80
Marwa Ahmed (Professor)
INTRODUCTION
In recent years, the rise of the knowledge
economy has created new challenges for
strategic management and made managing
intellectual capital an integral part of the
firm’s strategy, thus making the creation,
development and capturing of value from
knowledge and competencies a critical issue.
This development has led to a burst of
attention to knowledge assets in the
management, organization and strategy
literatures. The rise of competencies and
capabilities approaches to firm organization
during the last decade has been linked to the
knowledge economy and the increasing
importance of innovation, rapid technological
change, and knowledge assets, among other
things (Eisenhardt and Martin, 2000).
There are different levels of knowledge that
can be recognized and understood in an
organization’s knowledge resources
(Greenberg, 2003). The easiest form of
knowledge to understand is that of structured
knowledge.
The hardest form of knowledge (in terms of
understanding or detection) is tacit
Knowledge Management Concepts as viewed by
executive managers in Jordan
MARWA AHMED
 Philadelphia University
E-mail: marwaahmad@yahoo.com
ABSTRACT
What is the major knowledge management (KM) concept that the executive managers viewed?
To answer this question a total of 45   questionnaires were used in this research. SPSS, ANOVA,
T-test and sheffee test were used for the 45 respondents result .The chi-square goodness-of-fit
test was used for checking non-response bias.
The results of this research provide evidence that: KM concepts are statistically significant
and provide guidelines for future research. KM drives economic growth. The executive manager
who works with a Company with fewer than 50 employees feels that KM informs decision-
making. KM can be translated into a profitable venue is significant for the manager who
works in industry and Irbid City female and has a Bachelor degree.
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knowledge, which is the most transparent and
subjective form of knowledge (Griffith,
2003).
An organization’s knowledge resources have
pertinently been described as an iceberg
(Haldin-Herrgard, 2000). Structured, explicit
knowledge is the visible top of the iceberg.
This part of the knowledge resource is easy
to find and recognize, and therefore easy to
share.
Knowledge assets are very important to all
organizations as they play a major role in all
decision making. However, very little
attention is given to how knowledge is created
and how the knowledge creation process can
be managed. According to Teece, knowledge
assets cannot be bought and sold and need to
be built in-house by organizations, and “they
must also be exploited internally in order for
full value to be realized by the owner” (Teece,
2000, p. 36). He further argues that the nature
of knowledge itself makes organizational
knowledge difficult to transfer as it is
embedded in the organizational processes,
procedures, routines and structures
(Bogdanowicz, 2002). According to
Bloodgood and Salisbury, every organization
needs to identify where knowledge resides
in the organization. It is very important
especially when designing strategies “in order
to ensure knowledge is being created,
transferred and protected in the right way and
with the right individuals” (Bloodgood and
Salisbury, 2001, p. 55). With reliable
collections of knowledge assets, then
knowledge can be transferred to the
respective person at the right time and at the
right place with great accuracy so the
management of knowledge sharing across
organizational borders should focus on the
nature of knowledge integration and key
factors that facilitate it. Managers hoping to
create value by bringing together diverse
knowledge streams from others must
understand that economic action is embedded
within a social structure in which different
types of social capital obtain, impacting the
nature of knowledge integration. Therefore,
social structures of human interaction must
not be left as mere constructs to be
understood. Rather, they must be managed.
Conscious decisions must be made about the
design of roles, responsibilities, authority, and
coordination of activities to facilitate
knowledge integration.
Knowledge integration also depends on social
capital. The transfer of information and
knowledge at micro- and macro-levels
between individuals and between
organizations depends on people initiating
and facilitating those transfers (Denning,
2002; Hinds, 2003). Consequently, all those
things that encourage or inhibit inter-personal
communication affect knowledge transfer. Of
these, the importance of trust has long been
noted (Sole, 2000).
Knowledge management can be generally
understood as the understanding, regulation,
and creation of policy associated with
relevant information. The field has been
particularly studied within the context of the
enterprise, with a focus toward how certain
approaches toward the knowledge within the
organization can lead to a competitive
advantage.
