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Creatingtheenvironmentfordriverdistraction:Athematicframeworkof

sociotechnicalfactors

KatieJ.Parnell*a,NevilleA.Stanton

a,andKatherineL.Plant

a

*Correspondingauthor

aTransportationResearchGroup,FacultyofEngineeringandtheEnvironment,BoldrewoodInnovation

Campus,UniversityofSouthampton,BurgessRoad,Southampton,SO167QF,UnitedKingdom.

Abstract

Asmodernsocietybecomesmorereliantontechnology,itsusewithinthevehicleisbecomingaconcernfor

roadsafetyduetobothportableandbuilt-indevicesofferingsourcesofdistraction.Whiletheeffectsof

distractingtechnologiesarewelldocumented,littleisknownaboutthecausalfactorsthatleadtothedrivers’

engagement with technological devices. The relevance of the sociotechnical system within which the

behaviouroccursrequiresfurtherresearch.Thispaperpresentstwoexperiments,thefirstaimstoassessthe

driversself-reporteddecisiontoengagewithtechnologicaltaskswhiledrivingandtheirreasoningfordoing

so with respect to the wider sociotechnical system. This utilised a semi-structured interview method,

conductedwith30driverstoinitiateadiscussionontheirlikelihoodofengagingwith22differenttasksacross

7differentroadtypes.Inductivethematicanalysisprovidedahierarchicalthematicframeworkthatdetailed

the self-reported causal factors that influence the drivers’ use of technologywhilst driving. The second

experiment assessed the relevance of the hierarchical framework to a model of distraction that was

established from within the literature on the drivers use of distracting technologies while driving. The

findingsprovidevalidationforsomerelationshipsstudiedintheliterature,aswellasprovidinginsightsinto

relationships that require further study. The role of the sociotechnical system in the engagement of

distractions while driving is highlighted, with the causal factors reported by drivers suggesting the

importanceof considering thewider systemwithinwhich thebehaviour is occurringandhow itmaybe

creating theconditions fordistraction tooccur.This supportspreviousclaimsmadewithin the literature

basedmodel.Recommendationsareproposedthatencourageamovementawayfromindividualfocused

countermeasurestowardssystemicactors.

Keywords:In-vehicletechnology,Driverdistraction,Qualitativemethods,Sociotechnicalsystems.

1. Introduction

Technologicaldevelopmentsarelargelydrivenbyindustrialorcommercialrequirementswhich,Dorf(2001)

claims,areharnessedbymankindtochangeormanipulatetheirenvironment.Thedrivingenvironmenthas

changedmarkedlythroughtheimplementationoftechnologyoverrecentdecades(Walkeretal,2001).This

hashadramificationsforthedesignanduseofvehicles(Wierwille,1993;Walkeretal,2001).Driversnow

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expect thedesignof thevehicle to include technological facilities thatenableentertainment,navigation,

communication,connectivity,efficiencyandcomfortwhiledriving.Yet,thereisaneedtoensurethatthe

implementationof such technologiesdoesnotadverselyaffect roadsafety (Leeetal,2008;Youngetal,

2011).

The distractive effects of hand-held phones have been evidenced,with adverse consequences to driver

performancemetrics,suchasvehiclecontrol(Tsimhonietal,2004),attentiontunnelling(Reimer,2009),and

hazarddetection(Summalaetal,1998)amongothers.Yet,despitebeingmadeawareoftherisksposedby

mobile phoneswhile driving and legislation to ban their use acrossmany countries, drivers continue to

engagewiththem(Dingusetal,2006;Lerneretal,2008;Walshetal,2008;Zhouetal,2012;Young&Lenné,

2010;Metz et al, 2015; Tivesten&Dozza, 2015).While previous research has informed on the adverse

consequencesofmobilephones,thecontextualandmotivationalfactorsthatleadtoengagementinother

technological tasks isunder-researched (Young&Regan,2007;Youngetal,2008;Young&Lenné,2010;

Tivesten&Dozza,2015;Horreyetal,2017).

Some research has been conducted into the decisions that driversmake to engagewith distractions in

simulators(Metzetal,2011;Schömig&Metz,2013),ontesttracks(Horrey&Lesch,2009)andthroughthe

analysisofdataderivedfromnaturalisticstudies(Metzetal,2015;Tivesten&Dozza,2015).Achallengein

the assessment of driver distraction research is the dichotomy between high levels of control and the

naturalistic studyofbehaviour (Youngetal,2008), thus thebenefitsand limitationsof thesestudiesare

inherenttothevalidityofthefindings.Whilesimulatorsoffercontroloverexternalvariables,suchasroad

type and other road users, capturing realistic behaviour is compromised (Young et al, 2008). Yet, in

naturalistic studies the focus of data collection is on the driver and their triggered engagement with

secondary tasks as they allow very little control, and thusmeasurement of, the contextual factors that

influence drivers’ engagement with secondary tasks (Metz et al, 2015). TheWorld Health Organisation

(WHO)nowacknowledgesthesociotechnicalsystembasedapproachwhichidentifiesdriverbehaviour,not

asaproductoftheindividual,butasaproductofsystemicelementssuchastheroadlayout,roaddesign,

vehicledesign,andthecontextsurroundingthedrivingtask(WHO,2004).Despitethis,theapplicationof

systemsbasederrormanagementapproacheshavebeenlargely ignored(Salmonetal,2010).Thecausal

errortaxonomysuggestedbyStantonandSalmon(2009)statesfivekeyelementswithinthesociotechnical

systemwhichinfluencetheconditionsthatleadtoerror;thedriver,thevehicle,roadinfrastructure,other

roadusersandenvironmental conditions.Thus, it canbesuggested that thecauseofdistraction related

errorsisnotlimitedtothedriver,insteaditisinfluencedbyamultitudeofothersystemicactors.

Reviewingdistractionwiththesociotechnicalsystems‘riskmanagementframework’(RMF)developedby

Rasmussen (1997) revealed the impact that hierarchical levels of the systemhave on the emergence of

distraction.Actorswererevealedfromtheinternationalandnationalcommittees(Parnelletal,2017a)who

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setthelawsthatareenforcedbylocalgovernmentsandregulatorsthatthenfeeddowntheframeworkto

themanufacturersofdevicesandtheinteractiontheyhavewiththeenduser(Young&Salmon,2012;Parnell

etal,2017a).Ratherthanfocusingonthedrivers’decisiontoengageastheinitiationoferror,thesystems

approach gives an insight into the conditions through which the driver was permitted to engage with

distractingtechnologiesandhowthisbehaviourinfluencestheemergenceofsafetywithinthesystemasa

whole.Yet,appropriatemethodsarerequiredtoassessthesociotechnicalsystem(Youngetal,2013;Salmon

etal,2017).

Inafirstattempttoassessandmodeldriverdistractionfromasociotechnicalsystemsapproach,Parnellet

al(2016)developedthePARRC(Priority,Adapt,Resource,Regulate,Conflict)modelofdistraction,thefirst

model of the behaviour to account for the contribution of systemic factors. This encompasses five key

mechanismsthroughwhichin-vehicletechnologymayleadtodistractionacrossthesociotechnicalsystem,

including‘goalpriority’,‘adapttodemand’,‘resourceconstraints’,‘behaviouralregulation’and‘goalconflict’

(Parnell et al, 2016). The PARRC model was developed through grounded theory methodology which

determined thekey factors involved in theemergenceofdistractionasevolved from the literature. The

interconnections made between these mechanisms were shown to influence how distraction related

behaviouremergedfromthesystem,aswellastherelevanceofothersystemicactorsonthemechanisms.

Readers are directed to Parnell et al (2016) for further information. Application of the PARRC model

mechanismstoanAccimapanalysissuggestedhowactorsinthesystemmaybepreventingtheemergence

ofdistractionorconverselyleavingthesystemopentodistraction(Parnelletal,2017a).Thishighlightedthe

roleoflegislation,developedthroughinternationalandnationalcommitteesthatisthenenforcedthrough

national laws, that targets hand-held mobile phone use but is more ambiguous on the use of other

technologies.Theambiguityinlegislationwasshowntohaveledtotheadvancementoftechnologiesand

theirimplementationwithinthevehicle,despitealackofevidencetosuggestthemtobesaferthanhand-

heldmobilephones(Parnelletal,2017a).Yet,themechanismsofthePARRCmodelweredrawnfromthe

literatureusinggroundedtheoryandthereforerequirevalidationthroughtheirapplicationtootherdata

sources,methodsand/or investigatorsthroughtheprocessoftriangulation(Hignett,2005;Raffertyetal,

2010).

Thispaperseekstogaindatafromdriversontheirselfreportedreasonsforengagingwithtechnologywhile

driving. Questionnaires and online surveys have strived to gather responses on drivers’ frequency of

engagingwithdistractionsandtheirviewsontherisksindoingso(e.g.McEvoyetal,2006;Young&Lenné,

2010;Walshetal,2008;Zhouetal,2009;Zhouetal,2012;).Yet,theyareoftenprescriptive,posingclosed

questionsthatmaylimitthedatatotheagendaoftheresearcher(O’Cathain&Thomas,2004).Instead,the

causalfactorsthatdriversdeemtoinfluencetheirdecisiontoengagewithdistractions,andhowthismay

resultindistractionrelatedevents,isofinterest(Youngetal,2008;Young&Lenné,2010;Lee,2014).The

first experiment within this paper sought to obtain the drivers self-reported reasons for engaging with

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technology while driving using a semi-structured interview method to engage drivers in open-ended

discussionsonwhytheymaybemore,orless,likelytoengagewithvarioustypesoftechnologywhiledriving.

The inductive thematic analysis thatwas applied during the data analysis aimed to develop factors that

driversthemselvesdeemto influencetheirengagementwithtechnologicaltasks.Thesecondexperiment

aimedtoassesshowthecausalfactorsderivedfromthedriversintheinterviewstudyrelatedtothecausal

factorsthatweredevelopedfromtheliteratureinthedevelopmentofthePARRCmodel(Parnelletal,2016).

Thismodelisusedforitsabilitytoassessthesociotechnicalsystemsurroundingthebehaviour(Parnelletal,

2016;2017a).Thefindingsseektoassistintheprovisionofcountermeasuresthattargetthesourceofthe

issue,ratherthanobservingwithhindsighttheeffectsofdistraction.

