Computational Modelling of the Role of Leadership Style for Its Context-Sensitive Control Over Multilevel Organisational Learning

Gülay Canbaloğlu*, Jan Treur, Anna Wiewiora

*Corresponding author for this work

Research output: Chapter in Book/Conference proceedings/Edited volumeConference contributionScientificpeer-review

10 Citations (Scopus)
6 Downloads (Pure)

Abstract

This 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.
Original languageEnglish
Title of host publicationProceedings of 7th International Congress on Information and Communication Technology, ICICT 2022
EditorsXin-She Yang, Simon Sherratt, Nilanjan Dey, Amit Joshi
PublisherSpringer
Pages223-239
Number of pages17
ISBN (Print)978-981191606-9
DOIs
Publication statusPublished - 2023
Event7th International Congress on Information and Communication Technology, ICICT 2022 - Virtual, Online
Duration: 21 Feb 202224 Feb 2022

Publication series

NameLecture Notes in Networks and Systems
Volume447
ISSN (Print)2367-3370
ISSN (Electronic)2367-3389

Conference

Conference7th International Congress on Information and Communication Technology, ICICT 2022
CityVirtual, Online
Period21/02/2224/02/22

Bibliographical note

Green Open Access added to TU Delft Institutional Repository 'You share, we take care!' - Taverne project https://www.openaccess.nl/en/you-share-we-take-care
Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public.

Keywords

  • Computational modelling
  • Context-sensitive control
  • Leadership style
  • Organisational learning
  • Self-modelling networks

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