an extended model for measuring the technology transfer potential

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Southern Cross University

ePublications@SCU

Teses

2011

An extended model for measuring the technologytransfer potentials at the industrial levelSathayanarayanan PachamuthuSouthern Cross University, [email protected]
http://epubs.scu.edu.au/http://epubs.scu.edu.au/theseshttp://epubs.scu.edu.au/theseshttp://epubs.scu.edu.au/


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An Extended Model for Measuring the

Technology Transfer Potentials

at the Industrial Level

Sathayanarayanan PACHAMUTHU


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STATEMENT OF ORIGINAL AUTHORSHIP

I certify that the work presented in this thesis is, to the best of my knowledge and belief, original,except as acknowledged in the text and the material has not been submitted, either in whole or in

part, for a degree at this or any other university. I also certify that, to the best of my knowledge,any help received in preparing this thesis, and all sources used have been acknowledged in thisthesis.

Sathayanarayanan PACHAMUTHU

2011


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ACKNOWLEDGEMENTS

During this research, many individuals and institutions provided me great support and

guidance which helped me complete this study successfully. I would like to express my

sincere thanks to Southern Cross University (SCU) and Sydney College of Business and IT

(SCBIT) for providing me the opportunity to undertake my DBA study, and their

management and staff for supporting me throughout my study. I am grateful to SRMUniversity, India for giving me time and supporting my study financially.

My sincere thanks to the principal supervisor, Dr. Veerappan Jayaraman, who guided me

throughout this research study. His area of interest and research in technology transfer

modeling has inspired me to undertake this study. I greatly acknowledge his support,

guidance, and contributions throughout this study. I am very proud to have modified and

extended his quantitative technology transfer model that he developed in his research.

I have been taught the importance of education and value of knowledge by my parents in

the very early stage of my life. They provided me the education and great opportunities,

and I will always remain grateful to them.


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ABSTRACT

Technology contributes to the development of society and economy of the nation through

the invention, diffusion, transfer, and application of new knowledge. In the emerging

global economy of the 21st century, technology is a key to sustainable economic

prosperity. Transfer of technology is the key element for the industrialization, growth, and

economic development of the countries. The knowledge transferring capabilities of thetransferor, adaptation and assimilation capabilities of the transferee are important to a large

extent on the success of any transfer of technology.

The quantitative or mathematical modeling has not been significantly utilized in analyzing

the technology transfer process. Some of the well known quantitative studies on

technology transfer have been done by Haq (1979), Suckchareonpong (1979), Baruch Raz,

Gerald Steinberg, and Andrew Ruina (1983), Baruch Raz and Isak Assa (1988), Liu

(1993), Bhargava (1995), Jayaraman, Truong and Agrawal (1998), Truong (2002). It is

possible that more contribution to the knowledge relating to technology transfer can be

made by studying the process of technology transfer using quantitative methods.

The main focus of this study is to develop an extended quantitative model incorporating


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It is accepted in the literature on Technology Transfer (Haq 1979, Sharif-Haq 1981,

Sukchareonpong 1979, Jayaraman-Truong-Agrawal 1996) that the main factor governing

the technological transfer process, the transfer rate, at any time is proportional to the

current level of assimilation of such technology of the transferee and the level remaining to

be achieved by the transferee in the long run. However, there could be many other

important factors influencing the transfer rate such as the technological gap between

transferor and transferee, the potential technological distance between the transferor and

transferee, the geographical distance between locations etc.

In this study, an extended mathematical model is developed for measuring technology

transfer potentials. It is hypothesised that the rate of assimilation of a particular technology

of a transferee at a certain time, t, is proportional to: (a) the existing level of assimilation of

such technology of the transferee, (b) the level remaining to be achieved by the transferee

in the long run, and (c) a technology transfer function that incorporates the relative

technological gap (potential technological distance) between the transferor and transferee.

Using the technology transfer model developed, the level of assimilation that the transferee

can achieve with the selected transferor during the period of technology transfer is

measured. The boundary conditions for technology transfer model are then verified. The

boundary conditions are based on the fact that when the time tends to minus infinity, the

assimilation level of the transferee would be equal to zero and when time tends to plus


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Brazil, and France are presented. In this study, only a few countries are selected due to the

limitation on the availability and reliability of the data. As this study is on developing an

extended model for measuring the technology transfer potentials that exist between a

transferor and a transferee at the industrial level, the variables that influence and reflect the

performance of the given industry of various countries under study are identified and

collected for the past years. The variables used in this study are broadly categorized into

three groups, namely, (i) variables relevant to national technology climate conditions, (ii)

variables reflecting manufacturing technology climate conditions, and (iii) variables

pertinent to the specific industry technology climate conditions in a country.

In this study, the indicators that influence and reflect the performance of the given industry

such as Research and Development (R&D) Expenses per economically active population,

Output per employee in the manufacturing sector, Value added per employee of the

manufacturing sector, Output per employee of the specific industry, and Value added per

employee of the specific industry, are considered for formulating the technology index at

the industrial level. The national level technology climate variables and the manufacturing

sectoral level technology climate variables are assumed to have direct influence on the

growth of the specific industry. The Value added per employee in the specific industry is

considered as the technology assimilation parameter at the industrial level.


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Malaysia, 98.76% for Singapore, 93.63% for France, 91.26% for Brazil, 90.71% for UK,

90.60% for Korea, 90.51% for Germany, and 83.15% for China. It is found that the

technology transfer model developed in this study provides a very good fit in all the above

transfer situations. The fitness of the model is quite significant for countries such as Japan,

Singapore, Malaysia, Germany and France at the 0.01 level where as it is significant for

countries such as Korea, China, UK, and Brazil at the 0.05 level.

In the case study of technology transfer in Electronics industry in selected member

countries such as Korea, Japan, China, Singapore, Malaysia, UK, Germany, USA, Brazil,

and France, the historical data of the value added per employee (US$ in PPP terms) and the

predicted values are used to fit the technology transfer phenomenon in that industry. In

terms of technology transfer in Electronics industry, the model developed in this study

explains the variation in the prediction to the extent of 99.30% for Germany, 98.20% for

UK, 97.97% for France, 96.20% for Singapore, 95.44% for China, 90.59% for Brazil,

89.96% for Japan, 88.40% for Malaysia, and 76.87% for Korea. It is found that the

technology transfer model developed in this study provides a very good fit in all the above

transfer situations. The fitness of the model is quite significant for countries such as China,

Singapore, UK, Germany, and France at the 0.01 level where as it is significant for

countries such as Korea, Japan, Malaysia and Brazil at the 0.05 level.


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Singapore, Malaysia, UK, Germany, Brazil, and France at the 0.01 level where as it is

significant for country China, Korea and Japan at the 0.05 level.

Based on the results obtained from the case studies of technology transfer in automobile

industry, electronics industry, and computing industry for Korea, Japan, China, Singapore,

Malaysia, UK, Germany, USA, Brazil, and France, it is concluded that the hypothesis used

in this research that the rate of assimilation of a particular technology of a transferee at a

certain time, t, is proportional to: (a) the existing level of assimilation of such technology

of the transferee, (b) the level remaining to be achieved by the transferee in the long run,

and (c) a technology transfer function that incorporates the relative technological gap

(potential technological distance) between the transferor and transferee, is accepted at the

significance level of 0.05.

