239
1 INTRODUCTION
Maritimeemissionofgreenhousegases(GHG)inthe
shipping industryisprojectedtoincreaseby50%to
250%intheperiodto2050[1],withpotentiallyhuge
impactontheglobalenvironmentandhumanhealth
[2].Asadominatingtransportationsector,theglobal
shipping industry has been striving
hard to reduce
themaritimefootprintofitsGHGemissionsforyears
in recognition of the climate change challenges. In
2012shippingemitted938milliontonnesofCO
2with
2.6% of the total emission volume in the world, in
contrast with 1100 million tonnes of CO
2 from
shipping with 3.5% of the total global emission
volumein2007[1].Theincreaseofenergyefficiency
(EE) is a pivotal contributor behind the significant
changesinthesenumbers,asitcouldleadtoreduce
25%to75%ofCO
2emission,accordingtothesecond
IMOGHGstudy[3].
The Ship Energy Efficiency Management Plan
(SEEMP)[4]prescribedmandatoryregulationsabout
howenergyconservationshouldbeorganizedinthe
field, i.e. how to develop the best practices for fuel‐
efficient ship operations. A direct outcome is to
decreasefuelconsumption
whilemaintainingatleast
thesame level of transportation services. The strong
focus on optimization of energy consumption is
tightlyrelatedtotheeconomicfactors,thatimproving
energy efficiency can often lead to increased
profitability[5].However,ithasbeenobservedthatit
is the ship’s crew who usually have this
direct and
Maritime Energy Efficiency in a Sociotechnical System:
A Collaborative Learning Synergy via Mediating
Technologies
Y.Man,M.Lundh,&S.MacKinnon
ChalmersUniversityofTechnology,Gothenburg,Sweden
ABSTRACT: Previous research in the domain of maritime energy efficiency has mainly addressed concerns
regardingindividualexperiencesandorganizationalbarriers.Reflectiononthereciprocalhuman‐technology
relationship, interaction design and its impact on the practitioners’ learning and organizational decision‐
making process is rather scarce. Informed
by focus group interviews, this paper describes the essence of
practitioners’ activities and the nature of interaction design and proposed improved design for energy
efficiencymonitoringsystems.Findingssuggestknowledgesharingforamutualunderstandingonboardships
is critical to energy efficiency. Learning can go beyond the embodiment of individual
cognitive change but
becomesacollectiveandcollaborativeachievementmediatedbytechnology,whichinformsopportunitiesfor
interaction design. The design needs to consider the context in which knowledge mobilisation occurs and
facilitate collaborative learning. With more intelligent systems introduced to the shipping industry, it is
important to consider the impact of
mediating technologies in management practices and mediating
technologiescanbeintegratedintoabroadercollaborativelearningpa radigmemergingbetweentheshipand
shore. This study highlights those social‐cultural dimensions important to establishing a common ground
betweenpractitioners,managementandadvancedtechnologies.
http://www.transnav.eu
the International Journal
on Marine Navigation
and Safety of Sea Transportation
Volume 12
Number 2
June 2018
DOI:10.12716/1001.12.02.03
240
considerableimpacton EEthrough theiroperational
practice [6, 7]. This paper will review previous
relevant research work and describe practical ship
visits with respect to the ship’s crew’s EE
performance in the maritime domain and its
connectiontohumanfactorsandinteractiondesign.
2 REVIEWOFRELATEDLITERATURE
2.1
Gapsininter‐departmentcollaborations
The engine and bridge departments come with
differentskillsets:bridgeofficersareresponsiblefor
navigating the ship safely and efficiently with an
appropriate voyage plan, while ship engineers are
dealing predominantly with the engines and other
systemsforshippropulsionandpowergeneration[8].
Thus, communication is essential to help coordinate
information and meet operational goals in such
complex socio‐technical environments [9]. Previous
research [10] has attempted to explore the ship’s
interdepartmental communication via the joint
activity approach [11] which is concerned with
contextualizingcommunicationinthelensofcriteria,
requirementsandchoreography.
Kataria,Holder[10]has
informed inter‐departmental communication
scenarios(suchastopreparethevesseltobereadyfor
certainactionsorincidents)andidentifiedthatthere
were perceived fissures between the two
departments:“…alackofunderstandingoftheother
group’swork,whatengineersdo,andbridgeofficers
concerns
bridge. Bridge officers noted that they do
not always know the difficulties and concerns for
engineersrelatedtoamanoeuvresuchaswhenthey
want to go from full ahead to full astern…the
engineersmightaskwhysomanyengineorderswere
required.Therecanbeadifferenceinprioritiesin
the
twodepartments,namelyconcernfortheenginesvs.
optimalspeedofshiphandling”(p.173).
Shipenergyoptimisationrequiresancoordinating
effortbytwodepartments[6].Itisessentiallyajoint
activity, which is described as “behaviours that
carried out by an ensemble of people who are
coordinating with
each other”. As Klein, Feltovich
[11] describe, only by meeting criteria (i.e. intention
and interdependence), requirements
(interpredictability, common ground, directability)
and choreography (phases, signalling, coordination
devices, coordination costs), can a joint activity be
achieved successfully. Noteworthy is that
coordination depends on the ability to predict the
action of other parties
and most important basis for
interpredictability is common ground, a
communicating, mutual understanding establishing
processthatcanbesignificantlyinfluencedbymutual
knowledge [11, 12].How they can develop the
mutualunderstandingandknowledge,especiallyvia
the mediating tools (as part of coordination devices
[11,13])remainstobeelucidated
uponinthecontext
ofEE.
2.2 Collaborativelearningandknowledgesharing
Activity Theory [14‐16] has informed that the
meaningofthethingsorthegoalsthatmakepeople
act on reality has its profound root in social
interactionandthusknowledgeismaintainedinsuch
interaction. The production of
knowledge and
attention of the departments can be diverse, as they
have different socio‐culture experiences, which can
contributetothefissuredescribedbyKataria,Holder
[10].
Previous knowledge management literatures in
the EE domain that knowledge and skills are
identified to be the central social constructs [5],
shaping meanings
of things that people do. Other
studies have also recognized the importance of the
crew’s awareness, knowledge, motivation and ideas
in EE operations [17] and addressed concerns
regardingtheeducationandknowledgedevelopment
process[18].ViktoreliusandLundh[6]identifiedthat
there is a social boundary between the engine and
bridge
departments to share knowledge with each
other. For example, the engineers did have many
thoughtsonEEandexchangedideasoverlunchbut
theyseldomspoketothebridgeabouttheiropinions
[6]. In Kataria, Holder [10]’s study, the division
between the engine and deck was framed as “huge
Berlin
wall”. There is a certain need to overcome
barriers and create the context to allow knowledge
development.
The intuitive solution would be what Kataria,
Holder [10] proposed, using training to develop
knowledge,forpotentiallyenhancingcollaborationto
improve EE.But knowledge mobilisation in the
shipping domain is usually in a formal,
explicit,
salient professional form like what Viktorelius and
Lundh [6] observed: ‘crew members…had been sent
to a one day course for how to use the fuel
management system’. This is consistent with the
common perspective in knowledge management
research that knowledge is considered as object and
assetthuslearningbecomes
theprocessoftransferring
the objects from producer to receptacles to produce
actions[19‐21].Onelimitationisthatitbringsbenefits
mostlyatanindividuallevelbasedontheknowledge
networkframeworkprosedbyBüchelandRaub[22].
Additionally, the professional training can be
normative per se, infusing information to
the
practitionersabout“whatshouldbedone”insteadof
supporting their own ways of “muddling through”
[23]ineachoftheirsituatedcontext.Thoughthistype
offormaleducationmaybebeneficialindevelopinga
commongroundconcerningsystemusageinroutine
tasks, it may also have its intrinsic limitation
in
cultivating dynamic problem‐solving capacity
requiredunderunanticipatedsituations[23,24].
