Publication:
Computational modelling of the role of leadership style for its context-sensitive control over multilevel organisational learning

dc.contributor.coauthorTreur, Jan
dc.contributor.coauthorWiewiora, Anna
dc.contributor.departmentDepartment of Computer Engineering
dc.contributor.kuauthorCanbaloğlu, Gülay
dc.contributor.kuprofileUndergraduate Student
dc.contributor.otherDepartment of Computer Engineering
dc.contributor.schoolcollegeinstituteCollege of Engineering
dc.contributor.yokidN/A
dc.date.accessioned2024-11-09T23:28:16Z
dc.date.issued2023
dc.description.abstractThis paper addresses formalisation and computational modelling of context-sensitive control over multilevel organisational learning and in particular the role of the leadership style in influencing feed forward learning flows. It addresses a realistic case study with focus on the role of managers for control of multilevel organisational learning. To this end a second-order adaptive self-modelling network model is introduced and an example simulation for the case study is discussed.
dc.description.indexedbyScopus
dc.description.openaccessYES
dc.description.publisherscopeInternational
dc.description.volume447
dc.identifier.doi10.1007/978-981-19-1607-6_20
dc.identifier.isbn978--9811-9160-6-9
dc.identifier.issn2367-3370
dc.identifier.linkhttps://www.scopus.com/inward/record.uri?eid=2-s2.0-85135827508&doi=10.1007%2f978-981-19-1607-6_20&partnerID=40&md5=e42e2ab18cd5ede22917b0340faa3cf6
dc.identifier.scopus2-s2.0-85135827508
dc.identifier.urihttps://dx.doi.org/10.1007/978-981-19-1607-6_20
dc.identifier.urihttps://hdl.handle.net/20.500.14288/11861
dc.keywordsComputational modelling
dc.keywordsContext-sensitive control
dc.keywordsLeadership style
dc.keywordsOrganisational learning
dc.keywordsSelf-modelling networks
dc.languageEnglish
dc.publisherSpringer Nature
dc.sourceLecture Notes in Networks and Systems
dc.subjectComputer sciences
dc.titleComputational modelling of the role of leadership style for its context-sensitive control over multilevel organisational learning
dc.typeConference proceeding
dspace.entity.typePublication
local.contributor.authoridN/A
local.contributor.kuauthorCanbaloğlu, Gülay
relation.isOrgUnitOfPublication89352e43-bf09-4ef4-82f6-6f9d0174ebae
relation.isOrgUnitOfPublication.latestForDiscovery89352e43-bf09-4ef4-82f6-6f9d0174ebae

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