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FOSTERING SYNERGIES BETWEEN INFORMATION TECHNOLOGY AND
MANAGERIAL AND ORGANIZATIONAL COGNITION:
THE ROLE OF KNOWLEDGE MANAGEMENT
ELIAS G CARAYANNIS, PhD
MANAGEMENT OF SCIENCE, TECHNOLOGY AND INNOVATION
PROGRAM
DEPARTMENT OF MANAGEMENT SCIENCE
SCHOOL OF BUSINESS AND PUBLIC MANAGEMENT
THE GEORGE WASHINGTON UNIVERSITY
WASHINGTON, DC20052
(202) 994 4062
(202) 994 4930 (Fax)
Email: [email protected]
International Journal of Technovation, 12 / 1998
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FOSTERING SYNERGIES BETWEEN INFORMATION TECHNOLOGY AND
MANAGERIAL AND ORGANIZATIONAL COGNITION:
THE ROLE OF KNOWLEDGE MANAGEMENT
ABSTRACT
In this paper, we try to understand the role of knowledge management in fostering
a synergistic symbiosis between information technology and managerial and
organizational cognition. Both information technology and knowledge management can
be perceived as strategic enablers of managerial and organizational cognition.
We synthesize classical cognition concepts and recent empirical experience with
knowledge management applications to develop an organizational knowledge
management model (the Organizational Cognition Spiral or OCS) and tool (the
Organizational Knowledge Network or OK Net) for understanding and supporting
managerial and organizational cognition.
KEY CONCEPTS: Knowledge Management, Managerial and Organizational Cognition, Organizational Knowledge Management Networks, Collaborative Learning, Meta-Learning, Co-opetition, Information Technology Productivity Paradox, Next Generation Intelligent Agents
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Introduction
"We are drowning in information but starved of knowledge"
John Naisbitt, Megatrends
Individuals and organizations are faced with an avalanche of data and information
that may be critical in nature but hard to properly manage and leverage. Recent attempts
to address these challenges have resulted in the emergence of the increasingly popular
field of knowledge management.
In this paper, we try to understand the role of knowledge management in fostering
a synergistic symbiosis between information technology and managerial and
organizational cognition. Both information technology and knowledge management can
be perceived as strategic enablers of managerial and organizational cognition.
While information technology can be considered as a value-adding technological
infrastructure, knowledge management can be viewed as a sociotechnical system of tacit
and explicit business policies and practices. These are enabled by the strategic integration
of information technology tools, business processes, and intellectual, human, and social
capital (The Conference Board, 1996). The advent of internet-related information
technology such as intranets, extranets, and intelligent agents has contributed
significantly to the increased interest in knowledge management: "Organizations are
beginning to connect themselves in ways that they hadn't planned for or
expected...Groups, departments, and teams suddenly find themselves being able to share
information that they hadn't been able to share before." (Informationweek, 10/20/97).
Finally, managerial and organizational cognition can be perceived as the human and
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organizational capability for individual and collective reasoning, learning, emoting and
envisioning. Organizational memory, intelligence, and culture are important determinants
of cognition processes at both the individual and the organizational levels.
Multiple studies have shown that superimposing technology on organizations is
not the answer to cope with such challenges as information and cognitive overload
straining limited resources like intellectual bandwidth and available time: “Employees
will ignore, underuse, or subvert the most sophisticated technology of collaboration if
they do not trust and respect each other, or if they lack a sense of mutual interest in
common goals. The valuable potential of electronic knowledge tools can only be realized
in an environment that encourages and rewards their use...” (Conference Board, 1996). In
fact, mismatched information technologies and organizational settings have often given
rise to what is termed as the information technology productivity paradox (Brynjolfsson,
1993, 1994, 1995, 1996). The "one size fits all" solution that technology vendors promote
often does not work and it is in this very context that understanding the nature of
organizational culture and managerial and organizational cognition is key: “Many of the
most valuable efforts to encourage knowledge creation and distribution have little to do
with new technology, especially when the knowledge is complex and tacit, and therefore
resists codification. In fact, some of the most productive knowledge projects are low
tech.” (The Conference Board, 1996).
To better deal with these issues, we focus on two key questions:
a) What is the role of managerial cognition in the filtering, interpretation,
and sharing of vast amounts of data?, and
b) How can firms turn this data into knowledge?
