorrectlyinordertobeabletoutilizeinacollectivenetwork.Upuntilnow,itisanoftenongoingbattlefororganizationstofulfilltheserequirements,asmanyofthemusedifferentsystems(Bresnick,2017).Alsoandevenmoreimportant,thequalityofthecaptureddataisofimportanceasalowqualityofdatacanleadtomisleadingormisguidedconclusionsandshouldbecircumventedbyallmeans.


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DatastorageDatastorageisanimportantissuewhenitcomestocosts,securityandperformance.Asthevolumeofdata grows exponentially it is an often observed challenge tomanage the costs and impacts of datacenters.Whereason-premisedatastoragepromisescontroloversecurityandaccess,anon-sitenetworkcan be expansive to scale, difficult to maintain and challenging to produce and share data amongstdifferent stakeholders. Cloud storage, on the other hand, becomes increasingly cheap and reliable(Siddiqa,et.al.,2017).Still,organizationsmustbecautiouswhomtheysharetheirdatawith.

Security Asitwaspreviouslymentioned,securityisoneofthemostimportantfactorsintermsofdatasharinganddataanalytics.Inthehealthcaresector,wheredatasharingisanalreadyestablishedpractice,anumberof technical safeguards for organizations were developed. These safeguards include amongst othersproceduressuchasusinguptodateanti-virussoftware,settingupfirewallsandtheencryptionofsensitivedata(Bresnick,2017).

AdministratorshipOngoingadministratorshipandcurationof thedata isan importantconcern.For thedataownersandanalysts,itisimportanttounderstand,whenandbywhomthedatawascreatedandforwhatpurpose.Also,itshouldbeknown,whousedthedatapreviously,whyandhow.Therefore,havingatrustworthydataadministrator,whohandlesthedevelopmentandcurationofthedatatoensurethatallelementshavedefinedformatsandremainusefulforitspurpose,isveryimportant(Dunning&Friedman,2015).

UpdatingInmostcases,dataisnotstatic,especiallyinrelationtothiscasestudy.Thinkofsmarthomeswithconstantroomtemperaturecontrol.Thesedataupdatesmayoccureveryfewseconds,whereasotherinformationsuch a home addressmight only change once in several years.Understanding the volatility of data isthereforeofmajorconcernandoperatorsshouldknow,whichdatasetsneedmanualupdates,whereasotherdatasetscanbeautomated(Bresnick,2017).

SharingSharing data amongst external partners is essential for projects such as the Sandbox. However, thereluctancebetweendataownersisconsiderablyhigh.Distrust,thepossibilitytolosevaluedcustomerstocompetitors,orrevealingauniquecompetitiveadvantagearejustafewexamplestomentionthatareinfluencingdataownersandtheirwillingnesstosharedata.Inordertoestablishadata-sharingnetwork,it therefore requires clear guidelines and strategies,making it easier to share data securely (Olson&Downey,2001).The above-mentioned challenges just present a partial overview of factors to keep in mind whendevelopingabigdataexchangeecosystem.Inordertofunction,participantsmustbeabletoovercomeeachof thebeforementioned factors anddoing so takes time, commitmentand communication. ThesubsequentpartofthisresearchwillfocusontheviewpointsandconcernsofpotentialstakeholderswithintheSandboxprojectandwillhopefullydelivernew,additionalinsightsrelatedtodatasharinganddataanalytics.


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2.4 Summaryofliteraturereview

Theprevious literature reviewwasbrokendown into twomainareas.The first section focuseson thetheorybehind the logicof collectiveactionandwill in theupcoming studybeofmain interest,as theguidingresearchquestionfocusesonfactorsthatinfluencepotentialstakeholdersandtheirdecisiontoengageinadatasharingnetwork.Inthisparticularcase,theideaistoconnectdifferentstakeholdersthatcollectfire-relateddatainordertoimprovefiresafetyinSweden.Thisapproachrelatestothecollectiveactiontheory,asitinvolvesactionsthatarecarriedoutbyagroupoforganizationsthataretryingtoobtaina common goal, namely to enhance proactive fire safety. The subsequent mentioned benefits andchallenges,relatedtodatasharinganddataanalyticsshallthengiveadditionalinsightsandhelptoclarifythe viewpoints and concerns of potential stakeholders. Highlighting the benefits and challenges is ofimportanceastheymighthaveacrucialimpactonthestakeholder’sdecisiontoengageincollectiveaction. Therefore,boththeoreticalareasarehighlyrelatedtoeachotherastheyreinforcethemselves.

3. Methodology3.1 ResearchStrategy

Theaimofthisresearchistoidentifyfactorsthatarelikelytoaffectstakeholdersandtheirdecisiontosharefire-relateddata.Therefore,togetabetterunderstandingofhowinvolvedorganizationsoperate,includinghowandwhatdatatheycollectbutalsotounderstandtheirconcernsandterms,itissuitabletoconductanexternalanalysisamongstdifferentorganizations.Theappliedmethodologyisofqualitativenature,notonlytoobtainrichdataandinformationaboutprocesses,strategiesandapproachesbutalsotobeabletounderstandthebigpicture.Lookingintotheepistemologicalconsiderations,thefocusofthisstudyisoninterpretivismastheanticipatedtypeofinformationismostlikelytobefoundinthepersonalviewsandperspectivesofthepeopleandthereforerequiresaclose involvementwiththeinvestigatedpeople.Suchpersonalviewpointsareratherunobtainablethroughtheuseofquantitativeanalysessuchassurveys(Bryman&Bell,2011)andthereforeaquantitativeanalysisisnotapplicableforthisresearch.Moreover,aqualitativemethodologyallowsforacertaindegreeof flexibility,as forexample toadjustinterviewquestions,ortodivedeeperincertainareasinalignmentwithnewlymadediscoveries(Bryman&Bell,2011).Also,aninductiveviewoftherelationshipbetweentheoryandresearchisapplied,wherebytheformerisgeneratedoutofthelatter(Bryman&Bell,2011).Incontrasttotestinganestablishedtheory,thescopeofthisresearchistodevelopnewvaluableinsights,astherehasbeennotolittleresearchonthisspecificsetofdatasharingrelatedtofiresafetybefore.Atthispointitshouldbenoted,thataqualitativeresearchstrategyishighlyvulnerabletosubjectivismandgeneralization,basedontheresearcher’ssubjectiveobservationsandinterpretations(Bryman&Bell,2011). This obstacle cannot be fully avoided, however by frequently biasing the observations on atheoreticalframework,affectingpersonalinterpretationsandgeneralizationsshallbeminimized.


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3.2 ResearchDesign

Intheprocessofdatacollectionandanalysis,acasestudydesignhasbeenchosen.Conductingasinglecasestudyismotivatedbyvariousaspects,bothinrelationtotheexpectedoutcomeoftheresearchaswellasofpracticalimplications.Primarily,thecasestudydesignentailsthedetailedandintensiveanalysisofasinglecase(Bryman&Bell,2007).Asemphasizedintheresearchquestion,thefocusofthisresearchis to identify factors that affect data owners and their decisions to share data amongst differentstakeholders. One would argue that the examination of different stakeholders can be classified as amultiplecasestudy,howeverinrelationtotheresearchquestion,thisstudyistreatedasasinglecase,asthegoalistoprovideanin-depthelucidationofSandboxidea.Byfurtherlookingintothespecifictypeofcase,Yin(2003)generallydistinguishesfivetypesofcases.Consideringthedifferentclassifications,thisresearchcanbeconsideredasarevelatorycase,asthefocusisonaphenomenonthatwaspreviouslynotinvestigatedscientificallyandthereforegoesinlinewithaninductiveapproach.Shiftingthefocusonthepracticalsituation,asinglein-depthcasestudyissuitableforthisresearch,asitisconductedincooperationwithSBF.HavingacloserelationshipwiththeinitiatoroftheSandboxprojectisbeneficial,asitprovidesaccesstodetailedbackgroundinformationabouttheprojectbutalsonottodrifttoofarawayandstayfocused.

3.3 ResearchMethodsandDataCollection

Duetotheexplorativenatureofthisstudy,whichfocusesontheexperiencesandperceptionsofrelevantstakeholders, qualitative data collectionmethods are applied in this research as preferredmethod ofobtainingdata.Specifically,semi-structuredinterviewswithachosensampleofstakeholdersthatcollectfire-relateddatawereconducted.Semi-structuredinterviewshavetheadvantagethattheyareflexibleintheir process and therefore create the possibility to adapt and rephrase questions according to thesituationalcircumstances (Bryman&Bell,2011). Thedegreeof flexibilityandadjustability is themainmotivationbehindchoosing thismethod,as the chosen researchareawaspreviouslynot investigatedscientificallybutalsowiththeintentiontoexploretheintervieweesownperspectives.Also,byhavingtheopportunitytoadjust interviewquestions,richerandmorevaluable informationfromtherespondentscanbegathered,whichprovidesamorecompletepictureandincreasesthevalidityofthestudy(Bryman&Bell,2011).Further,semi-structuredinterviewsassureacertainleveloffocusandguidance,whichisespeciallyhelpfulforresearchersthatarerelativelyinexperiencedinthefieldofinterviewing(Bryman&Bell,2011).

3.3.1 SelectionofOrganizationsandRespondents

Theselectionofpart-takingorganizationsandrespondentsevolvedthroughouttheclosecooperationwithSBF.AlistofpotentialstakeholdersandcontactpersonswasprovidedbySBFastheyhavepreviouslybeenin contactwith a number of potential stakeholders. The criteria for being a relevant stakeholder andthereforebeingapotentialintervieweeweremainlyrelatedtotheorganizationsconnectiontofire-relateddataanditsgeographicallocationinSweden.


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Potentialintervieweeswerecontactedviaemail,whichincludedabriefinformationabouttheresearchareabutalsoanoverviewofotherpotentialpart-takingorganizations.Alsoitwasclearlystated,thattheresearchhasanacademicbackgroundinformofaMasterthesis.Intotal,eightemailstosevendifferentorganizationsweresentout.Incasethatnoresponsewasreceivedwithinaweek,afriendlyreminderwassentouttotheorganization,toemphasizetheinterestasthembeingpartoftheresearch.Overalleightresponseswerereceived,outofthese,sixrespondentsagreedtobeinterviewedaspartoftheresearch. Table 3.1 below provides an overview of the conducted interviews, followed by a brief backgroundinformationabouttheorganizationandinterviewee.

OrganizationPositionoftheInterviewee

Date(2018) Length Channel

SBFConsultant/Data

Expert4thofApril 50min F2F

SOSAlarm FireSpecialist 12thofApril 31min F2F

Länsförsäkringar InnovationManager 12thofApril 28min F2F

MSB Statistician 17thofApril 52min Skype

KarlstadUniversityProfessorinNatural

disastertheory18thofApril 30min Skype

GötaLejon RiskManager 24thofApril 34min F2FTable3.1OverviewoftheconductedinterviewsandorganizationsSBF isanover100yearsoldSwedishorganizationwithabout150employeesand100consultantsthatworkwith fire and safety inspection,writes rules and regulations and through their research, supportresearchersonfiresafety.SBFisanassociationthathasbothcommercialandsocialgoals.TheirmissionistomakesurethatnobodyinSwedendiesorgetshurtinfiresandthatnopropertyisdestroyed.TheinterviewwithinSBFwasconductedwithanITconsultantwhoworksforSBFsince1year.TheintervieweehasalotofexperienceintherealmofbigdataanddatabasesandsupportsSBFwiththeSandboxproject.Hecurrentlydevelopsaprototypetoestablishaproofofconcept.SOSAlarmistheSwedishhubthatcreatessafetyandsecurity.Formorethan60yearstheyhandlethenationalemergencyhotline112andmakesurethattheambulance,emergencyservicesandpolicecandotheirjob.SOSAlarmisanorganizationthathasauniqueaccesstoinformation,whichtheycontinuouslyconvertintoknowledgeandservices.TheorganizationispartlyownedbythestateandallofSweden’smunicipalitiesandcountycouncils.Theorganizationemployed947peoplein2016whilegeneratinganoperatingprofitof57.8millionSEKatthesametime.TheintervieweeworksforSOSAlarmformorethan12 years and has an in-depth knowledge regarding information handling and policies within theorganization.


