U.Syed Aktharsha Anisa.

Knowledge Management System and Learning Organizaion - An Empirical

Study at Engineering Organization
U. Syed Aktharsha, Email: akthar_jmc@yahoo.com

Associate Professor, Jamal Institute of Management,

Jamal Mohamed College,

Tiruchirappalli, Tamil Nadu.

H. Anisa, Email: anisa.akh @gmail.com

Assistant Professor, Jamal Institute of Management,

Jamal Mohamed College,

Tiruchirappalli, Tamil Nadu.

Abstract

Purpose – The purpose of this paper is to examine the effects of knowledge management
system on the characteristics of learning organization. This paper also attempts to
investigate the relationship between demographic profile and knowledge management
system and the relationship between demographic profile and learning organizations.
Design/ Methodology- A private engineering concern in a district has been chosen for
conducting this study and a sample of 65 managers and engineers were chosen from the
population of 180 managers and engineers together. Survey based instrument is used to
gather the responses from managers and engineers.
Findings - Some variations were observed on knowledge management system due to the
factors such as innovation, different market entry and market share. Some variations were
observed on the properties of learning organization due to the factors such knowledge
application, knowledge management process and shared vision.
Implications/limitations- The study is limited to one particular organization. The results
may not be applicable to other business organizations.
Originality/Value- Knowledge Management System refers to a system for managing
knowledge in organizations for supporting creation, capture, storage and dissemination of
information. Now a day, many organizations especially knowledge based organizations
have started realizing the importance and benefits of KMS and also the contribution of
KMS towards becoming learning organizations are well understood by the organizations.
Keywords: Knowledge, Knowledge Management system, learning organization

1. INTRODUCTION

Knowledge Management (KM) comprises a range of strategies and practices used

in an organization to identify, create, represent, distribute, and enable adoption of insights
and experiences. Such insights and experiences comprise knowledge, either embodied in

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individuals or embedded in organizational processes or practice. An established

discipline since 1991, KM includes courses taught in the fields of business
administration, information systems, management, and library and information sciences.
More recently, other fields have started contributing to KM research; these include
information and media, computer science, public health, and public policy.Many large
companies and non-profit organizations have resources dedicated to internal KM efforts,
often as a part of their 'business strategy', 'information technology', or 'human resource
management' departments. Several consulting companies also exist that provide strategy
and advice regarding KM to these organizations.Knowledge Management efforts
typically focus on organizational objectives such as improved performance, competitive
advantage, innovation, the sharing of lessons learned, integration and continuous
improvement of the organization. KM efforts overlap with organizational learning, and
may be distinguished from that by a greater focus on the management of knowledge as a
strategic asset and a focus on encouraging the sharing of knowledge. KM efforts can help
individuals and groups to share valuable organizational insights, to reduce redundant
work, to reduce training time for new employees, to retain intellectual capital as
employees turnover in an organization, and to adapt to changing environments and
markets.

Knowledge Management System (KM System) refers to a system for managing

knowledge in organizations for supporting creation, capture, storage and dissemination of
information. It can comprise a part of a Knowledge Management initiative.The idea of a
KM system is to enable employees to have ready access to the organization's documented
base of facts, sources of information, and solutions. For example a typical claim
justifying the creation of a KM system might run something like this: an engineer could
know the metallurgical composition of an alloy that reduces sound in gear systems.
Sharing this information organization wide can lead to more effective engine design and
it could also lead to ideas for new or improved equipment.

2. FEATURES OF A KNOWLEDGE MANAGEMENT SYSTEM

Purpose: a KMS will have an explicit Knowledge Management objective of some type
such as collaboration, sharing good practice or the like.
Context: One perspective on KMS would see knowledge is information that is
meaningfully organized, accumulated and embedded in a context of creation and
application.
Processes: KMS are developed to support and enhance knowledge-intensive processes,
tasks or projects of e.g., creation, construction, identification, capturing, acquisition,
selection, valuation, organization, linking, structuring, formalization, visualization,
transfer, distribution, retention, maintenance, refinement, revision, evolution, accessing,
retrieval and last but not least the application of knowledge, also called the knowledge
life cycle.
Participants: Users can play the roles of active, involved participants in knowledge
networks and communities fostered by KMS, although this is not necessarily the case.
KMS designs are held to reflect that knowledge is developed collectively and that the
“distribution” of knowledge leads to its continuous change, reconstruction and

