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6 Νοε 2013 (πριν από 3 χρόνια και 8 μήνες)

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P
H
.D D
ISSERTATION

O
RAL

I
NTEGRATED

K
NOWLEDGE

M
ANAGEMENT

F
RAMEWORK

FOR

A
DDRESSING

I
NFORMATION

T
ECHNOLOGY

P
ROJECT

F
AILURES

(IKMFAITPF)


Walden
University






1

T
HE

P
ROBLEM


Synthesis of a Knowledge Management (KM) framework for minimizing the Information
Technology (IT) project risks to increase project success rate, which impacts organizations
positively, while inculcating knowledge sharing culture among team members.

Drivers of the problem


IT projects, close to 71% (Harman, 2006; Sauer & Gemino & Reich, 2007), fail to deliver due to scope, time,
quality, staff and money related problems.


Lack of applicability of existing knowledge management (KM) frameworks that can be applied directly to
information technology (IT) projects
(Burgess, 2005; Ermine & Boughzala & Toukara, 2006; Fichman & Kell & Tiwana, 2005; Iyer & Shankarnarayan
& Wyner, 2006; Jewels & Ford, 2006; Haas, 006; Kalpic & Bernus, 2006; Kane & Pretorius & Steyn, 2005; Landaeta, 2008; Lee & A
nde
rson, 2006; Ragsdell &
Oppenhiem, 2006; Whelton & Ballard & Tommelein, 2002).


Mobile knowledge workforce and assets.


Increasing globalization and distributed teams.


Involvement of multiple cultures and interactions.


Complexity in IT projects along with fast changing industrial and organizational changes.


Lack of or poor software standards


Task complexity, project complexity, and software complexity


Flexibility of IT projects


Fluid building blocks


Knowledge assets


people, projects and process







2

T
HE

S
TUDY

Research Goals


G1
-

Identify and assess KM related project failures due to five major variables


scope, time, quality, money and
staff.


G2
-

Build integrated KM framework that addresses these failures and enhances the project success rates.


G3
-

Devise a measuring system that quantifies the value add of such a system.

Research Questions

Question for G1


Q1: How scope, time, quality, money and staff factors, that relate to knowledge management, impact IT projects to
fail?

Questions for G2


Q2: How can these factors be addressed to improve IT project success rate by using a knowledge management
framework?


Q3: How can the development of a KM framework help in increasing the rate of success of IT projects?

Question for G3


Q4: How can the development of performance management system measure the success of the framework?



3

T
HEORETICAL

F
RAMEWORK


Basic theories by Nonaka and Takeuchi (1994) and Boisot (1998) on
KM are the foundational theories for this research study


theoretical
lens through which this research looks at the problem.


SECI model


Socialization, Externalization, Combination, and
Internalization


I
-
Space


knowledge flow


Un
-
coded to codified


Concrete to abstract


Undiffused to diffused


4

C
ONCEPTUAL

MODEL

OF

THE

RESEARCH

Research Model

5

People
Projects
Process
Patterns of IT
projects failures that
relate to KM
Proposed KM Framework
Expanded Conceptual Framework of the Proposed Research
Data from
-

IT projects
Cases
Measuring the
Performance of the
Framework
Project Selection
-
using purposeful
sampling
R
ESEARCH

M
ETHOD



Qualitative Research Methodology


Multiple case study method.


Discovering the facts about an object, entity or unit of analysis


in this study


IT project about why and how of the story.


Purposeful sampling (sampling of IT projects and not people)


IT Projects implemented during 2007
-
2010 within financial service industry in the USA.


Projects that had worked through similar environment


Project/Software methodologies adopted


Lack of formal knowledge management infrastructure


Organizational level of maturity, Capability Maturity Model Integrated (CMMI)


Projects that were challenged (a subcategory of failed projects).


These projects were implemented successfully with additional project resources


scope, time, quality, staff and money.


Semi
-
structured Face
-
to
-
Face Interviews


Interviewed the project manager and the project lead for each of the five projects selected through purposeful sample method.


Questionnaire A
-

for purposeful sampling of the projects.


Questionnaire B


for capturing qualitative data from project managers.


Questionnaire C


for capturing qualitative data from project leads.


Transcription and coding


Interviews conducted, were audio recorded and then transcribed.


Coding of the transcripts were done based on the themes identified through qualitative data that related to KM activities and

ta
sks.