2.Experiment12.1Aim

This experiment aimed to understand the drivers self-reported reasons for engaging with technological

deviceswhiledrivingandtheinvolvementofthesociotechnicalintheirdecision-makingprocess.Previous

researchhassoughttocapturethedrivers’useoftechnologiesusingquestionnairesandonlinesurveys,yet

thisstudyaimstocapturethedrivers’subjectiveperspectiveintheirownwords.Thiswillinvolvetheuseof

semi-structured interviews toelicitdiscussionswithdriverson their likelihoodofengagingwithdifferent

technologicaltasksacrossdifferentroadtypes.

2.2.Method

2.2.1Participants

DriverswithexperienceofUKroadswerespecifiedastheroadtypesincludedwithinthesemi-structured

interviewsrelatedtothosecomprisingtheUKroadwaysystem(Walkeretal,2013).Atotalof30participants

wererecruited(15females,15males),acrossthreeagecategories(18-30,31-49,50-65),withfivefemales

andfivemalesineachcategory.ParticipantswererecruitedundertherequirementthattheyheldafullUK

drivinglicenseandhadaminimumof1-yearsexperiencedrivingonUKroads(meanyearsexperience=19.5,

SD=13.08).Theywerealsorequiredtobefrequentdrivers,drivingonaregularweeklybasisinorderforthem

tobeexposedtosituationswheretheymaybeinclinedtoengagewithtechnology(meanhoursspentdriving

aweek=9hrs45min,SD=6hrs20mins).Participationwasvoluntary.

2.2.2Datacollection

To obtain the drivers own views on why they engage with technological devices while driving, semi-

structuredinterviewswereconducted.Semi-structuredinterviewshavebeenusedeffectivelytoinvestigate

otheraspectsofdrivingbehaviour(Simon&Corbett,2007;Gardner&Abraham,2007;Tonetto&Desmet,

2016),buttheyhavenotbeenappliedtostudyhowdriverdistractionisviewedbydrivers.Theirapplication

within this research allowed for open-ended questions that enabled drivers to generate concepts they

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deemedimportanttotheiruseoftechnologicaldeviceswhilstfacilitatingastructureddatacollectionmethod

that could be reliably applied across all interviewees. They also allowed the researcher to probe into

interestingconceptsastheyarose(Cohen&Crabtree,2006).

The interviewswere structured around a table that encouraged the driver to discuss their likelihood of

engagingwitharangeofdifferenttechnologicaltaskswhiledrivingacrossdifferentUKroadtypesinorder

toprovideadiscussionsurroundingthesituationsandenvironmentswhichmayinfluencetheuseofdifferent

technologicaldevices.Table1presentsthelistoftechnologicaltasksposedtoparticipantsintheinterview.

Thetasksweredrawnfromthecurrentliteratureinvestigatingdistractionfromin-vehicletechnology(e.g.

Young&Lenné,2010;Nealeetal,2005;McEvoyetal,2006;Harveyetal,2011a),aswellasreportsfrom

roadsafetyorganisationsandpolicereports(RAC,2016;DepartmentforTransport,2015a).Theroadtypes

presentedhavebeenshowninpreviousresearchtoinfluencedrivers’situationalawareness(Walkeretal,

2013)andcrashrates(Bayliss,2009).Theyincludedmotorways,majorA/Broads,urbanroads,ruralroads,

residential roads and junctions. Participants were presented with a road type classification sheet with

definitions,imagesandcontextualinformationrelatingtoeachoftheroadtypesforclarity.

Table1.Listoftechnologies/specifictasksthatdriverswereaskedtoratetheirlikelihoodofengagingwith.

Technology Task

NavigationsystemMonitorroute

Enterdestination

Hands-freesystem

Findnumberfromaddressbook

Answeracall

Talktoother

In-vehiclesystem

Changeclimatecontrol

Changesong/radiostation

Adjustvolume

Listentomusic

Verballycommunicatewithin-builtsystem

Mobilephone/Portabledevice

Enterdestinationintonavigationapp

Monitornavigationapp

Write/sendatext

Readatext

Answerphonecall

Talkonthephone

Enter/Findanumber

Changesong/audiotrack

Usevoiceassistfeatures

Takeaphoto

Usesocialmediaapps

Checkyouremail

Participantswerefreetogeneratetheirownreasoningbehindwhytheymayormaynotengagewitharange

ofdifferenttechnologicaltaskswhiledrivingacrosstheroadtypes.Theresearcherprobedtheparticipantto

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expandontheirdiscussionpoints forclarityandfurther informationwherenecessary.Thesameprimary

researcherconductedtheinterviewswithallparticipantsforconsistency.

Apilotstudywasconductedtodetermineifthetechnologicaltaskswererepresentativeofthoseusedby

driversandtoestablishagreementonthedescriptionsoftheUKroadtypes.Thisrevealedanoverlapinsome

of thetechnological tasks, suchassearching forapointof interestandadestination inasat-nav. Italso

revealedthatwhendriversdiscussedtheirbehaviouratajunctiontheyseemedtodifferintheirdiscussions

surrounding the use of technologywhen stopped at a junction e.g. at traffic lights compared to driving

throughanintersection.Therefore,thejunctionroadtypewassplitintwotorepresentbothdrivingthrough

ajunctionandstoppedatajunction.

Theinterviewslastedapproximately30minutesalthoughthisvarieddependingonthediscussionsengaged

bytheparticipantandtheresearcher(average=34.21mins,SD=14.07).Interviewswereaudiorecordedand

transcribed. Due to the sensitive content, i.e. if they were likely to engage in an activity that may be

consideredillegalunderUKlawssuchasusingamobilephonewhiledriving,confidentialitywasensuredto

allow theparticipant to talkopenly.The interviewstudywasapprovedby the research institutesEthical

ResearchandGovernanceOrganisation(ERGOreference:24937).

2.2.3Dataanalysis

Transcriptionsoftheinterviewsprovidedthedatasetfromwhichtoanalyseanddrawinferencesonthe

causalfactorsthatdriversreportedtoinfluencetheirdecisiontoengageinthetechnologicaltaskswhile

drivinganddeterminetherelationshipofthefactorstothewidersociotechnicalsystem.Thematicanalysis

wasusedtoorganise,analyseandinterpretkeythemeswithinthedata(Braun&Clarke,2006).Atheme

wasdefinedas“…somethingimportantaboutthedatainrelationtotheresearchquestion,andrepresents

somelevelofpatternedresponseormeaningwithinthedataset”(Braun&Clarke,2006;p10).The

flexibilityofthemethodisbothanadvantage,infacilitatingadaptabilityacrossdifferentapproaches,anda

limitationduetocommentsoflimitedrigourfromunclearmethodologies(Braun&Clarke,2006).Yet,

BraunandClarke(2006)commentthatwithclearlydefinedmethodsandcommentary,thematicanalysis

canbeahighlyusefultoolindrawingmeaningfromqualitativedata.Themethodologyappliedtothedata

setisthereforegivenindetailwithinthispaper.

ThethematiccodingprocesswasconductedinNvivo11softwaretoaddrigourtothequalitativeresearch

(Richards&Richards,1991;Welsh,2002).Italsoassistedinreviewingthesub-themes(seeExperiment2),

andallowedforqueriestoberunonthecodeddatatointerrogatethecodesaftertheyweredeveloped.

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2.2.3.1Inductivethematiccoding

Thethematicanalysisutiliseda‘bottom-up’approach,wherebycontentwascodedwithoutapre-existing

framework, rather the frameworkdevelopedthroughtheanalysisof thedata (Boyatzis,1998).Thus, the

themesthatdevelopedwerestronglylinkedtothesourceofthedata(Patton,1990).Incontrasttodeductive

thematicanalysis,whichseekstolookataspectsofthedatathatrelatetotheresearchframeworkunder

investigation, inductive analysis provides a richer insight into the data set as a whole, using naturally

occurring themes (Braun & Clarke, 2006). It was not desirable to impose a framework on the drivers’

verbalisations,butratherdrawontheconceptsthattheparticipantsdeemedtobeimportant.Braunand

Clarke(2006)statethattheclarityofthemethodologyusedtodevelopthematiccodes isessential to its

validity, which led to the development of their own guidelines on conducting thematic coding. Their

guidelineswerefollowedandtheprocessofapplyingthemtothedatacollectedfromtheinterviewsisshown

inFigure1.Thesameprimaryresearcherthatconductedtheinterviewsalsoconductedthethematicanalysis,

astheywerefullyimmersedinthedataset.

Figure1.Stagesoftheinductivethematicanalysis.

Theiterativenatureofinductivethematicanalysismeantthattheinitial,descriptive,subthemeswerecoded

asmultiple individualconceptstodrawasmuchinformationfromthetranscriptsaspossible(Stages1-3,

Figure1).Thesewerecodedinthedriversownwords,in-vivo,tostaytruetothedata(Richie&Lewis,2003)

If framework does not fit data set return to further review and refine codes.

Stage 1Collect data - Interviews

Stage 2Transcribe data and familiarisation with data set

The primary researcher transcribed approximately 33% of interviews with the remainder sent to a transcription agency. The primary researcher spent time reading through all transcripts before coding

Stage 3Generate initial descriptive codes

The primary researcher conducted all interviews for consistency

Initial codes were generated based on the descriptions of the responses using the drivers own words where applicable. Braun and Clarke’s (2006) advice was followed: • Code as many themes as possible• Include context surrounding each excerpt• Allow exerts to be coded at more than one theme

Stage 4Review and collate themes into semantic themes

Stage 5Review and collate themesinto systemic themes

Re-read the the data extracts coded to each of the initial ‘descriptive’ themes generated at stage 3 and review to assess their coherence. Collate themes on their semantic meaning.

Once the descriptive and semantic themes are set, review for patterns across the themes that relate to systemic elements. This requires some interpretation.

Stage 6Contrast themes to data set as a whole

Go back to the initial data set with the developed thematic structure to observe its fit with the data set as a whole and code any further extracts that may have been missed.

DescriptionStageOutput

Audio recordings of the interviews with all participants

Complete transcriptions of all the interviews. Ready for input into Nvivo 11.

Extensive list of initial descriptive codes

Revised list of descriptive codes aggregated into semantic themes

Organised list of descriptive and semantic themes with systemic high-level themes that identify the patterns of the data

Refined theoretical framework encompassing the patterns observed in the data set

Stage 7Define and name themes appropriately

Review content coded to each theme to construct a meaningful definition of each theme and what it represents in the data. A codebook was generated encompassing the descriptive, semantic and systemic themes.

Clearly defined themes and subthemes that are easily identifiable and collated into a codebook.