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application in different contexts, by different participants with differing backgrounds and

experiences.
Instruments: KMS support KM instruments, e.g., the capture, creation and sharing of
the codifiable aspects of experience, the creation of corporate knowledge directories,
taxonomies or ontologies, expertise locators, skill management systems, collaborative
filtering and handling of interests used to connect people, the creation and fostering of
communities or knowledge networks.
A KMS offers integrated services to deploy KM instruments for networks of
participants, i.e. active knowledge workers, in knowledge-intensive business processes
along the entire knowledge life cycle. KMS can be used for a wide range of cooperative,
collaborative, adhocracy and hierarchy communities, virtual organizations, societies and
other virtual networks, to manage media contents; activities, interactions and work-flows
purposes; projects; works, networks, departments, privileges, roles, participants and other
active users in order to extract and generate new knowledge and to enhance, leverage and
transfer in new outcomes of knowledge providing new services using new formats and
interfaces and different communication channels.Some of the advantages claimed for KM
systems are:
1. Sharing of valuable organizational information throughout organizational
hierarchy.
2. Can avoid re-inventing the wheel, reducing redundant work.
3. May reduce training time for new employees.
4. Retention of Intellectual Property after the employee leaves if such knowledge
can be codified.
5. Cultivate innovation by encouraging the free flow of ideas.
6. Improve customer service by streamlining response time.
7. Improve employee retention rates by recognizing the value of employees'
knowledge and rewarding them for it.
8. Streamline operations and reduce costs by eliminating redundant or unnecessary
processes.

3. REVIEW OF LITERATURE
Jennifer Rowley (2000) had undertaken a study on “From Learning Organization to
Knowledge Entrepreneur”. He establishes the clear link between learning and
knowledge, and proposes a simple model, which makes this relationship explicit. A range
of definitions of the learning organization are drawn from the literature. Much of this
literature makes little reference to that which is being learned although those authors who
have introduced the concepts of the learning laboratory, the knowledge creating
organization and the knowing organization acknowledge the significance of knowledge in
organizational development and learning. Other perspectives on the organizational
processes associated with knowledge come from the recent literature on knowledge
management. It is argued that indiscriminate knowledge creation will not lead to
organizational learning, and that knowledge is not something that can be viewed as a
neutral tool in the learning process. A number of characteristics of knowledge need to be
recognized, and accommodated in learning processes and knowledge management.
Finally, the concept of a knowledge entrepreneur is proposed.

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Jonathan D. Pemberton, George H. Stonehouse (2000) conduted a research on

“Organizational learning and knowledge assets – an essential partnership”. The study
revelled that Competitive success is governed by an organization’s ability to develop new
knowledge assets that create core competences. While these exist in many forms,
organizational learning is an integral feature of any learning organization that exploits its
knowledge resources to generate superior performance. This paper explores the ideas and
links between organizational learning and knowledge management, making reference to a
number of sectors and companies, and specifically the airline industry, arguing that the
culture, structure and infrastructure of an organization are essential elements that
facilitate and nurture learning. As a consequence, core competences are built and
developed within the learning organizations which, in turn, contribute to its competitive
success.

Mireille Merx-Chermin, Wim J. Nijhof (2005) had done a study on “Factors influencing
knowledge creation and innovation in an organization”. The purpose of this study is to
gain a better understanding of the factors that influence the innovative power of
organizations. The concept of innovation and innovative power was examined by
analysing the relationship between the construct of the learning organization, knowledge
organization and innovative organization, and has resulted in an innovation process
model. This model consists of three processes: knowledge creation, innovation and
learning to learn. The factors that might influence this cycle are: added value for
stakeholders, leadership, climate, structure and strategic alignment. This is an exploratory
study that was conducted at Oce Technologies in The Netherlands. The case study
consisted of a qualitative and a quantitative stage and comprised a selection of two
innovation projects separated in time. The purpose of the first phase was to collect
information about the innovation spiral, through interviews with members of three
divisions in each innovation process. After this, a survey was designed and sent to all
employees and managers of the three divisions involved in the two innovation cases. On
the basis of a data analysis, factors explaining variance in terms of innovation, learning
and knowledge creation were identified. If innovation is discontinuous, the innovation
spiral is not valid; if innovation has strength in critical reflection on cases from the past to
mould the future, the model has some explanatory power. Using a survey technique to
retrieve data from a current innovation experiment has a set of possible risks like
maturation, forgetting, selection and a different context. Reflection and reconstruction,
however, are the only possible means to achieve this. A case study does not guarantee
generalization of results.By studying the model and the factors that can influence them,
organizations understand that it is necessary to integrate their initiatives in organizational
learning, knowledge creation and innovation for the benefit of the organization, to find a
better way to adjust to discontinuous change and finally gain innovative power.

