Title

Engaging layers of intangibles across intelligent learning ecosystems for competitive advantage

Document Type

Article

Publication Date

3-1-2018

Abstract

The Intelligent Learning Ecosystem (ILE) integrates all forms of intangible assets, recognizing not only tacit and explicit knowledge, but also big data and analytics/intelligence within and across organizations. The ILE structure provides a system for dynamic learning through the synthesis and analysis of intangible assets, creating decision-impacting intelligence across the organization and its partners. Here we extend our understanding of how this ecosystem works by also considering the learning dynamics of individuals and teams. As such, the ILE not only facilitates organizational and partner learning but also leverages the positive impact of intangibles management on employee development, team sophistication and company competitiveness. Consequently, this paper studies the place of knowledge assets in a wider conceptual framework. By managing that wider range of intangible inputs with a structure designed not only to exchange existing knowledge or data but also to create new learning and insights, decision-makers can accomplish several things. Initially, the range of potentially valuable inputs is increased, bringing in a more diverse set of intangibles that might have more relevance in specific industries or companies. Secondly, the structures can be designed not only to exchange knowledge or big data but to bring it all together, along with all other available intangibles, for analysis. As a result, new learning can take place as cross-functional teams derive insights from the inputs. Finally, such a structure can work not only within a single enterprise but across its wider network of collaborators. The resulting intelligence learning ecosystems bring an even wider range of inputs, diverse perspectives, and opportunities for new learning to all the partners. By looking more widely at these possibilities, knowledge assets can be employed even more productively than when considered only in traditional knowledge management systems.

Publication Name

Electronic Journal of Knowledge Management

Volume Number

16

First Page

36

Last Page

47

Issue Number

1

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