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A decision support system for evaluation of the
knowledge sharing crossing boundaries in agri-food value
chains
Biljana Mileva Boshokoska, Shaofeng Liu, Guoqing Zhao, Alejandro
Fernandez, Susana Gamboa, Mariana del Pino, Pascale Zaraté, Jorge E.
Hernández, Huilan Chen
To cite this version:
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2 6208
To cite this version:
Boshokoska, Biljana Mileva and Fernandez, Alejandro and Zhao, Guoqing and
Chen, Huilan and Hernández, Jorge E. and Del Pino, Mariana and Zaraté, Pascale
and Liu, Shaofeng and Gamboa, Susana A decision support system for
evaluation of the knowledge sharing crossing boundaries in agri-food value
chains.
(2019) Computers in Industry, 110. 64-80. ISSN 0166-3615.
Official URL:
https://doi.org/10.1016/j.compind.2019.04.012
A decision support system for evaluation of the knowledge sharing
crossing boundaries in agri-food value chains
Biljana Mileva Boshkoska
a,
b,
*
, Shaofeng Liu
C,
Guoqing Zhao
c, Alejandro Fernandez
d,
Susana Gamboa
d, Mariana del Pino
d, Pascale Zarate
e, Jorge Hernandez
r,
g
. Huilan Chen
ha jozef Stefan" lnstirute, Jamova cesta 39, S/-1000 Ljubljana, Slovenia
b Faculty of Information Studies in Novo mesto, SI-8000, Slovenia
c Plymoth Business School University of Plymouth, UK
d National University of La Plata, Argentina
e University of Toulouse 1, France
f Management School University of Liverpool, UK
g Universidad de La Frontera, Department of Systems Engineering, Chile, Temuco h Plymotlt Business Sd!ool, University of Plymouth, UK
ABSTRACT
Keywords:
Knowledge sharing Knowledge boundaries Decision support system Agricultural value chain
An agri food value chain (VC) represents a set of activities aimed at delivering highly valuable products to the market. Due to the diversity of actors in the agri food VCs accumulated knowledge is typically situated within the boundaries of each entity of the VC. Hence, the question is how to improve knowledge sharing in agri food VC, or more specifically how can knowledge flow and mobilize among clifferent actors in the VC. To answer this question, we present a decision support system (DSS) for evaluation of knowledge sharing crossing boundaries in agri food VC. The proposed DSS is developed through two phases: (i) identification of the most common knowledge boundaries by using machine learning and ontology technologies: ( ii) transformation of the obtained ontology into a DSS for the evaluation of existing knowledge boundaries. In particular, the developed DSS helps in identifying, evaluating and providing directions for improvement of the knowledge sharing crossing boundaries in agri food VC. We apply the DSS to evaluate three real VCs: a tomato VC in Argentina, a Chinese leafVC in China and a brassica VC in the UK. The comparative analysis across the three varied case studies and their evaluation with the proposed DSS lead to more insights into knowledge based decisions that a particular VC needs to address to improve its knowledge flow, in particular, to obtain insights in the transparency and interoperability of data and knowledge crossing boundaries in agri food VCs.
1. Introduction
Knowledge management within organizations and cross
organizational collaboration in value chains (VCs) have been
acknowledged as two important parts of crossing the organisation
barriers created by knowledge boundaries [
1
]. The need of crossing
organizational boundaries by knowledge sharing cornes from the
necessity to gain a better understanding of different cultures,
disciplines, and management practices, with the aim of developing
better and more comprehensive solutions. In particular, cross
organizational collaboration may lead to quicker understanding
• Corresponding author at: "Jozef Stefan" lnstitute, Jamova cesta 39, S1-1000 Ljubljana, Slovenia.
E-mail address: [email protected] (B.M. Boshkoska).
and grasping of newly developed trends in ait kinds of specialised
knowledge. However, crossing organizational and knowledge
boundaries is a difficult task.
ishowtoperformknowledgesharingcrossingboundariesinagri
foodvaluechains,ormorespecificallyhowcanknowledgeflow
andmobilizeamongdifferentactorsbothverticallyandhorizon
tally. Vertically, knowledge flow should be among the whole
agricultural value chain, from farm to fork, by freely crossing
boundariesbetweendifferentstagesofthevaluechain.Horizon
tally,knowledgeflowshouldbeabletocrossdifferentbodieseven
at the same stage of the chain but with different level of
knowledge.Oneofthekeychallengesofknowledgeflow,which
is a precondition for providing qualitydecisions, represent the
knowledge boundaries whether existing between different
domains,differentpractitioners’groups,orpeoplewithdifferent
levelofknowledgeevenwithinthesamedomainandgroup,such
as between novices and experienced practitioners. Knowledge
boundariesexistduetodifferencesinthewaywework,shareour
knowledge,expertise,differentorganizational culture,ordue to
theinvolvementofmanyactors,forexample,farmers,coopera
tives,foodprocessors,wholesalers, retailersand consumers[2].
Typicallythisknowledgeis situatedwithintheboundariesof a
specificlevel ofthe valuechain,hence it isimportant that the
knowledgeassets, whicharesituatedatonelevel,arelinkedto
another,asrepresentedinFig.1.
Thispaperreportspartoftheresearchworkassociatedwiththe
EUHorizon2020projectRUC APS(Enhancingandimplementing
knowledgebased ICT solutions withinhighRisk and Uncertain
ConditionsforAgricultureProductionSystems,https://ruc aps.eu/
https://ruc aps.eu/), aiming at development of a new decision
supportsystem(DSS)forcrossingknowledgeboundariesin the
domainofagriculturalvaluechain.
Themaincontributionsofthispaperarethreefold.Firstly,we
developanewontologyforknowledgesharingcrossingbound
ariesbasedonthereportedstate of the artliteraturereviewsin
journalpaperspublishedfrom2010 2018.Theobtainedontology
helpsinidentifyingthemostcommonlyreportedproblemsand
solutionsinthefieldinthelasteightyears,andaidsatgroupingthe
repeatedconceptsamongdifferentactorsinthefield.Secondly,the
ontologyisusedtodefineanewDSSandnewdecisionruleswhich
allow considering an extensive hierarchy of attributes for
knowledge sharing crossing boundaries. Thirdly, we explored
theuseof thedevelopedDSS for theevaluationof three value
chains investigated in the RUC APS project, in particular the
ChineseleafvaluechaininChina,tomatovaluechaininArgentina,
and brassicavaluechainintheUnitedKingdom.Attheend we
suggesthowtoimprovetheknowledgesharingcrossingbound
ariesintheevaluatedVCs.
Therestofthispaperisorganisedasfollows.Section2states
therelatedwork,Section3explainstheusedresearchmethodol
ogy,Section4discussesthedatapreparationprocessandSection5
develops ontologyforknowledge boundaryconcepts.Section6
discussesthenewlydevelopeddecisionsupportsystem.Section7
presentsandevaluatescasestudiesusingthreedifferentvegetable
valuechainsinagri foodindustryfromthreedifferentcontinents.
Finally,conclusionsaredrawninSection8.