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and allow an emerging framework to reflect the real-life experiences and thought processes of drivers

(Tonetto&Desmet,2016).Themultipledescriptivethemeswerethenanalysed,organisedandrefinedinto

semanticthemes(Stage4).Thisrequiredsomeinterpretationandinductivetheorisingonthesignificanceof

conceptsandtheirbroadermeaning(Patton,1990).

2.2.3.2Inter-raterreliabilitytest

Allowingothers,independenttotheresearchproject,toapplythecodethathasbeenappliedtoadataset

isausefulwayofassessingthereliabilityofthecoding(Boyatzis,1998).Totestthecodingframework10%

oftheparticipants’transcriptswererandomlychosenforinter-raterreliabilitytesting.Twocolleagueswithin

the Human Factors research team were recruited to test inter-rater reliability. They were given the

codebook, which presented the full thematic framework (see the Online Appendix), during a 45minute

trainingbriefingwheretheinterviewstudywasexplainedandthethemesweredescribedbeforetheywere

askedtoindependentlycodethesame10%ofthetranscripts.Theinter-raterswereonlyrequiredtocodeat

thesemanticlevel,whilereferencingthelowerleveldescriptivethemestoaidtheircoding.Nvivo11software

wasusedfortheinter-ratercodingwiththeresearcherscodinghidden.Percentageagreementwasusedto

assessthereliabilityscoresoftheinter-raters incontrasttotheresearchers initialcoding.Thismethodis

widelyappliedininter-raterreliabilitystudies(Boyatzis,1998;Plant&Stanton,2013)andwhilethereare

stillnoestablishedstandardsontheacceptablelevelofagreementbetweenraters,Boyatzis(1998)deems

70%agreementasanecessary levelofagreement.Bothratersreachedagreementpercentagesoverthis

level(rater1=81.24%,rater2=74.87%).Thusindicatingthattheraterswereabletousetheframeworkto

codethedataata level thatwasmuchhigherthanchance,attestingtothereliabilityof theresearchers

codingandtheapplicationofthethematicframework.

2.3Results

Inductivethematicanalysisresultedinthedevelopmentofahierarchicalframeworkofthemesthatreflected

thedrivers’self-reportedlikelihoodtoengageineachofthetechnologicaltasksoneachoftheroadtypes.

AnoverviewofthehighlevelsystemicandsemanticthemesisshowninTable2.Thefullthematicframework,

includingthedescriptive,semanticandsystemiclevelsthatweredevelopedthoughtheprocess(shownin

Figure1)ispresentedintheOnlineAppendix.

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Semanticsubthemes Description ExampleQuote

Driver Referencesmadebythedriverincludingtheirmental/physiologicalstate,experience,knowledge,skills,abilitiesandcontext-relatedbehaviour

(D1)Attitudeofdriver

Negative:Referencetonegativeattitudesofthedrivertowardsperformingthetaskwhiledriving “Because,Ijustthinkitistheworstthingintheworld,Ijustwouldn’tdoit,it’sterrible”Positive:Referencetopositiveattitudesofthedrivertowardsperformingthetaskwhiledriving “Idon’tseeanyproblemwithitpersonallywhatsoever”

Unnecessary:Referencetothedriverperceivingthetasktobeunnecessarytoperformwhiledriving “That’ssomething–it’sjustsomethingthatcanwaituntilwhenyougethomeIthink”

(D2)Tendency Referencetothedriversstatedtendencytoperformthetaskinthepastand/orthefutureasanindicatoroftheirlikelihoodtoengage “Ihavebeenknowntodothat”

(D3)Viewofself Referencetothedriversstatedviewofthemselvesandtheirownbehaviouraltendencieswhenstatingtheirlikelihoodtoperformthetask ‘ifIamstoppedIgenerallyamalittlebitmorenaughty”

(D4)Influenceofothers Referencetootherpeopleandtheirinfluenceonthedriverandtheirlikelihoodofperformingthetaskwhiledriving “theshameifyoudidsomethingbad,thateveryonewouldthinkyouaresostupid”Infrastructure Referencetothespecificroadtypewithintheroadtransportsystem,includingthelayout,contents,policy,andregulatedconditions

(I1)Perceptionsofsurroundingenvironment

Referencetothecontextsurroundingtheroadenvironmentofaspecificroadtypethatisinterpretedasaninfluencingfactorinthelikelihoodtoperformthetaskinthespecificroadenvironment

“fortheseroadsandjunctions,itwouldrequirealotmoreconcentration”

(I2)RoadLayout Referencetofeaturesofthespecificfixedroadenvironmentthatinfluencethedriver’slikelihoodtoperformthetask“becausetomeamotorway,onceyouareonit,itisallmovinginthesamedirectiongenerally”

(I3)Illegality Referencetothelegislationontheuseofthetaskwhiledriving “Iusuallyholditinalowposition,sothepolicecan’tsee”

(I4)Task-roadrelationshipReferencetotheinteractionbetweenfeaturesoftheroadandthetaskthatinfluencehowthetwomaybecompatiblesuchthatthelikelihoodofperformingthetaskisinfluenced.

“Yeahitwouldbestilted,Iwouldprobablymakethepersononthephoneaware,sayhangonaminutebutIwouldprobablysoundnotasengage”

(I5)RoadrelatedbehaviourReferencetotheactionsandresponsesthataretypicalorrequiredofthespecificroadtypewhichinfluencesthelikelihoodofperformingthetaskondifferentroads.

“urbanroadIthinkismorebusyaswellsoIthinkthemoresortofdecisionsyou’vegottomake”

Task Referencetothedetailssurroundingthespecifictaskandengagementwithit

(T1)Complexity Referencetothedifficultyoreaseofperformingthetaskwhiledriving “ifyouhavetounlockthephonescreenorwhateveretc.,itisnotassimple–wellitisquitedistracting”

(T2)Interaction Referencetophysicalfeaturesofthetaskthatrelatetotheinteractionrequiredtoperformthetaskwhiledriving.Thisrelatestotheinterfacedesign,devicelocationanddriverrequiredactionsinordertoengagewiththetask.“It’sonlyonebuttontopress,sothat’snotanissue”

(T3)Duration Referencetothetimeand/orlengthofthetask “ifit’salongtextyoumightnotreadit”

(T4)Desirability Referencetofeaturesofthetaskthatinfluencehowdesirableitmaybetoperformwhiledriving.Thismayincludeitsuse,performanceorqualityandoptionsforalternativemethodsofcompletingthetask.“Idon’treallyusemyphoneverymuchanywaysoit’sneverbeensomethingthatIhavefeltIhaveneeded”

(T5)EngagementregulationReferencetothefactorsthatinfluencetheconditionssurroundingtheonsetofthetask.Theymayrelatetothephysicality’softhetaskand/orthedrivers’motivationrelatingtothetask.

“IwillalwaysfigureoutwhatI’mgoingtolistentoandsetitgoingbeforeIleave”

(T6)Abilitytocomplete Referencetofeaturesofthetaskwhichinfluenceitsabilitytobecompletedinfullwhiledriving “becauseI’vehadthecarforages,Iknowwheretheswitchesare”

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Table2.Thesystemsandsemanticsubthemesofthethematicframework.

Semanticsubthemes Description ExampleQuote

Context Referencetothecircumstancessurroundingthebehaviourdescribed

(C1)JourneyContext Referencetocircumstancesthatformthesettingforajourneythatmayinfluencethelikelihoodtoperformthetask.“ifIaminastrangecity,IwouldbelesslikelytomessaroundbecauseIdon’tknowwhereIamgoing”

(C2)TaskContext Referencetocircumstancesthatformthesettingfortheuseofthetaskthatinfluencethedrivers’likelihoodtoengagewithit.

“It’sstuffwhenIactuallyfeellikeIneedtosendamessagequickly,soifI’veagreedtocomehomeatacertaintimeandI’mrunninglateforinstance”

(C3)Roadcontext Referencetocircumstancesthatformthesettingsurroundingtheroadingeneral(notrelatedtospecificinfrastructure)thatinfluencethelikelihoodtoperformthetask“Ithinkitwouldbesituationaldependent,justhowbusyisit?Ithink”

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ItisevidentfromthefulltablepresentedintheAppendixthattherewasanextensivelistofreasonsthat

drivers gave for engaging, or not engaging, with the technological tasks while driving. A total of 168

descriptive themes were iteratively generated and revised into 18 semantic thematic categories. The

generationofthesethemeswasalengthybutin-depthprocess.Clusteringthesesemanticthemesintohigher

level systemic actors that contribute to the occurrence of the causal factors gave another level to the

frameworkthatreadilydemonstratesthecontributionofthesystemwithinwhichdriverdistractionoccurs.

Inlinewithpreviousindividualfocusedapproaches,thedriveremergedasakeyactor.Thedrivercategory

suggests that the driver is influenced by their own attitudes (D1), perceptions of themselves (D3) and

tendencies(D2)intheirengagementwithdistractionswhiledriving,aswellashowtheyfeeltheymaybe

viewedbyothers(D4).Yet,thedevelopmentoftheothercategoriessuggeststhattheyarealsoinfluenced

byothersystemicactors.

Theroleofinfrastructurewasalsoreportedwhenrespondingtothedifferentroadtypesthatwereposedto

thedriverduringthesemi-structuredinterviews.Theirperceptionsoftheroadenvironment(I1)alteredhow

likelytheywouldbetoengageduetotherequirementsofthedrivingtaskintheseconditionse.g.increased

concentrationrequiredatjunctionsorreducedperceptionofriskonmotorways.Roadlayoutacrossroad

types(I2)wasalsowidelydiscussedwiththediscussionofcorners,roadturnings,androadvisibilitystated

aselementscontributingtothedecisiontoengage.Driversalsomadeconnectionsbetweentheroadand

theirbehaviouronit(I5),aswellasbetweenthetaskanditsuseinrelationtotheroad(I4).Forexample,the

speedofparticularroadsortheavailabilityofplacestostopwasdiscussedascontributingtotheirdecision

to engage while driving. Furthermore, the behaviour required in the driving task was also reported to

influence their ability to engage in the different secondary taskswhile driving, for example driving on a

motorwaywasdeemedtobeeasierbysomedriverswhichmotivatedtheirengagementinmorecomplex

secondarytasks,thaniftheywereinamorecomplexroadenvironment.Illegality(I3)wasathemethatis

alsomentionedbydriversandisincludedintheroadinfrastructuretheme,asitisinStantonandSalmon

(2009). Interestingly, the lawwasonlyoneof the168other factors thatdriversstated to influence their

decision to engage. This highlights the potential for the development of other techniques to tackle the

numerousothercontributingfactors,asafearofthelawwasonlyasmallcontributiontothedriversself-

reportedcausalfactors.

