submitted by adish kumar - m.tech divison grade/adish kumar ms200501... · 2015-04-24 · mr....

A Thesis on

SELECTION OF ARCHITECTURAL MODEL FOR THE IMPLEMENTATION OF KNOWLEDGE MANAGEMENT IN INDUSTIRES: AN ANP

APPROACH

A DISSERTATION Submitted in partial fulfillment of the requirements for the award of the degree

of

MASTER OF TECHNOLOGY

In

INFORMATION TECHNOLOGY (Specialization: SOFTWARE ENGINEERING)

Submitted by

ADISH KUMAR

(Enrol. No. – MS200501)

Under the Guidance of:

Mr. Manish Kumar IIIT-Allahabad

INDIAN INSTITUTE OF INFORMATION TECHNOLOGY, ALLAHABAD

(A University Established under sec.3 of UGC Act, 1956 vide Notification No.

F.9-4/99-U.3 Dated 04.08.2000 of the Govt. of India)

(A Centre of Excellence in Information Technology Established by Govt. of India)

IINNDDIIAANN IINNSSTTIITTUUTTEE OOFF IINNFFOORRMMAATTIIOONN TTEECCHHNNOOLLOOGGYY

AALLLLAAHHAABBAADD (A University Established under sec.3 of UGC Act, 1956 vide Notification No. F.9-4/99-U.3 Dated 04.08.2000

of the Govt. of India )


Date: ______________

I/WE DO HEREBY RECOMMEND THAT THE THESIS WORK PREPARED UNDER

MY/OUR SUPERVISION BY ADISH KUMAR ENTITLED “SELECTION OF

ARCHITECTURAL MODEL FOR THE IMPLEMENTATION OF KNOWLEDGE

MANAGEMENT IN INDUSTRIES: AN ANP APPROACH” BE ACCEPTED IN

PARTIAL FULFILMENT OF THE REQUIREMENTS FOR THE DEGREE OF

MASTER OF TECHNOLOGY IN INFORMATION TECHNOLOGY (SOFTWARE

ENGINEERING) FOR EXAMINATION.

COUNTERSIGNED

MR. MANISH KUMAR ______________________________ (THESIS ADVISER)

i

DR. U. S. TIWARY DEAN (ACADEMICS)





CERTIFICATE OF APPROVAL*

The foregoing thesis is hereby approved as a creditable study in the area of

knowledge management carried out and presented in a manner satisfactory to

warrant its acceptance as a pre-requisite to the degree for which it has been

submitted. It is understood that by this approval the undersigned do not

necessarily endorse or approve any statement made, opinion expressed or

conclusion drawn therein but approve the thesis only for the purpose for which it

is submitted.

COMMITTEE ON

FINAL EXAMINATION

FOR EVALUATION

OF THE THESIS

* Only in case the recommendation is concurred in

i

ii





CANDIDATE DECLARATION

This is to certify that Report entitled “Selection of Architectural Model

for the Implementation of Knowledge Management in Industries: An ANP

Approach” which is submitted by me in partial fulfillment of the requirement

for the completion of M.Tech. in Information Technology (with specialization in

Software Engineering) to Indian Institute of Information Technology, Allahabad

comprises only my original work and due acknowledgement has been made in

the text to all other material used.

Adish Kumar

(Enrol No. : MS200501)

Selection of the Architectural Model for KM Implementation: An ANP Approach

Indian Institute of Information Technology - Allahabad

iii

ACKNOWLEDGEMENT

It was a pleasure doing the thesis work which helped me

learn new things, coming out with solutions to the toughest problems and

develop my programming skills. First and foremost, I would like to thank

INDIAN INSTITUTE OF INFORMATION TECHNOLOGY,

ALLAHABAD for providing me such opportunity to carry out the

dissertation work.

I would like to express my sincere gratitude to my thesis

supervisor Mr. Manish Kumar, IIIT Allahabad for providing his precious

advices and suggestions. I would like to thank him for spending his

valuable time in correcting my mistakes and posing new challenges which

really made me work hard on it. With his valuable guidance, the work was

finally completed.

I would also like to express my sincere gratitude to Prof.

Rakesh Narain and Mr. Ravi Kant, M. N. N. I. T. Allahabad, for their

valuable suggestions and advices in carrying out this work.

I would like to thank my friends who always supported me

and encouraged me in my studies. I would like to pay my special thanks to

Mr. Vineet Chauhan and Mr. Prateek Dayal with whom I used to discuss

about the problems in my thesis and they were always there to listen to

me and give their suggestions. Mr. Nilesh Shukla, who was sort of a

teacher to me, for giving clues about how to solve problems; Mr. Anand

Arun Atre, whose hard working always left an impression on me; Mr.

Abhay Pawane, who taught me how to enjoy life and make maximum of it;

Mr. Kamal Sawan, whose working setup has always astonished me; Mr.

Prabhat Saheja, who taught me how to admire the beauty; Mr. Imran

Khan, who inspired me always to be perfect; Mr. Sampath Kumar, who



iv

taught me to how to enjoy things which you don’t understand; Mr. Pankaj

Kandpal, from whom I came to know how to live life to the fullest. I would

like to thank Mr. Kamal Singh, Mr. Anil Pandey and Mr. Dinh Ngoc Lan

who were always there whenever I needed them. I would also like to thank

my batch mates from M. Tech (WCC), M. Tech (IS) and M, Tech (BI) for

giving me such a beautiful time here in IIITA and make my this two years

a memorable journey.

I would like to thank my parents Dr. M. D. Singh and Smt.

Mala Singh, and my sister Ms. Aditi Singh, who always supported me and

encouraged me in my studies. They have been my strength and

encouragement in my life. I would not be writing this thesis today without

their constant support and devotion. I dedicate this thesis to them.

Thank you,

Adish Kumar



v

ABSTRACT

Globalization is generating product synergies, high

purchasing power, and access to world markets, which is ultimately

increasing the competition. To sustain in the today’s global business

environment, knowledge plays an important role in increasing the

performance and responsiveness of an organization. Due to the role of

knowledge, Knowledge Management (KM) has come into light. KM is

basically concerned with the creation, capture, storage, application and

reuse of knowledge. Keeping knowledge in mind, in this thesis, three

architectures have been proposed. These architectures are based on

knowledge extensive, knowledge intensive and the existing system, from

these one of the architectures are to be selected for the implementation of

KM in industries.

However, selecting a proper architecture for the

implementation of KM is a kind of multiple criteria decision making

(MCDM) problem required to consider a large number of complex factors.

The analytic network process (ANP) is a new MCDM method which can

deal with all kinds of dependencies systematically. Since the ANP has

these advantages, in this thesis work, we develop an effective framework

based on the ANP to help industries to evaluate and select the

architecture. ANP framework presented in this thesis work consists of

Cost, Competitiveness and Responsiveness as determinants for evaluation

of the architectures. The framework explores relationships among

Organizational Structure, Process Integration and Innovation, and also

among Knowledge Extensive, Knowledge Intensive and Existing system

architectures for implementation of KM. Additionally, evaluation matrices

are presented to illustrate the application of the proposed framework.

Also, sensitivity analysis is conducted for incorporating the variation’s in

expert’s opinion for relative importance of one determinant over the other

in relation to implementation of KM.



vi

TABLE OF CONTENTS Certificate …………………………………………………………………………… i

Declaration …………………………………………………………………………. ii

Acknowledgement …………………………………………………………………. iii

Abstract ……………………………………………………………………………… v

Table of Contents ………………………………..………………………………... vi

List of Figures ……………………………………………………………………… viii

List of Tables ……………………………………………………………………….. ix

List of Abbreviations ………………………………………………………………. x

1 INTRODUCTION ………………………………………………………………. 1

1.1 In Context of Knowledge Management in Industries …………………….. 2

1.2 In Context of Need of Architectural Model for Implementation of KM … 4

1.3 In Context of Need of Analytic Network Process (ANP) Approach in KM 5

2 LITERATURE REVIEW ……………………………………………………… 7

2.1 Literature Review for Knowledge and Knowledge Management ……….. 7

2.1.1 Data ………………………………………………………………………. 8

2.1.2 Information ……………………………………………………………… 9

2.1.3 Knowledge ………………………………………………………………. 11

2.1.3.1 Definition of Knowledge …………………………………... 12

2.1.3.2 Elements of Knowledge …………………………………… 13

2.2 What is Knowledge Management? …………………………………………… 15

2.3 Literature Review for Knowledge Management Enablers ……………….. 18

2.4 Literature Review for Models / Frameworks / Architectures for Implementation of KM ………………………………………………………….

23

2.5 Literature Review for Analytic Network Process (ANP) Approach for Strategic Decision Making ……………………………………………………..

33

2.6 Problem Discussion …………………………………………………………….. 37

3 RESEARCH MODEL FRAMEWORK............................................................... 39

3.1 Determinants ……………………………………………………………………. 39

3.1.1 Cost ………………………………………………………………………. 40

3.1.2 Competitiveness ………………………………………………………... 40

3.1.3 Responsiveness …………………………………………………………. 41

3.2 Dimensions & Attributes ……………………………………………………… 42

3.2.1 Organizational Structure ……………………………………………... 43

3.2.2 Process Integration …………………………………………………….. 45



vii

3.2.3 Innovation ……………………………………………………………….. 47

3.3 Alternatives ……………………………………………………………………… 48

3.3.1 Knowledge Extensive Architectural Model ………………………… 48

3.3.2 Knowledge Intensive Architectural Model …………………………. 52

3.3.3 The Existing System Architectural Model …………………………. 55

3.4 Comparison of the Three Architectural Models ……………………………. 59

4 METHODOLOGY: THE ANALYTIC NETWORK PROCESS (ANP) ……. 65

4.1 Analytic Network Process (ANP) …………………………………………….. 65

4.2 Advantages of ANP …………………………………………………………….. 67

4.3 Disadvantages of ANP …………………………………………………………. 68

4.4 Outline of the Steps of ANP useful for Calculation ……………………….. 69

5 APPLICATION OF ANP FRAMEWORK MODEL …………………………. 71

5.1 Model Development and Problem Formulation ...…………………………….. 71

5.2 Decision Making ………………….……………………………………………………. 72

5.2.1 Pair wise Comparison Matrices for Determinants …………………. 73

5.2.2 Pair wise Comparison Matrices for Dimensions …………………….. 74

5.2.3 Pair wise Comparison Matrices between Component / Attribute Level ………………………………………………………………………

75

5.2.4 Pair wise Comparison Matrices for Interdependencies................. 76

5.3 Supermatrix Formation and Analysis ...………………………………................. 78

5.4 Evaluation of Alternatives…….……………………………...................................... 80

5.5 Desirability Index for a Determinant …………………………………................. 81

5.6 Calculation of Architectural Weighted Index (AWI) ………………………… 84

6 RESULTS & DISCUSSIONS …………………………………………………... 85

6.1 Results …..……………………………………………………………………….. 85

6.2 Sensitivity Analysis …………………………………………………………….. 88

6.3 Future Scope of Work …………………………………………………………... 94

7 CONCLUSIONS ………………………………………………………………… 95

REFERENCES ……………………………………………………………………... 97

APPENDIX A (Consistency Ratio) …………………………...…………………. 109

APPENDIX B (Tables and Calculations) …………….………………………… 112



viii

LIST OF FIGURES

2.1 The relationship between Data, Information and Knowledge. 8 2.2 Conversion of Data to Knowledge……………………………….. 122.3 Pillars of Knowledge Management……………………………… 252.4 Core Capabilities and Knowledge Building Activities……….. 262.5 Organizational Knowledge Management (KM) Model……….. 272.6 Model of the Knowing Organization…………………………….. 282.7 A Framework for Knowledge Management……………………. 292.8 Intangible Assets Framework……………………………………. 302.9 Intellectual and Capital Model…………………………………… 31

2.10 KPMG Knowledge Management Process………………………. 312.11 Spiral of Organizational Knowledge Creation…………………. 323.1 ANP Framework Model…………………………………………… 463.2 Alternative 1: The Knowledge Extensive Architectural

Model…………………………………………………………………. 513.3 Alternative 2: The Knowledge Intensive Architectural

Model…………………………………………………………………. 543.4 Alternative 3: The Existing System Architectural Model……. 566.1 Variation in priority of KM Architectural Models with the

Changes in the Weights assigned to Cost with respect to Competitiveness…………………………………………………….. 89

6.2 Variation in priority of KM Architectural Models with the Changes in the Weights assigned to Cost with respect to Responsiveness……………………………………………………… 91

6.3 Variation in priority of KM Architectural Models with the Changes in the Weights assigned to Competitiveness with respect to Responsiveness………………………………………… 93



ix

LIST OF TABLES

2.1 Comparison of properties of Tacit vs Explicit Knowledge……….... 143.1 Comparison of the three architectural models……………………… 605.1 Pair wise Comparison Matrix for relative importance of

Determinants……………………………………………………………. 735.2 Pair wise Comparison Matrix for relative importance of

Dimensions on Determinant / Responsiveness…………………….. 755.3 Pair wise Comparison Matrix for attribute Enablers under

Determinant / Responsiveness and Dimension / Organizational Structure…………………………………………………………………. 76

5.4 Pair wise Comparison Matrix for attribute Enabler Culture under Determinant / Responsiveness and Dimension / Organizational Structure, OS/RESP/CU…………………………….. 77

5.5 Super Matrix formation for Responsiveness before Convergence.. 795.6 Super Matrix formation for Responsiveness after Convergence.... 795.7 Pair wise Comparison Matrix for the relative importance of

Alternatives on Enablers for RESP/OS/CU….……………………... 805.8 Architectural Weighted Desirability Index for Responsiveness…. 836.1 Architectural Weighted Index (AWI) for various alternative KM

Architectural Models under selection of the Architectural Model……………………………………………………………………… 87


Indian Institute of Information Technology - Allahabad x

LIST OF ABBREVIATIONS

AHP: Analytic Hierarchy Process ANP: Analytic Network Process

APQC: American Productivity and Quality Center AWI: Architectural Weighted Index COL: Collaboration

COMP: Competitiveness CSKB: Central Single Knowledge Base

CU: Culture HRD: Human Resource Department HRM: Human Resource Management

ICT: Information and Communication Technology INN: Innovation

IT: Information Technology ITW: Information Technology and Web KC: Knowledge Creation KF: Knowledge Flow KM: Knowledge Management

KPMG: Knowledge Process Management Group KS: Knowledge Sharing

KW: Knowledge Worker MCDM: Multi – Criteria Decision – Making

OS: Organizational Structure PI: Process Integration

PPA: Product & Process Architectures R&D: Research & Development

RESP: Responsiveness SC: Supply Chain SP: Strategic Planning TE: Training & Education

TMC: Top Management Commitment TQC: Total Quality Control

Selection of Architectural Model for KM Implementation: An ANP Approach


1

CHAPTER – 1

INTRODUCTION

Globalization is the interlocking of different countries in the

world in a political, economic, social and technological sense [1]. It has

become the most important phenomenon in our time. This process effects the

environment, culture, political systems, economic development and

prosperity, and human physical well – being in societies around the world.

Global reach brings many benefits. Operations support one another by

generating product synergies and sharing know – how. All benefit from the

improved purchasing power that scale offers. All enjoy access to world

markets through a sales and marketing network that touches every corner of

the globe so it increases the competition at both domestic and international

level. To sustain in the global market continuous innovation is required.

Knowledge is the core component of innovation not technology or finance [2].

It is the new core competence of organization as resource based economy has

been moved to the knowledge based economy. The globalization shifts the

business from production – based to knowledge – based economy [3].

In today’s competitive world, organizations need capacity to

retain, develop, organize and utilize their employee’s capabilities, in order to

remain competitive. It has become more difficult to survive in the market if

an organization is not willing to understand that technology based

competitive advantages are temporary and that the only sustainable

competitive advantage that they have is their employees. Drucker [4]



2

maintains that ‘‘Knowledge has become the key economic resource and the

dominant – and perhaps even the only source of competitive advantage’’.

Knowledge management (KM) is helpful in creating a competitive edge in

today’s global business environment. Thus, knowledge (explicit or tacit) and

knowledge management have emerged as increasingly important features for

organizational survival.

As quoted by Benjamin Franklin about knowledge... “If a man empties his purse into his head, no man can take it away

from him; an investment in knowledge always pays the best interest”.

As quoted by Ikujiro Nonaka about knowledge...

“In an organization where the only certainty is uncertainty, the one source of lasting competitive advantage is knowledge...successful companies are those that consistently create new knowledge, disseminate it widely throughout the organization, and quickly embody it in new technology and products”.

1.1 IN CONTEXT OF KNOWLEDGE MANAGEMENT (KM) IN INDUSTRIES

Knowledge Management (KM) helps management of the

information, knowledge and experiences available to an organization – its

creation, capture, storage, availability and utilization – in order that

organizational activities build on what is already known and extend it

further [5]. KM is also considered as an integrated systematic approach for

identifying, managing, and sharing all the information assets of the

organization, including databases, documents, policies and procedures as well



3

as previously unarticulated expertise and experience held by individuals,

groups and departments [6]. KM helps organizations to identify, select,

organize, disseminate, reuse and transfer important information and

expertise. These are necessary for problem – solving, dynamic learning,

strategic planning, and decision – making [7, 8].

Different industries are implementing KM, such industry types

are – banking, manufacturing, construction, electronics, textiles, mining, oil

& gas sector, pharmaceutical industry, aerospace and food/beverages. KM is

applied in manufacturing industries such as automobile, machine tools,

electronics–telecommunication–IT, and chemicals. Manufacturing

organization can apply KM to their planning and organizing, shop floor and

operations, R&D, marketing and finance, logistics and supply chain, and

human resource development. The reasons for using KM are:

Ensuring competitive advantage,

Creating new knowledge for the organization,

Managing resources effectively, and

Developing new technologies and products.

The use of KM may deliver strong benefits by way of improved

efficiency, decision – making, responsiveness, innovation, sharing of best

practices, lower cost and better judgments on task. KM also imparts other

benefits by way of providing faster information to professionals, avoiding

duplications, reducing cycle time, reusing information and knowledge,

increased sales, improved flexibility, brainstorming tools, reduced time – to –

market, and improved customization.



4

A chief concern in industries is that experts are leaving the

industries and new recruits are taking long time to acquire professionalism.

Also KM is necessary to enhance internal collaboration among employees, to

capture and share best practices, to manage legal property (patents, brands,

etc) and to maintain long term customer relationships.

Thus KM in industries is all about coming together, keeping

together and working together. Hence application of KM in industries will

lead to the ability to be flexible and more innovative as well as improving

decision making which ultimately leads to customer satisfaction.

1.2 IN CONTEXT OF NEED OF ARCHITECTURAL MODEL FOR THE IMPLEMENTATION OF KM

In the knowledge economy, a key source of sustainable

competitive advantage relies on the way to create, share, and utilize

knowledge [9]. Many models and frameworks have been proposed by many

researchers for the implementation of KM by considering different

approaches [10]. Three broad categories of Knowledge Management (KM)

models have been identified by McAdam and McCreedy [11], they are

namely:

Knowledge Category Models.

Intellectual Category Models.

Socially Constructed Models.

In this thesis, three architectural models are being proposed for

the implementation of Knowledge Management in the industries. They are

namely:



5

Knowledge Extensive System.

Knowledge Intensive System.

The Existing System.

These systems have been proposed keeping in mind the places

from where we can extract knowledge i.e. the architectures are knowledge –

centric. This will help the industry people in tracing out the places from

where they can extract maximum knowledge. This will also help the experts

who are working for the implementation of KM in getting the idea about

what are the factors that are to be taken care of while implementing KM in

an industry. It will also give them an idea about the factors which are to be

taken care of most and what are the factors which are to be considered later

while implementing KM. By using these architectures, we can easily get to

know the present scenario of the industry and also for saving time and cost

both, in the implementation of KM. Thus there is a need of architectural

model for the implementation of KM.

1.3 IN CONTEXT OF USE OF ANALYTIC NETWORK

PROCESS (ANP) APPROACH IN KM

ANP is a coupling of two parts, where the first consists of a

control hierarchy or network of criteria and sub – criteria that controls the

interactions, while the second part is a network of influences among the

elements and clusters [12]. Analytical Network Process (ANP) is a more

general form of AHP, incorporating feedback and interdependent

relationships among decision attributes and alternatives. ANP is a decision

making tool and it has been used in various fields for decision making. It has

been used by researchers in selection of R & D projects [13], in supply chain

management [14], in selection of process in chemical industry [15], in



6

accessing the performance factors [16], in decisions related to vendor

selection [17], in selection of strategies [18] for various purposes and many

more uses are there.

ANP is being used in the field of KM but not on a large scale. It

is used for fulfilling partial goals in the industries. The researchers have used

ANP to implement KM partially in industries but not considering the

industry as a whole. No one has ever given any decision making framework

for the selection of the architectural models and also the architectural models

for the implementation of KM in industries. In this thesis, ANP is being used

with KM for giving a decision making framework for the selection of the most

suitable proposed architecture for the implementation of KM in industries.