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In order to answer these questions we need to better understand how data and
information can be converted into knowledge and how knowledge can be effectively
managed and transferred within and across organizations. In other words we need to
focus on the following issues:
1. The role of managerial and organizational cognition in transforming data into
knowledge.
2. The role of information technology in reasoning, interpretation, and decision making.
3. Ways in which knowledge management can foster synergies between information
technology and managerial and organizational cognition and deal with the
information technology productivity paradox.
In this context, our paper tries to provide a conceptual framework on how
managers and organizations can think and learn better and faster or in other words,
manage and transfer knowledge more effectively and efficiently. This conceptual
framework can be operationalized by means of emerging information technology tools
and infrastructures such as WWW-enabled intra- and inter-organizational information
networks (intranets and extranets) and next generation intelligent agents. Moreover, this
framework can be perceived in its totality as an organizational knowledge management
network with both real and virtual components at the individual, intra-, and inter-
organizational levels. The organizational knowledge management network in question,
relies both on knowledge and meta-knowledge, or knowledge about the knowledge within
the network.
We perceive managerial and organizational cognition and knowledge
management as transitions across progressively higher levels of knowledge and meta-
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knowledge. These transitions are enabled by several human- and machine-centric
technologies. Specifically, we examine the role of several human- or machine-centric
technologies that may constitute part of an organizational knowledge management
network. Examples of human-centric technologies are: a) electronic meeting systems, b)
groupware tools, c) video-conferencing, d) internet search agents, e) speech recognition.
Examples of machine-centric technologies are: a) genetic algorithms, b) decision support
tools, c) expert systems, d) virtual reality, e) intelligent agent simulation, f) micro-worlds,
g) war-gaming, and h) system dynamics (Simulating Future Worlds, BiT3M Conference,
SRI Consulting, August 1997). Each one of these technologies supports a particular
managerial and/or organizational process, function, faculty and capability, such as
individual decision making, organizational culture, and individual and organizational
learning.
Definition of Terms: Cognition, Learning, Knowledge, Meta-cognition, Meta-
learning, Meta-knowledge, Knowledge Management
“We shall go nowhere without emergent learning alongside deliberate planning”
(Mintzberg, 1991)
Cognition / Meta-Cognition
Cognition is the human capacity to perceive, interpret, and reason about
environmental and conceptual environmental or organizational stimuli and meta-
cognition the capacity “to think about thinking, as meta-learning means to learn about
learning” (Carayannis, 1994: 8).
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Learning / Meta-Learning
The primary process by which firms change their capabilities to better fit the
environment is through learning. In the case of learning like the case of most other
fundamental concepts, there is little consensus as to what learning is and how it takes
place. In economics, learning is perceived as tangible, quantifiable improvements in
value-adding activities, in management, learning is seen as the source of "sustainable
competitive efficiency" (Dodgson, 1993: 376), whereas in the innovation literature,
learning is considered as a source of "comparative innovative efficiency" (ibid: 376). As
noted in Doz (1996), there is a distinction in the organizational context between cognitive
learning and behavioral learning. Cognitive learning occurs when members of a firm
realize that some change is needed in a given situation; behavioral learning occurs when
the organizational routines of that firm are actually changed (the implementation of
cognitive learning). Expanding the concept of learning further, organizational learning
occurs when the new behavior is replicated throughout the firm, leading to broad-based
organizational change (Teece et al, 1997: 525).
