ACCELERATED INNOVATION AND KM IMPACT
by Joseph M. Firestone, Ph.D.
Innovation, The Knowledge Life Cycle Model and Knowledge Management
Innovation acceleration is a "hot topic" in knowledge management . While
"first generation" or "supply-side" knowledge management focused mainly on
problems and concerns of managing knowledge storage and distribution, some
individuals in knowledge management have recently championed the cause of
"demand-side" knowledge processing . They argue that knowledge management
is broader than "supply-side" activities, and that, moreover, the KM value
proposition is greatly enhanced when we expand its focus to include knowledge
production activities, and in particular business innovation.
This new focus of KM is on innovation. It is about managing it, and
accelerating it, and it is about managing and accelerating innovations in
creating business innovations. But what is innovation? There are many ways to
define it, and I won't provide a definitional survey in this article. But my
definition is that innovation is a completed knowledge process life cycle
event, beginning with knowledge production and ending in incorporation of
knowledge structures within business structures. Innovation acceleration then,
involves continuous decrease in the cycle time of the knowledge process cycle.
To reason intelligently about innovation it is necessary to be clear on the
nature of the knowledge life cycle.
A Knowledge Life Cycle Model
Knowledge Production and Knowledge Integration are core knowledge processes
in the model. Knowledge Production produces Validated Knowledge Claims (VKCs),
Unvalidated Knowledge Claims (UKCs), and Invalidated Knowledge Claims (IKCs),
and information about the status of these. Organizational Knowledge (OK) is
composed of all of the foregoing results of knowledge production. It is what
is integrated into the enterprise by the Knowledge Integration process.
The Knowledge Life Cycle Model (Overview)
The knowledge integration process, in turn, produces the Distributed
Organizational Knowledge Base (DOKB) and the DOKB, in its turn, has a major
impact on structures incorporating organizational knowledge such as business
processes and information systems. Coupled with external sources these
structures then feed back to impact Knowledge Production at a later time --
which is why it's called the Knowledge Life Cycle (KLC) model.
Drilling down into knowledge production, the KLC view is that information
acquisition, and individual and group learning, impact on knowledge claim
formulation, which, in turn, produces Codified Knowledge Claims (CKCs). These,
in their turn, are tested in the knowledge validation sub-process, which
produces organizational knowledge. Individual and group learning may involve
knowledge production from the perspective of the individual or group, but from
the perspective of the enterprise, what the individuals and groups learn is
information, not knowledge. Similarly information acquired may be knowledge
from the perspective of the external parties it is acquired from.
The Components of Knowledge Production
Drilling down into knowledge integration, organizational knowledge is
integrated across the enterprise by the broadcasting, searching/retrieving,
teaching, and sharing sub-processes. These generally work in parallel rather
than sequentially. And not all are necessary to a specific instance of the
KLC. All may be based in personal non-electronic or electronic interactions.
Here is a glossary of the major terms used in the KLC Model.
The Components of Knowledge Integration: Glossary
Codified Knowledge Claims - Information that has been codified, and is claimed
to be true, but which has not yet been subjected to organizational validation.
Distributed Organizational Knowledge Base - an abstract construct representing
the outcome of knowledge integration. The DOKB is found everywhere in the
enterprise, not merely in electronic repositories.
Experiential Feedback Loops - Processes by which information concerning the
outcomes of organizational learning activities are fed back into the knowledge
production phase of an organization's knowledge life cycle as a useful
reference for future action.
Individual and Group Learning - A process involving human interaction,
knowledge claim formulation, and validation by which new individual and/or
group level knowledge is created.
Information About Invalidated Knowledge Claims - Information that asserts the
existence of invalidated knowledge claims and the circumstances under which
such knowledge was invalidated.
Information About Unvalidated Knowledge Claims - Information thats asserts the
existence of unvalidated knowledge claims, and the circumstances under which
such knowledge was tested and neither validated nor invalidated.
Information About Validated Knowledge Claims - Information that asserts the
existence of validated knowledge claims and the circumstances under which such
knowledge was validated.
Information Acquisition - A process by which an organization either
deliberately or serendipitously acquires knowledge claims or information
produced by others external to the organization.
Invalidated Knowledge - A collection of codified invalidated knowledge claims.
