DECLARATION

I, --------- student of Masters of Business Administration from SMU. Hereby declare that I

have completed Dissertation on “Knowledge management in learning organization” as part of

the course requirement.

I further declare that the information presented in this project is true and original to the best

of my knowledge.

Date: 24-5-11 Name:------------ Roll No.:------------

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CERTIFICATE

This to certify that Ms_______, a student of Masters of Business Administration, MBA HR

has undertaken a dissertation as a part of her academic curriculum.

The project named ‘Knowledge Management in Learning Organization’ has been diligently

done under my guidance and supervision. The project is prepared in partial fulfillment of

MBA (HR).

To the best of my knowledge, this piece of work is original and no part of this report has been

submitted by the student to any other Institute/University earlier.

Date: 25-5-11

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Executive Summary

Knowledge Management is about a systematic approach to managing intellectual assets and

other information in a way that provides the company with a competitive advantage.

Knowledge Management is a business optimization strategy, and not limited to a particular

technology or information. A wide variety of information technologies play a key role in a

KM initiative, simply because of the savings in time and effort they provide over manual

operations. If a company takes the digitized data and indexes it with a software program that

allows someone to search for specific content instead of manually paging through hundreds

of screens, it is practicing Knowledge Management.

Organizational learning could be defined as a process of 'coordinated systems change, with

mechanisms built in for individuals and groups to access, build and use organizational

memory, structure and culture to develop long term organizational capacity'.

The research checks into Knowledge Management system and Learning Organizations. The

effects of KM and what organizations do as a learning organization is studied through this

research.

Under the project title ‘Knowledge Management and Learning Organizations’ following

objectives have been served:

- Identifying the effects of KM and important criteria’s for measuring KM

success.

- Association between Knowledge Management and Learning Organization.

At end, the research differentiates between the factors which are more important and less

important. It tells how organizations are doing as learning organization. The important factors

have been elaborated and suggestions for further research are given.

It concludes with differentiation among significant and insignificant variables and what

factors are interrelated in Knowledge Management and Learning Organizations. A few are:

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Most useful criteria

- Decision Making

- Sharing best practices

- Enhanced Collaboration

- Improved Communication

Least useful criteria

- Increased market share

- Better staff attraction/retention

- Delegation of authority

- Return on investment of KM effort

At last suggestions for further research and references have been provided.

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ACKNOWLEDGEMENT

The dissertation project titled ‘Knowledge Management in Learning Organization’ has been

conducted by the under signed as an academic part of the 2 year MBA HR program to be

completed in 2010.

The project has been completed based on primary research under the guidance of Professor

Mr.Sanjay Kaul (HR), Sikkim Manipal University.

I am obliged and owe enormous intellectual debt to Mr. Sanjay Kaul for her guidance and

enriched thoughts from different perspectives. My increased spectrum of knowledge in this

field is a constant direction that has helped me to absorb relevant and high quality

information.

I am thankful to the employees who have taken out time from their busy schedule to fill up

the questionnaire, both in person and through mail.

Date: 25-5-11 Nisha Mishra

Roll No.: 511042381

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Contents Page No.Declaration………………………………………………………………… 1

Certification from Faculty Guide………………………………………….. 2

Executive Summary ……………………………………………………… 3

Acknowledgement ……………………………………………………….. 5

List of Tables and Figures ………………………………………………… 7

Chapter 1- Introduction to the present study 10

Chapter 2- Research Methodology 21

Chapter 3- Review of literature 24

Chapter 4- Research Analysis & Findings 28

Chapter 5- Suggestion for further research 64

Chapter 6- Conclusion 65

References…………………………………………………………………. 66

Annexure…………………………………………………………………... 68

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LIST OF FIGURES AND TABLES

S.NO. FIGURES AND TABLES PAGE

NO.

1 Figure 1: The KM/OM/OL model (Jennex & Olfman, 2002)

15

2 Figure 2 : Triple Loop Learning (Argyris & Schon, 1978) 18

3 TABLE & EXHIBIT 4.1- KM system helps in fast and better decision making

30

4 TABLE & EXHIBIT 4.2- KM helps in enhancing the productivity 31

5 TABLE & EXHIBIT 4.3- Implementing KM results in sharing best

practices

32

6 TABLE & EXHIBIT 4.4- KM makes it easy to enter different market

types

33

7 TABLE & EXHIBIT 4.5- KM helps in increased innovation 34

8 TABLE & EXHIBIT 4.6 - KM system results in increased market

share

35

9 TABLE & EXHIBIT 4.7- KM increases the learning/adaptation

capability of employees

36

10 TABLE & EXHIBIT 4.8- KM helps in better staff attraction/retention 37

11 TABLE & EXHIBIT 4.9- KM results in enhanced collaboration

within the organization

38

12 TABLE & EXHIBIT 4.10- KM helps to address the communication

gap in the organization

39

13 TABLE & EXHIBIT 4.11- KM helps in constant and continuous

transformation of individual learning to organizational Learning and

vice versa

40

14 TABLE & EXHIBIT 4.12- KM results in increased delegation of

authority and accountability to individuals

41

TABLE & EXHIBIT 4.13- KM helps to achieve better ROI 42

TABLE & EXHIBIT 4.14- Information is readily available on

required topics

43

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TABLE & EXHIBIT 4.15- Information can be uploaded in

organization’s database

44

TABLE & EXHIBIT 4.16- Personal best practices can be shared with

other employees

45

TABLE & EXHIBIT 4.17- Availability of hardware and software

technologies

46

TABLE & EXHIBIT 4.18- Processes for creation, capture, and

acquisition of knowledge

47

TABLE & EXHIBIT 4.19- Knowledge can be easily shared and acted

upon

48

TABLE & EXHIBIT 4.20- A cohering and powerful vision of the

organization is shared

49

TABLE & EXHIBIT 4.21- Enabling structures in terms of hierarchy

and communication flows exists

50

TABLE & EXHIBIT 4.22- There are cohesive teams in organization

which facilitates learning

51

TABLE & EXHIBIT 4.23- Organization provides incentives to use

KM system

52

TABLE & EXHIBIT 4.24- Organization strives for learning,

unlearning and re-learning for its employees

53

Table 1: Representing eigenvalues, variance explained, and

cumulative variance

54

Table 2: Representing Extracted Factors with Eigenvalues >1; (F =

Factor)

56

Table 3: Representing Variables that are interrelated to each other 57

Table 4: Representing Survey Results of KM Criteria 58

Table 5: Representing Survey results of Organizational Learning 59

Graph 1 - Scree Plot for Unrotated Factor Loadings 57

Graph 2: Line shape chart representing mean score and distinguishing highest & least variables (KM)

59

Graph 3: Line shape chart representing mean score and distinguishing 60

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highest & least variables (LO)

Figure 4.1 – Significant Knowledge Management Outcome 63

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CHAPTER 1

INTRODUCTION

Today’s business environment is characterized by continuous, often radical change. Such a

volatile climate demands a new attitude and approach within organizations—actions must be

anticipatory, adaptive, and based on a faster cycle of knowledge creation. Hendricks and

Vrien, 1999, suggest that the knowledge assets possessed by a company create the possibility

for a sustainable competitive advantage. This being the case, a company that manages

knowledge effectively will have a better chance of long-term survival than those which lack

in the same area (Nonaka and Takeuchi 1995).

Davenport and Prusak (1998) view knowledge as an evolving mix of framed experience,

values, contextual information and expert insight that provides a framework for evaluating

and incorporating new experiences and information. They found that in organizations,

knowledge often becomes embedded in documents or repositories and in organizational

routines, processes, practices and norms. They add that for knowledge to have value it must

include the elements of human context, experience and interpretation. Nonaka (1994)

expands this view by stating that knowledge is about meaning in the sense that it is context

specific. This implies that users of knowledge must understand and have experience with the

context (surrounding conditions and influences) in which the knowledge is generated and

used for it to be meaningful. As to compete effectively modern business organizations need

skilled managers and employees and further require methods for managing knowledge for its

people as it creates big impact on overall performance. Knowledge Management (KM)

attempts to secure and replenish the learning experiences, as well as the work products, of the

individuals who comprise an organization. KM is the ability to selectively capture, archive,

and access the best practices of work-related knowledge and decision making from

employees and managers for both individual and group behaviors. It is a systematic business

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enhancing strategy that selects, stores, organizes, packages, and communicates information

essential to the business in a manner that improves employee performance and corporate

competitiveness. A wide variety of information technologies play a key role in a KM

initiative, simply because of the savings in time and effort they provide over manual

operations. If a company takes the digitized data and indexes it with a software program that

allows someone to search for specific content instead of manually paging through hundreds

of screens, it is practicing Knowledge Management.