Jozef Loermans (2002) had taken up a study on “Synergizing the learning organization
and knowledge management. Many writers on management during the 1990s have stated
that we have neither a good understanding of the process of organizational learning nor a
good grasp of the concept of knowledge management. In his 1990 book The Fifth
Discipline, Peter Senge quoted others in asserting that “The most successful corporation
of the 1990s will be something called the learning organization and the ability to learn

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faster than your competitors may be the only sustainable means of achieving competitive
advantage”. More recently, writers such as Drucker, Davenport, Prusak, and Stewart have
made similar claims when describing the drivers for managing corporate knowledge. This
paper briefly looks at the overlaps and synergies between these concepts. It is argued that
the discipline of knowledge management at a corporate level and the phenomenon of the
learning organization are inextricably linked and should always be analysed and
discussed in concert.

Andrew J. Sense (2008) had undertaken a study on “Conceptions of learning and

managing the flow of knowledge in the project-based environment” The purpose of this
paper is to examine how people can conceive learning and knowledge management
processes within project teams and provides conceptual guidance on the most effective
way to managerially approach these important and often neglected project issues. This is
a conceptual paper which draws on and dissects a very broad and relevant literature on
learning and knowledge management. Based on the analysis conducted, and with an eye
to improving project learning, project outcomes and participant learning skills, the key
argument of this paper is that participants in project teams must acknowledge and pursue
a more socially oriented trajectory in their learning and knowledge management
activities. Therein, the participants, their project practices and the organization of the
project environment become the focal points of attention and action. This paper puts
forward a conceptually grounded argument for a greater practical emphasis to be placed
on the social systems in learning and knowledge management processes in projects. The
opportunity exists to test this argument in further empirical project studies. This paper
provides a foundation for project practitioners to critically reflect on their current learning
and knowledge management attitudes and practices, while encouraging their attention
towards the management of their project social systems.This paper confronts
conventional and limited perspectives about learning and managing the flow of
knowledge within projects, and serves to stimulate participant and researcher reflection
on more socially oriented approaches towards these project activities.

Bill Buckler (1998) had conducted a research on “Practical steps towards a learning
organization: applying academic knowledge to improvement and innovation in business
processes” This paper outlines research currently being carried out at the Nottingham
Trent University, in collaboration with a recently privatised utility. The aim of the
research is to synthesise a learning process model from relevant learning theory, and from
this, to derive a practical model, which can be used by organizations to facilitate
individual, team and organizational learning, resulting in continuous improvement and
innovation in business processes. The learning process model has been developed, and
was the subject of an article in The Learning Organization (Buckler, 1996). Workshops,
based on the model, have been held, with groups of managers, and feedback from these
has been used to assess the usefulness of the models in an organizational context. This
process has resulted in the design of a series of six workshops which aims to help
organizational management teams develop a deep understanding of the learning process.
This will lay the foundations for a systemic approach to learning within the organization,
and a move towards the elusive learning organization. Research is continuing, with
further field trials of the workshops, which will provide insight into the links between

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individual, team and organizational learning, the relationships between learning and
performance, systemic barriers to learning, and necessary leadership skills.

