Knowledge Management: Strategy For Corporate
Survival And Sustainable Global Development
Oyedokun
Agbeja, David O. Fajemisin, Obafemi
Awolowo University , Nigeria
ABSTRACT:
The engine for knowledge management (KM) is interconvertibility of tacit (TK) and explicit knowledge (EK) preceded by creation of TK. The cycle is repeated as existing knowledge is exhausted of its utility and scarcity. In an era of increasingly short product/service life cycle, complex adaptive behaviour becomes an organization’s survival strategy. Through the practice of KM an organization becomes a learning and knowledge creation entity. The KM is anchored on interconnected communities of practice (CoPs). Through CoPs, an organization can learn and unlearn as it exhibits complex adaptive behaviour. The KM process, innovation mechanism, and work process are linked at various points for innovative developments. For best results, the organization partners with the members of its ecosystem for innovative product/process/ service development through knowledge networking. Knowledge logistics is a pre-requisite for best organizational knowledge networking.
Keywords: Tacit knowledge, Explicit knowledge, CoP, Ecosystem, Knowledge
networking, Complex behaviour
1. Introduction
In recent time, knowledge management (KM) has become an important success factor for organizations. Increasing product / service complexity, rapidly decreasing product / service life cycle, globalization, increasing prominence of virtual organizations, and customer orientation are developments that require a thorough and systematic management of knowledge within an organization and among several cooperating organizations.
KM is the unwinding and sharing knowledge throughout the organization to leverage the competitive advantage (Sridharan, et al., 2004). KM is a systematic and integrative process of coordinating the organization wide activities of: creating, mapping, storing, and sharing knowledge by individuals and groups in pursuit of major organizational goals. It is the process through which organizations create and use their institutional and collective knowledge.
However, with time, systemic entropy deprives existing knowledge of essential utility and scarcity, prompting the condition for a new cycle of knowledge creating (Brinklow, 2004).
A KM strategy must accommodate and directly support organizational innovative processes to deliver sustainability and survival. KM can be harnessed to bring sustainable development to those countries in dire need of development.
2. Knowledge Management
Literature
We identify and highlight the following foremost four concepts in the KM literature:
¨ knowledge hierarchy
¨ information technology
¨ knowledge-based systems
¨ knowledge management life cycle.
2.1. Knowledge Hierarchy
Figure 1:
Knowledge Hierarchy (Source: Davenport
& Prusak, 1998; Nissen et al., 2000; Von krough et al., 2000)
Information is much more sparse than data and also enjoys greater action ability than the latter. Knowledge is near the top of the hierarchy and it is not only the most scarce of the three but it is also endowed with the ability to impact and engender desirable (effective) behaviors in people.
Tuomi (1999) argues for an inverted hierarchial relationship between data, information and knowledge. According to Tuomi (1999), this reverse order is prompted by the fact that the semantic structure needed for the representation of information is provided by knowledge. Also, without desire for information, there would be no need for data. Hence, the reverse order:
data à information à knowledge.
This is in agreement with Spiegler (2000) concept of transformation. That
is, data is transformed into information and the latter into knowledge. This
flow directionality is depicted in 2. The
captures the existence dependency of information and knowledge. While
knowledge is dependent on existent information, the latter is dependent on
existing data. Even data are dependent on existing need for information.
Figure 2: Knowledge
Flow Directionality (Source: Nissen, 2002)
Bellinger et al. (2004) proposed a different hierarchy comprising data, information, knowledge and wisdom. The key to this hierarchy is achieving levels of understanding. This hierarchy is depicted in Figure 3.
Figure
3: The Data, Information, Knowledge, And Wisdom Hierarchy (Source: Bellinger et
al., 2004).
The three hierarchies highlighted above elicit the following definitions:
¨ Data: Data may be viewed as some disconnected collection of facts about a domain that have little intrinsic interest (Brinklow, 2004).
¨ Information: Information emerges from a domain when relationships between the facts are established and understood (Brinklow, 2004).
