A Handbook of Inter Firm Technology Transfer and Organizational Performance Doc Xi Finale

Sazali A.W, Raduan C.R The Handbook of Inter-Firm Technology Transfer - An Integrated Perspective

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THE HANDBOOK OF INTER-FIRM TECHNOLOGY TRANSFER

- AN INTEGRATED PERSPECTIVE

http://www.pdfcomplete.com/cms/hppl/tabid/108/Default.aspx?r=q8b3uige22


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THE HANDBOOK OF INTER-FIRM TECHNOLOGY

TRANSFER - AN INTEGRATED PERSPECTIVE

By

SAZALI ABDUL WAHAB AND RADUAN CHE ROSE



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CONTENTS _________________________________

Contents iii

List of Abbreviations v

Preface vii

Introduction xii

1 - Technology and Technology Transfer: Defining the Concepts 1

Part I - Literature Review and Conceptual Models of Inter-Firm Technology 13

Transfer

2 - The Evolution and Development of Technology Transfer Models: The Knowledge- 14

Based View and Organizational Learning Perspectives

3 - The Effects of Inter Firm Technology Transfer Characteristics on Degree of 47

Technology Transfer in International Joint Ventures: A Framework

4 - A Holistic Model of Inter-Firm Technology Transfer Based on Integrated 79

Perspectives of Knowledge-Based View and Organizational Learning



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Part II - Technology Transfer Characteristics and Degree of Technology 97

Transfer

5 - The Effects of Knowledge Characteristics on Degree of Inter-Firm Technology 98

Transfer

6 - Measuring the Impact of Technology Suppliers’ Characteristics on Degree of Inter- 113

Firm Technology Transfer in International Joint Ventures

7 - The Effects of Technology Recipients’ Characteristics on Degree of Inter-Firm 129

Technology Transfer

8 - The Effects of Relationship Characteristics on Degree of Inter-Firm Technology 144

Transfer

Part III - Degree of Technology Transfer and Organizational Performance 161

9 - Measuring the Effect of Degree of Inter-Firm Technology Transfer on Local Firms’ 162

Performance

Part IV - The Significant Influence of Moderating Factors 178

10 - MNCs’ Size, Technology Recipients’ Characteristics and Degree of Inter-Firm 179

Technology Transfer

11 - Age of Joint Venture, Degree of Inter- Firm Technology Transfer and Local Firms’ 198

Performance

12 - MNCs’ Country of Origin, Degree Technology Transfer and Firms’ Performance 216

13 - MNCs’ Equity Ownership, Degree of Technology Transfer and Firms’ Performance 235



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LIST OF ABBREVIATIONS ACAP Absorptive Capacity CEO Chief Executive Officer COMPLX Complexity CPERF Corporate Performance EDA Exploratory Data Analysis EXPK Degree of Explicit Knowledge FDI Foreign Direct Investment FMM Federation of Malaysian Manufacturers GM General Manager GSM Graduate School of Management HRM Human Resource Management HRPERF Human Resource Performance ICV International Cooperative Venture IJV International Joint Venture IMP Industrial Master Plan JV Joint Venture JVAGE Age of Joint Venture KBV Knowledge-Based View KCHAR Knowledge Characteristics KCHARQ Knowledge Characteristics Questionnaire KT Knowledge Transfer LFP Local Firms’ Performance LFPQ Local Firms’ Performance Questionnaire MD Managing Director MI Manufacturing Industry MIDA Malaysian Industrial Development Authority MITI Ministry of International Trade and Industry MLR Multiple Linear Regression MMR Moderated Multiple Regression MNCs Multinational Corporations MNCCOO MNCs’ Country of Origin MNCIND MNCs’ Industries MNCSIZE Size of MNCs MT Mutual Trust OL Organizational Learning OLS Ordinary Least Square PPROTEC Partner Protectiveness RBV Resource-Based View RCHAR Relationship Characteristics RCHARQ Relationship Characteristics Questionnaire RCOL Recipient Collaborativeness RELQLTY Relationship Quality R&D Research and Development ROC Registrar of Companies



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ROI Return on Investment SD Standard Deviation SI Service Industry SPEC Specificity SPSS Statistical Package for the Social Science TCT Tacitness TCTK Degree of Tacit Knowledge TRANSCAP Transfer Capacity TT Technology Transfer TTDEG Degree of Technology Transfer TTDEGQ Degree of Technology Transfer Questionnaire TTCHARS Technology Transfer Characteristics TRCHAR Technology Recipient Characteristics TRCHARQ Technology Recipient Characteristics Questionnaire TSCHAR Technology Supplier Characteristics TSCHARQ Technology Supplier Characteristics Questionnaire U.S United States 9th MP The Ninth Malaysia Plan



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Preface _________________________________

The MNCs are regarded as the most efficient vehicle for transferring technology and knowledge

across organizational borders especially through strategic alliances and international joint

ventures (IJVs) (Tihanyi and Roath, 2002; Kagut and Zander, 1993). The inter-firm technology

transfer through IJVs have significantly contributed to a higher degree of local innovation

performance/capabilities, technological capabilities, competitive advantage, organizational

learning effectiveness, productivity, technological development of local industry, and the

economic growth of the host country. Realizing the high potentials of inter-firm technology

transfer initiatives, organizations in the developing countries are strategizing to collaborate, learn

and internalize their foreign partner’s technological knowledge by forming IJVs with foreign

MNCs to help speed up the process of strengthening their organizational competitiveness,

technological capabilities and local innovation.

Nevertheless, the inter-firm technology transfer processes in IJVs have often involved complex

tradeoffs between the technology suppliers’ willingness to transfer their considerable amount of

technologies, degree of protectiveness of proprietary technology (Inkpen, 2000), degree of

transparency (openness) (Hamel, 1991), and motivation to transfer (Szulanski, 1996).

Transferring technologies within IJVs has always been subjected to various facilitators, actors

and complex relationship between partners (Szulanski, 1996); which could contribute

tremendous impact on degree of technologies. Consistent with the inter-firm technology transfer

and organizational learning phenomenon, previous studies have also argued that the interplay

between complex relationship and competition between IJVs partners (Hamel, 1991) and the

tension between knowledge sharing and knowledge protection have caused a ‘learning paradox’

(Hau and Evangelista, 2007); where this paradox exists because the fact that the inter-firm

technology transfer is indeed an organizational learning process (Huber, 1991).



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Previous studies have also repeatedly cautioned that due to the risk of ‘technology spillovers’

and high transaction cost associated with technology transfer, the MNCs are perceived to be a

reluctant technology supplier and are quite slow in transferring technology and R&D expertise to

local industries (Narayanan and Lai, 2000; Muller and Schnitzer, 2006). On the other hand, the

MNCs have consistently contended that it is not a question of their willingness to transfer rather

the transferring process is hampered by low maturity level of the local industry; which is largely

due to insufficiency of skilled personnel and weak institutional support and business

environment (Rasiah and Anuwar, 1998). However, studies have shown that as compared to the

U.S MNCs, technology transfers by the Japanese MNCs have been found to be less intensive and

much slower (Raduan, 2002; Yamashita, 1991). Indeed, the Japanese MNCs, to some extent,

have no intention to transfer key aspects of their technology in order to maintain their dominance

in Southeast Asian economies (Taylor, 1995). Evidence have shown that the impressive record

of economic progress in Malaysia has not gone hand in hand with the technological progress of

proportionate magnitude due to weak relationship between technology transfer practice and

decision to innovate thus causing small effect on the country’s technological development

(Malairaja and Zawdie, 2004). Although theoretically technology transfer initiatives such as

strategic alliances and IJVs are considered as the most efficient mechanisms to internalize

foreign technologies, they do not sufficiently help improve local technological and innovative

capabilities (Bell et al., 1996).

Thus, the current issue of technology transfer in the developing countries is centered on the

effectiveness, efficiency and successful implementation of technology transfer process

(Narayanan and Lai, 2000; Lai and Narayanan, 1997), where the success has frequently been

associated with degree of technologies that are transferred to local firms (Pak and Park, 2004;

Yin and Bao, 2006). This is mainly because the technology transfer success is not merely

possessing the ability to operate, maintain or repair machineries at the production level

(transmission) but it also includes the ability to successfully learn, acquire, adopt and apply new

external technologies and knowledge (absorption) (Davenport and Prusak, 2000). Technology

transfer initiatives should not only act as a catalyst for national economic growth but more

importantly as a perfect mechanism to increase local organizations’ performance,

competitiveness, technological and innovation capabilities, and productivity.



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In their efforts to materialize these noble objectives, organizations in the developing countries

are seriously attempting not only to eliminate the inter-firm technology transfer barriers and

reduce technological gaps that inhibit a higher transfer of advance technologies; which are

attributed to knowledge, recipient, supplier and relationship characteristics, but they are also

trying to assess and estimate the extent of significant effect of these critical determinants of

technology transfer on degree of technology transfer. Moreover, organizations in the developing

countries are very much concerned about the significant role played by tacit and explicit

knowledge in strengthening both corporate and human resource performances. Realizing that

both tacit and explicit technologies are the main source of competitive advantage of foreign

MNCs therefore in order to increase and further improve their organizational performance, the

local organizations are facing a major challenge to fully optimize and extract the potential

benefits/opportunities of learning both tacit and explicit knowledge that arise from IJVs.

Although many studies have acknowledged the significant effect of knowledge transfer

determinants on knowledge transfer outcomes, nevertheless, the effects of technology transfer

characteristics on degree of technology transfer in inter-firm technology transfer could possibly

be influenced by other established factors such as MNCs’ size (MNCSIZE), age of JV (JVAGE),

MNCs’ country of origin (MNCCOO), and MNCs’ types of industry (MNCIND). Therefore, based

on the above scenarios, failure to appropriately understand, address, and manage the inter-firm

technology transfer factors and barriers holistically would certainly cause uncertainties of the

technology transfer outcomes, compromise the local firms’ global competitiveness, productivity

and the technological capability building process of local work force. This will definitely

undermine the government’s ambition to turn Malaysia into a developed nation in 2020. Frpm

the academic perspective, the primary objective of this handbook is to contribute to the existing

literature on inter-firm technology especially in providing empirical evidence on the significant

relationships between technology transfer characteristics, degree of technology transfer and

organizational performance.

Sazali Abdul Wahab

National Defence University of Malaysia

July, 2011



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REFERENCES

Bell, M., Hobday, M., Abdullah, S., Ariffin, N. & Malik, J. (1996). Aiming for the 2020: A Demand-

Driven Perspective on Industrial Technology Policy in Malaysia: Final Report to the Ministry of

Science, Technology and Environment, Malaysia, World Bank/ United Nation Development

Programme.

Davenport, T. H. & Prusak, L. (2000). Working Knowledge: How Organizations Manage What They

Know. Harvard Business School Press, Boston, MA.

Hamel G. (1991). Competition for Determinant and Interpartner Learning within International Strategic

Alliances. Strategic Management Journal, 12, p. 83–103.

Hau, L. N. & Evangelista, F. (2007). Acquiring Tacit and Explicit Marketing Knowledge from Foreign

Partners in IJVs. Journal of Business Research, 60, p. 1152-1165.

Huber, G. P. (1991). Organizational Learning: The Contributing Processes and the Literature,

Organization Science, 2 (1), p. 88-115.

Inkpen, A. C. (2000). Learning through Joint Ventures: A Framework of Knowledge Acquisition. Journal

of Management Studies, 37 (7), p. 1019-1043.

Lai, Y. W. & Narayanan, S. (1997). The Quest for Technological Competence via MNCs: A Malaysian

Case Study. Asian Economic Journal, 11 (4), p. 407-422.

Malairaja, C. & Zawdie, G. (2004). The ‘black box’ Syndrome in Technology Transfer and the Challenge

of Innovation in Developing Countries, International Journal of Technology Management and

Sustainable Development 3 (3), p. 233-251.

Muller, T. & Schnitzer, M. (2006). Technology Transfer and Spillovers in International Joint Ventures.

Journal of International Economics, 68, p. 456-468.

Narayanan, S. & Lai, Y. W. (2000). Technological Maturity and Development without Research: The

Challenge for Malaysian Manufacturing. Development and Change, 31, p. 435-457.

Kogut, B. & Zander, U. (1993). Knowledge of the Firm and the Evolutionary Theory of the Multinational

Corporation. Journal of International Business Studies, 24 (4), p. 625-646.

Pak, Y. & Park, Y. (2004). A Framework of Knowledge Transfer in Cross-Border Joint Ventures: An

Empirical Test of the Korean Context, Management International Review, 44 (4), p. 435-455.

Raduan, C. R. (2002). Japanese–Style Management Abroad, Prentice Hall.

Rasiah, R. & Anuar, A. (1998), Governing Industrial Technology Transfer in Malaysia, in Yusoff, I. and

Ismail, A. G, Malaysian Industrialization: Governance and the Technical Change, Penerbit

Universiti Kebangsaan Malaysia, Bangi.



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Szulanski, G. (1996). Exploring Internal Stickiness: Impediments to the Transfer of Best Practice within

the Firm, Strategic Management Journal, 17 (Winter Special Issue), p. 27–43.

Taylor, M. Z. (1995). Dominance Through Technology: Is Japan Creating a Yen Block in Southeast

Asia? Foreign Affairs, 74 (6), p.14-20.

Tihanyi, L. & Roath, A. S. (2002). Technology Transfer and Institutional Development in Central and

Eastern Europe. Journal of World Business, 37, p. 188-198.

Yamashita, S. (1991). Transfer of Japanese Technology and Management to ASEAN Countries,

University of Tokyo Press.

Yin, E. & Bao, Y. (2006). The Acquisition of Tacit Knowledge in China: An Empirical Analysis of the

‘Supplier-side Individual Level’ and ‘Recipient-side’ Factors. Management International Review,

46 (3), p. 327-348.



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Introduction

1 - Technology and Technology Transfer: Defining the Concepts



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1

Technology and Technology Transfer:

Defining the Concepts

CHAPTER OUTLINE

The dynamic nature of technology has contributed to the existence of various definitions and

concepts of technology by previous studies. Discussions on the concept of technology are crucial

in getting a clear understanding on the nature of technology before examining what exactly the

technology consists of. Building specifically on knowledge-based view (KBV) and organizational

learning (OL) perspectives, this work follows a stream of literature which suggests that 1)

knowledge as the critical element underlying technology, and 2) both technology and knowledge

are inter-dependent and inseparable in nature.

INTRODUCTION Reddy and Zhoa (1990) argue that defining the technology concept is not easy because

technology has been defined from various perspectives. The term ‘technology’ is inherently an

abstract concept; which is difficult to interpret, observe and evaluate (Blomstrom and Kokko,

1998). Regardless of the extensive research conducted on the subject, many of the literatures are

fragmented along different specialties. Thus, there is no commonly accepted paradigm (Reddy

and Zhoa, 1990). Due to this the concepts, variables and measures relevant to the study are

different from one study to another (Kumar et al., 1999).

THE TECHNOLOGY CONCEPT Past researchers have viewed and defined the term ‘technology’ from different perspectives; and

this has influenced the research design and results, negotiations around a transfer and

government policies in general (Reddy and Zhoa, 1990). From the cultural system perspective



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the researchers define technology as a cultural system which is concerned with the relationships

between humans and their environment (Tepstra and David, 1985). From the systems

perspective, technology is referred to as encompassing 1) the basic knowledge sub-system, 2) the

technical support system (software), and 3) the capital-embodied technology (hardware) (Afriyie,

1988). From the socio-technology perspective the researchers take a broader view by describing

technology to be meaningful only when it becomes a social fact (Levin, 1996; Rogers and

Shoemaker, 1971). Some researchers have even defined technology as the essential human

attribute (Pitt, 1999).

The early concept of technology as information holds that technology is generally easy to apply,

reproduce and reuse (Arrow, 1962). This view is inconsistent with the collection of literatures on

international TT literature which hold that technology is conceived as “firm-specific information

concerning the characteristics and performance properties of the production process and product

design” (Reddy and Zhoa, 1990). The production process or operation technology is embodied in

the equipment or the means to produce a defined product. On the other hand, the product design

or product technology is that which is manifested in the finished product (Reddy and Zhoa,

1990).

Technology is also viewed as a ‘configuration’ where the transfer object (the technology) must

rely on a subjectively determined but specifiable set of processes and products (Sahal, 1981).

Based on Sahal’s (1981, 1982) concept, technology and knowledge are inseparable because

when a technological product is transferred or diffused, the knowledge upon which its

composition is based is also diffused. The physical entity cannot be put to use without the

existence of a knowledge base which is inherent and not ancillary (Bozeman, 2000). Technology

does not only relate to technology embodied in the product. It is also associated with the

knowledge, information of its use, application and the process in developing the product (Lovell,

1998; Bozeman, 2000). Technology has always been connected with obtaining certain results,

resolving certain problems, completing certain tasks using particular skills, employing

knowledge, and exploiting assets and resources (Lan and Young, 1996).

Technology has been construed as “the firm’s intangible assets and it is firm-specific” which

forms the basis of a firm’s competitiveness and generally is released under special condition

(Caves, 1974; Dunning, 1981). Technology as the intangible assets of the firm is rooted in the

firm’s routines and is not easy to transfer due to the gradual learning process and higher cost



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associated with transferring tacit knowledge (Radosevic, 1999). Valuable technological

knowledge which is the intangible assets of the firm is never easily transferred from one firm to

another because technological learning process is needed to assimilate and internalize the

transferred technology (Lin, 2003). Technology is mainly differentiated knowledge about a

specific application, tacit, often uncodified and largely cumulative within firms (Pavit, 1985). It

can include information that is not easily reproducible and transferable (Tihanyi and Roath,

2002). Based on this argument, technology is seen as tacit knowledge, firm-specific secrets or

knowledge known by one organization (Polanyi, 1967; Nonaka, 1994). Technology has also

been referred to as “the integration of the physical objects or artifacts, the process of making the

objects and the meaning associated with the physical objects” (MacKenzie and Wajcman, 1985).

These elements are not distinctive and separable factors rather they form a ‘seamless web’ that

constitutes technology (Woolgar, 1987). All the three elements should be understood as being

connected to each other in which a change in one element will affect the other elements. Thus,

technology is broadly defined as embodied in people, materials, cognitive and physical

processes, facilities, machines and tools (Lin, 2003).

Kumar et al. (1999) categorize technology into two primary components: 1) a physical

component which comprises items such as products, tooling, equipments, blueprints, techniques,

and processes; and 2) the informational component which consists of know-how in management,

marketing, production, quality control, reliability, skilled labor and functional areas. Rosenberg

and Frischtak (1985) consider technology as firm-specific information, concerned with the

characteristics and performance properties of production processes and product designs, tacit and

cumulative in nature. Technology is the theoretical and practical knowledge, skills, and artifacts

that can be used to develop products and services as well as their production and delivery

systems (Burgelman et al., 1996). In extending the technology concept, Maskus (2003) defines

technology as “the information necessary to achieve a certain production outcome from a

particular means of combining or processing selected inputs which include production processes,

intra-firm organizational structures, management techniques and means of finance, marketing

methods or any of its combination”.

A review of the literature indicates that previous researchers have broadly categorized

technology in terms of its ‘explicit’ and ‘tacit’ nature (Polanyi, 1967). These two forms of

technology are also referred to as ‘hardware’ and ‘software’ technology. The term ‘technology’



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has been extensively debated by both hardware and software schools. Based on the definitions

given, the researchers from the hardware school define technology as “the construction and use

of machines, systems, processes or engineering” (Strassman, 1968; Jones, 1970; Hawthorne,

1971; Galbraith, 1972; Goulet, 1989; Lovell, 1998; Reisman, 2005). Generally, hardware

technology corresponds with explicit knowledge which refers to knowledge underlying

technology that can easily be codified, shared, transmitted, retrieved, reused, transferable in

formal or systematic language i.e. production manuals, academic papers, books, technical

specifications, designs and is only useful when tacit knowledge enables individuals and

organizations to use it (Techakanont and Terdudomtham, 2004). Software technology, on the

other hand, corresponds with implicit/tacit knowledge underlying technology that is difficult to

codify, communicate, transfer and is generally exchanged through action, commitments and

direct involvement such as face-to-face communication or on-the-job/apprenticeship type of

training (Ernst and Kim, 2002).

DEFINING THE CONCEPT OF TECHNOLOGY TRANSFER The definitions and concepts of TT have been discussed in many different ways based on the

disciplines, perspectives and purposes of research (Bozeman, 2000). TT is often a chaotic and

disorderly process involving groups and individuals who may hold different views about the

value and potential use of the technology, where the researchers, developers, and users are likely

to have different perceptions about technology (Gibson and Slimor, 1991). A review of TT

literature reveals that TT is a complex, difficult process even when it occurs across different

functions within a single product division of a single company (Zaltman et al., 1973; Kidder,

1981; Smith and Alexander, 1988) and needs time to evolve (Agmon and von Glinow, 1991).

However, economic theory for example Solow’s (1957) growth model has viewed technology as

given that is embodied in products or processes, where technology resembles blueprint,

machines, or materials that are easily replicated and transferred (Lin, 2003; Arrow, 1962).

A stream of literature describes TT from various dimensions for example TT as 1) the

transmission of know-how to suit local conditions with effective absorption and diffusion both

within and across countries (Chung, 2001; Kanyak, 1985; Teece, 1977; Roessner, 1993; Tihanyi

and Roath, 2002), 2) the transmission of know-how (knowledge) which enables the recipient



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enterprise to manufacture a particular product or provide a specific service (Baronson, 1970), 3)

the application of scientific principles to solve practical problems (Levin, 1996), 4) the

technology system in terms of technology embodied in people (person-embodied), things

(product-embodied) or processes (process-embodied) (Hall and Johnson, 1970), 5) the process

by which ideas and concepts are moved from the laboratory to marketplace (Phillips, 2002;

Williams and Gibson, 1990), 6) the transfer of knowledge and concept from developed to less

technologically developed countries (Putranto et al., 2003), 7) the transfer of inventive activities

to secondary users (Van Gigch, 1978), 8) the transmission or movement of knowledge as a

process where it involves the process how an organization or a country transfers scientific or

technological achievements, new uses for technology, designs, and the technical knowledge that

can be used in production (Chun 2007), 9) an intentional, goal-oriented interaction between two

or more social entities during which the pool of technological knowledge remains stable or

increases through the transfer of one or more components of technology (Autio and Laamanen,

1995), and 10) the transmission of embodied and disembodied technologies (Das, 1987).

Past researchers have identified that technology can be transferred from one place to another for

instance from university to enterprise, from organization to another organization, or from one

country to another (Solo and Rogers, 1972). In the context of developing countries TT needs to

be perceived in terms of achieving three core objectives: 1) the introduction of new techniques

by means of investment of new plants, (2) the improvement of existing techniques, and (3) the

generation of new knowledge (Hoffman and Girvan, 1990). In the context of manufacturing

processes, TT requires not only the transfer of technological knowledge in the form of process

sheets, blueprints, products, and materials specification but also the transfer of know-how of

high-caliber engineering and technical personnel (Farhang, 1996). Another stream of literature

views TT process which is not only about the transmission of knowledge but is also related to 1)

a learning process where technological knowledge is continually accumulated into human

resources that are engaged in production activities (Shiowattana, 1991), 2) the technology

recipient’s capability to learn and absorb technology into the production function (Maskus,

2003), 3) the capacity to master, develop and later produce autonomously the technology

underlying the products (Chesnais, 1986), 4) a sustained relationship between two enterprises

over a period of time which enables the receiving enterprise to produce the product with the



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desired level of quality standards and cost efficiency (Reddy and Zhoa, 1990), and 5) a deeper

and wider accumulation of knowledge (Shiowattana, 1991).

The literatures on TT and international TT are extensive and varied in perspectives from various

disciplines which include political science, economics, sociology, public policy, marketing and

management of technology (Kumar et al., 1999; Zhoa and Reisman, 1992). The economists often

define TT on the basis of the properties of generic knowledge; where the main focus is on

variables that relate to production and design (Arrow, 1969; Dosi, 1988). For the sociologist they

tend to link TT to innovation and view technology as “a design for instrumental action that

reduces the uncertainty of cause–effect relationships involved in achieving a desired outcome”

(Rogers and Shoemaker, 1971). The anthropologists tend to broadly view TT within the context

of cultural change and how technology affects changes (Foster, 1962; Service, 1971; Merrill,

1972). The social science perspective relates TT to socio-technical process; which implies the

transfer of cultural skills accompanying the movement of machinery, equipment and tools which

include the transfer of the physical movement of artifacts and the embedded cultural skills

(Levin, 1993). The business disciplines tend to concentrate on stages of TT, particularly stages

which are related to designs, productions and sales (Teese, 1976; Lake 1979). The bulk of TT

literature has been contributed by management researchers (Zhoa and Reisman, 1992).

Management researchers tend to focus on intra-sector transfer and relationships between TT and

strategy (Rabino, 1989; Chiesa and Manzini, 1996; Laamanen and Autio, 1996; Lambe and

Spekman, 1997). Most of the current literatures by management researchers have extensively

shifted their focus to TT within alliances and how alliances are crucial to the development of TT

(Zhoa and Reisman, 1992).

Based on various definitions, TT area is very wide and dynamic. The numbers of literature on the

subject are voluminous, extensive and varied in perspectives (Kumar et al., 1999; Zhoa and

Reisman, 1992). Past studies on TT and KT have clearly distinguished between intra and inter-

firm transfer. While this work focuses on inter-firm TT, the understanding on how both types of

transfer operate is useful since both intra and inter-firm transfers serve different purposes and

apply different mechanisms of transfer. Intra-firm TT and KT focus on how knowledge is

created and internally transferred between departments, units, divisions of the same organization,

or MNCs’ subsidiaries (Argote and Ingram, 2000). On the other hand, inter-firm TT or KT

involves the process of knowledge acquisition and transfer from outside of the organizational



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boundary especially within the strategic alliances or IJVs between two or more different

organizations (Huber, 1991; Inkpen, 1998a; Inkpen and Dinur, 1998; Inkpen, 2000). Thus,

consistent with the development of current TT literature this work specifically focuses on the

transfer of technologies and knowledge between unaffiliated organizations in IJVs.

TECHNOLOGY TRANSFER AND KNOWLEDGE TRANSFER A review of literature reveals that past studies have made little attempt to explain the difference

between knowledge transfer (KT) and technology transfer (TT). Many of the studies do not draw

clear distinctions between KT and TT where most of the studies have applied the terms

interchangeably in both TT and KT literatures. Most of the studies have treated KT and TT to

have similar meanings and dimensions. Based on various definitions from different disciplines of

research and background, most of the researchers have affirmed that knowledge as the critical

element underlying technology. TT is closely associated with the transfer of information, know-

how, technical knowledge embodied in the products, processes and managements (Hall and

Johnson, 1970; Kanyak, 1985; Shiowattana, 1987; Das, 1987; Williams and Gibson, 1990;

Hayden, 1992; Gibson and Rogers, 1994). Other definition makes direct reference to knowledge

as a critical element underlying the product technology, process technology and management

technology (Grosse, 1996). Many researchers have attempted to explain the interface between

TT and KT, and some even tried to draw distinctions between these two concepts. Kogut and

Zander (1992, 1993), in their study on knowledge transfer within the multinationals (MNCs), use

both terms interchangeably to establish a close association between TT and KT when suggesting

that TT within MNCs can be explained by the attributes of knowledge such as tacitness,

codifiability and teachability. Sinani and Meyer (2004) make no distinction between TT and KT

in studying ‘spillover effects’ of TT. Sung and Gibson (2000) connote TT and KT to have

similar meaning when suggesting KT and TT as the movement of knowledge and technology

through some channels from one individual or organization to another.

Studies have suggested that technology and knowledge are inseparable. For example Sahal

(1981, 1982) argues that technology as ‘configuration’, observing that the transfer object (the

technology) must rely on a subjectively determined but specifiable set of processes and products.

Bozeman (2000) asserts that Sahal’s (1981, 1982) concept has resolved a major analytical



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problem in distinguishing TT and KT. Both TT and KT are inseparable because when a

technological product is transferred or diffused, the knowledge upon which its composition is

based is also transferred (Bozeman, 2000). TT will not occur without knowledge transfer as

knowledge is the ‘key’ to control technology (Li-Hua, 2006). Thus, studies on KT turn almost

invariably to TT when empirical investigation is in order (Simonin, 1996b pg. 466). Dunning

(1994) argues that current studies tend to relate technology directly with knowledge. In the

context of TT through FDIs, Kogut and Zander (1993) have explicitly indicated that the transfer

of knowledge embodies a firm’s advantage, underlying technology, production, marketing or

other activities. Although TT and KT have been regularly used interchangeably in many

literatures as they are highly interactive, however, they serve different purposes. Gopalakrishnan

and Santoro (2004) distinguish TT and KT in term of their purposes. KT implies a broader and

have more inclusive construct which is directed more towards the “why” for change whereas TT

focuses on a narrower and more targeted construct that usually embodies certain tools for

changing the environment (Gopalakrishnan and Santoro, 2004). Even though it appears that there

are distinctions between their purposes, majority of the researchers are in consensus on the fact

that knowledge is the critical element underlying TT (Kogut and Zander, 1992, 1993; Sinani and

Meyer, 2004; Sung and Gibson, 2000; Sahal, 1981, 1982; Bozeman, 2000; Lin, 2003; Li-Hua,

2006).

REFERENCES Afriyie, K. (1988). A Technology Transfer Methodology for Developing Joint Production Strategies in

Varying Systems, In F. J. Contractor and P. Lorange (Eds.), Cooperative Strategies in International Business, p. 81-95.

Agmon, T. & von Glinow, M. (1991). Technology Transfer in International Business, Oxford: Oxford Universities Press.

Arrow, K. (1969). Classificatory Notes on the Production and Transmission of Technological Knowledge. American Economic Review, Papers and Proceedings, May, p. 244–250.

Arrow K. J. (1962). Economic Welfare and the Allocation of Resources for Invention, in: R. R. Nelson, The Rate and Direction of Inventive Activity, Princeton University Press, New Jersey, p. 609–625.

Autio, E. & Laamanen, T. (1995). Measurement and Evaluation of Technology Transfer: Review of Technology Transfer Mechanisms and Indicators. International Journal of Technology Transfer Management, 10(6), p. 643-664.

Baronson, J. (1970). Technology Transfer through the International Firms. American Economic Review Papers and Proceedings, p. 435-440.

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13

Part I

Literature Review and Conceptual Models of Inter-Firm

Technology Transfer

2 - The Evolution and Development of Technology Transfer Models: The Knowledge-

Based View and Organizational Learning Perspectives

3 - The Effects of Inter Firm Technology Transfer Characteristics on Degree of

Technology Transfer in International Joint Ventures: A Framework

4 - A Holistic Model of Inter-Firm Technology Transfer Based on Integrated

Perspectives of Knowledge-Based View and Organizational Learning



14

2

The Evolution and Development of Technology

Transfer Models: The Knowledge-Based View and

Organizational Learning Perspectives

CHAPTER OUTLINE

The main objective of this work is to contribute to the existing Technology Transfer (TT)

literature by reviewing the evolution and development of the previous TT models which include

the traditional TT model, models developed after 1990s, other related theoretical foundations

underlying TT models, and the current TT models which have strong influence of knowledge-

based view (KBV) and organizational learning (OL) perspectives. Since the current management

researchers have a strong focus on TT within strategic alliance and other collaborative ventures,

this review highlights the significant influence of KBV and OL perspectives on inter-firm TT

models.

INTRODUCTION Based on a review of literature, technology transfer (TT) is not a new thing. Researchers have

traced back TT process to the pre-history of the human species: where TT largely involved tacit

knowledge which is evolutionary prior to explicit knowledge (Donald, 1991; Mathews and

Roussel, 1997). Since there were no written languages until 3000 BC, TT had mainly occurred

through language; which were supplemented by equations and diagrams which constitute as the

major means of explicit transfer of technological knowledge (Gorman, 2002). The spoken

language and gestures have explicitly transferred technological knowledge in friendly

encounters. However, much of pre-historic TT between people occurred when people with



15

superior agricultural technology assimilated or eliminated those who could not reproduce as

rapidly (Diamond, 1997).

Segman (1989), who conducted a historical review of TT, traced the TT process from the

Neolithic times, the role of Arabs played in transferring technologies from East to West and the

transfer of English textile expertise to the American textile industry in the 18th and 19th Centuries

(Cameron, 1960; Irwin and Moore, 1991). In the 18th Century, despite the English law

preventing knowledge migration, France eventually managed to obtain ‘specialized steel making

know-how’ by importing English workers and through industrial espionage (Cameron, 1960;

Irwin and Moore, 1991). The success of the American textile industry in 18th and 19th Century

was due to the transfer of knowledge and expertise by the English textile industry (Cameron,

1960). Previous studies have shown that certain industries collapsed, for example the English

clock and watch industry, due to the industry resistance to the opportunities of TT (Irwin and

Moore, 1991). The main objective of this paper is to review the evolution and development of

TT models in terms of focus of each model, strengths and limitations of the models, and finally

to highlight the significance influence of knowledge-based view (KBV) and organizational

learning (OL) perspectives, which have strong theoretical foundation, on the current TT models.

This review limits its perimeter by focusing on the inter-firm TT between two unaffiliated

organizations.

APPROACHES TO TECHNOLOGY TRANSFER Previous studies on TT have employed different approaches to shape and govern the TT efforts.

TT as a domain covers all activities around technological development. Few TT models were

developed after the World War II to govern the implementation of TT activities and their

application to marketplace (Devine et al., 1987; Tenkasi and Mohrman, 1995). Among the

traditional TT models developed were the appropriability model, dissemination model,

knowledge utilization model, and communication model. In the 1970s studies have adopted “the

economic international trade approach” in developing a linear model of TT (Bessant and Francis,

2005). In the 1980s research on TT emphasized on the effectiveness of the specific technology

being transferred which in general is within a broader context of economic development (Hope,



16

1983). The 1990s approach emphasizes on the significance of learning at the organization level

as a key element in facilitating technology transfer (Figuereido, 2001).

In late 1980s and early 1990s TT models have started to absorb the principles of the organization

development movement (French and Bell, 1995). Strategic management researchers have further

contributed to the development of TT frameworks based on KBV and OL perspectives as these

perspectives have been found to have quite similar dimensions such as outcomes, processes,

barriers and facilitators (Daghfous, 2004). These perspectives have significantly contributed to

the expansion of TT models since literatures from both KBV and OL perspectives appear to

subsume most of the contributions of the TT literatures (Daghfous, 2004).

THE APPROPRIABILITY MODEL This model, which was developed in 1945-1950s, suggests that good or quality technologies sell

themselves (Gibson and Slimor, 1991). The model emphasizes on the importance of quality of

research, and competitive market pressure in achieving TT and promoting the use of research

findings (Devine et al., 1987; Gibson and Slimor, 1991; Tenkasi and Mohrman, 1995).

According to this model, TT process simply occurs when technology has found users or has been

discovered by the market. Purposive or deliberate TT mechanism is seen as unnecessary. This

model assumes that after the researchers develop the technology and make technologies available

through various forms of communications such as technical reports and professional journals, the

users will “automatically show up at the researcher’s door” (Devine et al., 1987).

Gibson and Slimor (1991), in their three-level TT model, describe the first level (technology

development level) as the most fundamental level; when technology process can be largely

passive through mediated means such as research reports, journal articles and computer tapes.

The underlying presumption of the appropriability approach is “viewing TT as the result of an

automatic process that began with scientific research and then moved to development, financing,

manufacturing and marketing. [One] need not necessarily be concerned with linkages in the

technology commercialization process” (Kozmetsky, 1990). However, previous studies have

acknowledged that over the years evidence has shown that quality technologies do not usually

sell well themselves (Devine et al., 1987; Gibson and Slimor, 1991).



17

THE DISSIMINATION MODEL This model, which was popularized by Rogers (1983) and Rogers and Kincaid (1982), is

developed in the 1960-1970s (Gibson and Slimor, 1991). This approach suggests the importance

of technology and innovation to be diffused or disseminated to the potential users by the experts

(Williams and Gibson, 1990). This model assumes that an expert will transfer specialized

knowledge to the willing user. The presumption underlying this model is that once the linkages

are established, the new technology will move from the expert to the non-expert “like water

through a pipe once the channel is opened” (Williams and Gibson, 1990; Gibson and Slimor,

1991). Gibson and Slimor (1991) describe this model as the second level of their model; the

technology acceptance level. Based on their model, this level includes the expert’s primary

responsibility to select technology and ensure the technology is available to a receptor that can

understand and potentially use the technology (Gibson and Slimor, 1991). However, this model

suffers from its one-way communication (unilateral) characteristic with no involvement from the

users (Devine et al., 1987; Gibson and Slimor, 1991).

THE KNOWLEDGE UTILIZATION MODEL This model, which was developed in late 1980s (Gibson and Slimor, 1991), has a significant

influence on TT literature (Szakonyi, 1990; Zacchea, 1992). The approach taken by this model is

its emphasis on 1) the important role of interpersonal communication between the technology

developers/researchers and technology users, and 2) the importance of organizational barriers or

facilitators of TT. The knowledge utilization approach represents an evolutionary step which

focuses on how to organize knowledge to effective use in the technology users setting (Backer,

1991). Gibson and Slimor (1991) view this model as the third level in their model; technology

application level. This level is the most involved level of TT where it includes the profitable use

of the technology in the market place as well as other application such as intra-firm processes.

While this approach indicates an appreciation of the complexities of the TT, researchers have

argued that the model suffers from a linear bias (Dimancescu and Botkin, 1986). The underlying

presumption of this model is that technology moves “hand-to-hand” to one direction, unilaterally

from the experts to the users, to become a developed idea and eventually a product (Gibson and



18

Slimor, 1991). This model reduces the complex transfer process to chronologically ordered

stages (Gibson and Slimor, 1991; Sung and Gibson, 2000). The appropriability, dissemination

and knowledge utilization models still suffer from inherent linear bias where these TT models

have limitations in terms of their limited application in transferring technology across

organizational boundaries (Tenkasi and Mohrman, 1995; Gibson and Slimor, 1991).

THE COMMUNICATION MODEL Departing from the previous three models, several researchers have suggested that the

communication model as a replacement of the earlier TT models (Williams and Gibson, 1990;

Gibson et al., 1990; Doheny-Farina, 1992). This model perceives TT as “a communication and

information flow process with communication understood to be concerned with full exchange

and sharing of meanings”. This model suggests technology as “an on-going process which

involves a two-way interactive process (non-linear) by continuously and simultaneously

exchanging ideas among the individuals involved” (Williams and Gibson, 1990). Consistent with

this approach, other researchers view communication model of TT follows the network

communication paradigm; where feedback is all pervasive and the participants in the TT process

are transceivers rather than the sources and receivers (Gibson and Slimor, 1991; Irwin and

Moore, 1991). Other researchers acknowledge that feedbacks help the participants in the transfer

process to reach convergence about the important dimensions of the technology (Rogers, 1983;

Rogers and Kincaid, 1981). To overcome the obstacles and barriers to the transfer process,

different sets of functions, activities, and network must occur simultaneously (Rogers, 1983;

Kozmetsky, 1988a, 1988b).

The communication model, which consists of characteristics such as two-way communication,

interactive, interpersonal/organizational communication, helps to explain the failures of the

previous TT strategies, which are based on one-way unidirectional communication, and

dissemination/diffusion models (Irwin and Moore, 1991). Two-way interactive communication is

primarily developed towards overcoming the communication barriers between the technology

developer group and the user group (Doheny-Farina, 1992; Dobrin, 1989). This model assumes

that there is “a body of information, of objective facts, just lying there waiting to be

communicated” (Dobrin, 1989). The underlying presumption is that knowledge is an object that



19

exists independently, valid, complete and has universal applicability (Tenkasi and Mohrman,

1995). The implementer (technology developer) is responsible for transferring knowledge

correctly through the appropriate channels for the user to understand; and failure to adopt

knowledge is simply because the users fail to understand (Tenkasi and Mohrman, 1995).

Although the communication model shows an appreciation of the complexities of TT, this model

is unable to provide explanations on 1) the complexities of TT in the context of knowledge

transferred through collaborative learning, 2) the subjectivity of knowledge, and 3) the need for

contextual adaptation, dialoging at the level of values, assumption, and beliefs that takes on more

acute proportions with soft or disembodied technologies (Tenkasi and Mohrman, 1995). This

view is consistent with the earlier studies on TT which suggests that the focus of the current

management researchers is on TT in strategic alliances/IJVs, and learning at the organizational

level in facilitating TT (Zhoa and Reisman, 1992; Figuereido, 2001).

TECHNOLOGY TRANSFER MODELS AFTER 1990s A review of the literature reveals that TT researchers have attempted to develop new technology

transfer model distinguishing from the traditional models developed earlier which mainly focus

on TT processes. The later models developed by researchers (Gibson and Slimor, 1991;

Rebentisch and Ferretti, 1995; Sung and Gibson, 2000) attempt to address the limitations that

arise from the traditional TT models in terms of the application in contemporary high-tech

industries (Gibson and Slimor, 1991). Several models developed after 1990s have emphasized on

1) the important element of communication between the technology developer and the receiver

or between different organizations, 2) the levels of TT, 3) the factors which influence TT and

KT, and 4) the TT processes in IJV (Gibson and Slimor, 1991; Sung and Gibson, 2000;

Rebentich and Ferretti, 1995).

GIBSON AND SLIMOR’S MODEL This model describes TT from the perspective of technology researchers and users through three

levels of involvement. The underlying theories of this model are the organization and



20

communication theories (Gibson and Slimor, 1991). This model proposes that TT consists of

three levels of involvement: Level I (Technology Development), Level II (Technology

Acceptance), and Level III (Technology Application). This model explains the levels of

technology transfer involvements and integrates the activities involved in the traditional models.

Technology Development is considered as the most important level where the transfer process is

viewed as passive through transfer means such as research reports, journal articles, and computer

tapes. This level relates to the appropriability model: where the emphasis is on the importance of

quality of research and competitive market pressure in achieving technology transfer (Gibson

and Slimor, 1991). Technology Acceptance level indicates more involvement of TT. During this

level the technology developer is responsible in making certain that the technology is made

available to the receptors that can understand and potentially use the technology. This level of

involvement relates to the dissemination model: where the concentration is on disseminating

innovations to individual users (Gibson and Slimor, 1991). Technology Application level is the

most involved level of TT. Technology application includes commercializing the use of

technology in the marketplace and other application such as intra-firm processes. This level

equates with knowledge utilization model: where emphases are on the critical element of

interpersonal communication between technology developers and users, and the organizational

barriers and facilitators of TT (Gibson and Slimor, 1991).

SUNG AND GIBSON’S MODEL This model is developed to have similar objectives as Gibson and Slimor’s (1991) model that is

to address limitations in the traditional TT models. As an expansion and improvement to the

three levels involvement model of TT (Gibson and Slimor, 1991), this model provides plausible

explanations as to the levels and factors affecting knowledge and TT by describing knowledge

and TT in four levels of involvements: Level I (Knowledge and Technology Creation), Level II

(Sharing), Level III (Implementation), and Level IV (Commercialization) (Sung and Gibson,

2000).

At the creation level, the technology developers conduct and develop research into knowledge

and make available of their result/finding through research publication, videotapes,

teleconference, news, and anecdotes. TT at this level is considered as a passive process where it



21

needs only minimum involvement of all participants (Sung and Gibson, 2000). Secondly, at the

sharing level, technology developers and users begin to share responsibility as the success of

technology transfer occurs when knowledge and technology are transferred across personal,

functional, or organizational, and knowledge and technology are well accepted and understood

by users (Sung and Gibson, 2000). Thirdly, at the implementation level, success is determined by

the timely and efficiency of knowledge and technology transfer, and the user’s resources ability

to implement. KT and TT may occur through manufacturing transfer, processes transfer or

services and best practice transfer (Sung and Gibson, 2000). Finally, at the commercialization

level, knowledge and technology is commercially utilized. The commercialization level is built

cumulatively on the success of creation, sharing, and implementation levels with the help of

market strength. Success of the implementation level is measured by return of investment (ROI)

and increased market share (Sung and Gibson, 2000).

REBENTISCH AND FERRETTI’S MODEL Rebentisch and Ferretti (1995) propose an integrated model of TT process developed from the

insights derived from the study of two IJVs. According to Rebentisch and Ferretti (1995) TT

areas require further investigation and integration particularly on 1) the effect of the

interdependencies between the technology characteristics and its organizational context, and 2)

the interface between the core competencies of the firm and its ability to adopt new technology.

The model (Figure 2.3) addresses the issues on 1) how much effort is required to transfer

different types of technologies, and 2) what impact the organization’s existing competencies

might have on that process. This model refers TT as “the transfer of the embodied knowledge

assets between organizations”. The TT process in this model consists of four categories that

include 1) Transfer Scope, 2) Transfer Method, 3) Knowledge Architecture, and 4)

Organizational Adaptive Ability.

The scope of transfer is determined by how much information is embodied in the technology and

what type of technologies a firm seeks to acquire from the source. Based on this model the

transfer scope consists of four types of technologies: General knowledge, Specific knowledge,

Hardware, and Behaviors. This model categorizes the transfer methods in the TT process as 1)

Impersonal communication, 2) Personal communication, 3) Group interaction, and 4) Physical



22

relocation. Knowledge architecture is defined as “a characterization of the structure and artifacts

into which knowledge has been embodied in the organization, and describes the way

organization stores and processes information” (Rebentisch and Ferretti, 1995). Knowledge

architecture has four critical elements which influence TT process 1) technology hardware, 2)

experience base, 3) procedures, and 4) organization power structures. These elements correspond

with the level of technology’s complexity and compatibility with the existing organization, the

costs and extent of change involved in implementing it, and the possibility of encountering any

opposition (Rebentisch and Ferretti, 1995). Organizational adaptive ability is “the adoption of the

organization’s ability to utilize its resources to make adaptations either to itself or to a new

technology” (Rebentisch and Ferretti, 1995). Organizational adaptive ability consists of staffing

and production flexibility. This model, which is developed based on two IJVs, nevertheless,

mainly offers the theoretical insights of TT process of hardware or embodied technology

(explicit knowledge) where no hypothesis testing and empirical examination has been conducted.

Since this model is developed from the transferring partner’s perspective thus it suffers from

inherent linear bias in which the relationship and contextual dimensions of JVs have not been

considered.

PREVIOUS STUDIES ON TECHNOLOGY TRANSFER Past studies have shown that most literature on TT are focusing on technologies which are

categorized as ‘hardware technology’ (embodied or explicit technology) such as manufacturing

control system, water purification processes and telephony switches (Saad, 2000; Bessant and

Francis, 2005). Previous research on TT has traditionally concentrated on the effective linkages

and information movement with no influence of management theory (Levinson and Moran,

1987). Early literature on TT have dealt with issues on, among others, TT process,

appropriateness of technology, cooperation and conflict between transfer countries, the success

of technology transfer, and the social and economic benefits of TT for both suppliers and

recipient countries (Katz, 1985; Lall, 1982; Lynn, 1985; Mytelka, 1985; Wallender, 1979).

However, in late 1980s and early 1990s TT literatures have begun to adopt some principles of

the organizational development movement (French and Bell, 1995), and management theory

(Sung and Gibson, 2000). Based on the limitations faced by the previous TT models and the



23

growing interest on organizational development, TT models have been developed to include

management theory (Bessant and Francis, 2005). Among the pioneer researchers who have

included management theory principles in their TT models are Creighton et al. (1985), Slimor et

al. (1990), Inman (1987), Pinkston (1989), Gibson and Rogers (1991), and Levinson and Moran

(1987). Creigton et al. (1985) in their TT model include nine elements such as organization,

project, documentation of information, distribution of information, linking, capacity to transport

or receive and to act, credibility of organizations in the transaction, willingness to transmit,

receive or implement ideas, and reward. Other researchers focus on the importance of difference

between consortia and their member companies in terms of academic and business value,

networking and information sharing, long vs. short-term perspectives, universal vs. particular

research objectives, and performance evaluation (Slimor et al., 1990).