Thetaskitselfgenerateddiscussiononhowitinfluencedthelikelihoodofthedrivertoengagewithitwhile

driving.Notably,thisincludedhowlongthetaskwouldtaketocomplete(T3),themethodthroughwhich

theycouldinteractwithit(T2),thecomplexityofthisinteraction(T1),itsdesirability(T4),theabilityforthe

task tobecompleted (T6)aswell ashow itsonsetmaybe regulated (T5). The task themehas themost

semantic subthemes,highlighting thenumberof variables relating to the task that influence thedrivers’

engagement with it. It was evident that drivers were aware of the differing complexities and ways of

interactingwiththedifferenttechnologicaltasksandhowtheycouldmanagethesewhiledriving.Therole

12

of the manufacturer in facilitating engagement and the influence of developments in Human Machine

Interface(HMI)designconceptswasparticularlyevidentthroughoutthesediscussions.

Theothersystemiccategorywasthewidercontextwithinwhichdriversdiscussedtheirengagementwith

thetechnologicaltasks.Theydiscussedthetypeofjourney(C1)thattheymaybegoingonthatmayrequire

themtoengagewiththetaskmore.Forexample,theuseofasat-navwasmorelikelyonalongerjourney

whentheydidn’tknowwheretheyweregoingoraphonecallmaybemorelikelyiftheywerecommuting.

The context of the task itself (C2)was also discussedwith the importance of the task to their priorities

referenced frequently. Notably, when discussing phone based tasks drivers reporting who was

communicatingwiththemandtheirperceivedimportanceofthecommunicationtogreatlyinfluencetheir

engagementwiththetask.

2.4Discussion

ThedevelopmentofthethematicframeworkinTable2isthefirstattempttodevelopanextensivelistofthe

drivers self-reported reasons for engagingwith technological tasks. Previous efforts to assess the causal

factorsofdrivererrorhavesuggestedthatthekeyfactorsrelatetothefollowingsystemicelements:road

infrastructure,thevehicle,thedriver,otherroadusersandenvironmentalconditions(Stanton&Salmon,

2009; Salmon et al, 2010). Thus far, the development of error taxonomies has been heavily theoretical,

emergingfromtheaggregationofpreviousliteratureandaccidentreports(e.g.Stanton&Salmon,2009).

Thehierarchicallevelsoftheframeworkthatwereinductivelygeneratedgaveaninsightintothehigherlevel

factorsthatarecloselytiedtothedriversowncommentsanddiscussions.Thehigh-levelfactorssuggestthe

importanceofthedriver,theroadinfrastructure,thetaskandthewidercontextonthedrivers’decisionto

usethetechnologicaltaskswhiledriving.DifferencestothecausalfactorstaxonomypresentedbyStanton

andSalmon, (2009) includeamorespecific focusonthetask in thethematic frameworkrather thanthe

vehicleasawhole.Thisislikelyduetothedesignandaimsofthestudywhichrequireddriverstotalkthrough

theirlikelihoodofengagingwithavarietyofdifferenttechnologicaltasks.Hadtheparticipantsbeenasked

todriveortalkmoreabouttheinteractionbetweencompletingthetaskwhiledriving,morereferencesto

thevehiclemayhaveemerged(Pedic&Ezrakhovich,1999).‘Otherroadusers’isalsoabsentfromthehigh-

level themes of the thematic framework, but does appear within the infrastructure theme under the

‘perceptionsofsurroundingenvironment’semanticsubtheme(seetheOnlineAppendix).Thiscouldsuggest

thatthedrivers’viewofotherroadusersistightlylinkedtoinfrastructureandthesurroundingenvironment

whendecidingtoengagewithtechnologicaltasks.Otherresearchconductedinanaturalisticdrivingstudy

has suggested that other vehicle in front of thedriver donot influencedrivers’ decision to engagewith

technologicaltasks(Tivesten&Dozza,2015).

Whiletherewasanevidentinvolvementofsystemicactorsthatinfluencedengagement,thedriveremerged

asakeysystemicthemeduetothereferencesthatparticipantsmadetotheirattitudes,perceptionsand

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viewsof engagingwith the technological tasks. This compliments other research that utilised surveys to

identifythatdrivers’intentiontoengageisstronglyinfluencedbytheirattitudetowardsthebehaviourand

theirperceived riskof the task (Welshetal,2008;Zhouetal, 2012).Thevoluntaryaspectofdistraction

(Beanland et al, 2013) and its self-regulatory association (Tivesten & Dozza, 2015) are inherent to the

behaviour, yet this should not be studied independently to the wider context and system within the

behaviouroccurs(Young&Salmon,2015;Parnelletal,2016).

Theroad infrastructurewasdiscussedextensively leading tomultiple themeswithin the framework.This

compliments theresearchconducted inanaturalisticstudybyTivestenandDozza (2015)whofoundthe

drivers’abilitytoanticipatetheroadinfrastructure,suchastightcornersorstraightroads, influencesthe

drivers’engagementwithdistractingtasks.Whiletheysuggestedthatotherroadconditionsdidnotinfluence

thedrivers’engagementastheycouldnotbeanticipated,thefindingsfromthisinterviewstudysuggestthat

road environment and the relationship between the task and the road is discussed as a causal factor in

engagementandthusdriverscananticipatetheeffectismayhaveontheirdrivingperformance,withinthe

interviewsetting.Otherthemesintheframeworkhavealsobeensuggestedintheliteraturesuchastask

context(Lerneretal,2008),taskcapabilities(Zhouetal,2009),journeycontextandtheinfluenceofothers

(Tivesten&Dozza,2015).Yet,theaggregationoffactorsthatwereinductivelygeneratedfromasampleof

driversisnovelandhasstrongtheoreticalapplications.

Theextensiverangeofcausalfactorswithintheframeworkincludesthecontributionof legislationtothe

drivers’decisiontoengage,yetthereareahostofothercontributingfactorsthatsuggestthepotentialfor

other measures through which to tackle the drivers’ engagement with technologies that can lead to

distractionrelatedevents.ThePARRCmodelofdistraction(Parnelletal,2016)highlightstherelevanceof

systemic actors to the causal factors that are attributed to driver distraction in the literature. The

developmentofthethematicframeworkinExperiment1offersthepossibilitytocontrastthecausalfactors

thatdriversreportintheinterviewstudytothosethatarereportedintheliterature.Furthermore,thisoffers

theopportunitytodeterminethefurtheravenuesfordistractionmitigation,asthePARRCmodelhassought

toachieveinthepast(Parnelletal,2016;17).ThisisexploredwithinExperiment2.

3.Experiment23.1.Aim

The inductive thematic analysis conducted in Experiment1allowed the causal factors that influence the

drivers’likelihoodofengagingwithtechnologiestobedirectlylinkedtothedrivers’discussions.ThePARRC

modelofdistractionsoughtcausalfactorsdirectlyfromtheliteratureusinggroundedtheorymethodology

(Parnell et al, 2016). Yet, like the thematic framework developed in Experiment 1, it highlights the

involvementofthewidersociotechnicalsysteminthedevelopmentofdistraction.TheaimofExperiment2

willbetodeterminetherelationofthedriversreportstotheclaimsmadeintheliteraturebyapplyingthe

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thematicframeworkinTable2tothePARRCmodelofdistractiondevelopedinParnelletal(2016).Thiswill

seektoassessthevalidationofthePARRCmodelthroughtriangulationwith itsapplicationtoalternative

datasources(Hignett,2005).Itwillalsoassessifthereareconceptsthatarereportedbythedriversthat

havenotbeenstudiedintheliterature.Itthereforeseekstopromotefutureresearchaswellasproviding

sociotechnicalsystemsrecommendationstothemitigationofdistraction,ashasbeenachievedwithprevious

applicationsofthePARRCmodel(Parnelletal,2016;2017b).

3.2.Method

The PARRCmodel of distraction (Parnell et al, 2016) was reviewed to assess how the literature-driven

mechanisms relate to causal factors stated by drivers. The process through which this was achieved is

detailedinFigure2.

Figure2.StagesintheapplicationofthethematicframeworktothePARRCmodelofdistraction.

The relationship of the semantic causal factors stated by drivers to the key factors identified from the

literatureinthePARRCmodelwasreviewedthroughdiscussionswithsubjectmatterexpertswithover40

yearsofHumanFactorsexperience(Stage1,Figure2).Therelationshipofthesemanticfactorstosystemic

Stage 1Code the thematic framework to the PARRC model at the semantic level using Nvivo11.

Stage 2Asses the interconnections made between the PARRC mechanisms across the causal framework using a matrix query in Nvivo11.

The semantic themes developed from the thematic coding of the interview transcripts were coded to the key mechanisms of the PARRC model; ‘Goal priority’, ‘Adapt to demand’, ‘Resource constraints’, ‘Behavioural regulation’ and ‘Goal priority’. This was conducted with the discussion of subject matter experts.

Coding of the transcripts to the PARRC model mechanisms with the semantic themes in Nvivo11 allowed a matrix query to be run in the software to calculate the number of references to co-occurring PARRC mechanisms. The frequency of connections made between the mechanisms can be used to infer the strength of the connections which can then be compared to the original PARRC model interconnections that were derived from observations in the literature.

Stage 3Review the content of the transcripts coded to interconnecting themes.

From the matrix queries in Nvivo11 the context of the transcripts that were coded to co-occurring PARRC mechanisms can be reviewed to understand what the interconnections mean to the driver themselves. These can then be contrasted to the interconnections that were identified by researchers in the literature that the PARRC model was grounded within.

DescriptionStageOutput

Expansion of the PARRC mechanisms to include drivers self-reported causal factors

Frequency counts on the number of references in the interview transcripts that make connections between mechanisms of the PARRC model.

An understanding of what the interconnections of the PARRC model relate to in the drivers discussion.

Stage 4Contrast the original PARRC model developed from the literature on driver distraction from technology to the model developed from the drivers self-reported reasons for engaging with technological tasks.

The PARRC model was constructed from the interconnections that were referenced by the drivers, with the frequency of the interconnections visualised through the strength of the interconnections. This was then contrasted to the original PARRC model whose interconnecting mechanisms were constructed from the number references and significant relationships made between the mechanisms in the literature. Comparisons between the drivers stated behaviour and the behaviour studied in the literature.