2.Relatedwork
Manystudieshavebeenconductedtofindouthowknowledge
ismanagedacrossorganizationalboundaries[1,3 7].Despitethe
availableknowledgeandunderstandingaboutthewaysofcreation
ofknowledgeboundariesindifferentareas[8],theevaluationsof
knowledge boundaries as well as the influence of knowledge
sharingoncrossingtheknowledgeboundariesinagri foodvalue
chainsremainsstillverylimitedintheliterature[9].Evaluationof
existingknowledgeboundariesrequiresintegrationofknowledge
management into decision support systems, which has been
investigated by many scholars resulting in the emergence for
development of expert systems and knowledge based decision
support systems [10]. To propose a suitable DSS based on the
availableresearchliteratureintheperiodfrom2012to2018we
applymethodsfromdatasciencethatdealwithtextanalysis.
Datascienceisconcernedwithanalysisofrelevantdatawith
thegoaloffiningcertainpatternsofdataandtheirtransformation
intorelevantinformationratherthanfocusingonthemethodology
onhowitwillachieveit.Thereforetherearedifferentmethods
which maybeused, includingstarte of the art LatentSemantic
Analysis (LSA),LatentDirichletAllocation(LDA)andassociation
rules. LSA is a method that is used for mining concepts from
documents.Itusesthemathematicaltechniqueofsingularvalue
decomposition to define concepts that connect the provided
documents. The limitations of LSA include difficulties in the
interpretationoftheresultingconceptsandinabilitytofinddirect
and indirectassociation aswell as higher orderco occurrences
amongtermswhenusingofbagofwordsmodel[11].LDAisawell
established method for defining concepts in natural language
processing.Howeversomeofitslimitationsinclude:fixednumber
of topics which must be known ahead of time, dirichlet topic
distributioncannotcapturecorrelations,non hierarchical,static,
bagofwords(assumeswordsareexchangeable,sentencestructure
isnotmodelled), unsupervised(sometimesweaksupervision is
desirable,e.g.in sentimentanalysis) [12]. Association rulesis a
technique for analysing patterns of data in a database [13].
However,associationruleminingoftenproducesalargenumberof
ruleswhichmakesitdifficultforuserstoanalysethemshemay
requireadditionalprocessinginordertoobtainotherproperties,
forexample,thehierarchyoftherules.
Thatbeingsaid,theproposedmethodologyinthemanuscript,
which is based on OntoGen and DEX allows: usage of BOWs,
finding hierarchical concepts, defining the number of topics
coveredwitheachoftheconcepts,interactivelyfindingthemost
suitablenumberofsubconcepts,visualisationofresults,easiness
ofinterpretationoftheresultsetc.OntoGenisindeedstate of the
artmethodinwhichinferenceandreasoningisbasedonlatent
semanticindexingfollowedbyk meansforthediscoveryoftopics
in BOWs [55,14]. Additionally, OntoGen allows the user to
manuallyeditthetopicsaddedtotheontologyaswellassuggests
the main keywords of the topics in two ways: using centroid
vectorsorusingsupportvectormachines.Theproposedontology
is followed with a DSS prepared with a well known decision
makingmethodDEX,implementedinafreeofcharge,userfriendly
toolcalledDEXi.DEXhasbeenusedinmanyareasfordevelopinga
qualitativedecisionmakingmodelssuchasinagriculture[15,16],
environment[17], medicine[18,19] etc.Theeasinessofusageof
bothtoolsleavetheuseronlytodealwiththedecisionofchoosing
the most suitable documents instead of thinking about the
difficultiesintheprogrammingimplementationofbothmethods.
Themainadvantageofourmethodologyisthatitusesstate of
the arttechniquesfrommachinelearninganddecisionanalysis,
whichareimplementedinwell knownfreeofcharge,userfriendly
softwaretools.Hencetheuseronlyneedsdocumentsinorderto
usethismethodologywithoutbeingconcernedwiththeadditional
programming. In addition, both OntoGen and DEXi provide
visualizationoftheresults,unlikemostoftheavailablemethod
ologieswhichfocusmainlyonthemathematicalpropertiesofthe
methodsandlacktheirimplementationinuserfriendlytools.
3. Researchmethodology
The research methodology follows our proposed three step
approach[20]:
Data preparation step which includes extraction of domain
relatedknowledge;
Constructionof ontologythat describesthe extractedknowl
edge;
Developmentofa DSS whosestructure followstheidentified
ontologyrules.
In our case,the preparation of domain related data includes
selectionofresearcharticleswhosecontentwillbeusedforextracting
knowledgeintheformofanontology.Inthesecondstep,anontology
isconstructedbasedonthekeywordsfromtheselectedarticles.The
resultofthisstepisasetofrulesthatdeterminetherelationbetween
certainconceptsfromthedomainspecificknowledge.Finally,the
generated DSS that closely matches the identifiedontologystructureis
employedfortheevaluationofknowledgesharingcrossingbound
aries in three agri food VCs. The details of the used research
methodologyareschematicallypresentedinFig.2.Inthefollowing,
eachofthestepsisdescribedindetail.
4. Datapreparation
Thedatapreparationstepiscrucialfortheeffectivenessofthe
overall system. We have firstly identified the key concepts in
bridging the knowledge boundaries. These concepts were
employedaskeywordsforsearching theWebofScience(WoS)
databaseforextractingpapersthatdealwiththetopicsofinterest.
InWoSwesearchedtheTitle,AbstractandAuthorkeywordsfields
within a record in order to obtain the required papers. The
resultingsetofpaperswasprunedbyremovingduplicatedarticles,
and articles that are out of interest (for example conference
articles,shortarticles,articlespublishedbeforeacertainyear,etc).
Intheprocessofidentificationofthescopeandresearchobjectives
weformulatetworesearchdirections.Thefirstoneistodevelopa
DSSmodelforevaluationofexistingknowledgesharingpractices,
asdescribedinthecurrentlyavailableresearcharticles,basedon
an ontology describing the current trends in the knowledge
sharingcrossingboundariesfield.Thesecondoneistoevaluate
threerealuse casesinagri foodVCdefinedwithintheRUC APS
project, and discuss thepossibilities of improving the existing
knowledgeboundariesinthoseusecases.
The researchdirections wereformulatedbased onconsulta
tionswiththreeexpertsinacademiaandagri foodindustry,who
arealsoinvolvedintheRUC UPSproject.
Inourprevious attempttopreparesuchaDSS[20],thedata
preparation step employed a low cardinality keyword set.
Consequently, this limited the granularity of the data hence
limitingthesensitivityofthecompletesystem.Thereforetoobtain
better ontology and DSS, in this work, the keyword set was
carefullyconstructedin ordertoimprovethekeyconceptsthat
comprise the terminology of “knowledge sharing crossing
boundaries”.Thestartingpointwerethefollowingconcepts:
1Learning,sustainability,development(networks)
2Crossboundarieseducation(networks).
3Innovation,boundaryobjects(knowledgetypes).
4Knowledgesharing,teams(networks).
5Organization,technology,human/tacitknowledge(knowledge
types,networks).
Theconceptswereusedtodefinethekeywordsforselectionof
themostrelevantarticlesinWoSasintersectionbetweenthekey
word “knowledge boundaries” and the above concepts. In the
processofpre processingweselectedthemostrelevantarticles,
weremovedduplicates,suchthatonearticlegoesonlyintoone
concept which lead to removal of the concept “Learning,
sustainability, development”. However, as shown later in Fig. 4,
theconceptoccursassubconceptof“Embededknowledgesharing”.