7

CHAPTER – 2

LITERATURE REVIEW

The critical review of the literature gives an idea that most of

the research work carried out has been concentrated on KM and its enablers

in industries. The literature review regarding Analytic Network Process

(ANP) describes it as a multi – criteria decision making tool which

incorporates interdependencies and feedback both. Thus the literature review

can be classified into following main areas:

Literature review for knowledge and KM

Literature review for KM enablers in industries.

Literature review for models / frameworks / architectures for

implementation of KM.

Literature review for Analytic Network Process (ANP) application for

strategic decision – making.

2.1 LITERATURE REVIEW FOR KNOWLEDGE & KNOWLEDGE MANAGEMENT

The basic building block of knowledge is data, the processing of

data resulting in information, and as a consequence of processing information

knowledge is derived. Knowledge is the next natural progression after

information; that is, a higher order than information. Fig.2.1 shows the

relationship between data, information and knowledge [19].



8

Figure 2.1 The relationships between Data, Information & Knowledge

2.1.1 DATA Data are discrete, objective, non – contextual facts about events.

In an organizational context, data is most usefully described as structured

records of transactions [20]. Data are used in and created by all daily

operations, from serving a customer, manufacturing a product, tracking

inventory, down times of machines, cycle times of production, turnover etc.

Modern organizations usually store data in some sort of technology system. It

is entered into the system by departments such as finance, accounting, R&D,

HRM, quality and marketing. Until recently, it has been managed by central

information systems department that respond to requests for data from

management and other parts of the organization. The company does not

know what data it has or understand the processes that create data and

transform them into useful information. Quantitatively there is too much

data of little or no value. Redundancy is high and unmanaged. Obsolete data

are not retired, diverting management attention from important data.

DATA



9

Organizations need data and some organizations are heavily

dependent on it. Banks, insurance companies, utilities, and government

agencies are obvious examples. Firms sometimes pile up data because it is

factual and therefore creates an illusion of scientific accuracy. Gather enough

data, the argument goes, and objectively correct decisions will automatically

suggest themselves. This is false on two counts. First, too much data can

make it harder to identify and make sense of the data that matters. Second,

and most fundamentally, there is no inherent meaning in data. Data

describes only a part of what happened. It provides no judgment or

interpretation and no sustainable basis of action. While the raw material of

decision – making may include data, it cannot tell you what to do. Data says

nothing about its own importance or irrelevance. But data is important to

organizations largely, of course, because it is essential raw material for

information.

2.1.2 INFORMATION The basic building block of knowledge is data, the processing of

data results in information. That is, adding context by organizing, or

categorizing it with a particular purpose in mind. Information requires a

sender and receiver. For example for any message, it has a sender and a

receiver. Information is meant to change the way the receiver perceives

something, to have an impact on his judgment and behavior. Information

moves around organization through hard and soft networks. A hard network

has a visible and definite infrastructure: wires, delivery vans, satellite dishes,

post offices, addresses, electronic mailboxes. The messages these networks

deliver include e – mail, traditional or "snail" mail, delivery – service

packages, and Internet transmissions. A soft network is less formal and

visible. It is ad hoc. Someone's handing you a note or a copy of an article



10

marked "FYI" (For Your Information) is an example of information

transmission via soft network. Unlike data, information has meaning the

"relevance and purpose". Not only does it potentially shape the receiver, it

has a shape: it is organized to some purpose. Data becomes information when

its creator adds meaning. We transform data into information by adding

value in various ways. Let's consider several important methods, all

beginning with the letter C [21]:

Calculated – the data may have been analyzed mathematically or

statistically.

Condensed – the data may have been summarized in a more concise

form.

Corrected – errors have been removed from the data.

Contextualized – we know for what purpose the data was gathered.

Categorized – we know the units of analysis or key components of the

data.

Note that computers can help to add these values and transform

data into information, but they can rarely help with context, and humans

must usually help with categorization, calculation, and condensing. In

general, data are considered as row facts, information is regarded as an

organized set of data, and knowledge is perceived as meaningful information

[22]. Information is only valuable to the extent that is structured. Because of

a lack of structure in its creation, distribution, and reception of information,

the information often does not arrive where it is needed and, therefore, is

useless [23]. Information has little value and will not become knowledge until

it is processed by the human mind [24].



11

2.1.3 KNOWLEDGE

A dictionary definition of knowledge is “the facts, feelings or

experience known by person or group of people”. Knowledge is derived from

information but it is richer and more meaningful than information. It

includes familiarity, awareness and understanding gained through

experience or study, and results from making comparisons, identifying

consequences and making connection. In organizational term, knowledge is

generally thought of as being “know how” or “applied action”.

Knowledge is a fluid mix of framed experiences, values,

contextual information, and expert insight that provides a framework for

evaluating and incorporating new experiences and information. It originates

and is applied in the minds of knower. In organizations, it often becomes

embedded not only in documents or repositories but also in organizations

routines, process, practices and norms. It is a mixture of various elements; it

is fluid as well as formally structured; it is intuitive and therefore hard to

capture in words or understand completely in logical terms. Knowledge exists

within people, part and parcel of human complexity and unpredictability. If

information is to become knowledge, humans must do virtually all the work.

This transformation happens through such C words [21].

Comparison – how does information about this situation compare to

other situations we have known?

Consequences – what implications does the information have for

decisions and actions?

Connections – how does this bit of knowledge relate to others?

Conversation – what do other people think about this information?



12

Thus from the fig 2.2 we can have an idea of how a data is

converted into knowledge [25],

Figure 2.2 Conversion of Data to Knowledge.

2.1.3.1 DEFINITION OF KNOWLEDGE Grey [26] noted that knowledge is the full utilization of

information and data, coupled with the potential of people’s skills,

competencies, ideas, intuitions, commitments and motivations. Knowledge is

people, money, leverage, learning, flexibility, power, and competitive

advantage; it is stored in the individual brain or encoded in organizational

processes, documents, products, services, facilities and systems. It is the

result of learning which provides the sustainable competitive advantage.

On the other hand, Zack [27] added that knowledge is that

which we come to believe and value, based on the meaningfully organized

accumulation of information (messages) through experience, communication

or inference. Davenport and Pursak [21] defined knowledge as “information

combined with experience, context interpretation and reflection. It is ‘high-

value’ from information that is ready to apply decisions and actions”.

DATA Unrecognized

numbers, words or images

INFORMATIONData processed into meaningful

patterns

KNOWLEDGE Information made into productive use

and made actionable.



13

Beijerse [28] defines knowledge here as follows: Knowledge is

seen here as the capability to interpret data and information through a

process of giving meaning to these data and information; and an attitude

aimed at wanting to do so. New information and knowledge are thus being

created, and tasks can be executed. The capability and the attitude are of

course the result of available sources of information, experience, skills,

culture, character, personality, feelings, etc.

2.1.3.2 ELEMENTS OF KNOWLEDGE

Knowledge can be categorized in two ways – Explicit and Tacit

knowledge [29]:

Explicit knowledge: It can be relatively easily to formulate and

transmit in formal languages, document forms, mathematical

equations or symbols. It can be expressed in form of manuals,

computer codes, verbal languages, etc. This kind of knowledge can be

readily transmitted between individuals formally and systematically.

It is easier to identify. It is reusable in a consistent and repeatable

manner. it is usually contained within tangible or concrete media.

Tacit knowledge: It includes subjective insights, intuitions and

hunches that are highly personal and hard to formalize [30]. Tacitness

is the property of the knower; that is very difficult to articulate and

express to others. It is deeply rooted in an individual’s actions and

experience, as well as in the ideals, values or emotions he or she

embraces. It encompasses the kind of informal personal skills or crafts

often referred to as “know – how.” It consists of beliefs, ideals, values



14

and mental models, which are deeply ingrained in us and which we

often take for granted.

The comparison of properties of Tacit vs. Explicit Knowledge is shown in table 2.1.

Table 2.1

Comparison of the properties of Tacit vs. Explicit Knowledge

S. No. Properties of Tacit Knowledge Properties of Explicit Knowledge

1. Ability to adapt, to deal

with new and exceptional situation.

Ability to disseminate, to reproduce, to access and to

reapply throughout the organization.

2. Expertise know - how, know - why and care - why. Ability to teach, to train.

3. Ability to collaborate, to

share a vision, to transmit a culture.

Ability to organize, to systematize, to translate a

vision into mission statement, into operational guidelines.

4. Coaching and mentoring to

transfer experimental knowledge.

Transfer of knowledge via product, services and

documented processes.



15

2.2 WHAT IS KNOWLEDGE MANAGEMENT?

Fundamentally, KM is about applying the collective knowledge

of the entire workforce to achieve specific goals of the organization. The aim

of knowledge management is not necessarily to manage all knowledge, just

the knowledge that is most important to the organization. It is about

ensuring that people have the knowledge they need, where they need it, when

they need it – right knowledge, in right place, at the right time. As knowledge

reside in mind, managing knowledge is difficult so the work of knowledge

management is to establish an environment in which people are encouraged

to create, learn, share and use knowledge together for the benefit of the

organization. KM is the systematic effort to capture, store, retrieve, reuse,

create, transfer and share knowledge assets within an organization, in a

measurable way completely integrated in its operational and business goals,

in order to maximize innovation and competitive advantage [31]. KM is the

spark that will ignite an organization’s ability to get the most from the

investments it has made in workforce and information technology, and to

harness the considerable intellectual capital within a company and its

partners [32]. "Knowledge Management caters to the critical issues of

organizational adaptation, survival and competence in face of increasingly

discontinuous environmental change. Essentially, it embodies organizational

processes that seek synergistic combination of data and information

processing capacity of information technologies and the creative and

innovative capacity of human beings" [33].

KM thus is about many different things like:

Gathering information and transforming them into knowledge.

Collecting knowledge and making it available to others.



16

Increasing the amount of knowledge by sharing and leveraging the

existing knowledge.

Making knowledge a tangible asset owned by the company.

Producing tangible results by managing this whole process.

There are different definitions of KM. Unfortunately, the term

KM is not easy to define because it contains multiple representations and

concepts. Many authors agree that KM requires a total organizational

transformation, including organizational culture, structure, and management

style [21]. The present research examines some of the definitions, for example

Snowden [34] defines KM as “the identification, optimization,

and active management of intellectual assets, either in the form of explicit

knowledge held in artifacts or as tacit knowledge possessed by individuals or

Communities”

Poynder [35] suggests that there are currently three major

schools of thought on what KM is. One such school recommends that KM is

mainly an IT issue, with networks of computers and groupware being the

keys. If one constructs widespread computer networks and adds

communication tools that allow group collaboration, people will be more

disposed to sharing information and knowledge

Grey [26] defines KM as “an audit of ‘intellectual assets’ that

highlights unique sources, critical functions and potential bottlenecks which

hinder knowledge flows to the point of use. It protects intellectual assets from

decay, seeks opportunities to enhance decisions, services and products

through adding intelligence, increasing value and providing flexibility”



17

Bertels [36] defines KM as “the management of the organization

towards the continuous renewal of the organizational knowledge base - this

means, e.g. creation of supportive organizational structures, facilitation of

organizational members, putting IT-instruments with emphasis on teamwork

and diffusion of knowledge (e.g. groupware) into place.”

Finneran [37] regards KM as a discipline that assists the spread

of knowledge of individuals or groups across companies in ways that directly

affect performance. KM envisions getting the Right Information within the

Right Context to the Right Person at the Right Time for the Right Business

Purpose.

According to Macintosh [38], “Knowledge management involves

the recognition and analysis of obtainable and required knowledge assets and

knowledge asset – related processes, and the ensuing planning and control of

actions to develop both the assets and the processes so as to fulfill

organizational objectives.”

Starr [39] states that KM is information or data management

with the added process of capturing the tacit experience of the individual to

be shared, used and built upon by the organization, leading to increased

productivity.

Liebowitz [40] gave a short definition of KM as the process of

creating value from an organization’s intangible assets.

Gupta and Iyer [7] define KM as process that assists

organizations to find, select, arrange, distribute, and transfer important

information and expertise essential for activities such as problem solving,

lively learning, strategic planning and decision making.



18

Morse [41] has stated that KM focuses on understanding how

knowledge is obtained, created, stored, and utilized within an organization.

Maier [42] defined Knowledge Management as the management

function responsible for the regular selection, implementation and evaluation

of goal oriented knowledge strategies that aim at improving an organization’s

way of handling knowledge internal and external to the organization in order

to improve organizational performance. The implementation of knowledge

strategies comprises all person oriented, organizational and technological

instruments suitable to dynamically optimize the organization wide level of

competencies, education and ability to learn of the members of the

organization as well as to develop collective intelligence.

2.3 LITERATURE REVIEW FOR KM ENABLERS

It was in mid 1980’s, when knowledge was appreciated among

the individuals and organizations as having role to play in the emerging

competitive environment [43]. Up till now there was no clear understanding

developed among the organizations as how to manage the knowledge, which

certainly is of two types: “explicit” knowledge and “tacit” knowledge. Explicit

knowledge is the knowledge that can be expressed externally through

codification and documentation and is later stored in the organizational

computer memories. Tacit knowledge on the other hand cannot be expressed

externally as it resides in the heads of the employees working in the

organization.

In regard to knowledge Alfred Marshall says...

“Knowledge is our most powerful engine for production”.



19

In organizations, the concept of documenting knowledge and

sharing knowledge is not new as the organizational policies, producers,

employee training and personality development programs, reports and

manuals are serving the same purpose for years [44].

As per Nonka and Huber [44]...

“Knowledge is a justified personal belief that increases an individual’s

capacity to take effective action”.

As organizations strive to improve their business performance

and capacity for innovation, their attention is increasingly focused on how

they manage knowledge effectively. Experience has shown that successful

KM implementations in business settings prioritize attention on soft issues –

including human and cultural aspects, personal motivations, change

management methodologies, new and improved business processes enabling

multidisciplinary knowledge sharing, communication and collaboration, and

sees technology as an enabler. There are some basic steps in the management

of knowledge as knowledge capture, knowledge development, knowledge

sharing and knowledge utilization without which any organization will not be

able to effectively manage their knowledge [45].

Many scholars and practitioners viewed that a supportive

organizational culture can enable the successful implementation of

knowledge management. Organizational culture can be more simply defined

as the character or the personality of an organization. It is often described as

“the ways things are done in an organization”. Routinized ways of doing

things that people accept and line by. Organization have norms and values

that influence how members effort or may encourage them to do so” [46].

People’s willingness to ask questions that reveal their ‘‘ignorance’’, disagree

with others in public, contradict known experts, discuss their problems,



20

follow others in the thread of conversation – all these behaviors vary greatly

across cultures [47]. Organizational culture is the way of organizational life

that enables and motivates people to create, share and utilize knowledge for

the benefit and enduring success of the organization [48]. “Motivating the

Employees “to share and use knowledge is essential for successful

implementation of KM. Reward and Incentives increases the confidence on

the part of the source and recipient. Incentives are critical to the knowledge

transfer process, acting as signals for employees to engage in knowledge

transfer [49]. Osterloh and Frey [50] conclude that intrinsic motivation

enables the transfer of tacit knowledge. Compensation is a primary vehicle

for indirect motivating employee in which money is a goal independent of

activity [51]. Communities of practices are about sharing experiences and

knowledge in creative ways and this can lead to new approaches to problem

solving and innovation [52]. Leadership is and has always been the principal

approach to convince and motivate employees to do what managers have

planned for them in advance [53]. Leadership, by its influence component,

facilitates the implementation of knowledge activities in an organization.

Communication competence is the ability to demonstrate knowledge of the

appropriate communication behavior to effectively achieve organizational

goals. Communication between individuals requires both the decoding and

encoding of messages. Communication decoding competence refers to a

recipient’s ability to listen, be attentive and respond quickly; communication

encoding competence refers to a source’s ability to express one’s ideas clearly,

have a good command of the language, and can be easily understood [51].

Learning is another important enabler which underpins the

creation of knowledge. Organizations have to learn constantly, in order to

respond flexibly to changes in the environment and to stay competitive [54].

Learning is viewed as a process that allows workers to advance from novices

to experts through three levels of knowledge acquisition, that are know –



21

what, know – how, know – why [55]. Learning improves the behavior and

capability of individuals so that the organization can more effectively respond

to its environment which increases the performance of manufacturing

organization [56]. Learning processes define the quality of knowledge

distributed across the organization as well as the effectiveness with which

knowledge is put in use [57]. The intangible knowledge resource, intellectual

capital is a key driver of innovation and competitive advantage in today’s

knowledge based economy [58].

“Information technology infrastructure” supports each step in

KM process and is very essential and helpful in successfully performing these

processes through Information Technology (IT) tools like – database systems,

intranet, extranet, internet, web, group support systems, multimedia

technologies, etc [2]. IT is a vital enabler of knowledge management in an

organization, which helps the firm to know “what they actually know”.

Sufficient time should be made available for the employees’ learning and

interaction, for their training related to complex technological systems, which

help them to communicate their creativity and ideas helpful in enhancing the

innovations of organization for new product development [22]. IT

infrastructure accelerates data transmission and reduces cost of

communication. It enhances knowledge sharing process to flourish

innovation. It helps in distant learning and access to market information.

Many organizations becoming dependent on it in order to satisfy their

business aims and meet their needs. Networking enables communication

between distant persons and technical devices through internet, intranet, etc.

It enhances the interaction of individuals group, organization and inter –

organization. For KM, the role of IT infrastructure is to support knowledge

repositories, enhance knowledge access and transfer, and facilitate the

knowledge environment [58].



22

Collaboration increase competitiveness by achieving flexibility,

innovation quality and speed [59]. Collaborators were people who “worked

with the enemy”, collaboration is a word of which, today, most people seem to

approve. “Collaboration seems to capture the spirit and represent one of the

underpinning tenets of knowledge management, that of working together to

achieve common goals and objectives” [60]. Commitment is another strong

enabler of the organization. Commitment improves the managerial

involvement which leads to collaboration. “Commitment by the Top

Management” is necessary for full and continual support for the KM

initiatives across the organization, also there should be a separate “budgetary

support” for bringing knowledge management into practice and it will

actually be a clear signal for the employees that KM is an integral part of the

organization and is not an extra-activity apart from the routine ones [61].

Innovation is a critical enabler in the success of industries and it

will mainly arise by collaboration of ideas and feedback from all phases of the

product life cycle. “Product Life Cycle Knowledge Management” will lead to

creation of innovative ideas after collecting them from people involved in

product development and associated processes, and will ensure that all the

useful knowledge is saved, easily reused, and stored into a structured

knowledge repository for the achievement of business benefits like – overall

improved business performance, improved working conditions, and employee

satisfaction, improved customer satisfaction, reduction of product

development cycle time and increased responsiveness of the firms [62]. In

small and medium – sized companies nine knowledge streams in knowledge

management are of chief importance, they are, determine the knowledge

necessary, determine the knowledge available, determine the knowledge gap,

knowledge development, knowledge acquisition, knowledge lock, knowledge

sharing, knowledge utilization and evolution of utilized knowledge [28].



23

Organizations need to identify, access, share, store and use

knowledge related to customers, competitors, suppliers and employees in

order to analyze the market demands and its own capabilities for enhancing

responsiveness and customer satisfaction [63]. This will enable the

organization to make better planning, decision making, manage

unanticipated changes made by the supplier and will allow reducing the

changes late in the product development process where the changes are more

expensive. “Success measures of Knowledge Management” enables in

knowing how successful was the KM efforts in the organization and these

successful indicators can be in terms of financial returns, froth in resources

linked to project and growth in volume of the knowledge content usage [64].

2.4 LITERATURE REVIEW FOR MODELS / FRAMEWORKS / ARCHTIECTURES FOR KNOWLEDGE MANAGEMENT

Every time a new technology is developed, for its

implementation researchers and scholars propose a number of models,

frameworks or architectures for its implementation. These models,

frameworks and architectures assist in successful implementation of the

technology. These can be designed for a specific purpose and can also be in a

generalized form which may be common for all. Knowledge Management is

also a new technology which is up coming in the market at a great pace.

For the successful implementation of KM, the researchers and

scholars have proposed a number of models, frameworks and architectures.

These models, frameworks and architectures have been designed for some

specific purpose as well as some of them are generalized. These have been

proposed for specific purposes as performance management in supply chain,

in improvement of customer relationship management, in human resource



24

management, in improving the business performance of the organizations

and many more.

The researchers and scholars are constantly working on

different aspects of KM and exploring the various possibilities of using it in

different fields. They have proposed a number of frameworks based on

different aspects related to KM. These frameworks can be broadly classified

into two categories: descriptive frameworks and prescriptive frameworks [65].

The descriptive frameworks are frameworks which try to describe the

phenomena of Knowledge Management and the prescriptive frameworks are

those frameworks which advise the methodologies to be followed in

conducting KM.

Descriptive frameworks can further be classified as: broad

descriptive and specific descriptive frameworks [65]. A broad framework is

that descriptive framework which endeavors to give the details of the whole

phenomenon of KM. A specific framework is that descriptive framework

whose center of attention is a particular aspect of this phenomenon.

There are a number of frameworks proposed by lots of

researchers and scholars. Some of the frameworks based on the above stated

classification form a part of the discussion. These frameworks are discussed

below.