Anona Armstrong, Patrick Foley (2003) did a study on “Foundations for a learning
organization: organization learning mechanisms” . This paper outlines the results of
research currently being carried out at Victoria University, Australia, into what is a
learning organization, how organizations learn, and how to develop a learning
organization. The objective of the present study was to identify the components that
underpin the development and operation of a learning organization, i.e. the foundations,
or organizational learning mechanisms, that support the development and maintenance of
a learning organization. The study identified four facilitating mechanisms: the learning
environment, identifying learning and development needs, meeting learning and
development needs and applying learning in the workplace. Factor analysis of the
learning environment questionnaire identified 12 scales that supported the structural
hypotheses, 11 of which had minimum reliability coefficients of 0.70 and above. This
research provides an instrument for systematically measuring and monitoring progress
towards achieving a learning organization

4. RESEARCH METHODOLOGY
The present study is undertaken to find out the following.
 To investigate the relationship between demographic profile and knowledge
management systems
 To investigate the relationship between demographic profile and learning
organization.
 To identify the variables and their grouping into factors that influence the
knowledge management system and learning organization.
The Sampling Design
A private engineering concern was chosen for conducting this study. The study has taken
into account the various aspects of knowledge management system and its contribution to
learning organization. The decision to choose this particular private company was taken
because the senior administrators of the concern permitted to couduct this study on
knowledge management and learning organization. A sample of 65 managers and
engineers has been chosen from the population of 180 managers and engineers together
using stratified random sampling method. The tabulated description of demographic
details of sample is presented in Table 1.
S.no Variables Number Frequency (%)
1 Age
Below 30 23 35
30-40 18 28
41-50 15 23
Above 50 9 14
2 Education Qualification
Diploma 35 54
UG 25 38
PG 5 8
3 Designation

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Engineer 45 69
Manager 20 31
4 Department
Engineering 21 33
Production 21 32
Quality Control 23 35
5 Experience
Below 10 29 45
10-20 23 35
20-30 6 9
Above 30 7 11
6 Income Level
Below 10,000 7 11
10,000-20,000 25 39
20,000-30,000 16 24
Above 30,000 17 26
Table 1. Frequency Distribution of sample demographics
Data Collection
The data was collected from the managers and engineers of the selected engineering
enterprise through a questionaire which had 3 major parts, namely;
1. Demographic characteristics
2. Effects of Knowledge Management System
3. Learning Organization characteristics.
Measurement Scale
The questionaire consisted of a series of statements, where the engineers and managers
were requested to provide answers in the form of agreement or disagreement to express
their perceptions towards knowledge management system and learning organization. A
Likert scale was used so that the respondent can select a numerical score ranging from 1
to 5 for each statements where 1, 2,3,4 and 5 denote “Strongly Disagree”, “Disagree”,
“Neutral”, “Agree” and Strong Agree” respectively in part 2 and 3.
Data Analysis
Realiability Analysis
Pre-testing techniques namely Cronbach’s Alpha and Hoteling’s T-square test were used
to check the reliability and equivalence of the variables used for research. The results of
this analysis are presented in Table 2.

Dimension name No of Cronbach’s Hoteling’s T- d.f

items Alpha square test

Effects of Knowledge 13 .798 491.263* 12,53

Management system (part II)

Learning organization(part III) 11 .695 256.390* 10,55

Table 2: Reliability Analysis

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The above results of Cronbach’s Alpha indicate that the two dimensions namely effects
of knowledge management system (Part II) and characteristics of learning organization
(Part III) achieved a high internal consistency of 79.8% and 69.5% respectively.
Similarly More over Hoteling’s t-squared test exhibits that the mean of items under all
dimensions were significantly different at 1% level. Thus it is known that in all items in
questionaire conveyed different meaning to the respondents.
Chi – Square Analysis
Chi- Square Test of Significance (Age and KMS)
Hypothesis
H0: There is no significant relation between age and KMS.
H1: There is significant relation between age and KMS.

Chi- Square Test of Significance (Qualifications and KMS)

Hypothesis
H0: There is no significant relation between qualifications and KMS.
H1: There is significant relation between qualifications and KMS.

Chi- Square Test of Significance (Department and KMS)

Hypothesis
H0: There is no significant relation between department and KMS.
H1: There is significant relation between department and KMS.