Wealsoremovedtheconferencearticles,whichfinallyresultedin
224articlesfromWoSbetween2010and2018,asshowninTable1.
5. Ontologyforknowledgesharingcrossingboundaries
Ontologiesareavisualandefficientwayofrepresentationof
domain knowledge encoded in large number of information
sources. The construction of the ontology comprises of pre
processingofthedownloadedarticlessothattheyareintheformat
thatissuitableforusageoftheOntoGensoftwaretool.Itisatool
thatoffersasemi automaticwayofconstructionofanontology
basedonautomatictopicextractionfromthedownloadedpapers
[55,14].Usuallydataaregivenasabag of wordswhichisatext
document in which each row represents one instance of data
containing,forexample,thetitle,abstractandkeywordsofone
paper.Based onthedeveloped bag of words,OntoGen software
toolautomaticallysuggestsconcepts,namesofconcepts,keywords
etc. Concepts are the central partin generating ontologies. To
generatetheconcepts,wehaveusedtheoptionofunsupervised
learningofferedbytheOntoGensoftware,whichisbasedonthe
latentsemanticindexingandk meansclusteringtechniques.User
isaskedtoenterthenumberofclusters(concepts)andasaresult
thepapersinthebag of wordsaredividedaccordingtosimilarity
inthewantednumberofconcepts.Thisisaniterativeprocedurein
whicheachoftheconceptsmaybefurtherdivideduntiltheuser
decidesonthegranularityoftheobtainedontology.
Theconceptofontologyallowsustoovercometheproblemof
organisationoflargenumberofdocumentsandtoprovideavisual
representation of the concepts. The visualisation of clusters
(concepts)inthedownloadeddocumentsispresentedasavisual
map in Fig. 3. The visual map shows three major clusters of
documents,representedwiththelightbluecolour.However,these
clustersof documentsare interconnected withdocumentsthat
dealwithmorethanoneselectedtopic,asrepresentedwithdarker
bluecolourinFig.3.Hence,thereareintersectionsofthedifferent
concepts,presentedasintersectionofellipsesinFig.3.
Using OntoGen, we extracted the following most frequently
researchedconceptsassubtopicsoftheknowledgeboundaries:
1Ontology
2Innovationandknowledgeboundaries
3Knowledgesharing
4Organizationnetworksforinnovationandlearning
Each ofthe conceptswas furtherdividedinto subconcepts,
someofwhichoccurringrepeatedly.Theprocessendedwiththe
developmentoftheontology,asshowninFig.4.Theintersection
documents that occur in more than one sub concept are
represented with dotted lines in Fig. 4. For example, the sub
concept“Organizationroleincommunication”isanimportantone
fortheevaluationofthe“Tacitknowledgesharing”inorganisations,
howeveritisalsoimportantfortheevaluationoftheformationof
“Organisationnetworksforinnovationandlearning”.
Theconcept Ontologyensures thattheexistentknowledgeis
formallydefinedthusallowingitssystematicstorageininformation
systems,itsarticulationandpossibilityofitsdissemination[21].
5.1.Innovationandknowledgeboundaries
The concept Innovation andknowledge boundariescomprises
threesubcategories:
1Cross functionalteams
2Boundaryobjectsininnovationcommunities
3Externalknowledgeintegrationfor networkedinnovation a
conceptthatalsooccursindefiningtheOrganizationnetworks
forinnovationandlearningconcept
Cross functional teams deals with existence of teams in
organizations that are responsible for transferring knowledge
fromoneteamtoanotherforminganinterdisciplinaryenviron
ment.Theseteamshaveadifficultroleofidentification,elabora
tion, confrontation the differences and dependencies across
knowledgeboundariesinparticular whenteamsarefacedwith
contemporaryknowledge[22,23].
Boundary objects examines the pragmatic view between
knowledge and boundaries and studies the representation of
knowledgethathelpscrosstheknowledgeboundaries[24 26,1].
In addition it explores how to overcome three progressively
complexknowledgeboundariesinorganizations/networks:syn
tactic,semantic,andpragmatic[27,28].
5.2.Knowledgesharing
The concept of knowledge sharing is divided into three
categories:
1Explicitknowledgesharing
2Tacitknowledgesharing
3Embeddedknowledgesharing
This concept groups various documents which deal with
knowledgeboundariesat newlyemerginginterfaces forknowl
edgesharing,knowledgesharingthroughlearning, inparticular
explorativeandexploitativeknowledgesharing[29],andbehav
iourofgroupsthatdealwiththecontradictionamongdistributed
knowledgeinboundary spanningcollaborativeprocesses[30].
The first category, the Explicit knowledge sharing,comprises
threeinterconnectedconcepts:
1Ontology a dependent sub concept from the developed
ontologysystemforknowledgeboundaries
Table1
TotalnumberofselectedarticlesfromWoS.
Intersectionofkeyconcepts Numberofarticles in
WoSbetween 2010–2018 ("knowledgeboundaries")AND(“crossboundary
education”)
3 ("knowledgeboundaries")AND(“innovation”)AND
(“boundaryobjects”)
Fig.3.VisualisationofallarticlesinOntoGenthatformthemainontologyconcepts.
2Systemsfordecisionmaking
3Managementculture
Systemsfordecision makingimprovethetotalprofitand due
date performance in organisations [31]. Management culture
defines therole of themanagement in knowledge sharing.For
example,managementthatallowsusageofICTtoolsforbottom up
knowledgeflowandmotivateteamworkaswellasencouragethe
intrinsicbehaviourof theiremployeesleadtobetterknowledge
sharinginorganizations.
The secondcategory, theTacit knowledge sharing,comprises
threeinterconnectedconcepts:
1Informalnetworksandinnovation
2Socialandindividualaspectsofcommunication
3Organisationsroleincommunication
These three concepts allow successful propagation of tacit
knowledgethroughoutanetwork.Studiesinthisfieldfocusontwo
types of propagation of tacit knowledge: through creation of
industry university links which would serve as a conceptual
bridgebetweeninternallabour marketsandnetwork organiza
tions;andidentificationofknowledgeboundariesthathappenin
projectsandestablishednetworks[8].
Thefirst concept, Informalnetworks andinnovation, is influ
encedby theexistence ofdifferenttypes of collaborationsthat
happen on informal level, however, may lead to unplanned
innovations. Another important aspect is the establishment of
socialnetworksthroughexistingsocialmediawhichallowsharing,
learninganddiscussingtacitknowledge.
Thesecondconcept,Socialandindividualaspectsofcommuni
cation,comprisestheideaofthesocialcapitaloftheemployees
andtheabilityoftheemployeestousestate of the arttoolsfor
formalorunformalcommunication.
ThelastconceptthatdefinesTacitknowledge,theOrganisations
roleincommunication,isimportantbecauseitdefinesthreeaspects
oforganisationalmanagement:organisationalculture,themoti
vation that organisations provide for sharing practices and
promotion of such activities with the aim of increasing the
awarenessofemployeesfor sharingtacit knowledge,aswellas
allowingafreeflowofcommunicationamongmembersbelonging
to different teams. Teams seem to have an important role in
knowledgesharing.Theexaminedpapersdiscusshowtocrossthe
boundariesbetweendifferentteammembers,orinparticularteam
leaders. The main boundaries are associated with different
knowledge backgrounds of the team members’ coming from
variousdisciplines[32,6,33],whenteamsarefacedwithnovelty,
andco locationofresearchanddevelopmentteamsinmulti space
environment[23,34].
