Chapter 9 Knowledge Management

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© 2005 Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition,
Turban, Aronson, and
Liang ,
arafatmy

9
-
1

Chapter 9

Knowledge Management

Turban, Aronson, and Liang
Decision Support Systems and Intelligent Systems,
Seventh Edition


© 2005 Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition,
Turban, Aronson, and Liang

9
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Learning Objectives


Define
knowledge
.


Learn the characteristics of knowledge management.


Describe organizational learning.


Understand the knowledge management cycle.


Understand knowledge management system technology and how it
is implemented.


Learn knowledge management approaches.


Understand the activities of the CKO and knowledge workers.


Describe the role of knowledge management in the organization.


Be able to evaluate intellectual capital.


Understand knowledge management systems implementation.


Illustrate the role of technology, people, and management with
regards to knowledge management.


Understand the benefits and problems of knowledge management
initiatives.


Learn how knowledge management can
change organizations
.

Knowledge Mgmt

Basically KM is collaborative computing
at the Organization level.

The goal is to
capture
,
store
,
maintain
,
and
deliver

useful knowledge in a
meaningful form to anyone who
needs it anyplace and anytime within
an org.

Knowledge = Intellectual Capital




© 2005 Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition,
Turban, Aronson, and Liang

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The Importance


of Managing Knowledge


Intellectual capital is appreciable assets, most
assets depreciate.


Knowledge work is increasing in importance as
much as the increase in service economy.


Employees with the most intellectual capital have
become volunteers to improve
B.P.
عادبلاا


Most managers ignore intellectual capital and lose
out on the benefits of its use.


Employees with the most intellectual capital are
often the least appreciated.


Knowledge mismanagement
.

© 2005 Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition,
Turban, Aronson, and Liang

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Encourage Knowledge

SHARING



Discouragement due to org. cultural or
technical barriers.


Educate people on the value of knowledge.


Refurbish reward and recognition system.


Act as a role model for sharing.


Make it a job requirement.


Make the tech. work for people; don’t expect the
people to work for the tech.
(machines in front of machines)


Educate people about the value in the know.


It is OK to make mistakes,
“No one is perfect”


© 2005 Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition,
Turban, Aronson, and Liang

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Why people do not share

KNOWLWDGE

It can cripple an org. As people refuse to share
what they know, a situation created by Org.
Culture and Barriers. Why people
(hide away)

their knowledge .?


Knowledge as a source of power and will
guarantee job security.


People wont get credit for sharing knowledge.


People don’t have time, or know how to share.


People don’t know the value or how much they
know.











© 2005 Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition,
Turban, Aronson, and Liang

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© 2005 Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition,
Turban, Aronson, and Liang

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Siemens Knows What It Knows Through
Knowledge Management


Knowledge management


Community of interest


Repositories (storehouse)


Communities of practice


Informal knowledge
-
sharing techniques
(Social Networks)


Employee initiated


Created ShareNet


Easy to share knowledge


Incentives for posting


Internal evangelists responsible for training,
monitoring, and assisting users


Top management support

© 2005 Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition,
Turban, Aronson, and Liang

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Knowledge Management


Process to help organization identify,
select, organize, disseminate, transfer
information


Structuring enables problem
-
solving,
dynamic learning, strategic planning,
decision
-
making and reduce redundancy


Leverage value of
intellectual capital
through reuse


The age of
knowledge worker
.


© 2005 Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition,
Turban, Aronson, and Liang

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9.3
Knowledge


Data

= collection of facts, measurements,
statistics


Information

= organized data


Knowledge

=
contextual, relevant, actionable info.


Strong experiential and reflective elements


Good leverage and increasing returns


Dynamic


Branches and fragments with growth


Knowledge is valuable when it is shared.


Uncertain value in sharing


Evolves over time with experience

© 2005 Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition,
Turban, Aronson, and Liang

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Knowledge


Explicit
(Leaky)

knowledge


Objective, rational, technical


Policies, goals, strategies, papers, reports


Codified


Leaky knowledge


Tacit
(sticky)

knowledge


Subjective, cognitive, experiential learning


Highly personalized


Difficult to formalize


Cumulative store of the experiences.


Within the brain of individuals or embedded in the
group interaction
.