A version of the PARRC model developed from the literature on driver distraction from technology and a version of a PARRC model developed from the drivers self reports in the interview.

15

actors(stage5oftheframeworkinFigure1)meantthatthecontributionofthesystemicactorstothecausal

mechanismsofthePARRCmodelcouldbeidentified.Furthermore,interconnectionsareimportantwithin

systemsmodelsassociotechnicalsystemsemphasiseemergenceofsafety fromthecomplex interactions

betweensystemicelements(Leveson,2004).TheinterconnectionsintheoriginalPARRCmodelwerederived

fromempiricallytestedconnectionsmadeintheliteratureaswellasassociationsmadebyauthorsinrelating

conceptstooneanother.Connectionsbetweenthecausalfactorsreportedbythedriversintheinterview

studyinExperiment1wereidentifiedusingamatrixqueryinNvivo11softwarethatwasusedtocodethe

data.Matrixqueriesallowthenumberofco-occurringcodedthemestobequantifiedandhighlighted,to

determinethenumberandtypeofdataexcerptsthatrelatetoco-occurringthemesofinterest.Stage3of

theinductivethematicprocess(Figure1),statestheprocessforgeneratingtheinitialdescriptivecodeswithin

thedata.Thisprocessrequiresexcerptstobecodedatmultiplethemes(Braun&Clarke,2006),allowingco-

occurringthemestobereviewedafterwards.ThelinkingofthesemanticsubthemestothePARRCmodel

mechanismsinFigure2,allowedthelinksbetweenthesubthemesrepresentingeachofthePARRCfactors

to be explored. The total number of interconnecting statements in the interviews between the PARRC

mechanism subthemes was calculated. The connections could then be reviewed through the Nvivo11

softwaretofurtheranalysetheconceptsthatwerecodedtotheinterconnectingPARRCmechanisms(Stage

3,Figure2).ComparisonscouldthenbemadebetweentheoriginalPARRCmodel,groundedintheliterature,

and the reconstructedPARRCmodelbuilt from thedrivers self-reporteddiscussionson their reasons for

engagingwithtechnologywhiledriving.

3.3Results

Application of the semantic themes detailed in Table 2 to the PARRC factors and assessment of the

interconnectionsreferencedbythedriversledtotheconstructionofthePARRCmodeldevelopedfromthe

drivers’selfreportedreasonsforengagingwithdistractivetechnologieswhiledriving(Figure2,Stage4).This

ispresentedinFigure3.InsightsthatweregainedfromtheapplicationofthePARRCmodelframeworkto

thethemesidentifiedandinductivelygeneratedfromtheinterviewsarediscussed.

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Figure3.Applicationofthethematicframework,referencedinTable2,tothePARRCmodelofdistraction.

EachofthePARRCfactorswerefoundtoberepresented inthethematicframeworkdevelopedfromthe

driversself-reportedreasonsforengagingwithdifferenttechnologicaltaskswhiledriving.Therelevanceof

thesethemestothefactorsisdiscussedbelow.

Adapttodemands:Driverssupportedthenotionthattheyadaptboththeirbehaviourinthedrivingtask

andthesecondarytaskinlinewithincreasedmentalandphysicaldemandwhendiscussingtheirlikelihood

ofengagingwithatechnologicaltaskwhiledriving.Thesemanticsubtheme‘Roadcontext’(C3)highlights

theneedtoalterandadapttheirdrivingbehaviourinlinewiththechangingdemandsoftheroad

environment.Thesemanticsubtheme‘Road-taskrelationship’(I4)suggeststhatdriversalsoadapttheir

behaviouracrossdifferentroadtypes,suchthattheyareawareofthedifferentdemandsofdifferentroads

andaltertheirengagementwithtechnologyaccordingly.Intermsoftheadaptionofthetechnologicaltask,

thesubtheme‘abilitytocomplete’(T6)suggeststhatdriversadaptthefunctionalityofthetask,adjusting

thecompletenessofitasillustratedintheexamplequote,inordertomeetthedemandsofthedriving

task.

Behaviouralregulation:Theinterviewsprovidedinformationonthecognitivethoughtprocessesofthe

driverandtheirperceptionsoftheirownbehaviourrelativetothesurroundingenvironment.Ithasshown

thatthedrivers‘attitude’(D1),‘tendencies’(D2),‘viewofself’(D3),andthe‘influenceofothers’(D4)are

keyfactorsthatrelatetotheregulationoftheirbehaviourwithrespecttoengagingintechnologywhilst

Examples:Not worth it

Too dangerousHappily do it

Examples:Drawn in IgnoreTriggered responseExamples:

Eyes off the roadHands on the wheel

Examples:Task TimeLength of text

Examples:Required attentionOther road users

Examples:Poor visibilityCorners

Examples:SpeedDriving task difficulty

Examples:Journey lengthJourney TypeFamiliarity

Examples:UrgencyNecessityIn a hurry

Examples:Do it all the timeDon’t like doing itTry not o

Examples:GuiltyStupidToo clumsy

Examples:Others do it tooSocial peer pressureOthers seeing you

Examples:Know I shouldn’t

Examples:Concentration required from drivingBusyness of road environment

Examples:Driving SlowerWait for right conditions

Examples:Adapt for driving functionalityLimited completion

Examples:Ease of taskCognitive processing required Examples:

UtilityReliabilityUse outside of car

Attitude

Driver (D1)

Engagement regulation

Task (T5)

Task (T6)Ability to complete

Task (T2)Interaction

Road Infrastructure (I5)

Road Related behaviour

Road Infrastructure (I2)

Road layout

Road Infrastructure (I1)Perceived surrounding

environment

Context (C2)

Task Context

Context (C1)

Journey Context

Tendency

Driver (D2)

View of self

Driver (D3)Influence of others

Driver (D4)

Context (C3)

Road Context

Road Infrastructure (I4)

Road-Task relationship

Illegality

Road Infrastructure (I3)Task (T3)

Duration

Task (T1)

Complexity

Desirability

Task (T4)

Adapt to demands

Behavioural Regulation

Goal Priority

Resource constraints

Goal Conflict

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driving.Furthermore,asisdemonstratedintheexamplequote(Table2,I4),thebehaviourofthedriveris

alsoshowntoberegulatedbytheroadinfrastructurewithinthetask-roadrelationshiptheme(I4),with

driversdiscussinghowtheyregulatetheirbehaviourinrelationtotheroadenvironmentandattainmentof

thedrivinggoal,whichisalteredacrossroadtypes.

Goalconflict:Driversdiscussedthelimitationsofrespondingtoco-occurringdrivingandtechnologicaltask

goalswithrespecttothefeaturesofthetechnologicaltasks(T4&T5)andtheirknowledgeonthelaws(I3)

whichstatetheconflictinggoalsshouldnotbeachievedinunison(i.e.drivingwhileusingahand-held

phoneunderUKlaw).Featuresofthetechnologicaltaskwhichrelatetoitspotentialtoconflictwiththe

drivingtaskwere‘desirability’(T4)and‘engagementregulation’(T5).Technologieswithinthevehiclehave

developedovertimetoprovidenovelinteractionsandfunctionalitiestothedriverthatwerenotpreviously

available.Thismakesthemdesirabletowould-beusers(Walkeretal,2001)andthereforeplacesthemin

conflictforattentionwiththemaindrivingtask.Driversdiscusstheutility(T4)ofthetechnologiesandhow

thisrelatestotheirusewhiledriving,asillustratedintheexamplequote(Table2,T4).Theyalsodiscussthe

featuresofthetaskthatdeterminehowabletheyaretoregulatetheonsetofthetask(T5)andthearising

conflictthismayhavewiththedrivingtask.Forexample,manydriverscommentedonthetriggered

responsethatoccurredwhentheyreceiveatextwhiledriving,e.g.“’Readingatext’,youseeIwouldreada

textjustbecauseofthenatureofthefactthatitflashesuponyourphone.”[Participant:7,Task:Readtext

onmobilephone].Thissuggeststhatdriversdidnotalwayswishtoengageinthetaskbutthedesignofthe

deviceallowedittocompeteforthedrivers’attention,divertingitawayfromthedrivingtask.

Goalpriority:Theprioritisationofthegoalswasfoundtobeinfluencedbythecontextandcircumstance

whichsurroundtheinteraction,notjusttheroadinfrastructurebutthespecificcircumstancesthatmay,or

maynot,leadtointeractionwithtechnology.Thejourneytypeand/orlength(C1)influencedthe

requirementtoengage.Forexample,longerjourneysmayleadtomoreinteractionswiththemusicsystem.

Thefamiliarity(C1)withtheroutewasalsosuggestedtoalterconfidenceinprioritisingthetechnological

task.Thecircumstancessurroundingthetechnologicaltaskwerealsoimportant(C2),suchashow

importantorurgentthetaskwas,ashighlightedintheexamplequote.Thecontextualfactorssuggestthere

aremanysituationsinwhichengagementwithtechnologyismoreorlesslikelytooccur.Thesedonot

relatetoroadtypeorenvironmentbutspecificmomentsandcircumstancesthatcannotbeforeseenand

directlyimpactondriver’swillingnesstoengage.

Resourceconstraints:Theattentionalresourcesofthedriverwerereportedtobeconstrainedbyfeatures

ofthetask(T1-T3)andfeaturesoftheroadenvironment(I1-I2).Theroadtypespresentedtothedrivers

ledthemintoadiscussiononthefeaturesoftheroadthatinfluencestheirdecisiontoengagewith

technology.Discussionsontheroadlayout(I2)andtheirinterpretationofthesurroundingenvironment(I1)

highlightedthedrivers’awarenessfortheelementsoftheroadenvironmentwhichmaylimittheir

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attention.Likewise,therewasalsoanin-depthdiscussiononthecharacteristicsofthetechnologicaltasks

presentedtodriversandhowfeaturessuchasits‘complexity’(T1),methodof‘interaction’(T2),and

‘duration’(T3)influencedtheattentionthatitrequired.Insomecases,suchastheexamplequote,the

perceivedresourcesrequiredtointeractareminimalwhichincreasesthelikelihoodofengaging,whereas

forothertaskstheperceivedresourcesaretoogreattocompletethetaskwhiledriving.