Some of the organizations in India that have adopted Knowledge Management are:

Wipro

Infosys

Compaq

Sapient

TCS

WockHardt,

L&T Infotech

Hewlett Packard etc.

Several professional services firms already have knowledge management roles in place.

McKinsey, Andersen Consulting, Ernst & Young, Price Waterhouse, and A.T. Kearney all

have "Chief Knowledge Officers" in place. Buckman Laboratories reoriented its Information

Systems organization to become managers of knowledge, and now calls the group the

Knowledge Transfer department. Hewlett-Packard created one knowledge management

group within its corporate Product Processes Organization, and another within its Computer

Systems marketing group.

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Elements in creating KM system are:

The elements that create a KM system are as follows:

Data

They are numbers. They are numerical values or other attributes derived from observation,

experiment, or calculation.

Information

It is data in context. Information is a collection of data and associated explanations,

interpretations, concerning a particular object, event, or process.

Metadata

It’s the data about information. Metadata includes descriptive summaries and categorization

of data and information. That is, metadata is information about the context in which

information is used.

Knowledge

It is information that is organized or summarized to enhance comprehension, awareness, or

understanding. That is, knowledge is a combination of metadata and an awareness of the

context in which the metadata can be applied successfully.

Instrumental understanding

It is the clear and complete idea of the nature, significance, and explanation of something. It

is about relating specific knowledge to various concepts.

Types of Knowledge:

Knowledge can be of three kinds:

Tacit knowledge

It is the knowledge that is acquired at a subconscious level and therefore difficult to explain

to others. For example, an expert machinist may be extremely skilled at operating a particular

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machine, but he might be unable to instruct a new joiner on how to duplicate his expertise.

Most knowledge involving pattern recognition skills fall under his category.

Implicit knowledge

This type of knowledge is controlled by experts. Implicit knowledge can be extracted from

the expert—through a process termed knowledge engineering. For example, a sales executive

can train a new joiner about the basic procedure and rules to follow while making sales. The

new joiner can gain same effectiveness as his trainer over the period of time. Thus knowledge

which can be imparted through instructions and procedures is Implicit Knowledge.

Explicit knowledge

It can be easily conveyed from someone proficient at a task to someone else through written

or verbal communications. For example, the recipe for a cake; the steps involved in bolting a

car door, list of ingredients required for a chemical process are all explicit knowledge. Unlike

tacit and implicit knowledge, explicit knowledge often can be found in a book or operating

manual.

The importance of KM becomes transparent when considering the different forms

which knowledge can take. Just as learning can be divided into the two distinct categories of

single- and

double-loop learning, knowledge can be classified as either tacit or explicit. Explicit

knowledge is just what it claims to be— knowledge that has been explicitly explained,

recorded, or documented (McInerney 2002). As explicit knowledge has, in some form, been

communicated and can be formally documented, it can be shared relatively easily among

individuals throughout an organization. The more difficult task of knowledge management

then becomes that of managing tacit knowledge. Tacit knowledge may be considered

intuitive knowledge guided by experience. This type of knowledge is based on experience,

mental models, and perspectives that are so deeply embedded in a person that the knowledge

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becomes second nature to an individual and, as such, is difficult to communicate. Central to

the concept of knowledge management is either: 1) putting individuals in touch with one

another to share their tacit knowledge; and/or 2) transforming individuals’ tacit knowledge

into explicit knowledge, which can be used by the entire organization. Failing to either share

tacit knowledge or create explicit knowledge from tacit knowledge can result not only in

losses to an organization but can also help to accelerate a competitor’s advantage. In other

words, by sharing and extracting an employee’s tacit knowledge a company multiplies the

value which that employee adds to the company. If one company is able to share and/or

extract tacit knowledge in this manner, it will excel above competitors who may have the

same knowledge within their grasp but fail to share it on a company-wide basis.

The Learning Organization

The concept of a learning organization, which is often associated with that of organizational

learning, is defined as reframing and learning from one’s own experiences in an effective

manner.

Organizational learning is concerned with the development of new knowledge or insights that

have the potential to influence behavior. It takes place within organizational context and

'refers to an organization's acquisition of understanding, know how, techniques and practices

of any kind and by any means. Organizational learning examines how individual and team

learning can be translated into an organizational resource and is therefore linked to processes

of knowledge management.

Organizational learning could be defined as a process of 'coordinated systems change, with

mechanisms built in for individuals and groups to access, build and use organizational

memory, structure and culture to develop long term organizational capacity'.

Organizational learning can be characterized as an intricate three stage process consisting of

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knowledge acquisition, dissemination and shared implementation. Knowledge may be

acquired from direct experience, the experience of others or organizational memory.

Knowledge Management and Organizational Learning

Figure 1.1: The KM/OM/OL model (Jennex & Olfman, 2002) – Knowledge Management in Modern Organizations

A better understanding of KM is obtained by understanding the concepts of organizational

memory (OM) and organizational learning (OL). The three areas are related and have an

impact on organizational effectiveness. Organizational effectiveness is how well the

organization does those activities which are critical to its survival. OL is the process which

organization uses to learn how to do these activities better. OL results when users utilize

knowledge. Effectiveness can improve, worse, or remain the same. Effectiveness influences

the feedback provided to the organization using the knowledge. KM and OM are the

processes used to identify and capture critical knowledge. Knowledge workers and their

organizations do KM. Above figure illustrate these relationships.

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Relationship between Knowledge management and Learning Organization

The greatest challenge organizations face today is how to manage their intellectual capital.

The business environment has now entered a knowledge era, where knowledge has become

power, and learning rapidly and building competency and capability in an organization has

become a preeminent strategy for success. Thus managing knowledge is rapidly becoming

more important to organizations than financial resources, market positions, technology and

other tangible assets. Various organizations are talking about this concept in terms of

intellectual capital, intellectual property, knowledge assets, and business intelligence.

Corporate knowledge is being viewed as one of the most important sustainable untapped

source of competitive advantage in business. There is always a new idea waiting to be

discovered, new ways of doing things, new products, new strategies, and new markets.

(McElroy W. Mark, 2000)

According to Peter Drucker, the collective knowledge residing in the minds of its employees,

customers, suppliers etc., is the most vital resource of an organization growth, even more than

the traditional factors of production (land, labour and capital). (Grossman Martin, 2006)

However, Knowledge Management does not happen by chance. A culture that promotes

knowledge creation and provides for appropriate support processes is necessary. Therefore, if

organizations are to fully benefit from the principles of Knowledge Management, they must

focus on how the cognitive capacity of their employees and support processes are aligned to

provide timely information for improvement. The cognitive (in the form of heuristics and

intuitions) and the support processes (such as culture, products and services) are the two most

important constructs with Knowledge Management.

The level of Knowledge Management in an organization whose employees perform

organizational tasks routinely can be determined from whether they are able or unable to

contribute for any improvements in their organizational business processes. Equally, the

support from the organization in providing the facilities that support and optimize knowledge

management is an important issue. Based on the level of cognitive capacities of employees

and support services existing in the organization, the condition can be classified as Integrated

Knowledge Management, Partial Knowledge Management and Absence of Knowledge

Management in an organization. Effective Knowledge management requires not only

addressing the mindset of the employees but also putting in place the necessary support

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services that facilitate an environment for knowledge creation and learning. An organization

should always seek employees who demonstrate stimulating behavior, acquire knowledge

and know how to adapt to change. Knowledge and learning are fundamental factors that

needs to be addressed if business excellence is to be achieved for competitive advantage (Jha,

Joshi, etal, 2006)

Knowledge acquisition process is twofold, inward and outward. Internal knowledge

acquisition owes much to Total Quality Management (TQM) idea of internal benchmarking

and learning from experience. External knowledge is acquired to bring in innovative ideas

and develop effective operating systems. The ability to learn from the internal and external

business environment has become one of the principle value adding resources for learning

organizations.

Peter Senge defines Learning Organization as "a group of people continuously enhancing

their capacity to create what they want to create". For organizations to anticipate and respond

to complexities and uncertainties, have to consciously and comprehensively gather, organize,

share, and analyze its knowledge in terms of resources, documents, and people skills. The

rate at which organizations learn and adapt to the changing environment may become the

sustainable source of competitive advantage.