© 2005 Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition,
Turban, Aronson, and Liang

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Knowledge Management


Systematic and active management
of ideas, information, and knowledge
residing within organization’s
employees


Knowledge management systems


Use of technologies to manage
knowledge


Used with turnover, change, downsizing


Provide consistent levels of service

© 2005 Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition,
Turban, Aronson, and Liang

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9.4
Organizational Learning


Learning organization


Ability to learn from past


To improve, organization must learn


3
Issues:


Meaning, Management, Measurement


5
Activities:


Problem
-
solving, Creative experimentation, learning from past,
learning from acknowledged best practices, transfer of knowledge
within organization

-
,


Organizational memory


way to save and share.


Must have organizational memory to have a learning Org.

Categories ( Well

)


1
-

Individual
2
-
information
3
-
Culture
4
-
Transformation
5
-
Structural

“Generally when a technology project fails, it is because
the technology doesn’t match the organization’s culture”


Organizational learning


Develop new knowledge that have the potential to influence
behavior.


Corporate memory is critical for success.


Org. Learning Process:


Know. Acquisition.


Know. Sharing.


Know. Utilization.


Organizational culture


Pattern of shared basic assumptions


Can cause KM success or failure.


Difficult to measure the impact of culture on org. (Strong culture
produce strong results(ROI, Net income, increase in stock price)



© 2005 Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition,
Turban, Aronson, and Liang

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KSF

To create an enterprise with a
culture of continuous change
where employees are not
threatened by change but are
encouraged by it because they
believe it will improve their
quality of life.


© 2005 Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition,
Turban, Aronson, and Liang

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© 2005 Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition,
Turban, Aronson, and Liang

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Knowledge Management
Initiatives


Aims


Make knowledge visible


Develop knowledge intensive culture


Build knowledge infrastructure


Surrounding processes


Creation of knowledge


Sharing of knowledge


Seeking out knowledge


Using knowledge

© 2005 Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition,
Turban, Aronson, and Liang

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Knowledge Management
Initiatives


Knowledge creation


Generating new ideas, routines, insights


Modes


Socialization, externalization, internalization,
combination


Knowledge sharing


Willing explanation to another directly or
through an intermediary


Knowledge seeking


Knowledge sourcing

Core competency linked to
Tacit and Explicit Knowledge

Core

Competencies
of the
organization

Explicit
Knowledge

Policies, Patents,
Decisions,
strategies

Tacit
Knowledge

Expertise, Know
-
how, Org. Culture,
Values

© 2005 Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition,
Turban, Aronson, and Liang

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Convert tacit know. To
measurable Explicit knowledge

Process of explication may
generate tacit knowledge


© 2005 Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition,
Turban, Aronson, and Liang

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Approaches to Knowledge
Management


Process Approach


Codifies knowledge


Formalized controls, approaches, technologies


Fails to capture most tacit knowledge


Practice Approach


Assumes that most knowledge is tacit


Informal systems


Social events, communities of practice, person
-
to
-
person contacts


Challenge to make tacit knowledge explicit, capture it,
add to it, transfer it

© 2005 Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition,
Turban, Aronson, and Liang

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Approaches to Knowledge
Management


Hybrid Approach


Practice approach initially used to store explicit
knowledge


Tacit knowledge primarily stored as contact information


Best practices captured and managed


Best practices


Methods that effective organizations use to operate and
manage functions


Knowledge repository


Place for capture and storage of knowledge


Different storage mechanisms depending upon data
captured


© 2005 Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition,
Turban, Aronson, and Liang

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9.6 Knowledge Management
System Cycle


Creates knowledge
through new ways of doing
things


Identifies and captures new
knowledge


Places knowledge into
context so it is usable


Stores knowledge in
repository


Reviews for accuracy and
relevance


Makes knowledge
available at all times to
anyone

Disseminate

©
2005
Prentice Hall, Decision Support Systems and Intelligent Systems,
7
th Edition,
Turban, Aronson, and Liang

9
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Components of Knowledge
Management Systems

KMS are developed using 3 sets of technologies:

1.
Communication


Access knowledge


Communicates with others

2.
Collaboration


Perform group work


Synchronous or asynchronous


Same place/different place

3.
Storage and retrieval


Capture, storing, retrieval, and management of both
explicit and tacit knowledge through collaborative
systems