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Länsförsäkringarisauniqueallianceof23customer-ownedregionalinsurancecompaniesinSweden.Allcompanieshaveastronglocalbaseintheirhomemarketandhavenoownershipinterestotherthanthoseoftheircustomers.Theirmissionistodevelopproducts,conceptsandsystemsupport,exclusivelyontheircustomer needs. In addition to the parent company, Länsförsäkringar AB, the group includesLänsförsäkringarSak,LänsförsäkringarBank,LänsförsäkringarFondlivandLänsförsäkringarLiv.Thegroupemploysmorethan2000peopleandtheoperatingprofitexceeded2.8billionSEKin2017.TheintervieweeworksasChiefInnovationManageratLänsförsäkringarandhasextensiveexperienceininnovationandtechnology.Asheintroducedhimself:“Iamdoingstuffthatwedon’tdotoday.”MSB,alsoknownastheSwedishCivilContingenciesAgencyistheresponsibleactorforissuesconcerningcivilprotection,publicsafety,emergencymanagementandcivildefenseaslongasnootherauthorityhasresponsibility.MSBhasaclosecooperationwiththemunicipalities,countycouncils,theprivatesectorandother organizations and works with knowledge enhancement, training, supports regulation andsupervision.Theirgoalistoachievegreatersecurityandsafetyatalllevelsinsociety.MSBissteeredbythe Swedish Government, specifying objectives and reporting requirements, while also allocatingresourcesforMSBadministrationandactivities.TheintervieweeworksasastatisticsproducerforMSBintheknowledgedevelopmentsection.HisroleistosupervisethefirebrigadesinSwedenbytakinginandanalyzingdatafromtheincidentreports,whichareprovidedbythefirebrigades.KarlstadUniversityThe contacted interviewee is a professor in the field of risk and environmental studies at KarlstadUniversity,Sweden.Withabackgroundinnatural-andgeoscience,theintervieweeworksamongstotherswithriskmanagementandiscurrentlyinvolvedinacatastrophemodelingprojectwiththeSwedishKK-Foundation.SimilartotheSandboxproject,theideaistodevelopnewtypesofdatacollectiontogetabetterunderstandingofhowlargeadamageisaftertherewasarainstorm.TheconsultedscientisthaspreviouslybeenincontactwithSBFandwasroughlyfamiliarwiththeSandboxproject.GötaLejonisaninsurancecompanythatworkswithlosspreventionandisresponsibleforallmunicipalownedadministrationsandcompaniesinGothenburg.Theirmissionistoofferinsurancesolutionsthatbenefittheentirecity,whilealsobeinganimportantcatalysttoreducethecitiesrisksandresponsibilitiesforefficientclaimsmanagement.GötaLejonactsasanon-profitorganizationandcurrentlyemploys12people.Theintervieweeworkswithintheorganization´s losspreventionandriskmanagementandhasseveralyearsofworkexperienceintherelatedfield.

3.3.2 Practicalities

Before conducting the interviews, an interview guide was constructed (Appendix 1). The guide wasstructured in accordance to thebuilding blocks thatwere identified in the theoretical framework andtherefore includedtwomainfieldsof interest:questions inrelationtothecollectiveactiontheoryandquestionsrelatedtodatasharinganddataanalytics.Overall,theinterviewguidecombinedsixtopicareas:personalbackgroundinformation,challengesandfuture,currentdatacollectionpractices,datasharinganddataanalytics,benefits&challengesofdatasharingandprojectSandbox.Foreachtopicarea,specificinterviewquestionswereformulated,fromwhichtheinterviewercouldchoosefrombutalsobeingable


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toadaptquestionsinagivensituation,astheinterviewwentalong.Preparinganinterviewguideassuredboth,askingtherightquestionsinrelevantareas,whilealsokeepinganadequatelevelofflexibility.ThefirstinterviewwasconductedwithSBF,whopickeduptheideaoftheSandboxproject,whilealsobeingastakeholderwithintheframework.TheinformationobtainedfromtheinterviewwithSBFwerecrucialforallsubsequentinterviewsastheyprovidedinitialin-depthinformationabouttheSandboxideaandcanthereforebeconsideredasbackgroundinformation.Even though, SBFwas in the previous contact with relevant stakeholders, not every intervieweewasprofound familiar with the Sandbox approach. Therefore, relevant information obtained from theinterviewwithSBFweresharedwithall subsequent intervieweespriorconducting the interview itself.Also,thephrasingofcertaininterviewquestionswasslightlychangedaftertheinterviewwithSBFasitbecame apparent that the interviewee did not grasp the full intention of the interview question. Inparticular,thephrasingofinterviewquestionsrelatedtoOstromscollectiveactiontheorywerechanged(Topicarea5,seeappendix1)toassureabetterqualitythroughoutconductingtheinterviews.Mostoftheinterviewswereconductedonaface-face(F2F)level,astheyhaveaseriesofadvantagesasopposedtotelephoneinterviews.Thisincludesamongstothersthelengthofaninterview,whichisusuallylimitedto20-25minutesviathephone,whereaspersonalinterviewscanbemuchlongerthanthis(Bryman&Bell,2011).Also,itisimpliedthatthederivedqualityofdatafromtelephoneinterviewsisinferiortothatofcomparable(F2F)interviews(Bryman&Bell,2011).However,duetogeographicaldistances,twooftheinterviewswereconductedviaSkype.Inordertomaintain,aggregateandanalyzethegathereddata,theinterviewswerewiththepermissionoftheintervieweevoicerecordedandlatertranscribed.Therebythemobileapplication“Wrappup”wasused,whichrecordsandtranscribessimultaneously.Thetoolwashelpfultobacktrackimportantsectionsof the interview and was of major importance for the analysis part. Besides using a recording tool,importanthandwrittennotesweretakenthroughouttheinterview.3.4 DataAnalysis

Miles(1979)oncedescribedqualitativedataasan“attractivenuisance”duetotheattractivenessof itsrichness but also the difficulty to find analytical lanes through that abundance. Bryman& Bell (2011)therefore state that the researcher must protect himself from being flooded by the richness of thecollecteddata,topreventfailingtogivenowidersignificancetothedata.Inordertopreventthisfailure,a thematic analysis was performed which focuses on the identification of patterned meaning acrossdatasetstoanswertheresearchquestion(Braun&Clarke,2006).Oneoftheadvantagesofathematicanalysisis,thatitisrelativelyflexible,butalsoitsuitsquestionsthatarerelatedtopeople’sexperiences,viewsandperceptionswhichisofspecialinterestinthisresearch.Theprocessofathematicanalysisstartswith data familiarization, followed by coding, searching for themes, revision of the themes and asubsequently defining andnamingof themes (Braun&Clarke, 2006). The intendedaimof a thematicanalysisistodetectpatternsthroughoutthegathereddata,inordertogainnewinsights,thatideallyhelpto answer the research question. By applying a thematic approach, it was possible to structure theplenitudeofgathereddataintothemeswhichsubsequentlysimplifiedtheanalysis.


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3.5 Qualityofthestudy

Inordertoreduceuncertainties,thequalityoftheresearchisveryimportant.Thisincludesahighlevelofreliabilityandvalidity.Thereisanoftendiscussedissueregardingthevalidityandreliabilityofqualitativeresearch,ascertainresearchersquestiontheirrelevanceforqualitativeresearch(Bryman&Bell,2011).However, one stance is to assimilate reliability and validity intoqualitative research as LeCompte andGoetz(1982)do.

3.5.1 Reliability

Thereliabilityreferstothedegreetowhichastudycanbereplicated(externalreliability).Inqualitativeresearch, this is in particular difficult, as creating the same environmental setting throughout theinvestigationisdifficult(Bryman&Bell,2011).Thisthesishoweveremployshightransparencybyprovidingadetailedexplanationofundertakendecisionsandprocedures,whichhopefullyaffectsthereplicabilityandthereforeincreasesthereliabilityofthisstudy.Theinternalreliabilitycanbeaffectedbythenumberofobserversinvolved.Asthisresearchwasconductedbyasingleperson,itisdifficulttoexchangeopinionsandfindconsistencies,howeverbyhavingaclosecollaborationwithSBFandafrequentreporting,thelackof internal reliability has been reduced. Further, a good preparation prior the interviews was ofimportance. In order to assure a good quality of the interviews within this study, the researcherfamiliarizedhimselfwiththeintervieweepriorconductingtheinterviewwhilealsoperforminginterviewpilotstogetfamiliarwiththequestioningandhowtoreactongivenanswers.

3.5.2 Validity

The internalvalidityofastudyreferstotheconsensusbetweentheresearchers’observationsandthetheoreticalideastheydevelop(Bryman&Bell,2011).Inqualitativeresearch,itisinmostcasesdifficulttomeasuresuchvalidityhoweverthevalidityofthisresearchwas increasedbyafrequentcomparisonoftheories and empirical observations throughout the research process. The level of external validity isanotheroftenconcernedproblemwithinqualitativeresearch.Toincreasetheexternalvalidityofastudyitisimportanttomaketheresultsgeneralizablesotheycanbeappliedtoothersocialsettings(Bryman&Bell,2011).Byformulatingaclearandwell-structuredresearchquestion,thelevelofvaliditywastriedtoincrease. Also by choosing an appropriate sample for the topic of interest, the validity was affectedpositively.Asitwasstatedearlier,theintervieweeswerecarefullychosenincooperationwithSBFandtheinterviews resulted in rich insightful findings. The validity was further increased by following a goodresearch practice,which included the importance to keep track of the research phases, supported byfrequentauditionsfromothers(regularmeetingswiththesupervisorandSBF)andagoodlevelofself-reflection.


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4. EmpiricalFindingsIn this section, the results of theempirical data collection areoutlined.Asmentioned in thepreviousmethodologypart,sixdifferentinterviewees,eachrepresentingadifferentorganizationwereinterviewed.Thefollowingchapterhighlightsrelevantempiricalfindings,resultedfromtheinterviewguide.Overall,five topicareaswere identifiedwhichare successivelypresented. Subsequently, themain findingsaresummarizedintable4.1.