Wiig gave one framework for KM known as framework of pillars

of knowledge management [66]. It is comprised of three functions which he

calls the KM pillars. These are the dominant functions which are required for

managing knowledge. Figure 2.3 shows that the pillars are set up on a broad

perception of knowledge creation, manifestation, use and transfer.



25

Figure 2.3 Pillars of Knowledge Management [66]

I

Survey & Categorize Knowledge

Analyze Knowledge & Related Activities

Elicit, Codify & Organize Knowledge

III

Synthesize Knowledge Related

Activities

Handle Use & Control Knowledge

Leverage, Distribute & Automate Knowledge

II

Appraise & Evaluate Value of Knowledge

&

Knowledge Related Actions

KNOWLEDGE MANAGEMENT

KNOWLEDGE MANAGEMENT FOUNDATION

BROAD UNDERSTANDING OF KNOWLEDGE

– CREATIONS – MANIFESTATIONS – USE – TRANSFER



26

1. Physical Systems. 2. Managerial Systems.

kills dge.

3. Employee Sand Knowle

4. Values and Norms.

Figure 2.4 Core Capabilities and Knowledge Building Activities [67]

Leonard – Barton presented a KM framework which included

four core capabilities and four knowledge building activities [67]. These are

decisive for Knowledge – based organizations. It is shown in figure 2.4 that

there are four knowledge building activities which encircle the core

capabilities. They are shared and creative problem solving (to produce

current products), experimenting and prototyping (to build capabilities for

the future) and importing and absorbing technologies from outside of the

firm’s knowledge. These activities are related to knowledge creation and

diffusion.

4

3

2

1

Problem Solving

Importing

Knowledge

Implementing

&

Integrating

Experimenting



27

Figure 2.5 Organizational Knowledge Management (KM) Model [68]

Arthur Anderson and American Productivity & Quality Center

presented a model for KM known as ‘Organizational Knowledge Management

(KM) Model’ [68]. It embodies the seven KM processes. These processes can

operate on the knowledge of an organization. The operations they can

perform is shown in figure 2.5. These are create, identify, collect, adapt,

organize, apply and share.

Choo presented a model known as “Model of Knowing

Organization” [69]. It is shown in figure 2.6 and this model supports that an

organization uses the information strategically. They use it for sense making,

knowledge creation and decision making.



28

Figure 2.6 Model of the Knowing Organization [69]

A framework is introduced by van der Spek and Spijkervet

known as Framework for Knowledge Management [70]. It characterizes a

cycle of four Knowledge Management stages. The stages identified are

Sense making

Information Interpretation

Knowledge Creation

Information Transformation

Decision Making

Informat cessing ion Pro

Organizational Action



29

conceptualize, reflect, act and retrospect. These stages govern the basic

operations on knowledge, as shown in figure 2.7.

Figure 2.7 A Framework for Knowledge Management [70]

Svieby supported the assumption of organizational knowledge as

intangible assets [71]. He gave a framework for this known as Intangible

Assets framework. As shown in figure 2.8, it is comprised of three

External and Internal External and Internal

Developments Developments

External and External and Internal Internal Developments Developments

Conceptualize

• Draw up inventory • Analyze strong & weak points

Retrospect

• Evaluate results achieved

• Compare old and new situation

Reflect

• Establish required improvement

• Plan the improvement process

Act

• Developing knowledge • Distributing knowledge • Combining knowledge • Holding knowledge



30

components. These components are external structure, internal structure and

employee competence.

Figure 2.8 Intangible Assets Framework [71]

Petrash introduced a model which involved three types of

organizational resources [72]. These resources are referred as intellectual

capital and the model is known as “Intellectual Capital Model”. These are as

shown in figure 2.9: human capital, organizational capital and customer

capital.

Alavi gave a framework known as Model of Knowledge

Management process [73]. It elaborates the KM process in a consulting firm,

KPMG Peat Marwick. It defines KM as creating, leveraging and sharing of

know – how and intellectual assets. These are done across the firm by all the

individuals in order to improve the services to the clients. KPMG has built –

up a KM process model which consisted of a sequence of six phases. These six

phases as shown in figure 2.10 are: acquisition, indexing, filtering, linking,

distribution and application.

INTANGIBLE ASSETS

EXTERNAL STRUCTURE

(brands, customer and supplier relationships)

INTERNAL STRUCTURES

(The organization: management, legal structure, manual systems, attitudes, R & D, software).

EMPLOYEE COMPETENCE

(Education, experience)



31

Figure 2.9 Intellectual Capital Model [72]

Figure 2.10 KPMG Knowledge Management Process [73]

Nonaka gave a model known as “Spiral of Organizational

Knowledge Creation” [74]. It recognizes four kinds of “knowledge conversion”

Human

Capital

Customer

Capital

Organizational

Capital

Value

DepictsKnowledge

Flow

Acquisition Indexing Filtering

Linking Distribution Application



32

that drives knowledge creation. These are as shown in figure 2.11:

socialization, externalization, internalization and combination.

Figure 2.11 Spiral of Organizational Knowledge Creation [74]

Beckham also introduced a perspective framework. In this

framework, an eight – sequence is recommended for administering knowledge

Combination

Socialization

Explici

Externalization

t

Tacit

Organization Individual Group Inter – Organization

Knowledge Level

Internalization



33

management. These eight stages are: identify, capture, select, store, apply,

create and sell [75].

Thus, these models are helpful in the implementation and form

the base of various other models and frameworks proposed for the

implementation of KM. These models and frameworks have helped in the

formation of the architectural models proposed in this thesis.

2.5 LITERATURE REVIEW FOR ANALYTIC NETWORK PROCESS (ANP) FOR STRATEGIC DECISION MAKING

Analytical Network process (ANP) is a special case of AHP

Analytical Hierarchy Process was developed by Saaty in 1996. AHP

maintains a unidirectional hierarchical relationship among decision level.

The overall goal of the problem is apex of ANP model. In this hierarchy, one

group of entities influences another set of entities. ANP allows more complex

interrelationships between decision levels and attributes. The major

difference between the AHP and ANP approaches is that ANP assumes that

the system has interdependent relationships among the attributes with no

strict hierarchical relationship. ANP allows a feedback relationship among

the criteria at different levels and interdependence between the criteria and

attributes at same sublevel through development of a super matrix.

Graphically summarizing an ANP model describes a model consisting of the

“ultimate goal” or “overall objective” at the top most level, followed by

“criteria” or “determinants”, which are followed by “Dimensions” and

“Alternatives” which are a means to achieve the overall objective. Criteria

have dominance over dimensions, dimensions dominate the attributes and

the whole model is dominated by the overall objective. The analytic network

process (ANP) is capable of taking into consideration the multiple dimensions



34

of information into the analysis, a powerful and necessary characteristic for

any strategic decision [18].

A numerous application of ANP have been published in the

literature, like- marketing, transportation projects, forecasting, supply chain

management, medical, military, manufacturing process selection, R&D

project selection and etc.. ANP allows qualitative values to be transformed

into quantitative values for comparative analysis. The ANP is a relatively

simple, intuitive approach that managers and other decision-makers can

accept [13]. ANP has been used for analyzing the alternatives for

improvements in supply chain performance thus presenting a framework for

deciding the priorities for the performance improvement of a supply chain

[76]. ANP may be differentiated into two kinds of models: the feedback

system model and the series system model. In the feedback system model,

evaluation clusters link one by one in turn as a network system. This kind of

model can capture effectively the complex effects of interplay in human

society, especially when risk and uncertainty are involved [30]. Meade and

Sarkis [77] suggested a decision methodology that applied ANP to evaluate

alternatives (e.g. projects) and to help organizations become more agile, with

a specific objective of improving the manufacturing business processes.

Cheng and Li [78] illustrate how to empirically prioritize a set of projects by

using a five-level project selection model. In order to utilize the high level

model of the relationships influencing the selection of a third party reverse

logistics provider an ANP methodology must be determined [77]. Analytic

network process (ANP) based decision model was developed for the

evaluation of various alternatives for end – of – life reverse logistics in the

computer hardware industries [10]. Meade suggested that ANP is an

approach utilizing quantitative, qualitative, tangible and intangible factors

pertaining to the decision of whether and which third party logistics provider

should be selected. The decision model, using the ANP, is capable of taking



35

into consideration the analysis, a powerful and necessary characteristic for

any strategic decision [79]. Momoh and Zhu [80] proposed an application of

AHP and ANP to enhance the selection of generating power units for

appropriate price allocation in a competitive power industry.

Sarkis [81] presented a systemic ANP model to evaluate

environmental practices and programs in analyzing various projects,

technological or business decision alternatives. Chung et al. [82] applied ANP

in selection of product mix for efficient manufacturing in a semiconductor

fabricator considering the aspects of product, equipment efficiency and

finance. ANP models problems involving systems in which the relationships

between the levels are not distinct (i.e. easily represented as higher or lower,

controlling or subordinate). These systems are known as ‘‘systems with

feedback’’ and refer to systems where a level may both dominate and be

dominated, directly or indirectly, by other decision attributes and levels [15].

ANP – based framework is to identify the level of impact of different factors

on total quality management (TQM) implementation and to assess the

readiness of the Turkish manufacturing industry to adopt TQM practices

[83]. Raisinghani and Meade [18] illustrated “what dimension of KM is most

important in developing an agile e – supply?” using the ANP research model,

and serves as the framework for our research study.

ANP is a coupling of two parts. The first consists of a control

hierarchy (or network) of criteria and sub criteria that controls the feedback

networks. The second part consists of the networks of influence that contain

the factors of the problem and the logical groupings of these factors into

clusters; control criterion (or sub – criterion) has a feedback network [17, 83].

A super matrix of limiting influence that gives the priorities of the factors in

the network is computed for each network. Each decision network is

composed of clusters, their elements, and links between the elements. A link



36

between an element (the ‘‘parent’’) and the elements it connects to in a given

cluster (its ‘‘children’’) makes up the usual AHP pair wise comparison set.

The interactions, feedback, influences, and dependencies in the system are

expressed through these links. Links between elements within the same

cluster are called inner dependencies, whereas links between a parent

element in one cluster and its children in another cluster are called outer

dependencies [84]. Inner and outer dependencies are the best way decision-

makers can capture and represent the concepts of influencing or being

influenced, between clusters and between elements with respect to a specific

element. Pair wise comparisons are made systematically for all combinations

using the fundamental comparison scale (1 – 9) of AHP that is used to

indicate how many times an element dominates another. Preference between

each pair of elements verbally as equally important, moderately more

important, strongly more important, very strongly more important, and

extremely more important. These descriptive preferences would then be

translated into numerical values 1, 3, 5, 7, 9, respectively with 2, 4, 6, and 8

as intermediate values for comparisons between two successive judgments.

Reciprocals of these values are used for the corresponding transpose

judgments. In making judgments, the decision maker can incorporate

experience, knowledge and hard data [85]. Tangibles can be included in the

model alongside intangibles.

Thus, we can say that ANP is a “multi – criteria, multi –

dimension and multi – attribute” decision making model which has been

successfully implemented for strategic decisions in varied fields.

2.6 PROBLEM DISCUSSION



37

The literature gives an idea that there is a lot of research work

is being done in the field of knowledge management, analytical hierarchy

process (AHP) and the Analytic Network Process (ANP) methodology. The

literature review gives support to the fact that an architectural model is

required for the implementation of knowledge management in industries.

Also ANP is a multi-criteria decision making tool which is used for strategic

decision making giving an idea to the managers about the interdependencies

and feedback between various attributes and helps in deciding which

alternative is best suited for the problem. Still, there is a necessity to

construct a framework describing how to determine the architectural model

for the implementation of KM in industries through the use of analytic

network process.

The research work in this field is going on, but ANP has not

been used much in the field of KM. While going through the papers related to

survey regarding KM in industries, the thought of proposing architectures

and using ANP for selecting the best architecture for the implementation of

KM. The variables taken are those which are used for the implementation of

KM and they play a major role in it.

Thus, this thesis work is intended to propose an ANP model

construct for the implementation of KM by determining the appropriate

architectural model. The architectural models that are to be used as

alternatives are knowledge extensive, knowledge intensive and the existing

systems. These are designed keeping in mind the dimensions and

determinants that have been chosen for achieving the goal. In order to

implement KM in industries, it needs to achieve Cost, Enhancing

Competence and Responsiveness which in this model are taken as criteria or

determinants. Organizational structure, Process integration and Innovation

are taken as dimensions. For this work ANP is adapted because, it is a

decision making tool which can determine the best architecture for the



38

implementation of KM by considering systematic characteristics of

determinants and dimensions.

CHAPTER – 3



39

RESEARCH MODEL FRAMEWORK

A model framework is being developed in this work to determine

the architectural model for the implementation of knowledge management in

the industries. ANP based framework is adopted which contains

determinants, dimensions, attributes and alternatives in different sublevels.

It finds the interdependencies and also feedback between the different levels.

This framework helps to take decisions regarding the selection of the

architectural model for the KM implementation. The framework showing the

defined variables for determinants, dimensions and attributes is shown, see

Figure 3.1.

3.1 DETERMINANTS

The determinants are those variables in the framework which

helps us in achieving the goal defined. In this work, the determinants for

achieving the goal i.e. “selection of the architectural model for the

implementation of knowledge management in Indian industries” are Cost,

Competitiveness and Responsiveness. These three determinants are most

suitable for selecting the architectural model for the implementation of KM

because they cover each and every aspect of an industry in wider prospects.

The three determinants are illustrated below in the following sub–sections:

3.1.1 COST



40

Today cost has become the most important determinant in the

implementation of knowledge management. Whenever a new thing is

implemented, first thing that is taken care of is the cost and it is also seen

that how the new implementation going to help the industry in terms of

profit and loss. The cost for an industry is a market winner. Since, after

implementation of KM in the industry lead time is reduced, response time

increases and thus decreasing the overall cost of the product without

affecting the quality of the product. This is because of the factors which have

improved due to the implementation of KM.

3.1.2 COMPETITIVENESS

Competitiveness is the degree to which business meet the high quality product with lower cost, with high customer service and utilizing resources efficiently. It increases the effectiveness and efficiency of the organizations. Grant [86] proposed that integration of individual’s specialized knowledge as a salient organizational capability to create and sustain competitive advantage. In a dynamic environment, competitive advantage is defined as “the ability to create unique advantage and to protect these advantages against imitation” [87]. To sustain in the global market organizations are finding means of differentiating its products in order to compete with off – shore manufacturers [88]. This can be achieved by improving customer service, productivity and quality of the product using minimum resources. The new mind set in organizations is competitive mind set. CEO across the country agrees: Increased competitiveness is the issue of the day. They have applied resources to improve it, but continue to degrade. To achieve competitiveness, business executives, advisors and allies must provide their organization with a methodology for changing from functional, performance – at – any – cost mindset to a new mindset driven by KM. Competitiveness acts as a fundamental driver and is the effectiveness measurement of the business process. Applying KM program in



41

organizations, removes the barriers which impede performance. Diversity between nations typically reflects different environmental conditions, which in turn affects the strategies, directions, and challenges of a specific industry. Information and communication technology (ICT) effectively managing information and knowledge have become a key element to improve the organization’s performance. ICT helps an organization to capture, distribute, and manage information effectively [89]. ICT works as knowledge enabling tool for supporting and enhancing the performance of the organizations to sustain the competitive edge. R&D, teaching and learning, developing knowledge workers, providing community services, supporting culture and enhancing competitiveness. It increases the performance by providing channels for storing, acquiring, transferring, exchanging, distributing, and reusing both internally and externally [90].

3.1.3 RESPONSIVENESS

The most critical capability for a manufacturing company today is its ability to manage change quickly. To survive in the global market, responsiveness decreases the affect by internal and external disturbances on production operation and increases the response of existing infrastructure. Responsiveness is the real time performance. It is the degree to which a business is able to meet the customer’s final needs within customer lead time. The crux of responsiveness is that make/market cycle time (the time it takes a company to forecast, schedule, acquire material, receive material, manufacture and distribute) is usually much longer than the customer’s lead time (the time from the customer’s last change to his required delivery). Likewise a long design/development cycle time relative to market intervals (the time in between significant new product introduction in the market) causes non – responsiveness. In short, the root cause of non – responsiveness is long cycle time in critical business processes. Non – responsiveness is usually blamed on three generic symptoms: poor forecasts, changing market needs and poor execution. Barley [91] defines Responsiveness as the ability



42

of a production system to respond to disturbances (originating inside or outside the manufacturing organization) which impact upon production goals. The following areas have been identified as being of importance in the development of more responsive manufacturing systems [92]:

Human organizational

Equipment processes

Decision, control and information systems

Responsiveness is a measure of how well it adapts to changes in consumer behavior, customer requirements and changing trade and competitive structure [93]. Externally responding to customer’s needs by rapidly designing and manufacturing products satisfying those needs and internally by reducing the lead times for all tasks in a company, resulting in improved quality, lower cost and of course quick response. By enhancing faster delivery of the mass customized product which helps in building goodwill in market thus results in increased market share, and enables accelerated time performance which leads to higher efficiency by economizing the production, lower overheads, reduced lead times, faster inventory and higher productivity. All this helps in improving the business performance, thus making responsiveness a key business objective which today every organization strives to achieve [93].

3.2 DIMENSIONS & ATTRIBUTES

In the present model framework, organizational structure, process integration and innovation have been considered as dimensions for selection of architectural model for the implementation of KM. These are actually the basic sub – criteria for the selection of the architectural model for implementation of KM but the attributes or enablers under these dimensions



43

may vary according to companies and process adopted in it. The following dimensions and attributes are considered for the selection purposes.

3.2.1 ORGANIZATIONAL STRUCTURE

Organizational structure consists of some sub – strategies in

itself or some standard set of principles or methodologies and culture in its

operations environment. Under the framework the dimension organizational

structure is characterized by culture, top management commitment, strategic

planning and IT and web for knowledge management. No knowledge

management strategy can lead to a success until it is well supported by the

organizational “culture” which should be such that, “the right person at the

right job at the right time”. Employees should “work for the organization

rather working for themselves” and also organization should go for a culture

of “knowledge sharing rather than knowledge hoarding”, this indeed creates

an atmosphere of trust organization wide.

The organizational culture effects personal attitudes and

working patterns, also it is one of the most important enablers of KM and IT

infrastructure requires more funds because without IT infrastructure, the

knowledge accessing, knowledge creating and knowledge sharing is not

possible in the present environment and all this requires sufficient financial

support [94]. Culture of the organization also includes motivating the

employees to share and reuse the knowledge which can be achieved by

rewards and incentives, through speeches and individual attention,

promotions of employees based on the amount of knowledge sharing and

usage and knowledge contribution by them [95].



44

Organizations top management must appreciate and encourage

cross-boundary learning and sharing [2]. In order to show their

“commitment” towards knowledge management organization’s top

management appoints a full time executive and his /her subordinates to

handle KM initiatives in the organization for e.g. CKO (Chief Knowledge

Officer), Knowledge Managers, etc. Top management should help in building

KM setups and also should define and develop the skills of learning from

other people, at the same time the top management should keep faith in

knowledge management initiatives and remain patient in expecting results

from KM [24].

Knowledge capture, knowledge development, knowledge

sharing, and knowledge utilization are the important knowledge based

activities performed generally in any organization for successfully

implementing KM so that the organization could derive real benefits for

business performance improvement [45]. Knowledge capture is a process by

which knowledge (both external and internal to the organization) is obtained

and stored in the information systems like- traditional database systems,

document management systems, etc. After knowledge capturing this

captured knowledge is required to be organized and analyzed for strategic or

tactical decision making through on – line analytical process, and intelligence

systems, etc. Once the knowledge is captured and developed the next stage is

to distribute this knowledge through group support systems, computer

assisted communication technologies like – internet, EDI, e – mails, voice –

mails, video – conferencing, etc.

3.2.2 PROCESS INTEGRATION


Indian Institute of Information Technology - Allahabad 45

Process integration consists of some sub – strategies in itself or

some standard set of principles or methodologies and culture in its operations

environment. Under the framework the dimension process integration is

characterized by product and process architecture, knowledge sharing in

supply chain and integration of core processes. Every enterprise is part of the

“connected economy” and as such one need to extend the enterprise all the

way to the suppliers (and in turn to the supplier’s supplier) and the business

partners like distributors, retailers and ultimately the end customer [96].

Formal training not only enables learning but also ensures that practices in

the alliances are consistent and that these lead to standard processes [97].

Product and process architectures are the platforms that provide the back-

end flexibility so as to enable the organization in maintaining the core areas

despite highly dynamic and competitive market force. The most frequent

problem occurs due to conflicts between or within the product and process

architectures [98]. Therefore it is essential to develop modularity in the

architectures for solving conflicts and integrating the processes.

“Collaboration seems to capture the spirit and represent one of

the underpinning tenets of knowledge management, that of working together

to achieve common goals and objectives.” In knowledge-focused organizations,

knowledge sharing is highly dependent on effective ongoing collaboration.