A key element in analyzing how technology
is transferred internally within the firm is to
consider the wider issue of knowledge and
information flows. Technology can be
transferred in terms of tangible assets, such
as new products, plant and equipment, and
in intangible form through formal
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Marwa Ahmed (Professor)
mechanisms, such as patents and licences,
and informally through information flows and
knowledge sharing (Menzler-Hokkanen,
1995). A more recent form of organizational
innovation in multinational firms is where
senior managers, planning intra-firm
technology transfer activities, have employed
different organizational designs to facilitate
the exchange and combination of knowledge
in the development of a technology transfer
process.
There are two predominant conceptions of
what is meant by knowledge: “exogenic” and
“endogenic”. One can differentiate these
conceptions by the difference between
“knowledge” and “knowing”.
The exogenic viewpoint views knowledge as
static, unchanging, facts, existing
independently of the knower, located in the
world as discoverable “truths”. In the
exogenic viewpoint there is little or no link
between knowledge and “knowing”
(Kakabadse, 2003).
The endogenic viewpoint views knowledge
as dynamic, changeable facts, cognitions,
feelings, and emotions, dependent on the
knower, located in the mind with possible
various “correct” versions of the truth.
Knowledge can be created and is inherently
subjective (Kakabadse, 2003).
A third, emerging viewpoint is social
constructivism where knowledge is neither
exogenic nor endogenic. Knowledge or
knowing is the product of the knowledge
system at the point where the knower interacts
with the world.
Tacit knowledge remains in people’s heads.
What we are trying to access on the other side
of the spectrum is how do we connect the
person who needs knowledge (or a particular
piece of information) with the person who
has that knowledge or information sitting in
his head. Because all knowledge is
experiential, it is something that the person
alone has acquired or cultivated because of
his or her past experience.
Knowledge is widely recognized as a key
competitive asset of organizations (Nonaka
and Takeuchi, 1995). Good management
practice must take into account the tacit
knowledge emerging from knowledge
deliberation and sharing. Knowledge of good
management practices may be stored in
human agents and in various information
artifacts, it also reside in organizational
actions as organizations learn through the
expansion of their repertoires of actions and
procedures (Cohen and Bacdayan, 1994;Lin,
1982). People in various organizational roles
access knowledge in different ways and for
different reasons. Their knowledge-related
activities are influenced by a number of
factors, including their areas of expertise,
trust and privacy among workers, and the
feasibility and effectiveness of knowledge
sharing. Thus, sharing the knowledge held
in organizational memory requires not only
identifying the location of the knowledge
sources, but also facilitating strategies of
knowledge retrieval and use.
Knowledge is used at different levels of the
organization in different contexts by different
types of know-how contributors. The middle
manager in an insurance company who makes
sure that organizational strategy is understood
and implemented by the employees in the
department; the butcher in the food section
of a department store who uses his expertise
to identify the requirements of his customers
and to select and prepare meat in such a way
83
as to meet those requirements; and the
machinist on the shop floor of a
manufacturing plant who uses a short-cut
based on experience, which reduces the
duration of the process without sacrificing
quality . What do all these different types of
know-how contributors have in common?
The fact is that they all use knowledge.
Literature review
The term “knowledge” is defined as the
boundaries encompassing job-related entities
(such as operational thoughts, behaviors,
standard operation procedures, organizational
routines, and competitor and customer
knowledge) and individuals’ insights and
their past working experience which is
relevant to the current job.
A significant point for management to
understand is that “a company’s knowledge
management strategy should reflect its
competitive strategy” (Hansen., 1999, p.).
The KM strategy chosen should create value
for the firm’s customers, turn a profit for the
firm, and focus on how the firm’s employees
deliver on the value and economics. Second,
the firms should not attempt to implement
and excel at both strategies. Rather, they
should use one strategy primarily and use the
second strategy to support the first.
The concept of knowledge is based on a
theoretical perspective whereby networks are
defined as social relationship among
individuals or groups  (Elsawy, 2001)
Tomi Hussi(2004) critically discusses the
definitions of knowledge management. The
analysis shows that different concepts
actually focus on different angles of the topic.