4.1 Topicareas

4.1.1 Topicarea1:ChallengesandFuture

Thefirsttopicareaisprimarilyconcernedwithchallengesthattheinterviewedorganizationisfacingtoday,butalsoanticipatedchallengesforthefuture.Theintervieweewasaskedtodescribethecurrentsituationwithintheorganizationandifapplicable,elaborateoninitiatedactionsinordertoembracethefuture.Whiletalkingaboutpresentchallengesandthefuture,theintervieweewasalsoaskedtorelatehisanswerstodatasharinganddataanalytics(ifpossible).SBFSBFisanorganizationwhichworkstowardsthegoalthatnooneinSwedendiesorgetshurtwithinafire.Formorethan20yearstheycollectfire-relateddatabutsofartheydidn’teffectivelyusethatdata.ItwasmorerecentlythatSBFstartedtolookintoareaswherethisdatacouldbeutilized–notonlyinternallybutalsoexternally.Generallyspeaking,SBF is themiddlemanbetweenthe firebrigadesandthe insurancecompaniesandinvoicestheinsurancecompaniesforthefirebrigadessalvageservices.Withinthisprocesstheycollectamultitudeoffire-relateddata,howeverthisdataonlyrepresentsasubsetoftheoveralldata.SBFsgoalistoconnecttootherentitiessuchasinsurancecompaniesandpropertyowners,inordertogetaccesstotheothersubsetofthedata.However,thementionedchallengerelatedtothisideaisthatmostentities are not willing to share their data, due to their fear of giving away valuable and sensitiveinformation.SOSAlarmSOSAlarmistheorganizationthathandlesall112callsandthereforecollectsanenormousamountofdatafromallfiresinwhichthefirebrigadeisinvolvedin.Theyarecurrentlylookingintonewwaysonhowtousethisdata.Specifically,theyareinterestedinbeingabletoseewhatmighthappennext.SOSAlarmisjustattheverybeginningofthisprocessbuttheyunderstoodthattheycandoalotmorewiththeirdata.Presently, SOS Alarm is sharing their datawith other organizations such asMSB, however theirmainchallengeistogiveotherentitiesonlytherightamountofaccesstotheirdata,asitcontainsundercertaincircumstances,privateandthereforeconfidentialinformation.LänsförsäkringarLänsförsäkringarhasaveryinnovatewayofthinking,withahighcustomer-focusinmind.Theirideaistochange their businessmodel, away from selling insurances as a product towards being proactive andpreventinginsurancecases.Theywanttofocusonservicesthathelptoeliminatetherisksthatpeoplehavefortheirhomes,propertyandhealth.Therefore,theywanttomovealltheirresourcesandmoney


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frombeingreactivetowardsbeingproactiveandseehowtheycanprovidesafetyfortheircustomersinthefuture.Withinthisapproach,onemajorchallengeistogatherasufficientamountofdata,butalsotobeabletounderstandhowtoconnectandusethisdatainordertohelptheircustomersandeliminaterisks.MSBMSB collects fire-related data from the fire brigades, however up until now this process is voluntary.Therefore,theirfuturegoalistomakethisprocedureobligatory,meaningthatthefirebrigadesareobligedby lawtoprovideMSBwithfire-relateddata.ThedrivingreasonrelatestothefactthatMSB ishavingproblemswithunder-reportingbutalsobecauseofavaryingqualityofthedatawhichlikewiseaffectsthereliabilityandusabilityofthedata.Also,MSBwouldbefurtherinterestinhavingaccesstoadditionaldata-setssuchasinformationregardingfire-relatedcosts.Todate,MSBonlyreceivesinstantreportsfromthefirebrigadeswhichincludesinformationaboutthecauseofafire,whenitstartedandwhichactionwastakenbythefirebrigade.However,theydon’tknowtheactualcostsofafirewhichwouldbeanadditionalreasonableproxy.MSBstatesthatitwouldgivethemmuchmorepowertotheiranalysesiftheywouldcooperatewithinsurancecompaniesandgetanactualnumberoflossesinKrona.KarlstadUniversityTheresearcherfromtheuniversitystatesthatthemainchallengeswithinadatasharingframeworkarerelated to regulations on data handling. Organizations are obliged to keep personal informationconfidentialnotonlyby lawbutalso tomaintain legitimacy towards their customers,bynot revealingprivateinformationaboutwhoisinsuredandinwhatway.Further,competitivereasonsarementionedasdataisconsideredasavaluableresourceandthereforesustainsthefirms’competitiveadvantage.GötaLejonGöta Lejon just recently introducedanewdata storage andhandling system,but themain challenge,mentionedbythe intervieweerelatestothefunctionalityofthesystemitself.Thesystemneedstobefilledwithparameters,inordertostorethedatacorrespondingly.However,inmanycases,collecteddatadoesn’t fit into the systemand later systemdesign changesare costlyanddifficult to implement. Theinterviewee mentions that it takes a lot of resources and time to develop a system which fits theorganizationspurposesaseachsystemisunique.

4.1.2 Topicarea2:Datacollectionpractices

Thissectionisconcernedwithcurrentdatacollectionpractices.Inparticular,theintervieweeswereaskedtospecifywhatkindofdatatheirorganizationiscollecting,whichtechniquestheyapplytocollectthedataandhowtheyusetheirdata.Asthisareaisquitesensibleandpotentiallyinvolvesconfidentialinformationbutalsobecauseoftherequiredtechnicalexpertiseandknowledge,notallrespondentsansweredwithanin-depthexplanation.SBFSBFcollectsfire-relateddataformorethan20years.Thedata isamongstothersgatheredfrompublicorganizations such asMSB, however up until now they didn’t make use of this data in the sense of


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proactivefiresafety.Inyearthe2015SBFstartedtodevelopanewprototypefordatacollection,whichcurrently involvesone insurancecompany.Thedata isstored inasimplestructureddatabasewiththeattempttogeneratenewinsightfulfindings.SOSAlarmSOS Alarm currently records all 112 calls as well as every communication with the fire brigades andambulances.Further,theyhaveaccesstoeveryfirereportwhichincludesallrelevantinformationaboutwhat,whenandwheresomethinghappened.Therefore,thispackageofinformationisextremelyvaluableforSOSAlarm.Thegathereddataisstoredinadatawarehousefor10yearsunlessitcontainspersonalinformationsuchasnamesor telephonenumberswhichneeds tobeerasedwithin13monthsdue tolegislationreasons.Todate,SOSAlarmhasnodailyuseforthisdata,ratheritisusedforspecificresearchprojectsandthegenerationofstatisticsasforinstanceregardingthenumberoffiresinSweden.Thesestatisticsarethencomparedwithseparatestaticsfromotherorganizationssuchasthefirebrigadesandinsurance companies. However, the interviewee stated that they never correspond. Therefore, theintervieweeexpressed theneed tomerge thisdatawithother sourcesas itwouldmost likelyprovideresultsthatareclosertothetruth.Länsförsäkringar The intervieweewasn’t too familiarwith the currentdata collectionpracticeswithin theorganization,howeverhementionedacollaborationwithastartupthatcurrentlydevelopedanInternetofThings(IoT)platformforLänsförsäkringar.Theprojectfocusesonmoisturesensorsthatare installed inhomesandsendsoutinformationinrealtime.Novelabouttheirideais,thatanotificationissentouttoaneighbororanyotherchosenperson,incasetheapartmentownerisnotathome.Around400housesareinvolvedinthispilotprojectandonebiggerdamageduetoawaterleakagewasalreadyprevented. MSBMSBusesathree-stagedsysteminordertohandletheirdata.ThefirststageisknownasareceivingsystemthroughwhichallfireinstantreportsinSwedenarecompiled.Aspreviouslymentioned,thefirebridgesaretheprovideroftheseinstantreports.Thesecondstageisthedatawarehouse,whereallthedataisaggregatedandstored.Theenvironmentismuchmoreuser-friendlyandallowsforeasieranalysesandanalyticalwork.Thethirdstageisaweb-basedsystem,whichallowsexternalorganizationstoaccessthedata.MSBcurrentlyusesthedataforcomparisonreasonse.g.analyzingdifferentregionalareasbutalsotoprovidethedatatothegeneralpublicsuchasjournalistsororganizationslikeSBFthatusethedataforresearchpurposes.GötaLejonGötaLejonjustrecentlyintroducedanewdatacollectinganddatastoragesystembasedonexcelsheets.The system isprimarilyused to store informationabout insuredbuildingsbut also for losspreventionprojects, which includes risk surveys and risk assessment results. The results of these surveys andassessmentsarestoredinthedatasystemandprovideinformationregardingthedateoftheassessment,identified flawsandundertakenactions.Further, thesystem isusedtocompileannualstatisticsaboutoriginated lossesdue to fires andotheroccurring incidents. The statistics are indented tobeused forcomparisonreasonsbutalsotobeabletomakerecommendationsforfutureimprovements.


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4.1.3 Topicarea3:Datasharing&dataanalytics

Thissectionrevolvesaroundtheterms;datasharinganddataanalytics. Inparticular, the intervieweeswereaskedtoexpresstheirpersonalassociationwiththesetermsandifthereisarelationtodatasharinganddataanalyticswithintheirorganization.SBFAsan ITexpert, the intervieweehasmanyyearsofexperience in the fieldofbigdataanddata-basedanalyses.AshedevelopstheprototypeforSBF,hementionedtheSandboxprojectasaconcreteexampletodemonstratehisassociationwithdatasharinganddataanalytics.ThereisahighrelationbetweenSBFandthetwotermsastheycurrentlytrytoinitiateanewframework,knownastheSandboxwhichinvolvesdatasharinganddataanalyticspractices.SOSAlarmTheintervieweementionedthewiderspectrumofdatasharingandthateveryindividualhasitsownviewinregardstotheseterms.However,forSOSAlarmitmeansthecollectionofdatafrommanydifferentsources.SOSAlarmconsidersdataasavaluablesourceforinnovation,duetoitsextensiveapplicability.ForthemomentSOSAlarmtriestoconnectdatafromopensourcesinordertobeabletopredictwhatwillhappeninthenexthour.LänsförsäkringarBeforetheintervieweestartedtoworkforLänsförsäkringar,hethoughtthatinsurancecompanieswouldbemuchmorematureinthewaytheyareusingtheirdata,howeveratthepresentstageitdoesn’tseemtobethecase.Theintervieweesideabehinddatasharinganddataanalyticsistodevelopandbuildnewpredictivemodelstoeliminaterisks.Inhisroleasinnovationmanager,itisimportantforhimtoshowhisorganizationwhattheycandowithdata.Theintervieweeimitatedseveralpilotprojectsrelatedtodatasharing,indicatingageneralinterestinthisfield.MSBAsagovernmentalownedorganization,MSBisobligedtosharedatawithindividualsandorganizationsthataskforspecificdata,unlessitcontainspersonalinformationandrevealsprivacy.Basedontheirdata,MSB produces statistics and performs basic analyses for customers such as the fire brigades. Theintervieweeitselfisunawareofallthepossibilitiesrelatedtodatasharinganddataanalytics,howeverhewouldliketoknowmoreaboutitspotential.KarlstadUniversityTheintervieweeisnotveryfamiliarwiththeterms;datasharinganddataanalytics,howeverhestartedtofollowgeneraldiscussionrelatedto this field.So farhecanonlyseea fractionof itspotentialusebutmentionsacatastrophemodelingproject,heiscurrentlyinvolvedin,todemonstratehisunderstanding.TheprojectissimilartotheSandboxidea,whichinvolvestheadvanceduseofdatacollectionpracticestogeneratenewfindings.


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GötaLejonTheintervieweesmainassociationwiththetermsdatasharinganddataanalyticsrelatestothebroaderavailability of information and increased simplicity to access data. Also, the intervieweementions thefeasibilityofbetteranalyses,asaccesstoabroaderdata-set,allowsfortheidentificationofconnectionswithinthedata.Asagovernmentalownedorganization,GötaLejonhastoactaccordingtotheprincipleofpublicaccess toofficial recordsandshares theirdatawithotherorganizationsand individualsuponrequest.Also,theyrequestinformationfromotherorganizationssuchastherescueservicesandstorethisdataintheirsystemsforreportingpurposes.