Collaboration can reduce the cost of communication while expanding its reach

(time and distance), increase the number and quality of alternatives while

decreasing the cost of transactions, enable tight integration between firms

while reducing the cost of coordination. It comprises an important knowledge

management process that offers significant opportunities for improving

economic performance and competitiveness of many companies.



Figure 3.1: ANP Framework Model

1. CULTURE 2. TOP MANAGEMENT

COMMITMENT 3. STRATEGIC

PLANNING 4. IT & WEB

5. PRODUCT AND PROCESS ARCHITECTURE

6. KNOWLEDGE FLOW 7. SUPPLY CHAIN 8. COLLABORATION

9. KNOWLEDGE CREATION 10. KNOWLEDGE WORKER 11. KNOWLEDGE SHARING 12. TRAINING &

EDUCATION

KNOWLEDGE INTENSIVE

EXISTING SYSTEM

KNOWLEDGE EXTENSIVE

Determination of the architectural model for the implementation of Knowledge Management in Indian Industries

ARCHITECTURAL WEIGHTED INDEX (AWI)

ORGANIZATIONAL PROCESS STRUCTURE INTEGRATION INNOVATION

COST COMPETITIVENESS RESPONSIVENESS

1

4 3

2 9

12 11

10 5

8 7

6



47

3.2.3 INNOVATION

Today innovation has become the main stay of business and is

important for improving the business performance of organizations. Only

those organizations are able to differentiate themselves from their

competitors who make “intelligent use” of knowledge assets of the

organization for development of new products which help in attracting

customer’s interest and will give a competitive edge to the organization over

its rivals. Innovation of new products and services not only makes the

organization “ pro – active” in meeting needs of the customer which they have

not yet faced but will surely be facing in future but also opens the door for a

new venture, thus allowing the organization to go for business expansion

plans. Traditionally the innovation process in organizations is centralized to

one department like the Research and Development (R&D) department but

today the organizations are going for decentralized innovation process which

are well connected through the knowledge networks for the transfer of ideas,

creativity, information, etc based on which innovation of new product

development has become more collaborative than ever before [99]. Leaders

recognize the skills of human resources and understand that high

motivational supports creativity which is essential for innovation, also today

the innovation and knowledge creation are required for competitive

initiatives such as improving customer satisfaction, developing new products

and markets providing faster response [94]. For giving considerable benefits

of innovation and new product development, an organization have to

consistently apply knowledge management practices in its business which

helps them to understand market trends and customer – requirements and

puts useful knowledge into action for its innovation processes which leads to

improvement of business performance [100]. The classification and

codification of organizational knowledge leads to organizational innovation



48

through two models on knowledge management for innovation, one is

“cognitive” model, in which knowledge for innovation is referred to as

objectively defined concepts and facts and the other is “community” model in

which knowledge for innovation is socially constructed and is based on

experience [101]. Thus, the selection of the right architectural model for the

implementation of KM is vital for the success of new innovations.

3.3 ALTERNATIVES

Alternatives are actually a way to achieve the overall objective

of the ANP model. The proposed work is to choose the best proposed

alternative through decision-making to select the architectural model for the

implementation of KM. Evaluation is carried out using ANP to determine the

best alternative. The three proposed alternatives are “Knowledge Extensive”

architectural model, “Knowledge Intensive” architectural model and

“Existing System” architectural model.

3.3.1 ALTERNATIVE 1: KNOWLEDGE EXTENSIVE ARCHITECTURAL MODEL

This architecture aims to implement KM on a wider frame,

which encompasses systems both within the organization and for its network

interactions. Recognizing the importance of KM at the basic level of

individual – group interactions, there is a need to develop individual and

group focus at all levels and interfacing of these is required with the view to

develop a culture of sharing in form of ‘working for company’ rather than



49

‘working for myself’. The knowledge intensive architecture for a firm is shown

in fig 3.2.

Communities of practice are established frequently to generate

problem’s solution. Group discussions are used to bring out the ambiguities

developed by the individuals of the group. Realizing the importance of idea

management and innovations at the core of the organization, the firm

encourages creativity by establishing enhanced access facilitation to the

knowledge sources. The tacit interactions or facilitation are in the form of

guidance by experts and the establishment of “virtual teams” [102, 103]

across the enterprise or the establishment of “virtual organization” [104] with

collaborators. On a need – based manner consultancies are hired by firms

with an aim to acquire content, which is both the tacit and explicit

knowledge. To facilitate access to experts, the managers carefully assess

them to put ‘right person at right spot’, IT enabled “expert pointers” [105] are

developed for quick tracing by individuals in their own right from their

desktop via the intranet. The documented knowledge content existing in the

form of details related to product, process and technology as well as the

explicit knowledge content of the collaborators is put to easy and personalized

access for the employees, right at the desktop via intranets and extranets.

The access to knowledge that lies outside the company’s knowledge bases and

the relevant information form the Internet play equally important role in the

industries.

A non – hierarchical managerial structure facilitates the

architecture to avoid idea decay or knowledge loss due to social and

organizational factors. Along with this, knowledge managers and technical

filters need to exist to assess the quality and value of knowledge. Knowledge

managers being intermediary to the sources of knowledge and the

repositories play a crucial role from in assigning the taxonomy to the broad



organizational knowledge and punitively deciding and developing channels

for knowledge flow [106].

The product and operational data is gathered and organized in

repositories for knowledge leverage and distribution. The warehoused atomic

data in the repositories is organized by database systems and extracted and

packaged by various IT tools like data mining and expert systems so as to

retrieve the data as knowledge at the appropriate time. This summarized

knowledge is presented with an intuitive, contextual and channelized access.

The repositories also serve to preserve knowledge by enabling capture and

storage of knowledge assets as well as experience and learning. These are put

to personalized access to enhance reuse, guarded by security systems and

privileged access to disallow misuse of knowledge. The filtration, storage and

analysis of historical knowledge are also done.

The product and process related data are distributed in

decentralized repositories for the functional components of modular product

and process architectures. They need to be integrated not only to support

concurrent engineering but also to enable a central single knowledge base

(CSKB) abstraction for enabling consistent and non – redundant behavior

among the processes and work teams [107]. “Knowledge Architecture”

identification allows improvement in functional components independently

and enables effective induction of technological advances. The modular

architectures allow leverage for likely changes due to environmental issues

and thus provide opportunity for the firm to remain competitive in the event

of stringent environmental regulations.



COLLABORATORPEERS

K

N

O

W

L

E

D

G

E

C

U

S

T

O

M

E

R

KNOWLEDGE OUTFLOW

EXTERNAL CONTENT

TACIT

VIRTUAL TEAMS, SUPPLY CHAIN

NETWORK, CONSULTANCY, AND

ACQUISITION.

EXPLICIT

CONTENT SHARING,

RESEARCH, NEWS

FEEDS.

INTERNAL CONTENT

TACIT

EXPERTS, VIRTUAL TEAMS &

COMMUNITIES OF PRACTICE,

KNOWLEDGE WORKER.

EXPLICIT

PRODUCT, PROCESS,

TECHNOLOGY,

EXPERTISE.

KNOWLEDGE MANAGER & TECHNICAL

FILTER

QUALITY ASSESSMENT,

VALUE ASSESSMENT,

TAXONOMY ASSIGNMENT,

CHANNEL ASSIGNMENT,

HISTORICAL KNOWLEDGE FILTRATION.

COMMUNICATION TOOLS

INTRANETS & VIRTUAL TEAMS,

EXTRANET & VIRTUAL ORGANIZATION,

INTERNET & VIRTUAL EXPERTISE.

INFORMATION PROCESSING

TOOLS

GROUPWARE,

EXPERT SYSTEMS,

DOCUMENT & FILE MANAGEMENT,

DATABASE MANAGEMENT,

DATA MINING.

ORGANIZATIONAL PROCESSES &

REPOSITORIES

CONCERN FOR ENVIRONMENT

AGGREGATE, INDEX,

CLASSIFY,

CATEGORIZE, SORT,

SEARCH, PACKAGE,

SUMMARIZE.

PRODUCT DATA

MANAGEMENT,

INTEGRATION OF

INTRA & INTER

DEPARTMENTAL

DATABASES, DATA

WAREHOUSE.

EASY & CONTEXTUAL

ACCESS, KNOWLEDGE INPUT

ALONG AND UPDATING AFTER

THE JOB, SECURITY, PRIVILEGE OF

ACCESS.

CHANNELS FOR KNOWLEDGE FLOW

INFORMATION & LEARNING

CONTEXTUAL

INSIGHTS,

INTERPRETATIONS &

ANALYSIS,

PERSONALIZED

KNOWLEDGE, Ad –

HOC & SUMMARIZED

ACCESS.

FOCUS & DIRECT

MARTS, INTEGRATED

SUPPORT FOR

COCURRENT

ENGINEERING,

ABSTRACTION.

INTERNAL & EXTERNAL COMMUNITY,

DAILY BRIEFS,

WEBNAIRS,

INTERACTIVE TOOLS.

Figure 3.2: Architecture 1- The Knowledge Extensive Architectural Model



52

Knowledge flow is channelized in the architecture for both

internal and external knowledge flow in the organization. Firm provides

personalized knowledge to employees. Experts’ insights and the summarized

analysis of information are available on a real time basis. The core processes

are facilitated through the access of knowledge both in the context they

require and also in an integrated manner with a view to provide consistent

information to work processes. Moreover, CSKB maintains consistent

knowledge of each customer, which enables personalized service. Knowledge

teams are set up to work as interface for potential bad customer experience.

Market knowledge is captured and integrated into the product data, which

can help in new product development. The learnings from the market test are

fed back to the tacit assets of the organization, which is useful in developing

multiple skills at all levels and improving intelligence of the organization.

These learnings are fed to the explicit assets for appropriate storage and

reuse. Off – site and on – site workers feed knowledge into the repositories

during their working. On completion of job they are encouraged to ‘rectify and

furnish’ the contents related to the specialty of job and process. Many

internal commentaries such as chairman’s speech, classroom sessions etc. are

continuously retained and disseminated. External commentary is in the form

of benchmarking data, and consultants’ involvement etc. Knowledge is shared

with the collaborators through extranets. Knowledge outflows for ‘non –

customers’ are mediated through whit papers, web portals and case studies.

3.3.2 ALTERNATIVE 2: KNOWLEDGE INTENSIVE ARCHITECTURAL MODEL

This architecture has inducted knowledge management

intensively within the organization, focuses market capture but

organizational interactions with collaborators are not perceived as a potential



area for knowledge management endeavor. The knowledge intensive

architecture for a firm is shown in fig 3.3.

Like architecture 1, this architecture also terminates at meeting

the customer requirements and organizational needs of knowledge. It

contains the technological and non – technological infrastructure to support

knowledge management implementation within the organization. The firm

invests into developing very agile processes to support diverse market

strategies and a strong market interface rather than investing in costly, IT

enabled, knowledge infrastructure with its collaborators. Some relevant

strategies are on – demand of the customer; self – initiative in bringing a

wider range of products to market, ‘future - forecasting’ of the market

conditions and ‘event – based’ occasions to create an opportunity for group

meeting interaction and knowledge sharing with the marketing channels [108, 109].

It has established repositories to support these strategies on the

basis of lifecycle of the information they store. The IT and web uses are not so

extensive due to which the architecture is unable to sustain sharing of

knowledge within the supply chain partners. Such architectures may

occasionally lose few opportunities by not exploiting the knowledge

architectural aspect of suppliers’ technical know – how and their ability to fill

gaps in areas of mutual concern. However, knowledge architectures that are

strong in identifying opportunities within the organization are capable in

determining and solving the know – how scarcity in the organization [110].



Knowledge Capture

Regular capture of tacit assets,

Capture of special events,

Capture from business environment.

Knowledge Storage

Integration through Data

Warehousing

Stable Storage

Dynamic Storage

Application Storage

Future Fore casting

Self – initiative

On – demand

Knowledge for Promotional

Purposes

Event – based Knowledge

Dissemination

C U S T O M E R

Information & Learning

Knowledge Refinement

Filtration Aggregation Sorting

De – redundancy Packaging

Dynamic Reprocessing

Periodic Review, Trend Analysis,

Value Added Knowledge Creation, Data Mining.

Figure 3.3: Alternative 2: The Knowledge Intensive Architectural Model



55

3.3.3 ALTERNATIVE 3: EXISTING SYSTEM ARCHITECTURAL MODEL

This architecture corresponds to the existing system in the

organization. Typically, it lacks knowledge management as a separate

system. The architecture for the existing system of a firm is shown in fig 3.4.

Knowledge traverses in the organization but is generally

unexplored and uncaptured causing unnoticed loss and decay of knowledge. It

lacks knowledge – oriented architecture and infrastructure needed for

managerial and technological needs of the firm. Knowledge sharing is

requirement – triggered event and the transfer of knowledge occurs through

beauracratic procedures. A hierarchical management structure exists causing

decay of ideas and leading to lesser initiatives for innovation. Use of IT is not

knowledge oriented. Knowledge losses are sometimes uncontrolled.

The distinction of knowledge as tacit and explicit is absent due

to established knowledge management practices. There is only accidental

reuse of knowledge because knowledge sources, tacit (experts) and explicit

(product or customer information), are not easily available and hence avoided

by the employees. Individuals and group are seen as the basic knowledge

sources only in departments of research i.e., innovations are restricted to the

R&D department and hence no ‘innovation – culture’ exists. The inter –

departmental innovations and sharing are scanty. Discussions and meetings

form the major source of interactions for the integration of individual ideas.

Human Resource Department (HRD) focuses on improving the performances

and efficiency of individuals rather than creating a knowledge culture.



S U P P L I E R S

C O M P E T I T O R S S T A K E H O L D E R S

HRD, R&D, SOCIETY, GOVERNMENT & TOP

MANAGEMENTCULTURE

C U S T O M E R S

Organizational Processes

Market Engagement Teams,

Equipments,

Flexibility and Integrity of

Processes,

Concurrent Engineering

Product Data Management

Processes

Collate & organize,

Integration of market

information into

product structure,

Integration of

databases in-and-

across Departments,

Easy retrieval of

product data, Security,

privilege of access,

and audit trials.

Tools

Data index,

Product structure

management,

File management,

Data translation and transfer,

Change and workflow control

and messages,

Application interfaces,

Reports and technology

Results

Data integrity,

Consistent and complete

information,

Reutilization of data,

Smooth information flow,

Non – redundancy of data,

Single source of data,

Integrated environment

support for concurrent

engineering.

INFORMATION & LEARNING

Figure 3.4: Alternative 3 – The Existing System Architectural Model


Indian Institute of Information Technology – Allahabad

57

Sharing knowledge with the supply chain partners is in form of

the assistance in technology and information emanating from customers.

Suppliers use their experience of supplying to several manufacturers under

varied market conditions. Knowledge pertaining to business environment is

collated via in – person reporting or through face – to – face interactions that

are generally time consuming and struggle in preserving knowledge for

reuse. The firms also hire consultancy firms and share their operational

knowledge.

The person seeking experts’ assistance has to put some efforts

on his part to get himself guided to the appropriate maturity that is needed

for getting communicable with experts. He may also reach a wrong person or

may be guided ineffectively resulting into a bad experience. So, next time he

would avoid this effort, leading to loss of ideas. Major reason for this is due to

less initiative taken on part of managers to put the ‘right person on the right

spot’ or lack of checking the relevancy and completeness of the knowledge

transferred. These would culminate into poor knowledge sharing culture and

inefficient knowledge management endeavors.

The firm loses huge knowledge in form of defections of

experienced employees for joining the competitor or the retirement of a top

manager. In the present system (architecture 3) there is no concept of storing

the knowledge of these experts for preservation. The historical knowledge in

papers or in computers is not filtered or analyzed exhaustively.

Flexible ‘product structure management’ and processes exist allowing

the firm to adapt to the changing market conditions. Data is collated in ‘separate

and independent’ product databases according to the product and process structures.

Production process integration is enabled by applications that facilitate interactions



58

among product and process segments and data translation and transfer tools for

supporting concurrent engineering by facilitating an integrated and transparent

view [107]. The homogeneous departmental databases are integrated with the

heterogeneous interdepartmental databases. However, less integration among

databases across various functional segments of the firm such as production,

marketing, sales act as a bottleneck in developing a single central source of

knowledge.

The firm has plans to inform the reengineered changes to the

employees. No structured channels exist for the flow of knowledge in the

organization. There is no concept of providing knowledge access on personal basis.

Security system is established by a password – based system for access to the

information. Audit trials are set up to avoid loss due to breakdown of databases.

Knowledge is exchanged with other organizations by face – to – face interactions

among the experts or by sharing strategies and technical know – how. As part of its’

marketing interface, sales and marketing teams are set up but some customer loss

does occur due to inconsistent employee behavior towards the customer. Major

reason behind this is either less interactions among multiple knowledge teams

handling an employee or non – availability of a CSKB providing consistent

information about the customer. Customer loss is also due to the sluggish policies

like identification of product design improvement captured only through complaints

and service teams, which is seldom through customer demands.

Sharing knowledge in the enterprise is restricted to expert visits and

video conferencing. Language barriers exist among the globally distributed branches

due to cultural differences. Application of strategies shared is not very successful

due to cost factors hindering the communication.



59

3.4 COMPARISON OF THE ALTERNATIVES

The comparison of the three architectural models is based on the

knowledge processes. KM process consists of three basic activities of creation,

organization and dissemination for application. The knowledge life cycle,

developed by APQC [111] describes the phases of organizational knowledge

from its creation to its application. At the core of Knowledge Management lie

the four processes: generating, organizing, developing and distribution [105].

Therefore, the three models are compared (Table 3.1) according to the type of

practices they establish corresponding to the KM activities at the identified

levels of individual or group, organization, market and enterprise level. The

categorical comparison is done according to [10]:

(i) creation of new knowledge at individual or group and organizational

level,

(ii) organization of knowledge at the individual or group and

organization level,

(iii) dissemination and application of knowledge at the individual or

group, organization and market level, and

(iv) creation, organization and distribution at enterprise level.



60

Table 3.1

Comparison of the three architectural models

S.No ISSUES KE KI ES

CREATION: Individual / Group & Organization Level

1 Group Interaction

Individual and group focus at all levels, Communities of practice is established

frequently to generate problem's solution. Through discussions

2 Depth of Innovations

Enhanced knowledge source access facilitation for enhanced innovations by all individuals at organization and enterprise

level. An 'Innovation Culture' exists.

Innovations localized to R & D, cross departmental

innovations are scanty.

3 Role of HRD HRD with focus on establishing the

knowledge - culture of sharing and reuse of knowledge.

HRD with focus on performance of

individual.

4 Supply Chain Aspect

Virtual teams, knowledge sharing,

supply chain network, virtual

organization, and interface plug filling

in modular architecture.

Knowledge management

unexplored along the supply chain.

Supply chain assistance in

technology and expertise.

5 Environmental Awareness

Knowledge oriented IT tools for scanning competitor specific knowledge.

Face to face interactions and in person reporting for

environmental scanning.

6 Interactions

with Peers and Collaborators

Consultancy acquisitions, and mergers not just aim of wider tacit

knowledge and greater customer breadth but also

able to share explicit knowledge

content.

Explicit knowledge not shared with the

collaborators.

Mergers, consultancy

acquisitions for better performance.



61

ORGANIZATION: Individual / Group & Organization Level

1 Expertise reach ability

Right person at right spot, Expert pointers for quick and effective tacit sharing.

Expertise source vagueness and

ambiguity.

2 Knowledge Capture

Capture and storage of knowledge assets fro preservation, easy access and availability of

explicit knowledge for reuse.

No defined concept of Knowledge assets

capture, hence its preservation and

reuse accidental and scanty.

3

Pointers to be captured

and organized

information

Pointers to enterprise wide data / information / knowledge and any

modifications available right at the desktop with the abstraction of a single source of

access.

Data index as pointers to data,

Data translation and transfer allowing transparency and Change control to identify range of

reengineering affected fields and

Messaging to broadcast resulting

changes in architectures.

4 Managerial Assessment

of Knowledge

Non - hierarchical managerial structure for no idea decay or knowledge loss, knowledge managers and technical filters for quality

and value assessment, taxonomy and channel assignment by the knowledge

managers.

No filter to access the value of

knowledge or to filter out the

relevant contents.

5 Historical Knowledge

Filtration, storage and analysis of historical

knowledge for reuse, Contextual access.

Issue history for historical data and

information.



62

6 Operational

Integration & Support

Knowledge architecture identification

allowing independent component

improvement, Integration of decentralized

databases ( for modularity and

concurrent engineering

support) to form a central single

knowledge base (CSKB) for enabling consistent and non - redundant behavior

among processes and work teams.

Knowledge architecture

determining the know - how scarcity in the organization but not exploring

the suppliers technical know -

how storage repositories established

according to the diverse customer

oriented strategies and hence lifetime of the information

Product structure management and

processes management exist

for flexibility but no concept of modular

knowledge architecture, Integrated

homogeneous and heterogeneous

databases across departments but no

central single source of access exists ensuring consistency of

processes and work teams.

7 Information Analysis & Processing

Data warehousing, Data mining tools and Expert systems.