Knowledgedevelopmentcouldalsotranscendthe
traditional notion if we take a distributed cognition
perspective, such as the socio‐cultural approach [25,
26] or situated cognition approach [27]. The
distributedcognitionperspectiveconcentratesonthat
human cognition is embedded
in the social‐cultural
context and the focus is from individual cognitive
changetodistributedfunctioningintheenvironment
of working groups [28, 29] and tools. Knowledge
mobilisation among the peers is not transferring the
objects but carried out in an implicit and tacit form
[30].ViktoreliusandLundh[6]
showedthattheship
engineers might come up with new ideas in an
unstructured form, which could shaped itself
differently from traditional classroom learning.
241
Knowledgesharingcanbecomeanaturalproductof
themutualengagement,ora‘dynamicby‐productof
interactions’[31].
InLaveandWenger[32]’sconceptincommunityof
practice,groups of workersshare a common concern
andlearn how to improve the ways of doing asthe
interactionbetween
and within the groups proceeds
onaregularbasis.Knowledgedevelopmentisseenas
collective and collaborative achievements in the
communitiesofpractice[30,32,33].Learningatwork
is essentially both a culturally and socially situated
activity [34]. The emphasis is that knowledge
development is achieved by increased participation,
which
refers to the process in which a ‘newcomer’
immerses himself or herself in the sociocultural
practicesofacommunity.His/hercompetencewould
growashe/sheismoreknowledgablyskilfulthrough
moreinteractions[32].Thebenefitofsituatedlearning
is that the characteristics of situatedness can breed
collaborations and innovations through
the
participation of multiple agents [35, 36]. A
collaborative learning approach is emerged in this
sense that it occurs “in a situation in which two or
more people (a small group, class, community or
society)learnorattempttolearnsomethingtogether”
[37] and collaborations among workers, the joint
intellectualeffort,
becomesawaytoachievelearning
[28]. Collaborative learning could lead to greater
problem‐solving success than individual learning
[38].
Therelationshipbetweenlearningasanindividual
phenomenonandasacollectivephenomenoncanbe
conceptualized considering an inter‐subjectivity
perspective [14, 15, 28, 39‐41]. Vygotsky [14] argued
that
the psychological development is from inter‐
psychologicaltointra‐psychological(e.g.anengineer
may progressively understand navigational
knowledge from the bridge department when he is
frequently interacting with them, thus “the
communityinfluencestheindividual”);butColeand
Engeström[41]contended thatit couldalsobefrom
intra‐psychological to inter
‐psychological (“the
individual influences the community”) as learning
could be a bi‐directional phenomenon (e.g. the
navigators could get influenced by the engineer). A
collaborativelearningexperienceisreflectedthrough
thesocio‐communicativeprocessinwhichpluralityof
perspectivesandsocialcoordinationispresented[28].
2.3 ProblemsinShip‐Shore
Management
Knowledge production and mobilisation onboard
ships at the sharp‐end can be influencedby shore‐
based support and management at the blunt‐end.
JohnsonandAndersson(2016)suggestedthatcurrent
organizational structures inhibit learning and
innovationinEE.Itwas foundthatthe practitioners
who directly influence energy consumption on
the
frontlinescouldbeorganisationallytoofarremoved
to be included in the crucial organizational decision
making process [7]. A body of energy efficiency
researchaddressedtheconcernsfromthemanageria l
perspective, e.g. the quality and awareness of
information [42], inaccuracy of information [43], etc.
This came along with
a broadened discussion
groundedinthesocietalandorganizationalviews to
understandthe gaps in energymanagement such as
managementstandards[44],informationasymmetries
and power structures within organizations [7], how
the organisational change influences human
behaviour [18], energy performance monitoring
competence, data accessibility, data analysis and
actionabilitiesofimprovedoperational
measures[45].
Thepractitioners’voiceswerenotnecessarilyheardin
the important ship‐shore communication [46] and
they usually get poor learning support from the
organization [6], as the shipping industry is
essentiallyatop‐downmanagementsystem[18].
2.4 ToolMediationandInteractionDesign
Collaborativelearningcanbein
variousformssuchas
face‐to‐face communication or computer mediated
[37]. Much knowledge management and
organisational research identified problems in the
shipEEcontext[7,44,46],compoundingthegapissue
ininter‐departmentcollaboration.However,solutions
regardingsuchissues,specifically,howcollaborative
learning activities and interactions between bridge
and
engineandevenbetweenshipandshorecanbe
supported have not been addressed. Is there any
“niche” for the supporting tools? If so, how should
thetoolsaddresstheusers’needs?
The working environment onboard is
characterised by task distribution across the ship’s
crew and accessibility of media ting tools
[47].
Mediationmeansthathumanexperienceisshapedby
the tools and it is the mediator (the tools) that
connects the human workers organically and
intimately to the world, so the technology use shall
not be limited to a mechanical input‐output relation
between human and machine [48]. Kataria, Holder
[10]
suggestedthattechnologymediatedcollaboration
is very important for interdepartmental interactions,
such as using shared displays to create better
situational awareness. In today’s context of EE,
technologies have been mainly exploited in the
manner of sensing complex environmental factors
contributing to EE and automatically manipulating
operationalconfigurationsforoptimizedperformance
[49‐51].Theyarenotnecessarilydesignedinawayto
supportthe practitioners’ awareness and assessment
ofEEperformanceorcollaborativelearningactivities,
which are considered crucial in energy saving
activities[42,43,52].
ViktoreliusandLundh[6]reportedthatastate‐of‐
the‐art performance monitoring system called ETA
‐
pilot had been installed on modern ferry vessels to
aggregate huge amount of energy consumption
related data to supposedly inform the crews about
energy efficiency. The authors of this paper
conducted several visits to the same modern ferry
shipsandinterviewedwiththedesigneroftheETA‐
pilotaspre‐
studiestounderstandhowtheexistingEE
monitoringsystemworkedintheoryandinpractice.
SailingspeedisdirectlyrelevanttoEE.TheETA‐pilot
takesin fixedparametersofship characteristics(e.g.
parameters of the ship hull etc.) and dynamic
parameters(e.g.draft,trimetc.),theprofileofwater
depth
during the voyage, forecasted weather
information, together with estimated time of arrival
anddistance.Proposedspeedprofilesinreal‐timeare
242
generated and the technology directly regulates the
speed,untilthenavigatorsintervene(e.g.whenthere
isatrafficsituationthatrequiresaspeedordirection
change). The fuel consumption is calculated in real‐
time (kg per nautical miles) and displayed as a
dynamiccurvealongwithotherdynamicparameters
in
acomplexgraph.Oncetheferryarrivestheport,a
number of EE related data has been generated and
wouldbetransferredtotheshore‐basedmanagement
officeforfurtheranalyses.Figure1illustrateshowthe
ETA‐pilot records how the fuel consumption varies
along the voyage together with a
total fuel
consumption(27816kg).
Inthepre‐study,oneoftheauthorsofthispaper
was informed by the designer of the ETA‐pilot that
therewasnouniversaloptimaluseofthetoolbuthad
to depend on the navigators’ own experience and
interpretation, as there are many factors
that the
current algorithm of the tool cannot universally
account for, e.g. different routes, different efficiency
fromdifferentpropellers,differentpotentialstosave
fuel on different ships. The designer also expected
thatthetoolcouldenhancethecollaborationbetween
bridge and engine departments and certain analysis
couldbedoneto
informfutureimprovementsonEE.
Figure1. The ETA‐pilot interface once the ship arrives the
destination.
However, this was not what the actual practices
reflected. One observed phenomenon was the users
have different “tool‐related competence”, a notion
describedas thecapability interms of tool usingby
Kaptelinin [53]. Viktorelius and Lundh [6]’s study
reported that some even claimed that by
disconnecting ETA‐pilot, navigating manually
could
contributemore fuelsaving. Inthe pre‐study,it had
verified that a lot of data were generated but “not
used by the crew members” [54]. The shipping
companyonlyusedtheEEdatasentfromETA‐pilot
to check the fuel consumption after major
maintenanceactivities likecleaning
thehull [54, 55].
Viktorelius and Lundh [6] concluded that this is a
problem of “underdevelopment of evaluating
activities”or“inadequateknowledge”.