Thethirdcategory,theEmbeddedknowledgesharing,comprises
twointerconnectedconcepts:
1Knowledgemanagementsystems
2Learningbehaviour
Sharingembeddedknowledgeinpoliciesandproductsneedsto
beallowedthroughtoolssuchasknowledgemanagementsystems.
Knowledgemanagementsystemsaredeterminedbytheexistence
of strategy for managing knowledgemanagement systems and
theirimplementationincompanies.Thesecondimportantfactor
inknowledgemanagementsystemsistheirscalabilityi.e.tobe
abletotransferknowledgefromalocalorganisationbranchtoits
othernationalorinternationalbranches.
Learningbehaviourisdeterminedbytwofactors.Thefirstoneis
thelearningbehaviourofemployeesinorganisationswhichisdueto
the developed trust, motivation, leadership style, workplace
spirituality and social networks embedded in the organization
[35]. The second one represents the learning practices in the
organisationi.e.whethertheorganisationsupportsonlyindividual
learning or alsoimplementsplatformsforcollaborativelearning[36].
5.3.Organisationalnetworksforinnovationandlearning
Organisational networks for innovation and learning and the
imposed cross boundariescan beanalysedthrougha varietyof
aspectssuchas:
1Interorganizationalnetworksforinnovations
2Externalknowledgeintegrationfornetworkedinnovation
Interorganizationalnetworksforinnovationsaredefinedthrough
twoattributes:theroleofdigitalizationincompaniesincreating
andsupportinginterorganizationalinnovations[49 50],andthe
boundarieswhichoccurduetoformingclustersinorganizations
responsibleforinterorganizationalinnovations.Thefirstattribute
contributestowardsbetterknowledgesharingandimpliesbetter
knowledgeflowwithintheorganization;thesecondoneimplies
forming groups where the knowledge is “hidden” within the
organisation.Externalknowledgeintegrationfornetworkedinnova
tion [37,38,24,39,40] dealswith external organisationalbound
ariesandisdefinedthroughtwoattributes:existenceofnetworks
between the organization and academics, and dynamics of
external network development. The first attribute, academics
and industry integration, describes the company’s needs and
possibilitiestoextendtheirexpertiseandknowledgeboundaries
into the offered markets of the universities with which they
collaborate,thusleadingtotheformationofintegratedresources
with work experiences that balance the two sectors [41]. It
provides insights of how organizations bridge the boundaries
betweentherequiredtechnologicalknowledgefoundexternally,
and how they align the obtained external knowledge and
organizationsstrategies associatedwithimproving current, and
developing future capabilities. It generalizes the academy
industry crossingof boundariesin awaythat theacademically
gained knowledge can be used both for work and academic
requirements[42,43].Fourlearningmechanismsaredefinedfor
crossing the academy industry boundaries: identification,
coordination,reflection,andtransformation[44,45,26].
The attribute dynamics of external network development
describes the company’s dynamics in developmentof external
networks with other parties of interest with common goal of
sharingpracticesthatmayleadtoinnovations[37].Itfocuseson
the knowledge exchanges across knowledge boundaries in
activities of different organisations, which aim to provide an
innovation[39,24],functioningofinnovationclustersandusageof
knowledge brokering activities to cross knowledge boundaries
[46]; and open innovations [5], which deals with obtaining
knowledgefromdistantknowledgesources.
6. ADSSforknowledgesharingcrossingboundaries
This step includes defining the basic concepts of the DSS
architecture:adatabase,amodel,andauserinterface.Thebasic
modelarchitectureinthisresearchisdirectlyobtainedfromthe
developedconceptsandrelationsintheontology.Next,themodel
providedirectionsof“how”toimprovetheevaluatedalternatives
athand.Inthisresearch,alternativesrepresentthreeuse casesof
agri foodvaluechain,whichwewouldliketoevaluateandfindout
howtoimprovetheirexistingknowledge.Theproblemathand
deals with qualitatively described concepts, thus usage of
qualitativedecisionsupporttechniquesis anaturalwayforthe
developmentoftheDSS.WehaveusedDEXmethod[47]inthis
researchtodeveloptheDSSbecauseithasbeenpreviouslyused
successfully in similar fields. In addition, DEX method is
implemented in DEXi software tool, which is freely available
andeasytouse[48].DEXmethodisarule basedqualitativemulti
attribute decision modelling methodology. To use DEX, the
decisionmakeruseshis/herexpertknowledgetodefine“if then”
rulesfortherelationamongtheattributesintheDSS(forexample
conceptsanditssubconcepts).Therulesleadtoutilityfunctions
given in tabular format that represent experts’ opinions,
preferences and/or knowledge. In DEX, several attributes are
aggregatedintoone,andtheaggregatedattributeispropagatedto
thenexthigherhierarchicallevelofthemodel.TheDEXmodel
consistsof:attributes,scalesofattributes(usuallyqualitativesetof
wordsorderedinapreferentialway,suchas:'developed','partially
developed, 'underdeveloped', etc.), hierarchy of attributes (that
representadecisiontree),anddecisionrules(interpretedas“if
then”rules).
Finally,theevaluationofoptionsisperformed.Inthisphasethe
userentersalloptionsinthedevelopedmodel,whichevaluates
them.InDEXthereisapossibilitytoperform“plus minus”analysis
whichallowstheusertoseehowthefinalevaluationofanoption
wouldchangeifsomeoftheattributesimprovestheirvalues.
TheontologypresentedinFig.4wasusedtodevelopaDSSfor
evaluationoftheknowledgeboundariesinagri foodvaluechains.
The structure of the proposed DSS, its attributes, scales of
Fig.5.Attributes,scalesofattributes,andhierarchyofattributesforevaluationofthelevelofknowledgeboundaries.
Table2
attributes,andhierarchyofattributesforevaluationofthelevelof
knowledgeboundariesaregiveninFig.5.Itisahierarchicalmodel,
wheretheattribute“Knowledgeboundaries”isevaluatedbasedon
thevaluesofitsdescendantattributes(sub concepts):“Ontology”,
“Innovationandknowledgeboundaries”,“Knowledgesharing”,and
“Organizationnetworksforinnovationandlearning”.Theseattrib
utes, withexceptionof the attribute “Ontology” areaggregated
attributes,also calleddependent attributes,meaning that their
valuesareobtainedindirectly,byusingaggregationfunctionover
thevaluesoftheinputattributes.Foreachaggregatedattribute,a
utility tableis defined by the decision maker in which he/she
defines the rules of aggregationfrom lower level attributes to
higherlevelattributes.