¨ Knowledge: Knowledge emerges when the patterns of relationships are identified and understood.Ackoff (1989) provides the following definitions which properly describe the Bellinger et al’s hierarchy:
¨ Data: These are raw symbols that just exist.
¨ Information: Data that are given meaning through relational connection.
¨ Knowledge: This is a useful collection of appropriate information.
¨ Understanding: This is cognitive and analytical synthesis of new knowledge from existing knowledge.
¨ Wisdom: To judge and by so doing produce understanding where there was no previous understanding.
Ackoff (1989) adds a time dimension to the hierarchy: information ages rapidly, knowledge ‘has a longer life-span’, understanding ‘has an aura of permanence’ and wisdom is permanent. Knowledge cannot be permanent since systemic entropy drains knowledge of any value as utility and scarcity are diminished (Boisot, 1995).
With the urge to establish the distinction between data, information and knowledge, Boisot (1998) proffered the following definitions:
¨ Data is simply the discernable difference between alternative states of a system
¨ Information is data that modifies expectations or condition readiness of the observer. the more expectations are modified, the greater the information quotient of the data
¨ Knowledge is the set of expectations and a disposition to act held by an observer
The relationship/ distinction between data, information, and knowledge could be summarized as follows:
¨ Knowledge structures are modified by the arrival of new information extracted from data generated from phenomena.
2.2. Information Technology
Database management systems (DBMS), data mining (DM), data warehouses (DW), group wares (GW), intranets, extranets are the most common information technologies (ITS) used in support of KM. Virtually all ITS currently applied in KM abstract at data level and not knowledge level. We catch data flow and not the flow of knowledge. Unfortunately, knowledge continues to be unevenly distributed in the organization. Instead, KM must be supported by ITS such as knowledge mining, knowledge capture and discovery, knowledge filtering, knowledge warehousing, ontology establishment and development and intelligent agents. Simply put, ITS in support of KM must abstract at knowledge level. Actionability and therefore progress might continue to be deterred at some decision points within the organization. Luckily, research efforts are in this direction (Zhuge, 2004; Hendler, 2001).
2.3. Knowledge–based
Systems
The knowledge-based systems (KBS) such as expert systems and intelligent agents are fairing much better than extant ITS applied in KM. For instance, much of the KBS are predicated on the capture, formalization, and application of strong domain knowledge. There is optimism that future KBS will adequately cover the six stages of KM life cycle.
2.4. Knowledge Management Life
Cycle
Nissen et al. (2000) observe a sense of process flow or a life cycle
associated with knowledge management. Integrating their survey of the
literature, (e.g., Gartner Group 1998,
Table 1
Knowledge
Management Life Cycle Models (Source: Nissen, 2002)
|
Model |
Phase 1 |
Phase 2 |
Phase 3 |
Phase 4 |
Phase 5 |
Phase 6 |
|
Despres & Chauvel |
Create |
Map/bundle |
Store |
Share/Transfer |
Reuse |
Evolve |
|
Gartner Group |
Create |
Organized |
Capture |
Access |
Use |
|
|
Davenport & Prusak |
Generate |
|
Codify |
Transfer |
|
|
|
Nissen |
Capture |
Organize |
Formalize |
Distribute |
Apply |
|
|
Amalgamated |
Create |
Organize |
Formalize |
Distribute |
Apply |
Evolve |
The Amalgamated Model integrates the key concepts and terms from the four life cycle models. The Amalgamated Model is more complete with its beginning at the creation step. Of the six phases in KM Life Cycle, only knowledge organization, formalization, and distribution seem to be effectively supported by current ITs (Nissen, 2002). The workflow and knowledge flow are respectively referred to as horizontal and vertical processes and conceptualized in 4.
There is need for full and powerful interaction between workflow and knowledge flow process. A non discontinuous interaction between the two provides opportunity for knowledge management to directly influence changes in the workflow promptly and desirably. This promptness may result in a lot of cost savings.