Knowledge / Meta-knowledge
Knowledge, learning and cognition, are classical terms that have been re-
discovered in the context of the information technology and knowledge management
revolutions. Beckman (1998) compiled a number of useful and relevant definitions of
knowledge and organizational knowledge:
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• Knowledge is organized information applicable to problem solving. (Woolf, 1990)
• Knowledge is information that has been organized and analyzed to make it
understandable and applicable to problem solving or decision making. (Turban,
1992)
• Knowledge encompasses the implicit and explicit restrictions placed upon objects
(entities), operations, and relationships along with general and specific heuristics and
inference procedures involved in the situation being modeled. (Sowa, 1984)]
• Knowledge consists of truths and beliefs, perspectives and concepts, judgments and
expectations, methodologies and know-how. (Wiig, 1993)
• Knowledge is the whole set of insights, experiences, and procedures which are
considered correct and true and which therefore guide the thoughts, behaviors, and
communication of people. (van der Spek and Spijkervet, 1997)
• Knowledge is reasoning about information to actively guide task execution, problem-
olving, and decision-making in order to perform, learn, and teach. (Beckman, 1997)
• Organizational knowledge is the collective sum of human-centered assets, intellectual
property assets, infrastructure assets, and market assets. (Brookings, 1996)
• Organizational knowledge is processed information embedded in routines and
processes which enable action. It is also knowledge captured by the organization’s
systems, processes, products, rules, and culture. (Myers, 1996)
The knowledge creation, transfer, selection, acquisition, storage, and retrieval
processes (see Table 1) can be viewed from an information theoretic (Shannon &
Weaver, 1949) and a meta-cognitive (Simon, 1969; Sternberg & Frensch, 1991;
Halpern, 1989) / linguistic perspective (Chomsky, 1971, 1993), where the human
problem solver and technology manager is seen as both a technician and a craftsman
(Schon, 1983), a "lumper" and a "splitter" (Mintzberg, 1989). Individuals, teams, and
organizations rely on multi-layered technological learning and unlearning (Carayannis,
1992, 1993, 1994a, 1994b, 1994c, 1994d; Dodgson, 1991, 1993) to create, maintain, and
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enhance the capacity of individuals, groups, and organizations to transfer and absorb
embodied and disembodied (von Hippel, 1988) technology in the form of artifacts,
beliefs, and evaluation routines (Garud & Rappa, 1994) and tacit and explicit knowledge
(Polanyi, 1958, 1966; Nonaka, 1988, 1994).
Moreover, it is critical to realize that individual and organizational learning and
knowledge are mutually complementing and reinforcing entities using the medium of
organizational memory:
To be organizational, rather than individual, learning, knowledge must be
accessible to others rather than the discoverer, subject to both their application or
use, and to their change and adaptation. ... Organizational learning, to be learning
rather than "mere adaptation" must be generalized. It must go beyond simple
replication to application, change, refinement. It must include "rules for learning"
and their change and adaptation, rather than the rote iteration of past successful
actions.... Finally, if learning is to include innovation, it must encompass a system
for governing the future as well as the present. (Jelinek, 1979: 162-163).
Moreover, this learning process must be endowed with an organizational memory
that is both, accurate and precise to build, maintain, and renew continuously the
firm's reservoir of skills and competencies: If an organization is to learn
anything, then the distribution of its memory, the accuracy of that memory, and
the conditions under which that memory is treated as a constraint become crucial
characteristics of organizing. (Weick, 1979: 206.) [in Carayannis, 1994d].
In this context, it is important to remember that “knowledge does not grow in a
linear way, through the accumulation of facts and the application of the hypothetico-
deductive method, but rather resembles an upward spiral, so that each time we reevaluate
a position or place we’ve been before, we do so from a new perspective” (Jaggar, 1983:
368) [In Carayannis, 1994d: 52]. This concept sets the scene for the development of the
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Organizational Cognition Spiral (OCS) we present later in the paper, as part of our
organizational knowledge management model. Organically relevant to these concepts is
intuition, which Weick defines as “compressed expertise” (Davenport & Prusak, 1998:
11) along with meta-knowledge as knowledge (awareness) about the knowledge you
possess.