An example of a utility table is provided in Table 2 for
the attribute “Inter organisational innovation”. The qualitative
valuesoftheattributeareobtainedbyaggregatingthevaluesof
theattributes“boundaryclusters”and “digitalizationandinnova
tion”.TheaggregationvaluesaregivenintheTable2,autilitytable
inwhicheachrowcanberepresentedasaneasilyunderstandable
“if then”rule.Forthegivenexamplewemayderivethefollowing
fourrules:
Rule1:
“IFboundaryclustersAREexistentANDdigitalizationboundary
clusters ARE existent AND digitalization and innovation ARE
unsupported THEN Inter organisational innovation IS strongly
bounded”.
Rule2:
“IF boundaryclustersAREnonexistentANDdigitalizationand
innovationAREunsupportedTHENInterorganisationalinnovation
HASlimitedbounded”.
Rule3:
“IF boundary clusters ARE existent AND digitalization and
innovation HAS VALUEGRATER THAN OR EQUALTO supported
THENInterorganisationalinnovationHASlimitedbounded”.
Rule4:
“IF boundaryclustersAREnonexistentANDdigitalizationand
innovation HAS VALUEGRATER THAN OR EQUALTO supported
THENInterorganisationalinnovationHASnoboundaries”.
Utility tables for all aggregated attributes in the developed
decisionsupportsystemaregiveninTablesA1 A16TablesA1 A16
inAppendixA.
7.EvaluationoftheDSS
To evaluate the proposed decision support system we have
chosenthreerealagri foodvaluechainsthatwerepartoftheRUC
APSproject:
ChineseleafvaluechaininChina;
TomatovaluechaininArgentina;
BrassicavaluechainintheUnitedKingdom.
These three cases are selected because the Chinese leaf,
Argentine (LaPlata) tomato,andUKbrassica valuechains deal
withverydifferentproductshencerequirevariedknowledgeto
flowthrough the chains. Furthermore, thethree countries are
located in three different continents with varied knowledge
sharing cultures. By undertaking comparative analysis across
threevaried casestudies, it allowsus toevaluatetheDSS and
obtainmoreinsightsintoknowledge baseddecisionsupport,in
particular,toobtaininsightsinthetransparencyandinteropera
bility of data andknowledge crossing boundaries in agri food
value chains. In Fig. 6 we present DEXi interface showing the
database with the three options and values of their input
attributes. Fig.7.ChineseleafvaluechaininChina.
Fig.8.Argentine(LaPlata)tomatovaluechain.
7.1.DescriptionoftheChineseleafvaluechain
The Chineseleaf value chain is schematically representedin
Fig.7.Agri foodresearchinstitutions/universitiesmainlytransfer
theirpestcontrolknowledgewithfarmers/producers. Seedand
agri chemicalsellersprovidetheinformationonwhichseedand
which agri chemical product are the best one for farmers/
producers.AfterharvestingChineseleaf,farmers/producerswould
selltheirpartofproductstothelocalconsumersdirectly.Some
large farmers/producers (more than 40 employees) have the
capabilitytoselltheChineseleafproductstothewholesalersin
otherplacesdirectlyinordertoearnmoremoney.Butmostofthe
productswouldbesoldbyfarmers/producerstothedistributorsor
wholesalersintheproducingarea.Then,theChineseleafproducts
wouldbesoldbylocaldistributors/wholesalerstothewholesalers
inotherplaces.Next,inotherplaces,theproductswouldbesold
by wholesalerstosmall retailers inthemarkets,supermarkets,
hotels, restaurants and government organizations (such as
military).Finally,consumerscanbuyproducts throughdifferent
ways.
7.2.DescriptionoffreshtomatovaluechaininLaPlata/BuenosAires
peri urbanregion,Argentina
The case of freshtomatovalue chaininLaPlata,Argentina is
presentedinFig.8.TheHorticulturalperi urbanof LaPlatahas
shown an interrupted economic, productive,technological, and
commercialgrowthandinthelastdecadesandthisquantitative
growth has been accompanied by a qualitative differentiation,
expressed in a better product quality, extension of the supply
Table3
periodandanincreaseinthenumberofproducers.Onehundred
percentofthetomatoproductioninthisregionisdestinedforfresh
consumption, mainly to the densest population centre in
Argentina, the Autonomous City of Buenos Aires and its
surroundingswhichcomprises15millionpeople.
In La Plata (Argentina), most tomatoes are cultivated in
greenhouses(1900ha)Medium andlarge(or morecapitalized)
farmers/producersaremorelikelytoproducetomatoes,whereas
smallfarmers aremore likelytoproduceleaf vegetables.Large
producerssellmostlyinsupermarketsandtotheCentralMarketof
Argentina(inBuenosAires).
All the products’ quality needsto be checked through two
differentways:(1)thereisaninspectorintheCentralMarketto
checkthequality;(2)takesomesamplestothelabtocheckthe
qualityoftheproduct.Inthecentralmarket,morethan50%sellers
arewholesalers,10 15% sellers areagent and restof them are
producersandcooperatives.ThebuyersintheCentralMarketcan
bedividedinto7differentgroups,whicharelargescaleretailers,
small retailers, wholesalers, restaurants, government organiza
tions, supermarkets and independent buyers. Finally, these
retailerswillselltomatoestoconsumers.Thelargeproducerssell
directlytosupermarkets.
7.3.DescriptionoftheUnitedKingdombrassicavaluechain
Fig.9showstheUnitedKingdom(UK)brassicavaluechain.Most
oftheinformationisthesameasinthecaseofChineseleafvalue
chainandArgentinetomatovaluechain.Theonlydifferenceisthe
retailer,meaningthatmostofthebrassicaaresoldthroughthe
supermarketssuchasTesco.
7.4.Evaluationofthethreeagri foodvaluechains
Theevaluationresultsofthethreeexamplesofagri foodvalue
chainsisgiveninTable3.Allattributesarecolourcodedsothatthe
greencolourrepresentsthemostpreferredattributevalueandthe
redcolourrepresentstheleastpreferredattributevalue.Thefinal
evaluationforknowledgeboundariesofleaf,tomatoandbrassica
valuechainsareweak,medium:weakandnone,respectively.
Theevaluationoftheattributesforeachofthevaluechainswas
performedbetweenadecisionanalystandaknowledgemanage
mentexpertinvolvedin theRUC UPS project.The rationalefor
evaluationoftheattributesisgivenincontinuation.
The evaluation of the attribute Innovation and knowledge
boundaries,comprises evaluationof threeotherattributes,from
whichtwodifferintheirevaluationsforthepresentedagri food
valuechains.Thefirstattribute,cross functionalteams,isevaluated
asexistent,forArgentinetomatoandUKbrassica,andaslimited,for
Chineseleaf.Inallthree agri foodvaluechainsfarmersusually
attenddifferenttrainingstolearnaboutnewtechnologiesusedin
the fields, for example how to use new chemicals. The main
differenceisthatChineseleaffarmisconsideredasasmallone,
whilefarmsforArgentinetomatoandUKbrassicaareconsideredas
largefarms.Thereisadifferencebetweensmallandlargefarms,in
theapproach thattheyusefor forming cross functionalteams.