Product structure management, MRP

based product planning.

8 Environmental Concern

Induction of projected concern

for environment in the interfaces.

Processes inflexible to the changes

required for environmental

concern.

Modification required in

processes to build in the concern for

environment, driven by legislation and external checks.

DISSEMINATION & APPLICATION: Individual / Group, Organization & Market Level

1 Channels for Knowledge flow in the

organization

Channels for knowledge

dissemination to enable context based leverage,

personalized knowledge.

Isolated intranets for knowledge dissemination.

No structured channel of

knowledge inflows or outflows.



63

2 Security Firewalls for selective inflow of

information, privilege of access to allow sharing and distribution of knowledge.

Security by password based

level access, audit trials and file management.

3 Organizational

knowledge exchange

Extranets set up for the collaborators

and peers knowledge sharing, knowledge outflow through company website and white

papers.

No extranets, just the intranets.

Face to face or report form

knowledge sharing with collaborators

and peers.

4 Customer

facing interface

Knowledge team as interface to avoid customer loss, and capturing greater

market share.

Strategically diverse interface for

greater customer capture.

Sales and marketing teams,

customer loss due to inconsistent

behavior on part of customer facing

teams.

5 Market awareness

Quick market knowledge capture and integration, expert tips available on a continual mode and integrated into

business processes.

Identification and integration into

system for product design modifications

captured through complaints and

service teams and sometimes through customer demands.

6 Intelligence loop

Complete diffusion and integration of learning into the organization for skill

development at all levels and knowledge preservation, knowledge input along the work and rectification and furnishing in

completion.

Learning localized to the customer

facing teams and on - site workers,

knowledge input along the job.

ISSUES AT ENTERPRISE LEVEL

1

Creation of new strategies

by sharing diverse market

knowledge

"Virtual teams" to share the strategies successful in their markets.

Sharing according to requirement, and restricted to expert

visits and video conferencing.



64

2

Centrally organizing

the enterprise knowledge

Knowledge hub and centers for centralized

innovations, and integrating and

multiplexing knowledge of enterprise's branches.

Sharing only on requirement basis.

3 Knowledge dissemination

Knowledge de -multiplexers to bring down the

language barriers, view of a common

culture establishment

across the enterprise.

IT to bring down the language barriers

and facilitating access to global

enterprise knowledge on

desktop.

Language barriers due to cultural

differences among the globally distributed branches.

4 Application of

Shared strategies

IT advances allowing the enhanced communication for effective strategy

application.

Less successful due to cost factors hindering the

communication.



65

CHAPTER 4 CHAPTER 4

DECISION ENVIRONMENT DECISION ENVIRONMENT

4.1 Analytic Network Process (ANP)4.1 Analytic Network Process (ANP)

The Analytic Hierarchy Process (AHP) was developed and

documented primarily by Thomas Saaty. The strength of the AHP method

lies in its ability to structure a complex, multi – person, multi – attribute,

and multi – period problem hierarchically. Pair wise comparisons of the

elements (usually, alternatives and attributes) can be established using scale

indicating the strength with which one element dominates another with

respect to a higher element. This scaling process can be translated into

priority weights (scores) for comparisons of alternatives. Analytic Network

Process (ANP) [112] is a comprehensive decision – making technique that

captures the outcome of the dependence and feedback within and between the

clusters of elements. Analytical Hierarchy Process (AHP) serves as a starting

point for ANP. Analytical Network Process (ANP) is a more general form of

AHP, incorporating feedback and interdependent relationships among

decision attributes and alternatives.

ANP is a coupling of two parts, where the first consists of a control

hierarchy or network of criteria and sub – criteria that controls the

interactions, while the second part is a network of influences among the

elements and clusters [12].



66

ANP based approach is used for selecting architectural model for the

implementation of Knowledge Management in Indian Industries. The reasons

for selecting ANP are: (i) selecting architectural model for the

implementation of KM is a multi – criteria decision – making problem,

(ii) many factors, enablers and criteria in decision environment are

interdependent on one another and (iii) most importantly some of the criteria,

enablers and dimensions are subjective due to which synthetic score through

simple weightage method is difficult to arrive at. Analytical Hierarchical

Process (AHP) is similar to ANP but it cannot capture interdependencies [12,

113].

Hierarchical representation is an important component of ANP,

however strict hierarchical structure is not recommended, as is the case with

AHP. The ANP technique allows for more complex relationships among the

decision levels and attributes. The ANP consists of coupling of two phases.

The first phase consists of a control hierarchy of network of criteria and sub-

criteria that control the interactions. The second phase is a network of

influences among the elements and clusters. The network varies from criteria

to criteria and thus different super matrices of limiting influence are

computed for each control criteria. Finally each one of these super matrices is

weighted by its priority of its control criteria and results are synthesized

through addition for the entire control criterion. [113].

Some of the fundamental ideas in support of ANP are [84]:

ANP is built on the widely used AHP technique.

ANP allows for interdependency, therefore ANP goes beyond AHP.

The ANP technique deals with dependence within a set of elements

(inner dependence) and among different sets of elements (outer

dependence).



67

The network structure of the ANP makes possible the representation

of any decision problem without concern for what criteria comes first

and what comes next as in a hierarchy.

The ANP is a non – linear structure that deals with sources, cycles and

sinks having a hierarchy of linear form with the goals in the top level

and the alternatives in the bottom level.

ANP portrays a real world representation of the problem under

consideration by prioritizing not only just the elements but also the

groups or clusters of elements as is often necessary.

The ANP utilizes the idea of a control hierarchy or a control network in

dealing with different criteria, eventually leading to the analysis of

benefits, opportunities, costs and risks.

4.2 Advantages of ANP

ANP is a comprehensive technique that allows for the inclusion of all

the relevant criteria; tangible as well as intangible, which have some

bearing on decision – making process [112].

AHP models a decision – making framework that assumes uni –

directional hierarchical relationship among decision levels, whereas

ANP allows for more complex relationship among the decision levels

and attributes as it does not require a strict hierarchical structure.

In decision – making problems, it is very important to consider the

interdependent relationship among criteria because of the

characteristics of interdependence that exists in real life problems.

The ANP methodology allows for the considerations of

interdependencies among and between levels of criteria and thus is an



68

attractive multi – criteria decision – making tool. This feature makes it

superior from AHP which fails to capture interdependencies among

different enablers, criteria and sub – criteria [113].

ANP methodology is beneficial in considering both qualitative as well

as quantitative characteristics which need to be considered, as well as

taking non – linear interdependent relationship among the attributes

into consideration [77].

ANP is unique in the sense that it provides synthetic scores, which is

an indicator of the relative ranking of different alternatives available

to the decision maker.

4.3 Disadvantages of ANP

Identifying the relative attributes of the problem and determining

their relative importance in decision – making process requires

extensive discussion and brainstorming sessions. Also, data acquisition

is a very time intensive process for ANP methodology.

ANP requires more calculations and formation of additional pair – wise

comparison matrices as compared to the AHP process. Thus, a careful

track of matrices and pair – wise comparisons of attributes is

necessary.

The pair – wise comparison of attributes under consideration can only

be subjectively performed, and hence their accuracy of the results

depends on the user’s expertise knowledge in the area concerned.



69

4.3 Outline of the steps of the ANP useful for calculation:

Determine the control hierarchies including their criteria for

comparing the components of the system and their sub criteria for

comparing the elements of the system.

For each control criterion or sub – criterion, determine the clusters of

the system with their elements.

Arrange the clusters and their elements in a convenient way (perhaps

in a column). Use the identical label to represent the same cluster and

the same elements for all the control criteria.

Perform paired comparisons on the clusters as they influence each

cluster and on those that it influences, with respect to that criterion.

Perform paired comparisons on the elements within the clusters

themselves according to their influence on each element in another

cluster they are connected to (or elements in their own cluster). The

comparisons are made with respect to a criterion or sub – criterion of

the control hierarchy.

Sum the values in each column of the pair – wise comparison matrix.

Divide each element in a column by the sum of its respective column.

The resultant matrix is referred to as the normalized pair wise

comparison matrix.

Sum the elements in each row of the normalized pair wise comparison

matrix, and divide the sum by the n elements in the row. These final

numbers provide an estimate of the relative priorities for the elements

being compared with respect to its upper level criterion.



70

Find the consistency ratio of each pair wise matrix. It should not be

above 0.05 for n = 3 and 0.08 for n = 4.

For each control criterion, construct the super matrix (M) by laying out

the clusters in the order they are numbered and all the elements in

each cluster both vertically in the left and horizontally at the top.

Assign value zero for no influence.

Compute the limiting priorities of each super matrix according to

whether it is irreducible (primitive or imprimitive [cyclic] or it is

reducible with one being a simple or a multiple root and whether the

system is cyclic or not.

Raise MW to stabilize the values in the super matrix.

Synthesize the limiting priorities by weighting each limiting super

matrix by the weight of its control criterion and adding the resulting

super matrices.

Repeat the synthesis for each of the four control hierarchies: one for

benefits, one for costs, one for opportunities and a fourth for risks.

Synthesize the results from the four control hierarchies by multiplying

the benefits by the opportunities and dividing by the costs multiplied

by the risks. Then, read off the highest priority alternative or the

desired mix of alternatives.



71

CHAPTER – 5

APPLICATION OF ANP FRAMEWORK MODEL

ANP methodology is applied in this thesis for the determination

of the architectural model for the implementation of KM in industries. The

analysis and implementation of the ANP model with the determinant

Responsiveness is illustrated as follows:

5.1 STEP 1: MODEL DEVELOPMENT & PROBLEM FORMULATION

In this step, the decision problem is structured into its

important components. The relevant criteria and alternatives are chosen on

the basis of the review of literature and discussions with the experts from the

academia and industry. The development of the model involves formulating

the problem and carefully selecting the attributes at different levels and

structuring them in the form of a control hierarchy where the criteria at the

top level in the model have the highest strategic value. The top – level

criteria in this model are Cost, Competitiveness (COMP) and Responsiveness

(RESP). These three criteria are termed as the determinants. In the second

level of hierarchy, three sub – criteria termed as dimensions of the model is

placed which supports all the three determinants at the top level of

hierarchy. These are Organizational Structure (OS), Process Integration (PI)

and Innovation (INN). In this ANP model, each of the four dimensions has

some enablers, which help to achieve that particular dimension. For example,



72

the dimension OS is supported by the enablers Culture (CU), Top

Management Commitment (TMC), Strategic Planning (SP) and Information

Technology & Web (ITW). These enablers also have some interdependency on

one another. The degree of interdependency may vary from case to case and

would be captured in later steps. The strength of the ANP model is the

feedback and the network structure of the ANP makes the representation of

the decision problem possible without much concern for what comes first and

what comes next in a hierarchy. The objective of this hierarchy is to select the

best possible alternative that will best meet the goal of implementing

knowledge management in industry. The ANP model so developed is

presented in Fig. 3.1. The alternatives that the decision maker wishes to

evaluate are shown at the bottom of the model. The opinion of the knowledge

worker of the company was sought in the comparisons of the relative

importance of the criteria and the formation of pair – wise comparison

matrices to be used in the ANP model. The results of all the three

determinants would be used in the calculation of Architectural Weighted

Index (AWI), which indicates the score assigned to an architectural model for

the implementation of KM in the industries. One of the determinants can

have more important criteria for a particular alternative, but here the

analysis was made by the combined effect of all the determinants.

5.2 STEP 2: DECISION MAKING

ANP requires decision making for the pair wise comparisons in

between different sublevel components and interdependency relationships

among attributes and dependency relationships across two different sublevel

hierarchies. Decision making is performed by critical review of the literature

and also by considering expert opinion from the academia and industry. On a

scale of one to nine (Saaty scale) [112], for a series of pair – wise comparisons



73

with respect to upper level criteria, decisions were given and in case of

interdependencies, components within the same level are viewed as control

components for each other.

5.2.1 PAIR – WISE COMPARISON OF DETERMINANTS

In this step, a pair wise comparison is made between the

determinants for obtaining the relative weights in between them. The

weighted priority (e–vector) is calculated after obtaining the relative weights

in between the determinants.

For obtaining the relative weights in between the determinants,

a question should be asked to make a decision. The question is like “what is the relative impact on selection of architectural model, when cost is compared to competitiveness?” The answer on a scale of 1 – 9 was 2 and this is placed

as a second entry of cost row. Similarly, for the remaining, the comparisons

are made and the weighted priority (e–vector) is calculated, see Table 5.1.

Table 5.1

Pair wise Comparison Matrix for relative importance of Determinants

COST COMP RESP e–Vector

COST 1 2 4 0.5714

COMP 0.5 1 2 0.2857

RESP 0.25 0.5 1 0.1429

Total 1.75 3.5 7 1

Consistency Ratio: 0 (See Appendix I).



74

The e–vectors (also referred to as local priority vector) are the

weighted priorities of the determinants and shown in the last column of the

matrix. A two–stage algorithm is used for computing e–vector. For the

computation of the e–vector, we first add the values in each column of the

matrix. Then, dividing each entry in each column by the total of that column,

the normalized matrix is obtained which permits the meaningful comparison

among elements. Finally, averaging over the rows is performed to obtain the

e–vectors. These e–vectors would be used in Table 6.1 for the calculation of

architectural weighted index of alternatives.

5.2.2 PAIR – WISE COMPARISON OF DIMENSIONS

In this step, a pair wise comparison matrix is prepared for

determining the relative importance of each of these dimensions in the

implementation of the architectural model clusters on the determinant. One

such matrix for the determinant responsiveness is shown in Table 5.2. There

will be two more matrices, one for each of the determinants cost and

competitiveness. One can also put up a question here like “how important is the organizational structure in comparison to process integration for selection of architectural model when considering control hierarchy for responsiveness?” From this table, the results of the comparison (e-vectors) of

the dimensions for the determinant responsiveness are carried as Pja in Table

5.8.



75

Table 5.2

Pair wise Comparison Matrix for relative importance of Dimensions on Determinant / Responsiveness:

OS PI INN e – vector

OS 1 4 6 0.6584

PI 0.2500 1 5 0.2618

INN 0.1667 0.2000 1 0.0798

Total 1.4167 5.2 12 1

5.2.3 PAIR – WISE COMAPRISON MATRICES BETWEEN COMPONENT / ATTRIBUTE LEVEL

In this step, for a determinant and within a given dimension cluster

pair – wise comparison is done between the applicable attributes. The pair –

wise comparison matrix for the dimension Organizational Structure under

the determinant Responsiveness is shown in Table 5.3. One can also put up a

question here like “what is the relative importance of CU in comparison to TMC in achieving responsiveness?” Under each determinant for all of the

dimension clusters, the dependency relationships between attribute enablers

are made.



76

Table 5.3

Pair wise Comparison Matrix for attribute enablers under determinant / Responsiveness and the dimension / Organizational Structure:

CU TMC SP ITW e – vector

CU 1 0.2500 4 0.3333 0.2121

TMC 4 1 0.5000 0.3333 0.2163

SP 0.2500 2 1 0.5000 0.1786

ITW 3 3 2 1 0.3930

Total 8.25 6.25 7.5 2.1666 1

In Table 5.3, the relative importance of CU when compared to

SP with respect to OS, in achieving the Responsiveness, is 4. From Table 5.3,

it is also observed that the enabler ITW has the maximum influence (0.3930)

on OS in improving the Responsiveness. Similarly, SP has the minimum

influence (0.1786) on OS in improving the Responsiveness. The number of

such pair – wise comparison matrices depends on the number of

determinants and the dimensions in the ANP model. In this model, 9 such

pair – wise comparison matrices are formed. The e – vectors obtained from

these matrices are imported as Adkja in Table 5.8.

5.2.4 PAIR – WISE COMAPRISON MATRICES FOR INTERDEPENDENCIES

In this step, pair wise comparisons are done to consider the

interdependency relationship in between the attribute enablers for a



77

particular dimension cluster. One such comparison under determinant

responsiveness is illustrated in Table 5.4.

It represents the result of OS / Responsiveness cluster with CU

as the control attribute over other enablers. The question asked to the

decision maker for evaluating the interdependencies is ‘when considering CU

with regards to increasing Responsiveness, “what is the relative impact of

enabler a when compared to enabler b?”. For example, ‘when considering CU,

with regards to increasing Responsiveness, what is the relative impact of

TMC when compared to SP?’

Table 5.4

Pair-wise comparison matrix for attribute enablers under determinant – Responsiveness and the Dimension, OS / Responsiveness / CU:


TMC 1 0.1667 4 0.2200

SP 6 1 5 0.6864

ITW 0.2500 0.2000 1 0.0936

Total 7.25 1.3667 10 1

From Table 5.4, it is observed that SP (0.6864) has the

maximum impact on OS/Responsiveness cluster with CU as the control

enabler over others. For each determinant, there will be 12 such matrices at

this level of relationship. The e – vectors from these matrices are used in the

formation of super matrices. As there are three determinants, 36 such

matrices will be formed. The e – vectors from matrix in Table 5.4 have been

used in 2nd row of the super matrix in Table 5.5.



78

5.3 STEP 3: SUPERMATRIX FORMATION AND ANALYSIS

The super matrix allows for a resolution of the

interdependencies that exist among the elements of a system. It is a

partitioned matrix where each sub–matrix is composed of a set of

relationships between and within the levels as represented by the decision

maker’s model. In this model, there are three super matrices for each of the

three determinants of knowledge management Analytical network, which

need to be evaluated. One such super matrix M, shown in Table 5.5, presents

the results of the relative importance measures for each of the enablers for

the determinant Responsiveness. The values of the elements of the super

matrix M have been imported from the pair–wise comparison matrices of

interdependencies (for example, Table 5.4). As there are 12 such pair-wise

comparison matrices, one for each of the interdependent enablers in the

competitiveness, there will be 12 non–zero columns in this super matrix.

Each of the non–zero values in the column is the relative importance weight

associated with the interdependent pair–wise comparison matrices. In the

next stage, the super matrix M is made to converge to obtain a long–term

stable set of weights. For convergence to occur, super matrix needs to be

‘column stochastic’, i.e. the sum total of each of the columns of the super

matrix needs to be one. Raising the super matrix M to the power Mk, where k

is an arbitrarily large number, allows for the convergence of the

interdependent relationships [77]. In this example, convergence is reached at

M69. The converged super matrix is shown in Table 5.6.



79

Table 5.5 Super matrix for Responsiveness before Convergence RES CU TMC SP ITW PPA KF SC COL KC KW KS TE

CU 0 0.3699 0.1018 0.3137 0 0 0 0 0 0 0 0

TMC 0.2200 0 0.5321 0.3331 0 0 0 0 0 0 0 0

SP 0.6864 0.2979 0 0.3532 0 0 0 0 0 0 0 0

ITW 0.0936 0.3323 0.3661 0 0 0 0 0 0 0 0 0

PPA 0 0 0 0 0 0.1199 0.1038 0.1560 0 0 0 0

KF 0 0 0 0 0.1279 0 0.6651 0.6196 0 0 0 0

SC 0 0 0 0 0.3601 0.2721 0 0.2243 0 0 0 0

COL 0 0 0 0 0.5120 0.6080 0.2311 0 0 0 0 0

KC 0 0 0 0 0 0 0 0 0 0.0952 0.1181 0.1199

KW 0 0 0 0 0 0 0 0 0.2635 0 0.2431 0.6080

KS 0 0 0 0 0 0 0 0 0.1275 0.2543 0 0.2721

TE 0 0 0 0 0 0 0 0 0.6091 0.6505 0.6389 0

Table 5.6 Super matrix for Responsiveness after Convergence (M69)

RES CU TMC SP ITW PPA KF SC COL KC KW KS TE

CU 0.2027 0.2027 0.2027 0.2027 0 0 0 0 0 0 0 0

TMC 0.2779 0.2779 0.2779 0.2779 0 0 0 0 0 0 0 0

SP 0.3001 0.3001 0.3001 0.3001 0 0 0 0 0 0 0 0

ITW 0.2212 0.22123 0.2212 0.2212 0 0 0 0 0 0 0 0

PPA 0 0 0 0 0.1142 0.1142 0.1142 0.1142 0 0 0 0

KF 0 0 0 0 0.3259 0.3259 0.3259 0.3259 0 0 0 0

SC 0 0 0 0 0.2093 0.2093 0.2093 0.2093 0 0 0 0

COL 0 0 0 0 0.3214 0.3214 0.3214 0.3214 0 0 0 0

KC 0 0 0 0 0 0 0 0 0.1001 0.1001 0.1001 0.1001

KW 0 0 0 0 0 0 0 0 0.3124 0.3124 0.3124 0.3124

KS 0 0 0 0 0 0 0 0 0.1986 0.1986 0.1986 0.1986

TE 0 0 0 0 0 0 0 0 0.3910 0.3910 0.3910 0.3910



80

5.4 STEP 4: EVALUATION OF ALTERNATIVES

The final set of pair–wise comparisons is made for the relative

impact of each of the alternatives Knowledge Extensive, Knowledge Intensive

and Existing System on the enablers in influencing the determinants. The

number of such pair–wise comparison matrices is dependent on the number

of enablers that are included in each of the determinants. In our present case,

there are 12 enablers for each of the determinants, which lead to 36 such

pair–wise matrices. One such pair–wise comparison matrix is shown in Table

5.7, where the impacts of three alternatives are evaluated on the enabler CU

in influencing the determinant Responsiveness. The e–vectors from this

matrix are used in columns 6 – 8 of compatibility desirability indices matrix

in Table 5.8. The columns 6 – 8 in Table 5.8 correspond to Knowledge

Extensive, Knowledge Intensive and Existing System architectural models

respectively.