The fitness of the technology transfer model is found to be very satisfactory in all the three

case studies done. The case studies indicate that the model can provide an effective means

for measuring the transfer potentials that exist between a transferor and a transferee. Since

the model predicts the level of assimilation that a transferee can achieve with a given

transferor in the long run, it is possible for this dynamic model to be used as a decision-

making tool by countries in determining the optimum partner for most effective technology

transfer


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TABLE OF CONTENTS

CHAPTER TITLE PAGE

Statement of Original Authorship ii

Acknowledgments iii

Abstract iv

Table of Contents ix

List of Tables xv

1 INTRODUCTION

1.1 Background 1


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2 LITERATURE REVIEW

2.1 Introduction 8

2.2 Definition of Technology 8

2.3 Measurement and Indices 9

2.3.1 Measurement 9

2.3.2 Indices 9

2.4 Methods Determining Weight 10

2.5 Technology Transfer 12

2.5.1 Technology Transfer Elements 13

2.5.2 Technology Transfer Mechanisms 14

2.5.3 Technology Transfer Effectiveness 15

2.6 Technology Diffusion and Technology Transfer 15

2.7 Technological Diffusion Models 15


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3.4 Technology Index - Normalization 41

3.5 Technology Index Function 41

3.6 Evaluating Technological Characteristics 42

3.7 Determinants of Technology Index 43

3.8 Summary 44

4 TECHNOLOGY TRANSFER MODEL DEVELOPMENT

4.1 Introduction 45

4.2 Assumptions for Developing Technology Transfer 45

Model

4.3 Technology Transfer - Generalized Model 46

4.4 Technology Transfer - Specific Model 50

4.5 Verification of Technology Transfer Model 53


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at the Industrial Level

5.2.2 Data Collection 67

5.2.3 Technology Index and Its Function 67

5.3 Technology Transfer in Automobile Industry in Selected 68

Countries

5.3.1 Determinants of Technological Capability 68

in Automobile Industry


5.3.3 Technology Index of Automobile Industry 70

5.3.4 Technology Index Function of Automobile Industry 82

5.3.5 Technology Transfer Model Incorporating 89

Time and a Dynamic Technological Potential Distance

in Automobile Industry


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in Electronics Industry

5.5 Technology Transfer in Computing Industry in Selected 124

Countries

5.5.1 Determinants of Technological Capability 125

in Computing Industry


5.5.3 Technology Index of Computing Industry 125

5.5.4 Technology Index Function of Computing Industry 136



in Computing Industry


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REFERENCES 168

APPENDIX 1RAW DATA 183

APPENDIX 2FIGURES 199


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LIST OF TABLES

TABLE TITLE PAGE

5.1 The Standardized Data of Technology Variables in 72

Automobile Industry

5.2 Correlation Coefficient Matrix of Technology Variables in 75

Automobile Industry

5.3 Factor Analysis and Loading Matrix of Technology 77

Variables in Automobile Industry

5.4 Calculated Indexes and Normalized Indexes of Selected 79

Countries in Automobile Industry

5.5 Technology Index Functions of Selected Countries in 83


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5.9 Predicted Value Added per Employee data and Historical 93

Value Added per Employee on technology transfer model in

Automobile industry for Selected Countries

5.10 The Fitness of the Technology Transfer Model in 97

Korea, Japan, China, Singapore, Malaysia, UK,

Germany, Brazil, and FranceAutomobile Industry


Electronics Industry

5.12 Correlation Coefficient Matrix of Technology Variables 103

In Electronics Industry


Variables in Electronics Industry

5.14 Calculated Indexes and Normalized Indexes of Selected 107


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5.18 Average Potential Technology Distances between 117

USA and other Selected Countries in Electronics Industry



Automobile industry for Selected Countries



Germany, Brazil, and FranceElectronics Industry


Computing Industry

5.22 Correlation Coefficient Matrix of Technology Variables in 129

Computing Industry


Variables in Computing Industry


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5.28 Average Potential Technology Distances between USA 143

and other Selected Countries in Computing Industry



Computing industry for Selected Countries



Germany, Brazil, and FranceComputing Industry


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CHAPTER 1: INTRODUCTION

1.1 Background

Technology is widely accepted as essential for improving the economy of a nation, in

particular, in developing countries where industrial growth has occupied a very important

role (Guan, Mok, Yam and Pun 2006). Evidences from many countries have shown that a

countrys international competitiveness and capacity to grow in the long term is dependent

on its ability to master technology and to manage and generate technological change

(Haque 1995). Technology can contribute to the development of society and economy of a

nation through the invention, diffusion, transfer, and application of new knowledge.

Technology development is the basic means through which firms, industries, and countries

can foster their competitive capabilities and increase their competitive advantage.

Traditionally, the concept of competitiveness has been analyzed at the firm, the industry

(or one sector of it), and the country level (Wang, Chien, and Kao 2007).

Technology is man-made and consists of two major components, namely, hardware and

software. The technology provides a powerful tool for socioeconomic development of a

nation (Sharif 1986). The modern development would have been essentially inconceivable

without the technology support (Technology Atlas Project Team 1987). Technology is


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The literature on technology management has numerous definitions of technology. In this

study, it is considered that technology consists of four basic components (Technology

Atlas Project Team 1989), namely, (i) Technoware (Facilities), (ii) Humanware (Abilities),

(iii) Inforware (Facts), and (iv) Orgaware (Framework). All the four components of

technology are required simultaneously in any transformation operation that involves in the

production of goods through the conversion of material inputs into outputs and they are

complementary to each another.

1.3 Technology Transfer

The international transfer of know-how, knowledge and technological expertise is growing

and they are increasingly important in the world economy (Archibugi, and Lundvall 2001,

Archibugi, and Pietrobelli 2003). Technology transfer (TT) suggests the movement of

technology from one entity to another, for example, from one organization to another, from

a university to an organization, or from one country to another (Solo and Rogers 1972).

The complexity of the technology transfer process depends on the type of technology, the

owner's capability of tranferring, the acquirer's capability of assimilating, and the complex

interaction between the two parties (Lee, Wang, and Lin 2010, Goc 2002).

The concepts of technology transfer have been defined in many different ways. However,

there is usually an agreement (Sung and Gibson 2000 Sung and Hyon 1998) that


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techniques by means of investment of new plants; (ii) the improvement of existing

techniques, and (iii) the generation of new knowledge.

It is emphasized that industrialization is the main path for economic growth and

development by many nations. However, its success is dependent on the availability of the

required technology and the capability to use technology effectively (Sharif and Haq

1979). For achieving rapid technological advancement, many countries emphasize the

"Transfer of Technology" as a rational way. Hence, in the developed and the developing

countries, technology transfer has become a subject of considerable research activity.

Technology transfer is a process in which a technology generated in one place is adapted

and utilized or diffused in other places. Technology is carried across the border of two

entities that can be nations, industries, firms, or even individuals, and it can be interpreted

as an active process (Autio and Laamanen, 1995). There must be a transferor and a

transferee for technology transfer to occur. The transferor has higher technological level

than the transferee. A technological level is defined as the capability of the technological

system that consists of technoware, humanware, inforware and orgaware. The

technological gap between the transferor and transferee offers a potential for technology

flow (Technology Atlas Project Team 1989). Unless the technology transferred to a

country from outside is efficiently and effectively assimilated within the country it would


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The technology innovations and their diffusion have been studied by many researchers

(Blackman 1971 & 1974; Fisher and Pry 1971; Gold, Peirce and Rosegger 1975; Hough

1975; Lakhani 1975; Mahajan and Schoeman 1979; Nielsen 1974; Sharif and Kabir

1976). Technological innovation performs a function in a better and efficient way, and it

contributes to technological substitution over time. Technology substitution is the process

of substitution of one technology for another. Technology substitution models have been

developed by Mansfield (1961), Fisher-Pry (1971), Blackman (1972), Floyd (1968),

Ayres-Noble-Overly (1967), Sharif-Kabir (1976). Number of studies (Nesbath and Ray

1974; Nielsen 1974; Swan 1973; Gold 1981; Metcalfe 1970; Ray 1969; Romeo 1975;

Sharif and Ramanathan 1982; Buzzelli 1982; Clark, Freeman and Soete 1981; Vickery

1981; Madeuf 1982) had focused the international diffusion of technological innovations.