Invalidated Knowledge Claims - Codified knowledge claims that have not
satisfied an organization's validation criteria. Falsehoods.
Knowledge Claim - A codified expression of potential knowledge which may be
held as validated knowledge at an individual and/or group level, but which has
not yet been subjected to a validation process at an organizational level.
Information. Knowledge claims are components of hierarchical networks of
rules, that if validated would become the basis for organizational or agent
Knowledge Claim Formulation - A process involving human interaction by which
new organizational knowledge claims are formulated.
Knowledge Integration - The process by which an organization introduces new
knowledge claims to its operating environment and retires old ones. Knowledge
Integration includes all knowledge transmission, teaching, knowledge sharing,
and other social activity that communicates either an understanding of
previously produced organizational knowledge to knowledge workers, or the
knowledge that certain sets of knowledge claims have been tested, and that
they and information about their validity strength is available in the
organizational knowledge base, or some degree of understanding between these
alternatives. Knowledge integration processes, therefore, may also include the
transmission and integration of information.
Knowledge Production - A process by which new organizational knowledge is
created, discovered, or made. Synonymous with "organizational learning."
Knowledge Validation Process - A process by which knowledge claims are
subjected to organizational criteria to determine their value and veracity.
Organizational Knowledge - A complex network of validated knowledge claims
held by an organization, consisting of declarative and procedural rules.
Organizational Learning - A process involving human interaction, knowledge
claim formulation, and validation by which new organizational knowledge is
(business) Structures Incorporating Organizational Knowledge - Outcomes of
organizational system interaction. The organization behaves through these
structures including business processes, strategic plans, authority
structures, information systems, policies and procedures, etc. Knowledge
structures exist within these business structures and are the particular
configurations of knowledge found in them.
Unvalidated Knowledge Claims - Codified knowledge claims that have not
satisfied an organization's validation criteria, but which were not
invalidated either. Knowledge claims requiring further study.
Validated Knowledge Claims - Codified knowledge claims that have best
satisfied an organization's validation criteria compared to other, competing,
knowledge claims. "Truth" as we currently know it.
Knowledge Management, the KLC, and Innovation When we look at innovation
through the KLC model, it is only an additional short step to recognize that
to manage innovation we need to manage the KLC and both of its master
processes. What is the relationship between managing the KLC and Knowledge
Management itself? To answer this question, we need to decide whether managing
knowledge refers to managing knowledge processes, managing the outcomes of
these processes, or managing both? It has recently been stated [2, P. 87] that
"It's not knowledge management, stupid, it's knowledge PROCESS management."
But this is surely too simple. While KM is a process that manages the
knowledge processes of the KLC, since those processes produce knowledge
outcomes including the knowledge base, it is also true that KM indirectly
manages knowledge outcomes. Or, to put the situation another way, knowledge
management is most directly knowledge process management, and only indirectly
knowledge base management. The knowledge processes in question are given in
the KLC. So knowledge management is both process and outcome management, and
so is innovation management.
The Nature of Knowledge Management
There are many available definitions of knowledge management , but few
specifications that bring the definitions a step closer to analysis and
measurement. I define KM as human activity that is part of the Knowledge
Management Process (KMP) of an agent or collective. This reduces KM to the
definition of KMP. And the KMP is an ongoing, persistent, purposeful
interaction among human-based agents through which the participating agents
aim at managing (handling, directing, governing, controlling, coordinating,
planning, organizing) other agents, components, and activities participating
in the basic knowledge processes (knowledge production and knowledge
integration) into a planned, directed, unified whole, producing, maintaining,
enhancing, acquiring, and transmitting the enterprise's distributed
organizational knowledge base. This definition is another way of stating the
idea that KM is management of the KLC and its outcomes. But it still needs
Let's note first that the KMP is a business process. Any business process
including the KMP may be viewed as a network of sequentially linked activities
governed by validated rule sets, or knowledge. A linked sequence of activities
performed by one or more agents sharing at least one objective is a Task. A
linked sequence of tasks governed by validated rule sets, producing results of
measurable value to the agent or agents performing the tasks is a Task
Pattern. A cluster of task patterns, not necessarily performed sequentially,
often performed iteratively and incrementally, is a Task Cluster. Finally, a
hierarchical network of interrelated, purposive, activities of intelligent
agents that transforms inputs into valued outcomes, a cluster of task
clusters, is a business process.