Chi- Square Test of Significance (Designation and KMS)

Hypothesis
H0: There is no significant relation between Designation and KMS.
H1: There is significant relation between Designation and KMS

Chi- Square Test of Significance (Experience and KMS)

Hypothesis
H0: There is no significant relation between experience and KMS
H1: There is significant relation between experience and KMS

Chi- Square Test of Significance (Income Level and KMS)

Hypothesis
H0: There is no significant relation between income level and KMS
H1: There is significant relation between income level system and KMS

The values of chi-square statistics obtained from chi-squre distribution table for all 6
combinations are 12.59, 9.49, 9.49, 5.99 , 12.59 and 12.59 in that order and the
calculated chi-square statistics values are 5.484, 2.421, 3.853, 2.596 , 4.975 and 5.983 in
that order which lies in the acceptance region. Thus, the null hypothesis can not be
rejected where as alternative hypothesis are rejected. So, it can be concluded that
demomograhpic characteristcs of managers and engineers and effects and usage of KMS
are independent on the basis of statistical evidence at 5 % level of significance. Results of
chi-square are presented in Table 3.

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S.no Variables Chi-square statistic

1 Age and KMS 5.484 < 12.59 ( Not Significant)
2 Qualifications and KMS 2.421 < 9.49 ( Not Significant)
3 Department and KMS 3.853 < 9.49 ( Not Significant)
4 Designation and KMS 2.596 < 5.99( Not Significant)
5 Experience and KMS 4.975 < 12.59 ( Not Significant)
6 Income Level and KMS 5.983 < 12.59 ( Not Significant)
Table 3: Results of Chi-square Analysis
Chi- Square Test of Significance (Age and Learning organization)
Hypothesis
H0: There is no significant relation between age and Learning organization.
H1: There is significant relation between age and Learning organization.

Chi- Square Test of Significance (Qualifications and Learning organization)

Hypothesis
H0: There is no significant relation between qualifications and Learning organization
H1: There is significant relation between qualifications and Learning organization.

Chi- Square Test of Significance (Department and Learning organization)

Hypothesis
H0: There is no significant relation between department and Learning organization.
H1: There is significant relation between department and Learning organization.

Chi- Square Test of Significance (Designation and Learning organization)

Hypothesis
H0: There is no significant relation between Designation and Learning organization.
H1: There is significant relation between Designation and Learning organization

Chi- Square Test of Significance (Experience and Learning organization)

Hypothesis
H0: There is no significant relation between experience and Learning organization
H1: There is significant relation between experience and Learning organization

Chi- Square Test of Significance (Income Level and Learning organization)

Hypothesis
H0: There is no significant relation between income level and Learning organization
H1: There is significant relation between income level and Learning organization

The values of chi-square statistics obtained from chi-squre distribution table for all 5
combinations are 7.82, 5.99, 5.99, 3.84 , 7.82 amd 7.82 in that order and the calculated
chi-square statistics values are 2.554, 2.696, 3.436, 0.685, 1.099 and 3.235 in that order
which lies in the acceptance region. Thus, the null hypothesis are accepted where as
alternative hypothesis are rejected. So, it can be concluded that demographic
characteristics of mangers and engineers and learning organization are independent on

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the basis of statistical evidence at 5 % level of significance. Results of chi-square are

presented in Table 4.

S.no Variables Chi-square statistic

1 Age and Learning organization 2.554 < 7.82 ( Not Significant)
2 Qualifications and Learning organization 2.696 < 5.99( Not Significant)
3 Department and Learning organization 3.436 < 5.99( Not Significant)
4 Designation and Learning organization 0.685 < 3.84 ( Not Significant)
5 Experience and Learning organization 1.099 < 7.82 ( Not Significant)
6 Income Level and Learning organization 3.235 < 7.82( Not Significant)
Table 4: Results of Chi-square Analysis
Factor Analysis
Dimensions: Effect of KM
Data validity for factor analysis was calculated using KMO Measure of sampling
adequacy. The minimum acceptable level is 0.5. Since calculated Kaiser-Meyer-Olkin
(0.777) is greater than 0.5, so it is appropriate to do factor analysis. Hence Bartlett’s test
of sphericity value is 299.589 it is also a kind of chi-square and it is significant. The
results of Kaiser-Meyer-Olkin and Bartlett’s test of sphericity are shown in table 5.