Many researchers (Balasubramanium 1973; Baster 1972; Gruber and Marquis 1969; Hall

and Johnson 1970; Hawthorne 1971; Joshi 1977; Spencer and Woroniak 1967; Teece

1981; Ramanathan 1994; Schwartz 1982; Simon 1991; Davidson 1980; Cusumano and

Elenkov 1994; Madeuf 1984; Jequier 1976; Mytelka 1985; Todaro 1985; Hoelscher

1975; Reddy and Zhao 1990; Mock 1974; Patel 1972; Streeten 1972; Barranson and

Roark 1995; Desai 1994; Katz 1985; Seaton and Cordey-Hayes 1993) studied on the

subject of technology transfer in descriptive nature. A number of qualitative analysis and

case studies of technology transfer between countries have been done (Barranson 1969 &


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technology transfer process quantitatively. A methodology to evaluate the technology

transfer potentials incorporating the time, technological levels and potential technology

distance between the transferor and transferee will help in understanding the complex

process of technology transfer. The quantitative models could give a defferent approach

and better understanding of the technology transfer process.

Technology transfer, usually, takes place at the firm level, between a firm in the supplier

country and a firm in the recipient country. However, the government policies on

technology transfer are generally formulated at the industrial level. The science and

technology agreements are signed between governments with the objective of facilitating

the technology transfer between an industry in the supplier country and the corresponding

industry in the recipient country.

1.4 Research Objectives

The main objectives of this research are to (i) develop a mathematical function that

evaluates the technological level of a country; (ii) develop a generalized mathematical

model for measuring the technology transfer potentials that exist between a transferor and

a transferee; (iii) develop a specific dynamic mathematical model incorporating time,technological level and potential technology distance for evaluating technology transfer

potentials; (iv) verify the technology transfer model for boundary conditions; (v) derive the


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Expenditure per Economically Active Population reflects the support of the nation towards

the development of science and technology climate which is a major input to the

development of the overall manufacturing sector that consists of various industries. The

output per employee in the manufacturing sector or each specific industry reflects the

technology and its sophistication employed in their production facilities. The value-added

per employee in the manufacturing sector or each specific industry reflects the

effectiveness of the human skills and technology employed in their production facilities.

The national technology climate conditions and manufacturing technology climate

conditions have direct influence on the growth of the specific industry in a country.

However, these influences may vary depending on the type of industry. In this research it

has been assumed that they will have same influence on all industries.

In this study, the technology level or capability of a particular industry in a given country

is measured through an index called the technology index. It indicates the knowledge of a

country in a particular industry. Technical knowledge or capability is cumulative in nature,

and in general, increases with respect to time (Patel 1972). Further, it is logical to assume

that this increase in the knowledge would be increasing at an increasing rate in the initialstage and increasing at a decreasing rate toward the latter stage. Thus it is assumed in this

research that the technology index is a function of time and having a form of S-curve or


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the application of the dynamic model is presented at the industry level not at the firm level

due to the limited availability of data. In this research, the value added per employee in a

specific industry is used as the assimilation parameter indicating the sophistication of the

application of technology in that industry.

Due to the limitation on the availability and consistency of the data, in this research, only

selected countries such as Korea, Japan, China, Singapore, Malaysia, UK, Germany, USA,

Brazil, and France are considered. The case studies are performed in automobile industry,

electronics industry, and computing industry. The data for the periods 2003 2007 are

collected for this study.

1.6 Organization of the Thesis

The organization of this study is summarized through various chapters. Chapter II deals

with the Literature Review. The literature outlining the concept of technology,

measurement and indexes, methods of determining weights including factor analysis, and

the concept of technology transfer are presented in this chapter. The review of existing

technology diffusion and transfer studies in literature are also presented in this chapter.

Chapter III provides the Research Methodology for measuring the technological level of acountry. Chapter IV presents a generalized mathematical model for measuring the

technology transfer potentials that exist between a transferor and a transferee In this


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CHAPTER 2: LITERATURE REVIEW

2.1 Introduction

The definition of technology, general overviews of measurement and indices, methods of

determining weights of the factors influencing the technology level, and the technology

transfer and various aspects of technology transfer process are presented in this chapter.

Some well-known technological diffusion models and technology transfer models are

reviewed in this chapter.

2.2 Definition of Technology

There are numerous definitions of technology are available in the literature. Some of thedefinitions have been compiled by Ramanathan (1990). Baesd on the various definitions of

technology by researchers, Malecki (1991) viewed technology as a transformer, tool, and

knowledge.

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Ramanathan (1994) quoting Sharif (1986), the Technology Atlas Team (1987) and Asian


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2.3 Measurement and Indices

2.3.1 Measurement

The concept of 'measurement' was originally explained by scientists by assigning

numerical values to properties of objects or events. Physicists, later, extended this concept

of measurement to selective non-additive physical properties. Behavioural scientists

applied formal measurement procedures to wider range of non-additive psychological and

psychophysical attributes. The concept of measurement has finally widened to encompass

the full range of non-additive and even non-orderable topological data with the

sophistication and flexibility of statistical techniques (Adelmen and Morris 1972).

Measurement is categorized into three different kinds (Torgerson 1960): (i) Derived

measurementIt is the measurement obtained through laws relating the property to other

properties; (ii) Measurement by fiat - It is the measurement obtained simply by arbitrary

definition. Ordinarily, it depends on presumed relationships between observations and

concept of interest; and (iii) Fundamental measurement It is a measurement by which

numbers can be assigned according to natural laws to represent the property and yet which

does not presuppose measurement of any other variables.

2.3.2 Indices

An index is something that points or indicates something else Indices may be


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Indices are used to describe trends or situations or compare the trends and situations. The

situations may be time periods (e.g. different years), or situations in a spatial sense (e.g.

different countries or regions), or groups of individuals (Baster 1972).

In cases, where the purpose of comparing the overall situation between a number of

countries, individuals or objects (as in the present study of comparing technological level

of different countries), it is appropriate to have a composite index, because a single

measure combining a number of factors which assumed to be causally related to the

situation, will be more meaningful. The formulation of composite index requires the

determination of the weight which gives the degree of relative importance of each

individual factor forming the composite index (Baster 1972).

2.4 Methods of Determining Weight

Some of the well known methods (Harbison, Marvhnic and Resnick 1970, Martino 1972,

Brown and Gibson 1972, Saaty and Rogers 1976) for determining weight or relative

importance of a large number of elements which altogether describes a particular

phenomenon or situation are: (i) Delphi Technique, (ii) Ranking Technique, (iii) Rating

Technique, (iv) Pairwise Comparison Technique, (v) Preference Theory Technique, (vi)Eigen Value Analysis, (vii) Taxonomic Analysis, and (viii) Factor Analysis. In this study,

Factor Analysis is used for determining weights or relative importance of the variables that


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In factor analysis, each of n observed variables is described in the term of m new

uncorrelated factors: F F Fm1 2, ......,, and unique factors Uj , (j = 1, 2, 3, ......, n)

Y a F a F a F b U m m1 11 1 12 2 1 1 1 .......

Y a F a F a F b U m m2 21 1 22 2 2 2 2 .......

.

.

.

Y a F a F a F b U n n n nm m n n 1 1 2 2 .......

