People-focused knowledge management

People Adopt New Mindsets!
Practitioners of deliberate and systematic KM develop widely
shared mindsets across their organizations. They focus on two
aspects:
1. The psychological, social, organizational, economic, and tech-
nical mechanisms that make knowledge and other IC assets
strengthen operational and strategic situation-handling and 
the effectiveness of resulting actions and enterprise perfor-
mance; and
2. How knowledge and other IC assets need to be managed from
competitiveness, investment, and enterprise renewal points of
points of view to support the enterprise and its stakeholders in
both the short term and the long term.
These mindsets embrace proactive, exploratory, and innovative
perspectives with notions of careful and responsible IC assets man-
agement. The mindsets are not prescriptive. Instead, they amount 
to a benevolent “Intellectual Capital Stewardship Mentality” (see
Chapter 7), which brings constructive and actionable knowledge per-
spectives to everyday situations — automatically, easily, and natu-
rally. The mentality is built by helping people in several ways. They
develop understanding of options for developing, obtaining, and
leveraging IC assets for everyday work. They are provided with role
models to understand the advantages for themselves, their customers
and stakeholders, and the enterprise. They build understanding and
motivation to think and react in the new way.
In organizations that pursue deliberate and systematic KM, the
mindsets have become a natural and automatized part of the daily
“Living the Job” and have resulted in adopting new operational 
considerations as routine. People are directly concerned with how 
to build, acquire, and apply the best possible IC assets such as ex-
pertise by experimenting, teaching, collaborating, discussing with
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People-Focused Knowledge Management
experts and peers, hiring, creating and using knowledge bases, com-
puter models, and in numerous other ways. From strategic perspec-
tives, it makes people consider options and tradeoffs for how to
invest time, effort, and resources to build IC assets for future needs.
Notes
1. It is important to keep in mind that “effectiveness” is not the same as
“efficiency.” We use these definitions: effective — producing a decided,
decisive, or desired effect or result; effectiveness — the quality or degree
of being effective; efficient — being productive without waste; efficiency
— the quality or degree of being efficient (Merriam-Webster 1986).
2. Systematic approaches, when applied to societal processes, emphasize
applying systems theory to deal with interconnectedness, effects over
time, parallelisms, and nonlinear behaviors.
3. Work: (a) something produced or accomplished by effort, exertion, or
exercise of skill; (b) something produced by creative talent or expendi-
ture of creative effort (Merriam-Webster 1986).
4. Service paradigms describe how the enterprise, separate units, and people
ideally should behave and do for external and internal customers and
how everyone should appear to customers through their actions as
explained in Chapter 7.
5. In the extreme, it should be remembered that a small percentage (
~
3–5
percent) of the general population have sociopathic tendencies. Many
become part of the workforce, in some instances at all levels.
6. Adapted from the Boston University 1987 North American Manufac-
turing Futures Survey.
7. Philadelphia Human Resource Planning Group 2002.
8. Completed staff work is the study of a problem and presentation of a
solution by a staff member in such form that all that remains to be done
on the part of the recipient is to approve or disapprove the recommended
action. (“Recommended action” must be emphasized since the more dif-
ficult the problem is, the more the tendency is to present the problem to
the recipient in a piecemeal fashion.)
9. Action Space — the domain that lies within the boundaries or the con-
straints that circumscribe the outer limits of actions within which the
person or enterprise operates comfortably (see Chapter 5).
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3
A
CTIONS 
A
RE INITIATED BY
K
NOWLEDGEABLE 
P
EOPLE: 
P
EOPLE 
M
AKE
D
ECISIONS AND 
A
CT 
U
SING 
D
IFFERENT
K
INDS OF 
M
ENTAL 
F
UNCTIONS
Premise 3-1: The Machinery of the Brain
Metaphor Is a Useful Beginning
From external observations of behavior, goal-oriented human 
reasoning can in part be described as a sequence of separate tasks.
These tasks receive information about some target situation and
apply knowledge to reason about them from the perspectives of a set
of objectives. The actual tasks and underlying mental mechanisms
are not known or understood with any clarity. Such a description of
human reasoning is similar to that of a “computing machine” and
provides an initial information theory model.
1
Premise 3-2: The Mind-As-Machine Metaphor Does
Not Cover Everything
As Lakoff (1987), and later Fauconnier and Turner (2002) and
others point out, the mind-as-machine view does not support many
observable operational functions that people perform with little
effort — complex categorization being one. Also, recent research
describes other human mental functions such as conceptual blending
(Fauconnier & Turner 2002), which involve capabilities far beyond
the realm of the mind-as-machine metaphor.
Whereas the human mind and its functions may be a mystery, we
characterize part of its behavior by augmenting cognitive sciences and
additional research results by borrowing from systems theory and
other areas. Consequently, many constructs for knowledge, its 
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People-Focused Knowledge Management
acquisition and application, presented in this book may be artificial
and even questionable. However, they serve to provide a knowledge-
based framework suitable for practical business considerations.
The Personal Reasoning Example
Peter Jones, an experienced design engineer, was drawing up the
specifications for an industrial heat exchanger. The problem was
complicated with information describing physical space constraints
and close exit temperature requirements over a wide operating range.
Peter had designed similar exchangers before and knew immediately
that the best solution would involve countercurrent flow with a par-
ticular geometric arrangement. Without thinking explicitly about it,
he knew precisely how to detail calculations and how to use the com-
puter analysis programs. In fact, he performed all initial specification
tasks without giving conscious thought as to how to do it or what 
to do next. He had a well-established script in his mind that he 
operationalized and activated nonconsciously, once given the infor-
mation describing the situation.
The technical specifications were easily done, and while working
alone, Peter completed them in a few hours. Then came the compli-
cated part — to design the specified capacity into a physical shape
that would fit into the available space. This was a new challenge.
Peter and two collaborators struggled for several days to solve the
problem by trying different geometric configurations. At first, none
seemed to work properly. The ones they could fit into the space posed
impossible manufacturing problems. After several attempts, they
remembered having seen an unusually shaped exchanger using
uncommon materials which had the needed manufacturing flexibil-
ity while also having the required thermal and physical properties.
By obtaining more information on the materials, the team finalized
the design and submitted it to the shop to be built. After completing
this project, Peter realized that using these materials would allow his
company to make a new line of heat exchangers for uses that they
previously had not been able to serve.
In performing his work, Peter drew on a wealth of personal knowl-
edge. He possessed mental reference models as automatized tacit
knowledge about creating heat exchanger specifications. Such work
had over time become routine for him. He reasoned rapidly, intu-
itively, and accurately with concepts, scripts, and facts that repre-
sented his tacit understanding of the detailed scientific principles and
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Actions Are Initiated by Knowledgeable People
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engineering methods pertaining to heat exchanger design. In addi-
tion, Peter and his team used other, less automatized knowledge to
explore design options for the physical configuration of the device.