The Activity to Business Process Hierarchy I break down the KMP  into
three task clusters: interpersonal behavior, knowledge processing behavior,
and decision making behavior. Interpersonal behavior may be further
Figurehead or ceremonial KM activity (focuses on performing formal KM acts
such as signing contracts, attending public functions on behalf of the
enterprise's KM process, and representing the KM process to dignitaries
visiting the enterprise);
Leadership (includes hiring, training, motivating, monitoring, and evaluating
staff. It also includes persuading non-KM agents within the enterprise of the
validity of KM process activities); and
Building external relationships -- another political activity designed to
build status and to cultivate external sources of support for KM.
Knowledge processing behavior includes:
KM knowledge production (different in that it is here that the rules for
knowledge production that are used at the level of knowledge processes are
KM Knowledge Integration (affected by KM knowledge production, and also
affects knowledge production activities by stimulating new ones).
Decision making behavior includes:
Changing knowledge process rules (involves making the decision to change such
rules and causing both the new rules and the mandate to use them to be
Crisis Handling (e.g., meeting CEO requests for new competitive intelligence
in an area of high strategic interest for an enterprise, and directing rapid
development of a KM support infrastructure in response to requests from high
Allocating Resources (KM support infrastructures, training, professional
conferences, salaries for KM staff, funds for new KM programs, etc.);
Negotiating agreements( with representatives of business processes over levels
of effort for KM, the shape of KM programs, the ROI expected of KM activities,
Further specification of KM involves breaking down the task patterns
further, but I don't need to do that for this discussion. In brief, the nature
of knowledge management is that it is a complex process composed of the above
task clusters and task patterns. To assess its impact on innovation, we need
to assess the changes in the state of the KM task clusters and task patterns
and the changes in the various components of the knowledge life cycle induced
by the changes in KM patterns. The changes in KM patterns are what we mean by
KM interventions. Later on I will provide examples of KM interventions.
Aspects of KM Impact on Innovation
I divide KM impact on innovation into three categories: KM impact on
knowledge processes; KM impact on knowledge process cycle times; and KM impact
on innovation rates and innovation relevance.
Impact on Knowledge Processes
The main point is that changes in KM cause changes in each of the components
of the two knowledge processes. KM impact on knowledge processes is a set of
impacts classifiable as impacts in information acquisition, individual and
group learning, knowledge claim formulation, and knowledge claim validation,
broadcasting, searching/retrieving, teaching and sharing. KM impact on
organizational knowledge, the distributed organizational knowledge base and
other outcomes incorporating knowledge structures is indirect. But changes in
these products of knowledge processes feedback to impact on future operations
of knowledge processes. Though not shown in the figure they may also feedback
to impact the KM process itself, provided a healthy KM process is in place.
KM Impact on Knowledge Processes
A more detailed classification of KM impacts can be developed from the
cross-classification of KM task patterns and KLC components. There are 72
types of KM impact resulting from this cross-classification. And many more
types would result if the KM task patterns were further broken down into
tasks. The types of impact can serve as a guide to hypothesis formation and
model construction. They provide a framework within which we can seek to
formulate and test hypotheses and rules and rule sets in models. The types of
KM impact can easily be laid out in a table, but I won't take the space to do
Impact on Knowledge Process Cycle Times
If changes in KM have an impact on changes in knowledge process components,
it is to be expected that they have this impact indirectly, through changes
they induce in the KM tasks comprising these components, and that these
changes, in turn, result in changes in knowledge processing cycle time. Figure
six illustrates this impact of changes in KM on knowledge process cycle times.
There is a cycle time for every component of the KLC. The total cycle time in
any instance of knowledge processing is the sum of the cycle times involved in
that instance of knowledge processing. Note that not every knowledge
processing component need be present in a given cyclical instance.
KM Impact on Cycle Times
Note also, that the impact of KM may be partitioned into separate impacts on
each of the cycle times associated with each of the components of the KLC.
Moreover, the impacts or changes in individual cycle times are additive in
determining the total cycle time changes in the KLC.