Used NVivo version8 for coding into themes and patterns of themes.



6

V
ALIDITY

-

I
NTERNAL

Conformability


Taken field notes and used to correct and minimize error in analysis.


Arranged multiple interview sessions to capture data based on the previous sessions.


Semi
-
structured questionnaires guided the researcher through asking questions where necessary and appropriate for
data consistency.

Member checking


At the end of the transcription, the researcher interacted with project managers and project leads to make sure that
what was said was interpreted correctly. In few cases the researcher corrected the information, although these were
found to be minor. For example project schedules, budget and how they view project success were elaborated,
corrected, and interpreted.

Transferability


The research was aided by semi
-
structured interview instrument which defined the scope of questions to be asked for
all the project managers and/or project leads.


Purposeful sampling with a defined criteria for the project selection provides transparency on the research method
used.



7

V
ALIDITY

-

E
XTERNAL

Generalizability


There was no selection of people in this research, although project managers and project leads were the
participants. So there were limited idiosyncratic settings within the sample.


Balanced
breadth
and
depth
of the data collected


Multiple cases were selected


Five projects were selected


Each of the interview sessions lasted for 60 to 90 minutes for project manager or project lead and spanned
into multiple sessions.


Research data covered multiple aspects of project failures to cover depth required.

Dependability


Used negative cases


Utilized multiple interview sessions and rephrased the same questions where appropriate.


Examined themes at the end of initial analysis and made sure that the there was no disconfirming evidence.








8

R
ELIABILITY


Multiple case
-

Five projects and each project constituted a
single case in this qualitative case study research.


Multiple organizations were involved from where the projects
were sampled.


Multiple listening's of the tapes with help of digital
recordings, and with flexibility of variations in the speed of
play of the audio recordings.


Multiple transcriptions (four times) of audio recorded files in
phases with reexamination the transcriptions.



9

R
ESULTS

-

1


Research Question 1: How do scope, time, quality, and staff factors relate to KM influence IT project failure?


Research Question 2: How can these factors be addressed through a knowledge management framework to
improve the success rate of IT projects?


Project 1


quality, scope, time, money and staff


Project 2


staff, quality, time, scope, and money


Project 3


scope, time, quality, and money


Project 5


time, quality, staff, and time


Project 6


money, staff, time, scope, and quality


Patterns identified from the data


Knowledge Area (KA)


Knowledge Producer (KP)


Knowledge Consumer (KC)


Knowledge Base (KB)


Knowledge Flow (KF)


Knowledge Inhibitors (KI)


Knowledge Accelerators (KA)


Knowledge Distribution (KD)


Un
-
codified and Un
-
abstracted Knowledge (UUK)


Training and Learning (TA)



10

R
ESULTS

-

2

11

S.
No

KM Pattern

Case 1


Case 2


Case 3


Case 5


Case 6


1

Knowledge
Area (KA)


Lack of systems
analysis skills


Lack of

systems
analysis skills

Unclear
deliverables


Lack of
systems
analysis
skills

Detailed scope
challenges



Mutually
dependent
projects’
deliverables

Detailed
scope
challenges

Lack of vendor
product knowledge

Lack of
vendor
product
knowledge

Lack of
b
usiness
systems
knowledge

Lack of new
application
knowledge
based on
vendor
product

Lack of
vendor
product
knowledge

Lack of quality
guidelines

Incomplete
and
informal
quality
guidelines



No formal
quality
guidelines

Needed technical,
procedural, so
ft
skill and process
knowledge


Needed
technical,
procedural,
soft skill and
process
knowledge

Needed to
follow
informal
processes

Needed to
follow
informal
processes


R
ESULTS

-

3

12

S.
No

KM Pattern

Case 1


Case 2


Case 3


Case 5


Case 6


2

Knowledge
Producer
(KP)


Ve
ndor team
had expertise on
vendor product

Business team
had domain
knowledge
expertise

Team relied
for expertise
on one person

Team
members
worked
independently
rather than
collaboratively

Project teams
had
technology
knowledge of
the vendor
product

Pr
oject team had
domain
knowledge
expertise

Technology
team had
expertise in
technology
areas

C
hallenges in
technical areas
such as
deployments
to end users


Client teams
had domain
knowledge

3

Knowledge
Consumer
(KC)