A Learning Organization is one that, according to Senge, has acquired "systems thinking" by

mastering the disciplines of 'shared values', 'personal mastery', 'mental models' and 'team

learning' (Senge, 1992) 'system thinking' has therefore become known as the fifth discipline

and is closely related to Deming's concept of 'knowledge system'. This system talks about

profound knowledge, which is knowledge universal to all businesses, large or small. Once the

individual understands the system of profound knowledge, he will apply its principles in

every kind of relationship with other people. This will enable him make better decisions for

organizational transformation.

A Learning Organization and its people make use of their experience and others to improve

their performance. Individuals learn to improve their performance through challenging their

assumptions; which in turn provides new insights and perspectives about organizational

improvement. Continuous learning is built into the system and the value of continuous

learning is espoused, driven and role-modeled by the top management leadership within the

organization. Further, communication within all the levels of management is open and

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widespread. People at all levels are included for decision-making process and are recognized

for their contribution towards learning and disseminating the knowledge acquired to other

employees. Some of the success stories that have shown the characteristics of a Learning

Organizations are GE, Johnson & Johnson, Toyota Motors, Southwest Airlines, Intel, Cisco

Systems, Tata Steel, Tata Motors, Infosys Technologies and many such organizations. What

are common to these companies are their founding values, and their desire to create new

products and markets, new approaches and greater customer value.

Since organizations learn only through individuals who learn (Senge 1992), it is necessary to

look closer at how individuals learn. In this respect, it is worth noting that individual learning

is a necessary but not sufficient condition for organizational learning. Hence, there is a need

for having "shared rules". Argyris and Schon distinguish between three different types of

learning: single-loop and double-loop as well as deutro-loop learning (Argyris & Schon,

1978)--or called triple-loop learning. This is shown below in Figure 2 .

Figure 1.2 : Triple Loop Learning (Argyris & Schon, 1978)

Practitioners of Organizational Learning, known as "organ learners," therefore, see a

difference between what individuals know and knowledge held collectively by groups of

individuals. Individual learning leads to individual knowledge; Organizational Learning leads

to collective knowledge. Conflict between them is bound to occur and can be seen as a

stimulant for innovation and creativity. Older ideas give way to newer, more effective ones as

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people in business, for e.g., attempt to resolve their individual and group differences.

There are several themes, which emerged from various perspectives on Learning

Organization

In order to grow, organizations need to continuously learn

Both individuals and organizations learn, using different methods, producing different

outcomes

Information storage, processing and sharing are important.

Context i.e. (structure and culture) contributes to organizational learning

These themes integrating knowledge and learning are a starting point for linking Knowledge

Management and Organizational Learning Practices.

It has been established that a Learning Organization generates new knowledge.

Knowledge Management System (KMS) at SAPIENT TECHNOLOGIES

The knowledge management system or process at Sapient is called as People Portal. It is

administered or managed by an internal team of experts. This team has different sub teams

which are managing various aspects of KM. Knowledge is managed and shared through

following at Sapient:

- Individual Blogs

- Interest Groups

- Community Forums

Although no online library or data bank exist at Sapient.

The other features of the system are as follows:

- Every piece of information is indexed and is easy to search.

- Required information on processes, domains, sectors is available.

- All documents relating to work are available on intranet.

- Users can share their experiences and practices through blogs.

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The information outside the company i.e. external information about competitors, new

products, markets is not available on the system.

Apart from this employees can put their requirements like grant for leave, complaints, and

other requirements on the local network system. It also displays the achievements, birthday,

and anniversaries, of employees. It is used as a recognition system in the company.

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CHAPTER2

RESEARCH METHODOLOGY

Research Objectives

Under the project title ‘Knowledge Management and Learning Organizations’ following

objectives have been served:

- To identify the effects of KM and important criteria’s for measuring KM

success.

This objective identifies which factors Knowledge Management is affecting most and what is

important for measuring success of Knowledge Management system

- To examine the association between Knowledge Management and Learning

Organization.

This objective examines how Knowledge Management and Learning Organizations are

interrelated and what factors associate them.

Rationale of the study

KM is a relatively new field of study and much of research has not been done on it. Only in

mid 90’s researchers have considered as a research topic. The theory and previous literature

on knowledge management tells about the factors that create KM system and which all

performance measures are affected by it. Also the model of Information Systems success

given by DeLone and McLean tells what creates KM system. Thus, the rationale is to study

these factors and identify which factors have maximum importance.

Research Design: Exploratory research design has been followed in this study. It was

conducted with a considerable understanding of the situation being studied. The objective of

the exploratory research is to explore or search through problem or situation to provide

insights and understanding.

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Sample Size: Population- Population consists of employees exposed to Knowledge

Management Systems and has relevant experience in it. In this study responses of 60

respondents is collected and surveyed.

Selection of Sample: The respondents selected from the population constitute what is

technically called a ‘sample’.

Technique used – Random Sampling

The Survey

The final survey was designed with the following features:

- There are 24 questions, which are divided into 2 parts i.e. knowledge management

and learning organization.

- A brief note about the purpose of the questionnaire is provided in the cover letter.

- Definitions are provided for important terms used in the questionnaire.

Sources of data

1. Secondary data…… communication medium like books, journals, magazines, internet.

2. Primary data….. Questionnaire

Questionnaire:

A questionnaire is a structured set of definite questions, with each question provided with

options, about which the respondents are required to present their views. Advantages of

questionnaire over other methods of data collection are as follow.

There is low cost even when the universe is large and widely spread geographically. It is free

from the bias of the interviewer: answers are the respondents own choice. Respondents have

adequate time to give well thought out answers.

Scaling technique

Likert Scale is used in the questionnaire administered for the study. Developed by Rensis

Likert, a Likert Scale is widely used rating scale that requires respondents to indicate a degree

of agreement or disagreement with each of a series of statements about the factors

undertaken. The scale has five response categories viz,

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1. Strongly Agree

2. Agree

3. Disagree

4. Strongly Disagree

5. Don’t know/ Can’t say

Thus, an employee will have the most favorable opinion towards the Knowledge

Management System with his response towards one end i.e. strongly agree and most

unfavorable opinion towards other end i.e. strongly disagree of the scale.

Action Plan and Data Collection

Study is undertaken according to following action plan:

Step 1. Developing an approach to the problem, it involved formulation of objective, making

rough information needs, what all data was required, analyzing secondary data and

discussions with staff that helped in giving inputs as and when required.

Step 2. Review of books, journal helped in the formation of objectives. It helped in better

defining the problem, what factors should be considered, helped in formulating research

design and also formed the basis of collecting Primary Data

Step 3. Conducting survey of respondents in various companies

Step 4. After collection of questionnaire Data analysis was done which is discussed in detail

later.

Step 5. Suggestion & recommendation are given at last.

Data Analyses

The questionnaire was designed for the employees and it was made very simple with all the

factors divided into simple components that they can understand it easily.

There were a total of 24 questions.

Each questionnaire form was inspected and analyzed carefully. The data collected from the

questionnaire was expressed in concise and logical form with the help of pie charts and

tables. The responses of 60 respondents over 24 questions are shown in the pie charts.

Different color schemes show different options in the pie charts.

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CHAPTER 3

REVIEW OF LITERATURE

The initial Research defines knowledge and knowledge management (KM) and establishes its

roots. KM is not a brand new topic, while organizational learning and organizational memory

are related topics that have been fields of research for many years. The chapter shows how

research has been done in field of Knowledge Management. Additionally, this chapter

explains that KM has become a research area due to a many trends that have made KM

necessary and technically useful. (Murray E. Jennex, 1999) The next in line ate the

arguments that show knowledge management (KM) is a discipline. Kuhn’s (1996) criteria for

being a discipline are used as a framework for providing information showing KM to be a

discipline. It was found that KM has interesting research questions, journals specific to KM, a

body of accepted knowledge, professional societies, its own jargon and ontology, and its own

degree programs. It also is concluded that KM is a young and growing discipline. (David

Croasdell, 1994)

Although knowledge management (KM) is maturing as a research topic, there is no

agreement on what constructs create its foundation. The topic has received increasing

attention in academic journals, it is important for researchers to be aware of the research

streams associated with KM. Accordingly, this chapter reviews the knowledge management

literature published in top-tier journals from 2000 to 2005. These articles then are classified

by knowledge management construct and by research methodology. The results indicate that

the majority of knowledge management research has examined the construct of knowledge

transfer. Trends of published KM research, gaps, and inconsistencies in the examined

literature and applied research methodologies are discussed. Knowledge management is a

complex field, divided into necessity or design. In this research, data that maps out a number

of the characteristics of the field is presented. (Todd Peachey, Dianne Hall, Casey

Cegielski, 2000) After that trends that indicate how knowledge management is evolving into

a discipline in its own right and present some thoughts on what the dominant characteristics

of that discipline need to be. Knowledge management (KM) initiatives are undertaken in

order to improve organizational performance. The goal of such improvement is to make an

organization more competitive in delivering value to its customers, employers, and

stakeholders. However, without a plan that links KM activities to organizational performance,

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the time, effort, and money devoted to a KM initiative may yield little benefit. Thus,

understanding this linkage is crucial to competitiveness of knowledge-based organizations.