Based on this, a model will be built that ties
all of them into a unitary entity. At the same
time, this model gives a reconfigured
definition of the concept of knowledge
management.
The study of (Mie Augier & Thorbjørn
Knudsen 2004) meets the challenge of
modeling the knowledge organization by
introducing a new, unifying way of thinking
about the organization of knowledge.
Building on ideas set forth in the behavioral
theory of the firm we present a modeling
framework in which the central idea is to
represent the organization of knowledge as a
structure that defines the flow of information
among members with limited levels of
cognitive skill. Such a structure is referred to
as architecture. The need to design
architectures that help their members make
less errors by rejecting bad alternatives and
accepting good ones is an important but
largely overlooked issue in knowledge
management. The present article offers an
approach to think about this issue in a
systematic way. The use of the proposed
modeling framework is illustrated through
examples. The article omits treatment of the
technical details of the proposed modeling
framework. A useful way of designing
organizations that make fewer errors is
outlined. This paper advances a new way of
thinking about knowledge organizations that
may be relevant for both researchers and
practitioners.
Katsuhiro Umemoto & others (2004)
examines the evolution of R&D knowledge
management at Japan’s business equipment
maker Fuji Xerox, from the sashimi system,
a Japanese origin of concurrent engineering,
to its successor
zen-in system, which is composed mainly of
a real high-tech discussion room equipped
with databases that provide technical
information and two 70-inch displays that
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Marwa Ahmed (Professor)
show virtual but real-size, three-dimensional
graphic models. We found that Fuji Xerox
has chosen the “hybridization strategy” that
mixes human-based and IT-based
knowledge-sharing techniques. We also argue
that concurrent engineering provides not only
efficiency benefits but also positive effects
on group and organizational creativity.
Finally we present a conceptual framework
of “how concurrent engineering works”, i.e.
uncertainty and diversity necessitate
concurrency which produces such benefits as
efficiency and creativity, and which in turn
realizes product integrity.
The paper of Lang&others (2004) argues that
social contexts and social capital enable
knowledge integration; that different social
contexts combined with different types of
social capital enable different types of
knowledge integration. Four types of social
contexts are distinguished based on the extent
of social embeddedness and closeness of
interorganizational coupling; four types of
social capital are also described. Based on
the diversity of knowledge streams, the extent
of tacitness of knowledge to be exchanged,
and value created through such exchanges,
four modes of knowledge integration are
identified, namely frontier, incremental,
combinative, and instrumental. This paper
provides new insights about the processes of
interorganizational transfer of knowledge: the
unique combination of a specific social
context with a specific type of social capital
means firms can achieve equally effective yet
highly differentiated approaches to different
modes of knowledge integration.
The vast majority of knowledge positions
require individuals to interpret, analyze and/
or synthesize information. Today, these terms
can be used as synonyms for managing
organizational processes. A process in which
humans become responsible for inferences,
diagnoses, judgments and decision making,
often under severe time limits (Dixon, 1999).
Since manipulating information is the
knowledge worker’s main task, everybody,
including senior management, is
“manipulating” information. Theoretically,
the knowledge worker does it for the benefit
of the organization, but in practice for his/
her own interests. In addition, members of
the organization have to integrate information
into the organizational context. Therefore an
infrastructure is needed to create an ongoing
collective interpretation that means another
tool to manipulate information.
Research problem
The research attempts to provide an in-depth
empirical investigation of the issue of the KM
concept as viewed by Jordanian excusive
managers whom they have some knowledge
about KM concept and know how to build a
knowledge base.
Research importance
Knowledge flow in informal mechanisms is
highly dynamic compared to formal
mechanisms, since links and connections are
not formally defined.  The strengths of
informal relationships can easily vanish or
persistently survive in this kind of network.
Human nodes are important to keep the
networks functioning because each human
node can be a highly capable search agent,
knowledge repository, etc.
(Crosse, 2000) suggested “to build better
networks, focus on who knows what”.