A stream of TT literature has identified variables affecting TT and knowledge transfer (KT)

success such as person-to-person contact, knowing whom to contact, variety of communication

channels, set up transfer office or committee, a sense of common purpose, understanding of

nature of business, attitude and values, awareness of transfer, concreteness of

knowledge/technology, a collaborative research program, clear definition of transfer, program

(training, demo, tutorials), incentive for transfer, share success stories, push and pull for

technology, and product champion (Creighton et al.,1985; Slimor et al.,1990; Inman, 1987;

Pinkston, 1989; Gibson and Rogers, 1991; Levinson and Moran, 1987).

Past studies on TT have also focused on several areas of TT such as 1) the characteristics of the

technology transferred, 2) methods of transfer, and 3) attributes of the adoption organization and

its technology environment (Rebentisch and Ferretti, 1995). A large stream of literature has

addressed the effect of characteristics of technology on adoption or transfer particularly on the

adoption and diffusion of innovations. Among the innovation variables that have been identified

to influence the adoption process are innovation cost (Ettlie and Vallenga, 1979), innovation

complexity, relative advantage, trialability, observability (Pelz, 1985; Rogers, 1983), reliability,

scientific status, importance, communicability, and flexibility (Tornatzky and Klien, 1982).

Research on the method of transfer have focused on the organizational linkages whether

hierarchical or structural (Nadler and Tushman, 1988), hierarchy as the simplest of linking

mechanisms, functions by formalizing the reporting or communication relationships between the

sources and recipients of technology or information (Lawrence and Lorsch, 1967), structural



24

linking mechanisms that link people and groups together non-hierarchically, using liaisons,

personnel transfer, informal roles, transfer groups, and project structures (Allen and Cooney,

1971; Roberts and Frohman, 1978; Allen et al., 1988; Jervis, 1975; Roberts, 1979; Larson and

Gobeli, 1988).

From a review of literature, a stream of literature has examined the effect of organizational

attributes on the adoption of new technologies which include size, centralization, formalization

(Ettlie et al., 1984), organizational complexity (Pelz, 1985), centralization of decision-making,

exposure to external information, managerial attitudes (Carter and Williams, 1959; Dewar and

Dutton, 1986), regulatory or union influence, and risk taking attributes (Ettlie and Vallenga,

1979). Another stream of literature suggests that organizational characteristics such as

centralization, formalization and complexity must correspond to characteristics of the new

technology for the transfer or adoption to be successful (Burns and Stalker, 1966; Lawrence and

Lorsch, 1967; Ettlie et al., 1984; Dewar and Dutton, 1986). Thus, based on large number of

previous studies on TT, several patterns of TT studies have been identified as follows:

1) Prior to 1990s many studies on TT concentrate on the adoption and diffusion of innovations

which were modeled based on appropriability, dissemination or knowledge utilization models

(Devine et al., 1987; Rogers, 1983; Rogers and Kincaid, 1981).

2) The pre 1990s studies on TT have focused less on two-way inter-personal communication or

relationship between the sender (supplier) and the receiver (recipient) (Devine et al., 1987;

Rogers, 1983; Rogers and Kincaid, 1981).

3) Prior to 1990s, fewer studies were conducted to determine the influence of the

interdependencies between technology and organizational and environmental context such as

organization and culture distance (Rebentisch and Ferretti, 1995).

4) Before 1990s less or no studies provide TT’s framework for inter-firm TT in strategic alliance

or IJVs (Zhoa and Reisman, 1992) instead many studies emphasized on the intra-firm TT

(Gibson and Slimor, 1991).

5) Prior to 1990s fewer studies on TT have examined the impact of ‘software technology’

(disembodied) or ‘tacit knowledge’ and its properties on TT. Many studies on TT have focused

on the ‘hardware technology’ (embodied technology) or ‘explicit technology’ (Bessant and

Francis, 2005).



25

6) Past studies on TT do not provide frameworks on organizational learning especially on how

technology is acquired, learned, shared and transferred within and outside the organizational

boundary (Sung and Gibson, 2000).

Thus, based on these limitations, in late 1980s and early 1990s studies on TT have attracted

researchers from the management fields to further develop TT models which provide answers to

questions on various TT issues. Among the management perspectives that provide valuable

contributions to TT literature are KBV and OL perspectives.

OTHER RELATED THEORETICAL FOUNDATIONS OF TECHNOLOGY TRANSFER Besides examining TT models from the previous literatures, an understanding of the related

theoretical perspectives is necessary to enable the readers to relate with the practical and

empirical aspects. From a review of literature, the other relevant theories which are found to be

related to TT are the international trade (IT) theory, foreign direct investment (FDI) theory, KBV

perspective and OL perspective.

The international trade theories, which consist of the classical trade theory (Ricardo, 1817;

Smith, 1776), the factor proportion theory (Hecksher and Ohlin, 1933), and the product life cycle

theory (Vernon, 1966, 1971; Wells, 1968, 1969), are related to TT studies as they provide

plausible explanations on how trades between countries contribute to the flow of productions or

goods and services which have brought along the technology embedded in them. The foreign

direct investment theories are related to TT studies as these theories provide explanations on how

FDIs by MNCs become the main channel for intra-firm technology transfer; where technology is

transferred to MNCs’ subsidiary or affiliates in the host countries. FDI theories consist of the

market imperfection theory (Hymer, 1960, 1970; Kindleberger, 1969; Caves, 1971), international

production theory (Dunning, 1980), internationalization theory (Buckley, 1982, 1985; Buckley

and Casson, 1976), and transaction cost theory (Williamson, 1975; Ouchi, 1980; Williamson and

Ouchi, 1981).

However, for the purpose of this review; which focuses on the cross border and inter-firm TT of

tacit and explicit knowledge (software and hardware technology), the relevant theories



26

underlying the current TT model are KBV and OL perspectives. This study contends that as TT

does not only require transmission of knowledge but also knowledge absorption and use

(Devanport and Prusak, 1998, 2000) these perspectives, which are interrelated, would enable this

study to capture and explain the distinct characteristics and behavioral factors of the actors and

facilitators/barriers involved such as the attributes of knowledge transferred, attitudes of both

technology supplier and recipient, as well as their relational and contextual factors (Szulanski,

1996). The streams of literature on TT, KBV and OL perspectives are quite similar along various

dimensions for example the outcomes, processes, barriers and facilitators (Daghfous, 2004).

KNOWLEDGE-BASED VIEW PERSPECTIVE AND TECHNOLOGY TRANSFER In the late 1980s and early 1990s, knowledge-based economies, which are based on the

production, distribution and use of knowledge and information, have emerged as the dominant

perspective in the management fields. Knowledge has been regarded as the catalyst of

organizational competitiveness and economic growth. KBV perspective, which is originally

developed from resource-based view of the firm (RBV) perspective (Wernerfelt, 1984; Barney,

1991), has been developed to response to the change in the environment; where in the

information era knowledge, especially tacit knowledge, is acknowledged as one of the most

important strategic resources (Grant and Baden-Fuller, 1995; Grant, 1996a, 1996b, 1997). These

researchers explain the relationship between a firm’s knowledge integration and its competitive

advantage based on the resource and capability perspective. Knowledge integration in a firm will

lead to the firm’s organization capability which in turn determines its competitive advantage.

KBV perspective suggests that firms as a bundle of knowledge emphasize on firm-specific,

intangible, non-tradable and inimitable knowledge as durable sources of sustainable competitive

advantage of the firm (Spender, 1996; Barney, 1991). Past studies on KBV perspective have

focused on knowledge as a key competitive asset. Many studies have emphasized on the capacity

of firms to integrate tacit knowledge (Grant and Baden-Fuller, 1995; Conner and Pralahad,

1996). KBV perspective is primarily concerned with human resource than other physical

resources of the firm since human resource plays an important role in the process of knowledge

creation, knowledge transfer and acquisition within organizations (Conner and Pralahad, 1996;

Kogut and Zander, 1996).



27

KBV perspective is critical in explaining how firms gradually grow and achieve sustainable

competitive advantage through knowledge creation and learning (Kogut and Zander, 1992, 1993;

Spender, 1996). As repository of knowledge, firms build their firm-specific knowledge and

accumulate it over time, which makes them specialize in specific product or service (Dierickx

and Cool, 1989; Kogut and Zander, 1993). Firms expanding abroad transfer to their foreign

subsidiaries firm-specific ownership advantages such as superior production, marketing and

technical knowledge because of the inherent disadvantage of operating in the host country’s

environment (Hymer, 1976). The intra-firm knowledge transfer of superior knowledge is viewed

as an effective means of replication and exploitation of the ownership advantage for economic

rents (Kogut and Zander, 1993).

However, recent studies have argued that firms are no longer seen as repository of knowledge

rather as an instrument to transfer knowledge across subsidiaries and contribute to knowledge

development (Gupta and Govindarajan, 2000; Holm and Pedersen, 2000). As firm-specific

knowledge is the important resource of the firm, tacit knowledge is more difficult to replicate

and transfer than explicit knowledge (Mowery and Rosenberg, 1989). The firm’s tacit knowledge

is not easily communicated and shared as it is highly personal deeply rooted in action and in an

individual’s involvement within a specific context (Nonaka, 1994). The individual’s insights and

skills that form tacit knowledge in human resource, which are gained through personal

experience, are hard or impossible to articulate or transfer (Kogut and Zander, 1993; Nelson and

Winter, 1982; Nonaka, 1994; Polanyi, 1962, Simonin, 1999a). Tacit knowledge acts as “the glue

that integrates mechanism in learning” (Dhanaraj et al., 2004). On the other hand, explicit

knowledge, which is highly codifiable and transmittable in formal and systematic language, acts

as “the building blocks” (Polanyi, 1967; Nonaka and Takeuchi, 1995).

A number of researchers such as Kogut and Zander (1992, 1993, 1996), Nonaka (1994), Nonaka

and Takeuchi (1995), Nonaka et al. (1996), Grant (1996a, 1996b, 1997), Spender (1996), and

Szulanski (1996) are among the researchers who have developed KBV perspective as a theory of

the firm and strategy. These researchers stress on knowledge as “the most important strategic

resource which emphasizes the roles of knowledge acquisition, storage, replication, transfer, and

creation in organizations”.



28

KOGUT AND ZANDER’S MODEL Kogut and Zander (1992) are among the first researchers who established the foundation for the

knowledge-based theory of the firm when emphasizing the strategic importance of knowledge as

a source of competitive advantage. Their work is focused on the idea that “what firms do better

than markets is the creation and transfer of knowledge within the organization”. Knowledge,

which consists of information and know-how, is not only held by individuals but is also

expressed in regularities by which members cooperate in a social community. Firms as social

communities act as “a repository of capabilities” determined by the social knowledge embedded

in enduring individual relationships structured by organizing principles (Kogut and Zander,

1992). The organizing principles refer to as “the organizing knowledge that establishes the

context of discourse and coordination among individuals with disparate expertise and that

replicates the organization over time in correspondence to the changing expectations and identity

of its members” (Kogut and Zander, 1996).

This view was further articulated and empirically tested in Kogut and Zander (1993). They assert

that 1) firms are efficient means by which knowledge is created and transferred, 2) a common

understanding is developed by individuals and groups in a firm through repeated interaction to

transfer knowledge from ideas into production and markets, 3) what a firm does is not depending

on the market’s failure rather the efficiency in the process of transformation relative to other

firms, and 4) the firm’s boundary is determined by the difference in knowledge and the

embedded capabilities between the creator and the users (possessed with complementary skills)

and not market failure. Kogut and Zander (1996) further extend their discussion on the concept

of identity by asserting that individuals are “unsocial sociality” where they have both a desire to

become a member of community and at the same time also have a desire to retain their own

individuality (Kogut and Zander, 1996). As firms provide a normative territory to which

members identify, costs of coordination, communication, and learning within firms are much

lower which allow more knowledge to be shared and created within firms.



29

NONAKA’S MODEL A stream of literatures has found consistent support for Kogut and Zander’s (1992) model of

organization knowledge creation and transfer (Nonaka, 1994; Nonaka and Takeuchi, 1995;

Nonaka et al., 1996) for example 1) knowledge should be the basic unit of analysis for

explaining a firm’s behavior, and 2) organization knowledge is socially constructed. This group

of researchers proposes a model of knowledge creation, which complements Kogut and Zander’s

(1992) model, by proposing a model for understanding the knowledge creation process in

organizations in which organizational knowledge is created through a continuous dialogue

between tacit and explicit knowledge.

This model proposes four modes of knowledge conversion 1) from tacit knowledge to tacit

knowledge (socialization); a process of personalized form of tacit knowledge growth in which

an individual passes on knowledge to another individual, 2) from tacit knowledge to explicit

knowledge (externalization); a process when individuals take existing knowledge, add their tacit

knowledge and create something new that can be shared throughout the organization, 3) from

explicit knowledge to explicit knowledge (combination); a process where knowledge is gained

by combining and synthesizing existing explicit knowledge from different sources, and 4) from

explicit to tacit knowledge (internalization); a process where new explicit knowledge is

internalized within members of the organization to create new tacit knowledge (Nonaka, 1994;

Nonaka and Takeuchi, 1995).

Even though each of these modes may independently create knowledge, the organizational

knowledge creation processes only occur when all the four modes are organizationally managed

and dynamically interacted. This process which is highly iterative constitutes ‘knowledge spiral’

which happens mainly through informal networks of relations in the organization starting from

the individual level, then moves up to the group (collective) level and eventually to the

organizational level. It creates a ‘spiraling effect’ of knowledge accumulation and growth which

promotes organization innovation and learning (Nonaka, 1994; Nonaka and Takeuchi, 1995).



30

GRANT’S MODEL Departing from Kogut and Zander (1992, 1993), Nonaka (1994) Nonaka and Takeuchi (1995),

and Nonaka et al. (1996), Grant (1996a, 1996b, 1997) propose a different model of knowledge

creation. Grant (1996a) has further articulated the theoretical arguments of knowledge-based

view, which considers knowledge creation as “an individual activity rather than an

organizational activity”. Several assumptions underlying the model are described as follows:

1) Knowledge is the important productive resource in terms of its contribution to value added

and its strategic significance.

2) Knowledge consists of information, technology, know-how, and skills. Thus, different types

of knowledge vary in their transferability. The critical distinction is between ‘explicit

knowledge’; which is capable of articulation and transferable at low cost, and ‘tacit knowledge’;

which is manifested only in its application and is not amenable to transfer. The ease with which

knowledge can be transferred also depends upon the capacity of the recipient to aggregate units

of knowledge.

3) Individuals are the primary agents of knowledge creation and the principal repositories of

knowledge especially tacit knowledge. If individual’s learning capacity is bounded, knowledge

creation requires specialization, where increased depth of knowledge normally requires

sacrificing breadth of knowledge. At the same time, production typically requires the application

of many types of knowledge.

4) Most knowledge is subject to economies of scale and scope. This is especially the case with

explicit knowledge that, once created, can be deployed in additional applications at low marginal

cost (Grant and Baden-Fuller, 1995; Grant, 1997).

While knowledge resides within individuals and firms consist of multiple individuals with

specialized knowledge, the firms’ role is to integrate this knowledge to enable it to produce

products and services. Firms exist because of their efficient ability in creating conditions where

many individuals can integrate their specialist knowledge (Grant, 1996a). Specialized knowledge

can be integrated within firms through four mechanisms 1) through rules and directives; where

rules are standards which regulate the interactions between individuals and directives are what

the specialists establish to guide the non-specialists, 2) through sequencing; a mechanism to

organize production activities in a time-patterned sequence such that each specialist’s input



31

occurs independently through being assigned a separate time slot, 3) through routines; where the

signals and responses developed by teams over time allow the complex interactions between

individuals in a relatively automatic fashion, and 4) through group problem solving and decision

making; a mechanism used to perform unusual, complex, and important tasks that requires

extensive personal interactions and communications. Common knowledge is important as a

means through which multiple individuals can communicate to integrate knowledge (Grant,

1996b).

SPENDER’S MODEL As opposed to traditional models of knowledge creation within organizations (Kogut and Zander,

1992; Nonaka, 1994; Grant, 1996a, 1996b), Spender (1996) proposes a dynamic rather than a

static knowledge-based theory of the firm. Knowledge is viewed as “a process or a competent

goal-oriented activity rather than as an observable and transferable resource” (Spender, 1996).

As knowledge is dynamic in nature and contained within actor network, a firm is a dynamic,

evolving, quasi-autonomous, organic system of knowledge production and application (Spender,

1996). A firm is a system of knowing activity and not a system of applied abstract knowledge

(Spender, 1996). Other proponents of this view are Blacker (1995) and Orlikowski (2002).

Blacker (1995) argues that traditional approach to knowledge is “compartmentalized and static”

and further suggests that rather than discussing knowledge, it is more beneficial to discuss the

process of knowing. Orlikowski (2002) suggests that the perspective which focuses on the

knowledgeability of action (perspective on knowing) that is on knowing may be value in a

perspective rather than knowledge.

SZULANSKI’S MODEL Szulanski (1995) adopts a different approach to KT by adopting a communication metaphor

(Shannon and Weaver, 1949) in analyzing intra-firm transfer of best practice in a manner

analogous to the transmission of a message from a source to a recipient within a given media or

context (Timbrell et al. , 2001). While knowledge transfer is a distinct experience rather than



32

diffusion, best practice transfer should be regarded as “a process rather than a transaction or

event” (Szulanski, 1995). Szulanski (1996) proposes an intra-firm transfer of best practice model

which views intra-firm transfer of best practice as “an unfolding process” in which

organizational routines are replicated through four stages of processes: 1) initiation, 2)

implementation, 3) ramp-up, and 4) integration.

Initiation is described as comprising all events that lead to the decision to transfer. A transfer

commences when both a need and the knowledge to meet that need coexist within the

organization, possibly undiscovered. When the need is discovered, it triggers a search for

potential solution; a search that leads to the discovery of superior knowledge (Szulanski, 1996).

Implementation begins with the decision to transfer in which resources flow between the

knowledge recipient and the source, the transfer-specific social ties between the source and the

knowledge recipient are established, and the transferred practice is normally adapted with the

objectives to suit the anticipated needs of the recipient to preempt problems experienced in a

previous transfer of the same practice, and to facilitate the introduction of new knowledge less

difficult to the recipient (Szulanski, 1996). Ramp-up commences when the recipient begins to

use the transferred knowledge. At this level the recipient’s primary concern is to identify and

resolve unexpected problems that restrict its ability to match or exceed the transfer performance

expectation (Szulanski, 1996). Integration starts when satisfactory result is achieved by the

recipient from the transferred knowledge and the transferred knowledge is converted into the

firm’s routine (Szulanski, 1996).

Szulanski (1996) has explored the origin of internal stickiness and identified four sets of factors

which are likely to have significant influence on the difficulty of knowledge transfer: i)

characteristics of the knowledge transferred, ii) the source, iii) the recipient, and iv) the context

in which the transfer takes place. Central to Szulanski’s (1996) model of intra-firm knowledge

transfer, which builds on the previous TT literature (Leonard-Barton, 1990; Teece, 1977; Rogers,

1983), is the importance of examining all the four sets of factors simultaneously in an eclectic

model. Few researchers have developed their intra and inter-firm knowledge transfer framework

based on this model (for example Szulanski, 2000, 2003; Gupta and Govindarajan, 2000;

Minbaeva, 2007; Simonin, 1999a, 1999b, 2004).



33

ORGANIZATIONAL LEARNING AND TECHNOLOGY TRANSFER Based on a review of literature, the term organizational learning (OL) has existed in the literature

since 1960s through researchers such as Argris (1964), Cangelosi and Dill (1965), and Cyret and

March (1963). Past studies have defined OL from three different views. The first view describes

OL as a process. Researchers who hold this view describe learning as cognition or information

processing. OL is defined as 1) a development of insights, knowledge and associations between

past actions, the effectiveness of those actions, and the future actions (Appelbaum and

Goransson, 1997), 2) the process by which the organizational knowledge base is developed and

shaped (Tsang, 1999), and 3) an organizational process, both intentional and unintentional,

enabling the acquisition of, access to, and revision of organizational memory (Robey et al.,

2000).

The second view emphasizes on the outcomes of OL such as change of behavior and

improvement of organizational effectiveness. Proponents of this view describe OL as 1) a change

in the behavior of individuals or groups within an organization leading to changes in the

behavior of the organization itself (Reynolds and Ablett, 1998), 2) increasing an organizational

capacity to take effective action (Kim, 1993), changes in the behavior of the organizations’

knowledge and value base leading to improved problem solving ability and capacity for action

(Probst et al.,1997), and 3) improving actions through better knowledge and understanding (Fiol

and Lyles, 1985).

The third view integrates both views that link the learning process and outcomes. Researchers

who hold this view describe OL as 1) a process that result in changed behavior in ways that lead

to improved performance (Buckler, 1998), and 2) the development or acquisition of new

knowledge or skills in response to internal and external stimuli that leads to a more or less

permanent change in collective behavior, enhancing organizational effectiveness (Sadler-Smith

et al., 2001).

Other researchers have described OL as 1) the environment adjustment process for achieving the

specific goals of an organization and a common learning method of procedure of the

organization (Lin, 2007), 2) the firm’s ability of evolution and action in response to the

stimulation from the internal and external environment (Meyers, 1990), 3) the process of

promoting organizational activities with better knowledge and understanding (Grant, 1996), and



34

4) the process of acquiring or internalizing the skills or know-how of the partners (Khanna et al.,

1998).

An entity/organization learns if through its processing of information the range of its potential

behaviors is changed, or if any of its unit acquires knowledge that it recognizes as potentially

useful to the organization (Huber, 1991). Building on Huber’s (1991) seminal work on OL,

Miner and Mezias (1996) suggest that OL involves three key questions or issues: 1) what are the

learning processes, 2) who or what is doing the learning, and 3) when is learning valuable. The

first question is closely related to the four constructs and processes of OL developed by Huber

(1991). The four constructs are integrally linked to explain the OL process. The four constructs

are described as follows:

1) Knowledge Acquisition: This construct refers to the process of how knowledge is acquired or

obtained. Knowledge acquisition consists of five stages of processes: i) congenital learning, ii)

experiential learning, iii) vicarious learning, iv) grafting and v) searching and noticing.

2) Information Distribution relates to the process by which information from different sources is

shared and thereby leads to new information or understanding.

3) Information Interpretation: This construct refers to a process by which distributed information

is given one or more commonly understood interpretations.

4) Organizational Memory: This construct refers to the means by which knowledge is stored for

future use (Huber, 1991).

On the second question, Miner and Mezias (1996) suggest three levels of learning 1) the

individual level; where individuals acquire and interpret information based on their personal

cognitive maps and frameworks, 2) the group level; where the group decision-making of the firm

will respond to performance feed-back with shared understanding and coordinated behavior; and

3) the organization level; where groups in the organization acquire knowledge through sharing of

experience. With regard to the third question, the factors affecting the learning impact include

learning rate, level of noise in the feedback process, numbers of the independent learning sub-

units and the timing of learning (Miner and Mezias, 1996).

Huber’s (1991) work on OL provides a useful framework in understanding how knowledge is

acquired by organizations in the inter-firm relationship from outside the organizational boundary

through grafting process (Inkpen, 2000). Through grafting, which is a sub process of knowledge

acquisition; organizations increase their store of knowledge by acquiring new knowledge not



35

previously available within the organization either by mergers, acquisition or alliance (Huber,

1991). Thus, the focus of the present study directly relates to the grafting process, which occurs

at the OL level to explain how organizations (the partner firms) in the inter-firm relationship

such as IJVs acquire and transfer knowledge between them (Huber, 1991; Inkpen, 2000; Tsang et

al., 2004; Hau and Evangelista, 2007).

Knowledge acquisition through alliance may occur in various organizational arrangements of

strategic alliance such as JVs, licensing agreements, distribution and supply agreements, research

and development partnerships and technical exchanges (Inkpen, 1998a). Knowledge acquisition

by organization through JVs is a multi-stage process (Inkpen and Dinur, 1998). The first stage

begins with the formation of the JV; where interactions between the individuals from two or

more JV partners occur. The second stage is the grafting process; where the knowledge is

transferred from the JV to the partners. In the final stage, for internalization to occur, the parent

firms must first attempt to transfer the partner’s skill-related knowledge from the JV to

themselves (Inkpen and Dinur, 1998).

Past studies on learning through alliance have acknowledged two main factors which affect

knowledge acquisition 1) the accessibility of knowledge, and 2) the firm’s effectiveness at

learning (Inkpen, 1998a, 2000). The accessibility to alliance’s knowledge is mainly depending

on knowledge tacitness and partner protectiveness (Inkpen, 1998a). Knowledge tacitness limits

knowledge accessibility when knowledge, which is embedded in personal beliefs, experiences

and values, is hard to formalize, not easily visible and difficult to communicate and share

(Inkpen, 2000). Partner protectiveness inhibits alliance knowledge acquisition when a high

competitive overlap exists between partners. The transferring firms will be reluctant to share or

transfer knowledge due to risk of knowledge spillovers to the opportunist learning partner

(Inkpen, 1998a). In addition, knowledge must be accessible before it can be acquired and even if

knowledge is accessible, it does not ensure acquisition. Thus, an effective knowledge acquisition

is determined by 1) the knowledge connections between the parent and its JV, and 2) the nature

of alliance knowledge and its relatedness to the parent (Inkpen, 2000).

Bapuji and Crossan (2004), however, suggest that external learning by the organizations occurs

in three forms: 1) congenital learning; where a new firm learns from the past experience of other

firms in the industry, 2) vicarious learning; where firms learn from the experience of other firms,

and 3) inter-organizational learning. In the inter-organizational learning, OL occurs through



36

vicarious learning when organizations interact with each other in alliances or joint ventures

(Bapuji and Crossan, 2004).

Few TT studies have suggested that OL perspective provides much needed rigor in the

conceptualization of the TT process in terms of its depth and breadth. Daghfous (2004) views OL

literature as necessary and a complementary component of the complete view of 1) TT as a

learning process; and 2) technology recipient organizations as learning system (Levin, 1993;

Daghfous, 2004; Bapuji and Crossan, 2004). A review of literature reveals that the previous

researchers have proposed several OL models which explain 1) how an organization learns

(Argyris and Schon, 1978), 2) the sources of knowledge (Mills and Friesen, 1992), 3) the OL

process (Nevis et al., 1995), 4) how organizational knowledge is created (Nonaka, 1994), 5) the

links between individual learning and OL (Kim, 1993), and 6) the OL and KT in international

joint ventures (IJVs) (Tiemessen et al., 1997).

ARGYRIS AND SCHON’S MODEL Argyris and Schon (1978) develop a three-fold typology of organizational learning: 1) single-

loop, 2) double-loop, and 3) triple-loop (deutero) learning. Single-loop learning is described as

“the error-detection-and-correction process; where errors are detected and corrected to allow an

organization to change its methods and rules to improve what is being done within existing

programs or policies”. As a result, the organization achieves its current objective more

efficiently. In addition to the error-detection-and-correction, double-loop learning involves

“change of the value of an organization’s theory-in-use”. This form of learning occurs when

errors are detected and corrected in ways that involves the changes in an organization’s

underlying norms, policies and objective. Triple-loop or deutero learning is “learning how to

learn”; where the organizational members’ cognitive changes as a result of reflecting and

inquiring into their previous learning experiences. Triple-loop learning is also a process how to

execute single and double-loop learning (Argyris and Schon, 1978).



37

MILLS AND FRIESEN’S MODEL This model describes the ways how organizations learn and focuses on the sources of

knowledge. The model explains that an organization learns through individuals in the

organization. These individuals are hired because of their specific competencies or knowledge

which may be gained through on the job training or formal training. Learning is an individual

phenomenon, which benefits the organization entirely through the individuals (Mills and Friesen,

1992). OL should involve systemizing knowledge into its practices, processes, and procedures

that are the reutilization of knowledge. When individuals do not use knowledge or resign, the

knowledge will still remain with the organization which constitutes OL. If an organization

acquires or merges with other organization, OL occurs when the acquiring organization absorbs

the acquired organization practices and procedures, or adds to its personnel the knowledge

embodied in the acquired organization’s processes and personnel (Mills and Friesen, 1992).

NEVIS, DiBELLA AND GOULD’S MODEL Nevis et al. (1995) propose a three-stage model of OL: 1) knowledge acquisition, 2) knowledge

sharing, and 3) knowledge utilization. Knowledge acquisition refers to the development or

creation of skills, insights, and relationship. Knowledge sharing relates to the dissemination of

knowledge that has been learned. Knowledge utilization is the integration of learning to make it

widely available; where it can be generalized to new environments. OL may occur in a planned

or informal ways. Knowledge and skill acquisition occur not only through acquisition but also

through knowledge sharing and utilization (Nevis et al., 1995).

NONAKA’S KNOWLEDGE SPIRAL MODEL Nonaka (1994) proposes a model describing how organizational knowledge is created through

different channels of interaction between tacit knowledge and explicit knowledge. Nonaka

(1994) suggests four modes how knowledge is created through: 1) socialization process (tacit to

tacit knowledge creation), 2) externalization process (tacit to explicit knowledge creation), 3)



38

combination process (explicit to explicit knowledge creation), and 4) internalization process

(explicit to tacit knowledge creation). A discussion on this model has been provided above.

KIM’S MODEL Kim (1993) proposes an integrative model describing the link between individual learning and

OL in which an organization learns through its individual members is affected either directly or

indirectly by individual learning. This model describes OL as not only a collective individual

learning but also involves the transfer mechanism between individual and OL; where individual

learning becomes embedded in an organization’s memory and structure. In this sense, individual

learning affects learning at organizational level by its influence on the organization’s shared

models. This model stresses that organization learns only through its members and learning does

not depend on any specific members. However, individuals can learn without the organization.

OL process is viewed from two perspectives: 1) the collective learning perspective, and 2) the

cognitive-outcome perspective. The collective learning perspective describes how knowledge

through individual learning becomes organization shared knowledge, and the cognitive outcome

perspective indicates that knowledge acquired through individual learning can lead directly to

individual action or indirectly to organizational action through knowledge sharing (Kim, 1993).

INTERNATIONAL JOINT VENTURE KNOWLEDGE MANAGEMENT MODEL Building on Parkhe (1993) and Toyne (1989), Tiemessen et al. (1997) propose a model of OL

and KT in IJVs based on input-process-output model. According to this model there are four

critical elements involved in OL and knowledge transfer in IJV: Structure, Conditions, Process,

and Outcomes. Tiemessen et al. (1997) propose three phases of inter-organizational learning in

JV.

The first phase is transfer process where two independent firms form a JV, both firms transfer

and contribute resources in terms of their existing stock of competencies. Transfer is described as

the movement/migration of knowledge between the parents firms, directly or indirectly, through

activities such as buying technology, observing and imitating technology used by the other JV’s



39

partner or modifying/changing the existing technologies based on the partner’s direction.

Transfer means “to accept the partner’s knowledge, to integrate knowledge into one’s own

systems or changing one’s own resources to imitate knowledge” (Tiemessen et al., 1997).

The second phase is transformation process where through joint activities these competencies are

then transformed and enhanced to reflect the combined pool of knowledge and skills as well as

new knowledge and skills created from the alliance. Knowledge transformation is the extension

of existing knowledge and the creation of new knowledge within the JV. Thus, transformation is

defined as the integration, application and leveraging of contributed knowledge, and the creation

of new knowledge as a result of IJV activities. The successful exploitation of an advantage

internationally may require an adaptation of the technology, system, or management practices, or

all of them to the local environment (Casson, 1993). Collaborating with local partners is crucial

in ensuring appropriate and correct adaptation, and opportunities to improve own capabilities.

Through adaptation process, resource integration and partnering knowledge are created

(Tiemessen et al., 1997).

The third phase is harvesting process where partners harvest knowledge and skills from IJV and

bring back to the parent firms. Harvesting is described as “a process of retrieving knowledge that

has already been created and tested from the IJV resources in which it resides, and internalizing

it into the parent firm so it can be retrieved back and used in other applications”. Knowledge

harvesting process is different from transfer and transformation process because the process is

more difficult and not straightforward (Tiemessen et al., 1997). Knowledge harvesting by the

parent firms is contingent upon the top management’s active role in JV and proper

communication with the JV managers (Lyles, 1988).

CONCLUSION This review significantly contributes to the existing TT literature by reviewing the evolution and

development of the previous TT models which include the traditional TT model, models

developed after 1990s, other related theoretical foundations underlying TT models, and the

current TT models which have strong influence of KBV and OL perspectives. This review could

help shape the direction of both future theoretical and empirical studies on inter-firm technology

transfer specifically 1) on how KBV and OL perspectives could play significant role in



40

explaining the complex relationships between the supplier and recipient in inter-firm technology

transfer 2) the tradeoffs that involve between properties of technology, protecting proprietary

technologies, competitiveness of the supplier, willingness to transfer technology, and learning

attitudes of the recipient in strategic alliances and JVs, and 3) on how KVB and OL perspectives

could be integrated in a holistic model to explain the relationships between knowledge

transferred, the recipient, the supplier, relationship characteristics and degree of technology

transfer.

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3

The Effects of Inter Firm Technology Transfer

Characteristics on Degree of

Technology Transfer in International Joint Ventures:

A Framework CHAPTER OUTLINE

The inter-firm technology transfers (TT) in collaborative joint ventures (JVs) often involve

tradeoffs between the willingness of technology supplier to transfer a considerable amount of

technologies to technology recipient and degree of protection of the proprietary technology,

knowledge and competencies as the source of the supplier’s competitive advantage. Thus,

technology transfers through JVs, although have been acknowledged in many studies as the

most efficient mechanism in internalizing the partner’s technology, knowledge and skill, have

frequently involved various facilitators, actors and complicated relationship between partners

that have direct impact on the degree or amount of technology transferred in JVs. Building on

the integrated knowledge-based view and organizational learning perspectives, and previous

TT models, this work proposes a holistic TT model in providing explanations on the relative

and simultaneous effects of technology transfer characteristics (TTCHARS) on degree of

technology transfer (TTDEG). Subsequently, the holistic TT model also conceptualizes the

effect of TTDEG on local firms’ performance dimensions namely corporate and human

resource performance, and the moderating effects of MNCs’ firm size, age of JV, MNCs’

country of origin, and types of industries in the TTCHARS-TTDEG relationship.

INTRODUCTION As a developing country, Malaysia for the past thirty years has transformed its resource-based

economy to industrial-based economy which resulted in a tremendous economic growth

(Malairaja and Zawdie, 2004). The Malaysian government, through its intensified efforts, has



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turned the national economy from labor intensive to capital intensive. The early success in

developing its industrial sectors was mainly owed to direct import of low technologies especially

from United States (U.S), Japan and Europe. The change in the economic policy has witnessed

intense efforts made by the Malaysian’s government in attracting foreign investment in industrial

sectors through foreign direct investments (FDI) and international joint ventures (IJVs) formed

between the multinational corporations (MNCs) and local companies. The total FDIs inflows for

the period 1986-1995 have increased from RM73.4bn to RM121.8bn in 1996-2005 (The Third

Industrial Master Plan). Therefore, in generating the economic growth, the FDIs will continue as

the primary source of foreign technology. In another example, the FDIs for the manufacturing

sector in 2006 have increased to RM14.7bn from RM13.5bn in 2005 (MIDA, 2007).

Since technology has been acknowledged as an important catalyst of corporate success and

national economic growth (Millman, 2001), Malaysia relied heavily on FDIs from MNCs as the

primary source of technology (Lee and Tan, 2006) to enhance its technological capabilities and

competitiveness of local industries. Like many of the developing countries, Malaysia has limited

resource capacities in terms of research and development (R & D) base, limited investment in

R&D, production and manufacturing capability, and weak infrastructure and technological

advantage (Lado and Vozikis, 1996; Tepstra and David, 1985). The presence of MNCs in

Malaysia as the technology supplier is crucial because not only they own, produce and control

bulk of the world technology but they have also undertaken almost 80% of all private R&D

expenditures worldwide (Dunning, 1993).

In order to realize its aspiration of becoming a developed and industrialized nation in 2020,

Malaysia has no alternative but to aggressively develop and sustain its own technology by

embarking on appropriate technology transfer (TT) strategies and initiatives. Thus, as a way to

build the technological capacity, strengthen their core competencies, and expend into

technological field which are critical for maintaining and developing the market share, the

Malaysian companies and industries greatly need foreign technologies to achieve this objectives

(Wagner and Yezril, 1999). The important role of MNCs as the main source of technology has

been affirmed by the previous studies. The presence of MNCs is regarded as the most efficient

vehicle for transferring technology and knowledge across border through FDIs and IJVs (Tihanyi

and Roath, 2002; Kagut and Zander, 1993).



49

Past studies have shown that foreign MNCs in Malaysia have successfully transferred their

technology to local industries (Lai and Narayanan, 1997, Narayanan and Lai, 2000). Through

technology transfers by MNCs the host country would benefits in achieving long term economic

growth (Marton, 1986; Blomstrom, 1990), providing a higher potentials of innovation

performance/capabilities (Guan et al., 2006; Kotabe et al., 2007)), increasing technological

capabilities (Kumar et al., 1999; Madanmohan et al., 2004), enhancing the competitive

advantage (Liao and Hu, 2007; Rodriguez and Rodriguez, 2005), enhancing the organizational

learning effectiveness (Inkpen, 2000; Inkpen and Dinur, 1998), providing a positive effect on

productivity (Caves, 1974; Xu, 2000; Liu and Wang, 2003), and increasing the technological

development of local industry (Markusen and Venables, 1999). In addition, other studies have

proposed TT as one mechanism by which developing countries can break vicious cycle of

economic underdevelopment (Lado and Vozikis, 1996; Samli, 1985).

However, few recent studies have concluded that the TT agent such as FDIs, JVs, and licensing

operations have not succeeded in helping to develop indigenous capabilities even though they

have significantly contributed to the impressive economic growth performance in Malaysia

(Malairaja and Zawdie, 2004). During the Asian economic crisis in 1997-1998, which has

brought the host country economy vulnerable to changes in investors’ sentiment and foreign

competitions, the international TT through FDIs do not sufficiently help to develop the

indigenous capabilities (Lee and Tan, 2006). Thus, learning from past experience, the companies

in the developing countries such as Malaysia should re-strategize their TT policies and strategies

by not only understanding, identifying and examining the critical TT determinants’

characteristics that may have significant effect on the TT outcome but also studying the

boundary conditions for the relationship. TT should not only focus on TT as an efficient vehicle

to generate economic growth performance but more importantly it must also be capable in

developing indigenous capabilities and organizational competitiveness.

Before embarking on any TT strategies and policies, there is a need to critically examine the

technology transfer characteristics (TTCHARS) that may have significant influence on the

successful and effective implementation of TT particularly technologies transferred through

IJVs. This is because TT success is determined by the substantial amount of technology

transferred (level of TT) and the technological capacity to absorb, assimilate, improve and

further develop the newly acquired technology (Madanmohan et al., 2004).



50

Based on a review of literature, bulks of the previous literature are concentrating more on the

macro-economic or institutional factors (Contractor and Sagafi-Nejad; 1981, Marton; 1986). Due

to the diverse environmental factors which impede TT success the factors that influence TT have

become important (Cui et al., 2006). Since the TT literature is extensive and varied in

perspective, this study has specifically focused on inter-firm TT across organizational boundaries

via IJVs based on the underlying knowledge-based view (KBV) and organizational learning

(OL) perspectives. Thus, while acknowledging the significance of other perspectives of TT they

are indeed beyond the scope of this study.

The presence of the MNCs through various formal market channels such as direct exporting of

capital goods and products, foreign direct investments, licensing, and IJVs with local firms have

become the primary sources of technology for the local technological development and national

economic growth. Many studies on intra and inter-firm TT have shown that TT involves a

complex and difficult process even when it occurs across different function within a single

product division of a single company. Thus, the current issue is centered on the effectiveness,

efficiency and successful implementation of TT and no longer on whether MNCs are transferring

their technologies to the Malaysian industries (Lai and Narayanan, 1997). TT success depends

heavily on interactive communications between the technology supplier and recipient which

requires both parties involvement (Gibson and Slimor, 1991).

Previous studies have also indicated that MNCs as the reluctant technology supplier have been

slow in transferring technology and R&D expertise to local industries due to the risk of

technology spillovers (Lai and Narayanan, 1997). The foreign MNCs often face a tradeoff

between transferring their valuable technologies to their counterpart and protecting the

technologies as the source of their competitive advantage. In this situation the MNCs have

repeatedly claimed that it is not a question of their willingness to transfer technologies rather the

transferring process is mainly hampered by low maturity level of the Malaysian industry which is

largely due to insufficiency of skilled personnel and weak institutional support and business

environment (Rasiah and Anuwar, 1998).

When compared to the U.S MNCs, the TT by the Japanese MNCs are found to be less intensive,

slower, and technologies are normally been transferred within their ‘keiretsu’ (Raduan, 2002;

Yamashita, 1991, Hamel 1991). In fact according to Taylor (1995) the Japanese MNCs, to some

extent, have no intention to transfer key aspects of their technology in order to maintain their



51

dominance in Southeast Asian economies. From KBV perspective studies have acknowledged

that MNCs tend to be more protective of their advance technology, knowledge and competencies

in products, processes and management because these strategic valuable resources and

competencies are their main sources of competitive advantage (Barney 1991; Peteraf, 1993;

Pralahad and Hamel, 1990; Dierickx and Cool, 1989). On the other hand, studies from the OL

perspective have suggested that the technology suppliers tend to protect their technology and

knowledge from the recipient when they become opportunistic in the collaborative relationship

(Inkpen, 1998a; Inkpen and Dinur, 1998; Child and Faulkner, 1998).

Based on the above scenarios, a number of TT issues require further theoretical and empirical

explanations. Specifically, in the context of inter-firm TT through IJVs, the existing question is

on the extent of TT by the supplier partner when transferring their advance technology to local

recipient partners, especially the transfer of tacit knowledge which has a high content of

ambiguity, complexity, and specificity. The current TT issue in JV thus focuses on the extent of

degree of technologies that being transferred by the technology supplier to technology recipient

partners in terms of tacit knowledge (new product/service development, managerial systems and

practice, process designs and new marketing expertise), and explicit knowledge

(manufacturing/service techniques/skills, promotion techniques/skills, distribution know-how,

and purchasing know-how).

Since JVs are frequently perceived as instable organization, the degree of technology transferred

in JVs often involves a tradeoff between transferring technology and protecting proprietary

technology/knowledge by the supplier. From the technology recipient’s perspective, TT success

includes the ability to learn, acquire, absorb and apply new external technologies and knowledge

embedded in product materials, physical assets, processes and production, and management

capabilities and not limited to possessing the ability to operate, maintain or repair the

machineries in the production level.

Secondly, past studies on intra and inter-firm knowledge transfer have established the significant

effects of technology actors and facilitators/barriers such as the characteristics of knowledge

transferred, source, recipient and contextual/relational in the knowledge transfer process

(Szulanski, 1996, 2000, 2003; Gupta and Govindarajan, 2000; Minbaeva, 2007). Thus, in the

context of inter-firm TT in JVs, where TT processes are more complicated, difficult and involved

the process of transferring technology between unaffiliated organizations, the remaining issue is



52

on the extent of significant effects of technology transfer characteristics (TTCHARS) on degree

or amount of technology transfer (TTDEG). To put it differently to what extent the TT

characteristics have significantly affected TT outcomes?

Thirdly, since JVs is one of the formal and externalized mechanisms of TT which directly affect

performance, the intriguing issue is on the extent of TTDEG in affecting the performance of

local firms (LFP); specifically on how TTDEG helps to improve the local companies’ corporate

and human resource/competencies performance.

Finally, although previous studies have acknowledged the significant effects of knowledge

transfer determinants on knowledge transfer outcomes, nevertheless the effects of TTCHARS on

TTDEG in inter-firm TT through JVs could have possibly be moderated by other important

factors such as size of MNCs, age of JV, MNCs’ country of origin, and MNCs’ types of industry.

Thus, in other words the variations in TTDEG’s outcome could have been significantly

influenced by these variables. Past studies have established that the issue of effectiveness and

efficiency of inter-firm TT between foreign MNCs and local recipient’s firm in JV with the

facilitators/barriers that impede TT (the transfer process) and knowledge acquisition (the

absorption process) are primarily attributed to the critical TTCHARS such as knowledge

transferred (knowledge attributes), technology-recipient (knowledge seeker attributes),

technology-supplier (source attributes), and supplier-recipient relationship (relational attributes)

characteristics (Leonard-Barton, 1990; Teece, 1977; Rogers, 1983; Szulanski, 1996).

In the strategic alliance set-up such as IJVs, these facilitators/barriers to transferability could

facilitate or impede inter-firm TT and knowledge acquisition resulting in the higher/lower degree

(amount) of technology transfer to the recipient. Since the co-existence of these determinants is

interrelated with each other, therefore the ineffectiveness of any characteristic would cause inter-

firm TT and knowledge acquisition in JVs to be less successful and effective. As inter-firm TT in

JVs involves more complex and difficult processes as compared to intra-firm TT, these

facilitators/barriers, which are attributed to the TTCHARS, require close theoretical explanations

to describe the relative influence of each characteristic of TT and their combine/joint effects on

TTDEG.

Through the conceptual explanations on the individual and joint effects of each TTCHARS

characteristic on TTDEG the recipients’ organizations/firms are unable to get first hand

information and understanding before reviewing, designing and formulating new TT policies and



53

strategies in order to achieve a higher level or amount of technology transfer in JVs, increase the

overall local companies’ competitiveness, productivity, and performance, enhance knowledge

acquisition by the local companies, help to develop indigenous technological capabilities of the

local work force, and stimulate local innovation capability. Thus, building on intra and inter-firm

TT literature, this study advances a holistic model of TTCHARS-TTDEG by proposing that 1)

all the TTCHARS, which include the knowledge transferred (KCHAR), technology recipient

(TRCHAR), technology supplier (TSCHAR), and relationship (RCHAR) characteristics, are

critical in affecting TTDEG in JVs, 2) TTCHARS-TTDEG relationship could possibly be

moderated by certain factors, and 3) TTDEG affects the LFP.

In this conceptual study, inter-firm TT is defined as “the transfer of technological knowledge,

information and know-how that are transferred across organizational border by the technology-

supplier; where the technology recipients’ firms have effectively acquired, learned and absorbed

knowledge and technology embedded in product materials, physical assets, processes and

production and managerial capabilities” (Kogut and Zander, 1992, 1993; Teese, 1976; Grant,

1996a; Szulanski, 1996; Inkpen, 1998, 2000; Inkpen and Dinur, 1998; Simonin, 1999a, 1999b,

2004).

Since technology is an abstract subject, TTDEG is operationalized as the degree of technological

knowledge from two dimensions: 1) tacit knowledge in terms of new product/service

development, managerial systems and practice, process designs and new marketing expertise,

and 2) explicit knowledge in terms of manufacturing/service techniques/skills, promotion

techniques/skills, distribution know-how, and purchasing know-how. JVs are referred to as “a

form of international collaborative/cooperative efforts which bring together two or more firms to

engage in a joint activity to which each member contributes resources with expectation to extract

resources of higher value, share their respective resources, skills and expertise” (Beamish and

Bedrow, 2003; Ibrahim and Mcguire, 2001).