This research uses the knowledge chain model as the theoretical base for an empirical study

of the linkage between KM activities and approaches to competitiveness. It finds that every

one of the nine knowledge chain activities can be performed in ways that improve

organizational competitiveness in any of four ways: enhanced productivity, agility,

innovation, and reputation. Apart from offering empirical support for the knowledge chain

model, the primary finding of this research is that each knowledge chain activity deserves to

be considered as a possible means for implementing each of these four approaches to

improving organization performance.

Another issue was capturing of tacit knowledge within organizations has risen in recent years.

However, the capture of explicit knowledge is relatively straightforward; methods for

eliciting tacit knowledge are less developed. (David G. Schwartz, 2001) This research

briefly overviews a number of strategies for eliciting tacit knowledge and then provides a

detailed examination of one of these strategies. The critical decision interview method can

assist expert respondents to articulate tacit knowledge by probing beyond their usual theories

about their actions to reveal their practice. Tacit knowledge then can be identified by

contrasting respondents’ practices with theoretical prescriptions for best practice in the field.

Knowledge management (KM) has gained increasing attention since the mid-1990s. A KM

strategy involves helping people share and put knowledge into action. However, before an

organization can realize the benefits of KM, a fundamental question needs to be asked: What

performance goals is the organization trying to achieve? In this research, a multi-level

framework that gives a view of the performance environment surrounding organizational

knowledge work. It explains the KM framework using two organizational case studies. Then,

based on the KM framework and further insights drawn from our case studies, it offer a series

of steps that may guide and assist organizations and practitioners as they undertake KM

initiatives. Research further demonstrates the applicability of these steps by examining KM

initiatives within a global software development company.

While the discipline of knowledge management (KM) is no longer emerging, some

organizations are still finding it difficult to fully take advantage of their intellectual assets.

Having proper organizational culture is an important barrier to knowledge management

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success. This empirical research project, conducted with data from 97 organizations involved

in KM, explores relationships between the level of organizational trust and the use of KM

methodologies, in particular the use of codification KM methodologies and personalization

KM methodologies. The presence of trust can be used as an indicator of KM initiative

success. The contribution of this research will help organizations seeking to launch a KM

initiative to choose which KM tools and technologies to use in order to maximize their

chances of success. (Vincent Ribière, 1994) A community of practice (CoP) is an

organizational form that promotes sense making, knowledge management, and learning. It is

important to understand how and why these communities form and grow over time. These

questions are explored in a qualitative analysis of a knowledge management (KM)

community of practice. This case study includes a description of how the organization

formed, survived, grew, and matured over a five-year period (1999-2004). Several practices

and structures related to CoP development are identified: operations, roles and

responsibilities, communications, subgroup structures, use of information technologies, and

other aspects of organizing. Using data from several sources (e.g., membership surveys,

interviews with key informants, document analysis), four sets of critical success factors are

identified: Individual factors, content factors, meeting factors, and organizational factors.

These factors are arranged into a descriptive model of the function and structure of CoPs over

the life cycle. This work also sheds light on how to set up and successfully grow a

community of practice. (Eric W. Ste n, 2003) Further research surveys knowledge

management (KM) and knowledge management system (KMS) success factors and models. It

also provides a framework for assessing KM and KMS success models. The framework uses

three criteria: how well the model fits actual KMS success factors, the degree to which the

model has a theoretical foundation, and if the model can be used for both types of KMS. The

framework then is applied to four KMS success models found in the literature and is

determined to be a useful framework for assessing KMS success models. For a company to

be focused consistently toward its customers and their processes, it needs to customize its

processes and systems. The solution is process-oriented portals that integrate companies’

systems and provide transparent access to information stored in these systems. A key problem

is finding relevant information objects in systems and whether knowledge is available at right

time and at the right place. A company’s competitive advantage is based on this knowledge

advantage as well as in the capability to transform this superior knowledge into market-

driven business processes. The questions addressed in this research are how the value of

information objects is affected by the context in which it is considered and how associated

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contexts can be uncovered for given situations. (Massey, Ramesh, & Montoya-Weiss, 2006)

Research introduces a continuum of context comprised of the relationships among data,

information objects, knowledge, and their contexts according to their degree and ease of

availability. In addition, research evaluate the full-text search, attribute-based search, and

topic maps as approaches for knowledge discovery through customer process-oriented portals

and providing patterns that indicate when to apply which approach. Two small case studies

are presented of knowledge discovery through such portals.

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CHAPTER 4

DATA ANALYSIS AND FINDINGS

This part contains the data collected from the respondents. This chapter is divided into three

parts i.e. Sample Description, Mean Scores and Factor Analysis. There are different charts

representing the employee opinion about a particular factor. The introduction to all tables is

given in order to represent the necessity of including the questions and providing the basis of

understanding the results. Measure of central tendency (Mean) has been used extensively to

analyze the collected data. Tables have been used to represent the analyzed data. Factor

Analysis has been done with the help of SPSS software. Responses have been analyzed to

arrive at the effectiveness of the factors.

Part 1: Sample Description

The sample size is of 60 respondents who are exposed to Knowledge Management system in

different organizations. They are either administering the KM system or are users of the

system.

Companies with which respondents belong to are:

- Wipro

- Sapient Technologies

- Infosys

16 respondents have filled the questionnaire from Wipro, 26 from Sapient Technologies and

18 from Infosys

The respondents belong to various positions such as:

- Associate Technology

- Developer

- Quality Analyst

- Lead Technology &

- Associates

All of them belong to Software Development and IT department in their respective

companies.

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Part 2: Mean Scores

This section represents the analysis of different variables along with pie charts. The mean

score is calculated for each variable and analysis for that variable is done.

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Decision Making

Fast and better decision making has been critical in businesses these days. Changing

conditions and increasing competition demands real time decision making. Knowledge

Management assists this decision making and makes information readily available.

Table and Exhibit 4.1 – Decision Making KM system helps in fast and better decision making

Responses Percentage of ResponseStrongly Agree 14Agree 28Disagree 10Strongly Disagree 04Dn/Cs 04Total Responses 60

The above chart represents that about 70% of employees agree that KM system helps in fast

and better decision making. About 23% disagree with the statement and rest didn’t reply.

Not significant but many respondents agreed with the statement. For this variable,

mean value is 2.733. Thus, decision making is an important criteria of judging KM

success and it effects the decision making of employees to some extent.

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Enhanced productivity or Service quality

Organizations continuously strive for increased productivity or improved service quality. KM

effects the working of employees and hence productivity also.

Table and Exhibit 4.2– Enhanced Productivity or Service quality KM helps in enhancing the productivityResponses Frequency of ResponseStrongly Agree 12Agree 20Disagree 12Strongly Disagree 08Dn/Cs 08Total Responses 60

The above chart represents that 53% of employees agree that KM system helps in increasing

productivity. About 34% disagree with the statement and rest didn’t reply.

For this factor, respondents consider it insignificant as mean value for this variable is

2.333. Productivity or service quality is not the most important criteria and KM does

not affect it significantly.

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Sharing Best Practices

KM helps in sharing experiences, knowledge, ideas and expertise. The best and successful

practices can be easily shared among the employees.

Table and Exhibit 4.3- Sharing best Practices

Implementing KM results in sharing best practicesResponses Frequency of ResponseStrongly Agree 28Agree 18Disagree 10Strongly Disagree 04Dn/Cs 00Total Responses 60

The above chart represents that about 77% of employees agree that KM system assists in

sharing best practices. About 23% of respondents disagree to the situation.

Most respondents are of the opinion that KM helps in sharing best practices. Mean

value for this variable is 3.166. Most respondents agree to the statement. It is one of the

important criteria affected by KM system.

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Entering new Markets

Expanding continuously is important for businesses in order to maintain its existence. For this

they enter new markets. KM provides instant information and makes changes easy in such

scenarios.