Informal role players who contribute to
knowledge integration capabilities might
affect competitive advantage by influencing
85
the type of knowledge integration. Grant
(1996) introduces three dimensions of
knowledge integration: efficiency, scope, and
flexibility. The efficiency of integration is the
level of accessibility of expertise knowledge
that resides across the organization. From all
of these the importance of the research comes
to help the decision makers to take advantage
of KM and apply it in their organization.
Research instrument
The first step in developing this research
instrument was an extensive and in-depth
literature review.  The questionnaire was
measured on a 1 to 5 Likert scale. Second,
many practising executive managers and
other experts critiqued the instrument. After
numerous iterations, improvements were
incorporated into the survey.
Three types of validity were carefully applied
to this instrument (Eisenhardt., 2000):
content, construct, and criterion-related
(predictive) validity. Content validily assesses
the concept of the designed scale such as
information technology. In this research,
content validity is established through an
extensive literature review, face-to-face
interviews with administrators and other
experts. Construct validity evaluates the
appropriateness of variables.
The questionnaire was sent to 50 executive
managers. Each questionnaire included a
personalized cover letter that promised
anonymity. To prompt questionnaire
participants to fill out the survey, a follow-
up letter was sent two weeks after the
questionnaire was mailed. A follow-up
telephone call was also made to each
administrator during the next two weeks.
These managers are the only ones who see
the overall picture of KM.
A total of 45   questionnaires were used in
this research. SPSS, ANOVA, T-test and
sheffee test were used for the 45 respondents
result .The chi-square goodness-of-fit test
was used for checking non-response bias.
The reliability of scales was assessed by
Cronbach’s coefficient alpha. Reliability is
an assessment of the internal consistency of
the construct indicators. The Cronbach alpha
values of 0.60 or higher are generally
considered to be acceptable (Nunnally, 1978).
The alpha values for this research scale are
0.79.
Sample Size
The sample size is shown in table 1
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Marwa Ahmed (Professor)
Table 1
Company Size (number of employees):
Less then 50=14
50 to less then 100=15
100 and more=16
Kind of services that the company offers:
Education=6
Medical=10
Industry=14
Public=15
Location of the company:
Amman=21
Zarka=13
Irbid=11
Years of experience of the executive manager:
Less than 10 years=17
10 years to less than 15 years=15
15 years and more=13
Sex of the executive manager:
Male = 33
Female= 12
Education of the executive manager:
Less than Bachelor’s degree= 8
Bachelor’s degree = 27
Graduate study = 10
Research limitations
There are several limitations of this research. First, this research is dependent on questionnaire
data.
The second is the small sample size. The small number of the sample could be said to be too
small to be statistically representative of the entire companies. Nevertheless, the findings
were not considered by the company’s management to be untypical of the company, and the
managers took the results seriously.
Hypotheses
The hypotheses are as follows:
H1: there are   statistical differences between KM concepts and company size at
 <=0.05
H2: there are   statistical differences between KM concepts and the kind of services
 at α <=0.05
87
H3: there are   statistical differences between KM concepts and location of the company
 at α <=0.05
H4: there are   statistical differences between KM concepts and the years of experience of the
executive managers at α <=0.05
H5: there are   statistical differences between KM concepts and the sex of the executive
managers at α <=0.05
H6: there are   statistical differences between KM concepts and the education of the executive
managers at α <=0.05
Statistical results
What is the major KM concept that the executive managers viewed? To answer this question
we find the mean for each variable (table 2). A quick review of the result in table 2 reveals
clearly that variable 7(KM drives economic growth) has the highest mean value (4.9556) and
this means that the executive managers feel very strongly that KM drives economic growth.
This should not come as a surprise. Indeed, the Jordanian executive managers feel that KM is
very necessary for economic growth.
 Variable 10 has the least mean value, which means that the executive managers feel very
weakly that KM is a strategic advantage for the company.
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Marwa Ahmed (Professor)
Table  2
Descriptive Statistics
Variables N Minimum Maximum Mean Std.