Where

Yi = a standardized form of a variable with known data,

ajm = a factor loading or weight for each factor,

Fm = a function of unknown variables,

Uj = a unique factor,

and bj = a unique factor weight.


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2.5 Technology Transfer

The international transfer of know-how, knowledge and technological expertise are

increasingly important in the world economy (Archibugi, and Lundvall 2001, Archibugi,

and Pietrobelli 2003). Technology transfer (TT) suggests the movement of technology

from one entity to another, for example, from one organization to another, from a

university to an organization, or from one country to another (Solo and Rogers 1972).

Technology transfer depends to a large extent on the complexity of the technology, the

owner's capability of transferring, the acquirer's capability of learning, and the complex

interaction between the two parties (Lee, Wang, and Lin 2010).

The concepts of technology transfer have been defined in many different ways. However,

there is usually agreement (Sung and Gibson 2000, Sung and Hyon 1998) that technology

transfer requires a profoundly human endeavor (Gibson and Smilor 1991). The transfer of

technology often requires collaborative activity between two or more individuals or

functional units who are separated by structural, cultural, and organizational boundaries.

Technology transfer is an interactive process with a great deal of back-and-forth exchange

among individuals over an extended period of time (Gibson and Smilor 1991). Technology

transfer has also been defined as being product-embodied, process-embodied or personnel-embodied (Chen 1996). It can be a lengthy, complex and dynamic process and its success

is influenced by various factors originating from many different sources (Kumar Kumar


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technology transfer process as a complex "communication process" involving a purveyor, a

message, an organised channel, a receiver, and feedback: from the receiver to the purveyor.

Nowadays the technology transfer issue revolves around the extent of degree of

technologies that are transferred by the transferors to transferees (Pak and Park, 2004;

Minbaeva, 2007). The question is no longer whether the transferors are transferring

technology to transferees instead the focus in the literature has shifted to questions on (i)

the level (sophistication) of the transferred technology, and (ii) the stage where the transfer

process has reached (Lai and Narayanan, 1997; Narayanan and Lai, 2000).

2.5.1 Technology Transfer Elements

Technology transfer process involves seven major elements including transferor (source),

transferee (receiver), technology being transferred, transfer mechanism, transferor

environment, transferee environment, and greater environment. The entity that possesses

the technology is known as the transferor. The entity seeking the technology is the

transferee. Technology is the combination of technoware, humanware, inforware and

orgaware. A technology transfer mechanism is any specific form of interaction between

two or more social entities during which technology is transferred. The transferorenvironment is the set of conditions such as economic status, its technological status, and

policies and commitment towards technology transfer activities under which the transferor


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2.5.2 Technology Transfer Mechanisms

Some of the major mechanisms of technology transfer that can be included under these two

categories are as follows (Ramanathan 1994):

Market Oriented Mechanisms: Purchases of plant, equipment and products; Direct foreign

investment; Joint ventures; Technical collaboration; Licensing; Technical services

agreements; Engineering and construction agreements; Subcontracting; Turnkey contracts;

Product-in-hand contracts; Management contracts; Production sharing; Joint research

ventures; and Expert services. Non-Market Oriented Mechanisms: Books, academic

journals, business magazines etc; Sales literature; Technical information services;

Industrial fairs and exhibitions; Informal personal contacts; Participation in conferences,

seminars and workshops; and Training.

The technology transfer mechanisms exhibit some limitations or deficiencies (Seaton and

Cordey-Hayes 1993) such as failure to recognise adequately the significance of recipient

organisations needs, failure to address service delivery aspects of the technology and

knowledge transfer process, and underestimate the importance of the interactive processesand mechanisms between the supplier and the recipient.


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2.5.3 Technology Transfer Effectiveness

The effectiveness of transfer activity is evaluated in several different ways in the literature

(Mason 1980, Teece 1981, Schwartz 1982, Madeuf 1984, Mytelka 1985). They measured

transfer effectiveness by calculating the transfer cost, the speed, scope and level of internal

versus external transfer activity, R&D sufficiency in the local facility, control of imported

technology, and whether the technology transferred can assist with the development of new

skills and technology that will alter the host countrys comparative advantage. They

suggested that the imported technology must be assimilated.

A number of factors that influence the effective transfer have been identified and examined

by Reddy and Zhao (1990). The factors include the supplier firms willingness and ability

to transfer technical knowledge, the supplier firms organisational structure, the absorptive

capacity of the recipient firm, the mode of transfer used, the relationship between

interacting countries and firms, and training.

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2.6 Technology Diffusion and Technology Transfer

The concepts of technology diffusion and transfer are very close and in some cases they


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of mathematical models have been developed by researchers (Mansfild 1961, Floyd 1968,

Fisher-Pry 1971, Blackman 1972, Sharif-Kabir 1976, Sharif-Haq 1979).

Mansfield (1961) developed a model that explains the differences among innovations in

the rate of imitation. Mansfield's model is recognizable as the Pearl Curve.

The model expression is as follows:

m tn

eij

ij

lij ijt( )

( )

1

where

mij(t) = number of firms which have adopted the j-th innovation in the i-th industry at

time t,

nij = number of firms in the i-th industry which adopted the j-th innovation,

lij = a constant determining the location of the innovation diffusion curve,

ij = a constant determining the innovation diffusion rate,

t = time.

Floyd (1968) developed a mathematical model permitting trend extrapolation of figure-of-


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Fisher and Pry (1971) developed a technological substitution model with the assumptions

that technological advances are considered as competitive substitutions of one method of

satisfying a need for another, the substitution will proceed to completion once it has

progressed as far as a few percent, and the rate of substitution of new for old is

proportional to the remaining amount of the old left to be substituted.

The model is given by:

f

ft t

12 0

exp ( )

where

f = market share of new product in fraction,

1 - f = market share remaining to be substituted,

= half the annual fractional growth in the early years,

t = time,

and to = time at which substitution is half complete, i.e. f = 1/2.


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where

f = the figure-of-merit achieved at time t,

F = the maximum attainable value of the figure-of-merit

f0 = the figure-of-merit at time t = to

= a constant which governs the rate of change of figure-of-merit,

and t = time.

Sharif-Kabir (1975) developed a generalized technological substitution model expressed

as:

lnf

F f

F

F fc c t

1 2

where

f = market share of a product at time t,

F = upper limit of the market share,


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Shariff-Haq (1979) developed a causal model for studying the behaviour of technological

substitution. The model is expressed as follows:

UAQ Z P S T G D K N K N K [ ( )( )]2

where

= a parameter which governs overall substitution rate

Z = a constant representative of a given industry

P = profitability index

S = investment index

T = time since innovation

G = annual rate of growth of industrial production

D = obsolescence effect multiplier

N = total market size

K = adopters of the new technology

U = utility adjusted price effect multiplier

A = advertising effectiveness multiplier

Q = quality effect multiplier

t t


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F = maximum level of the cumulative proportion of adoptors,

a, b = positive real constants,

t = time,

and Fo = cumulative proportion of adoptors at time t = to

A number of studies (Nesbath and Ray 1976; Nielsen 1976; Swan 1973) considered the

international diffusion of technology. Bundgaard-Nielsen (1976) presented a quantitative

analysis of the parameters that influence the international diffusion of new technology. He

used the well known Pearl Reed curve to describe the diffusion process as follows:

A tA

c t t( )

( )

exp[ ( )]

1 0

where

A(t) = number of adoptions of the new technology that have occurred in year t after

the introduction of the new technology.

A() = final number of adoptions

c = rate constant of the diffusion process

t0 = time when half of the final number of adoption has occurred.