Interconnections:JustasthemechanismsofthePARRCmodelwerefoundedingroundedtheory

methodology,theinterconnectionsbetweenthemwereidentifiedfromtheempiricallytestedconnections

madeintheliterature(Parnelletal,2016).Thetotalnumberofinterconnectingstatementsinthe

interviewsbetweenthePARRCmechanismsubthemesisshowninFigure4b.Inlinewithstage4inthe

applicationofthePARRCmodeltothethematicframeworkinFigure2,thisiscontrastedtothe

interconnectionsfoundduringthegroundedtheoryapproachintheoriginaldevelopmentofthePARRC

model(Figure4a).ThenumbersontheinterconnectionsinFigure4arelatetothenumberofstudies

empiricallytestingtherelationshipbetweenthefactors.Thesizeoftheconnectinglinesrepresentsthe

numberofconnectionsmadeinbothdiagrams.Thematrixcodingoftheinterviewdataonlystatesthe

frequencyofco-occurringsubthemes,notthedirectionofanyrelationshipthatmayoccur,sothe

connectionsareshownaslinesratherthandirectionalarrowsinFigure4b.

a) b)

Figure4.PARRCmodelscreatedusinga)empiricallytestedrelationshipsintheliterature(takenfromParnelletal,2016)b)interviewtranscripts.

Figure4contraststheinterconnectionsbetweenthePARRCfactorsintheoriginalmodel(Figure4a)and

theonebasedonthedriverscodedtranscripts(Figure4b).Asidefromthequantitiesoftotal

interconnectionsbeinghigherintheinterviewdrivenconnections(Figure4b),whichislikelytobedueto

richdatasourceoftheinterviewsincontrasttothe33studiesidentifiedintheliteraturereview(Parnellet

al,2016),therearesimilaritiesintheconfigurationsoftheinterconnections.Thissuggestssupportforthe

underlyingliteraturethatthePARRCmodelwasgroundedin,astheresearchistargetingconceptsthatare

alsogeneratedtobeimportantbydrivers.

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Connection Description Quote

Resourceconstraints GoalConflictConstrainedresourcesmeansdriverscannotperformtwotasksatoncesotheyareinconflictwitheachother.

Well,againitisjustIwouldfindittoodistracting,Iwouldn’tbeabletodriveandoperatethesoftwareonmyphoneinordertoenterthedestination,becauseIwouldn’tbeabletoseeproperly!

Resourceconstraints Adapttodemands Limitedresourcesrequiresadaptationofbehaviour.

Sothepicturethatyou’vegothaslotsofcarsonthesideoftheroadforexampleandhousesandI’dbethinking,“Ahacarisgoingtopulloutinfrontofme”or,“I’mgoingtogetveryclosetoacar”sotherefore100%needstobeontheroadatthatpoint.

Resourceconstraints Behaviouralregulation Driversmustregulatebehaviourinlinewiththeirlimitedresources.

Yes,Idon’tthinkthatcanbedoneinasafeamountoftime.ItwouldhavetobelessthanasecondIthinkandeventhen,eventhatonamotorwayisrisky.ButIdon’tthinkIcoulddothatinlessthanabout20seconds,evenifIknewthepostcodeandeverything.SoIjustwouldn’t

GoalConflict GoalPriority Driversprioritisetoovercomeconflict.Soalsodependingonwhoiscalling,soifforexampleworkiscallingI’llprobablyturnthatofforsomenumberthatIdon’tknow,whereasifit’ssomeonethatI’mmorelikelytoknoworsomeonewho’smorelikelytotellmesomethingimportantthenIwillanswermorereadily

GoalConflict Adapttodemands Driverscanadapttothedemandsoftheconflictingtasks.

Iwouldbehighlylikelytodothisonamotorway,becauseIthinkitisquiteeasy,againlikeusingaphoneIwoulddoitstaggeredtouchsomethinglookup,touchsomethingelse,lookup.

GoalConflict BehaviouralregulationTheabilitytoregulatebehaviourtowardsnewtasksthatconflictsthedrivingtaskinfluencestheconflictforattentionbetweenthetasks.

Justbecauseit’smoreofa–forme,forsomereasonit’sanautomaticresponse,sothephoneflashesandyounaturallyjustlookoverandthenyoureadthetext.

Adapttodemands GoalConflict Driversadaptbehaviourwhichcanleadtofurtherconflictbetweengoals.

Yeah,intothecarbutIlistentoplaylistssoit’sneverreally…actually,onthemotorwayit’sfindingasong,ifI’mfindingasong,searchingonSpotifywhereasifIwasonaruralroadoraresidentialarea,I’djustbeskippingasong.

Adapttodemands GoalPriority Driverscanadapttodemandsbyprioritisingtheirgoals.

TheonlytimeImightdoitisatajunction,stopped;there’sachanceImightifIreallyneedtomakeacall,butotherwiseIwouldn’tmoving,no.

Adapttodemands Behaviouralregulation Driverscanregulatetheirbehaviourtoadapttothedemandsoftheenvironmentandthetask.yeah,oksochangingclimatecontrolsIdefinitelywouldonthemotorway,aslongasyouarejustcruisingalong

Goalpriority AdapttodemandsTheprioritisationofgoalsrequiresadaptionoftheprimaryandsecondarytasksinlinewithcurrentdemands.

Ruralroadprobably,itdependshowrural,ifitareallyreallytinyroadIprobablydon’tansweritbecauseitisquitenicetobeabletoheartheroad

Additionalconnections

BehaviouralRegulation GoalConflict

Thedriversattitudetowardsthetaskandstatedintentiontoengagewithitwhiledrivinginfluencethepotentialforthetechnologicaltasktocomeintoconflictwiththedrivingtask

No,never,never,never,never,never.Readatest?Honestly,Iwouldneverdothisstuff.

Resourceconstraints GoalPriority Driverslendresourcestothedrivingtasktodetermineitsprioritybeforedecidingtoengagewithit.Iwouldneveropenupawholemessage,butImightglancedownandlookatwhoitisfromatleastandwhatiswrittenonit.

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Table3.InterconnectionsbetweenthePARRCfactorsasdrawnfromtheinterviewdata.

Theevidencefortheseconnectionsfoundwithinthedriversself-reportedlikelihoodofengagingwithtechnologies

providessupportforthePARRCmodel,andtheliteraturewithinwhichitwasdeveloped.Yet,somedifferencescanbe

seenbetweenthetwomodelsinFigure4.Thesewereassessedbylookingtowardsthetranscriptsthatwerecodedto

theconnectingthemesinthematrixqueryandunderstandinghowthedrivers’reportsmaydiffertothosestudiedin

theliterature.Theyincludethefollowing:

• Areductionintheprominenceoftheconnectionbetween‘goalconflict’and‘goalpriority’.

InthedevelopmentoftheoriginalPARRCmodelconnections,theliteraturewasfoundtofrequentlyconsidertheneed

toprioritise thegoalsof thedriving taskand thesecondary task inorder to resolveanygoalconflict thatmaybe

occurringbetweenthetwo(e.g.Doganetal,2011).Yet,thetranscriptsofthedriversverbalisedthoughtprocesson

theirlikelihoodtoengageintechnologicaltaskssuggestsprioritisationofgoalstobelessofaconcern.

• Anincreasedprominenceoftheconnectionbetween‘resourceconstraints’and‘adapttodemands’.

Adaption of behaviour tomanage resources was a well represented notion in the interview transcripts, with 72

statements connecting ‘resource constraints’ themes to adapt to demand themes. The connectionwas originally

deemedtoreflecttheideathatadaptionrelatestotheattentionalresourcesthatareavailable(Parnelletal,2016).

ThePARRCmodel alignswith the theory that the attentional resourcesof drivers are limited, such that both the

primaryandsecondarytasksmustcompeteforavailableresourcestofacilitatetheireffectiveperformance(Wickens,

2002).Onewayofensuringthatattentionalresourcesareefficientlyallocatedacrosstheprimaryandsecondarytask

isthroughadaption(Noyetal,1989;Cnossenetal,2000).Figure4bandtheexamplequotegiveninTable3suggest

thatdriversdoreporttheadaptionoftheirbehaviourinordertofacilitatetheirsecondarytaskgoal.Italsosuggests

thatthisadaptiontofacilitatemultiplegoalsismoreimportanttodriversthantheneedtoprioritiseonegoalover

another.

• Theadditionofaconnectionbetween‘resourceconstraints’and‘goalpriority’,whichwasnotfoundinthe

literaturethattheoriginalPARRCmodelwasdevelopedfrom.

AnotherkeydifferencebetweenFigure4aand4bistheadditionofanextraconnectionbetween‘resourceconstraints’

and‘goalpriority’,whichwasnotfoundintheliteraturethatthePARRCmodelwasdrawnfromandisthereforeabsent

intheoriginalmodel(Figure4a).Parnelletal(2016)reasonedthattheabsenceofthisconnectioncouldstemfroma

number of causes which relate to the other connections in themodel. These include the prominent connection

between‘goalpriority’,‘adapttodemands’and‘resourceconstraints’,whichpreventsresourcesfromconstraining

theprioritisedgoal,andfacilitatesadaptionofbehaviourinlinewithresourceavailabilityinordertoprioritiseone

goal over another (Parnell et al, 2016). Yet, the findings from the interview data suggest that drivers domake a

connection between ‘resource constraints’ and ‘goal priority’, although it is minimal. Table 3 gives an example

statement of the connection made by the drivers and suggests the connection relates to drivers lending some

21

attentionalresourcestowardsthetaskinordertodetermineifitrequiresprioritising.Thisisofinterestasitsuggests

a concept that was not previously been considered in the literature from which the original PARRC model was

conceived.

• Anincreasedprominenceofconnectionbetween‘goalconflict’and‘behaviouralregulation’.

Figure 4a suggested the connection between ‘behavioural regulation’ and ‘goal conflict’ reflected the bottom-up

processthroughwhichcertaingoalsmayresultintriggeredresponsesthatrealignstheconflictinggoals(Parnelletal,

2016). The interview data presents a stronger connection between ‘goal conflict’ and ‘behavioural regulation’, in

Figure4b.Areviewofthecodedtranscriptsconnectingthemechanismssuggestitmayreflectaconnectioninthe

oppositedirectiontotheoriginalPARRCmodel,goingfrom‘behaviouralregulation’to‘goalconflict’.Theexample

quotefromTable3highlightstheinfluencethatdrivers’attitudestowardsthetechnologicaltaskscanhaveontheir

potentialtoconflictwiththedrivingtask.Thedriverintheexampleisadamantthattheywillneverusethedevice

while driving, stating theywill turn it off so that itwill not pose a conflict to their driving goal. This illustrates a

reoccurring theme within the interview transcripts, many drivers held strong attitudes towards the use of

technologicaltaskswhiledrivingstatingthattheywouldnevereverattempttoengageinthetaskwhiledriving.

