Conference Information

ICICKM 2020: International Conference on Intellectual Capital, Knowledge Management & Organisational Learning

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ICICKM
Submission Date:
2020-04-08 Extended
Notification Date:
2020-07-23
Conference Date:
2020-10-15
Location:
Toronto, Ontario, Canada
Years:
17
Viewed: 14483   Tracked: 0   Attend: 0

Call For Papers

ICICKM 2020 (International Conference on Intellectual Capital, Knowledge Management & Organisational Learning) is an academic conference held in Toronto, Ontario, Canada on 2020-10-15. The paper submission deadline is 2020-04-08 (extended). Acceptance notifications are sent on 2020-07-23.

Academic Papers Case Studies Work in-Progress Papers Round Table Discussion Proposals PhD Papers Posters Non- Academic or Practitioner Contributions Conceptual frameworks, theories and models of intellectual capital management and knowledge management have been evolving for over two decades. Both academic researchers and practitioners have posited that their successful employ benefits organizations by improving their operations, personnel management, and strategy development. At this juncture it may be useful to assess the impact of all this work on actual performance outcomes. How has the theory and practice of intellectual capital and knowledge management actually influenced organizations? What are notable outcomes and how do they inform future thinking and design? Has the management of intangibles really paid off? ICICKM provides an opportunity for academics and practitioners who are involved in the study, management, development and implementation of IC and KM initiatives to come together from around the world in order to exchange ideas. All Papers accepted for ICICKM after the double-blind peer review process will be published in the conference proceedings, subject to author registration and payment. The Proceedings is a published book with an ISBN and ISSN. Key Topics Frameworks for conceptualising IC, KM and OL Managing IC, KM and OL The knowledge economy Intangible assets and tacit knowledge Analysing big data
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