Deviation
KM is a strategic advantages 45 1.00 5.00 4.4222 .91674
for the company
KM can be translated into a 45 3.00 5.00 4.4667 .69413
profitable venue
KM drives innovation 45 3.00 5.00 4.6667 .60302
KM informs decision making 45 4.00 5.00 4.7333 .44721
KM is a key for gaining a 45 2.00 5.00 4.7333 .65366
competitive advantage
KM is a human resource practice 45 3.00 5.00 4.8000 .45726
KM reduces future search costs 45 3.00 5.00 4.8889 .38271
KM involves knowing how to 45 4.00 5.00 4.9111 .28780
take data & share it
KM turned data into information 45 4.00 5.00 4.9111 .28780
KM drives economic growth 45 4.00 5.00 4.9556 .20841
Valid N (listwise) 45
To test the first hypothesis (H1: there are   statistical differences between KM concepts and
company size at α <=0.05) the researcher used the ANOVA test (Table 3).
Table  3
ANOVA For Company Size
Variables Sum of df Mean F Sig.
Squares Square
KM turned data Between 978 2 .489 7.700 .001*
into information Groups
Within Groups 2.667 42 .063
Total 3.644 44
KM informs Between 1.422 2 .711 4.048 .025
decision making Groups
Within Groups 7.378 42 .176
Total 8.800 44
KM reduces future Between 1.111 2 .556 4.375 .019
search costs Groups
Within Groups 5.333 42 .127
Total 6.444 44
The results shown in table 3 state that variables: 2, 3 and 9 are significant at α <=0.05. To
know which company’s size was higher than the others we used scheffe test (Table 4).
89
Table 4
Multiple Comparisons Scheffe for the size
Mean Std. Sig. 95%
Difference Error Confidence
 (I-J) Interval
Dependent Variable (I) (J) Lower Upper
SIZE SIZE Bound Bound
KM turned data 1.00 2.00 -.3333 .09759 .006 -.5810 -.0857
into information
3.00 -.3333 .09391 .004 -.5716 -.0950
2.00 1.00 .3333 .09759 .006 .0857 .5810
3.00 .0000 .08809 1.000 -.2235 .2235
3.00 1.00 .3333 .09391 .004 .0950 .5716
2.00 .0000 .08809 1.000 -.2235 .2235
KM informs 1.00 2.00 .2667 .16232 .270 -.1453 .6786
decision making
3.00 .4444 .15620 .025 .0481 .8408
2.00 1.00 -.2667 .16232 .270 -.6786 .1453
3.00 .1778 .14653 .485 -.1941 .5496
3.00 1.00 -.4444 .15620 .025 -.8408 -.0481
2.00 -.1778 .14653 .485 -.5496 .1941
KM reduces future 1.00 2.00 .3333 .13801 .065 -.0169 .6836
search costs
3.00 .0000 .13280 1.000 -.3370 .3370
2.00 1.00 -.3333 .13801 .065 -.6836 .0169
3.00 -.3333 .12458 .037 -.6495 -.0172
3.00 1.00 .0000 .13280 1.000 -.3370 .3370
2.00 .3333 .12458 .037 .0172 .6495
The mean difference is significant at the .05 level.
Table 4 shows that the executive manager who works in a company has which fewer than 50
employees feel s strongly that KM inform decision making more than the ones who worked in
a company with fewer than 50 employee.
Table 4 also shows that the executive manager who works in a company with 100 employees
or more feels strongly that KM turned data into information more than the ones who worked
in a company with 50 employees.
And that the executive manager who works in a company having 50 or fewer than 100 employee
feels strongly that KM reduces future search cost more than the ones who worked in a company
with 100 and more employees.
To test the second hypothesis (H2: there are   statistical differences between KM concepts and
the kind of services at ± <=0.05) the researcher used the ANOVA test (Table 5).
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Marwa Ahmed (Professor)
Table 5
ANOVA For Kind of services
Variables Sum of df Mean F Sig.