3 PURPOSEOFTHESTUDY
Thepurposeofthestudyistoinvestigateifandhow
the identified gaps in the EE practices can be
mitigatedthroughaddressingthe
end‐usersconcerns
in the EE context and exploring opportunities of
interaction design. The purpose of the paper is to
understand the relationships between collective
activities and mediating technologies and their
implicationstoknowledgetransferandmanagement
practicesin the contextof energy efficiency onboard
ships.
4 METHODS
4.1 Participant
demographics
In total, five male ship engineers and eight male
bridge officers were invited to participate two focus
groups that were conducted chronologically in this
study. All participants are Swedes. For engineers,
theiragesrangedfrom39to49yearswithameanage
44.4 (SD = 3.9) years. The engineers
had between 6
and96monthsofexperienceattheircurrentpositions
with a mean period of 52.5 (SD = 34.3) months. For
navigators,theiragesrangedfrom32to52yearsold
with a mean age 44.3 (SD = 6.2) years. They had
between 12 and 156 months of
experience at their
currentpositions withameanperiodof69(SD=47.9)
months.Mostofthemcomefromashippingcompany
thatisoneofthelargestferryoperatorsintheworld.
4.2 Procedurefordatacollection
Adigitalconsentformwasmailedtotheparticipants
toinformthem
abouthowthecollectionofthedataas
well as treatment of data and their identities would
fulfilthe ethicaldemands foracademic research. All
participants were aware that their participation was
voluntary,signedthewrittenconsentformsandfilled
in the demographic details prior to the focus group
interviews.
The focus groups were pre‐planned carefully
together with Sweship Energy, a platform created by
theSwedishMaritimeAssociationandsupportedby
theSwedishEnergyAuthorityandSwedishMaritime
Administration to organize recurring workshops for
knowledge sharing and learning among ship
practitioners with different knowledge backgrounds
andcapabilities.Thefocus
groupswerecarriedoutin
theworkshopsontwoseparateoccasions.Inthefirst
workshop, there were three engineers and four
navigators who signed up with the purpose of
sharing EE knowledge and participated the focus
group. In the second workshop, there were two
engineers and four navigators signed up and
participated the focus group. All participants are
already involved in various EE activities onboard.
These two focus groups have identical orchestration
withrespecttothetopic,posedquestions,controlled
process,fieldnotestaking,assistantsandmoderator.
The two focus groups were led by the same
moderator in a structural way
which lasted
approximately one hour. During the session, three
assistants were present to distribute va rious hand‐
outswiththequestionsfordiscussion,takediscussion
notes, and probe the participantsʹ responses when
appropriate.Eachfocusgroup wasdividedinto two
parts. The firstpartwas a brief discussion on a few
general
questions pertaining to EE operations
onboard, including today’s measure practices to
achieve energy optimization, plus an objective
prioritization of four aspects, i.e. ‘passenger/cargo
safety’, ‘ship safety’, ‘energy efficiency’ and
‘passengercomfort’.Thesecondpartwasanin‐depth
discussionon their perceived values, challenges and
expectationstowardsthedesignand
usageofanideal
digitalized EE monitoring tool in terms of the three
major phases of a voyage sailing, i.e. before the
voyage, during the voyage, after the voyage.
243
Noteworthyisthattheposedquestionswereframed
in a way to attempt to make them reflect upon the
gains and pains related to their current EE related
practices (i.e. how the engineers and navigators
interact with each other and how they engage with
the mediated computerised systems onboard to
improveEE),insteadofrequiringthemtowear hats
ofanITexpert.Eachquestionwasbothpresentedon
aprojectorscreenandthehandouts.Allparticipants
were informed to write their own opinions on the
handouts first and then discussed with the group
abouttheirknowledgeandideas.The
hand‐outswere
collecteduponcompletionofthefocusgroup.
4.3 Dataanalysisapproach
Considering the homogeneity of the two focus
groups, the gathered data were merged to form a
larger base to allow findings to be emerged. To
analyse the qualitative data, all hand‐outs were
analysed jointly with field
notes in an iterative
manner guided by the Grounded Theory [56].
Grounded Theory is a qualitative data analysis
method that strives to categorize themes in the
transcripts and generate explanatory propositions
that correspond to real‐world phenomena [56, 57].
Grounded theory allows theoretical concepts or
frameworkstobeemergedfrom
thedatathroughout
theresearchprocess[56]. ‘Opencoding’, aniterative
process of aligning pieces of text to different
categorieswithattached codememos [57],was used
to portray the relationships between emerging
concepts in pursuit of a higher‐level categorization
and theorizing. All participants’ responses were
imported into MAXQDA 12
(www.maxqda.com), a
computer assisted software mainly used for
qualitativedata analysis, to be coded for qualitative
textanalysis[58].
5 RESULTS
5.1 Briefdiscussionongoalprioritiesandpractical
operations
Due to the small size of the sample, it was easy to
observethatall participantsagreed‘ship safety’and
‘passenger/cargo safety’ were the top two in their
priority list and most of them thought ‘passenger
comfort’ was the least important. This was also
confirmed in the later discussion. All participants
agreed that safety outweighed EE, although EE was
alsoaveryimportantgoaltothem.
The main message derived from
the discussions
was that both the bridge and engine department
recognizedtheimportancetocollaborateandinspire
each other to improve EE. The mixed‐background
participantspointedoutthatitwascriticalforthemto
share information and learn from each other in a
distributed working environment (i.e. bridge and
engine),suchas‘whenwediscuss,weactuallycooperate
(…)usethebenefitsofthey(i.e.engineers)havinggreat
ideaandwehavebadidea’,‘openmind fornewthoughts
from the crew’, ‘have discussions regarding decisions be
made’, ‘display simple information so everybody can feel
that
theycanparticipating’,‘thinkingabouthowtoreduce
energyusageindifferentways’,etc.Theybelievedthat
bridge‐engine collaboration could become a useful
approach to increase personal awareness, especially
dealing with some ‘trivial’ details in operations
towardenergyoptimization.Forexample,anavigator
admittedthathe‘sometimes
(…)forget aboutadjusting
the speed pilot’ because they went to drink coffee.
Some engineers thought it could be beneficial to
communicate about how many engines would be
used(‘thoughtfulnessregardingthenumberofmachines’)
becauseitcaninfluencefuelconsumption.
The participants not just stated their motivations
about
collaboration and learning for improved EE,
they also made a link to a prominent factor
influencing effectiveness and efficiency of EE
performance – the mediating tools. They addressed
the concerns of losing transparency by introducing
complexautomatedtoolsliketheETA‐pilotthatthey
did not necessarily understand a system’s
status
whichmadeithardtocommunicatewiththeirpeers.
Some engineers even expressed that using ‘blunt
instruments’ like hands‐on tasks, sounds, vibrations,
slowly ticking measurement was more efficiently
contributing to their understanding of the
circumstancesintheolddays.Anengineermentioned
that the fuel monitoring system had
‘too much
information’tobecome usableandsupportive forthe
crewmemberstoplanthevoyage.
Reliability and functionalities of the tools were
also briefly discussed. As the direct users of the
automaticeco‐drivingsystems(includingETA‐pilot),
thenavigatorsparticularlyexpressed thattheir ship‐
handlingexperiencewas
thekeytoachievesufficient
situation awareness and deliver improved EE
performance, as the tools are ‘not as perfect as you
think’. Both engineers and navigators agreed that
usabilityofthetoolsandinformationsharingplaysan
importantroleintheiractivitiespertainingtoenergy
optimization, such as ‘whatever
information, has to be
simple, clear for both engine room and bridge’, ‘the most
important is that we have the same information on the
screens’,etc.
5.2 In‐depthdiscussiononactivities,values,expectations
andchallenges
The participants’ responses in the in‐depth
discussions from the two focus groups
were
organizedaroundthreephasesofavoyage,i.e.before
thejourney,duringthejourneyandafterthejourney.