4.1.4 Topicarea4:Benefits&challengesofdatasharinganddataanalytics

Thissectionrevolvesaroundthebenefitsandchallengesrelatedtodatasharinganddataanalytics.Theintervieweewasaskedtostatehispersonalopinionregardingthosetwoterms.SBFTheintervieweementionedtheexampleofafireinamulti-familyhomebuildingwith10differentfamilies.Intheworstcase,itcouldbethateachfamilyhasadifferentinsurancecompanyandthereforeitsownviewofthefireandwhathappened.Allthisdataneedstobecollectedinordertomakecost-predictions.However,ifinsurancecompanieswouldroutinelysharetheirdatainasystemasitistheapproachwithintheSandbox,itwouldbemucheasiertotracktheactualcostsofafire.Incontrasttohisexample,theintervieweementionsregulationsby law, thedifficultytosharepersonaldata,competitivereasonsforprivatecompaniesandthegeneralreluctanceoforganizationstosharetheirdataasoccurringchallengesrelatedtodatasharing.Also,theintervieweementions,thatdifferentscandalsinthenewsaffectpeople’ssentimentregardingbigdataanddatasharing.However,heispositivelyconvincedthatthebenefitsofdatasharingoutweighrelatedchallenges.SOSAlarmAs one of the biggest challenge, the interviewee mentioned privacy concerns. Sharing and analyzingpersonaldatacreatesthepossibilitytodevelopapictureofeverycitizeninthecountryandthequestionisifwereallywantthat?Asoneofthemainbenefits,theintervieweementionsthepossibilitytoconnectthingswithhappeningsfromthepastwhichmightalsocreatethepossibilitytobeabletopredictthingsin the future. Overall the interviewee considers the benefits as being more determining than thechallenges.Länsförsäkringar Regarding the benefits and challenges of data sharing and data analytics the interviewee once againmentionedtheintentiontochangetheorganizationalbusinessmodelfrombeingreactivetowardsbeingmoreproactive.Thishoweverrequirestheadvanceduseofdatainordertodeliverpersonalservices.Theintervieweespecificallyclarifies that theuseofpersonaldata isnomean topunish their customers, ifanything theywant to reduce the risksof anaccident.Asmajor challenges, the intervieweementionsregulationsbylawandthefactthatindividualsandorganizationsarestilltooafraidofdataandhowitwillbeusedagainstthem.Theintervieweesuggeststogivethepeoplethepoweroftheirowndatabackandmentionsblockchainasapossibletechnology.


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MSBThe interviewee stated thathe is unawareof all thepotential benefits and challenges related todatasharing and data analytics, however hementions the legal side and the quality of the data asmajorchallenges.GötaLejonAsoneofthemainbenefitsrelatedtodatasharinganddataanalytics,theintervieweementionstheabilitytobemoreproactive,astheaccesstoalargerdatabaseprovidesmoreinformationtoinitiateproactivesteps. Also, the increased level of reliability in relation to a larger database was mentioned as anadvantage.Asmajorchallenges,theintervieweementionedintegrityandtrust,duetotheoftenoccurringmisuseofdataanduncertaintyhowtheshareddataisusedintheend.

4.1.5 Topicarea5:ProjectSandbox

The last section revolves around the interviewees general opinion regarding the Sandbox project. Inparticular,questionswereaskedtoidentifymainincentivesandconcernsfororganizationstobepartoftheproject.Butalsoperceptionsregardingtheoverallsizeoftheproject,theconstellationofpartakingorganizationsandtheimportanceoftrustwerereviewed.SBFAsbeingtheinitiatoroftheSandboxproject,SBFhasaverypositiveattitudetowardstheproject.Theirgoalistoconnectallentitiesthatcollectfire-relateddataastheyseeitasanimportanttasktousefire-related data for more profound purposes. The potential benefits that would arise throughout theimplementationoftheSandboxprojectwouldnotjustbenefitallinvolvedorganizationsandthesociety,butalsostrengthenSBFsrole.Regardingthesizeoftheproject,theintervieweementions,thatalargerandmorediversifiednumberofstakeholderswouldbenefit theoverallproject,asagreaterandmorediverse amount of datawould providemore insights.However, he alsomentions that itwould benodisappointment to startwithonlyoneadditional stakeholder, to test andmodifyandbuildaproofofconcept.OnceotherorganizationsseethepotentialoftheSandboxapproach,theintervieweeisconfidentthatotherorganizationswill follow.Also, the intervieweementionspersonal trust asoneof themostimportantpillars,especiallyintheearlystageoftheproject.Theintervieweestated:“Someoneneedstohaveagoodpersonalrelationshipandthereforecommunicationisarequiredkeycompetence.”SOSAlarmSOSAlarmhasapositiveopinionregardingprojectsthatinvolvedatasharingandlearningpractices.Theirmaingoalistoprotectthelifeofthecitizenbylearningtopredictthingsbutalsotomaketheirworkmoreefficientlywhilealsosavingmoney.BeinginvolvedintheSandboxwouldcertainlyhelpSOSAlarmtoworktowardsthesegoals.Ontheotherhand,theyhavetobecarefulwhomtheysharetheirdatawithandtowhatextent.Ingeneral,theyhavenodoubtstosharetheirdatawithanorganizationsuchasSBF,howeverthe general set-up must be very secure to make sure that sensible data is not misused. Also, theintervieweementions the importanceof trust,as it lays the foundation for sharingsensitivedata.Theintervieweesuggeststocreatesomekindofindependentorganization,whereeachoftheinvolvedentitiesispartof,givingaccessandcontroltoeveryoneinvolved.Regardingthesizeoftheproject,theinterviewee


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mentions that a greater number of participantswouldmake it easier for SOS Alarm to reason for itsparticipation in such as framework. Also the interviewee comments on the diversity of involvedorganizations,arguingthatablendofdifferentorganizationsincreasesthelegitimacyoftheoverallidea.Conclusivelytheintervieweestates,thataprojectliketheSandboxcouldhavealreadybeenimplementedyearsago,butbacktheneveryonewasevenmoreafraidandunawareofthepowerofdata.Länsförsäkringar Startingtocollaboratewithdifferentkindofactorsandseeinghowwecancombinefire-relateddata,issomethingLänsförsäkringarwantstobepartof.Theirgoalistofocusonservicesthathelptoeliminatetherisksthatpeoplehavefortheirhomesandthereforetheytrytomoveawayfrombeingreactiveandpayingoutmoneywhenitsalreadytoolate,towardsbeingmoreproactiveandsupportprevention.Thiscertainly includes being part of a collaborative network. However, the interviewee also states that itsalreadydifficulttocombinedatawithintheirownorganization,thereforeit’simportantforthemtoseehow they can combine data in a project like the Sandbox. Further, the interviewee mentions theimportanceofcleanandaccuratedatabutalsoregulationsrelatedtodataandthewaydataisstored,asitconstitutesacomplexproblem.Theintervieweestated:“Oncewestarttocombinedatafromdifferentsourcesitbecomesevenmoreimportanttomakesurethatthequalityofthedataisadequateandgoodenough”. Time, resources and efforts also play an important role within this framework and theintervieweesuggeststostartthisprojectinachosenarea,toseehowitturnsoutanddevelops.Onceitshowspositiveresults,hesuggestsawiderroll-out.Regardingthediversityofinvolvedorganizations,theintervieweearguesthatamixofdifferentorganizationsisthewaytogo,howeverhealsostates,thatit’sachallengewhenorganizationswithinthesamebusinessareasharetheirdataduetocompetitivereason. MSBFromtheperspectiveofaresearcher,the intervieweeseesa lotofpotential intheSandbox idea,as itwouldcreatethepossibilityforMSBtocompareinsurancecompanydatawithoperationaldatafromthefirebrigades.Subsequently,thisdatacouldbeusedforcost-benefitanalyses,whichwouldresultinmoreeffectivefireprotection.Inrelationtothat,theintervieweementionsthatupuntilnowmanydecisionswithinthefirebrigadeservicearenotwellgroundedinscienceandprimarilybasedongutfeeling.Havingmore data and a scientific background would most likely lead to a more objective decision-making.Nevertheless,theintervieweealsomentionsarisingchallenges,whichrelatetodataprotectionregulationsandtheimportancenottogiveawayidentifiablepersonaldata.Asthebiggestchallenge,theintervieweementionsthedevelopmentoftrustamongsttheinsurancecompanies,asthereisthebiggestconflictofinterest.Asagovernmentalownedorganization,MSBisobligedtosharetheirdata.Insurancecompaniesontheotherhandareveryreluctanttosharetheirdataasitisoneoftheirmostvaluableassets.KarlstadUniversityTheintervieweeenvisionsgreatpotentialintheSandboxideaandiskeentoseewhatorganizationscandowiththiskindofapproach.Tohismind,itisimportantfororganizationstoturnintomoreknowledge-basedorganizations,asfromascientificpointofview,datahelpstounderstandaphenomenon,whichlikewisehelpstounderstandtheroot-causeforanyparticularincident.Theintervieweeishoweveralsoconcernedaboutarisingchallenges,includingtheamountofinformationorganizationsshould/shouldnotshareamongstdifferentstakeholders,butalsohowtheuseof informationwillaffectcertaingroups in


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society.Further,hementionstheimportanceoflegalrestrictionsandtrustamongstdifferentstakeholdersbut also thenecessity toestablish long-termcommitment and stable agreements among the involvedstakeholders.RegardingthesizeoftheSandbox,theintervieweehasatwo-sidedopinion.Hearguesthat,involving organizations within the same industry e.g. insurance companies, increases the difficulty toestablishtrustandjointwork.However,healsobelievesthatabroaderuseoftheSandbox,requirestheinvolvement of different companieswithin the same industry, as data is the key resourcewithin thisproject.GötaLejonThe intervieweesoverall perception towards theSandboxprojects is verypositive.Göta Lejonalreadycooperateswithmanydifferentorganizations,astheybelievethatsharingknowledgeisagreatadvantageandbysharingopinionsanddifferentstrengths,everyonecanbenefitfromeachother.Alsoduetoitsnon-profitcharacter,GötaLejonisobligedtosharetheirdatawithotherorganizations.Howeverasoneofthemainchallenges, therespondedmentions the importanceof trustandtheconcernthatdatamightbeused, not in linewith the organizations core values, whichwould harm the organizations reputation.Therefore, therespondentsuggeststostarttheprojectwithasmallcoreteamona local level,beforeexpanding the project after a successful test-phase. Also the interviewee believes that a mixture ofdifferentorganizationswouldbemorebeneficial for theoverallproject,asdifferentorganizationsadddifferentuniquevalueandinformation.

4.2 Summaryoftheempiricalfindings Topicarea1:Challenges&Future

SBF - SBFonlyhasaccesstoasubsetofallfirerelateddata- SofarSBFdidn’tmakeuseofthatdata- Developanewdatasharingframework

SOSAlarm - Collectsbigamountsoffirerelateddataandlookintonewwayshowtousethisdata- Challenge:onlygiveotherstherightamountofaccesstotheirdata

Länsförsäkringar - Mostlikelynotsellinginsurancesanymore- Shiftfrombeingreactivetowardsbeingproactive(preventinsurancecases)- Predictingwhatwillhappennext

MSB

- Datasharingforfirebrigadesisvoluntaryàchangetowardsobligatory- MSBhasproblemsofunder-reporting+qualityaffectsusability- Dataneedstobemoreuserfriendly

KarlstadUniversity - Tougherregulationsaffectdatasharingpractices- Datasharingislimitedduetocompetitivereasons

GötaLejon - Developadatahandlingsystem,whichallowsforincreasedflexibilityandthepossibilitytoadd/changeparametersafteritsintroduction

Topicarea2:Datacollectionpractices

SBF - Collectsfirerelateddataformorethan20yeas- Sofaraverysimplestructureddatabase

SOSAlarm - Recordsall112callsandradiocommunicationwiththefirebrigadesandambulances- Dataisstoredinadatawarehousefor10years- Nodailyuseforthatdata,ratherforspecificresearchprojects

Länsförsäkringar - CollaborationwithastartupthatdevelopsanIoTplatform(moisturesensors)MSB - 3-stagedsystemfordatacollectionanddatahandling(receiving,storage,analysis)

- DataisusedforcomparisonreasonsandaccessibletoeveryoneKarlstadUniversity - Noanswer


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GötaLejon - Justintroducedanewdatahandlingsystem,usedforreportingandcompilingofstatistics Topicarea3:Bigdatasharing&dataanalyticsSBF - Seesenormouspotentialindatasharinganddataanalytics

