Abstract
The Cynefin model (Kurtz & Snowden, 2003) is a common concept for designing the most logical response from decision-makers in certain situations. This model is general, so it can be used in several applications, such as knowledge transfer, which is the focus of this article. The author aims to describe in detail, both the characteristics of the domain and the decision-making model, with the concept of causal ambiguity (Reed & Defillippi, 1990), absorptive capacity (Zahra & George, 2002), and pragmatic view of knowledge (Carlile, 2004). One of the managers' common mistakes in managing organizational knowledge is the failure to identify situations accurately. Using the case study method, the results of this study are expected to help practitioners minimize these mistakes and determine the right decisions in forming a sustainable competitive advantage (SCA). The discussion of this paper is divided into several sections; dynamics of cynefin model, absorptive capacity, pragmatic view of knowledge, integration of concepts, and conclusions.
Highlights
The Cyne in model distinguishes ive domains based on cause and effect, namely known, knowable, complex, chaos, and disorder
We describe in detail the characteristics of cyne in domain and its decision -making model with the concept of causal ambiguity, absorptive capacity, and pragmatic view for the absorption of organizational knowledge to produce innovation
Concept Integration Based on the Astra Credit Companies (ACC) case study above, in the context of this knowledge management, the discussion starts from the knowledge environment (Cyne in model), knowledge characteristic, and knowledge integration
Summary
The Cyne in model distinguishes ive domains based on cause and effect, namely known, knowable, complex, chaos, and disorder. Each domain has speci ic characteristics and a speci ic decision-making approach model. This domain does not describe the knowledge that individuals or organizations have. It explains the situation based on the perception of the individual or organization (Kurtz & Snowden, 2003) as seen in igure 1-a. Known and knowable that are categorized as ordered domains have predictable causality.
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