Impact on Innovation Rates and Innovation Relevance
Changes in KM patterns cause changes in the KLC. Two results are changes in
component and total cycle times and changes in the relevance or value of new
innovations. Innovations are not automatically valuable, and increases in
innovation cycle times are not automatically beneficial. Innovation relevance
addresses these questions.
KM Impact on Innovation Rates and Relevance
Innovation acceleration -- the change in velocity divided by the change in
time; and Velocity -- the number of innovation cycles per unit time (It will
be a small number if time is measured in seconds, minutes, hours, days, or
even weeks). Velocity also = 1/[the sum of the initial (before KM
intervention) component cycle times and the change in the sum of cycle times
after KM intervention].
KM Interventions and KM Metrics
KM process interventions are changes made in the nine task patterns and
their relationships, and even more concretely, in tasks comprising the task
patterns. These changes impact knowledge process components such as
information acquisition, knowledge validation, knowledge sharing, and their
relationships, and therefore also impact the relevance, acceleration, and
velocity of innovations. In order to evaluate KM interventions it is necessary
to measure their impact. In turn, this requires metrics for both KMP and KLC
attributes. There are three categories of knowledge process and product
metrics necessary for measuring KM impact and evaluating any KM intervention:
(1) internal metrics measuring changes in the task clusters, patterns, tasks,
and activities of the KM Process, and the products of the KMP; (2) knowledge
life cycle metrics needed for measuring the impact of changes in KM on KLC
process components, relationships, Innovation Velocity (IV), Innovation
Acceleration (IA), and Innovation Relevance (IR); and (3) metrics for
measuring the impact of changes in IV, IA, and IR on the enterprise.
Still more specifically, to validate any KM intervention one needs to
analyze the impact attributable to it of changes in KM patterns on changes in
metrics related to: information acquisition, individual and group learning,
knowledge claim formulation, knowledge validation; broadcasting, searching,
teaching, sharing; innovation velocity; innovation acceleration, innovation
relevance; and indicators external to the knowledge life cycle such as Return
On Capital Employed (ROCE), ROI, Operating Margin, and numerous balanced
scorecard-type measures of organizational performance. This is the validation
context of all KM interventions or KM techniques designed to accelerate
innovation, or to otherwise improve the quality of the KLC. Here are some
examples of the three types of metrics needed to evaluate KM interventions.
KM Process Internal and Related Product Metrics
KM process metrics include change in KM knowledge production cycle times; KM
knowledge integration cycle times; Frequency of change in knowledge production
rules; Intensity of collaboration among KM agents, teams, and groups;
Cycle time in responding to requests for competitive intelligence KM product
metrics include change in: Breadth of distribution of KM knowledge within the
KM community of practice; Increase/decrease in extent of validation of various
components of the KM organizational enterprise knowledge model.
Knowledge Life Cycle Metrics
KLC Process Metrics include change in: KLC Component Cycle Times; Innovation
Velocity; Innovation Acceleration;
Intensity of Collaborative Activity in Knowledge Production
Some KLC Product Metrics include change in: Extent of Innovation Relevance;
Average level of measurement of attributes in knowledge base within and across
domains; Validation profile of various components of the knowledge Base
KM-Related Enterprise Metrics.
Some Enterprise Process Metrics include change in: Manufacturing Production
Cycle Times; Customer Service Cycle Time; Intensity of collaboration in
enterprise business processes; Some Enterprise Product Metrics include change
in: ROI; Profitability; Market Share; Customer Retention; and Employee
Some Examples of KM Interventions
The key to KM impact on innovation is KM intervention. I have argued that we
must begin to measure and evaluate the impact of KM interventions on the KLC
and on innovation if we want to be effective in accelerating innovation. By
way of concluding this discussion of accelerated innovation and KM impact it
may be helpful to provide some examples of what we mean by the kind of KM
interventions that will need to be evaluated.
Allocate KM Resources to Support Involvement in External Initiatives
This may include involvement in outside consortia, think tanks, research
initiatives, industry conferences, outside training programs, and industry
intelligence subscription services. Impacts on information acquisition,
individual and group learning, and knowledge claim formulation are likely.