- Currentlydevelopingofaprototypefordatasharing,involvingoneinsurancecompanySOSAlarm - Enormous amounts of fire related data is collected, but they didn’t uncover its full

potentialyet- Idea:connectdatafromopensourceswiththeirowndata

Länsförsäkringar - ManypilotprojectswithinLänsförsäkringarrelatedtobigdataanddatasharing- Buildnewpredictivemodelstoeliminaterisks

MSB - MSBgeneratesstatisticsanddoesbasicanalytics,basedonfirerelateddata- MSBisobligedtosharetheirdata

KarlstadUniversity - “Icanonlyseeafractionofthepotentialuse”GötaLejon - Moredataallowsforbetteranalysesandthepossibilitytomakeconnectionsamongst

differentdatasources- Dataisalreadysharedwithotherorganizations

Topicarea4:Benefits&ChallengesofbigdatasharingSBF - Datasharingwouldmakeiteasiertotracktheactualcostsofafire

- Challenges:regulationsbylaw,GDPR,legalandcompetitiveissuesforcompaniesSOSAlarm - Learntopredictthingsandmakeconnectionstothepast

- Challenge:Privacyconcernsandsurveillance;Dowereallywantthat?Länsförsäkringar - Usingdatatoinitiateactionsandreducetherisksofanaccident

- PeopleareafraidofdataandhowitisusedagainstthemàneedforchangeMSB - ChallengesrelatedtolegalissuesandthequalityofthedataKarlstadUniversity - NoanswerGötaLejon - Increasedreliabilityandopportunitytobemoreproactive

- Fearofdatamisuseandreputationaldamage Topicarea5:ProjectSandboxSBF - Strongbeliefintheproject,strengthentheroleofSBF

- Starttodevelopaproofofconcept,engageingoodpersonalrelationships- Personaltrustasoneofthemostimportantpillar

SOSAlarm - Theirmaingoal:protectthecitizen,maketheirworkmoreeffective,trytopredictwhatwillhappennext

- Datastorageandhandlingmustbeverysecure(needforanorganizationthateveryonecantrust)

- SuggestsamixofdifferentorganizationsLänsförsäkringar - Movingfrombeingreactivetowardsbeingproactive(requirescollaboration)

- Difficultytocombinedatafromdifferentsources(quality,usability)- Time,resources,effortsandtrustplayanimportantrole

MSB - Moreeffectivefireprotectionduetocost-benefit-analysis- Dataprotectionregulationsandotherlegalargumentsareveryimportant- Thebiggestchallengeistodeveloptrustamongtheinsurancecompanies

KarlstadUniversity - Seesgreatpotentialintheidea,turningmoreintoknowledge-basedorganizations- Withouttrust,longtermcommitmentandstableagreementswewon’tachieveanything- Suggeststoinvolveavarietyofdifferentorganizationsandscientists

GötaLejon - Seesitasagreatadvantagetoshareknowledge,opinionsandstrengths- Trustisimportanttoassurethatdataisnotmisused- Testingtheprojectwithcoreteamofmixedorganizationsonalocallevel

Table4.1Summaryoftheempiricalfindings


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5 AnalysisThe following section contains an analysis of the empirical findings in relation to the interviewedorganizations and areaswhichwere highlighted in the theoretical review. Thus, this section links theempirical findings from theprevious chapter and the theoretical areaswhichwere emphasized in theliteraturereview.Thefiveidentifiedtopicareas,astheywerepresentedinthepreviousempiricalfindingspartareonceagainoutlinedandanalyzedsuccessively.

5.1Analysisofthetopicareas

5.1.1 Topicarea1:ChallengesandFuture

Thefirsttopicarearevolvesaroundthecurrentsituationwithintheinterviewedorganization.Inparticular,questions were asked about current and upcoming challenges and how the organization is trying toembracethem.TheideawastogetabetterpictureoftheorganizationbutalsotoidentifyIfthereisarelationtodatasharinganddataanalyticswithintheorganization.

Bytakingacloserlookattheempiricalfindings,itbecomesapparentthatallinterviewedorganizationsshareonecommongoal–namelytoimprovefiresafetyandmakesurethatnooneinSwedendiesorgetshurtwithin a fire.Whilemost of the interviewed organizations are partly governmental owned, theirprimary goal seems plausible, interestingly however, Länsförsäkringar and SBF as privately heldorganizations indirectlyfollowsthesamemission.Withahighcustomer-centric focus,Länsförsäkringarwantstochangeitsbusinessmodel,notfocusingonsellinginsurancesasaproduct/serviceanymore,buthelptopreventinsurancecases.

“Weneedtomoveallourresourcesandmoneyfrombeingreactivetobeproactiveandseehowwecanprovidesafetyforourcustomersinthefuture.”

(Länsförsäkringar)

Thisnewwayofdoingbusiness isrootedinLänsförsäkringarsbeliefthataproactiveapproachismuchmorecost-effective,whilealsopreventingtheircustomerspain.Though,inordertoachievethisgoal,dataasaresourcebecomesmuchmoreimportanttothem,asitwasalsostatedbyMichaelPorter(2014),whoconsidersdataasbecominga coreasset formanybusinesses in the future. SBFas the initiatorof theSandboxideaactsaccordingtothesameprinciple.Afteryearsofcollectingdatawithouteffectivelyusingit, they recognized thepotential that canbeunlockedby sharingdata.Oneof thedriversbehind thischangeisasmentionedintheintroductorytheoryrelatedtoadvancesinICTcoupledwitharoutineofdatacollection.AsstatedbyDavies(2016),theamountofproduceddataisexpectedtodoubleeverytwoyears and this growth is amongstothers inducedbynewand cheaper solutions to store,manageandprocessdatabutalsoduetoadvancesincomputerpowerandthesprawlofdatacollectiondevices.Besides the joint organizational mission, namely to save people’s lives, also each of the organizationcollectsfire-relateddata.Although,theamountofcollectedfire-relateddataamongsttheorganizationsvaries,onecommonobstacleremains,namelythefactthateachoftheorganizationonlyhasaccesstoasubset of the overall data. However, the empirical findings also indicate, that the interviewedorganizationsarestartingtorealizethatthereisanactualneedtocollaborateinorderunifytheirdata.


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“Ourdataisjustalittlepieceofthebroaderpicture.”(SBF)

“Ifwecan’tseeourcustomer’sdataorotherorganizationsdata,wecannotmakepredictions.”(Länsförsäkringar)

Most of the governmental owned organizations such asMSB, SOS Alarm and Göta Lejon are alreadyobligedbylawtosharetheirdatawithotherorganizations,howeveratthesametime,theyfaceaproblemofunder-reporting.Inordertocompileadvancedstatisticsanddevelopbetteranalysesmoredatafromdifferentsourcesisrequired.

“Wewouldbeveryinterestsinthedatafrominsurancescompaniesregardingfirerelatedcosts,asitwouldaddareasonableproxytoourcost-benefitanalysis.”

(MSB)

Inordertobeabletodoso,closercollaborationisrequired.ThischallengerelatestoOstromscollectiveactiontheory,nonethelessinaslightlydifferentform.WhereasOstromsoriginaltheoryrelatestocommonpoolresourcessuchasfishery,whichisusuallyscarceandthereforerequirescautioustreatmentbythecollective,dataontheotherhandgrowsexponentially.Butitisalsoquestionableifdatacanbeconsideredasacommonpoolresource.ScholarssuchasMauthnerandParry(2009)arguethatresearchdata,asitistheprimaryusecasewithintheSandboxidea,shouldbeconsideredasacommonpoolresourceduetoscientific, moral, economic and political arguments. Throughout the years, this standpoint howeverchangedasMauthner(2012)startedtoquestionifdatasharingisactuallyinthepublicinterestandifitnecessarily constitutes good science. Nevertheless, the discussion if data should be considered as acommonpoolresourceisatthispointnegligible,asitisnotthemainfocusofthisresearcher.Rathertheidea is toapplyOstroms(2009)collectiveactiontheory inanewsetting, inorder identify factors, thatinfluencedataownersandtheirdecisiontosharedataandthereforeengageincollectiveaction.OstromscommonpoolfisheryexampleandtheSandboxideaarehowevertosomeextentconnectedasitrequiresin both cases collective action in order to gain collective benefits. Further, both examples share thechallengetotreattheresourcesofinterestcautiously,asitistheircommongoaltosustainit.

“Themainchallengeistogivetherightamountofaccesstothedatabecausethedataisverysensibleandclassified.Wehavetomakesuretohandlethedatainagoodway.”

(SOSAlarm)

Conclusively,all interviewedorganizationsstatedthattheywouldbewillingtosharetheirdata,as it istheircommongoaltoimprovefiresafetywhichconditionallyrequirestoengageindatasharingpractices.The above mentioned findings are crucial as they highlight that the nature of organization, theorganizationalmission,respectivelytheorganizationalbusinessmodelaredeterminingaspectsthataffectdataownersandtheirdecisiontosharefire-relateddata.


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5.1.2 Topicarea2:Datacollectionpractices

Inthissection,the intervieweeswereaskedtospecifywhatkindofdatatheirorganizationiscurrentlycollecting,whichtechniquestheyapplytocollectthedataandhowtheymakeuseoftheirdata.Theideawastogetabetteroverviewofhowtheorganizationsoperatebutalsotohighlighthowtheydifferandwhatconsequencesitmightimply.All interviewedorganizations thatwere part of this research are currently collecting fire-related data.However, the technical implementation of these data collection and analyzing procedures differsprofoundly.Whereas the recently introduced data collection system by Göta Lejon is based on Excelsheets,MSBon theother handuses a 3-stageddata handling system, including a receiving hub, datawarehouseandweb-basedsystem.Thosetwoexampleshighlighttheextremesanditisimportanttoshedlightonthetechnicalside,asthisaspectwillmostlikelypresentonemajorobstaclewithintherealizationoftheSandboxprojectandthereforerelatesthesub-researchquestion:“Whatarepotentialobstaclesandbenefitsthataffectdatasharinganddataanalytics?”AspreviouslymentionedbyBresnick(2017),datasharingpracticesoftencompriseissuesrelatedtothecompatibilityofthegathereddata.Thecaptureddataneeds tobe clean, complete, accurate and formatted correctly inorder tobe able toutilize in acollectivenetwork.However,astheempiricalfindingsindicate,thisdoesn’tseemtobethecaseyetandit therefore requiresa lotmorecommunicationandcommitmentby thecollective inorder toachievecompatibility.Duetothebackgroundoftheresearcher,furthertechnicalconsiderationsaremoreorlessneglectedwithin this study,hence it isevenmore important for future research to look into technicalconsiderationastheyseemtoportrayonemajorchallengewithindatasharinganddataanalytics.Besides the previously highlighted differences in data collection and data handling procedures, theinterviewed organizations seem to have a common interest in how they use their data. Amongst allrespondents,themainreasonfordatacollectionrelatestothepreparationofstatisticsandreporting.MSBforinstancecurrentlyusestheirdatatoanalyzepreviousfiresindifferentregionalareas.Also,theyprovidetheirdatatothegeneralpublicforjournalisticandresearchpurposes.SOSAlarmandGötaLejoncollectfire-relateddataforsimilarpurposes,interestinglyhowevernoneoftheinterviewedorganizationsusestheirdataproactivelytoimprovefiresafetyyet,whichhighlightstheimportanceandneedforprojectssuchastheSandbox.“Todaywedon’thaveadailyuseforthisdata,moreforspecificresearchprojectsorthegenerationof

statisticsaboutthenumberoffiresinSweden.Thosestatisticsarethencomparedwithseparatestatisticsfromthefirebrigadesandinsurancecompaniesbuttheynevercorrespond.”

(SOSAlarm)

Nevertheless, as itwasmentioned in the previous section,most of the interviewed organizations arestartingtorealizethattheirdatacanbeusedformuchmoreamplifiedpurposes.Insteadofcollectingdatawithout using it proactively the Sandbox idea demonstrates an innovative way to use data for moreadvanced purposes which will hopefully deliver new, valuable insights and improve the fire safety inSweden.