-
same as above
-

-
same as
above
-

-
same
as
above
-

-
same as
above
-

-
same as
above
-

4

Knowledge
Base (KB)


No formal or
structured
documentation

Process and
design
-
related
formal and
structured
documentation

Some formal
and structured
documentation

Some formal
and informal
documentation

Some
inf
ormal
documentation

Informal
documentation
did not cater to
the needs of the
project

Insufficient
documentation
for the project

Insufficient
documentation
for the project

Insufficient
documentation

Insufficient
documentation


R
ESULTS

-

4

13

S.
No

KM Pattern

Case 1


Case 2


Case 3


Case 5


Case 6


5

Knowledge
Flow (KF)

Insignificant or
minimal
knowledge transfer


Knowledge
transfer was
insignificant
or minimal

Poor or
insignificant
knowledge
transfer


Insufficient
knowledge
transfer


Insignificant
or minimal
knowl
edge
transfer

6

Knowledge
Inhibitors (KI)


Virtual team
environment

Virtual
environment

Virtual
environment

Virtual
environment

Virtual
environment

Multiple countries

Multiple
countries

Multiple
countries

Multiple
countries

Multiple
countries

Mult
iple project
assignments

Multiple
project
assignments


Multiple
project
assignments

Multiple
project
assignments

Initial

members
left and new
members joined

Initial

members
left and new
members
joined

Members left
the project

Initial

members left
and n
ew
members
joined

New
members
joined the
project

People left and
new people joined

People left
and new
people
joined

People left
and new
people joined

People left
and new
people joined

People left
and new
people
joined

Recurring
problems

Time an
d
budget
constraints





No rewards or
recognition


No rewards
or
recognition


No rewards or
recognition



No rewards
or
recognition



70% interactions
(average)

25%
interactions
(average)

50%
interactions
(average)

30
%
interactions

(average)

60%
inte
ractions
(average)


R
ESULTS

-

5

14

S.
No

KM Pattern

Case 1


Case 2


Case 3


Case 5


Case 6


7

Knowledge
Accelerators
(KA)



None


None


None

Five
instances of
rewards and
recognition


None

8

Knowledge
Distribution
(KD)



None


None


None


None


None

9

Un
-
codified
and
Un
-
abstracted
Knowledge
(UUK)


Needed procedural
knowledge


None


None


None


None

10

Training and
Learning (TA)


No formal training

No formal
training

No formal
training

No formal
training

Some
training on
technology

No e
-
learning
sessions

No e
-
learni
ng
sessions

No e
-
learning
sessions

No e
-
learning
sessions

No e
-
learning
sessions

No coaching

No coaching

No coaching

No coaching

Some level
of
mentoring


D
ATA

A
NALYSIS

-

1



Research Question 3: How can the development of
a KM framework help to improve the success of IT
projects?


15

T
HE

F
RAMEWORK

16

D
ATA

A
NALYSIS

-

3


Research Question 4: How can the development of
a performance
-
management system be used to
measure the success of the KM framework?


17

M
EASURING

M
ECHANISM

FOR

I
NTEGRATED

K
NOWLEDGE

M
ANAGEMENT

F
RAMEWORK

FOR

A
DDRESSING

I
NFORMATION
-
T
ECHNOLOGY

P
ROJECT

F
AILURES


18


S
.
No

W
ha
t

W
ho

G
oa
l
s

Com
m
e
nt
s

1

K
now
l
e
dge
ga
p
i
de
nt
i
f
i
c
a
t
i
on

KP

KC

L
i
s
t
t
he
know
l
e
dge
ga
p pre
s
e
nt
f
or t
he
proj
e
c
t
s

M
e
a
s
ure
t
hi
s

w
i
t
hi
n t
he
t
hre
e

a
re
a
s
-
t
e
c
hni
c
a
l
, proc
e
s
s
, a
nd
s
of
t
s
ki
l
l
.

M
e
a
s
ura
bl
e
goa
l
s
a
re
c
re
a
t
e
d.

S
urve
y t
he
know
l
e
dge

c
ons
um
e
rs
on how
m
uc
h t
he
y
ha
ve
l
e
a
rne
d.

S
urve
y know
l
e
dge
c
ons
um
e
rs

on how
m
uc
h t
he
y ha
ve
a
ppl
i
e
d
t
he
i
r know
l
e
dge
.