Table1summarizesthekeyfindingsandpresentsan
emergingcollaborativelearningsynergybetweenthe
bridge and engine departments in pursuit of
improved EE onboard ships via mediating
technology, based on the
user requirements
elicitation. To support the need of information
sharing, communicating and collaborative learning
viamediatingtechnologyonEEoptimizationbecame
the emerging theme, woven into the participants’
discourse of their activities (either experienced or
anticipated) throughout the three phrases. A more
detailedelucidationonthefindingsisfollowedby
the
table.
244
Table1.TheparticipantsaddressedthekeyactivitiesthatcontributetoEE,thevaluesofmediatingtechnologiesintermsof
howthey can contribute to mutual understanding and the common ground, as well as the potential barriers to achieve
improvedEEperformanceinthreephasesofavoyage.Noteworthyisthat
thephraseof“afterthe journey”wasmerely
anticipatedyetthestatedactivitieshadnotbeenperform.
__________________________________________________________________________________________________
BeforethejourneyDuringthejourney Afterthejourney
__________________________________________________________________________________________________
IdentifiedkeyactivityVoyageplanningEco‐drivingandsharing Performanceevaluationand
contributingtoEE(experiencedcollaborativelybythe situationalinformationlearningforfutureimprovedEE
or/andexpected)engineandbridgebetweentheengine practicestogether
departmentsandbridge
Expectationsonthemediating Sharinganoverviewofthe Sharingimportant Summarizingandanalysingthe
toolsandtheirvaluesinsystemsstatus,route situationalinformationjourney’sEEperformanceto
contributingtoamutualoptions,weathersuchascurrent,wind, provideaconcretemediumfor
understandingforbuildingthe information,estimatedfuel
traffic,speed/ETA, inter‐departmentaldiscussion
and
commongroundconsumptionandtime real‐timefuellearning
constraintsconsumptionandits
consumptionbenchmarks
Potentialchallenges(barrierstoSystemcomplexity,time MotivationandsystemLackofcapabilitiestoanalyse
the
achieveimprovedEEconstraints,datareliability complexity,data rawdataormotivations,time
performanceviaemployingthereliability,timeconstraints,technicaluncertainties
expectedtools)constraints,weather/
trafficdynamics
__________________________________________________________________________________________________
5.2.1 Beforethejourney
In the pre‐departure phase, the participants
expectedthatthekeyactivity,voyageplanningtasks,
shallbesupportedwiththeprovisionofanoverview
of the systems, route options, etc. Although the
engineandbridgecandifferentconcerns(navigators
addressedtheneedforspeed/routerecommendation
based
on the analysis of water depth, predicted
weather status, traffic forecast etc. while engineers
desired the estimated fuel consumption and time
constraints like estimated time of arrival (ETA) and
departure time), all participants agreed that it is
important the activity is done in collaborative way
supported by the tools. The engineer
participants
pointedoutthatitwasnecessaryto‘involvetheengine
departmentinthevoyageplanning’anditwascrucialto
‘always reconcile bridge and engine control room if
something deviates from the norm. For example,
maintenance, which means that not all machines are
available and when it is
expected to be completed’. The
navigatorparticipantsalsoexpressedthatinformation
sharingbetween departments cancontribute to their
learning too, e.g. ‘more people and more data so it is
betterdecisionbases’.
5.2.2 Duringthejourney
During the voyages, both the navigators and
engineersexpectedthatthetoolcan
supportrelevant
situationalinformationsharinglikeseacurrent,wind
andtrafficso that eco‐drivingcan be achievedvia a
teameffort.Anopencontactmediatedbytools(e.g.a
shared display and voice communications) were
appreciatedduringthevoyage,e.g.‘continuouscontact
with the control room and the discussion
of the same
information’ mentioned by a navigator. All engineer
participants believed such open contact could allow
the bridge and engine departments to diagnose
‘abnormal situations’ and increase mutual
understandingaboutdecisionmakinginthevoyage,
suchas‘whywedonotrunwithXnumberofmachines’,
‘howmanymachines…shouldbeconsulted onthebasisof
exhaustgasboiler,lubricativeoil,axlegenerator’.Interms
ofthefeaturesoftheshareddisplays,itwasnoticed
that that in addition to the key information of
speed/ETA, real‐time fuel consumption, the
participants also appreciated some sort of
benchmarkstobepresentedinthefuelconsumption
curves to not only support navigators navigational
decision making but also contribute to mutual
understandingoftheengine‐deckteam.
5.2.3 Afterthejourney
The post‐journey activities that they expected to
undertakewasthemostenlighteningpartofthefocus
group findings.
Performance evaluation and
collaborative learning experiences with the
engagement of the tool were highlighted by all the
participants yet they do not exist today as common
practiceor even standard operating procedures. The
participants acknowledged that there were no
analytical actions being taken once the ship arrived
theport, mainly
becausethey lacked thecapabilities
and opportunities (e.g. time constraints and the
existing tool did not support them to learn). They
suggested that the reality was that large amount of
operational and factual data were collected, plotted,
ignored while the shore‐based management did not
conduct effective analysis either. The most
appreciated and desired value of the tool was to
analyse the data and further support the inter‐
departmentalcollaborativelearningactivitieslateron,
suchasreviewthejourneyanddiscussabouthowto
doabetterjobbyeachdepartment.Theparticipants
considered the fuel consumption performance
evaluationprovidedby
thetoolasaconcretemedium
forthenavigatorsandengineerstoinspireeachother
andreflectupontheirpastbehavioursanddecisions,
such as ‘know what was good, what can be done
differently’,‘learnthingsforthenexttrip’,‘getfeedbackto
see if the trip has
been completed within the planned
parametersordeviatedfromthetheoreticalplan’,‘obtaina
summary…which parts of the trip cost more fuel’, ‘get
material for discussion at various meetings’, ‘feedback on
245
whattheconsumptionwasforacertainaction/driving’etc.
In the post‐journey analysis activity, communication
wasextremelyimportantforteamreflections,likeone
participant addressed, ‘… (The engineers and
navigators) should discuss that information (about the
most recently completed journey) during various
encountersonboard’.
5.2.4 Challenges
The participants
also discussed the barriers to
affect EE performance via employing the expected
tools. Besides the system complexity and technical
challenges,theparticipantspointedoutthattimeisa
crucialconstraintfactorinfluencingtheircollaborative
learning anddecision makingacross all three stages
ofa voyage. In addition, ease‐of‐use
of the tool was
also mentioned. The participants used such
expressions as ‘an easily understandable system’,
‘simplicityoperations’,‘easytouse’,‘presentthisingood
way’ to accentuate importance of their usability
requirements. This part appeared to be a further
elaboration of the brief discussion on the
‘blunt
instrument’ – they seem to prefer usability over
functionality in tool using. To them lack of
transparency due to increasing automation is a
problem.
6 DISCUSSION
6.1 Methodologicallimitations
While this size of the focus group (i.e. from six to
eight)isconsideredidealtobalancethecollection
of
fine details with a breadth of perspectives [59], the
participantsperfocusgroupismixedofbackgrounds,
consisting of engineers and navigators. The
heterogeneityofparticipantsisusuallytobeavoided
asthismightmakepeoplelesscomfortableinsharing
experiences and lead to less interaction between
people pertaining to
different professions [59].
However, the focused groups are arranged in the
workshops which are open to both engineers and
navigators, thus the sign‐up participants are
intrinsically mixed and it is hard to control prior to
the focus group study. Nevertheless, the
specializationoftheresearchscopeandcontextof
the
researchretainsacertaindegreeoflegitimacyinsuch
group settings. Focus groups are suitable for
identifying problems, finding desires, and va lues
from the stakeholders’ view [60]. Given the main
interestisthecollaborativeworkonenergyefficiency
between the bridge and engine control room, the
heterogeneity became advantageous when
plurality
of perspectives and backgrounds is presented. The
interaction between engineers and navigators was
found to be beneficial to develop a mutual
understanding of the concerns outside each of their
individual professional realm and build a common
ground shared in the EE context. Thus, an overall
comprehensionofthechallenges
inthewholesystem
and wider consensus of the system requirements
among the participants aiming at improving the EE
performance could be reached. In addition, the
heterogeneityismainlyreflectedintheprofession,as
all participants are Swedish males sharing the
Scandinaviannavigationalculture,plustheyallhave
considerable sea experience
and the majority had
experience dealing with energy performance
monitoringsystems.