Kaiser-Meyer-Olkin Measure of Sampling Adequacy. .777

Approx. Chi-Square 299.589
Bartlett's Test of Sphericity df 78.000
Sig. .000
Table 5: KMO and Bartlett's Test

Compo Initial Eigenvalues Extraction Sums of Squared Loadings

nent Total % of Variance Cumulative % Total % of Variance Cumulative %
1 4.610 35.460 35.460 4.610 35.460 35.460
2 1.927 14.826 50.286 1.927 14.826 50.286
3 1.187 9.130 59.416 1.187 9.130 59.416
4 1.000 7.693 67.108 1.000 7.693 67.108
5 .755 5.811 72.919
6 .695 5.348 78.268
7 .661 5.082 83.350
8 .557 4.285 87.635
9 .428 3.289 90.924
10 .366 2.816 93.740
11 .328 2.522 96.262
12 .260 2.003 98.265
13 .226 1.735 100.000
Table 6: Total Variance Explained

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Extraction Method: Principal Component Analysis.

The Table 6 reveals that 4 factors have been extracted out of 13 variables that exceed the
eigen value of one. The variables less than the Eigen value of one are not considered
during extraction method.
Total % of Variance Cumulative %
2.833 21.793 21.793
2.433 18.715 40.508
1.866 14.353 54.861
1.592 12.247 67.108
Table 7: Rotation Sums of Squared Loadings
The Table 7 shows that Factor 1, factor 2, factor 3 and factor 4 explain a variation of
21.793, 18.715, 14.353, and 12.247 respectively and together show the variance of
67.108.
Component
1 2 3 4
Collaboration .761 .063 .379 -.108
Innovation .731 .131 .185 .031
Adaptation capability .680 .213 .291 .041
Addressing of communication gap .655 .033 .242 .474
Better ROI .627 .073 .415 .165
Entry of different market types -.129 .852 .084 .242
Enhanced Productivity or Service Quality .147 .748 .332 .171
Sharing of Best Practices .410 .704 -.015 -.062
Delegation of authority and accountability .247 .261 .793 .065
Transformation of individual learning .018 -.032 .612 .581
Fast and Better Decision Making .215 .487 .495 .025
Better staff attraction .343 .153 .012 .727
Increased market share -.052 .499 .074 .602
Table 8: Rotated Component Matrix

Factor: 1 Factor: 2 Factor: 3 Factor: 4

Innovation through Different market Better decision Increase market
collaboration entry through making through share
enhanced delegation
productivity
Collaboration Entry of different Delegation of Better staff
market types authority and attraction
accountability
Innovation Enhanced Transformation of Increased market
Productivity or individual learning share

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Service Quality
Adaptation capability Sharing of Best Fast and Better
Practices Decision Making
Addressing of
communication gap
Better ROI
Table 9: Naming of factors
It is infered that Factor 1 consists of five variables of which collaboration and innovations
are found to be significant with a variation of 21.793%. Factor 2 consists of three
variables of which different market type are significant with a variation of 18.715%.
Factor 3 consists of three variables of which Delegation of authority and accountability
are significant with a variation of 14.353% .Factor 4 consists of two variables of which
Better staff attraction are significant with a variation of 12.247%. Based on the results of
factor loading (Table 8), the factors are named which is given in table 9.

Dimensions: Learning Organization

Data validity for factor analysis was calculated using KMO Measure of sampling
adequacy. The minimum acceptable level is 0.5. Since calculated Kaiser-Meyer-Olkin
(0.670) is greater than 0.5, so it is appropriate to do factor analysis. Hence Bartlett’s test
of sphericity value is 117.040 it is also a kind of chi-square and it is significant. The
results of Kaiser-Meyer-Olkin and Bartlett’s test of sphericity are shown in table 10.

Kaiser-Meyer-Olkin Measure of Sampling Adequacy. .670

Approx. Chi-Square 117.040
Bartlett's Test of Sphericity df 55.000
Sig. .000
Table 10: KMO and Bartlett's Test

Compo Initial Eigenvalues Extraction Sums of Squared Loadings

nent Total % of Variance Cumulative % Total % of Variance Cumulative %
1 2.905 26.406 26.406 2.905 26.406 26.406
2 1.377 12.516 38.921 1.377 12.516 38.921
3 1.254 11.400 50.321 1.254 11.400 50.321
4 1.133 10.304 60.625 1.133 10.304 60.625
5 .880 8.002 68.626
6 .748 6.803 75.429
7 .714 6.490 81.919
8 .646 5.876 87.796
9 .564 5.130 92.926
10 .478 4.345 97.271
11 .300 2.729 100.000

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Table 11: Total Variance Explained

Extraction Method: Principal Component Analysis.