Table and Exhibit 4.4 – Entering New Markets KM makes it easy to enter different market typesResponses Frequency of ResponseStrongly Agree 08Agree 14Disagree 18Strongly Disagree 12Dn/Cs 08Total Responses 60

The above chart represents only about 37% of employees agree that KM system helps in

entering to new markets and expansion. About 50% disagree to the statement and rest is

unsure of their opinion.

Entering new markets is one of the least important criteria affected by KM system.

Mean value for this variable is 2.033. Thus effect of KM on this factor is insignificant.

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Innovation

Innovation is important to maintain the competitive advantage in market. Knowledge is the

backbone of innovation and therefore KM is important.

Table and Exhibit 4.5 - InnovationKM helps in increased innovationResponses Frequency of ResponseStrongly Agree 08Agree 18Disagree 16Strongly Disagree 14Dn/Cs 04Total Responses 60

The chart represent about 43% employees agree that KM system results in increased

innovation. About 50% disagree to the statement and rest didn’t reply.

The mean value for this variable is 2.20. Thus, KM does not effect innovation

significantly but it may be of some use in measuring KM success.

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Market ShareTable and Exhibit 4.6 - Market Share

KM system results in increased market shareResponses Frequency of ResponseStrongly Agree 04Agree 08Disagree 16Strongly Disagree 26Dn/Cs 06Total Responses 60

The chart represents that about 20% employees believe that KM system helps in increasing

the market share. About 70% of employees disagree to the statement and rest didn’t reply.

The mean score for this variable is 1.633. Thus, Market share of a company is least

effected by the KM system and it is not an important measure for judging KM success.

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Learning and adaptation capability

Table and Exhibit 4.7 – Learning and adaption capability

KM increases the learning/adaptation capability of employeesResponses Frequency of ResponseStrongly Agree 06Agree 22Disagree 20Strongly Disagree 12Dn/Cs 00Total Responses 60

The chart represents about 47% employees agree that KM system helps in increased learning

and adapting to new situations. About 53% disagree to the statement.

KM supports learning and adaptation of employees to some extent. The mean score for

this variable is 2.366. Thus KM does not effect employee learning and adaptation in a

significant manner and it is of little importance in measuring KM success.

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Staff Attraction/Retention

Table and Exhibit 4.8 – Staff Attraction/ Retention

KM helps in better staff attraction/retentionResponses Frequency of ResponseStrongly Agree 06Agree 08Disagree 16Strongly Disagree 24Dn/Cs 06Total Responses 60

The chart represents about 23% employees believe that KM systems result in attracting and

retaining staff. About 67% are disagreeing to the statement and rest didn’t reply.

Most respondents are of the opinion that KM does not help in attracting or retaining

employees the mean score for this variable is 1.733. Thus KM has insignificant effect on

staff attraction and retention.

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Enhanced Collaboration

Table and Exhibit 4.9 – Enhanced Collaboration

KM results in enhanced collaboration within the organizationResponses Frequency of ResponseStrongly Agree 24Agree 14Disagree 16Strongly Disagree 06Dn/Cs 00Total Responses 60

The chart represents about 63% employees agree that KM system results in enhanced

collaboration i.e. improved coordination and teamwork. KM makes employees better

connected to each other. About 37% employees disagree to the situation.

Most respondents agree that KM system helps in better collaboration among employees

and departments. The mean score for this variable is 2.933. Thus it is one of the

important criteria in measuring KM success and KM improves employee collaboration

significantly.

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Communication among employees

Table and Exhibit 4.10 – Communication among employees

KM helps to address the communication gap in the organizationResponses Frequency of ResponseStrongly Agree 28Agree 12Disagree 14Strongly Disagree 06Dn/Cs 00Total Responses 60

The chart represent about 66% employees agree that KM system improves lines of

communication and makes other employees easily approachable. About 34% of employees

disagree to the situation.

KM helps in filling communication gaps and improves communication among

employees. Respondents feel that KM improves communication among employees in a

significant manner.

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Creation of Intellectual assets

Table and Exhibit 4.11 – Creation of Intellectual assets

KM helps in constant and continuous transformation of individual learning to organizational Learning and vice versaResponses Frequency of ResponseStrongly Agree 08Agree 18Disagree 16Strongly Disagree 08Dn/Cs 10Total Responses 60

The chart represents about 44% of employees agree that KM helps in creating knowledge

banks and also synchronize between employees and organization. About 40% of employees

disagree to the statement and rest didn’t reply.

Respondents disagree that KM builds information pool and knowledge banks. The

mean score for this variable is 2.1. Thus, KM builds the intellectual capital of an

organization to very small extent but it is not a significant criterion in measuring KM

success.

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Delegation of authority

Table and Exhibit 4.12 – Delegation of authority

KM results in increased delegation of authority and accountability to individualsResponses Frequency of ResponseStrongly Agree 04Agree 12Disagree 12Strongly Disagree 28Dn/Cs 04Total Responses 60

The chart represents about 27% of employees agree that KM system results in increased

delegation of authority and responsibility. About 66% of employees disagree to this statement

and rest 7% didn’t reply.

Most Respondents feel that KM does not effect delegation of authority. The mean value

for this variable is 1.733. Thus, delegation is an insignificant criteria for measuring KM

success and its effects.

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Return on Investment

Table and Exhibit 4.13 – Return on Investment

KM helps to achieve better ROIResponses Frequency of ResponseStrongly Agree 06Agree 08Disagree 16Strongly Disagree 24Dn/Cs 06Total Responses 60

The chart represents about 24% of employees agree that applying KM system results in

increased Return on Investment for the company. About 66% of employees disagree to this

and rest didn’t reply.

Most respondents are of the opinion that ROI is not the important criteria in measuring

KM success. The mean value for this variable is 1.733. Thus, contribution of KM system

in increasing the ROI is insignificant.

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Availability of Information

Table and Exhibit 4.14 – Availability of Information

The chart represents 34% of employees agree that information regarding specific processes

and publications are available for reference. About 66% of employees disagree to the

statement.

The mean value for this variable is 1.966. Thus we can say that most employees disagree

to the statement and KM system is not an important source of information. It is not an

important variable in making an organization a learning organization.

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Information is readily available on required topicsResponses Frequency of ResponseStrongly Agree 08Agree 12Disagree 10Strongly Disagree 30

Dn/Cs 00Total Responses 60

Uploading Information

Table and Exhibit 4.15 – Uploading Information

Information can be uploaded in organization’s databaseResponses Frequency of ResponseStrongly Agree 10Agree 24Disagree 14Strongly Disagree 08Dn/Cs 04Total Responses 60

The chart represents about 57% employees agree that they can upload the information

regarding the process on which they work. About 36% disagree to the statement and rest

didn’t reply..

The mean value for this variable is 2.466. Thus most employees are disagreeing that

they can upload information but some are satisfied with it.

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Sharing personal practices

Table and Exhibit 4.16 – Sharing personal practices

Personal best practices can be shared with other employeesResponses Frequency of ResponseStrongly Agree 14Agree 24Disagree 12Strongly Disagree 10Dn/Cs 00Total Responses 60

The chart represents that 64% of employees agree that they can share their personal practices

on KM system. About 36% employees disagree to this.

The mean value for this variable is 2.7. Thus, respondents somewhat agree that they can

share their practices with others. Sharing personal practices is somewhat important in

making an organization a learning organization.

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Availability of Technology

Table and Exhibit 4.17 – Availability of Technology

Availability of hardware and software technologiesResponses Frequency of ResponseStrongly Agree 12Agree 28Disagree 16Strongly Disagree 04Dn/Cs 00Total Responses 60

The chart represents that 66% of employees agree that required hardware and software

technology is available to support learning. About 34% of employees disagree with this

aspect.

The mean value for this variable is 2.8. Thus, we can say that most respondents are

satisfied with technology available and this is a important factor in building a learning

organization.

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Knowledge Acquisition

Table and Exhibit 4.18 – Knowledge Acquisition

Processes for creation, capture, and acquisition of knowledgeResponses Frequency of ResponseStrongly Agree 10Agree 14Disagree 20Strongly Disagree 08Dn/Cs 08Total Responses 60

The chart represents that 40% of employees agree that there are well defined processes for

creation, capture, and acquisition of knowledge. About 46% employees disagree with the

statement and rest didn’t reply.

The mean value for this variable is 2.166. Thus, most respondents disagree that proper

process for knowledge acquisition exists.