The data collection drew from a sample size of
thirteenparticipants.The rationaleto mergethe two
focusgroupsintoalargerbasefordataanalysisisthe
common context of the focus groups, participant’s
background composition and data
collection
approach. The focus of both workshops was to
facilitateknowledgesharingonEE.
Itisimportanttogeneratetheoreticalinsights,but
the authors would contend it is more important to
associate these views with design as a means to
contribute to problem solving in the field and user
experience improvement.
Flach and Voorhorst [61]
argued that the “experience” cannot be fully
understood from a perspective that considers the
users as objects independent from the artefacts
(conventional social science) or from a perspective
that considers the artefacts as objects independent
fromtheusers (conventionalengineering). Inthe EE
context, a critical
question is how to incorporate
collaborative learning into the practitioner’s daily
operations, which conventional engineering or
technology‐centricdesignseldomconsider.
6.2 Collaborativelearningsynergymediatedbythetools
6.2.1 Mutualunderstandinganddistributedcognition
Engineers and navigators work at distributed
places and thus can be considered as distributed
working communities
of practice. Previous research
[10] has addressed the inter‐departmental gaps, but
theydidnotstudythecollaborationinthecontextof
EE. Informed by the participants’ needs in
information and knowledge sharing in the three
stagesofavoyageforanimprovedEEperformance,
thispaperhasidentifieda
criticalpointintheirjoint
EE activity, i.e. how to facilitate their mutual
understanding in their distributed EE practices. The
resultssuggest that the distributedcommunities can
be interdependent upon each other’s work for
improved EE and need to communicate and
collaborate with each other in order to get the job
done better. For example, how many main engines
and auxiliary engines will be available depends on
the work in the ECR and the bridge must take this
factor into account in the voyage planning. The
engineers requested to be informed about ETA and
substantialmanoeuvringchangesmadebythebridge
so the engine department could properly adjust the
operational status, change configurations of engines.
Thedecisionmakingonthebridgeduringthevoyage
due to traffic situation or weather can influence the
engineers’ maintenance plan, which in turn might
affectenergyoptimisationperformance.
The results identified that the engineers and
navigators
sharethesameintentiontomaintainsafety
and save fuel (this is also probably because all the
workshop’sattendeesareactivepractitionersforfuel
savingonboard).Thus,theymayhaveafoundationto
share criteria for a joint activity [11]. Building a
commonground is acrucial step towards their
joint
activity‐the results verified that the engineers and
246
navigatorsmustunderstandeachother’sconcernsin
order to plan adaptively and perform efficiently for
improved EE. Knowledge sharing for a mutual
understandingonboardshipsisthusvitaltoEE.This
process needs to be incorporated into their common
practice.
The results regarding the post journey analysis
revealed how an
envisioned collaborative activity
mediated by technology could have potentials to
contribute to knowledge sharing. Taking the
distributed cognition perspective can allow us to
understand the value of this collaborative learning
activity.Learningisconceptualizedassituatedaction
thatisinherentlyintegratedwithhumanactivitiesin
certain context [32, 35]. Once
the journey is
completed, if the mediating tool could analyse the
data and shape a communication space for the
engineers and navigators to reflect upon the newly
finished voyage, then an opportunity might be
created to influence the aforementioned socio‐
boundary [6] between the two departments. This is
because, learning and
knowing are essentially
addressing‘relationsamongpeopleengageinactivity
in, with, and arising from the socially and cultural
structured world’ [27]. Collaborative learning can
provide opportunities to improve, evolve, reinforce,
and even innovate practices [35] and invite mutual
learningascollectivelyshapedactivity[33].Tosome
extent, the activities
at this stage could likely bring
much value in the improvement of their future
practices.Thenavigatorsandengineersmayusethis
processtonotonlyunderstandeachother’sconcerns
duringthepastjourney,butalsolearnideasandtacit
knowledge from the team that may result in more
fuel‐saving.
Inaddition, italsocreatesanopportunity
for the distributed working communities to have
computer‐mediated and face‐to‐face promotive
interactionatthe sametime,which are fundamental
elementsinvolvedincollaborativelearning[37,62].
The dynamic between the individual and the
social can also be addressed by an
inter‐subjective
perspective. One example is to assess of one’s
developmentthroughthezoneofproximaldevelopment
[14]. That is, the level of the development is not
grounded in that individual’s absolute performance,
but the distance between the level indicator of
independent problem solving and level indicator of
problem solving with
support from other capable
peers or mediating tools. One example is that the
engineers might not necessarily understand why an
extraengineisrequiredduetochangedweatherand
the navigators might not necessarily understand the
relationshipbetweenpitchandRPM(Revolutionsper
minute)andhowitscouplingcouldaffect
speedand
fuel saving. The results have suggest that the
(distributed) EE work needs joint intellectual effort
mediatedbythetools.Theengineersand navigators
must communicate with each other and depend on
each other, in which the process the mediating
technology need to consider how to support their
knowledgesharing
andformabasistoincreasetheir
mutual understanding. The study result has clearly
demonstratedthatintheEEdomain, thereisno“all‐
round” workers in either engine or bridge
department, but it is possible to shorten the distance
by increasing capabilities via more communication
and interactions, i.e. via
a collaborative learning
approach.
6.2.2 Implicationsforinteractiondesign
The results revealed many user requirements on
the EE monitoring systems in terms of collaborative
learningandinformationsharing.Itisimportantthat
the mediating tools need to support their
collaborative learning activities and development of
theircommonground.Butwhywould
ETA‐pilotfail
toaddress theseconcerns, asViktorelius andLundh
[6] reported that it was “not used by the crew
members”?Whywouldtheresultssuggestthatusers
are suffering from usability issues or lost
transparency nowadays? What lessons we can learn
forinteractiondesign?
What ETA‐pilot does
is to collect sensor
informationandplottingdozens ofparameters(wind,
trim,fuelconsumptionetc.)onthedisplay.Software
systemslikethishavebeenlargelydesignedtostore,
search, and process, formulate and visualize energy
consumptionrelatedinformation[63‐65].Thehuman‐
technology interaction process could be
conceptualizedasanindividual
learningactivity,i.e.
aresultofcognitivechangeinthehumanoperator’s
headstimulatedbythesoftware.Thefocusinthethe
industry has been predominantly on the causal
relationship between enhanced IT capabilities
(stimulus)andlearningoutcome(response),i.e.how
theintroducedfeaturesoftechnologiescouldsupport
the practitioners’
information processing activities
[66].Such designfashion has ahistorical root in the
traditional Human‐Machine Interaction framework
[67]thatitisadyadicsemioticparadigminaHuman‐
Artefactrelationship.Thisrelationshipusuallyrefers
‘meaning’(i.e.somethingahumanagentlearnt)to‘a
stateinternaltotheagent’[68].Therefore
learningas
such is considered a process of internalization of
knowledgetypicallyinindividuallearners.TheETA‐
pilot assumes that providing fuel‐consumption data
curve equals knowledge creation. That is, the tool
decontextualizes knowledge and deemphasizes the
contextinwhichtheknowledgecouldbeapplied.
Thisprobablyexplainswhythe
userswanttohave
some sort of benchmark displayed in the fuel
consumption monitoring system during the journey
andexpectthetoolstoanalysethedatainsomeways
toshapeadiscussionbasisafterthejourney,because
theywantinformationtobecontextualizedinwhich
process the numbers become value
‐creating
knowledgetowardstheirEEgoal.Forexample,inthe
ETA‐pilot,20 kg fuelconsumption per nautical mile
mightnotmeanmuchtothembutthetrendtospend
more than 5 kg fuel per nautical mile than the
“historical average consumption value” might do.
