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Title: Revealing intangible assets and archetypes for organisational change
Author: Michiotis, Stefanos
ISNI:       0000 0004 7963 3694
Awarding Body: University of Greenwich
Current Institution: University of Greenwich
Date of Award: 2016
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Organisational change is difficult to cope with, especially in the context of social complexity; for people see things differently, according to their assumptions, values, rationales or objectives. Adopting the complex adaptive character of human systems, this dissertation argues that non-linear change methodologies are more appropriate when dealing with cases of deep change or transition than traditional linear approaches. To this end, it undertakes the task to develop and test a new sensemaking tool, which will be able to reveal the intangible assets and archetypes in organisations or communities. Its conceptual model is derived from the theories of complexity and archetypes and is consistent with their fundamental considerations. After being adequately contextualized, the developed tool-prototype is successfully implemented in three different cases; both its process and findings have been positively evaluated by the users and the information delivered can be also used by them as stimuli for self-assessment. The results of the research validate the thesis and evolve the theoretical convergence of the theories of complexity and archetypes on a practical level. It is the first time that complex emergent methods have been combined with archetypal models, in order to create a sensemaking tool to be applied in transitional contexts and imprint key aspects of the collective perception and behaviour. Knowing such information, leaders can identify in a safer way where and how to move in order to reach the desired destination. Furthermore, the research shows that the combination of hitherto barely-related or seemingly unconnected scientific domains (e.g. archetypes, geometry and network analysis or qualitative research and software development) can open new areas and routes in scientific knowledge and create new diagnostic tools.
Supervisor: Cronin, Bruce ; Johnson, Leslie Sponsor: Not available
Qualification Name: Thesis (Ph.D.) Qualification Level: Doctoral
EThOS ID:  DOI: Not available
Keywords: HG Finance