© 2005 Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition,
Turban, Aronson, and Liang

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Components of Knowledge
Management Systems


Supporting technologies


Artificial intelligence


KM is a systems often have AI methods embedded in them


Expert systems, neural networks, fuzzy logic, intelligent
agents


Intelligent agents


Systems that learn how users work and provide assistance


Knowledge discovery in databases


Process used to search for and extract information


Internal = data and document mining


External = model marts and model warehouses


XML


Extensible Markup Language


Enables standardized representations of data


Better collaboration and communication through portals


©
2005
Prentice Hall, Decision Support Systems and Intelligent Systems,
7
th Edition,
Turban, Aronson, and Liang

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Knowledge Management
System Implementation


Challenge to identify and integrate components


Early systems developed with networks, groupware,
databases


Knowware

is
Technology tools that support KM

1.
Collaborative computing tools


Groupware

2.
Knowledge servers

3.
Enterprise knowledge portals

4.
Document management systems DMS

1.
Content management systems

5.
Knowledge harvesting tools

6.
Search engines

7.
Knowledge management suites


Complete out
-
of
-
the
-
box solutions


© 2005 Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition,
Turban, Aronson, and Liang

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9.7 Knowledge Management
System Implementation


Implementation


Software development companies.


Information systems vendors.


Consulting firms.



Application Service Providers ASP

Outsourcing,


©
2005
Prentice Hall, Decision Support Systems and Intelligent Systems,
7
th Edition,
Turban, Aronson, and Liang

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Knowledge Management System
Integration


Integration with enterprise and
information systems


DSS/BI


Integrates models and activates them for specific
problem


Artificial Intelligence


Expert system = if
-
then
-
else rules


Natural language processing = understanding
searches


Artificial neural networks = understanding text


Artificial intelligence based tools = identify and
classify expertise




© 2005 Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition,
Turban, Aronson, and Liang

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Knowledge Management System
Integration


Database


Knowledge discovery in databases


CRM


Provide tacit knowledge to users


Supply chain management systems


Can access combined tacit and explicit knowledge


Corporate intranets and extranets


Knowledge flows more freely in both directions


Capture knowledge directly with little user involvement


Deliver knowledge when system thinks it is needed


©
2005
Prentice Hall, Decision Support Systems and Intelligent Systems,
7
th Edition,
Turban, Aronson, and Liang

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9.8

Role of PEOPLE in KM


Chief knowledge officer


Senior level


Sets strategic priorities


Defines area of knowledge based on organization mission and goals


Creates infrastructure


Identifies knowledge champions


Manages content produced by groups


Adds to knowledge base


CEO


Champion knowledge management


Upper management


Ensures availability of resources to CKO


Communities of practice


Knowledge management system developers


Team members that develop system


Knowledge management system staff


Catalog and manage knowledge



© 2005 Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition,
Turban, Aronson, and Liang

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9.9 Valuation
ensuring success
KM


Asset
-
based approaches


Identifies intellectual assets


Focuses on increasing value


Knowledge linked to applications and
business benefits approaches


Balanced scorecard


Economic value added


Inclusive valuation methodology


Return on management ratio


Knowledge capital measure


Estimated sale price approach


©
2005
Prentice Hall, Decision Support Systems and Intelligent Systems,
7
th Edition,
Turban, Aronson, and Liang

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29

Metrics


Financial


ROI


Perceptual, rather than absolute


Intellectual capital not considered an asset


Non
-
financial


Value of intangibles


External relationship linkages capital


Structural capital


Human capital


Social capital


Environmental capital



© 2005 Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition,
Turban, Aronson, and Liang

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Factors Leading to Success and
Failure of Systems

Success


Companies must assess need


System needs technical and organizational infrastructure
to build on


System must have economic value to organization


Senior management support


Organization needs multiple channels for knowledge
transfer


Appropriate organizational culture

Failure


System does not meet organization’s needs


Lack of commitment


No incentive to use system


Lack of integration

End of ch
9
KM

Than You

©
2005
Prentice Hall, Decision Support Systems and Intelligent Systems,
7
th Edition,
Turban, Aronson, and Liang

9
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31