Table 5.7

Pair wise comparison matrix for the relative importance of alternatives on enablers for RESP / OS / CU

KE KI ES e – vector

KE 1 3 4 0.5940

KI 0.3333 1 4 0.2967

ES 0.2500 0.2500 1 0.1093

Total 1.5833 4.25 9 1



81

5.5 STEP 5: DESIRABILITY INDEX

The equation of desirability index, Dia for alternative i and

determinant a is defined as [113]:

(1) D I

ikjakja kjaja1 1

SA APjaj K

iai k

D= =

≡∑∑

Where

Pja is the relative importance weight of dimension j on the determinant of a,

ADkja is the relative importance weight attribute enabler k of dimension j in

the determinant of (D) relationships between component levels,

AIkja is the stabilized relative importance weight (determined by the super

matrix) of attribute enabler k of dimension j in the determinant of network

for interdependency (I) relationships within the knowledge management

attribute enablers’ component level,

Sikja is the relative impact of knowledge management implementation

alternative i on knowledge management enabler k of dimension j of

knowledge management control network a,

Kja is the index set of attribute enablers for dimension j in for determinant

control a, and J is the index set for the dimensions (same for all control

hierarchies).



82

Table 5.8 shows the desirability indices calculated for the

determinant (Di responsiveness). It is based on using the relative weights obtained

from the pair – wise comparison of alternatives, dimensions and weights of

enablers from the converged super matrix. These weights are used to

calculate a score for the determinants of architectural weighted index (AWI)

for each of the alternatives. In Table 5.8, the values of third column are

copied from Table 5.2 which is obtained by comparing the relative impact of

the dimensions on the determinant Responsiveness. For example, in

improving the responsiveness, the role of organizational structure is found to

be the most important (0.6584), which is followed by process integration

(0.2618) and innovation (0.0798). The values of the fourth column are copied

from Table 5.3 which is obtained by comparing the relative impact of the

enablers on the dimension Organizational Structure for the determinant

Responsiveness and values are also obtained from two more tables made for

the other two dimensions Process Integration and Innovation. The values in

the fifth column of Table 5.8 are the stable independent weights of the

enablers obtained from the converged super matrix (Table 5.6).

The next three columns are from the pair – wise comparison

matrices giving the relative impact of each of the alternatives on the

enablers. The final three columns represent the weighted values of the

alternatives (Pja× ADkja ×AIkja× Sikja) for each of the enablers.



83

Table 5.8

Architectural Weighted Desirability Index for Responsiveness

Dimension Enablers Pja ADkja AIkja S1 S2 S3 KE KI ES

CU 0.6584 0.2121 0.2027 0.5940 0.2967 0.1093 0.0168 0.0084 0.0031

OS TMC 0.6584 0.2163 0.2779 0.5516 0.2768 0.1716 0.0218 0.0110 0.0068

SP 0.6584 0.1786 0.3001 0.6777 0.2418 0.1281 0.0239 0.0085 0.0045

ITW 0.6584 0.3930 0.2212 0.6505 0.2543 0.0952 0.0372 0.0146 0.0054

PPA 0.2618 0.0735 0.1142 0.7093 0.2141 0.0958 0.0016 0.0005 0.0002

PI KF 0.2618 0.4890 0.3529 0.6194 0.2842 0.0964 0.0280 0.0128 0.0044

SC 0.2618 0.3049 0.2093 0.5963 0.3191 0.0846 0.0100 0.0053 0.0014

COL 0.2618 0.1326 0.3214 0.6807 0.2014 0.1179 0.0076 0.0022 0.0013

KC 0.0798 0.0816 0.1001 0.6505 0.2543 0.0952 0.0004 0.0002 0.0001

INN KW 0.0798 0.2344 0.3124 0.7071 0.2014 0.0915 0.0041 0.0012 0.0005

KS 0.0798 0.1744 0.1986 0.6853 0.2213 0.0934 0.0019 0.0006 0.0003

TE 0.0798 0.5097 0.3910 0.6080 0.2721 0.1199 0.0097 0.0043 0.0019

Total 0.1630 0.0696 0.0299

For the purpose of illustration, the value corresponding to the above formula is shown below:

(0.6584 × 0.2121 × 0.2027 × 0.5940 = 0.0168) Knowledge Extensive

(0.6584 × 0.2121 × 0.2027 × 0.2967 = 0.0084) Knowledge Intensive

(0.6584 × 0.2121 × 0.2027 × 0.1093 = 0.0031) Existing System



84

The summations of these results, for Responsiveness of each of

these alternatives, are presented in the final row of Table 5.8. These results

indicate that the Knowledge Extensive architectural model with a value of

0.1630 has maximum influence on the responsiveness. It is followed by

Knowledge Intensive architectural model (0.0696) and Existing System

architectural model (0.0299). Till this step, the analysis has been conducted

only for the determinant Responsiveness. Similar analysis is carried out for

other two determinants. In the next step, an index would be calculated to

capture the achievement of overall goal of selecting an alternative.

5.6 STEP 6: CALCULATION OF ARCHITECTURAL WEIGHTED INDEX (AWI)

The architectural weighted index (AWI) for an alternative i is

the summation of the products of the desirability indices (Dia) and the

relative importance weights of the determinants (Ca) for the selection of the

architectural model for the implementation of KM. The pair – wise

comparison relationships among different determinants from Table 5.1 shows

that the determinant Cost (0.5714) has got maximum importance in the

selection of the architectural model for the implementation of KM followed by

determinant Competitiveness (0.2857) and determinant Responsiveness

(0.1429). So the organization should focus on Cost by having good

infrastructure, good networking, good organizational structure, and also

should enhance their innovation for attracting customer’s interest and being

pro – active in meeting their demands.



85

CHAPTER – 6

RESULTS AND ANALYSIS

6.1 RESULTS

The Knowledge Extensive architectural model intends to

implement Knowledge Management (KM) in a wider prospect. It is comprised

of the systems both within the organization and for its network interactions.

It believes in developing a culture of sharing in the form of “working for the

industry” rather than “working for myself”. It supports non – hierarchical

managerial structure to avoid idea of decay of knowledge. It advocates

development of multiple skills at all levels and improving intelligence of the

organization. Thus, it works for the overall improvement of the individual

and the organization. The Knowledge Intensive architectural model

implements KM intensively within an organization. It focuses on the market

capture and developing very agile processes to support diverse market

strategies. It does not perceive organizational interaction with the

collaborators as a potential area for KM strives. It is capable of determining

and solving the know – how scarcity in the organization. The Existing System

architectural model represents the existing structure of an organization when

KM has not been implemented. In this model, everything is requirement

initiated meaning that whenever requirement arises for a particular thing

then it is done. It has a hierarchical management structure then causing

decay of ideas and leading to lesser initiatives for innovation. In this type of

system, innovation is restricted to the R & D department. It does not have a

concept of storing knowledge of the experts who are retiring for preservation,

so that it can be used in future.



86

The ANP model proposed is an aid to the people working for the

implementation of KM in arriving at prudent decisions when the complexity

of decision variables and multi – criteria decision environment make their

decision task quite complicated. This ANP model is used for the selection of

the architectural model best suited for the implementation of KM in

industries. This could serve as one of the important tools for taking a

strategic decision of this type. The criteria and attributes that are used in

this model focus on KM and the requirements for the selection of the

architectural model. Here, it is essential to discuss the priority values for the

determinants, which influence the decision of selecting the architectural

model for the implementation of KM.

It has been observed (from Table 5.1) that the cost (0.6232) is

the most important criteria in the selection of the architectural model for the

implementation of KM. This is followed by competitiveness (0.2305) and

responsiveness (0.1373). In the selection of the architectural model, it is seen

that the architectural model should take care of the Cost and should also

increase the Competitiveness. It means that the architectural model

supporting cost and competitiveness is considered as the favorable one.

Responsiveness is less supported because if an organization is having

responsiveness then only it can think of achieving competitiveness and also

take care of the cost.

The ANP model is capable of handling interdependencies and

present decision model provides values in the form of weighted index for the

three different architectural models in order to select the model for the

implementation of KM. The final normalized values for Architectural

Weighted Index (AWI) relationship (see Table 6.1) for Knowledge Extensive,

for Knowledge Intensive and for Existing system architectural model.



87

Table 6.1

Architectural Weighted Index (AWI) for various alternative KM architectural models under selection of the architectural model

Alternatives

(Models)

Criteria Calculated weights

for alternatives

Cost Competitiveness ResponsivenessAWI NORM

0.5714 0.2857 0.1429

Knowledge

Extensive 0.1661 0.1606 0.1630 0.1641 0.5758

Knowledge

Intensive 0.0938 0.0692 0.0696 0.0833 0.2924

Existing

System 0.0421 0.0323 0.0299 0.0376 0.1318

Total 0.285 1

For the successful implementation of KM, the ANP framework

suggests that with the existing priority levels of architectural weighted

determinants, normalized value for Knowledge Extensive architectural model

is higher than that of Knowledge Intensive or Existing System architectural

model.



88

6.2 SENSITIVITY ANALYSIS Sensitivity analysis is as important concept for the effective use

of any quantitative decision making model. Sensitivity analysis is carried out

in this present thesis work to analyze the changes in the Architectural

Weighted Index (AWI) for the Knowledge Extensive, Knowledge Intensive

and the Existing System architectural models with the variations in the

expert’s opinion towards Cost with respect to Competitiveness, Cost with

respect to Responsiveness and Competitiveness with respect to

Responsiveness. Overall objective of the sensitivity analysis is to see the

robustness of the proposed framework due to variation in the expert opinion

from academia in assigning the weights during comparison. Table 6.1

indicates how the architectural weighted index (AWI) for the proposed

framework of the three architectural models varies with changing priority of

cost, competitiveness and responsiveness. When the overall objective is to

reduce the cost, desirability indices are higher for Knowledge Extensive

architectural model than the Knowledge Intensive and Existing system

architectural model. In relation to enhancing the competitiveness, knowledge

extensive architectural model is having a higher desirability index than

Knowledge intensive and existing system architectural model. In an effort to

increase responsiveness, again the knowledge extensive architecture is

having a higher desirability index than knowledge intensive and existing

system architectural model. In Fig 6.1, X – axis represents the relative weight of cost

compared to competitiveness. The relative weights are in the scale of 1/9 – 9

(Saaty scale). Y – axis represents the normalized value of architectural

weighted index (AWI). These weights are obtained using the ANP framework,

which captures the interdependence among variables for selection of the

architectural model. In the present ANP framework, the experts have



89

assigned relative weight of 2 when cost is compared to competitiveness on

selection of architectural model. The graph shown in fig 6.1 is drawn using three sets of

normalized values for Knowledge Extensive, Knowledge Intensive and the

Existing System architectural models.

Figure 6.1 Variation in priority of KM Architectural Models with the Changes in the Weights assigned to Cost with respect to Competitiveness.

These values are plotted against the relative weights assigned

when cost is compared to competitiveness. When the graph is considered for



90

Knowledge Extensive architectural model, it shows that when the

competitiveness is increased, the cost remains constant initially but after

sometime it starts decreasing. When considered for Knowledge Intensive

architectural model, it shows that when competitiveness is increased, the cost

remains constant initially but after sometime it starts increasing. Similarly

for Existing System architectural model, when competitiveness is increased,

the cost remains stable. Thus, it can be concluded from the graph that

Knowledge Extensive architectural model is the model which increases the

competitiveness without increasing the cost and sometimes in long run, it

also reduces the cost.

In Fig 6.2, X – axis represents the relative weight of cost

compared to responsiveness. The relative weights are in the scale of 1/9 – 9

(Saaty scale). Y – axis represents the normalized value of architectural

weighted index (AWI). These weights are obtained using the ANP framework,

which captures the interdependence among variables for selection of the

architectural model. In the present ANP framework, the experts have

assigned relative weight of 4 when cost is compared to responsiveness on

selection of architectural model.

The graph shown in fig 6.2 is drawn using three sets of


Existing System architectural models. These values are plotted against the

relative weights assigned when cost is compared to responsiveness.



91

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

Normalized Weighted Index

Variation in the weights assigned when Cost is compared to Responsiveness

Knowledge ExtensiveKnowledge IntensiveExisting System

Figure 6.2 Variation in priority of KM Architectural Models with the Changes in the Weight assigned to Cost with respect to Responsiveness.

When the graph is considered for Knowledge Extensive

architectural model, it shows that when the responsiveness is increased, the

cost remains constant initially but after sometime it starts decreasing. When

considered for Knowledge Intensive architectural model, it shows that when

competitiveness is increased, the cost is increasing gradually. Similarly for

Existing System architectural model, when competitiveness is increased, the

cost remains stable. Thus, it can be concluded from the graph that Knowledge

Extensive architectural model is the model which increases the

responsiveness without increasing the cost and sometimes in long run, it also

reduces the cost.



92

In Fig 6.3, X – axis represents the relative weight of

competitiveness compared to responsiveness. The relative weights are in the

scale of 1/9 – 9 (Saaty scale). Y – axis represents the normalized value of

architectural weighted index (AWI). These weights are obtained using the

ANP framework, which captures the interdependence among variables for

selection of the architectural model. In the present ANP framework, the

experts have assigned relative weight of 2 when competitiveness is compared

to responsiveness on selection of architectural model. The graph shown in fig 6.3 is drawn using three sets of


Existing System architectural models. These values are plotted against the

relative weights assigned when competitiveness is compared to

responsiveness.

When the graph is considered for Knowledge Extensive

architectural model, it shows that the competitiveness and responsiveness

are stable and that they do not affect each other much. Therefore, the graph

is a straight line with not much variation. When considered for Knowledge

Intensive architectural model, it shows the same thing as for Knowledge

Extensive architectural model. Similarly for Existing System architectural

model, it gives the same result as for the other two architectural models.

Thus, it can be concluded from the graph that competitiveness and

responsiveness does not affect each other much but they go hand in hand. It

means that for increasing competitiveness you are required to increase

responsiveness.



93

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

Normalized Weighted Index

Variation in the weights assigned when Competitiveness is compared to Responsiveness

Knowledege ExtensiveKnowledge Intensive

Existing System

Figure 6.3 Variation in priority of KM Architectural Models with the Changes in Weight assigned to Competitiveness with respect to Responsiveness.

The final conclusion that can be drawn from the three graphs

shown above is that competitiveness and responsiveness affect the cost but

competitiveness and responsiveness go together. Therefore, the best

architectural model will be that model which increases competitiveness and

responsiveness without affecting the cost much. Then the best architectural

model for the successful implementation of KM is Knowledge Extensive

architectural model.



94

6.3 FUTURE SCOPE OF WORK

The analysis using ANP is relatively cumbersome as in the

present work nearly 90 pair – wise comparison matrices are required. It

requires long and exhaustive discussion with experts from the case

knowledge management to arrive at the relationship among enablers.

Therefore, the advantages of ANP technique could be derived for making

strategic decisions that are vital for the growth and survival of knowledge

management. The values for pair-wise comparisons depend on the knowledge

of the decision – makers. Therefore group of decision-makers should include

those experts who understand the implications of enablers for the selection of

the architectural model for the successful implementation of KM. The

proposed framework has been developed by considering a general scenario of

industries; it can be made specific by applying to specific industries. The

framework can also be improved by considering more variables for the

selection of the architectural models for the implementation of KM. The three

architectural models proposed can be improved by considering more aspects

of the industries that might have been missed out in the work. Thus, there is

a scope of improvement of the architectural models.



95

CHAPTER – 7

CONCLUSIONS

The effective implementation of KM largely depends on the

decision making framework and a proper architectural model. Hence, in order

to implement KM successfully and, in order to reduce cost and improve the

competitiveness and responsiveness of an organization we have to evaluate

and select a suitable architectural model. The selection of the architectural

model usually involves both subjective and quantitative judgment. It also

requires handling of several complex factors in a better objective and logical

manner. Thus, the selection of KM architectural model is a kind of multiple

criteria decision – making problem, and requires multiple criteria decision –

making methods to solve it appropriately. Unlike traditional methods which

are based on independent assumptions, the ANP is a relatively new multiple

criteria decision – making method which can deal with all kinds of

dependencies systematically.

In the context of industries, ANP method helps to evaluate and

select the architectural model successfully. The results of this analytic study

show that the most required purpose is to reduce the cost of implementation

of KM and therefore, the “Knowledge Extensive” architectural model is

preferred. Because the proposed ANP framework can handle the effects of

dependencies, it is relatively useful and makes the evaluation result to be

more reasonable. Additionally, this study has contributed to extend practical

applications of ANP in KM field. The ANP methodology adopted here arrives

at a synthetic score, which may be quite useful for the decision – makers.

Further, using the suggested analytical procedure, it can effectively handle



96

any problem of selection with multi – faceted criteria. It also analyzes the

relative impact of different enablers on the three KM architectural models

considered for an industry.



97

REFERENCES [1] Sjursen K, (2000), “Globalization”, Text book, Wilson Co.

[2] Daghfous, A. (2003), “How to make Knowledge management a firm’s core capability”, Journal of Knowledge Management Practice, Vol. 4, No. 4, pp. 337 – 345.

[3] Chong, S.C. and Choi, Y.S. (2005), “Critical factors in the successful implementation of knowledge management”, Journal of Knowledge Management Practice, Vol. 6.

[4] Drucker, P.F. (1993), “Post Capitalist Society”, New York, Harper Business, New York.

[5] Mayo, A. (1998), “Memory bankers”, People Management, Vol. 4, No. 2, pp. 34 – 8.

[6] Singh, M.D., Shankar, R., Narain, R. and Kumar, A. (2006), “Survey of Knowledge Management Practices in Indian Manufacturing Industries”, Journal of Knowledge Management, Vol. 10, No. 6, pp. 110 – 128.

[7] Gupta, B. and Iyer, L. (2000), “Knowledge Management Systems: An Imperative for supporting the E – commerce Customer”, Proceeding of the 2000 IRMA International Conference, Ancharge, Alaska.

[8] Singh, M. D. (2003), “Study of selected issues in knowledge management practices in Indian industries”, Ph.D. Thesis, MNNIT Allahabad.

[9] Desouza K. and Evaristo R., (2003), “Global Knowledge Management Strategies”, European Management Journal, Vol 2, No 1, pp 62 – 67.

[10] Shankar R. and Gupta A., (2005), "Towards framework for Knowledge Management Implementation”, Knowledge and Process Management, Vol. 12, No. 4, pp 259 – 277.

[11] McAdam R. and McCreedy S., (1999), “A critical review of knowledge management models", The Learning Organization Vol. 6, No. 3, pp 91 –100.



98

[12] Shankar, R., Ravi, V. and Tiwari, M. K., (2005), “Analyzing Alternatives in Reverse Logistics for end – of – life computers: ANP and Balanced Scorecard Approach”, Computer & Science Engineering, Vol. 48, pp 325 – 356.

[13] Meade, L. M. and Presley, A., (2002), “R & D Project Selection Using the Analytic Network Process”, IEEE Transactions on Engineering Management, Vol. 49, No. 1, pp 59 – 66.

[14] Ren Changri, Chai Yueting and Lin Yi, (2004), “Active Performance Management in Supply Chain”, IEEE International Conference on Systems, Man and Cybernetics.

[15] Partovi, F. Y. (2006), “An analytical model of process choice in the chemical industry”, International Journal of Production Economics, Vol. 103, No. 2. pp. 451 – 488.

[16] Kayakutlu, G., Buyukozkan, G., (2006), “Assessing Performance Factor for Logistics Companies”, International Conference on Service System and Service Management, IEEE Vol. 1, Oct 2006, pp 601 – 606.

[17] Bayazit, O. (2006), “Use of analytic network process in vendor selection decisions”, Benchmarking: An International Journal, Vol. 13, No. 5, pp. 566 – 579.

[18] Raisinghani, M. S. and Meade, L. L, (2005), “Strategic decisions in supply – chain intelligence using Knowledge Management: An Analytic – Network – Process Framework”, Supply Chain Management: An International Journal, Vol. 10, No. 2, pp.114 – 121.

[19] Hale, M. and Kalucy, L. (2002), “Knowledge Management: Maintaining Organizational Memory”, Journal Watch.

[20] Davenport, T. H., DeLong, D. W. and Beers, M C. (1997), “Building Successful Knowledge Management Projects”, Center for Business Innovation Working Paper, May.



99

[21] Davenport, T. and Prusak, L. (1998), “Working Knowledge: How Organizations Manage What They Know”, Harvard Business School Press, Boston, MASS, pp. 1 – 7.