Allocate KM Resources to Establish and Support Communities of Practice
This may include implementing Web-based collaborative processing IT
applications. The effects may include decentralizing innovation, encouraging
cross-disciplinary collaboration, decreasing cycle time in individual and
group learning, knowledge claim formulation, and knowledge claim validation.
Change Knowledge Processing Rules By Introducing a Formal Knowledge Production
Can impact individual and group learning, knowledge claim formulation, and
knowledge validation, including establishing new knowledge validation
criteria. Impact on innovation acceleration, velocity, and relevance may
result, and must be carefully evaluated.
Implement Training Programs for KM
Impact can include rapid increase in awareness of the components of both
knowledge processing and knowledge management. In turn, this can lead to
acceleration in the various components of knowledge production especially, and
to implementation of new IT infrastructure to support knowledge processing in
Allocate KM Resources to Implement an Enterprise Knowledge Portal
Implementing an EKP can have a comprehensive impact on all components of
knowledge processing. EKPs can accelerate information acquisition, individual
and group learning, knowledge claim formulation, and support all of the
knowledge integration sub-processes as well. Impact however, will depend on
the specific changes introduced by the EKP. A comprehensive EKP can support
communities of practice, introduce a formal knowledge production methodology,
and support a variety of information acquisition, knowledge validation, and
knowledge integration activities, as well as a variety of KM activities.
White Paper No. Fourteen
 Edward W. Swanstrom "21 st Century Knowledge Management," Financial
Knowledge Management (October 1999), P. 11.
 Mark McElroy, "The Second Generation of KM," Knowledge Management
(October, 1999), Pp. 86-88, also available at
 Edward Swanstrom, Joseph M. Firestone, Mark W. McElroy, Douglas T.
Weidner, and Steve Cavaleri, "The Age of The Metaprise," Knowledge Management
Consortium International, Gaithersburg, MD, 1999.
 In e-mail and telephone communications.
 See Yogesh Malhotra's compilation at www.brint.com
 See Henry Mintzberg, "A New Look at the Chief Executive's Job,"
Organizational Dynamics," (AMACOM, Winter, 1973)
Joseph M. Firestone, Ph.D.
CEO, Chief Scientist
Executive Information Systems Inc (EIS)
Joseph M. Firestone, Ph.D. is CEO and Chief Scientist of Executive Information Systems (EIS)
Inc. Joe has varied experience in consulting, management, information
technology, decision support, and social systems analysis. Currently, he
focuses on product, methodology, architecture, and solutions development in
Enterprise Information and knowledge Portals, where he performs Knowledge and
knowledge management audits, training, and facilitative systems planning,
requirements capture, analysis, and design. Joe was the first to define and
specify the Enterprise Knowledge Portal Concept. He is widely published in the
areas of Decision Support (especially Enterprise Information and Knowledge
Portals, Data Warehouses/Data Marts, and Data Mining), and Knowledge
Management, and has recently completed a full-length industry report entitled
Information Portals." Joe is a founding member of the Knowledge Management
Consortium International (KMCI), Editor of the new KMCI Journal, Chairperson
of the KMCIís Artificial Knowledge Management Systems SIG, a member of its
Executive Committee, its Metaprise Project, and the KMCI Institute Governing
Council. Joe is a frequent speaker at national conferences on KM and Portals.
He is also developer of the Web site www.dkms.com, one of the most widely visited
Web sites in the Portal and KM fields. DKMS.com has now reached a visitation
rate of 83,000 visits annually.
Executive Information Systems Inc
The Executive Information Systems (EIS) Enterprise Knowledge Portal (EKP) is
the only portal solution that provides the assurance that enterprise decision
making will be based on validated knowledge. EISís EKP lets enterprises avoid
the risk involved in Enterprise Information Portals which claim to offer
increases in competitive advantage, ROI, speed of innovation, productivity,
effectiveness and profitability, but have as a central vulnerability the fact
that they are only capable of managing data and information, not
Enterprises using EIP-based solutions when they could be using EKP-based ones,
are gambling that unvalidated information can produce promised EIP benefits.
The central value proposition of the EIS EKP is that it replaces gambling on
unvalidated information with knowledge-based decision making. That is why it
is much more likely to achieve the promised benefits of EIP-based solutions
than its EIP competitors.
For more information, see