Whilesmallfarmsusuallyattendtrainingsoutsidetheirfarms(in
trainingcentres,freeacademiacourses,freesessionsorganizedby
non governmentalorganisations)whichhappenrarely,largefarms
frequentlypaytoexpertsandprivateorganizationstocomeand
educatethem onthefield.Farmers workingonsmallfarmsare
willingtocooperateandgainknowledge,howeverduetofinances
theyhavelimitedcrossfunctionalteams.Hence,theevaluationof
thecross functionalteamsattributefortheChineseleafvaluechain
aslimited.Theseconddifferenceisintheevaluationoftheattribute
dynamicsofexternalnetworksdevelopment.Inparticular,forthecase
of Argentine tomato VC it is considered that the dynamics of
networksdevelopmentisslow,duetothefactthatfarmersarenot
encouragedtosharetheirpracticeswithotherparties.
TheevaluationshowedthatExplicitknowledgesharingisweak
forArgentinetomatoVC,mediumforChineseleafVCandstrongfor
UKbrassicaVC.Therationalisbasedonthreeattributes.Thefirst
one,systemsfordecisionsupport,isweakinArgentinetomatoVC.
Althoughasystemhasbeenprocuredforassessmentofweather
risks,andithasbeenconnectedtoasystemtoshareinformation
betweenfarmersasalarmsregardingtheconditionsofpests,still
thesystemisnotyetwidelyused.Ontheotherhand,inChinese
leafVCareinvitedtovisitthefarmersandhelptheminmaking
professional decisions. Finally, UK has in place advanced ICT
systemsthatfarmersuseforcommunication:thereisaweather
systeminplaceandasystemfordeterminingthepests.Thenext
differencesareintheevaluationoftheExplicitknowledgesharing
areintheemployees’behaviour.InArgentinetomatoVC,thereisa
rewardsystemtokeepskilledfarmersatwork,thusthereisno
needtoencouragethemtolearnothernewskills.InChina,farms
forChineseleafsareveryfrequent,thustheexistentknowledgeis
sufficient andthere isnoneedtogainfurtherknowledgeorto
explicitlyshareit.InUKbrassicaVC,itiscommonforfarmersto
visitotherfarmsandselltheirknowledge,forexample,farmers
frequently sell their knowledge about how they operate their
farms.
ThethirddifferenceamongVCsisintheusageofICTtoolsfor
knowledgesharing.Althoughtodayitisacommonunderstanding
thateveryonehasaccesstoICTtools,themanagementculturein
ChineseleafVCandArgentinetomatoVCissuchthatitisreluctant
touseICTforknowledgesharingasactorsintheVCsfrequently
regardtheirknowledgeabouttheprocessesintheVCsassecrets.
Ontheotherhand,inUKbrassicaVC,itisallowedtousestate of
the art tools for formal and unformal communication and all
actorsintheVCareencouragedtousetheminordertogain or
shareknowledgeamongthemselves.
Regarding tacit knowledge sharing, the three VCs differ in
evaluationofsixattributes.Thefirstone,socialnetworksandmedia,
isevaluatedasexistentinVCsforArgentinetomatoandChinese
leaf,howevertheyhappeninaninformalmanner.Theattribute
innovation through collaboration, is considered as weak for
ArgentinetomatoVC,wherefarmerscollaboratewithNGOsand
universities,andprojecttheircollaborationstheresuchastestinga
certainpest,orsearchingforwaystoreducethepestrisk.Dueto
verylimitedfinancessuchprojectionsarerare.
IntheChineseleafVCthesituationisthesameasinArgentine
tomatoVC,butin additiontheprojections happenona regular
basis. In UK brassica VC all companies in the value chain use
projectionswhicharenotlimitedonlytothecooperationbetween
academiaandfarmers.Thenextattribute,socialcapital,inChinese
leaf VCis evaluated asexistent since there are companiesthat
invest in agricultureleadingtoavailability ofnewtechnologies
thusmakingpossibilitiesfordevelopmentofthesocialcapital.In
ArgentinetomatoVCthereisalimitednumberofsuchcompanies
compared to China tomato VC. The attribute motivation and
awarenessforsharingpracticesinArgentinetomatoVCandChinese
leafVCs is consideredasweak asthesharingpractices happen
within the farms, however outside the organizations it is not
encouragedandsometimesitdoesnotexistsatall.Ontheother
handin UKbrassicaVCit is commonpracticetovisit different
farmstoobtainotherknowledgeaboutoperationpractices.The
same rationale applies for communication among members of
different teams, which is supported within organisations in
Argentina and China, however not encouraged between teams
fromdifferentorganisations. The last attributeis organizational
culture, which for Chineseleaf VCand Argentinetomato VCis
consideredasunderdeveloped,assimplythecultureofthetwoVCs
RegardingembeddedknowledgesharingthethreeVCsdifferin
the evaluation of two attributes. The attribute strategy for
managing knowledge management system is considered as non
existent, and the scalability of KMS are limited only to local
organizationalunitsfortheArgentinetomatoVC.
The Organisation networks differ in the evaluation of two
attributes.Thefirstone,digitalizationandinnovation,isevaluated
asunsupportedinArgentine tomatoVCdue totheapproachfor
spending theavailablefinances, which areusually dedicatedto
buying a new equipment for the fields,instead of investing in
knowledge management equipment and tools. Due to limited
finances the evaluation for Chinese leaf VC is evaluated as
supported. The next attribute, dynamics of external network
development, is also a part of the evaluation of the Innovation
andknowledgeboundaries,anditisalreadyexplainedearlier.
DEXisoftwareincorporatesplus minusanalysis,whichallows
toseetheeffectsofchangingeachbasicattributebyonevalue(if
possible),independentlyofotherattributes,ontheevaluationofa
selectedaggregatedattribute.
The evaluation showed that the best resultsfor knowledge
sharingcrossing boundariesarefortheUKbrassicaVC.Despite
sucharesult,theanalysisidentifiedtwoattributesthatmightbe
improved: motivationandawareness forsharing practicesand IT
toolsforcommunications.Thisisunderstandablegiventhefactthat
ITtoolsareperpetuallyimprovedandcompanieslaginadopting
thenewestpractices.
Theplus minusanalysisshowsthattheknowledgeboundariesof
theChineseleafVCmaybeimprovedforonevalueup(fromweakto
none), if at least two of the attributes cross functional teams,
boundariesobjectsandmotivationandawarenessforsharingpractices
improve.Thechangewouldleadtheevaluationoftheknowledge
boundaries from the interval weak to none. The knowledge
boundariesoftheArgentinetomatoVCmayimprovebyimproving
the value of the attribute digitalization and innovation from
unsupported toweak. The changewould lead theevaluation of
theknowledgeboundariesfromtheintervalmedium:weak,toonly
weak.
Finally,weconcludethat theproposedapproachenablesthe
evaluationofknowledgesharingagri foodcrossingboundariesin
agree foodvalueschainswithdifferentsizes.
8. Conclusion
The paperpresents a new DSS for evaluation of knowledge
boundariesinagri foodvaluechainsbasedonanewontologyand
newdecisionrulesfortheevaluationoftheconceptofknowledge
sharingcrossingboundaries.Byincreasingthegranularityofthe
ontologywewereable toobtainmoredetailed dependentand
independentrelationsamong concepts thatdefinethestate of
the artconceptsofknowledgesharingcrossingboundariesinagri
foodVCs.Suchan increasedgranularityledtowards a compre
hensiveDSSwith22inputattributes.