THEORETICAL DEVELOPMENT AND HYPOTHESES The main theories underpinning the relationships of variables in the conceptual framework of

this study are knowledge–based view (KBV) and organizational learning (OL) perspectives. The

perspective of KBV underlies the relationships between the KCHARS and sub- independent



54

variables: Tacitness (TCT), Complexity (COMPLX) and Specificity (SPEC), and dependent

variable - TTDEG. Both KBV and OL perspectives underlie the relationships between the

TRCHARS and sub-independent variables: Absorptive Capacity (ACAP) and Recipient

Collaborativeness (RCOL) and TTDEG. The relationships between the TSCHAR and sub-

independent variables: Partner Protectiveness (PPROTEC) and Transfer Capacity

(TRANSCAP), and TTDEG are governed by both KBV and OL perspective. The OL perspective

underlies the relationship between RCHAR and sub-variables: Relationship Quality (RELQLTY)

and Mutual Trust (MT). Figure 1 below depicts the relationships between the variables in the

study’s conceptual framework.

Figure 1: The Holistic Model of TTCHARS-TTDEG-LFP in IJV

KCHAR H1 H7 H1 H8

TRCHAR H2

H5 H6

TSCHAR H3 H3 H9

H10

RCHARS H4

. Tacitness *(KBV Perspective)

Moderating Variables . Firm Size . Age of JV

. Complexity *(KBV Perspective)

. Specificity *(KBV Perspective)

. Absorptive Capacity *(KBV Perspective)

Corporate

Performance Local Firms’ Performance Human Resource Performance

. Recipient Collaborativeness *(OL Perspective)

Degree of Tacit Technology Knowledge Transfer Explicit Knowledge . Partner

Protectiveness *(OL Perspective)

. Transfer Capacity *(KBV Perspective)

Moderating Variables . Country of Origin . Types of Industry

. Relationship Quality *(OL Perspective)

. Mutual Trust *(OL Perspective)



55

KNOWLEDGE CHARACTERISTICS AND DEGREE OF TECHNOLOGY TRANSFER The first characteristic of TT this study investigates is knowledge characteristics (KCHAR). The

KCHAR form the first group of TT characteristic in this study. Based on a literature review,

KCHAR that have been identified include tacitness, complexity, specificity (Kogut and Zander,

1993; Inkpen and Dinur, 1998, Simonin, 1999a, 1999b, 2004; Pak and Park, 2004; Inkpen, 2000;

Minbaeva, 2007; Makhija and Ganesh, 1997; Lei et al., 1997; Inkpen, 1998a, 1998b, 2000;

Parise and Handerson, 2001; Mohr and Sengupta, 2002), knowledge relatedness (Inkpen, 2000;

Lyles et al., 2003), desirability (Pak and Park, 2004) and availability (Minbaeva, 2007).

Knowledge tacitness, specificity and complexity have contributed significantly to knowledge

ambiguity in imitation (Reed and DeFillippi, 1990), and knowledge migration (Szulanski, 1996).

Building on the previous intra-firm knowledge transfer studies (Winter, 1987; Reed and

DeFillippi, 1990; Szulanski, 1996; Zander and Kogut, 1995; Kogut and Zander, 1993; Minbaeva,

2007) and inter-firm knowledge transfer studies (Lyles and Salk, 1996; Mowery et al., 1996;

Simonin, 1999a; Simonin, 1999b; Simonin, 2004; Inkpen, 1998a; Inkpen and Dinur, 1998; Pak

and Park, 2004), this study conceptualizes that the three critical dimensions of KCHAR:

Tacitness (TCT), Complexity (COMPLX) and Specificity (SPEC) have a significant negative

impact on degree of technology transfer (TTDEG).

Knowledge has been classified using many different dimensions and the dimension that appears

to be particularly relevant to TT is tacit vs. explicit dimension (Marcotte and Niosi, 2000; Grant,

1996a, 1996b, 1997). The concept of tacit knowledge (TCT) is derived from the famous work of

Polanyi (1962) who asserts that “we can know more than what we can tell”. Tacit knowledge is

knowledge that is non-verbalizable, intuitive and unarticulated, developed through the transfer of

context-specific knowledge, embedded in non-standardized and tailored process, and is difficult

to acquire and exploit (Polanyi, 1967). Tacit knowledge derives from the accumulated

experience, and is reflected in the expertise, skills and routines acquired by organizational

members over time (Winter, 1987). Past studies have established that tacit knowledge, which

includes insights, intuitions and hunches, rule of thumb, gut feeling, personal and organizational

skills (Nonaka, 1994), managerial and marketing expertise (Lane et al., 2001), is difficult to

codify: where it can only be observed through its application and acquired through practice.



56

Thus, tacit knowledge transfer between individuals is slow, costly and uncertain (Kogut and

Zander, 1992).

Acquiring tacit knowledge is subject to time-compression diseconomies: which means to

accelerate tacit knowledge learning is very difficult or perhaps not even possible no matter how

much efforts or resources are invested to acquire them within a short period of time (Dierickx

and Cool, 1989; Lin, 2003) because tacit knowledge is unique to the knowledge owner and not

codifiable in formulas or manuals and cannot be reverse-engineered easily (Zander and Kogut,

1995). Tacit knowledge which is hard to formalize, often sticky and not easily visible, is difficult

to communicate, transfer and share between the alliance partners as it involves 1) intangible

factors embedded in the personal beliefs, experiences, and values in an organization (Inkpen,

1998a, 2000), 2) internal individual processes like experience, reflection, internalization or

individual talents (Nonaka, 1994), and 3) high incremental cost of transferring the knowledge to

a specified location in a form usable by a given party (von Hippel, 1994).

A number of literature has described complexity (COMPLX) from many dimensions for

example: 1) COMPLX is closely associated with the amount of information required to

characterize the item of knowledge in question (Winter, 1987), 2) COMPLX is “a result of the

interdependent skills and assets: which arises from large numbers of technologies, organization

routines and individual or team-based experience” (Reed and DeFillippi, 1990), 3) COMPLX as

“the number of interdependent technologies, routines, individuals and resources linked to a

particular knowledge or assets” (Simonin, 1999a), 4) COMPLX as “the number of critical and

interacting elements embraced by an entity or activity” (Kogut and Zander, 1993), and 5)

COMPLX as “an applied system whose components have multiple interactions and constitutes a

non-decomposable whole” (Singh, 1997). COMPLX of human and technological systems

produce higher levels of ambiguity which restrains imitation and impedes transferability (Reed

and DeFillippi, 1990). It is argued that the higher the degree of COMPLX of the manufacturing

technology, the more difficult for knowledge to be transferred or imitated (Kogut and Zander,

1993).

Specificity (SPEC) originally refers to transaction costs asset specificity as popularized by

Williamson (1985). Asset SPEC which includes site, physical, dedicated and human assets refer

to durable investments that are undertaken in support of particular transaction (Williamson,

1985). Building on Williamson (1985), Reed and DeFillippi (1990) define SPEC as “transaction-



57

specific skills and assets that are utilized in production processes and provision of services for

particular customers”. Through firm-customer relationship, the business actions resulting from

the resource and skill deployment (competencies) are highly specific and inter-dependent with

the firm’s internal or external transaction partners (Reed and DeFillippi, 1990). Although sites or

physical assets create limited ambiguity to imitation by rivals, dedicated assets such as the plants

specifically designed for the production of goods and services for a specific customer, and

human asset SPEC is linearly and significantly related to ambiguity as these types of asset SPEC

create barriers to imitation and are protected by the security and exclusivity of the firm-customer

relationship (Reed and DeFillippi, 1990). Simonin (1999a, 1999b) narrowly views SPEC as

“durable investments in specialized equipment, facilities and skilled human resources”.

Asset SPEC is not only acted as a source of causal ambiguity and barrier to imitation, where

technology is difficult to be explicitly articulated (Lippman and Rumelt, 1982), but also as a

barrier to knowledge transferability (Simonin, 1999a). The firms’ resources and competencies,

which are highly specific, are difficult to imitate and transfer as they are embedded in context

and idiosyncrasy to the firm (Kogut and Zander, 1993). Firms create sustainable competitive

advantage by developing firms’ assets and competencies that are firm-specific, produce complex

social relationships i.e. firm-customer relationship, embedded in a firm’s history and culture,

generate organizational tacit knowledge and time consuming to develop (Lado and Wilson,

1994; Dierickx and Cool, 1989; Kogut and Zander, 1993).

H1: Knowledge characteristics, which comprise of tacitness, complexity, and specificity, have a

negative effect on degree of technology transfer in JVs.

TECHNOLOGY RECIPIENTS’ CHARACTERISTICS AND DEGREE OF TECHNOLOGY TRANSFER The characteristics of technology-recipient (TRCHAR) have been affirmed by many studies as

the important factors that affect knowledge transfer. The TRCHAR form the second group of

characteristic of TT in this study. The recipient’s characteristics that have been identified to

influence TT and KT are absorptive capacity (Cohen and Levinthal, 1990; Hamel, 1991; Lyles

and Salk, 1996; Mowery et al., 1996; Lane and Lubatkin, 1998; Lane et al., 2001; Gupta and



58

Govindarajan, 2000, Minbaeva et al., 2003, Minbaeva, 2007; Pak and Park, 2004), experience

(Simonin, 1999a, 1999b), prior knowledge and experience (Inkpen, 1998a, 1998b, 2000; Tsang,

2001), knowledge relatedness (Inkpen, 2000), learning capacity (Makhjia and Ganesh, 1997;

Parise and Henderson, 2001), receptivity (Hamel, 1991; Baughn et al., 1997), learning intent or

objectives (Beamish and Berdrow, 2003; Hamel, 1991; Simonin, 2004; Inkpen and Beamish,

1997; Baughn et al., 1997; Inkpen, 1998a; Mohr and Sengupta, 2002), managerial belief rigidity

(Inkpen and Crossan, 1995), and recipient collaborativeness, readiness and method

comprehensiveness (Yin and Bao, 2006). This study conceptualizes the two critical dimensions

of TRCHAR: Absorptive Capacity (ACAP) and Recipient Collaborativeness (RCOL) to have a

positive impact on TTDEG.

As TT involves the process of transmission and absorption of knowledge (Davenport and Prusak,

1998, 2000), the recipient firms’ ability to absorb the knowledge transferred depends on the

degree of their absorptive capacity (ACAP). Past studies have shown that a low degree of the

technology-recipient’s ACAP impedes both intra-firm and inter-firm knowledge transfer (Cohen

and Levinthal, 1990; Hamel, 1991; Lyles and Salk, 1996; Mowery et al., 1996; Lane and

Lubatkin, 1998; Lane et al., 2001; Gupta and Govindarajan, 2000; Minbaeva et al., 2003;

Minbaeva, 2007; Pak and Park, 2004; Simonin, 1999a, 1999b). The concept of ACAP has been

extensively examined in both theoretical and empirical studies. In their seminal paper, Cohen

and Levinthal (1990) define ACAP as “the firm’s ability to recognize the value of new external

information, assimilate it, and apply it to commercial ends”. ACAP of a firm is primarily a

function of the recipient firm’s level of prior related knowledge. Prior related knowledge is

closely related to the individuals units of knowledge available within the organization. The

accumulation of prior knowledge will increase the ability to make sense of, assimilate and use

new knowledge (Kim, 1998). The firms’ ACAP tends to be developed cumulatively, in which

absorptive capacity is more likely to be developed and maintained as a byproduct of routine

activity when the knowledge domain that the firm wishes to exploit is closely related to its

current knowledge base (Cohen and Lavinthal, 1990). Prior related knowledge, which includes

basic/minimal skills, a shared language, positive attitude towards learning, relevant prior

experience and up-to-date information on knowledge domain, is critical for organization to

assimilate and exploit new knowledge (Cohen and Lavinthal, 1990; Szulanski, 1996, 2003;

Minbaeva, 2007). By possessing sufficient prior related knowledge, which is closely associated



59

with new knowledge, the organization will have adequate ability to absorb new technological

and innovative competencies/capabilities (Cohen and Lavinthal, 1990).

The recipient collaborativeness (RCOL) is mostly involved inter-firm knowledge transfer

between partners in collaborative relationship such as strategic alliances and joint ventures. In

intra-firm knowledge transfer, firms are expected to encounter fewer problems when transferring

technology to their own subsidiaries and affiliates within the organizational boundaries. Strategic

alliances provide an ideal platform for organizational learning especially through IJVs where

partners’ firms can acquire, learn, create new knowledge, and transfer knowledge between them

(Inkpen, 2000). Nonetheless, strategic alliances face a tradeoff between the opportunities for

generating and sharing knowledge and the propensity that the partner may tend to behave

opportunistically (Child and Faulkner, 1998). Building on the concept of inter-partner learning

developed by Hamel (1991), RCOL is defined as “the recipient firms’ willingness to establish a

mutually beneficial and collaborative relationship: which requires the recipient firms’ honest

intention to create common benefits for both the supplier and recipient” (Yin and Bao, 2006).

Thus, learning in the collaborative relationship greatly depends on the partners’ intent; whether

the partners’ learning objective/intent is collaborative (complementary) or competitive (Child

and Faulkner, 1998).

H2: Technology recipient characteristics, which comprise of absorptive capacity and recipient

collaborativeness, have a positive effect on degree of technology transfer in JVs.

TECHNOLOGY SUPPLIERS’ CHARACTERISTICS AND DEGREE OF TECHNOLOGY TRANSFER The technology suppliers’ characteristic (TSCHAR) is the third group of TT characteristics in

this study. The two critical dimensions of TSCHAR under study are Partner Protectiveness

(PPROTEC) and Transfer Capacity (TRANSCAP). A stream of studies has identified numerous

TSCHAR such as motivation (Gupta and Govindarajan, 2000; Szulanski, 1996), partner

protectiveness (Simonin, 1999a, 1999b, 2004; Szulanski, 1996, Inkpen, 1998a, 1998b, 2000),

partner assistance (Lyles et al., 1999), partner transparency (Hamel, 1991), disseminative

capacity (Minbaeva and Michailova, 2004), control (Lyles et al., 2003), prior experience



60

(Subramaniam and Venkataraman, 2001), transferor’s commitment (Tsang et al., 2004),

articulated objective or goal clarity (Lyles and Salk,1996; Inkpen 2000) and source transfer

capacity (Szulanski, 1996; Martin and Solomon, 2003) to have a significant influence on

knowledge transfer.

A review of literature shows that TSCHAR have been studied from many aspects of suppliers’

behaviors. Previous studies on the suppliers’ behaviors, which are largely theoretical and case-

based, suggest different conclusions on the suppliers’ behaviors because there have been no

consensus on the appropriate definition and measure of the concept (Minbaeva, 2007). The

technology-supplier, as a source of knowledge, must be knowledgeable to form a knowledge gap

between the transferor and the transferee; where they are being perceived as reliable or valuable

sources of knowledge (Szulanski, 1996), and must also be willing to support and co-operate with

the local partner in transferring technological knowledge (Simonin, 1999a). This study

conceptualizes that the two critical behavioral characteristics namely, PPROTEC and

TRANSCAP, as the vital technology-supplier characteristics in facilitating inter-firm technology

transfer.

The ability of a firm to acquire knowledge in the cooperative arrangement such as joint venture

is not solely depending on its internal ACAP. The inter-firm learning opportunity provided by

strategic alliance is also subjected to degree of willingness of the technology-supplier to

cooperate or engage in sharing knowledge i.e. to reduce the level of protectiveness (Simonin,

1999a; Steensma and Lyles, 2000). One of the critical elements of technology-supplier

characteristic is partner protectiveness (PPROTEC) which is beyond the technology-recipient’s

control. PPROTEC has been found to have a significant impact on both intra and inter-firm

knowledge transfer (Simonin, 2004; Szulanski, 1996).

PPROTEC refers to as “the extent of protections/hurdles, intentionally or unintentionally,

imposed by the foreign partner on the local partner in an IJV which restrict the accessibility to

proprietary technology/knowledge” (Hau and Evangelista, 2007). PPROTEC is significantly

relates to the degree of transparency. Transparency is thus defined as “the degree of openness of

one partner (technology-supplier) and their willingness to transfer knowledge to the other partner

(technology-recipient)” (Hamel, 1991). In the context of intra-firm, openness is referred to as

“the degree to which relationship between business unit managers and corporate supervisors is



61

open and informal which promotes spontaneous and open exchange of information and ideas”

(Gupta, 1987).

Many theoretical studies have indicated that partners in the collaborative relationship such as JV

are expected to mutually exchange their valuable or proprietary assets, resources, information,

knowledge and technology between them in order to achieve mutual benefits (Inkpen, 2000;

Khanna et al., 1998; Child and Faulkner, 1998). These proprietary competencies are the sources

of sustainable competitive advantage of the supplier partner; and for fear of losing ownership, a

position of privilege and superiority of their valuable assets they tend to protect their hard-won

success and competencies from the opportunistic behaviors of the recipient partner (Parkhe,

1993; Steensma and Lyles, 2000; Szulanski, 1996). The foreign parent firm may intentionally

restricts knowledge flow to the JV because cooperation through JVs is viewed as a low cost

approach for the local firms to gain competencies (Hamel et al., 1989; Simonin, 1999a, 2004;

Steensma and Lyles, 2000) unless they have sufficient incentive to mitigate the cost typically

associated with the transfer (Dyer and Singh, 1998).

Partners in the strategic alliance, due to the risk of knowledge spillover/leakage, tend to be more

protective of their valuable knowledge resources as their competitiveness is very much

depending on these valuable resources (Barney, 1991). Valuable knowledge resources of the

firm, if not well protected will leak to potential competitors or competitors which eventually will

enable competitors to gain competitive advantage and use it against the proprietor or supplier

firms (Cohen and Lavinthal, 1990; Hamel et al., 1989; Simonin, 1999a, 2004; Steensma and

Lyles, 2000). Knowledge spillover to an alliance partner tends to shift the balance of bargaining

power between partners which lead to the initiation of changes in the partner relationship

(Inkpen, 2000). Due to asymmetries of knowledge between the alliance partners, PPROTEC and

knowledge accessibility will be correspondingly asymmetrical in which partners in an alliance

can be less transparent or open than the other partner (Hamel, 1991).

As technology and knowledge transfer involve the absorption and transmission of knowledge

(Devanport and Prusak, 1998, 2000), the ability of the technology-supplier to efficiently transfer

knowledge and technology to the recipient becomes critical in inter-firm TT. Several studies

have suggested that while firms differ in their ability in knowledge creation, they also differ in

their ability to transfer knowledge (TRANSCAP) within or outside of the organizational

boundary (Kogut and Zander, 1992, 1993; Szulanski, 1996). The efficiency in transmitting



62

technology or knowledge by the supplier is important in both intra and inter-firm knowledge

transfer as it affects the TT outcomes. The firms’ ability to transfer knowledge to their

subsidiaries efficiently and effectively can serve several objectives such as: 1) to facilitate their

expansion in foreign countries, 2) to maintain the firms’ competitiveness, and 3) to safeguard

their competencies from the competitors (Martin and Solomon, 2003).

In the context of strategic alliance, the firms’ ability to transfer knowledge facilitates the

organizational learning process and justifies their commitments in the collaborative relationship:

where all partners are expected to mutually contribute their knowledge, technologies, skills and

competencies to the JVs to gain mutual benefits (Inkpen, 1998a, Inkpen 2000; Khanna et al.,

1998; Child and Faulkner, 1998). Past studies have described TRANSCAP from many

dimensions for example: 1) the source (supplier) ‘not perceived as reliable’ (Szulanski, 1996), 2)

the firms’ ability to articulate uses of their own knowledge, assess the needs and capabilities of

the potential recipient, and transfer knowledge to different location (Martin and Solomon, 2003),

3) a disseminative capacity of the knowledge sender in terms of the source’s ability and

willingness to share knowledge (Minbaeva and Minhailova, 2004), 4) the sender’s ability to

articulate and communicate knowledge to the recipient (Minbaeva, 2007), 5) the parent firms’

capacity to knowledge transfer (Wang et al., 2004), and 6) the source’s motivational disposition

(Gupta and Govindarajan, 2000).

H3: Technology supplier characteristics, which comprise of low degree of partner protectiveness

and transfer capacity, have a positive effect on degree of technology transfer in JVs.

RELATIONSHIP CHARACTERISTICS AND DEGREE OF TECHNOLOGY TRANSFER A stream of literatures on intra and inter-firm knowledge transfer has identified many aspects of

RCHAR. The RCHAR form the fourth group of TT characteristic in this study. From a review of

literature among the RCHAR that have been identified are organizational distance (Simonin,

1999a, 1999b), cultural distance (Lyles and Salk, 1996; Mowery et al., 1996; Choi and Lee,

1997; Inkpen, 1998a, 1998b, Liu and Vince, 1999), organizational context (Kogut and Zander,

1993; Zander and Kogut, 1995), knowledge connection (Inkpen, 2000), organizational structure



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(Inkpen, 1997), ownership type (Kogut, 1988; Mowery et al., 1996), ownership equity (Pak and

Park, 2004), relationship openness (Hamel, 1991; Inkpen, 2000), partners attachment (Inkpen

and Beamish, 1997), inter-partner trust (Baughn et al., 1997; Morrison and Mezentseff, 1997;

Love and Gunasekaran, 1999, Inkpen, 2000), empathy (Buckley et al., 2002), relationship quality

and strength (Szulanski, 1996; Lin, 2005), relational openness (Wathne et al., 1996), relational

capital (Kale et al., 2000), informal relationship (Clarke et al., 1998), articulated goals and

management commitment (Choi and Lee, 1997; Morrison and Mezentseff, 1997), and legal,

political and technical differences (Marcotte and Niosi, 2000).

The present study conceptualizes that the two important aspects of RCHAR: Relationship

Quality (RELQLTY) and Mutual Trust (MT) are expected to have a positive impact on TTDEG.

In order to facilitate intra and inter-firm TT, both technology-supplier and technology-recipient

are expected not only to establish a close relationship between them but also develop relationship

quality (RELQLTY). For firms which have differences in terms of the organizational structures,

cultural backgrounds, experiences, capabilities, learning intent and technological resources,

transferring technology is rather a challenging process (Argote, 1999; Hamel, 1991). As

knowledge is a firm-specific, embedded in firm organizational context, personal quality in nature

and idiosyncrasy (Nonaka, 1994; Kogut and Zander, 1992, 1993), acquiring and transferring

technology require frequent and effective interactions between the supplier and recipient

(Bresman et al., 1999).

The importance of numerous individual exchanges in transferring tacit knowledge within

organization is achievable through “ease of communication and intimacy of relationship”

between the source and recipient unit and thus a problematic relationship between the source and

recipient will lead to hardships in transferring knowledge (Szulanski, 1996). Gupta and

Govindarajan (2000) argue that the existence and richness of transmission channels as an

important determinant of knowledge flows within MNCs. The richness of communications links

is captured and or operationalized as “informality, openness and density of communications”.

Informality, openness and communication density are closely related to relationship quality as

they indicate higher degree of involvement and interaction frequency between the sender and

receiver, increase the openness of communication, spontaneous and open exchange of

information and ideas between the interacting parties, and the potential for numerous individual

exchanges (Szulanski, 1996; Nonaka, 1994; Lin, 2005; Gupta, 1987). Wang et al. (2004) suggest



64

that effective transfer of managerial knowledge by MNCs to Chinese subsidiaries is not only

depending on the adequate presence of expatriates but also productive interaction between the

expatriates and their Chinese counterpart.

From the inter-firm transfer context, Lin (2005) categorizes quality interaction in terms of its

frequency, adequacy, amiability and constructiveness. Bresman et al. (1999) argue that

communication involves two distinct but overlapping stages. First, the post integration process:

which is largely depending on an effective, extensive and intensive communication between the

acquirer and acquired units; and second, the tacit knowledge transfer process: which requires

intensive communication and frequent interaction between the transmitting and receiving parties.

Strategic alliance literatures have explicitly indicated that RELQLTY or quality of interaction

between alliance partners promotes greater opportunity to learn, share and access to the alliance

partners’ strategic knowledge and competencies. RELQLTY creates higher relationship openness

which directly affects the willingness and ability of alliance partners to share information and

communicate openly (Inkpen, 1998a, 2000).

Inter-partner mutual trust (MT) is critical in the collaborative relationship as MT: 1) develops a

sense of openness and shared understanding between partners (Dyer and Nobeoka, 2000), 2)

facilitates greater accessibility to the alliance knowledge and knowledge acquisition (Inkpen,

1998a, 2000), 3) creates opportunities for a mutual inter-organizational learning: when partners

become more open and committed in sharing their knowledge and competencies (Inkpen and

Dinur, 1998; Inkpen and Beamish, 1997), 4) reduces the partners’ protectiveness of their

knowledge and promotes free exchange of information between partners (Inkpen, 2000), 5)

creates higher propensity of inter-partner learning as knowledge is more accessible due to free

exchange of information (Hamel, 1991; Doz, and Hamel, 1998; Inkpen, 2000), 6) reduces the

fear of opportunistic behaviors of the learning partner and promotes greater transparency

between the exchange processes (Gulati, 1995), 7) promotes knowledge acquisition and inter-

organizational learning (Glaister et al., 2003; Inkpen and Tsang, 2005), and 8) fosters norms of

reciprocity (Nahapiet and Ghoshal, 1998). MT reduces the fear of opportunistic behaviors of the

learning partner, promotes greater transparency between the exchange processes (Gulati, 1995)

and may mitigate partner protectiveness (Inkpen, 1998a). The partners’ openness or transparency

which determines the willingness to exchange, share and transfer knowledge between alliance



65

partners is primarily hindered by a mutual suspicion of opportunistic behaviors between them

(Kale et al., 2000).

In the cooperative ventures such as IJVs mutual trust, which derives from the existence of

personal attachment, contributes to more willingness to transfer knowledge between alliance

partners (Luo, 2001). High degree of MT indicates that the partners in a collaborative

relationship accept each other as an ally not as competitor (Powell et al., 1996), signifies the

partners’ commitment not to take advantage on the other partner’s weaknesses and or

vulnerabilities (Steensma and Lyles, 2000), and contributes to information learning and sharing:

when partners are less suspicious of the other partner’s opportunistic behaviors (Child and

Faulkner, 1998). Trust allows potential access to the alliance valuable resources and a

willingness to solve problems through mutual problem-solving (Uzzi, 1997).

A collaborative alliance with low degree of trust will reduce the partners’ openness or

transparency in knowledge sharing and learning, and limit the information’s accuracy,

comprehensiveness and timeliness (Zand, 1972; Kale et al., 2000) as the partners are unwilling to

face the risk associated with sharing more valuable information (Hedlund, 1994). A lack of inter-

partner trust may also generate inter-firm conflicts, increase partners’ opportunistic behaviors

and eventually erode mutual trust (Tsang et al., 2004). The inter-partner trust acts as an ongoing

social control mechanism and risk reduction device as it determines the extent of knowledge

exchange in IJVs and the efficiency with which it is exchanged (Lane et al., 2001). Trust is also

crucial in alliances and joint ventures as no contracts/agreements can cover all the variations and

conditions that can occur (Dhanaraj et al., 2004).

H4: Relationship characteristics, which comprise of relationship quality and mutual trust, have a

positive effect on degree of technology transfer in JVs.

TECHNOLOGY TRANSFER CHARACTERISTICS AND DEGREE OF TECHNOLOGY TRANSFER Building on intra and inter-firm knowledge transfer literature, TTCHARS which formed the

study’s conceptual framework, are viewed as both the critical facilitators/determinants of TT and

barriers to TT (Szulanski, 1996). The TTCHARS are inter-dependent, co-exist and closely



66

related to each other where failure to manage any of TT characteristic will affect the TT

outcomes. Previous studies on intra and inter-knowledge transfer have acknowledged the

significant influence of these facilitators on TT’s success or failure (Szulanski, 1996, 2000, 2003;

Gupta and Govindarajan, 2003; Minbaeva, 2007; Hamel, 1991; Inkpen, 1998, 2000).

For technology acquisition to happen in IJVs, technology must be first accessible by the learning

partner. In a collaborative/cooperative learning environment as opposed to competitive learning,

the transferring partner is more transparent and willing to share/transfer their proprietary

knowledge, competencies and skills although they are organizationally embedded in the

organization’s routines and processes (Hamel, 1991; Inkpen, 2000; Child and Faulkner, 1998).

As a result, this will reduce the degree of PPROTEC to allow for freer and greater flow of

information to the learning partner particularly the accessibility to tacit knowledge (Inkpen,

2000; Yan and Luo, 2001; Hamel, 1991; Doz and Hamel, 1998).

Relationship openness thus is influenced by the learning intent of the learning partner and inter-

partner MT (Inkpen, 2000; Inkpen and Beamish, 1997). If competitive overlap exists and for fear

of losing their proprietary technology/knowledge and risk of spillovers, the transferring partner is

likely to be less transparent, more protective of their technology either through explicit or active

measures, and restrict the information flow to the opportunistic partner who perceives JV as a

low cost approach to internalize partner’s competencies (Hamel, 1991; Simonin, 1999a, 2004;

Steensma and Lyles, 2000). The JV partner’s learning intent also determines the TRANSCAP of

the transferring partner in terms of motivation to transfer technology.

MT between IJV’s partners is important in reducing the fear of opportunistic behaviors of the

learning partner, promotes greater transparency which may contribute a higher degree of

accessibility to partner’s technological knowledge, and motivate the transferring partner to share

and transfer higher technology (Inkpen, 1998; 2000). As a result of the collaborative learning

intent, RELQLTY promotes a higher degree of MT and openness between partners resulting in a

higher degree of knowledge sharing and transfer of tacit knowledge (Inkpen, 2000; von Hippel,

1998; Marsden, 1990; Kale et al., 2000). On the other aspect, learning capability (ACAP)

promotes higher TTDEG if the learning partner has the capacity to recognize, absorb, assimilate

and apply new technology/knowledge to ensure a higher TTDEG (Cohen and Lavinthal, 1990;

Lane and Lubatkin, 1998). ACAP is closely related to knowledge connection and knowledge

relatedness between JV partners (Inkpen and Dinur, 1998; Inkpen, 2000). Acquiring tacit



67

knowledge involves various organizational levels and personal interactions between individuals

and groups. Thus, knowledge connection and knowledge relatedness between JV partners are

capable in creating potentials for the sharing of more personal observations and experiences (Von

Krogh, 1994; Inkpen 2000).

Although TCT, COMPLX and SPEC contribute to technology ambiguity and barriers to TT,

these barriers to technological gap between JV partners may be reduced or eliminated if the

learning partner has adequate prior related knowledge and intensity of learning efforts (Hamel,

1991; Inkpen, 2000; Szulanski, 1996; Kim, 1998). Building on the previous theoretical and

empirical studies, this study proposes the following hypothesis:

H5: Technology transfer characteristics, which comprise of knowledge transferred, technology

recipient, technology supplier and relationship characteristics, have a positive effect on

degree of technology transfer in JVs.

DEGREE OF TECHNOLOGY TRANSFER AND LOCAL FIRMS’ PERFORMANCE A review of literature reveals that most of the empirical studies on inter-firm technology and

knowledge transfer in strategic alliance, particularly IJVs, are limiting their focus on the

performance of the IJVs (for example Lyles and Salk, 1996; Lane et al., 2001; Tsang et al., 2004;

Dhanaraj et al., 2004; Steensma and Lyles, 2000). On the other hand, the performance of the

MNCs’ subsidiary and affiliate in the host countries has become the primary focus of intra-firm

knowledge transfer literature (for example Chen, 1996; Chung, 2001; Ofer and Potterovich,

2000; Cui et al., 2006; Lin, 2003). Most of the studies on strategic alliance and IJVs have

recorded positive impact of knowledge acquisition or transfer on IJVs’ performance for example:

1) knowledge acquisition has a positive impact on the IJVs’ human resource, general and

business performance (Lyles and Salk, 1996), 2) knowledge acquisition as a better predictor for

human-resource related performance than the general and business performance (Lyles and Salk,

1996), 3) knowledge acquisition from parent firms has a significant positive effect on IJVs’

performance (Lane et al., 2001; Tsang et al., 2004), and, 4) tacit and explicit knowledge

acquisition have a positive impact on IJVs’ performance (Dhanaraj et al., 2004). However, there



68

have been inadequate studies on direct effect of technology or knowledge transfer on LFP. Only

Yin and Bao (2006) find tacit knowledge acquisition has significantly affected LFP.

H6: Higher degrees of technology transfer in JVs, which comprise of degree of tacit and explicit

knowledge, has a positive effect on local firms’ performance.

MODERATING EFFECT OF SIZE OF MNCs Past studies have acknowledged the effect of MNCSIZE on both intra and inter-firm knowledge

transfer due to asymmetries in the availability of the firms’ resources (Kogut and Zander, 1992,

1993; Simonin, 1997, 1999a, 2004; Bresman et al., 1999; Minbaeva et al., 2003). Large firms,

because of the availability of high number of resources and expertise, are capable to transfer

more/higher technology and knowledge than small firms. Hagedoorn and Schakenraad (1994)

find a strong positive effect of MNCSIZE on the intensity of strategic partnering and

technological cooperation because large firms have substantial administrative, organizational and

monitoring supports to form an alliance. Generally, small firms do not have adequate resources

and are likely to transfer knowledge and technology through arm’s length licensing agreements

(Stobaugh, 1988). MNCSIZE affects the propensity of the firm to develop competitive advantage

and achieves the above-average performance (Porter, 1980). The strategy literatures also regard

MNCSIZE as the important contingency variable with respect to governance, levels of

diversification and resistance to organizational change (Hoskisson et al., 1994), influence intra-

firm knowledge transfer, and as an impediment to organizational learning (Marquardt and

Reynolds, 1994). In the context of strategic alliance, MNCSIZE has been considered as: 1) a

determinant of alliance participation, intensity of strategic partnering and technological

cooperation (Berg et al., 1982; Hagedoorn and Schkenraad, 1994), 2) a differentiating factor in

the motives for alliance formation (Glaister and Buckley, 1996), and 3) a source of asymmetric

bargaining power between partners in the alliance relationship (Khanna et al., 1998).

H7: The relationship between technology transfer characteristics and degree of technology

transfer is moderated by size of MNCs.



69

MODERATING EFFECT OF AGE OF JV JVAGE or JV’s duration is expected to influence the relationships between TTCHARS and

TTDEG (Foss and Pedersen, 2002). The longer the collaborative relationship, the greater

opportunity for JV’s partners to share, learns, and transfer technology and knowledge between

them (Kale et al., 2000). However, Kale et al. (2000) caution that longer duration of JV

relationship could increase the propensity of losing the valuable proprietary asset to the JV’s

partner. Gomez-Mejia and Palich (1997) posit that the subsidiary’ maturity in the local market

provides capabilities in overcoming the negative impacts of cultural difference through

deliberate strategies. From the strategic alliance context, studies have shown that age of alliance

as an important variable because, as the alliance sustains over the years, cultural distances tend to

decrease (Meschi, 1997), the inter-partner trust intensifies (Gulati, 1995), relative bargaining

power between partner changes (Yan and Gray, 1994), alliance partners develop personal

attachment (Inkpen and Beamish, 1997), partner becomes more familiar with each other’s

expertise and idiosyncrasies (Simonin, 1999a), and older IJVs are likely to leverage the acquired

knowledge and convert it to competitive advantage (Tsang et al., 2004). However, few

researchers have cautioned that as alliances are perceived as ‘a race to learn’; where alliances are

being regarded as unstable organizational forms (Porter, 1990; Hamel, 1991; Inkpen and

Beamish, 1997, Inkpen, 1998a; Yan and Gray, 1994), age of JV thus may contribute to a shift in

the partners’ bargaining power associated with the acquisition of knowledge and skills that

allows a firm to eliminate a partner dependency (Inkpen and Beamish, 1997). JVAGE moderates

the relationship between knowledge acquisition and performance for two reasons: 1) when a JV

can survive within a considerable period, firms become more practiced and more efficient at

what they already do, and 2) studies have shown that the relationship duration in JV is positively

associated with frequency of communication and information exchange between partners (Tsang

et al., 2004; Hallen et al., 1991). As the relationship develops, shared experience is able to

resolve inter-firm conflicts through open problem solving and compromise (Lin and Germain,

1998).


transfer is moderated by age of JV.



70

MODERATING EFFECT OF MNCs’ COUNTRY OF ORIGIN Many empirical studies have established that MNCCOO (nationality) has a significant impact

on: 1) the propensities of MNCs’ choice of global strategies, 2) organizational structures and

control system, 3) internal corporate cultures (Bartlett and Ghoshal, 1989; Egelhoff, 1984;

Franko, 1976; Porter, 1990; Yip et al., 1994), 4) expected outcomes (Harrigan, 1988b), 5)

alliance outcomes and performance (Parkhe, 1993), 6) partners’ learning and protection of

proprietary assets in an alliance (Kale et al., 2000), and 7) the way how the MNCs operate

(Gupta and Govindarajan, 2000). Problems related to cultural differences, opinions, beliefs, and

attitude tend to accelerate due to alliance partners’ nationality (Kale et al., 2001). The differences

in culture, language, educational background and distance with cross national partners; which act

as barriers to inter-organizational learning, impede the inter-partner learning and knowledge

transfer (Mowery et al. 1996). However, Yin and Bao (2006) find nationality of alliance’s

partners (the U.S, Japan and Western firms) has no significant effect on the relationships

between the supplier and recipient factors and tacit knowledge acquisition.


transfer is moderated by MNCs’ country of origin.

MODERATING EFFECT OF MNCs’ TYPES OF INDUSTRY Based on the economic theory, MNCs have become increasingly important due to

ineffectiveness and inefficiency of the external market to facilitate intra-knowledge transfer

(Caves, 1982; Hymer, 1960; Kindleberger, 1969). Empirical examination of the economic theory

has consistently found that industries characterized by greater degrees of knowledge intensities

(industries with higher R&D-to-sales-ratios and/or higher advertising-to-sales ratios) have the

propensity to become more global than other industries (Gupta and Govindarajan, 2000; Goedde,

1978; Grueber et al., 1967; Horst, 1972). Asymmetries in the industries characteristics indicate

that certain industries are more global and require a higher level of knowledge transfer than other

industries (Minbaeva et al., 2003). Past studies have categorized MNCIND in terms of: 1) fixed

asset intensity and advertising intensity industries (Gupta and Govindarajan, 2000), 2) metal and



71

electronic; food, pulp and paper; chemical; finance service; wholesale and retail; and hotel and

transportation industries (Minbaeva et al. 2003), 3) electronics, machinery and metals, and

chemical products industries (Cho and Lee, 2004), 4) biochemical and non-biochemical

industries (Lane and Lubatkin, 1998), 5) service and manufacturing industries (Lane et al.,

2001), and 6) industry sales growth (Luo, 2001).


transfer is moderated by MNCs’ types of industry.

CONCLUSION Building on intra and inter-firm KT literature (Szulanski, 1996; Gupta and Govindarajan, 2000;

Minbaeva, 2007; Tiemessen et al. 1997; Inkpen 2000), the present study extends the literature by

conceptualizing the effects of four critical dimensions of TTCHARS (KCHAR, TRCHAR,

TSCHAR, and RCHAR) on TTDEG in a single holistic model to identify the relative and

simultaneous influence of each group of TTCHARS on degree of inter-firm TT in IJVs.

Secondly, this study also extends the literature by responding to the limitations highlighted by

the researchers in the area that studies on inter-firm TT and KT, and knowledge acquisition

require more hypothesis development and testing (Huber, 1991; Fiol, 1994), the cross border TT

and KT from MNCs to local firms has not been extensively researched (Pak and Park, 2004), and

fewer studies adopt the local firms or recipient’s perspective (Yin and Bao, 2006).

Thirdly, previous studies on KT are limited to selected functional expertise such as technological

learning (Lin, 2007), managerial knowledge (Si and Bruton, 1999; Tsang 2001; Luo and Peng,

1999; Liu and Vince, 1999; Lin, 2005), managerial skills (Wong et al., 2002), technology or

manufacturing know how (Lam, 1997; Bresman et al., 1999), business environment and product

market knowledge (Geppert and Clark, 2003), marketing knowledge (Simonin, 1999b; Wong et

al., 2002), and research and development (Cummings and Teng, 2003; Minbaeva, 2007). Thus,

since technology is an abstract subject, this study has further extended the literature by

operationalizing TTDEG from two distinct dimensions namely degree of tacit and explicit

knowledge and conceptualized the effect of TTCHARS on TTDEG and its two dimensions.



72

Fourthly, based on a review of literature, except for Yin and Boa (2006) who examined the effect

of tacit knowledge acquisition on local firms’ performance, very few intra and inter-firm studies

have examined the impact of TT, specifically the effect of TTDEG on organizational

performance. For example empirical studies by Szulanski (1996), Minbaeva et al. (2003), Pak

and Park (2004), Hau and Evangelista (2007) and Minbaeva (2007) are mainly focusing on KT

outcomes. This study extends the inter-firm TT and KT literature by conceptualizing local firms’

performance from two dimensions namely corporate (CPERF) and human resource (HRPERF)

performance. In specific this study has contributed to the expansion of TT literature by

conceptualizing the effects of TTDEG on LFP in terms of corporate performance, and 2) human

resource/competencies performance.

Finally, from a review of literature there have been limited studies that have included moderating

variables in the previous TT frameworks. Thus, this study conceptualizes the moderating effects

of MNCSIZE, JVAGE, MNCCOO, and MNCIND on TTCHARS-TTDEG relationship. Since

the effects of TTCHARS and their dimensions on TTDEG and its dimensions have never been

previously examined, thus the inclusion of moderating variables (MNCSIZE, JVAGE,

MNCCOO, and MNCIND) in the framework also has extended inter-firm TT literature by

providing new plausible explanations on the boundary conditions of the TTCHARS-TTDEG

relationship.

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4

A Holistic Model of Inter-Firm Technology Transfer

Based on Integrated Perspectives of Knowledge-Based View

and Organizational Learning

CHAPTER OUTLINE

The objective of this work is to propose a holistic model of inter-firm technology transfer (TT)

based on an integrated knowledge-based-view (KBV) and organizational learning (OL)

perspectives in conceptualizing the critical links between technology transfer characteristics:

knowledge, technology recipient, technology supplier and relationship characteristics

(TTCHARS) and degree of technology transfer (TTDEG) in international joint ventures (IJVs).

Since the current TT issue now is centered on efficiency and effectiveness of technology transfer

as an efficient formal vehicle to internalize foreign technologies as compared to direct import of

goods, licensing and foreign direct investment, the advancement of the holistic model is to

provide a complete view on the significant effect of technology transfer characteristics and their

dimensions on degree of technology transfer in IJV which eventually could contribute to the

increase of local companies’ competitiveness, indigenous technical capabilities, technological

development, and potential for local innovation. In the study’s model, hypotheses are developed

to describe the relationships between TTCHARS and TTDEG based on the underlying KBV and

OL perspectives.

INTRODUCTION The presence of the MNCs through various formal market channels such as direct export of

capital goods and products, foreign direct investments, licensing, and IJVs with local firms have

become the primary sources of technology for local technological development and national

economic growth (Marton, 1986). The inter-firm technology transfers (TT) in collaborative joint



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ventures (JVs) often involve tradeoffs between the willingness of technology supplier to transfer

a considerable amount of their technologies to technology recipient and degree of protection of

the proprietary technology, knowledge and competencies as the source of the supplier’s

competitive advantage (Inkpen, 2000; Hamel, Doz, and Pralahad, 1989). Technology transfers

through JVs, although have been acknowledged in many studies as the most efficient mechanism

in internalizing the partner’s technologies, knowledge and skills, have frequently involved

various facilitators, actors and complex relationship between partners that have direct impact on

the degree or amount of technology transferred in JVs (Szulanski, 1996; Inkpen, 2000).

Past studies view that TT by MNCs to developing countries as a dynamic and on-going process

(Shiowattana, 1987; Methe, 1991). Many studies on intra and inter-firm TT are in consensus that

TT is a complex and difficult process even when it occurs across different functions within a

single product division of a single company (Gibson and Slimor, 1991; Kidder, 1981; Smith and

Alexander, 1988). The current issue is no longer whether MNCs are transferring their technology

to the Malaysian industries; rather the issues are centered on the effectiveness, efficiency and

success of implementation of TT (Lai and Narayanan, 1997). This is because TT’s success

heavily depends on interactive communications between the technology supplier and recipient

which requires both parties involvement (Gibson and Slimor, 1991). Previous studies have also

indicated that MNCs are said to be a reluctant technology supplier and have been slow in

transferring technology and R&D expertise to local industries due to the risk of technology

'spillovers’ (Narayanan and Lai, 2000; Ravenhill, 1999; Guyton, 1995; Muller and Schnitzer,

2006).

On the other hand, MNCs contend that it is not a question of their willingness to transfer

technologies rather the transferring process is mainly hampered by low maturity level of the

Malaysian industry which is largely due to insufficiency of skilled personnel and weak

institutional support and business environment (Rasiah and Anuwar, 1998). As compared to the

U.S MNCs, TT by the Japanese MNCs have been found to be less intensive and slower where

technologies are normally transferred within their ‘keiretsu’ (Raduan, 2002; Yamashita, 1991).

The Japanese MNCs for example, to some extent, have no intention to transfer key aspects of

their technology in order to maintain their dominance in Southeast Asian economies (Taylor,

1995). The impressive record of economic progress in Malaysia has not gone hand in hand with

the technological progress of proportionate magnitude due to weak relationship between the TT



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practice and the decision to innovate (Malairaja and Zawdie, 2004). In this aspect, the innovation

capabilities greatly depend on the local firms’ ability to understand, assimilate and apply new

technology transferred to them (Cohen and Lavinthal, 1990). Studies from KBV perspective

have acknowledged that MNCs tend to be more protective of their advance technology,

knowledge and competencies in products, processes and management because these strategic

valuable resources and competencies are their main sources of competitive advantage (Porter,

1985; Barney, 1991; Peteraf, 1993; Wernerfelt, 1984; Pralahad and Hamel, 1990). OL

perspective studies have suggested that technology and knowledge are protected by the supplier

when the recipients are opportunistic in the collaborative relationship (Inkpen, 1998a; Inkpen

and Dinur, 1998; Child and Faulkner, 1998). Thus, based on the above scenarios, three critical

issues require serious attention.

First, in the context of TT through IJVs, the remaining question is on the extent of TT by foreign

MNCs when transferring their advance technology to local recipient partner (Narayanan and Lai,

2000). While realizing that technologies, knowledge, and competencies are the supplier’s main

source of competitive advantage, the current TT issue in JV revolves around the extent of degree

of technologies that are being transferred by the suppliers to recipient partners in terms of tacit

knowledge (new product/service development, managerial systems and practice, process designs

and new marketing expertise), and explicit knowledge (manufacturing/service techniques/skills,

promotion techniques/skills, distribution know-how, and purchasing know-how) (Madanmohan

et al., 2004). This is because from the recipient’s perspective, TT success is not merely

possessing the ability to operate, maintain or repair the machineries at the production level

(transmission) but it also includes the ability to learn, acquire, absorb and apply new external

technologies and knowledge embedded in product materials, physical assets, processes and

production, and management capabilities (absorption) (Davenport and Prusak, 1998, 2000).

Second, previous studies on intra-firm knowledge transfer have acknowledged the significant

influence of technology actors and facilitators/barriers such as the characteristics of knowledge

transferred, source, recipient and contextual/relational in the knowledge transfer process

(Szulanski, 1996, 2000, 2003; Gupta and Govindarajan, 2000; Minbaeva, 2007). Thus, in the

context of inter-firm TT where technology transfer processes are more complex, difficult and

involve the process of transferring technology across the organizational boundary to unaffiliated

firms, the prevailing issue is on the extent of significant effects of TT characteristics



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(TTCHARS) in determining the degree or level of technology transfer (TTDEG). Specifically to

what extents do TT characteristics influenced TTDEG? And whether all TTCHARS and their

dimensions can predict and explain TTDEG? Finally, since JVs is one of the formal and

externalized mechanisms of TT which could directly affect performance, therefore the next

intriguing issue is on the extent of TTDEG in affecting the performance of local firms;

specifically on how TTDEG could help to improve the corporate and human

resource/competencies performance.