Figure 4: Horizontal And
Vertical Processes (Source: Nissen, 2002)
Briefly, the two horizontal directed graphs in
the delineate separate examples of a
work process (e.g., Step 1- 6 are performed at different points in time, space,
organization). The graph at the top of the
represents one particular example (e.g., performed a specific point in
time, location, organization) of this notional process, and the graph at the
bottom represents a different example (e.g., Performed at a separate point in
time, location, organization).
Both horizontal graphs represent the flow of work through the enterprise. The vertical graph represents a complementary set of processes responsible for the flow of knowledge. As stated above, knowledge is not evenly distributed through the enterprise, yet enterprise performance is dependent upon consistency and effectiveness across various workflows. The associated knowledge (e.g., process procedures, best practices, tool selection, and usage) flow across time, space and organizations. Such cross – process activities are seen as driving the flow of knowledge – as opposed to the flow of work − through the enterprise. Indeed, Nissen and Espino (2000) identify the following vertical processes among others: training; personnel assignment; and IT support. These processes interact in a complex manner that is not reflected by the simple, linear flow depicted in the . Nissen et al emphasize these vertical processes.
3. Knowledge Flow Dynamics
For knowledge creation and flow, the KM community
has relied on two dimensions: (1) epistemological and (2) ontological.
The epistemological dimension provides a contrast between explicit and tacit knowledge. That knowledge that can be formalized through artifacts such as books, letters, manuals, standard operating procedures, and other media is explicit. That which cannot on its own be formalized but only pertains to understanding and expertise that reside in the heads / minds of people is tacit. In fact, the orthogonal dimension of epistemology represents the dynamic relationship between tacit and explicit knowledge; in particular, the mutuality of tacit and explicit knowledge creation (Brinklow 2004).
Figure 5: Nonaka Knowledge Flow Theory
(Source: Nissen, 2002)
According to Nissen ( 2002) the ontological dimension depicts knowledge that is shared with others in groups or larger aggregations of people across an organization. In essence, Ontology is adopted to denote the dimension that represents the dissemination of knowledge throughout the diversity of strata describing an organization.
The interaction between the two dimensions constitutes the principal means of describing knowledge flow (Nonaka 1994). This is depicted in 5. According to Nonaka (1994), individuals in the organization create knew knowledge and the latter is of course tacit in nature. The flow of knowledge through the organization occurs in four stages.
The first stage of knowledge flow occurs through a process termed socialization. During this phase, members of a team share experience and perspectives in a manner analogous to communities of practice. Vector 1 in corresponds to socialization flow and corresponds to tacit knowledge (i.e., along the epistemological dimension) flowing from the individual to the group level (i.e., along the ontological dimension).
The second stage of knowledge flow (Vector 2) occurs through a process termed externalization. The latter entails the use of metaphors through dialog that leads to articulation of tacit knowledge and its subsequent formalization to make it concrete, explicit, and codifiable.
The third stage of knowledge flow (vector 3) occurs through a process termed combination. The latter represents coordination between different groups in the organization – along with documentation of existing knowledge – to combine new, intra–team concepts with other, explicit knowledge in the organization (Nissen 2002).
The fourth stage of knowledge flow (vector 4) occurs through a process termed internalization. The latter implies that diverse members in the organization apply the combined knowledge from above invariably, through trial and error. In turn, such knowledge is translated into tacit form at the organizational level.
Currently, KM life cycle model and Nonaka knowledge flow theory are separate and distinct from each other. The Amalgamated KM life cycle model and Nonaka knowledge theory should be interwoven / integrated to provide a veritable construct for dynamic flow of knowledge through the organization. With time also taken into cognizance, Nissen and Levitt(2002) construct a four – dimensional model which provides a powerful, vivid description of the dynamic flow phenomenon. This construct is depicted in Figure 6.