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SRSin = long-run equilibrium synthetic share of total rubber consumption, in country i,

0 1 SRSin .

a = constant term which positions logistic on time scale

b = rate of diffusion.

Therefore, in most of the technology diffusion or substitution studies, it is found to be valid

that the rate of diffusion at any time t is proportional to the present level of diffusion and

the amount yet to be achieved.

Mathematically, it can be expressed as

df t

dt

( ) f (t) (F - f (t))

where

f (t) = the present level of diffusion at time t.

F = The maximum level of diffusion that can be achieved.

F - f (t) = The amount of diffusion yet to be achieved.

Th b i b itt


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2.8 Technology Transfer Models

The well known quantitative models on technology transfer have been developed by Haq

(1979), Sukchareonpong (1979), Baruch, Gerald Steinberg, and Andrew Ruina (1983),

Baruch Raz and Isak Assa (1988), Liu (1993) and Jayaraman, Truong and Agrawal (1998).

Haq (1979) developed a time-level technology transfer model to describe the pattern of

technology transfer. The time-level technology transfer model has been shown as follows:

f (D, t) =F e

F e f D t

f D te

MAXD

MAXD

FMAXA eD

t t

1

0

0

20

( , )

( , )

. ( )

Where

f (D, t) = the level of assimilation at a location having potential technological distance

D at time t.

FMAX = the maximum level of assimilation that can be achieved by the most

developed source in the technology

f D t( , )0 = the level of assimilation at location having potential technological distance

D at time t0 .


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Sukchreonpong (1979) developed the technology transfer model assuming the

technological levels of transferor and transferee, and the technological gap between

transferor and transferee as functions of time. Moreover, the potential technological

distance was used as the average value rather than a constant that has been used by Haq

(1979).

The model is expressed as follows:

f tF

F f t

f t

a e

a e

a e

a eex

bxt

xbxt

kF

bx y

byt

y

byt

kF

bykF t t

( )

( )

( )

( )

1 0

00

0

0

where

f (t) = existing level of technology of a transferee at time t

F = Maximum level of technology that can be achieved in the long run by the

transferee

ax , bx, ay, by are positive real constants.

and t = time


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where

FMAX = maximum possible level of technology that the most developed country

(which has the highest technological level) can attain.

D = average potential technological distance of the transferee from the most developed

country.

Baruch Raz, Gerald Steinberg and Andrew Ruina (1983) developed a quantitative model

for the analysis of technology transfer which relates to the behaviours of the technological

leader and followers. In the model, they assumed that the rate of development of the

technological follower consists of two contributions:

dX

dtk f x f x xF F F L F 1 2( ) ( , )

The first contribution, kF , represents the indigenous development of the follower and the

second contribution represents that of technology transfer. In many cases, kF is

significantly less than the second transfer term and progress is, thus, dependent on the gap

between leader and follower. As the gap decreases, the contribution of technology transfer

ld l i ll b l i ifi t hil f l th f ll h l


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its indigenous development. There will be no contribution due to technology transfer. The

rate of progress of the follower would become

dX

dtkF F .

The analytic solution to the above model is given by

x t x k t k k k x x k k k eF L L L F T L F L F T kTt( ) ( ) / ( ) / 0 0 0

where x tF( ) = technological development of the follower at time t.

xL0 = technological development of the leader at time t = 0.

x F0

= technological development of the follower at time t = 0.kL = indigenous ability of the leader to develop.

kF = indigenous ability of the follower to develop.

kT = rate of technology transfer.

Later, Baruch Raz and Isak Assa (1988) improved the above model and the solution to the

above model is given by


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the leader.

UF = the upper limit of the technological development of

the follower.

The parameters such as technology transfer rate, KT , the rate of technological development

of the leader, KL , and the rate of technological development of the follower, KF, were

still assumed as constants.

In order to overcome some of the limitations of the previous work, Liu (1993) developed a

quantitative model for analysing technology transfer processes between countries in which

the absorbing capability of the follower has been taken into account explicitly.

Liu (1993) expressed the model by assuming that the growth rate of the follower is a

product of the absorbing capability (or transfer rate) K tT( ) and the current technological

gap between the leader and the follower as follows:

dX t

dtK t X t X t F

T L F

( )( )[ ( ) ( )]


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where KF is a constant reflecting the transfer rate or absorbing capability of the follower.

The technology transfer models that have been developed by Haq (1979) and

Sukchareonpong (1979) used the following hypothesis:

The rate of assimilation of a transferee in a particular technology during technology

transfer at a certain time, t, is proportional to:

the existing level of assimilation of the transferee in such technology

the level remaining to be achieved by the transferee

the function governing the technology transfer rate

The technology transfer models that have been developed by Raz et al. (1983 & 1988) and

Liu (1993) used the following hypothesis:

The rate of growth of a transferee in a particular technology during technology transfer at a

certain time, t, is proportional to:


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(t) =T t

T t

x

y

( )

( ) (Lius Model)

where

(t) is the function governing technology transfer rate.

Tx (t) is the technological level of the transferor.

Tx (t) =

1

1 a exbxt

Ty (t) is the technological level of the transferee.

Ty (t) =1

1

a eybyt

a a b bx y x y,

, , , are positive real constants.

G (t) is the technological gap between the transferor and transferee.

G (t) = Tx (t) - Ty (t)

D is the potential technological distance between the transferor and transferee

D =T T

T

x y

y

I ll h d l l di R l d l (1983 & 1988) h h l f


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f te

f tk e d

kF d

t

t

kF d

t

t

t

t

( )

( )( )

( )

( )

0

0

0

0

1

The above generalized mathematical model measures the level of technology assimilation

of a country in an industry during technology transfer with respect to time. It is a non-

linear function of time having a S-shape. The constant k can be obtained from the non-

linear regression analysis.

Jayaraman further developed the specific models for technology transfer using the

following functions governing the technology transfer rate:

(t) = (1 - G (t)).G(t)

(t) = R(t).G(t) =T t

T t

T t T t y

x

x y

( )

( )

( ( ) ( ))

T t T t( ) ( )


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Ty (t) =1

1

a eybyt

a a b bx y x y, , , are positive real constants.

G (t) is the technological gap between the transferor and transferee

at time t.

G (t) = Tx (t) - Ty (t)

R(t) = relative technology position of the transferee from the transferor at time t.

R tT t

T t

y

x

( )( )

( )

D(t) = potential technological distance between the transferor and transferee at time t.

D(t) =T t T t

T t

x y

y

( ) ( )

( )

In all the above functions governing the technology transfer, the following conditions

exist:

When the technology gap, G(t) = 1 ie. the technological level of the transferor, T tx ( ) , is

very high as compared to the technological level of the transferee, T ty ( ) , or in other words,

T ( ) 0 th th f ti i th t h l t f (t) b


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potential for transfer, but still that does not happen in reality because of the existence of

competition amongst themselves.

The specific models for technology transfer are expressed as

Model - 1df t

dtk G t G t f t F f t

( ).( ( )). ( ) ( )( ( )) 1

Model - 2df t

dtk R t G t f t F f t

( ). ( ). ( ) ( )( ( ))

Model - 3df t

dtk e G t f t F f t

D t( ). . ( ) ( )( ( ))

( )

where

k = a constant reflecting the rate of technology transfer

The specific models for technology transfer are developed as follows:


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Model 2:

f te

f tkG R e d

kF G R d

t

t

kF G R d

t

t

t

t

( )

( )( ). ( )

( ). ( )

( ). ( )

0

0

0

0

1

Model 3:

f te

f tkG e e d

kF G eD

d

t

t

D

kF G e D d

t

t

t

t

( )

( )( ).