G
e
t
t
he
f
a
c
t
s
f
r
o
m
t
he
proj
e
c
t

t
ha
t
a
re
i
m
pl
e
m
e
nt
e
d a
nd us
e
d
t
hi
s
f
ra
m
e
w
ork.

----------------------------

KP

K
now
l
e
dge
P
roduc
e
r

KC

K
now
l
e
dge
Cons
um
e
r

PM

P
roj
e
c
t
M
a
na
ge
r

2

K
now
l
e
dge

re
qui
re
m
e
nt
c
a
pt
ure

KP

KC

A
na
l
yz
e
t
he
ga
p i
nt
o re
qui
re
m
e
nt
s
w
i
t
h qua
l
i
t
y
l
e
ve
l
s

3

K
now
l
e
dge
-
a
rt
i
c
l
e

c
ont
ri
but
i
ons

KP

N
um
be
r of
ne
w
a
rt
i
c
l
e
s
t
hrough t
he
pe
ri
od

Ra
t
i
ng of
t
he
a
rt
i
c
l
e
s
f
rom
t
he
c
ons
um
e
rs

V
a
l
ue
a
dde
d t
o t
he
c
us
t
om
e
rs
f
or t
he
proj
e
c
t
s

4

T
ra
i
ni
ng a
nd l
e
a
rni
ng
c
ont
ri
but
i
ons

KC

A
dd va
l
ue
f
or t
he
c
ons
um
e
rs

F
e
e
dba
c
k f
rom
t
he
c
ons
um
e
rs

5

P
roj
e
c
t
be
ne
f
i
t
s

PM

H
ow
m
uc
h of
t
he
know
l
e
dge
i
s
us
e
f
ul
f
or t
he
proj
e
c
t

de
l
i
ve
ra
bl
e
s
.

T
hi
s

m
e
a
s
ure
m
e
nt

a
ggre
ga
t
e
s
a
l
l
t
he
m
e
a
s
ure
d
qua
nt
i
f
i
e
rs
f
rom
1 t
o 4 a
bove
.


Measuring the Framework

Project
Benefit and Overall Benefits

19


L
e
a
rni
ng

O
bj
e
c
t
s

K
now
l
e
dge


Ba
s
e
A
rt
i
c
l
e
s

Ra
t
i
ng

K
now
l
e
dge

Ba
s
e
A
rt
i
c
l
e
s

Ra
t
i
ng

T
ra
i
ni
ng a
nd
L
e
a
rni
ng

Ra
t
i
ng

P
roj
e
c
t

Be
ne
f
i
t

O
ve
ra
l
l
Be
ne
f
i
t

E
x
i
s
t
i
ng
Articles

Q
ual
i
t
y


Ne
w

Q
ual
i
t
y


Cons
um
e
r

V
al
ue

Q
ual
i
t
y




LO1

0

1

10
scale

0

0

1

10
scale

Y
1 =
0

0

1

10
scale

0

(X
1+Y
1)/
20

= P
B1

(X
1+Y
1+Z
1)/
30

= O
B1

1

X1

X1

1

10

Y
1 =
10

1

1

10
scale

Z1

LO2

0

1

10
scale

0

0

1

10
scale

Y
2 =
0

0

1

10
scale

0

(X
2+Y
2)/
20

= P
B2

(X
2+Y
2+Z
2)/
30

= O
B2

1

X2

X2

1

10

Y
2 =
10

1

1

10
scale

Z2



LO3

0

1

10
scale

0

0

1

10
scale

Y
3 =
0

0

1

10
scale

0

(X
3+Y
3)/
20

= P
B3

(X
3+Y
3+Z
3)/
30

= O
B3

1

X3

X3

1

10

Y
3 =
10

1

1

10
scale

Z3



……












L
e
a
rni
ng

O
bj
e
c
t
s

K
now
l
e
dge


Ba
s
e
A
rt
i
c
l
e
s

Ra
t
i
ng

K
now
l
e
dge

Ba
s
e
A
rt
i
c
l
e
s

Ra
t
i
ng

T
ra
i
ni
ng a
nd
L
e
a
rni
ng

Ra
t
i
ng

P
roj
e
c
t

Be
ne
f
i
t

O
ve
ra
l
l
Be
ne
f
i
t

E
x
i
s
t
i
ng
Articles

Q
ual
i
t
y


Ne
w

Q
ual
i
t
y


Cons
um
e
r

V
al
ue

Q
ual
i
t
y




LOn

0

1

10
scale

0

0

1

10
scale

Y
n =
0

0

1

10
scale

0

(X
n+Y
1)/
20

= P
Bn

(X
n+Y
n+Z
n)/
30

= O
Bn

1

Xn

Xn

1

10

Y
n =
10

1

1

10
scale

Zn




S
UMMARY

OF

F
INDINGS

AND

C
ONCLUSIONS

-

1


Teams worked in predominantly virtual environments.