THE INTER-FIRM TECHNOLOGY TRANSFER: THE INTEGRATED MODEL The main theories underpinning the relationships of variables in the conceptual framework

(Figure 1) of this study are knowledge–based view (KBV) and organizational learning (OL)

perspectives. The perspective of KBV underlies the relationships between KCHAR and their

sub-variables: tacitness (TCT), complexity (COMPLX) and specificity (SPEC), and dependent

variable TTDEG. Both KBV and OL perspectives underlie the relationships between the

TRCHAR and their sub-variables: absorptive capacity (ACAP) and recipient collaborativeness

(RCOL) and TTDEG. The relationships between the TSCHAR and sub-variables: partner

protectiveness (PPROTEC), and transfer capacity (TRANSCAP), and TTDEG are governed by

both KBV and OL perspectives. The OL perspective underlies the relationship between RCHAR

and their sub-variables: relationship quality (RELQLTY) and mutual trust (MT). For the

TTCHARS-TTDEG relationship, both KBV and OL perspectives are integrated to underlie the

relationship. As indicated in Figure 1 below, both KBV and OL perspectives have not only

provided a strong foundation for the relationships between all TTCHARS and TTDEG in this

framework but they also provide valuable arguments, theoretical insights, empirical findings,

hypotheses development, testable constructs and reliable measurements (Simonin, 1999a;

Minbaeva, 2007)



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Figure 1: The Inter-Firm TT Integrated Model of TTCHARS-TTDEG-LFP

Previous studies on knowledge transfer have acknowledged the significant effect of KCHAR

(TCT, COMPLX and SPEC) on knowledge transfer (Kogut and Zander, 1992, 1993; Simonin,

1999a, 1999b, Pak and Park, 2004; Minbaeva, 2007). KBV perspective suggests that tacit

knowledge/tacitness (TCT) is not easily replicable and transferable (Mowery and Rosenberg,

1989). A number of studies have established the role played by tacit knowledge as a barrier to

knowledge and TT (Kogut and Zander, 1996; Choi and Lee, 1997; Simonin, 1999a). Tacit

knowledge, which is context-specific, embedded in non-standardized and tailored process, is

difficult to acquire and exploit (Polanyi, 1962).

Tacit knowledge is the implicit and non-codifiable accumulation of skills resulting from learning

by doing, accumulated through experience and refined by practice (Reed and DeFillippi, 1990).

Hence, tacit knowledge which is highly personal, deeply rooted in action, commitment, and

involvement within a specific context, is hard to be formalized, communicated and shared

(Nonaka, 1994). Tacit knowledge is subject to time-compression diseconomies which means to

accelerate tacit knowledge learning is very difficult or perhaps not even possible no matter how

much effort or resources are invested to acquire them (Dierickx and Cool, 1989). In the context

of OL perspective, tacit knowledge, which is hard to formalize and not easily visible, is difficult

to be communicated and shared with the other partners as it involves intangible factors that is

embedded in the personal beliefs, experiences, and values of an organization (Inkpen, 1998a,

Inkpen and Dinur, 1998). Empirical studies have found support that TCT has a significant

Technology Recipient

Characteristics

Technology Supplier Characteristics

Relationship Characteristics

Tacit Degree of Knowledge Technology Transfer Explicit Knowledge

Corporate Performance Local Firms’ Performance Human Resource Performance

Knowledge Characteristics



84

negative impact which impedes inter-firm knowledge transfer (Simonin, 1999a, 1999b; Pak and

Park, 2004; Minbaeva, 2007).

Past studies have also affirmed the significant effect of knowledge complexity (COMPLX) on

knowledge transfer. COMPLX, as a result of the interdependent skills and assets, arises from

large numbers of technologies, organization routines, individual and team-based experience

(Reed and DeFillippi, 1990). COMPLX of human and technological systems produce a higher

level of ambiguity that restrains imitation and impedes transferability. COMPLX as the number

of interdependent technologies, routines, individuals and resources is linked to a particular

knowledge or assets (Simonin, 1999a). As COMPLX increases, knowledge or technology

becomes difficult to transfer or imitate (Kogut and Zander, 1993).

Empirical studies have found support that COMPLX has a significant negative effect on both

intra and inter-firm knowledge transfer (Simonin, 1999a, 1999b; Minbaeva, 2007). Specificity

(SPEC), as the third knowledge characteristic, refers to assets specificity that includes site,

physical, dedicated and human assets which are durable investments undertaken in support of

particular transaction (Williamson, 1985). Assets specificity as durable investments in

specialized equipment, facilities and skilled human resources is not only acting as a source of

ambiguity and barrier to imitation but also as a barrier to knowledge transferability (Simonin,

1999a; 1999b).

From the KBV perspective, a firm creates sustainable competitive advantage by developing

valuable assets and competencies which are firm-specific, produce complex social relationships,

embedded in the firm’s history and culture thus generating organizational tacit knowledge (Lado

and Wilson, 1994). As the firm’s source of competitive advantage, knowledge or technology

which is firm-specific is difficult to transfer (Kogut and Zander, 1993). Empirical studies have

established that SPEC has a negative effect on knowledge transfer (Simonin, 1999a; Pak and

Park, 2004; Minbaeva, 2007). Building on the theoretical and empirical studies, KCHAR which

consists of TCT, COMPLX and SPEC are predicted to have a significant negative effect on

TTDEG and its dimensions. This study advances the following hypotheses:

H1: Knowledge characteristics are significantly related to degree of technology transfer.

H1a: Knowledge characteristics are significantly related to degree of tacit knowledge.

H1b: Knowledge characteristics are significantly related to degree of explicit knowledge.



85

Many theoretical and empirical studies have found support for the positive effect of absorptive

capacity (ACAP) on knowledge transfer. A low degree of the technology recipient’s ACAP

impedes both intra and inter-firm knowledge transfer (Cohen and Lavinthal, 1990; Hamel, 1991;

Lyles and Salk, 1996; Mowery et al., 1996; Lane and Lubatkin, 1998; Lane et al., 2001; Gupta

and Govindarajan, 2000; Minbaeva et al., 2003, Minbaeva, 2007; Pak and Park, 2004; Simonin,

1999a, 1999b). ACAP is the firm’s ability to recognize, assimilate, and apply to commercial ends

the value of new external information (Cohen and Lavinthal, 1990). Prior related knowledge, as

the important element of ACAP, is critical for organizations to assimilate and exploit new

knowledge. By possessing sufficient prior related knowledge, organizations are able to have an

adequate ability to absorb new external knowledge (Cohen and Lavinthal, 1990).

OL literature suggests that another critical element of ACAP is the recipient’s firm intensity of

efforts. Intensity of effort is reflected on the amount of energy expended by organizational

members to solve problems through organization members directing their considerable time and

effort in learning how to solve problems before attempting to solve complex problems (Kim,

1998). Both intra and inter-firm knowledge transfer literature have found positive effect of

ACAP on knowledge transfer (Szulanski, 1996; Minbaeva, 2007; Lyles and Salk, 1996; Mowery

et al., 1996; Simonin, 1999a; Pak and Park, 2004; Yin and Bao, 2006).

Another important dimension of TRCHAR is recipient collaborative (RCOL). RCOL is closely

related to the recipient’s learning intent (competitive vs. collaborative intent). The technology

recipient firm’s willingness to establish a mutually beneficial and collaborative relationship

requires the recipient firm’s honest intention to create common benefits for both JV partners (Yin

and Bao, 2006). Studies on inter OL have suggested that cooperative/collective learning

encourages the alliance partners to work together by sharing their knowledge, benefit each

other‘s complementarities and provide mutual opportunities to extract potential synergies

between their respective competencies (Doz, 1996; Geringer, 1991).

Collaborative learning creates an access to the partner’s knowledge and skills such as product

and process technology, organizational skills, and knowledge about new environments (Inkpen,

1995a). In the collaborative learning environment where the recipient’s learning intent is crucial,

the transferring partner tends to be more open or transparent in terms of sharing and transferring

knowledge to the acquiring firm as it involves mutual exchange of valuable knowledge (Inkpen,

2000). RCOL, which is reflected on the partner’s learning intent (competitive vs. collaborative



86

intent), determines the degree of openness or transparency in knowledge sharing and knowledge

transfer (Inkpen, 2000). Few studies have found positive effect of RCOL on knowledge transfer

(Yin and Boa, 2006; Hamel, 1991). Building on the theoretical and empirical studies, TRCHAR

which consists of ACAP and recipient RCOL are expected to have a significant positive effect

on TTDEG and its dimensions. Thus, this study advances the following hypotheses:

H2: Technology recipient characteristics are significantly related to degree of technology

transfer.

H2a: Technology recipient characteristics are significantly related to degree of tacit knowledge.

H2b:Technology recipient characteristics are significantly related to degree of explicit

knowledge.

Partners in the collaborative relationship such as JV are expected to mutually exchange their

valuable assets, resources, information, knowledge and technology between them in order to

achieve mutual benefits (Inkpen, 2000; Khanna et al., 1998; Child and Faulkner, 1998). KBV

perspective suggests that since the firm’s competencies are the sources of technology-supplier’s

sustainable competitive advantage therefore for fear of losing ownership of their valuable assets

they tend to protect their competencies from the opportunist recipient partner (Barney, 1991;

Cohen and Lavinthal, 1990; Hamel, 1991). Partner protectiveness (PPROTEC) is closely related

to partner’s transparency or openness (Hamel, 1991). Relationship openness has been described

as “the willingness and ability of JVs’ partners to share information and communicate openly”

(Inkpen, 2000). Most of the researchers are in consensus that the extent of willingness of the

JV’s partners to share and transfer knowledge depends on the degree of partner protectiveness

and transparency (Hamel, 1991; Inkpen, 2000). Studies have suggested that if a situation of high

competitive overlap exists, an alliance partner may be very reluctant to share knowledge due to

risk of knowledge ‘spillovers’ to the other partner (Inkpen, 1998a; Inkpen, 2000; Yan and Luo,

2001). However, the theoretical and empirical studies have found inconsistent results of the

impact of PPROTEC on both intra and inter-firm knowledge transfer (Szulanski, 1996; Simonin,

2004).

As for the technology-supplier’s transfer capacity (TRANSCAP), many studies have suggested

that while firms differ in their ability in creating knowledge, they also differ in their ability to



87

transfer knowledge within or outside of the organization boundary (Kogut and Zander, 1992,

1993; Szulanski, 1996). The efficiency in transmitting knowledge by the technology supplier is

important in both intra and inter-firm knowledge transfer (Martin and Solomon, 2003). Studies

on inter-firm knowledge transfer suggest that the firm’s ability to transfer knowledge facilitates

the OL process as it justifies their commitments in the collaborative relationship (Inkpen, 1998a;

Inkpen 2000; Khanna et al., 1998; Child and Faulkner, 1998). Empirical studies have shown that

TRANSCAP has a significant positive impact on both intra and inter-firm knowledge transfer

(Szulanski, 1996; Gupta and Govindarajan, 2000; Minbaeva, 2007; Yin and Bao, 2006). Thus,

building on the theoretical and empirical studies, TSCHAR which consists of PROTEC and

TRANSCAP are expected to have a significant effect on TTDEG and its dimensions. Thus, this

study advances the following hypotheses:

H3: Technology supplier characteristics are significantly related to degree of technology

transfer.

H3a: Technology supplier characteristics are significantly related to degree of tacit knowledge.

H3b: Technology supplier characteristics are significantly related to degree of explicit

knowledge.

OL literature suggests that acquiring and transferring technology require frequent and effective

interactions between the supplier and recipient as knowledge is firm-specific, embedded in firm

organizational context, personal quality in nature and idiosyncrasy (Nonaka, 1994; Kogut and

Zander, 1992, 1993; Bresman et al., 1999). Studies have identified relationship quality

(RELQLTY) as the critical element of relationship characteristic in both intra and inter-firm

knowledge transfer (Szulanski, 1996; Gupta and Govindarajan, 2000; Lin, 2005; Gupta, 1987;

Wang et al., 2004; Bresman et al., 1999). RELQLTY promotes intimacy of relationship between

the source and recipient unit (Szulanski, 1996), informality, openness and density of

communication (Gupta and Govindarajan, 2000), and increases openness of communication,

spontaneous and open exchange of information between interacting parties (Gupta, 1987). In the

context of strategic alliance, RELQLTY promotes greater opportunities to learn, share and access

alliance partners’ strategic knowledge and competencies. It also creates higher relationship

openness which could directly affect the willingness of alliance partner to share information and



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communicate openly (Inkpen, 1998a, 2000). Consistent with the theoretical studies, empirical

studies have established that RELQLTY has a significant positive effect on both intra and inter-

firm knowledge transfer (Szulanski, 1996; Minbaeva, 2007; Hansen, 1999, 2002; Gupta and

Govindarajan, 2000; Lin, 2005; Bresman et al., 1999).

With respect to mutual trust (MT) between partners, previous studies have suggested that MT

creates opportunities for a mutual inter-organizational learning when partners become more open

and committed in sharing their knowledge and competencies, less protective of their knowledge,

and develop free exchange of information between partners (Inkpen, 2000). When the level of

transparency or openness between the alliance partners is high, the propensity for inter-partner

learning is also high as knowledge is more accessible due to free exchange of information

(Hamel, 1991; Doz and Hamel, 1998; Inkpen, 2000). MT encourages partners to be more open

and transparent in exchanging, sharing, and transferring knowledge and technology between

them due to non-existence of opportunistic behaviors (Kale et al., 2000; Gulati, 1995; Uzzi,

1997; Child and Faulkner, 1998; Steensma and Lyles, 2000; Lane et al., 2001). MT is found to

have reduced search cost, increased benefits and alliance’s performance (Gulati, 1995), increased

alliance’s cooperation, improved flexibility, reduced the coordinating activities cost, and

increased knowledge transfer and learning (Smith et al., 1995). Empirical studies have found

positive impact of mutual trust on inter-firm knowledge transfer (Nielsen, 2007; Kale et al.,

2000; Luo, 2001; Dhanaraj et al., 2004; Pak and Park, 2004). Building on the previous studies,

RELQLTY and MT are predicted to have a significant positive effect on TTDEG and its

dimensions. Thus, this study advances the following hypotheses:

H4: Relationship characteristics are significantly related to degree of technology transfer.

H4a: Relationship characteristics are significantly related to degree of tacit knowledge.

H4b: Relationship characteristics are significantly related to degree of explicit knowledge.

Building on intra and inter-firm knowledge transfer literature, all technology transfer

characteristics (TTCHARS) which formed the study’s conceptual framework, are viewed as both

the critical facilitators/determinants and barriers to TT (Szulanski, 1996). The TTCHARS are

inter-dependent, co-existed and closely related to each other; where failure to manage any of TT

characteristic will affect TT outcomes. Previous studies on intra and inter-knowledge transfer



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have acknowledged the significant influence of these facilitators/barriers on TT’s success or

failure (Szulanski, 1996, 2003; Gupta and Govindarajan, 2003; Minbaeva, 2007; Hamel, 1991;

Inkpen, 1998, 2000). For technology acquisition to occur in IJVs, technology must first be

accessible by the learning partner.

In a collaborative/cooperative learning environment as opposed to competitive learning, the

transferring partner is more transparent/open and willing to share and transfer their proprietary

knowledge, competencies and skills although they are organizationally embedded in the

organization’s routines and processes (Hamel, 1991; Inkpen, 2000; Child and Faulkner, 1998).

As a result, this will reduce the degree of PPROTEC to allow for freer and greater flow of

information to the learning partner particularly the accessibility to tacit knowledge (Inkpen,

2000; Yan and Luo, 2001; Hamel, 1991; Doz and Hamel, 1998). Relationship openness thus is

influenced by the learning intent of the recipient partner and inter-partner MT (Inkpen, 2000;

Inkpen and Beamish, 1997). If competitive overlap exists and for fear of losing their proprietary

technology/knowledge and risk of spillovers, the transferring partner is likely to be less

transparent, more protective of their technology either through explicit or active measures, and

restrict the information flow to the opportunist partner who perceives JV as a low cost approach

to internalize partner’s competencies (Hamel, 1991; Simonin, 1999a, 2004; Steensma and Lyles,

2000). The recipient partner’s learning intent also determines the TRANSCAP of the transferring

partner in terms of increasing motivation to transfer technology.

MT between JV partners is important in reducing the fear of opportunistic behaviors of the

recipient partner, promotes greater transparency which may contribute a higher degree of

accessibility to partner’s technological knowledge, and motivates the transferring partner to share

and transfer higher technology (Inkpen, 1998; 2000). As a result of the collaborative learning

intent (RCOL), RELQLTY promotes a higher degree of MT and openness between partners

resulting in a higher degree of knowledge sharing and transfer of tacit knowledge (Inkpen, 2000;

von Hippel, 1998; Marsden, 1990; Kale et al., 2000).

On the other aspect, learning capability (ACAP) promotes higher TTDEG if the learning partner

has the capacity to recognize, absorb, assimilate and apply new technology/knowledge (Cohen

and Lavinthal, 1990; Lane and Lubatkin, 1998). ACAP is closely related to knowledge

connection and knowledge relatedness between JV partners (Inkpen and Dinur, 1998; Inkpen,

2000). Acquiring tacit knowledge involves various organizational levels and personal



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interactions between individuals and groups. Thus, knowledge connection and knowledge

relatedness between JV partners are capable of creating potentials for the sharing of more

personal observations and experiences (Von Krogh, 1994; Inkpen 2000).

Although TCT, COMPLX and SPEC have greatly contributed to technology ambiguity, these

barriers to technological gap between JV partners may be reduced or eliminated if the learning

partner has adequate prior related knowledge and intensity of learning efforts (Hamel, 1991;

Inkpen, 2000; Szulanski, 1996; Kim, 1998). Building on previous theoretical and empirical

studies, this study proposes the following hypotheses:

H5: Technology transfer characteristics, which consist of knowledge, technology recipient,

technology supplier, and relationship characteristics, are significant predictors of degree

of technology transfer.

Most of the studies on strategic alliance operationalize performance as either the JV or MNCs’

subsidiary performance. Intra and inter-firm empirical studies on knowledge transfer and

acquisition have established that knowledge transfer and acquisition have a significant positive

effect on human resource, business and general performance (Lyles and Salk, 1996), operational

cost, operational efficiency, employee productivity, business volume, market share, market

penetration, product quality, customer service, and customer satisfaction (Lane et al., 2001;

Tsang et al., 2004; Dhanaraj et al., 2004; Cui et al., 2006). On the local firms’ performance

(LFP), tacit knowledge acquisition is found to have a significant positive effect on the recipient

firms’ performance in terms of increasing their productivity, revenue and market share (Yin and

Bao, 2006). Based on the empirical studies, this study proposes the following hypotheses:

H6: Degree of technology transfer, which consists of degree of tacit and explicit knowledge, is

significantly related to local firms’ performance.

H6a: Degree of technology transfer, which consists of degree of tacit and explicit knowledge, is

significantly related to local firms’ corporate performance.

H6b: Degree of technology transfer, which consists of degree of tacit and explicit knowledge, is

significantly related to local firms’ human resource performance.



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CONCLUSION Since many studies on inter-firm TT are theoretical (Hamel, 1991; Inkpen, 1998a; Inkpen and

Dinur, 1998) and have exclusively focused on a single or few dimensions of TT determinants

(Pak and Park, 2004; Yin and Bao, 2006; Hau and Evangelista, 2007) there is a need for more

hypothesis development and testing (Huber, 1991; Fiol, 1994). The major contribution of this

conceptual study to inter-firm TT literature is the development of “a holistic model of inter-firm

TT in IJVs”, which is based on the integrated perspectives of KBV and OL, in explaining the

relative relationship (effects) of TT characteristics and degree of TT in a single model

(Szulanski, 1996; Minbaeva, 2007).

In the context of inter-firm TT and knowledge transfer (KT), there are inadequate studies which

examined all the four TT characteristics in a single model. For example, in the context of

Korean’s IJVs, Pak and Park (2004) examine two determinants of knowledge transfer: 1)

relation-specific variables (equity ownership, conflict, and experience) and 2) knowledge-

specific determinants (tacitness and absorptive capacity). Another study by Yin and Boa (2006)

examines both supplier individual level and recipient’s factors that affect tacit knowledge

acquisition in China’s JVs. In the context of marketing knowledge acquisition, Hau and

Evangelista (2007) examine the effect of knowledge seekers, knowledge holders, and contextual

factors on explicit and tacit knowledge acquisition through IJVs in Vietnam. Hence, the holistic

model advanced by this study extends the theoretical scope of TT and KT literature. From a

review of literature, the holistic model of inter-firm TT advanced by this study has never been

conceptualized before by previous researchers.

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Part II

Technology Transfer Characteristics and Degree of Technology Transfer

5 - The Effects of Knowledge Characteristics on Degree of Inter-Firm Technology

Transfer

6 - Measuring the Impact of Technology Suppliers’ Characteristics on Degree of Inter-

Firm Technology Transfer in International Joint Ventures

7 - The Effects of Technology Recipients’ Characteristics on Degree of Inter-Firm

Technology Transfer

8 - The Effects of Relationship Characteristics on Degree of Inter-Firm Technology

Transfer



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5

The Effects of Knowledge Characteristics on Degree of

Inter-Firm Technology Transfer

CHAPTER OUTLINE

The current issue on inter-firm technology transfer (TT) in the developing countries is centered

on the efficiency and effectiveness of the transfer process by the multinationals (MNCs). Thus,

organizations in the developing countries are striving hard to collaborate, learn and internalize

their foreign partner’s technological knowledge by forming strategic alliances and international

joint ventures (IJVs) as an efficient mean to increase global competitiveness, technological

capabilities, and potential for local innovation. Knowledge as the critical element underlying

technology has become one of the main factors that affects the success and failure of inter-firm

technology transfer within IJVs which is frequently measured by the degree of technology

transferred to the recipient partners.

INTRODUCTION Past studies have acknowledged the important role of MNCs as the main source of technology.

MNCs have been regarded as the most efficient vehicle for transferring technology and

knowledge across borders through FDIs and IJVs (Tihanyi and Roath, 2002; Kagut and Zander,

1993). Previous literature has indicated that foreign MNCs in Malaysia have successfully

transferred their technology to local industries (Lai and Narayanan, 1997, Narayanan and Lai,

2000). The technologies transferred by MNCs benefit the host country in terms of achieving long

term economic growth (Marton, 1986; Blomstrom, 1990), providing a higher potentials of

innovation performance/capabilities (Guan, Mok, Yam and Pun, 2006; Kotabe, Dunlap-Hinkler,

Parente and Mishra, 2007)), increasing technological capabilities (Kumar, Kumar and Persaud,



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1999; Madanmohan, Kumar and Kumar, 2004), enhancing the competitive advantage (Liao and

Hu, 2007; Rodriguez and Rodriguez, 2005), enhancing the organizational learning effectiveness

(Inkpen, 2000; Inkpen and Dinur, 1998), providing a positive effect on productivity (Caves,

1974; Xu, 2000; Liu and Wang, 2003), and increasing the technological development of local

industry (Markusen and Venables, 1999).

Prior to formulating the appropriate TT strategies and policies, there is a need to critically

examine one of the important technology transfer characteristics: knowledge characteristics that

may have significant influence on the successful and effective implementation of TT particularly

technologies transferred through IJVs. In the context of inter-firm TT, success is determined by

substantial amount of technology transferred (level of TT) and its effect on the level of

technological capacity of the local firms to absorb, assimilate, improve and further develop the

newly acquired technology (Madanmohan et al., 2004). IJVs are viewed as the most efficient

mode to transfer technology and knowledge which are organizationally embedded and difficult

to transfer through licensing agreements (Kogut, 1988; Mowery, Oxley and Silverman, 1996).

Knowledge, as an important element underlying technology, can be learned and transferred

between IJVs’ partners. IJVs provide both MNCs and local partners an appropriate vehicle to

facilitate the transfer of organizational knowledge, particularly for knowledge which is hard to be

transferred without the setting up of a JV such as institutional and cultural knowledge (Harrigan,

1984). Previous studies on inter-firm knowledge transfer have suggested that: 1) although studies

on TT and KT in strategic alliance have contributed many interesting and valuable theories, they

remain empirically under-researched (Mjoen and Tallman, 1997), 2) studies on inter-firm KT

and knowledge acquisition by organizations require more hypothesis development and testing

(Huber, 1991; Fiol, 1994), 3) the cross-border TT and KT from MNCs to local firms have not

been extensively researched (Pak and Park, 2004), 6) studies on inter-firm knowledge acquisition

in alliance have focused heavily on the supplier’s, JV’s or KT’s perspective (Martin and

Solomon, 2003; Simonin, 1999a, 2004; Lyles and Salk, 1996; Tsang et al., 2004), and 7) fewer

studies adopt the local firms or recipient’s perspective (Yin and Bao, 2006).



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KNOWLEDGE CHARACTERISTICS AND DEGREE OF INTER-FIRM TECHNOLOGY TRANSFER From the literature review, a number of KCHAR that have been identified include tacitness,

complexity, specificity (Kogut and Zander, 1993; Inkpen and Dinur, 1998, Simonin, 1999a,

1999b, 2004; Pak and Park, 2004; Inkpen, 2000; Minbaeva, 2007; Makhija and Ganesh, 1997;

Lei et al., 1997; Inkpen, 1998a, 1998b, 2000; Parise and Handerson, 2001; Mohr and Sengupta,

2002), knowledge relatedness (Inkpen, 2000; Lyles et al., 2003), desirability (Pak and Park,

2004) and availability (Minbaeva, 2007). Knowledge tacitness, specificity and complexity have

contributed significantly to knowledge ambiguity in imitation (Reed and DeFillippi, 1990), and

knowledge migration (Szulanski, 1996). Building on the previous intra-firm knowledge transfer

studies (Winter, 1987; Reed and DeFillippi, 1990; Szulanski, 1996; Zander and Kogut, 1995;

Kogut and Zander, 1993; Minbaeva, 2007) and inter-firm knowledge transfer studies (Lyles and

Salk, 1996; Mowery et al., 1996; Simonin, 1999a; Simonin, 1999b; Simonin, 2004; Inkpen,

1998a; Inkpen and Dinur, 1998; Pak and Park, 2004), this study empirically examines the effects

of the three critical elements of KCHAR: tacitness (TCT), complexity (COMPLX) and

specificity (SPEC) on two distinct dimensions of degree of technology transfer (TTDEG):

degrees of tacit (TCTDEG) and explicit (EXPDEG) knowledge based on the underlying KBV

perspective.

TACITNESS AND DEGREE OF TECHNOLOGY TRANSFER The knowledge dimension that appears to be particularly relevant to TT is ‘tacit vs. explicit

dimension’ (Marcotte and Niosi, 2000; Grant, 1996a, 1996b, 1997). The concept of tacit

knowledge (TCT) is derived from the famous work of Polanyi (1962) who asserts that “we can

know more than what we can tell”. Tacit knowledge is knowledge that is non-verbalizable,

intuitive and unarticulated, developed through the transfer of context-specific knowledge,

embedded in non-standardized and tailored process, and is difficult to acquire and exploit

(Polanyi, 1967). Tacit knowledge derives from the accumulated experience, and is reflected in

the expertise, skills and routines acquired by organizational members over time (Winter, 1987).

Past studies have established that tacit knowledge, which includes insights, intuitions and



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hunches, rule of thumb, gut feeling, personal and organizational skills (Nonaka, 1994),

managerial and marketing expertise (Lane et al., 2001), is difficult to codify: where it can only

be observed through its application and acquired through practice. Thus, tacit knowledge transfer

between individuals is slow, costly and uncertain (Kogut and Zander, 1992). Acquiring tacit

knowledge is subject to time-compression diseconomies: which means to accelerate tacit

knowledge learning is rather difficult or perhaps not even possible no matter how much efforts or

resources are invested to acquire them within a short period of time (Dierickx and Cool, 1989;

Lin, 2003). This is because tacit knowledge is unique to the knowledge owner and not codifiable

in formulas or manuals and cannot be reverse-engineered easily (Zander and Kogut, 1995). Tacit

knowledge which is hard to formalize, often sticky and not easily visible, is difficult to

communicate, transfer and share between the alliance or JVs partners as it involves 1) intangible

factors embedded in the personal beliefs, experiences, and values in an organization (Inkpen,

1998a, 2000), 2) internal individual processes like experience, reflection, internalization or

individual talents (Nonaka, 1994), and 3) high incremental cost of transferring the knowledge to

a specified location in a form usable by a given party (von Hippel, 1994).

H1: Tacitness is negatively related to degree of inter-firm technology transfer in IJVs.

COMPLEXITY AND DEGREE OF TECHNOLOGY TRANSFER As the second critical element of knowledge characteristic, complexity (COMPLX) has been

described from many aspects for example: 1) COMPLX is closely associated with the amount of

information required to characterize the item of knowledge in question (Winter, 1987), 2)

COMPLX is “a result of the interdependent skills and assets: which arises from large numbers of

technologies, organization routines and individual or team-based experience” (Reed and

DeFillippi, 1990), 3) COMPLX as ‘the number of interdependent technologies, routines,

individuals and resources linked to a particular knowledge or assets’ (Simonin, 1999a), 4)

COMPLX as “the number of critical and interacting elements embraced by an entity or activity”

(Kogut and Zander, 1993), and 5) COMPLX as ‘an applied system whose components have

multiple interactions and constitutes a non-decomposable whole’ (Singh, 1997). COMPLX of

human and technological systems produce higher levels of ambiguity which restrains imitation



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and impedes transferability (Reed and DeFillippi, 1990). It is argued that the higher the degree of

COMPLX of the manufacturing technology, the more difficult for knowledge to be transferred or

imitated (Kogut and Zander, 1993).

H2: Complexity is negatively related to degree of inter-firm technology transfer in IJVs.

SPECIFICITY AND DEGREE OF TECHNOLOGY TRANSFER On the other hand, specificity (SPEC) originally refers to transaction costs asset specificity as

popularized by Williamson (1985). Asset SPEC which includes site, physical, dedicated and

human assets refer to durable investments that are undertaken in support of particular transaction

(Williamson, 1985). Building on Williamson (1985), Reed and DeFillippi (1990) defined SPEC

as ‘transaction-specific skills and assets that are utilized in production processes and provision of

services for particular customers’. Through firm-customer relationship, the business actions

resulting from the resource and skill deployment (competencies) are highly specific and inter-

dependent with the firm’s internal or external transaction partners (Reed and DeFillippi, 1990).

Although sites or physical assets create limited ambiguity to imitation by rivals, dedicated assets

such as the plants specifically designed for the production of goods and services for a specific

customer, and human asset SPEC is linearly and significantly related to ambiguity as these types

of asset SPEC create barriers to imitation and are protected by the security and exclusivity of the

firm-customer relationship (Reed and DeFillippi, 1990). Simonin (1999a, 1999b) narrowly views

SPEC as ‘durable investments in specialized equipment, facilities and skilled human resources’.

Asset SPEC is not only acting as a source of causal ambiguity and barrier to imitation, where

technology is difficult to be explicitly articulated (Lippman and Rumelt, 1982), but also as a

barrier to knowledge transferability (Simonin, 1999a). The firms’ resources and competencies,

which are highly specific, are difficult to imitate and transfer as they are embedded in context

and idiosyncrasy to the firm (Kogut and Zander, 1993). Firms create sustainable competitive

advantage by developing firms’ assets and competencies that are firm-specific, produce complex

social relationships i.e. firm-customer relationship, embedded in a firm’s history and culture,

generate organizational tacit knowledge and time consuming to develop (Lado and Wilson,

1994; Dierickx and Cool, 1989; Kogut and Zander, 1993).



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H3: Specificity as one of the critical elements of knowledge characteristic is negatively related

to degree of inter-firm technology transfer IJVs.

METHODOLOGY AND SAMPLE Based on the number of IJV companies registered with the Registrar of Companies (ROC) as at

1st January 2008, the number of IJVs currently operating in Malaysia is 1038. Out of this, 850

IJVs are considered as active IJVs and 103 IJVs are either dormant or have ceased operation.

Since the focus of this study is on inter-firm TT from foreign MNCs to local companies, 85 IJVs

were further eliminated from the population frame because only IJVs that have operated more

than 2 years and have at least twenty percent (20%) of foreign equity are eligible to participate in

the survey. Therefore, based on the list provided by ROC, which is considered as the most

official and original source of information on foreign investment in Malaysia, it was decided that

all IJVs (850) be included in the survey.

Data collection was conducted in the period from July 2008 to December 2008 using a self-

administered questionnaire. The questionnaires were mailed to 850 active JV companies as listed

with ROC using a cover letter. After one month from the posting date the response was not

encouraging. By mid July 2008 there were only 70 responses received from the respondents.

Thus, in order to increase the response rate the researcher followed-up through numerous phone

calls, e-mails, reminders via letters and personal visits to seek the respondents’ cooperation in the

survey. After intensive efforts were made, by mid November 2008 a total of 145 responses

(17.05%) were received. Based on literature review, the response rates for mailed questionnaires

are usually not encouraging and low (Newman, 2003; Sakaran, 2003). In the Malaysian context,

however, a response rate of 15% to 25% is still being considered appropriate and acceptable

(Mohammed, 1998; Rozhan, Rohayu and Rasidah, 2001; Norziha, 2004). From 145 responses

only 128 questionnaires were usable and the balance were returned blank, returned incomplete,

or replied but unable to participate in the study.



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INSTRUMENTS AND MEASUREMENT The main research instrument in this study is the questionnaire. Building on the previous studies

on KT and TT, the questionnaire adopts a multi-item scales which have been modified

accordingly to suit the context of the study: inter-firm TT. Except for TTDEG, all the variables

are measured using ten-point Likert Scale (1 = strongly disagree to 10 = strongly agree). For

TTDEG, this variable is measured using ten-point Likert Scale (1 = very low transfer to 10 =

substantial transfer). The ten-point Likert Scale was selected because 1) the wider distribution of

scores around the mean provides more discriminating power, 2) it is easy to establish covariance

between two variables with greater dispersion around their means, 3) it has been well established

in academic and industry research, and 4) from a model development perspective, a ten-point

scale is more preferred (Allen and Rao, 2000).

DEGREE OF TECHNOLOGY TRANSFER Following Lyles and Salk (1996), Lane et al. (2001), Gupta and Govindarajan (2000), Dhanaraj

et al. (2004), Pak and Park (2004), Yin and Boa (2006) and Minbaeva (2007), this study adopts

“a multi-dimensional operationalization approach” in measuring this construct. This study

operationalizes TTDEG as the transfer of technological knowledge in terms of two dimensions:

1) tacit knowledge (TCTDEG) in terms of new product/service development, managerial systems

and practice, process designs and new marketing expertise, and 2) explicit knowledge

(EXPDEG) in terms of manufacturing/service techniques/skills, promotion techniques/skills,

distribution know-how, and purchasing know-how. The respondents were asked to evaluate

TTDEG from MNCs to local firms in terms of tacit and explicit dimensions of technological

knowledge. The Cronbach Alphas for TCTK and EXPK were 0.96 and 0.97 respectively. The

results of Cronbach Alpha were quite similar to that of Hau and Evangelista (2007) and Yin and

Bao (2006).



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TACITNESS This study measures TCT in terms of its two constructs: codifiability and teachability (Kogut and

Zander, 1993; Simonin, 1999a, 1999b, 2004). For codifiability, multi-item scales are designed to

capture the extent to which the technology has been articulated in documents. Two (2) items are

adopted from Kogut and Zander (1993) and modified accordingly to suit the context of this study

which includes statements as to whether 1) the foreign JV partner’s manual describing the

technology can be written, and 2) large parts of the foreign JV partner’s technology are

embodied in standard software. Two (2) items are adopted from Simonin (1999a, 1999b, 2004)

which include statements whether 1) the foreign JV partner’s technology is easily codified, and

2) the foreign JV partner’s technology is more explicit than tacit. One (1) item is adopted from

Pak and Park (2004) inquiring whether the partner’s technology is hard to verbally transfer. For

teachability, the scales are designed to capture the ease by which technology can be learned by

the local JV partner. Three (3) items are adapted from Kogut and Zander (1993) and modified

accordingly to suit the context of the study which include statements whether 1) the local JV

firm’s personnel can easily learn the technology by communicating with the foreign JV partner’s

skilled personnel, 2) the local JV local firm’s personnel can easily learn the technology by

studying a complete set of blueprints, and 3) educating and training the JV local firms’ personnel

is a quick and easy process. The Cronbach Alpha for TCT was slightly higher (0.86) than

Simonin’s (1999a) Cronbach Alpha (0.72).

COMPLEXITY Following Simonin (1999a, 1999b, 2004) and Kogut and Zander (1992, 1993), this study adopts

a five (5) items scale in measuring COMPLX which include statements whether the JV partner’s

technology is the product of many interdependent techniques, routines, individuals, resources,

and processes. The Cronbach Alpha for COMPLX was also higher (0.84) as compared to Pak

and Park’s (2004) Cronbach Alpha (0.74).



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SPECIFICITY To capture SPEC this study adopts a two (2) items scale from Simonin (1999a, 1999b) in terms

of whether 1) the foreign JV partner has invested significantly in specialized equipment and

facilities in developing their technology, and 2) the foreign JV partner has invested significantly

in skilled human resources in developing their technology. Following Pak and Park (2004), this

study also adopts one (1) item scale which includes a statement on whether the technology is

difficult to access from the other company. For SPEC the Cronbach Alpha was slightly lower

(0.72) as compared to Pak and Park’s (2004) Cronbach Alpha (0.87).

RESULTS Table 1 shows the descriptive data of all the variables (Mean values, Standard Deviations,

Correlations) and correlation matrix. Table 2 presents the results of regression analysis.

Table 1: Descriptive Statistics and Correlation Matrix --------------------------------------------------------------------------------------------------------------------- Variable Mean SD 1 2 3 4 -------------------------------------------------------------------------------------------------------------------------------------------- TCT 5.36 1.58 1.000 COMPLX 6.24 1.34 -0.084 1.000 SPEC 3.55 1.07 0.068 0.400** 1.000 TCTDEG 5.91 1.45 -0.194* -0.207* -0.012 1.000 TCT 5.93 1.35 1.000 COMPLX 5.89 1.31 -0.084 1.000 SPEC 4.73 1.30 0.068 0.400** 1.000 EXPDEG 6.47 1.34 -0.225* -0.236* -0.118 1.000 -------------------------------------------------------------------------------------------------------------------------------------------- n = 128, * p < 0.05, ** p < 0.01 From Table 1 above, there are clearly some associations between independent variables. For all

the variables, it was found that there was no multicollinearity problem; where the T values were

ranged between 0.827 - 0.881 and the VIF values were between 1.020 - 1.209. Tacitness (TCT)

and complexity (COMPLX) were significantly correlated with degree of tacit knowledge

(TCTDEG) (p < 0.05). Although specificity (SPEC) showed a negative correlation with

TCTDEG, however, it was not statistically significant. The correlation results also indicated that

both TCT and COMPLX also had significant negative correlations with EXPDEG (p < 0.05 and



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p < 0.01 respectively). Again, although specificity (SPEC) showed a negative direction as

predicted, however, the statistical result was insignificance.

Using the multiple regression analysis, the effects of TCT, COMPLX, and SPEC on two

dimensions of degree of technology transfer (TCTDEG and EXPDEG) were estimated. As

shown in Table 2 below, TCT and COMPLX as two critical components of knowledge

characteristics had significant negative effect on both degrees of tacit (p < 0.05, Beta value = -

0.143 and p < p -0.355 respectively ) and explicit (p < 0.05, Beta value = -0.155 and p < 0.05,

Beta value = -0.333 respectively) knowledge in inter-firm TT. The regression results indicated

that both TCT and COMPLX had considerable and significant effects on both dimensions of

technology transfer. This is also evident by the results of the adjusted R-squared and F statistics.

As the critical elements of knowledge characteristics, both tacitness and complexity had negative

significant effect on both degrees of tacit and explicit knowledge (p < 0.05). Therefore, H1 and

H2 are supported thus indicating that the higher level of knowledge tacitness and complexity of

the foreign partners’ technology contributes to the lesser degree of tacit and explicit knowledge

that are being transferred to the recipients/local partners in IJVs. The results further indicate that

the effects of COMPLX on both TCTDEG and EXPDEG in Model 1 and 2 were stronger than

the effects on TCT on both TCTDEG and EXPDEG suggesting that as compared to knowledge

tacitness the combination of large number of technologies, organization routines and

interdependent skills and assets have more influence in determining a lower degrees of both tacit

and explicit knowledge in IJVs. Interestingly, although specificity has been strongly highlighted

by previous literature of its significance, it has failed to provide any significant effects on both

degree of tacit and explicit knowledge (p > 0.05). In this study, specificity as one of the critical

elements of knowledge characteristic has not really effected lower degrees of tacit and explicit

knowledge in inter-firm TT in IJVs though the direction was correctly predicted. Thus, H3 is not

supported.



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Table 2: Results of Categorical Regression Analysisª --------------------------------------------------------------------------------------------------------------------- Variable Degree of Tacit Knowledge Degree of Explicit Knowledge (Model 1) (Model 2) -------------------------------------------------------------------------------------------------------------------------------------------- (Constant) 36.359*** 41.583*** Tacitness -0.143* -0.155* Complexity -0.355* -0.333* Specificity 0.165 0.002 R-squared 0.098 0.116 Adjusted 0.064 0.082 R-squared F 2.840* 3.407* -------------------------------------------------------------------------------------------------------------------------------------------- ª Cell entries are standardised coefficient estimates (n = 128), * p < 0.05, ** p < 0.01, *** p < 0.001 DISCUSSION AND CONCLUSIONS Based on the underlying knowledge-based view perspective, this study attempts to provide

empirical evidence as to the effects of three critical knowledge characteristics (tacitness,

complexity, and specificity) on degrees of tacit and explicit knowledge in the inter-firm TT

through IJVs. This paper has specifically addressed the effects of knowledge characteristics on

generic knowledge attributes (tacitness and explicitness) as highlighted by Pak and Park (2004,

pg. 429). Besides examining the relationships between these key knowledge characteristics and

degree of technology transfer (degrees of tacit and explicit knowledge), this study also had

extended the previous findings on knowledge specific attributes (Kogut and Zander, 1993;

Simonin, 1999a) which suggest that tacitness or ambiguity of knowledge is rather difficult to

transfer between strategic alliance partners of joint ventures. The consistent results of the

significant effects of tacitness and complexity on both degrees of tacit and explicit knowledge

are in line Pak and Park’s (2004) findings; where they found that the effects of specificity and

desirability on manufacturing-processing (explicit knowledge) were more dominant than new

product development (tacit knowledge). The results in the present study were quite interesting

given that although explicit knowledge is mostly codified in the form of blueprints, instructions,

formulas or standard manuals by the supplier; which allows for more easy transfer of technology,

however, explicit knowledge still implicitly consists of an intrinsic tacit element/value (firm-



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specific) in which to accelerate the transfer of explicit knowledge would involve various

organizational and group levels of involvement (Inkpen, 2000). The results further suggest that

explicit knowledge transfer of a highly tacit and complex technology/knowledge requires not

only learning by doing by the recipient but also active involvement of the teacher/supplier (Lane

and Lubatkin, 1998). Overall, the findings also confirm and support previous empirical results of

the effect of KCHAR on knowledge transfer; where knowledge-specific attributes such as

tacitness or ambiguous knowledge are more difficult to transfer for international ventures (Reed

and DeFillippi, 1990; Kogut and Zander, 1993; Szulanski, 1996; Simonin, 1999a, 1999b; Pak

and Park, 2004; Minbaeva, 2007).

The results of present study also suggest that tacitness and complexity had negatively affected

the degree of technologies (TCTDEG and EXPDEG) that are intended to be transferred to the

recipient because the technology supplier’s technologies and knowledge were well embodied

within the component of their competencies, non-codifiable, highly personal, and deeply rooted

in action, commitment, and involvement within a specific context (Reed and DeFillippi, 1990;

Nonaka, 1994). Tacitness and complexity of technology involved the intangible factors

embedded in the personal beliefs, experiences, and values in an organization which caused the

technology/knowledge to be difficult to be formalized, communicated, transferred and shared

between the alliance or JV partners (Inkpen, 1998a; 2000). On the insignificance of specificity,

the results seemed to concur with Simonin’s (1999a, pg. 614) suggestion that the construct’s

(SPEC) lack of effect needs to be further investigated for other types of competencies thus

should not only be restricted to technological knowledge.

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6

Measuring the Impact of Technology Suppliers’

Characteristics on Degree of Inter-Firm

Technology Transfer in International Joint Ventures

CHAPTER OUTLINE

While technology transfers through JVs, have been acknowledged by many studies as the most

efficient formal mechanism in internalizing the partner’s technologies, knowledge and skills, the

transfer process has frequently involved various facilitators, actors and complex relationship

between partners which cause direct impact on degree of technology transfer. Based on the

underlying knowledge-based view (KBV) and organizational learning (OL) perspectives, the

main objective of this work is to empirically examine the effects of two critical elements of

technology supplier characteristics: partner protectiveness and transfer capacity on two

dimensions of degree of technology transfer: degree of tacit and explicit knowledge.

INTRODUCTION The inter-firm technology transfers (TT) in international joint ventures (IJVs) have often

involved tradeoffs between the technology suppliers’ willingness to transfer a considerable

amount of their technologies; which include both tacit and explicit knowledge, degree of

protection of the proprietary technology, knowledge and competencies as the source of the

supplier’s competitive advantage (Inkpen, 2000), degree of transparency (Hamel, 1991), and

motivation to transfer (Szulanski, 1996). Previous studies have also argued that the interplay

between complex relationship and competition between IJVs partners (Hamel, 1991) and the

tension between knowledge sharing and knowledge protection have caused a ‘learning paradox’

(Hau and Evangelista, 2007; Jordon and Lowe, 2004). This paradox exists because the inter-firm



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technology transfer in strategic alliances and IJVs is indeed an organizational learning process

(Huber, 1991). Technology transfers through IJVs, although have been acknowledged in many

studies as the most efficient mechanism in internalizing the partner’s technology, knowledge and

skill, have also frequently involved various facilitators, actors and complicated relationship

between partners (Szulanski, 1996); which eventually cause direct impact on the degree or

amount of technology transferred in JVs . Studies from the KBV perspective have acknowledged

that MNCs tend to become more protective of their advance technology, knowledge and

competencies in products, processes and management because these strategic valuable resources

and competencies are their main sources of sustainable competitive advantage (Porter, 1985;

Barney, 1991; Peteraf, 1993; Wernerfelt, 1984; Pralahad and Hamel). The OL perspective

studies have argued that technology and knowledge are protected by the suppliers when the

recipients are opportunistic in the collaborative relationship (Inkpen, 1998a; Inkpen and Dinur,

1998; Child and Faulkner, 1998).

A number of studies on inter-firm knowledge transfer (KT) have suggested that: 1) many studies

have focused more on conceptual work which involves either small-sample or in-depth studies of

few organizations; thus studies on how strategic alliances work and alliance partners learn are

empirically under researched (Simonin, 1999a), 2) studies on TT and KT in strategic alliance

have contributed many interesting and valuable theories, however, they remain empirically

under-researched (Mjoen and Tallman, 1997), 3) the cross-border TT and KT from MNCs to

local firms have not been extensively researched (Pak and Park, 2004), 4) studies on inter-firm

knowledge acquisition in alliance have heavily focused on the supplier’s, JV’s or KT’s

perspective (Martin and Solomon, 2003; Simonin, 1999a, 2004; Lyles and Salk, 1996; Tsang et

al., 2004), 5) fewer studies have adopted the local firms’ (recipient) perspective (Yin and Bao,

2006). Therefore, based on the above limitations, this study responds to the literature gaps in by

empirically examining the effects of two critical elements of technology supplier characteristics:

partner protectiveness (PPROTEC) and transfer capacity (TRANSCAP) on two dimension of

degree of technology transfer: degree of tacit (TCTDEG) and explicit (EXPDEG) knowledge

from the local (recipient) firms’ perspective using the underlying KBV and OL perspectives.