( ).( )

( )

( ). ( )

0

0

0

0

1

The above specific mathematical models measure the level of technology assimilation of

the transferee in a given industry with respect to time. It is a non-linear function of timehaving a S-shape. The constant k can be obtained from the non-linear regression analysis.


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quantitatively. A methodology to evaluate the technology transfer potentials incorporating

the time, technological levels and technology gap between the transferor and transferee

will help in understanding the complex process of technology transfer. The main emphasis

of this study is to develop a quantitative model incorporating time, technological level and

a dynamic potential technological distance for measuring the technology transfer potentials

that exist between a transferor and a transferee at the industrial level.

In developing technology transfer model, Haq (1979) assumed that the technology levels

of transferor and transferee, the technology gap between the transferor and transferee, and

the potential technological distance of the transferee from transferor as constants.

However, Sukchareonpong (1979) assumed the above parameters as functions of time.

Both Haq (1979) and Sukchareonpong (1979) measured the technology levels by way of

an index but used the actual values for measuring the level of assimilation. In developingtechnology transfer models, Raz et al. (1983 & 1988), Liu (1993), and Jayaraman, Troung

and Agrawal (1998) assumed that the technology levels of transferor and transferee, and

the technology gap between transferor and transferee as functions of time. They used

indexes for measuring the level of technology growth.

The technology transfer models that have been developed so far in the literature use the

following two types of hypothesis


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the salient feature governing the technological transfer process is that the transfer rate at

any time is proportional to the current level of assimilation of such technology of the

transferee and the level remaining to be achieved by the transferee in the long run.

However, there may be many other important factors influencing the transfer rate such as

the technological gap between transferor and transferee, the potential technological

distance between the transferor and transferee, the geographical distance between locations

etc.

In this study, in developing the technology transfer model, it is hypothesized that the rate

of assimilation of a particular technology of a transferee at a certain time, t, is proportional

to:

(a) the existing level of assimilation of such technology of the transferee,

(b) the level remaining to be achieved by the transferee in the long run, and

(c) a function that incorporates the factor that influences the assimilation rate in a

significant manner ( such as the relative technological gap between the transferor and

transferee). The potential technological distance is defined as the relative technological gap

between the transferor and transferee.

2.10 Summary

In this chapter the concept of technology is presented The definition of technology


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CHAPTER 3: RESEARCH METHODOLOGY

3.1 Introduction

The specific objectives of this research is to develop of a mathematical function that

determines the technological level of a country; develop a generalized mathematical model

for measuring the technology transfer potentials that exist between a transferor and atransferee; develop a specific dynamic mathematical model incorporating time and

technological level for evaluating technology transfer potentials; verify the technology

transfer model for boundary conditions; derive the technological transfer/change models as

a special case of the technology transfer model developed; and apply the technology

transfer model to study the technology transfer pattern in selected countries in certainindustries.

In this chapter, a mathematical function is developed using the logistic growth pattern to

determine the technological level of a country, in a given industry. This is measured by an

indicator called Technology Index. Considering the variables that influence and reflect

the performance of that industry, the technology index is computed using the factor

loadings obtained by the statistical technique factor analysis. Initially, the concept of


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distance, between the transferor and the transferee. A generalized mathematical model for

measuring the technology transfer potentials that exist between a transferor and a

transferee is developed initially in this study. Then, a specific dynamic technology transfer

model is developed incorporating time, technological level and dynamic potential

technological distance.

As this study is on developing a quantitative model for measuring the technology transfer

potentials that exist between a transferor and a transferee at the industrial level, the

variables that influence and reflect the performance of the given industry of various

countries under study are identified and collected for the past few years. The raw data

collected for various countries for the past few years are then converted into standardized

data. The factor analysis is used to determine the factor loadings of the variables for

formulating the technology index at the industrial level. The calculated technology index isthen normalized to have value between 0 and 1. The past technology indexes of each

country in the given industry are used to develop its technology index function by using

the logistic growth pattern. The technology index function developed for each country in

the given industry is then used for determining its technology level by way of an index.

To show the applicability and validity of the technology transfer model developed in this

study some countries are selected to study their technology transfer pattern in selected


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The development of a generalized mathematical model for measuring the technology

transfer potentials that exist between a transferor and a transferee and a specific dynamic

technology transfer model incorporating time, technological level and dynamic

technological gap, verification of the selected technology transfer model for boundary

conditions, and derivation of technology transfer/change models as a special case of the

technology transfer model developed are presented in the chapter 4. The case studies to

show the applicability and validity of the technology transfer model developed are

presented in chapter 5.

3.2 Technology Index

The technological level is defined as the capability of the technological system that

consists of technoware, humanware, inforware and orgaware (Technology Atlas Project

Team 1989) and it is measured through an index called Technology Index. It measuresthe technological level on a single measure by aggregating the related statistics. The

technology index measured at the firm level refers to the level of technological

components applied in the production facilities within a firm. The technology index

measured at the industrial level refers to the level of the technological components applied

in the different production facilities within an industry. The technology index measured atthe national level refers to the level of the technological components applied in the

different production facilities in various industries within a country


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aj1 = the first factor loading,

1 = eigenvalue for the first factor,

and Yj = a standard form of j-th variable.

The factor analysis helps to develop a scale on which individuals, groups or nations can be

rated and compared (Rummel 1970). One of the problems that would be encountered when

using many variables is that the dimensions of these measured values may be different.

Many methods are available to overcome this problem. However, in this study factor

analysis technique is used to perform the required integration of several variables to give a

technology index.

Factor analysis method derives common variation from the correlation of chosen variables,

identifies the measurement overlaps, and extracts a group of fundamental and hypothetical

factors, with a view to use the correlation among the factors to redefine the correlation

among several variables. This technique usually involves the following two steps:

calculation of the correlation coefficient matrix, and

extraction of the factor loading matrix.


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Factors

Variable F1 F2 F3 F4 F5 Communality

X1 F11 F12 F13 F14 F15 C Fj1 12

X2 F21 F22 F23 F24 F25 C F j2 22

X3 F31 F32 F33 F34 F35 C Fj3 32

X4 F41 F42 F43 F44 F45 C F j4 4

2

X5 F51 F52 F53 F54 F55 C Fj5 52

Eigen

value E Fi1 12

E Fi2 22

E Fi3 32

E Fi4 42

E Fi5 52

5

In principle, the five variables are respectively matched by the corresponding five factors,

because the meaning of each factor differs so long as the five variables are not identical. In

the factor loading matrix, the factor loading value, ( F i jij , ,2,..., ; ,2,..., 1 5 1 5 ), the

communality, the factor loading matrix, and the eigen value have the following relations:


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Suppose that F112 and F12

2 stand for the variation of X1 explained by factor 1 and that of

X1 explained by factor 2, respectively. The total variation of X1 explained by factor 1

through factor 5, ( F F F F F 112

122

132

142

152 ) amounts to 1.

The total variation of the five variables ( X1 ~X5 ) that is explained by factor 1 (

F F F F F 112

212

312

412

512 ) is called the eigen value. This value represents the

portion that factor 1 explains out of the total variation. For instance, if the eigen value is

4.0, the total variation of the five variables ( X1 ~X5 ) is 5, and therefore factor 1 explains

80% (4.0 out of 5) of the total variation.

Assume that F11 , F21 , F31 , F42 , and F52 have respectively the highest value in the

corresponding factor loading matrix of X1 ,X2 , X3 ,X4 , and X5 . In this case X1 ,X2 , and

X3 can be categorized as a group that shares an identical characteristic of factor 1, and so

can X4 and X5 as group that has the characteristic of factor 2. As a result, X1 ,X2 ,X3 ,X4

, and X5 can be divided into two groups represented by factor 1 and factor 2. Such a

process leads to the selection of variables required for the derivation of the index.