Inferior knowledge flow.


Inhibiting factors of knowledge flow identified


No significant knowledge accelerating factors recognized.


Documentation was not sufficient and/or did not meet the project teams’ needs.


The integrated KM framework addresses these through increasing acceleration
factors for knowledge flow and decreasing inhibitors.


Formalizing knowledge and structuring knowledge allows teams to interact
efficiently and share knowledge, via knowledge base, effectively in virtual
environments.


The return on investment (ROI) on the investment for the framework allows the
project leaders to assess value in implementing it.







20

S
UMMARY

OF

F
INDINGS

AND

C
ONCLUSIONS

-

2


Identify and divide knowledge specific areas into learning objectives that are
measurable.


Rate the knowledge articles based the value addition to the project teams and reward
the members of the teams who produced the knowledge.


Evaluate the overall project benefit through the measuring systems prescribed.


The value addition in incremental as more projects follow through the framework the
more knowledge artifacts get collected and the more knowledge equity for the
organization.


The entire framework should be considered as a process and must be treated just like
any other process within the project development environment.


21

S
UMMARY

OF

F
INDINGS

AND

C
ONCLUSIONS

-

3


The framework requires a onetime investment on the KM infrastructure.


Project managers should assume responsibility for implementing the framework for
their projects.


While the project leaders have an option on how much they want to invest in the KM
framework in setting time for KM activities, the recommendation of at least 5%
buffer time should be allowed to trigger formal knowledge flow.


The framework indirectly creates knowledge sharing culture and allows this culture
to grow into a standard and is a continuous process.


The knowledge culture gets embedded in people minds and accelerates the habit in
others while working with them.

22

F
UTURE

R
ESEARCH

P
OTENTIAL


Repeat the research in other industries such as
healthcare, hospitals, pharmaceuticals, automobile, and
education and compare the results.


Identify the primary KM component(s) for each
industry.


Identify inhibiting and accelerating factors or lack
there of.


The measuring method prescribed deserves its own
research and ample research opportunities in that area.


Contribute to benchmarking KM measurements.

23

R
EFERENCES

-

1


Nonaka, I. (1990). Redundant, overlapping organizations: A Japanese approach to
managing the innovation process,.
California Management Review, 32
(3), 27
-
38.


Nonaka, I. (1994). A dynamic theory of organizational knowledge creation.
Organization Science, 5
(1).


Nonaka, I., & Takeuchi, H. (1995).
The knowledge creating company: how Japanese
companies create the dynamics of innovation
. New York, NY: Oxford University
Press.


Boisot, M. H. (1998).
Knowledge assets: Securing competitive advantage in
information economy
: Oxford, England: Oxford University Press.


Burgess, D. (2005). What motivates employees to transfer knowledge outside their
work unit.
Journal of Business Communication, 42
(4), 324
-
348.


Ermine, J.
-
L., Boughzala, I., & Tounkara, T. (2006). Critical knowledge map as a
decision tool for knowledge transfer actions.
The Electronic Journal of Knowledge
Management, 4
(2), 129
-
140.


Iyer, B., Shankarnarayanan, G., & Wyner, G. (2006). Process coordination
requirements implications for the design of knowledge management systems.
Journal
of Computer Information Systems.


Jewels, T., & Ford, M. (2006). Factors influencing knowledge sharing in information
technology projects. e
-
Service Journal.




24

R
EFERENCES

-

2


Haas, M. R. (2006). Knowledge gathering, team capabilities, and project performance
in challenging work environments.
Management Science, 52
(8), 1170
-
1184.


Kalpic, B., & Bernus, P. (2006). Business process modeling through the knowledge
management perspective.
Journal of Knowledge Management, 10
(3), 40
-
56.


Kane, Hilary., & Ragsdell, Gillian., & Oppenhiem, Charles. (2006). Knowledge
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