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TECHNOLOGY SUPPLIERS’ CHARACTERISTICS A review of literature shows that technology supplier characteristics (TSCHAR) have been

studied from many dimensions of suppliers’ behaviors. A stream of studies has identified

numerous TSCHAR that have significant influence on KT such as motivation (Gupta and

Govindarajan, 2000; Szulanski, 1996), partner protectiveness (Simonin, 1999a, 1999b, 2004;

Szulanski, 1996, Inkpen, 1998a, 1998b, 2000), partner assistance (Lyles et al., 1999), partner

transparency (Hamel, 1991), disseminative capacity (Minbaeva and Michailova, 2004), control

(Lyles et al., 2003), prior experience (Subramaniam and Venkataraman, 2001), transferor’s

commitment (Tsang et al., 2004), articulated objective or goal clarity (Lyles and Salk,1996;

Inkpen 2000) and source transfer capacity (Szulanski, 1996; Martin and Solomon, 2003).

PARTNER PROTECTIVENESS AND DEGREE OF TECHNOLOGY TRANSFER The ability of a firm to acquire knowledge in the cooperative arrangement such as JVs does not

solely depend on its internal ACAP. The inter-firm learning opportunity provided by strategic

alliance is also subjected to the degree of willingness of the technology suppliers to cooperate or

engage in knowledge sharing i.e. by reducing the level of protectiveness (Simonin, 1999a;

Steensma and Lyles, 2000). Partner protectiveness (PPROTEC) refers to as ‘the extent of

protections/hurdles, intentionally or unintentionally, imposed by the foreign partner on the local

partner in an IJV which restrict the accessibility to proprietary technology/knowledge’ (Hau and

Evangelista, 2007). PPROTEC is closely related to the degree of transparency. Transparency is

thus defined as ‘the degree of openness of one partner (technology-supplier) and their

willingness to transfer knowledge to the other partner (technology-recipient)’ (Hamel, 1991). In

the context of intra-firm transfer within multinational corporation (MNCs), openness is referred

to as ‘the degree to which relationship between business unit managers and corporate supervisors

is open and informal which promotes spontaneous and open exchange of information and ideas’

(Gupta, 1987).

Many theoretical studies have stressed that partners in the collaborative relationship such as JVs

are expected to mutually exchange their valuable proprietary assets, resources, information,

knowledge and technologies between them to achieve mutual benefits (Inkpen, 2000; Khanna et



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al., 1998; Child and Faulkner, 1998). However, these proprietary competencies are the critical

sources of sustainable competitive advantage of the supplier partners; and for fear of losing

ownership, a position of privilege and superiority of their valuable assets they are duty bound to

protect their hard-won success and competencies from the opportunist recipient partners (Parkhe,

1993; Steensma and Lyles, 2000; Szulanski, 1996). Unless they receive sufficient incentive to

mitigate the cost which is typically associated with the transfer, the foreign parent firms may

intentionally restrict knowledge flow to the JV. This is because collaborations through JVs are

commonly viewed as a low cost approach for local firms to gain competencies in a short period

(Hamel et al., 1989; Simonin, 1999a, 2004; Steensma and Lyles, 2000; Dyer and Singh, 1998).

Due to the risk of knowledge spillover (leakage), partners in the strategic alliance and IJVs, tend

to be more protective of their valuable knowledge resources as their competitiveness (survival) is

very much depending on these valuable resources (Barney, 1991).

Valuable knowledge resources of the firm, if not well protected will leak to potential competitors

or competitors which eventually will enable them to gain competitive advantage and use it

against the proprietor or supplier firms (Cohen and Lavinthal, 1990; Hamel et al., 1989; Simonin,

1999a, 2004; Steensma and Lyles, 2000). This is also because knowledge spillover to an alliance

partner tends to shift the balance of bargaining power between JV partners thus leading to the

initiation of changes in the partner relationship (Inkpen, 2000). Because of asymmetries of

knowledge between alliance or JV partners, PPROTEC and knowledge accessibility will be

correspondingly asymmetrical; where partners in an alliance can be less transparent or open than

the other partner (Hamel, 1991). However, even though openness/transparency of the alliance’s

partner, to some extent, is a prerequisite for carrying out joint tasks in an alliance, there is a great

concern by the managers about the unintended and unanticipated transfer of knowledge where

knowledge is unintentionally transferred by default rather than by design (Hamel, 1991).

Therefore, in the context of knowledge sharing within alliance; where a high-competitive overlap

between partners exists, the partner’s knowledge protectiveness level is expected to be high

(Inkpen, 1998a; Inkpen, 2000; Yan and Luo, 2001). This causes foreign partners to have low

motivation to exchange, share and transfer knowledge (Szulanski, 1996; Hua and Evangelista,

2007). Although alliances and JVs provide a strategic avenue to internalize the other partner’s

technologies and competencies, they are also likely to be surrounded by greater levels of

protectiveness and mistrust which may discourage the transfer of tacit knowledge; especially



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when learning in alliances demand active teaching by the technology supplier (Simonin, 2004,

1999a; Dhanaraj et al., 2004; Marcotte and Niosi, 2000).

As discussed above, studies on PPROTEC are mostly theoretical or case study. Only few studies

have empirically examined the impact of PPROTEC on technology and knowledge transfer. The

empirical results are found to have mixed results where 1) in the context of marketing know-how

transfer in strategic alliance, partner PPROTEC had an insignificant relationship with knowledge

ambiguity (Simonin, 1999b), 2) PPROTEC indirectly, through the mediation of knowledge

ambiguity, was insignificantly related to inter-firm KT in strategic alliance (Simonin, 1999a), 3)

PPROTEC had a significant negative effect on inter-firm KT in strategic alliance (Simonin,

2004), and 4) a partner’s knowledge protectiveness has a significant negative influence on

explicit and tacit marketing knowledge acquisition (Hua and Evangelista, 2007).

H1: Partner protectiveness is negatively related to degree of tacit and explicit knowledge in

inter-firm technology transfer.

TRANSFER CAPACITY AND DEGREE OF TECHNOLOGY TRANSFER As technology and knowledge transfer involve the absorption and transmission of knowledge

(Devanport and Prusak, 1998, 2000), the ability of the technology supplier to efficiently transfer

knowledge and technology to technology recipient becomes critical in inter-firm TT. Few studies

have suggested that while firms differ in their ability in knowledge creation, they also differ in

their ability to transfer knowledge within and outside of the organizational boundary (Kogut and

Zander, 1992, 1993; Szulanski, 1996). The efficiency in transmitting technology or knowledge

by the supplier is important in both intra and inter-firm knowledge transfer as it affects the TT

outcomes. The firms’ ability to transfer knowledge to their subsidiaries efficiently and

effectively may serve several objectives such as 1) to facilitate their expansion in foreign

countries, 2) to maintain the firms’ competitiveness, and 3) to safeguard their competencies and

expertise from the competitors (Martin and Solomon, 2003). In the context of strategic alliance,

the supplier firms’ ability to transfer knowledge facilitates the organizational learning process

and justifies their commitments in the collaborative relationship; where all partners are expected



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to mutually contribute their knowledge, technologies, skills and competencies to the JVs to gain

mutual benefits (Inkpen, 1998a, Inkpen 2000; Khanna et al., 1998; Child and Faulkner, 1998).

Past studies have described TRANSCAP from many dimensions for example: 1) the supplier

firms’ ability to articulate uses of their own knowledge, assess the needs and capabilities of the

potential recipient, and transfer knowledge to different location (Martin and Solomon, 2003), 2)

a disseminative capacity of the knowledge sender in terms of the source’s ability and willingness

to share knowledge (Minbaeva and Minhailova, 2004), 3) the sender’s ability to articulate and

communicate knowledge to the recipient (Minbaeva, 2007), 4) the parent firms’ capacity to

knowledge transfer (Wang et al., 2004), and 5) the source’s motivational disposition (Gupta and

Govindarajan, 2000).

Szulanski (1996) identifies source/supplier ‘not perceived as reliable’ and lack of motivation as

the source’s characteristics that contribute to knowledge stickiness. When the source is perceived

as unreliable, it is not seen as trustworthy and knowledgeable. In addition, due to the source’s

lack of motivation; which can undermine the KT process, the source may be reluctant to share its

proprietary knowledge with the recipient thus unwilling to devote sufficient time and resources

to support the transfer for fear of losing ownership, privilege or superiority (Szulanski, 1996).

The decision to transfer knowledge is largely individual and driven by the ability and willingness

of the sender to share knowledge (Minbaeva, 2007; Kogut and Zander, 1992, 1993; Szulanski,

1996). Minbaeva (2007) argues that knowledge sender (source) should possess ‘well-develop

abilities to articulate and communicate knowledge’ to the recipient. However, although the

knowledge sender (source) is capable in transmitting knowledge, they may be unwilling to share

knowledge (Minbaeva, 2007). Wang et al. (2004) identify parent firms’ capacity; which include

the ability to impart the knowledge in a form that can be assimilated by the recipient, as an

important determinant of knowledge transfer by MNC parent to its subsidiary.

In the context of inter-firm KT, the strategic alliance and JV literature have implicitly associated

the technology supplier’s TRANSCAP with 1) the parent’s firm assistance (Lyles et al., 1999;

Hau and Evangalista, 2007), 2) the foreign parent’s active involvement (Lyles and Salk, 1996;

Cumming and Teng, 2003), and 3) the foreign parent’s commitment (Tsang et al., 2004). For

IJVs to succeed, the foreign firm is expected to assist the local partner firms by providing

adequate and sufficient assistance to the IJV management in terms of transferring a significant

amount of knowledge to local personnel through training programs or interactions/contacts



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between local and foreign employees (Hau and Evangalista, 2007). Simonin (1999b) argues that

the degree to which a foreign partner has explicit contribution in terms of training of the local

personnel should be positively associated with the degree to which an IJV acquires explicit

knowledge from its foreign parent. In the context of strategic alliance and JV literature, there are

no studies which have directly examined the relationship between the supplier’s TRANSCAP

and TTDEG. Nevertheless, as TRANSCAP has been closely associated with the foreign firm’s

active involvement, assistance and commitment, few empirical studies have offered evidence

that 1) partner assistance has a significant positive impact on the acquisition of explicit

marketing knowledge (Hau and Evangalista, 2007), 2) the degree of active involvement of the

foreign parent is significantly related to knowledge acquisition (Lyles and Salk, 1996), and 3) the

foreign parent’s commitment has a significant positive effect on knowledge acquisition (Tsang et

al., 2004).

H2: Transfer capacity is positively related to degree of tacit and explicit knowledge in inter-firm

technology transfer.

METHODOLOGY AND SAMPLE The sample frame was taken from the IJV companies registered with the Registrar of Companies

(ROC). As at 1st January 2008, the number of IJVs operating in Malaysia was 1038. Out of this,

850 IJVs were considered as active IJVs and 103 IJVs were either dormant or had ceased

operation. Since the focus of this study is on inter-firm TT from foreign MNCs to local

companies, 85 IJVs were further eliminated from the population frame because only IJVs that

have operated more than 2 years and have at least twenty percent (20%) of foreign equity are

eligible to participate in the survey. Therefore, based on the list provided by ROC, which is

considered as the most official and original source of information on foreign investment in

Malaysia, it was decided that all IJVs (850) be included in the survey. Data collection was

conducted in the period from July 2008 to December 2008 using a self-administered

questionnaire. The questionnaires were mailed to 850 active JV companies as listed with ROC

using a cover letter. After one month from the posting date the response was found not




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only 128 questionnaires were usable and 17 questionnaires were returned blank, returned

incomplete, or replied but unable to participate in the study.

INSTRUMENT AND MEASUREMENT The main research instrument in this study is the questionnaire. Building on the previous KT and

TT studies, the questionnaire adopts a multi-item scales which have been modified accordingly

to suit the context of the study: inter-firm TT. Except for TTDEG, all the variables are measured

using ten-point Likert Scale (1 = strongly disagree to 10 = strongly agree). For TTDEG, this

variable is measured using ten-point Likert Scale (1 = very low transfer to 10 = substantial

transfer). The ten-point Likert Scale was selected because 1) the wider distribution of scores

around the mean provides more discriminating power, 2) it is easy to establish covariance

between two variables with greater dispersion around their means, 3) it has been well established

in academic and industry research, and 4) from a model development perspective, a ten-point

scale is more preferred (Allen and Rao, 2000).

DEPENDENT VARIABLE - DEGREE OF TECHNOLOGY TRANSFER Following Lyles and Salk (1996), Lane et al. (2001), Gupta and Govindarajan (2000), Dhanaraj







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knowledge. The Cronbach Alphas for TCTK and EXPK were 0.96 and 0.97 respectively. The

results of Cronbach Alpha were quite similar to that of Hau and Evangelista (2007) and Yin and

Bao (2006).

INDEPENDENT VARIABLES PARTNER PROTECTIVENESS The measure of PPROTEC is adopted from Simonin (1999a, 1999b and 2004). In measuring this

construct, this study adopts a two (2) items scale which includes statements whether 1) the

foreign JV partner has intentional procedures, routine and policies to restrict the sharing of

relevant information concerning its technology know-how, and 2) the foreign JV partner is very

protective of its technology know-how (Simonin, 1999a). This study also adopts two (2) newly

developed items by Hau and Evangelista (2007) namely 1) the restrictive policy of the foreign

partner with respect to knowledge spillovers to the other partner; and 2) the unwillingness of the

foreign expatriates to share technological expertise with the local firms’ personnel. The

Cronbach Alpha for PPROTEC was slightly higher (0.84) than Simonin’s (1999a) Cronbach

Alpha (0.77).

TRANSFER CAPACITY To operationalize the ability of foreign JV’s partner to articulate, communicate and transfer

technology to local firms, this study employs a four (4) items scale which consists of two (2)

items adopted from Lyles et al. (1999), and two (2) items from Hau and Evangelista (2007). The

respondents are asked to indicate whether 1) the foreign JV partner has provided the local partner

with materials on procedures and guidelines for technology planning and decision making, 2) the

foreign JV partner has offered formal training programs such as seminars and lectures to the



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local firm’s personnel (Lyles et al., 1999), 3) the training programs provided by the foreign JV

partner have been very helpful to the local firm’s personnel, and 4) there have been many foreign

JV partner personnel working in the JV firm (Hau and Evangelista, 2007). The Cronbach Alpha

for TRANSCAP was higher (0.92) than Minbaeva’s (2007) Cronbach Alpha (0.66).


Correlations). The results of regression analysis are presented in Table 2 below.

Table 1: Descriptive Statistics and Correlation Matrix. -------------------------------------------------------------------------------------------------------------------------------------------- Variable Mean SD 1 2 3 -------------------------------------------------------------------------------------------------------------------------------------------- PPROTEC 5.46 1.94 1.000 TRANSCAP 5.55 1.77 -0.407** 1.000 TCTDEG 4.49 1.49 -0.418** 0.686** 1.000 PPROTEC 6.10 1.90 1.000 TRANSCAP 5.55 1.77 -0.407** 1.000 EXPDEG 4.49 1.77 -0.314** 0.791** 1.000 -------------------------------------------------------------------------------------------------------------------------------------------- n = 128, * p < 0.05, ** p < 0.01 From Table 1 above, there are clearly some associations between independent variables. For all


ranged between 0.801 - 0.834 and the VIF values were between 1.108 - 1.199. Both partner

protectiveness (PPROTEC) and transfer capacity (TRANSCAP) were strongly correlated with

degree of tacit knowledge (TCTDEG) (p < 0.01) and had negative and positive signs

respectively; which are consistent with the theoretical arguments in the literature. The

correlation results also indicated that both PPROTEC and TRANSCAP also had strong

correlations with EXPDEG (p < 0.01). In comparison, the correlation between PPROTEC and

TCTDEG was slightly higher than correlation between PPROTEC and EXPDEG thus suggesting

that tacit knowledge is more likely to be protected by the transferring partner in IJVs as

compared to explicit knowledge. On the other hand, the correlation results for TRANCAP



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suggested that due to the suppliers’ ability to transfer, it is likely more explicit knowledge will be

transferred than tacit knowledge in IJVs.

Using the multiple regression analysis, the effects of PPROTEC and TRANSCAP on two

dimensions of degree of technology transfer (TCTDEG and EXPDEG) were estimated. As

shown in Table 2 below, transfer capacity as a critical component of technology recipient

characteristics had significant effect on both degrees of tacit and explicit knowledge in inter-firm

TT. The regression results indicated that transfer capacity had a strong significant effect on both

TCTDEG (p < 0.001, Beta value = 618) and EXPDEG (p < 0.001, Beta value = 795). This was

also evident by the results of the adjusted R-squared in Model 1 and Model 2 (0.493 and 0.626

respectively), F statistics (38.480 and 66.099 respectively) and the highly significant

corresponding p values. As the critical elements of technology recipient characteristics,

TRANSCAP had a highly significant effect on both degrees of tacit and explicit knowledge (p <

0.001). Therefore, H2 is supported thus indicating that the higher level of transfer capacity,

which is directly reflected on the technology supplier’s ability and motivation to transfer,

contributes to a higher degree of tacit and explicit that are transferred to the recipient partners in

IJVs.

Table 2: Results of group Regression Analysisª -------------------------------------------------------------------------------------------------------------------------------------------- Variable Degree of Tacit Knowledge Degree of Explicit Knowledge (Model 1) (Model 2) -------------------------------------------------------------------------------------------------------------------------------------------- (Constant) 11.108*** 5.106*** Partner Protectiveness -0.167† -0.010 Transfer Capacity 0.618*** 0.795*** R-squared 0.493 0.626 Adjusted 0.481 0.616 R-squared F 38.480*** 66.099*** -------------------------------------------------------------------------------------------------------------------------------------------- ª Cell entries are standardised coefficient estimates (n = 128) † p < 0.10, * p < 0.05, ** p < 0.01, *** p < 0.001 Interestingly, although partner protectiveness has a strong theoretical foundation as emphasized

by previous literature, the regression results show that it failed to provide strong effects on both



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degrees of tacit and explicit knowledge (p > 0.05) thus contrary to this study prediction. In this

study, however, as compared to Model 2, partner protectiveness in Model 1, as one of the critical

elements of technology supplier characteristic, did contribute a low significant negative effect on

TCTDEG (p < 0.10) indicating a significantly low influence of PROTEC in determining degree

of tacit knowledge transfer to the recipient partner. In Model 2, partner protectiveness did not

contribute significantly to a lower degree of explicit knowledge in inter-firm TT though the

direction was correctly hypothesized. Thus, based on the two regression results, H1 is partially

supported. Although the effect of partner protectiveness on degree of tacit knowledge is

relatively low in significance, the results still suggest that the presence of partner protectiveness,

which is closely associated with the technology suppliers’ degree of transparency (openness) and

their willingness to transfer, has more significant influence on degree of tacit knowledge than

explicit knowledge in IJVs. The technology suppliers tend to protect their tacit knowledge more

as compared to explicit knowledge because tacit knowledge embodied in products, processes and

management competencies (skills) are regarded as strategic valuable resources which could

sustain their competitive advantage (Kogut and Zander 1993; Barney, 1991).

DISCUSSION AND CONCLUSION Based on the underlying integrated KBV and OL perspectives, this study has bridged the gaps in

the literature by providing empirical evidence on the effects of two critical elements of

technology supplier characteristics: partner protectiveness and transfer capacity on two distinct

dimensions of degree of inter-firm technology transfer: degree of tacit (TCTDEG) and explicit

(EXPDEG) knowledge in IJVs using the Malaysian sample. From the regression results, the

strong significant effects of transfer capacity (TRANSCAP) on both degrees of tacit and explicit

knowledge (p < 0.001) confirm the previous theory on the importance of TRANSCAP in

facilitating technology transfer within IJVs (Inkpen, 2000). This suggests that the greater the

ability to transfer by the supplier the higher the degree of tacit and explicit knowledge will be

transferred to local recipient firms. Consistent with recent development in knowledge transfer

literature (Szulanski and Cappetta, 2003; Minbaeva, 2007), the results confirm the theoretical

proposition which suggested that knowledge provider attributes has become one of the most

important determinant of knowledge transfer. Although the literature on the effect of



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TRANSCAP on TTDEG is rather limited, the results of this study are in line with Minbaeva

(2007), Szulanski (1996), and Gupta and Govindarajan (2000). The results further suggest that

TRANSCAP of the supplier in IJVs; which is reflected on the parent’s firm assistance, active

involvement, high commitment in terms of investment in resources, and availability of training

and support, is crucial in determining the volume/degree of technology transferred to local JV

partner. To increase the level of TT in IJVs the technology supplier should become a proficient

transferor with the ability to 1) articulate knowledge, 2) describe the potential uses of the

knowledge and condition as to what the knowledge could achieve, and 3) assess the recipient’s

degree of receptivity, assimilation and use of technology (Martin and Solomon, 2003). The

population sample also indirectly inferred that TRANSCAP is one of the major concerns that

require serious attention.

On the low significant effect of partner protectiveness (PPROTEC) on degree of tacit (p < 0.10)

and its insignificant effect on degree of explicit knowledge (p > 0.05), this study follows the

argument made by Simonin (1999a, pg.615) who argued that it is difficult to evaluate and assess

the true role and effect of PPROTEC on degree of tacit knowledge in IJVs ‘if data on failed joint

ventures was not obtained’. Thus, it is impossible to assess the relationship between lack of

knowledge and failure should data on failed IJVs was not available (Lyles and Salk, 1996).

PPROTEC as a well established variable of knowledge transfer has been extensively dealt with

in the literature (Hamel, 1991; Inkpen, 2000; Parkhe, 1993). The results are consistent with

Simonin’s (1999a) findings where PPROTEC was found insignificant because 1) PPROTEC

may not always be detectable or observable, and 2) the insignificance of PPROTEC may well be

rooted in the close interplay between protectiveness and opportunism. From the results, there is

high possibility that the true effect of PROTEC in the study’s models was superseded by the

overly strong effect of TRANSCAP on both degrees of tacit and explicit knowledge thus

‘overshadowing’ the significant role of PPROTEC. The results, nevertheless, are not in line with

the recent findings by Hau and Evangelista (2007) where PPROTEC had significantly affected

the acquisition of tacit and explicit marketing knowledge in Vietnamese IJVs.



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7

The Effects of Technology Recipients’ Characteristics

on Degree of Inter-Firm Technology Transfer

CHAPTER OUTLINE

As an efficient mean to increase global competitiveness, technological capabilities, and potential

for local innovation, organizations in the developing countries are working hard to collaborate,

learn and internalize their foreign partner’s technological knowledge by forming strategic

alliances and international joint ventures (IJVs). Technology recipient characteristics, as one of

the important actors/facilitators of inter-firm technology transfer, have increasingly become

crucial factor in determining the success or failure of inter-firm technology transfer within IJVs.

Since the current issue on inter-firm technology transfer (TT) in the developing countries is

centered on the efficiency and effectiveness of the transfer process by the multinationals (MNCs)

therefore the success is often associated with degree of technology transferred to local partners.

INTRODUCTION Studies from knowledge-based view (KBV) perspective have acknowledged that MNCs tend to

be more protective of their advance technology, knowledge and competencies embodied in

products, processes and management because these strategic valuable resources and

competencies are their main sources of competitive advantage (Porter, 1985; Barney, 1991;

Peteraf, 1993; Wernerfelt, 1984; Pralahad and Hamel). On the other hand, organizational

learning (OL) perspective studies have suggested that technology and knowledge are more likely

to be protected by the supplier when the recipients are opportunistic in the collaborative

relationship (Inkpen, 1998a; Inkpen and Dinur, 1998; Child and Faulkner, 1998). Thus, in the

context of inter-firm technology transfer (TT) through international joint ventures (IJVs), the

remaining question is on the extent of TT by foreign MNCs; especially when transferring their



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advance technology to local recipient partner (Narayanan and Lai, 2000). While realizing that

technologies, knowledge, and competencies are the supplier’s main source of competitive

advantage, the current TT issue in IJVs revolves around the extent of degree of technologies that

are transferred by the suppliers to recipient partners in terms of tacit knowledge (new

product/service development, managerial systems and practice, process designs and new

marketing expertise), and explicit knowledge (manufacturing/service techniques/skills,

promotion techniques/skills, distribution know-how, and purchasing know-how) (Madanmohan

et al., 2004). This is because from the recipient’s perspective, TT success is not merely

possessing the ability to operate, maintain or repair the machineries at the production level

(transmission) but it also includes the ability to learn, acquire, absorb and apply new external

technologies and knowledge that are organizationally embedded in product materials, physical

assets, processes and production, and management capabilities (absorption) (Davenport and

Prusak, 1998, 2000).

Previous studies on intra-firm knowledge transfer have acknowledged the significant influence

of technology actors and facilitators/barriers such as the characteristics of knowledge transferred,

source, recipient and contextual/relational in the knowledge transfer process (Szulanski, 1996,

2000, 2003; Gupta and Govindarajan, 2000; Minbaeva, 2007). Thus, the impending issue now is

on the extent of effects of TT characteristics (TTCHARS) in determining the degree or level of

technology transfer (TTDEG). Specifically to what extents do TT characteristics influenced

TTDEG? Based on the underlying KBV and OL perspectives, this paper attempts to address the

above issue in particular the effects of two critical elements of technology recipient

characteristics (TRCHAR): absorptive capacity (ACAP) and recipient collaborativeness (RCOL)

on degree of inter-firm technology transfer (TTDEG): degree of tacit (TCTDEG) and explicit

knowledge (EXPDEG) in IJVs.

TECHNOLOGY RECIPIENTS’ CHARACTERISTICS The technology recipient characteristics (TRCHAR) have been affirmed by many studies as one

of the important determinants that affect knowledge transfer (KT). Among the recipient

characteristic factors that have been identified by literature to influence TT and knowledge KT



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are absorptive capacity (Cohen and Levinthal, 1990; Hamel, 1991; Lyles and Salk, 1996;

Mowery et al., 1996; Lane and Lubatkin, 1998; Lane et al., 2001; Gupta and Govindarajan, 2000,

Minbaeva et al., 2003, Minbaeva, 2007; Pak and Park, 2004), experience (Simonin, 1999a,

1999b), prior knowledge and experience (Inkpen, 1998a, 1998b, 2000; Tsang, 2001), knowledge

relatedness (Inkpen, 2000), learning capacity (Makhjia and Ganesh, 1997; Parise and Henderson,

2001), receptivity (Hamel, 1991; Baughn et al., 1997), learning intent or objectives (Beamish and

Berdrow, 2003; Hamel, 1991; Simonin, 2004; Inkpen and Beamish, 1997; Baughn et al., 1997;

Inkpen, 1998a; Mohr and Sengupta, 2002), managerial belief rigidity (Inkpen and Crossan,

1995), and recipient collaborativeness, readiness and method comprehensiveness (Yin and Bao,

2006).

ABSORPTIVE CAPACITY AND DEGREE OF TECHNOLOGY TRANSFER As TT involves the process of transmission and absorption of knowledge (Davenport and Prusak,

1998, 2000), the recipient firm’s ability to absorb the knowledge transferred largely depends on

the degree of their absorptive capacity (ACAP). Past studies have shown that a low degree of

technology recipient’s ACAP impedes both intra and inter-firm KT (Cohen and Levinthal, 1990;

Hamel, 1991; Lyles and Salk, 1996; Mowery et al., 1996; Lane and Lubatkin, 1998; Lane et al.,

2001; Gupta and Govindarajan, 2000; Minbaeva et al., 2003; Minbaeva, 2007; Pak and Park,

2004; Simonin, 1999a, 1999b). The concept of ACAP has been extensively reviewed in both

theoretical and empirical studies. In their seminal paper, Cohen and Levinthal (1990) define

ACAP as “the firm’s ability to recognize the value of new external information, assimilate it, and

apply it to commercial ends”. ACAP of a firm is primarily a function of the recipient firm’s level

of prior related knowledge. Prior related knowledge is closely related to the individuals units of

knowledge available within the organizations. The accumulation of prior knowledge increases

the ability to make sense of, assimilate and use new knowledge (Kim, 1998). The firm’s ACAP

tends to be developed cumulatively in which ACAP is more likely to be developed and

maintained as a byproduct of routine activity when the knowledge domain that the firm wishes to

exploit is closely related to its current knowledge base (Cohen and Lavinthal, 1990). Prior related

knowledge, which includes basic/minimal skills, a shared language, positive attitude towards



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learning, relevant prior experience and up-to-date information on knowledge domain, is critical

for an organization to assimilate and exploit new external knowledge (Cohen and Lavinthal,

1990; Szulanski, 1996, 2003; Minbaeva, 2007). By possessing sufficient prior related

knowledge, which is closely associated with new knowledge, the organization will have adequate

ability to absorb new technological and innovative competencies/capabilities (Cohen and

Lavinthal, 1990).

A stream of strategic alliance literatures has dealt with the concept of ACAP (Hamel, 1991;

Simonin, 1999a, 1999b, Inkpen, 2000; Szulanski, 1996). Hamel (1991) applies the term

‘receptivity’ to have similar notion to ACAP in explaining the organization’s capacity to learn

from their partner. Several factors have been identified as determinants of receptivity: 1) the

appropriateness of resource deployment, 2) incentive systems, 3) attitudes towards learning, and

4) the propensity to unlearn (Hamel, 1991). In a similar vein, few researchers have expended the

concept of receptivity to include ‘local parent receptivity’ which refers to the readiness and

ability of local parent to appreciate and receive the knowledge brought in by the foreign parent

(Tsang et al., 2004; Gupta and Govindarajan, 2000). All partners are not equally adept at

learning because the capacity to learn in strategic alliance mainly depends on the degree of

receptivity of the partners. Inter-partner learning is also determined by 1) the sense of confidence

which relates to partners’ learning attitudes and the need to unlearn, 2) the degree of skills’ gap

with the industry leaders, 3) the absorptiveness of the receptors i.e. the ability to observe,

interpret, apply and improve upon partner skills, 4) the top management’s commitment to

learning and 5) the capacity of the receptor to turn individual learning into collective learning

(Hamel, 1991). Hamel (1991) further argues that learning becomes almost impossible if the skills

gap between partners is too great.

The other critical element of ACAP is intensity of effort. This concept is proposed by Kim

(1998). Intensity of effort is referred to as “the amount of energy expended by organizational

members to solve problems” (Kim, 1998). Intensity of effort is achieved through organizational

members focusing their considerable time and effort in learning how to solve problems before

attempting to solve complex problems (Kim, 1998). Zahra and George (2002) re-conceptualize

the concept by proposing ACAP to have four complementary dimensions capabilities that

include 1) knowledge acquisition, 2) knowledge assimilation, 3) knowledge transformation, and

4) knowledge exploitation. Knowledge acquisition and assimilation capabilities form ‘potential



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capacity’, whereas knowledge transformation and knowledge exploitation capabilities form

‘realized capacity’ (Zahra and George, 2002). A number of empirical studies on inter-firm KT

have offered strong evidence on the relationship between ACAP and KT where: 1) capacity to

learn is a strong indicator of knowledge acquisition from foreign partners (Lyles and Salk, 1996),

2) ACAP is critical in the acquisition of capabilities in strategic alliance; where ACAP of the

partners strongly depend on their prior experience in related technological fields (Mowery et al.,

1996), 3) prior experience has a negative impact on ambiguity which impedes KT; when the

greater/higher the levels of prior experience of knowledge seeker, the less ambiguous the

knowledge that are to be transferred (Simonin, 1999a), 4) a lack of ACAP is one of the barriers

to KT (Szulanski, 1996), 5) a higher ACAP in the local firms promotes more KT in new product

development and manufacturing skills/techniques (Pak and Park, 2004), 6) the recipient

readiness has a positive impact on tacit knowledge acquisition (Yin and Bao, 2006) and 7) the

local parent’s receptivity is positively related to the amount of knowledge acquired from foreign

partner (Tsang et al., 2004).

H1: Absorptive capacity is positively related to a higher degree of inter-firm technology

transfer within IJVs.

RECIPIENT COLLABORATIVENESS AND DEGREE OF TECHNOLOGY TRANSFER Recipient collaborativenss (RCOL) is mostly involved in inter-firm TT between partners in

collaborative relationship such as strategic alliances and JVs. In intra-firm KT, firms are

expected to encounter fewer problems when transferring knowledge and technology to their own

subsidiaries and affiliates within the organizational boundaries. Strategic alliances provide an

ideal platform for organizational learning especially through IJVs where partner firms can

acquire, learn, create new knowledge, and transfer knowledge between them (Inkpen, 2000).

Nonetheless, strategic alliances face a tradeoff between the opportunities for generating and

sharing knowledge and the propensity that the partner may tend to become opportunistic (Child

and Faulkner, 1998). Building on the concept of inter-partner learning developed by Hamel

(1991), RCOL is defined as ‘the recipient firms’ willingness to establish a mutually beneficial



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and collaborative relationship which requires the recipient firms’ honest intention to create

common benefits for both the supplier and recipient’ (Yin and Bao, 2006). Thus, learning in the

collaborative relationship greatly depends on the partners’ intent; whether the recipient partners’

learning objective/intent is collaborative (complementary) or competitive (Child and Faulkner,

1998).

A stream of studies on inter-firm KT has established that a key determinant of inter-

organizational learning is partner’s intent (collaborative vs. competitive intent) (Beamish and

Berdrow, 2003; Hamel, 1991; Simonin, 2004; Inkpen and Beamish, 1997; Baughn et al., 1997;

Inkpen, 1998a; Mohr and Sengupta, 2002). Learning intent has always been referred to as 1) an

opportunity to learn and the desire and will of an organization to internalize a partner’s skill and

competencies (Hamel, 1991), 2) the desire and will of the partner firm to acquire the other firm’s

knowledge and skills (Tsang, 2001; Story and Mohr, 1997; Inkpen and Beamish, 1997), and 3)

the key condition for knowledge creation (Nonaka and Takeuchi, 1995). Past studies have

contributed valuable theoretical arguments on partners’ learning intent (competitive vs.

collaborative intent) and its relationship with partners’ collaborative attitudes in knowledge

acquisition (Hamel, 1991; Lindholm, 1997; Child and Faulkner, 1998; Khanna et al., 1998;

Inkpen, 1995a; Inkpen, 1998a; Inkpen, 2000). Most of the partners in strategic alliance consider

their cooperative relationship as transitional devices where the primary objective is to learn and

subsequently internalize their partners’ skills and knowledge (Hamel, 1991).

The propensity of learning in an alliance is high if the partners adopt the collaborative learning

approach as it creates potentials for mutual learning, knowledge sharing and knowledge

acquisition between them (Hamel, 1991; Inkpen, 2000). Thus, if learning is considered as a

competitive acquisition of knowledge rather than a collaborative approach, the alliance/JV

becomes destabilized (Beamish and Bedrow, 2003), the partners’ bargaining power changes

(Hamel, 1991; Inkpen, 1998a), the cooperative venture will not be learning oriented (Galister et

al., 2003), the mutual trust between partners erodes (Child and Falkner, 1998), the partners may

tend to hold back their knowledge as a defensive measure (Pucik, 1991), and the transferring

partner becomes cautious, skeptical and tend to monitor and control the flow of the proprietary

information (Makhija and Ganesh, 1997). A review of literature shows that very little empirical

evidence is available with respect to the relationship between recipient collaborativeness and

TTDEG. In a case study of nine international alliances, Hamel (1991) found empirical evidence



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that partner’s intent was a key determinant of inter-organizational learning in alliance. The only

empirical evidence is provided by Yin and Boa (2006). In their study on the acquisition of tacit

knowledge in China through IJVs, three aspects of recipient factors have been examined: RCOL,

method comprehensiveness and recipient readiness. The result showed that among the recipient

factors, RCOL was the most significant factor that had a positive impact on the tacit knowledge

acquisition. Since learning in an alliance depends on the partner’s intent, Simonin (2004) found

learning intent was consistently emerged as the significant determinant of KT.

H2: Recipient collaborativeness is positively related to a higher degree of inter- firm technology

transfer within IJVs.

METHODOLOGY AND SAMPLE The sample frame was taken from the number of IJV companies registered with the Registrar of

Companies (ROC). As at 1st January 2008, the number of IJVs currently operating in Malaysia is

1038. Out of this, 850 IJVs are considered as active IJVs and 103 IJVs are either dormant or

have ceased operation. Since the focus of this study is on inter-firm TT from foreign MNCs to

local companies, 85 IJVs were further eliminated from the population frame because only IJVs

that have operated more than 2 years and have at least twenty percent (20%) of foreign equity are






using a cover letter. After one month from the posting date the response was not encouraging. By

mid July 2008 there were only 70 responses received from the respondents. Thus, in order to

increase the response rate the researcher followed-up through numerous phone calls, e-mails,

reminders via letters and personal visits to seek the respondents’ cooperation in the survey. After

intensive efforts were made, by mid November 2008 a total of 145 responses (17.05%) were

received. Based on literature review, the response rates for mailed questionnaires are usually not

encouraging and low (Newman, 2003; Sakaran, 2003). In the Malaysian context, however, a



136

response rate of 15% to 25% is still being considered appropriate and acceptable (Mohammed,

1998; Rozhan, Rohayu and Rasidah, 2001; Norziha, 2004). From 145 responses only 128

questionnaires were usable and the balance were returned blank, returned incomplete, or replied

but unable to participate in the study. The main research instrument in this study is the

questionnaire. Building on the previous studies on KT and TT, the questionnaire adopts a multi-

item scales which have been modified accordingly to suit the context of the study: inter-firm TT.

Except for TTDEG, all the variables are measured using ten-point Likert Scale (1 = strongly

disagree to 10 = strongly agree). For TTDEG, this variable is measured using ten-point Likert

Scale (1 = very low transfer to 10 = substantial transfer). The ten-point Likert Scale was selected

because 1) the wider distribution of scores around the mean provides more discriminating power,

2) it is easy to establish covariance between two variables with greater dispersion around their

means, 3) it has been well established in academic and industry research, and 4) from a model

development perspective, a ten-point scale is more preferred (Allen and Rao, 2000).










knowledge. The Cronbach Alphas for TCTDEG and EXPDEG were 0.96 and 0.97 respectively.

The results of Cronbach Alpha were quite similar to that of Hau and Evangelista (2007) and Yin

and Bao (2006).



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ABSORPTIVE CAPACITY Building on Lane et al. (2001), this study captures ACAP’s critical elements of ability to

understand, assimilate and apply new external knowledge. In capturing these critical elements,

this study adopts a multi-item scale previously used by the researchers (Lyles and Salk, 1996;

Simonin, 1999a; Pak and Park, 2004) to measure the constructs using seven (7) items with

respect to statements on the academic background, technical capacity, educational programs,

financial support for new ideas, overseas training opportunities, and commitment in terms of

personnel and resources (physical, financial, and logistic) to JV. Following Cohen and Lavinthal

(1990) and Lane et al. (2001), this study also includes one (1) item to assess the local firm’s

ability to understand, assimilate and apply new technology transferred by the foreign parent firm.

The Cronbach Alpha for ACAP was higher (0.94) than Simonin’s (2004) Cronbach Alpha (0.81).

RECIPIENT COLLABORATIVENESS This study measures RCOL in terms of the local partner firms’ learning intent and their

collaborative attitudes by using a five (5) items scale in terms of 1) the local partner’s learning

objective, 2) the local partner’s desire, determination and will to learn from foreign partner, 3)

the technology-recipient’s willingness to allow foreign partner to inspect and monitor the use of

knowledge acquired from JV, 4) the local partner’s commitment not to compete directly with the

foreign partner in the future, and 5) the local partner’s commitment in sharing with the foreign

partner the benefits of the critical knowledge acquired from the JV (Yin and Bao, 2006; Thuc

Anh et al., 2006; Hamel, 1991; Simonin, 2004). The Cronbach Alpha for RCOL was higher

(0.92) than Yin and Bao’s (2006) Cronbach Alpha (0.71).


Correlations) and correlation matrix. Table 2 presents the results of regression analysis.



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Table 1: Descriptive Statistics and Correlation Matrix -------------------------------------------------------------------------------------------------------------------------------------------- Variable Mean SD 1 2 3 -------------------------------------------------------------------------------------------------------------------------------------------- ACAP 6.47 1.34 1.000 RCOL 5.75 1.67 0.541** 1.000 TCTDEG 6.56 1.66 0.329** 0.460** 1.000 ACAP 5.86 1.07 1.000 RCOL 5.84 1.70 0.541** 1.000 EXPDEG 6.60 1.64 0.313** 0.517** 1.000 -------------------------------------------------------------------------------------------------------------------------------------------- n = 128, * p < 0.05, ** p < 0.01 From Table 1 above, there are clearly some associations between independent variables. For all


ranged between 0.707 - 0.811 and the VIF values were between 1.238 - 1.414. Both absorptive

capacity (ACAP) and recipient collaborativeness (RCOL) were significantly correlated with

degree of tacit knowledge (TCTDEG) (p < 0.01). The correlation results also indicated that both

ACAP and RCOL also had strong significant correlations with EXPDEG (p < 0.01). Using the

multiple regression analysis, the effects of ACAP and RCOL on two dimensions of degree of

technology transfer (TCTDEG and EXPDEG) were estimated. As shown in Table 2 below,

recipient collaborativeness as a critical component of technology recipient characteristics had

significant effect on both degrees of tacit (p < 0.001, Beta value = 0.399) and explicit knowledge

(p < 0.001, Beta value = 0.491) in inter-firm TT. The regression results indicated that recipient

collaborativeness had a strong significant effect on both dimensions of technology transfer. This

is also evident by the results of the adjusted R-squared (0.221 and 0.269) and F statistics (17.690

and 22.954). It is also noted that in terms of its significance strength, the effect of RCOL on

EXPDEG was stronger than its effect on TCTDEG. Therefore, H2 is supported thus indicating

that the higher level of recipient collaborativeness, which is directly reflected on the recipient

collaborative learning intent, contributes to a higher degree of tacit and explicit being transferred

by the technology supplier partners in IJVs.



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Table 2: Results of group Regression Analysisª --------------------------------------------------------------------------------------------------------------------- Variable Degree of Tacit Knowledge Degree of Explicit Knowledge (Model 1) (Model 2) -------------------------------------------------------------------------------------------------------------------------------------------- (Constant) 12.238*** 14.597*** Absorptive Capacity 0.113 0.047 Recipient Collaborativeness 0.399*** 0.491*** R-squared 0.221 0.269 Adjusted R-squared 0.208 0.257 F 17.690*** 22.954*** -------------------------------------------------------------------------------------------------------------------------------------------- ª Cell entries are standardised coefficient estimates (n = 128) * p < 0.05, ** p < 0.01, *** p < 0.001 Surprisingly, although absorptive capacity has a strong theoretical foundation as highlighted by

previous literature, nevertheless contrary to this study expectation, it has failed to provide any

significant effect on both degrees of tacit and explicit knowledge (p > 0.05). In this study,

absorptive capacity as one of the critical elements of technology recipient characteristics has not

really contributed to a higher degrees of tacit and explicit knowledge in inter-firm TT though the

direction was correctly hypothesized. Thus, H1 is not supported. The results suggest that the

presence of prior related knowledge about specific technology and intensity of effort, as critical

components of absorptive capacity, do not necessarily help to increase degrees of tacit and

explicit knowledge in IJVs. This unexpected outcome is probably due to the high degree of

partner protectiveness (organizational and skills) which will undermine the learning (transfer)

process, low organizational and individual learning commitment, and low motivation to learn.

DISCUSSION AND CONCLUSION Based on the underlying integrated KBV and OL perspectives and since many of the inter-firm

TT studies are theoritical and still under researched, this study has bridged the literature gaps by

providing empirical evidence on the effects of two critical elements of technology recipient

characteristics (ACAP and SPEC) on degree of inter-firm technology transfer and its two distinct

dimensions namely: degree of tacit (TCTDEG) and explicit (EXPDEG) knowledge in IJVs using



140

the Malaysia sample. From the regression results, the strong significant effects of RCOL on both

degrees of tacit and explicit knowledge confirm the previous theory on the importance of

recipient collaborativeness in facilitating TT through JVs (Inkpen, 2000). The results suggest that

the greater the degree of RCOL the higher the degree of tacit and explicit knowledge will be

transferred by the foreign JV partners. In the cooperative venture such as JVs, the partner’s

learning intent is therefore crucial in encouraging openness and transparency of the transferring

partner to share and transfer more technology (Inkpen, 2000). Thus, if learning in JVs is being

considered as competitive rather than collaborative, it restricts the flow of the required

technology as the learning partner is treated as competitor (Child and Faulkner, 1998). The

results also suggest that collaborative learning, which is based on the underlying spirit of inter-

partner collaboration, promotes knowledge sharing, mutual benefits, and opportunities to extract

potential synergy between partners in JVs (Doz, 1996; Geringer, 1991). Thus, with these

incentives the transferring partner in JVs would have high motivation to share their technology in

the collaborative environment and may not deliberately prevent the transfer of technology

(Inkpen, 1998a, 2000). The results are in line with the previous studies which found statistical

support for the effect of RCOL on degree of knowledge transfer (Yin and Bao, 2006).

On the insignificance effects of absorptive capacity on both degrees of tacit and explicit

knowledge (p > 0.05), the first plausible argument is that transferring technological knowledge in

strategic alliances and IJVs is an inter-partner organizational learning process; where it depends

not only on prior related knowledge and intensity of effort of the learning partner but also ‘other

preconditions for receptivity’ (absorptive capacity) of the learning partner such as sense of

confidence, need to first to unlearn, size of skills gap with industry, and ability to turn

institutional learning to individual learning (Hamel, 1991). Moreover, the receptivity or

absorptive capacity/capability of the learning partner (recipient) would not have significant effect

on learning if 1) the transferring partner’s (supplier) degree of partner protectiveness

(organizational and skills) is high thus frustrating the learning (transfer) process, 2) there is low

organizational and individual learning commitment, and 3) low motivation to learn. Secondly, by

looking at the results, there is a tendency that the effect of absorptive capacity in the study’s

models was superseded by the strong effect of recipient collaborativeness on both degrees of

tacit and explicit knowledge thus ‘overshadowing’ the significant role of absorptive capacity.



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The results are consistent with Lane et al. (2001) where the effect of prior knowledge on

knowledge learned from IJVs was found less significant in Hungarian IJVs.

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8

The Effects of Relationship Characteristics on Degree of Inter-Firm Technology Transfer

CHAPTER OUTLINE

The success of technology transfer (TT) within international joint ventures (IJVs) in the

developing countries has frequently been measured by the degree of technology that is

transferred to local partners. As compared to other formal technology transfer agents

such as foreign direct investments (FDIs) and licensing, technology transfer through

IJVs have been acknowledged by many studies as the most efficient mechanism to

internalize the foreign partner’s technologies, knowledge and skills which are

organizationally embedded. However, the transfer process has always involved a

complex relationship between IJV partners which may cause direct impact on degree of

technology transfer. The success of inter-firm TT requires a strong existence of a close

and intense communications between the technology supplier and recipient.

INTRODUCTION The current issue on inter-firm technology transfer (TT) in the developing countries is revolved

around the issue of efficiency and effectiveness of the transfer process by the multinationals

(MNCs) (Pak and Park, 2004; Yin and Bao, 2006). Therefore, organizations in the developing

countries are attempting to collaborate, learn and internalize their foreign partner’s technological

knowledge by forming strategic alliances and IJVs with foreign multinational corporations

(MNCs) as an efficient mean to enhance their global competitiveness, technological capabilities,

and potential for local innovation. Nevertheless, the inter-firm technology transfers (TT) in IJVs

have often involved tradeoffs between the technology suppliers’ willingness to transfer their

considerable amount of technologies; which include tacit and explicit knowledge, degree of

protection of the proprietary technology (knowledge and competencies) as the source of the



145

supplier’s competitive advantage (Inkpen, 2000), degree of transparency (Hamel, 1991), and

motivation to transfer (Szulanski, 1996).