Table 1
Knowledge Processes
Underlying Activities
1) Acquiring Knowledge:
• Extracting • Interpreting • Transferring
2) Selecting Knowledge:
• Locating • Retrieving • Transferring
3) Internalizing Knowledge:
• Assessing • Targeting • Depositing
4) Using Knowledge
5) Generating Knowledge:
• Monitoring • Evaluating • Producing • Transferring
6) Externalizing Knowledge:
• Targeting • Producing • Transferring
Carayannis (1994d: 683) proposed an experiential meta-cognitive map, linking
cognitive entities in the dynamic (transient) domain with cognitive entities in the time-
transcending (classical) domain (see Table 2):
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Table 2
Dynamic domain
(time transient form and content)
Time-transcending domain
(time immutable form and content)
• Data
• Information
• Knowledge
• Intelligence
• Memory
• Wisdom
• Values
• Myths
• Consciousness
• Culture
A number of other authors have also proposed knowledge typologies. Nonaka
and Takeuchi (1995) have divided knowledge accessibility into two categories: tacit and
explicit. Beckman (1998) identifies three stages of accessibility: tacit, implicit, and
explicit:
• Tacit (human mind, organization) -- accessible indirectly only with difficulty through knowledge elicitation and observation of behavior
• Implicit (human mind, organization) -- accessible through querying and discussion, but informal knowledge must first be located and then communicated
• Explicit (document, computer) -- readily accessible, as well as documented into formal knowledge sources that are often well-organized
Moreover, Nonaka and Takeuchi (ibid) suggest the following types of knowledge (see
Table 3):
Table 3
TACIT KNOWLEDGE EXPLICIT KNOWLEDGE
Knowledge of experience (body skills) Knowledge of rationality (mind)
Simultaneous knowledge (here and now) Sequential knowledge (there and then)
Analog knowledge (practice) Digital knowledge (theory)
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Nonaka (ibid) has also developed a matrix for knowledge conversion based on accessibility (see Table 4):
Table 4
Knowledge Conversion Tacit Knowledge TO Explicit Knowledge
Tacit Knowledge FROM
Socialization (Sympathized K)
Externalization (Conceptual K)
Explicit Knowledge Internalization (Operational K)
Combination (Systemic K)
There is a number of tacit and explicit organizational processes and activities that
encapsulate organizational knowledge at the individual and group levels (see Table 5):
Table 5 (Adapted from Carayannis, 1994d)
Individual Knowledge
Group Knowledge
Instances of Explicit Knowledge Carriers
• Rules of Thumb • Procedures • Design Rules • Design Meta-Rules
• Drills • Stories • Best Practices • Work Processes • Business Reengineering
Instances of Tacit Knowledge Carriers
• Common Sense • Good Judgment • Wisdom • Intuition • Know-how • Expertise
• Group Texture • Work Practice • Core Competences • Organizational
Intelligence
Beckman (1998) proposes a five level Knowledge Hierarchy in which knowledge can
often be transformed from a lower level to a more valuable higher level (Table 6):
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Table 6
Knowledge Hierarchy Typology
Data
Text, fact, code, image, sound
Information
Organized, structured, interpreted, summarized data
Knowledge
Case, rule, process, model
Expertise
Fast & accurate advice, explanation & justification of result & reasoning
Capability
Organizational expertise: knowledge repository, integrated
These tables provide us with the conceptual framework within which we will define and
construct our organizational knowledge network and organizational cognition spiral.
Knowledge Management
Knowledge management is defined as: “the systematic, explicit, and deliberate
building, renewal, and application of knowledge to maximize an enterprise’s knowledge-
related effectiveness and returns from its knowledge assets”. [Wiig, 1997].
Sveiby (1998) defines knowledge management is “the art of creating value from
an organization’s intangible assets” (see Table 6a).
Moreover, Sveiby (1998) identifies two main tracks of knowledge management
activities: one track focuses on knowledge management as the management of
information and the other track as management of people. In this paper, we synthesize
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these two perspectives to develop our Organizational Knowledge Network and
Organizational Cognition Spiral models.
Table 6a
Adapted from (Sveiby, 1998)
Knowledge Management
Track / Level
Knowledge = Object
Knowledge = Process
Organization Level
“Re-engineers”
“Organization Theorists”
Individual Level
“AI-specialists”
“Psychologists”
Holsapple and Joshi present an extensive framework consisting of six steps with
supporting activities that helps exemplify the underlying evolutionary knowledge
management stages for both OK Net and OCS (see Table 7):
Table 7
1) Acquiring Knowledge: Extracting Interpreting Transferring
2) Selecting Knowledge: Locating Retrieving Transferring
3) Internalizing Knowledge: Assessing Targeting Depositing
4) Using Knowledge
5) Generating Knowledge: Monitoring Evaluating Producing Transferring
6) Externalizing Knowledge: Targeting Producing Transferring
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A Model for Organizational Knowledge Management: The Organizational
Knowledge Network (OK Net) and the Organizational Cognition Spiral (OCS)
The OK Net Model
The OK Net Model would be an experimental testbed or technology platform for
designing and testing an organizational knowledge management network for the support,
monitoring, capturing, measurement, and enrichment of organizational cognition in an
eight stage process (Table 8) (Beckman, 1997). A key knowledge that would be required
is a database of interest / expertise profiles on the human capital of the firm (knowledge /
expertise maps / repositories).