Figure 6: Extended Model With Knowledge Flows (Source: adapted from Nissen, 2002)
4. Response To Paradigm
Shifts
The competitive landscape is now characterized by periods of tranquility interrupted by discontinuous episodes of turbulence and upheaval (Tushman et. al. 1986). Inspite of this, the traditional negative feedback loops which provide information for the control of activities at the operational level and the positive feedback loops which provide information for upper management to alter the course of plan(s), remain the attractors. An organization must respond to these attractors proportionately to avoid collapse. The tension emanating from the paradigm shift must be resolved between the two attractors if organization is to survive and attain sustainable development (Brinklow, 2004). As we navigate a trajectory between the dynamic borders of the attractors, we enter an unpredictable domain which is paradoxically stable and unstable (Brinklow, 2004). An organization must therefore be sufficiently flexible to adapt to the competing necessities of order and disorder. According to the complexity theory, corporate practice suggests three models of organizational behaviour:
1. Ordered
2. Complex
3. Chaotic.
In order to survive, an organization must not only sense environmental changes but must also reconfigure its behaviour to adequately meet the challenges of its environment.
The Ordered Behavioural Model:
Negative feedback exerts a damping influence that tends to endorse strategic
conventions and ease an organization towards the ordered realm.
The Chaotic Behavioural Model:
Positive feedback has an amplifying influence that generates an exaggerated
response, and drives the organization towards the chaotic realm in a state of
unstable equilibrium (Brinklow,2004).
The Complex Behavioural Model:
Both ordered and chaotic modes of behaviour invite failure. Complexity theory
permits the shifting of tensions between positive and negative feedback loops
as a strategy of survival. Thus, survival requires an organization to be
neither stable nor unstable, but a combination of both, resulting in a complex
behaviour.
5. Matching The Demand For
Complex Behaviour
An organization that is a learning and knowledge creating entity can, through an appropriate, well thought out knowledge management scheme constantly adapt to environmental changes no matter how drastic through adaptive – complex behaviour. The culture of complex behaviour can be enshrined in an organization through Communities of practice (CoPs).
5.1. Commmunities Of Practice
A community of practice (CoP) is an affinity group, an informal network or forum for exchange of tips and generation of ideas. Members of a CoP are bound together through exposure to a common class of problems, common pursuit of solutions, and thereby themselves embodying a store of knowledge.
In recent time, CoPs, have become associated with knowledge management and are seen as ways of developing social capital, nurturing new knowledge, stimulating innovation or sharing existing tacit knowledge within an organization. A CoP is now accepted as part of organizational development. Indeed, a CoP is a veritable enabler for organizational KM. The CoPs within an organization are interconnected, and are sustained by learning. Through its CoPs, an organization knows what it knows and becomes effective and valuable as an organization (Wenger, 1998).
The social learning within the interconnected CoPs is capable of accomplishing primacy as a means to interpret, inform, and implement corporate behaviour through the following disciplines:
(1) Personal Mastery
(2) Mental Models
(3) Shared Vision
(4) Team Learning
(5) Systemic Thinking.
Personal
Mastery: Enrichment through socialization; empowerment of
individuals to develop and extend personal division? while reconciling
intrinsic aspirations.
Mental Models: Provide a network of
cognitive processes to recognize and contextualize organizational environment.
Shared
Vision: Shared vision refers to a common sense of purpose and
commitment from which to forge focus and energy for organizational learning.
Team
Learning: A discipline that starts with “dialogue” is
viewed as the process of aligning and developing the capacities of a team to
create the results its members truly desire. It builds on personal mastery and
shared vision- but these are not enough. When teams learn together, members
will grow more rapidly than could have occurred otherwise.
Systemic
Thinking: A major tool of systems analysis is systems thinking.
Basically, systems thinking is a way of helping a person to view systems from a
broad perspective that includes seeing overall structures, patterns and cycles
in systems rather than seeing only specific events in the system. This broad
view enables the individual to quickly identify the real causes of
issues in an organization and know where to work to address them. Systemic
thinking has produced a variety of principles and tools for analyzing and
changing systems. By focusing on the entire system, experts can attempt to identify
solutions that address as many problems as possible in the system. The positive
effect of those solutions leverages improvement throughout the system. Thus,
they are called “leverages points” in the system. Thus, priority on
the entire system and its leverages points is called whole systems thinking.