Table 11 reveals that 4 factors have been extracted out of 11 variables that exceed the
Eigen value of one.The variables less than the Eigen value of one are not considered
during extraction method.

Total % of Variance Cumulative %

1.826 16.597 16.597
1.801 16.372 32.969
1.680 15.268 48.237
1.363 12.388 60.625
Table 12: Rotation Sums of Squared Loadings
The table 12 shows that Factor 1, factor 2, factor 3 and factor 4 explain a variation of
16.597%, 16.372%, 15.268%, and 12.388% respectively and together show the variance
of 60.625%.

Component
1 2 3 4
Easy uploading into database .844 .240 -.101 -.015
Readily available of information .796 .011 .369 .033
Sharing and acting upon knowledge .014 .766 .033 .218
Incentives for learning .239 .676 -.025 .155
Continuous learning .037 .674 .192 -.232
Sharing of experience and information -.176 .048 .715 .281
Technologic enabled learning .234 -.009 .706 -.024
Well defined KM process .283 .338 .525 -.194
Sharing best practices .028 .162 .067 .784
Learning through communication .270 .312 .401 .514
Sharing powerful vision of the organization across
.427 .076 .211 .490
the workforce
Table 13: Rotated Component Matrix

Factor: 1 Factor: 2 Factor: 3 Factor: 4

Better information Application of Knowledge Shared vision
knowledge management
process

Easy uploading into Sharing and acting Sharing of Sharing best

database upon knowledge experience and practices

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information
Readily available of Incentives for Technologic Learning through
information learning enabled learning communication
Continuous learning Well defined KM Sharing powerful
process vision of the
organization across
the workforce
Table 14: Naming of factors

It is also infered that Factor 1 consists of two variables of which Easy uploading into
database are found to be significant with a variation of 16.597%. Factor 2 consists of
three variables of which Sharing and acting upon knowledge are significant with a
variation of 16.372%. Factor 3 consists of three variables of which Sharing of experience
and information are significant with a variation of 15.268%. Factor 4 consists of two
variables of which Sharing best practices are significant with a variation of 12.388%.
Based on the results of factor loading (Table 13), the factors are named which is given in
table 14.

5. CONCLUSION
The conclusions derived in empirical analysis are summaried below.
 Most of respondents are aware of what knowledge management is.
 The knowledge management activities of an organization are greatly
influenced by the demographic characteristic of employees.
 The ability of an organization to learn mainly depends on the
individual characteristic of an employee.
 The factors like innovation through collaboration, different market
entry through enhanced productivity, better decision making through delegation,
increase market share causes the variance on knowledge management system.
 The factors like better information, application of knowledge,
knowledge management process, and shared vision it contributes greatly to the
properties of learning organization
 Knowledge management in the organization perks up better staff retention.
 Knowledge management in the organization strengthens the workers to
accomplish the task quickly.
 Knowledge management endeavors the business into different market type.
 Knowledge management in the organization trims down the communication gap
between employees.
 Knowledge management in the organization raises the adaptation capability
among the employees.
 Knowledge management in the organization smoothes the progress of learning.
 Knowledge management in the organization augments the continuous
transformation of individual learning.

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 Knowledge management in the organization affords readymade information to the

employees.
 Knowledge management in the organization strengthens the collaboration among
employees within the organization.
 Knowledge management makes every effort for learning and re-learning through
training modules in the organization.
 The practice of knowledge management in the organization makes way for
sharing the best practices among employees which results in enhanced
collaboration among employees.

Based on the findings, few suggestions have been made by researcher which is
summarized below:
 This study should be made every year to evaluate the new practices that can
bring in changes in the organization.
 Caring about those people who are innovative and always are ready for
giving new ideas.
 There should be coordination among employees that they think they are
working for the same goals and objectives.
 Management should care more about the staff’s communication that should
give the time for sharing informally and give a high priority to KM on the
agenda.
 There should be exchanges of experiences and knowledge among people of
organizations by creating online communities for this reason.