Squares Square
KM can be translated Between 4.171 3 1.390 3.348 .028*
into a profitable venue Groups
Within 17.029 41 .415
Groups
Total 21.200 44
∗α <=0.05
The results shown in Table 5 state that variable 6 is significant at α <=0.05. To know which
kinds of services are higher than the others we used the  scheffe test (Table 6).
Table 6
Multiple Comparisons
Scheffe for the services
Mean Std. Sig. 95%
Difference   Error Confidence
(I-J)  Interval
Dependent (I) (J)
Variable  Service Service Lower Upper
Bound  Bound
KM can be 1.00 2.00 .5000 .33280 .527 -.4702 1.4702
translated into a
profitable venue
3.00 .9286 .31447 .046 .0119 1.8453
4.00 .4000 .31130 .651 -.5075 1.3075
2.00 1.00 -.5000 .33280 .527 -1.4702 .4702
3.00 .4286 .26683 .470 -.3493 1.2064
4.00 -.1000 .26310 .986 -.8670 .6670
3.00 1.00 -.9286 .31447 .046 -1.8453 -.0119
2.00 -.4286 .26683 .470 -1.2064 .3493
4.00 -.5286 .23949 .199 -1.2267 .1696
4.00 1.00 -.4000 .31130 .651 -1.3075 .5075
2.00 .1000 .26310 .986 -.6670 .8670
3.00 .5286 .23949 .199 -.1696 1.2267
The mean difference is significant at the .05 levels.
91
Table 6 shows that the executive manager who works in an industrial company feels strongly
that KM can be translated into a profitable venue more than the ones who worked in education.
To test the third hypothesis  (H3: there are   statistical differences between KM concepts and
the location at α <=0.05) the researcher used the ANOVA test (Table 7).
Table 7
ANOVA For the location
Variables Sum of df Mean F Sig.
 Squares  Square
KM informs Between 2.589 2 1.294 8.753 .001*
decision making Groups
Within 6.211 42 .148
Groups
Total 8.800 44
KM is a human 1.339 2 .669 3.576 .037*
resource practice Between
Groups
Within 7.861 42 .187
Groups
Total 9.200 44
KM can be Between 4.547 2 2.274 5.734 .006*
translated into a Groups
profitable venue
Within 16.653 42 .396
Groups
Total 21.200 44
*α <=0.05
The results shown in Table 7 state that variables: 3,5 and 6 are significant at α <=0.05. To
know which locations are higher than the others we used the Scheffe test (Table 8).
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Marwa Ahmed (Professor)
Table 8
Multiple Comparisons
Scheffe for the location
Mean Std. Sig. 95%
Difference  Error Confidence
(I-J) Interval
Dependent (I) (J) Lower Upper
Variable Location Location Bound  Bound
KM informs 1.00 2.00 .5678 .13571 .001 .2234 .9122
decision making
3.00 .2251 .14313 .301 -.1381 .5883
2.00 1.00 -.5678 .13571 .001 -.9122 -.2234
3.00 -.3427 .15754 .106 -.7425 .0571
3.00 1.00 -.2251 .14313 .301 -.5883 .1381
2.00 .3427 .15754 .106 -.0571 .7425
KM is a human 1.00 2.00 -.3810 .15268 .055 -.7684 .0065
resource practice
3.00 -.2900 .16103 .210 -.6987 .1186
2.00 1.00 .3810 .15268 .055 -.0065 .7684
3.00 .0909 .17724 .877 -.3589 .5407
3.00 1.00 .2900 .16103 .210 -.1186 .6987
2.00 -.0909 .17724 .877 -.5407 .3589
KM can be 1.00 2.00 .0513 .22222 .974 -.5126 .6152
translated into
a profitable venue
3.00 .7576 .23436 .009 .1628 1.3523
2.00 1.00 -.0513 .22222 .974 -.6152 .5126
3.00 .7063 .25796 .032 .0517 1.3609
3.00 1.00 -.7576 .23436 .009 -1.3523 -.1628
2.00 -.7063 .25796 .032 -1.3609 -.0517
The mean difference is significant at the .05 levels.