Thepointisthat
the“providing toomuch information”
asmentionedbytheparticipantswouldnothelpifthe
designignoresthecontextinwhichknowledgewould
begenerated,disseminatedandapplied.
Theresultsofwhatusersexpectsafterthejourney
unfolds a richer notion for knowledge mobilisation
andindicateanimportantdirectionfor
theinteraction
design,i.e.howmediatingtechnologyshouldsupport
collaborative learning and communication. Situated
actions‘dependinessentialwaysonitsmaterialand
social circumstances’ [69]. The dependence on the
247
mediating material suggests the vast potentials of
technologies in influencing the social interaction
amongdistributedworkcommunitiesandcontextin
whichtheknowledgemobilisationoccurs.Learningis
conceptualizedbysocialinteractionandparticipation
[32]. This collective aspect of learning invites new
possibilitiestodesigntheEEmonitoringsoftwareasa
shared repertoire of the communities. By facilitating
communicationandlearning,itisexpectedtogreatly
influencetheindividual‐collectivedimension[16].
Hereweproposeonesimplifieddesignpossibility
opposed to the original design in ETA‐pilot (see
Figure 3). This is an attempt to associate theoretical
reflectionswithpracticaldesign
solutions.
Figure3. A simplified proposed design for post journey
analysis.
Theinterfacenotonlyliststheactualconsumption
for the journey but also provides historical data on
fuel consumptions for the purpose of enabling
comparativeanalysisandillustratinghowgooditwas
amongthejourneysdoneunderthesimilarconditions
(wind,current,trim,etc.).Thevoyagewasplottedon
theleft
sidetogive anoverviewaboutconsumption
situationforeachleg(e.g.whatareasareusuallythe
mostenergyconsumingarea).Thefuelconsumption
curvealongthejourneycouldfall“outsidethenormal
range” (i.e. consumes more than worst case or less
thanthebestcase),which introducesdiscussionand
learningopportunities.Onthebottomoftheinterface,
these periods were logged, listed in a table,
aggregated and populated inside an interactive
Electronic Chart Display and Information System
(ECDIS) for “voyage replay”. Engineers and
navigatorscanreplaywhathappenedineachperiod
of significant increase/decrease of fuel consumption
and reflect upon
‘what was good, what can be done
differently’.Whenanengineerexplainedtoanavigator
howtheextrause ofthemain enginecontributed to
the precipitous increase in the consumption curve
renderedbythetool,orthenavigatorexplainedtothe
engineer how the sea traffic situation of
a certain
period looked like, the IT system is essentially
facilitating their mutual understanding. Frequent
interactionsamongindividualspertainingtodifferent
professional realms breed innovations and new
knowledge [35]. The tool can shape a space for
interaction and functions as an open platform to
buttress communications and participation, to allow
practitioners
of cross‐functional communities to
exchange opinions, formulate questions, share
expertise, and discuss solutions based on the data
from the real‐world. Learning would be
conceptualizedas acollective achievementmediated
bythetools.
The aim of proposing an interface here is not to
provideadetailed panacea butto
inspirethedesign
directionsandreflecttheroleofmediatingtools:how
itcanbebeyondthetraditionalconceptofsupporting
“information processing”. Tools can be used to
supportcollaborativelearningamongthedistributed
communitiesofpractice.
The dynamism of learning and practice suggests
thattherelationshipbetweenpracticeandinteraction
designismutuallysustained[33].Practiceisnotonly
an output of a certain design but also an input for
interaction design [70]. Interaction design is thus
framedas‘aprocessthatisarrangedwithinexisting
resource constraints to create, shape and decide all
use‐oriented qualities of a digital
artefact for one or
many clients’ on a human‐artefact level [71], or
conceptualized as ‘shaping a communication space’
[72].Interaction design is more than embodying
human‐machine engagement. With the emphasis on
the collective aspect of learning and distributed
cognition,acollaborativelearningsupporttoolcould
beanopportunityto
chartapathtowardsthegoalof
ameliorating collaborations between bridge and
enginedepartment on EE. Thestudy results suggest
that the emerging paradigm, Computer Support for
CollaborativeLearning(CSCL)[73,74],isincreasingly
important.
6.2.3 Organisationalsupportandtechnologyinasocio‐
technicalsystem
Timeconstraintsalongwiththe
systemcomplexity
andtechnicalchallengeswerementionedinthefocus
group studies as prominent challenges. Participants
may have motivations to communicate, learn and
sharebutsuch practicesare currently notsupported
by the company’s operational guidelines or codes.
The collected EE performance data were not
evaluatedbythecompany.Thisactually
echoeswith
many previously identified gaps in the shore‐based
management [6, 7, 18, 45] etc. Here we employed a
holisticperspectivetounderstandthecomplexsocio‐
technicalsysteminshipping[47].
Today’s work demands onboard ships have
dramatically changed over timewith more
technological artefacts being introduced onboard; at
the same time the operational tasks becoming
unprecedentedly complex [75, 76]. It is increasingly
requiring the ship’s crew at the sharp‐end to
communicate and learn in apprentice‐like forms, in
actions and practice, to improve EE [55]. The major
problem with the shipping organisations is that the
management do not
have sufficient knowledge,
information or an effective monitoring mechanism
regarding EE performance, thus the considerations
generatedatthesharp‐endwouldberarelytakeninto
account in organisational decision making processes
[7].Ontheotherhand,moreartificialintelligence(AI)
applications have been developed in the shipping
domain,havinggreat
potentialimpactonknowledge
creation,conventionaloperationsandwholeindustry
[77,78].
248
JohnsonandAndersson[7]suggestthatasolution
for the shipping companies to address the
organisationalbarrierscould bethe establishmentof
“bestpractices”.Wewouldcontendthatthisprocess
musttake the impact of mediating technologies into
account and discuss how it can be integrated into a
broader collaborative
paradigm emerging between
thesharp‐end(i.e.theship’screw)andblunt‐end(the
managementorlargerregulatorybodies).Ifadvanced
data‐driven intelligence can effectively monitor the
EEperformanceandresolvetheissuesofinformation
asymmetry,thenitislikelygoingtoprovideameans
toshapethecommunication
spacebetweenshipand
shore and enable a bottom‐up approach for energy
management. The bottom‐up approach would be
valuable for the organisation to learn the process of
adopting new measures for EE and address the
knowledge gaps in organizational decision making
process [18].The value of the “best practice”
approach might be limited without considering
knowledge mobilisation across the whole
organisation,e.g.howtoevolvetheinformalandtacit
knowledgesharingonboardtoinstitutionalizedform
ofknowledge.
Aprofoundunderstandingonthehumanelement
about energy efficient operations should be multi‐
layered and intertwined with sociotechnical
perspectives [18]. It
is critical to employ a holistic
viewthattechnologyisnotisolatedfromthecontext
inwhichitgetsused[61].Thisisevenmoreimportant
when considering the data‐driven intelligence has
potentials to change the fundamental ways of how
organizations make decisions [79]. A collaborative
synergy between engineers and
navigators, between
practitioners and management, mediated by
technology, is emerging in the EE business of the
shippingindustry. Along vision isto find a wayto
maximizethejointeffortsofthewholesysteminthe
human‐machine reconfigurations. It will certainly
raise more important questions of how intellectual
assets are developed and maintained in the
intertwined relationship between advanced
automationsandhumanworkers,andhowtofinda
commongroundtosituatepractitioners,management
and advanced technologies in a system‐wide
‘collaborative work’. Such questions are vital to
address the maritime EE challenges and human
factors concern. The design
‐based research and
development should take a sociotechnical stance to
obtainadeepenedunderstandingoftheessentialrole
of technologies that work closely with the workers,
fortheworkers,bytheworkers.