Previous studies on intra-firm knowledge transfer have confirmed the significant influence of

technology actors and facilitators (barriers) such as the characteristics of knowledge transferred,

source, recipient and contextual/relational on knowledge transfer process (Szulanski, 1996, 2000,

2003; Gupta and Govindarajan, 2000; Minbaeva, 2007). Thus, in the context of inter-firm TT

where technology transfer processes involve more complex relationship, the impending issue

now is on the extent of effects of relationship characteristics (RCHAR) on degree or level of

technology transfer (TTDEG). Relationship characteristics, as one of the important TT

characteristics, have increasingly become dominant factors in determining the success or failure

of inter-firm technology transfer within IJVs (Pak and Park, 2004; Minbaeva, 2007). Studies

from the KBV perspective have acknowledged that MNCs tend to become more protective of

their advance technology, knowledge and competencies in products, processes and management

because these strategic valuable resources and competencies are their main sources of sustainable

competitive advantage (Porter, 1985; Barney, 1991; Peteraf, 1993; Wernerfelt, 1984; Pralahad

and Hamel). The OL perspective studies have also argued that technology and knowledge are

protected by the supplier when the recipients are opportunistic in the collaborative relationship

(Inkpen, 1998a; Inkpen and Dinur, 1998; Child and Faulkner, 1998). Motivated by limited

empirical studies on inter-firm technology transfer and in response to literature gaps, this study

empirically examine the effects of two critical elements of relationship characteristics:

relationship quality (RELQLTY) and mutual trust (MT) on two dimensions of degree of

technology transfer: degree of tacit (TCTDEG) and explicit (EXPDEG) knowledge from the

local (recipient) firms’ perspective based on the underlying KBV and OL perspectives.

RELATIONSHIP CHARACTERISTICS A large stream of literatures has identified the relationship characteristic (RCHAR); which

include JV’s characteristics, as organizational distance (Simonin, 1999a, 1999b), cultural

distance (Lyles and Salk, 1996; Mowery et al., 1996; Choi and Lee, 1997; Inkpen, 1998a, 1998b,

Liu and Vince, 1999), organizational context (Kogut and Zander, 1993; Zander and Kogut,

1995), knowledge connection (Inkpen, 2000), organizational structure (Inkpen, 1997), ownership



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type (Kogut, 1988; Mowery et al., 1996), ownership equity (Pak and Park, 2004), relationship

openness (Hamel, 1991; Inkpen, 2000), partners attachment (Inkpen and Beamish, 1997), inter-

partner trust (Baughn et al., 1997; Morrison and Mezentseff, 1997; Love and Gunasekaran, 1999,

Inkpen, 2000), empathy (Buckley et al., 2002), relationship quality and strength (Szulanski,

1996; Lin, 2005), relational openness (Wathne et al., 1996), relational capital (Kale et al., 2000),

informal relationship (Clarke et al., 1998), articulated goals and management commitment (Choi

and Lee, 1997; Morrison and Mezentseff, 1997), and legal, political and technical differences

(Marcotte and Niosi, 2000).

RELATIONSHIP QUALITY AND DEGREE OF TECHNOLOGY TRANSFER In order to facilitate the intra and inter-firm TT, both technology supplier and recipient are

expected to establish a close relationship between them. For firms which have differences in

terms of the organizational structures, cultural backgrounds, experiences, capabilities, learning

intent and technological resources, transferring technology is rather a challenging process

(Argote, 1999; Hamel, 1991). As knowledge is a firm-specific, embedded in firm organizational

context, personal in nature and idiosyncrasy (Nonaka, 1994; Kogut and Zander, 1992, 1993),

acquiring and transferring technology require frequent and effective interactions between the

supplier and recipient (Bresman et al., 1999). A review of literature on both intra and inter-firm

knowledge transfer (KT) reveals that RELQLTY has been operationalized from many

dimensions such as 1) ease of communication and intimacy of relationship between the source

and recipient unit (Szulanski, 1996), 2) existence and richness of transmission channels (Gupta

and Govindarajan, 2000), 3) degree of interaction frequency between the sender and receiver

(Lin, 2005), 4) openness of communication, spontaneous, and open exchange of information and

ideas between the interacting parties (Gupta, 1987), 5) numerous individual exchanges

(Szulanski, 1996; Nonaka, 1994), 6) frequency, adequacy, amiability and constructiveness in

interaction (Lin, 2005), 7) productive interaction/relationship (Wang et al., 2004; Inkpen, 1998a),

8) extensive and effective inter-personal communication (Bresman et al., 1999), 9) meaningful

and timely information (Anderson and Narus, 1990), 10) close and intense interaction between

individual members of the alliance partners (von Hippel, 1988; Marsden, 1990), 11) personal



147

attachment between partners (Luo, 2001), and 12) extensive communication between JV’s

partners (Inkpen, 2000).

The importance of numerous individual exchanges in transferring tacit knowledge within

organization is achievable through “ease of communication and intimacy of relationship”

between the source and recipient units thus a problematic relationship between the source and

recipient will lead to hardships in transferring knowledge (Szulanski, 1996). Gupta and

Govindarajan (2000) argue that the existence and richness of transmission channels as an

important determinant of knowledge flows within MNCs. Informality, openness and

communication density are closely related to relationship quality as they 1) indicate higher

degree of involvement and interaction frequency between the sender and receiver, 2) increase the

openness of communication, spontaneous and open exchange of information and ideas between

the interacting parties, and 3) create potential for numerous individual exchanges (Szulanski,

1996; Nonaka, 1994; Lin, 2005; Gupta, 1987). From the inter-firm KT context, Lin (2005)

categorizes quality interaction in terms of its frequency, adequacy, amiability and

constructiveness.

Strategic alliance literature has explicitly highlighted that RELQLTY or quality of interaction

between alliance partners promotes greater opportunity to learn, share and access to the alliance

partners’ strategic knowledge and competencies. RELQLTY creates higher relationship

openness; which directly affects the willingness and ability of alliance partners to share

information and communicate openly (Inkpen, 1998a, 2000). The inter-partner RELQLTY is

also reflected on the formal and informal sharing of meaningful and timely information

(Anderson and Narus, 1990). Relationship openness between collaborative partners is

acknowledged as 1) a key factor in determining the amount of information shared, degree of

accessibility of alliance knowledge and the success of knowledge acquisition by alliance partner

(Inkpen, 2000), and 2) an essential element in the organizational learning process; which suggest

that more resources are likely to be invested in learning by the parent firms that regard their

alliance relationship as open (Hamel, 1991). Thus, a close and intense interaction between

alliances partners act as an effective mechanism to acquire, transfer or learn tacit and explicit

knowledge across the organizational interface (von Hippel, 1988; Marsden, 1990; Kale et al.,

2000).



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Empirical studies examining the relationship between RELQLTY and knowledge transfer are

found to be very limited as most of them are theoretical although the previous researchers have

highlighted its importance effects on knowledge acquisition (Yin and Bao, 2006). Many intra-

firm studies have offered evidence that RELQLTY provides a positive impact on KT (Szulanski,

1996; Minbaeva, 2007; Hansen, 1999, 2002; Gupta and Govindarajan, 2000). Surprisingly, in the

context of inter-firm KT although RELQLTY has been extensively debated by the literature,

nevertheless, the empirical studies are still very limited. In the context of inter-firm KT,

RELQLTY has a significant positive impact on knowledge acquisition (Lin, 2005), and effective

interpersonal communication between supplier and recipient through visits and meetings has

significantly facilitated cross border KT (Bresman et al., 1999). As opposed to quality

interaction, the JV literature has established that conflicts in general may lead to instability and

poor JVs performance (Killing, 1983; Lane and Beamish, 1990) thus minimizing the flow of

information and knowledge (Fiol and Lyles, 1985; Lane and Beamish, 1990; Parkhe, 1993).

H1: Relationship quality between IJV partners is positively related to a higher degree of inter-

firm technology transfer.

MUTUAL TRUST Inter-partner mutual trust (MT) is critical in the collaborative relationship as MT 1) develops a

sense of openness and shared understanding between partners (Dyer and Nobeoka, 2000), 2)

facilitates greater accessibility to the alliance knowledge and knowledge acquisition (Inkpen,

1998a, 2000), 3) creates opportunities for a mutual inter-organizational learning; when partners

become more open and committed in sharing their knowledge and competencies (Inkpen and

Dinur, 1998; Inkpen and Beamish, 1997), 4) reduces the partners’ protectiveness of their

knowledge and promotes free exchange of information between partners (Inkpen, 2000), 5)

creates higher propensity of inter-partner learning as knowledge is more accessible (Hamel,

1991; Doz, and Hamel, 1998; Inkpen, 2000), 6) reduces the fear of opportunistic behaviors of the

learning partner and promotes greater transparency between the exchange processes (Gulati,

1995), 7) promotes knowledge acquisition (Glaister et al., 2003; Inkpen and Tsang, 2005), and 8)

fosters norms of reciprocity (Nahapiet and Ghoshal, 1998).



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High degree of MT in a collaborative relationship strongly indicates that the partners 1) accept

each other as an ally not as competitor (Powell et al., 1996), 2) signify their commitment by not

taking advantage on the other partner’s weaknesses or vulnerabilities (Steensma and Lyles,

2000), and 3) are more willing to provide information to knowledge learning and sharing;

particularly when partners are less suspicious of the other partner’s opportunistic behaviors

(Child and Faulkner, 1998). Trust develops potential access to the alliance’s valuable resources

and willingness to solve problems through mutual problem-solving (Uzzi, 1997). A collaborative

alliance with low degree of trust will reduce the partner openness (transparency) in knowledge

sharing and learning thus limiting the information’s accuracy, comprehensiveness and timeliness

(Zand, 1972; Kale et al., 2000) as the partners are reluctant to face the risk associated with

sharing more valuable information (Hedlund, 1994). A lack of inter-partner trust may generate

inter-firm conflicts, increase opportunistic behaviors; which eventually erode mutual

understanding (Tsang et al., 2004). The inter-partner trust also acts as an ongoing social control

mechanism and risk reduction device as it determines the extent of knowledge exchange in IJVs

and the efficiency with which it is exchanged (Lane et al., 2001). Trust is also crucial in alliances

and IJVs as no contracts/agreements can cover all the variations and conditions that can occur

(Dhanaraj et al., 2004).

Trust between partners may help to develop mutual partner understanding, increase knowledge

accessibility (Inkpen, 1998a) and contribute to a freer and greater exchange of information and

know-how between alliances partners (Kale et al., 2000). Past studies on MT are mainly

theoretical (Nielsen, 2007). Nevertheless, few empirical studies have suggested that MT between

alliance partners as an important determinant in the alliance performance in terms of 1) reducing

search cost, increasing efficiency, enhancing benefits and alliance’s performance (Gulati, 1995),

2) increasing alliance’s cooperation, improving flexibility, reducing coordinating activities cost

and increasing KT and learning (Smith et al., 1995) and, 3) reducing negotiating costs in

alliances and enhancing alliance performance (Zaheer et al., 1998). Few strategic alliance studies

on IJVs have also offered empirical evidence. In general, MT has a significant positive

relationships with 1) the alliance performance (Nielsen, 2007), 2) learning in strategic alliance

(Kale et al., 2000), 3) international cooperative ventures (ICVs) performance (Luo, 2001), and 4)

tacit knowledge transfer in IJVs (Dhanaraj et al., 2004). On the other hand, Pak and Park’s

(2004) findings suggest that inter-firm conflicts erode MT when the transferring partners tend to



150

be more protective of their knowledge and unwilling to share knowledge due to suspicion of

opportunistic behaviors of the recipient partners.

H2: Mutual Trust between IJV partners is positively related to a higher degree of inter-firm

technology transfer.

























151

INSTRUMENT AND MEASUREMENT The main research instrument in this study is the questionnaire. Building on the previous KT and


to suit the context of the study: inter-firm TT. Except for degree of technology transfer

(TTDEG), all the variables are measured using ten-point Likert Scale (1 = strongly disagree to 10

= strongly agree). For TTDEG, this variable is measured using ten-point Likert Scale (1 = very

low transfer to 10 = substantial transfer). The ten-point Likert Scale was selected because 1) the

wider distribution of scores around the mean provides more discriminating power, 2) it is easy to

establish covariance between two variables with greater dispersion around their means, 3) it has

been well established in academic and industry research, and 4) from a model development

perspective, a ten-point scale is more preferred (Allen and Rao, 2000).










knowledge. The Cronbach Alphas for TCTDEG and EXPDEG were 0.96 and 0.97 respectively.

The results of Cronbach Alpha were quite similar to that of Hau and Evangelista (2007) and Yin

and Bao (2006).



152

RELATIONSHIP QUALITY This study operationalizes RELQLTY in terms of relationship informality, openness and

communication density; which increases the exchange of information, technology and

knowledge between partners (Gupta, 1987; Gupta and Govindarajan, 2000; Lin, 2005). To

capture this construct, this study employs a four (4) items scale developed by Lin (2005) in

which the items are designed to capture 1) the local JV partner efforts in maintaining frequent

interaction with the foreign JV partner, 2) the adequacy of the interaction, 3) the local JV partner

effort in maintaining an amiable climate for the interaction, and 4) the local JV partner’s effort in

ensuring that interaction is a constructive mode. As RELQLTY (informality, openness and

communication density) is explained by the relationship strength, this study adopts a seven (7)

items scale adopted from Cavusgil et al. (2003), Chua (2002), and Fryxell et al. (2002).

RELQLTY is measured in terms of 1) the desire to maintain a good social relationship by the

foreign and local JV partners, 2) the foreign and local JV partners can freely talk to each other

about difficulties (in general) they encounter with JV and they know that their concern will be

addressed, 3) the foreign and local JV partners are confident in each other’s capabilities, 4) the

foreign and local JV partners are free to share their ideas, feelings and hope with each other, 5)

the foreign and local JV partners are supportive of each other and they respond constructively

and caringly to their partner’s concern about the JV, 6) the foreign and local JV partners share a

sense of togetherness, and 7) the foreign and local JV partners share organizational myths and

stories with each other. The Cronbach Alpha for RELQLTY was slightly higher (0.96) than that

of (Lin, 2005).

MUTUAL TRUST This study employs a six (6) items scale developed by Dhanaraj et al. (2004) and five (5) item

scales from Kale et al. (2000) to measure MT between JV partners which include statements

whether 1) the JV partners can understand each other well and quickly, 2) the JV partners have

the feeling of being mislead, 3) the JV partners make damaging demands, 4) the stronger JV

partner pursues its interest at all costs, 5) the informal agreement are perceived as significant as

formal agreement, and 6) the JV partners take advantage on the weakness of the other party



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(Dhanaraj et al. , 2004). In addition, five (5) more items are adopted from Kale et al. (2000) with

respect to statements whether the JV is characterized by close interaction, mutual respect, mutual

trust, personal friendship, and reciprocity between the JV partners at multiple levels. The

Cronbach Alpha was lower slightly lower (0.88) than Dhanaraj et al. (2004).



Table 1: Descriptive Statistics and Correlation Matrix. -------------------------------------------------------------------------------------------------------------------------------------------- Variable Mean SD 1 2 3 -------------------------------------------------------------------------------------------------------------------------------------------- RELQLTY 5.91 1.45 1.000 MT 7.07 1.35 0.449** 1.000 TCTDEG 6.29 1.31 0.481** 0.720** 1.000 RELQLTY 6.47 1.34 1.000 MT 7.07 1.35 0.564** 1.000 EXPDEG 6.29 1.31 0.512** 0.721** 1.000 -------------------------------------------------------------------------------------------------------------------------------------------- n = 128, * p < 0.05, ** p < 0.01

From Table 1 above, there are clearly some associations between independent variables. For all


ranged between 0.481 - 0.491 and the VIF values were between 2.077 - 2.177. Both relationship

quality (RELQLTY) and mutual trust (MT) were strongly correlated with degree of tacit

knowledge (TCTDEG) (p < 0.01). It is also noted that RELQLTY and MT had positive signs

indicating consistency with the theoretical arguments in the literature. The correlation results

also indicated that both RELQLTY and MT had recorded strong correlations with EXPDEG (p <

0.01).

Using multiple regression analysis, the effects of RELQLTY and MT on two dimensions of

degree of technology transfer (TCTDEG and EXPDEG) were estimated. As shown in Table 2

below, relationship quality as a critical component of relationship characteristics had significant

effect on both degrees of tacit and explicit knowledge in inter-firm TT. The regression results



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indicated that RELQLTY in Model 2 had a stronger positive significant effect on degree of

explicit knowledge (p < 0.05, Beta value = 0.406) as compared to its effect on degree of tacit

knowledge in Model 1 (p < 0.05, Beta value = 0.214). This was also evident by the results of the

adjusted R-squared in Model 1 and Model 2 (0.254 and 0.342 respectively) and the F statistics

(8.028 and 8.804 respectively). On the corresponding p values, both results were statistically

significance (p = 0.001). From the regression results H1 is supported thus indicating that the

greater degree of RELQLTY, which is directly reflected on close, intimate, and informal

relationship between IJV partners, contributes to a higher degree of both tacit and explicit

knowledge in inter-firm TT. Interestingly, the effect of RELQLTY on degree of explicit

knowledge is stronger than its effect on tacit knowledge. The finding suggests that even if

frequent and effective interactions, close relationship, and intimacy of relationship do exist

between IJV partners, however, transferring explicit knowledge is equally challenging simply

because although explicit knowledge has been explicitly standardized in blueprints, manuals,

procedures and instructions by the transferring partner, quite often, it still consists of a highly

tacit, complex and specific knowledge; which is difficult to be articulated and understood

without the existence of ‘teacher-student’ relationship.

Table 2: Results of group Regression Analysisª -------------------------------------------------------------------------------------------------------------------------------------------- Variable Degree of Tacit Knowledge Degree of Explicit Knowledge (Model 1) (Model 2) -------------------------------------------------------------------------------------------------------------------------------------------- (Constant) 8.028*** 8.804*** Relationship Quality 0.214* 0.406** Mutual Trust 0.327** 0.220* R-squared 0.254 0.342 Adjusted R-squared 0.242 0.331 F 8.028*** 8.804*** -------------------------------------------------------------------------------------------------------------------------------------------- ª Cell entries are standardised coefficient estimates (n = 128) † p < 0.10, * p < 0.05, ** p < 0.01, *** p < 0.001

Consistent with the study’s prediction, mutual trust which has strong theoretical foundation

showed a similar strong significant effects on both degrees of tacit and explicit knowledge (p <



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0.01 and p < 0.05 respectively). As compared to the effect of MT on EXPDEG (p < 0.05) in

Model 2, MT recorded slightly stronger and better effect on TCTDEG in Model 1 (p < 0.01)

indicating a higher degree of tacit knowledge will likely be transferred to recipient partner as

compared to explicit knowledge. This suggests that tacit knowledge in IJVs is more accessible

by the recipient partner when IJV partners trust each other where no suspicious feelings exist

between them. The results further suggest that when the learning partner adopts the collaborative

learning intent, the transferring partner will become less protective of their technologies and is

more likely to be transparent in sharing tacit knowledge than explicit knowledge. Thus, based on

the two regression results for Model 1 and Model 2 above, H2 is supported.

DISCUSSION AND CONCLUSION Building on the underlying integrated KBV and OL perspectives, this study has bridged the

literature gaps by providing empirical evidence on the effects of two critical elements of

relationship characteristics: relationship quality (RELQLTY) and mutual trust (MT) on two

distinct dimensions of degree of inter-firm technology transfer: degree of tacit (TCTDEG) and

explicit (EXPDEG) knowledge in IJVs using the Malaysian sample. The results are consistent

with recent propositions made by the literature; where knowledge attributes are not the only

important determinant of knowledge transfer (Minbaeva, 2007; Szulanski and Cappetta, 2003).

The presence of strong impact of RELQLTY on both degrees of technology transfer (TCTDEG

and EXPDEG) suggests that the greater the quality of relationship the higher the degree of

technology transfer within the JVs. The results further suggest that the quality of relationship in

JVs in terms of frequent and effective interactions between partners, openness, spontaneous, and

adequacy of communication could create potentials for numerous individual exchanges between

the JV partners; particularly when the transfers involve a highly specific technology and tacit

knowledge (Szulanski, 1996; Lin, 2005; Inkpen, 2000). More opportunities for sharing, learning,

and transferring technology will exist if both partners have a higher quality of interactions

(Inkpen, 1998). In the collaborative and constructive interactions, RELQLTY is viewed as an

effective mechanism to facilitate inter-firm technology transfer as both partners are motivated to

invest more resources in the JVs and committed to fulfill their commitments. The result also

supports and confirms recent empirical findings by Lin (2005); where quality interaction was



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found to have a significant effect on knowledge acquisition. In line with the current development

in the literature, the population data also suggests that 1) RELQLTY has become a major

determinant of technology transfer in facilitating a higher degree of technology transfer, and 2)

conflicts between JV partners are likely to lead to JV’s instability thus minimizing the flows of

information.

On the significant effect of mutual trust (MT) on both degrees of technology transfer, the results

were expected given that many theoretical studies have consistently highlighted the importance

of MT between JV partners (Gulati, 1995; Kale et al., 2000; Powell, 1996). The strong

significant positive effects of MT on TCTDEG and EXPDEG suggests that the greater the

mutual trust between JV partners the higher the degree of inter-firm technology transfer to local

partners. Obviously, this is because MT reduces the existence of suspicious feelings between

partners in JVs thus creating opportunities for close interactions, increasing confidence that both

partners would not take advantage on each other, and promoting transparency (Kale et al., 2000).

In the collaborative ventures such as strategic alliances and JVs, a low degree of trust

discourages openness/transparency between partners resulting in a limited accuracy and

comprehensiveness of information and technology (Zand, 1972; Kale et al., 2000).

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Part III

Degree of Technology Transfer and Organizational Performance 9 - Measuring the Effect of Degree of Inter-Firm Technology Transfer on Local Firms’

Performance



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9

Measuring the Effect of Degree of Inter-Firm Technology

Transfer on Local Firms’ Performance

CHAPTER OUTLINE

The inter-firm technology transfers (TT) through international joint ventures (IJVs), among

others, have significantly contributed to a higher degree of local innovation

performance/capabilities, technological capabilities, competitive advantage, organizational

learning effectiveness, productivity, technological development of local industry, and the

economic growth of the host country. Since the focus of inter-firm TT has shifted its focus to

degree of technology transfer, the organizations in the developing countries are attempting to

assess the significant role of tacit and explicit knowledge in strengthening their corporate and

human resource performances. While both tacit and explicit technologies are viewed as the main

source of competitive advantage of the technology supplier, therefore in order to increase and

further improve their performance, the major challenge faced by the local recipient

organizations in the transfer process is to optimize the potential benefits of learning for both

tacit and explicit knowledge which arise from IJVs.

INTRODUCTION In the context of developing country, technology is viewed as an important catalyst of corporate

success and national economic growth (Millman, 2001). Due to lack of resource capacities such

as a weak research and development (R & D) base, limited investment in R&D, production and

manufacturing capability, weak infrastructure and technological disadvantage (Lado and

Vozikis, 1996; Tepstra and David, 1985), Malaysia like other developing countries, mainly

depend on the multinational corporations (MNCs) as its primary source of technology to enhance

the technological capabilities and competitiveness of local industries (Lee and Tan, 2006). This

is because MNCs own, produce and control the bulk of world technology in which they



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undertake nearly 80% of all private R&D expenditures worldwide (Dunning, 1993). Past studies

have acknowledged the important role of MNCs as the main source of technology; where MNCs

are regarded as the most efficient vehicle for transferring technology and knowledge across

borders especially through international joint ventures (IJVs), (Tihanyi and Roath, 2002; Kogut

and Zander, 1993).

When compared to various forms of strategic alliance such as distribution and supply

agreements, research and development partnerships or technical and management contract, IJVs

are considered as the most efficient formal mechanism for technology transfer (TT) to occur

through inter-partner learning between foreign MNCs and local firms (Kogut and Zander, 1993;

Inkpen 1998a, 2000). IJVs are also viewed as the most efficient mode to transfer technology or

knowledge which is organizationally embedded and difficult to transfer through licensing

agreements (Kogut, 1988; Mowery, Oxley and Silverman, 1996). IJVs provide both MNCs and

local partners an appropriate avenue to facilitate the transfer of organizational knowledge,

particularly for knowledge which is hard to be transferred without the setting up of a JV, such as

institutional and cultural knowledge (Harrigan, 1984). Since JVs is one of the formal and

externalized mechanisms of TT which could directly affect organizational performance,

therefore the next intriguing issue is on the extent of degree of technology transfer (TTDEG) in

affecting the performance of local firms; specifically on how TTDEG could have significantly

influenced the corporate and human resource/competencies performance of local firms. Based on

the underlying knowledge-based view (KBV) and organizational learning (OL) perspectives, this

study expects to fill in the literature gaps by empirically examining the effects of two distinct

degrees of technology transfer: degrees of tacit (TCTDEG) and explicit (EXPDEG) on two

dimensions of local firms’ performance (LFP): corporate and human resource performances

using data generated from JV companies formed in Malaysia.

DEGREE OF TECHNOLOGY TRANSFER The current TT issue in IJVs revolves around the extent of degree of technologies that are

transferred (TTDEG) by the suppliers to recipient partners (Pak and Park, 2004; Minbaeva,



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2007). The question is no longer whether or not the MNCs are transferring technology to local

firms instead the focus in the literature has shifted to questions on 1) the level (sophistication) of

the transferred technology, and 2) the stage where the transfer process has reached (Lai and

Narayanan, 1997; Narayanan and Lai, 2000). Except for Pak and Park (2004) and Minbaeva

(2007), not many studies in both intra and inter-firm TT have focused on TTDEG as independent

or dependent variable. In general, bulk of the studies has focused more on technological

knowledge and knowledge acquisition ‘per se’ as the outcomes (dependant variables). For

example, the technology transfer, knowledge transfer (KT) and strategic alliance literature have

extensively examined the relationships between 1) knowledge attributes, source and recipient

and KT success (Cummings et al., 2003), 2) knowledge seekers, knowledge holder and

contextual factors and know-how acquisition (Hau and Evangelista, 2007), 3) IJVs

characteristics and knowledge acquisition (Lyles and Salk, 1996), 4) knowledge actors’

interaction and KT (Bresman et al., 1999), 5) organization motivation, learning capacity,

learning hindrance and KT (Simonin, 2004), 6) absorptive capacity and knowledge learned from

foreign firm (Lane et al., 2001), 7) the IJV characteristics and knowledge acquisition (Tsang et

al., 2004), 8) knowledge antecedents, ambiguity and knowledge transfer (Simonin, 1999a), 9)

learning intent, management control and managerial knowledge acquisition (Lin, 2005), 10)

relational embeddedness and tacit/explicit knowledge acquisition (Dhanaraj et al., 2004) , 11)

overseeing effort, management involvement and knowledge acquisition (Tsang et al., 2002), 12)

the supplier and recipient factors and tacit knowledge acquisition (Yin and Bao, 2006), and 13)

relation-specific determinants, knowledge specific determinants and degree of knowledge

transfer (Pak and Park, 2004).

Although the previous researchers have not specifically dealt with TTDEG as a variable,

however, a number of studies have operationalized degree (amount) of technology transferred to

the recipient firm in terms of the extent of type of technological knowledge that are transferred or

acquired for instance 1) the tacit and explicit marketing knowledge (Hau and Evangalista, 2007),

2) the tacit and explicit knowledge (Dhanaraj et al., 2004; Yin and Bao, 2006), 3) the marketing

know-how (Simonin, 1999b; Wong et al., 2002), 4) the technology in service industries (Grosse,

1996), 5) the knowledge on product development and foreign cultures (Lyles and Salk, 1996), 7)

the technological learning (Lin, 2007), 8) the managerial knowledge (Si and Bruton, 1999; Tsang

2001; Luo and Peng, 1999; Liu and Vince, 1999; Lin, 2005), 9) managerial skills (Wong et al.,



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2002), 10) the technology or manufacturing know how (Lam, 1997; Bresman et al., 1999), 11)

the business environment and product market knowledge (Geppert and Clark, 2003), and 12) the

research and development (Minbaeva, 2007). In the context of inter-firm technological

knowledge transfer in IJVs, only Pak and Park (2004) have directly dealt with degree of

knowledge transfer as the outcome (dependent variable) with respect to the transfer of new

product development and manufacturing skills/techniques.

DEGREE OF TECHNOLOGY TRANSFER AND LOCAL FIRMS’ PERFORMANCE A review of the literature indicates that previous researchers have broadly categorized

technology in terms of its ‘tacit vs. explicit’ dimension (Polanyi, 1967). These two forms of

technology are also referred to as ‘hardware’ and ‘software’ technology. The technology term

has been extensively debated by both hardware and software schools. Based on the definitions

given, the researchers from the hardware school define technology as “the construction and use

of machines, systems, processes or engineering” (Strassman, 1968; Jones, 1970; Hawthorne,

1971; Galbraith, 1972; Goulet, 1989; Lovell, 1998; Reisman, 2005). Generally, hardware

technology corresponds with explicit knowledge; which refers to knowledge underlying

technology that is easily codified, shared, transmitted, retrieved, reused, transferable in formal or

systematic language i.e. production manuals, academic papers, books, technical specifications,

and designs; and is only useful when tacit knowledge enables individuals and organizations to

use it (Techakanont and Terdudomtham, 2004). Software technology, on the other hand,

corresponds with implicit/tacit knowledge underlying technology that is difficult to codify,

communicate, transfer, and is generally exchanged through action, commitments and direct

involvement such as face-to-face communication or on-the-job/apprenticeship type of training

(Ernst and Kim, 2002).

The KBV studies have argued that tacit knowledge, which includes insights, intuitions, hunches,

rule of thumb, gut feeling, personal and organizational skills is difficult to codify; where it can

only be observed through its application and acquired through practice (Nonaka, 1994; Lane et

al., 2001). Thus, tacit knowledge transfer between individuals is slow, costly and uncertain

(Kogut and Zander, 1992). Acquiring tacit knowledge is subject to time-compression

diseconomies; which means to accelerate tacit knowledge learning is very difficult or perhaps



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not even possible no matter how much efforts or resources are invested to acquire them within a

short period of time (Dierickx and Cool, 1989; Lin, 2003). This is because tacit knowledge is

unique to the knowledge owner and not codifiable in formulas or manuals, and cannot be easily

reverse-engineered (Zander and Kogut, 1995). On the other hand, explicit knowledge such as

product technologies, physical distribution methods, and promotion techniques, lies in the

organization’s policies, systems, guidelines and standardized procedures, and could be acquired,

exploited and transferred inter-organizationally in a formal and systematic language (Polanyi,

1967; Nelson and Winter, 1982; Martin and Solomon, 2003). Explicit knowledge is referred to as

“knowledge that could be articulated, codified, shared and transferred in the form of data,

formulae and principles, accessed using verbal communication and written documents through

words and numbers, and is less likely to act as a firm’s competitive advantage” (Kogut and

Zander, 1992; Winter, 1987).

Other theoretical studies have argued that tacit knowledge is hard to formalize, often sticky, not

easily visible, and difficult to communicate, transfer and share between the alliance partners as it

involves 1) intangible factors embedded in the personal beliefs, experiences, and values in an

organization (Inkpen, 1998a, 2000), 2) internal individual processes like experience, reflection,

internalization or individual talents (Nonaka, 1994), and 3) high incremental cost of transferring

knowledge to a specified location in a form usable by a given party (von Hippel, 1994). The OL

literature theoretically deals with organization tacit knowledge from several dimensions for

instance: 1) tacit knowledge as an important key in building the organization’s competitiveness

(Inkpen, 1998a), 2) organizational learning mainly occurs through transfer of tacit knowledge

(Glaister et al. 2003), 3) organization capabilities often involve the acquisition of tacit

knowledge (Makhija and Ganesh, 1997), 4) learning in JV is concentrated on the acquisition of

tacit knowledge such as management skills and marketing know-how knowledge (Lane et al.

2001), and 5) knowledge tacitness determines the accessibility of alliance knowledge acquisition

by partners (Inkpen, 2000). The TT and KT literature have also acknowledged that a substantial

transfer of technology regardless whether tacit or explicit technology will positively 1) lead to a

higher potentials of innovation performance/capabilities (Guan et al., 2006; Kotabe et al., 2007)),

2) increase technological capabilities (Kumar et al., 1999; Madanmohan et al., 2004), 3) enhance

competitive advantage (Liao and Hu, 2007; Rodriguez and Rodriguez, 2005), 4) enhance

organizational learning effectiveness (Inkpen, 2000; Inkpen and Dinur, 1998), 5) improve



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productivity (Caves, 1974; Xu, 2000; Liu and Wang, 2003), 6) increase technological

development of local industry (Markusen and Venables, 1999), and 7) improve the economic

growth of the host country (Blomstrom, 1990).

Most of the studies on strategic alliance operationalize performance as either the JV’s or MNCs’

subsidiary performance. A review of literature reveals that most of the empirical studies on inter-

firm technology and knowledge transfer in strategic alliance, particularly on IJVs, are limiting

their focus on the performance of the IJVs (Lyles and Salk, 1996; Lane et al., 2001; Tsang et al.,

2004; Dhanaraj et al., 2004; Steensma and Lyles, 2000). In the context of intra-firm knowledge

transfer many studies concentrate on the performance of the MNCs’ subsidiary in the host

countries (Chen, 1996; Chung, 2001; Ofer & Potterovich, 2000, Cui et al., 2006; Lin, 2003).

Intra and inter-firm empirical studies on knowledge transfer and acquisition have established that

knowledge transfer and acquisition have a significant positive effect on human resource,

business and general performance (Lyles and Salk, 1996), operational cost, operational

efficiency, employee productivity, business volume, market share, market penetration, product

quality, customer service, and customer satisfaction (Lane et al., 2001; Tsang et al., 2004;

Dhanaraj et al., 2004; Cui et al., 2006). On the local firms’ performance (LFP), tacit knowledge

acquisition is found to have a significant positive effect on the recipient firms’ performance in

terms of increasing their productivity, revenue and market share (Yin and Bao, 2006). Based on

the empirical studies, this study proposes the following hypotheses:

H1: A higher degree of tacit and explicit knowledge in inter-firm technology transfer is positively

related to a higher degree of local firms’ corporate performance.

H2: A higher degree of tacit and explicit knowledge in inter-firm technology transfer is positively

related to a higher degree of local firms’ human resources performance.







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The main research instrument in this study is the questionnaire. Building on the previous KT and












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LOCAL FIRMS’ PERFORMANCE (LFP) This study operationalizes LFP from two dimensions of performances: 1) corporate performance

(CPERF), and 2) human resource (competencies) performance (HRPERF). Based on literature

review, the qualitative (objective) measures of companies’ performance are the most practical

and ideal measurement of performance. However, the concrete financial figures are neither

available nor reliable (Lyles and Barden, 2000; Tsang et al., 2004). Past studies have shown a

positive relationship between objective and perceptual (subjective) measures of firm’s

performance (Lyles and Salk, 1996; Dess and Robinson, 1984; Geringer and Hebert, 1989,

1991). Thus, this study applies subjective measures to measure LFP based on IJV’s top

management assessments using “a multi-dimensional performance indicators”. The CPERF, as

the first dimension of LFP, is measured by a four (4) items scale measuring business volume,

market share, planned goals and profits. For HRPERF, as the second dimension of LFP, four (4)

items are used to measure product/service quality, employees’ productivity, managerial

techniques/skills and operational efficiency (Tsang et al., 2004; Yin and Bao, 2006; Lane et al.,

2001; Lyles and Salk, 1996). The Cronbach Alphas for CPERF and HRPERF were 0.926 and

0.97 respectively. The results of Cronbach Alpha were well above of Lyles and Salk (1996).




operationalizes TTDEG as the transfer of technological knowledge from two dimensions: 1) tacit

knowledge (TCTDEG) in terms of new product/service development, managerial systems and

practice, process designs and new marketing expertise, and 2) explicit knowledge (EXPDEG) in

terms of manufacturing/service techniques/skills, promotion techniques/skills, distribution know-

how, and purchasing know-how. The respondents are asked to evaluate TTDEG from MNCs to

local firms in terms of tacit and explicit dimensions of technological knowledge. The Cronbach

Alphas for TCTDEG and EXPDEG were 0.96 and 0.97 respectively. The results of Cronbach

Alpha were quite similar to that of Hau and Evangelista (2007) and Yin and Bao (2006).



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Table 1: Descriptive Statistics and Correlation Matrix. -------------------------------------------------------------------------------------------------------------------------------------------- Variable Mean SD 1 2 3 -------------------------------------------------------------------------------------------------------------------------------------------- TCTDEG 5.72 1.74 1.000 EXPDEG 5.91 1.45 0.747** 1.000 CPERF 6.47 1.34 0.597** 0.660** 1.000 TCTDEG 6.08 1.48 1.000 EXPDEG 5.91 1.45 0.747** 1.000 HRPERF 6.47 1.34 0.639** 0.740** 1.000 -------------------------------------------------------------------------------------------------------------------------------------------- n = 128, * p < 0.05, ** p < 0.01

From Table 1 above, there are clearly some associations between independent variables. For all


ranged between 0.442 - 0.444 and the VIF values were between 2.262 - 2.377. Both degrees of

tacit (TCTDEG) and explicit (EXPDEG) knowledge were strongly correlated with corporate

performance (CPERF) (p < 0.01). It is also noted that TCTDEG and EXPDEG had positive signs

indicating consistency with the theoretical arguments in the literature. The correlation results

also indicated that both TCTDEG and EXPDEG had recorded strong correlations with HRPERF

(p < 0.01).

Using multiple regression analysis, the effects of TCTDEG and EXPDEG on two dimensions of

performance (CPERF and HRPERF) were estimated. As shown in Table 2 below, TCTDEG as a

critical component of degree of technology transfer had significant effect on both corporate and

human resource performances in inter-firm TT. The regression results indicated that TCTDEG in

Model 1 had a stronger positive significant effect on corporate performance (p < 0.001, Beta

value = 0.485) as compared to its effect on human resource performance in Model 2 (p < 0.05,

Beta value = 0.196). This was also evident by the results of the adjusted R-squared in Model 1

and Model 2 (0.452 and 0.557 respectively) and the F statistics (53.309 and 80.836 respectively).

On the corresponding p values, both results were statistically significance (p = 0.001). From the



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regression results H1 is supported thus indicating that greater degrees of tacit and explicit

knowledge transfer significantly contributes to a higher corporate and human resource

performance in inter-firm TT through IJVs. Interestingly, the effect of TCTDEG on corporate

performance is stronger than its effect on HRPERF. The finding suggests that technological

knowledge on new product/service development, managerial systems/practice, process designs

and new marketing expertise which are found to be more tacit, complex and firm-specific could

significantly generate a higher degree of corporate performance specifically the business volume,

market share, planned goals and profits of local recipient partners.

Table 2: Results of group Regression Analysisª -------------------------------------------------------------------------------------------------------------------------------------------- Variable Corporate Performance Human Resource Performance (Model 1) (Model 2) -------------------------------------------------------------------------------------------------------------------------------------------- (Constant) 1.366*** 8.804*** TCTDEG 0.485*** 0.196* EXPDEG 0.234** 0.593*** R-squared 0.460 0.564 Adjusted R-squared 0.452 0.557 F 53.309*** 80.836*** -------------------------------------------------------------------------------------------------------------------------------------------- ª Cell entries are standardised coefficient estimates (n = 128) † p < 0.10, * p < 0.05, ** p < 0.01, *** p < 0.001

Consistent with the study’s prediction, EXPDEG which has strong theoretical foundation showed

a similar strong significant effects on both corporate and human resource performances (p < 0.01

and p < 0.001 respectively). As compared to the effect of EXPDEG on CPERF (p < 0.01, Beta

value = 0.234) in Model 1, EXPDEG recorded an impressive stronger and better effect on

HRPERF in Model 2 (p < 0.001, Beta value = 0.593) indicating a higher degree of explicit

knowledge will likely to enhance more and better human resource performance as compared to

corporate performance. This suggests that technological knowledge on product/service quality,

employees’ productivity, managerial techniques/skills and operational efficiency; which are less

tacit, easier to articulate, and transferrable are more likely to generate a higher degree of human

resource performance specifically the product/service quality, employees’ productivity,



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managerial techniques/skills and operational efficiency. Thus, based on the two regression

results for Model 1 and Model 2 above, H2 is supported.

DISCUSSION AND CONCLUSION Building on the underlying KBV and OL perspectives, this study has bridged the literature gaps

by providing empirical evidence on the effects of two distinct degrees of technology transfer:

degrees of tacit and explicit knowledge on two dimensions of performance: corporate and human

resource performances using the Malaysia sample. The results suggest that the higher the degree

of technology transfer (both tacit and explicit knowledge) the greater the corporate and human

resource performance of local recipient firms. The results are consistent with the findings in the

previous literature which found that both tacit (TCTDEG) and explicit (EXPDEG) knowledge

are highly capable in increasing both corporate and human resource performances (Lane et al.,

2001; Lyles and Salk, 1996; Yin and Bao, 2006; Tsang et al., 2004) suggesting that a higher

degree of TCTDEG and EXPDEG contributes to high improvement and increment of 1) the local

firms’ business volume, market share, planned goals, and profit, and 2) the local firms’

product/service quality, employees’ productivity, managerial techniques/skills, and operational

efficiency (Dhanaraj et al., 2004). Interestingly, the results for significant effect of TCTDEG on

CPERF have indeed supported the theoretical argument which argued that since tacit knowledge

for new product/service development, managerial systems and practice, process designs and new

marketing expertise is organizationally embedded in the interdependent systems, expertise and

complex individuals and groups’ routines of the technology suppliers; therefore these strategic

valuable resources and competencies if transferred will significantly lead to increase of local

firms’ competitiveness, technological capabilities, organizational learning effectiveness,

productivity and create potentials for innovations (Inkpen, 2000; Inkpen and Dinur, 1998; Xu,

2000; Liu and Wang, 2003). The results have expanded the general findings by Dhanaraj et al.

(2004); where TCTDEG had a significant effect on IJV performance. The significant role of

degree of explicit knowledge (EXPDEG) on human resource performance is explained by its

explicit nature in manufacturing/service techniques/skills, promotion techniques/skills,

distribution know-how, and purchasing know-how; which is frequently standardized by the

technology supplier in the form of standard manuals, procedures, and blueprints thus making it



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less ‘stickier’ than tacit knowledge, easier to articulate and understand, and more easy to share,

communicate and transfer.

Comparable to other formal market channels such as direct exporting of capital goods, foreign

direct investments (FDIs), and licensing agreements, this study also provides empirical evidence

that the IJVs formed between the foreign MNCs and local companies indeed have significant

positive effects on performance of local companies in Malaysia. The results, however, are not in

line with a recent findings by Malairaja and Zawdie (2004); where their findings suggested that

the TT through JVs in Malaysia have not succeeded in developing the indigenous innovation

capabilities. This is particularly evident with the significant effect of degree of technology

transfer (both TCTDEG and EXPDEG) on human resource performance. The significant effect

of TCTDEG and EXPDEG on HRPERF in improving product quality and employees’

productivity will positively boost local innovation capabilities when the local employees’

absorptive capacity, which is based on the employees’ prior related knowledge and intensity of

effort, is capable of absorbing, assimilating, and improving new technologies extracted from JVs

(Cohen and Levintahl, 1990; Kim, 1998). The results are consistent with the previous TT

literature findings where substantial TT has positively led to a higher potential of innovation

performance/capabilities (Guan et al., 2006; Kotabe et al., 2007), and increase the technological

capabilities and competitiveness (Madanmohan et al., 2004; Liao and Hu, 2007; Rodriguez and

Rodriguez, 2005). The results has further extended the KT literature, in particular the general

findings by Minbeava (2007), Pak and Park (2004), and Hau and Evangelista (2007) on the

overall significant effects of knowledge transfer and acquisition on JV’s performance. Another

critical finding of this study is that the main objective of the local companies participating in JVs

was largely driven by their efforts to improve the product/service quality, employees’

productivity, managerial techniques, and operational efficiency. The results in this respect

support the findings by Lyles and Salk (1996) and Lane et al. (2001); where knowledge

acquisition from foreign parent has significantly affected business and human resource

performance. The results are also consistent with OL literature which views technology recipient

organizations as a learning system and technology transfer as an organizational learning process

(Daghfous, 2004; Bapuji and Crossan, 2004).



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Part IV

The Significant Influence of Moderating Factors

10 - MNCs’ Size, Technology Recipients’ Characteristics and Degree of Inter-Firm

Technology Transfer

11 - Age of Joint Venture, Degree of Inter- Firm Technology Transfer and Local Firms’

Performance

12 - MNCs’ Country of Origin, Degree Technology Transfer and Firms’ Performance

13 - MNCs’ Equity Ownership, Degree of Technology Transfer and Firms’ Performance



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10

MNCs’ Size, Technology Recipients’ Characteristics

and Degree of Inter-Firm Technology Transfer

CHAPTER OUTLINE

Following the recent approach in the strategic alliance literature and based on the underlying

knowledge-based view (KBV) and organizational learning (OL) perspectives, this work fills in the

literature gaps by specifically examining the effect of size of MNCs (large vs. medium/small

MNCs) as a moderating variable in the relationships between the technology recipients’

characteristics (TRCHAR) and two distinct dimensions of degree of technology transfer

(TTDEG): degrees of tacit (TCTDEG) and explicit (EXPDEG) knowledge. The primary objective

is to provide new insights and information on the boundary conditions of TRCHAR-TTDEG

relationship.

INTRODUCTION Many studies from knowledge-based view (KBV) perspective have established that MNCs tend

to be more protective of their advance technology, knowledge and competencies which

embodied in products, processes and management because these strategic valuable resources and

competencies are their main sources of competitive advantage (Porter, 1985; Barney, 1991;

Peteraf, 1993; Wernerfelt, 1984; Pralahad and Hamel). On the other hand, organizational

learning (OL) perspective studies have suggested that technology and knowledge are more likely

to be protected by the supplier when the recipients are opportunistic in the collaborative

relationship (Inkpen, 1998a; Inkpen and Dinur, 1998; Child and Faulkner, 1998). Thus, in the

context of inter-firm technology transfer (TT) through international joint ventures (IJVs), the

remaining question is on the extent of TT by foreign multinational corporations (MNCs);

especially when transferring their advance technology to local recipient partner (Narayanan and



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Lai, 2000). While realizing that technologies, knowledge, and competencies are the supplier’s

main source of competitive advantage, the current TT issue in IJVs revolves around the extent of

degree of technologies (TTDEG) that are transferred by the suppliers to recipient partners in

terms of tacit knowledge (new product/service development, managerial systems and practice,

process designs and new marketing expertise), and explicit knowledge (manufacturing/service

techniques/skills, promotion techniques/skills, distribution know-how, and purchasing know-

how) (Madanmohan et al., 2004). This is because from the recipient’s perspective, TT success is

not merely possessing the ability to operate, maintain or repair the machineries at the production

level (transmission) but it also includes the ability to learn, acquire, absorb and apply new

external technologies and knowledge that are organizationally embedded in product materials,

physical assets, processes and production, and management capabilities (absorption) (Davenport

and Prusak, 1998, 2000).