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Knowledge Transfer

Table and Exhibit 4.19 : Knowledge Transfer

Knowledge can be easily shared and acted uponResponses Frequency of ResponseStrongly Agree 06Agree 24Disagree 16Strongly Disagree 14Dn/Cs 00Total Responses 60

The chart represents that 50% of employees agree that knowledge can be easily shared and

acted upon. About 50% employees disagree with the statement.

The mean value for this variable is 2.366. Thus, on average respondents disagree that

knowledge can be easily shared. It is not a prime factor in a learning organization.

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Common VisionTable and Exhibit 4.20– Common Vision

A cohering and powerful vision of the organization is sharedResponses Frequency of ResponseStrongly Agree 16Agree 24Disagree 14Strongly Disagree 06Dn/Cs 00Total Responses 60

The chart represents that 66% of employees agree that cohering and powerful vision of the

organization is shared across the workforce. About 34% employees disagree with the

statement.

The mean value for this variable is 2.833. Thus, on average respondents agree that a

common vision exists in the organization and employees agree with it. It is also an

important factor in making organization a learning organization.

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Organization Structure

Table and Exhibit 4.21– Organization Structure

Enabling structures in terms of hierarchy and communication flows existsResponses Frequency of ResponseStrongly Agree 08Agree 28Disagree 12Strongly Disagree 06Dn/Cs 06Total Responses 60

The chart represents that 60% of employees agree that enabling structures in terms of

hierarchy and communication flows exists. About 30% employees disagree with the

statement and rest didn’t reply.

The mean value for this variable is 2.433. Thus, on average respondents disagree that

enabling organization structure exists in the organization but it is somewhat important

for learning organization.

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Teamwork

Table and Exhibit 4.22- TeamworkThere are cohesive teams in organization which facilitates learningResponses Frequency of ResponseStrongly Agree 14Agree 32Disagree 08Strongly Disagree 06Dn/Cs 00Total Responses 60

The chart represents 76% of employees agree that there are cohesive teams in organization

which facilitates sharing of experiences. About 24% employees disagree with the statement

and rest didn’t reply.

The mean value for this variable is 2.9 Thus, on average respondents agree that

working in teams result in better learning for employees and organization. It is an

important measure for a learning organization.

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Incentives to use KM

Table and Exhibit 4.23 : incentives to use KM

Organization provides incentives to use KM systemResponses Frequency of ResponseStrongly Agree 04Agree 10Disagree 26Strongly Disagree 16Dn/Cs 04Total Responses 60

The chart represents 24% of respondents agree that organization provides incentives to

motivate users to use KM system. About 70% employees disagree with the statement and rest

didn’t reply.

The mean value for this variable is 1.9 Thus, on average respondents disagree that there

is any incentive to use KM. It is not an important measure for a learning organization.

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Learning and Re-learning of employees

Table and Exhibit 4.24 – Learning and Re-learning of employees

Organization strives for learning, unlearning and re-learning for its employeesResponses Frequency of ResponseStrongly Agree 10Agree 16Disagree 22Strongly Disagree 12Dn/Cs 00Total Responses 60

The chart represents 43% of respondents agree that organization continuously strives for

learning, unlearning and re-learning for its employees. About 57% employees disagree with

the statement.

The mean value for this variable is 2.4. Thus, on average respondents disagree that

organization provides opportunity for continuous learning.

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Part 3: Factor Analysis

This part deals with the factor analysis among variables. Factor Analysis is a statistical tool

which is used as a method for data reduction or structure detection. The term factor analysis

was first introduced by Thurstone in 1931. The main purpose or function of factor analysis

technique is as follows:

(1) to diminish/moderate the number of variables and

(2) to detect structure in the relationship between variables , that is to classify variables

The Factor Analysis technique has been applied to analyse the 24 variables contained in the

questionnaire. This technique has been applied on these variables with the purpose to

(a) Reduce the number of variables and

(b) To detect a structure in the relationship between variables

The Factor Analysis technique has been applied to analyze the 24 variables contained in the

questionnaire.

Factor Analysis

Factor Analysis results for data collected from respondents. It is done for all 24 variables and

60 respondents. Factors were extracted by Principal Component Method from the correlation

matrix. All factors with eigen values greater than 1 are extracted.

Table 1: Table gives eigenvalues, variance explained, and cumulative variance

Explained Variance (Eigenvalues) 

Value Factor 1 Factor 2 Factor 3 Factor 4 Factor 5 Factor 6 Factor 7 Factor 8

Eigenvalue 4.849 3.878 2.911 2.741 2.356 1.450 1.376 1.046

% of Var. 20.203 16.156 12.128 11.423 9.817 6.040 5.732 4.358

Cum. % 20.203 36.360 48.488 59.911 69.727 75.767 81.499 85.857

Value Factor 9 Factor 10

Factor 11

Factor 12

Factor 13

Factor 14

Factor 15

Factor 16

Eigenvalue 0.863 0.752 0.681 0.506 0.357 0.236 0.000 0.000

% of Var. 3.596 3.135 2.837 2.106 1.487 0.982 0.000 0.000

Cum. % 89.453 92.588 95.425 97.531 99.018 100.000 100.000 100.000

Value Factor 17

Factor 18

Factor 19

Factor 20

Factor 21

Factor 22

Factor 23

Factor 24

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Eigenvalue 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000

% of Var. 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000

Cum. % 100.000 100.000 100.000 100.000 100.000 100.000 100.000 100.000

The above table gives eigenvalues, variance explained, and cumulative variance explained for

the concerned factor solution.

The first panel gives values based on initial eigenvalues. The variances extracted by the

factors are called the Eigenvalues. The sum of Eigenvalues is equal to the number of

variables. The sum of Eigen values is equal to the number of variables. For the initial

solution, there are as many components or factors as there are variables, i.e. there are 24

variables and thus 24 components also.

The "Total" column gives the amount of variance in the observed variables accounted for by

each component or factor.

The "% of Variance" column gives the percent of variance accounted for by each specific

factor or component, relative to the total variance in all the variables. The "Cumulative %"

column gives the percent of variance accounted for by all factors or components up to and

including the current one.

In a good factor analysis, there are a few factors that explain a lot of the variance and the rest

of the factors explain relatively small amounts of variance.

Now that we have a measure of how much variance each successive factor extracts, next we

have to decide on how many factors to retain. By its nature this is an arbitrary decision.

However, there are some guidelines that are commonly used, which, in practice, will yield the

best results.

The Kaiser criterion is one of the guidelines which we use as a guideline to decide how

many factors we would ultimately retain. This criterion was proposed by Kaiser in 1960 and

hence the name.

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The Extraction Sums of Squared Loadings group gives information regarding the extracted

factors or components. For principal components extraction, these values will be the same as

those reported under Initial Eigenvalues. According to this criterion we will ultimately retain

only those factors which have eigenvalues greater than 1.

Thus according to the Extraction Sums of Squared Loadings we will ultimately retain the first

8 factors as the Principal Components because only the first 8 factors have their eigenvalues

as more than 1. The Eigen values of the first 8 factors are 4.849, 3.878, 2.911, 2.741, 2.356,

1.450, 1.376 and 1.046 respectively

Table 2: Extracted Factors with Eigenvalues >1; (F = Factor)

Variable F 1 F 2 F 3 F 4 F 5 F 6 F 7 F 8Decision Making 0.170 -0.111 0.081 0.353 0.360 -0.091 -0.050 0.446Enhanced Productivity 0.152 -0.341 0.073 -0.454 0.276 -0.309 -0.122 -0.325Sharing best practices 0.190 0.093 -0.245 -0.383 0.126 -0.576 -0.145 0.298Entering new markets 0.816 -0.084 0.136 -0.074 -0.266 -0.073 -0.165 -0.172Innovation -0.210 0.652 0.452 -0.079 0.307 0.120 -0.318 0.094Market share -0.045 -0.217 0.636 -0.005 0.499 -0.270 0.412 -0.140Learning/adaptation 0.341 -0.252 -0.096 -0.412 0.594 0.473 -0.086 -0.011Staff attraction/retention -0.153 0.463 -0.693 -0.024 0.351 0.017 0.105 -0.163Collaboration 0.318 -0.365 -0.389 0.537 0.370 -0.256 -0.264 -0.099Communication gap 0.465 0.297 0.161 0.674 0.015 0.247 0.145 -0.202Intellectual asset 0.835 0.302 0.228 -0.114 -0.002 -0.062 -0.192 0.082Increased Delegation 0.671 0.286 -0.266 -0.338 -0.082 -0.015 0.433 0.063ROI -0.105 0.848 -0.004 0.044 0.103 -0.244 -0.013 -0.118Availability of Information