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5.1.3 Topicarea3:Datasharinganddataanalytics

Inthissectiontheintervieweeswereaskedtoexpresstheiropinionregardingtheterms;datasharinganddataanalytics.Theideawastoascertainiftheintervieweesarefamiliarwiththeseconceptsandiftherearesimilarities/differencesintheirperceptions.Theempiricalfindingshighlight,thatmanyoftheintervieweesarenotyettrulyfamiliarwiththeconceptsof data sharing anddata analytics. Even though theyhave a general ideawhat data sharing anddataanalytics involves,theycannotpicturethegreaterpotentialyet.Nonetheless,mostoftherespondentshaveasimilarassociationwiththetwotermsandmentionthepossibilitytoconnectdifferentdatasourcesandtheincreasedavailabilityofdata-setsforbetteranalysesasexamplestodemonstratetheirassociationwiththosetwoterms.

“Insteadofspecifyingwhatyouwouldliketoknow,youjustpulloutthedatayouareinterestedin.”(GötaLejon)

Thefactthatmanyoftheintervieweesdon’tseethegreaterpotentialofdatasharinganddataanalyticpracticesyet,mightalsopresentanother importantfactorthat influencestheirdecisiontoengageinadata sharing network. Beingmore familiarwith related benefitsmight further increase their interest.Therefore, it is a crucial step to communicate the possibilities of the Sandbox idea in order to obtaingreater attractiveness and encouragement. The importance of communication is also mentioned inOstroms (2009) collectiveaction framework,which is a keydeterminant for thedevelopmentof long-lasting relationships and conviction. Beneficially, most of the interviewed organizations are alreadycommunicatingwitheachother,butalsoallintervieweesmentionedthattheyareinterestedinlearningmoreabouttheconceptofdatasharing,astheywouldliketoknowmoreaboutitspotentialimplications.

Anadditionalinterestingfindingrelatestothestatement,givenbytheintervieweesfromLänsförsäkringarand SOSAlarm. Insteadof just being able to comparedata fromdifferentdata sources, theyhave anextendedviewpointonthetermsdatasharinganddataanalyticsandmentiontheaspirationtobeabletopredict things in the future. This is an important finding, as the resultingobjectives toengage indatasharingseemtodifferamongsttheinterviewedorganizations.WhereasSBF,MSBandGötaLejonperceivedatasharingasanopportunity to improvetheiranalysesandstatistics,LänsförsäkringarandSOSevenfurtherassumetobeabletocreatenewpredictivemodels.After conducting the interviews with the organizations representative, it is certainly not possible togeneralizebydrawingconclusionsontheorganizationsoverallknowledgeandanticipationinregardstodata sharing and data analytics. Future research should therefore further analyze towhat extent theorganizationalknowledgeinregardstodatasharinganddataanalyticsaffectstheirdecisiontosharefire-relateddatainacollectivenetwork.


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5.1.4 Topicarea4:Benefitsandchallengesofdatasharinganddataanalytics

Thissectionrevolvesaroundthebenefitsandchallengesofdatasharinganddataanalytics.Theideawasto identify if the respondents are familiar with the benefits and challenges of data sharing and dataanalyticsbutalsoandmoreimportantlytounderstandtheirpersonalviewpointsandconcerns.Eventhoughtheintervieweeswerenottrulyfamiliarwiththeterms;datasharinganddataanalytics,allrespondents had a personal opinion regarding related benefits and challenges. As themost commonbenefit, the intervieweesmentioned the increased access to data and the ability to connect differentdatasetwhichpresumablyenhancesproactiveactions.

“Theadvantageofdatasharingisthatit’seasiertobeproactivebecauseyouhavealargerdatabase.”(GötaLejon)

“Thebenefitsareofcoursethatwecanlearntopredictthings.”(SOSAlarm)

In particular, the interviewees mentioned examples of how data sharing will most likely create thepossibilitytosimplertrackfire-relatedcosts,butalsotheabilitytomakeactualpredictionsofwhatmighthappennext,asitwasstatedintheabovequotebySOSAlarm.Inrelationtotheabilitytoconnectdatasetsfromdifferentstakeholders,theintervieweefromGötaLejonalsoassociatesanincreasedreliabilityoftheirdatasets.Thisassumptionishoweverquestionableas itwasalsomentionedasapotentialchallengeandwillbefurtherdiscussedinthesubsequentpart.Interestingly,mostof thebenefits thatwerementionedbythe interviewees,alsocorrespondwiththebenefits that were reviewed in the literature. Being able to conduct larger scale analytics, theencouragement of collaboration which presumably results in increased productivity, benefits for thesocietybutalsothepromotionofinnovation(changingthebusinessmodeltowardsbeingmorepro-active)arebenefits,thatwerementionedbyboth,theintervieweesandintheliterature.Benefitsofdatasharing&dataanalytics

SBF Länsförs. SOSAlarm MSB GötaLejon

Increasedproductivity X X X XNewfactorofproduction X Largerscaleanalytics X X X XEncouragingcollaboration X X X XPromotionofInnovation X X XBenefitsforsociety X X X X X

Table5.1Mentionedbenefitsofdatasharing&dataanalyticsTable 5.1 above illustrates the correspondence between benefits of data sharing and data analytics,mentioned in the literature comparedwith the responses throughout the conducted interviews. Eventhoughtheintervieweesdidnotmentioneverybenefitspecificallybyname,theoverallinterviewitselfindicatesthattherespondentsarewellawareoftheabove-listedbenefits.Onlythebenefit:newfactorof


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productionwassolelymentionedbyoneinterviewee(Länsförsäkringar)ashespecificallytalkedabouttheorganizational change of their business model towards being more proactive which concurrentlyimplicatestheimportanceofdataasanewfactorofproduction.Shifting the focus towards mentioned challenges related to data sharing and data analytics, mostrespondents againhad a strongly correspondingopinion. Legal restrictions andprivacy concernswereaddressedbyallintervieweesandmostlikelyaffectsorganizationsandtheirdecisiontoengageindatasharingpractices.

“Thechallengesareofcourseconcerning.Wecangetapictureofeverycitizeninthecountryanddowereallywantthat?Idon’tthinkso.”

(SOSAlarm)

“Todaywearealittlebitafraidofdataandhowitwillbeusedagainstus.”(Länsförsäkringar)

Also,theintervieweesmentionthatrecentscandalsinthenewssuchastheexampleofFacebookanditsbigdatabusinessaffectpeople’ssentiment.Further,asitwasalreadyaddressedpreviously,mostofthefire-related data contains personal information and is therefore highly confidential. Davies (2016)emphasizes on the same personal data protection concerns, arguing that there are techniques topseudonymizepersonaldata,buthealsostressesthattherearetechniquestore-identifythisdata,whichmightleadtoanunwanteddisclosureofprivateinformation.Therefore,organizationshavetomakesurethattheirdataisonlysharedinanappropriateway.Affectivefromthe25thofMay2018,theEuropeanCommissionreleasednewregulationswithregardtotheprocessingofpersonaldata(eugdpr.org,2018).Theseregulationsareconsideredtobethehigheststandards intheworld,withthe intentiontofostertrustandconsequentlyleadtoanincreasedwillingnesstosharedata.Criticalvoicesarehoweverexpectingacontradictorydevelopmentasstricterregulationsmightoutweighefficiencygainsandthereforeleadtoadecreaseindatasharing(Ciriani,2015).Timewilltelliftougherregulationswillleadtoanincreaseindatasharing,butfornow,itcanbestatedthatprivacyandpersonaldataconcernsareclearlyaffectingdataownersandtheirdecisiontoengageindatasharing.Further, competitive reasons were mentioned as a major challenge. Especially non-governmentalorganizationssuchasinsurancecompanieshavetobecarefulwhomtheysharetheirdatawith,asotherinsurancecompaniescaneasilycalculatetheircompetitor’sinsuranceplans,whichinreverseaffectstheircompetitiveadvantage.Inrelationtothat,trustwasmentionedasacrucialfactor,whichisalsomentionedas a central variable within Ostroms collective action theory, as it is a cornerstone of collaboration.AccordingtoHolton(2001),trusthelpstounderstandtheotherpartiespositionandsensingwhetherthereisatruthfulopportunityforgiveandtake.Beforesomeonestartstoengageindatasharing,itisthereforeimportanttoestablishtrustwhichisinparticularofimportancefortherealizationoftheSandboxproject.Besideschallengesthatareratherrelatetothehumansphere,therespondentsalsomentionedconcernsrelatedtotechnologicalconsiderationsandwerealsoaddressedinaprevioussection.Especiallyintermsof data sharing amongst several stakeholders with differences in the organizational size, and datacollectingpractices,thewaydataiscaptured,cleanedandstoredisveryimportant.


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“Inordertocombinedatafromdifferentsources,youneedtowashandcleanit,becauseotherwiseitdoesn’tmakesense.”(Länsförsäkringar)

Butalsothequalityofthedatawasmentionedasanimportantfactor,asalowqualityofdatacanleadtomisleadingconclusions,whichespeciallyinregardstofiresafetyshouldbecircumventedbyallmeans.Theamountanddiversityofdatafromdifferentstakeholderscanthereforehaveatwo-sidedeffect.Incasethatshareddataisaccurate,completeandformattedcorrectly,itwillmostlikelyaddvalueandincreasethevalidityandreliabilityoffire-relatedstatisticsandproactive-actions.Ifdataontheotherhandislackingaccuracy,consistencyandastandardized format, itwillmost likelynotaddvalueandratherharmthecollectivenetwork.Bresnick(2017)thereforesuggestsanongoingcurationofthecollecteddata,involvingatrustworthydataadministratorwhohandlesthedevelopmentofthedatatoensurethatdataiskeptindefinedformatsandremainsusefulforitspurpose.Table5.2below illustrates the correspondencebetween challengesof data sharing anddata analyticsmentioned in the literaturecomparedwithcollected responses throughout the interviews. Itbecomesapparent that the interviewees are well aware of potential challenges, nevertheless the table alsoindicatesthatcertainchallengesarenotyettakenintoconsideration.Thisunderlinestheimportancethatorganizationsneedtobefurtherfamiliarizedwithrelatedchallenges.Oncetheyaremoreknowledgeable,theywillmostlikelybelessafraidtoengageindatasharing.Challengesofdatasharing&dataanalytics

SBF Länsförs. SOSAlarm MSB GötaLejon

Datasecurity&privacyandlegalrequirements

X X X X X

Dataownership Datastorage X X X XSecurity Administratorship X X Updating Sharing X X X X XTrust X X X X X

Table5.2Mentionedchallengesofdatasharing&dataanalyticsOverall,itshallhoweverbestatedthatmostoftheintervieweesconsiderthebenefitsofdatasharinganddata analytics as more determining than anticipated challenges. This indicates that even though therespondentsareawareofrelatedchallenges,thebenefitsaremoreimpactfulontheirdecisiontoengageindatasharingthanthechallenges.


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5.1.5 Topicarea6:ProjectSandbox

The last section revolves around the interviewees general opinion regarding the Sandbox project. Inparticular,questionswereaskedtoidentifymainincentivesandconcernsthataffecttheorganizationsandtheirdecisiontoengageinadatasharingnetworksuchastheSandbox.Also,perceptionsregardingtheoverallsizeoftheproject,thestructureofparticipantsandtheimportanceoftrustwerereviewed.After analyzing theempirical findings, it canbe stated that theoverall opinion regarding the Sandboxprojectandthereforetheacceptancetoengage inadatasharingnetwork, isverypositiveamongstallinterviewees.Therespondentsshowedahighlevelofinterestandweretosomeextentexcitedtosee,whattheSandboxapproachmightfacilitateinthefuture.

“IthinktheSandboxideahasgreatpotentialandIamreallykeentoseewhatwecandowiththiskindofapproach.”