The OK Net would be flexible and adaptive, endowed with the capacity to learn,
learn how-to-learn, and learn to learn-how-to-learn (Argyris & Schoen, 1978,
Argyris, 1990, Carayannis, 1994d, 1995, 1996, 1997, 1998, forthcoming) from system
users and stakeholders, team and organization customers, suppliers, complementors, and
competitors in a co-opetitive manner (Brandenburger & Nalebuff, 1996) through explicit
and tacit, active and passive interactions (questionnaires, intelligent agent performance
gauged, compared, and recorded, tracking of evolutionary paths of user profiles and
preferences, etc.).
The OK Net would thus serve as a catalyst for the creation of trans-disciplinary /
trans-functional organizational knowledge clusters within and across communities of
practice. Specifically, its objectives would be to:
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• catalyze the creation of trans-disciplinary and trans-functional knowledge clusters
across teams and organizations
• provide a more responsive information technology infrastructure supporting
knowledge workers, being able to design products and services which are in line
with current and emerging market needs
• enable better utilization of resources by reducing / eliminating redundancies and
identifying weaknesses and anticipating opportunities for change
• develop and foster new and promising areas of collaborative, inter-disciplinary,
and cross-functional knowledge work
There are two key parameters that need to be specified when designing a knowledge
cluster:
• One is the cluster structure which reflects emphasis of hierarchy versus
decentralization
• The other parameter is the culture that specifies the norms and values that guide
behavior within networks – culture can vary from bureaucratic to entrepreneurial
An evolving variable that may need to be monitored closely is the stakeholder
relationships within the cluster and these could vary from total conflict to a high level
of cooperation.
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Table 8
Stage 1
Identify
Determine core competencies, sourcing strategy, and knowledge domains.
Stage 2
Capture
Formalize existing knowledge.
Stage 3
Select
Assess knowledge relevance, value, and accuracy. Resolve conflicting knowledge.
Stage 4
Store
Represent corporate memory in Knowledge Repository with Various Knowledge schemata.
Stage 5
Share
Distribute knowledge automatically to users based on interest and work. Collaborate on knowledge work through virtual teams.
Stage 6
Apply
Retrieve and use knowledge in making decisions, solving problems, automating or supporting work, job aids, and training.
Stage 7
Create
Discover new knowledge through research, experimenting, and creative thinking.
Stage 8
Sell
Develop and market new knowledge-based products and services.
The OK Net would consist of individual and team knowledge repositories with
competences / skills personal and team profiles as well as research / project records, and
on-going individual and team knowledge generation, diffusion, acquisition, storage, and
renewal activities and interests as well as other categories as appopriate. For each of
these key attributes, hyper-links would provide access to related and useful sites
including people and teams across and within the organization with overlapping /
complementary knowledge profiles.
The OK Net functions would be enabled by intelligent agents specialized to deal with
each of the categories of tasks listed (the competences intelligent advisor, the research
intelligent advisor, the service intelligent advisor, the intra- and inter-organizational
knowledge activities guide, etc). This technology would enable the OK Net to semi-
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automatically and interactively propose the composition of a team or committee with the
requisite expertise to deal with a certain project or to respond to an RFP, to create an ad-
hoc knowledge team to respond to an opportunity or threat, to develop a new program, or
to respond to an external request for community service, thus enhancing organizational
cognition processes.
Moreover, the intelligent agents would not only be specialized but they would also
continually learn collaboratively (Maes, 1998) from each other. In this manner, the OK
Net would be a living, continually evolving, self-organizing “socio-technology” enabled
by next generation intelligent agents that would adapt itself to minimize its intrusiveness
and maximize its anticipatory capacity of its customer needs and wants (Carayannis,
1994, 1996, 1998). The set of intelligent agents that would constitute the backbone of the
OK Net would also comprise subscribing agents that would match individual
competences and research interests with upcoming conferences, trade conventions,
product announcements and other external events, scheduling agents that would match
individual’s schedules to facilitate meeting either as residents or visitors, research
enhancing agents that would inform researchers of announced RFPs, publications, and
conferences, Government-University-Industry partnerships agents that would enable
collaborations across academia, government, and industry, etc.
The proposed intelligent KM interface would allow free-format reporting of
knowledge and information that would then be classified by intelligent agents and
reported to the right recipients (consumers ) of such content in real time (see Figure 0).