7 CONCLUSIONS
Previous research has identified various barriers
concerningmaritimeEE,buttoolittleresearchexists
to provide
understanding of inter‐departmental
collaboration in the EE context, the role of tool
mediation, collaborative learning and management
practices. The research on practitioners’ demands,
activitiesanddesignconsiderationsintheEEdomain
is rather scarce. The practitioners’ demand and
requirements on the EE monitoring system plays a
pivotalroletoconnect
thetheoreticalexploitationand
practicaldesignimplications.Thispaperhasexplored
how the end‐users’ needs with regard to the EE
monitoring system can inform knowledge sharing
and interaction design. Informed by the users’
requirements, this paper uncovers the social
underpinnings of their situated work and identifies
that knowledge sharing
for a mutual understanding
onboard ships is critical to energy efficiency. The
practitioners’ collaborative practice and system
requirementsarepositionedwithinthesocial‐cultural
perspectives, which informs new opportunities for
interaction design and management practices. The
findingsrevealthatlearningonboardshipsintheEE
contextisacollectiveandcollaborative
achievement,
forming a collaborative learning synergy via
mediating technologies. This builds us a basis to
understandthe role of technology mediation and its
potentialvalues incollaborativelearning,knowledge
mobilisation and organizational decision‐making
processes. With more intelligent systems introduced
totheshippingindustry,itisimportanttoconcernthe
social‐culturaldimensionsimportanttoestablishinga
commongroundbetweenpractitioners,management
and advanced technologies. Thus, this study might
also shape a basis to discuss human‐computer
interactiondisciplinewithawiderscopeinthefuture.
ACKNOWLEDGEMENT
Theauthorsgratefullyacknowledgethesupportfrom
The Royal Society of Arts and Sciences
in Göteborg
(Kungl. Vetenskaps‐ och Vitterhets‐Samhället i
Göteborg,KVVS)andSweshipEnergy.
REFERENCES
1. IMO, Third IMO Greenhouse Gas Study: Executive
Summary and Final Report. 2014, International Maritime
Organization(IMO):London,UK.
2. Eyring, V., et al., Transport impacts on atmosphere and
climate: Shipping. Atmospheric Environment, 2010.
44(37):p.4735‐4771.
3. Buhaug, Ø., et al., Second IMO GHG Study. 2009,
International
MaritimeOrganization:London,UK.
4. IMO, ANNEX 19, RESOLUTION MEPC.203(62)‐
AMENDMENTSTOTHEANNEXOFTHEPROTOCOL
OF 1997 TO AMEND THE INTERNATIONAL
CONVENTION FOR THE PREVENTION OF
POLLUTION FROM SHIPS, 1973, AS MODIFIED BY
THEPROTOCOLOF1978RELATINGTHERETO2011.
5. Bännstrand,M.,etal.,Studyontheoptimization
ofenergy
consumption as part of implementation of a Ship Energy
EfficiencyManagementPlan(SEEMP).2016,International
MaritimeOrganisation(IMO):London,UK.
6. Viktorelius, M. and M. Lundh, The Role of Distributed
CognitioninShipEnergyOptimization,inEnergyEfficient
Ships.2016:London,UK.
7. Johnson, H. and K.
Andersson, Ba rriers to energy
efficiencyinshipping.WMUJournalofMaritimeAffairs,
2016.15(1):p.79‐96.
8. vanDokkum,K.,ShipKnowledge(8ed).2013,Enkhuizen,
TheNetherlands:DokmarMaritimePublishersBV.
9. Flin,R.,P.OʹConnor,andM.Crichton,Safetyatthesharp
end‐aguideto
non‐technicalskills.2008,London:Ashgate.
10.Kataria, A., et al., Exploring Bridge‐Engine Control Room
Collaborative Team Communication. TransNav‐the
International Journal on Marine Navigation and Safety
ofSeaTransportation,2015.9(2):p.169‐176.
249
11.Klein, G.,etal.,Commongroundandcoordinationinjoint
activity.Organzationalsimulation,ed. W.R.K.Boff.2005,
NewYork:Wiley.
12.Brennan, S.E.,Thegroundingprobleminconversationswith
andthroughcomputers.Socialandcognitivepsychological
approaches to interpersonal communication ed. S.R.
Fussel and R.J. Kreuz. 1998, Mahwah, NJ:
Lawrence
ErlbaumAssociates.
13.Clark,H.,Using language.1996,Cambridge:Cambridge
UniversityPress.
14.Vygotsky,L.,MindinSociety:TheDevelopmentofHigher
PsychologicalProcesses.1978, Cambridge,Massachusetts:
HarvardUniversityPress.
15.Leontʹev, A.N., Activity Consciousness and Peronality.
1978,EngelwoodCliffs,NJ:Prentice‐Hall.
16.Kaptelinin, V. and
B.A. Nardi, Acting with Technology:
ActivityTheory and Interaction Design. 2006, Cambridge:
MITPress.
17.Banks, C.,etal.,Seafarersʹscurrentawareness, knowledge,
motivation and ideas towards low carbon‐energy efficient
operations, in LCS 2012‐2nd International Conference on
Technologies, Operations, Logistics and Modelling for Low
Carbon Shipping.
2012: School of Marine Science and
Technology.
18.Kitada, M. and A. Ölçer. A Contemporary Outlook on
HumanElementinEnergyEfficientSeaborneTransportation.
in 16th IAMU Annual General Assembly. 2015. Opatija,
Croatia.
19.Schultze, U. and D.E. Leidner, Studying knowledge
managementininformationsystemsresearch:discoursesand
theoreticalassumptions.
MISQ.,2002.26(3):p.213‐242.
20.Röling,N.,Theemergenceofknowledgesystemsthinking:A
changing perception of relationships among innovation,
knowledge process and configuration. Knowledge and
Policy,1992.5(1):p.42‐64.
21.Dissanayake, W., Communication Models in Knowledge
Generation, Dissemination and utilization Activities, in
Knowledge
Generation,Exchange,andUtilization,G.M.B.e.
al.,Editor.1986,WestviewPress:Boulder,Colorado,.
22.Büchel,B.andS.Raub,BuildingKnowledge‐creatingValue
Networks.EuropeanManagementJournal,2002.20(6):p.
587‐596.
23.Flach,J.M.,etal.,Decisionmakinginpractice:Thedynamics
of muddling through. Applied Ergonomics, 2017. 63: p.
133‐141.
24.Vicente, K.J., Cognitive work analysis: Toward safe,
productive, and healthy computer‐based work. 1999,
Mahwah,NJ:LawrenceErlbaumAssociatesInc..
25.Engeström, Y., Learning by expanding: An activity‐
theoretical approach to developmental research. 1987,
Helsinki:Orienta‐KonsultitOy.
26.Engeström, Y., Expansive Learning at Work:
Toward an
activity theoretical reconceptualization. Journal of
EducationandWork,2001.14(1):p.133‐156.
27.Lave, J., Situating learning in communities of practice.
Perspectives on socially shared cognition, ed. J.L. L.
Resnick,andS.Teasley.1991,Washington,DC:APA.
28.Roselli,N.D.,Collaborativelearning:Theoreticalfoundat ions
and applicable strategies
to university. Journal of
EducationalPsychology,2016.4(1):p.219‐280.
29.Lave, J., Cognition in practice: Mind, mathematics and
cultureineverydaylife.1988,NewYork,US:Cambridge
UniversityPress.
30.Orr, J.E., Talking About Machines: An Ethnography of A
ModernJob1996,U.S.:CornellUniversityPress.
31.
Parent,R.,M.Roy,andD.St‐Jacques,A systems
‐
based
dynamic knowledge transfer capacity model. Journal of
KnowledgeManagement,2007.11(6):p.81‐93.
32.Lave, J. and E. Wenger, Situated Learning‐Legitimate
peripheral participation. 1991, Cambrige, UK: Cambridge
UniversityPress.
33.Lundin, J., Talking about work. Designing information
technology for learning in interaction, in Department of
Informatics.
2005,UniversityofGothenburg:Gothenburg
Sweden.
34.Leidner, D.E. and S.L. Jarvenpaa, The use of information
technology to enhance management school education: a
theoreticalview.MISQ.,1995.19(3):p.265‐291.
35.Wenger, E., Communities of practice: Learning, meaning,
andidentity.1998:Cambridgeuniversitypress.