Table 8 shows that the executive manager who works in Zarka City feels strongly that KM
informs decision making more than the ones who worked in Amman City    And the one who
works in Amman City feels strongly that KM is a human resource practice. But the executive
manager who works in Irbid City  feels strongly that KM can translate into a profitable venue
more than the ones who worked in Zarka City and Amman City.
To test the fourth hypothesis  (H4: there are   statistical differences between KM concepts and
the years of experience of the executive managers at α <=0.05) the researcher used the ANOVA
Test (Table 9).
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Table 9
ANOVA For years of experience
Variable Sum of df Mean F Sig.
Squares Square
KM is a strategic Between 6.886 2 3.443 4.806 .013*
advantages for the Groups
company
Within 30.092 42 .716
Groups
Total 36.978 44
∗α <=0.05
The results shown in Table 9 state that variable 10 is significant at α <=0.05. To know which
categories of the years of experience are higher than the others we used the Scheffe test (Table
10).
Table 10
Multiple Comparisons
Scheffe for the years of experience
Mean Std. Sig. 95%
Difference Error Confidence
  (I-J)  Interval
Dependent (I) (J) Lower Upper
Variable Experien Experien Bound Bound
KM is a strategic 1.00 2.00 .5373 .29985 .213 -.2237 1.2982
advantages for the
company
3.00 -.4525 .31186 .358 -1.2439 .3389
2.00 1.00 -.5373 .29985 .213 -1.2982 .2237
3.00 -.9897 .32075 .014 -1.8037 -.1758
3.00 1.00 .4525 .31186 .358 -.3389 1.2439
2.00 .9897 .32075 .014 .1758 1.8037
  The mean difference is significant at the .05 levels.
Table 10 shows that the executive manager who has 10 years of experience and more feels
strongly that KM is a strategic advantages for the company more than the ones who have 15
years of experience.
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Marwa Ahmed (Professor)
To test the fifth hypothesis  (H5: there are   statistical differences between KM concepts and
the sex of the executive managers at α <=0.05 ) the researcher used the  T-test (Table 11).
The results shown in Table 11 state that variables: 2,3 and 6 are significant at ± <=0.05.
Table 11
T-tst For Sex
SEX KM turned KM informs KM can be
data into decision translated into
information making a profitable venue
Male Mean 4.9697 4.6364 4.3333
N 33 33 33
Std. Deviation .17408 .48850 .69222
Female Mean 4.7500 5.0000 4.8333
N 12 12 12
Std. Deviation .45227 .00000 .57735
Total Mean 4.9111 4.7333 4.4667
N 45 45 45
Std. Deviation .28780 .44721 .69413
Table 11 shows that the female executive manager feels strongly that KM informs decision
making and can  be translated into a profitable venue more than the  male executive manager.
But the male executive manager feels strongly that KM turned data into information more
than the female manager.
To test the sixth hypothesis  (H6: there are   statistical differences between KM concepts and
the education of the executive managers at α <=0.05) the researcher used the  ANOVA test
(Table 12).
Table 12
ANOVA For Kind of education
Variable Sum of df Mean F Sig.
Squares Square
KM turned data Between Groups 1.244 2 .622 10.889 .000
into information
Within Groups 2.400 42 .057
Total 3.644 44
KM can be translated Between Groups 3.615 2 1.807 4.317 .020
into a profitable venue
Within Groups 17.585 42 .419
Total 21.200 44
95
The results shown in Table 12 state that variables: 2and 6 are significant at ± <=0.05. To know
which kind of education is higher than the others we used the Scheffe test (tTable 13).