Members of a CoP must have access to the resources necessary to learn what they need to learn in order to take actions and make decisions that fully engage their own knowledge ability. Indeed, an organization becomes a learning and knowledge creating entity through `the activities of its interconnected CoPs.
No two KMs are exactly alike, but they are anchored on communities of practice. It is the latter, that in reality, turn information into its actionable variety ─ knowledge. Communities of practice built around critical – key areas of the organization can engender new ideas suitable for innovative discoveries. A plethora of potential innovative discoveries abound in the organization’s ecosystem. Therefore, an organization that is thirsty for innovative discoveries must extend to its ecosystem and practice communities of practise on a larger scale. After all, we are in the midst of a major paradigm shift in the way we process and disseminate information ─ a shift to an integrated global market for trade, finance, and knowledge (World Bank, 1999, Part one).
6. Innovation, Knowledge Management And
Business Ecosystem
Various authors have argued that innovation is the use of new knowledge to offer a new product or service that customers want; that is invention plus commercialization (Freeman, 1982; Roberts, 1988; Albers and Brewer, 2003). New Knowledge is of two varieties:
¨ technological related knowledge
¨ market related knowledge.
The first variety refers to the organizational systems or their components (input processing, output and even feedback). The other variety refers to one or a combination of the following: distribution channels, customers’ expectation.
Since innovation is a major objective of knowledge management system, the latter most be linked at various stages with innovation mechanism as shown in Table 2.
Table 2
Relation
Between Knowledge Management And Innovation Mechanisms (Source: Albers
and Brewer, 2003)
|
KM Elements |
Innovation Mechanisms |
|
|
1. |
Creation |
Motivational carrots / Incentives Introduce change – settings, groups, viewpoints. Treat everything as temporary – teams, organizations procedures, product lines. Reject underlying values and beliefs (personal and organizational). Encourage experimentation and ignore exports. Environmental factors – working conditions, economic means, transfer mechanisms, mentors. Hire smart and different. Incite discomfort and dissatisfaction. |
|
2. |
Acquisition |
Encourage education and learning –often alternative Internal and External sources – user communities opportunism – look outside the box. Idea storage medium – enable storage of non-used or used ideas |
|
3. |
Integration |
Storage Direction Integration of functional knowledge with process knowledge Challenge existing practices Use different perspectives – idea sharing |
|
4. |
Distribution |
Connecting those that know with those that need to know Transfer mechanisms Encourage idea sharing Keep ideas alive – not just an achieve, make tangible if possible Spread information about who knows what subject matter experts |
|
5. |
Application |
Freedom to experiment – prototype, model, pilot, test good ideas Organizational acceptance of short term financial loss |
Figure 7: Business Ecosystem (Source:
Albers and Brewer, 2003)
In a world of cooperation necessitated by globalization, innovative ideas in one organization should be regarded as just an integral part of global reservoir of innovative ideas. According to this concept, the innovative ideas created within an organization constitute a subset of the innovative ideas available at the organization’s ecosystem at any point in time. Through regular and efficient networking, an organization can contemplate strategic partnerships through which it can innovate and share the risks of innovation with others in the ecosystem (Pennings and Harianto, 1992). This risk ─ sharing reduces cost of equity capital and promotes reduced average cost of capital. Figure 7 shows the business ecosystem indicating potential partners. Strategic alliances and partners in the ecosystem collaborate synchronously. A two-way communication is, invariably accomplished through the internet. Information in reservoir/databases can be shared with the aid of synchronous collaborative software(s).