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Figure 0
Context Dependent Measures
Context Independent Measures
KN Assessment
Stakeholder Groups 1 2 3 ….. n
Expectations/ Objectives/ Performance Measures
Knowledge Creation • Ease • Flexibility • Richness
Knowledge Securing • Consistency
checking • Tracking changes
Knowledge Distribution • Awareness • Timeliness • Fairness
Knowledge Retrieval • Ease of navigation
/searching
Network variables • Network size • Network structure • Network complexity
Task and process variables • Search • Communication • Collaborative
Operationalize
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OK Net Knowledge Management Metrics
The OK Net knowledge management metrics would serve to motivate a set of
questions that would help to allow an organization determine where it is on the
Organizational Cognition Spiral (OCS) (see Figure 5) and in which direction it is
moving. Moreover, the OCS would allow to interpret how the transitions should be
managed and how to optimize the path followed along the OCS.
The knowledge management metrics will consist of short-term and long-term, input,
intermediary, output, and outcome metrics operationalized through a number of actions:
• The complete and timely registering of knowledge into the system should be further
rewarded by a sort of “Malcolm Baldridge award for knowledge management” given
to each cluster and acknowledging invididuals for their contributions.
• Measuring knowledge generation, exchange, storage, maintenance and management
would consist of input, intermediate, and outcome measures as well as short and long
term measures would be key.
• For each individual person input/output ratios would provide performance
benchmarks.
• Not only the generation but also the sharing of knowledge would be monitored.
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The Organizational Cognition Spiral (OCS) Model
A model for understanding the key issues involved in organizational knowledge
management is proposed. The model defines different knowledge states that are a
function of two dimensions - knowledge (K) and meta-knowledge(MK) as defined earlier
and it consists of successive “knowledge cycles” where an individual or organization can
transition and traverse four stages of awareness or ignorance. As each cycle is traversed
and then a transition to the next cycle occurs, the overall level of knowledge and meta-
knowledge is rising (see Figures 3 and 5).
Typically, but not always as we see in Tables 9 and 10, the transition takes place
from ignorance of ignorance (you do not know what you do not know) to awareness of
ignorance (you know what you do not know) to awareness of awareness (you know what
you know: the result of search, discovery, and learning) to ignorance of awareness (you
do not know what you know: the result of routinized as well as tacit awareness).
For the sake of simplicity, the dimensions are assumed to be at two levels
representing the presence and absence of (meta) knowledge. The levels of the two
dimensions are thus represented as K and K, and MK and MK. These two levels of the
two dimensions result in a total of four knowledge states:
I. K, MK (Awareness of awareness)
II. K, MK (Ignorance of awareness)
III. K, MK (Awareness of ignorance)
IV. K, MK (Ignorance of ignorance)
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Organizations may exist in any one of these states that include current, desired,
and/or intermediate levels. The states can be represented in the following way (Figure 1):
Figure 1
Knowledge management can be viewed as the process of managing the
transitions across these four states. We now elaborate on the nature of these states
illustrating them with some examples.
For the organizational knowledge states, the key questions for determining to
which knowledge state a given organization belongs at a given point in time, are:
1. Characteristics of a set of typical borderline organizations that define the
boundaries of the organizational knowledge continuum.
2. Characteristics of each of the organizations we are examining.
3. Reasons for classifying them in a given manner.
4. Evidence of transition.
These questions are defined by the enabling process, content, and technologies
underlying each state and transition.
K
K
MK MK
I II
III IV
23
For example, the K, MK state, seems to represent the state of many information
technology (IT)-intensive organizations that have recently undertaken massive IT infra-
structural upgrades especially in the data warehousing arena. These organizations all
seem to have ready access to data sources but are at a loss to derive value (knowledge)
from them. A typical reaction in such an organization is "we wish we knew all that we
know"(Information Week, 10/97).
Although many different transitions are possible, we identify certain key
transitions that are shown in the figure above (Figure 2). We attempt to relate the roles of
technology and cognition in enabling these transitions and discuss them using Simon's
decision framework of intelligence, design, and choice.
Figure 2
• Transition A occurs from III to I.
• Transition B occurs from IV to III.
• Transition C occurs from III to II.
• Transition D occurs from II to I.