[22] Bhatt, G. D. (2001), “Knowledge Management in Organizations: Examining the Interaction between Technologies, Techniques, and People”, Journal of Knowledge Management, Vol. 5, No.1, pp. 63 – 75.

[23] Rowley, J. and Farrow, J. (2000), “Organizing Knowledge: An Introduction to Managing Access to Information”, Text book.

[24] Martensson, M. (2000), “A critical review of Knowledge Management as a management tool”, Journal of Knowledge Management, Vol. 4, No. 3, 204 – 216.

[25] Azmi, M. A. and Zairi, M. (2005), “Knowledge Management: A Proposed Taxonomy”, Working Paper No 05/31.

[26] Grey, D. (1996), “What Is Knowledge Management?” http://www.km-forum.org/what_is.htm, March 31.

[27] Zack, M. H. (1998), “Developing a Knowledge Strategy”, California Management Review, Vol. 41, No. 3, pp. 125-145.

[28] Beijerse, R. P. (2000), “Knowledge management in small and medium sized companies: knowledge management for entrepreneurs”, Journal of knowledge management, Vol. 4 No.2, pp. 162 – 179.

[29] Nonaka, I. and Takeuchi, H. (1995), “The Knowledge-Creating Company: How Japanese Companies Create the Dynamics of Innovation”, Oxford University Press, April 10.

[30] Wu, W. and Lee, Yu – T. (2006), “Selecting knowledge management strategies by using the analytic network process”, Expert Systems with Applications: An International Journal, Vol. 32, No. 3, pp. 841 – 847.

[31] Dayan, R. and Evans, S. (2006), “KM your way to CMMI”, Journal of Knowledge Management, Vol. 10, No. 1, pp. 69 – 80.

http://www.km-forum.org/what_is.htm




100

[32] Olla, P. and Holm, J. (2006), “The role of knowledge management in the space industry: important or superfluous?”, Journal of Knowledge Management, Vol. 10, No. 2, pp. 3 – 7.

[33] Malhotra, Y. (2004), “Why knowledge management system fails? Enablers and constraints of knowledge management in human enterprises”, American Society for Information Science and Technology Monograph Series.

[34] Snowden, D. (2000) “A Framework for Creating a Sustainable Knowledge Management Program”, In Hermans, J. (ed.). The Knowledge Management Yearbook, 1999–2000.

[35] Poynder, R. (1998), “Getting to the Nuts and Bolts of Knowledge Management Information”, World Review, Vol. 135, No. 1, pp. 20.

[36] Bertels, T. (1996), What Is Knowledge Management? http://www.km-forum.org/what_is.htm.

[37] Finneran, T. (1999), “A Component-Based Knowledge Management System”, Robert S. Seiner, http://tdan.com/i009hy04.htm.

[38] Macintosh, A. (1998), “Position Paper on Knowledge Asset Management”. http://www.aiai.ed.ac.uk/~alm/kam.html.

[39] Starr, J. (1999). “Knowledge Management Terminology”, http://www.kmtool.net/vocabulary.htm.

[40] Liebowitz, J. (1999), “Key Ingredients to the Success of an Organization’s Knowledge Management Strategy”, Knowledge and Process Management, Vol. 6, No.1, pp. 37 – 40.

[41] Morse, R. (2000), “Knowledge Management Systems: Using Technology to Enhance Organizational Learning”, Information Resources Management Ass0ciation (IRMA), International Conference, pp. 426 –429.

[42] Maier, R. (2002), “Knowledge Management Systems: Information and Communication Technologies for Knowledge Management”, Text Book.



http://tdan.com/i009hy04.htm

http://www.aiai.ed.ac.uk/%7Ealm/kam.html

http://www.kmtool.net/vocabulary.htm



101

[43] Wiig, K. M. (1997), “Knowledge Management: An introduction and perspective”, Journal of Knowledge Management, Vol. 4, No. 2.

[44] Alavi, M. and Leinder, D. (1999), “Knowledge Management Systems: Emerging Views and Practices from the Field”, In: Proceedings of the 32nd Hawaii International Conference on System Sciences, IEEE, pp 1-11.

[45] Lee, S. M. and Hong, S. (2002), “An enterprise – wide knowledge management system infrastructure”, Industrial Management and Data Systems, Vol. 102, pp 17 – 25.

[46] Park, H. and Ribiere, V. (2004), “Critical attributes of organization culture that promote knowledge management technology implementation success”, Journal of Knowledge Management, Vol. 8, No. 3, pp 106 – 117.

[47] Linde, C. (2006), “Learning from the Mars Rover Mission: scientific discovery, learning and memory”, Journal of Knowledge Management, Vol. 10, No. 2, pp. 90 – 102.

[48] Oliver, S. and Reddy, K. (2006), “How to develop knowledge culture in organizations? A multiple case study of large distributed organizations”, Journal of Knowledge Management, Vol. 10, No. 4, pp. 6 – 24.

[49] Leyland, M. and Lucas, V. (2006), “Things are not always what they seem”, The Learning Organization, Vol. 13, No. 1, pp. 7 – 24.

[50] Osterloh, M. and Frey, B. S. (2000). “Motivation, knowledge transfer, and organizational firms”, Organization Science, Vol. 11, pp. 538 – 550.

[51] Dong – Gil, K. and Laurie, J. K. (2005), “Antecedents of knowledge transfer from consultants to clients in enterprise system implementations”, MIS Quarterly, Vol. 29, No. 1, pp. 59 – 85.

[52] Martin, V. A. and Hatzakis, T. (2005), “Cultivating knowledge sharing through the relationship management maturity model”, The Learning Organization, Vol. 12, No. 4, pp. 340 – 354.



102

[53] Vincent, M. and Alesa S. (2003), “Critical role of leadership in nurturing a knowledge – supporting culture”, Knowledge Management Research & Practice, Vol. 1, pp 39 – 48.

[54] Mittendorff, K. and Geijsel, F. (2006), “Communities of practice as stimulating forces for collective learning”, Journal of Workplace Learning, Vol. 18, No. 5, pp. 298-312.

[55] Wang, F. K. (2002), “Designing a case – based e – learning system: What, how and why”, Journal of Workplace Learning, Vol. 14, No. 1, pp. 30 – 43.

[56] Hyland, P. (2002), “Learning to compete: the value of internal benchmarking”. Benchmarking, An International Journal, Vol. 9, No 3, pp. 293 – 304.

[57] Lopez, S. P. (2004), “Managing knowledge: the link between culture and organizational learning’, Journal of Knowledge Management, Vol. 8, No. 6, pp. 93 – 104.

[58] Karsten, H. (2002), “ITI as Enabler of Knowledge Management: Empirical Perspective from Research Organizations in sub – Saharan Africa”, Computer Society Proceedings of the 35th Hawaii International Conference on System Sciences.

[59] Chi, L. and Holsapple, W. (2005), “Understanding computer – mediated inter – organizational collaboration: a model and framework”, Journal of Knowledge Management, Vol. 9, No. 1, pp. 53 – 75.

[60] Laycock, M. (2005), “Collaborating to compete: achieving effective knowledge sharing in organizations” The Learning Organization, Vol. 12, No. 6, pp. 523 – 538.

[61] Wong, K. Y., et. al., (2005), “Development of knowledge management initiative and system: A case study”, Expert Systems with Applications, pp 1 – 9.



103

[62] Mikel, S., Dragan, S., Alvaro, G., Ljubisa, U. and Kostas S. (2005), “Knowledge management of manufacturing product / process issues”, ISIPM – 2005.

[63] Sunassee, N. N. and Sewry, D. A. (2002), “A theoretical framework for knowledge management”, Proceedings of SAICSIT, pp 235 – 245.

[64] De Long, D., Davenport, T. and Beers, M., (1997), “What is a knowledge management project?”, Working Paper, Ernst & Young Center for Business Innovation, February, 1997.

[65] Holsapple, C. W. and Joshi, K. D. (1999), "Description and Analysis of Existing Management Frameworks", In: Proceedings of the 32nd Hawaii International Conference on System Sciences – 1999.

[66] Wiig, K. (1993), “Knowledge Management Foundations”, Arlington: Schema Press, 1993.

[67] Leonard – Bartan, D. (1995), “Wellsprings of Knowledge”, Boston: Harvard Business School Press, 1995.

[68] Arthur Andersen and the American Productivity and Quality Center (1996). “The Knowledge Management Assessment Tool: External Benchmarking Version”, Winter, 1996.

[69] Choo, C. (1996), “An Integrated Information Model of the Organization: The Knowing Organization”, http://www.fis.utoronto.ca/people/faculty/ choo/FIS/KO/KO.html1#contents (Dec 7, 1996).

[70] van der Spek, R. and Spijkervet, A. (1997), “Knowledge Management: Dealing Intelligently with Knowledge”, Knowledge Management and Its Integrative Elements, eds (Leibowitz, J. and Wilcox, L.), New York: CRC Press, 1997.

[71] Svieby, K. (1997), “The New Organizational Wealth”, San Francisco: Berrett – Koehler, 1997.

[72] Petrash, G. (1996), “Dow's Journey to a Knowledge Value Management Culture”, European Management Journal, Vol. 14, No. 4, pp 365 – 373.

http://www.fis.utoronto.ca/people/faculty/ choo/FIS/KO/KO.html1#contents

http://www.fis.utoronto.ca/people/faculty/ choo/FIS/KO/KO.html1#contents



104

[73] Alavi, M. (1997), “KPMG Peat Marwick U.S.: One Giant Brain”. Harvard Business School (Case), 9 – 397 – 108, Rev. July 11, 1997.

[74] Nonaka, I. (1994), “Dynamic Theory of Organizational Knowledge Creation”, Organizational Science, Vol. 5, No. 1, pp 14 – 37.

[75] Beckham, T. (1997), “A methodology for Knowledge Management”, Proceedings of the IASTED International Conference on AI and Soft Computing.

[76] Agarwal, A., Shankar, R. and Tiwari, M.K. (2005), “Modeling the metrics of lean, agile, le – agile supply chain: An ANP based approach”, European Journal of Operational Research, article in press, Vol. 173, No. 1, pp 211 – 225.

[77] Meade, L. M. and Sarkis, J. (1999), “Analyzing organizational project alternatives for agile manufacturing processes: an analytical network approach”, International Journal of Production Res, Vol. 37, No. 2, pp. 241 – 261.

[78] Cheng, E. and Li, H. (2005), “Analytic Network Process Applied to Project Selection”, Journal of Construction Engineering and Management, Vol. 131, No. 4, pp. 459 – 466.

[79] Meade, L. and Sarkis, J. (2002), “A Conceptual model for selecting and evaluating third – party reverse logistics providers” , Supply Chain Management : An International Journal , Vol. 7, No. 5, pp. 283 – 295.

[80] Momoh, J. A. and Zhu, J. Z. (1998), “Application of AHP/ANP to unit commitment in the deregulated power industry”, 1998 IEEE International Conference on Systems, Man, and Cybernetics, Vol. 1, pp. 817 – 822.

[81] Sarkis, J. (2002), “Quantitative models for performance measurement systems—alternate considerations”, International Journal of Production Economics, Vol. 86, pp. 81–90.



105

[82] Chung, S. H, Lee, H. I. and Pearn, W. L. (2005), “Analytic network process (ANP) approach for product mix planning in semiconductor fabricator”, International Journal of Production Economics, Vol. 96, pp. 15 – 36.

[83] Bayazita, O. and Karpak, B. (2005), “An ANP process – based framework for successful total quality management (TQM): An assessment of Turkish manufacturing industry readiness”, International Journal of Production Economics, Vol. 4, No. 5.

[84] Saaty, T. L. (1999), “Fundamentals of Analytical Process”, ISAHP 1999, Kobe, Japan, Aug 12 – 14, 1999.

[85] Harker, P. T. and Vargas, L. G. (1990), Reply to ‘‘Remarks on the analytic hierarchy process’’, Management Science, Vol. 36, pp. 269 – 273.

[86] Grant, R. M. (1996), "Towards a knowledge based theory of the firm", Strategic Management Journal, Vol. 17, pp. 6 – 18.

[87] Awazu, Y. (2004), "Informal network players, knowledge integration, and competitive advantage", Journal of Knowledge Management, Vol. 8, No. 1, pp. 62 – 70.

[88] Danskin, P., Solomon, B. G. and Michael, R. (2005), "Knowledge Management as a competitive advantage: lessons from textile and apparel value chain", Journal of Knowledge Management, Vol. 9, No. 2, pp 91 – 102.

[89] Goh, L. and Andrew, S. (2005), "Harnessing knowledge for innovation: an integrated management framework", Journal of Knowledge Management, Vol. 9, No. 4, pp 6 – 18.

[90] Jhonny, C. F. and Bolloju, N. (2005), "Explaining the intentions to share and reuse knowledge in the context of IT service operations", Journal of Knowledge Management, Vol. 9, No. 6, pp 30 – 41.



106

[91] Barley, S. R. (1996), " Technicians in the workplace: ethnographic evidence for bringing work into Organizational studies", Administrative Science Quarterly, Vol. 41, pp 404 – 441.

[92] Matson, J. and McFarlane, D. (1998), "Tools for assessing the responsiveness of existing production operations", Engineering and Physical Sciences Research Council, Vol. 1, No. 6, pp 26 – 29.

[93] Schwarz, D. (2000), "Logistics responsiveness in the food industry", Van den Bergh Foods Ltd, Responsiveness in Manufacturing (Digest No. 1998/213), IEE, pp 16/1 – 16/3.

[94] Singh, M. D., Shankar, R., Narain, R. and Agarwal, A. (2003), "An Interpretive Structural Modeling of Knowledge management in Engineering industries", Journal of Advances in Management Research, Vol. (I), pp 28 – 40.

[95] Gillingham, H and Roberts, B. (2006), "Implementing Knowledge Management: A practical approach", Journal of Knowledge Management, Vol. 7, No. 1.

[96] Sadagopan, S. (2000), "Clicks that rejuvenate the bricks" by CIO, Knowledge Management Cover Story, Vol. 1, No. 10.

[97] Parise, S. and Sasson, L. (2002), "Leveraging knowledge management across strategic alliances", Ivey Business Journal.

[98] Hansen, P. K. and Thyssen, J. (2000), "Continuous Improvement and Modularization in the Product Development Process", Aalborg University, Department of Production / Center for Industrial Production, Fibigerstraede 16, 9220, Aalborg.

[99] Enkel, E., Gibbert, M., Makarevitch, A. and Vasiliadis (2002), "Innovation / Knowledge Creation, Customer Integration and entering new ventures", Perspective for our partners, Vol. 1.



107

[100] Goh, A. (2004), "Enhancing Organizational Performance through Knowledge Innovation: A proposed strategic management framework", Journal of Knowledge Management Practice, Vol. 4.

[101] Sorensen, C. and Snis, U. (2001), "Innovation through knowledge codification", Journal of Information Technology, Vol. 16, No 2, pp 83 – 87.

[102] Linpack, J. and Stamps, J. (1997), "Virtual Teams", John Wiley & Sons, 1997.

[103] Jarvenpaa, S. and Shaw, T. R. (1998), "Global Virtual Teams: Integrating Models of Trust", In Sieber P.; Griese J. (eds): Organizational Virtualness, Proceedings of the VoNet – workshop, Simowa – Verlag.

[104] Lemkem, B., Kahler, H. and Rittenbruch, M (2000), "Sustained knowledge management by organizational culture", Proceedings of the 33rd Hawaii International Conference on System Sciences, pp 64 – 76.

[105] Apostolou, D. and Mentzas, G. (1998), "Managing Corporate Knowledge: A Comparative Analysis of Experiences in Consulting Firms", In: Second International Conference on Practical Aspects of Knowledge Management, www.imu.iccs.ntua.gr/Papers/C36-PAKMCONS.pdf.

[106] Bickerstaff, M. J. and Massoud, R. A. (2000), KM World, Santa Clara, CA, www.infotoday.com/KMWorld2000/presentations/bickerstaff-massoud-handouts.ppt.

[107] Obank, A., Leaney, P. and Roberts, S. (1995), "Data management within a manufacturing organization", Integrated Manufacturing Systems, Vol. 6, No. 3, pp 37 – 43.

[108] Anonymous (2002), KM Binnen, Ernst & Young, Feb 2002 – NGI, www.ngi.nl/nh/ey_km.pdf.

[109] Ernst & Young (1997), Business Intelligence (Online), http://www.entovation.com/backgrnd/art.htm.

http://www.imu.iccs.ntua.gr/Papers/C36-PAKMCONS.pdf

http://www.infotoday.com/KMWorld2000/presentations/bickerstaff-massoud-handouts.ppt

http://www.infotoday.com/KMWorld2000/presentations/bickerstaff-massoud-handouts.ppt

http://www.ngi.nl/nh/ey_km.pdf

http://www.entovation.com/backgrnd/art.htm



108

[110] Sanchez, R. (1997), "Knowledge Management and Organizational Competence" by Ron Sanchez (editor), 1st edition (December 15, 2001), Chapter – 11, pp 231, 234, 237.

[111] APQC (1997), Using Information Technology to Support Knowledge Management, Consortium Benchmarking Study: Final Report, 1997.

[112] Saaty, T. L., (1996), "Decision Making with Dependence and Feedback: The Analytic Network Process", RWS Publications, Pittsburgh, P.A.

[113] Agarwal, A. and Shankar, R., (2002), "Performance Improvement in Supply Chain: An ANP approach", Work Study, Vol. 51, No 3.



109

APPENDIX 1

CONSISTENCY RATIO FOR THE RELATIVE COMPARISON MATRIX OF THE DETERMINANT

Comparison Matrix (A):

COST COMPETITIVENESS RESPONSIVENESS

COST 1 2 4

COMPETITIVENESS 0.5 1 2

RESPONSIVENESS 0.25 0.5 1

Total 1.75 3.5 7

Normalized Matrix (B):

COST COMPETITIVENESS RESPONSIVENESS WEIGHT(w)

COST 0.5714 0.5714 0.5714 0.5714

COMPETITIVENESS 0. 2857 0.2857 0.2857 0.2857

RESPONSIVENESS 0.1429 0.1429 0.1429 0.1429

Method of calculating the normalized matrix:

STEP 1:

Compute A*C (Comparison Matrix multiplied by Normalized Matrix)

and calculate EV as A*C is divided by individual priority weight of the

determinant.



110

A*C EV 1.7143 3 0.8571 3 0.4286 3

A*C = d1 = (1*0.5714) + (2*0.2857) + (4*0.1429) = 1.7143

d2 = (0.5*0.5714) + (1*0.2857) + (2*0.1429) = 0.8571

d3 = (0.25*0.5714) + (0.5*0.2857) + (1*0.1429) = 0.4286 EV = A*C / w

e1 = 1.7143 / 0.5714 = 3

e2 = 0.8571 / 0.2857 = 3

e3 = 0.4286 / 0.1429 = 3 STEP 2: Calculate δ δ = Σ EV / m Where, m is the number of variables in the criteria.

δ = (e1 + e2 + e3) / m (since m = 3)

= (3 + 3 + 3) / 3

= 3 STEP 3: Compute Consistency Index (CI) CI = (δ – m) / (m – 1)

= (3 – 3) / (3 – 1)

= 0 / 2

= 0.



111

STEP 4:

Comparing Consistency Index (CI) with Random Index (RI) for the

appropriate value of ‘m’ to determine the degree of consistency is satisfactory

or not.

CI / RI = [(δ – 3) / 2] / 0.58 (RI for m=3 is 0.58)

= 0 / 0.58

= 0

Table D: Consistency Ratio Random Index (RI) m 2 3 4 5 6 7 8 9 10 RI 0 0.58 0.9 1.12 1.24 1.32 1.41 1.45 1.51

If CI / RI is less than 0.1 then the degree of consistency is considered to

be satisfactory.