Figure 8: Porter’s Diamond Model (Source: Albers And Brewer, 2003)
Figure 8 indicates that all conditions within the ecosystem are interdependent. This implies that even organizations living different conditions in the ecosystem are, by implication, interdependent. Therefore, through networking, an organization must interconnect and collaborate with other organizations in the ecosystem in order to innovate strategically, and in all, attain competitive edge.
Figure 9: Eco-Innovation Model
(Source: Albers and Brewer, 2003)
The eco-Innovation model (EIM) is an extended community of practice in which members of an ecosystem collaborate, learn, and generate new ideas, new ways of doing things better and solving problems confronting industry practice. The EIM can be flexibly used to promote cooperation within a coalition of organizations. By extension, development agencies all over the world can form a coalition to foster innovative and sustainable development in regions in dire need of development. The least developed countries of the world (Table 3) can strategize with KM that prudently explores the resources of their appropriate ecosystems. Knowledge sharing by networking within eco-groups has great potential for corporate survival and sustainable global development. What is needed is that knowledge logistics be stated before the commencement of knowledge networking with partners in the ecosystem.
7. Knowledge Logistics In A
Network
Knowledge logistics is the
aggregate of methods and procedures involved in the provision of the
appropriate knowledge to the appropriate individual at the appropriate time,
using the internet, especially (Zhuge, 2006).
7.1. `Knowledge Networking
There
are two types of knowledge networking: (1) direct knowledge sharing where
knowledge is passed between nodes in a pure peer – to – peer
networking mode, and (2) hybrid knowledge sharing where all flow is through a
central repository.
In a knowledge network, the nodes are team members, software agents or knowledge portals that provide services, and the links are flows of knowledge between nodes (Tiwana, 2003). Property of a Node: One major property of a node of a knowledge flow network is its knowledge energy which reflects its cognitive and creative ability, and thus determines the node’s “rank” or “reputation” within the nodes in the network (Zhuge, 2006). The knowledge energy is the power to drive the knowledge flow and so appropriately referred as knowledge power or knowledge intensity (Zhuge, 2006). The total energy of the nodes in a team’s network indicates the team’s ability to solve problems and accomplish task (Zhuge, 2006). The effectiveness of teamwork is a measure of energy differences between nodes (Zhuge, 2006). The knowledge energy of a node is measured in two ways: (1) estimated through question-and-answer tests; (2) or computed from the energy of its predecessor and successor nodes according to the principle: the more nodes it passes knowledge to, the greater its energy; and, the more nodes it receives knowledge from, the greater its potential energy. The energy of a node may change through learning. According to Figure 10, Knowledge flow process, work process, innovation process and support process are inextricably intertwined for optimal and sustainable development.
Figure
10: Main Organization Processes (Source: Urban and Regional Innovation Research
Unit ─
8. Conclusion
Knowledge management (KM) is premised on the cyclic process of tacit knowledge (TK) creation, conversion of TK to explicit knowledge (EK) and the conversion of EK to TK. The cycle is repeated once existing knowledge is virtually exhausted of utility and scarcity by systemic entropy.
No two KMs are identical, but they are anchored on COPs. An organization becomes a learning and knowledge creating entity through the activities of its interconnected COPs. The culture of complex behaviour is enshrined in the organization through its COPs. In this way, the organization can constantly adapt to drastic environmental changes through complex adaptive behaviour. The KM process, the work process, and innovation mechanism are linked at various stages. By partnering with its ecosystem, the organization can achieve innovative, sustainable development at reduced cost.
Collaborative team work within the organization and with teams in the ecosystem is facilitated with knowledge networking established on appropriate knowledge logistics. Knowledge networking with partners in the ecosystem has great potential for developmental organizations whose goal is to bring lasting development to deprived regions of the world.
Democratic Sao Tome & Principe Lao People’s Democratic
Republic Timor-Leste
Table 3: Current Least Developed Countries (Source: Wikipedia,
2006)
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Contact the Authors:
Dr. Oyedokun Agbeja, Senior Lecturer, Department
of Management and Accounting, Obafemi Awolowo University, Ile-Ife, Osun State,
Nigeria.