Table13
Multiple Comparisons
Scheffe for the kind of  education
Mean Std. Sig. 95%
Difference Error Confidence
 (I-J) Interval
Dependent (I) (J) Lower Upper
Variable Education  Education Bound  Bound
KM turned data 1.00 2.00 .0000 .09623 1.000 -.2442 .2442
 into information
3.00 .4000 .11339 .004 .1123 .6877
2.00 1.00 .0000 .09623 1.000 -.2442 .2442
3.00 .4000 .08849 .000 .1754 .6246
3.00 1.00 -.4000 .11339 .004 -.6877 -.1123
2.00 -.4000 .08849 .000 -.6246 -.1754
KM can be 1.00 2.00 .7407 .26047 .025 .0798 1.4017
translated into a
profitable venue
3.00 .4000 .30693 .435 -.3789 1.1789
2.00 1.00 -.7407 .26047 .025 -1.4017 -.0798
3.00 -.3407 .23953 .372 -.9486 .2671
3.00 1.00 -.4000 .30693 .435 -1.1789 .3789
2.00 .3407 .23953 .372 -.2671 .9486
The mean difference is significant at the .05 levels.
Table 13 shows that the executive manager who has graduate study feels strongly that KM
turned data into information more than the ones who have a  Bachelor”s degree and less. And
the ones who have a Bachelor’s degree feel strongly that KM can translate into a profitable
venue more than the ones who have less than Bachelor’s degree.
Discussion
All the six hypotheses were statistically significant. We have provided empirical evidence of
the concept of KM, as viewed by executive managers.
Clearly, the KM concept should be separate entities (constructs) in future research
Results support our hypotheses
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Marwa Ahmed (Professor)
Conclusion
The results of this research provide evidence that:
KM concepts are statistically significant and provide guidelines for future research.
KM drives economic growth.
The executive manager who works with a Company with fewer than 50 employees feels that
KM informs decision-making.
KM can be translated into a profitable venue is significant for the manager who works in
industry and Irbid City female and has a Bachelor degree.
Further research
The objective of this study was to develop a measurement instrument that identifies
Knowledge management (KM) concept as viewed by Jordanian managers.
In fact, KM can be viewed as a conceptually complex, evolving, broad umbrella of issues and
viewpoints. Traditionally, organizational knowledge needs to become a stable resource if it
can be translated into a profitable venue for information sharing. There is a growing rate of
turnover among managers and/or knowledge workers who accumulate organization-specific
knowledge that is ultimately lost to the firm and possibly gained by their competitors.
Knowledge needs to be captured, interpreted, and eventually transferred in such a manner that
the knowledge will continue to serve the organization, regardless of the individuals’ original
purpose for collecting the information.
 Although little is known about the ways that the dynamics inherent in knowledge influence
the eventual production of knowledge, the perception that collaborative research emerging
through knowledge will make important contributions to a knowledge-based economy. In
order to drive economic growth, recent government policy in both developed and developing
nations, demands greater interaction among industry, the government, and institutions of science
in the production of knowledge. In spite of broad acknowledgement of the complex dynamics
found within these knowledge networks, there remains the expectation that research will
produce knowledge that is of immediate use to industry, which in turn will promote  the
economy.
97
Research References
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Management, MIT Press, Boston, MA.
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A Short Bio of Marwa Ahmed Ph.D.
Marwa Ahmed Ph.D. is a Professor in Business Administration and she is Dean of Faculty
of Administrative & Financial Sciences 2002-till now-Philadephia University. Her Main
Research interests are Public Relations, Leadership Development, Small Business
Management, Quality Assurance in Universities, Knowledge Management, Electronic
Management.
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Questionnaire
First: Personal Data
Company Size (number of employees):
Fewer than 50
50 to Fewer than 100
100 and more
Kind of services that your company offers:
Education
Medical
Industry
Public
Location of the company:
Amman
Zarka
Irbid
Years of experience of the executive manager:
Less than 10 years
10 years to less than 15 years
15 years and more
Sex of the executive manager:
Male
Female
Education of the executive manager:
Less than Bachelor’s degree
Bachelor’s degree
Graduate study
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Marwa Ahmed (Professor)
Second: KM Variables
Variables Strongly Agree Bias Disagree Strongly
agree disagree
1. KM involves knowing
how to take data & share it
2. KM turned data into
information
3. KM informs decision
making
4. KM is a key for gaining a
competitive advantage
5. KM is a human
resource practice
6. KM can be translated
into a profitable venue
7. KM drives economic growth
8. KM drives innovation
9. KM reduces future
search costs
10. KM is a strategic
advantages for the company