A K
K
MK MK
I II
III IV B
C D
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The possible transitions serve to delineate possible evolutionary paths that
organizations follow as they adopt and adapt information technology to serve their
knowledge management and organizational needs and thus achieve different (hopefully
higher ) levels of organizational knowledge and meta-knowledge through learning and
meta-learning by doing (see Figure 3).
There are two key alternative paths that organizations follow along this “journey
to knowledge and self-awareness” that are mapped in Figure 4 and called the
Connectivity and Interactivity Paths.
Figure 3
The connectivity path is enabled by information technologies and is the most
commonly followed path by organizations because is seems more straightforward and is
easier to gauge as it involves more tangible inputs and outputs. However, it is the path
IV
I
A
K
K
MK MK
II
III
B
C
D
25
that often leads to dysfunctional situations such as the information technology
productivity paradox (Brynjolfsson, 1996, 1997, Hitt & Brynjolfsson, 1996) because it
often overlooks the human dimension and thus often impedes rather than facilitates
individual and organizational cognition. This is the efficiency-driven path.
The interactivity path is enabled by what we call information socio-technologies
and its emphasis is on effectiveness and on tacit as well as tangible inputs and outputs.
In Tables 9 and 10, we map the process-enabled and technology-enabled knowledge
transitions across the connectivity and interactivity paths as well as content-enabled and
technology-enabled knowledge states, mapping human-centric and machine-centric
technologies to each of the above transitions.
Figure 4
A K
K
MK MK
I II
III IV B
C D
CONNECTIVITY PATH
INTERACTIVITY PATH
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The "intelligent" transitions D and B are problem definition phases that involve creativity
and intelligence (pattern identification). The paths B-A and C-D are somewhat
contrasting in that B-A's emphasis is on effectiveness (organizational cognition-focused)
rather than efficiency. It is a top-down approach that focuses on values and objectives
that are crucial in problem definition. The path C-D's emphasis is more on efficiency and
the heavy reliance on technology and infra-structure possibly leading to the information
technology productivity paradox.
Each of the three stages in the Organizational Cognition Spiral (Figure 5)
reflects the progression from data to information to knowledge. Within each stage stage,
there is progression from the ignorance of ignorance (K / K) stage to the awareness of
awareness (K / K) stage.
The evolutionary knowledge transformation process is both differential and
integrative in nature (Carayannis, 1994, 1995, 1996, 1997, 1998), in that it consists of
both unlearning, learning and meta-learning components, differentiating new against
old experience, selecting and retaining the currently useful knowledge modules and
integrating the lessons learned throughout. This process reflects the dynamic of the
synthetic progression at the individual and the organizational levels from data to
information to knowledge to wisdom to intuition. In this manner, increasingly broader and
deeper levels of organizational knowing (Chun Wei Choo, 1998) are attained and both
quantitative as well as qualitative transformations of the organizational and individual
knowledge stock and flow occur.
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Figure 5
The Organizational Cognition Spiral (OCS)
Stage 1
Stage 3
Stage 2
IV
I
A
K
K
M M
II
III
B
C
D IV
I
A
K
K
M M
II
III
B
C
DIV
I
A
K
K
M M
II
III
B
C
D
28
Table 9
Process and Technology-Enabled Knowledge Transitions Transition Enabling Processes Enabling Technologies
A (III→I) From Awareness of Ignorance to Awareness of Knowledge
• Problem Solving • Internally-driven knowledge discovery • Active learning • Focus on effectiveness
• Decision Support Tools • Interactive Modeling
B (IV→III) From Ignorance of Ignorance to Awareness of Ignorance
• Collaborative processes • Internally-driven meta-knowledge
discovery • Value elicitation • Objectives Identification • Facilitation • Active learning • Focus on effectiveness
• Groupware • GDSS • Videoconferencing • Brainstorming
C (IV→II) From Ignorance of Ignorance to Ignorance of Awareness
• Knowledge osmosis • Externally-driven knowledge discovery • Knowledge creation • Passive learning • Focus on efficiency • Bypassing of Knowledge & IT
Productivity Paradox
• Information Infrastructure • Access Mechanisms: Networks
• LANs • WANs • Internet and Intranet
• Data Sources • Data Warehouses • Distributed Databases
D (II→I) From Ignorance of Awareness to Awareness of Awareness