0.816 -0.084 0.136 -0.074 -0.266 -0.073 -0.165 -0.172

Uploading data -0.210 0.652 0.452 -0.079 0.307 0.120 -0.318 0.094Sharing of practices -0.045 -0.217 0.636 -0.005 0.499 -0.270 0.412 -0.140Enabling technologies 0.341 -0.252 -0.096 -0.412 0.594 0.473 -0.086 -0.011Knowledge acquisition -0.153 0.463 -0.693 -0.024 0.351 0.017 0.105 -0.163Knowledge transfer 0.318 -0.365 -0.389 0.537 0.370 -0.256 -0.264 -0.099Common vision 0.465 0.297 0.161 0.674 0.015 0.247 0.145 -0.202Enabling structures 0.835 0.302 0.228 -0.114 -0.002 -0.062 -0.192 0.082Teamwork 0.671 0.286 -0.266 -0.338 -0.082 -0.015 0.433 0.063Incentives -0.105 0.848 -0.004 0.044 0.103 -0.244 -0.013 -0.118Employee re-learning 0.324 0.011 -0.076 0.397 0.215 0.015 0.272 0.577

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Table 3: Variables that are interrelated to each other

Factor 1 Factor 2 Factor 3 Factor 4 Factor 5 Factor 8

Entering New Markets

Innovation Market Share Enhanced Productivity

Learning/ adaptation

Decision Making

Intellectual Asset

ROI Staff attraction/ retention

Collaboration Enabling Technologies

Employee Re learning

Increased Delegation

Uploading Data

Sharing of Practices

Improved Communication

Availability of Information

Incentive to use KM

Knowledge Acquisition

Knowledge Transfer

Enabling Structure

Common Vision

TeamworkMaximum number of variables lays in factor 1 i.e. six. It means that these six variables have

common attributes and are interrelated to each other. Similarly for other variables which fall

under different factors, they are interrelated to each other. Rest Factor 6 has one variable

falling under it and Factor 7 has none of the variable falling under it.

Graph 1 - Scree Plot for Unrotated Factor LoadingsKM Score Analysis

It’s been assumed that respondents have KM experience, consider them

fairly knowledgeable about KM, and are involved in KM initiative decision

making.

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Since all the respondents indicated that they have KM experience and that

they have answered KM-related questions, we can assume that these

organizations are involved in implementing KM.

Table 4: Survey Results of KM Criteria

S No.

Knowledge Management

Mean score

Agree (%)

Remarks

1 Decision Making 2.733 70 Less significant but many responses agreed with it. It shows decision making is an important criteria of judging KM success2 Enhanced Productivity 2.333 53 Productivity or service quality is not the most important criteria and KM does not affect it significantly

3 Sharing best practices 3.166 77 It helps in sharing best practices It is one of the important criteria affected by KM system4 Entering new markets 2.033 37 It is one of the least important criteria affected by KM system

5 Innovation 2.2 43 KM does not effect innovation significantly but it may be of some use in measuring KM success6 Market Share 1.633 20 Market share of a company is least effected by the KM system

7 Learning/adaptation 2.366 47 KM does not effect employee learning and adaptation in a significant manner and it is of little importance in measuring KM success8 Staff

Attraction/Retention1.733 23 KM has insignificant effect on staff

attraction and retention9 Collaboration 2.933 63 KM system helps in better

collaboration among employees and departments it is one of the important criteria in measuring KM success10 Communication Gap 3.033 66 Respondents feel that KM improves communication among employees in a significant manner

11 Intellectual asset 2.1 44 KM builds the intellectual capital of an organization to very small extent but it is not a significant criterion in measuring KM success12 Increased Delegation 1.733 27 Delegation is an insignificant criteria for measuring KM success and its effects

13 ROI 1.733 24 Contribution of KM system in increasing the ROI is insignificant

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Graph 2: Line shape chart representing mean score and distinguishing highest & least

variables

The results shown above tells that there are variables 1, 2, 9 and 10 (shown as a black

rhombus) that are most effected by knowledge management system, they are important

factors in judging KM success. The research result also shows that there are variables that are

least effected by knowledge management system and they are least important criteria’s in

judging KM success. These variables are 6, 8, 12, and 13 (shown as a black triangle).

Table 5: Survey results of Organizational Learning

S No.

Learning Organization

Mean Scor

Agree (%)

Remarks

14 Availability of Information

1.966 34 Most employees disagree to the statement not an important variable in making an organization a learning organization

15 Uploading data 2.466 57 Most employees are disagreeing that they can upload information but some are satisfied with it16 Sharing of practices 2.7 64 Sharing personal practices is somewhat important in making an organization a learning organization17 Enabling

technologies2.8 66 Most respondents are satisfied with

technology available and this is a important factor in building a learning organization

18 Knowledge acquisition

2.166 44 Most respondents disagree that proper process for knowledge acquisition exists

19 Knowledge transfer 2.366 50 On average respondents disagree that knowledge can be easily shared. It is not a prime factor in a learning organization

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20 Common vision 2.833 66 It is an important factor in making a learning organization

21 Enabling structures 2.433 60 It’s mean score shows that it is somewhat important for learning organization

22 Teamwork 2.9 76 Teams result in better learning for employees and organization. It is an important measure for a learning organization23 Incentives 1.9 24 It is not an important measure for a learning organization

24 Employee re-learning 2.4 43 On average respondents disagree that organization provides opportunity for continuous learning

Graph 3: Line shape chart representing mean score and distinguishing highest & least

variables

The result shown above tells that there are certain variables 16, 17, 20 and 22 (shown as a

black rhombus) which help in making an organization a learning one. Their activities

contribute in building learning organization. The research result also shows that there are

variables that are least contributing factors and the organization is not influenced by them.

These variables are 14 and 23 (shown as a black triangle).

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Most useful criteria

The research results show that there are variables that are most effected by knowledge

management system and they are important criteria’s in judging KM success. These variables

are given below.

- Decision Making

- Sharing best practices

- Enhanced Collaboration

- Improved Communication

Least useful criteria

The research results show that there are variables that are least effected by knowledge

management system and they are not important criteria’s in judging KM success. These

variables are given below.

- Increased market share

- Better staff attraction/retention

- Delegation of authority

- Return on investment of KM effort

Some variables are not very significant to check effects of KM system but on the other hand

they cannot be totally ignored. These are:

- Enhanced Productivity

- Innovation

- Learning/adaptation

- Building Intellectual assets

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Research findings also show that what activities organizations are doing well that contributes

in building learning organization and what else they can focus on for organization’s learning.

The factors on which organizations are doing well are:

- Sharing of best practices

- Availability of required technologies

- Common vision of the organization among the workforce

- Working in teams

Factors on which organizations are not working well are:

- Availability of information regarding processes and knowledge banks

- Providing incentives to take active interest in KM system

Other factors which need to be improved are:

- Build processes that enable knowledge acquisition/capture and transfer

- Enable employees to upload information in KM database

- Build learning culture and focus on continuous learning

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Significant KM outcomes

Figure 4.1 – Significant Knowledge Management Outcome

Through this research, both the most useful and the least useful criteria are established for

outcomes of KM initiatives. While the most useful outcomes are difficult to measure, the

least useful outcomes can be quantified and are easily measurable. The research identified

enhanced collaboration within organization, improved communication, and sharing best

practices as the top three outcomes, followed by improved productivity and better decision

making. All of them contribute to organizational performance.

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Enhanced Collaboration

Among peopleWithin/across processesWithin/across functionsWithin/across units

Improved Employee Skills

At task levelAt process levelAt function levelAt organization level

Improved Productivity Better Decision Making

Improved Communication

CHAPTER 5

SUGGESTION FOR FURTHER RESEARCH

Statistical analysis and research findings helped to identify the criteria for measuring KM

efforts, which can be described as desired outcomes. The research study also helped to

identify new areas of interest for further research. Some of these areas are as follows:

The important criteria’s and insignificant criteria’s can be checked through a

quantitative analysis that how they are impacting the performance of individuals and

organization as a whole. The performance of organization before and after applying

KM system can be checked.

The interrelated factors between KM and Learning Organization are to be checked for

different industries and sectors. This will tell applicability of KM in Different

companies.

The most useful criteria identified through this research can be developed further into

detailed measures of KM success. The research questions like what are the detailed

measures for enhanced collaboration within an organization? What are they for:

improved communication and improved employee skills?—are required to be

answered. The research effort will establish detailed measures for each useful

criterion, validating their relation to each other and validating their effectiveness.