TheeffectivenessofthedevelopedDSSwasevaluatedonthree
realagri foodvaluechainsinthreecontinents,whichareusedas
usecasesfromtheRUC APSproject.In particular,weevaluated
knowledge boundaries for Chinese leaf value chain, Argentine
tomatovaluechainandUKbrassicavaluechain.Inaddition,we
performedaplus minusanalysisthatexplainswhichofthesub
conceptsthatdefineknowledgeboundariesneedstobeimproved
inordertoimprovethecrossingofknowledgeboundariesinthe
threeagri foodvaluechains.
Regardlessoftheevaluatedcase,themethodologywasableto
identifythepointsthatneedimprovementinordertoadvancethe
knowledgesharingcrossingboundaries.ForthecaseofUKbrassica
VC,despitebeingevaluatedaswelldeveloped,theproposedDSS
wasabletoidentifytwoweakattributesthatshouldbesomewhat
improved.ForthecasesofArgentinetomatoandChineseleafVCs,
multipleweakpointswereidentifiedandtheplus minusanalysis
showedthatbothVCscanbesignificantlyimprovedbychanging
onlya fewattributessuchas:cross functionalteams,boundaries
objects for Chinese leaf VC, and motivation and awareness for
sharing practices,and digitalization andinnovation forArgentine
tomatoVC.
Althoughthepresentedresultscoveraspecificproblemofagri
foodVCs, theproposedmethodologyis broadly applicable.The
methodologyrequiresonlytwouserinputsduringthedevelop
mentstage:thedomainknowledgekeywordsetandtheif then
evaluationrules.Usingthedomainknowledgeset,theuserfirstly
needs to extract the relevant publications from well known
databases, suchas WoS.Nexttheuserhastoprepare thetexts
intothesuitableformatforprocessingwiththeOntogensoftware
tool.Finally,theusermayusetheobtainedontologyasabasisfor
developmentofanif thenrulesinaDEXbaseddecisionsupport
system. Consequently, the proposed approach can be easily
upgradedorevenextendedtodifferentareasandproblemsthat
include identification of knowledge management concepts by
carefullydefiningthedomainknowledgekeywordset,theif then
evaluation rules and by following the steps of the proposed
methodology.Inaddition,thefutureworkmayincludeimprove
mentoftheontologybyaddingothersourcesofresearcharticles,
forexampleaddingconferencepapers,oraddingresearcharticle
fromseveralotherdatabases.
Acknowledgements
TheworkreportedinthispaperhasbenefitedfromtheRUC
APSproject(EnhancingandimplementingKnowledgebasedICT
solutionswithinhighRiskandUncertainConditionsforAgricul
tureProductionSystems,https://ruc aps.eu/)fundedbyEuropean
CommissionundertheHorizon2020Programme(H2020 MSCA
RISEAwardNo.691249).Thefirstauthoracknowledgesfunding
from the Slovenian Research Agency via program Complex
NetworksP1 0383.
AppendixA.Utilitytablesforaggregatedattributesinthe
developeddecisionsupportsystem
Thestar“*”inallsubsequentutilitytablesstandsfor“anyvalue”
ofthescaleforthecorrespondingattribute.
TableA1
UtilitytableforKnowledgeboundaries.
TableA2
TableA3
UtilitytableforExternalknowledgeintegrationfornetworkedinnovation.
TableA4
UtilitytableforKnowledgesharing.
TableA5
UtilitytableforExplicitknowledgesharing.
TableA6
UtilitytableforManagementculture.
TableA7
TableA8
UtilitytableforInformalnetworksandinnovation.
TableA9
UtilitytableforSocialandindividualaspectsofcommunication.
TableA10
UtilitytableforOrganizationsroleincommunication.
TableA11
UtilitytableforEmbeddedknowledgesharing.
TableA12
References
[1]P.Carlile,Apragmaticviewofknowledgeandboundaries:boundaryobjectsin newproductdevelopment,Organ.Sci.(2002)442–455.
[2]H.Chen,S.Liu,F.Oderanti,Aknowledgenetworkandmobilisationframework forleansupplychaindecisionsinagri-foodindustry,Int.J.Decis.Support.Syst. Technol.(2017)37–48.
[3]P.Carlile,Transferring,translating,andtransforming:anintegrativeframework formanagingknowledgeacrossboundaries,Organ.Sci.(2004)555–568. [4]E. Hustad,Knowledgemanagement indistributedwork:implicationsfor
boundaryspanninganditsdesign2017,1111,J.Integr.Des.Process.Sci.21(1) (2017)25–41.
[5]M. Wilhelm, W. Dolfsma, Managing knowledge boundaries for open innovation-lessonsfromtheautomotiveindustry,Int.J.Oper.Prod. Manage.(2018)19.
[6]J.Lee,J.Min,H.Lee,Settingaknowledgeboundaryacrossteams:knowledge protectionregulationforinter-teamcoordinationandteamperformance,J. Knowl.Manage.21(2)(2017)254–274.
[7]H.Nguyen,in:FredrickTell,ChristianBerggren,StefanoBrusoni,AndrewVan deVen(Eds.),ManagingKnowledgeIntegrationAcrossBoundaries,Oxford UniversityPress,2017(2017,11),305pp.,£55.ISBN:9780198785972. IndustrialRelationsJournal,48(5-6),518-520.
[8]J.Swart,P.Harvey,Identifyingknowledgeboundaries:thecaseofnetworked projects,J.Knowl.Manage.(2011)703–721.
[9]F. Hartwich, M. Pérez, L. Ramos, J. Soto, Knowledge management for agriculturalinnovation:lessonsfromnetworkingeffortsintheBolivian agriculturaltechnologysystem,Knowl.Manage.Dev.J.(2007)21–37. [10]P.Zarate,S.Liu,Anewtrendforknowledge-baseddecisionsupportsystems
design,Int.J.Inf.Decis.Sci.(2016)305–324.
[11]V.Abedi,M.Yeasin,R.Zand,Empiricalstudyusingnetworkofsemantically relatedassociationsinbridgingtheknowledgegap,J.Transl.Med. 12(1)(2014) 324.
[12] V.Smolyakov,Limitation ofLDA (latentDirichletAllocation)(2016,421) Retrievedfrom,(2016).https://stats.stackexchange.com/q/208630. [13]R.Agrawal,R.Srikant,Fastalgorithmsforminingassociationrulesinlarge
databases,20thInternationalConferenceonVeryLargeDataBases,(1994),pp. 478–499.
[14]B.Fortuna,M.Grobelnik,D.Mladenic, D1.9.1SimultaneousOntologies,SEKT: SemanticallyEnabledKnowledgeTechnologies,Ljubljana,2005EU-ISTProject IST-2003-506826.
[15]M.Bohanec,B.Boshkoska,T.Prins,E.Kok,SIGMO:adecisionsupportSystem forIdentificationofgeneticallymodifiedfoodorfeedproducts2017,1,Food Control71(2017)168–177.
[16]D.Craheix,J.-E.Bergez,F.Angevin,C.Bockstaller,M.Bohanec,B.Colomb,etal., Guidelinestodesignmodelsassessingagriculturalsustainability,basedupon feedbacksfromtheDEXidecisionsupportsystem2015,1023,Agron.Sustain. Dev.35(4)(2015)1431–1447.