3.4.Discussion

Experiment2haspresentedtheapplicationofthethematicframeworkthatwasdevelopedinExperiment1tothe

PARRCmodelofdistractionwhichpresentsthecausalfactorsinvolvedindriverdistractionasstatedintheliterature.

ThishasvalidatedandextendedtheoriginalPARRCmodelandtheliteraturewithinwhichitiscomprised.Ithasshown

howthedrivers’verbalreportsrelatetothestudyofthebehaviourintheliterature.Driverdistractioncanbeadifficult

behaviour tostudy in itsnaturalenvironmentduetootherconfounding factorsandtheethical issuesofexposing

participantstodistractingactivities(Carstenetal,2013).Therefore,capturingthedriversreportedbehaviourthrough

open-endeddiscussionsandestablishingitsrelationtotheexistingliteratureprovidesmuchneededvalidationofthe

research.

ThecausalfactorsreportedwithintheinterviewsandtheirassociationwiththePARRCmodelfactorssupportsother

researchfromnaturalisticdrivingstudies.Thenotionofdriveradaptionisparticularlyevidentwithstudiesthathave

lookedatdrivers’ engagementwith secondary tasks,withdrivers slowingdown (Metzet al, 2015) and increasing

headway(Tivesten&Dozza,2015)whenengagingwithsecondarytasks.Thissupportstheadaptionofbehaviourat

thecontrollevelthathasalsobeenfoundinsimulatorstudies(Schömig&Metz,2013;Cnossenetal,2004).Yet,there

wasalsoasuggestionthatdriversstrategicallyplantheirengagementwiththetechnologicaltasksinadvance.The

notionthatdrivers’engagementwithtechnologydependsonjourneytypeandroadinfrastructurehavealsobeen

foundinnaturalisticdrivingstudies(Tivesten&Dozza,2015).

Therewere,however,commentsmadebydriversthatwerenotincludedinthedevelopmentoftheoriginalPARRC

model from the literature,which furtherhighlights the importanceofassessing thevalidationofmodelswith the

applicationtodifferentsources(Hignett,2005).Thisincludesanadjustmentwithinthestructureofthemodelformed

22

fromtheinterviewdatawhichsuggestedlessofafocusonprioritisinggoalsdirectlyandmorereportsofadapting

theirlimitedresourcepoolandattributingsomeresourcestodeterminingthepriorityofthesecondarytaskbefore

lendingitfurtherresourcestocarryoutthetask.Thishighlightstheeffectthattaskssuchastextmessageshavewhen

theydrawthedrivers’attentiontowardsthedeviceandleadthedrivertomakeadecisiontoprioritisethedrivingtask

orthetexttask.Ifthenotificationdidnotarisewhilethevehiclewasinmotiontheywouldnotbealertedtoitand

theywouldnotneedtore-establishtheirpriorities.

Conversely,theconnectionbetweenthe‘behaviouralregulation’and‘goalconflict’factorssuggeststhatsomedrivers

wereabletocontrolthegoalsthatconflictedwiththedrivingtaskduetotheirattitudes,perceptionsandbehaviour

that regulated their engagementwith the taskswhile driving. This reflects otherswho suggest the role of driver

attitudeintheirintentiontoengagewithdistractionswhiledriving(Walshetal,2008;Zhouetal,2009;Zhouetal,

2012).The suggestion thatdrivers’engagementwithdistractionsare largelyvoluntary (Lee,2014;Beanlandetal,

2013), suggest that they do have an element of control over their behaviour but that they choose to become

distracted.ThedevelopmentofthethematicframeworkanditsapplicationofthePARRCmodelaimstosuggestthat

thedrivers’decisiontoengageisnotentirelystraightforwardandthatbanningthebehaviourthroughlegislationis

nottheonlyoption.Insteadtherearenumerousfactorsandactorstoconsiderwithinthesociotechnicalsystem,which

arecomplexlyinterconnectedindeterminetheemergenceofdistraction.

4.GeneralDiscussion

It isunderstoodfrompreviousresearchthatthemisuseoftechnologicaldevices,suchassat-navs (Tsimhonietal,

2004),musicplayers(Youngetal,2012),hands-freephones(Horrey&Wickens,2006)mobilephones(McCarttetal,

2006)andwearabletechnologies(Sawyeretal,2014)poseathreattoroadsafety.Whilst,researchfocusesonthe

adverseeffectsoncethetechnologyisengagedwithbythedriver,itfailstoaccountforwhythedriverschooseto

engagewiththetechnology inthefirstplace.Anunderstandingofthekeyunderlyingcausalfactorsthatmotivate

driverstoengagewithtechnologiesiscriticalinprovidingrecommendationsandcountermeasurestolimittheadverse

effectsofdriverdistraction(Walshetal,2008;Atchleyetal,2011;Atchleyetal,2012;Zhouetal,2009;Young&Lenné,

2010).Furthermore,therelevanceofsystemsbasedmeasurestocounteringdriverdistractionarerequiredtoimprove

thesafetyoftheroadtransportsystemasawhole(Young&Salmon,2012;Salmonetal,2012;Parnelletal,2016).

Thesystemiclevelofthethematicframeworksuggestshowactorsoutsideofthedrivers’controlmaybecreatingthe

conditions for distractions to be engaged with. Application of the thematic framework to the PARRC model in

Experiment2suggestedhowtheunderlyingthemesrelatetothePARRCfactors,validatingthemwithconceptsderived

fromdriversreportsoftheirownbehaviour.Furthermore,explorationoftheinterconnectionsbetweenPARRCfactors

hassuggestedsomedifferentstructuralconnectionswithinthemodel.Astheoriginalmodelreflectedtheempirically

testedrelationshipsintheliterature,thedifferenceintheinterconnectionsfoundinthispapersuggestpotentialgaps

intheliterature.Theinductiveanalysishasprovidedfactorsthatdriversthemselvesreporttobeimportantintheir

decisiontoengagewithtechnologicaltaskswhiledriving.Thishassupportedtheagendaofpreviousresearchthathas

appliedbehaviouralintentionliteraturetodriverdistraction(Welshetal,2008;Zhouetal2009;Zhouetal,2012).Yet,

23

it has also highlighted the importance of the interacting elements in the road transport system in creating the

conditionsfordriverstomakethedecisiontoengagewithtechnologicalsecondarytaskswhiledriving.

4.1Recommendationstopractise

The adverse implications of using specific technological tasks while driving are known (Horrey &Wickens, 2006;

Tsimhonietal,2004;McCarttetal,2006;Youngetal,2012),yetthefacilitatingconditionsarelessacknowledged.By

targetingthecausalfactorsofdistraction,countermeasurescanbedevelopedthatfocusontheunderlyingcausesof

theissue,ratherthanlimitingitseffectsonceengagementhasbeeninitiated.Thethematicframeworkdevelopedin

Experiment 1 highlights the importance of the wider road transport sociotechnical system and its influence on

technologyuse,includingthedriver,task,contextandroadinfrastructure.Thissupportstheimportanceoflooking

beyondindividualfocusedmethodsoftargetingdriverdistraction(Young&Salmon,2012;Salmonetal,2012;Parnell

etal,2017).

Todeterminetheimportanceofsystemicactorstoissuesthatarefoundwithinsociotechnicalsystems,thehierarchy

ofthesystemcanbemappedusingtheRMF(Rasmussen,1997).Thisrepresentationofthesystemshierarchyisuseful

indetermininghowactorsinteractwitheachother(Rasmussen,1997),whichcanthenbeusedtoassessthepotential

forincidentaswellasidentifyingfuturesolutions(e.g.Young&Salmon,2012;Parnelletal,2017).Thelocationofkey

actors inthehierarchycanfacilitatetheprovisionofcountermeasuresthattargetelementshigherup,toproduce

widespreadchangeatlowerlevels(Branfordetal,2009).TheadaptedRMFhierarchy(Parnelletal,2017)thatincludes

anadditional twohigh level themes, thenational and international committees, alongside theoriginal levels (the

government,regulators,companyandmanagement;Rasmussen,1997)wasapplied.Theactorsrelevanttoeachof

thesystemiclevelsofthethematicframeworkwereassessedtodeterminetheactorsacrossthehierarchicallevels

thatcouldbetargetedforfuturecountermeasures.

24

Figure4. Systemsactorsacross thehierarchyof the sociotechnical systems thatare related to the fourhigh-level

systemicthemesidentifiedinthethematicanalysis.

4.1.1Driver

Thedrivers’attitudetowardstheuseofthetechnologywasfoundtoplayanimportantroleinlimitingitsconflictwith

thedrivinggoal.Elementsofthesystemthatimpactonthedriverandtheirattitudestowardstechnologicaldevices

appearacross thehierarchy fromthosedirectly interactingwiththedriver,suchaspassengersor thepresenceof

otherroaduserswhomaybewatching,tohigherlevelactorsuchaseducationalproviders,themediaandroadsafety

charitieswhocancontrolattitudesinamoretopdownmanner.ArecentroadsafetycampaignbyTHINK!intheUK

withthetag line‘maketheglovecompartmentthephonecompartment’,guidesthedriverawayfromplacingthe

phonegoal intoconflictwiththedrivinggoal (THINK!,2017).Thiswouldalsopreventthedriverfromdetermining

25

wherethetaskliesintheirgoalprioritiesandhowtheymayadapttheirbehaviouraccordingly.Furthermore,theviews

wehaveontheuseoftechnologywhiledrivingasasocietyareinfluencedbynationalcommitteeswhodeterminethe

importanceofbehaviourwithinnationalculture.Theissueofroadsafetyisasocialresponsibilitythatshouldbeshared

bythetoplevelofthesystem(Larssonetal,2010)andthereforetheuseoftechnologieswhiledrivingneedtobe

portrayedasanti-socialwhenitconflictswiththesafemonitoringofthedrivingtask.