Based on geographical location as well as industry type, the differences in KM criteria

can be analyzed using multiple factors. The research questions like what are the

differences in KM criteria based on geographical location? Are they industry specific

can be addressed in a follow-up research.

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CHAPTER 6

CONCLUSION

Through this research, both the most useful and the least useful criteria are established for

outcomes of KM initiatives. While the most useful outcomes are difficult to measure, the

least useful outcomes can be quantified and are easily measurable. The research identified

enhanced collaboration within organization, improved communication, and sharing best

practices as the top three outcomes, followed by improved productivity and better decision

making. All of them contribute to organizational performance.

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REFERENCES

Vincent Ribière, New York Institute of Technology, USA, Francis Tuggle, Chapman

University, USA, (1994). The Influence of Organizational Trust on the Use of KM

Systems, NJ: Prentice-Hall.

David Croasdell, (1994). Knowledge Management as a discipline, Organization

Science, 5(1), p14-37.

Stefan Kremer, The Information Management Group (IMG AG), Switzerland. The

Role of Context and Its Explication for Fostering Knowledge Transparency,

International Journal of Knowledge Management, 1(1), p i-v.

Hazel Taylor, (1995). Eliciting Tacit Knowledge Using the Critical Decision

Interview Method, Information Systems Research, 6(2), p85-117.

Murray E. Jennex, (1999, March-April) Knowledge Management, Harvard Business

Review, p106-116

Todd Peachey, Dianne Hall, Casey Cegielski, (2000). KM Systems - Are We Seeing

the Whole Picture? Harvard Business School Press.

David G. Schwartz, (2001). Creating a Disciplined Whole from Many

interdisciplinary Parts, Organization Science, 2(1), p88-115.

Clyde Holsapple, Meenu Singh,(2003). Knowledge Chain Evidence and Extensions,

Cambridge University Press.

Ji Hoon Song. International Journal of Training & Development, Dec2008, Vol. 12 Issue 4, p265-281

Essentials of Knowledge Management by Bryan Bergeron

Chang, S. C. and Lee, M. S. (2007) A study on relationship among leadership,

organizational culture, the operation of learning organization and employees’ job

satisfaction. The Learning Organization. 14 (2), 155-185.

Christensen, P. H. (2007) Knowledge sharing: moving away from the obsession with

best practices. Journal of Knowledge Management. 11 (1), 36-47.

Garvin, D A (1993) Building a Learning Organization. Harvard Business Review.

July-August 1993, 78-91.

Hansen, M., Nohria, N. and Tierney, T. (1999) What’s your strategy for managing

knowledge? Harvard Business Review. 77 (2), 106.

Kumar, N & Idris, K (2006) An Examination of Educational Institutions’ Knowledge

Performance: Analysis, implications and outlines for future research. The Learning

Organization. 13 (1), 96-116.

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McDermott, R. and O’Dell, C. (2001) Overcoming cultural barriers to knowledge

sharing. Journal of Knowledge Management. 5 (1). 76-85.

Morris, T. (2001) Asserting property rights: knowledge codification in the

professional service firm. Human Relations. 54 (7), 819-38.

Ortenbald, A. (2004) The learning organization: towards an integrated model. The

Learning Organization. 11 (2/3), 129.

Park, H., Ribeire, V. and Schulte, W. (2004) Critical attributes of organizational

culture that promote knowledge management implementation success. Journal of

Knowledge Management. 7(5), 55-66.

Patterson, G. (1999) The learning university. The Learning Organization. 6(1), 9.

Senge, P.M. (1990). The Fifth Discipline. New York: Doubleday.

Stata, R. (1989) Organizational learning-the key to management innovation. Sloan

Management Review. 30(3), 63-74.

Swan, J., Newell, S., Scarbrough, H. and Hislop, D. (1999) Knowledge Management

and innovation: networks and networking. Journal of Knowledge Management. 3(4),

262-75.

Watskins, K. and Marsick, V.(Eds) (1996) Creating the Learning Organization.

American Society for Training and Development, VA: Alexandria.

Books:

Essentials of Knowledge Management by Bryan Bergeron

Knowledge Management Systems - Value Shop Creation by Gottschalk P.

Websites:

http://en.wikipedia.org/wiki/Organizational_learning

http://en.wikipedia.org/wiki/Knowledge_management

http://www.citehr.com/1434-learning-organizations.html

http://www.about-goal-setting.com/KM-Library/knowledge-management-why-

important.html

http://www.itmweb.com/essay538.htm

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ANNEXURE

QUESTIONNAIRE

The major objective of this survey is to find out the effects of Knowledge Management System in an organization and its contribution to a Learning Organization. The information collected is only for research purpose and will be kept confidential.

Description of Rating ScalesValue Meaning Assigned

Strongly Agree You are in agreement with the statement to a very high extent

Agree You believe that statement is true to some extent

Disagree You believe that statement is not true to some extent

Strongly Disagree You totally disagree with the statement

Don’t know/Cant say(Dn/Cs) You do not know about it or can not say

PART 1 Following are the statements that reflect the effects of Knowledge Management system in an organization. According to your opinion please PUT (O) symbol in front of the suitable option.

1. The KM system helps in fast and better decision making.Strongly Agree Disagree Strongly Dn/Cs

Agree Disagree

2. KM helps in enhanced productivity or service quality. Strongly Agree Disagree Strongly Dn/Cs Agree Disagree

3. Implementing KM results in sharing best practices. Strongly Agree Disagree Strongly Dn/Cs

Agree Disagree 4. KM makes it easy to enter different market types.

Strongly Agree Disagree Strongly Dn/Cs Agree Disagree

5. KM helps in increased innovation by the employees.

Strongly Agree Disagree Strongly Dn/Cs Agree Disagree

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6. Application of KM system results in increased market share. Strongly Agree Disagree Strongly Dn/Cs

Agree Disagree

7. KM increases the learning/adaptation capability of employees.Strongly Agree Disagree Strongly Dn/Cs Agree Disagree

8. KM helps in better staff attraction/retention. Strongly Agree Disagree Strongly Dn/Cs Agree Disagree

9. KM results in enhanced collaboration within the organization. Strongly Agree Disagree Strongly Dn/Cs Agree Disagree

10. KM helps to address the communication gap in the organization. Strongly Agree Disagree Strongly Dn/Cs

Agree Disagree 11. KM helps in constant and continuous transformation of individual learning to organizational Learning and vice versa.

Strongly Agree Disagree Strongly Dn/Cs Agree Disagree

12. KM results in increased delegation of authority and accountability to individuals. Strongly Agree Disagree Strongly Dn/Cs

Agree Disagree

13. KM helps to achieve better ROI. Strongly Agree Disagree Strongly Dn/Cs

Agree Disagree

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PART 2

Following are the statements that reflect characteristics of any Learning Organization. According to your opinion please TICK in front of the option that best suit your organization.

1. Information is readily available on required topics from current publications to industry specific processes. Strongly Agree Disagree Strongly Dn/Cs

Agree Disagree 2. Information regarding process description can be uploaded in organization’s database. Strongly Agree Disagree Strongly Dn/Cs

Agree Disagree

3. Personal best practices can be shared with other employees. Strongly Agree Disagree Strongly Dn/Cs

Agree Disagree

4. Enabling hardware and software technologies are available to support learning rather than control it. Strongly Agree Disagree Strongly Dn/Cs

Agree Disagree

5. There are well defined processes for creation, capture, and acquisition of knowledge. Strongly Agree Disagree Strongly Dn/Cs

Agree Disagree

6. Useful knowledge can be easily shared and acted upon. Strongly Agree Disagree Strongly Dn/Cs

Agree Disagree

7. A cohering and powerful vision of the organization is shared across the workforce to promote need for strategic thinking at all levels. Strongly Agree Disagree Strongly Dn/Cs

Agree Disagree 8. There are enabling structures in terms of hierarchy and communication flows that facilitates learning. Strongly Agree Disagree Strongly Dn/Cs

Agree Disagree

9. There are cohesive teams in organization which facilitates sharing of experiences and Information among employees. Strongly Agree Disagree Strongly Dn/Cs

Agree Disagree

10. The organization provides incentives to motivate users to learn from experiences and

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use KM system. Strongly Agree Disagree Strongly Dn/Cs

Agree Disagree

11. The organization continuously strives for learning, unlearning and re-learning for its employees. Strongly Agree Disagree Strongly Dn/Cs

Agree Disagree

Thank You for your time and thoughtful responses

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