Technology recipient characteristics (TRCHAR) have increasingly become the dominant factors

in determining the success or failure of inter-firm technology transfer within IJVs (Inkpen, 2000;

Pak and Park, 2004; Minbaeva, 2007; Yin and Bao, 2006). Among the TRCHAR factors that

have been identified by literature to influence TT and knowledge KT are absorptive capacity

(Cohen and Levinthal, 1990; Hamel, 1991; Lyles and Salk, 1996; Mowery et al., 1996; Lane and

Lubatkin, 1998; Lane et al., 2001; Gupta and Govindarajan, 2000, Minbaeva et al., 2003,

Minbaeva, 2007; Pak and Park, 2004), experience (Simonin, 1999a, 1999b), prior knowledge and

experience (Inkpen, 1998a, 1998b, 2000; Tsang, 2001), knowledge relatedness (Inkpen, 2000),

learning capacity (Makhjia and Ganesh, 1997; Parise and Henderson, 2001), receptivity (Hamel,

1991; Baughn et al., 1997), learning intent or objectives (Beamish and Berdrow, 2003; Hamel,

1991; Simonin, 2004; Inkpen and Beamish, 1997; Baughn et al., 1997; Inkpen, 1998a; Mohr and

Sengupta, 2002), managerial belief rigidity (Inkpen and Crossan, 1995), and recipient

collaborativeness, readiness and method comprehensiveness (Yin and Bao, 2006). Although

many studies have empirically confirmed the significant effect of knowledge transfer

determinants on knowledge transfer outcomes, nevertheless, the effects of TRCHAR on TTDEG

in inter-firm TT could possibly have been moderated by other established factors such as size of

MNCs, age of JV, MNCs’ country of origin, and MNCs’ types of industry. In other words the

variations in TTDEG could have been significantly influenced by these variables.



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From a review of literature, a bulk of studies on knowledge transfer and acquisition in strategic

alliance (Szulanski, 1996; Gupta and Govindarajan, 2000; Minbaeva, 2007; Pak and Park, 2004;

Lin, 2005; Wang and Nicholas, 2005; Liao and Hu, 2007; Bresman et al., 1999; Mowery et al.,

1996; Lyles and Salk, 1996; Kogut and Zander, 1993; Grosse, 1996: Dhanaraj et al., 2004; Hau

and Evangelista, 2007) have not tested the impact (strength) of moderating variables on the

linear (direct) relationships between technology recipient characteristics and technology or

knowledge transfer. Nevertheless, a number of studies on inter-firm KT and knowledge

acquisition in strategic alliance and JVs have acknowledged the important role of moderating

variables such as: 1) collaborative know-how, learning capacity and alliance duration (Simonin,

1999a), 2) collaborative experience and firm size (Simonin, 1999b), 3) organizational culture,

firm size, alliance form, and competitive regime (Simonin, 2004), 4) age of JV (Mohr and

Sengupta, 2002), 5) alliance origin and alliance experience (Yin and Bao, 2006), 6) age of JV

(Tsang et al., 2004), and 7) environmental challenge (Hau and Evangelista, 2007). Following the

recent approach in the strategic alliance literature (Simonin, 1999a, 1999b, 2004; Yin and Bao,

2006; Tsang et al., 2004) and based on the underlying knowledge-based view (KBV) and

organizational learning (OL) perspectives, this work fills in the literature gaps by specifically

examining the effect of size of MNCs (large vs. medium/small MNCs) as a moderating variable

in the relationships between the TRCHAR and two distinct dimensions of degree of technology

transfer: degrees of tacit (TCTDEG) and explicit (EXPDEG) knowledge. The primary objective

is to provide new insights and information on the boundary conditions of TRCHAR-TTDEG

relationship (Aguinis, 2004).

SIZE OF MNCs, TECHNOLOGY RECIPIENTS’ CHARACTERISTICS AND DEGREE OF TECHNOLOGY TRANSFER As TT involves the process of transmission and absorption of knowledge (Davenport and Prusak,

1998, 2000), the recipient firm’s ability to absorb the knowledge transferred largely depends on

the degree of their absorptive capacity (ACAP). Absorptive capacity (ACAP) is the firm’s ability

to recognize, assimilate, and apply to commercial ends the value of new external information

(Cohen and Levinthal, 1990). Prior related knowledge, as the important element of ACAP, is

critical for organizations to assimilate and exploit new knowledge. By possessing sufficient prior



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related knowledge, organizations are able to have an adequate ability to absorb new external

knowledge (Cohen and Levinthal, 1990). OL literature suggests that another critical element of

ACAP is the recipient’s firm intensity of efforts. Intensity of effort is reflected on the amount of

energy expended by organizational members to solve problems through organization members

directing their considerable time and effort in learning how to solve problems before attempting

to solve complex problems (Kim, 1998).

Another important dimension of TRCHAR is recipient collaborative (RCOL). RCOL is closely

related to the recipient’s learning intent (competitive vs. collaborative intent). The technology

recipient firm’s willingness to establish a mutually beneficial and collaborative relationship

requires the recipient firm’s honest intention to create common benefits for both JV partners (Yin

and Bao, 2006). Studies on inter-organizational learning have suggested that

cooperative/collective learning encourages the alliance partners to work together by sharing their

knowledge, benefit each other’s complementarities thus providing mutual opportunities to

extract potential synergies between their respective competencies (Doz, 1996; Geringer, 1991).

Collaborative learning creates an access to the partner’s knowledge and skills such as product

and process technology, organizational skills, and knowledge about new environments (Inkpen,

1995a). In the collaborative learning environment; where the recipient’s learning intent is crucial,

the transferring partner tends to be more open or transparent in terms of sharing and transferring

knowledge to the acquiring partner as it involves mutual exchange of valuable knowledge

(Inkpen, 2000). RCOL, which is reflected on the partner’s learning intent, determines the degree

of openness or transparency in knowledge sharing and knowledge transfer (Inkpen, 2000).

Past studies have affirmed that MNCSIZE has a significant effect on the intensity of strategic

partnering and technological cooperation (Hagedoorn and Schakenraad, 1994), propensity of the

firm to develop competitive advantage and achieve the above-average performance (Porter,

1980), organizational learning (Marquardt and Reynolds, 1994), motives for alliance formation

(Glaister and Buckley, 1996), and asymmetric bargaining power between partners in the alliance

relationship (Khanna et al., 1998). Generally, as compared to large firms, small firms do not have

adequate resources and expertise to undertake inter-firm TT and are more likely to transfer

technology through licensing agreements (Stobaugh, 1998). Empirical studies have found that

MNCSIZE has a significant moderating effect on the relationships 1) between experience, know-

how and collaborative relationship, and 2) between knowledge tacitness and ambiguity



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(Simonin, 1997; Simonin, 2004; Dhanaraj et al., 2004; Bresman et al. 1999). However, other

studies have found MNCSIZE did not moderate 1) the knowledge-performance relationship, and

absorptive capacity, and 2) learning and IJV performance relationship (Tsang et al., 2004; Lane

et al., 2001).

H1: Size of MNCs moderates the relationship between technology recipient characteristics and

degree of tacit knowledge in IJVs.

H2: Size of MNCs moderates the relationship between technology recipient characteristics and

degree of explicit knowledge in IJVs.





















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(Mohammed, 1998; Rozhan, Rohayu and Rasidah, 2001).

From 145 responses only 128 questionnaires were usable and 17 questionnaires were returned

blank, returned incomplete, or replied but unable to participate in the study. The main research

instrument in this study is the questionnaire. Building on the previous TT and KT studies, the

questionnaire adopts a multi-item scales which have been modified accordingly to suit the

context of the study: inter-firm TT. Except for degree of technology transfer (TTDEG), all the

variables are measured using ten-point Likert Scale (1 = strongly disagree to 10 = strongly

agree). For TTDEG, this variable is measured using ten-point Likert Scale (1 = very low transfer

to 10 = substantial transfer). The ten-point Likert Scale was selected because 1) the wider

distribution of scores around the mean provides more discriminating power, 2) it is easy to











how, and purchasing know-how. The respondents were asked to evaluate TTDEG from MNCs to






185

TECHNOLOGY RECIPIENTS’ CHARACTERISTICS (TRCHAR) This study concentrates on two distinct elements of TRCHAR: absorptive capacity (ACAP) and

recipient collaborativeness (RCOL); which have strong theoretical and empirical supports

(Cohen and Levinthal, 1990; Hamel, 1991; Simonin, 1999a). This study captures the technology

recipients’ capability, willingness to absorb new knowledge and attitude towards learning.

ABSORPTIVE CAPACITY Building on Lane et al. (2001), this study captures absorptive capacity’s (ACAP) critical

elements of ability to understand, assimilate and apply new external knowledge. In capturing

these critical elements, this study adopts a multi-item scale previously used by the researchers

(Lyles and Salk, 1996; Simonin, 1999a; Pak and Park, 2004) to measure the constructs using

seven (7) items with respect to statements on the academic background, technical capacity,

educational programs, financial support for new ideas, overseas training opportunities, and

commitment in terms of personnel and resources (physical, financial, and logistic) to JV.

Following Cohen and Levinthal (1990) and Lane et al. (2001), this study also includes one (1)

item to assess the local firm’s ability to understand, assimilate and apply new technology

transferred by the foreign parent firm. The Cronbach Alpha for ACAP was higher (0.94) than

Simonin’s (2004) Cronbach Alpha (0.81).

RECIPIENT COLLABORATIVENESS This study measures RCOL in terms of the local partner firms’ learning intent and their

collaborative attitudes by using a five (5) items scale in terms of 1) the local partner’s learning

objective, 2) the local partner’s desire, determination and will to learn from foreign partner, 3)

the technology-recipient’s willingness to allow foreign partner to inspect and monitor the use of

knowledge acquired from JV, 4) the local partner’s commitment not to compete directly with the

foreign partner in the future, and 5) the local partner’s commitment in sharing with the foreign

partner the benefits of the critical knowledge acquired from the JV (Yin and Bao, 2006; Thuc



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Anh et al., 2006; Hamel, 1991; Simonin, 2004). The Cronbach Alpha for RCOL was higher

(0.92) than Yin and Bao’s (2006) Cronbach Alpha (0.71).

SIZE OF MNCs (MNCSIZE) Following the previous studies (Simonin, 1999a; Tsang et al., 2004; Dhanaraj et al., 2004),

MNCSIZE is measured by the total employees of the foreign JV partner based on items coded: 0 =

large MNCs (employees > 1000) and 1 = medium/small MNCs (employees < 1000) (Yin and

Bao, 2006).

MODEL AND ANALYSIS The moderated multiple regression (MMR) analysis is described as an inferential procedure

which consists of comparing two different least-squares regression equations (Aguinis, 2004;

Aiken and West, 1991; Cohen and Cohen, 1983; Jaccard et al., 1990). Using the MMR analysis,

the moderating effect of the variable (product term) was analyzed by interpreting 1) the R²

change in the models obtained from the model summaries, and 2) the regressions coefficients for

the product term obtained from the coefficients tables. Prior to conducting the MMR analysis,

preliminary analyses were conducted to ensure that there was no violation of the assumptions of

normality, linearity, homoscedasticity, and homogeneity of error variance. The population data

was carefully examined to avoid the occurrence of 1) Type 1 error; which is the error of rejecting

the true null hypotheses at a specified , and 2) Type 2 error (β); which is the error of failing to

reject a false null hypotheses at a specified power (Aguinis, 2004). In this study, Equation 1

below was used to represent the variables in the ordinary least-squares (OLS) model:

Equation 1 (OLS model): Y = β0 + β1X+ β2Z + e

To determine the presence of moderating effect, the OLS model was then compared with the

MMR model which was represented by Equation 2 below:



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Equation 2 (MMR model): Y = β0 + β1X+ β2Z + β3X*Z + e

where, Y = degree of technology of transfer (TCTDEG and EXPDEG as the dependent

variables), X = technology recipient characteristics (absorptive capacity and recipient

collaborativeness), Z = a hypothesized binary grouping moderator (MNCSIZE; large vs.

medium/small), X*Z = the product between the predictors (TRCHAR*MNCSIZE), β0 = the

intercept of the line-of-best-of-fit which represents the value of Y when X = 0, β1 = the least-

squares estimate of the population regression coefficient for X, β2 = the least-squares estimate of

the population regression coefficient for Z, β3 = the sample-base least-squares estimates of the

population regression coefficient for the product term, and e = the error term. The moderating

variable (product term) is a binary grouping moderator; where the moderating variable MNCSIZE

was coded using the dummy coding system; 0 = large MNCs, and 1 = medium/small MNCs.

This was done because of its simplicity and ease of interpretation of results when making

comparisons between different groups (Aguinis, 2004).

RESULTS Table 1 and Table 2 show the model summary for both degrees of tacit (TCTDEG) and explicit

(EXPDEG) knowledge. The coefficients for all variables for Model 1 and Model 2 (for both

TCTDEG and EXPDEG) are presented in Table 3 and Table 4 below.

Table 1: Model Summary � - Degree of Tacit Knowledge

Table 1 above shows that for Model 1, R = .488, R² = .238 and [F (2, 125) = 19.488, p = .0001].

This R² means that 23.8% of the variance in the TCTDEG is explained by TRCHAR scores and

Model Summaryc

.488a .238 .225 5.107 .238 19.488 2 125 .000

.522b .272 .255 5.010 .035 5.891 1 124 .017

Model12

R R SquareAdjustedR Square

Std. Error ofthe Estimate

R SquareChange F Change df1 df2 Sig. F Change

Change Statistics

Predictors: (Constant), MNCSIZE, TRCHARa.

Predictors: (Constant), MNCSIZE, TRCHAR, TRCHAR*MNCSIZEb.

Dependent Variable: TCTDEGc.



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MNCSIZE. Model 2 shows the results after the product term (TRCHAR*MNCSIZE) was included

in the equation. Table 1 also indicates that the inclusion of the product term resulted in an R²

change of .035, [F (1, 124) = 5.891, p < 0.01]. The results support the presence of a moderating

effect. To put it differently, the moderating effect of MNCSIZE explains 3.5% variance in the

TCTDEG above and beyond the variance by TRCHAR scores and MNCSIZE. Thus, it can

reasonably be concluded that hypothesis H1 is supported.

Table 2: Model Summary � - Degree of Explicit Knowledge


This R² means that 22.2% of the variance in the EXPDEG is explained by TRCHAR scores and

MNCSIZE. Model 2 also shows the results after the product term (TRCHAR*MNCSIZE) was

included in the equation. Table 2 above indicates that the inclusion of the product term resulted

in an R² change of .009, [F (1, 124) = 1.467, p > 0.05]. The results show no presence of

significant moderating effect. To put it differently, the moderating effect of MNCSIZE explains

only 0.9% variance in the EXPDEG above and beyond the variance by TRCHAR scores and

MNCSIZE. Thus, it can safely be concluded that hypothesis H2 is not supported. The coefficients

table for TCTDEG as shown in Table 3 below depicts the results of the regressions equation for

Model 1 and Model 2.

Model Summaryc

.471a .222 .210 4.783 .222 17.831 2 125 .000

.481b .231 .212 4.774 .009 1.467 1 124 .228

Model12




Change Statistics

Predictors: (Constant), MNCSIZE, TRCHARa.

Predictors: (Constant), MNCSIZE, TRCHAR, TRCHAR*MNCSIZEb.

Dependent Variable: EXPDEGc.



189

Table 3: Coefficientsª - Degree of Tacit Knowledge

Model 1 indicates that TRCHAR was statistically significant (p < 0.001; Beta value = 0.376);

and MNCSIZE was statistically significant (p < 0.05; Beta value = -0.239). Equation 3 below

shows that for a 1-point increase in TRCHAR, the TCTDEG is predicted to have a difference by

.114, given that the MNCSIZE is held constant. The regression coefficient associated with

MNCSIZE means that the difference in TCTDEG between large and medium/small MNCs is -

2.811, given that TRCHAR is held constant.

Equation 3: TCTDEG = 16.348 + .114TRCHAR - 2.811MNCSIZE

The high-order of interaction effects of the MMR test was conducted to differentiate the degree

of technology transferred by large and medium/small MNCs. Model 2 shows the results after the

product term (TRCHAR*MNCSIZE) was included in the equation. As indicated in Table 1 the

inclusion of product term resulted in an R² change of .035, [F (1, 124) = 5.891, p < 0.01]. Model

2 shows TRCHAR are highly significant (p < 0.001; Beta value = .685). Both MNCSIZE and

TRCHAR*MNCSIZE were also found to be significant (p < 0.10; Beta value = 0.655 and p <

0.01; Beta value = -0.899 respectively). The results support the presence of a moderating effect.

Table 3 also reveals information on the regression coefficients after the inclusion of product term

in the equation. The equation for Model 2 is as follows:

Equation 4: TCTDEG = 8.503 + .207TRCHAR + 7.709MNCSIZE - .129TRCHAR*MNCSIZE

Coefficientsa

16.348 2.150 7.603 .000 12.092 20.603.114 .024 .376 4.698 .000 .066 .161

-2.811 .942 -.239 -2.984 .003 -4.676 -.9478.503 3.859 2.203 .029 .865 16.142.207 .045 .685 4.579 .000 .118 .297

7.709 4.432 .655 1.740 .084 -1.062 16.481-.129 .053 -.899 -2.427 .017 -.234 -.024

(Constant)TRCHARMNCSIZE(Constant)TRCHARMNCSIZETRCHAR*MNCSIZE

Model1

2

B Std. Error

UnstandardizedCoefficients

Beta

StandardizedCoefficients

t Sig. Lower Bound Upper Bound95% Confidence Interval for B

Dependent Variable: TCTDEGa.



190

As indicated above, the interpretation of the regression coefficients is based on the fact that the

binary moderator was coded using the dummy code system. The result for Model 2 indicates that

for a 1-point increase in the TRCHAR, the TCTDEG is predicted to have a difference by .207,

given that MNCSIZE is held constant. The interpretation of the regression coefficients for the

product term in Equation 4 is that there is a -.129 difference between the slope of TCTDEG on

TRCHAR between large and medium/small MNCs. In other words, the slope regressing

TCTDEG on TRCHAR is steeper for medium/small MNCs as compared to large MNCs. The

TRCHAR and TCTDEG relationship for large and medium/small MNCs is shown in Figure 1

below by creating a graph displaying the relationships for each of the groups (Aguinis, 2004).

From the results of descriptive statistics, the value of the mean score for TRCHAR is 6.57; and

for the standard deviation (SD) is 1.60. Following Aguinis (2004), the value 1 SD above the

mean is 8.17 and the value 1 SD below the mean is 4.97. Thus, using the value of 1 (SD) above

and 1 (SD) below mean in Equation 4 yields the graph shown in Figure 1. Results based on

Equation 4 led to the conclusion that there was a moderating effect of MNCSIZE. Figure 1 below

shows that the TRCHAR-TCTDEG relationship is stronger (i.e. steeper slope) for medium/small

MNCs as compared to large MNCs. The coefficients table for EXPDEG as shown in Table 4

below depicts the results of the regressions equation for Model 1 and Model 2.

Table 4: Coefficientsª - Degree of Explicit Knowledge

Model 1 indicates that TRCHAR was statistically significant (p < 0.001; Beta value = 0.405);

and MNCSIZE was also statistically significant (p < 0.05; Beta value = -0.168). Equation 5 below

shows that for a 1-point increase in TRCHAR, the EXPDEG is predicted to have a difference by

.113, given that the MNCSIZE is held constant. The regression coefficient associated with

Coefficientsa

18.056 2.014 8.967 .000 14.071 22.041.113 .023 .405 5.008 .000 .069 .158

-1.834 .882 -.168 -2.079 .040 -3.580 -.08814.326 3.678 3.895 .000 7.047 21.604

.158 .043 .564 3.663 .000 .073 .2433.169 4.223 .290 .750 .454 -5.190 11.528-.061 .051 -.461 -1.211 .228 -.161 .039

(Constant)TRCHARMNCSIZE(Constant)TRCHARMNCSIZETRCHAR*MNCSIZE

Model1

2

B Std. Error


Beta



Dependent Variable: EXPDEGa.



191

MNCSIZE means that the difference in EXPDEG between large and medium/small MNCs is -

1.834, given that TRCHAR is held constant.

Equation 5: EXPDEG = 18.056 + .113TRCHAR - 1.834MNCSIZE

Model 2 shows the results after the product term (TRCHAR*MNCSIZE) was included in the

equation. As indicated in Table 2 the inclusion of product term resulted in an R² change of .009,

[F (1, 124) = 1.467, p > 0.05]. TRCHAR was found statistically significant (p < 0.001; Beta

value = 0.564); whereas both MNCSIZE and TRCHAR*MNCSIZE were not statistically significant

(both at p > 0.05). The results did not show the presence of a significant moderating effect. Table

4 also reveals information on the regression coefficients after the inclusion of product term in the

equation. The equation for Model 2 is as follows:

Equation 6: EXPDEG = 14.326 + .158TRCHAR + 3.169MNCSIZE - .061TRCHAR*MNCSIZE

The result for Model 2 indicates that for a 1-point increase in the TRCHAR, the EXPDEG is

predicted to have a difference by .158, given that MNCSIZE is held constant. The interpretation of

the regression coefficients for the product term in Equation 6 is that there was a .158 difference

between the slope of EXPDEG on TRCHAR between large MNCs and medium/small MNCs.

The slope regressing EXPDEG on TRCHAR is steeper for medium/small MNCs as compared to

large MNCs. The TRCHAR and EXPDEG relationship for large and medium/small MNCs is

also shown in Figure 1 below. The value of the mean score for TRCHAR is 6.57 and for the

standard deviation (SD) is 1.60. The value 1 SD above the mean is 8.17, and the value 1 SD

below the mean is 4.97. Thus, using the value of 1 (SD) above and 1 (SD) below mean in

Equation 6 yields the graph shown in Figure 1. Results based on Equation 6 led to the conclusion

that there was no significant moderating effect of MNCSIZE. Although insignificant, Figure 1

below indicates that the TRCHAR-EXPDEG relationship is slightly stronger (i.e. steeper slope)

for medium/small MNCs as compared to large MNCs.



192

Figure 1: Slopes for both TCTDEG and EXPDEG on TRCHAR for MNCSIZE


by providing empirical evidence on the significant moderating effect of size of MNCs on the

relationships between technology transfer characteristics (absorptive capacity and recipient

collaborativeness) and two dimensions of degree of technology transfer: degrees of tacit and

explicit knowledge using the Malaysia sample. The results suggest that, in comparison, the

inclusion of MNCSIZE (large vs. medium/small MNCs) in TRCHAR-TCTDEG relationship has a

significant moderating effect in changing the degree (volume) of tacit knowledge only (p <

0.001; R- squared change of 0.035) not degree of explicit knowledge (p > 0.05; R- squared

change of 0.009). The moderating effect of MNCSIZE is shown to be capable of changing the

nature of relationship and explains under what conditions TRCHAR causes TCTDEG. The

presence of significant moderating effect of MNCSIZE (large and medium/small MNCs) exceeded

the linear relationship between TRCHAR and TCTDEG.

30

25

20

15

10

5

0

-5

Low TCTDEG / EXPDEG (1 SD below mean) High TCTDEG / EXPDEG (1 SD above mean) medium and small MNCs (TCTDEG) large MNCs (TCTDEG) medium and small MNCs (EXPDEG) large MNCs (EXPDEG)



193

This is rather interesting given the fact that MNCSIZE received strong theoretical support in the

literature (Hagedoorn and Schakenraad, 1994; Glaister and Buckley, 1996; Hoskisson et al.,

1994). The results further suggest that MNCSIZE; whether large or medium/small MNCs, has

been established to provide a significant moderating impact in TRCHAR- TCTDEG relationship

in the JVs. The slopes for TCTDEG on TRCHAR for large and medium/small MNCs (Figure 1)

indicated that the relationship appeared to be stronger for medium/small MNCs as compared to

large MNCs. The results also provide critical information in such that although transferring tacit

knowledge in IJVs requires the recipient partners to have 1) a sufficient prior related knowledge

about the suppliers’ technologies and their intensity of effort in organizational learning; where

the recipient partners have the ability to assimilate and exploit new knowledge (Cohen and

Levinthal, 1990; Hamel, 1991) and 2) the collaborative learning intent; which could encourage

more openness and transparency of the supplier partners to share and transfer more technology

(Inkpen, 2000), however, due to the nature of knowledge which is highly tacit, complex and

firm-specific both large and medium/small MNCs are unlikely to transfer higher degree of tacit

knowledge.

As compared to medium/small MNCs, although large MNCs are known for having abundant

supply of resources, expertise and vast JVs experience to undertake technology transfer,

however, due to their inherent advantage, a higher bargaining power and less dependent on local

partners they are unlikely to transfer a higher degree or technology to local partners. This is also

because large MNCs have the propensity to regard their JV as one-way learning processes thus

having little to share with local partners (Liu and Vince, 1999; Danis and Park, 2002). Since

learning in IJVs is asymmetrical, large MNCs tend to perceive learning as solely the task of the

knowledge-disadvantaged local partners (Lin, 2005). Moreover, the MNCs from the developed

countries would normally request for a higher equity ownership in order to increase their

bargaining power and have full control of the systems, methods and decisions in the JVs

(Makhija and Ganesh, 1997). Large MNCs typically form JVs as the mechanism towards

extracting knowledge and information on local business, economics, and political stability

(Sinha, 2001). In this sense, in order to maintain their dominance, large MNCs are reluctant to

transfer their technologies to local JV partners instead they are more inclining to protect their

proprietary technologies and competencies (Taylor, 1995). On the other hand, although

medium/small MNCs in IJVs are more likely to compromise with local partners in terms of



194

equity ownership to maintain a balanced bargaining power, however, due to their limited

resources, expertise and lack of IJVs experience they are most unlikely to undertake technology

transfer of tacit knowledge than large MNCs particularly if the transfer involves technologies

which form the strategic valuable resources, competencies and source of sustainable competitive

advantage of the MNCs (Porter, 1985; Barney, 1991; Peteraf, 1993; Wernerfelt, 1984; Pralahad

and Hamel, 1990). The results further support and extend the empirical findings by Simonin

(1997), Simonin (2004), Dhanaraj et al. (2004) and Bresman et al. (1999). The findings also

extend technology transfer literature by establishing that MNCSIZE moderates the relationship

between TRCHAR and degree of tacit knowledge transfer.

Since the IJV literature has highlighted the high instability rate of IJVs in developing countries,

future studies could be directed to empirically examine the moderating effect of size of MNCs on

the relationships between degree of inter-firm TT and conflicts, learning outcomes, asymmetric

bargaining power, stability of JV, and equity ownership. Finally, future studies could further

investigate the effects of few other established moderating variables such as organizational

culture, collaborative know-how, prior JV experience, and learning capacity on the above

relationships to provide new insights and information on the boundary conditions of technology

recipient characteristics-degree of technology transfer relationship.

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Allen, D. R. & Rao, T. R. (2000). Analysis of Customer Satisfaction Data. United States of America: America Society for Quality.

Aguinis, H. (2004), Regression Analysis for Categorical Moderators, New York, The Gilford Press. Barney, J.B (1991). Firm Resources and Sustained Competitive Advantage. Journal of Management, 17,


International Alliances, Journal of World Business, 32(2), p. 103 –17. Beamish, P.W. & Berdrow, I. (2003). Learning from International Joint Ventures - the Unintended

Outcome, Long Range Planning, 36, p. 285–303. Bresman, H., Birkinshaw, J. & Nobel, R. (1999). Knowledge Transfer in International Acquisitions.

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11 Age of Joint Venture, Degree of Inter- Firm Technology

Transfer and Local Firms’ Performance.

CHAPTER OUTLINE

Although many studies have acknowledged the significant effect of knowledge transfer on

performance outcomes, nevertheless, studies which examine the effects of degree of technology

transfer (TTDEG) on both local firms’ corporate (CPERF) and human resource (HRPERF)

performances in inter-firm TT are still scarce. Moreover, the relationships between TTDEG and

both CPERF and HRPERF of local firms could possibly be influenced by other established

moderating factors such as size of MNCs, age of JV, MNCs’ country of origin, and MNCs’ types

of industry.

INTRODUCTION When compared to various forms of strategic alliance such as distribution and supply

agreements, research and development partnerships or technical and management contract, the

IJVs are considered as the most efficient formal mechanism for technology transfer (TT) to occur

through inter-partner learning between foreign MNCs and local firms (Kogut and Zander, 1993;

Inkpen 1998a, 2000). IJVs are also viewed as the most efficient mode to transfer technology or

knowledge which is organizationally embedded and difficult to transfer through licensing

agreements (Kogut, 1988; Mowery, Oxley and Silverman, 1996). IJVs provide both MNCs and

local partners an appropriate avenue to facilitate the transfer of organizational knowledge,

particularly for knowledge which is hard to be transferred without the setting up of a JV, such as

institutional and cultural knowledge (Harrigan, 1984).



199

A review of literature reveals that most of the empirical studies on inter-firm technology and

knowledge transfer in strategic alliance, particularly IJVs, are limiting their focus on the

performance of the IJVs (for example Lyles and Salk, 1996; Lane et al., 2001; Tsang et al.,

2004; Dhanaraj et al., 2004; Steensma and Lyles, 2000). On the other hand, the performance of

the MNCs’ subsidiary and affiliate in the host countries has become the primary focus of intra-

firm knowledge transfer literature (for example Chen, 1996; Chung, 2001; Cui et al., 2006; Lin,

2003). Most of the studies on strategic alliance and IJVs have recorded positive relationship

between knowledge acquisition or transfer and IJVs’ performance for example 1) knowledge

acquisition has a positive impact on the IJVs’ human resource, general and business performance

(Lyles and Salk, 1996), 2) knowledge acquisition as a better predictor for human-resource related

performance than the general and business performance (Lyles and Salk, 1996), 3) knowledge

acquisition from parent firms has a significant positive effect on IJVs’ performance (Lane et al.,

2001; Tsang et al., 2004), 4) explicit knowledge acquisition have a positive impact on IJVs’

performance (Dhanaraj et al., 2004), and 5) tacit knowledge about overseas information was

positively related to new product development capacities (Subramaniam and Venkatraman,

2001). In addition, Yin and Bao (2006) found tacit knowledge acquisition had significantly

affected local firms’ performance (LFP). Dhanaraj et al. (2004) found tacit knowledge was

negatively related to IJVs’ performance.

As indicated above, although many studies have acknowledged the significant effect of

knowledge transfer on performance outcomes, nevertheless, except for Yin and Bao (2006)

studies which examine the effects of degree of technology transfer (TTDEG) on both local firms’

corporate (CPERF) and human resource (HRPERF) performances in inter-firm TT are still

scarce. Moreover, the relationships between TTDEG and both CPERF and HRPERF of local

firms could possibly have been influenced by other established moderating factors such as size of


variations in CPERF and HRPERF could have been significantly influenced or explained by

these variables. Thus, this work fills in the literature gaps by specifically examining the effect of

age of joint ventures (old vs. young JVs) as a moderating variable in the relationships between

degree of technology transfer (TTDEG) and two distinct dimensions of local firms’ performance

(LFP): corporate (CPERF) and human resource (HRPERF) performances. The primary objective



200

is to provide new insights and information on the boundary conditions for TTDEG-LFP

relationship (Aguinis, 2004).

AGE OF JOINT VENTURE, DEGREE OF TECHNOLOGY TRANSFER AND LOCAL FIRMS’ PERFORMANCE The current TT issue in IJVs revolves around the extent of degree of technologies that are

transferred (TTDEG) by the suppliers to recipient partners (Pak and Park, 2004; Minbaeva,

2007). The question is no longer whether or not the MNCs are transferring technology to local

firms instead the focus in the literature has shifted to questions on 1) the level (sophistication) of

the transferred technology, and 2) the stage where the transfer process has reached (Lai and

Narayanan, 1997; Narayanan and Lai, 2000). Except for Pak and Park (2004) and Minbaeva

(2007), not many studies in both intra and inter-firm TT have focused on TTDEG as independent

or dependent variable. In general, bulk of the studies has focused more on technological

knowledge and knowledge acquisition ‘per se’ as the outcomes (dependant variables). For

example, the technology transfer, knowledge transfer (KT) and strategic alliance literature have

extensively examined the relationships between 1) knowledge attributes, source and recipient

and KT success (Cummings et al., 2003), 2) knowledge seekers, knowledge holder and

contextual factors and know-how acquisition (Hau and Evangelista, 2007), 3) IJVs














201
















The TT and KT literature have also acknowledged that a substantial transfer of technology

regardless whether tacit or explicit technology will positively 1) lead to a higher potentials of

innovation performance/capabilities (Guan et al., 2006; Kotabe et al., 2007)), 2) increase

technological capabilities (Kumar et al., 1999; Madanmohan et al., 2004), 3) enhance

competitive advantage (Liao and Hu, 2007; Rodriguez and Rodriguez, 2005), 4) enhance

organizational learning effectiveness (Inkpen, 2000; Inkpen and Dinur, 1998), 5) improve

productivity (Caves, 1974; Liu and Wang, 2003), 6) increase technological development of local

industry (Markusen and Venables, 1999), and 7) improve the economic growth of the host

country (Blomstrom, 1990).

The IJV literature suggests that the longer the collaborative relationships the greater the

opportunity for JV partners to share, learn and transfer technology and knowledge between them.

This is because the duration of relationship is positively associated with frequency of

communication and information exchange between partners (Kale et al., 2000; Hallen et al.,

1991; Foss and Pedersen, 2002). Nevertheless, duration of JV could also increase the propensity

of losing the valuable proprietary asset to the other JV partner (Kale et al., 2000). From the

strategic alliance perspective, as an alliance sustains overtime; JVAGE provides several effects



202

such as it intensifies inter-partner trust, changes the bargaining power between partners, and

develops partners’ personal attachment (Gulati, 1995; Yan and Gray, 1994; Inkpen and Beamish,

1997). Empirical studies have found that the moderating effect of JVAGE has mixed results. Few

empirical studies on inter-firm knowledge transfer in IJVs find JVAGE is insignificant in

relationship between 1) knowledge acquisition-performance relationship, and 2) organizational

characteristics, structural mechanisms, contextual factors, and knowledge acquisition

relationship (Tsang et al., 2004; Lin, 2005; Lyles and Salk, 1996). Nevertheless, empirical

studies have also recorded significant moderating effect of JVAGE on 1) ambiguity-knowledge

transfer relationship, and 2) knowledge characteristics-marketing knowledge transfer relationship

(Simonin, 1999a, 1999b). Therefore, this study posits as follows:

H1: The relationship between degree of inter-firm technology transfer and local firms’ corporate

performance is moderated by age of joint venture.

H2: The relationship between degree of inter-firm technology transfer and local firms’ human

resource performance is moderated by age of joint venture.
















203







(Mohammed, 1998; Rozhan, Rohayu and Rasidah, 2001). From 145 responses only 128

questionnaires were usable and 17 questionnaires were returned blank, returned incomplete, or

replied but unable to participate in the study.

The main research instrument in this study is the questionnaire. Building on the previous TT and

KT studies, the questionnaire adopts a multi-item scales which have been modified accordingly









LOCAL FIRMS’ PERFORMANCE (LFP) This study operationalizes LFP from two dimensions of performances: 1) corporate performance











204


















AGE OF JOINT VENTURE (JVAGE)

In measuring JVAGE this study required the respondents to indicate the JV’s number of years in

operation based on items coded: 0 = old joint ventures (number of years > 10 years) and 1 =

young joint ventures (number of years < 10 years) (Tsang et al., 2004; Lin, 2005; Simonin,

1999a; Luo, 2001).



205

MODEL AND ANALYSIS The moderated multiple regression (MMR) analysis is described as an inferential procedure

which consists of comparing two different least-squares regression equations (Aguinis, 2004;

Aiken and West, 1991; Cohen and Cohen, 1983; Jaccard et al., 1990). Using the MMR analysis,

the moderating effect of the variable (product term) was analyzed by interpreting 1) the R²

change in the models obtained from the model summaries, and 2) the regressions coefficients for

the product term obtained from the coefficients tables. Prior to conducting the MMR analysis,











where, Y = local firms’ performance (CPERF and HRPERF as the dependent variables), X =

degree of technology transfer (TCTDEG and EXPDEG), Z = a hypothesized binary grouping

moderator (Age of Joint Venture; old vs. young), X*Z = the product between the predictors

(TTDEG*JVAGE), β0 = the intercept of the line-of-best-of-fit which represents the value of Y

when X = 0, β1 = the least-squares estimate of the population regression coefficient for X, β2 =

the least-squares estimate of the population regression coefficient for Z, β3 = the sample-base

least-squares estimates of the population regression coefficient for the product term, and e = the

error term. The moderating variable (product term) is a binary grouping moderator; where the

moderating variable JVAGE was coded using the dummy coding system; 0 = old JVs, and 1 =



206

young JVs. This was done because of its simplicity and ease of interpretation of results when

making comparisons between different groups (Aguinis, 2004).

RESULTS Table 1 and Table 2 show the model summary for both corporate (CPERF) and human resource

(HRPERF) performances. The coefficients for all variables for Model 1 and Model 2 (for both

CPERF and HRPERF) are presented in Table 3 and Table 4 below.

Table 1: Model Summary � - Corporate Performance


This R² means that 45.9% of the variance in the CPERF is explained by TTDEG scores and

JVAGE. Model 2 shows the results after the product term (CPERF*JVAGE) was included in the

equation. Table 1 also indicates that the inclusion of the product term resulted in an R² change of

.032, [F (1, 124) = 7.796, p < 0.01]. The results support the presence of a moderating effect. To

put it differently, the moderating effect of JVAGE explains 3.5% variance in the CPERF above

and beyond the variance by TTDEG scores and JVAGE. Thus, it can reasonably be concluded

that hypothesis H1 is supported.

Table 2: Model Summary � - Human Resource Performance

Model Summaryc

.678a .459 .451 5.186 .459 53.060 2 125 .000

.701b .491 .479 5.051 .032 7.796 1 124 .006

Model12




Change Statistics

Predictors: (Constant), TTDEG, JVAGEa.

Predictors: (Constant), TTDEG, JVAGE, TTDEG*JVAGEb.

Dependent Variable: CPERFc.

Model Summaryc

.736a .541 .534 4.067 .541 73.710 2 125 .000

.754b .568 .557 3.962 .027 7.662 1 124 .007

Model12




Change Statistics

Predictors: (Constant), TTDEG, JVAGEa.

Predictors: (Constant), TTDEG, JVAGE, TTDEG*JVAGEb.

Dependent Variable: HRPERFc.



207


This R² means that 54.1% of the variance in the HRPERF is explained by TTDEG scores and

JVAGE. Model 2 also shows the results after the product term (TTDEG*JVAGE) was included

in the equation. Table 2 above indicates that the inclusion of the product term resulted in an R²

change of .027, [F (1, 124) = 7.662, p < 0.05]. The results also show a presence of significant

moderating effect. To put it differently, the moderating effect of JVAGE explains 2.7% variance

in the HRPERF above and beyond the variance by TTDEG scores and JVAGE. Thus, it can

safely be concluded that hypothesis H2 is supported. The coefficients table for CPERF as shown

in Table 3 below depicts the results of the regressions equation for Model 1 and Model 2.

Table 3: Coefficientsª - Corporate Performance

Model 1 indicates that TTDEG was statistically significant (p < 0.001; Beta value = 0.651);

however JVAGE was not statistically significant (p > 0.05). Equation 3 below shows that for a 1-

point increase in TTDEG, the CPERF is predicted to have a difference by .436, given that the

JVAGE is held constant. The regression coefficient associated with JVAGE means that the

difference in CPERF between old and young JVs is -1.055, given that TTDEG is held constant.

Equation 3: CPERF = 1.968 + .436TTDEG - 1.055JVAGE

The high-order of interaction effects of the MMR test was conducted to differentiate the extent

of CPERF that was influenced by old and young JVs. Model 2 shows the results after the product

term (TTDEG*JVAGE) was included in the equation. As indicated in Table 1 the inclusion of

product term resulted in an R² change of .032, [F (1, 124) = 7.796, p < 0.01]. Model 2 shows

TTDEG are highly significant (p < 0.001; Beta value = .953). Both JVAGE and

Coefficientsa

10.968 2.493 3.789 .000 -2.967 6.902.436 .046 .651 9.455 .000 .345 .527

-1.055 .964 -.075 -1.094 .276 -2.964 .8534.390 2.578 1.703 .091 -.713 9.493

.638 .085 .953 7.493 .000 .469 .806-8.000 2.659 -.571 -3.009 .003 -13.262 -2.738

-.051 .018 -.677 -2.792 .006 -.088 -.015

(Constant)TTDEGJVAGE(Constant)TTDEGJVAGETTDEG*JVAGE

Model1

2

B Std. Error


Beta


t Sig. Lower Bound Upper Bound

95% Confidence Interval for B

Dependent Variable: CPERFa.



208

TTDEG*JVAGE were also found to be significant (p < 0.01; Beta value = -0.571 and p < 0.01;

Beta value = -0.677 respectively). The results support the presence of a moderating effect. Table

3 also reveals information on the regression coefficients after the inclusion of product term in the


Equation 4: CPERF = 4.390 + .638TTDEG -8.000JVAGE - .051TTDEG*JVAGE



for a 1-point increase in the TTDEG, the CPERF is predicted to have a difference by .638, given

that JVAGE is held constant. The interpretation of the regression coefficients for the product

term in Equation 4 is that there is a -.051 difference between the slope of CPERF on TTDEG

between old and young JVs. In other words, the slope regressing CPERF on TTDEG is steeper

for young JVs as compared to old JVs. The TTDEG and CPERF relationship for old and young

JVs is shown in Figure 1 below by creating a graph displaying the relationships for each of the

groups (Aguinis, 2004). From the results of descriptive statistics, the value of the mean score for

TTDEG is 6.19; and for the standard deviation (SD) is 1.30. Following Aguinis (2004), the value

1 SD above the mean is 7.49 and the value 1 SD below the mean is 4.89. Thus, using the value of

1 (SD) above and 1 (SD) below mean in Equation 4 yields the graph shown in Figure 1. Results

based on Equation 4 led to the conclusion that there was a moderating effect of JVAGE. Figure 1

below shows that the TTDEG-CPERF relationship is stronger (i.e. steeper slope) for old JVs as

compared to young JVs. The coefficients table for HRPERF as shown in Table 4 below depicts

the results of the regressions equation for Model 1 and Model 2.

Table 4: Coefficientsª - Human Resource Performance

Coefficientsa

3.338 1.955 1.707 .090 -.531 7.207.422 .036 .741 11.675 .000 .351 .494.215 .756 .018 .285 .776 -1.281 1.712

5.222 2.023 2.582 .011 1.218 9.225.579 .067 1.016 8.671 .000 .447 .711

-5.186 2.086 -.435 -2.486 .014 -9.314 -1.057-.040 .014 -.618 -2.768 .007 -.069 -.011

(Constant)TTDEGJVAGE(Constant)TTDEGJVAGETTDEG*JVAGE

Model1

2

B Std. Error


Beta


t Sig. Lower Bound Upper Bound

95% Confidence Interval for B

Dependent Variable: HRPERFa.



209

Model 1 indicates that TTDEG was statistically significant (p < 0.001; Beta value = .741);

however JVAGE was not statistically significant (p > 0.05). Equation 5 below shows that for a 1-

point increase in TTDEG, the HRPERF is predicted to have a difference by .422, given that the

JVAGE is held constant. The regression coefficient associated with JVAGE means that the

difference in HRPERF between old and young JVs is .215, given that TTDEG is held constant.

Equation 5: = 3.338 + .422TTDEG + .215JVAGE

Model 2 shows the results after the product term (TTDEG*JVAGE) was included in the


[F (1, 124) = 7.662, p < 0.05]. TTDEG was found highly significant (p < 0.001; Beta value =

1.016); whereas both JVAGE and TTDEG*JVAGE were also statistically significant (both at p <

0.05, Beta value = -0.435; p < 0.01, Beta value = -0.618). The results show the presence of a

significant moderating effect. Table 4 also reveals information on the regression coefficients

after the inclusion of product term in the equation. The equation for Model 2 is as follows:

Equation 6: HRPERF = 5.222 + .579TTDEG - 5.186JVAGE - .040TTDEG*JVAGE

The result for Model 2 indicates that for a 1-point increase in the TTDEG, the HRPERF is

predicted to have a difference by .579, given that JVAGE is held constant. The interpretation of

the regression coefficients for the product term in Equation 6 is that there was a -.040 difference

between the slope of HRPERF on TTDEG between old and young JVs. The slope regressing

HRPERF on TTDEG is steeper for young JVs as compared to old JVs. The TTDEG and

HRPERF relationship for old and young JVs is also shown in Figure 1 below. The value of the

mean score for TTDEG is 6.19 and for the standard deviation (SD) is 1.30. The value 1 SD above

the mean is 7.49, and the value 1 SD below the mean is 4.89. Thus, using the value of 1 (SD)

above and 1 (SD) below mean in Equation 6 yields the graph shown in Figure 1. Results based

on Equation 6 led to the conclusion that there was also a significant moderating effect of

JVAGE. Figure 1 below indicates that the TTDEG-HRPERF relationship is slightly stronger (i.e.

steeper slope) for old JVs as compared to young JVs.



210

Figure 1: Slopes for both CPERF and HRPERF on TTDEG for JVAGE

30

25

20

15

10

5

0

-5


by providing empirical evidence on the significant moderating effects of age of joint ventures on

the relationships between degree of inter-firm technology transfer (degrees of tacit and explicit

knowledge) and two dimensions of local firms’ performance: corporate and human resource

performances using the Malaysia sample. The results suggest that, in comparison, the inclusion

of JVAGE (old and young JVs) in TTDEG-LFP relationship has a significant moderating effect

in changing the local firms’ corporate performance (CPERF) (p < 0.01; R- squared change of

0.032) and the local firms’ human resource performance (HRPERF) (p < 0.05; R- squared

change of 0.027). The moderating effect of JVAGE is shown to be capable of changing the

nature of relationship and further explains under what conditions TTDEG causes CPERF and

HRPERF. The presence of significant moderating effect of JVAGE (old and young JVs)

exceeded the linear relationship between TTDEG and both CPERF and HRPERF. The result are

Low CPERF / HRPERF (1 SD below mean) High CPERF / HRPERF (1 SD above mean) Young JVs (CPERF) Old JVs (CPERF) Young JVs (HRPERF) Old JVs (HRPERF)



211

consistent with recent literature which has strongly supported the significant role of JVAGE

(Foss and Pedersen, 2002; Kale et al., 2000; Tsang et al., 2004; Simonin, 2004). The results also

suggest that JVAGE; whether old or young JVs, has been established to provide a significant

moderating impact in TTDEG-CPERF and TTDEG-HRPERF relationships in the JVs; where the

relationships are stronger for old JVs as compared to young JVs.

The results provide critical information in such that although a successful technology transfer in

IJVs; which includes the transfer of substantial tacit and explicit knowledge could have

significantly increased 1) the corporate performance in terms the local firms’ business volume,

market share, planned goals and profits, and 2) the human resource performance in terms of local

firms’ product/service quality, employees’ productivity, managerial techniques/skills and

operational efficiency, nevertheless, since the technologies which are transferred to local firms

mostly originated from the sophisticated and competitive foreign MNCs, the propensity of

increasing both CPERF and HRPERF is unlikely to maximize the local firms’ performance. This

is simply because although a longer period of collaborative relationship in JVs could escalate the

opportunity to share, learn, and transfer technologies between JV partners; which is resulted

from the decrease of cultural distances, increase of inter-partner trust and personal attachment

between partners (Gulati 1995; Yan and Gray, 1994), however, the formation of alliances and

JVs have frequently been perceived as ‘a race to learn’ and are closely associated with JVs’

instability.