When X X and X1 2 3 are grouped together the weight is calculated from factor loading


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TF

F F FZ

F

F F FZ

F

F F FZ

11

2

112

212

312 1

212

112

212

312 2

312

112

212

312 3

* * *

where Z Z Z1 2 3, , = the Z-scores of X X X1 2 3, , , respectively, in the standard normal

distribution.

3.4 Technology Index - Normalization

The technology index, T, obtained from factor analysis ranges from - to +. Since the

variables are assumed to be normally distributed, the technology index, T, which is the

sum of standardized normal variates is also normally distributed. It is appropriate to have a

standardized normal variates (i.e., with mean 0 and variance 1) than the non-standardized

ones. By , employing the cumulative normal distribution table, the technology index, T, is

normalized.

Since Technology index, T is a Z-score, it has positive or negative values. The

methodology of transforming an index with positive or negative values into an index with

nonnegative value between 0 and 1 is known as ZX-score method. The Z-score has the

average of 0 and a variance of 1. The probability that a value of less than 0 can occur is

0.5, which is also the probability area under the normal curve that a value of more than 0


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T tae

bt( )

1

1

where T(t) is the technology index function at year t with 0 T t( ) 1 ; and the constants a

and b have positive values.

The technology index function can be estimated by taking the natural logarithm on both

sides and converting the resultant expression into linear regression equation as shown

below:

Y t

where

YT t

ln(( )

)1

1

ln ,a < <

= - b, < 0.


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3.7 Determinants of Technology Index

The technological capability of a country at the industrial level is generally influenced by

both the national and manufacturing technology climate factors.

In this study, the factors that determine the technological capability of a country at the

industrial level are broadly based upon the following three categories:

National Technology Climate Factors.

Manufacturing Technology Climate Factors.

Specific Industry Technology Climate Factors.

Due to the limitation on the availability of data, the following variables are considered for

measuring the technology index at the industrial level:

National Technology Climate Factors:

Research and Development Expenditure per Economically Active Population.

Manufacturing Technology Climate Factors: Output per Employee in Manufacturing Sector.

V l dd d E l i M f i


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The output per employee in the manufacturing sector or each specific industry reflects the

technology and its sophistication employed in their production facilities. The value-added

per employee in the manufacturing sector or each specific industry reflects the

effectiveness of the human skills and technology employed in their production facilities.

The national technology climate conditions and manufacturing technology climate

conditions have direct influence on the growth of the specific industry in a country.

However, these influences may vary depending on the type of industry. In this research it

has been assumed that they will have same influence on all industries.

3.8 Summary

In this chapter, the concept of technology index, the methodology used to construct the

technology index, the normalization of technology index and development of technologyindex function are presented. The determinants of technology index at the industrial level,

used for this study, are identified. In this chapter, the factor analysis is proposed to

determine the factor loadings of the variables for formulating the technology index at the

industrial level. The calculated technology index is then normalized to have value between

0 and 1. The technology indexes data are used to develop its technology index function by

using the logistic growth pattern. The technology index function is then used for

determining its technology level by way of an index


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CHAPTER 4: TECHNOLOGY TRANSFER MODEL

DEVELOPMENT

4.1 Introduction

To develop the technology transfer model, it is hypothesised in this study that the

technology assimilation rate of the transferee is proportional to its existing level of

assimilation, the level remaining to be achieved in the long run and the function governingthe technology transfer rate. The function governing the technology transfer rate is

assumed to be the function of relative technological gap, namely potential technological

distance, between the transferor and the transferee. A generalized mathematical model for

measuring the technology transfer potentials that exist between a transferor and a

transferee is developed initially in this study. Then, a specific dynamic technology transfer

model is developed incorporating time, technological level and dynamic technological gap.

Finally, the model is verified for boundary conditions. The Haqs model, Blackmans

model, Fisher-Pry model, Bhargava model, Mansfield model, and Jayaraman-Truong-

Agrawal model are derived to be the special cases of the technology transfer model

developed.


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(iii) The transferee must have the required capability to adapt the technology. The

capability of the transferee to exploit a particular technology is determined by its

technological level measured by its technological index.

(iv)The result of the spatial diffusion will be technology assimilation by the transferee.

The level of the technology transferred at time t is determined by the diffusion of that

technology until the time t.

(v)Technology is assumed to be specific. Over time, the mode of production is considered

to remain the same. The model does not allow for revolutionary changes or

breakthrough in the production of similar product.

(vi)If the technology gap between the transferor and transferee in the given industry is too

high, less or even none of technology can be transferred since the transferees industry

does not have enough capability to assimilate the technology. If the technology gap is

too close, the transfer potential is again low because they have very little to transfer.The countries having close technological level in an industry produce the same kind of

product (or technology), hence they are competitors. Although in this case there is a

small potential for transfer, but still that does not happen in reality because of the

existence of the competition amongst themselves and their desire to maintain a

competitive edge.

(vii)The maximum level of the technology that is to be assimilated by the transferee will

be determined by the relative technological gap between the transferor and transferee


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proportional to the current level of assimilation of such technology of the transferee and

the level remaining to be achieved by the transferee in the long run. However, there may be

many other important factors influencing the transfer rate such as the technological gap

between transferor and transferee, the potential technological distance between the

transferor and transferee, the geographical distance between locations etc.

In developing the model, the function governing the technology transfer rate is considered

as dependent on the relative technological gap between the transferor and the transferee

which is the potential technological distance. In many spatial diffusion studies, the

geographical distance between the transferor and the transferee acts as a major determinant

of the transfer process. The greater the distance, the lesser the impact of innovation.

Generally the geographical distance has insignificant bearing upon technological

innovation diffusion. For example, Japan is far away from USA as compared to SouthAmerican countries, but in case of automobile technology, electronics technology and

computing technology, Japan is far ahead of the South American countries. This suggests

that geographical distance is not necessarily an important factor for studying the

technology transfer process. Instead it will be more relevant to consider the relative

technological distance between the transferor and the transferee.

Therefore in this study in developing the technology transfer model it is hypothesized


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Thus the general form of the technology transfer model can be mathematically expressed

as

)]()()[()( tftFtftdt

df

or

)]()()[()( tftFtftkdt

df (1)

where

)(t is a function governing the technology transfer rate and k is a positive proportionality real

constant.

In the literature (Haq 1979, Sharif-Haq 1981, Sukchareonpong 1979, Jayaraman-Truong-

Agrawal 1996, Truong 2002) so far, both )(tF and )(t are taken to be constant. For

instance, Sharif-Haq (1979) introduces the technological gap asy

yx

T

TTD

and assume


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dttktfFf

df)(

)]([

and then integrating both sides, noting that the integral of the left hand side is

)(

)(ln

1

tfF

tf

F.

In this study, we will assume )(tF is a function of t.

dt

df= 2)()()()()( tftktftFtk

)()(

)()()(

12

tktf

tFtkdtdf

tf

or

- )()(

)()()(

1 2 tktftFtk

dtdf

tf


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Notice that in the special case where FtF )( constant, the general solution (2)

becomes:

t

tdkF

etf

tfF

Ftf

0

)(

0

0

)(

)(1

)(

(3)

Equation (3) is the formula representing the flow of the transferred technology with respect

to time in the case where F is a constant.

4.4 Technology Transfer - Specific Model

In this study, an extended dynamic model of technology transfer is developed by giving

both )(t and )(tF special functional forms and taking into account the influence on thetransfer rate by the most significant factor, the potential technological distance.