• Individual privacy protection • Externally-driven meta-knowledge
discovery • Intellectual capital management • Passive learning • Focus on efficiency
• Intelligent Agent Technologies • Collaborative Filters • Data Mining • Neural Networks
E (III→II) From Awareness of Ignorance to Ignorance of Awareness
• Top down tacit learning • Knowledge internalization / routinization • Externally & internally-driven conceptual
paradigm emergence and crystallization • Transition of focus from effectiveness to
efficiency • Bypassing of Knowledge & IT
Productivity Paradox
• Tools for making technology infrastructure decisions
• Access Mechanisms: Networks • LANs • WANs • Internet and Intranet
• Data Sources • Data Warehouses • Distributed Databases
• Groupware • GDSS • Videoconferencing • Brainstorming
F (II→III) From Ignorance of Awareness to Awareness of Ignorance
• Bottom up explicit learning • Knowledge obsolescence / substitution • Internally & externally-driven conceptual
“gestalt switches” / paradigm shifts • Transition of focus from efficiency to
effectiveness • Resolution of Knowledge & IT
Productivity Paradox
• Groupware • GDSS • Videoconferencing • Brainstorming • Learning-capable intelligent agents or
interfaces
29
Table 10
Content and Technology-Enabled Knowledge States State Enabling Content Enabling Technologies
State I: K, MK Awareness of Awareness
• • Internally-driven knowledge discovery • Active learning • Focus on effectiveness
• Decision Support Tools • Interactive Modeling
State II: K, MK Ignorance of Awareness
• Collaborative processes • Internally-driven meta-knowledge
discovery • Value elicitation • Objectives Identification • Facilitation • Active learning • Focus on effectiveness
• Groupware • GDSS • Videoconferencing • Brainstorming
State III: K, MK Awareness of Ignorance
• Knowledge osmosis • Externally-driven knowledge discovery • Knowledge creation • Passive learning • Focus on efficiency • Bypassing of Knowledge & IT
Productivity Paradox
• Information Infrastructure • Access Mechanisms: Networks
• LANs • WANs • Internet and Intranet
• Data Sources • Data Warehouses • Distributed Databases
State IV: K, MK Ignorance of Ignorance
• Individual privacy protection • Externally-driven meta-knowledge
discovery • Intellectual capital management • Passive learning • Focus on efficiency
• Intelligent Agent Technologies • Collaborative Filters • Data Mining • Neural Networks
E (III→II) From Awareness of Ignorance to Ignorance of Awareness
• Top down tacit learning • Knowledge internalization / routinization • Externally & internally-driven conceptual
paradigm emergence and crystallization • Transition of focus from effectiveness to
efficiency • Bypassing of Knowledge & IT
Productivity Paradox
• Tools for making technology infrastructure decisions
• Access Mechanisms: Networks • LANs • WANs • Internet and Intranet
• Data Sources • Data Warehouses • Distributed Databases
• Groupware • GDSS • Videoconferencing • Brainstorming
F (II→III) From Ignorance of Awareness to Awareness of Ignorance
• Bottom up explicit learning • Knowledge obsolescence / substitution • Internally & externally-driven conceptual
“gestalt switches” / paradigm shifts • Transition of focus from efficiency to
effectiveness • Resolution of Knowledge & IT
Productivity Paradox
• Groupware • GDSS • Videoconferencing • Brainstorming • Learning-capable intelligent agents or
interfaces
30
Conclusions
By looking at the beginning and ending organizational knowledge states and then
examining the events and processes that enable the organizational knowledge transitions
thus tracking the transitions an organization is making, and then examining why and how
they happened, we can get very important insights on how to anticipate knowledge
transitions, facilitate desirable ones and avoid undesirable ones (proactive, anticipatory
management).
Moreover, by gaining a better understanding on how and why past transitions
occurred, we may be able to better focus on the key drivers of present knowledge
transitions and thus assess and influence more effectively present and future transitions.
In this manner, organizations can achieve a closer match between their current perceived
and actual knowledge state and transition and thus avoid harboring costly delusions of
“knowledge grandeur” or eschewing emerging strategic opportunities or threats.
With the OK Net and the OCS, we propose an organizational knowledge
architecture for optimal “impedance matching” between organizational knowledge
capabilities and needs and managerial cognition functions and processes. In this way,
organizations can better match their knowledge capabilities and needs with their
managerial cognition functions and processes..
31
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