(KarlstadUniversity)

Primaryincentives,affectingstakeholdersandtheirdecisiontoengageinprojectssuchastheSandbox,mainlyrelatetothenatureoftheorganizationbutalsototheirbusinessmodelandorganizationalmission.As it was previously mentioned, four of the interviewed organizations (SOS Alarm,MSB, Göta Lejon,KarlstadUniversity)arepartlyownedbythegovernmentandthereforeobligedbylawtosharetheirdatawithotherorganizations.Further,theorganizationalbusinessmodelappearstohaveanimpactonthedecision to share data amongst different stakeholders. Länsförsäkringar as a non-governmentalorganizationhasastrongcustomer-centricfocuswiththedefinedmissiontomakesurethatnoneoftheircustomersgets involved inanaccident.Bychangingtheirbusinessmodel frombeingreactivetowardsbeing proactive and circumventing accidents, they attempt to achieve this goal. They are thereforecommittedtoengageinadatasharingnetwork,ashavingaccesstoabroaderdatanetworkfosterstheinitiationofproactiveactions.OtherinterviewedorganizationssuchasSBFfollowasimilarapproachandthereforetheorganizationalmissionandbusinessmodelaredrivingfactorstoengageinadatasharingnetwork.Thefindingsfurtherindicatethattheinterviewedorganizationsstarttorecognizethebenefitofknowledge, rooted indataand therefore intend to turn intomoreknowledge-basedorganizations.Upuntilnowmanydecisionswithinthefirebrigadeservicearenotverywellgroundedinscienceandmanydecisionsarebasedongutfeeling.However,withanextendedscientificbackground,decisionscouldbemademoreobjectively,whichpotentiallyalsoresultsinamoreeffectivefireprotection.

Asmainconcerns,theintervieweesmentionedtheimportanceofasecureset-upoftheSandboxtomakesurethatnodataismisused.Thisisaconsiderablefactorasmostoftheinvolvedorganizationsrelyontheirreputation.Incaseofadata-misuse,theoverallprojectwillmost-likelycollapse,asthereputationaldamagewouldbetoogravely.Also,as itwaspreviouslymentionedandreviewedinthe literature;thequality,compatibilityandneatnessofthedatawerestatedasimportantfactorstoassurethefeasibilityoftheproject.Further,considerationsregardingpersonaldataprotectionregulationsandtheimportanceoftrustamongstinvolvedstakeholderswerementionedaskeypillarswhichmost-likelyaffectthesuccessof the Sandbox project. This consideration goes in linewithMerson (2015)who refers to trust as animportant variable amongst those that engage in data sharing practices. Most of the interviewedorganizationsalreadycommunicateand interactwitheachother,which isa fittingattitude inordertoestablishtrust.


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Concerning the overall size of the Sandbox project and the structure of involved participants, theintervieweesaltogetherhadastronglycorrespondingopinion.ItwassuggestedtotesttheSandboxwithaselectedcoreteaminadefinedregiontodevelopaproofofconcept.Afterasuccessfultestphase,theprojectshouldthenberolled-outonalargerscale.Theintervieweesfurtherproposedtoinvolveagreaternumberofparticipants, asa largernumberof involvedorganizationswouldmost likely result inmorediversedata-sets.Theintervieweeshoweveralsomentionedrelatedconcernsregardingthenumberofinvolvedorganizations,arguingthatanincreasedgroupsizemightnegativelyaffecttheproject,duetoarisingchallenges,suchastheincompatibilityofdata-setsandtheincreasedchancethatdataismisused.This concern coincideswithMancurOlson’s (1965) notion regarding the group size and the collectiveactiontheory,arguingthatanincreasedgroupsizenegativelyaffectstheprobabilitytoachieveacollectivebenefitduetofree-ridereffects.OtherscholarssuchasBatesandShepsle(1995)ontheotherhandarguewiththeoppositeprediction,statingthatcollectiveactionispositivelycorrelatingwiththegroupsizeandtherefore results in a two-sided viewpoint. Regarding the structure of involved organizations, theinterviewees suggest a diversified number of organizations, as each organization contributes with itsuniquedataset.Atthesametime,itishoweveralsomentionedasanoccurringchallenge,especiallywhenstakeholderswithinthesamebusinessareagettogether.

“Ithinkamixtureoforganizationsisperfectbutit’salwaysachallengewhenyouhavestakeholdersinthesamebusinessarea.Thequestioniswhoishelpingwhohereandwhoisgainingthemostbenefits?”

(Länsförsäkringar)Inthereviewedtheory,scholarshaveasimilar,two-sidedopinionregardingtheheterogeneityofinvolvedparticipants,arguingwiththesameconcernsanditisthereforedifficulttoreasonforeitherside.Itmostlikely requires somekindof testing inorder togivea clearanswerwhetheraheterogeneousormorediversegroupisbeneficialornot.

Conclusively itcanbestatedthatall intervieweesshowedahigh interest intheSandboxproject.Eventhoughtheintervieweesmentionrelevantconcerns,theoverallsentimentregardingtheprojectismorethanpositiveanditbecameapparentthatallinterviewedorganizationsarecurrentlytryingtogetmoreengaged indata sharingpractices.As Schalenkamp (2014)put it: In today’s digital economy,datahasbecomeincreasinglyvaluableasitrealizesenormouspotentialthatcanbeunlockedbydatasharinganddataanalyticsandcurrentdevelopments,technologicalbutalsoorganizational, indicatethatit isabouttime.“Thequestionisifwewanttodothisandtheanswerisyes.Wecouldhavedonethisyearsbeforebut

backtheneveryonewasscaredaboutitandnotknowingifwecandoit.Butnowweseethepossibilitiestodothis(….)Todayeverygrocerystorehasmoreknowledgeabouttheircustomer’sfuturebuyingbehaviorbutwedon’thavetheknowledgetopredictwhatmighthappennext.Butweshouldhave.”

(SOSAlarm)


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5.2 Results

Afteranalyzingtheempiricalfindings,thefollowingresearchquestionandrelevantsubordinateresearchquestionwhichwereguidingthisstudyshallnowbeanswered:

GuidingResearchQuestion:• Whatfactorsinfluencestakeholders,makingthemwillingtosharetheirdataforthemutualbenefit

intermsoffiresafetyinSweden?

SubordinateResearchQuestion:• Whatarepotentialbenefitsandobstaclesthataffectdatasharinganddataanalytics?Inordertoanswertheresearchquestions,anewmodelderivedfromtheintroductoryliteraturereviewandthesubsequentanalysisoftheempiricalfindingswasdeveloped.Themodeldistinguishesbetweenmotivationalanddiscouragingfactorsthatarelikelytoaffectdatasharingandwillbefurtherdiscussedinthesubsequentpart.Themotivationalfactorsaremainlyrelatedtotheorganizationitselfbutalsototheidentifiedbenefitsofdatasharinganddataanalytics.Throughoutthisresearch,differentorganizationswereinterviewedandthefindingsindicate,thatthenatureoftheorganizationispresumedtobeanimportantfactorwhichislikely to affect data sharing. Out of six interviewed organizations, four (MSB, SOS Alarm, Göta Lejon,KarlstadUniversity)arepartlyownedbythegovernment,whichdirectlyaffectstheirobligationtosharetheir data according to the principal of public access to official records. Three out of these fourgovernmental owned organizations are currently collecting fire-related data and act according to theprinciplebysharingtheirdatawithotherorganizations.Theremainingtwointerviewedorganizations;SBFandLänsförsäkringararenotgovernmentalownedandthereforenotobligedtosharetheirdata,howeverthefindingsindicatethattheirorganizationalmissionandbusinessmodelarelikelytoaffecttheirdecisiontoengageindatasharingpractices.Asanover100-year-oldorganization,SBFhasthedefinedmissiontomakesure thatnobody inSwedendiesorgetshurt in fires.Alsoasbeing the initiatorof theSandboxprojectSBFshowshighcommitmentandwillingnesstosharetheirdataamongstdifferentstakeholdersandisthereforestronglydrivenbyitsorganizationalmission.Länsförsäkringarontheotherhandhasaverycustomer-centricfocusandiscurrentlychangingtheirbusinessmodelfrombeingreactivetowardsbeingmoreproactiveandtryingtoreducethenumberofinsurancecases.Inordertoachievethischange,Länsförsäkringarexpressedtheneedtoengageindatasharingpractices,asdataisakeyprerequisitetoinitiateproactiveactionsandhencedirectlyaffectedbyitsorganizationalbusinessmodel.Furthermotivationalfactorsrelatetotheidentifiedbenefitsofdatasharinganddataanalytics.Reviewedin the literature but also confirmed throughout the interviews, respondentsmentioned the ability toconductlargerscaleanalyticsduetothebroaderaccesstodata-sets,increasedproductivitygains;asnewlygained insightsare likely leading to thedesignofnewproductsandservices,and the improvementofcurrentprocesseswhichisconcurrentlylinkedtothepromotionofinnovation.Further,asitwasindirectlymentioned by the interviewee from Länsförsäkringar, data acts as a new factor of production and isespeciallyofimportancethroughoutthetransformationtowardsamoreproactivebusinessmodel.Lastly,


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theencouragementofcollaborationwasmentioned,whichisdirectlyreflectedbySBFsinitiatedSandboxproject.Besides thepreviousexpressedbenefits, the research further indicates that the following twovariablesaccordingtoOstromscollectiveactiontheoryarepresumedtoaffectdatasharing;Thenumber,aswellastheheterogeneityofinvolvedparticipants,werementionedasimportantfactorsthroughouttheinterviews,arguingthatagreaterandmorediversifiednumberofparticipantswillresultinmorevariedandinsightfuldata.Atthesametimehowever,therespondentsalsomentionedconcernsduetothefactthatagreaternumberofparticipantsmightalsonegativelyaffectdatasharing.Itisthereforenotpossibleto clearly state whether the number and heterogeneity of participants are truly motivational ordiscouragingfactors,nevertheless,theconductedinterviewsleavetheimpressionthatthenumberandheterogeneityofparticipantsshouldratherbeconsideredasmotivationalthandiscouragingfactors.Theidentifieddiscouragingfactorsaremainlyrelatedtothechallengesofdatasharinganddataanalyticsbut also to the concern of trust as a key variablewithin Elinor Ostroms collective action theory. Theimportanceofdatasecurityandprivacywhichalsoincludeslegalrestrictionswerementionedthroughouteachinterviewandarethereforeconsideredasthemostdeterminingdiscouragingfactors.Withoutclearguidelinesonhowcollectivedataisusedandclarificationwhichdataisshared,projectsliketheSandboxwillmost likelynot succeed in the long run.Also, it remains tobe seenhow thenewlyeffectivedataregulationsby the EUwill impact data sharing in the future as itwas appraiseddifferently by variousscholars.Further,thehandlingandstorageofdataisacrucialfactorasitwasexpressedasamajorconcernamongsttheinterviewees.Particularlyinaframeworkwheremanydifferentorganizationscombinetheirdata,itisimportanttomakesurethatthequalityofthedata isappropriateandgoodenough.Thecurrentdatacollectionandhandlingpracticesamongsttheinvolvedorganizationsvarygreatly,rangingfrombasicexcelsheets towards advanceddatawarehouses. Itwill thereforebe amajor challengewithin the Sandboxprojecttoaggregateandformatthedatacorrectlyinordertoensureagoodqualityofthedata.Relatedtothedatacollectionpracticalities,therelevanceofdataadministratorshipwasidentifiedasanadditionalfactorwhichislikelytoaffectdatasharing.Theintervieweesreferredtotheproblemthatdataisoftenmisusedandinmanycases,itprevailsuncertainhowtheshareddataisusedintheend.Therefore,itwassuggested to create an independent organization, acting as a data administrator, who handles thedevelopmentandcurationof thedatatoensurethat ithasdefinedformatsandremainsuseful for itspurposes. Last but not least, trustwasmentioned as an important variable throughout all conductedinterviewswhilealsorepresentingakeyvariablewithinOstromscollectiveactiontheory.Withouttrust,dataownerswon’tbewillingtosharetheirdataanditthereforerequiresagoodlevelofcollaborationwithagreatdealofcommunicationinordertocreateanenvironmentwhereotherscanlearntotrusteachother.