[17]T.Ravnikar,M.Bohanec,G.Muri,Monitoringandassessmentofanthropogenic activitiesinmountainlakes:acaseoftheFifthTriglavLakeintheJulianAlps 2016,430,Environ.Monit.Assess.188(4)(2016)253.
[18]M.Bohanec,D.Miljkovic, A.Valmarska,B.MilevaBoshkoska,E.Gasparoli,G. Gentile,etal.,AdecisionsupportsystemforParkinsondiseasemanagement: expertmodelsforsuggestingmedicationchange2018,515,J.Decis.Syst.27 (sup1))(2018)164–172.
[19]A.Baert,E.Clays,L.Bolliger,D.DeSmedt,M.Lustrek,A.Vodopija,etal.,A personaldecisionsupportsystemforheartfailuremanagement(HeartMan): studyprotocoloftheHeartManrandomizedcontrolledtrial2018,1227,BMC Cardiovasc.Disord.18(1)(2018)186.
TableA13
UtilitytableforLearningbehaviour.
TableA14
UtilitytableforOrganizationnetworks.
TableA15
UtilitytableforInterorganizationalinnovation.
TableA16
[20]B. MilevaBoshkoska,S.Liu,H.Chen,Towards aknowledgemanagement frameworkforcrossingknowledgeboundariesinagriculturalvaluechain,J. Decis.Syst.27(2018).
[21]I.Nonaka,Adynamictheoryoforganizationalknowledgecreation,Organ.Sci. (1994).
[22]J. Kotlarsky, B. van den Hooff,L. Houtman, Arewe onthe samepage? Knowledgeboundariesandtransactivememorysystemdevelopmentin cross-functionalteams2015,426,Commun.Res.42(3)(2015)319–344. [23]A.Majchrzak,P.More,S.Faraj,Transcendingknowledgedifferencesin
cross-functionalteams,Organ.Sci.23(4)(2012)951–970.
[24]P.Smith,Boundaryemergenceininter-organizationalinnovation,Eur.J.Innov. Manage.19(1)(2016)47–71.
[25]M.Marheineke,H.Habicht,K.Moslein,Bridgingknowledgeboundaries:the useofboundaryobjectsinvirtualinnovationcommunities,RDManage. (2016)11.
[26]W.Barley,Anticipatorywork:howtheneedtorepresentknowledgeacross boundariesshapesworkpracticeswithinthem,Organ.Sci.(2015)17. [27]R. Abraham,S.Aier,R. Winter,Crossingtheline:overcomingknowledge
boundariesinenterprisetransformation,Bus.Inf.Syst.Eng.(2015)11. [28]C.Rau,A.Neyer,K.Moslein,Innovationpracticesandtheirboundary-crossing
mechanisms:areviewandproposalsforthefuture,Technol.Anal.Strateg. Manage.(2012)37.
[29]G. Im, A. Rai, Knowledge sharing ambidexterity in long-term interorganizationalrelationships,Manage.Sci.(2008)1281–1296. [30]S. Gasson, The dynamics of sensemaking, knowledge, and expertise in
collaborative,boundary-spanningdesign,J.Comput.Commun.(2005). [31]M.Buenemann,C.Martius,J.Jones,S.Herrmann,D.Klein,M.Mulligan,etal.,
Integrativegeospatialapproachesforthecomprehensivemonitoringand assessmentoflandmanagementsustainability:rationale,potentials,and characteristics,LandDegrad.Dev.22(2)(2011)226–239.
[32]R. Fitzgerald, C. Rowley, How have Japanese multinational companies changed?Competitiveness,managementandsubsidiaries,AsiaPacificBus. Rev.(2015)8.
[33]D. Wannenmacher, A. Antoine, Management of innovative collaborative projects:momentsoftensionandthepeer-mediationprocess-acase-study approach,Knowl.Manage.Res.Pract.(2016)11.
[34]A.Coradi,M.Heinzen,R.Boutellier,Designingworkspacesforcross-functional knowledge-sharinginR&D:the"co-locationpilot"ofNovartis,J.Knowl. Manage.(2015)21.
[35]M.Rahman,A.Osman-Gani,M.Momen,N.Islam,Testingknowledgesharing effectiveness:trust,motivation,leadershipstyle,workplacespiritualityand
socialnetworkembeddedmodel2015,121,Manage.Mark.10(4)(2015)284– 303.
[36]M.Anshari,Y.Alas,L.Guan,Pervasiveknowledge,socialnetworks,andcloud computing:E-learning2.0,EurasiaJ.Math.Sci.Technol.Educ.(2015). [37]T.Burström,J.Harri,T.Wilson,Nascententrepreneursmanaginginnetworks:
equivocality,multiplexityandtieformation2018,3,J.EnterprisingCult.26 (01) (2018)51–83.
[38]S.Mäenpää,A.Suominen,R.Breite,Boundaryobjectsaspartofknowledge integrationfornetworkedinnovation,Technol.Innov.Manage.Rev.(2016). [39]S.-V. Rehm, L. Goel, The emergence of boundary clusters in
inter-organizationalinnovation2015,1,Inf.Organ.25(1)(2015)27–51. [40]P. Valkering, C. Beumer,J. de Kraker, C. Ruelle,An analysis of learning
interactionsinacross-bordernetworkforsustainableurbanneighbourhood development2013,6,J.Clean.Prod.49(2013)85–94.
[41]A.Lam,Knowledgenetworksandcareers:academicscientistsin industry-universitylinks,J.Manage.Stud.(2007).
[42]J.Garraway,Knowledgeboundariesandboundary-crossinginthedesignof work-responsiveuniversitycurricula,Teach.High.Educ.(2010)211–222. [43]M.Young,J.Muller,Threeeducationalscenariosforthefuture:lessonsfrom
thesociology,Eur.J.Educ.(2010)17.
[44]S.F.Akkerman,A.Bakker,Boundarycrossingandboundaryobjects,Rev.Educ. Res.(2011)132–169.
[45]J.Hong,R.Snell,Boundary-crossingandthelocalizationofcapabilitiesina Japanesemultinationalfirm,AsiaPacificBus.Rev.(2015)19.
[46]L. Castro,Strategizing across boundaries: revisitingknowledge brokering activitiesinFrenchinnovationclusters,J.Knowl.Manage.(2015)21. [47]M. Bohanec, V. Rajkovi9c,I.Bratko,B. Zupan, M.Žnidarši9c,DEXmethodology:three
decadesofqualitativemulti-attributemodelling,Informatica49(2013)54. [48] M. Bohanec, DEXi: Program for Multi-attribute Decision Making, User’s
Manual,Version5.00IJSReportDP-11897.Ljubljana.Retrievedfrom,Jožef StefanInstitute,Ljubljana,2015.http://kt.ijs.si/MarkoBohanec/pub/ DEXiManual500.pdf.
[49] Alin,P.,Iorio,J.,&Taylor,J.(2013).Digitalboundaryobjectsasnegotiation facilitators:Spanningboundariesinvirtualengineeringprojectnetworks. ProjectManagementJournal,16.
[50] Dougherty&Dunne,2012Dougherty,D.,&Dunne,D.(2012).Digitalscience andknowledgeboundariesincomplexinnovation.OrganizationScience,18. [GoogleScholar]).