112

APPENDIX – II (Tables and Calculations)

Determinant – COST (a) Pair – wise comparison matrices for the relative importance of

dimensions on determinant / COST: (Pja)

OS PI INN e – vector OS 1 5 8 0.7020 PI 0.2000 1 6 0.2382

INN 0.1250 0.1667 1 0.0630 Total 1.3250 6.1667 15 1

(b) Pair – wise comparison matrices for dependencies in between

enablers: (ADkja)

i) Pair – wise comparison matrix for attribute enablers under determinant /

COST and the dimension / ORGANIZATIONAL STRUCTURE:


CU 1 0.1250 0.5000 0.5000 0.0832 TMC 8 1 4 2 0.5353 SP 2 0.2500 1 0.3333 0.1229

ITW 2 0.5000 3 1 0.2586 Total 13 1.8750 8.5 3.8333 1

ii) Pair – wise comparison matrix for attribute enablers under determinant /

COST and the dimension / PROCESS INTEGRATION:

PPA KF SC COL e – vector

PPA 1 0.2500 0.1429 0.2000 0.0629 KF 4 1 3 4 0.4847 SC 7 0.3333 1 3 0.2957

COL 5 0.2500 0.3333 1 0.1568 Total 17 1.8333 4.4762 8.2 1



113

iii) Pair – wise comparison matrix for attribute enablers under determinant /

COST and the dimension / INNOVATION:

KC KW KS TE e – vector

KC 1 0.2000 0.2500 0.1667 0.0580 KW 5 1 0.3333 0.1429 0.1383 KS 4 3 1 0.2500 0.2143 TE 6 7 4 1 0.5894

Total 16 11.2 5.5833 1.5596 1 (c) Pair–wise comparison matrices for Interdependencies among

Enablers: (AIkja)

i) OS / COST / CU

CU TMC SP ITW e – vector TMC 1 3 0.5000 0.3656 SP 0.3333 1 4 0.3542

ITW 2 0.2500 1 0.2802 Total 3.3333 4.25 5.5 1

ii) OS / COST / TMC

TMC CU SP ITW e – vector CU 1 0.3333 0.2500 0.1275 SP 3 1 5 0.6091

ITW 4 0.2000 1 0.2635 Total 8 1.5333 6.25 1

iii) OS / COST / SP

SP CU TMC ITW e – vector CU 1 0.2000 0.3333 0.1038

TMC 5 1 4 0.6651 ITW 3 0.2500 1 0.2311 Total 9 1.45 5.3333 1

iv) OS / COST / ITW

ITW CU TMC SP e – vector CU 1 0.2000 3 0.2585

TMC 5 1 2 0.5703 SP 0.3333 0.5000 1 0.1711

Total 6.3333 1.7000 6 1



114

v) PI / COST / PPA

PPA KF SC COL e – vector KF 1 5 4 0.6597 SC 0.2000 1 3 0.2236

COL 0.2500 0.3333 1 0.1167 Total 1.45 6.3333 8 1

vi) PI / COST / KF

KF PPA SC COL e – vector PPA 1 0.3333 0.2500 0.1199 SC 3 1 0.3333 0.2721

COL 4 3 1 0.6080 Total 8 4.3333 1.5833 1

vii) PI / COST / SC

SC PPA KF COL e – vector PPA 1 0.2000 0.2500 0.0962 KF 5 1 4 0.6505

COL 4 0.2500 1 0.2543 Total 10 1.45 5.25 1

viii) PI / COST / COL

COL PPA KF SC e – vector PPA 1 0.2500 0.3333 0.1199 KF 4 1 3 0.6080 SC 3 0.3333 1 0.2721

Total 8 1.5833 4.3333 1 ix) INN / COST / KC

KC KW KS TE e – vector KW 1 4 0.2000 0.2635 KS 0.2500 1 0.3333 0.1275 TE 5 3 1 0.6091

Total 6.25 8 1.5333 1

x) INN / COST / KW

KW KC KS TE e – vector KC 1 0.3333 0.25 0.1181 KS 3 1 0.25 0.2431 TE 4 4 1 0.6389

Total 8 5.3333 1.5 1



115

xi) INN / COST / KS

KS KC KW TE e – vector KC 1 0.2500 0.2000 0.0936 KW 4 1 0.1667 0.2200 TE 5 6 1 0.6864

Total 10 7.25 1.3667 1 xii) INN / COST / TE

TE KC KW KS e – vector KC 1 0.2000 0.1667 0.0768 KW 5 1 0.2500 0.2618 KS 6 4 1 0.6584

Total 12 5.2000 1.4167 1 (d) i) Super Matrix for COST before Convergence: RES CU TMC SP ITW PPA KF SC COL KC KW KS TE

CU 0 0.1275 0.1038 0.2585 0 0 0 0 0 0 0 0

TMC 0.3656 0 0.6651 0.5703 0 0 0 0 0 0 0 0

SP 0.3542 0.6091 0 0.1711 0 0 0 0 0 0 0 0

ITW 0.2802 0.2635 0.2311 0 0 0 0 0 0 0 0 0

PPA 0 0 0 0 0 0.1199 0.0952 0.1373 0 0 0 0

KF 0 0 0 0 0.6597 0 0.6505 0.6232 0 0 0 0

SC 0 0 0 0 0.2236 0.2721 0 0.2335 0 0 0 0

COL 0 0 0 0 0.1167 0.6080 0.2543 0 0 0 0 0

KC 0 0 0 0 0 0 0 0 0 0.1181 0.0936 0.0798

KW 0 0 0 0 0 0 0 0 0.2635 0 0.2200 0.2618

KS 0 0 0 0 0 0 0 0 0.1275 0.2431 0 0.6584

TE 0 0 0 0 0 0 0 0 0.6091 0.6389 0.6864 0



116

ii) Super Matrix for COST after Convergence:


CU 0.1304 0.1304 0.1304 0.1304 0 0 0 0 0 0 0 0

TMC 0.3649 0.3649 0.3649 0.3649 0 0 0 0 0 0 0 0

SP 0.3031 0.3031 0.3031 0.3031 0 0 0 0 0 0 0 0

ITW 0.2027 0.2027 0.2027 0.2027 0 0 0 0 0 0 0 0

PPA 0 0 0 0 0.1204 0.1204 0.1204 0.1204 0 0 0 0

KF 0 0 0 0 0.3870 0.3870 0.3870 0.3870 0 0 0 0

SC 0 0 0 0 0.2100 0.2100 0.2100 0.2100 0 0 0 0

COL 0 0 0 0 0.3006 0.3006 0.3006 0.3006 0 0 0 0

KC 0 0 0 0 0 0 0 0 0.0852 0.0852 0.0852 0.0852

KW 0 0 0 0 0 0 0 0 0.1974 0.1974 0.1974 0.1974

KS 0 0 0 0 0 0 0 0 0.3211 0.3211 0.3211 0.3211

TE 0 0 0 0 0 0 0 0 0.3984 0.3984 0.3984 0.3984

(e) Pair – wise comparison matrices for the relative importance of

alternatives on enablers for the determinant / COST:

i) COST / OS / CU

KE KI ES e – vector KE 1 2 3 0.5247 KI 0.5000 1 3 0.3338 ES 0.3333 0.3333 1 0.1416

Total 1.8333 3.3333 7 1



117

ii) COST / OS / TMC


-Total 2 3.3333 6 1 iii) COST / OS / SP


Total 1.45 5.3333 9 1 iv) COST / OS / ITW


Total 1.75 3.25 9 1

v) COST / PI / PPA


Total 1.5833 4.5 7 1 vi) COST / PI / KF


Total 1.5333 4.3333 9 1 vii) COST / PI / SC


Total 1.5833 4.3333 8 1



118

viii) COST / PI / COL


Total 1.5 4.25 11 1 ix) COST / INN / KC


Total 1.75 3.3333 8 1

x) COST / INN / KW


Total 2 3.3333 6 1 xi) COST / INN / KS


Total 1.4167 5.2 12 1 xii) COST / INN / TE


Total 1.5833 4.5 7 1



119

(f) Architectural Weighted Desirability Index for COST:


CU 0.6996 0.0832 0.1304 0.5247 0.3338 0.1416 0.0040 0.0025 0.0011

OS TMC 0.6996 0.5353 0.3649 0.4778 0.3500 0.1722 0.0653 0.0478 0.0235

SP 0.6996 0.1229 0.3031 0.6651 0.2311 0.1038 0.0173 0.0060 0.0027

ITW 0.6996 0.2586 0.2027 0.5437 0.3459 0.1103 0.0199 0.0127 0.0040

PPA 0.2375 0.0629 0.1204 0.6232 0.2395 0.1373 0.0011 0.0004 0.0002

PI KF 0.2375 0.4847 0.3870 0.6334 0.2605 0.1061 0.0282 0.0116 0.0047

SC 0.2375 0.2957 0.2100 0.6772 0.2952 0.1276 0.0100 0.0044 0.0019

COL 0.2375 0.1568 0.3006 0.6394 0.2737 0.0869 0.0072 0.0031 0.0010

KC 0.0627 0.0580 0.0852 0.5571 0.3202 0.1226 0.0002 0.0001 0.0004

INN KW 0.0627 0.1383 0.1974 0.4778 0.3500 0.1722 0.0008 0.0006 0.0003

KS 0.0627 0.2143 0.3240 0.6584 0.2618 0.0798 0.0029 0.0011 0.0003

TE 0.0627 0.5894 0.3984 0.6232 0.2395 0.1373 0.0092 0.0035 0.0020

Total 0.1661 0.0938 0.0421



120

Determinant – COMPETITIVENESS (a) Pair – wise comparison matrices for the relative importance of

dimensions on determinant / COMPETITIVENESS: (Pja)

OS PI INN e – vector OS 1 4 3 0.6080 PI 0.2500 1 0.3333 0.1199

INN 0.3333 3 1 0.2721 Total 1.5833 8 4.3333 1

(b) Pair – wise comparison matrices for dependencies in between

enablers: (ADkja)

i) Pair – wise comparison matrix for attribute enablers under determinant / COMPETITIVENESS and the dimension / ORGANIZATIONAL STRUCTURE:


CU 1 4 3 0.2500 0.2710 TMC 0.2500 1 4 0.3333 0.1815 SP 0.3333 0.2500 1 0.5000 0.1075

ITW 4 3 2 1 0.4400 Total 5.5833 8.25 1 2.0833 1

ii) Pair – wise comparison matrix for attribute enablers under determinant /

COMPETITIVENESS and the dimension / PROCESS INTEGRATION:

PPA KF SC COL e – vector PPA 1 0.2500 0.3333 0.3333 0.0829 KF 4 1 4 3 0.5060 SC 3 0.2500 1 3 0.2487

COL 3 0.3333 0.3333 1 0.1624 Total 11 1.8333 5.6666 7.3333 1


COMPETITIVENESS and the dimension / INNOVATION:

KC KW KS TE e – vector KC 1 0.2000 0.3333 0.1667 0.0583 KW 5 1 4 0.1429 0.2430 KS 3 0.2500 1 0.2500 0.1243 TE 6 7 4 1 0.5746

Total 15 8.45 9.3333 1.5596 1



121

(c) Pair–wise comparison matrices for Interdependencies among Enablers: (AIkja)

i) OS / COMP / CU

CU TMC SP ITW e – vector TMC 1 0.3333 0.2000 0.1200 SP 3 1 4 0.5780

ITW 5 0.25 1 0.3019 Total 9 1.5833 5.2 1

ii) OS / COMP / TMC

TMC CU SP ITW e – vector CU 1 4 3 0.6196 SP 0.2500 1 2 0.2243

ITW 0.3333 0.5000 1 0.1560 Total 1.5833 5.5 6 1

iii) OS / COMP / SP

SP CU TMC ITW e – vector CU 1 0.2500 3 0.2311

TMC 4 1 5 0.6651 ITW 0.3333 0.2000 1 0.1038 Total 5.3333 1.4500 9 1

iv) OS / COMP / ITW

ITW CU TMC SP e – vector CU 1 4 0.3333 0.2842

TMC 0.2500 1 0.2000 0.0964 SP 3 5 1 0.6194

Total 4.25 10 1.5333 1

v) PI / COMP / PPA

PPA KF SC COL e – vector KF 1 5 4 0.6768 SC 0.2000 1 2 0.1925

COL 0.2500 0.5000 1 0.1307 Total 1.45 6.5 7 1



122

vi) PI / COMP / KF

KF PPA SC COL e – vector PPA 1 0.2500 0.3333 0.1181 SC 4 1 4 0.6389

COL 3 0.2500 1 0.2431 Total 8 1.5 5.3333 1

vii) PI / COMP / SC

SC PPA KF COL e – vector PPA 1 0.2000 0.3333 0.1200 KF 5 1 0.2500 0.3019

COL 3 4 1 0.5780 Total 9 5.2 1.5833 1

viii) PI / COMP / COL


Total 8 1.5833 4.3333 1 ix) INN / COMP / KC


Total 6.25 9 1.45 1

x) INN / COMP / KW


Total 10 7.25 1.3667 1 xi) INN / COMP / KS


Total 10 6.1667 1.5333 1



123

xii) INN / COMP / TE

TE KC KW KS e – vector KC 1 0.2000 0.3333 0.1038 KW 5 1 4 0.6651 KS 3 0.2500 1 0.2311

Total 9 1.45 5.3333 1 (d)

i) Supermatrix for COMPETITIVENESS before Convergence:


CU 0 0.6196 0.2311 0.2842 0 0 0 0 0 0 0 0

TMC 0.1200 0 0.6651 0.0964 0 0 0 0 0 0 0 0

SP 0.5780 0.2243 0 0.6194 0 0 0 0 0 0 0 0

ITW 0.3019 0.1560 0.1038 0 0 0 0 0 0 0 0 0

PPA 0 0 0 0 0 0.1181 0.1200 0.1199 0 0 0 0

KF 0 0 0 0 0.6768 0 0.3019 0.6080 0 0 0 0

SC 0 0 0 0 0.1925 0.6389 0 0.2721 0 0 0 0

COL 0 0 0 0 0.1307 0.2431 0.5780 0 0 0 0 0

KC 0 0 0 0 0 0 0 0 0 0.0936 0.1148 0.1038

KW 0 0 0 0 0 0 0 0 0.2474 0 0.2975 0.6651

KS 0 0 0 0 0 0 0 0 0.1078 0.2200 0 0.2311

TE 0 0 0 0 0 0 0 0 0.6447 0.6864 0.6877 0



124

ii) Supermatrix for COMPETITIVENESS after Convergence:


CU 0.2746 0.2746 0.2746 0.2746 0 0 0 0 0 0 0 0

TMC 0.2555 0.2555 0.2555 0.2555 0 0 0 0 0 0 0 0

SP 0.3122 0.3122 0.3122 0.3122 0 0 0 0 0 0 0 0

ITW 0.1552 0.1552 0.1552 0.1552 0 0 0 0 0 0 0 0

PPA 0 0 0 0 0.1066 0.1066 0.1066 0.1066 0 0 0 0

KF 0 0 0 0 0.3254 0.3254 0.3254 0.3254 0 0 0 0

SC 0 0 0 0 0.3011 0.3011 0.3011 0.3011 0 0 0 0

COL 0 0 0 0 0.2671 0.2671 0.2671 0.2671 0 0 0 0

KC 0 0 0 0 0 0 0 0 0.0906 0.0906 0.0906 0.0906

KW 0 0 0 0 0 0 0 0 0.3366 0.3366 0.3366 0.3366

KS 0 0 0 0 0 0 0 0 0.1755 0.1755 0.1755 0.1755

TE 0 0 0 0 0 0 0 0 0.3969 0.3969 0.3969 0.3969

(e) Pair–wise comparison matrices for the relative importance of alternatives on enablers for the determinant / COMPETITIVENESS:

i) COMP / OS / CU


Total 1.5833 4.5 7 1



125

ii) COMP / OS / TMC


KE 1 2 3 0.5390 KI 0.5000 1 2 0.2972 ES 0.3333 0.5000 1 0.1638

Total 1.8333 3.5 6 1 iii) COMP / OS / SP


Total 1.45 5.5 8 1 iv) COMP / OS / ITW


Total 1.4167 5.2 12 1

v) COMP / PI / PPA


Total 1.5833 5.3333 7 1 vi) COMP / PI / KF


Total 1.5333 4.25 10 1



126

vii) COMP / PI / SC


Total 1.4167 5.2 12 1

viii) COMP / PI / COL


Total 1.5833 4.3333 8 1 ix) COMP / INN / KC


Total 1..5333 4.3333 9 1

x) COMP / INN / KW


Total 1.5 5.3333 8 1 xi) COMP / INN / KS


Total 1.7 3.3333 9 1 xii) COM / INN / TE


Total 1.75 3.5 7 1



127

(f) Architectural Weighted Desirability Index for COMPETITIVENESS:


CU 0.6080 0.2710 0.2746 0.6232 0.2395 0.1373 0.0282 0.0108 0.0062

OS TMC 0.6080 0.1815 0.2555 0.5390 0.2972 0.1638 0.0152 0.0084 0.0046

SP 0.6080 0.1075 0.3122 0.6807 0.2014 0.1179 0.0139 0.0041 0.0024

ITW 0.6080 0.4400 0.1552 0.6584 0.2618 0.0798 0.0273 0.0109 0.0033

PPA 0.1199 0.0829 0.1066 0.6034 0.2580 0.1386 0.0006 0.0003 0.0001

PI KF 0.1199 0.5060 0.3254 0.6194 0.2842 0.0964 0.0122 0.0056 0.0019

SC 0.1199 0.2487 0.3011 0.6584 0.2618 0.0798 0.0059 0.0024 0.0007

COL 0.1199 0.1624 0.2671 0.6080 0.2721 0.1233 0.0032 0.0014 0.0006

KC 0.2721 0.0583 0.0905 0.6334 0.2605 0.1061 0.0009 0.0004 0.0002

INN KW 0.2721 0.2430 0.3366 0.6389 0.2431 0.1181 0.0142 0.0054 0.0026

KS 0.2721 0.1243 0.1755 0.5813 0.3091 0.1096 0.0035 0.0018 0.0007

TE 0.2721 0.5746 0.3969 0.5714 0.2857 0.1429 0.0355 0.0177 0.0089

Total 0.1606 0.0692 0.0323



128

Determinant – RESPONSIVENESS (a) Pair – wise comparison matrices for dependencies in between

enablers: (ADkja) ii) Pair – wise comparison matrix for attribute enablers under determinant /

RESPONSIVENESS and the dimension / PROCESS INTEGRATION:

PPA KF SC COL e – vector

PPA 1 0.2500 0.2000 0.3333 0.0735 KF 4 1 3 4 0.4890 SC 5 0.3333 1 4 0.3049

COL 3 0.2500 0.2500 1 0.1326 Total 13 1.8333 4.45 9.3333 1


RESPONSIVENESS and the dimension / INNOVATION:

KC KW KS TE e – vector

KC 1 0.2500 0.2000 0.3333 0.0816 KW 4 1 3 0.2000 0.2344 KS 5 0.3333 1 0.2500 0.1744 TE 3 5 4 1 0.5097

Total 13 6.5833 8.2 1.7833 1

(b) Pair – wise comparison matrices for Interdependencies among

Enablers: (AIkja) ii) OS / RESP / TMC

TMC CU SP ITW e – vector CU 1 3 0.5000 0.3699 SP 0.3333 1 2 0.2979

ITW 2 0.5000 1 0.3323 Total 3.3333 4.5 3.5 1



129

iii) OS / RESP / SP

SP CU TMC ITW e – vector CU 1 0.2500 0.2000 0.1018

TMC 4 1 2 0.5321 ITW 5 0.5000 1 0.3661 Total 10 1.75 3.2 1

iv) OS / RESP / ITW

ITW CU TMC SP e – vector CU 1 0.2500 3 0.3137

TMC 4 1 0.2500 0.3331 SP 0.3333 4 1 0.3532

Total 5.3333 5.25 4.25 1

v) PI / RESP / PPA

PPA KF SC COL e – vector KF 1 0.2500 0.3333 0.1279 SC 4 1 0.5000 0.3601

COL 3 2 1 0.5120 Total 8 3.25 1.8333 1

vi) PI / RESP / KF

KF PPA SC COL e – vector

PPA 1 0.3333 0.2500 0.1199 SC 3 1 0.3333 0.2721

COL 4 3 1 0.6080 Total 8 4.3333 1.5833 1

vii) PI / RESP / SC

SC PPA KF COL e – vector PPA 1 0.2000 0.3333 0.1038 KF 5 1 4 0.6651

COL 3 0.2500 1 0.2311 Total 9 1.45 5.3333 1

viii) PI / RESP / COL


Total 6 1.5833 5.5 1



130

ix) INN / RESP / KC


Total 6.25 8 1.5333 1

x) INN / RESP / KW


Total 10 5.25 1.45 1 xi) INN / RESP / KS


Total 8 5.3333 1.5 1 xii) INN / RESP / TE

TE KC KW KS e – vector KC 1 0.2500 0.3333 0.1199 KW 4 1 3 0.6080 KS 3 0.3333 1 0.2721

Total 8 1.5833 4.3333 1 (e) Pair – wise comparison matrices for the relative importance of

alternatives on enablers for the determinant / RESPONSIVENESS: ii) RESP / OS / TMC


KE 1 4 2 0.5516 KI 0.2500 1 3 0.2768 ES 0.5000 0.3333 1 0.1716

Total 1.75 5.3333 6 1



131

iii) RESP / OS / SP


Total 1.5333 4.5 8 1 iv) RESP / OS / ITW


Total 1.45 5.25 10 1

v) RESP / PI / PPA


Total 1.3429 6.25 12 1 vi) RESP / PI / KF


Total 1.5333 4.25 10 1

vii) RESP / PI / SC


Total 1.5333 4.1667 12 1

viii) RESP / PI / COL


Total 1.45 5.5 8 1


Indian Institute of Information Technology – Allahabad 132

ix) RESP / INN / KC


Total 1.45 5.25 10 1

x) RESP / INN / KW


Total 1.3667 6.3333 10 1 xi) RESP / INN / KS


Total 1.4167 5.3333 10 1 xii) RESP / INN / TE


KE 1 3 4 0.6080 KI 0.3333 1 3 0.2721 ES 0.2500 0.3333 1 0.1199

Total 1.5833 4.3333 8 1

submitted by adish kumar - m.tech divison grade/adish kumar ms200501... · 2015-04-24 · mr....

Documents