It is concluded that the KMS helps the organization in improving its performance
in terms of innovation and better decision making. Also it paves the path for organization
to transform into learning organization. So the organization should continuously pay
focus on KM efforts.

6. LIMITATIONS AND FUTHER RESEARCH

The results obtained in this study could be subject to some limitations as mentioned
below:
 The study is restricted only to a particular engineering firm in a district.
 The population belongs to only managers and engineers and samples are
drawn from particular departments of a selected organization.
 Identifying managers and engineers who are really familiar and
experienced with KMS are found to be difficult.

Some avenues for further research are as follows:

 The relationship between knowledge mangement system and Organizational
culture
 The relationship between knowledge management system and Knowledge sharing
 The relationship between knowledge management system and knowledge seeking
practices
 The relationship between knowledge management system and intellectual capital
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U.Syed Aktharsha Anisa.H

 The relationship between knowledge management system and task characteristics.

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U.Syed Aktharsha Anisa.H

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APPENDIX - QUESTIONNAIRE
KNOWLEDGE MANAGEMENT SYSTEM AND LEARNING ORGANIZAION
- AN EMPIRICAL STUDY AT ENGINEERING ORGANIZATION

PART1: DEMOGRAPHIC PROFILE

Gender:
Femal
MALE e
Age:
Below 30 30-40 41-50 Above 50
Department:
Engineerin
Production Quality Control
g
Designation:
Engineer Manager
Education Qualification:

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U.Syed Aktharsha Anisa.H

Diploma UG PG
Experience:
Below 10 10-20 20-30 Above 30
Income Level:
Below 10,000 10,000-20,000 20,000-30,000 Above 30,000

PART - II: THE EFFECTS OF KNOWLEDGE MANAGEMENT SYSTEM IN AN

ORGANIZATION.

Please put tick mark in the appropriate box matching your opinion

SA – Strongly Agree; A – Agree;N – Neutral;DA – Disagree;SDA – Strongly

Disagree

Questions SA A N DA SDA
1. The KM system helps in fast and better decision making. 1 2 3 4 5
2. KM helps in enhanced productivity or service quality. 1 2 3 4 5
3. Implementing KM results in sharing best practices. 1 2 3 4 5
4. KM makes it easy to enter different market types. 1 2 3 4 5
5. KM helps in increased innovation by the employees. 1 2 3 4 5
6. Application of KM system results in increased market 1 2 3 4 5
share

7. KM increases the learning/adaptation capability of 1 2 3 4 5

employees.

8. KM helps in better staff attraction/retention. 1 2 3 4 5

9. KM results in enhanced collaboration within the 1 2 3 4 5
organization
10. KM helps to address the communication gap in the 1 2 3 4 5
organization.
11. KM helps in constant and continuous transformation of 1 2 3 4 5
individual
Learning to organizational Learning and vice versa.
12. KM results in increased delegation of authority and 1 2 3 4 5
accountability to individuals.
13. KM helps to achieve better ROI. 1 2 3 4 5

PART – III: CHARACTERISTICS OF LEARNING ORGANIZATION.

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Questions SA A N DA SDA
14. Information is readily available on required topics from 1 2 3 4 5
current Publications to industry specific processes.
15. Information regarding process description can be 1 2 3 4 5
uploaded in Organization’s database.
16. Personal best practices can be shared with other 1 2 3 4 5
employees.
17. Enabling hardware and software technologies are 1 2 3 4 5
available to support learning rather than control it.
18. There are well defined processes for creation, capture, 1 2 3 4 5
and acquisition of knowledge.
19. Useful knowledge can be easily shared and acted upon. 1 2 3 4 5

20. A cohering and powerful vision of the organization is 1 2 3 4 5

shared across the workforce to promote need for strategic
thinking at all levels.
21. There are enabling structures in terms of hierarchy and 1 2 3 4 5
communication flows that facilitates learning.
22. There are cohesive teams in organization which 1 2 3 4 5
facilitates sharing of experiences and Information among
employees.
23. The organization provides incentives to motivate users 1 2 3 4 5
to learn from experiences and use KM system.
24. The organization continuously strives for learning, 1 2 3 4 5
unlearning and re-learning for its employees.

International Journal of Business Research and Management (IJBRM)