An Adaptive ICT-Enabled Model for Knowledge Identification and Management for Enterprise Development

An Adaptive ICT-Enabled Model for Knowledge Identification and Management for Enterprise Development

Agnes Mindila (Jomo Kenyatta University of Agriculture and Technology, Nairobi, Kenya), Anthony Rodrigues (Jaramogi Oginga Odinga University of Science and Technology, Nairobi, Kenya), Dorothy McCormick (University of Nairobi, Nairobi, Kenya) and Ronald Mwangi (Jomo Kenyatta University of Agriculture and Technology, Nairobi, Kenya)
Copyright: © 2014 |Pages: 19
DOI: 10.4018/ijsda.2014010104


Knowledge is vital in achieving enterprise growth and development. This paper argues that treating knowledge management as a Complex Adaptive System (CAS) presents an alternative lens within which processes within knowledge management can be better understood and hence allows scholars in enterprise development design successful intervention programs. The paper presents a conceptual and system dynamic model that reveals the structural underpinnings of knowledge identification and management and in so doing makes clear influence points where interventions can be made. The paper presents a systematic strategy of employing Information and Communication Technologies (ICTs) as interventions in the structural underpinnings of knowledge identification and management and models them within the system dynamic model. The system dynamic model developed is presented as a learning tool for researchers who can further modify it and apply in different scenarios. The validation of a section of the system dynamic model is done on a Micro and Small Enterprises (MSEs) association. The validation reveals conformity to the structural representation of the developed model in a real life scenario. However, differences are noticed in the ICT interventions that are employed. The paper also presents researchers and practitioners in enterprise development with a model that they can use to design intervention programs in knowledge management.
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Knowledge activities are dynamic in nature in the sense that they are embedded in social relationships. These activities involve a myriad of agents involved in interactions that results in self re-organization of knowledge and integration. Knowledge emerges in the culture, practices and concepts used by the agents involved in the interactions (McElroy, 2000; Tommassini, 2002; Lin & Tseng, 2005).

In this paper complexity theory is used to provide a new perspective that presents an adaptive model for identifying and managing knowledge for Micro and Small Enterprises (MSEs). In essence positing that knowing and learning are delivered in a Complex Adaptive System (CAS) environment (McElroy, 2000). Learning involves relationships that provide mechanisms by which culture is communicated and adapted because culture exists within and between individuals and within and between any groups, where content of culture is what the individuals know. The learning process is characterized by dropping or adding particular relationships and as this process continues culture is created and maintained (Reich & Kaarst-Brown, 2003). It is the learning that facilitates enterprise growth and development (Styhre, 2003). Knowledge cannot be separated from the process, a process known as knowing, unlike information which can be separated from the process. This process represents the practical and interactive use of knowledge (Tommassini, 2002). Viewing knowledge identification and management as a CAS enables the process of knowing and learning to be managed. If the process is well managed then the end result in this case knowledge is well managed.

A CAS is non-linear as posited by Mendenhall, et al. (2000, pp. 193) that “the relationship between variables are interrelationships meaning variable x does not have one and only one effect on variable y. variable x and y have mutual influences on each other and on a myriad of other coexisting variables as well. That being non-linear it means that causality can only be understood by analysing the complexity of interconnections among all the variables in the system.

In a CAS many rules are active simultaneously and each of the rules may participate in influencing an outcome and each may influence the actions of other parts. This distributed many-ruled organization of a CAS places strong requirements on computer simulation. The most direct approach is to provide a simulation in which many rules are active simultaneously enabling parallel computation (Holland, 1992).

CAS has been used extensively to assist in the understanding of complex feedback systems and to solve dynamic tasks (Forrester, 1969; Sterman, 2000). Dynamically complex means that the time evolutionary behaviour is complex. Dynamic complexity arises in quite simple systems by delayed influences and non-linear interactions between different agents. This implies that agents transform in ways that are surprising by transforming, mutating and adapting to changes (Sterman, 2000).

Holland (1992) argues that to make parallel simulation of CASs the simulation must directly mimic the ongoing parallel interactions of the CAS and there must be a visual game like user interface that provides natural controls without requiring any knowledge of the underlying computations. Besides, the aggregate behaviour is of interest and needs to be understood so that attempts to modify it can be achieved. To achieve this there has to be an understanding of how the aggregate behaviour emerges from the interaction of parts (Holland, 1992).

In view of achieving the necessary representation and simulation this study employs system dynamics. System dynamics is used since it is a system based approach that provides tools which aid in the understanding of complex systems. System dynamics spans the gap between purely holistic methodologies and purely reductionist methodologies. It works between the two extremes hence poses both holistic and reductionist properties. The reductionist property it possesses is as far as helping with understanding of phenomenon rather than problem solving (Sterman, 2000; Coyle, 1996; Forrester, 1969).

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