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Figure5.1Newdatasharingmodel

Theabove-shownmodelsummarizesanddemonstratesthefactorsthatarelikelytoaffectdatasharinganddataanalytics,inacollectivenetworkwhichinvolvesdifferentstakeholdersthatcollectfire-relateddata. Themodel was developed in order to answer the guiding research questions of this study andhighlightsanumberofmotivationalanddiscouragingfactorswhilealsoincorporatingrelatedobstaclesandbenefitsofdatasharingwhichrelatestothesubordinateresearchquestion.Certainfactorssuchasthe nature of the organization and the importance of a secure set-up and trust seem to be moredetermining than other factors, however they conclusively act together as guiding elements for therealizationofacollectivedatasharingnetworkasitistheideawithintheSandboxproject.

6 Conclusion&FutureResearch

6.1 Concludingdiscussion

Thisresearchpresentsnewfindingsrelatedtoaninnovativeframeworkwhichfocusesondatasharingandfiresafety.Data-Driveninnovationistheoverarchingtermwhichispredictedtobeakeypillarinthe21stcenturysourcesofgrowth,astheexploitationofdatapromisesthecreationofnew,innovativeproducts,services andbusinessmodels,while also stimulating greater competitiveness.An incorporatedprojectcalledSandbox,emphasizesonthisterm,which’scentral idea istoconnectdifferentstakeholdersthatcollectfire-relateddata.Subsequently,thatdatashouldbeusedinordertogeneratenewfindingswiththegoaltoenhanceproactivefiresafety.Therelevanceofthisresearchisdeterminedbytwomajortrends– namelytheincreaseofurbanizationwhichimpliestheneedforproactivesafetyandsignificantadvancesininformationtechnology,whichenablestheimplementationofdatasharingnetworks.Bothtrendswerefurther discussed at the beginning of this research and deciding factors to commence this study. Anintroductoryliteraturereviewreferstothelaterfocusofthisresearchandisbrokendownintotwomainareas:thelogicofcollectiveactionandthebenefitsandchallengesofdatasharinganddataanalytics.Bothsubjectsarecloselyrelatedtoeachotherandwerefurtherelaborateduponthroughoutthisstudy.


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Theguidingresearchquestion:“Whatfactorsinfluencestakeholders,makingthemwillingtosharetheirdataforthemutualbenefitintermsoffiresafetyinSweden?”andrelevantsubordinateresearchquestion“Whatarepotentialbenefitsandobstaclesaffectdatasharinganddataanalytics?”wereansweredwiththeendorsementofanewlydevelopedmodelwhich incorporatescharacteristics fromexisting theory,mergedwiththeempiricalfindingsthatwereidentifiedthroughoutconductingthisqualitativeresearch.Thedevelopedmodeldistinguishesbetweenmotivationalanddiscouragingfactorsthatarelikelytoaffectdatasharing.Identifiedmotivationalfactorsprimarilyrelatetotheorganizationitself;includingthenatureoforganizatione.g.iftheorganizationisgovernmentalownedandthereforeobligedtosharetheirdata,theorganizationalmissionandbusinessmodel,anticipatedbenefitsofdatasharingwhichincludeamongstothers theability to conduct larger scaleanalyticsdue to theavailabilityof largerdata-sets, increasedproductivityduetotheabilitytocombinedatafromdifferentsourceswhichcanthenbeusedtodesignnew products and services, but also the encouragement of collaboration which is likely to enhanceproactivefiresafetyandthereforeconstitutesabenefitforthesociety.Lastlythesizeoftheoveralldatasharing network including the number and heterogeneity of involved participants were identified asmotivationalfactors,asagreaterandmorediversenumberofinvolvedactorspresumablyresultinmoreversatiledata-sets.The identifieddiscouraging factors that influencedatasharinganddataanalyticsmainly relate todatasecurityandprivacyconcerns,asfire-relateddataoftenincludesconfidentialinformationandthereforepointsouttheneedthatnodataismisused,legalrestrictionsandtheannouncementofnewlaws,astheyaffect the overall sentiment and decision to engage in a data sharing network, but also technicalconsiderationsintermsofhowdataishandled,administratedandformattedcorrectlytoensurethatdataremainsusefulforitspurposes.Lastbutnotleast,theimportanceoftrustwasidentifiedasadeterminingfactorasitisacentralvariablefortheestablishmentofcollaborationandthereforedirectlyaffectingdataownersandtheirdecisiontoengageinadatasharingnetwork.

Figure5.1Newdatasharingmodel


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The identified motivational and discouraging factors provide guidance to answer the main researchquestionofthisstudybyhighlightingrelevantdeterminantsthatinfluencestakeholdersandtheirdecisionto share fire-related data. In conclusion it can be stated, that it is a combination ofmotivational anddiscouragingfactorsthataffectdataownersandtheirdecisiontoengageinadatasharingnetwork.Theempiricalfindingshoweveralsoindicate,thatmostoftheintervieweesconsiderthebenefits(belongingto themotivational factors) of data sharing as beingmore determining than the discouraging factors,whichlikewiseaffectstheirdecisiontosharetheirdata.Alsoitshouldbementionedthateventhoughtheidentifieddiscouragingfactorsrepresentmajorobstacles,theycanoutrightbedealtwith.ThroughoutthedevelopmentofadatasharingnetworksuchastheSandboxitisthereforesuggestedtopayparticularlyattention to a secure set-up, which incorporates latest data regulations and privacy concerns. Also,additionalattentionneedstobepaidontechnicalconsiderationsastheempiricalfindingsindicatethatdatacollecting-,handling-andstorage-practicescanvarytremendously.Toassureanaccurateflowofthecollectivedata,clean,completeandformattedcorrectlydataisaprerequisite.Thisfurtherdemandsforatrustworthyadministrativeentity,toensureacontinuouscurationofthecollectivedata.Also,itneedstobeemphasizedontheestablishmentof trustamongst involvedorganizations,as it isakeyelementtoestablishlong-termcommitmentandcollaboration.Conclusively,itissuggestedtoexplicitlycommunicaterelatedbenefitsandchallengesofdatasharinganddataanalytics,asatthispointmanyoftheinterviewedorganizationsarenotyetthoroughlyfamiliarwithrelatedbenefitsandchallenges,which inturnmightfurtherstrengthentheirinteresttoengageinadatasharingnetwork.Thesubordinate researchquestion,which revolvesaroundpotentialbenefitsandobstacles thataffectdatasharinganddataanalyticsisconcurrentlyansweredwiththeunderpinnedmodel,astheidentifiedmotivationalanddiscouragingfactorsincorporatepotentialobstaclesandbenefits.


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6.2 FutureResearch

The focusof this researchwas toprovidenew insightson factors thatare likely toaffectdatasharingamongstdifferentstakeholdersthatcollectfire-relateddata.Theaimofthestudywasnottoprovidein-depth informationabouteachof the interviewedorganizationsbut rather to identify andpresent theoverallopinionsandvoicesinrelationtodatasharingandfiresafety.Nevertheless,onemajorlimitationofthisresearchconstitutestheamountofexaminedintervieweesandorganizations,whichwasmainlylimited due to time constraints. Someone should not neglect the fact that one interviewee does notrepresenttheoverallviewpointofanorganization.Also,noteveryintervieweewasprofoundlyfamiliarwiththeconceptofdatasharingwhilealsonotbeingtrulyfamiliarwithtechnicalconsiderations.Futureresearchshouldthereforefocusonexpandingthecircleofintervieweesinordertogetabroaderpictureoftheorganizationinstudybutalsotoemphasizeonimportanttechnicalconsideration.Futureresearchshouldalsofurtherfocusontheidentifiedmotivationalanddiscouragingfactorsthatarelikelytoaffectdatasharing,asthisresearchdoesnotmeasuretowhatextenteachoftheidentifiedfactorsaffectdataownersandtheirdecisiontoengageindatasharing.Sofar,itcanonlybestatedthatcertainfactorsseemtobemoredeterminingthanothers,howeveritwouldbeworthwhiletobeabletoranktheidentifiedmotivationalanddiscouragingfactorsinordertodeterminetheirdistantimportance.Therefore,additionalquantitativeresearchissuggested,whichratherfocusesonnumbersthanonwords.Also,as itwasstated inthebeginningofthisresearch,theunderlying ideaoftheSandboxmodel istodevelopaviablebusinessmodel,whichinvolvesdifferentfire-relatedstakeholdersandSBF,representingthebackboneoftheframework.Thefindingsofthisstudyarenotdirectlyakinwiththerequirementstodevelop a businessmodel, indirectly however this studyprovides supportive information and lays thefoundationforfutureresearch,relatedtothedevelopmentofabusinessmodel.Lastbutnotleast,thechosenareaofstudywithinthisresearchisveryunique.Notonlybecauseoftheconnection between data sharing and the fire safety, but also because of the previously mentionedcombination of studied organizations. Future research is therefore suggested to test if the developedmodelaspresentedintheanalysisandconclusion,canalsobeappliedinotherareaswheredatasharinganddataanalyticspracticesarepresumedtobeeffectivetools.


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8 Appendices

Appendix1:InterviewGuide

Personalbackground

- Couldyoupleasestartandintroduceyourselfshortly?(name,positionwithinthecompany,etc.)

Topicarea1:ChallengesandFuture- Whatarethemainchallengesthatyourorganizationisfacingtodayandinthefuture?(Istherea

relationtobigdata,datasharing)- Whatcapabilitiesdoyouthinkwillbeofimportance?

Topicarea2:Currentdatacollectionpractices(sensible/optional)

- Areyourcurrentlycollectingdatawhichisrelatedtofiresafety?(e.g.datafrompreviousfires,real-timedatagatheredthroughsensorsasappliedinsmarthomes)Ifyes,couldyoupleasebemorespecific?

- Howdoyoucollectthisdata?- Where/Howdoyoustorethisdata?- Howdoyouanalyzethisdata,whatisyouruseforthisdata?

Topicarea3:Bigdatasharing&dataanalytics

- Whatdoyouassociatewiththetermdatasharinganddataanalytics?- Istherearelationtodatasharing&dataanalyticswithinyourorganization?

Topicarea4:Benefits&Challengesofbigdatasharing

- Areyoufamiliarwiththebenefitsandchallengesofdatasharing&dataanalytics?- Whatbenefitsandchallengesinregardstodatasharinganddataanalyticsdoyouconsideras

mostimportant?- Doyouconsiderthebenefitsasbeingmoredeterminingthanthechallenges,ortheotherway

round?Topicarea5:ProjectSandbox

- WhatisyourgeneralopinionregardingtheSandboxproject?(consensus/rejection)- Whatwouldbethemainincentivesforyourorganizationtobepartofthisproject?- Whatwouldbeyourmainexpectations/concerns,whenparticipatinginthisproject?- WouldthesizeoftheoverallprojectaffectyourdecisionforbeingpartoftheSandboxproject?- Wouldtheheterogeneityoftheinvolvedorganizationsaffectyourdecisiontobepartofthe

sandboxproject?(e.g.onlyinsurancecompaniesoramixofdifferentorganizationssuchasprivatesafetyfirms,governmentalagencies,insurancecompanies)

- Inregardstotheprojectanddatasharing:Howimportantistrustforyourorganization?- WouldyougenerallybeinterestinparticipatingintheSandboxproject?

data sharing in the fire industry€¦ · predictive policing is a similar, already existing...

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