36.Wenger,E.,R.A.
McDermott,andW.Snyder,Cultivating
communities of practice: A guide to managing knowledge.
2002:HarvardBusinessPress.
37.Dillenbourg, P., What do you mean by collaborative
learning? Collaborative‐learning: Cognitive and.
Computational Approaches, ed. P. Dillenbourg. 1999,
Oxford,UK:ElsevierPublishing.
38.Johnson, D.W., et al., Impact of group processing
on
achievement in cooperative groups. The Journal of Social
Psychology,1990.130(4):p.507‐516.
39.Vygotsky, L., Thought and language. 1962, Cambridge,
MA:MITPress.
40.Leontʹev, A.N., Problems of the development of the mind
1981,Moscow:Progress.
41.Cole, M. and Y. Engeström, A Cultural‐Historical
ApproachtoDistributedCognition.DistributedCognitions:
Psychological and Educational Considerations, ed. G.
Salomon.1993,NewYork:CambridgeUniversityPress.
42.Acciaro,M.,P.N.Hoffmann,andM.S.Eide,TheEnergy
EfficiencyGapinMaritimeTransport.JournalofShipping
andOceanEngineering,2013.3(10):p.1‐10.
43.Jafarzadeh, S. and I.B.
Utne, A framework to bridge the
energy efficiency gap in shipping. Energy, 2014.
69(SupplementC):p.603‐612.
44.Johnson, H., et al., Will the ship energy efficiency
managementplanreduceCO2emissions?Acomparisonwith
ISO 50001 and the ISM code. Maritime Policy &
Management,2013.40(2):p.
177‐190.
45.Poulsen,R.T.andH.Johnson,Thelogicofbusinessvs.the
logic of energy management practice: understanding the
choicesandeffectsofenergyconsumptionmonitoringsystems
in shipping companies. Journal of Cleaner Production,
2016.112:p.3785‐3797.
46.Johnson,H.,M.Johansson,andK.Andersson,Barriers
to
improvingenergy efficiency inshort sea shipping: an action
researchcasestudy.JournalofCleanerProduction,2014.
66(SupplementC):p.317‐327.
47.Hetherington, C., R. Flin, and K. Mearns, Safety in
shipping:Thehuman element.JournalofSafetyResearch,
2006.37(4):p.401‐411.
48.Nardi, B.A., Context
and Consciousness: Activity Theory
and Human‐computer Interaction. 1996, Cambridge: MIT
Press.
49.Baldi, F., Improving Ship Energy Efficiency through a
Systems Perspective, in Institutionen för sjöfart och marin
teknik,MaritimeOperations.2013,ChalmersUniversityof
Technology:Göteborg.
50.Vesting, F., et al., Procedure for Application‐Oriented
Optimisation of
Marine Propellers. Journal of Marine
ScienceandEngineering,2016.4(4):p.83.
51.UNECLAC, Measures to improve energy efficiency in
shipping. FACILITATION OF TRANSPORT AND
TRADEINLATINAMERICAANDTHECARIBBEAN,
2013.8(324).
52.Bazari, Z. and T. Longva, Assessment of IMO Mandated
EnergyEfficiencyMeasuresforInternational
Shipping.2011:
London.
53.Kaptelinin,V.,Distributionofcognitionbetweenmindsand
artifacts:Augmentationofmediation?AI&SOCIETY,1996.
10(1):p.15‐25.
54.Viktorelius, M., Expanding practice theory in energy
research‐a cultural‐historical activity perspective. Energy
ResearchandSocialScience,2017.Underreviewing..
55.Viktorelius,M.,
Thehumanandsocialdimensionofenergy
efficient ship operation, in The International Conference on
MaritimeEnergyManagement.2017:Malmö,Sweden.
56.Corbin,J.andA.Strauss,BasicsofQualitativeResearch3e.
2008:SAGEPublications,Inc.
57.Patton, M.Q., Qualitative research & evaluation methods.
2002,ThousandOaksCa.:
Sage.xxiv,598p.
250
58.Kuckartz, U., Qualitative Text Analysis: A Guide to
Methods,PracticeandUsingSoftware.2014,London,UK:
SAGEPublicationsInc.
59.Goodman,E.,M.Kuniavsky,andA. Moed,Observingthe
User Experience, Second Edition: A Practitionerʹs Guide to
UserResearch.2012,USA:MorganKaufmannPublishers
Inc.608.
60.Ivey,
J.,Focus Groups. Pediatric Nursing, 2011. 37(5): p.
251.
61.Flach, J.M. and F. Voorhorst, What Matters: Putting
CommonSensetoWork.2016,Dayton,USA:WrightState
UniversityLibraries.
62.Laal, M. and M. Laal, Collaborative learning: what is it?
Procedia‐Social and Behavioral Sciences, 2012. 31: p.
491
‐495.
63.Storey, V.C. and R.C. Goldstein, Knowledge‐based
approachestodatabasedesign.MISQ.,1993.17(1):p.25‐46.
64.Obretenov, D., et al., A knowledge‐based approach to
relational database design. Data & Knowledge
Engineering,1988.3(3):p.173‐180.
65.Lee,S.andR.Oʹkeefe,
Anexperimentalinvestigationinto
the process of knowledge‐based systems development.
EuropeanJournalofInformationSystems,1996.5(4):p.
233‐249.
66.Alavi, M. and D.E. Leidner, Research Commentary:
Technology‐Mediated Learning—A Call for Greater Depth
and Breadth of Research. Information Systems Research,
2001.12(1):p.1‐10.
67.Johannsen,
G., Human‐machine interaction., in
Encyclopedia of Life Support Systems (EOLSS): Control
Systems, Robotics, and Automation. 2007, EOLSS
Publishers:Paris,France.
68.Flach, J.M., Situation Awareness: Context Matters! A
CommentaryonEndsley.JournalofCognitiveEngineering
andDecisionMaking,2015.9(1):p.59‐72.
69.Suchman, L.A., Human‐Machine
Reconfigurations: Plans
and Situated Actions. 2007, NY, USA: Cambridge
UniversityPress.
70.Carroll, J.M., W.A.Kellogg,andM.B.Rosson, The task‐
artifact cycle, in Designing interaction. 1991, Cambridge
UniversityPress.p.74‐102.
71.Löwgren, J. and E. Stolterman, Thoughtful Interaction
Design: A Design Perspective on Information Technology.
2004:TheMITPress.
72.Winograd, T., Bringing Design to Software. 1996, US:
Addison‐Wesley.
73.Koschmann, T., Paradigm Shifts and Instructional
Technology. CSCL: Theory and practice of an emerging
paradigm, ed. T. Koschmann. 1996, Mahwah, NJ:
LawrenceErlbaum.
74.Stahl, G., T. Koschmann, and D. Suthers, Computer‐
supported collaborative
learning: An historical perspective.
Cambridgehandbookofthelearningsciences, ed. R.K.
Sawyer. 2006, Cambridge, UK: Cambridge University
Press.
75.Lundh, M., et al., Working conditions in the engine
department – A qualitative study among engine room
personnel on board Swedish merchant ships. Applied
Ergonomics,2011. 42(2):p.384‐
390.
76.Lundh,M.andL.W.Rydstedt,Astaticorganizationina
dynamiccontext–Aqualitativestudyofchangesinworking
conditionsforSwedishengineofficers.AppliedErgonomics,
2016.55:p.1‐7.
77.Viktor Mayer‐Schönberger and K. Cukier, Big Data: A
Revolution That Will Transform How We
Live, Work, and
Think.2013,UK:JohnMurray.
78.Rødseth, Ø.J., L.P. Perera, and B. Mo. Big Data in
Shipping‐Challenges and Opportunities. in 15th
InternationalConferenceonComputerandITApplicationsin
theMaritimeIndustries‐COMPITʹ16.2016.Lecce,Italy:
TechnischeUniverstatHamburg.
79.PwC,Thehumanfactor:
Workingwithmachinestomakebig
decisions, in PwCʹs Global Data and Analytics 2016: Big
Data.2016,PwC:US.
