A decision support system for evaluation of the knowledge sharing crossing boundaries in agri-food value chains

OATAO is an open access repository that collects the work of Toulouse

researchers and makes it freely available over the web where possible

Any correspondence concerning this service should be sent

to the repository administrator:

tech-oatao@listes-diff.inp-toulouse.fr

This is a author’s version published in:

https://oatao.univ-toulouse.fr/

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

_,

_,

_*

_{, Shaofeng Liu}

_{, Guoqing Zhao}

_{, Alejandro Fernandez}

_,

Susana Gamboa

_{, Mariana del Pino}

_{, Pascale Zarate}

_{, Jorge Hernandez}

_,

_g

_{. Huilan Chen}

a 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: biljana.mileva@ijs.si (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.

An agri food VC is formed by a chain of network actors,

including different size of producers (responsible for growing food

commodities ), cooperatives, food processors (responsible for

processing, manufacturing and marketing food products), distrib

utors/wholesalers, retailers (responsible for marketing and sell

ing), consumers ( end us ers who purchase and consume food), and

government/non government organizations (such as research

institutions, universities, communities responsible for research,

development and knowledge transfer and management among

different actors in the agri food value chain). The diversity of actors

in the agri food value chain naturally leads to varied knowledge

which is typically situated within the boundaries of a specific

entity of the value chain. Hence, the question that we try to answer

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 dif_ficulties in the interpretationoftheresultingconceptsandinabilitytofinddirect and indirectassociation aswell as higher orderco occurrences Fig.1.Knowledgesharinginvaluechain.

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”)

9 ("knowledgeboundaries")AND(“organization”) 39 ("embeddedknowledgesharing") 76 ("explicitknowledgesharing") 51 ("tacitknowledgesharing") 46

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 requiresdefinitionofrules thatwouldgoverntheconceptsand

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 Fig.6.DEXiinterfaceshowingthedatabasewiththreeoptionsandvaluesoftheirinputattributes.

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 issuchthatsharingtacitknowledgeisnotsupported.

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, multipleweakpointswereidenti_fiedandtheplus 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]).

[55]B. Fortuna,M.Grobelnik,D. Mladenic, D1.7.1OntologyGeneration From Scratch,SEKTDeliverable,SEKT:SemanticallyEnabledKnowledge Technologies,Ljubljana,2005EU-ISTProjectIST-2003-506826.