Therefore, a longer duration of JVs may probably cause a shift (increase) in the supplier

partners’ bargaining power thus eliminating their partner dependency on the recipient partners

(Inkpen and Beamish, 1997). As a result, this will indeed frustrate the recipient partners’

organizational learning process; when the supplier partners become more protective of their

strategic valuable asset and reluctant to transfer higher technologies. On the other hand, the

MNCs in young JVs are normally reluctant to invest a higher degree of resources (both capital

and human resources) in the newly formed JVs. Their attitude is closely associated with the

skeptical feelings towards the recipient partners’ true learning intent (whether competitive vs.

collaborative) thus limiting the flows of their valuable technologies to recipient partners (Child

and Falkner, 1998; Khanna et al., 1998; Hamel, 1991). In this circumstance, as technology flows

are strictly restricted and controlled, even if technologies are intentionally transferred, their

effects on local firms’ performance could be very nominal. The results are explicitly consistent



212

with Dhanaraj et al.’s (2004) who reasoned that tacit knowledge could negatively affected IJVs’

performance because 1) tacit knowledge has lagged relationship with IJVs’ performance (Lyles

and Salk, 1996), 2) the foreign tacit knowledge needs to be adapted to the IJVs and current

environment (Martin and Solomon, 2003a, b), and 3) learning and utilizing tacit knowledge

utilization are interdependent but distinct (Lane et al., 2001).

REFERENCES

Aiken, L. S. & West, S. G. (1991). Multiple Regression: Testing and Interpreting Interacting, Newbury

Park, CA: Sage. Allen, D. R. & Rao, T. R. (2000). Analysis of Customer Satisfaction Data. United States of America:

America Society for Quality. Aguinis, H. (2004), Regression Analysis for Categorical Moderators, New York, The Gilford Press. Blomstrom, M. (1990). Transnational Corporations and Manufacturing Exports from Developing

Countries. New York, United Nations. Bresman, H., Birkinshaw, J. & Nobel, R. (1999). Knowledge Transfer in International Acquisitions.

Journal of International Business Studies, 30(3), p. 439–62. Caves, R.E. (1974). Multinational Firms, Competition and Productivity in Host-Country Markets.

Economica, 41, p. 176-193. Chen, E.K.Y. (1996). Transnational Corporations and Technology Transfer to Developing Countries in

UNCTAD, Transnational Corporations and World Development, p. 181-214, London, UK: Thompson Business Press.



Cohen, J. & Cohen, P. (1983). Applied Multiple Regression/Correlational Analysis for the Behavioral Sciences (2nd ed.). Hillsdale, NJ: Erlbaum.





Foss, N.J. & Pedersen, T. (2002). Sources of Subsidiary Knowledge and Knowledge Transfer in MNCs. In: Lundan, S., (Eds.). Network Knowledge in International Business, Edward Elgar, Cheltenham, p. 91–114.

Geppert, M. & Clark, E. (2003). Knowledge and Learning in Transnational Ventures: An Actor-Centred Approach. Management Decision, 41(5), pp.433-442.



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Geringer, J. M. & Hebert, L. (1991). Measuring Performance of International Joint Ventures, Journal of International Business Studies, 22(2), p. 249 - 63.


Gulati, R., (1995). Does Familiarity Breed Trust? The Implications of Repeated Ties for Contractual Choice in Alliances. Academy of Management Journal 38(1), p. 85–112.


Hallen, L, Johanson, J. & Seyed-Mohamed, N. (1991). Interfirm Adaptation in Business Markets. Journal of Marketing, 55, p. 29–37.





















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Martin, X.Y.F. & Salomon, R. (2003a). Tacitness, Learning , and International Expansion: A Study of Foreign Direct Investments in A Knowledge-Intensive Industry. Organization Science, 14 (3), p. 297-311.

Martin, X.Y.F. & Salomon, R. (2003b). Knowledge Transfer Capacity and its Implications for the Theory of the Multinational Corporation. Journal of International Business Studies, 34(4), 356-373.















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Yan, A. M. & Gray, B. (1994). Bargaining Power, Management Control, and Performance in United States-China Joint Ventures: A Comparative Case-Study. Academy of Management Journal, 37(6), p. 1478-1517.




216

12 MNCs’ Country of Origin, Degree of Inter-Firm Technology

Transfer and Firms’ Performance CHAPTER OUTLINE

This work fills in the literature gaps by specifically examining the effect of MNCs’ country of

origin (Western vs. Asian MNCs) as a moderating variable in the relationships between degree of

technology transfer (TTDEG) and two distinct dimensions of local firms’ performance (LFP):

corporate (CPERF) and human resource (HRPERF) performances. The primary objective is to

provide new insights and information on the boundary conditions for TTDEG-LFP relationship.

INTRODUCTION When compared to various forms of strategic alliance such as distribution and supply

agreements, research and development partnerships or technical and management contract, the

international joint ventures (IJVs) are considered as the most efficient formal mechanism for

technology transfer (TT) to occur via inter-partner learning between foreign MNCs and local

firms (Kogut and Zander, 1993; Inkpen 1998a, 2000). IJVs are also viewed as the most efficient

mode to transfer technology and knowledge which are organizationally embedded and difficult

to transfer through licensing agreements (Kogut, 1988; Mowery, Oxley and Silverman, 1996).

IJVs provide both MNCs and local partners an appropriate avenue to facilitate the transfer of

organizational knowledge, particularly for knowledge which is hard to be transferred without the

setting up of a JV such as institutional and cultural knowledge (Harrigan, 1984).



217

A review of literature reveals that majority of empirical studies on inter-firm technology and

knowledge transfer in strategic alliance particularly IJVs are limiting their focus on the















affected local firms’ performance (LFP). Interestingly, Dhanaraj et al. (2004) found tacit

knowledge was negatively related to IJVs’ performance.


knowledge transfer on performance outcomes, nevertheless except for Yin and Bao (2006),




firms could possibly be influenced by other established moderating factors such as size of


variations in CPERF and HRPERF could have been significantly influenced or explained by

these variables. Thus, this work fills in the literature gaps by specifically examining the effect of

MNCs’ country of origin (Western vs. Asian MNCs) as a moderating variable in the

relationships between degree of technology transfer (TTDEG) and two distinct dimensions of

local firms’ performance (LFP): corporate (CPERF) and human resource (HRPERF)



218

performances. The primary objective is to provide new insights and information on the boundary

conditions for TTDEG-LFP relationship (Aguinis, 2004).

DEGREE OF TECHNOLOGY TRANSFER, FIRMS’ PERFORMANCE AND MNCs’ COUNTRY OF ORIGIN The current TT issue in developing countries revolves around the extent of degree of

technologies that are transferred (TTDEG) by the suppliers to recipient partners (Pak and Park,

2004; Minbaeva, 2007). The question is no longer whether or not the MNCs are transferring

technology to local firms; instead the focus in the literature has shifted to questions on 1) the

level (sophistication) of the transferred technology, and 2) the stage where the transfer process

has reached (Lai and Narayanan, 1997; Narayanan and Lai, 2000). Except for Pak and Park

(2004) and Minbaeva (2007), not many studies in both intra and inter-firm TT have focused on

TTDEG as independent or dependent variable. In general, bulk of the studies has focused more

on technological knowledge and knowledge acquisition ‘per se’ as the outcomes (dependant

variables). For example, the technology transfer, knowledge transfer (KT) and strategic alliance

literature have extensively examined the relationships between 1) knowledge attributes, source

and recipient and KT success (Cummings et al., 2003), 2) knowledge seekers, knowledge holder

and contextual factors and know-how acquisition (Hau and Evangelista, 2007), 3) IJVs














219









2001; Liu and Vince, 1999; Lin, 2005), 9) managerial skills (Wong et al., 2002), 10) the

technology or manufacturing know how (Lam, 1997; Bresman et al., 1999), 11) the business

environment and product market knowledge (Geppert and Clark, 2003), and 12) the research and

development (Minbaeva, 2007). In the context of inter-firm technological knowledge transfer in

IJVs, only Pak and Park (2004) have directly dealt with degree of knowledge transfer as the

outcome (dependent variable) with respect to the transfer of new product development and

manufacturing skills/techniques.

The inter-firm TT and KT literature have also acknowledged that a substantial transfer of

technology regardless whether tacit or explicit technology will positively 1) lead to a higher

potentials of innovation performance/capabilities (Guan et al., 2006; Kotabe et al., 2007)), 2)

increase technological capabilities (Kumar et al., 1999; Madanmohan et al., 2004), 3) enhance

organizations’ competitive advantage (Liao and Hu, 2007; Rodriguez and Rodriguez, 2005), 4)

enhance organizational learning effectiveness (Inkpen, 2000; Inkpen and Dinur, 1998), 5)

improve productivity (Caves, 1974; Liu and Wang, 2003), 6) increase technological development

of local industry (Markusen and Venables, 1999), and 7) improve the economic growth of the

host country (Blomstrom, 1990).

Many empirical studies have established that MNCCOO (nationality) has a significant impact on

1) the propensities of MNCs’ choice of global strategies, 2) organizational structures and control

system (Bartlett and Ghoshal, 1989), 3) internal corporate cultures (Egelhoff, 1984; Porter,

1985), 4) expected outcomes (Harrigan, 1988b), 5) alliance outcomes and performance (Parkhe,

1993), 6) partners’ learning and protection of proprietary assets in an alliance (Kale et al., 2000),

and 7) the way how the MNCs operate (Gupta and Govindarajan, 2000). Problems related to

cultural differences, opinions, beliefs, and attitude tend to accelerate due to alliance/JV partners’



220

nationality (Kale et al., 2000). The differences in culture, language, educational background and

distance with cross national partners; which act as barriers to inter-organizational learning,

impede the inter-partner learning and knowledge transfer (Mowery et al. 1996). However, Yin

and Bao (2006) found nationality of alliance’s partners (the U.S, Japan and Western firms) has

no significant effect on the relationships between the supplier and recipient factors and tacit

knowledge acquisition.

H1: The relationship between degree of technology transfer and local firms’ corporate

performance is moderated by the MNCs’ country of origin.

H2: The relationship between degree of technology transfer and local firms’ human resource

performance is moderated by the MNCs’ country of origin.


















221



are usually not encouraging and low (Sekaran, 2003).

In the Malaysian context, however, a response rate of 15% to 25% is still being considered

appropriate and acceptable (Mohammed, 1998; Rozhan, Rohayu and Rasidah, 2001). From 145

responses only 128 questionnaires were usable and 17 questionnaires were returned blank,

returned incomplete, or replied but unable to participate in the study. The main research

instrument in this study is the questionnaire. Building on the previous TT and KT studies, the

questionnaire adopts a multi-item scales which have been modified accordingly to suit the

context of the study: inter-firm TT. Except for degree of technology transfer (TTDEG), all the

variables are measured using ten-point Likert Scale (1 = strongly disagree to 10 = strongly

agree). For TTDEG, this variable is measured using ten-point Likert Scale (1 = very low transfer

to 10 = substantial transfer). The ten-point Likert Scale was selected because 1) the wider

distribution of scores around the mean provides more discriminating power, 2) it is easy to




LOCAL FIRMS’ PERFORMANCE This study operationalizes local firms’ performance (LFP) from two dimensions of

performances: 1) corporate performance (CPERF), and 2) human resource (competencies)

performance (HRPERF). Based on literature review, the qualitative (objective) measures of

companies’ performance are the most practical and ideal measurement of performance.

However, the concrete financial figures are neither available nor reliable (Lyles and Barden,

2000; Tsang et al., 2004). Past studies have shown a positive relationship between objective and

perceptual (subjective) measures of firm’s performance (Lyles and Salk, 1996; Dess and

Robinson, 1984; Geringer and Hebert, 1989, 1991). Thus, this study applies subjective measures



222

to measure LFP based on IJV’s top management assessments using “a multi-dimensional

performance indicators”. The CPERF, as the first dimension of LFP, is measured by a four (4)

items scale measuring business volume, market share, planned goals and profits. For HRPERF,

as the second dimension of LFP, four (4) items are used to measure product/service quality,

employees’ productivity, managerial techniques/skills and operational efficiency (Tsang et al.,

2004; Yin and Bao, 2006; Lane et al., 2001; Lyles and Salk, 1996). The Cronbach Alphas for

CPERF and HRPERF were 0.926 and 0.97 respectively. The results of Cronbach Alpha were

well above of Lyles and Salk (1996).

DEGREE OF TECHNOLOGY TRANSFER Following Lyles and Salk (1996), Lane et al., (2001), Gupta and Govindarajan (2000), Dhanaraj













223

MNCs’ COUNTRY OF ORIGIN (MNCCOO) Following the previous studies (Yin and Bao, 2006; Mowery et al., 1996; Kale et al., 2000),

MNCCOO is measured by the nationality of the MNCs foreign JV partners based on item coded: 0

= Western MNCs (Unites States and European countries) and 1 = Asian MNCs (Japan and other

Asian countries).

MODEL AND ANALYSIS The moderated multiple regression (MMR) analysis is defined as an inferential procedure which

consists of comparing two different least-squares regression equations (Aguinis, 2004; Aiken and

West, 1991; Cohen and Cohen, 1983; Jaccard et al., 1990). Using the MMR analysis, the

moderating effect of the variable (product term) was analyzed by interpreting 1) the R² change in

the models obtained from the model summaries, and 2) the regressions coefficients for the

product term obtained from the coefficients tables. Prior to conducting the MMR analysis,












degree of technology transfer (TCTDEG and EXPDEG), Z = a hypothesized binary grouping



224

moderator (MNCCOO; Western vs. Asian MNCs), X*Z = the product between the predictors

(TTDEG*MNCCOO), β0 = the intercept of the line-of-best-of-fit which represents the value of Y

when X = 0, β1 = the least-squares estimate of the population regression coefficient for X, β2 =

the least-squares estimate of the population regression coefficient for Z, β3 = the sample-base

least-squares estimates of the population regression coefficient for the product term, and e = the

error term. The moderating variable (product term) is a binary grouping moderator; where the

moderating variable MNCCOO was coded using the dummy coding system; 0 = Western MNCs,

and 1 = Asian MNCs. This was done because of its simplicity and ease of interpretation of

results when making comparisons between different groups (Aguinis, 2004).

RESULTS Table 1 and Table 2 show the model summary for both corporate (CPERF) and human resource






MNCCOO. Model 2 shows the results after the product term (TTDEG*MNCCOO) was included in

the equation. Table 1 also indicates that the inclusion of the product term resulted in an R²

change of .018, [F (1, 124) = 7.796, p < 0.05]. The results support for the small presence of a

significant moderating effect. To put it differently, the moderating effect of MNCCOO explains

Model Summaryc

.695a .482 .474 5.074 .482 58.257 2 125 .000

.707b .500 .488 5.006 .018 4.414 1 124 .038

Model12




Change Statistics

Predictors: (Constant), MNCCOO, TTDEGa.

Predictors: (Constant), MNCCOO, TTDEG, TTDEG*MNCCOOb.




225

1.8% variance in the CPERF above and beyond the variance by TTDEG scores and MNCCOO.

Thus, it can reasonably be concluded that hypothesis H1 is supported.




MNCCOO. Model 2 shows the results after the product term (TTDEG*MNCCOO) was included in

the equation. Table 1 also indicates that the inclusion of the product term resulted in an R²

change of only .008, [F (1, 124) = 2.644, p > 0.05]. The results show no significant presence of

moderating effect. To put it differently, the moderating effect of MNCCOO explains only 0.8%

variance in the HRPERF above and beyond the variance by TTDEG scores and MNCCOO. Thus,

it can safely be concluded that hypothesis H2 is not supported. The coefficients table for CPERF

as shown in Table 3 below depicts the results of the regressions equation for Model 1 and Model

2.


Model 1 indicates that TTDEG was statistically significant (p < 0.001; Beta value = .640); and

MNCCOO was also statistically significant (p < 0.01). Equation 3 below shows that for a 1-point

Model Summaryc

.772a .596 .590 3.814 .596 92.373 2 125 .000

.778b .605 .595 3.789 .008 2.644 1 124 .106

Model12




Change Statistics

Predictors: (Constant), MNCCOO, TTDEGa.

Predictors: (Constant), MNCCOO, TTDEG, TTDEG*MNCCOOb.


Coefficientsa

.889 2.182 .408 .684 -3.428 5.207

.428 .044 .640 9.741 .000 .341 .5152.442 .932 .172 2.622 .010 .599 4.286

-2.370 2.653 -.893 .373 -7.621 2.881.496 .054 .741 9.165 .000 .389 .603

12.042 4.661 .849 2.584 .011 2.817 21.268-.190 .090 -.716 -2.101 .038 -.369 -.011

(Constant)TTDEGMNCCOO(Constant)TTDEGMNCCOOTTDEG*MNCCOO

Model1

2

B Std. Error


Beta






226

increase in TTDEG, the CPERF is predicted to have a difference by .428, given that the

MNCCOO is held constant. The regression coefficient associated with MNCCOO means that the

difference in CPERF between Western and Asian MNCs is 2.442, given that TTDEG is held

constant.

Equation 3: CPERF = .889 + .428TTDEG + 2.442MNCCOO


of CPERF that was influenced by Western and Asian MNCs. Model 2 shows the results after the

product term (TTDEG*MNCCOO) was included in the equation. As indicated in Table 1 the

inclusion of product term resulted in an R² change of .018, [F (1, 124) = 7.796, p < 0.05]. Model

2 shows TTDEG, MNCCOO and TTDEG*MNCCOO were significant (p < 0.001, Beta value =

.741; p < 0.01, Beta value = .849; p < 0.05, Beta value = -.716, respectively). The results did

support for the presence of a significant moderating effect. Table 3 also reveals information on

the regression coefficients after the inclusion of product term in the equation. The equation for

Model 2 is as follows:

Equation 4: CPERF = -2.370 + .496TTDEG + 12.042MNCCOO - .190TTDEG*MNCCOO




that MNCCOO is held constant. The interpretation of the regression coefficients for the product


between Western and Asian MNCs. In other words, the slope regressing CPERF on TTDEG is

steeper for Asian MNCs as compared to Western MNCs. The TTDEG and CPERF relationship

for Western and Asian MNCs is shown in Figure 1 below by creating a graph displaying the

relationships for each of the groups (Aguinis, 2004). From the results of descriptive statistics, the

value of the mean score for TTDEG is 6.19; and for the standard deviation (SD) is 1.30.

Following Aguinis (2004), the value 1 SD above the mean is 7.49 and the value 1 SD below the

mean is 4.89. Thus, using the value of 1 SD above and 1 SD below mean in Equation 4 yields the



227

graph shown in Figure 1. Results based on Equation 4 led to the conclusion that there was no

significant moderating effect of MNCCOO. Figure 1 below shows that the TTDEG-CPERF

relationship is stronger (i.e. steeper slope) for Asian MNCs as compared to Western MNCs. The

coefficients table for HRPERF as shown in Table 4 below depicts the results of the regressions

equation for Model 1 and Model 2.


Model 1 indicates that both TTDEG and MNCCOO were statistically significant (p < 0.001; Beta

value = .688; p < 0.001, Beta value = .241, respectively); Equation 5 below shows that for a 1-

point increase in TTDEG, the HRPERF is predicted to have a difference by .392, given that the

MNCCOO is held constant. The regression coefficient associated with MNCCOO means that the

difference in HRPERF between Western and Asian MNCs is 2.906, given that TTDEG is held

constant.

Equation 5: = 3.757 + .392TTDEG + 2.906MNCCOO

Model 2 shows the results after the product term (TTDEG*MNCCOO) was included in the


[F (1, 124) = 2.644, p > 0.05]. TTDEG and MNCCOO were found significant (p < 0.001, Beta

value = .758; p < 0.01, Beta value = .706, respectively). However, TTDEG*MNCCOO was found

insignificant (p > 0.05). The results show there was no presence of a significant moderating

effect. Table 4 also reveals information on the regression coefficients after the inclusion of

product term in the equation. The equation for Model 2 is as follows:

Coefficientsa

3.757 1.640 2.291 .024 .512 7.002.392 .033 .688 11.860 .000 .326 .457

2.906 .700 .241 4.150 .000 1.520 4.2921.848 2.008 .920 .359 -2.127 5.823.432 .041 .758 10.534 .000 .351 .513

8.530 3.528 .706 2.418 .017 1.547 15.513-.111 .068 -.493 -1.626 .106 -.247 .024

(Constant)TTDEGMNCCOO(Constant)TTDEGMNCCOOTTDEG*MNCCOO

Model1

2

B Std. Error


Beta






228

Equation 6: HRPERF = 1.848 + .432TTDEG + 8.530MNCCOO - .111TTDEG*MNCCOO


predicted to have a difference by .432, given that MNCCOO is held constant. The interpretation of

the regression coefficients for the product term in Equation 6 is that there was a -.111 difference

between the slope of HRPERF on TTDEG between Western and Asian MNCs. The slope

regressing HRPERF on TTDEG is steeper for Asian MNCs as compared to Western MNCs. The

TTDEG-HRPERF relationship for Western and Asian MNCs is also shown in Figure 1 below.

The value of the mean score for TTDEG is 6.19 and for the standard deviation (SD) is 1.30. The

value 1 SD above the mean is 7.49, and the value 1 SD below the mean is 4.89. Thus, using the

value of 1 SD above and 1 SD below mean in Equation 6 yields the graph shown in Figure 1.

Results based on Equation 6 led to the conclusion that there was no significant moderating effect

of MNCCOO. Although insignificant, Figure 1 below indicates that the TTDEG-HRPERF

relationship is stronger (i.e. steeper slope) for Asian MNCs as compared to Western MNCs.

Figure 1: Slopes for both CPERF and HRPERF on TTDEG for MNCCOO

30

25

20

15

10

5

0

-5

Low CPERF/ HRPERF (1 SD below mean) High CPERF / HRPERF (1 SD above mean) Asian MNCs (CPERF) Western MNCs (CPERF) Asian MNCs (HRPERF) Western MNCs (HRPERF)



229


by providing empirical evidence and new insights on the significant moderating effects of

MNCs’ country of origin in the relationships between degree of inter-firm technology transfer

and two dimensions of local firms’ performance: corporate and human resource performances

using the Malaysia sample. In comparison, the results clearly suggest that the inclusion of

MNCCOO (Western vs. Asian MNCs) in TTDEG-LFP relationship has significant moderating

effects in changing only the local firms’ corporate performance (CPERF) (p < 0.05; R- squared

change of 0.018) not human resource performance (HRPERF) (p > 0.05). The moderating effect

of MNCCOO is shown to be capable of changing the nature of relationship and further explains

under what conditions TTDEG causes CPERF. This means the presence of significant

moderating effect of MNCCOO (Western and Asian MNCs) exceeded the linear relationship

between TTDEG and CPERF. The results are consistent with literature which has strongly

supported for the significant role of MNCs’ size (Bartlett and Ghoshal, 1989; Egelhoff, 1984;

Porter, 1985). The results also suggest that MNCCOO; whether Western or Asian MNCs has been

established to provide a significant moderating impact in TTDEG-HRPERF relationship in the

IJVs; where the relationship was found stronger for Asian MNCs as compared to Western

MNCs.

The results further provide critical information in such that although a successful technology

transfer in IJVs; which includes the transfer of substantial tacit and explicit knowledge could

have significantly increased the corporate performance (CPERF) in terms of local firms’

business volume, market share, planned goals and profits, nevertheless, since the technologies

which are transferred to local firms mostly originated from the sophisticated and competitive

foreign MNCs, the outcome of the inter-firm technology transfer does not necessarily help to

improve local firms’ corporate performance. The plausible reason is that, due to cultural

differences (distances), attitudes towards outsiders (clannishness) and the fact that knowledge in

Oriental cultures is more contextual than Western cultures, Asian MNCs in IJVs are relatively

more protective of their technologies and knowledge as compared to Western MNCs; which are

quite transparent and more open in facilitating knowledge assimilation and acquisition (Hamel,

1991). Since inter-firm technology transfer in IJVs is an organizational learning process



230

(Daghfous, 2004), the recipient partners’ absorptive capacity, intensity of effort and collaborative

learning intent are not the only preconditions for a successful technology acquisition (Hamel,

1991). Because of the cultural distances, Asian MNCs are most unlikely to transfer a higher

degree or technology to local partners. As a result, this will indeed frustrate and dampen the

recipient partners’ organizational learning process; especially when the Asian MNCs become

less transparent in the transfer process. In this circumstance, as technology flows are cautiously

transferred and controlled, even if technology transfers do take place, their effects on local firms’

corporate performance could be very nominal. On the other hand, although Western MNCs in

IJVs are found to be more transparent in terms of knowledge sharing and knowledge openness,

however, due to their ‘technology superiority’ Western MNCs tend to regard their JV as one-way

learning processes thus having little to share with local partners (Liu and Vince, 1999; Danis and

Parkhe, 2002). Since learning in IJVs is asymmetrical, Western MNCs view technological

learning as solely the task of the knowledge-disadvantaged local partners (Lin, 2005). Moreover,

they are also unlikely to seriously undertake technology transfer particularly if the transfer

involves technologies which form the strategic valuable resources, competencies and source of

sustainable competitive advantage of the MNCs (Porter, 1985; Barney, 1991; Peteraf, 1993;

Wernerfelt, 1984; Pralahad and Hamel, 1990). The results further extend the empirical findings

by Hamel (1991) who found Japanese JV partners were relatively less transparent in inter-firm

organizational learning and knowledge transfer when compared to Western JV partners.

REFERENCE

Aiken, L. S. & West, S. G. (1991). Multiple Regression: Testing and Interpreting Interacting,

Newbury Park, CA: Sage. Allen, D. R. & Rao, T. R. (2000). Analysis of Customer Satisfaction Data. United States of

America: America Society for Quality. Aguinis, H. (2004), Regression Analysis for Categorical Moderators, New York, The Gilford

Press. Barney, J.B (1991). Firm Resources and Sustained Competitive Advantage. Journal of

Management, 17, p. 151-166. Blomstrom, M. (1990). Transnational Corporations and Manufacturing Exports from

Developing Countries. New York, United Nations. Bresman, H., Birkinshaw, J. & Nobel, R. (1999). Knowledge Transfer in International

Acquisitions. Journal of International Business Studies, 30(3), p. 439–62.



231


Chen, E.K.Y. (1996). Transnational Corporations and Technology Transfer to Developing Countries in UNCTAD, Transnational Corporations and World Development, p. 181-214, London, UK: Thompson Business Press.










Egelhoff, W.G. (1984). Patterns of Control in US, UK, and European Multinational Corporations, Journal of International Business Studies, 15, p. 73–83.










232





















233















Sekaran, U. (2003). Research Methods for Business, Fourth Edition, John Wiley & Sons, Inc. Si, S. X. & Bruton, G. D. (1999). Knowledge Transfer in International Joint Ventures in

Transitional Economy: The China Experience, The Academy of Management Executive, 13(1), p. 83-90.






234










235

13 MNCs’ Equity Ownership, Degree of Technology Transfer

and Firms’ Performance CHAPTER OUTLINE

Since the focus of inter-firm TT in developing countries has shifted to degree of technology

transfer, organizations in developing countries are attempting to assess not only the significant

role of technology transfer in strengthening their corporate and human resource performance

but also the influence of other critical variables such as MNCs’ size, age of JVs, country of

origin, MNCs’ equity ownership (MNCEQTY) and MNC’s type of industries that could

significantly moderate the relationship. This work fills in the literature gaps by specifically

examining the effect of equity ownership of MNCs (50/50 equal ownership between MNCs and

local JV partners vs. minor/majority ownership by MNCs) as a moderating variable in the

relationships between degree of technology transfer (TTDEG) and two distinct dimensions of

local firms’ performance (LFP): corporate (CPERF) and human resource (HRPERF)

performances.

INTRODUCTION A review of literature reveals that majority of empirical studies on inter-firm technology and

knowledge transfer in strategic alliance particularly IJVs are limiting their focus on the









236









affected local firms’ performance (LFP). Interestingly, Dhanaraj et al. (2004) found tacit

knowledge was negatively related to IJVs’ performance.


knowledge transfer on performance outcomes, nevertheless except for Yin and Bao (2006),




firms could possibly be influenced by other established moderating factors such as size of

MNCs, age of JV, MNCs’ country of origin, MNCs’ equity ownership, and MNCs’ types of

industry. In other words the variations in CPERF and HRPERF could have been significantly

influenced or explained by these variables. Thus, this study fills in the literature gaps by

specifically examining the effect of equity ownership of MNCs (50/50 equal ownership between

MNCs and local JV partners vs. minor/majority ownership by MNCs) as a moderating variable

in the relationships between degree of technology transfer (TTDEG) and two distinct dimensions

of local firms’ performance (LFP): corporate (CPERF) and human resource (HRPERF)

performances. The primary objective is to provide new insights and information on the boundary

conditions for TTDEG-LFP relationship (Aguinis, 2004).

EQUITY OWNERSHIP, DEGREE OF TECHNOLOGY TRANSFER AND LOCAL FIRMS’ PERFORMANCE The current TT issue in developing countries revolves around the extent of degree of

technologies that are transferred (TTDEG) by the suppliers to recipient partners (Pak and Park,



237

2004; Minbaeva, 2007). The question is no longer whether or not the MNCs are transferring

technology to local firms; instead the focus in the literature has shifted to questions on 1) the

level (sophistication) of the transferred technology, and 2) the stage where the transfer process

has reached (Lai and Narayanan, 1997; Narayanan and Lai, 2000). Except for Pak and Park

(2004) and Minbaeva (2007), not many studies in both intra and inter-firm TT have focused on

TTDEG as independent or dependent variable. In general, bulk of the studies has focused more

on technological knowledge and knowledge acquisition ‘per se’ as the outcomes (dependant

variables). For example, the technology transfer, knowledge transfer (KT) and strategic alliance

literature have extensively examined the relationships between 1) knowledge attributes, source

and recipient and KT success (Cummings et al., 2003), 2) knowledge seekers, knowledge holder

and contextual factors and know-how acquisition (Hau and Evangelista, 2007), 3) IJVs























238







The inter-firm TT and KT literature have also acknowledged that a substantial transfer of

technology regardless whether tacit or explicit technology will positively 1) lead to a higher

potentials of innovation performance/capabilities (Guan et al., 2006; Kotabe et al., 2007), 2)

increase technological capabilities (Kumar et al., 1999; Madanmohan et al., 2004), 3) enhance

organizations’ competitive advantage (Liao and Hu, 2007; Rodriguez and Rodriguez, 2005), 4)

enhance organizational learning effectiveness (Inkpen, 2000; Inkpen and Dinur, 1998), 5)

improve productivity (Caves, 1974; Liu and Wang, 2003), 6) increase technological development

of local industry (Markusen and Venables, 1999), and 7) improve the economic growth of the

host country (Blomstrom, 1990).

Both knowledge acquisition and knowledge transfer literatures have argued that equity

ownership in IJVs, particularly shared management IJVs; where the partners’ equity/share is split

50/50 between IJV partners, could significantly affect the success of knowledge acquisition in

IJVs when 1) appropriate controls can facilitate the organizational learning process by managing

the dynamic internal processes of IJVs such as a balanced bargaining power and different need-

configurations of partners (Makhija and Ganesh, 1997), 2) equity ownership enables the JV

partners to interact and communicate easily thus creating opportunities to share and easy access

to each partner’s technologies, knowledge and competencies (Pak and Park, 2004), 3) shared

management IJVs provides a strong strategic rationale of transferring and acquiring knowledge

and skills of both partners (Salk, 1992), 4) acquiring tacit knowledge from a JV partner is less

difficult through shared management as compared to simple contract-based relationship

(Mowery et al., 1996), and 5) it determines the degree of resource commitment or equity

interests as control is closely associated with ‘the partners’ ability to influence systems, methods,

and decisions (Anderson and Gatignon, 1986). Nevertheless, few other researchers have also

stressed that if no single partner has dominant controls in IJVs, the 50/50 ownership structure

could also 1) escalate difficulties when cultural differences are present (Killing, 1983), and 2)



239

create parental tensions (Killing, 1983; Salk, 1992). Pak and Park (2004) further argued that even

if the local JV partners were to have 49% equity in IJVs that does not guarantee them to have

higher influence in making decision related to major business operations; which include learning

and acquiring new knowledge. On the other hand, quite often, a dominant equity ownership

(more than 50% equity ownership) by local JV partners will only discourage higher resource

commitment by foreign MNCs thus reducing the possibility for a higher technological

knowledge transfer to local JV partners (Pak and Park, 2004). Studies have shown that equity

ownership structure in IJVs has moderated 1) the relationships between cultural

misunderstandings and written goal and knowledge acquisition from foreign parents (Lyles and

Salk, 1996), and 2) articulated goal (Harrigan, 1986; Salk, 1992).

H1: The equity ownership of MNCs moderates the relationship between degree of inter-firm

technology transfer and local firms’ corporate performance.

H2: The equity ownership of MNCs moderates the relationship between degree of inter-firm

technology transfer and local firms’ human resource performance.














240







are usually not encouraging and low (Sekaran, 2003). In the Malaysian context, however, a

response rate of 15% to 25% is still being considered appropriate and acceptable (Mohammed,

1998; Rozhan, Rohayu and Rasidah, 2001). From 145 responses only 128 questionnaires were

usable and 17 questionnaires were returned blank, returned incomplete, or replied but unable to

participate in the study.

INSTRUMENT AND MEASUREMENT The main research instrument in this study is the questionnaire. Building on the previous TT and

KT studies, the questionnaire adopts a multi-item scales which have been modified accordingly









LOCAL FIRMS’ PERFORMANCE This study operationalizes LFP from two dimensions of performances: 1) corporate performance





241













DEGREE OF TECHNOLOGY TRANSFER Following Lyles and Salk (1996), Lane et al., (2001), Gupta and Govindarajan (2000), Dhanaraj













242

EQUITY OWNERSHIP OF MNCs Following the previous studies (Lyles and Salk, 1996; Pak and Park, 2004), MNCEQTY is

measured by the ownership structure of the JVs companies that were registered with ROC based

on items coded: 0 = 50/50 equal ownership between MNCs and local JV partners and 1 =

minor/majority ownership by MNCs (Yin and Bao, 2006).

MODEL AND ANALYSIS The moderated multiple regression (MMR) analysis is defined as an inferential procedure which

consists of comparing two different least-squares regression equations (Aguinis, 2004; Aiken and

West, 1991; Cohen and Cohen, 1983; Jaccard et al., 1990). Using the MMR analysis, the

moderating effect of the variable (product term) was analyzed by interpreting 1) the R² change in

the models obtained from the model summaries, and 2) the regressions coefficients for the

product term obtained from the coefficients tables. Prior to conducting the MMR analysis,












degree of technology transfer, Z = a hypothesized binary grouping moderator (MNCEQTY: 50/50



243

equal ownership between MNCs and local JV partners vs. minor/majority ownership by MNCs),

X*Z = the product between the predictors (TTDEG*MNCEQTY), β0 = the intercept of the line-of-

best-of-fit which represents the value of Y when X = 0, β1 = the least-squares estimate of the

population regression coefficient for X, β2 = the least-squares estimate of the population

regression coefficient for Z, β3 = the sample-base least-squares estimates of the population

regression coefficient for the product term, and e = the error term. The moderating variable

(product term) is a binary grouping moderator; where the moderating variable MNCEQTY was

coded using the dummy coding system; 0 = 50/50 equal ownership between JV partners and 1 =

minor/majority ownership by MNCs. This was done because of its simplicity and ease of

interpretation of results when making comparisons between different groups (Aguinis, 2004).

RESULTS

Table 1 and Table 2 show the model summary for both corporate (CPERF) and human resource






MNCEQTY. Model 2 shows the results after the product term (TTDEG*MNCEQTY) was included

in the equation. Table 1 also indicates that the inclusion of the product term resulted in an R²

change of .013, [F (1, 124) = 3.072, p < 0.10]. The results support for the presence of a small

Model Summaryc

.674a .455 .446 5.207 .455 52.137 2 125 .000

.684b .468 .455 5.165 .013 3.072 1 124 .082

Model12




Change Statistics

Predictors: (Constant), MNCEQTY, TTDEGa.

Predictors: (Constant), MNCEQTY, TTDEG, TTDEG*MNCEQTYb.




244

significant moderating effect. To put it differently, the moderating effect of MNCEQTY explains

only 1.3% variance in the CPERF above and beyond the variance by TTDEG scores and

MNCEQTY. Thus, it can reasonably be concluded that hypothesis H1 is supported.




MNCEQTY. Model 2 also shows the results after the product term (TTDEG*MNCEQTY) was

included in the equation. Table 2 above indicates that the inclusion of the product term resulted

in an R² change of .035, [F (1, 124) = 10.385, p < 0.01]. The results show a presence of

significant moderating effect. To put it differently, the moderating effect of MNCEQTY explains

3.5% variance in the HRPERF above and beyond the variance by TTDEG scores and MNCEQTY.

Thus, it can safely be concluded that hypothesis H2 is supported. The coefficients table for

CPERF as shown in Table 3 below depicts the results of the regressions equation for Model 1

and Model 2.


Model Summaryc

.736a .542 .535 4.062 .542 73.995 2 125 .000

.760b .577 .567 3.918 .035 10.385 1 124 .002

Model12




Change Statistics

Predictors: (Constant), MNCEQTY, TTDEGa.

Predictors: (Constant), MNCEQTY, TTDEG, TTDEG*MNCEQTYb.


Coefficientsa

.509 2.304 .221 .825 -4.050 5.069

.445 .046 .665 9.662 .000 .354 .537

.145 .334 .030 .435 .664 -.517 .807-10.042 6.439 -1.560 .121 -22.788 2.703

.666 .134 .995 4.972 .000 .401 .9312.873 1.591 .591 1.806 .073 -.276 6.022-.056 .032 -.736 -1.753 .082 -.119 .007

(Constant)TTDEGMNCEQTY(Constant)TTDEGMNCEQTYTTDEG*MNCEQTY

Model1

2

B Std. Error


Beta






245


however MNCEQTY was not statistically significant (p > 0.05). Equation 3 below shows that for a

1-point increase in TTDEG, the CPERF is predicted to have a difference by .445, given that the

MNCEQTY is held constant. The regression coefficient associated with MNCEQTY means that the

difference in CPERF between 50/50 equal ownership between JV partners and minor/majority

ownership by MNCs is .145, given that TTDEG is held constant.

Equation 3: CPERF = .509 + .445TTDEG + .145MNCEQTY


of CPERF that was influenced by 50/50 equal ownership between JV partners and

minor/majority ownership by MNCs. Model 2 shows the results after the product term

(TTDEG*MNCEQTY) was included in the equation. As indicated in Table 1 the inclusion of

product term resulted in an R² change of .013, [F (1, 124) = 3.072, p < 0.10]. Model 2 shows

TTDEG and MNCEQTY were significant (p < 0.001, Beta value = .995; p < 0.10, Beta value =

.591, respectively). Similarly, TTDEG*MNCEQTY was also found to be significant (p < 0.10).

The results did support for the presence of a small significant moderating effect. Table 3 also

reveals information on the regression coefficients after the inclusion of product term in the


Equation 4: CPERF = -10.042 + .666TTDEG + 2.873MNCEQTY - .056TTDEG*MNCEQTY




that MNCEQTY is held constant. The interpretation of the regression coefficients for the product


between 50/50 equal ownership between JV partners and minor/majority ownership by MNCs.

In other words, the slope regressing CPERF on TTDEG is steeper for minor/majority ownership

by MNCs as compared to 50/50 equal ownership between JV partners. The TTDEG and CPERF

relationship for 50/50 equal ownership between MNCs and local JV partners and minor/majority



246

ownership by MNCs is shown in Figure 1 below by creating a graph displaying the relationships

for each of the groups (Aguinis, 2004). From the results of descriptive statistics, the value of the

mean score for TTDEG is 6.19; and for the standard deviation (SD) is 1.30. Following Aguinis

(2004), the value 1 SD above the mean is 7.49 and the value 1 SD below the mean is 4.89. Thus,

using the value of 1 SD above and 1 SD below mean in Equation 4 yields the graph shown in

Figure 1. Results based on Equation 4 led to the conclusion that there was a small significant

moderating effect of MNCEQTY. Figure 1 below shows that the TTDEG-CPERF relationship is

stronger (i.e. steeper slope) for minor/majority ownership by MNCs as compared to 50/50 equal

ownership between JV partners. The coefficients table for HRPERF as shown in Table 4 below

depicts the results of the regressions equation for Model 1 and Model 2.



however MNCEQTY was not statistically significant (p > 0.05). Equation 5 below shows that for a

1-point increase in TTDEG, the HRPERF is predicted to have a difference by .425, given that the

MNCEQTY is held constant. The regression coefficient associated with MNCEQTY means that the

difference in HRPERF between 50/50 equal ownership between MNCs and local JV partners and

minor/majority ownership by MNCs is -.153, given that TTDEG is held constant.

Equation 5: = 3.836 + .425TTDEG -.153MNCEQTY

Coefficientsa

3.836 1.797 2.134 .035 .279 7.393.425 .036 .746 11.820 .000 .354 .496

-.153 .261 -.037 -.585 .559 -.669 .364-10.882 4.885 -2.228 .028 -20.551 -1.213

.733 .102 1.286 7.212 .000 .532 .9343.651 1.207 .883 3.025 .003 1.262 6.040-.078 .024 -1.206 -3.223 .002 -.126 -.030

(Constant)TTDEGMNCEQTY(Constant)TTDEGMNCEQTYTTDEG*MNCEQTY

Model1

2

B Std. Error


Beta






247

Model 2 shows the results after the product term (TTDEG*MNCEQTY) was included in the


[F (1, 124) = 10.385, p < 0.01]. TTDEG, MNCEQTY and TTDEG*MNCEQTY were found highly

significant (p < 0.001, Beta value = 1.286; p < 0.05, Beta value = .883; p < 0.05, Beta value = -

1.206, respectively). The results show the presence of a significant moderating effect. Table 4

also reveals information on the regression coefficients after the inclusion of product term in the


Equation 6: HRPERF = -10.882 + .733TTDEG + 3.651MNCEQTY - .078TTDEG*MNCEQTY


predicted to have a difference by .733, given that MNCEQTY is held constant. The interpretation

of the regression coefficients for the product term in Equation 6 is that there was a -.078

difference between the slopes of HRPERF on TTDEG between 50/50 equal ownership between

JV partners and minor/majority ownership by MNCs. The slope regressing HRPERF on TTDEG

is steeper for minor/majority ownership by MNCs as compared to 50/50 equal ownership

between JV partners. The TTDEG-HRPERF relationship for 50/50 equal ownership between JV

partners and minor/majority ownership by MNC is also shown in Figure 1 below. The value of

the mean score for TTDEG is 6.19 and for the standard deviation (SD) is 1.30. The value 1 SD

above the mean is 7.49, and the value 1 SD below the mean is 4.89. Thus, using the value of 1

SD above and 1 SD below mean in Equation 6 yields the graph shown in Figure 1. Results based

on Equation 6 led to the conclusion that there was a significant moderating effect of MNCEQTY.

Figure 1 below indicates that the TTDEG-HRPERF relationship is stronger (i.e. steeper slope)

for 50/50 equal ownership between JV partners as compared to minor/majority ownership by

MNCs.



248

Figure 1: Slopes for both CPERF and HRPERF on TTDEG for MNCEQTY


by providing empirical evidence and new insights on the significant moderating effects of

MNCs’ equity ownership in the relationships between degree of inter-firm technology transfer

and two dimensions of local firms’ performance: corporate and human resource performances

using the Malaysia sample. In comparison, the results suggest that the inclusion of MNCEQTY

(50/50 equal ownership between MNCs and local JV partners vs. minor/majority ownership by

MNCs) in TTDEG-LFP relationship has similar significant moderating effects in changing both

local firms’ corporate performance (CPERF) (p < 0.10; R- squared change of 0.013) and local

firms’ human resource performance (HRPERF) (p < 0.01; R- squared change of 0.035). The

moderating effect of MNCEQTY is shown to be capable of changing the nature of relationship and

further explains under what conditions TTDEG causes CPERF and HRPERF. This means the

30

25

20

15

10

5

0

-5

-10

Low CPERF / HRPERF (1 SD below mean) High CPERF / HRPERF (1 SD above mean) Minor/Majority ownership by MNCs (CPERF) 50/50 ownership by JV partners (CPERF) Minor/Majority ownership by MNCs (HRPERF) 50/50 ownership by JV partners (HRPERF)



249

presence of significant moderating effect of MNCEQTY (50/50 equal ownership between JV

partners and minor/majority ownership by MNCs) exceeded the linear relationship between

TTDEG and both CPERF and HRPERF. The results are consistent with literature which has

strongly supported the significant role of MNCEQTY (Lyles and Salk, 1996; Harrigan, 1986; Salk,

1992).

The results also suggest that MNCEQTY; whether 50/50 equal ownership between MNCs and

local JV partners or minor/majority ownership by MNCs, has been established to provide a

significant moderating effect in 1) TTDEG-CPERF relationship; where the relationship was

found stronger for minor/majority ownership by MNCs as compared to 50/50 equal ownership

between JV partners, and 2) TTDEG-HRPERF relationship; where the relationship was found

stronger for 50/50 equal ownership between JV partners.

The results provide critical information in such that although a successful technology transfer in

IJVs; which includes the transfer of substantial tacit and explicit knowledge could have

significantly increased 1) the corporate performance in terms the local firms’ business volume,

market share, planned goals and profits, and 2) the human resource performance in terms of local

firms’ product/service quality, employees’ productivity, managerial techniques/skills and

operational efficiency, nevertheless, organizational learning and acquiring new technological

knowledge in JVs with shared management is rather difficult when there is no clear dominant

control by any partner in the decision making process especially on the day-to-day managerial

affairs and management of assets and resources. The IJVs with shared management are more

likely to encounter difficulties when cultural differences are present. The differences in culture,

language, educational background and distance with cross national partners could act as barriers

to inter-organizational learning thus impeding knowledge transfer in IJVs (Mowery et al. 1996).

Due to these cultural distances, a shared management IJVs is more unlikely to transfer a higher

degree or technology to local partners. As a result, this will indeed frustrate and dampen the

recipient partners’ organizational learning process; especially when conflicts; which are most

likely to arise from cultural differences, inhibit the transfer process.

On the other hand, the MNCs with minor equity ownership in IJVs will normally become

cautious and selective to transfer their strategic valuable resources, competencies and source of

sustainable competitive advantage of the MNCs (Porter, 1985; Barney, 1991; Peteraf, 1993;

Wernerfelt, 1984; Pralahad and Hamel, 1990); especially when they have less control/ability to



250

influence the systems, methods and decisions; which is frequently associated with higher

resources commitment and equity interests. In this circumstance, as technology flows are strictly

restricted and controlled, therefore even if technologies are intentionally transferred their effects

on local firms’ corporate and human resource performances could be very nominal. In

comparison, due to their ‘technology superiority’, the MNCs with majority equity ownership in

IJVs have frequently perceived that their JVs as one-way learning processes thus having little to

share with local partners (Liu and Vince, 1999; Danis and Parkhe, 2002). Since learning in IJVs

is asymmetrical, the MNCs with majority equity ownership view organizational learning as

solely the task of the knowledge-disadvantaged local partners (Lin, 2005). By limiting the flows

of their valuable technologies, the local JV partners might not have much to learn thus resulting

in no significant improvement on local firms’ corporate and human resource performances. The

results further extend the findings made by Lyles and Salk (1996) and Pak and Park (2004) by

empirically establishing that MNCEQTY has significantly influenced the relationship between

degree of inter-firm technology transfer and local firms’ performance.

REFERENCES

Aiken, L. S. & West, S. G. (1991). Multiple Regression: Testing and Interpreting Interacting, Newbury

Park, CA: Sage. Aguinis, H. (2004), Regression Analysis for Categorical Moderators, New York, The Gilford Press. Allen, D. R. & Rao, T. R. (2000). Analysis of Customer Satisfaction Data. United States of America:

America Society for Quality. Anderson, E & Gatignon, H. (1986). Models of Foreign Entry: A Transaction Cost Analysis and

Propositions. Journal of International Studies, 17 (3), p. 1-26. Barney, J.B (1991). Firm Resources and Sustained Competitive Advantage. Journal of Management, 17,

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