4.1.2Infrastructure

Asan integralpartofthetransportationsystem,road infrastructure isregulatedatthe international,nationaland

governmental levels with the aim to develop an efficient road transport system (e.g. Department for Transport,

2015b).Theinteractionofroadtypewithtechnologicalengagementhasbeenexploredheretoidentifythatdrivers

doconsidertheroadenvironmentwhentheydecidetousetechnologywhiledriving,ashasalsobeenidentifiedin

naturalisticdrivingstudies (Tivesten&Dozza,2015).Onmotorways,descriptivethemessuchas ‘justcruising’and

‘consistent’roadlayoutssuggestthatdriversdeemthedrivingtasktobelessdemandingontheseroadscomparedto

ruralroadswhere‘poorvisibility’and‘corners’mayincreasedemandinthesecondarytask.Yet,attheindustrialand

resource providers level there are no actors directly influencing which tasks are compatible with different road

infrastructures (Salmonetal, 2012). Future research shoulddetermine if certain tasksand technologiesaremore

compatiblewithcertainroadenvironments.Forexample,interactingwithasat-navmaybeeasieronamotorwaybut

ithaslimitedusehereasthereisgenerallymoreroadsideinformationandthedistancebetweenjunctionsisgreater

thanonruralorurbanroads,whichmaybemoredemandingbutholdagreaterrequirementtoengagewiththesat-

nav to navigate through fast changing environments. Tivesten andDozza (2015) also came to similar conclusions

suggestingthepotentialforsomeroadareaswherephoneusemayberegulated,ratherthanbanned.

4.1.3Task

Thetechnological task isassociatedwithanumberofsystemselements fromtheverytopofthehierarchytothe

bottom.Thedesignoftechnologicaltasksisinfluencedbyanumberofguidelines,standardsandcriteriathatstem

frominternationalandnationalactors,whicharethenfeddowntotheindividualmanufacturersanddevelopers.Yet,

thereisalsoaneedtorepresenttheviewsoftheenduserandapplyiterativedesignproceduresthatallowforthe

evaluationofin-builtsystemsusabilitywithrespecttothedriverandthecontextofuse(Harveyetal,2011b).The

driversthatwereinterviewedmadenumerousreferencestothetaskfeatures,suchashowitmaytaketheireyesoff

theroadortheirhandsoffthewheelunderthe‘interaction’subtheme,aswellasreferencingthe‘complexity’and

‘duration’ofthetask.Thissuggestsdrivershadanunderstandingoftheattentionalrequirementsofthetechnological

tasks. Design standards and guidelines have aimed to inform what is achievable while driving, yet they do not

necessarilytakeintoconsiderationthedesirethatdrivershavetousethetechnologyattheenduserlevel.Indeed,

otherresearchpresentedinthisjournalsuggeststhatdifferentdriversneedanddesiredifferentinformationunder

different contexts (Davidsson & Alm, 2014). By facilitating the functioning of the technology in the vehicle, the

temptationforthedrivertoengagewillendure,thisisparticularlytrueofmobilephones(Nelsonetal,2009).The

multi-functionalityofphonesprovidesextratemptationforthedrivertoengagewithitwhiledrivingandthisshould

berespondedtobydevicedevelopersbylimitingfunctionalitywhiledriving.Therewerenumerouscommentsrelating

26

tothenotificationsreceivedonmobilephonewhendriversreceivedtextsorphonecallswhichtriggeraresponse

fromthedriver.Thepresenceoftechnologieswithcapabilitiestotriggeraresponsethattakesthedriversattention

awayfromthedrivingtask,evenmomentarily,shouldberevisedbydevicemanufacturersasitforcestheenduserto

assess theirprioritieswhichshouldpredominately focusonthedriving taskandroadsafety (Leeetal,2008).The

manufacturerApplehastakenstepstowardsthiswiththeirrecentphoneupdate(ios11,releasedSeptember2017)

thatincludesa‘donotdisturbwhiledriving’modethatcansensewhenthedeviceisinamovingvehicleand,once

promptedbytheuser,willturnoffnotifications(Apple,2017).Thereis,therefore,thepotentialfornewregulations

that target the desires and engagement regulation of technology use to stem from the very top levels of the

sociotechnicalsystemandfocusontheinfluencesacrossthelevelsofthesystem,notjustthedriver.

4.1.4Context

The framework highlighted the importance of circumstance in the drivers’ decision to engage. There are many

complexlyinteractingfactorsinfluencingtheuseoftechnologywhiledrivingthatrelatetotheroadenvironment,the

driverandthetaskitself.Theinformationthatdriversrequireanddesireunderdifferentcontextualdemandsislikely

todiffer(Davidsson&Alm,2014;Tivesten&Dozza,2015).Theeffectsofcontextoccurinapredominantlybottomup

fashionwithinthesystemhierarchyastheyaredeterminedbytheinteractionswiththesurroundingenvironmental

conditions.Figure5showsthelackofhighlevelactorsoncontextwithinthesociotechnicalsystemwhichisreflective

ofthelimitedcontroloverthecomplexlyintegratedfactorsthatcompriseindividualcircumstances.Roadconditions

arehardtocontrolastheyareinfluencedbyenvironmentalconditionssuchastimeofday,roadtypeandweather

conditions.Taskconditionsrelatingtotheurgencyornecessitytointeractsuggestthatdriversassesstheirpriorities

astasksandrequirementsarise.Yet,determiningandsettingprioritiesinadvance,orpredefiningsituationswhere

engagementwouldbemoreor lessnecessarycouldlooktocontroltechnologyengagementathigher levels inthe

system.Researchcentresofferingfacilitiestotestdifferentcontextualfactorsthroughtheuseofdrivingsimulators

andhighlycontrolledenvironmentscanofferpromisinginsightsintotheroleofcontextondriverengagement(e.g.

Konstantopoulosetal,2008)andfutureworkshouldassesthecomplexlyinteractingconditionsthatinfluencedrivers

desiretoengage.

4.2EvaluationandFuturework

Theframeworkwasdevelopedfromtheself-reportedbehavioursofasampleof30participants,which issmall in

contrast to the number of participants that can be recruited from online surveys that facilitate far-reaching

recruitmentdatabases.However, thedataobtainedwasmuchricher,withover17hoursofaudio recordingsand

transcribeddata.Furthermore,thesamplestrivedtoincludedriversofanequalrangeofageandgenderstogenerate

a framework based on a representative sample.While the research focused on UK drivers, the laws relating to

technologyuseinthevehiclearesimilaracrossEurope,Australia,NewZealand,JapanandIndiawhospecificallyban

theuseofmobilephones.However,futureworkshouldseektoexplorehowtheframeworkofcausalfactorsmay

alterwithindividualandculturaldifferences,asthismayinfluencetheuseoftechnologybothinsideandoutsideof

thevehicle(e.g.McEvoyetal,2007;Shineretal,2005;McEvoyetal,2006;Young&Lenné,2010;Horberryetal,2006).

27

Anadvantageofthethematicframework is itsgrounding intheself-reportedbehavioursofdrivers,suchthatthe

causalfactorsaredirectlyinformedbythosewhoexperiencethem.Theuseoftheinterviewsettingallowedthedrivers

toopenlydiscussallfactorsthattheyfeltinfluencedtheirlikelihoodofengaging.Whileotherstudieshaveexplored

thedrivers’willingness toengagewithdistractions insimulators (Metzetal,2011;Schömig&Metz,2013)and in

naturalistic driving settings (Metz et al, 2015; Tivesten & Dozza, 2015) they have looked at the drivers’ physical

engagementwiththetaskrelativetothecontextoftheroadenvironment,ratherthanenablingthedriverstodiscuss

theirmotivationstoengage.ThestudyconductedbyLerneretal(2008)facilitateddriverstodiscusstheirwillingness

toengagewithdistractionswithinfocusgroups,yetthismayhavebeeneffectedbythesocialdynamicsandbiases

whendiscussingbehavioursthatbeundesirable(Smithson,2000;Lajunen&Summala,2003).Thedrivers’discussions

inExperiment1wereconductedwithconfidentialityandanonymitywhichencourageddriverstorevealtheirtrue

views.Thisdiscussionalsoallowedforinsightsintothewidersociotechnicalsystemanditsinvolvementintheissue

ofdriverdistractiontoberevealed,aconceptthatwasnotobtainedbytheobjectivemeasurementsinsimulatoror

naturalisticstudies(e.g.Metzetal,2011;Schömig&Metz,2013;Metzetal,2015;Tivesten&Dozza,2015).Whileit

is important to review the drivers’ willingness to engagewithin the context that the behaviour arises, the open

discussionswithdrivers,promptedwithdifferentroadtypesandtechnologicaltasks,hasuncoveredavarietyoffactors

that influence theirwillingness to engage that has not been explored previously. Furthermore, this has provided

validationofpreviousresearch,aswellasproposingthepossibilityoffutureexplorationsofthebehaviourandways

tomitigateagainstit.

5.Conclusions

Thispaperhaspresentedthedevelopmentofathematic frameworkofthecausal factorsthatmotivatedriversto

engagewithtechnologywhiledriving.Theuseofsemi-structuredinterviewsenableddriverstodiscusstheirlikelihood

ofengagingwithavarietyoftechnologicaltasksacrosscommonUKroadtypes.Thestructureoftheinterviewsallowed

allparticipantstodiscussthesametasksandroadtypeswhilefreelygeneratingthekeyconceptsthatwereimportant

to their perceived likelihood of performing the task. Inductive thematic analysis facilitated the development of a

hierarchicalframeworkofcausalfactorsthatwasdrivenfromthedriverowninterpretations,ratherthanapplying

predefinedtheories.Thishasshowntheinfluenceofsystemicactorsonthecausalfactorsinfluencingtechnologyuse

whiledriving,highlightinghowtheroadtransportsystemmaybecreatingtheconditionsfordriverdistractiontooccur.

Thisshouldencourageamovementawayfromindividualfocusedcountermeasuresandtowardstheroleofsystemic

actors,whichhasbeenhighlightedthroughtheapplicationofthethematicframeworktotheinteractingmechanisms

ofdistractionfromthePARRCmodel(Parnelletal,2016).ThethematicanalysishasshowntherelevanceofthePARRC

factors to thesystemicandsemantic themesthatdrivers report to influencetheirengagementwith technological

devices. This has supportedprevious literature in the field,whilst also suggesting additional concepts of interest.

Assessmentoftheactorsimpactingonthesystemicthemesidentifiedfromtheinterviewsacrossthehierarchyofthe

sociotechnicalsystemshashighlightedfutureareasforresearchandcountermeasureimplementation.

28

6.Acknowledgements

This researchwas fundedby theEngineeringandPhysical ScienceResearchCouncil (EPSRC)grantEP/G036896/1,

undertheIndustryDoctoralTrainingCentreinTransportandtheEnvironment.

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