Substituting )(')( tDt for 0t , )()( tDFetF in the generalised technology

transfer model developed in (2),

dDekF

e

t

t

D

0

)()('


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time having a S-shape. The constant k can be obtained from the non-linear regression

analysis.

In the above model, the values of D and F are functions of time. For simplicity in

computation, the potential technological distance between the transferor and the transferee

can be reasonably approximated to their average values of D in the time period of t1 and t2

. The average value of D is computed by dividing the relative average of technological gap

between the transferor and the transferee in the time period of t1 and t2 , by the average

technological level of the transferee in the time period of t1 and t2 . It is further assumed

that F is the maximum level of assimilation that the most developed country can achieve

in the long run.

The potential technological distance is redefined as,

DT T

T

x y

y

where


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substituting the values of Tx and Ty in the above Eqn., we get

Dt t

T t dt t t

T t dt

t tT t dt

x

t

t

y

t

t

y

t

t

1 1

1

2 11

2

2 11

2

2 11

2

( ) ( )

( )

D

T t dt T t dt

T t dt

x

t

t

y

t

t

y

t

t

( ) ( )

( )

1

2

1

2

1

2

Substituting the average value of D as D

T t dt T t dt

T t dt

x

t

t

y

t

t

y

t

t

( ) ( )

( )

1

2

1

2

1

2and F = maxF in the

technology transfer model


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)(

0

0max

max

02

max

)(

)(1

)(ttekF

D

D

etf

tfF

eFtf

Notice that this equation can be easily re-written as:

)0(2max

)()(

)(

)(

0max

0

max

ttDekF

De

tfF

tf

tfeF

tf

Taking logarithm both sides:

)(2

max

0max

0

max

0

)()(ln

)()(ln

ttD

DDekF

tfeFtf

tfeFtf

Letting: 02

max

0max

0

1)(

)(ln tekF

tfeF

tfc

D

D

, and DekFc 2max2

we get:

tcctf

21

)(ln


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where

maxF is the maximum level of assimilation that the transferor will achieve

at t ; and

D is the potential technological distance between the transferor and transferee.

Assuming the average value of D as D

T t dt T t dt

T t dt

x

t

t

y

t

t

y

t

t

( ) ( )

( )

1

2

1

2

1

2and F = maxF in the

technology transfer model

)()(0

0)0(

)0()(

)(

1)(

tDtDkFe

eekF

kFeEikFeEiketf

etf

tD

tDtD

the model is rewritten as


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(i) at t ; lim )(tf = 0.

t

(ii) at t ; lim )(tf = F e Dmax

t

The boundary conditions are applied to the model as follows:

)(

0

0max

max

02

max

)(

)(1

)(ttekF

D

D

etf

tfF

eFtf

At t ; on taking the limits of both sides in Eqn. as t , one obtains,

etf

tfeF

eFtf

D

D

)(

)(1

)(

0

0max

max


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D

D

eFeF

tf

maxmax

01)(

The technology transfer model,)(

0

0max

max

02

max

)(

)(1

)(ttekF

D

D

etf

tfF

eFtf

is thus verified as shown above.

4.6 Derivation of Previous Technology Transfer/Change Models in the Literature

The dynamic technology transfer model developed in this study can be used to generate the

time-level technology transfer models (Haq-Shariff model, Sukchareonpong model, and

Jayaraman-Truong-Agrawal model), and the models of technological change (namely,

Blackmans model, Fisher-Pry model, Mansfields model, and Bhargava model) by

making certain assumptions. The previous models developed in the literature are shown to

be the derived cases of the dynamic model developed in this study.

Fisher-Pry model (Fisher-Pry 1975):

In this case , take )(t = 1, )(tF = 1, )( 0tf =2

1, and 2k .


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)(2 0)(1

)( tte

tf

tf

( Fisher-Pry model).

Bhargava model (Bhargava 1995):

111)()( ccc ctbbtct , )(tF = 1, )( 0tf =2

1, and bk .

Then the solution as given by:

)(

)(

0

10

10

011

0

11

11)(

1

)(

ccc

ccc

t

tcc

t

t

cc

ttb

ttb

dtctbb

dtctbb

e

e

etf

etf

Putting cbta )( 0 , we have:

cbca

etf

)(

)(


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Blackman model (Blackman 1974):

Here, we take )(t = 1, )(tF = maxF , and Ak .


tt

t

t

dAF

dAF

eFFtf

etf0

max

0max

1

maxmax0

1

11

)(

1)(

)(

maxmax0

)(

0max

0max

11

)(

1)( ttAF

ttAF

eFFtf

e

tf

)(max

0max

)(

0max

0max

0max

1)(

)(

ttAF

ttAF

eF

tfF

etfF


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)]([)(

)(

0maxmax

max

max tfFF

F

tfF

tf

=

)(

)(

0max

)(

00max

tfF

etfttAF


)(

)(

)(ln

)(

)(ln 0max

0max

0

max

ttAF

tfF

tf

tfF

tf

Let 0max0max

0

1)(

)(ln tAF

tfF

tfc

, and max2 AFc , then

tcctfF

tf21

max )(

)(ln

(Blackman model)

Mansfield model (Mansfield 1961):

Here, )(t = 1, )(tF = 1, and Ak . This is case 3 with 1max F .


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= Atce 1 , where 01 Atcc .

Re-arranging, one gets:

)( 11

1)(

Atce

tf

(Mansfield model).

Sharif-Haq model (Haq 1979, Sharif-Haq 1981):

)(t = De , )(tF = DeF max , and Ak ,

wherey

yx

T

TTD

is treated as a constant.


t

t

DDdeeAF

D

etfF

eFtf

0max)(

1

)(

0max

max


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)(2

max

0max

0

max

0

)(

)(ln

)(

)(ln

ttD

DDeAF

tfeF

tf

tfeF

tf

Letting:)(2

max0max

0

1

0

)(

)(ln

ttD

D

eAFtfeF

tfc

, and DeAFc 2

max2

we get:

tcctfeF

tfD 21

max )(

)(ln

(Sharif-Haq model)

Sukchareonpong model (Sukchareonpong 1979):

)(t = )(1 tG , )(tF = F , and 'kk , where 'k is a positive constant.


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= t

t

b

x

y

b

x

x

yx eab

eab

kF

0

ln1

ln1

=

00

lnln)( 0 tby

tb

y

x

tb

x

tb

x

xy

y

x

x

ea

ea

b

kF

ea

ea

b

kFttkF

t

tdGkF

e 0)](1[

= )( 000

ttkF

bkF

tb

y

tb

ybkF

tb

x

tb

x eea

ea

ea

ea y

y

yx

x

x

Hence:

e)(

)(1

)(

)t-F(t-

0

0 0

00

k

b

kF

tb

y

tb

yb

kF

tb

x

tb

xy

y

yx

y

y

ea

ea

ea

ea

tf

tfF

Ftf

(Sukchareonpong model)

Jayaraman-Truong-Agrawal model (Jayaraman-Truong-Agrawal 1996):


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But

yxyx b

y

b

x

b

y

b

x eaeaeaeaGG

1

1

1

11

1

1

1

1)](1)[(

=

yxyxyx bybxbybxb

y

b

x eaeaeaeaeaea 11

2

1

1

1

1

1

1

1

122

Hence:

t

t

t

t b

yx

yb

xx

xby

y

kFeab

kFaeab

kFaeabkFdGGkF

yx

y

0

0

)(2)()(

)ln(2)](1)[(

where

t

an extended model for measuring the technology transfer potential

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